{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sample():\n",
    "    x = np.linspace(-10, 10, 1000)\n",
    "    for sigma in range(1, 7):\n",
    "        plt.plot(x, norm.pdf(x, 0, sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viele Packages erweitern Matplotlib\n",
    "\n",
    "- [Seaborn](http://seaborn.pydata.org/tutorial/aesthetics.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "plot_sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `set_style`\n",
    "- `color_palette`, `set_palette`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_data = norm.rvs(size=1000)\n",
    "sns.distplot(norm_data, kde=False, fit=norm)\n",
    "norm.fit(norm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,10,100)\n",
    "y = x + norm.rvs(size=100)\n",
    "sns.regplot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "def valstr_to_date(valstr):\n",
    "    t = float(valstr)\n",
    "    year = int(t)\n",
    "    seconds = int((t % 1) * 365 * 24 * 60 * 60)\n",
    "    return dt.datetime(year=year, month=1, day=1) + dt.timedelta(seconds=seconds)\n",
    "\n",
    "data = pd.read_table('data/temperatures.txt', delim_whitespace=True, header=None, names=[ \"Date\", \"Temperature\" ], index_col=0, na_values=[ \"99\", \"-99\" ], date_parser=valstr_to_date)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots in LaTeX einbinden\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.use('pgf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_sample()\n",
    "plt.savefig(\"plots/vector_plot.pgf\")\n",
    "!tail plots/vector_plot.pgf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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