test_install.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# [NTDS'18]: test your installation\n",
    "[ntds'18]: https://github.com/mdeff/ntds_2018\n",
    "\n",
    "[Michaël Defferrard](http://deff.ch), [EPFL LTS2](http://lts2.epfl.ch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a mini \"test\" Jupyter notebook to make sure the main packages we'll use are installed.\n",
    "Run it after following the [installation instructions](https://github.com/mdeff/ntds_2018#installation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standalone dependencies\n",
    "\n",
    "If you get a `command not found` error, try to run `conda install <package-name>` (in the `ntds_2018` environment, i.e., after `conda activate ntds_2018`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!jupyter --version\n",
    "!jupyter-notebook --version\n",
    "# !jupyter-lab --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ipython --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python packages\n",
    "\n",
    "If you get a `ModuleNotFoundError` error, try to run `conda install <package-name>` (in the `ntds_2018` environment, i.e., after `conda activate ntds_2018`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "scipy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "nx.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygsp\n",
    "pygsp.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Small test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = pygsp.graphs.Logo()\n",
    "graph.estimate_lmax()\n",
    "filt = pygsp.filters.Heat(graph, tau=100)\n",
    "DELTAS = [20, 30, 1090]\n",
    "signal = np.zeros(graph.N)\n",
    "signal[DELTAS] = 1\n",
    "signal = filt.filter(signal)\n",
    "graph.plot_signal(signal, highlight=DELTAS)"
   ]
  }
 ],
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