{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "name": "Allegro Trains matplotlib example.ipynb",
   "provenance": [],
   "collapsed_sections": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NKas2cYws8F6",
    "colab_type": "text"
   },
   "source": [
    "# Allegro Trains matplotlib example\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allegroai/trains/blob/master/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb)\n",
    "\n",
    "This tutorial introduce Trains with matplotlib functionality. You can find more frameworks examples [here](https://github.com/allegroai/trains/tree/master/examples/frameworks)."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "72lCj7MJmRkQ",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "!pip install trains\n",
    "!pip install numpy\n",
    "!pip install seaborn"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b8jtq0iSt3-U",
    "colab_type": "text"
   },
   "source": [
    "### Create a new task.\n",
    "Task object should be provided `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). A link to the newly generated task will be printed and the task will be updated real time in the Trains demo server.\n",
    "\n",
    "You can read about task in the docs [here](https://allegro.ai/docs/task.html)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "ses67ulJkGPq",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from trains import Task\n",
    "\n",
    "# Start a new task\n",
    "task = Task.init(project_name=\"Colab notebooks\", task_name=\"Matplotlib example\")\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "whxX3Xjmy1PI",
    "colab_type": "text"
   },
   "source": [
    "This example was generated based on [trains matplotlib example](https://github.com/allegroai/trains/blob/master/examples/frameworks/matplotlib/matplotlib_example.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2l-0WvJt_yo",
    "colab_type": "text"
   },
   "source": [
    "### Matplotlib support\n",
    "\n",
    "Matplotlib plots are automatically logged. Data available in the task 'Results' section\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "a-nOyg9xlxiR",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# create plot\n",
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = (30 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "iV4BtqRFmi0N",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# create another plot - with a name\n",
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, 'o', color='black')\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yKT5UjDk6DGB",
    "colab_type": "text"
   },
   "source": [
    "Notice imshow will cause the plot images to apear as Images in the debug sample section"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "IVzUScalmio-",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# create unitlted image plot\n",
    "m = np.eye(256, 256, dtype=np.uint8)\n",
    "plt.imshow(m)\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "mioKlXpimib1",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# create image plot - with a name\n",
    "m = np.eye(256, 256, dtype=np.uint8)\n",
    "plt.imshow(m)\n",
    "plt.title('Image Title')\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "AE7Gbm3GfvvK",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# create plot with savefig\n",
    "N = 10\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = (30 * np.random.rand(N))**2\n",
    "plt.title('savefig Image')\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.savefig(\"plot.png\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jcRWq9Xc56fX",
    "colab_type": "text"
   },
   "source": [
    "Seaborn example:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "j-usk2d_mqS4",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "# Load an example dataset with long-form data\n",
    "fmri = sns.load_dataset(\"fmri\")\n",
    "# Plot the responses for different events and regions\n",
    "sns.lineplot(x=\"timepoint\", y=\"signal\",\n",
    "             hue=\"region\", style=\"event\",\n",
    "             data=fmri)\n",
    "plt.show()"
   ],
   "execution_count": null,
   "outputs": []
  }
 ]
}