Tag1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Olaf-007/crashkurs_python/blob/main/Tag1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "0ZGPjSmc-8F3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdc94330-179c-4b7e-e242-739e0b48e855"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bla\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(\"Bla\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Warum? Hier ist *die* Erklärung.\n",
        "\n",
        "$\\sum_8^6$\n",
        "\n",
        "```python\n",
        "print(\"Hello World\",7)\n",
        "```\n",
        "\n",
        "![test.jpg]()"
      ],
      "metadata": {
        "id": "ERosIw0j_XW1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: plot a cross on a matplotlib graph, make backrground blue to yellow\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "\n",
        "# Create a figure and axes\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# Plot a cross\n",
        "ax.plot([0, 1], [0.5, 0.5], color='black', linewidth=2)\n",
        "ax.plot([0.5, 0.5], [0, 1], color='black', linewidth=2)\n",
        "\n",
        "\n",
        "# Set the background color to a gradient from blue to yellow\n",
        "gradient = np.linspace(0, 1, 256)\n",
        "cmap = cm.get_cmap('viridis')\n",
        "for i in range(256):\n",
        "    ax.axvspan(i / 256, (i + 1) / 256, facecolor=cmap(gradient[i]), alpha=1)\n",
        "\n",
        "\n",
        "# Customize the plot\n",
        "ax.set_xlim([0, 1])\n",
        "ax.set_ylim([0, 1])\n",
        "ax.set_aspect('equal')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7FoJV7ou_TaD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "5afa3b74-c539-4db0-efe1-123723b3d897"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-915330ea9d33>:17: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
            "  cmap = cm.get_cmap('viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RVFEt6hXfWZM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}