kantel/legends.ipynb Secret

## legends.ipynb
{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Übungen mit Matplotlib"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Legenden"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Legenden in Matplotlib-Plots kann man mit dem Positionsargument `loc` (zum Beispiel `\"upper right\"` positionieren. In vielen Fällen weiß man jedoch nicht, wie das Ergebnis aussehen wird, bevor man es geplottet hat und die Legende verdeckt unter Umständen einen (wichtigen) Teil des Plots. Daher kann man alternativ das Positionsargument `best` verwenden. Matplotlib versucht dann, automatisch die bestmögliche Position für die Legende zu finden:"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T10:47:58.097Z",
          "iopub.execute_input": "2020-05-13T10:47:58.102Z",
          "iopub.status.idle": "2020-05-13T10:47:59.151Z",
          "shell.execute_reply": "2020-05-13T10:47:59.155Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(0, 25, 1000)\n",
        "f1 = np.sin(0.5*x)\n",
        "f2 = 3*np.cos(0.8*x)"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T10:49:37.517Z",
          "iopub.execute_input": "2020-05-13T10:49:37.521Z",
          "iopub.status.idle": "2020-05-13T10:49:37.526Z",
          "shell.execute_reply": "2020-05-13T10:49:37.529Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x, f1, label = \"$sin(0.5*x)$\")\n",
        "plt.plot(x, f2, label = \"$3sin(x)$\")\n",
        "plt.legend(loc = \"best\")\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T11:16:08.743Z",
          "iopub.execute_input": "2020-05-13T11:16:08.755Z",
          "iopub.status.idle": "2020-05-13T11:16:09.075Z",
          "shell.execute_reply": "2020-05-13T11:16:09.089Z"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In den folgenden beiden Beispielen kann man sehen, daß `loc = \"best\"`  in der Regel sehr gut funktioniert:"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(-2*np.pi, 2*np.pi, 70, endpoint = True)\n",
        "f1 = np.sin(0.5*x)\n",
        "f2 = -3*np.cos(0.8*x)"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T10:57:35.521Z",
          "iopub.execute_input": "2020-05-13T10:57:35.527Z",
          "iopub.status.idle": "2020-05-13T10:57:35.536Z",
          "shell.execute_reply": "2020-05-13T10:57:35.540Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xticks([-6.28, -3.14, 0, 3.14, 6.28],\n",
        "           [r\"$-2\\pi$\", r\"$-\\pi$\", r\"$0$\", r\"$+\\pi$\", r\"$+2\\pi$\"])\n",
        "plt.yticks([-3, -1, 0, 1, 3])\n",
        "plt.plot(x, f1, label = \"$sin(0.5x)$\")\n",
        "plt.plot(x, f2, label = \"$-3cos(0.8x)$\")\n",
        "plt.legend(loc = \"best\")\n",
        "plt.show()\n",
        "         "
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T11:05:33.798Z",
          "iopub.execute_input": "2020-05-13T11:05:33.804Z",
          "iopub.status.idle": "2020-05-13T11:05:33.984Z",
          "shell.execute_reply": "2020-05-13T11:05:33.993Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(-2*np.pi, 2*np.pi, 70, endpoint = True)\n",
        "f1 = np.sin(0.5*x)\n",
        "f2 = 3*np.cos(0.8*x)"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T11:06:42.551Z",
          "iopub.execute_input": "2020-05-13T11:06:42.555Z",
          "iopub.status.idle": "2020-05-13T11:06:42.563Z",
          "shell.execute_reply": "2020-05-13T11:06:42.568Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xticks([-6.28, -3.14, 0, 3.14, 6.28],\n",
        "           [r\"$-2\\pi$\", r\"$-\\pi$\", r\"$0$\", r\"$+\\pi$\", r\"$+2\\pi$\"])\n",
        "plt.yticks([-3, -1, 0, 1, 3])\n",
        "plt.plot(x, f1, label = \"$sin(0.5x)$\")\n",
        "plt.plot(x, f2, label = \"$3cos(0.8x)$\")\n",
        "plt.legend(loc = \"best\")\n",
        "plt.show()\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-05-13T11:07:18.505Z",
          "iopub.execute_input": "2020-05-13T11:07:18.512Z",
          "iopub.status.idle": "2020-05-13T11:07:18.675Z",
          "shell.execute_reply": "2020-05-13T11:07:18.687Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "outputExpanded": false,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.2",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "/Users/admin/anaconda3/bin/python",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}