Find_Intercept.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Find Intercept"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-10-09T03:38:22.003Z",
          "iopub.execute_input": "2021-10-09T03:38:22.005Z",
          "iopub.status.idle": "2021-10-09T03:38:22.241Z",
          "shell.execute_reply": "2021-10-09T03:38:22.246Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "x = np.linspace(-5,5,2)\n",
        "ax.spines['left'].set_position('center')\n",
        "ax.spines['bottom'].set_position('center')\n",
        "ax.spines['right'].set_color('none')\n",
        "ax.spines['top'].set_color('none')\n",
        "ax.xaxis.set_ticks_position('bottom')\n",
        "ax.yaxis.set_ticks_position('left')\n",
        "plt.plot(x, -x+3, '-r', label='y=-x+3')\n",
        "plt.legend(loc='upper left')\n",
        "plt.ylim([-3, 3])\n",
        "plt.show()\n",
        "\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": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-10-09T03:38:22.251Z",
          "iopub.execute_input": "2021-10-09T03:38:22.257Z",
          "iopub.status.idle": "2021-10-09T03:38:22.349Z",
          "shell.execute_reply": "2021-10-09T03:38:22.352Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}