McKenlly/maria.ipynb

## maria.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN+bY9C3F9XshaqHQHgRMLa",
      "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/gist/McKenlly/6755391b4fd7d52245aa1fa73cc39e77/maria.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "bo7iZnL31rAA"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_name_func1 = 'func1.csv'\n",
        "file_name_func2 = 'func2.csv'"
      ],
      "metadata": {
        "id": "7qsEXGiS3BDk"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "func1_coords = pd.read_csv (file_name_func1)\n",
        "func2_coords = pd.read_csv (file_name_func2)\n",
        "func2_coords['y'] = func2_coords.apply(lambda row : row[1] if row[1] > 0 else 0, axis=1)\n",
        "func1_coords['y'] = func1_coords.apply(lambda row : row[1] if row[1] > 0 else 0, axis=1)\n",
        "\n",
        "print (func1_coords)\n",
        "print (func2_coords)\n",
        "# print(func1_coords.headers())\n",
        "# print (func2_coords['x'].min())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48YMGnUr2o4l",
        "outputId": "d12c9597-059f-41a2-aff3-2dcf4206007b"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           x      y        y\n",
            "0     219.74  33274  33274.0\n",
            "1     219.99  32776  32776.0\n",
            "2     220.24  33133  33133.0\n",
            "3     220.49  33561  33561.0\n",
            "4     220.74  33211  33211.0\n",
            "...      ...    ...      ...\n",
            "1917  699.00   -236      0.0\n",
            "1918  699.25   -149      0.0\n",
            "1919  699.50    -40      0.0\n",
            "1920  699.75    -74      0.0\n",
            "1921  700.00   -221      0.0\n",
            "\n",
            "[1922 rows x 3 columns]\n",
            "         x       y         y\n",
            "0    560.0  261067  261067.0\n",
            "1    560.5  287778  287778.0\n",
            "2    561.0  310654  310654.0\n",
            "3    561.5  347731  347731.0\n",
            "4    562.0  370579  370579.0\n",
            "..     ...     ...       ...\n",
            "476  798.0   28186   28186.0\n",
            "477  798.5   28210   28210.0\n",
            "478  799.0   29041   29041.0\n",
            "479  799.5   27561   27561.0\n",
            "480  800.0   27020   27020.0\n",
            "\n",
            "[481 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (func1_coords['x'].min())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2x8rltqvMn4t",
        "outputId": "5c117258-5ea0-4c51-fc17-1c32148a4d51"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "219.74\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax1 = plt.subplots()\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Wavelength, nm')\n",
        "ax1.set_ylabel(r'Molar Extinction, $\\frac{см^{-1}}{М}$', color=color)\n",
        "ax1.plot('x', 'y', data=func1_coords, color='r')\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel(r'Fluorescence intensity, $10^{6}$', color=color)\n",
        "ax2.plot('x', 'y', data=func2_coords, color='b')\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "fig.tight_layout() \n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "EhAtLwV8BQXF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "81799070-6b1b-48f5-be96-f97037dd391d"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: Second argument 'y' is ambiguous: could be a format string but is in 'data'; using as data.  If it was intended as data, set the format string to an empty string to suppress this warning.  If it was intended as a format string, explicitly pass the x-values as well.  Alternatively, rename the entry in 'data'.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: Second argument 'y' is ambiguous: could be a format string but is in 'data'; using as data.  If it was intended as data, set the format string to an empty string to suppress this warning.  If it was intended as a format string, explicitly pass the x-values as well.  Alternatively, rename the entry in 'data'.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot('x', 'y', data=func1_coords, color='r', label='Первый график')\n",
        "# plt.plot('x', 'y', data=func2_coords, color='b', label='второй график')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "t3pNAWrh3H-g",
        "outputId": "dfe0de86-323b-40a4-9a11-d0879692d202"
      },
      "execution_count": 76,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "func2_coords_y = func2_coords\n",
        "func2_coords_y['y'] = func2_coords_y.apply(lambda row : row[1] if row[1] > 0 else 0, axis=1)\n",
        "# plt.plot('x', 'y', data=func1_coords, color='r', label='Первый график')\n",
        "plt.plot('x', 'y', data=func2_coords_y, color='b', label='второй график')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "jknZICnn5Pr2",
        "outputId": "09260f93-6720-4542-bfdc-8a1a5d691c71"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: Second argument 'y' is ambiguous: could be a format string but is in 'data'; using as data.  If it was intended as data, set the format string to an empty string to suppress this warning.  If it was intended as a format string, explicitly pass the x-values as well.  Alternatively, rename the entry in 'data'.\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ytP4A3N9-46J"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}