{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4eb1ee6b-58d5-4f68-8c56-645b4ce23705",
   "metadata": {},
   "source": [
    "# pyopenms"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7b7f7b40-9948-4035-9d1e-6013e327d49f",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/html",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "<a href=\"https://notebooks.gesis.org/binder/v2/gh/OpenMS/pyopenms_viz/main?filepath=docs/Data Formatting/pyopenms.ipynb\" target=\"_blank\">\n",
    "    <img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Launch on Binder\" style=\"width: 150px;\"/>\n",
    "</a>\n",
    "<br></br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67055e7c-e4e2-4648-8df6-ea04f17e1809",
   "metadata": {},
   "source": [
    "## Install pyopenms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70b62198-0dba-4573-8dbe-19f31a10c7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install pyopenms --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c68afe3-cd59-424a-b822-46ffca16456a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File test.mzML downloaded successfully!\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/levitsky/pyteomics/master/tests/test.mzML'\n",
    "file_name = 'test.mzML'\n",
    "\n",
    "# # Send a GET request to the URL\n",
    "response = requests.get(url)\n",
    "\n",
    "# # Save the content of the response to a file\n",
    "with open(file_name, 'wb') as file:\n",
    "    file.write(response.content)\n",
    "\n",
    "print(f'File {file_name} downloaded successfully!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53eabf53-784c-4147-b589-e989ed17f9a3",
   "metadata": {},
   "source": [
    "## Load `.mzML` file and convert to `pd.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61f85915-0450-499d-a451-38c6d26b4315",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RT</th>\n",
       "      <th>mz</th>\n",
       "      <th>inty</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <th>39823</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>39824</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>39825</th>\n",
       "      <td>0.3561</td>\n",
       "      <td>1999.961548</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>39826</th>\n",
       "      <td>0.3561</td>\n",
       "      <td>1999.985718</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>39827</th>\n",
       "      <td>0.3561</td>\n",
       "      <td>2000.009888</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39828 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           RT           mz  inty\n",
       "0      0.2961   200.000183   0.0\n",
       "1      0.2961   200.000427   0.0\n",
       "2      0.2961   200.000671   0.0\n",
       "3      0.2961   200.000916   0.0\n",
       "4      0.2961   202.605835   0.0\n",
       "...       ...          ...   ...\n",
       "39823  0.3561  1999.913086   0.0\n",
       "39824  0.3561  1999.937256   0.0\n",
       "39825  0.3561  1999.961548   0.0\n",
       "39826  0.3561  1999.985718   0.0\n",
       "39827  0.3561  2000.009888   0.0\n",
       "\n",
       "[39828 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyopenms as oms\n",
    "input_file = \"./test.mzML\"\n",
    "\n",
    "# open mzML file\n",
    "exp = oms.MSExperiment()\n",
    "oms.MzMLFile().load(input_file, exp)\n",
    "\n",
    "# convert to pd.DataFrame\n",
    "df = exp.get_df(long=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7955e0ff-25f4-4de8-8f88-0170ebe0167f",
   "metadata": {},
   "source": [
    "## Convert a single Spectrum as a `pd.DataFrame`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b560a0-5135-4c05-952f-3b6083802595",
   "metadata": {},
   "source": [
    "This can be achieved by filtering the dataframe above to the desired spectrum or can be done with the pyopenms interface. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1c7d799-8bad-4075-86c4-20689ea15259",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <th>19910</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "      <td>FTMS + p ESI Full ms [200.00-2000.00]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19911</th>\n",
       "      <td>1999.961513</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NONE&gt;</td>\n",
       "      <td>1</td>\n",
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       "      <td>200</td>\n",
       "      <td>2000</td>\n",
       "      <td>FTMS + p ESI Full ms [200.00-2000.00]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19912</th>\n",
       "      <td>1999.985729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NONE&gt;</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>controllerType=0 controllerNumber=1 scan=1</td>\n",
       "      <td></td>\n",
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       "      <td>810</td>\n",
       "      <td>1471973</td>\n",
       "      <td>15245068</td>\n",
       "      <td>200</td>\n",
       "      <td>2000</td>\n",
       "      <td>FTMS + p ESI Full ms [200.00-2000.00]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19913</th>\n",
       "      <td>2000.009947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NONE&gt;</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>controllerType=0 controllerNumber=1 scan=1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>810</td>\n",
       "      <td>1471973</td>\n",
       "      <td>15245068</td>\n",
       "      <td>200</td>\n",
       "      <td>2000</td>\n",
       "      <td>FTMS + p ESI Full ms [200.00-2000.00]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19914 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mz  ...  preset scan configuration\n",
       "0       200.000188  ...                          1\n",
       "1       200.000430  ...                          1\n",
       "2       200.000673  ...                          1\n",
       "3       200.000915  ...                          1\n",
       "4       202.605829  ...                          1\n",
       "...            ...  ...                        ...\n",
       "19909  1999.913081  ...                          1\n",
       "19910  1999.937296  ...                          1\n",
       "19911  1999.961513  ...                          1\n",
       "19912  1999.985729  ...                          1\n",
       "19913  2000.009947  ...                          1\n",
       "\n",
       "[19914 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spectrum = oms.MSSpectrum(exp.getSpectrum(0))\n",
    "df = spectrum.get_df()\n",
    "df"
   ]
  }
 ],
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