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‎pr-95/API.html‎

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‎pr-95/Data Formatting/Data Formatting.html‎

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‎pr-95/Data Formatting/alphatims.html‎

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‎pr-95/Data Formatting/alphatims.ipynb‎

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‎pr-95/Data Formatting/pymzml.html‎

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‎pr-95/Data Formatting/pymzml.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f8776c6e-5d39-4f3e-9d9a-152dd0359c93",
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"metadata": {},
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"source": [
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"# pymzml"
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]
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},
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{
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"cell_type": "raw",
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"id": "5ea454b7-3fb4-4d87-86ac-62dcbb2671b1",
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"metadata": {
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"editable": true,
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"raw_mimetype": "text/html",
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"source": [
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"<a href=\"https://notebooks.gesis.org/binder/v2/gh/OpenMS/pyopenms_viz/main?filepath=docs/Data Formatting/pymzml.ipynb\" target=\"_blank\">\n",
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" <img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Launch on Binder\" style=\"width: 150px;\"/>\n",
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"</a>\n",
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"<br></br>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "efb4dce4-75eb-464d-b839-bd3e93819142",
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"metadata": {},
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"source": [
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"## Install pymzml"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6944da94-fa96-4f8e-bb68-798ca545e810",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install pymzml --quiet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "44c8ed73-b62f-4c05-ab3a-f00fd8ba84c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import pymzml"
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]
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},
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{
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"cell_type": "markdown",
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"id": "29a93547-1b8c-4b93-91c0-17c7caf932fd",
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"metadata": {},
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"source": [
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"## Download the test file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "02396b2f-50c4-4e7c-b938-74a8f4c28dd8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"File test.mzML downloaded successfully!\n"
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]
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}
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],
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"source": [
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"# # Download test file\n",
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"import requests\n",
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"\n",
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"url = 'https://raw.githubusercontent.com/levitsky/pyteomics/master/tests/test.mzML'\n",
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"file_name = 'test.mzML'\n",
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"\n",
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"# Send a GET request to the URL\n",
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"response = requests.get(url)\n",
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"\n",
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"# Save the content of the response to a file\n",
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"with open(file_name, 'wb') as file:\n",
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" file.write(response.content)\n",
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"\n",
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"print(f'File {file_name} downloaded successfully!')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c601f0e-9118-44e5-a8d0-96b62ef1e65e",
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"metadata": {},
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"source": [
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"## Load `.mzML` file and convert to `pd.DataFrame`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3cfbb66c-2dcc-4293-a0f3-4e747ec5c880",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>mz</th>\n",
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" <th>int</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>204.760065</td>\n",
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" <td>3901.930681</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>204.778823</td>\n",
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" <td>3431.441349</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>204.790975</td>\n",
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" <td>3703.068912</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>204.795946</td>\n",
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" <td>4317.227255</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>204.797708</td>\n",
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" <td>4267.166873</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1735</th>\n",
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" <td>1998.647154</td>\n",
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" <td>2567.956920</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1736</th>\n",
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" <td>1998.908241</td>\n",
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" <td>5539.517009</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1737</th>\n",
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" <td>1999.254369</td>\n",
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" <td>14109.211636</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1738</th>\n",
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" <td>1999.510959</td>\n",
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" <td>8432.185933</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1739</th>\n",
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" <td>1999.782814</td>\n",
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" <td>1875.846606</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>1740 rows × 2 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" mz int\n",
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"0 204.760065 3901.930681\n",
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"1 204.778823 3431.441349\n",
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"2 204.790975 3703.068912\n",
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"3 204.795946 4317.227255\n",
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"4 204.797708 4267.166873\n",
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"... ... ...\n",
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"1735 1998.647154 2567.956920\n",
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"1736 1998.908241 5539.517009\n",
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"1737 1999.254369 14109.211636\n",
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"1738 1999.510959 8432.185933\n",
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"1739 1999.782814 1875.846606\n",
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"\n",
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"[1740 rows x 2 columns]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"input_file = \"./test.mzML\"\n",
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"\n",
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"run = pymzml.run.Reader(input_file)\n",
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"# Get the first spec\n",
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"spec = run.next()\n",
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"# Convert to Pandas DataFrame\n",
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"df = pd.DataFrame(spec.centroidedPeaks).rename(columns={0:'mz', 1:'int'})\n",
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"df"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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