{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "polish-inquiry", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "from masskit.utils.tablemap import ArrowLibraryMap\n", "from masskit.test_fixtures.demo_fixtures import cho_uniq_short_parquet" ] }, { "attachments": {}, "cell_type": "markdown", "id": "dutch-vehicle", "metadata": {}, "source": [ "# Use of the spectrum object\n", "Masskit is a software library for doing computations associated with mass spectrometry and a good place to start our description of Masskit is to examine the usage of the [`Spectrum`](../masskit.spectrum.html#masskit.spectrum.spectrum.Spectrum) class that creates a spectrum object. Let's begin by loading two spectra objects from a table in a file and then taking a look at a thumbnail of one of the spectra:" ] }, { "cell_type": "code", "execution_count": 2, "id": "economic-attitude", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:created chunk 1 with 100 records\n", "INFO:root:processing batch 0 with size 100\n", "INFO:root:created chunk 1 with 0 records\n" ] }, { "data": { "image/png": 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contains the precursor and product ions for a spectrum, a dictionary of properties, and a variety of functions to operate on the ions.\n", "## Accessing the spectrum ions " ] }, { "cell_type": "code", "execution_count": 3, "id": "d700c22b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The precursor m/z of spectrum 1 is 855.4538\n", "The first five product ion m/z's of spectrum 1 are [143.0823 153.2575 159.0917 162.5977 169.0972]\n", "The first five product ion intensities's of spectrum 1 are [143.0823 153.2575 159.0917 162.5977 169.0972]\n" ] } ], "source": [ "print('The precursor m/z of spectrum 1 is', spectrum1.precursor.mz)\n", "print(\"The first five product ion m/z's of spectrum 1 are\", spectrum1.products.mz[0:5])\n", "print(\"The first five product ion intensities's of spectrum 1 are\", spectrum1.products.mz[0:5])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "f8b6eed4", "metadata": {}, "source": [ "You'll note that the product ion mz's and intensities are stored in arrays, where each peak in the spectrum corresponds to a position in spectrum1.products.mz with a corresponding intensity in the same position in `spectrum1.products.mz`. The precursor m/z is stored in `spectrum1.precursor.mz`. We use arrays of numbers as modern processors work quickly on arrays of the same type. Behind the scenes, we use numpy or arrow arrays to speed computations." ] }, { "attachments": {}, "cell_type": "markdown", "id": "exclusive-bosnia", "metadata": {}, "source": [ "## Spectrum properties\n", "Each spectrum has properties associated with it, such as a name or collision energy. These are accessed as attributes of the spectrum object:" ] }, { "cell_type": "code", "execution_count": 4, "id": "intelligent-europe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The name of the spectrum is AAAACALTPGPLADLAAR/2_1(4,C,CAM) and the collision energy is 46.0\n" ] } ], "source": [ "print(\"The name of the spectrum is\", spectrum1.name, \"and the collision energy is\", spectrum1.ev)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "d52ebd46", "metadata": {}, "source": [ "## Spectrum mass tolerance information\n", "The spectrum object supports mass tolerances measured in both ppm's and Daltons. Mass tolerance information is kept in objects of class [`MassInfo`](../masskit.spectrum.html#masskit.spectrum.spectrum.MassInfo), and is applied to ions by using the `tolerance` array of `Spectrum` that show the mz tolerance for each ion. Optionally, the `tolerance` array values can be set individually for each ion to allow for nonstandard mass tolerances. The arrya is also used to perform quick interval arithmetic when matching spectra." ] }, { "cell_type": "code", "execution_count": 5, "id": "acf0eb6b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20.0 ppm\n", "minimum mz values [143.07943835 153.25443485 159.08851817 162.59444805 169.09381806]\n", "maximum mz values [143.08516165 153.26056515 159.09488183 162.60095195 169.10058194]\n" ] } ], "source": [ "print(spectrum1.products.mass_info.tolerance, spectrum1.products.mass_info.tolerance_type)\n", "print(\"minimum mz values\", spectrum1.products.tolerance[0:5])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "brave-height", "metadata": {}, "source": [ "## Operations on spectra\n", "There are a variety of operations that can be applied to spectra, including normalization, noise filtering, merging, masking, shifting, plotting, and comparison. These operations can be conveniently chained, and by default, create new spectrum objects. A list of methods can be found in [`Spectrum`](../masskit.spectrum.html#masskit.spectrum.spectrum.Spectrum). For example, to normalize the intensity to 1.0 and noise filter a spectrum, one could do:" ] }, { "cell_type": "code", "execution_count": 6, "id": "d7dd0b4c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(,\n", " )" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PCAAANGotvV1AbYWEhMjPz6/SLlJBQUGl3aYLLV++XJMmTdKKFSs0fPhw074tWrTQtddea7qzZLVaZbVaa188AADwWT6zsxQQEKD4+Hilp6d7tKenpysxMbHa85YuXaoHH3xQ77//vkaNGlXj9zEMQ1lZWYqIiLjkmgEAgO/zmZ0lSZo5c6bsdrsGDBighIQELVy4UDk5OZo6daqkirfH8vLytGTJEkkVQen+++/XK6+8osGDB7t3pVq1aiWbzSZJevbZZzV48GB169ZNLpdLr776qrKysvT66697Z5IAAKBR8amwNH78eJ04cUKzZ89Wfn6+YmNjtWbNGkVHR0uS8vPzPe659Oc//1lnz57Vr3/9a/361792tz/wwANavHixJOnUqVN66KGH5HA4ZLPZFBcXpy1btmjgwIGXdW4AAKBx8qn7LDVW3GcJQEPzlXsHcZ8l+JImd58lAAAAbyAsAQAAmCAsAQAAmCAsAQBwGRQVSRZLxVFU5O1qcDEISwAAACYISwAAACYISwAAACYISwAAACYISwAAACbqFJYefPBBbdmypb5rAQAAaHTqFJZOnz6tpKQkdevWTc8//7zy8vLquy40AVwmCwCXB79vG1adwlJaWpry8vI0bdo0rVixQjExMbrlllv0t7/9TaWlpfVdIwAAddKmDeEBl67On1nq2LGjZsyYoczMTO3atUtXXXWV7Ha7IiMj9cgjj+jw4cP1WScAAIBXXPIHvPPz87Vu3TqtW7dOfn5+uvXWW3Xw4EH16tVLc+bMqY8aAQAAvKZOYam0tFRpaWkaPXq0oqOjtWLFCj3yyCPKz8/X22+/rXXr1umdd97R7Nmz67teAACAy6plXU6KiIhQeXm57rnnHu3atUv9+vWr1GfEiBFq167dJZYHAADgXXUKS3PmzNHYsWMVGBhYbZ/27dsrOzu7zoUBAAA0BnV6G27jxo1VXvVWVFSkX/ziF5dcFAAAQGNRp7D09ttv64cffqjU/sMPP2jJkiWXXBTQHHGfFNQWl8MDl9dFvQ3ncrlkGIYMw9Dp06c93oYrKyvTmjVrFBoaWu9FArVVVFTxQiJJhYVSUJB36wEA1E1j+n1+UWGpXbt2slgsslgsuvrqqys9b7FY9Oyzz9ZbcUB1GtM/Il/BmgHedf5uYFER/wZ9yUWFpY0bN8owDN14441KS0tThw4d3M8FBAQoOjpakZGR9V4kAACAt1xUWLr++uslSdnZ2ercubMsFkuDFAUAAOqmTRt2j+tbrcPS/v37FRsbqxYtWsjpdOqzzz6rtm/fvn3rpTgAAABvB8Bah6V+/frJ4XAoNDRU/fr1k8VikWEYlfpZLBaVlZXVa5FAY8HnfgCg+al1WMrOztYVV1zh/hpoqghEAIDz1TosRUdHV/k1cLEII6wBGg4/W0D9q/NNKT/66CP349/85jdq166dEhMT9e2339ZbcUB1LrwEFwCAhlKnsPT888+rVatWkqSMjAzNmzdPL774okJCQvTII4/Ua4EAvKsh7yzOXcsB+II6/SHd3NxcXXXVVZKkv//97/rZz36mhx56SNddd52GDRtWn/WhkWjqW/vnz+/oUe/Wguanqf/7AnxdnXaW2rRpoxMnTkiS1q1bp+HDh0uSAgMDq/ybcWicGuv/1TfWui7F+XMqKPjxhbG+x24q63W5sYYAzNQpLN18882aPHmyJk+erK+++kqjRo2SJB08eFAxMTH1WV8l8+fPV5cuXRQYGKj4+Hht3brVtP/mzZsVHx+vwMBAde3aVW+88UalPmlpaerVq5esVqt69eqlVatWNVT5PuHCF3aL5eJf3M//P+Wmwhfm1KZN43nBb4wBpDHW5Ksux1ry3wsNqahIstlq17dOYen1119XQkKCjh07prS0NHXs2FGStGfPHt1zzz11GbJWli9fruTkZD399NPKzMzUkCFDdMsttygnJ6fK/tnZ2br11ls1ZMgQZWZm6qmnntL06dOVlpbm7pORkaHx48fLbrdr3759stvtGjdunHbu3Nlg80DDaawf/Darq6o6q/ur8vX54nH+WI3lxai6+fGi2Tw11n/PuDS++O+5Tp9ZateunebNm1epvaH/iO7LL7+sSZMmafLkyZKkuXPn6pNPPtGCBQuUmppaqf8bb7yhzp07a+7cuZKknj17avfu3frTn/6ku+++2z3GzTffrJSUFElSSkqKNm/erLlz52rp0qUXVV9RUZH8/PwuYYaX14W/iIqKpLAwzz4XPq7qvKr6/PvfVZ9zYf/qAkF1z1f1/Y4d+/EzHlXN6fxzjh6t+vMg1X3PNm1+PKegoPpzanJ+3++/r7r+C8crKJC6dv2x7qKiHx+fP2Z1YevcedWtzYXO/37nXPj5rdquZ3Xf78Jzq+tvVm91634p/529/aJcm+9f25+/usylqv8u9bWWl7KetR3L7OfqwnEupZba/NzXNMb5X9fnz1pt/iesMajNa0B1zzXEBSZS7Qa1GFXdhrsWTp06pV27dqmgoEDl5eU/DmixyG6312VIUyUlJWrdurVWrFihO++8090+Y8YMZWVlafPmzZXOGTp0qOLi4vTKK6+421atWqVx48bp+++/l7+/vzp37qxHHnnE4yq+OXPmaO7cudXeBqG4uFjFxcXuxy6XS1FRUfUxTQAAcJk5nU4FBwdX+3yddpY+/PBDTZw4UUVFRWrbtq3HH9RtqLB0/PhxlZWVKeyCbYWwsDA5HI4qz3E4HFX2P3v2rI4fP66IiIhq+1Q3piSlpqY2+C4aAABoHOoUlh599FH94he/0PPPP6/WrVvXd02mzg9mkmQYRqW2mvpf2H6xY6akpGjmzJnux+d2lr777jvTZAoAdVYf7wNdDue/n/vvf0uhoZe/Bl9ZK3idy+VSZGRkjf3qFJby8vI0ffr0yxqUQkJC5OfnV2nHp6CgoNLO0Dnh4eFV9m/ZsqX7Q+nV9aluTEmyWq2yWq2V2oOCghTEP0oADS0oqPEGgPPragx1NoYa0GiVlZXVql+droYbMWKEdu/eXZdT6ywgIEDx8fFKT0/3aE9PT1diYmKV5yQkJFTqv27dOg0YMED+/v6mfaobEwAANC912lkaNWqUHn/8cX3++efq06ePO3icM2bMmHop7kIzZ86U3W7XgAEDlJCQoIULFyonJ0dTp06VVPH2WF5enpYsWSJJmjp1qubNm6eZM2dqypQpysjI0KJFizyucpsxY4aGDh2qF154Qbfffrs++OADrV+/Xtu2bWuQOQAAAB9j1IHFYqn2aNGiRV2GrLXXX3/diI6ONgICAoz+/fsbmzdvdj/3wAMPGNdff71H/02bNhlxcXFGQECAERMTYyxYsKDSmCtWrDC6d+9u+Pv7Gz169DDS0tIuqian02lIMpxOZ53mBAA1Kiw0DKniKCz0djXVO3r0xzqPHvVODb6yVvC62r5+1/nWAfiRy+WSzWar8dJDAKgzX/kDcgUFnh+u9tYHvH1hreB1tX39rtNnls535syZSx0CAACg0apTWCorK9Mf/vAHXXnllWrTpo3+/f+3a/7d736nRYsW1WuBAAAA3lSnsPTcc89p8eLFevHFFxUQEOBu79Onj/73f/+33ooDAADwtjqFpSVLlmjhwoWaOHGix99C69u3r7788st6Kw4AAMDb6hSW8vLydNVVV1VqLy8vV2lp6SUXBQAA0FjUKSz17t1bW7durdS+YsUKxcXFXXJRAAAAjUWdbkr5+9//Xna7XXl5eSovL9fKlSt16NAhLVmyRP/4xz/qu0YAAACvqdPO0m233ably5drzZo1slgseuaZZ/TFF1/oww8/1M0331zfNQIAAHhNnXaWpIq/DzdixIj6rAUAAKDRqdPOUteuXXXixIlK7adOnVLXrl0vuSgAAIDGok5h6ZtvvlFZWVml9uLiYuXl5V1yUQAAAI3FRb0Nt3r1avfXn3zyiWw2m/txWVmZNmzYoJiYmHorDgAAwNsuKizdcccdkiSLxaIHHnjA4zl/f3/FxMTopZdeqrfiAAAAvO2iwlJ5ebkkqUuXLvr0008VEhLSIEUBAAA0FnW6Gi47O7u+6wAAAGiU6nzrgA0bNmjDhg0qKChw7zid85e//OWSCwMAAGgM6hSWnn32Wc2ePVsDBgxQRESELBZLfdcFAADQKNQpLL3xxhtavHix7HZ7fdcDAADQqNTpPkslJSVKTEys71oAAAAanTqFpcmTJ+v999+v71oAAAAanTq9DXfmzBktXLhQ69evV9++feXv7+/x/Msvv1wvxQEAAHhbncLS/v371a9fP0nSgQMH6rMeAACARqVOYWnjxo31XQcAAECjdFFh6a677qqxj8ViUVpaWp0LAgAAaEwuKiyd/4dzAQAAmoOLCktvvfVWQ9UBAADQKNXp1gEAAADNBWEJAADABGEJAADABGEJAADABGEJAADAhM+EpZMnT8put8tms8lms8lut+vUqVPV9i8tLdUTTzyhPn36KCgoSJGRkbr//vv13XffefQbNmyYLBaLxzFhwoQGng0AAPAVPhOW7r33XmVlZWnt2rVau3atsrKyZLfbq+3//fffa+/evfrd736nvXv3auXKlfrqq680ZsyYSn2nTJmi/Px89/HnP/+5IacCAAB8SJ3+3Mnl9sUXX2jt2rXasWOHBg0aJEl68803lZCQoEOHDql79+6VzrHZbEpPT/doe+211zRw4EDl5OSoc+fO7vbWrVsrPDy8YScBAAB8kk/sLGVkZMhms7mDkiQNHjxYNptN27dvr/U4TqdTFotF7dq182h/7733FBISot69e+uxxx7T6dOnTccpLi6Wy+XyOAAAQNPkEztLDodDoaGhldpDQ0PlcDhqNcaZM2f05JNP6t5771VwcLC7feLEierSpYvCw8N14MABpaSkaN++fZV2pc6XmpqqZ5999uInAgAAfI5Xd5ZmzZpV6cPVFx67d++WVPEHei9kGEaV7RcqLS3VhAkTVF5ervnz53s8N2XKFA0fPlyxsbGaMGGC/va3v2n9+vXau3dvteOlpKTI6XS6j9zc3IucOQAA8BVe3VmaNm1ajVeexcTEaP/+/Tp69Gil544dO6awsDDT80tLSzVu3DhlZ2frn//8p8euUlX69+8vf39/HT58WP3796+yj9VqldVqNR0HAAA0DV4NSyEhIQoJCamxX0JCgpxOp3bt2qWBAwdKknbu3Cmn06nExMRqzzsXlA4fPqyNGzeqY8eONX6vgwcPqrS0VBEREbWfCAAAaLJ84gPePXv21MiRIzVlyhTt2LFDO3bs0JQpUzR69GiPK+F69OihVatWSZLOnj2rn/3sZ9q9e7fee+89lZWVyeFwyOFwqKSkRJL0r3/9S7Nnz9bu3bv1zTffaM2aNRo7dqzi4uJ03XXXeWWuAACgcfGJsCRVXLHWp08fJSUlKSkpSX379tU777zj0efQoUNyOp2SpCNHjmj16tU6cuSI+vXrp4iICPdx7gq6gIAAbdiwQSNGjFD37t01ffp0JSUlaf369fLz87vscwQAAI2PT1wNJ0kdOnTQu+++a9rHMAz31zExMR6PqxIVFaXNmzfXS30AAKBp8pmdJQAAAG8gLAEAAJggLAEAAJggLAEAAJggLAEAAJjwmavhAAColaAgqYaroYGLwc4SAACACcISAACACcISAACACcISAACACcISAACACcISAACACcISAACACcISAACACcISAACACcISAACACcISAKD+BAVV/TXgwwhLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJghLAAAAJlp6uwAAQC0EBUmG4e0qgGaJnSUAAAAThCUAAAAThCUAAAAThCUAAAATPhOWTp48KbvdLpvNJpvNJrvdrlOnTpme8+CDD8pisXgcgwcP9uhTXFyshx9+WCEhIQoKCtKYMWN05MiRBpwJAADwJT4Tlu69915lZWVp7dq1Wrt2rbKysmS322s8b+TIkcrPz3cfa9as8Xg+OTlZq1at0rJly7Rt2zYVFhZq9OjRKisra6ipAAAAH+ITtw744osvtHbtWu3YsUODBg2SJL355ptKSEjQoUOH1L1792rPtVqtCg8Pr/I5p9OpRYsW6Z133tHw4cMlSe+++66ioqK0fv16jRgxov4nAwAAfIpP7CxlZGTIZrO5g5IkDR48WDabTdu3bzc9d9OmTQoNDdXVV1+tKVOmqKCgwP3cnj17VFpaqqSkJHdbZGSkYmNjTcctLi6Wy+XyOAAAQNPkE2HJ4XAoNDS0UntoaKgcDke1591yyy1677339M9//lMvvfSSPv30U914440qLi52jxsQEKD27dt7nBcWFmY6bmpqqvuzUzabTVFRUXWcGQAAaOy8GpZmzZpV6QPYFx67d++WJFkslkrnG4ZRZfs548eP16hRoxQbG6vbbrtNH3/8sb766it99NFHpnXVNG5KSoqcTqf7yM3NreWMAQCAr/HqZ5amTZumCRMmmPaJiYnR/v37dfTo0UrPHTt2TGFhYbX+fhEREYqOjtbhw4clSeHh4SopKdHJkyc9dpcKCgqUmJhY7ThWq1VWq7XW3xcAAPgur4alkJAQhYSE1NgvISFBTqdTu3bt0sCBAyVJO3fulNPpNA01Fzpx4oRyc3MVEREhSYqPj5e/v7/S09M1btw4SVJ+fr4OHDigF198sQ4zAgAATY1PfGapZ8+eGjlypKZMmaIdO3Zox44dmjJlikaPHu1xJVyPHj20atUqSVJhYaEee+wxZWRk6JtvvtGmTZt02223KSQkRHfeeackyWazadKkSXr00Ue1YcMGZWZm6r777lOfPn3cV8cBAIDmzSduHSBJ7733nqZPn+6+cm3MmDGaN2+eR59Dhw7J6XRKkvz8/PTZZ59pyZIlOnXqlCIiInTDDTdo+fLlatu2rfucOXPmqGXLlho3bpx++OEH3XTTTVq8eLH8/Pwu3+QAAECjZTEMw/B2Eb7O5XLJZrPJ6XQqODjY2+UAgPcUFUlt2lR8XVgoBQV5tx7ARG1fv33ibTgAAABvISwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACYICwBAACY8JmwdPLkSdntdtlsNtlsNtntdp06dcr0HIvFUuXxP//zP+4+w4YNq/T8hAkTGng2AADAV7T0dgG1de+99+rIkSNau3atJOmhhx6S3W7Xhx9+WO05+fn5Ho8//vhjTZo0SXfffbdH+5QpUzR79mz341atWtVj5QAAwJf5RFj64osvtHbtWu3YsUODBg2SJL355ptKSEjQoUOH1L179yrPCw8P93j8wQcf6IYbblDXrl092lu3bl2pLwAAgOQjb8NlZGTIZrO5g5IkDR48WDabTdu3b6/VGEePHtVHH32kSZMmVXruvffeU0hIiHr37q3HHntMp0+fNh2ruLhYLpfL4wAAAE2TT+wsORwOhYaGVmoPDQ2Vw+Go1Rhvv/222rZtq7vuusujfeLEierSpYvCw8N14MABpaSkaN++fUpPT692rNTUVD377LMXNwkAAOCTvLqzNGvWrGo/hH3u2L17t6SKD2tfyDCMKtur8pe//EUTJ05UYGCgR/uUKVM0fPhwxcbGasKECfrb3/6m9evXa+/evdWOlZKSIqfT6T5yc3MvYtYAAMCXeHVnadq0aTVeeRYTE6P9+/fr6NGjlZ47duyYwsLCavw+W7du1aFDh7R8+fIa+/bv31/+/v46fPiw+vfvX2Ufq9Uqq9Va41gAAMD3eTUshYSEKCQkpMZ+CQkJcjqd2rVrlwYOHChJ2rlzp5xOpxITE2s8f9GiRYqPj9c111xTY9+DBw+qtLRUERERNU8AAAA0eT7xAe+ePXtq5MiRmjJlinbs2KEdO3ZoypQpGj16tMeVcD169NCqVas8znW5XFqxYoUmT55cadx//etfmj17tnbv3q1vvvlGa9as0dixYxUXF6frrruuwecFAAAaP58IS1LFFWt9+vRRUlKSkpKS1LdvX73zzjsefQ4dOiSn0+nRtmzZMhmGoXvuuafSmAEBAdqwYYNGjBih7t27a/r06UpKStL69evl5+fXoPMBAAC+wWIYhuHtInydy+WSzWaT0+lUcHCwt8sBAO8pKpLatKn4urBQCgrybj2Aidq+fvvMzhIAAIA3EJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABM+ExYeu6555SYmKjWrVurXbt2tTrHMAzNmjVLkZGRatWqlYYNG6aDBw969CkuLtbDDz+skJAQBQUFacyYMTpy5EgDzAAAAPginwlLJSUlGjt2rH71q1/V+pwXX3xRL7/8subNm6dPP/1U4eHhuvnmm3X69Gl3n+TkZK1atUrLli3Ttm3bVFhYqNGjR6usrKwhpgEAAHyMxTAMw9tFXIzFixcrOTlZp06dMu1nGIYiIyOVnJysJ554QlLFLlJYWJheeOEF/fKXv5TT6dQVV1yhd955R+PHj5ckfffdd4qKitKaNWs0YsSIWtXkcrlks9nkdDoVHBx8SfMDAJ9WVCS1aVPxdWGhFBTk3XoAE7V9/faZnaWLlZ2dLYfDoaSkJHeb1WrV9ddfr+3bt0uS9uzZo9LSUo8+kZGRio2NdfepSnFxsVwul8cBAACapiYblhwOhyQpLCzMoz0sLMz9nMPhUEBAgNq3b19tn6qkpqbKZrO5j6ioqHquHgB8VFCQZBgVB7tKaCK8GpZmzZoli8VieuzevfuSvofFYvF4bBhGpbYL1dQnJSVFTqfTfeTm5l5SjQAAoPFq6c1vPm3aNE2YMMG0T0xMTJ3GDg8Pl1SxexQREeFuLygocO82hYeHq6SkRCdPnvTYXSooKFBiYmK1Y1utVlmt1jrVBQAAfItXw1JISIhCQkIaZOwuXbooPDxc6enpiouLk1RxRd3mzZv1wgsvSJLi4+Pl7++v9PR0jRs3TpKUn5+vAwcO6MUXX2yQugAAgG/xali6GDk5OfrPf/6jnJwclZWVKSsrS5J01VVXqc3/X3nRo0cPpaam6s4775TFYlFycrKef/55devWTd26ddPzzz+v1q1b695775Uk2Ww2TZo0SY8++qg6duyoDh066LHHHlOfPn00fPhwb00VAAA0Ij4Tlp555hm9/fbb7sfndos2btyoYcOGSZIOHTokp9Pp7vOb3/xGP/zwg/7rv/5LJ0+e1KBBg7Ru3Tq1bdvW3WfOnDlq2bKlxo0bpx9++EE33XSTFi9eLD8/v8szMQAA0Kj53H2WGiPuswQAgO9p9vdZAgAAqA+EJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABOEJQAAABM+8+dOGrNzN0F3uVxergQAANTWudftmv6YCWGpHpw4cUKSFBUV5eVKAADAxTp9+rRsNlu1zxOW6kGHDh0kSTk5OaaL3dS5XC5FRUUpNze3Wf+NPNbhR6xFBdahAuvwI9aigrfXwTAMnT59WpGRkab9CEv1oEWLio9+2Wy2Zv1Df05wcDDrINbhfKxFBdahAuvwI9aigjfXoTabHHzAGwAAwARhCQAAwARhqR5YrVb9/ve/l9Vq9XYpXsU6VGAdfsRaVGAdKrAOP2ItKvjKOliMmq6XAwAAaMbYWQIAADBBWAIAADBBWAIAADBBWAIAADBBWLpE8+fPV5cuXRQYGKj4+Hht3brV2yXVq9TUVF177bVq27atQkNDdccdd+jQoUMefQzD0KxZsxQZGalWrVpp2LBhOnjwoEef4uJiPfzwwwoJCVFQUJDGjBmjI0eOXM6p1KvU1FRZLBYlJye725rLOuTl5em+++5Tx44d1bp1a/Xr10979uxxP99c1uHs2bP67W9/qy5duqhVq1bq2rWrZs+erfLycnefprgWW7Zs0W233abIyEhZLBb9/e9/93i+vuZ88uRJ2e122Ww22Ww22e12nTp1qoFnV3tm61BaWqonnnhCffr0UVBQkCIjI3X//ffru+++8xijKayDVPPPxPl++ctfymKxaO7cuR7tjX4tDNTZsmXLDH9/f+PNN980Pv/8c2PGjBlGUFCQ8e2333q7tHozYsQI46233jIOHDhgZGVlGaNGjTI6d+5sFBYWuvv88Y9/NNq2bWukpaUZn332mTF+/HgjIiLCcLlc7j5Tp041rrzySiM9Pd3Yu3evccMNNxjXXHONcfbsWW9M65Ls2rXLiImJMfr27WvMmDHD3d4c1uE///mPER0dbTz44IPGzp07jezsbGP9+vXG119/7e7THNbBMAzjv//7v42OHTsa//jHP4zs7GxjxYoVRps2bYy5c+e6+zTFtVizZo3x9NNPG2lpaYYkY9WqVR7P19ecR44cacTGxhrbt283tm/fbsTGxhqjR4++XNOskdk6nDp1yhg+fLixfPly48svvzQyMjKMQYMGGfHx8R5jNIV1MIyafybOWbVqlXHNNdcYkZGRxpw5czyea+xrQVi6BAMHDjSmTp3q0dajRw/jySef9FJFDa+goMCQZGzevNkwDMMoLy83wsPDjT/+8Y/uPmfOnDFsNpvxxhtvGIZR8YvD39/fWLZsmbtPXl6e0aJFC2Pt2rWXdwKX6PTp00a3bt2M9PR04/rrr3eHpeayDk888YTx05/+tNrnm8s6GIZhjBo1yvjFL37h0XbXXXcZ9913n2EYzWMtLnxhrK85f/7554YkY8eOHe4+GRkZhiTjyy+/bOBZXTyzgHDOrl27DEnu/5luiutgGNWvxZEjR4wrr7zSOHDggBEdHe0RlnxhLXgbro5KSkq0Z88eJSUlebQnJSVp+/btXqqq4TmdTkk//vHg7OxsORwOj3WwWq26/vrr3euwZ88elZaWevSJjIxUbGysz63Vr3/9a40aNUrDhw/3aG8u67B69WoNGDBAY8eOVWhoqOLi4vTmm2+6n28u6yBJP/3pT7VhwwZ99dVXkqR9+/Zp27ZtuvXWWyU1r7U4p77mnJGRIZvNpkGDBrn7DB48WDabzSfXRar43WmxWNSuXTtJzWsdysvLZbfb9fjjj6t3796VnveFteAP6dbR8ePHVVZWprCwMI/2sLAwORwOL1XVsAzD0MyZM/XTn/5UsbGxkuSea1Xr8O2337r7BAQEqH379pX6+NJaLVu2THv37tWnn35a6bnmsg7//ve/tWDBAs2cOVNPPfWUdu3apenTp8tqter+++9vNusgSU888YScTqd69OghPz8/lZWV6bnnntM999wjqfn8TJyvvubscDgUGhpaafzQ0FCfXJczZ87oySef1L333uv+Y7HNaR1eeOEFtWzZUtOnT6/yeV9YC8LSJbJYLB6PDcOo1NZUTJs2Tfv379e2bdsqPVeXdfCltcrNzdWMGTO0bt06BQYGVtuvqa9DeXm5BgwYoOeff16SFBcXp4MHD2rBggW6//773f2a+jpI0vLly/Xuu+/q/fffV+/evZWVlaXk5GRFRkbqgQcecPdrDmtxofqYc1X9fXFdSktLNWHCBJWXl2v+/Pk19m9q67Bnzx698sor2rt370XX3JjWgrfh6igkJER+fn6VEm1BQUGl/6tqCh5++GGtXr1aGzduVKdOndzt4eHhkmS6DuHh4SopKdHJkyer7dPY7dmzRwUFBYqPj1fLli3VsmVLbd68Wa+++qpatmzpnkdTX4eIiAj16tXLo61nz57KycmR1Hx+HiTp8ccf15NPPqkJEyaoT58+stvteuSRR5Samiqpea3FOfU15/DwcB09erTS+MeOHfOpdSktLdW4ceOUnZ2t9PR0966S1HzWYevWrSooKFDnzp3dvzu//fZbPfroo4qJiZHkG2tBWKqjgIAAxcfHKz093aM9PT1diYmJXqqq/hmGoWnTpmnlypX65z//qS5dung836VLF4WHh3usQ0lJiTZv3uxeh/j4ePn7+3v0yc/P14EDB3xmrW666SZ99tlnysrKch8DBgzQxIkTlZWVpa5duzaLdbjuuusq3Triq6++UnR0tKTm8/MgSd9//71atPD8Fern5+e+dUBzWotz6mvOCQkJcjqd2rVrl7vPzp075XQ6fWZdzgWlw4cPa/369erYsaPH881lHex2u/bv3+/xuzMyMlKPP/64PvnkE0k+shYN/hHyJuzcrQMWLVpkfP7550ZycrIRFBRkfPPNN94urd786le/Mmw2m7Fp0yYjPz/ffXz//ffuPn/84x8Nm81mrFy50vjss8+Me+65p8pLhTt16mSsX7/e2Lt3r3HjjTc26suja+P8q+EMo3msw65du4yWLVsazz33nHH48GHjvffeM1q3bm28++677j7NYR0MwzAeeOAB48orr3TfOmDlypVGSEiI8Zvf/MbdpymuxenTp43MzEwjMzPTkGS8/PLLRmZmpvsqr/qa88iRI42+ffsaGRkZRkZGhtGnT59Gdcm82TqUlpYaY8aMMTp16mRkZWV5/O4sLi52j9EU1sEwav6ZuNCFV8MZRuNfC8LSJXr99deN6OhoIyAgwOjfv7/7kvqmQlKVx1tvveXuU15ebvz+9783wsPDDavVagwdOtT47LPPPMb54YcfjGnTphkdOnQwWrVqZYwePdrIycm5zLOpXxeGpeayDh9++KERGxtrWK1Wo0ePHsbChQs9nm8u6+ByuYwZM2YYnTt3NgIDA42uXbsaTz/9tMeLYVNci40bN1b5O+GBBx4wDKP+5nzixAlj4sSJRtu2bY22bdsaEydONE6ePHmZZlkzs3XIzs6u9nfnxo0b3WM0hXUwjJp/Ji5UVVhq7GthMQzDaPj9KwAAAN/EZ5YAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAoJa+/fZbWa1WuVwub5cC4DIiLAFALX3wwQcaNmyYgoODvV0KgMuIsASg2Rk2bJgefvhhJScnq3379goLC9PChQtVVFSkn//852rbtq1+8pOf6OOPP/Y474MPPtCYMWMkSRaLpdIRExPjhdkAaGiEJQDN0ttvv62QkBDt2rVLDz/8sH71q19p7NixSkxM1N69ezVixAjZ7XZ9//33kqRTp05p69at7rCUn5/vPr7++mtdddVVGjp0qDenBKCBWAzDMLxdBABcTsOGDVNZWZm2bt0qSSorK5PNZtNdd92lJUuWSJIcDociIiKUkZGhwYMH6/3339dLL72kPXv2eIxlGIbuvvtu5eTkaOvWrWrVqtVlnw+AhtXS2wUAgDf07dvX/bWfn586duyoPn36uNvCwsIkSQUFBZI834I731NPPaWMjAx9+umnBCWgieJtOADNkr+/v8dji8Xi0WaxWCRJ5eXlKi0t1dq1a3X77bd7nPPuu+9qzpw5WrVqlTp16tTwRQPwCsISANRg48aNateunfr16+duy8jI0OTJk/XnP/9ZgwcP9l5xABocb8MBQA1Wr17t8Racw+HQnXfeqQkTJmjEiBFyOBySKt7Ou+KKK7xVJoAGws4SANRg9erVHm/Bffnllzp69KjefvttRUREuI9rr73Wi1UCaChcDQcAJvbu3asbb7xRx44dq/Q5JwDNAztLAGDi7Nmzeu211whKQDPGzhIAAIAJdpYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABMEJYAAABM/B+vkPHE1HcdcgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spectrum1b = spectrum1.norm(1.0).filter(min_intensity=0.2)\n", "spectrum1.norm(1.0).plot(axes=plt.gca(), mirror_spectrum=spectrum1b)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "cutting-mention", "metadata": {}, "source": [ "## Operations on pairs of spectra" ] }, { "attachments": {}, "cell_type": "markdown", "id": "geological-renewal", "metadata": {}, "source": [ "### Cosine score between two spectra" ] }, { "cell_type": "code", "execution_count": 7, "id": "loaded-accessory", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "586.6037181010105" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spectrum1.cosine_score(spectrum2)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "amber-enhancement", "metadata": {}, "source": [ "### Cosine score between mz subranges" ] }, { "cell_type": "code", "execution_count": 8, "id": "signed-halloween", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "599.529099567551" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spectrum1.filter(min_mz=500, max_mz=1000).cosine_score(spectrum2.filter(min_mz=500, max_mz=1000))" ] }, { "attachments": {}, "cell_type": "markdown", "id": "tutorial-oliver", "metadata": {}, "source": [ "### Merge two spectra, first by creating a filter spectrum" ] }, { "cell_type": "code", "execution_count": 9, "id": "continent-modem", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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hIWrdmzZtEkVRZM+ePVJaWipZWVkSFRUlbrdbXcby5cslOjpa8vPzpbi4WGbNmiWJiYly9epVtczcuXMlISFBHA6HOBwOiY+Pl8zMTHX61atXJS4uTh588EEpLi6W/Px8iY6OlpUrV97yOo2Eq7B6z1NTw6uwRrLen29Njb9bQ77wdduj20n0u+++G4cPH0ZMTIzH+HfffRf33HOPXtXg4sWLyMrKwpdffolJkyYhOTkZx44dU+t97rnn0NLSghUrVqC+vh4zZ85EXl6eeg8IAGzZsgW33XYbfvjDH6KlpQUPPvggduzY4bHn9Oabb2L16tXq1VoLFy7E1q1b1eljxozBhx9+iBUrVuC+++5DUFAQFi9ejJdfflm3dSUiMjLdHue+b98+2O12rFu3Dhs3bsTzzz+Pzz//HLt27cI///lPzJ49W49qRqSR8Dj33vPU1AA9p4EG60m5fBqv/9TWXvt8a2oALxcs0jDh67ZHt3MgCxYswDvvvIP9+/fDZDLhN7/5DU6fPo19+/YxPIiIRiBd7wPJyMhARkaGnoskIiKD0m0PZOrUqairq+s3vqGhAVOnTtWrGiIiMgjdAuT8+fNeH1nS1taGqqoqvaohIiKD8PkQ1gcffKC+/uijj6Aoivq+s7MTH3/8MaZMmeJrNUREZDA+B8j3v/99AN2P9Vi6dKnHNLPZjClTpuCVV17xtRoiIjIYnwOkq6sLABAbG4vjx48jPDzc50YREZHx6XYVVnl5uV6LIiKiYUDXy3g//vhjfPzxx6itrVX3THr85S9/0bMqIiLyM90C5Pnnn8fGjRtx7733Iioq6oaPNCciouFNtwDZtm0bduzYAbvdrtciiYjIwHS7D6S9vV191DnRreLj4ImGH90CZNmyZXjrrbf0WhwRERmcboewWltbsX37duTn5yMhIQFms9ljenZ2tl5VERGRAegWIJ999hlmzJgBADh58qReiyUiIoPSLUAKCgr0WhSNAn1/z4OIhh+fA2TRokU3LGMymbB7925fq6JRru+PVhGRf/kcIL0fnkhERKOHzwHy17/+VY92kIHx52NJb/ybGhl0u4yXiIhGFwYIERFpwgAhIiJNGCCki96PH+GjSIhGBwYI9cMwIKKbwQAhIiJNGCBERKQJA4SIiDRhgNCwwXMzdD38PRn/YIAQEZEmDBAiItKEATJK+WuXv6fenucgEdHwpdvvgRANlt4P3iMi4+AeCBH51bhx/jnxzRPvvmOAkO6uXLn2erD+MXvX4Q03DkSDjwEyCui9MeXGmYgABgh5MRLutxgJ60BkdAyQUWg0nJRmgBANPgYIDejKlZsLm94b6Rudn/A270gPNBo6/jopPxoxQEaZkbKxHooT9SMRz1+RnhggpIveG/Svvrr2WstGihs2ouGBATIKGP18gF5t6v3t+lYPo9HoM5j/F6NlT48BQobn7R/Q170cIvIdA4QG3ADzmzxpNVq+hd+s2tpr/VFb6+/W6IMBQqqef/ipU6+Na2nxbZlXrnhuRLxtSLSEVGur5/u+Fwf0XubUqdfqHskbNF/Wb6T3DQ0OBsgoc72NtT+vztJjL2c0b/SG4gnHDBjyhgEyysTF+TZ/36Dp2bD0Xm7fvQOt9dzsBmvqVCAycuAyvDfAv8aNG9rw4Wc9NBggOnn99dcRGxuLsWPHIikpCYcPH/Z3k25aZKR/7w3x9TCZr3qH1Uj/hj2UV+QN137k3tbNY4Do4J133sGaNWvwq1/9CiUlJbj//vsxb948VFRU+Ltpuuv7Tf9W/sHGjbvxnoI33g5xff/7A8/T+yqtgYyUGysHW+/P+csvvY+n0YcBooPs7Gz8+Mc/xrJly/DNb34Tv//97zF58mT84Q9/8HfT/KKh4drrmzlklpY28HQt50h6t6GHlvDq62a+nRrlG6xR2tHbUB/K8hej33ulFwaIj9rb21FUVIQ5c+Z4jJ8zZw6OHj3qp1bp50Yb995XbPV48knf6mxu9rzM8bvf9W153vRsyAba++i7Afa2t+Lr4a/RdPist77reTPr3ffzGC6XxUZGav9sjb6O/ElbH3355Zfo7OxEZJ+vt5GRkXA6nV7naWtrQ1tbm/re5XIBANxu96C0sbFxUBY7aLyF0q26TtfflJ6Q+OILz+VNm9a/jhuNczqBSZO6Xzc3AzabZ/l//7t/3V98cW0Zly4BwcH923j6NJCc7H0ZPfXefvu192430Nnp+bfQ2AiMHdv9+vJlzzb06N2WHk1N117X1l77vHra6m09++r7N9m7LdfTewPsdve/VLvv8jo7r7XjRm1zOq/V37eezs5r4/su73q8/c/1DoC+6+tt2X3bOlAf3UrbPOfp3uaIyI1n8EbIJ1VVVQJAjh496jH+hRdekK9//ete51m/fr0A4MCBAwdDDJWVlZq2f9wD8VF4eDjGjBnTb2+jtra2315Jj3Xr1uHZZ59V3zc0NCAmJgYVFRVQFGVQ2zsaud1uTJ48GZWVlQgNDfV3c0Yc9u/gGsz+FRE0NjbCdqNdxutggPgoMDAQSUlJOHDgAB555BF1/IEDB/Dwww97ncdiscBisfQbrygK/wEHUWhoKPt3ELF/B9dg9a8vX1oZIDp49tlnYbfbce+99yIlJQXbt29HRUUFli9f7u+mERENGgaIDh5//HHU1dVh48aNqK6uRlxcHPbv34+YmBh/N42IaNAwQHSyYsUKrFixQtO8FosF69ev93pYi3zH/h1c7N/BZeT+NYlovX6LiIhGM95ISEREmjBAiIhIEwYIERFpwgDxs+H8GHh/2rBhA0wmk8dgtVrV6SKCDRs2wGazISgoCOnp6SgrK/NYRltbG1atWoXw8HAEBwdj4cKFuHjx4lCviiEcOnQICxYsgM1mg8lkwnvvvecxXa/+rK+vh91uh6IoUBQFdrsdDd6efDnC3Kh/n3zyyX5/z8k9z6r5f0bsXwaIH42mx8APhrvvvhvV1dXqUFpaqk7bvHkzsrOzsXXrVhw/fhxWqxWzZ89GY6+HFK1ZswZ79+5FTk4Ojhw5gqamJmRmZqKz5+FHo0hzczMSExOxdetWr9P16s/FixfjxIkTyM3NRW5uLk6cOAG73T7o6+dvN+pfAJg7d67H3/P+/fs9phuyfzU9AIV08d3vfleWL1/uMe4b3/iG/OIXv/BTi4aP9evXS2JiotdpXV1dYrVaZdOmTeq41tZWURRFtm3bJiIiDQ0NYjabJScnRy1TVVUlAQEBkpubO6htNzoAsnfvXvW9Xv156tQpASDHjh1TyzgcDgEgZ86cGeS1Mo6+/SsisnTpUnn44YevO49R+5d7IH4y0h8DPxTOnj0Lm82G2NhYPPHEEzh37hwAoLy8HE6n06NvLRYL0tLS1L4tKipCR0eHRxmbzYa4uDj2fx969afD4YCiKJg5c6ZaJjk5GYqisM8BfPLJJ4iIiMD06dPxk5/8BLW9Ht9r1P5lgPiJlsfA0zUzZ87Erl278NFHH+FPf/oTnE4nUlNTUVdXp/bfQH3rdDoRGBiIsLCw65ahbnr1p9PpRERERL/lR0REjPo+nzdvHt588038+9//xiuvvILjx49j1qxZ6s8+GLV/eSe6n5lMJo/3ItJvHPU3b9489XV8fDxSUlJw1113YefOnerJRy19y/6/Pj3601t59nn345B6xMXF4d5770VMTAw+/PBDLFq06Lrz+bt/uQfiJ1oeA0/XFxwcjPj4eJw9e1a9GmugvrVarWhvb0d9ff11y1A3vfrTarWipqam3/IvX77MPu8jKioKMTExOHv2LADj9i8DxE96Pwa+twMHDiA1NdVPrRq+2tracPr0aURFRSE2NhZWq9Wjb9vb23Hw4EG1b5OSkmA2mz3KVFdX4+TJk+z/PvTqz5SUFLhcLhQWFqplPv30U7hcLvZ5H3V1daisrERUVBQAA/fvoJyap5uSk5MjZrNZ3njjDTl16pSsWbNGgoOD5fz58/5umuGtXbtWPvnkEzl37pwcO3ZMMjMzJSQkRO27TZs2iaIosmfPHiktLZWsrCyJiooSt9utLmP58uUSHR0t+fn5UlxcLLNmzZLExES5evWqv1bLbxobG6WkpERKSkoEgGRnZ0tJSYlcuHBBRPTrz7lz50pCQoI4HA5xOBwSHx8vmZmZQ76+Q22g/m1sbJS1a9fK0aNHpby8XAoKCiQlJUXuuOMOw/cvA8TPXnvtNYmJiZHAwED59re/LQcPHvR3k4aFxx9/XKKiosRsNovNZpNFixZJWVmZOr2rq0vWr18vVqtVLBaLPPDAA1JaWuqxjJaWFlm5cqVMmDBBgoKCJDMzUyoqKoZ6VQyhoKDA60+dLl26VET068+6ujpZsmSJhISESEhIiCxZskTq6+uHaC39Z6D+vXLlisyZM0cmTZokZrNZ7rzzTlm6dGm/vjNi//JpvEREpAnPgRARkSYMECIi0oQBQkREmjBAiIhIEwYIERFpwgAhIiJNGCBERKQJA4SIiDRhgBARkSYMECIi0oQBQjSCXLhwARaLBW63299NoVGAAUI0grz//vtIT09HaGiov5tCowADhMiA0tPTsWrVKqxZswZhYWGIjIzE9u3b0dzcjB/96EcICQnBXXfdhX/9618e873//vtYuHAhgO5fp+s7TJkyxQ9rQyMVA4TIoHbu3Inw8HAUFhZi1apV+OlPf4rHHnsMqampKC4uRkZGBux2O65cuQIAaGhowOHDh9UAqa6uVocvvvgC06ZNwwMPPODPVaIRho9zJzKg9PR0dHZ24vDhwwCAzs5OKIqCRYsWYdeuXQC6f2I2KioKDocDycnJeOutt/DKK6+gqKjIY1kigkcffRQVFRU4fPgwgoKChnx9aGS6zd8NICLvEhIS1NdjxozBxIkTER8fr47r+Z3r2tpaAJ6Hr3r75S9/CYfDgePHjzM8SFc8hEVkUGaz2eO9yWTyGGcymQAAXV1d6OjoQG5uLh5++GGPef7+979jy5Yt2Lt3L6Kjowe/0TS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