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synthetic isotope pattern default to
averagine
1. examples/synthetics.ipynb
+ Started with example on synthetics 🔵⚪⚪⚪⚪⚪⚪⚪⚪⚪
2. src/pystoms/synthetics.py:
+ Started to refactor synthetics module 🔵⚪⚪⚪⚪⚪⚪⚪⚪⚪- Loading branch information
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pystoms.synthetics as syn\n", | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "import scipy as sp" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "iso = syn.Isotopic_Averagine_Distribution()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "ename": "ValueError", | ||
| "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||
| "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", | ||
| "Cell \u001b[0;32mIn [8], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m num_peaks \u001b[39m=\u001b[39m \u001b[39m6\u001b[39m\n\u001b[1;32m 5\u001b[0m x \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m30900\u001b[39m,\u001b[39m32000\u001b[39m)\u001b[39m/\u001b[39m\u001b[39m100\u001b[39m\n\u001b[0;32m----> 6\u001b[0m iso\u001b[39m.\u001b[39;49mpdf(x,mass,charge,sigma,num_peaks)\n", | ||
| "File \u001b[0;32m~/miniconda3/envs/pymc_dev/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:2094\u001b[0m, in \u001b[0;36mrv_continuous.pdf\u001b[0;34m(self, x, *args, **kwds)\u001b[0m\n\u001b[1;32m 2092\u001b[0m goodargs \u001b[39m=\u001b[39m argsreduce(cond, \u001b[39m*\u001b[39m((x,)\u001b[39m+\u001b[39margs\u001b[39m+\u001b[39m(scale,)))\n\u001b[1;32m 2093\u001b[0m scale, goodargs \u001b[39m=\u001b[39m goodargs[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m], goodargs[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n\u001b[0;32m-> 2094\u001b[0m place(output, cond, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_pdf(\u001b[39m*\u001b[39;49mgoodargs) \u001b[39m/\u001b[39m scale)\n\u001b[1;32m 2095\u001b[0m \u001b[39mif\u001b[39;00m output\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 2096\u001b[0m \u001b[39mreturn\u001b[39;00m output[()]\n", | ||
| "File \u001b[0;32m~/Workspaces/pystoms/src/pystoms/synthetics.py:77\u001b[0m, in \u001b[0;36mIsotopic_Averagine_Distribution._pdf\u001b[0;34m(self, x, mass, charge, sigma, num_peaks)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[39m\"\"\"Calculates probability density function (PDF)\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \n\u001b[1;32m 51\u001b[0m \u001b[39mOverwrites scipy.rv_continuous._pdf. Is internally called by Isotopic_Averagine_Distribution.pdf\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[39m Supported are \"averagine\" and \"non_averagine\".\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 76\u001b[0m \u001b[39m# means of normal distributions are [mass/charge,(mass+1)/charge,...,(mass+num_peaks-1)/charge]\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m means \u001b[39m=\u001b[39m (np\u001b[39m.\u001b[39mrepeat(mass, num_peaks) \u001b[39m+\u001b[39m np\u001b[39m.\u001b[39;49marange(num_peaks)) \u001b[39m/\u001b[39m charge\n\u001b[1;32m 78\u001b[0m \u001b[39m# sigmas of normal distributions are sigma for all\u001b[39;00m\n\u001b[1;32m 79\u001b[0m sigmas \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrepeat(sigma, num_peaks)\n", | ||
| "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "mass = 310.1\n", | ||
| "charge = 2\n", | ||
| "sigma = 1\n", | ||
| "num_peaks = 6\n", | ||
| "x = np.arange(30900,32000)/100\n", | ||
| "iso.pdf(x,mass,charge,sigma,num_peaks)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 9, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "ename": "ValueError", | ||
| "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||
| "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", | ||
| "Cell \u001b[0;32mIn [9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m syn_pep \u001b[39m=\u001b[39m syn\u001b[39m.\u001b[39;49mSyntheticPeptideFeature(\u001b[39m310\u001b[39;49m,\u001b[39m2\u001b[39;49m,\u001b[39m12\u001b[39;49m,\u001b[39m1\u001b[39;49m,\u001b[39m1\u001b[39;49m,)\n", | ||
| "File \u001b[0;32m~/Workspaces/pystoms/src/pystoms/synthetics.py:294\u001b[0m, in \u001b[0;36mSyntheticPeptideFeature.__init__\u001b[0;34m(self, mass, charge, emg_μ, emg_σ, emg_λ, elution_noise, isotopic_noise, num_peaks, isotopic_sigma, scan_intervall, ms_resolution, isotopic_pattern_model, correctBaseline)\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39misotopic_sigma \u001b[39m=\u001b[39m isotopic_sigma\n\u001b[1;32m 293\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcorrectBaseline \u001b[39m=\u001b[39m correctBaseline\n\u001b[0;32m--> 294\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfeature_data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_generate()\n", | ||
| "File \u001b[0;32m~/Workspaces/pystoms/src/pystoms/synthetics.py:342\u001b[0m, in \u001b[0;36mSyntheticPeptideFeature._generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[39m# from first m/z peak -1 to last m/z peak +1\u001b[39;00m\n\u001b[1;32m 337\u001b[0m m_z_axis \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\n\u001b[1;32m 338\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmass \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcharge \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m,\n\u001b[1;32m 339\u001b[0m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmass \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcharge \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m \u001b[39m+\u001b[39m \u001b[39m1\u001b[39m \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresolution,\n\u001b[1;32m 340\u001b[0m \u001b[39m1\u001b[39m \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresolution,\n\u001b[1;32m 341\u001b[0m )\n\u001b[0;32m--> 342\u001b[0m m_z_intensity \u001b[39m=\u001b[39m iso\u001b[39m.\u001b[39;49mpdf(\n\u001b[1;32m 343\u001b[0m m_z_axis, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmass, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcharge, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49misotopic_sigma, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mn\n\u001b[1;32m 344\u001b[0m )\n\u001b[1;32m 345\u001b[0m measurements_per_frame \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(m_z_axis)\n\u001b[1;32m 347\u001b[0m \u001b[39m# for each rt timepoint draw new noise samples and add them on isotopic dist.\u001b[39;00m\n\u001b[1;32m 348\u001b[0m \u001b[39m# then multiply with elution profile at this timepoint\u001b[39;00m\n", | ||
| "File \u001b[0;32m~/miniconda3/envs/pymc_dev/lib/python3.10/site-packages/scipy/stats/_distn_infrastructure.py:2094\u001b[0m, in \u001b[0;36mrv_continuous.pdf\u001b[0;34m(self, x, *args, **kwds)\u001b[0m\n\u001b[1;32m 2092\u001b[0m goodargs \u001b[39m=\u001b[39m argsreduce(cond, \u001b[39m*\u001b[39m((x,)\u001b[39m+\u001b[39margs\u001b[39m+\u001b[39m(scale,)))\n\u001b[1;32m 2093\u001b[0m scale, goodargs \u001b[39m=\u001b[39m goodargs[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m], goodargs[:\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n\u001b[0;32m-> 2094\u001b[0m place(output, cond, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_pdf(\u001b[39m*\u001b[39;49mgoodargs) \u001b[39m/\u001b[39m scale)\n\u001b[1;32m 2095\u001b[0m \u001b[39mif\u001b[39;00m output\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 2096\u001b[0m \u001b[39mreturn\u001b[39;00m output[()]\n", | ||
| "File \u001b[0;32m~/Workspaces/pystoms/src/pystoms/synthetics.py:77\u001b[0m, in \u001b[0;36mIsotopic_Averagine_Distribution._pdf\u001b[0;34m(self, x, mass, charge, sigma, num_peaks)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[39m\"\"\"Calculates probability density function (PDF)\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \n\u001b[1;32m 51\u001b[0m \u001b[39mOverwrites scipy.rv_continuous._pdf. Is internally called by Isotopic_Averagine_Distribution.pdf\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[39m Supported are \"averagine\" and \"non_averagine\".\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 76\u001b[0m \u001b[39m# means of normal distributions are [mass/charge,(mass+1)/charge,...,(mass+num_peaks-1)/charge]\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m means \u001b[39m=\u001b[39m (np\u001b[39m.\u001b[39mrepeat(mass, num_peaks) \u001b[39m+\u001b[39m np\u001b[39m.\u001b[39;49marange(num_peaks)) \u001b[39m/\u001b[39m charge\n\u001b[1;32m 78\u001b[0m \u001b[39m# sigmas of normal distributions are sigma for all\u001b[39;00m\n\u001b[1;32m 79\u001b[0m sigmas \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mrepeat(sigma, num_peaks)\n", | ||
| "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "syn_pep = syn.SyntheticPeptideFeature(310,2,12,1,1,)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 13, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "norm = sp.stats.norm()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "pymc_dev", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.6" | ||
| }, | ||
| "orig_nbformat": 4, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "e72a8d8ce8364635d68c11ed264ecd033ec926576ac60b4957a81f7389674ed5" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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