Mahdi_Results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df3b72b8-1bfc-48d1-9e9d-98f95350fb32",
   "metadata": {},
   "source": [
    "# Mahdi Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b713840a-cebc-40df-a881-65bce1419821",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from fmc import *\n",
    "from pathlib import Path\n",
    "from operator import itemgetter\n",
    "\n",
    "data = Path(\"data\")\n",
    "figures = Path(\"figures\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55155c40-d294-4b38-b060-b638ada806d2",
   "metadata": {},
   "source": [
    "### Define Samples, Filenames, Labels and Mass Spec Peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17ce8d99-0e9f-402e-bc1d-7f25d919da00",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = ['Control','E1','E2','E3','E4','E5','E6','M1','M2','M3','M4','M5','M6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "459994bd-2a1f-429b-a8e2-5b2afec67fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Control': PosixPath('data/2024_04_08_C-mer_Control_0_A9_1.txt'),\n",
       " 'E1': PosixPath('data/2024_04_08_E1_K2S2O5_ETNH2_pH_7.7_30_min_0_C8_1.txt'),\n",
       " 'E2': PosixPath('data/2024_04_08_E2_K2S2O5_ETNH2_pH_7.5_30_min_0_D9_1.txt'),\n",
       " 'E3': PosixPath('data/2024_04_08_E3_K2S2O5_ETNH2_pH_7.3_30_min_0_E9_1.txt'),\n",
       " 'E4': PosixPath('data/2024_04_08_E4_K2S2O5_ETNH2_pH_7.7_60_min_0_I8_1.txt'),\n",
       " 'E5': PosixPath('data/2024_04_08_E5_K2S2O5_ETNH2_pH_7.5_60_min_0_J8_1.txt'),\n",
       " 'E6': PosixPath('data/2024_04_08_E6_K2S2O5_ETNH2_pH_7.3_60_min_0_K9_1.txt'),\n",
       " 'M1': PosixPath('data/2024_04_08_M1_K2S2O5_MENH2_pH_7.7_30_min_0_F9_1.txt'),\n",
       " 'M2': PosixPath('data/2024_04_08_M2_K2S2O5_MENH2_pH_7.5_30_min_0_G10_1.txt'),\n",
       " 'M3': PosixPath('data/2024_04_08_M3_K2S2O5_MENH2_pH_7.3_30_min_0_H9_1.txt'),\n",
       " 'M4': PosixPath('data/2024_04_08_M4_K2S2O5_MENH2_pH_7.7_60_min_0_L9_1.txt'),\n",
       " 'M5': PosixPath('data/2024_04_08_M5_K2S2O5_MENH2_pH_7.5_60_min_0_M7_1.txt'),\n",
       " 'M6': PosixPath('data/2024_04_08_M6_K2S2O5_MENH2_pH_7.3_60_min_0_N7_1.txt')}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for correct correspondence between samples and data.glob('*.txt') \n",
    "fns = {s: fn for s, fn in zip(samples,sorted(data.glob('*.txt')))}\n",
    "fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43690a31-e613-44d1-a50f-bb3a35bd6951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Control': '2024_04_08_C-mer_Control_0_A9_1min',\n",
       " 'E1': '2024_04_08_E1_K2S2O5_ETNH2_pH_7.7_30_min',\n",
       " 'E2': '2024_04_08_E2_K2S2O5_ETNH2_pH_7.5_30_min',\n",
       " 'E3': '2024_04_08_E3_K2S2O5_ETNH2_pH_7.3_30_min',\n",
       " 'E4': '2024_04_08_E4_K2S2O5_ETNH2_pH_7.7_60_min',\n",
       " 'E5': '2024_04_08_E5_K2S2O5_ETNH2_pH_7.5_60_min',\n",
       " 'E6': '2024_04_08_E6_K2S2O5_ETNH2_pH_7.3_60_min',\n",
       " 'M1': '2024_04_08_M1_K2S2O5_MENH2_pH_7.7_30_min',\n",
       " 'M2': '2024_04_08_M2_K2S2O5_MENH2_pH_7.5_30_min',\n",
       " 'M3': '2024_04_08_M3_K2S2O5_MENH2_pH_7.3_30_min',\n",
       " 'M4': '2024_04_08_M4_K2S2O5_MENH2_pH_7.7_60_min',\n",
       " 'M5': '2024_04_08_M5_K2S2O5_MENH2_pH_7.5_60_min',\n",
       " 'M6': '2024_04_08_M6_K2S2O5_MENH2_pH_7.3_60_min'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = {s:fn.stem.split('min')[0]+'min' for s, fn in fns.items()}\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8e66a01-6e59-41c0-afec-9dd7252776e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'E1': 'pH 7.7 30 min',\n",
       " 'E2': 'pH 7.5 30 min',\n",
       " 'E3': 'pH 7.3 30 min',\n",
       " 'E4': 'pH 7.7 60 min',\n",
       " 'E5': 'pH 7.5 60 min',\n",
       " 'E6': 'pH 7.3 60 min',\n",
       " 'M1': 'pH 7.7 30 min',\n",
       " 'M2': 'pH 7.5 30 min',\n",
       " 'M3': 'pH 7.3 30 min',\n",
       " 'M4': 'pH 7.7 60 min',\n",
       " 'M5': 'pH 7.5 60 min',\n",
       " 'M6': 'pH 7.3 60 min'}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pat = r'(pH.*)'\n",
    "labels_short = {s:  ' '.join(re.search(pat, l).group().split('_')) for s,l in labels.items() if re.search(pat, l)}\n",
    "labels_short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0be7efb1-d98c-4764-bafd-0b03467c0717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Control': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([ 80, 125, 160, ...,  52,  87, 118])),\n",
       " 'E1': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([24, 45, 61, ..., 35, 47, 52])),\n",
       " 'E2': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([13,  7,  8, ..., 11, 16, 17])),\n",
       " 'E3': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([14,  3,  6, ..., 32, 20, 15])),\n",
       " 'E4': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([118, 101,  87, ..., 169, 151, 133])),\n",
       " 'E5': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([36, 49, 54, ..., 61, 44, 43])),\n",
       " 'E6': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([ 31,  13,  19, ..., 110,  96,  51])),\n",
       " 'M1': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([11,  7, 32, ..., 34, 35, 26])),\n",
       " 'M2': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([ 52,  49,  35, ..., 173, 172, 200])),\n",
       " 'M3': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([ 84, 123, 157, ...,  42,  40,  71])),\n",
       " 'M4': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([ 91, 135, 106, ...,  47,  42,  41])),\n",
       " 'M5': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([14,  8,  7, ...,  8, 21, 40])),\n",
       " 'M6': (array([1400.03 , 1400.043, 1400.056, ..., 1739.985, 1740.   , 1740.014]),\n",
       "  array([112,  82,  66, ...,  51,  70,  82]))}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peaks = {s: read_ms_txt(fn) for s, fn in fns.items()}\n",
    "peaks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bd30987-f491-4680-bf54-ee1643113a30",
   "metadata": {},
   "source": [
    "### Choose Sample Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2e44420-ccc2-43e7-867e-58e6cd1c233c",
   "metadata": {},
   "outputs": [],
   "source": [
    "e1_m3_30 = ['Control','E1','E2','E3','M1','M2','M3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5f919f4-20e3-468c-ab91-ffa0d7801a8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "e4_m6_60 = ['Control','E4','E5','E6','M4','M5','M6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24806819-29a2-418e-9f69-d754775c8f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "es = ['E1','E2','E3','E4','E5','E6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "239ba416-c952-46b4-8844-f6f8e22098bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ms = ['M1','M2','M3','M4','M5','M6']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3008bbc1-c825-4bfa-9102-e603bf5e9952",
   "metadata": {},
   "source": [
    "### Plot Mass Spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3965699b-f5ca-4d37-bda0-bff0f35d79b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mplot_spec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpeaks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msave_as\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mxrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mannotate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
       "\u001b[0;31mFile:\u001b[0m      ~/uofc/Projects/BAT-seq/Experiments/2024_04_08/ms_peaks/fmc.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spec?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ed5a402-cdf0-4e88-9841-9846fc5c82fc",
   "metadata": {},
   "outputs": [
    {
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      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_spec(peaks['Control'],title='Control',figsize=(20,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f3107ae-9af3-4aec-a94d-50cadf9dcb7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mplot_multiple_specs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpeaks_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlabels_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msave_as\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mxrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mannotate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m <no docstring>\n",
       "\u001b[0;31mFile:\u001b[0m      ~/uofc/Projects/BAT-seq/Experiments/2024_04_08/ms_peaks/fmc.py\n",
       "\u001b[0;31mType:\u001b[0m      function"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_specs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2c10c8df-4afd-412e-8906-14d1260c8327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_specs(itemgetter(*e1_m3_30)(peaks), itemgetter(*e1_m3_30)(labels))\n",
    "plot_multiple_specs(itemgetter(*e1_m3_30)(peaks), itemgetter(*e1_m3_30)(labels), save_as = figures/'ms_E1_M3_30_min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f82c730-d899-4128-a9ab-c1d36d18add5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_specs(itemgetter(*e4_m6_60)(peaks), itemgetter(*e4_m6_60)(labels))\n",
    "plot_multiple_specs(itemgetter(*e4_m6_60)(peaks), itemgetter(*e4_m6_60)(labels), save_as = figures/'ms_E4_M6_60_min')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86283552-a3d7-4009-a88b-af23a68eeb18",
   "metadata": {},
   "source": [
    "### Calculate Efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "50c30d1c-0e53-4b90-85ff-39dc81d2b68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.71, 0.63, 0.71, 0.94, 0.93, 0.97]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C-> 4eC\n",
    "es_eff = list(map(lambda s: efficiency_fec(s), itemgetter(*es)(peaks)))\n",
    "es_eff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5dee95f7-7328-4575-9760-c90f7ffd9a20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.63, 0.71, 0.8, 0.94, 0.92, 0.97]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C -> 4mc\n",
    "ms_eff = list(map(lambda s: efficiency_fmc(s), itemgetter(*ms)(peaks)))\n",
    "ms_eff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0463d601-e2d9-4c87-9ad7-8897cedd4a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('pH 7.7 30 min',\n",
       " 'pH 7.5 30 min',\n",
       " 'pH 7.3 30 min',\n",
       " 'pH 7.7 60 min',\n",
       " 'pH 7.5 60 min',\n",
       " 'pH 7.3 60 min')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itemgetter(*es)(labels_short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ef205e24-3c9f-40ab-bd34-3f14d86d3263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'E1': 'E1: pH 7.7 30 min',\n",
       " 'E2': 'E2: pH 7.5 30 min',\n",
       " 'E3': 'E3: pH 7.3 30 min',\n",
       " 'E4': 'E4: pH 7.7 60 min',\n",
       " 'E5': 'E5: pH 7.5 60 min',\n",
       " 'E6': 'E6: pH 7.3 60 min',\n",
       " 'M1': 'M1: pH 7.7 30 min',\n",
       " 'M2': 'M2: pH 7.5 30 min',\n",
       " 'M3': 'M3: pH 7.3 30 min',\n",
       " 'M4': 'M4: pH 7.7 60 min',\n",
       " 'M5': 'M5: pH 7.5 60 min',\n",
       " 'M6': 'M6: pH 7.3 60 min'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "81819e9c-871d-483f-9427-c22ee1a39794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.DataFrame({'Labels': itemgetter(*es)(labels_short), 'EthylAmine': es_eff, 'MethlyAmine': ms_eff})\n",
    "data_long = pd.melt(data, id_vars='Labels', var_name='Metric', value_name='Value')\n",
    "plt.figure(figsize=(10, 4)) \n",
    "sns.barplot(x='Labels', y='Value', hue='Metric', data=data_long, width=.3)\n",
    "plt.ylabel('(%) Efficiency')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.savefig('figures/efficiency.png')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "435a0b69-753b-4418-9802-42690cc9086e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Labels  EthylAmine  MethlyAmine\n",
      "0  pH 7.7 30 min        0.71         0.63\n",
      "1  pH 7.5 30 min        0.63         0.71\n",
      "2  pH 7.3 30 min        0.71         0.80\n",
      "3  pH 7.7 60 min        0.94         0.94\n",
      "4  pH 7.5 60 min        0.93         0.92\n",
      "5  pH 7.3 60 min        0.97         0.97\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "a010307b-f011-4cf4-a475-e8f03cc5a1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('figures/efficiency.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5e06b670-fad6-4dc5-90c0-83d746320973",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_markdown('figures/efficiency.md',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55e7c22d-a6f4-4d3b-b7be-1179a7231044",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}