Load and verify matlab data, convert for python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16f6e1cf-c95f-4580-90ef-13e915c40a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "import pickle\n",
    "from rdkit import Chem\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cd52754-1225-4bab-ac35-58a91d0908d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "struct = sio.loadmat('odormappingdata_simple_05_2022.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46c76f11-5ffb-4e54-b691-f93e9d696c6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "allconcs_prep\n",
      "allrespmats\n",
      "odornameslist\n",
      "respmatrix_omp111L_dF\n",
      "respmatrix_omp111R_dF\n",
      "respmatrix_omp112L_dF\n",
      "respmatrix_omp112R_dF\n",
      "respmatrix_omp113L_dF\n",
      "respmatrix_omp113R_dF\n",
      "respmatrix_omp114L_dF\n",
      "respmatrix_omp114R_dF\n"
     ]
    }
   ],
   "source": [
    "for k in struct.keys():\n",
    "    if not k.startswith(\"_\"):\n",
    "        print(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a8f0041-4970-457e-8830-e3b2dde703c6",
   "metadata": {},
   "source": [
    "## load concentrations\n",
    "from the description:\n",
    "\n",
    " 'allconcs_prep': Calculated final delivered concentration of each odorant, indexed in the same order as response matrices and odornames list, for each mouse, in mols/L. Note that concentrations estimated from vapor pressure, calibrated air dilution, and liquid dilution, assuming ideal behavior. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae02c0c0-d477-4bae-a4ec-e08223d77ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "allconcs_prep = struct['allconcs_prep']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f00429d7-e59c-48da-858b-8dabe496ce99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(185, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allconcs_prep.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28293e8a-534b-43c3-b773-48bba36e6afe",
   "metadata": {},
   "source": [
    "## load response matrices\n",
    "From the description: \n",
    "\n",
    "'allrespmatrices_dF': Structure array consists of odorant response matrices and ROI positions for each OB. \n",
    "Variables are indexed in the following order: \n",
    " 1. omp111L, \n",
    " 2. omp111R, \n",
    " 3. omp112L, \n",
    " 4. omp112R, \n",
    " 5. omp113L, \n",
    " 6. omp113R, \n",
    " 7. omp114L, \n",
    " 8. omp114R. \n",
    " \n",
    "All odorants are in the same order for all response matrices (see ‘odornameslist’ variable). \n",
    "\n",
    "ROI positions indicate centroid of each ROI, after visual registration by aligning the midline and caudal sinus. Units are microns, reference (zero) is midline (for ‘Xpos’) and caudal sinus (for ‘Ypos’). Note that the Xposition for ROIs from the left OB is negative relative to midline.\n",
    "\n",
    "\n",
    "the fields in each of the struct elements are:\n",
    "1. respmat\n",
    "2. lsparseness\n",
    "3. numeffodorsperglom\n",
    "4. roidynrange\n",
    "5. normrespmat\n",
    "6. dim\n",
    "7. xnormrespmat\n",
    "8. maxgloms\n",
    "9. psparseness\n",
    "10. numrespglomsperodor\n",
    "11. rho_corrx\n",
    "12. ROIPos\n",
    "13. ROImaxresp\n",
    "\n",
    "Note that `ROIPos` are stored as a matlab table which `scipy.io.loadmat` cannot load. Therefore, these are provided in separate files here, see below. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bdedf62",
   "metadata": {},
   "outputs": [],
   "source": [
    "allrespmats = struct['allrespmats']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fd35856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1004"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allresprows = sum([allrespmats[0,i][0].shape[0] for i in range(allrespmats.shape[1])])\n",
    "allresprows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0887051d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ranges = np.zeros((allrespmats.shape[1],2),dtype=int)\n",
    "allresp = np.zeros((allresprows,187))\n",
    "allrespnorm = np.zeros_like(allresp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fd97631",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 0\n",
    "for i in range(allrespmats.shape[1]):\n",
    "    assert allrespmats[0,i][0].shape == allrespmats[0,i][4].shape\n",
    "    ysize = allrespmats[0,i][0].shape[0]\n",
    "    allresp[y:y+ysize,:] = allrespmats[0,i][0]\n",
    "    allrespnorm[y:y+ysize,:] = allrespmats[0,i][4]\n",
    "    ranges[i,:] = [y,y+ysize]\n",
    "    y += ysize"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b05b797",
   "metadata": {},
   "source": [
    "Finally, we should add a list of names of mice so we can tell them apart later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf301831",
   "metadata": {},
   "outputs": [],
   "source": [
    "mousenames = [\n",
    "    \"omp111L\",\n",
    "    \"omp111R\",\n",
    "    \"omp112L\",\n",
    "    \"omp112R\",\n",
    "    \"omp113L\",\n",
    "    \"omp113R\",\n",
    "    \"omp114L\",\n",
    "    \"omp114R\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d560efb",
   "metadata": {},
   "source": [
    "### load ROIPos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee1dc11b",
   "metadata": {},
   "outputs": [],
   "source": [
    "roiposmat = sio.loadmat('roipos.mat')\n",
    "roipos = np.zeros((allresprows,2))\n",
    "for i in range(8):\n",
    "    roipos_i = roiposmat['ROIPos{:d}'.format(i)]\n",
    "    roiposrows_i = roipos_i.shape[0]\n",
    "    allresprows_i = allrespmats[0,i][0].shape[0]\n",
    "    assert roiposrows_i == allresprows_i\n",
    "    roipos[ranges[i,0]:ranges[i,1],:] = roipos_i\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "065cd9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1441e0d90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(roipos[:,0],roipos[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e16f9c6",
   "metadata": {},
   "source": [
    "## odor names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3cf5e8c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "odornameslist = [on[0][0] for on in struct['odornameslist']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "068f79c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C7 propionic acid',\n",
       " 'C1 butyric acid',\n",
       " 'C2 2-methylbutyric acid',\n",
       " 'C3 valeric acid',\n",
       " 'C6 isovaleric acid',\n",
       " 'C9 2-methyl-2-pentenoic acid',\n",
       " 'C5 hexanoic acid',\n",
       " 'C4 heptanoic acid',\n",
       " 'D15 methacrolein',\n",
       " 'D16 butyraldehyde',\n",
       " 'D13 isobutyraldehyde',\n",
       " 'D1 2-methylbutyraldehyde',\n",
       " 'D2 trans-2-methyl-2-butenal',\n",
       " 'D12 methional',\n",
       " 'D8 valeraldehyde',\n",
       " 'D7 isovaleraldehyde',\n",
       " 'D5 2-methylvaleraldehyde',\n",
       " 'D14 2-methyl-2-pentenal',\n",
       " 'D6 hexanal',\n",
       " 'D3 heptanal',\n",
       " 'D4 octanal',\n",
       " 'D9 trans-2-nonenal',\n",
       " 'D18 trans-2,cis-6-nonadienal',\n",
       " 'D10 trans-2-dodecenal',\n",
       " 'D17 2-hexyl-2-decenal',\n",
       " 'E1 butyl acetate',\n",
       " 'E7 S-methyl thiobutanoate',\n",
       " 'E11 isoamyl acetate',\n",
       " 'E8 hexyl acetate',\n",
       " 'E15 3-mercaptohexyl acetate',\n",
       " 'E2 ethyl butyrate',\n",
       " 'E9 methyl 2-methylbutyrate',\n",
       " 'E3 vinyl butyrate',\n",
       " 'E4 methyl valerate',\n",
       " 'E12 1-octen-3-yl butyrate',\n",
       " 'E14 methyl tiglate',\n",
       " 'E5 ethyl tiglate',\n",
       " 'E10 isopropyl tiglate',\n",
       " 'E6 hexyl tiglate',\n",
       " 'E13 isobornyl isovalerate',\n",
       " 'F1 furfuryl mercaptan',\n",
       " 'F2 2-pentylfuran',\n",
       " 'F4 difurfuryl disulfide',\n",
       " 'F5 5-ethyl-4-hydroxy-2-methyl-3(2H)-furanone',\n",
       " 'F6 5-methylfurfural',\n",
       " 'F7 furfuryl methyl sulfide',\n",
       " 'S19 2-methyl-3-tetrahydrofuranthiol',\n",
       " 'F8 1-furfurylpyrrole',\n",
       " 'K13 diacetyl',\n",
       " 'K5 2-butanone',\n",
       " 'K6 2-pentanone',\n",
       " 'K3 4-methyl-3-penten-2-one',\n",
       " 'K1 2-hexanone',\n",
       " 'K15 3-hepten-2-one',\n",
       " 'K17 5-methyl-2-hepten-4-one',\n",
       " 'K2 2-octanone',\n",
       " 'K11 3-octen-2-one',\n",
       " 'K14 2-nonanone',\n",
       " 'L2 1-hexanol',\n",
       " 'L8 cis-3-hexenol',\n",
       " 'L11 3-(methylthio)-1-hexanol',\n",
       " 'L4 geraniol',\n",
       " 'L5 champignol',\n",
       " 'K16 ethyl maltol',\n",
       " 'L3 (+-)-geosmin',\n",
       " 'N8 ethylenediamine',\n",
       " 'N6 butylamine',\n",
       " 'N11 2-methylbutylamine',\n",
       " 'N15 N-butyldimethylamine',\n",
       " 'N4 isopentylamine',\n",
       " 'N9 cadaverine',\n",
       " 'N7 octylamine',\n",
       " 'N13 N,N-dimethyloctylamine',\n",
       " 'N1 cyclohexylamine',\n",
       " 'N14 N,N-dimethylcyclohexylamine',\n",
       " 'N2 phenethylamine',\n",
       " 'R34 benzylamine',\n",
       " 'R33 tyramine',\n",
       " 'R35 N,N-dimethyl-2-phenethylamine',\n",
       " 'R39 3-phenylpropylamine',\n",
       " 'N3 N-methyl piperidine',\n",
       " 'P12 2-acetylpyridine',\n",
       " 'P13 4-tert-butylpyridine',\n",
       " 'P10 5,6,7,8-tetrahydroquinoxaline',\n",
       " 'P15 5-methylquinoxaline',\n",
       " 'R19 indole',\n",
       " 'R25 3-methylindole',\n",
       " 'M1 5-methyl heptan-3-one oxime',\n",
       " 'O1 gamma-undecalactone',\n",
       " 'P26 pyrazine',\n",
       " 'P14 2-acetylpyrazine',\n",
       " 'P25 2-methylpyrazine',\n",
       " 'P23 2-methoxypyrazine',\n",
       " 'P27 2-ethylpyrazine',\n",
       " 'P1 2,3-diethylpyrazine',\n",
       " 'P24 2-chloropyrazine',\n",
       " 'P2 2-methoxy-3-methylpyrazine',\n",
       " 'P4 2-ethyl-3-methylpyrazine',\n",
       " 'P16 2,3-dimethylpyrazine',\n",
       " 'P17 2,5-dimethylpyrazine',\n",
       " 'P8 2,6-dimethylpyrazine',\n",
       " 'P9 2,3,5-trimethylpyrazine',\n",
       " 'P11 2,3,5,6-tetramethylpyrazine',\n",
       " 'P29 2-ethyl-5-methylpyrazine',\n",
       " 'P28 2-ethyl-3-methoxypyrazine',\n",
       " 'P3 2-methoxy-3(5 or 6)-isopropylpyrazine',\n",
       " 'P5 2-isobutyl-3-methoxypyrazine',\n",
       " 'P6 2-methoxy-3-(1-methylpropyl)pyrazine',\n",
       " 'P7 2-isopropyl-3-methoxypyrazine',\n",
       " 'P19 2-acetyl-3,(5 or 6)-dimethylpyrazine',\n",
       " 'P20 2-isobutyl-3-methylpyrazine',\n",
       " 'P21 2-acetyl-3-ethylpyrazine',\n",
       " 'P22 2-acetyl-3-methylpyrazine',\n",
       " 'S18 pyrazineethanethiol',\n",
       " 'P18 5H-5-methyl-6,7-dihydrocyclopenta[b]pyrazine',\n",
       " 'R1 benzaldehyde',\n",
       " 'D11 trans-cinnamaldehyde',\n",
       " 'R2 cuminaldehyde',\n",
       " 'R27 p-anisaldehyde',\n",
       " 'R8 vanillin',\n",
       " 'R41 piperonal',\n",
       " 'R11 acetophenone',\n",
       " 'R12 2-hydroxyacetophenone',\n",
       " 'R13 2,4-dimethylacetophenone',\n",
       " 'R14 4-aminoacetophenone',\n",
       " 'R15 4-methylacetophenone',\n",
       " 'R21 4-methoxyacetophenone',\n",
       " 'R30 2-methylacetophenone',\n",
       " 'R31 propiophenone',\n",
       " 'R32 butyrophenone',\n",
       " 'R17 4-phenyl-2-butanone',\n",
       " 'K12 4-(4-hydroxyphenyl)-2-butanone',\n",
       " 'R3 methyl salicylate',\n",
       " 'R4 methyl benzoate',\n",
       " 'R26 benzyl acetate',\n",
       " 'R28 ethyl benzoate',\n",
       " 'R20 benzyl benzoate',\n",
       " 'R29 methyl anthranilate',\n",
       " 'R5 dimethyl anthranilate',\n",
       " 'R37 phenylacetate',\n",
       " 'R22 ethyl phenylacetate',\n",
       " 'R16 allyl phenylacetate',\n",
       " 'R40 phenyl propionate',\n",
       " 'R9 4-methylanisole',\n",
       " 'R10 m-cresol',\n",
       " 'R23 guaiacol',\n",
       " 'R24 2,6-dimethoxyphenol',\n",
       " 'R6 eugenol',\n",
       " 'L10 isoeugenol',\n",
       " 'R36 methyl eugenol',\n",
       " 'R38 methyl isoeugenol',\n",
       " 'R7 eugenyl acetate',\n",
       " 'R44 elemicin',\n",
       " 'R43 carvacrol',\n",
       " 'R42 coumarin',\n",
       " 'S1 2-isobutylthiazole',\n",
       " 'S2 2-acetylthiazole',\n",
       " 'S3 2-isopropyl-4-methylthiazole',\n",
       " 'S5 2-methyl-2-thiazoline',\n",
       " 'S7 2,4,5-trimethylthiazole',\n",
       " 'S13 ethyl-2,5-dihydro-4-methylthiazole',\n",
       " 'S17 4-methylthiazole',\n",
       " 'S16 dimethyl trisulfide',\n",
       " 'S11 allyl disulfide',\n",
       " 'S14 4-methoxy-2-methyl-2-butanethiol',\n",
       " 'S15 2-methyl-4-propyl-1,3-oxathiane',\n",
       " 'T5 fenchol',\n",
       " 'L7 terrasol',\n",
       " 'T3 alpha-pinene',\n",
       " 'T4 1,8-cineole',\n",
       " 'X1 (R)-(+)-limonene',\n",
       " 'K10 L-carvone',\n",
       " 'K7 beta-ionone',\n",
       " 'K8 beta-damascone',\n",
       " 'K9 damascenone',\n",
       " 'L1 (+-)-menthol',\n",
       " 'K4 menthone',\n",
       " 'K18 (R)-(+)-pulegone',\n",
       " 'F9 (+)-menthofuran',\n",
       " 'K19 (+)-isomenthone',\n",
       " 'L9 (+)-neomenthol',\n",
       " 'T2 nootkatone',\n",
       " 'F3 (-)-ambroxide',\n",
       " 'Y2 myrcene',\n",
       " 'Y1 1,3,5-undecatriene',\n",
       " 'Z4 empty',\n",
       " 'Z3 triglyceride']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odornameslist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b3121a",
   "metadata": {},
   "source": [
    "## save for convenience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2945f7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "datadict = {\n",
    "    'allresp': allresp,\n",
    "    'allrespnorm': allrespnorm,\n",
    "    'roipos': roipos, \n",
    "    'ranges': ranges,\n",
    "    'mousenames': mousenames,\n",
    "    'odornameslist': 'odornameslist'\n",
    "}\n",
    "with open('elife22data_python.pkl','wb') as f:\n",
    "    pickle.dump(datadict, f)"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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