SMG-epi-blasticidin-calc-ratio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5618354b-6d2b-4107-bdaf-76387afa00cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time, os, sys, glob\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.dpi'] = 300\n",
    "# from cellpose import models, io\n",
    "# from cellpose import plot, utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "058cedfc-62a4-444c-b9e9-f78e3fcfe130",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pos_ratio(npy_file, channel=0, threshold=None):\n",
    "    '''\n",
    "    Inputs:\n",
    "    -------\n",
    "        - npy_file: segmentation file output from Cellpose\n",
    "        - threshold: the value beyond which a segmentation is called positive\n",
    "        - channel: the target channel to evaluate\n",
    "        \n",
    "    Outputs:\n",
    "    --------\n",
    "        - pos_ratio: a float number of the ratio of postivie segmentations\n",
    "    '''\n",
    "    dat = np.load(npy_file, allow_pickle=True).item()\n",
    "    if len(dat['img'].shape) == 2:\n",
    "        img = dat['img']\n",
    "    else:\n",
    "        img = dat['img'][channel]\n",
    "    masks = dat['masks']\n",
    "    n_label = np.max(masks)\n",
    "    \n",
    "    # get mean intensities for all masks\n",
    "    mask_mean = []\n",
    "    for i in range(1, n_label+1):\n",
    "        temp = np.mean(img, where=(masks==i))\n",
    "        mask_mean.append(temp)\n",
    "    \n",
    "    if threshold is None:\n",
    "        out_mean = np.mean(img, where=(masks==0))\n",
    "        out_std = np.std(img, where=(masks==0))\n",
    "        threshold = out_mean + out_std\n",
    "        \n",
    "    n_pos = np.sum( np.array(mask_mean)>threshold )\n",
    "    pos_ratio = n_pos / n_label\n",
    "    \n",
    "    return pos_ratio, n_pos, n_label, threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08f8a42d-ed83-4593-9c54-bf5734b58820",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the ratio of cells/nuclei with positive NLS-mScarlet signals\n",
    "\n",
    "# specify the data folder\n",
    "data_folder = '../data/220601-X-188-blast-test/220601-X-188-add-blast-40x-output'\n",
    "\n",
    "# the target channel NLS-mScarlet is channel 3 (2 due to 0-indexing)\n",
    "CHANNEL = 2\n",
    "\n",
    "# empirically, the background signal of NLS-mScarlet is ~10 (in the bud),\n",
    "# we set the THRESHOLD to be higher to exclude some of the really dim ones\n",
    "THRESHOLD = 50\n",
    "\n",
    "# empty lists to store the output data\n",
    "file_names, pos_ratio_blast, n_pos_blast, n_label_blast = [], [], [], []\n",
    "\n",
    "npy_files = glob.glob(os.path.join(data_folder, '*.npy'))\n",
    "for npy_file in npy_files:\n",
    "    file_name = os.path.basename(npy_files[0])\n",
    "    pos_ratio, n_pos, n_label, threshold = get_pos_ratio(npy_file,\n",
    "                                                         channel=CHANNEL,\n",
    "                                                         threshold=THRESHOLD)\n",
    "    file_names.append(file_name)\n",
    "    pos_ratio_blast.append(pos_ratio)\n",
    "    n_pos_blast.append(n_pos)\n",
    "    n_label_blast.append(n_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bbd96679-37d4-4998-8383-b139fd03e155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate per sample positive ratios\n",
    "\n",
    "# for the blast treated groups, I acquired 3 FOVs per sample,\n",
    "# where I have 10 unique samples\n",
    "N = 10\n",
    "\n",
    "# create a zipped array \n",
    "n_pos_n_label = np.array(list(zip(n_pos_blast, n_label_blast)))\n",
    "# split by sample for calculation\n",
    "n_per_FOV_grouped = np.array_split(n_pos_n_label, N)\n",
    "# calculate the total numbers per sample by summing up all FOVs\n",
    "n_per_sample = np.sum(n_per_FOV_grouped, axis=1)\n",
    "# calculate the ratios  \n",
    "ratio_per_sample_blast = n_per_sample[:,0] / n_per_sample[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "429a7268-2117-4e06-84f8-58ee512fae37",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = '../data/220601-X-188-blast-test/220601-X-188-add-blast-40x-output'\n",
    "output_file = os.path.join(data_folder, 'ratio_per_sample_blast.csv')\n",
    "np.savetxt(output_file, ratio_per_sample_blast, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eebe1b24-a6b8-40c3-b49c-a586b0e220a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.array(list(zip(pos_ratio_blast, n_pos_blast, n_label_blast)))\n",
    "output_file = os.path.join(data_folder, 'ratio_Ns_per_file_blast.csv')\n",
    "np.savetxt(output_file, temp, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bb46837-b6f7-40c5-98ad-b8b32daa7c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb9d82a1eb0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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BBRes6PUAAAAAYFyEFABjtmHDhpx11lmLstbZZ5+d9evXL8paAAAAADBuQgqACbjoootW1DoAAAAAMAlCCoAJ2LRp08S3Ydq8eXPOO++8ia4BAAAAAJMkpACYkDe+8Y1Zt27dROZet25dLrvssonMDQAAAACLRUgBMCFr167NVVddlampqbHOOzU1lauuuipr164d67wAAAAAsNiEFAATtGHDhlxzzTVje6Ji3bp1ueaaa7Jhw4axzAcAAAAAfRJSAEzYhg0bsnPnzmzevHlB82zevDk7d+4UUAAAAACwYggpABbB2rVrc+WVV+bd7353zj777JHGnn322dm2bVuuvPJKWzwBAAAAsKIc23cBAEeTTZs2ZdOmTbnhhhuydevW7NixI9ddd1327t173z1TU1M588wzs3HjxlxwwQVZv359jxUDAAAAwOQIKQB6sH79+rz2ta9NklRV9u3bl7vuuiurV6/O8ccfn9ZazxUCAAAAwOQJKQB61lrLmjVrsmbNmr5LAQAAAIBF5UwKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF0IKAAAAAACgF8f2XQAr1qrhxk033dRXHQAAAAAAHMYMn92umum+SWlVtZjrcZRorX1fknf2XQcAAAAAACN5XlW9a7EWs90TAAAAAADQCyEFAAAAAADQC9s9MRGttROSnDPU9dkk+3sqZ7E9NgdvdfW8JB/vqRZgZfI+A0ya9xlg0rzPAJPmfQbmblWSbx1qX1NVty3W4g7OZiKm/5940fYtW0paa92uj1fVjX3UAqxM3meASfM+A0ya9xlg0rzPwMj+pa+FbfcEAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD0QkgBAAAAAAD04ti+C4AV6MtJXt1pA4yT9xlg0rzPAJPmfQaYNO8zsEy0quq7BgAAAAAA4ChkuycAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXQgoAAAAAAKAXx/ZdAKw0rbXHJtmY5FFJViXZm+QjST5QVXf2WRuwPLXWWpJTkmzI4L3lxCR3ZfD+8m9J/sn7CwCwHLXWHp/kOzL4GefBSe5I8qUkH0vyr1V1V4/lActQa211kicleUKSqSTHJflqkpuTXJ/kpqqq/ioEupr/TcJ4tNaen+SXk5xxmFv2Jbkiyaur6pZFKgtYplprU0men+Q5SZ6Z5GFHuP3uJNuS/E5VXTP56oCjSWtta5L/2On+dFWd0kM5wArQWluT5BVJfjzJqUe4dX+SHUneXlVvWIzagOWrtXZmkp9J8sIkq49w6+eTvDnJG6rqK4tRG3BkQgpYoOmE/s1JXjLHIV9O8sKqunZyVQHLWWvtdzP4pX3VPIb/SZJXVNVXx1sVcDRqrX1vknfNcElIAcxLa+38JG9K8i0jDPtSVT1iQiUBy1xr7QFJXpfklRlta/svJfnRqrpqIoUBcyakgAWY/g/hXyR5XufSPUk+k+S2DL4ZdELn+teTPLuq/nHiRQLLTmvtn5OcOcOle5J8IYMfph+Y5NE59P0lGXzj8FlVtW9iRQIrXmvthCQ3JjlphstCCmBkrbWfSfJbSVrn0p1Jdie5JYNtWR6Zg58iFVIAh9Va+6MMvuTV9fUkH89gG7m1SR6TQ99/9id5flW9Z6JFAkfk4GxYmFfm0IDi95OcXFWPqaonJXlokhdkEFoc8OAkb53+5R/gSG5NcnmSTUmmqupbq+rJVfUdGfyg/Ywk2ztjNmawvRzAQrw+9wcUX+uzEGD5a629NMn/k4M/IHxPkucmObGqHltVT62q06vqmzN4//mhJO/I4ENEgEO01l6YQwOKD2Xw+9MJ0+8pT62qx2XwBNercvB7yqokfzy93S7QE09SwDy11tYm+WSSNUPdv1BVv3mY+09K8g8ZHH57wK9V1asmViSwLE0/SbE2yWuSbKmqO2a5/5gMgoyXdS49s6qunkyVwErWWjs3yd9n8GHivUkuTnLp0C2epADmrLX2uCS7kjxouuvuJD9SVVvnOH6qqvZOqj5g+Wqt7Uqyfqjrn5OcW1WH/YJFa+2ZSf4mybFD3b9YVb8xmSqB2XiSAubv53NwQHFtkksOd3NVfT6Hpvs/Mx12AAx7VZLHV9WbZwsokqSq7kny8gx+IB820yPPAEfUWjsug/3iD3zb+Y1J/qm/ioAV4A9zf0CRJC+Za0CRJAIKYCattcfk4IAiSV5+pIAiSarq7zM4W3TY946zNmA0QgqYh+mzKH6s0/2rNcujSVX1dzl4W5Y1SV485vKAZa6qtlXVSNsaTAcVl3a6v2d8VQFHkV9P8tjpv38myX/rsRZgmWutPS+D7SkPeFtVva2veoAV5fGd9ueqaq5frHhHp/24MdQDzJOQAubn6Um+eaj9iSTvm+PYblr//DHUA5AcejbF2tbag3upBFiWWmtPSfJfh7p+qqr29VQOsDJ0t6N8dS9VACvRQzvtz44w9jOd9okLKwVYCCEFzM+mTvu9sz1FMXxvp31ua+0hY6gJYKatEE5Y9CqAZam19sAMvkxxzHTX26rq3T2WBCxz0+fyDT/Z+cGqurGveoAV57ZO+7gRxnbvvWWBtQALIKSA+fnOTvsDcx1YVbuTfGqoa1WSJy68JICcNEPfnkWvAliufiHJhum/35rkwv5KAVaI5+T+4DNJru6rEGBF+mCn/YQRvgS6sdPesfBygPkSUsD8PKHT/tCI47v3d+cDmI+zOu1Pj3q2BXB0aq09MckvDXVdVFVf7KseYMV4Sqf9rwf+0lp7Umvtstbav7bW9rbWvt5a+1Rr7b2ttZ+bfgoD4LCq6nM5+EujqzOHL1m01lbn4O0tk0O35gYWkZACRtRaOy7JyZ3uUfY9nOn+7mFPAPPxnzrtv+6lCmBZaa09IINfzFdNd21P8kf9VQSsIN2Q4hOtteNba29Ocn2SVyQ5PYO94I9L8ugkz07y+iT/1lp73fRWdACHc1GSe4fav9Za+5HD3dxaOzHJ23Pwl0X/qqr+ajLlAXNxbN8FwDL0sCRtqH13kptHnOPznfbDF1QRcNRrrZ2X5OxO9xU9lAIsPxcm+T+m/74/yctGOGsL4Ege12nfm+TaJE+aw9jjMtiG7imttRdU1e3jLg5Y/qrqH1prP53kdzP4rObYJFe01n4qyV8k+WiSOzL4LOepSTbn4AO335vkgkUtGjiEkAJGd3yn/fV5/CL/tVnmBJiz1tpDk/xBp/t/VpV9VYEjaq2dmuQ1Q12/UVUf6aseYOWYfkprTaf7stwfUFSSd2fw5Ofnkjxk+toPJVk3NObZGXzx4gcmWC6wjFXV77XWPprBe8xp091PyaFPcw37RJJLk/xRVd17hPuARWC7JxhdN1C4cx5z3DHLnABzMv0BwJ8ledRQ921x4C0wN3+YwQeDSfKRJK/rsRZgZTkhBz+BniRnTP/fPUnOqarvq6rfr6p3V9VbquriDLbC3dIZ94LW2g9PuF5gGauqv88glPjvSe6Z5fbPTN+3RUABS4OQAkb3oE57PofS3tVpHzfPWgBen+S5nb7/XFWjnpUDHGVaay/N4BvKyeAbzS+rqvn8XAMwk8N9EeueJJuqavtMF6tqXwZPU/x/nUu/2Frrhh4ASZLW2k8k+XiSn0tyzCy3n5zk8iSfaq11z/UDeiCkgNF1n5xYNeNdR7Z6ljkBZtVauzDJz3a6L62qt/RRD7B8tNYemcE3CA940+E+MASYp8P9jvOmqvpfRxo4/c3mn8zBh+E+Psk5Y6oNWCFaaw9srb09ye8leeR091eS/FqSjUmmMvjcZl2S70vylxl8OSMZnE3x5tba6xe1aOAQQgoY3b5Ou/tkxVx0n5zozglwRK21zUl+p9N9RZKLF70YYDn63SQnTv/9i0l+vr9SgBXqcL/j/NFcBlfVJ5L8badbSAF0/V4OPrNmR5LTqupVVfVPVXVrVd1dVV+oqr+qqhckeX4ODlJ/rrX2Y4tYM9AhpIDRdX/YfvA8Hjt+SKctpADmrLV2fpI/zsH7PP9Fkh+vqpp5FMBAa+1FSb5/qOu/VNWtPZUDrFBVdUcO3Rf+9iT/MsI013TaT15QUcCK0lo7N8lLh7puTnJ+VX3xSOOq6l1JfqrT/frWmq24oSdCChjdLbn/0cAkeWCSh484x0md9s0Lqgg4arTWnpHkbUmOHep+b5ILqmq2A+IAksFZNgdsq6q39lYJsNJ1f8+5acRDaj/aaY/6exewsl3Yaf9OVX15jmOvSPKxofbaJC8YR1HA6IQUMKLpbwR9ptN98ojTdO//yPwrAo4WrbWnJnlXDt5m7gNJvt9ht8AIThz6+6bWWs32J8nVnTkePcN937lo/wJgufhwp/3VEcd3759aQC3ACjK9o8UzO91/Ndfx04Hptk732QutC5gfIQXMTzdUeOKI458wy3wAB2mtnZ7kPUmOH+r+lyTnVdXX+qkKAOCIPtRprx5xfPf8v68voBZgZZlKckKn75MjztG9v7vrBbBIhBQwPx/stJ8+14GttUcmOWWo6+4c+sM7wH1aa4/PYEun4W8PfjjJ91TVbf1UBQAwq+s77W8ZcXx3e6c9C6gFWFlmCj2/MeIcd3fax8yzFmCBjp39FmAG705y0VD72a21NscDa7+70766qhycDcyotfboJH+bg39J/2SS7xphv1WAYc/L4EytUXxHkv8+1P5Skh/s3HPTQooCVqRtSe7N/V+QPLW19tCq+socx5/ZaXfPqACOXjOFlusy2tMU3Scn/H4FPRFSwPx8IIMDtB823X5MknNz6H7NM3lpp/3O8ZUFrCTTT179XZJHDXV/Psmzqurz/VQFLHdVdc2oY1pr3W8m3llVfzumkoAVqqpubq29P8lZQ90vSPKm2ca21o5N8v2d7veNrzpgOauq/a21LyR55FD3M5O8eYRpntVpf3zBhQHzYrsnmIfpA5au6HS/avrgpsNqrT0rB/+AfnuSt463OmAlaK09NIMtnh471P3lDJ6gGHWvVQCAvvxBp/3K1tpczqb4v5I8Yqj91SR/M7aqgJXg7zrt/zodcM6qtXZOkqfNMh+wSIQUMH+XJBnepumcHLwF1EFaayfl0G8MvaGqbplAbcAy1lpbk+SqJKcNdd+a5Lur6sO9FAUAMD9bk+waan97kj9orR3284jW2lOTXNrpvtxZXEDHn3Xa65NcfqT3lyRprT0uyZZO978l+ccx1gaMoM1tC31gJq21X0jyuk737yV5TVXtnr7nAUm+L8kbkpw8dN/uJKdV1a2LUCqwjLTWrs5gC7lhv5L5/dB8XVXtXXBRwFGttXZuDt7W8tNVdUovxQDLzvQT5e9NMvzk+d8mubiqrhu674QMtsd9dZLjh+79WJInV9Xti1AusIy01v4+yTM63f+Q5FeTXFNV3xi6d22SH03yy0lO6Ix5UVW9fXKVAkcipIAFmA4g3pnk/M6le5J8OsltSU5NcmLn+h0ZbNny/knXCCw/rbVx/sf5GVX1vjHOBxyFhBTAQrXWLkrymzNc+mKSzyV5SAbbXK7qXN+Twc8zu7oDAVprj8jg3NBTZ7i8L4ODtO9IsjaD80Rn2qb7t6rq5yZWJDAr2z3BAkyfTfGiJH/euXRMBv/xe1IODSj2JDlPQAEAABwtquqSJBcmubtz6RFJnpzkCTk0oPhokqcJKIDDqaovZrD99vtmuHx8kg1JNmYQgnYDiruTXJzklRMsEZgDIQUsUFXdWVUXJHlhkg8e4davJbk8yRN9qxkAADjaVNUbk5ye5C05NKwY9skk/yXJ6VX1b4tRG7B8VdVnkzwryYszCCvunWXIbRls1b2hqi4p28xA72z3BGM2fQDTU5OclME3gW5N8uEk76+qO3ssDQAAYElorX1Tkqcn+bYM9obfl+RLSa6vqo/2WRuwvLXW1mTwhNZjMtjd4kFJvprBzhY7k3xoemcMYIkQUgAAAAAAAL2w3RMAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANALIQUAAAAAANCL/w3+uy+2JSy4gwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ratio_per_sample_blast, 'ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5fd4113-bba5-4f25-bb30-d2059662297d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the ratio of cells/nuclei with positive NLS-mScarlet signals\n",
    "\n",
    "# specify the data folder\n",
    "data_folder = '../data/220601-X-188-blast-test/220601-X-188-no-blast-40x-output'\n",
    "\n",
    "# the target channel NLS-mScarlet is channel 3 (2 due to 0-indexing)\n",
    "CHANNEL = 2\n",
    "\n",
    "# empirically, the background signal of NLS-mScarlet is ~10 (in the bud),\n",
    "# we set the THRESHOLD to be higher to exclude some of the really dim ones\n",
    "THRESHOLD = 50\n",
    "\n",
    "# empty lists to store the output data\n",
    "pos_ratio_no_blast, n_pos_no_blast, n_label_no_blast = [], [], []\n",
    "\n",
    "npy_files = glob.glob(os.path.join(data_folder, '*.npy'))\n",
    "for npy_file in npy_files:\n",
    "    # file_name = os.path.basename(npy_files[0])\n",
    "    pos_ratio, n_pos, n_label, threshold = get_pos_ratio(npy_file,\n",
    "                                                         channel=CHANNEL,\n",
    "                                                         threshold=THRESHOLD)\n",
    "    pos_ratio_no_blast.append(pos_ratio)\n",
    "    n_pos_no_blast.append(n_pos)\n",
    "    n_label_no_blast.append(n_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "700b26ae-6324-4cd3-b56a-ad73bf2fa5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate per sample positive ratios\n",
    "\n",
    "# for the blast treated groups, I acquired 2 FOVs per sample,\n",
    "# where I have 10 unique samples\n",
    "N = 10\n",
    "\n",
    "# create a zipped array \n",
    "n_pos_n_label = np.array(list(zip(n_pos_no_blast, n_label_no_blast)))\n",
    "# split by sample for calculation\n",
    "n_per_FOV_grouped = np.array_split(n_pos_n_label, N)\n",
    "# calculate the total numbers per sample by summing up all FOVs\n",
    "n_per_sample = np.sum(n_per_FOV_grouped, axis=1)\n",
    "# calculate the ratios  \n",
    "ratio_per_sample_no_blast = n_per_sample[:,0] / n_per_sample[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "caf26bcc-7b68-4745-b2c1-676b84acb97e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fba28584f70>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ratio_per_sample_no_blast, 'ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97d0029c-3b30-4ec1-b462-61614fc538df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_per_sample_no_blast.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b74bd21e-2a18-44fe-871e-29278d3d9625",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = '../data/220601-X-188-blast-test/220601-X-188-no-blast-40x-output'\n",
    "output_file = os.path.join(data_folder, 'ratio_per_sample_no_blast.csv')\n",
    "np.savetxt(output_file, ratio_per_sample_no_blast, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd1d7805-7f86-454f-ae83-e0fce4a72963",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.array(list(zip(pos_ratio_no_blast, n_pos_no_blast, n_label_no_blast)))\n",
    "output_file = os.path.join(data_folder, 'ratio_Ns_per_file_no_blast.csv')\n",
    "np.savetxt(output_file, temp, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d7fc0d-17e8-4eba-bca1-dfea5829dde7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
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