filter_by_scale.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cryosparc.tools import CryoSPARC\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotnine\n",
    "from plotnine import ggplot, aes\n",
    "with open('/u/rposert/instance-info.json', 'r') as f:\n",
    "    instance_info = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection succeeded to CryoSPARC command_core at http://localhost:40002\n",
      "Connection succeeded to CryoSPARC command_vis at http://localhost:40003\n",
      "Connection succeeded to CryoSPARC command_rtp at http://localhost:40005\n"
     ]
    }
   ],
   "source": [
    "cs = CryoSPARC(**instance_info)\n",
    "assert cs.test_connection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_number = 'P296'\n",
    "job_number = 'J91'\n",
    "project = cs.find_project(project_number)\n",
    "refinement = project.find_job(job_number)\n",
    "particles = refinement.load_output('particles')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (640 x 480)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(particles.rows())\n",
    "(ggplot(\n",
    "    df,\n",
    "    aes(x = 'alignments3D/alpha')\n",
    ")\n",
    "+ plotnine.theme_minimal()\n",
    "+ plotnine.geom_histogram(binwidth = 0.025)\n",
    "+ plotnine.coord_cartesian(xlim = [0.5, 1.75])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_threshold = 0.9\n",
    "filtered_particles = particles.query(\n",
    "    lambda row: row['alignments3D/alpha'] >= scale_threshold\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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alybClrdZ7/Fa658vDf+WWBydcWySu21GG2Da5vEzwfcX5tUs+st2+v4iAGTL6Lru+CRnjU/fsVyZWutlSRYT+7OPRLsy/Gdxq3H9nSuUWdx+TA7+5wKbZob9ZfGvZ59L4p4xAEzTDRPrO2bWCtj6fH+Bg7bN9xeXALOV3D1JGdcvWaXcJUnOTHLGIZ7nTV3XfV2Gvy5fmeT9SX6z1vq2FcovnufTtdZlb/xZa72y67rPZLgX2xlJVpzBDqbkSPWXL+q67qgkTxifXlBrXekGuYvO7rrun5J8VYYb6v5zkj9O8qpa62o36QVge3rAuPx8kn9cpZzPF7Yb31/gEGy37y9GALKVnDqxvneVcpcvU34jzsrQN27OMAvWo5O8teu6N3Vdd6tlyi+eZ7U2TaNdsBFHqr9MenCGWRqT9c3edVqSO2W4R+HxSe6T5PlJLu267kiN4AVgCxgvQVyc+OP3aq3XrFLc5wvbje8vcGi21fcXASBbyfET6zesWOrgvo3cq+LGJK9O8h1JTqy1fkWt9dgk90jy+rHM9yV51SrtWq1Nh9ouOFSb2V9Wsjh8/l+TvHuVch9I8uNJvjrJMbXWk5OclOGehVckOTnJW7quu+sU2gTAFtd1XUlyfoZg47NJ/r8Vivp8YTvx/QUO37b6/uISYFpU1i7ypWqt+5I8bZntlyR5Qtd1VyY5L8mTu657ea31o0eiXXAETOV92XXdbZM8cnx6/mo3ca+1vnKZbdcm+e2u6/4iyQeT3DbJC5MsTKN9AGxp/zvDiKZbkjxphYmtfL6wrfj+AodnO35/MQKQreS6ifVjVym3uO/aKZ77pzPcFLQkefgK7VqtTZvVLljJke4vCxluEt1nGKVxSGqt/5qDf6l++HhfDgC2qa7rfjrJczJ8vvxIrfUth1KPzxe2Id9fYHXb7vvL3DYMlnHFxPruVcot7rtilTIbUmu9PgcnUrjzCu1arU2b0i5YxZHuL4vD5/+s1vrJw6zr/ePyxCRfeZh1AbBFdV33vCQvGp8+rdb6m4dZpc8Xtg3fX2BN2+77iwCQreQjGdL5ZJi1dCWL+y7dpHYsHRq8eJ47dF13ynIHdF13uwwzaG1mu2DSEesvXdfdM8k3jk8P98vZUisOxQegXV3X/bckPzc+/cla669O+RQ+X9hOfH+BCdv1+4sAkC2j1npdkgvHpw9drkzXdafl4LT2q93Ec0O6rjsuB4OSTy7Z/RdJDozrD1mhigePy88n+ctptQtWcoT7yw+Ny6uTXHAY9Sz6z+Py2iT7p1AfAFtI13XPTvK/xqfPrbW+YkpV+3xh2/D9BVa1Lb+/CADZavaMy8d2XXf6Mvt/KsN9Lq7IBgKNcXa51bwwyW0ypPlvm9xRa716Ytt5XdftWFL30RluwDsWr9est11wmDalv0zqum5nkh8cn/5OrfVza5Rfta91XfdVOXhD6z+utd5yKO0CYGvquu4nk/zC+PT5tdb/vc7jfL6wrfj+AodmO39/MQswW82vJ3lWkjsleWvXdY+vtX6o67rbJHlmkqeP5V5Qaz0weWDXdZ/MMG33a2utT1xS73u6rntHkrcmubTW+oXxmLtnuPH04v0BfqvW+pFl2vXTGW6ue58MMwE9q9a6r+u6XUleMW7/fJKfOcR/NxyKzeovkx6R5Hbj+m+to02P67runCSvT/IXtdarxvMdl6TL8KXv5CTXZ/jFFbaklS6pSnLCkn1XL+1/sBUc6nt8tc+XruuenuTl49MX1Vp/Luvn84W5tRn9Jb6/0KhN6i+Ttu33FwEgW0qt9cau67oMo5XuleSiruuuyTBD1eL7+Vdqra/ZYNVfneE+Mz+X5Atd112d5NZJjpso87tJfnyFdl3cdd0PZ7h/wH9J8v1jHV+RYYTVgSRPqrW6fwZHzCb2l0mLv1xeWmt9/6olBzuSPHp8pOu665LclOS2OTgq/aokj621fvQw2gWzduUK21+35PkDk7xnc5sCm2Iz3uOvnFh/Std1T1ml7DNrrb878dznC/NsM/qL7y+0arN/h9q2319cAsyWU2u9OMP9LF6R5OMZpu6+Jsm7kjym1vr0lY9e0XOS/J8kH8zQeU/I8MH38SS/neS7aq0/UGu9aZV2vT7JfZP8TpJ9GT589yV5Y5L71lr3rHQsbJZN6i9Jkq7r7pDku8en6/nrWZL8WZLnJ3l7kn9JckuGXzT/I8n7kjwvyd1qrX96qO0CYMuavMzqDms8brPkWJ8vbDe+v8AGbffvL6Xv53aCEgAAAADgMBkBCAAAAAANEwACAAAAQMMEgAAAAADQMAEgAAAAADRMAAgAAAAADRMAAgAAAEDDBIAAAAAA0DABIAAAAAA0TAAIAAAAAA0TAAIAAABAwwSAAAAAANAwASAAAAAANEwACAAAAAANEwACAAAAQMMEgAAAAADQMAEgAAAAADRMAAgAbAullCeWUvrx8cQVynxy3P/JI9s6aNuR7FullDtN9PXzN/t8ALAVHD3rBgAAwLSNIe+dkqTv+xcegfN9bZJvTXJWknsn2ZXklCTHJ7k2yb8k+eskr+v7/sI16npAkj9bYfdNSa5Ock2STyT5YJK/TfLHfd9/7hDbfpck/zQ+fXLf9685lHoAgPklAAQAoEVPTHL/cf2FR+B8v5Tk4Svsu22SbxwfTy+l/E6SH+77/oZDOM8xSW4/Pu6S5EHj9s+WUt6Q5Gf6vt+/wTrPGZe3JPmjQ2gTADDnBIAAAKO+7+806zawpd2Q5MIk/5Bkb5J9SW7MMBrw2zMEbUcn+YEktyulPKjv+36NOi9J8vyJ50cl+YokX5lhpOG3JfnqJCcleXqS7y2lnNv3/Ts30O5Hjsu/7vv+Mxs4DgDYIgSAAABw+P5rkk/0ff/5Ffa/spTyDUn+X4bw7uwk35vk99ao96q+79+y0s5SSkny0CQvTXJGhrDxD0sp39n3/V+v1ehSyu2SfMv49A/XKg8AbE0mAQEAgMPU9/3HVgn/Fst8MMn/nNj0PVM4b9/3/dsz3Hvw7ePmWye5oJRy7Dqq+J4c/E7wlsNtDwAwnwSAAMDcK6XcupTyyFLKK0spf1VKubKUcqCUcm0p5Z9KKa8vpTxo7ZrWPM+6ZiotpewspTy9lPKXpZT9pZTrSykfG9t3l7HMCydmIn3AMnU8YGL/C8dtu0sp/7OUckkp5bpSyjWllH8opfxMKeXEjbR9bONTSinvK6V8Zmzjh0spLy6l3HbJsceXUn6ilPI342v7ubENLyylHLeB1+97SimvK6X88/izuaGU8olSyhtKKd+1xrFTeT1KKe8ppfQ5eP+/TNQ7+Th/mWO/vpTyqlLKRaWUq8f32GfG8/9xKeXZpZQz1/t6rOCSifU7HGZdXzTeT/CxST41btqV5KnrOPSccfmRvu//aenOUspdSynnlVL+YOxr15VSPj++Lu8tpTy/lHLK4bZ/uf5SSvnOUsqbSin/Wkq5sZSyr5TytlLKow6h/pNKKc8rpXyglPLZsT98pJTy0lLKmj+HI/U6AMBmcQkwALAVXJrka5bZfnyGiRDukuRxpZQ/TPK4vu+v26yGlFJ2JfmTJF+/ZNddx8cPlVLOPYR6H5zkjUlOXrLr68fH40opD+z7/rJ11HX7JDXJf16y68zx8dhSygP6vr98DCz/KMndlpQ9I8nPJDlnPO9/rHK+05K8Kck3L7P7TuPjB0spb07yhPVMfjHN12M9SikvyDBZyNI/kN9ufJyR5LszTC5yj8M41V0m1vcdRj1fpu/7q0spL80wIUmS/EiSl61UvgwjBBeD2bcss/8JSV67wuGLr8u3J3lOKWWh7/u3HWLTl2vby5Kct2TzHZI8LMnDxvfSwlqjLse67pPkD5J81ZJddxsfjy/D/Rg/tMLxM3sdAGBaBIAAwFZwbJLPJnl3kg8m+dcMEy6cmOReSf5LklMzTGbwm0m+fzMaUUq5dYZ7uC0GQFcleU2SDyW5VYYQ4PFJ3pDkHRuo+t5Jnp1kZ4ag4X1Jrs0QKD41ye4MwdFrM9w7bjVHJ3lzhvDvPUkuSPLpJKcn+dGxzrskeW0p5XuTvCtDMFIzXEK6f9z/tPG8X5/k5UmetNzJSimnJ3l/htc/ST48nv+fktw8nu8JSb42yWOSHFdKedgak18czuvx/CSnJPnZDGFnkiw3YuzfJv4N35PkxePTGzO8Fn+R5MoMgeCpSb4hB2fcPSSllLsm+e8Tm9a6/9+h+O0cDAD/UyllV9/3KwWND0lym3F9ufv/HZukT3JRkvcm+WiG90eSnJYhPHxohn745lLKt/R9/4HD/yfkGUkenaHP/2aSD2R4X39rhvfSMRneS6/P0PdXc1qSt2WYNfnNGfrv/gwTpzw5yX8a9/1uKeVefd8fWKaOWb0OADA9fd97eHh4eHh4eMz1I8PIq52r7D82Q9DVj49vW6bMEyf2P3GFej457v/kCvtfNFHHxUluv0yZb8oQXPQTjwcsU+4BS8pcnuTMZcrdbqJdfZJvXKPti4+nLlPmhAyXoC6WuTDJTUkesUzZUzMEh32SA0l2LVOmJPmrscwtGUZslWXK3SpDKLp43icfgdfjPYtl1vH+eutY9vNJvmGVcjuSfOs66jsjw6W152SY6OOpGcKqGyfa/epVjp98Ld5zCP3lYxPHd6uUO38ss3eFn9uZSe6yxrnOTnL9WM+frlJu8We2Ut964ZKf/8eS3HGZcvfKEMwulvveZcrcaUld1yT5jmXKHTv2gcVyj1mhbVN7HTw8PDw8PGb1cA9AAGDu9X3/9n75kTmL+2/IEPBdP27a8CW4ayml3CrJj49Pv5Dkv/R9/5ll2vJ3GWaE3ajH931/ydKNfd9fmeTnJjZ99zrqOr/v+19dpq5rk7xkYtM3JfnZvu/fukzZK5L88vj06By8VHTS9+TgZb+v6Pv+5X3ff9nIvn64TPOHknxi3LSe12ear8daFi/L/Yd+mKhjWX3f39z3/V+uo74nZLjk9A8yjPJ7dZLHZRi5dmmSH+77/sdXPvywfXJi/fbLFSil7EjyiPFpXeHndknf9/+82on6vn9XDl5mfHYp5Y4bb+6XuSVD/7p8mfN9KMmPTWz6qXXU96y+79+7TF03JHnexKZl30szfB0AYGoEgABAE/q+vybDqLwkud8mnOLbMlxamiTv7Pv+0lXKvi7Jv2+g7ov6vn/3KvvfNbG+nkkoXrHKvvdNrN+c5FXrLLvceReD1j7JL6zWoDEE/J3x6d1KKUvvxzZp2q/HWhaD468tpZw0hfpWckOSP81wGftmmrxf49J7KC76tiRfOa6/5TDPNxmKTqPvvbPv+39YaWff9xckWQzkzlrjvfTvGfrjSt6bIdBPDv+9NO3XAQCmxj0AAYAtoQwz1/5ghntt3SNDeHFchstQlzptE5pw1sT6n61WsO/7A6WUv0zSrbPuv1pj/+RIqNuuWGpwQ4Z7Eq5k8n5w/9ivMrnHkrLLnfc7xuX+JP+5lOV+FF9iso4zMnEfviWm+XqsxzuT3CdDWPbeUsovJHnrGq/Nivq+f26S5yZJKeWYDO/Hs5M8J8lPJHlaKeW/933/v6fQ9uWs+YPIwdl/r81wb82VKyvl2zLMMHzfJHfOcCn5zhWKT6Pv/ek6yrwrB0du3jcrv5f+tu/7L6ywL33f31RKuSrDrMmrvpdm8DoAwNQIAAGAuVdKeWSGyTa+cq2yoxM3oRm7J9Y/vo7y6ymz6KrVdo4hxeLTW69R178vdznnCnWtet4M9wdc9ryllONycETkV2a43HUjVhqZtma7Nvh6rMf/SvLwJPccH69L0pdSLskwCvLPk7x9HGW6IX3f35ThvfDxUsobkvxxkvsn+V+llGuWu1R7CiaDrJVGoj5yXL69X2Em3VLK8RnuXXjOBs49jb73Txsss3vFUmu/x5OD7/Nl30szfB0AYGoEgADAXCulfHOS38/B31s+lGGE0D9nuNTxpgyXoCYHZ37djNucHDexfsM6yq+nzKJbNtiWadV1OOc96TCOTYaJQVYyzddjTX3fXz2+z56d5EeS3DHDKLp7jI+nJrmxlPKaJP+97/urD/E8N5RSnpjkX8b6f7qU8ut930/73/s1E+tXLt1ZSrnXRJnlZv9d9LtJHjauX59hNt0PZpg05IYcvHT2Hkn+x7i+49Ca/CWuX7vIl5Q5YZVy03htZ/U6AMDUCAABgHn34hz8neVpfd+/eqWCpZT/vontmAwcjl1H+ePWLrKlXTex/g9933/DzFoyBX3fX5/kRaWUF2cYBfitSb4lw6W7p2YYHfa0JPcvpdxvLH8o5/lkKeXSDEH1riT/KclHpvBPSJKUUk7JwUtjk+T9yxRbHP13IMOIxOXq+dYcDL0uTvLgvu/3rVB2xQl6DtF6+s5kmWunfP4vmvHrAABTYxIQAGBulVJ2JnnA+PTvVwv/RnfaxObsnVj/2nWUv/NmNWQejKPgFoOXrxtnSd7y+sGH+r7/1b7vH59hNOBDknxqLHKPJE85zNNMBlYnHWZdSz1uYv0jfd9/epky54zLP+/7/rMr1PPgifXnrRR6jb5mlX2H4i5rF/mSMntXLHX4Zvk6AMDUCAABgHl2Sg6O/vvn1QqWUu6bg/ek2wwXTqw/cI227Mwwgqx1fz4uj8sQks2TL176WdYxO8lKxkDwnRkm71j07YdaXynlqHxpgPxll+geRt0nJTlvYtP/WabMaRkmPElWn/1318T6qn0vB0fITcuD1lHm7In15UY5TsssXwcAmBoBIAAwzyYvs1xrVNCLNrMhSf4iBycUeHAp5YxVyj4h65+wZCs7f2L9f5RSpjEhx7RMXqI8jcuxPzGxfji30Xl0ktuN6/sy3A/wsJVSjk2yJ8np46a9SX59maLnTKzXVapcV98rpXxLhpm5p+lB430KVzrnI5N83fj0b/u+/9RKZadglq8DAEyNABAAmFvjrKv/OD79xlLK9y4tU0rZUUr5xWzyl+9xptTFS5CPTvK7pZTbL9Oeb0ryss1syxy5IMlfj+tfn+QPSym3W6lwKeWoUsqDSinPPwJtmwzs7rNiqSSllP+7RuBUkvz4xKZ/WLL/u0op5651GXQp5bszzGa96FcPdwKQMnhokr9N8t3j5s8leXTf959b5pDF+//9/RrB2eSI159ZLtwdX7PfyzChyTTtyNC/Tl3mnPdI8n8nNv38lM+91CxfBwCYGpOAAADz7hU5GLy9qZTyuxkuPf2PDCNyfjDJ3ZN8OMOMwN+4iW15SYYRXIuzw14yzgx7UYZZbb8jyeMzXH5ak3TjcUd0Vtsjpe/7vpTymAwh4FdnuF/aJ0opv5/hsswrkxyT4TLKe2e4tPMOSd6VYcbmzfSnOXjZ7mtKKa9I8skkN4/bLu/7/uJx/clJnlxK+WiSd2d4L/17hok/vjrJ92WYGCQZ3ne/uuRcpyX5rSSvLKW8M8kHklyeYfTYcRnepw9Jct+JY/48yf9ax7/jlFLKORPPj0pyYpKTM7ym354vvfflFUke3/f9l10WO14ifP/x6Wqz/yZDuPtvSb4qyTcl+Vgp5TcyXAZ77FjPDyTZmeS1Sc5dx79lvd6c5DFJLh371wcyfG/51vE8x4zl3tT3/ZuneN7lzPJ1AICpEQACAPPu1zJ88f6hDCNsfmB8TLo4w8im39rMhvR9f2Mp5cFJ/iTJvTLcc/C/LSl2Q5InjfsXA8BNm6V01vq+v2Ic9Xh+kodnCLzOzepByGVHoGl/nCFku3+GAO5VS/a/NskTx/U+w3vrbuNjJf+W5DF931++wv4Tk3zv+FjJzRkCxJ8aR5Wu5cwkf7COcp9N8vokP9P3/X+sUOZhGYKqZPX7/6Xv+5tKKY/O8F4/JUMA9uIlxW5O8twMYe80g69XZRjB+ewk/3WFMhdkuNR+U834dQCAqREAAgBzre/7PskPl1LeluRHM4SBJ2YYofWxDJfevWYM545EexYDrx9LspBh9OExGUZ8vTPJK/u+/1gpZXKikP2b3rAZ6vv+qiSPKKX85wwjMr89w73oTkpyY4Z73X0kw30U39r3/SVHoE03l1IekuTpSR6V4ed0Ypb//XdXhtmmH5jh/fU1Sb4iQ7BzZZIPZRgx9/oVLqt9Q4Z/3/1zcETe7TOM0rsxw8//0iTvS7Kn7/tPHsY/7fNJrklydYYRjR/IEDy9re/7G9c49pxx+S8Tox9X1Pf934+Xt/7XJI/IMBryCxnuL/hnSf5P3/cfKKU8YMP/irXP/ZxSyp9k6Gf3yzBy9Ookf5fkN/q+v2Da51ylLTN7HQBgWsrwOzUAANNUSvn7DPee+2ySk3u/dDFD4/0Jr0pyQpJf7Pv+vDUOOaJKKS9M8jPj0wf2ff+e2bUGANpjEhAAgCkrpXxzDk488R7hH3Pg7AzhX7L2/f8AgMYIAAEANqCU8g2llBNX2X9GkjdObFo6YQTMwuLsv/+e4VJsAGAbcQ9AAICNOTfDjLHvzHDvtX/LcD+wXRlmAT4nB3/H+p2+7985i0bCpL7vn5LkKbNuBwAwGwJAAICNOy7DxBKPWqXMbyf54SPTHAAAWJkAEABgY34hw+zDD0lytyRfmWG22+szzAr6F0nO7/v+r2bVQAAAmGQWYAAAAABomElAAAAAAKBhAkAAAAAAaJgAEAAAAAAaJgAEAAAAgIYJAAEAAACgYQJAAAAAAGiYABAAAAAAGiYABAAAAICGCQABAAAAoGECQAAAAABomAAQAAAAABomAAQAAACAhgkAAQAAAKBhAkAAAAAAaJgAEAAAAAAaJgAEAAAAgIYJAAEAAACgYQJAAAAAAGiYABAAAAAAGiYABAAAAICGCQABAAAAoGECQAAAAABomAAQAAAAABomAAQAAACAhgkAAQAAAKBh/z/1uTdmzUcuaAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure Size: (640 x 480)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df = pd.DataFrame(filtered_particles.rows())\n",
    "(ggplot(\n",
    "    filtered_df,\n",
    "    aes(x = 'alignments3D/alpha')\n",
    ")\n",
    "+ plotnine.theme_minimal()\n",
    "+ plotnine.geom_histogram(binwidth = 0.025)\n",
    "+ plotnine.coord_cartesian(xlim = [0.5, 1.75])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'J92'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workspace_number = 'W2'\n",
    "cs.save_external_result(\n",
    "    project_number,\n",
    "    workspace_number,\n",
    "    filtered_particles,\n",
    "    type = 'particle',\n",
    "    passthrough = (job_number, 'particles'),\n",
    "    title = f\"Filtered per-particle scale >= {scale_threshold}\"\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cryosparc-tools",
   "language": "python",
   "name": "cryosparc-tools"
  },
  "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}