C-TRAP force cruve export.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2300957e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lumicks import pylake\n",
    "import numpy as np\n",
    "import sys\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import gaussian_kde\n",
    "from scipy.signal import savgol_filter\n",
    "#from skimage import io\n",
    "import os\n",
    "import matplotlib.animation as animation\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "172ca630",
   "metadata": {},
   "outputs": [],
   "source": [
    "def strip_ext(file_name):\n",
    "    file_name_split = file_name.split(\".h5\")\n",
    "    file_name_no_ext = file_name_split[0]\n",
    "    return(file_name_no_ext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bfa2a51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1/4: 20220805-185708 Scan 35MT7(12 uM Tub).h5\n",
      "Loaded 2/4: 20220909-161538 Scan 20 mt 10NM HSET_2.h5\n",
      "Loaded 3/4: 20230602-221543 Scan taxol_eb_hset_1.h5\n",
      "Loaded 4/4: 20230602-223838 Scan taxol_eb_hset_2.h5\n"
     ]
    }
   ],
   "source": [
    "#Load data\n",
    "data_folder = \"../data/\" #Set as folder containing data to process\n",
    "output_folder = (\"../output/\") #Folder used for outputs\n",
    "exp_id_split = data_folder.split(\"/\")\n",
    "exp_id = exp_id_split[-2]\n",
    "\n",
    "#Create output folder\n",
    "if not os.path.exists(output_folder + exp_id):\n",
    "    os.makedirs(output_folder + exp_id)\n",
    "\n",
    "#Get All Files from data folder\n",
    "filenames = sorted(os.listdir(data_folder)) #Sorted based on file names\n",
    "\n",
    "#Define empty lists to store files of interest\n",
    "kymo_list = []\n",
    "kymo_list_names = []\n",
    "fd_list = []\n",
    "fd_list_names = []\n",
    "tiff_list = []\n",
    "tiff_list_names = []\n",
    "marker_list = []\n",
    "marker_list_names = []\n",
    "scan_list = []\n",
    "scan_list_names = []\n",
    "calibration_list = []\n",
    "\n",
    "#Load all Kymographs, FD curves, .tiff, marker and scan files to lists\n",
    "x = 0\n",
    "\n",
    "load_verbose = \"TRUE\"\n",
    "\n",
    "for file in filenames:\n",
    "    if file.endswith(\".h5\") and \"Kymograph\" in file: #Loads Kymograph .h5 files\n",
    "        x += 1\n",
    "        kymo_list.append(pylake.File(data_folder + file))\n",
    "        kymo_list_names.append(file)\n",
    "        if load_verbose == \"TRUE\":\n",
    "            print(\"Loaded \" + str(x) + \"/\" + str(len(filenames)) + \": \" + file)\n",
    "    if file.endswith(\".h5\") and \"FD Curve\" in file:\n",
    "        x += 1\n",
    "        fd_list.append(pylake.File(data_folder + file)) #Loads FD .h5 files\n",
    "        fd_list_names.append(file)\n",
    "        if load_verbose == \"TRUE\":\n",
    "            print(\"Loaded \" + str(x) + \"/\" + str(len(filenames)) + \": \" + file)\n",
    "    if file.endswith(\".tiff\"): #Load .tiff files\n",
    "        x += 1\n",
    "        tiff_list.append(io.imread(data_folder + file))\n",
    "        #tiff_list_names.append(file)\n",
    "        if load_verbose == \"TRUE\":\n",
    "            print(\"Loaded \" + str(x) + \"/\" + str(len(filenames)) + \": \" + file)\n",
    "    if file.endswith(\".h5\") and \"Marker\" in file:\n",
    "        x += 1\n",
    "        marker_list.append(pylake.File(data_folder + file)) #Loads FD .h5 files\n",
    "        marker_list_names.append(file)\n",
    "        if load_verbose == \"TRUE\":\n",
    "            print(\"Loaded \" + str(x) + \"/\" + str(len(filenames)) + \": \" + file)\n",
    "    if file.endswith(\".h5\") and \"Scan\" in file:\n",
    "        x += 1\n",
    "        scan_list.append(pylake.File(data_folder + file)) #Loads FD .h5 files\n",
    "        scan_list_names.append(file)\n",
    "        if load_verbose == \"TRUE\":\n",
    "            print(\"Loaded \" + str(x) + \"/\" + str(len(filenames)) + \": \" + file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8a95ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, file in enumerate(scan_list):\n",
    "    force2x = file.downsampled_force2x.data\n",
    "    force2y = file.downsampled_force2y.data\n",
    "    distance = (file.distance1.data) - file.distance1.data[0]  #Convert from um to nm/bp\n",
    "    time = (file.distance1.timestamps - min(file.distance1.timestamps))/1e9\n",
    "   \n",
    "    plt.plot(time, 1000*distance) \n",
    "    plt.xlim(0,400)\n",
    "    plt.ylim(-250,250)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7121403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Axial calibration': 0.0, 'Bead diameter (um)': 2.0, 'D (V^2/s)': 0.024415264555058325, 'Fit range (max.) (Hz)': 23000.0, 'Fit range (min.) (Hz)': 100.0, 'Fit tolerance': 1e-07, 'Hydrodynamic correction enabled': 0.0, 'Kind': 'Reset offset to zero', 'Max iterations': 10000.0, 'Number of samples': 781250.0, 'Offset (pN)': -14.592834230617767, 'Points per block': 2000.0, 'Rd (um/V)': 3.170171563809053, 'Response (pN/V)': 1171.3555370244014, 'Rf (pN/V)': 1171.3555370244014, 'Sample rate (Hz)': 78125.0, 'Sign': -1.0, 'Start time (ns)': 1659711893346110000, 'Stop time (ns)': 1659711903346110000, 'Temperature (C)': 25.0, 'Viscosity (Pa*s)': 0.00089, 'alpha': 0.411627167263163, 'backing (%)': 99.89993715800897, 'chi_squared_per_deg': 1.4505577292178906, 'err_D (V^2/s)': 0.0004603479876831984, 'err_alpha': 0.006650528012571295, 'err_f_diode (Hz)': 322.45185708443097, 'err_fc (Hz)': 43.156937153315624, 'f_diode (Hz)': 10977.992037391918, 'fc (Hz)': 3505.3789200703277, 'gamma_0 (kg/s)': 1.6776104770169493e-08, 'kappa (pN/nm)': 0.36949279035768773}]\n",
      "[{'Axial calibration': 0.0, 'Bead diameter (um)': 2.0, 'D (V^2/s)': 0.033430173795182594, 'Fit range (max.) (Hz)': 23000.0, 'Fit range (min.) (Hz)': 100.0, 'Fit tolerance': 1e-07, 'Hydrodynamic correction enabled': 0.0, 'Kind': 'Reset offset to zero', 'Max iterations': 10000.0, 'Number of samples': 781250.0, 'Offset (pN)': -7.869840850133374, 'Points per block': 2000.0, 'Rd (um/V)': 2.709219333325876, 'Response (pN/V)': 998.1729827901678, 'Rf (pN/V)': 998.1729827901678, 'Sample rate (Hz)': 78125.0, 'Sign': -1.0, 'Start time (ns)': 1659711893346110000, 'Stop time (ns)': 1659711903346110000, 'Temperature (C)': 25.0, 'Viscosity (Pa*s)': 0.00089, 'alpha': 0.41504478312571447, 'backing (%)': 73.56827451129271, 'chi_squared_per_deg': 1.0921690272092919, 'err_D (V^2/s)': 0.0006427067926028042, 'err_alpha': 0.006124494844920747, 'err_f_diode (Hz)': 313.3534455307273, 'err_fc (Hz)': 43.64254141981146, 'f_diode (Hz)': 10740.861091720815, 'fc (Hz)': 3495.349815683912, 'gamma_0 (kg/s)': 1.6776104770169493e-08, 'kappa (pN/nm)': 0.3684356487906782}]\n"
     ]
    }
   ],
   "source": [
    "file_number = 0\n",
    "file = scan_list[file_number]\n",
    "print (file.downsampled_force2x.calibration)\n",
    "print (file.downsampled_force2y.calibration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c56ebb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 50.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JJ56QJFVVVUmSEhISgp6XkJDgLquqqlJUVJQGHjcu7/ia+BP8hsXHx7s1x8vJyXHP53EcR8OHD2/LpgHwoOnT7Qmh27fbjwqAL7JsmfS739lzaP7f/5MuuKBzX8/ns1dPfv99e30en0969lkpKckORd+5s3NfvzdpU9BpamrSRRddpKVLl+rCCy/ULbfconnz5mnlypVBdT6fL+h7Y0yLecc7vuZE9Sdbz+LFixUIBNypgstRAr2e49jhwRJHdfDF/vQnO2xcskdZrrii61575EjpiSfsdXeuukpqarJD0c89V7rjDqm6uut68ao2BZ2hQ4fq/PPPD5o3ZswY7dq1S5Lk9/slqcVRl+rqavcoj9/vV2Njo2pqak5as3fv3havv2/fvhZHi5pFR0crLi4uaAIAPr7CyZSU2CMqkrRwoXTbbeHpIylJeu45e+L8178uNTba0DVypL0WDxcbbL82BZ2LL75YH3zwQdC8v//97zrzzDMlSSNGjJDf71dBQYG7vLGxUYWFhZo4caIkKSUlRZGRkUE1lZWV2rJli1uTlpamQCCgjRs3ujUbNmxQIBBwawCgNZqDzttv25E0QLNdu+z1lg4ftvelWrEi3B1JEyZIr71m74o+frx08KD0i1/YwPOf/2nvpYU2asuZyxs3bjQRERHml7/8pdmxY4dZvXq16d+/v3nyySfdmmXLlhnHccyzzz5rNm/ebL797W+boUOHmrq6Orfm1ltvNcOGDTOvvvqqeffdd83ll19uxo0bZz777DO3Zvr06Wbs2LGmqKjIFBUVmeTkZJORkdHqXhl1BaDZmDF2dMsf/hDuTtBd1NUZM3as/b1ITrbfdzdNTcY888yx31/JmMREY1atMqaxMdzddZ5Qv3+3KegYY8yLL75okpKSTHR0tDnvvPPMww8/HLS8qanJ3Hvvvcbv95vo6Ghz2WWXmc2bNwfVHDp0yNx+++1m0KBBJiYmxmRkZJhdu3YF1ezfv9/Mnj3bxMbGmtjYWDN79mxTU1PT6j4JOgCa3X23fZPIzAx3J+gOPvvMmCuvtL8TCQnG7NwZ7o5O7rPPjHnsMWPOOONY4Dn7bDuvoSHc3YVeqN+/fcZ481JFdXV1chxHgUCA83WAXm7dOnt12tNPtyd3RkaGuyOE0w9+IP3Xf9l7VhUW2qHdPUFDg/Tww/ajrOaTlIcNs9szb54UGxve/kIl1O/f3OsKgOelpUmDB9tzdP7613B3g3BatcqGHMmOduopIUeSoqPtkPR//lP61a8kv1/avVtatEg680zppz+VysvD3WX3Q9AB4Hl9+9p7FkncPbo3W7tWuv12+/gXv5CuvTa8/bTXaadJd98tffSR9Mgj9oahNTXSL39pT1qeNEn6n//hxqHNCDoAeoXmu5m/8AL3FuqNtm61weboUenf/u3YdXN6suho6Xvfsxcd/NOfpKlT7YUH33pLmjvXXtk5I8N+3FVZGe5uw4dzdAD0CgcO2I+vGhvtm95xlwSDh1VXS6mp9gjIpZfaodvR0eHuqnPs3i09+aS9yvP27cHLLrpIuvhiO4R9wgT7cVffvuHp82RC/f5N0AHQa8yYIeXn28v9/+hH4e4GXeHwYWnKFGn9eunss6XiYmnIkHB31fmMkbZtk55/3h7F3LChZU1EhD2ZOSEhOPCMGiUlJx+b/H57pKirEHRaiaAD4HgrV9or306cyEnJvUFTkzR7tpSXZ0fcFRVJ550X7q7Co7LSjjArKrJhb9Mm6ciR1j13yBBp7Fg7JSfbr+efL/Xv3zm9EnRaiaAD4Hi7d0vDh9v/nVZVdd7dqdE9/Oxn0n/8hz1y8cor0uWXh7uj7uPoUenjj+3NQ/fvt6FQsuHn/felLVukzZulHTuOLfu8Pn3s/bguushewTklRbrwwtAMcSfotBJBB8CJXHSR/d/s//yPvUs0vOmJJ6Q5c+xj9nX7HTpkPwJ77z0bfN57T/rb36RPPmlZ6/PZ8JOScmy68EKprW/BoX7/jujwGgCgB5k50wadF1/kzc+rCgvtaCRJ+vGP2c8dERNzLLQ0M0bau9f+HZWWHpsqKqQPPrDTU08dqz9R+HGcrtsGjugA6FXefdf+Y9u/vz1k369fuDtCKL3/vnTJJdK//iVdc4309NP2YxZ0vupq+/dVWiq9886x8HMiZ5xx7Hyf5GRp9Gh7EnRsLB9dtRpBB8CJGGPP09mzR3rpJXvXanjDRx/ZkLNnj73i8Ztv2iMSCJ99+4KP+pSW2rvGf5GEBGnEiDoVF/PRFQC0i88nffOb9lYAL7xA0PGKvXuladNsyBkzxoZYQk74felL0vTpdmpWU2NPdm4+76f5pOd9++x+3Ls3tD1wRAdAr/PyyzbgJCbakVhdeY0QhF5trTR5sj1J9qyz7E1cv/zlMDeFNgsEbOD529/q9L3vcVNPAGi3r39dGjDADq99991wd4OO2L/f3vrgb3+zH3sUFBByeirHsUPVQ30PMoIOgF6nXz/piivsY27y2XNVV9vQWlpqL2pXUCCdc064u0J3Q9AB0Ct9/iaf6Hl27rQfV23ebG9RUFhoR+8AxyPoAOiVvvENO+y4rMyO1kHPsWGDvUnn++/bezW99RY3acUXY9QVgF7pS1+yd7IuLJSefVa6885wd9Q9GCP94x82TPzzn/Zk7U8/tbcBMMaOZDrtNGngQDtM/4wz7HT22VJUVOf3l5dnLwB4+LA0bpy98OPw4Z3/uui5CDoAeq1rrrFB509/IugUF0tPPml/Fu0Z3hsRYc+POf98O7z7/PPtR0lJSaEZ1XbwoHTHHdKjj9rvr7xS+sMfQnNvJXgbw8sB9Foff3xshE5Fhf0YpLdZu9be+HLdumPzoqPtPcEuuMD+TGJjpb597bJPP7XTvn32Z1ZRIZWXSwcOnHj9I0dK110nXX+9PQLTntDz4os25JSX2+cvXizdd9+xnuAtXBm5lQg6AFrjkkukv/5VeuABaeHCcHfTdXbtsuHhuefs91FRNpBkZtq7fEdHt35dxtgL9W3bZqf337df333XhqJmo0ZJU6bYk4gvv9x+fHiydb76qvSrX0mvvWbnDRtmb9b59a+3dWvRkxB0WomgA6A1HnhAys62geftt8PdTdd46inpllvsUZiICOn226Uf/tBeQDGUDh60Vyh++mn7taHh2LK+fe3VcmfNsvc7GjTI3in7ww/tycXPP2/PEZJsj4sWST/9qT0/CN5G0Gklgg6A1qiosCfT+nz2xNtQv9l3Jw0NNtT89rf2+4kTpYcfth9Rdbb6enuEprBQev11Oyz8VPr3t3ch/8EP7BWP0TsQdFqJoAOgtdLS7Mm4ubnS/Pnh7qZz1NRIV19tb3Tp80n//u/Sz34WvvNctm+3R5bWrZP+/nd7G4eYGPvx1Fe+ImVk2Is6cgSn9wn1+zejrgD0etdcY4POn/7kzaCzc6e9btC2bfbE4j/+8diVocPlvPPsCcVAZ+OCgQB6vWuusV/feiv0d04Ot/Jye72gbdvsx3Jvvx3+kAN0JYIOgF7vzDOlr37VXhTvmWfC3U3ofPSRHaFUUSGNHm2PWo0bF+6ugK5F0AEASTfcYL8++WR4+wiVigobcnbutMO6X3+dKwijdyLoAIDs9WP69JGKiqQdO8LdTcfU1EgzZtgjOuecI73xhrdHkwEnQ9ABANk7YKen28c9+ajO4cP22jRbt9pw8+qrx67+DPRGBB0A+F9ZWfbr739vr8zb0zQ1Sf/2b/ak6rg46eWX7flHQG/WpqCzZMkS+Xy+oMnv97vLjTFasmSJEhMTFRMTo8mTJ2vr1q1B62hoaNCCBQs0ZMgQDRgwQDNnztTu3buDampqapSVlSXHceQ4jrKyslRbW9v+rQSAVpg1y163pbzc3haip8nJsUPHIyPtrR3Gjg13R0D4tfmIzgUXXKDKykp32vy5y1suX75cK1asUG5urkpKSuT3+zVt2jTV19e7NdnZ2VqzZo3y8vK0bt06HThwQBkZGTp69Khbk5mZqbKyMuXn5ys/P19lZWXKav6vFgB0kv79jw01//3vw9tLW+Xn24sAStLKldwPCnCZNrj33nvNuHHjTrisqanJ+P1+s2zZMnfe4cOHjeM4ZtWqVcYYY2pra01kZKTJy8tza/bs2WP69Olj8vPzjTHGbNu2zUgyxcXFbk1RUZGRZLZv397qXgOBgJFkAoFAWzYRQC/32mvGSMacfroxn34a7m5a55//NGbgQNv3LbeEuxugY0L9/t3mIzo7duxQYmKiRowYoRtuuEEffvihJKm8vFxVVVVKbz6bT1J0dLQmTZqk9evXS5JKS0t15MiRoJrExEQlJSW5NUVFRXIcR6mpqW7NhAkT5DiOW3MiDQ0NqqurC5oAoK0mT7bntdTW2o+BurtPP7W3dqipkVJT7U1KARzTpqCTmpqqJ554Qq+88ooeeeQRVVVVaeLEidq/f7+qqqokSQkJCUHPSUhIcJdVVVUpKipKAwcOPGlNfHx8i9eOj493a04kJyfHPafHcRwN54IRANqhTx/p5pvt45Urw9vLqRgjzZsn/e1vUny8vYVFdHS4uwK6lzYFnRkzZuhb3/qWkpOTNXXqVL300kuSpN/97ndujc/nC3qOMabFvOMdX3Oi+lOtZ/HixQoEAu5UUVHRqm0CgOPNnWtP6C0ulsrKwt3NF/u//9feGLNvX3v0adiwcHcEdD8dGl4+YMAAJScna8eOHe7oq+OPulRXV7tHefx+vxobG1VTU3PSmr0nuNnMvn37Whwt+rzo6GjFxcUFTQDQHgkJ9uMgSVq1Kry9fJG33pLuvNM+/vWvpcsuC28/QHfVoaDT0NCg999/X0OHDtWIESPk9/tVUFDgLm9sbFRhYaEmTpwoSUpJSVFkZGRQTWVlpbZs2eLWpKWlKRAIaOPGjW7Nhg0bFAgE3BoA6Gzf/779+uST9vyX7mTPHunaa6WjR+0VnRcuDHdHQPfVpqBz1113qbCwUOXl5dqwYYOuueYa1dXVac6cOfL5fMrOztbSpUu1Zs0abdmyRTfddJP69++vzMxMSZLjOJo7d64WLVqk1157TZs2bdKNN97ofhQmSWPGjNH06dM1b948FRcXq7i4WPPmzVNGRoZGjx4d+p8AAJzAZZfZ69AcPCj95jfh7uaYhgY7BL662vb3yCPSKc4OAHq3tgzRuv76683QoUNNZGSkSUxMNFdffbXZunWru7ypqcnce++9xu/3m+joaHPZZZeZzZs3B63j0KFD5vbbbzeDBg0yMTExJiMjw+zatSuoZv/+/Wb27NkmNjbWxMbGmtmzZ5uampo2DSdjeDmAjlq92g7Z/tKXjDl4MNzdWLfccmz4+z//Ge5ugNAL9fu3z5ieeKHzU6urq5PjOAoEApyvA6BdPvvM3vn7o4+k3Fxp/vzw9vPoo9L3vmeP4Lz0kr1xJ+A1oX7/5l5XAPAFIiKkH/7QPv7P/7QfG4VLScmxoHXffYQcoLUIOgBwEt/5jr2z+c6d4buuzt690re+ZYPWzJnSPfeEpw+gJyLoAMBJxMRI//Ef9vF993X9CKzGRnvycUWFdO650hNP2IsaAmgd/lwA4BS+8x0pKcmGnKVLu/a1Fy6U1q2T4uKk55+XHKdrXx/o6Qg6AHAKffvac3Qkey+p997rmtddtUp66CF78vFTT0nnndc1rwt4CUEHAFrhiiukq66SjhyR5syxHyl1ptdflxYssI+XLpWuvLJzXw/wKoIOALSCz2ePrgwebO9/1XzeTmcoK5NmzbLD22+4QfrRjzrvtQCvI+gAQCslJBwbefXLX9pr2YTaRx/ZoeP19dKkSdJjj3HlY6AjCDoA0AbXXivNmycZY4+2bN4cunXv2SOlp0tVVVJysvTcc1K/fqFbP9AbEXQAoI1yc6XJk6UDB6RvfEPasaPj69y9265zxw7pzDOll1+WTj+94+sFejuCDgC0UVSU9Mwz0pgxNqBMmiRt29b+9b3/vnTppdI//mFDzptvSl/+csjaBXo1gg4AtMOgQTaQJCdLlZXSxRfb69y0VUGBlJZmz80ZOVIqLJTOOivEzQK9GEEHANopPl564w0bVGpr7Uip+fOlf/3r1M89dEj6wQ/sOTmBgA1KGzbYIzoAQoegAwAdMHiwPbJz5532+9/8RjrnHCknR/rkk5b1n35qR1JdcIH0X/9l582bJ736qjRkSFd1DfQePmOMCXcTnSHUt3kHgFN59VUbeJpHYkVESBMm2HN5IiOl8nJ7O4f6ert86FDpkUe4GCDweaF+/yboAEAIHT0qrV4tPfigVFp64pqzzpJuu81+zNW/f5e2B3R7BJ1WIugACLcPP5TeftueaNzUZC84+LWvSRddxB3IgS8S6vfviBD0BAA4gZEj7QQgfPg/BQAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8CyCDgAA8KwOBZ2cnBz5fD5lZ2e784wxWrJkiRITExUTE6PJkydr69atQc9raGjQggULNGTIEA0YMEAzZ87U7t27g2pqamqUlZUlx3HkOI6ysrJUW1vbkXYBAEAv0+6gU1JSoocfflhjx44Nmr98+XKtWLFCubm5Kikpkd/v17Rp01RfX+/WZGdna82aNcrLy9O6det04MABZWRk6OjRo25NZmamysrKlJ+fr/z8fJWVlSkrK6u97QIAgN7ItEN9fb0ZNWqUKSgoMJMmTTJ33HGHMcaYpqYm4/f7zbJly9zaw4cPG8dxzKpVq4wxxtTW1prIyEiTl5fn1uzZs8f06dPH5OfnG2OM2bZtm5FkiouL3ZqioiIjyWzfvr1VPQYCASPJBAKB9mwiAAAIg1C/f7friM78+fN15ZVXaurUqUHzy8vLVVVVpfT0dHdedHS0Jk2apPXr10uSSktLdeTIkaCaxMREJSUluTVFRUVyHEepqaluzYQJE+Q4jltzvIaGBtXV1QVNAACgd4to6xPy8vL07rvvqqSkpMWyqqoqSVJCQkLQ/ISEBO3cudOtiYqK0sCBA1vUND+/qqpK8fHxLdYfHx/v1hwvJydHP//5z9u6OQAAwMPadESnoqJCd9xxh5588kn169fvC+t8Pl/Q98aYFvOOd3zNiepPtp7FixcrEAi4U0VFxUlfDwAAeF+bgk5paamqq6uVkpKiiIgIRUREqLCwUA8++KAiIiLcIznHH3Wprq52l/n9fjU2NqqmpuakNXv37m3x+vv27WtxtKhZdHS04uLigiYAANC7tSnoTJkyRZs3b1ZZWZk7jR8/XrNnz1ZZWZlGjhwpv9+vgoIC9zmNjY0qLCzUxIkTJUkpKSmKjIwMqqmsrNSWLVvcmrS0NAUCAW3cuNGt2bBhgwKBgFsDAABwKm06Ryc2NlZJSUlB8wYMGKDBgwe787Ozs7V06VKNGjVKo0aN0tKlS9W/f39lZmZKkhzH0dy5c7Vo0SINHjxYgwYN0l133aXk5GT35OYxY8Zo+vTpmjdvnh566CFJ0s0336yMjAyNHj26wxsNAAB6hzafjHwqd999tw4dOqTbbrtNNTU1Sk1N1dq1axUbG+vW3H///YqIiNB1112nQ4cOacqUKXr88cfVt29ft2b16tVauHChOzpr5syZys3NDXW7AADAw3zGGBPuJjpDXV2dHMdRIBDgfB0AAHqIUL9/c68rAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWQQdAADgWW0KOitXrtTYsWMVFxenuLg4paWl6eWXX3aXG2O0ZMkSJSYmKiYmRpMnT9bWrVuD1tHQ0KAFCxZoyJAhGjBggGbOnKndu3cH1dTU1CgrK0uO48hxHGVlZam2trb9WwkAAHqlNgWdYcOGadmyZXrnnXf0zjvv6PLLL9dVV13lhpnly5drxYoVys3NVUlJifx+v6ZNm6b6+np3HdnZ2VqzZo3y8vK0bt06HThwQBkZGTp69Khbk5mZqbKyMuXn5ys/P19lZWXKysoK0SYDAIBew3TQwIEDzW9/+1vT1NRk/H6/WbZsmbvs8OHDxnEcs2rVKmOMMbW1tSYyMtLk5eW5NXv27DF9+vQx+fn5xhhjtm3bZiSZ4uJit6aoqMhIMtu3b291X4FAwEgygUCgo5sIAAC6SKjfv9t9js7Ro0eVl5engwcPKi0tTeXl5aqqqlJ6erpbEx0drUmTJmn9+vWSpNLSUh05ciSoJjExUUlJSW5NUVGRHMdRamqqWzNhwgQ5juPWnEhDQ4Pq6uqCJgAA0Lu1Oehs3rxZp512mqKjo3XrrbdqzZo1Ov/881VVVSVJSkhICKpPSEhwl1VVVSkqKkoDBw48aU18fHyL142Pj3drTiQnJ8c9p8dxHA0fPrytmwYAADymzUFn9OjRKisrU3Fxsb7//e9rzpw52rZtm7vc5/MF1RtjWsw73vE1J6o/1XoWL16sQCDgThUVFa3dJAAA4FFtDjpRUVE655xzNH78eOXk5GjcuHF64IEH5Pf7JanFUZfq6mr3KI/f71djY6NqampOWrN3794Wr7tv374WR4s+Lzo62h0N1jwBAIDercPX0THGqKGhQSNGjJDf71dBQYG7rLGxUYWFhZo4caIkKSUlRZGRkUE1lZWV2rJli1uTlpamQCCgjRs3ujUbNmxQIBBwawAAAFojoi3F99xzj2bMmKHhw4ervr5eeXl5evPNN5Wfny+fz6fs7GwtXbpUo0aN0qhRo7R06VL1799fmZmZkiTHcTR37lwtWrRIgwcP1qBBg3TXXXcpOTlZU6dOlSSNGTNG06dP17x58/TQQw9Jkm6++WZlZGRo9OjRId58AADgZW0KOnv37lVWVpYqKyvlOI7Gjh2r/Px8TZs2TZJ0991369ChQ7rttttUU1Oj1NRUrV27VrGxse467r//fkVEROi6667ToUOHNGXKFD3++OPq27evW7N69WotXLjQHZ01c+ZM5ebmhmJ7AQBAL+IzxphwN9EZ6urq5DiOAoEA5+sAANBDhPr9m3tdAQAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAzyLoAAAAz2pT0MnJydFXv/pVxcbGKj4+XrNmzdIHH3wQVGOM0ZIlS5SYmKiYmBhNnjxZW7duDappaGjQggULNGTIEA0YMEAzZ87U7t27g2pqamqUlZUlx3HkOI6ysrJUW1vbvq0EAAC9UpuCTmFhoebPn6/i4mIVFBTos88+U3p6ug4ePOjWLF++XCtWrFBubq5KSkrk9/s1bdo01dfXuzXZ2dlas2aN8vLytG7dOh04cEAZGRk6evSoW5OZmamysjLl5+crPz9fZWVlysrKCsEmAwCAXsN0QHV1tZFkCgsLjTHGNDU1Gb/fb5YtW+bWHD582DiOY1atWmWMMaa2ttZERkaavLw8t2bPnj2mT58+Jj8/3xhjzLZt24wkU1xc7NYUFRUZSWb79u2t6i0QCBhJJhAIdGQTAQBAFwr1+3eHztEJBAKSpEGDBkmSysvLVVVVpfT0dLcmOjpakyZN0vr16yVJpaWlOnLkSFBNYmKikpKS3JqioiI5jqPU1FS3ZsKECXIcx605XkNDg+rq6oImAADQu7U76BhjdOedd+qSSy5RUlKSJKmqqkqSlJCQEFSbkJDgLquqqlJUVJQGDhx40pr4+PgWrxkfH+/WHC8nJ8c9n8dxHA0fPry9mwYAADyi3UHn9ttv13vvvac//OEPLZb5fL6g740xLeYd7/iaE9WfbD2LFy9WIBBwp4qKitZsBgAA8LB2BZ0FCxbohRde0BtvvKFhw4a58/1+vyS1OOpSXV3tHuXx+/1qbGxUTU3NSWv27t3b4nX37dvX4mhRs+joaMXFxQVNAACgd2tT0DHG6Pbbb9ezzz6r119/XSNGjAhaPmLECPn9fhUUFLjzGhsbVVhYqIkTJ0qSUlJSFBkZGVRTWVmpLVu2uDVpaWkKBALauHGjW7NhwwYFAgG3BgAA4FQi2lI8f/58PfXUU3r++ecVGxvrHrlxHEcxMTHy+XzKzs7W0qVLNWrUKI0aNUpLly5V//79lZmZ6dbOnTtXixYt0uDBgzVo0CDdddddSk5O1tSpUyVJY8aM0fTp0zVv3jw99NBDkqSbb75ZGRkZGj16dCi3HwAAeFibgs7KlSslSZMnTw6a/9hjj+mmm26SJN199906dOiQbrvtNtXU1Cg1NVVr165VbGysW3///fcrIiJC1113nQ4dOqQpU6bo8ccfV9++fd2a1atXa+HChe7orJkzZyo3N7c92wgAAHopnzHGhLuJzlBXVyfHcRQIBDhfBwCAHiLU79/c6woAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHgWQQcAAHhWm4POW2+9pW9+85tKTEyUz+fTc889F7TcGKMlS5YoMTFRMTExmjx5srZu3RpU09DQoAULFmjIkCEaMGCAZs6cqd27dwfV1NTUKCsrS47jyHEcZWVlqba2ts0bCAAAeq82B52DBw9q3Lhxys3NPeHy5cuXa8WKFcrNzVVJSYn8fr+mTZum+vp6tyY7O1tr1qxRXl6e1q1bpwMHDigjI0NHjx51azIzM1VWVqb8/Hzl5+errKxMWVlZ7dhEAADQa5kOkGTWrFnjft/U1GT8fr9ZtmyZO+/w4cPGcRyzatUqY4wxtbW1JjIy0uTl5bk1e/bsMX369DH5+fnGGGO2bdtmJJni4mK3pqioyEgy27dvb1VvgUDASDKBQKAjmwgAALpQqN+/Q3qOTnl5uaqqqpSenu7Oi46O1qRJk7R+/XpJUmlpqY4cORJUk5iYqKSkJLemqKhIjuMoNTXVrZkwYYIcx3FrjtfQ0KC6urqgCQAA9G4hDTpVVVWSpISEhKD5CQkJ7rKqqipFRUVp4MCBJ62Jj49vsf74+Hi35ng5OTnu+TyO42j48OEd3h4AANCzdcqoK5/PF/S9MabFvOMdX3Oi+pOtZ/HixQoEAu5UUVHRjs4BAICXhDTo+P1+SWpx1KW6uto9yuP3+9XY2KiampqT1uzdu7fF+vft29fiaFGz6OhoxcXFBU0AAKB3C2nQGTFihPx+vwoKCtx5jY2NKiws1MSJEyVJKSkpioyMDKqprKzUli1b3Jq0tDQFAgFt3LjRrdmwYYMCgYBbAwAAcCoRbX3CgQMH9I9//MP9vry8XGVlZRo0aJDOOOMMZWdna+nSpRo1apRGjRqlpUuXqn///srMzJQkOY6juXPnatGiRRo8eLAGDRqku+66S8nJyZo6daokacyYMZo+fbrmzZunhx56SJJ08803KyMjQ6NHjw7FdgMAgF6gzUHnnXfe0de//nX3+zvvvFOSNGfOHD3++OO6++67dejQId12222qqalRamqq1q5dq9jYWPc5999/vyIiInTdddfp0KFDmjJlih5//HH17dvXrVm9erUWLlzojs6aOXPmF167BwAA4ER8xhgT7iY6Q11dnRzHUSAQ4HwdAAB6iFC/f3OvKwAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FkEHQAA4FndPuj85je/0YgRI9SvXz+lpKTo7bffDndLAACgh+jWQefpp59Wdna2fvKTn2jTpk269NJLNWPGDO3atSvcrQEAgB7AZ4wx4W7ii6Smpuqiiy7SypUr3XljxozRrFmzlJOTc9Ln1tXVyXEcvbLlFQ2IHdDq1/T5fG3q0ac21vfw9XfFa/S29XfFa/T09XfFa7Rl/d1p3V/kVP+0G51i+Ume35Hnevm1O6I9/xZLXfO31JHXas/rHag/oK+d/TUFAgHFxcW16zU/L6LDa+gkjY2NKi0t1Y9//OOg+enp6Vq/fn2L+oaGBjU0NLjf19XVSZKuePIKqV/n9goAAELkcGhX122DzieffKKjR48qISEhaH5CQoKqqqpa1Ofk5OjnP/95i/kjB45U35i+p3y9tib1thwI68x1t3X9nbnutq6/p667revnZ97xdbd1/b1p3Sf73/LJ/gfe1c8Lx2uGo9e26G6/S22q78T1N6lJtapt0/pPptsGnWbH/0IZY074S7Z48WLdeeed7vd1dXUaPny4Nt26KSSHvgAAQOerq6uTs8QJ2fq6bdAZMmSI+vbt2+LoTXV1dYujPJIUHR2t6OjormoPAAD0AN121FVUVJRSUlJUUFAQNL+goEATJ04MU1cAAKAn6bZHdCTpzjvvVFZWlsaPH6+0tDQ9/PDD2rVrl2699dZwtwYAAHqAbh10rr/+eu3fv1/33XefKisrlZSUpL/85S8688wzw90aAADoAbr1dXQ6ovk6OqEahw8AADpfqN+/u+05OgAAAB1F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ5F0AEAAJ4VEe4GOosxRpJUV1cX5k4AAEBrNb9vN7+Pd5Rng87+/fslScOHDw9zJwAAoK32798vx3E6vB7PBp1BgwZJknbt2hWSHxTar66uTsOHD1dFRYXi4uLC3U6vx/7oPtgX3Qf7ovsIBAI644wz3PfxjvJs0OnTx55+5DgOv7TdRFxcHPuiG2F/dB/si+6DfdF9NL+Pd3g9IVkLAABAN0TQAQAAnuXZoBMdHa17771X0dHR4W6l12NfdC/sj+6DfdF9sC+6j1DvC58J1fgtAACAbsazR3QAAAAIOgAAwLMIOgAAwLMIOgAAwLM8G3R+85vfaMSIEerXr59SUlL09ttvh7slz3vrrbf0zW9+U4mJifL5fHruueeClhtjtGTJEiUmJiomJkaTJ0/W1q1bw9Osx+Xk5OirX/2qYmNjFR8fr1mzZumDDz4IqmF/dI2VK1dq7Nix7oXo0tLS9PLLL7vL2Q/hk5OTI5/Pp+zsbHce+6NrLFmyRD6fL2jy+/3u8lDuB08GnaefflrZ2dn6yU9+ok2bNunSSy/VjBkztGvXrnC35mkHDx7UuHHjlJube8Lly5cv14oVK5Sbm6uSkhL5/X5NmzZN9fX1Xdyp9xUWFmr+/PkqLi5WQUGBPvvsM6Wnp+vgwYNuDfujawwbNkzLli3TO++8o3feeUeXX365rrrqKvcfbfZDeJSUlOjhhx/W2LFjg+azP7rOBRdcoMrKSnfavHmzuyyk+8F40Ne+9jVz6623Bs0777zzzI9//OMwddT7SDJr1qxxv29qajJ+v98sW7bMnXf48GHjOI5ZtWpVGDrsXaqrq40kU1hYaIxhf4TbwIEDzW9/+1v2Q5jU19ebUaNGmYKCAjNp0iRzxx13GGP4u+hK9957rxk3btwJl4V6P3juiE5jY6NKS0uVnp4eND89PV3r168PU1coLy9XVVVV0H6Jjo7WpEmT2C9dIBAISDp2s1v2R3gcPXpUeXl5OnjwoNLS0tgPYTJ//nxdeeWVmjp1atB89kfX2rFjhxITEzVixAjdcMMN+vDDDyWFfj947qaen3zyiY4ePaqEhISg+QkJCaqqqgpTV2j+2Z9ov+zcuTMcLfUaxhjdeeeduuSSS5SUlCSJ/dHVNm/erLS0NB0+fFinnXaa1qxZo/PPP9/9R5v90HXy8vL07rvvqqSkpMUy/i66Tmpqqp544gmde+652rt3r37xi19o4sSJ2rp1a8j3g+eCTjOfzxf0vTGmxTx0PfZL17v99tv13nvvad26dS2WsT+6xujRo1VWVqba2lo988wzmjNnjgoLC93l7IeuUVFRoTvuuENr165Vv379vrCO/dH5ZsyY4T5OTk5WWlqazj77bP3ud7/ThAkTJIVuP3juo6shQ4aob9++LY7eVFdXt0iH6DrNZ9OzX7rWggUL9MILL+iNN97QsGHD3Pnsj64VFRWlc845R+PHj1dOTo7GjRunBx54gP3QxUpLS1VdXa2UlBRFREQoIiJChYWFevDBBxUREeH+zNkfXW/AgAFKTk7Wjh07Qv534bmgExUVpZSUFBUUFATNLygo0MSJE8PUFUaMGCG/3x+0XxobG1VYWMh+6QTGGN1+++169tln9frrr2vEiBFBy9kf4WWMUUNDA/uhi02ZMkWbN29WWVmZO40fP16zZ89WWVmZRo4cyf4Ik4aGBr3//vsaOnRo6P8u2nz6cg+Ql5dnIiMjzaOPPmq2bdtmsrOzzYABA8xHH30U7tY8rb6+3mzatMls2rTJSDIrVqwwmzZtMjt37jTGGLNs2TLjOI559tlnzebNm823v/1tM3ToUFNXVxfmzr3n+9//vnEcx7z55pumsrLSnT799FO3hv3RNRYvXmzeeustU15ebt577z1zzz33mD59+pi1a9caY9gP4fb5UVfGsD+6yqJFi8ybb75pPvzwQ1NcXGwyMjJMbGys+z4dyv3gyaBjjDH//d//bc4880wTFRVlLrroIndYLTrPG2+8YSS1mObMmWOMsUMG7733XuP3+010dLS57LLLzObNm8PbtEedaD9IMo899phbw/7oGt/97nfdf4u+9KUvmSlTprghxxj2Q7gdH3TYH13j+uuvN0OHDjWRkZEmMTHRXH311Wbr1q3u8lDuB58xxnTwiBMAAEC35LlzdAAAAJoRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGcRdAAAgGf9fwOwxtMoADYSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_number = 0\n",
    "file = scan_list[file_number]\n",
    "force2x = file.downsampled_force2x.data\n",
    "force2y = file.downsampled_force2y.data\n",
    "force = file.downsampled_force2.data\n",
    "distance = (file.distance1.data) #Convert from um to nm/bp\n",
    "time = (file.distance1.timestamps - min(file.distance1.timestamps))/1e9\n",
    "\n",
    "distance_smooth = 1000*savgol_filter(np.ravel(distance), 100, 3)\n",
    "force_smooth = savgol_filter(np.ravel(force), 10, 3)\n",
    "\n",
    "plt.plot(time, force, color = \"orange\") #plot Force 2X\n",
    "plt.show()\n",
    "\n",
    "plt.plot(time, force2x) #plot Force 2X\n",
    "plt.show()\n",
    "    \n",
    "plt.plot(time, force2y, color = \"red\") #plot Force 2Y\n",
    "plt.show()\n",
    "\n",
    "plt.plot(time, distance, color = \"green\")\n",
    "plt.plot(time, distance_smooth, color = \"blue\")\n",
    "plt.xlim(0,50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36dd0a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.signal import find_peaks\n",
    "#from scipy.signal import find_peaks_cwt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c92fb64",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'find_peaks' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m peaks_time \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      3\u001b[0m peaks_force \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m----> 4\u001b[0m indices \u001b[38;5;241m=\u001b[39m \u001b[43mfind_peaks\u001b[49m(\u001b[38;5;241m-\u001b[39mdistance_smooth, prominence \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(time, distance_smooth, color \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlim(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m200\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'find_peaks' is not defined"
     ]
    }
   ],
   "source": [
    "peaks = []\n",
    "peaks_time = []\n",
    "peaks_force = []\n",
    "indices = find_peaks(-distance_smooth, prominence = 2)\n",
    "\n",
    "plt.plot(time, distance_smooth, color = \"blue\")\n",
    "plt.xlim(0,200)\n",
    "\n",
    "i = 0\n",
    "for x in indices[0]:\n",
    "    peaks.append(distance_smooth[x])\n",
    "    peaks_time.append(time[x])\n",
    "    peaks_force.append(force_smooth[x])\n",
    "plt.scatter(peaks_time,peaks, color = \"red\")\n",
    "plt.show()\n",
    "plt.plot(time,force)\n",
    "plt.plot(time,force_smooth)\n",
    "plt.scatter(peaks_time,peaks_force, color = \"red\", s =100)\n",
    "plt.xlim(0,200)\n",
    "plt.show()\n",
    "#plt.hist(peaks_force)\n",
    "print(peaks_force)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "153579fb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, file in enumerate(scan_list):\n",
    "    force2x = file.downsampled_force2x.data\n",
    "    force2y = file.downsampled_force2y.data\n",
    "    distance = (file.distance1.data) - file.distance1.data[0]  #Convert from um to nm/bp\n",
    "    time = (file.distance1.timestamps - min(file.distance1.timestamps))/1e9\n",
    "   \n",
    "    plt.plot(time, 1000*distance) \n",
    "    plt.xlim(0,400)\n",
    "    plt.ylim(-250,250)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3b1c0929",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "221543 Scan taxol_eb_hset_1\n",
      "223838 Scan taxol_eb_hset_2\n"
     ]
    }
   ],
   "source": [
    "#Process 2D Scans\n",
    "\n",
    "#Stand alone scans\n",
    "for index, x in enumerate(scan_list):\n",
    "    for scan in x.scans:\n",
    "        sc = x.scans[scan]\n",
    "        sc.save_tiff(output_folder + exp_id + \"/\" + strip_ext(scan_list_names[index]) + \"_\" + scan + \".tiff\")\n",
    "    file_name = strip_ext(scan_list_names[index])\n",
    "    file_name_split_date = file_name.split(\"-\")\n",
    "    file_name_short_date = file_name_split_date[-1]\n",
    "    file_name_split_marker = file_name_short_date.split(\" Marker\")\n",
    "    file_name_short = file_name_split_marker[0]\n",
    "    print(file_name_short_date)\n",
    "    x = scan_list[index]\n",
    "\n",
    "    force = x.downsampled_force2.data\n",
    "    distance = (x.distance1.data) #Convert from um to nm/bp\n",
    "    time = (x.distance1.timestamps - min(x.distance1.timestamps))/1e9\n",
    "\n",
    "    export = np.column_stack((time, distance, force))\n",
    "    column_names = ((file_name_short + \"_t\") + \",\" + (file_name_short + \"_d\") + \",\" + (file_name_short + \"_f\"))\n",
    "\n",
    "    np.savetxt(output_folder + exp_id + \"/\" + file_name_short_date + \".txt\", export, delimiter=\",\",header=column_names)\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "#Process force time traces\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a041b56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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