Merge tau v q data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d22c01d",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cd2cf57",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np \n",
    "import xarray as xr \n",
    "import yaml\n",
    "from yaml import CLoader as Loader, CDumper as Dumper\n",
    "from pathlib import Path\n",
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"Y:\\\\Philip\\\\PyDDM\\\\PyDDM\") #must point to the PyDDM folder\n",
    "import ddm_analysis_and_fitting as ddm\n",
    "\n",
    "import gc\n",
    "import statistics\n",
    "\n",
    "import scipy\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b59380",
   "metadata": {},
   "source": [
    "## Gather info from every roi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "708e3d34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\0min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pneill\\Anaconda3\\lib\\site-packages\\xarray\\backends\\plugins.py:65: RuntimeWarning: Engine 'cfgrib' loading failed:\n",
      "Cannot find the ecCodes library\n",
      "  warnings.warn(f\"Engine {name!r} loading failed:\\n{ex}\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\100min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\10min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\110min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\120min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\130min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\140min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\150min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\160min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\170min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\180min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\190min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\200min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\20min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\210min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\220min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\230min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\240min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\30min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\40min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\50min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\60min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\70min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\80min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae\\90min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\0min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\100min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\10min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\110min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\120min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\130min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\140min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\150min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\160min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\170min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\180min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\190min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\200min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\20min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\210min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\220min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\230min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\240min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\30min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\40min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\50min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\60min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\70min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\80min\n",
      "Analyzing directory: Y:\\Philip\\Nat\\0.5hae(2)\\90min\n"
     ]
    }
   ],
   "source": [
    "master_dict = {}\n",
    "    \n",
    "for i in range(0,241,10):\n",
    "    the_time = str(i)+'min'\n",
    "    master_dict[the_time] = {}\n",
    "\n",
    "locations = ['Y:\\\\Philip\\\\Nat\\\\0.5hae','Y:\\\\Philip\\\\Nat\\\\0.5hae(2)']\n",
    "for location in locations:\n",
    "    time_locations = glob.glob(location+\"\\\\*min\")\n",
    "    for times_dir in time_locations:\n",
    "        print(\"Analyzing directory: %s\" % times_dir)\n",
    "        the_time = times_dir.split(\"\\\\\")[-1]\n",
    "\n",
    "        ISF_files = glob.glob('%s\\\\*)\\\\*.nc' % times_dir)\n",
    "        for i,files in enumerate(ISF_files):\n",
    "            master_dict[the_time][files] = {}\n",
    "\n",
    "            stuff = xr.load_dataset(files)\n",
    "\n",
    "            master_dict[the_time][files]['tau'] = stuff.parameters.loc['Tau']\n",
    "            master_dict[the_time][files]['q'] = stuff.q\n",
    "            master_dict[the_time][files]['amp'] = stuff.A\n",
    "            master_dict[the_time][files]['stretch'] = stuff.parameters.loc['StretchingExp']\n",
    "            master_dict[the_time][files]['diffcoeff'] = stuff.effective_diffusion_coeff\n",
    "            master_dict[the_time][files]['diffcoeff_std'] = stuff.diffusion_coeff_std\n",
    "            master_dict[the_time][files]['alpha'] = stuff.msd_alpha\n",
    "            master_dict[the_time][files]['slope'] = stuff.tau_vs_q_slope\n",
    "        \n",
    "np.save(locations[0]+'_master_dict.npy', master_dict, allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae601dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "#master_dict = np.load(locations[0]+'_master_dict.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96686aec",
   "metadata": {},
   "source": [
    "## Set parameters and trim data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd5cb18d",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_min = 3\n",
    "q_max = 11\n",
    "q_range = slice(q_min,q_max)\n",
    "\n",
    "slope_min = -2.2\n",
    "slope_max = -1.8\n",
    "\n",
    "y_floor1 = 0\n",
    "y_ceil1 = 4\n",
    "\n",
    "y_floor2 = 0.15\n",
    "y_ceil2 = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a8ecb05",
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmed_dict = {}\n",
    "\n",
    "for i in range(0,241,10):\n",
    "    the_time = str(i)+'min'\n",
    "    trimmed_dict[the_time] = {}\n",
    "    for files in master_dict[the_time]:\n",
    "        if y_floor1 <= master_dict[the_time][files]['tau'][q_min] <= y_ceil1 and \\\n",
    "        y_floor2 <= master_dict[the_time][files]['tau'][q_max] <= y_ceil2:\n",
    "            \n",
    "            trimmed_dict[the_time][files] = {}\n",
    "            \n",
    "            trimmed_dict[the_time][files]['tau'] = master_dict[the_time][files]['tau']\n",
    "            trimmed_dict[the_time][files]['q'] = master_dict[the_time][files]['q']\n",
    "            trimmed_dict[the_time][files]['amp'] = master_dict[the_time][files]['amp']\n",
    "            trimmed_dict[the_time][files]['stretch'] = master_dict[the_time][files]['stretch']\n",
    "            trimmed_dict[the_time][files]['diffcoeff'] = master_dict[the_time][files]['diffcoeff']\n",
    "            trimmed_dict[the_time][files]['diffcoeff_std'] = master_dict[the_time][files]['diffcoeff_std']\n",
    "            trimmed_dict[the_time][files]['alpha'] = master_dict[the_time][files]['alpha']\n",
    "            trimmed_dict[the_time][files]['slope'] = master_dict[the_time][files]['slope']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445dba6f",
   "metadata": {},
   "source": [
    "## Average values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4eab5afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = ['tau','q','amp','stretch']\n",
    "for variable in variables:\n",
    "\n",
    "    time_list = []\n",
    "    for i in range(0,241,10):\n",
    "        the_time = str(i)+'min'\n",
    "        avg_list = []\n",
    "        for qs in range(64):\n",
    "            value_list = []\n",
    "            for files in trimmed_dict[the_time]:\n",
    "                value = trimmed_dict[the_time][files][variable][qs].values.tolist()\n",
    "                value_list.append(value)\n",
    "            try:\n",
    "                avg = sum(value_list)/len(value_list)\n",
    "            except:\n",
    "                avg = 0\n",
    "            avg_list.append(avg)\n",
    "        time_list.append(avg_list)\n",
    "    df = pd.DataFrame(time_list)\n",
    "    df.to_excel(locations[0]+'\\\\_'+variable+'.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c6f991d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = ['diffcoeff','diffcoeff_std','alpha','slope']\n",
    "for variable in variables:\n",
    "\n",
    "    time_list = []\n",
    "    for i in range(0,241,10):\n",
    "        the_time = str(i)+'min'\n",
    "        value_list = []\n",
    "        for files in trimmed_dict[the_time]:\n",
    "            value = trimmed_dict[the_time][files][variable]\n",
    "            value_list.append(value)\n",
    "        try:\n",
    "            time_val = sum(value_list)/len(value_list)\n",
    "        except:\n",
    "            time_val = 0\n",
    "        time_list.append(time_val)\n",
    "    df = pd.DataFrame(time_list)\n",
    "    df.to_excel(locations[0]+'\\\\_'+variable+'.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d00886d1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unfiltered: 150 --> Filtered: 114\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%matplotlib notebook\n",
    "\n",
    "the_time = '70min'\n",
    "\n",
    "xbound = [0.8,8]\n",
    "ybound = [0.01,100]\n",
    "\n",
    "plt.figure()\n",
    "for n,files in enumerate(master_dict[the_time]):\n",
    "    plt.xlabel('q (um-1)')\n",
    "    plt.ylabel('tau (s)')\n",
    "    plt.title('Unfiltered')\n",
    "    plt.xlim(xbound)\n",
    "    plt.ylim(ybound)\n",
    "    plt.loglog(master_dict[the_time][files]['q'], master_dict[the_time][files]['tau'], 'bo', alpha = 0.01)\n",
    "    plt.loglog(master_dict[the_time][files]['q'][q_min], master_dict[the_time][files]['tau'][q_min], 'go', alpha = 0.05)\n",
    "    plt.loglog(master_dict[the_time][files]['q'][q_max], master_dict[the_time][files]['tau'][q_max], 'go', alpha = 0.05)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "for i, files in enumerate(trimmed_dict[the_time]):\n",
    "    plt.xlabel('q (um-1)')\n",
    "    plt.ylabel('tau (s)')\n",
    "    plt.title('Filtered')\n",
    "    plt.xlim(xbound)\n",
    "    plt.ylim(ybound)\n",
    "    plt.loglog(trimmed_dict[the_time][files]['q'], trimmed_dict[the_time][files]['tau'], 'ro', alpha = 0.01)\n",
    "    plt.loglog(trimmed_dict[the_time][files]['q'][q_min], master_dict[the_time][files]['tau'][q_min], 'go', alpha = 0.05)\n",
    "    plt.loglog(trimmed_dict[the_time][files]['q'][q_max], master_dict[the_time][files]['tau'][q_max], 'go', alpha = 0.05)\n",
    "    #plt.loglog(trimmed_dict[the_time][files]['q'], time_list[16], 'bo', alpha = 0.01)\n",
    "    \n",
    "print('Unfiltered: '+ str(n+1),'--> Filtered: ' + str(i+1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f3903fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "D_list = []\n",
    "D_std_list = []\n",
    "amplitude_list = []\n",
    "#D = []\n",
    "\n",
    "for j in range(0,241,10):\n",
    "    the_time = str(j)+'min'\n",
    "    for i, files in enumerate(trimmed_dict[the_time]):\n",
    "        D = 1/(trimmed_dict[the_time][files]['tau'][q_range]*trimmed_dict[the_time][files]['q'][q_range]**2)\n",
    "        \n",
    "    #Deez = np.array(D)\n",
    "    means = D.mean()\n",
    "    stds = D.std()\n",
    "    \n",
    "    D_list.append(means)\n",
    "    D_std_list.append(stds)\n",
    "\n",
    "Diff_calc = pd.DataFrame(np.array(D_list))    \n",
    "Diff_std_calc = pd.DataFrame(np.array(D_std_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c11eaa87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0\n",
      "0.416522\n",
      "0.405221\n",
      "0.343719\n",
      "0.400218\n",
      "0.358578\n",
      "0.363962\n",
      "0.355678\n",
      "0.315571\n",
      "0.361559\n",
      "0.284536\n",
      "0.318445\n",
      "0.284389\n",
      "0.262888\n",
      "0.248007\n",
      "0.376176\n",
      "0.223041\n",
      "0.410512\n",
      "0.261082\n",
      "0.267288\n",
      "0.249754\n",
      "0.229232\n",
      "0.282869\n",
      "0.303434\n",
      "0.285425\n",
      "0.281710\n"
     ]
    }
   ],
   "source": [
    "print(Diff_calc.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "acc70618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0\n",
      "0.035061\n",
      "0.064067\n",
      "0.062208\n",
      "0.065266\n",
      "0.047229\n",
      "0.040015\n",
      "0.047585\n",
      "0.036968\n",
      "0.045765\n",
      "0.030227\n",
      "0.052786\n",
      "0.040630\n",
      "0.033600\n",
      "0.031365\n",
      "0.099238\n",
      "0.026075\n",
      "0.067951\n",
      "0.040341\n",
      "0.055916\n",
      "0.037533\n",
      "0.037477\n",
      "0.052492\n",
      "0.052957\n",
      "0.062691\n",
      "0.048134\n"
     ]
    }
   ],
   "source": [
    "print(Diff_std_calc.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80865108",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}