analyseScript/Joschka/Parameter_guessing_comp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "\n",
    "import glob\n",
    "\n",
    "import xrft\n",
    "import finufft\n",
    "\n",
    "from uncertainties import ufloat\n",
    "from uncertainties import unumpy as unp\n",
    "from uncertainties import umath\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.rcParams['font.size'] = 18\n",
    "\n",
    "from scipy.ndimage import gaussian_filter\n",
    "import matplotlib as mpl\n",
    "from scipy.optimize import curve_fit\n",
    "mpl.rc('xtick', labelsize=8)\n",
    "mpl.rc('ytick', labelsize=8)\n",
    "\n",
    "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
    "from Analyser.FitAnalyser import FitAnalyser\n",
    "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
    "from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
    "from ToolFunction.ToolFunction import *\n",
    "\n",
    "import time\n",
    "\n",
    "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
    "xr.plot.dataarray_plot.errorbar = errorbar\n",
    "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
    "\n",
    "imageAnalyser = ImageAnalyser()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#dataSet"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:09:57.820003800Z",
     "start_time": "2023-07-19T14:09:56.001606300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Some functions"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# get center of thresholded image\n",
    "\n",
    "def calc_cen(thresh1):\n",
    "    \"\"\"\n",
    "    returns array: [Y_center,X_center]\n",
    "    \"\"\"\n",
    "    cen = np.zeros(2)\n",
    "    (Y,X) = np.shape(thresh1)\n",
    "\n",
    "\n",
    "    thresh1 = thresh1 /np.sum(thresh1)\n",
    "\n",
    "    # marginal distributions\n",
    "    dx = np.sum(thresh1, 0)\n",
    "    dy = np.sum(thresh1, 1)\n",
    "\n",
    "    # expected values\n",
    "    cen[0] = np.sum(dx * np.arange(X))\n",
    "    cen[1] = np.sum(dy * np.arange(Y))\n",
    "    return cen\n",
    "\n",
    "def calc_cen_bulk(thresh):\n",
    "    \"\"\"\n",
    "    returns array in shape of input, containing array with [Y_center,X_center] for each image\n",
    "    \"\"\"\n",
    "    shape = np.shape(thresh)\n",
    "    cen = np.zeros((shape[0], shape[1], 2))\n",
    "    for i in range(0, shape[0]):\n",
    "        for j in range(0, shape[1]):\n",
    "            cen[i,j] = calc_cen(thresh[i,j])\n",
    "    return cen\n",
    "\n",
    "def guess_BEC_width(thresh, center):\n",
    "    \"\"\"\n",
    "    returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image\n",
    "    \"\"\"\n",
    "    shape = np.shape(thresh)\n",
    "    BEC_width_guess = np.zeros((shape[0], shape[1], 2))\n",
    "\n",
    "    for i in range(0, shape[0]):\n",
    "        for j in range(0, shape[1]):\n",
    "            BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])\n",
    "            BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])\n",
    "\n",
    "    return BEC_width_guess\n",
    "\n",
    "\n",
    "\n",
    "def gaussian(x, x0, sigma, A):\n",
    "    return A * np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
    "\n",
    "def polylog(power, numerator, order = 15):\n",
    "\n",
    "    dataShape = numerator.shape\n",
    "    numerator = np.tile(numerator, (order, 1))\n",
    "    numerator = np.power(numerator.T, np.arange(1, order+1)).T\n",
    "\n",
    "    denominator = np.arange(1, order+1)\n",
    "    denominator = np.tile(denominator, (dataShape[0], 1))\n",
    "    denominator = denominator.T\n",
    "\n",
    "    data = numerator/ np.power(denominator, power)\n",
    "\n",
    "    return np.sum(data, axis=0)\n",
    "\n",
    "def thermal(x, x0, amp, sigma, order = 15):\n",
    "    res = np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
    "    return amp * polylog(2, res, order)\n",
    "\n",
    "def Thomas_Fermi_1d(x, x0, amp, sigma):\n",
    "    res = (1-(( x - x0 ) / sigma) **2) **3/2\n",
    "    return amp * np.where(res > 0, res, 0)\n",
    "\n",
    "def density_1d(x, x0_bec, x0_th, amp_bec, amp_th, sigma_bec, sigma_th, polyorder=15):\n",
    "    return thermal(x, x0_th, amp_th, sigma_th, polyorder) + Thomas_Fermi_1d(x, x0_bec, amp_bec, sigma_bec)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:09:59.764265200Z",
     "start_time": "2023-07-19T14:09:59.697445300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import Data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "\n",
    "# import data\n",
    "img_dir = '//DyLabNAS/Data/'\n",
    "SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
    "folderPath = img_dir + SequenceName + '2023/04/17'# get_date()\n",
    "\n",
    "\n",
    "shotNum = \"import\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
    "dataSet = read_hdf5_file(filePath, \"images/MOT_3D_Camera/in_situ_absorption\")\n",
    "dataSet = swap_xy(dataSet)\n",
    "\n",
    "# get the scan axis name of the shot\n",
    "scanAxis = get_scanAxis(dataSet)\n",
    "\n",
    "# calculate the absorption imaging\n",
    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
    "\n",
    "dataSet\n",
    "OD = dataSet[\"OD\"]\n",
    "OD_np = OD.to_numpy()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:10:07.730652800Z",
     "start_time": "2023-07-19T14:10:05.350091900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 700x1500 with 11 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imageAnalyser.center = (960, 890)\n",
    "imageAnalyser.span = (150, 200)\n",
    "imageAnalyser.fraction = (0.1, 0.1)\n",
    "\n",
    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
    "cropOD = dataSet_cropOD.to_numpy()\n",
    "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[1], row=scanAxis[0])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:10:10.343320700Z",
     "start_time": "2023-07-19T14:10:07.733644900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Guess center\n",
    "\n",
    "ToDo: Crop from center guess"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting time: 133.1346035003662 ms\n"
     ]
    }
   ],
   "source": [
    "# from opencv import moments\n",
    "shape = np.shape(cropOD)\n",
    "sigma = 0.4\n",
    "blurred = gaussian_filter(cropOD, sigma=sigma)\n",
    "\n",
    "thresh = np.zeros(shape)\n",
    "for i in range(0,shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.5,0,1)\n",
    "\n",
    "center = calc_cen_bulk(thresh)\n",
    "\n",
    "BEC_width_guess = guess_BEC_width(thresh, center)\n",
    "\n",
    "X_guess_og = np.zeros((shape[0], shape[1], shape[3]))\n",
    "# Y_guess_og = np.zeros((shape[0], shape[1], shape[2]))\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        X_guess_og[i,j] = np.sum(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2) , :], 0) / len(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2),0])\n",
    "\n",
    "        # Y_guess_og[i,j] = np.sum(cropOD[i,j, :, round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)], 1) / len(cropOD[i,j,0,round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)])\n",
    "\n",
    "#[nr images x, nr images y, X / Y, center / sigma]\n",
    "x = np.linspace(0,149,150)\n",
    "y = np.linspace(0,199, 200)\n",
    "\n",
    "popt = np.zeros((shape[0], shape[1], 6))\n",
    "\n",
    "p0 = np.ones((shape[0], shape[1], 6))\n",
    "\n",
    "max = np.zeros((shape[0], shape[1]))\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    max[i] = np.ndarray.max(X_guess_og[i],axis=1)\n",
    "\n",
    "\n",
    "p0[:, :, 0] = center[:, :, 0]  # center BEC\n",
    "p0[:, :, 1] = center[:, :, 0]   # center th\n",
    "p0[:, :, 2] = 0.7 * max          # amp BEC\n",
    "p0[:, :, 3] = 0.3 * max         # amp th\n",
    "p0[:, :, 4] = BEC_width_guess[:, :, 0] / 1.22  # sigma BEC\n",
    "p0[:, :, 5] = BEC_width_guess[:, :, 0] * 3          # sigma th\n",
    "\n",
    "start = time.time()\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        popt[i,j], pcov = curve_fit(density_1d, x, X_guess_og[i,j] , p0[i, j], nan_policy='omit')\n",
    "stop = time.time()\n",
    "\n",
    "print(f'fitting time: {(stop-start)*1e3} ms')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:10:14.430793Z",
     "start_time": "2023-07-19T14:10:14.234569Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Bimodal 1d Fit"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1500x1000 with 10 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fsize=(15,10)\n",
    "vmax = 1.5\n",
    "fig, ax = plt.subplots(shape[0],shape[1],figsize=fsize)\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        lab = f\"A$_{{BEC}}$ = {popt[i,j,0]:.1f} \\n A$_{{th}}$ = {popt[i,j,1]:.1f} \"\n",
    "        ax[i,j].plot(x, X_guess_og[i,j])\n",
    "        ax[i,j].plot(x, density_1d(x, *popt[i,j]), label = lab)\n",
    "        ax[i,j].plot(x, thermal(x, popt[i,j,1], popt[i,j, 3], popt[i,j, 5]))\n",
    "\n",
    "\n",
    "        #ax[i,j].legend(fontsize=10)\n",
    "        ax[i,j].set_facecolor('#FFFFFF')\n",
    "plt.show()\n",
    "\n",
    "# fig, ax = plt.subplots(shape[0],shape[1],figsize=fsize)\n",
    "# for i in range(0,shape[0]):\n",
    "#     for j in range(0,shape[1]):\n",
    "#         ax[i][j].pcolormesh(cropOD[i][j], cmap='jet', vmin=0, vmax=vmax)\n",
    "#         #ax[i][j].plot(max[i,j,1],max[i,j,0], marker='x', markersize=12)\n",
    "#         ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', color='g', markersize=12)\n",
    "# plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:11:19.303385Z",
     "start_time": "2023-07-19T14:11:17.704358600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[76.98612316 76.98612316  0.22849011  0.09792433 24.59016393 90.        ]\n",
      "[ 7.54164943e+01  7.71704306e+01  7.15133560e-02  1.76207641e-01\n",
      "  1.30666164e+01 -3.38844382e+01]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,1):\n",
    "    for j in range(0,1):\n",
    "        print(p0[i,j])\n",
    "        print(popt[i,j])\n",
    "        print(\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-19T14:13:10.622384700Z",
     "start_time": "2023-07-19T14:13:10.578499800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}