Fig 13 - Effect of IPD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.interpolate\n",
    "import scipy.ndimage\n",
    "import scipy.special\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the user data\n",
    "luna_df = pd.read_csv('perceptual_data.csv')\n",
    "luna_df['nonuniformity'] = (100-luna_df['uniformity'])/(100+luna_df['uniformity'])\n",
    "luna_df['strongly_worse'] = (luna_df['convresp'] >= 2)\n",
    "luna_df['any_worse'] = (luna_df['convresp'] >= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate averages for the different conditions\n",
    "df = luna_df.copy()\n",
    "\n",
    "x = {}\n",
    "y = {}\n",
    "for crit in ['strongly_worse', 'any_worse']:\n",
    "    for where in ('nasal', 'temporal'):\n",
    "        for AR, luning in (('monoar', 'monolune'),\n",
    "                           ('binoar', 'binolune')):\n",
    "            df_subset = df.loc[(df['AR'] == AR) &\n",
    "                               (((df['luning'] == luning) & (df['where'] == where)) |\n",
    "                               ((df['luning'] == 'nolune') & (pd.isna(df['where']))))]\n",
    "\n",
    "            x[crit, where, AR], y[crit, where, AR] = [], []\n",
    "            for n_idx, n in enumerate(sorted(set(df_subset['nonuniformity']))):\n",
    "                dat = df_subset[df_subset['nonuniformity']==n][crit]\n",
    "                x[crit, where, AR].append(n)\n",
    "                y[crit, where, AR].append(dat.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Gaussian CDF fit, which will be fit to the user data\n",
    "def cdf_2param(x, mu, sigma):\n",
    "    return 1/2*(1+math.erf((x-mu)/(sigma*np.sqrt(2))))\n",
    "cdf_v = np.vectorize(cdf_2param)\n",
    "\n",
    "# Calculate fits\n",
    "fits_mean = {}\n",
    "for crit in ['any_worse', 'strongly_worse']:\n",
    "    for where in ('nasal', 'temporal'):\n",
    "        for AR in ('monoar', 'binoar'):\n",
    "            t = np.linspace(min(x[crit, where, AR]), max(x[crit, where, AR]), 1000)\n",
    "            fit_params = curve_fit(cdf_v, x[crit, where, AR],\n",
    "                                          y[crit, where, AR], p0=[0.5, 2])[0]\n",
    "            fits_mean[crit, where, AR] = fit_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the nonuniformity for a given threshold\n",
    "def f_nonuniformity(P, mu, sigma):\n",
    "    return np.sqrt(2)*sigma*scipy.special.erfinv(2*P-1)+mu\n",
    "\n",
    "target_nonuniformity = lambda crit, where, AR, x, : f_nonuniformity(x, *fits_mean[crit, where, AR])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the eyepiece nonuniformities\n",
    "eyepiece_csv = pd.read_csv('eyepiece_nonuniformities_without_eyeroll.csv', header=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add columns with the calculated nonuinformity for the left and right sources\n",
    "for (idx, row) in eyepiece_csv.iterrows():\n",
    "    eyepiece_csv.loc[idx, ('source_max')] = np.max([row[f'source_{loc}'] for loc in\n",
    "                                                   ['center', 'left', 'right']])\n",
    "    eyepiece_csv.loc[idx, ('source_min')] = np.min([row[f'source_{loc}'] for loc in\n",
    "                                                   ['center', 'left', 'right']])\n",
    "\n",
    "eyepiece_csv['nonuniformity_left']  = ((eyepiece_csv['source_max'] - eyepiece_csv['source_left'])/\n",
    "                                       (eyepiece_csv['source_max'] + eyepiece_csv['source_left']))\n",
    "eyepiece_csv['nonuniformity_right'] = ((eyepiece_csv['source_max'] - eyepiece_csv['source_right'])/\n",
    "                                       (eyepiece_csv['source_max'] + eyepiece_csv['source_right']))\n",
    "eyepiece_csv['nonuniformity_horz']  = ((eyepiece_csv['source_max'] - eyepiece_csv['source_min'])/\n",
    "                                       (eyepiece_csv['source_max'] + eyepiece_csv['source_min']))\n",
    "eyepiece_csv['nonuniformity_left'].fillna(0, inplace=True)\n",
    "eyepiece_csv['nonuniformity_right'].fillna(0, inplace=True)\n",
    "eyepiece_csv['nonuniformity_horz'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescale_data(eyepiece, data_source=None, scale_data=None):\n",
    "    if data_source is None:\n",
    "        data_source = 'nonuniformity'\n",
    "    if scale_data is None:\n",
    "        scale_data = 5\n",
    "\n",
    "    x_orig = eyepiece[['x']].to_numpy().squeeze()\n",
    "    y_orig = eyepiece[['y']].to_numpy().squeeze()\n",
    "    z_orig = eyepiece[['z']].to_numpy().squeeze()\n",
    "    m_orig = eyepiece[[data_source]].to_numpy().squeeze()\n",
    "\n",
    "    x = np.array(list(sorted(np.unique(x_orig))))\n",
    "    y = np.array(list(sorted(np.unique(y_orig))))\n",
    "    z = np.array(list(sorted(np.unique(z_orig))))\n",
    "\n",
    "    X,Y,Z = np.meshgrid(x, y, z)\n",
    "\n",
    "    M = scipy.interpolate.griddata((x_orig, y_orig, z_orig), m_orig, (X, Y, Z), method='linear')\n",
    "\n",
    "    if scale_data:\n",
    "        M = scipy.ndimage.zoom(M, [scale_data, scale_data, scale_data], output=None, order=3,\n",
    "                               mode='nearest', prefilter=True)\n",
    "        x = np.linspace(min(x), max(x), int(len(x)*scale_data))\n",
    "        y = np.linspace(min(y), max(y), int(len(y)*scale_data))\n",
    "        z = np.linspace(min(z), max(z), int(len(z)*scale_data))\n",
    "        \n",
    "    return [x, y, z, M]\n",
    "\n",
    "# Calculate the smoothed data\n",
    "nonuniformity_l = rescale_data(eyepiece_csv, data_source='nonuniformity_left')\n",
    "nonuniformity_r = rescale_data(eyepiece_csv, data_source='nonuniformity_right')\n",
    "nonuniformity_h = rescale_data(eyepiece_csv, data_source='nonuniformity_horz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to flip arrays left/right to account for IPD mismatch direction\n",
    "nonuniformity_l[3] = np.fliplr(nonuniformity_l[3])\n",
    "nonuniformity_r[3] = np.fliplr(nonuniformity_r[3])\n",
    "nonuniformity_h[3] = np.fliplr(nonuniformity_h[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x237.6 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "idx_x = nonuniformity_h[3].shape[0]//2\n",
    "thres_p=0.75\n",
    "    \n",
    "idx_x = int(idx_x)\n",
    "data_h = nonuniformity_h[3][idx_x,:,:].copy().T\n",
    "data_l = nonuniformity_l[3][idx_x,:,:].copy().T\n",
    "data_r = nonuniformity_r[3][idx_x,:,:].copy().T\n",
    "\n",
    "data_diff = data_l-data_r\n",
    "\n",
    "f, axs = plt.subplots(1, 3, figsize=(10, 3.3), dpi=72)\n",
    "spacing_y = np.mean(np.diff(nonuniformity_h[1]))\n",
    "spacing_z = np.mean(np.diff(nonuniformity_h[2]))\n",
    "extent=(min(nonuniformity_h[1])-spacing_y/2, max(nonuniformity_h[1])+spacing_y/2,  # y\n",
    "        min(nonuniformity_h[2])-spacing_z/2, max(nonuniformity_h[2])+spacing_z/2)  # z\n",
    "\n",
    "# -------------------------\n",
    "# Nasal\n",
    "ax = axs[0]\n",
    "img = ax.imshow(data_l, interpolation='nearest', origin='lower', vmin=0, vmax=1, extent=extent, zorder=1,\n",
    "                cmap=plt.get_cmap('viridis_r'))\n",
    "CS = ax.contour(data_l, [target_nonuniformity('any_worse', 'nasal', 'binoar', thres_p),],\n",
    "                extent=extent, colors=['w'], linestyles='dashed', zorder=3, linewidths=[3,])\n",
    "ax.set_title('Nasal source')\n",
    "ax.set_ylabel('Relative eye relief (mm)')\n",
    "ax.set_yticks(45.2 + np.linspace(-10, 10, 5))\n",
    "ax.set_yticklabels(np.linspace(-10, 10, 5))\n",
    "ax.set_xticks(np.linspace(min(nonuniformity_h[1]), max(nonuniformity_h[1]), 5))\n",
    "ax.set_xticklabels([f'{i:.0f}' for i in np.linspace(min(nonuniformity_h[1]), max(nonuniformity_h[1]), 5)*2])\n",
    "\n",
    "# -------------------------\n",
    "# Temporal\n",
    "ax = axs[1]\n",
    "img = ax.imshow(data_r, interpolation='nearest', origin='lower', vmin=0, vmax=1, extent=extent, zorder=1,\n",
    "                cmap=plt.get_cmap('viridis_r'))\n",
    "CS = ax.contour(data_r, [target_nonuniformity('any_worse', 'temporal', 'binoar', thres_p),],\n",
    "                extent=extent, colors=['w'], zorder=3, linewidths=[3,])\n",
    "ax.set_title('Temporal Source')\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks(np.linspace(min(nonuniformity_h[1]), max(nonuniformity_h[1]), 5))\n",
    "ax.set_xticklabels([f'{i:.0f}' for i in np.linspace(min(nonuniformity_h[1]), max(nonuniformity_h[1]), 5)*2])\n",
    "ax.set_xlabel('IPD mismatch (mm)')\n",
    "\n",
    "# -------------------------\n",
    "# Both\n",
    "ax = axs[2]\n",
    "img = ax.imshow(data_h, interpolation='nearest', origin='lower', vmin=0, vmax=1, extent=extent, zorder=1,\n",
    "                cmap=plt.get_cmap('viridis_r'))\n",
    "CS = ax.contour(data_l, [target_nonuniformity('any_worse', 'nasal', 'binoar', thres_p),],\n",
    "                extent=extent, colors=['w'], linestyles='dashed', zorder=3, linewidths=[3,])\n",
    "CS = ax.contour(data_r, [target_nonuniformity('any_worse', 'temporal', 'binoar', thres_p),],\n",
    "                extent=extent, colors=['w'], zorder=3, linewidths=[3,])\n",
    "ax.set_title('Nonuniformity')\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks(np.linspace(min(nonuniformity_h[1]), max(nonuniformity_h[1]), 5))\n",
    "ax.set_xticklabels([f'{i:.0f}' for i in np.linspace(min(nonuniformity_h[1]), max(nonuniformity_h[1]), 5)*2])\n",
    "\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "plt.colorbar(img, cax=cax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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