data_violinplots_ssim_dcx2_vs_degx3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78cf33ef-5d5f-4ef4-8b9a-05d0c62f7ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71683726-4bc2-45e7-b71c-244546fb7330",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'W:/NPC_adult_new/quantification_data/ssim_quantification/'\n",
    "# data_path = '/mnt/e/Data/contrast_enhancement_paper/quantification_data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d243ded-8625-43cf-8797-92a32846129c",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'dcx2_vs_degx3_all_ssim_20230725.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "652cbe9c-3ce1-4bb0-9849-2088accd2c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(os.path.join(data_path, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e96ecefd-4808-4521-950b-c57c1b81778f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_filtered = data.loc[data['Real Z'] < 300]\n",
    "data_filtered = data_filtered.loc[data_filtered['Real Z'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "13322995-5ee7-4101-8c9b-eac3263d66ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1832b31e400>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ref line properties\n",
    "ref_alpha = 0.2\n",
    "ref_color = 'k'\n",
    "ref_style = 'dashed'\n",
    "\n",
    "#Plot 1\n",
    "# fig = plt.figure(figsize=(12, 6))\n",
    "# fig.add_subplot(1, 2, 1)\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(6,6))\n",
    "#Z depth reference line, qualitative results\n",
    "# plt.vlines(x=[11.1, 50.4, 90.9], ymin=0, ymax=10, colors=ref_color, alpha=ref_alpha, linestyles=ref_style)\n",
    "\n",
    "sns.lineplot(\n",
    "    data=data_filtered, x='Real Z scaled', #x=[x for x in range(len(perc_ci_mean))], \n",
    "    y=\"SSIM\", \n",
    "    # hue=\"Condition\",\n",
    "    errorbar='se',\n",
    "    linewidth=3,\n",
    "    # palette=[\"tab:grey\",\"tab:blue\", \"tab:green\", \"tab:orange\"],\n",
    "    # palette=[\"grey\",\"blue\", \"darkblue\", \"lightgreen\", \"mediumseagreen\", \"darkgreen\", \"sandybrown\", \"orange\", \"darkorange\"],\n",
    "    # palette=[\"tab:grey\", \"b\", \"g\", \"orange\"],\n",
    "    ax=ax1,\n",
    ")\n",
    "sns.despine()\n",
    "# plt.ylabel('Percentile Contrast Index')\n",
    "plt.ylabel(' ')\n",
    "plt.xlabel(' ')\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "ax1.legend(\n",
    "    handles=handles,\n",
    "    labels=labels,\n",
    "    # [\"Raw\", \"CLAHE\", \"Deconvolution\", \"FCE-Net\", \"DeepContrast\"],\n",
    "    # labelcolor = [\"grey\",\"blue\", \"darkblue\", \"seagreen\", \"red\", \"darkorange\"],\n",
    "    loc='upper right',\n",
    "    bbox_to_anchor=(1.15, 1.10),\n",
    "    # fontsize=font_size,\n",
    ")\n",
    "# plt.ylim(0.5, 3.0)\n",
    "# plt.xlim(5.7, 93)\n",
    "\n",
    "# fig.savefig(data_path+'figures/contrast_quantification_alldata_avg_se_pci_withZref.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93fd489-2789-420a-83a2-850e6cc47ae1",
   "metadata": {},
   "source": [
    "## Getting some simple statistics per condition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa83a48d-c091-4e74-bb35-c242ddc78a5a",
   "metadata": {},
   "source": [
    "### Percentile Contrast Index stats per condition\n",
    "\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d5e7f3c1-a71e-410d-a8f4-8ff20ae0c971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Position\n",
       "Intermediate    0.837074\n",
       "Surface         0.828912\n",
       "undefined       0.831596\n",
       "Name: SSIM, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssim_mean = data_filtered.groupby(['Position'])['SSIM'].mean()\n",
    "ssim_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29788037-a481-4396-906d-e42e679cb9b2",
   "metadata": {},
   "source": [
    "standard Deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66afa544-97e1-457b-b826-9a32baf89e0c",
   "metadata": {},
   "source": [
    "___\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4eb0ce2e-9a1a-4e81-a04b-ad9fd948bd0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.831605571228835"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ssim_all = data_filtered['SSIM'].mean()\n",
    "data_ssim_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8feb4341-0263-4a82-b5cf-7058b3d53a7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssim_verydeep = data_filtered.loc[data_filtered['Real Z scaled'] > 85]\n",
    "# data_psnr = data_psnr.loc[data_psnr['Real Z scaled'] > 93]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6340b6b7-f5a7-4bb7-be53-36e172716724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7992917771291667\n",
      "0.056895008469596206\n"
     ]
    }
   ],
   "source": [
    "ssim_mean = data_ssim_verydeep['SSIM'].mean()\n",
    "print(ssim_mean)\n",
    "ssim_sd = data_ssim_verydeep['SSIM'].std()\n",
    "print(ssim_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f34999b-66f6-4641-90b1-79de449e19de",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssim_deep = data_filtered.loc[data_filtered['Real Z scaled'] < 85]\n",
    "data_ssim_deep = data_ssim_deep.loc[data_ssim_deep['Real Z scaled'] > 70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "61a1e022-1e9f-4f80-8658-c671f75c7543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8177581741866667\n",
      "0.06822465405947441\n"
     ]
    }
   ],
   "source": [
    "ssim_mean = data_ssim_deep['SSIM'].mean()\n",
    "print(ssim_mean)\n",
    "ssim_sd = data_ssim_deep['SSIM'].std()\n",
    "print(ssim_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcbfbd70-0b54-4d57-90a6-13f83d59f93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssim_intermediate = data_filtered.loc[data_filtered['Real Z scaled'] < 70]\n",
    "data_ssim_intermediate = data_ssim_intermediate.loc[data_ssim_intermediate['Real Z scaled'] > 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa0eea4b-745e-4863-b6fc-4c9110331596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8412227341152883\n",
      "0.03207847581287844\n"
     ]
    }
   ],
   "source": [
    "ssim_mean = data_ssim_intermediate['SSIM'].mean()\n",
    "print(ssim_mean)\n",
    "ssim_sd = data_ssim_intermediate['SSIM'].std()\n",
    "print(ssim_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18ee702b-bdcf-4a9a-84a1-b50d12d71d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssim_shallow = data_filtered.loc[data_filtered['Real Z scaled'] > 15]\n",
    "data_ssim_shallow = data_ssim_shallow.loc[data_ssim_shallow['Real Z scaled'] < 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "63cb526e-946c-4840-8a66-a0a4783da50f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8346886235061225\n",
      "0.02640441504601859\n"
     ]
    }
   ],
   "source": [
    "ssim_mean = data_ssim_shallow['SSIM'].mean()\n",
    "print(ssim_mean)\n",
    "ssim_sd = data_ssim_shallow['SSIM'].std()\n",
    "print(ssim_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "acfceaeb-6a19-4f5c-83c0-2267a59bce2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssim_veryshallow = data_filtered.loc[data_filtered['Real Z scaled'] < 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5c96e298-c496-4eeb-b808-ca1b5b547fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8257795498769231\n",
      "0.02115515269578024\n"
     ]
    }
   ],
   "source": [
    "ssim_mean = data_ssim_veryshallow['SSIM'].mean()\n",
    "print(ssim_mean)\n",
    "ssim_sd = data_ssim_veryshallow['SSIM'].std()\n",
    "print(ssim_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "376d9867-56ca-4ae8-9a2b-1212162d01cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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