data_violinplots_ssim_raw_vs_degx1.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 = 'raw_vs_1deg1pred_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 0x21ad8813d00>"
      ]
     },
     "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    0.547657\n",
       "Surface         0.598123\n",
       "undefined       0.562895\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": null,
   "id": "4eb0ce2e-9a1a-4e81-a04b-ad9fd948bd0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ssim_all = data_filtered['PSNR'].mean()\n",
    "data_ssim_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
      "0.5051432419583333\n",
      "0.06959426851589942\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": [
      "0.5193266684986666\n",
      "0.07390223492391014\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": [
      "0.5644854806020051\n",
      "0.05610653016394681\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": 17,
   "id": "63cb526e-946c-4840-8a66-a0a4783da50f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6032230605387755\n",
      "0.06235067576923806\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": 18,
   "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": 19,
   "id": "5c96e298-c496-4eeb-b808-ca1b5b547fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.585077587157265\n",
      "0.07498630843218483\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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