data_violinplots_ssim_dcx1_vs_degx2.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 = 'dcx1_vs_degx2_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 0x1ef52ada280>"
      ]
     },
     "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": 8,
   "id": "d5e7f3c1-a71e-410d-a8f4-8ff20ae0c971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Position\n",
       "Intermediate    0.753602\n",
       "Surface         0.797983\n",
       "undefined       0.765423\n",
       "Name: SSIM, dtype: float64"
      ]
     },
     "execution_count": 8,
     "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": 10,
   "id": "4eb0ce2e-9a1a-4e81-a04b-ad9fd948bd0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7654951771536332"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ssim_all = data_filtered['SSIM'].mean()\n",
    "data_ssim_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "id": "6340b6b7-f5a7-4bb7-be53-36e172716724",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7045112525625001\n",
      "0.07506882273592072\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": 13,
   "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": 14,
   "id": "61a1e022-1e9f-4f80-8658-c671f75c7543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7179216334719999\n",
      "0.08817865781126294\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": 15,
   "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": 16,
   "id": "aa0eea4b-745e-4863-b6fc-4c9110331596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7694167237889725\n",
      "0.05672699485707282\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": 17,
   "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": 18,
   "id": "63cb526e-946c-4840-8a66-a0a4783da50f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8010575571891156\n",
      "0.0520739904052131\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": 19,
   "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": 20,
   "id": "5c96e298-c496-4eeb-b808-ca1b5b547fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7918207946974359\n",
      "0.05674874985648138\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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