data_violinplots_ssim_dcx3_vs_degx4.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 = 'dcx3_vs_degx4_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 0x2ddccb3e1f0>"
      ]
     },
     "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    0.773712\n",
       "Surface         0.717429\n",
       "undefined       0.756841\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": [
       "0.7567634449012688"
      ]
     },
     "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": [
      "0.7789852869958334\n",
      "0.05747693499489223\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": [
      "0.7978673483279999\n",
      "0.06463281624988265\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": [
      "0.7643598417062657\n",
      "0.05975628301730289\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": [
      "0.7131023318571429\n",
      "0.07764690556600912\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": [
      "0.7259264699555555\n",
      "0.0681393969228397\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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