python/validation/deconvolution/validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\anaconda3\\envs\\codex\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import papermill as pm\n",
    "import pandas as pd\n",
    "import plotnine as pn\n",
    "from collections import OrderedDict\n",
    "import os\n",
    "import json\n",
    "from tqdm import tqdm, TqdmSynchronisationWarning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=TqdmSynchronisationWarning)\n",
    "nb_dir = 'results'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deconvolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset bars-samp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input Notebook:  deconvolution.ipynb\n",
      "Output Notebook: results\\deconvolution\\bars-samp.ipynb\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 27/27 [00:37<00:00,  1.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset bars-crop\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input Notebook:  deconvolution.ipynb\n",
      "Output Notebook: results\\deconvolution\\bars-crop.ipynb\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 27/27 [00:37<00:00,  1.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset microtubules-samp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input Notebook:  deconvolution.ipynb\n",
      "Output Notebook: results\\deconvolution\\microtubules-samp.ipynb\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 27/27 [00:42<00:00,  1.58s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset microtubules-crop\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input Notebook:  deconvolution.ipynb\n",
      "Output Notebook: results\\deconvolution\\microtubules-crop.ipynb\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 27/27 [00:41<00:00,  1.53s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset bead-samp\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input Notebook:  deconvolution.ipynb\n",
      "Output Notebook: results\\deconvolution\\bead-samp.ipynb\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 27/27 [00:42<00:00,  1.56s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset celegans-dapi-crop\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Input Notebook:  deconvolution.ipynb\n",
      "Output Notebook: results\\deconvolution\\celegans-dapi-crop.ipynb\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 27/27 [01:58<00:00,  4.38s/it]\n"
     ]
    }
   ],
   "source": [
    "args = OrderedDict()\n",
    "\n",
    "# Crop slices (@25%):\n",
    "# 128 - 48:80 (n=32)\n",
    "# 256 - 96:160 (n=64)\n",
    "# 512 - 192:320 (n=128)\n",
    "# WARNING: Make sure that crop slices are centered or PSFs will not \n",
    "# be symmetric and lead to nonsense results\n",
    "\n",
    "# Bars (Orig: 128x256x256)\n",
    "args['bars-samp'] = dict(\n",
    "    downsample_factor=.25,\n",
    "    n_iter=25, dataset_name='bars'\n",
    ")\n",
    "args['bars-crop'] = dict(\n",
    "    crop_slice='(slice(48, 80), slice(96, 160), slice(96, 160))', \n",
    "    n_iter=25, dataset_name='bars'\n",
    ")\n",
    "\n",
    "# Microtubules (Orig: 128x256x512)\n",
    "args['microtubules-samp'] = dict(\n",
    "    downsample_factor=.25, \n",
    "    dataset_name='microtubules', n_iter=25\n",
    ")\n",
    "args['microtubules-crop'] = dict(\n",
    "    crop_slice='(slice(48, 80), slice(96, 160), slice(192, 320))',\n",
    "    dataset_name='microtubules', n_iter=25\n",
    ")\n",
    "\n",
    "# Bead (256, 256, 256)\n",
    "args['bead-samp'] = dict(n_iter=25, downsample_factor=.25, dataset_name='bead')\n",
    "\n",
    "# CElegans (Orig: 104x712x672)\n",
    "args['celegans-dapi-crop'] = dict(\n",
    "    n_iter=25, \n",
    "    # Crop slice to truncate signals at borders as a test of edge artifacts\n",
    "    crop_slice='(slice(36, 68), slice(228, 484), slice(208, 464))', \n",
    "    dataset_name='celegans-dapi'\n",
    ") \n",
    "\n",
    "os.makedirs(nb_dir, exist_ok=True)\n",
    "for k, params in args.items():\n",
    "    print('Processing dataset', k)\n",
    "    out_path = os.path.join(nb_dir, k + '.ipynb')\n",
    "    pm.execute_notebook('template.ipynb', out_path, parameters=params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>type</th>\n",
       "      <th>filename</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>crop_slice</td>\n",
       "      <td>(slice(48, 80), slice(96, 160), slice(96, 160))</td>\n",
       "      <td>parameter</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dataset_name</td>\n",
       "      <td>bars</td>\n",
       "      <td>parameter</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>n_iter</td>\n",
       "      <td>25</td>\n",
       "      <td>parameter</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>scores</td>\n",
       "      <td>[{\"lib\":\"dl2\",\"algo\":\"lw\",\"nrmse\":-1.842583111...</td>\n",
       "      <td>record</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "      <td>bars-crop.ipynb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dataset_name</td>\n",
       "      <td>bars</td>\n",
       "      <td>parameter</td>\n",
       "      <td>bars-samp.ipynb</td>\n",
       "      <td>bars-samp.ipynb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                                              value       type  \\\n",
       "0    crop_slice    (slice(48, 80), slice(96, 160), slice(96, 160))  parameter   \n",
       "1  dataset_name                                               bars  parameter   \n",
       "2        n_iter                                                 25  parameter   \n",
       "3        scores  [{\"lib\":\"dl2\",\"algo\":\"lw\",\"nrmse\":-1.842583111...     record   \n",
       "4  dataset_name                                               bars  parameter   \n",
       "\n",
       "          filename              key  \n",
       "0  bars-crop.ipynb  bars-crop.ipynb  \n",
       "1  bars-crop.ipynb  bars-crop.ipynb  \n",
       "2  bars-crop.ipynb  bars-crop.ipynb  \n",
       "3  bars-crop.ipynb  bars-crop.ipynb  \n",
       "4  bars-samp.ipynb  bars-samp.ipynb  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pm.read_notebooks(out_dir).dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algo</th>\n",
       "      <th>comp_type</th>\n",
       "      <th>lib</th>\n",
       "      <th>nrmse</th>\n",
       "      <th>psnr</th>\n",
       "      <th>ssim</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lw</td>\n",
       "      <td>ground_truth</td>\n",
       "      <td>dl2</td>\n",
       "      <td>-1.842583</td>\n",
       "      <td>7.551699</td>\n",
       "      <td>0.116184</td>\n",
       "      <td>bars-crop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nif</td>\n",
       "      <td>ground_truth</td>\n",
       "      <td>dl2</td>\n",
       "      <td>-2.997178</td>\n",
       "      <td>3.325989</td>\n",
       "      <td>0.021215</td>\n",
       "      <td>bars-crop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rif</td>\n",
       "      <td>ground_truth</td>\n",
       "      <td>dl2</td>\n",
       "      <td>-1.870676</td>\n",
       "      <td>7.420269</td>\n",
       "      <td>0.131307</td>\n",
       "      <td>bars-crop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rl-npad</td>\n",
       "      <td>ground_truth</td>\n",
       "      <td>dl2</td>\n",
       "      <td>-1.406720</td>\n",
       "      <td>9.896085</td>\n",
       "      <td>0.203789</td>\n",
       "      <td>bars-crop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rl-wpad</td>\n",
       "      <td>ground_truth</td>\n",
       "      <td>dl2</td>\n",
       "      <td>-1.514692</td>\n",
       "      <td>9.253757</td>\n",
       "      <td>0.187872</td>\n",
       "      <td>bars-crop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      algo     comp_type  lib     nrmse      psnr      ssim    dataset\n",
       "0       lw  ground_truth  dl2 -1.842583  7.551699  0.116184  bars-crop\n",
       "1      nif  ground_truth  dl2 -2.997178  3.325989  0.021215  bars-crop\n",
       "2      rif  ground_truth  dl2 -1.870676  7.420269  0.131307  bars-crop\n",
       "3  rl-npad  ground_truth  dl2 -1.406720  9.896085  0.203789  bars-crop\n",
       "4  rl-wpad  ground_truth  dl2 -1.514692  9.253757  0.187872  bars-crop"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = []\n",
    "for k, g in df.groupby('key'):\n",
    "    dataset = k.replace('.ipynb', '')\n",
    "    json_df = g.set_index('name').loc['scores']['value']\n",
    "    scores.append(pd.DataFrame.from_records(json.loads(json_df)).assign(dataset=dataset))\n",
    "scores = pd.concat(scores)\n",
    "scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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wymgAAAAAAAr3H/rWA0XyZCReAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1600x1600 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371958826418635)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    pn.ggplot(\n",
    "        scores.melt(id_vars=['algo', 'lib', 'comp_type', 'dataset']), \n",
    "        pn.aes(x='algo', y='value', fill='lib')\n",
    "    ) +\n",
    "    pn.geom_bar(stat='identity', position='dodge') +\n",
    "    pn.facet_grid('dataset~variable', scales='free') +\n",
    "    pn.theme(figure_size=(16, 16))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scores(df, algo, sort_metric, title):\n",
    "    df = df[df['algo'] == algo].drop('algo', axis=1)\n",
    "    order = df.groupby('dataset')[sort_metric].mean().sort_values().index\n",
    "    df = df.melt(id_vars=['lib', 'comp_type', 'dataset'])\n",
    "    df['dataset'] = pd.Categorical(df['dataset'], ordered=True, categories=order)\n",
    "    \n",
    "    return (\n",
    "        pn.ggplot(df, pn.aes(x='dataset', y='value', fill='lib')) +\n",
    "        pn.geom_bar(stat='identity', position='dodge') +\n",
    "        pn.facet_wrap('~variable', scales='free', ncol=1) +\n",
    "        pn.ggtitle(title) +\n",
    "        pn.scale_fill_brewer(type='qual', palette='Set1', guide=pn.guide_legend(title='Library')) +\n",
    "        pn.xlab('Dataset Name') +\n",
    "        pn.ylab('Score Value (higher is better)') +\n",
    "        pn.theme(figure_size=(12, 8))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (78030769023)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_scores(scores, 'rl-npad', 'ssim', 'Scores by Library and Dataset')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}