From c5544ba020c0731a3e5934a93d6b7b4cb36bd58e Mon Sep 17 00:00:00 2001
From: Elizabeth Berrigan <berri104@gmail.com>
Date: Sun, 31 Mar 2024 12:42:09 -0700
Subject: [PATCH] Delete notebooks for now

---
 MultiDicotPipeline.ipynb | 733 ---------------------------------------
 1 file changed, 733 deletions(-)
 delete mode 100644 MultiDicotPipeline.ipynb

diff --git a/MultiDicotPipeline.ipynb b/MultiDicotPipeline.ipynb
deleted file mode 100644
index 7939493..0000000
--- a/MultiDicotPipeline.ipynb
+++ /dev/null
@@ -1,733 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sleap_roots import Series, find_all_series\n",
-    "from sleap_roots import MultipleDicotPipeline\n",
-    "from sleap_roots.trait_pipelines import Pipeline\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import json\n",
-    "\n",
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "csv_path = \"tests/data/multiple_arabidopsis_11do/merged_proofread_samples_03122024.csv\" # For sample information (count, group)\n",
-    "folder_path = \"tests/data/multiple_arabidopsis_11do\" # Location of h5 files and predictions\n",
-    "primary_name = \"primary\" # For loading primary root predictions\n",
-    "lateral_name = \"lateral\" # For loading lateral root predictions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['tests/data/multiple_arabidopsis_11do/6039_1.h5',\n",
-       " 'tests/data/multiple_arabidopsis_11do/7327_2.h5',\n",
-       " 'tests/data/multiple_arabidopsis_11do/9535_1.h5',\n",
-       " 'tests/data/multiple_arabidopsis_11do/997_1.h5']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Find all h5 files in the folder\n",
-    "all_h5s = find_all_series(folder_path)\n",
-    "all_h5s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Series(h5_path='tests/data/multiple_arabidopsis_11do/6039_1.h5', primary_labels=Labels(labeled_frames=67, videos=1, skeletons=1, tracks=0), lateral_labels=Labels(labeled_frames=68, videos=1, skeletons=1, tracks=0), crown_labels=None, video=Video(filename=\"tests/data/multiple_arabidopsis_11do/6039_1.h5\", shape=(72, 1088, 2048, 1), dataset=vol, backend=HDF5Video), csv_path='tests/data/multiple_arabidopsis_11do/merged_proofread_samples_03122024.csv'),\n",
-       " Series(h5_path='tests/data/multiple_arabidopsis_11do/7327_2.h5', primary_labels=Labels(labeled_frames=43, videos=1, skeletons=1, tracks=0), lateral_labels=Labels(labeled_frames=31, videos=1, skeletons=1, tracks=0), crown_labels=None, video=Video(filename=\"tests/data/multiple_arabidopsis_11do/7327_2.h5\", shape=(72, 1088, 2048, 1), dataset=vol, backend=HDF5Video), csv_path='tests/data/multiple_arabidopsis_11do/merged_proofread_samples_03122024.csv'),\n",
-       " Series(h5_path='tests/data/multiple_arabidopsis_11do/9535_1.h5', primary_labels=Labels(labeled_frames=42, videos=1, skeletons=1, tracks=0), lateral_labels=Labels(labeled_frames=36, videos=1, skeletons=1, tracks=0), crown_labels=None, video=Video(filename=\"tests/data/multiple_arabidopsis_11do/9535_1.h5\", shape=(72, 1088, 2048, 1), dataset=vol, backend=HDF5Video), csv_path='tests/data/multiple_arabidopsis_11do/merged_proofread_samples_03122024.csv'),\n",
-       " Series(h5_path='tests/data/multiple_arabidopsis_11do/997_1.h5', primary_labels=Labels(labeled_frames=72, videos=1, skeletons=1, tracks=0), lateral_labels=Labels(labeled_frames=72, videos=1, skeletons=1, tracks=0), crown_labels=None, video=Video(filename=\"tests/data/multiple_arabidopsis_11do/997_1.h5\", shape=(72, 1088, 2048, 1), dataset=vol, backend=HDF5Video), csv_path='tests/data/multiple_arabidopsis_11do/merged_proofread_samples_03122024.csv')]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the cylinder series (one per h5 file)\n",
-    "all_series = [Series.load(h5_path=h5, primary_name=primary_name, lateral_name=lateral_name, csv_path=csv_path) for h5 in all_h5s]\n",
-    "all_series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the first series in the list\n",
-    "series = all_series[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "First sample has name 6039_1\n",
-      "First sample has genotype 6039\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"First sample has name {series.series_name}\")\n",
-    "print(f\"First sample has genotype {series.group}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize the pipeline\n",
-    "pipeline = MultipleDicotPipeline()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Aggregated traits saved to 6039_1.all_frames_traits.json\n",
-      "Summary statistics saved to 6039_1.all_frames_summary.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the traits of the first sample\n",
-    "first_sample_traits = pipeline.compute_multiple_dicots_traits(series=series, write_json=True, write_csv=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lateral_count_min</th>\n",
-       "      <th>lateral_count_max</th>\n",
-       "      <th>lateral_count_mean</th>\n",
-       "      <th>lateral_count_median</th>\n",
-       "      <th>lateral_count_std</th>\n",
-       "      <th>lateral_count_p5</th>\n",
-       "      <th>lateral_count_p25</th>\n",
-       "      <th>lateral_count_p75</th>\n",
-       "      <th>lateral_count_p95</th>\n",
-       "      <th>lateral_lengths_min</th>\n",
-       "      <th>...</th>\n",
-       "      <th>network_distribution_ratio_p95</th>\n",
-       "      <th>network_solidity_min</th>\n",
-       "      <th>network_solidity_max</th>\n",
-       "      <th>network_solidity_mean</th>\n",
-       "      <th>network_solidity_median</th>\n",
-       "      <th>network_solidity_std</th>\n",
-       "      <th>network_solidity_p5</th>\n",
-       "      <th>network_solidity_p25</th>\n",
-       "      <th>network_solidity_p75</th>\n",
-       "      <th>network_solidity_p95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>5.08209</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.240176</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3.777593</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.757133</td>\n",
-       "      <td>0.041121</td>\n",
-       "      <td>0.150504</td>\n",
-       "      <td>0.062255</td>\n",
-       "      <td>0.057276</td>\n",
-       "      <td>0.01982</td>\n",
-       "      <td>0.042815</td>\n",
-       "      <td>0.048231</td>\n",
-       "      <td>0.070095</td>\n",
-       "      <td>0.098175</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 315 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   lateral_count_min  lateral_count_max  lateral_count_mean  \\\n",
-       "0                  1                  7             5.08209   \n",
-       "\n",
-       "   lateral_count_median  lateral_count_std  lateral_count_p5  \\\n",
-       "0                   5.0           1.240176               3.0   \n",
-       "\n",
-       "   lateral_count_p25  lateral_count_p75  lateral_count_p95  \\\n",
-       "0                4.0                6.0                7.0   \n",
-       "\n",
-       "   lateral_lengths_min  ...  network_distribution_ratio_p95  \\\n",
-       "0             3.777593  ...                        0.757133   \n",
-       "\n",
-       "   network_solidity_min  network_solidity_max  network_solidity_mean  \\\n",
-       "0              0.041121              0.150504               0.062255   \n",
-       "\n",
-       "   network_solidity_median  network_solidity_std  network_solidity_p5  \\\n",
-       "0                 0.057276               0.01982             0.042815   \n",
-       "\n",
-       "   network_solidity_p25  network_solidity_p75  network_solidity_p95  \n",
-       "0              0.048231              0.070095              0.098175  \n",
-       "\n",
-       "[1 rows x 315 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame([first_sample_traits[\"summary_stats\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing series '6039_1'\n",
-      "Finished processing group '6039'\n",
-      "Aggregated traits for group 6039 saved to 6039.grouped_traits.json\n",
-      "Finished processing group '6039'\n",
-      "Summary statistics for group 6039 saved to 6039.grouped_summary.csv\n",
-      "Processing series '7327_2'\n",
-      "Finished processing group '7327'\n",
-      "Aggregated traits for group 7327 saved to 7327.grouped_traits.json\n",
-      "Finished processing group '7327'\n",
-      "Summary statistics for group 7327 saved to 7327.grouped_summary.csv\n",
-      "Processing series '9535_1'\n",
-      "Finished processing group '9535'\n",
-      "Aggregated traits for group 9535 saved to 9535.grouped_traits.json\n",
-      "Finished processing group '9535'\n",
-      "Summary statistics for group 9535 saved to 9535.grouped_summary.csv\n",
-      "Processing series '997_1'\n",
-      "Finished processing group '997'\n",
-      "Aggregated traits for group 997 saved to 997.grouped_traits.json\n",
-      "Finished processing group '997'\n",
-      "Summary statistics for group 997 saved to 997.grouped_summary.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the traits grouped by genotype\n",
-    "grouped_traits = pipeline.compute_multiple_dicots_traits_for_groups(series_list=list(all_series), write_json=True, write_csv=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(grouped_traits)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lateral_count_min</th>\n",
-       "      <th>lateral_count_max</th>\n",
-       "      <th>lateral_count_mean</th>\n",
-       "      <th>lateral_count_median</th>\n",
-       "      <th>lateral_count_std</th>\n",
-       "      <th>lateral_count_p5</th>\n",
-       "      <th>lateral_count_p25</th>\n",
-       "      <th>lateral_count_p75</th>\n",
-       "      <th>lateral_count_p95</th>\n",
-       "      <th>lateral_lengths_min</th>\n",
-       "      <th>...</th>\n",
-       "      <th>network_distribution_ratio_p95</th>\n",
-       "      <th>network_solidity_min</th>\n",
-       "      <th>network_solidity_max</th>\n",
-       "      <th>network_solidity_mean</th>\n",
-       "      <th>network_solidity_median</th>\n",
-       "      <th>network_solidity_std</th>\n",
-       "      <th>network_solidity_p5</th>\n",
-       "      <th>network_solidity_p25</th>\n",
-       "      <th>network_solidity_p75</th>\n",
-       "      <th>network_solidity_p95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>5.08209</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.240176</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3.777593</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.757133</td>\n",
-       "      <td>0.041121</td>\n",
-       "      <td>0.150504</td>\n",
-       "      <td>0.062255</td>\n",
-       "      <td>0.057276</td>\n",
-       "      <td>0.01982</td>\n",
-       "      <td>0.042815</td>\n",
-       "      <td>0.048231</td>\n",
-       "      <td>0.070095</td>\n",
-       "      <td>0.098175</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 315 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   lateral_count_min  lateral_count_max  lateral_count_mean  \\\n",
-       "0                  1                  7             5.08209   \n",
-       "\n",
-       "   lateral_count_median  lateral_count_std  lateral_count_p5  \\\n",
-       "0                   5.0           1.240176               3.0   \n",
-       "\n",
-       "   lateral_count_p25  lateral_count_p75  lateral_count_p95  \\\n",
-       "0                4.0                6.0                7.0   \n",
-       "\n",
-       "   lateral_lengths_min  ...  network_distribution_ratio_p95  \\\n",
-       "0             3.777593  ...                        0.757133   \n",
-       "\n",
-       "   network_solidity_min  network_solidity_max  network_solidity_mean  \\\n",
-       "0              0.041121              0.150504               0.062255   \n",
-       "\n",
-       "   network_solidity_median  network_solidity_std  network_solidity_p5  \\\n",
-       "0                 0.057276               0.01982             0.042815   \n",
-       "\n",
-       "   network_solidity_p25  network_solidity_p75  network_solidity_p95  \n",
-       "0              0.048231              0.070095              0.098175  \n",
-       "\n",
-       "[1 rows x 315 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame([grouped_traits[0][\"summary_stats\"]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grouped_summary_df = pd.DataFrame([grouped_trait[\"summary_stats\"] for grouped_trait in grouped_traits])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>lateral_count_min</th>\n",
-       "      <th>lateral_count_max</th>\n",
-       "      <th>lateral_count_mean</th>\n",
-       "      <th>lateral_count_median</th>\n",
-       "      <th>lateral_count_std</th>\n",
-       "      <th>lateral_count_p5</th>\n",
-       "      <th>lateral_count_p25</th>\n",
-       "      <th>lateral_count_p75</th>\n",
-       "      <th>lateral_count_p95</th>\n",
-       "      <th>lateral_lengths_min</th>\n",
-       "      <th>...</th>\n",
-       "      <th>network_distribution_ratio_p95</th>\n",
-       "      <th>network_solidity_min</th>\n",
-       "      <th>network_solidity_max</th>\n",
-       "      <th>network_solidity_mean</th>\n",
-       "      <th>network_solidity_median</th>\n",
-       "      <th>network_solidity_std</th>\n",
-       "      <th>network_solidity_p5</th>\n",
-       "      <th>network_solidity_p25</th>\n",
-       "      <th>network_solidity_p75</th>\n",
-       "      <th>network_solidity_p95</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>5.082090</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.240176</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3.777593</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.757133</td>\n",
-       "      <td>0.041121</td>\n",
-       "      <td>0.150504</td>\n",
-       "      <td>0.062255</td>\n",
-       "      <td>0.057276</td>\n",
-       "      <td>0.019820</td>\n",
-       "      <td>0.042815</td>\n",
-       "      <td>0.048231</td>\n",
-       "      <td>0.070095</td>\n",
-       "      <td>0.098175</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3.434109</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.825260</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>4.345694</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.679840</td>\n",
-       "      <td>0.024168</td>\n",
-       "      <td>0.293489</td>\n",
-       "      <td>0.092920</td>\n",
-       "      <td>0.087395</td>\n",
-       "      <td>0.062009</td>\n",
-       "      <td>0.030521</td>\n",
-       "      <td>0.041196</td>\n",
-       "      <td>0.125539</td>\n",
-       "      <td>0.214581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>6.007937</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3.027640</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>4.431438</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.677514</td>\n",
-       "      <td>0.032377</td>\n",
-       "      <td>0.166538</td>\n",
-       "      <td>0.055098</td>\n",
-       "      <td>0.048888</td>\n",
-       "      <td>0.023023</td>\n",
-       "      <td>0.033393</td>\n",
-       "      <td>0.038470</td>\n",
-       "      <td>0.065840</td>\n",
-       "      <td>0.092981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>1.914854</td>\n",
-       "      <td>4.25</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>8.75</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>17.140351</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.550392</td>\n",
-       "      <td>0.017635</td>\n",
-       "      <td>0.028867</td>\n",
-       "      <td>0.021103</td>\n",
-       "      <td>0.019285</td>\n",
-       "      <td>0.004037</td>\n",
-       "      <td>0.017699</td>\n",
-       "      <td>0.017987</td>\n",
-       "      <td>0.022816</td>\n",
-       "      <td>0.027564</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4 rows × 315 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   lateral_count_min  lateral_count_max  lateral_count_mean  \\\n",
-       "0                  1                  7            5.082090   \n",
-       "1                  1                  9            3.434109   \n",
-       "2                  1                 13            6.007937   \n",
-       "3                  4                  9            7.000000   \n",
-       "\n",
-       "   lateral_count_median  lateral_count_std  lateral_count_p5  \\\n",
-       "0                   5.0           1.240176              3.00   \n",
-       "1                   1.0           2.825260              1.00   \n",
-       "2                   6.0           3.027640              1.00   \n",
-       "3                   7.5           1.914854              4.25   \n",
-       "\n",
-       "   lateral_count_p25  lateral_count_p75  lateral_count_p95  \\\n",
-       "0                4.0               6.00                7.0   \n",
-       "1                1.0               6.00                8.0   \n",
-       "2                4.0               8.00               11.0   \n",
-       "3                5.5               8.75                9.0   \n",
-       "\n",
-       "   lateral_lengths_min  ...  network_distribution_ratio_p95  \\\n",
-       "0             3.777593  ...                        0.757133   \n",
-       "1             4.345694  ...                        0.679840   \n",
-       "2             4.431438  ...                        0.677514   \n",
-       "3            17.140351  ...                        0.550392   \n",
-       "\n",
-       "   network_solidity_min  network_solidity_max  network_solidity_mean  \\\n",
-       "0              0.041121              0.150504               0.062255   \n",
-       "1              0.024168              0.293489               0.092920   \n",
-       "2              0.032377              0.166538               0.055098   \n",
-       "3              0.017635              0.028867               0.021103   \n",
-       "\n",
-       "   network_solidity_median  network_solidity_std  network_solidity_p5  \\\n",
-       "0                 0.057276              0.019820             0.042815   \n",
-       "1                 0.087395              0.062009             0.030521   \n",
-       "2                 0.048888              0.023023             0.033393   \n",
-       "3                 0.019285              0.004037             0.017699   \n",
-       "\n",
-       "   network_solidity_p25  network_solidity_p75  network_solidity_p95  \n",
-       "0              0.048231              0.070095              0.098175  \n",
-       "1              0.041196              0.125539              0.214581  \n",
-       "2              0.038470              0.065840              0.092981  \n",
-       "3              0.017987              0.022816              0.027564  \n",
-       "\n",
-       "[4 rows x 315 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped_summary_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing series '6039_1'\n",
-      "Finished processing group '6039'\n",
-      "Aggregated traits for group 6039 saved to 6039.grouped_traits.json\n",
-      "Finished processing group '6039'\n",
-      "Processing series '7327_2'\n",
-      "Finished processing group '7327'\n",
-      "Aggregated traits for group 7327 saved to 7327.grouped_traits.json\n",
-      "Finished processing group '7327'\n",
-      "Processing series '9535_1'\n",
-      "Finished processing group '9535'\n",
-      "Aggregated traits for group 9535 saved to 9535.grouped_traits.json\n",
-      "Finished processing group '9535'\n",
-      "Processing series '997_1'\n",
-      "Finished processing group '997'\n",
-      "Aggregated traits for group 997 saved to 997.grouped_traits.json\n",
-      "Finished processing group '997'\n",
-      "Computed traits for all groups saved to group_summarized_traits.csv\n"
-     ]
-    }
-   ],
-   "source": [
-    "grouped_summary_df = pipeline.compute_batch_multiple_dicots_traits_for_groups(all_series=list(all_series), write_json=True, write_csv=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'6039'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped_traits[0][\"group\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4, 316)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped_summary_df.shape"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "sleap_roots",
-   "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": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}