diff --git a/.gitignore b/.gitignore
index a20fd7a..ddb3e5b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 __pycache__/
 build/
 *.egg-info/
+*.ipynb_checkpoints/
 
 # Documentation cruft
 docs/_build
@@ -10,3 +11,6 @@ docs/sg_execution_times.rst
 
 # Editor cruft
 *.swp
+
+# Apple cruft
+*.DS_Store
diff --git a/paper_figures/eval_results/all_results.txt b/paper_figures/eval_results/all_results.txt
new file mode 100644
index 0000000..f0e9359
--- /dev/null
+++ b/paper_figures/eval_results/all_results.txt
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new file mode 100644
index 0000000..222e4a9
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new file mode 100644
index 0000000..345bfcb
--- /dev/null
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index 0000000..2e0feb6
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new file mode 100644
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diff --git a/paper_figures/eval_results/cellsam/general/omnipose.txt b/paper_figures/eval_results/cellsam/general/omnipose.txt
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new file mode 100644
index 0000000..80b9a7e
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index 0000000..8b79703
--- /dev/null
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diff --git a/paper_figures/livecell/cellsam/general/LIVECell_good.txt b/paper_figures/livecell/cellsam/general/LIVECell_good.txt
new file mode 100644
index 0000000..6366012
--- /dev/null
+++ b/paper_figures/livecell/cellsam/general/LIVECell_good.txt
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diff --git a/paper_figures/livecell/cellsam/general_FS_10_FT/LIVECell_good.txt b/paper_figures/livecell/cellsam/general_FS_10_FT/LIVECell_good.txt
new file mode 100644
index 0000000..11ed6db
--- /dev/null
+++ b/paper_figures/livecell/cellsam/general_FS_10_FT/LIVECell_good.txt
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diff --git a/paper_figures/metadata_LIVECELL.csv b/paper_figures/metadata_LIVECELL.csv
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diff --git a/paper_figures/paper_figures.ipynb b/paper_figures/paper_figures.ipynb
new file mode 100644
index 0000000..09882c1
--- /dev/null
+++ b/paper_figures/paper_figures.ipynb
@@ -0,0 +1,1565 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results for Generalist Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "name_map = {\n",
+    "    \"Gendarme_BriFi\": \"BriFiSeg\",\n",
+    "    \"cellpose\": \"Cellpose\",\n",
+    "    \"ep_phase_microscopy_all\": \"Phase400\",\n",
+    "    \"H_and_E\": \"H&E\",\n",
+    "    \"tissuenet_wholecell\": \"TissueNet\",\n",
+    "    \"YeaZ\": \"YeaZ\",\n",
+    "    \"YeastNet\": \"YeastNet\",\n",
+    "    \"dsb_fixed\": \"DSB\",\n",
+    "    \"deepbacs\": \"DeepBacs\",\n",
+    "    \"omnipose\": \"OmniPose\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "datasets = [\n",
+    "    'Gendarme_BriFi',\n",
+    "    'H_and_E',\n",
+    "    'YeaZ',\n",
+    "    'YeastNet',\n",
+    "    'cellpose',\n",
+    "    'deepbacs',\n",
+    "    'dsb_fixed',\n",
+    "    'ep_phase_microscopy_all',\n",
+    "    'omnipose',\n",
+    "    'tissuenet_wholecell',\n",
+    "]\n",
+    "\n",
+    "# colors for the plots\n",
+    "c1 = \"#fdbb84\"\n",
+    "c2 = \"#e34a33\"\n",
+    "c3 = '#deebf7'\n",
+    "c4 = '#3182bd'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## define paths and load results\n",
+    "\n",
+    "\n",
+    "\n",
+    "cellsam_path = Path.cwd() / \"eval_results/cellsam\"\n",
+    "cellpose_path = Path.cwd() / \"eval_results/cellpose\"\n",
+    "\n",
+    "cellpose_generalist_path = cellpose_path / 'general'\n",
+    "cellsam_generalist_path = cellsam_path / 'general'\n",
+    "\n",
+    "\n",
+    "cp_generalist_dict = {}\n",
+    "\n",
+    "for file in cellpose_generalist_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cp_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "cs_generalist_dict = {}\n",
+    "\n",
+    "for file in cellsam_generalist_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cs_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose vs CellSAM - Generalist Models by Dataset\n",
+    "\"\"\"\n",
+    "\n",
+    "cp_means = []; cs_means = []\n",
+    "cp_sems = []; cs_sems = []\n",
+    "\n",
+    "for ds in datasets:\n",
+    "    cp_data = cp_generalist_dict[ds]\n",
+    "    cs_data = cs_generalist_dict[ds]\n",
+    "    # 1 - mean for the bar\n",
+    "    cp_m = 1 - np.mean(cp_data)\n",
+    "    cs_m = 1 - np.mean(cs_data)\n",
+    "    # standard error of the mean for the error bar\n",
+    "    cp_sem = np.std(cp_data, ddof=1) / np.sqrt(len(cp_data))\n",
+    "    cs_sem = np.std(cs_data, ddof=1) / np.sqrt(len(cs_data))\n",
+    "\n",
+    "    cp_means.append(cp_m)\n",
+    "    cs_means.append(cs_m)\n",
+    "    cp_sems.append(cp_sem)\n",
+    "    cs_sems.append(cs_sem)\n",
+    "\n",
+    "# Plot as a bar chart\n",
+    "x = np.arange(len(datasets))\n",
+    "width = 0.35  # width of each bar\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "# Plot CP bars slightly left, CS bars slightly right\n",
+    "bars_cp = ax.bar(x - width/2, cp_means, width, \n",
+    "                 edgecolor='black', linewidth=1,\n",
+    "                 yerr=cp_sems, capsize=5, label='Cellpose', color=c2)\n",
+    "bars_cs = ax.bar(x + width/2, cs_means, width, \n",
+    "                 edgecolor='black', linewidth=1,\n",
+    "                 yerr=cs_sems, capsize=5, label='CellSAM', color=c4)\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([name_map[ds] for ds in datasets], rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_dataset_comparison_cp_reg.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose vs CellSAM - Generalist Models by Data Type\n",
+    "\"\"\"\n",
+    "\n",
+    "dataset_agg_map = {\n",
+    "    \"Tissue\": [\"tissuenet_wholecell\"],\n",
+    "    \"Cell Culture\": [\"cellpose\", \"ep_phase_microscopy_all\", \"Gendarme_BriFi\"],\n",
+    "    \"H&E\": [\"H_and_E\"],\n",
+    "    \"Bacteria\": [\"deepbacs\", \"omnipose\"],\n",
+    "    \"Yeast\": [\"YeaZ\", \"YeastNet\"],\n",
+    "    \"Nuclear\": [\"dsb_fixed\"],\n",
+    "}\n",
+    "\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "cp_group_means = []\n",
+    "cp_group_sems  = []\n",
+    "cs_group_means = []\n",
+    "cs_group_sems  = []\n",
+    "\n",
+    "for group in group_names:\n",
+    "    # Get all datasets that belong to this group\n",
+    "    datasets_for_group = dataset_agg_map[group]\n",
+    "    \n",
+    "    # Gather all F1 arrays and concatenate them\n",
+    "    cp_all = np.concatenate([cp_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    cs_all = np.concatenate([cs_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    \n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cp_mean = 1 - np.mean(cp_all)\n",
+    "    cs_mean = 1 - np.mean(cs_all)\n",
+    "    cp_group_means.append(cp_mean)\n",
+    "    cs_group_means.append(cs_mean)\n",
+    "    \n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cp_sem = np.std(cp_all, ddof=1) / np.sqrt(len(cp_all))\n",
+    "    cs_sem = np.std(cs_all, ddof=1) / np.sqrt(len(cs_all))\n",
+    "    cp_group_sems.append(cp_sem)\n",
+    "    cs_group_sems.append(cs_sem)\n",
+    "\n",
+    "# Now plot side‐by‐side bars for the groups\n",
+    "x = np.arange(len(group_names))\n",
+    "width = 0.35\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "\n",
+    "bars_cp = ax.bar(\n",
+    "    x - width/2, cp_group_means, width,\n",
+    "    yerr=cp_group_sems, edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='Cellpose', color=c2\n",
+    ")\n",
+    "bars_cs = ax.bar(\n",
+    "    x + width/2, cs_group_means, width,\n",
+    "    yerr=cs_group_sems, edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='CellSAM', color=c4\n",
+    ")\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(group_names, rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_general_grouped_comparison_cp_reg.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose vs CellSAM - Generalist Models by Data Type + Neurips Challenge\n",
+    "\"\"\"\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "dataset_agg_map = {\n",
+    "    \"Tissue\": [\"tissuenet_wholecell\"],\n",
+    "    \"Cell Culture\": [\"cellpose\", \"ep_phase_microscopy_all\", \"Gendarme_BriFi\"],\n",
+    "    \"H&E\": [\"H_and_E\"],\n",
+    "    \"Bacteria\": [\"deepbacs\", \"omnipose\"],\n",
+    "    \"Yeast\": [\"YeaZ\", \"YeastNet\"],\n",
+    "    \"Nuclear\": [\"dsb_fixed\"],\n",
+    "}\n",
+    "\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "cp_group_means = []\n",
+    "cp_group_sems  = []\n",
+    "cs_group_means = []\n",
+    "cs_group_sems  = []\n",
+    "\n",
+    "for group in group_names:\n",
+    "    # Get all datasets that belong to this group\n",
+    "    datasets_for_group = dataset_agg_map[group]\n",
+    "    \n",
+    "    # Gather all F1 arrays and concatenate them\n",
+    "    cp_all = np.concatenate([cp_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    cs_all = np.concatenate([cs_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    \n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cp_mean = 1 - np.mean(cp_all)\n",
+    "    cs_mean = 1 - np.mean(cs_all)\n",
+    "    cp_group_means.append(cp_mean)\n",
+    "    cs_group_means.append(cs_mean)\n",
+    "    \n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cp_sem = np.std(cp_all, ddof=1) / np.sqrt(len(cp_all))\n",
+    "    cs_sem = np.std(cs_all, ddof=1) / np.sqrt(len(cs_all))\n",
+    "    cp_group_sems.append(cp_sem)\n",
+    "    cs_group_sems.append(cs_sem)\n",
+    "\n",
+    "# Existing group names\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "group_names.append(\"Neurips Challenge\")\n",
+    "\n",
+    "# neurips challenge results\n",
+    "cp_new_val = 1 - 0.8612  \n",
+    "cs_new_val = 1 - 0.8723 \n",
+    "\n",
+    "cp_group_means.append(cp_new_val)\n",
+    "cs_group_means.append(cs_new_val)\n",
+    "# Since there's no variability reported, just pass 0 or None\n",
+    "cp_group_sems.append(0)\n",
+    "cs_group_sems.append(0)\n",
+    "\n",
+    "# Plotting\n",
+    "x = np.arange(len(group_names))  # Now has len() = old groups + 1\n",
+    "width = 0.35\n",
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "\n",
+    "bars_cp = ax.bar(\n",
+    "    x[:-1] - width/2, cp_group_means[:-1], width,\n",
+    "    yerr=cp_group_sems[:-1], edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='Cellpose', color=c2\n",
+    ")\n",
+    "bars_cs = ax.bar(\n",
+    "    x[:-1] + width/2, cs_group_means[:-1], width,\n",
+    "    yerr=cs_group_sems[:-1], edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='CellSAM', color=c4\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Now create the last bar group closer to the existing ones\n",
+    "offset = 0.25            \n",
+    "separator_x = x[-1] - .3 \n",
+    "last_index = x[-1] + offset\n",
+    "\n",
+    "bars_cp_new = ax.bar(\n",
+    "    last_index - width/2, cp_group_means[-1], width,\n",
+    "    yerr=cp_group_sems[-1], edgecolor='black', linewidth=1, \n",
+    "    color=c2\n",
+    ")\n",
+    "bars_cs_new = ax.bar(\n",
+    "    last_index + width/2, cs_group_means[-1], width,\n",
+    "    yerr=cs_group_sems[-1], edgecolor='black', linewidth=1, \n",
+    "    color=c4\n",
+    ")\n",
+    "\n",
+    "# Then move the dotted line accordingly\n",
+    "ax.axvline(separator_x, color='gray', linestyle='--', linewidth=1)\n",
+    "xtick_positions = np.concatenate((x[:-1], [last_index]))\n",
+    "ax.set_xticks(xtick_positions)\n",
+    "ax.set_xticklabels(group_names, rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_general_grouped_comparison_cp_reg.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Results for Individual Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "name_map = {\n",
+    "    \"Gendarme_BriFi\": \"BriFiSeg\",\n",
+    "    \"cellpose\": \"Cellpose\",\n",
+    "    \"ep_phase_microscopy_all\": \"Phase400\",\n",
+    "    \"H_and_E\": \"H&E\",\n",
+    "    \"tissuenet_wholecell\": \"TissueNet\",\n",
+    "    \"YeaZ\": \"YeaZ\",\n",
+    "    \"YeastNet\": \"YeastNet\",\n",
+    "    \"dsb_fixed\": \"DSB\",\n",
+    "    \"deepbacs\": \"DeepBacs\",\n",
+    "    \"omnipose\": \"OmniPose\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "datasets = [\n",
+    "    'Gendarme_BriFi',\n",
+    "    'H_and_E',\n",
+    "    'YeaZ',\n",
+    "    'YeastNet',\n",
+    "    'cellpose',\n",
+    "    'deepbacs',\n",
+    "    'dsb_fixed',\n",
+    "    'ep_phase_microscopy_all',\n",
+    "    'omnipose',\n",
+    "    'tissuenet_wholecell',\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# colors for the plots\n",
+    "c1 = \"#fdbb84\"\n",
+    "c2 = \"#e34a33\"\n",
+    "c3 = '#deebf7'\n",
+    "c4 = '#3182bd'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cellpose_path = Path.cwd() / \"eval_results/cellpose\"\n",
+    "cellpose_individual_path = cellpose_path / 'individual'\n",
+    "\n",
+    "# load results from txt files\n",
+    "cp_individual_dict = {}\n",
+    "for file in cellpose_individual_path.glob(\"*.txt\"):  # Selects only .txt files\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)  # Load as NumPy array\n",
+    "        cp_individual_dict[file.stem] = data  # Store in dictionary with filename (without extension)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "cellsam_individual_path = cellsam_path / 'individual'\n",
+    "\n",
+    "cs_individual_dict = {}\n",
+    "for file in cellsam_individual_path.glob(\"*.txt\"):  # Selects only .txt files\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)  # Load as NumPy array\n",
+    "        cs_individual_dict[file.stem] = data  # Store in dictionary with filename (without extension)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose vs CellSAM - Individual Models by Dataset\n",
+    "\"\"\"\n",
+    "\n",
+    "cp_means = []; cs_means = []\n",
+    "cp_sems = []; cs_sems = []\n",
+    "\n",
+    "for ds in datasets:\n",
+    "    cp_data = cp_individual_dict[ds]\n",
+    "    cs_data = cs_individual_dict[ds]\n",
+    "    # 1 - mean for the bar\n",
+    "    cp_m = 1 - np.mean(cp_data)\n",
+    "    cs_m = 1 - np.mean(cs_data)\n",
+    "    # standard error of the mean for the error bar\n",
+    "    cp_sem = np.std(cp_data, ddof=1) / np.sqrt(len(cp_data))\n",
+    "    cs_sem = np.std(cs_data, ddof=1) / np.sqrt(len(cs_data))\n",
+    "\n",
+    "    cp_means.append(cp_m)\n",
+    "    cs_means.append(cs_m)\n",
+    "    cp_sems.append(cp_sem)\n",
+    "    cs_sems.append(cs_sem)\n",
+    "\n",
+    "# Plot as a bar chart\n",
+    "x = np.arange(len(datasets))\n",
+    "width = 0.35  # width of each bar\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "# Plot CP bars slightly left, CS bars slightly right\n",
+    "bars_cp = ax.bar(x - width/2, cp_means, width,\n",
+    "                 edgecolor='black', linewidth=1,\n",
+    "                 yerr=cp_sems, capsize=5, label='Cellpose', color=c2)\n",
+    "bars_cs = ax.bar(x + width/2, cs_means, width, \n",
+    "                 edgecolor='black', linewidth=1,\n",
+    "                 yerr=cs_sems, capsize=5, label='CellSAM', color=c4)\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([name_map[ds] for ds in datasets], rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_general_grouped_comparison_cp_reg.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose vs CellSAM - Individual Models by Data Type\n",
+    "\"\"\"\n",
+    "# Your mapping from group -> list of dataset keys:\n",
+    "dataset_agg_map = {\n",
+    "    \"Tissue\": [\"tissuenet_wholecell\"],\n",
+    "    \"Cell Culture\": [\"cellpose\", \"ep_phase_microscopy_all\", \"Gendarme_BriFi\"],\n",
+    "    \"H&E\": [\"H_and_E\"],\n",
+    "    \"Bacteria\": [\"deepbacs\", \"omnipose\"],\n",
+    "    \"Yeast\": [\"YeaZ\", \"YeastNet\"],\n",
+    "    \"Nuclear\": [\"dsb_fixed\"],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "\n",
+    "cp_group_means = []\n",
+    "cp_group_sems  = []\n",
+    "cs_group_means = []\n",
+    "cs_group_sems  = []\n",
+    "\n",
+    "for group in group_names:\n",
+    "    # Get all datasets that belong to this group\n",
+    "    datasets_for_group = dataset_agg_map[group]\n",
+    "    \n",
+    "    # Gather all F1 arrays and concatenate them\n",
+    "    cp_all = np.concatenate([cp_individual_dict[ds] for ds in datasets_for_group])\n",
+    "    cs_all = np.concatenate([cs_individual_dict[ds] for ds in datasets_for_group])\n",
+    "    \n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cp_mean = 1 - np.mean(cp_all)\n",
+    "    cs_mean = 1 - np.mean(cs_all)\n",
+    "    cp_group_means.append(cp_mean)\n",
+    "    cs_group_means.append(cs_mean)\n",
+    "    \n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cp_sem = np.std(cp_all, ddof=1) / np.sqrt(len(cp_all))\n",
+    "    cs_sem = np.std(cs_all, ddof=1) / np.sqrt(len(cs_all))\n",
+    "    cp_group_sems.append(cp_sem)\n",
+    "    cs_group_sems.append(cs_sem)\n",
+    "\n",
+    "# Now plot side‐by‐side bars for the groups\n",
+    "x = np.arange(len(group_names))\n",
+    "width = 0.35\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "\n",
+    "bars_cp = ax.bar(\n",
+    "    x - width/2, cp_group_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cp_group_sems, capsize=5, \n",
+    "    label='Cellpose', color=c2\n",
+    ")\n",
+    "bars_cs = ax.bar(\n",
+    "    x + width/2, cs_group_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cs_group_sems, capsize=5, \n",
+    "    label='CellSAM', color=c4\n",
+    ")\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(group_names, rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "# ax.set_title('Grouped Comparison of Mean Error')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_general_grouped_comparison_cp_reg.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CellSAM Generalist vs Specialist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "name_map = {\n",
+    "    \"Gendarme_BriFi\": \"BriFiSeg\",\n",
+    "    \"cellpose\": \"Cellpose\",\n",
+    "    \"ep_phase_microscopy_all\": \"Phase400\",\n",
+    "    \"H_and_E\": \"H&E\",\n",
+    "    \"tissuenet_wholecell\": \"TissueNet\",\n",
+    "    \"YeaZ\": \"YeaZ\",\n",
+    "    \"YeastNet\": \"YeastNet\",\n",
+    "    \"dsb_fixed\": \"DSB\",\n",
+    "    \"deepbacs\": \"DeepBacs\",\n",
+    "    \"omnipose\": \"OmniPose\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "datasets = [\n",
+    "    'Gendarme_BriFi',\n",
+    "    'H_and_E',\n",
+    "    'YeaZ',\n",
+    "    'YeastNet',\n",
+    "    'cellpose',\n",
+    "    'deepbacs',\n",
+    "    'dsb_fixed',\n",
+    "    'ep_phase_microscopy_all',\n",
+    "    'omnipose',\n",
+    "    'tissuenet_wholecell',\n",
+    "]\n",
+    " \n",
+    "c1 = \"#fdbb84\"\n",
+    "c2 = \"#e34a33\"\n",
+    "c3 = '#deebf7'\n",
+    "c4 = '#3182bd'\n",
+    "\n",
+    "cellsam_path = Path.cwd() / \"eval_results/cellsam\"\n",
+    "cellpose_path = Path.cwd() / \"eval_results/cellpose\"\n",
+    "\n",
+    "cellsam_generalist_path = cellsam_path / 'general'\n",
+    "cellsam_individual_path = cellsam_path / 'individual'\n",
+    "\n",
+    "\n",
+    "cellsam_generalist_dict = {}\n",
+    "for file in cellsam_generalist_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cellsam_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "\n",
+    "cellsam_specific_dict = {}\n",
+    "for file in cellsam_individual_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cellsam_specific_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "CellSAM Specialist vs Generalist by Dataset\n",
+    "\"\"\"\n",
+    "\n",
+    "cs_general_means = []; cs_specific_means = []\n",
+    "cs_general_sems = []; cs_specific_sems = []\n",
+    "\n",
+    "for ds in datasets:\n",
+    "    cs_general_data = cellsam_generalist_dict[ds]\n",
+    "    cs_specific_data = cellsam_specific_dict[ds]\n",
+    "    \n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cs_general_mean = 1 - np.mean(cs_general_data)\n",
+    "    cs_specific_mean = 1 - np.mean(cs_specific_data)\n",
+    "    cs_general_means.append(cs_general_mean)\n",
+    "    cs_specific_means.append(cs_specific_mean)\n",
+    "    \n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cs_general_sem = np.std(cs_general_data, ddof=1) / np.sqrt(len(cs_general_data))\n",
+    "    cs_specific_sem = np.std(cs_specific_data, ddof=1) / np.sqrt(len(cs_specific_data))\n",
+    "    cs_general_sems.append(cs_general_sem)\n",
+    "    cs_specific_sems.append(cs_specific_sem)\n",
+    "\n",
+    "\n",
+    "x = np.arange(len(datasets))\n",
+    "width = 0.35\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "bars_cs_specific = ax.bar(\n",
+    "    x - width/2, cs_specific_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cs_specific_sems, capsize=5, \n",
+    "    label='Specialist', color=c3\n",
+    ")\n",
+    "\n",
+    "bars_cs_general = ax.bar(\n",
+    "    x + width/2, cs_general_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cs_general_sems, capsize=5, \n",
+    "    label='Generalist', color=c4\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([name_map[ds] for ds in datasets], rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "# ax.set_title('Generalist Model Comparison of Mean Error')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_dataset_comparison_cellsam_specific_general.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "CellSAM Specialist vs Generalist by Data Type\n",
+    "\"\"\"\n",
+    "\n",
+    "dataset_agg_map = {\n",
+    "    \"Tissue\": [\"tissuenet_wholecell\"],\n",
+    "    \"Cell Culture\": [\"cellpose\", \"ep_phase_microscopy_all\", \"Gendarme_BriFi\"],\n",
+    "    \"H&E\": [\"H_and_E\"],\n",
+    "    \"Bacteria\": [\"deepbacs\", \"omnipose\"],\n",
+    "    \"Yeast\": [\"YeaZ\", \"YeastNet\"],\n",
+    "    \"Nuclear\": [\"dsb_fixed\"],\n",
+    "}\n",
+    "\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "\n",
+    "\n",
+    "cs_specifc_group_means = []\n",
+    "cs_general_group_means = []\n",
+    "cs_specifc_group_sems = []\n",
+    "cs_general_group_sems = []\n",
+    "\n",
+    "\n",
+    "for group in group_names:\n",
+    "    # Get all datasets that belong to this group\n",
+    "    datasets_for_group = dataset_agg_map[group]\n",
+    "    \n",
+    "    # Gather all F1 arrays and concatenate them\n",
+    "    cs_specific_all = np.concatenate([cellsam_specific_dict[ds] for ds in datasets_for_group])\n",
+    "    cs_general_all = np.concatenate([cellsam_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "\n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cs_specific_mean = 1 - np.mean(cs_specific_all)\n",
+    "    cs_general_mean = 1 - np.mean(cs_general_all)\n",
+    "    cs_specifc_group_means.append(cs_specific_mean)\n",
+    "    cs_general_group_means.append(cs_general_mean)\n",
+    "\n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cs_specific_sem = np.std(cs_specific_all, ddof=1) / np.sqrt(len(cs_specific_all))\n",
+    "    cs_general_sem = np.std(cs_general_all, ddof=1) / np.sqrt(len(cs_general_all))\n",
+    "    cs_specifc_group_sems.append(cs_specific_sem)\n",
+    "    cs_general_group_sems.append(cs_general_sem)\n",
+    "\n",
+    "x = np.arange(len(group_names))\n",
+    "width = 0.35\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "bars_cs_specific = ax.bar(\n",
+    "    x - width/2, cs_specifc_group_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cs_specifc_group_sems, capsize=5, \n",
+    "    label='Specialist', color=c3\n",
+    ")\n",
+    "\n",
+    "bars_cs_general = ax.bar(\n",
+    "    x + width/2, cs_general_group_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cs_general_group_sems, capsize=5, \n",
+    "    label='Generalist', color=c4\n",
+    ")\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(group_names, rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_grouped_comparison_specific_general.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cellpose Generalist vs Specialist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "name_map = {\n",
+    "    \"Gendarme_BriFi\": \"BriFiSeg\",\n",
+    "    \"cellpose\": \"Cellpose\",\n",
+    "    \"ep_phase_microscopy_all\": \"Phase400\",\n",
+    "    \"H_and_E\": \"H&E\",\n",
+    "    \"tissuenet_wholecell\": \"TissueNet\",\n",
+    "    \"YeaZ\": \"YeaZ\",\n",
+    "    \"YeastNet\": \"YeastNet\",\n",
+    "    \"dsb_fixed\": \"DSB\",\n",
+    "    \"deepbacs\": \"DeepBacs\",\n",
+    "    \"omnipose\": \"OmniPose\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "datasets = [\n",
+    "    'Gendarme_BriFi',\n",
+    "    'H_and_E',\n",
+    "    'YeaZ',\n",
+    "    'YeastNet',\n",
+    "    'cellpose',\n",
+    "    'deepbacs',\n",
+    "    'dsb_fixed',\n",
+    "    'ep_phase_microscopy_all',\n",
+    "    'omnipose',\n",
+    "    'tissuenet_wholecell',\n",
+    "]\n",
+    "\n",
+    "c1 = \"#fdbb84\"\n",
+    "c2 = \"#e34a33\"\n",
+    "c3 = '#deebf7'\n",
+    "c4 = '#3182bd'\n",
+    "\n",
+    "cellpose_path = Path.cwd() / \"eval_results/cellpose\"\n",
+    "cellpose_generalist_path = cellpose_path / 'general'\n",
+    "cellpose_individual_path = cellpose_path / 'individual'\n",
+    "\n",
+    "\n",
+    "cellpose_generalist_dict = {}\n",
+    "for file in cellpose_generalist_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cellpose_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "\n",
+    "cellpose_specific_dict = {}\n",
+    "for file in cellpose_individual_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cellpose_specific_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose Specialist vs Generalist by Dataset\n",
+    "\"\"\"\n",
+    "\n",
+    "cp_general_means = []; cp_specific_means = []\n",
+    "cp_general_sems = []; cp_specific_sems = []\n",
+    "\n",
+    "for ds in datasets:\n",
+    "    cp_general_data = cellpose_generalist_dict[ds]\n",
+    "    cp_specific_data = cellpose_specific_dict[ds]\n",
+    "    \n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cp_general_mean = 1 - np.mean(cp_general_data)\n",
+    "    cp_specific_mean = 1 - np.mean(cp_specific_data)\n",
+    "    cp_general_means.append(cp_general_mean)\n",
+    "    cp_specific_means.append(cp_specific_mean)\n",
+    "    \n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cp_general_sem = np.std(cp_general_data, ddof=1) / np.sqrt(len(cp_general_data))\n",
+    "    cp_specific_sem = np.std(cp_specific_data, ddof=1) / np.sqrt(len(cp_specific_data))\n",
+    "    cp_general_sems.append(cp_general_sem)\n",
+    "    cp_specific_sems.append(cp_specific_sem)\n",
+    "\n",
+    "\n",
+    "x = np.arange(len(datasets))\n",
+    "width = 0.35\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "bars_cp_specific = ax.bar(\n",
+    "    x - width/2, cp_specific_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cp_specific_sems, capsize=5, \n",
+    "    label='Specialist', color=c1\n",
+    ")\n",
+    "\n",
+    "bars_cp_general = ax.bar(\n",
+    "    x + width/2, cp_general_means, width,\n",
+    "    edgecolor='black', linewidth=1,\n",
+    "    yerr=cp_general_sems, capsize=5, \n",
+    "    label='Generalist', color=c2\n",
+    ")\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([name_map[ds] for ds in datasets], rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "# ax.set_title('Generalist Model Comparison of Mean Error')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_dataset_comparison_CELLPOSE_specific_general.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Cellpose Specialist vs Generalist by Data Type\n",
+    "\"\"\"\n",
+    "\n",
+    "dataset_agg_map = {\n",
+    "    \"Tissue\": [\"tissuenet_wholecell\"],\n",
+    "    \"Cell Culture\": [\"cellpose\", \"ep_phase_microscopy_all\", \"Gendarme_BriFi\"],\n",
+    "    \"H&E\": [\"H_and_E\"],\n",
+    "    \"Bacteria\": [\"deepbacs\", \"omnipose\"],\n",
+    "    \"Yeast\": [\"YeaZ\", \"YeastNet\"],\n",
+    "    \"Nuclear\": [\"dsb_fixed\"],\n",
+    "}\n",
+    "\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "cp_group_general_means = []\n",
+    "cp_group_general_sems  = []\n",
+    "cp_group_specific_means = []\n",
+    "cp_group_specific_sems  = []\n",
+    "\n",
+    "for group in group_names:\n",
+    "    datasets_for_group = dataset_agg_map[group]\n",
+    "    cp_general_all = np.concatenate([cellpose_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    cp_specific_all = np.concatenate([cellpose_specific_dict[ds] for ds in datasets_for_group])\n",
+    "    \n",
+    "    cp_general_mean = 1 - np.mean(cp_general_all)\n",
+    "    cp_specific_mean = 1 - np.mean(cp_specific_all)\n",
+    "    cp_group_general_means.append(cp_general_mean)\n",
+    "    cp_group_specific_means.append(cp_specific_mean)\n",
+    "    \n",
+    "    cp_general_sem = np.std(cp_general_all, ddof=1) / np.sqrt(len(cp_general_all))\n",
+    "    cp_specific_sem = np.std(cp_specific_all, ddof=1) / np.sqrt(len(cp_specific_all))\n",
+    "    cp_group_general_sems.append(cp_general_sem)\n",
+    "    cp_group_specific_sems.append(cp_specific_sem)\n",
+    "\n",
+    "\n",
+    "\n",
+    "x = np.arange(len(group_names))\n",
+    "width = 0.35\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "\n",
+    "bars_cp_specific = ax.bar(\n",
+    "    x - width/2, cp_group_specific_means, width,\n",
+    "    yerr=cp_group_specific_sems, edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='Specialist', color=c1\n",
+    ")\n",
+    "\n",
+    "bars_cp_general = ax.bar(\n",
+    "    x + width/2, cp_group_general_means, width,\n",
+    "    yerr=cp_group_general_sems, edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='Generalist', color=c2\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(group_names, rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_grouped_comparison_CELLPOSE_specific_general.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### CellSAM vs Cellpose - Cyto3 Comparision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "name_map = {\n",
+    "    \"Gendarme_BriFi\": \"BriFiSeg\",\n",
+    "    \"cellpose\": \"Cellpose\",\n",
+    "    \"ep_phase_microscopy_all\": \"Phase400\",\n",
+    "    \"H_and_E\": \"H&E\",\n",
+    "    \"tissuenet_wholecell\": \"TissueNet\",\n",
+    "    \"YeaZ\": \"YeaZ\",\n",
+    "    \"YeastNet\": \"YeastNet\",\n",
+    "    \"dsb_fixed\": \"DSB\",\n",
+    "    \"deepbacs\": \"DeepBacs\",\n",
+    "    \"omnipose\": \"OmniPose\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "datasets = [\n",
+    "    'Gendarme_BriFi',\n",
+    "    'H_and_E',\n",
+    "    'YeaZ',\n",
+    "    'YeastNet',\n",
+    "    'cellpose',\n",
+    "    'deepbacs',\n",
+    "    'dsb_fixed',\n",
+    "    'ep_phase_microscopy_all',\n",
+    "    'omnipose',\n",
+    "    'tissuenet_wholecell',\n",
+    "]\n",
+    "\n",
+    "c1 = \"#fdbb84\"\n",
+    "c2 = \"#e34a33\"\n",
+    "c3 = '#deebf7'\n",
+    "c4 = '#3182bd'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cellsam_path = Path.cwd() / \"eval_results/cellsam\"\n",
+    "cellpose_path = Path.cwd() / \"eval_results/cellpose\"\n",
+    "\n",
+    "cellpose_generalist_path = cellpose_path / 'cyto3'\n",
+    "cellsam_generalist_path = cellsam_path / 'general'\n",
+    "\n",
+    "\n",
+    "cp_generalist_dict = {}\n",
+    "\n",
+    "for file in cellpose_generalist_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cp_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "cs_generalist_dict = {}\n",
+    "\n",
+    "for file in cellsam_generalist_path.glob(\"*.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cs_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "CellSAM vs Cellpose - Cyto3 Comparision by Dataset\n",
+    "\"\"\"\n",
+    "\n",
+    "cp_means = []; cs_means = []\n",
+    "cp_sems = []; cs_sems = []\n",
+    "\n",
+    "for ds in datasets:\n",
+    "    cp_data = cp_generalist_dict[ds]\n",
+    "    cs_data = cs_generalist_dict[ds]\n",
+    "    # 1 - mean for the bar\n",
+    "    cp_m = 1 - np.mean(cp_data)\n",
+    "    cs_m = 1 - np.mean(cs_data)\n",
+    "    # standard error of the mean for the error bar\n",
+    "    cp_sem = np.std(cp_data, ddof=1) / np.sqrt(len(cp_data))\n",
+    "    cs_sem = np.std(cs_data, ddof=1) / np.sqrt(len(cs_data))\n",
+    "\n",
+    "    cp_means.append(cp_m)\n",
+    "    cs_means.append(cs_m)\n",
+    "    cp_sems.append(cp_sem)\n",
+    "    cs_sems.append(cs_sem)\n",
+    "\n",
+    "# Plot as a bar chart\n",
+    "x = np.arange(len(datasets))\n",
+    "width = 0.35  # width of each bar\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "\n",
+    "# Plot CP bars slightly left, CS bars slightly right\n",
+    "bars_cp = ax.bar(x - width/2, cp_means, width, \n",
+    "                 edgecolor='black', linewidth=1,\n",
+    "                 yerr=cp_sems, capsize=5, label='Cellpose - Cyto3', color=c2)\n",
+    "bars_cs = ax.bar(x + width/2, cs_means, width, \n",
+    "                 edgecolor='black', linewidth=1,\n",
+    "                 yerr=cs_sems, capsize=5, label='CellSAM', color=c4)\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([name_map[ds] for ds in datasets], rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "# ax.set_title('Generalist Model Comparison of Mean Error')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_dataset_comparison_cyto3.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "CellSAM vs Cellpose - Cyto3 Comparision by Data Type\n",
+    "\"\"\"\n",
+    "\n",
+    "dataset_agg_map = {\n",
+    "    \"Tissue\": [\"tissuenet_wholecell\"],\n",
+    "    \"Cell Culture\": [\"cellpose\", \"ep_phase_microscopy_all\", \"Gendarme_BriFi\"],\n",
+    "    \"H&E\": [\"H_and_E\"],\n",
+    "    \"Bacteria\": [\"deepbacs\", \"omnipose\"],\n",
+    "    \"Yeast\": [\"YeaZ\", \"YeastNet\"],\n",
+    "    \"Nuclear\": [\"dsb_fixed\"],\n",
+    "}\n",
+    "\n",
+    "group_names = list(dataset_agg_map.keys())\n",
+    "cp_group_means = []\n",
+    "cp_group_sems  = []\n",
+    "cs_group_means = []\n",
+    "cs_group_sems  = []\n",
+    "\n",
+    "for group in group_names:\n",
+    "    # Get all datasets that belong to this group\n",
+    "    datasets_for_group = dataset_agg_map[group]\n",
+    "    \n",
+    "    # Gather all F1 arrays and concatenate them\n",
+    "    cp_all = np.concatenate([cp_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    cs_all = np.concatenate([cs_generalist_dict[ds] for ds in datasets_for_group])\n",
+    "    \n",
+    "    # Compute (1 - mean(F1)) for the group\n",
+    "    cp_mean = 1 - np.mean(cp_all)\n",
+    "    cs_mean = 1 - np.mean(cs_all)\n",
+    "    cp_group_means.append(cp_mean)\n",
+    "    cs_group_means.append(cs_mean)\n",
+    "    \n",
+    "    # Standard error of the mean (SEM) for the group\n",
+    "    cp_sem = np.std(cp_all, ddof=1) / np.sqrt(len(cp_all))\n",
+    "    cs_sem = np.std(cs_all, ddof=1) / np.sqrt(len(cs_all))\n",
+    "    cp_group_sems.append(cp_sem)\n",
+    "    cs_group_sems.append(cs_sem)\n",
+    "\n",
+    "# Now plot side‐by‐side bars for the groups\n",
+    "x = np.arange(len(group_names))\n",
+    "width = 0.35\n",
+    "plt.rcParams['svg.fonttype'] = 'none' \n",
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "\n",
+    "bars_cp = ax.bar(\n",
+    "    x - width/2, cp_group_means, width,\n",
+    "    yerr=cp_group_sems, edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='Cellpose - Cyto3', color=c2\n",
+    ")\n",
+    "bars_cs = ax.bar(\n",
+    "    x + width/2, cs_group_means, width,\n",
+    "    yerr=cs_group_sems, edgecolor='black', linewidth=1, capsize=5, \n",
+    "    label='CellSAM', color=c4\n",
+    ")\n",
+    "\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(group_names, rotation=45, ha='right')\n",
+    "ax.set_ylabel('Mean Error (1 - F1)')\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(\n",
+    "    loc='upper center',\n",
+    "    bbox_to_anchor=(0.5, 1.15),\n",
+    "    ncol=2,\n",
+    "    prop={'size': 14},\n",
+    "    frameon=False\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_grouped_comparison_cyto3.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LiveCell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "\n",
+    "cellsam_path = Path.cwd() / \"livecell/cellsam\"\n",
+    "cellpose_path = Path.cwd() / \"livecell/cellpose\"\n",
+    "\n",
+    "\n",
+    "c1 = \"#fdbb84\"\n",
+    "c2 = \"#e34a33\"\n",
+    "c3 = '#deebf7'\n",
+    "c4 = '#3182bd'\n",
+    "\n",
+    "\n",
+    "cellpose_generalist_path = cellpose_path / 'general/'\n",
+    "cellsam_generalist_path = cellsam_path / 'general/'\n",
+    "cellsam_fewshot_path = cellsam_path / 'general_FS_10_FT/'\n",
+    "\n",
+    "\n",
+    "cp_generalist_dict = {}\n",
+    "for file in cellpose_generalist_path.glob(\"LIVECell_good.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cp_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "cs_generalist_dict = {}\n",
+    "for file in cellsam_generalist_path.glob(\"LIVECell_good.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cs_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "\n",
+    "cs_fewshot_dict = {}\n",
+    "for file in cellsam_fewshot_path.glob(\"LIVECell_good.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cs_fewshot_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "dataset = 'LIVECell_good'  # Single dataset name\n",
+    "\n",
+    "# Extract data from dictionaries (replace with your actual data extraction logic)\n",
+    "cp_data = cp_generalist_dict.get(dataset, [])\n",
+    "cs_data = cs_generalist_dict.get(dataset, [])\n",
+    "cs_fewshot_data = cs_fewshot_dict.get(dataset, [])\n",
+    "\n",
+    "# Compute mean and standard error\n",
+    "cp_mean = 1 - np.mean(cp_data)\n",
+    "cs_mean = 1 - np.mean(cs_data)\n",
+    "cs_fewshot_mean = 1 - np.mean(cs_fewshot_data)\n",
+    "\n",
+    "cp_sem = np.std(cp_data, ddof=1) / np.sqrt(len(cp_data)) \n",
+    "cs_sem = np.std(cs_data, ddof=1) / np.sqrt(len(cs_data))    \n",
+    "cs_fewshot_sem = np.std(cs_fewshot_data, ddof=1) / np.sqrt(len(cs_fewshot_data)) \n",
+    "\n",
+    "# Set up bar positions\n",
+    "x = np.array([0])  # Single data point\n",
+    "bar_spacing = 0.05  # Space between bars\n",
+    "width = 0.2  # Bar width\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(5, 6))  # Adjust figure size\n",
+    "\n",
+    "# Adjust positions of each bar for even spacing\n",
+    "bars_cp = ax.bar(x - width - bar_spacing, cp_mean, width, yerr=cp_sem, capsize=5, \n",
+    "                 label='Cellpose', color=c2, edgecolor='black')\n",
+    "\n",
+    "bars_cs = ax.bar(x, cs_mean, width, yerr=cs_sem, capsize=5, \n",
+    "                 label='CellSAM', color=c3, edgecolor='black')  # Adjusted color for distinction\n",
+    "\n",
+    "bars_csf = ax.bar(x + width + bar_spacing, cs_fewshot_mean, width, yerr=cs_fewshot_sem, capsize=5, \n",
+    "                  label='CellSAM Fewshot', color=c4, edgecolor='black')\n",
+    "\n",
+    "# Labels and styling\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([\"LIVECell\"], ha='center')\n",
+    "ax.set_ylabel(\"Mean Error (1 - F1)\")\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "# Adjust legend to accommodate three categories\n",
+    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=3, prop={'size': 12}, frameon=False)\n",
+    "ax.set_xlim([-0.5, 0.5])  \n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_livecell_dataset.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "cellpose_generalist_path = cellpose_path / 'general/'\n",
+    "cellsam_generalist_path = cellsam_path / 'general/'\n",
+    "cellsam_fewshot_path = cellsam_path / 'general_FS_10_FT/'\n",
+    "\n",
+    "cp_generalist_dict = {}\n",
+    "for file in cellpose_generalist_path.glob(\"LIVECell_good.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cp_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "cs_generalist_dict = {}\n",
+    "for file in cellsam_generalist_path.glob(\"LIVECell_good.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cs_generalist_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "\n",
+    "cs_fewshot_dict = {}\n",
+    "for file in cellsam_fewshot_path.glob(\"LIVECell_good.txt\"):\n",
+    "    try:\n",
+    "        data = np.loadtxt(file)\n",
+    "        cs_fewshot_dict[file.stem] = data\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error reading {file.name}: {e}\")\n",
+    "        \n",
+    "dataset = ['LIVECell_good']\n",
+    "\n",
+    "# Load cell types\n",
+    "celltypes = pd.read_csv(\n",
+    "    Path.cwd() / \"metadata_LIVECELL.csv\", \n",
+    "    header=None\n",
+    ")\n",
+    "celltypes_array = celltypes.values.flatten()\n",
+    "\n",
+    "# Load errors (already doing 1 - value)\n",
+    "cp_data        = 1 - cp_generalist_dict.get(\"LIVECell_good\", [])\n",
+    "cs_data        = 1 - cs_generalist_dict.get(\"LIVECell_good\", [])\n",
+    "cs_fewshotData = 1 - cs_fewshot_dict.get(\"LIVECell_good\", [])\n",
+    "\n",
+    "print(len(celltypes), len(cp_data), len(cs_data), len(cs_fewshotData))\n",
+    "\n",
+    "# Build DataFrame\n",
+    "df_cp = pd.DataFrame({\n",
+    "    'error':    cp_data,\n",
+    "    'method':   'CellPose',\n",
+    "    'celltype': celltypes_array\n",
+    "})\n",
+    "df_cs = pd.DataFrame({\n",
+    "    'error':    cs_data,\n",
+    "    'method':   'CellSam',\n",
+    "    'celltype': celltypes_array\n",
+    "})\n",
+    "df_csf = pd.DataFrame({\n",
+    "    'error':    cs_fewshotData,\n",
+    "    'method':   'CellSam Fewshot',\n",
+    "    'celltype': celltypes_array\n",
+    "})\n",
+    "df_all = pd.concat([df_cp, df_cs, df_csf], ignore_index=True)\n",
+    "\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 6))\n",
+    "\n",
+    "# Get unique cell types in the order they appear\n",
+    "unique_celltypes = df_all['celltype'].unique()\n",
+    "x_positions = np.arange(len(unique_celltypes))  \n",
+    "offset  = 0.25\n",
+    "v_width = 0.2  \n",
+    "\n",
+    "for i, ct in enumerate(unique_celltypes):\n",
+    "    cp_vals  = df_all.query(\"celltype == @ct & method == 'CellPose'\")['error']\n",
+    "    cs_vals  = df_all.query(\"celltype == @ct & method == 'CellSam'\")['error']\n",
+    "    csf_vals = df_all.query(\"celltype == @ct & method == 'CellSam Fewshot'\")['error']\n",
+    "    \n",
+    "    vio_cp = ax.violinplot(\n",
+    "        [cp_vals],                \n",
+    "        positions=[x_positions[i] - offset],\n",
+    "        widths=v_width,\n",
+    "        showmeans=True,\n",
+    "        showextrema=False,\n",
+    "        showmedians=False\n",
+    "    )\n",
+    "    for b in vio_cp['bodies']:\n",
+    "        b.set_facecolor(c2)       \n",
+    "        b.set_edgecolor('black')\n",
+    "        b.set_alpha(1.0)          \n",
+    "    if 'cmeans' in vio_cp:\n",
+    "        vio_cp['cmeans'].set_color('black')\n",
+    "        vio_cp['cmeans'].set_linewidth(2.0)\n",
+    "\n",
+    "    vio_cs = ax.violinplot(\n",
+    "        [cs_vals],\n",
+    "        positions=[x_positions[i]],\n",
+    "        widths=v_width,\n",
+    "        showmeans=True,\n",
+    "        showextrema=False,\n",
+    "        showmedians=False\n",
+    "    )\n",
+    "    for b in vio_cs['bodies']:\n",
+    "        b.set_facecolor(c3)       \n",
+    "        b.set_edgecolor('black')\n",
+    "        b.set_alpha(1.0)\n",
+    "    if 'cmeans' in vio_cs:\n",
+    "        vio_cs['cmeans'].set_color('black')\n",
+    "        vio_cs['cmeans'].set_linewidth(2.0)\n",
+    "\n",
+    "    vio_csf = ax.violinplot(\n",
+    "        [csf_vals],\n",
+    "        positions=[x_positions[i] + offset],\n",
+    "        widths=v_width,\n",
+    "        showmeans=True,\n",
+    "        showextrema=False,\n",
+    "        showmedians=False\n",
+    "    )\n",
+    "    for b in vio_csf['bodies']:\n",
+    "        b.set_facecolor(c4)       \n",
+    "        b.set_edgecolor('black')\n",
+    "        b.set_alpha(1.0)\n",
+    "    if 'cmeans' in vio_csf:\n",
+    "        vio_csf['cmeans'].set_color('black')\n",
+    "        vio_csf['cmeans'].set_linewidth(2.0)\n",
+    "\n",
+    "# Set x‐labels, y‐labels, title\n",
+    "ax.set_xticks(x_positions)\n",
+    "ax.set_xticklabels(unique_celltypes, rotation=45, ha='right')\n",
+    "ax.set_ylabel(\"Mean Error (1 - F1)\")\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "\n",
+    "# Build a custom legend that matches the violin colors exactly\n",
+    "legend_patches = [\n",
+    "    mpatches.Patch(facecolor=c2, edgecolor='black', label='Cellpose'),\n",
+    "    mpatches.Patch(facecolor=c3, edgecolor='black', label='CellSAM'),\n",
+    "    mpatches.Patch(facecolor=c4, edgecolor='black', label='CellSAM Fewshot')\n",
+    "]\n",
+    "ax.legend(handles=legend_patches, loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=3, prop={'size': 12}, frameon=False)\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_dataset_livecell_dataset.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Neurips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cp_mean = 1- 0.8612\n",
+    "cs_mean = 1- 0.8723\n",
+    "\n",
+    "x = np.array([0])  # Single data point\n",
+    "bar_spacing = 0.05 \n",
+    "width = 0.2  # Width of bars\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(4, 6))\n",
+    "\n",
+    "# Bar plots with error bars\n",
+    "bars_cp = ax.bar(x - (width/2 + bar_spacing), cp_mean, width,  label='CellPose', color=c2, edgecolor='black')\n",
+    "bars_cs = ax.bar(x + (width/2 + bar_spacing), cs_mean, width,  label='CellSam', color=c4, edgecolor='black')\n",
+    "\n",
+    "# Labels and styling\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([\"Neurips Challenge\"], ha='center')\n",
+    "ax.set_ylabel(\"Mean Error (1 - F1)\")\n",
+    "ax.spines[\"top\"].set_visible(False)\n",
+    "ax.spines[\"right\"].set_visible(False)\n",
+    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=2, prop={'size': 12}, frameon=False)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# fig.savefig(\"mean_error_neurips_dataset.svg\", format=\"svg\", dpi=300)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}