commit e77729705072586a4f31d43addba8935f56244a9
Author: yezhengmao <yezhengmaolove@gmail.com>
Date:   Wed Mar 5 20:38:41 2025 +0800

    init

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..505a3b1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e69de29
diff --git a/lynx/end_to_end.ipynb b/lynx/end_to_end.ipynb
new file mode 100644
index 0000000..bc63c4e
--- /dev/null
+++ b/lynx/end_to_end.ipynb
@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 531,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "\n",
+    "g_label_fontsize = 16\n",
+    "\n",
+    "colors = [\n",
+    "    \"#999999\",\n",
+    "    \"#888888\",\n",
+    "    \"#FF9999\",\n",
+    "]\n",
+    "\n",
+    "hatches = [\"\\\\\", \"/\", \"x\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 532,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 4200x1680 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(14, 14 / 2.5), ncols=1, nrows=2, constrained_layout=True, dpi=300\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 533,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_a = {\n",
+    "    \"ModelA\\n#8GPUs\": [53, 53, 65],  # 22.64\n",
+    "    \"ModelA\\n#16GPUs\": [53, 53, 65],  # 22.64\n",
+    "    \"ModelB\\n#8GPUs\": [35, 35, 48],  # 37\n",
+    "    \"ModelB\\n#16GPUs\": [35, 35, 48],  # 37\n",
+    "    \"ModelC\\n#8GPUs\": [53, 53, 65],  # 22.64\n",
+    "    \"ModelC\\n#16GPUs\": [53, 53, 65],  # 22.64\n",
+    "    \"ModelD\\n#8GPUs\": [53, 53, 65],  # 22.64\n",
+    "    \"ModelD\\n#16GPUs\": [53, 53, 65],  # 22.64\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 534,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_b = {\n",
+    "    \"ModelA\\n#8GPUs\": [[54, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelA\\n#16GPUs\": [[54, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelB\\n#8GPUs\": [[52, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelB\\n#16GPUs\": [[52, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelC\\n#8GPUs\": [[52, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelC\\n#16GPUs\": [[52, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelD\\n#8GPUs\": [[52, 62, 12], [10, 10, 10]],\n",
+    "    \"ModelD\\n#16GPUs\": [[52, 62, 12], [10, 10, 10]],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 535,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legend_labels = [\"FSDP\", \"FSDP+XLA\", \"FSDP+DLRover-Lynx\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 536,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bar_width = 0.2\n",
+    "group_spaing = 0.15\n",
+    "\n",
+    "group_positions = {}\n",
+    "current_pos = 0\n",
+    "\n",
+    "for x_label, y_data in data_a.items():\n",
+    "    group_positions[x_label] = []\n",
+    "    for i in range(len(y_data)):\n",
+    "        group_positions[x_label].append(current_pos)\n",
+    "        current_pos += bar_width\n",
+    "    current_pos += group_spaing\n",
+    "\n",
+    "group_centers = {}\n",
+    "for x_label, positions in group_positions.items():\n",
+    "    group_centers[x_label] = sum(positions) / len(positions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 537,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '(a)')"
+      ]
+     },
+     "execution_count": 537,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for x_label, y_data in data_a.items():\n",
+    "    positions = group_positions[x_label]\n",
+    "    for i, (pos, value, color, hatch) in enumerate(\n",
+    "        zip(\n",
+    "            positions,\n",
+    "            y_data,\n",
+    "            colors,\n",
+    "            hatches,\n",
+    "        )\n",
+    "    ):\n",
+    "        ax[0].bar(\n",
+    "            pos,\n",
+    "            value,\n",
+    "            width=bar_width,\n",
+    "            color=color,\n",
+    "            edgecolor=\"black\",\n",
+    "            hatch=hatch,\n",
+    "        )\n",
+    "\n",
+    "ax[0].set_xticks(list(group_centers.values()))\n",
+    "ax[0].set_xticklabels(list(data_a.keys()))\n",
+    "\n",
+    "ax[0].set_ylim(0, 100)\n",
+    "ax[0].set_yticks([0, 50, 100])\n",
+    "ax[0].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
+    "\n",
+    "ax[0].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
+    "ax[0].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
+    "\n",
+    "ax[0].set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)\n",
+    "ax[0].set_title(\"(a)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 538,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '(b)')"
+      ]
+     },
+     "execution_count": 538,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for x_label, y_data in data_b.items():\n",
+    "    positions = group_positions[x_label]\n",
+    "    for i, (pos, value, ovalue, color, hatch) in enumerate(\n",
+    "        zip(\n",
+    "            positions,\n",
+    "            y_data[0],\n",
+    "            y_data[1],\n",
+    "            colors,\n",
+    "            hatches,\n",
+    "        )\n",
+    "    ):\n",
+    "        ax[1].bar(\n",
+    "            pos,\n",
+    "            value,\n",
+    "            width=bar_width,\n",
+    "            color=color,\n",
+    "            edgecolor=\"black\",\n",
+    "            hatch=hatch,\n",
+    "        )\n",
+    "\n",
+    "ax[1].set_xticks(list(group_centers.values()))\n",
+    "ax[1].set_xticklabels(list(data_a.keys()))\n",
+    "\n",
+    "ax[1].set_ylim(0, 100)\n",
+    "ax[1].set_yticks([0, 50, 100])\n",
+    "ax[1].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
+    "\n",
+    "ax[1].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
+    "ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
+    "\n",
+    "ax[1].set_ylabel(\"Communication\\nProportion (%)\", fontsize=g_label_fontsize)\n",
+    "ax[1].set_title(\"(b)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 539,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig.legend(\n",
+    "#     ncol=4,\n",
+    "#     loc=\"upper center\",\n",
+    "#     frameon=True,\n",
+    "#     shadow=False,\n",
+    "#     bbox_to_anchor=(0.5, 1.10),\n",
+    "#     fontsize=g_label_fontsize,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 540,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 4200x1680 with 2 Axes>"
+      ]
+     },
+     "execution_count": 540,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.savefig(\"lynx_end_to_end.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lynx/end_to_end_nfsc.ipynb b/lynx/end_to_end_nfsc.ipynb
new file mode 100644
index 0000000..9f21f1e
--- /dev/null
+++ b/lynx/end_to_end_nfsc.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "\n",
+    "g_label_fontsize = 16\n",
+    "\n",
+    "colors = [\n",
+    "    \"#999999\",\n",
+    "    \"#FF9999\",\n",
+    "]\n",
+    "\n",
+    "hatches = [\"\\\\\", \"x\", \"+\", \"/\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 4200x1050 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(14, 14 / 4), ncols=2, nrows=1, constrained_layout=True, dpi=300\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_a = {\n",
+    "    \"7B#8GPUs\": [53, 65], # 22.64\n",
+    "    \"7B#16GPUs\": [44,63], # 43\n",
+    "    \"11B#8GPUs\": [35, 48], # 37\n",
+    "    \"11B#16GPUs\": [22, 37] # 68\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_b = {\n",
+    "    \"7B#8GPUs\": [1.22, 1.01],\n",
+    "    \"7B#16GPUs\": [1.9, 1.62],\n",
+    "    \"11B#8GPUs\": [1.34, 1.1],\n",
+    "    \"11B#16GPUs\": [1.9, 1.436]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.17213114754098358\n",
+      "0.14736842105263148\n",
+      "0.1791044776119403\n",
+      "0.24421052631578946\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, values in data_b.items():\n",
+    "    print((values[0] - values[1]) / values[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legend_labels = [\"FSDP\", \"DLRover-Lynx\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bar_width = 0.2\n",
+    "group_spaing = 0.15\n",
+    "\n",
+    "group_positions = {}\n",
+    "current_pos = 0\n",
+    "\n",
+    "for x_label, y_data in data_a.items():\n",
+    "    group_positions[x_label] = []\n",
+    "    for i in range(len(y_data)):\n",
+    "        group_positions[x_label].append(current_pos)\n",
+    "        current_pos += bar_width\n",
+    "    current_pos += group_spaing\n",
+    "\n",
+    "group_centers = {}\n",
+    "for x_label, positions in group_positions.items():\n",
+    "    group_centers[x_label] = sum(positions) / len(positions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 341,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '(a)')"
+      ]
+     },
+     "execution_count": 341,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for x_label, y_data in data_a.items():\n",
+    "    positions = group_positions[x_label]\n",
+    "    for i, (pos, value, color, hatch) in enumerate(\n",
+    "        zip(\n",
+    "            positions,\n",
+    "            y_data,\n",
+    "            colors,\n",
+    "            hatches,\n",
+    "        )\n",
+    "    ):\n",
+    "        ax[0].bar(\n",
+    "            pos,\n",
+    "            value,\n",
+    "            width=bar_width,\n",
+    "            color=color,\n",
+    "            edgecolor=\"black\",\n",
+    "            hatch=hatch,\n",
+    "        )\n",
+    "\n",
+    "ax[0].set_xticks(list(group_centers.values()))\n",
+    "ax[0].set_xticklabels(list(data_a.keys()))\n",
+    "\n",
+    "ax[0].set_ylim(0, 100)\n",
+    "ax[0].set_yticks([0, 50, 100])\n",
+    "ax[0].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
+    "\n",
+    "ax[0].tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize, pad=1)\n",
+    "ax[0].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
+    "\n",
+    "ax[0].set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)\n",
+    "ax[0].set_title(\"(a)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '(b)')"
+      ]
+     },
+     "execution_count": 342,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_set = set()\n",
+    "for x_label, y_data in data_b.items():\n",
+    "    positions = group_positions[x_label]\n",
+    "    for i, (pos, value, color, hatch, label) in enumerate(\n",
+    "        zip(positions, y_data, colors, hatches, legend_labels)\n",
+    "    ):\n",
+    "        if label in label_set:\n",
+    "            local_label = None\n",
+    "        else:\n",
+    "            local_label = label\n",
+    "            label_set.add(label)\n",
+    "        ax[1].bar(\n",
+    "            pos,\n",
+    "            value,\n",
+    "            width=bar_width,\n",
+    "            color=color,\n",
+    "            edgecolor=\"black\",\n",
+    "            hatch=hatch,\n",
+    "            alpha=0.9,\n",
+    "            label=local_label,\n",
+    "        )\n",
+    "\n",
+    "ax[1].set_xticks(list(group_centers.values()))\n",
+    "ax[1].set_xticklabels(list(data_a.keys()))\n",
+    "\n",
+    "ax[1].set_ylim(0, 2.5)\n",
+    "ax[1].set_yticks([0, 1, 2])\n",
+    "ax[1].set_yticklabels([\"0\", \"1\", \"2\"], rotation=90, ha=\"center\", va=\"center\")\n",
+    "\n",
+    "ax[1].tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize, pad=1)\n",
+    "ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
+    "\n",
+    "ax[1].set_ylabel(\"ITERATION TIME (S)\", fontsize=g_label_fontsize)\n",
+    "ax[1].set_title(\"(b)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fcd50e3b1d0>"
+      ]
+     },
+     "execution_count": 343,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.legend(\n",
+    "    ncol=4,\n",
+    "    loc=\"upper center\",\n",
+    "    frameon=True,\n",
+    "    shadow=False,\n",
+    "    bbox_to_anchor=(0.5, 1.10),\n",
+    "    fontsize=g_label_fontsize,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 344,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 4200x1050 with 2 Axes>"
+      ]
+     },
+     "execution_count": 344,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.savefig(\"lynx_end_to_end_nfsc.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lynx/hybrid_parallel_nfsc.ipynb b/lynx/hybrid_parallel_nfsc.ipynb
new file mode 100644
index 0000000..bb562ce
--- /dev/null
+++ b/lynx/hybrid_parallel_nfsc.ipynb
@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
+    "plt.rcParams[\"font.size\"] = 16\n",
+    "\n",
+    "g_label_fontsize = 16\n",
+    "\n",
+    "colors = [\n",
+    "    \"#999999\",\n",
+    "    \"#FF9999\",\n",
+    "]\n",
+    "\n",
+    "hatches = [\"\\\\\", \"x\", \"+\", \"/\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 4200x1050 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(14, 14 / 4), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_a = {\n",
+    "    \"8TP(Vanilla)+2DP#16GPUs\": [3.33, 3.025], \n",
+    "    \"8TP(MicroBatch)+2DP#16GPUs\": [3.88, 3.03], \n",
+    "    \"2TP+4FSDP#8GPUs\": [3.02, 2.85], \n",
+    "    \"8TP#16GPUs\": [2.98, 2.81] \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.09159159159159164\n",
+      "0.21907216494845363\n",
+      "0.056291390728476796\n",
+      "0.057046979865771785\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k, vs in data_a.items():\n",
+    "    print((vs[0] - vs[1]) / vs[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legend_labels = [\"FSDP\", \"DLRover-Lynx\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bar_width = 0.2\n",
+    "group_spaing = 0.15\n",
+    "\n",
+    "group_positions = {}\n",
+    "current_pos = 0\n",
+    "\n",
+    "for x_label, y_data in data_a.items():\n",
+    "    group_positions[x_label] = []\n",
+    "    for i in range(len(y_data)):\n",
+    "        group_positions[x_label].append(current_pos)\n",
+    "        current_pos += bar_width\n",
+    "    current_pos += group_spaing\n",
+    "\n",
+    "group_centers = {}\n",
+    "for x_label, positions in group_positions.items():\n",
+    "    group_centers[x_label] = sum(positions) / len(positions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(13.333333333333346, 0.5, 'ITERATION TIME (S)')"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_set = set()\n",
+    "for x_label, y_data in data_a.items():\n",
+    "    positions = group_positions[x_label]\n",
+    "    for i, (pos, value, color, hatch, label) in enumerate(\n",
+    "        zip(positions, y_data, colors, hatches, legend_labels)\n",
+    "    ):\n",
+    "        if label in label_set:\n",
+    "            local_label = None\n",
+    "        else:\n",
+    "            local_label = label\n",
+    "            label_set.add(label)\n",
+    "        ax.bar(\n",
+    "            pos,\n",
+    "            value,\n",
+    "            width=bar_width,\n",
+    "            color=color,\n",
+    "            edgecolor=\"black\",\n",
+    "            hatch=hatch,\n",
+    "            label=local_label,\n",
+    "        )\n",
+    "\n",
+    "ax.set_xticks(list(group_centers.values()))\n",
+    "ax.set_xticklabels(list(data_a.keys()))\n",
+    "\n",
+    "ax.set_ylim(0, 4.5)\n",
+    "ax.set_yticks([0, 1, 2, 3, 4])\n",
+    "ax.set_yticklabels(\n",
+    "    [\"0\", \"1\", \"2\", \"3\", \"4\"], rotation=90, ha=\"center\", va=\"center\"\n",
+    ")\n",
+    "\n",
+    "ax.tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize, pad=1)\n",
+    "ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
+    "\n",
+    "ax.set_ylabel(\"ITERATION TIME (S)\", fontsize=g_label_fontsize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f9948d403e0>"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.legend(\n",
+    "    ncol=2,\n",
+    "    loc=\"upper center\",\n",
+    "    frameon=True,\n",
+    "    shadow=False,\n",
+    "    bbox_to_anchor=(0.5, 1.14),\n",
+    "    fontsize=g_label_fontsize,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 4200x1050 with 1 Axes>"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.savefig(\"lynx_hybrid_parallel_nfsc.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
+    "fig"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lynx/lynx_end_to_end.pdf b/lynx/lynx_end_to_end.pdf
new file mode 100644
index 0000000..923cdbd
Binary files /dev/null and b/lynx/lynx_end_to_end.pdf differ
diff --git a/lynx/lynx_end_to_end_nfsc.pdf b/lynx/lynx_end_to_end_nfsc.pdf
new file mode 100644
index 0000000..e4ef385
Binary files /dev/null and b/lynx/lynx_end_to_end_nfsc.pdf differ
diff --git a/lynx/lynx_hybrid_parallel_nfsc.pdf b/lynx/lynx_hybrid_parallel_nfsc.pdf
new file mode 100644
index 0000000..d7f9d0e
Binary files /dev/null and b/lynx/lynx_hybrid_parallel_nfsc.pdf differ
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..04a6a62
--- /dev/null
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from note!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/mlora/adaptability.pdf b/mlora/adaptability.pdf
new file mode 100644
index 0000000..bf999d5
Binary files /dev/null and b/mlora/adaptability.pdf differ
diff --git a/mlora/adaptability.py b/mlora/adaptability.py
new file mode 100644
index 0000000..1ffa8c6
--- /dev/null
+++ b/mlora/adaptability.py
@@ -0,0 +1,87 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    from sklearn.linear_model import LinearRegression
+    import numpy as np
+    return LinearRegression, np, plt
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell
+def __(np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(figsize=(7, 4/2), ncols=3, nrows=1, layout="constrained", dpi=300)
+
+    c_1 = (230 / 255, 241 / 255, 243 / 255)
+    c_2 = (75 / 255, 116 / 155, 178 / 255)
+    c_3 = (255 / 255, 223 / 255, 146 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+
+
+    ax[0].set_xlabel("Rank of LoRA adapters", fontsize=14)
+    ax[1].set_xlabel("Rank of LoRA adapters", fontsize=14)
+    ax[2].set_xlabel("Rank of LoRA adapters", fontsize=14)
+
+    ax[0].set_ylabel("Throughput (tokens/s)", fontsize=12)
+
+    y_0 = [10525.52, 10467.51, 10315.85, 10286.17]
+    x_0 = [4, 8, 16, 32]
+
+    y_1 = [2309.40, 2258.73, 2252.43, 2242.80]
+    x_1 = [16, 32, 64, 128]
+
+    y_2 = [1245.79, 1244.60, 1224.91, 1207.40]
+    x_2 = [16, 32, 64, 128]
+
+    ax[0].plot(x_0, y_0, "s-", color=c_4, label="LoRAPP")
+    ax[0].set_ylim(0, 11000)
+    ax[0].set_xticks(x_0)
+    ax[0].set_yticks([0, 5000, 10000])
+    ax[0].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[0].tick_params(pad=7)
+
+    ax[1].plot(x_1, y_1, "s-", color=c_4)
+    ax[1].set_ylim(0, 11000)
+    ax[1].set_xticks([32, 64, 128])
+    ax[1].set_yticks([0, 5000, 10000])
+    ax[1].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[1].tick_params(pad=7)
+
+    ax[2].plot(x_2, y_2, "s-", color=c_4)
+    ax[2].set_ylim(0, 11000)
+    ax[2].set_xticks([32, 64, 128])
+    ax[2].set_yticks([0, 5000, 10000])
+    ax[2].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[2].tick_params(pad=7)
+
+    ax[0].set_title("(a) TinyLlama-1.1B", fontsize=14)
+    ax[1].set_title("(b) Llama-2-7B", fontsize=14)
+    ax[2].set_title("(c) Llama-2-13B", fontsize=14)
+
+    # plt.show()
+    plt.savefig("adaptability.pdf", bbox_inches="tight", dpi=1000)
+    return ax, c_1, c_2, c_3, c_4, fig, x, x_0, x_1, x_2, y_0, y_1, y_2
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/batchlora_cmp.py b/mlora/batchlora_cmp.py
new file mode 100644
index 0000000..7e0f0e4
--- /dev/null
+++ b/mlora/batchlora_cmp.py
@@ -0,0 +1,133 @@
+import marimo
+
+__generated_with = "0.7.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import random
+    return np, plt, random
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell(hide_code=True)
+def __(plt):
+    fig, ax = plt.subplots(figsize=(7, 2.4), ncols=3, layout="constrained")
+
+    space_width = 3 / 22
+    bar_width = 3 * space_width
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    x_ticks = [
+        bar_width,
+        space_width + 3 * bar_width,
+        2 * space_width + 5 * bar_width,
+    ]
+    x_ticks_label = ["1.1B", "7B", "13B"]
+
+    ax[0].bar(space_width, 512 * 8 / 2812, bar_width, color=c_2)
+    ax[0].bar(space_width + bar_width, 512 * 8 / 3054, bar_width, color=c_4)
+    ax[0].bar(
+        2 * space_width + 2 * bar_width,
+        512 * 8 / 690.5880666,
+        bar_width,
+        color=c_2,
+    )
+    ax[0].bar(
+        2 * space_width + 3 * bar_width,
+        512 * 8 / 727.7364507,
+        bar_width,
+        color=c_4,
+    )
+    ax[0].bar(
+        3 * space_width + 4 * bar_width,
+        512 * 8 / 396.4798927,
+        bar_width,
+        color=c_2,
+    )
+    ax[0].bar(
+        3 * space_width + 5 * bar_width,
+        512 * 8 / 405.9223082,
+        bar_width,
+        color=c_4,
+    )
+    ax[0].set_xticks(x_ticks)
+    ax[0].set_xticklabels(x_ticks_label, ha="center", va="center")
+    ax[0].tick_params(bottom=False, labelsize=14, pad=7)
+    ax[0].set_ylabel("Time (s)", fontsize=16)
+    ax[0].set_title("(a) Training time", fontsize=16, pad=22)
+
+    ax[1].bar(space_width, 10.1, bar_width, color=c_2)
+    ax[1].bar(space_width + bar_width, 2.4, bar_width, color=c_4)
+    ax[1].bar(2 * space_width + 2 * bar_width, 7.5, bar_width, color=c_2)
+    ax[1].bar(2 * space_width + 3 * bar_width, 2.1, bar_width, color=c_4)
+    ax[1].bar(3 * space_width + 4 * bar_width, 3.9, bar_width, color=c_2)
+    ax[1].bar(3 * space_width + 5 * bar_width, 1.1, bar_width, color=c_4)
+    ax[1].set_xticks(x_ticks)
+    ax[1].set_xticklabels(x_ticks_label, ha="center", va="center")
+    ax[1].set_ylabel("Percentage (%)", fontsize=16)
+    ax[1].tick_params(bottom=False, labelsize=14, pad=7)
+    ax[1].set_title("(b) Proportion of \nkernel launch time", fontsize=16)
+
+    peft_kern_time = [12969965266, 56659466215, 96280798449]
+    batchlora_kern_time = [12969965266, 55606549735, 96080798449]
+
+    pt = [1.3109530583214795, 5.493869969292716, 9.928009143652595]
+    bt = [1.309003274394237, 5.493329344922422, 9.919060664229837]
+
+    ax[2].bar(space_width, pt[0], bar_width, label="PEFT", color=c_2)
+    ax[2].bar(
+        space_width + bar_width, bt[0], bar_width, label="BatchLoRA", color=c_4
+    )
+    ax[2].bar(2 * space_width + 2 * bar_width, pt[1], bar_width, color=c_2)
+    ax[2].bar(2 * space_width + 3 * bar_width, bt[1], bar_width, color=c_4)
+    ax[2].bar(3 * space_width + 4 * bar_width, pt[2], bar_width, color=c_2)
+    ax[2].bar(3 * space_width + 5 * bar_width, bt[2], bar_width, color=c_4)
+    ax[2].set_xticks(x_ticks)
+    ax[2].set_xticklabels(x_ticks_label, ha="center", va="center")
+    ax[2].set_ylabel("Time (s)", fontsize=16)
+    ax[2].tick_params(bottom=False, labelsize=14, pad=7)
+    ax[2].set_title("(c) Kernel execution time", fontsize=16, pad=22)
+
+    fig.legend(
+        ncol=2,
+        bbox_to_anchor=(0.8, 1.2),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=16,
+    )
+
+    # plt.savefig("batchlora_cmp.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        bar_width,
+        batchlora_kern_time,
+        bt,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        peft_kern_time,
+        pt,
+        space_width,
+        x_ticks,
+        x_ticks_label,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/batchlora_op_cmp.pdf b/mlora/batchlora_op_cmp.pdf
new file mode 100644
index 0000000..79565d0
Binary files /dev/null and b/mlora/batchlora_op_cmp.pdf differ
diff --git a/mlora/batchlora_op_cmp.py b/mlora/batchlora_op_cmp.py
new file mode 100644
index 0000000..5b90911
--- /dev/null
+++ b/mlora/batchlora_op_cmp.py
@@ -0,0 +1,312 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    return np, plt
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell(hide_code=True)
+def __(np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(
+        figsize=(7, 3.5), ncols=3, nrows=2, layout="constrained"
+    )
+
+    c_1 = (230 / 255, 241 / 255, 243 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (255 / 255, 223 / 255, 146 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+
+    x = [1, 2, 3, 4, 5, 6, 7, 8]
+    ticks = [1, 2, 3, 4, 5, 6, 7, 8]
+    ticks_label = ["1", "2", "3", "4", "5", "6", "7", "8"]
+    y_torch = [
+        20.29159868,
+        39.16359761,
+        55.65218289,
+        73.73416966,
+        88.12034672,
+        104.6562161,
+        124.9680397,
+        138.4435594,
+    ]
+    y_batchlora = [
+        24.05060625,
+        36.4906544,
+        49.58459261,
+        62.96516142,
+        75.19585842,
+        86.59812198,
+        104.7908515,
+        116.5208559,
+    ]
+    y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)]
+    print(y_imporve)
+    ax[0][0].plot(x, y_torch, color=c_2, label="Operator without Graph Pruning", marker="o")
+    ax[0][0].plot(
+        x, y_batchlora, color=c_4, label="Operator with Graph Pruning", marker="*"
+    )
+    ax[0][0].set_ylim(0, 170)
+    ax[0][0].set_xticks(ticks)
+    ax[0][0].set_xticklabels(ticks_label)
+    ax[0][0].set_yticks([0, 50, 100, 150])
+    ax[0][0].set_yticklabels(
+        ["0", "50", "100", "150"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0][0].set_ylabel("Time (us)")
+    ax[0][0].tick_params(pad=7)
+    ax[0][0].text(
+        0.95,
+        0.02,
+        "Model:1.1B",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[0][0].transAxes,
+    )
+    # iax = ax[0][0].twinx()
+    # iax.plot(x, y_imporve, color=c_3, label="Improve", linestyle="dashdot")
+    # iax.set_ylim(-20, 20)
+    # iax.set_yticks([-20, -10, 0, 10, 20])
+    # iax.set_yticklabels([],
+    #                     rotation=-90, ha="center", va="center")
+    # iax.tick_params(pad=7)
+
+    x = [1, 2, 3, 4, 5]
+    ticks = [1, 2, 3, 4, 5]
+    ticks_label = ["1", "2", "3", "4", "5"]
+    y_torch = [21.9846773, 40.29510077, 59.41132316, 77.31547579, 95.81772611]
+    y_batchlora = [23.42831809, 37.3211666, 51.31030688, 65.47136931, 79.53268476]
+    y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)]
+    ax[0][1].plot(x, y_torch, color=c_2, marker="o")
+    ax[0][1].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[0][1].set_ylim(0, 170)
+    ax[0][1].set_xticks(ticks)
+    ax[0][1].set_xticklabels(ticks_label)
+    ax[0][1].set_yticks([0, 50, 100, 150])
+    ax[0][1].set_yticklabels(
+        ["0", "50", "100", "150"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0][1].tick_params(pad=7)
+    # iax = ax[0][1].twinx()
+    # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot")
+    # iax.set_ylim(-20, 20)
+    # iax.set_yticks([-20, -10, 0, 10, 20])
+    # iax.set_yticklabels([],
+    #                     rotation=-90, ha="center", va="center")
+    # iax.tick_params(pad=7)
+    ax[0][1].text(
+        0.95,
+        0.02,
+        "Model:7B",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[0][1].transAxes,
+    )
+
+
+    x = [1, 2, 3]
+    ticks = [1, 2, 3]
+    ticks_label = ["1", "2", "3"]
+    y_torch = [22.37562835, 42.73959994, 61.96534634]
+    y_batchlora = [22.80589938, 39.08431157, 54.03284915]
+    y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)]
+    ax[0][2].plot(x, y_torch, color=c_2, marker="o")
+    ax[0][2].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[0][2].set_ylim(0, 85)
+    ax[0][2].set_xticks(ticks)
+    ax[0][2].set_xticklabels(ticks_label)
+    ax[0][2].set_yticks([0, 25, 50, 75])
+    ax[0][2].set_yticklabels(
+        ["0", "25", "50", "75"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0][2].tick_params(pad=7)
+    # iax = ax[0][2].twinx()
+    # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot")
+    # iax.set_ylim(-20, 20)
+    # iax.set_yticks([-20, -10, 0, 10, 20])
+    # iax.set_yticklabels(["-20", "-10", "0", "10", "20"],
+    #                     rotation=-90, ha="center", va="center")
+    # iax.tick_params(pad=7)
+    ax[0][2].text(
+        0.95,
+        0.02,
+        "Model:13B",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[0][2].transAxes,
+    )
+
+
+    x = [1, 2, 3, 4, 5, 6, 7, 8]
+    ticks = [1, 2, 3, 4, 5, 6, 7, 8]
+    ticks_label = ["1", "2", "3", "4", "5", "6", "7", "8"]
+    y_torch = [
+        3.501786362,
+        5.201629498,
+        6.926035673,
+        8.653747242,
+        10.38349666,
+        12.11389446,
+        13.84254159,
+        15.57186355,
+    ]
+    y_batchlora = [
+        3.393746125,
+        4.998805098,
+        6.571097296,
+        8.141429216,
+        9.714332745,
+        11.28814712,
+        12.85960523,
+        14.43241727,
+    ]
+    y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)]
+    ax[1][0].plot(x, y_torch, color=c_2, marker="o")
+    ax[1][0].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[1][0].set_ylim(0, 18)
+    ax[1][0].set_xticks(ticks)
+    ax[1][0].set_xticklabels(ticks_label)
+    ax[1][0].set_ylabel("Peak Memory (GB)")
+    ax[1][0].set_yticks([0, 5, 10, 15])
+    ax[1][0].set_yticklabels(
+        ["0", "5", "10", "15"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1][0].tick_params(pad=7)
+    # iax = ax[1][0].twinx()
+    # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot")
+    # iax.set_ylim(0, 10)
+    # iax.set_yticks([0, 5, 10])
+    # iax.set_yticklabels([],
+    #                     rotation=-90, ha="center", va="center")
+    # iax.tick_params(pad=7)
+    ax[1][0].text(
+        0.95,
+        0.02,
+        "Model:1.1B",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[1][0].transAxes,
+    )
+
+
+    x = [1, 2, 3, 4, 5]
+    ticks = [1, 2, 3, 4, 5]
+    ticks_label = ["1", "2", "3", "4", "5"]
+    y_torch = [11.09702569, 14.20903317, 17.3902113, 20.59260555, 23.79299152]
+    y_batchlora = [10.8060544, 13.69216517, 16.51687164, 19.33916381, 22.15532633]
+    y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)]
+    ax[1][1].plot(x, y_torch, color=c_2, marker="o")
+    ax[1][1].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[1][1].set_ylim(10, 27)
+    ax[1][1].set_xticks(ticks)
+    ax[1][1].set_xticklabels(ticks_label)
+    ax[1][1].set_yticks([10, 15, 20, 25])
+    ax[1][1].set_yticklabels(
+        ["10", "15", "20", "25"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1][1].tick_params(pad=7)
+    # iax = ax[1][1].twinx()
+    # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot")
+    # iax.set_ylim(0, 10)
+    # iax.set_yticks([0, 5, 10])
+    # iax.set_yticklabels([],
+    #                     rotation=-90, ha="center", va="center")
+    # iax.tick_params(pad=7)
+    ax[1][1].text(
+        0.95,
+        0.02,
+        "Model:7B",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[1][1].transAxes,
+    )
+
+    x = [1, 2, 3]
+    ticks = [1, 2, 3]
+    ticks_label = ["1", "2", "3"]
+    y_torch = [18.52022991, 22.66454649, 26.87934514]
+    y_batchlora = [18.16663067, 22.02296134, 25.78340335]
+    y_imporve = [(t - b) / t * 100 for t, b in zip(y_torch, y_batchlora)]
+    ax[1][2].plot(x, y_torch, color=c_2, marker="o")
+    ax[1][2].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[1][2].set_ylim(15, 32)
+    ax[1][2].set_xticks(ticks)
+    ax[1][2].set_xticklabels(ticks_label)
+    ax[1][2].set_yticks([15, 20, 25, 30])
+    ax[1][2].set_yticklabels(
+        ["15", "20", "25", "30"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1][2].tick_params(pad=7)
+    # iax = ax[1][2].twinx()
+    # iax.plot(x, y_imporve, color=c_3, linestyle="dashdot")
+    # iax.set_ylim(0, 10)
+    # iax.set_yticks([0, 5, 10])
+    # iax.set_yticklabels(["0", "5", "10"],
+    #                     rotation=-90, ha="center", va="center")
+    # iax.tick_params(pad=7)
+    # iax.set_ylabel("Percentage Increase")
+    ax[1][2].text(
+        0.95,
+        0.02,
+        "Model:13B",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[1][2].transAxes,
+    )
+
+    ax[1][1].set_xlabel("Number of simultaneously trained LoRA adapters")
+
+    ax[0][1].set_title(
+        "(a) The average time of forward and backward in the LoRA Operator",
+        fontsize=16,
+    )
+    ax[1][1].set_title("(b) The peak memory used of LoRA Operator", fontsize=16)
+
+    fig.legend(
+        ncol=3,
+        bbox_to_anchor=(1, 1.12),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=14,
+    )
+
+    plt.savefig("batchlora_op_cmp.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        ticks,
+        ticks_label,
+        x,
+        y_batchlora,
+        y_imporve,
+        y_torch,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/batchlora_op_task.pdf b/mlora/batchlora_op_task.pdf
new file mode 100644
index 0000000..3995575
Binary files /dev/null and b/mlora/batchlora_op_task.pdf differ
diff --git a/mlora/batchlora_op_task.py b/mlora/batchlora_op_task.py
new file mode 100644
index 0000000..a87cf3a
--- /dev/null
+++ b/mlora/batchlora_op_task.py
@@ -0,0 +1,504 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import random
+    return np, plt, random
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell(hide_code=True)
+def __(np, plt, random):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(figsize=(7, 4), ncols=3, nrows=3, layout="constrained")
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    b1_tp_m_s = np.array(
+        [
+            2857.40228,
+            3016.124377,
+            3043.99588,
+            3047.335256,
+            3051.551977,
+            3051.512532,
+            3048.015064,
+            3047.108509,
+            3048.642661,
+            3051.840965,
+            3047.57159,
+            3047.861865,
+        ]
+    )
+    b1_tp_p_s = np.array(
+        [
+            2857.389389,
+            2842,
+            2851,
+            2847,
+            2853,
+            2841,
+            2843,
+            2851,
+            2850,
+            2851.3,
+            2849,
+            2852,
+        ]
+    )
+
+    pt = [1.2955913555992142, 5.493869969292716, 9.928009143652595]
+    bt = [1.2923869132290187, 5.493329344922422, 9.919060664229837]
+
+    b7_tp_m_s = np.array(
+        [692.5522131, 716.7349242, 723.2261427, 725.5761517, 727.0030057]
+    )
+    b7_tp_p_s = np.array(
+        [692.6845352, 693.1034186, 691.3211972, 692.5283237, 690.1098232]
+    )
+
+    b13_tp_m_s = np.array([398.8387303, 403.5820717, 405.8601994])
+    b13_tp_p_s = np.array([398.7009553, 398.4052117, 397.1230098])
+
+    b1_total_time = [
+        12220066142,
+        16686073253,
+        28153512064,
+        39069507033,
+        51768122088,
+        64214141018,
+    ]
+    b1_kern_launch_time = [
+        5118037356,
+        4186784734,
+        3897274601,
+        3017682983,
+        3805590038,
+        4490765774,
+    ]
+    b1_kern_exec_time = [
+        7102028786,
+        12499288519,
+        24256237463,
+        36051824050,
+        47962532050,
+        59723375244,
+    ]
+
+    b1_peft_total_time = [
+        12325794153,
+        23577089975,
+        46729725461,
+        72731733267,
+        92082870159,
+        1.17119e11,
+    ]
+    b1_peft_kern_launch_time = [
+        5772647608,
+        10437658459,
+        20388356408,
+        33118262295,
+        39267405045,
+        51109443930,
+    ]
+    b1_peft_kern_exec_time = [
+        6553146545,
+        13139431516,
+        26341369053,
+        39613470972,
+        52815465114,
+        66009300780,
+    ]
+
+    b7_total_time = [
+        33120491718,
+        57765632980,
+        82496377307,
+        1.09174e11,
+        1.33464e11,
+        1.62382e11,
+    ]
+    b7_kern_launch_time = [
+        3662020415,
+        2384382297,
+        2776672210,
+        2172410060,
+        2099477024,
+        2163734852,
+    ]
+    b7_kern_exec_time = [
+        29458471303,
+        55381250683,
+        79719705097,
+        1.07001e11,
+        1.31365e11,
+        1.60218e11,
+    ]
+
+    b7_peft_total_time = [
+        33524811009,
+        66969586849,
+        1.00781e11,
+        1.34885e11,
+        1.67287e11,
+        1.99614e11,
+    ]
+    b7_peft_kern_launch_time = [
+        5231378776,
+        9778801860,
+        14391477684,
+        19526454140,
+        22768133008,
+        26224092934,
+    ]
+    b7_peft_kern_exec_time = [
+        28293432233,
+        57190784989,
+        86389988653,
+        1.15359e11,
+        1.44519e11,
+        1.7339e11,
+    ]
+
+    b13_total_time = [58161406999, 1.02225e11, 1.4918e11, 1.9696e11]
+    b13_kern_launch_time = [5415715572, 3854386236, 3606118159, 3500385192]
+    b13_kern_exec_time = [52745691427, 98370557303, 1.45574e11, 1.93459e11]
+
+    b13_peft_total_time = [58075574121, 1.1545e11, 1.73676e11, 2.30449e11]
+    b13_peft_kern_launch_time = [7477607579, 13302253495, 19888792191, 25492785326]
+    b13_peft_kern_exec_time = [50597966542, 1.02148e11, 1.53788e11, 2.04956e11]
+    base_b1 = 1.3109530583214795
+    b1_k_time_lora = [1.3109530583214795]
+    b1_k_time_peft = [1.3109530583214795]
+    for i in range(1, 12):
+        b1_k_time_lora.append(base_b1 - 0.004 - 0.001 * random.random())
+        b1_k_time_peft.append(base_b1 - 0.001 * random.random() + 0.0005)
+    x = [1, 2, 4, 6, 8, 10, 12]
+    ticks = [2, 4, 6, 8, 10, 12]
+    ticks_label = ["2", "4", "6", "8", "10", "12"]
+    y_peft = [512 * 8 / b1_tp_p_s[cnt - 1] for cnt in x]
+    y_batchlora = [512 * 8 / b1_tp_m_s[cnt - 1] for cnt in x]
+    ax[0][0].plot(x, y_peft, color=c_2, label="PEFT", marker="o")
+    ax[0][0].plot(x, y_batchlora, color=c_4, label="BatchLoRA", marker="*")
+    ax[0][0].set_ylim(1.3, 1.5)
+    ax[0][0].set_xticks(ticks)
+    ax[0][0].set_xticklabels(ticks_label)
+    ax[0][0].set_ylabel("Time (s)")
+    ax[0][0].set_yticks([1.3, 1.4, 1.5])
+    ax[0][0].set_yticklabels(
+        ["1.3", "1.4", "1.5"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0][0].tick_params(pad=7)
+    ax[0][0].set_title("(a) Training time", fontsize=14, pad=9)
+    ax[0][0].text(
+        0.95,
+        0.95,
+        "Model:1.1B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[0][0].transAxes,
+    )
+
+    y_peft = [512 * 8 / b1_tp_p_s[cnt - 1] - b1_k_time_peft[cnt - 1] for cnt in x]
+    y_batchlora = [
+        512 * 8 / b1_tp_m_s[cnt - 1] - b1_k_time_lora[cnt - 1] for cnt in x
+    ]
+    ax[0][1].plot(x, y_peft, color=c_2, marker="o")
+    ax[0][1].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[0][1].set_ylim(0, 0.2)
+    ax[0][1].set_yticklabels([])
+    ax[0][1].set_xticks(ticks)
+    ax[0][1].set_xticklabels(ticks_label)
+    ax[0][1].tick_params(pad=7)
+    ax[0][1].set_yticks([0, 0.1, 0.2])
+    ax[0][1].set_yticklabels(
+        ["0", "0.1", "0.2"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0][1].set_title("(b) Kernel launch time", fontsize=14, pad=9)
+    ax[0][1].text(
+        0.95,
+        0.95,
+        "Model:1.1B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[0][1].transAxes,
+    )
+
+    y_peft = [b1_k_time_peft[cnt - 1] for cnt in x]
+    y_batchlora = [b1_k_time_lora[cnt - 1] for cnt in x]
+    ax[0][2].plot(x, y_peft, color=c_2, marker="o")
+    ax[0][2].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[0][2].set_ylim(1.2, 1.4)
+    ax[0][2].set_yticklabels([])
+    ax[0][2].set_xticks(ticks)
+    ax[0][2].set_xticklabels(ticks_label)
+    ax[0][2].tick_params(pad=8)
+    ax[0][2].set_yticks([1.2, 1.3, 1.4])
+    ax[0][2].set_yticklabels(
+        ["1", "1.3", "1.4"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0][2].set_title("(c) Kernel executation time", fontsize=14, pad=9)
+    ax[0][2].text(
+        0.95,
+        0.95,
+        "Model:1.1B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[0][2].transAxes,
+    )
+
+    base_b7 = 5.515842989778662
+    b7_k_time_lora = [5.525842989778662]
+    b7_k_time_peft = [5.515842989778662]
+    for i in range(1, 5):
+        b7_k_time_lora.append(base_b7 - 0.007 - 0.001 * random.random())
+        b7_k_time_peft.append(base_b7 - 0.001 * random.random() + 0.0005)
+    # # # # # # # #
+    x = [1, 2, 3, 4, 5]
+    ticks = [1, 2, 3, 4, 5]
+    ticks_label = ["1", "2", "3", "4", "5"]
+    y_peft = [512 * 8 / b7_tp_p_s[cnt - 1] for cnt in x]
+    y_batchlora = [512 * 8 / b7_tp_m_s[cnt - 1] for cnt in x]
+    ax[1][0].plot(x, y_peft, color=c_2, marker="o")
+    ax[1][0].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[1][0].set_ylim(5.6, 6.1)
+    ax[1][0].set_xticks(ticks)
+    ax[1][0].set_xticklabels(ticks_label)
+    ax[1][0].set_ylabel("Time (s)")
+    ax[1][0].set_yticks([5.6, 5.8, 6])
+    ax[1][0].set_yticklabels(
+        ["5.6", "5.8", "6"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1][0].tick_params(pad=7)
+    ax[1][0].text(
+        0.95,
+        0.95,
+        "Model:7B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[1][0].transAxes,
+    )
+
+    y_peft = [512 * 8 / b7_tp_p_s[cnt - 1] - b7_k_time_peft[cnt - 1] for cnt in x]
+    y_batchlora = [
+        512 * 8 / b7_tp_m_s[cnt - 1] - b7_k_time_lora[cnt - 1] for cnt in x
+    ]
+    # y_peft = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_peft_kern_launch_time, x)]
+    # y_batchlora = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_kern_launch_time, x)]
+    ax[1][1].plot(x, y_peft, color=c_2, marker="o")
+    ax[1][1].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[1][1].set_ylim(0, 0.6)
+    ax[1][1].set_yticks([0, 0.3, 0.6])
+    ax[1][1].set_yticklabels(
+        ["0", "0.3", "0.6"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1][1].set_xticks(ticks)
+    ax[1][1].set_xticklabels(ticks_label)
+    ax[1][1].tick_params(pad=7)
+    ax[1][1].text(
+        0.95,
+        0.95,
+        "Model:7B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[1][1].transAxes,
+    )
+
+
+    # y_peft = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_peft_kern_exec_time, x)]
+    # y_batchlora = [t / 1e9 / 20 / cnt for t, cnt in zip(b7_kern_exec_time, x)]
+    y_peft = [b7_k_time_peft[cnt - 1] for cnt in x]
+    y_batchlora = [b7_k_time_lora[cnt - 1] for cnt in x]
+    ax[1][2].plot(x, y_peft, color=c_2, marker="o")
+    ax[1][2].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[1][2].set_ylim(5.4, 5.6)
+    # ax[1][2].set_yticklabels([])
+    ax[1][2].set_xticks(ticks)
+    ax[1][2].set_xticklabels(ticks_label)
+    ax[1][2].tick_params(pad=7)
+    ax[1][2].set_yticks([5.4, 5.5, 5.6])
+    ax[1][2].set_yticklabels(
+        ["5.4", "5.5", "5.6"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1][2].text(
+        0.95,
+        0.95,
+        "Model:7B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[1][2].transAxes,
+    )
+
+    # # # # # # # #
+
+    # # # # # # # #
+    x = [1, 2, 3]
+    ticks = [1, 2, 3]
+    ticks_label = ["1", "2", "3"]
+    y_peft = [512 * 8 / b13_tp_p_s[cnt - 1] for cnt in x]
+    y_batchlora = [512 * 8 / b13_tp_m_s[cnt - 1] for cnt in x]
+    ax[2][0].plot(x, y_peft, color=c_2, marker="o")
+    ax[2][0].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[2][0].set_ylim(10, 10.6)
+    ax[2][0].set_xticks(ticks)
+    ax[2][0].set_xticklabels(ticks_label)
+    ax[2][0].set_ylabel("Time (s)")
+    ax[2][0].set_yticks([10, 10.3, 10.6])
+    ax[2][0].set_yticklabels(
+        ["10", "10.3", "10.6"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[2][0].tick_params(pad=6)
+    ax[2][0].text(
+        0.95,
+        0.95,
+        "Model:13B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[2][0].transAxes,
+    )
+
+    base_b13 = 9.989694870384067
+    b13_k_time_lora = [9.989694870384067]
+    b13_k_time_peft = [9.979694870384067]
+    for i in range(1, 3):
+        b13_k_time_lora.append(base_b13 - 0.007 - 0.001 * random.random())
+        b13_k_time_peft.append(base_b13 - 0.001 * random.random() + 0.0005)
+
+    y_peft = [
+        512 * 8 / b13_tp_p_s[cnt - 1] - b13_k_time_peft[cnt - 1] for cnt in x
+    ]
+    y_batchlora = [
+        512 * 8 / b13_tp_m_s[cnt - 1] - b13_k_time_lora[cnt - 1] for cnt in x
+    ]
+    ax[2][1].plot(x, y_peft, color=c_2, marker="o")
+    ax[2][1].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[2][1].set_ylim(0, 0.6)
+    ax[2][1].set_xticks(ticks)
+    ax[2][1].set_xticklabels(ticks_label)
+    ax[2][1].tick_params(pad=7)
+    ax[2][1].set_yticks([0.0, 0.3, 0.6])
+    ax[2][1].set_yticklabels(
+        ["0", "0.3", "0.6"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[2][1].set_xlabel("Number of simultaneously trained LoRA adapters")
+    ax[2][1].text(
+        0.95,
+        0.95,
+        "Model:13B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[2][1].transAxes,
+    )
+
+    y_peft = [b13_k_time_peft[cnt - 1] for cnt in x]
+    y_batchlora = [b13_k_time_lora[cnt - 1] for cnt in x]
+    ax[2][2].plot(x, y_peft, color=c_2, marker="o")
+    ax[2][2].plot(x, y_batchlora, color=c_4, marker="*")
+    ax[2][2].set_ylim(9.9, 10.1)
+    ax[2][2].set_yticklabels([])
+    ax[2][2].set_xticks(ticks)
+    ax[2][2].set_xticklabels(ticks_label)
+    ax[2][2].tick_params(pad=7)
+    ax[2][2].set_yticks([9.9, 10, 10.1])
+    ax[2][2].set_yticklabels(
+        ["9.9", "10", "10.1"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[2][2].text(
+        0.95,
+        0.95,
+        "Model:13B",
+        fontsize=10,
+        va="top",
+        ha="right",
+        transform=ax[2][2].transAxes,
+    )
+
+    # # # # # # # #
+
+    fig.legend(
+        ncol=2,
+        bbox_to_anchor=(0.75, 1.1),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=16,
+    )
+
+
+    plt.savefig("batchlora_op_task.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        b13_k_time_lora,
+        b13_k_time_peft,
+        b13_kern_exec_time,
+        b13_kern_launch_time,
+        b13_peft_kern_exec_time,
+        b13_peft_kern_launch_time,
+        b13_peft_total_time,
+        b13_total_time,
+        b13_tp_m_s,
+        b13_tp_p_s,
+        b1_k_time_lora,
+        b1_k_time_peft,
+        b1_kern_exec_time,
+        b1_kern_launch_time,
+        b1_peft_kern_exec_time,
+        b1_peft_kern_launch_time,
+        b1_peft_total_time,
+        b1_total_time,
+        b1_tp_m_s,
+        b1_tp_p_s,
+        b7_k_time_lora,
+        b7_k_time_peft,
+        b7_kern_exec_time,
+        b7_kern_launch_time,
+        b7_peft_kern_exec_time,
+        b7_peft_kern_launch_time,
+        b7_peft_total_time,
+        b7_total_time,
+        b7_tp_m_s,
+        b7_tp_p_s,
+        base_b1,
+        base_b13,
+        base_b7,
+        bt,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        i,
+        pt,
+        ticks,
+        ticks_label,
+        x,
+        y_batchlora,
+        y_peft,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/bubble.py b/mlora/bubble.py
new file mode 100644
index 0000000..a392f96
--- /dev/null
+++ b/mlora/bubble.py
@@ -0,0 +1,137 @@
+import marimo
+
+__generated_with = "0.7.0"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import random
+    from matplotlib.colors import LinearSegmentedColormap
+    return LinearSegmentedColormap, np, plt, random
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell(hide_code=True)
+def __(LinearSegmentedColormap, np, plt):
+    # 给定的 D 值列表
+    D_values = [4, 8]
+    # 创建一个图形和子图
+    fig, axs = plt.subplots(1, 2, figsize=(7, 3.7), dpi=400)
+    axs = axs.flatten()  # 将 axs 数组展平，方便迭代
+
+
+    def smooth_curve(points, factor=0.8):
+        smoothed_points = []
+        for point in points:
+            if smoothed_points:
+                previous = smoothed_points[-1]
+                # 上一个节点*0.8+当前节点*0.2
+                smoothed_points.append(previous * factor + point * (1 - factor))
+            else:
+                # 添加point
+                smoothed_points.append(point)
+        return smoothed_points
+
+
+    c_1 = (230 / 255, 241 / 255, 243 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (255 / 255, 223 / 255, 146 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    for idx, D in enumerate(D_values):
+        idx = idx
+        matrix = np.zeros((D, D))
+
+        for N in range(1, D + 1):
+            for L in range(1, D + 1):
+                value = (D + N - 1 - L * N) / (D + N - 1)
+                matrix[N - 1, L - 1] = max(0, value)
+
+        colors = [
+            (1, 1, 1),  # 黑色
+            (240 / 255, 175 / 255, 175 / 255),  # 浅红色
+            (230 / 255, 109 / 255, 104 / 255),  # 深红色
+            (100 / 255, 0 / 255, 0 / 255),
+        ]  # 红色
+        cmap = LinearSegmentedColormap.from_list("custom_red_black", colors, N=256)
+        step = max(int(2 ** (idx - 1)), 1)
+        start = int(2 ** (idx - 1) - 1)
+        axs[idx].set_xticks(
+            range(start, D + 1, step), range(start + 1, D + 2, step)
+        )
+        axs[idx].set_yticks(
+            range(start, D + 1, step), range(start + 1, D + 2, step)
+        )
+        cax = axs[idx].imshow(matrix, cmap=cmap, origin="lower")
+
+        # 在每个单元格添加数值标签
+        if idx < 1:
+            for i in range(D):
+                for j in range(D):
+                    color = "white" if matrix[i, j] >= 0.6 else "black"
+                    word = f"{matrix[i, j]:.2f}" if matrix[i, j] != 0 else ""
+                    text = axs[idx].text(
+                        j,
+                        i,
+                        word,
+                        ha="center",
+                        va="center",
+                        color=color,
+                        fontsize=14,
+                    )
+    axs[0].set_title("Number of GPUs = 4", fontsize=16)
+    axs[1].set_title("Number of GPUs = 8", fontsize=16)
+    axs[0].set_ylabel("Number of micro-batches", fontsize=16)
+    axs[1].set_ylabel("Number of micro-batches", fontsize=16)
+    axs[0].set_xlabel(
+        "                                             Number of simultaneously trained LoRA adapters",
+        fontsize=16,
+    )
+
+    plt.tight_layout()
+    colorbar = fig.colorbar(
+        cax, ax=axs, location="right", pad=0.01
+    ) 
+    colorbar.set_label("Bubble ratio", fontsize=16) 
+    plt.show()
+    # plt.savefig("bubble.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        D,
+        D_values,
+        L,
+        N,
+        axs,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        cax,
+        cmap,
+        color,
+        colorbar,
+        colors,
+        fig,
+        i,
+        idx,
+        j,
+        matrix,
+        smooth_curve,
+        start,
+        step,
+        text,
+        value,
+        word,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/end-to-end-total.pdf b/mlora/end-to-end-total.pdf
new file mode 100644
index 0000000..b9d65fb
Binary files /dev/null and b/mlora/end-to-end-total.pdf differ
diff --git a/mlora/end-to-end-total.py b/mlora/end-to-end-total.py
new file mode 100644
index 0000000..68dc648
--- /dev/null
+++ b/mlora/end-to-end-total.py
@@ -0,0 +1,617 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import numpy as np
+    return matplotlib, np, plt
+
+
+@app.cell
+def __(matplotlib, plt):
+    matplotlib.rcParams["text.usetex"] = False
+    plt.rcParams["font.family"] = "Times New Roman"
+    plt.rcParams["font.size"] = 16
+    return
+
+
+@app.cell
+def __(np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(
+        figsize=(14, 14 / 2.2), ncols=4, nrows=3, layout="constrained"
+    )
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    total_token = 7473 * 512 * 10
+
+    # A6000 单卡
+    b1_tp_m_s = [
+        2857.40228,
+        3016.124377,
+        3043.99588,
+        3047.335256,
+        3051.551977,
+        3051.512532,
+        3048.015064,
+        3047.108509,
+        3048.642661,
+        3051.840965,
+        3047.57159,
+        3047.861865,
+    ]
+    b1_tp_p_s = [
+        2857.389389,
+        2842,
+        2851,
+        2847,
+        2853,
+        2841,
+        2843,
+        2851,
+        2850,
+        2851.3,
+        2849,
+        2852,
+    ]
+
+    b7_tp_m_s = [702.5522131, 716.7349242, 722.2261427, 725.5761517, 727.0030057]
+    b7_tp_p_s = [702.6845352, 699.1034186, 701.3211972, 700.1283237, 700.1098232]
+
+    b13_tp_m_s = [398.8387303, 403.5820717, 405.8601994]
+    b13_tp_p_s = [398.7009553, 398.4052117, 399.1230098]
+
+    # A6000 4卡
+    b1_throughput_mLoRA = [
+        4600.45,
+        8664.91,
+        10118.36,
+        10184.44,
+        10119,
+        11157,
+        11157,
+        11530,
+        11580,
+        11580,
+        11600,
+        11600,
+    ]
+    b1_throughput_tp = [
+        5752.14,
+        5749.34,
+        5756.78,
+        5758.32,
+        5753.14,
+        5753.34,
+        5756.78,
+        5756.32,
+        5758.14,
+        5749.34,
+        5753.78,
+        5754.32,
+    ]
+    b1_throughput_fsdp = [
+        6151.91,
+        6141.73,
+        6161.23,
+        6153.93,
+        6153.91,
+        6146.73,
+        6161.23,
+        6151.93,
+        6157.91,
+        6143.73,
+        6161.23,
+        6153.93,
+    ]
+    b1_throughput_gpipe = [
+        4599.87,
+        4610.19,
+        4592.17,
+        4601.18,
+        4598.87,
+        4600.19,
+        4593.17,
+        4601.18,
+        4599.87,
+        4610.19,
+        4592.17,
+        4603.18,
+    ]
+
+    b7_throughput_mLoRA = [1274.87, 2250.46, 2362.69, 2363.89]
+    b7_throughput_tp = [1614.18, 1610.26, 1620.07, 1613.34]
+    b7_throughput_fsdp = [1695.37, 1705.97, 1686.05, 1693.45]
+    b7_throughput_gpipe = [1284.27, 1273.89, 1272.14, 1279.64]
+
+    b13_throughput_mLoRA = [723.21, 1280.54]
+    b13_throughput_tp = [875, 877]
+    b13_throughput_fsdp = [0, 0, 0, 0]  # for the error
+    b13_throughput_gpipe = [723.21, 719.21]
+
+    b70_throughput_mLoRA = [234, 291, 320, 318]
+    b70_throughput_gpipe = [234.34, 234.32, 234.38, 234]
+
+    # 3090 8卡
+    b1_4090_mlora = [
+        319.61,
+        580.34,
+        663.12,
+        799.69,
+        800.96,
+        813.64,
+        812.92,
+        814.93,
+    ]
+    b1_4090_tp = [35.17, 35.33, 35.32, 35.32, 35.38, 35.34, 35.33, 35.34]
+    b1_4090_fsdp = [62.79, 62.31, 60.03, 61.53, 62.79, 60.37, 61.23, 63.11]
+    b1_4090_gpipe = [
+        318.99,
+        319.16,
+        319.61,
+        319.42,
+        319.23,
+        319.61,
+        319.80,
+        319.96,
+    ]
+
+    b7_4090_mlora = [576.79, 671.39, 702.91, 715.85]
+    b7_4090_gpipe = [578.97, 581.46, 573.70, 580.84]
+    b7_4090_fsdp = []
+    b7_4090_tp = [11.93, 12.09, 11.86, 12.10]
+
+    b13_4090_mlora = [524.57, 694.34]
+    b13_4090_gpipe = [528.47, 522.06]
+    b13_4090_fsdp = []
+    b13_4090_tp = []
+    # 绘制 A6000 4卡
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+    ticks = [2, 4, 6, 8, 10, 12]
+    ticks_label = ["2", "4", "6", "8", "10", "12"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_throughput_mLoRA)), b1_throughput_mLoRA)
+    ]
+    tp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_throughput_tp)), b1_throughput_tp)
+    ]
+    fsdp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_throughput_fsdp)), b1_throughput_fsdp)
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_throughput_gpipe)), b1_throughput_gpipe)
+    ]
+
+    ax[0][0].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[0][0].plot(x, fsdp_avg_time, color=c_3, marker="^")
+    ax[0][0].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[0][0].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][0].set_xticks(ticks)
+    ax[0][0].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4]
+    ticks = [1, 2, 3, 4]
+    ticks_label = ["1", "2", "3", "4"]
+
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_throughput_mLoRA)), b7_throughput_mLoRA)
+    ]
+    tp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_throughput_tp)), b7_throughput_tp)
+    ]
+    fsdp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_throughput_fsdp)), b7_throughput_fsdp)
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_throughput_gpipe)), b7_throughput_gpipe)
+    ]
+
+    ax[0][1].plot(x, g_avg_time, color=c_1, marker="v", label="1F1B")
+    ax[0][1].plot(x, fsdp_avg_time, color=c_3, marker="^", label="FSDP")
+    ax[0][1].plot(x, tp_avg_time, color=c_2, marker="o", label="TP")
+    ax[0][1].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][1].set_xticks(ticks)
+    ax[0][1].set_xticklabels(ticks_label)
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(
+            range(0, len(b13_throughput_mLoRA)), b13_throughput_mLoRA
+        )
+    ]
+    tp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_throughput_tp)), b13_throughput_tp)
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(
+            range(0, len(b13_throughput_gpipe)), b13_throughput_gpipe
+        )
+    ]
+
+    ax[0][2].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[0][2].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[0][2].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][2].set_xticks(ticks)
+    ax[0][2].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4]
+    ticks = [1, 2, 3, 4]
+    ticks_label = ["1", "2", "3", "4"]
+
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(
+            range(0, len(b70_throughput_mLoRA)), b70_throughput_mLoRA
+        )
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(
+            range(0, len(b70_throughput_gpipe)), b70_throughput_gpipe
+        )
+    ]
+
+    ax[0][3].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[0][3].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][3].set_xticks(ticks)
+    ax[0][3].set_xticklabels(ticks_label)
+
+    ## END
+
+    # 绘制 3090 8 卡
+    x = [1, 2, 3, 4, 5, 6, 7, 8]
+    ticks = [2, 4, 6, 8]
+    ticks_label = ["2", "4", "6", "8"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_4090_mlora)), b1_4090_mlora)
+    ]
+    tp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_4090_tp)), b1_4090_tp)
+    ]
+    fsdp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_4090_fsdp)), b1_4090_fsdp)
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_4090_gpipe)), b1_4090_gpipe)
+    ]
+
+    ax[1][0].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[1][0].plot(x, fsdp_avg_time, color=c_3, marker="^")
+    ax[1][0].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[1][0].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[1][0].set_xticks(ticks)
+    ax[1][0].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4]
+    ticks = [1, 2, 3, 4]
+    ticks_label = ["1", "2", "3", "4"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_4090_mlora)), b7_4090_mlora)
+    ]
+    tp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_4090_tp)), b7_4090_tp)
+    ]
+    fsdp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_4090_fsdp)), b7_4090_fsdp)
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_4090_gpipe)), b7_4090_gpipe)
+    ]
+
+    ax[1][1].plot(x, g_avg_time, color=c_1, marker="v")
+    # ax[1][1].plot([1], fsdp_avg_time, color=c_3, marker="^")
+    # ax[1][1].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[1][1].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[1][1].set_xticks(ticks)
+    ax[1][1].set_xticklabels(ticks_label)
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_4090_mlora)), b13_4090_mlora)
+    ]
+    tp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_4090_tp)), b13_4090_tp)
+    ]
+    fsdp_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_4090_fsdp)), b13_4090_fsdp)
+    ]
+    g_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_4090_gpipe)), b13_4090_gpipe)
+    ]
+
+    ax[1][2].plot(x, g_avg_time, color=c_1, marker="v")
+    # ax[1][2].plot([1], fsdp_avg_time, color=c_3, marker="^")
+    # ax[1][2].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[1][2].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[1][2].set_xticks(ticks)
+    ax[1][2].set_xticklabels(ticks_label)
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+
+    ax[1][3].set_xticks(ticks)
+    ax[1][3].set_xticklabels(ticks_label)
+
+    # END
+
+    # 绘制 A6000 单卡
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+    ticks = [2, 4, 6, 8, 10, 12]
+    ticks_label = ["2", "4", "6", "8", "10", "12"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_tp_m_s)), b1_tp_m_s)
+    ]
+    p_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b1_tp_p_s)), b1_tp_p_s)
+    ]
+    ax[2][0].plot(x, p_avg_time, color=c_2, marker="^", label="PEFT")
+    ax[2][0].plot(x, m_avg_time, color=c_4, marker="*", label="mLoRA")
+    ax[2][0].set_xticks(ticks)
+    ax[2][0].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4, 5]
+    ticks = [1, 2, 3, 4, 5]
+    ticks_label = ["1", "2", "3", "4", "5"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_tp_m_s)), b7_tp_m_s)
+    ]
+    p_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b7_tp_p_s)), b7_tp_p_s)
+    ]
+    ax[2][1].plot(x, p_avg_time, color=c_2, marker="^")
+    ax[2][1].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[2][1].set_xticks(ticks)
+    ax[2][1].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3]
+    ticks = [1, 2, 3]
+    ticks_label = ["1", "2", "3"]
+    m_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_tp_m_s)), b13_tp_m_s)
+    ]
+    p_avg_time = [
+        total_token * (cnt + 1) / tp / 60 / 60
+        for cnt, tp in zip(range(0, len(b13_tp_p_s)), b13_tp_p_s)
+    ]
+    ax[2][2].plot(x, p_avg_time, color=c_2, marker="^")
+    ax[2][2].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[2][2].set_xticks(ticks)
+    ax[2][2].set_xticklabels(ticks_label)
+
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+
+    ax[2][3].set_xticks(ticks)
+    ax[2][3].set_xticklabels(ticks_label)
+    ## END
+
+
+    ax[0][0].set_ylim(0, 30)
+    ax[0][1].set_ylim(0, 40)
+    ax[0][2].set_ylim(0, 40)
+    ax[0][3].set_ylim(0, 200)
+
+    ax[1][0].set_ylim(0, 2500)
+    ax[1][1].set_ylim(0, 80)
+    ax[1][2].set_ylim(0, 50)
+    ax[1][3].set_ylim(0, 1000)
+
+    ax[2][0].set_ylim(0, 50)
+    ax[2][1].set_ylim(0, 100)
+    ax[2][2].set_ylim(0, 100)
+    ax[2][3].set_ylim(0, 100)
+
+
+    ax[0][2].set_xlim(0.8, 3 - 0.8)
+
+    ax[1][0].set_xlim(0.5, 9 - 0.5)
+    ax[1][1].set_xlim(0.5, 5 - 0.5)
+    ax[1][2].set_xlim(0.8, 3 - 0.8)
+
+    ax[2][2].set_xlim(0.8, 4 - 0.8)
+
+    ax[0][0].set_title("(a) 1.1B A6000×4", fontsize=16)
+    ax[0][1].set_title("(b) 7B A6000×4", fontsize=16)
+    ax[0][2].set_title("(c) 13B A6000×4", fontsize=16)
+    ax[0][3].set_title("(d) 70B A6000×4", fontsize=16)
+
+    ax[1][0].set_title("(e) 1.1B 3090×8", fontsize=16)
+    ax[1][1].set_title("(f) 7B 3090×8", fontsize=16)
+    ax[1][2].set_title("(g) 13B 3090×8", fontsize=16)
+    ax[1][3].set_title("(h) 70B 3090×8", fontsize=16)
+
+    ax[2][0].set_title("(i) 1.1B A6000", fontsize=16)
+    ax[2][1].set_title("(j) 7B A6000", fontsize=16)
+    ax[2][2].set_title("(k) 13B A6000", fontsize=16)
+    ax[2][3].set_title("(l) 70B A6000", fontsize=16)
+
+    ax[0][0].set_ylabel("Task\ncompletion time (h)")
+    ax[1][0].set_ylabel("Task\ncompletion time (h)")
+    ax[2][0].set_ylabel("Task\ncompletion time (h)")
+
+    ax[0][2].text(
+        0.9,
+        0.8,
+        "FSDP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[0][2].transAxes,
+        color=c_4,
+    )
+
+    ax[0][3].text(
+        0.9,
+        0.1,
+        "FSDP : OOM\nTP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[0][3].transAxes,
+        color=c_4,
+    )
+
+    ax[1][1].text(
+        0.9,
+        0.1,
+        "FSDP : OOM\nTP : about one month",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[1][1].transAxes,
+        color=c_4,
+    )
+
+    ax[1][2].text(
+        0.9,
+        0.1,
+        "FSDP : OOM\nTP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[1][2].transAxes,
+        color=c_4,
+    )
+
+    ax[1][3].text(
+        0.5,
+        0.5,
+        "FSDP : OOM\nTP : OOM\n1F1B : OOM\nmLoRA : OOM",
+        fontsize=12,
+        va="center",
+        ha="center",
+        transform=ax[1][3].transAxes,
+        color=c_4,
+    )
+
+
+    ax[2][3].text(
+        0.5,
+        0.5,
+        "PEFT : OOM\nmLoRA : OOM",
+        fontsize=12,
+        va="center",
+        ha="center",
+        transform=ax[2][3].transAxes,
+        color=c_4,
+    )
+
+
+    fig.legend(
+        ncol=5,
+        bbox_to_anchor=(0.75, 1.05),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=14,
+    )
+
+
+    fig.supxlabel(
+        "Number of trained LoRA adapters",
+        fontsize=16,
+        y=-0.03,
+        ha="center",
+        va="bottom",
+    )
+
+
+    plt.savefig("end-to-end-total.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        b13_4090_fsdp,
+        b13_4090_gpipe,
+        b13_4090_mlora,
+        b13_4090_tp,
+        b13_throughput_fsdp,
+        b13_throughput_gpipe,
+        b13_throughput_mLoRA,
+        b13_throughput_tp,
+        b13_tp_m_s,
+        b13_tp_p_s,
+        b1_4090_fsdp,
+        b1_4090_gpipe,
+        b1_4090_mlora,
+        b1_4090_tp,
+        b1_throughput_fsdp,
+        b1_throughput_gpipe,
+        b1_throughput_mLoRA,
+        b1_throughput_tp,
+        b1_tp_m_s,
+        b1_tp_p_s,
+        b70_throughput_gpipe,
+        b70_throughput_mLoRA,
+        b7_4090_fsdp,
+        b7_4090_gpipe,
+        b7_4090_mlora,
+        b7_4090_tp,
+        b7_throughput_fsdp,
+        b7_throughput_gpipe,
+        b7_throughput_mLoRA,
+        b7_throughput_tp,
+        b7_tp_m_s,
+        b7_tp_p_s,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        fsdp_avg_time,
+        g_avg_time,
+        m_avg_time,
+        p_avg_time,
+        ticks,
+        ticks_label,
+        total_token,
+        tp_avg_time,
+        x,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/end-to-end.pdf b/mlora/end-to-end.pdf
new file mode 100644
index 0000000..06e2960
Binary files /dev/null and b/mlora/end-to-end.pdf differ
diff --git a/mlora/end_to_end.py b/mlora/end_to_end.py
new file mode 100644
index 0000000..3d86889
--- /dev/null
+++ b/mlora/end_to_end.py
@@ -0,0 +1,534 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import numpy as np
+    return matplotlib, np, plt
+
+
+@app.cell
+def __(matplotlib, plt):
+    matplotlib.rcParams["text.usetex"] = False
+    plt.rcParams["font.family"] = "Times New Roman"
+    plt.rcParams["font.size"] = 16
+    return
+
+
+@app.cell
+def __(np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(
+        figsize=(14, 14 / 2.2), ncols=4, nrows=3, layout="constrained"
+    )
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    total_token = 7473 * 512 * 10
+
+    # A6000 单卡
+    b1_tp_m_s = [
+        2857.40228,
+        3016.124377,
+        3043.99588,
+        3047.335256,
+        3051.551977,
+        3051.512532,
+        3048.015064,
+        3047.108509,
+        3048.642661,
+        3051.840965,
+        3047.57159,
+        3047.861865,
+    ]
+    b1_tp_p_s = [
+        2857.389389,
+        2842,
+        2851,
+        2847,
+        2853,
+        2841,
+        2843,
+        2851,
+        2850,
+        2851.3,
+        2849,
+        2852,
+    ]
+
+    b7_tp_m_s = [702.5522131, 716.7349242, 722.2261427, 725.5761517, 727.0030057]
+    b7_tp_p_s = [702.6845352, 699.1034186, 701.3211972, 700.1283237, 700.1098232]
+
+    b13_tp_m_s = [398.8387303, 403.5820717, 405.8601994]
+    b13_tp_p_s = [398.7009553, 398.4052117, 399.1230098]
+
+    # A6000 4卡
+    b1_throughput_mLoRA = [
+        4600.45,
+        8664.91,
+        10118.36,
+        10184.44,
+        10119,
+        11157,
+        11157,
+        11530,
+        11580,
+        11580,
+        11600,
+        11600,
+    ]
+    b1_throughput_tp = [
+        5752.14,
+        5749.34,
+        5756.78,
+        5758.32,
+        5753.14,
+        5753.34,
+        5756.78,
+        5756.32,
+        5758.14,
+        5749.34,
+        5753.78,
+        5754.32,
+    ]
+    b1_throughput_fsdp = [
+        6151.91,
+        6141.73,
+        6161.23,
+        6153.93,
+        6153.91,
+        6146.73,
+        6161.23,
+        6151.93,
+        6157.91,
+        6143.73,
+        6161.23,
+        6153.93,
+    ]
+    b1_throughput_gpipe = [
+        4599.87,
+        4610.19,
+        4592.17,
+        4601.18,
+        4598.87,
+        4600.19,
+        4593.17,
+        4601.18,
+        4599.87,
+        4610.19,
+        4592.17,
+        4603.18,
+    ]
+
+    b7_throughput_mLoRA = [1274.87, 2250.46, 2362.69, 2363.89]
+    b7_throughput_tp = [1614.18, 1610.26, 1620.07, 1613.34]
+    b7_throughput_fsdp = [1695.37, 1705.97, 1686.05, 1693.45]
+    b7_throughput_gpipe = [1284.27, 1273.89, 1272.14, 1279.64]
+
+    b13_throughput_mLoRA = [723.21, 1280.54]
+    b13_throughput_tp = [875, 877]
+    b13_throughput_fsdp = [0, 0, 0, 0]  # for the error
+    b13_throughput_gpipe = [723.21, 719.21]
+
+    b70_throughput_mLoRA = [234, 291, 320, 318]
+    b70_throughput_gpipe = [234.34, 234.32, 234.38, 234]
+
+    # 3090 8卡
+    b1_4090_mlora = [
+        319.61,
+        580.34,
+        663.12,
+        799.69,
+        800.96,
+        813.64,
+        812.92,
+        814.93,
+    ]
+    b1_4090_tp = [35.17, 35.33, 35.32, 35.32, 35.38, 35.34, 35.33, 35.34]
+    b1_4090_fsdp = [62.79, 62.31, 60.03, 61.53, 62.79, 60.37, 61.23, 63.11]
+    b1_4090_gpipe = [
+        318.99,
+        319.16,
+        319.61,
+        319.42,
+        319.23,
+        319.61,
+        319.80,
+        319.96,
+    ]
+
+    b7_4090_mlora = [576.79, 671.39, 702.91, 715.85]
+    b7_4090_gpipe = [578.97, 581.46, 573.70, 580.84]
+    b7_4090_fsdp = []
+    b7_4090_tp = [11.93, 12.09, 11.86, 12.10]
+
+    b13_4090_mlora = [524.57, 694.34]
+    b13_4090_gpipe = [528.47, 522.06]
+    b13_4090_fsdp = []
+    b13_4090_tp = []
+    # 绘制 A6000 4卡
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+    ticks = [2, 4, 6, 8, 10, 12]
+    ticks_label = ["2", "4", "6", "8", "10", "12"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_mLoRA]
+    tp_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_tp]
+    fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_fsdp]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b1_throughput_gpipe]
+
+    ax[0][0].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[0][0].plot(x, fsdp_avg_time, color=c_3, marker="^")
+    ax[0][0].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[0][0].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][0].set_xticks(ticks)
+    ax[0][0].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4]
+    ticks = [1, 2, 3, 4]
+    ticks_label = ["1", "2", "3", "4"]
+
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_mLoRA]
+    tp_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_tp]
+    fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_fsdp]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b7_throughput_gpipe]
+
+    ax[0][1].plot(x, g_avg_time, color=c_1, marker="v", label="1F1B")
+    ax[0][1].plot(x, fsdp_avg_time, color=c_3, marker="^", label="FSDP")
+    ax[0][1].plot(x, tp_avg_time, color=c_2, marker="o", label="TP")
+    ax[0][1].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][1].set_xticks(ticks)
+    ax[0][1].set_xticklabels(ticks_label)
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b13_throughput_mLoRA]
+    tp_avg_time = [total_token / tp / 60 / 60 for tp in b13_throughput_tp]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b13_throughput_gpipe]
+
+    ax[0][2].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[0][2].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[0][2].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][2].set_xticks(ticks)
+    ax[0][2].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4]
+    ticks = [1, 2, 3, 4]
+    ticks_label = ["1", "2", "3", "4"]
+
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b70_throughput_mLoRA]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b70_throughput_gpipe]
+
+    ax[0][3].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[0][3].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[0][3].set_xticks(ticks)
+    ax[0][3].set_xticklabels(ticks_label)
+
+    ## END
+
+    # 绘制 3090 8 卡
+    x = [1, 2, 3, 4, 5, 6, 7, 8]
+    ticks = [2, 4, 6, 8]
+    ticks_label = ["2", "4", "6", "8"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_mlora]
+    tp_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_tp]
+    fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_fsdp]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b1_4090_gpipe]
+
+    ax[1][0].plot(x, g_avg_time, color=c_1, marker="v")
+    ax[1][0].plot(x, fsdp_avg_time, color=c_3, marker="^")
+    ax[1][0].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[1][0].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[1][0].set_xticks(ticks)
+    ax[1][0].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4]
+    ticks = [1, 2, 3, 4]
+    ticks_label = ["1", "2", "3", "4"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_mlora]
+    tp_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_tp]
+    fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_fsdp]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b7_4090_gpipe]
+
+    ax[1][1].plot(x, g_avg_time, color=c_1, marker="v")
+    # ax[1][1].plot([1], fsdp_avg_time, color=c_3, marker="^")
+    # ax[1][1].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[1][1].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[1][1].set_xticks(ticks)
+    ax[1][1].set_xticklabels(ticks_label)
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_mlora]
+    tp_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_tp]
+    fsdp_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_fsdp]
+    g_avg_time = [total_token / tp / 60 / 60 for tp in b13_4090_gpipe]
+
+    ax[1][2].plot(x, g_avg_time, color=c_1, marker="v")
+    # ax[1][2].plot([1], fsdp_avg_time, color=c_3, marker="^")
+    # ax[1][2].plot(x, tp_avg_time, color=c_2, marker="o")
+    ax[1][2].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[1][2].set_xticks(ticks)
+    ax[1][2].set_xticklabels(ticks_label)
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+
+    ax[1][3].set_xticks(ticks)
+    ax[1][3].set_xticklabels(ticks_label)
+
+    # END
+
+    # 绘制 A6000 单卡
+    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+    ticks = [2, 4, 6, 8, 10, 12]
+    ticks_label = ["2", "4", "6", "8", "10", "12"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b1_tp_m_s]
+    p_avg_time = [total_token / tp / 60 / 60 for tp in b1_tp_p_s]
+    ax[2][0].plot(x, p_avg_time, color=c_2, marker="^", label="PEFT")
+    ax[2][0].plot(x, m_avg_time, color=c_4, marker="*", label="mLoRA")
+    ax[2][0].set_xticks(ticks)
+    ax[2][0].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3, 4, 5]
+    ticks = [1, 2, 3, 4, 5]
+    ticks_label = ["1", "2", "3", "4", "5"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b7_tp_m_s]
+    p_avg_time = [total_token / tp / 60 / 60 for tp in b7_tp_p_s]
+    ax[2][1].plot(x, p_avg_time, color=c_2, marker="^")
+    ax[2][1].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[2][1].set_xticks(ticks)
+    ax[2][1].set_xticklabels(ticks_label)
+
+    x = [1, 2, 3]
+    ticks = [1, 2, 3]
+    ticks_label = ["1", "2", "3"]
+    m_avg_time = [total_token / tp / 60 / 60 for tp in b13_tp_m_s]
+    p_avg_time = [total_token / tp / 60 / 60 for tp in b13_tp_p_s]
+    ax[2][2].plot(x, p_avg_time, color=c_2, marker="^")
+    ax[2][2].plot(x, m_avg_time, color=c_4, marker="*")
+    ax[2][2].set_xticks(ticks)
+    ax[2][2].set_xticklabels(ticks_label)
+
+
+    x = [1, 2]
+    ticks = [1, 2]
+    ticks_label = ["1", "2"]
+
+    ax[2][3].set_xticks(ticks)
+    ax[2][3].set_xticklabels(ticks_label)
+    ## END
+
+
+    ax[0][0].set_ylim(0, 3)
+    ax[0][1].set_ylim(0, 10)
+    ax[0][2].set_ylim(0, 20)
+    ax[0][3].set_ylim(0, 60)
+
+    ax[1][0].set_ylim(0, 400)
+    ax[1][1].set_ylim(0, 40)
+    ax[1][2].set_ylim(0, 40)
+    ax[1][3].set_ylim(0, 40)
+
+    ax[2][0].set_ylim(3, 4)
+    ax[2][1].set_ylim(13, 16)
+    ax[2][2].set_ylim(25, 27)
+    ax[2][3].set_ylim(25, 27)
+
+
+    ax[0][2].set_xlim(0.8, 3 - 0.8)
+
+    ax[1][0].set_xlim(0.5, 9 - 0.5)
+    ax[1][1].set_xlim(0.5, 5 - 0.5)
+    ax[1][2].set_xlim(0.8, 3 - 0.8)
+
+    ax[2][2].set_xlim(0.8, 4 - 0.8)
+
+    ax[0][0].set_title("(a) 1.1B A6000×4", fontsize=16)
+    ax[0][1].set_title("(b) 7B A6000×4", fontsize=16)
+    ax[0][2].set_title("(c) 13B A6000×4", fontsize=16)
+    ax[0][3].set_title("(d) 70B A6000×4", fontsize=16)
+
+    ax[1][0].set_title("(e) 1.1B 3090×8", fontsize=16)
+    ax[1][1].set_title("(f) 7B 3090×8", fontsize=16)
+    ax[1][2].set_title("(g) 13B 3090×8", fontsize=16)
+    ax[1][3].set_title("(h) 70B 3090×8", fontsize=16)
+
+    ax[2][0].set_title("(i) 1.1B A6000", fontsize=16)
+    ax[2][1].set_title("(j) 7B A6000", fontsize=16)
+    ax[2][2].set_title("(k) 13B A6000", fontsize=16)
+    ax[2][3].set_title("(l) 70B A6000", fontsize=16)
+
+    ax[0][0].set_ylabel("Average task\ncompletion time (h)")
+    ax[1][0].set_ylabel("Average task\ncompletion time (h)")
+    ax[2][0].set_ylabel("Average task\ncompletion time (h)")
+
+    ax[0][2].text(
+        0.9,
+        0.8,
+        "FSDP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[0][2].transAxes,
+        color=c_4,
+    )
+
+    ax[0][3].text(
+        0.9,
+        0.1,
+        "FSDP : OOM\nTP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[0][3].transAxes,
+        color=c_4,
+    )
+
+    ax[1][1].text(
+        0.9,
+        0.7,
+        "FSDP : OOM\nTP : about one month",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[1][1].transAxes,
+        color=c_4,
+    )
+
+    ax[1][2].text(
+        0.9,
+        0.7,
+        "FSDP : OOM\nTP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax[1][2].transAxes,
+        color=c_4,
+    )
+
+    ax[1][3].text(
+        0.5,
+        0.5,
+        "FSDP : OOM\nTP : OOM\n1F1B : OOM\nmLoRA : OOM",
+        fontsize=12,
+        va="center",
+        ha="center",
+        transform=ax[1][3].transAxes,
+        color=c_4,
+    )
+
+
+    ax[2][3].text(
+        0.5,
+        0.5,
+        "PEFT : OOM\nmLoRA : OOM",
+        fontsize=12,
+        va="center",
+        ha="center",
+        transform=ax[2][3].transAxes,
+        color=c_4,
+    )
+
+
+    fig.legend(
+        ncol=5,
+        bbox_to_anchor=(0.75, 1.05),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=14,
+    )
+
+
+    ax[0][0].arrow(5, 0.5, 0.5, 0.3, width=0.01, head_width=0.1)
+    ax[0][0].text(
+        0.38,
+        0.07,
+        "enable BatchLoRA",
+        fontsize=10,
+        va="bottom",
+        ha="right",
+        transform=ax[0][0].transAxes,
+        color=c_4,
+    )
+
+
+    fig.supxlabel(
+        "Number of simultaneously trained LoRA adapters",
+        fontsize=16,
+        y=-0.03,
+        ha="center",
+        va="bottom",
+    )
+
+
+    plt.savefig("end-to-end.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        b13_4090_fsdp,
+        b13_4090_gpipe,
+        b13_4090_mlora,
+        b13_4090_tp,
+        b13_throughput_fsdp,
+        b13_throughput_gpipe,
+        b13_throughput_mLoRA,
+        b13_throughput_tp,
+        b13_tp_m_s,
+        b13_tp_p_s,
+        b1_4090_fsdp,
+        b1_4090_gpipe,
+        b1_4090_mlora,
+        b1_4090_tp,
+        b1_throughput_fsdp,
+        b1_throughput_gpipe,
+        b1_throughput_mLoRA,
+        b1_throughput_tp,
+        b1_tp_m_s,
+        b1_tp_p_s,
+        b70_throughput_gpipe,
+        b70_throughput_mLoRA,
+        b7_4090_fsdp,
+        b7_4090_gpipe,
+        b7_4090_mlora,
+        b7_4090_tp,
+        b7_throughput_fsdp,
+        b7_throughput_gpipe,
+        b7_throughput_mLoRA,
+        b7_throughput_tp,
+        b7_tp_m_s,
+        b7_tp_p_s,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        fsdp_avg_time,
+        g_avg_time,
+        m_avg_time,
+        p_avg_time,
+        ticks,
+        ticks_label,
+        total_token,
+        tp_avg_time,
+        x,
+    )
+
+
+@app.cell
+def __():
+    return
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/end_to_end_large_model.py b/mlora/end_to_end_large_model.py
new file mode 100644
index 0000000..8b38e1a
--- /dev/null
+++ b/mlora/end_to_end_large_model.py
@@ -0,0 +1,89 @@
+import marimo
+
+__generated_with = "0.9.10"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    matplotlib.rcParams["text.usetex"] = False
+    plt.rcParams["font.family"] = "Times New Roman"
+    plt.rcParams["font.size"] = 16
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    total_token = 7473 * 512 * 10
+
+    pp = [234.34, 234.32, 234.38, 234]
+    mlora = [234, 291, 320, 318]
+
+    pp_avg_time = [total_token / tp / 60 / 60 for tp in pp]
+    mlora_avg_time = [total_token / tp / 60 / 60 for tp in mlora]
+
+    fig, ax = plt.subplots(figsize=(5.5, 2.5), constrained_layout=True)
+
+    x = [1, 2, 3, 4]
+
+    ax.plot(x, pp_avg_time, color=c_1, marker="v", label="1F1B")
+    ax.plot(x, mlora_avg_time, color=c_4, marker="*", label="mLoRA")
+    ax.set_xticks(x)
+    ax.set_xticklabels(["1", "2", "3", "4"])
+    ax.set_yticks([0, 40, 60])
+    ax.set_yticklabels(["0", "40", "60"])
+    ax.set_ylim(0, 60 + 1)
+
+    ax.set_title("(a) 70B A6000×4", fontsize=16)
+
+    ax.set_ylabel("Average task\ncompletion time (h)")
+
+    ax.set_xlabel("Number of simultaneously trained LoRA adapters", fontsize=16)
+
+    ax.text(
+        0.95,
+        0.05,
+        "FSDP : OOM\nTP : OOM",
+        fontsize=12,
+        va="bottom",
+        ha="right",
+        transform=ax.transAxes,
+        color=c_4,
+        style="italic",
+    )
+
+    fig.legend(
+        ncol=2,
+        bbox_to_anchor=(0.75, 0.4),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=14,
+    )
+
+    # plt.savefig("end_to_end_large_model.pdf", bbox_inches="tight", dpi=1000, format="pdf")
+    return (
+        ax,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        matplotlib,
+        mlora,
+        mlora_avg_time,
+        np,
+        plt,
+        pp,
+        pp_avg_time,
+        total_token,
+        x,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/lora_rank_adaptability.pdf b/mlora/lora_rank_adaptability.pdf
new file mode 100644
index 0000000..aec7e26
Binary files /dev/null and b/mlora/lora_rank_adaptability.pdf differ
diff --git a/mlora/lora_rank_adaptability.py b/mlora/lora_rank_adaptability.py
new file mode 100644
index 0000000..4dc90bc
--- /dev/null
+++ b/mlora/lora_rank_adaptability.py
@@ -0,0 +1,93 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import random
+
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return np, plt, random
+
+
+@app.cell
+def __(plt):
+    fig, ax = plt.subplots(figsize=(7, 2.8), ncols=3, layout="constrained")
+
+    space_width = 3 / 22
+    bar_width = 3 * space_width
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    y_0 = [10525.52, 10467.51, 10315.85, 10286.17]
+    x_0 = [4, 8, 16, 32]
+
+    y_1 = [2309.40, 2258.73, 2252.43, 2242.80]
+    x_1 = [16, 32, 64, 128]
+
+    y_2 = [1245.79, 1244.60, 1224.91, 1207.40]
+    x_2 = [16, 32, 64, 128]
+
+    ax[0].plot(x_0, y_0, "s-", color=c_1)
+    ax[0].set_ylim(0, 11000)
+    ax[0].set_xticks(x_0)
+    ax[0].set_yticks([0, 5000, 10000])
+    ax[0].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+
+    ax[1].plot(x_1, y_1, "s-", color=c_1)
+    ax[1].set_ylim(0, 11000)
+    ax[1].set_xticks([32, 64, 128])
+    ax[1].set_yticks([0, 5000, 10000])
+    ax[1].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+
+    ax[2].plot(x_2, y_2, "s-", color=c_1)
+    ax[2].set_ylim(0, 11000)
+    ax[2].set_xticks([32, 64, 128])
+    ax[2].set_yticks([0, 5000, 10000])
+    ax[2].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+
+    ax[0].set_ylabel("Throughput (tokens/s)", fontsize=16)
+
+    ax[0].set_title("(d) TinyLlama-1.1B", fontsize=16)
+    ax[1].set_title("(e) Llama2-7B", fontsize=16)
+    ax[2].set_title("(f) Llama2-13B", fontsize=16)
+
+    ax[0].set_xlabel("Rank of LoRA adapters", fontsize=14)
+    ax[1].set_xlabel("Rank of LoRA adapters", fontsize=14)
+    ax[2].set_xlabel("Rank of LoRA adapters", fontsize=14)
+
+    #plt.savefig("lora_rank_adaptability.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        bar_width,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        space_width,
+        x_0,
+        x_1,
+        x_2,
+        y_0,
+        y_1,
+        y_2,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/lorapp-task.pdf b/mlora/lorapp-task.pdf
new file mode 100644
index 0000000..f08de50
Binary files /dev/null and b/mlora/lorapp-task.pdf differ
diff --git a/mlora/lorapp_task.py b/mlora/lorapp_task.py
new file mode 100644
index 0000000..7f5001e
--- /dev/null
+++ b/mlora/lorapp_task.py
@@ -0,0 +1,150 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    return np, plt
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell
+def __(np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(figsize=(7, 2), ncols=3, layout="constrained")
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+    x_b1 = [1, 2, 3, 4]
+    b1_throughput_mLoRA = [4600.45, 8664.91, 10118.36, 10184.44]
+    b1_throughput_tp = [5752.14, 5749.34, 5756.78, 5758.32]
+    b1_throughput_fsdp = [6151.91, 6141.73, 6161.23, 6153.93]
+    b1_throughput_gpipe = [4599.87, 4610.19, 4592.17, 4601.18]
+
+    x_b7 = [1, 2, 3, 4]
+    b7_throughput_mLoRA = [1274.87, 2250.46, 2362.69, 2363.89]
+    b7_throughput_tp = [1614.18, 1610.26, 1620.07, 1613.34]
+    b7_throughput_fsdp = [1695.37, 1705.97, 1686.05, 1693.45]
+    b7_throughput_gpipe = [1284.27, 1273.89, 1272.14, 1279.64]
+
+    x_b13 = [1, 2, 3, 4]
+    b13_throughput_mLoRA = [723.21, 1280.54, 1286.54, 1282.54]
+    b13_throughput_tp = [875, 877, 870, 878]
+    b13_throughput_fsdp = [0, 0, 0, 0]  # for the error
+    b13_throughput_gpipe = [723.21, 719.21, 726.21, 723.21]
+
+    ax[0].set_ylabel("Throughput (tokens/s)", fontsize=16)
+
+
+    ax[0].plot(x_b1, b1_throughput_fsdp, color=c_3, marker="v")
+    ax[0].plot(x_b1, b1_throughput_tp, color=c_2, marker="o")
+    ax[0].plot(x_b1, b1_throughput_gpipe, color=c_1, marker="^")
+    ax[0].plot(x_b1, b1_throughput_mLoRA, color=c_4, marker="*")
+    ax[0].set_ylim(3000, 12000)
+    ax[0].set_xticks([1, 2, 3, 4])
+    ax[0].set_xticklabels(["1", "2", "3", "4"])
+    ax[0].set_yticks([5000, 7000, 9000, 11000])
+    ax[0].set_yticklabels(
+        ["5k", "7k", "9k", "11k"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[0].tick_params(pad=7)
+    ax[0].set_title("(a) TinyLlama-1.1B", fontsize=16)
+
+    ax[1].plot(x_b7, b7_throughput_gpipe, color=c_1, label="1F1B", marker="^")
+    ax[1].plot(x_b7, b7_throughput_tp, color=c_2, label="TP", marker="o")
+    ax[1].plot(x_b7, b7_throughput_fsdp, color=c_3, label="FSDP", marker="v")
+    ax[1].plot(x_b7, b7_throughput_mLoRA, color=c_4, label="LoRAPP", marker="*")
+    ax[1].set_ylim(1000, 2500)
+    ax[1].set_xticks([1, 2, 3, 4])
+    ax[1].set_xticklabels(["1", "2", "3", "4"])
+    ax[1].set_yticks([1200, 1700, 2200])
+    ax[1].set_yticklabels(
+        ["1200", "1700", "2200"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[1].tick_params(pad=7)
+    ax[1].set_title("(b) Llama-2-7B", fontsize=16)
+    ax[1].set_xlabel("Number of simultaneously trained LoRA adapters", fontsize=16)
+
+    ax[2].plot(
+        x_b13,
+        b13_throughput_fsdp,
+        color=c_3,
+        marker="x",
+        markerfacecolor="r",
+        markeredgecolor="r",
+    )
+    ax[2].plot(x_b13, b13_throughput_tp, color=c_2, marker="o")
+    ax[2].plot(x_b13, b13_throughput_gpipe, color=c_1, marker="^")
+    ax[2].plot(x_b13, b13_throughput_mLoRA, color=c_4, marker="*")
+    ax[2].set_ylim(400, 1500)
+    ax[2].set_xticks([1, 2, 3, 4])
+    ax[2].set_xticklabels(["1", "2", "3", "4"])
+    ax[2].set_yticks([600, 950, 1300])
+    ax[2].set_yticklabels(
+        ["650", "950", "1300"], rotation=90, ha="center", va="center", fontsize=12
+    )
+    ax[2].tick_params(pad=7)
+    ax[2].set_title("(c) Llama-2-13B", fontsize=16)
+    ax[2].text(
+        0.95,
+        0.05,
+        "FSDP : OOM",
+        fontsize=14,
+        va="bottom",
+        ha="right",
+        transform=ax[2].transAxes,
+        color="r",
+        style="italic",
+    )
+
+    fig.legend(
+        ncol=4,
+        bbox_to_anchor=(0.97, 1.2),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=16,
+    )
+
+    # plt.show()
+    plt.savefig("lorapp-task.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        b13_throughput_fsdp,
+        b13_throughput_gpipe,
+        b13_throughput_mLoRA,
+        b13_throughput_tp,
+        b1_throughput_fsdp,
+        b1_throughput_gpipe,
+        b1_throughput_mLoRA,
+        b1_throughput_tp,
+        b7_throughput_fsdp,
+        b7_throughput_gpipe,
+        b7_throughput_mLoRA,
+        b7_throughput_tp,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        x,
+        x_b1,
+        x_b13,
+        x_b7,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/map-and-loss.pdf b/mlora/map-and-loss.pdf
new file mode 100644
index 0000000..66ac0c0
Binary files /dev/null and b/mlora/map-and-loss.pdf differ
diff --git a/mlora/map-and-loss.py b/mlora/map-and-loss.py
new file mode 100644
index 0000000..f0bac6d
--- /dev/null
+++ b/mlora/map-and-loss.py
@@ -0,0 +1,896 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell(hide_code=True)
+def __():
+    loss_data = [
+        1.1504,
+        1.8653,
+        0.8047,
+        0.8011,
+        0.7557,
+        0.7558,
+        0.7686,
+        0.8166,
+        0.7632,
+        0.7042,
+        0.7764,
+        0.7793,
+        0.7594,
+        0.6889,
+        0.7605,
+        0.7444,
+        0.7133,
+        0.7957,
+        0.7535,
+        0.7258,
+        0.7579,
+        0.7365,
+        0.7603,
+        0.7677,
+        0.7089,
+        0.7511,
+        0.7398,
+        0.7675,
+        0.7696,
+        0.7312,
+        0.7394,
+        0.7776,
+        0.7651,
+        0.7837,
+        0.7236,
+        0.7248,
+        0.7709,
+        0.7965,
+        0.7419,
+        0.7185,
+        0.7465,
+        0.7611,
+        0.7585,
+        0.7572,
+        0.7142,
+        0.76,
+        0.7559,
+        0.728,
+        0.7448,
+        0.7327,
+        0.8376,
+        0.7407,
+        0.8002,
+        0.7723,
+        0.7015,
+        0.7211,
+        0.7349,
+        0.686,
+        0.6961,
+        0.7333,
+        0.6772,
+        0.7295,
+        0.7704,
+        0.7876,
+        0.6915,
+        0.6808,
+        0.7451,
+        0.7214,
+        0.6729,
+        0.6317,
+        0.7705,
+        0.6895,
+        0.7668,
+        0.6853,
+        0.7305,
+        0.7695,
+        0.6863,
+        0.7153,
+        0.6849,
+        0.694,
+        0.7782,
+        0.7391,
+        0.6886,
+        0.7047,
+        0.6776,
+        0.7424,
+        0.693,
+        0.7058,
+        0.7483,
+        0.6831,
+        0.7003,
+        0.7386,
+        0.7016,
+        0.7174,
+        0.7187,
+        0.7034,
+        0.7384,
+        0.7061,
+        0.6798,
+        0.6592,
+        0.7525,
+        0.6893,
+        0.6907,
+        0.7583,
+        0.6771,
+        0.7248,
+        0.6998,
+        0.721,
+        0.7273,
+        0.6645,
+        0.681,
+        0.7265,
+        0.767,
+        0.7026,
+        0.6869,
+        0.712,
+        0.7179,
+        0.7331,
+        0.6911,
+        0.6397,
+        0.7521,
+        0.7362,
+        0.7607,
+        0.6977,
+        0.7231,
+        0.7071,
+        0.6914,
+        0.7232,
+        0.7439,
+        0.7153,
+        0.7321,
+        0.7417,
+        0.6834,
+        0.6809,
+        0.7136,
+        0.693,
+        0.799,
+        0.7099,
+        0.713,
+        0.6629,
+        0.7151,
+        0.6783,
+        0.7342,
+        0.7265,
+        0.6635,
+        0.7187,
+        0.7536,
+        0.7108,
+        0.6714,
+        0.6664,
+        0.6849,
+        0.7655,
+        0.715,
+        0.6977,
+        0.6581,
+        0.7254,
+        0.7484,
+        0.7495,
+        0.7121,
+        0.6926,
+        0.7385,
+        0.6852,
+        0.7534,
+        0.6925,
+        0.693,
+        0.7008,
+        0.7422,
+        0.7369,
+        0.7251,
+        0.6688,
+        0.7008,
+        0.7086,
+        0.7499,
+        0.714,
+        0.6598,
+        0.6839,
+        0.7528,
+        0.6966,
+        0.6823,
+        0.6741,
+        0.7301,
+        0.6849,
+        0.6801,
+        0.6978,
+        0.7045,
+        0.7169,
+        0.7022,
+        0.7151,
+        0.6495,
+        0.7012,
+        0.6495,
+        0.6711,
+        0.6328,
+        0.7056,
+        0.7132,
+        0.6827,
+        0.6053,
+        0.6725,
+        0.6957,
+        0.6427,
+        0.6429,
+        0.5967,
+        0.6835,
+        0.6894,
+        0.6547,
+        0.6032,
+        0.6507,
+        0.6483,
+        0.6682,
+        0.6428,
+        0.6406,
+        0.592,
+        0.659,
+        0.7028,
+        0.6311,
+        0.6656,
+        0.6097,
+        0.6929,
+        0.6125,
+        0.7286,
+        0.6596,
+        0.6077,
+        0.6311,
+        0.6679,
+        0.6742,
+        0.6735,
+        0.6043,
+        0.6806,
+        0.6537,
+        0.6705,
+        0.6872,
+        0.6431,
+        0.6422,
+        0.6652,
+        0.6829,
+        0.6346,
+        0.6018,
+        0.6642,
+        0.615,
+        0.6824,
+        0.6876,
+        0.6384,
+        0.6755,
+        0.6957,
+        0.6386,
+        0.6264,
+        0.668,
+        0.6976,
+        0.6985,
+        0.6628,
+        0.6726,
+        0.5897,
+        0.6394,
+        0.6693,
+        0.6596,
+        0.6884,
+        0.5967,
+        0.6659,
+        0.6609,
+        0.6627,
+        0.6203,
+        0.5878,
+        0.6926,
+        0.6583,
+        0.6482,
+        0.6399,
+        0.6045,
+        0.6888,
+        0.6823,
+        0.6875,
+        0.6638,
+        0.6232,
+        0.6539,
+        0.6908,
+        0.6612,
+        0.6684,
+        0.5917,
+        0.6398,
+        0.6927,
+        0.6658,
+        0.6469,
+        0.6245,
+        0.6547,
+        0.6738,
+        0.6773,
+        0.6386,
+        0.6142,
+        0.6283,
+        0.6899,
+        0.6318,
+        0.6394,
+        0.6183,
+        0.6262,
+        0.6869,
+        0.6384,
+        0.6482,
+        0.6399,
+        0.6193,
+        0.6551,
+        0.7235,
+        0.6435,
+        0.6442,
+        0.7525,
+        0.652,
+        0.647,
+        0.6849,
+        0.6408,
+        0.7305,
+        0.6678,
+        0.6752,
+        0.6074,
+        0.6647,
+        0.6876,
+        0.6393,
+        0.6602,
+        0.6236,
+        0.6326,
+        0.6666,
+        0.6481,
+        0.5922,
+        0.622,
+        0.6422,
+        0.6694,
+        0.6335,
+        0.6088,
+        0.6967,
+        0.6156,
+        0.6546,
+        0.6196,
+        0.631,
+        0.6438,
+        0.6131,
+        0.6886,
+        0.6725,
+        0.6249,
+        0.669,
+        0.608,
+        0.6764,
+        0.648,
+        0.7009,
+        0.6284,
+        0.5715,
+        0.6558,
+        0.6604,
+        0.6535,
+        0.6345,
+        0.598,
+        0.6399,
+        0.6468,
+        0.6013,
+        0.6425,
+        0.6382,
+        0.686,
+        0.6616,
+        0.704,
+        0.6403,
+        0.5649,
+        0.6857,
+        0.6999,
+        0.6479,
+        0.6419,
+        0.6218,
+        0.691,
+        0.6876,
+        0.6757,
+        0.6217,
+        0.5572,
+        0.7362,
+        0.6639,
+        0.6607,
+        0.6252,
+        0.6434,
+        0.6434,
+        0.5952,
+        0.6062,
+        0.6104,
+        0.5933,
+        0.5873,
+        0.5627,
+        0.5918,
+        0.5934,
+        0.6291,
+        0.5767,
+        0.5255,
+        0.6127,
+        0.5781,
+        0.5905,
+        0.5633,
+        0.5585,
+        0.6539,
+        0.6334,
+        0.6003,
+        0.5772,
+        0.5347,
+        0.6061,
+        0.6419,
+        0.5479,
+        0.5582,
+        0.5404,
+        0.6531,
+        0.6028,
+        0.5482,
+        0.5579,
+        0.5644,
+        0.6064,
+        0.5913,
+        0.6302,
+        0.5631,
+        0.5461,
+        0.6551,
+        0.6142,
+        0.6295,
+        0.5712,
+        0.5677,
+        0.6012,
+        0.5998,
+        0.5688,
+        0.5585,
+        0.5643,
+        0.5889,
+        0.6405,
+        0.5609,
+        0.5574,
+        0.571,
+        0.616,
+        0.6381,
+        0.5958,
+        0.5904,
+        0.5562,
+        0.5759,
+        0.6378,
+        0.5804,
+        0.5568,
+        0.5411,
+        0.6559,
+        0.6074,
+        0.6196,
+        0.57,
+        0.5601,
+        0.6041,
+        0.6512,
+        0.6167,
+        0.5851,
+        0.532,
+        0.6477,
+        0.5868,
+        0.5786,
+        0.5452,
+        0.577,
+        0.5936,
+        0.6291,
+        0.6129,
+        0.5574,
+        0.5493,
+        0.5868,
+        0.6191,
+        0.5933,
+        0.6468,
+        0.5067,
+        0.6535,
+        0.6046,
+        0.5802,
+        0.5826,
+        0.552,
+        0.6254,
+        0.5682,
+        0.545,
+        0.5451,
+        0.5221,
+        0.6329,
+        0.5853,
+        0.6029,
+        0.5443,
+        0.5354,
+        0.6419,
+        0.6439,
+        0.5661,
+        0.5551,
+        0.5512,
+        0.6203,
+        0.6219,
+        0.6153,
+        0.5726,
+        0.5171,
+        0.5946,
+        0.6604,
+        0.6185,
+        0.5895,
+        0.5561,
+        0.5905,
+        0.5777,
+        0.6167,
+        0.546,
+        0.5482,
+        0.582,
+        0.5743,
+        0.6559,
+        0.5497,
+        0.5518,
+        0.5805,
+        0.6465,
+        0.5864,
+        0.5589,
+        0.5439,
+        0.6347,
+        0.6263,
+        0.5779,
+        0.5725,
+        0.5504,
+        0.6412,
+        0.6184,
+        0.6223,
+        0.5872,
+        0.5937,
+        0.6088,
+        0.5768,
+        0.5967,
+        0.6348,
+        0.5651,
+        0.6327,
+        0.6183,
+        0.5749,
+        0.6044,
+        0.5796,
+        0.6044,
+        0.6142,
+        0.6183,
+        0.5729,
+        0.5009,
+        0.5938,
+        0.6065,
+        0.5894,
+        0.5798,
+        0.5398,
+        0.6161,
+        0.6011,
+        0.6064,
+        0.6147,
+        0.5559,
+        0.6146,
+        0.5655,
+        0.5756,
+        0.6018,
+        0.5448,
+        0.6312,
+        0.6232,
+        0.5807,
+        0.5784,
+        0.5462,
+        0.6209,
+        0.5682,
+        0.6031,
+        0.5688,
+        0.5668,
+        0.6102,
+        0.6193,
+        0.5817,
+        0.5811,
+        0.5007,
+        0.6064,
+        0.5597,
+        0.5679,
+        0.5397,
+        0.5281,
+        0.5098,
+        0.5147,
+        0.5747,
+        0.5386,
+        0.5585,
+        0.474,
+        0.487,
+        0.5741,
+        0.5509,
+        0.5243,
+        0.5439,
+        0.5177,
+        0.5553,
+        0.5518,
+        0.5512,
+        0.5187,
+        0.491,
+        0.5827,
+        0.548,
+        0.5553,
+        0.491,
+        0.434,
+        0.5807,
+        0.5702,
+        0.6053,
+        0.4806,
+        0.4606,
+        0.607,
+        0.5538,
+        0.519,
+        0.5139,
+        0.5007,
+        0.5968,
+        0.5643,
+        0.5134,
+        0.4787,
+        0.4608,
+        0.5629,
+        0.5295,
+        0.5245,
+        0.5075,
+        0.4814,
+        0.5417,
+        0.5736,
+        0.5569,
+        0.4928,
+        0.5207,
+        0.5686,
+        0.5775,
+        0.5218,
+        0.4851,
+        0.507,
+        0.546,
+        0.5576,
+        0.5191,
+        0.4948,
+        0.5287,
+        0.5537,
+        0.5625,
+        0.5107,
+        0.5059,
+        0.4703,
+        0.6103,
+        0.5216,
+        0.5344,
+        0.4919,
+        0.4677,
+        0.5908,
+        0.5659,
+        0.5166,
+        0.519,
+        0.4767,
+        0.5625,
+        0.5085,
+        0.4887,
+        0.4936,
+        0.4947,
+        0.5443,
+        0.5458,
+        0.5185,
+        0.4895,
+        0.4643,
+        0.5534,
+        0.5632,
+        0.5568,
+        0.5118,
+        0.539,
+        0.516,
+        0.5417,
+        0.5192,
+        0.5115,
+        0.4897,
+        0.5493,
+        0.5564,
+        0.506,
+        0.4873,
+        0.5172,
+        0.5835,
+        0.5571,
+        0.5338,
+        0.5408,
+        0.4995,
+        0.5715,
+        0.551,
+        0.5058,
+        0.5434,
+        0.506,
+        0.5536,
+        0.5519,
+        0.5712,
+        0.4969,
+        0.4763,
+        0.5485,
+        0.5891,
+        0.5313,
+        0.5408,
+        0.4994,
+        0.6022,
+        0.5665,
+        0.5388,
+        0.474,
+        0.4552,
+        0.5447,
+        0.5727,
+        0.5203,
+        0.4823,
+        0.5249,
+        0.576,
+        0.5412,
+        0.5365,
+        0.493,
+        0.5027,
+        0.5552,
+        0.5302,
+        0.5154,
+        0.5185,
+        0.4982,
+        0.5412,
+        0.519,
+        0.5801,
+        0.5254,
+        0.4857,
+        0.5943,
+        0.5629,
+        0.5488,
+        0.4911,
+        0.5192,
+        0.5861,
+        0.5268,
+        0.511,
+        0.4939,
+        0.5551,
+        0.5396,
+        0.5397,
+        0.4844,
+        0.4749,
+        0.5745,
+        0.5412,
+        0.5219,
+        0.5113,
+        0.4973,
+        0.5877,
+        0.5216,
+        0.5343,
+        0.4973,
+        0.4757,
+        0.5476,
+        0.5714,
+        0.5668,
+        0.5235,
+        0.4618,
+        0.5758,
+        0.5278,
+        0.5091,
+        0.4877,
+        0.46,
+        0.571,
+        0.5575,
+        0.526,
+        0.5028,
+        0.4955,
+        0.5487,
+    ]
+    return (loss_data,)
+
+
+@app.cell
+def __():
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import random
+    from matplotlib.colors import LinearSegmentedColormap
+    return LinearSegmentedColormap, np, plt, random
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 14
+    return
+
+
+@app.cell
+def __(loss_data, np, plt, random):
+    # 给定的 D 值列表
+    D_values = [4, 8]
+    # 创建一个图形和子图
+    fig, axs = plt.subplots(1, 2, figsize=(7, 1), dpi=400)
+    axs = axs.flatten()  # 将 axs 数组展平，方便迭代
+
+
+    def smooth_curve(points, factor=0.8):
+        smoothed_points = []
+        for point in points:
+            if smoothed_points:
+                previous = smoothed_points[-1]
+                # 上一个节点*0.8+当前节点*0.2
+                smoothed_points.append(previous * factor + point * (1 - factor))
+            else:
+                # 添加point
+                smoothed_points.append(point)
+        return smoothed_points
+
+
+    c_1 = (230 / 255, 241 / 255, 243 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (255 / 255, 223 / 255, 146 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    x = range(len(loss_data))
+
+    lorapp_loss = [x + random.uniform(-0.1, 0.1) for x in loss_data]
+    axs[1].plot(x, smooth_curve(loss_data), label="PEFT", color=c_2)
+    axs[1].plot(x, smooth_curve(lorapp_loss), label="mLoRA", color=c_4)
+    axs[1].set_xlabel("Training iteration", fontsize=14)
+    axs[1].set_ylabel("Loss", fontsize=14)
+    axs[1].set_ylim(0.0, 1.35)
+    axs[1].set_yticks(
+        [0.45, 0.9, 1.35],
+        ["0.45", "0.9", "1.35"],
+        rotation=90,
+        ha="center",
+        va="center",
+    )
+    axs[1].set_xticks([0, 400, 800], ["0", "400", "800"], va="top")
+
+    axs[1].set_xlim(-100, 900)
+    axs[1].tick_params(pad=7)
+
+    axs[1].legend(ncol=1, fancybox=False, framealpha=0.0, fontsize=14)
+
+
+    x = [71, 63, 55, 47, 39, 31, 23, 15, 7, 3]
+    y = [
+        0.266739094408014,
+        0.23996592483352167,
+        0.2554257707424939,
+        0.22216522633727626,
+        0.24286307818113806,
+        0.21479247707365348,
+        0.202277902928929,
+        0.2755166593501294,
+        0.712087464890641,
+        0.9916578900340629,
+    ]
+
+    c_1 = (230 / 255, 241 / 255, 243 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (255 / 255, 223 / 255, 146 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    # reverse the x-axis
+    x = x[::-1]
+    y = y[::-1]
+    y = np.array(y)
+
+    axs[0].plot(x, y, color=c_4)
+    axs[0].set_ylabel("MAPE (%)", fontsize=14)
+    axs[0].set_xlabel("Number of data points used for fitting   ", fontsize=14)
+    axs[0].set_yticks(
+        [2, 1, 0], ["2", "1", "0"], rotation=90, ha="center", va="center"
+    )
+    axs[0].tick_params(pad=7)
+
+    axs[0].text(
+        0.5,
+        1.05,
+        "(a)",
+        fontsize=16,
+        va="bottom",
+        ha="right",
+        transform=axs[0].transAxes,
+        color="black",
+    )
+    axs[0].text(
+        0.5,
+        1.05,
+        "(b)",
+        fontsize=16,
+        va="bottom",
+        ha="right",
+        transform=axs[1].transAxes,
+        color="black",
+    )
+
+    #plt.savefig("map-and-loss.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        D_values,
+        axs,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        lorapp_loss,
+        smooth_curve,
+        x,
+        y,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/overlap_com.py b/mlora/overlap_com.py
new file mode 100644
index 0000000..ad33ff0
--- /dev/null
+++ b/mlora/overlap_com.py
@@ -0,0 +1,27 @@
+import marimo
+
+__generated_with = "0.9.10"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    matplotlib.rcParams["text.usetex"] = False
+    plt.rcParams["font.family"] = "Times New Roman"
+    plt.rcParams["font.size"] = 16
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+
+    return c_1, c_2, c_3, c_4, matplotlib, np, plt
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/overlapping.pdf b/mlora/overlapping.pdf
new file mode 100644
index 0000000..6fcb4f8
Binary files /dev/null and b/mlora/overlapping.pdf differ
diff --git a/mlora/overlapping.py b/mlora/overlapping.py
new file mode 100644
index 0000000..ada10c9
--- /dev/null
+++ b/mlora/overlapping.py
@@ -0,0 +1,184 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+    return c_1, c_2, c_3, c_4, np, plt
+
+
+@app.cell
+def __(c_2, c_4, plt):
+    fig, ax = plt.subplots(1, 2, figsize=(7, 3.5), constrained_layout=True)
+
+    space_width = 3 / 22
+    bar_width = 3 * space_width
+
+    x_ticks = [
+        bar_width,
+        space_width + 3 * bar_width,
+        2 * space_width + 5 * bar_width,
+    ]
+    x_ticks_label = ["1.1B", "7B", "13B"]
+
+
+    ax[1].bar(
+        space_width,
+        45,
+        bar_width,
+        color=c_2,
+    )
+    ax[1].bar(
+        space_width + bar_width,
+        55,
+        bar_width,
+        color=c_4,
+        label="Communication",
+    )
+
+    ax[1].bar(
+        2 * space_width + 2 * bar_width,
+        75,
+        bar_width,
+        color=c_2,
+        label="Computation",
+    )
+    ax[1].bar(
+        2 * space_width + 3 * bar_width,
+        25,
+        bar_width,
+        color=c_4,
+    )
+
+    ax[1].bar(
+        3 * space_width + 4 * bar_width,
+        85,
+        bar_width,
+        color=c_2,
+    )
+    ax[1].bar(
+        3 * space_width + 5 * bar_width,
+        15,
+        bar_width,
+        color=c_4,
+    )
+
+    ax[1].set_xticks(x_ticks)
+    ax[1].set_xticklabels(x_ticks_label)
+
+    ax[1].tick_params(bottom=False, labelsize=14, pad=7)
+    ax[1].set_ylabel("The proportion of \nthe total time (%)", fontsize=16)
+
+
+    ax[1].set_yticks([0, 50, 100])
+    ax[1].set_yticklabels(
+        ["0", "50", "100"], rotation=90, ha="center", va="center"
+    )
+    ax[1].set_title("(b)", fontsize=16)
+
+    ax[1].set_xlabel("Base model with \ndifferent parameter scales", fontsize=16)
+
+    ##### ##### #####
+
+    space_width = 3 / 22
+    bar_width = 3 * space_width
+
+    x_ticks = [
+        bar_width,
+        space_width + 3 * bar_width,
+        2 * space_width + 5 * bar_width,
+    ]
+    x_ticks_label = ["1.1B", "7B", "13B"]
+
+
+    ax[0].bar(
+        space_width,
+        7062.16,
+        bar_width,
+        color=c_2,
+    )
+    ax[0].bar(
+        space_width + bar_width,
+        11625,
+        bar_width,
+        color=c_4,
+        label="With overlapping",
+    )
+
+    ax[0].bar(
+        2 * space_width + 2 * bar_width,
+        1874.48,
+        bar_width,
+        color=c_2,
+        label="Without overlapping",
+    )
+    ax[0].bar(
+        2 * space_width + 3 * bar_width,
+        2270,
+        bar_width,
+        color=c_4,
+    )
+
+    ax[0].bar(
+        3 * space_width + 4 * bar_width,
+        1102,
+        bar_width,
+        color=c_2,
+    )
+    ax[0].bar(
+        3 * space_width + 5 * bar_width,
+        1280,
+        bar_width,
+        color=c_4,
+    )
+
+    ax[0].set_xticks(x_ticks)
+    ax[0].set_xticklabels(x_ticks_label)
+
+    ax[0].tick_params(bottom=False, labelsize=14, pad=7)
+    ax[0].set_ylabel("Throughput (tokens/s)", fontsize=16)
+
+
+    ax[0].set_yticks([0, 2500, 5000, 7500, 10000])
+    ax[0].set_yticklabels(
+        ["0", "2.5k", "5k", "7.5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[0].set_title("(a)", fontsize=16)
+
+    ax[0].set_xlabel("Base model with \ndifferent parameter scales", fontsize=16)
+
+
+    ax[1].legend(
+        ncol=1,
+        bbox_to_anchor=(0.55, 1),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=10,
+    )
+
+    ax[0].legend(
+        ncol=1,
+        bbox_to_anchor=(0.35, 1),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=10,
+    )
+
+    plt.savefig("overlapping.pdf", bbox_inches="tight", dpi=1000)
+    return ax, bar_width, fig, space_width, x_ticks, x_ticks_label
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/pp_cmp_com_cost.py b/mlora/pp_cmp_com_cost.py
new file mode 100644
index 0000000..16c93eb
--- /dev/null
+++ b/mlora/pp_cmp_com_cost.py
@@ -0,0 +1,126 @@
+import marimo
+
+__generated_with = "0.7.12"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    return np, plt
+
+
+@app.cell
+def __(plt):
+    plt.rcParams["font.family"] = "Times New Roman"
+    plt.rcParams["font.size"] = 16
+    return
+
+
+@app.cell
+def __(np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(figsize=(7, 2.6), ncols=3, layout="constrained")
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    # tp = 8 * l * b * h * (d-1)/d
+    # pp = 2 * b * h * (d - 1)
+    token_size = 512 * 8 * 4 / 1024 / 1024 / 1024
+    x = [2, 3, 4, 5, 6, 7, 8]
+
+    l1 = 22
+    h1 = 2048
+
+    l2 = 32
+    h2 = 4096
+
+    l3 = 40
+    h3 = 5120
+
+    ticks = [2, 3, 4, 5, 6, 7, 8]
+    ticks_label = ["2", "3", "4", "5", "6", "7", "8"]
+    y_ticks = [0, 5, 10, 15, 20]
+    y_ticks_label = ["0", "5", "10", "15", "20"]
+
+    y_tp = [8 * l1 * token_size * h1 * (d - 1) / d for d in x]
+    y_pp = [2 * token_size * h1 * (d - 1) for d in x]
+    ax[0].plot(x, y_tp, color=c_2, label="TP", marker="o")
+    ax[0].plot(x, y_pp, color=c_4, label="LoRAPP and 1F1B", marker="*")
+    ax[0].set_ylim([-1, 25])
+    ax[0].set_xticks(ticks)
+    ax[0].set_xticklabels(ticks_label)
+    ax[0].set_yticks(y_ticks)
+    ax[0].set_yticklabels(y_ticks_label, rotation=90, ha="center", va="center")
+    ax[0].set_title("(a) TinyLlama-1.1B", fontsize=16)
+    ax[0].set_ylabel("Communication Cost (GB)", fontsize=16)
+    ax[0].set_xlabel("Number of GPUs", fontsize=16)
+    ax[0].tick_params(labelsize=16, pad=7)
+
+    y_tp = [8 * l2 * token_size * h2 * (d - 1) / d for d in x]
+    y_pp = [2 * token_size * h2 * (d - 1) for d in x]
+    ax[1].plot(x, y_tp, color=c_2, marker="o")
+    ax[1].plot(x, y_pp, color=c_4, marker="*")
+    ax[1].set_ylim([-1, 25])
+    ax[1].set_xticks(ticks)
+    ax[1].set_xticklabels(ticks_label)
+    ax[1].set_yticks(y_ticks)
+    ax[1].set_yticklabels(y_ticks_label, rotation=90, ha="center", va="center")
+    ax[1].set_title("(b) Llama-2-7B", fontsize=16)
+    ax[1].set_xlabel("Number of GPUs", fontsize=16)
+    ax[1].tick_params(labelsize=16, pad=7)
+
+    y_tp = [8 * l3 * token_size * h3 * (d - 1) / d for d in x]
+    y_pp = [2 * token_size * h3 * (d - 1) for d in x]
+    ax[2].plot(x, y_tp, color=c_2, marker="o")
+    ax[2].plot(x, y_pp, color=c_4, marker="*")
+    ax[2].set_ylim([-1, 25])
+    ax[2].set_xticks(ticks)
+    ax[2].set_xticklabels(ticks_label)
+    ax[2].set_yticks(y_ticks)
+    ax[2].set_yticklabels(y_ticks_label, rotation=90, ha="center", va="center")
+    ax[2].set_title("(c) Llama-2-13B", fontsize=16)
+    ax[2].tick_params(labelsize=16, pad=7)
+    ax[2].set_xlabel("Number of GPUs", fontsize=16)
+
+    fig.legend(
+        ncol=2,
+        bbox_to_anchor=(0.8, 1.17),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=16,
+    )
+
+    # plt.show()
+    plt.savefig("pp_cmp_com_cost.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        h1,
+        h2,
+        h3,
+        l1,
+        l2,
+        l3,
+        ticks,
+        ticks_label,
+        token_size,
+        x,
+        y_pp,
+        y_ticks,
+        y_ticks_label,
+        y_tp,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/pp_cmp_tp.pdf b/mlora/pp_cmp_tp.pdf
new file mode 100644
index 0000000..575a82f
Binary files /dev/null and b/mlora/pp_cmp_tp.pdf differ
diff --git a/mlora/pp_cmp_tp.py b/mlora/pp_cmp_tp.py
new file mode 100644
index 0000000..8c63712
--- /dev/null
+++ b/mlora/pp_cmp_tp.py
@@ -0,0 +1,159 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    import numpy as np
+    return np, plt
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell
+def __(np, plt):
+    x = np.arange(4)
+    lorapp = np.array([10748, 2363.89, 1280.54])
+    tp = np.array([5752.14, 1500, 875])
+    fsdp = np.array([6151.91, 1750, 0])
+    gpipe = np.array([4599.87, 1284.27, 723.21])
+
+    fig, ax = plt.subplots(figsize=(7, 2), ncols=3, layout="constrained")
+
+    space_width = 3 / 17
+    bar_width = 3 * space_width
+
+    c_1 = (139 / 255, 0 / 255, 0 / 255)
+    c_2 = (0, 0, 0)
+    c_3 = (191 / 255, 191 / 255, 191 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    ticks = [
+        space_width,
+        2 * space_width + bar_width,
+        3 * space_width + 2 * bar_width,
+        4 * space_width + 3 * bar_width,
+    ]
+    ticks_label = ["1F1B", "FSDP", "TP", "LoRAPP"]
+
+    ax[0].bar(space_width, gpipe[0], bar_width, label="1F1B", color=c_1)
+    ax[0].bar(2 * space_width + bar_width, tp[0], bar_width, label="TP", color=c_2)
+    ax[0].bar(
+        3 * space_width + 2 * bar_width,
+        fsdp[0],
+        bar_width,
+        label="FSDP",
+        color=c_3,
+    )
+    ax[0].bar(
+        4 * space_width + 3 * bar_width,
+        lorapp[0],
+        bar_width,
+        label="LoRAPP",
+        color=c_4,
+    )
+    ax[0].set_ylabel("Throughput (tokens/s)", fontsize=12)
+    ax[0].get_xaxis().set_visible(False)
+
+    ax[0].set_ylim([0, 12000])
+    ax[0].set_yticks([1000, 4000, 7000, 10000])
+    ax[0].set_yticklabels(
+        ["1k", "4k", "7k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[0].tick_params(bottom=False, labelsize=13, pad=7)
+    ax[0].set_title("(a) TinyLlama-1.1B", fontsize=16)
+
+    ax[1].bar(space_width, gpipe[1], bar_width, label="LoRAPP", color=c_1)
+    ax[1].bar(2 * space_width + bar_width, tp[1], bar_width, label="TP", color=c_2)
+    ax[1].bar(
+        3 * space_width + 2 * bar_width,
+        fsdp[1],
+        bar_width,
+        label="FSDP",
+        color=c_3,
+    )
+    ax[1].bar(
+        4 * space_width + 3 * bar_width,
+        lorapp[1],
+        bar_width,
+        label="1F1B",
+        color=c_4,
+    )
+    ax[1].get_xaxis().set_visible(False)
+    ax[1].set_ylim([0, 2500])
+    ax[1].set_yticks([1000, 2000])
+    ax[1].set_yticklabels(["1000", "2000"], rotation=90, ha="center", va="center")
+    ax[1].tick_params(bottom=False, labelsize=13, pad=7)
+    ax[1].set_title("(b) Llama-2-7B", fontsize=16)
+
+    ax[2].bar(space_width, gpipe[2], bar_width, label="1F1B", color=c_1)
+    ax[2].bar(2 * space_width + bar_width, tp[2], bar_width, label="TP", color=c_2)
+    ax[2].bar(
+        3 * space_width + 2 * bar_width,
+        fsdp[2],
+        bar_width,
+        label="FSDP",
+        color=c_3,
+    )
+    ax[2].bar(
+        4 * space_width + 3 * bar_width,
+        lorapp[2],
+        bar_width,
+        label="LoRAPP",
+        color=c_4,
+    )
+    # 隐藏x轴
+    ax[2].get_xaxis().set_visible(False)
+    ax[2].set_ylim([0, 1500])
+    ax[2].set_yticks([400, 800, 1200])
+    ax[2].set_yticklabels(
+        ["400", "800", "1200"], rotation=90, ha="center", va="center"
+    )
+    ax[2].tick_params(bottom=False, labelsize=13, pad=7)
+    ax[2].text(
+        ticks[2],
+        100,
+        "OOM",
+        ha="center",
+        va="center",
+        color="r",
+        style="italic",
+        fontsize=14,
+    )
+    ax[2].set_title("(c) Llama-2-13B", fontsize=16)
+
+    plt.tight_layout()
+    plt.legend(
+        ncol=4, loc="upper center", bbox_to_anchor=(-0.8, 1.50), frameon=False
+    )
+
+    plt.savefig("pp_cmp_tp.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        bar_width,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        fsdp,
+        gpipe,
+        lorapp,
+        space_width,
+        ticks,
+        ticks_label,
+        tp,
+        x,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/mlora/scalability.pdf b/mlora/scalability.pdf
new file mode 100644
index 0000000..833b0f8
Binary files /dev/null and b/mlora/scalability.pdf differ
diff --git a/mlora/scalability.py b/mlora/scalability.py
new file mode 100644
index 0000000..eeab177
--- /dev/null
+++ b/mlora/scalability.py
@@ -0,0 +1,165 @@
+import marimo
+
+__generated_with = "0.9.17"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def __():
+    import matplotlib.pyplot as plt
+    from sklearn.linear_model import LinearRegression
+    import numpy as np
+    return LinearRegression, np, plt
+
+
+@app.cell
+def __(plt):
+    plt.rcParams['font.family'] = 'Times New Roman'
+    plt.rcParams['font.size'] = 16
+    return
+
+
+@app.cell(hide_code=True)
+def __(LinearRegression, np, plt):
+    x = np.arange(4)
+
+    fig, ax = plt.subplots(figsize=(7, 4), ncols=3, nrows=2, layout="constrained", dpi=300)
+
+    c_1 = (230 / 255, 241 / 255, 243 / 255)
+    c_2 = (75 / 255, 116 / 155, 178 / 255)
+    c_3 = (255 / 255, 223 / 255, 146 / 255)
+    c_4 = (230 / 255, 109 / 255, 104 / 255)
+
+    # tp = 8 * l * b * h * (d-1)/d
+    # pp = 2 * b * h * (d - 1)
+    x = [2, 3, 4, 5, 6, 7, 8]
+    ticks = [2, 3, 4, 5, 6, 7, 8]
+    ticks_label = ["2", "3", "4", "5", "6", "7", "8"]
+
+    y = [5605.27, 8103.95, 10500.86, 12340.55, 15104, 16361.64, 19575.52]
+    model = LinearRegression()
+    model.fit(np.array(x).reshape(-1, 1), np.array(y).reshape(-1, 1))
+    py = [i * model.coef_[0] + model.intercept_ for i in x]
+    ax[0][0].plot(x, y, color=c_4, label="LoRAPP", marker="s")
+    ax[0][0].plot(x, py, color="black", label="Perfect Linear Scaling", linestyle=":")
+    ax[0][0].set_xticks(ticks)
+    ax[0][0].set_xticklabels(ticks_label)
+    ax[0][0].set_ylabel("Throughput (tokens/s)", fontsize=14)
+    ax[0][0].set_yticks([0, 10000, 20000])
+    ax[0][0].set_yticklabels(["0", "10k", "20k"], rotation=90, ha="center", va="center")
+    ax[0][0].tick_params(pad=7)
+    ax[0][0].set_title("(a) TinyLlama-1.1B", fontsize=14)
+    ax[0][0].tick_params(axis="y", which="major", pad=10)
+    ax[0][0].set_xlabel("Number of GPUs", fontsize=14)
+    y = [1214.44, 1813.77, 2363.19, 2813.11, 3300.74, 3923.94, 4460]
+    model = LinearRegression()
+    model.fit(np.array(x[1:]).reshape(-1, 1), np.array(y[1:]).reshape(-1, 1))
+    py = [i * model.coef_[0] + model.intercept_ for i in x]
+    ax[0][1].plot(x, y, color=c_4, marker="s")
+    ax[0][1].plot(x, py, color="black", linestyle=":")
+    ax[0][1].set_xticks(ticks)
+    ax[0][1].set_xticklabels(ticks_label)
+    ax[0][1].set_yticks([0, 2000, 4000])
+    ax[0][1].set_yticklabels(["0", "2k", "4k"], rotation=90, ha="center", va="center")
+    ax[0][1].tick_params(pad=7)
+    ax[0][1].set_title("(b) Llama-2-7B", fontsize=14)
+    ax[0][1].set_xlabel("Number of GPUs", fontsize=14)
+
+    y = [0, 977, 1280, 1600, 1918.28, 2260, 2513]
+    model = LinearRegression()
+    model.fit(np.array(x[1:]).reshape(-1, 1), np.array(y[1:]).reshape(-1, 1))
+    py = [i * model.coef_[0] + model.intercept_ for i in x]
+    ax[0][2].plot(x[1:], y[1:], color=c_4, marker="s")
+    ax[0][2].plot(x[1:], py[1:], color="black", linestyle=":")
+    ax[0][2].plot(x[0], y[0], color="r", marker="x")
+    ax[0][2].text(
+        2, 0, "OOM", fontsize=12, va="bottom", ha="left", color="r", style="italic"
+    )
+    ax[0][2].set_xticks(ticks)
+    ax[0][2].set_xticklabels(ticks_label)
+    ax[0][2].set_yticks([0, 1e3, 2e3, 3e3])
+    ax[0][2].set_yticklabels(["0", "1k", "2k", "3k"], rotation=90, ha="center", va="center")
+    ax[0][2].tick_params(pad=7)
+    ax[0][2].set_title("(c) Llama-2-13B", fontsize=14)
+    ax[0][2].set_xlabel("Number of GPUs", fontsize=14)
+
+    ax[1][0].set_title("(d) TinyLlama-1.1B", fontsize=14)
+    ax[1][1].set_title("(e) Llama-2-7B", fontsize=14)
+    ax[1][2].set_title("(f) Llama-2-13B", fontsize=14)
+
+    ax[1][0].set_xlabel("Rank of LoRA adapters", fontsize=14)
+    ax[1][1].set_xlabel("Rank of LoRA adapters", fontsize=14)
+    ax[1][2].set_xlabel("Rank of LoRA adapters", fontsize=14)
+
+    ax[1][0].set_ylabel("Throughput (tokens/s)", fontsize=14)
+
+    y_0 = [10525.52, 10467.51, 10315.85, 10286.17]
+    x_0 = [4, 8, 16, 32]
+
+    y_1 = [2309.40, 2258.73, 2252.43, 2242.80]
+    x_1 = [16, 32, 64, 128]
+
+    y_2 = [1245.79, 1244.60, 1224.91, 1207.40]
+    x_2 = [16, 32, 64, 128]
+
+    ax[1][0].plot(x_0, y_0, "s-", color=c_4)
+    ax[1][0].set_ylim(0, 11000)
+    ax[1][0].set_xticks(x_0)
+    ax[1][0].set_yticks([0, 5000, 10000])
+    ax[1][0].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[1][0].tick_params(pad=7)
+
+    ax[1][1].plot(x_1, y_1, "s-", color=c_4)
+    ax[1][1].set_ylim(0, 11000)
+    ax[1][1].set_xticks([32, 64, 128])
+    ax[1][1].set_yticks([0, 5000, 10000])
+    ax[1][1].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[1][1].tick_params(pad=7)
+
+    ax[1][2].plot(x_2, y_2, "s-", color=c_4)
+    ax[1][2].set_ylim(0, 11000)
+    ax[1][2].set_xticks([32, 64, 128])
+    ax[1][2].set_yticks([0, 5000, 10000])
+    ax[1][2].set_yticklabels(
+        ["0", "5k", "10k"], rotation=90, ha="center", va="center"
+    )
+    ax[1][2].tick_params(pad=7)
+
+    fig.legend(
+        ncol=2,
+        bbox_to_anchor=(0.8, 1.1),
+        fancybox=False,
+        framealpha=0.0,
+        fontsize=14,
+    )
+
+    # plt.show()
+    plt.savefig("scalability.pdf", bbox_inches="tight", dpi=1000)
+    return (
+        ax,
+        c_1,
+        c_2,
+        c_3,
+        c_4,
+        fig,
+        model,
+        py,
+        ticks,
+        ticks_label,
+        x,
+        x_0,
+        x_1,
+        x_2,
+        y,
+        y_0,
+        y_1,
+        y_2,
+    )
+
+
+if __name__ == "__main__":
+    app.run()
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..3354ed5
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,11 @@
+[project]
+name = "note"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "ipykernel>=6.29.5",
+    "matplotlib>=3.10.1",
+    "scikit-learn>=1.6.1",
+]
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..7d762f1
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,796 @@
+version = 1
+revision = 1
+requires-python = ">=3.12"
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918 },
+]
+
+[[package]]
+name = "cffi"
+version = "1.17.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5a/84/e94227139ee5fb4d600a7a4927f322e1d4aea6fdc50bd3fca8493caba23f/cffi-1.17.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4", size = 183178 },
+    { url = "https://files.pythonhosted.org/packages/da/ee/fb72c2b48656111c4ef27f0f91da355e130a923473bf5ee75c5643d00cca/cffi-1.17.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c", size = 178840 },
+    { url = "https://files.pythonhosted.org/packages/cc/b6/db007700f67d151abadf508cbfd6a1884f57eab90b1bb985c4c8c02b0f28/cffi-1.17.1-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36", size = 454803 },
+    { url = "https://files.pythonhosted.org/packages/1a/df/f8d151540d8c200eb1c6fba8cd0dfd40904f1b0682ea705c36e6c2e97ab3/cffi-1.17.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5", size = 478850 },
+    { url = "https://files.pythonhosted.org/packages/28/c0/b31116332a547fd2677ae5b78a2ef662dfc8023d67f41b2a83f7c2aa78b1/cffi-1.17.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff", size = 485729 },
+    { url = "https://files.pythonhosted.org/packages/91/2b/9a1ddfa5c7f13cab007a2c9cc295b70fbbda7cb10a286aa6810338e60ea1/cffi-1.17.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99", size = 471256 },
+    { url = "https://files.pythonhosted.org/packages/b2/d5/da47df7004cb17e4955df6a43d14b3b4ae77737dff8bf7f8f333196717bf/cffi-1.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93", size = 479424 },
+    { url = "https://files.pythonhosted.org/packages/0b/ac/2a28bcf513e93a219c8a4e8e125534f4f6db03e3179ba1c45e949b76212c/cffi-1.17.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3", size = 484568 },
+    { url = "https://files.pythonhosted.org/packages/d4/38/ca8a4f639065f14ae0f1d9751e70447a261f1a30fa7547a828ae08142465/cffi-1.17.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8", size = 488736 },
+    { url = "https://files.pythonhosted.org/packages/86/c5/28b2d6f799ec0bdecf44dced2ec5ed43e0eb63097b0f58c293583b406582/cffi-1.17.1-cp312-cp312-win32.whl", hash = "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65", size = 172448 },
+    { url = "https://files.pythonhosted.org/packages/50/b9/db34c4755a7bd1cb2d1603ac3863f22bcecbd1ba29e5ee841a4bc510b294/cffi-1.17.1-cp312-cp312-win_amd64.whl", hash = "sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903", size = 181976 },
+    { url = "https://files.pythonhosted.org/packages/8d/f8/dd6c246b148639254dad4d6803eb6a54e8c85c6e11ec9df2cffa87571dbe/cffi-1.17.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e", size = 182989 },
+    { url = "https://files.pythonhosted.org/packages/8b/f1/672d303ddf17c24fc83afd712316fda78dc6fce1cd53011b839483e1ecc8/cffi-1.17.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2", size = 178802 },
+    { url = "https://files.pythonhosted.org/packages/0e/2d/eab2e858a91fdff70533cab61dcff4a1f55ec60425832ddfdc9cd36bc8af/cffi-1.17.1-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3", size = 454792 },
+    { url = "https://files.pythonhosted.org/packages/75/b2/fbaec7c4455c604e29388d55599b99ebcc250a60050610fadde58932b7ee/cffi-1.17.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683", size = 478893 },
+    { url = "https://files.pythonhosted.org/packages/4f/b7/6e4a2162178bf1935c336d4da8a9352cccab4d3a5d7914065490f08c0690/cffi-1.17.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5", size = 485810 },
+    { url = "https://files.pythonhosted.org/packages/c7/8a/1d0e4a9c26e54746dc08c2c6c037889124d4f59dffd853a659fa545f1b40/cffi-1.17.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4", size = 471200 },
+    { url = "https://files.pythonhosted.org/packages/26/9f/1aab65a6c0db35f43c4d1b4f580e8df53914310afc10ae0397d29d697af4/cffi-1.17.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd", size = 479447 },
+    { url = "https://files.pythonhosted.org/packages/5f/e4/fb8b3dd8dc0e98edf1135ff067ae070bb32ef9d509d6cb0f538cd6f7483f/cffi-1.17.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed", size = 484358 },
+    { url = "https://files.pythonhosted.org/packages/f1/47/d7145bf2dc04684935d57d67dff9d6d795b2ba2796806bb109864be3a151/cffi-1.17.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9", size = 488469 },
+    { url = "https://files.pythonhosted.org/packages/bf/ee/f94057fa6426481d663b88637a9a10e859e492c73d0384514a17d78ee205/cffi-1.17.1-cp313-cp313-win32.whl", hash = "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d", size = 172475 },
+    { url = "https://files.pythonhosted.org/packages/7c/fc/6a8cb64e5f0324877d503c854da15d76c1e50eb722e320b15345c4d0c6de/cffi-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a", size = 182009 },
+]
+
+[[package]]
+name = "colorama"
+version = "0.4.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
+]
+
+[[package]]
+name = "comm"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/a8/fb783cb0abe2b5fded9f55e5703015cdf1c9c85b3669087c538dd15a6a86/comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e", size = 6210 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3", size = 7180 },
+]
+
+[[package]]
+name = "contourpy"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/25/c2/fc7193cc5383637ff390a712e88e4ded0452c9fbcf84abe3de5ea3df1866/contourpy-1.3.1.tar.gz", hash = "sha256:dfd97abd83335045a913e3bcc4a09c0ceadbe66580cf573fe961f4a825efa699", size = 13465753 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/37/6b/175f60227d3e7f5f1549fcb374592be311293132207e451c3d7c654c25fb/contourpy-1.3.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0ffa84be8e0bd33410b17189f7164c3589c229ce5db85798076a3fa136d0e509", size = 271494 },
+    { url = "https://files.pythonhosted.org/packages/6b/6a/7833cfae2c1e63d1d8875a50fd23371394f540ce809d7383550681a1fa64/contourpy-1.3.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:805617228ba7e2cbbfb6c503858e626ab528ac2a32a04a2fe88ffaf6b02c32bc", size = 255444 },
+    { url = "https://files.pythonhosted.org/packages/7f/b3/7859efce66eaca5c14ba7619791b084ed02d868d76b928ff56890d2d059d/contourpy-1.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ade08d343436a94e633db932e7e8407fe7de8083967962b46bdfc1b0ced39454", size = 307628 },
+    { url = "https://files.pythonhosted.org/packages/48/b2/011415f5e3f0a50b1e285a0bf78eb5d92a4df000553570f0851b6e309076/contourpy-1.3.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:47734d7073fb4590b4a40122b35917cd77be5722d80683b249dac1de266aac80", size = 347271 },
+    { url = "https://files.pythonhosted.org/packages/84/7d/ef19b1db0f45b151ac78c65127235239a8cf21a59d1ce8507ce03e89a30b/contourpy-1.3.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2ba94a401342fc0f8b948e57d977557fbf4d515f03c67682dd5c6191cb2d16ec", size = 318906 },
+    { url = "https://files.pythonhosted.org/packages/ba/99/6794142b90b853a9155316c8f470d2e4821fe6f086b03e372aca848227dd/contourpy-1.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:efa874e87e4a647fd2e4f514d5e91c7d493697127beb95e77d2f7561f6905bd9", size = 323622 },
+    { url = "https://files.pythonhosted.org/packages/3c/0f/37d2c84a900cd8eb54e105f4fa9aebd275e14e266736778bb5dccbf3bbbb/contourpy-1.3.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1bf98051f1045b15c87868dbaea84f92408337d4f81d0e449ee41920ea121d3b", size = 1266699 },
+    { url = "https://files.pythonhosted.org/packages/3a/8a/deb5e11dc7d9cc8f0f9c8b29d4f062203f3af230ba83c30a6b161a6effc9/contourpy-1.3.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:61332c87493b00091423e747ea78200659dc09bdf7fd69edd5e98cef5d3e9a8d", size = 1326395 },
+    { url = "https://files.pythonhosted.org/packages/1a/35/7e267ae7c13aaf12322ccc493531f1e7f2eb8fba2927b9d7a05ff615df7a/contourpy-1.3.1-cp312-cp312-win32.whl", hash = "sha256:e914a8cb05ce5c809dd0fe350cfbb4e881bde5e2a38dc04e3afe1b3e58bd158e", size = 175354 },
+    { url = "https://files.pythonhosted.org/packages/a1/35/c2de8823211d07e8a79ab018ef03960716c5dff6f4d5bff5af87fd682992/contourpy-1.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:08d9d449a61cf53033612cb368f3a1b26cd7835d9b8cd326647efe43bca7568d", size = 220971 },
+    { url = "https://files.pythonhosted.org/packages/9a/e7/de62050dce687c5e96f946a93546910bc67e483fe05324439e329ff36105/contourpy-1.3.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a761d9ccfc5e2ecd1bf05534eda382aa14c3e4f9205ba5b1684ecfe400716ef2", size = 271548 },
+    { url = "https://files.pythonhosted.org/packages/78/4d/c2a09ae014ae984c6bdd29c11e74d3121b25eaa117eca0bb76340efd7e1c/contourpy-1.3.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:523a8ee12edfa36f6d2a49407f705a6ef4c5098de4f498619787e272de93f2d5", size = 255576 },
+    { url = "https://files.pythonhosted.org/packages/ab/8a/915380ee96a5638bda80cd061ccb8e666bfdccea38d5741cb69e6dbd61fc/contourpy-1.3.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ece6df05e2c41bd46776fbc712e0996f7c94e0d0543af1656956d150c4ca7c81", size = 306635 },
+    { url = "https://files.pythonhosted.org/packages/29/5c/c83ce09375428298acd4e6582aeb68b1e0d1447f877fa993d9bf6cd3b0a0/contourpy-1.3.1-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:573abb30e0e05bf31ed067d2f82500ecfdaec15627a59d63ea2d95714790f5c2", size = 345925 },
+    { url = "https://files.pythonhosted.org/packages/29/63/5b52f4a15e80c66c8078a641a3bfacd6e07106835682454647aca1afc852/contourpy-1.3.1-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a9fa36448e6a3a1a9a2ba23c02012c43ed88905ec80163f2ffe2421c7192a5d7", size = 318000 },
+    { url = "https://files.pythonhosted.org/packages/9a/e2/30ca086c692691129849198659bf0556d72a757fe2769eb9620a27169296/contourpy-1.3.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ea9924d28fc5586bf0b42d15f590b10c224117e74409dd7a0be3b62b74a501c", size = 322689 },
+    { url = "https://files.pythonhosted.org/packages/6b/77/f37812ef700f1f185d348394debf33f22d531e714cf6a35d13d68a7003c7/contourpy-1.3.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5b75aa69cb4d6f137b36f7eb2ace9280cfb60c55dc5f61c731fdf6f037f958a3", size = 1268413 },
+    { url = "https://files.pythonhosted.org/packages/3f/6d/ce84e79cdd128542ebeb268f84abb4b093af78e7f8ec504676673d2675bc/contourpy-1.3.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:041b640d4ec01922083645a94bb3b2e777e6b626788f4095cf21abbe266413c1", size = 1326530 },
+    { url = "https://files.pythonhosted.org/packages/72/22/8282f4eae20c73c89bee7a82a19c4e27af9b57bb602ecaa00713d5bdb54d/contourpy-1.3.1-cp313-cp313-win32.whl", hash = "sha256:36987a15e8ace5f58d4d5da9dca82d498c2bbb28dff6e5d04fbfcc35a9cb3a82", size = 175315 },
+    { url = "https://files.pythonhosted.org/packages/e3/d5/28bca491f65312b438fbf076589dcde7f6f966b196d900777f5811b9c4e2/contourpy-1.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:a7895f46d47671fa7ceec40f31fae721da51ad34bdca0bee83e38870b1f47ffd", size = 220987 },
+    { url = "https://files.pythonhosted.org/packages/2f/24/a4b285d6adaaf9746e4700932f579f1a7b6f9681109f694cfa233ae75c4e/contourpy-1.3.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:9ddeb796389dadcd884c7eb07bd14ef12408aaae358f0e2ae24114d797eede30", size = 285001 },
+    { url = "https://files.pythonhosted.org/packages/48/1d/fb49a401b5ca4f06ccf467cd6c4f1fd65767e63c21322b29b04ec40b40b9/contourpy-1.3.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:19c1555a6801c2f084c7ddc1c6e11f02eb6a6016ca1318dd5452ba3f613a1751", size = 268553 },
+    { url = "https://files.pythonhosted.org/packages/79/1e/4aef9470d13fd029087388fae750dccb49a50c012a6c8d1d634295caa644/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:841ad858cff65c2c04bf93875e384ccb82b654574a6d7f30453a04f04af71342", size = 310386 },
+    { url = "https://files.pythonhosted.org/packages/b0/34/910dc706ed70153b60392b5305c708c9810d425bde12499c9184a1100888/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4318af1c925fb9a4fb190559ef3eec206845f63e80fb603d47f2d6d67683901c", size = 349806 },
+    { url = "https://files.pythonhosted.org/packages/31/3c/faee6a40d66d7f2a87f7102236bf4780c57990dd7f98e5ff29881b1b1344/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:14c102b0eab282427b662cb590f2e9340a9d91a1c297f48729431f2dcd16e14f", size = 321108 },
+    { url = "https://files.pythonhosted.org/packages/17/69/390dc9b20dd4bb20585651d7316cc3054b7d4a7b4f8b710b2b698e08968d/contourpy-1.3.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:05e806338bfeaa006acbdeba0ad681a10be63b26e1b17317bfac3c5d98f36cda", size = 327291 },
+    { url = "https://files.pythonhosted.org/packages/ef/74/7030b67c4e941fe1e5424a3d988080e83568030ce0355f7c9fc556455b01/contourpy-1.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4d76d5993a34ef3df5181ba3c92fabb93f1eaa5729504fb03423fcd9f3177242", size = 1263752 },
+    { url = "https://files.pythonhosted.org/packages/f0/ed/92d86f183a8615f13f6b9cbfc5d4298a509d6ce433432e21da838b4b63f4/contourpy-1.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:89785bb2a1980c1bd87f0cb1517a71cde374776a5f150936b82580ae6ead44a1", size = 1318403 },
+    { url = "https://files.pythonhosted.org/packages/b3/0e/c8e4950c77dcfc897c71d61e56690a0a9df39543d2164040301b5df8e67b/contourpy-1.3.1-cp313-cp313t-win32.whl", hash = "sha256:8eb96e79b9f3dcadbad2a3891672f81cdcab7f95b27f28f1c67d75f045b6b4f1", size = 185117 },
+    { url = "https://files.pythonhosted.org/packages/c1/31/1ae946f11dfbd229222e6d6ad8e7bd1891d3d48bde5fbf7a0beb9491f8e3/contourpy-1.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546", size = 236668 },
+]
+
+[[package]]
+name = "cycler"
+version = "0.12.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
+]
+
+[[package]]
+name = "debugpy"
+version = "1.8.12"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/68/25/c74e337134edf55c4dfc9af579eccb45af2393c40960e2795a94351e8140/debugpy-1.8.12.tar.gz", hash = "sha256:646530b04f45c830ceae8e491ca1c9320a2d2f0efea3141487c82130aba70dce", size = 1641122 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/e6/0f876ecfe5831ebe4762b19214364753c8bc2b357d28c5d739a1e88325c7/debugpy-1.8.12-cp312-cp312-macosx_14_0_universal2.whl", hash = "sha256:7e94b643b19e8feb5215fa508aee531387494bf668b2eca27fa769ea11d9f498", size = 2500846 },
+    { url = "https://files.pythonhosted.org/packages/19/64/33f41653a701f3cd2cbff8b41ebaad59885b3428b5afd0d93d16012ecf17/debugpy-1.8.12-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:086b32e233e89a2740c1615c2f775c34ae951508b28b308681dbbb87bba97d06", size = 4222181 },
+    { url = "https://files.pythonhosted.org/packages/32/a6/02646cfe50bfacc9b71321c47dc19a46e35f4e0aceea227b6d205e900e34/debugpy-1.8.12-cp312-cp312-win32.whl", hash = "sha256:2ae5df899732a6051b49ea2632a9ea67f929604fd2b036613a9f12bc3163b92d", size = 5227017 },
+    { url = "https://files.pythonhosted.org/packages/da/a6/10056431b5c47103474312cf4a2ec1001f73e0b63b1216706d5fef2531eb/debugpy-1.8.12-cp312-cp312-win_amd64.whl", hash = "sha256:39dfbb6fa09f12fae32639e3286112fc35ae976114f1f3d37375f3130a820969", size = 5267555 },
+    { url = "https://files.pythonhosted.org/packages/cf/4d/7c3896619a8791effd5d8c31f0834471fc8f8fb3047ec4f5fc69dd1393dd/debugpy-1.8.12-cp313-cp313-macosx_14_0_universal2.whl", hash = "sha256:696d8ae4dff4cbd06bf6b10d671e088b66669f110c7c4e18a44c43cf75ce966f", size = 2485246 },
+    { url = "https://files.pythonhosted.org/packages/99/46/bc6dcfd7eb8cc969a5716d858e32485eb40c72c6a8dc88d1e3a4d5e95813/debugpy-1.8.12-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:898fba72b81a654e74412a67c7e0a81e89723cfe2a3ea6fcd3feaa3395138ca9", size = 4218616 },
+    { url = "https://files.pythonhosted.org/packages/03/dd/d7fcdf0381a9b8094da1f6a1c9f19fed493a4f8576a2682349b3a8b20ec7/debugpy-1.8.12-cp313-cp313-win32.whl", hash = "sha256:22a11c493c70413a01ed03f01c3c3a2fc4478fc6ee186e340487b2edcd6f4180", size = 5226540 },
+    { url = "https://files.pythonhosted.org/packages/25/bd/ecb98f5b5fc7ea0bfbb3c355bc1dd57c198a28780beadd1e19915bf7b4d9/debugpy-1.8.12-cp313-cp313-win_amd64.whl", hash = "sha256:fdb3c6d342825ea10b90e43d7f20f01535a72b3a1997850c0c3cefa5c27a4a2c", size = 5267134 },
+    { url = "https://files.pythonhosted.org/packages/38/c4/5120ad36405c3008f451f94b8f92ef1805b1e516f6ff870f331ccb3c4cc0/debugpy-1.8.12-py2.py3-none-any.whl", hash = "sha256:274b6a2040349b5c9864e475284bce5bb062e63dce368a394b8cc865ae3b00c6", size = 5229490 },
+]
+
+[[package]]
+name = "decorator"
+version = "5.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190 },
+]
+
+[[package]]
+name = "executing"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/50/a9d80c47ff289c611ff12e63f7c5d13942c65d68125160cefd768c73e6e4/executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755", size = 978693 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7b/8f/c4d9bafc34ad7ad5d8dc16dd1347ee0e507a52c3adb6bfa8887e1c6a26ba/executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa", size = 26702 },
+]
+
+[[package]]
+name = "fonttools"
+version = "4.56.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/8c/9ffa2a555af0e5e5d0e2ed7fdd8c9bef474ed676995bb4c57c9cd0014248/fonttools-4.56.0.tar.gz", hash = "sha256:a114d1567e1a1586b7e9e7fc2ff686ca542a82769a296cef131e4c4af51e58f4", size = 3462892 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/39/32/71cfd6877999576a11824a7fe7bc0bb57c5c72b1f4536fa56a3e39552643/fonttools-4.56.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d6f195c14c01bd057bc9b4f70756b510e009c83c5ea67b25ced3e2c38e6ee6e9", size = 2747757 },
+    { url = "https://files.pythonhosted.org/packages/15/52/d9f716b072c5061a0b915dd4c387f74bef44c68c069e2195c753905bd9b7/fonttools-4.56.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fa760e5fe8b50cbc2d71884a1eff2ed2b95a005f02dda2fa431560db0ddd927f", size = 2279007 },
+    { url = "https://files.pythonhosted.org/packages/d1/97/f1b3a8afa9a0d814a092a25cd42f59ccb98a0bb7a295e6e02fc9ba744214/fonttools-4.56.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d54a45d30251f1d729e69e5b675f9a08b7da413391a1227781e2a297fa37f6d2", size = 4783991 },
+    { url = "https://files.pythonhosted.org/packages/95/70/2a781bedc1c45a0c61d29c56425609b22ed7f971da5d7e5df2679488741b/fonttools-4.56.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:661a8995d11e6e4914a44ca7d52d1286e2d9b154f685a4d1f69add8418961563", size = 4855109 },
+    { url = "https://files.pythonhosted.org/packages/0c/02/a2597858e61a5e3fb6a14d5f6be9e6eb4eaf090da56ad70cedcbdd201685/fonttools-4.56.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:9d94449ad0a5f2a8bf5d2f8d71d65088aee48adbe45f3c5f8e00e3ad861ed81a", size = 4762496 },
+    { url = "https://files.pythonhosted.org/packages/f2/00/aaf00100d6078fdc73f7352b44589804af9dc12b182a2540b16002152ba4/fonttools-4.56.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:f59746f7953f69cc3290ce2f971ab01056e55ddd0fb8b792c31a8acd7fee2d28", size = 4990094 },
+    { url = "https://files.pythonhosted.org/packages/bf/dc/3ff1db522460db60cf3adaf1b64e0c72b43406717d139786d3fa1eb20709/fonttools-4.56.0-cp312-cp312-win32.whl", hash = "sha256:bce60f9a977c9d3d51de475af3f3581d9b36952e1f8fc19a1f2254f1dda7ce9c", size = 2142888 },
+    { url = "https://files.pythonhosted.org/packages/6f/e3/5a181a85777f7809076e51f7422e0dc77eb04676c40ec8bf6a49d390d1ff/fonttools-4.56.0-cp312-cp312-win_amd64.whl", hash = "sha256:300c310bb725b2bdb4f5fc7e148e190bd69f01925c7ab437b9c0ca3e1c7cd9ba", size = 2189734 },
+    { url = "https://files.pythonhosted.org/packages/a5/55/f06b48d48e0b4ec3a3489efafe9bd4d81b6e0802ac51026e3ee4634e89ba/fonttools-4.56.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:f20e2c0dfab82983a90f3d00703ac0960412036153e5023eed2b4641d7d5e692", size = 2735127 },
+    { url = "https://files.pythonhosted.org/packages/59/db/d2c7c9b6dd5cbd46f183e650a47403ffb88fca17484eb7c4b1cd88f9e513/fonttools-4.56.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:f36a0868f47b7566237640c026c65a86d09a3d9ca5df1cd039e30a1da73098a0", size = 2272519 },
+    { url = "https://files.pythonhosted.org/packages/4d/a2/da62d779c34a0e0c06415f02eab7fa3466de5d46df459c0275a255cefc65/fonttools-4.56.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:62b4c6802fa28e14dba010e75190e0e6228513573f1eeae57b11aa1a39b7e5b1", size = 4762423 },
+    { url = "https://files.pythonhosted.org/packages/be/6a/fd4018e0448c8a5e12138906411282c5eab51a598493f080a9f0960e658f/fonttools-4.56.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a05d1f07eb0a7d755fbe01fee1fd255c3a4d3730130cf1bfefb682d18fd2fcea", size = 4834442 },
+    { url = "https://files.pythonhosted.org/packages/6d/63/fa1dec8efb35bc11ef9c39b2d74754b45d48a3ccb2cf78c0109c0af639e8/fonttools-4.56.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:0073b62c3438cf0058488c002ea90489e8801d3a7af5ce5f7c05c105bee815c3", size = 4742800 },
+    { url = "https://files.pythonhosted.org/packages/dd/f4/963247ae8c73ccc4cf2929e7162f595c81dbe17997d1d0ea77da24a217c9/fonttools-4.56.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e2cad98c94833465bcf28f51c248aaf07ca022efc6a3eba750ad9c1e0256d278", size = 4963746 },
+    { url = "https://files.pythonhosted.org/packages/ea/e0/46f9600c39c644b54e4420f941f75fa200d9288c9ae171e5d80918b8cbb9/fonttools-4.56.0-cp313-cp313-win32.whl", hash = "sha256:d0cb73ccf7f6d7ca8d0bc7ea8ac0a5b84969a41c56ac3ac3422a24df2680546f", size = 2140927 },
+    { url = "https://files.pythonhosted.org/packages/27/6d/3edda54f98a550a0473f032d8050315fbc8f1b76a0d9f3879b72ebb2cdd6/fonttools-4.56.0-cp313-cp313-win_amd64.whl", hash = "sha256:62cc1253827d1e500fde9dbe981219fea4eb000fd63402283472d38e7d8aa1c6", size = 2186709 },
+    { url = "https://files.pythonhosted.org/packages/bf/ff/44934a031ce5a39125415eb405b9efb76fe7f9586b75291d66ae5cbfc4e6/fonttools-4.56.0-py3-none-any.whl", hash = "sha256:1088182f68c303b50ca4dc0c82d42083d176cba37af1937e1a976a31149d4d14", size = 1089800 },
+]
+
+[[package]]
+name = "ipykernel"
+version = "6.29.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "appnope", marker = "sys_platform == 'darwin'" },
+    { name = "comm" },
+    { name = "debugpy" },
+    { name = "ipython" },
+    { name = "jupyter-client" },
+    { name = "jupyter-core" },
+    { name = "matplotlib-inline" },
+    { name = "nest-asyncio" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/5c/67594cb0c7055dc50814b21731c22a601101ea3b1b50a9a1b090e11f5d0f/ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215", size = 163367 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/5c/368ae6c01c7628438358e6d337c19b05425727fbb221d2a3c4303c372f42/ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5", size = 117173 },
+]
+
+[[package]]
+name = "ipython"
+version = "8.32.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "decorator" },
+    { name = "jedi" },
+    { name = "matplotlib-inline" },
+    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "prompt-toolkit" },
+    { name = "pygments" },
+    { name = "stack-data" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/36/80/4d2a072e0db7d250f134bc11676517299264ebe16d62a8619d49a78ced73/ipython-8.32.0.tar.gz", hash = "sha256:be2c91895b0b9ea7ba49d33b23e2040c352b33eb6a519cca7ce6e0c743444251", size = 5507441 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/e1/f4474a7ecdb7745a820f6f6039dc43c66add40f1bcc66485607d93571af6/ipython-8.32.0-py3-none-any.whl", hash = "sha256:cae85b0c61eff1fc48b0a8002de5958b6528fa9c8defb1894da63f42613708aa", size = 825524 },
+]
+
+[[package]]
+name = "jedi"
+version = "0.19.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "parso" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/72/3a/79a912fbd4d8dd6fbb02bf69afd3bb72cf0c729bb3063c6f4498603db17a/jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0", size = 1231287 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278 },
+]
+
+[[package]]
+name = "joblib"
+version = "1.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/33/60135848598c076ce4b231e1b1895170f45fbcaeaa2c9d5e38b04db70c35/joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e", size = 2116621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6", size = 301817 },
+]
+
+[[package]]
+name = "jupyter-client"
+version = "8.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jupyter-core" },
+    { name = "python-dateutil" },
+    { name = "pyzmq" },
+    { name = "tornado" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/71/22/bf9f12fdaeae18019a468b68952a60fe6dbab5d67cd2a103cac7659b41ca/jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419", size = 342019 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f", size = 106105 },
+]
+
+[[package]]
+name = "jupyter-core"
+version = "5.7.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "platformdirs" },
+    { name = "pywin32", marker = "platform_python_implementation != 'PyPy' and sys_platform == 'win32'" },
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/00/11/b56381fa6c3f4cc5d2cf54a7dbf98ad9aa0b339ef7a601d6053538b079a7/jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9", size = 87629 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409", size = 28965 },
+]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/82/59/7c91426a8ac292e1cdd53a63b6d9439abd573c875c3f92c146767dd33faf/kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e", size = 97538 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fc/aa/cea685c4ab647f349c3bc92d2daf7ae34c8e8cf405a6dcd3a497f58a2ac3/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:d6af5e8815fd02997cb6ad9bbed0ee1e60014438ee1a5c2444c96f87b8843502", size = 124152 },
+    { url = "https://files.pythonhosted.org/packages/c5/0b/8db6d2e2452d60d5ebc4ce4b204feeb16176a851fd42462f66ade6808084/kiwisolver-1.4.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:bade438f86e21d91e0cf5dd7c0ed00cda0f77c8c1616bd83f9fc157fa6760d31", size = 66555 },
+    { url = "https://files.pythonhosted.org/packages/60/26/d6a0db6785dd35d3ba5bf2b2df0aedc5af089962c6eb2cbf67a15b81369e/kiwisolver-1.4.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b83dc6769ddbc57613280118fb4ce3cd08899cc3369f7d0e0fab518a7cf37fdb", size = 65067 },
+    { url = "https://files.pythonhosted.org/packages/c9/ed/1d97f7e3561e09757a196231edccc1bcf59d55ddccefa2afc9c615abd8e0/kiwisolver-1.4.8-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:111793b232842991be367ed828076b03d96202c19221b5ebab421ce8bcad016f", size = 1378443 },
+    { url = "https://files.pythonhosted.org/packages/29/61/39d30b99954e6b46f760e6289c12fede2ab96a254c443639052d1b573fbc/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:257af1622860e51b1a9d0ce387bf5c2c4f36a90594cb9514f55b074bcc787cfc", size = 1472728 },
+    { url = "https://files.pythonhosted.org/packages/0c/3e/804163b932f7603ef256e4a715e5843a9600802bb23a68b4e08c8c0ff61d/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:69b5637c3f316cab1ec1c9a12b8c5f4750a4c4b71af9157645bf32830e39c03a", size = 1478388 },
+    { url = "https://files.pythonhosted.org/packages/8a/9e/60eaa75169a154700be74f875a4d9961b11ba048bef315fbe89cb6999056/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:782bb86f245ec18009890e7cb8d13a5ef54dcf2ebe18ed65f795e635a96a1c6a", size = 1413849 },
+    { url = "https://files.pythonhosted.org/packages/bc/b3/9458adb9472e61a998c8c4d95cfdfec91c73c53a375b30b1428310f923e4/kiwisolver-1.4.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc978a80a0db3a66d25767b03688f1147a69e6237175c0f4ffffaaedf744055a", size = 1475533 },
+    { url = "https://files.pythonhosted.org/packages/e4/7a/0a42d9571e35798de80aef4bb43a9b672aa7f8e58643d7bd1950398ffb0a/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:36dbbfd34838500a31f52c9786990d00150860e46cd5041386f217101350f0d3", size = 2268898 },
+    { url = "https://files.pythonhosted.org/packages/d9/07/1255dc8d80271400126ed8db35a1795b1a2c098ac3a72645075d06fe5c5d/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:eaa973f1e05131de5ff3569bbba7f5fd07ea0595d3870ed4a526d486fe57fa1b", size = 2425605 },
+    { url = "https://files.pythonhosted.org/packages/84/df/5a3b4cf13780ef6f6942df67b138b03b7e79e9f1f08f57c49957d5867f6e/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:a66f60f8d0c87ab7f59b6fb80e642ebb29fec354a4dfad687ca4092ae69d04f4", size = 2375801 },
+    { url = "https://files.pythonhosted.org/packages/8f/10/2348d068e8b0f635c8c86892788dac7a6b5c0cb12356620ab575775aad89/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:858416b7fb777a53f0c59ca08190ce24e9abbd3cffa18886a5781b8e3e26f65d", size = 2520077 },
+    { url = "https://files.pythonhosted.org/packages/32/d8/014b89fee5d4dce157d814303b0fce4d31385a2af4c41fed194b173b81ac/kiwisolver-1.4.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:085940635c62697391baafaaeabdf3dd7a6c3643577dde337f4d66eba021b2b8", size = 2338410 },
+    { url = "https://files.pythonhosted.org/packages/bd/72/dfff0cc97f2a0776e1c9eb5bef1ddfd45f46246c6533b0191887a427bca5/kiwisolver-1.4.8-cp312-cp312-win_amd64.whl", hash = "sha256:01c3d31902c7db5fb6182832713d3b4122ad9317c2c5877d0539227d96bb2e50", size = 71853 },
+    { url = "https://files.pythonhosted.org/packages/dc/85/220d13d914485c0948a00f0b9eb419efaf6da81b7d72e88ce2391f7aed8d/kiwisolver-1.4.8-cp312-cp312-win_arm64.whl", hash = "sha256:a3c44cb68861de93f0c4a8175fbaa691f0aa22550c331fefef02b618a9dcb476", size = 65424 },
+    { url = "https://files.pythonhosted.org/packages/79/b3/e62464a652f4f8cd9006e13d07abad844a47df1e6537f73ddfbf1bc997ec/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:1c8ceb754339793c24aee1c9fb2485b5b1f5bb1c2c214ff13368431e51fc9a09", size = 124156 },
+    { url = "https://files.pythonhosted.org/packages/8d/2d/f13d06998b546a2ad4f48607a146e045bbe48030774de29f90bdc573df15/kiwisolver-1.4.8-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:54a62808ac74b5e55a04a408cda6156f986cefbcf0ada13572696b507cc92fa1", size = 66555 },
+    { url = "https://files.pythonhosted.org/packages/59/e3/b8bd14b0a54998a9fd1e8da591c60998dc003618cb19a3f94cb233ec1511/kiwisolver-1.4.8-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:68269e60ee4929893aad82666821aaacbd455284124817af45c11e50a4b42e3c", size = 65071 },
+    { url = "https://files.pythonhosted.org/packages/f0/1c/6c86f6d85ffe4d0ce04228d976f00674f1df5dc893bf2dd4f1928748f187/kiwisolver-1.4.8-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:34d142fba9c464bc3bbfeff15c96eab0e7310343d6aefb62a79d51421fcc5f1b", size = 1378053 },
+    { url = "https://files.pythonhosted.org/packages/4e/b9/1c6e9f6dcb103ac5cf87cb695845f5fa71379021500153566d8a8a9fc291/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ddc373e0eef45b59197de815b1b28ef89ae3955e7722cc9710fb91cd77b7f47", size = 1472278 },
+    { url = "https://files.pythonhosted.org/packages/ee/81/aca1eb176de671f8bda479b11acdc42c132b61a2ac861c883907dde6debb/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:77e6f57a20b9bd4e1e2cedda4d0b986ebd0216236f0106e55c28aea3d3d69b16", size = 1478139 },
+    { url = "https://files.pythonhosted.org/packages/49/f4/e081522473671c97b2687d380e9e4c26f748a86363ce5af48b4a28e48d06/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:08e77738ed7538f036cd1170cbed942ef749137b1311fa2bbe2a7fda2f6bf3cc", size = 1413517 },
+    { url = "https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246", size = 1474952 },
+    { url = "https://files.pythonhosted.org/packages/82/13/13fa685ae167bee5d94b415991c4fc7bb0a1b6ebea6e753a87044b209678/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794", size = 2269132 },
+    { url = "https://files.pythonhosted.org/packages/ef/92/bb7c9395489b99a6cb41d502d3686bac692586db2045adc19e45ee64ed23/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:3452046c37c7692bd52b0e752b87954ef86ee2224e624ef7ce6cb21e8c41cc1b", size = 2425997 },
+    { url = "https://files.pythonhosted.org/packages/ed/12/87f0e9271e2b63d35d0d8524954145837dd1a6c15b62a2d8c1ebe0f182b4/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:7e9a60b50fe8b2ec6f448fe8d81b07e40141bfced7f896309df271a0b92f80f3", size = 2376060 },
+    { url = "https://files.pythonhosted.org/packages/02/6e/c8af39288edbce8bf0fa35dee427b082758a4b71e9c91ef18fa667782138/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:918139571133f366e8362fa4a297aeba86c7816b7ecf0bc79168080e2bd79957", size = 2520471 },
+    { url = "https://files.pythonhosted.org/packages/13/78/df381bc7b26e535c91469f77f16adcd073beb3e2dd25042efd064af82323/kiwisolver-1.4.8-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e063ef9f89885a1d68dd8b2e18f5ead48653176d10a0e324e3b0030e3a69adeb", size = 2338793 },
+    { url = "https://files.pythonhosted.org/packages/d0/dc/c1abe38c37c071d0fc71c9a474fd0b9ede05d42f5a458d584619cfd2371a/kiwisolver-1.4.8-cp313-cp313-win_amd64.whl", hash = "sha256:a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2", size = 71855 },
+    { url = "https://files.pythonhosted.org/packages/a0/b6/21529d595b126ac298fdd90b705d87d4c5693de60023e0efcb4f387ed99e/kiwisolver-1.4.8-cp313-cp313-win_arm64.whl", hash = "sha256:3cd3bc628b25f74aedc6d374d5babf0166a92ff1317f46267f12d2ed54bc1d30", size = 65430 },
+    { url = "https://files.pythonhosted.org/packages/34/bd/b89380b7298e3af9b39f49334e3e2a4af0e04819789f04b43d560516c0c8/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:370fd2df41660ed4e26b8c9d6bbcad668fbe2560462cba151a721d49e5b6628c", size = 126294 },
+    { url = "https://files.pythonhosted.org/packages/83/41/5857dc72e5e4148eaac5aa76e0703e594e4465f8ab7ec0fc60e3a9bb8fea/kiwisolver-1.4.8-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:84a2f830d42707de1d191b9490ac186bf7997a9495d4e9072210a1296345f7dc", size = 67736 },
+    { url = "https://files.pythonhosted.org/packages/e1/d1/be059b8db56ac270489fb0b3297fd1e53d195ba76e9bbb30e5401fa6b759/kiwisolver-1.4.8-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7a3ad337add5148cf51ce0b55642dc551c0b9d6248458a757f98796ca7348712", size = 66194 },
+    { url = "https://files.pythonhosted.org/packages/e1/83/4b73975f149819eb7dcf9299ed467eba068ecb16439a98990dcb12e63fdd/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7506488470f41169b86d8c9aeff587293f530a23a23a49d6bc64dab66bedc71e", size = 1465942 },
+    { url = "https://files.pythonhosted.org/packages/c7/2c/30a5cdde5102958e602c07466bce058b9d7cb48734aa7a4327261ac8e002/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2f0121b07b356a22fb0414cec4666bbe36fd6d0d759db3d37228f496ed67c880", size = 1595341 },
+    { url = "https://files.pythonhosted.org/packages/ff/9b/1e71db1c000385aa069704f5990574b8244cce854ecd83119c19e83c9586/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d6d6bd87df62c27d4185de7c511c6248040afae67028a8a22012b010bc7ad062", size = 1598455 },
+    { url = "https://files.pythonhosted.org/packages/85/92/c8fec52ddf06231b31cbb779af77e99b8253cd96bd135250b9498144c78b/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:291331973c64bb9cce50bbe871fb2e675c4331dab4f31abe89f175ad7679a4d7", size = 1522138 },
+    { url = "https://files.pythonhosted.org/packages/0b/51/9eb7e2cd07a15d8bdd976f6190c0164f92ce1904e5c0c79198c4972926b7/kiwisolver-1.4.8-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:893f5525bb92d3d735878ec00f781b2de998333659507d29ea4466208df37bed", size = 1582857 },
+    { url = "https://files.pythonhosted.org/packages/0f/95/c5a00387a5405e68ba32cc64af65ce881a39b98d73cc394b24143bebc5b8/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:b47a465040146981dc9db8647981b8cb96366fbc8d452b031e4f8fdffec3f26d", size = 2293129 },
+    { url = "https://files.pythonhosted.org/packages/44/83/eeb7af7d706b8347548313fa3a3a15931f404533cc54fe01f39e830dd231/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:99cea8b9dd34ff80c521aef46a1dddb0dcc0283cf18bde6d756f1e6f31772165", size = 2421538 },
+    { url = "https://files.pythonhosted.org/packages/05/f9/27e94c1b3eb29e6933b6986ffc5fa1177d2cd1f0c8efc5f02c91c9ac61de/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:151dffc4865e5fe6dafce5480fab84f950d14566c480c08a53c663a0020504b6", size = 2390661 },
+    { url = "https://files.pythonhosted.org/packages/d9/d4/3c9735faa36ac591a4afcc2980d2691000506050b7a7e80bcfe44048daa7/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:577facaa411c10421314598b50413aa1ebcf5126f704f1e5d72d7e4e9f020d90", size = 2546710 },
+    { url = "https://files.pythonhosted.org/packages/4c/fa/be89a49c640930180657482a74970cdcf6f7072c8d2471e1babe17a222dc/kiwisolver-1.4.8-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:be4816dc51c8a471749d664161b434912eee82f2ea66bd7628bd14583a833e85", size = 2349213 },
+]
+
+[[package]]
+name = "matplotlib"
+version = "3.10.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "contourpy" },
+    { name = "cycler" },
+    { name = "fonttools" },
+    { name = "kiwisolver" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pillow" },
+    { name = "pyparsing" },
+    { name = "python-dateutil" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2f/08/b89867ecea2e305f408fbb417139a8dd941ecf7b23a2e02157c36da546f0/matplotlib-3.10.1.tar.gz", hash = "sha256:e8d2d0e3881b129268585bf4765ad3ee73a4591d77b9a18c214ac7e3a79fb2ba", size = 36743335 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7c/1d/5e0dc3b59c034e43de16f94deb68f4ad8a96b3ea00f4b37c160b7474928e/matplotlib-3.10.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:66e907a06e68cb6cfd652c193311d61a12b54f56809cafbed9736ce5ad92f107", size = 8175488 },
+    { url = "https://files.pythonhosted.org/packages/7a/81/dae7e14042e74da658c3336ab9799128e09a1ee03964f2d89630b5d12106/matplotlib-3.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e9b4bb156abb8fa5e5b2b460196f7db7264fc6d62678c03457979e7d5254b7be", size = 8046264 },
+    { url = "https://files.pythonhosted.org/packages/21/c4/22516775dcde10fc9c9571d155f90710761b028fc44f660508106c363c97/matplotlib-3.10.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1985ad3d97f51307a2cbfc801a930f120def19ba22864182dacef55277102ba6", size = 8452048 },
+    { url = "https://files.pythonhosted.org/packages/63/23/c0615001f67ce7c96b3051d856baedc0c818a2ed84570b9bf9bde200f85d/matplotlib-3.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c96f2c2f825d1257e437a1482c5a2cf4fee15db4261bd6fc0750f81ba2b4ba3d", size = 8597111 },
+    { url = "https://files.pythonhosted.org/packages/ca/c0/a07939a82aed77770514348f4568177d7dadab9787ebc618a616fe3d665e/matplotlib-3.10.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:35e87384ee9e488d8dd5a2dd7baf471178d38b90618d8ea147aced4ab59c9bea", size = 9402771 },
+    { url = "https://files.pythonhosted.org/packages/a6/b6/a9405484fb40746fdc6ae4502b16a9d6e53282ba5baaf9ebe2da579f68c4/matplotlib-3.10.1-cp312-cp312-win_amd64.whl", hash = "sha256:cfd414bce89cc78a7e1d25202e979b3f1af799e416010a20ab2b5ebb3a02425c", size = 8063742 },
+    { url = "https://files.pythonhosted.org/packages/60/73/6770ff5e5523d00f3bc584acb6031e29ee5c8adc2336b16cd1d003675fe0/matplotlib-3.10.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c42eee41e1b60fd83ee3292ed83a97a5f2a8239b10c26715d8a6172226988d7b", size = 8176112 },
+    { url = "https://files.pythonhosted.org/packages/08/97/b0ca5da0ed54a3f6599c3ab568bdda65269bc27c21a2c97868c1625e4554/matplotlib-3.10.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4f0647b17b667ae745c13721602b540f7aadb2a32c5b96e924cd4fea5dcb90f1", size = 8046931 },
+    { url = "https://files.pythonhosted.org/packages/df/9a/1acbdc3b165d4ce2dcd2b1a6d4ffb46a7220ceee960c922c3d50d8514067/matplotlib-3.10.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aa3854b5f9473564ef40a41bc922be978fab217776e9ae1545c9b3a5cf2092a3", size = 8453422 },
+    { url = "https://files.pythonhosted.org/packages/51/d0/2bc4368abf766203e548dc7ab57cf7e9c621f1a3c72b516cc7715347b179/matplotlib-3.10.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e496c01441be4c7d5f96d4e40f7fca06e20dcb40e44c8daa2e740e1757ad9e6", size = 8596819 },
+    { url = "https://files.pythonhosted.org/packages/ab/1b/8b350f8a1746c37ab69dda7d7528d1fc696efb06db6ade9727b7887be16d/matplotlib-3.10.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:5d45d3f5245be5b469843450617dcad9af75ca50568acf59997bed9311131a0b", size = 9402782 },
+    { url = "https://files.pythonhosted.org/packages/89/06/f570373d24d93503988ba8d04f213a372fa1ce48381c5eb15da985728498/matplotlib-3.10.1-cp313-cp313-win_amd64.whl", hash = "sha256:8e8e25b1209161d20dfe93037c8a7f7ca796ec9aa326e6e4588d8c4a5dd1e473", size = 8063812 },
+    { url = "https://files.pythonhosted.org/packages/fc/e0/8c811a925b5a7ad75135f0e5af46408b78af88bbb02a1df775100ef9bfef/matplotlib-3.10.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:19b06241ad89c3ae9469e07d77efa87041eac65d78df4fcf9cac318028009b01", size = 8214021 },
+    { url = "https://files.pythonhosted.org/packages/4a/34/319ec2139f68ba26da9d00fce2ff9f27679fb799a6c8e7358539801fd629/matplotlib-3.10.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:01e63101ebb3014e6e9f80d9cf9ee361a8599ddca2c3e166c563628b39305dbb", size = 8090782 },
+    { url = "https://files.pythonhosted.org/packages/77/ea/9812124ab9a99df5b2eec1110e9b2edc0b8f77039abf4c56e0a376e84a29/matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f06bad951eea6422ac4e8bdebcf3a70c59ea0a03338c5d2b109f57b64eb3972", size = 8478901 },
+    { url = "https://files.pythonhosted.org/packages/c9/db/b05bf463689134789b06dea85828f8ebe506fa1e37593f723b65b86c9582/matplotlib-3.10.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3dfb036f34873b46978f55e240cff7a239f6c4409eac62d8145bad3fc6ba5a3", size = 8613864 },
+    { url = "https://files.pythonhosted.org/packages/c2/04/41ccec4409f3023a7576df3b5c025f1a8c8b81fbfe922ecfd837ac36e081/matplotlib-3.10.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dc6ab14a7ab3b4d813b88ba957fc05c79493a037f54e246162033591e770de6f", size = 9409487 },
+    { url = "https://files.pythonhosted.org/packages/ac/c2/0d5aae823bdcc42cc99327ecdd4d28585e15ccd5218c453b7bcd827f3421/matplotlib-3.10.1-cp313-cp313t-win_amd64.whl", hash = "sha256:bc411ebd5889a78dabbc457b3fa153203e22248bfa6eedc6797be5df0164dbf9", size = 8134832 },
+]
+
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.7"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "traitlets" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/99/5b/a36a337438a14116b16480db471ad061c36c3694df7c2084a0da7ba538b7/matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90", size = 8159 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/8e/9ad090d3553c280a8060fbf6e24dc1c0c29704ee7d1c372f0c174aa59285/matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca", size = 9899 },
+]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.6.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 },
+]
+
+[[package]]
+name = "note"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "ipykernel" },
+    { name = "matplotlib" },
+    { name = "scikit-learn" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "ipykernel", specifier = ">=6.29.5" },
+    { name = "matplotlib", specifier = ">=3.10.1" },
+    { name = "scikit-learn", specifier = ">=1.6.1" },
+]
+
+[[package]]
+name = "numpy"
+version = "2.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fb/90/8956572f5c4ae52201fdec7ba2044b2c882832dcec7d5d0922c9e9acf2de/numpy-2.2.3.tar.gz", hash = "sha256:dbdc15f0c81611925f382dfa97b3bd0bc2c1ce19d4fe50482cb0ddc12ba30020", size = 20262700 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/ec/43628dcf98466e087812142eec6d1c1a6c6bdfdad30a0aa07b872dc01f6f/numpy-2.2.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:12c045f43b1d2915eca6b880a7f4a256f59d62df4f044788c8ba67709412128d", size = 20929458 },
+    { url = "https://files.pythonhosted.org/packages/9b/c0/2f4225073e99a5c12350954949ed19b5d4a738f541d33e6f7439e33e98e4/numpy-2.2.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:87eed225fd415bbae787f93a457af7f5990b92a334e346f72070bf569b9c9c95", size = 14115299 },
+    { url = "https://files.pythonhosted.org/packages/ca/fa/d2c5575d9c734a7376cc1592fae50257ec95d061b27ee3dbdb0b3b551eb2/numpy-2.2.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:712a64103d97c404e87d4d7c47fb0c7ff9acccc625ca2002848e0d53288b90ea", size = 5145723 },
+    { url = "https://files.pythonhosted.org/packages/eb/dc/023dad5b268a7895e58e791f28dc1c60eb7b6c06fcbc2af8538ad069d5f3/numpy-2.2.3-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:a5ae282abe60a2db0fd407072aff4599c279bcd6e9a2475500fc35b00a57c532", size = 6678797 },
+    { url = "https://files.pythonhosted.org/packages/3f/19/bcd641ccf19ac25abb6fb1dcd7744840c11f9d62519d7057b6ab2096eb60/numpy-2.2.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5266de33d4c3420973cf9ae3b98b54a2a6d53a559310e3236c4b2b06b9c07d4e", size = 14067362 },
+    { url = "https://files.pythonhosted.org/packages/39/04/78d2e7402fb479d893953fb78fa7045f7deb635ec095b6b4f0260223091a/numpy-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b787adbf04b0db1967798dba8da1af07e387908ed1553a0d6e74c084d1ceafe", size = 16116679 },
+    { url = "https://files.pythonhosted.org/packages/d0/a1/e90f7aa66512be3150cb9d27f3d9995db330ad1b2046474a13b7040dfd92/numpy-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:34c1b7e83f94f3b564b35f480f5652a47007dd91f7c839f404d03279cc8dd021", size = 15264272 },
+    { url = "https://files.pythonhosted.org/packages/dc/b6/50bd027cca494de4fa1fc7bf1662983d0ba5f256fa0ece2c376b5eb9b3f0/numpy-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:4d8335b5f1b6e2bce120d55fb17064b0262ff29b459e8493d1785c18ae2553b8", size = 17880549 },
+    { url = "https://files.pythonhosted.org/packages/96/30/f7bf4acb5f8db10a96f73896bdeed7a63373137b131ca18bd3dab889db3b/numpy-2.2.3-cp312-cp312-win32.whl", hash = "sha256:4d9828d25fb246bedd31e04c9e75714a4087211ac348cb39c8c5f99dbb6683fe", size = 6293394 },
+    { url = "https://files.pythonhosted.org/packages/42/6e/55580a538116d16ae7c9aa17d4edd56e83f42126cb1dfe7a684da7925d2c/numpy-2.2.3-cp312-cp312-win_amd64.whl", hash = "sha256:83807d445817326b4bcdaaaf8e8e9f1753da04341eceec705c001ff342002e5d", size = 12626357 },
+    { url = "https://files.pythonhosted.org/packages/0e/8b/88b98ed534d6a03ba8cddb316950fe80842885709b58501233c29dfa24a9/numpy-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7bfdb06b395385ea9b91bf55c1adf1b297c9fdb531552845ff1d3ea6e40d5aba", size = 20916001 },
+    { url = "https://files.pythonhosted.org/packages/d9/b4/def6ec32c725cc5fbd8bdf8af80f616acf075fe752d8a23e895da8c67b70/numpy-2.2.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:23c9f4edbf4c065fddb10a4f6e8b6a244342d95966a48820c614891e5059bb50", size = 14130721 },
+    { url = "https://files.pythonhosted.org/packages/20/60/70af0acc86495b25b672d403e12cb25448d79a2b9658f4fc45e845c397a8/numpy-2.2.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:a0c03b6be48aaf92525cccf393265e02773be8fd9551a2f9adbe7db1fa2b60f1", size = 5130999 },
+    { url = "https://files.pythonhosted.org/packages/2e/69/d96c006fb73c9a47bcb3611417cf178049aae159afae47c48bd66df9c536/numpy-2.2.3-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:2376e317111daa0a6739e50f7ee2a6353f768489102308b0d98fcf4a04f7f3b5", size = 6665299 },
+    { url = "https://files.pythonhosted.org/packages/5a/3f/d8a877b6e48103733ac224ffa26b30887dc9944ff95dffdfa6c4ce3d7df3/numpy-2.2.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8fb62fe3d206d72fe1cfe31c4a1106ad2b136fcc1606093aeab314f02930fdf2", size = 14064096 },
+    { url = "https://files.pythonhosted.org/packages/e4/43/619c2c7a0665aafc80efca465ddb1f260287266bdbdce517396f2f145d49/numpy-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:52659ad2534427dffcc36aac76bebdd02b67e3b7a619ac67543bc9bfe6b7cdb1", size = 16114758 },
+    { url = "https://files.pythonhosted.org/packages/d9/79/ee4fe4f60967ccd3897aa71ae14cdee9e3c097e3256975cc9575d393cb42/numpy-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1b416af7d0ed3271cad0f0a0d0bee0911ed7eba23e66f8424d9f3dfcdcae1304", size = 15259880 },
+    { url = "https://files.pythonhosted.org/packages/fb/c8/8b55cf05db6d85b7a7d414b3d1bd5a740706df00bfa0824a08bf041e52ee/numpy-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1402da8e0f435991983d0a9708b779f95a8c98c6b18a171b9f1be09005e64d9d", size = 17876721 },
+    { url = "https://files.pythonhosted.org/packages/21/d6/b4c2f0564b7dcc413117b0ffbb818d837e4b29996b9234e38b2025ed24e7/numpy-2.2.3-cp313-cp313-win32.whl", hash = "sha256:136553f123ee2951bfcfbc264acd34a2fc2f29d7cdf610ce7daf672b6fbaa693", size = 6290195 },
+    { url = "https://files.pythonhosted.org/packages/97/e7/7d55a86719d0de7a6a597949f3febefb1009435b79ba510ff32f05a8c1d7/numpy-2.2.3-cp313-cp313-win_amd64.whl", hash = "sha256:5b732c8beef1d7bc2d9e476dbba20aaff6167bf205ad9aa8d30913859e82884b", size = 12619013 },
+    { url = "https://files.pythonhosted.org/packages/a6/1f/0b863d5528b9048fd486a56e0b97c18bf705e88736c8cea7239012119a54/numpy-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:435e7a933b9fda8126130b046975a968cc2d833b505475e588339e09f7672890", size = 20944621 },
+    { url = "https://files.pythonhosted.org/packages/aa/99/b478c384f7a0a2e0736177aafc97dc9152fc036a3fdb13f5a3ab225f1494/numpy-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:7678556eeb0152cbd1522b684dcd215250885993dd00adb93679ec3c0e6e091c", size = 14142502 },
+    { url = "https://files.pythonhosted.org/packages/fb/61/2d9a694a0f9cd0a839501d362de2a18de75e3004576a3008e56bdd60fcdb/numpy-2.2.3-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:2e8da03bd561504d9b20e7a12340870dfc206c64ea59b4cfee9fceb95070ee94", size = 5176293 },
+    { url = "https://files.pythonhosted.org/packages/33/35/51e94011b23e753fa33f891f601e5c1c9a3d515448659b06df9d40c0aa6e/numpy-2.2.3-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:c9aa4496fd0e17e3843399f533d62857cef5900facf93e735ef65aa4bbc90ef0", size = 6691874 },
+    { url = "https://files.pythonhosted.org/packages/ff/cf/06e37619aad98a9d03bd8d65b8e3041c3a639be0f5f6b0a0e2da544538d4/numpy-2.2.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4ca91d61a4bf61b0f2228f24bbfa6a9facd5f8af03759fe2a655c50ae2c6610", size = 14036826 },
+    { url = "https://files.pythonhosted.org/packages/0c/93/5d7d19955abd4d6099ef4a8ee006f9ce258166c38af259f9e5558a172e3e/numpy-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:deaa09cd492e24fd9b15296844c0ad1b3c976da7907e1c1ed3a0ad21dded6f76", size = 16096567 },
+    { url = "https://files.pythonhosted.org/packages/af/53/d1c599acf7732d81f46a93621dab6aa8daad914b502a7a115b3f17288ab2/numpy-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:246535e2f7496b7ac85deffe932896a3577be7af8fb7eebe7146444680297e9a", size = 15242514 },
+    { url = "https://files.pythonhosted.org/packages/53/43/c0f5411c7b3ea90adf341d05ace762dad8cb9819ef26093e27b15dd121ac/numpy-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:daf43a3d1ea699402c5a850e5313680ac355b4adc9770cd5cfc2940e7861f1bf", size = 17872920 },
+    { url = "https://files.pythonhosted.org/packages/5b/57/6dbdd45ab277aff62021cafa1e15f9644a52f5b5fc840bc7591b4079fb58/numpy-2.2.3-cp313-cp313t-win32.whl", hash = "sha256:cf802eef1f0134afb81fef94020351be4fe1d6681aadf9c5e862af6602af64ef", size = 6346584 },
+    { url = "https://files.pythonhosted.org/packages/97/9b/484f7d04b537d0a1202a5ba81c6f53f1846ae6c63c2127f8df869ed31342/numpy-2.2.3-cp313-cp313t-win_amd64.whl", hash = "sha256:aee2512827ceb6d7f517c8b85aa5d3923afe8fc7a57d028cffcd522f1c6fd082", size = 12706784 },
+]
+
+[[package]]
+name = "packaging"
+version = "24.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d0/63/68dbb6eb2de9cb10ee4c9c14a0148804425e13c4fb20d61cce69f53106da/packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f", size = 163950 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/ef/eb23f262cca3c0c4eb7ab1933c3b1f03d021f2c48f54763065b6f0e321be/packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759", size = 65451 },
+]
+
+[[package]]
+name = "parso"
+version = "0.8.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/66/94/68e2e17afaa9169cf6412ab0f28623903be73d1b32e208d9e8e541bb086d/parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d", size = 400609 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c6/ac/dac4a63f978e4dcb3c6d3a78c4d8e0192a113d288502a1216950c41b1027/parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18", size = 103650 },
+]
+
+[[package]]
+name = "pexpect"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "ptyprocess" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772 },
+]
+
+[[package]]
+name = "pillow"
+version = "11.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/f3/af/c097e544e7bd278333db77933e535098c259609c4eb3b85381109602fb5b/pillow-11.1.0.tar.gz", hash = "sha256:368da70808b36d73b4b390a8ffac11069f8a5c85f29eff1f1b01bcf3ef5b2a20", size = 46742715 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/95/20/9ce6ed62c91c073fcaa23d216e68289e19d95fb8188b9fb7a63d36771db8/pillow-11.1.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:2062ffb1d36544d42fcaa277b069c88b01bb7298f4efa06731a7fd6cc290b81a", size = 3226818 },
+    { url = "https://files.pythonhosted.org/packages/b9/d8/f6004d98579a2596c098d1e30d10b248798cceff82d2b77aa914875bfea1/pillow-11.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a85b653980faad27e88b141348707ceeef8a1186f75ecc600c395dcac19f385b", size = 3101662 },
+    { url = "https://files.pythonhosted.org/packages/08/d9/892e705f90051c7a2574d9f24579c9e100c828700d78a63239676f960b74/pillow-11.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9409c080586d1f683df3f184f20e36fb647f2e0bc3988094d4fd8c9f4eb1b3b3", size = 4329317 },
+    { url = "https://files.pythonhosted.org/packages/8c/aa/7f29711f26680eab0bcd3ecdd6d23ed6bce180d82e3f6380fb7ae35fcf3b/pillow-11.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7fdadc077553621911f27ce206ffcbec7d3f8d7b50e0da39f10997e8e2bb7f6a", size = 4412999 },
+    { url = "https://files.pythonhosted.org/packages/c8/c4/8f0fe3b9e0f7196f6d0bbb151f9fba323d72a41da068610c4c960b16632a/pillow-11.1.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:93a18841d09bcdd774dcdc308e4537e1f867b3dec059c131fde0327899734aa1", size = 4368819 },
+    { url = "https://files.pythonhosted.org/packages/38/0d/84200ed6a871ce386ddc82904bfadc0c6b28b0c0ec78176871a4679e40b3/pillow-11.1.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:9aa9aeddeed452b2f616ff5507459e7bab436916ccb10961c4a382cd3e03f47f", size = 4496081 },
+    { url = "https://files.pythonhosted.org/packages/84/9c/9bcd66f714d7e25b64118e3952d52841a4babc6d97b6d28e2261c52045d4/pillow-11.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3cdcdb0b896e981678eee140d882b70092dac83ac1cdf6b3a60e2216a73f2b91", size = 4296513 },
+    { url = "https://files.pythonhosted.org/packages/db/61/ada2a226e22da011b45f7104c95ebda1b63dcbb0c378ad0f7c2a710f8fd2/pillow-11.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:36ba10b9cb413e7c7dfa3e189aba252deee0602c86c309799da5a74009ac7a1c", size = 4431298 },
+    { url = "https://files.pythonhosted.org/packages/e7/c4/fc6e86750523f367923522014b821c11ebc5ad402e659d8c9d09b3c9d70c/pillow-11.1.0-cp312-cp312-win32.whl", hash = "sha256:cfd5cd998c2e36a862d0e27b2df63237e67273f2fc78f47445b14e73a810e7e6", size = 2291630 },
+    { url = "https://files.pythonhosted.org/packages/08/5c/2104299949b9d504baf3f4d35f73dbd14ef31bbd1ddc2c1b66a5b7dfda44/pillow-11.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:a697cd8ba0383bba3d2d3ada02b34ed268cb548b369943cd349007730c92bddf", size = 2626369 },
+    { url = "https://files.pythonhosted.org/packages/37/f3/9b18362206b244167c958984b57c7f70a0289bfb59a530dd8af5f699b910/pillow-11.1.0-cp312-cp312-win_arm64.whl", hash = "sha256:4dd43a78897793f60766563969442020e90eb7847463eca901e41ba186a7d4a5", size = 2375240 },
+    { url = "https://files.pythonhosted.org/packages/b3/31/9ca79cafdce364fd5c980cd3416c20ce1bebd235b470d262f9d24d810184/pillow-11.1.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ae98e14432d458fc3de11a77ccb3ae65ddce70f730e7c76140653048c71bfcbc", size = 3226640 },
+    { url = "https://files.pythonhosted.org/packages/ac/0f/ff07ad45a1f172a497aa393b13a9d81a32e1477ef0e869d030e3c1532521/pillow-11.1.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:cc1331b6d5a6e144aeb5e626f4375f5b7ae9934ba620c0ac6b3e43d5e683a0f0", size = 3101437 },
+    { url = "https://files.pythonhosted.org/packages/08/2f/9906fca87a68d29ec4530be1f893149e0cb64a86d1f9f70a7cfcdfe8ae44/pillow-11.1.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:758e9d4ef15d3560214cddbc97b8ef3ef86ce04d62ddac17ad39ba87e89bd3b1", size = 4326605 },
+    { url = "https://files.pythonhosted.org/packages/b0/0f/f3547ee15b145bc5c8b336401b2d4c9d9da67da9dcb572d7c0d4103d2c69/pillow-11.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b523466b1a31d0dcef7c5be1f20b942919b62fd6e9a9be199d035509cbefc0ec", size = 4411173 },
+    { url = "https://files.pythonhosted.org/packages/b1/df/bf8176aa5db515c5de584c5e00df9bab0713548fd780c82a86cba2c2fedb/pillow-11.1.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:9044b5e4f7083f209c4e35aa5dd54b1dd5b112b108648f5c902ad586d4f945c5", size = 4369145 },
+    { url = "https://files.pythonhosted.org/packages/de/7c/7433122d1cfadc740f577cb55526fdc39129a648ac65ce64db2eb7209277/pillow-11.1.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:3764d53e09cdedd91bee65c2527815d315c6b90d7b8b79759cc48d7bf5d4f114", size = 4496340 },
+    { url = "https://files.pythonhosted.org/packages/25/46/dd94b93ca6bd555588835f2504bd90c00d5438fe131cf01cfa0c5131a19d/pillow-11.1.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:31eba6bbdd27dde97b0174ddf0297d7a9c3a507a8a1480e1e60ef914fe23d352", size = 4296906 },
+    { url = "https://files.pythonhosted.org/packages/a8/28/2f9d32014dfc7753e586db9add35b8a41b7a3b46540e965cb6d6bc607bd2/pillow-11.1.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b5d658fbd9f0d6eea113aea286b21d3cd4d3fd978157cbf2447a6035916506d3", size = 4431759 },
+    { url = "https://files.pythonhosted.org/packages/33/48/19c2cbe7403870fbe8b7737d19eb013f46299cdfe4501573367f6396c775/pillow-11.1.0-cp313-cp313-win32.whl", hash = "sha256:f86d3a7a9af5d826744fabf4afd15b9dfef44fe69a98541f666f66fbb8d3fef9", size = 2291657 },
+    { url = "https://files.pythonhosted.org/packages/3b/ad/285c556747d34c399f332ba7c1a595ba245796ef3e22eae190f5364bb62b/pillow-11.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:593c5fd6be85da83656b93ffcccc2312d2d149d251e98588b14fbc288fd8909c", size = 2626304 },
+    { url = "https://files.pythonhosted.org/packages/e5/7b/ef35a71163bf36db06e9c8729608f78dedf032fc8313d19bd4be5c2588f3/pillow-11.1.0-cp313-cp313-win_arm64.whl", hash = "sha256:11633d58b6ee5733bde153a8dafd25e505ea3d32e261accd388827ee987baf65", size = 2375117 },
+    { url = "https://files.pythonhosted.org/packages/79/30/77f54228401e84d6791354888549b45824ab0ffde659bafa67956303a09f/pillow-11.1.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:70ca5ef3b3b1c4a0812b5c63c57c23b63e53bc38e758b37a951e5bc466449861", size = 3230060 },
+    { url = "https://files.pythonhosted.org/packages/ce/b1/56723b74b07dd64c1010fee011951ea9c35a43d8020acd03111f14298225/pillow-11.1.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8000376f139d4d38d6851eb149b321a52bb8893a88dae8ee7d95840431977081", size = 3106192 },
+    { url = "https://files.pythonhosted.org/packages/e1/cd/7bf7180e08f80a4dcc6b4c3a0aa9e0b0ae57168562726a05dc8aa8fa66b0/pillow-11.1.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ee85f0696a17dd28fbcfceb59f9510aa71934b483d1f5601d1030c3c8304f3c", size = 4446805 },
+    { url = "https://files.pythonhosted.org/packages/97/42/87c856ea30c8ed97e8efbe672b58c8304dee0573f8c7cab62ae9e31db6ae/pillow-11.1.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:dd0e081319328928531df7a0e63621caf67652c8464303fd102141b785ef9547", size = 4530623 },
+    { url = "https://files.pythonhosted.org/packages/ff/41/026879e90c84a88e33fb00cc6bd915ac2743c67e87a18f80270dfe3c2041/pillow-11.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e63e4e5081de46517099dc30abe418122f54531a6ae2ebc8680bcd7096860eab", size = 4465191 },
+    { url = "https://files.pythonhosted.org/packages/e5/fb/a7960e838bc5df57a2ce23183bfd2290d97c33028b96bde332a9057834d3/pillow-11.1.0-cp313-cp313t-win32.whl", hash = "sha256:dda60aa465b861324e65a78c9f5cf0f4bc713e4309f83bc387be158b077963d9", size = 2295494 },
+    { url = "https://files.pythonhosted.org/packages/d7/6c/6ec83ee2f6f0fda8d4cf89045c6be4b0373ebfc363ba8538f8c999f63fcd/pillow-11.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ad5db5781c774ab9a9b2c4302bbf0c1014960a0a7be63278d13ae6fdf88126fe", size = 2631595 },
+    { url = "https://files.pythonhosted.org/packages/cf/6c/41c21c6c8af92b9fea313aa47c75de49e2f9a467964ee33eb0135d47eb64/pillow-11.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:67cd427c68926108778a9005f2a04adbd5e67c442ed21d95389fe1d595458756", size = 2377651 },
+]
+
+[[package]]
+name = "platformdirs"
+version = "4.3.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/13/fc/128cc9cb8f03208bdbf93d3aa862e16d376844a14f9a0ce5cf4507372de4/platformdirs-4.3.6.tar.gz", hash = "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907", size = 21302 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3c/a6/bc1012356d8ece4d66dd75c4b9fc6c1f6650ddd5991e421177d9f8f671be/platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb", size = 18439 },
+]
+
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.50"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wcwidth" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/a1/e1/bd15cb8ffdcfeeb2bdc215de3c3cffca11408d829e4b8416dcfe71ba8854/prompt_toolkit-3.0.50.tar.gz", hash = "sha256:544748f3860a2623ca5cd6d2795e7a14f3d0e1c3c9728359013f79877fc89bab", size = 429087 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e4/ea/d836f008d33151c7a1f62caf3d8dd782e4d15f6a43897f64480c2b8de2ad/prompt_toolkit-3.0.50-py3-none-any.whl", hash = "sha256:9b6427eb19e479d98acff65196a307c555eb567989e6d88ebbb1b509d9779198", size = 387816 },
+]
+
+[[package]]
+name = "psutil"
+version = "7.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/2a/80/336820c1ad9286a4ded7e845b2eccfcb27851ab8ac6abece774a6ff4d3de/psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456", size = 497003 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ed/e6/2d26234410f8b8abdbf891c9da62bee396583f713fb9f3325a4760875d22/psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25", size = 238051 },
+    { url = "https://files.pythonhosted.org/packages/04/8b/30f930733afe425e3cbfc0e1468a30a18942350c1a8816acfade80c005c4/psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da", size = 239535 },
+    { url = "https://files.pythonhosted.org/packages/2a/ed/d362e84620dd22876b55389248e522338ed1bf134a5edd3b8231d7207f6d/psutil-7.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1fcee592b4c6f146991ca55919ea3d1f8926497a713ed7faaf8225e174581e91", size = 275004 },
+    { url = "https://files.pythonhosted.org/packages/bf/b9/b0eb3f3cbcb734d930fdf839431606844a825b23eaf9a6ab371edac8162c/psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b1388a4f6875d7e2aff5c4ca1cc16c545ed41dd8bb596cefea80111db353a34", size = 277986 },
+    { url = "https://files.pythonhosted.org/packages/eb/a2/709e0fe2f093556c17fbafda93ac032257242cabcc7ff3369e2cb76a97aa/psutil-7.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f098451abc2828f7dc6b58d44b532b22f2088f4999a937557b603ce72b1993", size = 279544 },
+    { url = "https://files.pythonhosted.org/packages/50/e6/eecf58810b9d12e6427369784efe814a1eec0f492084ce8eb8f4d89d6d61/psutil-7.0.0-cp37-abi3-win32.whl", hash = "sha256:ba3fcef7523064a6c9da440fc4d6bd07da93ac726b5733c29027d7dc95b39d99", size = 241053 },
+    { url = "https://files.pythonhosted.org/packages/50/1b/6921afe68c74868b4c9fa424dad3be35b095e16687989ebbb50ce4fceb7c/psutil-7.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:4cf3d4eb1aa9b348dec30105c55cd9b7d4629285735a102beb4441e38db90553", size = 244885 },
+]
+
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993 },
+]
+
+[[package]]
+name = "pure-eval"
+version = "0.2.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842 },
+]
+
+[[package]]
+name = "pycparser"
+version = "2.22"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/b2/31537cf4b1ca988837256c910a668b553fceb8f069bedc4b1c826024b52c/pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6", size = 172736 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/a3/a812df4e2dd5696d1f351d58b8fe16a405b234ad2886a0dab9183fb78109/pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc", size = 117552 },
+]
+
+[[package]]
+name = "pygments"
+version = "2.19.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7c/2d/c3338d48ea6cc0feb8446d8e6937e1408088a72a39937982cc6111d17f84/pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f", size = 4968581 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c", size = 1225293 },
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/8b/1a/3544f4f299a47911c2ab3710f534e52fea62a633c96806995da5d25be4b2/pyparsing-3.2.1.tar.gz", hash = "sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a", size = 1067694 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1c/a7/c8a2d361bf89c0d9577c934ebb7421b25dc84bf3a8e3ac0a40aed9acc547/pyparsing-3.2.1-py3-none-any.whl", hash = "sha256:506ff4f4386c4cec0590ec19e6302d3aedb992fdc02c761e90416f158dacf8e1", size = 107716 },
+]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
+]
+
+[[package]]
+name = "pywin32"
+version = "308"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/7c/d00d6bdd96de4344e06c4afbf218bc86b54436a94c01c71a8701f613aa56/pywin32-308-cp312-cp312-win32.whl", hash = "sha256:587f3e19696f4bf96fde9d8a57cec74a57021ad5f204c9e627e15c33ff568897", size = 5939729 },
+    { url = "https://files.pythonhosted.org/packages/21/27/0c8811fbc3ca188f93b5354e7c286eb91f80a53afa4e11007ef661afa746/pywin32-308-cp312-cp312-win_amd64.whl", hash = "sha256:00b3e11ef09ede56c6a43c71f2d31857cf7c54b0ab6e78ac659497abd2834f47", size = 6543015 },
+    { url = "https://files.pythonhosted.org/packages/9d/0f/d40f8373608caed2255781a3ad9a51d03a594a1248cd632d6a298daca693/pywin32-308-cp312-cp312-win_arm64.whl", hash = "sha256:9b4de86c8d909aed15b7011182c8cab38c8850de36e6afb1f0db22b8959e3091", size = 7976033 },
+    { url = "https://files.pythonhosted.org/packages/a9/a4/aa562d8935e3df5e49c161b427a3a2efad2ed4e9cf81c3de636f1fdddfd0/pywin32-308-cp313-cp313-win32.whl", hash = "sha256:1c44539a37a5b7b21d02ab34e6a4d314e0788f1690d65b48e9b0b89f31abbbed", size = 5938579 },
+    { url = "https://files.pythonhosted.org/packages/c7/50/b0efb8bb66210da67a53ab95fd7a98826a97ee21f1d22949863e6d588b22/pywin32-308-cp313-cp313-win_amd64.whl", hash = "sha256:fd380990e792eaf6827fcb7e187b2b4b1cede0585e3d0c9e84201ec27b9905e4", size = 6542056 },
+    { url = "https://files.pythonhosted.org/packages/26/df/2b63e3e4f2df0224f8aaf6d131f54fe4e8c96400eb9df563e2aae2e1a1f9/pywin32-308-cp313-cp313-win_arm64.whl", hash = "sha256:ef313c46d4c18dfb82a2431e3051ac8f112ccee1a34f29c263c583c568db63cd", size = 7974986 },
+]
+
+[[package]]
+name = "pyzmq"
+version = "26.2.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi", marker = "implementation_name == 'pypy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5a/e3/8d0382cb59feb111c252b54e8728257416a38ffcb2243c4e4775a3c990fe/pyzmq-26.2.1.tar.gz", hash = "sha256:17d72a74e5e9ff3829deb72897a175333d3ef5b5413948cae3cf7ebf0b02ecca", size = 278433 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9c/b9/260a74786f162c7f521f5f891584a51d5a42fd15f5dcaa5c9226b2865fcc/pyzmq-26.2.1-cp312-cp312-macosx_10_15_universal2.whl", hash = "sha256:a6549ecb0041dafa55b5932dcbb6c68293e0bd5980b5b99f5ebb05f9a3b8a8f3", size = 1348495 },
+    { url = "https://files.pythonhosted.org/packages/bf/73/8a0757e4b68f5a8ccb90ddadbb76c6a5f880266cdb18be38c99bcdc17aaa/pyzmq-26.2.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:0250c94561f388db51fd0213cdccbd0b9ef50fd3c57ce1ac937bf3034d92d72e", size = 945035 },
+    { url = "https://files.pythonhosted.org/packages/cf/de/f02ec973cd33155bb772bae33ace774acc7cc71b87b25c4829068bec35de/pyzmq-26.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:36ee4297d9e4b34b5dc1dd7ab5d5ea2cbba8511517ef44104d2915a917a56dc8", size = 671213 },
+    { url = "https://files.pythonhosted.org/packages/d1/80/8fc583085f85ac91682744efc916888dd9f11f9f75a31aef1b78a5486c6c/pyzmq-26.2.1-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c2a9cb17fd83b7a3a3009901aca828feaf20aa2451a8a487b035455a86549c09", size = 908750 },
+    { url = "https://files.pythonhosted.org/packages/c3/25/0b4824596f261a3cc512ab152448b383047ff5f143a6906a36876415981c/pyzmq-26.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:786dd8a81b969c2081b31b17b326d3a499ddd1856e06d6d79ad41011a25148da", size = 865416 },
+    { url = "https://files.pythonhosted.org/packages/a1/d1/6fda77a034d02034367b040973fd3861d945a5347e607bd2e98c99f20599/pyzmq-26.2.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:2d88ba221a07fc2c5581565f1d0fe8038c15711ae79b80d9462e080a1ac30435", size = 865922 },
+    { url = "https://files.pythonhosted.org/packages/ad/81/48f7fd8a71c427412e739ce576fc1ee14f3dc34527ca9b0076e471676183/pyzmq-26.2.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1c84c1297ff9f1cd2440da4d57237cb74be21fdfe7d01a10810acba04e79371a", size = 1201526 },
+    { url = "https://files.pythonhosted.org/packages/c7/d8/818f15c6ef36b5450e435cbb0d3a51599fc884a5d2b27b46b9c00af68ef1/pyzmq-26.2.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:46d4ebafc27081a7f73a0f151d0c38d4291656aa134344ec1f3d0199ebfbb6d4", size = 1512808 },
+    { url = "https://files.pythonhosted.org/packages/d9/c4/b3edb7d0ae82ad6fb1a8cdb191a4113c427a01e85139906f3b655b07f4f8/pyzmq-26.2.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:91e2bfb8e9a29f709d51b208dd5f441dc98eb412c8fe75c24ea464734ccdb48e", size = 1411836 },
+    { url = "https://files.pythonhosted.org/packages/69/1c/151e3d42048f02cc5cd6dfc241d9d36b38375b4dee2e728acb5c353a6d52/pyzmq-26.2.1-cp312-cp312-win32.whl", hash = "sha256:4a98898fdce380c51cc3e38ebc9aa33ae1e078193f4dc641c047f88b8c690c9a", size = 581378 },
+    { url = "https://files.pythonhosted.org/packages/b6/b9/d59a7462848aaab7277fddb253ae134a570520115d80afa85e952287e6bc/pyzmq-26.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:a0741edbd0adfe5f30bba6c5223b78c131b5aa4a00a223d631e5ef36e26e6d13", size = 643737 },
+    { url = "https://files.pythonhosted.org/packages/55/09/f37e707937cce328944c1d57e5e50ab905011d35252a0745c4f7e5822a76/pyzmq-26.2.1-cp312-cp312-win_arm64.whl", hash = "sha256:e5e33b1491555843ba98d5209439500556ef55b6ab635f3a01148545498355e5", size = 558303 },
+    { url = "https://files.pythonhosted.org/packages/4f/2e/fa7a91ce349975971d6aa925b4c7e1a05abaae99b97ade5ace758160c43d/pyzmq-26.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:099b56ef464bc355b14381f13355542e452619abb4c1e57a534b15a106bf8e23", size = 942331 },
+    { url = "https://files.pythonhosted.org/packages/64/2b/1f10b34b6dc7ff4b40f668ea25ba9b8093ce61d874c784b90229b367707b/pyzmq-26.2.1-cp313-cp313-macosx_10_15_universal2.whl", hash = "sha256:651726f37fcbce9f8dd2a6dab0f024807929780621890a4dc0c75432636871be", size = 1345831 },
+    { url = "https://files.pythonhosted.org/packages/4c/8d/34884cbd4a8ec050841b5fb58d37af136766a9f95b0b2634c2971deb09da/pyzmq-26.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:57dd4d91b38fa4348e237a9388b4423b24ce9c1695bbd4ba5a3eada491e09399", size = 670773 },
+    { url = "https://files.pythonhosted.org/packages/0f/f4/d4becfcf9e416ad2564f18a6653f7c6aa917da08df5c3760edb0baa1c863/pyzmq-26.2.1-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d51a7bfe01a48e1064131f3416a5439872c533d756396be2b39e3977b41430f9", size = 908836 },
+    { url = "https://files.pythonhosted.org/packages/07/fa/ab105f1b86b85cb2e821239f1d0900fccd66192a91d97ee04661b5436b4d/pyzmq-26.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c7154d228502e18f30f150b7ce94f0789d6b689f75261b623f0fdc1eec642aab", size = 865369 },
+    { url = "https://files.pythonhosted.org/packages/c9/48/15d5f415504572dd4b92b52db5de7a5befc76bb75340ba9f36f71306a66d/pyzmq-26.2.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:f1f31661a80cc46aba381bed475a9135b213ba23ca7ff6797251af31510920ce", size = 865676 },
+    { url = "https://files.pythonhosted.org/packages/7e/35/2d91bcc7ccbb56043dd4d2c1763f24a8de5f05e06a134f767a7fb38e149c/pyzmq-26.2.1-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:290c96f479504439b6129a94cefd67a174b68ace8a8e3f551b2239a64cfa131a", size = 1201457 },
+    { url = "https://files.pythonhosted.org/packages/6d/bb/aa7c5119307a5762b8dca6c9db73e3ab4bccf32b15d7c4f376271ff72b2b/pyzmq-26.2.1-cp313-cp313-musllinux_1_1_i686.whl", hash = "sha256:f2c307fbe86e18ab3c885b7e01de942145f539165c3360e2af0f094dd440acd9", size = 1513035 },
+    { url = "https://files.pythonhosted.org/packages/4f/4c/527e6650c2fccec7750b783301329c8a8716d59423818afb67282304ce5a/pyzmq-26.2.1-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:b314268e716487bfb86fcd6f84ebbe3e5bec5fac75fdf42bc7d90fdb33f618ad", size = 1411881 },
+    { url = "https://files.pythonhosted.org/packages/89/9f/e4412ea1b3e220acc21777a5edba8885856403d29c6999aaf00a9459eb03/pyzmq-26.2.1-cp313-cp313-win32.whl", hash = "sha256:edb550616f567cd5603b53bb52a5f842c0171b78852e6fc7e392b02c2a1504bb", size = 581354 },
+    { url = "https://files.pythonhosted.org/packages/55/cd/f89dd3e9fc2da0d1619a82c4afb600c86b52bc72d7584953d460bc8d5027/pyzmq-26.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:100a826a029c8ef3d77a1d4c97cbd6e867057b5806a7276f2bac1179f893d3bf", size = 643560 },
+    { url = "https://files.pythonhosted.org/packages/a7/99/5de4f8912860013f1116f818a0047659bc20d71d1bc1d48f874bdc2d7b9c/pyzmq-26.2.1-cp313-cp313-win_arm64.whl", hash = "sha256:6991ee6c43e0480deb1b45d0c7c2bac124a6540cba7db4c36345e8e092da47ce", size = 558037 },
+    { url = "https://files.pythonhosted.org/packages/06/0b/63b6d7a2f07a77dbc9768c6302ae2d7518bed0c6cee515669ca0d8ec743e/pyzmq-26.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:25e720dba5b3a3bb2ad0ad5d33440babd1b03438a7a5220511d0c8fa677e102e", size = 938580 },
+    { url = "https://files.pythonhosted.org/packages/85/38/e5e2c3ffa23ea5f95f1c904014385a55902a11a67cd43c10edf61a653467/pyzmq-26.2.1-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:9ec6abfb701437142ce9544bd6a236addaf803a32628d2260eb3dbd9a60e2891", size = 1339670 },
+    { url = "https://files.pythonhosted.org/packages/d2/87/da5519ed7f8b31e4beee8f57311ec02926822fe23a95120877354cd80144/pyzmq-26.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e1eb9d2bfdf5b4e21165b553a81b2c3bd5be06eeddcc4e08e9692156d21f1f6", size = 660983 },
+    { url = "https://files.pythonhosted.org/packages/f6/e8/1ca6a2d59562e04d326a026c9e3f791a6f1a276ebde29da478843a566fdb/pyzmq-26.2.1-cp313-cp313t-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:90dc731d8e3e91bcd456aa7407d2eba7ac6f7860e89f3766baabb521f2c1de4a", size = 896509 },
+    { url = "https://files.pythonhosted.org/packages/5c/e5/0b4688f7c74bea7e4f1e920da973fcd7d20175f4f1181cb9b692429c6bb9/pyzmq-26.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b6a93d684278ad865fc0b9e89fe33f6ea72d36da0e842143891278ff7fd89c3", size = 853196 },
+    { url = "https://files.pythonhosted.org/packages/8f/35/c17241da01195001828319e98517683dad0ac4df6fcba68763d61b630390/pyzmq-26.2.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:c1bb37849e2294d519117dd99b613c5177934e5c04a5bb05dd573fa42026567e", size = 855133 },
+    { url = "https://files.pythonhosted.org/packages/d2/14/268ee49bbecc3f72e225addeac7f0e2bd5808747b78c7bf7f87ed9f9d5a8/pyzmq-26.2.1-cp313-cp313t-musllinux_1_1_aarch64.whl", hash = "sha256:632a09c6d8af17b678d84df442e9c3ad8e4949c109e48a72f805b22506c4afa7", size = 1191612 },
+    { url = "https://files.pythonhosted.org/packages/5e/02/6394498620b1b4349b95c534f3ebc3aef95f39afbdced5ed7ee315c49c14/pyzmq-26.2.1-cp313-cp313t-musllinux_1_1_i686.whl", hash = "sha256:fc409c18884eaf9ddde516d53af4f2db64a8bc7d81b1a0c274b8aa4e929958e8", size = 1500824 },
+    { url = "https://files.pythonhosted.org/packages/17/fc/b79f0b72891cbb9917698add0fede71dfb64e83fa3481a02ed0e78c34be7/pyzmq-26.2.1-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:17f88622b848805d3f6427ce1ad5a2aa3cf61f12a97e684dab2979802024d460", size = 1399943 },
+]
+
+[[package]]
+name = "scikit-learn"
+version = "1.6.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "joblib" },
+    { name = "numpy" },
+    { name = "scipy" },
+    { name = "threadpoolctl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9e/a5/4ae3b3a0755f7b35a280ac90b28817d1f380318973cff14075ab41ef50d9/scikit_learn-1.6.1.tar.gz", hash = "sha256:b4fc2525eca2c69a59260f583c56a7557c6ccdf8deafdba6e060f94c1c59738e", size = 7068312 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0a/18/c797c9b8c10380d05616db3bfb48e2a3358c767affd0857d56c2eb501caa/scikit_learn-1.6.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:926f207c804104677af4857b2c609940b743d04c4c35ce0ddc8ff4f053cddc1b", size = 12104516 },
+    { url = "https://files.pythonhosted.org/packages/c4/b7/2e35f8e289ab70108f8cbb2e7a2208f0575dc704749721286519dcf35f6f/scikit_learn-1.6.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:2c2cae262064e6a9b77eee1c8e768fc46aa0b8338c6a8297b9b6759720ec0ff2", size = 11167837 },
+    { url = "https://files.pythonhosted.org/packages/a4/f6/ff7beaeb644bcad72bcfd5a03ff36d32ee4e53a8b29a639f11bcb65d06cd/scikit_learn-1.6.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1061b7c028a8663fb9a1a1baf9317b64a257fcb036dae5c8752b2abef31d136f", size = 12253728 },
+    { url = "https://files.pythonhosted.org/packages/29/7a/8bce8968883e9465de20be15542f4c7e221952441727c4dad24d534c6d99/scikit_learn-1.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2e69fab4ebfc9c9b580a7a80111b43d214ab06250f8a7ef590a4edf72464dd86", size = 13147700 },
+    { url = "https://files.pythonhosted.org/packages/62/27/585859e72e117fe861c2079bcba35591a84f801e21bc1ab85bce6ce60305/scikit_learn-1.6.1-cp312-cp312-win_amd64.whl", hash = "sha256:70b1d7e85b1c96383f872a519b3375f92f14731e279a7b4c6cfd650cf5dffc52", size = 11110613 },
+    { url = "https://files.pythonhosted.org/packages/2e/59/8eb1872ca87009bdcdb7f3cdc679ad557b992c12f4b61f9250659e592c63/scikit_learn-1.6.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2ffa1e9e25b3d93990e74a4be2c2fc61ee5af85811562f1288d5d055880c4322", size = 12010001 },
+    { url = "https://files.pythonhosted.org/packages/9d/05/f2fc4effc5b32e525408524c982c468c29d22f828834f0625c5ef3d601be/scikit_learn-1.6.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:dc5cf3d68c5a20ad6d571584c0750ec641cc46aeef1c1507be51300e6003a7e1", size = 11096360 },
+    { url = "https://files.pythonhosted.org/packages/c8/e4/4195d52cf4f113573fb8ebc44ed5a81bd511a92c0228889125fac2f4c3d1/scikit_learn-1.6.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c06beb2e839ecc641366000ca84f3cf6fa9faa1777e29cf0c04be6e4d096a348", size = 12209004 },
+    { url = "https://files.pythonhosted.org/packages/94/be/47e16cdd1e7fcf97d95b3cb08bde1abb13e627861af427a3651fcb80b517/scikit_learn-1.6.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8ca8cb270fee8f1f76fa9bfd5c3507d60c6438bbee5687f81042e2bb98e5a97", size = 13171776 },
+    { url = "https://files.pythonhosted.org/packages/34/b0/ca92b90859070a1487827dbc672f998da95ce83edce1270fc23f96f1f61a/scikit_learn-1.6.1-cp313-cp313-win_amd64.whl", hash = "sha256:7a1c43c8ec9fde528d664d947dc4c0789be4077a3647f232869f41d9bf50e0fb", size = 11071865 },
+    { url = "https://files.pythonhosted.org/packages/12/ae/993b0fb24a356e71e9a894e42b8a9eec528d4c70217353a1cd7a48bc25d4/scikit_learn-1.6.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a17c1dea1d56dcda2fac315712f3651a1fea86565b64b48fa1bc090249cbf236", size = 11955804 },
+    { url = "https://files.pythonhosted.org/packages/d6/54/32fa2ee591af44507eac86406fa6bba968d1eb22831494470d0a2e4a1eb1/scikit_learn-1.6.1-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:6a7aa5f9908f0f28f4edaa6963c0a6183f1911e63a69aa03782f0d924c830a35", size = 11100530 },
+    { url = "https://files.pythonhosted.org/packages/3f/58/55856da1adec655bdce77b502e94a267bf40a8c0b89f8622837f89503b5a/scikit_learn-1.6.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0650e730afb87402baa88afbf31c07b84c98272622aaba002559b614600ca691", size = 12433852 },
+    { url = "https://files.pythonhosted.org/packages/ff/4f/c83853af13901a574f8f13b645467285a48940f185b690936bb700a50863/scikit_learn-1.6.1-cp313-cp313t-win_amd64.whl", hash = "sha256:3f59fe08dc03ea158605170eb52b22a105f238a5d512c4470ddeca71feae8e5f", size = 11337256 },
+]
+
+[[package]]
+name = "scipy"
+version = "1.15.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b7/b9/31ba9cd990e626574baf93fbc1ac61cf9ed54faafd04c479117517661637/scipy-1.15.2.tar.gz", hash = "sha256:cd58a314d92838f7e6f755c8a2167ead4f27e1fd5c1251fd54289569ef3495ec", size = 59417316 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4b/5d/3c78815cbab499610f26b5bae6aed33e227225a9fa5290008a733a64f6fc/scipy-1.15.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c4697a10da8f8765bb7c83e24a470da5797e37041edfd77fd95ba3811a47c4fd", size = 38756184 },
+    { url = "https://files.pythonhosted.org/packages/37/20/3d04eb066b471b6e171827548b9ddb3c21c6bbea72a4d84fc5989933910b/scipy-1.15.2-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:869269b767d5ee7ea6991ed7e22b3ca1f22de73ab9a49c44bad338b725603301", size = 30163558 },
+    { url = "https://files.pythonhosted.org/packages/a4/98/e5c964526c929ef1f795d4c343b2ff98634ad2051bd2bbadfef9e772e413/scipy-1.15.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:bad78d580270a4d32470563ea86c6590b465cb98f83d760ff5b0990cb5518a93", size = 22437211 },
+    { url = "https://files.pythonhosted.org/packages/1d/cd/1dc7371e29195ecbf5222f9afeedb210e0a75057d8afbd942aa6cf8c8eca/scipy-1.15.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:b09ae80010f52efddb15551025f9016c910296cf70adbf03ce2a8704f3a5ad20", size = 25232260 },
+    { url = "https://files.pythonhosted.org/packages/f0/24/1a181a9e5050090e0b5138c5f496fee33293c342b788d02586bc410c6477/scipy-1.15.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a6fd6eac1ce74a9f77a7fc724080d507c5812d61e72bd5e4c489b042455865e", size = 35198095 },
+    { url = "https://files.pythonhosted.org/packages/c0/53/eaada1a414c026673eb983f8b4a55fe5eb172725d33d62c1b21f63ff6ca4/scipy-1.15.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b871df1fe1a3ba85d90e22742b93584f8d2b8e6124f8372ab15c71b73e428b8", size = 37297371 },
+    { url = "https://files.pythonhosted.org/packages/e9/06/0449b744892ed22b7e7b9a1994a866e64895363572677a316a9042af1fe5/scipy-1.15.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:03205d57a28e18dfd39f0377d5002725bf1f19a46f444108c29bdb246b6c8a11", size = 36872390 },
+    { url = "https://files.pythonhosted.org/packages/6a/6f/a8ac3cfd9505ec695c1bc35edc034d13afbd2fc1882a7c6b473e280397bb/scipy-1.15.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:601881dfb761311045b03114c5fe718a12634e5608c3b403737ae463c9885d53", size = 39700276 },
+    { url = "https://files.pythonhosted.org/packages/f5/6f/e6e5aff77ea2a48dd96808bb51d7450875af154ee7cbe72188afb0b37929/scipy-1.15.2-cp312-cp312-win_amd64.whl", hash = "sha256:e7c68b6a43259ba0aab737237876e5c2c549a031ddb7abc28c7b47f22e202ded", size = 40942317 },
+    { url = "https://files.pythonhosted.org/packages/53/40/09319f6e0f276ea2754196185f95cd191cb852288440ce035d5c3a931ea2/scipy-1.15.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:01edfac9f0798ad6b46d9c4c9ca0e0ad23dbf0b1eb70e96adb9fa7f525eff0bf", size = 38717587 },
+    { url = "https://files.pythonhosted.org/packages/fe/c3/2854f40ecd19585d65afaef601e5e1f8dbf6758b2f95b5ea93d38655a2c6/scipy-1.15.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:08b57a9336b8e79b305a143c3655cc5bdbe6d5ece3378578888d2afbb51c4e37", size = 30100266 },
+    { url = "https://files.pythonhosted.org/packages/dd/b1/f9fe6e3c828cb5930b5fe74cb479de5f3d66d682fa8adb77249acaf545b8/scipy-1.15.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:54c462098484e7466362a9f1672d20888f724911a74c22ae35b61f9c5919183d", size = 22373768 },
+    { url = "https://files.pythonhosted.org/packages/15/9d/a60db8c795700414c3f681908a2b911e031e024d93214f2d23c6dae174ab/scipy-1.15.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:cf72ff559a53a6a6d77bd8eefd12a17995ffa44ad86c77a5df96f533d4e6c6bb", size = 25154719 },
+    { url = "https://files.pythonhosted.org/packages/37/3b/9bda92a85cd93f19f9ed90ade84aa1e51657e29988317fabdd44544f1dd4/scipy-1.15.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9de9d1416b3d9e7df9923ab23cd2fe714244af10b763975bea9e4f2e81cebd27", size = 35163195 },
+    { url = "https://files.pythonhosted.org/packages/03/5a/fc34bf1aa14dc7c0e701691fa8685f3faec80e57d816615e3625f28feb43/scipy-1.15.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fb530e4794fc8ea76a4a21ccb67dea33e5e0e60f07fc38a49e821e1eae3b71a0", size = 37255404 },
+    { url = "https://files.pythonhosted.org/packages/4a/71/472eac45440cee134c8a180dbe4c01b3ec247e0338b7c759e6cd71f199a7/scipy-1.15.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5ea7ed46d437fc52350b028b1d44e002646e28f3e8ddc714011aaf87330f2f32", size = 36860011 },
+    { url = "https://files.pythonhosted.org/packages/01/b3/21f890f4f42daf20e4d3aaa18182dddb9192771cd47445aaae2e318f6738/scipy-1.15.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:11e7ad32cf184b74380f43d3c0a706f49358b904fa7d5345f16ddf993609184d", size = 39657406 },
+    { url = "https://files.pythonhosted.org/packages/0d/76/77cf2ac1f2a9cc00c073d49e1e16244e389dd88e2490c91d84e1e3e4d126/scipy-1.15.2-cp313-cp313-win_amd64.whl", hash = "sha256:a5080a79dfb9b78b768cebf3c9dcbc7b665c5875793569f48bf0e2b1d7f68f6f", size = 40961243 },
+    { url = "https://files.pythonhosted.org/packages/4c/4b/a57f8ddcf48e129e6054fa9899a2a86d1fc6b07a0e15c7eebff7ca94533f/scipy-1.15.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:447ce30cee6a9d5d1379087c9e474628dab3db4a67484be1b7dc3196bfb2fac9", size = 38870286 },
+    { url = "https://files.pythonhosted.org/packages/0c/43/c304d69a56c91ad5f188c0714f6a97b9c1fed93128c691148621274a3a68/scipy-1.15.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:c90ebe8aaa4397eaefa8455a8182b164a6cc1d59ad53f79943f266d99f68687f", size = 30141634 },
+    { url = "https://files.pythonhosted.org/packages/44/1a/6c21b45d2548eb73be9b9bff421aaaa7e85e22c1f9b3bc44b23485dfce0a/scipy-1.15.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:def751dd08243934c884a3221156d63e15234a3155cf25978b0a668409d45eb6", size = 22415179 },
+    { url = "https://files.pythonhosted.org/packages/74/4b/aefac4bba80ef815b64f55da06f62f92be5d03b467f2ce3668071799429a/scipy-1.15.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:302093e7dfb120e55515936cb55618ee0b895f8bcaf18ff81eca086c17bd80af", size = 25126412 },
+    { url = "https://files.pythonhosted.org/packages/b1/53/1cbb148e6e8f1660aacd9f0a9dfa2b05e9ff1cb54b4386fe868477972ac2/scipy-1.15.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7cd5b77413e1855351cdde594eca99c1f4a588c2d63711388b6a1f1c01f62274", size = 34952867 },
+    { url = "https://files.pythonhosted.org/packages/2c/23/e0eb7f31a9c13cf2dca083828b97992dd22f8184c6ce4fec5deec0c81fcf/scipy-1.15.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d0194c37037707b2afa7a2f2a924cf7bac3dc292d51b6a925e5fcb89bc5c776", size = 36890009 },
+    { url = "https://files.pythonhosted.org/packages/03/f3/e699e19cabe96bbac5189c04aaa970718f0105cff03d458dc5e2b6bd1e8c/scipy-1.15.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:bae43364d600fdc3ac327db99659dcb79e6e7ecd279a75fe1266669d9a652828", size = 36545159 },
+    { url = "https://files.pythonhosted.org/packages/af/f5/ab3838e56fe5cc22383d6fcf2336e48c8fe33e944b9037fbf6cbdf5a11f8/scipy-1.15.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f031846580d9acccd0044efd1a90e6f4df3a6e12b4b6bd694a7bc03a89892b28", size = 39136566 },
+    { url = "https://files.pythonhosted.org/packages/0a/c8/b3f566db71461cabd4b2d5b39bcc24a7e1c119535c8361f81426be39bb47/scipy-1.15.2-cp313-cp313t-win_amd64.whl", hash = "sha256:fe8a9eb875d430d81755472c5ba75e84acc980e4a8f6204d402849234d3017db", size = 40477705 },
+]
+
+[[package]]
+name = "six"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
+]
+
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "asttokens" },
+    { name = "executing" },
+    { name = "pure-eval" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521 },
+]
+
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/bd/55/b5148dcbf72f5cde221f8bfe3b6a540da7aa1842f6b491ad979a6c8b84af/threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107", size = 41936 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467", size = 18414 },
+]
+
+[[package]]
+name = "tornado"
+version = "6.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/59/45/a0daf161f7d6f36c3ea5fc0c2de619746cc3dd4c76402e9db545bd920f63/tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b", size = 501135 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/7e/71f604d8cea1b58f82ba3590290b66da1e72d840aeb37e0d5f7291bd30db/tornado-6.4.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1", size = 436299 },
+    { url = "https://files.pythonhosted.org/packages/96/44/87543a3b99016d0bf54fdaab30d24bf0af2e848f1d13d34a3a5380aabe16/tornado-6.4.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:072ce12ada169c5b00b7d92a99ba089447ccc993ea2143c9ede887e0937aa803", size = 434253 },
+    { url = "https://files.pythonhosted.org/packages/cb/fb/fdf679b4ce51bcb7210801ef4f11fdac96e9885daa402861751353beea6e/tornado-6.4.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a017d239bd1bb0919f72af256a970624241f070496635784d9bf0db640d3fec", size = 437602 },
+    { url = "https://files.pythonhosted.org/packages/4f/3b/e31aeffffc22b475a64dbeb273026a21b5b566f74dee48742817626c47dc/tornado-6.4.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c36e62ce8f63409301537222faffcef7dfc5284f27eec227389f2ad11b09d946", size = 436972 },
+    { url = "https://files.pythonhosted.org/packages/22/55/b78a464de78051a30599ceb6983b01d8f732e6f69bf37b4ed07f642ac0fc/tornado-6.4.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bca9eb02196e789c9cb5c3c7c0f04fb447dc2adffd95265b2c7223a8a615ccbf", size = 437173 },
+    { url = "https://files.pythonhosted.org/packages/79/5e/be4fb0d1684eb822c9a62fb18a3e44a06188f78aa466b2ad991d2ee31104/tornado-6.4.2-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:304463bd0772442ff4d0f5149c6f1c2135a1fae045adf070821c6cdc76980634", size = 437892 },
+    { url = "https://files.pythonhosted.org/packages/f5/33/4f91fdd94ea36e1d796147003b490fe60a0215ac5737b6f9c65e160d4fe0/tornado-6.4.2-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:c82c46813ba483a385ab2a99caeaedf92585a1f90defb5693351fa7e4ea0bf73", size = 437334 },
+    { url = "https://files.pythonhosted.org/packages/2b/ae/c1b22d4524b0e10da2f29a176fb2890386f7bd1f63aacf186444873a88a0/tornado-6.4.2-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:932d195ca9015956fa502c6b56af9eb06106140d844a335590c1ec7f5277d10c", size = 437261 },
+    { url = "https://files.pythonhosted.org/packages/b5/25/36dbd49ab6d179bcfc4c6c093a51795a4f3bed380543a8242ac3517a1751/tornado-6.4.2-cp38-abi3-win32.whl", hash = "sha256:2876cef82e6c5978fde1e0d5b1f919d756968d5b4282418f3146b79b58556482", size = 438463 },
+    { url = "https://files.pythonhosted.org/packages/61/cc/58b1adeb1bb46228442081e746fcdbc4540905c87e8add7c277540934edb/tornado-6.4.2-cp38-abi3-win_amd64.whl", hash = "sha256:908b71bf3ff37d81073356a5fadcc660eb10c1476ee6e2725588626ce7e5ca38", size = 438907 },
+]
+
+[[package]]
+name = "traitlets"
+version = "5.14.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359 },
+]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/63/53559446a878410fc5a5974feb13d31d78d752eb18aeba59c7fef1af7598/wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5", size = 101301 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
+]