demo/summarize_used_libraries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1413fb18-3c0e-4cb7-ba25-42d3eeae09c8",
   "metadata": {},
   "source": [
    "# Summarizing used Python libraries\n",
    "This notebook demonstrates how one can dive into summarizing generated code, in this case which Python libraries were used. The `_result.jsonl` files contain a column `completion`, which contains Python code as string. These code snipptes can be summarized for each model as shown here, e.g. by searching for common Python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da4ac394-a726-42c6-be64-be200c63bd13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45984da5-ba46-4c0c-b7d1-e842bd3e9725",
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = \"../data/\"\n",
    "# if you want to investigate a single model only, add its name here:\n",
    "search_term = \"\"\n",
    "\n",
    "# Enter the terms to search for here\n",
    "common_libraries = [\n",
    "'numpy',\n",
    "'scipy',\n",
    "'skimage',\n",
    "'cv2',\n",
    "'pandas',\n",
    "'pyclesperanto_prototype',\n",
    "'vedo',\n",
    "'umap',\n",
    "'dask',\n",
    "'zarr',\n",
    "'circle_fit',\n",
    "'statsmodels',\n",
    "'xarray',\n",
    "'pillow',\n",
    "'nibabel',\n",
    "'SimpleITK',\n",
    "'trimesh',\n",
    "'itk']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4166e77-38fa-4954-a9d7-0015efd47f8f",
   "metadata": {},
   "source": [
    "First we collect all results and the corresponding models from the jsonl files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2abe33e-1d70-4ce4-b003-3923c4ff1fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = []\n",
    "for filename in os.listdir(directory):\n",
    "    if search_term in filename and filename.endswith(\"_results.jsonl\"):\n",
    "        df = pd.read_json(directory + filename, lines=True)\n",
    "        df['model'] = filename.replace(\"samples_\",\"\").replace(\"_results\",\"\").replace(\".jsonl\",\"\")\n",
    "        collection.append(df)\n",
    "\n",
    "if len(collection) == 1:\n",
    "    df = collection[0]\n",
    "else:\n",
    "    df = pd.concat(collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fda0a750-37b4-48da-8fdf-f27f22f14695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_id</th>\n",
       "      <th>completion</th>\n",
       "      <th>full_response</th>\n",
       "      <th>result</th>\n",
       "      <th>passed</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>../test_cases/apply_otsu_threshold_and_count_p...</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import required libraries (cv2...</td>\n",
       "      <td>Here's the completed code with a step-by-step ...</td>\n",
       "      <td>failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...</td>\n",
       "      <td>False</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>../test_cases/binary_closing.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import necessary libraries (nu...</td>\n",
       "      <td>Here's the completed code with a plan, necessa...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>../test_cases/binary_skeleton.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import necessary libraries (sk...</td>\n",
       "      <td>Here's the completed code with a step-by-step ...</td>\n",
       "      <td>failed: Input must be a 2D numpy array of bool...</td>\n",
       "      <td>False</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>../test_cases/bland_altman.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import required libraries (pan...</td>\n",
       "      <td>Here's the completed code with a plan, necessa...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>../test_cases/combine_columns_of_tables.ipynb</td>\n",
       "      <td>\\n# Plan:\\n# 1. Import required library (panda...</td>\n",
       "      <td>Here's the completed code with a step-by-step ...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>claude-3-5-sonnet-20240620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>../test_cases/workflow_batch_process_folder_me...</td>\n",
       "      <td>\\n    import os\\n    import pandas as pd\\n    ...</td>\n",
       "      <td>\\n    import os\\n    import pandas as pd\\n    ...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>../test_cases/workflow_segmentation_counting.i...</td>\n",
       "      <td>\\n    import skimage\\n    import numpy as np\\n...</td>\n",
       "      <td>\\n    import skimage\\n    import numpy as np\\n...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>../test_cases/workflow_segmentation_measuremen...</td>\n",
       "      <td>\\n    import skimage\\n    import numpy as np\\n...</td>\n",
       "      <td>\\n    import skimage\\n    import numpy as np\\n...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>../test_cases/workflow_segment_measure_umap.ipynb</td>\n",
       "      <td>\\n    import numpy as np\\n    import pandas as...</td>\n",
       "      <td>\\n    import numpy as np\\n    import pandas as...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>../test_cases/workflow_watershed_segmentation_...</td>\n",
       "      <td>\\n    import skimage\\n    blurred = skimage.fi...</td>\n",
       "      <td>\\n    import skimage\\n    blurred = skimage.fi...</td>\n",
       "      <td>passed</td>\n",
       "      <td>True</td>\n",
       "      <td>reference</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15390 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               task_id  \\\n",
       "0    ../test_cases/apply_otsu_threshold_and_count_p...   \n",
       "1                   ../test_cases/binary_closing.ipynb   \n",
       "2                  ../test_cases/binary_skeleton.ipynb   \n",
       "3                     ../test_cases/bland_altman.ipynb   \n",
       "4        ../test_cases/combine_columns_of_tables.ipynb   \n",
       "..                                                 ...   \n",
       "565  ../test_cases/workflow_batch_process_folder_me...   \n",
       "566  ../test_cases/workflow_segmentation_counting.i...   \n",
       "567  ../test_cases/workflow_segmentation_measuremen...   \n",
       "568  ../test_cases/workflow_segment_measure_umap.ipynb   \n",
       "569  ../test_cases/workflow_watershed_segmentation_...   \n",
       "\n",
       "                                            completion  \\\n",
       "0    \\n# Plan:\\n# 1. Import required libraries (cv2...   \n",
       "1    \\n# Plan:\\n# 1. Import necessary libraries (nu...   \n",
       "2    \\n# Plan:\\n# 1. Import necessary libraries (sk...   \n",
       "3    \\n# Plan:\\n# 1. Import required libraries (pan...   \n",
       "4    \\n# Plan:\\n# 1. Import required library (panda...   \n",
       "..                                                 ...   \n",
       "565  \\n    import os\\n    import pandas as pd\\n    ...   \n",
       "566  \\n    import skimage\\n    import numpy as np\\n...   \n",
       "567  \\n    import skimage\\n    import numpy as np\\n...   \n",
       "568  \\n    import numpy as np\\n    import pandas as...   \n",
       "569  \\n    import skimage\\n    blurred = skimage.fi...   \n",
       "\n",
       "                                         full_response  \\\n",
       "0    Here's the completed code with a step-by-step ...   \n",
       "1    Here's the completed code with a plan, necessa...   \n",
       "2    Here's the completed code with a step-by-step ...   \n",
       "3    Here's the completed code with a plan, necessa...   \n",
       "4    Here's the completed code with a step-by-step ...   \n",
       "..                                                 ...   \n",
       "565  \\n    import os\\n    import pandas as pd\\n    ...   \n",
       "566  \\n    import skimage\\n    import numpy as np\\n...   \n",
       "567  \\n    import skimage\\n    import numpy as np\\n...   \n",
       "568  \\n    import numpy as np\\n    import pandas as...   \n",
       "569  \\n    import skimage\\n    blurred = skimage.fi...   \n",
       "\n",
       "                                                result  passed  \\\n",
       "0    failed: OpenCV(4.9.0) D:\\a\\opencv-python\\openc...   False   \n",
       "1                                               passed    True   \n",
       "2    failed: Input must be a 2D numpy array of bool...   False   \n",
       "3                                               passed    True   \n",
       "4                                               passed    True   \n",
       "..                                                 ...     ...   \n",
       "565                                             passed    True   \n",
       "566                                             passed    True   \n",
       "567                                             passed    True   \n",
       "568                                             passed    True   \n",
       "569                                             passed    True   \n",
       "\n",
       "                          model  \n",
       "0    claude-3-5-sonnet-20240620  \n",
       "1    claude-3-5-sonnet-20240620  \n",
       "2    claude-3-5-sonnet-20240620  \n",
       "3    claude-3-5-sonnet-20240620  \n",
       "4    claude-3-5-sonnet-20240620  \n",
       "..                          ...  \n",
       "565                   reference  \n",
       "566                   reference  \n",
       "567                   reference  \n",
       "568                   reference  \n",
       "569                   reference  \n",
       "\n",
       "[15390 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20c4c64f-6612-425c-86af-e2a52f109ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\n# Plan:\\n# 1. Import required libraries (cv2 for image processing, numpy for array operations)\\n# 2. Convert the input image to grayscale if it\\'s not already\\n# 3. Apply Otsu\\'s thresholding method to create a binary image\\n# 4. Count the number of positive pixels (white pixels) in the binary image\\n# 5. Return the count of positive pixels\\n\\nimport cv2\\nimport numpy as np\\n\\ndef apply_otsu_threshold_and_count_postiive_pixels(image):\\n    \"\"\"\\n    Takes an image, applies Otsu\\'s threshold method to it to create a binary image and \\n    counts the positive pixels.\\n    \"\"\"\\n    # Convert image to grayscale if it\\'s not already\\n    if len(image.shape) > 2:\\n        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n    else:\\n        gray = image\\n\\n    # Apply Otsu\\'s thresholding\\n    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\\n\\n    # Count positive pixels (white pixels)\\n    positive_pixel_count = np.sum(binary == 255)\\n\\n    return positive_pixel_count\\n',\n",
       " '\\n# Plan:\\n# 1. Import necessary libraries (numpy for array operations, scipy for morphological operations)\\n# 2. Create a square-shaped structuring element (kernel) with the given radius\\n# 3. Apply binary dilation to the input image\\n# 4. Apply binary erosion to the result of dilation\\n# 5. Return the final closed image\\n\\nimport numpy as np\\nfrom scipy import ndimage\\n\\ndef binary_closing(binary_image, radius:int=1):\\n    \"\"\"\\n    Applies binary closing to a binary_image with a square footprint with a given radius.\\n    \"\"\"\\n    # Create a square-shaped structuring element\\n    footprint = np.ones((2*radius+1, 2*radius+1), dtype=bool)\\n    \\n    # Apply binary dilation\\n    dilated = ndimage.binary_dilation(binary_image, structure=footprint)\\n    \\n    # Apply binary erosion to the dilated image\\n    closed = ndimage.binary_erosion(dilated, structure=footprint)\\n    \\n    return closed\\n',\n",
       " '\\n# Plan:\\n# 1. Import necessary libraries (skimage for skeletonization)\\n# 2. Check if the input is a valid 2D binary image\\n# 3. Apply skeletonization using skimage.morphology.skeletonize\\n# 4. Return the skeletonized image\\n\\n# Import required libraries\\nimport numpy as np\\nfrom skimage.morphology import skeletonize\\n\\ndef binary_skeleton(binary_image):\\n    \"\"\"\\n    Applies skeletonization to a 2D binary image.\\n    \"\"\"\\n    # Check if the input is a valid 2D binary image\\n    if not isinstance(binary_image, np.ndarray) or binary_image.ndim != 2 or binary_image.dtype != bool:\\n        raise ValueError(\"Input must be a 2D numpy array of boolean type\")\\n\\n    # Apply skeletonization\\n    skeleton = skeletonize(binary_image)\\n\\n    # Return the skeletonized image\\n    return skeleton\\n']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just some examples to get an idea\n",
    "df.head(3)['completion'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d134c4f6-0277-426a-ab80-81362aa08c5c",
   "metadata": {},
   "source": [
    "## Searching for common terms\n",
    "First, we search the generated code for the common libraries as specified above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a840bf73-6480-4b95-9ace-4097361fe0d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rober\\AppData\\Local\\Temp\\ipykernel_24664\\760873324.py:7: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  lib_counts = df.groupby('model').apply(count_libs, lib_list=common_libraries)\n"
     ]
    }
   ],
   "source": [
    "# Define the function to count errors\n",
    "def count_libs(group, lib_list):\n",
    "    counts = {lib: group['completion'].str.contains(lib, regex=False).sum() for lib in lib_list}\n",
    "    return pd.Series(counts)\n",
    "\n",
    "# Apply the function to each model group\n",
    "lib_counts = df.groupby('model').apply(count_libs, lib_list=common_libraries)\n",
    "\n",
    "# Transpose the result for the desired format: models as columns, libs as rows\n",
    "lib_counts = lib_counts.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c14cd389-fa56-45ef-8052-f5db1133105e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reference',\n",
       " 'claude-3-5-sonnet-20241022',\n",
       " 'claude-3-5-sonnet-20240620',\n",
       " 'gpt-4o-2024-08-06',\n",
       " 'o1-mini-2024-09-12',\n",
       " 'gpt-4o-2024-05-13',\n",
       " 'gpt-4-turbo-2024-04-09',\n",
       " 'claude-3-opus-20240229',\n",
       " 'gpt-4-1106-preview',\n",
       " 'gemini-1.5-pro-001',\n",
       " 'meta-llama-3.1-405b-instruct',\n",
       " 'gpt-4o-mini-2024-07-18',\n",
       " 'deepseek-coder-v2',\n",
       " 'llama3-70b-instruct-q8_0',\n",
       " 'llama3-70b-instruct-q4_0',\n",
       " 'gpt-3.5-turbo-1106',\n",
       " 'gemini-1.5-flash-001',\n",
       " 'codegemma-7b-instruct-fp16',\n",
       " 'mixtral-8x22b-instruct-v0.1-q4_0',\n",
       " 'mixtral-8x7b-instruct-v0.1-q5_0',\n",
       " 'phi3-3.8b-mini-instruct-4k-fp16',\n",
       " 'codellama-70b-instruct-q4_0',\n",
       " 'gemini-pro',\n",
       " 'mistral-nemo',\n",
       " 'llama3-8b-instruct-fp16',\n",
       " 'command-r-plus-104b-q4_0',\n",
       " 'codellama']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reorder columns to be similar to evaluation result plots\n",
    "with open(\"model_order.txt\", 'r') as f:\n",
    "    columns_in_order = f.readlines()\n",
    "    columns_in_order = [c.strip() for c in columns_in_order]\n",
    "columns_in_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2cef3f79-8f60-435d-9b6d-59b9f546426a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>reference</th>\n",
       "      <th>claude-3-5-sonnet-20241022</th>\n",
       "      <th>claude-3-5-sonnet-20240620</th>\n",
       "      <th>gpt-4o-2024-08-06</th>\n",
       "      <th>o1-mini-2024-09-12</th>\n",
       "      <th>gpt-4o-2024-05-13</th>\n",
       "      <th>gpt-4-turbo-2024-04-09</th>\n",
       "      <th>claude-3-opus-20240229</th>\n",
       "      <th>gpt-4-1106-preview</th>\n",
       "      <th>gemini-1.5-pro-001</th>\n",
       "      <th>...</th>\n",
       "      <th>codegemma-7b-instruct-fp16</th>\n",
       "      <th>mixtral-8x22b-instruct-v0.1-q4_0</th>\n",
       "      <th>mixtral-8x7b-instruct-v0.1-q5_0</th>\n",
       "      <th>phi3-3.8b-mini-instruct-4k-fp16</th>\n",
       "      <th>codellama-70b-instruct-q4_0</th>\n",
       "      <th>gemini-pro</th>\n",
       "      <th>mistral-nemo</th>\n",
       "      <th>llama3-8b-instruct-fp16</th>\n",
       "      <th>command-r-plus-104b-q4_0</th>\n",
       "      <th>codellama</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>numpy</th>\n",
       "      <td>220</td>\n",
       "      <td>497</td>\n",
       "      <td>487</td>\n",
       "      <td>453</td>\n",
       "      <td>435</td>\n",
       "      <td>442</td>\n",
       "      <td>434</td>\n",
       "      <td>453</td>\n",
       "      <td>398</td>\n",
       "      <td>322</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>478</td>\n",
       "      <td>392</td>\n",
       "      <td>450</td>\n",
       "      <td>426</td>\n",
       "      <td>165</td>\n",
       "      <td>403</td>\n",
       "      <td>432</td>\n",
       "      <td>412</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scipy</th>\n",
       "      <td>70</td>\n",
       "      <td>118</td>\n",
       "      <td>170</td>\n",
       "      <td>104</td>\n",
       "      <td>112</td>\n",
       "      <td>118</td>\n",
       "      <td>123</td>\n",
       "      <td>131</td>\n",
       "      <td>141</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>168</td>\n",
       "      <td>82</td>\n",
       "      <td>138</td>\n",
       "      <td>118</td>\n",
       "      <td>31</td>\n",
       "      <td>133</td>\n",
       "      <td>155</td>\n",
       "      <td>82</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skimage</th>\n",
       "      <td>220</td>\n",
       "      <td>124</td>\n",
       "      <td>115</td>\n",
       "      <td>110</td>\n",
       "      <td>108</td>\n",
       "      <td>102</td>\n",
       "      <td>129</td>\n",
       "      <td>125</td>\n",
       "      <td>132</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>118</td>\n",
       "      <td>102</td>\n",
       "      <td>129</td>\n",
       "      <td>151</td>\n",
       "      <td>116</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>131</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv2</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "      <td>66</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>107</td>\n",
       "      <td>120</td>\n",
       "      <td>82</td>\n",
       "      <td>137</td>\n",
       "      <td>192</td>\n",
       "      <td>31</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>98</td>\n",
       "      <td>72</td>\n",
       "      <td>99</td>\n",
       "      <td>81</td>\n",
       "      <td>52</td>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pyclesperanto_prototype</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vedo</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umap</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dask</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zarr</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circle_fit</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statsmodels</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xarray</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pillow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nibabel</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SimpleITK</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trimesh</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>itk</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model                    reference  claude-3-5-sonnet-20241022  \\\n",
       "numpy                          220                         497   \n",
       "scipy                           70                         118   \n",
       "skimage                        220                         124   \n",
       "cv2                              0                          56   \n",
       "pandas                          60                          95   \n",
       "pyclesperanto_prototype         40                           0   \n",
       "vedo                            20                           0   \n",
       "umap                            20                          20   \n",
       "dask                            10                           0   \n",
       "zarr                            10                          10   \n",
       "circle_fit                      10                           0   \n",
       "statsmodels                      0                           0   \n",
       "xarray                           0                           0   \n",
       "pillow                           0                           0   \n",
       "nibabel                         10                          13   \n",
       "SimpleITK                        0                           7   \n",
       "trimesh                          0                           0   \n",
       "itk                              0                           7   \n",
       "\n",
       "model                    claude-3-5-sonnet-20240620  gpt-4o-2024-08-06  \\\n",
       "numpy                                           487                453   \n",
       "scipy                                           170                104   \n",
       "skimage                                         115                110   \n",
       "cv2                                              45                 51   \n",
       "pandas                                          100                 98   \n",
       "pyclesperanto_prototype                           0                  0   \n",
       "vedo                                              0                  0   \n",
       "umap                                             20                 20   \n",
       "dask                                              0                  0   \n",
       "zarr                                             10                 10   \n",
       "circle_fit                                        0                  1   \n",
       "statsmodels                                       0                  0   \n",
       "xarray                                            0                  0   \n",
       "pillow                                            0                  0   \n",
       "nibabel                                          12                 18   \n",
       "SimpleITK                                        10                  2   \n",
       "trimesh                                           0                  0   \n",
       "itk                                              10                  2   \n",
       "\n",
       "model                    o1-mini-2024-09-12  gpt-4o-2024-05-13  \\\n",
       "numpy                                   435                442   \n",
       "scipy                                   112                118   \n",
       "skimage                                 108                102   \n",
       "cv2                                      31                 66   \n",
       "pandas                                   97                 99   \n",
       "pyclesperanto_prototype                   0                  0   \n",
       "vedo                                      0                  0   \n",
       "umap                                     20                 20   \n",
       "dask                                      0                  0   \n",
       "zarr                                     10                 10   \n",
       "circle_fit                                0                  0   \n",
       "statsmodels                               0                  0   \n",
       "xarray                                    0                  0   \n",
       "pillow                                    0                  0   \n",
       "nibabel                                  15                 16   \n",
       "SimpleITK                                 5                  7   \n",
       "trimesh                                   0                  0   \n",
       "itk                                       5                  6   \n",
       "\n",
       "model                    gpt-4-turbo-2024-04-09  claude-3-opus-20240229  \\\n",
       "numpy                                       434                     453   \n",
       "scipy                                       123                     131   \n",
       "skimage                                     129                     125   \n",
       "cv2                                          63                      44   \n",
       "pandas                                      100                      99   \n",
       "pyclesperanto_prototype                       0                       0   \n",
       "vedo                                          0                       0   \n",
       "umap                                         20                      20   \n",
       "dask                                          0                       0   \n",
       "zarr                                         10                      10   \n",
       "circle_fit                                    0                       0   \n",
       "statsmodels                                   0                       0   \n",
       "xarray                                        1                       0   \n",
       "pillow                                        2                       0   \n",
       "nibabel                                      17                      10   \n",
       "SimpleITK                                     2                      10   \n",
       "trimesh                                       0                       0   \n",
       "itk                                           2                      10   \n",
       "\n",
       "model                    gpt-4-1106-preview  gemini-1.5-pro-001  ...  \\\n",
       "numpy                                   398                 322  ...   \n",
       "scipy                                   141                  65  ...   \n",
       "skimage                                 132                 149  ...   \n",
       "cv2                                      57                  52  ...   \n",
       "pandas                                   97                  68  ...   \n",
       "pyclesperanto_prototype                   0                   0  ...   \n",
       "vedo                                      0                   0  ...   \n",
       "umap                                     20                  20  ...   \n",
       "dask                                      0                   0  ...   \n",
       "zarr                                     10                  10  ...   \n",
       "circle_fit                                0                   0  ...   \n",
       "statsmodels                               0                   0  ...   \n",
       "xarray                                    0                   0  ...   \n",
       "pillow                                    0                   0  ...   \n",
       "nibabel                                  20                  10  ...   \n",
       "SimpleITK                                 1                   4  ...   \n",
       "trimesh                                   0                   0  ...   \n",
       "itk                                       0                   3  ...   \n",
       "\n",
       "model                    codegemma-7b-instruct-fp16  \\\n",
       "numpy                                           298   \n",
       "scipy                                            76   \n",
       "skimage                                         154   \n",
       "cv2                                              43   \n",
       "pandas                                           74   \n",
       "pyclesperanto_prototype                           0   \n",
       "vedo                                              0   \n",
       "umap                                             20   \n",
       "dask                                              0   \n",
       "zarr                                             10   \n",
       "circle_fit                                        0   \n",
       "statsmodels                                       1   \n",
       "xarray                                            0   \n",
       "pillow                                            0   \n",
       "nibabel                                          14   \n",
       "SimpleITK                                         2   \n",
       "trimesh                                           1   \n",
       "itk                                               4   \n",
       "\n",
       "model                    mixtral-8x22b-instruct-v0.1-q4_0  \\\n",
       "numpy                                                 478   \n",
       "scipy                                                 168   \n",
       "skimage                                               118   \n",
       "cv2                                                    90   \n",
       "pandas                                                 98   \n",
       "pyclesperanto_prototype                                 0   \n",
       "vedo                                                    0   \n",
       "umap                                                   20   \n",
       "dask                                                    0   \n",
       "zarr                                                   10   \n",
       "circle_fit                                              0   \n",
       "statsmodels                                             0   \n",
       "xarray                                                  0   \n",
       "pillow                                                  3   \n",
       "nibabel                                                11   \n",
       "SimpleITK                                               7   \n",
       "trimesh                                                 1   \n",
       "itk                                                     8   \n",
       "\n",
       "model                    mixtral-8x7b-instruct-v0.1-q5_0  \\\n",
       "numpy                                                392   \n",
       "scipy                                                 82   \n",
       "skimage                                              102   \n",
       "cv2                                                   76   \n",
       "pandas                                                72   \n",
       "pyclesperanto_prototype                                0   \n",
       "vedo                                                   0   \n",
       "umap                                                  20   \n",
       "dask                                                   0   \n",
       "zarr                                                  10   \n",
       "circle_fit                                             0   \n",
       "statsmodels                                            0   \n",
       "xarray                                                 0   \n",
       "pillow                                                 1   \n",
       "nibabel                                               10   \n",
       "SimpleITK                                             10   \n",
       "trimesh                                                0   \n",
       "itk                                                   10   \n",
       "\n",
       "model                    phi3-3.8b-mini-instruct-4k-fp16  \\\n",
       "numpy                                                450   \n",
       "scipy                                                138   \n",
       "skimage                                              129   \n",
       "cv2                                                  107   \n",
       "pandas                                                99   \n",
       "pyclesperanto_prototype                                0   \n",
       "vedo                                                   0   \n",
       "umap                                                  20   \n",
       "dask                                                   0   \n",
       "zarr                                                  10   \n",
       "circle_fit                                             0   \n",
       "statsmodels                                            0   \n",
       "xarray                                                 0   \n",
       "pillow                                                 1   \n",
       "nibabel                                               11   \n",
       "SimpleITK                                              0   \n",
       "trimesh                                                0   \n",
       "itk                                                    0   \n",
       "\n",
       "model                    codellama-70b-instruct-q4_0  gemini-pro  \\\n",
       "numpy                                            426         165   \n",
       "scipy                                            118          31   \n",
       "skimage                                          151         116   \n",
       "cv2                                              120          82   \n",
       "pandas                                            81          52   \n",
       "pyclesperanto_prototype                            0           0   \n",
       "vedo                                               0           0   \n",
       "umap                                              16          20   \n",
       "dask                                               3           0   \n",
       "zarr                                              10          10   \n",
       "circle_fit                                         0           0   \n",
       "statsmodels                                        0           0   \n",
       "xarray                                             4           2   \n",
       "pillow                                             1           0   \n",
       "nibabel                                           10          10   \n",
       "SimpleITK                                          9           7   \n",
       "trimesh                                            0           1   \n",
       "itk                                                9           7   \n",
       "\n",
       "model                    mistral-nemo  llama3-8b-instruct-fp16  \\\n",
       "numpy                             403                      432   \n",
       "scipy                             133                      155   \n",
       "skimage                            60                       68   \n",
       "cv2                               137                      192   \n",
       "pandas                             97                       98   \n",
       "pyclesperanto_prototype             0                        0   \n",
       "vedo                                0                        0   \n",
       "umap                               20                       20   \n",
       "dask                                0                        0   \n",
       "zarr                               10                       10   \n",
       "circle_fit                          0                        0   \n",
       "statsmodels                         0                        0   \n",
       "xarray                              4                        1   \n",
       "pillow                              1                        1   \n",
       "nibabel                            10                       11   \n",
       "SimpleITK                           8                        0   \n",
       "trimesh                             0                        0   \n",
       "itk                                 6                        2   \n",
       "\n",
       "model                    command-r-plus-104b-q4_0  codellama  \n",
       "numpy                                         412        454  \n",
       "scipy                                          82        114  \n",
       "skimage                                       131         96  \n",
       "cv2                                            31        137  \n",
       "pandas                                         89         95  \n",
       "pyclesperanto_prototype                         0          0  \n",
       "vedo                                            0          0  \n",
       "umap                                           19         20  \n",
       "dask                                            0          0  \n",
       "zarr                                           10         10  \n",
       "circle_fit                                      0          0  \n",
       "statsmodels                                     1          2  \n",
       "xarray                                          4          2  \n",
       "pillow                                          1          0  \n",
       "nibabel                                        17         12  \n",
       "SimpleITK                                       0          1  \n",
       "trimesh                                         0          0  \n",
       "itk                                             0          1  \n",
       "\n",
       "[18 rows x 27 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib_counts = lib_counts[columns_in_order]\n",
    "lib_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6407686e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13.2\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "print(sns.__version__)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "df = lib_counts\n",
    "\n",
    "# Separate the numerical data for plotting and the string column for y-axis labels\n",
    "numerical_data = df.select_dtypes(include=[np.number])\n",
    "mean_values = numerical_data.mean().sort_values(ascending=False)\n",
    "\n",
    "# Plotting\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "#plot square heatmap entries\n",
    "sns.heatmap(numerical_data, annot=True, linewidths=1, fmt=\".0f\", cbar_kws={'shrink': 1, 'aspect': 2*16}, cmap=\"Blues\", robust=True)\n",
    "plt.gca().set_xlabel('')\n",
    "\n",
    "plt.savefig(\"../docs/paper/used_libraries_heatmap.png\", dpi=300,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad373812-7323-4e18-9512-95bb2e01c5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>\\rot{reference}</th>\n",
       "      <th>\\rot{claude-3-5-sonnet-20241022}</th>\n",
       "      <th>\\rot{claude-3-5-sonnet-20240620}</th>\n",
       "      <th>\\rot{gpt-4o-2024-08-06}</th>\n",
       "      <th>\\rot{o1-mini-2024-09-12}</th>\n",
       "      <th>\\rot{gpt-4o-2024-05-13}</th>\n",
       "      <th>\\rot{gpt-4-turbo-2024-04-09}</th>\n",
       "      <th>\\rot{claude-3-opus-20240229}</th>\n",
       "      <th>\\rot{gpt-4-1106-preview}</th>\n",
       "      <th>\\rot{gemini-1.5-pro-001}</th>\n",
       "      <th>...</th>\n",
       "      <th>\\rot{codegemma-7b-instruct-fp16}</th>\n",
       "      <th>\\rot{mixtral-8x22b-instruct-v0.1-q4_0}</th>\n",
       "      <th>\\rot{mixtral-8x7b-instruct-v0.1-q5_0}</th>\n",
       "      <th>\\rot{phi3-3.8b-mini-instruct-4k-fp16}</th>\n",
       "      <th>\\rot{codellama-70b-instruct-q4_0}</th>\n",
       "      <th>\\rot{gemini-pro}</th>\n",
       "      <th>\\rot{mistral-nemo}</th>\n",
       "      <th>\\rot{llama3-8b-instruct-fp16}</th>\n",
       "      <th>\\rot{command-r-plus-104b-q4_0}</th>\n",
       "      <th>\\rot{codellama}</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>numpy</th>\n",
       "      <td>220</td>\n",
       "      <td>497</td>\n",
       "      <td>487</td>\n",
       "      <td>453</td>\n",
       "      <td>435</td>\n",
       "      <td>442</td>\n",
       "      <td>434</td>\n",
       "      <td>453</td>\n",
       "      <td>398</td>\n",
       "      <td>322</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>478</td>\n",
       "      <td>392</td>\n",
       "      <td>450</td>\n",
       "      <td>426</td>\n",
       "      <td>165</td>\n",
       "      <td>403</td>\n",
       "      <td>432</td>\n",
       "      <td>412</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scipy</th>\n",
       "      <td>70</td>\n",
       "      <td>118</td>\n",
       "      <td>170</td>\n",
       "      <td>104</td>\n",
       "      <td>112</td>\n",
       "      <td>118</td>\n",
       "      <td>123</td>\n",
       "      <td>131</td>\n",
       "      <td>141</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>168</td>\n",
       "      <td>82</td>\n",
       "      <td>138</td>\n",
       "      <td>118</td>\n",
       "      <td>31</td>\n",
       "      <td>133</td>\n",
       "      <td>155</td>\n",
       "      <td>82</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skimage</th>\n",
       "      <td>220</td>\n",
       "      <td>124</td>\n",
       "      <td>115</td>\n",
       "      <td>110</td>\n",
       "      <td>108</td>\n",
       "      <td>102</td>\n",
       "      <td>129</td>\n",
       "      <td>125</td>\n",
       "      <td>132</td>\n",
       "      <td>149</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>118</td>\n",
       "      <td>102</td>\n",
       "      <td>129</td>\n",
       "      <td>151</td>\n",
       "      <td>116</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>131</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cv2</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>51</td>\n",
       "      <td>31</td>\n",
       "      <td>66</td>\n",
       "      <td>63</td>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>90</td>\n",
       "      <td>76</td>\n",
       "      <td>107</td>\n",
       "      <td>120</td>\n",
       "      <td>82</td>\n",
       "      <td>137</td>\n",
       "      <td>192</td>\n",
       "      <td>31</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pandas</th>\n",
       "      <td>60</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "      <td>99</td>\n",
       "      <td>97</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>74</td>\n",
       "      <td>98</td>\n",
       "      <td>72</td>\n",
       "      <td>99</td>\n",
       "      <td>81</td>\n",
       "      <td>52</td>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pyclesperanto_prototype</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vedo</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umap</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dask</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zarr</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circle_fit</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statsmodels</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xarray</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pillow</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nibabel</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SimpleITK</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trimesh</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>itk</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         \\rot{reference}  \\rot{claude-3-5-sonnet-20241022}  \\\n",
       "numpy                                220                               497   \n",
       "scipy                                 70                               118   \n",
       "skimage                              220                               124   \n",
       "cv2                                    0                                56   \n",
       "pandas                                60                                95   \n",
       "pyclesperanto_prototype               40                                 0   \n",
       "vedo                                  20                                 0   \n",
       "umap                                  20                                20   \n",
       "dask                                  10                                 0   \n",
       "zarr                                  10                                10   \n",
       "circle_fit                            10                                 0   \n",
       "statsmodels                            0                                 0   \n",
       "xarray                                 0                                 0   \n",
       "pillow                                 0                                 0   \n",
       "nibabel                               10                                13   \n",
       "SimpleITK                              0                                 7   \n",
       "trimesh                                0                                 0   \n",
       "itk                                    0                                 7   \n",
       "\n",
       "                         \\rot{claude-3-5-sonnet-20240620}  \\\n",
       "numpy                                                 487   \n",
       "scipy                                                 170   \n",
       "skimage                                               115   \n",
       "cv2                                                    45   \n",
       "pandas                                                100   \n",
       "pyclesperanto_prototype                                 0   \n",
       "vedo                                                    0   \n",
       "umap                                                   20   \n",
       "dask                                                    0   \n",
       "zarr                                                   10   \n",
       "circle_fit                                              0   \n",
       "statsmodels                                             0   \n",
       "xarray                                                  0   \n",
       "pillow                                                  0   \n",
       "nibabel                                                12   \n",
       "SimpleITK                                              10   \n",
       "trimesh                                                 0   \n",
       "itk                                                    10   \n",
       "\n",
       "                         \\rot{gpt-4o-2024-08-06}  \\rot{o1-mini-2024-09-12}  \\\n",
       "numpy                                        453                       435   \n",
       "scipy                                        104                       112   \n",
       "skimage                                      110                       108   \n",
       "cv2                                           51                        31   \n",
       "pandas                                        98                        97   \n",
       "pyclesperanto_prototype                        0                         0   \n",
       "vedo                                           0                         0   \n",
       "umap                                          20                        20   \n",
       "dask                                           0                         0   \n",
       "zarr                                          10                        10   \n",
       "circle_fit                                     1                         0   \n",
       "statsmodels                                    0                         0   \n",
       "xarray                                         0                         0   \n",
       "pillow                                         0                         0   \n",
       "nibabel                                       18                        15   \n",
       "SimpleITK                                      2                         5   \n",
       "trimesh                                        0                         0   \n",
       "itk                                            2                         5   \n",
       "\n",
       "                         \\rot{gpt-4o-2024-05-13}  \\\n",
       "numpy                                        442   \n",
       "scipy                                        118   \n",
       "skimage                                      102   \n",
       "cv2                                           66   \n",
       "pandas                                        99   \n",
       "pyclesperanto_prototype                        0   \n",
       "vedo                                           0   \n",
       "umap                                          20   \n",
       "dask                                           0   \n",
       "zarr                                          10   \n",
       "circle_fit                                     0   \n",
       "statsmodels                                    0   \n",
       "xarray                                         0   \n",
       "pillow                                         0   \n",
       "nibabel                                       16   \n",
       "SimpleITK                                      7   \n",
       "trimesh                                        0   \n",
       "itk                                            6   \n",
       "\n",
       "                         \\rot{gpt-4-turbo-2024-04-09}  \\\n",
       "numpy                                             434   \n",
       "scipy                                             123   \n",
       "skimage                                           129   \n",
       "cv2                                                63   \n",
       "pandas                                            100   \n",
       "pyclesperanto_prototype                             0   \n",
       "vedo                                                0   \n",
       "umap                                               20   \n",
       "dask                                                0   \n",
       "zarr                                               10   \n",
       "circle_fit                                          0   \n",
       "statsmodels                                         0   \n",
       "xarray                                              1   \n",
       "pillow                                              2   \n",
       "nibabel                                            17   \n",
       "SimpleITK                                           2   \n",
       "trimesh                                             0   \n",
       "itk                                                 2   \n",
       "\n",
       "                         \\rot{claude-3-opus-20240229}  \\\n",
       "numpy                                             453   \n",
       "scipy                                             131   \n",
       "skimage                                           125   \n",
       "cv2                                                44   \n",
       "pandas                                             99   \n",
       "pyclesperanto_prototype                             0   \n",
       "vedo                                                0   \n",
       "umap                                               20   \n",
       "dask                                                0   \n",
       "zarr                                               10   \n",
       "circle_fit                                          0   \n",
       "statsmodels                                         0   \n",
       "xarray                                              0   \n",
       "pillow                                              0   \n",
       "nibabel                                            10   \n",
       "SimpleITK                                          10   \n",
       "trimesh                                             0   \n",
       "itk                                                10   \n",
       "\n",
       "                         \\rot{gpt-4-1106-preview}  \\rot{gemini-1.5-pro-001}  \\\n",
       "numpy                                         398                       322   \n",
       "scipy                                         141                        65   \n",
       "skimage                                       132                       149   \n",
       "cv2                                            57                        52   \n",
       "pandas                                         97                        68   \n",
       "pyclesperanto_prototype                         0                         0   \n",
       "vedo                                            0                         0   \n",
       "umap                                           20                        20   \n",
       "dask                                            0                         0   \n",
       "zarr                                           10                        10   \n",
       "circle_fit                                      0                         0   \n",
       "statsmodels                                     0                         0   \n",
       "xarray                                          0                         0   \n",
       "pillow                                          0                         0   \n",
       "nibabel                                        20                        10   \n",
       "SimpleITK                                       1                         4   \n",
       "trimesh                                         0                         0   \n",
       "itk                                             0                         3   \n",
       "\n",
       "                         ...  \\rot{codegemma-7b-instruct-fp16}  \\\n",
       "numpy                    ...                               298   \n",
       "scipy                    ...                                76   \n",
       "skimage                  ...                               154   \n",
       "cv2                      ...                                43   \n",
       "pandas                   ...                                74   \n",
       "pyclesperanto_prototype  ...                                 0   \n",
       "vedo                     ...                                 0   \n",
       "umap                     ...                                20   \n",
       "dask                     ...                                 0   \n",
       "zarr                     ...                                10   \n",
       "circle_fit               ...                                 0   \n",
       "statsmodels              ...                                 1   \n",
       "xarray                   ...                                 0   \n",
       "pillow                   ...                                 0   \n",
       "nibabel                  ...                                14   \n",
       "SimpleITK                ...                                 2   \n",
       "trimesh                  ...                                 1   \n",
       "itk                      ...                                 4   \n",
       "\n",
       "                         \\rot{mixtral-8x22b-instruct-v0.1-q4_0}  \\\n",
       "numpy                                                       478   \n",
       "scipy                                                       168   \n",
       "skimage                                                     118   \n",
       "cv2                                                          90   \n",
       "pandas                                                       98   \n",
       "pyclesperanto_prototype                                       0   \n",
       "vedo                                                          0   \n",
       "umap                                                         20   \n",
       "dask                                                          0   \n",
       "zarr                                                         10   \n",
       "circle_fit                                                    0   \n",
       "statsmodels                                                   0   \n",
       "xarray                                                        0   \n",
       "pillow                                                        3   \n",
       "nibabel                                                      11   \n",
       "SimpleITK                                                     7   \n",
       "trimesh                                                       1   \n",
       "itk                                                           8   \n",
       "\n",
       "                         \\rot{mixtral-8x7b-instruct-v0.1-q5_0}  \\\n",
       "numpy                                                      392   \n",
       "scipy                                                       82   \n",
       "skimage                                                    102   \n",
       "cv2                                                         76   \n",
       "pandas                                                      72   \n",
       "pyclesperanto_prototype                                      0   \n",
       "vedo                                                         0   \n",
       "umap                                                        20   \n",
       "dask                                                         0   \n",
       "zarr                                                        10   \n",
       "circle_fit                                                   0   \n",
       "statsmodels                                                  0   \n",
       "xarray                                                       0   \n",
       "pillow                                                       1   \n",
       "nibabel                                                     10   \n",
       "SimpleITK                                                   10   \n",
       "trimesh                                                      0   \n",
       "itk                                                         10   \n",
       "\n",
       "                         \\rot{phi3-3.8b-mini-instruct-4k-fp16}  \\\n",
       "numpy                                                      450   \n",
       "scipy                                                      138   \n",
       "skimage                                                    129   \n",
       "cv2                                                        107   \n",
       "pandas                                                      99   \n",
       "pyclesperanto_prototype                                      0   \n",
       "vedo                                                         0   \n",
       "umap                                                        20   \n",
       "dask                                                         0   \n",
       "zarr                                                        10   \n",
       "circle_fit                                                   0   \n",
       "statsmodels                                                  0   \n",
       "xarray                                                       0   \n",
       "pillow                                                       1   \n",
       "nibabel                                                     11   \n",
       "SimpleITK                                                    0   \n",
       "trimesh                                                      0   \n",
       "itk                                                          0   \n",
       "\n",
       "                         \\rot{codellama-70b-instruct-q4_0}  \\rot{gemini-pro}  \\\n",
       "numpy                                                  426               165   \n",
       "scipy                                                  118                31   \n",
       "skimage                                                151               116   \n",
       "cv2                                                    120                82   \n",
       "pandas                                                  81                52   \n",
       "pyclesperanto_prototype                                  0                 0   \n",
       "vedo                                                     0                 0   \n",
       "umap                                                    16                20   \n",
       "dask                                                     3                 0   \n",
       "zarr                                                    10                10   \n",
       "circle_fit                                               0                 0   \n",
       "statsmodels                                              0                 0   \n",
       "xarray                                                   4                 2   \n",
       "pillow                                                   1                 0   \n",
       "nibabel                                                 10                10   \n",
       "SimpleITK                                                9                 7   \n",
       "trimesh                                                  0                 1   \n",
       "itk                                                      9                 7   \n",
       "\n",
       "                         \\rot{mistral-nemo}  \\rot{llama3-8b-instruct-fp16}  \\\n",
       "numpy                                   403                            432   \n",
       "scipy                                   133                            155   \n",
       "skimage                                  60                             68   \n",
       "cv2                                     137                            192   \n",
       "pandas                                   97                             98   \n",
       "pyclesperanto_prototype                   0                              0   \n",
       "vedo                                      0                              0   \n",
       "umap                                     20                             20   \n",
       "dask                                      0                              0   \n",
       "zarr                                     10                             10   \n",
       "circle_fit                                0                              0   \n",
       "statsmodels                               0                              0   \n",
       "xarray                                    4                              1   \n",
       "pillow                                    1                              1   \n",
       "nibabel                                  10                             11   \n",
       "SimpleITK                                 8                              0   \n",
       "trimesh                                   0                              0   \n",
       "itk                                       6                              2   \n",
       "\n",
       "                         \\rot{command-r-plus-104b-q4_0}  \\rot{codellama}  \n",
       "numpy                                               412              454  \n",
       "scipy                                                82              114  \n",
       "skimage                                             131               96  \n",
       "cv2                                                  31              137  \n",
       "pandas                                               89               95  \n",
       "pyclesperanto_prototype                               0                0  \n",
       "vedo                                                  0                0  \n",
       "umap                                                 19               20  \n",
       "dask                                                  0                0  \n",
       "zarr                                                 10               10  \n",
       "circle_fit                                            0                0  \n",
       "statsmodels                                           1                2  \n",
       "xarray                                                4                2  \n",
       "pillow                                                1                0  \n",
       "nibabel                                              17               12  \n",
       "SimpleITK                                             0                1  \n",
       "trimesh                                               0                0  \n",
       "itk                                                   0                1  \n",
       "\n",
       "[18 rows x 27 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate over the columns and format new names\n",
    "new_column_names = [f\"\\\\rot{{{col}}}\" for col in lib_counts.columns]\n",
    "\n",
    "# Update the DataFrame columns\n",
    "lib_counts.columns = new_column_names\n",
    "lib_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6c3519-80ba-4ff9-9197-fda947f3212b",
   "metadata": {},
   "source": [
    "Next we save the dataframe as tex to disk, so that we can include it in the manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2af7782d-3e9a-4b20-a966-0b0738d5d7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert DataFrame to TeX format string\n",
    "tex_str = lib_counts.to_latex()\n",
    "tex_str = tex_str.replace(\"_\", \"\\\\_\")\n",
    "tex_str = tex_str.replace(\"prototype\", \"\\\\\\\\ prototype\")\n",
    "\n",
    "# Define filename for the TeX file\n",
    "tex_filename = '../docs/paper/library_counts_table.tex'\n",
    "\n",
    "# Write the TeX format string to a file\n",
    "with open(tex_filename, 'w') as tex_file:\n",
    "    tex_file.write(tex_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7ee0a1-58ec-469e-a062-c8c9643b4bc3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}