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": [
    {
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       "<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>14820 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",
       "[14820 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
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  {
   "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_38492\\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-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": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>reference</th>\n",
       "      <th>claude-3-5-sonnet-20240620</th>\n",
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       "      <th>gpt-4-turbo-2024-04-09</th>\n",
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       "      <th>gpt-4-1106-preview</th>\n",
       "      <th>gemini-1.5-pro-001</th>\n",
       "      <th>meta-llama-3.1-405b-instruct</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",
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       "      <td>220</td>\n",
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       "      <td>434</td>\n",
       "      <td>453</td>\n",
       "      <td>398</td>\n",
       "      <td>322</td>\n",
       "      <td>475</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>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>126</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>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>119</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>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>40</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>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>100</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>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>...</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>1</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>1</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>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>2</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>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>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>18</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>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>2</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>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>2</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 × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "model                    reference  claude-3-5-sonnet-20240620  \\\n",
       "numpy                          220                         487   \n",
       "scipy                           70                         170   \n",
       "skimage                        220                         115   \n",
       "cv2                              0                          45   \n",
       "pandas                          60                         100   \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                          12   \n",
       "SimpleITK                        0                          10   \n",
       "trimesh                          0                           0   \n",
       "itk                              0                          10   \n",
       "\n",
       "model                    gpt-4o-2024-08-06  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",
       "model                    gpt-4o-2024-05-13  gpt-4-turbo-2024-04-09  \\\n",
       "numpy                                  442                     434   \n",
       "scipy                                  118                     123   \n",
       "skimage                                102                     129   \n",
       "cv2                                     66                      63   \n",
       "pandas                                  99                     100   \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                       1   \n",
       "pillow                                   0                       2   \n",
       "nibabel                                 16                      17   \n",
       "SimpleITK                                7                       2   \n",
       "trimesh                                  0                       0   \n",
       "itk                                      6                       2   \n",
       "\n",
       "model                    claude-3-opus-20240229  gpt-4-1106-preview  \\\n",
       "numpy                                       453                 398   \n",
       "scipy                                       131                 141   \n",
       "skimage                                     125                 132   \n",
       "cv2                                          44                  57   \n",
       "pandas                                       99                  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                                    0                   0   \n",
       "statsmodels                                   0                   0   \n",
       "xarray                                        0                   0   \n",
       "pillow                                        0                   0   \n",
       "nibabel                                      10                  20   \n",
       "SimpleITK                                    10                   1   \n",
       "trimesh                                       0                   0   \n",
       "itk                                          10                   0   \n",
       "\n",
       "model                    gemini-1.5-pro-001  meta-llama-3.1-405b-instruct  \\\n",
       "numpy                                   322                           475   \n",
       "scipy                                    65                           126   \n",
       "skimage                                 149                           119   \n",
       "cv2                                      52                            40   \n",
       "pandas                                   68                           100   \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                             1   \n",
       "xarray                                    0                             0   \n",
       "pillow                                    0                             0   \n",
       "nibabel                                  10                            18   \n",
       "SimpleITK                                 4                             2   \n",
       "trimesh                                   0                             0   \n",
       "itk                                       3                             2   \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 26 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": {
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       "<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-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>\\rot{meta-llama-3.1-405b-instruct}</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",
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       "      <th>\\rot{llama3-8b-instruct-fp16}</th>\n",
       "      <th>\\rot{command-r-plus-104b-q4_0}</th>\n",
       "      <th>\\rot{codellama}</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>numpy</th>\n",
       "      <td>220</td>\n",
       "      <td>487</td>\n",
       "      <td>453</td>\n",
       "      <td>435</td>\n",
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       "      <td>412</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scipy</th>\n",
       "      <td>70</td>\n",
       "      <td>170</td>\n",
       "      <td>104</td>\n",
       "      <td>112</td>\n",
       "      <td>118</td>\n",
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       "      <td>131</td>\n",
       "      <td>141</td>\n",
       "      <td>65</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>skimage</th>\n",
       "      <td>220</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>119</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>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>40</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>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>100</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>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>...</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>1</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>1</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>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>2</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>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>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>18</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>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>2</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>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>2</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 × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         \\rot{reference}  \\rot{claude-3-5-sonnet-20240620}  \\\n",
       "numpy                                220                               487   \n",
       "scipy                                 70                               170   \n",
       "skimage                              220                               115   \n",
       "cv2                                    0                                45   \n",
       "pandas                                60                               100   \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                                12   \n",
       "SimpleITK                              0                                10   \n",
       "trimesh                                0                                 0   \n",
       "itk                                    0                                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{meta-llama-3.1-405b-instruct}  ...  \\\n",
       "numpy                                                   475  ...   \n",
       "scipy                                                   126  ...   \n",
       "skimage                                                 119  ...   \n",
       "cv2                                                      40  ...   \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                                               1  ...   \n",
       "xarray                                                    0  ...   \n",
       "pillow                                                    0  ...   \n",
       "nibabel                                                  18  ...   \n",
       "SimpleITK                                                 2  ...   \n",
       "trimesh                                                   0  ...   \n",
       "itk                                                       2  ...   \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 26 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": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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