diff --git a/backend/console/heuristic_filter.py b/backend/console/heuristic_filter.py
index cc5e06d..13a381f 100644
--- a/backend/console/heuristic_filter.py
+++ b/backend/console/heuristic_filter.py
@@ -45,7 +45,8 @@ class FilterPropertyName:
 	property_methanol_perm = 'methanol permeability'
 	property_n2_perm = 'N_{2} permeability'
 	property_ch4_perm = 'CH_{4} permeability'
-	
+	property_water_perm = 'water permeability'
+	# water_perm_hf_sel1k = 'water permeability'
 
 def add_args(subparsers: _SubParsersAction):
 	parser: ArgumentParser = subparsers.add_parser(
@@ -114,6 +115,7 @@ def run(args: ArgumentParser):
 	'''
 
 	# #Query for sel1k
+	# log.note("Filter for select-1k")
 	# query = '''
 	# SELECT pt.id AS para_id FROM paper_texts pt
 	# JOIN filtered_papers fp ON fp.doi = pt.doi
diff --git a/backend/console/llm_pipeline.py b/backend/console/llm_pipeline.py
index fdef675..0814023 100644
--- a/backend/console/llm_pipeline.py
+++ b/backend/console/llm_pipeline.py
@@ -60,9 +60,10 @@ def run(args: ArgumentParser):
         polyai.api.api_key = sett.LLMPipeline.polyai_key
 
     else:
-        log.critical("Unrecognized api '{}' defined in the method {}",
-                     method.api, args.method)
-        exit(1)
+        log.warn("No api being used.")
+        # log.critical("Unrecognized api '{}' defined in the method {}",
+        #              method.api, args.method)
+        # exit(1)
 
 
     para_filter_name = method.para_subset
diff --git a/backend/console/ps_ner_filter.py b/backend/console/ps_ner_filter.py
index 2c934ec..8ee985e 100644
--- a/backend/console/ps_ner_filter.py
+++ b/backend/console/ps_ner_filter.py
@@ -46,6 +46,8 @@ class HeuristicFilterName:
 	n2_perm_ner_full = "property_n2_perm" 
 	ch4_perm_ner_full = "property_ch4_perm" 
 	methanol_perm_ner_full = "property_methanol_perm"
+	water_perm_ner_full = 'property_water_perm'
+	# water_perm_ner_sel1k = 'water_perm_hf_sel1k'
 
 
 def add_args(subparsers: _SubParsersAction):
diff --git a/backend/prompt_extraction/exllama_model.py b/backend/prompt_extraction/exllama_model.py
new file mode 100644
index 0000000..881c283
--- /dev/null
+++ b/backend/prompt_extraction/exllama_model.py
@@ -0,0 +1,101 @@
+from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Cache, ExLlamaV2Tokenizer
+from exllamav2.generator import ExLlamaV2StreamingGenerator, ExLlamaV2Sampler
+from transformers import AutoTokenizer
+import time
+import pylogg as log
+
+class ExLlamaV2Model:
+    def __init__(self, model_directory, model_name, max_new_tokens=150, max_seq_len = 8000):
+        self.model_directory = model_directory
+        # self.template_path = template_path
+        self.model_name = model_name #option between [llama3, phi3]
+        self.max_new_tokens = max_new_tokens
+        self.max_seq_len = max_seq_len
+        self.model, self.cache, self.tokenizer, self.chat_tokenizer, self.config = self.initialize_model()
+        self.generator, self.settings = self.setup_generator()
+
+    def initialize_model(self):
+        """Initializes the model, tokenizer, cache, and other settings."""
+        print(f"Loading model: {self.model_directory}")
+        # template = "".join([line.strip() for line in open(self.template_path)])
+        chat_tokenizer = AutoTokenizer.from_pretrained(self.model_directory, use_fast = False)
+
+        # Sanity Check
+        if not chat_tokenizer.chat_template:
+            raise ValueError("Chat template not specified in 'tokenizer_config.json'")
+        
+        # chat_tokenizer.chat_template = template
+
+        config = ExLlamaV2Config(self.model_directory)
+        model = ExLlamaV2(config)
+        cache = ExLlamaV2Cache(model, max_seq_len = self.max_seq_len, lazy=True)
+        model.load_autosplit(cache, progress = True)
+        tokenizer = ExLlamaV2Tokenizer(config)
+
+        tokenizer.eos_token = ''
+        tokenizer.eos_token_id = 128009
+
+        return model, cache, tokenizer, chat_tokenizer, config
+
+    def setup_generator(self):
+        """Sets up the generator with the given model, cache, and tokenizer."""
+        settings = ExLlamaV2Sampler.Settings()
+        settings.temperature = 0.85
+        settings.top_k = 50
+        settings.top_p = 0.8
+        settings.token_repetition_penalty = 1.01
+
+        generator = ExLlamaV2StreamingGenerator(self.model, self.cache, self.tokenizer)
+        generator.warmup()
+
+        generator.set_stop_conditions([self.chat_tokenizer.eos_token_id])
+        
+        if self.model_name == 'llama3':
+            stop_conditions = [self.tokenizer.eos_token_id]
+            generator.set_stop_conditions(stop_conditions)
+        # elif self.model_name =='phi3':
+        #     generator.set_stop_conditions([self.chat_tokenizer.eos_token_id])
+
+        return generator, settings
+
+    def generate_text(self, chat):
+        """Generates text based on the chat history."""
+        prompt = self.chat_tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+        log.info(f"Prompt: {prompt}")
+        # input_ids = self.tokenizer.encode(prompt, add_bos=True)
+
+        input_ids = self.chat_tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+
+        self.generator.begin_stream_ex(input_ids, self.settings, loras = None, decode_special_tokens = True)
+
+        # if self.model_name=='phi3':
+        generated_tokens = []
+        output  = ''
+
+        while True:
+            res = self.generator.stream_ex()
+            chunk = res["chunk"]
+            output += chunk
+            eos = res["eos"]
+
+            generated_tokens += res['chunk_token_ids'][0].tolist()
+            print(chunk, end="", flush=True)
+
+            if eos or len(generated_tokens) == self.max_new_tokens:
+                print("\n")
+                break
+
+        return output
+
+        # generated_tokens = 0
+        # output = ''
+        # while True:
+        #     res = self.generator.stream_ex()
+        #     chunk = res["chunk"]
+        #     output += chunk
+        #     eos = res["eos"]
+        #     generated_tokens += 1
+        #     if eos or generated_tokens == self.max_new_tokens:
+        #         break
+
+        # return output
diff --git a/backend/prompt_extraction/prompt_extractor.py b/backend/prompt_extraction/prompt_extractor.py
index c21cae8..b1f9125 100644
--- a/backend/prompt_extraction/prompt_extractor.py
+++ b/backend/prompt_extraction/prompt_extractor.py
@@ -14,9 +14,17 @@
 from backend.text.normalize import TextNormalizer
 from backend.prompt_extraction.shot_selection import ShotSelector
 from backend.postgres.orm import APIRequests, PaperTexts, ExtractionMethods
+from backend.prompt_extraction.exllama_model import ExLlamaV2Model
 
 log = pylogg.New('llm')
 
+model_name = 'llama3'
+max_seq_len = 4000
+max_new_tokens = 512
+model_directory= "/data/sonakshi/SynthesisRecipes/models/Llama-3-8B-Instruct-exl2"
+model = ExLlamaV2Model(model_directory, max_new_tokens= max_new_tokens, max_seq_len = max_seq_len, model_name=model_name)
+
+
 class LLMExtractor:
     PROMPTS = [
         "Extract all numbers in JSONL format with 'material', 'property', 'value', 'condition' columns.",
@@ -184,16 +192,52 @@ def _ask_llm(self, para : PaperTexts, prompt : str,
             reqinfo.status = 'failed'
             log.error("API request failed.")
         else:
+            # reqinfo.status = 'done'
+            # try:
+            #     reqinfo.response_obj = json.loads(str(output))
+            #     str_output = output["choices"][0]["message"]["content"]
+            #     reqtok = output["usage"]["prompt_tokens"]
+            #     resptok = output["usage"]["completion_tokens"]
+            #     reqinfo.response = str_output
+            #     reqinfo.request_tokens = reqtok
+            #     reqinfo.response_tokens = resptok
+            #     reqinfo.status = 'ok'
             reqinfo.status = 'done'
             try:
                 reqinfo.response_obj = json.loads(str(output))
-                str_output = output["choices"][0]["message"]["content"]
-                reqtok = output["usage"]["prompt_tokens"]
-                resptok = output["usage"]["completion_tokens"]
+                
+                # Assuming the output is in the expected format
+                if isinstance(output, dict) and "choices" in output and output["choices"]:
+                    str_output = output["choices"][0]["message"]["content"]
+                    reqtok = output["usage"]["prompt_tokens"]
+                    resptok = output["usage"]["completion_tokens"]
+                else:
+                    # If the expected structure is not present (i.e. using Exllama)
+                    str_output = str(output)
+                    reqtok = None
+                    resptok = None
+
                 reqinfo.response = str_output
                 reqinfo.request_tokens = reqtok
                 reqinfo.response_tokens = resptok
                 reqinfo.status = 'ok'
+                
+            except json.JSONDecodeError:
+                log.error("Failed to decode JSON from output.")
+                reqinfo.status = 'json decode error'
+                reqinfo.response_obj = str(output)
+                reqinfo.response = str(output)
+                reqinfo.request_tokens = None
+                reqinfo.response_tokens = None
+
+            except KeyError as err:
+                log.error("Key error when parsing output: {}", err)
+                reqinfo.status = 'key error'
+                reqinfo.response_obj = str(output)
+                reqinfo.response = str(output)
+                reqinfo.request_tokens = None
+                reqinfo.response_tokens = None
+
             except Exception as err:
                 log.error("Failed to parse API output: {}", err)
                 reqinfo.status = 'output parse error'
@@ -227,14 +271,18 @@ def _make_request(self, messages : list[dict]) -> dict:
                     messages = messages
                 )
         else:
-            raise NotImplementedError("Unknown API", self.api)
+            response = model.generate_text(messages)
+            # raise NotImplementedError("Unknown API", self.api)
 
         return response
     
     def _extract_data(self, response : dict) -> list[dict]:
         """ Post process the LLM output and extract the embedded data. """
         data = []
-        str_output = response["choices"][0]["message"]["content"]
+        try:
+            str_output = response["choices"][0]["message"]["content"]
+        except:
+            str_output = response #when using exllama
         log.trace("Parsing LLM output: {}", str_output)
 
         try:
diff --git a/notebooks/Abstract-Extracted_Data.ipynb b/notebooks/Abstract-Extracted_Data.ipynb
index 1c37190..298e043 100644
--- a/notebooks/Abstract-Extracted_Data.ipynb
+++ b/notebooks/Abstract-Extracted_Data.ipynb
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +168,7 @@
        "[82 rows x 2 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,17 +196,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.save('notebooks/Abstract-Extracted_Data.csv')\n",
-    "propcount.save('notebooks/Abstract-Property_Count.csv')"
+    "df.save('data/Abstract-Extracted_Data.csv')\n",
+    "propcount.save('data/Abstract-Property_Count.csv')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +217,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/notebooks/Analyze-Select-1k.ipynb b/notebooks/Analyze-Select-1k.ipynb
index 1e2a69c..3c63da2 100644
--- a/notebooks/Analyze-Select-1k.ipynb
+++ b/notebooks/Analyze-Select-1k.ipynb
@@ -41,7 +41,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.111 s)\n"
+      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.071 s)\n"
      ]
     }
    ],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
     "try:\n",
     "    plt.style.use(\"~/matplotlib.mplstyle\")\n",
     "    plt.rcParams['font.family'] = 'cursive'\n",
-    "    plt.rcParams['axes.labelsize'] = 'medium'\n",
+    "    plt.rcParams['axes.labelsize'] = 'large'\n",
     "except:\n",
     "    try:\n",
     "        plt.style.use(\"PromptDataExtraction/notebooks/matplotlib.mplstyle\")\n",
@@ -310,9 +310,16 @@
     "execute(\"\"\"select * from extraction_methods order by dataset Limit 10\"\"\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Paper Count"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -353,7 +360,7 @@
        "0   1000"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -366,6 +373,514 @@
     "\"\"\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>directory</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>acs</td>\n",
+       "      <td>457468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>aip</td>\n",
+       "      <td>177932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ecs</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>elsevier</td>\n",
+       "      <td>907234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>hindawi</td>\n",
+       "      <td>6951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>informa_uk</td>\n",
+       "      <td>27900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>iop_publishing</td>\n",
+       "      <td>8007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>nature</td>\n",
+       "      <td>198487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>rsc</td>\n",
+       "      <td>392347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>springer</td>\n",
+       "      <td>111360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>wiley</td>\n",
+       "      <td>213349</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         directory   count\n",
+       "0              acs  457468\n",
+       "1              aip  177932\n",
+       "2              ecs      42\n",
+       "3         elsevier  907234\n",
+       "4          hindawi    6951\n",
+       "5       informa_uk   27900\n",
+       "6   iop_publishing    8007\n",
+       "7           nature  198487\n",
+       "8              rsc  392347\n",
+       "9         springer  111360\n",
+       "10           wiley  213349"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.rcParams['font.family'] = 'cursive'\n",
+    "plt.rcParams['axes.labelsize'] = 'large'\n",
+    "plt.rcParams['xtick.labelsize'] = 'large'\n",
+    "\n",
+    "df = execute(\"\"\"\n",
+    "             SELECT directory, count(*) FROM paper_corpus pc\n",
+    "             GROUP BY directory;\n",
+    "\"\"\")\n",
+    "total = df['count'].sum()\n",
+    "fig, ax = plt.subplots(figsize=(3.25, 2.4))\n",
+    "df.set_index('directory').plot.bar(ax=ax)\n",
+    "ax.set(ylabel='Document count', xlabel='')\n",
+    "ax.legend([f\"Total: {total:,}\"])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('notebooks/files_per_directory.png', dpi=600)\n",
+    "plt.show()\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x264 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>directory</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>acs</td>\n",
+       "      <td>202</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>aip</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>elsevier</td>\n",
+       "      <td>357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>hindawi</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nature</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>rsc</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>springer</td>\n",
+       "      <td>62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>wiley</td>\n",
+       "      <td>232</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  directory  count\n",
+       "0       acs    202\n",
+       "1       aip      8\n",
+       "2  elsevier    357\n",
+       "3   hindawi      4\n",
+       "4    nature     10\n",
+       "5       rsc    125\n",
+       "6  springer     62\n",
+       "7     wiley    232"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = execute(\"\"\"\n",
+    "    SELECT pc.directory, count(*) FROM filtered_papers fp \n",
+    "    JOIN paper_corpus pc ON fp.doi = pc.doi \n",
+    "    WHERE fp.filter_name = 'select-1k'\n",
+    "    GROUP BY pc.directory;\n",
+    "\"\"\")\n",
+    "\n",
+    "\n",
+    "total = df['count'].sum()\n",
+    "fig, ax = plt.subplots(figsize=(3.25, 2.2))\n",
+    "df.set_index('directory').plot.bar(ax=ax)\n",
+    "ax.set(ylabel='Document count', xlabel='')\n",
+    "ax.legend([f\"Total: {total:,}\"])\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('notebooks/files_select-1k.png', dpi=600)\n",
+    "plt.show()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>directory</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acs</th>\n",
+       "      <td>0.044156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aip</th>\n",
+       "      <td>0.004496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ecs</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>elsevier</th>\n",
+       "      <td>0.039350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hindawi</th>\n",
+       "      <td>0.057546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>informa_uk</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>iop_publishing</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nature</th>\n",
+       "      <td>0.005038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rsc</th>\n",
+       "      <td>0.031860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>springer</th>\n",
+       "      <td>0.055675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wiley</th>\n",
+       "      <td>0.108742</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count\n",
+       "directory               \n",
+       "acs             0.044156\n",
+       "aip             0.004496\n",
+       "ecs                  NaN\n",
+       "elsevier        0.039350\n",
+       "hindawi         0.057546\n",
+       "informa_uk           NaN\n",
+       "iop_publishing       NaN\n",
+       "nature          0.005038\n",
+       "rsc             0.031860\n",
+       "springer        0.055675\n",
+       "wiley           0.108742"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = execute(\"\"\"\n",
+    "    SELECT pc.directory, count(*) FROM filtered_papers fp \n",
+    "    JOIN paper_corpus pc ON fp.doi = pc.doi \n",
+    "    WHERE fp.filter_name = 'select-1k'\n",
+    "    GROUP BY pc.directory;\n",
+    "\"\"\").set_index('directory')\n",
+    "\n",
+    "df2 = execute(\"\"\"\n",
+    "             SELECT directory, count(*) FROM paper_corpus pc\n",
+    "             GROUP BY directory;\n",
+    "\"\"\").set_index('directory')\n",
+    "df = 100 * df1/df2\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IqD5SRHIupVKpmJyJSFaOk+Me63NJu3rX6nhFDGsQESkNkzMRkQFiciYiMkBMzkREBojJmYjIAHERChGRAVJEcg4JCcG7775bq8887nSYmqjtlBk57N69Gy+88AKsrKz03RQiegyKGNaYNWsWLl68qO9mGJTdu3fj/v37+m4GET0mRSRnOzs7dOvWTd/NeCzr16+Hq6srevbsiQ0bNuDQoUPo1asX3NzcsGXLFgDAjRs30LdvX+kzrVq1kt738fHBtGnT4OjoKP31MGXKFJw7dw7Dhw/H5MmTdX9SRFRnihjWqK9OnDiBb7/9FufOnYORkRESExMxbtw4/PTTTzAxMUFgYCDc3NxgZ2dXZR0///wzDhw4gPfffx8uLi5YunQptm/fjoSEBOzevRsdOnTQ3QkRkdYooudcXx06dAjBwcEwMnr4O/LIkSMYMWIEmjdvDmNjY0ycOBFff/21xjqcnZ3RunVrWFtb66LJRKQjTM56VnYParVajZKSEul1cXExiouLNX7exMREes59RYiUQ2fJWa1WIygoCJ6enti8eXOF8gcPHmDmzJk1Pl4Jhg8fjh07dqC4uBhCCPTs2ROhoaHIyspCQUEBtm/fjoCAADRq1EhK2vfu3cO9e/eqrdvY2Bj37t3jRUGiekpnyTk8PBxOTk6IjY1FVFQUMjIypLLbt2/jhRdewLFjx2p0vFL4+vrC398fvXr1gqurK86dO4dFixbB29sbHh4eGDVqFHx8fNCmTRsYGxtjzpw5+OCDD9CsWbMa1T1y5EjMnj1bB2dCRNqmsy1D//3vf+Pll19G7969sW7dOnTv3h1+fn7ljunfvz9OnjxZo+Mrm51x6dIl/mlPRLJS3Jah6enpsLCwAABYWVkhJSVFq8cTESmJzqbSGRsbS+OmarUaBQUFdTo+Pj6+wmd4g1ciUgqd9ZxtbGyk/S/S0tKqnfpV2+OJiJREZ8nZ3d0dkZGRAICYmBj06dNHq8cTESmJzpJzYGAgEhIS4OHhAX9/f1haWmLOnDkAgIyMDAwYMAB//PEHBgwYgLt371Y43sHBQVdNJSLSO97glYioFhQ3W4OIiGqOyZmIyAApYlc63gmFiJRGET3nkJAQdO/eXd/NICLSGkUkZ94JhYiURhHDGnZ2dho3pCciqm8U0XMmIlIaJmciIgPE5ExEZICYnImIDBCTMxGRAVLEbA0uQiEipVFEz5mLUIhIaRSRnLkIhYiURhHDGlyEQkRKo4ieMxGR0jA5ExEZICZnIiIDxORMRGSAmJyJiAyQImZrcBEKESmNInrOXIRCREqjiOTMRShEpDSKGNbgIhQiUhpF9JyJiJSGyZmIyAAxORMRGSAmZyIiA6Sz5KxWqxEUFARPT09s3ry52rKcnByMGjUKvr6+eOutt3TVTCIig6Cz5BweHg4nJyfExsYiKioKGRkZGsv27NmDWbNmISYmBkZGRvjzzz911VQiIr3T2VS62NhYvPzyywAAb29vxMXFwc/Pr8oyGxsbKYE/ePAAFhYW5err1q2brppORKRzOus5p6enSwnWysoKKSkpGsvGjBmDPXv24Mknn4StrS0cHBx01VQiIr3TWc/Z2NgYJSUlAB6OMRcUFGgsO3jwIAYOHIiIiAiMHz8eN2/eRPv27aXPxMfHV4ihUqlkPgsiIt3QWc/ZxsZG2pwoLS0N1tbWGsuOHTsGf39/qFQqeHl54fTp07pqKhGR3uksObu7uyMyMhIAEBMTgz59+mgsc3Z2xvnz5wEA586dQ5cuXXTVVCIivdNZcg4MDERCQgI8PDzg7+8PS0tLzJkzp9IyBwcHTJ06Fd999x18fHzQqlUr9OrVS1dNJSLSO5UQQui7EdqiUqmgoNMhIgPkODnusT6XtKt3rY7nCkEiIgPE5ExEZIAUsZ8zb1NFREpTo55zfHw8Dh8+DABISEhAQkKCrI2qLd6mioiUptrkPH/+fBw4cAALFy4EAJiammLatGmyN6w2eJsqIlKaaoc1oqOjERcXh2+//RYA4ODggPT0dNkbVhu8TRURKU21Pec2bdrg1KlT0tLobdu2oWPHjrI3jIioIas2Oe/atQtbtmzBnTt30LFjR8TGxmLnzp26aBsRUYNV7bDG+fPnsXfv3grv//333ygsLGQvmohIBtX2nNevXw8hBNLS0qT3Dh48iGeffVba1pOIiLSr2p6zvb09unbtipYtWyI/Px8fffQRduzYgYiICNjY2MDLywsTJ07URVuJiBqMapPzuXPn8Pvvv8PExAT379+Hn58f1Gp1ub2V9Y2LUIhIaaod1mjbti22bNmC6OhofPLJJ2jRogXu3buHu3fv4s8//4S5ubku2qkRF6EQkdJUuytdTk4Otm/fjitXrqB3797Iy8uDiYkJNm7ciKKiIuzduxdPP/20rtpbqdKec/fu3bkrHRHJSle70nHLUCKiWtBVcq52zDk9PR0bNmzA7du3yyU+znUmIpJPtWPOL774Iho3boyRI0fi5MmTGDp0KP755x9dtI2IqMGqNjlnZGRg6dKlePbZZ2Fvb4/nn38e2dnZumgbEVGDVW1y7tSpE2JjYwEAw4YNw6xZs3Djxg2520VE1KBVe0GwoKAAjRs3hpGREfLy8rB161b4+vriqaee0lUba4wXBIlIbgZzD8GdO3fCyOjhdUNTU1NMmjQJMTExj9U4uaSmpiI+Pl7fzSAi0ppqk/O2bdvKvba0tDS4mRpchEJESlNlct6wYQMcHR1x6dIldOzYER07doSjoyPs7e3h4OCgyzZWi3dCISKlqXbM2c3NDb/++quu2lMnHHMmIrkZzJhzfUnMRERKUu0KwcjISGzZsgWZmZkQQkAIAZVKZXAXBYmIlKTa5Dx16lRs3boVjo6OumgPERGhBsMabm5u8Pf3R5cuXco9akutViMoKAienp7YvHlzjcoOHTqEp59+Gv379691PCKi+qxGd0J5+umn4ePjAxMTE+n9VatW1SpQeHg4nJycsHPnTowYMQJjx45FixYtqixr1KgR5s+fj7Nnz6J58+a1PC0iovqt2uTcr18/9OvXr86BYmNj8fLLLwMAvL29ERcXBz8/vyrLrl+/jvHjx1eZmLt161bnNhERGapqk/PEiRNx8eJFJCUlISAgAAkJCY8VKD09HRYWFgAAKysrpKSkaCy7fPkycnJyMHjwYFhbWyM0NPSx4hIR1UfVjjnPnz8fBw8exMKFCwE8XMI9bdq0WgcyNjZGSUkJgIdjzAUFBRrL8vPz4eTkhOjoaLRq1UrafKlUfHx8hQcRkVJUm5yjo6OxfPlyGBsbAwAcHByQnp5e60A2NjbSTVjT0tJgbW2tsczW1haurq4AgB49eiApKanWMYmI6qtqk3ObNm1w6tQpqFQqAA/32ujYsWOtA7m7uyMyMhIAEBMTgz59+mgs8/T0xMmTJwEA58+fR48ePWodk4iovqo2Oe/atQtbtmzBnTt30LFjR8TGxj7WxkeBgYFISEiAh4cH/P39YWlpiTlz5lRa5uDggGHDhiE5ORkDBgxA69atubERETUo1e6tkZKSgubNm8PMzAwAcPv2bajVarRt21YnDawN7q2hPLrax4Copgxmb41x48YhJydHeq1WqzFlypTat4yIiGqsRvcQtLW1lV63bdsWqampsjaKiKihq3aes62tLY4fPw5vb28AwLFjx9CyZUvZG1Ybqamp0mwPImpYlDr0VW1y3rlzJyZNmoTbt29DpVLB3t4eu3fv1kHTai4kJATvvvuuvptBRKQ11Sbn1NRUREdHIycnB0IINGvWTBftqpVZs2Zh9OjRnNFBRIpR7ZjzzJkzkZmZCXNzc4NMzABgZ2fHvTaISFGq7TmPHj0a/v7+CAoKQvv27aXFKEOGDJG9cUREDVW1yTk+Ph7Ozs6IjY2V9rdQqVRMzkREMqo2Oe/atUsX7SAiojKqHXP+6aefMG7cOGlM9+jRo3jttddkbxgRUUNWoy1Dd+zYgSZNmgAABg4cKG1IRERE8qg2ORcXF0OlUkkXAm/fvo1Gjar9GBER1UG1Y87Lly/HkCFDkJycjNmzZyM6OhohISG6aFuNcYUgESlNtcl52LBh6Nu3L06dOoVGjRphxYoVsLS01EXbaowrBIlIaapNzrdv38aKFSsQGxsLY2NjDBw4EIsWLTKoBM0VgkSkNNUOHvv7+6NFixb4/vvv8c0336CoqAhjx47VRdtqjCsEiUhpqu05W1lZYeXKldLrdevWwc3NTdZGERE1dNUm5969eyMyMhKOjo4AgGvXrqFPnz64evWqdIyTk5N8LSQiaoCqTc4XLlzAhQsXKrw/ffp0AA+XcsfExGi/ZUREDVi1yfno0aO6aAcREZXB1SRERAao2p5zfcBFKESkNIroOYeEhHCOMxEpiiJ6zjVZhKLUm0ASkTIpYstQLkIhIqXhlqFERAaIW4YSERkgRWwZSkSkNNV2gYcNG4ZDhw5h+/btGDp0KH7++WcMHDiw1oHUajWCgoLg6emJzZs317iMiKghqjQ5R0VFlXucPXsWTZo0gbGxMX755RdERUXVOlB4eDicnJwQGxuLqKgoZGRk1KhszZo1GD9+/GOcGhFR/VXpsMYXX3yh8UMqlQpDhgypVaDY2Fi8/PLLAABvb2/ExcXBz89PY9n169eRnJxcaX2cnUFESlZpct61a5fWA6Wnp8PCwgLAw21IU1JSqi1buXIl5syZgzVr1mi9PUREhqzS5Lx161ZMmzYNALBs2bJKP7hkyZJaBTI2NkZJSQmAh2PMBQUFGsuOHDmCXr16SUn7UfHx8RXeK51RQvJ6nAU9XMxDVDuVjjkLIaTn9vb2lT5qy8bGRtr/Ii0tDdbW1hrLIiMjcezYMcyePRsnT57Ejh07ah2TiKi+qrTn/OKLL0rPS/dtrit3d3dERkbCy8sLMTEx5S7yVVY2cuRIAMCNGzewePFiBAcHa6UdRET1QaU950cv9r3zzjt1DhQYGIiEhAR4eHjA398flpaWmDNnTqVlDg4OdY5HRFSfVdpzLjusAQARERF1TtCNGjXCgQMHyr23fv36KstKdejQAfv27atTbCKi+qbSnvOjF9YeTdZERCSvSnvOhYWFuHXrlpSUi4qKyr0GgHbt2ummhUREDVClyfnBgwfw8fEpl4y9vb2l5yqVComJifK3roZ4JxQiUppKk/ONGzd03Iy6CQkJwbvvvqvvZhARaY0i9v6cNWsWLl68qO9mEBFpjSJuU2VnZwc7Ozt9N4OISGsU0XMmIlIaJmciIgPE5ExEZIAUMeZMRIblcXYuBLh7YVnsORMRGSBF9Jy5CIWIlEYRPeeQkBB0795d380gItIaRSRnLkIhIqVRxLAGF6EQkdIooudMRKQ0TM5ERAaIyZmIyAAxORMRGSAmZyIiA6SI2RpchEJESqOInjMXoRCR0igiOXMRChEpjSKGNbgIhYiURhE9ZyIipWFyJiIyQEzOREQGiMmZiMgA6Sw5q9VqBAUFwdPTE5s3b662LDMzE8899xz69OmDbdu26aqZREQGQWfJOTw8HE5OToiNjUVUVBQyMjI0lm3evBnr16/HqVOn8NlnnyEtLU1XTSUi0judTaWLjY3Fyy+/DADw9vZGXFwc/Pz8qiwbOXIkOnfuDAB45plncOXKFdjY2Ej1devWTVdNJyLSOZ31nNPT02FhYQEAsLKyQkpKisYyJycnqfzGjRtSoiYiagh01nM2NjZGSUkJgIdjzAUFBTUqi4mJQY8ePWBvb1+uvvj4+AoxVCqVHE0nItI5nfWcbWxspM2J0tLSYG1tXW3Z5cuXcejQIbz11lu6aiYRkUHQWXJ2d3dHZGQkgIe94T59+mgsu3//PtauXYsPP/xQV00kIjIYOkvOgYGBSEhIgIeHB/z9/WFpaYk5c+ZUWubg4IA1a9bg7Nmz8PPzw4ABAxAXF6erphIR6Z3OxpwbNWqEAwcOlHtv/fr1VZatWrUKq1at0lXziIgMClcIEhEZICZnIiIDpIj9nHmbKiJSGkX0nHmbKiJSGkUkZ96mioiURhHDGrxNFREpjSJ6zkRESsPkTERkgJiciYgMEJMzEZEBYnImIjJAipitwUUoRKQ0iug5cxEKESmNIpIzF6EQkdIoYliDi1CISGkU0XMmIlIaJmciIgPE5ExEZICYnImIDBCTMxGRAVLEbA0uQiEipVFEcg4JCcG7776r72YQGSzHyXGP9bmkXb213BKqKUUk51mzZmH06NEGtUrwcX4Y+INARKUUkZy5CIWIlIYXBImIDBCTMxGRAWJyJiIyQDpLzmq1GkFBQfD09MTmzZurLdN0PBGR0uksOYeHh8PJyQmxsbGIiopCRkaGxjJNxxMRKZ3OknNsbCz8/PwAAN7e3oiLi9NYpul4AOjWrVuFBxGRUuhsKl16ejosLCwAAFZWVkhJSdFYpul4TVQqlVbbrdqt1eoMJpau6frclPxd6pKS/90M6dyEEBXe01nP2djYGCUlJQAejicXFBRoLNN0PADEx8dXeAghIISQ7opy8eJFje8JIeDi4gIXF5dy71V1bG3qrez9ymJpo16eG8+N51a/z60yOkvONjY20v4XaWlpsLa21lim6fjq2NraYunSpbC1tdX4Xm0+X9t66xpPG23gufHceG7159wepRJVpW0tCw8PR1xcHFasWAF/f3/s3LkTDg4OVZadOXOmyuO1qXSsOj4+Xut16zOWruPx3OpnPJ6b4cbTWc85MDAQCQkJ8PDwgL+/PywtLTFnzpxKyxwcHCp9j4ioodBZz5mIiGqOKwSJiAwQkzMRkQFiciYiMkBMzgp07do1CCGQmZmJnTt34ubNm/pukmI8Ot9e27766qtyr9VqNQ4fPoysrCxZ4+pCVlaWNKc3Ly8PaWlpssUqLCyUre5HpaamylJvg03OJ06cgBACt27dwpIlS3Du3DlZ46WlpWHVqlWYPn061q1bh5ycHNlivfrqq1CpVJg5cyaMjY0xceJE2WIBwKpVq2StHwCOHTsmPY+JianwkMsbb7whJZTDhw8jMDBQtlilMTZt2oTdu3ejsLAQ77zzDq5du4YZM2ZoPdbVq1exYMECTJo0Cbm5udi0aZPWY5T10ksvoaioSHodFBQkW6yAgIAqF3do27x58zBmzBgcPHhQq7+8G2xyXr58OVQqFV5//XX4+flJ0/rk4ufnBxsbG7zxxhto3LixrD/k+fn5yMjIQElJCSZMmIAmTZrIFgt42JvctWsXsrOzoVaroVartR6j7NzREydOlHucPHlS6/FK+fr6YsyYMXjppZfw+++/IyIiQrZYAHDz5k307t0bTzzxBKZOnYozZ87g9ddfx99//631WLNmzcLChQvx119/wczMDIcOHdJ6jLLy8/Ol/4umpqbIzs6WLZaLiwsGDx6MjRs3YufOndi5c6dssfbs2YPdu3fDyMgIwcHBmD59Ok6cOFHnehVxm6rHkZubiwsXLqB58+bw8vKCqamprPGcnJwwbdo0AEDXrl0RFRUlW6ygoCBMnToVq1atQnZ2Njw8PGSLBQDnz5/HiRMnsHfvXgAP9zfRdm/21VdflZ43btwYQ4YMgbu7u9b3UgGAxMRE6Xm3bt0wduxYfPDBB5g3bx7+/vtvdOzYUesxSzVu3BidOnVCVlYWMjIypARmZKT9H9X8/Hw8ePAAAKRfrHLq1asXFi5cCC8vL5w4cQI9e/aULZarqytcXV2l13L8PymVnZ2N/fv347vvvkPnzp0xceJEJCUlYfLkydi1a9dj19tg5zlHRUXhu+++w4IFC9CsWTPs2bMHr732mmzxhg4dim7dusHc3BxZWVm4cOEC+vfvDwBYtmyZVmNFRERg8ODBMDMzQ1paGk6cOIERI0ZoNcajhBBITk5GmzZtUFBQABMTE9lixcbG4scff8Rvv/0GMzMz9OvXD9OnT9da/ZMnT66yTKVSydoLS0xMxKZNm2BmZgYrKyvk5+fj119/hb+/P6ZOnarVWHFxcXj99deRkJCAZ555BsuWLZP9Jsk//PADfvvtN/To0QP+/v6yxcnPz0dERAT++ecfBAcHIy4uDt7e3rLECggIwPjx4zFixIhyf6WuXbsWc+fOffyKRQO1ZcsWkZOTI4QQ4u7duyIkJETWeMeOHavyoW0DBw4s99rX11frMcratGmTmDlzpnB3dxf5+fnipZdekjWeEEIkJiaK7du3i6lTp4pRo0bJHk+JIiIidBrvrbfeEmq1Wnz++efCx8dHLF26VLZYo0aNEjExMWLAgAFCCCH8/f1li5WXlycOHjwoNmzYIHJycsRPP/2klXob7JjzgQMHYGZmBuDhxkuPXiXXhsuXL0vP27ZtW+Hh4+MDHx8frcctKiqSLkzk5+fLPsPgyy+/REhICMzNzdG0aVNpwyq5ODs7Y968eejcuTM2b96M0NBQ2WJ5eXnB29sb3t7ecHd3x9NPPy1bLACYOnUqevfuDW9vbym2XEJDQ3HlyhXZ6n9UXFwcVCoV9u3bh2PHjmllXLYqycnJGDBggDSckZubK1usCRMmwNbWFuHh4TAzM9PaBfIGO+YshEBaWhpsbGyQlpaG4uJircf4/PPPsXz5cgD/uwBZllx/Hi9btgyDBg2CmZkZcnNzsXDhQlnilGrdujU+/PBD5Obm4uDBg2jfvr2s8Y4dO4YffvgBYWFhCAsLg6OjI9544w1ZYpVNIEIIrFmzRpY4pa5du1bhxhJy+euvv+Dq6goXFxeYmZlBpVLh+PHjssacO3cufHx8oFarZRlHLzVlyhT06dMH//zzD0aPHl3umoW2lf4iKP1Z19YvggabnD/++GNMmDABeXl5aNKkCT7++GOtxyj9xwIeztb45JNPkJaWBktLS1nnYf7555946qmnEBUVhT/++APBwcF49tlnZYu3e/du7NixA+7u7njw4AG2bNkiWywAaNKkCZo2bYri4mKkpqaiUSP5/gAse3FQCFHutRx69OiBNWvWwNXVVUpevr6+ssQqOz1RF4qKijB69Gh4enoiPz9flp+5Uv369UNgYCASExPx5JNPwtLSEpmZmbC0tNR6rODgYFl+ETTY5Hzx4kVkZmbi7t27sLKywiuvvILff/9dtnibNm3CyZMn4efnh+joaIwZM0a2WF9++SWOHTsGX19fmJmZyTLMcPnyZXTt2hUAcPv2bQwdOhRDhw4F8LBHJueMhlmzZmHw4MFYsGAB2rVrJ1scoPwvWJVKJfuwRsuWLZGXl4fY2FgpplzJubIL0UuWLJElFgA4OjoiODgYw4YNg7m5eZVt0Ibly5djwYIFaNmyJSwtLfHZZ5/h8OHDePLJJ7Fy5UqtxgoKCsILL7xQ7heBNjTY5PzJJ5/oLFkCD2+1pVKp0LNnT3zzzTey7jFbOsyQk5Mj2zDDo0M2Zck9o2HHjh04fPgwoqOjpffkWtBQl6lQj2Pp0qU6i9WvXz/peW5uruxzuCdMmIAJEybIGqNUSkoKIiIikJubC1NTU/zyyy84cOCA1maIvP322xqn52njl06DTc66TJYA8Omnn6KoqAhLly7F2rVrsXHjRtli6WKYoWxCfvHFFzF06FBZxxDLGj16NAYPHozt27fjtddew4EDB2RdbVaVd955B++8806d6xk1apR0QdrLy0v6oRdCyDoOPGjQoHKvr169KkucUnJc/K5Kfn4+Fi5ciOLiYvj7+0OtVsPU1BTGxsZaqX/w4MFaqUeTBjvP+datW7C3t0deXh7Wrl0Lb2/vCv9ZqWY2b96M6OhotGjRAiNGjMCQIUNkTdSDBw9GdHQ0AgIC8OWXX+L555/HDz/8IFu8qvj6+sq6dFxuZX8RqNVqNGvWDEeOHNFzq7Tj8OHDOHjwIBo3bgxTU1PcuXNH+gtSG2PdZYf1KrsOoY1hvQabnEn7MjIysH79euzatQu3bt2SLc6aNWswe/ZsHD16FKtXr4aFhQW+++472eJVZeDAgTh69KhW64yOjkZYWFi56Y9yDhE1JH///TccHBy0slrw7bfflv56NDIygp+fH1q3bi2Va+PfjMmZ6uzixYsIDQ3F6dOn0a1bN4wZMwbPPPOMTmIXFRVp7U/V2pKj5+zi4oLPP/8cFhYW0nudOnXSaoxSr7/+OjZs2CC9njdvHj744ANZYunarl27cPDgwXKzouT6K2f//v04evQo7t69i86dO2Pw4MHSxfG6YHKmOpszZ47sCXnChAkaezyl+3rokhzJee7cuZg7dy6eeOIJrdZb1q+//orz58/jww8/xLx58wA8vCC4ceNGnS5KkVPPnj0RExMDKysr6b3GjRvLFi8/Px+nTp3CF198gd9//x1nz56tc50N9oIgac/MmTOxa9cubNu2DZs2bcKuXbu0Pul/xYoV0vPVq1cjODgYFhYWSEtLwxdffKHVWLpWduy3sLAQ4eHhsLa2RuPGjaFSqXDmzBmtxmvUqJF0TaA0YVlaWuLAgQNajaNPgYGBuH79ern54nIZOnQoWrZsieeeew4rV66Evb29Vuplz5nqbPDgwQgLC8OIESMQExODIUOGyLrrnoeHR7mE9cwzz+D06dOyxatKamoq7OzsZKv/+vXr6NChg2w9vj///BPOzs6y1K1vw4YNQ35+vvRajp0SS505cwZRUVG4ffs2TE1N0a9fP7z44ot1rpc9Z6ozXW89GRgYCB8fH/Tp0wfnz5/HkCFDZI135swZ6fyA/63YkyMxBwQEICIiAvPnz0d6ejru3buHr7/+WutxAOC3335DcHBwuVWrci7E0qXvv/9eZ7E8PDygUqlw9OhRnDt3Dvv379dKcmbPmepMH1tP3r17F0lJSejQoYOsvddx48bB1tYW3377LUaNGoUff/xR1rvm+Pv74/vvv8cLL7yAiIgIWf8K6devH06cOFFuIZacm0jJbcGCBXjvvfcAVH6NQq7rEkOHDoW7uzv8/Pzg6emptQvU7DlTnVlYWOD48ePIyspCWFgYmjdvLmu8qKgofP311+WuxMs13Sw1NRX79+/H1atXsXLlSly8eFGWOKU6deoEPz8/vPHGG8jOzpb1JhCPLsSS+9zkVna/67LXKOQm19xw9pypzkp7d+PGjcOwYcOwY8cOWTfVcXZ2xr59+8pdiZdrutmbb76JFStWIDQ0FBEREfjrr7+0foFOk9JVgnK4desWbt++jZSUFKxbtw5LliyRbR8PXSu76rK+Ys+Z6uzRexZ+9tlnssbz9/dHu3btYGtrK2scAPjoo48AAK+88gq8vLzQtm1brceYNGkSdu/eDUC3y7dHjRoFDw8PWFlZwcvLS9osSwm6du2KSZMm4cUXX5Q2Wapv58bkTHU2efJknd6z8MKFC+jTpw9atWolTTeTK4FFRUXhm2++Kbff99atW7Uao+xdr+XcgP5Rbdq0kXWPF30yNjaGo6Mjzp8/D0De3f3kwmENeiyPzsh49M9vOfdY1iUnJyds27at3KIQbQ+hVDdeLtemTmPHjkV2dna5ucBybeGpD6dPn8aNGzfg7OwMNzc3fTen1thzpscyaNCgcslYrVYjOTkZrVu3RmFhobQfsbasX78ec+bMAVD5do1yJZXhw4fjqaeeQosWLWSpH5B35Zoms2bN0ktcXRgzZgyMjIzg7e2NFStWoF27dli3bp2+m1UrTM70WMpu+LNp0yZcvHgROTk5iIyMxKRJk7Qez93dXXrev39//Pbbb/D09JT1ghnwcG/szp07o2vXrrINoUycOFF6XlRUhLCwMCQlJcHZ2RmBgYFajVWWLrfw1LW0tDRp0cmMGTN0tteLNjE5U52FhoZKF5NMTEyQnp6u9RhlN4ZfsmQJ+vTpI20TqlKpZLsRakREBO7evauz3u2wYcPQv39/9O3bFydPnsSBAwcUtaxaVxwdHXH48GGYm5sjKysLjo6OUrKuL2PPTM5UZ4/eeUXuW0c5ODjIev+5snx8fBAWFlZuXFbOW3AZGxtLG/gPHToUfn5+ssVSsqSkJJw9exa3b99GmzZtkJmZiRMnTtSrC4NMzlRnurjzStlx5uLiYgwaNAg9e/aUEqZcd8W+c+cO7ty5Iy0HluMWXGXrMzMzw3vvvQcLCws8ePAATZs21WqshsLIyAjLli3DiBEjMGnSJBw9elSntwDTBiZnqrOmTZvKfnGp7G2BdHnHmhEjRuD555+XNUbZIZOysczNzTF69GhZYytVbm4uLly4AAsLC3h5ecm60lIuTM5UL+jr4tVXX32FLl26oEuXLrLFKHtB8K+//pItTkOyZMkSbN++He+99x6ys7NlvbAqF85zJtJgwIAB+OWXX+Di4gIzMzNZF7wA/9uwRwiB3NxcXL9+Hb/99pts8chwMTkTGbCPP/4Y//d//6fvZpAeMDkTaZCSkoL58+fjxo0bsLe3x4cffijL/hqlyl4cFELg66+/1svNa0n/OOZMpMGUKVOwbt06dO7cGZcuXcLUqVNl2yISKH9xUKVSKWo5NdWOMjZAIJJJdnY2OnfuDODhnbFzcnJkjffSSy/B2NgYycnJaN68OXr16iVrPDJcTM5EGrzwwguYPHkyQkJCMG7cONmn1fn7++PixYtwc3PDiRMnMHbsWFnjkeHimDPRI6ZNmyZtC3rt2jUIIXDhwgW4uLjAxcVF1tgBAQE4fPiw9HrQoEH48ccfZY1JholjzkSPuHLlCi5cuABzc3O88sor+Pzzz6XhhcTERFmXb5uammLjxo3SnhDNmjWTLhLKtXUoGSb2nIkekZSUhJCQEKSlpeHIkSMYNmwYSn9M5Fi+XdaePXuqLCu7WIWUj8mZSIPjx4/LtuMdkSYc1iDSID8/HzNmzNDJnb6r88ILL+Cbb77RS2zSPSZnIg3mzJmDvXv3wtraWt9NQWZmpr6bQDrE5EykwZAhQ9CuXTvY2dnpuymy3vGFDA+TM5EGFy5cgKenJ+zt7aW9o+Xc+IioFJMz0SPK3ky2X79+5W6RRaQrTM5Ejyh7M9mym/zrW+vWrfXdBNIhTqUjIjJA3FuDyICcOnUKw4YNg5eXF0aMGIH4+Hh9N4n0hD1nIgPi5OSEQ4cOwdnZGb/++ismTZqE33//Xd/NIj1gz5nIgPj6+sLZ2RkA4ObmBldXV/02iPSGPWciA1J6r0JTU1NkZ2cjNzcX9vb2st+7kAwPkzMRkQHisAaRAUlJScGkSZMwYMAAjB07Frdu3dJ3k0hP2HMmMiABAQHl7lk4d+5cWe9ZSIaLPWciA6LrexaS4eIKQSIDUnrPQnd3d5w8eRKBgYH6bhLpCXvORHq2fv166Xl6ejoKCgoQEhKC/Px8ZGVl6a9hpFfsORPp2aN7eRjSfh6kP7wgSERkgDisQURkgJiciYgMEJMzEZEBYnImIjJATM5ERAaIyZkUr0OHDkhOTsbw4cP13RSiGmNypgahRYsW+O9//1vner755hvcuHGj7g0iqgaTM1EtMDmTrjA5k+Ko1WrMmDEDnTt3xrRp05Cbmwvg4fAGABw7dgz/+te/MGbMGCxevBglJSWYPXs2evfujcDAQGnJ9Pr16+Hq6oqePXtiw4YNWLJkCSIiIjBlyhT4+/sDAC5fvgwfHx+4ubnh1VdfRVFREQBgwIABCAkJgaurK/bs2YPp06dL7VuyZAn27dunw2+E6iVBpDDh4eHC19dX5OXlifT0dGFubi7y8vJE+/bthRBCHD16VDRp0kRER0cLIYTYtm2bWLZsmRBCiB9++EF8/PHH4vjx42LQoEGiqKhICCHE9evXhRBCTJw4URw9elQIIURxcbF4+umnxeXLl4UQQixbtkysWbNGCCGEj4+PCAgIEDk5OaKwsFA4OTmJgoICIYQQbm5uIisrSyffBdVf3FuDFOenn35CUFAQTExMYGJigmbNmlU4xs3NDYMGDQIAfP/997h06RLCwsIAAP7+/rh58yaCg4NhZPTwR6Rjx44V6rhy5QpsbGzQpUsXAMC0adMQGBiIefPmAQBmzZoFMzMzqc7IyEg4OTmhQ4cOaN68ufZPnBSFyZkURwghJdWqtG/fXnquUqmwd+/echsQ/fvf/4ZKpdJYh1qtRklJifS6uLgYxcXFlcaYMGECNm7cCFdXV4wdO7bG50INF8ecSXG8vLywd+9eFBcX4+bNm7h3716FY8om3ueeew4bNmyAEAJ5eXn4+eefMXz4cOzYsQPFxcUQQiA2NhYAYGxsjHv37uH+/fvo2rUr7ty5g/j4eADAJ598goCAgEpjuLu7IzExEbGxsXjuuefkOnVSECZnUpwRI0bA1tYWPXr0wPr16ysd1ijrlVdeQZs2bdCjRw/069cPaWlp8PX1hb+/P3r16gVXV1f8/PPPAB5e6Hvttdfw8ssvQwiBL774AtOnT0fPnj1x7949LFiwoMo4zz77LMzNzWFubq7V8yVl4pahRDqyb98+mJmZYeTIkfpuCtUD7DkT6ciRI0e4SpFqjMmZSGaXLl2Cj48PevfuDRMTE303h+oJDmsQERkg9pyJiAwQkzMRkQFiciYiMkBMzkREBojJmYjIAP0/MQrxffhkDYUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 390x264 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>directory</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>acs</th>\n",
+       "      <td>0.044156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aip</th>\n",
+       "      <td>0.004496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ecs</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>elsevier</th>\n",
+       "      <td>0.039350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hindawi</th>\n",
+       "      <td>0.057546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>informa_uk</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>iop_publishing</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nature</th>\n",
+       "      <td>0.005038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rsc</th>\n",
+       "      <td>0.031860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>springer</th>\n",
+       "      <td>0.055675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wiley</th>\n",
+       "      <td>0.108742</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count\n",
+       "directory               \n",
+       "acs             0.044156\n",
+       "aip             0.004496\n",
+       "ecs                  NaN\n",
+       "elsevier        0.039350\n",
+       "hindawi         0.057546\n",
+       "informa_uk           NaN\n",
+       "iop_publishing       NaN\n",
+       "nature          0.005038\n",
+       "rsc             0.031860\n",
+       "springer        0.055675\n",
+       "wiley           0.108742"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = df['count'].sum()\n",
+    "fig, ax = plt.subplots()\n",
+    "df.plot.bar(ax=ax)\n",
+    "ax.set(ylabel='File percentage', title=f\"All Files, Total: {total:,}\")\n",
+    "# plt.savefig('notebooks/files_per_directory.png', dpi=600)\n",
+    "plt.show()\n",
+    "df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -427,33 +942,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb Cell 10\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Number of paragraphs from the select 1k.\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m execute(\u001b[39m\"\"\"\u001b[39;49m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m    SELECT count(*) FROM paper_texts pt \u001b[39;49m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m    JOIN filtered_papers fp ON fp.doi = pt.doi \u001b[39;49m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m    WHERE fp.filter_name = \u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mselect-1k\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m;\u001b[39;49m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;49m)\n",
-      "\u001b[1;32m/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb Cell 10\u001b[0m line \u001b[0;36m5\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexecute\u001b[39m(sql, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m pd\u001b[39m.\u001b[39mDataFrame:\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\" Query the database using raw sql.\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m        Return a pandas dataframe containing the results.\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m     results \u001b[39m=\u001b[39m postgres\u001b[39m.\u001b[39;49mraw_sql(sql, kwargs)\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m results:\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Btyrion2.mse.gatech.edu/data/sonakshi/PromptDataExtraction/notebooks/Analyze-Select-1k.ipynb#X12sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/backend/postgres/__init__.py:95\u001b[0m, in \u001b[0;36mraw_sql\u001b[0;34m(query, params, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(\u001b[39mdict\u001b[39m(params))\n\u001b[1;32m     94\u001b[0m sess \u001b[39m=\u001b[39m session()\n\u001b[0;32m---> 95\u001b[0m results \u001b[39m=\u001b[39m sess\u001b[39m.\u001b[39;49mexecute(sa\u001b[39m.\u001b[39;49mtext(query), kwargs)\n\u001b[1;32m     96\u001b[0m sess\u001b[39m.\u001b[39mclose()\n\u001b[1;32m     98\u001b[0m Row \u001b[39m=\u001b[39m namedtuple(\u001b[39m'\u001b[39m\u001b[39mRow\u001b[39m\u001b[39m'\u001b[39m, results\u001b[39m.\u001b[39mkeys())\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/orm/scoping.py:738\u001b[0m, in \u001b[0;36mscoped_session.execute\u001b[0;34m(self, statement, params, execution_options, bind_arguments, _parent_execute_state, _add_event)\u001b[0m\n\u001b[1;32m    670\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexecute\u001b[39m(\n\u001b[1;32m    671\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    672\u001b[0m     statement: Executable,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    678\u001b[0m     _add_event: Optional[Any] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m    679\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Result[Any]:\n\u001b[1;32m    680\u001b[0m \u001b[39m    \u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"Execute a SQL expression construct.\u001b[39;00m\n\u001b[1;32m    681\u001b[0m \n\u001b[1;32m    682\u001b[0m \u001b[39m    .. container:: class_bases\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    735\u001b[0m \n\u001b[1;32m    736\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m  \u001b[39m# noqa: E501\u001b[39;00m\n\u001b[0;32m--> 738\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_proxied\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m    739\u001b[0m         statement,\n\u001b[1;32m    740\u001b[0m         params\u001b[39m=\u001b[39;49mparams,\n\u001b[1;32m    741\u001b[0m         execution_options\u001b[39m=\u001b[39;49mexecution_options,\n\u001b[1;32m    742\u001b[0m         bind_arguments\u001b[39m=\u001b[39;49mbind_arguments,\n\u001b[1;32m    743\u001b[0m         _parent_execute_state\u001b[39m=\u001b[39;49m_parent_execute_state,\n\u001b[1;32m    744\u001b[0m         _add_event\u001b[39m=\u001b[39;49m_add_event,\n\u001b[1;32m    745\u001b[0m     )\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/orm/session.py:2262\u001b[0m, in \u001b[0;36mSession.execute\u001b[0;34m(self, statement, params, execution_options, bind_arguments, _parent_execute_state, _add_event)\u001b[0m\n\u001b[1;32m   2201\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mexecute\u001b[39m(\n\u001b[1;32m   2202\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m   2203\u001b[0m     statement: Executable,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2209\u001b[0m     _add_event: Optional[Any] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   2210\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Result[Any]:\n\u001b[1;32m   2211\u001b[0m \u001b[39m    \u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"Execute a SQL expression construct.\u001b[39;00m\n\u001b[1;32m   2212\u001b[0m \n\u001b[1;32m   2213\u001b[0m \u001b[39m    Returns a :class:`_engine.Result` object representing\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2260\u001b[0m \n\u001b[1;32m   2261\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2262\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_execute_internal(\n\u001b[1;32m   2263\u001b[0m         statement,\n\u001b[1;32m   2264\u001b[0m         params,\n\u001b[1;32m   2265\u001b[0m         execution_options\u001b[39m=\u001b[39;49mexecution_options,\n\u001b[1;32m   2266\u001b[0m         bind_arguments\u001b[39m=\u001b[39;49mbind_arguments,\n\u001b[1;32m   2267\u001b[0m         _parent_execute_state\u001b[39m=\u001b[39;49m_parent_execute_state,\n\u001b[1;32m   2268\u001b[0m         _add_event\u001b[39m=\u001b[39;49m_add_event,\n\u001b[1;32m   2269\u001b[0m     )\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/orm/session.py:2153\u001b[0m, in \u001b[0;36mSession._execute_internal\u001b[0;34m(self, statement, params, execution_options, bind_arguments, _parent_execute_state, _add_event, _scalar_result)\u001b[0m\n\u001b[1;32m   2144\u001b[0m     result: Result[Any] \u001b[39m=\u001b[39m compile_state_cls\u001b[39m.\u001b[39morm_execute_statement(\n\u001b[1;32m   2145\u001b[0m         \u001b[39mself\u001b[39m,\n\u001b[1;32m   2146\u001b[0m         statement,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2150\u001b[0m         conn,\n\u001b[1;32m   2151\u001b[0m     )\n\u001b[1;32m   2152\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 2153\u001b[0m     result \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m   2154\u001b[0m         statement, params \u001b[39mor\u001b[39;49;00m {}, execution_options\u001b[39m=\u001b[39;49mexecution_options\n\u001b[1;32m   2155\u001b[0m     )\n\u001b[1;32m   2157\u001b[0m \u001b[39mif\u001b[39;00m _scalar_result:\n\u001b[1;32m   2158\u001b[0m     \u001b[39mreturn\u001b[39;00m result\u001b[39m.\u001b[39mscalar()\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1412\u001b[0m, in \u001b[0;36mConnection.execute\u001b[0;34m(self, statement, parameters, execution_options)\u001b[0m\n\u001b[1;32m   1410\u001b[0m     \u001b[39mraise\u001b[39;00m exc\u001b[39m.\u001b[39mObjectNotExecutableError(statement) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   1411\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1412\u001b[0m     \u001b[39mreturn\u001b[39;00m meth(\n\u001b[1;32m   1413\u001b[0m         \u001b[39mself\u001b[39;49m,\n\u001b[1;32m   1414\u001b[0m         distilled_parameters,\n\u001b[1;32m   1415\u001b[0m         execution_options \u001b[39mor\u001b[39;49;00m NO_OPTIONS,\n\u001b[1;32m   1416\u001b[0m     )\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/sql/elements.py:515\u001b[0m, in \u001b[0;36mClauseElement._execute_on_connection\u001b[0;34m(self, connection, distilled_params, execution_options)\u001b[0m\n\u001b[1;32m    513\u001b[0m     \u001b[39mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m    514\u001b[0m         \u001b[39massert\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m, Executable)\n\u001b[0;32m--> 515\u001b[0m     \u001b[39mreturn\u001b[39;00m connection\u001b[39m.\u001b[39;49m_execute_clauseelement(\n\u001b[1;32m    516\u001b[0m         \u001b[39mself\u001b[39;49m, distilled_params, execution_options\n\u001b[1;32m    517\u001b[0m     )\n\u001b[1;32m    518\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    519\u001b[0m     \u001b[39mraise\u001b[39;00m exc\u001b[39m.\u001b[39mObjectNotExecutableError(\u001b[39mself\u001b[39m)\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1635\u001b[0m, in \u001b[0;36mConnection._execute_clauseelement\u001b[0;34m(self, elem, distilled_parameters, execution_options)\u001b[0m\n\u001b[1;32m   1623\u001b[0m compiled_cache: Optional[CompiledCacheType] \u001b[39m=\u001b[39m execution_options\u001b[39m.\u001b[39mget(\n\u001b[1;32m   1624\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mcompiled_cache\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine\u001b[39m.\u001b[39m_compiled_cache\n\u001b[1;32m   1625\u001b[0m )\n\u001b[1;32m   1627\u001b[0m compiled_sql, extracted_params, cache_hit \u001b[39m=\u001b[39m elem\u001b[39m.\u001b[39m_compile_w_cache(\n\u001b[1;32m   1628\u001b[0m     dialect\u001b[39m=\u001b[39mdialect,\n\u001b[1;32m   1629\u001b[0m     compiled_cache\u001b[39m=\u001b[39mcompiled_cache,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1633\u001b[0m     linting\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdialect\u001b[39m.\u001b[39mcompiler_linting \u001b[39m|\u001b[39m compiler\u001b[39m.\u001b[39mWARN_LINTING,\n\u001b[1;32m   1634\u001b[0m )\n\u001b[0;32m-> 1635\u001b[0m ret \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_execute_context(\n\u001b[1;32m   1636\u001b[0m     dialect,\n\u001b[1;32m   1637\u001b[0m     dialect\u001b[39m.\u001b[39;49mexecution_ctx_cls\u001b[39m.\u001b[39;49m_init_compiled,\n\u001b[1;32m   1638\u001b[0m     compiled_sql,\n\u001b[1;32m   1639\u001b[0m     distilled_parameters,\n\u001b[1;32m   1640\u001b[0m     execution_options,\n\u001b[1;32m   1641\u001b[0m     compiled_sql,\n\u001b[1;32m   1642\u001b[0m     distilled_parameters,\n\u001b[1;32m   1643\u001b[0m     elem,\n\u001b[1;32m   1644\u001b[0m     extracted_params,\n\u001b[1;32m   1645\u001b[0m     cache_hit\u001b[39m=\u001b[39;49mcache_hit,\n\u001b[1;32m   1646\u001b[0m )\n\u001b[1;32m   1647\u001b[0m \u001b[39mif\u001b[39;00m has_events:\n\u001b[1;32m   1648\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdispatch\u001b[39m.\u001b[39mafter_execute(\n\u001b[1;32m   1649\u001b[0m         \u001b[39mself\u001b[39m,\n\u001b[1;32m   1650\u001b[0m         elem,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1654\u001b[0m         ret,\n\u001b[1;32m   1655\u001b[0m     )\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1844\u001b[0m, in \u001b[0;36mConnection._execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m   1839\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exec_insertmany_context(\n\u001b[1;32m   1840\u001b[0m         dialect,\n\u001b[1;32m   1841\u001b[0m         context,\n\u001b[1;32m   1842\u001b[0m     )\n\u001b[1;32m   1843\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1844\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_exec_single_context(\n\u001b[1;32m   1845\u001b[0m         dialect, context, statement, parameters\n\u001b[1;32m   1846\u001b[0m     )\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1984\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m   1981\u001b[0m     result \u001b[39m=\u001b[39m context\u001b[39m.\u001b[39m_setup_result_proxy()\n\u001b[1;32m   1983\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m-> 1984\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_handle_dbapi_exception(\n\u001b[1;32m   1985\u001b[0m         e, str_statement, effective_parameters, cursor, context\n\u001b[1;32m   1986\u001b[0m     )\n\u001b[1;32m   1988\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/base.py:2342\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001b[0m\n\u001b[1;32m   2340\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2341\u001b[0m         \u001b[39massert\u001b[39;00m exc_info[\u001b[39m1\u001b[39m] \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 2342\u001b[0m         \u001b[39mraise\u001b[39;00m exc_info[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mwith_traceback(exc_info[\u001b[39m2\u001b[39m])\n\u001b[1;32m   2343\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m   2344\u001b[0m     \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reentrant_error\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1965\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m   1963\u001b[0m                 \u001b[39mbreak\u001b[39;00m\n\u001b[1;32m   1964\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m evt_handled:\n\u001b[0;32m-> 1965\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdialect\u001b[39m.\u001b[39;49mdo_execute(\n\u001b[1;32m   1966\u001b[0m             cursor, str_statement, effective_parameters, context\n\u001b[1;32m   1967\u001b[0m         )\n\u001b[1;32m   1969\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_has_events \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mengine\u001b[39m.\u001b[39m_has_events:\n\u001b[1;32m   1970\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdispatch\u001b[39m.\u001b[39mafter_cursor_execute(\n\u001b[1;32m   1971\u001b[0m         \u001b[39mself\u001b[39m,\n\u001b[1;32m   1972\u001b[0m         cursor,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1976\u001b[0m         context\u001b[39m.\u001b[39mexecutemany,\n\u001b[1;32m   1977\u001b[0m     )\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/sqlalchemy/engine/default.py:921\u001b[0m, in \u001b[0;36mDefaultDialect.do_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m    920\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdo_execute\u001b[39m(\u001b[39mself\u001b[39m, cursor, statement, parameters, context\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m--> 921\u001b[0m     cursor\u001b[39m.\u001b[39;49mexecute(statement, parameters)\n",
-      "File \u001b[0;32m/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/encodings/utf_8.py:15\u001b[0m, in \u001b[0;36mdecode\u001b[0;34m(input, errors)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[39m### Codec APIs\u001b[39;00m\n\u001b[1;32m     13\u001b[0m encode \u001b[39m=\u001b[39m codecs\u001b[39m.\u001b[39mutf_8_encode\n\u001b[0;32m---> 15\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecode\u001b[39m(\u001b[39minput\u001b[39m, errors\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mstrict\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m     16\u001b[0m     \u001b[39mreturn\u001b[39;00m codecs\u001b[39m.\u001b[39mutf_8_decode(\u001b[39minput\u001b[39m, errors, \u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     18\u001b[0m \u001b[39mclass\u001b[39;00m \u001b[39mIncrementalEncoder\u001b[39;00m(codecs\u001b[39m.\u001b[39mIncrementalEncoder):\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "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>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>37434</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count\n",
+       "0  37434"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -465,6 +997,13 @@
     "\"\"\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Method Lists"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -501,7 +1040,7 @@
     "    50: 'water contact angle',\n",
     "    51: 'dielectric constant',\n",
     "    52: 'density',\n",
-    "    53:  'bandgap',\n",
+    "    53: 'bandgap',\n",
     "    54: 'limiting oxygen index',\n",
     "    55: 'hardness',\n",
     "    56: 'lower critical solution temperature',\n",
@@ -567,7 +1106,7 @@
     "    156: 'water contact angle',\n",
     "    129: 'dielectric constant',\n",
     "    134: 'density',\n",
-    "    131:  'bandgap',\n",
+    "    131: 'bandgap',\n",
     "    158: 'limiting oxygen index',\n",
     "    170: 'hardness',\n",
     "    173: 'lower critical solution temperature',\n",
@@ -616,6 +1155,86 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parity Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from sklearn.metrics import r2_score, mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "propcode = \"tg\"\n",
+    "propname = \"Tg (K)\"\n",
+    "method_mbt = 38\n",
+    "method_gpt = 164\n",
+    "prop_lower = 100\n",
+    "prop_higher = 700\n",
+    "\n",
+    "# propcode = 'bandgap'\n",
+    "# propname = \"Bandgap (eV)\"\n",
+    "# method_mbt = 53\n",
+    "# method_gpt = 131\n",
+    "# prop_lower = 0\n",
+    "# prop_higher = 10\n",
+    "\n",
+    "\n",
+    "saveas = f\"notebooks/sel1k-methods-parity.{propcode}.png\"\n",
+    "\n",
+    "data = execute(f\"\"\"\n",
+    "        SELECT * FROM (\n",
+    "            SELECT em.entity_name AS material, em.para_id,\n",
+    "                max(CASE WHEN ep.method_id = :gpt THEN ep.numeric_value END) AS gpt,\n",
+    "                max(CASE WHEN ep.method_id = :mbt  THEN ep.numeric_value END) AS mbt\n",
+    "            FROM extracted_properties ep\n",
+    "            JOIN extracted_materials em ON em.id = ep.material_id\n",
+    "            WHERE (ep.method_id = :gpt OR ep.method_id = :mbt)\n",
+    "            AND ep.numeric_value >= :low AND ep.numeric_value <= :high\n",
+    "            GROUP BY em.entity_name, em.para_id\n",
+    "        ) AS ft\n",
+    "        WHERE ft.gpt IS NOT NULL and ft.mbt IS NOT NULL;\n",
+    "    \"\"\", gpt=method_gpt, mbt=method_mbt, low = prop_lower, high = prop_higher)\n",
+    "\n",
+    "rmse = mean_squared_error(data.mbt, data.gpt, squared=False)\n",
+    "r2 = r2_score(data.mbt, data.gpt)\n",
+    "\n",
+    "metrics = f\"RMSE = {rmse:0.2f}\\n$R^2$ = {r2:0.2f}\"\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3.25, 2.5))\n",
+    "ax.plot(data.mbt, data.gpt, '.', label = metrics)\n",
+    "ax.set(xlabel=f\"MaterialsBERT, {propname}\", ylabel=f\"GPT-3.5, {propname}\")\n",
+    "ax.legend(loc='upper left', frameon=True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(saveas, dpi=600)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2184,7 +2803,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data4 - General NER - GPT 3.5 - Similar Pipeline."
+    "# Data4 - General NER - GPT 3.5 - Similar Pipeline."
    ]
   },
   {
@@ -2724,7 +3343,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data3 - Property-specific NER - BERT Pipeline."
+    "# Data3 - Property-specific NER - BERT Pipeline."
    ]
   },
   {
@@ -3090,7 +3709,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data1 - Property-specific NER-GPT-Random Pipeline."
+    "# Data1 - Property-specific NER-GPT-Random Pipeline."
    ]
   },
   {
@@ -3658,7 +4277,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data2 - Property-specific Llama2 Pipeline."
+    "# Data2 - Property-specific Llama2 Pipeline."
    ]
   },
   {
@@ -3987,7 +4606,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data1 - Property-specific NER-GPT-Similar Pipeline."
+    "# Data1 - Property-specific NER-GPT-Similar Pipeline."
    ]
   },
   {
@@ -4489,7 +5108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Summary Plots"
+    "# Summary Plots"
    ]
   },
   {
@@ -4724,7 +5343,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Summary Plots v2"
+    "# Summary Plots v2"
    ]
   },
   {
diff --git a/notebooks/Analyze-Valid-Data.ipynb b/notebooks/Analyze-Valid-Data.ipynb
index 0b69988..ce0379b 100644
--- a/notebooks/Analyze-Valid-Data.ipynb
+++ b/notebooks/Analyze-Valid-Data.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -33,14 +33,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.058 s)\n"
+      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.068 s)\n"
      ]
     }
    ],
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -135,7 +135,7 @@
        "25"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,116 +174,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tg bandgap sd hardness td co2_perm cs ct eab fs h2_perm iec is lcst loi meoh_perm o2_perm ri tm ts ucst wca wu tc ym "
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "\nsd ($%$)\n    ^\nParseException: Expected end of text, found '$'  (at char 4), (line:1, col:5)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[29], line 25\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m     23\u001b[0m     ax\u001b[38;5;241m.\u001b[39mhist(df\u001b[38;5;241m.\u001b[39mvalue, bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m)\n\u001b[0;32m---> 25\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtight_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m# plt.savefig(f'notebooks/hist.all.png', dpi=300)\u001b[39;00m\n\u001b[1;32m     27\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/pyplot.py:2349\u001b[0m, in \u001b[0;36mtight_layout\u001b[0;34m(pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   2347\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[38;5;241m.\u001b[39mtight_layout)\n\u001b[1;32m   2348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtight_layout\u001b[39m(\u001b[38;5;241m*\u001b[39m, pad\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.08\u001b[39m, h_pad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, w_pad\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, rect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 2349\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgcf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtight_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mh_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrect\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/figure.py:3544\u001b[0m, in \u001b[0;36mFigure.tight_layout\u001b[0;34m(self, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   3542\u001b[0m previous_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_layout_engine()\n\u001b[1;32m   3543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_layout_engine(engine)\n\u001b[0;32m-> 3544\u001b[0m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3545\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(previous_engine, TightLayoutEngine) \\\n\u001b[1;32m   3546\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m previous_engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3547\u001b[0m     _api\u001b[38;5;241m.\u001b[39mwarn_external(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe figure layout has changed to tight\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/layout_engine.py:184\u001b[0m, in \u001b[0;36mTightLayoutEngine.execute\u001b[0;34m(self, fig)\u001b[0m\n\u001b[1;32m    182\u001b[0m renderer \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39m_get_renderer()\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m--> 184\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mget_tight_layout_figure\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    185\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_subplotspec_list\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    186\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mh_pad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw_pad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    187\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrect\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs:\n\u001b[1;32m    189\u001b[0m     fig\u001b[38;5;241m.\u001b[39msubplots_adjust(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/_tight_layout.py:266\u001b[0m, in \u001b[0;36mget_tight_layout_figure\u001b[0;34m(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m {}\n\u001b[1;32m    262\u001b[0m     span_pairs\u001b[38;5;241m.\u001b[39mappend((\n\u001b[1;32m    263\u001b[0m         \u001b[38;5;28mslice\u001b[39m(ss\u001b[38;5;241m.\u001b[39mrowspan\u001b[38;5;241m.\u001b[39mstart \u001b[38;5;241m*\u001b[39m div_row, ss\u001b[38;5;241m.\u001b[39mrowspan\u001b[38;5;241m.\u001b[39mstop \u001b[38;5;241m*\u001b[39m div_row),\n\u001b[1;32m    264\u001b[0m         \u001b[38;5;28mslice\u001b[39m(ss\u001b[38;5;241m.\u001b[39mcolspan\u001b[38;5;241m.\u001b[39mstart \u001b[38;5;241m*\u001b[39m div_col, ss\u001b[38;5;241m.\u001b[39mcolspan\u001b[38;5;241m.\u001b[39mstop \u001b[38;5;241m*\u001b[39m div_col)))\n\u001b[0;32m--> 266\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[43m_auto_adjust_subplotpars\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    267\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmax_nrows\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_ncols\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mspan_pairs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mspan_pairs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43msubplot_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubplot_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43max_bbox_list\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max_bbox_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mpad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mh_pad\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw_pad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mw_pad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[38;5;66;03m# kwargs can be none if tight_layout fails...\u001b[39;00m\n\u001b[1;32m    274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m rect \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    275\u001b[0m     \u001b[38;5;66;03m# if rect is given, the whole subplots area (including\u001b[39;00m\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;66;03m# labels) will fit into the rect instead of the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;66;03m# auto_adjust_subplotpars twice, where the second run\u001b[39;00m\n\u001b[1;32m    281\u001b[0m     \u001b[38;5;66;03m# with adjusted rect parameters.\u001b[39;00m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/_tight_layout.py:82\u001b[0m, in \u001b[0;36m_auto_adjust_subplotpars\u001b[0;34m(fig, renderer, shape, span_pairs, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m subplots:\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m---> 82\u001b[0m         bb \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m [\u001b[43mmartist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_tightbbox_for_layout_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[1;32m     84\u001b[0m tight_bbox_raw \u001b[38;5;241m=\u001b[39m Bbox\u001b[38;5;241m.\u001b[39munion(bb)\n\u001b[1;32m     85\u001b[0m tight_bbox \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39mtransFigure\u001b[38;5;241m.\u001b[39minverted()\u001b[38;5;241m.\u001b[39mtransform_bbox(tight_bbox_raw)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:1415\u001b[0m, in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1409\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1410\u001b[0m \u001b[38;5;124;03mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[1;32m   1411\u001b[0m \u001b[38;5;124;03m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[1;32m   1412\u001b[0m \u001b[38;5;124;03mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[1;32m   1413\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1415\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfor_layout_only\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1416\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   1417\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mget_tightbbox(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/axes/_base.py:4385\u001b[0m, in \u001b[0;36m_AxesBase.get_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[0m\n\u001b[1;32m   4383\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m   4384\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxison \u001b[38;5;129;01mand\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m-> 4385\u001b[0m         ba \u001b[38;5;241m=\u001b[39m \u001b[43mmartist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_tightbbox_for_layout_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4386\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m ba:\n\u001b[1;32m   4387\u001b[0m             bb\u001b[38;5;241m.\u001b[39mappend(ba)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:1415\u001b[0m, in \u001b[0;36m_get_tightbbox_for_layout_only\u001b[0;34m(obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1409\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1410\u001b[0m \u001b[38;5;124;03mMatplotlib's `.Axes.get_tightbbox` and `.Axis.get_tightbbox` support a\u001b[39;00m\n\u001b[1;32m   1411\u001b[0m \u001b[38;5;124;03m*for_layout_only* kwarg; this helper tries to use the kwarg but skips it\u001b[39;00m\n\u001b[1;32m   1412\u001b[0m \u001b[38;5;124;03mwhen encountering third-party subclasses that do not support it.\u001b[39;00m\n\u001b[1;32m   1413\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1415\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfor_layout_only\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1416\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   1417\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mget_tightbbox(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/axis.py:1341\u001b[0m, in \u001b[0;36mAxis.get_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1339\u001b[0m \u001b[38;5;66;03m# take care of label\u001b[39;00m\n\u001b[1;32m   1340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m-> 1341\u001b[0m     bb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1342\u001b[0m     \u001b[38;5;66;03m# for constrained/tight_layout, we want to ignore the label's\u001b[39;00m\n\u001b[1;32m   1343\u001b[0m     \u001b[38;5;66;03m# width/height because the adjustments they make can't be improved.\u001b[39;00m\n\u001b[1;32m   1344\u001b[0m     \u001b[38;5;66;03m# this code collapses the relevant direction\u001b[39;00m\n\u001b[1;32m   1345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m for_layout_only:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:959\u001b[0m, in \u001b[0;36mText.get_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m    955\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    956\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwant to call \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure.draw_without_rendering()\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m first.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    958\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m--> 959\u001b[0m     bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_unitless_position()\n\u001b[1;32m    961\u001b[0m     x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform((x, y))\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:386\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    384\u001b[0m clean_line, ismath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_math(line)\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[0;32m--> 386\u001b[0m     w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    388\u001b[0m \u001b[43m        \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    390\u001b[0m     w \u001b[38;5;241m=\u001b[39m h \u001b[38;5;241m=\u001b[39m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:97\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweakref\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:105\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(\u001b[38;5;241m4096\u001b[39m)\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:230\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_text_width_height_descent(s, prop, ismath)\n\u001b[1;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[1;32m    229\u001b[0m     ox, oy, width, height, descent, font_image \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m--> 230\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmathtext_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[1;32m    233\u001b[0m font \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_font(prop)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/mathtext.py:226\u001b[0m, in \u001b[0;36mMathTextParser.parse\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;66;03m# lru_cache can't decorate parse() directly because prop\u001b[39;00m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;66;03m# is mutable; key the cache using an internal copy (see\u001b[39;00m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;66;03m# text._get_text_metrics_with_cache for a similar case).\u001b[39;00m\n\u001b[1;32m    225\u001b[0m prop \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m prop \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/mathtext.py:247\u001b[0m, in \u001b[0;36mMathTextParser._parse_cached\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mParser()\n\u001b[0;32m--> 247\u001b[0m box \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    248\u001b[0m output \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mship(box)\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvector\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/_mathtext.py:1995\u001b[0m, in \u001b[0;36mParser.parse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   1992\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expression\u001b[38;5;241m.\u001b[39mparseString(s)\n\u001b[1;32m   1993\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   1994\u001b[0m     \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[0;32m-> 1995\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m ParseException\u001b[38;5;241m.\u001b[39mexplain(err, \u001b[38;5;241m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1996\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state_stack \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1997\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_in_subscript_or_superscript \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "\u001b[0;31mValueError\u001b[0m: \nsd ($%$)\n    ^\nParseException: Expected end of text, found '$'  (at char 4), (line:1, col:5)"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error in callback <function _draw_all_if_interactive at 0x7fd69a225ab0> (for post_execute):\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "\nsd ($%$)\n    ^\nParseException: Expected end of text, found '$'  (at char 4), (line:1, col:5)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/pyplot.py:120\u001b[0m, in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_draw_all_if_interactive\u001b[39m():\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m matplotlib\u001b[38;5;241m.\u001b[39mis_interactive():\n\u001b[0;32m--> 120\u001b[0m         \u001b[43mdraw_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/_pylab_helpers.py:132\u001b[0m, in \u001b[0;36mGcf.draw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m manager \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force \u001b[38;5;129;01mor\u001b[39;00m manager\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mstale:\n\u001b[0;32m--> 132\u001b[0m         \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/backend_bases.py:2082\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[1;32m   2081\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[0;32m-> 2082\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:400\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[1;32m    397\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m RendererAgg\u001b[38;5;241m.\u001b[39mlock, \\\n\u001b[1;32m    398\u001b[0m      (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[1;32m    399\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    401\u001b[0m     \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[1;32m    403\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/axis.py:1384\u001b[0m, in \u001b[0;36mAxis.draw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1382\u001b[0m \u001b[38;5;66;03m# Shift label away from axes to avoid overlapping ticklabels.\u001b[39;00m\n\u001b[1;32m   1383\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_label_position(renderer)\n\u001b[0;32m-> 1384\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_offset_text_position(tlb1, tlb2)\n\u001b[1;32m   1387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mset_text(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajor\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mget_offset())\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:752\u001b[0m, in \u001b[0;36mText.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    749\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cm_set(text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_wrapped_text()):\n\u001b[0;32m--> 752\u001b[0m     bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    753\u001b[0m     trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    755\u001b[0m     \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[1;32m    756\u001b[0m     \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:386\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    384\u001b[0m clean_line, ismath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_math(line)\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[0;32m--> 386\u001b[0m     w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    388\u001b[0m \u001b[43m        \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    390\u001b[0m     w \u001b[38;5;241m=\u001b[39m h \u001b[38;5;241m=\u001b[39m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:97\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweakref\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:105\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(\u001b[38;5;241m4096\u001b[39m)\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:230\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_text_width_height_descent(s, prop, ismath)\n\u001b[1;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[1;32m    229\u001b[0m     ox, oy, width, height, descent, font_image \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m--> 230\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmathtext_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[1;32m    233\u001b[0m font \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_font(prop)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/mathtext.py:226\u001b[0m, in \u001b[0;36mMathTextParser.parse\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;66;03m# lru_cache can't decorate parse() directly because prop\u001b[39;00m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;66;03m# is mutable; key the cache using an internal copy (see\u001b[39;00m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;66;03m# text._get_text_metrics_with_cache for a similar case).\u001b[39;00m\n\u001b[1;32m    225\u001b[0m prop \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m prop \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/mathtext.py:247\u001b[0m, in \u001b[0;36mMathTextParser._parse_cached\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mParser()\n\u001b[0;32m--> 247\u001b[0m box \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    248\u001b[0m output \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mship(box)\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvector\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/_mathtext.py:1995\u001b[0m, in \u001b[0;36mParser.parse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   1992\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expression\u001b[38;5;241m.\u001b[39mparseString(s)\n\u001b[1;32m   1993\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   1994\u001b[0m     \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[0;32m-> 1995\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m ParseException\u001b[38;5;241m.\u001b[39mexplain(err, \u001b[38;5;241m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1996\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state_stack \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1997\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_in_subscript_or_superscript \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "\u001b[0;31mValueError\u001b[0m: \nsd ($%$)\n    ^\nParseException: Expected end of text, found '$'  (at char 4), (line:1, col:5)"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "\nsd ($%$)\n    ^\nParseException: Expected end of text, found '$'  (at char 4), (line:1, col:5)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/axis.py:1384\u001b[0m, in \u001b[0;36mAxis.draw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1382\u001b[0m \u001b[38;5;66;03m# Shift label away from axes to avoid overlapping ticklabels.\u001b[39;00m\n\u001b[1;32m   1383\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_label_position(renderer)\n\u001b[0;32m-> 1384\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_offset_text_position(tlb1, tlb2)\n\u001b[1;32m   1387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mset_text(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmajor\u001b[38;5;241m.\u001b[39mformatter\u001b[38;5;241m.\u001b[39mget_offset())\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:752\u001b[0m, in \u001b[0;36mText.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    749\u001b[0m renderer\u001b[38;5;241m.\u001b[39mopen_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_gid())\n\u001b[1;32m    751\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cm_set(text\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_wrapped_text()):\n\u001b[0;32m--> 752\u001b[0m     bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    753\u001b[0m     trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n\u001b[1;32m    755\u001b[0m     \u001b[38;5;66;03m# don't use self.get_position here, which refers to text\u001b[39;00m\n\u001b[1;32m    756\u001b[0m     \u001b[38;5;66;03m# position in Text:\u001b[39;00m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:386\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    384\u001b[0m clean_line, ismath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_math(line)\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[0;32m--> 386\u001b[0m     w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    387\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    388\u001b[0m \u001b[43m        \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    389\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    390\u001b[0m     w \u001b[38;5;241m=\u001b[39m h \u001b[38;5;241m=\u001b[39m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:97\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[0;32m---> 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweakref\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/text.py:105\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(\u001b[38;5;241m4096\u001b[39m)\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:230\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mget_text_width_height_descent(s, prop, ismath)\n\u001b[1;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[1;32m    229\u001b[0m     ox, oy, width, height, descent, font_image \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m--> 230\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmathtext_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m width, height, descent\n\u001b[1;32m    233\u001b[0m font \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_font(prop)\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/mathtext.py:226\u001b[0m, in \u001b[0;36mMathTextParser.parse\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;66;03m# lru_cache can't decorate parse() directly because prop\u001b[39;00m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;66;03m# is mutable; key the cache using an internal copy (see\u001b[39;00m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;66;03m# text._get_text_metrics_with_cache for a similar case).\u001b[39;00m\n\u001b[1;32m    225\u001b[0m prop \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m prop \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 226\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_cached\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/mathtext.py:247\u001b[0m, in \u001b[0;36mMathTextParser._parse_cached\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# Cache the parser globally.\u001b[39;00m\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m_parser \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mParser()\n\u001b[0;32m--> 247\u001b[0m box \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    248\u001b[0m output \u001b[38;5;241m=\u001b[39m _mathtext\u001b[38;5;241m.\u001b[39mship(box)\n\u001b[1;32m    249\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvector\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[0;32m/data/akhlak/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/matplotlib/_mathtext.py:1995\u001b[0m, in \u001b[0;36mParser.parse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   1992\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_expression\u001b[38;5;241m.\u001b[39mparseString(s)\n\u001b[1;32m   1993\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ParseBaseException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m   1994\u001b[0m     \u001b[38;5;66;03m# explain becomes a plain method on pyparsing 3 (err.explain(0)).\u001b[39;00m\n\u001b[0;32m-> 1995\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m ParseException\u001b[38;5;241m.\u001b[39mexplain(err, \u001b[38;5;241m0\u001b[39m)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1996\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state_stack \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1997\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_in_subscript_or_superscript \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
-      "\u001b[0;31mValueError\u001b[0m: \nsd ($%$)\n    ^\nParseException: Expected end of text, found '$'  (at char 4), (line:1, col:5)"
+      "tg "
      ]
     },
     {
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",
       "text/plain": [
        "<Figure size 870x660 with 25 Axes>"
       ]
@@ -317,6 +220,8 @@
     "        continue\n",
     "    ax.hist(df.value, bins=50)\n",
     "\n",
+    "    break\n",
+    "\n",
     "plt.tight_layout()\n",
     "# plt.savefig(f'notebooks/hist.all.png', dpi=300)\n",
     "plt.show()"
@@ -324,12 +229,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 390x264 with 1 Axes>"
       ]
@@ -339,7 +244,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 390x264 with 1 Axes>"
       ]
diff --git a/notebooks/GPT_vs_MBert.ipynb b/notebooks/GPT_vs_MBert.ipynb
index 2bb5c59..818f28d 100644
--- a/notebooks/GPT_vs_MBert.ipynb
+++ b/notebooks/GPT_vs_MBert.ipynb
@@ -40,7 +40,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.065 s)\n"
+      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.071 s)\n"
      ]
     }
    ],
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,18 +134,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "absdata = pd.read_csv('notebooks/Abstract-Extracted_Data.csv', index_col=0)\n",
-    "abstg = absdata[absdata['prop'] == 'tg']\n",
+    "absdata = pd.read_csv('data/Abstract-Extracted_Data.csv', index_col=0)\n",
+    "abstg = absdata[absdata['prop'] == 'glass transition temperature']\n",
     "abseg = absdata[absdata['prop'] == 'bandgap']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -163,12 +163,12 @@
      "output_type": "stream",
      "text": [
       "Bandgap:\n",
-      "\tgpt_total: 63361\n",
-      "\tmbt_total: 24898\n",
+      "\tgpt_total: 62883\n",
+      "\tmbt_total: 24598\n",
       "\tabs_total: 2532\n",
       "Tg:\n",
-      "\tgpt_total: 125391\n",
-      "\tmbt_total: 62596\n",
+      "\tgpt_total: 124915\n",
+      "\tmbt_total: 62178\n",
       "\tabs_total: 6898\n"
      ]
     }
@@ -187,12 +187,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 390x198 with 2 Axes>"
       ]
@@ -203,12 +203,14 @@
    ],
    "source": [
     "plt.rcParams['font.family'] = 'cursive'\n",
+    "plt.rcParams['axes.labelsize'] = 'medium'\n",
+    "\n",
     "n = 85\n",
     "\n",
     "fig, ax = plt.subplots(1, 2, dpi=120, figsize=(3.25, 1.65))\n",
-    "ax[0].hist(gpttg.value, color='#e0809d', bins=n, label = 'GPT-3.5')\n",
-    "ax[0].hist(mbttg.value, color='#009d9a', bins=n, label = 'MaterialsBERT')\n",
-    "ax[0].hist(abstg.value, color='#ee0000', bins=n, label = 'Abstracts')\n",
+    "ax[0].hist(gpttg.value, color='#e0809d', bins=n, label = 'GPT-3.5\\nFull text')\n",
+    "ax[0].hist(mbttg.value, color='#009d9a', bins=n, label = 'MaterialsBERT\\nFull text')\n",
+    "ax[0].hist(abstg.value, color='#ee0000', bins=n, label = 'MaterialsBERT\\nAbstracts')\n",
     "ax[0].set(xlabel='Tg (°C)', xlim=(-200, 600))\n",
     "\n",
     "ax[1].hist(gpteg.value, color='#e0809d', bins=n, label = 'GPT-3.5')\n",
@@ -217,7 +219,7 @@
     "ax[1].set(xlabel='Bandgap (eV)', xlim=(0, 7))\n",
     "\n",
     "lines, labels = ax[0].get_legend_handles_labels()\n",
-    "fig.legend(lines, labels, loc='upper center', bbox_to_anchor=(0.5, 1.06), bbox_transform=plt.gcf().transFigure, ncol=3)\n",
+    "fig.legend(lines, labels, loc='upper center', bbox_to_anchor=(0.5, 1.15), bbox_transform=plt.gcf().transFigure, ncol=3)\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.subplots_adjust(wspace=0.2)\n",
diff --git a/notebooks/Pairwise-Plots.ipynb b/notebooks/Pairwise-Plots.ipynb
index 0b0394b..c57a536 100644
--- a/notebooks/Pairwise-Plots.ipynb
+++ b/notebooks/Pairwise-Plots.ipynb
@@ -40,7 +40,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.096 s)\n"
+      "\u001b[1;36mNOTE  --\u001b[0m postgres_ Connected to PostGres DB: polylet (took 0.073 s)\n"
      ]
     }
    ],
@@ -59,6 +59,8 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "from sklearn.metrics import r2_score, mean_squared_error\n",
+    "\n",
     "try:\n",
     "    plt.style.use(\"~/matplotlib.mplstyle\")\n",
     "except: raise"
@@ -82,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +142,7 @@
     "    else:\n",
     "        fig = None\n",
     "\n",
-    "    # Group by only common materails or both materail and doi\n",
+    "    # Group by only common materails or both material and doi\n",
     "    groupby = 'ed.material, ed.doi' if same_doi else 'ed.material'\n",
     "\n",
     "    # Group by doi\n",
@@ -202,69 +204,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 288x300 with 1 Axes>"
+       "<Figure size 390x264 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(     material   prop1     prop2\n",
-       " 0         (1)  103.50  2.980000\n",
-       " 1          10   68.10  2.302030\n",
-       " 2         10a  191.50  2.100000\n",
-       " 3         10b   76.50  2.100000\n",
-       " 4          11   73.55  2.207819\n",
-       " ...       ...     ...       ...\n",
-       " 1872       Z0  289.50  3.270000\n",
-       " 1873       Z3  283.40  1.850000\n",
-       " 1874       Z4   56.20  1.550000\n",
-       " 1875   Z-PDPF  185.00  2.810000\n",
-       " 1876  γ-Al2O3 -102.00  6.200000\n",
-       " \n",
-       " [1877 rows x 3 columns],\n",
-       "        material       prop1     prop2\n",
-       " 0            +}  -33.050000  1.940000\n",
-       " 1           0.5   69.875000  3.000000\n",
-       " 2            15    5.182500  1.800000\n",
-       " 3           1-b  111.000000  2.475000\n",
-       " 4            4)  131.000000  1.900000\n",
-       " ..          ...         ...       ...\n",
-       " 908         ZnO  105.218333  3.139828\n",
-       " 909  ZnO-Ag NCs   23.000000  3.285000\n",
-       " 910         ZnS   64.000000  3.403286\n",
-       " 911          Zr  -26.000000  0.806000\n",
-       " 912     ZrO_{2}  -42.000000  4.125833\n",
-       " \n",
-       " [913 rows x 3 columns])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot_pairwise('tg', 'bandgap', same_doi=False)"
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "plot_pairwise('wu', 'wca', ax=ax, same_doi=True)\n",
+    "ax.set(xlabel='Water uptake (wt %)', ylabel='Water contact angle (°)', title=None)\n",
+    "ax.set_xscale(\"log\", base=10)\n",
+    "# ax.set_yscale(\"log\", base=10)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 588x324 with 6 Axes>"
       ]
@@ -286,10 +258,9 @@
     "ax.set(xlabel='Tg (°C)', ylabel='Crystallization\\ntemperature (°C)', title=None)\n",
     "\n",
     "ax = axes[2]\n",
-    "plot_pairwise('wu', 'sd', ax = ax)\n",
-    "ax.set(xlabel='Water uptake (wt %)', ylabel='Swelling degree (%)', title=None)\n",
+    "plot_pairwise('wu', 'wca', ax = ax)\n",
+    "ax.set(xlabel='Water uptake (wt %)', ylabel='Water contact angle (°)', title=None)\n",
     "ax.set_xscale(\"log\", base=10)\n",
-    "ax.set_yscale(\"log\", base=10)\n",
     "\n",
     "ax = axes[3]\n",
     "plot_pairwise('ri', 'bandgap', ax = ax)\n",
@@ -314,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,12 +294,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 390x264 with 1 Axes>"
       ]
@@ -353,6 +324,67 @@
     "plt.plot(polyline, model1(polyline), color='k')\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parity plot between methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 390x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "propcode = 'tg'\n",
+    "propname = \"Tg (K)\"\n",
+    "\n",
+    "# propcode = 'bandgap'\n",
+    "# propname = \"Bandgap (eV)\"\n",
+    "\n",
+    "\n",
+    "saveas = f\"notebooks/methods-parity.{propcode}.png\"\n",
+    "\n",
+    "data = execute(f\"\"\"\n",
+    "        SELECT * FROM (\n",
+    "            SELECT ed.material, ed.doi,\n",
+    "                avg(CASE WHEN ed.\"method\" = 'GPT-3.5' THEN ed.value END) AS gpt,\n",
+    "                avg(CASE WHEN ed.\"method\" = 'MaterialsBERT' THEN ed.value END) AS mbt\n",
+    "            FROM extracted_data ed \n",
+    "            WHERE ed.property = :p1 AND ed.confidence = 1\n",
+    "            GROUP BY ed.material, ed.doi\n",
+    "            ORDER BY ed.material\n",
+    "        ) AS ft\n",
+    "        WHERE ft.gpt IS NOT NULL and ft.mbt IS NOT NULL;\n",
+    "    \"\"\", p1=propcode)\n",
+    "\n",
+    "rmse = mean_squared_error(data.mbt, data.gpt, squared=False)\n",
+    "r2 = r2_score(data.mbt, data.gpt)\n",
+    "\n",
+    "metrics = f\"RMSE = {rmse:0.2f}\\n$R^2$ = {r2:0.2f}\"\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3.25, 2.5))\n",
+    "ax.plot(data.mbt, data.gpt, '.', label = metrics)\n",
+    "ax.set(xlabel=f\"MaterialsBERT, {propname}\", ylabel=f\"GPT-3.5, {propname}\")\n",
+    "ax.legend(loc='upper left', frameon=True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(saveas, dpi=600)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/Test-NER-Pipeline.ipynb b/notebooks/Test-NER-Pipeline.ipynb
index 6ca5de1..a3ad210 100644
--- a/notebooks/Test-NER-Pipeline.ipynb
+++ b/notebooks/Test-NER-Pipeline.ipynb
@@ -5,6 +5,16 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import os \n",
+    "os.chdir(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Para ID 615 from polylet\n",
     "text = \"\"\"\n",
@@ -36,12 +46,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "os.chdir(\"..\")\n",
+    "os.chdir(\"/data/sonakshi/PromptDataExtraction\")\n",
     "assert 'PromptDataExtract' in os.getcwd()"
    ]
   },
@@ -49,7 +59,16 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/sonakshi/PromptDataExtraction/_conda_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "from backend import postgres, sett\n",
     "from backend.record_extraction import bert_model, record_extractor\n",
@@ -78,7 +97,22 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Device set to use cuda:0\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;34m  OK  --\u001b[0m bert_ Loaded bert model to device 0 (took 0.476 s)\n"
+     ]
+    }
+   ],
    "source": [
     "bert = bert_model.MaterialsBERT()\n",
     "bert.init_local_model(model=sett.NERPipeline.model, device=sett.NERPipeline.pytorch_device)"
@@ -186,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.16"
   },
   "orig_nbformat": 4
  },
diff --git a/pranav/Run_Output/output/bandgap/dataset_ground_embeddings.pt b/pranav/Run_Output/output/bandgap/dataset_ground_embeddings.pt
new file mode 100644
index 0000000..4617852
Binary files /dev/null and b/pranav/Run_Output/output/bandgap/dataset_ground_embeddings.pt differ
diff --git a/scripts/filter_polymer_papers.py b/scripts/filter_polymer_papers.py
index 4153a6d..b3f8fac 100644
--- a/scripts/filter_polymer_papers.py
+++ b/scripts/filter_polymer_papers.py
@@ -6,17 +6,15 @@
 """
 
 import os
-import sett
+from backend import postgres, sett
 import pylogg as log
 
-from backend import postgres
 from backend.postgres.orm import Papers, FilteredPapers
 
 sett.load_settings()
 postgres.load_settings()
 db = postgres.connect()
 
-
 def add_to_postgres(doi : str, filter_name : str, filter_desc : str):
     """ Add a paper to the list of a specific filter.
         Ignore if exists in the database.
@@ -115,7 +113,7 @@ def init_logger(run_name, log_level = 8):
 if __name__ == '__main__':
     runName = "runs/filters/polymer_papers"
     os.makedirs(runName, exist_ok=True)
-    t1 = init_logger(runName, log.INFO)
+    t1 = init_logger(runName)
 
     find_papers(debugCount = 0)
 
diff --git a/scripts/heuristic-filter-specific.py b/scripts/heuristic-filter-specific.py
new file mode 100644
index 0000000..ec237c0
--- /dev/null
+++ b/scripts/heuristic-filter-specific.py
@@ -0,0 +1,229 @@
+#!/usr/bin/env python
+"""
+Filter paragraphs for thermoset-related content using specific keywords
+"""
+
+import os
+import sys
+import pylogg as log
+from tqdm import tqdm
+import argparse
+from collections import defaultdict
+
+from backend import postgres, sett
+from backend.postgres.orm import Papers, FilteredPapers, PaperTexts, FilteredParagraphs
+from backend.utils import checkpoint
+
+
+sett.load_settings()
+postgres.load_settings()
+db = postgres.connect()
+
+# Dictionary to track filtering statistics
+filtration_dict = defaultdict(int)
+
+THERMOSET_KEYWORDS = [
+    'Thermoset',
+    'Acrylate',
+    'Methacrylate', 
+    'Crosslinker',
+    'PEGDA',
+    'Thermomechanical',
+    'UV',
+    'BMA',
+    '2-HEA',
+    '2-HEMA',
+    'TMPETA',
+    'IBOA',
+    'IDA',
+    'BCOE',
+    'DEGDMA',
+    'Bisphenol A ethoxylate dimethacrylate',
+    'EEMA'
+]
+
+
+WATER_TRANSPORT_KEYWORDS = [
+    'Water Vapor',
+    'water vapor separation',
+    'water vapor activities',
+    'water vapor activity', 
+    'water permeability',
+    'water vapor permeability',
+    'water vapour permeability',
+    'water diffusivity',
+    'water diffusion',
+    'water vapor diffusion',
+    'water vapour diffusion',
+    'water vapor diffusivity',
+    'water vapour diffusivity',
+    'water vapour diffusion coefficient',
+    'water vapour solubility coefficient',
+    'water vapor diffusion coefficient',
+    'water vapor solubility coefficient',
+    'water diffusion coefficient',
+    'water permeability coefficient',
+    'water solubility coefficient',
+    'water solubility',
+    'Water Vapor solubility',
+    'Water Vapour solubility',
+    'Water Vapor absorption',
+    'Water Vapor adsorption', 
+    'Water Vapor Sorption',
+    'water sorption',
+    'Water vapour absorption',
+    'Water vapour adsorption',
+    'Water vapour',
+    'water vapour separation',
+    'water vapour activities',
+    'water vapour activity',
+    'moisture permeation coefficient',
+    'moisture solubility coefficient',
+    'Moisture permeability',
+    'Moisture diffusivity', 
+    'Moisture diffusion',
+    'solubility of water molecules',
+    'diffusivity of water molecules'
+]
+
+SWELLING_KEYWORDS = [
+    'Swelling degree',
+    'Swelling ratio',
+    'Uptake sorption',
+    'Mass uptake',
+    'Organic solvents',
+    'Polymer solvent interaction parameter',
+    'Flory Huggins interaction parameter',
+    'Solubility'
+]
+
+def add_to_filtered_paragraphs(para_id, filter_name):
+    """Add a paragraph to the filtered list if not already present"""
+    paragraph = FilteredParagraphs().get_one(db, {'para_id': para_id, 'filter_name': filter_name})
+    if paragraph is not None:
+        log.trace(f"Paragraph in PostGres: {para_id}. Skipped.")
+        return False
+    
+    obj = FilteredParagraphs()
+    obj.para_id = para_id
+    obj.filter_name = filter_name
+    obj.insert(db)
+    
+    log.trace(f"Added to PostGres: {para_id}")
+    return True
+
+def keyword_filter(keyword_list, para):
+    """Check if paragraph contains any keywords"""
+    return any(keyword.lower() in para.text.lower() for keyword in keyword_list)
+
+def process_keyword_content(para, category, KEYWORDS):
+    """Process paragraph for thermoset content in specific category"""
+    if keyword_filter(KEYWORDS, para):
+        filtration_dict[f'{category}_paragraphs'] += 1
+        return True
+    return False
+
+def keyword_filter_check(category, filter_name, KEYWORDS):
+    """Main function to check paragraphs for thermoset content"""
+    
+    last_processed_id = checkpoint.get_last(db, name=filter_name, table=PaperTexts.__tablename__)
+    log.info("Last run row ID: {}", last_processed_id)
+
+    # Query to get paragraphs from thermoset papers
+    query = '''
+    SELECT pt.id AS para_id FROM paper_texts pt
+    JOIN filtered_papers fp ON fp.doi = pt.doi
+    WHERE pt.id > :last_processed_id
+    AND fp.filter_name = 'polymer_papers'
+    ORDER BY pt.id
+    LIMIT :limit;
+    '''
+
+    log.info("Querying list of non-processed paragraphs.")
+    records = postgres.raw_sql(query, {'last_processed_id': last_processed_id, 'limit': 10000000, 'filter_name': filter_name})
+    log.note("Found {} paragraphs not processed.", len(records))
+
+    if len(records) == 0:
+        return
+    else:
+        log.note("Unprocessed Row IDs: {} to {}", records[0].para_id, records[-1].para_id)
+    
+    relevant_paras = 0
+    
+    for row in tqdm(records):
+        if row.para_id < last_processed_id:
+            continue
+
+        if sett.Run.debugCount > 0 and filtration_dict['total_paragraphs'] > sett.Run.debugCount:
+            break
+
+        filtration_dict['total_paragraphs'] += 1
+
+        # Fetch the paragraph text
+        para = PaperTexts().get_one(db, {'id': row.para_id})
+
+        if process_keyword_content(para, category, KEYWORDS):
+            log.note(f"{para.id} contains relevant {category} content")
+            relevant_paras += 1
+
+            if add_to_filtered_paragraphs(para_id=row.para_id, filter_name=filter_name):
+                if relevant_paras % 20 == 0:
+                    db.commit()
+
+        else:
+            log.info(f"{para.id} did not contain relevant {category} content")
+
+        if filtration_dict['total_paragraphs'] % 100 == 0 or filtration_dict['total_paragraphs'] == len(records):
+            log.info(f'Total paragraphs processed: {filtration_dict["total_paragraphs"]}')
+            log.info(f'Paragraphs with thermoset content: {filtration_dict[f"{category}_paragraphs"]}')
+    
+    log.info(f'Last processed para_id: {row.para_id}')
+
+    checkpoint.add_new(db, name=filter_name, table=PaperTexts.__tablename__, row=row.para_id,
+                      comment={'user': sett.Run.userName, 'filter': filter_name,
+                              'debug': True if sett.Run.debugCount > 0 else False})
+    
+    db.commit()
+
+def log_run_info(category, filter_name):
+    """Log information about the current run"""
+    t1 = log.note(f"{filter_name} Filter Run for category: {category}")
+    log.info("CWD: {}", os.getcwd())
+    log.info("Host: {}", os.uname())
+
+    if sett.Run.debugCount > 0:
+        log.note("Debug run. Will parse maximum {} files.", sett.Run.debugCount)
+    else:
+        log.note("Production run. Will parse all files.")
+
+    log.info("Using loglevel = {}", sett.Run.logLevel)
+    return t1
+
+if __name__ == '__main__':
+    filter_name = 'water_transport_brandon'
+    category = 'water_transport'
+    keywords = WATER_TRANSPORT_KEYWORDS
+
+
+    # parser = argparse.ArgumentParser(description='Run water transport filter')
+    # parser.add_argument('--filter-name', '-f', required=True,
+    #                   help='Name of the filter')
+    # parser.add_argument('--category', '-c', required=True,
+    #                   help='Category to filter for')
+    # parser.add_argument('--keywords', '-k', required=True,
+    #                   help='Keywords to filter for')
+    
+    # args = parser.parse_args()
+    # filter_name = args.filter_name
+    # category = args.category
+    # keywords = args.keywords
+    
+    os.makedirs(sett.Run.directory, exist_ok=True)
+    log.setFile(open(sett.Run.directory + f"/{filter_name}.log", "w+"))
+    log.setLevel(sett.Run.logLevel)
+    log.setFileTimes(show=True)
+    log.setConsoleTimes(show=True)
+    
+    t1 = log_run_info(category, filter_name)
+    keyword_filter_check(category=category, filter_name=filter_name, KEYWORDS=keywords)
+    t1.done("All Done.") 
\ No newline at end of file
diff --git a/scripts/ner-filter-specific.py b/scripts/ner-filter-specific.py
new file mode 100644
index 0000000..5a56a45
--- /dev/null
+++ b/scripts/ner-filter-specific.py
@@ -0,0 +1,143 @@
+#!/usr/bin/env python
+"""
+Filter paragraphs for water transport-related content using NER model
+"""
+
+import os
+import sys
+import pylogg as log
+from tqdm import tqdm
+from collections import defaultdict
+
+from backend import postgres, sett
+from backend.postgres.orm import Papers, FilteredPapers, PaperTexts, FilteredParagraphs, PropertyMetadata
+from backend.utils import checkpoint
+import torch
+from backend.record_extraction import bert_model
+
+sett.load_settings()
+postgres.load_settings()
+db = postgres.connect()
+
+# Dictionary to track filtering statistics
+filtration_dict = defaultdict(int)
+
+material_entity_types = ['POLYMER', 'POLYMER_FAMILY', 'MONOMER', 'ORGANIC']
+
+# Load NER model
+if torch.cuda.is_available():
+    log.info('GPU device found')
+    device = 0
+else:
+    device = 'cpu'
+
+# Load Materials bert to GPU
+bert = bert_model.MaterialsBERT()
+bert.init_local_model(
+    sett.NERPipeline.model, device)
+ner_pipeline = bert.pipeline
+
+def add_to_filtered_paragraphs(para_id, ner_filter_name):
+    """Add a paragraph to the filtered list if not already present"""
+    paragraph = FilteredParagraphs().get_one(db, {'para_id': para_id, 'filter_name': ner_filter_name})
+    if paragraph is not None:
+        log.trace(f"Paragraph in PostGres: {para_id}. Skipped.")
+        return False
+    obj = FilteredParagraphs()
+    obj.para_id = para_id
+    obj.filter_name = ner_filter_name
+    obj.insert(db)
+    log.trace(f"Added to PostGres: {para_id}")
+    return True
+
+def ner_filter(para_text, unit_list=None, ner_output=None):
+    """Pass paragraph through NER pipeline to check whether it contains relevant information"""
+    if ner_output is None:
+        ner_output = ner_pipeline(para_text)
+    mat_flag = False
+    prop_name_flag = False
+    prop_value_flag = False
+    for entity in ner_output:
+        if entity['entity_group'] in material_entity_types:
+            mat_flag = True
+        elif entity['entity_group'] == 'PROP_NAME':
+            prop_name_flag = True
+        elif entity['entity_group'] == 'PROP_VALUE':
+            prop_value_flag = True
+    output_flag = mat_flag and prop_name_flag and prop_value_flag
+    return ner_output, output_flag
+
+def ner_filter_check(category, filter_name):
+    """Main function to check paragraphs for water transport content using NER"""
+    ner_filter_name = f"{filter_name}_ner"
+    last_processed_id = checkpoint.get_last(db, name=ner_filter_name, table=PaperTexts.__tablename__)
+    log.info("Last run row ID: {}", last_processed_id)
+
+    query = '''
+	SELECT fp.para_id, pt.text
+	FROM filtered_paragraphs fp
+	JOIN paper_texts pt ON fp.para_id = pt.id
+	WHERE fp.filter_name = :filter_name
+	AND fp.para_id > :last_processed_id ORDER BY fp.para_id LIMIT :limit;
+	'''
+
+    log.info("Querying list of non-processed paragraphs.")
+    records = postgres.raw_sql(query, {'last_processed_id': last_processed_id, 'limit': 10000000, 'filter_name': filter_name})
+    log.note("Found {} paragraphs not processed.", len(records))
+
+    if len(records) == 0:
+        return
+    else:
+        log.note("Unprocessed Row IDs: {} to {}", records[0].para_id, records[-1].para_id)
+
+    relevant_paras = 0
+
+    for row in tqdm(records):
+        if row.para_id < last_processed_id:
+            continue
+        if sett.Run.debugCount > 0 and filtration_dict['total_paragraphs'] > sett.Run.debugCount:
+            break
+        filtration_dict['total_paragraphs'] += 1
+        para = PaperTexts().get_one(db, {'id': row.para_id})
+        ner_output, ner_filter_output = ner_filter(para_text=para.text)
+        if ner_filter_output:
+            log.note(f"{para.id} contains relevant {category} content (NER)")
+            filtration_dict[f'{category}_paragraphs_ner'] += 1
+            relevant_paras += 1
+            if add_to_filtered_paragraphs(para_id=row.para_id, ner_filter_name=ner_filter_name):
+                if relevant_paras % 20 == 0:
+                    db.commit()
+        else:
+            log.info(f"{para.id} did not contain relevant {category} content (NER)")
+        if filtration_dict['total_paragraphs'] % 100 == 0 or filtration_dict['total_paragraphs'] == len(records):
+            log.info(f'Total paragraphs processed: {filtration_dict["total_paragraphs"]}')
+            log.info(f'Paragraphs with {category} content (NER): {filtration_dict[f"{category}_paragraphs_ner"]}')
+    log.info(f'Last processed para_id: {row.para_id}')
+    checkpoint.add_new(db, name=ner_filter_name, table=PaperTexts.__tablename__, row=row.para_id,
+                      comment={'user': sett.Run.userName, 'filter': ner_filter_name,
+                              'debug': True if sett.Run.debugCount > 0 else False})
+    db.commit()
+
+def log_run_info(category, filter_name):
+    """Log information about the current run"""
+    t1 = log.note(f"{filter_name}_ner NER Filter Run for category: {category}")
+    log.info("CWD: {}", os.getcwd())
+    log.info("Host: {}", os.uname())
+    if sett.Run.debugCount > 0:
+        log.note("Debug run. Will parse maximum {} files.", sett.Run.debugCount)
+    else:
+        log.note("Production run. Will parse all files.")
+    log.info("Using loglevel = {}", sett.Run.logLevel)
+    return t1
+
+if __name__ == '__main__':
+    filter_name = 'water_transport_brandon'
+    category = 'water_transport'
+    os.makedirs(sett.Run.directory, exist_ok=True)
+    log.setFile(open(sett.Run.directory + f"/{filter_name}_ner.log", "w+"))
+    log.setLevel(sett.Run.logLevel)
+    log.setFileTimes(show=True)
+    log.setConsoleTimes(show=True)
+    t1 = log_run_info(category, filter_name)
+    ner_filter_check(category=category, filter_name=filter_name)
+    t1.done("All Done.") 
\ No newline at end of file
diff --git a/scripts/run_gpu_check.sh b/scripts/run_gpu_check.sh
new file mode 100644
index 0000000..51fc5d9
--- /dev/null
+++ b/scripts/run_gpu_check.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+# Set a memory threshold (in MiB)
+MEM_THRESHOLD=6000  
+COMMAND="python scripts/ner-filter-specific.py "
+while true; do
+    TOTAL_GPU_MEM=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | awk '{sum+=$1} END {print sum}')
+    TOTAL_USED_MEM=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | awk '{sum+=$1} END {print sum}')
+    FREE_MEM=$((TOTAL_GPU_MEM - TOTAL_USED_MEM))
+
+    if [ "$FREE_MEM" -gt "$MEM_THRESHOLD" ]; then
+        echo "Enough GPU memory available ($FREE_MEM MiB). Running the command..."
+        eval $COMMAND
+        break 
+    else
+        echo "Not enough GPU memory available ($FREE_MEM MiB). Retrying in 30 seconds..."
+        sleep 10
+    fi
+done