diff --git a/README.md b/README.md
index 499f31d..5354cbf 100644
--- a/README.md
+++ b/README.md
@@ -106,8 +106,7 @@ Some of the figures in the manuscript were created during the main pipeline step
 - Figure 2: located at `./analysis/figures/AA_propensities.png` created in notebook `./analysis/probe_model.ipynb`
 - Figure 3: located at `./analysis/figures/disulfide_logits.png` created in notebook `./analysis/probe_model.ipynb`
 - Figure 4: located at `./analysis/figures/estimated_shift_thermo_gen.png` created in notebook `./analysis/dataset_stats.ipynb`
-- Figure 5: located at `./analysis/figures/mAF_scores.png` created in notebook `./analysis/compare_estimated_stability.ipynb`
-- Figure 6: see repo https://zenodo.org/records/10625583
+- Figure 5: see repo https://zenodo.org/records/10625583
 - Figure 7: located at './data/plots/exp_tm_scores.png' created in script `./scripts/zero_shot_experiment.py`
 
 ## License
diff --git a/analysis/figures/data_redundancy.png b/analysis/figures/data_redundancy.png
deleted file mode 100644
index 9e632f2..0000000
Binary files a/analysis/figures/data_redundancy.png and /dev/null differ
diff --git a/data/nomelt-model-full/benchmark_results.json b/data/nomelt-model-full/benchmark_results.json
new file mode 100644
index 0000000..f534efb
--- /dev/null
+++ b/data/nomelt-model-full/benchmark_results.json
@@ -0,0 +1,20 @@
+{
+  "new": {
+    "lipa": {
+      "pearson_coef": 0.2890140057583261,
+      "pearson_p": 4.670293896734481e-43,
+      "spearman_coef": 0.3205967878373402,
+      "spearman_p": 4.1698546948049733e-53,
+      "frac_qualitative_sig": 0.6286221472483685
+    }
+  },
+  "original": {
+    "lipa": {
+      "pearson_coef": 0.0070886669820527425,
+      "pearson_p": 0.7412644748916395,
+      "spearman_coef": 0.001625281365076926,
+      "spearman_p": 0.9396560014153784,
+      "frac_qualitative_sig": 0.49945479059432124
+    }
+  }
+}
\ No newline at end of file
diff --git a/data/nomelt-model-full/fireprotdb_benchmark_results.json b/data/nomelt-model-full/fireprotdb_benchmark_results.json
new file mode 100644
index 0000000..099e394
--- /dev/null
+++ b/data/nomelt-model-full/fireprotdb_benchmark_results.json
@@ -0,0 +1,12 @@
+{
+  "New": {
+    "spearman_coef": 0.32183325388483064,
+    "spearman_p": 2.11588628138216e-218,
+    "auroc": 0.6122423981479029
+  },
+  "Original": {
+    "spearman_coef": -0.016768034527282,
+    "spearman_p": 0.10965961456982194,
+    "auroc": 0.5013025291347248
+  }
+}
\ No newline at end of file
diff --git a/data/nomelt-model-full/fireprotdb_new_detailed_results.csv b/data/nomelt-model-full/fireprotdb_new_detailed_results.csv
new file mode 100644
index 0000000..97557a1
--- /dev/null
+++ b/data/nomelt-model-full/fireprotdb_new_detailed_results.csv
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diff --git a/data/nomelt-model-full/fireprotdb_original_detailed_results.csv b/data/nomelt-model-full/fireprotdb_original_detailed_results.csv
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index 0000000..e01a03c
--- /dev/null
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diff --git a/data/nomelt-model-full/meltome_benchmark_results.json b/data/nomelt-model-full/meltome_benchmark_results.json
new file mode 100644
index 0000000..bf713b5
--- /dev/null
+++ b/data/nomelt-model-full/meltome_benchmark_results.json
@@ -0,0 +1,14 @@
+{
+  "Original": {
+    "spearman_correlation": -0.1340010992676487,
+    "spearman_p_value": 1.0461819369876712e-37,
+    "pearson_correlation": -0.00571109525792792,
+    "pearson_p_value": 0.586060502266146
+  },
+  "New": {
+    "spearman_correlation": 0.0643856191766336,
+    "spearman_p_value": 7.970986911625336e-10,
+    "pearson_correlation": -0.037172837582440874,
+    "pearson_p_value": 0.00039167343648485395
+  }
+}
\ No newline at end of file
diff --git a/data/nomelt-model-full/zero_shot_estimated.json b/data/nomelt-model-full/zero_shot_estimated.json
index 63faa50..e5f7114 100644
--- a/data/nomelt-model-full/zero_shot_estimated.json
+++ b/data/nomelt-model-full/zero_shot_estimated.json
@@ -1 +1,84 @@
-{"lovd": {"temps": [38.5, 39.5, 44.0, 44.3, 45.0, 46.0, 52.0, 49.0, 49.3, 50.8], "scores": [0.0, -1.7846342231352177, 5.768416195356515, 10.28385920146837, 11.560601332554814, 15.913306315032823, 26.523630078307946, 22.467910635148996, 20.186244017803944, 17.10234541509284], "spearman": 0.9393939393939393, "p": 5.484052998513666e-05}, "lipa": {"temps": [56.0, 61.2, 67.4, 71.2, 78.2], "scores": [0.0, -5.604306400828901, -7.999710763806732, -7.288183090682358, -3.629957930221515], "spearman": -0.3, "p": 0.6238376647810728}}
\ No newline at end of file
+{
+  "original": {
+    "temperatures": [
+      38.5,
+      39.5,
+      44.0,
+      44.3,
+      45.0,
+      46.0,
+      52.0,
+      49.0,
+      49.3,
+      50.8
+    ],
+    "scores": [
+      0.0,
+      -18.994323695779503,
+      -112.9062081064811,
+      -134.18577013179666,
+      -173.751525966394,
+      -290.4352513447698,
+      -396.07049920894536,
+      -454.85826668603545,
+      -497.9485341623336,
+      -578.5951367674965
+    ],
+    "spearman": -0.9272727272727272,
+    "p": 0.00011203450639397582,
+    "random": [
+      -190.25619694344803,
+      -219.2303771656412,
+      -214.7284087555191,
+      -199.4984073037123,
+      -218.53714937531805,
+      -213.50481216018238,
+      -233.83415788779413,
+      -194.63392453896464,
+      -220.91808502051956,
+      -218.43287662825725
+    ],
+    "random_mean": -212.35743957793565
+  },
+  "new": {
+    "temperatures": [
+      38.5,
+      39.5,
+      44.0,
+      44.3,
+      45.0,
+      46.0,
+      52.0,
+      49.0,
+      49.3,
+      50.8
+    ],
+    "scores": [
+      0.0,
+      -1.7846342231352177,
+      5.768416195356515,
+      10.28385920146837,
+      11.560601332554814,
+      15.913306315032823,
+      26.523630078307946,
+      22.467910635148996,
+      20.186244017803944,
+      17.10234541509284
+    ],
+    "spearman": 0.9393939393939393,
+    "p": 5.484052998513666e-05,
+    "random": [
+      -46.3755245724553,
+      -36.80330686390008,
+      -28.523209203281958,
+      -30.92111942891394,
+      -26.978543226181834,
+      -44.06408132530037,
+      -45.88558775966179,
+      -39.976432593982224,
+      -25.854521196963653,
+      -21.43537590859003
+    ],
+    "random_mean": -34.681770207923115
+  }
+}
\ No newline at end of file
diff --git a/data/plots/fireprotdb_new_performance.png b/data/plots/fireprotdb_new_performance.png
new file mode 100644
index 0000000..e445028
Binary files /dev/null and b/data/plots/fireprotdb_new_performance.png differ
diff --git a/data/plots/fireprotdb_original_performance.png b/data/plots/fireprotdb_original_performance.png
new file mode 100644
index 0000000..9382a1a
Binary files /dev/null and b/data/plots/fireprotdb_original_performance.png differ
diff --git a/data/plots/new_lipa.png b/data/plots/new_lipa.png
new file mode 100644
index 0000000..ed952ec
Binary files /dev/null and b/data/plots/new_lipa.png differ
diff --git a/data/plots/new_lovd_scores.png b/data/plots/new_lovd_scores.png
new file mode 100644
index 0000000..9c03350
Binary files /dev/null and b/data/plots/new_lovd_scores.png differ
diff --git a/data/plots/new_meltome_scores.png b/data/plots/new_meltome_scores.png
new file mode 100644
index 0000000..8146a69
Binary files /dev/null and b/data/plots/new_meltome_scores.png differ
diff --git a/data/plots/original_lipa.png b/data/plots/original_lipa.png
new file mode 100644
index 0000000..96b52af
Binary files /dev/null and b/data/plots/original_lipa.png differ
diff --git a/data/plots/original_lovd_scores.png b/data/plots/original_lovd_scores.png
new file mode 100644
index 0000000..6244e63
Binary files /dev/null and b/data/plots/original_lovd_scores.png differ
diff --git a/data/plots/original_meltome_scores.png b/data/plots/original_meltome_scores.png
new file mode 100644
index 0000000..5e61cd1
Binary files /dev/null and b/data/plots/original_meltome_scores.png differ
diff --git a/dvc.yaml b/dvc.yaml
index b48ff1f..f3db482 100644
--- a/dvc.yaml
+++ b/dvc.yaml
@@ -278,6 +278,24 @@ stages:
     plots:
       - ./data/plots/lipa_gym.png: # density plot of exp vs model scores
           cache: false
+
+  meltome_benchmark:
+    wdir: ./
+    cmd: python scripts/meltome_benchmark.py
+    deps:
+      - ./scripts/meltome_benchmark.py
+      - ./data/nomelt-model-full/model/
+    params:
+      - model.generation_max_length
+      - model.model_hyperparams
+    outs:
+      - ./data/nomelt-model-full/meltome_benchmark_results.json:
+          cache: false
+    plots:
+      - ./data/plots/original_meltome_scores.png:
+          cache: false
+      - ./data/plots/new_meltome_scores.png:
+          cache: false
 # proof of principle scripts
 #################################
 
diff --git a/nomelt/model.py b/nomelt/model.py
index 61e608c..dac7a9a 100644
--- a/nomelt/model.py
+++ b/nomelt/model.py
@@ -204,7 +204,49 @@ def score_variants(self, wt: str, variants: Iterable[str], batch_size: int = 5,
         wt_probs = [float(wt_probs_all[i][vocab['▁' + wt[i]]]) for i in range(len(wt))]
         wt_score = self.compute_metric(wt_probs, wt_probs, normalize=indels)
         return wt_score, variant_scores
-
+    
+    def score_wts(self, sequences: Iterable[str], batch_size: int = 5) -> list:
+        """
+        Score a list of sequences by computing the mean softmax log logits.
+        
+        Args:
+            sequences (Iterable[str]): List of protein sequences to score.
+            batch_size (int): Batch size for processing.
+        
+        Returns:
+            list: List of mean softmax log logits scores for each input sequence.
+        """
+        # Use the same sequence for both input and output
+        logit_data = self.predict(sequences, sequences, batch_size=batch_size)
+        
+        scores = []
+        vocab = self.tokenizer.get_vocab()
+        
+        for i, seq in enumerate(sequences):
+            logits = torch.tensor(logit_data[i]['logits'])
+            
+            # Compute softmax
+            softmax_logits = torch.nn.functional.log_softmax(logits, dim=-1)
+            
+            # Get the actual sequence length (without padding)
+            seq_length = len(seq)
+            
+            # Extract probabilities for observed tokens
+            observed_probs = []
+            for j, char in enumerate(seq):
+                token = '▁' + char  # Adjust this if your tokenizer uses a different prefix
+                if token in vocab:
+                    observed_probs.append(softmax_logits[j, vocab[token]].item())
+                else:
+                    # Handle the case where the token is not in the vocabulary
+                    observed_probs.append(1e-10)  # Small probability for unknown tokens
+            
+            # Compute mean across the actual sequence length
+            mean_log_prob = np.mean(observed_probs)
+            
+            scores.append(mean_log_prob)
+
+        return scores
 
 
 
diff --git a/scripts/fireprot_zero.py b/scripts/fireprot_zero.py
new file mode 100644
index 0000000..9c997d3
--- /dev/null
+++ b/scripts/fireprot_zero.py
@@ -0,0 +1,206 @@
+import os
+import requests
+import json
+import logging
+import io
+import re
+from typing import List, Tuple
+
+import pandas as pd
+import numpy as np
+from scipy.stats import spearmanr
+from sklearn.metrics import roc_auc_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+from tqdm import tqdm
+from yaml import safe_load
+import codecarbon
+
+from nomelt.model import NOMELTModel
+
+# Seaborn settings
+sns.set_context("talk")
+sns.set_style("whitegrid")
+
+# Logger setup
+logger = logging.getLogger(__name__)
+LOGLEVEL = os.getenv('LOGLEVEL', 'INFO')
+LOGFILE = f'./logs/fireprotdb_benchmark.log'
+
+def setup_logging():
+    logger.setLevel(getattr(logging, LOGLEVEL))
+    fh = logging.FileHandler(LOGFILE, mode='w')
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    fh.setFormatter(formatter)
+    logger.addHandler(fh)
+
+def download_fireprotdb_data(url: str, output_file: str) -> pd.DataFrame:
+    if os.path.exists(output_file):
+        logger.info(f"Loading FireProtDB data from file: {output_file}")
+        return pd.read_csv(output_file)
+    
+    logger.info(f"Downloading FireProtDB data from {url}")
+    payload = {"searchData": {"type": "expr", "key": "all", "value": " ", "checkOptions": []}, "filter": {"filterKey": "ddG", "order": "asc"}}
+    response = requests.post(url, json=payload)
+    content = response.content.decode('utf-8')
+    df = pd.read_csv(io.StringIO(content))
+    df.to_csv(output_file, index=False)
+    return df
+
+def process_fireprotdb_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    wt_data = {}
+    variant_data = []
+
+    for _, row in df.iterrows():
+        uniprot_id = row['uniprot_id']
+        if len(row['sequence']) > 400:
+            continue
+        if uniprot_id not in wt_data:
+            wt_data[uniprot_id] = {
+                'sequence': row['sequence'],
+                'tm': row['tm'] - row['dTm']  # Calculate wildtype Tm
+            }
+        
+        # Check if delta Tm is available
+        if pd.isna(row['dTm']):
+            continue
+
+        # Verify that the mutation matches the sequence
+        wt_aa = row['wild_type']
+        mut_aa = row['mutation']
+        pos = row['position'] - 1  # Convert to 0-based index
+        
+        if pos >= len(row['sequence']) or row['sequence'][pos] != wt_aa:
+            logger.warning(f"Mutation {wt_aa}{row['position']}{mut_aa} does not match sequence for {uniprot_id}")
+            continue
+
+        variant_data.append({
+            'uniprot_id': uniprot_id,
+            'wildtype_sequence': row['sequence'],
+            'mutation': f"{wt_aa}{row['position']}{mut_aa}",
+            'delta_tm': row['dTm']
+        })
+
+    wt_df = pd.DataFrame.from_dict(wt_data, orient='index')
+    wt_df.reset_index(inplace=True)
+    wt_df.rename(columns={'index': 'uniprot_id'}, inplace=True)
+
+    variant_df = pd.DataFrame(variant_data)
+    
+    # Drop any remaining rows with NaN values
+    variant_df = variant_df.dropna()
+
+    logger.info(f"Processed {len(wt_df)} wildtype sequences and {len(variant_df)} variants")
+    
+    return wt_df, variant_df
+
+def apply_mutation(sequence: str, mutation: str) -> str:
+    match = re.match(r'([A-Z])(\d+)([A-Z])', mutation)
+    if not match:
+        raise ValueError(f"Invalid mutation format: {mutation}")
+    wt, pos, mut = match.groups()
+    pos = int(pos) - 1
+    if sequence[pos] != wt:
+        raise ValueError(f"Mutation {mutation} does not match sequence at position {pos+1}")
+    return sequence[:pos] + mut + sequence[pos+1:]
+
+def evaluate_model(model: NOMELTModel, wt_df: pd.DataFrame, variant_df: pd.DataFrame) -> dict:
+    results = []
+    counter = 0
+
+    for _, wt_row in tqdm(wt_df.iterrows(), total=len(wt_df), desc="Evaluating proteins"):
+        wt_sequence = wt_row['sequence']
+        variants = variant_df[variant_df['uniprot_id'] == wt_row['uniprot_id']]
+        if len(variants) == 0:
+            continue
+        counter +=1
+        mutated_sequences = [apply_mutation(wt_sequence, mut) for mut in variants['mutation']]
+        wt_score, variant_scores = model.score_variants(wt_sequence, mutated_sequences, batch_size=5, indels=False)
+        
+        for _, label, score in zip(variants.itertuples(), variants['delta_tm'], variant_scores):
+            results.append({
+                'delta_tm': label,
+                'nomelt_score': score
+            })
+
+    results_df = pd.DataFrame(results)
+    
+    x_data = results_df['delta_tm']
+    y_data = results_df['nomelt_score']
+    
+    spearman_coef, spearman_p = spearmanr(x_data, y_data)
+    auroc = roc_auc_score((x_data > 0).astype(int), y_data)
+
+    return {
+        'spearman_coef': spearman_coef,
+        'spearman_p': spearman_p,
+        'auroc': auroc,
+        'results_df': results_df
+    }
+
+def plot_results(results: dict, model_name: str, output_dir: str):
+    x_data = results['results_df']['delta_tm']
+    y_data = results['results_df']['nomelt_score']
+    
+    fig, ax = plt.subplots(figsize=(8, 8))
+    sns.kdeplot(x=x_data, y=y_data, fill=True, cmap="YlGnBu", ax=ax)
+    ax.set_xlabel('ΔTm [°C]')
+    ax.set_ylabel('NOMELT score')
+    ax.set_title(f'{model_name} Model Performance on FireProtDB')
+    
+    textstr = f"Spearman: {results['spearman_coef']:.3f} (p={results['spearman_p']:.2e})\nAUROC: {results['auroc']:.3f}"
+    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+    ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=12,
+            verticalalignment='top', bbox=props)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(output_dir, f'fireprotdb_{model_name.lower()}_performance.png'), dpi=300)
+    plt.close()
+
+def main():
+    setup_logging()
+
+    # Initialize codecarbon
+    tracker = codecarbon.OfflineEmissionsTracker(
+        project_name="fireprotdb_benchmark",
+        output_dir='./data',
+        country_iso_code="USA",
+        region="washington")
+    tracker.start()
+
+    # Load parameters
+    with open('./params.yaml', 'r') as f:
+        params = safe_load(f)
+
+    hyperparams = params['model']['model_hyperparams']
+
+    # Download and process data
+    url = "https://loschmidt.chemi.muni.cz/fireprotdb/v1/export?searchType=advanced&type=csv"
+    output_file = "./data/fireprotdb_data.csv"
+    df = download_fireprotdb_data(url, output_file)
+    wt_df, variant_df = process_fireprotdb_data(df)
+
+    # Initialize models
+    new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams)
+    original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams)
+
+    # Evaluate models
+    results = {}
+    for model_name, model in [("New", new_model), ("Original", original_model)]:
+        logger.info(f"Evaluating {model_name} model")
+        results[model_name] = evaluate_model(model, wt_df, variant_df)
+        plot_results(results[model_name], model_name, './data/plots')
+
+    # Save results
+    with open('./data/nomelt-model-full/fireprotdb_benchmark_results.json', 'w') as f:
+        json.dump({k: {kk: vv for kk, vv in v.items() if kk != 'results_df'} for k, v in results.items()}, f, indent=2)
+
+    # Save detailed results
+    for model_name, result in results.items():
+        result['results_df'].to_csv(f'./data/nomelt-model-full/fireprotdb_{model_name.lower()}_detailed_results.csv', index=False)
+
+    logger.info("Benchmark completed. Results saved.")
+    tracker.stop()
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py
new file mode 100644
index 0000000..721c2e5
--- /dev/null
+++ b/scripts/meltome_benchmark.py
@@ -0,0 +1,90 @@
+import os
+import io
+import requests
+import zipfile
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import json
+from scipy.stats import spearmanr, pearsonr
+from yaml import safe_load
+from nomelt.model import NOMELTModel
+
+def download_and_read_csv(url, csv_name, cache_dir='./tmp/'):
+    # Create cache directory if it doesn't exist
+    cache_file = os.path.join(cache_dir, csv_name)
+    os.makedirs(os.path.dirname(cache_file), exist_ok=True)
+
+    if os.path.exists(cache_file):
+        print(f"Loading data from cache: {cache_file}")
+        return pd.read_csv(cache_file)
+    else:
+        print(f"Downloading data from {url}")
+        response = requests.get(url)
+        z = zipfile.ZipFile(io.BytesIO(response.content))
+        df = pd.read_csv(z.open(csv_name))
+        
+        # Save to cache
+        df.to_csv(cache_file, index=False)
+        print(f"Data cached to {cache_file}")
+        
+        return df
+
+def evaluate_model(model, sequences, temperatures):
+    scores = []
+    for i in range(0, len(sequences), 50):
+        seq_batch = sequences[i:i+50]
+        scores.extend(model.score_wts(seq_batch, batch_size=2))
+    spearman_corr, spearman_p = spearmanr(temperatures, scores)
+    pearson_corr, pearson_p = pearsonr(temperatures, scores)
+    return scores, spearman_corr, spearman_p, pearson_corr, pearson_p
+
+def plot_results(temperatures, scores, spearman_corr, pearson_corr, model_name):
+    plt.figure(figsize=(10, 6))
+    sns.scatterplot(x=temperatures, y=scores)
+    plt.xlabel('Melting Temperature (°C)')
+    plt.ylabel(f'{model_name} NOMELT Score')
+    plt.title(f'{model_name} Model: NOMELT Score vs Melting Temperature\n'
+              f'Spearman R: {spearman_corr:.3f}, Pearson R: {pearson_corr:.3f}')
+    plt.savefig(f'./data/plots/{model_name.lower()}_meltome_scores.png', dpi=300, bbox_inches='tight')
+    plt.close()
+
+def main():
+    # Load parameters
+    with open('./params.yaml', 'r') as f:
+        params = safe_load(f)
+
+    # Download and read data
+    url = "https://github.com/J-SNACKKB/FLIP/raw/main/splits/meltome/splits.zip"
+    df = download_and_read_csv(url, "splits/mixed_split.csv")
+    # remove real long sequences
+    df = df[df['sequence'].str.len()<=300]
+
+    # Initialize models
+    hyperparams = params['model']['model_hyperparams']
+    original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams)
+    new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams)
+
+    sequences = df['sequence'].tolist()
+    temperatures = df['target'].tolist()
+
+    results = {}
+    for model, model_name in [(original_model, "Original"), (new_model, "New")]:
+        scores, spearman_corr, spearman_p, pearson_corr, pearson_p = evaluate_model(model, sequences, temperatures)
+        
+        results[model_name] = {
+            'spearman_correlation': float(spearman_corr),
+            'spearman_p_value': float(spearman_p),
+            'pearson_correlation': float(pearson_corr),
+            'pearson_p_value': float(pearson_p)
+        }
+        
+        plot_results(temperatures, scores, spearman_corr, pearson_corr, model_name)
+
+    # Save results
+    os.makedirs('./data/nomelt-model-full', exist_ok=True)
+    with open('./data/nomelt-model-full/meltome_benchmark_results.json', 'w') as f:
+        json.dump(results, f, indent=2)
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/protein_gym_benchmark.py b/scripts/protein_gym_benchmark.py
index 93a57b2..aebe095 100644
--- a/scripts/protein_gym_benchmark.py
+++ b/scripts/protein_gym_benchmark.py
@@ -1,9 +1,6 @@
 """
-Protein Gym original data source for LipA T50:
-https://pubs.acs.org/doi/10.1021/acs.jcim.9b00954
-
+Compare original to finteuned model weights on thermal stability ProteinGym benchmark
 """
-# System dependencies
 import io
 import os
 import sys
@@ -14,7 +11,8 @@
 import tqdm
 from yaml import safe_load
 
-# Library dependencies
+ 
+
 import duckdb as ddb
 import numpy as np
 import pandas as pd
@@ -24,12 +22,13 @@
 import seaborn as sns
 import codecarbon
 
-# Local dependencies
 from nomelt.model import NOMELTModel
 
+
 # Seaborn settings
 sns.set_context("talk")
 sns.set_style("whitegrid")
+ 
 
 # Environment variables and logger setup
 logger = logging.getLogger(__name__)
@@ -37,14 +36,15 @@
 LOGFILE = f'./logs/{os.path.basename(__file__)}.log'
 os.environ['SLURM_JOBID'] = os.environ.get('SLURM_JOB_ID', '')
 
+
 # Constants
 ANALYSIS_DIR = "./data/gym/analysis/"
+ 
 
 # Functions definitions
 def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> list:
     """
     Generate a list of mutated sequences from a mutation table DataFrame, each with a single mutation applied to the wild-type.
-
     Parameters:
     - wt_sequence: The wild-type protein sequence.
     - mutation_df: DataFrame containing the mutation table.
@@ -62,7 +62,7 @@ def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> l
     for mutation in mutations:
         # Create a fresh copy of wt for each mutation
         mutated_sequence = list(wt_sequence)
-        
+
         match = re.match(r'([A-Z]+)(\d+)([A-Z]+)', mutation)
         if not match:
             continue # Skip any mutations that don't match the expected format
@@ -82,6 +82,7 @@ def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> l
 
     return all_mutated_sequences
 
+
 def pairwise_t_test(df, alpha=0.05):
     """
     Perform pairwise t-tests on a dataframe of measurements.
@@ -95,6 +96,7 @@ def pairwise_t_test(df, alpha=0.05):
     """
     # Extract measurements
     measurements = df[['dms_mean', 'dms_std']]
+
     # Create combinations of all pairs
     pairs = list(itertools.combinations(measurements.index, 2))
 
@@ -104,18 +106,20 @@ def pairwise_t_test(df, alpha=0.05):
     for i, j in tqdm.tqdm(pairs, desc="Performing pairwise t-tests"):
         mean_i, std_i = measurements.loc[i].values
         mean_j, std_j = measurements.loc[j].values
+
         # Calculate t-statistic and p-value
         t_stat, p_val = stats.ttest_ind_from_stats(mean1=mean_i, std1=std_i, nobs1=3,
                                                    mean2=mean_j, std2=std_j, nobs2=3)
 
         # Check if the difference is significant
         if p_val < alpha:
-            significant_pairs.append((i, j, mean_i - mean_j))
 
+            significant_pairs.append((i, j, mean_i - mean_j))
     return significant_pairs
 
-# Adjusting the 'Variants of BsLipA' to match the 'mutant' format by adding 31 to the numeric part
+ 
 
+# Adjusting the 'Variants of BsLipA' to match the 'mutant' format by adding 31 to the numeric part
 # Function to adjust the numeric part of the variant names
 # this is because the authors reported the mutations starting from a specific region
 # of LipA instead of the start of the uniprot seq
@@ -126,6 +130,59 @@ def adjust_variant(variant, offset=31):
     else:
         return variant
 
+ 
+
+def load_and_process_data(file_path, sheet_name=0):
+    """Load and process data from an Excel file."""
+    data = pd.read_excel(file_path, sheet_name=sheet_name, usecols='A:C')
+    data.columns = ['mutation', 'dms_mean', 'dms_std']
+    data['mutation_shifted'] = data['mutation'].apply(adjust_variant)
+    return data
+
+ 
+
+def evaluate_model(model, wt, data, benchmark_name):
+    """Evaluate the model on a given dataset and return metrics."""
+    mutated_sequences_list = generate_mutated_sequences(wt, data)
+    data['mutated_sequence'] = mutated_sequences_list
+
+    wt_score, variant_scores = model.score_variants(wt, mutated_sequences_list, batch_size=5, indels=False)
+    data['nomelt_score'] = variant_scores
+
+    sig_pairs = pairwise_t_test(data)
+    mask = []
+    for i, j, i_sub_j_dms in sig_pairs:
+        i_sub_j_nomelt = data.loc[i, 'nomelt_score'] - data.loc[j, 'nomelt_score']
+        mask.append(np.sign(i_sub_j_nomelt) == np.sign(i_sub_j_dms))
+
+    x_data = data['dms_mean']
+    y_data = data['nomelt_score']
+    pearson_coef, pearson_p = pearsonr(x_data, y_data)
+    spearman_coef, spearman_p = spearmanr(x_data, y_data)
+
+    plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, f'./data/plots/{benchmark_name}.png')
+
+    return {
+        'pearson_coef': pearson_coef,
+        'pearson_p': pearson_p,
+        'spearman_coef': spearman_coef,
+        'spearman_p': spearman_p,
+        'frac_qualitative_sig': sum(mask)/len(sig_pairs)
+    }
+
+ 
+
+def plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, filename):
+    """Plot and save the results."""
+    fig, ax = plt.subplots(figsize=(5, 5))
+    sns.kdeplot(x=x_data, y=y_data, fill=True, bw_adjust=0.5, cmap="Blues", ax=ax)
+    ax.set_xlabel('DMS score [C]')
+    ax.set_ylabel('NOMELT score')
+    plt.text(0.05, 0.95, f'Pearson: {pearson_coef:.2f} (p={pearson_p:.2e})\nSpearman: {spearman_coef:.2f} (p={spearman_p:.2e})',
+             fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', alpha=0.5), transform=plt.gca().transAxes)
+    plt.savefig(filename, dpi=300, bbox_inches='tight')
+
+ 
 
 if __name__ == "__main__":
     # Logger initialization
@@ -138,6 +195,8 @@ def adjust_variant(variant, offset=31):
     utils_logger.setLevel(getattr(logging, LOGLEVEL))
     utils_logger.addHandler(fh)
 
+ 
+
     # initialize codecarbon
     tracker = codecarbon.OfflineEmissionsTracker(
         project_name="estimate_gym",
@@ -147,76 +206,35 @@ def adjust_variant(variant, offset=31):
 
     tracker.start()
 
-    # Data reading and processing
-    logger.info("Reading data...")
-    data_stds = pd.read_excel("./data/gym/ci9b00954_si_002.xlsx", usecols='A:C')
-    data_stds.columns = ['mutation', 'dms_mean', 'dms_std']
-    data_stds['mutation_shifted'] = data_stds['mutation'].apply(adjust_variant)
+    # Data reading and processing 
+    # Load data for both benchmarks
+    data_lipa = load_and_process_data("./data/gym/ci9b00954_si_002.xlsx")
+    # data_second = load_and_process_data("./path/to/second_benchmark.xlsx")  # Replace with actual path
 
-    # Designating WT sequence
+    # Designating WT sequence (assuming it's the same for both benchmarks, adjust if needed)
     wt = "MKFVKRRIIALVTILMLSVTSLFALQPSAKAAEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN"
 
-    # Generate mutated sequences
-    logger.info("Generating mutated sequences")
-    mutated_sequences_list = generate_mutated_sequences(wt, data_stds)
-    data_stds['mutated_sequence'] = mutated_sequences_list
-    logger.info(f"Generated {len(mutated_sequences_list)} mutated sequences")
+ 
 
-    # Model initialization
-    logger.info("Initializing model")
+    # Model initialization for new and original weights
     with open('./params.yaml', 'r') as f:
         params = safe_load(f)
-    hyperparams = params['model']['model_hyperparams']
-    model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams)
-    
-    # Score mutated sequences
-    logger.info("Scoring mutated sequences")
-    wt_score, variant_scores = model.score_variants(wt, mutated_sequences_list, batch_size=5, indels=False)
-    data_stds['nomelt_score'] = variant_scores
-    logger.info("model scores computed!")
-
-    # Analysis
-    logger.info("Analyzing scores...")
-    sig_pairs = pairwise_t_test(data_stds)
-    logger.info(f"Found {len(sig_pairs)} significant pairs out of {len(data_stds)**2}")
-
-    # get boolean mask for whenever nomelt properly predicts the effect of a mutation wualitatively
-    # only for the significant pairs
-    mask = []
-    for i, j, i_sub_j_dms in sig_pairs:
-        i_sub_j_nomelt = data_stds.loc[i, 'nomelt_score'] - data_stds.loc[j, 'nomelt_score']
-        mask.append(np.sign(i_sub_j_nomelt) == np.sign(i_sub_j_dms))
-    logger.info(f"Found {sum(mask)} out of {len(sig_pairs)} significant pairs where NOMELT qualitatively predicts the effect of the mutation")
 
-    # plotting
-    x_data = data_stds['dms_mean']
-    y_data = data_stds['nomelt_score']
-    # Calculate Pearson and Spearman coefficients and p-values
-    pearson_coef, pearson_p = pearsonr(x_data, y_data)
-    spearman_coef, spearman_p = spearmanr(x_data, y_data)
+    hyperparams = params['model']['model_hyperparams']
+    new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams)
+    original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams)
 
-    logger.info(f"Pearson correlation coefficient: {pearson_coef}, p-value: {pearson_p}")
-    logger.info(f"Spearman correlation coefficient: {spearman_coef}, p-value: {spearman_p}")
+    # Evaluate both models on both datasets
+    results = {}
 
-    fig, ax= plt.subplots(figsize=(5, 5))
-    # create a density plot
-    sns.kdeplot(x=x_data, y=y_data, fill=True, bw_adjust=0.5, cmap="Blues", ax=ax)
-    # adding a regression line
-    m, b = np.polyfit(x_data, y_data, 1)
-    x_grid = np.linspace(x_data.min(), x_data.max(), 10)
-    # ax.plot(x_grid, m*x_grid + b, color='red', linestyle='--', label='Regression line')
-    ax.set_xlabel('DMS score [C]')
-    ax.set_ylabel('NOMELT score')
-
-    # annotate with Pearson and Spearman coefficients and p-values
-    plt.text(0.05, 0.95, f'Pearson: {pearson_coef:.2f} (p={pearson_p:.2e})\nSpearman: {spearman_coef:.2f} (p={spearman_p:.2e})', 
-             fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', alpha=0.5), transform=plt.gca().transAxes)
-    plt.savefig('./data/plots/lipa_gym.png', dpi=300, bbox_inches='tight')
+    for model_name, model in [("new", new_model), ("original", original_model)]:
+        results[model_name] = {}
+        for benchmark_name, data in [("lipa", data_lipa)]: # , ("second_benchmark", data_second)
+            results[model_name][benchmark_name] = evaluate_model(model, wt, data, f"{model_name}_{benchmark_name}")
 
-    with open('./data/nomelt-model-full/lipa_gym_zero_shot.json', 'w') as f:
-        json.dump({'pearson_coef': pearson_coef, 'pearson_p': pearson_p, 'spearman_coef': spearman_coef, 'spearman_p': spearman_p, 'frac_qualitative_sig': sum(mask)/len(sig_pairs)
-         }, f)
+    # Save results
+    with open('./data/nomelt-model-full/benchmark_results.json', 'w') as f:
+        json.dump(results, f, indent=2)
+ 
     # CodeCarbon tracker stop
-    tracker.stop()
-
-
+    tracker.stop()
\ No newline at end of file
diff --git a/scripts/zero_shot_experiment.py b/scripts/zero_shot_experiment.py
index b0ce548..a765c19 100644
--- a/scripts/zero_shot_experiment.py
+++ b/scripts/zero_shot_experiment.py
@@ -1,134 +1,90 @@
 """Use NOMELT as a zero shot estimator and compare to experimental data.
 """
-import pandas as pd
+from nomelt.model import NOMELTModel
+from yaml import safe_load
+import json
+import os
 import numpy as np
+from scipy.stats import spearmanr
 import matplotlib.pyplot as plt
 import seaborn as sns
-import os
-import json
-import MDAnalysis as mda
-import io
-import copy
-from scipy.stats import spearmanr
 
-from nomelt.model import NOMELTModel
-from yaml import safe_load
+def main(sequences, temperatures, system_name):
+    # Load parameters from params.yaml
+    with open('./params.yaml', 'r') as f:
+        params = safe_load(f)
 
-sns.set_style('ticks')
-sns.set_context('talk')
+    hyperparams = params['model']['model_hyperparams']
 
-# variant sets from https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202101349
+    # Load both models
+    original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams)
+    new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams)
+
+    results = {}
 
-LovD_seqs = {
-    'LovD': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSLTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
-    'LovD1': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
-    'LovD2': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
-    'LovD3': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
-    'LovD4': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
-    'LovD5': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
-    'LovD6': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEKAIYAQYQQG",
-    'LovD7': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRVDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG",
-    'LovD8': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG",
-    'LovD9': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVLMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVRLDETVDRLLPDLCAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVMRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG"
-}
-LovD_temperatures = [38.5, 39.5, 44.0, 44.3, 45.0, 46.0, 52.0, 49.0, 49.3, 50.8]
+    for model, model_name in [(original_model, "original"), (new_model, "new")]:
+        wt_score, variant_scores = model.score_variants(sequences[0], sequences[1:])
+        scores = [wt_score] + variant_scores
+
+        # Calculate Spearman correlation
+        spear_r, spear_p = spearmanr(temperatures, scores)
+
+        results[model_name] = {
+            'temperatures': temperatures,
+            'scores': scores,
+            'spearman': float(spear_r),
+            'p': float(spear_p)
+        }
+
+        # generate random mutations and check scores
+        wt_seq = sequences[0]
+        variants = []
+        for i in range(10):
+            positions = np.random.choice(list(range(len(wt_seq))), size=10, replace=False)
+            var_ = list(wt_seq)
+            for p in positions:
+                var_[p] = str(np.random.choice(list('ACDEFGHIKLMNPQRSTVWY')))
+            variants.append(''.join(var_))
+
+        _, random_scores = model.score_variants(sequences[0], variants)
+
+        results[model_name]['random'] = random_scores
+        results[model_name]['random_mean'] = np.mean(random_scores)
+
+        # Create plot
+        fig, ax = plt.subplots(figsize=(4, 4))
+        sns.regplot(x=temperatures, y=scores, ax=ax)
+        ax.set_xlabel('Melting Temperature (°C)')
+        ax.set_ylabel(f'{model_name.upper()} NOMELT Logit Score')
+        ax.hlines(np.mean(random_scores), xmin=min(temperatures), xmax=max(temperatures), label='10 random mutations', color='r')
+        plt.savefig(f'./data/plots/{model_name}_{system_name}_scores.png', dpi=300, bbox_inches='tight')
+        plt.close()
+
+
+    return results
+
+# variant sets from https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202101349
 
 if __name__ == "__main__":
-    with open('./params.yaml', 'r') as f:
-        params = safe_load(f)
 
-    hyperparams = params['model']['model_hyperparams']
-    model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams)
-
-    # start with LovD
-    lovd_variants = list(LovD_seqs.values())[1:]
-    lovd_wt = LovD_seqs['LovD']  
-
-    wt_score, variant_scores = model.score_variants(lovd_wt, lovd_variants)
-    lovd_scores = [0.0] + variant_scores
-
-    fig, ax = plt.subplots(figsize=(4, 4))
-    sns.regplot(x=LovD_temperatures, y=lovd_scores, ax=ax)
-    ax.set_xlabel('Melting Temperature (°C)')
-    ax.set_ylabel('NOMELT Logit Score')
-
-    plt.savefig('./data/plots/lovd_scores.png', dpi=300, bbox_inches='tight')
-    # get spearmans
-    lovd_spear_r, lovd_spear_p = spearmanr(LovD_temperatures, lovd_scores)
-
-    # now do lipa
-    LipA = "AEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN"
-    LipA_mutation_table = pd.read_csv(io.StringIO("""
-    Name,Mutations,TM (°C)
-    WT,,56.0
-    TM,L114P A132D N166Y,61.2
-    2D9,F17S N89Y L114P A132D I157M N166Y,67.4
-    4D3,A15S F17S A20E N89Y G111D L114P A132D I157M N166Y,71.2
-    6B,A15S F17S A20E N89Y G111D L114P A132D M134E M137P I157M S163P N166Y,78.2
-
-    """))
-
-    def parse_mutation(mutation_str):
-        """
-        Parse a mutation string to get the original amino acid, position, and new amino acid.
-
-        Parameters:
-            mutation_str (str): The mutation string (e.g., "A132D")
-
-        Returns:
-            tuple: original amino acid (str), position (int), new amino acid (str)
-        """
-        original_aa = mutation_str[0]
-        new_aa = mutation_str[-1]
-        position = int(mutation_str[1:-1]) - 1  # Convert to Python 0-based index
-
-        return original_aa, position, new_aa
+    LovD_seqs = {
+        'LovD': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSLTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
+        'LovD1': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
+        'LovD2': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
+        'LovD3': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
+        'LovD4': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
+        'LovD5': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG",
+        'LovD6': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEKAIYAQYQQG",
+        'LovD7': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRVDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG",
+        'LovD8': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG",
+        'LovD9': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVLMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVRLDETVDRLLPDLCAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVMRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG"
+    }
+    LovD_temperatures = [38.5, 39.5, 44.0, 44.3, 45.0, 46.0, 52.0, 49.0, 49.3, 50.8]
     
-    def check_mutation_table(df, wt):
-        df = df.copy()
-        df['Mutations'] = df['Mutations'].fillna('')
-        df['Mutations'] = df['Mutations'].apply(lambda s: s.split())
-
-        all_mutations = np.unique(np.hstack(df['Mutations'].values))
-        print(all_mutations)
-        for m in all_mutations:
-            a, pos, b = parse_mutation(m)
-            assert wt[pos] == a, m
-        return df
-    LipA_mutation_table = check_mutation_table(LipA_mutation_table, LipA)
-    def convert_seq(mutations):
-        seq = list(copy.copy(LipA))
-        for m in mutations:
-            _, pos, new = parse_mutation(m)
-            seq[pos] = new
-        return ''.join(seq)
-    LipA_mutation_table['seq'] = LipA_mutation_table['Mutations'].apply(convert_seq)
-    LipA_temps = LipA_mutation_table['TM (°C)'].values
-    LipA_seqs = LipA_mutation_table['seq'].values
-    LipA_wt = LipA_seqs[0]
-    LipA_variants = LipA_seqs[1:]
-    lipa_wt_score, lipa_v_scores = model.score_variants(LipA_wt, LipA_variants, indels=False)
-    lipa_scores = [lipa_wt_score] + lipa_v_scores
-    # get spearmans
-    lipa_spear_r, lipa_spear_p = spearmanr(LipA_temps, lipa_scores)
-
-    # make plot
-    fig, ax = plt.subplots(2,1, figsize=(4,8), sharex=True)
-    sns.regplot(x=LovD_temperatures, y=lovd_scores, ax=ax[0])
-    sns.regplot(x=LipA_temps, y=lipa_scores, ax=ax[1])
-    ax[0].hlines(y=0.0, xmin=ax[0].get_xlim()[0], xmax=ax[0].get_xlim()[1], color='k')
-    ax[1].hlines(y=0.0, xmin=ax[0].get_xlim()[0], xmax=ax[0].get_xlim()[1], color='k')
-    ax[1].set_xlabel('Experimental TM [C]')
-    ax[1].set_ylabel('Model logP score')
-    ax[0].set_ylabel('Model logP score')
-    ax[0].set_title('LovD Variants')
-    ax[1].set_title('LipA Variants')
-    plt.savefig('./data/plots/exp_tm_scores.png', dpi=400, bbox_inches='tight')
-
-    # save the data too
+    if not os.path.exists('./data/plots'):
+        os.makedirs('./data/plots', exist_ok=True)
+
+    results = main(list(LovD_seqs.values()), LovD_temperatures, 'lovd')
+
     with open('./data/nomelt-model-full/zero_shot_estimated.json', 'w') as f:
-        json.dump(
-            {'lovd': {'temps': list(LovD_temperatures), 'scores': list(lovd_scores), 'spearman': float(lovd_spear_r), 'p': float(lovd_spear_p)},
-             'lipa': {'temps': list(LipA_temps), 'scores': list(lipa_scores), 'spearman': float(lipa_spear_r), 'p': float(lipa_spear_p)}},
-            f
-        )
\ No newline at end of file
+        json.dump(results, f, indent=2)