import pandas as pd

# Load the dataset
df = pd.read_parquet("aqora://aqora/magic-state-cultivation-with-lattice-surgery/v0.0.0")

# Display basic info
print(f"Total runs: {len(df)}")
print(f"Columns: {len(df.columns)}")
print(f"Experiment types: {df['experiment_type'].nunique()}")

# View first few rows
print(df.head())

# Get summary statistics
print(df.describe())

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df = pd.read_parquet("aqora://aqora/magic-state-cultivation-with-lattice-surgery/v0.0.0")

# Create visualization
plt.figure(figsize=(12, 6))
sns.boxplot(data=df, x='experiment_type', y='logical_error_rate')
plt.xticks(rotation=45, ha='right')
plt.title('Logical Error Rate Distribution by Experiment Type')
plt.ylabel('Logical Error Rate')
plt.xlabel('Experiment Type')
plt.tight_layout()
plt.show()

# Print statistics
stats = df.groupby('experiment_type')['logical_error_rate'].describe()
print(stats)

import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_parquet("aqora://aqora/magic-state-cultivation-with-lattice-surgery/v0.0.0")

# Calculate fidelity (1 - error_rate)
df['fidelity'] = 1 - df['logical_error_rate']

# Scatter plot
plt.figure(figsize=(10, 6))
for exp_type in df['experiment_type'].unique():
    subset = df[df['experiment_type'] == exp_type]
    plt.scatter(subset['qubitrounds_cost'], subset['fidelity'], 
                label=exp_type, alpha=0.7, s=100)

plt.xlabel('Qubit-Rounds Cost')
plt.ylabel('Logical Fidelity (1 - error rate)')
plt.title('Fidelity vs Resource Cost Tradeoff')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load data
df = pd.read_parquet("aqora://aqora/magic-state-cultivation-with-lattice-surgery/v0.0.0")

# Group by code distance
distance_analysis = df.groupby('code_distance').agg({
    'logical_error_rate': ['mean', 'std'],
    'num_data_qubits': 'first',
    'acceptance_rate': 'mean'
}).round(6)

print("Impact of Code Distance:")
print(distance_analysis)

# Visualization
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))

# Error rate vs distance
df.groupby('code_distance')['logical_error_rate'].mean().plot(
    kind='bar', ax=ax1, color='coral')
ax1.set_title('Average Logical Error Rate by Code Distance')
ax1.set_ylabel('Logical Error Rate')
ax1.set_xlabel('Code Distance')

# Qubit count vs distance
df.groupby('code_distance')['num_data_qubits'].first().plot(
    kind='bar', ax=ax2, color='skyblue')
ax2.set_title('Data Qubits Required by Code Distance')
ax2.set_ylabel('Number of Data Qubits')
ax2.set_xlabel('Code Distance')

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_parquet("aqora://aqora/magic-state-cultivation-with-lattice-surgery/v0.0.0")

# Extract stage success rates
stages = ['injection_success_rate', 'stabilize1_success_rate', 
          'surgery_success_rate', 'stabilize2_success_rate', 
          'epilogue_success_rate']

stage_names = ['Injection', 'Stabilize 1', 'Surgery', 'Stabilize 2', 'Epilogue']

# Calculate means
stage_means = [df[stage].mean() for stage in stages]

# Create bar plot
plt.figure(figsize=(10, 6))
bars = plt.bar(stage_names, stage_means, color='steelblue', alpha=0.7)
plt.axhline(y=0.95, color='red', linestyle='--', label='95% threshold')
plt.ylabel('Success Rate')
plt.title('Average Success Rate by Protocol Stage')
plt.ylim(0.85, 1.0)
plt.legend()
plt.grid(True, alpha=0.3, axis='y')

# Add value labels on bars
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height,
             f'{height:.3f}',
             ha='center', va='bottom')

plt.tight_layout()
plt.show()

print("\nStage-by-Stage Performance:")
for name, rate in zip(stage_names, stage_means):
    print(f"{name:15s}: {rate:.4f}")