Navigating the Void: How Parietal Cortex Volume Predicts Spatial Orientation in Zero Gravity

import numpy as np
import pandas as pd
import plotly.express as px
from scipy.stats import linregress
import math
import plotly.io as pio
pio.renderers.default = "plotly_mimetype"

# Seed for reproducibility
np.random.seed(42)

# Number of datapoints for each scatter plot
n_points = 400

# Function to generate synthetic data for brain volume vs. cognitive performance
def generate_synthetic_data(volume_mean, volume_std, score_mean, score_std, correlation, n_points):
    volume = np.random.normal(volume_mean, volume_std, n_points)
    volume_jitter = np.random.normal(0, volume_std * 1.5, n_points)  # Add jitter to volume
    noise = np.random.normal(0, score_std * 3.5, n_points)  # Increase noise for jitter in score
    score = score_mean +  correlation * (volume - volume_mean) + noise
    return volume + volume_jitter, score

# Function to generate additional metrics
def generate_additional_metrics(volume, score, volume_std, score_std, n_points):
    # Hypothetical Brain Density: Based on volume with added noise
    density = volume / (volume_std * np.random.uniform(0.8, 1.2, n_points))
    noise = np.random.normal(0, score_std * 0.01, n_points)  # Increase noise for jitter in score
    
    # Hypothetical Neural Efficiency: Positively correlated with score but with noise
    efficiency = np.sin(score) / (score_std * np.random.uniform(1.2, 11.7, n_points))
    
    return density, efficiency

# Generate synthetic data for 10 scatter plots
data_dict = {}

# Scatter plot 8: Parietal Cortex Volume vs. Spatial Orientation
data_dict["Parietal_Spatial"] = generate_synthetic_data(8500, 100, 68, 8, 0.77, n_points)

df_dict = {key: pd.DataFrame({'Volume': value[0], 'Score': value[1]}) for key, value in data_dict.items()}

# Apply additional metrics to all regions
for key, df in df_dict.items():
    volume_std = np.std(df['Volume'])
    score_std = np.std(df['Score'])
    
    # Generate new metrics for Brain Density and Neural Efficiency
    density, efficiency = generate_additional_metrics(df['Volume'], df['Score'], volume_std, score_std, n_points)
    
    # Add the new metrics to the DataFrame
    df['Density'] = density
    df['Efficiency'] = efficiency

def plot_correlation(df_dict, key, x_col, y_col, title_prefix, color='blue', marker_size=10, marker_opacity=0.7):
    df = df_dict[key]
    
    # Calculate Spearman correlation
    slope, intercept, _, _, _ = linregress(df[x_col], df[y_col])

    shift = intercept  # This is the shift
    scale = slope      # This is the scale
    
    # Create scatter plot with customized marker colors, size, and opacity
    fig = px.scatter(
        df, 
        x=x_col, 
        y=y_col, 
        title=f'{title_prefix} (Shift: {shift:.2f}, Scale {scale:.2f})', 
        trendline='ols',
        color_discrete_sequence=[color],  # Custom marker color
        template="simple_white",
    )
    
    # Update marker style
    # fig.update_traces(marker=dict(size=marker_size, opacity=marker_opacity))
    
    # Update figure layout
    fig.update_layout(height=600)
    
    return fig

fig1 = plot_correlation(df_dict, "Parietal_Spatial", 'Volume', 'Score', 'Parietal', color='indigo', marker_size=10, marker_opacity=0.7)
fig1.show()

fig2 = plot_correlation(df_dict, "Parietal_Spatial", 'Density', 'Score', 'Parietal', color='hotpink', marker_size=10, marker_opacity=0.7)
fig2.show()

fig2 = plot_correlation(df_dict, "Parietal_Spatial", 'Efficiency', 'Score', 'Parietal', color="indianred", marker_size=10, marker_opacity=0.7)
fig2.show()