"""
RTX-Neuro Quickstart Guide

RTX-Neuro is a high-performance MEG/EEG analysis library implemented in Rust
with Python bindings via PyO3.

Installation:
    pip install rtx-neuro

Or build from source:
    cd crates/specialized/rtx-neuro-python
    maturin develop  # For development
    maturin build    # For release wheel
"""

import rtx_neuro as rtx
import numpy as np

# Check version
print(f"RTX-Neuro version: {rtx.__version__}")

# ============================================================
# Available Modules
# ============================================================

print("\nAvailable modules:")
print("- rtx.io: File format readers")
print("- rtx.stats: Statistical analysis")

# ============================================================
# Quick Example: Complete Analysis Pipeline
# ============================================================

print("\n" + "=" * 50)
print("Quick Example: Statistical Analysis")
print("=" * 50)

# Generate synthetic data (simulating ERP amplitudes)
np.random.seed(42)

# Control group: N=30 subjects
control = np.random.normal(loc=0.0, scale=1.0, size=30)

# Treatment group: N=30 subjects with a 0.8 effect
treatment = np.random.normal(loc=0.8, scale=1.0, size=30)

# Perform statistical test
t_stat, p_value = rtx.stats.ttest_ind(control, treatment)

print(f"\nIndependent t-test:")
print(f"  Control mean: {np.mean(control):.3f}")
print(f"  Treatment mean: {np.mean(treatment):.3f}")
print(f"  t-statistic: {t_stat:.3f}")
print(f"  p-value: {p_value:.6f}")
print(f"  Result: {'Significant' if p_value < 0.05 else 'Not significant'} at alpha=0.05")

# ============================================================
# Quick Example: Multiple Comparison Correction
# ============================================================

print("\n" + "=" * 50)
print("Quick Example: FDR Correction")
print("=" * 50)

# Simulate p-values from 50 statistical tests
p_values = np.concatenate([
    np.random.uniform(0.001, 0.01, 5),   # 5 true effects
    np.random.uniform(0.1, 1.0, 45)      # 45 null effects
])
np.random.shuffle(p_values)

# Apply FDR correction
rejected, corrected_p = rtx.stats.fdr_correction(p_values, alpha=0.05)

print(f"\n50 statistical tests:")
print(f"  Uncorrected significant: {np.sum(p_values < 0.05)}")
print(f"  FDR-corrected significant: {np.sum(rejected)}")
print(f"  Smallest corrected p-value: {np.min(corrected_p):.6f}")

# ============================================================
# API Reference
# ============================================================

print("\n" + "=" * 50)
print("API Reference")
print("=" * 50)

print("""
IO Module (rtx.io):
  read_raw_edf(path)   - Read EDF/EDF+ files
  read_raw_fif(path)   - Read Elekta/Neuromag FIF files
  read_raw_ctf(path)   - Read CTF MEG datasets (.ds)
  read_raw_bti(path)   - Read 4D-Neuroimaging/BTi data
  read_raw_kit(path)   - Read Yokogawa/KIT data (.con, .sqd)
  read_raw_egi(path)   - Read EGI data (.raw, .mff)

RawData Properties:
  .sfreq        - Sampling frequency (Hz)
  .n_channels   - Number of channels
  .n_samples    - Number of samples
  .duration     - Recording duration (seconds)
  .ch_names     - List of channel names
  .path         - File path

RawData Methods:
  .get_data(tmin, tmax)     - Get numpy array [n_channels x n_samples]
  .pick_channels(ch_names)  - Get indices for specific channels

Stats Module (rtx.stats):
  ttest_1samp(data, popmean=0.0)          - One-sample t-test
  ttest_ind(a, b)                          - Independent samples t-test
  ttest_rel(a, b)                          - Paired samples t-test
  permutation_t_test(a, b, n_permutations, seed)  - Permutation test
  fdr_correction(p_values, alpha=0.05)     - FDR (BH) correction
  bonferroni_correction(p_values, alpha=0.05)  - Bonferroni correction
""")

print("=" * 50)
print("For more examples, see the examples/ directory.")