#!/usr/bin/env python3

"""

Quick test of visualization with mock data.

"""

import matplotlib

matplotlib.use('Agg') # Non-interactive backend

import matplotlib.pyplot as plt

import numpy as np

from datetime import datetime, timedelta

# Import our Tufte style

import sys

import os

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from oilpriceapi.visualization import TufteStyle

def create_tufte_demo():

"""Create a demo chart following Tufte principles."""

# Generate mock data

days = 30

dates = [datetime.now() - timedelta(days=i) for i in range(days, 0, -1)]

base_price = 70

prices = base_price + np.cumsum(np.random.randn(days) * 0.5)

# Create figure

fig, ax = plt.subplots(figsize=(10, 6), dpi=100)

# Apply Tufte style

TufteStyle.apply(ax)

# Plot data

ax.plot(

dates, prices,

color=TufteStyle.COLORS['primary'],

linewidth=1.5

)

# Add annotations for max and min

max_idx = np.argmax(prices)

min_idx = np.argmin(prices)

ax.scatter([dates[max_idx]], [prices[max_idx]],

color=TufteStyle.COLORS['accent'], s=20, zorder=5)

ax.annotate(

f'High: ${prices[max_idx]:.2f}',

xy=(dates[max_idx], prices[max_idx]),

xytext=(10, 10),

textcoords='offset points',

fontsize=9,

color=TufteStyle.COLORS['accent']

)

ax.scatter([dates[min_idx]], [prices[min_idx]],

color=TufteStyle.COLORS['secondary'], s=20, zorder=5)

ax.annotate(

f'Low: ${prices[min_idx]:.2f}',

xy=(dates[min_idx], prices[min_idx]),

xytext=(10, -15),

textcoords='offset points',

fontsize=9,

color=TufteStyle.COLORS['secondary']

)

# Labels

ax.set_xlabel('Date', fontsize=10)

ax.set_ylabel('Price (USD)', fontsize=10)

ax.set_title('Oil Price Visualization (Tufte Style)', fontsize=14, pad=20)

# Format x-axis

import matplotlib.dates as mdates

ax.xaxis.set_major_formatter(mdates.DateFormatter('%m/%d'))

fig.autofmt_xdate(rotation=45, ha='right')

# Add data source note

fig.text(

0.99, 0.01,

f'Mock Data | Generated {datetime.now().strftime("%Y-%m-%d")}',

fontsize=7,

color='gray',

ha='right',

va='bottom',

transform=fig.transFigure

)

# Add statistics

mean_price = np.mean(prices)

std_price = np.std(prices)

ax.text(

0.02, 0.98,

f'Mean: ${mean_price:.2f}\nStd Dev: ${std_price:.2f}',

transform=ax.transAxes,

fontsize=9,

verticalalignment='top',

bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.8, edgecolor='none')

)

plt.tight_layout()

# Save figure

output_file = 'tufte_style_demo.png'

fig.savefig(output_file, dpi=150, bbox_inches='tight')

print(f'Chart saved to: {output_file}')

return fig

def create_sparkline_demo():

"""Create sparklines following Tufte's design."""

# Create multiple sparklines

fig, axes = plt.subplots(3, 1, figsize=(3, 3), dpi=100)

commodities = ['Brent Crude', 'Natural Gas', 'Gold']

for idx, (ax, commodity) in enumerate(zip(axes, commodities)):

# Generate mock data

data = 50 + np.cumsum(np.random.randn(50) * 0.5)

# Remove all axes

ax.axis('off')

# Plot line

ax.plot(data, color=TufteStyle.COLORS['primary'], linewidth=1)

# Add end point

ax.plot(len(data)-1, data[-1], 'o',

color=TufteStyle.COLORS['accent'], markersize=3)

# Add label and value

ax.text(0, data[0], commodity, fontsize=8, va='center')

ax.text(len(data)-1, data[-1], f'${data[-1]:.1f}',

fontsize=7, ha='right', va='center')

plt.tight_layout(pad=0.5)

output_file = 'sparklines_demo.png'

fig.savefig(output_file, dpi=100, bbox_inches='tight')

print(f'Sparklines saved to: {output_file}')

return fig

if __name__ == '__main__':

print('Creating Tufte-style visualizations...')

print('=' * 50)

# Create main chart

print('1. Creating main price chart...')

create_tufte_demo()

# Create sparklines

print('2. Creating sparklines...')

create_sparkline_demo()

print('\nDone! Check the generated PNG files.')