pybiorythm

Output Formats

PyBiorythm supports multiple output formats to suit different use cases, from simple visualization to data analysis.

Chart Formats

Vertical Chart (Default)

The traditional biorhythm chart format with time flowing top-to-bottom:

python main.py -y 1990 -m 5 -d 15 --orientation vertical

Example Output:

=== BIORHYTHM CHART ===
Birth Date: 1990-05-15
Chart Date: 2025-08-07 (12837 days alive)

⚠️  SCIENTIFIC WARNING ⚠️
Biorhythm theory is PSEUDOSCIENCE with no proven validity!
Use for entertainment and educational purposes only.

Key: Physical (p), Emotional (e), Intellectual (i)
Critical days marked when cycles cross zero line (:)

Thu Aug  7    p     :     e i    
Fri Aug  8       p  :  e      i  
Sat Aug  9          : p    e   i 
Sun Aug 10          :   p     e i
Mon Aug 11          :      p e  i
Tue Aug 12          :       p e i
Wed Aug 13      p   :        e i
Thu Aug 14   p      :         ei

Features:

• ✅ Easy to read date-by-date
• ✅ Clear critical day identification
• ✅ Compact representation
• ✅ Traditional format users expect

Horizontal Chart (Timeline)

Wave-like visualization with time flowing left-to-right:

python main.py -y 1990 -m 5 -d 15 --orientation horizontal

Example Output:

=== BIORHYTHM WAVE (Physical + Emotional + Intellectual) ===

                    e               
            p               i       
                        e           
      p                     i       
                e                   
 p                           i      
                    e               
                                i   

Features:

• ✅ Wave pattern visualization
• ✅ Shows cycle relationships
• ✅ Good for trend analysis
• ✅ Compact for long periods

JSON Formats

JSON Vertical

Structured data with vertical chart metadata:

python main.py -y 1990 -m 5 -d 15 --orientation json-vertical

JSON Horizontal

Structured data with horizontal chart metadata:

python main.py -y 1990 -m 5 -d 15 --orientation json-horizontal

JSON Schema Reference

Both JSON formats follow the same schema with different metadata:

{
  "meta": {
    "generator": "biorythm",
    "version": "1.2.1",
    "birthdate": "1990-05-15",
    "plot_date": "2025-08-07", 
    "days_alive": 12837,
    "cycle_lengths_days": {
      "physical": 23,
      "emotional": 28,
      "intellectual": 33
    },
    "chart_orientation": "vertical",
    "days": 29,
    "width": 55,
    "scientific_warning": "⚠️ SCIENTIFIC WARNING ⚠️..."
  },
  "cycle_repeats": {
    "physical_emotional_repeat_in_days": 644,
    "all_cycles_repeat_in_days": 21252
  },
  "critical_days": [
    {
      "date": "2025-08-05",
      "cycles": "Physical cycle(s) near zero"
    }
  ],
  "data": [
    {
      "date": "2025-07-24",
      "days_alive": 12823,
      "physical": -0.8987940462991669,
      "emotional": 0.9744583088414919,
      "intellectual": -0.9510565162951536,
      "critical_cycles": []
    }
  ]
}

Metadata Fields

Meta Section

Field	Type	Description
generator	string	Software identifier
version	string	Software version
birthdate	string	Birth date (ISO format)
plot_date	string	Chart start date (ISO format)
days_alive	integer	Days between birth and plot date
cycle_lengths_days	object	Standard cycle lengths
chart_orientation	string	Chart type metadata
days	integer	Number of days in dataset
width	integer	Chart width (characters)
scientific_warning	string	Pseudoscience disclaimer

Cycle Repeats

Field	Type	Description
physical_emotional_repeat_in_days	integer	When P+E cycles repeat (644 days)
all_cycles_repeat_in_days	integer	When all cycles repeat (21,252 days)

Critical Days

Array of critical day events:

Field	Type	Description
date	string	Date of critical event
cycles	string	Which cycle(s) are critical

Data Points

Each data point contains:

Field	Type	Range	Description
date	string		Date (ISO format)
days_alive	integer	0+	Days since birth
physical	float	-1.0 to +1.0	Physical cycle value
emotional	float	-1.0 to +1.0	Emotional cycle value
intellectual	float	-1.0 to +1.0	Intellectual cycle value
critical_cycles	array		List of cycles near zero

Use Cases by Format

ASCII Charts

Best for:

• ✅ Quick visual reference
• ✅ Terminal/console applications
• ✅ Email or text-based reports
• ✅ Debugging and development
• ✅ Traditional biorhythm users

Examples:

# Daily check
python main.py -y 1990 -m 5 -d 15 --days 7

# Monthly overview  
python main.py -y 1990 -m 5 -d 15 --days 30 --orientation horizontal

# Extended analysis
python main.py -y 1990 -m 5 -d 15 --days 90

JSON Data

Best for:

• ✅ Data analysis and visualization
• ✅ Machine learning training data
• ✅ API integrations
• ✅ Statistical analysis
• ✅ Custom application development

Examples:

import json
import pandas as pd
import matplotlib.pyplot as plt

# Load data
data = json.loads(json_output)
df = pd.DataFrame(data['data'])

# Analysis
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)

# Visualization
df[['physical', 'emotional', 'intellectual']].plot(figsize=(12, 6))
plt.title('Biorhythm Cycles Over Time')
plt.axhline(y=0, color='black', linestyle='--', alpha=0.3)
plt.show()

Output Customization

Chart Size

# Narrow chart (minimum width: 12)
python main.py -y 1990 -m 5 -d 15 --orientation vertical  # Default width: 55

# Wide chart (programmatic)
calc = BiorhythmCalculator(width=100)

Time Period

# One week
python main.py -y 1990 -m 5 -d 15 --days 7

# One month  
python main.py -y 1990 -m 5 -d 15 --days 30

# One year (JSON recommended for large datasets)
python main.py -y 1990 -m 5 -d 15 --days 365 --orientation json-vertical

File Output

Save to File

# ASCII chart to file
python main.py -y 1990 -m 5 -d 15 > biorhythm_chart.txt

# JSON data to file
python main.py -y 1990 -m 5 -d 15 --orientation json-vertical > data.json

# Multiple formats
python main.py -y 1990 -m 5 -d 15 --orientation vertical > chart.txt
python main.py -y 1990 -m 5 -d 15 --orientation json-vertical > data.json

Batch Processing

#!/bin/bash
# Generate multiple formats for analysis

birthdate="1990-05-15"
IFS='-' read -r year month day <<< "$birthdate"

# Generate all formats
python main.py -y "$year" -m "$month" -d "$day" --orientation vertical > "${birthdate}_chart.txt"
python main.py -y "$year" -m "$month" -d "$day" --orientation horizontal > "${birthdate}_wave.txt"  
python main.py -y "$year" -m "$month" -d "$day" --orientation json-vertical > "${birthdate}_data.json"

Integration Examples

Pandas Analysis

import pandas as pd
import json

# Load JSON data
with open('biorhythm_data.json', 'r') as f:
    data = json.load(f)

# Convert to DataFrame
df = pd.DataFrame(data['data'])
df['date'] = pd.to_datetime(df['date'])

# Statistical analysis
stats = df[['physical', 'emotional', 'intellectual']].describe()
correlation = df[['physical', 'emotional', 'intellectual']].corr()

print("Descriptive Statistics:")
print(stats)
print("\nCorrelation Matrix:")
print(correlation)

Web API Integration

from flask import Flask, jsonify
from biorythm import BiorhythmCalculator
from datetime import datetime

app = Flask(__name__)

@app.route('/biorhythm/<int:year>/<int:month>/<int:day>')
def get_biorhythm(year, month, day):
    calc = BiorhythmCalculator(days=30)
    birthdate = datetime(year, month, day)
    json_data = calc.generate_timeseries_json(birthdate)
    
    return jsonify(json.loads(json_data))

if __name__ == '__main__':
    app.run(debug=True)

Visualization

import matplotlib.pyplot as plt
import numpy as np

# Custom plotting function
def plot_biorhythm(json_data):
    data = json.loads(json_data)
    df = pd.DataFrame(data['data'])
    
    fig, ax = plt.subplots(figsize=(15, 8))
    
    # Plot cycles
    ax.plot(df['date'], df['physical'], 'r-', label='Physical (23d)', linewidth=2)
    ax.plot(df['date'], df['emotional'], 'b-', label='Emotional (28d)', linewidth=2)
    ax.plot(df['date'], df['intellectual'], 'g-', label='Intellectual (33d)', linewidth=2)
    
    # Mark critical days
    critical_dates = [cd['date'] for cd in data['critical_days']]
    for date in critical_dates:
        ax.axvline(pd.to_datetime(date), color='orange', linestyle='--', alpha=0.7)
    
    # Formatting
    ax.axhline(y=0, color='black', linestyle='-', alpha=0.3)
    ax.set_ylim(-1.1, 1.1)
    ax.set_ylabel('Cycle Value')
    ax.set_title(f'Biorhythm Chart - Born {data["meta"]["birthdate"]}')
    ax.legend()
    ax.grid(True, alpha=0.3)
    
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()

Performance Considerations

Format	Speed	Memory	Best for
Vertical ASCII	Fastest	Lowest	Quick checks, small periods
Horizontal ASCII	Fast	Low	Trend visualization, medium periods
JSON	Medium	Medium	Data analysis, large periods
JSON + Pandas	Slower	Higher	Statistical analysis, visualization

Next Steps

• Learn CLI usage: Command Line Interface
• See more examples: Usage Examples
• API integration: Calculator API
• Data analysis: JSON Schema

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