astrokundali 0.2.6

Generate and plot North-Indian Lagna Kundali charts using pyswisseph. Flexible astrokundali package with Different house calculation methods and interpretation JSONs

AstroKundali

AstroKundali is a lightweight and modular Python library for generating Vedic astrology charts using Swiss Ephemeris. It supports traditional North Indian chart plotting, divisional charts (D1 to D60), planetary dispositions, drishti (aspect) logic, and advanced calculations like Ashtakavarga. Designed for flexibility and clarity, it enables both astrologers and developers to compute and visualize personalized Kundalis with precision.

Request: If anyone would like to collaborate or discuss new features, please send me an email. I will get in touch with you.
If you want access to this project's source code, please do the same. I am planning to make this project fully open source in the near future.

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USER OPTIONS

Ayanāṃśa Options

• fagan_bradley
  Fagan–Bradley Ayanāṃśa: a Western sidereal offset fixed at 24°02′31″ (January 1 1950) and widely used in Western sidereal astrology.

• lahiri (default)
  Lahiri (Chitra‑Paksha/Rohini) Ayanāṃśa: India’s official Vedic ayanāṃśa, zeroed at Spica (Chitra), and by far the most prevalent in Hindu astrology.

• deluce
  de Luce Ayanāṃśa: proposed by Robert DeLuce (1877–1964), theoretically set to 1 CE but in practice anchored to ~26°24′47″ (1900) using Newcomb’s precession theory.

• raman
  Raman Ayanāṃśa: introduced by B. V. Raman to better align with traditional nakṣatra positions, favored by his school of Vedic astrology.

• krishnamurti
  Krishnamurti (KP) Ayanāṃśa: developed by K. S. Krishnamurti for the KP system, emphasizing precise divisional‑chart timing.

• sassanian
  Sassanian Ayanāṃśa: reconstructs pre‑Islamic Persian/Sassanian zodiac data (via Al‑Biruni), often used with whole‑sign houses for ancient chart revival.

• aldebaran
  Aldebaran Ayanāṃśa: fixes zero at the bright star Aldebaran (15° Taurus), popular in fixed‑star and esoteric charting.

• galcenter
  Galcenter Ayanāṃśa: anchors the zodiac at the Milky Way’s center (~5° Sagittarius), used in modern “galactic” sidereal astrology.

House‑System Options

• equal
  Equal Houses: twelve 30° houses measured from the exact Ascendant degree, offering simplicity and uniformity across latitudes.

• whole_sign (default)
  Whole‑Sign: the sign containing the Ascendant becomes the 1st house, the next sign the 2nd, etc.; the oldest division from Hellenistic to Vedic traditions.

• porphyry
  Porphyry: each quadrant (Ascendant→MC, MC→Descendant, etc.) is trisected into three equal parts, creating houses by trisection of angle arcs; described by Vettius Valens in the 2nd CE.

• placidus
  Placidus: the most common Western quadrant system, trisecting diurnal/nocturnal arcs between the four angles to set cusps; can fail near polar circles.

• koch
  Koch: a time‑based quadrant method dividing right‑ascension arcs equally between angles; popular in research and German circles, defined only within ~66° latitude.

• campanus
  Campanus: divides the prime vertical into twelve equal segments, projecting them onto the ecliptic; emphasizes Earth‑centered spatial divisions.

• regiomontanus
  Regiomontanus: splits the celestial equator into twelve equal arcs then projects those onto the ecliptic; a medieval Renaissance favorite attributed to Johann Müller.

Example Usage

  pip install astrokundali
  or
  !pip install astrokundali [Notebook]
    

Configure AstroData

    from astrokundali import AstroData
    data = AstroData(2009,3,30,9,36,0,5,30,19.0760,72.8777,ayanamsa='lahiri')
  

Plot Lagna Chart

    from astrokundali import plot_lagna_chart
    houses = plot_lagna_chart(data, house_system= 'whole_sign') 
      or 
    houses = plot_lagna_chart(data) 
  

Plot Moon/Chandra Chart

from astrokundali import plot_moon_chart
houses = plot_moon_chart(data, house_system= 'whole_sign')
or
houses = plot_moon_chart(data)

Plot Navamsa/Navamsha/D9 Chart

    from astrokundali import plot_navamsa_chart
    houses = plot_navamsa_chart(data, house_system= 'whole_sign') 
      or 
    houses = plot_navamsa_chart(data)
  

Read Astrokundali House Objects

   from astrokundali import format_houses        
   import json
   readable = format_houses(houses) 
   print(json.dumps(readable, indent=2))
  

Get Planetary Positions and Dispositions

    from astrokundali import get_dispositions
    import json
    disp = get_dispositions(data, house_system= 'whole_sign')
      or
    disp = get_dispositions(data)
    print(json.dumps(report, ensure_ascii=False, indent=2)) 
  

Get Interpretation Report

    from astrokundali import generate_report, json_sanitize
    import json
    report  = json_sanitize(generate_report(data, house_system= 'whole_sign'))
      or
    report  = json_sanitize(generate_report(data))
    print(json.dumps(report, ensure_ascii=False, indent=2))
  

• to use json_sanitize function please install ftfy using pip install ftfy
  Marriage Matching

    from astrokundali import AstroData, match_kundli
    boy = AstroData(1990,1,1,10,0,0,5,30,19.07,72.88)
    girl = AstroData(1992,6,15,16,30,0,5,30,28.61,77.23)
    from pprint import pprint
    pprint(match_kundli(boy, girl))
      or
    from astrokundali import AstroData, match_kundli
    boy = AstroData(1990,1,1,10,0,0,5,30,19.07,72.88,ayanamsa='lahiri')
    girl = AstroData(1992,6,15,16,30,0,5,30,28.61,77.23,ayanamsa='lahiri')
    from pprint import pprint
    pprint(match_kundli(boy, girl, house_system= 'whole_sign')) [Version should be >=0.2.1]
  

Format Match Table in Pandas DataFrame

    from astrokundali import AstroData, match_kundli
    boy = AstroData(1990,1,1,10,0,0,5,30,19.07,72.88)
    girl = AstroData(1992,6,15,16,30,0,5,30,28.61,77.23)
    astro_match = match_kundli(boy, girl)
    print(astro_match['interpretation'])
    import pandas as pd
    df = pd.DataFrame(astro_match['table'])
    df