Human design (HD) engine

HD engine is a script for calculation of single, composite and multiple human design features in python.

Supported Features

Calculations of single features

• Basic values from planetary positions
  • longitude
  • gate
  • line
  • color
  • tone
  • base
• profile
• inner authority
• typ
• active chakras
• active channels
• split
• variables

Calculations of composite features

• composite channels
• composite chakras

Calculations of mutliple instances

Based on a list of birth times, for each birth time all single features are calculated

Examples

Single instance calculations

import hd_features as hd
import hd_constants

  #examples for birth time format
  zone = 'Europe/Berlin'
  birth_time= 1876,1,5,10,2,4 #konrad adenauer
  zone = 'Asia/Shanghai'
  birth_time= 1935,7,6,4,48,0 #dalai lama

  #automatic timezone(tz) offset calculation (for all avl. timezones see pytz.all_timezones)
  hours = hd.get_utc_offset_from_tz(birth_time,zone)

  ##manual time_zone offset calculation
  hours=8

  timestamp = tuple(list(birth_time) + [hours])
  single_result = hd.calc_single_hd_features(timestamp,report=True,channel_meaning=True)

Composite instance calculations

  hours=2 #time_zone offset
  #define persons you want to combine
  persons_dict = {"1":(1990,1,30,1,2,0,hours),
                 "2":(2010,2,30,1,2,0,hours),
                 "3":(2020,6,30,1,2,0,hours),
  			           }
  #composite channels and chakras
  hd.get_composite_combinations(persons_dict)
  #full view, with readable meanings
  hd.get_composite_combinations(persons_dict).explode(["new_channels","new_ch_meaning"])
  #composite gates matching penta 
  hd.get_penta(persons_dict)

Multiple instance calculations

  start_date = 1781-30,1,1,1,1
  end_date = 2027+30, 12, 31,23,1
  percentage = 1 #percentage of processed list (1=100%,0=0%)
  time_unit = "days" #years,months,days,hours,minutes are supported
  intervall = 10 #every X unit, note gates changing every ~5 days, lines every 0.9 day
  num_cpu = 6
  result,timestamp_list = hd.calc_mult_hd_features(
  						start_date,end_date,percentage,time_unit,intervall,num_cpu)
  result_lists = hd.unpack_mult_features(result,full=True) #lists are structured as dict

  #Example for simple "Typ"-distribution
  typ_dist = pd.Series(result_lists["typ_list"]).value_counts()/len(result_lists["typ_list"])
  labels=typ_dist.index
  plt.pie(typ_dist,labels=labels,autopct='%1.1f%%',startangle=90);