def get_month_sunrise(self, daily_panchaanga):
""" Assigns Lunar months to days in the period
:return:
"""
solstice_tropical_month_span = zodiac.get_previous_solstice(
jd=daily_panchaanga.jd_sunrise)
solstice_lunar_month = SolsticePostDark10AdhikaAssigner._get_solstice_lunar_month(
solstice_tropical_month_span=solstice_tropical_month_span)
is_solstice_lunar_month_adhika = str(
solstice_lunar_month.index).endswith(".5")
if is_solstice_lunar_month_adhika:
lunar_month = self._month_from_previous_jd_month(
jd=daily_panchaanga.jd_sunrise,
prev_jd=solstice_tropical_month_span.jd_start,
prev_jd_month=solstice_lunar_month)
return lunar_month
else:
# At this point, we're sure that there was no previous postDark10 solstice.
# Are we in a lunar month containing solstice?
tropical_month = zodiac.get_tropical_month(
jd=daily_panchaanga.jd_sunrise)
if tropical_month.index in [3, 9]:
anga_span_finder = AngaSpanFinder.get_cached(
ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0,
anga_type=AngaType.SIDEREAL_MONTH)
solstice_tropical_month_span = anga_span_finder.find(
jd1=daily_panchaanga.jd_sunrise,
jd2=daily_panchaanga.jd_sunrise + 32,
target_anga_id=daily_panchaanga.tropical_date_sunset.month
+ 1)
span_finder = AngaSpanFinder.get_cached(
anga_type=AngaType.TITHI,
ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0)
tithi_1_jds = span_finder.get_spans_in_period(
jd_start=daily_panchaanga.jd_sunrise,
jd_end=solstice_tropical_month_span.jd_start,
target_anga_id=1)
if len(tithi_1_jds) == 0:
solstice_lunar_month = SolsticePostDark10AdhikaAssigner._get_solstice_lunar_month(
solstice_tropical_month_span=
solstice_tropical_month_span)
return solstice_lunar_month
# The default case.
lunar_month = self._month_from_previous_jd_month(
jd=daily_panchaanga.jd_sunrise,
prev_jd=solstice_tropical_month_span.jd_start,
prev_jd_month=solstice_lunar_month)
return lunar_month
def get_panchaanga_for_shaka_year(city,
year,
precomputed_json_dir="~/Documents/jyotisha",
computation_system: ComputationSystem = None,
allow_precomputed=True):
fname = os.path.expanduser(
'%s/%s__shaka_%s__%s.json' %
(precomputed_json_dir, city.name, year, computation_system))
if os.path.isfile(fname) and allow_precomputed:
fn = lambda: get_panchaanga_for_shaka_year(
city=city,
year=year,
precomputed_json_dir=precomputed_json_dir,
computation_system=computation_system,
allow_precomputed=False)
panchaanga = load_panchaanga(fname=fname, fallback_fn=fn)
# Fest repos to be used might have changed in this call.
panchaanga.computation_system = computation_system
panchaanga.update_festival_details()
return panchaanga
else:
logging.info(
'No precomputed data available. Computing panchaanga...\n')
SHAKA_CIVIL_ERA_DIFF = 78
start_year_civil = year + era.get_year_0_offset(era_id=era.ERA_SHAKA)
anga_span_finder = AngaSpanFinder.get_cached(
ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0,
anga_type=AngaType.SIDEREAL_MONTH)
start_equinox = anga_span_finder.find(jd1=time.utc_gregorian_to_jd(
Date(year=start_year_civil, month=3, day=1)),
jd2=time.utc_gregorian_to_jd(
Date(year=start_year_civil,
month=5,
day=1)),
target_anga_id=1)
end_equinox = anga_span_finder.find(jd1=time.utc_gregorian_to_jd(
Date(year=start_year_civil + 1, month=3, day=1)),
jd2=time.utc_gregorian_to_jd(
Date(year=start_year_civil + 1,
month=5,
day=1)),
target_anga_id=1)
tz = Timezone(city.timezone)
panchaanga = periodical.Panchaanga(
city=city,
start_date=tz.julian_day_to_local_time(
julian_day=start_equinox.jd_start),
end_date=tz.julian_day_to_local_time(
julian_day=end_equinox.jd_start),
computation_system=computation_system)
panchaanga.year = year
# Festival data may be updated more frequently and a precomputed panchaanga may go out of sync. Hence we keep this method separate.
logging.info('Writing computed panchaanga to %s...\n' % fname)
try:
panchaanga.dump_to_file(filename=fname)
except EnvironmentError:
logging.warning("Not able to save.")
logging.error(traceback.format_exc())
return panchaanga
def test_get_tithis_in_period():
span_finder = AngaSpanFinder.get_cached(anga_type=AngaType.TITHI, ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0)
spans = span_finder.get_spans_in_period(jd_start=time.ist_timezone.local_time_to_julian_day(Date(year=2020, month=1, day=1)), jd_end=time.ist_timezone.local_time_to_julian_day(Date(year=2020, month=6, day=30)), target_anga_id=30)
jds = [x.jd_start for x in spans]
numpy.testing.assert_array_almost_equal(jds, [2458872.36655025,
2458902.0647052005,
2458931.792117506,
2458961.5055956016,
2458991.1712410315,
2459020.765607745], decimal=3)
def test_get_anga_span_solar_month(): from jyotisha.panchaanga.temporal import time span_finder = AngaSpanFinder.get_cached(anga_type=AngaType.SIDEREAL_MONTH, ayanaamsha_id=Ayanamsha.CHITRA_AT_180) numpy.testing.assert_array_almost_equal(span_finder.find(jd1=2458222.0333434483-32, jd2=2458222.0333434483 + 4, target_anga_id=12,).to_tuple(), (2458192.24785228, 2458222.6026552585), decimal=3) jd2 = time.ist_timezone.local_time_to_julian_day(time.Date(2020, 4, 16)) assert span_finder.find(jd1=jd2-32, jd2=jd2, target_anga_id=1).to_tuple() == (2458953.109659805, None) assert span_finder.find(jd1=2458133.0189002366-32, jd2=2458133.0189002366, target_anga_id=10).to_tuple() == (2458132.8291680976, None)
def _month_from_previous_jd_month(cls, jd, prev_jd, prev_jd_month):
span_finder = AngaSpanFinder.get_cached(
anga_type=AngaType.TITHI,
ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0)
tithi_1_jds = span_finder.get_spans_in_period(jd_start=prev_jd,
jd_end=jd,
target_anga_id=1)
is_prev_month_adhika = str(prev_jd_month.index).endswith(".5")
if is_prev_month_adhika:
lunar_month = prev_jd_month + max(0, len(tithi_1_jds) - 0.5)
else:
lunar_month = prev_jd_month + len(tithi_1_jds)
return lunar_month
def test_get_anga_span_tithi():
span_finder = AngaSpanFinder.get_cached(
anga_type=AngaType.TITHI, ayanaamsha_id=Ayanamsha.CHITRA_AT_180)
assert span_finder.find(
jd1=2458102.5, jd2=2458108.5,
target_anga_id=30).to_tuple() == (2458104.6663699686,
2458105.771125107)
assert span_finder.find(
jd1=2444959.54042, jd2=2444963.54076,
target_anga_id=27).to_tuple() == (2444960.4924699212,
2444961.599213224)
def test_get_yogas_in_period():
span_finder = AngaSpanFinder.get_cached(
anga_type=AngaType.KARANA, ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0)
spans = span_finder.get_spans_in_period(
jd_start=time.ist_timezone.local_time_to_julian_day(
Date(year=2020, month=1, day=1)),
jd_end=time.ist_timezone.local_time_to_julian_day(
Date(year=2020, month=6, day=30)),
target_anga_id=15)
jds = [x.jd_start for x in spans]
numpy.testing.assert_array_almost_equal(jds, [
2458851.146, 2458881.029, 2458910.808, 2458940.431, 2458969.882,
2458999.185, 2459028.392
],
decimal=3)
def test_get_karanas_in_period():
span_finder = AngaSpanFinder.get_cached(
anga_type=AngaType.KARANA, ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0)
spans = span_finder.get_spans_in_period(
jd_start=time.ist_timezone.local_time_to_julian_day(
Date(year=2020, month=1, day=1)),
jd_end=time.ist_timezone.local_time_to_julian_day(
Date(year=2020, month=6, day=30)),
target_anga_id=30)
jds = [x.jd_start for x in spans]
numpy.testing.assert_array_almost_equal(jds, [
2458858.845, 2458888.379, 2458917.821, 2458947.19, 2458976.52,
2459005.852
],
decimal=3)
def get_month_sunrise(self, daily_panchaanga):
""" Assigns Lunar months to days in the period
:return:
"""
next_tithi_30_span = AngaSpanFinder.get_cached(
anga_type=AngaType.TITHI,
ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0).get_spans_in_period(
jd_start=daily_panchaanga.jd_sunrise,
jd_end=daily_panchaanga.jd_sunrise + 35,
target_anga_id=30)[0]
tropical_solar_month_span = self._get_previous_post_dark_10_tropical_solar_month_span(
jd=next_tithi_30_span.jd_end)
if tropical_solar_month_span.jd_start >= daily_panchaanga.jd_sunrise:
return tropical_solar_month_span.anga + 0.5
else:
return self._month_from_previous_jd_month_provisional(
jd=daily_panchaanga.jd_sunrise,
prev_jd=tropical_solar_month_span.jd_start,
prev_jd_month=tropical_solar_month_span.anga + 0.5)
def compute_sun_moon_transitions(self,
previous_day_panchaanga=None,
force_recomputation=False):
"""
:param previous_day_panchaanga: Panchangam for previous day, to avoid unnecessary calculations. (rise_trans calculations can be time consuming.)
:param force_recomputation: Boolean indicating if the transitions should be recomputed. (rise_trans calculations can be time consuming.)
:return:
"""
if force_recomputation or self.jd_sunrise is None:
if previous_day_panchaanga is not None and previous_day_panchaanga.jd_next_sunrise is not None:
self.jd_sunrise = previous_day_panchaanga.jd_next_sunrise
else:
self.jd_sunrise = self.city.get_rising_time(
julian_day_start=self.julian_day_start, body=Graha.SUN)
if force_recomputation or self.jd_sunset is None:
self.jd_sunset = self.city.get_setting_time(
julian_day_start=self.jd_sunrise, body=Graha.SUN)
if force_recomputation or self.jd_previous_sunset is None:
if previous_day_panchaanga is not None and previous_day_panchaanga.jd_sunset is not None:
self.jd_previous_sunset = previous_day_panchaanga.jd_sunset
else:
self.jd_previous_sunset = self.city.get_setting_time(
julian_day_start=self.jd_sunrise - 1, body=Graha.SUN)
if force_recomputation or self.jd_next_sunrise is None:
self.jd_next_sunrise = self.city.get_rising_time(
julian_day_start=self.jd_sunset, body=Graha.SUN)
if self.jd_sunset == 0.0:
logging.error('No sunset was computed!')
raise (ValueError(
'No sunset was computed. Perhaps the co-ordinates are beyond the polar circle (most likely a LAT-LONG swap! Please check your inputs.'
))
if force_recomputation or self.jd_moonrise is None:
self.jd_moonrise = self.city.get_rising_time(
julian_day_start=self.jd_sunrise, body=Graha.MOON)
if force_recomputation or self.jd_moonset is None:
self.jd_moonset = self.city.get_setting_time(
julian_day_start=self.jd_sunrise, body=Graha.MOON)
if force_recomputation or self.sunrise_day_angas is None:
self.sunrise_day_angas = DayAngas()
# Deliberately passing ASHVINI_STARTING_0 below since it is cheapest. Tithi is independent of ayanAmsha.
self.sunrise_day_angas.tithis_with_ends = AngaSpanFinder.get_cached(
ayanaamsha_id=Ayanamsha.ASHVINI_STARTING_0,
anga_type=zodiac.AngaType.TITHI).get_all_angas_in_period(
jd1=self.jd_sunrise, jd2=self.jd_next_sunrise)
self.sunrise_day_angas.tithi_at_sunrise = self.sunrise_day_angas.tithis_with_ends[
0].anga
self.sunrise_day_angas.nakshatras_with_ends = AngaSpanFinder.get_cached(
ayanaamsha_id=self.computation_system.ayanaamsha_id,
anga_type=zodiac.AngaType.NAKSHATRA).get_all_angas_in_period(
jd1=self.jd_sunrise, jd2=self.jd_next_sunrise)
self.sunrise_day_angas.nakshatra_at_sunrise = self.sunrise_day_angas.nakshatras_with_ends[
0].anga
self.sunrise_day_angas.yogas_with_ends = AngaSpanFinder.get_cached(
ayanaamsha_id=self.computation_system.ayanaamsha_id,
anga_type=zodiac.AngaType.YOGA).get_all_angas_in_period(
jd1=self.jd_sunrise, jd2=self.jd_next_sunrise)
self.sunrise_day_angas.yoga_at_sunrise = self.sunrise_day_angas.yogas_with_ends[
0].anga
self.sunrise_day_angas.karanas_with_ends = AngaSpanFinder.get_cached(
ayanaamsha_id=self.computation_system.ayanaamsha_id,
anga_type=zodiac.AngaType.KARANA).get_all_angas_in_period(
jd1=self.jd_sunrise, jd2=self.jd_next_sunrise)
self.sunrise_day_angas.raashis_with_ends = AngaSpanFinder.get_cached(
ayanaamsha_id=self.computation_system.ayanaamsha_id,
anga_type=zodiac.AngaType.RASHI).get_all_angas_in_period(
jd1=self.jd_sunrise, jd2=self.jd_next_sunrise)
self.sunrise_day_angas.solar_nakshatras_with_ends = AngaSpanFinder.get_cached(
ayanaamsha_id=self.computation_system.ayanaamsha_id,
anga_type=zodiac.AngaType.SOLAR_NAKSH).get_all_angas_in_period(
jd1=self.jd_sunrise, jd2=self.jd_next_sunrise)
def get_panchaanga_for_kali_year(city,
year,
precomputed_json_dir="~/Documents/jyotisha",
computation_system: ComputationSystem = None,
allow_precomputed=True,
recompute_festivals=True):
year = int(year)
fname = os.path.expanduser(
'%s/%s__kali_%s__%s.json' %
(precomputed_json_dir, city.name, year, computation_system))
if os.path.isfile(fname) and allow_precomputed:
fn = lambda: get_panchaanga_for_kali_year(
city=city,
year=year,
precomputed_json_dir=precomputed_json_dir,
computation_system=computation_system,
allow_precomputed=False)
panchaanga = load_panchaanga(fname=fname, fallback_fn=fn)
# Fest repos to be used might have changed in this call.
panchaanga.computation_system = computation_system
if recompute_festivals:
panchaanga.update_festival_details()
return panchaanga
else:
logging.info(
'No precomputed data available or allowed. Computing panchaanga...\n'
)
start_year_civil = year - era.get_year_0_offset(era_id=era.ERA_KALI)
anga_span_finder = AngaSpanFinder.get_cached(
ayanaamsha_id=Ayanamsha.CHITRA_AT_180,
anga_type=AngaType.SIDEREAL_MONTH)
start_mesha = anga_span_finder.find(jd1=time.utc_gregorian_to_jd(
Date(year=start_year_civil, month=3, day=1)),
jd2=time.utc_gregorian_to_jd(
Date(year=start_year_civil,
month=5,
day=1)),
target_anga_id=1)
jd_next_sunset_start_mesha = city.get_setting_time(
julian_day_start=start_mesha.jd_start, body=Graha.SUN)
end_mina = anga_span_finder.find(jd1=time.utc_gregorian_to_jd(
Date(year=start_year_civil + 1, month=3, day=1)),
jd2=time.utc_gregorian_to_jd(
Date(year=start_year_civil + 1,
month=5,
day=1)),
target_anga_id=1)
jd_preceding_sunset_end_mina = city.get_setting_time(
julian_day_start=end_mina.jd_start - 1, body=Graha.SUN)
tz = Timezone(city.timezone)
panchaanga = periodical.Panchaanga(
city=city,
start_date=tz.julian_day_to_local_time(
julian_day=jd_next_sunset_start_mesha),
end_date=tz.julian_day_to_local_time(
julian_day=jd_preceding_sunset_end_mina),
computation_system=computation_system)
panchaanga.year = year
# Festival data may be updated more frequently and a precomputed panchaanga may go out of sync. Hence we keep this method separate.
logging.info('Writing computed panchaanga to %s...\n' % fname)
try:
panchaanga.dump_to_file(filename=fname)
except EnvironmentError:
logging.warning("Not able to save.")
logging.error(traceback.format_exc())
return panchaanga