suncalc-py

A fast, vectorized Python implementation of suncalc.js for calculating sun position and sunlight phases (times for sunrise, sunset, dusk, etc.) for the given location and time.

While other similar libraries exist, I didn't originally encounter any that met my requirements of being both openly-licensed and vectorized ¹

Install

pip install suncalc

Using

Example

suncalc is designed to work both with single values and with arrays of values.

First, import the module:

from suncalc import get_position, get_times
from datetime import datetime

There are currently two methods: get_position, to get the sun azimuth and altitude for a given date and position, and get_times, to get sunlight phases for a given date and position.

date = datetime.now()
lon = 20
lat = 45
get_position(date, lon, lat)
# {'azimuth': -0.8619668996997687, 'altitude': 0.5586446727994595}

get_times(date, lon, lat)
# {'solar_noon': Timestamp('2020-11-20 08:47:08.410863770'),
#  'nadir': Timestamp('2020-11-19 20:47:08.410863770'),
#  'sunrise': Timestamp('2020-11-20 03:13:22.645455322'),
#  'sunset': Timestamp('2020-11-20 14:20:54.176272461'),
#  'sunrise_end': Timestamp('2020-11-20 03:15:48.318936035'),
#  'sunset_start': Timestamp('2020-11-20 14:18:28.502791748'),
#  'dawn': Timestamp('2020-11-20 02:50:00.045539551'),
#  'dusk': Timestamp('2020-11-20 14:44:16.776188232'),
#  'nautical_dawn': Timestamp('2020-11-20 02:23:10.019832520'),
#  'nautical_dusk': Timestamp('2020-11-20 15:11:06.801895264'),
#  'night_end': Timestamp('2020-11-20 01:56:36.144269287'),
#  'night': Timestamp('2020-11-20 15:37:40.677458252'),
#  'golden_hour_end': Timestamp('2020-11-20 03:44:46.795967773'),
#  'golden_hour': Timestamp('2020-11-20 13:49:30.025760010')}

These methods also work for arrays of data, and since the implementation is vectorized it's much faster than a for loop in Python.

import pandas as pd

df = pd.DataFrame({
    'date': [date] * 10,
    'lon': [lon] * 10,
    'lat': [lat] * 10
})
pd.DataFrame(get_position(df['date'], df['lon'], df['lat']))
# azimuth	altitude
# 0	-1.485509	-1.048223
# 1	-1.485509	-1.048223
# ...

pd.DataFrame(get_times(df['date'], df['lon'], df['lat']))['solar_noon']
# 0   2020-11-20 08:47:08.410863872+00:00
# 1   2020-11-20 08:47:08.410863872+00:00
# ...
# Name: solar_noon, dtype: datetime64[ns, UTC]

If you want to join this data back to your DataFrame, you can use pd.concat:

times = pd.DataFrame(get_times(df['date'], df['lon'], df['lat']))['solar_noon']
pd.concat([df, times], axis=1)

API

get_position

Calculate sun position for a given date and latitude/longitude

• date (datetime or a pandas series of datetimes): date and time to find sun position of
• lng (float or numpy array of float): longitude to find sun position of
• lat (float or numpy array of float): latitude to find sun position of

get_times

• date (datetime or a pandas series of datetimes): date and time to find sunlight phases of

• lng (float or numpy array of float): longitude to find sunlight phases of

• lat (float or numpy array of float): latitude to find sunlight phases of

• height (float or numpy array of float, default 0): observer height in meters

• times (Iterable[Tuple[float, str, str]]): an iterable defining the angle above the horizon and strings for custom sunlight phases. The default is:

  # (angle, morning name, evening name)
  DEFAULT_TIMES = [
      (-0.833, 'sunrise', 'sunset'),
      (-0.3, 'sunrise_end', 'sunset_start'),
      (-6, 'dawn', 'dusk'),
      (-12, 'nautical_dawn', 'nautical_dusk'),
      (-18, 'night_end', 'night'),
      (6, 'golden_hour_end', 'golden_hour')
  ]
  

Benchmark

This benchmark is to show that the vectorized implementation is nearly 100x faster than a for loop in Python.

First set up a DataFrame with random data. Here I create 100,000 rows.

from suncalc import get_position, get_times
import pandas as pd

def random_dates(start, end, n=10):
    """Create an array of random dates"""
    start_u = start.value//10**9
    end_u = end.value//10**9
    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')

start = pd.to_datetime('2015-01-01')
end = pd.to_datetime('2018-01-01')
dates = random_dates(start, end, n=100_000)

lons = np.random.uniform(low=-179, high=179, size=(100_000,))
lats = np.random.uniform(low=-89, high=89, size=(100_000,))

df = pd.DataFrame({'date': dates, 'lat': lats, 'lon': lons})

Then compute SunCalc.get_position two ways: the first using the vectorized implementation and the second using df.apply, which is equivalent to a for loop. The first is more than 100x faster than the second.

%timeit get_position(df['date'], df['lon'], df['lat'])
# 41.4 ms ± 437 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit df.apply(lambda row: get_position(row['date'], row['lon'], row['lat']), axis=1)
# 4.89 s ± 184 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Likewise, compute SunCalc.get_times the same two ways: first using the vectorized implementation and the second using df.apply. The first is 2800x faster than the second!

%timeit get_times(df['date'], df['lon'], df['lat'])
# 55.3 ms ± 1.91 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%time df.apply(lambda row: get_times(row['date'], row['lon'], row['lat']), axis=1)
# CPU times: user 2min 33s, sys: 288 ms, total: 2min 34s
# Wall time: 2min 34s

1: pyorbital looks great but is GPL3-licensed; pysolar is also GPL3-licensed. pyEphem is LGPL3-licensed. I recently discovered sunpy and astropy, both of which probably would've worked but I didn't see them at first...

Changelog

[0.1.0] - 2020-11-19

• Initial release on PyPI