# suncalc Release 0.1.1

A fast, vectorized Python port of suncalc.js

Keywords
suncalc, sun, numpy, sunrise, sunset
MIT
Install
``` pip install suncalc==0.1.1 ```

# 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'),
#  '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']))
pd.concat([df, times], axis=1)```

### API

#### `get_position`

Calculate sun position (azimuth and altitude) for a given date and latitude/longitude

• `date` (`datetime` or a pandas series of datetimes): date and time to find sun position of. Datetime must be in UTC.
• `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

Returns a `dict` with two keys: `azimuth` and `altitude`. If the input values were singletons, the `dict`'s values will be floats. Otherwise they'll be numpy arrays of floats.

#### `get_times`

• `date` (`datetime` or a pandas series of datetimes): date and time to find sunlight phases of. Datetime must be in UTC.

• `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')
]```

Returns a `dict` where the keys are `solar_noon`, `nadir`, plus any keys passed in the `times` argument. If the input values were singletons, the `dict`'s values will be of type `datetime.datetime` (or `pd.Timestamp` if you have pandas installed, which is a subclass of and therefore compatible with `datetime.datetime`). Otherwise they'll be pandas `DateTime` series. The returned times will be in UTC.

## 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! Some of the difference here is that under the hood the non-vectorized approach uses `pd.to_datetime` while the vectorized implementation uses `np.astype('datetime64[ns, UTC]')`. `pd.to_datetime` is really slow!!

```%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. `suncalcPy` is another port of `suncalc.js`, and is MIT-licensed, but doesn't use Numpy and thus isn't vectorized. I recently discovered `sunpy` and `astropy`, both of which probably would've worked but I didn't see them at first and they look quite complex for this simple task...