solposx

https://solposx.readthedocs.io
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solposx is a Python package that provides reference implementations of solar position algorithms and...
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### Install solposx via pip

Source: https://solposx.readthedocs.io/en/stable/installation.html

Standard installation command to install the package from PyPI.

```bash
pip install solposx
```

--------------------------------

### Install solposx with optional dependencies

Source: https://solposx.readthedocs.io/en/stable/installation.html

Command to install the package along with optional dependencies like skyfield and sg2.

```bash
pip install solposx[optional]
```

--------------------------------

### nasa_horizons(latitude, longitude, start, end, elevation=0.0, *, time_step='1h', refraction_correction=False, url='https://ssd.jpl.nasa.gov/api/horizons.api')

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.nasa_horizons.html

Retrieves solar positions from NASA's Horizons web service. The function requires a start time, end time, and frequency, and returns a DataFrame with solar position angles.

```APIDOC
## nasa_horizons(latitude, longitude, start, end, elevation=0.0, *, time_step='1h', refraction_correction=False, url='https://ssd.jpl.nasa.gov/api/horizons.api')

### Description
Retrieve solar positions from NASA's Horizons web service. The function requires specifying a time period with start, end, and frequency, and returns a pandas DataFrame with solar position angles.

### Method
Function call

### Endpoint
Not applicable (SDK function)

### Parameters
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **start** (datetime-like) - Required - Start of the requested time period.
- **end** (datetime-like) - Required - End of the requested time period.
- **elevation** (float, optional) - Optional - Elevation of the point of interest [m]. The default is 0 m.
- **time_step** (str, optional) - Optional - Time step size of the requested time series. '1m' for minutes, '1h' for hours, '1d' for days, '1mo' for months, and '1y' for years. The default is '1h'.
- **refraction_correction** (bool, optional) - Optional - Whether to return apparent solar position angles corrected for atmospheric refraction. The default is False.
- **url** (str, optional) - Optional - API endpoint. The default is 'https://ssd.jpl.nasa.gov/api/horizons.api'.

### Returns
pandas.DataFrame with the following columns in degrees (note that all columns except azimuth are prefixed with 'apparent_' if refraction_correction=True):
- right_ascension
- declination
- azimuth
- elevation

### Notes
The refraction correction algorithm is the same as solposx.refraction.spa(). See [3] for more information.
```

--------------------------------

### Compute solar positions and RMSD comparison

Source: https://solposx.readthedocs.io/en/stable/solarposition_introduction.html

Creates hourly timestamps for the year 2025, computes solar positions using the Michalsky and Skyfield algorithms, and calculates the RMSD between them using solposx.tools.calc_error.

```python
times = pd.date_range('2025-01-01 00:00:00', '2025-12-31 23:59:00', freq='h', tz='UTC')

michalsky = solposx.solarposition.michalsky(times, site.latitude, site.longitude)
skyfield = solposx.solarposition.skyfield(times, site.latitude, site.longitude)

comparison = solposx.tools.calc_error(michalsky['elevation'], michalsky['azimuth'], skyfield['elevation'], skyfield['azimuth'])

```

--------------------------------

### solposx.solarposition.walraven

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.walraven.html

Documentation for the walraven function in the solposx.solarposition module.

```APIDOC
## Function: solposx.solarposition.walraven

### Description
Calculate solar position using the Walraven algorithm. Walraven's algorithm has a stated accuracy of 0.01 degrees. The implementation accounts for the 1979 Erratum and the correct azimuth quadrant selection.

### Signature
`solposx.solarposition.walraven(times, latitude, longitude)`

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]

### Returns
- **pandas.DataFrame** - DataFrame with the following columns (all values in degrees):
  - elevation: actual sun elevation (not accounting for refraction).
  - zenith: actual sun zenith (not accounting for refraction).
  - azimuth: sun azimuth, east of north.

### References
- Walraven's algorithm [1]
- 1979 Erratum [2]
- Correct azimuth quadrant selection [3]
```

--------------------------------

### Skyfield de440.bsp download progress

Source: https://solposx.readthedocs.io/en/stable/solarposition_introduction.html

Shows the progress output from downloading the de440.bsp ephemeris file, which is required by the Skyfield algorithm.

```text
[                                 ]   0% de440.bsp

```

```text
[####                             ]  13% de440.bsp

```

```text
[############                     ]  36% de440.bsp

```

```text
[##################               ]  55% de440.bsp

```

```text
[#######################          ]  71% de440.bsp

```

```text
[#############################    ]  88% de440.bsp

```

```text
[#################################] 100% de440.bsp

```

--------------------------------

### solarposition.walraven(times, latitude, ...)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the Walraven algorithm.

```APIDOC
## solarposition.walraven(times, latitude, ...)

### Description
Calculate solar position using the Walraven algorithm.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### michalsky()

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.michalsky.html

Documentation for the michalsky() function, which calculates solar position using the Michalsky algorithm.

```APIDOC
## michalsky()

### Description
Calculates solar position using the Michalsky algorithm with a stated accuracy of 0.01 degrees from 1950 to 2050. The function returns a DataFrame with solar elevation, zenith, and azimuth angles.

### Method
Function call

### Endpoint
`solposx.solarposition.michalsky(times, latitude, longitude, spencer_correction=True, julian_date='original')`

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps, must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly; for such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **spencer_correction** (bool, default True) - Optional - Applies the correction suggested by Spencer so the algorithm works for all latitudes.
- **julian_date** (string, default 'original') - Optional - Julian date calculation. Can be one of the following:
  - `'original'`: calculation based on Michalsky's paper.
  - `'pandas'`: calculation using a pandas built-in function.

### Returns
pandas.DataFrame with the following columns (all values in degrees):
- elevation: actual sun elevation (not accounting for refraction).
- apparent_elevation: sun elevation, accounting for atmospheric refraction.
- zenith: actual sun zenith (not accounting for refraction).
- apparent_zenith: sun zenith, accounting for atmospheric refraction.
- azimuth: sun azimuth, east of north.

### Raises
- **ValueError** - An error is raised if the julian_date calculation is not 'original' or 'pandas'.

### Notes
The Michalsky algorithm is based on equations in the Astronomical Almanac. The original algorithm did not work for the southern hemisphere; this implementation includes by default the Spencer correction to work for all latitudes. Minor clarifications to the original paper have been published as errata. The Julian date calculation in the original paper ensures the stated accuracy only for 1950-2050; outside this period, the 'pandas' option can be used.
```

--------------------------------

### solarposition.spa(time, latitude, longitude)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using a python implementation of the NREL SPA algorithm.

```APIDOC
## solarposition.spa(time, latitude, longitude)

### Description
Calculate the solar position using a python implementation of the NREL SPA algorithm.

### Parameters
- **time** - The time for which to calculate the solar position.
- **latitude** - The latitude of the location.
- **longitude** - The longitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### Calculate solar position with solposx.michalsky and plot elevation/azimuth

Source: https://solposx.readthedocs.io/en/stable/solarposition_introduction.html

Calculates solar elevation and azimuth angles at the Technical University of Denmark on 2025/06/21 using the Michalsky algorithm from solposx. It creates a Location object with pvlib, generates an hourly time range, and plots the resulting elevation and azimuth angles over time.

```python
import pandas as pd
import matplotlib.pyplot as plt
import solposx
from pvlib.location import Location
import matplotlib.dates as mdates

# Definition of Location object
site = Location(55.79, 12.52, 'UTC', 50, 'DTU, Denmark') # latitude, longitude, time_zone, altitude, name

# Definition of a time range of simulation
times = pd.date_range('2025-06-21 00:00:00', '2025-06-22 00:00:00', freq='h', tz=site.tz)

# Calculate solar position
solpos = solposx.solarposition.michalsky(times, site.latitude, site.longitude)

# Plots for solar zenith and solar azimuth angles
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
fig.suptitle('Solar Position Estimation at the Technical University of Denmark on 2025/06/21')

# plot for solar elevation angle
axes[0].plot(solpos.elevation)
axes[0].set_ylabel('Solar elevation angle [°]')

# plot for solar azimuth angle
axes[1].plot(solpos.azimuth)
axes[1].set_ylabel('Solar azimuth angle [°]')

# format plot
for ax in axes:
    ax.set_xlabel('Time (UTC)')
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H'))
    ax.set_xlim(pd.Timestamp('2025-06-21 00:00:00'), pd.Timestamp('2025-06-21 23:59:00'))

plt.tight_layout()
plt.show()
```

--------------------------------

### solposx.solarposition.sg2_c

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.sg2_c.html

Documentation for the sg2_c() function, which calculates solar position using the SG2 Python package.

```APIDOC
## solposx.solarposition.sg2_c

### Description
Calculates solar position using the SG2 Python package, which wraps the official C-code. The SG2 algorithm has a stated accuracy of 0.003 degrees from years 1980 to 2030.

### Method
Function call

### Signature
`solposx.solarposition.sg2_c(times, latitude, longitude, elevation=0, *, pressure=101325, temperature=12)`

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Must be localized.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **elevation** (float, default: 0) - Optional - Altitude of the location of interest. [m]
- **pressure** (float, default: 101325) - Optional - Annual average air pressure. [Pa]
- **temperature** (float, default: 12) - Optional - Annual average air temperature. [°C]

### Returns
pandas.DataFrame with the following columns (all values in degrees):
- elevation: actual sun elevation (not accounting for refraction).
- apparent_elevation: sun elevation, accounting for atmospheric refraction.
- zenith: actual sun zenith (not accounting for refraction).
- apparent_zenith: sun zenith, accounting for atmospheric refraction.
- azimuth: sun azimuth, east of north.

### Return Type
pandas.DataFrame

### See Also
- `solposx.solarposition.sg2`

### References
- [1] SG2 Python package
- [2] SG2 algorithm
```

--------------------------------

### solarposition.michalsky(times, latitude, ...)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the Michalsky algorithm.

```APIDOC
## solarposition.michalsky(times, latitude, ...)

### Description
Calculate solar position using the Michalsky algorithm.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### usno(times, latitude, longitude, *, delta_t=67.0, gmst_option=1)

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.usno.html

Calculates solar position using the USNO algorithm. Returns a DataFrame with elevation, zenith, and azimuth angles in degrees.

```APIDOC
## usno(times, latitude, longitude, *, delta_t=67.0, gmst_option=1)

### Description
Calculates solar position using the USNO algorithm. Returns a DataFrame with elevation, zenith, and azimuth angles in degrees.

### Method
Function call

### Signature
`usno(times, latitude, longitude, *, delta_t=67.0, gmst_option=1)`

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **delta_t** (numeric, default: 67.0) - Optional - Difference between terrestrial time and UT1. If `delta_t` is None, uses `pvlib.spa.calculate_deltat()` using `times.year` and `times.month` from pandas.DatetimeIndex. For most simulations the default `delta_t` is sufficient. The USNO has historical and forecasted `delta_t`. [seconds]
- **gmst_option** (int, default: 1) - Optional - Different ways of calculating the Greenwich mean sidereal time. gmst_option needs to be either 1 or 2.

### Returns
pandas.DataFrame with the following columns (all values in degrees):
- **elevation** : actual sun elevation (not accounting for refraction).
- **zenith** : actual sun zenith (not accounting for refraction).
- **azimuth** : sun azimuth, east of north.

### Return Type
pandas.DataFrame

### References
[1] U.S. Naval Observatory algorithm reference.
```

--------------------------------

### solposx.solarposition.sg2

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.sg2.html

Documentation for the sg2() function, which calculates solar position using the SG2 algorithm.

```APIDOC
## solposx.solarposition.sg2

### Description
Calculate solar position using the SG2 algorithm. The SG2 algorithm has a stated accuracy of 0.003 degrees from years 1980 to 2030.

### Method
Function call

### Signature
```python
solposx.solarposition.sg2(times, latitude, longitude, elevation=0, *, pressure=101325, temperature=12)
```

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **elevation** (float, default: 0) - Optional - Altitude of the location of interest. [m]
- **pressure** (float, default: 101325) - Optional - Annual average air pressure. [Pa]
- **temperature** (float, default: 12) - Optional - Annual average air temperature. [°C]

### Returns
pandas.DataFrame with the following columns (all values in degrees):
- **elevation** : actual sun elevation (not accounting for refraction).
- **apparent_elevation** : sun elevation, accounting for atmospheric refraction.
- **zenith** : actual sun zenith (not accounting for refraction).
- **apparent_zenith** : sun zenith, accounting for atmospheric refraction.
- **azimuth** : sun azimuth, east of north.

### Notes
The SG2 equations as described in [1] had a number of typos and errors. These errors have been corrected in the present implementation.

### See Also
- `solposx.solarposition.sg2_c`
```

--------------------------------

### solarposition.usno(times, latitude, longitude, *)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the USNO algorithm.

```APIDOC
## solarposition.usno(times, latitude, longitude, *)

### Description
Calculate solar position using the USNO algorithm.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.
- **longitude** - The longitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### spa()

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.spa.html

Calculate the solar position using a python implementation of the NREL SPA algorithm. The function accepts a pandas.DatetimeIndex, latitude, longitude, and optional parameters for elevation, air pressure, temperature, delta_t, and atmospheric refraction. It returns a pandas DataFrame with solar position values.

```APIDOC
## spa()

### Description
Calculate the solar position using a python implementation of the NREL SPA algorithm. The details of the NREL SPA algorithm are described in [1], [2]. If numba is installed, the functions can be compiled to machine code and the function can be multithreaded. Without numba, the function evaluates via numpy with a slight performance hit.

### Method
Function call

### Signature
`solposx.solarposition.spa(time, latitude, longitude, elevation=0.0, *, air_pressure=101325.0, temperature=12.0, delta_t=67.0, atmos_refract=None, **kwargs)`

### Parameters
- **time** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **elevation** (float, default: 0) - Optional - Altitude of the location of interest. [m]
- **air_pressure** (float, default: 101325) - Optional - Annual average air pressure. [Pa]
- **temperature** (float, default: 12) - Optional - Annual average air temperature. [°C] negative to west. [degrees]
- **delta_t** (numeric, default: 67.0) - Optional - Difference between terrestrial time and UT1. If `delta_t` is None, uses `pvlib.spa.calculate_deltat()` using `times.year` and `times.month` from pandas.DatetimeIndex. For most simulations the default `delta_t` is sufficient. The USNO has historical and forecasted `delta_t` [3]. [seconds]
- **atmos_refract** (float, optional) - Optional - The approximate atmospheric refraction (in degrees) at sunrise and sunset.
- **how** (str, optional, default 'numpy') - Optional - Options are 'numpy' or 'numba'. If numba >= 0.17.0 is installed, how='numba' will compile the spa functions to machine code and run them multithreaded.
- **numthreads** (int, optional, default 4) - Optional - Number of threads to use if how == 'numba'.

### Returns
pandas.DataFrame with the following columns (all values in degrees):
- **elevation** : actual sun elevation (not accounting for refraction). [°]
- **apparent_elevation** : sun elevation, accounting for atmospheric refraction. [°]
- **zenith** : actual sun zenith (not accounting for refraction). [°]
- **apparent_zenith** : sun zenith, accounting for atmospheric refraction. [°]
- **azimuth** : sun azimuth, east of north. [°]
- **equation_of_time** : [minutes]

### Return Type
pandas.DataFrame

### References
[1] NREL SPA algorithm details
[2] NREL SPA algorithm details
[3] USNO historical and forecasted delta_t
```

--------------------------------

### Plot elevation and azimuth differences

Source: https://solposx.readthedocs.io/en/stable/solarposition_introduction.html

Creates two side-by-side plots showing the difference in solar elevation and azimuth angles between the Michalsky and Skyfield models over the year.

```python
fig, axes = plt.subplots(1, 2, figsize=(10, 4.5), facecolor='w', edgecolor='k')

axes[0].plot(michalsky['elevation']-skyfield['elevation'])
axes[1].plot(michalsky['azimuth']-skyfield['azimuth'])

axes[0].set_ylabel('Difference in solar elevation angle [°]')
axes[0].set_ylim(-0.011, 0.011)

axes[1].set_ylabel('Difference in solar azimuth angle [°]')
axes[1].set_ylim(-0.015, 0.015)

for ax in axes:
    ax.set_xlabel('Month')
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
    ax.grid(alpha=0.5, zorder=-2)

plt.tight_layout()
plt.show()

```

--------------------------------

### solarposition.nasa_horizons(latitude, ...)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Retrieves solar positions from NASA's Horizons web service.

```APIDOC
## solarposition.nasa_horizons(latitude, ...)

### Description
Retrieve solar positions from NASA's Horizons web service.

### Parameters
- **latitude** - The latitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### solarposition.sg2(times, latitude, longitude)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the SG2 algorithm.

```APIDOC
## solarposition.sg2(times, latitude, longitude)

### Description
Calculate solar position using the SG2 algorithm.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.
- **longitude** - The longitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### solarposition.psa(times, latitude, longitude, *)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the PSA algorithm.

```APIDOC
## solarposition.psa(times, latitude, longitude, *)

### Description
Calculate solar position using the PSA algorithm.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.
- **longitude** - The longitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### iqbal(times, latitude, longitude)

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.iqbal.html

Calculates solar position using the Iqbal algorithm. Returns a DataFrame with elevation, zenith, and azimuth angles in degrees.

```APIDOC
## iqbal(times, latitude, longitude)

### Description
Calculates solar position using the Iqbal algorithm. The Iqbal algorithm is based on equations in [2].

### Method
Function call

### Signature
`solposx.solarposition.iqbal(times, latitude, longitude)`

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]

### Returns
DataFrame with the following columns (all values in degrees):
- **elevation** : actual sun elevation (not accounting for refraction).
- **zenith** : actual sun zenith (not accounting for refraction).
- **azimuth** : sun azimuth, east of north.

### Return Type
pandas.DataFrame

### References
[1] Iqbal algorithm reference. [2] Equations reference.
```

--------------------------------

### Calculate refraction for six models with solposx

Source: https://solposx.readthedocs.io/en/stable/refraction_introduction.html

Calculates refraction angles for six models (Archer, Bennett, Hughes, Michalsky, SG2, SPA) over solar elevation angles from -5 to 90 degrees in 0.1-degree steps. The resulting DataFrame is used for the comparison plots.

```python
import numpy as np
import pandas as pd
import solposx

functions = {
    'Archer': solposx.refraction.archer,
    'Bennett': solposx.refraction.bennett,
    'Hughes': solposx.refraction.hughes,
    'Michalsky': solposx.refraction.michalsky,
    'SG2': solposx.refraction.sg2,
    'SPA': solposx.refraction.spa,
}

elevation = np.arange(-5, 90, 0.1)

refraction = pd.DataFrame(
    data={name: function(elevation) for name, function in functions.items()},
    index=elevation)
```

--------------------------------

### solarposition.skyfield(times, latitude, ...)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the Skyfield Python package.

```APIDOC
## solarposition.skyfield(times, latitude, ...)

### Description
Calculate solar position using the Skyfield Python package.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### solarposition.noaa(times, latitude, longitude, *)

Source: https://solposx.readthedocs.io/en/stable/solarposition.html

Calculates the solar position using the NOAA algorithm.

```APIDOC
## solarposition.noaa(times, latitude, longitude, *)

### Description
Calculate solar position using the NOAA algorithm.

### Parameters
- **times** - The time(s) for which to calculate the solar position.
- **latitude** - The latitude of the location.
- **longitude** - The longitude of the location.

### Returns
- Solar zenith, elevation, and azimuth angles.
```

--------------------------------

### psa()

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.psa.html

Calculate solar position using the PSA algorithm. The algorithm was developed at Plataforma Solar de Almería (PSA) and can use two sets of coefficients: 2001 (tuned to 1999-2015) and 2020 (tuned to 2020-2050).

```APIDOC
## psa()

### Description
Calculate solar position using the PSA algorithm. The algorithm was developed at Plataforma Solar de Almería (PSA) and can use two sets of coefficients: 2001 (tuned to 1999-2015) and 2020 (tuned to 2020-2050).

### Method
Function call

### Signature
`solposx.solarposition.psa(times, latitude, longitude, *, coefficients=2020)`

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **coefficients** (int or list, default 2020) - Optional - Coefficients for the solar position algorithm. Available options include 2001 or 2020. Alternatively a list of custom coefficients can be specified.

### Raises
- **ValueError** - Raises an error if `coefficients` is not in [2001, 2020] or a list of the 15 coefficients.

### Returns
DataFrame with the following columns (all values in degrees):
- **elevation** - actual sun elevation (not accounting for refraction).
- **azimuth** - sun azimuth, east of north.
- **zenith** - actual sun zenith (not accounting for refraction).

Return type: pandas.DataFrame

```

--------------------------------

### skyfield(times, latitude, longitude, *, de='de440.bsp')

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.solarposition.skyfield.html

Calculates solar position using the Skyfield Python package. Returns a DataFrame with elevation, zenith, and azimuth in degrees.

```APIDOC
## skyfield(times, latitude, longitude, *, de='de440.bsp')

### Description
Calculate solar position using the Skyfield Python package. Skyfield is a Python package that can calculate high precision position of stars, planets, and satellites in orbit around the Earth based on ephemerides. Calculated positions should agree with the Astronomical Almanac to within 0.0005 arcseconds.

### Method
Function call

### Signature
```python
skyfield(times, latitude, longitude, *, de='de440.bsp')
```

### Parameters
- **times** (pandas.DatetimeIndex) - Required - Timestamps - must be localized. Prior to 1970 and far in the future UTC and UT1 may deviate significantly. For such use cases, UT1 times should be provided.
- **latitude** (float) - Required - Latitude in decimal degrees. Positive north of equator, negative to south. [degrees]
- **longitude** (float) - Required - Longitude in decimal degrees. Positive east of prime meridian, negative to west. [degrees]
- **de** (str or Skyfield SpiceKernel, optional, default: 'de440.bsp') - Ephemeris of choice.

### Returns
pandas.DataFrame with the following columns (all values in degrees):
- **elevation** : actual sun elevation (not accounting for refraction).
- **zenith** : actual sun zenith (not accounting for refraction).
- **azimuth** : sun azimuth, east of north.

### Return Type
pandas.DataFrame

### References
- [1] Skyfield documentation
- [2] Skyfield package
```

--------------------------------

### hughes(elevation, pressure=101325.0, temperature=12.0)

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.refraction.hughes.html

Computes the atmospheric refraction correction based on the Hughes algorithm. Takes the true solar elevation angle and optional pressure and temperature values, returning the refraction angle in degrees.

```APIDOC
## Function: hughes(elevation, pressure=101325.0, temperature=12.0)

### Description
Computes the atmospheric refraction correction based on the Hughes algorithm. This function is used to calculate the refraction correction in the NOAA solar position algorithm.

### Parameters
- **elevation** (array-like) - Required - True solar elevation angle (not accounting for refraction). [degrees]
- **pressure** (numeric, default 101325) - Optional - Annual average atmospheric pressure. [Pascal]
- **temperature** (numeric, default 12) - Optional - Annual average temperature. The default in this code deviates from [1], which used 10 C. [C]

### Returns
- **refraction_angle** (np.array or pd.Series) - Atmospheric refraction angle. [degrees]

### Notes
The equation to calculate the refraction correction is given by:
5°<el≤90°: 58.1*tan(el) - 0.07*tan(el)^3 + 8.6e-5*tan(el)^5 - 0.5
-0.575°<el≤5°: el*(-518.2 + el*(103.4 + el*(-12.79 + el*0.711))) + 1735
el≤-0.575°: -20.774*tan(el)
where el is the true (unrefracted) solar elevation angle.

### References
[1] G. Hughes, [2] SUNAEP software
```

--------------------------------

### RuntimeWarning from pvlib.tools.py during refraction calculation

Source: https://solposx.readthedocs.io/en/stable/refraction_introduction.html

RuntimeWarning emitted by pvlib.tools.py when arccos encounters an invalid value, likely due to input outside the valid domain. This warning appears during the refraction calculation.

```text
/home/docs/checkouts/readthedocs.org/user_builds/solposx/envs/stable/lib/python3.13/site-packages/pvlib/tools.py:100: RuntimeWarning: invalid value encountered in arccos
  res = np.degrees(np.arccos(number))
```

--------------------------------

### Print RMSD values

Source: https://solposx.readthedocs.io/en/stable/solarposition_introduction.html

Prints the RMSD values for elevation, azimuth, and combined vector angle between the Michalsky and Skyfield models.

```python
print(f'The RMSD of the Michalsky model to Skyfield for elevation angle is: {comparison['zenith_rmsd']:.4f} degrees')
print(f'The RMSD of the Michalsky model to Skyfield for azimuth angle is: {comparison['azimuth_rmsd']:.4f} degrees')
print(f'The RMSD of the Michalsky model to Skyfield for combined vector angle is: {comparison['combined_rmsd']:.4f} degrees')

```

--------------------------------

### solposx.refraction.sg2

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.refraction.sg2.html

Documentation for the sg2 function, which computes atmospheric refraction correction for solar elevation angles.

```APIDOC
## solposx.refraction.sg2(elevation, pressure=101325.0, temperature=12.0)

### Description
Calculates the atmospheric refraction correction of the solar elevation angle using the algorithm from SG2, based on the method by Ph. Blanc and L. Wald.

### Parameters
- **elevation** (array-like) - Required - True solar elevation angle (not accounting for refraction) in degrees.
- **pressure** (numeric, default 101325) - Optional - Annual average atmospheric pressure in Pascal.
- **temperature** (numeric, default 12) - Optional - Annual average air temperature in degrees Celsius.

### Returns
- **np.array or pd.Series** - Atmospheric refraction angle in degrees.

### Notes
The refraction correction is computed using piecewise formulas depending on the elevation angle. For elevation > -0.01 rad, the formula is:

\(\frac{P}{1010} \cdot \frac{283}{273+T} \cdot \frac{2.96706 \times 10^{-4}}{\tan(el + 0.0031376 \cdot (el + 0.089186)^{-1})}\)

For elevation < -0.01 rad, the formula is:

\(\frac{P}{1010} \cdot \frac{283}{273+T} \cdot \frac{1.005516 \times 10^{-4}}{\tan(el)}\)

where \(el\) is the true solar elevation angle, \(P\) is the local air pressure, and \(T\) is the local air temperature.

### References
- [1] Ph. Blanc and L. Wald, "The SG2 algorithm for a fast and accurate computation of the position of the Sun for multi-decadal time period", Solar Energy, 2012.
```

--------------------------------

### michalsky(elevation)

Source: https://solposx.readthedocs.io/en/stable/generated/solposx.refraction.michalsky.html

Calculates the atmospheric refraction correction for a given true solar elevation angle using the Michalsky algorithm. Returns the refraction angle in degrees.

```APIDOC
## michalsky(elevation)

### Description
Calculates the atmospheric refraction correction for a given true solar elevation angle using the Michalsky algorithm.

### Method
Function call

### Endpoint
`solposx.refraction.michalsky(elevation)`

### Parameters
- **elevation** (array-like) - Required - True solar elevation angle (not accounting for refraction). [degrees]

### Returns
- **np.array or pd.Series** - Atmospheric refraction angle. [degrees]

### Notes
The equation to calculate the refraction correction is given by:
ref = 3.51561 * (0.1594 + 0.0196 * el + 0.00002 * el^2) / (1 + 0.505 * el + 0.0845 * el^2)
where el is the true (uncorrected) solar elevation angle.

### References
- Michalsky [1]
```

--------------------------------

### RMSD output

Source: https://solposx.readthedocs.io/en/stable/solarposition_introduction.html

Shows the printed RMSD values for the comparison.

```text
The RMSD of the Michalsky model to Skyfield for elevation angle is: 0.0043 degrees
The RMSD of the Michalsky model to Skyfield for azimuth angle is: 0.0048 degrees
The RMSD of the Michalsky model to Skyfield for combined vector angle is: 0.0049 degrees

```
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