"""
This module contains the Location class.
"""
# Will Holmgren, University of Arizona, 2014-2016.
import os
import datetime
import warnings
import pandas as pd
import pytz
import h5py
from pvlib import solarposition, clearsky, atmosphere, irradiance
from pvlib.tools import _degrees_to_index
[docs]class Location:
"""
Location objects are convenient containers for latitude, longitude,
timezone, and altitude data associated with a particular
geographic location. You can also assign a name to a location object.
Location objects have two timezone attributes:
* ``tz`` is a IANA timezone string.
* ``pytz`` is a pytz timezone object.
Location objects support the print method.
Parameters
----------
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
tz : str, int, float, or pytz.timezone, default 'UTC'.
See
http://en.wikipedia.org/wiki/List_of_tz_database_time_zones
for a list of valid time zones.
pytz.timezone objects will be converted to strings.
ints and floats must be in hours from UTC.
altitude : float, default 0.
Altitude from sea level in meters.
name : None or string, default None.
Sets the name attribute of the Location object.
See also
--------
pvlib.pvsystem.PVSystem
"""
[docs] def __init__(self, latitude, longitude, tz='UTC', altitude=0, name=None):
self.latitude = latitude
self.longitude = longitude
if isinstance(tz, str):
self.tz = tz
self.pytz = pytz.timezone(tz)
elif isinstance(tz, datetime.timezone):
self.tz = 'UTC'
self.pytz = pytz.UTC
elif isinstance(tz, datetime.tzinfo):
self.tz = tz.zone
self.pytz = tz
elif isinstance(tz, (int, float)):
self.tz = tz
self.pytz = pytz.FixedOffset(tz*60)
else:
raise TypeError('Invalid tz specification')
self.altitude = altitude
self.name = name
def __repr__(self):
attrs = ['name', 'latitude', 'longitude', 'altitude', 'tz']
return ('Location: \n ' + '\n '.join(
f'{attr}: {getattr(self, attr)}' for attr in attrs))
[docs] @classmethod
def from_tmy(cls, tmy_metadata, tmy_data=None, **kwargs):
"""
Create an object based on a metadata
dictionary from tmy2 or tmy3 data readers.
Parameters
----------
tmy_metadata : dict
Returned from tmy.readtmy2 or tmy.readtmy3
tmy_data : None or DataFrame, default None
Optionally attach the TMY data to this object.
Returns
-------
Location
"""
# not complete, but hopefully you get the idea.
# might need code to handle the difference between tmy2 and tmy3
# determine if we're dealing with TMY2 or TMY3 data
tmy2 = tmy_metadata.get('City', False)
latitude = tmy_metadata['latitude']
longitude = tmy_metadata['longitude']
if tmy2:
name = tmy_metadata['City']
else:
name = tmy_metadata['Name']
tz = tmy_metadata['TZ']
altitude = tmy_metadata['altitude']
new_object = cls(latitude, longitude, tz=tz, altitude=altitude,
name=name, **kwargs)
# not sure if this should be assigned regardless of input.
if tmy_data is not None:
new_object.tmy_data = tmy_data
new_object.weather = tmy_data
return new_object
[docs] @classmethod
def from_epw(cls, metadata, data=None, **kwargs):
"""
Create a Location object based on a metadata
dictionary from epw data readers.
Parameters
----------
metadata : dict
Returned from epw.read_epw
data : None or DataFrame, default None
Optionally attach the epw data to this object.
Returns
-------
Location object (or the child class of Location that you
called this method from).
"""
latitude = metadata['latitude']
longitude = metadata['longitude']
name = metadata['city']
tz = metadata['TZ']
altitude = metadata['altitude']
new_object = cls(latitude, longitude, tz=tz, altitude=altitude,
name=name, **kwargs)
if data is not None:
new_object.weather = data
return new_object
[docs] def get_solarposition(self, times, pressure=None, temperature=12,
**kwargs):
"""
Uses the :py:func:`pvlib.solarposition.get_solarposition` function
to calculate the solar zenith, azimuth, etc. at this location.
Parameters
----------
times : pandas.DatetimeIndex
Must be localized or UTC will be assumed.
pressure : None, float, or array-like, default None
If None, pressure will be calculated using
:py:func:`pvlib.atmosphere.alt2pres` and ``self.altitude``.
temperature : None, float, or array-like, default 12
kwargs
passed to :py:func:`pvlib.solarposition.get_solarposition`
Returns
-------
solar_position : DataFrame
Columns depend on the ``method`` kwarg, but always include
``zenith`` and ``azimuth``. The angles are in degrees.
"""
if pressure is None:
pressure = atmosphere.alt2pres(self.altitude)
return solarposition.get_solarposition(times, latitude=self.latitude,
longitude=self.longitude,
altitude=self.altitude,
pressure=pressure,
temperature=temperature,
**kwargs)
[docs] def get_clearsky(self, times, model='ineichen', solar_position=None,
dni_extra=None, **kwargs):
"""
Calculate the clear sky estimates of GHI, DNI, and/or DHI
at this location.
Parameters
----------
times: DatetimeIndex
model: str, default 'ineichen'
The clear sky model to use. Must be one of
'ineichen', 'haurwitz', 'simplified_solis'.
solar_position : None or DataFrame, default None
DataFrame with columns 'apparent_zenith', 'zenith',
'apparent_elevation'.
dni_extra: None or numeric, default None
If None, will be calculated from times.
kwargs
Extra parameters passed to the relevant functions. Climatological
values are assumed in many cases. See source code for details!
Returns
-------
clearsky : DataFrame
Column names are: ``ghi, dni, dhi``.
"""
if dni_extra is None:
dni_extra = irradiance.get_extra_radiation(times)
try:
pressure = kwargs.pop('pressure')
except KeyError:
pressure = atmosphere.alt2pres(self.altitude)
if solar_position is None:
solar_position = self.get_solarposition(times, pressure=pressure)
apparent_zenith = solar_position['apparent_zenith']
apparent_elevation = solar_position['apparent_elevation']
if model == 'ineichen':
try:
linke_turbidity = kwargs.pop('linke_turbidity')
except KeyError:
interp_turbidity = kwargs.pop('interp_turbidity', True)
linke_turbidity = clearsky.lookup_linke_turbidity(
times, self.latitude, self.longitude,
interp_turbidity=interp_turbidity)
try:
airmass_absolute = kwargs.pop('airmass_absolute')
except KeyError:
airmass_absolute = self.get_airmass(
times, solar_position=solar_position)['airmass_absolute']
cs = clearsky.ineichen(apparent_zenith, airmass_absolute,
linke_turbidity, altitude=self.altitude,
dni_extra=dni_extra, **kwargs)
elif model == 'haurwitz':
cs = clearsky.haurwitz(apparent_zenith)
elif model == 'simplified_solis':
cs = clearsky.simplified_solis(
apparent_elevation, pressure=pressure, dni_extra=dni_extra,
**kwargs)
else:
raise ValueError('{} is not a valid clear sky model. Must be '
'one of ineichen, simplified_solis, haurwitz'
.format(model))
return cs
[docs] def get_airmass(self, times=None, solar_position=None,
model='kastenyoung1989'):
"""
Calculate the relative and absolute airmass.
Automatically chooses zenith or apparant zenith
depending on the selected model.
Parameters
----------
times : None or DatetimeIndex, default None
Only used if solar_position is not provided.
solar_position : None or DataFrame, default None
DataFrame with with columns 'apparent_zenith', 'zenith'.
model : str, default 'kastenyoung1989'
Relative airmass model. See
:py:func:`pvlib.atmosphere.get_relative_airmass`
for a list of available models.
Returns
-------
airmass : DataFrame
Columns are 'airmass_relative', 'airmass_absolute'
See also
--------
pvlib.atmosphere.get_relative_airmass
"""
if solar_position is None:
solar_position = self.get_solarposition(times)
if model in atmosphere.APPARENT_ZENITH_MODELS:
zenith = solar_position['apparent_zenith']
elif model in atmosphere.TRUE_ZENITH_MODELS:
zenith = solar_position['zenith']
else:
raise ValueError(f'{model} is not a valid airmass model')
airmass_relative = atmosphere.get_relative_airmass(zenith, model)
pressure = atmosphere.alt2pres(self.altitude)
airmass_absolute = atmosphere.get_absolute_airmass(airmass_relative,
pressure)
airmass = pd.DataFrame(index=solar_position.index)
airmass['airmass_relative'] = airmass_relative
airmass['airmass_absolute'] = airmass_absolute
return airmass
[docs] def get_sun_rise_set_transit(self, times, method='pyephem', **kwargs):
"""
Calculate sunrise, sunset and transit times.
Parameters
----------
times : DatetimeIndex
Must be localized to the Location
method : str, default 'pyephem'
'pyephem', 'spa', or 'geometric'
kwargs are passed to the relevant functions. See
solarposition.sun_rise_set_transit_<method> for details.
Returns
-------
result : DataFrame
Column names are: ``sunrise, sunset, transit``.
"""
if method == 'pyephem':
result = solarposition.sun_rise_set_transit_ephem(
times, self.latitude, self.longitude, **kwargs)
elif method == 'spa':
result = solarposition.sun_rise_set_transit_spa(
times, self.latitude, self.longitude, **kwargs)
elif method == 'geometric':
sr, ss, tr = solarposition.sun_rise_set_transit_geometric(
times, self.latitude, self.longitude, **kwargs)
result = pd.DataFrame(index=times,
data={'sunrise': sr,
'sunset': ss,
'transit': tr})
else:
raise ValueError('{} is not a valid method. Must be '
'one of pyephem, spa, geometric'
.format(method))
return result
[docs]def lookup_altitude(latitude, longitude):
"""
Look up location altitude from low-resolution altitude map
supplied with pvlib. The data for this map comes from multiple open data
sources with varying resolutions aggregated by Mapzen.
More details can be found here
https://github.com/tilezen/joerd/blob/master/docs/data-sources.md
Altitudes from this map are a coarse approximation and can have
significant errors (100+ meters) introduced by downsampling and
source data resolution.
Parameters
----------
latitude : float.
Positive is north of the equator.
Use decimal degrees notation.
longitude : float.
Positive is east of the prime meridian.
Use decimal degrees notation.
Returns
-------
altitude : float
The altitude of the location in meters.
Notes
-----------
Attributions:
* ArcticDEM terrain data DEM(s) were created from DigitalGlobe, Inc.,
imagery and funded under National Science Foundation awards 1043681,
1559691, and 1542736;
* Australia terrain data © Commonwealth of Australia
(Geoscience Australia) 2017;
* Austria terrain data © offene Daten Österreichs - Digitales
Geländemodell (DGM) Österreich;
* Canada terrain data contains information licensed under the Open
Government Licence - Canada;
* Europe terrain data produced using Copernicus data and information
funded by the European Union - EU-DEM layers;
* Global ETOPO1 terrain data U.S. National Oceanic and Atmospheric
Administration
* Mexico terrain data source: INEGI, Continental relief, 2016;
* New Zealand terrain data Copyright 2011 Crown copyright (c) Land
Information New Zealand and the New Zealand Government
(All rights reserved);
* Norway terrain data © Kartverket;
* United Kingdom terrain data © Environment Agency copyright and/or
database right 2015. All rights reserved;
* United States 3DEP (formerly NED) and global GMTED2010 and SRTM
terrain data courtesy of the U.S. Geological Survey.
References
----------
.. [1] `Mapzen, Linux foundation project for open data maps
<https://www.mapzen.com/>`_
.. [2] `Joerd, tool for downloading and processing DEMs, Used by Mapzen
<https://github.com/tilezen/joerd/>`_
.. [3] `AWS, Open Data Registry Terrain Tiles
<https://registry.opendata.aws/terrain-tiles/>`_
"""
pvlib_path = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(pvlib_path, 'data', 'Altitude.h5')
latitude_index = _degrees_to_index(latitude, coordinate='latitude')
longitude_index = _degrees_to_index(longitude, coordinate='longitude')
with h5py.File(filepath, 'r') as alt_h5_file:
alt = alt_h5_file['Altitude'][latitude_index, longitude_index]
# 255 is a special value that means nodata. Fallback to 0 if nodata.
if alt == 255:
return 0
# Altitude is encoded in 28 meter steps from -450 meters to 6561 meters
# There are 0-254 possible altitudes, with 255 reserved for nodata.
alt *= 28
alt -= 450
return float(alt)