Source code for pvlib.location

"""
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)