Package Overview

Introduction

The core mission of pvlib-python is to provide open, reliable, interoperable, and benchmark implementations of PV system models.

There are at least as many opinions about how to model PV systems as there are modelers of PV systems, so pvlib-python provides several modeling paradigms.

Modeling paradigms

The backbone of pvlib-python is well-tested procedural code that implements PV system models. pvlib-python also provides a collection of classes for users that prefer object-oriented programming. These classes can help users keep track of data in a more organized way, provide some “smart” functions with more flexible inputs, and simplify the modeling process for common situations. The classes do not add any algorithms beyond what’s available in the procedural code, and most of the object methods are simple wrappers around the corresponding procedural code.

Let’s use each of these pvlib modeling paradigms to calculate the yearly energy yield for a given hardware configuration at a handful of sites listed below.

In [1]: import pandas as pd

In [2]: import matplotlib.pyplot as plt

# seaborn makes the plots look nicer
In [3]: import seaborn as sns

In [4]: sns.set_color_codes()

In [5]: naive_times = pd.DatetimeIndex(start='2015', end='2016', freq='1h')

# very approximate
# latitude, longitude, name, altitude, timezone
In [6]: coordinates = [(30, -110, 'Tucson', 700, 'Etc/GMT+7'),
   ...:                (35, -105, 'Albuquerque', 1500, 'Etc/GMT+7'),
   ...:                (40, -120, 'San Francisco', 10, 'Etc/GMT+8'),
   ...:                (50, 10, 'Berlin', 34, 'Etc/GMT-1')]
   ...: 

In [7]: import pvlib

# get the module and inverter specifications from SAM
In [8]: sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')

In [9]: sapm_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')

In [10]: module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']

In [11]: inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']

# specify constant ambient air temp and wind for simplicity
In [12]: temp_air = 20

In [13]: wind_speed = 0

Procedural

The straightforward procedural code can be used for all modeling steps in pvlib-python.

The following code demonstrates how to use the procedural code to accomplish our system modeling goal:

In [14]: system = {'module': module, 'inverter': inverter,
   ....:           'surface_azimuth': 180}
   ....: 

In [15]: energies = {}

In [16]: for latitude, longitude, name, altitude, timezone in coordinates:
   ....:     times = naive_times.tz_localize(timezone)
   ....:     system['surface_tilt'] = latitude
   ....:     solpos = pvlib.solarposition.get_solarposition(times, latitude, longitude)
   ....:     dni_extra = pvlib.irradiance.extraradiation(times)
   ....:     dni_extra = pd.Series(dni_extra, index=times)
   ....:     airmass = pvlib.atmosphere.relativeairmass(solpos['apparent_zenith'])
   ....:     pressure = pvlib.atmosphere.alt2pres(altitude)
   ....:     am_abs = pvlib.atmosphere.absoluteairmass(airmass, pressure)
   ....:     tl = pvlib.clearsky.lookup_linke_turbidity(times, latitude, longitude)
   ....:     cs = pvlib.clearsky.ineichen(solpos['apparent_zenith'], am_abs, tl,
   ....:                                  dni_extra=dni_extra, altitude=altitude)
   ....:     aoi = pvlib.irradiance.aoi(system['surface_tilt'], system['surface_azimuth'],
   ....:                                solpos['apparent_zenith'], solpos['azimuth'])
   ....:     total_irrad = pvlib.irradiance.total_irrad(system['surface_tilt'],
   ....:                                                system['surface_azimuth'],
   ....:                                                solpos['apparent_zenith'],
   ....:                                                solpos['azimuth'],
   ....:                                                cs['dni'], cs['ghi'], cs['dhi'],
   ....:                                                dni_extra=dni_extra,
   ....:                                                model='haydavies')
   ....:     temps = pvlib.pvsystem.sapm_celltemp(total_irrad['poa_global'],
   ....:                                          wind_speed, temp_air)
   ....:     effective_irradiance = pvlib.pvsystem.sapm_effective_irradiance(
   ....:         total_irrad['poa_direct'], total_irrad['poa_diffuse'],
   ....:         am_abs, aoi, module)
   ....:     dc = pvlib.pvsystem.sapm(effective_irradiance, temps['temp_cell'], module)
   ....:     ac = pvlib.pvsystem.snlinverter(dc['v_mp'], dc['p_mp'], inverter)
   ....:     annual_energy = ac.sum()
   ....:     energies[name] = annual_energy
   ....: 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-da00fd02098b> in <module>()
      2     times = naive_times.tz_localize(timezone)
      3     system['surface_tilt'] = latitude
----> 4     solpos = pvlib.solarposition.get_solarposition(times, latitude, longitude)
      5     dni_extra = pvlib.irradiance.extraradiation(times)
      6     dni_extra = pd.Series(dni_extra, index=times)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in get_solarposition(time, latitude, longitude, altitude, pressure, method, temperature, **kwargs)
     98         ephem_df = spa_python(time, latitude, longitude, altitude,
     99                               pressure, temperature,
--> 100                               how='numpy', **kwargs)
    101     elif method == 'pyephem':
    102         ephem_df = pyephem(time, latitude, longitude, pressure, temperature,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in spa_python(time, latitude, longitude, altitude, pressure, temperature, delta_t, atmos_refract, how, numthreads, **kwargs)
    324     app_zenith, zenith, app_elevation, elevation, azimuth, eot = spa.solar_position(
    325         unixtime, lat, lon, elev, pressure, temperature, delta_t,
--> 326         atmos_refract, numthreads)
    327 
    328     result = pd.DataFrame({'apparent_zenith': app_zenith, 'zenith': zenith,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1149     result = do_calc(unixtime, lat, lon, elev, pressure,
   1150                      temp, delta_t, atmos_refract, numthreads,
-> 1151                      sst, esd)
   1152 
   1153     if not isinstance(result, np.ndarray):

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position_numpy(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1058         return v, alpha, delta
   1059     m = sun_mean_longitude(jme)
-> 1060     eot = equation_of_time(m, alpha, delta_psi, epsilon)
   1061     H = local_hour_angle(v, lon, alpha)
   1062     xi = equatorial_horizontal_parallax(R)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in equation_of_time(sun_mean_longitude, geocentric_sun_right_ascension, longitude_nutation, true_ecliptic_obliquity)
    892     less = E < -20
    893     other = (E <= 20) & (E >= -20)
--> 894     E = greater * (E - 1440) + less * (E + 1440) + other * E
    895     return E
    896 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _evaluate_numeric_binop(self, other)
   3458 
   3459                 from pandas.tseries.offsets import DateOffset
-> 3460                 other = self._validate_for_numeric_binop(other, op, opstr)
   3461 
   3462                 # handle time-based others

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _validate_for_numeric_binop(self, other, op, opstr)
   3435             other = _values_from_object(other)
   3436             if other.dtype.kind not in ['f', 'i']:
-> 3437                 raise TypeError("cannot evaluate a numeric op "
   3438                                 "with a non-numeric dtype")
   3439         elif isinstance(other, (DateOffset, np.timedelta64,

TypeError: cannot evaluate a numeric op with a non-numeric dtype

In [17]: energies = pd.Series(energies)

# based on the parameters specified above, these are in W*hrs
In [18]: print(energies.round(0))
Series([], dtype: float64)

In [19]: energies.plot(kind='bar', rot=0)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-19-2f97a4fe1d4b> in <module>()
----> 1 energies.plot(kind='bar', rot=0)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   3597                            colormap=colormap, table=table, yerr=yerr,
   3598                            xerr=xerr, label=label, secondary_y=secondary_y,
-> 3599                            **kwds)
   3600     __call__.__doc__ = plot_series.__doc__
   3601 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   2671                  yerr=yerr, xerr=xerr,
   2672                  label=label, secondary_y=secondary_y,
-> 2673                  **kwds)
   2674 
   2675 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
   2467         plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
   2468 
-> 2469     plot_obj.generate()
   2470     plot_obj.draw()
   2471     return plot_obj.result

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
   1039     def generate(self):
   1040         self._args_adjust()
-> 1041         self._compute_plot_data()
   1042         self._setup_subplots()
   1043         self._make_plot()

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _compute_plot_data(self)
   1148         if is_empty:
   1149             raise TypeError('Empty {0!r}: no numeric data to '
-> 1150                             'plot'.format(numeric_data.__class__.__name__))
   1151 
   1152         self.data = numeric_data

TypeError: Empty 'DataFrame': no numeric data to plot

In [20]: plt.ylabel('Yearly energy yield (W hr)')
Out[20]: <matplotlib.text.Text at 0x7feee2ebc210>

pvlib-python provides a basic_chain() function that implements much of the code above. Use this function with a full understanding of what it is doing internally!

In [21]: from pvlib.modelchain import basic_chain

In [22]: energies = {}

In [23]: for latitude, longitude, name, altitude, timezone in coordinates:
   ....:     dc, ac = basic_chain(naive_times.tz_localize(timezone),
   ....:                          latitude, longitude,
   ....:                          module, inverter,
   ....:                          altitude=altitude,
   ....:                          orientation_strategy='south_at_latitude_tilt')
   ....:     annual_energy = ac.sum()
   ....:     energies[name] = annual_energy
   ....: 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-23-bb5c37b8c90f> in <module>()
      4                          module, inverter,
      5                          altitude=altitude,
----> 6                          orientation_strategy='south_at_latitude_tilt')
      7     annual_energy = ac.sum()
      8     energies[name] = annual_energy

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/modelchain.py in basic_chain(times, latitude, longitude, module_parameters, inverter_parameters, irradiance, weather, surface_tilt, surface_azimuth, orientation_strategy, transposition_model, solar_position_method, airmass_model, altitude, pressure, **kwargs)
    129                                                      altitude=altitude,
    130                                                      pressure=pressure,
--> 131                                                      **kwargs)
    132 
    133     # possible error with using apparent zenith with some models

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in get_solarposition(time, latitude, longitude, altitude, pressure, method, temperature, **kwargs)
     98         ephem_df = spa_python(time, latitude, longitude, altitude,
     99                               pressure, temperature,
--> 100                               how='numpy', **kwargs)
    101     elif method == 'pyephem':
    102         ephem_df = pyephem(time, latitude, longitude, pressure, temperature,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in spa_python(time, latitude, longitude, altitude, pressure, temperature, delta_t, atmos_refract, how, numthreads, **kwargs)
    324     app_zenith, zenith, app_elevation, elevation, azimuth, eot = spa.solar_position(
    325         unixtime, lat, lon, elev, pressure, temperature, delta_t,
--> 326         atmos_refract, numthreads)
    327 
    328     result = pd.DataFrame({'apparent_zenith': app_zenith, 'zenith': zenith,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1149     result = do_calc(unixtime, lat, lon, elev, pressure,
   1150                      temp, delta_t, atmos_refract, numthreads,
-> 1151                      sst, esd)
   1152 
   1153     if not isinstance(result, np.ndarray):

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position_numpy(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1058         return v, alpha, delta
   1059     m = sun_mean_longitude(jme)
-> 1060     eot = equation_of_time(m, alpha, delta_psi, epsilon)
   1061     H = local_hour_angle(v, lon, alpha)
   1062     xi = equatorial_horizontal_parallax(R)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in equation_of_time(sun_mean_longitude, geocentric_sun_right_ascension, longitude_nutation, true_ecliptic_obliquity)
    892     less = E < -20
    893     other = (E <= 20) & (E >= -20)
--> 894     E = greater * (E - 1440) + less * (E + 1440) + other * E
    895     return E
    896 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _evaluate_numeric_binop(self, other)
   3458 
   3459                 from pandas.tseries.offsets import DateOffset
-> 3460                 other = self._validate_for_numeric_binop(other, op, opstr)
   3461 
   3462                 # handle time-based others

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _validate_for_numeric_binop(self, other, op, opstr)
   3435             other = _values_from_object(other)
   3436             if other.dtype.kind not in ['f', 'i']:
-> 3437                 raise TypeError("cannot evaluate a numeric op "
   3438                                 "with a non-numeric dtype")
   3439         elif isinstance(other, (DateOffset, np.timedelta64,

TypeError: cannot evaluate a numeric op with a non-numeric dtype

In [24]: energies = pd.Series(energies)

# based on the parameters specified above, these are in W*hrs
In [25]: print(energies.round(0))
Series([], dtype: float64)

In [26]: energies.plot(kind='bar', rot=0)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-26-2f97a4fe1d4b> in <module>()
----> 1 energies.plot(kind='bar', rot=0)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   3597                            colormap=colormap, table=table, yerr=yerr,
   3598                            xerr=xerr, label=label, secondary_y=secondary_y,
-> 3599                            **kwds)
   3600     __call__.__doc__ = plot_series.__doc__
   3601 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   2671                  yerr=yerr, xerr=xerr,
   2672                  label=label, secondary_y=secondary_y,
-> 2673                  **kwds)
   2674 
   2675 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
   2467         plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
   2468 
-> 2469     plot_obj.generate()
   2470     plot_obj.draw()
   2471     return plot_obj.result

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
   1039     def generate(self):
   1040         self._args_adjust()
-> 1041         self._compute_plot_data()
   1042         self._setup_subplots()
   1043         self._make_plot()

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _compute_plot_data(self)
   1148         if is_empty:
   1149             raise TypeError('Empty {0!r}: no numeric data to '
-> 1150                             'plot'.format(numeric_data.__class__.__name__))
   1151 
   1152         self.data = numeric_data

TypeError: Empty 'DataFrame': no numeric data to plot

In [27]: plt.ylabel('Yearly energy yield (W hr)')
Out[27]: <matplotlib.text.Text at 0x7feee2d580d0>

Object oriented (Location, PVSystem, ModelChain)

The first object oriented paradigm uses a model where a PVSystem object represents an assembled collection of modules, inverters, etc., a Location object represents a particular place on the planet, and a ModelChain object describes the modeling chain used to calculate PV output at that Location. This can be a useful paradigm if you prefer to think about the PV system and its location as separate concepts or if you develop your own ModelChain subclasses. It can also be helpful if you make extensive use of Location-specific methods for other calculations.

The following code demonstrates how to use Location, PVSystem, and ModelChain objects to accomplish our system modeling goal:

In [28]: from pvlib.pvsystem import PVSystem

In [29]: from pvlib.location import Location

In [30]: from pvlib.modelchain import ModelChain

In [31]: system = PVSystem(module_parameters=module,
   ....:                   inverter_parameters=inverter)
   ....: 

In [32]: energies = {}

In [33]: for latitude, longitude, name, altitude, timezone in coordinates:
   ....:     location = Location(latitude, longitude, name=name, altitude=altitude,
   ....:                         tz=timezone)
   ....:     mc = ModelChain(system, location,
   ....:                     orientation_strategy='south_at_latitude_tilt')
   ....:     mc.run_model(naive_times.tz_localize(timezone))
   ....:     annual_energy = mc.ac.sum()
   ....:     energies[name] = annual_energy
   ....: 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-33-ea2c6c6ff63f> in <module>()
      4     mc = ModelChain(system, location,
      5                     orientation_strategy='south_at_latitude_tilt')
----> 6     mc.run_model(naive_times.tz_localize(timezone))
      7     annual_energy = mc.ac.sum()
      8     energies[name] = annual_energy

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/modelchain.py in run_model(self, times, irradiance, weather)
    710         """
    711 
--> 712         self.prepare_inputs(times, irradiance, weather)
    713         self.aoi_model()
    714         self.spectral_model()

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/modelchain.py in prepare_inputs(self, times, irradiance, weather)
    630         self.times = times
    631 
--> 632         self.solar_position = self.location.get_solarposition(self.times)
    633 
    634         self.airmass = self.location.get_airmass(

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/location.py in get_solarposition(self, times, pressure, temperature, **kwargs)
    165                                                pressure=pressure,
    166                                                temperature=temperature,
--> 167                                                **kwargs)
    168 
    169 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in get_solarposition(time, latitude, longitude, altitude, pressure, method, temperature, **kwargs)
     98         ephem_df = spa_python(time, latitude, longitude, altitude,
     99                               pressure, temperature,
--> 100                               how='numpy', **kwargs)
    101     elif method == 'pyephem':
    102         ephem_df = pyephem(time, latitude, longitude, pressure, temperature,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in spa_python(time, latitude, longitude, altitude, pressure, temperature, delta_t, atmos_refract, how, numthreads, **kwargs)
    324     app_zenith, zenith, app_elevation, elevation, azimuth, eot = spa.solar_position(
    325         unixtime, lat, lon, elev, pressure, temperature, delta_t,
--> 326         atmos_refract, numthreads)
    327 
    328     result = pd.DataFrame({'apparent_zenith': app_zenith, 'zenith': zenith,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1149     result = do_calc(unixtime, lat, lon, elev, pressure,
   1150                      temp, delta_t, atmos_refract, numthreads,
-> 1151                      sst, esd)
   1152 
   1153     if not isinstance(result, np.ndarray):

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position_numpy(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1058         return v, alpha, delta
   1059     m = sun_mean_longitude(jme)
-> 1060     eot = equation_of_time(m, alpha, delta_psi, epsilon)
   1061     H = local_hour_angle(v, lon, alpha)
   1062     xi = equatorial_horizontal_parallax(R)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in equation_of_time(sun_mean_longitude, geocentric_sun_right_ascension, longitude_nutation, true_ecliptic_obliquity)
    892     less = E < -20
    893     other = (E <= 20) & (E >= -20)
--> 894     E = greater * (E - 1440) + less * (E + 1440) + other * E
    895     return E
    896 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _evaluate_numeric_binop(self, other)
   3458 
   3459                 from pandas.tseries.offsets import DateOffset
-> 3460                 other = self._validate_for_numeric_binop(other, op, opstr)
   3461 
   3462                 # handle time-based others

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _validate_for_numeric_binop(self, other, op, opstr)
   3435             other = _values_from_object(other)
   3436             if other.dtype.kind not in ['f', 'i']:
-> 3437                 raise TypeError("cannot evaluate a numeric op "
   3438                                 "with a non-numeric dtype")
   3439         elif isinstance(other, (DateOffset, np.timedelta64,

TypeError: cannot evaluate a numeric op with a non-numeric dtype

In [34]: energies = pd.Series(energies)

# based on the parameters specified above, these are in W*hrs
In [35]: print(energies.round(0))
Series([], dtype: float64)

In [36]: energies.plot(kind='bar', rot=0)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-36-2f97a4fe1d4b> in <module>()
----> 1 energies.plot(kind='bar', rot=0)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   3597                            colormap=colormap, table=table, yerr=yerr,
   3598                            xerr=xerr, label=label, secondary_y=secondary_y,
-> 3599                            **kwds)
   3600     __call__.__doc__ = plot_series.__doc__
   3601 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   2671                  yerr=yerr, xerr=xerr,
   2672                  label=label, secondary_y=secondary_y,
-> 2673                  **kwds)
   2674 
   2675 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
   2467         plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
   2468 
-> 2469     plot_obj.generate()
   2470     plot_obj.draw()
   2471     return plot_obj.result

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
   1039     def generate(self):
   1040         self._args_adjust()
-> 1041         self._compute_plot_data()
   1042         self._setup_subplots()
   1043         self._make_plot()

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _compute_plot_data(self)
   1148         if is_empty:
   1149             raise TypeError('Empty {0!r}: no numeric data to '
-> 1150                             'plot'.format(numeric_data.__class__.__name__))
   1151 
   1152         self.data = numeric_data

TypeError: Empty 'DataFrame': no numeric data to plot

In [37]: plt.ylabel('Yearly energy yield (W hr)')
Out[37]: <matplotlib.text.Text at 0x7feee2ad0650>

See Will Holmgren’s ModelChain gist for more discussion about new features in ModelChain.

Object oriented (LocalizedPVSystem)

The second object oriented paradigm uses a model where a LocalizedPVSystem represents a PV system at a particular place on the planet. This can be a useful paradigm if you’re thinking about a power plant that already exists.

The following code demonstrates how to use a LocalizedPVSystem object to accomplish our modeling goal:

In [38]: from pvlib.pvsystem import LocalizedPVSystem

In [39]: energies = {}

In [40]: for latitude, longitude, name, altitude, timezone in coordinates:
   ....:     localized_system = LocalizedPVSystem(module_parameters=module,
   ....:                                          inverter_parameters=inverter,
   ....:                                          surface_tilt=latitude,
   ....:                                          surface_azimuth=180,
   ....:                                          latitude=latitude,
   ....:                                          longitude=longitude,
   ....:                                          name=name,
   ....:                                          altitude=altitude,
   ....:                                          tz=timezone)
   ....:     times = naive_times.tz_localize(timezone)
   ....:     clearsky = localized_system.get_clearsky(times)
   ....:     solar_position = localized_system.get_solarposition(times)
   ....:     total_irrad = localized_system.get_irradiance(solar_position['apparent_zenith'],
   ....:                                                   solar_position['azimuth'],
   ....:                                                   clearsky['dni'],
   ....:                                                   clearsky['ghi'],
   ....:                                                   clearsky['dhi'])
   ....:     temps = localized_system.sapm_celltemp(total_irrad['poa_global'],
   ....:                                            wind_speed, temp_air)
   ....:     aoi = localized_system.get_aoi(solar_position['apparent_zenith'],
   ....:                                    solar_position['azimuth'])
   ....:     airmass = localized_system.get_airmass(solar_position=solar_position)
   ....:     effective_irradiance = localized_system.sapm_effective_irradiance(
   ....:         total_irrad['poa_direct'], total_irrad['poa_diffuse'],
   ....:         airmass['airmass_absolute'], aoi)
   ....:     dc = localized_system.sapm(effective_irradiance, temps['temp_cell'])
   ....:     ac = localized_system.snlinverter(dc['v_mp'], dc['p_mp'])
   ....:     annual_energy = ac.sum()
   ....:     energies[name] = annual_energy
   ....: 
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-782e66a18e4f> in <module>()
     10                                          tz=timezone)
     11     times = naive_times.tz_localize(timezone)
---> 12     clearsky = localized_system.get_clearsky(times)
     13     solar_position = localized_system.get_solarposition(times)
     14     total_irrad = localized_system.get_irradiance(solar_position['apparent_zenith'],

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/location.py in get_clearsky(self, times, model, solar_position, dni_extra, **kwargs)
    204         if solar_position is None:
    205             solar_position = self.get_solarposition(times, pressure=pressure,
--> 206                                                     **kwargs)
    207 
    208         apparent_zenith = solar_position['apparent_zenith']

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/location.py in get_solarposition(self, times, pressure, temperature, **kwargs)
    165                                                pressure=pressure,
    166                                                temperature=temperature,
--> 167                                                **kwargs)
    168 
    169 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in get_solarposition(time, latitude, longitude, altitude, pressure, method, temperature, **kwargs)
     98         ephem_df = spa_python(time, latitude, longitude, altitude,
     99                               pressure, temperature,
--> 100                               how='numpy', **kwargs)
    101     elif method == 'pyephem':
    102         ephem_df = pyephem(time, latitude, longitude, pressure, temperature,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/solarposition.py in spa_python(time, latitude, longitude, altitude, pressure, temperature, delta_t, atmos_refract, how, numthreads, **kwargs)
    324     app_zenith, zenith, app_elevation, elevation, azimuth, eot = spa.solar_position(
    325         unixtime, lat, lon, elev, pressure, temperature, delta_t,
--> 326         atmos_refract, numthreads)
    327 
    328     result = pd.DataFrame({'apparent_zenith': app_zenith, 'zenith': zenith,

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1149     result = do_calc(unixtime, lat, lon, elev, pressure,
   1150                      temp, delta_t, atmos_refract, numthreads,
-> 1151                      sst, esd)
   1152 
   1153     if not isinstance(result, np.ndarray):

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in solar_position_numpy(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)
   1058         return v, alpha, delta
   1059     m = sun_mean_longitude(jme)
-> 1060     eot = equation_of_time(m, alpha, delta_psi, epsilon)
   1061     H = local_hour_angle(v, lon, alpha)
   1062     xi = equatorial_horizontal_parallax(R)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/checkouts/pvsystem_tutport/pvlib/spa.py in equation_of_time(sun_mean_longitude, geocentric_sun_right_ascension, longitude_nutation, true_ecliptic_obliquity)
    892     less = E < -20
    893     other = (E <= 20) & (E >= -20)
--> 894     E = greater * (E - 1440) + less * (E + 1440) + other * E
    895     return E
    896 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _evaluate_numeric_binop(self, other)
   3458 
   3459                 from pandas.tseries.offsets import DateOffset
-> 3460                 other = self._validate_for_numeric_binop(other, op, opstr)
   3461 
   3462                 # handle time-based others

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/indexes/base.pyc in _validate_for_numeric_binop(self, other, op, opstr)
   3435             other = _values_from_object(other)
   3436             if other.dtype.kind not in ['f', 'i']:
-> 3437                 raise TypeError("cannot evaluate a numeric op "
   3438                                 "with a non-numeric dtype")
   3439         elif isinstance(other, (DateOffset, np.timedelta64,

TypeError: cannot evaluate a numeric op with a non-numeric dtype

In [41]: energies = pd.Series(energies)

# based on the parameters specified above, these are in W*hrs
In [42]: print(energies.round(0))
Series([], dtype: float64)

In [43]: energies.plot(kind='bar', rot=0)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-43-2f97a4fe1d4b> in <module>()
----> 1 energies.plot(kind='bar', rot=0)

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in __call__(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   3597                            colormap=colormap, table=table, yerr=yerr,
   3598                            xerr=xerr, label=label, secondary_y=secondary_y,
-> 3599                            **kwds)
   3600     __call__.__doc__ = plot_series.__doc__
   3601 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_series(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)
   2671                  yerr=yerr, xerr=xerr,
   2672                  label=label, secondary_y=secondary_y,
-> 2673                  **kwds)
   2674 
   2675 

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _plot(data, x, y, subplots, ax, kind, **kwds)
   2467         plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
   2468 
-> 2469     plot_obj.generate()
   2470     plot_obj.draw()
   2471     return plot_obj.result

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
   1039     def generate(self):
   1040         self._args_adjust()
-> 1041         self._compute_plot_data()
   1042         self._setup_subplots()
   1043         self._make_plot()

/home/docs/checkouts/readthedocs.org/user_builds/pvlib-python-dacoex/conda/pvsystem_tutport/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _compute_plot_data(self)
   1148         if is_empty:
   1149             raise TypeError('Empty {0!r}: no numeric data to '
-> 1150                             'plot'.format(numeric_data.__class__.__name__))
   1151 
   1152         self.data = numeric_data

TypeError: Empty 'DataFrame': no numeric data to plot

In [44]: plt.ylabel('Yearly energy yield (W hr)')
Out[44]: <matplotlib.text.Text at 0x7fef07dcd190>

User extensions

There are many other ways to organize PV modeling code. We encourage you to build on these paradigms and to share your experiences with the pvlib community via issues and pull requests.

Getting support

The best way to get support is to make an issue on our GitHub issues page .

How do I contribute?

We’re so glad you asked! Please see our wiki for information and instructions on how to contribute. We really appreciate it!

Credits

The pvlib-python community thanks Sandia National Lab for developing PVLIB Matlab and for supporting Rob Andrews of Calama Consulting to port the library to Python. Will Holmgren thanks the DOE EERE Postdoctoral Fellowship program for support. The pvlib-python maintainers thank all of pvlib’s contributors of issues and especially pull requests. The pvlib-python community thanks all of the maintainers and contributors to the PyData stack.