move modeling paradigms to intro examples

docs/sphinx/source/index.rst

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    :maxdepth: 1
 
    package_overview
+   introexamples
    whatsnew
    installation
    contributing

docs/sphinx/source/introexamples.rst

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+.. _introexamples:
+
+Intro Examples
+==============
+
+This page contains introductory examples of pvlib python usage.
+
+.. _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.
+
+.. ipython:: python
+
+    import pandas as pd
+    import matplotlib.pyplot as plt
+
+    naive_times = pd.DatetimeIndex(start='2015', end='2016', freq='1h')
+
+    # very approximate
+    # latitude, longitude, name, altitude, timezone
+    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')]
+
+    import pvlib
+
+    # get the module and inverter specifications from SAM
+    sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
+    sapm_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')
+    module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']
+    inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
+
+    # specify constant ambient air temp and wind for simplicity
+    temp_air = 20
+    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:
+
+.. ipython:: python
+
+    system = {'module': module, 'inverter': inverter,
+              'surface_azimuth': 180}
+
+    energies = {}
+
+    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.get_extra_radiation(times)
+        airmass = pvlib.atmosphere.get_relative_airmass(solpos['apparent_zenith'])
+        pressure = pvlib.atmosphere.alt2pres(altitude)
+        am_abs = pvlib.atmosphere.get_absolute_airmass(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.get_total_irradiance(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
+
+    energies = pd.Series(energies)
+
+    # based on the parameters specified above, these are in W*hrs
+    print(energies.round(0))
+
+    energies.plot(kind='bar', rot=0)
+    @savefig proc-energies.png width=6in
+    plt.ylabel('Yearly energy yield (W hr)')
+    @suppress
+    plt.close();
+
+
+.. _object-oriented:
+
+Object oriented (Location, PVSystem, ModelChain)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The first object oriented paradigm uses a model where a
+:py:class:`~pvlib.pvsystem.PVSystem` object represents an assembled
+collection of modules, inverters, etc., a
+:py:class:`~pvlib.location.Location` object represents a particular
+place on the planet, and a :py:class:`~pvlib.modelchain.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. pvlib-python
+also includes a :py:class:`~pvlib.tracking.SingleAxisTracker` class that
+is a subclass of :py:class:`~pvlib.pvsystem.PVSystem`.
+
+The following code demonstrates how to use
+:py:class:`~pvlib.location.Location`,
+:py:class:`~pvlib.pvsystem.PVSystem`, and
+:py:class:`~pvlib.modelchain.ModelChain` objects to accomplish our
+system modeling goal. ModelChain objects provide convenience methods
+that can provide default selections for models and can also fill
+necessary input data with modeled data. In our example below, we use
+convenience methods. For example, no irradiance data is provided as
+input, so the ModelChain object substitutes irradiance from a clear-sky
+model via the prepare_inputs method. Also, no irradiance transposition
+model is specified (keyword argument `transposition` for ModelChain) so
+the ModelChain defaults to the `haydavies` model. In this example,
+ModelChain infers the DC power model from the module provided by
+examining the parameters defined for module.
+
+.. ipython:: python
+
+    from pvlib.pvsystem import PVSystem
+    from pvlib.location import Location
+    from pvlib.modelchain import ModelChain
+
+    system = PVSystem(module_parameters=module,
+                      inverter_parameters=inverter)
+
+    energies = {}
+    for latitude, longitude, name, altitude, timezone in coordinates:
+        times = naive_times.tz_localize(timezone)
+        location = Location(latitude, longitude, name=name, altitude=altitude,
+                            tz=timezone)
+        # very experimental
+        mc = ModelChain(system, location,
+                        orientation_strategy='south_at_latitude_tilt')
+        # model results (ac, dc) and intermediates (aoi, temps, etc.)
+        # assigned as mc object attributes
+        mc.run_model(times)
+        annual_energy = mc.ac.sum()
+        energies[name] = annual_energy
+
+    energies = pd.Series(energies)
+
+    # based on the parameters specified above, these are in W*hrs
+    print(energies.round(0))
+
+    energies.plot(kind='bar', rot=0)
+    @savefig modelchain-energies.png width=6in
+    plt.ylabel('Yearly energy yield (W hr)')
+    @suppress
+    plt.close();
+
+
+Object oriented (LocalizedPVSystem)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The second object oriented paradigm uses a model where a
+:py:class:`~pvlib.pvsystem.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 :py:class:`~pvlib.pvsystem.LocalizedPVSystem` inherits from both
+:py:class:`~pvlib.pvsystem.PVSystem` and
+:py:class:`~pvlib.location.Location`, while the
+:py:class:`~pvlib.tracking.LocalizedSingleAxisTracker` inherits from
+:py:class:`~pvlib.tracking.SingleAxisTracker` (itself a subclass of
+:py:class:`~pvlib.pvsystem.PVSystem`) and
+:py:class:`~pvlib.location.Location`. The
+:py:class:`~pvlib.pvsystem.LocalizedPVSystem` and
+:py:class:`~pvlib.tracking.LocalizedSingleAxisTracker` classes may
+contain bugs due to the relative difficulty of implementing multiple
+inheritance. The :py:class:`~pvlib.pvsystem.LocalizedPVSystem` and
+:py:class:`~pvlib.tracking.LocalizedSingleAxisTracker` may be deprecated
+in a future release. We recommend that most modeling workflows implement
+:py:class:`~pvlib.location.Location`,
+:py:class:`~pvlib.pvsystem.PVSystem`, and
+:py:class:`~pvlib.modelchain.ModelChain`.
+
+The following code demonstrates how to use a
+:py:class:`~pvlib.pvsystem.LocalizedPVSystem` object to accomplish our
+modeling goal:
+
+.. ipython:: python
+
+    from pvlib.pvsystem import LocalizedPVSystem
+
+    energies = {}
+    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
+
+    energies = pd.Series(energies)
+
+    # based on the parameters specified above, these are in W*hrs
+    print(energies.round(0))
+
+    energies.plot(kind='bar', rot=0)
+    @savefig localized-pvsystem-energies.png width=6in
+    plt.ylabel('Yearly energy yield (W hr)')
+    @suppress
+    plt.close();