style fixes

pvlib/atmosphere.py

@@ -317,8 +317,8 @@ def gueymard94_pw(temp_air, relative_humidity):
        1294-1300.
     """
 
-    T = temp_air + 273.15  # Convert to Kelvin
-    RH = relative_humidity
+    T = temp_air + 273.15  # Convert to Kelvin                  # noqa: N806
+    RH = relative_humidity                                      # noqa: N806
 
     theta = T / 273.15
 
@@ -473,7 +473,7 @@ def first_solar_spectral_correction(pw, airmass_absolute, module_type=None,
     _coefficients['cigs'] = (
         0.85252, -0.022314, -0.0047216, 0.13666, 0.013342, -0.0008945)
     _coefficients['asi'] = (
-        1.12094, -0.047620, -0.0083627, -0.10443, 0.098382,-0.0033818)
+        1.12094, -0.047620, -0.0083627, -0.10443, 0.098382, -0.0033818)
 
     if module_type is not None and coefficients is None:
         coefficients = _coefficients[module_type.lower()]

pvlib/clearsky.py

@@ -12,7 +12,7 @@
 import numpy as np
 import pandas as pd
 
-from pvlib import tools, atmosphere, solarposition, irradiance
+from pvlib import atmosphere, tools
 
 
 def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
@@ -204,7 +204,8 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
 
     lt_h5_file = tables.open_file(filepath)
     try:
-        lts = lt_h5_file.root.LinkeTurbidity[latitude_index, longitude_index, :]
+        lts = lt_h5_file.root.LinkeTurbidity[latitude_index,
+                                             longitude_index, :]
     except IndexError:
         raise IndexError('Latitude should be between 90 and -90, '
                          'longitude between -180 and 180.')
@@ -364,8 +365,9 @@ def haurwitz(apparent_zenith):
 
     cos_zenith = tools.cosd(apparent_zenith.values)
     clearsky_ghi = np.zeros_like(apparent_zenith.values)
-    clearsky_ghi[cos_zenith>0] = 1098.0 * cos_zenith[cos_zenith>0] * \
-                                    np.exp(-0.059/cos_zenith[cos_zenith>0])
+    cos_zen_gte_0 = cos_zenith > 0
+    clearsky_ghi[cos_zen_gte_0] = (1098.0 * cos_zenith[cos_zen_gte_0] *
+                                   np.exp(-0.059/cos_zenith[cos_zen_gte_0]))
 
     df_out = pd.DataFrame(index=apparent_zenith.index,
                           data=clearsky_ghi,
@@ -675,14 +677,14 @@ def detect_clearsky(measured, clearsky, times, window_length,
     if len(unique_deltas) == 1:
         sample_interval = unique_deltas[0]
     else:
-        raise NotImplementedError('algorithm does not yet support unequal ' \
+        raise NotImplementedError('algorithm does not yet support unequal '
                                   'times. consider resampling your data.')
 
     samples_per_window = int(window_length / sample_interval)
 
     # generate matrix of integers for creating windows with indexing
     from scipy.linalg import hankel
-    H = hankel(np.arange(samples_per_window),
+    H = hankel(np.arange(samples_per_window),                   # noqa: N806
                np.arange(samples_per_window-1, len(times)))
 
     # calculate measurement statistics
@@ -729,14 +731,16 @@ def detect_clearsky(measured, clearsky, times, window_length,
         previous_alpha = alpha
         clear_meas = measured[clear_samples]
         clear_clear = clearsky[clear_samples]
+
         def rmse(alpha):
             return np.sqrt(np.mean((clear_meas - alpha*clear_clear)**2))
+
         alpha = minimize_scalar(rmse).x
         if round(alpha*10000) == round(previous_alpha*10000):
             break
     else:
         import warnings
-        warnings.warn('failed to converge after %s iterations' \
+        warnings.warn('failed to converge after %s iterations'
                       % max_iterations, RuntimeWarning)
 
     # be polite about returning the same type as was input
@@ -765,17 +769,18 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
 
     Based on NREL Excel implementation by Daryl R. Myers [1, 2].
 
-    Bird and Hulstrom define the zenith as the "angle between a line to the sun
-    and the local zenith". There is no distinction in the paper between solar
-    zenith and apparent (or refracted) zenith, but the relative airmass is
-    defined using the Kasten 1966 expression, which requires apparent zenith.
-    Although the formulation for calculated zenith is never explicitly defined
-    in the report, since the purpose was to compare existing clear sky models
-    with "rigorous radiative transfer models" (RTM) it is possible that apparent
-    zenith was obtained as output from the RTM. However, the implentation
-    presented in PVLIB is tested against the NREL Excel implementation by Daryl
-    Myers which uses an analytical expression for solar zenith instead of
-    apparent zenith.
+    Bird and Hulstrom define the zenith as the "angle between a line to
+    the sun and the local zenith". There is no distinction in the paper
+    between solar zenith and apparent (or refracted) zenith, but the
+    relative airmass is defined using the Kasten 1966 expression, which
+    requires apparent zenith. Although the formulation for calculated
+    zenith is never explicitly defined in the report, since the purpose
+    was to compare existing clear sky models with "rigorous radiative
+    transfer models" (RTM) it is possible that apparent zenith was
+    obtained as output from the RTM. However, the implentation presented
+    in PVLIB is tested against the NREL Excel implementation by Daryl
+    Myers which uses an analytical expression for solar zenith instead
+    of apparent zenith.
 
     Parameters
     ----------
@@ -824,7 +829,8 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
 
     `SERI/TR-642-761 <http://rredc.nrel.gov/solar/pubs/pdfs/tr-642-761.pdf>`_
 
-    `Error Reports <http://rredc.nrel.gov/solar/models/clearsky/error_reports.html>`_
+    `Error Reports
+     <http://rredc.nrel.gov/solar/models/clearsky/error_reports.html>`_
     """
     etr = dni_extra  # extraradiation
     ze_rad = np.deg2rad(zenith)  # zenith in radians

pvlib/forecast.py

@@ -574,19 +574,19 @@ def isobaric_to_ambient_temperature(self, data):
             Temperature in K
         """
 
-        P = data['pressure'] / 100.0
-        Tiso = data['temperature_iso']
-        Td = data['temperature_dew_iso'] - 273.15
+        P = data['pressure'] / 100.0                            # noqa: N806
+        Tiso = data['temperature_iso']                          # noqa: N806
+        Td = data['temperature_dew_iso'] - 273.15               # noqa: N806
 
         # saturation water vapor pressure
         e = 6.11 * 10**((7.5 * Td) / (Td + 273.3))
 
         # saturation water vapor mixing ratio
         w = 0.622 * (e / (P - e))
 
-        T = Tiso - ((2.501 * 10.**6) / 1005.7) * w
+        temperature = Tiso - ((2.501 * 10.**6) / 1005.7) * w
 
-        return T
+        return temperature
 
     def uv_to_speed(self, data):
         """
@@ -670,13 +670,19 @@ def __init__(self, resolution='half', set_type='best'):
             'wind_speed_gust': 'Wind_speed_gust_surface',
             'wind_speed_u': 'u-component_of_wind_isobaric',
             'wind_speed_v': 'v-component_of_wind_isobaric',
-            'total_clouds': 'Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average',
-            'low_clouds': 'Total_cloud_cover_low_cloud_Mixed_intervals_Average',
-            'mid_clouds': 'Total_cloud_cover_middle_cloud_Mixed_intervals_Average',
-            'high_clouds': 'Total_cloud_cover_high_cloud_Mixed_intervals_Average',
-            'boundary_clouds': 'Total_cloud_cover_boundary_layer_cloud_Mixed_intervals_Average',
+            'total_clouds':
+                'Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average',
+            'low_clouds':
+                'Total_cloud_cover_low_cloud_Mixed_intervals_Average',
+            'mid_clouds':
+                'Total_cloud_cover_middle_cloud_Mixed_intervals_Average',
+            'high_clouds':
+                'Total_cloud_cover_high_cloud_Mixed_intervals_Average',
+            'boundary_clouds': ('Total_cloud_cover_boundary_layer_cloud_'
+                                'Mixed_intervals_Average'),
             'convect_clouds': 'Total_cloud_cover_convective_cloud',
-            'ghi_raw': 'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average', }
+            'ghi_raw': ('Downward_Short-Wave_Radiation_Flux_'
+                        'surface_Mixed_intervals_Average')}
 
         self.output_variables = [
             'temp_air',
@@ -716,7 +722,7 @@ def process_data(self, data, cloud_cover='total_clouds', **kwargs):
         return data[self.output_variables]
 
 
-class HRRR_ESRL(ForecastModel):
+class HRRR_ESRL(ForecastModel):                                 # noqa: N801
     """
     Subclass of the ForecastModel class representing
     NOAA/GSD/ESRL's HRRR forecast model.
@@ -925,7 +931,8 @@ def __init__(self, set_type='best'):
             'low_clouds': 'Low_cloud_cover_low_cloud',
             'mid_clouds': 'Medium_cloud_cover_middle_cloud',
             'high_clouds': 'High_cloud_cover_high_cloud',
-            'condensation_height': 'Geopotential_height_adiabatic_condensation_lifted'}
+            'condensation_height':
+                'Geopotential_height_adiabatic_condensation_lifted'}
 
         self.output_variables = [
             'temp_air',

pvlib/irradiance.py

@@ -125,7 +125,7 @@ def _handle_extra_radiation_types(datetime_or_doy, epoch_year):
     # a better way to do it.
     if isinstance(datetime_or_doy, pd.DatetimeIndex):
         to_doy = tools._pandas_to_doy  # won't be evaluated unless necessary
-        to_datetimeindex = lambda x: datetime_or_doy
+        def to_datetimeindex(x): return x                       # noqa: E306
         to_output = partial(pd.Series, index=datetime_or_doy)
     elif isinstance(datetime_or_doy, pd.Timestamp):
         to_doy = tools._pandas_to_doy
@@ -139,12 +139,12 @@ def _handle_extra_radiation_types(datetime_or_doy, epoch_year):
             tools._datetimelike_scalar_to_datetimeindex
         to_output = tools._scalar_out
     elif np.isscalar(datetime_or_doy):  # ints and floats of various types
-        to_doy = lambda x: datetime_or_doy
+        def to_doy(x): return x                                 # noqa: E306
         to_datetimeindex = partial(tools._doy_to_datetimeindex,
                                    epoch_year=epoch_year)
         to_output = tools._scalar_out
     else:  # assume that we have an array-like object of doy
-        to_doy = lambda x: datetime_or_doy
+        def to_doy(x): return x                                 # noqa: E306
         to_datetimeindex = partial(tools._doy_to_datetimeindex,
                                    epoch_year=epoch_year)
         to_output = tools._array_out
@@ -1972,7 +1972,7 @@ def _gti_dirint_lt_90(poa_global, aoi, aoi_lt_90, solar_zenith, solar_azimuth,
         # we are here because we ran out of coeffs to loop over and
         # therefore we have exceeded max_iterations
         import warnings
-        failed_points = best_diff[aoi_lt_90][best_diff_lte_1_lt_90 == False]
+        failed_points = best_diff[aoi_lt_90][~best_diff_lte_1_lt_90]
         warnings.warn(
             ('%s points failed to converge after %s iterations. best_diff:\n%s'
              % (len(failed_points), max_iterations, failed_points)),

pvlib/modelchain.py

@@ -116,8 +116,6 @@ def basic_chain(times, latitude, longitude,
         raise ValueError('orientation_strategy or surface_tilt and '
                          'surface_azimuth must be provided')
 
-    times = times
-
     if altitude is None and pressure is None:
         altitude = 0.
         pressure = 101325.
@@ -126,12 +124,9 @@ def basic_chain(times, latitude, longitude,
     elif pressure is None:
         pressure = atmosphere.alt2pres(altitude)
 
-    solar_position = solarposition.get_solarposition(times, latitude,
-                                                     longitude,
-                                                     altitude=altitude,
-                                                     pressure=pressure,
-                                                     method=solar_position_method,
-                                                     **kwargs)
+    solar_position = solarposition.get_solarposition(
+        times, latitude, longitude, altitude=altitude, pressure=pressure,
+        method=solar_position_method, **kwargs)
 
     # possible error with using apparent zenith with some models
     airmass = atmosphere.get_relative_airmass(
@@ -372,12 +367,11 @@ def dc_model(self, model):
             if model in DC_MODEL_PARAMS.keys():
                 # validate module parameters
                 missing_params = DC_MODEL_PARAMS[model] - \
-                                        set(self.system.module_parameters.keys())
-                if missing_params: # some parameters are not in module.keys()
+                                 set(self.system.module_parameters.keys())
+                if missing_params:  # some parameters are not in module.keys()
                     raise ValueError(model + ' selected for the DC model but '
-                                         'one or more required parameters '
-                                         'are missing : ' +
-                                         str(missing_params))
+                                     'one or more required parameters are '
+                                     'missing : ' + str(missing_params))
                 if model == 'sapm':
                     self._dc_model = self.sapm
                 elif model == 'desoto':

pvlib/pvsystem.py

@@ -6,8 +6,8 @@
 from __future__ import division
 
 from collections import OrderedDict
-import os
 import io
+import os
 try:
     from urllib2 import urlopen
 except ImportError:
@@ -16,10 +16,9 @@
 import numpy as np
 import pandas as pd
 
-from pvlib import tools
+from pvlib import atmosphere, irradiance, tools, singlediode as _singlediode
 from pvlib.tools import _build_kwargs
 from pvlib.location import Location
-from pvlib import irradiance, atmosphere, singlediode as _singlediode
 
 
 # a dict of required parameter names for each DC power model
@@ -333,7 +332,7 @@ def calcparams_desoto(self, effective_irradiance, temp_cell, **kwargs):
         kwargs = _build_kwargs(['a_ref', 'I_L_ref', 'I_o_ref', 'R_sh_ref',
                                 'R_s', 'alpha_sc', 'EgRef', 'dEgdT',
                                 'irrad_ref', 'temp_ref'],
-                                self.module_parameters)
+                               self.module_parameters)
 
         return calcparams_desoto(effective_irradiance, temp_cell, **kwargs)
 
@@ -361,7 +360,7 @@ def calcparams_pvsyst(self, effective_irradiance, temp_cell):
                                 'R_s', 'alpha_sc', 'EgRef',
                                 'irrad_ref', 'temp_ref',
                                 'cells_in_series'],
-                                self.module_parameters)
+                               self.module_parameters)
 
         return calcparams_pvsyst(effective_irradiance, temp_cell, **kwargs)
 
@@ -511,7 +510,7 @@ def first_solar_spectral_loss(self, pw, airmass_absolute):
         """
 
         if 'first_solar_spectral_coefficients' in \
-                               self.module_parameters.keys():
+                self.module_parameters.keys():
             coefficients = \
                    self.module_parameters['first_solar_spectral_coefficients']
             module_type = None
@@ -552,7 +551,6 @@ def _infer_cell_type(self):
                            'mc-Si': 'multisi',
                            'c-Si': 'multisi',
                            'Si-Film': 'asi',
-                           'CdTe': 'cdte',
                            'EFG mc-Si': 'multisi',
                            'GaAs': None,
                            'a-Si / mono-Si': 'monosi'}
@@ -1133,7 +1131,7 @@ def calcparams_desoto(effective_irradiance, temp_cell,
          * EgRef = 1.121
          * dEgdT = -0.0002677
 
-         >>> M = np.polyval([-1.26E-4, 2.816E-3, -0.024459, 0.086257, 0.918093],
+         >>> M = np.polyval([-1.26E-4, 2.816E-3, -0.024459, 0.086257, 0.9181],
          ...                AMa) # doctest: +SKIP
 
          Source: [1]
@@ -1210,7 +1208,7 @@ def calcparams_desoto(effective_irradiance, temp_cell,
     # equivalent to the product of S (irradiance reaching a module's cells) *
     # M (spectral adjustment factor) as described in [1].
     IL = effective_irradiance / irrad_ref * \
-              (I_L_ref + alpha_sc * (Tcell_K - Tref_K))
+        (I_L_ref + alpha_sc * (Tcell_K - Tref_K))
     I0 = (I_o_ref * ((Tcell_K / Tref_K) ** 3) *
           (np.exp(EgRef / (k*(Tref_K)) - (E_g / (k*(Tcell_K))))))
     # Note that the equation for Rsh differs from [1]. In [1] Rsh is given as
@@ -1346,16 +1344,17 @@ def calcparams_pvsyst(effective_irradiance, temp_cell,
     nNsVth = gamma * k / q * cells_in_series * Tcell_K
 
     IL = effective_irradiance / irrad_ref * \
-              (I_L_ref + alpha_sc * (Tcell_K - Tref_K))
+        (I_L_ref + alpha_sc * (Tcell_K - Tref_K))
 
     I0 = I_o_ref * ((Tcell_K / Tref_K) ** 3) * \
-          (np.exp((q * EgRef) / (k * gamma) * (1 / Tref_K - 1 / Tcell_K)))
+        (np.exp((q * EgRef) / (k * gamma) * (1 / Tref_K - 1 / Tcell_K)))
 
-    Rsh_tmp = (R_sh_ref - R_sh_0 * np.exp(-R_sh_exp)) / (1.0 - np.exp(-R_sh_exp))
+    Rsh_tmp = \
+        (R_sh_ref - R_sh_0 * np.exp(-R_sh_exp)) / (1.0 - np.exp(-R_sh_exp))
     Rsh_base = np.maximum(0.0, Rsh_tmp)
 
     Rsh = Rsh_base + (R_sh_0 - Rsh_base) * \
-              np.exp(-R_sh_exp * effective_irradiance / irrad_ref)
+        np.exp(-R_sh_exp * effective_irradiance / irrad_ref)
 
     Rs = R_s
 
@@ -1723,8 +1722,7 @@ def sapm_celltemp(poa_global, wind_speed, temp_air,
 
     if isinstance(model, str):
         model = temp_models[model.lower()]
-    elif isinstance(model, list):
-        model = model
+
     elif isinstance(model, (dict, pd.Series)):
         model = [model['a'], model['b'], model['deltaT']]