Remove redundant .keys() calls for dicts

Author: eumiro

pvlib/iam.py

@@ -633,7 +633,7 @@ def marion_diffuse(model, surface_tilt, **kwargs):
     try:
         iam_model = models[model]
     except KeyError:
-        raise ValueError('model must be one of: ' + str(list(models.keys())))
+        raise ValueError('model must be one of: ' + str(list(models)))
 
     iam_function = functools.partial(iam_model, **kwargs)
     iam = {}

pvlib/iotools/acis.py

@@ -69,7 +69,7 @@ def _get_acis(start, end, params, map_variables, url, **kwargs):
     if map_variables:
         df = df.rename(columns=VARIABLE_MAP)
 
-        for key in list(metadata.keys()):
+        for key in list(metadata):
             if key in VARIABLE_MAP:
                 metadata[VARIABLE_MAP[key]] = metadata.pop(key)
 

pvlib/iotools/bsrn.py

@@ -307,7 +307,7 @@ def parse_bsrn(fbuf, logical_records=('0100',)):
     dfs = []  # Initialize empty list for dataframe
 
     # Parse LR0100 - basic measurements including GHI, DNI, DHI and temperature
-    if ('0100' in lr_startrow.keys()) & ('0100' in logical_records):
+    if ('0100' in lr_startrow) & ('0100' in logical_records):
         fbuf.seek(0)  # reset buffer to start of file
         LR_0100 = pd.read_fwf(fbuf, skiprows=lr_startrow['0100'] + 1,
                               nrows=lr_nrows['0100'], header=None,
@@ -327,7 +327,7 @@ def parse_bsrn(fbuf, logical_records=('0100',)):
         dfs.append(LR_0100)
 
     # Parse LR0300 - other time series data, including upward and net radiation
-    if ('0300' in lr_startrow.keys()) & ('0300' in logical_records):
+    if ('0300' in lr_startrow) & ('0300' in logical_records):
         fbuf.seek(0)  # reset buffer to start of file
         LR_0300 = pd.read_fwf(fbuf, skiprows=lr_startrow['0300']+1,
                               nrows=lr_nrows['0300'], header=None,
@@ -340,7 +340,7 @@ def parse_bsrn(fbuf, logical_records=('0100',)):
         dfs.append(LR_0300)
 
     # Parse LR0500 - UV measurements
-    if ('0500' in lr_startrow.keys()) & ('0500' in logical_records):
+    if ('0500' in lr_startrow) & ('0500' in logical_records):
         fbuf.seek(0)  # reset buffer to start of file
         LR_0500 = pd.read_fwf(fbuf, skiprows=lr_startrow['0500']+1,
                               nrows=lr_nrows['0500'], na_values=[-99.9],

pvlib/iotools/sodapro.py

@@ -162,7 +162,7 @@ def get_cams(latitude, longitude, start, end, email, identifier='mcclear',
         time_step_str = TIME_STEPS_MAP[time_step]
     except KeyError:
         raise ValueError(f'Time step not recognized. Must be one of '
-                         f'{list(TIME_STEPS_MAP.keys())}')
+                         f'{list(TIME_STEPS_MAP)}')
 
     if (verbose) and ((time_step != '1min') or (time_ref != 'UT')):
         verbose = False
@@ -276,7 +276,7 @@ def parse_cams(fbuf, integrated=False, label=None, map_variables=True):
             metadata[line.split(': ')[0].lstrip('# ')] = line.split(': ')[1]
 
     # Convert latitude, longitude, and altitude values from strings to floats
-    for k_old in list(metadata.keys()):
+    for k_old in list(metadata):
         k_new = k_old.lstrip().split(' ')[0].lower()
         if k_new in ['latitude', 'longitude', 'altitude']:
             metadata[k_new] = float(metadata.pop(k_old))

pvlib/modelchain.py

@@ -720,7 +720,7 @@ def dc_model(self, model):
         # Set model and validate parameters
         if isinstance(model, str):
             model = model.lower()
-            if model in _DC_MODEL_PARAMS.keys():
+            if model in _DC_MODEL_PARAMS:
                 # validate module parameters
                 module_parameters = tuple(
                     array.module_parameters for array in self.system.arrays)
@@ -861,7 +861,7 @@ def ac_model(self, model):
 
     def infer_ac_model(self):
         """Infer AC power model from system attributes."""
-        inverter_params = set(self.system.inverter_parameters.keys())
+        inverter_params = set(self.system.inverter_parameters)
         if _snl_params(inverter_params):
             return self.sandia_inverter
         if _adr_params(inverter_params):
@@ -2034,11 +2034,9 @@ def _all_same_index(data):
 def _common_keys(dicts):
     """Return the intersection of the set of keys for each dictionary
     in `dicts`"""
-    def _keys(x):
-        return set(x.keys())
     if isinstance(dicts, tuple):
-        return set.intersection(*map(_keys, dicts))
-    return _keys(dicts)
+        return set.intersection(*map(set, dicts))
+    return set(dicts)
 
 
 def _tuple_from_dfs(dfs, name):

pvlib/pvsystem.py

@@ -699,8 +699,7 @@ def first_solar_spectral_loss(self, pw, airmass_absolute):
         pw = self._validate_per_array(pw, system_wide=True)
 
         def _spectral_correction(array, pw):
-            if 'first_solar_spectral_coefficients' in \
-                    array.module_parameters.keys():
+            if 'first_solar_spectral_coefficients' in array.module_parameters:
                 coefficients = \
                     array.module_parameters[
                         'first_solar_spectral_coefficients'
@@ -1037,10 +1036,10 @@ def _infer_cell_type(self):
                            'GaAs': None,
                            'a-Si / mono-Si': 'monosi'}
 
-        if 'Technology' in self.module_parameters.keys():
+        if 'Technology' in self.module_parameters:
             # CEC module parameter set
             cell_type = _cell_type_dict[self.module_parameters['Technology']]
-        elif 'Material' in self.module_parameters.keys():
+        elif 'Material' in self.module_parameters:
             # Sandia module parameter set
             cell_type = _cell_type_dict[self.module_parameters['Material']]
         else:

pvlib/tests/iotools/test_psm3.py

@@ -193,9 +193,9 @@ def test_get_psm3_attribute_mapping(nrel_api_key):
     expected_columns = [
         'Year', 'Month', 'Day', 'Hour', 'Minute', 'ghi', 'wind_speed']
     pd.testing.assert_index_equal(pd.Index(expected_columns), data.columns)
-    assert 'latitude' in meta.keys()
-    assert 'longitude' in meta.keys()
-    assert 'altitude' in meta.keys()
+    assert 'latitude' in meta
+    assert 'longitude' in meta
+    assert 'altitude' in meta
 
 
 @pytest.mark.remote_data

pvlib/tests/iotools/test_pvgis.py

@@ -378,7 +378,7 @@ def _compare_pvgis_tmy_json(expected, month_year_expected, inputs_expected,
                             meta_expected, pvgis_data):
     data, months_selected, inputs, meta = pvgis_data
     # check each column of output separately
-    for outvar in meta_expected['outputs']['tmy_hourly']['variables'].keys():
+    for outvar in meta_expected['outputs']['tmy_hourly']['variables']:
         assert np.allclose(data[outvar], expected[outvar])
     assert np.allclose(
         [_['month'] for _ in months_selected], np.arange(1, 13, 1))
@@ -430,7 +430,7 @@ def test_get_pvgis_tmy_basic(expected, meta_expected):
 def _compare_pvgis_tmy_basic(expected, meta_expected, pvgis_data):
     data, _, _, _ = pvgis_data
     # check each column of output separately
-    for outvar in meta_expected['outputs']['tmy_hourly']['variables'].keys():
+    for outvar in meta_expected['outputs']['tmy_hourly']['variables']:
         assert np.allclose(data[outvar], expected[outvar])
 
 
@@ -447,7 +447,7 @@ def _compare_pvgis_tmy_csv(expected, month_year_expected, inputs_expected,
                            meta_expected, csv_meta, pvgis_data):
     data, months_selected, inputs, meta = pvgis_data
     # check each column of output separately
-    for outvar in meta_expected['outputs']['tmy_hourly']['variables'].keys():
+    for outvar in meta_expected['outputs']['tmy_hourly']['variables']:
         assert np.allclose(data[outvar], expected[outvar])
     assert np.allclose(
         [_['month'] for _ in months_selected], np.arange(1, 13, 1))

pvlib/tests/ivtools/test_sdm.py

@@ -112,15 +112,15 @@ def test_fit_desoto_sandia(cec_params_cansol_cs5p_220p):
     sim_ivcurves.update(v=v.T, i=i.T, ee=ee, tc=tc)
 
     result = sdm.fit_desoto_sandia(sim_ivcurves, specs)
-    modeled = pd.Series(index=params.keys(), data=np.nan)
+    modeled = pd.Series(index=params, data=np.nan)
     modeled['a_ref'] = result['a_ref']
     modeled['I_L_ref'] = result['I_L_ref']
     modeled['I_o_ref'] = result['I_o_ref']
     modeled['R_s'] = result['R_s']
     modeled['R_sh_ref'] = result['R_sh_ref']
     expected = pd.Series(params)
-    assert np.allclose(modeled[params.keys()].values,
-                       expected[params.keys()].values, rtol=5e-2)
+    assert np.allclose(modeled[list(params)].values,
+                       expected[list(params)].values, rtol=5e-2)
 
 
 def _read_iv_curves_for_test(datafile, npts):
@@ -261,7 +261,7 @@ def test_fit_pvsyst_sandia(npts=3000):
     assert np.allclose(
         ivcurves['v_oc'], iv_res['v_oc'], equal_nan=True, rtol=0.019)
     # cells_in_series, alpha_sc, beta_voc, descr
-    assert all((iv_specs[k] == pvsyst_specs[k]) for k in iv_specs.keys())
+    assert all(v == pvsyst_specs[k] for k, v in iv_specs.items())
     # I_L_ref, I_o_ref, EgRef, R_sh_ref, R_sh_0, R_sh_exp, R_s, gamma_ref,
     # mu_gamma
     assert np.isclose(modeled['I_L_ref'], pvsyst['I_L_ref'], rtol=6.5e-5)

pvlib/tests/test_irradiance.py

@@ -467,9 +467,8 @@ def test_get_total_irradiance_scalars(model):
         model=model,
         surface_type='urban')
 
-    assert list(total.keys()) == ['poa_global', 'poa_direct',
-                                  'poa_diffuse', 'poa_sky_diffuse',
-                                  'poa_ground_diffuse']
+    assert list(total) == ['poa_global', 'poa_direct', 'poa_diffuse',
+                           'poa_sky_diffuse', 'poa_ground_diffuse']
     # test that none of the values are nan
     assert np.isnan(np.array(list(total.values()))).sum() == 0
 
@@ -493,9 +492,8 @@ def test_get_total_irradiance_missing_airmass():
         dhi=100,
         dni_extra=1400,
         model='perez')
-    assert list(total.keys()) == ['poa_global', 'poa_direct',
-                                  'poa_diffuse', 'poa_sky_diffuse',
-                                  'poa_ground_diffuse']
+    assert list(total) == ['poa_global', 'poa_direct', 'poa_diffuse',
+                           'poa_sky_diffuse', 'poa_ground_diffuse']
 
 
 def test_poa_components(irrad_data, ephem_data, dni_et, relative_airmass):