Replace all instances of OrderedDict with dict

docs/sphinx/source/contributing/style_guide.rst

@@ -58,7 +58,7 @@ allows for multiple input types to work for many parameters. pvlib uses
 the following generic descriptors as short-hand to indicate which
 specific types may be used:
 
-* dict-like : dict, OrderedDict, pd.Series
+* dict-like : dict, pd.Series
 * numeric : scalar, np.array, pd.Series. Typically int or float dtype.
 * array-like : np.array, pd.Series. Typically int or float dtype.
 

docs/sphinx/source/user_guide/modeling_topics/clearsky.rst

@@ -297,7 +297,7 @@ A clear sky time series using only basic pvlib functions.
 
 
 The input data types determine the returned output type. Array input
-results in an OrderedDict of array output, and Series input results in a
+results in an dict of array output, and Series input results in a
 DataFrame output. The keys are 'ghi', 'dni', and 'dhi'.
 
 Grid with a clear sky irradiance for a few turbidity values.
@@ -437,7 +437,7 @@ A clear sky time series using only basic pvlib functions.
     In [1]: plt.close();
 
 The input data types determine the returned output type. Array input
-results in an OrderedDict of array output, and Series input results in a
+results in an dict of array output, and Series input results in a
 DataFrame output. The keys are 'ghi', 'dni', and 'dhi'.
 
 Irradiance as a function of solar elevation.
@@ -518,7 +518,7 @@ Contour plots of irradiance as a function of both PW and AOD.
 
     In [1]: aod700, precipitable_water = np.meshgrid(aod700, precipitable_water)
 
-    # inputs are arrays, so solis is an OrderedDict
+    # inputs are arrays, so solis is an dict
     In [1]: solis = clearsky.simplified_solis(apparent_elevation, aod700,
        ...:                                   precipitable_water, pressure,
        ...:                                   dni_extra)

docs/sphinx/source/whatsnew/v0.16.0.rst

@@ -34,6 +34,25 @@ Breaking Changes
   and :py:func:`pvlib.iam.schlick_diffuse` from tuple to ``dict``, to be
   consistent with :py:func:`pvlib.iam.marion_diffuse`. (:issue:`2837`,
   :pull:`2842`)
+* All ``OrderedDict`` outputs are now ``dict``.  The following functions
+  are affected (:issue:`1684`, :pull:`2856`):
+
+  * :py:func:`~pvlib.clearsky.ineichen`
+  * :py:func:`~pvlib.clearsky.simplified_solis`
+  * :py:func:`~pvlib.clearsky.detect_clearsky`
+  * :py:func:`~pvlib.clearsky.bird`
+  * :py:func:`~pvlib.irradiance.disc`
+  * :py:func:`~pvlib.irradiance.gti_dirint`
+  * :py:func:`~pvlib.irradiance.erbs`
+  * :py:func:`~pvlib.irradiance.erbs_driesse`
+  * :py:func:`~pvlib.irradiance.orgill_hollands`
+  * :py:func:`~pvlib.irradiance.boland`
+  * :py:func:`~pvlib.irradiance.campbell_norman`
+  * :py:func:`~pvlib.irradiance.louche`
+  * :py:func:`~pvlib.pvsystem.sapm`
+  * :py:func:`~pvlib.pvsystem.max_power_point`
+
+
 
 Deprecations
 ~~~~~~~~~~~~

pvlib/clearsky.py

@@ -4,7 +4,6 @@
 """
 
 import os
-from collections import OrderedDict
 import calendar
 
 import numpy as np
@@ -56,7 +55,7 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
 
     Returns
     -------
-    clearsky : DataFrame (if Series input) or OrderedDict of arrays
+    clearsky : DataFrame (if Series input) or dict of arrays
         Contains the columns/keys ``'dhi', 'dni', 'ghi'``, with the same
         unit as the input parameter ``dni_extra``.
 
@@ -138,7 +137,7 @@ def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
 
     dhi = ghi - dni*cos_zenith
 
-    irrads = OrderedDict()
+    irrads = {}
     irrads['ghi'] = ghi
     irrads['dni'] = dni
     irrads['dhi'] = dhi
@@ -363,8 +362,8 @@ def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.,
 
     Returns
     -------
-    clearsky : DataFrame (if Series input) or OrderedDict of arrays
-        DataFrame/OrderedDict contains the columns/keys
+    clearsky : DataFrame (if Series input) or dict of arrays
+        DataFrame/dict contains the columns/keys
         ``'dhi', 'dni', 'ghi'``.
 
     References
@@ -408,7 +407,7 @@ def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.,
     ghi = i0p * np.exp(-taug/sin_elev**g) * sin_elev
     dhi = i0p * np.exp(-taud/sin_elev**d)
 
-    irrads = OrderedDict()
+    irrads = {}
     irrads['ghi'] = ghi
     irrads['dni'] = dni
     irrads['dhi'] = dhi
@@ -737,7 +736,7 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
         Boolean array or Series of whether or not the given time is
         clear. Return type is the same as the input type.
 
-    components : OrderedDict, optional
+    components : dict, optional
         Dict of arrays of whether or not the given time window is clear
         for each condition. Only provided if ``return_components`` is True.
 
@@ -905,7 +904,7 @@ def detect_clearsky(measured, clearsky, times=None, infer_limits=False,
         clear_samples = pd.Series(clear_samples, index=times)
 
     if return_components:
-        components = OrderedDict()
+        components = {}
         components['mean_diff_flag'] = c1
         components['max_diff_flag'] = c2
         components['line_length_flag'] = c3
@@ -971,8 +970,8 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
 
     Returns
     -------
-    clearsky : DataFrame (if Series input) or OrderedDict of arrays
-        DataFrame/OrderedDict contains the columns/keys
+    clearsky : DataFrame (if Series input) or dict of arrays
+        DataFrame/dict contains the columns/keys
         ``'dhi', 'dni', 'ghi', 'direct_horizontal'`` in  [W/m^2].
 
     See also
@@ -1039,8 +1038,8 @@ def bird(zenith, airmass_relative, aod380, aod500, precipitable_water,
     gh = (id_nh + ias) / (1.0 - albedo * rs)
     diffuse_horiz = gh - id_nh
     # TODO: be DRY, use decorator to wrap methods that need to return either
-    # OrderedDict or DataFrame instead of repeating this boilerplate code
-    irrads = OrderedDict()
+    # dict or DataFrame instead of repeating this boilerplate code
+    irrads = {}
     irrads['direct_horizontal'] = id_nh
     irrads['ghi'] = gh
     irrads['dni'] = id_

pvlib/irradiance.py

@@ -5,7 +5,6 @@
 """
 
 import datetime
-from collections import OrderedDict
 from functools import partial
 
 import numpy as np
@@ -1958,7 +1957,7 @@ def disc(ghi, solar_zenith, datetime_or_doy, pressure=101325,
 
     Returns
     -------
-    output : OrderedDict or DataFrame
+    output : dict or DataFrame
         Contains the following keys:
 
         * ``dni``: The modeled direct normal irradiance
@@ -2000,7 +1999,7 @@ def disc(ghi, solar_zenith, datetime_or_doy, pressure=101325,
     bad_values = (solar_zenith > max_zenith) | (ghi < 0) | (dni < 0)
     dni = np.where(bad_values, 0, dni)
 
-    output = OrderedDict()
+    output = {}
     output['dni'] = dni
     output['kt'] = kt
     output['airmass'] = am
@@ -2479,7 +2478,7 @@ def gti_dirint(poa_global, aoi, solar_zenith, solar_azimuth, times,
 
     Returns
     -------
-    data : DataFrame
+    data : dict or DataFrame
         Contains the following keys/columns:
 
         * ``ghi``: the modeled global horizontal irradiance. [Wm⁻²]
@@ -2517,7 +2516,7 @@ def gti_dirint(poa_global, aoi, solar_zenith, solar_azimuth, times,
         ghi_gte_90, dni_gte_90, dhi_gte_90 = np.nan, np.nan, np.nan
 
     # put the AOI < 90 and AOI >= 90 conditions together
-    output = OrderedDict()
+    output = {}
     output['ghi'] = ghi.where(aoi_lt_90, ghi_gte_90)
     output['dni'] = dni.where(aoi_lt_90, dni_gte_90)
     output['dhi'] = dhi.where(aoi_lt_90, dhi_gte_90)
@@ -2776,7 +2775,7 @@ def erbs(ghi, zenith, datetime_or_doy, min_cos_zenith=0.065, max_zenith=87):
 
     Returns
     -------
-    data : OrderedDict or DataFrame
+    data : dict or DataFrame
         Contains the following keys/columns:
 
         * ``dni``: the modeled direct normal irradiance. [Wm⁻²]
@@ -2823,7 +2822,7 @@ def erbs(ghi, zenith, datetime_or_doy, min_cos_zenith=0.065, max_zenith=87):
     # ensure that closure relationship remains valid
     dhi = np.where(bad_values, ghi, dhi)
 
-    data = OrderedDict()
+    data = {}
     data['dni'] = dni
     data['dhi'] = dhi
     data['kt'] = kt
@@ -2885,7 +2884,7 @@ def erbs_driesse(ghi, zenith, datetime_or_doy=None, dni_extra=None,
 
     Returns
     -------
-    data : OrderedDict or DataFrame
+    data : dict or DataFrame
         Contains the following keys/columns:
 
         * ``dni``: the modeled direct normal irradiance. [Wm⁻²]
@@ -2960,7 +2959,7 @@ def erbs_driesse(ghi, zenith, datetime_or_doy=None, dni_extra=None,
     # ensure that closure relationship remains valid
     dhi = np.where(bad_values, ghi, dhi)
 
-    data = OrderedDict()
+    data = {}
     data['dni'] = dni
     data['dhi'] = dhi
     data['kt'] = kt
@@ -3012,7 +3011,7 @@ def orgill_hollands(ghi, zenith, datetime_or_doy, dni_extra=None,
 
     Returns
     -------
-    data : OrderedDict or DataFrame
+    data : dict or DataFrame
         Contains the following keys/columns:
 
         * ``dni``: the modeled direct normal irradiance. [Wm⁻²]
@@ -3058,7 +3057,7 @@ def orgill_hollands(ghi, zenith, datetime_or_doy, dni_extra=None,
     # ensure that closure relationship remains valid
     dhi = np.where(bad_values, ghi, dhi)
 
-    data = OrderedDict()
+    data = {}
     data['dni'] = dni
     data['dhi'] = dhi
     data['kt'] = kt
@@ -3113,7 +3112,7 @@ def boland(ghi, solar_zenith, datetime_or_doy, a_coeff=8.645, b_coeff=0.613,
 
     Returns
     -------
-    data : OrderedDict or DataFrame
+    data : dict or DataFrame
         Contains the following keys/columns:
 
         * ``dni``: the modeled direct normal irradiance. [Wm⁻²]
@@ -3169,7 +3168,7 @@ def boland(ghi, solar_zenith, datetime_or_doy, a_coeff=8.645, b_coeff=0.613,
     # ensure that closure relationship remains valid
     dhi = np.where(bad_values, ghi, dhi)
 
-    data = OrderedDict()
+    data = {}
     data['dni'] = dni
     data['dhi'] = dhi
     data['kt'] = kt
@@ -3204,7 +3203,7 @@ def campbell_norman(zenith, transmittance, pressure=101325.0,
 
     Returns
     -------
-    irradiance: DataFrame
+    irradiance: dict or DataFrame
         Modeled direct normal irradiance, direct horizontal irradiance,
         and global horizontal irradiance. [Wm⁻²]
 
@@ -3223,7 +3222,7 @@ def campbell_norman(zenith, transmittance, pressure=101325.0,
     dhi = 0.3 * (1.0 - tau**airmass) * dni_extra * cos_zen
     ghi = dhi + dni * cos_zen
 
-    irrads = OrderedDict()
+    irrads = {}
     irrads['ghi'] = ghi
     irrads['dni'] = dni
     irrads['dhi'] = dhi
@@ -3902,7 +3901,7 @@ def louche(ghi, solar_zenith, datetime_or_doy, max_zenith=90):
 
     Returns
     -------
-    data: OrderedDict or DataFrame
+    data: dict or DataFrame
         Contains the following keys/columns:
 
         * ``dni``: the modeled direct normal irradiance, see :term:`dni`.
@@ -3934,7 +3933,7 @@ def louche(ghi, solar_zenith, datetime_or_doy, max_zenith=90):
     # ensure that closure relationship remains valid
     dhi = np.where(bad_values, ghi, dhi)
 
-    data = OrderedDict()
+    data = {}
     data['dni'] = dni
     data['dhi'] = dhi
     data['kt'] = Kt

pvlib/pvsystem.py

@@ -3,7 +3,6 @@
 performance of PV modules and inverters.
 """
 
-from collections import OrderedDict
 import functools
 import io
 import itertools
@@ -2262,7 +2261,7 @@ def sapm(effective_irradiance, temp_cell, module, *, temperature_ref=25,
 
     Returns
     -------
-    A DataFrame with the columns:
+    A dict or DataFrame with the columns:
 
         * i_sc : Short-circuit current (A)
         * i_mp : Current at the maximum-power point (A)
@@ -2369,7 +2368,7 @@ def sapm(effective_irradiance, temp_cell, module, *, temperature_ref=25,
     # avoid repeated __getitem__
     cells_in_series = module['Cells_in_Series']
 
-    out = OrderedDict()
+    out = {}
 
     out['i_sc'] = (
         module['Isco'] * Ee * (1 + module['Aisc']*(temp_cell -
@@ -2684,7 +2683,7 @@ def max_power_point(photocurrent, saturation_current, resistance_series,
 
     Returns
     -------
-    OrderedDict or pandas.DataFrame
+    dict or pandas.DataFrame
         ``(i_mp, v_mp, p_mp)``
 
     Notes
@@ -2702,7 +2701,7 @@ def max_power_point(photocurrent, saturation_current, resistance_series,
         ivp = {'i_mp': i_mp, 'v_mp': v_mp, 'p_mp': p_mp}
         out = pd.DataFrame(ivp, index=photocurrent.index)
     else:
-        out = OrderedDict()
+        out = {}
         out['i_mp'] = i_mp
         out['v_mp'] = v_mp
         out['p_mp'] = p_mp