Merge branch 'development' of https://github.com/NREL/rdtools into de…

…velopment

docs/sphinx/source/changelog/v3.0.0.rst

@@ -43,9 +43,9 @@ Deprecations
 
 Bug fixes
 ---------
-* Set marker linewidth to zero in `rdtools.plotting.degradation_summary_plots` (:pull:`433`)
+* Set marker linewidth to zero in `rdtools.plotting.degradation_summary_plots` (:issue:`408`, :pull:`433`)
 * Fix :py:func:`~rdtools.normalization.energy_from_power` returns incorrect index for shifted hourly data (:issue:`370`, :pull:`437`)
-* Add warning to clearsky workflow when ``power_expected`` is passed by user (:pull:`439`)
+* Add warning to clearsky workflow when ``power_expected`` is passed by user (:issue:`271`, :pull:`439`)
 * Fix different results with Nan's and Zeros in power series (:issue:`313`, :pull:`442`)
 * Fix pandas deprecation warnings in tests (:pull:`444`)
 * Fix typos in citation section of the readme file (:issue:`414`, :pull:`421`)

docs/sphinx/source/index.rst

@@ -53,7 +53,7 @@ The preferred method for degradation rate estimation is the year-on-year
 (YOY) approach (Jordan 2018), available in :py:func:`.degradation.degradation_year_on_year`.
 The YOY calculation yields a distribution of degradation rates, the
 central tendency of which is the most representative of the true
-degradation. We note that the workflow described above and implimented in 
+degradation. We note that the workflow described above and implemented in 
 :py:class:`.analysis_chains.TrendAnalysis` provides an estimate of degradation rate,
 not performance loss rate (PLR). PLR includes losses that are explicitly filtered
 out by the primary workflow (Deceglie 2023).

rdtools/bootstrap.py

@@ -19,7 +19,7 @@ def _make_time_series_bootstrap_samples(
     decomposition_type='multiplicative', bootstrap_seed=None
 ):
     '''
-    Generate bootstrap samples based a time series signal and its model fit
+    Generate bootstrap samples based on a time series signal and its model fit
     using circular block bootstrapping.
 
     Parameters