update overview

docs/overview.rst → docs/overview.md

@@ -1,25 +1,25 @@
-Overview
---------
+# Overview
+
 FLASC provides a rich suite of analysis tools for SCADA data filtering & 
 analysis, wind farm model validation, field experiment design, and field 
 experiment monitoring. The repository is centrally built around NRELs
-in-house `floris <https://github.com/nrel/floris>`_ wake modeling utility.
+in-house [floris](https://github.com/nrel/floris) wake modeling utility.
 FLASC also largely relies on the energy ratio to, among others, quantify wake
 losses in synthetic and historical data, to perform turbine northing
 calibrations, and for model parameter estimation.
 
-Literature
-==========
-See :cite:`Doekemeijer2022a` and :cite:`Bay2022a` for a practical
+# Literature
+
+See a particular wind rose, e.g., for annual operation. See
+{cite:p}`Doekemeijer2022a` and {cite:p}`Bay2022a`for a practical
 example of how the flasc repository is used or processing and analyses of
 historical SCADA data of three offshore wind farms.
 
-    .. bibliography:: references.bib
-        :style: unsrt
-        :filter: docname in docnames
+  ```{bibliography}
+  ```
+
+# Citation
 
-Citation
-========
 
 If FLASC played a role in your research, please cite it. This software can be
 cited as:
@@ -28,7 +28,6 @@ cited as:
 
 For LaTeX users:
 
-.. code-block:: latex
 
     @misc{flasc2022,
       author = {NREL},
@@ -40,66 +39,59 @@ For LaTeX users:
     }
 
 
-Questions
-=========
+# Questions
+
 For technical questions regarding FLASC usage, please post your questions to
-`GitHub Discussions <https://github.com/NREL/flasc/discussions>`_ on the
+[GitHub Discussions](https://github.com/NREL/flasc/discussions) on the
 FLASC repository. Alternatively, email the NREL FLASC team at
 `[email protected] <mailto:[email protected]>`_ or
 `[email protected] <mailto:[email protected]>`_.
 
 
-Module overview
-=================
+# Module overview
+
 FLASC consists of multiple modules, including:
 
-++++++++++++++++++++++++++
-flasc.dataframe_operations
-++++++++++++++++++++++++++
+
+## flasc.dataframe_operations
+
 This module includes functionality to easily manipulate Pandas DataFrames.
 Functions include filtering data by wind direction, wind speed an/or TI,
 deriving the ambient conditions from the upstream turbines, all the while
 dealing with angle wrapping for angular variables.
 
-++++++++++++++++++++++++++
-flasc.energy_ratio
-++++++++++++++++++++++++++
+
+## flasc.energy_ratio
+
 this module contains classes to calculate and visualize the energy ratio as
 defined by Fleming et al. (2019). The energy ratio is a very useful quantity
 in SCADA data analysis and related model validation. It represents the amount
 of energy produced by a turbine relative to what that turbine would have
-produced if no wakes were present. Various classes are included in this model,
-from classes used to calculate and plot the energy ratio for a single dataset,
-a class for multiple datasets, and a class that calculates the wind direction
-bias for every turbine by maximizing the energy ratio fit between FLORIS and
-SCADA data. Various visualization methods are included such as energy ratio
-plots and automated generation of detailed excel spreadsheets to determine
-where and which turbines performed differently than expected. These methods
-can be used both for model validation and for processing field campaign data,
-e.g. baseline vs optimized operation.
-
-++++++++++++++++++++++++++
-flasc.floris_tools
-++++++++++++++++++++++++++
+produced if no wakes were present.  See [energy ratio](energy_ratio) for more
+details.
+
+
+## flasc.floris_tools
+
 This module contains functions that leverage the floris model directly. This
 includes functions to calculate a large set of floris simulations (with MPI,
 optionally) for different atmospheric conditions, yaw misalignments and/or
 model parameters. It also includes two functions to precalculate and
 respectively interpolate from a large set of model solutions to speed up
 further postprocessing.
 
-++++++++++++++++++++++++++
-flasc.model_estimation
-++++++++++++++++++++++++++
+
+## flasc.model_estimation
+
 This is a module related to the estimation of parameters in the floris wind
 farm model. One class herein, called floris_sensitivity_analysis, performs
 Sobol parameter sensitivity studies to determine which parameters are most
 sensitive in various situations (atmospheric conditions, turbine settings,
 wind farm layouts).
 
-++++++++++++++++++
-flasc.optimization
-++++++++++++++++++
+
+## flasc.optimization
+
 The optimization module includes functions to estimate the timeshift between
 two sources of data, for example, to sychronize measurements from a met mast
 with measurements from SCADA data. The module also includes a function to
@@ -109,9 +101,9 @@ correct calibration of at least one other wind turbine. Finally, this module
 also contains a function to estimate the atmospheric turbulence intensity
 based on the power measurements of the turbines inside a wind farm.
 
-+++++++++++++++++++++++
-flasc.raw_data_handling
-+++++++++++++++++++++++
+
+## flasc.raw_data_handling
+
 This module contains functions that supports importing and processing raw
 SCADA data files. Specifically, it provides a class called
 "sql_database_manager" which can be used to up- and download data between
@@ -122,21 +114,19 @@ format for optimal balance of storage size and load/write speed.
 Additionally, can split one large dataframe into multiple dataframes and
 feather files.
 
-+++++++++++++++++++++++
-flasc.time_operations
-+++++++++++++++++++++++
+
+## flasc.time_operations
+
 This module allows the user to easily downsample, upsample and calculate
 moving averages of a data frame with SCADA and/or FLORIS data. These functions
 allow the user to specify which columns contain angular variables, and
 consequently 360 deg wrapping is taken care of. It also allows the user
 to calculate the min, max, std and median for downsampled data frames. It
 leverages efficient functions inherent in pandas to maximize performance.
 
-+++++++++++++++++++++++
-flasc.turbine_analysis
-+++++++++++++++++++++++
+
+## flasc.turbine_analysis
+
 this module allows the user to analyze SCADA data on a turbine level. Outliers
 can be detected and removed. Filtering methods include sensor-stuck type of
 fault detection and analysis of the turbine wind speed-power curve.
-
-.. seealso:: `Return to table of contents <index.html>`_