Feature 1998 standard sections

met/docs/Users_Guide/appendixB.rst

@@ -1,10 +1,11 @@
 .. _appendixB:
 
+************************************************
 Appendix B Map Projections, Grids, and Polylines
-================================================
+************************************************
 
 Map Projections
-_______________
+===============
 
 The following map projections are currently supported in MET:
 
@@ -23,7 +24,7 @@ The following map projections are currently supported in MET:
 * Gaussian Projection
 
 Grid Specification Strings
-__________________________
+==========================
 
 Several configuration file and command line options support the definition of grids as a grid specification string. A description of the that string for each of the supported grid types is provided below.
 
@@ -82,14 +83,14 @@ For a Gaussian grid, the syntax is
 The parameters **Nx** and **Ny** are as before, while **lon_zero** defines the first longitude.
 
 Grids
-_____
+=====
 
 The majority of NCEP's pre-defined grids that reside on one of the projections listed above are implemented in MET. The user may specify one of these NCEP grids in the configuration files as "GNNN" where NNN is the 3-digit NCEP grid number. Defining a new masking grid in MET would involve modifying the vx_data_grids library and recompiling.
 
 Please see `NCEP's website for a description and plot of these predefined grids <http://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html>`_.
 
 Polylines for NCEP Regions
-__________________________
+==========================
 
 Many of NCEP's pre-defined verification regions are implemented in MET as lat/lon polyline files. The user may specify one of these NCEP verification regions in the configuration files by pointing to the lat/lon polyline file in the installed *share/met/poly* directory. Users may also easily define their own lat/lon polyline files.
 

met/docs/Users_Guide/appendixD.rst

@@ -2,8 +2,9 @@
 
 .. _App_D-Confidence-Intervals:
 
+*******************************
 Appendix D Confidence Intervals
-===============================
+*******************************
 
 A single verification statistic is statistically meaningless without associated uncertainty information in accompaniment. There can be numerous sources of uncertainty associated with such a statistic including observational, physical uncertainties about the underlying processes governing the equation, sample uncertainty, etc. Although all of the sources of uncertainty can be important, the most heavily researched, and easiest to calculate, is that of sampling uncertainty. It is this source of uncertainty that can presently be obtained with MET, and the techniques for deriving these estimates are described here. Sampling uncertainty through MET is gleaned by way of confidence intervals (CIs) as these are generally most informative. A :math:`(1 - \alpha) \cdot 100\%` confidence interval is interpreted, somewhat awkwardly, in the following way. If the test were repeated 100 times (so that we have 100 such intervals), then we expect the true value of the statistics to fall inside :math:`(1-\alpha)\cdot 100` of these intervals. For example, if :math:`\alpha=0.05` then we expect the true value to fall within 95 of the intervals.
 

met/docs/Users_Guide/appendixE.rst

@@ -1,7 +1,8 @@
 .. _appendixE:
 
+**********************
 Appendix E WWMCA Tools
-======================
+**********************
 
 There are two WWMCA tools available. The WWMCA-Plot tool makes a PostScript plot of one or more WWMCA cloud percent files and the WWMCA-Regrid tool regrids WWMCA cloud percent files and reformats them into netCDF files that the other MET tools can read.
 

met/docs/Users_Guide/appendixF.rst

@@ -1,15 +1,16 @@
 .. _appendixF:
 
+***************************
 Appendix F Python Embedding
-===========================
+***************************
 
 Introduction
-____________
+============
 
 MET includes the ability to embed Python to a limited degree. Users may use Python scripts and whatever associated Python packages they wish in order to prepare 2D gridded data fields, point observations, and matched pairs as input to the MET tools. We fully expect that this degree of embedding will increase in the future. In addition, plans are in place to extend Python with MET in upcoming releases, allowing users to invoke MET tools directly from their Python script. While MET version 8.0 was built on Python 2.x, MET versions 9.0 and beyond are built on Python 3.6+.
 
 Compiling Python Support
-________________________
+========================
 
 In order to use Python embedding, the user's local Python installation must have the C-language Python header files and libraries. Sometimes when Python is installed locally, these header files and libraries are deleted at the end of the installation process, leaving only the binary executable and run-time shared object files. But the Python header files and libraries must be present to compile support in MET for Python embedding. Assuming the requisite Python files are present, and that Python embedding is enabled when building MET (which is done by passing the **--enable-python** option to the **configure** command line), the MET C++ code will use these in the compilation process to link directly to the Python libraries.
 
@@ -25,7 +26,7 @@ Make sure that these are set as environment variables or that you have included
 
 
 MET_PYTHON_EXE
-______________
+==============
 
 When Python embedding support is compiled, MET instantiates the Python interpreter directly. However, for users of highly configurable Conda environments, the Python instance set at compilation time may not be sufficient. Users may want to switch between Conda environments for which different packages are available. MET version 9.0 has been enhanced to address this need.
 
@@ -48,7 +49,7 @@ With this approach, users should be able to execute Python scripts in their own
 .. _pyembed-2d-data:
 
 Python Embedding for 2D data
-____________________________
+============================
 
 We now describe how to write Python scripts so that the MET tools may extract 2D gridded data fields from them. Currently, MET offers two ways to interact with Python scripts: by using NumPy N-dimensional arrays (ndarrays) or by using Xarray DataArrays. The interface to be used (NumPy or Xarray) is specified on the command line (more on this later). The user's scripts can use any Python libraries that are supported by the local Python installation, or any personal or institutional libraries or code that are desired in order to implement the Python script, so long as the data has been loaded into either a NumPy ndarray or an Xarray DataArray by the end of the script. This offers advantages when using data file formats that MET does not directly support. If there is Python code to read the data format, the user can use those tools to read the data, and then copy the data into a NumPy ndarray or an Xarray DataArray. MET can then ingest the data via the Python script. Note that whether a NumPy ndarray or an Xarray DataArray is used, the data should be stored as double precision floating point numbers. Using different data types, such as integers or single precision floating point numbers, will lead to unexpected results in MET.
 
@@ -236,7 +237,7 @@ The Ensemble-Stat, Series-Analysis, and MTD tools support the use of file lists
     -title "Python enabled plot_data_plane"
 
 Python Embedding for Point Observations
-_______________________________________
+=======================================
 
 
 The ASCII2NC tool supports the "-format python" option. With this option, point observations may be passed as input. An example of this is provided in :numref:`ascii2nc-pyembed`. That example uses the **read_ascii_point.py** sample script which is included with the MET code. It reads ASCII data in MET's 11-column point observation format and stores it in a Pandas dataframe to be read by the ASCII2NC tool with Python.
@@ -248,7 +249,7 @@ The **read_ascii_point.py** sample script can be found in:
 • `MET GitHub repository <https://github.com/dtcenter/MET>`_ in *met/scripts/python*.
 
 Python Embedding for MPR data
-_____________________________
+=============================
 
 The Stat-Analysis tool supports the "-lookin python" option. With this option, matched pair (MPR) data may be passed as input. An example of this is provided in :numref:`StA-pyembed`. That example uses the **read_ascii_mpr.py** sample script which is included with the MET code. It reads MPR data and stores it in a Pandas dataframe to be read by the Stat-Analysis tool with Python.
 
@@ -260,7 +261,7 @@ The **read_ascii_mpr.py** sample script can be found in:
 
 
 Python Embedding for Point Observations as input
-________________________________________________
+================================================
 
 
 The point2grid, plot_point_obs, ensemble_stat, and point_stat tools use MET point observation NetCDF. They support the python embedding by the prefix 'PYTHON_NUMPY=" and followed by a python script name instead of the MET point observastion NetCDF filename. The customized python script is expected to extend MET_BASE/python/met_point_obs.py and to produce the python variable, **met_point_data**,  which is the dictionary of the MET point observation data. They are defined at MET_BASE/python/met_point_obs.py.

met/docs/Users_Guide/appendixG.rst

@@ -1,7 +1,8 @@
 .. _appendixG:
 
+****************************************
 Appendix G Vectors and Vector Statistics
-========================================
+****************************************
 
 In this appendix, we discuss some basic properties of vectors, concentrating on the two-dimensional case. To keep the discussion simple, we will assume we are using a Cartesian coordinate system.