Repository: changliao1025/pye3sm (language: python)

Issue:
Merge main to dev



Discussion / hints:

Fix patch:
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 0000000..5672769
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,34 @@
+version: 2
+
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+    # You can also specify other tool versions:
+    # nodejs: "19"
+    # rust: "1.64"
+    # golang: "1.19"
+
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  builder: html
+  configuration: docs/source/conf.py
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+formats:
+  - pdf
+  - epub
+
+# Optionally declare the Python requirements required to build your docs
+python:
+   install:
+   - requirements: docs/requirements.txt
\ No newline at end of file
diff --git a/README.md b/README.md
index bb38650..a51c66f 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
 # PyE3SM
 
 PyE3SM is a Python library to assist E3SM model simulation.
-Currently, it only supports the land and river components.
+Currently, it only supports the `land` and `river` components.
+Acknowledgment
 
-
-# Acknowledgement
 This research was supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research at Pacific Northwest National Laboratory. The study was also partly supported by U.S. Department of Energy Office of Science Biological and Environmental Research through the Earth and Environmental System Modeling program as part of the Energy Exascale Earth System Model (E3SM) project. 
 
 # Install
+
 1. pip install pye3sm
 
 
diff --git a/pye3sm/elm/__init__.py b/docs/figures/readme.md
old mode 100755
new mode 100644
similarity index 100%
rename from pye3sm/elm/__init__.py
rename to docs/figures/readme.md
diff --git a/docs/source/Doxyfile.in b/docs/source/Doxyfile.in
new file mode 100644
index 0000000..da6b0f7
--- /dev/null
+++ b/docs/source/Doxyfile.in
@@ -0,0 +1,2565 @@
+# Doxyfile 1.8.15
+
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+# doxygen (www.doxygen.org) for a project.
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+# For lists, items can also be appended using:
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+PROJECT_LOGO           =
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+OUTPUT_DIRECTORY       = "@DOXYGEN_OUTPUT_DIR@"
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+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES, upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# and Mac users are advised to set this option to NO.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = NO
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES, the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# append additional text to a page's title, such as Class Reference. If set to
+# YES the compound reference will be hidden.
+# The default value is: NO.
+
+HIDE_COMPOUND_REFERENCE= NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
+# list. This list is created by putting \todo commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
+# list. This list is created by putting \test commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES, the
+# list will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            =
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO, doxygen will only warn about wrong or incomplete
+# parameter documentation, but not about the absence of documentation. If
+# EXTRACT_ALL is set to YES then this flag will automatically be disabled.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = NO
+
+# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
+# a warning is encountered.
+# The default value is: NO.
+
+WARN_AS_ERROR          = NO
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  = "@DOXYGEN_INPUT_DIR@"
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: https://www.gnu.org/software/libiconv/) for the list of
+# possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# read by doxygen.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
+# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
+# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
+# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,
+# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf, *.qsf and *.ice.
+
+FILE_PATTERNS          = *.c \
+                         *.cc \
+                         *.cxx \
+                         *.cpp \
+                         *.c++ \
+                         *.java \
+                         *.ii \
+                         *.ixx \
+                         *.ipp \
+                         *.i++ \
+                         *.inl \
+                         *.idl \
+                         *.ddl \
+                         *.odl \
+                         *.h \
+                         *.hh \
+                         *.hxx \
+                         *.hpp \
+                         *.h++ \
+                         *.cs \
+                         *.d \
+                         *.php \
+                         *.php4 \
+                         *.php5 \
+                         *.phtml \
+                         *.inc \
+                         *.m \
+                         *.markdown \
+                         *.md \
+                         *.mm \
+                         *.dox \
+                         *.py \
+                         *.pyw \
+                         *.f90 \
+                         *.f95 \
+                         *.f03 \
+                         *.f08 \
+                         *.f \
+                         *.for \
+                         *.tcl \
+                         *.vhd \
+                         *.vhdl \
+                         *.ucf \
+                         *.qsf \
+                         *.ice
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = YES
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       =
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        =
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       = *
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE =
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# entity all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see https://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+# The default value is: NO.
+
+CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_OPTIONS          =
+
+# If clang assisted parsing is enabled you can provide the clang parser with the
+# path to the compilation database (see:
+# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) used when the files
+# were built. This is equivalent to specifying the "-p" option to a clang tool,
+# such as clang-check. These options will then be passed to the parser.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+
+CLANG_DATABASE_PATH    =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+COLS_IN_ALPHA_INDEX    = 5
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            =
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefore more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the style sheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# https://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to YES can help to show when doxygen was last run and thus if the
+# documentation is up to date.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = NO
+
+# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
+# documentation will contain a main index with vertical navigation menus that
+# are dynamically created via Javascript. If disabled, the navigation index will
+# consists of multiple levels of tabs that are statically embedded in every HTML
+# page. Disable this option to support browsers that do not have Javascript,
+# like the Qt help browser.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_MENUS     = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: https://developer.apple.com/xcode/), introduced with OSX
+# 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
+# genXcode/_index.html for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler (hhc.exe). If non-empty,
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated
+# (YES) or that it should be included in the master .chm file (NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated
+# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: http://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# http://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# https://www.mathjax.org) which uses client side Javascript for the rendering
+# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = YES
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from https://www.mathjax.org before deployment.
+# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using Javascript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: https://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: https://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = YES
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when not enabling USE_PDFLATEX the default is latex when enabling
+# USE_PDFLATEX the default is pdflatex and when in the later case latex is
+# chosen this is overwritten by pdflatex. For specific output languages the
+# default can have been set differently, this depends on the implementation of
+# the output language.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         =
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# Note: This tag is used in the Makefile / make.bat.
+# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file
+# (.tex).
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to
+# generate index for LaTeX.
+# Note: This tag is used in the generated output file (.tex).
+# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.
+# The default value is: \makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_MAKEINDEX_CMD    = \makeindex
+
+# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. The package can be specified just
+# by its name or with the correct syntax as to be used with the LaTeX
+# \usepackage command. To get the times font for instance you can specify :
+# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
+# To use the option intlimits with the amsmath package you can specify:
+# EXTRA_PACKAGES=[intlimits]{amsmath}
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
+# string, for the replacement values of the other commands the user is referred
+# to HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# LaTeX style sheets that are included after the standard style sheets created
+# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list).
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_STYLESHEET =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES, to get a
+# higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_TIMESTAMP        = NO
+
+# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
+# path from which the emoji images will be read. If a relative path is entered,
+# it will be relative to the LATEX_OUTPUT directory. If left blank the
+# LATEX_OUTPUT directory will be used.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EMOJI_DIRECTORY  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's
+# configuration file, i.e. a series of assignments. You only have to provide
+# replacements, missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's configuration file. A template extensions file can be
+# generated using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
+# with syntax highlighting in the RTF output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_SOURCE_CODE        = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+#...
+GENERATE_XML           = YES
+#...
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
+# namespace members in file scope as well, matching the HTML output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_NS_MEMB_FILE_SCOPE = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
+# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# the structure of the code including all documentation. Note that this feature
+# is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO, the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_MAKEVAR_PREFIX =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the preprocessor
+#---------------------------------------------------------------------------
+
+# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all
+# C-preprocessor directives found in the sources and include files.
+# The default value is: YES.
+
+ENABLE_PREPROCESSING   = YES
+
+# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names
+# in the source code. If set to NO, only conditional compilation will be
+# performed. Macro expansion can be done in a controlled way by setting
+# EXPAND_ONLY_PREDEF to YES.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+MACRO_EXPANSION        = NO
+
+# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then
+# the macro expansion is limited to the macros specified with the PREDEFINED and
+# EXPAND_AS_DEFINED tags.
+# The default value is: NO.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_ONLY_PREDEF     = NO
+
+# If the SEARCH_INCLUDES tag is set to YES, the include files in the
+# INCLUDE_PATH will be searched if a #include is found.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SEARCH_INCLUDES        = YES
+
+# The INCLUDE_PATH tag can be used to specify one or more directories that
+# contain include files that are not input files but should be processed by the
+# preprocessor.
+# This tag requires that the tag SEARCH_INCLUDES is set to YES.
+
+INCLUDE_PATH           =
+
+# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard
+# patterns (like *.h and *.hpp) to filter out the header-files in the
+# directories. If left blank, the patterns specified with FILE_PATTERNS will be
+# used.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+INCLUDE_FILE_PATTERNS  =
+
+# The PREDEFINED tag can be used to specify one or more macro names that are
+# defined before the preprocessor is started (similar to the -D option of e.g.
+# gcc). The argument of the tag is a list of macros of the form: name or
+# name=definition (no spaces). If the definition and the "=" are omitted, "=1"
+# is assumed. To prevent a macro definition from being undefined via #undef or
+# recursively expanded use the := operator instead of the = operator.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+PREDEFINED             =
+
+# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this
+# tag can be used to specify a list of macro names that should be expanded. The
+# macro definition that is found in the sources will be used. Use the PREDEFINED
+# tag if you want to use a different macro definition that overrules the
+# definition found in the source code.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+EXPAND_AS_DEFINED      =
+
+# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will
+# remove all references to function-like macros that are alone on a line, have
+# an all uppercase name, and do not end with a semicolon. Such function macros
+# are typically used for boiler-plate code, and will confuse the parser if not
+# removed.
+# The default value is: YES.
+# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.
+
+SKIP_FUNCTION_MACROS   = YES
+
+#---------------------------------------------------------------------------
+# Configuration options related to external references
+#---------------------------------------------------------------------------
+
+# The TAGFILES tag can be used to specify one or more tag files. For each tag
+# file the location of the external documentation should be added. The format of
+# a tag file without this location is as follows:
+# TAGFILES = file1 file2 ...
+# Adding location for the tag files is done as follows:
+# TAGFILES = file1=loc1 "file2 = loc2" ...
+# where loc1 and loc2 can be relative or absolute paths or URLs. See the
+# section "Linking to external documentation" for more information about the use
+# of tag files.
+# Note: Each tag file must have a unique name (where the name does NOT include
+# the path). If a tag file is not located in the directory in which doxygen is
+# run, you must also specify the path to the tagfile here.
+
+TAGFILES               =
+
+# When a file name is specified after GENERATE_TAGFILE, doxygen will create a
+# tag file that is based on the input files it reads. See section "Linking to
+# external documentation" for more information about the usage of tag files.
+
+GENERATE_TAGFILE       =
+
+# If the ALLEXTERNALS tag is set to YES, all external class will be listed in
+# the class index. If set to NO, only the inherited external classes will be
+# listed.
+# The default value is: NO.
+
+ALLEXTERNALS           = NO
+
+# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed
+# in the modules index. If set to NO, only the current project's groups will be
+# listed.
+# The default value is: YES.
+
+EXTERNAL_GROUPS        = YES
+
+# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in
+# the related pages index. If set to NO, only the current project's pages will
+# be listed.
+# The default value is: YES.
+
+EXTERNAL_PAGES         = YES
+
+# The PERL_PATH should be the absolute path and name of the perl script
+# interpreter (i.e. the result of 'which perl').
+# The default file (with absolute path) is: /usr/bin/perl.
+
+PERL_PATH              = /usr/bin/perl
+
+#---------------------------------------------------------------------------
+# Configuration options related to the dot tool
+#---------------------------------------------------------------------------
+
+# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram
+# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to
+# NO turns the diagrams off. Note that this option also works with HAVE_DOT
+# disabled, but it is recommended to install and use dot, since it yields more
+# powerful graphs.
+# The default value is: YES.
+
+CLASS_DIAGRAMS         = YES
+
+# You can define message sequence charts within doxygen comments using the \msc
+# command. Doxygen will then run the mscgen tool (see:
+# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the
+# documentation. The MSCGEN_PATH tag allows you to specify the directory where
+# the mscgen tool resides. If left empty the tool is assumed to be found in the
+# default search path.
+
+MSCGEN_PATH            =
+
+# You can include diagrams made with dia in doxygen documentation. Doxygen will
+# then run dia to produce the diagram and insert it in the documentation. The
+# DIA_PATH tag allows you to specify the directory where the dia binary resides.
+# If left empty dia is assumed to be found in the default search path.
+
+DIA_PATH               =
+
+# If set to YES the inheritance and collaboration graphs will hide inheritance
+# and usage relations if the target is undocumented or is not a class.
+# The default value is: YES.
+
+HIDE_UNDOC_RELATIONS   = YES
+
+# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is
+# available from the path. This tool is part of Graphviz (see:
+# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent
+# Bell Labs. The other options in this section have no effect if this option is
+# set to NO
+# The default value is: NO.
+
+HAVE_DOT               = NO
+
+# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed
+# to run in parallel. When set to 0 doxygen will base this on the number of
+# processors available in the system. You can set it explicitly to a value
+# larger than 0 to get control over the balance between CPU load and processing
+# speed.
+# Minimum value: 0, maximum value: 32, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_NUM_THREADS        = 0
+
+# When you want a differently looking font in the dot files that doxygen
+# generates you can specify the font name using DOT_FONTNAME. You need to make
+# sure dot is able to find the font, which can be done by putting it in a
+# standard location or by setting the DOTFONTPATH environment variable or by
+# setting DOT_FONTPATH to the directory containing the font.
+# The default value is: Helvetica.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTNAME           = Helvetica
+
+# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of
+# dot graphs.
+# Minimum value: 4, maximum value: 24, default value: 10.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTSIZE           = 10
+
+# By default doxygen will tell dot to use the default font as specified with
+# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set
+# the path where dot can find it using this tag.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_FONTPATH           =
+
+# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for
+# each documented class showing the direct and indirect inheritance relations.
+# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CLASS_GRAPH            = YES
+
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+
+UML_LIMIT_NUM_FIELDS   = 10
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+# collaboration graphs will show the relations between templates and their
+# instances.
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+# This tag requires that the tag HAVE_DOT is set to YES.
+
+TEMPLATE_RELATIONS     = NO
+
+# If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to
+# YES then doxygen will generate a graph for each documented file showing the
+# direct and indirect include dependencies of the file with other documented
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+# This tag requires that the tag HAVE_DOT is set to YES.
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+# set to YES then doxygen will generate a graph for each documented file showing
+# the direct and indirect include dependencies of the file with other documented
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+# This tag requires that the tag HAVE_DOT is set to YES.
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+
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+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable call graphs for selected
+# functions only using the \callgraph command. Disabling a call graph can be
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+# This tag requires that the tag HAVE_DOT is set to YES.
+
+CALL_GRAPH             = NO
+
+# If the CALLER_GRAPH tag is set to YES then doxygen will generate a caller
+# dependency graph for every global function or class method.
+#
+# Note that enabling this option will significantly increase the time of a run.
+# So in most cases it will be better to enable caller graphs for selected
+# functions only using the \callergraph command. Disabling a caller graph can be
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+# This tag requires that the tag HAVE_DOT is set to YES.
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+CALLER_GRAPH           = NO
+
+# If the GRAPHICAL_HIERARCHY tag is set to YES then doxygen will graphical
+# hierarchy of all classes instead of a textual one.
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+# This tag requires that the tag HAVE_DOT is set to YES.
+
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+
+# If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the
+# dependencies a directory has on other directories in a graphical way. The
+# dependency relations are determined by the #include relations between the
+# files in the directories.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DIRECTORY_GRAPH        = YES
+
+# The DOT_IMAGE_FORMAT tag can be used to set the image format of the images
+# generated by dot. For an explanation of the image formats see the section
+# output formats in the documentation of the dot tool (Graphviz (see:
+# http://www.graphviz.org/)).
+# Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order
+# to make the SVG files visible in IE 9+ (other browsers do not have this
+# requirement).
+# Possible values are: png, jpg, gif, svg, png:gd, png:gd:gd, png:cairo,
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+# png:gdiplus:gdiplus.
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+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_IMAGE_FORMAT       = png
+
+# If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to
+# enable generation of interactive SVG images that allow zooming and panning.
+#
+# Note that this requires a modern browser other than Internet Explorer. Tested
+# and working are Firefox, Chrome, Safari, and Opera.
+# Note: For IE 9+ you need to set HTML_FILE_EXTENSION to xhtml in order to make
+# the SVG files visible. Older versions of IE do not have SVG support.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+INTERACTIVE_SVG        = NO
+
+# The DOT_PATH tag can be used to specify the path where the dot tool can be
+# found. If left blank, it is assumed the dot tool can be found in the path.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_PATH               =
+
+# The DOTFILE_DIRS tag can be used to specify one or more directories that
+# contain dot files that are included in the documentation (see the \dotfile
+# command).
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOTFILE_DIRS           =
+
+# The MSCFILE_DIRS tag can be used to specify one or more directories that
+# contain msc files that are included in the documentation (see the \mscfile
+# command).
+
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+
+# The DIAFILE_DIRS tag can be used to specify one or more directories that
+# contain dia files that are included in the documentation (see the \diafile
+# command).
+
+DIAFILE_DIRS           =
+
+# When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the
+# path where java can find the plantuml.jar file. If left blank, it is assumed
+# PlantUML is not used or called during a preprocessing step. Doxygen will
+# generate a warning when it encounters a \startuml command in this case and
+# will not generate output for the diagram.
+
+PLANTUML_JAR_PATH      =
+
+# When using plantuml, the PLANTUML_CFG_FILE tag can be used to specify a
+# configuration file for plantuml.
+
+PLANTUML_CFG_FILE      =
+
+# When using plantuml, the specified paths are searched for files specified by
+# the !include statement in a plantuml block.
+
+PLANTUML_INCLUDE_PATH  =
+
+# The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of nodes
+# that will be shown in the graph. If the number of nodes in a graph becomes
+# larger than this value, doxygen will truncate the graph, which is visualized
+# by representing a node as a red box. Note that doxygen if the number of direct
+# children of the root node in a graph is already larger than
+# DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note that
+# the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH.
+# Minimum value: 0, maximum value: 10000, default value: 50.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_GRAPH_MAX_NODES    = 50
+
+# The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the graphs
+# generated by dot. A depth value of 3 means that only nodes reachable from the
+# root by following a path via at most 3 edges will be shown. Nodes that lay
+# further from the root node will be omitted. Note that setting this option to 1
+# or 2 may greatly reduce the computation time needed for large code bases. Also
+# note that the size of a graph can be further restricted by
+# DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction.
+# Minimum value: 0, maximum value: 1000, default value: 0.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+MAX_DOT_GRAPH_DEPTH    = 0
+
+# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent
+# background. This is disabled by default, because dot on Windows does not seem
+# to support this out of the box.
+#
+# Warning: Depending on the platform used, enabling this option may lead to
+# badly anti-aliased labels on the edges of a graph (i.e. they become hard to
+# read).
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_TRANSPARENT        = NO
+
+# Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output
+# files in one run (i.e. multiple -o and -T options on the command line). This
+# makes dot run faster, but since only newer versions of dot (>1.8.10) support
+# this, this feature is disabled by default.
+# The default value is: NO.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_MULTI_TARGETS      = NO
+
+# If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page
+# explaining the meaning of the various boxes and arrows in the dot generated
+# graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+GENERATE_LEGEND        = YES
+
+# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot
+# files that are used to generate the various graphs.
+# The default value is: YES.
+# This tag requires that the tag HAVE_DOT is set to YES.
+
+DOT_CLEANUP            = YES
\ No newline at end of file
diff --git a/docs/source/authors.rst b/docs/source/authors.rst
new file mode 100644
index 0000000..ff6a507
--- /dev/null
+++ b/docs/source/authors.rst
@@ -0,0 +1,7 @@
+#######
+Authors
+#######
+
+* Chang Liao (Pacific Northwest National Laboratory)
+
+
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 0000000..33b7017
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,288 @@
+# -*- coding: utf-8 -*-
+#
+# Read the Docs Template documentation build configuration file, created by
+# sphinx-quickstart on Tue Aug 26 14:19:49 2014.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#sys.path.insert(0, os.path.abspath('.'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+import subprocess, os
+
+def configureDoxyfile(input_dir, output_dir):
+    with open('Doxyfile.in', 'r') as file :
+        filedata = file.read()
+
+    filedata = filedata.replace('@DOXYGEN_INPUT_DIR@', input_dir)
+    filedata = filedata.replace('@DOXYGEN_OUTPUT_DIR@', output_dir)
+
+    with open('Doxyfile', 'w') as file:
+        file.write(filedata)
+
+# Check if we're running on Read the Docs' servers
+read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True'
+
+breathe_projects = {}
+
+if read_the_docs_build:
+    input_dir = '../../pye3sm'
+    output_dir = './_build'
+    configureDoxyfile(input_dir, output_dir)
+    subprocess.call('doxygen', shell=True)
+    breathe_projects['pye3sm'] = output_dir + '/xml'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+#...
+extensions = ['sphinx.ext.autodoc', "breathe" ]
+#...
+
+# Breathe Configuration
+breathe_default_project = "pye3sm"
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix of source filenames.
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'pye3sm'
+copyright = u'2020, Pacific Northwest National Laboratory'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '1.0'
+# The full version, including alpha/beta/rc tags.
+release = '1.0'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['_build', 'breathe/*']
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+#show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+#keep_warnings = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = 'sphinx_rtd_theme'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+#html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+#html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+#html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'ReadtheDocsTemplatedoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+# The paper size ('letterpaper' or 'a4paper').
+#'papersize': 'letterpaper',
+
+# The font size ('10pt', '11pt' or '12pt').
+#'pointsize': '10pt',
+
+# Additional stuff for the LaTeX preamble.
+#'preamble': '',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+  ('index', 'ReadtheDocsTemplate.tex', u'Read the Docs Template Documentation',
+   u'Read the Docs', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    ('index', 'readthedocstemplate', u'Read the Docs Template Documentation',
+     [u'Read the Docs'], 1)
+]
+
+# If true, show URL addresses after external links.
+#man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+  ('index', 'ReadtheDocsTemplate', u'Read the Docs Template Documentation',
+   u'Read the Docs', 'ReadtheDocsTemplate', 'One line description of project.',
+   'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+#texinfo_appendices = []
+
+# If false, no module index is generated.
+#texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+#texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+#texinfo_no_detailmenu = False
+
diff --git a/docs/source/contribution.rst b/docs/source/contribution.rst
new file mode 100644
index 0000000..cbbfe22
--- /dev/null
+++ b/docs/source/contribution.rst
@@ -0,0 +1,10 @@
+
+############
+Contribution
+############
+
+
+PyE3SM was developed and maintained by
+
+* Chang Liao (Pacific Northwest National Laboratory)
+
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
new file mode 100644
index 0000000..52495b9
--- /dev/null
+++ b/docs/source/faq.rst
@@ -0,0 +1,32 @@
+###########################
+Frequently Asked Questions
+###########################
+
+1. Why my `conda` cannot create environment?
+   
+   Turn off the VPN or bypass it.
+
+2. Why import `GDAL` failed?
+   
+   Consider using the `conda-forge` channel.
+
+3. `proj` related issue https://github.com/OSGeo/gdal/issues/1546, 
+   
+   Make sure you correctly set up the `PROJ_LIB`
+
+   Because the `GDAL` library is used by this project and the `proj` library is often not configured correctly automatically. 
+   On Linux or Mac, you can set it up using the `.bash_profile` such as:
+
+   Anaconda:
+
+   `export PROJ_LIB=/people/user/.conda/envs/hexwatershed/share/proj`
+
+   Miniconda:
+
+   `export PROJ_LIB=/opt/miniconda3/envs/hexwatershed/share/proj`
+
+4. What if my model doesn't produce the correct or expected answer?
+   
+   Answer: There are several hidden assumptions within the workflow. For example, if you provide the DEM and river network for two different regions, the program won't be able to tell you that. A visual inspection of your data is important.
+   
+   Optionally, you can turn on the `iFlag_debug` option in the configuration file to output the `intermediate files`.
\ No newline at end of file
diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst
new file mode 100644
index 0000000..97bf8a2
--- /dev/null
+++ b/docs/source/glossary.rst
@@ -0,0 +1,28 @@
+########
+Glossary
+########
+
+
+************
+Great circle
+************
+
+In mathematics, a great circle or orthodrome is the circular intersection of a sphere and a plane passing through the sphere's center point.
+
+****
+DGGS
+****
+
+A discrete global grid (DGG) is a mosaic that covers the entire Earth's surface. Mathematically it is a space partitioning: it consists of a set of non-empty regions that form a partition of the Earth's surface. In a usual grid-modeling strategy, to simplify position calculations, each region is represented by a point, abstracting the grid as a set of region-points. Each region or region-point in the grid is called a cell.
+
+****
+TIN
+****
+
+In computer graphics, a triangulated irregular network (TIN) is a representation of a continuous surface consisting entirely of triangular facets (a triangle mesh), used mainly as Discrete Global Grid in primary elevation modeling.
+
+****
+MPAS
+****
+
+Model for Prediction Across Scales.
\ No newline at end of file
diff --git a/docs/source/history.rst b/docs/source/history.rst
new file mode 100644
index 0000000..b56d98e
--- /dev/null
+++ b/docs/source/history.rst
@@ -0,0 +1,7 @@
+#######
+History
+#######
+
+* 2020-10-01: Design
+
+
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 0000000..4806cda
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,38 @@
+.. pyflowline documentation master file, created by
+   sphinx-quickstart on Sun Jan 15 01:20:02 2023.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to pye3sm's documentation!
+======================================
+
+.. toctree::
+   :maxdepth: 3
+   :numbered:
+   
+   readme
+   quickstart
+   installation   
+   references
+   history
+   authors
+   support
+   contribution
+   faq
+   api   
+   
+
+Addendum
+--------
+
+.. toctree::
+   :maxdepth: 3
+
+   glossary
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
new file mode 100644
index 0000000..5d47163
--- /dev/null
+++ b/docs/source/installation.rst
@@ -0,0 +1,3 @@
+############
+Installation
+############
\ No newline at end of file
diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst
new file mode 100644
index 0000000..98b406e
--- /dev/null
+++ b/docs/source/quickstart.rst
@@ -0,0 +1,3 @@
+#####################
+Quick Start
+#####################
diff --git a/docs/source/readme.rst b/docs/source/readme.rst
new file mode 100644
index 0000000..b299d70
--- /dev/null
+++ b/docs/source/readme.rst
@@ -0,0 +1,19 @@
+#####################
+What is PyE3SM?
+#####################
+
+*********
+Overview
+*********
+
+
+***********
+Development
+***********
+
+
+
+*********
+Objective
+*********
+
diff --git a/docs/source/references.rst b/docs/source/references.rst
new file mode 100644
index 0000000..3eec1fa
--- /dev/null
+++ b/docs/source/references.rst
@@ -0,0 +1,5 @@
+############
+References
+############
+
+
diff --git a/docs/source/support.rst b/docs/source/support.rst
new file mode 100644
index 0000000..e4d9dfb
--- /dev/null
+++ b/docs/source/support.rst
@@ -0,0 +1,6 @@
+#######
+Support
+#######
+
+Support is provided through Github issue(https://github.com/changliao1025/pye3sm/issues).
+
diff --git a/pye3sm/elm/grid/__init__.py b/pye3sm/atm/__init__.py
similarity index 100%
rename from pye3sm/elm/grid/__init__.py
rename to pye3sm/atm/__init__.py
diff --git a/pye3sm/elm/grid/structured/__init__.py b/pye3sm/atm/general/__init__.py
similarity index 100%
rename from pye3sm/elm/grid/structured/__init__.py
rename to pye3sm/atm/general/__init__.py
diff --git a/pye3sm/atm/general/atm_retrieve_forcing_data_info.py b/pye3sm/atm/general/atm_retrieve_forcing_data_info.py
index 6e2efc5..8939d16 100644
--- a/pye3sm/atm/general/atm_retrieve_forcing_data_info.py
+++ b/pye3sm/atm/general/atm_retrieve_forcing_data_info.py
@@ -23,6 +23,6 @@ def atm_retrieve_forcing_data_info(oCase_in, sVariable_in):
             sFilename = sWorkspace_simulation_case_run + slash + dummy
             
             #parse xml
-            sFolder ,sField, aFilename = parse_xml_file_atm(sFilename,'fileNames')
+            sFolder ,aField, aFilename = parse_xml_file_atm(sFilename,'fileNames')
         
-    return sFolder, sField, aFilename
\ No newline at end of file
+    return sFolder, aField, aFilename
\ No newline at end of file
diff --git a/pye3sm/elm/grid/unstructured/__init__.py b/pye3sm/atm/general/structured/__init__.py
similarity index 100%
rename from pye3sm/elm/grid/unstructured/__init__.py
rename to pye3sm/atm/general/structured/__init__.py
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/__init__.py b/pye3sm/atm/general/structured/twod/__init__.py
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/__init__.py
rename to pye3sm/atm/general/structured/twod/__init__.py
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/fan/__init__.py b/pye3sm/atm/general/structured/twod/map/__init__.py
old mode 100644
new mode 100755
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/fan/__init__.py
rename to pye3sm/atm/general/structured/twod/map/__init__.py
diff --git a/pye3sm/tools/atm/forcing/map_forcing_data_2d.py b/pye3sm/atm/general/structured/twod/map/map_forcing_data_2d.py
similarity index 100%
rename from pye3sm/tools/atm/forcing/map_forcing_data_2d.py
rename to pye3sm/atm/general/structured/twod/map/map_forcing_data_2d.py
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/grace/__init__.py b/pye3sm/atm/general/structured/twod/save/__init__.py
old mode 100644
new mode 100755
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/grace/__init__.py
rename to pye3sm/atm/general/structured/twod/save/__init__.py
diff --git a/pye3sm/tools/atm/forcing/save_forcing_data_2d.py b/pye3sm/atm/general/structured/twod/save/save_forcing_data_2d.py
similarity index 100%
rename from pye3sm/tools/atm/forcing/save_forcing_data_2d.py
rename to pye3sm/atm/general/structured/twod/save/save_forcing_data_2d.py
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/situ/__init__.py b/pye3sm/atm/general/structured/twod/stats/__init__.py
old mode 100644
new mode 100755
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/situ/__init__.py
rename to pye3sm/atm/general/structured/twod/stats/__init__.py
diff --git a/pye3sm/tools/atm/forcing/stat_forcing_data_2d.py b/pye3sm/atm/general/structured/twod/stats/stat_forcing_data_2d.py
similarity index 100%
rename from pye3sm/tools/atm/forcing/stat_forcing_data_2d.py
rename to pye3sm/atm/general/structured/twod/stats/stat_forcing_data_2d.py
diff --git a/pye3sm/elm/h2sc/model/__init__.py b/pye3sm/atm/general/unstructured/singlegrid/plot/__init__.py
old mode 100644
new mode 100755
similarity index 100%
rename from pye3sm/elm/h2sc/model/__init__.py
rename to pye3sm/atm/general/unstructured/singlegrid/plot/__init__.py
diff --git a/pye3sm/tools/atm/forcing/tsplot_forcing_data_2d_singlegrid.py b/pye3sm/atm/general/unstructured/singlegrid/plot/tsplot_forcing_data_2d_singlegrid.py
similarity index 100%
rename from pye3sm/tools/atm/forcing/tsplot_forcing_data_2d_singlegrid.py
rename to pye3sm/atm/general/unstructured/singlegrid/plot/tsplot_forcing_data_2d_singlegrid.py
diff --git a/pye3sm/elm/h2sc/model/adjust/__init__.py b/pye3sm/atm/mesh/__init__.py
old mode 100644
new mode 100755
similarity index 100%
rename from pye3sm/elm/h2sc/model/adjust/__init__.py
rename to pye3sm/atm/mesh/__init__.py
diff --git a/pye3sm/case/e3sm_create_case.py b/pye3sm/case/e3sm_create_case.py
index c73a5a6..ba63f68 100644
--- a/pye3sm/case/e3sm_create_case.py
+++ b/pye3sm/case/e3sm_create_case.py
@@ -52,7 +52,10 @@ def e3sm_create_case(oE3SM_in, \
     sFilename_elm_domain = oCase_in.sFilename_elm_domain
     sFilename_elm_surfacedata = oCase_in.sFilename_elm_surfacedata
 
-    sFilename_user_datm_prec = '/compyfs/liao313/04model/e3sm/amazon/user_datm.streams.txt.CLMGSWP3v1.Precip'
+    sFilename_user_datm_prec = '/compyfs/liao313/04model/e3sm/amazon/user_datm.streams.txt.CLMGSWP3v1.Precip_parflow'
+    sFilename_user_datm_solar = '/compyfs/liao313/04model/e3sm/amazon/user_datm.streams.txt.CLMGSWP3v1.Solar_parflow'
+    sFilename_user_datm_temp = '/compyfs/liao313/04model/e3sm/amazon/user_datm.streams.txt.CLMGSWP3v1.TPQW_parflow'
+
     sFilename_user_dlnd = '/qfs/people/liao313/data/e3sm/sag/mosart/dlnd.streams.txt.lnd.gpcc'
     #GIT_HASH=`git log -n 1 --format=%h`
 
@@ -186,6 +189,8 @@ def e3sm_create_case(oE3SM_in, \
                 sCommand = sCommand.lstrip()
                 p = subprocess.Popen(sCommand, shell= True)
                 p.wait()
+
+                
                 
                 pass
             else: ##branch run
@@ -229,20 +234,19 @@ def e3sm_create_case(oE3SM_in, \
                 p.wait()
                 pass
             
+                        
             
-            
-            
-            sCommand = sPython + ' ./xmlchange DATM_MODE=CLMGSWP3v1' + '\n'
-            sCommand = sCommand.lstrip()
-            p = subprocess.Popen(sCommand, shell= True)
-            p.wait()
+            #sCommand = sPython + ' ./xmlchange DATM_MODE=CLMGSWP3v1' + '\n'
+            #sCommand = sCommand.lstrip()
+            #p = subprocess.Popen(sCommand, shell= True)
+            #p.wait()
     
-            sCommand = sPython + ' ./xmlchange -file env_run.xml -id DOUT_S             -val FALSE' + '\n'
+            sCommand = sPython + ' ./xmlchange --file env_run.xml --id DOUT_S        --val FALSE' + '\n'
             sCommand = sCommand.lstrip()
             p = subprocess.Popen(sCommand, shell= True)
             p.wait()
     
-            sCommand = sPython + ' ./xmlchange -file env_run.xml -id INFO_DBUG          -val 2' + '\n'
+            sCommand = sPython + ' ./xmlchange --file env_run.xml --id INFO_DBUG       --val 2' + '\n'
             sCommand = sCommand.lstrip()
             p = subprocess.Popen(sCommand, shell= True)
             p.wait()
@@ -283,10 +287,11 @@ def e3sm_create_case(oE3SM_in, \
             p = subprocess.Popen(sCommand, shell= True)
             p.wait()
             #we will generate clm name list in real time
-            sCommand = 'cp ' + sFilename_elm_namelist + ' ./user_nl_elm' + '\n'
-            sCommand = sCommand.lstrip()
-            p = subprocess.Popen(sCommand, shell= True)
-            p.wait()        
+            if iFlag_elm ==1:
+                sCommand = 'cp ' + sFilename_elm_namelist + ' ./user_nl_elm' + '\n'
+                sCommand = sCommand.lstrip()
+                p = subprocess.Popen(sCommand, shell= True)
+                p.wait()        
     
             if(iFlag_elm_spinup==1):
                 sCommand = 'cp ' + sFilename_datm_namelist + ' ./user_nl_datm' + '\n'
@@ -301,7 +306,7 @@ def e3sm_create_case(oE3SM_in, \
             #Build and submit
             if (iFlag_debug == 1):
             
-                sCommand = sPython + ' ./xmlchange -file env_build.xml DEBUG=TRUE' + '\n'
+                sCommand = sPython + ' ./xmlchange --file env_build.xml DEBUG=TRUE' + '\n'
                 sCommand = sCommand.lstrip()
                 p = subprocess.Popen(sCommand, shell= True)
                 p.wait()
@@ -340,7 +345,7 @@ def e3sm_create_case(oE3SM_in, \
             
             else:
                 #debug,
-                sCommand = sPython + ' ./xmlchange -file env_build.xml DEBUG=TRUE' + '\n'
+                sCommand = sPython + ' ./xmlchange --file env_build.xml DEBUG=TRUE' + '\n'
                 sCommand = sCommand.lstrip()
                 p = subprocess.Popen(sCommand)
                 p.wait()
@@ -530,11 +535,15 @@ def e3sm_create_case(oE3SM_in, \
             ofs.write(sLine)
             pass
 
-        sLine =  ' ./xmlchange -file env_run.xml -id DOUT_S -val FALSE' + '\n'
+        sLine =  ' ./xmlchange CALENDAR=NO_LEAP' + '\n'
         sLine = sLine.lstrip()
         ofs.write(sLine)
 
-        sLine =  ' ./xmlchange -file env_run.xml -id INFO_DBUG -val 2' + '\n'
+        sLine =  ' ./xmlchange --file env_run.xml --id DOUT_S --val FALSE' + '\n'
+        sLine = sLine.lstrip()
+        ofs.write(sLine)
+
+        sLine =  ' ./xmlchange --file env_run.xml --id INFO_DBUG --val 2' + '\n'
         sLine = sLine.lstrip()
         ofs.write(sLine)
         
@@ -591,6 +600,12 @@ def e3sm_create_case(oE3SM_in, \
                 sLine = 'cp ' + sFilename_user_datm_prec + ' ./user_datm.streams.txt.CLMGSWP3v1.Precip' + '\n'
                 sLine = sLine.lstrip()
                 ofs.write(sLine) 
+                sLine = 'cp ' + sFilename_user_datm_solar + ' ./user_datm.streams.txt.CLMGSWP3v1.Solar' + '\n'
+                sLine = sLine.lstrip()
+                ofs.write(sLine) 
+                sLine = 'cp ' + sFilename_user_datm_temp + ' ./user_datm.streams.txt.CLMGSWP3v1.TPQW' + '\n'
+                sLine = sLine.lstrip()
+                ofs.write(sLine) 
         else:
             sLine = 'cp ' + sFilename_elm_namelist + ' ./user_nl_dlnd' + '\n'
             sLine = sLine.lstrip()
@@ -618,7 +633,7 @@ def e3sm_create_case(oE3SM_in, \
         #Build and submit
         if (iFlag_debug == 1):
         
-            sLine = ' ./xmlchange -file env_build.xml DEBUG=TRUE' + '\n'
+            sLine = ' ./xmlchange --file env_build.xml DEBUG=TRUE' + '\n'
             sLine = sLine.lstrip()
             ofs.write(sLine)
             pass
diff --git a/pye3sm/case/e3sm_rename_case.py b/pye3sm/case/e3sm_rename_case.py
index 390523f..151a3c6 100644
--- a/pye3sm/case/e3sm_rename_case.py
+++ b/pye3sm/case/e3sm_rename_case.py
@@ -8,47 +8,99 @@
 
 from pyearth.system.define_global_variables import *
 
-from pye3sm.shared import oE3SM
-
-from pye3sm.shared.e3sm_read_configuration_file import e3sm_read_configuration_file
 
+from pye3sm.shared.case import pycase
 
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
 
 def e3sm_rename_case(sFilename_configuration_in, sDate, sDate_new, \
-    iFlag_continue_in = None,\
-         iFlag_debug_in = None,\
-   iFlag_resubmit_in=None, \
-       iFlag_short_in=None, \
-           iCase_index_in = None, \
-         sFilename_clm_namelist_in = None):
     
-
-    e3sm_read_configuration_file(sFilename_configuration_in,sDate_in=sDate, iFlag_continue_in = iFlag_continue_in ,\
-        iFlag_debug_in = iFlag_debug_in, iFlag_resubmit_in = iFlag_resubmit_in, iFlag_short_in= iFlag_short_in, \
-        iCase_index_in = iCase_index_in, sFilename_clm_namelist_in = sFilename_clm_namelist_in)
+           iCase_index_in = None, sModel_in = None, sRegion_in = None):
     
-    sCase = oE3SM.sCase
 
-    sWorkspace_case = oE3SM.sWorkspace_case
-    sWorkspace_simulation_case = oE3SM.sWorkspace_simulation_case
-    sWorkspace_analysis_case = oE3SM.sWorkspace_analysis_case
-    sFilename_nl = oE3SM.sDirectory_case + slash + 'user_nl_clm_' + oE3SM.sCase
+    aParameter_case = pye3sm_read_case_configuration_file(sFilename_configuration_in,\
+        sDate_in=sDate, \
+        iCase_index_in = iCase_index_in, sModel_in = sModel_in, sRegion_in = sRegion_in)
+
+    oCase = pycase(aParameter_case)
+    sCase = oCase.sCase
+
+    sWorkspace_case = oCase.sWorkspace_case
+    sWorkspace_simulation_case = oCase.sWorkspace_simulation_case
+    sWorkspace_analysis_case = oCase.sWorkspace_analysis_case
+    
     
     sCase_new = sModel+sDate_new+ "{:03d}".format(iCase)
-    sWorkspace_case_new  = oE3SM.sDirectory_case + slash + sCase_new
-    sWorkspace_simulation_case_new  = oE3SM.sDirectory_run + slash + sCase_new
-    sWorkspace_analysis_case_new  = oE3SM.sWorkspace_analysis + slash + sCase_new
-    sFilename_nl_new = oE3SM.sDirectory_case + 'user_nl_clm_' + sCase_new
+    sWorkspace_case_aux = oCase.sWorkspace_case_aux
+    sWorkspace_case_aux_new = oCase.sDirectory_case_aux + slash + sCase_new
+    sWorkspace_case_new  = oCase.sDirectory_case + slash + sCase_new
+    sWorkspace_simulation_case_new  = oCase.sDirectory_run + slash + sCase_new
+    sWorkspace_analysis_case_new  = oCase.sWorkspace_analysis + slash + sCase_new
+
+    if not os.path.isdir(sWorkspace_case_aux):
+        print('This path does not exist:' + sWorkspace_case_aux)
+        return
+        
+    if not os.path.isdir(sWorkspace_case):
+        print('This path does not exist:' + sWorkspace_case)
+        return
+        
+
+    if not os.path.isdir(sWorkspace_simulation_case):
+        print('This path does not exist:' + sWorkspace_simulation_case)
+        return
+        
+
+    if not os.path.isdir(sWorkspace_analysis_case):
+        print('This path does not exist:' + sWorkspace_analysis_case)
+        return
+
+    if os.path.isdir(sWorkspace_case_aux_new):
+        print('This path already exist:' + sWorkspace_case_aux_new)
+        return
+        
+    if os.path.isdir(sWorkspace_case_new):
+        print('This path already exist:' + sWorkspace_case_new)
+        return
+        
+
+    if os.path.isdir(sWorkspace_simulation_case_new):
+        print('This path already exist:' + sWorkspace_simulation_case_new)
+        return
+        
+
+    if os.path.isdir(sWorkspace_analysis_case_new):
+        print('This path already exist:' + sWorkspace_analysis_case_new)
+        return
+        
+
+    #rename folders
+    os.rename(sWorkspace_case_aux, sWorkspace_case_aux_new)
+    os.rename(sWorkspace_case, sWorkspace_case_new)
+    os.rename(sWorkspace_simulation_case, sWorkspace_simulation_case_new)
+    os.rename(sWorkspace_analysis_case, sWorkspace_analysis_case_new)
+
+    #rename subfolders?
+    sWorkspace_simulation_case_build =oCase.sWorkspace_simulation_case_build
+    sWorkspace_simulation_case_run = oCase.sWorkspace_simulation_case_run
+    
     
 
+    return
 
 
+    #sFilename_nl_new = oCase.sDirectory_case + 'user_nl_elm_' + sCase_new
+    
     #shutil.move(sWorkspace_case, sWorkspace_case_new)
 
-    shutil.move(sFilename_nl, sFilename_nl_new)
-    sWorkspace_simulation_case_run = oE3SM.sWorkspace_simulation_case_run
+    #shutil.move(sFilename_nl, sFilename_nl_new)
+
+    #rename files
+    
+    
     sPatterns = [sCase+'*']
-    sWorkspace_simulation_case_run='/compyfs/liao313/e3sm_scratch/h2sc20200402001/run'
+    
     for sPattern in sPatterns:
         sFilepaths = sWorkspace_simulation_case_run + slash + sPattern
         aFilenames =  glob.glob(sFilepaths, recursive = False)
@@ -64,24 +116,18 @@ def e3sm_rename_case(sFilename_configuration_in, sDate, sDate_new, \
     
     #move result
 
-    
-  
-    
-
 
     print('finished')
 if __name__ == '__main__':
-    sModel = 'h2sc'
-    sRegion ='global'
+    sModel = 'e3sm'
+    sRegion ='amazon'
    
     sVariable = 'ZWT'
-    sFilename_configuration = sWorkspace_configuration + slash + \
-        sModel + slash \
-            + sRegion + slash + 'h2sc_configuration_' + sVariable.lower() + sExtension_txt
+    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
     
    
-    iCase_start = 1
-    iCase_end = 1
+    iCase_start = 59
+    iCase_end = 59
 
     iFlag_debug = 0
     iFlag_continue = 0
@@ -89,16 +135,12 @@ def e3sm_rename_case(sFilename_configuration_in, sDate, sDate_new, \
 
     
 
-    sDate='202000402'
-    sDate_new = '20200402'
+    sDate='20220701'
+    sDate_new = '20220601'
     #write the clm namelist file
     for iCase in range( iCase_start , iCase_end+1):
 
-        #sCase =   "{:03d}".format(iCase- iCase_start +1)
-
-
-        #sCase_new = sModel + sDate + sCase 
-
 
-        e3sm_rename_case(sFilename_configuration , sDate, sDate_new, iCase_index_in = iCase)
+        e3sm_rename_case(sFilename_case_configuration , sDate, sDate_new, iCase_index_in = iCase, \
+            sModel_in = sModel, sRegion_in = sRegion)
     
\ No newline at end of file
diff --git a/pye3sm/elm/general/structured/twod/extract/elm_prepare_gp_input_data.py b/pye3sm/elm/general/structured/twod/extract/elm_prepare_gp_input_data.py
index 6f0d37d..cec8631 100644
--- a/pye3sm/elm/general/structured/twod/extract/elm_prepare_gp_input_data.py
+++ b/pye3sm/elm/general/structured/twod/extract/elm_prepare_gp_input_data.py
@@ -29,8 +29,8 @@ def elm_prepare_gp_input_data(oE3SM_in, oCase_in):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
     sWorkspace_scratch = '/compyfs/liao313'
     sWorkspace_analysis = oCase_in.sWorkspace_analysis
diff --git a/pye3sm/elm/general/structured/twod/extract/elm_train_mogp.py b/pye3sm/elm/general/structured/twod/extract/elm_train_mogp.py
index 2261a4a..ca4f055 100644
--- a/pye3sm/elm/general/structured/twod/extract/elm_train_mogp.py
+++ b/pye3sm/elm/general/structured/twod/extract/elm_train_mogp.py
@@ -53,8 +53,8 @@ def elm_train_mogp(oE3SM_in, oCase_in):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
     nCase = 40
 
     
diff --git a/pye3sm/elm/general/structured/twod/extract/elm_train_sogp.py b/pye3sm/elm/general/structured/twod/extract/elm_train_sogp.py
index 218c170..c975d5e 100644
--- a/pye3sm/elm/general/structured/twod/extract/elm_train_sogp.py
+++ b/pye3sm/elm/general/structured/twod/extract/elm_train_sogp.py
@@ -25,8 +25,8 @@ def elm_train_sogp(oE3SM_in, oCase_in):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
 
     # Create some sample data.
diff --git a/pye3sm/elm/general/structured/twod/map/elm_map_variable_2d.py b/pye3sm/elm/general/structured/twod/map/elm_map_variable_2d.py
index ae26349..0e7f29d 100644
--- a/pye3sm/elm/general/structured/twod/map/elm_map_variable_2d.py
+++ b/pye3sm/elm/general/structured/twod/map/elm_map_variable_2d.py
@@ -4,25 +4,39 @@
 import datetime
 
 from pyearth.system.define_global_variables import *
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
 from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
-from pyearth.visual.color.create_diverge_rgb_color_hex import create_diverge_rgb_color_hex
 
-from pyearth.visual.map.map_raster_data import map_raster_data
-
-from pyearth.toolbox.data.remove_outliers import remove_outliers
-from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+from pyearth.visual.map.raster.map_raster_data import map_raster_data
+from pye3sm.elm.mesh.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
  
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
 
 def elm_map_variable_2d(oE3SM_in, \
                                           oCase_in,\
                                         iFlag_scientific_notation_colorbar_in =None,   \
+                                            iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
                                           dData_max_in = None,\
                                           dData_min_in = None,
+                                          sColormap_in=None,\
                                          sUnit_in=None,\
-                                          sTitle_in =None):
+                                          sTitle_in =None, \
+                                            aLegend_in = None):
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =1
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
 
     sModel = oCase_in.sModel
     sRegion = oCase_in.sRegion
@@ -55,13 +69,8 @@ def elm_map_variable_2d(oE3SM_in, \
     dLat_max = np.max(aLat)
     dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
     dResolution_y = (dLat_max - dLat_min) / (nrow-1)
-    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
-
-    print('Prepare the map grid')
-   
-    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
-    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
-    
+    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, \
+        dLat_min -dResolution_y,  dLat_max+dResolution_y]
 
     dates = list()
     nyear = iYear_end - iYear_start + 1
@@ -81,22 +90,6 @@ def elm_map_variable_2d(oE3SM_in, \
     dates=np.array(dates)
     dates_subset = dates[subset_index]
     nstress_subset= len(dates_subset)
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
-    #read the stack data
-    sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
-
-    if os.path.exists(sFilename):
-        #print("Yep, I can read that file: " + sFilename)                
-        pass
-    else:
-        print(sFilename + ' is missing')
-        print("Nope, the path doesn't reach your file. Go research filepath in python")
-        return
-
-    aData_all = gdal_read_envi_file_multiple_band(sFilename)
-    aVariable_total = aData_all[0]
-    aVariable_total_subset = aVariable_total[subset_index,:,:]
-
 
     sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
     if not os.path.exists(sWorkspace_analysis_case_variable):
@@ -107,32 +100,85 @@ def elm_map_variable_2d(oE3SM_in, \
         os.makedirs(sWorkspace_analysis_case_region)
         pass
 
-   
-    for i in np.arange(nstress_subset):
-        aImage = aVariable_total_subset[i, :,:]
-        #get date
-        pDate = dates_subset[i]
-        sDate = pDate.strftime('%Y%m%d')
-        sLabel_legend = sRegion.title() + sDate
-        sFilename_out = sWorkspace_analysis_case_region + slash \
-        + sVariable + '_map_' + sDate +'.png'         
-
-        #aData_all = np.log10(aData_all)
-        ##set inf to min
-        #bad_index = np.where( np.isinf(  aData_all) == True  )
-        #aData_all[bad_index] = dMin_y_in
-        aData_all = np.array(aImage)                  
+    if iFlag_monthly ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_monthly_in = 1)
+        for i in np.arange(nstress_subset):
+            aImage = aData_ret[i]
+            #get date
+            pDate = dates_subset[i]
+            sDate = pDate.strftime('%Y%m%d')
+            
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_' + sDate +'.png'                   
+
+            
+            map_raster_data(aImage, aImage_extent,\
+                                    sFilename_out,\
+                                    sColormap_in=sColormap_in,\
+                                    sTitle_in = sTitle_in,\
+                                    sUnit_in=sUnit_in,\
+                                    iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                    iFlag_contour_in = 1,\
+                                    dData_max_in = dData_max_in,\
+                                    dData_min_in = dData_min_in,
+                                    dMissing_value_in = -9999,\
+                                    aLabel_legend_in = aLegend_in)
+
+            
+
+    #mean or total
+
+    if iFlag_annual_mean ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_mean_in = 1)
+        #annual mean
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            aImage = aData_ret[iYear-iYear_start]
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_mean_'+ sYear +'.png'
+            
+            map_raster_data(aImage,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                        sColormap_in=sColormap_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                        iFlag_contour_in = 1,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                        aLabel_legend_in = aLegend_in)
+
+        pass
+    
+    if iFlag_annual_total ==1: #annual total
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_total_in = 1)
+        for iYear in range(iYear_start, iYear_end + 1):
+            aImage = aData_ret[iYear-iYear_start]
+            sYear = "{:04d}".format(iYear)            
+            
+            
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_annual_total_'+ sYear +'.png'
+            
+            if dData_max_in is not None:
+                dData_max_in2 = dData_max_in * 10
+            else: 
+                dData_max_in2 = dData_max_in
+
+            map_raster_data(aImage,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                        sColormap_in=sColormap_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                        iFlag_contour_in = 1,\
+                                           dData_max_in = dData_max_in2,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                        aLabel_legend_in = aLegend_in)
+        pass
         
-        map_raster_data(aData_all,  aImage_extent,\
-                              sFilename_out,\
-                                  sTitle_in = sTitle_in,\
-                                      sUnit_in=sUnit_in,\
-                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
-                                       dData_max_in = dData_max_in,\
-                                          dData_min_in = dData_min_in,
-                                  dMissing_value_in = -9999)
-                                
-        print(sDate)
 
 
 
diff --git a/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_2d.py b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_2d.py
new file mode 100644
index 0000000..407e2cb
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_2d.py
@@ -0,0 +1,254 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+from pyearth.visual.color.create_diverge_rgb_color_hex import create_diverge_rgb_color_hex
+
+from pyearth.visual.map.raster.map_raster_data import map_raster_data
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+ 
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+def elm_map_variable_difference_2d(oE3SM_in, \
+                                          oCase_x_in,\
+                                            oCase_y_in,\
+                                        iFlag_scientific_notation_colorbar_in =None,   \
+                                                iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
+                                          dData_max_in = None,\
+                                          dData_min_in = None,
+                                          sExtend_in= None,\
+                                            sColormap_in=None,\
+                                         sUnit_in=None,\
+                                          sTitle_in =None, \
+                                            aLegend_in = None):
+
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =1
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    sModel = oCase_x_in.sModel
+    sRegion = oCase_x_in.sRegion
+    iFlag_same_grid = oCase_x_in.iFlag_same_grid
+
+    iYear_start = oCase_x_in.iYear_start
+    iYear_end = oCase_x_in.iYear_end
+
+    iYear_subset_start = oCase_x_in.iYear_subset_start
+    iYear_subset_end = oCase_x_in.iYear_subset_end
+
+    sLabel_Y = oCase_x_in.sLabel_y
+    dConversion = oCase_x_in.dConversion
+    sVariable = oCase_x_in.sVariable
+    sCase = oCase_x_in.sCase
+    sWorkspace_simulation_case_run = oCase_x_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case_x = oCase_x_in.sWorkspace_analysis_case
+    sWorkspace_analysis_case_y = oCase_y_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat,aMask = elm_retrieve_case_dimension_info(oCase_x_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
+
+    print('Prepare the map grid')
+   
+    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
+    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+  
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable
+    sWorkspace_analysis_case_variable_y = sWorkspace_analysis_case_y + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable_x):
+        os.makedirs(sWorkspace_analysis_case_variable_x)
+
+    sWorkspace_analysis_case_region_y = sWorkspace_analysis_case_variable_y + slash + 'map_diff'
+    if not os.path.exists(sWorkspace_analysis_case_region_y):
+        os.makedirs(sWorkspace_analysis_case_region_y)
+        pass
+
+
+
+    if iFlag_monthly ==1 :
+
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =1,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 0  )
+        aData_all_y = elm_retrieve_variable_2d(  oCase_y_in,\
+                iFlag_monthly_in = 1,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 0  )
+
+        for i in np.arange(nstress_subset):
+            aImage_x = aData_all_x[i]
+            aImage_y = aData_all_y[i]
+            #get date
+            pDate = dates_subset[i]
+            sDate = pDate.strftime('%Y%m%d')
+            sLabel_legend = sRegion.title() + sDate
+            sFilename_out = sWorkspace_analysis_case_region_y + slash \
+            + sVariable + '_map_diff_' + sDate +'.png'                 
+
+            aImage_x = np.array(aImage_x)  
+            aImage_y = np.array(aImage_y)  
+            nan_index = np.where(aImage_x == -9999)
+            aImage = aImage_y - aImage_x
+
+            aData_all = np.array(aImage)                 
+            aData_all = aData_all * dConversion
+            aData_all[nan_index] =np.nan
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                    sExtend_in = sExtend_in,\
+                                      sTitle_in = sTitle_in,\
+                                          sColormap_in=sColormap_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           iFlag_contour_in= 1,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+
+            print(sDate)
+    
+    if iFlag_annual_mean ==1:
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=1,\
+                iFlag_annual_total_in= 0  )
+        aData_all_y = elm_retrieve_variable_2d(  oCase_y_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=1,\
+                iFlag_annual_total_in= 0  )
+        for iYear in range(iYear_start, iYear_end + 1):
+            
+            sYear = "{:04d}".format(iYear)         
+
+            aData_dummy= np.array(aData_all_x[0])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aData_dummy_x= np.array(aData_all_x[iYear - iYear_start])           
+            aImage_x = np.reshape(aData_dummy_x, (nrow, ncolumn))           
+            aData_dummy_y= np.array(aData_all_y[iYear - iYear_start])      
+            aImage_y = np.reshape(aData_dummy_y, (nrow, ncolumn))         
+
+            aImage = aImage_y - aImage_x
+            aData_all = np.array(aImage)                  
+            aData_all = aData_all * dConversion
+            aData_all[nan_index] = np.nan
+            
+            sFilename_out = sWorkspace_analysis_case_region_y + slash \
+            + sVariable + '_map_diff_mean_'+ sYear +'.png'
+
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                    sExtend_in = sExtend_in,\
+                                      sTitle_in = sTitle_in,\
+                                        sColormap_in=sColormap_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           iFlag_contour_in= 1,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+
+          
+
+
+    if iFlag_annual_total ==1:
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 1  )
+        aData_all_y = elm_retrieve_variable_2d(  oCase_y_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 1  )
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)           
+
+            aData_dummy= np.array(aData_all_x[0])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aData_dummy_x= np.array(aData_all_x[iYear - iYear_start])           
+            aImage_x = np.reshape(aData_dummy_x, (nrow, ncolumn))          
+            aData_dummy_y= np.array(aData_all_y[iYear - iYear_start])       
+            aImage_y = np.reshape(aData_dummy_y, (nrow, ncolumn))         
+            aImage = aImage_y - aImage_x
+            aData_all = np.array(aImage)                  
+            aData_all = aData_all * dConversion
+            aData_all[nan_index]  =np.nan
+
+            sFilename_out = sWorkspace_analysis_case_region_y + slash \
+            + sVariable + '_map_diff_annual_'+ sYear +'.png'
+            if dData_max_in is not None:
+                dData_max_in2 = dData_max_in * 10
+            else: 
+                dData_max_in2 = dData_max_in
+
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                    sColormap_in=sColormap_in,\
+                                    sExtend_in = sExtend_in,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           iFlag_contour_in= 1,\
+                                           dData_max_in = dData_max_in2,\
+                                              dData_min_in = -1*dData_max_in2,
+                                      dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+
+            
+
+
+    print("finished")
+
+
diff --git a/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_dc_2d.py b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_dc_2d.py
new file mode 100644
index 0000000..68f6ad3
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_dc_2d.py
@@ -0,0 +1,250 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+
+from pyearth.visual.map.raster.map_raster_data_dc import map_raster_data_dc
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+def elm_map_variable_difference_dc_2d(oE3SM_in, \
+                                          oCase_x_in,\
+                                            oCase_y_in,\
+                                        iFlag_scientific_notation_colorbar_in =None,   \
+                                                iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
+                                                    aInterval_in = None,\
+                aColor_in = None,\
+                                          dData_max_in = None,\
+                                          dData_min_in = None,
+                                          sExtend_in= None,\
+                                         sUnit_in=None,\
+                                          sTitle_in =None, \
+                                            aLegend_in = None):
+
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =1
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    sModel = oCase_x_in.sModel
+    sRegion = oCase_x_in.sRegion
+    iFlag_same_grid = oCase_x_in.iFlag_same_grid
+
+    iYear_start = oCase_x_in.iYear_start
+    iYear_end = oCase_x_in.iYear_end
+
+    iYear_subset_start = oCase_x_in.iYear_subset_start
+    iYear_subset_end = oCase_x_in.iYear_subset_end
+
+    sLabel_Y = oCase_x_in.sLabel_y
+    dConversion = oCase_x_in.dConversion
+    sVariable = oCase_x_in.sVariable
+    sCase = oCase_x_in.sCase
+    sWorkspace_simulation_case_run = oCase_x_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case_x = oCase_x_in.sWorkspace_analysis_case
+    sWorkspace_analysis_case_y = oCase_y_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat,aMask = elm_retrieve_case_dimension_info(oCase_x_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
+
+    print('Prepare the map grid')
+   
+    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
+    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable
+    sWorkspace_analysis_case_variable_y = sWorkspace_analysis_case_y + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable_x):
+        os.makedirs(sWorkspace_analysis_case_variable_x)
+
+    sWorkspace_analysis_case_region_y = sWorkspace_analysis_case_variable_y + slash + 'map_diff_dc'
+    if not os.path.exists(sWorkspace_analysis_case_region_y):
+        os.makedirs(sWorkspace_analysis_case_region_y)
+        pass
+
+    if iFlag_monthly ==1 :
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =1,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 0  )
+        aData_all_y = elm_retrieve_variable_2d(  oCase_y_in,\
+                iFlag_monthly_in = 1,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 0  )
+        for i in np.arange(nstress_subset):
+            aImage_x = aData_all_x[i]
+            aImage_y = aData_all_y[i]
+            #get date
+            pDate = dates_subset[i]
+            sDate = pDate.strftime('%Y%m%d')
+            sLabel_legend = sRegion.title() + sDate
+            sFilename_out = sWorkspace_analysis_case_region_y + slash \
+            + sVariable + '_map_diff_dc_' + sDate +'.png'         
+            aImage_x = np.array(aImage_x)  
+            aImage_y = np.array(aImage_y)  
+            nan_index = np.where(aImage_x == -9999)
+            aImage = aImage_y - aImage_x
+            aData_all = np.array(aImage)                 
+            aData_all = aData_all * dConversion
+            aData_all[nan_index] =np.nan
+            map_raster_data_dc(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                    sExtend_in = sExtend_in,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           iFlag_contour_in= 1,\
+                                             aInterval_in = aInterval_in,\
+                aColor_in = aColor_in,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+
+            print(sDate)
+    
+    if iFlag_annual_mean ==1:
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=1,\
+                iFlag_annual_total_in= 0  )
+        aData_all_y = elm_retrieve_variable_2d(  oCase_y_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=1,\
+                iFlag_annual_total_in= 0  )
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            aData_dummy= np.array(aData_all_x[0])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aData_dummy_x= np.array(aData_all_x[iYear - iYear_start])           
+            aImage_x = np.reshape(aData_dummy_x, (nrow, ncolumn))           
+            aData_dummy_y= np.array(aData_all_y[iYear - iYear_start])      
+            aImage_y = np.reshape(aData_dummy_y, (nrow, ncolumn))         
+          
+            aImage = aImage_y - aImage_x
+            aData_all = np.array(aImage)                  
+            aData_all = aData_all * dConversion
+            aData_all[nan_index] = np.nan
+
+            sFilename_out = sWorkspace_analysis_case_region_y + slash \
+            + sVariable + '_map_diff_mean_dc_'+ sYear +'.png'
+
+            map_raster_data_dc(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                    sExtend_in = sExtend_in,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           iFlag_contour_in= 1,\
+                                            aInterval_in = aInterval_in,\
+                aColor_in = aColor_in,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+
+          
+
+
+    if iFlag_annual_total ==1:
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 1  )
+        aData_all_y = elm_retrieve_variable_2d(  oCase_y_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=0,\
+                iFlag_annual_total_in= 1  )
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            aData_dummy= np.array(aData_all_x[0])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aData_dummy_x= np.array(aData_all_x[iYear - iYear_start])           
+            aImage_x = np.reshape(aData_dummy_x, (nrow, ncolumn))          
+            aData_dummy_y= np.array(aData_all_y[iYear - iYear_start])       
+            aImage_y = np.reshape(aData_dummy_y, (nrow, ncolumn))    
+
+            aImage = aImage_y - aImage_x
+            aData_all = np.array(aImage)                  
+            aData_all = aData_all * dConversion
+            aData_all[nan_index]  =np.nan
+
+            sFilename_out = sWorkspace_analysis_case_region_y + slash \
+            + sVariable + '_map_diff_annual_dc_'+ sYear +'.png'
+            if dData_max_in is not None:
+                dData_max_in2 = dData_max_in * 10
+            else: 
+                dData_max_in2 = dData_max_in
+
+            map_raster_data_dc(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                    sExtend_in = sExtend_in,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           iFlag_contour_in= 1,\
+                                             aInterval_in = aInterval_in,\
+                aColor_in = aColor_in,\
+                                           dData_max_in = dData_max_in2,\
+                                              dData_min_in = -1*dData_max_in2,
+                                      dMissing_value_in = -9999, \
+                                        aLabel_legend_in=aLegend_in)
+
+            
+
+
+    print("finished")
+
+
diff --git a/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_w_observation_2d.py b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_w_observation_2d.py
new file mode 100644
index 0000000..510a97a
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_w_observation_2d.py
@@ -0,0 +1,150 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+from pyearth.visual.color.create_diverge_rgb_color_hex import create_diverge_rgb_color_hex
+
+from pyearth.visual.map.raster.map_raster_data import map_raster_data
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+ 
+
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+def elm_map_variable_difference_w_observation_2d(oE3SM_in, \
+                                          oCase_x_in,\
+                                            aData_y_in,\
+                                        iFlag_scientific_notation_colorbar_in =None,   \
+                                          dData_max_in = None,\
+                                          dData_min_in = None,
+                                          sExtend_in= None,\
+                                            sFormat_contour_in=None,\
+                                         sUnit_in=None,\
+                                          sTitle_in =None, \
+                                            aLegend_in = None):
+
+    sModel = oCase_x_in.sModel
+    sRegion = oCase_x_in.sRegion
+    iFlag_same_grid = oCase_x_in.iFlag_same_grid
+
+    iYear_start = oCase_x_in.iYear_start
+    iYear_end = oCase_x_in.iYear_end
+
+    iYear_subset_start = oCase_x_in.iYear_subset_start
+    iYear_subset_end = oCase_x_in.iYear_subset_end
+
+    sLabel_Y = oCase_x_in.sLabel_y
+    dConversion = oCase_x_in.dConversion
+    sVariable = oCase_x_in.sVariable
+    sCase = oCase_x_in.sCase
+    sWorkspace_simulation_case_run = oCase_x_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case_x = oCase_x_in.sWorkspace_analysis_case
+
+
+    #new approach
+    aLon, aLat,aMask = elm_retrieve_case_dimension_info(oCase_x_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
+
+    print('Prepare the map grid')
+   
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+    sWorkspace_variable_dat_x = sWorkspace_analysis_case_x + slash + sVariable +  slash + 'dat'
+   
+    #read the stack data
+    sFilename_x = sWorkspace_variable_dat_x + slash + sVariable  + sExtension_envi
+
+    if os.path.exists(sFilename_x):
+        #print("Yep, I can read that file: " + sFilename_x)                
+        pass
+    else:
+        print(sFilename_x + ' is missing')
+        print("Nope, the path doesn't reach your file. Go research filepath in python")
+        return
+
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable
+  
+    if not os.path.exists(sWorkspace_analysis_case_variable_x):
+        os.makedirs(sWorkspace_analysis_case_variable_x)
+
+    sWorkspace_analysis_case_region_x = sWorkspace_analysis_case_variable_x + slash + 'map_diff_w_obs'
+    if not os.path.exists(sWorkspace_analysis_case_region_x):
+        os.makedirs(sWorkspace_analysis_case_region_x)
+        pass
+
+    aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                iFlag_monthly_in =0,\
+                    iFlag_annual_mean_in=1,\
+                iFlag_annual_total_in= 0  )
+    
+    aImage_x= aData_all_x.pop()  
+    aImage_x = np.reshape(aImage_x , (nrow, ncolumn)) 
+    #get date
+
+    sFilename_out = sWorkspace_analysis_case_region_x + slash \
+        + sVariable + '_map_diff_w_obs'  +'.png'         
+   
+    aImage_x = np.array(aImage_x)  
+    aImage_y = np.array(aData_y_in)  
+    nan_index = np.where(aData_y_in == -9999)
+    aImage = (aImage_x - aImage_y)#/aImage_y
+    
+    aData_all = np.array(aImage)                  
+    
+    aData_all = aData_all * dConversion
+    aData_all[nan_index] = -9999    
+    
+    map_raster_data(aData_all,  aImage_extent,\
+                          sFilename_out,\
+                              iFlag_contour_in= 1,\
+                            sExtend_in = sExtend_in,\
+                              sTitle_in = sTitle_in,\
+                                  sUnit_in=sUnit_in,\
+                                    sFormat_contour_in = sFormat_contour_in,\
+                              iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                   dData_max_in = dData_max_in,\
+                                      dData_min_in = dData_min_in,
+                              dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+                            
+    
+
+
+
+    print("finished")
+
+
diff --git a/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_w_observation_dc_2d.py b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_w_observation_dc_2d.py
new file mode 100644
index 0000000..ccd61ec
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/map/elm_map_variable_difference_w_observation_dc_2d.py
@@ -0,0 +1,171 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+from pyearth.visual.color.create_diverge_rgb_color_hex import create_diverge_rgb_color_hex
+
+from pyearth.visual.map.raster.map_raster_data_dc import map_raster_data_dc
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.mesh.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+ 
+
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+def elm_map_variable_difference_w_observation_dc_2d(oE3SM_in, \
+                                          oCase_x_in,\
+                                            aData_y_in,\
+                                        iFlag_scientific_notation_colorbar_in =None,   \
+                                              iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
+                                                       aInterval_in = None,\
+                aColor_in = None,\
+                                          dData_max_in = None,\
+                                          dData_min_in = None,
+                                          sExtend_in= None,\
+                                         sUnit_in=None,\
+                                          sTitle_in =None, \
+                                            aLegend_in = None):
+
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    sModel = oCase_x_in.sModel
+    sRegion = oCase_x_in.sRegion
+    iFlag_same_grid = oCase_x_in.iFlag_same_grid
+
+    iYear_start = oCase_x_in.iYear_start
+    iYear_end = oCase_x_in.iYear_end
+
+    iYear_subset_start = oCase_x_in.iYear_subset_start
+    iYear_subset_end = oCase_x_in.iYear_subset_end
+
+    sLabel_Y = oCase_x_in.sLabel_y
+    dConversion = oCase_x_in.dConversion
+    sVariable = oCase_x_in.sVariable
+    sCase = oCase_x_in.sCase
+    sWorkspace_simulation_case_run = oCase_x_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case_x = oCase_x_in.sWorkspace_analysis_case
+
+
+    #new approach
+    aLon, aLat,aMask = elm_retrieve_case_dimension_info(oCase_x_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
+
+    print('Prepare the map grid')
+   
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+    sWorkspace_variable_dat_x = sWorkspace_analysis_case_x + slash + sVariable +  slash + 'dat'
+   
+    #read the stack data
+    sFilename_x = sWorkspace_variable_dat_x + slash + sVariable  + sExtension_envi
+
+    if os.path.exists(sFilename_x):
+        #print("Yep, I can read that file: " + sFilename_x)                
+        pass
+    else:
+        print(sFilename_x + ' is missing')
+        print("Nope, the path doesn't reach your file. Go research filepath in python")
+        return
+
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable
+  
+    if not os.path.exists(sWorkspace_analysis_case_variable_x):
+        os.makedirs(sWorkspace_analysis_case_variable_x)
+
+    sWorkspace_analysis_case_region_x = sWorkspace_analysis_case_variable_x + slash + 'map_diff_w_obs_dc'
+    if not os.path.exists(sWorkspace_analysis_case_region_x):
+        os.makedirs(sWorkspace_analysis_case_region_x)
+        pass
+
+    if iFlag_annual_mean ==1:
+        aData_all_x = elm_retrieve_variable_2d(  oCase_x_in,\
+                    iFlag_monthly_in =0,\
+                        iFlag_annual_mean_in=1,\
+                    iFlag_annual_total_in= 0  )
+
+        aImage_x= aData_all_x.pop()  
+        aImage_x = np.reshape(aImage_x , (nrow, ncolumn)) 
+        #get date
+
+        sFilename_out = sWorkspace_analysis_case_region_x + slash \
+            + sVariable + '_map_diff_w_obs_dc'  +'.png'         
+    
+      
+        aImage_y = np.array(aData_y_in)  
+        nan_index = np.where(aData_y_in == -9999)
+        aImage = (aImage_x - aImage_y)  #/aImage_y
+
+        aData_all = np.array(aImage)                  
+
+        aData_all = aData_all * dConversion
+        aData_all[nan_index] = -9999    
+
+        map_raster_data_dc(aData_all,  aImage_extent,\
+                              sFilename_out,\
+                                sExtend_in = sExtend_in,\
+                                  sTitle_in = sTitle_in,\
+                                      sUnit_in=sUnit_in,\
+                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                        iFlag_contour_in= 1,\
+                                               aInterval_in = aInterval_in,\
+                aColor_in = aColor_in,\
+                                       dData_max_in = dData_max_in,\
+                                          dData_min_in = dData_min_in,
+                                  dMissing_value_in = -9999, aLabel_legend_in=aLegend_in)
+                            
+    
+
+
+
+    print("finished")
+
+
diff --git a/pye3sm/elm/general/structured/twod/map/elm_map_variable_halfdegree.py b/pye3sm/elm/general/structured/twod/map/elm_map_variable_halfdegree.py
deleted file mode 100644
index 860b55d..0000000
--- a/pye3sm/elm/general/structured/twod/map/elm_map_variable_halfdegree.py
+++ /dev/null
@@ -1,149 +0,0 @@
-
-#no longer used because we will use IDL for this step
-import os
-import sys
-
-import numpy as np
-import matplotlib as mpl
-
-import matplotlib.pyplot as plt
-import matplotlib.ticker as ticker
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-
-from cartopy.mpl.geoaxes import GeoAxes
-from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
-from mpl_toolkits.axes_grid1 import AxesGrid
-
-from pyearth.system.define_global_variables import *
-from pyearth.gis.gdal.gdal_read_envi_file import gdal_read_envi_file_multiple
-
-def fomatter(x, pos):
-    a, b = '{:.2e}'.format(x).split('e')
-    b = int(b)
-    return r'${} \times 10^{{{}}}$'.format(a, b)
-
-def elm_map_variable_halfdegree(sFilename_configuration_in, \
-    iCase_index, \
-    iFlag_same_grid_in = None, \
-    iYear_start_in = None, \
-    iYear_end_in = None, \
-    sDate_in = None):
-    #local
-    e3sm_read_configuration_file(sFilename_configuration_in, iCase_index_in = iCase_index,\
-         iYear_start_in = iYear_start_in, \
-    iYear_end_in = iYear_end_in, \
-         sDate_in= sDate_in)      
-    sModel = 'vsfm'
-    sVariable ='qdrai'
-    mms2mmd = 24 * 3600.0
-    dConversion = mms2mmd
-    iCase = 11
-    sCase = sModel + "{:02d}".format(iCase)
-    iYear_start = 1948
-    iYear_end = 2013
-    iMonth_start = 1
-    iMonth_end = 12
-
-
-    #for the sake of simplicity, all directory will be the same, no matter on mac or    cluster
-    sWorkspace_data = home + slash + 'data'
-    sWorkspace_simulation = sWorkspace_scratch + slash + 'csmruns'
-    sWorkspace_analysis = sWorkspace_scratch + slash + '03model' + slash \
-        + sModel + slash + 'analysis'
-        
-    if not os.path.isdir(sWorkspace_analysis):
-        os.makedirs(sWorkspace_analysis)
-
-
-    sWorkspace_simulation_case = sWorkspace_simulation + slash  + sCase + slash + 'run'
-
-    sWorkspace_analysis_case = sWorkspace_analysis + slash + sCase 
-
-    if not os.path.exists(sWorkspace_analysis_case):
-        os.makedirs(sWorkspace_analysis_case)
-
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable.lower() +    slash + 'dat'
-
-    sWorkspace_variable_png = sWorkspace_analysis_case + slash + sVariable.lower() +    slash + 'png'
-    if not os.path.exists(sWorkspace_variable_png):
-                        os.makedirs(sWorkspace_variable_png)
-
-
-    #read the stack data
-
-    sFilename = sWorkspace_variable_dat + slash + sVariable.lower()  + sExtension_envi
-
-    aData_all = gdal_read_envi_file_multiple(sFilename)
-
-    for iYear in range(iYear_start, iYear_end + 1):
-        sYear = str(iYear).zfill(4)
-        for iMonth in range(iMonth_start, iMonth_end + 1):
-            sMonth = str(iMonth).zfill(2)
-            i = (iYear-iYear_start) * 12 + iMonth-iMonth_start
-            img=(aData_all[0])[i]
-            min_value = -4
-            max_value = 1.5
-            aMask1 = np.where(  img != missing_value )  
-            aMask2 = np.where(  img == missing_value )
-            aMask3 = np.where(  img == 0.0 )
-
-            img = img * dConversion
-            img[aMask2] = 10**(min_value )
-            img[aMask3] = 10**(min_value )
-            img = np.log10(img) 
-            print(np.max(img))
-            print(np.min(img))
-            img[ np.where(  img < min_value )  ] = min_value
-            img[ np.where(  img > max_value )  ] = max_value
-            img[aMask2] = np.nan               
-            img_extent=(-180,180,-90,90)
-            #plot
-            fig = plt.figure(figsize=(12,9),  dpi=100 )
-            cmap = plt.get_cmap('rainbow') 
-            projection = ccrs.PlateCarree()
-            axes_class = (GeoAxes,
-                      dict(map_projection=projection))
-            axgr = AxesGrid(fig, 111, axes_class=axes_class,
-                        nrows_ncols=(1,1),
-                        axes_pad=0.6,
-                        cbar_location='right',
-                        cbar_mode='single',
-                        cbar_pad=0.2,
-                        cbar_size='1.5%',
-                        label_mode='')  # note the empty label_mode
-
-            for i, ax in enumerate(axgr):
-                #ax.coastlines()
-                ax.axis('off')
-                #ax.gridlines() 
-                #ax.add_feature(cfeature.OCEAN, zorder=0)
-                #ax.add_feature(cfeature.LAND, zorder=0, edgecolor='black')
-                #ax.add_feature(cfeature.COASTLINE)
-                ax.set_global()
-                ax.set_xmargin(0.05)
-                ax.set_ymargin(0.10)
-                ax.set_xticks(np.linspace(-180, 180, 5), crs=projection)
-                ax.set_yticks(np.linspace(-90, 90, 5), crs=projection)
-                lon_formatter = LongitudeFormatter(zero_direction_label=True)
-                lat_formatter = LatitudeFormatter()
-                ax.xaxis.set_major_formatter(lon_formatter)
-                ax.yaxis.set_major_formatter(lat_formatter)
-                ax.set_title('Total Drainage (mm/day)', loc='center')
-                implot = ax.imshow(img, extent = img_extent, origin='upper',cmap=cmap , \
-                 vmax = max_value, vmin = min_value , transform = projection ) 
-
-            #axgr.cbar_axes[0].colorbar(p)
-            cb_label_log = 1 * np.arange( (max_value - min_value + 1) , dtype= float) +     min_value
-            cb_label = np.power(10.0, cb_label_log)
-            cb = plt.colorbar(implot, cax = axgr.cbar_axes[0], extend = 'both')
-            tick_locs   = cb_label_log
-            tick_labels = ['{:.0e}'.format(x) for x in cb_label]
-            cb.locator     = ticker.FixedLocator(tick_locs)
-            cb.formatter   = ticker.FixedFormatter(tick_labels)       
-            cb.update_ticks()
-            #plt.show()
-            sFilename_png= sWorkspace_variable_png + slash + sVariable.lower() + sYear +    sMonth + sExtension_png
-            plt.savefig(sFilename_png, bbox_inches = 'tight')
-            print(sFilename_png)
-            plt.close('all')
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_histogram_variable_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_histogram_variable_2d.py
new file mode 100644
index 0000000..8505ca4
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_histogram_variable_2d.py
@@ -0,0 +1,249 @@
+import os, sys
+import numpy as np
+
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+
+from pyearth.visual.histogram.histogram_plot import histogram_plot
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+def elm_histogram_variable_2d(oE3SM_in, \
+                              oCase_in,\
+                              iFlag_log_in = None,\
+                              iFlag_scientific_notation_in=None,\
+                              iFlag_monthly_in = None,\
+                              iFlag_annual_mean_in = None,\
+                              iFlag_annual_total_in = None,\
+                              dMax_x_in = None,\
+                              dMin_x_in = None,\
+                              dSpace_x_in = None,\
+                                sFormat_x_in= None,\
+                              sLabel_x_in=None,\
+                              sLabel_y_in = None,\
+                              sTitle_in =None,\
+                                aLegend_in=None):
+
+    if iFlag_log_in is not None:
+        iFlag_log = iFlag_log_in
+    else: 
+        iFlag_log = 0
+
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    sModel = oCase_in.sModel
+    sRegion = oCase_in.sRegion
+    iFlag_same_grid = oCase_in.iFlag_same_grid
+
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    sLabel_Y = oCase_in.sLabel_y
+    dConversion = oCase_in.dConversion
+    sVariable = oCase_in.sVariable
+    sCase = oCase_in.sCase
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat,aMask =elm_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+
+    print('Prepare the map grid')
+   
+    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
+    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
+
+    #read the stack data
+
+    sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
+
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable):
+        os.makedirs(sWorkspace_analysis_case_variable)
+
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'histogram'
+    if not os.path.exists(sWorkspace_analysis_case_region):
+        os.makedirs(sWorkspace_analysis_case_region)
+        pass
+
+
+    if iFlag_monthly ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_monthly_in = 1)
+        for i in np.arange(0, nstress_subset, 1):           
+            pDate = dates_subset[i]
+            sDate = pDate.strftime('%Y%m%d')
+            aImage = aData_ret[i]
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            nan_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[nan_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)           
+            aData = np.array(dummy1)
+            if iFlag_log  == 1:
+                aData = np.log10(aData)
+                #set inf to min
+                good_index = np.where( np.isinf(  aData) == False  )
+                aData = aData[good_index] 
+
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+                + sVariable + '_histo_' + sDate +'.png'    
+
+            aData_all = [aData]    
+            
+            histogram_plot( aData_all,\
+                                  sFilename_out,\
+                                    iFlag_log_in = iFlag_log_in,\
+                                  ncolumn_in =1,\
+                                    dMin_x_in=dMin_x_in,\
+                                  dMax_x_in = dMax_x_in,\
+                                  sTitle_in = sTitle_in, \
+                                           sLabel_x_in= sLabel_x_in,\
+                                  sLabel_y_in= sLabel_y_in,\
+                                  sFormat_x_in= sFormat_x_in ,\
+                                  aLabel_legend_in = aLegend_in, \
+                                  #aColor_in = ['black'],\
+                                  sLocation_legend_in = 'upper left' ,\
+                                  aLocation_legend_in = (0.0, 1.0),\
+                                  iSize_x_in = 12,\
+                                  iSize_y_in = 5)
+
+    if iFlag_annual_mean ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_mean_in = 1)
+        #annual mean
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            aImage = aData_ret[iYear-iYear_start]
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            nan_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[nan_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)           
+            aData = np.array(dummy1)
+
+            if iFlag_log  == 1:
+                aData = np.log10(aData)
+                #set inf to min
+                good_index = np.where( np.isinf(  aData) == False  )
+                aData = aData[good_index] 
+
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_histo_mean_'+ sYear +'.png'
+
+            aData_all = [aData]    
+          
+            
+            histogram_plot( aData_all,\
+                                  sFilename_out,\
+                                    iFlag_log_in = iFlag_log_in,\
+                                  ncolumn_in =1,\
+                                    dMin_x_in=dMin_x_in,\
+                                  dMax_x_in = dMax_x_in,\
+                                  sTitle_in = sTitle_in, \
+                                           sLabel_x_in= sLabel_x_in,\
+                                  sLabel_y_in= sLabel_y_in,\
+                                  sFormat_x_in= sFormat_x_in ,\
+                                  aLabel_legend_in = aLegend_in, \
+                                  #aColor_in = ['black'],\
+                                  sLocation_legend_in = 'upper left' ,\
+                                  aLocation_legend_in = (0.0, 1.0),\
+                                  iSize_x_in = 12,\
+                                  iSize_y_in = 5)
+
+        pass
+    
+    if iFlag_annual_total == 1: #annual total
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_total_in = 1)
+        for iYear in range(iYear_start, iYear_end + 1):
+            aImage = aData_ret[iYear-iYear_start]
+            sYear = "{:04d}".format(iYear)    
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            nan_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[nan_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)           
+            aData = np.array(dummy1)
+            if iFlag_log  == 1:
+                aData = np.log10(aData)
+                #set inf to min
+                good_index = np.where( np.isinf(  aData) == False  )
+                aData = aData[good_index]   
+
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_histo_annual_total_'+ sYear +'.png'       
+
+            aData_all = [aData]    
+          
+
+            histogram_plot( aData_all,\
+                                  sFilename_out,\
+                                    iFlag_log_in = iFlag_log_in,\
+                                  ncolumn_in =1,\
+                                    dMin_x_in=dMin_x_in,\
+                                  dMax_x_in = dMax_x_in,\
+                                  sTitle_in = sTitle_in, \
+                                    sLabel_x_in= sLabel_x_in,\
+                                  sLabel_y_in= sLabel_y_in,\
+                                  sFormat_x_in= sFormat_x_in ,\
+                                  aLabel_legend_in = aLegend_in, \
+                                  sLocation_legend_in = 'upper left' ,\
+                                  aLocation_legend_in = (0.0, 1.0),\
+                                  iSize_x_in = 12,\
+                                  iSize_y_in = 5)
+        pass
+
+    print("finished")
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variable_x_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variable_x_2d.py
new file mode 100644
index 0000000..f5739d7
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variable_x_2d.py
@@ -0,0 +1,148 @@
+import os, sys
+import numpy as np
+import datetime
+
+import scipy.stats
+
+
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file_multiple_band
+from pyearth.visual.scatter.scatter_plot_data_density import scatter_plot_data_density
+from pyearth.visual.scatter.scatter_plot_data import scatter_plot_data
+
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+def elm_scatterplot_variable_x_2d(oE3SM_in,\
+                                         oCase_x_in,\
+                                         aData_y_in, \
+                                          sVariable_y_in,\
+                                         iFlag_scientific_notation_x_in=None,\
+                                         iFlag_scientific_notation_y_in=None,\
+                                         iFlag_log_x_in=None,\
+                                         iFlag_log_y_in=None,\
+                                         dMin_x_in = None, \
+                                         dMax_x_in = None, \
+                                         dMin_y_in = None, \
+                                         dMax_y_in = None, \
+                                         dSpace_x_in = None, \
+                                         dSpace_y_in = None, \
+                                         sLabel_x_in = None, \
+                                         sLabel_y_in = None,\
+                                         sLabel_legend_in =None   ):
+
+
+
+    sModel = oCase_x_in.sModel
+    sRegion = oCase_x_in.sRegion
+
+    iYear_start = oCase_x_in.iYear_start
+    iYear_end = oCase_x_in.iYear_end
+    iFlag_same_grid = oCase_x_in.iFlag_same_grid
+
+    dConversion = oCase_x_in.dConversion
+    sVariable_x = oCase_x_in.sVariable
+ 
+    sCase = oCase_x_in.sCase
+
+    sWorkspace_analysis_case_x = oCase_x_in.sWorkspace_analysis_case
+  
+
+   
+    #plot
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable_x
+
+    sWorkspace_analysis_case_grid = sWorkspace_analysis_case_variable_x + slash + 'scatterplot'
+    if not os.path.exists(sWorkspace_analysis_case_grid):
+        os.makedirs(sWorkspace_analysis_case_grid)
+        pass
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case_x + slash + sVariable_x + slash + 'tiff'
+    #read the stack data
+    sFilename_x = sWorkspace_variable_dat + slash + sVariable_x  + sExtension_tiff
+    aData_all_x = gdal_read_geotiff_file_multiple_band(sFilename_x)
+    aVariable_x = np.array(aData_all_x[0]).astype(float)
+
+    
+
+    #reshape the data
+    #pick a year and month as a snapshot
+    iYear = 2000
+    iMonth = 8
+    iIndex  = ( iYear - iYear_start ) * 12 + iMonth
+    x = aVariable_x[iIndex, :, :]
+   
+    y = aData_y_in
+
+    #remove missing value
+    good_index = np.where(  (x != missing_value)&(y != missing_value)  )
+    x= x[good_index]
+    y= y[good_index]
+
+    aCorrelation = scipy.stats.kendalltau(x, y)
+    print(aCorrelation)
+
+    x = x * oCase_x_in.dConversion
+ 
+
+    x.flatten()
+    y.flatten()
+
+    
+
+    if iFlag_log_y_in == 1:
+        aData_y = np.log10(y)
+        dMin_y_dummy = aData_y[np.isfinite(aData_y)].min()
+        #set inf to min
+        bad_index = np.where( np.isinf(  aData_y) == True  )
+        aData_y[bad_index] = dMin_y_dummy
+        #we can also calculate correlation here
+        y= aData_y
+
+    #we could have two options to produce simple scatter plot or density map
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '_' + sVariable_y_in + '_scatterplot.png'
+    scatter_plot_data(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
+                              iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
+                              iFlag_log_x_in=iFlag_log_x_in,\
+                              iFlag_log_y_in=iFlag_log_y_in,\
+                                iFlag_lowess_in=1,\
+                              dMin_x_in = dMin_x_in, \
+                              dMax_x_in = dMax_x_in, \
+                              dMin_y_in = dMin_y_in, \
+                              dMax_y_in = dMax_y_in, \
+                              dSpace_x_in = dSpace_x_in, \
+                              dSpace_y_in = dSpace_y_in, \
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x_in,\
+                              sLabel_y_in= sLabel_y_in,\
+                              sLabel_legend_in = sLabel_legend_in)
+
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '_' + sVariable_y_in + '_densityplot.png'
+    scatter_plot_data_density(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
+                              iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
+                              iFlag_log_x_in=iFlag_log_x_in,\
+                              iFlag_log_y_in=iFlag_log_y_in,\
+                              dMin_x_in = dMin_x_in, \
+                              dMax_x_in = dMax_x_in, \
+                              dMin_y_in = dMin_y_in, \
+                              dMax_y_in = dMax_y_in, \
+                              dSpace_x_in = dSpace_x_in, \
+                              dSpace_y_in = dSpace_y_in, \
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x_in,\
+                              sLabel_y_in= sLabel_y_in,\
+                              sLabel_legend_in = sLabel_legend_in)
+
+    print("finished")
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variable_y_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variable_y_2d.py
new file mode 100644
index 0000000..ddb97c1
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variable_y_2d.py
@@ -0,0 +1,150 @@
+import os, sys
+import numpy as np
+import datetime
+
+import scipy.stats
+
+
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file_multiple_band
+from pyearth.visual.scatter.scatter_plot_data_density import scatter_plot_data_density
+from pyearth.visual.scatter.scatter_plot_data import scatter_plot_data
+
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+def elm_scatterplot_variable_y_2d(oE3SM_in,\
+                                         aData_x_in,\
+                                         oCase_y_in, \
+                                         iFlag_scientific_notation_x_in=None,\
+                                         iFlag_scientific_notation_y_in=None,\
+                                         iFlag_log_x_in=None,\
+                                         iFlag_log_y_in=None,\
+                                         dMin_x_in = None, \
+                                         dMax_x_in = None, \
+                                         dMin_y_in = None, \
+                                         dMax_y_in = None, \
+                                         dSpace_x_in = None, \
+                                         dSpace_y_in = None, \
+                                         sLabel_x_in = None, \
+                                         sLabel_y_in = None,\
+                                         sLabel_legend_in =None   ):
+
+
+
+    sModel = oCase_y_in.sModel
+    sRegion = oCase_y_in.sRegion
+
+    iYear_start = oCase_y_in.iYear_start
+    iYear_end = oCase_y_in.iYear_end
+    iFlag_same_grid = oCase_y_in.iFlag_same_grid
+
+    dConversion = oCase_y_in.dConversion
+    sVariable_x = oCase_y_in.sVariable
+    sVariable_y = oCase_y_in.sVariable
+    sCase = oCase_y_in.sCase
+
+    sWorkspace_analysis_case_x = oCase_y_in.sWorkspace_analysis_case
+    sWorkspace_analysis_case_y = oCase_y_in.sWorkspace_analysis_case
+
+   
+    #plot
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable_x
+    sWorkspace_analysis_case_variable_y = sWorkspace_analysis_case_y + slash + sVariable_y
+    sWorkspace_analysis_case_grid = sWorkspace_analysis_case_variable_y + slash + 'scatterplot'
+    if not os.path.exists(sWorkspace_analysis_case_grid):
+        os.makedirs(sWorkspace_analysis_case_grid)
+        pass
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case_x + slash + sVariable_x + slash + 'tiff'
+    #read the stack data
+    sFilename_x = sWorkspace_variable_dat + slash + sVariable_x  + sExtension_tiff
+    aData_all_x = gdal_read_geotiff_file_multiple_band(sFilename_x)
+    aVariable_x = np.array(aData_all_x[0]).astype(float)
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case_y + slash + sVariable_y +  slash + 'tiff'
+    #read the stack data
+    sFilename_y = sWorkspace_variable_dat + slash + sVariable_y  + sExtension_tiff
+    aData_all_y = gdal_read_geotiff_file_multiple_band(sFilename_y)
+    aVariable_y = np.array(aData_all_y[0]).astype(float)
+
+    #reshape the data
+    #pick a year and month as a snapshot
+    iYear = 2000
+    iMonth = 8
+    iIndex  = ( iYear - iYear_start ) * 12 + iMonth
+    x = aVariable_x[iIndex, :, :]
+    y = aVariable_y[iIndex, :, :]
+
+    #remove missing value
+    good_index = np.where(  (x != missing_value)&(y != missing_value)  )
+    x= x[good_index]
+    y= y[good_index]
+
+    aCorrelation = scipy.stats.kendalltau(x, y)
+    print(aCorrelation)
+
+    x = x * oCase_y_in.dConversion
+    y = y * oCase_y_in.dConversion
+
+    x.flatten()
+    y.flatten()
+
+    
+
+    if iFlag_log_y_in == 1:
+        aData_y = np.log10(y)
+        dMin_y_dummy = aData_y[np.isfinite(aData_y)].min()
+        #set inf to min
+        bad_index = np.where( np.isinf(  aData_y) == True  )
+        aData_y[bad_index] = dMin_y_dummy
+        #we can also calculate correlation here
+        y= aData_y
+
+    #we could have two options to produce simple scatter plot or density map
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '_' + sVariable_y + '_scatterplot.png'
+    scatter_plot_data(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
+                              iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
+                              iFlag_log_x_in=iFlag_log_x_in,\
+                              iFlag_log_y_in=iFlag_log_y_in,\
+                                iFlag_lowess_in=1,\
+                              dMin_x_in = dMin_x_in, \
+                              dMax_x_in = dMax_x_in, \
+                              dMin_y_in = dMin_y_in, \
+                              dMax_y_in = dMax_y_in, \
+                              dSpace_x_in = dSpace_x_in, \
+                              dSpace_y_in = dSpace_y_in, \
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x_in,\
+                              sLabel_y_in= sLabel_y_in,\
+                              sLabel_legend_in = sLabel_legend_in)
+
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '_' + sVariable_y + '_densityplot.png'
+    scatter_plot_data_density(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
+                              iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
+                              iFlag_log_x_in=iFlag_log_x_in,\
+                              iFlag_log_y_in=iFlag_log_y_in,\
+                              dMin_x_in = dMin_x_in, \
+                              dMax_x_in = dMax_x_in, \
+                              dMin_y_in = dMin_y_in, \
+                              dMax_y_in = dMax_y_in, \
+                              dSpace_x_in = dSpace_x_in, \
+                              dSpace_y_in = dSpace_y_in, \
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x_in,\
+                              sLabel_y_in= sLabel_y_in,\
+                              sLabel_legend_in = sLabel_legend_in)
+
+    print("finished")
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_2d.py
new file mode 100644
index 0000000..b224384
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_2d.py
@@ -0,0 +1,151 @@
+import os, sys
+import numpy as np
+import datetime
+
+import scipy.stats
+
+
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file_multiple_band
+from pyearth.visual.scatter.scatter_plot_data_density import scatter_plot_data_density
+from pyearth.visual.scatter.scatter_plot_data import scatter_plot_data
+
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+def elm_scatterplot_variables_2d(oE3SM_in,\
+                                         oCase_x_in,\
+                                         oCase_y_in, \
+                                         iFlag_scientific_notation_x_in=None,\
+                                         iFlag_scientific_notation_y_in=None,\
+                                         iFlag_log_x_in=None,\
+                                         iFlag_log_y_in=None,\
+                                         dMin_x_in = None, \
+                                         dMax_x_in = None, \
+                                         dMin_y_in = None, \
+                                         dMax_y_in = None, \
+                                         dSpace_x_in = None, \
+                                         dSpace_y_in = None, \
+                                         sLabel_x_in = None, \
+                                         sLabel_y_in = None,\
+                                          sFormat_y_in=None,\
+                                         sLabel_legend_in =None   ):
+
+
+
+    sModel = oCase_x_in.sModel
+    sRegion = oCase_x_in.sRegion
+
+    iYear_start = oCase_x_in.iYear_start
+    iYear_end = oCase_x_in.iYear_end
+    iFlag_same_grid = oCase_x_in.iFlag_same_grid
+
+    dConversion = oCase_x_in.dConversion
+    sVariable_x = oCase_x_in.sVariable
+    sVariable_y = oCase_y_in.sVariable
+    sCase = oCase_x_in.sCase
+
+    sWorkspace_analysis_case_x = oCase_x_in.sWorkspace_analysis_case
+    sWorkspace_analysis_case_y = oCase_y_in.sWorkspace_analysis_case
+
+   
+    #plot
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable_x
+    sWorkspace_analysis_case_variable_y = sWorkspace_analysis_case_y + slash + sVariable_y
+    sWorkspace_analysis_case_grid = sWorkspace_analysis_case_variable_y + slash + 'scatterplot'
+    if not os.path.exists(sWorkspace_analysis_case_grid):
+        os.makedirs(sWorkspace_analysis_case_grid)
+        pass
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case_x + slash + sVariable_x + slash + 'tiff'
+    #read the stack data
+    sFilename_x = sWorkspace_variable_dat + slash + sVariable_x  + sExtension_tiff
+    aData_all_x = gdal_read_geotiff_file_multiple_band(sFilename_x)
+    aVariable_x = np.array(aData_all_x[0]).astype(float)
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case_y + slash + sVariable_y +  slash + 'tiff'
+    #read the stack data
+    sFilename_y = sWorkspace_variable_dat + slash + sVariable_y  + sExtension_tiff
+    aData_all_y = gdal_read_geotiff_file_multiple_band(sFilename_y)
+    aVariable_y = np.array(aData_all_y[0]).astype(float)
+
+    #reshape the data
+    #pick a year and month as a snapshot
+    iYear = 2000
+    iMonth = 8
+    iIndex  = ( iYear - iYear_start ) * 12 + iMonth
+    x = aVariable_x[iIndex, :, :]
+    y = aVariable_y[iIndex, :, :]
+
+    #remove missing value
+    good_index = np.where(  (x != missing_value)&(y != missing_value)  )
+    x= x[good_index]
+    y= y[good_index]
+
+    aCorrelation = scipy.stats.kendalltau(x, y)
+    print(aCorrelation)
+
+    x = x * oCase_x_in.dConversion
+    y = y * oCase_y_in.dConversion
+
+    x.flatten()
+    y.flatten()    
+
+    if iFlag_log_y_in == 1:
+        aData_y = np.log10(y)
+        dMin_y_dummy = aData_y[np.isfinite(aData_y)].min()
+        #set inf to min
+        bad_index = np.where( np.isinf(  aData_y) == True  )
+        aData_y[bad_index] = dMin_y_dummy
+        #we can also calculate correlation here
+        y= aData_y
+
+    #we could have two options to produce simple scatter plot or density map
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '_' + sVariable_y + '_scatterplot.png'
+    scatter_plot_data(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
+                              iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
+                              iFlag_log_x_in=iFlag_log_x_in,\
+                              iFlag_log_y_in=iFlag_log_y_in,\
+                                iFlag_lowess_in=1,\
+                              dMin_x_in = dMin_x_in, \
+                              dMax_x_in = dMax_x_in, \
+                              dMin_y_in = dMin_y_in, \
+                              dMax_y_in = dMax_y_in, \
+                              dSpace_x_in = dSpace_x_in, \
+                              dSpace_y_in = dSpace_y_in, \
+                                sFormat_y_in=sFormat_y_in,\
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x_in,\
+                              sLabel_y_in= sLabel_y_in,\
+                              sLabel_legend_in = sLabel_legend_in)
+
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '_' + sVariable_y + '_densityplot.png'
+    scatter_plot_data_density(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
+                              iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
+                              iFlag_log_x_in=iFlag_log_x_in,\
+                              iFlag_log_y_in=iFlag_log_y_in,\
+                              dMin_x_in = dMin_x_in, \
+                              dMax_x_in = dMax_x_in, \
+                              dMin_y_in = dMin_y_in, \
+                              dMax_y_in = dMax_y_in, \
+                              dSpace_x_in = dSpace_x_in, \
+                              dSpace_y_in = dSpace_y_in, \
+                                sFormat_y_in=sFormat_y_in,\
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x_in,\
+                              sLabel_y_in= sLabel_y_in,\
+                              sLabel_legend_in = sLabel_legend_in)
+
+    print("finished")
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_halfdegree.py b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_2d_multi_case.py
similarity index 73%
rename from pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_halfdegree.py
rename to pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_2d_multi_case.py
index 00ab743..b3822ca 100644
--- a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_halfdegree.py
+++ b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_2d_multi_case.py
@@ -1,37 +1,36 @@
 import os, sys
 import numpy as np
-import datetime
 
+import datetime
 import scipy.stats
 
-
-
 from pyearth.system.define_global_variables import *
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file_multiple_band
-from pyearth.visual.scatter.scatter_plot_data_density import scatter_plot_data_density
 
-from pyearth.visual.scatter.scatter_plot_data import scatter_plot_data
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file, gdal_read_geotiff_file_multiple_band
+
+from pyearth.visual.scatter.scatter_plot_multiple_data import scatter_plot_multiple_data
 
 
 from pye3sm.shared.e3sm import pye3sm
 from pye3sm.shared.case import pycase
 
-def elm_scatterplot_variables_halfdegree(oE3SM_in,\
-                                         oCase_x_in,\
-                                         oCase_y_in, \
-                                         iFlag_scientific_notation_x_in=None,\
-                                         iFlag_scientific_notation_y_in=None,\
-                                         iFlag_log_x_in=None,\
-                                         iFlag_log_y_in=None,\
-                                         dMin_x_in = None, \
-                                         dMax_x_in = None, \
-                                         dMin_y_in = None, \
-                                         dMax_y_in = None, \
-                                         dSpace_x_in = None, \
-                                         dSpace_y_in = None, \
-                                         sLabel_x_in = None, \
-                                         sLabel_y_in = None,\
-                                         sLabel_legend_in =None   ):
+def elm_scatterplot_variables_2d_multi_case(oE3SM_in,\
+    iCase_start_in, iCase_end_in,\
+                                                oCase_x_in,\
+                                                oCase_y_in, \
+                                                iFlag_scientific_notation_x_in=None,\
+                                                iFlag_scientific_notation_y_in=None,\
+                                                iFlag_log_x_in=None,\
+                                                iFlag_log_y_in=None,\
+                                                dMin_x_in = None, \
+                                                dMax_x_in = None, \
+                                                dMin_y_in = None, \
+                                                dMax_y_in = None, \
+                                                dSpace_x_in = None, \
+                                                dSpace_y_in = None,\
+                                                sLabel_x_in = None, \
+                                                sLabel_y_in = None,\
+                                                sLabel_legend_in =None):
 
 
 
@@ -108,7 +107,7 @@ def elm_scatterplot_variables_halfdegree(oE3SM_in,\
 
     #we could have two options to produce simple scatter plot or density map
 
-    scatter_plot_data_density(x, y,\
+    scatter_plot_multiple_data(x, y,\
                               sFilename_out,\
                               iSize_x_in = 8,\
                               iSize_y_in = 8, \
@@ -130,5 +129,4 @@ def elm_scatterplot_variables_halfdegree(oE3SM_in,\
     print("finished")
 
 
-if __name__ == '__main__':
-    import argparse
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_halfdegree_domain.py b/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_halfdegree_domain.py
deleted file mode 100644
index 5392523..0000000
--- a/pye3sm/elm/general/structured/twod/plot/elm_scatterplot_variables_halfdegree_domain.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import os, sys
-import numpy as np
-
-import datetime
-import scipy.stats
-
-from pyearth.system.define_global_variables import *
-
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file, gdal_read_geotiff_file_multiple_band
-
-from pyearth.visual.scatter.scatter_plot_data import scatter_plot_data
-from pyearth.visual.scatter.scatter_plot_data_density import scatter_plot_data_density
-
-
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-
-def elm_scatterplot_variables_halfdegree_domain(oE3SM_in,\
-                                                oCase_x_in,\
-                                                oCase_y_in, \
-                                                iFlag_scientific_notation_x_in=None,\
-                                                iFlag_scientific_notation_y_in=None,\
-                                                iFlag_log_x_in=None,\
-                                                iFlag_log_y_in=None,\
-                                                dMin_x_in = None, \
-                                                dMax_x_in = None, \
-                                                dMin_y_in = None, \
-                                                dMax_y_in = None, \
-                                                dSpace_x_in = None, \
-                                                dSpace_y_in = None,\
-                                                sLabel_x_in = None, \
-                                                sLabel_y_in = None,\
-                                                sLabel_legend_in =None):
-
-
-
-    sModel = oCase_x_in.sModel
-    sRegion = oCase_x_in.sRegion
-
-    iYear_start = oCase_x_in.iYear_start
-    iYear_end = oCase_x_in.iYear_end
-    iFlag_same_grid = oCase_x_in.iFlag_same_grid
-    dConversion = oCase_x_in.dConversion
-    sVariable_x = oCase_x_in.sVariable
-    sVariable_y = oCase_y_in.sVariable
-    sCase = oCase_x_in.sCase
-
-    sWorkspace_analysis_case = oCase_x_in.sWorkspace_analysis_case
-
-    iFlag_optional = 1
-
-
-    nrow = 360
-    ncolumn = 720
-
-    sWorkspace_data_auxiliary_basin = sWorkspace_data + slash + sModel + slash + sRegion + slash \
-        + 'auxiliary' + slash + 'basins'
-    aBasin = ['amazon','congo','mississippi','yangtze']
-
-    nDomain = len(aBasin)
-
-
-
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable_x +    slash + 'tiff'
-    #read the stack data
-
-    sFilename_x = sWorkspace_variable_dat + slash + sVariable_x   + sExtension_tiff
-
-    aData_all_x = gdal_read_geotiff_file_multiple_band(sFilename_x)
-    aVariable_x = aData_all_x[0]
-
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable_y  +    slash + 'tiff'
-    #read the stack data
-
-    sFilename_y = sWorkspace_variable_dat + slash + sVariable_y   + sExtension_tiff
-
-    aData_all_y = gdal_read_geotiff_file_multiple_band(sFilename_y)
-    aVariable_y = aData_all_y[0]
-
-
-    #plot
-    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable_x
-
-
-    sWorkspace_analysis_case_grid = sWorkspace_analysis_case_variable + slash + 'scatterplot'
-    if not os.path.exists(sWorkspace_analysis_case_grid):
-        os.makedirs(sWorkspace_analysis_case_grid)
-        pass
-
-    #reshape the data
-    #pick a year and month
-    iYear = 2000
-    iMonth = 8
-    iIndex  = ( iYear - iYear_start ) * 12 + iMonth
-    x0 = aVariable_x[iIndex, :, :]
-    y0 = aVariable_y[iIndex, :, :]
-
-
-    for iDomain in np.arange(nDomain):
-        sDomain = aBasin[iDomain]
-        sLabel_legend = sDomain.title()
-        sFilename_basin = sWorkspace_data_auxiliary_basin + slash + sDomain + slash + sDomain + '.tif'
-        dummy = gdal_read_geotiff_file(sFilename_basin)
-        dummy_mask1 = dummy[0]
-
-
-        dummy_index = np.where(dummy_mask1 ==1)
-        x1 = x0[dummy_index]
-        y1 = y0[dummy_index]
-
-        #remove missing value
-        good_index = np.where(  (x1 != missing_value)&(y1 != missing_value)  )
-        x2= x1[good_index]
-        y2= y1[good_index]
-
-        aCorrelation = scipy.stats.kendalltau(x2, y2)
-        print(aCorrelation)
-
-        x = x2 * oCase_x_in.dConversion
-        y = y2 * oCase_y_in.dConversion
-
-        if iFlag_log_y_in == 1:
-            aData_y = np.log10(y)
-            #set inf to min
-            bad_index = np.where( np.isinf(  aData_y) == True  )
-            aData_y[bad_index] = dMin_y_in
-            y=aData_y
-
-
-
-        sFilename_out = sWorkspace_analysis_case_grid + slash \
-            + sVariable_x + '-' +  sVariable_y +'-'+ sDomain + '_scatterplot.png'
-
-        scatter_plot_data(x, y,\
-                          sFilename_out,\
-                          iSize_x_in = 8,\
-                          iSize_y_in = 8, \
-                          iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
-                          iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
-                          iFlag_log_x_in=iFlag_log_x_in,\
-                          iFlag_log_y_in=iFlag_log_y_in,\
-                          dMin_x_in = dMin_x_in, \
-                          dMax_x_in = dMax_x_in, \
-                          dMin_y_in = dMin_y_in, \
-                          dMax_y_in = dMax_y_in, \
-                          dSpace_x_in = dSpace_x_in, \
-                          dSpace_y_in = dSpace_y_in, \
-                          sTitle_in = '', \
-                          sLabel_x_in= sLabel_x_in,\
-                          sLabel_y_in= sLabel_y_in,\
-                          sLabel_legend_in = sLabel_legend)
-        pass
-
-    print("finished")
-
-
-if __name__ == '__main__':
-    import argparse
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_ts_analysis_plot_variable_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_ts_analysis_plot_variable_2d.py
new file mode 100644
index 0000000..5738de2
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_ts_analysis_plot_variable_2d.py
@@ -0,0 +1,253 @@
+import os
+import numpy as np
+import datetime
+
+from pyearth.system.define_global_variables import *
+
+from pyearth.visual.timeseries.analysis.plot_time_series_analysis import plot_time_series_analysis
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+from pyearth.toolbox.data.cgpercentiles import cgpercentiles
+ 
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+
+def elm_ts_analysis_plot_variable_2d(oE3SM_in,\
+                                                    oCase_in, \
+                                                    iFlag_log_in = None,\
+                                                        iFlag_scientific_notation_in=None,\
+                                                           iFlag_monthly_in = None,\
+                                          iFlag_annual_mean_in = None,\
+                                          iFlag_annual_total_in = None,\
+                                              iFlag_median_in=None,\
+                                                    iReverse_y_in =None,\
+                                                    dMin_x_in = None, \
+                                                    dMax_x_in = None, \
+                                                    dMin_y_in = None, \
+                                                    dMax_y_in = None, \
+                                                    dSpace_x_in = None, \
+                                                    dSpace_y_in = None, \
+                                                        sFormat_y_in=None,\
+                                                    sLabel_x_in=None,
+                                                    sLabel_y_in = None,\
+                                                    sTitle_in =None,\
+                                                        aLabel_legend_in=None):
+
+
+
+
+    if iFlag_log_in is not None:
+        iFlag_log = iFlag_log_in
+    else: 
+        iFlag_log = 0
+    
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+    
+    if iFlag_median_in is None:
+        iFlag_median = 0
+    else:
+        iFlag_median = iFlag_median_in    
+
+
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    sLabel_Y = oCase_in.sLabel_y
+    dConversion = oCase_in.dConversion
+    sVariable = oCase_in.sVariable
+    
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat, aMask =elm_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    dates = list()
+    dates_year=list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        dSimulation0 = datetime.datetime(iYear, 6, 30)
+        dates_year.append( dSimulation0 )
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable):
+        os.makedirs(sWorkspace_analysis_case_variable)
+
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'tsaplot'
+    if not os.path.exists(sWorkspace_analysis_case_region):
+        os.makedirs(sWorkspace_analysis_case_region)
+        pass
+
+
+    if iFlag_monthly ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_monthly_in = 1)        
+        aDataTs = np.full(nstress_subset, -9999, dtype=float)
+       
+        for i in np.arange(0, nstress_subset, 1):
+            aImage = aData_ret[i]     
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]          
+          
+        
+            if iFlag_median ==1:                
+                aDataTs[i] = np.median(dummy1)             
+            
+            else:
+                aDataTs[i] = np.mean(dummy1)                                  
+
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)          
+
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsaplot_monthly' +'.png'
+
+
+        plot_time_series_analysis(dates_subset, \
+                                  aDataTs,\
+                                  sFilename_out,\
+                                  sLabel_y_in,\
+                                  iFlag_without_raw_in =1,\
+                                  iFlag_log_in =iFlag_log_in,\
+                                         iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                                  iReverse_y_in = iReverse_y_in, \
+                                  dMin_x_in = dMin_x_in, \
+                                  dMax_x_in = dMax_x_in, \
+                                  dMin_y_in = dMin_y_in, \
+                                  dMax_y_in = dMax_y_in, \
+                                  dSpace_x_in = dSpace_x_in, \
+                                  dSpace_y_in = dSpace_y_in, \
+                                  sLabel_x_in = sLabel_x_in,\
+                                        sFormat_y_in=sFormat_y_in,\
+                                            sTitle_in=sTitle_in,\
+                                              aLabel_legend_in=aLabel_legend_in,\
+                                  iSize_x_in = 10,\
+                                  iSize_y_in = 9)
+
+    if iFlag_annual_mean ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_mean_in = 1)        
+        aDataTs = np.full(nyear, -9999, dtype=float)
+        for iYear in range(iYear_start, iYear_end + 1):            
+            aImage = aData_ret[iYear-iYear_start]            
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            dummy1 = remove_outliers(dummy1, 0.05)
+            aDataTs[iYear-iYear_start] = np.nanmean(dummy1) 
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsaplot_annual_mean' +'.png'
+    
+
+        plot_time_series_analysis(dates_subset, \
+                                  aDataTs,\
+                                  sFilename_out,\
+                                  sLabel_y_in,\
+                                  iFlag_without_raw_in =1,\
+                                  iFlag_log_in =iFlag_log_in,\
+                                         iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                                  iReverse_y_in = iReverse_y_in, \
+                                  dMin_x_in = dMin_x_in, \
+                                  dMax_x_in = dMax_x_in, \
+                                  dMin_y_in = dMin_y_in, \
+                                  dMax_y_in = dMax_y_in, \
+                                  dSpace_x_in = dSpace_x_in, \
+                                  dSpace_y_in = dSpace_y_in, \
+                                  sLabel_x_in = sLabel_x_in,\
+                                    sFormat_y_in=sFormat_y_in,\
+                                              sTitle_in=sTitle_in,\
+                                        aLabel_legend_in=aLabel_legend_in,\
+                                  iSize_x_in = 10,\
+                                  iSize_y_in = 9)
+
+    if iFlag_annual_total ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_total_in = 1)        
+        aDataTs = np.full(nyear, -9999, dtype=float)
+        for iYear in range(iYear_start, iYear_end + 1):
+            aImage = aData_ret[iYear-iYear_start]
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)
+            aDataTs[iYear-iYear_start] = np.nanmean(dummy1) 
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsaplot_annual_total' +'.png'               
+
+        plot_time_series_analysis(dates_subset, \
+                                  aDataTs,\
+                                  sFilename_out,\
+                                  sLabel_y_in,\
+                                  iFlag_without_raw_in =1,\
+                                  iFlag_log_in =iFlag_log_in,\
+                                    iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                                  iReverse_y_in = iReverse_y_in, \
+                                  dMin_x_in = dMin_x_in, \
+                                  dMax_x_in = dMax_x_in, \
+                                  dMin_y_in = dMin_y_in, \
+                                  dMax_y_in = dMax_y_in, \
+                                  dSpace_x_in = dSpace_x_in, \
+                                  dSpace_y_in = dSpace_y_in, \
+                                  sLabel_x_in = sLabel_x_in,\
+                                        sFormat_y_in=sFormat_y_in,\
+                                                  sTitle_in=sTitle_in,\
+                                              aLabel_legend_in=aLabel_legend_in,\
+                                  iSize_x_in = 10,\
+                                  iSize_y_in = 9)
+
+    print("finished")
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_ts_analysis_plot_variable_halfdegree_domain.py b/pye3sm/elm/general/structured/twod/plot/elm_ts_analysis_plot_variable_halfdegree_domain.py
deleted file mode 100644
index 480feaa..0000000
--- a/pye3sm/elm/general/structured/twod/plot/elm_ts_analysis_plot_variable_halfdegree_domain.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os, sys
-import numpy as np
-import numpy.ma as ma
-import datetime
-
-from pyearth.system.define_global_variables import *
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
-from pyearth.visual.timeseries.analysis.plot_time_series_analysis import plot_time_series_analysis
-
-from pyearth.toolbox.data.remove_outliers import remove_outliers
-
-
-def elm_ts_analysis_plot_variable_halfdegree_domain(oE3SM_in,\
-                                                    oCase_in, \
-                                                    iFlag_log_in = None,\
-                                                    iReverse_y_in =None,\
-                                                    dMin_x_in = None, \
-                                                    dMax_x_in = None, \
-                                                    dMin_y_in = None, \
-                                                    dMax_y_in = None, \
-                                                    dSpace_x_in = None, \
-                                                    dSpace_y_in = None, \
-                                                    sLabel_x_in=None,
-                                                    sLabel_y_in = None,\
-                                                    sTitle_in =None):
-
-
-
-
-    sModel = oCase_in.sModel
-    sRegion = oCase_in.sRegion
-
-    iYear_start = oCase_in.iYear_start
-
-    iYear_end = oCase_in.iYear_end
-
-    iFlag_same_grid = oCase_in.iFlag_same_grid
-
-
-    iYear_subset_start = oCase_in.iYear_subset_start
-
-    iYear_subset_end = oCase_in.iYear_subset_end
-
-    print('The following model is processed: ', sModel)
-
-    dConversion = oCase_in.dConversion
-    sVariable = oCase_in.sVariable
-    sCase = oCase_in.sCase
-    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
-    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
-
-    nrow = 360
-    ncolumn = 720
-
-    #read basin mask
-    sWorkspace_data_auxiliary_basin = sWorkspace_data + slash + sModel + slash + sRegion + slash \
-        + 'auxiliary' + slash + 'basins'
-    aBasin = ['amazon','congo','mississippi','yangtze']
-
-    nDomain = len(aBasin)
-
-    dates = list()
-    nyear = iYear_end - iYear_start + 1
-    for iYear in range(iYear_start, iYear_end + 1):
-        for iMonth in range(1,13):
-            dSimulation = datetime.datetime(iYear, iMonth, 1)
-            dates.append( dSimulation )
-            pass
-
-    nstress = nyear * nmonth
-
-    iMonth = 1
-    index_start = (iYear_subset_start - iYear_start)* 12 + iMonth - 1
-    index_end = (iYear_subset_end + 1 - iYear_start)* 12 + iMonth - 1
-    subset_index = np.arange(index_start , index_end , 1 )
-
-
-    dates=np.array(dates)
-    dates_subset = dates[subset_index]
-    nstress_subset= len(dates_subset)
-
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable.lower() +  slash + 'dat'
-
-
-    #read the stack data
-
-    sFilename = sWorkspace_variable_dat + slash + sVariable.lower()  + sExtension_envi
-
-    aData_all = gdal_read_envi_file_multiple_band(sFilename)
-    aVariable_total = aData_all[0]
-    aVariable_total_subset = aVariable_total[subset_index,:,:]
-
-
-    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
-    if not os.path.exists(sWorkspace_analysis_case_variable):
-        os.makedirs(sWorkspace_analysis_case_variable)
-        pass
-
-    sWorkspace_analysis_case_domain = sWorkspace_analysis_case_variable + slash + 'tsaplot'
-    if not os.path.exists(sWorkspace_analysis_case_domain):
-        os.makedirs(sWorkspace_analysis_case_domain)
-        pass
-
-    #aData_all=[]
-
-    for iDomain in np.arange(1, nDomain+1, 1):
-
-        sDomain = aBasin[iDomain-1]
-        sFilename_basin = sWorkspace_data_auxiliary_basin + slash + sDomain + slash + sDomain + '.tif'
-        dummy = gdal_read_geotiff_file(sFilename_basin)
-        dummy_mask1 = dummy[0]
-        sLabel_legend = sDomain.title()
-
-        pShape = aVariable_total_subset.shape
-        aVariable0= np.full(pShape, np.nan, dtype=float)
-        aVariable2 = np.full(nstress_subset, -9999, dtype=float)
-        for i in np.arange(0, pShape[0], 1):
-            aVariable0[i, :,:] = aVariable_total_subset[i, :,:]
-            aVariable0[i][dummy_mask1!=1] = np.nan
-            dummy1 = aVariable0[i, :,:]
-            dummy2 = remove_outliers(dummy1, 0.05)
-            aVariable2[i] = np.nanmean(dummy2)
-            pass
-
-        x = dates_subset
-        y = aVariable2
-
-        if iFlag_log_in == 1:
-            aData_y = np.log10(y)
-            #set inf to min
-            bad_index = np.where( np.isinf(  aData_y) == True  )
-            aData_y[bad_index] = dMin_y_in
-            y=aData_y
-
-        sFilename_out = sWorkspace_analysis_case_domain + slash \
-            + sVariable + '_tsaplot_' + sDomain +'.png'
-
-
-
-
-        plot_time_series_analysis(x, \
-                                  y,\
-                                  sFilename_out,\
-                                  sLabel_y_in,\
-                                  iFlag_without_raw_in =1,\
-                                  iFlag_log_in =iFlag_log_in,\
-                                  iReverse_y_in = iReverse_y_in, \
-                                  dMin_x_in = dMin_x_in, \
-                                  dMax_x_in = dMax_x_in, \
-                                  dMin_y_in = dMin_y_in, \
-                                  dMax_y_in = dMax_y_in, \
-                                  dSpace_x_in = dSpace_x_in, \
-                                  dSpace_y_in = dSpace_y_in, \
-                                  sTitle_in = sDomain, \
-                                  sLabel_x_in = sLabel_x_in,\
-                                  iSize_x_in = 10,\
-                                  iSize_y_in = 9)
-
-    print("finished")
-
-
-if __name__ == '__main__':
-    import argparse
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_total_water_storage_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_total_water_storage_2d.py
new file mode 100644
index 0000000..f60a036
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_total_water_storage_2d.py
@@ -0,0 +1,223 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+import calendar
+import scipy.ndimage as ndimage
+
+from pyearth.system.define_global_variables import *
+from pyearth.toolbox.reader.text_reader_string import text_reader_string
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+from pyearth.visual.timeseries.plot_time_series_data import plot_time_series_data
+from pyearth.visual.color.create_qualitative_rgb_color_hex import create_qualitative_rgb_color_hex
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.mesh.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+
+def elm_tsplot_total_water_storage_2d(oE3SM_in, \
+                                                     oCase_in, \
+                                                     dMax_y_in = None,\
+                                                     dMin_y_in =None):
+
+
+    sModel = oCase_in.sModel
+    sRegion = oCase_in.sRegion
+    iFlag_same_grid = oCase_in.iFlag_same_grid
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+    sVariable = oCase_in.sVariable
+    sCase = oCase_in.sCase
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+    sWorkspace_analysis_case_domain = sWorkspace_analysis_case + slash + 'tsplot_tws'
+    if not os.path.exists(sWorkspace_analysis_case_domain):
+        os.makedirs(sWorkspace_analysis_case_domain)
+        pass    
+
+    #new approach
+    aLon, aLat , aMask_ll= elm_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    aMask_ul = np.flip(aMask_ll, 0)
+    nrow = np.array(aMask_ll).shape[0]
+    ncolumn = np.array(aMask_ll).shape[1]
+    aMask_index_ll = np.where(aMask_ll==1)
+    aMask_index_ul = np.where(aMask_ul==1)
+    nrow_extract, ncolumn_extract = aLon.shape
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn_extract-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow_extract-1)
+
+    iRow_index_top = int( ( 90-0.5*dResolution_y  - dLat_max ) /  dResolution_y )
+    iRow_index_bot = int(( 90-0.5*dResolution_y  - dLat_min ) /  dResolution_y )
+    iColumn_index_left = int( (dLon_min - ( -180+0.5*dResolution_x   ) ) / dResolution_x)
+    iColumn_index_right = int( (dLon_max - ( -180+0.5*dResolution_x   ) ) / dResolution_x)
+
+    aDate = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            aDate.append( dSimulation )
+
+    nstress = nyear * nmonth
+    iMonth = 1
+    index_start = (iYear_subset_start - iYear_start)* 12 + iMonth - 1
+    index_end = (iYear_subset_end + 1 - iYear_start)* 12 + iMonth - 1
+    subset_index = np.arange(index_start , index_end , 1 )
+    aDate=np.array(aDate)
+    aDate_subset = aDate[subset_index]
+    nstress_subset= len(aDate_subset)
+
+    #read greace date list
+    sFilename_grace_lut = '/qfs/people/liao313/data/h2sc/global/auxiliary/grace/data_date.txt'
+    dummy = text_reader_string(sFilename_grace_lut, cDelimiter_in=',')
+    grace_date0 = dummy[:,0]
+    grace_date1 = dummy[:,1]
+    grace_date2 = dummy[:,2]
+    grace_date3 = dummy[:,3]
+    grace_date4 = dummy[:,4]
+    aData_grace=np.full( (nstress_subset, nrow_extract, ncolumn_extract), -9999, dtype=float )
+    sFilename_pre = 'GRD-3_'
+    sFilename_suf = '_GRAC_JPLEM_BA01_0600_LND_v03.tif'
+    sWorkspace_grace  = '/compyfs/liao313/00raw/grace/RL06/v03/JPL'
+    iStress = 1
+    for iYear in np.arange( iYear_subset_start, iYear_subset_end +1):
+        for iMonth in np.arange(1, 13):
+            sYear = "{:04d}".format(iYear)
+            sYear2 = sYear[2:4]
+            sMonth =  "{:02d}".format(iMonth)
+            sMonth2 = calendar.month_abbr[iMonth]
+            sDate  = sMonth2 + sYear2
+            dummy_index = np.where( grace_date0 == sDate )
+            sDummy = grace_date4[dummy_index][0]
+            sDummy1 = sDummy.strip()
+            sDummy2 = sYear + sDummy1[5:8] + '-' + sYear + sDummy1[-3:]
+            sFilename_grace = sWorkspace_grace + slash \
+                + sFilename_pre + sDummy2 + sFilename_suf
+            print(sFilename_grace)
+            #read the file
+            aData_dummy0  = gdal_read_geotiff_file(sFilename_grace)
+            aData_dummy0 = aData_dummy0[0]
+
+            #resample the data because resolution is different
+            aData_dummy1 = np.flip(aData_dummy0, 0) #flip the data first
+            aData_dummy1 = np.roll(aData_dummy1, 180, axis=1) # right
+            #because the dimensions, we use a simple way to resample
+            #the grace resolution is 1.0 degree
+            
+            zoom_level = (1.0 / dResolution_x )
+            aData_dummy2 = ndimage.zoom(aData_dummy1, zoom_level, order=0)
+
+            aData_dummy2[np.where(aData_dummy2==-99999)] = -9999
+            aData_dummy2[np.where(aData_dummy2==-9999)] = np.nan
+            #extract 
+            aData_dummy3 = aData_dummy2[ iRow_index_top:iRow_index_bot+1, iColumn_index_left:iColumn_index_right+1 ]
+            
+            aData_grace[iStress - 1,:,:]=aData_dummy3
+            iStress=iStress+1
+
+
+    #read the stack data for each variable
+    aVariable = ['rain','snow', 'qdrai','qvegt',  'qvege','qsoil', 'qover']
+    nvariable = len(aVariable)
+    aVariable_tws = np.full(( nvariable, nstress_subset, nrow, ncolumn ),-9999, dtype=float)
+
+    #aVariable_begin_end =['tws_month_begin','tws_month_end']
+
+    for i in np.arange(1, nvariable+1):
+        sVariable = aVariable[i-1]
+        sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
+        sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
+        aData_all = gdal_read_envi_file_multiple_band(sFilename)
+        aVariable_total = aData_all[0]
+        aVariable_tws[i-1, :, :, :] = aVariable_total[subset_index,:,:]
+
+
+    aVariable_all = np.full((nvariable, nstress_subset), -9999, dtype=float)
+    for i in np.arange(1, nvariable+1):
+        sVariable = aVariable[i-1]
+        aVariable_total_subset = aVariable_tws[i-1, :, : , :]
+        aVariable0 = aVariable_total_subset.reshape(nstress_subset,nrow , ncolumn)
+        aVariable2 = np.full(nstress_subset, -9999, dtype=float)
+        for iStress in  np.arange(1,nstress_subset+1):
+            dummy = aVariable0[iStress-1, :,:]
+            dummy1 = dummy[aMask_index_ul]
+            
+            aVariable2[iStress-1] = np.nanmean(dummy1)
+        #shoule we shift one time step
+        #aVariable2 = np.roll(aVariable2, 1)
+        aVariable_all[i-1, :] = aVariable2
+        #use the equation here
+        #S = Rain + Snow - (qsoil + qvege + qvegt) - (Runoff)
+        #grace
+    iStress = 1
+    aVariable6 = np.full(nstress_subset, -9999, dtype=float)
+    for iStress in np.arange(1,nstress_subset+1):
+        dummy = aData_grace[iStress-1, :,:]
+      
+        dummy1 = dummy[aMask_index_ul]
+        dummy1[dummy1==-9999] = np.nan
+        aVariable6[iStress-1] = np.nanmean(dummy1)
+        #use regional mean instead of grid
+    if np.isnan(aVariable_all).all():
+        pass
+    else:
+        #separated
+        sFilename_out = sWorkspace_analysis_case_domain + slash \
+            + 'flux_tsplot_' + sRegion +'.png'
+        aDate_ts = np.tile(aDate_subset,(nvariable,1))
+        aData_ts = aVariable_all 
+        sLabel_Y = r'Water flux ($mm \, s^{-1}$)'
+        sTitle= sRegion.title()
+        aColor = create_qualitative_rgb_color_hex(7)
+        plot_time_series_data(aDate_ts, \
+                              aData_ts,\
+                              sFilename_out,\
+                              iFlag_scientific_notation_in=1, \
+                                #iFlag_log_in=1,\
+                              ncolumn_in= 7,\
+                              #dMax_y_in = dMax_y_in,\
+                              #dMin_y_in = dMin_y_in,\
+                              sTitle_in = sTitle, \
+                              sLabel_y_in= sLabel_Y,\
+                              sFormat_y_in = '%.1e',\
+                              sLocation_legend_in = 'upper right' ,\
+                              aColor_in= aColor,\
+                              aLabel_legend_in = aVariable, \
+                              iSize_x_in = 12,\
+                              iSize_y_in = 5)
+        #combined
+        aDate_ts = [aDate_subset, aDate_subset ]
+        #be careful
+        aTWS_ts = aVariable_all[0,:]+ aVariable_all[1,:] \
+            - (aVariable_all[2,:]+ aVariable_all[3,:] + aVariable_all[4,:])\
+            - (aVariable_all[5,:]+ aVariable_all[6,:])
+        #aTWs_ts_end = aVariable_all[8,:]
+        #aTWs_ts_begin = np.roll(aTWs_ts_end, -1)
+        #aTWS_ts = aTWs_ts_end - aTWs_ts_begin
+        aData_ts = [aTWS_ts *31* 24*3600/1000, aVariable6]
+        sLabel_legend = sRegion.title()
+        sFilename_out = sWorkspace_analysis_case_domain + slash \
+            + 'tws_tsplot_' + sRegion +'.png'
+        sLabel_Y = r'Total water storage variation (m)'
+        plot_time_series_data(aDate_ts, aData_ts,\
+                              sFilename_out,\
+                              ncolumn_in=2,\
+                              dMax_y_in = 0.3,\
+                              dMin_y_in = -0.3,\
+                              sTitle_in = sTitle, \
+                              sLabel_y_in= sLabel_Y,\
+                              aLabel_legend_in = ['ELM-HLGF', 'GRACE'], \
+                              aMarker_in=['+','*'],\
+                              iSize_x_in = 12,\
+                              iSize_y_in = 5)
+
+    print("finished")
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_total_water_storage_halfdegree_domain.py b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_total_water_storage_halfdegree_domain.py
deleted file mode 100644
index a6f2492..0000000
--- a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_total_water_storage_halfdegree_domain.py
+++ /dev/null
@@ -1,248 +0,0 @@
-import os, sys
-import numpy as np
-import numpy.ma as ma
-import datetime
-import calendar
-import scipy.ndimage as ndimage
-
-from pyearth.system.define_global_variables import *
-from pyearth.toolbox.reader.text_reader_string import text_reader_string
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
-from pyearth.visual.timeseries.plot_time_series_data import plot_time_series_data
-from pyearth.visual.color.create_qualitative_rgb_color_hex import create_qualitative_rgb_color_hex
-from pyearth.toolbox.data.remove_outliers import remove_outliers
-
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-def elm_tsplot_total_water_storage_halfdegree_domain(oE3SM_in, \
-                                                     oCase_in, \
-                                                     dMax_y_in = None,\
-                                                     dMin_y_in =None):
-
-
-    sModel = oCase_in.sModel
-    sRegion = oCase_in.sRegion
-    iFlag_same_grid = oCase_in.iFlag_same_grid
-    iYear_start = oCase_in.iYear_start
-    iYear_end = oCase_in.iYear_end
-
-
-    iYear_subset_start = oCase_in.iYear_subset_start
-
-    iYear_subset_end = oCase_in.iYear_subset_end
-
-
-    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
-
-
-    dConversion = oCase_in.dConversion
-    sVariable = oCase_in.sVariable
-    sCase = oCase_in.sCase
-    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
-    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
-    sWorkspace_analysis_case_domain = sWorkspace_analysis_case + slash + 'tsplot_tws_domain'
-    if not os.path.exists(sWorkspace_analysis_case_domain):
-        os.makedirs(sWorkspace_analysis_case_domain)
-        pass
-
-    nrow = 360
-    ncolumn = 720
-
-    #read basin mask
-    sWorkspace_data_auxiliary_basin = sWorkspace_data + slash  \
-        + sModel + slash + sRegion + slash \
-        + 'auxiliary' + slash + 'basins'
-    aBasin = ['amazon','congo','mississippi','yangtze']
-
-    nDomain = len(aBasin)
-
-
-    aDate = list()
-    nyear = iYear_end - iYear_start + 1
-    for iYear in range(iYear_start, iYear_end + 1):
-        for iMonth in range(1,13):
-            dSimulation = datetime.datetime(iYear, iMonth, 15)
-            aDate.append( dSimulation )
-
-    nstress = nyear * nmonth
-
-    iMonth = 1
-    index_start = (iYear_subset_start - iYear_start)* 12 + iMonth - 1
-    index_end = (iYear_subset_end + 1 - iYear_start)* 12 + iMonth - 1
-    subset_index = np.arange(index_start , index_end , 1 )
-    aDate=np.array(aDate)
-    aDate_subset = aDate[subset_index]
-    nstress_subset= len(aDate_subset)
-
-    #read greace date list
-    sFilename_grace_lut = '/qfs/people/liao313/data/h2sc/global/auxiliary/grace/data_date.txt'
-    dummy = text_reader_string(sFilename_grace_lut, cDelimiter_in=',')
-    grace_date0 = dummy[:,0]
-    grace_date1 = dummy[:,1]
-    grace_date2 = dummy[:,2]
-    grace_date3 = dummy[:,3]
-    grace_date4 = dummy[:,4]
-    aData_grace=np.full( (nstress_subset, nrow, ncolumn), -9999, dtype=float )
-    sFilename_pre = 'GRD-3_'
-    sFilename_suf = '_GRAC_JPLEM_BA01_0600_LND_v03.tif'
-    sWorkspace_grace  = '/compyfs/liao313/00raw/grace/RL06/v03/JPL'
-    iStress = 1
-    for iYear in np.arange( iYear_subset_start, iYear_subset_end +1):
-        for iMonth in np.arange(1, 13):
-            sYear = "{:04d}".format(iYear)
-            sYear2 = sYear[2:4]
-            sMonth =  "{:02d}".format(iMonth)
-            sMonth2 = calendar.month_abbr[iMonth]
-            sDate  = sMonth2 + sYear2
-            dummy_index = np.where( grace_date0 == sDate )
-            sDummy = grace_date4[dummy_index][0]
-            sDummy1 = sDummy.strip()
-            sDummy2 = sYear + sDummy1[5:8] + '-' + sYear + sDummy1[-3:]
-            sFilename_grace = sWorkspace_grace + slash \
-                + sFilename_pre + sDummy2 + sFilename_suf
-            print(sFilename_grace)
-            #read the file
-            aData_dummy0  = gdal_read_geotiff_file(sFilename_grace)
-            aData_dummy0 = aData_dummy0[0]
-
-            #resample the data because resolution is different
-            aData_dummy1 = np.flip(aData_dummy0, 0)
-            aData_dummy1 = np.roll(aData_dummy1, 180, axis=1) # right
-            #because the dimensions, we use simple way to resample
-            aData_dummy2 = ndimage.zoom(aData_dummy1, 2, order=0)
-
-            aData_dummy2[np.where(aData_dummy2==-99999)] = -9999
-            aData_dummy2[np.where(aData_dummy2==-9999)] = np.nan
-            #plt.imshow(aData_dummy2)
-            #plt.show()
-            aData_grace[iStress - 1,:,:]=aData_dummy2
-            iStress=iStress+1
-
-
-    #read the stack data for each variable
-    aVariable = ['rain','snow', 'qdrai','qvegt',  'qvege','qsoil', 'qover']
-    nvariable = len(aVariable)
-    aVariable_tws = np.full(( nvariable, nstress_subset, nrow, ncolumn ),-9999, dtype=float)
-
-    #aVariable_begin_end =['tws_month_begin','tws_month_end']
-
-    for i in np.arange(1, nvariable+1):
-        sVariable = aVariable[i-1]
-        sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
-        sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
-        aData_all = gdal_read_envi_file_multiple_band(sFilename)
-        aVariable_total = aData_all[0]
-        aVariable_tws[i-1, :, :, :] = aVariable_total[subset_index,:,:]
-
-    for iDomain in np.arange(1, nDomain+1):
-
-        sDomain = aBasin[iDomain-1]
-        sTitle = sDomain.title()
-        sFilename_basin = sWorkspace_data_auxiliary_basin + slash + sDomain + slash + sDomain + '.tif'
-        dummy = gdal_read_geotiff_file(sFilename_basin)
-        dummy_mask1 = dummy[0]
-
-
-        aVariable_all = np.full((nvariable, nstress_subset), -9999, dtype=float)
-        for i in np.arange(1, nvariable+1):
-            sVariable = aVariable[i-1]
-            aVariable_total_subset = aVariable_tws[i-1, :, : , :]
-            aVariable0 = aVariable_total_subset.reshape(nstress_subset,nrow , ncolumn)
-            aVariable2 = np.full(nstress_subset, -9999, dtype=float)
-
-            for iStress in  np.arange(1,nstress_subset+1):
-                dummy = aVariable0[iStress-1, :,:]
-                dummy1 = dummy[dummy_mask1 == 1]
-                #remove outlier
-                if(i==3):
-                    dummy1 = remove_outliers(dummy1, 0.1)
-                    pass
-
-                aVariable2[iStress-1] = np.nanmean(dummy1)
-
-            #shoule we shift one time step
-
-            aVariable2 = np.roll(aVariable2, 1)
-
-            aVariable_all[i-1, :] = aVariable2
-            #use the equation here
-            #S = Rain + Snow - (qsoil + qvege + qvegt) - (Runoff)
-            #grace
-        iStress = 1
-
-        aVariable6 = np.full(nstress_subset, -9999, dtype=float)
-        for iStress in np.arange(1,nstress_subset+1):
-            dummy = aData_grace[iStress-1, :,:]
-
-            #plt.imshow(dummy)
-            #plt.show()
-            #print(dummy)
-            dummy1 = dummy[dummy_mask1 == 1]
-            dummy1[dummy1==-9999] = np.nan
-            aVariable6[iStress-1] = np.nanmean(dummy1)
-            #use regional mean instead of grid
-
-        if np.isnan(aVariable_all).all():
-            pass
-        else:
-            #separated
-            sFilename_out = sWorkspace_analysis_case_domain + slash \
-                + 'flux_tsplot_' + sDomain +'.png'
-
-            aDate_ts = np.tile(aDate_subset,(nvariable,1))
-            aData_ts = aVariable_all
-            sLabel_Y = r'Water flux ($mm  s^{-1}$)'
-            aColor = create_qualitative_rgb_color_hex(7)
-            plot_time_series_data(aDate_ts, \
-                                  aData_ts,\
-                                  sFilename_out,\
-                                  iFlag_scientific_notation_in=1, \
-                                  ncolumn_in= 7,\
-                                  dMax_y_in = dMax_y_in,\
-                                  dMin_y_in = dMin_y_in,\
-                                  sTitle_in = sTitle, \
-                                  sLabel_y_in= sLabel_Y,\
-                                  sFormat_y_in = '%.1e',\
-                                  sLocation_legend_in = 'upper right' ,\
-                                  aColor_in= aColor,\
-                                  aLabel_legend_in = aVariable, \
-                                  iSize_x_in = 12,\
-                                  iSize_y_in = 5)
-            #combined
-            aDate_ts = [aDate_subset, aDate_subset ]
-            #be careful
-
-            aTWS_ts = aVariable_all[0,:]+ aVariable_all[1,:] \
-                - (aVariable_all[2,:]+ aVariable_all[3,:] + aVariable_all[4,:])\
-                - (aVariable_all[5,:]+ aVariable_all[6,:])
-
-
-            #aTWs_ts_end = aVariable_all[8,:]
-            #aTWs_ts_begin = np.roll(aTWs_ts_end, -1)
-            #aTWS_ts = aTWs_ts_end - aTWs_ts_begin
-
-            aData_ts = [aTWS_ts *31* 24*3600/1000, aVariable6]
-            sLabel_legend = sDomain.title()
-            sFilename_out = sWorkspace_analysis_case_domain + slash \
-                + 'tws_tsplot_' + sDomain +'.png'
-
-            sLabel_Y = r'Total water storage variation (m)'
-
-            plot_time_series_data(aDate_ts, aData_ts,\
-                                  sFilename_out,\
-                                  ncolumn_in=2,\
-                                  dMax_y_in = 0.3,\
-                                  dMin_y_in = -0.3,\
-                                  sTitle_in = sTitle, \
-                                  sLabel_y_in= sLabel_Y,\
-                                  aLabel_legend_in = ['ELM-H2SC', 'GRACE'], \
-                                  aMarker_in=['+','*'],\
-                                  iSize_x_in = 12,\
-                                  iSize_y_in = 5)
-
-    print("finished")
-
-
-if __name__ == '__main__':
-    import argparse
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d.py b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d.py
index 8795823..d501255 100644
--- a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d.py
+++ b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d.py
@@ -1,30 +1,54 @@
 import os, sys
 import numpy as np
-import numpy.ma as ma
+
 import datetime
 
 from pyearth.system.define_global_variables import *
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
-from pyearth.visual.color.create_diverge_rgb_color_hex import create_diverge_rgb_color_hex
+
 
 from pyearth.visual.timeseries.plot_time_series_data import plot_time_series_data
+from pyearth.visual.timeseries.plot_time_series_data_w_variation import plot_time_series_data_w_variation
 
 from pyearth.toolbox.data.remove_outliers import remove_outliers
 from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
  
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
 
 def elm_tsplot_variable_2d(oE3SM_in, \
                                           oCase_in,\
+                                            iReverse_y_in=None,\
+                                        iFlag_log_in = None,\
+                                             iFlag_scientific_notation_in=None,\
+                                                   iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
                                           dMax_y_in = None,\
                                           dMin_y_in = None,
                                           dSpace_y_in = None,\
                                           sLabel_x_in=None,\
-                                          sLabel_z_in = None,\
+                                          sLabel_y_in = None,\
                                           sTitle_in =None):
 
+    if iFlag_log_in is not None:
+        iFlag_log = iFlag_log_in
+    else: 
+        iFlag_log = 0
+    
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
     sModel = oCase_in.sModel
     sRegion = oCase_in.sRegion
     iFlag_same_grid = oCase_in.iFlag_same_grid
@@ -43,29 +67,18 @@ def elm_tsplot_variable_2d(oE3SM_in, \
     sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
 
     #new approach
-    aLon, aLat,aMask =elm_retrieve_case_dimension_info(oCase_in)
+    aLon, aLat, aMask =elm_retrieve_case_dimension_info(oCase_in)
     #dimension
     nrow = np.array(aMask).shape[0]
     ncolumn = np.array(aMask).shape[1]
     aMask = np.where(aMask==0)
 
-    #resolution
-    dLon_min = np.min(aLon)
-    dLon_max = np.max(aLon)
-    dLat_min = np.min(aLat)
-    dLat_max = np.max(aLat)
-    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
-    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
-
-    print('Prepare the map grid')
-   
-    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
-    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
-    
-
     dates = list()
+    dates_year=list()
     nyear = iYear_end - iYear_start + 1
     for iYear in range(iYear_start, iYear_end + 1):
+        dSimulation0 = datetime.datetime(iYear, 6, 30)
+        dates_year.append( dSimulation0 )
         for iMonth in range(1,13):
             dSimulation = datetime.datetime(iYear, iMonth, 15)
             dates.append( dSimulation )
@@ -78,92 +91,156 @@ def elm_tsplot_variable_2d(oE3SM_in, \
     subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
     subset_index = np.arange( subset_index_start,subset_index_end, 1 )
 
-
     dates=np.array(dates)
     dates_subset = dates[subset_index]
     nstress_subset= len(dates_subset)
 
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
-
-
-    #read the stack data
-
-    sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
-
-    aData_all = gdal_read_envi_file_multiple_band(sFilename)
-    aVariable_total = aData_all[0]
-    aVariable_total_subset = aVariable_total[subset_index,:,:]
-
-
     sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
     if not os.path.exists(sWorkspace_analysis_case_variable):
         os.makedirs(sWorkspace_analysis_case_variable)
 
-    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'tsplot_domain'
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'tsplot'
     if not os.path.exists(sWorkspace_analysis_case_region):
         os.makedirs(sWorkspace_analysis_case_region)
         pass
 
-    aData_all=[]
-    aLabel_legend=[]
-    
-    
-
-    sLabel_legend = sRegion.title()
-    sFilename_out = sWorkspace_analysis_case_region + slash \
-        + sVariable + '_tsplot_' + sDomain +'.png'
 
+    if iFlag_monthly ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_monthly_in = 1)        
+        aDataTs = np.full(nstress_subset, -9999, dtype=float)
+        for i in np.arange(0, nstress_subset, 1):
+            aImage = aData_ret[i]     
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)
+            aDataTs[i] = np.nanmean(dummy1) 
     
-    pShape = aVariable_total_subset.shape
-    aVariable0= np.full(pShape, np.nan, dtype=float)
-    aVariable2 = np.full(nstress_subset, -9999, dtype=float)
-
-    for i in np.arange(0, pShape[0], 1):
-        aVariable0[i, :,:] = aVariable_total_subset[i, :,:]
-        
-        dummy1 = aVariable0[i, :,:]
-        #dummy2 = remove_outliers(dummy1, 0.05)
-        aVariable2[i] = np.nanmean(dummy2)
-        pass
-    aData_all.append(aVariable2)
-    aLabel_legend.append(sLabel_legend)
-    pass
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
 
-    #aData_all = np.log10(aData_all)
-    ##set inf to min
-    #bad_index = np.where( np.isinf(  aData_all) == True  )
-    #aData_all[bad_index] = dMin_y_in
-    aData_all = np.array(aData_all)    
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsplot_monthly' +'.png'
 
+        aDate_all = [dates_subset]
+        aData_all = [aDataTs]    
+        aLabel_legend=[ sVariable + ' monthly' ]
 
-    sFilename_out = sWorkspace_analysis_case_region + slash \
-        + sVariable + '_tsplot' +'.png'
+        plot_time_series_data(aDate_all,
+                          aData_all,\
+                          sFilename_out,\
+                          iReverse_y_in = iReverse_y_in, \
+                          iFlag_log_in = iFlag_log_in,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in =1,\
+                          dMax_y_in = dMax_y_in,\
+                          dMin_y_in = dMin_y_in,\
+                          dSpace_y_in = dSpace_y_in, \
+                          sTitle_in = sTitle_in, \
+                          sLabel_y_in= sLabel_y_in,\
+                          sFormat_y_in= '%.2f' ,\
+                          aLabel_legend_in = aLabel_legend, \
+                          aColor_in = ['black'],\
+                          aMarker_in = ['o'],\
+                          sLocation_legend_in = 'lower right' ,\
+                          aLocation_legend_in = (1.0, 0.0),\
+                          aLinestyle_in = ['-'],\
+                          iSize_x_in = 12,\
+                          iSize_y_in = 5)
 
-    aDate_all = np.array([dates_subset])
+    if iFlag_annual_mean ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_mean_in = 1)        
+        aDataTs = np.full(nyear, -9999, dtype=float)
+        for iYear in range(iYear_start, iYear_end + 1):            
+            aImage = aData_ret[iYear-iYear_start]            
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            dummy1 = remove_outliers(dummy1, 0.05)
+            aDataTs[iYear-iYear_start] = np.nanmean(dummy1) 
     
-    plot_time_series_data(aDate_all,
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsplot_annual_mean' +'.png'
+
+        aDate_all = [dates_year]
+        aData_all = [aDataTs]    
+        aLabel_legend=[ sVariable + ' annual_mean' ]
+        plot_time_series_data(aDate_all,
                           aData_all,\
                           sFilename_out,\
-                          iReverse_y_in = 1, \
-                          #iFlag_log_in = 1,\
-                          ncolumn_in = 4,\
+                          iReverse_y_in = iReverse_y_in, \
+                          iFlag_log_in = iFlag_log_in,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in =1,\
                           dMax_y_in = dMax_y_in,\
                           dMin_y_in = dMin_y_in,\
                           dSpace_y_in = dSpace_y_in, \
                           sTitle_in = sTitle_in, \
-                          sLabel_y_in= sLabel_Y,\
-                          sFormat_y_in= '%1d' ,\
+                          sLabel_y_in= sLabel_y_in,\
+                          sFormat_y_in= '{:.3f}' ,\
                           aLabel_legend_in = aLabel_legend, \
-                          aColor_in = aColor,\
-                          aMarker_in = ['o','.','*','+'],\
+                          aColor_in = ['black'],\
+                          aMarker_in = ['o'],\
                           sLocation_legend_in = 'lower right' ,\
                           aLocation_legend_in = (1.0, 0.0),\
-                          aLinestyle_in = ['-','--','-.' ,'solid'],\
+                          aLinestyle_in = ['-'],\
                           iSize_x_in = 12,\
                           iSize_y_in = 5)
 
+    if iFlag_annual_total ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_total_in = 1)        
+        aDataTs = np.full(nyear, -9999, dtype=float)
+        for iYear in range(iYear_start, iYear_end + 1):
+            aImage = aData_ret[iYear-iYear_start]
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)
+            aDataTs[iYear-iYear_start] = np.nanmean(dummy1) 
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsplot_annual_total' +'.png'
+
+        aDate_all = [dates_year]
+        aData_all = [aDataTs]    
+        aLabel_legend=[ sVariable + ' annual_total' ]
+        plot_time_series_data(aDate_all,
+                          aData_all,\
+                          sFilename_out,\
+                          iReverse_y_in = iReverse_y_in, \
+                          iFlag_log_in = iFlag_log_in,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in =1,\
+                          dMax_y_in = dMax_y_in,\
+                          dMin_y_in = dMin_y_in,\
+                          dSpace_y_in = dSpace_y_in, \
+                          sTitle_in = sTitle_in, \
+                          sLabel_y_in= sLabel_y_in,\
+                          sFormat_y_in= '{:.3f}' ,\
+                          aLabel_legend_in = aLabel_legend, \
+                          aColor_in = ['black'],\
+                          aMarker_in = ['o'],\
+                          sLocation_legend_in = 'lower right' ,\
+                          aLocation_legend_in = (1.0, 0.0),\
+                          aLinestyle_in = ['-'],\
+                          iSize_x_in = 12,\
+                          iSize_y_in = 5)
     print("finished")
 
 
-if __name__ == '__main__':
-    import argparse
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_singlegrid.py b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_singlegrid.py
index 6ec0b30..0b29e92 100644
--- a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_singlegrid.py
+++ b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_singlegrid.py
@@ -18,7 +18,7 @@
 
 def elm_tsplot_variable_2d_singlegrid(oE3SM_in, \
                                           oCase_in, \
-                                           
+                                                aLon_in=  None  ,   aLat_in= None,
                                                    iReverse_y_in= None,\
                                           dMax_y_in = None,\
                                           dMin_y_in = None,
@@ -51,8 +51,8 @@ def elm_tsplot_variable_2d_singlegrid(oE3SM_in, \
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
     dLon_min = np.min(aLon)
     dLon_max = np.max(aLon)
     dLat_min = np.min(aLat)
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_w_variation.py b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_w_variation.py
new file mode 100644
index 0000000..89da0e3
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_2d_w_variation.py
@@ -0,0 +1,291 @@
+import os
+import numpy as np
+import datetime
+from pyearth.system.define_global_variables import *
+from pyearth.visual.timeseries.plot_time_series_data_w_variation import plot_time_series_data_w_variation
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+from pyearth.toolbox.data.cgpercentiles import cgpercentiles
+from pye3sm.elm.general.structured.twod.retrieve.elm_retrieve_variable_2d import elm_retrieve_variable_2d
+
+def elm_tsplot_variable_2d_w_variation(oE3SM_in, \
+                                          oCase_in,\
+                                          iReverse_y_in=None,\
+                                          iFlag_log_in = None,\
+                                          iFlag_scientific_notation_in=None,\
+                                          iFlag_monthly_in = None,\
+                                          iFlag_annual_mean_in = None,\
+                                          iFlag_annual_total_in = None,\
+                                            iFlag_median_in=None,\
+                                          dMax_y_in = None,\
+                                          dMin_y_in = None,
+                                          dSpace_y_in = None,\
+                                          aColor_in = None,\
+                                          sFormat_y_in=None,\
+                                          sLabel_x_in=None,\
+                                          sLabel_y_in = None,\
+                                          sTitle_in =None,\
+                                            aLegend_in=None):
+
+    if iFlag_log_in is not None:
+        iFlag_log = iFlag_log_in
+    else: 
+        iFlag_log = 0
+    
+    if iFlag_monthly_in is None:
+        iFlag_monthly  =0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    if iFlag_median_in is None:
+        iFlag_median = 0
+    else:
+        iFlag_median = iFlag_median_in    
+    
+    sModel = oCase_in.sModel
+    sRegion = oCase_in.sRegion
+    iFlag_same_grid = oCase_in.iFlag_same_grid
+
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    sLabel_Y = oCase_in.sLabel_y
+    dConversion = oCase_in.dConversion
+    sVariable = oCase_in.sVariable
+    sCase = oCase_in.sCase
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat, aMask =elm_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    dates = list()
+    dates_year=list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        dSimulation0 = datetime.datetime(iYear, 6, 30)
+        dates_year.append( dSimulation0 )
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable):
+        os.makedirs(sWorkspace_analysis_case_variable)
+
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'tsplot'
+    if not os.path.exists(sWorkspace_analysis_case_region):
+        os.makedirs(sWorkspace_analysis_case_region)
+        pass
+
+
+    if iFlag_monthly ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_monthly_in = 1)        
+        aDataTs = np.full(nstress_subset, -9999, dtype=float)
+        aUpper=np.full(nstress_subset, -9999, dtype=float)
+        aLower=np.full(nstress_subset, -9999, dtype=float)
+        for i in np.arange(0, nstress_subset, 1):
+            aImage = aData_ret[i]     
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)
+            #aDataTs[i] = np.nanmean(dummy1) 
+            dRange = 25
+            aPercentiles_in = np.arange(50-dRange*0.5, 50+dRange*0.5 + 1, 25)
+            aInterval = cgpercentiles(dummy1, aPercentiles_in, missing_value_in = -9999)             
+          
+           
+            if iFlag_median ==1:
+                aDataTs[i] = np.median(dummy1) 
+                aUpper[i ] =aInterval[1]
+                aLower[i ] =aInterval[0]
+            
+            else:
+                dummy0 = np.std(dummy1)    
+                aDataTs[i] = np.mean(dummy1) 
+                aUpper[i ] =aDataTs[i] + 0.5*dummy0
+                aLower[i ] =aDataTs[i] - 0.5*dummy0
+
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            aUpper = np.log10(aUpper)
+            #aLower0=aLower
+            aLower = np.log10(aLower)
+
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsplot_monthly_w_variation' +'.png'
+
+        aDate_all = [dates_subset]
+        aData_all = [aDataTs]    
+        aData_upper_all= [aUpper]
+        aData_lower_all=[aLower]
+        
+
+        plot_time_series_data_w_variation(aDate_all,
+                          aData_all,\
+                              aData_upper_all,\
+                                aData_lower_all,\
+                          sFilename_out,\
+                          iReverse_y_in = iReverse_y_in, \
+                          iFlag_log_in = iFlag_log_in,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in =1,\
+                          dMax_y_in = dMax_y_in,\
+                          dMin_y_in = dMin_y_in,\
+                          dSpace_y_in = dSpace_y_in, \
+                          sTitle_in = sTitle_in, \
+                          sLabel_y_in= sLabel_y_in,\
+                          sFormat_y_in= sFormat_y_in ,\
+                          aLabel_legend_in = aLegend_in, \
+                          aColor_in = aColor_in,\
+                          aMarker_in = ['o'],\
+                          sLocation_legend_in = 'upper left' ,\
+                          aLocation_legend_in = (0.0, 1.0),\
+                          aLinestyle_in = ['-'],\
+                          iSize_x_in = 12,\
+                          iSize_y_in = 5)
+
+    if iFlag_annual_mean ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_mean_in = 1)        
+        aDataTs = np.full(nyear, -9999, dtype=float)
+        for iYear in range(iYear_start, iYear_end + 1):            
+            aImage = aData_ret[iYear-iYear_start]            
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)
+            aDataTs[iYear-iYear_start] = np.nanmean(dummy1) 
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsplot_annual_mean' +'.png'
+
+        aDate_all = [dates_year]
+        aData_all = [aDataTs]    
+
+        plot_time_series_data_w_variation(aDate_all,
+                          aData_all,\
+                              aData_upper_all,\
+                                aData_lower_all,\
+                          sFilename_out,\
+                          iReverse_y_in = iReverse_y_in, \
+                          iFlag_log_in = iFlag_log_in,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in =1,\
+                          dMax_y_in = dMax_y_in,\
+                          dMin_y_in = dMin_y_in,\
+                          dSpace_y_in = dSpace_y_in, \
+                          sTitle_in = sTitle_in, \
+                          sLabel_y_in= sLabel_y_in,\
+                          sFormat_y_in= sFormat_y_in ,\
+                          aLabel_legend_in = aLegend_in, \
+                          aColor_in = aColor_in,\
+                          aMarker_in = ['o'],\
+                          sLocation_legend_in = 'lower right' ,\
+                          aLocation_legend_in = (1.0, 0.0),\
+                          aLinestyle_in = ['-'],\
+                          iSize_x_in = 12,\
+                          iSize_y_in = 5)
+
+    if iFlag_annual_total ==1:
+        aData_ret = elm_retrieve_variable_2d( oCase_in, iFlag_annual_total_in = 1)        
+        aDataTs = np.full(nyear, -9999, dtype=float)
+        aUpper=np.full(nstress_subset, -9999, dtype=float)
+        aLower=np.full(nstress_subset, -9999, dtype=float)
+        for iYear in range(iYear_start, iYear_end + 1):
+            aImage = aData_ret[iYear-iYear_start]
+            dummy1 = np.reshape(aImage, (nrow, ncolumn))
+            good_index = np.where(dummy1 != -9999)
+            dummy1=dummy1[good_index]
+            #dummy1 = remove_outliers(dummy1, 0.05)
+            
+            if iFlag_median ==1:
+                aDataTs[iYear-iYear_start] = np.median(dummy1) 
+                             
+            
+            else:
+                
+                dummy0 = np.std(dummy1)    
+                aDataTs[iYear-iYear_start] = np.nanmean(dummy1) 
+                
+                
+    
+        if iFlag_log  == 1:
+            aDataTs = np.log10(aDataTs)
+            #set inf to min
+            bad_index = np.where( np.isinf(  aDataTs) == True  )
+            aDataTs[bad_index] = dMin_y_in
+
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_tsplot_annual_total' +'.png'
+
+        aDate_all = [dates_year]
+        aData_all = [aDataTs]    
+
+        plot_time_series_data_w_variation(aDate_all,
+                          aData_all,\
+                              aData_upper_all,\
+                                aData_lower_all,\
+                          sFilename_out,\
+                          iReverse_y_in = iReverse_y_in, \
+                          iFlag_log_in = iFlag_log_in,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in =1,\
+                          dMax_y_in = dMax_y_in,\
+                          dMin_y_in = dMin_y_in,\
+                          dSpace_y_in = dSpace_y_in, \
+                          sTitle_in = sTitle_in, \
+                          sLabel_y_in= sLabel_y_in,\
+                          sFormat_y_in= sFormat_y_in ,\
+                          aLabel_legend_in = aLegend_in, \
+                          aColor_in =aColor_in,\
+                          aMarker_in = ['o'],\
+                          sLocation_legend_in = 'lower right' ,\
+                          aLocation_legend_in = (1.0, 0.0),\
+                          aLinestyle_in = ['-'],\
+                          iSize_x_in = 12,\
+                          iSize_y_in = 5)
+    print("finished")
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_halfdegree.py b/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_halfdegree.py
deleted file mode 100644
index bd50882..0000000
--- a/pye3sm/elm/general/structured/twod/plot/elm_tsplot_variable_halfdegree.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os, sys
-import numpy as np
-import datetime
- 
-from pyearth.system.define_global_variables import *
-from pyearth.gis.envi.envi_write_header import envi_write_header
-from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple
-from pyearth.visual.timeseries.plot_time_series_data import plot_time_series_data
-
-def elm_tsplot_variable_halfdegree(oE3SM_in, \
-                                          oCase_in,\
-                                              iFlag_log_y_in=None,\
-                                          dMax_y_in = None,\
-                                          dMin_y_in = None,
-                                          dSpace_y_in = None,\
-                                          sLabel_x_in=None,\
-                                              sLabel_y_in=None,\
-                                          sTitle_in =None):
-
-    
-
-    sModel = oCase_in.sModel
-    sRegion = oCase_in.sRegion
-    iYear_start = oCase_in.iYear_start
-    iYear_end = oCase_in.iYear_end
-
-    iYear_subset_start = oCase_in.iYear_subset_start
-    iYear_subset_end = oCase_in.iYear_subset_end    
-
-    print('The following model is processed: ', sModel)
-    if (sModel == 'h2sc'):
-        pass
-    else:
-        if (sModel == 'vsfm'):
-            aDimension = [96, 144]
-        else:
-            pass
-
-    dConversion = oCase_in.dConversion
-    sVariable = oCase_in.sVariable
-    sCase = oCase_in.sCase
-    sWorkspace_simulation_case_run =oCase_in.sWorkspace_simulation_case_run
-    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
-
-    iFlag_optional = 1
-
-
-    nrow = 360
-    ncolumn = 720
-
-    dates = list()
-    nyear = iYear_end - iYear_start + 1
-    for iYear in range(iYear_start, iYear_end + 1):
-        for iMonth in range(1,13):
-            dSimulation = datetime.datetime(iYear, iMonth, 15)
-            dates.append( dSimulation )
-
-    iStress = 1
-
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable.lower() +    slash + 'dat'
-
-
-    #read the stack data
-
-    sFilename = sWorkspace_variable_dat + slash + sVariable.lower()  + sExtension_envi
-
-    aData_all = gdal_read_envi_file_multiple(sFilename)
-    aVariable_total = aData_all[0]
-
-
-    #plot
-    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
-    if not os.path.exists(sWorkspace_analysis_case_variable):
-        os.makedirs(sWorkspace_analysis_case_variable)
-
-    sWorkspace_analysis_case_grid = sWorkspace_analysis_case_variable + slash + 'tsplot_grid'
-    if not os.path.exists(sWorkspace_analysis_case_grid):
-        os.makedirs(sWorkspace_analysis_case_grid)
-
-    sLabel_Y =r'Water table depth (m)'
-    sLabel_legend = 'Simulated water table depth'
-    for iRow in np.arange(1, nrow+1, 10):
-        sRow = "{:03d}".format(iRow)
-        for iColumn in np.arange(1, ncolumn+1, 10):
-            sColumn = "{:03d}".format(iColumn)
-
-            sGrid =  sRow + '_' +sColumn
-
-            sFilename_out = sWorkspace_analysis_case_grid + slash + 'wtd_tsplot_' + sGrid +'.png'
-
-
-            aVariable= aVariable_total[:, iRow-1, iColumn-1]
-            if np.isnan(aVariable).all():
-                pass
-            else:
-
-                plot_time_series_data(dates, aVariable,\
-                                              sFilename_out,\
-                                              iReverse_y_in = 1, \
-                                              sTitle_in = '', \
-                                              sLabel_y_in= sLabel_Y,\
-                                              sLabel_legend_in = sLabel_legend, \
-                                              iSize_x_in = 12,\
-                                              iSize_y_in = 5)
-
-    print("finished")
-
-
diff --git a/pye3sm/mosart/grid/structured/__init__.py b/pye3sm/elm/general/structured/twod/retrieve/__init__.py
old mode 100755
new mode 100644
similarity index 100%
rename from pye3sm/mosart/grid/structured/__init__.py
rename to pye3sm/elm/general/structured/twod/retrieve/__init__.py
diff --git a/pye3sm/elm/general/structured/twod/retrieve/elm_retrieve_variable_2d.py b/pye3sm/elm/general/structured/twod/retrieve/elm_retrieve_variable_2d.py
new file mode 100644
index 0000000..bafa817
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/retrieve/elm_retrieve_variable_2d.py
@@ -0,0 +1,156 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+
+from pye3sm.elm.mesh.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info
+ 
+
+
+def elm_retrieve_variable_2d(  oCase_in,\
+                                            iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None          ):
+    if iFlag_monthly_in is None:
+        iFlag_monthly  = 0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    sModel = oCase_in.sModel
+    sRegion = oCase_in.sRegion
+    iFlag_same_grid = oCase_in.iFlag_same_grid
+
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    sLabel_Y = oCase_in.sLabel_y
+    dConversion = oCase_in.dConversion
+    sVariable = oCase_in.sVariable
+    sCase = oCase_in.sCase
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat,aMask_ll = elm_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    aMask_ul = np.flip(aMask_ll, 0)
+    nrow = np.array(aMask_ul).shape[0]
+    ncolumn = np.array(aMask_ul).shape[1]
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
+
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
+    #read the stack data
+    sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
+
+    if os.path.exists(sFilename):
+        #print("Yep, I can read that file: " + sFilename)                
+        pass
+    else:
+        print(sFilename + ' is missing')
+        print("Nope, the path doesn't reach your file. Go research filepath in python")
+        return
+
+    aData_all = gdal_read_envi_file_multiple_band(sFilename)
+    aVariable_total = aData_all[0]
+    aVariable_total_subset = aVariable_total[subset_index,:,:]
+
+
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable):
+        os.makedirs(sWorkspace_analysis_case_variable)
+
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'map'
+    if not os.path.exists(sWorkspace_analysis_case_region):
+        os.makedirs(sWorkspace_analysis_case_region)
+        pass
+
+    aData_out = list()
+    if iFlag_monthly ==1 :
+        for i in np.arange(nstress_subset):
+            aImage = aVariable_total_subset[i, :,:]
+            #get date          
+            aData_all = np.array(aImage)                  
+            nan_index = np.where(aData_all == -9999)
+            aData_all = aData_all * dConversion
+            aData_all[nan_index] = -9999     
+            aData_out.append(aData_all)
+
+    #mean or total
+
+    if iFlag_annual_mean ==1:
+        #annual mean
+        for iYear in range(iYear_start, iYear_end + 1):            
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.mean(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))            
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999            
+            aData_out.append(aData_all)
+        pass
+    
+    if iFlag_annual_total ==1: #annual total
+        for iYear in range(iYear_start, iYear_end + 1):          
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.sum(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))           
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999                        
+            aData_out.append(aData_all)
+            pass
+
+
+    return aData_out    
+        
+
+
+
+
diff --git a/pye3sm/elm/general/structured/twod/save/elm_save_variable_2d.py b/pye3sm/elm/general/structured/twod/save/elm_save_variable_2d.py
index 63a5493..b964769 100644
--- a/pye3sm/elm/general/structured/twod/save/elm_save_variable_2d.py
+++ b/pye3sm/elm/general/structured/twod/save/elm_save_variable_2d.py
@@ -8,7 +8,7 @@
 from pyearth.system.define_global_variables import *    
 from pyearth.gis.gdal.write.gdal_write_envi_file import gdal_write_envi_file_multiple_band
 from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file_multiple_band
-from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info 
+from pye3sm.elm.mesh.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info 
 
 from pye3sm.shared.e3sm import pye3sm
 from pye3sm.shared.case import pycase
@@ -50,8 +50,8 @@ def elm_save_variable_2d(oE3SM_in, oCase_in):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
     #resolution
     dLon_min = np.min(aLon)
@@ -149,7 +149,7 @@ def elm_save_variable_2d(oE3SM_in, oCase_in):
                     dummy_index = np.where( aData_ll == missing_value1 ) 
                     aData_ll[dummy_index] = missing_value
                     
-                    aData_ll[aMask_ll_index] = missing_value
+                    aData_ll[aMask_index_ll] = missing_value
                     aData_ul = np.flip(aData_ll, 0)   
                     #save output
                     
diff --git a/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_band_effect_2d.py b/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_band_effect_2d.py
new file mode 100644
index 0000000..12e74a4
--- /dev/null
+++ b/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_band_effect_2d.py
@@ -0,0 +1,144 @@
+import os, sys
+import numpy as np
+import datetime
+
+ 
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.envi.envi_write_header import envi_write_header
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+from pyearth.visual.timeseries.plot_time_series_data import plot_time_series_data
+from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info 
+
+from pye3sm.elm.general.elm_retrieve_surface_data_info import elm_retrieve_surface_data_info
+def elm_calculate_slope_band_effect_2d(oE3SM_in, oCase_in , sVariable_in   ):
+
+    
+
+    sModel = oCase_in.sModel
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+     #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+    #read case info
+
+    #new approach
+    aLon, aLat , aMask_ll= elm_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    aMask_ul = np.flip(aMask_ll, 0)
+    nrow = np.array(aMask_ll).shape[0]
+    ncolumn = np.array(aMask_ll).shape[1]
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
+
+
+    #read slope info 
+    #slope is store within the surface data
+    sVariable='hslp'
+    aSlope0 = elm_retrieve_surface_data_info(oCase_in, sVariable)
+    
+
+    #read variable of interest
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+    
+    
+    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable_in.lower() +    slash + 'dat'
+    #read the stack data
+
+    sFilename = sWorkspace_variable_dat + slash + sVariable_in.lower()  + sExtension_envi
+
+    aData_all = gdal_read_envi_file_multiple_band(sFilename)
+    aVariable = aData_all[0]
+
+ 
+    
+
+
+    #plot
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable_in.lower()
+    
+    #classify the slope
+
+    aSlope = aSlope0[np.where(aSlope0 != -9999)]
+
+    upper_quartile = np.percentile(aSlope, 67)
+    lower_quartile = np.percentile(aSlope,33)
+
+    aIndex = np.where( aSlope0 >= upper_quartile )
+    bIndex = np.where( (aSlope0 >= lower_quartile) & (aSlope0 < upper_quartile) )
+    cIndex = np.where( (aSlope0 < lower_quartile) & (aSlope0 != -9999))
+
+    for iStress in range(nstress_subset):
+        dummy_data = np.flip(aVariable[iStress],0)
+        dummy1 = dummy_data[aIndex]
+        a= np.mean(dummy1[np.where(dummy1!=-9999)])
+        dummy2 = dummy_data[bIndex]
+        b= np.mean(dummy2[np.where(dummy2!=-9999)])
+        dummy3 = dummy_data[cIndex]
+        c=np.mean( dummy3[np.where(dummy3!=-9999)])
+        aData[iStress] =a
+        bData[iStress] =b
+        cData[iStress] =c
+
+
+    #plot all in one
+    aDate_all = np.array( [dates_subset,dates_subset,dates_subset] )
+    aData_all = np.array([aData, bData, cData])
+
+    sFilename_out  = sWorkspace_analysis_case_variable + slash + 'slope_effect.png'
+    print(sFilename_out)
+    sTitle_in = ''
+    sLabel_Y='Drainage'
+    aLabel_legend=['High','Moderate','Low']
+    aColor=['b','r','green']
+    dMax_y_in = np.max(aData_all) * 1.3
+    iFlag_scientific_notation_in=1
+    plot_time_series_data(aDate_all,
+                          aData_all,\
+                          sFilename_out,\
+                          iReverse_y_in = 0, \
+                          iFlag_log_in = 0,\
+                          iFlag_scientific_notation_in=iFlag_scientific_notation_in,\
+                          ncolumn_in = 4,\
+                          dMax_y_in = dMax_y_in,\
+                          dMin_y_in = 0,\
+                          dSpace_y_in = None, \
+                          sTitle_in = sTitle_in, \
+                          sLabel_y_in= sLabel_Y,\
+                          sFormat_y_in= '%1d' ,\
+                          aLabel_legend_in = aLabel_legend, \
+                          aColor_in = aColor,\
+                          aMarker_in = ['o','.','*'],\
+                          sLocation_legend_in = 'upper right' ,\
+                          aLocation_legend_in = (1.0, 0.0),\
+                          aLinestyle_in = ['-','--','-.' ],\
+                          iSize_x_in = 12,\
+                          iSize_y_in = 5)
+
+
+
+
+
+    print("finished")
+
+
diff --git a/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_effect_2d.py b/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_band_effect_2d_singlegrid.py
similarity index 95%
rename from pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_effect_2d.py
rename to pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_band_effect_2d_singlegrid.py
index 2f124f7..c0eb4e7 100644
--- a/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_effect_2d.py
+++ b/pye3sm/elm/general/structured/twod/stats/elm_calculate_slope_band_effect_2d_singlegrid.py
@@ -11,9 +11,7 @@
 from pye3sm.elm.grid.elm_retrieve_case_dimension_info import elm_retrieve_case_dimension_info 
 
 from pye3sm.elm.general.elm_retrieve_surface_data_info import elm_retrieve_surface_data_info
-def elm_calculate_slope_effect_2d(oE3SM_in, oCase_in   , sVariable_in   ):
-
-    
+def elm_calculate_slope_effect_2d(oE3SM_in, oCase_in , sVariable_in   ):
 
     sModel = oCase_in.sModel
     iYear_start = oCase_in.iYear_start
@@ -48,8 +46,8 @@ def elm_calculate_slope_effect_2d(oE3SM_in, oCase_in   , sVariable_in   ):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
 
     #read slope info 
@@ -105,7 +103,7 @@ def elm_calculate_slope_effect_2d(oE3SM_in, oCase_in   , sVariable_in   ):
 
 
     #plot all in one
-    aDate_all =np.array( [dates_subset,dates_subset,dates_subset] )
+    aDate_all = np.array( [dates_subset,dates_subset,dates_subset] )
     aData_all = np.array([aData, bData, cData])
 
     sFilename_out  = sWorkspace_analysis_case_variable + slash + 'slope_effect.png'
diff --git a/pye3sm/elm/general/structured/twod/stats/elm_calculate_variable_signature_2d.py b/pye3sm/elm/general/structured/twod/stats/elm_calculate_variable_signature_2d.py
index c014a1b..822cabb 100644
--- a/pye3sm/elm/general/structured/twod/stats/elm_calculate_variable_signature_2d.py
+++ b/pye3sm/elm/general/structured/twod/stats/elm_calculate_variable_signature_2d.py
@@ -43,8 +43,8 @@ def elm_calculate_variable_signature_2d(oE3SM_in, oCase_in):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
 
     #get comlumn and row number
diff --git a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_observation_variables_2d.py b/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_observation_variables_2d.py
new file mode 100644
index 0000000..5132e13
--- /dev/null
+++ b/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_observation_variables_2d.py
@@ -0,0 +1,140 @@
+import os, sys
+import argparse
+import numpy as np
+
+import scipy.stats
+
+ 
+
+from pyearth.system.define_global_variables import *
+
+ 
+ 
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file_multiple_band
+from pyearth.visual.scatter.scatter_plot_data_density import scatter_plot_data_density
+
+
+iFlag_debug = 1    
+iIndex_start = 52
+iIndex_end = 52    
+sModel = 'e3sm'
+sRegion = 'amazon'
+sDate = '20220701'
+iYear_start = 2000
+iYear_end = 2009
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)    
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)   
+
+#read the observation data
+sWorkspace_data = '/people/liao313/data'
+sFilename_wtd = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'elm' + slash + 'wtd_extract.tif'
+
+pWTD = gdal_read_geotiff_file(sFilename_wtd)
+aWTD_obs = pWTD[0]
+
+for iCase_index in (aCase_index):
+
+    #read slope from an existing case
+    sVariable = 'sur_slp'
+    sLabel_x = r'Surface slope (percent)'
+    dConversion = 100
+    aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                           iCase_index_in =  iCase_index ,\
+                                                           iYear_start_in = iYear_start, \
+                                                           iYear_end_in = iYear_end,\
+                                                           dConversion_in= dConversion,\
+                                                         
+                                                             sDate_in= sDate,\
+                                                           sModel_in = sModel,\
+                                                              sRegion_in = sRegion,\
+                                                           sVariable_in = sVariable )
+
+    oCase_x  = pycase(aParameter_case)        
+    sWorkspace_analysis_case_x = oCase_x.sWorkspace_analysis_case
+    sVariable_x= oCase_x.sVariable
+    
+
+   
+    #plot
+    sWorkspace_analysis_case_variable_x = sWorkspace_analysis_case_x + slash + sVariable_x
+    
+    sWorkspace_analysis_case_grid = sWorkspace_analysis_case_variable_x + slash + 'scatterplot'
+    if not os.path.exists(sWorkspace_analysis_case_grid):
+        os.makedirs(sWorkspace_analysis_case_grid)
+        pass
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case_x + slash + sVariable_x + slash + 'tiff'
+    #read the stack data
+    sFilename_x = sWorkspace_variable_dat + slash + sVariable_x  + sExtension_tiff
+    aData_all_x = gdal_read_geotiff_file_multiple_band(sFilename_x)
+    aVariable_x = np.array(aData_all_x[0]).astype(float)
+    
+    dMin_x = 0
+    dMax_x= 10
+    dMin_y = 0
+    dMax_y= 40
+    #dMin_y = -6
+    #dMax_y= -3
+    dSpace_x = 2
+    dSpace_y =10
+    iFlag_log_x = 0
+    iFlag_log_y = 0
+    iFlag_scientific_notation_x = 0
+    iFlag_scientific_notation_y = 0
+
+    iYear = 2000
+    iMonth = 8
+    iIndex  = ( iYear - iYear_start ) * 12 + iMonth
+    x = aVariable_x[iIndex, :, :]
+    y = aWTD_obs
+
+    #remove missing value
+    good_index = np.where(  (x != missing_value)&(y != missing_value)  )
+    x= x[good_index]
+    y= y[good_index]
+
+    aCorrelation = scipy.stats.kendalltau(x, y)
+    print(aCorrelation)
+
+    x = x * oCase_x.dConversion
+    
+
+    x.flatten()
+    y.flatten()
+    sFilename_out = sWorkspace_analysis_case_grid + slash + sVariable_x + '-wtd'   + '_scatterplot.png'
+
+    sLabel_x = r'Surface slope (percent)'
+    sLabel_y='Water table depth'
+    sLabel_legend_in= 'Amazon'
+    scatter_plot_data_density(x, y,\
+                              sFilename_out,\
+                              iSize_x_in = 8,\
+                              iSize_y_in = 8, \
+                              iFlag_scientific_notation_x_in=0,\
+                              iFlag_scientific_notation_y_in=0,\
+                              iFlag_log_x_in=0,\
+                              iFlag_log_y_in=0,\
+                              dMin_x_in = 0, \
+                              dMax_x_in = 20, \
+                              dMin_y_in = 0, \
+                              dMax_y_in = 10, \
+                              dSpace_x_in = 2, \
+                              dSpace_y_in = 1, \
+                              sTitle_in = '', \
+                              sLabel_x_in= sLabel_x,\
+                              sLabel_y_in= sLabel_y,\
+                              sLabel_legend_in = sLabel_legend_in)
+    pass
+print('finished')
diff --git a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_variables_halfdegree.py b/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_variables_halfdegree.py
deleted file mode 100644
index 68e3e0e..0000000
--- a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_variables_halfdegree.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import os, sys
-import argparse
-import numpy as np
-
-
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
- 
-
-from pyearth.system.define_global_variables import *
-
- 
- 
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-
-from pye3sm.elm.general.halfdegree.plot.elm_scatterplot_variables_halfdegree import elm_scatterplot_variables_halfdegree
-
-def h2sc_scatterplot_variables_halfdegree(oE3SM_in, \
-                                          oCase_x_in, \
-                                          oCase_y_in,\
-                                               iFlag_scientific_notation_x_in=None,\
-                        iFlag_scientific_notation_y_in=None,\
-                                                     iFlag_log_x_in=None,\
-                                                       iFlag_log_y_in=None,\
-                                          dMin_x_in = None, \
-                                          dMax_x_in = None, \
-                                          dMin_y_in = None, \
-                                          dMax_y_in = None, \
-                                          dSpace_x_in = None, \
-                                          dSpace_y_in = None, \
-                                          sLabel_x_in=None,\
-                                          sLabel_y_in=None,\
-                                          sLabel_legend_in=None):
-
-    elm_scatterplot_variables_halfdegree(oE3SM_in,\
-                                         oCase_x_in,\
-                                         oCase_y_in,\
-                                            iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
-                                                        iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
-                                                               iFlag_log_x_in=iFlag_log_x_in,\
-                                                    iFlag_log_y_in=iFlag_log_y_in,\
-                                         dMin_x_in = dMin_x_in, \
-                                         dMax_x_in = dMax_x_in, \
-                                         dMin_y_in = dMin_y_in, \
-                                         dMax_y_in = dMax_y_in, \
-                                         dSpace_x_in = dSpace_x_in, \
-                                         dSpace_y_in = dSpace_y_in, \
-                                         sLabel_x_in = sLabel_x_in, \
-                                         sLabel_y_in= sLabel_y_in,\
-                                        sLabel_legend_in = sLabel_legend_in)
-
-if __name__ == '__main__':
-    iFlag_debug = 1
-    if iFlag_debug == 1:
-        iIndex_start = 9
-        iIndex_end = 9
-    else:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--iIndex_start", help = "the path",   type = int)
-        parser.add_argument("--iIndex_end", help = "the path",   type = int)
-        pArgs = parser.parse_args()
-        iIndex_start = pArgs.iIndex_start
-        iIndex_end = pArgs.iIndex_end
-
-    sModel = 'h2sc'
-    sRegion = 'global'
-    sDate = '20200924'
-
-    iYear_start = 1979
-    iYear_end = 2008
-
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/case.xml'
-
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
-    print(aParameter_e3sm)
-    oE3SM = pye3sm(aParameter_e3sm)
-
-    
-
-    iCase_index_start = iIndex_start
-    iCase_index_end = iIndex_end
-    aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-
-        #iCase_index = 240
-    for iCase_index in (aCase_index):
-        sVariable = 'sur_slp'
-        sLabel_x = r'Surface slope (percent)'
-        dConversion = 100
-        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
-                                                               iCase_index_in =  iCase_index ,\
-                                                               iYear_start_in = iYear_start, \
-                                                               iYear_end_in = iYear_end,\
-                                                               dConversion_in= dConversion,\
-                                                               sDate_in= sDate,\
-                                                               sVariable_in = sVariable )
-        #print(aParameter_case)
-        oCase_x  = pycase(aParameter_case)
-        
-        sVariable = 'qdrai'
-        sVariable = 'zwt'
-        
-        
-        sLabel_y = r'Groundwater drainage ($mm \times s^{-1}$)'
-        sLabel_y = r'Water table depth (m)'
-        dConversion = 1.0
-        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
-                                                               iCase_index_in =  iCase_index ,\
-                                                               iYear_start_in = iYear_start, \
-                                                               iYear_end_in = iYear_end,\
-                                                               sDate_in= sDate,\
-                                                               sVariable_in = sVariable )
-        #print(aParameter_case)
-        oCase_y  = pycase(aParameter_case)
-        dMin_x = 0
-        dMax_x= 10
-        dMin_y = 0
-        dMax_y= 40
-        #dMin_y = -6
-        #dMax_y= -3
-        dSpace_x = 2
-        dSpace_y =10
-        iFlag_log_x = 0
-        iFlag_log_y = 0
-        iFlag_scientific_notation_x = 0
-        iFlag_scientific_notation_y = 0
-        h2sc_scatterplot_variables_halfdegree(oE3SM, \
-                                              oCase_x,\
-                                              oCase_y,\
-                                             iFlag_scientific_notation_x_in=iFlag_scientific_notation_x,\
-                                                     iFlag_scientific_notation_y_in =iFlag_scientific_notation_y,\
-                                                     iFlag_log_x_in= iFlag_log_x,\
-                                                     iFlag_log_y_in = iFlag_log_y,\
-                                              dMin_x_in = dMin_x,\
-                                              dMax_x_in = dMax_x, \
-                                              dMin_y_in = dMin_y, \
-                                              dMax_y_in = dMax_y, \
-                                              dSpace_x_in = dSpace_x, \
-                                              dSpace_y_in = dSpace_y, \
-                                              sLabel_x_in = sLabel_x, \
-                                              sLabel_y_in = sLabel_y,\
-                                              sLabel_legend_in = 'Global')
-        pass
-
-    print('finished')
diff --git a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_variables_halfdegree_domain.py b/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_variables_halfdegree_domain.py
deleted file mode 100644
index b282c81..0000000
--- a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_scatterplot_variables_halfdegree_domain.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import os, sys
-import argparse
-import numpy as np
-
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
- 
-from pyearth.system.define_global_variables import *
-
- 
- 
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-from pye3sm.elm.general.halfdegree.plot.elm_scatterplot_variables_halfdegree_domain import elm_scatterplot_variables_halfdegree_domain
-
-def h2sc_scatterplot_variables_halfdegree_domain(oE3SM_in, \
-                                                 oCase_x_in, \
-                                                 oCase_y_in,\
-                                                      iFlag_scientific_notation_x_in=None,\
-                        iFlag_scientific_notation_y_in=None,\
-                                                     iFlag_log_x_in=None,\
-                                                       iFlag_log_y_in=None,\
-                                                 dMin_x_in = None, \
-                                                 dMax_x_in = None, \
-                                                 dMin_y_in = None, \
-                                                 dMax_y_in = None, \
-                                                 dSpace_x_in = None, \
-                                                 dSpace_y_in = None, \
-                                                 sLabel_x_in=None,\
-                                                 sLabel_y_in=None,\
-                                                     sLabel_legend_in=None):
-
-    elm_scatterplot_variables_halfdegree_domain(oE3SM_in, \
-                                                oCase_x_in, \
-                                                oCase_y_in,\
-                                                iFlag_scientific_notation_x_in=iFlag_scientific_notation_x_in,\
-                                                        iFlag_scientific_notation_y_in=iFlag_scientific_notation_y_in,\
-                                                               iFlag_log_x_in=iFlag_log_x_in,\
-                                                    iFlag_log_y_in=iFlag_log_y_in,\
-                                                dMin_x_in = dMin_x_in, \
-                                                dMax_x_in = dMax_x_in, \
-                                                dMin_y_in = dMin_y_in, \
-                                                dMax_y_in = dMax_y_in, \
-                                                dSpace_x_in = dSpace_x_in, \
-                                                dSpace_y_in = dSpace_y_in, \
-                                                sLabel_x_in = sLabel_x_in, \
-                                                sLabel_y_in= sLabel_y_in,\
-                                                    sLabel_legend_in=sLabel_legend_in)
-
-if __name__ == '__main__':
-    iFlag_debug = 1
-    if iFlag_debug == 1:
-        iIndex_start = 3
-        iIndex_end = 3
-    else:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--iIndex_start", help = "the path",   type = int)
-        parser.add_argument("--iIndex_end", help = "the path",   type = int)
-        pArgs = parser.parse_args()
-        iIndex_start = pArgs.iIndex_start
-        iIndex_end = pArgs.iIndex_end
-
-    sModel = 'h2sc'
-    sRegion = 'global'
-    sDate = '20200924'
-    sDate = '20210108'
-
-    iYear_start = 1979
-    iYear_end = 2008
-
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/case.xml'
-
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
-    print(aParameter_e3sm)
-    oE3SM = pye3sm(aParameter_e3sm)
-
-    
-    iCase_index_start = iIndex_start
-    iCase_index_end = iIndex_end
-    aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-
-        #iCase_index = 240
-    for iCase_index in (aCase_index):
-        sVariable ='rain'
-        #sVariable = 'sur_slp'
-        sVariable = 'gage_height'
-        sLabel_x = r'Precipitation ($mm \times s^{-1}$)'
-        #sLabel_x = r'Surface slope (percent)'
-        sLabel_x = r'Gage height (m)'
-        dConversion = 100
-        dConversion = 1.0
-        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
-                                                               iCase_index_in =  iCase_index ,\
-                                                               iYear_start_in = iYear_start, \
-                                                               iYear_end_in = iYear_end,\
-                                                               dConversion_in= dConversion,\
-                                                               sDate_in= sDate,\
-                                                               sVariable_in = sVariable )
-        #print(aParameter_case)
-        oCase_x  = pycase(aParameter_case)
-        
-        sVariable = 'qdrai'
-        sVariable = 'zwt'
-        #sVariable = 'wt_slp'
-        
-        sLabel_y = r'Water table depth (m)'
-        #sLabel_y = r'Groundwater drainage ($mm \times s^{-1}$)'
-        #sLabel_y = r'Water table slope (percent)'
-        dConversion = 1.00
-        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
-                                                               iCase_index_in =  iCase_index ,\
-                                                               iYear_start_in = iYear_start, \
-                                                               iYear_end_in = iYear_end,\
-                                                            dConversion_in= dConversion,\
-                                                               sDate_in= sDate,\
-                                                               sVariable_in = sVariable )
-        #print(aParameter_case)
-        oCase_y  = pycase(aParameter_case)
-        dMin_x = 0
-        dMax_x = 10
-        
-        dMin_y = 0
-        dMax_y = 40
-        #dMin_y = -6
-        #dMax_y= -3
-        dSpace_x = 2
-        dSpace_y = 10
-        iFlag_log_x = 0
-        iFlag_log_y = 0
-        iFlag_scientific_notation_x = 0
-        iFlag_scientific_notation_y = 0
-        h2sc_scatterplot_variables_halfdegree_domain(oE3SM, \
-                                                     oCase_x,\
-                                                     oCase_y,\
-                                                    iFlag_scientific_notation_x_in=iFlag_scientific_notation_x,\
-                                                     iFlag_scientific_notation_y_in =iFlag_scientific_notation_y,\
-                                                     iFlag_log_x_in= iFlag_log_x,\
-                                                     iFlag_log_y_in = iFlag_log_y,\
-                                                     dMin_x_in = dMin_x,\
-                                                     dMax_x_in = dMax_x, \
-                                                     dMin_y_in = dMin_y, \
-                                                     dMax_y_in = dMax_y, \
-                                                     dSpace_x_in = dSpace_x, \
-                                                     dSpace_y_in = dSpace_y, \
-                                                     sLabel_x_in = sLabel_x, \
-                                                     sLabel_y_in = sLabel_y)
-
-    print('finished')
diff --git a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_tsplot_total_water_storage_halfdegree_domain.py b/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_tsplot_total_water_storage_halfdegree_domain.py
deleted file mode 100644
index 86b1ed3..0000000
--- a/pye3sm/elm/h2sc/analysis/structured/twod/plot/h2sc_tsplot_total_water_storage_halfdegree_domain.py
+++ /dev/null
@@ -1,88 +0,0 @@
-#this script should be run using Python 2.7.8 instead of Python 3
-#module load python/2.7.8
-#maybe I was wrong? 20200305 Chang Liao (chang.liao@pnnl.gov)
-
-import os, sys
-import argparse
-import numpy as np
-
-
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
- 
-
-from pyearth.system.define_global_variables import *
-
-
- 
- 
-
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-
-from pye3sm.elm.general.halfdegree.plot.elm_tsplot_total_water_storage_halfdegree_domain import elm_tsplot_total_water_storage_halfdegree_domain
-
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-
-def h2sc_tsplot_total_water_storage_halfdegree_domain(oE3SM_in, \
-    oCase_in, \
-                                                    
-                                                      dMax_y_in = None,\
-                                                      dMin_y_in= None ):
-
-    elm_tsplot_total_water_storage_halfdegree_domain(oE3SM_in, oCase_in, \
-                                        
-                                                     dMax_y_in = dMax_y_in,\
-                                                     dMin_y_in = dMin_y_in )
-
-if __name__ == '__main__':
-    iFlag_debug = 1
-    if iFlag_debug == 1:
-        iIndex_start = 2
-        iIndex_end = 2
-    else:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--iIndex_start", help = "the path",   type = int)
-        parser.add_argument("--iIndex_end", help = "the path",   type = int)
-        pArgs = parser.parse_args()
-        iIndex_start = pArgs.iIndex_start
-        iIndex_end = pArgs.iIndex_end
-
-    iFlag_same_grid = 1
-    sModel = 'h2sc'
-    sRegion = 'global'
-    sDate = '20200924'
-    sDate = '20201214'
-   
-
-    iYear_start = 1979
-    iYear_end = 2008
-
-
-    iCase_index_start = iIndex_start
-    iCase_index_end = iIndex_end
-    aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-
-    #iCase_index = 240
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/case.xml'
-
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
-
-    oE3SM = pye3sm(aParameter_e3sm)
-    for iCase_index in (aCase_index):
-
-        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
-                                                               iCase_index_in =  iCase_index ,\
-                                                               iYear_subset_start_in = 2004, \
-                                                               iYear_subset_end_in =2008,\
-                                                               iFlag_same_grid_in = iFlag_same_grid, \
-                                                               iYear_start_in = iYear_start, \
-                                                               iYear_end_in = iYear_end,\
-                                                               sDate_in= sDate)
-
-        oCase = pycase(aParameter_case)
-
-        h2sc_tsplot_total_water_storage_halfdegree_domain(oE3SM,   oCase    )
-
-    print('finished')
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/fan/h2sc_evaluate_water_table_depth_halfdegree.py~ b/pye3sm/elm/h2sc/evaluation/halfdegree/fan/h2sc_evaluate_water_table_depth_halfdegree.py~
deleted file mode 100644
index bd5c8f2..0000000
--- a/pye3sm/elm/h2sc/evaluation/halfdegree/fan/h2sc_evaluate_water_table_depth_halfdegree.py~
+++ /dev/null
@@ -1,98 +0,0 @@
-import os, sys
-import argparse
-import numpy as np
-
-
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
-sys.path.extend(sSystem_paths)
-
-from pyes.system.define_global_variables import *
-
-
-sPath_pye3sm = sWorkspace_code +  slash + 'python' + slash + 'e3sm' + slash + 'e3sm_python'
-sys.path.append(sPath_pye3sm)
-from e3sm.elm.general.halfdegree.plot.elm_scatterplot_variables_halfdegree import elm_scatterplot_variables_halfdegree
-
-def h2sc_evaluate_water_table_depth_halfdegree(sFilename_configuration_in, \
-                                    iCase_index,\
-                                               iYear_start_in = None, \
-                                               iYear_end_in =None,\
-                                               dMin_x_in = None, \
-                                               dMax_x_in = None, \
-                                               dMin_y_in = None, \
-                                               dMax_y_in = None, \
-                                               dSpace_x_in = None, \
-                                               dSpace_y_in = None, \
-                                               sDate_in = None,\
-                                               sLabel_x_in=None,\
-                                               sLabel_y_in=None):
-    elm_scatterplot_variables_halfdegree(sFilename_configuration_x,\
-                                         sFilename_configuration_y,\
-                                         iCase_index, \
-                                         iFlag_same_grid_in = 1,\
-                                         iYear_start_in = iYear_start_in,\
-                                         iYear_end_in = iYear_end_in,\
-
-                                       dMin_x_in = 0, \
-                                         dMax_x_in = dMax_x_in, \
-                                         dMin_y_in = dMin_y_in, \
-                                         dMax_y_in = dMax_y_in, \
-
-    dSpace_x_in = dSpace_x_in, \
-                                         dSpace_y_in = dSpace_y_in, \
-                                         sDate_in = sDate_in, \
-                                         sLabel_x_in = sLabel_x_in, \
-                                         sLabel_y_in= sLabel_y_in)
-
-if __name__ == '__main__':
-    iFlag_debug = 1
-    if iFlag_debug == 1:
-        iIndex_start = 1
-        iIndex_end = 1
-    else:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--iIndex_start", help = "the path",   type = int)
-        parser.add_argument("--iIndex_end", help = "the path",   type = int)
-        pArgs = parser.parse_args()
-        iIndex_start = pArgs.iIndex_start
-        iIndex_end = pArgs.iIndex_end
-
-    sModel = 'h2sc'
-    sRegion = 'global'
-    sDate = '20200421'
-
-    iYear_start = 1980
-    iYear_end = 2008
-
-
-
-    sVariable = 'zwt'
-    sFilename_configuration = sWorkspace_configuration + slash + sModel + slash \
-        + sRegion + slash + 'h2sc_configuration_' + sVariable.lower() + sExtension_txt
-
-
-    sLabel = 'Water table depth (m)'
-
-
-
-
-    iCase_index_start = iIndex_start
-    iCase_index_end = iIndex_end
-    aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-
-        #iCase_index = 240
-    for iCase_index in (aCase_index):
-        h2sc_evaluate_water_table_depth_halfdegree(sFilename_configuration,\
-                                                   iCase_index,\
-                                                   iYear_start_in = iYear_start, \
-                                                   iYear_end_in =iYear_end,\
-                                                   dMin_x_in = 0,\
-                                                   dMax_x_in = 0.1, \
-                                                   dMin_y_in = 0, \
-                                                   dMax_y_in = 40, \
-                                                   dSpace_x_in = 0.02, \
-                                                   dSpace_y_in = 10, \
-                                                   sDate_in= sDate, \
-                                                   sLabel_in = sLabel_y)
-
-    print('finished')
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/grace/unused/h2sc_evaluate_total_water_storage_with_grace_halfdegree_domain.py b/pye3sm/elm/h2sc/evaluation/halfdegree/grace/unused/h2sc_evaluate_total_water_storage_with_grace_halfdegree_domain.py
deleted file mode 100644
index d320a3a..0000000
--- a/pye3sm/elm/h2sc/evaluation/halfdegree/grace/unused/h2sc_evaluate_total_water_storage_with_grace_halfdegree_domain.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import os, sys
-import argparse
-import numpy as np
-import numpy.ma as ma
-import datetime
-import calendar
-import scipy.ndimage as ndimage
-from netCDF4 import Dataset #it maybe be replaced by gdal 
-import matplotlib.pyplot as plt
-
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
-sys.path.extend(sSystem_paths)
-
-from pyes.toolbox.reader.text_reader_string import text_reader_string
-from pyes.system.define_global_variables import *
-from pyes.gis.gdal.read.gdal_read_geotiff import gdal_read_geotiff
-
-from pyes.toolbox.data.remove_outliers import remove_outliers
-
-from pyes.gis.gdal.read.gdal_read_envi_file_multiple_band import gdal_read_envi_file_multiple_band
-
-from pyes.visual.timeseries.plot_time_series_data import plot_time_series_data
-
-
-sPath_pye3sm = sWorkspace_code +  slash + 'python' + slash + 'e3sm' + slash + 'e3sm_python'
-sys.path.append(sPath_pye3sm)
-from e3sm.shared import oE3SM
-from e3sm.shared.e3sm_read_configuration_file import e3sm_read_configuration_file
-
-def h2sc_evaluate_total_water_storage_with_grace_halfdegree_domain(oE3SM_in, \
-                                              oCase_in, \
-                                               dMin_in = None, \
-                                               dMax_in = None, \
-                                               dMin_x_in = None, \
-                                               dMax_x_in = None, \
-                                               dSpace_x_in = None, \                                             
-                                               sLabel_x_in = None, \
-                                               sLabel_y_in = None, \
-                                               aLabel_legend_in = None, \
-                                               sTitle_in=None):
-
-
-
-
-    sModel = oE3SM.sModel
-    sRegion = oE3SM.sRegion
-    sCase = oE3SM.sCase
-    nrow = 360
-    ncolumn = 720
-   
-    iYear_start = oCase_in.iYear_start    
-    iYear_end = oCase_in.iYear_end 
-
-    #read obs 
-    sFilename_mask = sWorkspace_data + slash \
-        + 'h2sc' + slash +  sRegion + slash + 'raster' + slash + 'dem' + slash \
-        + 'MOSART_Global_half_20180606c.chang_9999.nc'
-    #read in mask
-    aDatasets = Dataset(sFilename_mask)
-    netcdf_format = aDatasets.file_format
-    print(netcdf_format)
-    print("Print dimensions:")
-    print(aDatasets.dimensions.keys())
-    print("Print variables:")
-    print(aDatasets.variables.keys())
-    for sKey, aValue in aDatasets.variables.items():
-        if "ele0" == sKey:
-            aEle0 = (aValue[:]).data
-            break
-
-    aMask = np.where(aEle0 == missing_value)
-    aMask1 = np.where(aEle0 != missing_value)
-     #read basin mask
-    sWorkspace_data_auxiliary_basin = sWorkspace_data + slash + sModel + slash + sRegion + slash \
-        + 'auxiliary' + slash + 'basins' 
-    aBasin = ['amazon','congo','mississippi','yangtze']
-
-    #set point index
-    aLon=-55
-    aLat = -2.5
-
-    aX = int( (90- aLat) / 0.5 )
-    aY = int( (aLon - (-180.0) )/ 0.5 )
-
-
-    nDomain = len(aBasin)
-    aMask_domain = np.full( (nDomain, nrow, ncolumn), 0, dtype=int)
-    for i in range(nDomain):
-        sFilename_basin = sWorkspace_data_auxiliary_basin + slash + aBasin[i] + slash + aBasin[i] + '.tif'
-        dummy = gdal_read_geotiff(sFilename_basin)
-        dummy1 = dummy[0]
-        dummy2 = ndimage.binary_erosion(dummy1, iterations =5).astype(
-            dummy1.dtype)
-        aMask_domain[i, :,:] = dummy2
-        #test image process 
-        pass
-
-
-    dates = list()
-    nyear = iYear_end - iYear_start + 1
-    for iYear in range(iYear_start, iYear_end + 1):
-        for iMonth in range(1,13):
-            dSimulation = datetime.datetime(iYear, iMonth, 15)
-            dates.append( dSimulation )
-    dates=np.array(dates)
-    iYear_subset_start = 2004
-    iYear_subset_end = 2008
-    iMonth = 1
-
-    subset_index_start = (iYear_subset_start-iYear_start) * 12 + iMonth-1 
-    subset_index_end = (iYear_subset_end+1-iYear_start) * 12 + iMonth-1
-    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
-    dates_subset = dates[subset_index]
-    nstress_subset = len(dates_subset)
-    #read simulated 
-    sWorkspace_analysis_case = oE3SM.sWorkspace_analysis_case
-
-    #we will have P ET, Runoff for new analysis
-    aVariable = ['rain', 'snow', 'qsoil','qvege', 'qve']
-    nVariable = len(aVariable)
-    for i in np.arange(nVariable):
-        sVariable = aVariable[i].lower()
-        sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
-        sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable.lower() +    slash + 'dat'
-        #read the stack data
-
-        sFilename = sWorkspace_variable_dat + slash + sVariable.lower()  + sExtension_envi
-
-        aData_all = gdal_read_envi_file_multiple_band(sFilename)
-        aVariable_all = aData_all[0]
-
-        aVariable_total_subset = aVariable_all[ subset_index,:,:]
-        pass
- 
-    
-    sWorkspace_analysis_case_domain = sWorkspace_analysis_case_variable + slash + 'tsplot'
-    if not os.path.exists(sWorkspace_analysis_case_domain):
-        os.makedirs(sWorkspace_analysis_case_domain)
-        pass
-
-
-    #read greace date list
-    sFilename_grace_lut = '/qfs/people/liao313/data/h2sc/global/auxiliary/grace/data_date.txt'
-    dummy = text_reader_string(sFilename_grace_lut, cDelimiter_in=',')
-    grace_date0 = dummy[:,0]
-    grace_date1 = dummy[:,1]
-    grace_date2 = dummy[:,2]
-    grace_date3 = dummy[:,3]
-    grace_date4 = dummy[:,4]
-    aData_grace=np.full( (nstress_subset, nrow, ncolumn), -9999, dtype=float )
-
-    #now process GRACE data
-
-    sFilename_pre = 'GRD-3_'
-    sFilename_suf = '_GRAC_JPLEM_BA01_0600_LND_v03.tif'
-    sWorkspace_grace  = '/compyfs/liao313/00raw/grace/RL06/v03/JPL'
-    iStress = 1
-    for iYear in np.arange( iYear_subset_start, iYear_subset_end +1):
-        for iMonth in np.arange(1, 13):
-            sYear = "{:04d}".format(iYear)
-            sYear2 = sYear[2:4]
-            sMonth =  "{:02d}".format(iMonth)
-            sMonth2 = calendar.month_abbr[iMonth]
-            sDate  = sMonth2 + sYear2
-            dummy_index = np.where( grace_date0 == sDate )
-            sDummy = grace_date4[dummy_index][0]
-            sDummy1 = sDummy.strip()
-            sDummy2 = sYear + sDummy1[5:8] + '-' + sYear + sDummy1[-3:]
-            sFilename_grace = sWorkspace_grace + slash \
-                + sFilename_pre + sDummy2 + sFilename_suf 
-            print(sFilename_grace)
-            #read the file
-            aData_dummy0  = gdal_read_geotiff(sFilename_grace)
-            aData_dummy0 = aData_dummy0[0]
-            
-            #resample the data because resolution is different
-            aData_dummy1 = np.flip(aData_dummy0, 0)
-            aData_dummy1 = np.roll(aData_dummy1, 180, axis=1) # right
-            #because the dimensions, we use simple way to resample
-            aData_dummy2 = ndimage.zoom(aData_dummy1, 2, order=0)
-            
-            aData_dummy2[np.where(aData_dummy2==-99999)] = -9999
-            aData_dummy2[np.where(aData_dummy2==-9999)] = np.nan
-            #plt.imshow(aData_dummy2)
-            #plt.show()
-            aData_grace[iStress - 1,:,:]=aData_dummy2
-            iStress=iStress+1
-            
-    aTime = dates_subset
-    for iDomain in np.arange(1,2): 
-
-        sDomain = aBasin[iDomain-1]
-        sLabel_legend = sDomain.title()        
-        dummy_mask0 = aMask_domain[iDomain-1, :, :]
-        dummy_mask1 = np.reshape(dummy_mask0, (nrow, ncolumn))
-        dummy_mask1 = 1 - dummy_mask1
-       
-        dummy_mask = np.repeat(dummy_mask1[np.newaxis,:,:], nstress_subset, axis=0)
-        
-        aVariable0 = ma.masked_array(aVariable_total_subset, mask= dummy_mask)
-        aVariable1 = aVariable0.reshape(nstress_subset, nrow , ncolumn)        
-        #get reference lwe
-        aVariable3 = np.full(nstress_subset, -9999, dtype=float)
-        aVariable2_mean = np.mean(aVariable1, axis=0)
-        for iStress in range(1,nstress_subset+1):
-            dummy = aVariable1[iStress-1, :,:]
-            dummy1 = dummy - aVariable2_mean
-            dummy1 = dummy1[dummy1.mask == False]
-            #remove outlier
-            dummy1 = remove_outliers(dummy1[np.where(dummy1 != -9999)], 0.1)
-            aVariable3[iStress-1] = np.nanmean(dummy1)
-            
-    
-        iStress = 1
-        #apply mask
-        aVariable4 = ma.masked_array(aData_grace, mask= dummy_mask)
-        #aVariable4 = aData_grace
-        aVariable5 = aVariable4.reshape(nstress_subset, nrow, ncolumn)        
-        aVariable6 = np.full(nstress_subset, -9999, dtype=float)
-        for iStress in range(1,nstress_subset+1):
-            dummy = aVariable5[iStress-1, :,:]
-            #plt.imshow(dummy)
-            #plt.show()
-            #print(dummy)
-            dummy1 = dummy[dummy.mask == False]
-            dummy1[np.where(dummy1==-9999)] = np.nan 
-            #aVariable6[iStress-1] = np.nanmean(dummy[aX,aY])
-            aVariable6[iStress-1] = np.nanmean(dummy1)
-            #use regional mean instead of grid            
-            print(np.nanmax(dummy1))
-
-        #now we can comparre
-        print(aVariable3)
-        print(aVariable6)
-        print('finished')
-
-        #plot and save
-        aTime_all = [aTime, aTime]
-        aData_all = [-1.0*aVariable3, aVariable6]
-        sFilename_out = sWorkspace_analysis_case_domain + slash \
-            + sVariable +'_'+ sDomain + '_wtd_grace_tsplot' +'.png'
-
-      
-        plot_time_series_data(aTime_all, aData_all, \
-                                  sFilename_out,\
-                                  iSize_x_in = 12, \
-                                  iSize_y_in = 5, \
-                                  dMax_y_in =1.3, \
-                                  dMin_y_in = -1.3, \
-                                  dSpace_y_in=0.4,\
-                                  sLabel_y_in = 'TWS variations (m)', \
-                                aColor_in = ['red', 'blue'],\
-                                aMarker_in = ['o','+'],\
-                                    aLinestyle_in = ['dotted','dashed'],\
-                                  aLabel_legend_in = ['Simulation WTD','GRACE TWS'])
-
-
-if __name__ == '__main__':
-    iFlag_debug = 1
-    if iFlag_debug == 1:
-        iIndex_start = 1
-        iIndex_end = 1
-    else:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--iIndex_start", help = "the path",   type = int)
-        parser.add_argument("--iIndex_end", help = "the path",   type = int)
-        pArgs = parser.parse_args()
-        iIndex_start = pArgs.iIndex_start
-        iIndex_end = pArgs.iIndex_end
-
-    sModel = 'h2sc'
-    sRegion = 'global'
-    sDate = '20200421'
-    #sDate = '20200602'
-
-    iYear_start = 1980
-    iYear_end = 2008
-
-    sVariable = 'zwt'
-    sFilename_configuration = sWorkspace_configuration + slash + sModel + slash \
-        + sRegion + slash + 'h2sc_configuration_' + sVariable.lower() + sExtension_txt
-
-
-    sLabel = 'Water table depth (m)'
-
-
-    aLabel_legend = [  'Observed WTD','Simulated WTD' ]
-
-    iCase_index_start = iIndex_start
-    iCase_index_end = iIndex_end
-    aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-
-        #iCase_index = 240
-    for iCase_index in (aCase_index):
-        h2sc_evaluate_water_table_depth_with_grace_halfdegree_domain(sFilename_configuration,\
-                                                   iCase_index,\
-                                                   
-                                                   dMin_in = 0, \
-                                                   dMax_in = 80, \
-                                                   sDate_in= sDate, \
-                                                sLabel_x_in=sLabel,\
-                                                #sLabel_y_in='Distribution [%]',\
-                                                   #aLabel_legend_in = aLabel_legend,\
-                                                   )
-
-    print('finished')
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/grace/unused/h2sc_evaluate_water_table_depth_with_grace_halfdegree_domain.py b/pye3sm/elm/h2sc/evaluation/halfdegree/grace/unused/h2sc_evaluate_water_table_depth_with_grace_halfdegree_domain.py
deleted file mode 100644
index 98ba615..0000000
--- a/pye3sm/elm/h2sc/evaluation/halfdegree/grace/unused/h2sc_evaluate_water_table_depth_with_grace_halfdegree_domain.py
+++ /dev/null
@@ -1,322 +0,0 @@
-import os, sys
-import argparse
-import numpy as np
-import numpy.ma as ma
-import datetime
-import calendar
-import scipy.ndimage as ndimage
-from netCDF4 import Dataset #it maybe be replaced by gdal 
-import matplotlib.pyplot as plt
-
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
-sys.path.extend(sSystem_paths)
-
-from pyes.toolbox.reader.text_reader_string import text_reader_string
-from pyes.system.define_global_variables import *
-from pyes.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file, gdal_read_envi_file_multiple_band
-
-from pyes.toolbox.data.remove_outliers import remove_outliers
-
-from pyes.visual.timeseries.plot_time_series_data import plot_time_series_data
-
-
-sPath_pye3sm = sWorkspace_code +  slash + 'python' + slash + 'e3sm' + slash + 'e3sm_python'
-sys.path.append(sPath_pye3sm)
-from e3sm.shared import oE3SM
-from e3sm.shared.e3sm_read_configuration_file import e3sm_read_configuration_file
-
-def h2sc_evaluate_water_table_depth_with_grace_halfdegree_domain(sFilename_configuration_in, \
-                                               iCase_index,\
-                                               iYear_start_in = None, \
-                                               iYear_end_in =None,\
-                                               dMin_in = None, \
-                                               dMax_in = None, \
-                                               dMin_x_in = None, \
-                                               dMax_x_in = None, \
-                                               dSpace_x_in = None, \
-                                               sDate_in = None, \
-                                               sLabel_x_in = None, \
-                                               sLabel_y_in = None, \
-                                               aLabel_legend_in = None, \
-                                               sTitle_in=None):
-
-
-    e3sm_read_configuration_file(sFilename_configuration_in,\
-                                 iCase_index_in = iCase_index, \
-                                 iYear_start_in = iYear_start_in,\
-                                 iYear_end_in = iYear_end_in,\
-                                 sDate_in= sDate_in)
-
-    sModel = oE3SM.sModel
-    sRegion = oE3SM.sRegion
-    sCase = oE3SM.sCase
-    nrow = 360
-    ncolumn = 720
-    if iYear_start_in is not None:
-        iYear_start = iYear_start_in
-    else:
-        iYear_start = oE3SM.iYear_start
-    if iYear_end_in is not None:
-        iYear_end = iYear_end_in
-    else:
-        iYear_end = oE3SM.iYear_end
-    #read obs 
-    sFilename_mask = sWorkspace_data + slash \
-        + 'h2sc' + slash +  sRegion + slash + 'raster' + slash + 'dem' + slash \
-        + 'MOSART_Global_half_20180606c.chang_9999.nc'
-    #read in mask
-    aDatasets = Dataset(sFilename_mask)
-    netcdf_format = aDatasets.file_format
-    print(netcdf_format)
-    print("Print dimensions:")
-    print(aDatasets.dimensions.keys())
-    print("Print variables:")
-    print(aDatasets.variables.keys())
-    for sKey, aValue in aDatasets.variables.items():
-        if "ele0" == sKey:
-            aEle0 = (aValue[:]).data
-            break
-    aMask = np.where(aEle0 == missing_value)
-    aMask1 = np.where(aEle0 != missing_value)
-     #read basin mask
-    sWorkspace_data_auxiliary_basin = sWorkspace_data + slash + sModel + slash + sRegion + slash \
-        + 'auxiliary' + slash + 'basins' 
-    aBasin = ['amazon','congo','mississippi','yangtze']
-
-    #set point index
-    aLon=-55
-    aLat = -2.5
-
-    aX = int( (90- aLat) / 0.5 )
-    aY = int( (aLon - (-180.0) )/ 0.5 )
-
-
-    nDomain = len(aBasin)
-    aMask_domain = np.full( (nDomain, nrow, ncolumn), 0, dtype=int)
-    for i in range(nDomain):
-        sFilename_basin = sWorkspace_data_auxiliary_basin + slash + aBasin[i] + slash + aBasin[i] + '.tif'
-        dummy = gdal_read_geotiff(sFilename_basin)
-        dummy1 = dummy[0]
-        dummy2 = ndimage.binary_erosion(dummy1, iterations =5).astype(
-            dummy1.dtype)
-        aMask_domain[i, :,:] = dummy2
-        #test image process 
-
-
-    dates = list()
-    nyear = iYear_end - iYear_start + 1
-    for iYear in range(iYear_start, iYear_end + 1):
-        for iMonth in range(1,13):
-            dSimulation = datetime.datetime(iYear, iMonth, 15)
-            dates.append( dSimulation )
-    dates=np.array(dates)
-    iYear_subset_start = 2004
-    iYear_subset_end = 2008
-    iMonth = 1
-
-    subset_index_start = (iYear_subset_start-iYear_start) * 12 + iMonth-1 
-    subset_index_end = (iYear_subset_end+1-iYear_start) * 12 + iMonth-1
-
-    #read simulated 
-    sWorkspace_analysis_case = oE3SM.sWorkspace_analysis_case
-    sVariable = oE3SM.sVariable.lower()
-    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
-    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable.lower() +    slash + 'dat'
-    #read the stack data
-
-    sFilename = sWorkspace_variable_dat + slash + sVariable.lower()  + sExtension_envi
-
-    aData_all = gdal_read_envi_file_multiple_band(sFilename)
-    aVariable_all = aData_all[0]
-    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
-    
-    dates_subset = dates[subset_index]
-    nstress_subset= len(dates_subset)
-
-
-    aVariable_total_subset = aVariable_all[ subset_index,:,:]
- 
-    
-    sWorkspace_analysis_case_domain = sWorkspace_analysis_case_variable + slash + 'tsplot'
-    if not os.path.exists(sWorkspace_analysis_case_domain):
-        os.makedirs(sWorkspace_analysis_case_domain)
-
-
-    #read greace date list
-    sFilename_grace_lut = '/qfs/people/liao313/data/h2sc/global/auxiliary/grace/data_date.txt'
-    dummy = text_reader_string(sFilename_grace_lut, cDelimiter_in=',')
-    grace_date0 = dummy[:,0]
-    grace_date1 = dummy[:,1]
-    grace_date2 = dummy[:,2]
-    grace_date3 = dummy[:,3]
-    grace_date4 = dummy[:,4]
-    aData_grace=np.full( (nstress_subset, nrow, ncolumn), -9999, dtype=float )
-
-    #now process GRACE data
-
-    sFilename_pre = 'GRD-3_'
-    sFilename_suf = '_GRAC_JPLEM_BA01_0600_LND_v03.tif'
-    sWorkspace_grace  = '/compyfs/liao313/00raw/grace/RL06/v03/JPL'
-    iStress = 1
-    for iYear in np.arange( iYear_subset_start, iYear_subset_end +1):
-        for iMonth in np.arange(1, 13):
-            sYear = "{:04d}".format(iYear)
-            sYear2 = sYear[2:4]
-            sMonth =  "{:02d}".format(iMonth)
-            sMonth2 = calendar.month_abbr[iMonth]
-            sDate  = sMonth2 + sYear2
-            dummy_index = np.where( grace_date0 == sDate )
-            sDummy = grace_date4[dummy_index][0]
-            sDummy1 = sDummy.strip()
-            sDummy2 = sYear + sDummy1[5:8] + '-' + sYear + sDummy1[-3:]
-            sFilename_grace = sWorkspace_grace + slash \
-                + sFilename_pre + sDummy2 + sFilename_suf 
-            print(sFilename_grace)
-            #read the file
-            aData_dummy0  = gdal_read_geotiff(sFilename_grace)
-            aData_dummy0 = aData_dummy0[0]
-            
-            #resample the data because resolution is different
-            aData_dummy1 = np.flip(aData_dummy0, 0)
-            aData_dummy1 = np.roll(aData_dummy1, 180, axis=1) # right
-            
-            #because the dimensions, we use simple way to resample
-            
-            aData_dummy2 = ndimage.zoom(aData_dummy1, 2, order=0)
-            
-            
-            aData_dummy2[np.where(aData_dummy2==-99999)] = -9999
-            aData_dummy2[np.where(aData_dummy2==-9999)] = np.nan
-            #plt.imshow(aData_dummy2)
-            #plt.show()
-            aData_grace[iStress - 1,:,:]=aData_dummy2
-            
-            iStress=iStress+1
-            
-    aTime = dates_subset
-    for iDomain in np.arange(1,2): 
-
-        sDomain = aBasin[iDomain-1]
-        sLabel_legend = sDomain.title()        
-        dummy_mask0 = aMask_domain[iDomain-1, :, :]
-        dummy_mask1 = np.reshape(dummy_mask0, (nrow, ncolumn))
-        dummy_mask1 = 1 - dummy_mask1
-       
-        dummy_mask = np.repeat(dummy_mask1[np.newaxis,:,:], nstress_subset, axis=0)
-        
-        aVariable0 = ma.masked_array(aVariable_total_subset, mask= dummy_mask)
-        aVariable1 = aVariable0.reshape(nstress_subset, nrow , ncolumn)        
-        #get reference lwe
-        aVariable3 = np.full(nstress_subset, -9999, dtype=float)
-        aVariable2_mean = np.mean(aVariable1, axis=0)
-        for iStress in range(1,nstress_subset+1):
-            dummy = aVariable1[iStress-1, :,:]
-            dummy1 = dummy - aVariable2_mean
-            dummy1 = dummy1[dummy1.mask == False]
-            #remove outlier
-            dummy1 = remove_outliers(dummy1[np.where(dummy1 != -9999)], 0.1)
-            aVariable3[iStress-1] = np.nanmean(dummy1)
-            
-    
-        iStress = 1
-        #apply mask
-        aVariable4 = ma.masked_array(aData_grace, mask= dummy_mask)
-        #aVariable4 = aData_grace
-
-        aVariable5 = aVariable4.reshape(nstress_subset, nrow, ncolumn)        
-        
-
-        aVariable6 = np.full(nstress_subset, -9999, dtype=float)
-       
-        for iStress in range(1,nstress_subset+1):
-            dummy = aVariable5[iStress-1, :,:]
-            #plt.imshow(dummy)
-            #plt.show()
-            #print(dummy)
-            dummy1 = dummy[dummy.mask == False]
-            dummy1[np.where(dummy1==-9999)] = np.nan
-            
-            #aVariable6[iStress-1] = np.nanmean(dummy[aX,aY])
-            aVariable6[iStress-1] = np.nanmean(dummy1)
-            #use regional mean instead of grid
-
-
-            
-            print(np.nanmax(dummy1))
-
-        #now we can comparre
-        print(aVariable3)
-        print(aVariable6)
-        print('finished')
-
-        #plot and save
-        aTime_all = [aTime, aTime]
-        aData_all = [-1.0*aVariable3, aVariable6]
-        sFilename_out = sWorkspace_analysis_case_domain + slash \
-            + sVariable +'_'+ sDomain + '_wtd_grace_tsplot' +'.png'
-
-      
-        plot_time_series_data(aTime_all, aData_all, \
-                                  sFilename_out,\
-                                  iSize_x_in = 12, \
-                                  iSize_y_in = 5, \
-                                  dMax_y_in =1.3, \
-                                  dMin_y_in = -1.3, \
-                                  dSpace_y_in=0.4,\
-                                  sLabel_y_in = 'TWS variations (m)', \
-                                aColor_in = ['red', 'blue'],\
-                                aMarker_in = ['o','+'],\
-                                    aLinestyle_in = ['dotted','dashed'],\
-                                  aLabel_legend_in = ['Simulation WTD','GRACE TWS'])
-
-
-if __name__ == '__main__':
-    iFlag_debug = 1
-    if iFlag_debug == 1:
-        iIndex_start = 1
-        iIndex_end = 1
-    else:
-        parser = argparse.ArgumentParser()
-        parser.add_argument("--iIndex_start", help = "the path",   type = int)
-        parser.add_argument("--iIndex_end", help = "the path",   type = int)
-        pArgs = parser.parse_args()
-        iIndex_start = pArgs.iIndex_start
-        iIndex_end = pArgs.iIndex_end
-
-    sModel = 'h2sc'
-    sRegion = 'global'
-    sDate = '20200421'
-    #sDate = '20200602'
-
-    iYear_start = 1980
-    iYear_end = 2008
-
-    sVariable = 'zwt'
-    sFilename_configuration = sWorkspace_configuration + slash + sModel + slash \
-        + sRegion + slash + 'h2sc_configuration_' + sVariable.lower() + sExtension_txt
-
-
-    sLabel = 'Water table depth (m)'
-
-
-    aLabel_legend = [  'Observed WTD','Simulated WTD' ]
-
-    iCase_index_start = iIndex_start
-    iCase_index_end = iIndex_end
-    aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-
-        #iCase_index = 240
-    for iCase_index in (aCase_index):
-        h2sc_evaluate_water_table_depth_with_grace_halfdegree_domain(sFilename_configuration,\
-                                                   iCase_index,\
-                                                   iYear_start_in = iYear_start, \
-                                                   iYear_end_in =iYear_end,\
-                                                   dMin_in = 0, \
-                                                   dMax_in = 80, \
-                                                   sDate_in= sDate, \
-                                                sLabel_x_in=sLabel,\
-                                                #sLabel_y_in='Distribution [%]',\
-                                                   #aLabel_legend_in = aLabel_legend,\
-                                                   )
-
-    print('finished')
diff --git a/pye3sm/mosart/grid/structured/twod/__init__.py b/pye3sm/elm/h2sc/evaluation/twod/__init__.py
similarity index 100%
rename from pye3sm/mosart/grid/structured/twod/__init__.py
rename to pye3sm/elm/h2sc/evaluation/twod/__init__.py
diff --git a/pye3sm/elm/h2sc/evaluation/twod/fan/__init__.py b/pye3sm/elm/h2sc/evaluation/twod/fan/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/fan/h2sc_evaluate_water_table_depth_halfdegree.py b/pye3sm/elm/h2sc/evaluation/twod/fan/h2sc_evaluate_water_table_depth_2d.py
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/fan/h2sc_evaluate_water_table_depth_halfdegree.py
rename to pye3sm/elm/h2sc/evaluation/twod/fan/h2sc_evaluate_water_table_depth_2d.py
diff --git a/pye3sm/elm/h2sc/evaluation/twod/grace/__init__.py b/pye3sm/elm/h2sc/evaluation/twod/grace/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/grace/readme.md b/pye3sm/elm/h2sc/evaluation/twod/grace/readme.md
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/grace/readme.md
rename to pye3sm/elm/h2sc/evaluation/twod/grace/readme.md
diff --git a/pye3sm/elm/h2sc/evaluation/twod/situ/__init__.py b/pye3sm/elm/h2sc/evaluation/twod/situ/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree.py b/pye3sm/elm/h2sc/evaluation/twod/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree.py
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree.py
rename to pye3sm/elm/h2sc/evaluation/twod/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree.py
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree_multiple_cases.py b/pye3sm/elm/h2sc/evaluation/twod/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree_multiple_cases.py
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree_multiple_cases.py
rename to pye3sm/elm/h2sc/evaluation/twod/situ/h2sc_evaluate_water_table_depth_with_situ_halfdegree_multiple_cases.py
diff --git a/pye3sm/elm/h2sc/evaluation/halfdegree/situ/h2sc_prepare_site_location.py b/pye3sm/elm/h2sc/evaluation/twod/situ/h2sc_prepare_site_location.py
similarity index 100%
rename from pye3sm/elm/h2sc/evaluation/halfdegree/situ/h2sc_prepare_site_location.py
rename to pye3sm/elm/h2sc/evaluation/twod/situ/h2sc_prepare_site_location.py
diff --git a/pye3sm/elm/h2sc/model/adjust/h2sc_change_sand_percentage.py b/pye3sm/elm/h2sc/model/adjust/h2sc_change_sand_percentage.py
deleted file mode 100644
index 56e90c2..0000000
--- a/pye3sm/elm/h2sc/model/adjust/h2sc_change_sand_percentage.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os #operate folder
-import sys
-
-import numpy as np
-
-from netCDF4 import Dataset #it maybe be replaced by gdal 
-#maybe not needed
-
-#import library
-sSystem_paths = os.environ['PATH'].split(os.pathsep)
- 
-#import global variable
-from pyearth.system.define_global_variables import *    
-
-from pyearth.gis.gdal.read.gdal_read_geotiff import gdal_read_geotiff
-from pyearth.toolbox.data.replace_variable_in_netcdf import replace_variable_in_netcdf
-
- 
- 
-
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-
-def h2sc_change_sand_percentage(oE3SM_in, oCase_in):
-    sModel = oCase_in.sModel
-    sRegion = oCase_in.sRegion
-    nrow = 360
-    ncolumn = 720
-    #read basin mask
-    sWorkspace_data_auxiliary_basin = sWorkspace_data + slash + sModel + slash + sRegion + slash \
-        + 'auxiliary' + slash + 'basins'
-    aBasin = ['amazon']
-
-    nDomain = len(aBasin)
-    aMask = np.full((nrow, ncolumn), 0, dtype=np.int32)
-    for i in range(nDomain):
-        sFilename_basin = sWorkspace_data_auxiliary_basin + slash + aBasin[i] + slash + aBasin[i] + '.tif'
-        dummy = gdal_read_geotiff(sFilename_basin)
-        aMask[:,:] = dummy[0]
-
-    sVariable = 'PCT_SAND'
-    sFilename_old = '/compyfs/inputdata/lnd/clm2/surfdata_map' + slash + 'surfdata_0.5x0.5_simyr2010_c191025_log10.nc'
-    
-    sFilename_new= '/compyfs/inputdata/lnd/clm2/surfdata_map' + slash + 'surfdata_0.5x0.5_simyr2010_c191025_log10_adjust_sand.nc'
-    pDatasets_in = Dataset(sFilename_old)
-    netcdf_format = pDatasets_in.file_format
-    sandpct = pDatasets_in.variables[sVariable]
-
-    
-    pShape = sandpct.shape
-    aData_out= np.full(pShape, 0, dtype=float) 
-    for i in np.arange(0, pShape[0], 1):
-        aData_out[i, :,:] = sandpct[i, :,:]
-        #a = np.where(aMask ==1)
-        #b= aData_out[i, :,:]
-        #c = np.flip(b, 0) 
-        #c[a] = c[a]+5
-        #c[c>100]=100
-        #d = np.flip(c, 0) 
-        #aData_out[i, :,:] = d
-        aData_out[i][aMask==1] += 10
-        pass
-    aData_out[aData_out>100] = 100
-    pDatasets_in.close()
-
-    replace_variable_in_netcdf(sFilename_old, sFilename_new, aData_out, sVariable)
-    print('finshed')
-
-    return
-if __name__ == '__main__':
-
-
-
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/e3sm/pye3sm/pye3sm/shared/case.xml'
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration    )
-    sDate = '20200906'
-    oE3SM = pye3sm(aParameter_e3sm)
-    aParameter_case = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
-                                                     iYear_start_in = 1979, \
-                                                              iYear_end_in = 2008,\
-                                                              sDate_in = sDate )
-    oCase = pycase(aParameter_case)   
-    oCase.iYear_subset_start = 2000
-    oCase.iYear_subset_end = 2008
-    h2sc_change_sand_percentage(oE3SM, oCase)
\ No newline at end of file
diff --git a/pye3sm/elm/h2sc/model/angle.py b/pye3sm/elm/h2sc/model/angle.py
deleted file mode 100644
index 5ff8001..0000000
--- a/pye3sm/elm/h2sc/model/angle.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import numpy as np
-
-dSlope = 0.08
-
-
-a = np.power(dSlope, 2)
-print(a)
-
-b= np.tan(dSlope)
-
-c =np.power(b, 2)
-
-print(c)
\ No newline at end of file
diff --git a/pye3sm/elm/h2sc/model/h2sc_calculate_downslope_drainage.py b/pye3sm/elm/h2sc/model/h2sc_calculate_downslope_drainage.py
deleted file mode 100644
index b728700..0000000
--- a/pye3sm/elm/h2sc/model/h2sc_calculate_downslope_drainage.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import math
-#dResolution_grid_in =1406 * 1000 #m to mm
-dDummy_slope = 5.699999999999999E-003    #ratio. ,not degree or radian
-sand = 10
-xksat         = 0.0070556 *( 10.**(-0.884+0.0153*sand) )
-dSaturate_hydraulic_conductivity_in= 1 * 1.0E-3 #m/s
-dSaturate_hydraulic_conductivity_in = 0.107872755330942 
-
-dHeight_below_river_in=42*1000
-    
-dDummy0 =  1406 * 1000
-#head pressure delta H, average  
-dDummy1 = dDummy0 * (dDummy_slope)
-#cross area A
-dDummy2 = dHeight_below_river_in  * 8350000  * 1000
-#q = k   A   delta H/ L
-
-#delta h/L
-dDummy3 = dDummy1 / dDummy0
-
-dDummy4 =  dSaturate_hydraulic_conductivity_in * dDummy2 * dDummy3 
-
-
-#drainage area, normalization
-dDummy5 = 8350000  * 1406 * 1000000 
-
-
-
-dFlow_downslope_out = dDummy4 /  dDummy5 #mm/s 
-
-dFlow_downslope_out = dFlow_downslope_out
-#unit?
-
-print(dFlow_downslope_out)
-
-
diff --git a/pye3sm/elm/h2sc/model/h2sc_calculate_seepage_drainage.py b/pye3sm/elm/h2sc/model/h2sc_calculate_seepage_drainage.py
deleted file mode 100644
index 0fcc3e6..0000000
--- a/pye3sm/elm/h2sc/model/h2sc_calculate_seepage_drainage.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import numpy as np
-
-dSlope_surface_in = 5.699999999999999E-003
-dSaturate_hydraulic_conductivity_in= 0.01
-dLength_hillslope = 3000  * 1000 
-
-dWidth_hillslope_in= 50000 * 1000
-dLength_seepage = 10 * 1000
-
-dArea_hillslope_in = dWidth_hillslope_in*dLength_hillslope
-
-
-#head pressure delta H, average  
-dDummy1 = dLength_hillslope * dSlope_surface_in
-#cross area A
-dDummy2 = dLength_seepage  * dWidth_hillslope_in  
-#q = k   A   delta H/ L
-
-#delta h/L
-dDummy3 = dSlope_surface_in
-
-dDummy4 =  dSaturate_hydraulic_conductivity_in * dDummy2 * dDummy3 
-
-
-dDummy5 = dArea_hillslope_in
-dFlow_downslope_out = dDummy4 /  dDummy5 #mm/s 
-
-dFlow_seepage_out = dSaturate_hydraulic_conductivity_in \
-         * dLength_seepage * np.power(dSlope_surface_in,1) * dWidth_hillslope_in / dArea_hillslope_in
-
-print(dFlow_downslope_out)
-print(dFlow_seepage_out)
\ No newline at end of file
diff --git a/pye3sm/elm/h2sc/model/h2sc_calculate_seepage_extent.py b/pye3sm/elm/h2sc/model/h2sc_calculate_seepage_extent.py
deleted file mode 100644
index 224599a..0000000
--- a/pye3sm/elm/h2sc/model/h2sc_calculate_seepage_extent.py
+++ /dev/null
@@ -1,4 +0,0 @@
-def h2sc_calculate_seepage_extent(nElevation_interval_in, \
-       dElevation_water_table_in, 
-       aElevation_profile_in ):
-       
diff --git a/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope.py b/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope.py
deleted file mode 100644
index 2f10435..0000000
--- a/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import os
-import math
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.pyplot as plt
-from matplotlib import cm
-from matplotlib.ticker import LinearLocator, FormatStrFormatter
-import numpy as np
-import matplotlib.ticker as mtick
-
-dDistance_in = 50000  /2.
-
-ninterval = 10
-
-aElevation1 =   np.arange(ninterval) * 200 - 300
-aElevation2 =   np.arange(ninterval) * 220 - 200
-print(aElevation1)
-print(aElevation2)
-
-dThickness_critical_zone_in = 50.0
-#dHeight1 = 40.0
-#dHeight2= - 40
-ndrop = 9
-aHeight1 =   np.arange(ndrop) * 10 + 10
-aHeight1 =   (np.arange(ndrop) + 1)*  dThickness_critical_zone_in / (ndrop+1)
-#aHeight2 =   np.arange(ndrop) * 10 + 10
-
-dSlope_mosart = 0.01
-
-dRatio0 = 0.5  #for bedrock slope
-dRatio1 = 0.25  #for transition slope
-
-
-dRatio2 = 1.0 #above seepage
-dRatio3 =1.0 #below transition
-
-X, Y = np.meshgrid(aElevation1, aElevation2)
-
-#for i in range(1,20):
-    #print(math.atan(i /ninterval))
-    #print(math.atan( (i+1.0)/ ninterval) - math.atan(i /ninterval) )
-
-for i in range(ninterval):
-    aHeight2 =   (np.arange(ndrop)+1) * (aElevation2[i]-aElevation1[i]) /(ndrop+1)
-    #print(aElevation1[i] ,aElevation2[i] )
-    #elevation difference reference
-    dummy0 = (aElevation2[i] - (aElevation1[i] ) ) 
-    dElevation_difference = dummy0
-    #slope
-    dDummy1 =  dElevation_difference  / dDistance_in
-    A1 = dDummy1
-    dSlope_surface = A1
-
-    dRatio1 = 1.0 - np.power(A1, 0.8)
-    print(A1, dRatio1)
-    dSlope_surface_radian = math.atan(A1)
-
-    #print(dSlope_surface / math.pi * 180)
-    
-    #dDummy2 = dElevation_difference / dDistance_in
-    #dDummy2 = dThickness_critical_zone_in * (1 - pow(dDummy2, 0.25 ))
-    #dDummy2 =  (aElevation2[i] - dDummy2) - (aElevation1[i] - dThickness_critical_zone_in) 
-    #dDummy3 =  dDummy2 / dDistance_in
-    dDummy2 = (aElevation2[i] - dThickness_critical_zone_in * dRatio0) - ( (aElevation1[i] ) - dThickness_critical_zone_in  ) 
-    dDummy3 =  dDummy2 / dDistance_in
-    A4 = dDummy3
-    dSlope_bedrock = A4
-    dSlope_bedrock_radian = math.atan(A4)
-    #print(dSlope_bedrock / math.pi * 180)
-
-    # start from here , we have another loop for WT dynamics
-    fig = plt.figure()
-    plt.cla()
-    ax = plt.axes()
-    B1 = aElevation1[i]
-    B4 = aElevation1[i]-dThickness_critical_zone_in
-    x=np.array([0, dDistance_in])
-    
-    y = x * A1 + B1
-    ax.set_xlabel('Distance (m)', fontsize=10)
-    ax.set_ylabel('Elevation (m)', fontsize=10)
-    
-    fmt = '%.1E' # Format you want the ticks, e.g. '40%'
-    xticks = mtick.FormatStrFormatter(fmt)
-    ax.xaxis.set_major_formatter(xticks)
-    
-    ax.plot(x,y ,'red',label='Surface')
-    ax.set_xlim(x[0], x[1])
-    y = x * A4 + B4
-    ax.plot(x,y, 'yellow',label='Bed rock')
-
-    a1_degree = np.arctan(A1) 
-    a1_degree = np.rad2deg( a1_degree)
-    #a1_degree = np.arctan(0.5)
-    dummy0 = np.array((a1_degree,)) 
-    dummy1 = np.array( [ 0, aElevation1[i] ] ) 
-    dummy2 = dummy1.reshape((1, 2))
-    trans_angle = ax.transData.transform_angles(dummy0, dummy2, False )[0]
-    #plt.show()
-
-    ax.text(-0.03 * dDistance_in , aElevation1[i] - dThickness_critical_zone_in, "Downslope end", color ='blue', rotation = 90)
-    trans_angle = trans_angle
-    ax.text(0.06* dDistance_in, aElevation1[i], "Seepage face", color ='green', rotation = trans_angle)
-    iFlag_once = 1 
-
-    A3 = ( dRatio1 ) * (aElevation2[i]  - aElevation1[i] ) / dDistance_in
-
-    B3 = aElevation1[i]
-    C3 = dDistance_in
-    D3 = A3 * C3 + B3
-    #left range
-    dRange0 = dThickness_critical_zone_in
-    dRange1 = dThickness_critical_zone_in + A3 * C3
-  #right range
-    dRange2 = dElevation_difference
-    dRange3 = aElevation2[i] - D3    
-
-#draw transition 
-    G34 = (B3-B4) / (A4-A3)
-    H34 = A4 * G34 + B4
-    if(G34 < dDistance_in):
-        x=np.array([0, G34])
-        y = x * A3 + B3
-        ax.plot(x,y, 'brown',label ='Water table transition')
-        x= np.array([G34, dDistance_in])
-        y = x * A3 + B3
-        ax.plot(x,y, 'brown', linestyle='--' )
-    else:
-        x=np.array([0, dDistance_in])
-        y = x * A3 + B3
-        ax.plot(x,y, 'brown',label ='Water table transition')
-        
-
-    for j in range(ndrop):
-        dHeight1 = aHeight1[j]
-        dHeight2 = aHeight2[j]
-        #transition slope
-        
-        #water table below minimal elevation
-        dWt1 =  aElevation1[i] - dHeight1        
-        dDummy1 = dHeight1 / dThickness_critical_zone_in
-        dDummy1 = pow( dDummy1, dRatio3)
-        dDummy2 = dRange1 * dDummy1
-
-
-        D5 = D3 -  dDummy2
-        F5 = dWt1
-        dDummy3 = (D5 - F5) / dDistance_in        
-       
-        A5 = dDummy3   
-        dSlope_watertable1 = math.atan( A5 )
-        #print(dSlope_watertable1 / math.pi * 180)
-        #intersect watertable below minimal elevation (A4 with A5)
-        #y2 = A4 * x + B4 
-        #y3 = A5 * x + B5         
-        B5 = aElevation1[i]-dHeight1
-        #x = (B5-B4) / (A4-A5)
-        G45 = (B5-B4) / (A4-A5)
-        H45 = A4 * G45 + B4
-        dLength_watertable1 = G45
-        #=========================================
-        #water table above minimal elevation/seepage          
-        #=========================================
-        dWt2 = aElevation1[i] + dHeight2
-        dDummy4 = dHeight2 / dRange2
-        #dDummy4 = dHeight2 / ( aElevation2[i]-dWt2)
-        dDummy5 = pow( dDummy4, dRatio2)
-        #dDummy5 = pow( dDummy4, 2)
-        dDummy6 = dRange3 * dDummy5
-        #dDummy6 =  ( aElevation2[i]-dWt2) * dDummy5
-        D2 = D3 + dDummy6
-        #D2 = dWt2 + dDummy6
-                
-        H12 = dWt2 
-        #H12 = A1 * G12 + B1
-        G12 = (H12 - B1)  /  A1
-        C2 = dDistance_in
-        A2 = ( D2 - H12)/( C2 - G12 )
-
-        #print( D2 )
-        #ratio
-        dSlope_watertable2 = math.atan( A2  )
-        #intersect above elevation (A4 A2)
-        #y2 = A4 * x + B4
-        #y4 = A2 * x + B2
-        #y41 = A2 * x41  + B2, where x41 and y41 are intersect between A1 and A2
-        #B2 unknown, but it could be calculated from:
-        
-        B2 = H12 - A2 * G12
-        
-        G24 = (B2-B4) / (A4-A2)
-        H24 = A4 * G24 + B4
-        dLength_watertable2 = G24 
-        #print(dLength_watertable2)
-        #intersect between two watertable
-        G25 = (B2-B5) / (A5-A2)
-        H25 = A5 * G25 + B5
-        #print(G45, G24, G25)
-        #print(A4, A1, A2, A3, A5)
-        
-        
-        ci = 'C' + str(j)
-        if iFlag_once == 1:
-            x=np.array([0, G45])
-            y = x * A5 + B5
-            ax.plot(x,y, 'blue',label ='Water table without seepage')
-            x= np.array([G45, dDistance_in])
-            y = x * A5 + B5
-            ax.plot(x,y, 'blue', linestyle='--' )
-            
-        else:
-            x=np.array([0, G45])
-            y = x * A5 + B5
-            ax.plot(x,y, 'blue')
-            x= np.array([G45, dDistance_in])
-            y = x * A5 + B5
-            ax.plot(x,y, 'blue', linestyle='--')
-            
-        #plot part
-        x_1 = [0, G12]
-        y_1 = [H12, H12]
-        #intersect as well
-        G24 = (B2-B4) / (A4-A2)
-        H24 = A4 * G24 + B4
-        if(G24 < dDistance_in):
-            x_2 = np.array([G12, G24])
-            y_2 = x_2 * A2 + B2
-            x_22 = np.array([G24, dDistance_in])
-            y_22 = x_22 * A2 + B2 
-            if iFlag_once == 1:
-                ax.plot(x_1,y_1, 'green', linestyle='--')
-                ax.plot(x_2,y_2, 'green', label ='Water table with seepage')
-                ax.plot(x_22,y_22, 'green', linestyle='--')
-            
-            else:
-                ax.plot(x_1,y_1, 'green', linestyle='--')
-                ax.plot(x_2,y_2, 'green')
-                ax.plot(x_22,y_22, 'green', linestyle='--')
-                
-        else: 
-            x_2 = np.array([G12, dDistance_in])
-            y_2 = x_2 * A2 + B2
-            if iFlag_once == 1:
-                ax.plot(x_1,y_1, 'green', linestyle='--')
-                ax.plot(x_2,y_2, 'green', label ='Water table with seepage')
-            else:
-                ax.plot(x_1,y_1, 'green', linestyle='--')
-                ax.plot(x_2,y_2, 'green')
-                
-
-        
-        
-        
-        iFlag_once = 0    
-        
-        
-        #print(y_2[1])
-        #plt.show()
-    ax.legend()
-    print("=============")    
-        #plt.savefig( 'slope_' +  "{:.0f}".format(dHeight1) +'_' +"{:.0f}".format(dHeight2) +  '_' +   str(i).zfill(2) +'.png')
-    #print('__file__:    ', __file__)
-    #print('basename:    ', os.path.basename(__file__))
-    #print('dirname:     ', os.path.dirname(__file__))
-
-    sFilename = os.path.dirname(__file__) + '/slope_' + str(i).zfill(2) + str(j).zfill(2)+ '.png'
-    plt.savefig(sFilename )
-
diff --git a/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope_animation.py b/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope_animation.py
deleted file mode 100644
index 54836f2..0000000
--- a/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope_animation.py
+++ /dev/null
@@ -1,185 +0,0 @@
-import math
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.pyplot as plt
-from matplotlib import cm
-from matplotlib.ticker import LinearLocator, FormatStrFormatter
-import numpy as np
-import matplotlib.ticker as mtick
-
-from matplotlib import animation
-
-dDistance_in = 50000  /2.
-
-ninterval = 5
-
-aElevation1 =   np.arange(ninterval) * 200 - 300
-aElevation2 =   np.arange(ninterval) * 210 - 200
-print(aElevation1)
-print(aElevation2)
-
-dThickness_critical_zone_in = 50.0
-#dHeight1 = 40.0
-#dHeight2= - 40
-ndrop = 5
-aHeight1 =   np.arange(ndrop) * 10 + 10
-aHeight2 =   np.flip(np.arange(ndrop) * 10 + 10)
-
-X, Y = np.meshgrid(aElevation1, aElevation2)
-aLength = np.full( (ninterval,ninterval), 0.0, dtype=float)
-
-#for i in range(1,20):
-    #print(math.atan(i /ninterval))
-    #print(math.atan( (i+1.0)/ ninterval) - math.atan(i /ninterval) )
-
-
-for i in range(ninterval):
-    print(aElevation2[i] ,aElevation1[i] )
-    #elevation difference reference
-    dummy0 = (aElevation2[i] - (aElevation1[i] ) ) 
-    dElevation_difference = dummy0
-    #slope
-    dDummy1 =  dElevation_difference  / dDistance_in
-    a1 = dDummy1
-    dSlope_surface = math.atan(a1)
-    #print(dSlope_surface / math.pi * 180)
-    aLength[i,i] = dSlope_surface
-    #dDummy2 = dElevation_difference / dDistance_in
-    #dDummy2 = dThickness_critical_zone_in * (1 - pow(dDummy2, 0.25 ))
-    #dDummy2 =  (aElevation2[i] - dDummy2) - (aElevation1[i] - dThickness_critical_zone_in) 
-    #dDummy3 =  dDummy2 / dDistance_in
-    dDummy2 = (aElevation2[i] - dThickness_critical_zone_in ) - ( (aElevation1[i] ) - dThickness_critical_zone_in * 0.95 ) 
-    dDummy3 =  dDummy2 / dDistance_in
-    a2= dDummy3
-
-    dSlope_bedrock = math.atan(a2)
-    #print(dSlope_bedrock / math.pi * 180)
-
-
-    for j in range(ndrop):
-        # start from here , we have another loop for WT dynamics
-        fig = plt.figure()
-        plt.cla()
-        ax = plt.axes()
-        b1 = aElevation1[i]
-        b2 = aElevation1[i]-dThickness_critical_zone_in
-        x=np.array([0, dDistance_in])
-
-        y = x * a1 + b1
-        ax.set_xlabel('Distance unit: m', fontsize=10)
-        ax.set_ylabel('Elevation unit: m', fontsize=10)
-
-        fmt = '%.1E' # Format you want the ticks, e.g. '40%'
-        xticks = mtick.FormatStrFormatter(fmt)
-        ax.xaxis.set_major_formatter(xticks)
-        ax.plot(x,y ,'red',label='Surface')
-        ax.set_xlim(x[0], x[1])
-        y = x * a2 + b2
-        ax.plot(x,y, 'yellow',label='Bed rock')
-        #plt.show()
-        iFlag_once = 1 
-        a5 = a1 * 0.8
-            #y5 =  a5 * x5 + b5 
-            #when x5 = 0 b5= surface elevation
-        b5 = aElevation1[i]
-        x5_2 = dDistance_in
-        y5_2 = a5 * x5_2 + b5
-    #left range
-        dRange0 = dThickness_critical_zone_in
-        dRange1 = dThickness_critical_zone_in + a5 * x5_2
-    
-        dRange2 = dElevation_difference
-        dRange3 = aElevation2[i] - y5_2   
-        dHeight1 = aHeight1[j]
-        dHeight2 = aHeight2[j]
-        #transition slope
-        
-        #water table below minimal elevation
-        dWt1 =  aElevation1[i] - dHeight1        
-        dDummy1 = dHeight1 / dThickness_critical_zone_in
-        dDummy1 = pow( dDummy1, 1)
-        dDummy2 = dRange1 * dDummy1
-
-
-        y3_2 = y5_2 -  dDummy2
-        y3_1 = dWt1
-        dDummy3 = (y3_2 - y3_1) / dDistance_in        
-       
-        a3 = dDummy3   
-        dSlope_watertable1 = math.atan( a3 )
-        #print(dSlope_watertable1 / math.pi * 180)
-        #intersect watertable below minimal elevation (a2 with a3)
-        #y2 = a2 * x + b2 
-        #y3 = a3 * x + b3         
-        b3 = aElevation1[i]-dHeight1
-        #x = (b3-b2) / (a2-a3)
-        x23 = (b3-b2) / (a2-a3)
-        y23 = a2 * x23 + b2
-        dLength_watertable1 = x23
-        #=========================================
-        #water table above minimal elevation            
-        #=========================================
-        dWt2 = aElevation1[i] + dHeight2
-        dDummy4 = dHeight2 / dRange2
-        dDummy4 = pow( dDummy4, 0.5)
-        dDummy5 = dRange3 * dDummy4
-        y4_2 = y5_2 + dDummy5
-                
-        y14 = dWt2 
-        #y14 = a1 * x14 + b1
-        x14 = (y14 - b1)  /  a1
-        x4_2 = dDistance_in
-        a4 = ( y4_2 - y14)/( x4_2 - x14 )
-
-        #print( y4_2 )
-        #ratio
-        dSlope_watertable2 = math.atan( a4  )
-        #intersect above elevation (a2 a4)
-        #y2 = a2 * x + b2
-        #y4 = a4 * x + b4
-        #y41 = a4 * x41  + b4, where x41 and y41 are intersect between a1 and a4
-        #b4 unknown, but it could be calculated from:
-        
-        b4 = y14 - a4 * x14
-        
-        x24 = (b4-b2) / (a2-a4)
-        y24 = a2 * x24 + b2
-        dLength_watertable2 = x24 
-        #print(dLength_watertable2)
-        #intersect between two watertable
-        x34 = (b4-b3) / (a3-a4)
-        y34 = a3 * x34 + b3
-        print(x23, x24, x34)
-        
-        
-        y = x * a3 + b3
-        ci = 'C' + str(j)
-        if iFlag_once == 1:
-            ax.plot(x,y, 'blue',label ='Water table without seepage')
-        else:
-            ax.plot(x,y, 'blue')
-        #print(y_2[1])
-        #plt.show()
-        ax.legend()
-        print("=============")    
-        #plt.savefig( 'slope_' +  "{:.0f}".format(dHeight1) +'_' + "{:.0f}".format(dHeight2) +  '_' + str(i).zfill(2) + '.png')
-        plt.savefig( 'slope_' + str(i).zfill(2) + str(j+4).zfill(2)+ '.png')
-
-        #x_1 = [0, x14]
-        #y_1 = [y14, y14]
-        #x_2 = np.array([x14, dDistance_in])
-        #y_2 = x_2 * a4 + b4
-        #if iFlag_once == 1:
-        #    ax.plot(x_1,y_1, 'green', label ='Water table with seepage')
-        #else:
-        #    ax.plot(x_1,y_1, 'green')
-        #iFlag_once = 0
-        #ax.plot(x_2,y_2, 'green')
-        ##print(y_2[1])
-        ##plt.show()
-        #ax.legend()
-        #print("=============")    
-        ##plt.savefig( 'slope_' +  "{:.0f}".format(dHeight1) +'_' + "{:.0f}".format(dHeight2) #+  '_' + str(i).zfill(2) + '.png')
-        #plt.savefig( 'slope_' + str(i).zfill(2) + str(j).zfill(2)+ '.png')
-        
-        
-
diff --git a/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope_old.py b/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope_old.py
deleted file mode 100644
index 7475f1e..0000000
--- a/pye3sm/elm/h2sc/model/h2sc_calculate_water_table_slope_old.py
+++ /dev/null
@@ -1,165 +0,0 @@
-import math
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.pyplot as plt
-from matplotlib import cm
-from matplotlib.ticker import LinearLocator, FormatStrFormatter
-import numpy as np
-from matplotlib import animation
-
-dDistance_in = 50000  /2.
-
-ninterval = 10
-
-aElevation1 =   np.arange(ninterval) * 200 -300
-aElevation2 =   np.arange(ninterval) * 250 + 600
-print(aElevation1)
-print(aElevation2)
-
-dThickness = 120.0
-#dHeight1 = 40.0
-#dHeight2= - 40
-ndrop = 5 
-aHeight1 =   np.arange(ndrop) * 20 + 10
-aHeight2 =   np.arange(ndrop) * 20 + 10
-
-X, Y = np.meshgrid(aElevation1, aElevation2)
-aLength = np.full( (ninterval,ninterval), 0.0, dtype=float)
-
-#for i in range(1,20):
-    #print(math.atan(i /ninterval))
-    #print(math.atan( (i+1.0)/ ninterval) - math.atan(i /ninterval) )
-
-for i in range(ninterval):
-    print(aElevation2[i] ,aElevation1[i] )
-    #elevation difference reference
-    dummy0 = (aElevation2[i] - (aElevation1[i] ) ) 
-    dElevation_difference = dummy0
-    #slope
-    dummy1 =  dElevation_difference  / dDistance_in
-    a1 = dummy1
-    dSlope_surface = math.atan(a1)
-    #print(dSlope_surface / math.pi * 180)
-    aLength[i,i] = dSlope_surface
-    dummy2 =  aElevation2[i] - (aElevation1[i] - 0.5 * dThickness) 
-    dummy3 =  dummy2 / dDistance_in
-    a2= dummy3
-    dSlope_bedrock = math.atan(a2)
-    #print(dSlope_bedrock / math.pi * 180)
-
-    # start from here , we have another loop for WT dynamics
-    fig = plt.figure()
-    plt.cla()
-    ax = plt.axes()
-    b1 = aElevation1[i]
-    b2 = aElevation1[i]-dThickness
-    x=np.array([0, dDistance_in])
-    
-    y = x * a1 + b1
-    ax.plot(x,y ,'red',label='Surface')
-    ax.set_xlim(x[0], x[1])
-    y = x * a2 + b2
-    ax.plot(x,y, 'yellow',label='Bed rock')
-    #plt.show()
-    iFlag_once = 1 
-    for j in range(ndrop):
-        dHeight1 = aHeight1[j]
-        dHeight2 = aHeight2[j]
-
-        #water table below minimal elevation
-        dWt1 =  aElevation1[i] - dHeight1
-        #relation with depth
-        dummy3 = aElevation1[i] - dThickness
-        dummy3 = dWt1- dummy3 
-        dummy3 = dummy3 / dThickness
-        dummy3 = 1-pow(dummy3, 4)
-        #relation with mean elevation
-        if (aElevation1[i]  > 0) :
-            #dummy4 =  dWt1 / (aElevation2[i] + aElevation1[i]) 
-            dummy4 = dHeight1 / dElevation_difference
-            dummy4 = pow(dummy4, 0.3)
-        else:
-            dummy4 = 1
-        
-        dummy5 = dElevation_difference * dummy3 * dummy4 #the smaller dummy3 and dummy4 are, the larger the slope is
-        dummy6 = aElevation1[i] + dummy5 
-        dummy6 = aElevation2[i] - dummy6
-        dummy6 = dummy6 / dDistance_in
-       
-        a3 = dummy6
-        print(dummy3, dummy4, dummy5, dummy6)    
-        dSlope_watertable1 = math.atan( a3 )
-        #print(dSlope_watertable1 / math.pi * 180)
-        #intersect watertable below minimal elevation (a2 with a3)
-        #y2 = a2 * x + b2 
-        #y3 = a3 * x + b3         
-        b3 = aElevation1[i]-dHeight1
-        #x = (b3-b2) / (a2-a3)
-        x23 = (b3-b2) / (a2-a3)
-        y23 = a2 * x23 + b2
-        dLength_watertable1 = x23
-
-        #water table above minimal elevation        
-        dWt2 = aElevation1[i] + dHeight2
-        if(aElevation1[i] > 0):
-            
-            #dummy7 =  dWt2 / (aElevation2[i] + aElevation1[i])             
-            dummy7 =  dHeight2 / dElevation_difference     
-            dummy8 = 1- pow(dummy7, 0.4)
-            dummy9 = dummy8 * dElevation_difference
-            
-        else:
-            dummy7 = dHeight2 - aElevation1[i]
-            dummy7 =  dHeight2 / dElevation_difference                 
-            dummy8 = 1 -pow(dummy7, 0.3)
-            dummy9 = dummy8 * dElevation_difference  
-
-        dummy10 =   aElevation1[i]  +  dummy9
-        #dummy10 =   dWt2 +  dummy9
-        y14 = dWt2 
-        #y14 = a1 * x14 + b1
-        x14 = (y14 - b1)  /  a1
-        x4 = dDistance_in
-        a4 = (y14 - dummy10)/( x14 - x4 )
-        #ratio
-        dSlope_watertable2 = math.atan( a4  )
-        #intersect above elevation (a2 a4)
-        #y2 = a2 * x + b2
-        #y4 = a4 * x + b4
-        #y41 = a4 * x41  + b4, where x41 and y41 are intersect between a1 and a4
-        #b4 unknown, but it could be calculated from:
-        
-        b4 = y14 - a4 * x14
-        
-        x24 = (b4-b2) / (a2-a4)
-        y24 = a2 * x24 + b2
-        dLength_watertable2 = x24 
-        #print(dLength_watertable2)
-        #intersect between two watertable
-        x34 = (b4-b3) / (a3-a4)
-        y34 = a3 * x34 + b3
-        #print(x23, y23, x24, y24, x34,y34)
-        
-        
-        y = x * a3 + b3
-        ci = 'C' + str(j)
-        if iFlag_once == 1:
-            ax.plot(x,y, 'blue',label ='Water table without seepage')
-        else:
-            ax.plot(x,y, 'blue')
-        x_1 = [0, x14]
-        y_1 = [y14, y14]
-        x_2 = np.array([x14, dDistance_in])
-        y_2 = x_2 * a4 + b4
-        if iFlag_once == 1:
-            ax.plot(x_1,y_1, 'green', label ='Water table with seepage')
-        else:
-            ax.plot(x_1,y_1, 'green')
-        iFlag_once = 0
-        ax.plot(x_2,y_2, 'green')
-        #print(y_2[1])
-        #plt.show()
-    ax.legend()
-    print("=============")    
-    #plt.savefig( 'slope_' +  "{:.0f}".format(dHeight1) +'_' + "{:.0f}".format(dHeight2) + '_' + str(i).zfill(2) + '.png')
-    plt.savefig( 'slope_' + str(i).zfill(2) + '.png')
-
diff --git a/pye3sm/elm/h2sc/model/slope_0008.png b/pye3sm/elm/h2sc/model/slope_0008.png
deleted file mode 100644
index 1bdc87e..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0108.png b/pye3sm/elm/h2sc/model/slope_0108.png
deleted file mode 100644
index 1656dfc..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0208.png b/pye3sm/elm/h2sc/model/slope_0208.png
deleted file mode 100644
index 4b73389..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0308.png b/pye3sm/elm/h2sc/model/slope_0308.png
deleted file mode 100644
index eb34ff0..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0408.png b/pye3sm/elm/h2sc/model/slope_0408.png
deleted file mode 100644
index a76eeb9..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0508.png b/pye3sm/elm/h2sc/model/slope_0508.png
deleted file mode 100644
index d40ae4d..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0608.png b/pye3sm/elm/h2sc/model/slope_0608.png
deleted file mode 100644
index 25db900..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0708.png b/pye3sm/elm/h2sc/model/slope_0708.png
deleted file mode 100644
index 8e6ca01..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0808.png b/pye3sm/elm/h2sc/model/slope_0808.png
deleted file mode 100644
index 83f42dc..0000000
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diff --git a/pye3sm/elm/h2sc/model/slope_0908.png b/pye3sm/elm/h2sc/model/slope_0908.png
deleted file mode 100644
index 203456d..0000000
Binary files a/pye3sm/elm/h2sc/model/slope_0908.png and /dev/null differ
diff --git a/pye3sm/elm/mesh/__init__.py b/pye3sm/elm/mesh/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/pye3sm/elm/grid/create_customized_elm_domain.py b/pye3sm/elm/mesh/create_customized_elm_domain.py
similarity index 85%
rename from pye3sm/elm/grid/create_customized_elm_domain.py
rename to pye3sm/elm/mesh/create_customized_elm_domain.py
index eb38b35..bc6ee00 100644
--- a/pye3sm/elm/grid/create_customized_elm_domain.py
+++ b/pye3sm/elm/mesh/create_customized_elm_domain.py
@@ -6,14 +6,14 @@
 import getpass
 from netCDF4 import Dataset
 
-from pye3sm.elm.grid.unstructured.ReadConfigurationFile import ReadConfigurationFile
+from pye3sm.elm.mesh.unstructured.ReadConfigurationFile import ReadConfigurationFile
 
-from pye3sm.elm.grid.structured.ComputeLatLonAtVertex import ComputeLatLonAtVertex
+from pye3sm.elm.mesh.structured.ComputeLatLonAtVertex import ComputeLatLonAtVertex
 
-from pye3sm.elm.grid.structured.twod.create_customized_elm_surface_file_2d import create_customized_elm_surface_file_2d
-from pye3sm.elm.grid.structured.twod.create_customized_elm_domain_file_2d import create_customized_elm_domain_file_2d
-from pye3sm.elm.grid.unstructured.create_customized_elm_surface_file_1d import create_customized_elm_surface_file_1d
-from pye3sm.elm.grid.unstructured.create_customized_elm_domain_file_1d import create_customized_elm_domain_file_1d
+from pye3sm.elm.mesh.structured.twod.create_customized_elm_surface_file_2d import create_customized_elm_surface_file_2d
+from pye3sm.elm.mesh.structured.twod.create_customized_elm_domain_file_2d import create_customized_elm_domain_file_2d
+from pye3sm.elm.mesh.unstructured.create_customized_elm_surface_file_1d import create_customized_elm_surface_file_1d
+from pye3sm.elm.mesh.unstructured.create_customized_elm_domain_file_1d import create_customized_elm_domain_file_1d
 
 def create_customized_elm_domain( aLon, aLat, aMask_in, dLon, dLat, \
         sFilename_configuration, \
diff --git a/pye3sm/elm/grid/elm_extract_grid_latlon_from_mosart.py b/pye3sm/elm/mesh/elm_extract_grid_latlon_from_mosart.py
similarity index 100%
rename from pye3sm/elm/grid/elm_extract_grid_latlon_from_mosart.py
rename to pye3sm/elm/mesh/elm_extract_grid_latlon_from_mosart.py
diff --git a/pye3sm/elm/grid/elm_retrieve_case_dimension_info.py b/pye3sm/elm/mesh/elm_retrieve_case_dimension_info.py
similarity index 100%
rename from pye3sm/elm/grid/elm_retrieve_case_dimension_info.py
rename to pye3sm/elm/mesh/elm_retrieve_case_dimension_info.py
diff --git a/pye3sm/elm/grid/elm_sparse_grid.cfg b/pye3sm/elm/mesh/elm_sparse_grid.cfg
similarity index 100%
rename from pye3sm/elm/grid/elm_sparse_grid.cfg
rename to pye3sm/elm/mesh/elm_sparse_grid.cfg
diff --git a/pye3sm/elm/grid/ex/icom_14x1_sparse_grid.cfg b/pye3sm/elm/mesh/ex/icom_14x1_sparse_grid.cfg
similarity index 100%
rename from pye3sm/elm/grid/ex/icom_14x1_sparse_grid.cfg
rename to pye3sm/elm/mesh/ex/icom_14x1_sparse_grid.cfg
diff --git a/pye3sm/elm/grid/ex/icom_14x1_sparse_grid_latlon.txt b/pye3sm/elm/mesh/ex/icom_14x1_sparse_grid_latlon.txt
similarity index 100%
rename from pye3sm/elm/grid/ex/icom_14x1_sparse_grid_latlon.txt
rename to pye3sm/elm/mesh/ex/icom_14x1_sparse_grid_latlon.txt
diff --git a/pye3sm/elm/grid/ex/icom_14x1_sparse_grid_latv.txt b/pye3sm/elm/mesh/ex/icom_14x1_sparse_grid_latv.txt
similarity index 100%
rename from pye3sm/elm/grid/ex/icom_14x1_sparse_grid_latv.txt
rename to pye3sm/elm/mesh/ex/icom_14x1_sparse_grid_latv.txt
diff --git a/pye3sm/elm/grid/ex/icom_14x1_sparse_grid_lonv.txt b/pye3sm/elm/mesh/ex/icom_14x1_sparse_grid_lonv.txt
similarity index 100%
rename from pye3sm/elm/grid/ex/icom_14x1_sparse_grid_lonv.txt
rename to pye3sm/elm/mesh/ex/icom_14x1_sparse_grid_lonv.txt
diff --git a/pye3sm/elm/grid/hpc/clump.png b/pye3sm/elm/mesh/hpc/clump.png
similarity index 100%
rename from pye3sm/elm/grid/hpc/clump.png
rename to pye3sm/elm/mesh/hpc/clump.png
diff --git a/pye3sm/elm/grid/hpc/decompInit.py b/pye3sm/elm/mesh/hpc/decompInit.py
similarity index 100%
rename from pye3sm/elm/grid/hpc/decompInit.py
rename to pye3sm/elm/mesh/hpc/decompInit.py
diff --git a/pye3sm/elm/grid/hpc/land_mask.png b/pye3sm/elm/mesh/hpc/land_mask.png
similarity index 100%
rename from pye3sm/elm/grid/hpc/land_mask.png
rename to pye3sm/elm/mesh/hpc/land_mask.png
diff --git a/pye3sm/elm/grid/icom/create_clm_surface_data.py b/pye3sm/elm/mesh/icom/create_clm_surface_data.py
similarity index 100%
rename from pye3sm/elm/grid/icom/create_clm_surface_data.py
rename to pye3sm/elm/mesh/icom/create_clm_surface_data.py
diff --git a/pye3sm/elm/grid/icom/icom_14x1_sparse_grid.cfg b/pye3sm/elm/mesh/icom/icom_14x1_sparse_grid.cfg
similarity index 100%
rename from pye3sm/elm/grid/icom/icom_14x1_sparse_grid.cfg
rename to pye3sm/elm/mesh/icom/icom_14x1_sparse_grid.cfg
diff --git a/pye3sm/elm/grid/icom/icom_14x1_sparse_grid_latlon.txt b/pye3sm/elm/mesh/icom/icom_14x1_sparse_grid_latlon.txt
similarity index 100%
rename from pye3sm/elm/grid/icom/icom_14x1_sparse_grid_latlon.txt
rename to pye3sm/elm/mesh/icom/icom_14x1_sparse_grid_latlon.txt
diff --git a/pye3sm/elm/grid/icom/icom_14x1_sparse_grid_latv.txt b/pye3sm/elm/mesh/icom/icom_14x1_sparse_grid_latv.txt
similarity index 100%
rename from pye3sm/elm/grid/icom/icom_14x1_sparse_grid_latv.txt
rename to pye3sm/elm/mesh/icom/icom_14x1_sparse_grid_latv.txt
diff --git a/pye3sm/elm/grid/icom/icom_14x1_sparse_grid_lonv.txt b/pye3sm/elm/mesh/icom/icom_14x1_sparse_grid_lonv.txt
similarity index 100%
rename from pye3sm/elm/grid/icom/icom_14x1_sparse_grid_lonv.txt
rename to pye3sm/elm/mesh/icom/icom_14x1_sparse_grid_lonv.txt
diff --git a/pye3sm/elm/grid/structured/ComputeLatLonAtVertex.py b/pye3sm/elm/mesh/structured/ComputeLatLonAtVertex.py
similarity index 100%
rename from pye3sm/elm/grid/structured/ComputeLatLonAtVertex.py
rename to pye3sm/elm/mesh/structured/ComputeLatLonAtVertex.py
diff --git a/pye3sm/elm/mesh/structured/__init__.py b/pye3sm/elm/mesh/structured/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/pye3sm/elm/grid/structured/twod/create_customized_elm_domain_file_2d.py b/pye3sm/elm/mesh/structured/twod/create_customized_elm_domain_file_2d.py
similarity index 100%
rename from pye3sm/elm/grid/structured/twod/create_customized_elm_domain_file_2d.py
rename to pye3sm/elm/mesh/structured/twod/create_customized_elm_domain_file_2d.py
diff --git a/pye3sm/elm/grid/structured/twod/create_customized_elm_surface_file_2d.py b/pye3sm/elm/mesh/structured/twod/create_customized_elm_surface_file_2d.py
similarity index 99%
rename from pye3sm/elm/grid/structured/twod/create_customized_elm_surface_file_2d.py
rename to pye3sm/elm/mesh/structured/twod/create_customized_elm_surface_file_2d.py
index da4c3a6..fe74c54 100644
--- a/pye3sm/elm/grid/structured/twod/create_customized_elm_surface_file_2d.py
+++ b/pye3sm/elm/mesh/structured/twod/create_customized_elm_surface_file_2d.py
@@ -5,7 +5,7 @@
 import getpass
 from netCDF4 import Dataset
 
-from pye3sm.elm.grid.unstructured.PerformFractionCoverCheck import PerformFractionCoverCheck
+from pye3sm.elm.mesh.unstructured.PerformFractionCoverCheck import PerformFractionCoverCheck
 def create_customized_elm_surface_file_2d( aLon_region, aLat_region, aMask_in,\
     sFilename_surface_data_in, \
     sFilename_surface_data_out, \
diff --git a/pye3sm/elm/grid/structured/twod/readme.md b/pye3sm/elm/mesh/structured/twod/readme.md
similarity index 100%
rename from pye3sm/elm/grid/structured/twod/readme.md
rename to pye3sm/elm/mesh/structured/twod/readme.md
diff --git a/pye3sm/elm/grid/unstructured/PerformFractionCoverCheck.py b/pye3sm/elm/mesh/unstructured/PerformFractionCoverCheck.py
similarity index 100%
rename from pye3sm/elm/grid/unstructured/PerformFractionCoverCheck.py
rename to pye3sm/elm/mesh/unstructured/PerformFractionCoverCheck.py
diff --git a/pye3sm/elm/grid/unstructured/ReadConfigurationFile.py b/pye3sm/elm/mesh/unstructured/ReadConfigurationFile.py
similarity index 100%
rename from pye3sm/elm/grid/unstructured/ReadConfigurationFile.py
rename to pye3sm/elm/mesh/unstructured/ReadConfigurationFile.py
diff --git a/pye3sm/elm/grid/unstructured/ReadLatLon.py b/pye3sm/elm/mesh/unstructured/ReadLatLon.py
similarity index 100%
rename from pye3sm/elm/grid/unstructured/ReadLatLon.py
rename to pye3sm/elm/mesh/unstructured/ReadLatLon.py
diff --git a/pye3sm/elm/mesh/unstructured/__init__.py b/pye3sm/elm/mesh/unstructured/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/pye3sm/elm/grid/unstructured/create_customized_elm_domain_file_1d.py b/pye3sm/elm/mesh/unstructured/create_customized_elm_domain_file_1d.py
similarity index 100%
rename from pye3sm/elm/grid/unstructured/create_customized_elm_domain_file_1d.py
rename to pye3sm/elm/mesh/unstructured/create_customized_elm_domain_file_1d.py
diff --git a/pye3sm/elm/grid/unstructured/create_customized_elm_surface_file_1d.py b/pye3sm/elm/mesh/unstructured/create_customized_elm_surface_file_1d.py
similarity index 99%
rename from pye3sm/elm/grid/unstructured/create_customized_elm_surface_file_1d.py
rename to pye3sm/elm/mesh/unstructured/create_customized_elm_surface_file_1d.py
index b521688..ed01094 100644
--- a/pye3sm/elm/grid/unstructured/create_customized_elm_surface_file_1d.py
+++ b/pye3sm/elm/mesh/unstructured/create_customized_elm_surface_file_1d.py
@@ -3,7 +3,7 @@
 from datetime import datetime
 import getpass
 from netCDF4 import Dataset
-from pye3sm.elm.grid.unstructured.PerformFractionCoverCheck import PerformFractionCoverCheck
+from pye3sm.elm.mesh.unstructured.PerformFractionCoverCheck import PerformFractionCoverCheck
 def create_customized_elm_surface_file_1d( aLon_region, aLat_region, \
     sFilename_surface_data_in, \
     sFilename_surface_data_out, \
diff --git a/pye3sm/mosart/evaluation/halfdegree/mosart_evaluate_stream_discharge_gsim.py b/pye3sm/mosart/evaluation/halfdegree/mosart_evaluate_stream_discharge_gsim.py
index f97e082..e463be9 100644
--- a/pye3sm/mosart/evaluation/halfdegree/mosart_evaluate_stream_discharge_gsim.py
+++ b/pye3sm/mosart/evaluation/halfdegree/mosart_evaluate_stream_discharge_gsim.py
@@ -6,47 +6,24 @@
 from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file_multiple_band
 from pyearth.toolbox.reader.text_reader_string import text_reader_string
 from pyearth.visual.timeseries.plot_time_series_data import plot_time_series_data
-from pye3sm.mosart.grid.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info 
+from pye3sm.mosart.mesh.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info 
 
-from pye3sm.tools.gsim.read_gsim_data import read_gsim_data
+from pye3sm.tools.mosart.gsim.read_gsim_data import read_gsim_data
+from pye3sm.mosart.general.structured.twod.retrieve.mosart_retrieve_variable_2d import mosart_retrieve_variable_2d
+from pye3sm.mosart.general.mosart_prepare_outlet_coordinates_with_gsim_filenames import mosart_prepare_outlet_coordinates_with_gsim_filenames
 
-#evaluate mosart stream discharge
-def mosart_prepare_outlet_coordinates_with_gsim_filenames(sFilename_mosart_gsim_info, iSkipline_in=1):
+def mosart_evaluate_stream_discharge_gsim(oE3SM_in,  oCase_in,  sFilename_mosart_gsim_info):
     """
-    extract global outlet indicex for gsim evaluation
-    """
-    #read the text file
-    data_all = text_reader_string(sFilename_mosart_gsim_info, iSkipline_in=1)
-    aBasin = data_all[:, 0]
-    aFilename_gsim = data_all[:, 1]
-
-    aLon = data_all[:, 2]
-    aLon = aLon.astype(np.float)
-
-    aLat =  data_all[:, 3]
-    aLat = aLat.astype(np.float)
-    
-    aID = data_all[:, 4]
-    aID = aID.astype(np.long)
-    
-    
+    Evaluate the mosart discharge using the gsim dataset
 
-    return aBasin, aLat, aLon, aID, aFilename_gsim
-
-    return
-
-def mosart_evaluate_stream_discharge_gsim(oE3SM_in, \
-                                          oCase_in, \
-                                              sFilename_mosart_gsim_info):
-
-    """
-    evaluate the mosart discharge using the gsim dataset
+    Args:
+        oE3SM_in (object): the E3SM class object
+        oCase_in (object): the Case class object
+        sFilename_mosart_gsim_info (str): The filename of the gsim data
     """
 
-
     #read the time series mosat output at the grid
-    sModel = oCase_in.sModel
-    sRegion = oCase_in.sRegion
+    
     iFlag_same_grid = oCase_in.iFlag_same_grid
     iYear_start = oCase_in.iYear_start
     iYear_end = oCase_in.iYear_end
@@ -57,21 +34,19 @@ def mosart_evaluate_stream_discharge_gsim(oE3SM_in, \
     sLabel_Y = oCase_in.sLabel_y
     dConversion = oCase_in.dConversion
     sVariable = oCase_in.sVariable
-    sCase = oCase_in.sCase
-    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    
     sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
     sWorkspace_variable_data = sWorkspace_analysis_case + slash + sVariable +  slash + 'tiff'
 
-    nrow = 360
-    ncolumn = 720
+    
      #new approach
     aLon, aLat , aMask_ll= mosart_retrieve_case_dimension_info(oCase_in)
     #dimension
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
     #resolution
     dLon_min = np.min(aLon)
@@ -103,94 +78,92 @@ def mosart_evaluate_stream_discharge_gsim(oE3SM_in, \
     dates_subset2 = dates[subset_index2]
     nstress_subset= len(dates_subset1)
 
-    #read the stack data
-
-    sFilename = sWorkspace_variable_data + slash + sVariable  + sExtension_tiff
-
-    aData_all = gdal_read_geotiff_file_multiple_band(sFilename)
-    aVariable_total = aData_all[0]
-    aVariable_total_subset = aVariable_total[subset_index1,:,:]
-
-    
     sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
     if not os.path.exists(sWorkspace_analysis_case_variable):
         os.makedirs(sWorkspace_analysis_case_variable)
-
-    sWorkspace_analysis_case_domain = sWorkspace_analysis_case_variable + slash + 'tsplot_domain'
+    sWorkspace_analysis_case_domain = sWorkspace_analysis_case_variable + slash + 'tsplot'
     if not os.path.exists(sWorkspace_analysis_case_domain):
         os.makedirs(sWorkspace_analysis_case_domain)
         pass
 
-    aBasin, aLat, aLon, aID, aFilename_gsim = mosart_prepare_outlet_coordinates_with_gsim_filenames(sFilename_mosart_gsim_info)
+    #read the stack data
+    iFlag_monthly=1
+    if iFlag_monthly ==1:
+        aData_ret = mosart_retrieve_variable_2d( oCase_in, iFlag_monthly_in = 1)
+        for i in np.arange(nstress_subset):        
+            aVariable_total_subset = aData_ret[subset_index1]
+
+
+            aBasin, aLat, aLon, aID, aFilename_gsim = mosart_prepare_outlet_coordinates_with_gsim_filenames(sFilename_mosart_gsim_info)
 
-    nsite = len(aFilename_gsim)
+            nsite = len(aFilename_gsim)
 
-    sWorkspace_gsim = '/compyfs/liao313/00raw/hydrology/gsim/GSIM_indices/TIMESERIES/monthly'
+            sWorkspace_gsim = '/compyfs/liao313/00raw/hydrology/gsim/GSIM_indices/TIMESERIES/monthly'
 
-    for iSite in np.arange(1, nsite+1, 1):
-        dLatitude = aLat[iSite-1]
-        dLongitude = aLon[iSite-1]
+            for iSite in np.arange(1, nsite+1, 1):
+                dLatitude = aLat[iSite-1]
+                dLongitude = aLon[iSite-1]
 
-        if dLatitude >=dLat_min and dLatitude<=dLat_max and dLongitude >=dLon_min and dLongitude<=dLon_max:
-            pass
-        else:
-            continue
-        sFilename_gsim = aFilename_gsim[iSite-1] + '.mon'
+                if dLatitude >=dLat_min and dLatitude<=dLat_max and dLongitude >=dLon_min and dLongitude<=dLon_max:
+                    pass
+                else:
+                    continue
+                sFilename_gsim = aFilename_gsim[iSite-1] + '.mon'
 
-        sFilename_gsim = 'BR_0000244.mon'
+                sFilename_gsim = 'BR_0000244.mon'
 
-        sFilename_gsim =  os.path.join(sWorkspace_gsim, sFilename_gsim) 
+                sFilename_gsim =  os.path.join(sWorkspace_gsim, sFilename_gsim) 
 
-        print(sFilename_gsim)
-        #continue
-        aData = read_gsim_data(sFilename_gsim,iYear_start, iYear_end )
+                print(sFilename_gsim)
+                #continue
+                aData = read_gsim_data(sFilename_gsim,iYear_start, iYear_end )
 
 
-        #the location of the grid
+                #the location of the grid
 
 
-        row_index = int( (dLat_max-dLatitude)/0.5 )
-        column_index =int ((dLongitude -dLon_min)/0.5)
+                row_index = int( (dLat_max-dLatitude)/0.5 )
+                column_index =int ((dLongitude -dLon_min)/0.5)
 
-        aData_all=[]
-        aLabel_legend=[]
-        sDomain = aFilename_gsim[iSite-1]
-        sLabel_legend = sDomain.title()
-        sFilename_out = sWorkspace_analysis_case_domain + slash \
-            + sVariable + '_tsplot_' + sDomain +'.png'
+                aData_all=[]
+                aLabel_legend=[]
+                sDomain = aFilename_gsim[iSite-1]
+                sLabel_legend = sDomain.title()
+                sFilename_out = sWorkspace_analysis_case_domain + slash \
+                    + sVariable + '_tsplot_' + sDomain +'.png'
 
-        pShape = aVariable_total_subset.shape
-        aVariable2 = np.full(nstress_subset, -9999, dtype=float)
-        for i in np.arange(0, pShape[0], 1):
+                pShape = aVariable_total_subset.shape
+                aVariable2 = np.full(nstress_subset, -9999, dtype=float)
+                for i in np.arange(0, pShape[0], 1):
 
-            dummy1 = aVariable_total_subset[i,:,:]
-            dummy2 = dummy1[ row_index, column_index ]
-            #dummy3 =  area[row_index, column_index]
-            aVariable2[i] = dummy2 #* dummy3 /1000.0
-            pass
+                    dummy1 = aVariable_total_subset[i,:,:]
+                    dummy2 = dummy1[ row_index, column_index ]
+                    #dummy3 =  area[row_index, column_index]
+                    aVariable2[i] = dummy2 #* dummy3 /1000.0
+                    pass
 
-        sTitle_in=''
-        sLabel_Y=''
+                sTitle_in=''
+                sLabel_Y=''
 
-        #generate the time series plot using the pyes library
-        aDate_all = np.array([dates_subset1, dates_subset1])
-        aData_all = np.array([aVariable2,aData[subset_index2] ])
-        aLabel_legend=['Modeled river discharge','Observed river discharge']
-        plot_time_series_data(aDate_all,
-                              aData_all ,\
-                              sFilename_out,\
-                              iReverse_y_in = 0, \
+                #generate the time series plot using the pyes library
+                aDate_all = np.array([dates_subset1, dates_subset1])
+                aData_all = np.array([aVariable2,aData[subset_index2] ])
+                aLabel_legend=['Modeled river discharge','Observed river discharge']
+                plot_time_series_data(aDate_all,
+                                      aData_all ,\
+                                      sFilename_out,\
+                                      iReverse_y_in = 0, \
 
-                              sTitle_in = sTitle_in, \
-                              sLabel_y_in= sLabel_Y,\
+                                      sTitle_in = sTitle_in, \
+                                      sLabel_y_in= sLabel_Y,\
 
-                              aLabel_legend_in = aLabel_legend, \
+                                      aLabel_legend_in = aLabel_legend, \
 
-                              sLocation_legend_in = 'lower right' ,\
-                              aLocation_legend_in = (1.0, 0.0),\
+                                      sLocation_legend_in = 'lower right' ,\
+                                      aLocation_legend_in = (1.0, 0.0),\
 
-                              iSize_x_in = 12,\
-                              iSize_y_in = 5)
+                                      iSize_x_in = 12,\
+                                      iSize_y_in = 5)
 
 
 
diff --git a/pye3sm/mosart/general/mosart_prepare_outlet_coordinates_with_gsim_filenames.py b/pye3sm/mosart/general/mosart_prepare_outlet_coordinates_with_gsim_filenames.py
new file mode 100644
index 0000000..5966356
--- /dev/null
+++ b/pye3sm/mosart/general/mosart_prepare_outlet_coordinates_with_gsim_filenames.py
@@ -0,0 +1,29 @@
+import numpy as np
+from pyearth.toolbox.reader.text_reader_string import text_reader_string
+
+def mosart_prepare_outlet_coordinates_with_gsim_filenames(sFilename_mosart_gsim_info, iSkipline_in=1):
+    """
+    Extract global outlet indicex for gsim discahrge evaluation
+
+    Args:
+        sFilename_mosart_gsim_info (str): The gsim filename
+        iSkipline_in (int, optional): how many line to be skipped. Defaults to 1.
+
+    Returns:
+        tuple[list, list, list, list, list]: aBasin, aLat, aLon, aID, aFilename_gsim
+    """
+    #read the text file
+    data_all = text_reader_string(sFilename_mosart_gsim_info, iSkipline_in=1)
+    aBasin = data_all[:, 0]
+    aFilename_gsim = data_all[:, 1]
+
+    aLon = data_all[:, 2]
+    aLon = aLon.astype(np.float)
+
+    aLat =  data_all[:, 3]
+    aLat = aLat.astype(np.float)
+    
+    aID = data_all[:, 4]
+    aID = aID.astype(np.long)        
+
+    return aBasin, aLat, aLon, aID, aFilename_gsim
\ No newline at end of file
diff --git a/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_2d.py b/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_2d.py
index 2964b0a..8712b2a 100644
--- a/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_2d.py
+++ b/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_2d.py
@@ -19,11 +19,28 @@
 def mosart_map_variable_2d(oE3SM_in, \
                                           oCase_in,\
                                         iFlag_scientific_notation_colorbar_in =None,   \
+                                             iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
                                           dData_max_in = None,\
                                           dData_min_in = None,
                                          sUnit_in=None,\
-                                          sTitle_in =None):
+                                          sTitle_in =None,aLegend_in=None):
 
+    if iFlag_monthly_in is None:
+        iFlag_monthly_in =1
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
     sModel = oCase_in.sModel
     sRegion = oCase_in.sRegion
     iFlag_same_grid = oCase_in.iFlag_same_grid
@@ -104,32 +121,96 @@ def mosart_map_variable_2d(oE3SM_in, \
         os.makedirs(sWorkspace_analysis_case_region)
         pass
 
-   
-    for i in np.arange(nstress_subset):
-        aImage = aVariable_total_subset[i, :,:]
-        #get date
-        pDate = dates_subset[i]
-        sDate = pDate.strftime('%Y%m%d')
-        sLabel_legend = sRegion.title() + sDate
-        sFilename_out = sWorkspace_analysis_case_region + slash \
-        + sVariable + '_map_' + sDate +'.png'         
-
-        #aData_all = np.log10(aData_all)
-        ##set inf to min
-        #bad_index = np.where( np.isinf(  aData_all) == True  )
-        #aData_all[bad_index] = dMin_y_in
-        aData_all = np.array(aImage)                  
-        
-        map_raster_data(aData_all,  aImage_extent,\
-                              sFilename_out,\
-                                  sTitle_in = sTitle_in,\
-                                      sUnit_in=sUnit_in,\
-                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
-                                       dData_max_in = dData_max_in,\
-                                          dData_min_in = dData_min_in,
-                                  dMissing_value_in = -9999)
-                                
-        print(sDate)
+    if iFlag_monthly ==1 :
+        for i in np.arange(nstress_subset):
+            aImage = aVariable_total_subset[i, :,:]
+            #get date
+            pDate = dates_subset[i]
+            sDate = pDate.strftime('%Y%m%d')
+            sLabel_legend = sRegion.title() + sDate
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_' + sDate +'.png'         
+
+            #aData_all = np.log10(aData_all)
+            ##set inf to min
+            #bad_index = np.where( np.isinf(  aData_all) == True  )
+            #aData_all[bad_index] = dMin_y_in
+            aData_all = np.array(aImage)                  
+
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999)
+
+            print(sDate)
+    
+    if iFlag_annual_mean ==1:
+        #annual mean
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.mean(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_mean'+ sYear +'.png'
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                        aLegend_in = aLegend_in)
+
+        pass
+    
+    if iFlag_annual_total ==1: #annual total
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.sum(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_annual_total'+ sYear +'.png'
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999
+            if dData_max_in is not None:
+                dData_max_in2 = dData_max_in * 10
+            else: 
+                dData_max_in2 = dData_max_in
+         
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           dData_max_in = dData_max_in2,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                        aLegend_in = aLegend_in)
 
 
 
diff --git a/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_history_2d.py b/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_history_2d.py
new file mode 100644
index 0000000..61e1461
--- /dev/null
+++ b/pye3sm/mosart/general/structured/twod/map/mosart_map_variable_history_2d.py
@@ -0,0 +1,255 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file, gdal_read_envi_file_multiple_band
+
+from pyearth.visual.map.raster.map_raster_data import map_raster_data
+
+from pyearth.toolbox.data.remove_outliers import remove_outliers
+from pye3sm.mosart.grid.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info
+ 
+
+def mosart_map_variable_history_2d(oE3SM_in, \
+                                          oCase_in,\
+                                        iFlag_scientific_notation_colorbar_in =None,   \
+                                            iFlag_history_in = None,\
+                                             iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None,\
+                                          dData_max_in = None,\
+                                          dData_min_in = None,
+                                         sUnit_in=None,\
+                                            sColormap_in=None,\
+                                          sTitle_in =None,aLegend_in=None):
+
+    if iFlag_history_in is None:
+        iFlag_history =0
+    else:
+        iFlag_history = iFlag_history_in
+
+    if iFlag_monthly_in is None:
+        iFlag_monthly =0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+    sModel = oCase_in.sModel
+    sRegion = oCase_in.sRegion
+    iFlag_same_grid = oCase_in.iFlag_same_grid
+
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    sLabel_Y = oCase_in.sLabel_y
+    dConversion = oCase_in.dConversion
+    sVariable = oCase_in.sVariable
+    sCase = oCase_in.sCase
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat,aMask = mosart_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    nrow = np.array(aMask).shape[0]
+    ncolumn = np.array(aMask).shape[1]
+    aMask = np.where(aMask==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
+
+    print('Prepare the map grid')
+   
+    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
+    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+
+    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
+
+
+    #read the stack data
+
+    sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
+
+    if iFlag_history ==1:
+        aData_all = gdal_read_envi_file(sFilename)
+        aVariable_total = aData_all[0]
+   
+    else:
+        aData_all = gdal_read_envi_file_multiple_band(sFilename)
+        aVariable_total = aData_all[0]
+        aVariable_total_subset = aVariable_total[subset_index,:,:]
+
+
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable):
+        os.makedirs(sWorkspace_analysis_case_variable)
+
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'map'
+    if not os.path.exists(sWorkspace_analysis_case_region):
+        os.makedirs(sWorkspace_analysis_case_region)
+        pass
+
+    if iFlag_history ==1:
+        aImage = aVariable_total
+        #get date
+        sYear = "{:04d}".format(iYear_end+1)
+        
+        sDate = sYear + '_01_01'
+        sLabel_legend = sRegion.title() + sDate
+        sFilename_out = sWorkspace_analysis_case_region + slash \
+        + sVariable + '_map_' + sDate +'.png'         
+       
+        aData_all = np.array(aImage)                  
+        map_raster_data(aData_all,  aImage_extent,\
+                              sFilename_out,\
+                                  sTitle_in = sTitle_in,\
+                                      sUnit_in=sUnit_in,\
+                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                       dData_max_in = dData_max_in,\
+                                          dData_min_in = dData_min_in,
+                                  dMissing_value_in = -9999,\
+                                    sColormap_in=sColormap_in,\
+                                        aLabel_legend_in = aLegend_in)
+        pass
+
+    if iFlag_monthly ==1 :
+        for i in np.arange(nstress_subset):
+            aImage = aVariable_total_subset[i, :,:]
+            #get date
+            pDate = dates_subset[i]
+            sDate = pDate.strftime('%Y%m%d')
+            sLabel_legend = sRegion.title() + sDate
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_' + sDate +'.png'         
+
+            #aData_all = np.log10(aData_all)
+            ##set inf to min
+            #bad_index = np.where( np.isinf(  aData_all) == True  )
+            #aData_all[bad_index] = dMin_y_in
+            aData_all = np.array(aImage)                  
+
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                          sColormap_in=sColormap_in,\
+                                        aLabel_legend_in = aLegend_in)
+
+            print(sDate)
+    
+    if iFlag_annual_mean ==1:
+        #annual mean
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.mean(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_mean_'+ sYear +'.png'
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           dData_max_in = dData_max_in,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                          sColormap_in=sColormap_in,\
+                                        aLabel_legend_in = aLegend_in)
+
+        pass
+    
+    if iFlag_annual_total ==1: #annual total
+        for iYear in range(iYear_start, iYear_end + 1):
+            sYear = "{:04d}".format(iYear)
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.sum(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))
+            sFilename_out = sWorkspace_analysis_case_region + slash \
+            + sVariable + '_map_annual_total_'+ sYear +'.png'
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999
+            if dData_max_in is not None:
+                dData_max_in2 = dData_max_in * 10
+            else: 
+                dData_max_in2 = dData_max_in
+         
+            map_raster_data(aData_all,  aImage_extent,\
+                                  sFilename_out,\
+                                      sTitle_in = sTitle_in,\
+                                          sUnit_in=sUnit_in,\
+                                      iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
+                                           dData_max_in = dData_max_in2,\
+                                              dData_min_in = dData_min_in,
+                                      dMissing_value_in = -9999,\
+                                          sColormap_in=sColormap_in,\
+                                        aLabel_legend_in = aLegend_in)
+
+
+
+    print("finished")
+
+
diff --git a/pye3sm/mosart/general/structured/twod/retrieve/mosart_retrieve_variable_2d.py b/pye3sm/mosart/general/structured/twod/retrieve/mosart_retrieve_variable_2d.py
new file mode 100644
index 0000000..d03a739
--- /dev/null
+++ b/pye3sm/mosart/general/structured/twod/retrieve/mosart_retrieve_variable_2d.py
@@ -0,0 +1,158 @@
+import os, sys
+import numpy as np
+import numpy.ma as ma
+import datetime
+
+from pyearth.system.define_global_variables import *
+from pyearth.gis.gdal.read.gdal_read_envi_file import gdal_read_envi_file_multiple_band
+
+from pye3sm.mosart.mesh.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info
+ 
+
+
+def mosart_retrieve_variable_2d(  oCase_in,\
+                                            iFlag_monthly_in = None,\
+                                            iFlag_annual_mean_in = None,\
+                                                iFlag_annual_total_in = None          ):
+    if iFlag_monthly_in is None:
+        iFlag_monthly  = 0
+    else:
+        iFlag_monthly = iFlag_monthly_in
+
+    if iFlag_annual_mean_in is None:
+        iFlag_annual_mean = 0
+    else:
+        iFlag_annual_mean = iFlag_annual_mean_in
+    
+    if iFlag_annual_total_in is None:
+        iFlag_annual_total = 0
+    else:
+        iFlag_annual_total = iFlag_annual_total_in
+
+    sModel = oCase_in.sModel
+    sRegion = oCase_in.sRegion
+    iFlag_same_grid = oCase_in.iFlag_same_grid
+
+    iYear_start = oCase_in.iYear_start
+    iYear_end = oCase_in.iYear_end
+
+    iYear_subset_start = oCase_in.iYear_subset_start
+    iYear_subset_end = oCase_in.iYear_subset_end
+
+    sLabel_Y = oCase_in.sLabel_y
+    dConversion = oCase_in.dConversion
+    sVariable = oCase_in.sVariable
+    sCase = oCase_in.sCase
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+
+    #new approach
+    aLon, aLat,aMask_ll = mosart_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    nrow = np.array(aMask_ll).shape[0]
+    ncolumn = np.array(aMask_ll).shape[1]
+    aMask_index_ll = np.where(aMask_ll==0)
+
+    aMask_ul = np.flip(aMask_ll, 0)
+    aMask_index_ul = np.where(aMask_ul==0)
+
+    
+
+    dates = list()
+    nyear = iYear_end - iYear_start + 1
+    for iYear in range(iYear_start, iYear_end + 1):
+        for iMonth in range(1,13):
+            dSimulation = datetime.datetime(iYear, iMonth, 15)
+            dates.append( dSimulation )
+
+    nstress = nyear * nmonth
+
+    #take the subset
+    iMonth = 1
+    subset_index_start = (iYear_subset_start - iYear_start) * 12 + iMonth-1
+    subset_index_end = (iYear_subset_end + 1 - iYear_start) * 12 + iMonth-1
+    subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+    dates=np.array(dates)
+    dates_subset = dates[subset_index]
+    nstress_subset= len(dates_subset)
+    sWorkspace_variable_dat = sWorkspace_analysis_case + slash + sVariable +  slash + 'dat'
+    #read the stack data
+    sFilename = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
+
+    if os.path.exists(sFilename):
+        #print("Yep, I can read that file: " + sFilename)                
+        pass
+    else:
+        print(sFilename + ' is missing')
+        print("Nope, the path doesn't reach your file. Go research filepath in python")
+        return
+
+    aData_all = gdal_read_envi_file_multiple_band(sFilename)
+    aVariable_total = aData_all[0]
+    aVariable_total_subset = aVariable_total[subset_index,:,:]
+
+
+    sWorkspace_analysis_case_variable = sWorkspace_analysis_case + slash + sVariable
+    if not os.path.exists(sWorkspace_analysis_case_variable):
+        os.makedirs(sWorkspace_analysis_case_variable)
+
+    sWorkspace_analysis_case_region = sWorkspace_analysis_case_variable + slash + 'map'
+    if not os.path.exists(sWorkspace_analysis_case_region):
+        os.makedirs(sWorkspace_analysis_case_region)
+        pass
+
+    aData_out = list()
+    if iFlag_monthly ==1 :
+        for i in np.arange(nstress_subset):
+            aImage = aVariable_total_subset[i, :,:]
+            #get date          
+            aData_all = np.array(aImage)                  
+            nan_index = np.where(aData_all == -9999)
+            aData_all = aData_all * dConversion
+            aData_all[nan_index] = -9999     
+            aData_out.append(aData_all)
+
+    #mean or total
+
+    if iFlag_annual_mean ==1:
+        #annual mean
+        for iYear in range(iYear_start, iYear_end + 1):            
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.mean(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))            
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999            
+            aData_out.append(aData_all)
+        pass
+    
+    if iFlag_annual_total ==1: #annual total
+        for iYear in range(iYear_start, iYear_end + 1):          
+            subset_index_start = (iYear - iYear_start) * 12 + iMonth-1
+            subset_index_end = (iYear + 1 - iYear_start) * 12 + iMonth-1
+            subset_index = np.arange( subset_index_start,subset_index_end, 1 )
+
+            aData_dummy= np.array(aVariable_total_subset[0, :,:])
+            aData_dummy = np.reshape(aData_dummy, (nrow, ncolumn))
+            nan_index = np.where(aData_dummy == -9999)
+            aVariable_total_annual = aVariable_total_subset[subset_index, :,:]
+            aImage = np.sum(aVariable_total_annual, axis=0)
+            aImage = np.reshape(aImage, (nrow, ncolumn))           
+            aData_all = aImage * dConversion
+            aData_all[nan_index] = -9999                        
+            aData_out.append(aData_all)
+            pass
+
+
+    return aData_out    
+        
+
+
+
+
diff --git a/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_2d.py b/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_2d.py
index 25f831c..2636711 100644
--- a/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_2d.py
+++ b/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_2d.py
@@ -1,14 +1,12 @@
-import os , sys
-
+import os
 import numpy as np
-from numpy.lib.function_base import _average_dispatcher
 from scipy.interpolate import griddata #generate grid
 from netCDF4 import Dataset #read netcdf
-from osgeo import gdal, osr #the default operator
+from osgeo import  osr #the default operator
 from pyearth.system.define_global_variables import *    
 from pyearth.gis.gdal.write.gdal_write_envi_file import gdal_write_envi_file_multiple_band
 from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file_multiple_band
-from pye3sm.mosart.grid.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info 
+from pye3sm.mosart.mesh.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info 
 
 from pye3sm.shared.e3sm import pye3sm
 from pye3sm.shared.case import pycase
@@ -43,8 +41,8 @@ def mosart_save_variable_2d(oE3SM_in, oCase_in):
     aMask_ul = np.flip(aMask_ll, 0)
     nrow = np.array(aMask_ll).shape[0]
     ncolumn = np.array(aMask_ll).shape[1]
-    aMask_ll_index = np.where(aMask_ll==0)
-    aMask_ul_index = np.where(aMask_ul==0)
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
 
     #resolution
     dLon_min = np.min(aLon)
@@ -143,7 +141,7 @@ def mosart_save_variable_2d(oE3SM_in, oCase_in):
                     dummy_index = np.where( aData_ll == missing_value1 ) 
                     aData_ll[dummy_index] = missing_value
                     
-                    aData_ll[aMask_ll_index] = missing_value
+                    aData_ll[aMask_index_ll] = missing_value
                     aData_ul = np.flip(aData_ll, 0)   
                     #save output
                     
diff --git a/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_history_2d.py b/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_history_2d.py
new file mode 100644
index 0000000..4a689a9
--- /dev/null
+++ b/pye3sm/mosart/general/structured/twod/save/mosart_save_variable_history_2d.py
@@ -0,0 +1,170 @@
+import os , sys
+import numpy as np
+
+from netCDF4 import Dataset #read netcdf
+from osgeo import gdal, osr #the default operator
+from pyearth.system.define_global_variables import *    
+from pyearth.gis.gdal.write.gdal_write_envi_file import gdal_write_envi_file
+from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file
+from pye3sm.mosart.grid.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info 
+
+
+
+def mosart_save_variable_history_2d(oE3SM_in, oCase_in):
+
+    sModel  = oCase_in.sModel
+    sRegion = oCase_in.sRegion               
+    iYear_start = oCase_in.iYear_start        
+    iYear_end = oCase_in.iYear_end          
+    iFlag_same_grid = oCase_in.iFlag_same_grid 
+    print('The following model is processed: ', sModel)
+    
+        
+    dConversion = oCase_in.dConversion   
+    sVariable  = oCase_in.sVariable
+    #for the sake of simplicity, all directory will be the same, no matter on mac or cluster
+   
+    sCase = oCase_in.sCase
+    #we only need to change the case number, all variables will be processed one by one    
+    
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+    
+    if not os.path.exists(sWorkspace_analysis_case):
+        os.makedirs(sWorkspace_analysis_case)    
+
+    #new approach
+    aLon, aLat , aMask_ll= mosart_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    aMask_ul = np.flip(aMask_ll, 0)
+    nrow = np.array(aMask_ll).shape[0]
+    ncolumn = np.array(aMask_ll).shape[1]
+    aMask_index_ll = np.where(aMask_ll==-9999)
+    aMask_index_ul = np.where(aMask_ul==-9999)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+
+    print('Prepare the map grid')
+   
+    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
+    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
+    grid_x, grid_y = np.meshgrid(longitude, latitude)
+    #prepare the header in
+    pHeaderParameters = {}    
+    pHeaderParameters['ncolumn'] = "{:0d}".format(ncolumn)
+    pHeaderParameters['nrow'] = "{:0d}".format(nrow)
+    pHeaderParameters['ULlon'] = "{:0f}".format(dLon_min-0.5 * dResolution_x)
+    pHeaderParameters['ULlat'] = "{:0f}".format(dLat_max+0.5 * dResolution_y)
+    pHeaderParameters['pixelSize'] = "{:0f}".format(dResolution_x)
+    pHeaderParameters['nband'] = '1'
+    pHeaderParameters['offset'] = '0'
+    pHeaderParameters['data_type'] = '4'
+    pHeaderParameters['bsq'] = 'bsq'
+    pHeaderParameters['byte_order'] = '0'
+    pHeaderParameters['missing_value'] = '-9999'
+    
+    iFlag_optional = 1 
+
+    #save netcdf
+    sWorkspace_variable = sWorkspace_analysis_case + slash \
+        + sVariable 
+    sWorkspace_variable_netcdf = sWorkspace_variable + slash + 'netcdf'
+    if not os.path.exists(sWorkspace_variable_netcdf):
+        os.makedirs(sWorkspace_variable_netcdf)
+    sWorkspace_variable_dat = sWorkspace_variable + slash + 'dat'
+    if not os.path.exists(sWorkspace_variable_dat):
+        os.makedirs(sWorkspace_variable_dat)
+    sWorkspace_variable_tiff = sWorkspace_analysis_case + slash \
+        + sVariable + slash + 'tiff'
+    if not os.path.exists(sWorkspace_variable_tiff):
+        os.makedirs(sWorkspace_variable_tiff)
+        
+    sFilename_output = sWorkspace_variable_netcdf + slash + sVariable +  sExtension_netcdf
+    #should we use the same netcdf format? 
+    pFile = Dataset(sFilename_output, 'w', format = 'NETCDF4') 
+    pDimension_longitude = pFile.createDimension('lon', ncolumn) 
+    pDimension_latitude = pFile.createDimension('lat', nrow) 
+
+    nmonth = (iYear_end - iYear_start +1) * 12
+    aGrid_stack= np.full((nmonth, nrow, ncolumn), -9999.0, dtype= float)
+    i=0
+    #for iYear in range(iYear_start, iYear_end + 1):
+    sYear = "{:04d}".format(iYear_end+1) #str(iYear).zfill(4)
+    
+        #for iMonth in range(iMonth_start, iMonth_end + 1):
+        #    sMonth = str(iMonth).zfill(2)
+    
+    sDummy = '.mosart.r.' + sYear + '-' + '01-01-00000' + sExtension_netcdf
+    sFilename = sWorkspace_simulation_case_run + slash + sCase + sDummy
+
+
+    #read before modification
+    if os.path.exists(sFilename):
+        #print("Yep, I can read that file: " + sFilename)                
+        pass
+    else:
+        print(sFilename + ' is missing')
+        print("Nope, the path doesn't reach your file. Go research filepath in python")
+        return
+    aDatasets = Dataset(sFilename)    
+    #read the actual data
+
+    sVariable = oCase_in.sVariable
+    for sKey, aValue in aDatasets.variables.items():
+        if sKey.lower() == sVariable.lower() :
+                           
+            aData_ll = (aValue[:]).data                                    
+            #missing_value1 = np.max(aData_ll)  
+
+            #dimension
+            aData_ll = np.array(aData_ll)
+            ndim = aData_ll.ndim
+            nTs = aData_ll.shape            
+            
+            aData_ll = aData_ll.reshape(nrow, ncolumn)                          
+            dummy_index = np.where( aData_ll == -9999 ) 
+            aData_ll[dummy_index] = missing_value
+            aData_ll[aMask_index_ll] = missing_value
+            aData_ul = np.flip(aData_ll, 0)   
+            #save output
+            sDummy = sVariable + sYear + '_01_01' 
+            pVar = pFile.createVariable( sDummy , 'f4', ('lat' , 'lon'),fill_value=-9999) 
+            pVar[:] = aData_ll
+            pVar.description = sDummy
+            pVar.unit = 'm' 
+            iFlag_netcdf_first = 0        
+        
+            #close netcdf file   
+            pFile.close()
+            #write envi and geotiff file
+
+            pSpatial = osr.SpatialReference()
+            pSpatial.ImportFromEPSG(4326)
+            sFilename_envi = sWorkspace_variable_dat + slash + sVariable  + sExtension_envi
+
+            gdal_write_envi_file(sFilename_envi, aData_ul,\
+                float(pHeaderParameters['pixelSize']),\
+                 float(pHeaderParameters['ULlon']),\
+                      float(pHeaderParameters['ULlat']),\
+                          -9999.0, pSpatial)
+
+            sFilename_tiff = sWorkspace_variable_tiff + slash + sVariable + sExtension_tiff
+
+            gdal_write_geotiff_file(sFilename_tiff, aData_ul,\
+                float(pHeaderParameters['pixelSize']),\
+                 float(pHeaderParameters['ULlon']),\
+                      float(pHeaderParameters['ULlat']),\
+                          -9999.0, pSpatial)
+
+
+            print("finished")
+
+
+
+    
diff --git a/pye3sm/mosart/general/structured/twod/stream/mosart_generate_stream_file_2d.py b/pye3sm/mosart/general/structured/twod/stream/mosart_generate_stream_file_2d.py
new file mode 100644
index 0000000..ffe5e91
--- /dev/null
+++ b/pye3sm/mosart/general/structured/twod/stream/mosart_generate_stream_file_2d.py
@@ -0,0 +1,164 @@
+import os 
+import numpy as np
+from netCDF4 import Dataset #read netcdf
+from osgeo import  osr #the default operator
+from pyearth.system.define_global_variables import *    
+from pye3sm.mosart.mesh.mosart_retrieve_case_dimension_info import mosart_retrieve_case_dimension_info 
+
+
+def mosart_generate_stream_file_2d(oE3SM_in, oCase_in):
+    """
+    Generate the stream file for the drof compset
+
+    Args:
+        oE3SM_in (pye3sm object): _description_
+        oCase_in (pycase object): _description_
+    """
+
+    sModel  = oCase_in.sModel
+    sRegion = oCase_in.sRegion               
+    iYear_start = oCase_in.iYear_start        
+    iYear_end = oCase_in.iYear_end          
+    iFlag_same_grid = oCase_in.iFlag_same_grid 
+    print('The following model is processed: ', sModel)
+    
+        
+    dConversion = oCase_in.dConversion   
+    sVariable  = oCase_in.sVariable
+    #for the sake of simplicity, all directory will be the same, no matter on mac or cluster
+   
+    sCase = oCase_in.sCase
+    #we only need to change the case number, all variables will be processed one by one
+    
+    
+    sWorkspace_simulation_case_run = oCase_in.sWorkspace_simulation_case_run
+    sWorkspace_analysis_case = oCase_in.sWorkspace_analysis_case
+    
+    if not os.path.exists(sWorkspace_analysis_case):
+        os.makedirs(sWorkspace_analysis_case)    
+
+    #new approach
+    aLon, aLat , aMask_ll= mosart_retrieve_case_dimension_info(oCase_in)
+    #dimension
+    aMask_ul = np.flip(aMask_ll, 0)
+    nrow = np.array(aMask_ll).shape[0]
+    ncolumn = np.array(aMask_ll).shape[1]
+    #be careful with the mask 0 or 1 
+    aMask_index_ll = np.where(aMask_ll==0)
+    aMask_index_ul = np.where(aMask_ul==0)
+
+    #resolution
+    dLon_min = np.min(aLon)
+    dLon_max = np.max(aLon)
+    dLat_min = np.min(aLat)
+    dLat_max = np.max(aLat)
+    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
+    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
+
+    print('Prepare the map grid')
+   
+    longitude = np.arange(dLon_min, dLon_max , dResolution_x)
+    latitude = np.arange( dLat_max, dLat_min, -1*dResolution_y)
+    grid_x, grid_y = np.meshgrid(longitude, latitude)
+   
+    
+    iFlag_optional = 1 
+
+    #save netcdf
+    sWorkspace_variable = sWorkspace_analysis_case + slash \
+        + sVariable 
+    
+    #where should we save the stream file?
+    sWorkspace_variable_netcdf = sWorkspace_variable + slash + 'netcdf'
+    if not os.path.exists(sWorkspace_variable_netcdf):
+        os.makedirs(sWorkspace_variable_netcdf)
+   
+    #how often should be output file is stored, per year is preferred similar to other stream file?
+        
+    sFilename_output = sWorkspace_variable_netcdf + slash + sVariable +  sExtension_netcdf
+    #should we use the same netcdf format? 
+    pFile = Dataset(sFilename_output, 'w', format = 'NETCDF4') 
+    pDimension_longitude = pFile.createDimension('lon', ncolumn) 
+    pDimension_latitude = pFile.createDimension('lat', nrow) 
+
+    nmonth = (iYear_end - iYear_start +1) * 12
+    aGrid_stack= np.full((nmonth, nrow, ncolumn), -9999.0, dtype= float)
+    i=0
+    for iYear in range(iYear_start, iYear_end + 1):
+        sYear = "{:04d}".format(iYear) #str(iYear).zfill(4)
+    
+        for iMonth in range(iMonth_start, iMonth_end + 1):
+            sMonth = str(iMonth).zfill(2)
+    
+            sDummy = '.mosart.h0.' + sYear + '-' + sMonth + sExtension_netcdf
+            sFilename = sWorkspace_simulation_case_run + slash + sCase + sDummy
+    
+            #read before modification
+    
+            if os.path.exists(sFilename):
+                #print("Yep, I can read that file: " + sFilename)                
+                pass
+            else:
+                print(sFilename + ' is missing')
+                print("Nope, the path doesn't reach your file. Go research filepath in python")
+                return
+    
+            aDatasets = Dataset(sFilename)
+    
+            for sKey, aValue in aDatasets.variables.items():
+            
+                if (sKey == 'lon'):                   
+                    aLongitude = (aValue[:]).data
+                    continue
+                if (sKey == 'lat'):                    
+                    aLatitude = (aValue[:]).data
+                    continue
+            
+            #quality control the longitude data
+            dummy_index = np.where(aLongitude > 180)
+            aLongitude[dummy_index] = aLongitude[dummy_index] - 360.0
+    
+            #read the actual data
+            sVariable_discharge  = 'RIVER_DISCHARGE_OVER_LAND_LIQ'
+            for sKey, aValue in aDatasets.variables.items():
+                if sKey.lower() == sVariable_discharge.lower() :
+                                   
+                    aData_ll = (aValue[:]).data                                    
+                    missing_value1 = np.max(aData_ll)  
+                    aData_ll = aData_ll.reshape(nrow, ncolumn)                          
+                    dummy_index = np.where( aData_ll == missing_value1 ) 
+                    aData_ll[dummy_index] = missing_value
+                    
+                    aData_ll[aMask_index_ll] = missing_value
+                    aData_ul = np.flip(aData_ll, 0)   
+                    #save output
+                    
+
+                    sDummy = sVariable + sYear + sMonth
+                    pVar = pFile.createVariable( sDummy , 'f4', ('lat' , 'lon'),fill_value=-9999) 
+                    pVar[:] = aData_ll
+                    pVar.description = sDummy
+                    pVar.unit = 'm' 
+                    iFlag_netcdf_first = 0
+                    
+                    if(iFlag_optional == 1):
+                        #stack data
+                        aGrid_stack[i, :,: ] =  aData_ul
+                        i=i+1
+                    break
+                else:
+                    pass
+    
+    
+    #close netcdf file   
+    pFile.close()
+ 
+    
+    
+
+
+    print("finished")
+
+
+
+    
diff --git a/pye3sm/mosart/general/structured/twod/stream/readme.md b/pye3sm/mosart/general/structured/twod/stream/readme.md
new file mode 100644
index 0000000..22a22ec
--- /dev/null
+++ b/pye3sm/mosart/general/structured/twod/stream/readme.md
@@ -0,0 +1,1 @@
+This function is used to generate the DROF stream file for different compsets.
\ No newline at end of file
diff --git a/pye3sm/mosart/plot/mosart_create_flow_accumulation_map.py b/pye3sm/mosart/map/mosart_map_flow_accumulation.py
similarity index 97%
rename from pye3sm/mosart/plot/mosart_create_flow_accumulation_map.py
rename to pye3sm/mosart/map/mosart_map_flow_accumulation.py
index 4b2b230..3afda39 100644
--- a/pye3sm/mosart/plot/mosart_create_flow_accumulation_map.py
+++ b/pye3sm/mosart/map/mosart_map_flow_accumulation.py
@@ -7,7 +7,7 @@
 from osgeo import gdal, osr
 
 
-def mosart_create_flow_accumulation_map(sFilename_netcdf, sFilename_shapefile_output):
+def mosart_map_flow_accumulation(sFilename_netcdf, sFilename_shapefile_output):
     
     if os.path.exists(sFilename_netcdf):
         print("Yep, I can read that file!")
diff --git a/pye3sm/mosart/plot/mosart_create_flow_direction_map.py b/pye3sm/mosart/map/mosart_map_flow_direction.py
similarity index 76%
rename from pye3sm/mosart/plot/mosart_create_flow_direction_map.py
rename to pye3sm/mosart/map/mosart_map_flow_direction.py
index b035796..6dead96 100644
--- a/pye3sm/mosart/plot/mosart_create_flow_direction_map.py
+++ b/pye3sm/mosart/map/mosart_map_flow_direction.py
@@ -5,8 +5,10 @@
 #import shapefile
 from osgeo import ogr
 from osgeo import gdal, osr
+import cartopy.crs as ccrs
+from pyearth.visual.map.vector.map_vector_polyline_data import map_vector_polyline_data
 
-def mosart_create_flow_direction_map(sFilename_netcdf_in, sFilename_shapefile_output):
+def mosart_map_flow_direction(sFilename_netcdf_in, sFilename_shapefile_output, sFilename_png):
     
     if os.path.exists(sFilename_netcdf_in):
         print("Yep, I can read that file!")
@@ -99,6 +101,28 @@ def mosart_create_flow_direction_map(sFilename_netcdf_in, sFilename_shapefile_ou
     #Save and close everything
     
     pDataset = pLayer = pFeature  = None      
+    pProjection = ccrs.PlateCarree()
+    aLegend=list()
+    aLegend.append(r'Region: Amazon')
+    aLegend.append(r'Resolution: $0.5^{\circ}$')
+    sColormap = 'Spectral_r'
+    map_vector_polyline_data(2,\
+    sFilename_shapefile_output, \
+    sFilename_png,\
+        iFlag_thickness_in =1,\
+            sField_thickness_in='dAccu',\
+         aExtent_in = None, \
+       iFlag_scientific_notation_colorbar_in=None,\
+    sColormap_in = sColormap,\
+        sTitle_in = 'River network', \
+    iDPI_in = None,\
+    dMissing_value_in=None,\
+    dData_max_in = None, \
+    dData_min_in = None,\
+        sExtend_in =None,\
+        sUnit_in=None,\
+            aLegend_in = aLegend,\
+                pProjection_map_in = pProjection)
                 
 
 
diff --git a/pye3sm/mosart/grid/Readme.md b/pye3sm/mosart/mesh/Readme.md
similarity index 100%
rename from pye3sm/mosart/grid/Readme.md
rename to pye3sm/mosart/mesh/Readme.md
diff --git a/pye3sm/mosart/grid/create_customized_mosart_domain.py b/pye3sm/mosart/mesh/create_customized_mosart_domain.py
similarity index 89%
rename from pye3sm/mosart/grid/create_customized_mosart_domain.py
rename to pye3sm/mosart/mesh/create_customized_mosart_domain.py
index 4dbd34e..5f7d9c6 100644
--- a/pye3sm/mosart/grid/create_customized_mosart_domain.py
+++ b/pye3sm/mosart/mesh/create_customized_mosart_domain.py
@@ -1,8 +1,8 @@
 import os
 import numpy as np
-from pye3sm.mosart.grid.unstructured.find_mosart_cell import find_mosart_cell
+from pye3sm.mosart.mesh.unstructured.find_mosart_cell import find_mosart_cell
 
-from pye3sm.mosart.grid.structured.twod.extract_mosart_by_cellid_2d_to_2d import extract_mosart_by_cellid_2d_to_2d
+from pye3sm.mosart.mesh.structured.twod.extract_mosart_by_cellid_2d_to_2d import extract_mosart_by_cellid_2d_to_2d
 
 
 
diff --git a/pye3sm/mosart/grid/create_global_river_mosart_information.py b/pye3sm/mosart/mesh/create_global_river_mosart_information.py
similarity index 100%
rename from pye3sm/mosart/grid/create_global_river_mosart_information.py
rename to pye3sm/mosart/mesh/create_global_river_mosart_information.py
diff --git a/pye3sm/mosart/grid/global_river.csv b/pye3sm/mosart/mesh/global_river.csv
similarity index 100%
rename from pye3sm/mosart/grid/global_river.csv
rename to pye3sm/mosart/mesh/global_river.csv
diff --git a/pye3sm/mosart/grid/mosart_retrieve_case_dimension_info.py b/pye3sm/mosart/mesh/mosart_retrieve_case_dimension_info.py
similarity index 100%
rename from pye3sm/mosart/grid/mosart_retrieve_case_dimension_info.py
rename to pye3sm/mosart/mesh/mosart_retrieve_case_dimension_info.py
diff --git a/pye3sm/mosart/mesh/structured/__init__.py b/pye3sm/mosart/mesh/structured/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/pye3sm/mosart/grid/structured/oned/extract_mosart_by_cellid_1d_to_1d.py b/pye3sm/mosart/mesh/structured/oned/extract_mosart_by_cellid_1d_to_1d.py
similarity index 100%
rename from pye3sm/mosart/grid/structured/oned/extract_mosart_by_cellid_1d_to_1d.py
rename to pye3sm/mosart/mesh/structured/oned/extract_mosart_by_cellid_1d_to_1d.py
diff --git a/pye3sm/mosart/mesh/structured/twod/__init__.py b/pye3sm/mosart/mesh/structured/twod/__init__.py
new file mode 100755
index 0000000..e69de29
diff --git a/pye3sm/mosart/grid/structured/twod/convert_index_between_array.py b/pye3sm/mosart/mesh/structured/twod/convert_index_between_array.py
similarity index 100%
rename from pye3sm/mosart/grid/structured/twod/convert_index_between_array.py
rename to pye3sm/mosart/mesh/structured/twod/convert_index_between_array.py
diff --git a/pye3sm/mosart/grid/structured/twod/extract_mosart_by_cellid_2d_to_1d.py b/pye3sm/mosart/mesh/structured/twod/extract_mosart_by_cellid_2d_to_1d.py
similarity index 99%
rename from pye3sm/mosart/grid/structured/twod/extract_mosart_by_cellid_2d_to_1d.py
rename to pye3sm/mosart/mesh/structured/twod/extract_mosart_by_cellid_2d_to_1d.py
index 45a0887..15e7000 100644
--- a/pye3sm/mosart/grid/structured/twod/extract_mosart_by_cellid_2d_to_1d.py
+++ b/pye3sm/mosart/mesh/structured/twod/extract_mosart_by_cellid_2d_to_1d.py
@@ -70,7 +70,7 @@ def extract_mosart_by_cellid_2d_to_1d(sFilenamae_mosart_in, sFilename_netcdf_out
     datasets_out.createDimension('gridcell', ncell_extract )
     for sKey, aValue in aDatasets.variables.items():            
         aDimenion_value = aValue.shape 
-        if len(aDimenion_value) ==1:
+        if len(aDimenion_value) == 1:
             #save later     
             pass
         else:
diff --git a/pye3sm/mosart/grid/structured/twod/extract_mosart_by_cellid_2d_to_2d.py b/pye3sm/mosart/mesh/structured/twod/extract_mosart_by_cellid_2d_to_2d.py
similarity index 99%
rename from pye3sm/mosart/grid/structured/twod/extract_mosart_by_cellid_2d_to_2d.py
rename to pye3sm/mosart/mesh/structured/twod/extract_mosart_by_cellid_2d_to_2d.py
index d45ea0d..173d0c1 100644
--- a/pye3sm/mosart/grid/structured/twod/extract_mosart_by_cellid_2d_to_2d.py
+++ b/pye3sm/mosart/mesh/structured/twod/extract_mosart_by_cellid_2d_to_2d.py
@@ -3,7 +3,7 @@
 
 from netCDF4 import Dataset
 
-from pye3sm.mosart.grid.structured.twod.convert_index_between_array import convert_index_between_array
+from pye3sm.mosart.mesh.structured.twod.convert_index_between_array import convert_index_between_array
 
 def extract_mosart_by_cellid_2d_to_2d(sFilename_mosart_in, sFilename_netcdf_out, aCellID_in):
 
diff --git a/pye3sm/mosart/grid/structured/twod/extract_mosart_elevation_profile_for_elm.py b/pye3sm/mosart/mesh/structured/twod/extract_mosart_elevation_profile_for_elm.py
similarity index 100%
rename from pye3sm/mosart/grid/structured/twod/extract_mosart_elevation_profile_for_elm.py
rename to pye3sm/mosart/mesh/structured/twod/extract_mosart_elevation_profile_for_elm.py
diff --git a/pye3sm/mosart/grid/unstructured/find_mosart_cell.py b/pye3sm/mosart/mesh/unstructured/find_mosart_cell.py
similarity index 100%
rename from pye3sm/mosart/grid/unstructured/find_mosart_cell.py
rename to pye3sm/mosart/mesh/unstructured/find_mosart_cell.py
diff --git a/pye3sm/shared/__pycache__/case.cpython-38.pyc b/pye3sm/shared/__pycache__/case.cpython-38.pyc
index 9deff97..2b2329c 100644
Binary files a/pye3sm/shared/__pycache__/case.cpython-38.pyc and b/pye3sm/shared/__pycache__/case.cpython-38.pyc differ
diff --git a/pye3sm/shared/__pycache__/pye3sm_read_configuration_file.cpython-38.pyc b/pye3sm/shared/__pycache__/pye3sm_read_configuration_file.cpython-38.pyc
index d1c46e0..d1acd85 100644
Binary files a/pye3sm/shared/__pycache__/pye3sm_read_configuration_file.cpython-38.pyc and b/pye3sm/shared/__pycache__/pye3sm_read_configuration_file.cpython-38.pyc differ
diff --git a/pye3sm/shared/case.py b/pye3sm/shared/case.py
index d36b8dd..5897079 100644
--- a/pye3sm/shared/case.py
+++ b/pye3sm/shared/case.py
@@ -35,6 +35,7 @@ class pycase(object):
     sWorkspace_cases=''
     sLabel_y='' #the y label for plotting purpose    
     sWorkspace_case=''
+    sWorkspace_case_aux=''
     sWorkspace_analysis_case=''
     sWorkspace_simulation_case=''
     sWorkspace_simulation_case_build=''
@@ -206,6 +207,8 @@ def __init__(self, aParameter):
 
         sCase_index = "{:03d}".format( self.iCase_index )
         sCase = self.sModel + self.sDate + sCase_index
+
+        self.sWorkspace_case_aux = self.sDirectory_case_aux + '/' + sCase
         self.sCase = sCase
         self.nyear = self.iYear_end - self.iYear_start + 1
         pass
diff --git a/pye3sm/shared/e3sm_global.py b/pye3sm/shared/e3sm_global.py
index 765dee4..b1b9506 100644
--- a/pye3sm/shared/e3sm_global.py
+++ b/pye3sm/shared/e3sm_global.py
@@ -5,7 +5,7 @@
 global iFlag_short
 global iCase_index
 global sCase
-global sFilename_clm_namelist
+global sFilename_elm_namelist
 global sModel
 global sDirectory_case
 global sDirectory_run
diff --git a/pye3sm/shared/pye3sm_read_configuration_file.py b/pye3sm/shared/pye3sm_read_configuration_file.py
index 91c0d29..667ce53 100644
--- a/pye3sm/shared/pye3sm_read_configuration_file.py
+++ b/pye3sm/shared/pye3sm_read_configuration_file.py
@@ -18,6 +18,7 @@ def pye3sm_read_e3sm_configuration_file(sFilename_configuration_in,\
                                         iFlag_resubmit_in = None,\
                                             RES_in = None,\
                                             COMPSET_in= None,\
+                                                Project_in = None,\
                                         sCIME_directory_in = None ):
 
     #read the default configuration
@@ -60,6 +61,11 @@ def pye3sm_read_e3sm_configuration_file(sFilename_configuration_in,\
         COMPSET = COMPSET_in
     else:
         COMPSET = 'IELM'
+    
+    if Project_in is not None:
+        Project = Project_in
+    else:
+        Project = 'e3sm'
 
 
     if sCIME_directory_in is not None:
@@ -82,6 +88,7 @@ def pye3sm_read_e3sm_configuration_file(sFilename_configuration_in,\
     config['iFlag_short'] = "{:01d}".format(iFlag_short)
     config['RES'] = RES
     config['COMPSET'] = COMPSET
+    config['PROJECT'] = Project
     config['sCIME_directory'] = sCIME_directory
 
     return config
@@ -292,13 +299,13 @@ def pye3sm_read_case_configuration_file(sFilename_configuration_in,\
 
     #several namelist maybe used if we need to change parameters
     if sFilename_elm_namelist_in is not None:
-        sFilename_clm_namelist = sFilename_elm_namelist_in
+        sFilename_elm_namelist = sFilename_elm_namelist_in
     else:
-        sFilename_clm_namelist = sWorkspace_scratch + slash + '04model' + slash \
+        sFilename_elm_namelist = sWorkspace_scratch + slash + '04model' + slash \
             + sModel + slash + sRegion + slash \
-            + 'cases' + slash + 'user_nl_clm'
-        if os.path.exists(sFilename_clm_namelist):
-            sLine = 'A default ELM namelist was found at: ' + sFilename_clm_namelist + ', and it will be used for simulation if needed. If other version is desired, please specify it.'
+            + 'cases' + slash + 'user_nl_elm'
+        if os.path.exists(sFilename_elm_namelist):
+            sLine = 'A default ELM namelist was found at: ' + sFilename_elm_namelist + ', and it will be used for simulation if needed. If other version is desired, please specify it.'
             print(sLine)
         else:
             print('A default ELM namelist was not found, you will not be able to use it without specifying it first.' )
@@ -368,7 +375,7 @@ def pye3sm_read_case_configuration_file(sFilename_configuration_in,\
    
     config['sFilename_datm_namelist'] = sFilename_datm_namelist
 
-    config['sFilename_elm_namelist'] = sFilename_clm_namelist
+    config['sFilename_elm_namelist'] = sFilename_elm_namelist
     config['sFilename_elm_domain'] = sFilename_elm_domain
 
     config['sFilename_mosart_namelist'] = sFilename_mosart_namelist 
diff --git a/pye3sm/tools/atm/forcing/generate_forcing_data_2d.py b/pye3sm/tools/atm/forcing/generate_forcing_data_2d.py
index d1e46f4..2a02028 100644
--- a/pye3sm/tools/atm/forcing/generate_forcing_data_2d.py
+++ b/pye3sm/tools/atm/forcing/generate_forcing_data_2d.py
@@ -137,13 +137,7 @@ def generate_forcing_data_2d(oE3SM_in, oCase_in, sVariable_forcing_in,   \
                 aLatitude_origin = np.flip(aLatitude_origin, 0)
          
                 aData_out_extract = np.full((nts, nrow_new, ncolumn_new), missing_value, dtype=float)
-                #for i in range(nrow_new):
-                #    for j in range(ncolumn_new):                             
-                #        dLon = aLongitude_origin[i,j]   
-                #        dLat = aLatitude_origin[i,j]             
-                #        iIndex = int( (90.0-(dLat)) / dResoultion_forcing )
-                #        jIndex = int( (dLon-(0.0)) / dResoultion_forcing )
-                #        aData_out_extract[:,i,j] = aData0[:,iIndex, jIndex]
+                
 
                 for iStep in range(nts):
                     dummy0 = aData0[iStep, :,:]
diff --git a/pye3sm/tools/atm/readme.md b/pye3sm/tools/atm/readme.md
new file mode 100644
index 0000000..e69de29
diff --git a/pye3sm/tools/elm/grace/data_date.txt b/pye3sm/tools/elm/grace/data_date.txt
deleted file mode 100644
index 1535879..0000000
--- a/pye3sm/tools/elm/grace/data_date.txt
+++ /dev/null
@@ -1,60 +0,0 @@
-Jan04, 2004_001–2004_013,	2004_001–2004_013,	2004_001–2004_013,	2004_001–2004_013
-Feb04, 2004_035–2004_060,	2004_035–2004_060,	2004_035–2004_060,	2004_035–2004_060
-Mar04, 2004_061–2004_091,	2004_061–2004_091,	2004_061–2004_091,	2004_061–2004_091
-Apr04, 2004_092–2004_121,	2004_092–2004_121,	2004_092–2004_121,	2004_092–2004_121
-May04, 2004_122–2004_152,	2004_122–2004_152,	2004_122–2004_152,	2004_122–2004_152
-Jun04, 2004_153–2004_182,	2004_153–2004_182,	2004_153–2004_182,	2004_153–2004_182
-Jul04, 2004_183–2004_213,	2004_183–2004_213,	2004_183–2004_213,	2004_183–2004_213
-Aug04, 2004_214–2004_244,	2004_214–2004_244,	2004_214–2004_244,	2004_214–2004_244
-Sep04, 2004_245–2004_274,	2004_245–2004_274,	2004_245–2004_274,	2004_245–2004_274
-Oct04, 2004_275–2004_305,	2004_275–2004_305,	2004_275–2004_305,	2004_275–2004_305
-Nov04, 2004_306–2004_335,	2004_306–2004_335,	2004_306–2004_335,	2004_306–2004_335
-Dec04, 2004_336–2004_366,	2004_336–2004_366,	2004_336–2005_000,	2004_336–2004_366
-Jan05, 2005_001–2005_031,	2005_001–2005_031,	2005_001–2005_031,	2005_001–2005_031
-Feb05, 2005_032–2005_059,	2005_032–2005_059,	2005_032–2005_059,	2005_032–2005_059
-Mar05, 2005_060–2005_090,	2005_061–2005_090,	2005_060–2005_090,	2005_060–2005_090
-Apr05, 2005_091–2005_120,	2005_091–2005_120,	2005_091–2005_120,	2005_091–2005_120
-May05, 2005_121–2005_151,	2005_121–2005_151,	2005_121–2005_151,	2005_121–2005_151
-Jun05, 2005_152–2005_181,	2005_152–2005_181,	2005_152–2005_181,	2005_152–2005_181
-Jul05, 2005_182–2005_212,	2005_182–2005_212,	2005_182–2005_212,	2005_182–2005_212
-Aug05, 2005_213–2005_243,	2005_213–2005_243,	2005_213–2005_243,	2005_213–2005_243
-Sep05, 2005_244–2005_273,	2005_244–2005_273,	2005_244–2005_273,	2005_244–2005_273
-Oct05, 2005_274–2005_304,	2005_274–2005_304,	2005_274–2005_304,	2005_274–2005_304
-Nov05, 2005_305–2005_334,	2005_305–2005_334,	2005_305–2005_334,	2005_305–2005_334
-Dec05, 2005_335–2005_365,	2005_335–2005_365,	2005_335–2005_364,	2005_335–2005_365
-Jan06, 2006_001–2006_031,	2006_001–2006_031,	2006_001–2006_031,	2006_001–2006_031
-Feb06, 2006_032–2006_059,	2006_032–2006_059,	2006_032–2006_059,	2006_032–2006_059
-Mar06, 2006_060–2006_090,	2006_060–2006_090,	2006_060–2006_090,	2006_060–2006_090
-Apr06, 2006_091–2006_120,	2006_091–2006_120,	2006_091–2006_120,	2006_091–2006_120
-May06, 2006_121–2006_151,	2006_121–2006_151,	2006_121–2006_151,	2006_121–2006_151
-Jun06, 2006_152–2006_181,	2006_152–2006_181,	2006_152–2006_181,	2006_152–2006_181
-Jul06, 2006_182–2006_212,	2006_182–2006_212,	2006_182–2006_212,	2006_182–2006_212
-Aug06, 2006_213–2006_243,	2006_213–2006_243,	2006_213–2006_243,	2006_213–2006_243
-Sep06, 2006_244–2006_273,	2006_244–2006_273,	2006_245–2006_273,	2006_244–2006_273
-Oct06, 2006_274–2006_304,	2006_274–2006_304,	2006_274–2006_304,	2006_274–2006_304
-Nov06, 2006_305–2006_334,	2006_305–2006_334,	2006_305–2006_334,	2006_305–2006_334
-Dec06, 2006_335–2006_365,	2006_335–2006_365,	2006_335–2007_000,	2006_335–2006_365
-Jan07, 2007_001–2007_031,	2007_001–2007_031,	2007_001–2007_031,	2007_001–2007_031
-Feb07, 2007_032–2007_058,	2007_032–2007_059,	2007_032–2007_058,	2007_032–2007_059
-Mar07, 2007_060–2007_090,	2007_060–2007_090,	2007_060–2007_090,	2007_060–2007_090
-Apr07, 2007_091–2007_120,	2007_091–2007_120,	2007_091–2007_120,	2007_091–2007_120
-May07, 2007_121–2007_151,	2007_121–2007_151,	2007_121–2007_151,	2007_121–2007_151
-Jun07, 2007_152–2007_181,	2007_152–2007_181,	2007_152–2007_181,	2007_152–2007_181
-Jul07, 2007_182–2007_212,	2007_182–2007_212,	2007_182–2007_212,	2007_182–2007_212
-Aug07, 2007_213–2007_243,	2007_213–2007_243,	2007_213–2007_243,	2007_213–2007_243
-Sep07, 2007_244–2007_273,	2007_244–2007_273,	2007_244–2007_273,	2007_244–2007_273
-Oct07, 2007_274–2007_304,	2007_274–2007_304,	2007_278–2007_304,	2007_274–2007_304
-Nov07, 2007_305–2007_334,	2007_305–2007_334,	2007_305–2007_334,	2007_305–2007_334
-Dec07, 2007_335–2007_365,	2007_335–2007_365,	2007_335–2008_000,	2007_335–2007_365
-Jan08, 2008_001–2008_031,	2008_001–2008_031,	2008_001–2008_031,	2008_001–2008_031
-Feb08, 2008_032–2008_060,	2008_032–2008_060,	2008_032–2008_060,	2008_032–2008_060
-Mar08, 2008_061–2008_091,	2008_061–2008_091,	2008_061–2008_091,	2008_061–2008_091
-Apr08, 2008_092–2008_121,	2008_092–2008_121,	2008_092–2008_121,	2008_092–2008_121
-May08, 2008_122–2008_152,	2008_122–2008_152,	2008_122–2008_152,	2008_122–2008_152
-Jun08, 2008_153–2008_182,	2008_153–2008_182,	2008_153–2008_182,	2008_153–2008_182
-Jul08, 2008_183–2008_213,	2008_183–2008_213,	2008_183–2008_213,	2008_183–2008_213
-Aug08, 2008_214–2008_244,	2008_214–2008_244,	2008_214–2008_244,	2008_214–2008_244
-Sep08, 2008_245–2008_274,	2008_245–2008_274,	2008_245–2008_274,	2008_245–2008_274
-Oct08, 2008_275–2008_305,	2008_275–2008_305,	2008_275–2008_305,	2008_275–2008_305
-Nov08, 2008_306–2008_335,	2008_306–2008_335,	2008_306–2008_335,	2008_306–2008_335
-Dec08, 2008_336–2008_366,	2008_336–2008_366,	2008_336–2008_365,	2008_336–2008_366
\ No newline at end of file
diff --git a/pye3sm/tools/elm/grace/download.txt b/pye3sm/tools/elm/grace/download.txt
deleted file mode 100644
index 985840e..0000000
--- a/pye3sm/tools/elm/grace/download.txt
+++ /dev/null
@@ -1,978 +0,0 @@
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-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003091-2003120_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
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-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003121-2003141_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003121-2003141_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003121-2003141_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003182-2003212_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003182-2003212_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003182-2003212_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003182-2003212_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003182-2003212_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2003182-2003212_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
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-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016129-2016152_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016153-2016182_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016153-2016182_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016153-2016182_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016153-2016182_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016153-2016182_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016153-2016182_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016183-2016211_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016183-2016211_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016183-2016211_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016183-2016211_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016183-2016211_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016183-2016211_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016221-2016247_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016221-2016247_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016221-2016247_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016221-2016247_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016221-2016247_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016221-2016247_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016319-2016345_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016319-2016345_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016319-2016345_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016319-2016345_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016319-2016345_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016319-2016345_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016346-2017006_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016346-2017006_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016346-2017006_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016346-2017006_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016346-2017006_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2016346-2017006_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017007-2017034_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017007-2017034_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017007-2017034_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017007-2017034_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017007-2017034_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017007-2017034_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017076-2017104_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017076-2017104_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017076-2017104_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017076-2017104_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017076-2017104_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017076-2017104_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017100-2017128_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017100-2017128_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017100-2017128_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017100-2017128_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017100-2017128_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017100-2017128_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017123-2017142_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017123-2017142_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017123-2017142_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017123-2017142_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017123-2017142_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017123-2017142_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017143-2017179_GRAC_UTCSR_BA01_0600_LND_v03.nc 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017143-2017179_GRAC_UTCSR_BA01_0600_LND_v03.nc.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017143-2017179_GRAC_UTCSR_BA01_0600_LND_v03.tif 											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017143-2017179_GRAC_UTCSR_BA01_0600_LND_v03.tif.md5											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017143-2017179_GRAC_UTCSR_BA01_0600_LND_v03.txt											
-https://podaac-opendap.jpl.nasa.gov/opendap/allData/tellus/L3/grace/land_mass/RL06/v03/CSR/GRD-3_2017143-2017179_GRAC_UTCSR_BA01_0600_LND_v03.txt.md5											
\ No newline at end of file
diff --git a/pye3sm/tools/elm/runoff/plot_ansi.py b/pye3sm/tools/elm/runoff/plot_ansi.py
deleted file mode 100644
index f5bf961..0000000
--- a/pye3sm/tools/elm/runoff/plot_ansi.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-import numpy as np
-from osgeo import ogr, osr, gdal
-import matplotlib.cm as cm
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-
-from pyearth.system.define_global_variables import *   
-crs=ccrs.PlateCarree()
-def plot_ansi():
-    sFilename_runoff= '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/kansi.tif'
-    
-    #sFilename_runoff = '/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract' + sExtension_tiff
-    if os.path.exists(sFilename_runoff):
-   
-        fig = plt.figure(dpi=300)
-        fig.set_figwidth( 4 )
-        fig.set_figheight( 4 )
-
-       
-         
-        dummy = gdal_read_geotiff_file(sFilename_runoff)
-        dResolution_x = dummy[1]
-        pProjection = dummy[8]
-        pSpatial_reference_source = dummy[9]
-        
-        ax_dem = fig.add_axes([0.3, 0.15, 0.6, 0.55] , projection=crs )
-        ax_dem.set_xmargin(0.05)
-        ax_dem.set_ymargin(0.10)          
-        
-        missing_value = dummy[6]
-
-        
-        dOriginX=dummy[2]
-        dOriginY=dummy[3]
-        nrow=dummy[4]
-        ncolumn=dummy[5]
-        dLon_min = dOriginX
-        dLon_max = dOriginX + ncolumn * dResolution_x
-        dLat_max = dOriginY
-        dLat_min = dOriginY - nrow * dResolution_x
-        aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
-        pSpatial_reference_target = osr.SpatialReference()  
-        pSpatial_reference_target.ImportFromEPSG(4326)
-        aLon= list()
-        aLat=list()
-        aLon.append(dLon_min)
-        aLon.append(dLon_max)
-        aLat.append(dLat_min)
-        aLat.append(dLat_max)
-        
-        aImage_in = dummy[0]
-        aImage_in[np.where(aImage_in == missing_value)] = np.nan
-        aImage_in=np.power(10, aImage_in)
-        # 
-        demplot = ax_dem.imshow(aImage_in, origin='upper', extent=aImage_extent,cmap=cm.terrain,transform=crs) 
-        # change all spines
-        for axis in ['top','bottom','left','right']:
-            ax_dem.spines[axis].set_linewidth(0.5)
-
-        # increase tick width
-        ax_dem.tick_params(width=0.5)
-       
-        gl = ax_dem.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                      linewidth=1, color='gray', alpha=0.3, linestyle='--')
-        gl.left_labels=False
-        gl.top_labels=False        
-       
-         # declare text size
-        XTEXT_SIZE = 8
-        YTEXT_SIZE = 8
-        # to facilitate text rotation at bottom edge, ...
-        # text justification: 'ha':'right' is used to avoid clashing with map's boundary
-        # default of 'ha' is center, often causes trouble when text rotation is not zero
-        gl.xlabel_style = {'size': XTEXT_SIZE, 'color': 'k', 'rotation':0, 'ha':'right'}
-        gl.ylabel_style = {'size': YTEXT_SIZE, 'color': 'k', 'rotation':90,'weight': 'normal'}
-
-        ax_cb= fig.add_axes([0.2, 0.2, 0.02, 0.5])
-        
-        cb = plt.colorbar(demplot, cax = ax_cb, extend = 'max')
-        cb.ax.get_yaxis().set_ticks_position('left')
-        cb.ax.get_yaxis().labelpad = 10
-        cb.ax.set_ylabel('Unit: ratio', rotation=270)
-        cb.ax.tick_params(labelsize=6) 
-
-        sFilename_out = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/ansi.png'
-        plt.savefig(sFilename_out, bbox_inches='tight')
-
-        
-
-if __name__ == '__main__':
-    #preprcess_mingpan_runoff2()
-    plot_ansi()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/runoff/plot_fover.py b/pye3sm/tools/elm/runoff/plot_fover.py
deleted file mode 100644
index 10d802a..0000000
--- a/pye3sm/tools/elm/runoff/plot_fover.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-import numpy as np
-from osgeo import ogr, osr, gdal
-import matplotlib.cm as cm
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-
-from pyearth.system.define_global_variables import *   
-crs=ccrs.PlateCarree()
-def plot_fover():
-    
-    
-    sFilename_runoff= '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/fover.tif'
-    if os.path.exists(sFilename_runoff):
-   
-        fig = plt.figure(dpi=300)
-        fig.set_figwidth( 4 )
-        fig.set_figheight( 4 )
-
-       
-         
-        dummy = gdal_read_geotiff_file(sFilename_runoff)
-        dResolution_x = dummy[1]
-        pProjection = dummy[8]
-        pSpatial_reference_source = dummy[9]
-        
-        ax_dem = fig.add_axes([0.3, 0.15, 0.6, 0.55] , projection=crs )
-        ax_dem.set_xmargin(0.05)
-        ax_dem.set_ymargin(0.10)          
-        
-        missing_value = dummy[6]
-
-        
-        dOriginX=dummy[2]
-        dOriginY=dummy[3]
-        nrow=dummy[4]
-        ncolumn=dummy[5]
-        dLon_min = dOriginX
-        dLon_max = dOriginX + ncolumn * dResolution_x
-        dLat_max = dOriginY
-        dLat_min = dOriginY - nrow * dResolution_x
-        aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
-        pSpatial_reference_target = osr.SpatialReference()  
-        pSpatial_reference_target.ImportFromEPSG(4326)
-        aLon= list()
-        aLat=list()
-        aLon.append(dLon_min)
-        aLon.append(dLon_max)
-        aLat.append(dLat_min)
-        aLat.append(dLat_max)
-        
-        aImage_in = dummy[0]
-        aImage_in[np.where(aImage_in == missing_value)] = np.nan
-        #aImage_in=aImage_in/30
-        # 
-        demplot = ax_dem.imshow(aImage_in, origin='upper', extent=aImage_extent,cmap=cm.terrain,transform=crs) 
-        # change all spines
-        for axis in ['top','bottom','left','right']:
-            ax_dem.spines[axis].set_linewidth(0.5)
-
-        # increase tick width
-        ax_dem.tick_params(width=0.5)
-       
-        gl = ax_dem.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                      linewidth=1, color='gray', alpha=0.3, linestyle='--')
-        gl.left_labels=False
-        gl.top_labels=False        
-       
-         # declare text size
-        XTEXT_SIZE = 8
-        YTEXT_SIZE = 8
-        # to facilitate text rotation at bottom edge, ...
-        # text justification: 'ha':'right' is used to avoid clashing with map's boundary
-        # default of 'ha' is center, often causes trouble when text rotation is not zero
-        gl.xlabel_style = {'size': XTEXT_SIZE, 'color': 'k', 'rotation':0, 'ha':'right'}
-        gl.ylabel_style = {'size': YTEXT_SIZE, 'color': 'k', 'rotation':90,'weight': 'normal'}
-
-        ax_cb= fig.add_axes([0.2, 0.2, 0.02, 0.5])
-        
-        cb = plt.colorbar(demplot, cax = ax_cb, extend = 'max')
-        cb.ax.get_yaxis().set_ticks_position('left')
-        cb.ax.get_yaxis().labelpad = 10
-        cb.ax.set_ylabel('', rotation=270)
-        cb.ax.tick_params(labelsize=6) 
-
-        sFilename_out = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/fover.png'
-        plt.savefig(sFilename_out, bbox_inches='tight')
-
-        
-
-if __name__ == '__main__':
-    #preprcess_mingpan_runoff2()
-    plot_fover()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/runoff/plot_runoff.py b/pye3sm/tools/elm/runoff/plot_runoff.py
deleted file mode 100644
index b64a211..0000000
--- a/pye3sm/tools/elm/runoff/plot_runoff.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import os
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-import numpy as np
-from osgeo import ogr, osr, gdal
-import matplotlib.cm as cm
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-
-from pyearth.system.define_global_variables import *   
-crs=ccrs.PlateCarree()
-def plot_runoff():
-    sFilename_runoff = ''
-    sFilename_runoff = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/' + 'minpan_runoff_extract' + sExtension_tiff
-    if os.path.exists(sFilename_runoff):
-   
-        fig = plt.figure(dpi=300)
-        fig.set_figwidth( 4 )
-        fig.set_figheight( 4 )
-
-       
-         
-        dummy = gdal_read_geotiff_file(sFilename_runoff)
-        dResolution_x = dummy[1]
-        pProjection = dummy[8]
-        pSpatial_reference_source = dummy[9]
-        
-        ax_dem = fig.add_axes([0.3, 0.15, 0.6, 0.55] , projection=crs )
-        ax_dem.set_xmargin(0.05)
-        ax_dem.set_ymargin(0.10)          
-        
-        missing_value = dummy[6]
-
-        
-        dOriginX=dummy[2]
-        dOriginY=dummy[3]
-        nrow=dummy[4]
-        ncolumn=dummy[5]
-        dLon_min = dOriginX
-        dLon_max = dOriginX + ncolumn * dResolution_x
-        dLat_max = dOriginY
-        dLat_min = dOriginY - nrow * dResolution_x
-        aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
-        pSpatial_reference_target = osr.SpatialReference()  
-        pSpatial_reference_target.ImportFromEPSG(4326)
-        aLon= list()
-        aLat=list()
-        aLon.append(dLon_min)
-        aLon.append(dLon_max)
-        aLat.append(dLat_min)
-        aLat.append(dLat_max)
-        
-        aImage_in = dummy[0]
-        aImage_in[np.where(aImage_in == missing_value)] = np.nan
-        aImage_in=aImage_in/30
-        # 
-        demplot = ax_dem.imshow(aImage_in, origin='upper', extent=aImage_extent,cmap=cm.terrain,transform=crs) 
-        # change all spines
-        for axis in ['top','bottom','left','right']:
-            ax_dem.spines[axis].set_linewidth(0.5)
-
-        # increase tick width
-        ax_dem.tick_params(width=0.5)
-       
-        gl = ax_dem.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                      linewidth=1, color='gray', alpha=0.3, linestyle='--')
-        gl.left_labels=False
-        gl.top_labels=False        
-       
-         # declare text size
-        XTEXT_SIZE = 8
-        YTEXT_SIZE = 8
-        # to facilitate text rotation at bottom edge, ...
-        # text justification: 'ha':'right' is used to avoid clashing with map's boundary
-        # default of 'ha' is center, often causes trouble when text rotation is not zero
-        gl.xlabel_style = {'size': XTEXT_SIZE, 'color': 'k', 'rotation':0, 'ha':'right'}
-        gl.ylabel_style = {'size': YTEXT_SIZE, 'color': 'k', 'rotation':90,'weight': 'normal'}
-
-        ax_cb= fig.add_axes([0.2, 0.2, 0.02, 0.5])
-        
-        cb = plt.colorbar(demplot, cax = ax_cb, extend = 'max')
-        cb.ax.get_yaxis().set_ticks_position('left')
-        cb.ax.get_yaxis().labelpad = 10
-        cb.ax.set_ylabel('Units: mm/s', rotation=270)
-        cb.ax.tick_params(labelsize=6) 
-
-        sFilename_out = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/ming_runoff.png'
-        plt.savefig(sFilename_out, bbox_inches='tight')
-
-        
-
-if __name__ == '__main__':
-    #preprcess_mingpan_runoff2()
-    plot_runoff()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/runoff/plot_wtd.py b/pye3sm/tools/elm/runoff/plot_wtd.py
deleted file mode 100644
index 2fb6736..0000000
--- a/pye3sm/tools/elm/runoff/plot_wtd.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import os
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-import numpy as np
-from osgeo import ogr, osr, gdal
-import matplotlib.cm as cm
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-
-from pyearth.system.define_global_variables import *   
-crs=ccrs.PlateCarree()
-def plot_wtd():
-    
-    
-    sFilename_runoff = '/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract' + sExtension_tiff
-    if os.path.exists(sFilename_runoff):
-   
-        fig = plt.figure(dpi=300)
-        fig.set_figwidth( 4 )
-        fig.set_figheight( 4 )
-
-       
-         
-        dummy = gdal_read_geotiff_file(sFilename_runoff)
-        dResolution_x = dummy[1]
-        pProjection = dummy[8]
-        pSpatial_reference_source = dummy[9]
-        
-        ax_dem = fig.add_axes([0.3, 0.15, 0.6, 0.55] , projection=crs )
-        ax_dem.set_xmargin(0.05)
-        ax_dem.set_ymargin(0.10)          
-        
-        missing_value = dummy[6]
-
-        
-        dOriginX=dummy[2]
-        dOriginY=dummy[3]
-        nrow=dummy[4]
-        ncolumn=dummy[5]
-        dLon_min = dOriginX
-        dLon_max = dOriginX + ncolumn * dResolution_x
-        dLat_max = dOriginY
-        dLat_min = dOriginY - nrow * dResolution_x
-        aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
-        pSpatial_reference_target = osr.SpatialReference()  
-        pSpatial_reference_target.ImportFromEPSG(4326)
-        aLon= list()
-        aLat=list()
-        aLon.append(dLon_min)
-        aLon.append(dLon_max)
-        aLat.append(dLat_min)
-        aLat.append(dLat_max)
-        
-        aImage_in = dummy[0]
-        aImage_in[np.where(aImage_in == missing_value)] = np.nan
-        aImage_in[np.where(aImage_in > 12)] = 12
-        #aImage_in=aImage_in/30
-
-        cmap = cm.get_cmap('rainbow')
-        # 
-        demplot = ax_dem.imshow(aImage_in, origin='upper', extent=aImage_extent,cmap = cmap,transform=crs) 
-        # change all spines
-        for axis in ['top','bottom','left','right']:
-            ax_dem.spines[axis].set_linewidth(0.5)
-
-        # increase tick width
-        ax_dem.tick_params(width=0.5)
-       
-        gl = ax_dem.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                      linewidth=1, color='gray', alpha=0.3, linestyle='--')
-        gl.left_labels=False
-        gl.top_labels=False
-       
-         # declare text size
-        XTEXT_SIZE = 8
-        YTEXT_SIZE = 8
-        # to facilitate text rotation at bottom edge, ...
-        # text justification: 'ha':'right' is used to avoid clashing with map's boundary
-        # default of 'ha' is center, often causes trouble when text rotation is not zero
-        gl.xlabel_style = {'size': XTEXT_SIZE, 'color': 'k', 'rotation':0, 'ha':'right'}
-        gl.ylabel_style = {'size': YTEXT_SIZE, 'color': 'k', 'rotation':90,'weight': 'normal'}
-
-        ax_cb= fig.add_axes([0.2, 0.2, 0.02, 0.5])
-        
-        cb = plt.colorbar(demplot, cax = ax_cb, extend = 'max')
-        cb.ax.get_yaxis().set_ticks_position('left')
-        cb.ax.get_yaxis().labelpad = 10
-        cb.ax.set_ylabel('Unit: m', rotation=270)
-        cb.ax.tick_params(labelsize=6) 
-
-        sFilename_out = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/wtd.png'
-        plt.savefig(sFilename_out, bbox_inches='tight')
-
-        
-
-if __name__ == '__main__':
-    #preprcess_mingpan_runoff2()
-    plot_wtd()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/runoff/preprocess_mingpan_runoff.py b/pye3sm/tools/elm/runoff/preprocess_mingpan_runoff.py
deleted file mode 100644
index fed5e50..0000000
--- a/pye3sm/tools/elm/runoff/preprocess_mingpan_runoff.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import os , sys
-import datetime
-import numpy as np
-from numpy.lib.function_base import _average_dispatcher
-from scipy.interpolate import griddata #generate grid
-from netCDF4 import Dataset #read netcdf
-import scipy.io
-import pandas as pd
-from statsmodels.tsa.seasonal import STL
-from osgeo import gdal, osr #the default operator
-from pyearth.system.define_global_variables import *   
-from pyearth.toolbox.date.leap_year import leap_year 
-from pyearth.gis.gdal.write.gdal_write_envi_file import gdal_write_envi_file_multiple_band
-from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file, gdal_write_geotiff_file_multiple_band
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file, gdal_read_geotiff_file_multiple_band 
-from pyearth.toolbox.data.convert_time_series_daily_to_monthly import convert_time_series_daily_to_monthly
-
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-from pye3sm.elm.grid.elm_extract_grid_latlon_from_mosart import elm_extract_grid_latlon_from_mosart
-def preprcess_mingpan_runoff():
-    """
-    Preprocess the runoff data
-    """
-    sPath = '/compyfs/inputdata/lnd/dlnd7/mingpan'
-    sPath2 = '/qfs/people/xudo627/ming/runoff'
-
-    iYear_start_in = 2000
-    iYear_end_in = 2008
-    iDay_start_in =1
-    iDay_end_in = 366
-    for iYear in range(iYear_start_in, iYear_end_in+1, 1):
-        sYear = "{:04d}".format(iYear)
-        if iYear == iYear_start_in:
-            iDay_start = iDay_start_in
-        else: 
-            iDay_start = 1
-        
-        if iYear == iYear_end_in :
-            iDay_end = iDay_end_in
-        else: 
-            if leap_year(iYear):
-
-                iDay_end = 366
-            else:
-                iDay_end = 365
-
-
-        for iDay in range( iDay_start ,iDay_end+1, 1):
-
-            sDay = "{:0d}".format(iDay)
-            sFilename_output = sPath2 + slash + 'RUNOFF05_' + sYear + '_' + sDay +  '.mat'
-            
-            mat = scipy.io.loadmat(sFilename_output)
-            print(type(mat))
-            aData_all = np.array(mat['ro05'])
-            print(aData_all.size)
-            
-
-    return
-def preprcess_mingpan_runoff2():
-    """
-    Preprocess the runoff data
-    """
-    sPath = '/compyfs/inputdata/lnd/dlnd7/mingpan'
-    nrow=360
-    ncolumn=720
-    pHeaderParameters = {}    
-    pHeaderParameters['ncolumn'] = "{:0d}".format(ncolumn)
-    pHeaderParameters['nrow'] = "{:0d}".format(nrow)
-    pHeaderParameters['ULlon'] = "{:0f}".format(-180.0)
-    pHeaderParameters['ULlat'] = "{:0f}".format(90.0)
-    pHeaderParameters['pixelSize'] = "{:0f}".format(0.5)
-    pHeaderParameters['nband'] = '1'
-    pHeaderParameters['offset'] = '0'
-    pHeaderParameters['data_type'] = '4'
-    pHeaderParameters['bsq'] = 'bsq'
-    pHeaderParameters['byte_order'] = '0'
-    pHeaderParameters['missing_value'] = '-9999'
-
-    iYear_start_in = 2000
-    iYear_end_in = 2008
-    iDay_start_in =1
-    iDay_end_in = 366
-    
-    nYear = iYear_end_in-iYear_start_in + 1
-    aData_all = np.full((nYear, 12,nrow, ncolumn),-9999, dtype =float)
-    for iYear in range(iYear_start_in, iYear_end_in+1, 1):
-        sYear = "{:04d}".format(iYear)
-        if iYear == iYear_start_in:
-            iDay_start = iDay_start_in
-        else: 
-            iDay_start = 1
-        
-        if leap_year(iYear):
-            iDay_end = 30
-        else:
-            iDay_end = 31
-            
-        sFilename = sPath + slash + 'ming_daily_'+  sYear + sExtension_netcdf
-        aDatasets = Dataset(sFilename) 
-        for sKey, aValue in aDatasets.variables.items():
-            if (sKey == 'lon'):                   
-                aLongitude = (aValue[:]).data
-                continue
-            if (sKey == 'lat'):                    
-                aLatitude = (aValue[:]).data
-                continue
-            if (sKey.lower() == 'qrunoff'):                    
-                aRunoff = (aValue[:]).data
-                continue
-
-        aData_year = np.full((12,nrow, ncolumn),-9999, dtype =float)
-        
-        for i in range(nrow):
-            for j in range(ncolumn):
-                aData_daily_in = aRunoff[:,i,j]
-                if np.max(aData_daily_in)==0:
-                    aData_year[:,i,j] = -9999
-                else:
-                    data_monthly = convert_time_series_daily_to_monthly(aData_daily_in, \
-                                iYear, 1, 1, \
-                                iYear, 12, iDay_end , sType_in = 'sum'  )
-                
-                    aData_year[:,i,j] = data_monthly
-        
-        #save as geotiff 
-        aData_year = np.flip(aData_year, 1)
-        for k in range(12):
-            array_2d = aData_year[k,:,:]
-            array_2d = np.reshape(array_2d, (nrow, ncolumn))
-            array_2d = np.roll(array_2d, int(0.5*ncolumn), axis=1)
-            aData_year[k,:,:] = array_2d
-        
-        pSpatial = osr.SpatialReference()
-        pSpatial.ImportFromEPSG(4326)
-        sFilename_tiff = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/' + sYear + sExtension_tiff
-
-        gdal_write_geotiff_file_multiple_band(sFilename_tiff, aData_year,\
-            float(pHeaderParameters['pixelSize']),\
-            float(pHeaderParameters['ULlon']),\
-            float(pHeaderParameters['ULlat']),\
-                  -9999.0, pSpatial)
-
-        aData_all[iYear-iYear_start_in, :,:] = aData_year
-    #save output 
-        
-
-
-
-    #convert_time_series_daily_to_monthly(aData_daily_in, iYear_start_in, iMonth_start_in, \
-    #    iDay_start_in, iYear_end_in, iMonth_end_in, iDay_end_in , sType_in = None  ):
-  
-            
-    sFilename_out = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/obs.nc'        
-
-    return
-def convert_time_series_to_moment():
-    nrow=360
-    ncolumn=720
-    pHeaderParameters = {}    
-    pHeaderParameters['ncolumn'] = "{:0d}".format(ncolumn)
-    pHeaderParameters['nrow'] = "{:0d}".format(nrow)
-    pHeaderParameters['ULlon'] = "{:0f}".format(-180.0)
-    pHeaderParameters['ULlat'] = "{:0f}".format(90.0)
-    pHeaderParameters['pixelSize'] = "{:0f}".format(0.5)
-    pHeaderParameters['nband'] = '1'
-    pHeaderParameters['offset'] = '0'
-    pHeaderParameters['data_type'] = '4'
-    pHeaderParameters['bsq'] = 'bsq'
-    pHeaderParameters['byte_order'] = '0'
-    pHeaderParameters['missing_value'] = '-9999'
-    sFilename_mosart_netcdf_out = '/qfs/people/liao313/data/e3sm/amazon/mosart/' + 'mosart_20220201040.nc'
-    aLon, aLat, aMask = elm_extract_grid_latlon_from_mosart(sFilename_mosart_netcdf_out)
-    nrow_extract, ncolumn_extract = aLon.shape
-    iYear_start = 2000
-    iYear_end = 2008
-    nyear= iYear_end- iYear_start + 1
-    dates = list()
-    nyear = iYear_end - iYear_start + 1
-    for iYear in range(iYear_start, iYear_end + 1):
-        for iMonth in range(1,13):
-            dSimulation = datetime.datetime(iYear, iMonth, 1)
-            dates.append( dSimulation )
-            pass
-    dates=np.array(dates)
-        
-    aData_all = np.full((9,12, nrow, ncolumn), -9999, dtype=float)
-    for iYear in range(iYear_start, iYear_end+1, 1):
-        sYear = "{:04d}".format(iYear)
-        for iMonth in range(12):
-            sMonth = "{:04d}".format(iYear)
-        sFilename_tiff = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/' + sYear + sExtension_tiff
-        dummy = gdal_read_geotiff_file_multiple_band(sFilename_tiff)
-        #aData = dummy[0]
-        aData_all[ iYear-iYear_start, :,:,: ] = dummy[0]
-
-    #reshape
-    aData = np.reshape(aData_all, (9*12, nrow, ncolumn))
-    aData_out = np.full((nrow, ncolumn), -9999, dtype=float)
-    aData_out_extract = np.full((nrow_extract, ncolumn_extract), -9999, dtype=float)
-    for i in range(nrow):
-        for j in range(ncolumn):
-            aVariable_ts = aData[:, i, j]
-            if np.max(aVariable_ts) > 0 and np.min(aVariable_ts) != -9999:
-                aData_tsa = pd.Series(aVariable_ts, index=pd.date_range(dates[0], \
-                                                     periods=len(dates), freq='M'), name = 'runoff')
-                stl = STL(aData_tsa, seasonal=13)
-                aTSA = stl.fit()
-                dSeason = np.array(aTSA.seasonal)
-                dTrend = np.array(aTSA.trend)
-                dResi = np.array(aTSA.resid)
-                #dummy = np.array([np.min(dTrend), np.max(dTrend)])
-                aData_out[i, j] = np.mean(dTrend)
-                if (aData_out[i, j]<0):
-                    print('err')
-                pass
-            else:
-                aData_out[i, j] = -9999.0
-    #write whole 
-    pSpatial = osr.SpatialReference()
-    pSpatial.ImportFromEPSG(4326)
-    sFilename_tiff = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/' + 'minpan_runoff' + sExtension_tiff
-
-    gdal_write_geotiff_file(sFilename_tiff, aData_out,\
-            float(pHeaderParameters['pixelSize']),\
-            float(pHeaderParameters['ULlon']),\
-            float(pHeaderParameters['ULlat']),\
-                  -9999.0, pSpatial)
-
-    #a = gdal_read_geotiff_file(sFilename_tiff)              
-    #aData_out = a[0]
-
-    #extract
-    aLat= np.flip(aLat, 0)
-    for i in range(nrow_extract):
-        for j in range(ncolumn_extract):
-            dLon = aLon[i,j]-0.25
-            dLat = aLat[i,j]+0.25
-            #locate it
-            
-            iIndex = int( (90-(dLat)) / 0.5 )
-            jIndex = int( (dLon-(-180)) / 0.5 )
-            aData_out_extract[i,j] = aData_out[iIndex, jIndex]
-
-    pSpatial = osr.SpatialReference()
-    pSpatial.ImportFromEPSG(4326)
-    sFilename_tiff = '/compyfs/liao313/04model/e3sm/amazon/analysis/gp/' + 'minpan_runoff_extract' + sExtension_tiff
-
-    gdal_write_geotiff_file(sFilename_tiff, aData_out_extract,\
-            float(pHeaderParameters['pixelSize']),\
-            np.min(aLon)-0.25,\
-            np.max(aLat)+0.25,\
-                  -9999.0, pSpatial)    
-    return
-if __name__ == '__main__':
-    #preprcess_mingpan_runoff2()
-    convert_time_series_to_moment()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/soil/preprocess_soil_ksat_data.py b/pye3sm/tools/elm/soil/preprocess_soil_ksat_data.py
deleted file mode 100644
index c9bbd3e..0000000
--- a/pye3sm/tools/elm/soil/preprocess_soil_ksat_data.py
+++ /dev/null
@@ -1,31 +0,0 @@
-
-import numpy as np
-from osgeo import gdal, osr #the default operator
-from pyearth.system.define_global_variables import *   
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file
-
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-
-def prepare_ksat_data():
-    
-    
-    sFilename_tiff = '/qfs/people/liao313/data/e3sm/amazon/elm/ksat_high.tif'
-    a = gdal_read_geotiff_file(sFilename_tiff)  
-
-    aData = a[0]
-    data_min = np.min(aData)
-
-    dummy_index = np.where(aData == data_min)
-
-    aData[dummy_index] = -9999
-
-    pSpatial = osr.SpatialReference()
-    pSpatial.ImportFromEPSG(4326)
-    sFilename_tiff = '/qfs/people/liao313/data/e3sm/amazon/elm/' + 'ksat_new' + sExtension_tiff
-
-    gdal_write_geotiff_file(sFilename_tiff, aData,\
-            0.5,-79.5      ,5.5   ,         -9999.0, pSpatial)    
-
-if __name__ == '__main__':    
-    prepare_ksat_data()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/wtd/convert_observation_wtd_data_to_halfdegree.py b/pye3sm/tools/elm/wtd/convert_observation_wtd_data_to_halfdegree.py
deleted file mode 100644
index 8948d7f..0000000
--- a/pye3sm/tools/elm/wtd/convert_observation_wtd_data_to_halfdegree.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import os
-import sys
-import numpy as np
-
-from netCDF4 import Dataset #it maybe be replaced by gdal 
-from osgeo import gdal #the default operator
-from scipy.interpolate import griddata #generate grid
-
-from pyearth.system.define_global_variables import *
-from pyearth.gis.envi.envi_write_header import envi_write_header
-
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
- 
-
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-
-def convert_observation_wtd_data_to_halfdegree(oE3SM_in, oCase_in):
-    
-    sModel  = oCase_in.sModel    
-    sRegion = oCase_in.sRegion
-    sWorkspace_data=  '/qfs/people/liao313/data'
-    dConversion = 1.0
-    sVariable = 'wtd'
-    sFilename_mosart_input = '/qfs/people/liao313/data/h2sc/global/raster/dem/MOSART_Global_half_20180606c.chang_9999.nc'
-
-    sFilename_wtd = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-        + 'wtd' + slash + 'Global_wtd_lowres.nc'
-
-    if os.path.isfile(sFilename_mosart_input):
-        pass
-    else:
-        error_code = 0
-        exit()
-    aDatasets = Dataset(sFilename_mosart_input)
-    netcdf_format = aDatasets.file_format
-    print(netcdf_format)
-    print("Print dimensions:")
-    print(aDatasets.dimensions.keys())
-    print("Print variables:")
-    print(aDatasets.variables.keys())
-    for sKey, aValue in aDatasets.variables.items():
-        if "ele0" == sKey:
-            aEle0 = (aValue[:]).data
-            break
-    nrow_new = 360
-    ncolumn_new = 720
-    aEle0 = aEle0.reshape(nrow_new, ncolumn_new)
-    #remember that mask latitude start from -90, so need to flip it    
-    aEle0 = np.flip(aEle0, 0) 
-    aMask = np.where(aEle0 == missing_value)
-    #read obs
-    if os.path.isfile(sFilename_wtd):
-        pass
-    else:
-        error_code = 0
-        exit()
-    aDatasets = Dataset(sFilename_wtd)
-    netcdf_format = aDatasets.file_format
-    print(netcdf_format)
-    print("Print dimensions:")
-    print(aDatasets.dimensions.keys())
-    print("Print variables:")
-    print(aDatasets.variables.keys())
-
-    for sKey, aValue in aDatasets.variables.items():
-        if "lat" == sKey:
-            aLatitude = (aValue[:]).data
-            print(aValue.datatype)
-            print(aValue.dimensions)
-        if "lon" == sKey:
-            aLongitude = (aValue[:]).data
-            print(aValue.datatype)
-            print(aValue.dimensions)
-        if "WTD" == sKey:
-            aWTD = (aValue[:]).data
-            print(aValue.datatype)
-            print(aValue.dimensions)
-
-    print('prepare grid')
-
-#new grid
-    longitude = np.arange(-179.75, 180., 0.5)
-    latitude = np.arange(89.75, -90, -0.5)
-    grid_x, grid_y = np.meshgrid(longitude, latitude)
-    
-
-    nrow_obs = len(aLatitude)
-    ncol_obs = len(aLongitude)
-
-    dummy = np.array(aLongitude)
-    aLongitude = np.transpose( np.tile(dummy.reshape(ncol_obs, 1), (1, nrow_obs)) )
-
-    dummy = np.array(aLatitude)
-    aLatitude =  np.tile(dummy.reshape(nrow_obs, 1), (1, ncol_obs))
-
-    aWTD = aWTD.reshape(nrow_obs, ncol_obs)
-    missing_value1 = np.min(aWTD)
-    dummy_index = np.where( (aLongitude < 180 ) & ( aLatitude < 90 ) \
-                          &(aWTD != missing_value1 ) )
-    aLongitude_subset = aLongitude[dummy_index]
-    aLatitude_subset = aLatitude[dummy_index]
-    aData_subset = aWTD[dummy_index] 
-    points = np.vstack((aLongitude_subset, aLatitude_subset))
-    points = np.transpose(points)
-    values = aData_subset 
-    aGrid_data = griddata(  points, values, (grid_x, grid_y), method='linear')
-    aGrid_data[aMask] = missing_value
-
-    #save output
-    #save netcdf
-    sFilename_output = sWorkspace_data + slash + sModel + slash +  sRegion + slash + 'raster' + slash \
-        + 'wtd' + slash  + sVariable.lower() + '_halfdegree' + sExtension_netcdf
-    pFile = Dataset(sFilename_output, 'w', format='NETCDF4') 
-
-    pDimension_longitude = pFile.createDimension('lon', ncolumn_new) 
-    pDimension_latitude = pFile.createDimension('lat', nrow_new) 
-    pVar = pFile.createVariable('wtd', 'f4', ('lat' , 'lon')) 
-    pVar[:] = aGrid_data
-    pVar.description = 'Water table depth' 
-    pVar.unit = 'm' 
-    pFile.close()
-    
-
-    #optional
-    iFlag_optional = 1
-    if(iFlag_optional ==1):
-        #save as envi
-        pHeaderParameters = {}
-        pHeaderParameters['ncolumn'] = '720'
-        pHeaderParameters['nrow'] = '360'
-        pHeaderParameters['ULlon'] = '-180'
-        pHeaderParameters['ULlat'] = '90'
-        pHeaderParameters['pixelSize'] = '0.5'
-        pHeaderParameters['nband'] = '1'
-        pHeaderParameters['offset'] = '0'
-        pHeaderParameters['data_type'] = '4'
-        pHeaderParameters['bsq'] = 'bsq'
-        pHeaderParameters['byte_order'] = '0'
-        pHeaderParameters['missing_value'] = '-9999'
-        sFilename_envi = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-            + 'wtd' + slash  + sVariable.lower() + '_halfdegree'  + sExtension_envi
-
-        aGrid_data.astype('float32').tofile(sFilename_envi)
-        #write header
-        sFilename_header = sWorkspace_data + slash + sModel + slash + sRegion+ slash + 'raster' + slash \
-            + 'wtd' + slash  + sVariable.lower() + '_halfdegree'  + sExtension_header
-        pHeaderParameters['sFilename'] = sFilename_header
-        envi_write_header(sFilename_header, pHeaderParameters)
-
-
-        #Open output format driver, see gdal_translate --formats for list
-        src_ds = gdal.Open( sFilename_envi )
-        sFormat = "GTiff"
-        driver = gdal.GetDriverByName( sFormat )
-        #Output to new format
-        sFilename_tiff = sWorkspace_data + slash + sModel +  slash + sRegion + slash + 'raster' + slash \
-            + 'wtd' + slash  + sVariable.lower() + '_halfdegree' + sExtension_tiff
-        dst_ds = driver.CreateCopy( sFilename_tiff, src_ds, 0 )
-        #Properly close the datasets to flush to disk
-        dst_ds = None
-        src_ds = None
-
-if __name__ == '__main__':
-    
-    
-    sModel = 'h2sc'
-    sRegion ='global'
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration )
-
-
-    aParameter_case = pye3sm_read_case_configuration_file(sFilename_case_configuration)
-    oE3SM = pye3sm(aParameter_e3sm)
-    oCase = pycase(aParameter_case)
-    convert_observation_wtd_data_to_halfdegree(oE3SM, oCase)
-
-    
-    print('finished')
\ No newline at end of file
diff --git a/pye3sm/tools/elm/wtd/convert_observation_wtd_data_to_raster.py b/pye3sm/tools/elm/wtd/convert_observation_wtd_data_to_raster.py
deleted file mode 100644
index dcd13c4..0000000
--- a/pye3sm/tools/elm/wtd/convert_observation_wtd_data_to_raster.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import os
-import sys
-import numpy as np
-
-from netCDF4 import Dataset #it maybe be replaced by gdal 
-from osgeo import gdal #the default operator
-from osgeo import gdal, osr
-
-from pyearth.system.define_global_variables import *
-
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
-from pyearth.gis.gdal.write.gdal_write_envi_file import gdal_write_envi_file
-from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-
-def convert_observation_wtd_data_to_raster(oE3SM_in, oCase_in):
-    
-    sModel  = oCase_in.sModel    
-    sRegion = oCase_in.sRegion
-    sWorkspace_data=  '/qfs/people/liao313/data'
-    dConversion = 1.0
-    sVariable = 'wtd'
-
-    sFilename_wtd = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-        + 'wtd' + slash + 'Global_wtd_lowres.nc'
-
-    
-    
-    if os.path.isfile(sFilename_wtd):
-        pass
-    else:
-        error_code = 0
-        exit()
-    aDatasets = Dataset(sFilename_wtd)
-    netcdf_format = aDatasets.file_format
-    print(netcdf_format)
-    print("Print dimensions:")
-    print(aDatasets.dimensions.keys())
-    print("Print variables:")
-    print(aDatasets.variables.keys())
-
-    for sKey, aValue in aDatasets.variables.items():
-        if "lat" == sKey:
-            aLatitude = (aValue[:]).data
-            print(aValue.datatype)
-            print(aValue.dimensions)
-        if "lon" == sKey:
-            aLongitude = (aValue[:]).data
-            print(aValue.datatype)
-            print(aValue.dimensions)
-        if "WTD" == sKey:
-            aWTD = (aValue[:]).data
-            print(aValue.datatype)
-            print(aValue.dimensions)
-
-    print('prepare grid')
-
-    nrow=580
-    ncolumn=1436
-
-    dLat_min=np.min(aLatitude)
-    dLat_max=np.max(aLatitude)
-    dLon_min=np.min(aLongitude)
-    dLon_max=np.max(aLongitude)
-
-    dResolution=(dLat_max-dLat_min)/(nrow-1)
-    dResolution1=(dLon_max-dLon_min)/(ncolumn-1)
-
-    ULlon = dLon_min - dResolution/2.0
-    ULlat = dLat_max + dResolution/2.0
-    aWTD=np.reshape(aWTD,(nrow,ncolumn))
-    aGrid_data = np.flip(aWTD, 0)
-    aGrid_data[np.where(aGrid_data<0)]=-9999
-
-    #optional
-    iFlag_optional = 1
-    if(iFlag_optional ==1):
-        #save as envi
-        pHeaderParameters = {}
-        pHeaderParameters['ncolumn'] = '1436'
-        pHeaderParameters['nrow'] = '580'
-        pHeaderParameters['ULlon'] = ULlon
-        pHeaderParameters['ULlat'] = ULlat
-        pHeaderParameters['pixelSize'] = '0.25'
-        pHeaderParameters['nband'] = '1'
-        pHeaderParameters['offset'] = '0'
-        pHeaderParameters['data_type'] = '4'
-        pHeaderParameters['bsq'] = 'bsq'
-        pHeaderParameters['byte_order'] = '0'
-        pHeaderParameters['missing_value'] = '-9999'
-        sFilename_envi = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-            + 'wtd' + slash  + sVariable.lower() + '_025'  + sExtension_envi
-        pSpatial = osr.SpatialReference()
-        pSpatial.ImportFromEPSG(4326)
-        gdal_write_envi_file(sFilename_envi, aGrid_data, dResolution,float(pHeaderParameters['ULlon']),\
-              float(pHeaderParameters['ULlat']),\
-                  -9999.0, pSpatial   )
-
-        sFilename_tiff = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-            + 'wtd' + slash  + sVariable.lower() + '_025'  + sExtension_tiff
-        gdal_write_geotiff_file(sFilename_tiff, aGrid_data,\
-        float(pHeaderParameters['pixelSize']),\
-         float(pHeaderParameters['ULlon']),\
-              float(pHeaderParameters['ULlat']),\
-                  -9999.0, pSpatial)
-
-
-        
-
-if __name__ == '__main__':
-    
-    
-    sModel = 'h2sc'
-    sRegion ='global'
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration )
-
-
-    aParameter_case = pye3sm_read_case_configuration_file(sFilename_case_configuration)
-    oE3SM = pye3sm(aParameter_e3sm)
-    oCase = pycase(aParameter_case)
-    convert_observation_wtd_data_to_raster(oE3SM, oCase)
-
-    
-    print('finished')
\ No newline at end of file
diff --git a/pye3sm/tools/elm/wtd/create_mosart_boundary.py b/pye3sm/tools/elm/wtd/create_mosart_boundary.py
deleted file mode 100644
index 9a813a0..0000000
--- a/pye3sm/tools/elm/wtd/create_mosart_boundary.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-import sys
-import numpy as np
-
-from netCDF4 import Dataset #it maybe be replaced by gdal 
-from osgeo import gdal #the default operator
-from scipy.interpolate import griddata #generate grid
-from osgeo import gdal, osr
-from pyearth.system.define_global_variables import *
-from pyearth.gis.envi.envi_write_header import envi_write_header
-
-from pyearth.gis.gdal.write.gdal_write_envi_file import gdal_write_envi_file
-from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file
-from pye3sm.shared.e3sm import pye3sm
-from pye3sm.shared.case import pycase
- 
-
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
-from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-from pye3sm.elm.grid.elm_extract_grid_latlon_from_mosart import elm_extract_grid_latlon_from_mosart
-def create_mosart_boundary(oE3SM_in, oCase_in):
-    
-    sModel  = oCase_in.sModel    
-    sRegion = oCase_in.sRegion
-    sWorkspace_data=  '/qfs/people/liao313/data'
-    dConversion = 1.0
-    sFilename_mosart_netcdf_out = '/qfs/people/liao313/data/e3sm/amazon/mosart/' + 'mosart_20220201040.nc'
-    aLongitude, aLatitude, aMask = elm_extract_grid_latlon_from_mosart(sFilename_mosart_netcdf_out)
-    nrow, ncolumn = aLongitude.shape
-
-
-    dLat_min=np.min(aLatitude)
-    dLat_max=np.max(aLatitude)
-    dLon_min=np.min(aLongitude)
-    dLon_max=np.max(aLongitude)
-
-    dResolution=(dLat_max-dLat_min)/(nrow-1)
-    dResolution1=(dLon_max-dLon_min)/(ncolumn-1)
-
-    ULlon = dLon_min - dResolution/2.0
-    ULlat = dLat_max + dResolution/2.0
-    aMask=np.reshape(aMask,(nrow,ncolumn))
-    aGrid_data = np.flip(aMask, 0)
-    aGrid_data[np.where(aGrid_data<0)]=-9999
-    #optional
-    iFlag_optional = 1
-    if(iFlag_optional ==1):
-        #save as envi
-        pHeaderParameters = {}
-        pHeaderParameters['ncolumn'] = ncolumn
-        pHeaderParameters['nrow'] = nrow
-        pHeaderParameters['ULlon'] = ULlon
-        pHeaderParameters['ULlat'] = ULlat
-        pHeaderParameters['pixelSize'] = dResolution
-        pHeaderParameters['nband'] = '1'
-        pHeaderParameters['offset'] = '0'
-        pHeaderParameters['data_type'] = '4'
-        pHeaderParameters['bsq'] = 'bsq'
-        pHeaderParameters['byte_order'] = '0'
-        pHeaderParameters['missing_value'] = '-9999'
-        
-        sFilename_envi = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-            + 'wtd' + slash  +  'mosart_05'  + sExtension_envi
-        pSpatial = osr.SpatialReference()
-        pSpatial.ImportFromEPSG(4326)
-        gdal_write_envi_file(sFilename_envi, aGrid_data, dResolution,float(pHeaderParameters['ULlon']),\
-              float(pHeaderParameters['ULlat']),\
-                  -9999.0, pSpatial   )
-
-        sFilename_tiff = sWorkspace_data + slash + sModel + slash + sRegion + slash + 'raster' + slash \
-            + 'wtd' + slash  +  'mosart_05'  + sExtension_tiff
-        gdal_write_geotiff_file(sFilename_tiff, aGrid_data,\
-        float(pHeaderParameters['pixelSize']),\
-         float(pHeaderParameters['ULlon']),\
-              float(pHeaderParameters['ULlat']),\
-                  -9999.0, pSpatial)
-
-    return
-
-if __name__ == '__main__':
-    
-    
-    sModel = 'h2sc'
-    sRegion ='global'
-    sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
-    sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
-    aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration )
-
-
-    aParameter_case = pye3sm_read_case_configuration_file(sFilename_case_configuration)
-    oE3SM = pye3sm(aParameter_e3sm)
-    oCase = pycase(aParameter_case)
-    create_mosart_boundary(oE3SM, oCase)
-
-    
-    print('finished')
\ No newline at end of file
diff --git a/pye3sm/tools/elm/wtd/map_observation_wtd.py b/pye3sm/tools/elm/wtd/map_observation_wtd.py
deleted file mode 100644
index 08d6608..0000000
--- a/pye3sm/tools/elm/wtd/map_observation_wtd.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import numpy as np
-from osgeo import gdal, osr #the default operator
-from pyearth.system.define_global_variables import *   
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file
-from pye3sm.elm.grid.elm_extract_grid_latlon_from_mosart import elm_extract_grid_latlon_from_mosart
-from pyearth.visual.map.map_raster_data import map_raster_data
-def map_observation_wtd():
-
-    #new wtd
-    sFilename_in = '/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract' + sExtension_tiff
-    
-    aData_out1, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection,  pSpatial_reference = gdal_read_geotiff_file(sFilename_in)
-
-    dLon_min = dOriginX
-    dResolution_x= dPixelWidth
-    dLon_max = dLon_min + ncolumn * dPixelWidth
-    dLat_min =  dOriginY - nrow * dPixelWidth
-    dLat_max= dOriginY
-
-    aImage_extent= [dLon_min ,dLon_max , dLat_min ,  dLat_max]
-
-    sFilename_out ='/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract' + sExtension_png
-    sTitle_in = 'Water table depth'
-    sUnit_in = 'Unit: m'
-    iFlag_scientific_notation_colorbar_in= 0 
-    dData_max_in =20
-    dData_min_in = 0
-    map_raster_data(aData_out1,  aImage_extent,\
-                              sFilename_out,\
-                                  sTitle_in = sTitle_in,\
-                                      sUnit_in=sUnit_in,\
-                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
-                                       dData_max_in = dData_max_in,\
-                                          dData_min_in = dData_min_in,
-                                  dMissing_value_in = -9999)
-    #new wtd
-    sFilename_in = '/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract_new' + sExtension_tiff
-    
-    aData_out2, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection,  pSpatial_reference = gdal_read_geotiff_file(sFilename_in)
-
-    dLon_min = dOriginX
-    dResolution_x= dPixelWidth
-    dLon_max = dLon_min + ncolumn * dPixelWidth
-    dLat_min =  dOriginY - nrow * dPixelWidth
-    dLat_max= dOriginY
-
-    aImage_extent= [dLon_min ,dLon_max , dLat_min ,  dLat_max]
-
-    sFilename_out ='/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract_new' + sExtension_png
-    sTitle_in = 'Water table depth'
-    sUnit_in = 'Unit: m'
-    iFlag_scientific_notation_colorbar_in= 0 
-    dData_max_in = 20
-    dData_min_in = 0
-    map_raster_data(aData_out2,  aImage_extent,\
-                              sFilename_out,\
-                                  sTitle_in = sTitle_in,\
-                                      sUnit_in=sUnit_in,\
-                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
-                                       dData_max_in = dData_max_in,\
-                                          dData_min_in = dData_min_in,
-                                  dMissing_value_in = -9999)
-
-    index = np.where(aData_out1 == -9999)
-    aData_out = aData_out1- aData_out2
-
-    aData_out[index]=-9999
-    sFilename_out ='/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract_diff' + sExtension_png
-    sTitle_in = 'Water table depth difference'
-    sUnit_in = 'Unit: m'
-    iFlag_scientific_notation_colorbar_in= 0 
-    dData_max_in = 5
-    dData_min_in = -5
-    map_raster_data(aData_out,  aImage_extent,\
-                              sFilename_out,\
-                                  sTitle_in = sTitle_in,\
-                                      sUnit_in=sUnit_in,\
-                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
-                                       dData_max_in = dData_max_in,\
-                                          dData_min_in = dData_min_in,
-                                  dMissing_value_in = -9999,\
-                                      sExtend_in='both')
-    
-    return
-
-if __name__ == '__main__':
-    map_observation_wtd()
\ No newline at end of file
diff --git a/pye3sm/tools/elm/wtd/prepare_water_table.py b/pye3sm/tools/elm/wtd/prepare_water_table.py
deleted file mode 100644
index 8871498..0000000
--- a/pye3sm/tools/elm/wtd/prepare_water_table.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import numpy as np
-from osgeo import gdal, osr #the default operator
-from pyearth.system.define_global_variables import *   
-from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pyearth.gis.gdal.write.gdal_write_geotiff_file import gdal_write_geotiff_file
-from pye3sm.elm.grid.elm_extract_grid_latlon_from_mosart import elm_extract_grid_latlon_from_mosart
-from pyearth.visual.map.map_raster_data import map_raster_data
-def prepare_water_table():
-
-    
-    nrow=360
-    ncolumn=720
-    pHeaderParameters = {}    
-    pHeaderParameters['ncolumn'] = "{:0d}".format(ncolumn)
-    pHeaderParameters['nrow'] = "{:0d}".format(nrow)
-    pHeaderParameters['ULlon'] = "{:0f}".format(-180.0)
-    pHeaderParameters['ULlat'] = "{:0f}".format(90.0)
-    pHeaderParameters['pixelSize'] = "{:0f}".format(0.5)
-    pHeaderParameters['nband'] = '1'
-    pHeaderParameters['offset'] = '0'
-    pHeaderParameters['data_type'] = '4'
-    pHeaderParameters['bsq'] = 'bsq'
-    pHeaderParameters['byte_order'] = '0'
-    pHeaderParameters['missing_value'] = '-9999'
-    sFilename_mosart_netcdf_out = '/qfs/people/liao313/data/e3sm/amazon/mosart/' + 'mosart_20220201040.nc'
-    aLon, aLat, aMask = elm_extract_grid_latlon_from_mosart(sFilename_mosart_netcdf_out)
-    nrow_extract, ncolumn_extract = aLon.shape
-
-    #flip data
-    aLon = np.flip(aLon, 0) 
-    aLat = np.flip(aLat, 0) 
-    aMask = np.flip(aMask, 0) 
-    dLon_min = np.min(aLon)
-    dLon_max = np.max(aLon)
-    dLat_min = np.min(aLat)
-    dLat_max = np.max(aLat)
-
-
-    sFilename_tiff = '/qfs/people/liao313/data/h2sc/global/raster/wtd/' + 'wtd_halfdegree' + sExtension_tiff
-    a = gdal_read_geotiff_file(sFilename_tiff)              
-    aData_out = a[0]
-    
-    aData_out_extract = np.full((nrow_extract, ncolumn_extract), -9999, dtype=float)
-    for i in range(nrow_extract):
-        for j in range(ncolumn_extract):
-            dLon = aLon[i,j]-0.25
-            dLat = aLat[i,j]+0.25
-            #locate it
-            iMask = aMask[i,j]
-            if iMask >=1:
-            
-                iIndex = int( (90-(dLat)) / 0.5 )
-                jIndex = int( (dLon-(-180)) / 0.5 )
-                aData_out_extract[i,j] = aData_out[iIndex, jIndex]
-
-    pSpatial = osr.SpatialReference()
-    pSpatial.ImportFromEPSG(4326)
-    sFilename_tiff = '/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract' + sExtension_tiff
-
-    gdal_write_geotiff_file(sFilename_tiff, aData_out_extract,\
-            float(pHeaderParameters['pixelSize']),\
-            np.min(aLon)-0.25,\
-            np.max(aLat)+0.25,\
-                  -9999.0, pSpatial)    
-    
-
-    aData_all = np.array(aData_out_extract)    
-    dResolution_x = (dLon_max - dLon_min) / (ncolumn-1)
-    dResolution_y = (dLat_max - dLat_min) / (nrow-1)
-    aImage_extent =  [dLon_min- dResolution_x ,dLon_max + dResolution_x, dLat_min -dResolution_x,  dLat_max+dResolution_x]
-
-    sFilename_out ='/qfs/people/liao313/data/e3sm/amazon/elm/' + 'wtd_extract' + sExtension_png
-    sTitle_in = 'Water table depth'
-    sUnit_in = 'Unit: m'
-    iFlag_scientific_notation_colorbar_in= 0 
-    dData_max_in =20
-    dData_min_in = 0
-    map_raster_data(aData_all,  aImage_extent,\
-                              sFilename_out,\
-                                  sTitle_in = sTitle_in,\
-                                      sUnit_in=sUnit_in,\
-                                  iFlag_scientific_notation_colorbar_in =  iFlag_scientific_notation_colorbar_in,\
-                                       dData_max_in = dData_max_in,\
-                                          dData_min_in = dData_min_in,
-                                  dMissing_value_in = -9999)
-    
-    return
-
-if __name__ == '__main__':
-    prepare_water_table()
\ No newline at end of file


Test patch:
diff --git a/tests/configurations/e3sm.xml b/tests/configurations/e3sm.xml
index f8890f0..0545f83 100644
--- a/tests/configurations/e3sm.xml
+++ b/tests/configurations/e3sm.xml
@@ -63,7 +63,7 @@
         The workspace of pest calibration RES='ne30_oEC'
       </desc>
     </entry>
-    <entry id="COMPSET" value="ICLM45">
+    <entry id="COMPSET" value="IELM45">
       <type>char</type>
       <desc>
         The workspace of pest calibration
diff --git a/tests/example/case/example_create_customized_elm_mosart_case.py b/tests/example/case/example_create_customized_elm_mosart_case.py
index f11ecf8..ac17be0 100644
--- a/tests/example/case/example_create_customized_elm_mosart_case.py
+++ b/tests/example/case/example_create_customized_elm_mosart_case.py
@@ -9,17 +9,17 @@
 from pyearth.gis.location.convert_lat_lon_range import convert_180_to_360
 from pyearth.toolbox.data.beta.add_variable_to_netcdf import add_multiple_variable_to_netcdf
 from pyearth.gis.gdal.read.gdal_read_geotiff_file import gdal_read_geotiff_file
-from pye3sm.elm.grid.create_customized_elm_domain import create_customized_elm_domain
+from pye3sm.elm.mesh.create_customized_elm_domain import create_customized_elm_domain
 
 from pye3sm.case.e3sm_create_case import e3sm_create_case
 from pye3sm.shared.e3sm import pye3sm
 from pye3sm.shared.case import pycase
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
-from pye3sm.mosart.grid.create_customized_mosart_domain import create_customized_mosart_domain
-from pye3sm.mosart.grid.structured.twod.extract_mosart_elevation_profile_for_elm import extract_mosart_elevation_profile_for_elm, extract_mosart_variable_for_elm
+from pye3sm.mosart.mesh.create_customized_mosart_domain import create_customized_mosart_domain
+from pye3sm.mosart.mesh.structured.twod.extract_mosart_elevation_profile_for_elm import extract_mosart_elevation_profile_for_elm, extract_mosart_variable_for_elm
 
-from pye3sm.elm.grid.elm_extract_grid_latlon_from_mosart import elm_extract_grid_latlon_from_mosart
+from pye3sm.elm.mesh.elm_extract_grid_latlon_from_mosart import elm_extract_grid_latlon_from_mosart
 sModel = 'e3sm'
 sRegion = 'site'
 sRegion = 'sag'
@@ -31,7 +31,7 @@
 #dLongitude =  -60
 #dLatitude =  -11
 sRegion ='amazon'
-iCase = 56
+iCase = 63
 iFlag_replace_datm_forcing=0
 iFlag_replace_dlnd_forcing=0
 
@@ -50,14 +50,13 @@
         iFlag_elmmosart = 0        
 else:
     iFlag_elmmosart = 0
-    
 
 
 iFlag_2d_to_1d = 0 
 iFlag_create_case = 1 
 iFlag_submit_case = 0
 
-iFlag_default = 0
+iFlag_default = 1
 iFlag_debug = 0 #is this a debug run
 iFlag_branch = 0
 iFlag_initial = 1 #use restart file as initial
@@ -134,9 +133,11 @@
 
 sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
 sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
-sCIME_directory ='/qfs/people/liao313/workspace/fortran/e3sm/E3SM/cime/scripts'
-sCIME_directory ='/qfs/people/liao313/workspace/fortran/e3sm/E3SM_H2SC/cime/scripts'
-sFilename_configuration = '/people/liao313/workspace/python/pye3sm/pye3sm/elm/grid/elm_sparse_grid.cfg'
+if iFlag_default ==1:
+    sCIME_directory ='/qfs/people/liao313/workspace/fortran/e3sm/E3SM/cime/scripts'
+else:
+    sCIME_directory ='/qfs/people/liao313/workspace/fortran/e3sm/E3SM_H2SC/cime/scripts'
+sFilename_configuration = '/people/liao313/workspace/python/pye3sm/pye3sm/elm/mesh/elm_sparse_grid.cfg'
 
 
 
@@ -445,7 +446,7 @@
                 ofs.write(sLine)
                 sLine = 'hist_empty_htapes = .true.' + '\n'
                 ofs.write(sLine)
-                sLine = "hist_fincl1 = 'QOVER', 'QDRAI', 'QRUNOFF', 'ZWT', 'QCHARGE','hk_sat','anisotropy','sur_elev','sur_slp','wt_slp','gage_height' "  + '\n'
+                sLine = "hist_fincl1 = 'QOVER', 'QDRAI', 'QRUNOFF', 'ZWT', 'QCHARGE','hk_sat','anisotropy','sur_elev','sur_slp','wt_slp','gage_height', 'RAIN','SNOW','QSOIL', 'QVEGE','QVEGT' "  + '\n'
                 ofs.write(sLine)
 
             #this is a case that use existing restart file
@@ -473,6 +474,9 @@
         ofs.write(sLine)
         sLine = 'inundflag = .false.'+ '\n'
         ofs.write(sLine)
+        sLine = 'rtmhist_nhtfrq = -24,-24'+ '\n'
+        ofs.write(sLine)
+        
         #opt_elevprof = 1
         ofs.close()
 
diff --git a/tests/example/case/example_e3sm_create_batch_case.py b/tests/example/case/example_e3sm_create_batch_case.py
index 6c7c703..d08d503 100644
--- a/tests/example/case/example_e3sm_create_batch_case.py
+++ b/tests/example/case/example_e3sm_create_batch_case.py
@@ -53,9 +53,9 @@
     
     
     sCase =  sModel + sDate+ "{:03d}".format(iCase)
-    sFilename_clm_namelist = sWorkspace_scratch + slash + '04model' + slash \
+    sFilename_elm_namelist = sWorkspace_scratch + slash + '04model' + slash \
         + sModel + slash + sRegion + slash \
-        + 'cases' + slash + 'user_nl_clm_' + sCase
+        + 'cases' + slash + 'user_nl_elm_' + sCase
 
     sFilename_datm_namelist = sWorkspace_scratch + slash + '04model' + slash \
         + sModel + slash + sRegion + slash \
@@ -64,7 +64,7 @@
 
     if (iFlag_initial !=1):
         #normal case,      
-        ofs = open(sFilename_clm_namelist, 'w')
+        ofs = open(sFilename_elm_namelist, 'w')
         sCommand_out = "fsurdat = " + "'" \
             + '/compyfs/inputdata/lnd/clm2/surfdata_map/surfdata_0.5x0.5_simyr2010_c191025_log10.nc' + "'" + '\n'
         ofs.write(sCommand_out)
@@ -74,7 +74,7 @@
         ofs.write(sLine)
         ofs.close()
     else:
-        ofs = open(sFilename_clm_namelist, 'w')
+        ofs = open(sFilename_elm_namelist, 'w')
         sCommand_out = "fsurdat = " + "'" \
             + '/compyfs/inputdata/lnd/clm2/surfdata_map/surfdata_0.5x0.5_simyr2010_c191025_log10.nc' + "'" + '\n'
         ofs.write(sCommand_out)
@@ -113,7 +113,7 @@
                                                               iYear_data_start_in = 1979   ,\
                                                               iCase_index_in = iCase, \
                                                               sDate_in = sDate, \
-                                                              sFilename_clm_namelist_in = sFilename_clm_namelist, \
+                                                              sFilename_elm_namelist_in = sFilename_elm_namelist, \
                                                               sFilename_datm_namelist_in = sFilename_datm_namelist )
         
     else:
@@ -125,7 +125,7 @@
                                                               iYear_data_start_in = 1979   , \
                                                               iCase_index_in = iCase, \
                                                               sDate_in = sDate, \
-                                                              sFilename_clm_namelist_in = sFilename_clm_namelist )
+                                                              sFilename_elm_namelist_in = sFilename_elm_namelist )
     
     oCase = pycase(aParameter_case)
     e3sm_create_case(oE3SM, oCase )
diff --git a/tests/example/elm/postprocess/twod/example_elm_slope_effect_2d.py b/tests/example/elm/postprocess/twod/example_elm_slope_effect_2d.py
index d72f4c1..2408bc3 100644
--- a/tests/example/elm/postprocess/twod/example_elm_slope_effect_2d.py
+++ b/tests/example/elm/postprocess/twod/example_elm_slope_effect_2d.py
@@ -4,7 +4,7 @@
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
 
-from pye3sm.elm.general.structured.twod.stats.elm_calculate_slope_effect_2d import elm_calculate_slope_effect_2d
+from pye3sm.elm.general.structured.twod.stats.elm_calculate_slope_band_effect_2d import elm_calculate_slope_effect_2d
 sModel = 'e3sm'
 sRegion ='amazon'
 sDate = '20220410'
diff --git a/tests/example/elm/postprocess/twod/example_elm_map_forcing_data.py b/tests/example/elm/postprocess/twod/map/example_elm_map_forcing_data.py
similarity index 99%
rename from tests/example/elm/postprocess/twod/example_elm_map_forcing_data.py
rename to tests/example/elm/postprocess/twod/map/example_elm_map_forcing_data.py
index bc91519..e2f58b2 100644
--- a/tests/example/elm/postprocess/twod/example_elm_map_forcing_data.py
+++ b/tests/example/elm/postprocess/twod/map/example_elm_map_forcing_data.py
@@ -6,7 +6,6 @@
 import numpy as np
 import multiprocessing
 
-
 from pyearth.system.define_global_variables import *
  
 from pye3sm.shared.e3sm import pye3sm
diff --git a/tests/example/elm/postprocess/twod/example_elm_map_variable_2d.py b/tests/example/elm/postprocess/twod/map/example_elm_map_variable_2d.py
similarity index 98%
rename from tests/example/elm/postprocess/twod/example_elm_map_variable_2d.py
rename to tests/example/elm/postprocess/twod/map/example_elm_map_variable_2d.py
index 5ccd10b..894e1a2 100644
--- a/tests/example/elm/postprocess/twod/example_elm_map_variable_2d.py
+++ b/tests/example/elm/postprocess/twod/map/example_elm_map_variable_2d.py
@@ -32,7 +32,7 @@
 sTitle = r'Water table depth'
 #sTitle = r'Perched water table depth (m)'
 #sTitle=r'Overland runoff'
-#sTitle=r'Subsurface runoff'
+sTitle=r'Subsurface runoff'
 
 sUnit = r'Units: mm/s'
 sUnit = r'Unit: m'
diff --git a/tests/example/elm/postprocess/twod/map/example_elm_map_variable_2d_batch.py b/tests/example/elm/postprocess/twod/map/example_elm_map_variable_2d_batch.py
new file mode 100644
index 0000000..f9ac738
--- /dev/null
+++ b/tests/example/elm/postprocess/twod/map/example_elm_map_variable_2d_batch.py
@@ -0,0 +1,127 @@
+
+import numpy as np
+from pyearth.system.define_global_variables import *
+ 
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.elm.general.structured.twod.map.elm_map_variable_2d import elm_map_variable_2d
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+
+sDate = '20220701'
+
+
+iFlag_debug = 1
+
+iIndex_start = 59
+iIndex_end = 59
+
+
+#start loop
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+
+iFlag_scientific_notation_colorbar_in = 0
+iYear_start = 2000
+iYear_end = 2009
+sModel = 'e3sm'
+sRegion='amazon'
+
+aVariable = ['ZWT','QDRAI','QOVER','QRUNOFF']
+aFlag_scientific_notation_colorbar=[0,1,1,1]
+
+
+
+aTitle= [ 'Water table depth','Subsurface runoff','Overland runoff','Total runoff' ]
+aUnit = [r'Unit: m',r'Units: mm/s',r'Units: mm/s',r'Units: mm/s']
+aData_min = [0,0,0,0]
+aData_max = [25, 1E-5 ,1E-5,2E-5]
+aConversion = [1,1,1,1]
+aFlag_monthly = [0,0,0,0]
+aFlag_annual_mean = [1, 0,0,0]
+aFlag_annual_total = [0, 1,1,1]
+aColormap= [ 'rainbow','gist_rainbow','gist_rainbow','gist_rainbow' ]
+aColormap= [ 'Spectral_r','Spectral','Spectral','Spectral' ]
+
+#for tws analysis
+aVariable = ['RAIN','SNOW','QSOIL', 'QVEGE','QVEGT']
+aFlag_scientific_notation_colorbar=[1,1,1,1, 1]
+aTitle= [ 'Rain','Snow','Soil evaporation','Vegetation evaporation','Vegetation Transpiration' ]
+aUnit = [r'Units: mm/s',r'Units: mm/s',r'Units: mm/s',r'Units: mm/s', r'Units: mm/s']
+aData_min = [0,0,0,0, 0]
+aData_max = [None, None ,None,None, None]
+aConversion = [1,1,1,1,1]
+aFlag_monthly = [1,1,1,1,1]
+aFlag_annual_mean = [0,0, 0,0,0]
+aFlag_annual_total = [0, 0,0,0,0]
+aColormap= [ 'rainbow','gist_rainbow','gist_rainbow','gist_rainbow' ]
+aColormap= [ 'Spectral_r','Spectral','Spectral','Spectral', 'Spectral' ]
+
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+aCase_index = np.array([59, 61])
+nvariable = len(aVariable)
+
+ncase = len(aCase_index)
+aText1=['Default ELM', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG']
+aText2=['None', 'dynamic', 'dynamic (surface slope x 10)', 'constant (surface slope)', 'dynamic', 'dynamic', 'dynamic', 'constant (surface slope)', 'dynamic', 'dynamic']
+aText3=['None', 'dynamic', 'dynamic','constant (1.0)',  'dynamic', 'dynamic',  'dynamic','dynamic', 'dynamic', 'dynamic']
+aText4=['None', 'dynamic', 'dynamic','dynamic', 'dynamic', 'constant (1.0 m)','constant (10.0 m)','constant (10.0 m)', 'dynamic', 'dynamic']
+
+for i in range(ncase):
+    iCase_index = aCase_index[i]
+    for iVariable in np.arange(2, 5):
+        sVariable = aVariable[iVariable]
+        sUnit = aUnit[iVariable]
+        sTitle = aTitle[iVariable]
+        dData_min = aData_min[iVariable]
+        dData_max = aData_max[iVariable]
+        #dData_max = None
+        dConversion = aConversion[iVariable]
+        iFlag_monthly = aFlag_monthly[iVariable]
+        iFlag_annual_mean = aFlag_annual_mean[iVariable]
+        iFlag_annual_total = aFlag_annual_total[iVariable]
+        sColormap = aColormap[iVariable]
+        iFlag_scientific_notation_colorbar = aFlag_scientific_notation_colorbar[iVariable]
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                            dConversion_in= dConversion,\
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+
+        oCase = pycase(aParameter_case)
+
+        aLegend = list()
+        sCase = "{:0d}".format(iCase_index -50 )
+        sText = 'Case ' + sCase 
+        aLegend.append(sText)
+
+        aLegend.append( 'Water table slope: ' + aText2[iCase_index -51])
+        aLegend.append( 'Anisotropy ratio: ' + aText3[iCase_index  -51])
+        aLegend.append( 'River gage height: ' + aText4[iCase_index -51])
+        
+
+        elm_map_variable_2d(oE3SM, oCase ,  dData_min_in=dData_min, dData_max_in=dData_max,\
+            iFlag_scientific_notation_colorbar_in = iFlag_scientific_notation_colorbar, \
+                iFlag_monthly_in = iFlag_monthly,\
+                    iFlag_annual_mean_in= iFlag_annual_mean,\
+                          iFlag_annual_total_in= iFlag_annual_total,\
+            sUnit_in = sUnit,\
+                sColormap_in = sColormap,\
+            sTitle_in=  sTitle,\
+                aLegend_in= aLegend )
+        pass
+
+print('finished')
diff --git a/tests/example/elm/postprocess/twod/plot/example_elm_histogram_variable_2d.py b/tests/example/elm/postprocess/twod/plot/example_elm_histogram_variable_2d.py
new file mode 100644
index 0000000..5bceb37
--- /dev/null
+++ b/tests/example/elm/postprocess/twod/plot/example_elm_histogram_variable_2d.py
@@ -0,0 +1,95 @@
+
+import numpy as np
+from pyearth.system.define_global_variables import *
+ 
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.elm.general.structured.twod.plot.elm_histogram_variable_2d import elm_histogram_variable_2d
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+sDate = '20220701'
+iIndex_start = 51
+iIndex_end = 58
+
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+
+ncase = len(aCase_index)
+
+iYear_start = 2000
+iYear_end = 2009
+sModel = 'e3sm'
+sRegion='amazon'
+aVariable = ['ZWT','QDRAI','QOVER','QRUNOFF']
+
+aLabel_x =[ r'Water table depth (m)', r'Subsurface runoff (mm/s)',\
+  r'Overland runoff (mm/s)',r'Total runoff (mm/s)'  ]
+
+aConversion = [1,1,1,1]
+aFlag_log = [0,1,1,1]
+aFlag_scientific_notation_colorbar=[0,1,1,1]
+
+iFlag_scientific_notation=0
+aFlag_monthly = [0,0,0,0]
+aFlag_annual_mean = [1, 0,0,0]
+aFlag_annual_total = [0, 1,1,1]
+
+aData_min = [0,-8,-8, -8]
+aData_max = [10, -4 ,-4,-4]
+
+aFormat_x = ['{:.1f}','{:.0f}','{:.0f}','{:.0f}']
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+for i in range(ncase):
+    iCase_index = aCase_index[i]
+    for iVariable in np.arange(1, 2):
+        sVariable = aVariable[iVariable]
+        
+        iFlag_log = aFlag_log[iVariable]
+        iFlag_scientific_notation_colorbar = aFlag_scientific_notation_colorbar[iVariable]
+        iFlag_monthly = aFlag_monthly[iVariable]
+        iFlag_annual_mean = aFlag_annual_mean[iVariable]
+        iFlag_annual_total = aFlag_annual_total[iVariable]
+
+        dData_min = aData_min[iVariable]
+        dData_max = aData_max[iVariable]
+
+        sLabel_x = aLabel_x[iVariable]
+        sFormat_x = aFormat_x[iVariable]
+        aParameter_case = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+
+        oCase = pycase(aParameter_case)
+
+        aLegend=list()
+        sCase = "{:0d}".format(iCase_index -50 )
+        sText = 'Case ' + sCase 
+        aLegend.append(sText)
+
+        elm_histogram_variable_2d(oE3SM, oCase,  \
+           iFlag_monthly_in= iFlag_monthly,\
+            iFlag_annual_mean_in=iFlag_annual_mean,\
+              iFlag_annual_total_in=iFlag_annual_total,\
+            dMin_x_in= dData_min,\
+            dMax_x_in = dData_max,\
+            iFlag_log_in= iFlag_log,\
+            iFlag_scientific_notation_in=iFlag_scientific_notation,\
+                sFormat_x_in= sFormat_x,\
+                                                       sLabel_x_in=sLabel_x, \
+                                                        sLabel_y_in = 'Frequency', aLegend_in = aLegend )
+print('finished')
diff --git a/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_2d_multi_case.py b/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_2d_multi_case.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_x_2d.py b/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_x_2d.py
new file mode 100644
index 0000000..9a81731
--- /dev/null
+++ b/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_x_2d.py
@@ -0,0 +1,131 @@
+
+
+import os, sys
+import argparse
+import subprocess
+import numpy as np
+import multiprocessing
+
+from pyearth.system.define_global_variables import *
+ 
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.elm.general.structured.twod.plot.elm_scatterplot_variables_2d import elm_scatterplot_variables_2d
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+
+sDate = '20220701'
+
+iIndex_start = 51
+iIndex_end = 58
+
+
+#start loop
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+
+aVariable = ['QDRAI','ZWT','QOVER','QRUNOFF']
+
+aFlag_scientific_notation_y=[0,0,0,0]
+iYear_start = 2000
+iYear_end = 2009
+sModel = 'e3sm'
+sRegion='amazon'
+
+aTitle= ['Subsurface runoff' , 'Water table depth','Overland runoff','Total runoff' ]
+
+aUnit = [r'Units: mm/s', r'Unit: m',r'Units: mm/s',r'Units: mm/s']
+
+aData_min = [-8,0,-8,-8]
+aData_max = [None ,10, None,None]
+
+aSpace_y = [1,2,1,1]
+aFlag_log_y = [1,0,1,1]
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+nvariable = len(aVariable)
+
+dMin_x = 0
+dMax_x= 20
+dSpace_x = 2
+               
+for iCase_index in (aCase_index):
+    sCase_index =  "{:0d}".format( iCase_index - 50)
+    for iVariable in np.arange(0, nvariable):
+        if iCase_index == 51:
+            iCase_index_x = 52
+        else:
+            iCase_index_x = iCase_index
+        iCase_index_y = iCase_index
+        #the x variable, by default, it is the surface slope
+        sVariable = 'sur_slp'
+        sLabel_x = r'Surface slope (percent)'
+        dConversion = 100
+        iFlag_scientific_notation_x = 0
+        iFlag_log_x = 0
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index_x ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                        dConversion_in= dConversion,\
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+        oCase_x_in = pycase(aParameter_case)
+        #the y variable
+        sVariable = aVariable[iVariable]
+        sUnit = aUnit[iVariable]
+        
+        dMin_y = aData_min[iVariable]
+        dMax_y = aData_max[iVariable]
+        iFlag_scientific_notation_y = aFlag_scientific_notation_y[iVariable]
+               
+        sLabel_y = aTitle[iVariable] + ' (' +  sUnit + ')'
+
+        dSpace_y = aSpace_y[iVariable]
+        iFlag_log_y = aFlag_log_y[iVariable]
+        
+        dConversion = 1.0
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index_y ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                            dConversion_in= dConversion,\
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+        oCase_y_in = pycase(aParameter_case)
+
+        sLabel_legend = 'Case ' + sCase_index        
+
+        elm_scatterplot_variables_2d(oE3SM,\
+                                         oCase_x_in,\
+                                         oCase_y_in, \
+                                         iFlag_scientific_notation_x_in=iFlag_scientific_notation_x,\
+                                         iFlag_scientific_notation_y_in=iFlag_scientific_notation_y,\
+                                          iFlag_log_x_in= iFlag_log_x,\
+                                        iFlag_log_y_in = iFlag_log_y,\
+                                         dMin_x_in = dMin_x, \
+                                         dMax_x_in = dMax_x, \
+                                         dMin_y_in = dMin_y, \
+                                         dMax_y_in = dMax_y, \
+                                         dSpace_x_in = dSpace_x, \
+                                         dSpace_y_in = dSpace_y, \
+                                         sLabel_x_in = sLabel_x, \
+                                         sLabel_y_in = sLabel_y,\
+                                         sLabel_legend_in = sLabel_legend )
+        pass
+
+print('finished')
diff --git a/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_y_2d.py b/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_y_2d.py
new file mode 100644
index 0000000..513ace0
--- /dev/null
+++ b/tests/example/elm/postprocess/twod/plot/example_elm_scatterplot_variable_y_2d.py
@@ -0,0 +1,137 @@
+
+
+import os, sys
+import argparse
+import subprocess
+import numpy as np
+
+
+from pyearth.system.define_global_variables import *
+ 
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.elm.general.structured.twod.plot.elm_scatterplot_variables_2d import elm_scatterplot_variables_2d
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+
+sDate = '20220701'
+
+iIndex_start = 59
+iIndex_end = 59
+
+
+#start loop
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+
+
+
+aVariable = ['QDRAI','ZWT','QOVER','QRUNOFF']
+
+aFlag_scientific_notation_y=[0,0,0,0]
+iYear_start = 2000
+iYear_end = 2009
+sModel = 'e3sm'
+sRegion='amazon'
+
+aTitle= ['Subsurface runoff' , 'Water table depth','Overland runoff','Total runoff' ]
+
+aUnit = [r'mm/s', r'm',r'mm/s',r'mm/s']
+
+aFormat_y = ['{:.0f}','{:.1f}','{:.0f}','{:.0f}']
+
+aData_min = [-8,0,-8,-8]
+aData_max = [None ,10, None,None]
+
+aSpace_y = [1,2,1,1]
+aFlag_log_y = [1,0,1,1]
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+nvariable = len(aVariable)
+
+dMin_x = 0
+dMax_x= 20
+dSpace_x = 2
+               
+for iCase_index in (aCase_index):
+    sCase_index =  "{:0d}".format( iCase_index - 50)
+    for iVariable in np.arange(0, 2):
+        if iCase_index == 51:
+            iCase_index_x = 52
+        else:
+            iCase_index_x = iCase_index
+        iCase_index_y = iCase_index
+        #the x variable, by default, it is the surface slope
+        sVariable = 'sur_slp'
+        sLabel_x = r'Surface slope (percent)'
+        dConversion = 100
+        iFlag_scientific_notation_x = 0
+        iFlag_log_x = 0
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index_x ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                        dConversion_in= dConversion,\
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+        oCase_x_in = pycase(aParameter_case)
+        #the y variable
+        sVariable = aVariable[iVariable]
+        sUnit = aUnit[iVariable]
+      
+        dMin_y = aData_min[iVariable]
+        dMax_y = aData_max[iVariable]
+        iFlag_scientific_notation_y = aFlag_scientific_notation_y[iVariable]
+               
+        sLabel_y = aTitle[iVariable] + ' (' +  sUnit + ')'
+        sFormat_y = aFormat_y[iVariable]
+
+        dSpace_y = aSpace_y[iVariable]
+        iFlag_log_y = aFlag_log_y[iVariable]
+        
+        dConversion = 1.0
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index_y ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                            dConversion_in= dConversion,\
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+        oCase_y_in = pycase(aParameter_case)
+
+        sLabel_legend = 'Case ' + sCase_index        
+
+        elm_scatterplot_variables_2d(oE3SM,\
+                                         oCase_x_in,\
+                                         oCase_y_in, \
+                                         iFlag_scientific_notation_x_in=iFlag_scientific_notation_x,\
+                                         iFlag_scientific_notation_y_in=iFlag_scientific_notation_y,\
+                                          iFlag_log_x_in= iFlag_log_x,\
+                                        iFlag_log_y_in = iFlag_log_y,\
+                                         dMin_x_in = dMin_x, \
+                                         dMax_x_in = dMax_x, \
+                                         dMin_y_in = dMin_y, \
+                                         dMax_y_in = dMax_y, \
+                                         dSpace_x_in = dSpace_x, \
+                                         dSpace_y_in = dSpace_y, \
+                                         sLabel_x_in = sLabel_x, \
+                                         sLabel_y_in = sLabel_y,\
+                                            sFormat_y_in=sFormat_y,\
+                                         sLabel_legend_in = sLabel_legend )
+        pass
+
+print('finished')
diff --git a/tests/example/elm/postprocess/twod/example_elm_tsplot_forcing_2d_singlegrid.py b/tests/example/elm/postprocess/twod/plot/example_elm_tsplot_forcing_2d_singlegrid.py
similarity index 100%
rename from tests/example/elm/postprocess/twod/example_elm_tsplot_forcing_2d_singlegrid.py
rename to tests/example/elm/postprocess/twod/plot/example_elm_tsplot_forcing_2d_singlegrid.py
diff --git a/tests/example/elm/postprocess/twod/plot/example_elm_tsplot_variable_2d.py b/tests/example/elm/postprocess/twod/plot/example_elm_tsplot_variable_2d.py
new file mode 100644
index 0000000..c3bfdb3
--- /dev/null
+++ b/tests/example/elm/postprocess/twod/plot/example_elm_tsplot_variable_2d.py
@@ -0,0 +1,128 @@
+
+import numpy as np
+
+from pyearth.system.define_global_variables import *
+from pyearth.visual.color.create_diverge_rgb_color_hex import create_diverge_rgb_color_hex
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.elm.general.structured.twod.plot.elm_tsplot_variable_2d_w_variation import elm_tsplot_variable_2d_w_variation
+from pye3sm.elm.general.structured.twod.plot.elm_ts_analysis_plot_variable_2d import elm_ts_analysis_plot_variable_2d
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+sDate = '20220701'
+iIndex_start = 51
+iIndex_end = 58
+
+#start loop
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+
+ncase = len(aCase_index)
+
+iYear_start = 2000
+iYear_end = 2009
+sModel = 'e3sm'
+sRegion='amazon'
+iFlag_uq =1
+iFlag_tsa =1
+
+aVariable = ['ZWT','QDRAI','QOVER','QRUNOFF']
+aLabel_y =[ r'Water table depth (m)', r'Subsurface runoff (mm/s)',\
+  r'Overland runoff (mm/s)',r'Total runoff (mm/s)'  ]
+
+aReverse_y=[1,0,0,0]
+
+aConversion = [1,1,1,1]
+aFlag_log = [0,1,1,1]
+aFlag_scientific_notation_colorbar=[0,1,1,1]
+
+iFlag_scientific_notation=0
+aFlag_monthly = [0,1,1,1]
+aFlag_annual_mean = [1, 0,0,0]
+aFlag_annual_total = [0, 0,0,0]
+aFlag_median=[1,1,1,1]
+
+aData_min = [0,-7,-7, -7]
+aData_max = [10, -4 ,-4,-4]
+
+aFormat_y = ['{:.1f}','{:.0f}','{:.0f}','{:.0f}']
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+
+sTitle=''
+
+aColoar = create_diverge_rgb_color_hex(8)
+for i in range(0,ncase):
+    iCase_index = aCase_index[i]
+    for iVariable in np.arange(0, 2):
+        sVariable = aVariable[iVariable]
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                           iCase_index_in =  iCase_index ,\
+                                                           iYear_start_in = iYear_start, \
+                                                           iYear_end_in = iYear_end,\
+                                                            iYear_subset_start_in = iYear_start, \
+                                                             iYear_subset_end_in = iYear_end, \
+                                                           sDate_in= sDate,\
+                                                           sModel_in = sModel, \
+                                                               sRegion_in=sRegion,\
+                                                           sVariable_in = sVariable )
+
+        oCase = pycase(aParameter_case)
+       
+        iReverse_y = aReverse_y[iVariable]
+        iFlag_median = aFlag_median[iVariable]
+        iFlag_log = aFlag_log[iVariable]
+        iFlag_scientific_notation_colorbar = aFlag_scientific_notation_colorbar[iVariable]
+        iFlag_monthly = aFlag_monthly[iVariable]
+        iFlag_annual_mean = aFlag_annual_mean[iVariable]
+        iFlag_annual_total = aFlag_annual_total[iVariable]
+
+        dData_min = aData_min[iVariable]
+        dData_max = aData_max[iVariable]
+
+        sLabel_y = aLabel_y[iVariable]
+        sFormat_y = aFormat_y[iVariable]
+
+        aLegend=list()
+        sCase = "{:0d}".format(iCase_index -50 )
+        sText = 'Case ' + sCase 
+        aLegend.append(sText)
+
+        if iFlag_uq ==1:
+            elm_tsplot_variable_2d_w_variation(oE3SM, oCase,  \
+                                              iReverse_y_in= iReverse_y,\
+                                             iFlag_monthly_in= iFlag_monthly,\
+                                            iFlag_annual_mean_in=iFlag_annual_mean,\
+                                            iFlag_annual_total_in=iFlag_annual_total,\
+                                                iFlag_median_in = iFlag_median,\
+                                              iFlag_log_in= iFlag_log,\
+                                              iFlag_scientific_notation_in=0,\
+                                                dMax_y_in = dData_max,\
+                                              dMin_y_in =dData_min,\
+                                              sFormat_y_in = sFormat_y,\
+                                              sLabel_y_in=sLabel_y ,\
+                                                  aLegend_in=aLegend)
+        if iFlag_tsa ==1:
+            elm_ts_analysis_plot_variable_2d(oE3SM, oCase,  \
+                                            iReverse_y_in= iReverse_y,\
+                                             iFlag_monthly_in= iFlag_monthly,\
+                                            iFlag_annual_mean_in=iFlag_annual_mean,\
+                                            iFlag_annual_total_in=iFlag_annual_total,\
+                                                 iFlag_median_in = iFlag_median,\
+                                            iFlag_log_in= iFlag_log,\
+                                            iFlag_scientific_notation_in=0,\
+                                            dMin_y_in =dData_min,\
+                                            dMax_y_in = dData_max,\
+                                            sFormat_y_in = sFormat_y,\
+                                            sLabel_y_in=sLabel_y ,\
+                                              sTitle_in=sTitle,\
+                                            aLabel_legend_in=aLegend)
+
+print('finished')
diff --git a/tests/example/elm/postprocess/twod/example_elm_tsplot_variable_2d_singlegrid.py b/tests/example/elm/postprocess/twod/plot/example_elm_tsplot_variable_2d_singlegrid.py
similarity index 100%
rename from tests/example/elm/postprocess/twod/example_elm_tsplot_variable_2d_singlegrid.py
rename to tests/example/elm/postprocess/twod/plot/example_elm_tsplot_variable_2d_singlegrid.py
diff --git a/tests/example/elm/postprocess/twod/example_tsplot_forcing_2d_singlegrid.py b/tests/example/elm/postprocess/twod/plot/example_tsplot_forcing_2d_singlegrid.py
similarity index 100%
rename from tests/example/elm/postprocess/twod/example_tsplot_forcing_2d_singlegrid.py
rename to tests/example/elm/postprocess/twod/plot/example_tsplot_forcing_2d_singlegrid.py
diff --git a/tests/example/elm/postprocess/twod/plot/example_tsplot_total_water_storage_2d.py b/tests/example/elm/postprocess/twod/plot/example_tsplot_total_water_storage_2d.py
new file mode 100644
index 0000000..92bff95
--- /dev/null
+++ b/tests/example/elm/postprocess/twod/plot/example_tsplot_total_water_storage_2d.py
@@ -0,0 +1,59 @@
+#this script should be run using Python 2.7.8 instead of Python 3
+#module load python/2.7.8
+#maybe I was wrong? 20200305 Chang Liao (chang.liao@pnnl.gov)
+
+import os, sys
+
+import numpy as np
+
+from pyearth.system.define_global_variables import * 
+
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+from pye3sm.elm.general.structured.twod.plot.elm_tsplot_total_water_storage_2d import elm_tsplot_total_water_storage_2d
+
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+
+
+
+iFlag_same_grid = 1
+sModel = 'e3sm'
+sRegion = 'amazon'
+sDate = '20220701'
+
+
+iYear_start = 2000
+iYear_end = 2009
+dConversion = 1.0
+#iCase_index_start = iIndex_start
+##iCase_index_end = iIndex_end
+#aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+
+aCase_index = np.array([59, 61])
+#iCase_index = 240
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+
+
+for iCase_index in (aCase_index):
+    
+    aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = 2004, \
+                                                         iYear_subset_end_in = 2008, \
+                                                        dConversion_in= dConversion,\
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion)
+    oCase = pycase(aParameter_case)
+    elm_tsplot_total_water_storage_2d(oE3SM, oCase )
+print('finished')
diff --git a/tests/example/elm/postprocess/twod/example_elm_save_forcing_data_2d.py b/tests/example/elm/postprocess/twod/save/example_elm_save_forcing_data_2d.py
similarity index 100%
rename from tests/example/elm/postprocess/twod/example_elm_save_forcing_data_2d.py
rename to tests/example/elm/postprocess/twod/save/example_elm_save_forcing_data_2d.py
diff --git a/tests/example/elm/postprocess/twod/example_elm_save_variable_2d.py b/tests/example/elm/postprocess/twod/save/example_elm_save_variable_2d.py
similarity index 100%
rename from tests/example/elm/postprocess/twod/example_elm_save_variable_2d.py
rename to tests/example/elm/postprocess/twod/save/example_elm_save_variable_2d.py
diff --git a/tests/example/elm/postprocess/twod/example_elm_save_variable_2d_batch.py b/tests/example/elm/postprocess/twod/save/example_elm_save_variable_2d_batch.py
similarity index 80%
rename from tests/example/elm/postprocess/twod/example_elm_save_variable_2d_batch.py
rename to tests/example/elm/postprocess/twod/save/example_elm_save_variable_2d_batch.py
index 087958b..3eef6ee 100644
--- a/tests/example/elm/postprocess/twod/example_elm_save_variable_2d_batch.py
+++ b/tests/example/elm/postprocess/twod/save/example_elm_save_variable_2d_batch.py
@@ -15,27 +15,22 @@
 from pye3sm.elm.general.structured.twod.save.elm_save_variable_2d import elm_save_variable_2d
 
 iFlag_debug = 1
-if iFlag_debug == 1:
-    iIndex_start = 51
-    iIndex_end = 56
-else:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--iIndex_start", help = "the path",   type = int)
-    parser.add_argument("--iIndex_end", help = "the path",   type = int)
-    pArgs = parser.parse_args()
-    iIndex_start = pArgs.iIndex_start
-    iIndex_end = pArgs.iIndex_end
+
+iIndex_start = 61
+iIndex_end = 61
+
 
 sModel = 'e3sm'
 sRegion ='amazon'
 sDate = '20220701'
 
 aVariable = ['ZWT','QOVER','QRUNOFF','QDRAI','QCHARGE']
+#aVariable = ['QRUNOFF']
 #aVariable = ['hk_sat','anisotropy']
 #aVariable = ['wt_slp']#,'TWS_MONTH_BEGIN','TWS_MONTH_END']
 
-#aVariable = ['RAIN','SNOW','QSOIL', 'QVEGE','QVEGT', 'QOVER','QDRAI','QCHARGE', \
-  #  'wt_slp','sur_slp','ZWT']
+aVariable = ['RAIN','SNOW','QSOIL', 'QVEGE','QVEGT']
+#aVariable = [ 'sur_slp','wt_slp']
 nvariable = len(aVariable)
 #start loop
 iCase_index_start = iIndex_start
@@ -43,7 +38,7 @@
 iYear_start = 2000
 iYear_end = 2009
 aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-#aCase_index = np.array([2,4,8,16])
+
 
 sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
 sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
@@ -51,8 +46,11 @@
 sWorkspace_scratch = '/compyfs/liao313/'
 
 oE3SM = pye3sm(aParameter_e3sm)
+ncase = len(aCase_index)
+
+for i in range(ncase):
+    iCase_index = aCase_index[i]
 
-for iCase_index in (aCase_index):
     for iVariable in np.arange(nvariable):
         sVariable = aVariable[iVariable]
         aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
diff --git a/tests/example/mosart/evaluation/example_mosart_evaluate_stream_discharge_gsim.py b/tests/example/mosart/evaluation/example_mosart_evaluate_stream_discharge_gsim.py
index 925e24b..7e65958 100644
--- a/tests/example/mosart/evaluation/example_mosart_evaluate_stream_discharge_gsim.py
+++ b/tests/example/mosart/evaluation/example_mosart_evaluate_stream_discharge_gsim.py
@@ -8,8 +8,8 @@
 
 iFlag_debug = 1
 if iFlag_debug == 1:
-    iIndex_start = 24
-    iIndex_end = 24
+    iIndex_start = 52
+    iIndex_end = 57
 else:
     parser = argparse.ArgumentParser()
     parser.add_argument("--iIndex_start", help = "the path",   type = int)
@@ -23,7 +23,7 @@
 sModel = 'e3sm'
 sRegion = 'amazon'
 
-sDate = '20220410'
+sDate = '20220701'
 
 sVariable = 'discharge'
 sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
diff --git a/tests/example/mosart/postprocess/twod/example_mosart_map_variable_2d.py b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_2d.py
similarity index 96%
rename from tests/example/mosart/postprocess/twod/example_mosart_map_variable_2d.py
rename to tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_2d.py
index e230ea4..559010a 100644
--- a/tests/example/mosart/postprocess/twod/example_mosart_map_variable_2d.py
+++ b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_2d.py
@@ -1,11 +1,8 @@
 
 
 import os, sys
-import argparse
-import subprocess
-import numpy as np
-import multiprocessing
 
+import numpy as np
 
 from pyearth.system.define_global_variables import *
  
@@ -17,7 +14,8 @@
 
 
 sDate = '20220410'
-iCase_index = 13
+iIndex_start = 52
+iIndex_end = 57
 
 
 iYear_start = 2000
diff --git a/tests/example/elm/postprocess/twod/example_elm_map_variable_2d_batch.py b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_2d_batch.py
similarity index 57%
rename from tests/example/elm/postprocess/twod/example_elm_map_variable_2d_batch.py
rename to tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_2d_batch.py
index 754a35a..51247c7 100644
--- a/tests/example/elm/postprocess/twod/example_elm_map_variable_2d_batch.py
+++ b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_2d_batch.py
@@ -11,65 +11,66 @@
  
 from pye3sm.shared.e3sm import pye3sm
 from pye3sm.shared.case import pycase
-from pye3sm.elm.general.structured.twod.map.elm_map_variable_2d import elm_map_variable_2d
+from pye3sm.mosart.general.structured.twod.map.mosart_map_variable_2d import mosart_map_variable_2d
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
 
 
 sDate = '20220701'
-iCase_index = 4
-
-iFlag_debug = 1
-if iFlag_debug == 1:
-    iIndex_start = 51
-    iIndex_end = 56
-else:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--iIndex_start", help = "the path",   type = int)
-    parser.add_argument("--iIndex_end", help = "the path",   type = int)
-    pArgs = parser.parse_args()
-    iIndex_start = pArgs.iIndex_start
-    iIndex_end = pArgs.iIndex_end
-
-#start loop
-iCase_index_start = iIndex_start
-iCase_index_end = iIndex_end
+iIndex_start = 52
+iIndex_end = 58
 
-iFlag_scientific_notation_colorbar_in = 0
 
-aVariable = ['ZWT','QOVER','QRUNOFF','QDRAI']
-aFlag_scientific_notation_colorbar=[0,1,1,1]
 iYear_start = 2000
 iYear_end = 2009
 sModel = 'e3sm'
 sRegion='amazon'
 
 
-aTitle= [ 'Water table depth','Overland runoff','Total runoff','Subsurface runoff' ]
 
-aUnit = [r'Unit: m',r'Units: mm/s',r'Units: mm/s',r'Units: mm/s']
+aVariable = ['discharge','']
+aFlag_scientific_notation_colorbar=[0,1,1,1]
 
-aData_min = [0,0,0,0]
-aData_max = [20, None ,None,None]
 
+nvariable = len(aVariable)
+
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+sTitle = r'River discharge'
 
 
+sUnit = r'Units: m3/s'
+ncase = len(aCase_index)
+aText1=['H2SC', 'H2SC', 'H2SC', 'H2SC', 'H2SC', 'H2SC']
+aText2=['dynamic', 'constant (surface slope)', 'dynamic', 'dynamic', 'dynamic', 'constant (surface slope)']
+aText3=['dynamic', 'dynamic','constant (1.0)',  'dynamic', 'dynamic', 'dynamic']
+aText4=['dynamic', 'dynamic','dynamic', 'constant (1.0 m)','constant (10.0 m)','constant (10.0 m)']
+dData_min_in=0
+
+dData_max_in=2.0E5
+
+iFlag_scientific_notation_colorbar_in = 1
+
 sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
 sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
 aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
 print(aParameter_e3sm)
 oE3SM = pye3sm(aParameter_e3sm)
-aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
-nvariable = len(aVariable)
-for iCase_index in (aCase_index):
+for i in range(ncase):
+    iCase_index = aCase_index[i]
     for iVariable in np.arange(nvariable):
         sVariable = aVariable[iVariable]
-        sUnit = aUnit[iVariable]
-        sTitle = aTitle[iVariable]
-        dData_min = aData_min[iVariable]
-        dData_max = aData_max[iVariable]
-        iFlag_scientific_notation_colorbar = aFlag_scientific_notation_colorbar[iVariable]
 
+        aLegend = list()
+        sCase = "{:0d}".format(i+2 )
+        sText = 'Case index: ' + sCase 
+        aLegend.append(sText)
+
+        #aLegend.append( 'Model: ' + aText1[i])
+        aLegend.append( 'Water table slope: ' + aText2[i])
+        aLegend.append( 'Anisotropy ratio: ' + aText3[i])
+        aLegend.append( 'River gage height: ' + aText4[i])
         aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
                                                        iCase_index_in =  iCase_index ,\
                                                        iYear_start_in = iYear_start, \
@@ -80,12 +81,14 @@
                                                        sModel_in = sModel, \
                                                            sRegion_in=sRegion,\
                                                        sVariable_in = sVariable )
-#print(aParameter_case)
-        oCase = pycase(aParameter_case)
-        elm_map_variable_2d(oE3SM, oCase ,  dData_min_in=dData_min, dData_max_in=dData_max,\
-            iFlag_scientific_notation_colorbar_in = iFlag_scientific_notation_colorbar, \
-            sUnit_in = sUnit,\
-            sTitle_in=  sTitle )
-        pass
 
+        oCase = pycase(aParameter_case)
+        mosart_map_variable_2d(oE3SM, oCase ,  dData_min_in=dData_min_in, dData_max_in=dData_max_in,\
+       iFlag_scientific_notation_colorbar_in = iFlag_scientific_notation_colorbar_in, \
+            iFlag_monthly_in = 0,\
+                    iFlag_annual_mean_in= 0,\
+                          iFlag_annual_total_in= 1,\
+        sUnit_in = sUnit,\
+      sTitle_in=  sTitle,
+      aLegend_in= aLegend )
 print('finished')
diff --git a/tests/example/elm/postprocess/twod/example_elm_tsplot_variable_2d.py b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_history_2d.py
similarity index 64%
rename from tests/example/elm/postprocess/twod/example_elm_tsplot_variable_2d.py
rename to tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_history_2d.py
index bcf17f9..7ed17cd 100644
--- a/tests/example/elm/postprocess/twod/example_elm_tsplot_variable_2d.py
+++ b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_history_2d.py
@@ -1,41 +1,44 @@
 
 
 import os, sys
-import argparse
-import subprocess
-import numpy as np
-import multiprocessing
 
+import numpy as np
 
 from pyearth.system.define_global_variables import *
  
 from pye3sm.shared.e3sm import pye3sm
 from pye3sm.shared.case import pycase
-from pye3sm.elm.general.structured.twod.plot.elm_tsplot_variable_2d import elm_tsplot_variable_2d
+from pye3sm.mosart.general.structured.twod.map.mosart_map_variable_history_2d import mosart_map_variable_history_2d
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
 from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
 
-sDate = '20220314'
 
-dLon_in = -60.0
-dLat_in = -2.9
-iCase_index = 9
+sDate = '20220701'
+iIndex_start = 52
+iIndex_end = 57
+
+iCase_index = iIndex_start
+
+
 iYear_start = 2000
-iYear_end = 2010
+iYear_end = 2009
 sModel = 'e3sm'
 sRegion='amazon'
-sVariable = 'zwt'
-#sVariable = 'zwt_perch'
-#sVariable='qrunoff'
-#sVariable='qover'
-#sVariable='qdrai'
-sLabel_y = r'Water table depth (m)'
-#sLabel_y = r'Perched water table depth (m)'
-#sLabel_y=r'Overland runoff (mm/s)'
-#sLabel_y=r'Subsurface runoff (mm/s)'
-iReverse_y=1
-dMin_y=0
-dMax_y=10
+
+
+sVariable = 'RTM_YR_LIQ'
+
+sTitle = r'River gage height'
+
+
+sUnit = r'Unit: m'
+
+dData_min_in=0
+dData_max_in =15
+#dData_max_in=None
+
+iFlag_scientific_notation_colorbar_in = 0
+
 sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
 sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
 aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
@@ -53,12 +56,7 @@
                                                        sVariable_in = sVariable )
 #print(aParameter_case)
 oCase = pycase(aParameter_case)
-elm_tsplot_variable_2d(oE3SM, oCase,  \
-  aLon_in=  [dLon_in]  ,   aLat_in= [dLat_in],\
-  iReverse_y_in= iReverse_y,\
-     dMax_y_in = dMax_y,\
-      #iFlag_scientific_notation_in=1,\
-                                                     dMin_y_in =dMin_y,\
-                                                       sLabel_y_in=sLabel_y,\
-  aLabel_legend_in= [ sLabel_y] )
+mosart_map_variable_history_2d(oE3SM, oCase , iFlag_history_in =1, dData_min_in=dData_min_in, dData_max_in=dData_max_in,\
+  iFlag_scientific_notation_colorbar_in = iFlag_scientific_notation_colorbar_in, sUnit_in = sUnit,\
+ sTitle_in=  sTitle )
 print('finished')
diff --git a/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_history_2d_batch.py b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_history_2d_batch.py
new file mode 100644
index 0000000..08b0f71
--- /dev/null
+++ b/tests/example/mosart/postprocess/twod/map/example_mosart_map_variable_history_2d_batch.py
@@ -0,0 +1,91 @@
+
+
+
+import numpy as np
+
+from pyearth.system.define_global_variables import *
+ 
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+from pye3sm.mosart.general.structured.twod.map.mosart_map_variable_history_2d import mosart_map_variable_history_2d
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+
+sDate = '20220701'
+iIndex_start = 52
+iIndex_end = 58
+
+iCase_index = iIndex_start
+
+
+iYear_start = 2000
+iYear_end = 2009
+sModel = 'e3sm'
+sRegion='amazon'
+
+
+sVariable = 'RTM_YR_LIQ'
+aVariable = ['RTM_YR_LIQ']
+sTitle = r'MOSART river gage height'
+
+
+sUnit = r'Unit: m'
+
+dData_min_in=0
+dData_max_in =15
+dData_max_in=None
+
+iFlag_scientific_notation_colorbar_in = 0
+
+nvariable = len(aVariable)
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+ncase = len(aCase_index)
+
+sColormap=  'Spectral'
+
+#aText1=['Default ELM', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG', 'HLG']
+aText2=['None', 'dynamic', 'dynamic (surface slope x 10)', 'constant (surface slope)', 'dynamic', 'dynamic', 'dynamic', 'constant (surface slope)']
+aText3=['None', 'dynamic', 'dynamic','constant (1.0)',  'dynamic', 'dynamic',  'dynamic','dynamic']
+aText4=['None', 'dynamic', 'dynamic','dynamic', 'dynamic', 'constant (1.0 m)','constant (10.0 m)','constant (10.0 m)']
+
+
+for i in range(ncase):
+    iCase_index = aCase_index[i]
+    for iVariable in np.arange(nvariable):
+        sVariable = aVariable[iVariable]
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                        iYear_subset_start_in = iYear_start, \
+                                                         iYear_subset_end_in = iYear_end, \
+                                                       sDate_in= sDate,\
+                                                       sModel_in = sModel, \
+                                                           sRegion_in=sRegion,\
+                                                       sVariable_in = sVariable )
+
+        oCase = pycase(aParameter_case)
+
+        aLegend = list()
+        sCase = "{:0d}".format(iCase_index -50 )
+        sText = 'Case ' + sCase 
+        aLegend.append(sText)
+
+        #aLegend.append( 'Model: ' + aText1[iCase_index -51])
+        aLegend.append( 'Water table slope: ' + aText2[iCase_index -51])
+        aLegend.append( 'Anisotropy ratio: ' + aText3[iCase_index -51])
+        aLegend.append( 'River gage height: ' + aText4[iCase_index -51])
+        mosart_map_variable_history_2d(oE3SM, oCase , iFlag_history_in =1,  dData_min_in=dData_min_in, dData_max_in=dData_max_in,\
+          iFlag_scientific_notation_colorbar_in = iFlag_scientific_notation_colorbar_in, sUnit_in = sUnit,\
+              sColormap_in = sColormap,\
+          sTitle_in=  sTitle , aLegend_in = aLegend)
+print('finished')
diff --git a/tests/example/mosart/postprocess/twod/example_mosart_save_variable_2d.py b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_2d.py
similarity index 82%
rename from tests/example/mosart/postprocess/twod/example_mosart_save_variable_2d.py
rename to tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_2d.py
index dfdd345..0af7599 100644
--- a/tests/example/mosart/postprocess/twod/example_mosart_save_variable_2d.py
+++ b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_2d.py
@@ -7,29 +7,15 @@
 from pye3sm.mosart.general.structured.twod.save.mosart_save_variable_2d import mosart_save_variable_2d
 sModel = 'e3sm'
 sRegion ='amazon'
-sDate = '20220410'
+sDate = '20220701'
 
-iCase_index = 24
+iCase_index = 57
 iYear_start = 2000
 iYear_end = 2009
 #from now, to maintain consistancy, we will the same variable name for all processes.
 #use the new naming method
 sVariable = 'discharge'
-#sVariable = 'wt_slp'
-#aVariable = ['TWS_MONTH_END','TWS_MONTH_BEGIN']
-#sVariable = 'TWS_MONTH_END'
-#sVariable = 'sur_slp'
-#P
-#sVariable = 'RAIN'
-#sVariable = 'SNOW'
-#ET
-#sVariable = 'QSOIL'
-#sVariable = 'QVEGE'
-#sVariable = 'QVEGT'
-#runoff
-#sVariable = 'QDRAI'
-#sVariable = 'DRARI_h2sc'
-#sVariable = 'QOVER'
+
 
 sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
 sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
diff --git a/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_2d_batch.py b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_2d_batch.py
new file mode 100644
index 0000000..d97de3a
--- /dev/null
+++ b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_2d_batch.py
@@ -0,0 +1,48 @@
+import numpy as np
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+from pye3sm.mosart.general.structured.twod.save.mosart_save_variable_2d import mosart_save_variable_2d
+sModel = 'e3sm'
+sRegion ='amazon'
+sDate = '20220701'
+
+
+iIndex_start = 59
+iIndex_end = 59
+
+iYear_start = 2000
+iYear_end = 2009
+#from now, to maintain consistancy, we will the same variable name for all processes.
+#use the new naming method
+aVariable = ['discharge']
+
+
+nvariable = len(aVariable)
+
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+for iCase_index in (aCase_index):
+    for iVariable in np.arange(nvariable):
+        sVariable = aVariable[iVariable]
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                       sDate_in= sDate,\
+                                                           sModel_in = sModel,\
+                                                              sRegion_in = sRegion,\
+                                                       sVariable_in = sVariable )
+
+        oCase = pycase(aParameter_case)
+        mosart_save_variable_2d(oE3SM, oCase )
+print('finished')
\ No newline at end of file
diff --git a/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_history_2d.py b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_history_2d.py
new file mode 100644
index 0000000..7d18405
--- /dev/null
+++ b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_history_2d.py
@@ -0,0 +1,36 @@
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+from pye3sm.mosart.general.structured.twod.save.mosart_save_variable_history_2d import mosart_save_variable_history_2d
+sModel = 'e3sm'
+sRegion ='amazon'
+sDate = '20220701'
+
+iCase_index = 52
+iYear_start = 2000
+iYear_end = 2009
+#from now, to maintain consistancy, we will the same variable name for all processes.
+#use the new naming method
+sVariable = 'RTM_YR_LIQ'
+
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                       sDate_in= sDate,\
+                                                           sModel_in = sModel,\
+                                                              sRegion_in = sRegion,\
+                                                       sVariable_in = sVariable )
+#print(aParameter_case)
+oCase = pycase(aParameter_case)
+mosart_save_variable_history_2d(oE3SM, oCase )
+print('finished')
\ No newline at end of file
diff --git a/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_history_2d_batch.py b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_history_2d_batch.py
new file mode 100644
index 0000000..b7df9a8
--- /dev/null
+++ b/tests/example/mosart/postprocess/twod/save/example_mosart_save_variable_history_2d_batch.py
@@ -0,0 +1,49 @@
+import numpy as np
+from pye3sm.shared.e3sm import pye3sm
+from pye3sm.shared.case import pycase
+
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_e3sm_configuration_file
+from pye3sm.shared.pye3sm_read_configuration_file import pye3sm_read_case_configuration_file
+
+from pye3sm.mosart.general.structured.twod.save.mosart_save_variable_history_2d import mosart_save_variable_history_2d
+sModel = 'e3sm'
+sRegion ='amazon'
+sDate = '20220701'
+
+
+iIndex_start = 52
+iIndex_end = 58
+iYear_start = 2000
+iYear_end = 2009
+#from now, to maintain consistancy, we will the same variable name for all processes.
+#use the new naming method
+aVariable = ['RTM_YR_LIQ']
+nvariable = len(aVariable)
+iCase_index_start = iIndex_start
+iCase_index_end = iIndex_end
+aCase_index = np.arange(iCase_index_start, iCase_index_end + 1, 1)
+
+
+sFilename_e3sm_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/e3sm.xml'
+sFilename_case_configuration = '/qfs/people/liao313/workspace/python/pye3sm/pye3sm/case.xml'
+aParameter_e3sm = pye3sm_read_e3sm_configuration_file(sFilename_e3sm_configuration)
+print(aParameter_e3sm)
+oE3SM = pye3sm(aParameter_e3sm)
+ncase = len(aCase_index)
+
+for i in range(ncase):
+    iCase_index = aCase_index[i]
+    for iVariable in np.arange(nvariable):
+        sVariable = aVariable[iVariable]
+        aParameter_case  = pye3sm_read_case_configuration_file(sFilename_case_configuration,\
+                                                       iCase_index_in =  iCase_index ,\
+                                                       iYear_start_in = iYear_start, \
+                                                       iYear_end_in = iYear_end,\
+                                                       sDate_in= sDate,\
+                                                           sModel_in = sModel,\
+                                                              sRegion_in = sRegion,\
+                                                       sVariable_in = sVariable )
+#print(aParameter_case)
+        oCase = pycase(aParameter_case)
+        mosart_save_variable_history_2d(oE3SM, oCase )
+print('finished')
\ No newline at end of file
diff --git a/tests/example/mosart/preprocess/example_mosart_create_flow_direction_map.py b/tests/example/mosart/preprocess/example_mosart_create_flow_direction_map.py
deleted file mode 100644
index 5dea2db..0000000
--- a/tests/example/mosart/preprocess/example_mosart_create_flow_direction_map.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import os
-
-from pye3sm.mosart.plot.mosart_create_flow_direction_map import mosart_create_flow_direction_map
-
-
-sFilename_netcdf = '/compyfs/inputdata/rof/mosart/MOSART_Global_half_20210616.nc'
-sFilename_netcdf = '/qfs/people/liao313/data/e3sm/mosart/amazon/mosart_half_degree.nc'
-    
-sFilename_out='/qfs/people/liao313/data/e3sm/mosart/amazon/mosart_amazon_half_degree_flow_direction.shp'
-
-mosart_create_flow_direction_map(sFilename_netcdf, sFilename_out)
-
-    
diff --git a/tests/example/mosart/preprocess/example_mosart_create_flow_accumulation_map.py b/tests/example/mosart/preprocess/example_mosart_map_flow_accumulation.py
similarity index 73%
rename from tests/example/mosart/preprocess/example_mosart_create_flow_accumulation_map.py
rename to tests/example/mosart/preprocess/example_mosart_map_flow_accumulation.py
index 08e5c92..a75cbd8 100644
--- a/tests/example/mosart/preprocess/example_mosart_create_flow_accumulation_map.py
+++ b/tests/example/mosart/preprocess/example_mosart_map_flow_accumulation.py
@@ -1,6 +1,6 @@
 import os
 
-from pye3sm.mosart.plot.mosart_create_flow_accumulation_map import mosart_create_flow_accumulation_map
+from pye3sm.mosart.map.mosart_map_flow_accumulation import mosart_create_flow_accumulation_map
 
 
 sFilename_netcdf = '/compyfs/inputdata/rof/mosart/MOSART_Global_half_20210616.nc'
diff --git a/tests/example/mosart/preprocess/example_mosart_map_flow_direction.py b/tests/example/mosart/preprocess/example_mosart_map_flow_direction.py
new file mode 100644
index 0000000..bdf7e8b
--- /dev/null
+++ b/tests/example/mosart/preprocess/example_mosart_map_flow_direction.py
@@ -0,0 +1,15 @@
+import os
+
+from pye3sm.mosart.map.mosart_map_flow_direction import mosart_map_flow_direction
+
+
+
+sFilename_netcdf = '/qfs/people/liao313/data/e3sm/amazon/mosart/mosart_half_degree.nc'
+    
+sFilename_out='/qfs/people/liao313/data/e3sm/amazon/mosart/mosart_amazon_half_degree_flow_direction.shp'
+
+sFilename_png = '/qfs/people/liao313/data/e3sm/amazon/mosart/flow_direction.png'
+
+mosart_map_flow_direction(sFilename_netcdf, sFilename_out, sFilename_png)
+
+