Compare commits
8 Commits
| Author | SHA1 | Date |
|---|---|---|
 Dan Howe
|
64d6ca3015 | 5 years ago |
|
Dan Howe
|
c8fb90d30f | 5 years ago |
|
Dan Howe
|
e8ba383dfd | 5 years ago |
|
Dan Howe
|
4857456b8a | 5 years ago |
|
Dan Howe
|
d336196e24 | 5 years ago |
|
Dan Howe
|
02511aebc2 | 5 years ago |
|
Dan Howe
|
4f6fb13ff2 | 5 years ago |
|
Dan Howe
|
4cb144d483 | 5 years ago |
@ -1,2 +0,0 @@
|
||||
# Ignore folder content
|
||||
tests/*
|
||||
@ -1,20 +0,0 @@
|
||||
|
||||
engines:
|
||||
duplication:
|
||||
enabled: true
|
||||
- mass = 50
|
||||
config:
|
||||
languages:
|
||||
python:
|
||||
mass_threshold: 40
|
||||
fixme:
|
||||
enabled: true
|
||||
radon:
|
||||
enabled: true
|
||||
ratings:
|
||||
paths:
|
||||
- "**.py"
|
||||
exclude_paths:
|
||||
- wafo/tests/
|
||||
- wafo/MSPPT.py
|
||||
- wafo/MSO.py
|
||||
@ -1,25 +0,0 @@
|
||||
# .coveragerc to control coverage.py
|
||||
[run]
|
||||
branch = True
|
||||
source = wafo
|
||||
omit =
|
||||
tests/*
|
||||
source/*
|
||||
|
||||
[report]
|
||||
# Regexes for lines to exclude from consideration
|
||||
exclude_lines =
|
||||
# Have to re-enable the standard pragma
|
||||
pragma: no cover
|
||||
|
||||
# Don't complain about missing debug-only code:
|
||||
def __repr__
|
||||
if self\.debug
|
||||
|
||||
# Don't complain if tests don't hit defensive assertion code:
|
||||
raise AssertionError
|
||||
raise NotImplementedError
|
||||
|
||||
# Don't complain if non-runnable code isn't run:
|
||||
if 0:
|
||||
if __name__ == .__main__.:
|
||||
@ -1,42 +0,0 @@
|
||||
# Temporary and binary files
|
||||
*~
|
||||
*.py[cod]
|
||||
*.so
|
||||
*.cfg
|
||||
!setup.cfg
|
||||
*.orig
|
||||
*.log
|
||||
*.pot
|
||||
__pycache__/*
|
||||
.cache/*
|
||||
.*.swp
|
||||
*.mod
|
||||
|
||||
# Project files
|
||||
.ropeproject
|
||||
.project
|
||||
.pydevproject
|
||||
.settings
|
||||
.idea
|
||||
|
||||
# Package files
|
||||
*.egg
|
||||
*.eggs/
|
||||
.installed.cfg
|
||||
*.egg-info
|
||||
|
||||
# Unittest and coverage
|
||||
htmlcov/*
|
||||
.coverage
|
||||
.tox
|
||||
junit.xml
|
||||
coverage.xml
|
||||
|
||||
# Build and docs folder/files
|
||||
build/*
|
||||
dist/*
|
||||
sdist/*
|
||||
docs/_rst/*
|
||||
docs/_build/*
|
||||
cover/*
|
||||
MANIFEST
|
||||
@ -1,23 +0,0 @@
|
||||
doc-warnings: yes
|
||||
test-warnings: no
|
||||
strictness: high
|
||||
|
||||
pylint:
|
||||
disable:
|
||||
- too-few-public-methods
|
||||
- no-self-use
|
||||
- too-many-instance-attributes
|
||||
- invalid-name
|
||||
- missing-docstring
|
||||
- star-args
|
||||
- logging-format-interpolation
|
||||
- bad-builtin
|
||||
ignore-paths:
|
||||
- docs
|
||||
pep8:
|
||||
disable:
|
||||
- E731
|
||||
- E126
|
||||
- E128
|
||||
- E115
|
||||
- N802
|
||||
@ -1,400 +0,0 @@
|
||||
[MASTER]
|
||||
|
||||
# Specify a configuration file.
|
||||
#rcfile=
|
||||
|
||||
# Python code to execute, usually for sys.path manipulation such as
|
||||
# pygtk.require().
|
||||
#init-hook=
|
||||
|
||||
# Add files or directories to the blacklist. They should be base names, not
|
||||
# paths.
|
||||
ignore=CVS
|
||||
|
||||
# Add files or directories matching the regex patterns to the blacklist. The
|
||||
# regex matches against base names, not paths.
|
||||
ignore-patterns=
|
||||
|
||||
# Pickle collected data for later comparisons.
|
||||
persistent=yes
|
||||
|
||||
# List of plugins (as comma separated values of python modules names) to load,
|
||||
# usually to register additional checkers. (PAB)
|
||||
load-plugins=pylint.extensions.mccabe
|
||||
|
||||
# Maximum threshold for cyclomatic complexity (PAB)
|
||||
max-complexity=10
|
||||
|
||||
# (PAB)
|
||||
consider-iterating-dictionary=yes
|
||||
|
||||
# Use multiple processes to speed up Pylint.
|
||||
jobs=1
|
||||
|
||||
# Allow loading of arbitrary C extensions. Extensions are imported into the
|
||||
# active Python interpreter and may run arbitrary code.
|
||||
unsafe-load-any-extension=no
|
||||
|
||||
# A comma-separated list of package or module names from where C extensions may
|
||||
# be loaded. Extensions are loading into the active Python interpreter and may
|
||||
# run arbitrary code (PAB)
|
||||
extension-pkg-whitelist=numpy
|
||||
|
||||
# Allow optimization of some AST trees. This will activate a peephole AST
|
||||
# optimizer, which will apply various small optimizations. For instance, it can
|
||||
# be used to obtain the result of joining multiple strings with the addition
|
||||
# operator. Joining a lot of strings can lead to a maximum recursion error in
|
||||
# Pylint and this flag can prevent that. It has one side effect, the resulting
|
||||
# AST will be different than the one from reality. This option is deprecated
|
||||
# and it will be removed in Pylint 2.0.
|
||||
optimize-ast=no
|
||||
|
||||
|
||||
[MESSAGES CONTROL]
|
||||
|
||||
# Only show warnings with the listed confidence levels. Leave empty to show
|
||||
# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED
|
||||
confidence=
|
||||
|
||||
# Enable the message, report, category or checker with the given id(s). You can
|
||||
# either give multiple identifier separated by comma (,) or put this option
|
||||
# multiple time (only on the command line, not in the configuration file where
|
||||
# it should appear only once). See also the "--disable" option for examples.
|
||||
#enable=
|
||||
|
||||
# Disable the message, report, category or checker with the given id(s). You
|
||||
# can either give multiple identifiers separated by comma (,) or put this
|
||||
# option multiple times (only on the command line, not in the configuration
|
||||
# file where it should appear only once).You can also use "--disable=all" to
|
||||
# disable everything first and then reenable specific checks. For example, if
|
||||
# you want to run only the similarities checker, you can use "--disable=all
|
||||
# --enable=similarities". If you want to run only the classes checker, but have
|
||||
# no Warning level messages displayed, use"--disable=all --enable=classes
|
||||
# --disable=W"
|
||||
# disable=locally-disabled
|
||||
disable=import-star-module-level,old-octal-literal,oct-method,print-statement,unpacking-in-except,parameter-unpacking,backtick,old-raise-syntax,old-ne-operator,long-suffix,dict-view-method,dict-iter-method,metaclass-assignment,next-method-called,raising-string,indexing-exception,raw_input-builtin,long-builtin,file-builtin,execfile-builtin,coerce-builtin,cmp-builtin,buffer-builtin,basestring-builtin,apply-builtin,filter-builtin-not-iterating,using-cmp-argument,useless-suppression,range-builtin-not-iterating,suppressed-message,no-absolute-import,old-division,cmp-method,reload-builtin,zip-builtin-not-iterating,intern-builtin,unichr-builtin,reduce-builtin,standarderror-builtin,unicode-builtin,xrange-builtin,coerce-method,delslice-method,getslice-method,setslice-method,input-builtin,round-builtin,hex-method,nonzero-method,map-builtin-not-iterating,W0201
|
||||
|
||||
|
||||
[REPORTS]
|
||||
|
||||
# Set the output format. Available formats are text, parseable, colorized, msvs
|
||||
# (visual studio) and html. You can also give a reporter class, eg
|
||||
# mypackage.mymodule.MyReporterClass.
|
||||
output-format=parseable
|
||||
# output-format=text
|
||||
|
||||
# Put messages in a separate file for each module / package specified on the
|
||||
# command line instead of printing them on stdout. Reports (if any) will be
|
||||
# written in a file name "pylint_global.[txt|html]". This option is deprecated
|
||||
# and it will be removed in Pylint 2.0.
|
||||
files-output=no
|
||||
|
||||
# Tells whether to display a full report or only the messages
|
||||
reports=yes
|
||||
|
||||
# Python expression which should return a note less than 10 (10 is the highest
|
||||
# note). You have access to the variables errors warning, statement which
|
||||
# respectively contain the number of errors / warnings messages and the total
|
||||
# number of statements analyzed. This is used by the global evaluation report
|
||||
# (RP0004).
|
||||
evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)
|
||||
|
||||
# Template used to display messages. This is a python new-style format string
|
||||
# used to format the message information. See doc for all details
|
||||
#msg-template=
|
||||
|
||||
|
||||
[BASIC]
|
||||
|
||||
# List of builtins function names that should not be used, separated by a comma
|
||||
bad-functions=map,filter,input
|
||||
|
||||
# Good variable names which should always be accepted, separated by a comma
|
||||
good-names=i,j,k,m,n,ex, Run, _, x, y, z, u, v, w, a, b, c, f, g, h, p, q, r, s, t
|
||||
|
||||
# Bad variable names which should always be refused, separated by a comma
|
||||
bad-names=foo,bar,baz,toto,tutu,tata
|
||||
|
||||
# Colon-delimited sets of names that determine each other's naming style when
|
||||
# the name regexes allow several styles.
|
||||
name-group=
|
||||
|
||||
# Include a hint for the correct naming format with invalid-name
|
||||
#include-naming-hint=no
|
||||
include-naming-hint=yes
|
||||
|
||||
# List of decorators that produce properties, such as abc.abstractproperty. Add
|
||||
# to this list to register other decorators that produce valid properties.
|
||||
property-classes=abc.abstractproperty
|
||||
|
||||
# Regular expression matching correct function names
|
||||
function-rgx=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Naming hint for function names
|
||||
function-name-hint=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Regular expression matching correct variable names
|
||||
variable-rgx=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Naming hint for variable names
|
||||
variable-name-hint=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Regular expression matching correct dummy-variable names (PAB)
|
||||
dummy-variables-rgx=^_
|
||||
|
||||
# Regular expression matching correct constant names
|
||||
const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$
|
||||
|
||||
# Naming hint for constant names
|
||||
const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$
|
||||
|
||||
# Regular expression matching correct attribute names
|
||||
attr-rgx=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Naming hint for attribute names
|
||||
attr-name-hint=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Regular expression matching correct argument names
|
||||
argument-rgx=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Naming hint for argument names
|
||||
argument-name-hint=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Regular expression matching correct class attribute names
|
||||
class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
|
||||
|
||||
# Naming hint for class attribute names
|
||||
class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$
|
||||
|
||||
# Regular expression matching correct inline iteration names
|
||||
inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$
|
||||
|
||||
# Naming hint for inline iteration names
|
||||
inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$
|
||||
|
||||
# Regular expression matching correct class names
|
||||
class-rgx=[A-Z_][a-zA-Z0-9]+$
|
||||
|
||||
# Naming hint for class names
|
||||
class-name-hint=[A-Z_][a-zA-Z0-9]+$
|
||||
|
||||
# Regular expression matching correct module names
|
||||
module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
|
||||
|
||||
# Naming hint for module names
|
||||
module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$
|
||||
|
||||
# Regular expression matching correct method names
|
||||
method-rgx=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Naming hint for method names
|
||||
method-name-hint=[a-z_][a-z0-9_]{2,30}$
|
||||
|
||||
# Regular expression which should only match function or class names that do
|
||||
# not require a docstring.
|
||||
no-docstring-rgx=^_
|
||||
|
||||
# Minimum line length for functions/classes that require docstrings, shorter
|
||||
# ones are exempt.
|
||||
docstring-min-length=-1
|
||||
|
||||
|
||||
[ELIF]
|
||||
|
||||
# Maximum number of nested blocks for function / method body
|
||||
max-nested-blocks=5
|
||||
|
||||
|
||||
[FORMAT]
|
||||
|
||||
# Maximum number of characters on a single line.
|
||||
max-line-length=100
|
||||
|
||||
# Regexp for a line that is allowed to be longer than the limit.
|
||||
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
|
||||
|
||||
# Allow the body of an if to be on the same line as the test if there is no
|
||||
# else.
|
||||
single-line-if-stmt=no
|
||||
|
||||
# List of optional constructs for which whitespace checking is disabled. `dict-
|
||||
# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}.
|
||||
# `trailing-comma` allows a space between comma and closing bracket: (a, ).
|
||||
# `empty-line` allows space-only lines.
|
||||
no-space-check=trailing-comma,dict-separator
|
||||
|
||||
# Maximum number of lines in a module
|
||||
max-module-lines=1000
|
||||
|
||||
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
|
||||
# tab).
|
||||
indent-string=' '
|
||||
|
||||
# Number of spaces of indent required inside a hanging or continued line.
|
||||
indent-after-paren=4
|
||||
|
||||
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
|
||||
expected-line-ending-format=
|
||||
|
||||
|
||||
[LOGGING]
|
||||
|
||||
# Logging modules to check that the string format arguments are in logging
|
||||
# function parameter format
|
||||
logging-modules=logging
|
||||
|
||||
|
||||
[MISCELLANEOUS]
|
||||
|
||||
# List of note tags to take in consideration, separated by a comma.
|
||||
notes=FIXME,XXX,TODO
|
||||
|
||||
|
||||
[SIMILARITIES]
|
||||
|
||||
# Minimum lines number of a similarity.
|
||||
min-similarity-lines=4
|
||||
|
||||
# Ignore comments when computing similarities.
|
||||
ignore-comments=yes
|
||||
|
||||
# Ignore docstrings when computing similarities.
|
||||
ignore-docstrings=yes
|
||||
|
||||
# Ignore imports when computing similarities.
|
||||
ignore-imports=no
|
||||
|
||||
|
||||
[SPELLING]
|
||||
|
||||
# Spelling dictionary name. Available dictionaries: none. To make it working
|
||||
# install python-enchant package.
|
||||
spelling-dict=
|
||||
|
||||
# List of comma separated words that should not be checked.
|
||||
spelling-ignore-words=
|
||||
|
||||
# A path to a file that contains private dictionary; one word per line.
|
||||
spelling-private-dict-file=
|
||||
|
||||
# Tells whether to store unknown words to indicated private dictionary in
|
||||
# --spelling-private-dict-file option instead of raising a message.
|
||||
spelling-store-unknown-words=no
|
||||
|
||||
|
||||
[TYPECHECK]
|
||||
|
||||
# Tells whether missing members accessed in mixin class should be ignored. A
|
||||
# mixin class is detected if its name ends with "mixin" (case insensitive).
|
||||
ignore-mixin-members=yes
|
||||
|
||||
# List of module names for which member attributes should not be checked
|
||||
# (useful for modules/projects where namespaces are manipulated during runtime
|
||||
# and thus existing member attributes cannot be deduced by static analysis. It
|
||||
# supports qualified module names, as well as Unix pattern matching.
|
||||
ignored-modules=
|
||||
|
||||
# List of class names for which member attributes should not be checked (useful
|
||||
# for classes with dynamically set attributes). This supports the use of
|
||||
# qualified names.
|
||||
ignored-classes=optparse.Values,thread._local,_thread._local
|
||||
|
||||
# List of members which are set dynamically and missed by pylint inference
|
||||
# system, and so shouldn't trigger E1101 when accessed. Python regular
|
||||
# expressions are accepted.
|
||||
generated-members=
|
||||
|
||||
# List of decorators that produce context managers, such as
|
||||
# contextlib.contextmanager. Add to this list to register other decorators that
|
||||
# produce valid context managers.
|
||||
contextmanager-decorators=contextlib.contextmanager
|
||||
|
||||
|
||||
[CLASSES]
|
||||
|
||||
# List of method names used to declare (i.e. assign) instance attributes.
|
||||
defining-attr-methods=__init__,__new__,setUp
|
||||
|
||||
# List of valid names for the first argument in a class method.
|
||||
valid-classmethod-first-arg=cls
|
||||
|
||||
# List of valid names for the first argument in a metaclass class method.
|
||||
valid-metaclass-classmethod-first-arg=mcs
|
||||
|
||||
# List of member names, which should be excluded from the protected access
|
||||
# warning.
|
||||
exclude-protected=_asdict,_fields,_replace,_source,_make
|
||||
|
||||
|
||||
[DESIGN]
|
||||
|
||||
# Maximum number of arguments for function / method
|
||||
max-args=5
|
||||
|
||||
# Argument names that match this expression will be ignored. Default to name
|
||||
# with leading underscore
|
||||
ignored-argument-names=_.*
|
||||
|
||||
# Maximum number of locals for function / method body
|
||||
max-locals=15
|
||||
|
||||
# Maximum number of return / yield for function / method body
|
||||
max-returns=6
|
||||
|
||||
# Maximum number of branch for function / method body
|
||||
max-branches=12
|
||||
|
||||
# Maximum number of statements in function / method body
|
||||
max-statements=50
|
||||
|
||||
# Maximum number of parents for a class (see R0901).
|
||||
max-parents=7
|
||||
|
||||
# Maximum number of attributes for a class (see R0902).
|
||||
max-attributes=7
|
||||
|
||||
# Minimum number of public methods for a class (see R0903).
|
||||
min-public-methods=2
|
||||
|
||||
# Maximum number of public methods for a class (see R0904).
|
||||
max-public-methods=20
|
||||
|
||||
# Maximum number of boolean expressions in a if statement
|
||||
max-bool-expr=5
|
||||
|
||||
|
||||
[IMPORTS]
|
||||
|
||||
# Deprecated modules which should not be used, separated by a comma
|
||||
deprecated-modules=regsub,TERMIOS,Bastion,rexec
|
||||
|
||||
# Create a graph of every (i.e. internal and external) dependencies in the
|
||||
# given file (report RP0402 must not be disabled)
|
||||
import-graph=
|
||||
|
||||
# Create a graph of external dependencies in the given file (report RP0402 must
|
||||
# not be disabled)
|
||||
ext-import-graph=
|
||||
|
||||
# Create a graph of internal dependencies in the given file (report RP0402 must
|
||||
# not be disabled)
|
||||
int-import-graph=
|
||||
|
||||
# Force import order to recognize a module as part of the standard
|
||||
# compatibility libraries.
|
||||
known-standard-library=
|
||||
|
||||
# Force import order to recognize a module as part of a third party library.
|
||||
known-third-party=enchant
|
||||
|
||||
# Analyse import fallback blocks. This can be used to support both Python 2 and
|
||||
# 3 compatible code, which means that the block might have code that exists
|
||||
# only in one or another interpreter, leading to false positives when analysed.
|
||||
analyse-fallback-blocks=no
|
||||
|
||||
|
||||
[EXCEPTIONS]
|
||||
|
||||
# Exceptions that will emit a warning when being caught. Defaults to
|
||||
# "Exception"
|
||||
overgeneral-exceptions=Exception
|
||||
@ -1,61 +0,0 @@
|
||||
# Travis configuration file using the build matrix feature
|
||||
# Read more under http://docs.travis-ci.com/user/build-configuration/
|
||||
# THIS SCRIPT IS SUPPOSED TO BE AN EXAMPLE. MODIFY IT ACCORDING TO YOUR NEEDS!
|
||||
sudo: true
|
||||
language: python
|
||||
python:
|
||||
- 2.7
|
||||
# - 3.4 # Drop support for python 3.4
|
||||
- 3.5
|
||||
- 3.6
|
||||
notifications:
|
||||
email: false
|
||||
|
||||
# Setup anaconda
|
||||
before_install:
|
||||
- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
|
||||
wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh;
|
||||
else
|
||||
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
|
||||
fi
|
||||
- chmod +x miniconda.sh
|
||||
- ./miniconda.sh -b
|
||||
- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
|
||||
export PATH=/home/travis/miniconda2/bin:$PATH;
|
||||
else
|
||||
export PATH=/home/travis/miniconda3/bin:$PATH;
|
||||
fi
|
||||
- conda update --yes conda
|
||||
- conda install --yes conda-build
|
||||
- conda create --yes -n condaenv python=$TRAVIS_PYTHON_VERSION
|
||||
- conda install --yes -n condaenv pip
|
||||
- conda config --add channels https://conda.anaconda.org/omnia
|
||||
- conda config --add channels https://conda.anaconda.org/pbrod
|
||||
- source activate condaenv
|
||||
- sudo apt-get update
|
||||
- sudo apt-get install gfortran
|
||||
# Install packages
|
||||
install:
|
||||
- conda install --yes python=$TRAVIS_PYTHON_VERSION numpy scipy nose pytest matplotlib pytest-pep8 pytest-cov mpmath pillow numba statsmodels
|
||||
- pip install funcsigs
|
||||
- pip install numdifftools
|
||||
- pip install coveralls
|
||||
- pip install codecov
|
||||
- python setup.py build
|
||||
- python setup.py install
|
||||
# - conda build conda_recipe
|
||||
# - conda install wafo --use-local
|
||||
before_script:
|
||||
- "export DISPLAY=:99.0"
|
||||
- "sh -e /etc/init.d/xvfb start"
|
||||
- sleep 3 # give xvfb some time to start
|
||||
- git config --global user.email "per.andreas.brodtkorb@gmail.com"
|
||||
- git config --global user.name "pbrod"
|
||||
script:
|
||||
- coverage run --source=wafo setup.py test
|
||||
# - py.test wafo --cov --pep8 --doctest-modules
|
||||
after_success:
|
||||
- coveralls
|
||||
- codecov
|
||||
cache:
|
||||
- apt
|
||||
@ -1,8 +0,0 @@
|
||||
==========
|
||||
Developers
|
||||
==========
|
||||
|
||||
* Per A. Brodtkorb
|
||||
* Georg Lindgren
|
||||
* David Verelst
|
||||
|
||||
@ -1,11 +0,0 @@
|
||||
=========
|
||||
Changelog
|
||||
=========
|
||||
|
||||
Version 0.1
|
||||
===========
|
||||
|
||||
- Feature A added
|
||||
- FIX: nasty bug #1729 fixed
|
||||
- add your changes here!
|
||||
|
||||
@ -1,131 +0,0 @@
|
||||
environment:
|
||||
global:
|
||||
# SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
|
||||
# /E:ON and /V:ON options are not enabled in the batch script intepreter
|
||||
# See: http://stackoverflow.com/a/13751649/163740
|
||||
CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
|
||||
|
||||
matrix:
|
||||
|
||||
# Python 2.7.10 is the latest version and is not pre-installed.
|
||||
|
||||
- PYTHON: "C:\\Python27.10"
|
||||
PYTHON_VERSION: "2.7.10"
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python27.10-x64"
|
||||
PYTHON_VERSION: "2.7.10"
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
# Pre-installed Python versions, which Appveyor may upgrade to
|
||||
# a later point release.
|
||||
# See: http://www.appveyor.com/docs/installed-software#python
|
||||
|
||||
- PYTHON: "C:\\Python27"
|
||||
PYTHON_VERSION: "2.7.x" # currently 2.7.9
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python27-x64"
|
||||
PYTHON_VERSION: "2.7.x" # currently 2.7.9
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
- PYTHON: "C:\\Python33"
|
||||
PYTHON_VERSION: "3.3.x" # currently 3.3.5
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python33-x64"
|
||||
PYTHON_VERSION: "3.3.x" # currently 3.3.5
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
- PYTHON: "C:\\Python34"
|
||||
PYTHON_VERSION: "3.4.x" # currently 3.4.3
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python34-x64"
|
||||
PYTHON_VERSION: "3.4.x" # currently 3.4.3
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
# Python versions not pre-installed
|
||||
|
||||
# Python 2.6.6 is the latest Python 2.6 with a Windows installer
|
||||
# See: https://github.com/ogrisel/python-appveyor-demo/issues/10
|
||||
|
||||
- PYTHON: "C:\\Python266"
|
||||
PYTHON_VERSION: "2.6.6"
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python266-x64"
|
||||
PYTHON_VERSION: "2.6.6"
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
- PYTHON: "C:\\Python35"
|
||||
PYTHON_VERSION: "3.5.0"
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python35-x64"
|
||||
PYTHON_VERSION: "3.5.0"
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
# Major and minor releases (i.e x.0.0 and x.y.0) prior to 3.3.0 use
|
||||
# a different naming scheme.
|
||||
|
||||
- PYTHON: "C:\\Python270"
|
||||
PYTHON_VERSION: "2.7.0"
|
||||
PYTHON_ARCH: "32"
|
||||
|
||||
- PYTHON: "C:\\Python270-x64"
|
||||
PYTHON_VERSION: "2.7.0"
|
||||
PYTHON_ARCH: "64"
|
||||
|
||||
install:
|
||||
- ECHO "Filesystem root:"
|
||||
- ps: "ls \"C:/\""
|
||||
|
||||
- ECHO "Installed SDKs:"
|
||||
- ps: "ls \"C:/Program Files/Microsoft SDKs/Windows\""
|
||||
|
||||
# Install Python (from the official .msi of http://python.org) and pip when
|
||||
# not already installed.
|
||||
- ps: if (-not(Test-Path($env:PYTHON))) { & appveyor\install.ps1 }
|
||||
|
||||
# Prepend newly installed Python to the PATH of this build (this cannot be
|
||||
# done from inside the powershell script as it would require to restart
|
||||
# the parent CMD process).
|
||||
- "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%"
|
||||
|
||||
# Check that we have the expected version and architecture for Python
|
||||
- "python --version"
|
||||
- "python -c \"import struct; print(struct.calcsize('P') * 8)\""
|
||||
|
||||
# Upgrade to the latest version of pip to avoid it displaying warnings
|
||||
# about it being out of date.
|
||||
- "pip install --disable-pip-version-check --user --upgrade pip"
|
||||
|
||||
# Install the build dependencies of the project. If some dependencies contain
|
||||
# compiled extensions and are not provided as pre-built wheel packages,
|
||||
# pip will build them from source using the MSVC compiler matching the
|
||||
# target Python version and architecture
|
||||
- "%CMD_IN_ENV% pip install -r requirements.txt"
|
||||
|
||||
build_script:
|
||||
# Build the compiled extension
|
||||
- "%CMD_IN_ENV% python setup.py build"
|
||||
|
||||
test_script:
|
||||
# Run the project tests
|
||||
- "%CMD_IN_ENV% python setup.py test"
|
||||
|
||||
after_test:
|
||||
# If tests are successful, create binary packages for the project.
|
||||
- "%CMD_IN_ENV% python setup.py bdist_wheel"
|
||||
- "%CMD_IN_ENV% python setup.py bdist_wininst"
|
||||
- "%CMD_IN_ENV% python setup.py bdist_msi"
|
||||
- ps: "ls dist"
|
||||
|
||||
artifacts:
|
||||
# Archive the generated packages in the ci.appveyor.com build report.
|
||||
- path: dist\*
|
||||
|
||||
#on_success:
|
||||
# - TODO: upload the content of dist/*.whl to a public wheelhouse
|
||||
#
|
||||
@ -1 +0,0 @@
|
||||
collect_ignore = ["setup.py", "build_all.py"]
|
||||
@ -1,177 +0,0 @@
|
||||
# Makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line.
|
||||
SPHINXOPTS =
|
||||
SPHINXBUILD = sphinx-build
|
||||
PAPER =
|
||||
BUILDDIR = _build
|
||||
|
||||
# User-friendly check for sphinx-build
|
||||
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $?), 1)
|
||||
$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
|
||||
endif
|
||||
|
||||
# Internal variables.
|
||||
PAPEROPT_a4 = -D latex_paper_size=a4
|
||||
PAPEROPT_letter = -D latex_paper_size=letter
|
||||
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
|
||||
# the i18n builder cannot share the environment and doctrees with the others
|
||||
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
|
||||
|
||||
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext
|
||||
|
||||
help:
|
||||
@echo "Please use \`make <target>' where <target> is one of"
|
||||
@echo " html to make standalone HTML files"
|
||||
@echo " dirhtml to make HTML files named index.html in directories"
|
||||
@echo " singlehtml to make a single large HTML file"
|
||||
@echo " pickle to make pickle files"
|
||||
@echo " json to make JSON files"
|
||||
@echo " htmlhelp to make HTML files and a HTML help project"
|
||||
@echo " qthelp to make HTML files and a qthelp project"
|
||||
@echo " devhelp to make HTML files and a Devhelp project"
|
||||
@echo " epub to make an epub"
|
||||
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
|
||||
@echo " latexpdf to make LaTeX files and run them through pdflatex"
|
||||
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
|
||||
@echo " text to make text files"
|
||||
@echo " man to make manual pages"
|
||||
@echo " texinfo to make Texinfo files"
|
||||
@echo " info to make Texinfo files and run them through makeinfo"
|
||||
@echo " gettext to make PO message catalogs"
|
||||
@echo " changes to make an overview of all changed/added/deprecated items"
|
||||
@echo " xml to make Docutils-native XML files"
|
||||
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
|
||||
@echo " linkcheck to check all external links for integrity"
|
||||
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
|
||||
|
||||
clean:
|
||||
rm -rf $(BUILDDIR)/*
|
||||
|
||||
html:
|
||||
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
|
||||
|
||||
dirhtml:
|
||||
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
|
||||
|
||||
singlehtml:
|
||||
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
|
||||
@echo
|
||||
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
|
||||
|
||||
pickle:
|
||||
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
|
||||
@echo
|
||||
@echo "Build finished; now you can process the pickle files."
|
||||
|
||||
json:
|
||||
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
|
||||
@echo
|
||||
@echo "Build finished; now you can process the JSON files."
|
||||
|
||||
htmlhelp:
|
||||
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
|
||||
@echo
|
||||
@echo "Build finished; now you can run HTML Help Workshop with the" \
|
||||
".hhp project file in $(BUILDDIR)/htmlhelp."
|
||||
|
||||
qthelp:
|
||||
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
|
||||
@echo
|
||||
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
|
||||
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
|
||||
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/pywafo.qhcp"
|
||||
@echo "To view the help file:"
|
||||
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/pywafo.qhc"
|
||||
|
||||
devhelp:
|
||||
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
|
||||
@echo
|
||||
@echo "Build finished."
|
||||
@echo "To view the help file:"
|
||||
@echo "# mkdir -p $HOME/.local/share/devhelp/pywafo"
|
||||
@echo "# ln -s $(BUILDDIR)/devhelp $HOME/.local/share/devhelp/pywafo"
|
||||
@echo "# devhelp"
|
||||
|
||||
epub:
|
||||
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
|
||||
@echo
|
||||
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
|
||||
|
||||
latex:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo
|
||||
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
|
||||
@echo "Run \`make' in that directory to run these through (pdf)latex" \
|
||||
"(use \`make latexpdf' here to do that automatically)."
|
||||
|
||||
latexpdf:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo "Running LaTeX files through pdflatex..."
|
||||
$(MAKE) -C $(BUILDDIR)/latex all-pdf
|
||||
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
|
||||
|
||||
latexpdfja:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo "Running LaTeX files through platex and dvipdfmx..."
|
||||
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
|
||||
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
|
||||
|
||||
text:
|
||||
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
|
||||
@echo
|
||||
@echo "Build finished. The text files are in $(BUILDDIR)/text."
|
||||
|
||||
man:
|
||||
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
|
||||
@echo
|
||||
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
|
||||
|
||||
texinfo:
|
||||
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
|
||||
@echo
|
||||
@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
|
||||
@echo "Run \`make' in that directory to run these through makeinfo" \
|
||||
"(use \`make info' here to do that automatically)."
|
||||
|
||||
info:
|
||||
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
|
||||
@echo "Running Texinfo files through makeinfo..."
|
||||
make -C $(BUILDDIR)/texinfo info
|
||||
@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
|
||||
|
||||
gettext:
|
||||
$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
|
||||
@echo
|
||||
@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
|
||||
|
||||
changes:
|
||||
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
|
||||
@echo
|
||||
@echo "The overview file is in $(BUILDDIR)/changes."
|
||||
|
||||
linkcheck:
|
||||
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
|
||||
@echo
|
||||
@echo "Link check complete; look for any errors in the above output " \
|
||||
"or in $(BUILDDIR)/linkcheck/output.txt."
|
||||
|
||||
doctest:
|
||||
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
|
||||
@echo "Testing of doctests in the sources finished, look at the " \
|
||||
"results in $(BUILDDIR)/doctest/output.txt."
|
||||
|
||||
xml:
|
||||
$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
|
||||
@echo
|
||||
@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
|
||||
|
||||
pseudoxml:
|
||||
$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
|
||||
@echo
|
||||
@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
|
||||
@ -1,2 +0,0 @@
|
||||
.. _authors:
|
||||
.. include:: ../AUTHORS.rst
|
||||
@ -1,2 +0,0 @@
|
||||
.. _changes:
|
||||
.. include:: ../CHANGES.rst
|
||||
@ -1,238 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# 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
|
||||
import inspect
|
||||
from sphinx import apidoc
|
||||
|
||||
|
||||
__location__ = os.path.join(os.getcwd(), os.path.dirname(
|
||||
inspect.getfile(inspect.currentframe())))
|
||||
|
||||
package = "wafo"
|
||||
namespace = []
|
||||
namespace_pkg = ".".join([namespace[-1], package]) if namespace else package
|
||||
|
||||
# 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'
|
||||
|
||||
# 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', 'sphinx.ext.intersphinx', 'sphinx.ext.todo',
|
||||
'sphinx.ext.autosummary', 'sphinx.ext.viewcode', 'sphinx.ext.coverage',
|
||||
'sphinx.ext.doctest', 'sphinx.ext.ifconfig', 'sphinx.ext.pngmath']
|
||||
|
||||
# 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'pywafo'
|
||||
copyright = u'2015, davidovitch'
|
||||
|
||||
# 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 = '' # Is set by calling `setup.py docs`
|
||||
# The full version, including alpha/beta/rc tags.
|
||||
release = '' # Is set by calling `setup.py docs`
|
||||
|
||||
# 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']
|
||||
|
||||
# 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 = 'alabaster'
|
||||
|
||||
# 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".
|
||||
try:
|
||||
from namespace_pkg import __version__ as version
|
||||
except ImportError:
|
||||
pass
|
||||
else:
|
||||
release = version
|
||||
|
||||
# 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 = ""
|
||||
|
||||
# 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']
|
||||
|
||||
# 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 = 'wafo-doc'
|
||||
|
||||
|
||||
# -- 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]).
|
||||
latex_documents = [
|
||||
('index', 'user_guide.tex', u'pywafo Documentation',
|
||||
u'davidovitch', 'manual'),
|
||||
]
|
||||
|
||||
# The name of an image file (relative to this directory) to place at the top of
|
||||
# the title page.
|
||||
# latex_logo = ""
|
||||
|
||||
# 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
|
||||
|
||||
# -- External mapping ------------------------------------------------------------
|
||||
python_version = '.'.join(map(str, sys.version_info[0:2]))
|
||||
intersphinx_mapping = {
|
||||
'sphinx': ('http://sphinx.pocoo.org', None),
|
||||
'python': ('http://docs.python.org/' + python_version, None),
|
||||
'matplotlib': ('http://matplotlib.sourceforge.net', None),
|
||||
'numpy': ('http://docs.scipy.org/doc/numpy', None),
|
||||
'sklearn': ('http://scikit-learn.org/stable', None),
|
||||
'pandas': ('http://pandas.pydata.org/pandas-docs/stable', None),
|
||||
'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
|
||||
}
|
||||
@ -1,45 +0,0 @@
|
||||
======
|
||||
pywafo
|
||||
======
|
||||
|
||||
This is the documentation of **pywafo**.
|
||||
|
||||
.. note::
|
||||
|
||||
This is the main page of your project's `Sphinx <http://sphinx-doc.org/>`_
|
||||
documentation. It is formatted in `reStructuredText
|
||||
<http://sphinx-doc.org/rest.html>`__. Add additional pages by creating
|
||||
rst-files in ``docs`` and adding them to the `toctree
|
||||
<http://sphinx-doc.org/markup/toctree.html>`_ below. Use then
|
||||
`references <http://sphinx-doc.org/markup/inline.html>`__ in order to link
|
||||
them from this page, e.g. :ref:`authors <authors>` and :ref:`changes`.
|
||||
It is also possible to refer to the documentation of other Python packages
|
||||
with the `Python domain syntax
|
||||
<http://sphinx-doc.org/domains.html#the-python-domain>`__. By default you
|
||||
can reference the documentation of `Sphinx <http://sphinx.pocoo.org>`__,
|
||||
`Python <http://docs.python.org/>`__, `matplotlib
|
||||
<http://matplotlib.sourceforge.net>`__, `NumPy
|
||||
<http://docs.scipy.org/doc/numpy>`__, `Scikit-Learn
|
||||
<http://scikit-learn.org/stable>`__, `Pandas
|
||||
<http://pandas.pydata.org/pandas-docs/stable>`__, `SciPy
|
||||
<http://docs.scipy.org/doc/scipy/reference/>`__. You can add more by
|
||||
extending the ``intersphinx_mapping`` in your Sphinx's ``conf.py``.
|
||||
|
||||
Contents
|
||||
========
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
License <license>
|
||||
Authors <authors>
|
||||
Changelog <changes>
|
||||
Module Reference <api/modules>
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
@ -1,7 +0,0 @@
|
||||
.. _license:
|
||||
|
||||
=======
|
||||
License
|
||||
=======
|
||||
|
||||
.. literalinclude:: ../LICENSE.txt
|
||||
@ -1,110 +1,10 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Setup file for wafo.
|
||||
|
||||
This file was generated with PyScaffold 2.4.2, a tool that easily
|
||||
puts up a scaffold for your new Python project. Learn more under:
|
||||
http://pyscaffold.readthedocs.org/
|
||||
|
||||
Usage:
|
||||
Run all tests:
|
||||
python setup.py test
|
||||
|
||||
python setup.py doctests
|
||||
|
||||
Build documentation
|
||||
|
||||
python setup.py docs
|
||||
|
||||
Install
|
||||
python setup.py install [, --prefix=$PREFIX]
|
||||
|
||||
Build
|
||||
|
||||
python setup.py bdist_wininst
|
||||
|
||||
python setup.py bdist_wheel --universal
|
||||
|
||||
python setup.py sdist
|
||||
|
||||
PyPi upload:
|
||||
twine upload dist/*
|
||||
|
||||
"""
|
||||
|
||||
from __future__ import division, absolute_import, print_function
|
||||
|
||||
# numpy.distutils will figure out if setuptools is available when imported
|
||||
# this allows us to combine setuptools use_pyscaffold=True and f2py extensions
|
||||
import setuptools
|
||||
from numpy.distutils.core import setup
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
|
||||
import sys
|
||||
|
||||
|
||||
def setup_package_pyscaffold():
|
||||
|
||||
config = Configuration('wafo')
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# c_library
|
||||
config.add_extension('c_library',
|
||||
sources=['wafo/source/c_library/c_library.pyf',
|
||||
'wafo/source/c_library/c_functions.c'])
|
||||
# -------------------------------------------------------------------------
|
||||
# mvn
|
||||
config.add_extension('mvn',
|
||||
sources=['wafo/source/mvn/mvn.pyf',
|
||||
'wafo/source/mvn/mvndst.f'])
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# mvnprdmod
|
||||
lib_mvnprdmod_src = ['wafo/source/mvnprd/mvnprd.f',
|
||||
'wafo/source/mvnprd/mvnprodcorrprb.f']
|
||||
config.add_library('_mvnprdmod', sources=lib_mvnprdmod_src)
|
||||
config.add_extension('mvnprdmod',
|
||||
sources=['wafo/source/mvnprd/mvnprd_interface.f'],
|
||||
libraries=['_mvnprdmod'],
|
||||
depends=(lib_mvnprdmod_src))
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# cov2mod
|
||||
lib_cov2mod_src = ['wafo/source/mreg/dsvdc.f',
|
||||
'wafo/source/mreg/mregmodule.f',
|
||||
'wafo/source/mreg/intfcmod.f']
|
||||
config.add_library('_cov2mod', sources=lib_cov2mod_src)
|
||||
config.add_extension('cov2mod',
|
||||
sources=['wafo/source/mreg/cov2mmpdfreg_intfc.f'],
|
||||
libraries=['_cov2mod'],
|
||||
include_dirs=['wafo/source/mreg/'],
|
||||
depends=(lib_cov2mod_src))
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# rindmod
|
||||
lib_rindmod_src = ['wafo/source/rind2007/intmodule.f',
|
||||
'wafo/source/rind2007/jacobmod.f',
|
||||
'wafo/source/rind2007/swapmod.f',
|
||||
'wafo/source/rind2007/fimod.f',
|
||||
'wafo/source/rind2007/rindmod.f',
|
||||
'wafo/source/rind2007/rind71mod.f']
|
||||
config.add_library('_rindmod', sources=lib_rindmod_src)
|
||||
config.add_extension('rindmod',
|
||||
sources=['wafo/source/rind2007/rind_interface.f'],
|
||||
libraries=['_rindmod'],
|
||||
include_dirs=['wafo/source/mreg/'],
|
||||
depends=(lib_rindmod_src))
|
||||
|
||||
config.add_data_dir(('data', 'wafo/data'))
|
||||
|
||||
needs_sphinx = {'build_sphinx', 'upload_docs'}.intersection(sys.argv)
|
||||
sphinx = ['sphinx'] if needs_sphinx else []
|
||||
setup(setup_requires=['six', 'pyscaffold>=2.4rc1,<2.5a0'] + sphinx,
|
||||
tests_require=['pytest_cov', 'pytest'],
|
||||
use_pyscaffold=True,
|
||||
**config.todict())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
setup_package_pyscaffold()
|
||||
setuptools.setup(
|
||||
name='wafo',
|
||||
version='0.11',
|
||||
author='WAFO-group',
|
||||
author_email=None,
|
||||
description='Wave Analysis for Fatigue and Oceanography',
|
||||
packages=setuptools.find_packages(),
|
||||
)
|
||||
|
||||
@ -1,69 +0,0 @@
|
||||
<map version="0.8.1">
|
||||
<!-- To view this file, download free mind mapping software FreeMind from http://freemind.sourceforge.net -->
|
||||
<node CREATED="1268907205249" ID="Freemind_Link_1834025809" MODIFIED="1268920224244" STYLE="fork" TEXT="WAFO Objects">
|
||||
<font NAME="SansSerif" SIZE="11"/>
|
||||
<node CREATED="1268919537155" ID="Freemind_Link_823244205" MODIFIED="1268920190865" POSITION="right" STYLE="bubble" TEXT="SpecData1D" VSHIFT="36">
|
||||
<node CREATED="1268908695254" ID="Freemind_Link_834391422" MODIFIED="1268908707582" TEXT="bandwidth"/>
|
||||
<node CREATED="1268908733082" ID="Freemind_Link_1433599166" MODIFIED="1268908736348" TEXT="characteristic"/>
|
||||
<node CREATED="1268908690441" ID="Freemind_Link_1284342306" MODIFIED="1268908694941" TEXT="normalize"/>
|
||||
<node CREATED="1268908669144" ID="Freemind_Link_1815459961" MODIFIED="1268908677316" TEXT="nyquist_freq"/>
|
||||
<node CREATED="1268908664160" ID="Freemind_Link_703739784" MODIFIED="1268919800253" TEXT="moment" VSHIFT="4"/>
|
||||
<node CREATED="1268908678394" ID="Freemind_Link_1387269779" MODIFIED="1268919203464" TEXT="sampling_period" VSHIFT="3"/>
|
||||
<node CREATED="1268908643159" ID="Freemind_Link_895051601" MODIFIED="1268908655410" TEXT="stats_nl"/>
|
||||
<node CREATED="1268907362986" ID="Freemind_Link_1959199999" MODIFIED="1268920763548" TEXT="sim">
|
||||
<arrowlink DESTINATION="Freemind_Link_1110096845" ENDARROW="Default" ENDINCLINATION="-26;-470;" ID="Freemind_Arrow_Link_658989110" STARTARROW="None" STARTINCLINATION="134;20;"/>
|
||||
</node>
|
||||
<node CREATED="1268908639894" ID="Freemind_Link_881848843" MODIFIED="1268920768236" TEXT="sim_nl" VSHIFT="-116">
|
||||
<arrowlink DESTINATION="Freemind_Link_1110096845" ENDARROW="Default" ENDINCLINATION="-12;-203;" ID="Freemind_Arrow_Link_194892354" STARTARROW="None" STARTINCLINATION="286;65;"/>
|
||||
</node>
|
||||
<node CREATED="1268908656097" ID="Freemind_Link_932055041" MODIFIED="1268920049331" TEXT="testgaussian" VSHIFT="-11"/>
|
||||
<node CREATED="1268908524408" ID="Freemind_Link_451836141" MODIFIED="1268920637967" TEXT="tocovdata" VSHIFT="122">
|
||||
<arrowlink DESTINATION="Freemind_Link_1298097150" ENDARROW="Default" ENDINCLINATION="22;-61;" ID="Freemind_Arrow_Link_459039584" STARTARROW="None" STARTINCLINATION="155;-17;"/>
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Binary file not shown.
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Before Width: | Height: | Size: 240 KiB |
@ -1,97 +0,0 @@
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||||
"""
|
||||
Module extending the bitoperator capabilites of numpy
|
||||
"""
|
||||
|
||||
from numpy import (bitwise_and, bitwise_or,
|
||||
bitwise_not, binary_repr, # @UnusedImport
|
||||
bitwise_xor, where, arange) # @UnusedImport
|
||||
__all__ = ['bitwise_and', 'bitwise_or', 'bitwise_not', 'binary_repr',
|
||||
'bitwise_xor', 'getbit', 'setbit', 'getbits', 'setbits']
|
||||
|
||||
|
||||
def getbit(i, bit):
|
||||
"""
|
||||
Get bit at specified position
|
||||
|
||||
Parameters
|
||||
----------
|
||||
i : array-like of uints, longs
|
||||
value to
|
||||
bit : array-like of ints or longs
|
||||
bit position between 0 and the number of bits in the uint class.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> import numpy as np
|
||||
>>> binary_repr(13)
|
||||
'1101'
|
||||
>>> getbit(13,np.arange(3,-1,-1))
|
||||
array([1, 1, 0, 1])
|
||||
>>> getbit(5, np.r_[0:4])
|
||||
array([1, 0, 1, 0])
|
||||
"""
|
||||
return bitwise_and(i, 1 << bit) >> bit
|
||||
|
||||
|
||||
def getbits(i, numbits=8):
|
||||
"""
|
||||
Returns bits of i in a list
|
||||
"""
|
||||
return getbit(i, arange(0, numbits))
|
||||
|
||||
|
||||
def setbit(i, bit, value=1):
|
||||
"""
|
||||
Set bit at specified position
|
||||
|
||||
Parameters
|
||||
----------
|
||||
i : array-like of uints, longs
|
||||
value to
|
||||
bit : array-like of ints or longs
|
||||
bit position between 0 and the number of bits in the uint class.
|
||||
value : array-like of 0 or 1
|
||||
value to set the bit to.
|
||||
|
||||
Examples
|
||||
--------
|
||||
Set bit fifth bit in the five bit binary binary representation of 9 (01001)
|
||||
yields 25 (11001)
|
||||
>>> setbit(9,4)
|
||||
array(25)
|
||||
"""
|
||||
val1 = 1 << bit
|
||||
val0 = bitwise_not(val1)
|
||||
return where((value == 0) & (i == i) & (bit == bit), bitwise_and(i, val0),
|
||||
bitwise_or(i, val1))
|
||||
|
||||
|
||||
def setbits(bitlist):
|
||||
"""
|
||||
Set bits of val to values in bitlist
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> setbits([1,1])
|
||||
3
|
||||
>>> setbits([1,0])
|
||||
1
|
||||
"""
|
||||
val = 0
|
||||
for i, j in enumerate(bitlist):
|
||||
val |= j << i
|
||||
return val
|
||||
|
||||
if __name__ == '__main__':
|
||||
from wafo.testing import test_docstrings
|
||||
test_docstrings(__file__)
|
||||
|
||||
# t = set(np.arange(8),1,1)
|
||||
# t=get(0x84,np.arange(0,8))
|
||||
# t=getbyte(0x84)
|
||||
# t=get(0x84,[0, 1, 2, 3, 4, 5, 6, 7])
|
||||
# t=get(0x20, 6)
|
||||
# bit = [0 for i in range(8)]
|
||||
# bit[7]=1
|
||||
# t = setbits(bit)
|
||||
# print(hex(t))
|
||||
@ -1,3 +0,0 @@
|
||||
# TODO Fix doctests in fig.py
|
||||
collect_ignore = ["fig.py", "MSO.py", "MSPPT.py", "powerpoint.py",
|
||||
"win32_utils.py"]
|
||||
@ -1,3 +0,0 @@
|
||||
from __future__ import absolute_import
|
||||
from .info import __doc__
|
||||
from .info import *
|
||||
@ -1,582 +0,0 @@
|
||||
5.4829629629629641e+00
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4.3614999999999986e+00
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4.4896296296296301e+00
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3.3681395348837206e+00
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4.0259523809523818e+00
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4.3966666666666665e+00
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6.0100000000000007e+00
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3.7118181818181823e+00
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8.1500000000000004e+00
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4.5788000000000011e+00
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4.1022222222222222e+00
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5.7300000000000004e+00
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2.4837209302325594e+00
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4.7172093023255792e+00
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3.2725000000000000e+00
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5.7482608695652173e+00
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4.5774074074074083e+00
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5.3236363636363633e+00
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5.9372499999999988e+00
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5.0633333333333335e+00
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4.9976923076923079e+00
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5.0788888888888879e+00
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4.1337209302325579e+00
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4.6752272727272706e+00
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2.9566666666666670e+00
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1.1678124999999998e+01
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1.0795000000000000e+01
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6.3430000000000009e+00
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5.9580952380952388e+00
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9.2199999999999989e+00
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6.3524999999999991e+00
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6.3756000000000004e+00
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7.0014285714285718e+00
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5.0088372093023246e+00
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6.2643243243243232e+00
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6.1133333333333333e+00
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5.3249999999999993e+00
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2.7474074074074073e+00
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4.6804545454545430e+00
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5.7633333333333336e+00
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5.0625000000000000e+00
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4.9824999999999999e+00
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6.3411999999999997e+00
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4.5371428571428583e+00
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3.5171428571428587e+00
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3.1532558139534879e+00
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5.2366666666666664e+00
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5.1399999999999988e+00
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8.4735714285714288e+00
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7.1446666666666658e+00
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3.5134090909090903e+00
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2.9665909090909079e+00
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6.0766666666666671e+00
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3.8666666666666667e+00
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4.9534615384615384e+00
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4.8718518518518508e+00
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4.8771428571428563e+00
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6.2160975609756104e+00
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6.8499999999999996e+00
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4.2533333333333330e+00
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4.9648148148148143e+00
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4.9369230769230761e+00
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5.1770833333333330e+00
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3.1988372093023254e+00
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3.6213636363636370e+00
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9.4099999999999984e+00
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7.7975000000000003e+00
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6.8578947368421055e+00
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7.5875000000000004e+00
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1.9634090909090893e+00
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2.4779545454545455e+00
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5.7725000000000000e+00
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5.6566666666666663e+00
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3.5634615384615382e+00
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3.4437037037037039e+00
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2.9709090909090894e+00
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2.8075000000000006e+00
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5.7266666666666666e+00
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5.0633333333333335e+00
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5.6592307692307688e+00
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4.8769230769230765e+00
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4.5544186046511639e+00
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5.0542105263157877e+00
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8.0066666666666677e+00
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6.9049999999999994e+00
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3.1661538461538474e+00
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2.7900000000000000e+00
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2.2986363636363647e+00
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2.8874418604651164e+00
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3.5874999999999999e+00
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4.4849999999999994e+00
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9.3166666666666682e+00
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9.7884615384615401e+00
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3.9845454545454548e+00
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3.8721052631578958e+00
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4.7133333333333338e+00
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3.8250000000000002e+00
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4.3996153846153847e+00
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3.8052000000000006e+00
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4.9980952380952370e+00
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4.2854545454545452e+00
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3.9000000000000004e+00
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3.5349999999999997e+00
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2.4480769230769228e+00
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3.1014814814814819e+00
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8.3771428571428572e+00
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9.8775000000000013e+00
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3.9299999999999997e+00
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||||
5.6400000000000006e+00
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||||
9.2333333333333343e+00
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7.3440909090909097e+00
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||||
5.8252380952380962e+00
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||||
6.1797674418604664e+00
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||||
4.2533333333333330e+00
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||||
4.2750000000000004e+00
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||||
4.2941666666666665e+00
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4.6457692307692309e+00
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4.7833333333333332e+00
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4.3255813953488396e+00
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5.7267500000000009e+00
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1.9861363636363627e+00
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2.6293181818181823e+00
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||||
4.6733333333333329e+00
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
8.1230000000000011e+00
|
||||
5.8008571428571418e+00
|
||||
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|
||||
1.0175000000000001e+01
|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1.2693333333333333e+01
|
||||
6.8272222222222219e+00
|
||||
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|
||||
4.7619999999999996e+00
|
||||
3.9049999999999998e+00
|
||||
6.0947500000000012e+00
|
||||
5.3353333333333337e+00
|
||||
4.9464705882352931e+00
|
||||
2.7939999999999992e+00
|
||||
3.6475000000000000e+00
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
2.6910810810810815e+00
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||||
4.6559090909090921e+00
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||||
8.3445454545454538e+00
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
7.3470967741935489e+00
|
||||
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||||
7.0499999999999980e+00
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||||
3.7619999999999991e+00
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||||
5.0675757575757592e+00
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||||
6.7249999999999988e+00
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3.5806666666666662e+00
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3.7788636363636381e+00
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||||
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3.7105882352941166e+00
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||||
5.3206818181818178e+00
|
||||
2.9713333333333343e+00
|
||||
2.7563888888888886e+00
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||||
7.6529629629629641e+00
|
||||
6.4521428571428556e+00
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||||
5.8050000000000006e+00
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||||
2.0128571428571429e+00
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||||
4.2888888888888896e+00
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5.9328571428571450e+00
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||||
1.0609999999999999e+01
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||||
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||||
3.1337500000000009e+00
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||||
3.2837837837837829e+00
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||||
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5.9644444444444451e+00
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||||
1.0795999999999999e+01
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||||
8.9992592592592597e+00
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||||
7.1279310344827573e+00
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||||
4.2302272727272721e+00
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||||
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4.2258823529411762e+00
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3.2204444444444449e+00
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||||
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4.4771428571428586e+00
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||||
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||||
4.2769565217391312e+00
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||||
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||||
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||||
3.6694444444444434e+00
|
||||
1.1215238095238094e+01
|
||||
9.1850000000000005e+00
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||||
5.3722222222222227e+00
|
||||
6.2425000000000042e+00
|
||||
7.3538461538461544e+00
|
||||
3.0506250000000006e+00
|
||||
7.5833333333333321e+00
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||||
4.0663636363636355e+00
|
||||
3.4388235294117653e+00
|
||||
3.5664444444444445e+00
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||||
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||||
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||||
3.7924444444444458e+00
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||||
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||||
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||||
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||||
3.8061111111111114e+00
|
||||
4.5399999999999991e+00
|
||||
3.6055555555555543e+00
|
||||
3.9333333333333367e+00
|
||||
7.2324324324324314e+00
|
||||
2.5419999999999998e+00
|
||||
3.4575675675675668e+00
|
||||
5.8765116279069751e+00
|
||||
9.4742857142857133e+00
|
||||
3.7115217391304345e+00
|
||||
4.8933333333333326e+00
|
||||
6.4018181818181805e+00
|
||||
4.4633333333333329e+00
|
||||
3.6568750000000003e+00
|
||||
4.3422727272727268e+00
|
||||
4.2013333333333325e+00
|
||||
4.1880555555555556e+00
|
||||
3.4113636363636362e+00
|
||||
4.9605882352941180e+00
|
||||
4.7213333333333320e+00
|
||||
2.1199999999999992e+00
|
||||
3.2251351351351345e+00
|
||||
7.0423333333333336e+00
|
||||
4.8461111111111101e+00
|
||||
3.5000000000000000e+00
|
||||
4.7093333333333343e+00
|
||||
6.8564516129032258e+00
|
||||
5.3060465116279092e+00
|
||||
3.3566666666666656e+00
|
||||
4.4884444444444425e+00
|
||||
3.9066666666666667e+00
|
||||
4.9237837837837830e+00
|
||||
2.8633333333333337e+00
|
||||
2.3141176470588238e+00
|
||||
3.9447727272727278e+00
|
||||
3.9740000000000006e+00
|
||||
3.1935135135135138e+00
|
||||
7.9008333333333338e+00
|
||||
1.2878000000000000e+01
|
||||
7.3702380952380944e+00
|
||||
8.6866666666666656e+00
|
||||
5.4328571428571442e+00
|
||||
5.2213953488372109e+00
|
||||
9.0155555555555562e+00
|
||||
3.6631707317073179e+00
|
||||
3.6888888888888900e+00
|
||||
4.1529545454545467e+00
|
||||
3.5973333333333337e+00
|
||||
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|
||||
3.3593333333333320e+00
|
||||
3.2805882352941182e+00
|
||||
4.6777777777777771e+00
|
||||
6.9690909090909088e+00
|
||||
5.7182758620689640e+00
|
||||
2.7732608695652186e+00
|
||||
3.6272222222222235e+00
|
||||
6.5997368421052620e+00
|
||||
3.5273333333333339e+00
|
||||
3.3930555555555548e+00
|
||||
1.0433571428571428e+01
|
||||
9.1080000000000005e+00
|
||||
8.6382608695652188e+00
|
||||
8.7341666666666669e+00
|
||||
7.3107692307692300e+00
|
||||
3.3476744186046501e+00
|
||||
2.6300000000000003e+00
|
||||
4.5486666666666657e+00
|
||||
3.9621428571428572e+00
|
||||
5.5491176470588242e+00
|
||||
2.5150000000000010e+00
|
||||
2.5094444444444446e+00
|
||||
2.1204651162790711e+00
|
||||
4.4307142857142860e+00
|
||||
4.5968571428571421e+00
|
||||
5.1136585365853655e+00
|
||||
4.6394444444444449e+00
|
||||
6.1210000000000004e+00
|
||||
2.0293750000000004e+00
|
||||
3.0340540540540539e+00
|
||||
3.5041304347826112e+00
|
||||
3.1037499999999993e+00
|
||||
5.1755813953488401e+00
|
||||
3.9831250000000002e+00
|
||||
4.4294594594594594e+00
|
||||
4.3844444444444450e+00
|
||||
3.9411111111111108e+00
|
||||
6.6910526315789474e+00
|
||||
8.6833333333333318e+00
|
||||
5.3197058823529408e+00
|
||||
6.9751612903225810e+00
|
||||
5.9539999999999997e+00
|
||||
4.9557777777777785e+00
|
||||
3.4481250000000006e+00
|
||||
4.9451428571428577e+00
|
||||
6.9590322580645161e+00
|
||||
8.6592307692307688e+00
|
||||
8.1816666666666666e+00
|
||||
5.0956249999999992e+00
|
||||
8.1888000000000005e+00
|
||||
1.0237058823529411e+01
|
||||
5.3599999999999994e+00
|
||||
3.6475555555555559e+00
|
||||
3.2793333333333341e+00
|
||||
5.1406060606060597e+00
|
||||
5.8326190476190503e+00
|
||||
5.1949999999999985e+00
|
||||
1.0530500000000000e+01
|
||||
6.6633333333333340e+00
|
||||
5.4306060606060624e+00
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,485 +0,0 @@
|
||||
"""
|
||||
Data package in WAFO Toolbox.
|
||||
|
||||
Contents
|
||||
--------
|
||||
atlantic - Significant wave-height data recorded in the Atlantic Ocean
|
||||
gfaks89 - Surface elevation measured at Gullfaks C 24.12.1989
|
||||
gfaksr89 - Reconstructed surface elevation measured at Gullfaks C 24.12.1989.
|
||||
japansea - coastline map of The Japan Sea
|
||||
northsea - coastline map of The Nortsea
|
||||
sea - Surface elevation dataset used in WAT version 1.1.
|
||||
sfa89 - Wind measurements at Statfjord A 24.12.1989
|
||||
sn - Fatigue experiment, constant-amplitude loading.
|
||||
yura87 - Surface elevation measured off the coast of Yura
|
||||
|
||||
|
||||
|
||||
This module gives gives detailed information and easy access to all datasets
|
||||
included in WAFO
|
||||
|
||||
"""
|
||||
import numpy as np
|
||||
|
||||
import os
|
||||
__path2data = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
__all__ = ['atlantic', 'gfaks89', 'gfaksr89', 'japansea', 'northsea', 'sea',
|
||||
'sfa89', 'sn', 'yura87']
|
||||
|
||||
_NANS = set(['nan', 'NaN', '-1.#IND00+00', '1.#IND00+00', '-1.#INF00+00'])
|
||||
|
||||
|
||||
def _tofloat(x):
|
||||
return np.nan if x in _NANS else float(x or 0)
|
||||
|
||||
|
||||
_MYCONVERTER = {}
|
||||
for i in range(2):
|
||||
_MYCONVERTER[i] = _tofloat
|
||||
|
||||
|
||||
def _load(file): # @ReservedAssignment
|
||||
""" local load function
|
||||
"""
|
||||
return np.loadtxt(os.path.join(__path2data, file))
|
||||
|
||||
|
||||
def _loadnan(file): # @ReservedAssignment
|
||||
""" local load function accepting nan's
|
||||
"""
|
||||
return np.loadtxt(os.path.join(__path2data, file), converters=_MYCONVERTER)
|
||||
|
||||
|
||||
def atlantic():
|
||||
"""
|
||||
Return Significant wave-height data recorded in the Atlantic Ocean
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 582 X 1
|
||||
Sampling Rate : ~ 14 times a month
|
||||
Device :
|
||||
Source :
|
||||
Format : ascii
|
||||
|
||||
Description
|
||||
------------
|
||||
atlantic.dat contains average significant wave-height data recorded
|
||||
approximately 14 times a month in December-February during 7 years and
|
||||
at 2 locations in the Atlantic Ocean
|
||||
|
||||
Example
|
||||
--------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> Hs = wafo.data.atlantic()
|
||||
>>> np.allclose(Hs[:3], [ 5.48296296, 4.3615 , 5.26023256])
|
||||
True
|
||||
|
||||
h = pylab.plot(Hs)
|
||||
|
||||
Acknowledgement:
|
||||
---------------
|
||||
This dataset were made available by Dr. David Carter
|
||||
and Dr. David Cotton, Satellite Observing Systems, UK.
|
||||
"""
|
||||
return _load('atlantic.dat')
|
||||
|
||||
|
||||
def gfaks89():
|
||||
"""
|
||||
Return Surface elevation measured at Gullfaks C 24.12.1989
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 39000 X 2
|
||||
Sampling Rate : 2.5 Hz
|
||||
Device : EMI laser
|
||||
Source : STATOIL
|
||||
Format : ascii, c1: time c2: surface elevation
|
||||
|
||||
Description
|
||||
------------
|
||||
The wave data was measured 24th December 1989 at the Gullfaks C platform
|
||||
in the North Sea from 17.00 to 21.20. The period from 20.00 to 20.20
|
||||
is missing and contains NaNs. The water depth of 218 m is
|
||||
regarded as deep water for the most important wave components.
|
||||
There are two EMI laser sensors named 219 and 220. This data set is
|
||||
obtained from sensor 219, which is located in the Northwest
|
||||
corner approximately two platform leg diameters away from
|
||||
the closest leg.
|
||||
Thus the wave elevation is not expected to be significantly
|
||||
affected by diffraction effects for incoming waves in the western sector.
|
||||
The wind direction for this period is from the south.
|
||||
Some difficulties in calibration of the instruments have been reported
|
||||
resulting in several consecutive measured values being equal or almost
|
||||
equal in the observed data set.
|
||||
|
||||
This dataset is for non-commercial use only.
|
||||
|
||||
Hm0 = 6.8m, Tm02 = 8s, Tp = 10.5
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> x = wafo.data.gfaks89()
|
||||
>>> np.allclose(x[:3, 1], [-0.19667949, -0.46667949, -0.38667949])
|
||||
True
|
||||
|
||||
h = pylab.plot(x[:,0],x[:,1])
|
||||
|
||||
Acknowledgement:
|
||||
---------------
|
||||
This dataset were prepared and made available by Dr. S. Haver,
|
||||
STATOIL, Norway
|
||||
|
||||
See also
|
||||
--------
|
||||
gfaksr89, northsea
|
||||
|
||||
"""
|
||||
return _loadnan('gfaks89.dat')
|
||||
|
||||
|
||||
def gfaksr89():
|
||||
"""
|
||||
Return a reconstruction of surface elevation measured at Gullfaks C
|
||||
24.12.1989.
|
||||
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 39000 X 2
|
||||
Sampling Rate : 2.5 Hz
|
||||
Device : EMI laser
|
||||
Source : STATOIL
|
||||
Format : ascii, c1: time c2: surface elevation
|
||||
|
||||
Description
|
||||
-----------
|
||||
This is a reconstructed version of the data in the GFAKS89.DAT file.
|
||||
The following calls were made to reconstruct the data:
|
||||
|
||||
inds = findoutliers(gfaks89,.02,2,1.23);
|
||||
gfaksr89 = reconstruct(gfaks89,inds,6);
|
||||
|
||||
The wave data was measured 24th December 1989 at the Gullfaks C platform
|
||||
in the North Sea from 17.00 to 21.20. The period from 20.00 to 20.20
|
||||
is missing in the original data. The water depth of 218 m is
|
||||
regarded as deep water for the most important wave components.
|
||||
There are two EMI laser sensors named 219 and 220. This data set is
|
||||
obtained from sensor 219, which is located in the Northwest
|
||||
corner approximately two platform leg diameters away from
|
||||
the closest leg.
|
||||
Thus the wave elevation is not expected to be significantly
|
||||
affected by diffraction effects for incoming waves in the western sector.
|
||||
The wind direction for this period is from the south.
|
||||
Some difficulties in calibration of the instruments have been reported
|
||||
resulting in several consecutive measured values being equal or almost
|
||||
equal in the observed data set.
|
||||
|
||||
Hm0 = 6.8m, Tm02 = 8s, Tp = 10.5
|
||||
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> x = wafo.data.gfaksr89()
|
||||
|
||||
h = pylab.plot(x[:,0],x[:,1])
|
||||
|
||||
See also
|
||||
--------
|
||||
gfaks89
|
||||
"""
|
||||
return _loadnan('gfaksr89.dat')
|
||||
|
||||
|
||||
def japansea():
|
||||
"""
|
||||
Return coastline map of The Japan Sea
|
||||
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 692 X 2
|
||||
Sampling Rate :
|
||||
Device :
|
||||
Source : http://crusty.er.usgs.gov/coast/getcoast.html
|
||||
Format : ascii, c1: longitude c2: latitude
|
||||
|
||||
Description
|
||||
-----------
|
||||
JAPANSEA.DAT contains data for plotting a map of The Japan Sea.
|
||||
The data is obtained from USGS coastline extractor.
|
||||
|
||||
Example:
|
||||
-------
|
||||
#the map is seen by
|
||||
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> map1 = wafo.data.japansea()
|
||||
>>> np.allclose(map1[1:4, 0], [ 141.960057, 142.058624, 142.103214])
|
||||
True
|
||||
|
||||
h = pylab.plot(map1[:,0],map1[:,1])
|
||||
lon_loc = [131,132,132,135,139.5,139]
|
||||
lat_loc = [46, 43, 40, 35, 38.3, 35.7]
|
||||
loc = ['China','Vladivostok','Japan Sea', 'Japan', 'Yura','Tokyo']
|
||||
algn = 'right'
|
||||
for lon, lat, name in zip(lon_loc,lat_loc,loc):
|
||||
pylab.text(lon,lat,name,horizontalalignment=algn)
|
||||
|
||||
|
||||
# If you have the m_map toolbox (see http://www.ocgy.ubc.ca/~rich/):
|
||||
m_proj('lambert','long',[130 148],'lat',[30 48]);
|
||||
m_line(map(:,1),map(:,2));
|
||||
m_grid('box','fancy','tickdir','out');
|
||||
m_text(131,46,'China');
|
||||
m_text(132,43,'Vladivostok');
|
||||
m_text(132,40,'Japan Sea');
|
||||
m_text(135,35,'Japan');
|
||||
m_text(139.5,38.3,'Yura');
|
||||
m_text(139,35.7,'Tokyo');
|
||||
"""
|
||||
return _loadnan('japansea.dat')
|
||||
|
||||
|
||||
def northsea():
|
||||
"""
|
||||
NORTHSEA coastline map of The Nortsea
|
||||
|
||||
Data summary
|
||||
-------------
|
||||
Size : 60646 X 2
|
||||
Sampling Rate :
|
||||
Device :
|
||||
Source : http://crusty.er.usgs.gov/coast/getcoast.html
|
||||
Format : ascii, c1: longitude c2: latitude
|
||||
|
||||
Description
|
||||
-----------
|
||||
NORTHSEA.DAT contains data for plotting a map of The Northsea.
|
||||
The data is obtained from USGS coastline extractor.
|
||||
|
||||
Example
|
||||
-------
|
||||
# the map is seen by
|
||||
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> map1 = wafo.data.northsea()
|
||||
>>> np.allclose(map1[1:4, 0], [ 1.261996, 1.264064, 1.268171])
|
||||
True
|
||||
|
||||
h = pylab.plot(map1[:,0],map1[:,1])
|
||||
lon_pltfrm = [1.8, 2.3, 2., 1.9, 2.6]
|
||||
lat_pltfrm = [61.2, 61.2, 59.9, 58.4, 57.7]
|
||||
pltfrm = ['Statfjord A', 'Gullfaks C', 'Frigg', 'Sleipner', 'Draupner']
|
||||
h = pylab.scatter(lon_pltfrm,lat_pltfrm);
|
||||
algn = 'right'
|
||||
for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
|
||||
pylab.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
|
||||
|
||||
|
||||
lon_city = [10.8, 10.8, 5.52, 5.2]
|
||||
lat_city = [59.85, 63.4, 58.9, 60.3]
|
||||
city = ['Oslo','Trondheim','Stavanger', 'Bergen']
|
||||
h = pylab.scatter(lon_city,lat_city);
|
||||
algn = 'right'
|
||||
for lon, lat, name in zip(lon_city,lat_city,city):
|
||||
pylab.text(lon,lat,name,horizontalalignment=algn)
|
||||
|
||||
# If you have the mpl_toolkits.basemap installed
|
||||
from mpl_toolkits.basemap import Basemap
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
# setup Lambert Conformal basemap.
|
||||
m = Basemap(width=1200000,height=900000,projection='lcc',
|
||||
resolution='f',lat_1=56.,lat_2=64,lat_0=58,lon_0=5.)
|
||||
m.drawcoastlines()
|
||||
h = m.scatter(lon_pltfrm,lat_pltfrm);
|
||||
algn = 'right'
|
||||
for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
|
||||
m.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
|
||||
m.scatter(lon_city,lat_city)
|
||||
algn = 'right'
|
||||
for lon, lat, name in zip(lon_city,lat_city,city):
|
||||
m.text(lon,lat,name,horizontalalignment=algn)
|
||||
"""
|
||||
return _loadnan('northsea.dat')
|
||||
|
||||
|
||||
def sea():
|
||||
"""
|
||||
Return Surface elevation dataset used in WAT version 1.1.
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 9524 X 2
|
||||
Sampling Rate : 4.0 Hz
|
||||
Device : unknown
|
||||
Source : unknown
|
||||
Format : ascii, c1: time c2: surface elevation
|
||||
|
||||
Description
|
||||
-----------
|
||||
The wave data was used in one of WAFO predecessors, i.e. the Wave
|
||||
Analysis Toolbox version 1.1 (WAT)
|
||||
Hm0 = 1.9m, Tm02 = 4.0s, Tp2 = 11.5s Tp1=5.6s
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> x = wafo.data.sea()
|
||||
>>> np.allclose(x[:3,1],[-1.2004945 , -1.0904945 , -0.79049454])
|
||||
True
|
||||
|
||||
h = pylab.plot(x[:,0],x[:,1])
|
||||
"""
|
||||
return _load('sea.dat')
|
||||
|
||||
|
||||
def sfa89():
|
||||
"""
|
||||
Return Wind measurements at Statfjord A 24.12.1989
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 144 X 3
|
||||
Sampling Rate : 1/600 Hz
|
||||
Device :
|
||||
Source : DNMI (The Norwegian Meteorological Institute)
|
||||
Format : ascii, c1: time (hours)
|
||||
c2: velocity (m/s)
|
||||
c3: direction (degrees)
|
||||
Description
|
||||
-----------
|
||||
The registration of wind speeds at the Gullfaks field
|
||||
started up on Statfjord A in 1978 and continued until 1990.
|
||||
The dataregistration was transferred to Gullfaks C in Nov 1989.
|
||||
Due to some difficulties of the windregistration on Gullfaks C in
|
||||
the beginning, they continued to use the registered data from
|
||||
Statfjord A.
|
||||
The windspeed is measured in (meter/second), 110 m above mean water
|
||||
level (MWL) and the wind direction is given in degrees for the data.
|
||||
The data are a mean value of every 10 minutes.
|
||||
Wind directions are defined in the meteorological convention, i.e.,
|
||||
0 degrees = wind approaching from North, 90 degrees = wind from East, etc.
|
||||
This dataset is for non-commercial use only.
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> x = wafo.data.sfa89()
|
||||
|
||||
h = pylab.plot(x[:,0],x[:,1])
|
||||
|
||||
Acknowledgement
|
||||
----------------
|
||||
These data are made available by Knut A. Iden, DNMI.
|
||||
|
||||
See also
|
||||
--------
|
||||
northsea
|
||||
"""
|
||||
return _load('sfa89.dat')
|
||||
|
||||
|
||||
def sn():
|
||||
"""
|
||||
Return SN Fatigue experiment, constant-amplitude loading.
|
||||
|
||||
|
||||
Data summary
|
||||
------------
|
||||
Size : 40 X 2
|
||||
Source : unknown
|
||||
Format : ascii, c1: Amplitude MPa c2: Number of cycles
|
||||
|
||||
Description
|
||||
-----------
|
||||
A fatigue experiment with constant amplitudes at five levels:
|
||||
10,15,20,25 and 30 MPa. For each level is related 8 observations of
|
||||
the number of cycles to failure.
|
||||
|
||||
The origin of the data is unknown.
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> x = wafo.data.sn()
|
||||
|
||||
h = pylab.plot(x[:,0],x[:,1])
|
||||
|
||||
See also
|
||||
--------
|
||||
The same data appear in the directory wdemos/itmkurs/
|
||||
as SN.mat.
|
||||
|
||||
"""
|
||||
return _load('sn.dat')
|
||||
|
||||
|
||||
def yura87():
|
||||
"""
|
||||
Return Surface elevation measured off the coast of Yura.
|
||||
|
||||
|
||||
Data summary
|
||||
-----------
|
||||
Size : 85547 X 4
|
||||
Sampling Rate : 1 Hz
|
||||
Device : ultrasonic wave gauges
|
||||
Source : SRI, Ministry of Transport, Japan
|
||||
Format : ascii, c1: time (sec) c2-4: surface elevation (m)
|
||||
|
||||
Description
|
||||
-----------
|
||||
The wave data was measured at the Poseidon platform
|
||||
in the Japan Sea from 24th November 1987 08.12 hours to 25th November
|
||||
1987 07.57 hours. Poseidon was located 3 km off the coast of Yura
|
||||
in the Yamagata prefecture, in the Japan Sea during the measurements.
|
||||
The most important wave components are to some extent influenced by the
|
||||
water depth of 42 m. The data are measured with three ultrasonic wave
|
||||
gauges located at the sea floor and the relative coordinates of the
|
||||
gauges are as follows (x-axis points to the East, y-axis points to
|
||||
the North):
|
||||
X (m) Y (m)
|
||||
c2: -4.93, 25.02
|
||||
c3: 5.80, 92.12
|
||||
c4: 0.00, 0.00
|
||||
|
||||
This dataset is for non-commercial use only.
|
||||
|
||||
Hm0 = 5.1m, Tm02 = 7.7s, Tp = 12.8s
|
||||
Example
|
||||
-------
|
||||
>>> import pylab
|
||||
>>> import wafo
|
||||
>>> x = wafo.data.yura87()
|
||||
|
||||
h = pylab.plot(x[:,0],x[:,1])
|
||||
|
||||
Acknowledgement:
|
||||
-----------------
|
||||
This dataset were prepared and made available by Dr. Sc. H. Tomita,
|
||||
Ship Research Institute, Ministry of Transport, Japan.
|
||||
|
||||
See also
|
||||
--------
|
||||
japansea
|
||||
"""
|
||||
return _load('yura87.dat')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
@ -1,692 +0,0 @@
|
||||
nan nan
|
||||
141.960057 40.022926
|
||||
142.058624 39.818752
|
||||
142.103214 39.640392
|
||||
142.157191 39.469073
|
||||
142.136070 39.332957
|
||||
142.070358 39.229696
|
||||
142.046890 39.074805
|
||||
141.927201 38.955116
|
||||
141.805166 38.948076
|
||||
141.727720 38.765023
|
||||
141.638541 38.542073
|
||||
141.638541 38.368408
|
||||
141.629153 38.316777
|
||||
141.396817 38.333205
|
||||
141.143358 38.194742
|
||||
141.054179 37.934243
|
||||
141.087034 37.671397
|
||||
141.143358 37.453142
|
||||
141.143358 37.143360
|
||||
141.164480 36.957960
|
||||
140.976733 36.826537
|
||||
140.854698 36.603588
|
||||
140.812455 36.533182
|
||||
140.756131 36.258603
|
||||
140.744396 35.944127
|
||||
140.887553 35.791583
|
||||
140.922756 35.683628
|
||||
140.767865 35.603836
|
||||
140.589505 35.359765
|
||||
140.479204 35.162631
|
||||
140.237480 35.007740
|
||||
nan nan
|
||||
139.984022 35.233036
|
||||
140.016877 35.369152
|
||||
140.148300 35.495881
|
||||
140.192890 35.622610
|
||||
nan nan
|
||||
139.972288 39.161638
|
||||
140.070855 39.323569
|
||||
140.138913 39.530091
|
||||
140.148300 39.750693
|
||||
140.016877 39.844567
|
||||
nan nan
|
||||
140.237480 35.007740
|
||||
139.993409 34.862236
|
||||
139.951166 35.007740
|
||||
139.984022 35.233036
|
||||
nan nan
|
||||
140.192890 35.622610
|
||||
139.951166 35.622610
|
||||
139.894842 35.451291
|
||||
139.805662 35.305788
|
||||
139.784541 35.153243
|
||||
139.653118 35.233036
|
||||
139.409047 35.261198
|
||||
139.298746 35.115694
|
||||
139.254156 34.918560
|
||||
139.209566 34.726119
|
||||
139.089878 34.618165
|
||||
138.956108 34.636940
|
||||
138.911518 34.871623
|
||||
138.946721 35.035902
|
||||
138.791830 35.052329
|
||||
138.625204 34.925600
|
||||
138.460926 34.763669
|
||||
138.348278 34.580616
|
||||
138.315422 34.554800
|
||||
138.195733 34.571228
|
||||
137.996252 34.608778
|
||||
137.731060 34.618165
|
||||
137.477602 34.618165
|
||||
137.256999 34.554800
|
||||
137.212409 34.554800
|
||||
137.278121 34.636940
|
||||
137.388422 34.709692
|
||||
137.266387 34.726119
|
||||
137.167820 34.744894
|
||||
137.113842 34.798871
|
||||
137.057518 34.709692
|
||||
136.935483 34.817646
|
||||
136.926096 35.017127
|
||||
136.836916 34.981924
|
||||
136.714880 34.754281
|
||||
136.628048 34.618165
|
||||
136.771204 34.536026
|
||||
136.968339 34.416337
|
||||
136.968339 34.179307
|
||||
136.836916 34.270833
|
||||
136.714880 34.216856
|
||||
136.583458 34.198081
|
||||
136.449688 34.134717
|
||||
136.372243 34.040844
|
||||
136.351121 33.977479
|
||||
136.229086 33.813201
|
||||
136.064807 33.555049
|
||||
135.853592 33.454135
|
||||
135.701048 33.498725
|
||||
135.579012 33.555049
|
||||
135.478098 33.702899
|
||||
135.290352 33.803813
|
||||
135.236374 33.977479
|
||||
135.248108 34.134717
|
||||
135.248108 34.261446
|
||||
135.334941 34.334198
|
||||
135.501567 34.564188
|
||||
135.522688 34.636940
|
||||
135.478098 34.662755
|
||||
135.325554 34.643980
|
||||
135.147195 34.608778
|
||||
134.926592 34.681530
|
||||
134.684868 34.726119
|
||||
134.464266 34.716732
|
||||
134.264785 34.580616
|
||||
134.142749 34.526638
|
||||
133.978471 34.435112
|
||||
133.955002 34.425725
|
||||
133.901025 34.463274
|
||||
133.835314 34.489089
|
||||
133.680423 34.444499
|
||||
133.579509 34.435112
|
||||
133.403496 34.371747
|
||||
133.281461 34.362360
|
||||
133.161772 34.308383
|
||||
133.016268 34.280221
|
||||
132.851990 34.233284
|
||||
132.664243 34.198081
|
||||
132.530473 34.298995
|
||||
132.420172 34.270833
|
||||
132.300484 34.015028
|
||||
132.244160 33.885952
|
||||
132.154980 33.867178
|
||||
131.924990 33.932889
|
||||
131.758365 34.005641
|
||||
131.570618 33.949317
|
||||
131.427461 33.949317
|
||||
131.338281 33.914114
|
||||
131.162269 33.968092
|
||||
130.986256 33.996254
|
||||
130.974522 34.207469
|
||||
130.997990 34.317770
|
||||
131.195124 34.362360
|
||||
131.460317 34.416337
|
||||
131.636329 34.571228
|
||||
131.814689 34.681530
|
||||
132.122124 34.827033
|
||||
132.321605 35.045289
|
||||
132.563329 35.179059
|
||||
132.718220 35.387927
|
||||
132.917701 35.486494
|
||||
133.138304 35.549859
|
||||
133.304929 35.521697
|
||||
133.459820 35.477107
|
||||
133.800111 35.505269
|
||||
134.077038 35.495881
|
||||
134.309374 35.540471
|
||||
134.583954 35.594448
|
||||
134.795169 35.631998
|
||||
134.959448 35.639038
|
||||
135.137807 35.693015
|
||||
135.325554 35.702403
|
||||
135.313820 35.559246
|
||||
135.412387 35.531084
|
||||
135.733903 35.477107
|
||||
135.820736 35.495881
|
||||
135.909916 35.568633
|
||||
136.031951 35.631998
|
||||
136.142253 35.693015
|
||||
136.142253 35.944127
|
||||
136.151640 36.176464
|
||||
136.440301 36.382985
|
||||
136.682025 36.631750
|
||||
136.836916 36.932144
|
||||
136.848650 37.143360
|
||||
136.893240 37.312332
|
||||
137.233531 37.469570
|
||||
137.388422 37.453142
|
||||
137.355566 37.312332
|
||||
137.212409 37.248967
|
||||
137.057518 37.126932
|
||||
137.036397 37.082342
|
||||
137.113842 36.915717
|
||||
137.113842 36.737357
|
||||
137.224144 36.727970
|
||||
137.433012 36.756132
|
||||
137.609025 36.906329
|
||||
137.853095 37.011937
|
||||
138.151144 37.091729
|
||||
138.470313 37.284170
|
||||
138.681528 37.453142
|
||||
138.836419 37.654970
|
||||
139.012432 37.847410
|
||||
139.233035 37.960058
|
||||
139.244769 37.969446
|
||||
139.254156 37.976486
|
||||
139.355070 37.976486
|
||||
139.507614 38.152499
|
||||
139.531083 38.307390
|
||||
139.575672 38.438813
|
||||
139.695361 38.628906
|
||||
139.850252 38.835428
|
||||
139.927698 38.997359
|
||||
139.972288 39.161638
|
||||
nan nan
|
||||
140.016877 39.844567
|
||||
139.805662 39.912625
|
||||
139.906576 39.980683
|
||||
nan nan
|
||||
138.604083 38.272187
|
||||
138.580614 38.098521
|
||||
138.625204 38.056278
|
||||
138.514903 37.812208
|
||||
138.360012 37.802820
|
||||
138.327156 37.950671
|
||||
138.449192 38.204129
|
||||
138.604083 38.272187
|
||||
nan nan
|
||||
134.276519 33.268735
|
||||
134.065304 33.379036
|
||||
133.800111 33.454135
|
||||
133.570121 33.397811
|
||||
133.391762 33.296897
|
||||
133.293195 33.111497
|
||||
133.105448 32.879160
|
||||
133.084326 32.712535
|
||||
132.905967 32.693760
|
||||
132.751076 32.804061
|
||||
132.685365 32.879160
|
||||
132.553942 33.083335
|
||||
132.575063 33.167821
|
||||
132.509352 33.278122
|
||||
132.387316 33.360261
|
||||
132.223038 33.360261
|
||||
132.488230 33.536274
|
||||
132.697099 33.756876
|
||||
132.896580 33.977479
|
||||
133.105448 33.949317
|
||||
133.239218 33.895340
|
||||
133.436352 33.932889
|
||||
133.624099 34.005641
|
||||
133.689810 34.134717
|
||||
133.823580 34.226244
|
||||
133.933881 34.317770
|
||||
134.065304 34.334198
|
||||
134.264785 34.289608
|
||||
134.452531 34.198081
|
||||
134.619157 34.188694
|
||||
134.661400 33.996254
|
||||
134.717724 33.822588
|
||||
134.762314 33.803813
|
||||
134.771701 33.794426
|
||||
134.762314 33.766264
|
||||
134.652012 33.665350
|
||||
134.551098 33.618413
|
||||
134.452531 33.517499
|
||||
134.363352 33.369649
|
||||
134.276519 33.268735
|
||||
nan nan
|
||||
135.060362 34.571228
|
||||
135.027506 34.526638
|
||||
134.872615 34.371747
|
||||
134.762314 34.207469
|
||||
134.872615 34.179307
|
||||
134.982916 34.226244
|
||||
135.004038 34.390522
|
||||
135.060362 34.571228
|
||||
nan nan
|
||||
130.864220 33.876565
|
||||
130.920544 33.885952
|
||||
130.831365 33.895340
|
||||
130.676474 33.848403
|
||||
130.521583 33.721674
|
||||
130.423015 33.564436
|
||||
130.279859 33.564436
|
||||
130.178945 33.470563
|
||||
130.035788 33.416585
|
||||
nan nan
|
||||
130.000000 32.676758
|
||||
130.146089 32.712535
|
||||
130.324448 32.628049
|
||||
130.411281 32.759472
|
||||
130.289246 32.813449
|
||||
130.235269 32.897935
|
||||
130.235269 33.083335
|
||||
130.333836 33.149046
|
||||
130.444137 32.944872
|
||||
130.577907 32.766512
|
||||
130.577907 32.599887
|
||||
130.622496 32.581112
|
||||
130.631884 32.581112
|
||||
130.622496 32.386325
|
||||
130.521583 32.179803
|
||||
130.401894 32.095317
|
||||
130.300980 31.935732
|
||||
130.289246 31.710436
|
||||
130.345570 31.464019
|
||||
130.289246 31.323208
|
||||
130.401894 31.229335
|
||||
130.655352 31.123727
|
||||
130.709329 31.341983
|
||||
130.631884 31.520343
|
||||
130.688208 31.663500
|
||||
130.852486 31.625950
|
||||
130.775041 31.569626
|
||||
130.753919 31.445244
|
||||
130.810243 31.248110
|
||||
130.810243 31.011079
|
||||
130.941666 31.048629
|
||||
131.096557 31.161277
|
||||
131.108291 31.304434
|
||||
131.251448 31.435857
|
||||
131.361750 31.388920
|
||||
131.483785 31.625950
|
||||
131.549496 31.879408
|
||||
131.615208 32.114092
|
||||
131.725509 32.386325
|
||||
131.835810 32.552950
|
||||
131.967233 32.712535
|
||||
132.023557 32.794674
|
||||
132.035291 32.841611
|
||||
132.023557 32.998849
|
||||
131.978967 33.073947
|
||||
131.924990 33.174861
|
||||
131.680919 33.221798
|
||||
131.648063 33.306284
|
||||
131.746630 33.517499
|
||||
131.692653 33.627801
|
||||
131.537762 33.583211
|
||||
131.239714 33.573823
|
||||
131.051967 33.766264
|
||||
131.040233 33.857790
|
||||
130.962788 33.857790
|
||||
130.864220 33.876565
|
||||
nan nan
|
||||
130.146089 32.477851
|
||||
130.092112 32.358163
|
||||
130.080378 32.217353
|
||||
130.113233 32.142254
|
||||
130.247003 32.421527
|
||||
130.146089 32.477851
|
||||
nan nan
|
||||
131.117679 30.630892
|
||||
131.084823 30.764662
|
||||
131.030846 30.630892
|
||||
130.962788 30.410290
|
||||
131.030846 30.344578
|
||||
131.117679 30.630892
|
||||
nan nan
|
||||
130.545051 30.353966
|
||||
130.512195 30.410290
|
||||
130.455871 30.325804
|
||||
130.512195 30.210809
|
||||
130.676474 30.192034
|
||||
130.676474 30.325804
|
||||
130.545051 30.353966
|
||||
nan nan
|
||||
130.897076 37.539975
|
||||
130.843099 37.495385
|
||||
130.843099 37.478957
|
||||
130.885342 37.453142
|
||||
130.974522 37.504772
|
||||
130.897076 37.539975
|
||||
nan nan
|
||||
130.035788 33.416585
|
||||
130.000000 33.445983
|
||||
nan nan
|
||||
142.678694 48.000000
|
||||
142.654720 47.880130
|
||||
142.666454 47.650140
|
||||
142.809611 47.448313
|
||||
142.952768 47.335665
|
||||
143.107659 47.194854
|
||||
143.140515 47.028229
|
||||
143.206226 46.915581
|
||||
143.307140 46.840482
|
||||
143.462031 46.772424
|
||||
143.516009 46.802933
|
||||
143.572333 46.755996
|
||||
143.626310 46.582330
|
||||
143.670900 46.415705
|
||||
143.649778 46.225612
|
||||
143.548864 46.070720
|
||||
143.494887 46.270201
|
||||
143.417442 46.537741
|
||||
143.173371 46.605799
|
||||
142.943381 46.687938
|
||||
142.699310 46.697325
|
||||
142.523297 46.558862
|
||||
142.434118 46.361728
|
||||
142.356672 46.148166
|
||||
142.258105 45.955725
|
||||
142.025768 46.049599
|
||||
141.969444 46.354687
|
||||
141.960057 46.657429
|
||||
142.025768 46.983639
|
||||
142.070358 47.194854
|
||||
142.091480 47.441272
|
||||
142.124335 47.671262
|
||||
142.157191 47.894211
|
||||
142.248875 48.000000
|
||||
nan nan
|
||||
148.000000 44.948933
|
||||
147.909284 44.948933
|
||||
147.789595 44.885569
|
||||
147.679294 44.754146
|
||||
147.566646 44.697822
|
||||
147.435223 44.620376
|
||||
147.313188 44.524156
|
||||
147.214621 44.446711
|
||||
147.071464 44.446711
|
||||
147.125441 44.540584
|
||||
147.235742 44.650885
|
||||
147.280332 44.768227
|
||||
147.381246 44.801083
|
||||
147.590114 45.002910
|
||||
147.733271 45.113212
|
||||
147.864694 45.160148
|
||||
147.953874 45.291571
|
||||
148.000000 45.306694
|
||||
nan nan
|
||||
146.937694 43.843574
|
||||
147.015140 43.820106
|
||||
146.928307 43.756741
|
||||
146.761682 43.691030
|
||||
146.663115 43.730926
|
||||
146.761682 43.796637
|
||||
146.937694 43.843574
|
||||
nan nan
|
||||
146.210175 44.446711
|
||||
146.231297 44.486607
|
||||
146.287621 44.446711
|
||||
146.365066 44.399774
|
||||
146.419044 44.303554
|
||||
146.320477 44.249576
|
||||
146.120996 44.097032
|
||||
145.912127 43.939794
|
||||
145.724380 43.796637
|
||||
145.614079 43.667561
|
||||
145.536634 43.796637
|
||||
145.703259 43.946835
|
||||
145.900393 44.113460
|
||||
146.067018 44.280085
|
||||
146.144464 44.430283
|
||||
146.210175 44.446711
|
||||
nan nan
|
||||
145.083695 44.066523
|
||||
145.149406 44.113460
|
||||
145.370008 44.256617
|
||||
145.447454 44.209680
|
||||
145.348887 44.003159
|
||||
145.182262 43.780209
|
||||
145.250320 43.620625
|
||||
145.426332 43.540832
|
||||
145.426332 43.339004
|
||||
145.646935 43.331964
|
||||
145.912127 43.404716
|
||||
145.813560 43.249825
|
||||
145.492044 43.146564
|
||||
145.226851 43.001060
|
||||
145.050839 42.977592
|
||||
144.952272 43.017488
|
||||
144.841970 42.904840
|
||||
144.508720 42.935349
|
||||
144.234140 42.911880
|
||||
143.957214 42.799232
|
||||
143.692021 42.538734
|
||||
143.516009 42.285275
|
||||
143.450297 42.048245
|
||||
143.328262 41.923863
|
||||
143.051335 42.081101
|
||||
142.842467 42.196096
|
||||
142.478708 42.294663
|
||||
142.180659 42.440166
|
||||
141.960057 42.529346
|
||||
141.650275 42.555161
|
||||
141.417938 42.473022
|
||||
141.253660 42.367415
|
||||
141.054179 42.383842
|
||||
140.845310 42.505878
|
||||
140.556650 42.489450
|
||||
140.404105 42.311091
|
||||
140.502672 42.153853
|
||||
140.702153 42.097529
|
||||
140.922756 42.015389
|
||||
141.110503 41.900394
|
||||
141.241925 41.808868
|
||||
141.176214 41.710301
|
||||
140.955612 41.726729
|
||||
140.788986 41.719688
|
||||
140.624708 41.635202
|
||||
140.523794 41.520207
|
||||
140.270336 41.388784
|
||||
140.138913 41.421640
|
||||
140.082589 41.562450
|
||||
140.160034 41.801827
|
||||
140.216358 41.982534
|
||||
140.028612 42.120997
|
||||
nan nan
|
||||
139.927698 42.522306
|
||||
140.094323 42.660769
|
||||
140.326660 42.749949
|
||||
140.514407 42.904840
|
||||
140.568384 43.057384
|
||||
140.469817 43.162992
|
||||
140.514407 43.282680
|
||||
140.744396 43.242784
|
||||
140.964999 43.179420
|
||||
141.152746 43.179420
|
||||
141.363961 43.186460
|
||||
141.495384 43.378901
|
||||
141.462528 43.557260
|
||||
141.462528 43.716845
|
||||
141.596297 43.810718
|
||||
141.727720 44.043055
|
||||
141.739454 44.303554
|
||||
141.805166 44.547624
|
||||
141.870877 44.721290
|
||||
141.805166 44.862100
|
||||
141.727720 45.035766
|
||||
141.683130 45.160148
|
||||
141.694865 45.284531
|
||||
141.793432 45.354936
|
||||
141.903733 45.432381
|
||||
141.992913 45.462890
|
||||
142.124335 45.376057
|
||||
142.136070 45.361976
|
||||
142.180659 45.284531
|
||||
142.347285 45.190657
|
||||
142.490442 45.042807
|
||||
142.610130 44.918424
|
||||
142.765021 44.768227
|
||||
142.931647 44.643845
|
||||
143.140515 44.493647
|
||||
143.339996 44.383346
|
||||
143.516009 44.280085
|
||||
143.736611 44.193252
|
||||
143.936092 44.129888
|
||||
144.144960 44.082951
|
||||
144.332707 44.059483
|
||||
144.443009 43.970303
|
||||
144.654224 43.906938
|
||||
144.863092 43.939794
|
||||
145.083695 44.066523
|
||||
nan nan
|
||||
141.241925 45.230553
|
||||
141.131624 45.167189
|
||||
141.241925 45.096784
|
||||
141.340492 45.089743
|
||||
141.363961 45.230553
|
||||
141.241925 45.230553
|
||||
nan nan
|
||||
141.209070 41.372356
|
||||
141.230191 41.379397
|
||||
141.363961 41.372356
|
||||
141.485996 41.379397
|
||||
141.528239 41.280830
|
||||
141.518852 41.137673
|
||||
141.495384 40.947579
|
||||
141.518852 40.722283
|
||||
141.605685 40.501681
|
||||
141.793432 40.325668
|
||||
141.903733 40.130881
|
||||
141.960057 40.022926
|
||||
nan nan
|
||||
139.906576 39.980683
|
||||
140.082589 40.182511
|
||||
140.070855 40.435969
|
||||
140.061467 40.611982
|
||||
140.148300 40.729324
|
||||
140.282070 40.787995
|
||||
140.314926 40.787995
|
||||
140.336047 40.863093
|
||||
140.347781 41.022678
|
||||
140.413493 41.130632
|
||||
140.547262 41.179916
|
||||
140.657563 41.071962
|
||||
140.744396 40.879521
|
||||
140.821842 40.797382
|
||||
140.943877 40.931151
|
||||
141.075300 40.888908
|
||||
141.197336 40.895949
|
||||
141.319371 41.088389
|
||||
141.331105 41.245627
|
||||
141.185601 41.156448
|
||||
140.943877 41.104817
|
||||
140.878166 41.264402
|
||||
140.943877 41.428681
|
||||
141.042444 41.454496
|
||||
141.209070 41.372356
|
||||
nan nan
|
||||
132.729954 44.838632
|
||||
132.786278 44.909037
|
||||
132.828521 45.113212
|
||||
132.741689 45.244634
|
||||
132.521086 45.284531
|
||||
132.178448 45.237594
|
||||
132.089268 45.120252
|
||||
132.199570 44.941893
|
||||
132.199570 44.777614
|
||||
132.300484 44.667313
|
||||
132.476496 44.596908
|
||||
132.697099 44.681394
|
||||
132.729954 44.838632
|
||||
nan nan
|
||||
139.676457 48.000000
|
||||
139.620262 47.962270
|
||||
139.420781 47.814419
|
||||
139.265890 47.619632
|
||||
139.122733 47.441272
|
||||
138.934986 47.291075
|
||||
138.747240 47.140877
|
||||
138.592349 46.960171
|
||||
138.538371 46.817014
|
||||
138.460926 46.643348
|
||||
138.404602 46.521313
|
||||
138.261445 46.324179
|
||||
138.139409 46.162247
|
||||
138.261445 46.392237
|
||||
138.172265 46.256120
|
||||
138.085432 46.110617
|
||||
137.885951 45.979194
|
||||
137.787384 45.817262
|
||||
137.587903 45.655331
|
||||
137.379035 45.509827
|
||||
137.212409 45.361976
|
||||
137.012928 45.230553
|
||||
136.869772 45.113212
|
||||
136.804060 45.035766
|
||||
136.682025 44.901997
|
||||
136.538868 44.808123
|
||||
136.395711 44.643845
|
||||
136.297144 44.500688
|
||||
136.097663 44.413855
|
||||
135.921650 44.289473
|
||||
135.776146 44.146316
|
||||
135.755025 44.033668
|
||||
135.632989 43.899898
|
||||
135.513301 43.763781
|
||||
135.346676 43.597156
|
||||
135.093217 43.404716
|
||||
134.872615 43.282680
|
||||
134.628544 43.162992
|
||||
134.330496 43.008101
|
||||
134.065304 42.846169
|
||||
133.877557 42.782804
|
||||
133.746134 42.782804
|
||||
133.546653 42.740561
|
||||
133.326051 42.677197
|
||||
133.105448 42.700665
|
||||
132.995147 42.749949
|
||||
132.929435 42.740561
|
||||
132.762810 42.829741
|
||||
132.563329 42.855556
|
||||
132.410785 42.846169
|
||||
132.377929 42.895452
|
||||
132.366195 43.033916
|
||||
132.399051 43.137177
|
||||
132.399051 43.242784
|
||||
132.288749 43.209928
|
||||
132.068147 43.090240
|
||||
132.002435 43.146564
|
||||
132.089268 43.282680
|
||||
131.934377 43.299108
|
||||
131.913256 43.418797
|
||||
131.847544 43.292068
|
||||
131.781833 43.106668
|
||||
131.648063 42.984632
|
||||
131.558884 42.928308
|
||||
131.504906 42.822701
|
||||
131.382871 42.740561
|
||||
131.284304 42.611485
|
||||
131.073089 42.611485
|
||||
130.908810 42.620873
|
||||
130.753919 42.595058
|
||||
130.810243 42.529346
|
||||
130.843099 42.423739
|
||||
130.622496 42.562202
|
||||
nan nan
|
||||
140.028612 42.120997
|
||||
139.883108 42.212523
|
||||
139.894842 42.334559
|
||||
139.927698 42.522306
|
||||
nan nan
|
||||
130.699942 42.374455
|
||||
130.732798 42.278235
|
||||
130.610762 42.301703
|
||||
130.476993 42.268848
|
||||
130.324448 42.130384
|
||||
130.103846 41.956718
|
||||
nan nan
|
||||
130.103846 41.956718
|
||||
130.000000 41.866852
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,16 +0,0 @@
|
||||
%SEA Surface elevation dataset used in WAT version 1.1.
|
||||
%
|
||||
% CALL: xn = load('sea.dat');
|
||||
%
|
||||
% Size : 9524 X 2
|
||||
% Sampling Rate : 4.0 Hz
|
||||
% Device : unknown
|
||||
% Source : unknown
|
||||
% Format : ascii, c1: time c2: surface elevation
|
||||
% Description :
|
||||
% The wave data was used in one of WAFO predecessors, i.e. the Wave
|
||||
% Analysis Toolbox version 1.1 (WAT)
|
||||
%
|
||||
% Hm0 = 1.9m, Tm02 = 4.0s, Tp2 = 11.5s Tp1=5.6s
|
||||
%
|
||||
% See also
|
||||
@ -1,144 +0,0 @@
|
||||
0.0000000e+00 1.2400000e+01 2.0700000e+02
|
||||
1.6666667e-01 1.1800000e+01 2.0480000e+02
|
||||
3.3333333e-01 1.1300000e+01 1.9940000e+02
|
||||
5.0000000e-01 1.1800000e+01 1.9560000e+02
|
||||
6.6666667e-01 1.3000000e+01 1.9500000e+02
|
||||
8.3333333e-01 1.3400000e+01 1.9550000e+02
|
||||
1.0000000e+00 1.2000000e+01 1.9250000e+02
|
||||
1.1666667e+00 1.3300000e+01 1.9250000e+02
|
||||
1.3333333e+00 1.4900000e+01 1.9450000e+02
|
||||
1.5000000e+00 1.4400000e+01 1.9790000e+02
|
||||
1.6666667e+00 1.3600000e+01 1.9800000e+02
|
||||
1.8333333e+00 1.2400000e+01 1.8510000e+02
|
||||
2.0000000e+00 1.5100000e+01 2.0260000e+02
|
||||
2.1666667e+00 1.5000000e+01 2.0240000e+02
|
||||
2.3333333e+00 1.4400000e+01 2.0540000e+02
|
||||
2.5000000e+00 1.4600000e+01 2.0430000e+02
|
||||
2.6666667e+00 1.2800000e+01 2.0370000e+02
|
||||
2.8333333e+00 1.2600000e+01 2.0620000e+02
|
||||
3.0000000e+00 1.5500000e+01 2.1350000e+02
|
||||
3.1666667e+00 1.6700000e+01 2.1370000e+02
|
||||
3.3333333e+00 1.7100000e+01 2.1490000e+02
|
||||
3.5000000e+00 1.6500000e+01 2.1430000e+02
|
||||
3.6666667e+00 1.6800000e+01 2.1440000e+02
|
||||
3.8333333e+00 1.5100000e+01 2.1060000e+02
|
||||
4.0000000e+00 1.5600000e+01 2.1050000e+02
|
||||
4.1666667e+00 1.4700000e+01 2.0960000e+02
|
||||
4.3333333e+00 1.3900000e+01 2.0860000e+02
|
||||
4.5000000e+00 1.4200000e+01 2.0660000e+02
|
||||
4.6666667e+00 1.4100000e+01 2.0500000e+02
|
||||
4.8333333e+00 1.4800000e+01 2.0500000e+02
|
||||
5.0000000e+00 1.4600000e+01 2.0050000e+02
|
||||
5.1666667e+00 1.5100000e+01 1.9520000e+02
|
||||
5.3333333e+00 1.5000000e+01 1.9360000e+02
|
||||
5.5000000e+00 1.5600000e+01 1.9170000e+02
|
||||
5.6666667e+00 1.6900000e+01 1.8940000e+02
|
||||
5.8333333e+00 1.5800000e+01 1.8690000e+02
|
||||
6.0000000e+00 1.6500000e+01 1.8230000e+02
|
||||
6.1666667e+00 1.8100000e+01 1.8330000e+02
|
||||
6.3333333e+00 1.8400000e+01 1.8080000e+02
|
||||
6.5000000e+00 1.7900000e+01 1.7960000e+02
|
||||
6.6666667e+00 1.8900000e+01 1.7910000e+02
|
||||
6.8333333e+00 2.0000000e+01 1.7830000e+02
|
||||
7.0000000e+00 2.0000000e+01 1.7830000e+02
|
||||
7.1666667e+00 2.1400000e+01 1.7780000e+02
|
||||
7.3333333e+00 2.0800000e+01 1.7530000e+02
|
||||
7.5000000e+00 2.1800000e+01 1.7380000e+02
|
||||
7.6666667e+00 2.1800000e+01 1.7130000e+02
|
||||
7.8333333e+00 2.2600000e+01 1.7220000e+02
|
||||
8.0000000e+00 2.2700000e+01 1.7260000e+02
|
||||
8.1666667e+00 2.4100000e+01 1.7300000e+02
|
||||
8.3333333e+00 2.3500000e+01 1.7180000e+02
|
||||
8.5000000e+00 2.4100000e+01 1.7080000e+02
|
||||
8.6666667e+00 2.4400000e+01 1.6950000e+02
|
||||
8.8333333e+00 2.4900000e+01 1.7010000e+02
|
||||
9.0000000e+00 2.6500000e+01 1.7290000e+02
|
||||
9.1666667e+00 2.6500000e+01 1.7210000e+02
|
||||
9.3333333e+00 2.6300000e+01 1.7200000e+02
|
||||
9.5000000e+00 2.7300000e+01 1.7250000e+02
|
||||
9.6666667e+00 2.7300000e+01 1.7520000e+02
|
||||
9.8333333e+00 2.7800000e+01 1.7220000e+02
|
||||
1.0000000e+01 2.7200000e+01 1.7460000e+02
|
||||
1.0166667e+01 2.7900000e+01 1.7790000e+02
|
||||
1.0333333e+01 2.8400000e+01 1.7830000e+02
|
||||
1.0500000e+01 2.8300000e+01 1.7590000e+02
|
||||
1.0666667e+01 2.7300000e+01 1.7460000e+02
|
||||
1.0833333e+01 2.7600000e+01 1.7310000e+02
|
||||
1.1000000e+01 2.9300000e+01 1.7520000e+02
|
||||
1.1166667e+01 2.9600000e+01 1.7580000e+02
|
||||
1.1333333e+01 2.8700000e+01 1.7430000e+02
|
||||
1.1500000e+01 2.8200000e+01 1.7300000e+02
|
||||
1.1666667e+01 2.8100000e+01 1.7000000e+02
|
||||
1.1833333e+01 2.9100000e+01 1.6820000e+02
|
||||
1.2000000e+01 2.8800000e+01 1.7040000e+02
|
||||
1.2166667e+01 2.9600000e+01 1.7150000e+02
|
||||
1.2333333e+01 2.9900000e+01 1.7160000e+02
|
||||
1.2500000e+01 2.9500000e+01 1.7210000e+02
|
||||
1.2666667e+01 3.0100000e+01 1.7560000e+02
|
||||
1.2833333e+01 3.0900000e+01 1.7520000e+02
|
||||
1.3000000e+01 3.0900000e+01 1.7550000e+02
|
||||
1.3166667e+01 3.0000000e+01 1.7590000e+02
|
||||
1.3333333e+01 3.0500000e+01 1.7490000e+02
|
||||
1.3500000e+01 3.0500000e+01 1.7340000e+02
|
||||
1.3666667e+01 3.0100000e+01 1.7190000e+02
|
||||
1.3833333e+01 3.1500000e+01 1.7180000e+02
|
||||
1.4000000e+01 3.0700000e+01 1.7190000e+02
|
||||
1.4166667e+01 2.9200000e+01 1.6820000e+02
|
||||
1.4333333e+01 3.1200000e+01 1.6770000e+02
|
||||
1.4500000e+01 3.1900000e+01 1.6970000e+02
|
||||
1.4666667e+01 3.1300000e+01 1.7140000e+02
|
||||
1.4833333e+01 3.1300000e+01 1.7500000e+02
|
||||
1.5000000e+01 3.1500000e+01 1.7470000e+02
|
||||
1.5166667e+01 3.1800000e+01 1.7430000e+02
|
||||
1.5333333e+01 3.1500000e+01 1.7460000e+02
|
||||
1.5500000e+01 3.2800000e+01 1.7610000e+02
|
||||
1.5666667e+01 3.2700000e+01 1.7710000e+02
|
||||
1.5833333e+01 3.1000000e+01 1.7970000e+02
|
||||
1.6000000e+01 3.0100000e+01 1.8010000e+02
|
||||
1.6166667e+01 3.0000000e+01 1.7880000e+02
|
||||
1.6333333e+01 2.9200000e+01 1.7930000e+02
|
||||
1.6500000e+01 2.9900000e+01 1.7890000e+02
|
||||
1.6666667e+01 3.0300000e+01 1.7940000e+02
|
||||
1.6833333e+01 3.0700000e+01 1.7930000e+02
|
||||
1.7000000e+01 3.0600000e+01 1.8020000e+02
|
||||
1.7166667e+01 3.0300000e+01 1.7970000e+02
|
||||
1.7333333e+01 3.1100000e+01 1.7920000e+02
|
||||
1.7500000e+01 2.8900000e+01 1.8200000e+02
|
||||
1.7666667e+01 3.0300000e+01 1.8100000e+02
|
||||
1.7833333e+01 2.9900000e+01 1.7940000e+02
|
||||
1.8000000e+01 3.0800000e+01 1.7920000e+02
|
||||
1.8166667e+01 2.9500000e+01 1.7950000e+02
|
||||
1.8333333e+01 3.0600000e+01 1.7900000e+02
|
||||
1.8500000e+01 3.0800000e+01 1.7880000e+02
|
||||
1.8666667e+01 3.0900000e+01 1.8070000e+02
|
||||
1.8833333e+01 3.0700000e+01 1.8040000e+02
|
||||
1.9000000e+01 3.0700000e+01 1.7990000e+02
|
||||
1.9166667e+01 2.9400000e+01 1.8020000e+02
|
||||
1.9333333e+01 2.9100000e+01 1.7990000e+02
|
||||
1.9500000e+01 3.0600000e+01 1.8040000e+02
|
||||
1.9666667e+01 3.0100000e+01 1.8110000e+02
|
||||
1.9833333e+01 3.0000000e+01 1.8130000e+02
|
||||
2.0000000e+01 2.9300000e+01 1.8300000e+02
|
||||
2.0166667e+01 3.0500000e+01 1.8390000e+02
|
||||
2.0333333e+01 3.0800000e+01 1.8470000e+02
|
||||
2.0500000e+01 2.9600000e+01 1.8530000e+02
|
||||
2.0666667e+01 2.8800000e+01 1.8600000e+02
|
||||
2.0833333e+01 2.9100000e+01 1.8640000e+02
|
||||
2.1000000e+01 2.9800000e+01 1.8570000e+02
|
||||
2.1166667e+01 2.8700000e+01 1.8750000e+02
|
||||
2.1333333e+01 2.8700000e+01 1.8640000e+02
|
||||
2.1500000e+01 2.9300000e+01 1.8610000e+02
|
||||
2.1666667e+01 2.8500000e+01 1.8800000e+02
|
||||
2.1833333e+01 2.7200000e+01 1.9140000e+02
|
||||
2.2000000e+01 2.8100000e+01 1.9130000e+02
|
||||
2.2166667e+01 2.7000000e+01 1.9330000e+02
|
||||
2.2333333e+01 2.6300000e+01 1.9900000e+02
|
||||
2.2500000e+01 2.6100000e+01 2.0270000e+02
|
||||
2.2666667e+01 2.4000000e+01 2.1240000e+02
|
||||
2.2833333e+01 2.4000000e+01 2.1840000e+02
|
||||
2.3000000e+01 2.1600000e+01 2.1300000e+02
|
||||
2.3166667e+01 1.8800000e+01 2.0440000e+02
|
||||
2.3333333e+01 1.7700000e+01 1.9810000e+02
|
||||
2.3500000e+01 1.8500000e+01 1.9840000e+02
|
||||
2.3666667e+01 1.9600000e+01 1.9660000e+02
|
||||
2.3833333e+01 1.9200000e+01 1.9410000e+02
|
||||
@ -1,40 +0,0 @@
|
||||
1.0000000000000000e+01 1.2075320000000000e+06
|
||||
1.0000000000000000e+01 1.0013290000000000e+06
|
||||
1.0000000000000000e+01 1.1642510000000000e+06
|
||||
1.0000000000000000e+01 1.0521420000000000e+06
|
||||
1.0000000000000000e+01 1.3143320000000000e+06
|
||||
1.0000000000000000e+01 9.2781600000000000e+05
|
||||
1.0000000000000000e+01 8.5991500000000000e+05
|
||||
1.0000000000000000e+01 9.8150100000000000e+05
|
||||
1.5000000000000000e+01 1.9180900000000000e+05
|
||||
1.5000000000000000e+01 3.5513600000000000e+05
|
||||
1.5000000000000000e+01 2.5113800000000000e+05
|
||||
1.5000000000000000e+01 3.2085900000000000e+05
|
||||
1.5000000000000000e+01 4.2688000000000000e+05
|
||||
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<title
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Before Width: | Height: | Size: 24 KiB |
File diff suppressed because it is too large
Load Diff
@ -1,310 +0,0 @@
|
||||
"""
|
||||
WAFO defintions and numenclature
|
||||
|
||||
crossings :
|
||||
cycle_pairs :
|
||||
turning_points :
|
||||
wave_amplitudes :
|
||||
wave_periods :
|
||||
waves :
|
||||
|
||||
Examples
|
||||
--------
|
||||
In order to view the documentation do the following in an ipython window:
|
||||
|
||||
import wafo.definitions as wd
|
||||
wd.crossings()
|
||||
|
||||
or
|
||||
|
||||
wd.crossings?
|
||||
|
||||
|
||||
"""
|
||||
|
||||
|
||||
def wave_amplitudes():
|
||||
r"""
|
||||
Wave amplitudes and heights definitions and nomenclature
|
||||
|
||||
Definition of wave amplitudes and wave heights
|
||||
---------------------------------------------
|
||||
|
||||
<----- Direction of wave propagation
|
||||
|
||||
|
||||
|..............c_..........|
|
||||
| /| \ |
|
||||
Hd | _/ | \ | Hu
|
||||
M | / | \ |
|
||||
/ \ | M / Ac | \_ | c_
|
||||
F \ | / \m/ | \ | / \
|
||||
------d----|---u------------------d---|---u----d------ level v
|
||||
\ | /| \ | / \L
|
||||
\_ | / | At \_|_/
|
||||
\|/..| t
|
||||
t
|
||||
|
||||
Parameters
|
||||
----------
|
||||
Ac : crest amplitude
|
||||
At : trough amplitude
|
||||
Hd : wave height as defined for down crossing waves
|
||||
Hu : wave height as defined for up crossing waves
|
||||
|
||||
See also
|
||||
--------
|
||||
waves, crossings, turning_points
|
||||
"""
|
||||
print(wave_amplitudes.__doc__)
|
||||
|
||||
|
||||
def crossings():
|
||||
r"""
|
||||
Level v crossing definitions and nomenclature
|
||||
|
||||
Definition of level v crossings
|
||||
-------------------------------
|
||||
M
|
||||
. . M M
|
||||
. . . . . .
|
||||
F d . . L
|
||||
-----------------------u-------d-------o----------------- level v
|
||||
. . . . u
|
||||
. m
|
||||
m
|
||||
|
||||
Let the letters 'm', 'M', 'F', 'L','d' and 'u' in the
|
||||
figure above denote local minimum, maximum, first value, last
|
||||
value, down- and up-crossing, respectively. The remaining
|
||||
sampled values are indicated with a '.'. Values that are identical
|
||||
with v, but do not cross the level is indicated with the letter 'o'.
|
||||
We have a level up-crossing at index, k, if
|
||||
|
||||
x(k) < v and v < x(k+1)
|
||||
or if
|
||||
x(k) == v and v < x(k+1) and x(r) < v for some di < r <= k-1
|
||||
|
||||
where di is the index to the previous downcrossing.
|
||||
Similarly there is a level down-crossing at index, k, if
|
||||
|
||||
x(k) > v and v > x(k+1)
|
||||
or if
|
||||
x(k) == v and v > x(k+1) and x(r) > v for some ui < r <= k-1
|
||||
|
||||
where ui is the index to the previous upcrossing.
|
||||
|
||||
The first (F) value is a up crossing if x(1) = v and x(2) > v.
|
||||
Similarly, it is a down crossing if x(1) = v and x(2) < v.
|
||||
|
||||
See also
|
||||
--------
|
||||
wave_periods, waves, turning_points, findcross, findtp
|
||||
"""
|
||||
print(crossings.__doc__)
|
||||
|
||||
|
||||
def cycle_pairs():
|
||||
r"""
|
||||
Cycle pairs definitions and numenclature
|
||||
|
||||
Definition of Max2min and min2Max cycle pair
|
||||
--------------------------------------------
|
||||
A min2Max cycle pair (mM) is defined as the pair of a minimum
|
||||
and the following Maximum. Similarly a Max2min cycle pair (Mm)
|
||||
is defined as the pair of a Maximum and the following minimum.
|
||||
(all turning points possibly rainflowfiltered before pairing into cycles.)
|
||||
|
||||
See also
|
||||
--------
|
||||
turning_points
|
||||
"""
|
||||
print(cycle_pairs.__doc__)
|
||||
|
||||
|
||||
def wave_periods():
|
||||
r"""
|
||||
Wave periods (lengths) definitions and nomenclature
|
||||
|
||||
Definition of wave periods (lengths)
|
||||
------------------------------------
|
||||
|
||||
|
||||
<----- Direction of wave propagation
|
||||
|
||||
<-------Tu--------->
|
||||
: :
|
||||
<---Tc-----> :
|
||||
: : : <------Tcc---->
|
||||
M : c : : : :
|
||||
/ \ : M / \_ : : c_ c
|
||||
F \ :/ \m/ \: :/ \ / \
|
||||
------d--------u----------d-------u----d--------u---d-------- level v
|
||||
\ / \ / :\_ _/: :\_ L
|
||||
\_ / \_t_/ : \t_/ : : \m/
|
||||
\t/ : : : :
|
||||
: : <---Tt---> :
|
||||
<--------Ttt-------> : :
|
||||
<-----Td----->
|
||||
Tu = Up crossing period
|
||||
Td = Down crossing period
|
||||
Tc = Crest period, i.e., period between up crossing and
|
||||
the next down crossing
|
||||
Tt = Trough period, i.e., period between down crossing and
|
||||
the next up crossing
|
||||
Ttt = Trough2trough period
|
||||
Tcc = Crest2crest period
|
||||
|
||||
|
||||
<----- Direction of wave propagation
|
||||
|
||||
<--Tcf-> Tuc
|
||||
: : <-Tcb-> <->
|
||||
M : c : : : :
|
||||
/ \ : M / \_ c_ : : c
|
||||
F \ :/ \m/ \ / \___: :/ \
|
||||
------d---------u----------d---------u-------d--------u---d------ level v
|
||||
:\_ / \ __/: \_ _/ \_ L
|
||||
: \_ / \_t_/ : \t_/ \m/
|
||||
: \t/ : :
|
||||
: : : :
|
||||
<-Ttf-> <-Ttb->
|
||||
|
||||
|
||||
Tcf = Crest front period, i.e., period between up crossing and crest
|
||||
Tcb = Crest back period, i.e., period between crest and down crossing
|
||||
Ttf = Trough front period, i.e., period between down crossing and trough
|
||||
Ttb = Trough back period, i.e., period between trough and up crossing
|
||||
Also note that Tcf and Ttf can also be abbreviated by their crossing
|
||||
marker, e.g. Tuc (u2c) and Tdt (d2t), respectively. Similar applies
|
||||
to all the other wave periods and wave lengths.
|
||||
|
||||
(The nomenclature for wave length is similar, just substitute T and
|
||||
period with L and length, respectively)
|
||||
|
||||
<----- Direction of wave propagation
|
||||
|
||||
<--TMm-->
|
||||
<-TmM-> : :
|
||||
M : : M :
|
||||
/ \ : M /:\_ : M_ M
|
||||
F \ : / \m/ : \ : /: \ / \
|
||||
\ : / : \ : / : \ / \
|
||||
\ : / : \ : / : \_ _/ \_ L
|
||||
\_ : / : \_m_/ : \m_/ \m/
|
||||
\m/ : : : :
|
||||
<-----TMM-----> <----Tmm----->
|
||||
|
||||
|
||||
TmM = Period between minimum and the following Maximum
|
||||
TMm = Period between Maximum and the following minimum
|
||||
TMM = Period between Maximum and the following Maximum
|
||||
Tmm = Period between minimum and the following minimum
|
||||
|
||||
See also
|
||||
--------
|
||||
waves,
|
||||
wave_amplitudes,
|
||||
crossings,
|
||||
turning_points
|
||||
"""
|
||||
print(wave_periods.__doc__)
|
||||
|
||||
|
||||
def turning_points():
|
||||
r"""
|
||||
Turning points definitions and numenclature
|
||||
|
||||
Definition of turningpoints
|
||||
---------------------------
|
||||
<----- Direction of wave propagation
|
||||
|
||||
M M
|
||||
/ \ .... M /:\_ M_ M
|
||||
F \ | / \m/ : \ /: \ / \
|
||||
\ h | / : \ / : \ / \
|
||||
\ | / : \ / : \_ _/ \_ L
|
||||
\_ | / : \_m_/ : \m_/ \m/
|
||||
\m/ : : : :
|
||||
<------Mw-----> <-----mw----->
|
||||
|
||||
Local minimum or maximum are indicated with the
|
||||
letters 'm' or 'M'. Turning points in this connection are all
|
||||
local max (M) and min (m) and the last (L) value and the
|
||||
first (F) value if the first local extremum is a max.
|
||||
|
||||
(This choice is made in order to get the exact up-crossing intensity
|
||||
from rfc by mm2lc(tp2mm(rfc)) )
|
||||
|
||||
|
||||
See also
|
||||
--------
|
||||
waves,
|
||||
crossings,
|
||||
cycle_pairs
|
||||
findtp
|
||||
|
||||
"""
|
||||
print(turning_points.__doc__)
|
||||
|
||||
|
||||
def waves():
|
||||
r"""
|
||||
Wave definitions and nomenclature
|
||||
|
||||
Definition of trough and crest
|
||||
------------------------------
|
||||
A trough (t) is defined as the global minimum between a
|
||||
level v down-crossing (d) and the next up-crossing (u)
|
||||
and a crest (c) is defined as the global maximum between a
|
||||
level v up-crossing and the following down-crossing.
|
||||
|
||||
Definition of down- and up -crossing waves
|
||||
------------------------------------------
|
||||
A level v-down-crossing wave (dw) is a wave from a
|
||||
down-crossing to the following down-crossing.
|
||||
Similarly, a level v-up-crossing wave (uw) is a wave from an up-crossing
|
||||
to the next up-crossing.
|
||||
|
||||
Definition of trough and crest waves
|
||||
------------------------------------
|
||||
A trough-to-trough wave (tw) is a wave from a trough (t) to the
|
||||
following trough. The crest-to-crest wave (cw) is defined similarly.
|
||||
|
||||
|
||||
Definition of min2min and Max2Max wave
|
||||
--------------------------------------
|
||||
A min2min wave (mw) is defined starting from a minimum (m) and
|
||||
ending in the following minimum.
|
||||
Similarly a Max2Max wave (Mw) is thus a wave from a maximum (M)
|
||||
to the next maximum (all waves optionally rainflow filtered).
|
||||
|
||||
<----- Direction of wave propagation
|
||||
|
||||
|
||||
<------Mw-----> <----mw---->
|
||||
M : : c :
|
||||
/ \ M : / \_ : c_ c
|
||||
F \ / \m/ \ : /: \ /:\
|
||||
------d--------u----------d-------u----d--------u---d------ level v
|
||||
\ /: \ : /: : :\_ _/ : :\_ L
|
||||
\_ / : \_t_/ : : : \t_/ : : \m/
|
||||
\t/ <-------uw---------> : <-----dw----->
|
||||
: : : :
|
||||
<--------tw--------> <------cw----->
|
||||
|
||||
(F=first value and L=last value).
|
||||
|
||||
See also
|
||||
--------
|
||||
turning_points,
|
||||
crossings,
|
||||
wave_periods
|
||||
findtc,
|
||||
findcross
|
||||
"""
|
||||
print(waves.__doc__)
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
|
||||
@ -1,146 +0,0 @@
|
||||
"""
|
||||
Created on 20. jan. 2011
|
||||
|
||||
@author: pab
|
||||
"""
|
||||
import numpy as np
|
||||
from numpy import exp, meshgrid
|
||||
__all__ = ['peaks', 'humps', 'magic']
|
||||
|
||||
|
||||
def _magic_odd_order(n):
|
||||
ix = np.arange(n) + 1
|
||||
J, I = np.meshgrid(ix, ix)
|
||||
A = np.mod(I + J - (n + 3) / 2, n)
|
||||
B = np.mod(I + 2 * J - 2, n)
|
||||
M = n * A + B + 1
|
||||
return M
|
||||
|
||||
|
||||
def _magic_doubly_even_order(n):
|
||||
M = np.arange(1, n * n + 1).reshape(n, n)
|
||||
ix = np.mod(np.arange(n) + 1, 4) // 2
|
||||
J, I = np.meshgrid(ix, ix)
|
||||
iz = np.flatnonzero(I == J)
|
||||
M.put(iz, n * n + 1 - M.flat[iz])
|
||||
return M
|
||||
|
||||
|
||||
def _magic_even_order(n):
|
||||
p = n // 2
|
||||
M0 = magic(p)
|
||||
M = np.hstack((np.vstack((M0, M0 + 3 * p * p)),
|
||||
np.vstack((M0 + 2 * p * p, M0 + p * p))))
|
||||
if n > 2:
|
||||
k = (n - 2) // 4
|
||||
jvec = np.hstack((np.arange(k), np.arange(n - k + 1, n)))
|
||||
for i in range(p):
|
||||
for j in jvec:
|
||||
temp = M[i][j]
|
||||
M[i][j] = M[i + p][j]
|
||||
M[i + p][j] = temp
|
||||
|
||||
i = k
|
||||
j = 0
|
||||
temp = M[i][j]
|
||||
M[i][j] = M[i + p][j]
|
||||
M[i + p][j] = temp
|
||||
j = i
|
||||
temp = M[i + p][j]
|
||||
M[i + p][j] = M[i][j]
|
||||
M[i][j] = temp
|
||||
return M
|
||||
|
||||
|
||||
def magic(n):
|
||||
"""
|
||||
Return magic square for n of any orders > 2.
|
||||
|
||||
A magic square has the property that the sum of every row and column,
|
||||
as well as both diagonals, is the same number.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> np.allclose(magic(3),
|
||||
... [[8, 1, 6],
|
||||
... [3, 5, 7],
|
||||
... [4, 9, 2]])
|
||||
True
|
||||
|
||||
>>> np.allclose(magic(4),
|
||||
... [[16, 2, 3, 13],
|
||||
... [ 5, 11, 10, 8],
|
||||
... [ 9, 7, 6, 12],
|
||||
... [ 4, 14, 15, 1]])
|
||||
True
|
||||
|
||||
>>> np.allclose(magic(6),
|
||||
... [[35, 1, 6, 26, 19, 24],
|
||||
... [ 3, 32, 7, 21, 23, 25],
|
||||
... [31, 9, 2, 22, 27, 20],
|
||||
... [ 8, 28, 33, 17, 10, 15],
|
||||
... [30, 5, 34, 12, 14, 16],
|
||||
... [ 4, 36, 29, 13, 18, 11]])
|
||||
True
|
||||
"""
|
||||
if (n < 3):
|
||||
raise ValueError('n must be greater than 2.')
|
||||
|
||||
if np.mod(n, 2) == 1:
|
||||
return _magic_odd_order(n)
|
||||
elif np.mod(n, 4) == 0:
|
||||
return _magic_doubly_even_order(n)
|
||||
return _magic_even_order(n)
|
||||
|
||||
|
||||
def peaks(x=None, y=None, n=51):
|
||||
"""
|
||||
Return the "well" known MatLab (R) peaks function
|
||||
evaluated in the [-3,3] x,y range
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> x,y,z = peaks()
|
||||
|
||||
h = plt.contourf(x,y,z)
|
||||
|
||||
"""
|
||||
if x is None:
|
||||
x = np.linspace(-3, 3, n)
|
||||
if y is None:
|
||||
y = np.linspace(-3, 3, n)
|
||||
|
||||
[x1, y1] = meshgrid(x, y)
|
||||
|
||||
z = (3 * (1 - x1) ** 2 * exp(-(x1 ** 2) - (y1 + 1) ** 2) -
|
||||
10 * (x1 / 5 - x1 ** 3 - y1 ** 5) * exp(-x1 ** 2 - y1 ** 2) -
|
||||
1. / 3 * exp(-(x1 + 1) ** 2 - y1 ** 2))
|
||||
|
||||
return x1, y1, z
|
||||
|
||||
|
||||
def humps(x=None):
|
||||
"""
|
||||
Computes a function that has three roots, and some humps.
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> x = np.linspace(0,1)
|
||||
>>> y = humps(x)
|
||||
|
||||
h = plt.plot(x,y)
|
||||
"""
|
||||
if x is None:
|
||||
y = np.linspace(0, 1)
|
||||
else:
|
||||
y = np.asarray(x)
|
||||
|
||||
return 1.0 / ((y - 0.3) ** 2 + 0.01) + 1.0 / ((y - 0.9) ** 2 + 0.04) + \
|
||||
2 * y - 5.2
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from wafo.testing import test_docstrings
|
||||
test_docstrings(__file__)
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -1,73 +0,0 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "WAFO Chapter 3"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
"worksheets": [
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"CHAPTER3 Demonstrates distributions of wave characteristics\n",
|
||||
"=============================================================\n",
|
||||
"\n",
|
||||
"Chapter3 contains the commands used in Chapter3 in the tutorial.\n",
|
||||
" \n",
|
||||
"Some of the commands are edited for fast computation. \n",
|
||||
"\n",
|
||||
"Section 3.2 Estimation of wave characteristics from data\n",
|
||||
"----------------------------------------------------------\n",
|
||||
"Example 1\n",
|
||||
"~~~~~~~~~~ "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"speed = 'fast'\n",
|
||||
"#speed = 'slow'\n",
|
||||
"\n",
|
||||
"import wafo.data as wd\n",
|
||||
"import wafo.misc as wm\n",
|
||||
"import wafo.objects as wo\n",
|
||||
"xx = wd.sea() \n",
|
||||
"xx[:,1] = wm.detrendma(xx[:,1],len(xx))\n",
|
||||
"ts = wo.mat2timeseries(xx)\n",
|
||||
"Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)\n",
|
||||
"Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AssertionError",
|
||||
"evalue": "",
|
||||
"output_type": "pyerr",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-12-5b70e90102e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mxx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetrendma\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmat2timeseries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mTcrcr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwave_periods\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c2c'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tw'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[0mTc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mixc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwave_periods\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'u2d'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwdef\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tw'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mc:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\objects.pyc\u001b[0m in \u001b[0;36mwave_periods\u001b[1;34m(self, vh, pdef, wdef, index, rate)\u001b[0m\n\u001b[0;32m 1980\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mrate\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1981\u001b[0m \u001b[0mti\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1982\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstineman_interp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mti\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1983\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1984\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32mC:\\Python27\\lib\\site-packages\\matplotlib\\mlab.pyc\u001b[0m in \u001b[0;36mstineman_interp\u001b[1;34m(xi, x, y, yp)\u001b[0m\n\u001b[0;32m 2932\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2933\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2934\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2935\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2936\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0myp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mAssertionError\u001b[0m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 12
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -1,192 +0,0 @@
|
||||
import wafo.plotbackend.plotbackend as plt
|
||||
import numpy as np
|
||||
# pyreport -o chapter1.html chapter1.py
|
||||
|
||||
#! CHAPTER1 demonstrates some applications of WAFO
|
||||
#!================================================
|
||||
#!
|
||||
#! CHAPTER1 gives an overview through examples some of the capabilities of
|
||||
#! WAFO. WAFO is a toolbox of Matlab routines for statistical analysis and
|
||||
#! simulation of random waves and loads.
|
||||
#!
|
||||
#! The commands are edited for fast computation.
|
||||
|
||||
|
||||
#! Section 1.4 Some applications of WAFO
|
||||
#!---------------------------------------
|
||||
#! Section 1.4.1 Simulation from spectrum, estimation of spectrum
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! Simulation of the sea surface from spectrum
|
||||
#! The following code generates 200 seconds of data sampled with 10Hz from
|
||||
#! the Torsethaugen spectrum
|
||||
import wafo.spectrum.models as wsm
|
||||
S = wsm.Torsethaugen(Hm0=6, Tp=8)
|
||||
S1 = S.tospecdata()
|
||||
S1.plot()
|
||||
plt.show()
|
||||
|
||||
|
||||
##
|
||||
import wafo.objects as wo
|
||||
xs = S1.sim(ns=2000, dt=0.1)
|
||||
ts = wo.mat2timeseries(xs)
|
||||
ts.plot_wave('-')
|
||||
plt.show()
|
||||
|
||||
|
||||
#! Estimation of spectrum
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! A common situation is that one wants to estimate the spectrum for wave
|
||||
#! measurements. The following code simulate 20 minutes signal sampled at 4Hz
|
||||
#! and compare the spectral estimate with the original Torsethaugen spectum.
|
||||
plt.clf()
|
||||
Fs = 4
|
||||
xs = S1.sim(ns=np.fix(20 * 60 * Fs), dt=1. / Fs)
|
||||
ts = wo.mat2timeseries(xs)
|
||||
Sest = ts.tospecdata(L=400)
|
||||
S1.plot()
|
||||
Sest.plot('--')
|
||||
plt.axis([0, 3, 0, 5])
|
||||
plt.show()
|
||||
|
||||
#! Section 1.4.2 Probability distributions of wave characteristics.
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! Probability distribution of wave trough period:
|
||||
#! WAFO gives the possibility of computing the exact probability
|
||||
#! distributions for a number of characteristics given a spectral density.
|
||||
#! In the following example we study the trough period extracted from the
|
||||
#! time series and compared with the theoretical density computed with exact
|
||||
#! spectrum, S1, and the estimated spectrum, Sest.
|
||||
plt.clf()
|
||||
import wafo.misc as wm
|
||||
dtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)
|
||||
dtyest = Sest.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)
|
||||
|
||||
T, index = ts.wave_periods(vh=0, pdef='d2u')
|
||||
bins = wm.good_bins(T, num_bins=25, odd=True)
|
||||
wm.plot_histgrm(T, bins=bins, normed=True)
|
||||
|
||||
dtyex.plot()
|
||||
dtyest.plot('-.')
|
||||
plt.axis([0, 10, 0, 0.35])
|
||||
plt.show()
|
||||
|
||||
#! Section 1.4.3 Directional spectra
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! Here are a few lines of code, which produce directional spectra
|
||||
#! with frequency independent and frequency dependent spreading.
|
||||
plt.clf()
|
||||
plotflag = 1
|
||||
Nt = 101 # number of angles
|
||||
th0 = np.pi / 2 # primary direction of waves
|
||||
Sp = 15 # spreading parameter
|
||||
|
||||
D1 = wsm.Spreading(type='cos', theta0=th0, method=None)
|
||||
D12 = wsm.Spreading(type='cos', theta0=0, method='mitsuyasu')
|
||||
|
||||
SD1 = D1.tospecdata2d(S1)
|
||||
SD12 = D12.tospecdata2d(S1)
|
||||
SD1.plot()
|
||||
SD12.plot() # linestyle='dashdot')
|
||||
plt.show()
|
||||
|
||||
#! 3D Simulation of the sea surface
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! The simulations show that frequency dependent spreading leads to
|
||||
#! much more irregular surface so the orientation of waves is less
|
||||
#! transparent compared to the frequency independent case.
|
||||
#
|
||||
#! Frequency independent spreading
|
||||
#plotflag = 1; iseed = 1;
|
||||
#
|
||||
#Nx = 2 ^ 8;Ny = Nx;Nt = 1;dx = 0.5; dy = dx; dt = 0.25; fftdim = 2;
|
||||
#randn('state', iseed)
|
||||
#Y1 = seasim(SD1, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);
|
||||
#wafostamp('', '(ER)')
|
||||
#axis('fill')
|
||||
#disp('Block = 6'), pause(pstate)
|
||||
#
|
||||
###
|
||||
## Frequency dependent spreading
|
||||
#randn('state', iseed)
|
||||
#Y12 = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, fftdim, plotflag);
|
||||
#wafostamp('', '(ER)')
|
||||
#axis('fill')
|
||||
#disp('Block = 7'), pause(pstate)
|
||||
#
|
||||
#! Estimation of directional spectrum
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! The figure is not shown in the Tutorial
|
||||
#
|
||||
# Nx = 3; Ny = 2; Nt = 2 ^ 12; dx = 10; dy = 10;dt = 0.5;
|
||||
# F = seasim(SD12, Nx, Ny, Nt, dx, dy, dt, 1, 0);
|
||||
# Z = permute(F.Z, [3 1 2]);
|
||||
# [X, Y] = meshgrid(F.x, F.y);
|
||||
# N = Nx * Ny;
|
||||
# types = repmat(sensortypeid('n'), N, 1);
|
||||
# bfs = ones(N, 1);
|
||||
# pos = [X(:), Y(:), zeros(N, 1)];
|
||||
# h = inf;
|
||||
# nfft = 128;
|
||||
# nt = 101;
|
||||
# SDe = dat2dspec([F.t Z(:, :)], [pos types, bfs], h, nfft, nt);
|
||||
#plotspec(SDe), hold on
|
||||
#plotspec(SD12, '--'), hold off
|
||||
#disp('Block = 8'), pause(pstate)
|
||||
|
||||
#! Section 1.4.4 Fatigue, Load cycles and Markov models.
|
||||
#! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! Switching Markow chain of turningpoints
|
||||
#! In fatigue applications the exact sample path is not important, but
|
||||
#! only the tops and bottoms of the load, called the sequence of turning
|
||||
#! points (TP). From the turning points one can extract load cycles, from
|
||||
#! which damage calculations and fatigue life predictions can be
|
||||
#! performed.
|
||||
#!
|
||||
#! The commands below computes the intensity of rainflowcycles for
|
||||
#! the Gaussian model with spectrum S1 using the Markov approximation.
|
||||
#! The rainflow cycles found in the simulated load signal are shown in the
|
||||
#! figure.
|
||||
|
||||
#clf()
|
||||
#paramu = [-6 6 61];
|
||||
#frfc = spec2cmat(S1, [], 'rfc', [], paramu);
|
||||
#pdfplot(frfc);
|
||||
#hold on
|
||||
#tp = dat2tp(xs);
|
||||
#rfc = tp2rfc(tp);
|
||||
#plot(rfc(:, 2), rfc(:, 1), '.')
|
||||
#wafostamp('', '(ER)')
|
||||
#hold off
|
||||
#disp('Block = 9'), pause(pstate)
|
||||
|
||||
#! Section 1.4.5 Extreme value statistics
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
# Plot of yura87 data
|
||||
plt.clf()
|
||||
import wafo.data as wd
|
||||
xn = wd.yura87()
|
||||
#xn = load('yura87.dat');
|
||||
plt.subplot(211)
|
||||
plt.plot(xn[::30, 0] / 3600, xn[::30, 1], '.')
|
||||
plt.title('Water level')
|
||||
plt.ylabel('(m)')
|
||||
|
||||
#! Formation of 5 min maxima
|
||||
yura = xn[:85500, 1]
|
||||
yura = np.reshape(yura, (285, 300)).T
|
||||
maxyura = yura.max(axis=0)
|
||||
plt.subplot(212)
|
||||
plt.plot(xn[299:85500:300, 0] / 3600, maxyura, '.')
|
||||
plt.xlabel('Time (h)')
|
||||
plt.ylabel('(m)')
|
||||
plt.title('Maximum 5 min water level')
|
||||
plt.show()
|
||||
|
||||
#! Estimation of GEV for yuramax
|
||||
plt.clf()
|
||||
import wafo.stats as ws
|
||||
phat = ws.genextreme.fit2(maxyura, method='ml')
|
||||
phat.plotfitsummary()
|
||||
plt.show()
|
||||
#disp('Block = 11, Last block')
|
||||
@ -1,327 +0,0 @@
|
||||
import wafo.plotbackend.plotbackend as plt
|
||||
import numpy as np
|
||||
|
||||
# pyreport -o chapter2.html chapter2.py
|
||||
|
||||
#! CHAPTER2 Modelling random loads and stochastic waves
|
||||
#!=======================================================
|
||||
#!
|
||||
#! Chapter2 contains the commands used in Chapter 2 of the tutorial and
|
||||
#! present some tools for analysis of random functions with
|
||||
#! respect to their correlation, spectral and distributional properties.
|
||||
#! The presentation is divided into three examples:
|
||||
#!
|
||||
#! Example1 is devoted to estimation of different parameters in the model.
|
||||
#! Example2 deals with spectral densities and
|
||||
#! Example3 presents the use of WAFO to simulate samples of a Gaussian
|
||||
#! process.
|
||||
#!
|
||||
#! Some of the commands are edited for fast computation.
|
||||
#!
|
||||
#! Section 2.1 Introduction and preliminary analysis
|
||||
#!====================================================
|
||||
#! Example 1: Sea data
|
||||
#!----------------------
|
||||
#! Observed crossings compared to the expected for Gaussian signals
|
||||
|
||||
import wafo
|
||||
import wafo.objects as wo
|
||||
xx = wafo.data.sea()
|
||||
me = xx[:, 1].mean()
|
||||
sa = xx[:, 1].std()
|
||||
xx[:, 1] -= me
|
||||
ts = wo.mat2timeseries(xx)
|
||||
tp = ts.turning_points()
|
||||
|
||||
cc = tp.cycle_pairs()
|
||||
lc = cc.level_crossings()
|
||||
lc.plot()
|
||||
plt.show()
|
||||
|
||||
#! Average number of upcrossings per time unit
|
||||
#!----------------------------------------------
|
||||
#! Next we compute the mean frequency as the average number of upcrossings
|
||||
#! per time unit of the mean level (= 0); this may require interpolation in the
|
||||
#! crossing intensity curve, as follows.
|
||||
T = xx[:, 0].max() - xx[:, 0].min()
|
||||
f0 = np.interp(0, lc.args, lc.data, 0) / T # zero up-crossing frequency
|
||||
print('f0 = %g' % f0)
|
||||
|
||||
#! Turningpoints and irregularity factor
|
||||
#!----------------------------------------
|
||||
|
||||
fm = len(tp.data) / (2 * T) # frequency of maxima
|
||||
alfa = f0 / fm # approx Tm24/Tm02
|
||||
|
||||
print('fm = %g, alpha = %g, ' % (fm, alfa))
|
||||
|
||||
#! Visually examine data
|
||||
#!------------------------
|
||||
#! We finish this section with some remarks about the quality
|
||||
#! of the measured data. Especially sea surface measurements can be
|
||||
#! of poor quality. We shall now check the quality of the dataset {\tt xx}.
|
||||
#! It is always good practice to visually examine the data
|
||||
#! before the analysis to get an impression of the quality,
|
||||
#! non-linearities and narrow-bandedness of the data.
|
||||
#! First we shall plot the data and zoom in on a specific region.
|
||||
#! A part of sea data is visualized with the following commands
|
||||
plt.clf()
|
||||
ts.plot_wave('k-', tp, '*', nfig=1, nsub=1)
|
||||
|
||||
plt.axis([0, 2, -2, 2])
|
||||
plt.show()
|
||||
|
||||
#! Finding possible spurious points
|
||||
#!------------------------------------
|
||||
#! However, if the amount of data is too large for visual examinations one
|
||||
#! could use the following criteria to find possible spurious points. One
|
||||
#! must be careful using the criteria for extremevalue analysis, because
|
||||
#! it might remove extreme waves that are OK and not spurious.
|
||||
|
||||
import wafo.misc as wm
|
||||
dt = ts.sampling_period()
|
||||
# dt = np.diff(xx[:2,0])
|
||||
dcrit = 5 * dt
|
||||
ddcrit = 9.81 / 2 * dt * dt
|
||||
zcrit = 0
|
||||
inds, indg = wm.findoutliers(ts.data, zcrit, dcrit, ddcrit, verbose=True)
|
||||
|
||||
#! Section 2.2 Frequency Modeling of Load Histories
|
||||
#!----------------------------------------------------
|
||||
#! Periodogram: Raw spectrum
|
||||
#!
|
||||
plt.clf()
|
||||
Lmax = 9500
|
||||
S = ts.tospecdata(L=Lmax)
|
||||
S.plot()
|
||||
plt.axis([0, 5, 0, 0.7])
|
||||
plt.show()
|
||||
|
||||
#! Calculate moments
|
||||
#!-------------------
|
||||
mom, text = S.moment(nr=4)
|
||||
print('sigma = %g, m0 = %g' % (sa, np.sqrt(mom[0])))
|
||||
|
||||
#! Section 2.2.1 Random functions in Spectral Domain - Gaussian processes
|
||||
#!--------------------------------------------------------------------------
|
||||
#! Smoothing of spectral estimate
|
||||
#!----------------------------------
|
||||
#! By decreasing Lmax the spectrum estimate becomes smoother.
|
||||
|
||||
plt.clf()
|
||||
Lmax0 = 200
|
||||
Lmax1 = 50
|
||||
S1 = ts.tospecdata(L=Lmax0)
|
||||
S2 = ts.tospecdata(L=Lmax1)
|
||||
S1.plot('-.')
|
||||
S2.plot()
|
||||
plt.show()
|
||||
|
||||
#! Estimated autocovariance
|
||||
#!----------------------------
|
||||
#! Obviously knowing the spectrum one can compute the covariance
|
||||
#! function. The following code will compute the covariance for the
|
||||
#! unimodal spectral density S1 and compare it with estimated
|
||||
#! covariance of the signal xx.
|
||||
plt.clf()
|
||||
Lmax = 85
|
||||
R1 = S1.tocovdata(nr=1)
|
||||
Rest = ts.tocovdata(lag=Lmax)
|
||||
R1.plot('.')
|
||||
Rest.plot()
|
||||
plt.axis([0, 25, -0.1, 0.25])
|
||||
plt.show()
|
||||
|
||||
#! We can see in Figure below that the covariance function corresponding to
|
||||
#! the spectral density S2 significantly differs from the one estimated
|
||||
#! directly from data.
|
||||
#! It can be seen in Figure above that the covariance corresponding to S1
|
||||
#! agrees much better with the estimated covariance function
|
||||
|
||||
plt.clf()
|
||||
R2 = S2.tocovdata(nr=1)
|
||||
R2.plot('.')
|
||||
Rest.plot()
|
||||
plt.show()
|
||||
|
||||
#! Section 2.2.2 Transformed Gaussian models
|
||||
#!-------------------------------------------
|
||||
#! We begin with computing skewness and kurtosis
|
||||
#! for the data set xx and compare it with the second order wave approximation
|
||||
#! proposed by Winterstein:
|
||||
import wafo.stats as ws
|
||||
rho3 = ws.skew(xx[:, 1])
|
||||
rho4 = ws.kurtosis(xx[:, 1])
|
||||
|
||||
sk, ku = S1.stats_nl(moments='sk')
|
||||
|
||||
#! Comparisons of 3 transformations
|
||||
plt.clf()
|
||||
import wafo.transform.models as wtm
|
||||
gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku).trdata()
|
||||
g = wtm.TrLinear(mean=me, sigma=sa).trdata() # Linear transformation
|
||||
glc, gemp = lc.trdata(mean=me, sigma=sa)
|
||||
|
||||
glc.plot('b-') # Transf. estimated from level-crossings
|
||||
gh.plot('b-.') # Hermite Transf. estimated from moments
|
||||
g.plot('r')
|
||||
plt.grid('on')
|
||||
plt.show()
|
||||
|
||||
#! Test Gaussianity of a stochastic process
|
||||
#!------------------------------------------
|
||||
#! TESTGAUSSIAN simulates e(g(u)-u) = int (g(u)-u)^2 du for Gaussian processes
|
||||
#! given the spectral density, S. The result is plotted if test0 is given.
|
||||
#! This is useful for testing if the process X(t) is Gaussian.
|
||||
#! If 95% of TEST1 is less than TEST0 then X(t) is not Gaussian at a 5% level.
|
||||
#!
|
||||
#! As we see from the figure below: none of the simulated values of test1 is
|
||||
#! above 1.00. Thus the data significantly departs from a Gaussian distribution.
|
||||
plt.clf()
|
||||
test0 = glc.dist2gauss()
|
||||
#! the following test takes time
|
||||
N = len(xx)
|
||||
test1 = S1.testgaussian(ns=N, cases=50, test0=test0)
|
||||
is_gaussian = sum(test1 > test0) > 5
|
||||
print(is_gaussian)
|
||||
plt.show()
|
||||
|
||||
#! Normalplot of data xx
|
||||
#!------------------------
|
||||
#! indicates that the underlying distribution has a "heavy" upper tail and a
|
||||
#! "light" lower tail.
|
||||
plt.clf()
|
||||
import pylab
|
||||
ws.probplot(ts.data.ravel(), dist='norm', plot=pylab)
|
||||
plt.show()
|
||||
#! Section 2.2.3 Spectral densities of sea data
|
||||
#!-----------------------------------------------
|
||||
#! Example 2: Different forms of spectra
|
||||
#!
|
||||
import wafo.spectrum.models as wsm
|
||||
plt.clf()
|
||||
Hm0 = 7
|
||||
Tp = 11
|
||||
spec = wsm.Jonswap(Hm0=Hm0, Tp=Tp).tospecdata()
|
||||
spec.plot()
|
||||
plt.show()
|
||||
|
||||
#! Directional spectrum and Encountered directional spectrum
|
||||
#! Directional spectrum
|
||||
plt.clf()
|
||||
D = wsm.Spreading('cos2s')
|
||||
Sd = D.tospecdata2d(spec)
|
||||
Sd.plot()
|
||||
plt.show()
|
||||
|
||||
|
||||
##!Encountered directional spectrum
|
||||
##!---------------------------------
|
||||
#clf()
|
||||
#Se = spec2spec(Sd,'encdir',0,10);
|
||||
#plotspec(Se), hold on
|
||||
#plotspec(Sd,1,'--'), hold off
|
||||
##!wafostamp('','(ER)')
|
||||
#disp('Block = 17'),pause(pstate)
|
||||
#
|
||||
##!#! Frequency spectra
|
||||
#clf
|
||||
#Sd1 =spec2spec(Sd,'freq');
|
||||
#Sd2 = spec2spec(Se,'enc');
|
||||
#plotspec(spec), hold on
|
||||
#plotspec(Sd1,1,'.'),
|
||||
#plotspec(Sd2),
|
||||
##!wafostamp('','(ER)')
|
||||
#hold off
|
||||
#disp('Block = 18'),pause(pstate)
|
||||
#
|
||||
##!#! Wave number spectrum
|
||||
#clf
|
||||
#Sk = spec2spec(spec,'k1d')
|
||||
#Skd = spec2spec(Sd,'k1d')
|
||||
#plotspec(Sk), hold on
|
||||
#plotspec(Skd,1,'--'), hold off
|
||||
##!wafostamp('','(ER)')
|
||||
#disp('Block = 19'),pause(pstate)
|
||||
#
|
||||
##!#! Effect of waterdepth on spectrum
|
||||
#clf
|
||||
#plotspec(spec,1,'--'), hold on
|
||||
#S20 = spec;
|
||||
#S20.S = S20.S.*phi1(S20.w,20);
|
||||
#S20.h = 20;
|
||||
#plotspec(S20), hold off
|
||||
##!wafostamp('','(ER)')
|
||||
#disp('Block = 20'),pause(pstate)
|
||||
#
|
||||
##!#! Section 2.3 Simulation of transformed Gaussian process
|
||||
##!#! Example 3: Simulation of random sea
|
||||
##! The reconstruct function replaces the spurious points of seasurface by
|
||||
##! simulated data on the basis of the remaining data and a transformed Gaussian
|
||||
##! process. As noted previously one must be careful using the criteria
|
||||
##! for finding spurious points when reconstructing a dataset, because
|
||||
##! these criteria might remove the highest and steepest waves as we can see
|
||||
##! in this plot where the spurious points is indicated with a '+' sign:
|
||||
##!
|
||||
#clf
|
||||
#[y, grec] = reconstruct(xx,inds);
|
||||
#waveplot(y,'-',xx(inds,:),'+',1,1)
|
||||
#axis([0 inf -inf inf])
|
||||
##!wafostamp('','(ER)')
|
||||
#disp('Block = 21'),pause(pstate)
|
||||
#
|
||||
##! Compare transformation (grec) from reconstructed (y)
|
||||
##! with original (glc) from (xx)
|
||||
#clf
|
||||
#trplot(g), hold on
|
||||
#plot(gemp(:,1),gemp(:,2))
|
||||
#plot(glc(:,1),glc(:,2),'-.')
|
||||
#plot(grec(:,1),grec(:,2)), hold off
|
||||
#disp('Block = 22'),pause(pstate)
|
||||
#
|
||||
##!#!
|
||||
#clf
|
||||
#L = 200;
|
||||
#x = dat2gaus(y,grec);
|
||||
#Sx = dat2spec(x,L);
|
||||
#disp('Block = 23'),pause(pstate)
|
||||
#
|
||||
##!#!
|
||||
#clf
|
||||
#dt = spec2dt(Sx)
|
||||
#Ny = fix(2*60/dt) #! = 2 minutes
|
||||
#Sx.tr = grec;
|
||||
#ysim = spec2sdat(Sx,Ny);
|
||||
#waveplot(ysim,'-')
|
||||
##!wafostamp('','(CR)')
|
||||
#disp('Block = 24'),pause(pstate)
|
||||
#
|
||||
#! Estimated spectrum compared to Torsethaugen spectrum
|
||||
#!-------------------------------------------------------
|
||||
|
||||
plt.clf()
|
||||
fp = 1.1
|
||||
dw = 0.01
|
||||
H0 = S1.characteristic('Hm0')[0]
|
||||
St = wsm.Torsethaugen(Hm0=H0,Tp=2*np.pi/fp).tospecdata(np.arange(0,5+dw/2,dw))
|
||||
S1.plot()
|
||||
St.plot('-.')
|
||||
plt.axis([0, 6, 0, 0.4])
|
||||
plt.show()
|
||||
|
||||
|
||||
#! Transformed Gaussian model compared to Gaussian model
|
||||
#!--------------------------------------------------------
|
||||
dt = St.sampling_period()
|
||||
va, sk, ku = St.stats_nl(moments='vsk')
|
||||
#sa = sqrt(va)
|
||||
gh = wtm.TrHermite(mean=me, sigma=sa, skew=sk, kurt=ku, ysigma=sa)
|
||||
|
||||
ysim_t = St.sim(ns=240, dt=0.5)
|
||||
xsim_t = ysim_t.copy()
|
||||
xsim_t[:, 1] = gh.gauss2dat(ysim_t[:, 1])
|
||||
|
||||
ts_y = wo.mat2timeseries(ysim_t)
|
||||
ts_x = wo.mat2timeseries(xsim_t)
|
||||
ts_y.plot_wave(sym1='r.', ts=ts_x, sym2='b', sigma=sa, nsub=5, nfig=1)
|
||||
plt.show()
|
||||
@ -1,615 +0,0 @@
|
||||
from wafo.plotbackend import plotbackend as plt
|
||||
import numpy as np
|
||||
|
||||
#! CHAPTER3 Demonstrates distributions of wave characteristics
|
||||
#!=============================================================
|
||||
#!
|
||||
#! Chapter3 contains the commands used in Chapter3 in the tutorial.
|
||||
#!
|
||||
#! Some of the commands are edited for fast computation.
|
||||
#!
|
||||
#! Section 3.2 Estimation of wave characteristics from data
|
||||
#!----------------------------------------------------------
|
||||
#! Example 1
|
||||
#!~~~~~~~~~~
|
||||
|
||||
speed = 'fast'
|
||||
#speed = 'slow'
|
||||
import scipy.signal as ss
|
||||
import wafo.data as wd
|
||||
import wafo.misc as wm
|
||||
import wafo.objects as wo
|
||||
import wafo.stats as ws
|
||||
import wafo.spectrum.models as wsm
|
||||
xx = wd.sea()
|
||||
xx[:, 1] = ss.detrend(xx[:, 1])
|
||||
ts = wo.mat2timeseries(xx)
|
||||
Tcrcr, ix = ts.wave_periods(vh=0, pdef='c2c', wdef='tw', rate=8)
|
||||
Tc, ixc = ts.wave_periods(vh=0, pdef='u2d', wdef='tw', rate=8)
|
||||
|
||||
#! Histogram of crestperiod compared to the kernel density estimate
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
import wafo.kdetools as wk
|
||||
plt.clf()
|
||||
print(Tc.mean())
|
||||
print(Tc.max())
|
||||
|
||||
t = np.linspace(0.01,8,200);
|
||||
ftc = wk.TKDE(Tc, L2=0, inc=128)
|
||||
|
||||
plt.plot(t,ftc.eval_grid(t), t, ftc.eval_grid_fast(t),'-.')
|
||||
wm.plot_histgrm(Tc, normed=True)
|
||||
plt.title('Kernel Density Estimates')
|
||||
plt.xlabel('Tc [s]')
|
||||
plt.axis([0, 8, 0, 0.5])
|
||||
plt.show()
|
||||
|
||||
#! Extreme waves - model check: the highest and steepest wave
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
plt.clf()
|
||||
S, H = ts.wave_height_steepness(kind=0)
|
||||
indS = S.argmax()
|
||||
indH = H.argmax()
|
||||
ts.plot_sp_wave([indH, indS],'k.')
|
||||
plt.show()
|
||||
|
||||
#! Does the highest wave contradict a transformed Gaussian model?
|
||||
#!----------------------------------------------------------------
|
||||
|
||||
# TODO: Fix this
|
||||
|
||||
#clf
|
||||
#inds1 = (5965:5974)'; #! points to remove
|
||||
#Nsim = 10;
|
||||
#[y1, grec1, g2, test, tobs, mu1o, mu1oStd] = ...
|
||||
# reconstruct(xx,inds1,Nsim);
|
||||
#spwaveplot(y1,indA-10)
|
||||
#hold on
|
||||
#plot(xx(inds1,1),xx(inds1,2),'+')
|
||||
#lamb = 2.;
|
||||
#muLstd = tranproc(mu1o-lamb*mu1oStd,fliplr(grec1));
|
||||
#muUstd = tranproc(mu1o+lamb*mu1oStd,fliplr(grec1));
|
||||
#plot (y1(inds1,1), [muLstd muUstd],'b-')
|
||||
#axis([1482 1498 -1 3]),
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 6'),
|
||||
#pause(pstate)
|
||||
#
|
||||
##!#! Expected value (solid) compared to data removed
|
||||
#clf
|
||||
#plot(xx(inds1,1),xx(inds1,2),'+'), hold on
|
||||
#mu = tranproc(mu1o,fliplr(grec1));
|
||||
#plot(y1(inds1,1), mu), hold off
|
||||
#disp('Block = 7'), pause(pstate)
|
||||
|
||||
#! Crest height PDF
|
||||
#!------------------
|
||||
#! Transform data so that kde works better
|
||||
plt.clf()
|
||||
wave_data = ts.wave_parameters()
|
||||
Ac = wave_data['Ac']
|
||||
L2 = 0.6
|
||||
|
||||
ws.probplot(Ac**L2, dist='norm', plot=plt)
|
||||
plt.show()
|
||||
|
||||
#!#!
|
||||
plt.clf()#
|
||||
fac = wk.TKDE(Ac,L2=L2)(np.linspace(0.01,3,200), output='plot')
|
||||
fac.plot()
|
||||
# wafostamp([],'(ER)')
|
||||
print(fac.integrate(a=0.01, b=3))
|
||||
print(fac.integrate())
|
||||
print('Block = 8'),
|
||||
# pause(pstate)
|
||||
|
||||
#!#! Empirical crest height CDF
|
||||
plt.clf()
|
||||
Fac = fac.to_cdf()
|
||||
Femp = ws.edf(Ac)
|
||||
Fac.plot()
|
||||
Femp.plot()
|
||||
plt.axis([0, 2, 0, 1])
|
||||
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 9'), pause(pstate)
|
||||
|
||||
#!#! Empirical crest height CDF compared to a Transformed Rayleigh approximation
|
||||
|
||||
# facr = trraylpdf(fac.x{1},'Ac',grec1);
|
||||
# Facr = cumtrapz(facr.x{1},facr.f);
|
||||
# hold on
|
||||
# plot(facr.x{1},Facr,'.')
|
||||
# axis([1.25 2.25 0.95 1])
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 10'), pause(pstate)
|
||||
|
||||
#!#! Joint pdf of crest period and crest amplitude
|
||||
plt.clf()
|
||||
Tcf = wave_data['Tcf']
|
||||
Tcb = wave_data['Tcb']
|
||||
Tc = Tcf + Tcb
|
||||
fTcAc = wk.TKDE([Tc, Ac],L2=0.5, inc=256).eval_grid_fast(output='plot')
|
||||
fTcAc.labels.labx = 'Tc [s]'
|
||||
fTcAc.labels.laby = 'Ac [m]'
|
||||
fTcAc.plot()
|
||||
plt.hold(True)
|
||||
plt.plot(Tc, Ac,'k.')
|
||||
plt.hold(False)
|
||||
plt.show()
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 11'), pause(pstate)
|
||||
|
||||
#!#! Example 4: Simple wave characteristics obtained from Jonswap spectrum
|
||||
plt.clf()
|
||||
S = wsm.Jonswap(Hm0=5, Tp=10).tospecdata()
|
||||
m, mt = S.moment(nr=4, even=False)
|
||||
print(m)
|
||||
print(mt)
|
||||
# disp('Block = 12'), pause(pstate)
|
||||
|
||||
plt.clf()
|
||||
S.bandwidth(['alpha'])
|
||||
ch, Sa2, chtxt = S.characteristic(['Hm0', 'Tm02'])
|
||||
|
||||
# disp('Block = 13'), pause(pstate)
|
||||
|
||||
#!#! Section 3.3.2 Explicit form approximations of wave characteristic densities
|
||||
#!#! Longuett-Higgins model for Tc and Ac
|
||||
# plt.clf()
|
||||
# t = np.linspace(0,15,100)
|
||||
# h = np.linspace(0,6,100)
|
||||
# flh = lh83pdf(t, h, [m[0],m[1], m[2])
|
||||
# #disp('Block = 14'), pause(pstate)
|
||||
#
|
||||
# #!#! Transformed Longuett-Higgins model for Tc and Ac
|
||||
# clf
|
||||
# [sk, ku ]=spec2skew(S);
|
||||
# sa = sqrt(m(1));
|
||||
# gh = hermitetr([],[sa sk ku 0]);
|
||||
# flhg = lh83pdf(t,h,[m(1),m(2),m(3)],gh);
|
||||
# disp('Block = 15'), pause(pstate)
|
||||
|
||||
#!#! Cavanie model for Tc and Ac
|
||||
# clf
|
||||
# t = np.linspace(0,10,100);
|
||||
# h = np.linspace(0,7,100);
|
||||
# fcav = cav76pdf(t,h,[m(1) m(2) m(3) m(5)],[]);
|
||||
# disp('Block = 16'), pause(pstate)
|
||||
#
|
||||
# #!#! Example 5 Transformed Rayleigh approximation of crest- vs trough- amplitude
|
||||
# clf
|
||||
# xx = load('sea.dat');
|
||||
# x = xx;
|
||||
# x(:,2) = detrend(x(:,2));
|
||||
# SS = dat2spec2(x);
|
||||
# [sk, ku, me, si ] = spec2skew(SS);
|
||||
# gh = hermitetr([],[si sk ku me]);
|
||||
# Hs = 4*si;
|
||||
# r = (0:0.05:1.1*Hs)';
|
||||
# fac_h = trraylpdf(r,'Ac',gh);
|
||||
# fat_h = trraylpdf(r,'At',gh);
|
||||
# h = (0:0.05:1.7*Hs)';
|
||||
# facat_h = trraylpdf(h,'AcAt',gh);
|
||||
# pdfplot(fac_h)
|
||||
# hold on
|
||||
# pdfplot(fat_h,'--')
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 17'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# TC = dat2tc(xx, me);
|
||||
# tc = tp2mm(TC);
|
||||
# Ac = tc(:,2);
|
||||
# At = -tc(:,1);
|
||||
# AcAt = Ac+At;
|
||||
# disp('Block = 18'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# Fac_h = [fac_h.x{1} cumtrapz(fac_h.x{1},fac_h.f)];
|
||||
# subplot(3,1,1)
|
||||
# Fac = plotedf(Ac,Fac_h);
|
||||
# hold on
|
||||
# plot(r,1-exp(-8*r.^2/Hs^2),'.')
|
||||
# axis([1. 2. 0.9 1])
|
||||
# title('Ac CDF')
|
||||
#
|
||||
# Fat_h = [fat_h.x{1} cumtrapz(fat_h.x{1},fat_h.f)];
|
||||
# subplot(3,1,2)
|
||||
# Fat = plotedf(At,Fat_h);
|
||||
# hold on
|
||||
# plot(r,1-exp(-8*r.^2/Hs^2),'.')
|
||||
# axis([1. 2. 0.9 1])
|
||||
# title('At CDF')
|
||||
#
|
||||
# Facat_h = [facat_h.x{1} cumtrapz(facat_h.x{1},facat_h.f)];
|
||||
# subplot(3,1,3)
|
||||
# Facat = plotedf(AcAt,Facat_h);
|
||||
# hold on
|
||||
# plot(r,1-exp(-2*r.^2/Hs^2),'.')
|
||||
# axis([1.5 3.5 0.9 1])
|
||||
# title('At+Ac CDF')
|
||||
#
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 19'), pause(pstate)
|
||||
#
|
||||
# #!#! Section 3.4 Exact wave distributions in transformed Gaussian Sea
|
||||
# #!#! Section 3.4.1 Density of crest period, crest length or encountered crest period
|
||||
# clf
|
||||
# S1 = torsethaugen([],[6 8],1);
|
||||
# D1 = spreading(101,'cos',pi/2,[15],[],0);
|
||||
# D12 = spreading(101,'cos',0,[15],S1.w,1);
|
||||
# SD1 = mkdspec(S1,D1);
|
||||
# SD12 = mkdspec(S1,D12);
|
||||
# disp('Block = 20'), pause(pstate)
|
||||
#
|
||||
# #!#! Crest period
|
||||
# clf
|
||||
# tic
|
||||
# f_tc = spec2tpdf(S1,[],'Tc',[0 11 56],[],4);
|
||||
# toc
|
||||
# pdfplot(f_tc)
|
||||
# wafostamp([],'(ER)')
|
||||
# simpson(f_tc.x{1},f_tc.f)
|
||||
# disp('Block = 21'), pause(pstate)
|
||||
#
|
||||
# #!#! Crest length
|
||||
#
|
||||
# if strncmpi(speed,'slow',1)
|
||||
# opt1 = rindoptset('speed',5,'method',3);
|
||||
# opt2 = rindoptset('speed',5,'nit',2,'method',0);
|
||||
# else
|
||||
# #! fast
|
||||
# opt1 = rindoptset('speed',7,'method',3);
|
||||
# opt2 = rindoptset('speed',7,'nit',2,'method',0);
|
||||
# end
|
||||
#
|
||||
#
|
||||
# clf
|
||||
# if strncmpi(speed,'slow',1)
|
||||
# NITa = 5;
|
||||
# else
|
||||
# disp('NIT=5 may take time, running with NIT=3 in the following')
|
||||
# NITa = 3;
|
||||
# end
|
||||
# #!f_Lc = spec2tpdf2(S1,[],'Lc',[0 200 81],opt1); #! Faster and more accurate
|
||||
# f_Lc = spec2tpdf(S1,[],'Lc',[0 200 81],[],NITa);
|
||||
# pdfplot(f_Lc,'-.')
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 22'), pause(pstate)
|
||||
#
|
||||
#
|
||||
# f_Lc_1 = spec2tpdf(S1,[],'Lc',[0 200 81],1.5,NITa);
|
||||
# #!f_Lc_1 = spec2tpdf2(S1,[],'Lc',[0 200 81],1.5,opt1);
|
||||
#
|
||||
# hold on
|
||||
# pdfplot(f_Lc_1)
|
||||
# wafostamp([],'(ER)')
|
||||
#
|
||||
# disp('Block = 23'), pause(pstate)
|
||||
# #!#!
|
||||
# clf
|
||||
# simpson(f_Lc.x{1},f_Lc.f)
|
||||
# simpson(f_Lc_1.x{1},f_Lc_1.f)
|
||||
#
|
||||
# disp('Block = 24'), pause(pstate)
|
||||
# #!#!
|
||||
# clf
|
||||
# tic
|
||||
#
|
||||
# f_Lc_d1 = spec2tpdf(rotspec(SD1,pi/2),[],'Lc',[0 300 121],[],NITa);
|
||||
# f_Lc_d12 = spec2tpdf(SD12,[],'Lc',[0 200 81],[],NITa);
|
||||
# #! f_Lc_d1 = spec2tpdf2(rotspec(SD1,pi/2),[],'Lc',[0 300 121],opt1);
|
||||
# #! f_Lc_d12 = spec2tpdf2(SD12,[],'Lc',[0 200 81],opt1);
|
||||
# toc
|
||||
# pdfplot(f_Lc_d1,'-.'), hold on
|
||||
# pdfplot(f_Lc_d12), hold off
|
||||
# wafostamp([],'(ER)')
|
||||
#
|
||||
# disp('Block = 25'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
#
|
||||
#
|
||||
# clf
|
||||
# opt1 = rindoptset('speed',5,'method',3);
|
||||
# SD1r = rotspec(SD1,pi/2);
|
||||
# if strncmpi(speed,'slow',1)
|
||||
# f_Lc_d1_5 = spec2tpdf(SD1r,[], 'Lc',[0 300 121],[],5);
|
||||
# pdfplot(f_Lc_d1_5), hold on
|
||||
# else
|
||||
# #! fast
|
||||
# disp('Run the following example only if you want a check on computing time')
|
||||
# disp('Edit the command file and remove #!')
|
||||
# end
|
||||
# f_Lc_d1_3 = spec2tpdf(SD1r,[],'Lc',[0 300 121],[],3);
|
||||
# f_Lc_d1_2 = spec2tpdf(SD1r,[],'Lc',[0 300 121],[],2);
|
||||
# f_Lc_d1_0 = spec2tpdf(SD1r,[],'Lc',[0 300 121],[],0);
|
||||
# #!f_Lc_d1_n4 = spec2tpdf2(SD1r,[],'Lc',[0 400 161],opt1);
|
||||
#
|
||||
# pdfplot(f_Lc_d1_3), hold on
|
||||
# pdfplot(f_Lc_d1_2)
|
||||
# pdfplot(f_Lc_d1_0)
|
||||
# #!pdfplot(f_Lc_d1_n4)
|
||||
#
|
||||
# #!simpson(f_Lc_d1_n4.x{1},f_Lc_d1_n4.f)
|
||||
#
|
||||
# disp('Block = 26'), pause(pstate)
|
||||
#
|
||||
# #!#! Section 3.4.2 Density of wave period, wave length or encountered wave period
|
||||
# #!#! Example 7: Crest period and high crest waves
|
||||
# clf
|
||||
# tic
|
||||
# xx = load('sea.dat');
|
||||
# x = xx;
|
||||
# x(:,2) = detrend(x(:,2));
|
||||
# SS = dat2spec(x);
|
||||
# si = sqrt(spec2mom(SS,1));
|
||||
# SS.tr = dat2tr(x);
|
||||
# Hs = 4*si
|
||||
# method = 0;
|
||||
# rate = 2;
|
||||
# [S, H, Ac, At, Tcf, Tcb, z_ind, yn] = dat2steep(x,rate,method);
|
||||
# Tc = Tcf+Tcb;
|
||||
# t = linspace(0.01,8,200);
|
||||
# ftc1 = kde(Tc,{'L2',0},t);
|
||||
# pdfplot(ftc1)
|
||||
# hold on
|
||||
# #! f_t = spec2tpdf(SS,[],'Tc',[0 8 81],0,4);
|
||||
# f_t = spec2tpdf(SS,[],'Tc',[0 8 81],0,2);
|
||||
# simpson(f_t.x{1},f_t.f)
|
||||
# pdfplot(f_t,'-.')
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 27'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# tic
|
||||
#
|
||||
# if strncmpi(speed,'slow',1)
|
||||
# NIT = 4;
|
||||
# else
|
||||
# NIT = 2;
|
||||
# end
|
||||
# #! f_t2 = spec2tpdf(SS,[],'Tc',[0 8 81],[Hs/2],4);
|
||||
# tic
|
||||
# f_t2 = spec2tpdf(SS,[],'Tc',[0 8 81],Hs/2,NIT);
|
||||
# toc
|
||||
#
|
||||
# Pemp = sum(Ac>Hs/2)/sum(Ac>0)
|
||||
# simpson(f_t2.x{1},f_t2.f)
|
||||
# index = find(Ac>Hs/2);
|
||||
# ftc1 = kde(Tc(index),{'L2',0},t);
|
||||
# ftc1.f = Pemp*ftc1.f;
|
||||
# pdfplot(ftc1)
|
||||
# hold on
|
||||
# pdfplot(f_t2,'-.')
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 28'), pause(pstate)
|
||||
#
|
||||
# #!#! Example 8: Wave period for high crest waves
|
||||
# #! clf
|
||||
# tic
|
||||
# f_tcc2 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[0],-1);
|
||||
# toc
|
||||
# simpson(f_tcc2.x{1},f_tcc2.f)
|
||||
# f_tcc3 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[0],3,5);
|
||||
# #! f_tcc3 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[0],1,5);
|
||||
# simpson(f_tcc3.x{1},f_tcc3.f)
|
||||
# pdfplot(f_tcc2,'-.')
|
||||
# hold on
|
||||
# pdfplot(f_tcc3)
|
||||
# hold off
|
||||
# toc
|
||||
# disp('Block = 29'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# [TC tc_ind v_ind] = dat2tc(yn,[],'dw');
|
||||
# N = length(tc_ind);
|
||||
# t_ind = tc_ind(1:2:N);
|
||||
# c_ind = tc_ind(2:2:N);
|
||||
# Pemp = sum(yn(t_ind,2)<-Hs/2 & yn(c_ind,2)>Hs/2)/length(t_ind)
|
||||
# ind = find(yn(t_ind,2)<-Hs/2 & yn(c_ind,2)>Hs/2);
|
||||
# spwaveplot(yn,ind(2:4))
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 30'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# Tcc = yn(v_ind(1+2*ind),1)-yn(v_ind(1+2*(ind-1)),1);
|
||||
# t = linspace(0.01,14,200);
|
||||
# ftcc1 = kde(Tcc,{'kernel' 'epan','L2',0},t);
|
||||
# ftcc1.f = Pemp*ftcc1.f;
|
||||
# pdfplot(ftcc1,'-.')
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 31'), pause(pstate)
|
||||
#
|
||||
# tic
|
||||
# f_tcc22_1 = spec2tccpdf(SS,[],'t>',[0 12 61],[Hs/2],[Hs/2],-1);
|
||||
# toc
|
||||
# simpson(f_tcc22_1.x{1},f_tcc22_1.f)
|
||||
# hold on
|
||||
# pdfplot(f_tcc22_1)
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 32'), pause(pstate)
|
||||
#
|
||||
# disp('The rest of this chapter deals with joint densities.')
|
||||
# disp('Some calculations may take some time.')
|
||||
# disp('You could experiment with other NIT.')
|
||||
# #!return
|
||||
#
|
||||
# #!#! Section 3.4.3 Joint density of crest period and crest height
|
||||
# #!#! Example 9. Some preliminary analysis of the data
|
||||
# clf
|
||||
# tic
|
||||
# yy = load('gfaksr89.dat');
|
||||
# SS = dat2spec(yy);
|
||||
# si = sqrt(spec2mom(SS,1));
|
||||
# SS.tr = dat2tr(yy);
|
||||
# Hs = 4*si
|
||||
# v = gaus2dat([0 0],SS.tr);
|
||||
# v = v(2)
|
||||
# toc
|
||||
# disp('Block = 33'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# tic
|
||||
# [TC, tc_ind, v_ind] = dat2tc(yy,v,'dw');
|
||||
# N = length(tc_ind);
|
||||
# t_ind = tc_ind(1:2:N);
|
||||
# c_ind = tc_ind(2:2:N);
|
||||
# v_ind_d = v_ind(1:2:N+1);
|
||||
# v_ind_u = v_ind(2:2:N+1);
|
||||
# T_d = ecross(yy(:,1),yy(:,2),v_ind_d,v);
|
||||
# T_u = ecross(yy(:,1),yy(:,2),v_ind_u,v);
|
||||
#
|
||||
# Tc = T_d(2:end)-T_u(1:end);
|
||||
# Tt = T_u(1:end)-T_d(1:end-1);
|
||||
# Tcf = yy(c_ind,1)-T_u;
|
||||
# Ac = yy(c_ind,2)-v;
|
||||
# At = v-yy(t_ind,2);
|
||||
# toc
|
||||
# disp('Block = 34'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# tic
|
||||
# t = linspace(0.01,15,200);
|
||||
# kopt3 = kdeoptset('hs',0.25,'L2',0);
|
||||
# ftc1 = kde(Tc,kopt3,t);
|
||||
# ftt1 = kde(Tt,kopt3,t);
|
||||
# pdfplot(ftt1,'k')
|
||||
# hold on
|
||||
# pdfplot(ftc1,'k-.')
|
||||
# f_tc4 = spec2tpdf(SS,[],'Tc',[0 12 81],0,4,5);
|
||||
# f_tc2 = spec2tpdf(SS,[],'Tc',[0 12 81],0,2,5);
|
||||
# f_tc = spec2tpdf(SS,[],'Tc',[0 12 81],0,-1);
|
||||
# pdfplot(f_tc,'b')
|
||||
# hold off
|
||||
# legend('kde(Tt)','kde(Tc)','f_{tc}')
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 35'), pause(pstate)
|
||||
#
|
||||
# #!#! Example 10: Joint characteristics of a half wave:
|
||||
# #!#! position and height of a crest for a wave with given period
|
||||
# clf
|
||||
# tic
|
||||
# ind = find(4.4<Tc & Tc<4.6);
|
||||
# f_AcTcf = kde([Tcf(ind) Ac(ind)],{'L2',[1 .5]});
|
||||
# pdfplot(f_AcTcf)
|
||||
# hold on
|
||||
# plot(Tcf(ind), Ac(ind),'.');
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 36'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# tic
|
||||
# opt1 = rindoptset('speed',5,'method',3);
|
||||
# opt2 = rindoptset('speed',5,'nit',2,'method',0);
|
||||
#
|
||||
# f_tcfac1 = spec2thpdf(SS,[],'TcfAc',[4.5 4.5 46],[0:0.25:8],opt1);
|
||||
# f_tcfac2 = spec2thpdf(SS,[],'TcfAc',[4.5 4.5 46],[0:0.25:8],opt2);
|
||||
#
|
||||
# pdfplot(f_tcfac1,'-.')
|
||||
# hold on
|
||||
# pdfplot(f_tcfac2)
|
||||
# plot(Tcf(ind), Ac(ind),'.');
|
||||
#
|
||||
# simpson(f_tcfac1.x{1},simpson(f_tcfac1.x{2},f_tcfac1.f,1))
|
||||
# simpson(f_tcfac2.x{1},simpson(f_tcfac2.x{2},f_tcfac2.f,1))
|
||||
# f_tcf4=spec2tpdf(SS,[],'Tc',[4.5 4.5 46],[0:0.25:8],6);
|
||||
# f_tcf4.f(46)
|
||||
# toc
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 37'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# f_tcac_s = spec2thpdf(SS,[],'TcAc',[0 12 81],[Hs/2:0.1:2*Hs],opt1);
|
||||
# disp('Block = 38'), pause(pstate)
|
||||
#
|
||||
# clf
|
||||
# tic
|
||||
# mom = spec2mom(SS,4,[],0);
|
||||
# t = f_tcac_s.x{1};
|
||||
# h = f_tcac_s.x{2};
|
||||
# flh_g = lh83pdf(t',h',[mom(1),mom(2),mom(3)],SS.tr);
|
||||
# clf
|
||||
# ind=find(Ac>Hs/2);
|
||||
# plot(Tc(ind), Ac(ind),'.');
|
||||
# hold on
|
||||
# pdfplot(flh_g,'k-.')
|
||||
# pdfplot(f_tcac_s)
|
||||
# toc
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 39'), pause(pstate)
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# #! f_tcac = spec2thpdf(SS,[],'TcAc',[0 12 81],[0:0.2:8],opt1);
|
||||
# #! pdfplot(f_tcac)
|
||||
# disp('Block = 40'), pause(pstate)
|
||||
#
|
||||
# #!#! Section 3.4.4 Joint density of crest and trough height
|
||||
# #!#! Section 3.4.5 Min-to-max distributions Markov method
|
||||
# #!#! Example 11. (min-max problems with Gullfaks data)
|
||||
# #!#! Joint density of maximum and the following minimum
|
||||
# clf
|
||||
# tic
|
||||
# tp = dat2tp(yy);
|
||||
# Mm = fliplr(tp2mm(tp));
|
||||
# fmm = kde(Mm);
|
||||
# f_mM = spec2mmtpdf(SS,[],'mm',[],[-7 7 51],opt2);
|
||||
#
|
||||
# pdfplot(f_mM,'-.')
|
||||
# hold on
|
||||
# pdfplot(fmm,'k-')
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 41'), pause(pstate)
|
||||
#
|
||||
# #!#! The joint density of still water separated maxima and minima.
|
||||
# clf
|
||||
# tic
|
||||
# ind = find(Mm(:,1)>v & Mm(:,2)<v);
|
||||
# Mmv = abs(Mm(ind,:)-v);
|
||||
# fmmv = kde(Mmv);
|
||||
# f_vmm = spec2mmtpdf(SS,[],'vmm',[],[-7 7 51],opt2);
|
||||
# clf
|
||||
# pdfplot(fmmv,'k-')
|
||||
# hold on
|
||||
# pdfplot(f_vmm,'-.')
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 42'), pause(pstate)
|
||||
#
|
||||
#
|
||||
# #!#!
|
||||
# clf
|
||||
# tic
|
||||
# facat = kde([Ac At]);
|
||||
# f_acat = spec2mmtpdf(SS,[],'AcAt',[],[-7 7 51],opt2);
|
||||
# clf
|
||||
# pdfplot(f_acat,'-.')
|
||||
# hold on
|
||||
# pdfplot(facat,'k-')
|
||||
# hold off
|
||||
# wafostamp([],'(ER)')
|
||||
# toc
|
||||
# disp('Block = 43'), pause(pstate)
|
||||
|
||||
@ -1,407 +0,0 @@
|
||||
|
||||
#! CHAPTER4 contains the commands used in Chapter 4 of the tutorial
|
||||
#!=================================================================
|
||||
#!
|
||||
#! CALL: Chapter4
|
||||
#!
|
||||
#! Some of the commands are edited for fast computation.
|
||||
#! Each set of commands is followed by a 'pause' command.
|
||||
#!
|
||||
#! This routine also can print the figures;
|
||||
#! For printing the figures on directory ../bilder/ edit the file and put
|
||||
#! printing=1;
|
||||
|
||||
#! Tested on Matlab 5.3
|
||||
#! History
|
||||
#! Revised pab sept2005
|
||||
#! Added sections -> easier to evaluate using cellmode evaluation.
|
||||
#! revised pab Feb2004
|
||||
#! updated call to lc2sdat
|
||||
#! Created by GL July 13, 2000
|
||||
#! from commands used in Chapter 4
|
||||
#!
|
||||
|
||||
#! Chapter 4 Fatigue load analysis and rain-flow cycles
|
||||
#!------------------------------------------------------
|
||||
|
||||
printing = 0
|
||||
|
||||
|
||||
#! Section 4.3.1 Crossing intensity
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
import numpy as np
|
||||
from wafo.plotbackend import plotbackend as plt
|
||||
import wafo.data as wd
|
||||
import wafo.objects as wo
|
||||
|
||||
xx_sea = wd.sea()
|
||||
ts = wo.mat2timeseries(xx_sea)
|
||||
tp = ts.turning_points()
|
||||
mM = tp.cycle_pairs(kind='min2max')
|
||||
lc = mM.level_crossings(intensity=True)
|
||||
T_sea = ts.args[-1]-ts.args[0]
|
||||
|
||||
plt.subplot(1,2,1)
|
||||
lc.plot()
|
||||
plt.subplot(1,2,2)
|
||||
lc.setplotter(plotmethod='step')
|
||||
lc.plot()
|
||||
plt.show()
|
||||
|
||||
|
||||
m_sea = ts.data.mean()
|
||||
f0_sea = np.interp(m_sea, lc.args,lc.data)
|
||||
extr_sea = len(tp.data)/(2*T_sea)
|
||||
alfa_sea = f0_sea/extr_sea
|
||||
print('alfa = %g ' % alfa_sea)
|
||||
|
||||
#! Section 4.3.2 Extraction of rainflow cycles
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
#! Min-max and rainflow cycle plots
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
mM_rfc = tp.cycle_pairs(h=0.3)
|
||||
|
||||
plt.clf()
|
||||
plt.subplot(122),
|
||||
mM.plot()
|
||||
plt.title('min-max cycle pairs')
|
||||
plt.subplot(121),
|
||||
mM_rfc.plot()
|
||||
plt.title('Rainflow filtered cycles')
|
||||
plt.show()
|
||||
|
||||
#! Min-max and rainflow cycle distributions
|
||||
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
import wafo.misc as wm
|
||||
ampmM_sea = mM.amplitudes()
|
||||
ampRFC_sea = mM_rfc.amplitudes()
|
||||
plt.clf()
|
||||
plt.subplot(121)
|
||||
wm.plot_histgrm(ampmM_sea,25)
|
||||
ylim = plt.gca().get_ylim()
|
||||
plt.title('min-max amplitude distribution')
|
||||
plt.subplot(122)
|
||||
wm.plot_histgrm(ampRFC_sea,25)
|
||||
plt.gca().set_ylim(ylim)
|
||||
plt.title('Rainflow amplitude distribution')
|
||||
plt.show()
|
||||
|
||||
#!#! Section 4.3.3 Simulation of rainflow cycles
|
||||
#!#! Simulation of cycles in a Markov model
|
||||
# n = 41
|
||||
# param_m = [-1, 1, n]
|
||||
# param_D = [1, n, n]
|
||||
# u_markov=levels(param_m);
|
||||
# G_markov=mktestmat(param_m,[-0.2, 0.2],0.15,1);
|
||||
# T_markov=5000;
|
||||
#xxD_markov=mctpsim({G_markov [,]},T_markov);
|
||||
#xx_markov=[(1:T_markov)' u_markov(xxD_markov)'];
|
||||
#clf
|
||||
#plot(xx_markov(1:50,1),xx_markov(1:50,2))
|
||||
#title('Markov chain of turning points')
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 5'),pause(pstate)
|
||||
#
|
||||
#
|
||||
##!#! Rainflow cycles in a transformed Gaussian model
|
||||
##!#! Hermite transformed wave data and rainflow filtered turning points, h = 0.2.
|
||||
#me = mean(xx_sea(:,2));
|
||||
#sa = std(xx_sea(:,2));
|
||||
#Hm0_sea = 4*sa;
|
||||
#Tp_sea = 1/max(lc_sea(:,2));
|
||||
#spec = jonswap([],[Hm0_sea Tp_sea]);
|
||||
#
|
||||
#[sk, ku] = spec2skew(spec);
|
||||
#spec.tr = hermitetr([],[sa sk ku me]);
|
||||
#param_h = [-1.5 2 51];
|
||||
#spec_norm = spec;
|
||||
#spec_norm.S = spec_norm.S/sa^2;
|
||||
#xx_herm = spec2sdat(spec_norm,[2^15 1],0.1);
|
||||
##! ????? PJ, JR 11-Apr-2001
|
||||
##! NOTE, in the simulation program spec2sdat
|
||||
##!the spectrum must be normalized to variance 1
|
||||
##! ?????
|
||||
#h = 0.2;
|
||||
#[dtp,u_herm,xx_herm_1]=dat2dtp(param_h,xx_herm,h);
|
||||
#clf
|
||||
#plot(xx_herm(:,1),xx_herm(:,2),'k','LineWidth',2); hold on;
|
||||
#plot(xx_herm_1(:,1),xx_herm_1(:,2),'k--','Linewidth',2);
|
||||
#axis([0 50 -1 1]), hold off;
|
||||
#title('Rainflow filtered wave data')
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 6'),pause(pstate)
|
||||
#
|
||||
##!#! Rainflow cycles and rainflow filtered rainflow cycles in the transformed Gaussian process.
|
||||
#tp_herm=dat2tp(xx_herm);
|
||||
#RFC_herm=tp2rfc(tp_herm);
|
||||
#mM_herm=tp2mm(tp_herm);
|
||||
#h=0.2;
|
||||
#[dtp,u,tp_herm_1]=dat2dtp(param_h,xx_herm,h);
|
||||
#RFC_herm_1 = tp2rfc(tp_herm_1);
|
||||
#clf
|
||||
#subplot(121), ccplot(RFC_herm)
|
||||
#title('h=0')
|
||||
#subplot(122), ccplot(RFC_herm_1)
|
||||
#title('h=0.2')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_8.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 7'),pause(pstate)
|
||||
#
|
||||
##!#! Section 4.3.4 Calculating the rainflow matrix
|
||||
#
|
||||
#
|
||||
#Grfc_markov=mctp2rfm({G_markov []});
|
||||
#clf
|
||||
#subplot(121), cmatplot(u_markov,u_markov,G_markov), axis('square')
|
||||
#subplot(122), cmatplot(u_markov,u_markov,Grfc_markov), axis('square')
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 8'),pause(pstate)
|
||||
#
|
||||
##!#!
|
||||
#clf
|
||||
#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},3)
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 9'),pause(pstate)
|
||||
#
|
||||
##!#! Min-max-matrix and theoretical rainflow matrix for test Markov sequence.
|
||||
#cmatplot(u_markov,u_markov,{G_markov Grfc_markov},4)
|
||||
#subplot(121), axis('square'), title('min2max transition matrix')
|
||||
#subplot(122), axis('square'), title('Rainflow matrix')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_9.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 10'),pause(pstate)
|
||||
#
|
||||
##!#! Observed and theoretical rainflow matrix for test Markov sequence.
|
||||
#n=length(u_markov);
|
||||
#Frfc_markov=dtp2rfm(xxD_markov,n);
|
||||
#clf
|
||||
#cmatplot(u_markov,u_markov,{Frfc_markov Grfc_markov*T_markov/2},3)
|
||||
#subplot(121), axis('square'), title('Observed rainflow matrix')
|
||||
#subplot(122), axis('square'), title('Theoretical rainflow matrix')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_10.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 11'),pause(pstate)
|
||||
#
|
||||
##!#! Smoothed observed and calculated rainflow matrix for test Markov sequence.
|
||||
#tp_markov=dat2tp(xx_markov);
|
||||
#RFC_markov=tp2rfc(tp_markov);
|
||||
#h=1;
|
||||
#Frfc_markov_smooth=cc2cmat(param_m,RFC_markov,[],1,h);
|
||||
#clf
|
||||
#cmatplot(u_markov,u_markov,{Frfc_markov_smooth Grfc_markov*T_markov/2},4)
|
||||
#subplot(121), axis('square'), title('Smoothed observed rainflow matrix')
|
||||
#subplot(122), axis('square'), title('Theoretical rainflow matrix')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_11.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 12'),pause(pstate)
|
||||
#
|
||||
##!#! Rainflow matrix from spectrum
|
||||
#clf
|
||||
##!GmM3_herm=spec2mmtpdf(spec,[],'Mm',[],[],2);
|
||||
#GmM3_herm=spec2cmat(spec,[],'Mm',[],param_h,2);
|
||||
#pdfplot(GmM3_herm)
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 13'),pause(pstate)
|
||||
#
|
||||
#
|
||||
##!#! Min-max matrix and theoretical rainflow matrix for Hermite-transformed Gaussian waves.
|
||||
#Grfc_herm=mctp2rfm({GmM3_herm.f []});
|
||||
#u_herm=levels(param_h);
|
||||
#clf
|
||||
#cmatplot(u_herm,u_herm,{GmM3_herm.f Grfc_herm},4)
|
||||
#subplot(121), axis('square'), title('min-max matrix')
|
||||
#subplot(122), axis('square'), title('Theoretical rainflow matrix')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_12.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 14'),pause(pstate)
|
||||
#
|
||||
##!#!
|
||||
#clf
|
||||
#Grfc_direct_herm=spec2cmat(spec,[],'rfc',[],[],2);
|
||||
#subplot(121), pdfplot(GmM3_herm), axis('square'), hold on
|
||||
#subplot(122), pdfplot(Grfc_direct_herm), axis('square'), hold off
|
||||
#if (printing==1), print -deps ../bilder/fig_mmrfcjfr.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 15'),pause(pstate)
|
||||
#
|
||||
#
|
||||
##!#! Observed smoothed and theoretical min-max matrix,
|
||||
##!#! (and observed smoothed and theoretical rainflow matrix for Hermite-transformed Gaussian waves).
|
||||
#tp_herm=dat2tp(xx_herm);
|
||||
#RFC_herm=tp2rfc(tp_herm);
|
||||
#mM_herm=tp2mm(tp_herm);
|
||||
#h=0.2;
|
||||
#FmM_herm_smooth=cc2cmat(param_h,mM_herm,[],1,h);
|
||||
#Frfc_herm_smooth=cc2cmat(param_h,RFC_herm,[],1,h);
|
||||
#T_herm=xx_herm(end,1)-xx_herm(1,1);
|
||||
#clf
|
||||
#cmatplot(u_herm,u_herm,{FmM_herm_smooth GmM3_herm.f*length(mM_herm) ; ...
|
||||
# Frfc_herm_smooth Grfc_herm*length(RFC_herm)},4)
|
||||
#subplot(221), axis('square'), title('Observed smoothed min-max matrix')
|
||||
#subplot(222), axis('square'), title('Theoretical min-max matrix')
|
||||
#subplot(223), axis('square'), title('Observed smoothed rainflow matrix')
|
||||
#subplot(224), axis('square'), title('Theoretical rainflow matrix')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_13.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 16'),pause(pstate)
|
||||
#
|
||||
##!#! Section 4.3.5 Simulation from crossings and rainflow structure
|
||||
#
|
||||
##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
|
||||
##!#! for simulated process with irregularity factor 0.25.
|
||||
#clf
|
||||
#cross_herm=dat2lc(xx_herm);
|
||||
#alpha1=0.25;
|
||||
#alpha2=0.75;
|
||||
#xx_herm_sim1=lc2sdat(cross_herm,500,alpha1);
|
||||
#cross_herm_sim1=dat2lc(xx_herm_sim1);
|
||||
#subplot(211)
|
||||
#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))
|
||||
#hold on
|
||||
#stairs(cross_herm_sim1(:,1),...
|
||||
# cross_herm_sim1(:,2)/max(cross_herm_sim1(:,2)))
|
||||
#hold off
|
||||
#title('Crossing intensity, \alpha = 0.25')
|
||||
#subplot(212)
|
||||
#plot(xx_herm_sim1(:,1),xx_herm_sim1(:,2))
|
||||
#title('Simulated load, \alpha = 0.25')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_14_25.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 16'),pause(pstate)
|
||||
#
|
||||
##!#! Crossing spectrum (smooth curve) and obtained spectrum (wiggled curve)
|
||||
##!#! for simulated process with irregularity factor 0.75.
|
||||
#xx_herm_sim2=lc2sdat(cross_herm,500,alpha2);
|
||||
#cross_herm_sim2=dat2lc(xx_herm_sim2);
|
||||
#subplot(211)
|
||||
#plot(cross_herm(:,1),cross_herm(:,2)/max(cross_herm(:,2)))
|
||||
#hold on
|
||||
#stairs(cross_herm_sim2(:,1),...
|
||||
# cross_herm_sim2(:,2)/max(cross_herm_sim2(:,2)))
|
||||
#hold off
|
||||
#title('Crossing intensity, \alpha = 0.75')
|
||||
#subplot(212)
|
||||
#plot(xx_herm_sim2(:,1),xx_herm_sim2(:,2))
|
||||
#title('Simulated load, \alpha = 0.75')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_14_75.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 17'),pause(pstate)
|
||||
#
|
||||
##!#! Section 4.4 Fatigue damage and fatigue life distribution
|
||||
##!#! Section 4.4.1 Introduction
|
||||
#beta=3.2; gam=5.5E-10; T_sea=xx_sea(end,1)-xx_sea(1,1);
|
||||
#d_beta=cc2dam(RFC_sea,beta)/T_sea;
|
||||
#time_fail=1/gam/d_beta/3600 #!in hours of the specific storm
|
||||
#disp('Block 18'),pause(pstate)
|
||||
#
|
||||
##!#! Section 4.4.2 Level crossings
|
||||
##!#! Crossing intensity as calculated from the Markov matrix (solid curve) and from the observed rainflow matrix (dashed curve).
|
||||
#clf
|
||||
#mu_markov=cmat2lc(param_m,Grfc_markov);
|
||||
#muObs_markov=cmat2lc(param_m,Frfc_markov/(T_markov/2));
|
||||
#clf
|
||||
#plot(mu_markov(:,1),mu_markov(:,2),muObs_markov(:,1),muObs_markov(:,2),'--')
|
||||
#title('Theoretical and observed crossing intensity ')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_15.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 19'),pause(pstate)
|
||||
#
|
||||
##!#! Section 4.4.3 Damage
|
||||
##!#! Distribution of damage from different RFC cycles, from calculated theoretical and from observed rainflow matrix.
|
||||
#beta = 4;
|
||||
#Dam_markov = cmat2dam(param_m,Grfc_markov,beta)
|
||||
#DamObs1_markov = cc2dam(RFC_markov,beta)/(T_markov/2)
|
||||
#DamObs2_markov = cmat2dam(param_m,Frfc_markov,beta)/(T_markov/2)
|
||||
#disp('Block 20'),pause(pstate)
|
||||
#
|
||||
#Dmat_markov = cmat2dmat(param_m,Grfc_markov,beta);
|
||||
#DmatObs_markov = cmat2dmat(param_m,Frfc_markov,beta)/(T_markov/2);
|
||||
#clf
|
||||
#subplot(121), cmatplot(u_markov,u_markov,Dmat_markov,4)
|
||||
#title('Theoretical damage matrix')
|
||||
#subplot(122), cmatplot(u_markov,u_markov,DmatObs_markov,4)
|
||||
#title('Observed damage matrix')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_16.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 21'),pause(pstate)
|
||||
#
|
||||
#
|
||||
##!#!
|
||||
##!Damplus_markov = lc2dplus(mu_markov,beta)
|
||||
#pause(pstate)
|
||||
#
|
||||
##!#! Section 4.4.4 Estimation of S-N curve
|
||||
#
|
||||
##!#! Load SN-data and plot in log-log scale.
|
||||
#SN = load('sn.dat');
|
||||
#s = SN(:,1);
|
||||
#N = SN(:,2);
|
||||
#clf
|
||||
#loglog(N,s,'o'), axis([0 14e5 10 30])
|
||||
##!if (printing==1), print -deps ../bilder/fatigue_?.eps end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 22'),pause(pstate)
|
||||
#
|
||||
#
|
||||
##!#! Check of S-N-model on normal probability paper.
|
||||
#
|
||||
#normplot(reshape(log(N),8,5))
|
||||
#if (printing==1), print -deps ../bilder/fatigue_17.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 23'),pause(pstate)
|
||||
#
|
||||
##!#! Estimation of S-N-model on linear scale.
|
||||
#clf
|
||||
#[e0,beta0,s20] = snplot(s,N,12);
|
||||
#title('S-N-data with estimated N(s)','FontSize',20)
|
||||
#set(gca,'FontSize',20)
|
||||
#if (printing==1), print -deps ../bilder/fatigue_18a.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 24'),pause(pstate)
|
||||
#
|
||||
##!#! Estimation of S-N-model on log-log scale.
|
||||
#clf
|
||||
#[e0,beta0,s20] = snplot(s,N,14);
|
||||
#title('S-N-data with estimated N(s)','FontSize',20)
|
||||
#set(gca,'FontSize',20)
|
||||
#if (printing==1), print -deps ../bilder/fatigue_18b.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 25'),pause(pstate)
|
||||
#
|
||||
##!#! Section 4.4.5 From S-N curve to fatigue life distribution
|
||||
##!#! Damage intensity as function of $\beta$
|
||||
#beta = 3:0.1:8;
|
||||
#DRFC = cc2dam(RFC_sea,beta);
|
||||
#dRFC = DRFC/T_sea;
|
||||
#plot(beta,dRFC), axis([3 8 0 0.25])
|
||||
#title('Damage intensity as function of \beta')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_19.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 26'),pause(pstate)
|
||||
#
|
||||
##!#! Fatigue life distribution with sea load.
|
||||
#dam0 = cc2dam(RFC_sea,beta0)/T_sea;
|
||||
#[t0,F0] = ftf(e0,dam0,s20,0.5,1);
|
||||
#[t1,F1] = ftf(e0,dam0,s20,0,1);
|
||||
#[t2,F2] = ftf(e0,dam0,s20,5,1);
|
||||
#plot(t0,F0,t1,F1,t2,F2)
|
||||
#title('Fatigue life distribution function')
|
||||
#if (printing==1), print -deps ../bilder/fatigue_20.eps
|
||||
#end
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block 27, last block')
|
||||
@ -1,238 +0,0 @@
|
||||
## CHAPTER5 contains the commands used in Chapter 5 of the tutorial
|
||||
#
|
||||
# CALL: Chapter5
|
||||
#
|
||||
# Some of the commands are edited for fast computation.
|
||||
# Each set of commands is followed by a 'pause' command.
|
||||
#
|
||||
|
||||
# Tested on Matlab 5.3
|
||||
# History
|
||||
# Added Return values by GL August 2008
|
||||
# Revised pab sept2005
|
||||
# Added sections -> easier to evaluate using cellmode evaluation.
|
||||
# Created by GL July 13, 2000
|
||||
# from commands used in Chapter 5
|
||||
#
|
||||
|
||||
## Chapter 5 Extreme value analysis
|
||||
|
||||
## Section 5.1 Weibull and Gumbel papers
|
||||
from __future__ import division
|
||||
import numpy as np
|
||||
import scipy.interpolate as si
|
||||
from wafo.plotbackend import plotbackend as plt
|
||||
import wafo.data as wd
|
||||
import wafo.objects as wo
|
||||
import wafo.stats as ws
|
||||
import wafo.kdetools as wk
|
||||
pstate = 'off'
|
||||
|
||||
# Significant wave-height data on Weibull paper,
|
||||
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
Hs = wd.atlantic()
|
||||
wei = ws.weibull_min.fit(Hs)
|
||||
tmp = ws.probplot(Hs, wei, ws.weibull_min, plot=ax)
|
||||
plt.show()
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 1'),pause(pstate)
|
||||
|
||||
##
|
||||
# Significant wave-height data on Gumbel paper,
|
||||
plt.clf()
|
||||
ax = fig.add_subplot(111)
|
||||
gum = ws.gumbel_r.fit(Hs)
|
||||
tmp1 = ws.probplot(Hs, gum, ws.gumbel_r, plot=ax)
|
||||
#wafostamp([],'(ER)')
|
||||
plt.show()
|
||||
#disp('Block = 2'),pause(pstate)
|
||||
|
||||
##
|
||||
# Significant wave-height data on Normal probability paper,
|
||||
plt.clf()
|
||||
ax = fig.add_subplot(111)
|
||||
phat = ws.norm.fit2(np.log(Hs))
|
||||
phat.plotresq()
|
||||
#tmp2 = ws.probplot(np.log(Hs), phat, ws.norm, plot=ax)
|
||||
|
||||
#wafostamp([],'(ER)')
|
||||
plt.show()
|
||||
#disp('Block = 3'),pause(pstate)
|
||||
|
||||
##
|
||||
# Return values in the Gumbel distribution
|
||||
plt.clf()
|
||||
T = np.r_[1:100000]
|
||||
sT = gum[0] - gum[1] * np.log(-np.log1p(-1./T))
|
||||
plt.semilogx(T, sT)
|
||||
plt.hold(True)
|
||||
# ws.edf(Hs).plot()
|
||||
Nmax = len(Hs)
|
||||
N = np.r_[1:Nmax + 1]
|
||||
|
||||
plt.plot(Nmax/N, sorted(Hs, reverse=True), '.')
|
||||
plt.title('Return values in the Gumbel model')
|
||||
plt.xlabel('Return period')
|
||||
plt.ylabel('Return value')
|
||||
#wafostamp([],'(ER)')
|
||||
plt.show()
|
||||
#disp('Block = 4'),pause(pstate)
|
||||
|
||||
## Section 5.2 Generalized Pareto and Extreme Value distributions
|
||||
## Section 5.2.1 Generalized Extreme Value distribution
|
||||
|
||||
# Empirical distribution of significant wave-height with estimated
|
||||
# Generalized Extreme Value distribution,
|
||||
gev = ws.genextreme.fit2(Hs)
|
||||
gev.plotfitsummary()
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 5a'),pause(pstate)
|
||||
|
||||
plt.clf()
|
||||
x = np.linspace(0,14,200)
|
||||
kde = wk.TKDE(Hs, L2=0.5)(x, output='plot')
|
||||
kde.plot()
|
||||
plt.hold(True)
|
||||
plt.plot(x, gev.pdf(x),'--')
|
||||
# disp('Block = 5b'),pause(pstate)
|
||||
|
||||
# Analysis of yura87 wave data.
|
||||
# Wave data interpolated (spline) and organized in 5-minute intervals
|
||||
# Normalized to mean 0 and std = 1 to get stationary conditions.
|
||||
# maximum level over each 5-minute interval analysed by GEV
|
||||
xn = wd.yura87()
|
||||
XI = np.r_[1:len(xn):0.25] - .99
|
||||
N = len(XI)
|
||||
N = N - np.mod(N, 4*60*5)
|
||||
|
||||
YI = si.interp1d(xn[:, 0], xn[:, 1], kind='linear')(XI)
|
||||
YI = YI.reshape(4*60*5, N/(4*60*5)) # Each column holds 5 minutes of
|
||||
# interpolated data.
|
||||
Y5 = (YI - YI.mean(axis=0)) / YI.std(axis=0)
|
||||
Y5M = Y5.maximum(axis=0)
|
||||
Y5gev = ws.genextreme.fit2(Y5M,method='mps')
|
||||
Y5gev.plotfitsummary()
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 6'),pause(pstate)
|
||||
|
||||
## Section 5.2.2 Generalized Pareto distribution
|
||||
|
||||
# Exceedances of significant wave-height data over level 3,
|
||||
gpd3 = ws.genpareto.fit2(Hs[Hs>3]-3, floc=0)
|
||||
gpd3.plotfitsummary()
|
||||
#wafostamp([],'(ER)')
|
||||
|
||||
##
|
||||
plt.figure()
|
||||
# Exceedances of significant wave-height data over level 7,
|
||||
gpd7 = ws.genpareto.fit2(Hs(Hs>7), floc=7)
|
||||
gpd7.plotfitsummary()
|
||||
# wafostamp([],'(ER)')
|
||||
# disp('Block = 6'),pause(pstate)
|
||||
|
||||
##
|
||||
#Simulates 100 values from the GEV distribution with parameters (0.3, 1, 2),
|
||||
# then estimates the parameters using two different methods and plots the
|
||||
# estimated distribution functions together with the empirical distribution.
|
||||
Rgev = ws.genextreme.rvs(0.3,1,2,size=100)
|
||||
gp = ws.genextreme.fit2(Rgev, method='mps');
|
||||
gm = ws.genextreme.fit2(Rgev, *gp.par.tolist(), method='ml')
|
||||
gm.plotfitsummary()
|
||||
|
||||
gp.plotecdf()
|
||||
plt.hold(True)
|
||||
plt.plot(x, gm.cdf(x), '--')
|
||||
plt.hold(False)
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block =7'),pause(pstate)
|
||||
|
||||
##
|
||||
# ;
|
||||
Rgpd = ws.genpareto.rvs(0.4,0, 1,size=100)
|
||||
gp = ws.genpareto.fit2(Rgpd, method='mps')
|
||||
gml = ws.genpareto.fit2(Rgpd, method='ml')
|
||||
|
||||
gp.plotecdf()
|
||||
x = sorted(Rgpd)
|
||||
plt.hold(True)
|
||||
plt.plot(x, gml.cdf(x))
|
||||
# gm = fitgenpar(Rgpd,'method','mom','plotflag',0);
|
||||
# plot(x,cdfgenpar(x,gm),'g--')
|
||||
#gw = fitgenpar(Rgpd,'method','pwm','plotflag',0);
|
||||
#plot(x,cdfgenpar(x,gw),'g:')
|
||||
#gml = fitgenpar(Rgpd,'method','ml','plotflag',0);
|
||||
#plot(x,cdfgenpar(x,gml),'--')
|
||||
#gmps = fitgenpar(Rgpd,'method','mps','plotflag',0);
|
||||
#plot(x,cdfgenpar(x,gmps),'r-.')
|
||||
plt.hold(False)
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 8'),pause(pstate)
|
||||
|
||||
##
|
||||
# Return values for the GEV distribution
|
||||
T = np.logspace(1, 5, 10);
|
||||
#[sT, sTlo, sTup] = invgev(1./T,Y5gev,'lowertail',false,'proflog',true);
|
||||
|
||||
#T = 2:100000;
|
||||
#k=Y5gev.params(1); mu=Y5gev.params(3); sigma=Y5gev.params(2);
|
||||
#sT1 = invgev(1./T,Y5gev,'lowertail',false);
|
||||
#sT=mu + sigma/k*(1-(-log(1-1./T)).^k);
|
||||
plt.clf()
|
||||
#plt.semilogx(T,sT,T,sTlo,'r',T,sTup,'r')
|
||||
#plt.hold(True)
|
||||
#N = np.r_[1:len(Y5M)]
|
||||
#Nmax = max(N);
|
||||
#plot(Nmax./N, sorted(Y5M,reverse=True), '.')
|
||||
#plt.title('Return values in the GEV model')
|
||||
#plt.xlabel('Return priod')
|
||||
#plt.ylabel('Return value')
|
||||
#plt.grid(True)
|
||||
#disp('Block = 9'),pause(pstate)
|
||||
|
||||
## Section 5.3 POT-analysis
|
||||
|
||||
# Estimated expected exceedance over level u as function of u.
|
||||
plt.clf()
|
||||
|
||||
mrl = ws.reslife(Hs,'umin',2,'umax',10,'Nu',200);
|
||||
mrl.plot()
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 10'),pause(pstate)
|
||||
|
||||
##
|
||||
# Estimated distribution functions of monthly maxima
|
||||
#with the POT method (solid),
|
||||
# fitting a GEV (dashed) and the empirical distribution.
|
||||
|
||||
# POT- method
|
||||
gpd7 = ws.genpareto.fit2(Hs(Hs>7)-7, method='mps', floc=0)
|
||||
khat, loc, sigmahat = gpd7.par
|
||||
|
||||
muhat = len(Hs[Hs>7])/(7*3*2)
|
||||
bhat = sigmahat/muhat**khat
|
||||
ahat = 7-(bhat-sigmahat)/khat
|
||||
x = np.linspace(5,15,200);
|
||||
plt.plot(x,ws.genextreme.cdf(x, khat,bhat,ahat))
|
||||
# disp('Block = 11'),pause(pstate)
|
||||
|
||||
##
|
||||
# Since we have data to compute the monthly maxima mm over
|
||||
#42 months we can also try to fit a
|
||||
# GEV distribution directly:
|
||||
mm = np.zeros((1,41))
|
||||
for i in range(41):
|
||||
mm[i] = max(Hs[((i-1)*14+1):i*14])
|
||||
|
||||
|
||||
gev = ws.genextreme.fit2(mm)
|
||||
|
||||
|
||||
plt.hold(True)
|
||||
gev.plotecdf()
|
||||
|
||||
plt.hold(False)
|
||||
#wafostamp([],'(ER)')
|
||||
#disp('Block = 12, Last block'),pause(pstate)
|
||||
|
||||
@ -1,176 +0,0 @@
|
||||
from tutor_init import *
|
||||
import itertools
|
||||
# import sys
|
||||
log = logging.getLogger(__name__)
|
||||
log.setLevel(logging.DEBUG)
|
||||
|
||||
MARKERS = ('o', 'x', '+', '.', '<', '>', '^', 'v')
|
||||
|
||||
|
||||
def plot_varying_symbols(x, y, color='red', size=5):
|
||||
"""
|
||||
Create a plot with varying symbols
|
||||
Parameters
|
||||
----------
|
||||
x : numpy array with x data of the points
|
||||
y : numpy array with y data of the points
|
||||
color : color of the symbols
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
"""
|
||||
markers = itertools.cycle(MARKERS)
|
||||
for q, p in zip(x, y):
|
||||
plt.plot(q, p, marker=markers.next(), linestyle='', color=color,
|
||||
markersize=size)
|
||||
|
||||
|
||||
def damage_vs_S(S, beta, K):
|
||||
"""
|
||||
Calculate the damage 1/N for a given stress S
|
||||
|
||||
Parameters
|
||||
----------
|
||||
S : Stress [Pa]
|
||||
beta : coefficient, typically 3
|
||||
K : constant
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
"""
|
||||
return K * np.power(S, beta)
|
||||
|
||||
# Section 4.3.1 Crossing intensity
|
||||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
import wafo.data as wd
|
||||
import wafo.objects as wo
|
||||
import wafo.misc as wm
|
||||
|
||||
xx_sea = wd.sea()
|
||||
|
||||
Tlength = xx_sea[-1, 0] - xx_sea[0, 0]
|
||||
beta = 3
|
||||
K1 = 6.5e-31
|
||||
Np = 200
|
||||
Tp = Tlength / Np
|
||||
A = 100e6
|
||||
log.info("setting sin wave with Tp={} and T={}".format(Tp, Tlength))
|
||||
Nc = 1.0 / damage_vs_S(A, beta, K1)
|
||||
damage = float(Np) / float(Nc)
|
||||
log.info("budget at S={} N={}: damage = {} ".format(A, Nc, damage))
|
||||
#xx_sea[:, 1] = A * np.cos(2 * np.pi * xx_sea[:, 0]/Tp)
|
||||
xx_sea[:, 1] *= 500e6
|
||||
|
||||
log.info("loaded sea time series {}".format(xx_sea.shape))
|
||||
ts = wo.mat2timeseries(xx_sea)
|
||||
|
||||
tp = ts.turning_points()
|
||||
mM = tp.cycle_pairs(kind='min2max')
|
||||
Mm = tp.cycle_pairs(kind='max2min')
|
||||
lc = mM.level_crossings(intensity=True)
|
||||
T_sea = ts.args[-1] - ts.args[0]
|
||||
|
||||
# for i in dir(mM):
|
||||
# print(i)
|
||||
|
||||
|
||||
ts1 = wo.mat2timeseries(xx_sea[:, :])
|
||||
tp1 = ts1.turning_points()
|
||||
sig_tp = ts.turning_points(h=0, wavetype='astm')
|
||||
try:
|
||||
sig_cp = sig_tp.cycle_astm()
|
||||
log.info("Successfully used cycle_astm")
|
||||
except AttributeError:
|
||||
log.warning("Could use cycle_astm")
|
||||
sig_cp = None
|
||||
tp1 = ts1.turning_points()
|
||||
tp2 = ts1.turning_points(wavetype='Mw')
|
||||
mM1 = tp1.cycle_pairs(kind='min2max')
|
||||
Mm1 = tp1.cycle_pairs(kind='max2min')
|
||||
|
||||
tp_rfc = tp1.rainflow_filter(h=100e6)
|
||||
mM_rfc = tp_rfc.cycle_pairs()
|
||||
try:
|
||||
mM_rfc_a = tp1.cycle_astm()
|
||||
except AttributeError:
|
||||
mM_rfc_a = None
|
||||
tc1 = ts1.trough_crest()
|
||||
min_to_max = True
|
||||
rfc_plot = True
|
||||
if min_to_max:
|
||||
m1, M1 = mM1.get_minima_and_maxima()
|
||||
i_min_start = 0
|
||||
else:
|
||||
m1, M1 = Mm1.get_minima_and_maxima()
|
||||
i_min_start = 2
|
||||
|
||||
m_rfc, M_rfc = mM_rfc.get_minima_and_maxima()
|
||||
# m_rfc_a, M_rfc_a = mM_rfc_a.get_minima_and_maxima()
|
||||
ts1.plot('b-')
|
||||
if rfc_plot:
|
||||
plot_varying_symbols(tp_rfc.args[0::2], m_rfc, color='red', size=10)
|
||||
plot_varying_symbols(tp_rfc.args[1::2], M_rfc, color='green', size=10)
|
||||
else:
|
||||
plot_varying_symbols(tp.args[i_min_start::2], m1, color='red', size=10)
|
||||
plot_varying_symbols(tp.args[1::2], M1, color='green', size=10)
|
||||
|
||||
set_windows_title("Sea time series", log)
|
||||
|
||||
plt.figure()
|
||||
plt.subplot(122),
|
||||
mM.plot()
|
||||
plt.title('min-max cycle pairs')
|
||||
plt.subplot(121),
|
||||
mM_rfc.plot()
|
||||
|
||||
title = 'Rainflow filtered cycles'
|
||||
plt.title(title)
|
||||
set_windows_title(title)
|
||||
|
||||
|
||||
# Min-max and rainflow cycle distributions
|
||||
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
# import wafo.misc as wm
|
||||
ampmM_sea = mM.amplitudes()
|
||||
ampRFC_sea = mM_rfc.amplitudes()
|
||||
plt.figure()
|
||||
title = "s_n_curve"
|
||||
set_windows_title(title)
|
||||
S = np.linspace(1e6, 1000e6)
|
||||
plt.loglog(S, damage_vs_S(S, beta, K1))
|
||||
plt.figure()
|
||||
plt.subplot(121)
|
||||
stress_range = (1, 1e9)
|
||||
n_bins = 100
|
||||
wm.plot_histgrm(ampmM_sea, bins=n_bins, range=stress_range)
|
||||
plt.xlim(stress_range)
|
||||
ylim = plt.gca().get_ylim()
|
||||
plt.title('min-max amplitude distribution')
|
||||
plt.subplot(122)
|
||||
if sig_cp is not None:
|
||||
wm.plot_histgrm(sig_cp[:, 0], bins=n_bins, range=stress_range)
|
||||
plt.gca().set_ylim(ylim)
|
||||
title = 'Rainflow amplitude distribution'
|
||||
plt.title(title)
|
||||
plt.semilogy
|
||||
set_windows_title(title)
|
||||
|
||||
hist, bin_edges = np.histogram(
|
||||
sig_cp[
|
||||
:, 0], bins=n_bins, range=stress_range)
|
||||
|
||||
plt.figure()
|
||||
title = "my_bins"
|
||||
plt.title(title)
|
||||
plt.title(title)
|
||||
set_windows_title(title)
|
||||
plt.semilogy
|
||||
plt.bar(bin_edges[:-1], hist, width=stress_range[1] / n_bins)
|
||||
|
||||
print("damage min/max : {}".format(mM_rfc.damage([beta], K1)))
|
||||
|
||||
damage_rfc = K1 * np.sum(sig_cp[:, 0] ** beta)
|
||||
print("damage rfc : {}".format(damage_rfc))
|
||||
plt.show('hold')
|
||||
@ -1,27 +0,0 @@
|
||||
import logging
|
||||
import scipy as sp
|
||||
import numpy as np
|
||||
from numpy import pi, reshape
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib
|
||||
matplotlib.use('Qt4Agg')
|
||||
from matplotlib import rcParams
|
||||
rcParams.update({"font.size": 10})
|
||||
|
||||
|
||||
try:
|
||||
from win32api import LoadResource
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
log = logging.basicConfig(
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
level=logging.DEBUG)
|
||||
|
||||
|
||||
def set_windows_title(title, log=None):
|
||||
if log is not None:
|
||||
log.info("Set windows title {}".format(title))
|
||||
fig = plt.gcf()
|
||||
fig.canvas.set_window_title(title)
|
||||
plt.show()
|
||||
@ -1,65 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
f2py c_library.pyf c_functions.c -c
|
||||
|
||||
See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
|
||||
|
||||
def which(program):
|
||||
"""
|
||||
Return filepath to program if it exists
|
||||
|
||||
In order to test if a certain executable exists, it will search for the
|
||||
program name in the environment variables.
|
||||
If program is a full path to an executable, it will check it exists
|
||||
|
||||
Copied from:
|
||||
http://stackoverflow.com/questions/377017/test-if-executable-exists-in-python/
|
||||
It is supposed to mimic the UNIX command "which"
|
||||
"""
|
||||
|
||||
def is_exe(fpath):
|
||||
return os.path.exists(fpath) and os.access(fpath, os.X_OK)
|
||||
|
||||
fpath, unused_fname = os.path.split(program)
|
||||
if fpath:
|
||||
if is_exe(program):
|
||||
return program
|
||||
else:
|
||||
for path in os.environ["PATH"].split(os.pathsep):
|
||||
exe_file = os.path.join(path, program)
|
||||
if is_exe(exe_file):
|
||||
return exe_file
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def f2py_call_str():
|
||||
'''Return which f2py callable is in the path regardless of platform'''
|
||||
|
||||
# define possible options:
|
||||
# on Arch Linux, python and f2py are the calls corresponding to python 3
|
||||
# and python2/f2py2 for python 2
|
||||
# other Linux versions might still use python/f2py for python 2
|
||||
|
||||
if os.path.basename(sys.executable).endswith('2'):
|
||||
options = ('f2py2', 'f2py2.6', 'f2py2.7',)
|
||||
else: # on Windows and other Linux using python/f2py
|
||||
options = ('f2py.exe', 'f2py.bat', 'f2py', 'f2py2.6', 'f2py2.7',
|
||||
'f2py.py',)
|
||||
for k in options:
|
||||
if which(k):
|
||||
# Found the f2py path, no need to look further
|
||||
f2py_call = k
|
||||
f2py_path = which(k)
|
||||
break
|
||||
|
||||
try:
|
||||
print('found f2py in:', f2py_path)
|
||||
return f2py_call
|
||||
except NameError:
|
||||
raise UserWarning('Couldn\'t locate f2py. '
|
||||
'Should be part of NumPy installation.')
|
||||
@ -1,908 +0,0 @@
|
||||
# /usr/bin/env python
|
||||
'''
|
||||
Module FIG
|
||||
------------
|
||||
Module for manipulating windows/figures created using
|
||||
pylab or enthought.mayavi.mlab on the windows platform.
|
||||
|
||||
Figure manipulation involves
|
||||
maximization, minimization, hiding, closing, stacking or tiling.
|
||||
|
||||
It is assumed that the figures are uniquely numbered in the following way:
|
||||
Figure 1
|
||||
Figure 2
|
||||
....
|
||||
or
|
||||
TVTK scene 1
|
||||
TVTK scene 2
|
||||
TVTK scene 3
|
||||
...
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(6):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.stack('all')
|
||||
>>> fig.stack(1,2)
|
||||
>>> fig.hide(1)
|
||||
>>> fig.restore(1)
|
||||
>>> fig.tile()
|
||||
>>> fig.pile()
|
||||
>>> fig.maximize(4)
|
||||
>>> fig.close('all')
|
||||
'''
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
# import win32api
|
||||
import win32gui
|
||||
import win32con
|
||||
import wx
|
||||
import numpy
|
||||
|
||||
from win32gui import (EnumWindows, MoveWindow, GetWindowRect, FindWindow,
|
||||
ShowWindow, BringWindowToTop)
|
||||
|
||||
__all__ = ['close', 'cycle', 'hide', 'keep', 'maximize', 'minimize', 'pile',
|
||||
'restore', 'stack', 'tile', 'find_all_figure_numbers', 'set_size']
|
||||
|
||||
# Figure format strings to recognize in window title
|
||||
FIGURE_TITLE_FORMATS = ('Figure', 'TVTK Scene', 'Chaco Plot Window: Figure')
|
||||
_SCREENSIZE = None
|
||||
|
||||
|
||||
class CycleDialog(wx.Dialog):
|
||||
|
||||
def _get_buttons(self):
|
||||
hbox = wx.BoxSizer(wx.HORIZONTAL)
|
||||
buttons = ['Forward', 'Back', 'Cancel']
|
||||
callbacks = [self.on_forward, self.on_backward, self.on_cancel]
|
||||
for button, callback in zip(buttons, callbacks):
|
||||
button = wx.Button(self, -1, button, size=(70, 30))
|
||||
self.Bind(wx.EVT_BUTTON, callback, button)
|
||||
hbox.Add(button, 1, wx.ALIGN_CENTER)
|
||||
return hbox
|
||||
|
||||
def _get_message(self):
|
||||
label = ('Press back or forward to display previous or next figure(s),'
|
||||
' respectively. Press cancel to quit.')
|
||||
message = wx.StaticText(self, label=label, size=(240, 25))
|
||||
return message
|
||||
|
||||
def __init__(self, parent, interval=None, title='Cycle dialog'):
|
||||
super(CycleDialog, self).__init__(parent, title=title, size=(260, 130))
|
||||
if isinstance(interval, (float, int)):
|
||||
self.interval_milli_sec = interval * 1000
|
||||
else:
|
||||
self.interval_milli_sec = 30
|
||||
|
||||
self.timer = wx.Timer(self)
|
||||
self.Bind(wx.EVT_TIMER, self.on_forward, self.timer)
|
||||
|
||||
vbox = wx.BoxSizer(wx.VERTICAL)
|
||||
vbox.Add(self._get_message(), 0, wx.ALIGN_CENTER | wx.TOP, 20)
|
||||
vbox.Add(self._get_buttons(), 1, wx.ALIGN_CENTER | wx.TOP | wx.BOTTOM, 10)
|
||||
self.SetSizer(vbox)
|
||||
|
||||
def ShowModal(self, *args, **kwargs):
|
||||
self.timer.Start(self.interval_milli_sec, oneShot=True)
|
||||
return super(CycleDialog, self).ShowModal(*args, **kwargs)
|
||||
|
||||
def on_forward(self, evt):
|
||||
self.EndModal(wx.ID_FORWARD)
|
||||
|
||||
def on_backward(self, evt):
|
||||
self.EndModal(wx.ID_BACKWARD)
|
||||
|
||||
def on_cancel(self, evt):
|
||||
self.EndModal(wx.ID_CANCEL)
|
||||
|
||||
|
||||
def _get_cycle_dialog(parent=None, interval=None):
|
||||
app = wx.GetApp()
|
||||
if not app:
|
||||
app = wx.App(redirect=False)
|
||||
frame = wx.Frame(None)
|
||||
app.SetTopWindow(frame)
|
||||
dlg = CycleDialog(parent, interval)
|
||||
return dlg
|
||||
|
||||
|
||||
def get_window_position_and_size(window_handle):
|
||||
pos = GetWindowRect(window_handle)
|
||||
return pos[0], pos[1], pos[2] - pos[0], pos[3] - pos[1]
|
||||
|
||||
|
||||
def get_screen_position_and_size(window_handles):
|
||||
"""Return screen position; X, Y and size; width, height.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
window_handles: list of handles to open window figures
|
||||
(Note: only needed the first time)
|
||||
|
||||
Returns
|
||||
--------
|
||||
X : coordinate of the left side of the screen.
|
||||
Y : coordinate of the top of the screen.
|
||||
width : screen horizontal size
|
||||
height : screen vertical size
|
||||
|
||||
"""
|
||||
# pylint: disable=global-statement
|
||||
global _SCREENSIZE
|
||||
if _SCREENSIZE is None:
|
||||
window_handle = window_handles[0]
|
||||
pos = get_window_position_and_size(window_handle)
|
||||
_show_windows((window_handle,), win32con.SW_SHOWMAXIMIZED)
|
||||
_SCREENSIZE = get_window_position_and_size(window_handle)
|
||||
MoveWindow(window_handle, pos[0], pos[1], pos[2], pos[3], 1)
|
||||
return _SCREENSIZE
|
||||
|
||||
|
||||
def _get_screen_size(wnds):
|
||||
screen_width, screen_height = get_screen_position_and_size(wnds)[2:4]
|
||||
return screen_width, screen_height
|
||||
|
||||
|
||||
def _windowEnumerationHandler(handle, result_list):
|
||||
"""Pass to win32gui.EnumWindows() to generate list of window handle, window
|
||||
text tuples."""
|
||||
# pylint: disable=no-member
|
||||
if win32gui.IsWindowVisible(handle):
|
||||
result_list.append((handle, win32gui.GetWindowText(handle)))
|
||||
|
||||
|
||||
def _find_window_handles_and_titles(wantedTitle=None):
|
||||
"""Return list of window handle and window title tuples.
|
||||
|
||||
Parameter
|
||||
---------
|
||||
wantedTitle:
|
||||
|
||||
"""
|
||||
handles_n_titles = []
|
||||
EnumWindows(_windowEnumerationHandler, handles_n_titles)
|
||||
if wantedTitle is None:
|
||||
return handles_n_titles
|
||||
else:
|
||||
return [(handle, title)
|
||||
for handle, title in handles_n_titles
|
||||
if title.startswith(wantedTitle)]
|
||||
|
||||
|
||||
def find_figure_handles(*figure_numbers):
|
||||
"""Find figure handles from figure numbers."""
|
||||
wnd_handles = []
|
||||
for figure_number in _parse_figure_numbers(*figure_numbers):
|
||||
for format_ in FIGURE_TITLE_FORMATS:
|
||||
winTitle = format_ + ' %d' % figure_number
|
||||
handle = FindWindow(None, winTitle)
|
||||
if not handle == 0:
|
||||
wnd_handles.append(handle)
|
||||
return wnd_handles
|
||||
|
||||
|
||||
def find_all_figure_numbers():
|
||||
"""Return list of all figure numbers.
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> import fig
|
||||
>>> import pylab as p
|
||||
>>> for ix in range(5):
|
||||
... f = p.figure(ix)
|
||||
... p.draw()
|
||||
|
||||
fig.find_all_figure_numbers()
|
||||
[0, 1, 2, 3, 4]
|
||||
|
||||
>>> fig.close()
|
||||
|
||||
"""
|
||||
figure_numbers = []
|
||||
for wantedTitle in FIGURE_TITLE_FORMATS:
|
||||
handles_n_titles = _find_window_handles_and_titles(wantedTitle)
|
||||
for _handle, title in handles_n_titles:
|
||||
try:
|
||||
number = int(title.split()[-1])
|
||||
figure_numbers.append(number)
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
# pylint: disable=no-member
|
||||
return numpy.unique(figure_numbers).tolist()
|
||||
|
||||
|
||||
def _parse_figure_numbers(*args):
|
||||
figure_numbers = []
|
||||
for arg in args:
|
||||
if isinstance(arg, (list, tuple, set)):
|
||||
for val in arg:
|
||||
figure_numbers.append(int(val))
|
||||
elif isinstance(arg, int):
|
||||
figure_numbers.append(arg)
|
||||
elif arg == 'all':
|
||||
figure_numbers = find_all_figure_numbers()
|
||||
break
|
||||
else:
|
||||
raise TypeError('Only integers arguments accepted!')
|
||||
|
||||
if len(figure_numbers) == 0:
|
||||
figure_numbers = find_all_figure_numbers()
|
||||
return figure_numbers
|
||||
|
||||
|
||||
def _show_figure(figure_numbers, command):
|
||||
"""Sets the specified figure's show state.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers: list of figure numbers
|
||||
command: one of following commands:
|
||||
SW_FORCEMINIMIZE:
|
||||
Minimizes a window, even if the thread that owns the window is not
|
||||
responding. This flag should only be used when minimizing windows
|
||||
from a different thread.
|
||||
SW_HIDE:
|
||||
Hides the window and activates another window.
|
||||
SW_MAXIMIZE:
|
||||
Maximizes the specified window.
|
||||
SW_MINIMIZE:
|
||||
Minimizes the specified window and activates the next top-level window
|
||||
in the Z order.
|
||||
SW_RESTORE:
|
||||
Activates and displays the window. If the window is minimized or
|
||||
maximized, the system restores it to its original size and position.
|
||||
An application should specify this flag when restoring a minimized
|
||||
window.
|
||||
SW_SHOW:
|
||||
Activates the window and displays it in its current size and position.
|
||||
SW_SHOWDEFAULT:
|
||||
Sets the show state based on the SW_ value specified in the STARTUPINFO
|
||||
structure passed to the CreateProcess function by the program that
|
||||
started the application.
|
||||
SW_SHOWMAXIMIZED:
|
||||
Activates the window and displays it as a maximized window.
|
||||
SW_SHOWMINIMIZED:
|
||||
Activates the window and displays it as a minimized window.
|
||||
SW_SHOWMINNOACTIVE:
|
||||
Displays the window as a minimized window. This value is similar to
|
||||
SW_SHOWMINIMIZED, except the window is not activated.
|
||||
SW_SHOWNA:
|
||||
Displays the window in its current size and position. This value is
|
||||
similar to SW_SHOW, except the window is not activated.
|
||||
SW_SHOWNOACTIVATE:
|
||||
Displays a window in its most recent size and position. This value is
|
||||
similar to SW_SHOWNORMAL, except the window is not actived.
|
||||
SW_SHOWNORMAL:
|
||||
Activates and displays a window. If the window is minimized or
|
||||
maximized, the system restores it to its original size and position.
|
||||
An application should specify this flag when displaying the window for
|
||||
the first time.
|
||||
|
||||
"""
|
||||
for number in _parse_figure_numbers(*figure_numbers):
|
||||
for format_ in FIGURE_TITLE_FORMATS:
|
||||
title = format_ + ' %d' % number
|
||||
handle = FindWindow(None, title)
|
||||
if not handle == 0:
|
||||
BringWindowToTop(handle)
|
||||
ShowWindow(handle, command)
|
||||
|
||||
|
||||
def _show_windows(handles, command, redraw_now=False):
|
||||
"""Sets the specified window's show state.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
handles: list of window handles
|
||||
command: one of following commands:
|
||||
SW_FORCEMINIMIZE:
|
||||
Minimizes a window, even if the thread that owns the window is not
|
||||
responding. This flag should only be used when minimizing windows
|
||||
from a different thread.
|
||||
SW_HIDE:
|
||||
Hides the window and activates another window.
|
||||
SW_MAXIMIZE:
|
||||
Maximizes the specified window.
|
||||
SW_MINIMIZE:
|
||||
Minimizes the specified window and activates the next top-level window
|
||||
in the Z order.
|
||||
SW_RESTORE:
|
||||
Activates and displays the window. If the window is minimized or
|
||||
maximized, the system restores it to its original size and position.
|
||||
An application should specify this flag when restoring a minimized
|
||||
window.
|
||||
SW_SHOW:
|
||||
Activates the window and displays it in its current size and position.
|
||||
SW_SHOWDEFAULT:
|
||||
Sets the show state based on the SW_ value specified in the STARTUPINFO
|
||||
structure passed to the CreateProcess function by the program that
|
||||
started the application.
|
||||
SW_SHOWMAXIMIZED:
|
||||
Activates the window and displays it as a maximized window.
|
||||
SW_SHOWMINIMIZED:
|
||||
Activates the window and displays it as a minimized window.
|
||||
SW_SHOWMINNOACTIVE:
|
||||
Displays the window as a minimized window. This value is similar to
|
||||
SW_SHOWMINIMIZED, except the window is not activated.
|
||||
SW_SHOWNA:
|
||||
Displays the window in its current size and position. This value is
|
||||
similar to SW_SHOW, except the window is not activated.
|
||||
SW_SHOWNOACTIVATE:
|
||||
Displays a window in its most recent size and position. This value is
|
||||
similar to SW_SHOWNORMAL, except the window is not actived.
|
||||
SW_SHOWNORMAL:
|
||||
Activates and displays a window. If the window is minimized or
|
||||
maximized, the system restores it to its original size and position.
|
||||
An application should specify this flag when displaying the window for
|
||||
the first time.
|
||||
|
||||
redraw_now :
|
||||
|
||||
"""
|
||||
# pylint: disable=no-member
|
||||
for handle in handles:
|
||||
if not handle == 0:
|
||||
BringWindowToTop(handle)
|
||||
ShowWindow(handle, command)
|
||||
if redraw_now:
|
||||
rect = GetWindowRect(handle)
|
||||
win32gui.RedrawWindow(handle, rect, None, win32con.RDW_UPDATENOW)
|
||||
|
||||
|
||||
def keep(*figure_numbers):
|
||||
"""Keeps figure windows of your choice and closes the rest.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers specifying which figures to keep.
|
||||
|
||||
Example:
|
||||
--------
|
||||
# keep only figures 1,2,3,5 and 7
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(10):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.keep( range(1,4), 5, 7)
|
||||
|
||||
or
|
||||
fig.keep([range(1,4), 5, 7])
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.close
|
||||
|
||||
"""
|
||||
figs2keep = []
|
||||
for fig in figure_numbers:
|
||||
if isinstance(fig, (list, tuple, set)):
|
||||
for val in fig:
|
||||
figs2keep.append(int(val))
|
||||
elif isinstance(fig, int):
|
||||
figs2keep.append(fig)
|
||||
else:
|
||||
raise TypeError('Only integers arguments accepted!')
|
||||
|
||||
if len(figs2keep) > 0:
|
||||
allfigs = set(find_all_figure_numbers())
|
||||
figs2delete = allfigs.difference(figs2keep)
|
||||
close(figs2delete)
|
||||
|
||||
|
||||
def close(*figure_numbers):
|
||||
""" Close figure window(s)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to close (default 'all').
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(5):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.close(3,4) # close figure 3 and 4
|
||||
>>> fig.close('all') # close all remaining figures
|
||||
|
||||
or even simpler
|
||||
fig.close() # close all remaining figures
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.keep
|
||||
|
||||
"""
|
||||
# pylint: disable=no-member
|
||||
for handle in find_figure_handles(*figure_numbers):
|
||||
if win32gui.SendMessage(handle, win32con.WM_CLOSE, 0, 0):
|
||||
win32gui.SendMessage(handle, win32con.WM_DESTROY, 0, 0)
|
||||
|
||||
|
||||
def restore(*figure_numbers):
|
||||
"""Restore figures window size and position to its default value.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to restor (default 'all').
|
||||
|
||||
Description
|
||||
-----------
|
||||
RESTORE Activates and displays the window. If the window is minimized
|
||||
or maximized, the system restores it to its original size and position.
|
||||
|
||||
Examples
|
||||
---------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(5):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.restore('all') #Restores all figures
|
||||
>>> fig.restore() #same as restore('all')
|
||||
>>> fig.restore(p.gcf().number) #Restores the current figure
|
||||
>>> fig.restore(3) #Restores figure 3
|
||||
>>> fig.restore([2, 4]) #Restores figures 2 and 4
|
||||
|
||||
or alternatively
|
||||
fig.restore(2, 4)
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.close,
|
||||
fig.keep
|
||||
|
||||
"""
|
||||
SW_RESTORE = win32con.SW_RESTORE
|
||||
# SW_RESTORE = win32con.SW_SHOWDEFAULT
|
||||
# SW_RESTORE = win32con.SW_SHOWNORMAL
|
||||
_show_figure(figure_numbers, SW_RESTORE)
|
||||
|
||||
|
||||
def hide(*figure_numbers):
|
||||
"""hide figure(s) window.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to hide (default 'all').
|
||||
|
||||
Examples:
|
||||
--------
|
||||
>>> import wafo.fig as fig
|
||||
>>> import pylab as p
|
||||
>>> for ix in range(5):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.hide('all') #hides all unhidden figures
|
||||
>>> fig.hide() #same as hide('all')
|
||||
>>> fig.hide(p.gcf().number) #hides the current figure
|
||||
>>> fig.hide(3) #hides figure 3
|
||||
>>> fig.hide([2, 4]) #hides figures 2 and 4
|
||||
|
||||
or alternatively
|
||||
fig.hide(2, 4)
|
||||
>>> fig.restore(list(range(5)))
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle,
|
||||
fig.keep,
|
||||
fig.restore
|
||||
|
||||
"""
|
||||
_show_figure(figure_numbers, win32con.SW_HIDE)
|
||||
|
||||
|
||||
def minimize(*figure_numbers):
|
||||
"""Minimize figure(s) window size.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to minimize (default 'all').
|
||||
|
||||
Examples:
|
||||
---------
|
||||
>>> import wafo.fig as fig
|
||||
>>> import pylab as p
|
||||
>>> for ix in range(5):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.minimize('all') #Minimizes all unhidden figures
|
||||
>>> fig.minimize() #same as minimize('all')
|
||||
>>> fig.minimize(p.gcf().number) #Minimizes the current figure
|
||||
>>> fig.minimize(3) #Minimizes figure 3
|
||||
>>> fig.minimize([2, 4]) #Minimizes figures 2 and 4
|
||||
|
||||
or alternatively
|
||||
fig.minimize(2, 4)
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle,
|
||||
fig.keep,
|
||||
fig.restore
|
||||
|
||||
"""
|
||||
_show_figure(figure_numbers, win32con.SW_SHOWMINIMIZED)
|
||||
|
||||
|
||||
def maximize(*figure_numbers):
|
||||
"""Maximize figure(s) window size.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to maximize (default 'all').
|
||||
|
||||
Examples:
|
||||
---------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(5):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.maximize('all') #Maximizes all unhidden figures
|
||||
>>> fig.maximize() #same as maximize('all')
|
||||
>>> fig.maximize(p.gcf().number) #Maximizes the current figure
|
||||
>>> fig.maximize(3) #Maximizes figure 3
|
||||
>>> fig.maximize([2, 4]) #Maximizes figures 2 and 4
|
||||
|
||||
or alternatively
|
||||
fig.maximize(2, 4)
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle,
|
||||
fig.keep,
|
||||
fig.restore
|
||||
|
||||
"""
|
||||
_show_figure(figure_numbers, win32con.SW_SHOWMAXIMIZED)
|
||||
|
||||
|
||||
def pile(*figure_numbers, **kwds):
|
||||
"""Pile figure windows.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to pile (default 'all').
|
||||
kwds : dict with the following keys
|
||||
position :
|
||||
width :
|
||||
height :
|
||||
|
||||
Description
|
||||
-------------
|
||||
PILE piles all open figure windows on top of eachother
|
||||
with complete overlap. PILE(FIGS) can be used to specify which
|
||||
figures that should be piled. Figures are not sorted when specified.
|
||||
|
||||
Example:
|
||||
--------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(7):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.pile() # pile all open figures
|
||||
>>> fig.pile(range(1,4), 5, 7) # pile figure 1,2,3,5 and 7
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle, fig.keep, fig.maximize, fig.restore,
|
||||
fig.stack, fig.tile
|
||||
|
||||
"""
|
||||
wnds = find_figure_handles(*figure_numbers)
|
||||
numfigs = len(wnds)
|
||||
if numfigs > 0:
|
||||
screen_width, screen_height = _get_screen_size(wnds)
|
||||
pos = kwds.get(
|
||||
'position', (int(screen_width / 5), int(screen_height / 4)))
|
||||
width = kwds.get('width', int(screen_width / 2.5))
|
||||
height = kwds.get('height', int(screen_height / 2))
|
||||
|
||||
for wnd in wnds:
|
||||
MoveWindow(wnd, pos[0], pos[1], width, height, 1)
|
||||
BringWindowToTop(wnd)
|
||||
|
||||
|
||||
def set_size(*figure_numbers, **kwds):
|
||||
"""Set size for figure windows.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to pile (default 'all').
|
||||
kwds : dict with the following keys
|
||||
width :
|
||||
height :
|
||||
|
||||
Description
|
||||
-------------
|
||||
Set size sets the size of all open figure windows. SET_SIZE(FIGS)
|
||||
can be used to specify which figures that should be resized.
|
||||
Figures are not sorted when specified.
|
||||
|
||||
Example:
|
||||
--------
|
||||
>>> import pylab as p
|
||||
>>> import fig
|
||||
>>> for ix in range(7):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.set_size(7, width=150, height=100)
|
||||
>>> fig.set_size(range(1,4), 5,width=250, height=170)
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle, fig.keep, fig.maximize, fig.restore,
|
||||
fig.stack, fig.tile
|
||||
|
||||
"""
|
||||
handles = find_figure_handles(*figure_numbers)
|
||||
numfigs = len(handles)
|
||||
if numfigs > 0:
|
||||
screen_width, screen_height = _get_screen_size(handles)
|
||||
width = kwds.get('width', int(screen_width / 2.5))
|
||||
height = kwds.get('height', int(screen_height / 2))
|
||||
new_pos = kwds.get('position', None)
|
||||
pos = new_pos
|
||||
for handle in handles:
|
||||
if not new_pos:
|
||||
pos = get_window_position_and_size(handle)
|
||||
MoveWindow(handle, pos[0], pos[1], width, height, 1)
|
||||
BringWindowToTop(handle)
|
||||
|
||||
|
||||
def stack(*figure_numbers, **kwds):
|
||||
"""Stack figure windows.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to stack (default 'all').
|
||||
kwds : dict with the following keys
|
||||
figs_per_stack :
|
||||
number of figures per stack (default depends on screenheight)
|
||||
|
||||
Description
|
||||
-----------
|
||||
STACK stacks all open figure windows on top of eachother
|
||||
with maximum overlap. STACK(FIGS) can be used to specify which
|
||||
figures that should be stacked. Figures are not sorted when specified.
|
||||
|
||||
Example:
|
||||
--------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(7):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.stack() # stack all open figures
|
||||
>>> fig.stack(range(1,4), 5, 7) # stack figure 1,2,3,5 and 7
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle, fig.keep, fig.maximize, fig.restore,
|
||||
fig.pile, fig.tile
|
||||
|
||||
"""
|
||||
wnds = find_figure_handles(*figure_numbers)
|
||||
numfigs = len(wnds)
|
||||
if numfigs > 0:
|
||||
screenpos = get_screen_position_and_size(wnds)
|
||||
y_step = 25
|
||||
x_step = border = 5
|
||||
|
||||
figs_per_stack = kwds.get(
|
||||
'figs_per_stack',
|
||||
int(numpy.fix(0.7 * (screenpos[3] - border) / y_step)))
|
||||
|
||||
for iy in range(numfigs):
|
||||
pos = get_window_position_and_size(wnds[iy])
|
||||
# print('[x, y, w, h] = ', pos)
|
||||
ix = iy % figs_per_stack
|
||||
ypos = int(screenpos[1] + ix * y_step + border)
|
||||
xpos = int(screenpos[0] + ix * x_step + border)
|
||||
MoveWindow(wnds[iy], xpos, ypos, pos[2], pos[3], 1)
|
||||
BringWindowToTop(wnds[iy])
|
||||
|
||||
|
||||
def tile(*figure_numbers, **kwds):
|
||||
"""Tile figure windows.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to tile (default 'all').
|
||||
kwds : dict with key pairs
|
||||
specifying how many pairs of figures that are tiled at a time
|
||||
|
||||
Description
|
||||
-----------
|
||||
TILE places all open figure windows around on the screen with no
|
||||
overlap. TILE(FIGS) can be used to specify which figures that
|
||||
should be tiled. Figures are not sorted when specified.
|
||||
|
||||
Example:
|
||||
--------
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(7):
|
||||
... f = p.figure(ix)
|
||||
>>> fig.tile() # tile all open figures
|
||||
>>> fig.tile( range(1,4), 5, 7) # tile figure 1,2,3,5 and 7
|
||||
>>> fig.tile(range(1,11), pairs=2) # tile figure 1 to 10 two at a time
|
||||
>>> fig.tile(range(1,11), pairs=3) # tile figure 1 to 10 three at a time
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.cycle, fig.keep, fig.maximize, fig.minimize
|
||||
fig.restore, fig.pile, fig.stack
|
||||
|
||||
"""
|
||||
wnds = find_figure_handles(*figure_numbers)
|
||||
|
||||
nfigs = len(wnds)
|
||||
# Number of windows.
|
||||
|
||||
if nfigs > 0:
|
||||
nfigspertile = kwds.get('pairs', nfigs)
|
||||
|
||||
ceil = numpy.ceil
|
||||
sqrt = numpy.sqrt
|
||||
maximum = numpy.maximum
|
||||
|
||||
nlayers = int(ceil(nfigs / nfigspertile))
|
||||
|
||||
# Number of figures horisontally.
|
||||
nh = maximum(int(ceil(sqrt(nfigspertile))), 2)
|
||||
# Number of figures vertically.
|
||||
nv = maximum(int(ceil(nfigspertile / nh)), 2)
|
||||
|
||||
screenpos = get_screen_position_and_size(wnds)
|
||||
screen_width, screen_heigth = screenpos[2:4]
|
||||
|
||||
hspc = 10 # Horisontal space.
|
||||
topspc = 20 # Space above top figure.
|
||||
medspc = 10 # Space between figures.
|
||||
botspc = 20 # Space below bottom figure.
|
||||
|
||||
figwid = (screen_width - (nh + 1) * hspc) / nh
|
||||
fighgt = (screen_heigth - (topspc + botspc) - (nv - 1) * medspc) / nv
|
||||
|
||||
figwid = int(numpy.round(figwid))
|
||||
fighgt = int(numpy.round(fighgt))
|
||||
|
||||
idx = 0
|
||||
for unused_ix in range(nlayers):
|
||||
for row in range(nv):
|
||||
figtop = int(screenpos[1] + topspc + row * (fighgt + medspc))
|
||||
for col in range(nh):
|
||||
if (row) * nh + col < nfigspertile:
|
||||
if idx < nfigs:
|
||||
figlft = int(
|
||||
screenpos[0] + (col + 1) * hspc + col * figwid)
|
||||
fighnd = wnds[idx]
|
||||
MoveWindow(fighnd, figlft, figtop, figwid, fighgt,
|
||||
1)
|
||||
# Set position.
|
||||
BringWindowToTop(fighnd)
|
||||
idx += 1
|
||||
|
||||
|
||||
class _CycleGenerator(object):
|
||||
|
||||
"""Cycle through figure windows.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
figure_numbers : list of integers or string
|
||||
specifying which figures to cycle through (default 'all').
|
||||
kwds : dict with the following keys
|
||||
pairs : number of figures to cycle in pairs (default 1)
|
||||
maximize: If True maximize figure when viewing (default False)
|
||||
interval : pause interval in seconds
|
||||
|
||||
Description
|
||||
-----------
|
||||
CYCLE brings up all open figure in ascending order and pauses after
|
||||
each figure. Press escape to quit cycling, backspace to display previous
|
||||
figure(s) and press any other key to display next figure(s)
|
||||
When done, the figures are sorted in ascending order.
|
||||
|
||||
CYCLE(maximize=True) does the same thing, except figures are maximized.
|
||||
CYCLE(pairs=2) cycle through all figures in pairs of 2.
|
||||
|
||||
Examples:
|
||||
>>> import pylab as p
|
||||
>>> import wafo.fig as fig
|
||||
>>> for ix in range(4):
|
||||
... f = p.figure(ix)
|
||||
|
||||
fig.cycle(range(3), interval=1) # Cycle trough figure 0 to 2
|
||||
|
||||
# Cycle trough figure 0 to 2 with figures maximized
|
||||
fig.cycle(range(3), maximize=True, interval=1)
|
||||
fig.cycle(interval=1) # Cycle through all figures one at a time
|
||||
fig.tile(pairs=2, interval=1)
|
||||
fig.cycle(pairs=2, interval=2) # Cycle through all figures two at a time
|
||||
|
||||
fig.cycle(pairs=2) # Manually cycle through all figures two at a time
|
||||
>>> fig.close()
|
||||
|
||||
See also
|
||||
--------
|
||||
fig.keep, fig.maximize, fig.restore, fig.pile,
|
||||
fig.stack, fig.tile
|
||||
|
||||
"""
|
||||
escape_key = chr(27)
|
||||
backspace_key = chr(8)
|
||||
|
||||
def __init__(self, **kwds):
|
||||
self.dialog = None
|
||||
maximize = kwds.get('maximize', False)
|
||||
pairs = kwds.get('pairs', 1)
|
||||
self.interval = kwds.get('interval', 'user_defined')
|
||||
self.nfigspercycle = 1
|
||||
if maximize:
|
||||
self.command = win32con.SW_SHOWMAXIMIZED
|
||||
else:
|
||||
self.command = win32con.SW_SHOWNORMAL
|
||||
if pairs is not None:
|
||||
self.nfigspercycle = pairs
|
||||
|
||||
def _set_options(self, kwds):
|
||||
self.__init__(**kwds)
|
||||
|
||||
def _iterate(self, handles):
|
||||
i = 0
|
||||
numfigs = len(handles)
|
||||
self.dialog = _get_cycle_dialog(parent=None, interval=self.interval)
|
||||
while 0 <= i and i < numfigs:
|
||||
iu = min(i + self.nfigspercycle, numfigs)
|
||||
yield handles[i:iu]
|
||||
i = self.next_index(i)
|
||||
self.dialog.Destroy()
|
||||
raise StopIteration
|
||||
|
||||
def next_index(self, i):
|
||||
result = self.dialog.ShowModal()
|
||||
if result == wx.ID_FORWARD:
|
||||
i += self.nfigspercycle
|
||||
elif result == wx.ID_BACKWARD:
|
||||
i -= self.nfigspercycle
|
||||
else:
|
||||
i = -1
|
||||
return i
|
||||
|
||||
def __call__(self, *figure_numbers, **kwds):
|
||||
handles = find_figure_handles(*figure_numbers)
|
||||
numfigs = len(handles)
|
||||
if numfigs > 0:
|
||||
self._set_options(kwds)
|
||||
for handle in self._iterate(handles):
|
||||
_show_windows(handle, self.command, redraw_now=True)
|
||||
|
||||
_show_windows(handles, win32con.SW_SHOWNORMAL)
|
||||
|
||||
cycle = _CycleGenerator()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from wafo.testing import test_docstrings
|
||||
import matplotlib
|
||||
matplotlib.interactive(True)
|
||||
test_docstrings(__file__)
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,538 +0,0 @@
|
||||
"""
|
||||
Created on 20. aug. 2015
|
||||
|
||||
@author: pab
|
||||
"""
|
||||
from __future__ import division
|
||||
from collections import namedtuple
|
||||
import warnings
|
||||
import numdifftools as nd
|
||||
import numdifftools.nd_algopy as nda
|
||||
from numdifftools.extrapolation import dea3
|
||||
from numdifftools.limits import Limit
|
||||
import numpy as np
|
||||
from numpy import linalg
|
||||
from numpy.polynomial.chebyshev import chebval, Chebyshev
|
||||
from numpy.polynomial import polynomial
|
||||
from wafo.misc import piecewise, findcross, ecross
|
||||
|
||||
_FINFO = np.finfo(float)
|
||||
EPS = _FINFO.eps
|
||||
_EPS = EPS
|
||||
_TINY = _FINFO.tiny
|
||||
_HUGE = _FINFO.max
|
||||
|
||||
|
||||
def _assert(cond, msg):
|
||||
if not cond:
|
||||
raise ValueError(msg)
|
||||
|
||||
|
||||
def _assert_warn(cond, msg):
|
||||
if not cond:
|
||||
warnings.warn(msg)
|
||||
|
||||
|
||||
class PolyBasis(object):
|
||||
@staticmethod
|
||||
def _derivative(c, m):
|
||||
return polynomial.polyder(c, m)
|
||||
|
||||
@staticmethod
|
||||
def eval(t, c):
|
||||
return polynomial.polyval(t, c)
|
||||
|
||||
@staticmethod
|
||||
def _coefficients(k):
|
||||
c = np.zeros(k + 1)
|
||||
c[k] = 1
|
||||
return c
|
||||
|
||||
def derivative(self, t, k, n=1):
|
||||
c = self._coefficients(k)
|
||||
d_c = self._derivative(c, m=n)
|
||||
return self.eval(t, d_c)
|
||||
|
||||
def __call__(self, t, k):
|
||||
return t**k
|
||||
|
||||
|
||||
poly_basis = PolyBasis()
|
||||
|
||||
|
||||
class ChebyshevBasis(PolyBasis):
|
||||
|
||||
@staticmethod
|
||||
def _derivative(c, m):
|
||||
cheb = Chebyshev(c)
|
||||
dcheb = cheb.deriv(m=m)
|
||||
return dcheb.coef
|
||||
|
||||
@staticmethod
|
||||
def eval(t, c):
|
||||
return chebval(t, c)
|
||||
|
||||
def __call__(self, t, k):
|
||||
c = self._coefficients(k)
|
||||
return self.eval(t, c)
|
||||
|
||||
|
||||
chebyshev_basis = ChebyshevBasis()
|
||||
|
||||
|
||||
def richardson(q_val, k):
|
||||
# license BSD
|
||||
# Richardson extrapolation with parameter estimation
|
||||
c = np.real((q_val[k - 1] - q_val[k - 2]) / (q_val[k] - q_val[k - 1])) - 1.
|
||||
# The lower bound 0.07 admits the singularity x.^-0.9
|
||||
c = max(c, 0.07)
|
||||
return q_val[k] + (q_val[k] - q_val[k - 1]) / c
|
||||
|
||||
|
||||
def evans_webster_weights(omega, g, d_g, x, basis, *args, **kwds):
|
||||
|
||||
def psi(t, k):
|
||||
return d_g(t, *args, **kwds) * basis(t, k)
|
||||
|
||||
j_w = 1j * omega
|
||||
n = len(x)
|
||||
a_matrix = np.zeros((n, n), dtype=complex)
|
||||
rhs = np.zeros((n,), dtype=complex)
|
||||
|
||||
dbasis = basis.derivative
|
||||
lim_g = Limit(g)
|
||||
b_1 = np.exp(j_w * lim_g(1, *args, **kwds))
|
||||
if np.isnan(b_1):
|
||||
b_1 = 0.0
|
||||
a_1 = np.exp(j_w * lim_g(-1, *args, **kwds))
|
||||
if np.isnan(a_1):
|
||||
a_1 = 0.0
|
||||
|
||||
lim_psi = Limit(psi)
|
||||
for k in range(n):
|
||||
rhs[k] = basis(1, k) * b_1 - basis(-1, k) * a_1
|
||||
a_matrix[k] = (dbasis(x, k, n=1) + j_w * lim_psi(x, k))
|
||||
|
||||
solution = linalg.lstsq(a_matrix, rhs)
|
||||
return solution[0]
|
||||
|
||||
|
||||
def osc_weights(omega, g, d_g, x, basis, a_b, *args, **kwds):
|
||||
def _g(t):
|
||||
return g(scale * t + offset, *args, **kwds)
|
||||
|
||||
def _d_g(t):
|
||||
return scale * d_g(scale * t + offset, *args, **kwds)
|
||||
|
||||
w = []
|
||||
|
||||
for a, b in zip(a_b[::2], a_b[1::2]):
|
||||
scale = (b - a) / 2
|
||||
offset = (a + b) / 2
|
||||
|
||||
w.append(evans_webster_weights(omega, _g, _d_g, x, basis))
|
||||
|
||||
return np.asarray(w).ravel()
|
||||
|
||||
|
||||
class _Integrator(object):
|
||||
info = namedtuple('info', ['error_estimate', 'n'])
|
||||
|
||||
def __init__(self, f, g, dg=None, a=-1, b=1, basis=chebyshev_basis, s=1,
|
||||
precision=10, endpoints=True, full_output=False):
|
||||
self.f = f
|
||||
self.g = g
|
||||
self.dg = nd.Derivative(g) if dg is None else dg
|
||||
self.basis = basis
|
||||
self.a = a
|
||||
self.b = b
|
||||
self.s = s
|
||||
self.endpoints = endpoints
|
||||
self.precision = precision
|
||||
self.full_output = full_output
|
||||
|
||||
|
||||
class QuadOsc(_Integrator):
|
||||
def __init__(self, f, g, dg=None, a=-1, b=1, basis=chebyshev_basis, s=15,
|
||||
precision=10, endpoints=False, full_output=False, maxiter=17):
|
||||
self.maxiter = maxiter
|
||||
super(QuadOsc, self).__init__(f, g, dg=dg, a=a, b=b, basis=basis, s=s,
|
||||
precision=precision, endpoints=endpoints,
|
||||
full_output=full_output)
|
||||
|
||||
@staticmethod
|
||||
def _change_interval_to_0_1(f, g, d_g, a, _b):
|
||||
def f_01(t, *args, **kwds):
|
||||
den = 1 - t
|
||||
return f(a + t / den, *args, **kwds) / den ** 2
|
||||
|
||||
def g_01(t, *args, **kwds):
|
||||
return g(a + t / (1 - t), *args, **kwds)
|
||||
|
||||
def d_g_01(t, *args, **kwds):
|
||||
den = 1 - t
|
||||
return d_g(a + t / den, *args, **kwds) / den ** 2
|
||||
return f_01, g_01, d_g_01, 0., 1.
|
||||
|
||||
@staticmethod
|
||||
def _change_interval_to_m1_0(f, g, d_g, _a, b):
|
||||
def f_m10(t, *args, **kwds):
|
||||
den = 1 + t
|
||||
return f(b + t / den, *args, **kwds) / den ** 2
|
||||
|
||||
def g_m10(t, *args, **kwds):
|
||||
return g(b + t / (1 + t), *args, **kwds)
|
||||
|
||||
def d_g_m10(t, *args, **kwds):
|
||||
den = 1 + t
|
||||
return d_g(b + t / den, *args, **kwds) / den ** 2
|
||||
return f_m10, g_m10, d_g_m10, -1.0, 0.0
|
||||
|
||||
@staticmethod
|
||||
def _change_interval_to_m1_1(f, g, d_g, _a, _b):
|
||||
def f_m11(t, *args, **kwds):
|
||||
den = (1 - t**2)
|
||||
return f(t / den, *args, **kwds) * (1 + t**2) / den ** 2
|
||||
|
||||
def g_m11(t, *args, **kwds):
|
||||
den = (1 - t**2)
|
||||
return g(t / den, *args, **kwds)
|
||||
|
||||
def d_g_m11(t, *args, **kwds):
|
||||
den = (1 - t**2)
|
||||
return d_g(t / den, *args, **kwds) * (1 + t**2) / den ** 2
|
||||
return f_m11, g_m11, d_g_m11, -1., 1.
|
||||
|
||||
def _get_functions(self):
|
||||
a, b = self.a, self.b
|
||||
reverse = np.real(a) > np.real(b)
|
||||
if reverse:
|
||||
a, b = b, a
|
||||
f, g, dg = self.f, self.g, self.dg
|
||||
|
||||
if a == b:
|
||||
pass
|
||||
elif np.isinf(a) | np.isinf(b):
|
||||
# Check real limits
|
||||
if ~np.isreal(a) | ~np.isreal(b) | np.isnan(a) | np.isnan(b):
|
||||
raise ValueError('Infinite intervals must be real.')
|
||||
# Change of variable
|
||||
if np.isfinite(a) & np.isinf(b):
|
||||
f, g, dg, a, b = self._change_interval_to_0_1(f, g, dg, a, b)
|
||||
elif np.isinf(a) & np.isfinite(b):
|
||||
f, g, dg, a, b = self._change_interval_to_m1_0(f, g, dg, a, b)
|
||||
else: # -inf to inf
|
||||
f, g, dg, a, b = self._change_interval_to_m1_1(f, g, dg, a, b)
|
||||
|
||||
return f, g, dg, a, b, reverse
|
||||
|
||||
def __call__(self, omega, *args, **kwds):
|
||||
f, g, dg, a, b, reverse = self._get_functions()
|
||||
|
||||
val, err = self._quad_osc(f, g, dg, a, b, omega, *args, **kwds)
|
||||
|
||||
if reverse:
|
||||
val = -val
|
||||
if self.full_output:
|
||||
return val, err
|
||||
return val
|
||||
|
||||
@staticmethod
|
||||
def _get_best_estimate(k, q_0, q_1, q_2):
|
||||
if k >= 5:
|
||||
q_v = np.hstack((q_0[k], q_1[k], q_2[k]))
|
||||
q_w = np.hstack((q_0[k - 1], q_1[k - 1], q_2[k - 1]))
|
||||
elif k >= 3:
|
||||
q_v = np.hstack((q_0[k], q_1[k]))
|
||||
q_w = np.hstack((q_0[k - 1], q_1[k - 1]))
|
||||
else:
|
||||
q_v = np.atleast_1d(q_0[k])
|
||||
q_w = q_0[k - 1]
|
||||
errors = np.atleast_1d(abs(q_v - q_w))
|
||||
j = np.nanargmin(errors)
|
||||
return q_v[j], errors[j]
|
||||
|
||||
def _extrapolate(self, k, q_0, q_1, q_2):
|
||||
if k >= 4:
|
||||
q_1[k] = dea3(q_0[k - 2], q_0[k - 1], q_0[k])[0]
|
||||
q_2[k] = dea3(q_1[k - 2], q_1[k - 1], q_1[k])[0]
|
||||
elif k >= 2:
|
||||
q_1[k] = dea3(q_0[k - 2], q_0[k - 1], q_0[k])[0]
|
||||
# # Richardson extrapolation
|
||||
# if k >= 4:
|
||||
# q_1[k] = richardson(q_0, k)
|
||||
# q_2[k] = richardson(q_1, k)
|
||||
# elif k >= 2:
|
||||
# q_1[k] = richardson(q_0, k)
|
||||
q, err = self._get_best_estimate(k, q_0, q_1, q_2)
|
||||
return q, err
|
||||
|
||||
def _quad_osc(self, f, g, dg, a, b, omega, *args, **kwds):
|
||||
if a == b:
|
||||
q_val = b - a
|
||||
err = np.abs(b - a)
|
||||
return q_val, err
|
||||
|
||||
abseps = 10**-self.precision
|
||||
max_iter = self.maxiter
|
||||
basis = self.basis
|
||||
if self.endpoints:
|
||||
x_n = chebyshev_extrema(self.s)
|
||||
else:
|
||||
x_n = chebyshev_roots(self.s)
|
||||
# x_n = tanh_sinh_open_nodes(self.s)
|
||||
|
||||
# One interval
|
||||
hh = (b - a) / 2
|
||||
x = (a + b) / 2 + hh * x_n # Nodes
|
||||
|
||||
dtype = complex
|
||||
val0 = np.zeros((max_iter, 1), dtype=dtype) # Quadrature
|
||||
val1 = np.zeros((max_iter, 1), dtype=dtype) # First extrapolation
|
||||
val2 = np.zeros((max_iter, 1), dtype=dtype) # Second extrapolation
|
||||
|
||||
lim_f = Limit(f)
|
||||
a_b = np.hstack([a, b])
|
||||
wq = osc_weights(omega, g, dg, x_n, basis, a_b, *args, **kwds)
|
||||
val0[0] = hh * np.sum(wq * lim_f(x, *args, **kwds))
|
||||
|
||||
# Successive bisection of intervals
|
||||
nq = len(x_n)
|
||||
n = nq
|
||||
for k in range(1, max_iter):
|
||||
n += nq * 2**k
|
||||
|
||||
hh = hh / 2
|
||||
x = np.hstack([x + a, x + b]) / 2
|
||||
a_b = np.hstack([a_b + a, a_b + b]) / 2
|
||||
wq = osc_weights(omega, g, dg, x_n, basis, a_b, *args, **kwds)
|
||||
|
||||
val0[k] = hh * np.sum(wq * lim_f(x, *args, **kwds))
|
||||
|
||||
q_val, err = self._extrapolate(k, val0, val1, val2)
|
||||
|
||||
converged = (err <= abseps) | ~np.isfinite(q_val)
|
||||
if converged:
|
||||
break
|
||||
_assert_warn(converged, 'Max number of iterations reached '
|
||||
'without convergence.')
|
||||
_assert_warn(np.isfinite(q_val),
|
||||
'Integral approximation is Infinite or NaN.')
|
||||
|
||||
# The error estimate should not be zero
|
||||
err += 2 * np.finfo(q_val).eps
|
||||
return q_val, self.info(err, n)
|
||||
|
||||
|
||||
def adaptive_levin_points(m, delta):
|
||||
m_1 = m - 1
|
||||
prm = 0.5
|
||||
while prm * m_1 / delta >= 1:
|
||||
delta = 2 * delta
|
||||
k = np.arange(m)
|
||||
x = piecewise([k < prm * m_1, k == np.ceil(prm * m_1)],
|
||||
[-1 + k / delta, 0 * k, 1 - (m_1 - k) / delta])
|
||||
return x
|
||||
|
||||
|
||||
def open_levin_points(m, delta):
|
||||
return adaptive_levin_points(m + 2, delta)[1:-1]
|
||||
|
||||
|
||||
def chebyshev_extrema(m, delta=None):
|
||||
k = np.arange(m)
|
||||
x = np.cos(k * np.pi / (m - 1))
|
||||
return x
|
||||
|
||||
|
||||
def tanh_sinh_nodes(m, delta=None, tol=_EPS):
|
||||
tmax = np.arcsinh(np.arctanh(1 - _EPS) * 2 / np.pi)
|
||||
# tmax = 3.18
|
||||
m_1 = int(np.floor(-np.log2(tmax / max(m - 1, 1)))) - 1
|
||||
h = 2.0**-m_1
|
||||
t = np.arange((m + 1) // 2 + 1) * h
|
||||
x = np.tanh(np.pi / 2 * np.sinh(t))
|
||||
k = np.flatnonzero(np.abs(x - 1) <= 10 * tol)
|
||||
y = x[:k[0] + 1] if len(k) else x
|
||||
return np.hstack((-y[:0:-1], y))
|
||||
|
||||
|
||||
def tanh_sinh_open_nodes(m, delta=None, tol=_EPS):
|
||||
return tanh_sinh_nodes(m + 1, delta, tol)[1:-1]
|
||||
|
||||
|
||||
def chebyshev_roots(m, delta=None):
|
||||
k = np.arange(1, 2 * m, 2) * 0.5
|
||||
x = np.cos(k * np.pi / m)
|
||||
return x
|
||||
|
||||
|
||||
class AdaptiveLevin(_Integrator):
|
||||
"""Return integral for the Levin-type and adaptive Levin-type methods"""
|
||||
|
||||
@staticmethod
|
||||
def _a_levin(omega, f, g, d_g, x, s, basis, *args, **kwds):
|
||||
|
||||
def psi(t, k):
|
||||
return d_g(t, *args, **kwds) * basis(t, k)
|
||||
|
||||
j_w = 1j * omega
|
||||
nu = np.ones((len(x),), dtype=int)
|
||||
nu[0] = nu[-1] = s
|
||||
S = np.cumsum(np.hstack((nu, 0)))
|
||||
S[-1] = 0
|
||||
n = int(S[-2])
|
||||
a_matrix = np.zeros((n, n), dtype=complex)
|
||||
rhs = np.zeros((n,))
|
||||
dff = Limit(nda.Derivative(f))
|
||||
d_psi = Limit(nda.Derivative(psi))
|
||||
dbasis = basis.derivative
|
||||
for r, t in enumerate(x):
|
||||
for j in range(S[r - 1], S[r]):
|
||||
order = ((j - S[r - 1]) % nu[r]) # derivative order
|
||||
dff.fun.n = order
|
||||
rhs[j] = dff(t, *args, **kwds)
|
||||
d_psi.fun.n = order
|
||||
for k in range(n):
|
||||
a_matrix[j, k] = (dbasis(t, k, n=order + 1) +
|
||||
j_w * d_psi(t, k))
|
||||
k1 = np.flatnonzero(1 - np.isfinite(rhs))
|
||||
if k1.size > 0: # Remove singularities
|
||||
warnings.warn('Singularities detected! ')
|
||||
a_matrix[k1] = 0
|
||||
rhs[k1] = 0
|
||||
solution = linalg.lstsq(a_matrix, rhs)
|
||||
v = basis.eval([-1, 1], solution[0])
|
||||
|
||||
lim_g = Limit(g)
|
||||
g_b = np.exp(j_w * lim_g(1, *args, **kwds))
|
||||
if np.isnan(g_b):
|
||||
g_b = 0
|
||||
g_a = np.exp(j_w * lim_g(-1, *args, **kwds))
|
||||
if np.isnan(g_a):
|
||||
g_a = 0
|
||||
return v[1] * g_b - v[0] * g_a
|
||||
|
||||
def _get_integration_limits(self, omega, args, kwds):
|
||||
a, b = self.a, self.b
|
||||
M = 30
|
||||
ab = [a]
|
||||
scale = (b - a) / 2
|
||||
n = 30
|
||||
x = np.linspace(a, b, n + 1)
|
||||
dg_x = np.asarray([scale * omega * self.dg(xi, *args, **kwds)
|
||||
for xi in x])
|
||||
i10 = findcross(dg_x, M)
|
||||
i1 = findcross(dg_x, 1)
|
||||
i0 = findcross(dg_x, 0)
|
||||
im1 = findcross(dg_x, -1)
|
||||
im10 = findcross(dg_x, -M)
|
||||
x10 = ecross(x, dg_x, i10, M) if len(i10) else ()
|
||||
x1 = ecross(x, dg_x, i1, 1) if len(i1) else ()
|
||||
x0 = ecross(x, dg_x, i0, 0) if len(i0) else ()
|
||||
xm1 = ecross(x, dg_x, im1, -1) if len(im1) else ()
|
||||
xm10 = ecross(x, dg_x, im10, -M) if len(im10) else ()
|
||||
|
||||
for i in np.unique(np.hstack((x10, x1, x0, xm1, xm10))):
|
||||
if x[0] < i < x[n]:
|
||||
ab.append(i)
|
||||
ab.append(b)
|
||||
return ab
|
||||
|
||||
def __call__(self, omega, *args, **kwds):
|
||||
ab = self._get_integration_limits(omega, args, kwds)
|
||||
s = self.s
|
||||
val = 0
|
||||
n = 0
|
||||
err = 0
|
||||
for ai, bi in zip(ab[:-1], ab[1:]):
|
||||
vali, infoi = self._QaL(s, ai, bi, omega, *args, **kwds)
|
||||
val += vali
|
||||
err += infoi.error_estimate
|
||||
n += infoi.n
|
||||
if self.full_output:
|
||||
info = self.info(err, n)
|
||||
return val, info
|
||||
return val
|
||||
|
||||
@staticmethod
|
||||
def _get_num_points(s, prec, betam):
|
||||
return 1 if s > 1 else int(prec / max(np.log10(betam + 1), 1) + 1)
|
||||
|
||||
def _QaL(self, s, a, b, omega, *args, **kwds):
|
||||
"""if s>1,the integral is computed by Q_s^L"""
|
||||
scale = (b - a) / 2
|
||||
offset = (a + b) / 2
|
||||
prec = self.precision # desired precision
|
||||
|
||||
def ff(t, *args, **kwds):
|
||||
return scale * self.f(scale * t + offset, *args, **kwds)
|
||||
|
||||
def gg(t, *args, **kwds):
|
||||
return self.g(scale * t + offset, *args, **kwds)
|
||||
|
||||
def dgg(t, *args, **kwds):
|
||||
return scale * self.dg(scale * t + offset, *args, **kwds)
|
||||
dg_a = abs(omega * dgg(-1, *args, **kwds))
|
||||
dg_b = abs(omega * dgg(1, *args, **kwds))
|
||||
g_a = abs(omega * gg(-1, *args, **kwds))
|
||||
g_b = abs(omega * gg(1, *args, **kwds))
|
||||
delta, alpha = min(dg_a, dg_b), min(g_a, g_b)
|
||||
|
||||
betam = delta # * scale
|
||||
if self.endpoints:
|
||||
if delta < 10 or alpha <= 10 or s > 1:
|
||||
points = chebyshev_extrema
|
||||
else:
|
||||
points = adaptive_levin_points
|
||||
elif delta < 10 or alpha <= 10 or s > 1:
|
||||
points = chebyshev_roots
|
||||
else:
|
||||
points = open_levin_points # tanh_sinh_open_nodes
|
||||
|
||||
m = self._get_num_points(s, prec, betam)
|
||||
abseps = 10 * 10.0**-prec
|
||||
num_collocation_point_list = m * 2**np.arange(1, 5) + 1
|
||||
basis = self.basis
|
||||
|
||||
q_val = 1e+300
|
||||
num_function_evaluations = 0
|
||||
n = 0
|
||||
for num_collocation_points in num_collocation_point_list:
|
||||
n_old = n
|
||||
q_old = q_val
|
||||
x = points(num_collocation_points, betam)
|
||||
n = len(x)
|
||||
if n > n_old:
|
||||
q_val = self._a_levin(omega, ff, gg, dgg, x, s, basis, *args,
|
||||
**kwds)
|
||||
num_function_evaluations += n
|
||||
err = np.abs(q_val - q_old)
|
||||
if err <= abseps:
|
||||
break
|
||||
info = self.info(err, num_function_evaluations)
|
||||
return q_val, info
|
||||
|
||||
|
||||
class EvansWebster(AdaptiveLevin):
|
||||
"""Return integral for the Evans Webster method"""
|
||||
|
||||
def __init__(self, f, g, dg=None, a=-1, b=1, basis=chebyshev_basis, s=8,
|
||||
precision=10, endpoints=False, full_output=False):
|
||||
super(EvansWebster,
|
||||
self).__init__(f, g, dg=dg, a=a, b=b, basis=basis, s=s,
|
||||
precision=precision, endpoints=endpoints,
|
||||
full_output=full_output)
|
||||
|
||||
def _a_levin(self, omega, ff, gg, dgg, x, s, basis, *args, **kwds):
|
||||
w = evans_webster_weights(omega, gg, dgg, x, basis, *args, **kwds)
|
||||
|
||||
f = Limit(ff)(x, *args, **kwds)
|
||||
return np.sum(f * w)
|
||||
|
||||
def _get_num_points(self, s, prec, betam):
|
||||
return 8 if s > 1 else int(prec / max(np.log10(betam + 1), 1) + 1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
tanh_sinh_nodes(16)
|
||||
@ -1,532 +0,0 @@
|
||||
'''
|
||||
|
||||
All the software contained in this library is protected by copyright.
|
||||
Permission to use, copy, modify, and distribute this software for any
|
||||
purpose without fee is hereby granted, provided that this entire notice
|
||||
is included in all copies of any software which is or includes a copy
|
||||
or modification of this software and in all copies of the supporting
|
||||
documentation for such software.
|
||||
|
||||
THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED
|
||||
WARRANTY. IN NO EVENT, NEITHER THE AUTHORS, NOR THE PUBLISHER, NOR ANY
|
||||
MEMBER OF THE EDITORIAL BOARD OF THE JOURNAL "NUMERICAL ALGORITHMS",
|
||||
NOR ITS EDITOR-IN-CHIEF, BE LIABLE FOR ANY ERROR IN THE SOFTWARE, ANY
|
||||
MISUSE OF IT OR ANY DAMAGE ARISING OUT OF ITS USE. THE ENTIRE RISK OF
|
||||
USING THE SOFTWARE LIES WITH THE PARTY DOING SO.
|
||||
|
||||
ANY USE OF THE SOFTWARE CONSTITUTES ACCEPTANCE OF THE TERMS OF THE
|
||||
ABOVE STATEMENT.
|
||||
|
||||
|
||||
AUTHORS:
|
||||
Per A Brodtkorb
|
||||
Python code Based on matlab code written by:
|
||||
|
||||
Marco Caliari
|
||||
University of Verona, Italy
|
||||
E-mail: marco.caliari@univr.it
|
||||
|
||||
Stefano de Marchi, Alvise Sommariva, Marco Vianello
|
||||
University of Padua, Italy
|
||||
E-mail: demarchi@math.unipd.it, alvise@math.unipd.it,
|
||||
marcov@math.unipd.it
|
||||
|
||||
Reference
|
||||
---------
|
||||
Padua2DM: fast interpolation and cubature at the Padua points in Matlab/Octave
|
||||
NUMERICAL ALGORITHMS, 56 (2011), PP. 45-60
|
||||
|
||||
|
||||
Padua module
|
||||
------------
|
||||
In polynomial interpolation of two variables, the Padua points are the first
|
||||
known example (and up to now the only one) of a unisolvent point set
|
||||
(that is, the interpolating polynomial is unique) with minimal growth of their
|
||||
Lebesgue constant, proven to be O(log2 n).
|
||||
This module provides all the functions needed to perform interpolation and
|
||||
cubature at the Padua points, together with the functions and the demos used
|
||||
in the paper.
|
||||
|
||||
pdint.m : main function for interpolation at the Padua points
|
||||
pdcub.m : main function for cubature at the Padua points
|
||||
pdpts.m : function for the computation of the Padua points
|
||||
padua_fit.m : function for the computation of the interpolation
|
||||
coefficients by FFT (recommended)
|
||||
pdcfsMM.m : function for the computation of the interpolation
|
||||
coefficients by matrix multiplications
|
||||
pdval.m : function for the evaluation of the interpolation
|
||||
polynomial
|
||||
pdwtsFFT.m : function for the computation of the cubature
|
||||
weights by FFT
|
||||
pdwtsMM.m : function for the computation of the cubature
|
||||
weights by matrix multiplications (recommended)
|
||||
funct.m : function containing some test functions
|
||||
demo_pdint.m : demo script for pdint
|
||||
demo_cputime_pdint.m : demo script for the computation of CPU time for
|
||||
interpolation
|
||||
demo_errors_pdint.m : demo script for the comparison of interpolation with
|
||||
coefficients computed by FFT or by matrix
|
||||
multiplications
|
||||
demo_pdcub : demo script for pdcub
|
||||
demo_cputime_pdcub.m : demo script for the computation of CPU time for
|
||||
cubature
|
||||
demo_errors_pdcub.m : demo script for the comparison of cubature with
|
||||
weights computed by FFT or by matrix multiplications
|
||||
demo_errors_pdcub_gl.m : demo script for the comparison of different cubature
|
||||
formulas
|
||||
cubature_square.m : function for the computation of some cubature
|
||||
formulas for the square
|
||||
omelyan_solovyan_rule.m : function for the computation of Omelyan-Solovyan
|
||||
cubature points and weights
|
||||
Contents.m : Contents file for Matlab
|
||||
|
||||
|
||||
'''
|
||||
from __future__ import absolute_import, division
|
||||
import numpy as np
|
||||
from numpy.fft import fft
|
||||
from wafo.dctpack import dct
|
||||
from wafo.polynomial import map_from_interval, map_to_interval
|
||||
# from scipy.fftpack.realtransforms import dct
|
||||
|
||||
|
||||
class _ExampleFunctions(object):
|
||||
'''
|
||||
Computes test function in the points (x, y)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x,y : array-like
|
||||
evaluate the function in the points (x,y)
|
||||
i : scalar int (default 0)
|
||||
defining which test function to use. Options are
|
||||
0: franke
|
||||
1: half_sphere
|
||||
2: poly_degree20
|
||||
3: exp_fun1
|
||||
4: exp_fun100
|
||||
5: cos30
|
||||
6: constant
|
||||
7: exp_xy
|
||||
8: runge
|
||||
9: abs_cubed
|
||||
10: gauss
|
||||
11: exp_inv
|
||||
|
||||
Returns
|
||||
-------
|
||||
z : array-like
|
||||
value of the function in the points (x,y)
|
||||
'''
|
||||
@staticmethod
|
||||
def franke(x, y):
|
||||
'''Franke function.
|
||||
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500, is
|
||||
2.1547794245591083e+000 with an estimated absolute error of 8.88e-016.
|
||||
|
||||
The value of the definite integral on the square [0,1] x [0,1],
|
||||
obtained using a Padua Points cubature formula of degree 500, is
|
||||
4.06969589491556e-01 with an estimated absolute error of 8.88e-016.
|
||||
|
||||
Maple: 0.40696958949155611906
|
||||
'''
|
||||
def _exp(x, y, loc, scale, p2=2):
|
||||
return np.exp(- (x - loc[0])**2 / scale[0] - (y - loc[1])**p2 / scale[1])
|
||||
# exp = np.exp
|
||||
x9, y9 = 9. * x, 9. * y
|
||||
return (3. / 4 * _exp(x9, y9, [2, 2], [4, 4]) +
|
||||
3. / 4 * _exp(x9, y9, [-1, -1], [49, 10], p2=1) +
|
||||
1. / 2 * _exp(x9, y9, [7, 3], [4, 4]) -
|
||||
1. / 5 * _exp(x9, y9, [4, 7], [1, 1]))
|
||||
|
||||
@staticmethod
|
||||
def half_sphere(x, y):
|
||||
'''The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 2000, is
|
||||
3.9129044444568244e+000 with an estimated absolute error of 3.22e-010.
|
||||
'''
|
||||
return ((x - 0.5)**2 + (y - 0.5)**2)**(1. / 2)
|
||||
|
||||
@staticmethod
|
||||
def poly_degree20(x, y):
|
||||
''''Bivariate polynomial having moderate degree.
|
||||
The value of the definite integral on the square [-1,1] x
|
||||
[-1,1], up to machine precision, is 18157.16017316017 (see ref. 6).
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500,
|
||||
is 1.8157160173160162e+004.
|
||||
|
||||
2D modification of an example by L.N.Trefethen (see ref. 7), f(x)=x^20.
|
||||
'''
|
||||
return (x + y)**20
|
||||
|
||||
@staticmethod
|
||||
def exp_fun1(x, y):
|
||||
''' The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 2000,
|
||||
is 2.1234596326670683e+001 with an estimated absolute error of
|
||||
7.11e-015.
|
||||
'''
|
||||
return np.exp((x - 0.5)**2 + (y - 0.5)**2)
|
||||
|
||||
@staticmethod
|
||||
def exp_fun100(x, y):
|
||||
'''The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 2000,
|
||||
is 3.1415926535849605e-002 with an estimated absolute error of
|
||||
3.47e-017.
|
||||
'''
|
||||
return np.exp(-100 * ((x - 0.5)**2 + (y - 0.5)**2))
|
||||
|
||||
@staticmethod
|
||||
def cos30(x, y):
|
||||
''' The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500,
|
||||
is 4.3386955120336568e-003 with an estimated absolute error of
|
||||
2.95e-017.
|
||||
'''
|
||||
return np.cos(30 * (x + y))
|
||||
|
||||
@staticmethod
|
||||
def constant(x, y):
|
||||
'''Constant.
|
||||
To test interpolation and cubature at degree 0.
|
||||
The value of the definite integral on the square [-1,1] x [-1,1]
|
||||
is 4.
|
||||
'''
|
||||
return np.ones(np.shape(x + y))
|
||||
|
||||
@staticmethod
|
||||
def exp_xy(x, y):
|
||||
'''The value of the definite integral on the square [-1,1] x [-1,1]
|
||||
is up to machine precision is 5.524391382167263 (see ref. 6).
|
||||
2. The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500,
|
||||
is 5.5243913821672628e+000 with an estimated absolute error of
|
||||
0.00e+000.
|
||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
||||
f(x)=exp(x).
|
||||
'''
|
||||
return np.exp(x + y)
|
||||
|
||||
@staticmethod
|
||||
def runge(x, y):
|
||||
''' Bivariate Runge function: as 1D complex function is analytic
|
||||
in a neighborhood of [-1; 1] but not throughout the complex plane.
|
||||
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
up to machine precision, is 0.597388947274307 (see ref. 6).
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500,
|
||||
is 5.9738894727430725e-001 with an estimated absolute error of
|
||||
0.00e+000.
|
||||
|
||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
||||
f(x)=1/(1+16*x^2).
|
||||
'''
|
||||
return 1. / (1 + 16 * (x**2 + y**2))
|
||||
|
||||
@staticmethod
|
||||
def abs_cubed(x, y):
|
||||
'''Low regular function.
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
up to machine precision, is 2.508723139534059 (see ref. 6).
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500,
|
||||
is 2.5087231395340579e+000 with an estimated absolute error of
|
||||
0.00e+000.
|
||||
|
||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
||||
f(x)=abs(x)^3.
|
||||
'''
|
||||
return (x**2 + y**2)**(3 / 2)
|
||||
|
||||
@staticmethod
|
||||
def gauss(x, y):
|
||||
'''Bivariate gaussian: smooth function.
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
up to machine precision, is 2.230985141404135 (see ref. 6).
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 500,
|
||||
is 2.2309851414041333e+000 with an estimated absolute error of
|
||||
2.66e-015.
|
||||
|
||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
||||
f(x)=exp(-x^2).
|
||||
'''
|
||||
return np.exp(-x**2 - y**2)
|
||||
|
||||
@staticmethod
|
||||
def exp_inv(x, y):
|
||||
'''Bivariate example stemming from a 1D C-infinity function.
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
up to machine precision, is 0.853358758654305 (see ref. 6).
|
||||
The value of the definite integral on the square [-1,1] x [-1,1],
|
||||
obtained using a Padua Points cubature formula of degree 2000,
|
||||
is 8.5335875865430544e-001 with an estimated absolute error of
|
||||
3.11e-015.
|
||||
|
||||
2D modification of an example by L.N.Trefethen (see ref. 7),
|
||||
f(x)=exp(-1/x^2).
|
||||
'''
|
||||
arg_z = (x**2 + y**2)
|
||||
# Avoid cases in which "arg_z=0", setting only in those instances
|
||||
# "arg_z=eps".
|
||||
arg_z = arg_z + (1 - np.abs(np.sign(arg_z))) * 1.e-100
|
||||
arg_z = 1. / arg_z
|
||||
return np.exp(-arg_z)
|
||||
|
||||
def __call__(self, x, y, i=0):
|
||||
s = self
|
||||
test_function = [s.franke, s.half_sphere, s.poly_degree20, s.exp_fun1,
|
||||
s.exp_fun100, s.cos30, s.constant, s.exp_xy, s.runge,
|
||||
s.abs_cubed, s.gauss, s.exp_inv]
|
||||
return test_function[i](x, y)
|
||||
|
||||
|
||||
example_functions = _ExampleFunctions()
|
||||
|
||||
|
||||
def _find_m(n):
|
||||
ix = np.r_[1:(n + 1) * (n + 2):2]
|
||||
if np.mod(n, 2) == 0:
|
||||
n2 = n // 2
|
||||
offset = np.array([[0, 1] * n2 + [0, ]] * (n2 + 1))
|
||||
ix = ix - offset.ravel(order='F')
|
||||
return ix
|
||||
|
||||
|
||||
def padua_points(n, domain=(-1, 1, -1, 1)):
|
||||
''' Return Padua points
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : scalar integer
|
||||
interpolation degree
|
||||
domain : vector [a,b,c,d]
|
||||
defining the rectangle [a,b] x [c,d]. (default domain = (-1,1,-1,1))
|
||||
|
||||
Returns
|
||||
-------
|
||||
pad : array of shape (2 x (n+1)*(n+2)/2) such that
|
||||
(pad[0,:], pad[1,: ]) defines the Padua points in the domain
|
||||
rectangle [a,b] x [c,d].
|
||||
or
|
||||
X1,Y1,X2,Y2 : arrays
|
||||
Two subgrids X1,Y1 and X2,Y2 defining the Padua points
|
||||
-------------------------------------------------------------------------------
|
||||
'''
|
||||
a, b, c, d = domain
|
||||
t0 = [np.pi] if n == 0 else np.linspace(0, np.pi, n + 1)
|
||||
t1 = np.linspace(0, np.pi, n + 2)
|
||||
zn = map_to_interval(np.cos(t0), a, b)
|
||||
zn1 = map_to_interval(np.cos(t1), c, d)
|
||||
|
||||
Pad1, Pad2 = np.meshgrid(zn, zn1)
|
||||
ix = _find_m(n)
|
||||
return np.vstack((Pad1.ravel(order='F')[ix],
|
||||
Pad2.ravel(order='F')[ix]))
|
||||
|
||||
|
||||
def error_estimate(C0f):
|
||||
''' Return interpolation error estimate from Padua coefficients
|
||||
'''
|
||||
n = C0f.shape[1]
|
||||
C0f2 = np.fliplr(C0f)
|
||||
errest = sum(np.abs(np.diag(C0f2)))
|
||||
if (n >= 1):
|
||||
errest = errest + sum(np.abs(np.diag(C0f2, -1)))
|
||||
if (n >= 2):
|
||||
errest = errest + sum(np.abs(np.diag(C0f2, -2)))
|
||||
return 2 * errest
|
||||
|
||||
|
||||
def padua_fit(Pad, fun, *args):
|
||||
'''
|
||||
Computes the Chebyshevs coefficients
|
||||
|
||||
so that f(x, y) can be approximated by:
|
||||
|
||||
f(x, y) = sum cjk*Tjk(x, y)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
Pad : array-like
|
||||
Padua points, as computed with padua_points function.
|
||||
fun : function to be interpolated in the form
|
||||
fun(x, y, *args), where *args are optional arguments for fun.
|
||||
|
||||
Returns
|
||||
-------
|
||||
coefficents: coefficient matrix
|
||||
abs_err : interpolation error estimate
|
||||
|
||||
Example
|
||||
------
|
||||
>>> import numpy as np
|
||||
>>> import wafo.padua as wp
|
||||
>>> domain = [0, 1, 0, 1]
|
||||
>>> a, b, c, d = domain
|
||||
>>> points = wp.padua_points(21, domain)
|
||||
>>> C0f, abs_error = wp.padua_fit(points, wp.example_functions.franke)
|
||||
>>> x1 = np.linspace(a, b, 100)
|
||||
>>> x2 = np.linspace(c, d, 101)
|
||||
>>> val = wp.padua_val(x1, x2, C0f, domain, use_meshgrid=True)
|
||||
>>> X1, X2 = np.meshgrid(x1, x2)
|
||||
>>> true_val = wp.example_functions.franke(X1, X2)
|
||||
|
||||
>>> np.allclose(val, true_val, atol=10*abs_error)
|
||||
True
|
||||
>>> np.allclose(np.abs(val-true_val).max(), 0.0073174614275738296)
|
||||
True
|
||||
>>> np.allclose(abs_error, 0.0022486904061664046)
|
||||
True
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
plt.contour(x1, x2, val)
|
||||
|
||||
'''
|
||||
|
||||
N = np.shape(Pad)[1]
|
||||
# recover the degree n from N = (n+1)(n+2)/2
|
||||
n = int(round(-3 + np.sqrt(1 + 8 * N)) / 2)
|
||||
C0f = fun(Pad[0], Pad[1], *args)
|
||||
if (n > 0):
|
||||
ix = _find_m(n)
|
||||
GfT = np.zeros((n + 2) * (n + 1))
|
||||
GfT[ix] = C0f * 2 / (n * (n + 1))
|
||||
GfT = GfT.reshape(n + 1, n + 2)
|
||||
GfT = GfT.T
|
||||
GfT[0] = GfT[0] / 2
|
||||
GfT[n + 1] = GfT[n + 1] / 2
|
||||
GfT[:, 0] = GfT[:, 0] / 2
|
||||
GfT[:, n] = GfT[:, n] / 2
|
||||
Gf = GfT.T
|
||||
# compute the interpolation coefficient matrix C0f by FFT
|
||||
Gfhat = np.real(fft(Gf, 2 * n, axis=0))
|
||||
Gfhathat = np.real(fft(Gfhat[:n + 1, :], 2 * (n + 1), axis=1))
|
||||
C0f = 2 * Gfhathat[:, 0:n + 1]
|
||||
C0f[0] = C0f[0, :] / np.sqrt(2)
|
||||
C0f[:, 0] = C0f[:, 0] / np.sqrt(2)
|
||||
C0f = np.fliplr(np.triu(np.fliplr(C0f)))
|
||||
C0f[n] = C0f[n] / 2
|
||||
|
||||
return C0f, error_estimate(C0f)
|
||||
|
||||
|
||||
def paduavals2coefs(f):
|
||||
m = len(f)
|
||||
n = int(round(-1.5 + np.sqrt(.25 + 2 * m)))
|
||||
x = padua_points(n)
|
||||
idx = _find_m(n)
|
||||
w = 0 * x[0] + 1. / (n * (n + 1))
|
||||
idx1 = np.all(np.abs(x) == 1, axis=0)
|
||||
w[idx1] = .5 * w[idx1]
|
||||
idx2 = np.all(np.abs(x) != 1, axis=0)
|
||||
w[idx2] = 2 * w[idx2]
|
||||
|
||||
G = np.zeros(idx.max() + 1)
|
||||
G[idx] = 4 * w * f
|
||||
|
||||
use_dct = 100 < n
|
||||
if use_dct:
|
||||
C = np.rot90(dct(dct(G.T).T)) # , axis=1)
|
||||
else:
|
||||
t1 = np.r_[0:n + 1].reshape(-1, 1)
|
||||
Tn1 = np.cos(t1 * t1.T * np.pi / n)
|
||||
t2 = np.r_[0:n + 2].reshape(-1, 1)
|
||||
Tn2 = np.cos(t2 * t2.T * np.pi / (n + 1))
|
||||
C = np.dot(Tn2, np.dot(G, Tn1))
|
||||
|
||||
C[0] = .5 * C[0]
|
||||
C[:, 1] = .5 * C[:, 1]
|
||||
C[0, -1] = .5 * C[0, -1]
|
||||
del C[-1]
|
||||
|
||||
# Take upper-left triangular part:
|
||||
return np.fliplr(np.triu(np.fliplr(C)))
|
||||
# C = triu(C(:,end:-1:1));
|
||||
# C = C(:,end:-1:1);
|
||||
|
||||
|
||||
# TODO: padua_fit2 does not work correctly yet.
|
||||
def padua_fit2(Pad, fun, *args):
|
||||
# N = np.shape(Pad)[1]
|
||||
# recover the degree n from N = (n+1)(n+2)/2
|
||||
# _n = int(round(-3 + np.sqrt(1 + 8 * N)) / 2)
|
||||
C0f = fun(Pad[0], Pad[1], *args)
|
||||
return paduavals2coefs(C0f)
|
||||
|
||||
|
||||
def _compute_moments(n):
|
||||
k = np.r_[0:n:2]
|
||||
mom = 2 * np.sqrt(2) / (1 - k ** 2)
|
||||
mom[0] = 2
|
||||
return mom
|
||||
|
||||
|
||||
def padua_cubature(coefficients, domain=(-1, 1, -1, 1)):
|
||||
'''
|
||||
Compute the integral through the coefficient matrix.
|
||||
'''
|
||||
n = coefficients.shape[1]
|
||||
mom = _compute_moments(n)
|
||||
M1, M2 = np.meshgrid(mom, mom)
|
||||
M = M1 * M2
|
||||
C0fM = coefficients[0:n:2, 0:n:2] * M
|
||||
a, b, c, d = domain
|
||||
integral = (b - a) * (d - c) * C0fM.sum() / 4
|
||||
return integral
|
||||
|
||||
|
||||
def padua_val(X, Y, coefficients, domain=(-1, 1, -1, 1), use_meshgrid=False):
|
||||
'''
|
||||
Evaluate polynomial in padua form at X, Y.
|
||||
|
||||
Evaluate the interpolation polynomial defined through its coefficient
|
||||
matrix coefficients at the target points X(:,1),X(:,2) or at the
|
||||
meshgrid(X1,X2)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X, Y: array-like
|
||||
evaluation points.
|
||||
coefficients : array-like
|
||||
coefficient matrix
|
||||
domain : a vector [a,b,c,d]
|
||||
defining the rectangle [a,b] x [c,d]
|
||||
use_meshgrid: bool
|
||||
If True interpolate at the points meshgrid(X, Y)
|
||||
|
||||
Returns
|
||||
-------
|
||||
fxy : array-like
|
||||
evaluation of the interpolation polynomial at the target points
|
||||
'''
|
||||
def transform(tn, x, a, b):
|
||||
xn = map_from_interval(x, a, b).clip(min=-1, max=1).reshape(1, -1)
|
||||
tx = np.cos(tn * np.arccos(xn)) * np.sqrt(2)
|
||||
tx[0] = 1
|
||||
return tx
|
||||
|
||||
X, Y = np.atleast_1d(X, Y)
|
||||
original_shape = X.shape
|
||||
a, b, c, d = domain
|
||||
n = np.shape(coefficients)[1]
|
||||
|
||||
tn = np.r_[0:n][:, None]
|
||||
tx1 = transform(tn, X.ravel(), a, b)
|
||||
tx2 = transform(tn, Y.ravel(), c, d)
|
||||
|
||||
if use_meshgrid: # eval on meshgrid points
|
||||
return np.dot(tx1.T, np.dot(coefficients, tx2)).T
|
||||
# scattered points
|
||||
val = np.sum(np.dot(tx1.T, coefficients) * tx2.T, axis=1)
|
||||
return val.reshape(original_shape)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from wafo.testing import test_docstrings
|
||||
test_docstrings(__file__)
|
||||
@ -1,18 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sun Oct 25 14:55:34 2015
|
||||
|
||||
@author: dave
|
||||
"""
|
||||
|
||||
|
||||
def configuration(parent_package='', top_path=None):
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
config = Configuration('wafo', parent_package, top_path)
|
||||
config.add_subpackage('source')
|
||||
config.make_config_py()
|
||||
return config
|
||||
|
||||
if __name__ == "__main__":
|
||||
from numpy.distutils.core import setup
|
||||
setup(**configuration(top_path='').todict())
|
||||
@ -1,2 +0,0 @@
|
||||
from wafo.sg_filter._core import * # pylint: disable=wildcard-import
|
||||
from wafo.sg_filter import demos
|
||||
File diff suppressed because one or more lines are too long
@ -1,488 +0,0 @@
|
||||
import numpy as np
|
||||
from scipy.sparse.linalg import expm
|
||||
from scipy.signal import medfilt
|
||||
from wafo.plotbackend import plotbackend as plt
|
||||
from wafo.sg_filter._core import (SavitzkyGolay, smoothn, Kalman,
|
||||
HodrickPrescott, HampelFilter)
|
||||
|
||||
|
||||
def demo_savitzky_on_noisy_chirp():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_savitzky_on_noisy_chirp()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
plt.figure(figsize=(7, 12))
|
||||
|
||||
# generate chirp signal
|
||||
tvec = np.arange(0, 6.28, .02)
|
||||
true_signal = np.sin(tvec * (2.0 + tvec))
|
||||
true_d_signal = (2 + tvec) * np.cos(tvec * (2.0 + tvec))
|
||||
|
||||
# add noise to signal
|
||||
noise = np.random.normal(size=true_signal.shape)
|
||||
signal = true_signal + .15 * noise
|
||||
|
||||
# plot signal
|
||||
plt.subplot(311)
|
||||
plt.plot(signal)
|
||||
plt.title('signal')
|
||||
|
||||
# smooth and plot signal
|
||||
plt.subplot(312)
|
||||
savgol = SavitzkyGolay(n=8, degree=4)
|
||||
s_signal = savgol.smooth(signal)
|
||||
s2 = smoothn(signal, robust=True)
|
||||
plt.plot(s_signal)
|
||||
plt.plot(s2)
|
||||
plt.plot(true_signal, 'r--')
|
||||
plt.title('smoothed signal')
|
||||
|
||||
# smooth derivative of signal and plot it
|
||||
plt.subplot(313)
|
||||
savgol1 = SavitzkyGolay(n=8, degree=1, diff_order=1)
|
||||
|
||||
dt = tvec[1] - tvec[0]
|
||||
d_signal = savgol1.smooth(signal) / dt
|
||||
|
||||
plt.plot(d_signal)
|
||||
plt.plot(true_d_signal, 'r--')
|
||||
plt.title('smoothed derivative of signal')
|
||||
|
||||
|
||||
def demo_kalman_voltimeter():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_kalman_voltimeter()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
V0 = 12
|
||||
h = np.atleast_2d(1) # voltimeter measure the voltage itself
|
||||
q = 1e-9 # variance of process noise as the car operates
|
||||
r = 0.05 ** 2 # variance of measurement error
|
||||
b = 0 # no system input
|
||||
u = 0 # no system input
|
||||
filt = Kalman(R=r, A=1, Q=q, H=h, B=b)
|
||||
|
||||
# Generate random voltages and watch the filter operate.
|
||||
n = 50
|
||||
truth = np.random.randn(n) * np.sqrt(q) + V0
|
||||
z = truth + np.random.randn(n) * np.sqrt(r) # measurement
|
||||
x = np.zeros(n)
|
||||
|
||||
for i, zi in enumerate(z):
|
||||
x[i] = filt(zi, u) # perform a Kalman filter iteration
|
||||
|
||||
_hz = plt.plot(z, 'r.', label='observations')
|
||||
# a-posteriori state estimates:
|
||||
_hx = plt.plot(x, 'b-', label='Kalman output')
|
||||
_ht = plt.plot(truth, 'g-', label='true voltage')
|
||||
plt.legend()
|
||||
plt.title('Automobile Voltimeter Example')
|
||||
|
||||
|
||||
def lti_disc(F, L=None, Q=None, dt=1):
|
||||
"""LTI_DISC Discretize LTI ODE with Gaussian Noise.
|
||||
|
||||
Syntax:
|
||||
[A,Q] = lti_disc(F,L,Qc,dt)
|
||||
|
||||
In:
|
||||
F - NxN Feedback matrix
|
||||
L - NxL Noise effect matrix (optional, default identity)
|
||||
Qc - LxL Diagonal Spectral Density (optional, default zeros)
|
||||
dt - Time Step (optional, default 1)
|
||||
|
||||
Out:
|
||||
A - Transition matrix
|
||||
Q - Discrete Process Covariance
|
||||
|
||||
Description:
|
||||
Discretize LTI ODE with Gaussian Noise. The original
|
||||
ODE model is in form
|
||||
|
||||
dx/dt = F x + L w, w ~ N(0,Qc)
|
||||
|
||||
Result of discretization is the model
|
||||
|
||||
x[k] = A x[k-1] + q, q ~ N(0,Q)
|
||||
|
||||
Which can be used for integrating the model
|
||||
exactly over time steps, which are multiples
|
||||
of dt.
|
||||
|
||||
"""
|
||||
n = np.shape(F)[0]
|
||||
if L is None:
|
||||
L = np.eye(n)
|
||||
|
||||
if Q is None:
|
||||
Q = np.zeros((n, n))
|
||||
# Closed form integration of transition matrix
|
||||
A = expm(F * dt)
|
||||
|
||||
# Closed form integration of covariance
|
||||
# by matrix fraction decomposition
|
||||
|
||||
Phi = np.vstack((np.hstack((F, np.dot(np.dot(L, Q), L.T))),
|
||||
np.hstack((np.zeros((n, n)), -F.T))))
|
||||
AB = np.dot(expm(Phi * dt), np.vstack((np.zeros((n, n)), np.eye(n))))
|
||||
# Q = AB[:n, :] / AB[n:(2 * n), :]
|
||||
Q = np.linalg.solve(AB[n:(2 * n), :].T, AB[:n, :].T)
|
||||
return A, Q
|
||||
|
||||
|
||||
def demo_kalman_sine():
|
||||
"""Kalman Filter demonstration with sine signal.
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> demo_kalman_sine()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
sd = 0.5
|
||||
dt = 0.1
|
||||
w = 1
|
||||
T = np.arange(0, 30 + dt / 2, dt)
|
||||
n = len(T)
|
||||
X = 3 * np.sin(w * T)
|
||||
Y = X + sd * np.random.randn(n)
|
||||
|
||||
''' Initialize KF to values
|
||||
x = 0
|
||||
dx/dt = 0
|
||||
with great uncertainty in derivative
|
||||
'''
|
||||
M = np.zeros((2, 1))
|
||||
P = np.diag([0.1, 2])
|
||||
R = sd ** 2
|
||||
H = np.atleast_2d([1, 0])
|
||||
q = 0.1
|
||||
F = np.atleast_2d([[0, 1],
|
||||
[0, 0]])
|
||||
A, Q = lti_disc(F, L=None, Q=np.diag([0, q]), dt=dt)
|
||||
|
||||
# Track and animate
|
||||
m = M.shape[0]
|
||||
_MM = np.zeros((m, n))
|
||||
_PP = np.zeros((m, m, n))
|
||||
'''In this demonstration we estimate a stationary sine signal from noisy
|
||||
measurements by using the classical Kalman filter.'
|
||||
'''
|
||||
filt = Kalman(R=R, x=M, P=P, A=A, Q=Q, H=H, B=0)
|
||||
|
||||
# Generate random voltages and watch the filter operate.
|
||||
# n = 50
|
||||
# truth = np.random.randn(n) * np.sqrt(q) + V0
|
||||
# z = truth + np.random.randn(n) * np.sqrt(r) # measurement
|
||||
truth = X
|
||||
z = Y
|
||||
x = np.zeros((n, m))
|
||||
|
||||
for i, zi in enumerate(z):
|
||||
x[i] = np.ravel(filt(zi, u=0))
|
||||
|
||||
_hz = plt.plot(z, 'r.', label='observations')
|
||||
# a-posteriori state estimates:
|
||||
_hx = plt.plot(x[:, 0], 'b-', label='Kalman output')
|
||||
_ht = plt.plot(truth, 'g-', label='true voltage')
|
||||
plt.legend()
|
||||
plt.title('Automobile Voltimeter Example')
|
||||
|
||||
|
||||
# for k in range(m):
|
||||
# [M,P] = kf_predict(M,P,A,Q);
|
||||
# [M,P] = kf_update(M,P,Y(k),H,R);
|
||||
#
|
||||
# MM(:,k) = M;
|
||||
# PP(:,:,k) = P;
|
||||
#
|
||||
# %
|
||||
# % Animate
|
||||
# %
|
||||
# if rem(k,10)==1
|
||||
# plot(T,X,'b--',...
|
||||
# T,Y,'ro',...
|
||||
# T(k),M(1),'k*',...
|
||||
# T(1:k),MM(1,1:k),'k-');
|
||||
# legend('Real signal','Measurements','Latest estimate',
|
||||
# 'Filtered estimate')
|
||||
# title('Estimating a noisy sine signal with Kalman filter.');
|
||||
# drawnow;
|
||||
#
|
||||
# pause;
|
||||
# end
|
||||
# end
|
||||
#
|
||||
# clc;
|
||||
# disp('In this demonstration we estimate a stationary sine signal '
|
||||
# 'from noisy measurements by using the classical Kalman filter.');
|
||||
# disp(' ');
|
||||
# disp('The filtering results are now displayed sequantially for 10 time '
|
||||
# 'step at a time.');
|
||||
# disp(' ');
|
||||
# disp('<push any key to see the filtered and smoothed results together>')
|
||||
# pause;
|
||||
# %
|
||||
# % Apply Kalman smoother
|
||||
# %
|
||||
# SM = rts_smooth(MM,PP,A,Q);
|
||||
# plot(T,X,'b--',...
|
||||
# T,MM(1,:),'k-',...
|
||||
# T,SM(1,:),'r-');
|
||||
# legend('Real signal','Filtered estimate','Smoothed estimate')
|
||||
# title('Filtered and smoothed estimate of the original signal');
|
||||
#
|
||||
# clc;
|
||||
# disp('The filtered and smoothed estimates of the signal are now '
|
||||
# 'displayed.')
|
||||
# disp(' ');
|
||||
# disp('RMS errors:');
|
||||
# %
|
||||
# % Errors
|
||||
# %
|
||||
# fprintf('KF = %.3f\nRTS = %.3f\n',...
|
||||
# sqrt(mean((MM(1,:)-X(1,:)).^2)),...
|
||||
# sqrt(mean((SM(1,:)-X(1,:)).^2)));
|
||||
|
||||
|
||||
def demo_hampel():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_hampel()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
randint = np.random.randint
|
||||
Y = 5000 + np.random.randn(1000)
|
||||
outliers = randint(0, 1000, size=(10,))
|
||||
Y[outliers] = Y[outliers] + randint(1000, size=(10,))
|
||||
YY, res = HampelFilter(dx=3, t=3, fulloutput=True)(Y)
|
||||
YY1, res1 = HampelFilter(dx=1, t=3, adaptive=0.1, fulloutput=True)(Y)
|
||||
YY2, res2 = HampelFilter(dx=3, t=0, fulloutput=True)(Y) # median
|
||||
plt.figure(1)
|
||||
plot_hampel(Y, YY, res)
|
||||
plt.title('Standard HampelFilter')
|
||||
plt.figure(2)
|
||||
plot_hampel(Y, YY1, res1)
|
||||
plt.title('Adaptive HampelFilter')
|
||||
plt.figure(3)
|
||||
plot_hampel(Y, YY2, res2)
|
||||
plt.title('Median filter')
|
||||
|
||||
|
||||
def plot_hampel(Y, YY, res):
|
||||
X = np.arange(len(YY))
|
||||
plt.plot(X, Y, 'b.') # Original Data
|
||||
plt.plot(X, YY, 'r') # Hampel Filtered Data
|
||||
plt.plot(X, res['Y0'], 'b--') # Nominal Data
|
||||
plt.plot(X, res['LB'], 'r--') # Lower Bounds on Hampel Filter
|
||||
plt.plot(X, res['UB'], 'r--') # Upper Bounds on Hampel Filter
|
||||
i = res['outliers']
|
||||
plt.plot(X[i], Y[i], 'ks') # Identified Outliers
|
||||
|
||||
|
||||
def demo_tide_filter():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_tide_filter()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
# import statsmodels.api as sa
|
||||
import wafo.spectrum.models as sm
|
||||
sd = 10
|
||||
Sj = sm.Jonswap(Hm0=4. * sd)
|
||||
S = Sj.tospecdata()
|
||||
|
||||
q = (0.1 * sd) ** 2 # variance of process noise s the car operates
|
||||
r = (100 * sd) ** 2 # variance of measurement error
|
||||
b = 0 # no system input
|
||||
u = 0 # no system input
|
||||
|
||||
from scipy.signal import butter, filtfilt, lfilter_zi # lfilter,
|
||||
freq_tide = 1. / (12 * 60 * 60)
|
||||
freq_wave = 1. / 10
|
||||
freq_filt = freq_wave / 10
|
||||
dt = 1.
|
||||
freq = 1. / dt
|
||||
fn = (freq / 2)
|
||||
|
||||
P = 10 * np.diag([1, 0.01])
|
||||
R = r
|
||||
H = np.atleast_2d([1, 0])
|
||||
|
||||
F = np.atleast_2d([[0, 1],
|
||||
[0, 0]])
|
||||
A, Q = lti_disc(F, L=None, Q=np.diag([0, q]), dt=dt)
|
||||
|
||||
t = np.arange(0, 60 * 12, 1. / freq)
|
||||
w = 2 * np.pi * freq # 1 Hz
|
||||
tide = 100 * np.sin(freq_tide * w * t + 2 * np.pi / 4) + 100
|
||||
y = tide + S.sim(len(t), dt=1. / freq)[:, 1].ravel()
|
||||
# lowess = sa.nonparametric.lowess
|
||||
# y2 = lowess(y, t, frac=0.5)[:,1]
|
||||
|
||||
filt = Kalman(R=R, x=np.array([[tide[0]], [0]]), P=P, A=A, Q=Q, H=H, B=b)
|
||||
filt2 = Kalman(R=R, x=np.array([[tide[0]], [0]]), P=P, A=A, Q=Q, H=H, B=b)
|
||||
# y = tide + 0.5 * np.sin(freq_wave * w * t)
|
||||
# Butterworth filter
|
||||
b, a = butter(9, (freq_filt / fn), btype='low')
|
||||
# y2 = [lowess(y[max(i-60,0):i + 1], t[max(i-60,0):i + 1], frac=.3)[-1,1]
|
||||
# for i in range(len(y))]
|
||||
# y2 = [lfilter(b, a, y[:i + 1])[i] for i in range(len(y))]
|
||||
# y3 = filtfilt(b, a, y[:16]).tolist() + [filtfilt(b, a, y[:i + 1])[i]
|
||||
# for i in range(16, len(y))]
|
||||
# y0 = medfilt(y, 41)
|
||||
_zi = lfilter_zi(b, a)
|
||||
# y2 = lfilter(b, a, y)#, zi=y[0]*zi) # standard filter
|
||||
y3 = filtfilt(b, a, y) # filter with phase shift correction
|
||||
y4 = []
|
||||
y5 = []
|
||||
for _i, j in enumerate(y):
|
||||
tmp = np.ravel(filt(j, u=u))
|
||||
tmp = np.ravel(filt2(tmp[0], u=u))
|
||||
# if i==0:
|
||||
# print(filt.x)
|
||||
# print(filt2.x)
|
||||
y4.append(tmp[0])
|
||||
y5.append(tmp[1])
|
||||
_y0 = medfilt(y4, 41)
|
||||
# print(filt.P)
|
||||
# plot
|
||||
|
||||
plt.plot(t, y, 'r.-', linewidth=2, label='raw data')
|
||||
# plt.plot(t, y2, 'b.-', linewidth=2, label='lowess @ %g Hz' % freq_filt)
|
||||
# plt.plot(t, y2, 'b.-', linewidth=2, label='filter @ %g Hz' % freq_filt)
|
||||
plt.plot(t, y3, 'g.-', linewidth=2, label='filtfilt @ %g Hz' % freq_filt)
|
||||
plt.plot(t, y4, 'k.-', linewidth=2, label='kalman')
|
||||
# plt.plot(t, y5, 'k.', linewidth=2, label='kalman2')
|
||||
plt.plot(t, tide, 'y-', linewidth=2, label='True tide')
|
||||
plt.legend(frameon=False, fontsize=14)
|
||||
plt.xlabel("Time [s]")
|
||||
plt.ylabel("Amplitude")
|
||||
|
||||
|
||||
def demo_savitzky_on_exponential():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_savitzky_on_exponential()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
t = np.linspace(-4, 4, 500)
|
||||
y = np.exp(-t ** 2) + np.random.normal(0, 0.05, np.shape(t))
|
||||
n = 11
|
||||
ysg = SavitzkyGolay(n, degree=1, diff_order=0)(y)
|
||||
plt.plot(t, y, t, ysg, '--')
|
||||
|
||||
|
||||
def demo_smoothn_on_1d_cos():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_smoothn_on_1d_cos()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
x = np.linspace(0, 100, 2 ** 8)
|
||||
y = np.cos(x / 10) + (x / 50) ** 2 + np.random.randn(np.size(x)) / 10
|
||||
y[np.r_[70, 75, 80]] = np.array([5.5, 5, 6])
|
||||
z = smoothn(y) # Regular smoothing
|
||||
zr = smoothn(y, robust=True) # Robust smoothing
|
||||
_h0 = plt.subplot(121),
|
||||
_h = plt.plot(x, y, 'r.', x, z, 'k', linewidth=2)
|
||||
plt.title('Regular smoothing')
|
||||
plt.subplot(122)
|
||||
plt.plot(x, y, 'r.', x, zr, 'k', linewidth=2)
|
||||
plt.title('Robust smoothing')
|
||||
|
||||
|
||||
def demo_smoothn_on_2d_exp_sin():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_smoothn_on_2d_exp_sin()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
xp = np.arange(0, 1, 0.02) # np.r_[0:1:0.02]
|
||||
[x, y] = np.meshgrid(xp, xp)
|
||||
f = np.exp(x + y) + np.sin((x - 2 * y) * 3)
|
||||
fn = f + np.random.randn(*f.shape) * 0.5
|
||||
_fs, s = smoothn(fn, fulloutput=True)
|
||||
fs2 = smoothn(fn, s=2 * s)
|
||||
_h = plt.subplot(131),
|
||||
_h = plt.contourf(xp, xp, fn)
|
||||
_h = plt.subplot(132),
|
||||
_h = plt.contourf(xp, xp, fs2)
|
||||
_h = plt.subplot(133),
|
||||
_h = plt.contourf(xp, xp, f)
|
||||
|
||||
|
||||
def _cardioid(n=1000):
|
||||
t = np.linspace(0, 2 * np.pi, n)
|
||||
x0 = 2 * np.cos(t) * (1 - np.cos(t))
|
||||
y0 = 2 * np.sin(t) * (1 - np.cos(t))
|
||||
x = x0 + np.random.randn(x0.size) * 0.1
|
||||
y = y0 + np.random.randn(y0.size) * 0.1
|
||||
return x, y, x0, y0
|
||||
|
||||
|
||||
def demo_smoothn_on_cardioid():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_smoothn_on_cardioid()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
x, y, x0, y0 = _cardioid()
|
||||
z = smoothn(x + 1j * y, robust=False)
|
||||
plt.plot(x0, y0, 'y',
|
||||
x, y, 'r.',
|
||||
np.real(z), np.imag(z), 'k', linewidth=2)
|
||||
|
||||
|
||||
def demo_hodrick_on_cardioid():
|
||||
"""
|
||||
Example
|
||||
-------
|
||||
>>> demo_hodrick_on_cardioid()
|
||||
|
||||
>>> plt.close()
|
||||
"""
|
||||
x, y, x0, y0 = _cardioid()
|
||||
|
||||
smooth = HodrickPrescott(w=20000)
|
||||
# smooth = HampelFilter(adaptive=50)
|
||||
xs, ys = smooth(x), smooth(y)
|
||||
plt.plot(x0, y0, 'y',
|
||||
x, y, 'r.',
|
||||
xs, ys, 'k', linewidth=2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from wafo.testing import test_docstrings
|
||||
test_docstrings(__file__)
|
||||
# demo_savitzky_on_noisy_chirp()
|
||||
# plt.show('hold') # show plot
|
||||
# demo_kalman_sine()
|
||||
# demo_tide_filter()
|
||||
# demo_hampel()
|
||||
# demo_kalman_voltimeter()
|
||||
# demo_savitzky_on_exponential()
|
||||
# plt.figure(1)
|
||||
# demo_hodrick_on_cardioid()
|
||||
# plt.figure(2)
|
||||
# # demo_smoothn_on_1d_cos()
|
||||
# demo_smoothn_on_cardioid()
|
||||
# plt.show('hold')
|
||||
@ -1,11 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Dummy conftest.py for wafo.
|
||||
|
||||
If you don't know what this is for, just leave it empty.
|
||||
Read more about conftest.py under:
|
||||
https://pytest.org/latest/plugins.html
|
||||
"""
|
||||
from __future__ import print_function, absolute_import, division
|
||||
import pytest # @UnusedImport
|
||||
File diff suppressed because one or more lines are too long
@ -1,25 +0,0 @@
|
||||
"""
|
||||
f2py c_library.pyf c_functions.c -c
|
||||
|
||||
See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
from wafo.f2py_tools import f2py_call_str
|
||||
|
||||
def compile_all():
|
||||
f2py_call = f2py_call_str()
|
||||
print '=' * 75
|
||||
print 'compiling c_codes'
|
||||
print '=' * 75
|
||||
|
||||
compile_format = f2py_call + ' %s %s -c'
|
||||
|
||||
pyfs = ('c_library.pyf',)
|
||||
files = ('c_functions.c',)
|
||||
|
||||
for pyf, file_ in zip(pyfs, files):
|
||||
os.system(compile_format % (pyf, file_))
|
||||
|
||||
if __name__ == '__main__':
|
||||
compile_all()
|
||||
@ -1,779 +0,0 @@
|
||||
#include "math.h"
|
||||
/*
|
||||
* Install gfortran and run the following to build the module on windows:
|
||||
* f2py c_library.pyf c_functions.c -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71
|
||||
*/
|
||||
|
||||
/*
|
||||
* findrfc.c -
|
||||
*
|
||||
* Returns indices to RFC turningpoints of a vector
|
||||
* of turningpoints
|
||||
*
|
||||
* 1998 by Per Andreas Brodtkorb.
|
||||
*/
|
||||
|
||||
void findrfc(double *y1,double hmin, int *ind, int n,int *info) {
|
||||
double xminus, xplus, Tpl, Tmi, *y, Tstart;
|
||||
int i, j, ix=0, NC, iy;
|
||||
info[0] = 0;
|
||||
if (*(y1+0)> *(y1+1)){
|
||||
/* if first is a max , ignore the first max*/
|
||||
y=&(*(y1+1));
|
||||
NC=floor((n-1)/2);
|
||||
Tstart=1;
|
||||
}
|
||||
else {
|
||||
y=y1;
|
||||
NC=floor(n/2);
|
||||
Tstart=0;
|
||||
}
|
||||
|
||||
if (NC<1){
|
||||
return; /* No RFC cycles*/
|
||||
}
|
||||
|
||||
|
||||
if (( *(y+0) > *(y+1)) && ( *(y+1) > *(y+2)) ){
|
||||
info[0] = -1;
|
||||
return; /*This is not a sequence of turningpoints, exit */
|
||||
}
|
||||
if ((*(y+0) < *(y+1)) && (*(y+1)< *(y+2))){
|
||||
info[0]=-1;
|
||||
return; /*This is not a sequence of turningpoints, exit */
|
||||
}
|
||||
|
||||
|
||||
for (i=0; i<NC; i++) {
|
||||
|
||||
Tmi=Tstart+2*i;
|
||||
Tpl=Tstart+2*i+2;
|
||||
xminus=*(y+2*i);
|
||||
xplus=*(y+2*i+2);
|
||||
|
||||
if(i!=0){
|
||||
j=i-1;
|
||||
while((j>=0) && (*(y+2*j+1)<=*(y+2*i+1))){
|
||||
if( (*(y+2*j)<xminus) ){
|
||||
xminus=*(y+2*j);
|
||||
Tmi=Tstart+2*j;
|
||||
} /*if */
|
||||
j--;
|
||||
} /*while j*/
|
||||
} /*if i */
|
||||
if ( xminus >= xplus){
|
||||
if ( (*(y+2*i+1)-xminus) >= hmin){
|
||||
*(ind+ix)=Tmi;
|
||||
ix++;
|
||||
*(ind+ix)=(Tstart+2*i+1);
|
||||
ix++;
|
||||
} /*if*/
|
||||
goto L180;
|
||||
}
|
||||
|
||||
j=i+1;
|
||||
while((j<NC) ) {
|
||||
if (*(y+2*j+1) >= *(y+2*i+1)) goto L170;
|
||||
if( (*(y+2*j+2) <= xplus) ){
|
||||
xplus=*(y+2*j+2);
|
||||
Tpl=(Tstart+2*j+2);
|
||||
}/*if*/
|
||||
j++;
|
||||
} /*while*/
|
||||
|
||||
|
||||
if ( (*(y+2*i+1)-xminus) >= hmin) {
|
||||
*(ind+ix)=Tmi;
|
||||
ix++;
|
||||
*(ind+ix)=(Tstart+2*i+1);
|
||||
ix++;
|
||||
|
||||
} /*if*/
|
||||
goto L180;
|
||||
L170:
|
||||
if (xplus <= xminus ) {
|
||||
if ( (*(y+2*i+1)-xminus) >= hmin){
|
||||
*(ind+ix)=Tmi;
|
||||
ix++;
|
||||
*(ind+ix)=(Tstart+2*i+1);
|
||||
ix++;
|
||||
} /*if*/
|
||||
/*goto L180;*/
|
||||
}
|
||||
else{
|
||||
if ( (*(y+2*i+1)-xplus) >= hmin) {
|
||||
*(ind+ix)=(Tstart+2*i+1);
|
||||
ix++;
|
||||
*(ind+ix)=Tpl;
|
||||
ix++;
|
||||
} /*if*/
|
||||
} /*elseif*/
|
||||
L180:
|
||||
iy=i;
|
||||
} /* for i */
|
||||
info[0] = ix;
|
||||
return ;
|
||||
}
|
||||
|
||||
|
||||
|
||||
/*
|
||||
* findcross.c -
|
||||
*
|
||||
* Returns indices to level v crossings of argument vector
|
||||
*
|
||||
* 1998 by Per Andreas Brodtkorb. last modified 23.06-98
|
||||
*/
|
||||
|
||||
|
||||
void findcross(double *y, double v, int *ind, int n, int *info)
|
||||
{ int i,start, ix=0,dcross=0;
|
||||
start=0;
|
||||
if ( y[0]< v){
|
||||
dcross=-1; /* first is a up-crossing*/
|
||||
}
|
||||
else if ( y[0]> v){
|
||||
dcross=1; /* first is a down-crossing*/
|
||||
}
|
||||
else if ( y[0]== v){
|
||||
/* Find out what type of crossing we have next time.. */
|
||||
for (i=1; i<n; i++) {
|
||||
start=i;
|
||||
if ( y[i]< v){
|
||||
ind[ix] = i-1; /* first crossing is a down crossing*/
|
||||
ix++;
|
||||
dcross=-1; /* The next crossing is a up-crossing*/
|
||||
goto L120;
|
||||
}
|
||||
else if ( y[i]> v){
|
||||
ind[ix] = i-1; /* first crossing is a up-crossing*/
|
||||
ix++;
|
||||
dcross=1; /*The next crossing is a down-crossing*/
|
||||
goto L120;
|
||||
}
|
||||
}
|
||||
}
|
||||
L120:
|
||||
for (i=start; i<n-1; i++) {
|
||||
if (( (dcross==-1) && (y[i]<=v) && (y[i+1] > v) ) || ((dcross==1 ) && (y[i]>=v) && (y[i+1] < v) ) ) {
|
||||
|
||||
ind[ix] = i;
|
||||
ix++;
|
||||
dcross=-dcross;
|
||||
}
|
||||
}
|
||||
info[0] = ix;
|
||||
return;
|
||||
}
|
||||
|
||||
|
||||
/*
|
||||
* DISUFQ Is an internal function to spec2nlsdat
|
||||
*
|
||||
* CALL: disufq(rvec,ivec,rA,iA, w,kw,h,g,nmin,nmax,m,n)
|
||||
*
|
||||
* rvec, ivec = real and imaginary parts of the resultant (size m X n).
|
||||
* rA, iA = real and imaginary parts of the amplitudes (size m X n).
|
||||
* w = vector with angular frequencies (w>=0)
|
||||
* kw = vector with wavenumbers (kw>=0)
|
||||
* h = water depth (h >=0)
|
||||
* g = constant acceleration of gravity
|
||||
* nmin = minimum index where rA(:,nmin) and iA(:,nmin) is
|
||||
* greater than zero.
|
||||
* nmax = maximum index where rA(:,nmax) and iA(:,nmax) is
|
||||
* greater than zero.
|
||||
* m = size(rA,1),size(iA,1)
|
||||
* n = size(rA,2),size(iA,2), or size(rvec,2),size(ivec,2)
|
||||
*
|
||||
* DISUFQ returns the summation of difference frequency and sum
|
||||
* frequency effects in the vector vec = rvec +sqrt(-1)*ivec.
|
||||
* The 2'nd order contribution to the Stokes wave is then calculated by
|
||||
* a simple 1D Fourier transform, real(FFT(vec)).
|
||||
*
|
||||
* Install gfortran and run the following to build the module:
|
||||
* f2py diffsumfunq.pyf disufq1.c -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71
|
||||
*
|
||||
* by Per Andreas Brodtkorb 15.08.2001
|
||||
* revised pab 14.03.2002, 01.05.2002 22.07.2002, oct 2008
|
||||
*/
|
||||
|
||||
void disufq(double *rvec, double *ivec,
|
||||
double *rA, double *iA,
|
||||
double *w, double *kw,
|
||||
double h, double g,
|
||||
int nmin, int nmax,
|
||||
int m, int n)
|
||||
{
|
||||
double Epij, Edij;
|
||||
double tmp1, tmp2, tmp3, tmp4, kfact;
|
||||
double w1, w2, kw1, kw2, Cg;
|
||||
double rrA, iiA, riA, irA;
|
||||
int i,jy,ix,iz1,iv1,ixi,jyi;
|
||||
//int iz2, iv2;
|
||||
//Initialize rvec and ivec to zero
|
||||
for (ix=0;ix<n*m;ix++) {
|
||||
rvec[ix] = 0.0;
|
||||
ivec[ix] = 0.0;
|
||||
}
|
||||
|
||||
// kfact is set to 2 in order to exploit the symmetry.
|
||||
// If you set kfact to 1, you must uncomment all statements
|
||||
// including the expressions: rvec[iz2], rvec[iv2], ivec[iz2] and ivec[iv2].
|
||||
|
||||
kfact = 2.0;
|
||||
if (h>10000){ /* deep water /Inifinite water depth */
|
||||
for (ix = nmin-1;ix<nmax;ix++) {
|
||||
ixi = ix*m;
|
||||
iz1 = 2*ixi;
|
||||
//iz2 = n*m-ixi;
|
||||
kw1 = kw[ix];
|
||||
Epij = kw1;
|
||||
for (i=0;i<m;i++,ixi++,iz1++) {
|
||||
rrA = rA[ixi]*rA[ixi]; ///
|
||||
iiA = iA[ixi]*iA[ixi]; ///
|
||||
riA = rA[ixi]*iA[ixi]; ///
|
||||
|
||||
/// Sum frequency effects along the diagonal
|
||||
tmp1 = kfact*(rrA-iiA)*Epij;
|
||||
tmp2 = kfact*2.0*riA*Epij;
|
||||
rvec[iz1] += tmp1;
|
||||
ivec[iz1] += tmp2;
|
||||
|
||||
//rvec[iz2] += tmp1;
|
||||
//ivec[iz2] -= tmp2;
|
||||
//iz2++;
|
||||
|
||||
/// Difference frequency effects are zero along the diagonal
|
||||
/// and are thus not contributing to the mean.
|
||||
}
|
||||
for (jy = ix+1;jy<nmax;jy++){
|
||||
kw2 = kw[jy];
|
||||
Epij = 0.5*(kw2 + kw1);
|
||||
Edij = -0.5*(kw2 - kw1);
|
||||
//printf("Edij = %f Epij = %f \n", Edij,Epij);
|
||||
|
||||
ixi = ix*m;
|
||||
jyi = jy*m;
|
||||
iz1 = ixi+jyi;
|
||||
iv1 = jyi-ixi;
|
||||
//iz2 = (n*m-iz1);
|
||||
//iv2 = (n*m-iv1);
|
||||
for (i = 0;i<m;i++,ixi++,jyi++,iz1++,iv1++) {
|
||||
|
||||
rrA = rA[ixi]*rA[jyi]; ///rrA = rA[i][ix]*rA[i][jy];
|
||||
iiA = iA[ixi]*iA[jyi]; ///iiA = iA[i][ix]*iA[i][jy];
|
||||
riA = rA[ixi]*iA[jyi]; ///riA = rA[i][ix]*iA[i][jy];
|
||||
irA = iA[ixi]*rA[jyi]; ///irA = iA[i][ix]*rA[i][jy];
|
||||
|
||||
/* Sum frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA-iiA)*Epij;
|
||||
tmp2 = kfact*2.0*(riA+irA)*Epij;
|
||||
rvec[iz1] += tmp1;///rvec[i][ix+jy] += tmp1;
|
||||
ivec[iz1] += tmp2;///ivec[i][ix+jy] += tmp2;
|
||||
//rvec[iz2] += tmp1;///rvec[i][n*m-(ix+jy)] += tmp1;
|
||||
//ivec[iz2] -= tmp2;///ivec[i][n*m-(ix+jy)] -= tmp2;
|
||||
// iz2++;
|
||||
|
||||
/* Difference frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA+iiA)*Edij;
|
||||
tmp2 = kfact*2.0*(riA-irA)*Edij;
|
||||
|
||||
rvec[iv1] += tmp1;///rvec[i][jy-ix] += tmp1;
|
||||
ivec[iv1] += tmp2;///ivec[i][jy-ix] += tmp2;
|
||||
|
||||
//rvec[iv2] += tmp1;///rvec[i][n*m-(jy-ix)] += tmp1;
|
||||
//ivec[iv2] -= tmp2;///ivec[i][n*m-(jy-ix)] -= tmp2;
|
||||
//iv2++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
else{ /* Finite water depth */
|
||||
for (ix = nmin-1;ix<nmax;ix++) {
|
||||
kw1 = kw[ix];
|
||||
w1 = w[ix];
|
||||
tmp1 = tanh(kw1*h);
|
||||
/// Cg, wave group velocity
|
||||
Cg = 0.5*g*(tmp1 + kw1*h*(1.0- tmp1*tmp1))/w1; /// OK
|
||||
tmp1 = 0.5*g*(kw1/w1)*(kw1/w1);
|
||||
tmp2 = 0.5*w1*w1/g;
|
||||
tmp3 = g*kw1/(w1*Cg);
|
||||
|
||||
if (kw1*h<300.0){
|
||||
tmp4 = kw1/sinh(2.0*kw1*h);
|
||||
}
|
||||
else{ // To ensure sinh does not overflow.
|
||||
tmp4 = 0.0;
|
||||
}
|
||||
// Difference frequency effects finite water depth
|
||||
Edij = (tmp1-tmp2+tmp3)/(1.0-g*h/(Cg*Cg))-tmp4; /// OK
|
||||
|
||||
// Sum frequency effects finite water depth
|
||||
Epij = (3.0*(tmp1-tmp2)/(1.0-tmp1/kw1*tanh(2.0*kw1*h))+3.0*tmp2-tmp1); /// OK
|
||||
//printf("Edij = %f Epij = %f \n", Edij,Epij);
|
||||
|
||||
ixi = ix*m;
|
||||
iz1 = 2*ixi;
|
||||
//iz2 = n*m-ixi;
|
||||
for (i=0;i<m;i++,ixi++,iz1++) {
|
||||
|
||||
rrA = rA[ixi]*rA[ixi]; ///
|
||||
iiA = iA[ixi]*iA[ixi]; ///
|
||||
riA = rA[ixi]*iA[ixi]; ///
|
||||
|
||||
|
||||
/// Sum frequency effects along the diagonal
|
||||
rvec[iz1] += kfact*(rrA-iiA)*Epij;
|
||||
ivec[iz1] += kfact*2.0*riA*Epij;
|
||||
//rvec[iz2] += kfact*(rrA-iiA)*Epij;
|
||||
//ivec[iz2] -= kfact*2.0*riA*Epij;
|
||||
//iz2++;
|
||||
|
||||
/// Difference frequency effects along the diagonal
|
||||
/// are only contributing to the mean
|
||||
rvec[i] += 2.0*(rrA+iiA)*Edij;
|
||||
}
|
||||
for (jy = ix+1;jy<nmax;jy++) {
|
||||
// w1 = w[ix];
|
||||
// kw1 = kw[ix];
|
||||
w2 = w[jy];
|
||||
kw2 = kw[jy];
|
||||
tmp1 = g*(kw1/w1)*(kw2/w2);
|
||||
tmp2 = 0.5/g*(w1*w1+w2*w2+w1*w2);
|
||||
tmp3 = 0.5*g*(w1*kw2*kw2+w2*kw1*kw1)/(w1*w2*(w1+w2));
|
||||
tmp4 = (1-g*(kw1+kw2)/(w1+w2)/(w1+w2)*tanh((kw1+kw2)*h));
|
||||
Epij = (tmp1-tmp2+tmp3)/tmp4+tmp2-0.5*tmp1; /* OK */
|
||||
|
||||
tmp2 = 0.5/g*(w1*w1+w2*w2-w1*w2); /*OK*/
|
||||
tmp3 = -0.5*g*(w1*kw2*kw2-w2*kw1*kw1)/(w1*w2*(w1-w2));
|
||||
tmp4 = (1.0-g*(kw1-kw2)/(w1-w2)/(w1-w2)*tanh((kw1-kw2)*h));
|
||||
Edij = (tmp1-tmp2+tmp3)/tmp4+tmp2-0.5*tmp1; /* OK */
|
||||
//printf("Edij = %f Epij = %f \n", Edij,Epij);
|
||||
|
||||
ixi = ix*m;
|
||||
jyi = jy*m;
|
||||
iz1 = ixi+jyi;
|
||||
iv1 = jyi-ixi;
|
||||
// iz2 = (n*m-iz1);
|
||||
// iv2 = n*m-iv1;
|
||||
for (i=0;i<m;i++,ixi++,jyi++,iz1++,iv1++) {
|
||||
rrA = rA[ixi]*rA[jyi]; ///rrA = rA[i][ix]*rA[i][jy];
|
||||
iiA = iA[ixi]*iA[jyi]; ///iiA = iA[i][ix]*iA[i][jy];
|
||||
riA = rA[ixi]*iA[jyi]; ///riA = rA[i][ix]*iA[i][jy];
|
||||
irA = iA[ixi]*rA[jyi]; ///irA = iA[i][ix]*rA[i][jy];
|
||||
|
||||
/* Sum frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA-iiA)*Epij;
|
||||
tmp2 = kfact*2.0*(riA+irA)*Epij;
|
||||
rvec[iz1] += tmp1;///rvec[i][jy+ix] += tmp1;
|
||||
ivec[iz1] += tmp2;///ivec[i][jy+ix] += tmp2;
|
||||
//rvec[iz2] += tmp1;///rvec[i][n*m-(jy+ix)] += tmp1;
|
||||
//ivec[iz2] -= tmp2;///ivec[i][n*m-(jy+ix)] -= tmp2;
|
||||
//iz2++;
|
||||
|
||||
/* Difference frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA+iiA)*Edij;
|
||||
tmp2 = kfact*2.0*(riA-irA)*Edij;
|
||||
rvec[iv1] += tmp1;///rvec[i][jy-ix] += tmp1;
|
||||
ivec[iv1] += tmp2;///ivec[i][jy-ix] -= tmp2;
|
||||
|
||||
//rvec[iv2] += tmp1;
|
||||
//ivec[iv2] -= tmp2;
|
||||
//iv2++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
//return i;
|
||||
}
|
||||
/*
|
||||
* DISUFQ2 Is an internal function to spec2nlsdat
|
||||
*
|
||||
* CALL: disufq2(rsvec,isvec,rdvec,idvec,rA,iA, w,kw,h,g,nmin,nmax,m,n)
|
||||
*
|
||||
* rsvec, isvec = real and imaginary parts of the sum frequency
|
||||
* effects (size m X n).
|
||||
* rdvec, idvec = real and imaginary parts of the difference frequency
|
||||
* effects (size m X n).
|
||||
* rA, iA = real and imaginary parts of the amplitudes (size m X n).
|
||||
* w = vector with angular frequencies (w>=0)
|
||||
* kw = vector with wavenumbers (kw>=0)
|
||||
* h = water depth (h >=0)
|
||||
* g = constant acceleration of gravity
|
||||
* nmin = minimum index where rA(:,nmin) and iA(:,nmin) is
|
||||
* greater than zero.
|
||||
* nmax = maximum index where rA(:,nmax) and iA(:,nmax) is
|
||||
* greater than zero.
|
||||
* m = size(rA,1),size(iA,1)
|
||||
* n = size(rA,2),size(iA,2), or size(rvec,2),size(ivec,2)
|
||||
*
|
||||
* DISUFQ2 returns the summation of sum and difference frequency
|
||||
* frequency effects in the vectors svec = rsvec +sqrt(-1)*isvec and
|
||||
* dvec = rdvec +sqrt(-1)*idvec.
|
||||
* The 2'nd order contribution to the Stokes wave is then calculated by
|
||||
* a simple 1D Fourier transform, real(FFT(svec+dvec)).
|
||||
*
|
||||
*
|
||||
* This is a MEX-file for MATLAB.
|
||||
* by Per Andreas Brodtkorb 15.08.2001
|
||||
* revised pab 14.03.2002, 01.05.2002
|
||||
*/
|
||||
|
||||
void disufq2(double *rsvec, double *isvec,
|
||||
double *rdvec, double *idvec,
|
||||
double *rA, double *iA,
|
||||
double *w, double *kw,
|
||||
double h, double g,
|
||||
int nmin, int nmax,
|
||||
int m, int n)
|
||||
{
|
||||
double Epij, Edij;
|
||||
double tmp1, tmp2, tmp3, tmp4, kfact;
|
||||
double w1, w2, kw1, kw2, Cg;
|
||||
double rrA, iiA, riA, irA;
|
||||
int i,jy,ix,iz1,iv1,ixi,jyi;
|
||||
//int iz2,iv2
|
||||
|
||||
//Initialize rvec and ivec to zero
|
||||
for (ix=0;ix<n*m;ix++) {
|
||||
rsvec[ix] = 0.0;
|
||||
isvec[ix] = 0.0;
|
||||
rdvec[ix] = 0.0;
|
||||
idvec[ix] = 0.0;
|
||||
}
|
||||
|
||||
// kfact is set to 2 in order to exploit the symmetry.
|
||||
// If you set kfact to 1, you must uncomment all statements
|
||||
// including the expressions: rvec[iz2], rvec[iv2], ivec[iz2] and ivec[iv2].
|
||||
|
||||
kfact = 2.0;
|
||||
if (h>10000){ /* deep water /Inifinite water depth */
|
||||
for (ix = nmin-1;ix<nmax;ix++) {
|
||||
ixi = ix*m;
|
||||
iz1 = 2*ixi;
|
||||
//iz2 = n*m-ixi;
|
||||
kw1 = kw[ix];
|
||||
Epij = kw1;
|
||||
for (i=0;i<m;i++,ixi++,iz1++) {
|
||||
rrA = rA[ixi]*rA[ixi]; ///
|
||||
iiA = iA[ixi]*iA[ixi]; ///
|
||||
riA = rA[ixi]*iA[ixi]; ///
|
||||
|
||||
/// Sum frequency effects along the diagonal
|
||||
tmp1 = kfact*(rrA-iiA)*Epij;
|
||||
tmp2 = kfact*2.0*riA*Epij;
|
||||
rsvec[iz1] += tmp1;
|
||||
isvec[iz1] += tmp2;
|
||||
|
||||
//rsvec[iz2] += tmp1;
|
||||
//isvec[iz2] -= tmp2;
|
||||
//iz2++;
|
||||
|
||||
/// Difference frequency effects are zero along the diagonal
|
||||
/// and are thus not contributing to the mean.
|
||||
}
|
||||
for (jy = ix+1;jy<nmax;jy++){
|
||||
kw2 = kw[jy];
|
||||
Epij = 0.5*(kw2 + kw1);
|
||||
Edij = -0.5*(kw2 - kw1);
|
||||
//printf("Edij = %f Epij = %f \n", Edij,Epij);
|
||||
|
||||
ixi = ix*m;
|
||||
jyi = jy*m;
|
||||
iz1 = ixi+jyi;
|
||||
iv1 = jyi-ixi;
|
||||
//iz2 = (n*m-iz1);
|
||||
//iv2 = (n*m-iv1);
|
||||
for (i = 0;i<m;i++,ixi++,jyi++,iz1++,iv1++) {
|
||||
|
||||
rrA = rA[ixi]*rA[jyi]; ///rrA = rA[i][ix]*rA[i][jy];
|
||||
iiA = iA[ixi]*iA[jyi]; ///iiA = iA[i][ix]*iA[i][jy];
|
||||
riA = rA[ixi]*iA[jyi]; ///riA = rA[i][ix]*iA[i][jy];
|
||||
irA = iA[ixi]*rA[jyi]; ///irA = iA[i][ix]*rA[i][jy];
|
||||
|
||||
/* Sum frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA-iiA)*Epij;
|
||||
tmp2 = kfact*2.0*(riA+irA)*Epij;
|
||||
rsvec[iz1] += tmp1; ///rvec[i][ix+jy] += tmp1;
|
||||
isvec[iz1] += tmp2; ///ivec[i][ix+jy] += tmp2;
|
||||
//rsvec[iz2] += tmp1;///rvec[i][n*m-(ix+jy)] += tmp1;
|
||||
//isvec[iz2] -= tmp2;///ivec[i][n*m-(ix+jy)] += tmp2;
|
||||
//iz2++;
|
||||
|
||||
/* Difference frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA+iiA)*Edij;
|
||||
tmp2 = kfact*2.0*(riA-irA)*Edij;
|
||||
|
||||
rdvec[iv1] += tmp1;///rvec[i][jy-ix] += tmp1;
|
||||
idvec[iv1] += tmp2;///ivec[i][jy-ix] += tmp2;
|
||||
|
||||
//rdvec[iv2] += tmp1;///rvec[i][n*m-(jy-ix)] += tmp1;
|
||||
//idvec[iv2] -= tmp2;///ivec[i][n*m-(jy-ix)] -= tmp2;
|
||||
// iv2++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
else{ /* Finite water depth */
|
||||
for (ix = nmin-1;ix<nmax;ix++) {
|
||||
kw1 = kw[ix];
|
||||
w1 = w[ix];
|
||||
tmp1 = tanh(kw1*h);
|
||||
/// Cg, wave group velocity
|
||||
Cg = 0.5*g*(tmp1 + kw1*h*(1.0- tmp1*tmp1))/w1; /// OK
|
||||
tmp1 = 0.5*g*(kw1/w1)*(kw1/w1);
|
||||
tmp2 = 0.5*w1*w1/g;
|
||||
tmp3 = g*kw1/(w1*Cg);
|
||||
|
||||
if (kw1*h<300.0){
|
||||
tmp4 = kw1/sinh(2.0*kw1*h);
|
||||
}
|
||||
else{ // To ensure sinh does not overflow.
|
||||
tmp4 = 0.0;
|
||||
}
|
||||
// Difference frequency effects finite water depth
|
||||
Edij = (tmp1-tmp2+tmp3)/(1.0-g*h/(Cg*Cg))-tmp4; /// OK
|
||||
|
||||
// Sum frequency effects finite water depth
|
||||
Epij = (3.0*(tmp1-tmp2)/(1.0-tmp1/kw1*tanh(2.0*kw1*h))+3.0*tmp2-tmp1); /// OK
|
||||
//printf("Edij = %f Epij = %f \n", Edij,Epij);
|
||||
|
||||
ixi = ix*m;
|
||||
iz1 = 2*ixi;
|
||||
//iz2 = n*m-ixi;
|
||||
for (i=0;i<m;i++,ixi++,iz1++) {
|
||||
|
||||
rrA = rA[ixi]*rA[ixi]; ///
|
||||
iiA = iA[ixi]*iA[ixi]; ///
|
||||
riA = rA[ixi]*iA[ixi]; ///
|
||||
|
||||
|
||||
/// Sum frequency effects along the diagonal
|
||||
rsvec[iz1] += kfact*(rrA-iiA)*Epij;
|
||||
isvec[iz1] += kfact*2.0*riA*Epij;
|
||||
//rsvec[iz2] += kfact*(rrA-iiA)*Epij;
|
||||
//isvec[iz2] -= kfact*2.0*riA*Epij;
|
||||
|
||||
/// Difference frequency effects along the diagonal
|
||||
/// are only contributing to the mean
|
||||
//printf(" %f \n",2.0*(rrA+iiA)*Edij);
|
||||
rdvec[i] += 2.0*(rrA+iiA)*Edij;
|
||||
}
|
||||
for (jy = ix+1;jy<nmax;jy++) {
|
||||
// w1 = w[ix];
|
||||
// kw1 = kw[ix];
|
||||
w2 = w[jy];
|
||||
kw2 = kw[jy];
|
||||
tmp1 = g*(kw1/w1)*(kw2/w2);
|
||||
tmp2 = 0.5/g*(w1*w1+w2*w2+w1*w2);
|
||||
tmp3 = 0.5*g*(w1*kw2*kw2+w2*kw1*kw1)/(w1*w2*(w1+w2));
|
||||
tmp4 = (1-g*(kw1+kw2)/(w1+w2)/(w1+w2)*tanh((kw1+kw2)*h));
|
||||
Epij = (tmp1-tmp2+tmp3)/tmp4+tmp2-0.5*tmp1; /* OK */
|
||||
|
||||
tmp2 = 0.5/g*(w1*w1+w2*w2-w1*w2); /*OK*/
|
||||
tmp3 = -0.5*g*(w1*kw2*kw2-w2*kw1*kw1)/(w1*w2*(w1-w2));
|
||||
tmp4 = (1.0-g*(kw1-kw2)/(w1-w2)/(w1-w2)*tanh((kw1-kw2)*h));
|
||||
Edij = (tmp1-tmp2+tmp3)/tmp4+tmp2-0.5*tmp1; /* OK */
|
||||
//printf("Edij = %f Epij = %f \n", Edij,Epij);
|
||||
|
||||
ixi = ix*m;
|
||||
jyi = jy*m;
|
||||
iz1 = ixi+jyi;
|
||||
iv1 = jyi-ixi;
|
||||
// iz2 = (n*m-iz1);
|
||||
// iv2 = (n*m-iv1);
|
||||
for (i=0;i<m;i++,ixi++,jyi++,iz1++,iv1++) {
|
||||
rrA = rA[ixi]*rA[jyi]; ///rrA = rA[i][ix]*rA[i][jy];
|
||||
iiA = iA[ixi]*iA[jyi]; ///iiA = iA[i][ix]*iA[i][jy];
|
||||
riA = rA[ixi]*iA[jyi]; ///riA = rA[i][ix]*iA[i][jy];
|
||||
irA = iA[ixi]*rA[jyi]; ///irA = iA[i][ix]*rA[i][jy];
|
||||
|
||||
/* Sum frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA-iiA)*Epij;
|
||||
tmp2 = kfact*2.0*(riA+irA)*Epij;
|
||||
rsvec[iz1] += tmp1;///rsvec[i][jy+ix] += tmp1;
|
||||
isvec[iz1] += tmp2;///isvec[i][jy+ix] += tmp2;
|
||||
//rsvec[iz2] += tmp1;///rsvec[i][n*m-(jy+ix)] += tmp1;
|
||||
//isvec[iz2] -= tmp2;///isvec[i][n*m-(jy-ix)] += tmp2;
|
||||
//iz2++;
|
||||
|
||||
/* Difference frequency effects */
|
||||
tmp1 = kfact*2.0*(rrA+iiA)*Edij;
|
||||
tmp2 = kfact*2.0*(riA-irA)*Edij;
|
||||
rdvec[iv1] += tmp1;
|
||||
idvec[iv1] += tmp2;
|
||||
|
||||
//rdvec[iv2] += tmp1;
|
||||
//idvec[iv2] -= tmp2;
|
||||
// iv2++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// return i;
|
||||
}
|
||||
|
||||
|
||||
/* ++++++++++ BEGIN RF3 [ampl ampl_mean nr_of_cycle] */
|
||||
/* ++++++++++ Rain flow without time analysis */
|
||||
//By Adam Nieslony
|
||||
//Visit the MATLAB Central File Exchange for latest version
|
||||
//http://www.mathworks.com/matlabcentral/fileexchange/3026
|
||||
void findrfc3_astm(double *array_ext, double *array_out, int n, int *nout) {
|
||||
|
||||
double *pr, *po, a[16384], ampl, mean;
|
||||
int tot_num, index, j, cNr1, cNr2;
|
||||
|
||||
tot_num = n;
|
||||
|
||||
// pointers to the first element of the arrays
|
||||
pr = &array_ext[0];
|
||||
po = &array_out[0];
|
||||
|
||||
// The original rainflow counting by Nieslony, unchanged
|
||||
j = -1;
|
||||
cNr1 = 1;
|
||||
for (index=0; index<tot_num; index++) {
|
||||
a[++j]=*pr++;
|
||||
while ( (j >= 2) && (fabs(a[j-1]-a[j-2]) <= fabs(a[j]-a[j-1])) ) {
|
||||
ampl=fabs( (a[j-1]-a[j-2])/2 );
|
||||
switch(j) {
|
||||
case 0: { break; }
|
||||
case 1: { break; }
|
||||
case 2: {
|
||||
mean=(a[0]+a[1])/2;
|
||||
a[0]=a[1];
|
||||
a[1]=a[2];
|
||||
j=1;
|
||||
if (ampl > 0) {
|
||||
*po++=ampl;
|
||||
*po++=mean;
|
||||
*po++=0.50;
|
||||
}
|
||||
break;
|
||||
}
|
||||
default: {
|
||||
mean=(a[j-1]+a[j-2])/2;
|
||||
a[j-2]=a[j];
|
||||
j=j-2;
|
||||
if (ampl > 0) {
|
||||
*po++=ampl;
|
||||
*po++=mean;
|
||||
*po++=1.00;
|
||||
cNr1++;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
cNr2 = 1;
|
||||
for (index=0; index<j; index++) {
|
||||
ampl=fabs(a[index]-a[index+1])/2;
|
||||
mean=(a[index]+a[index+1])/2;
|
||||
if (ampl > 0){
|
||||
*po++=ampl;
|
||||
*po++=mean;
|
||||
*po++=0.50;
|
||||
cNr2++;
|
||||
}
|
||||
}
|
||||
// array of ints nout is outputted
|
||||
nout[0] = cNr1;
|
||||
nout[1] = cNr2;
|
||||
}
|
||||
/* ++++++++++ END RF3 */
|
||||
|
||||
|
||||
// ++ BEGIN RF5 [ampl ampl_mean nr_of_cycle cycle_begin_time cycle_period_time]
|
||||
/* ++++++++++ Rain flow with time analysis */
|
||||
//By Adam Nieslony
|
||||
//Visit the MATLAB Central File Exchange for latest version
|
||||
//http://www.mathworks.com/matlabcentral/fileexchange/3026
|
||||
void
|
||||
findrfc5_astm(double *array_ext, double *array_t, double *array_out, int n, int *nout) {
|
||||
double *pr, *pt, *po, a[16384], t[16384], ampl, mean, period, atime;
|
||||
int tot_num, index, j, cNr1, cNr2;
|
||||
|
||||
|
||||
// tot_num = mxGetM(array_ext) * mxGetN(array_ext);
|
||||
tot_num = n;
|
||||
|
||||
// pointers to the first element of the arrays
|
||||
pr = &array_ext[0];
|
||||
pt = &array_t[0];
|
||||
po = &array_out[0];
|
||||
|
||||
// array_out = mxCreateDoubleMatrix(5, tot_num-1, mxREAL);
|
||||
|
||||
// The original rainflow counting by Nieslony, unchanged
|
||||
j = -1;
|
||||
cNr1 = 1;
|
||||
for (index=0; index<tot_num; index++) {
|
||||
a[++j]=*pr++;
|
||||
t[j]=*pt++;
|
||||
while ( (j >= 2) && (fabs(a[j-1]-a[j-2]) <= fabs(a[j]-a[j-1])) ) {
|
||||
ampl=fabs( (a[j-1]-a[j-2])/2 );
|
||||
switch(j)
|
||||
{
|
||||
case 0: { break; }
|
||||
case 1: { break; }
|
||||
case 2: {
|
||||
mean=(a[0]+a[1])/2;
|
||||
period=(t[1]-t[0])*2;
|
||||
atime=t[0];
|
||||
a[0]=a[1];
|
||||
a[1]=a[2];
|
||||
t[0]=t[1];
|
||||
t[1]=t[2];
|
||||
j=1;
|
||||
if (ampl > 0) {
|
||||
*po++=ampl;
|
||||
*po++=mean;
|
||||
*po++=0.50;
|
||||
*po++=atime;
|
||||
*po++=period;
|
||||
}
|
||||
break;
|
||||
}
|
||||
default: {
|
||||
mean=(a[j-1]+a[j-2])/2;
|
||||
period=(t[j-1]-t[j-2])*2;
|
||||
atime=t[j-2];
|
||||
a[j-2]=a[j];
|
||||
t[j-2]=t[j];
|
||||
j=j-2;
|
||||
if (ampl > 0) {
|
||||
*po++=ampl;
|
||||
*po++=mean;
|
||||
*po++=1.00;
|
||||
*po++=atime;
|
||||
*po++=period;
|
||||
cNr1++;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
cNr2 = 1;
|
||||
for (index=0; index<j; index++) {
|
||||
ampl=fabs(a[index]-a[index+1])/2;
|
||||
mean=(a[index]+a[index+1])/2;
|
||||
period=(t[index+1]-t[index])*2;
|
||||
atime=t[index];
|
||||
if (ampl > 0){
|
||||
*po++=ampl;
|
||||
*po++=mean;
|
||||
*po++=0.50;
|
||||
*po++=atime;
|
||||
*po++=period;
|
||||
cNr2++;
|
||||
}
|
||||
}
|
||||
// /* free the memeory !!!*/
|
||||
// mxSetN(array_out, tot_num - cNr);
|
||||
nout[0] = cNr1;
|
||||
nout[1] = cNr2;
|
||||
}
|
||||
/* ++++++++++ END RF5 */
|
||||
@ -1,24 +0,0 @@
|
||||
Copyright (c) 2003, Adam Niesłony
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions are
|
||||
met:
|
||||
|
||||
* Redistributions of source code must retain the above copyright
|
||||
notice, this list of conditions and the following disclaimer.
|
||||
* Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in
|
||||
the documentation and/or other materials provided with the distribution
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
|
||||
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
||||
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||||
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
||||
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
@ -1,86 +0,0 @@
|
||||
! File c_code.pyf
|
||||
python module c_library
|
||||
interface
|
||||
subroutine findrfc(y1, hmin, ind, n, info)
|
||||
intent(c) findrfc ! findrfc is a C function
|
||||
intent(c) ! all findrfc arguments are considered as C based
|
||||
integer intent(hide), depend(y1) :: n=len(y1)
|
||||
double precision dimension(n), intent(in) :: y1 ! input array
|
||||
double precision intent(in) :: hmin
|
||||
integer dimension(n), intent(out) :: ind ! output array,
|
||||
integer dimension(1), intent(out) :: info
|
||||
end subroutine findrfc
|
||||
subroutine findcross(y, v, ind, n, info)
|
||||
intent(c) findcross ! findcross is a C function
|
||||
intent(c) ! all findcross arguments are considered as C based
|
||||
integer intent(hide), depend(y) :: n=len(y)
|
||||
double precision dimension(n), intent(in) :: y ! input array
|
||||
double precision intent(in) :: v
|
||||
integer dimension(n), intent(out) :: ind ! output array,
|
||||
integer dimension(1),intent(out) :: info
|
||||
end subroutine findcross
|
||||
subroutine disufq(rvec, ivec, rA, iA, w, kw, h, g,nmin,nmax, m, n)
|
||||
intent(c) disufq ! disufq is a C function
|
||||
intent(c) ! all disufq arguments are considered as C based
|
||||
!integer intent(hide), depend(rA),check(n*m==len(iA)) :: n=len(rA)/m
|
||||
!integer intent(hide), depend(rA), check(m==shape(iA,1)) :: m=shape(rA,1)
|
||||
double precision dimension(n*m), intent(in) :: rA, iA ! input array
|
||||
double precision dimension(n/2+1), intent(in) :: w, kw ! input array
|
||||
double precision intent(in) :: h, g
|
||||
integer intent(in) :: nmin, nmax
|
||||
double precision dimension(n*m), intent(out) :: rvec, ivec ! output array,
|
||||
end subroutine disufq
|
||||
subroutine disufq2(rsvec, isvec,rdvec, idvec, rA, iA, w, kw, h, g,nmin,nmax, m, n)
|
||||
intent(c) disufq2 ! disufq2 is a C function
|
||||
intent(c) ! all disufq2 arguments are considered as C based
|
||||
!integer intent(hide), depend(rA),check(n*m==len(iA)) :: n=len(rA)/m
|
||||
!integer intent(hide), depend(rA), check(m==shape(iA,1)) :: m=shape(rA,1)
|
||||
double precision dimension(n*m), intent(in) :: rA, iA ! input array
|
||||
double precision dimension(n/2+1), intent(in) :: w, kw ! input array
|
||||
double precision intent(in) :: h, g
|
||||
integer intent(in) :: nmin, nmax
|
||||
double precision dimension(n*m), intent(out) :: rsvec, isvec, rdvec, idvec ! output array,
|
||||
end subroutine disufq2
|
||||
|
||||
! ===== START NIESLONY RAINFLOW FUNCTIONS
|
||||
! RAINFLOW Revision: 1.1
|
||||
! by Adam Nieslony, 2009
|
||||
subroutine findrfc3_astm(array_ext, array_out, n, nout)
|
||||
intent(c) findrfc3_astm ! rf3 is a C function
|
||||
intent(c) ! all rf3 arguments are
|
||||
! considered as C based
|
||||
|
||||
! n is the length of the input array array_ext
|
||||
integer intent(hide), depend(array_ext) :: n=len(array_ext)
|
||||
|
||||
! of input array x
|
||||
double precision intent(in) :: array_ext(n)
|
||||
|
||||
! the output array
|
||||
double precision intent(out) :: array_out(n,3)
|
||||
|
||||
! nout array, to output additional ints
|
||||
integer dimension(2), intent(out) :: nout
|
||||
end subroutine findrfc3_astm
|
||||
|
||||
subroutine findrfc5_astm(array_ext, array_t, array_out, n, nout)
|
||||
intent(c) findrfc5_astm ! rf5 is a C function
|
||||
intent(c) ! all rf5 arguments are
|
||||
! considered as C based
|
||||
|
||||
! n is the length of the input array array_ext
|
||||
integer intent(hide), depend(array_ext) :: n=len(array_ext)
|
||||
|
||||
! of input array x
|
||||
double precision intent(in) :: array_ext(n), array_t(n)
|
||||
|
||||
! the output array
|
||||
double precision intent(out) :: array_out(n,5)
|
||||
|
||||
! nout array, to output additional ints
|
||||
integer dimension(2), intent(out) :: nout
|
||||
end subroutine findrfc5_astm
|
||||
! ===== END NIESLONY RAINFLOW FUNCTIONS
|
||||
|
||||
end interface
|
||||
end python module c_library
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,18 +0,0 @@
|
||||
'''
|
||||
python setup.py build_src build_ext --inplace
|
||||
|
||||
See also http://www.scipy.org/Cookbook/CompilingExtensionsOnWindowsWithMinGW
|
||||
'''
|
||||
# File setup.py
|
||||
|
||||
|
||||
def configuration(parent_package='', top_path=None):
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
config = Configuration('', parent_package, top_path)
|
||||
|
||||
config.add_extension('c_library',
|
||||
sources=['c_library.pyf', 'c_functions.c'])
|
||||
return config
|
||||
if __name__ == "__main__":
|
||||
from numpy.distutils.core import setup
|
||||
setup(**configuration(top_path='').todict())
|
||||
@ -1,450 +0,0 @@
|
||||
PROGRAM sp2Acdf1
|
||||
C***********************************************************************
|
||||
C This program computes upper and lower bounds for: *
|
||||
C *
|
||||
C density of T_i, for Ac <=h, in a gaussian process i.e. *
|
||||
C *
|
||||
C half wavelength (up-crossing to downcrossing) for crests <h *
|
||||
C or half wavelength (down-crossing to upcrossing) for trough >h *
|
||||
C I.R. 27 Dec. 1999 *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
&NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansrup
|
||||
double precision, dimension(:,:),allocatable :: ansrlo
|
||||
double precision, dimension(: ),allocatable :: ex,CY
|
||||
double precision, dimension(:,:),allocatable :: xc,fxind
|
||||
double precision, dimension(: ),allocatable :: h
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nstart,Ntime,tn,ts,speed,ph,def,seed1,seed_size,icy
|
||||
integer ::it1,it2,status
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! f90 sp2Acdf1.f rind50.f
|
||||
|
||||
CALL INIT_LEVELS(U,def,Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
!print *,'U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
if (abs(def).GT.1) THEN
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
!CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
endif
|
||||
allocate(h(1:Nx))
|
||||
CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
CALL INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
|
||||
NI=4; Nd=2
|
||||
Nc=3; Mb=2
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
|
||||
allocate(CY(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY(icy)=exp(-0.5*h(icy)*h(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate BIG'
|
||||
end if
|
||||
allocate(ex(1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate ex'
|
||||
end if
|
||||
allocate(ansrup(1:Ntime,1:Nx))
|
||||
allocate(ansrlo(1:Ntime,1:Nx))
|
||||
ansrup=0.d0
|
||||
ansrlo=0.d0
|
||||
allocate(fxind(1:Nx,1:2))
|
||||
fxind=0.d0 !this is not needed
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
|
||||
|
||||
allocate(a_up(Mb,NI-1))
|
||||
allocate(a_lo(Mb,NI-1))
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
xc(1,1:Nx)=h(1:Nx)
|
||||
xc(2,1:Nx)=u
|
||||
xc(3,1:Nx)=u
|
||||
|
||||
if (def.GT.0) then
|
||||
a_up(1,1)=0.d0
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XdInf
|
||||
a_lo(1,3)=-XdInf
|
||||
a_up(2,1)=1.d0
|
||||
else
|
||||
a_up(1,1)=u
|
||||
a_lo(1,1)=0.d0
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)= XdInf
|
||||
a_lo(2,1)=1.d0
|
||||
endif
|
||||
!print *,'Nstart',Nstart
|
||||
Nstart=MAX(3,Nstart)
|
||||
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
|
||||
!print *,'loop starts'
|
||||
do Ntd=Nstart,Ntime
|
||||
|
||||
Ntdc=Ntd+Nc
|
||||
ex=0.d0
|
||||
BIG=0.d0
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,-1,R0,R1,R2,R3,R4) ! positive wave period
|
||||
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
!print *,'test',fxind/CY(1:Nx)
|
||||
|
||||
do icy=1,Nx
|
||||
ansrup(Ntd,icy)=fxind(icy,1)*CC/CY(icy)
|
||||
ansrlo(Ntd,icy)=fxind(icy,2)*CC/CY(icy)
|
||||
enddo
|
||||
if (SCIS.GT.0) then
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
if((Nx.gt.4).or.NIT.gt.4) print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
goto 300
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansrup(ts,ph),ansrlo(ts,ph)
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 continue
|
||||
deallocate(BIG)
|
||||
deallocate(ex)
|
||||
deallocate(fxind)
|
||||
deallocate(ansrup)
|
||||
deallocate(ansrlo)
|
||||
deallocate(xc)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
|
||||
if (allocated(R3)) then
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
ENDIF
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,def,Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: def,Ntime,Nstart,NIT,speed,Nx
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) def
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
else
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
endif
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,def,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: def
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
!if (def.LT.0) THEN
|
||||
! H=-H
|
||||
!endif
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime,def
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
|
||||
close(4)
|
||||
close(5)
|
||||
endif
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,shft,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! For ts>1:
|
||||
! X(t2)..X(ts),..X(tn-1) X''(ts) X'(t1) X'(tn) X(ts) X(t1) X(tn) X'(ts)
|
||||
! = [Xt Xd Xc]
|
||||
!
|
||||
! For ts<=1:
|
||||
! X(t2)..,..X(tn-1) X'(t1) X'(tn) Y X(t1) X(tn)
|
||||
! = [Xt Xd Xc]
|
||||
!Add Y Condition : Y=h
|
||||
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
if (ts.LE.1) THEN
|
||||
Ntd1=tn
|
||||
N=Ntd1+Nc;
|
||||
shft=0 ! def=1 want only crest period Tc
|
||||
else
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+4
|
||||
shft=1 ! def=2 or 3 want Tc Ac or Tcf, Ac
|
||||
endif
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1+shft) = 0.d0 !cov(X(ti+1),Y)
|
||||
BIG(i ,Ntd1+2+shft) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+3+shft) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X' (t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X' (tn))
|
||||
enddo
|
||||
!call echo(big(1:tn,1:tn),tn)
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
|
||||
!cov(Xc)
|
||||
!print *,'t'
|
||||
BIG(Ntd1+1+shft,Ntd1+1+shft) = 100.d0!100.d0 ! cov(Y,Y)
|
||||
BIG(Ntd1+1+shft,Ntd1+2+shft) = 0.d0
|
||||
BIG(Ntd1+1+shft,Ntd1+3+shft) = 0.d0
|
||||
BIG(Ntd1+2+shft,Ntd1+2+shft) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+2+shft,Ntd1+3+shft) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+3+shft,Ntd1+3+shft) = R0(1) ! cov(X(tn),X (tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1+shft) = 0.d0 !cov(X'(tn),Y)
|
||||
BIG(Ntd1 ,Ntd1+2+shft) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+3+shft) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+1+shft) = 0.d0 !cov(X'(t1),Y)
|
||||
BIG(Ntd1-1,Ntd1+2+shft) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+3+shft) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
|
||||
|
||||
!call echo(big(1:N,1:N),N)
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
enddo
|
||||
!if (tn.eq.3) then
|
||||
!do j=1,N
|
||||
! do i=j,N
|
||||
! print *,'test',j,i,BIG(j,i)
|
||||
! enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
!enddo
|
||||
!endif
|
||||
!call echo(big(1:N,1:N),N)
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2Acdf1
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,356 +0,0 @@
|
||||
PROGRAM sp2mM1
|
||||
C***********************************************************************************
|
||||
C Computes upper lower bounds for density of maximum and the following minimum *
|
||||
C***********************************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
&NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansrup
|
||||
double precision, dimension(:,:),allocatable :: ansrlo
|
||||
double precision, dimension(: ),allocatable :: ex,h
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(:,:),allocatable :: fxind
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nstart,Ntime,tn,ts,speed,seed1,seed_size
|
||||
integer :: status,i,j,ij,Nx1
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
|
||||
! f90 sp2AmM1.f rind52.f
|
||||
|
||||
CALL INIT_LEVELS(Ntime,Nstart,NIT,speed,Nx1,dT)
|
||||
Nx=Nx1*(Nx1-1)/2
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
|
||||
CALL INITDATA(speed)
|
||||
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
allocate(h(1:Nx1))
|
||||
|
||||
CALL INIT_AMPLITUDES(h,Nx1)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
! Y= X'(t2)...X'(tn-1)||X''(t1) X''(tn)||X(t1) X(tn) X'(t1) X'(tn) !!
|
||||
! = [ Xt Xd Xc ] !!
|
||||
! !!
|
||||
! Nt=tn-2, Nd=2, Nc=4 !!
|
||||
! !!
|
||||
! Xt= contains Nt time points in the indicator function !!
|
||||
! Xd= " Nd derivatives !!
|
||||
! Xc= " Nc variables to condition on !!
|
||||
! !!
|
||||
! There are 3 ( NI=4) regions with constant bariers: !!
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] Y(i)<0. !!
|
||||
! (indI(2)=Nt) ; for i\in (indI(2)+1,indI(3)], Y(i)<0 (deriv. X''(t1)) !!
|
||||
! (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn)) !!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
||||
NI=4; Nd=2
|
||||
Nc=4; Mb=1
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(R4(1))
|
||||
XtInf=10.d0*SQRT(-R2(1))
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R2(1)/R4(1))
|
||||
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate BIG'
|
||||
end if
|
||||
allocate(ex(1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate ex'
|
||||
end if
|
||||
allocate(ansrup(1:Nx1,1:Nx1))
|
||||
ansrup=0.d0
|
||||
allocate(ansrlo(1:Nx1,1:Nx1))
|
||||
ansrlo=0.d0
|
||||
allocate(fxind(1:Nx,1:2))
|
||||
fxind=0.d0 !this is not needed
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
|
||||
|
||||
allocate(a_up(Mb,NI-1))
|
||||
allocate(a_lo(Mb,NI-1))
|
||||
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
|
||||
ij=0
|
||||
do i=2,Nx1
|
||||
do j=1,i-1
|
||||
ij=ij+1
|
||||
xc(1,ij)=h(i)
|
||||
xc(2,ij)=h(j)
|
||||
enddo
|
||||
enddo
|
||||
xc(3,1:Nx)=0.d0
|
||||
xc(4,1:Nx)=0.d0
|
||||
|
||||
a_lo(1,1)=-Xtinf
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)=+XdInf
|
||||
|
||||
|
||||
Nstart=MAX(2,Nstart)
|
||||
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
|
||||
do Ntd=Nstart,Ntime
|
||||
|
||||
Ntdc=Ntd+Nc
|
||||
ex=0.d0
|
||||
BIG=0.d0
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,R0,R1,R2,R3,R4) ! positive wave period
|
||||
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
ij=0
|
||||
do i=2,Nx1
|
||||
do j=1,i-1
|
||||
ij=ij+1
|
||||
ansrup(i,j)=ansrup(i,j)+fxind(ij,1)*CC*dt
|
||||
ansrlo(i,j)=ansrlo(i,j)+fxind(ij,2)*CC*dt
|
||||
enddo
|
||||
enddo
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
goto 300
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
do i=1,Nx1
|
||||
do j=1,Nx1
|
||||
write(11,*) ansrup(i,j),ansrlo(i,j)
|
||||
enddo
|
||||
enddo
|
||||
close(11)
|
||||
900 continue
|
||||
deallocate(BIG)
|
||||
deallocate(ex)
|
||||
deallocate(fxind)
|
||||
deallocate(ansrup)
|
||||
deallocate(ansrlo)
|
||||
deallocate(xc)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,Nstart,NIT,speed,Nx
|
||||
double precision ,intent(out) :: dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
READ (14,*) dT
|
||||
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
close(3)
|
||||
close(5)
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn
|
||||
integer :: i,j,N
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X(t1),X(tn),X'(t1),X'(tn)
|
||||
! = [ Xt | Xd | Xc ]
|
||||
!
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
N=tn+4
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = -R2(j-i+1) ! cov(X'(ti+1),X'(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,tn+1) = R1(i+1) !cov(X'(ti+1),X(t1))
|
||||
BIG(tn-1-i ,tn+2) = -R1(i+1) !cov(X'(ti+1),X(tn))
|
||||
BIG(i ,tn+3) = -R2(i+1) !cov(X'(ti+1),X'(t1))
|
||||
BIG(tn-1-i ,tn+4) = -R2(i+1) !cov(X'(ti+1),X'(tn))
|
||||
!Cov(Xt,Xd)
|
||||
BIG(i,tn-1) = R3(i+1) !cov(X'(ti+1),X''(t1))
|
||||
BIG(tn-1-i,tn) =-R3(i+1) !cov(X'(ti+1),X''(tn))
|
||||
enddo
|
||||
|
||||
!cov(Xd)
|
||||
BIG(tn-1 ,tn-1 ) = R4(1)
|
||||
BIG(tn-1,tn ) = R4(tn) !cov(X''(t1),X''(tn))
|
||||
BIG(tn ,tn ) = R4(1)
|
||||
|
||||
!cov(Xc)
|
||||
BIG(tn+1,tn+1) = R0(1) ! cov(X(t1),X(t1))
|
||||
BIG(tn+1,tn+2) = R0(tn) ! cov(X(t1),X(tn))
|
||||
BIG(tn+1,tn+3) = 0.d0 ! cov(X(t1),X'(t1))
|
||||
BIG(tn+1,tn+4) = R1(tn) ! cov(X(t1),X'(tn))
|
||||
BIG(tn+2,tn+2) = R0(1) ! cov(X(tn),X(tn))
|
||||
BIG(tn+2,tn+3) =-R1(tn) ! cov(X(tn),X'(t1))
|
||||
BIG(tn+2,tn+4) = 0.d0 ! cov(X(tn),X'(tn))
|
||||
BIG(tn+3,tn+3) =-R2(1) ! cov(X'(t1),X'(t1))
|
||||
BIG(tn+3,tn+4) =-R2(tn) ! cov(X'(t1),X'(tn))
|
||||
BIG(tn+4,tn+4) =-R2(1) ! cov(X'(tn),X'(tn))
|
||||
!Xc=X(t1),X(tn),X'(t1),X'(tn)
|
||||
!Xd=X''(t1),X''(tn)
|
||||
!cov(Xd,Xc)
|
||||
BIG(tn-1 ,tn+1) = R2(1) !cov(X''(t1),X(t1))
|
||||
BIG(tn-1 ,tn+2) = R2(tn) !cov(X''(t1),X(tn))
|
||||
BIG(tn-1 ,tn+3) = 0.d0 !cov(X''(t1),X'(t1))
|
||||
BIG(tn-1 ,tn+4) = R3(tn) !cov(X''(t1),X'(tn))
|
||||
BIG(tn ,tn+1) = R2(tn) !cov(X''(tn),X(t1))
|
||||
BIG(tn ,tn+2) = R2(1) !cov(X''(tn),X(tn))
|
||||
BIG(tn ,tn+3) =-R3(tn) !cov(X''(tn),X'(t1))
|
||||
BIG(tn ,tn+4) = 0.d0 !cov(X''(tn),X'(tn))
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
enddo
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
|
||||
END PROGRAM sp2mM1
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,504 +0,0 @@
|
||||
PROGRAM sp2tccpdf1
|
||||
C***********************************************************************
|
||||
C This program computes upper and lower bounds for the: *
|
||||
C *
|
||||
C density of T= T_1+T_2 in a gaussian process i.e. *
|
||||
C *
|
||||
C wavelengthes for crests <h1 and troughs >h2 *
|
||||
C *
|
||||
C Sylvie and Igor 7 dec. 1999 *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
& NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansrup
|
||||
double precision, dimension(:,:),allocatable :: ansrlo
|
||||
double precision, dimension(: ),allocatable :: ex,CY1,CY2
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(:,:),allocatable ::fxind
|
||||
double precision, dimension(: ),allocatable :: h1,h2
|
||||
double precision, dimension(: ),allocatable :: hh1,hh2
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Ntime,N0,tn,ts,speed,ph,seed1,seed_size,Nx1,Nx2
|
||||
integer :: icy,icy2
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! DIGITAL:
|
||||
! f90 -g2 -C -automatic -o ~/WAT/V4/sp2tthpdf.exe rind48.f sp2tthpdf.f
|
||||
! SOLARIS:
|
||||
!f90 -g -O -w3 -Bdynamic -fixed -o ../sp2tthpdf.exe rind48.f sp2tthpdf.f
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
CALL INIT_LEVELS(U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
|
||||
!print *,'U,Ntime,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,Ntime,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!Nx1=1
|
||||
!Nx2=1
|
||||
|
||||
Nx=Nx1*Nx2
|
||||
!print *,'NN',Nx1,Nx2,Nx
|
||||
|
||||
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
|
||||
allocate(h1(1:Nx1))
|
||||
allocate(h2(1:Nx2))
|
||||
CALL INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
|
||||
|
||||
allocate(hh1(1:Nx))
|
||||
allocate(hh2(1:Nx))
|
||||
!h transformation
|
||||
do icy=1,Nx1
|
||||
do icy2=1,Nx2
|
||||
hh1((icy-1)*Nx2+icy2)=h1(icy);
|
||||
hh2((icy-1)*Nx2+icy2)=h2(icy2);
|
||||
enddo
|
||||
enddo
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
!h1(1)=XtInf
|
||||
!h2(1)=XtInf
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
allocate(CY1(1:Nx))
|
||||
allocate(CY2(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY1(icy)=exp(-0.5*hh1(icy)*hh1(icy)/100)/(10*sqrt(twopi))
|
||||
CY2(icy)=exp(-0.5*hh2(icy)*hh2(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
!print *,CY1
|
||||
allocate(ansrup(1:Ntime,1:Nx))
|
||||
allocate(ansrlo(1:Ntime,1:Nx))
|
||||
ansrup=0.d0
|
||||
ansrlo=0.d0
|
||||
allocate(fxind(1:Nx,1:2))
|
||||
!fxind=0.d0 this is not needed
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
! Y={X(t2)..,X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)} !!
|
||||
! = [Xt Xd Xc] !!
|
||||
! !!
|
||||
! Nt=tn-2, Nd=3, Nc=2+3 !!
|
||||
! !!
|
||||
! Xt= contains Nt time points in the indicator function !!
|
||||
! Xd= " Nd derivatives !!
|
||||
! Xc= " Nc variables to condition on !!
|
||||
! (Y1,Y2) dummy variables ind. of all other v. inputing h1,h2 into rindd !!
|
||||
! !!
|
||||
! There are 6 ( NI=7) regions with constant bariers: !!
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] u<Y(i)<h1 !!
|
||||
! (indI(2)=ts-2); for i\in (indI(2),indI(2)], inf<Y(i)<inf (no restr.) !!
|
||||
! (indI(3)=ts-1); for i\in (indI(3),indI(4)], h2 <Y(i)<u !!
|
||||
! (indI(4)=Nt) ; for i\in (indI(4),indI(5)], Y(i)<0 (deriv. X'(ts)) !!
|
||||
! (indI(5)=Nt+1); for i\in (indI(5),indI(6)], Y(i)>0 (deriv. X'(t1)) !!
|
||||
! (indI(6)=Nt+2); for i\in (indI(6),indI(7)], Y(i)>0 (deriv. X'(tn)) !!
|
||||
! (indI(7)=Nt+3); NI=7. !!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
||||
NI=7; Nd=3
|
||||
Nc=5; Mb=3
|
||||
allocate(a_up(1:Mb,1:(NI-1)))
|
||||
allocate(a_lo(1:Mb,1:(NI-1)))
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
allocate(BIG(1:(Ntime+Nc+1),1:(Ntime+Nc+1)))
|
||||
ALLOCATE(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:(Ntime+Nc+1)))
|
||||
!print *,size(ex),Ntime
|
||||
ex=0.d0
|
||||
!print *,size(ex),ex
|
||||
xc(1,1:Nx)=hh1(1:Nx)
|
||||
xc(2,1:Nx)=hh2(1:Nx)
|
||||
xc(3,1:Nx)=u
|
||||
xc(4,1:Nx)=u
|
||||
xc(5,1:Nx)=u
|
||||
! upp- down- upp-crossings at t1,ts,tn
|
||||
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(1,3)=u
|
||||
|
||||
|
||||
a_lo(1,4)=-XdInf
|
||||
a_up(1,5)= XdInf
|
||||
a_up(1,6)= XdInf
|
||||
|
||||
a_up(2,1)=1.d0
|
||||
a_lo(3,3)=1.d0 !signe a voir!!!!!!
|
||||
! print *,a_up
|
||||
! print *,a_lo
|
||||
do tn=N0,Ntime,1
|
||||
! do tn=Ntime,Ntime,1
|
||||
Ntd=tn+1
|
||||
Nt=Ntd-Nd
|
||||
Ntdc=Ntd+Nc
|
||||
indI(4)=Nt
|
||||
indI(5)=Nt+1
|
||||
indI(6)=Nt+2
|
||||
indI(7)=Ntd
|
||||
if (SCIS.gt.0) then
|
||||
if (SCIS.EQ.2) then
|
||||
Nj=max(Nt,0)
|
||||
else
|
||||
Nj=min(max(Nt-5, 0),0)
|
||||
endif
|
||||
endif
|
||||
do ts=3,tn-2
|
||||
!print *,'ts,tn' ,ts,tn,Ntdc
|
||||
CALL COV_INPUT(Big(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2)!positive wave period
|
||||
indI(2)=ts-2
|
||||
indI(3)=ts-1
|
||||
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
|
||||
ds=dt
|
||||
do icy=1,Nx
|
||||
! ansr(tn,:)=ansr(tn,:)+fxind*CC*ds./(CY1.*CY2)
|
||||
ansrup(tn,icy)=ansrup(tn,icy)+fxind(icy,1)*CC*ds
|
||||
& /(CY1(icy)*CY2(icy))
|
||||
ansrlo(tn,icy)=ansrlo(tn,icy)+fxind(icy,2)*CC*ds
|
||||
& /(CY1(icy)*CY2(icy))
|
||||
enddo
|
||||
enddo ! ts
|
||||
print *,'Ready: ',tn,' of ',Ntime
|
||||
|
||||
enddo !tn
|
||||
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
!write(11,*) ansrup(ts,ph),ansrlo(ts,ph)
|
||||
write(11,111) ansrup(ts,ph),ansrlo(ts,ph)
|
||||
enddo
|
||||
enddo
|
||||
111 FORMAT(2x,F12.8,2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansrup)
|
||||
deallocate(ansrlo)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
deallocate(h1)
|
||||
deallocate(h2)
|
||||
deallocate(hh1)
|
||||
deallocate(hh2)
|
||||
deallocate(a_up)
|
||||
deallocate(a_lo)
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,N0,NIT,speed,Nx1,Nx2,SCIS,seed1
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) N0
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
|
||||
|
||||
READ (14,*) Nx1,Nx2
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.5) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h1,h2
|
||||
integer, intent(in) :: Nx1,Nx2
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx1
|
||||
READ (4,*) H1(ix)
|
||||
enddo
|
||||
do ix=1,Nx2
|
||||
READ (4,*) H2(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
!
|
||||
! ||X(t2)..X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)||
|
||||
! = [Xt Xd Xc]
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+Nc
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1) = 0.d0 !cov(X(ti+1),Y1)
|
||||
BIG(i ,Ntd1+2) = 0.d0 !cov(X(ti+1),Y2)
|
||||
BIG(i ,Ntd1+4) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+5) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X'(t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X'(tn))
|
||||
enddo
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1-1) = -R2(ts) !cov(X'(ts),X'(t1))
|
||||
BIG(Ntd1-2,Ntd1-2) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1 ) = -R2(tn+1-ts) !cov(X'(ts),X'(tn))
|
||||
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1,Ntd1+1) = 100.d0 ! cov(Y1 Y1)
|
||||
BIG(Ntd1+1,Ntd1+2) = 0.d0 ! cov(Y1 Y2)
|
||||
BIG(Ntd1+1,Ntd1+3) = 0.d0 ! cov(Y1 X(ts))
|
||||
BIG(Ntd1+1,Ntd1+4) = 0.d0 ! cov(Y1 X(t1))
|
||||
BIG(Ntd1+1,Ntd1+5) = 0.d0 ! cov(Y1 X(tn))
|
||||
BIG(Ntd1+2,Ntd1+2) = 100.d0 ! cov(Y2 Y2)
|
||||
BIG(Ntd1+2,Ntd1+3) = 0.d0 ! cov(Y2 X(ts))
|
||||
BIG(Ntd1+2,Ntd1+4) = 0.d0 ! cov(Y2 X(t1))
|
||||
BIG(Ntd1+2,Ntd1+5) = 0.d0 ! cov(Y2 X(tn))
|
||||
|
||||
BIG(Ntd1+3,Ntd1+3) = R0(1) ! cov(X(ts),X (ts)
|
||||
BIG(Ntd1+3,Ntd1+4) = R0(ts) ! cov(X(ts),X (t1))
|
||||
BIG(Ntd1+3,Ntd1+5) = R0(tn+1-ts) ! cov(X(ts),X (tn))
|
||||
BIG(Ntd1+4,Ntd1+4) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+4,Ntd1+5) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+5,Ntd1+5) = R0(1) ! cov(X(tn),X (tn))
|
||||
|
||||
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1) = 0.d0 !cov(X'(tn),Y1)
|
||||
BIG(Ntd1 ,Ntd1+2) = 0.d0 !cov(X'(tn),Y2)
|
||||
BIG(Ntd1-1 ,Ntd1+1) = 0.d0 !cov(X'(t1),Y1)
|
||||
BIG(Ntd1-1 ,Ntd1+2) = 0.d0 !cov(X'(t1),Y2)
|
||||
BIG(Ntd1-2 ,Ntd1+1) = 0.d0 !cov(X'(ts),Y1)
|
||||
BIG(Ntd1-2 ,Ntd1+2) = 0.d0 !cov(X'(ts),Y2)
|
||||
|
||||
BIG(Ntd1 ,Ntd1+4) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+5) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+4) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+5) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
BIG(Ntd1 ,Ntd1+3) = R1(tn+1-ts) !cov(X'(tn),X (ts))
|
||||
BIG(Ntd1-1,Ntd1+3) =-R1(ts) !cov(X'(t1),X (ts))
|
||||
BIG(Ntd1-2,Ntd1+3) = 0.d0 !cov(X'(ts),X (ts)
|
||||
BIG(Ntd1-2,Ntd1+4) = R1(ts) !cov(X'(ts),X (t1))
|
||||
BIG(Ntd1-2,Ntd1+5) = -R1(tn+1-ts) !cov(X'(ts),X (tn))
|
||||
|
||||
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
!cov(Xt,Xc)
|
||||
BIG(i,Ntd1+3) = R0(j+1) !cov(X(ti+1),X(ts))
|
||||
!Cov(Xt,Xd)
|
||||
if ((i+1-ts).lt.0) then
|
||||
BIG(i,Ntd1-2) = R1(j+1)
|
||||
else !cov(X(ti+1),X'(ts))
|
||||
BIG(i,Ntd1-2) = -R1(j+1)
|
||||
endif
|
||||
enddo
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
enddo
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2tccpdf1
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,497 +0,0 @@
|
||||
PROGRAM sp2tthpdf1
|
||||
C***********************************************************************
|
||||
C This program computes: *
|
||||
C *
|
||||
C density of T= T_1+T_2 in a gaussian process i.e. *
|
||||
C *
|
||||
C wavelengthes for crests <h1 and troughs >h2 *
|
||||
C *
|
||||
C Sylvie and Igor 7 dec. 1999 *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
& NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex,CY1,CY2
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h1,h2
|
||||
double precision, dimension(: ),allocatable :: hh1,hh2
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Ntime,tn,ts,speed,ph,seed1,seed_size,Nx1,Nx2,N0
|
||||
integer :: icy,icy2
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! DIGITAL:
|
||||
! f90 -g2 -C -automatic -o ~/WAT/V4/sp2tthpdf1.exe rind49.f sp2tthpdf1.f
|
||||
! SOLARIS:
|
||||
!f90 -g -O -w3 -Bdynamic -fixed -o ../sp2tthpdf.exe rind49.f sp2tthpdf1.f
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
CALL INIT_LEVELS(U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
|
||||
!print *,'U,Ntime,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,Ntime,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!Nx1=1
|
||||
!Nx2=1
|
||||
|
||||
Nx=Nx1*Nx2
|
||||
!print *,'NN',Nx1,Nx2,Nx
|
||||
|
||||
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
|
||||
allocate(h1(1:Nx1))
|
||||
allocate(h2(1:Nx2))
|
||||
CALL INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
|
||||
|
||||
allocate(hh1(1:Nx))
|
||||
allocate(hh2(1:Nx))
|
||||
!h transformation
|
||||
do icy=1,Nx1
|
||||
do icy2=1,Nx2
|
||||
hh1((icy-1)*Nx2+icy2)=h1(icy);
|
||||
hh2((icy-1)*Nx2+icy2)=h2(icy2);
|
||||
enddo
|
||||
enddo
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
!h1(1)=XtInf
|
||||
!h2(1)=XtInf
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
allocate(CY1(1:Nx))
|
||||
allocate(CY2(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY1(icy)=exp(-0.5*hh1(icy)*hh1(icy)/100)/(10*sqrt(twopi))
|
||||
CY2(icy)=exp(-0.5*hh2(icy)*hh2(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
!print *,CY1
|
||||
allocate(ansr(1:Ntime,1:Nx))
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
fxind=0.d0
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
! Y={X(t2)..,X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)} !!
|
||||
! = [Xt Xd Xc] !!
|
||||
! !!
|
||||
! Nt=tn-2, Nd=3, Nc=2+3 !!
|
||||
! !!
|
||||
! Xt= contains Nt time points in the indicator function !!
|
||||
! Xd= " Nd derivatives !!
|
||||
! Xc= " Nc variables to condition on !!
|
||||
! (Y1,Y2) dummy variables ind. of all other v. inputing h1,h2 into rindd !!
|
||||
! !!
|
||||
! There are 6 ( NI=7) regions with constant bariers: !!
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] u<Y(i)<h1 !!
|
||||
! (indI(2)=ts-2); for i\in (indI(2),indI(2)], inf<Y(i)<inf (no restr.) !!
|
||||
! (indI(3)=ts-1); for i\in (indI(3),indI(4)], h2 <Y(i)<u !!
|
||||
! (indI(4)=Nt) ; for i\in (indI(4),indI(5)], Y(i)<0 (deriv. X'(ts)) !!
|
||||
! (indI(5)=Nt+1); for i\in (indI(5),indI(6)], Y(i)>0 (deriv. X'(t1)) !!
|
||||
! (indI(6)=Nt+2); for i\in (indI(6),indI(7)], Y(i)>0 (deriv. X'(tn)) !!
|
||||
! (indI(7)=Nt+3); NI=7. !!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
||||
NI=7; Nd=3
|
||||
Nc=5; Mb=3
|
||||
allocate(a_up(1:Mb,1:(NI-1)))
|
||||
allocate(a_lo(1:Mb,1:(NI-1)))
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
allocate(BIG(1:(Ntime+Nc+1),1:(Ntime+Nc+1)))
|
||||
ALLOCATE(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:(Ntime+Nc+1)))
|
||||
!print *,size(ex),Ntime
|
||||
ex=0.d0
|
||||
!print *,size(ex),ex
|
||||
xc(1,1:Nx)=hh1(1:Nx)
|
||||
xc(2,1:Nx)=hh2(1:Nx)
|
||||
xc(3,1:Nx)=u
|
||||
xc(4,1:Nx)=u
|
||||
xc(5,1:Nx)=u
|
||||
! upp- down- upp-crossings at t1,ts,tn
|
||||
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(1,3)=u
|
||||
|
||||
|
||||
a_lo(1,4)=-XdInf
|
||||
a_up(1,5)= XdInf
|
||||
a_up(1,6)= XdInf
|
||||
|
||||
a_up(2,1)=1.d0
|
||||
a_lo(3,3)=1.d0 !signe a voir!!!!!!
|
||||
! print *,a_up
|
||||
! print *,a_lo
|
||||
do tn=N0,Ntime,1
|
||||
! do tn=Ntime,Ntime,1
|
||||
Ntd=tn+1
|
||||
Nt=Ntd-Nd
|
||||
Ntdc=Ntd+Nc
|
||||
indI(4)=Nt
|
||||
indI(5)=Nt+1
|
||||
indI(6)=Nt+2
|
||||
indI(7)=Ntd
|
||||
if (SCIS.gt.0) then
|
||||
if (SCIS.EQ.2) then
|
||||
Nj=max(Nt,0)
|
||||
else
|
||||
Nj=min(max(Nt-5, 0),0)
|
||||
endif
|
||||
endif
|
||||
do ts=3,tn-2
|
||||
!print *,'ts,tn' ,ts,tn,Ntdc
|
||||
CALL COV_INPUT(Big(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2)!positive wave period
|
||||
indI(2)=ts-2
|
||||
indI(3)=ts-1
|
||||
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
|
||||
ds=dt
|
||||
do icy=1,Nx
|
||||
! ansr(tn,:)=ansr(tn,:)+fxind*CC*ds./(CY1.*CY2)
|
||||
ansr(tn,icy)=ansr(tn,icy)+fxind(icy)*CC*ds/(CY1(icy)*CY2(icy))
|
||||
enddo
|
||||
enddo ! ts
|
||||
print *,'Ready: ',tn,' of ',Ntime
|
||||
|
||||
enddo !tn
|
||||
!print *,'ansr',ansr
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
!print *, ansr
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansr(ts,ph),hh1(ph),hh2(ph)
|
||||
! write(11,111) ansr(ts,ph)
|
||||
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
deallocate(h1)
|
||||
deallocate(h2)
|
||||
deallocate(hh1)
|
||||
deallocate(hh2)
|
||||
deallocate(a_up)
|
||||
deallocate(a_lo)
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,N0,NIT,speed,Nx1,Nx2,SCIS,seed1
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) N0
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
|
||||
|
||||
READ (14,*) Nx1,Nx2
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h1,h2
|
||||
integer, intent(in) :: Nx1,Nx2
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx1
|
||||
READ (4,*) H1(ix)
|
||||
enddo
|
||||
do ix=1,Nx2
|
||||
READ (4,*) H2(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
!
|
||||
! ||X(t2)..X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)||
|
||||
! = [Xt Xd Xc]
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+Nc
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1) = 0.d0 !cov(X(ti+1),Y1)
|
||||
BIG(i ,Ntd1+2) = 0.d0 !cov(X(ti+1),Y2)
|
||||
BIG(i ,Ntd1+4) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+5) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X'(t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X'(tn))
|
||||
enddo
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1-1) = -R2(ts) !cov(X'(ts),X'(t1))
|
||||
BIG(Ntd1-2,Ntd1-2) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1 ) = -R2(tn+1-ts) !cov(X'(ts),X'(tn))
|
||||
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1,Ntd1+1) = 100.d0 ! cov(Y1 Y1)
|
||||
BIG(Ntd1+1,Ntd1+2) = 0.d0 ! cov(Y1 Y2)
|
||||
BIG(Ntd1+1,Ntd1+3) = 0.d0 ! cov(Y1 X(ts))
|
||||
BIG(Ntd1+1,Ntd1+4) = 0.d0 ! cov(Y1 X(t1))
|
||||
BIG(Ntd1+1,Ntd1+5) = 0.d0 ! cov(Y1 X(tn))
|
||||
BIG(Ntd1+2,Ntd1+2) = 100.d0 ! cov(Y2 Y2)
|
||||
BIG(Ntd1+2,Ntd1+3) = 0.d0 ! cov(Y2 X(ts))
|
||||
BIG(Ntd1+2,Ntd1+4) = 0.d0 ! cov(Y2 X(t1))
|
||||
BIG(Ntd1+2,Ntd1+5) = 0.d0 ! cov(Y2 X(tn))
|
||||
|
||||
BIG(Ntd1+3,Ntd1+3) = R0(1) ! cov(X(ts),X (ts)
|
||||
BIG(Ntd1+3,Ntd1+4) = R0(ts) ! cov(X(ts),X (t1))
|
||||
BIG(Ntd1+3,Ntd1+5) = R0(tn+1-ts) ! cov(X(ts),X (tn))
|
||||
BIG(Ntd1+4,Ntd1+4) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+4,Ntd1+5) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+5,Ntd1+5) = R0(1) ! cov(X(tn),X (tn))
|
||||
|
||||
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1) = 0.d0 !cov(X'(tn),Y1)
|
||||
BIG(Ntd1 ,Ntd1+2) = 0.d0 !cov(X'(tn),Y2)
|
||||
BIG(Ntd1-1 ,Ntd1+1) = 0.d0 !cov(X'(t1),Y1)
|
||||
BIG(Ntd1-1 ,Ntd1+2) = 0.d0 !cov(X'(t1),Y2)
|
||||
BIG(Ntd1-2 ,Ntd1+1) = 0.d0 !cov(X'(ts),Y1)
|
||||
BIG(Ntd1-2 ,Ntd1+2) = 0.d0 !cov(X'(ts),Y2)
|
||||
|
||||
BIG(Ntd1 ,Ntd1+4) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+5) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+4) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+5) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
BIG(Ntd1 ,Ntd1+3) = R1(tn+1-ts) !cov(X'(tn),X (ts))
|
||||
BIG(Ntd1-1,Ntd1+3) =-R1(ts) !cov(X'(t1),X (ts))
|
||||
BIG(Ntd1-2,Ntd1+3) = 0.d0 !cov(X'(ts),X (ts)
|
||||
BIG(Ntd1-2,Ntd1+4) = R1(ts) !cov(X'(ts),X (t1))
|
||||
BIG(Ntd1-2,Ntd1+5) = -R1(tn+1-ts) !cov(X'(ts),X (tn))
|
||||
|
||||
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
!cov(Xt,Xc)
|
||||
BIG(i,Ntd1+3) = R0(j+1) !cov(X(ti+1),X(ts))
|
||||
!Cov(Xt,Xd)
|
||||
if ((i+1-ts).lt.0) then
|
||||
BIG(i,Ntd1-2) = R1(j+1)
|
||||
else !cov(X(ti+1),X'(ts))
|
||||
BIG(i,Ntd1-2) = -R1(j+1)
|
||||
endif
|
||||
enddo
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
enddo
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2tthpdf1
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,445 +0,0 @@
|
||||
PROGRAM sp2Acdf
|
||||
C***********************************************************************
|
||||
C This program computes: *
|
||||
C *
|
||||
C density of T_i, for Ac <=h, in a gaussian process i.e. *
|
||||
C *
|
||||
C half wavelength (up-crossing to downcrossing) for crests <h *
|
||||
C or half wavelength (down-crossing to upcrossing) for trough >h *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
&NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex,CY
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nstart,Ntime,tn,ts,speed,ph,def,seed1,seed_size,icy
|
||||
integer ::it1,it2,status
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! f90 sp2Acdf.f rind51.f
|
||||
|
||||
CALL INIT_LEVELS(U,def,Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
!print *,'U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
if (abs(def).GT.1) THEN
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
!CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
endif
|
||||
allocate(h(1:Nx))
|
||||
CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
CALL INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
|
||||
NI=4; Nd=2
|
||||
Nc=3; Mb=2
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
|
||||
allocate(CY(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY(icy)=exp(-0.5*h(icy)*h(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate BIG'
|
||||
end if
|
||||
allocate(ex(1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate ex'
|
||||
end if
|
||||
allocate(ansr(1:Ntime,1:Nx))
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
fxind=0.d0 !this is not needed
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
|
||||
|
||||
allocate(a_up(Mb,NI-1))
|
||||
allocate(a_lo(Mb,NI-1))
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
xc(1,1:Nx)=h(1:Nx)
|
||||
xc(2,1:Nx)=u
|
||||
xc(3,1:Nx)=u
|
||||
|
||||
if (def.GT.0) then
|
||||
a_up(1,1)=0.d0
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XdInf
|
||||
a_lo(1,3)=-XdInf
|
||||
a_up(2,1)=1.d0
|
||||
else
|
||||
a_up(1,1)=u
|
||||
a_lo(1,1)=0.d0
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)= XdInf
|
||||
a_lo(2,1)=1.d0
|
||||
endif
|
||||
!print *,'Nstart',Nstart
|
||||
Nstart=MAX(3,Nstart)
|
||||
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
|
||||
!print *,'loop starts'
|
||||
do Ntd=Nstart,Ntime
|
||||
|
||||
Ntdc=Ntd+Nc
|
||||
ex=0.d0
|
||||
BIG=0.d0
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,-1,R0,R1,R2,R3,R4) ! positive wave period
|
||||
C CALL ECHO(BIG(1:2,1:2))
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
!print *,'test',fxind/CY(1:Nx)
|
||||
|
||||
do icy=1,Nx
|
||||
ansr(Ntd,icy)=fxind(icy)*CC/CY(icy)
|
||||
enddo
|
||||
if (SCIS.GT.0) then
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
if((Nx.gt.4).or.NIT.gt.5) print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
goto 300
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
|
||||
!print *, ansr
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansr(ts,ph)
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 continue
|
||||
deallocate(BIG)
|
||||
deallocate(ex)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
|
||||
if (allocated(R3)) then
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
ENDIF
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,def,Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: def,Ntime,Nstart,NIT,speed,Nx
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) def
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
else
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
endif
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,def,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: def
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
!if (def.LT.0) THEN
|
||||
! H=-H
|
||||
!endif
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime,def
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
|
||||
close(4)
|
||||
close(5)
|
||||
endif
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,shft,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! For ts>1:
|
||||
! X(t2)..X(ts),..X(tn-1) X''(ts) X'(t1) X'(tn) X(ts) X(t1) X(tn) X'(ts)
|
||||
! = [Xt Xd Xc]
|
||||
!
|
||||
! For ts<=1:
|
||||
! X(t2)..,..X(tn-1) X'(t1) X'(tn) Y X(t1) X(tn)
|
||||
! = [Xt Xd Xc]
|
||||
!Add Y Condition : Y=h
|
||||
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
if (ts.LE.1) THEN
|
||||
Ntd1=tn
|
||||
N=Ntd1+Nc;
|
||||
shft=0 ! def=1 want only crest period Tc
|
||||
else
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+4
|
||||
shft=1 ! def=2 or 3 want Tc Ac or Tcf, Ac
|
||||
endif
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1+shft) = 0.d0 !cov(X(ti+1),Y)
|
||||
BIG(i ,Ntd1+2+shft) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+3+shft) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X' (t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X' (tn))
|
||||
enddo
|
||||
!call echo(big(1:tn,1:tn),tn)
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
|
||||
!cov(Xc)
|
||||
!print *,'t'
|
||||
BIG(Ntd1+1+shft,Ntd1+1+shft) = 100.d0!100.d0 ! cov(Y,Y)
|
||||
BIG(Ntd1+1+shft,Ntd1+2+shft) = 0.d0
|
||||
BIG(Ntd1+1+shft,Ntd1+3+shft) = 0.d0
|
||||
BIG(Ntd1+2+shft,Ntd1+2+shft) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+2+shft,Ntd1+3+shft) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+3+shft,Ntd1+3+shft) = R0(1) ! cov(X(tn),X (tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1+shft) = 0.d0 !cov(X'(tn),Y)
|
||||
BIG(Ntd1 ,Ntd1+2+shft) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+3+shft) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+1+shft) = 0.d0 !cov(X'(t1),Y)
|
||||
BIG(Ntd1-1,Ntd1+2+shft) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+3+shft) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
|
||||
|
||||
!call echo(big(1:N,1:N),N)
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
enddo
|
||||
!if (tn.eq.3) then
|
||||
!do j=1,N
|
||||
! do i=j,N
|
||||
! print *,'test',j,i,BIG(j,i)
|
||||
! enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
!enddo
|
||||
!endif
|
||||
!call echo(big(1:N,1:N),N)
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2Acdf
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,357 +0,0 @@
|
||||
PROGRAM cov2mmpdf
|
||||
C*******************************************************************************
|
||||
C This program computes joint density of maximum and the following minimum *
|
||||
C*******************************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
&NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nstart,Ntime,tn,ts,speed,seed1,seed_size
|
||||
integer :: status,i,j,ij,Nx1
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
|
||||
! f90 cov2mmpdf.f rind51.f
|
||||
|
||||
CALL INIT_LEVELS(Ntime,Nstart,NIT,speed,Nx1,dT)
|
||||
Nx=Nx1*(Nx1-1)/2
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
|
||||
CALL INITDATA(speed)
|
||||
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
allocate(h(1:Nx1))
|
||||
|
||||
CALL INIT_AMPLITUDES(h,Nx1)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
! Y= X'(t2)...X'(tn-1)||X''(t1) X''(tn)|| X'(t1) X'(tn) X(t1) X(tn) !!
|
||||
! = [ Xt Xd Xc ] !!
|
||||
! !!
|
||||
! Nt=tn-2, Nd=2, Nc=4 !!
|
||||
! !!
|
||||
! Xt= contains Nt time points in the indicator function !!
|
||||
! Xd= " Nd derivatives !!
|
||||
! Xc= " Nc variables to condition on !!
|
||||
! !!
|
||||
! There are 3 ( NI=4) regions with constant bariers: !!
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] Y(i)<0. !!
|
||||
! (indI(2)=Nt) ; for i\in (indI(2)+1,indI(3)], Y(i)<0 (deriv. X''(t1)) !!
|
||||
! (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn)) !!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
||||
NI=4; Nd=2
|
||||
Nc=4; Mb=1
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(R4(1))
|
||||
XtInf=10.d0*SQRT(-R2(1))
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R2(1)/R4(1))
|
||||
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate BIG'
|
||||
end if
|
||||
allocate(ex(1:Ntime+Nc),stat=status)
|
||||
if (status.ne.0) then
|
||||
print *,'can not allocate ex'
|
||||
end if
|
||||
if (Nx.gt.1) then
|
||||
allocate(ansr(1:Nx1,1:Nx1))
|
||||
else
|
||||
allocate(ansr(1,1:Ntime))
|
||||
end if
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
fxind=0.d0 !this is not needed
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
|
||||
|
||||
allocate(a_up(Mb,NI-1))
|
||||
allocate(a_lo(Mb,NI-1))
|
||||
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
|
||||
ij=0
|
||||
do i=2,Nx1
|
||||
do j=1,i-1
|
||||
ij=ij+1
|
||||
xc(3,ij)=h(i)
|
||||
xc(4,ij)=h(j)
|
||||
enddo
|
||||
enddo
|
||||
xc(1,1:Nx)=0.d0
|
||||
xc(2,1:Nx)=0.d0
|
||||
|
||||
a_lo(1,1)=-Xtinf
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)=+XdInf
|
||||
|
||||
|
||||
Nstart=MAX(2,Nstart)
|
||||
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
|
||||
do Ntd=Nstart,Ntime
|
||||
|
||||
Ntdc=Ntd+Nc
|
||||
ex=0.d0
|
||||
BIG=0.d0
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,R0,R1,R2,R3,R4) ! positive wave period
|
||||
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
ij=0
|
||||
if (Nx .gt. 1) then
|
||||
do i=2,Nx1
|
||||
do j=1,i-1
|
||||
ij=ij+1
|
||||
ansr(i,j)=ansr(i,j)+fxind(ij)*CC*dt
|
||||
enddo
|
||||
enddo
|
||||
else
|
||||
ansr(1,Ntd)=fxind(1)*CC
|
||||
end if
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
goto 300
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
if (Nx.gt.1) then
|
||||
do i=1,Nx1
|
||||
do j=1,Nx1
|
||||
write(11,*) ansr(i,j)
|
||||
enddo
|
||||
enddo
|
||||
else
|
||||
do j=1,Ntime
|
||||
write(11,*) ansr(1,j)
|
||||
enddo
|
||||
end if
|
||||
close(11)
|
||||
900 continue
|
||||
deallocate(BIG)
|
||||
deallocate(ex)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,Nstart,NIT,speed,Nx
|
||||
double precision ,intent(out) :: dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
READ (14,*) dT
|
||||
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
close(3)
|
||||
close(5)
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn
|
||||
integer :: i,j,N
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X'(t1),X'(tn),X(t1),X(tn)
|
||||
! = [ Xt | Xd | Xc ]
|
||||
!
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
N=tn+4
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = -R2(j-i+1) ! cov(X'(ti+1),X'(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,tn+3) = R1(i+1) !cov(X'(ti+1),X(t1))
|
||||
BIG(tn-1-i ,tn+4) = -R1(i+1) !cov(X'(ti+1),X(tn))
|
||||
BIG(i ,tn+1) = -R2(i+1) !cov(X'(ti+1),X'(t1))
|
||||
BIG(tn-1-i ,tn+2) = -R2(i+1) !cov(X'(ti+1),X'(tn))
|
||||
!Cov(Xt,Xd)
|
||||
BIG(i,tn-1) = R3(i+1) !cov(X'(ti+1),X''(t1))
|
||||
BIG(tn-1-i,tn) =-R3(i+1) !cov(X'(ti+1),X''(tn))
|
||||
enddo
|
||||
|
||||
!cov(Xd)
|
||||
BIG(tn-1 ,tn-1 ) = R4(1)
|
||||
BIG(tn-1,tn ) = R4(tn) !cov(X''(t1),X''(tn))
|
||||
BIG(tn ,tn ) = R4(1)
|
||||
|
||||
!cov(Xc)
|
||||
BIG(tn+3,tn+3) = R0(1) ! cov(X(t1),X(t1))
|
||||
BIG(tn+3,tn+4) = R0(tn) ! cov(X(t1),X(tn))
|
||||
BIG(tn+1,tn+3) = 0.d0 ! cov(X(t1),X'(t1))
|
||||
BIG(tn+2,tn+3) = R1(tn) ! cov(X(t1),X'(tn))
|
||||
BIG(tn+4,tn+4) = R0(1) ! cov(X(tn),X(tn))
|
||||
BIG(tn+1,tn+4) =-R1(tn) ! cov(X(tn),X'(t1))
|
||||
BIG(tn+2,tn+4) = 0.d0 ! cov(X(tn),X'(tn))
|
||||
BIG(tn+1,tn+1) =-R2(1) ! cov(X'(t1),X'(t1))
|
||||
BIG(tn+1,tn+2) =-R2(tn) ! cov(X'(t1),X'(tn))
|
||||
BIG(tn+2,tn+2) =-R2(1) ! cov(X'(tn),X'(tn))
|
||||
!Xc=X(t1),X(tn),X'(t1),X'(tn)
|
||||
!Xd=X''(t1),X''(tn)
|
||||
!cov(Xd,Xc)
|
||||
BIG(tn-1 ,tn+3) = R2(1) !cov(X''(t1),X(t1))
|
||||
BIG(tn-1 ,tn+4) = R2(tn) !cov(X''(t1),X(tn))
|
||||
BIG(tn-1 ,tn+1) = 0.d0 !cov(X''(t1),X'(t1))
|
||||
BIG(tn-1 ,tn+2) = R3(tn) !cov(X''(t1),X'(tn))
|
||||
BIG(tn ,tn+3) = R2(tn) !cov(X''(tn),X(t1))
|
||||
BIG(tn ,tn+4) = R2(1) !cov(X''(tn),X(tn))
|
||||
BIG(tn ,tn+1) =-R3(tn) !cov(X''(tn),X'(t1))
|
||||
BIG(tn ,tn+2) = 0.d0 !cov(X''(tn),X'(tn))
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
enddo
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
|
||||
END PROGRAM cov2mmpdf
|
||||
|
||||
@ -1,769 +0,0 @@
|
||||
PROGRAM sp2mmt
|
||||
C*******************************************************************************
|
||||
C This program computes joint density of the maximum and the following *
|
||||
C minimum or level u separated maxima and minima + period/wavelength *
|
||||
C*******************************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
&NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), allocatable :: BIG
|
||||
double precision, dimension(:,:,:),allocatable :: ansr
|
||||
double precision, dimension(: ), allocatable :: ex
|
||||
double precision, dimension(:,:), allocatable :: xc
|
||||
double precision, dimension(: ), allocatable :: fxind,h
|
||||
double precision, dimension(: ), allocatable :: R0,R1,R2,R3,R4
|
||||
double precision :: CC,U,XdInf,XtInf
|
||||
double precision, dimension(1,4) :: a_up,a_lo ! size Mb X NI-1
|
||||
integer , dimension(: ), allocatable :: seed
|
||||
integer ,dimension(5) :: indI = 0 ! length NI
|
||||
integer :: Nstart,Ntime,ts,tn,speed,seed1,seed_size
|
||||
integer :: status,i,j,ij,Nx0,Nx1,DEF,isOdd !,TMP
|
||||
LOGICAL :: SYMMETRY=.FALSE.
|
||||
double precision :: dT ! lag spacing for covariances
|
||||
|
||||
! f90 -gline -fieee -Nl126 -C -o intmodule.f rind60.f sp2mmt.f
|
||||
|
||||
CALL INIT_LEVELS(Ntime,Nstart,NIT,speed,SCIS,SEED1,Nx1,dT,u,def)
|
||||
CALL INITDATA(speed)
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
!allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
if (ALLOCATED(COV)) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
endif
|
||||
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
|
||||
Nx0 = Nx1 ! just plain Mm
|
||||
IF (def.GT.1) Nx0=2*Nx1 ! level v separated max2min densities wanted
|
||||
|
||||
|
||||
allocate(h(1:Nx0))
|
||||
|
||||
CALL INIT_AMPLITUDES(h,Nx0)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
|
||||
! For DEF = 0,1 : (Maxima, Minima and period/wavelength)
|
||||
! = 2,3 : (Level v separated Maxima and Minima and period/wavelength between them)
|
||||
! If Nx==1 then the conditional density for period/wavelength between Maxima and Minima
|
||||
! given the Max and Min is returned
|
||||
!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
! Y= X'(t2)..X'(ts)..X'(tn-1)||X''(t1) X''(tn)|| X'(t1) X'(tn) X(t1) X(tn)
|
||||
! = [ Xt Xd Xc ]
|
||||
!
|
||||
! Nt = tn-2, Nd = 2, Nc = 4
|
||||
!
|
||||
! Xt= contains Nt time points in the indicator function
|
||||
! Xd= " Nd derivatives in Jacobian
|
||||
! Xc= " Nc variables to condition on
|
||||
!
|
||||
! There are 3 (NI=4) regions with constant barriers:
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] Y(i)<0.
|
||||
! (indI(2)=Nt) ; for i\in (indI(2)+1,indI(3)], Y(i)<0 (deriv. X''(t1))
|
||||
! (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn))
|
||||
!
|
||||
!
|
||||
! For DEF = 4,5 (Level v separated Maxima and Minima and period/wavelength from Max to crossing)
|
||||
! If Nx==1 then the conditional joint density for period/wavelength between Maxima, Minima and Max to
|
||||
! level v crossing given the Max and the min is returned
|
||||
!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
! Y= X'(t2)..X'(ts)..X'(tn-1)||X''(t1) X''(tn) X'(ts)|| X'(t1) X'(tn) X(t1) X(tn) X(ts)
|
||||
! = [ Xt Xd Xc ]
|
||||
!
|
||||
! Nt = tn-2, Nd = 3, Nc = 5
|
||||
!
|
||||
! Xt= contains Nt time points in the indicator function
|
||||
! Xd= " Nd derivatives
|
||||
! Xc= " Nc variables to condition on
|
||||
!
|
||||
! There are 4 (NI=5) regions with constant barriers:
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] Y(i)<0.
|
||||
! (indI(2)=Nt) ; for i\in (indI(2)+1,indI(3)], Y(i)<0 (deriv. X''(t1))
|
||||
! (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn))
|
||||
! (indI(4)=Nt+2); for i\in (indI(4)+1,indI(5)], Y(i)<0 (deriv. X'(ts))
|
||||
!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
!
|
||||
!Revised pab 22.04.2000
|
||||
! - added mean separated min/max + (Tdm, TMd) period distributions
|
||||
! - added scis
|
||||
|
||||
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf = 10.d0*SQRT(R4(1))
|
||||
XtInf = 10.d0*SQRT(-R2(1))
|
||||
|
||||
Nc = 4
|
||||
NI=4; Nd=2;
|
||||
Mb=1 ;
|
||||
Nj = 0
|
||||
indI(1) = 0
|
||||
Nstart=MAX(2,Nstart)
|
||||
|
||||
isOdd = MOD(Nx1,2)
|
||||
IF (def.LE.1) THEN ! just plain Mm
|
||||
Nx = Nx1*(Nx1-1)/2
|
||||
IJ = (Nx1+isOdd)/2
|
||||
IF (H(1)+H(Nx1).EQ.0.AND.
|
||||
& (H(IJ).EQ.0.OR.H(IJ)+H(IJ+1).EQ.0) ) THEN
|
||||
SYMMETRY=.FALSE.
|
||||
PRINT *,' Integration region symmetric'
|
||||
! May save Nx1-isOdd integrations in each time step
|
||||
! This is not implemented yet.
|
||||
!Nx = Nx1*(Nx1-1)/2-Nx1+isOdd
|
||||
ENDIF
|
||||
|
||||
CC = TWOPI*SQRT(-R2(1)/R4(1)) ! normalizing constant = 1/ expected number of zero-up-crossings of X'
|
||||
|
||||
ELSE ! level u separated Mm
|
||||
Nx = (Nx1-1)*(Nx1-1)
|
||||
IF ( ABS(u).LE.1D-8.AND.H(1)+H(Nx1+1).EQ.0.AND.
|
||||
& (H(Nx1)+H(2*Nx1).EQ.0) ) THEN
|
||||
SYMMETRY=.FALSE.
|
||||
PRINT *,' Integration region symmetric'
|
||||
! Not implemented for DEF <= 3
|
||||
!IF (DEF.LE.3) Nx = (Nx1-1)*(Nx1-2)/2
|
||||
ENDIF
|
||||
|
||||
IF (DEF.GT.3) THEN
|
||||
Nstart = MAX(Nstart,3)
|
||||
Nc = 5
|
||||
NI=5; Nd=3;
|
||||
ENDIF
|
||||
CC = TWOPI*SQRT(-R0(1)/R2(1))*exp(0.5D0*u*u/R0(1)) ! normalizing constant= 1/ expected number of u-up-crossings of X
|
||||
ENDIF
|
||||
|
||||
!print *,'def',def
|
||||
IF (Nx.GT.1) THEN
|
||||
IF ((DEF.EQ.0.OR.DEF.EQ.2)) THEN ! (M,m) or (M,m)v distribution wanted
|
||||
allocate(ansr(Nx1,Nx1,1),stat=status)
|
||||
ELSE ! (M,m,TMm), (M,m,TMm)v (M,m,TMd)v or (M,M,Tdm)v distributions wanted
|
||||
allocate(ansr(Nx1,Nx1,Ntime),stat=status)
|
||||
ENDIF
|
||||
ELSEIF (DEF.GT.3) THEN ! Conditional distribution for (TMd,TMm)v or (Tdm,TMm)v given (M,m) wanted
|
||||
allocate(ansr(1,Ntime,Ntime),stat=status)
|
||||
ELSE ! Conditional distribution for (TMm) or (TMm)v given (M,m) wanted
|
||||
allocate(ansr(1,1,Ntime),stat=status)
|
||||
ENDIF
|
||||
if (status.ne.0) print *,'can not allocate ansr'
|
||||
allocate(BIG(Ntime+Nc+1,Ntime+Nc+1),stat=status)
|
||||
if (status.ne.0) print *,'can not allocate BIG'
|
||||
allocate(ex(1:Ntime+Nc+1),stat=status)
|
||||
if (status.ne.0) print *,'can not allocate ex'
|
||||
allocate(fxind(Nx),xc(Nc,Nx))
|
||||
|
||||
|
||||
! Initialization
|
||||
!~~~~~~~~~~~~~~~~~
|
||||
|
||||
BIG = 0.d0
|
||||
ex = 0.d0
|
||||
ansr = 0.d0
|
||||
a_up = 0.d0
|
||||
a_lo = 0.d0
|
||||
|
||||
xc(:,:) = 0.d0
|
||||
!xc(:,1:Nx) = 0.d0
|
||||
!xc(2,1:Nx) = 0.d0
|
||||
|
||||
a_lo(1,1) = -Xtinf
|
||||
a_lo(1,2) = -XdInf
|
||||
a_up(1,3) = +XdInf
|
||||
a_lo(1,4) = -Xtinf
|
||||
ij = 0
|
||||
IF (DEF.LE.1) THEN ! Max2min and period/wavelength
|
||||
do I=2,Nx1
|
||||
J = IJ+I-1
|
||||
xc(3,IJ+1:J) = h(I)
|
||||
xc(4,IJ+1:J) = h(1:I-1)
|
||||
IJ = J
|
||||
enddo
|
||||
ELSE
|
||||
! Level u separated Max2min
|
||||
xc(Nc,:) = u
|
||||
! H(1) = H(Nx1+1)= u => start do loop at I=2 since by definition we must have: minimum<u-level<Maximum
|
||||
do i=2,Nx1
|
||||
J = IJ+Nx1-1
|
||||
xc(3,IJ+1:J) = h(i) ! Max > u
|
||||
xc(4,IJ+1:J) = h(Nx1+2:2*Nx1) ! Min < u
|
||||
IJ = J
|
||||
enddo
|
||||
|
||||
!CALL ECHO(transpose(xc(3:5,:)))
|
||||
if (DEF.GT.3) GOTO 200
|
||||
ENDIF
|
||||
do Ntd = Nstart,Ntime
|
||||
!Ntd=tn
|
||||
Ntdc = Ntd+Nc
|
||||
Nt = Ntd-Nd;
|
||||
indI(2) = Nt;
|
||||
indI(3) = Nt+1;
|
||||
indI(4) = Ntd;
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),Ntd,0,R0,R1,R2,R3,R4) ! positive wave period
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
IF (Nx.LT.2) THEN
|
||||
! Density of TMm given the Max and the Min. Note that the density is not scaled to unity
|
||||
ansr(1,1,Ntd) = fxind(1)*CC
|
||||
GOTO 100
|
||||
ENDIF
|
||||
IJ = 0
|
||||
SELECT CASE (DEF)
|
||||
CASE(:0)
|
||||
! joint density of (M,m)
|
||||
!~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
do i = 2, Nx1
|
||||
J = IJ+i-1
|
||||
ansr(1:i-1,i,1) = ansr(1:i-1,i,1)+fxind(ij+1:J)*CC*dt
|
||||
IJ=J
|
||||
enddo
|
||||
CASE (1)
|
||||
! joint density of (M,m,TMm)
|
||||
do i = 2, Nx1
|
||||
J = IJ+i-1
|
||||
ansr(1:i-1,i,Ntd) = fxind(ij+1:J)*CC
|
||||
IJ = J
|
||||
enddo
|
||||
CASE (2)
|
||||
! joint density of level v separated (M,m)v
|
||||
do i = 2,Nx1
|
||||
J = IJ+Nx1-1
|
||||
ansr(2:Nx1,i,1) = ansr(2:Nx1,i,1)+fxind(ij+1:J)*CC*dt
|
||||
IJ = J
|
||||
enddo
|
||||
CASE (3:)
|
||||
! joint density of level v separated (M,m,TMm)v
|
||||
do i = 2,Nx1
|
||||
J = IJ+Nx1-1
|
||||
ansr(2:Nx1,i,Ntd) = ansr(2:Nx1,i,Ntd)+fxind(ij+1:J)*CC
|
||||
IJ = J
|
||||
enddo
|
||||
END SELECT
|
||||
|
||||
100 if (ALLOCATED(COV)) then
|
||||
write(11,*) COV(:) ! save coefficient of variation
|
||||
endif
|
||||
print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
|
||||
goto 800
|
||||
|
||||
200 do tn = Nstart,Ntime
|
||||
Ntd = tn+1
|
||||
Ntdc = Ntd + Nc
|
||||
Nt = Ntd - Nd;
|
||||
indI(2) = Nt;
|
||||
indI(3) = Nt + 1;
|
||||
indI(4) = Nt + 2;
|
||||
indI(5) = Ntd;
|
||||
!CALL COV_INPUT2(BIG(1:Ntdc,1:Ntdc),tn,-2,R0,R1,R2,R3,R4) ! positive wave period
|
||||
IF (SYMMETRY) GOTO 300
|
||||
|
||||
do ts = 2,tn-1
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2,R3,R4) ! positive wave period
|
||||
!print *,'Big='
|
||||
!CALL ECHO(BIG(1:Ntdc,1:MIN(Ntdc,10)))
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
|
||||
SELECT CASE (def)
|
||||
CASE (:4)
|
||||
IF (Nx.EQ.1) THEN
|
||||
! Joint density (TMd,TMm) given the Max and the min. Note the density is not scaled to unity
|
||||
ansr(1,ts,tn) = fxind(1)*CC
|
||||
|
||||
ELSE
|
||||
! 4, gives level u separated Max2min and wave period from Max to the crossing of level u (M,m,TMd).
|
||||
ij = 0
|
||||
do i = 2,Nx1
|
||||
J = IJ+Nx1-1
|
||||
ansr(2:Nx1,i,ts) = ansr(2:Nx1,i,ts)+
|
||||
& fxind(ij+1:J)*CC*dt
|
||||
IJ = J
|
||||
enddo
|
||||
ENDIF
|
||||
CASE (5:)
|
||||
IF (Nx.EQ.1) THEN
|
||||
! Joint density (Tdm,TMm) given the Max and the min. Note the density is not scaled to unity
|
||||
ansr(1,tn-ts+1,tn) = fxind(1)*CC
|
||||
ELSE
|
||||
|
||||
! 5, gives level u separated Max2min and wave period from the crossing of level u to the min (M,m,Tdm).
|
||||
ij = 0
|
||||
do i = 2,Nx1
|
||||
J = IJ+Nx1-1
|
||||
ansr(2:Nx1,i,tn-ts+1)=ansr(2:Nx1,i,tn-ts+1)+
|
||||
& fxind(ij+1:J)*CC*dt
|
||||
IJ = J
|
||||
enddo
|
||||
ENDIF
|
||||
END SELECT
|
||||
if (ALLOCATED(COV)) then
|
||||
write(11,*) COV(:) ! save coefficient of variation
|
||||
endif
|
||||
enddo
|
||||
GOTO 400
|
||||
300 do ts = 2,FLOOR(DBLE(Ntd)/2.d0) ! Using the symmetry since U = 0 and the transformation is linear
|
||||
CALL COV_INPUT(BIG(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2,R3,R4) ! positive wave period
|
||||
!print *,'Big='
|
||||
!CALL ECHO(BIG(1:Ntdc,1:Ntdc))
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex,xc,indI,a_lo,a_up)
|
||||
IF (Nx.EQ.1) THEN
|
||||
! Joint density of (TMd,TMm),(Tdm,TMm) given the max and the min. Note that the density is not scaled to unity
|
||||
ansr(1,ts,tn) = fxind(1)*CC
|
||||
IF (ts.LT.tn-ts+1) THEN
|
||||
ansr(1,tn-ts+1,tn) = fxind(1)*CC
|
||||
ENDIF
|
||||
GOTO 350
|
||||
ENDIF
|
||||
IJ = 0
|
||||
SELECT CASE (def)
|
||||
CASE (:4)
|
||||
|
||||
! 4, gives level u separated Max2min and wave period from Max to the crossing of level u (M,m,TMd).
|
||||
do i = 2,Nx1
|
||||
j = ij+Nx1-1
|
||||
ansr(2:Nx1,i,ts) = ansr(2:Nx1,i,ts)+
|
||||
& fxind(ij+1:J)*CC*dt
|
||||
IF (ts.LT.tn-ts+1) THEN
|
||||
ansr(i,2:Nx1,tn-ts+1) =
|
||||
& ansr(i,2:Nx1,tn-ts+1)+fxind(ij+1:J)*CC*dt ! exploiting the symmetry
|
||||
ENDIF
|
||||
IJ = J
|
||||
enddo
|
||||
CASE (5:)
|
||||
! 5, gives level u separated Max2min and wave period from the crossing of level u to min (M,m,Tdm).
|
||||
do i = 2,Nx1
|
||||
J = IJ+Nx1-1
|
||||
|
||||
ansr(2:Nx1,i,tn-ts+1)=ansr(2:Nx1,i,tn-ts+1)+
|
||||
& fxind(ij+1:J)*CC*dt
|
||||
IF (ts.LT.tn-ts+1) THEN
|
||||
ansr(i,2:Nx1,ts) = ansr(i,2:Nx1,ts)+
|
||||
& fxind(ij+1:J)*CC*dt ! exploiting the symmetry
|
||||
ENDIF
|
||||
IJ = J
|
||||
enddo
|
||||
END SELECT
|
||||
350 enddo
|
||||
400 print *,'Ready: ',tn,' of ',Ntime
|
||||
enddo
|
||||
|
||||
|
||||
|
||||
|
||||
800 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
!print *,'ans, IJ,def', shape(ansr),IJ,DEF
|
||||
if (Nx.GT.1) THEN
|
||||
ij = 1
|
||||
IF (DEF.GT.2.OR.DEF.EQ.1) IJ = Ntime
|
||||
!print *,'ans, IJ,def', size(ansr),IJ,DEF
|
||||
do ts = 1,ij
|
||||
do j=1,Nx1
|
||||
do i=1,Nx1
|
||||
write(11,*) ansr(i,j,ts)
|
||||
enddo
|
||||
enddo
|
||||
enddo
|
||||
ELSE
|
||||
ij = 1
|
||||
IF (DEF.GT.3) IJ = Ntime
|
||||
!print *,'ans, IJ,def', size(ansr),IJ,DEF
|
||||
do ts = 1,Ntime
|
||||
do j = 1,ij
|
||||
write(11,*) ansr(1,j,ts)
|
||||
enddo
|
||||
enddo
|
||||
ENDIF
|
||||
close(11)
|
||||
900 continue
|
||||
deallocate(BIG)
|
||||
deallocate(ex)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (Ntime,Nstart,NIT,speed,SCIS,SEED1,Nx,dT,u,def)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,Nstart,NIT,speed,Nx,DEF,SCIS,SEED1
|
||||
double precision ,intent(out) :: dT,U
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
READ (14,*) dT
|
||||
READ (14,*) U
|
||||
READ (14,*) DEF
|
||||
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
close(3)
|
||||
close(5)
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,tn,ts,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,N,shft
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! for ts <= 1:
|
||||
! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X'(t1),X'(tn),X(t1),X(tn)
|
||||
! = [ Xt | Xd | Xc ]
|
||||
!
|
||||
! for ts > =2:
|
||||
! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X'(t1),X'(tn),X(t1),X(tn) X(ts)
|
||||
! = [ Xt | Xd | Xc ]
|
||||
!
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
if (ts.GT.1) THEN
|
||||
! Assumption: a previous call to covinput has been made
|
||||
! need only to update the last row and column of big:
|
||||
N=tn+5
|
||||
!Cov(Xt,Xc)
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
BIG(i,N) = -sign(R1(j+1),R1(j+1)*dble(ts-i-1)) !cov(X'(ti+1),X(ts))
|
||||
enddo
|
||||
!Cov(Xc)
|
||||
BIG(N ,N) = R0(1) ! cov(X(ts),X(ts))
|
||||
BIG(tn+3 ,N) = R0(ts) ! cov(X(t1),X(ts))
|
||||
BIG(tn+4 ,N) = R0(tn-ts+1) ! cov(X(tn),X(ts))
|
||||
BIG(tn+1 ,N) = -R1(ts) ! cov(X'(t1),X(ts))
|
||||
BIG(tn+2 ,N) = R1(tn-ts+1) ! cov(X'(tn),X(ts))
|
||||
!Cov(Xd,Xc)
|
||||
BIG(tn-1 ,N) = R2(ts) !cov(X''(t1),X(ts))
|
||||
BIG(tn ,N) = R2(tn-ts+1) !cov(X''(tn),X(ts))
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
BIG(N,j) = BIG(j,N)
|
||||
enddo
|
||||
return
|
||||
endif
|
||||
IF (ts.LT.0) THEN
|
||||
shft = 1
|
||||
N=tn+5;
|
||||
ELSE
|
||||
shft = 0
|
||||
N=tn+4;
|
||||
ENDIF
|
||||
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = -R2(j-i+1) ! cov(X'(ti+1),X'(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,tn+3) = R1(i+1) !cov(X'(ti+1),X(t1))
|
||||
BIG(tn-1-i ,tn+4) = -R1(i+1) !cov(X'(ti+1),X(tn))
|
||||
BIG(i ,tn+1) = -R2(i+1) !cov(X'(ti+1),X'(t1))
|
||||
BIG(tn-1-i ,tn+2) = -R2(i+1) !cov(X'(ti+1),X'(tn))
|
||||
!Cov(Xt,Xd)
|
||||
BIG(i,tn-1) = R3(i+1) !cov(X'(ti+1),X''(t1))
|
||||
BIG(tn-1-i,tn) =-R3(i+1) !cov(X'(ti+1),X''(tn))
|
||||
enddo
|
||||
|
||||
!cov(Xd)
|
||||
BIG(tn-1 ,tn-1 ) = R4(1)
|
||||
BIG(tn-1 ,tn ) = R4(tn) !cov(X''(t1),X''(tn))
|
||||
BIG(tn ,tn ) = R4(1)
|
||||
|
||||
!cov(Xc)
|
||||
BIG(tn+3 ,tn+3) = R0(1) ! cov(X(t1),X(t1))
|
||||
BIG(tn+3 ,tn+4) = R0(tn) ! cov(X(t1),X(tn))
|
||||
BIG(tn+1 ,tn+3) = 0.d0 ! cov(X(t1),X'(t1))
|
||||
BIG(tn+2 ,tn+3) = R1(tn) ! cov(X(t1),X'(tn))
|
||||
BIG(tn+4 ,tn+4) = R0(1) ! cov(X(tn),X(tn))
|
||||
BIG(tn+1 ,tn+4) =-R1(tn) ! cov(X(tn),X'(t1))
|
||||
BIG(tn+2 ,tn+4) = 0.d0 ! cov(X(tn),X'(tn))
|
||||
BIG(tn+1 ,tn+1) =-R2(1) ! cov(X'(t1),X'(t1))
|
||||
BIG(tn+1 ,tn+2) =-R2(tn) ! cov(X'(t1),X'(tn))
|
||||
BIG(tn+2 ,tn+2) =-R2(1) ! cov(X'(tn),X'(tn))
|
||||
!Xc=X(t1),X(tn),X'(t1),X'(tn)
|
||||
!Xd=X''(t1),X''(tn)
|
||||
!cov(Xd,Xc)
|
||||
BIG(tn-1 ,tn+3) = R2(1) !cov(X''(t1),X(t1))
|
||||
BIG(tn-1 ,tn+4) = R2(tn) !cov(X''(t1),X(tn))
|
||||
BIG(tn-1 ,tn+1) = 0.d0 !cov(X''(t1),X'(t1))
|
||||
BIG(tn-1 ,tn+2) = R3(tn) !cov(X''(t1),X'(tn))
|
||||
BIG(tn ,tn+3) = R2(tn) !cov(X''(tn),X(t1))
|
||||
BIG(tn ,tn+4) = R2(1) !cov(X''(tn),X(tn))
|
||||
BIG(tn ,tn+1) =-R3(tn) !cov(X''(tn),X'(t1))
|
||||
BIG(tn ,tn+2) = 0.d0 !cov(X''(tn),X'(tn))
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
BIG(i,j) = BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,N,shft, tnold = 0
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! for ts <= 1:
|
||||
! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X'(t1),X'(tn),X(t1),X(tn)
|
||||
! = [ Xt | Xd | Xc ]
|
||||
!
|
||||
! for ts > =2:
|
||||
! X'(t2)..X'(ts),...,X'(tn-1) X''(t1),X''(tn) X'(ts) X'(t1),X'(tn),X(t1),X(tn) X(ts)
|
||||
! = [ Xt | Xd | Xc ]
|
||||
!
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s)) = r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
SAVE tnold
|
||||
|
||||
if (ts.GT.1) THEN
|
||||
shft = 1
|
||||
N=tn+5+shft
|
||||
!Cov(Xt,Xc)
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
BIG(i,N) = -sign(R1(j+1),R1(j+1)*dble(ts-i-1)) !cov(X'(ti+1),X(ts))
|
||||
enddo
|
||||
!Cov(Xc)
|
||||
BIG(N ,N) = R0(1) ! cov(X(ts),X(ts))
|
||||
BIG(tn+shft+3 ,N) = R0(ts) ! cov(X(t1),X(ts))
|
||||
BIG(tn+shft+4 ,N) = R0(tn-ts+1) ! cov(X(tn),X(ts))
|
||||
BIG(tn+shft+1 ,N) = -R1(ts) ! cov(X'(t1),X(ts))
|
||||
BIG(tn+shft+2 ,N) = R1(tn-ts+1) ! cov(X'(tn),X(ts))
|
||||
!Cov(Xd,Xc)
|
||||
BIG(tn-1 ,N) = R2(ts) !cov(X''(t1),X(ts))
|
||||
BIG(tn ,N) = R2(tn-ts+1) !cov(X''(tn),X(ts))
|
||||
|
||||
!ADD a level u crossing at ts
|
||||
|
||||
!Cov(Xt,Xd)
|
||||
do i = 1,tn-2
|
||||
j = abs(i+1-ts)
|
||||
BIG(i,tn+shft) = -R2(j+1) !cov(X'(ti+1),X'(ts))
|
||||
enddo
|
||||
!Cov(Xd)
|
||||
BIG(tn+shft,tn+shft) = -R2(1) !cov(X'(ts),X'(ts))
|
||||
BIG(tn-1 ,tn+shft) = R3(ts) !cov(X''(t1),X'(ts))
|
||||
BIG(tn ,tn+shft) = -R3(tn-ts+1) !cov(X''(tn),X'(ts))
|
||||
|
||||
!Cov(Xd,Xc)
|
||||
BIG(tn+shft ,N ) = 0.d0 !cov(X'(ts),X(ts))
|
||||
BIG(tn+shft,tn+shft+3) = R1(ts) ! cov(X'(ts),X(t1))
|
||||
BIG(tn+shft,tn+shft+4) = -R1(tn-ts+1) ! cov(X'(ts),X(tn))
|
||||
BIG(tn+shft,tn+shft+1) = -R2(ts) ! cov(X'(ts),X'(t1))
|
||||
BIG(tn+shft,tn+shft+2) = -R2(tn-ts+1) ! cov(X'(ts),X'(tn))
|
||||
|
||||
|
||||
|
||||
IF (tnold.EQ.tn) THEN ! A previous call to covinput with tn==tnold has been made
|
||||
! need only to update row and column N and tn+1 of big:
|
||||
! make lower triangular part equal to upper and then return
|
||||
do j=1,tn+shft
|
||||
BIG(N,j) = BIG(j,N)
|
||||
BIG(tn+shft,j) = BIG(j,tn+shft)
|
||||
enddo
|
||||
do j=tn+shft+1,N-1
|
||||
BIG(N,j) = BIG(j,N)
|
||||
BIG(j,tn+shft) = BIG(tn+shft,j)
|
||||
enddo
|
||||
return
|
||||
ENDIF
|
||||
tnold = tn
|
||||
ELSE
|
||||
N = tn+4
|
||||
shft = 0
|
||||
endif
|
||||
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = -R2(j-i+1) ! cov(X'(ti+1),X'(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,tn+shft+3) = R1(i+1) !cov(X'(ti+1),X(t1))
|
||||
BIG(tn-1-i ,tn+shft+4) = -R1(i+1) !cov(X'(ti+1),X(tn))
|
||||
BIG(i ,tn+shft+1) = -R2(i+1) !cov(X'(ti+1),X'(t1))
|
||||
BIG(tn-1-i ,tn+shft+2) = -R2(i+1) !cov(X'(ti+1),X'(tn))
|
||||
!Cov(Xt,Xd)
|
||||
BIG(i,tn-1) = R3(i+1) !cov(X'(ti+1),X''(t1))
|
||||
BIG(tn-1-i,tn) =-R3(i+1) !cov(X'(ti+1),X''(tn))
|
||||
enddo
|
||||
|
||||
!cov(Xd)
|
||||
BIG(tn-1 ,tn-1 ) = R4(1)
|
||||
BIG(tn-1 ,tn ) = R4(tn) !cov(X''(t1),X''(tn))
|
||||
BIG(tn ,tn ) = R4(1)
|
||||
|
||||
!cov(Xc)
|
||||
BIG(tn+shft+3 ,tn+shft+3) = R0(1) ! cov(X(t1),X(t1))
|
||||
BIG(tn+shft+3 ,tn+shft+4) = R0(tn) ! cov(X(t1),X(tn))
|
||||
BIG(tn+shft+1 ,tn+shft+3) = 0.d0 ! cov(X(t1),X'(t1))
|
||||
BIG(tn+shft+2 ,tn+shft+3) = R1(tn) ! cov(X(t1),X'(tn))
|
||||
BIG(tn+shft+4 ,tn+shft+4) = R0(1) ! cov(X(tn),X(tn))
|
||||
BIG(tn+shft+1 ,tn+shft+4) =-R1(tn) ! cov(X(tn),X'(t1))
|
||||
BIG(tn+shft+2 ,tn+shft+4) = 0.d0 ! cov(X(tn),X'(tn))
|
||||
BIG(tn+shft+1 ,tn+shft+1) =-R2(1) ! cov(X'(t1),X'(t1))
|
||||
BIG(tn+shft+1 ,tn+shft+2) =-R2(tn) ! cov(X'(t1),X'(tn))
|
||||
BIG(tn+shft+2 ,tn+shft+2) =-R2(1) ! cov(X'(tn),X'(tn))
|
||||
!Xc=X(t1),X(tn),X'(t1),X'(tn)
|
||||
!Xd=X''(t1),X''(tn)
|
||||
!cov(Xd,Xc)
|
||||
BIG(tn-1 ,tn+shft+3) = R2(1) !cov(X''(t1),X(t1))
|
||||
BIG(tn-1 ,tn+shft+4) = R2(tn) !cov(X''(t1),X(tn))
|
||||
BIG(tn-1 ,tn+shft+1) = 0.d0 !cov(X''(t1),X'(t1))
|
||||
BIG(tn-1 ,tn+shft+2) = R3(tn) !cov(X''(t1),X'(tn))
|
||||
BIG(tn ,tn+shft+3) = R2(tn) !cov(X''(tn),X(t1))
|
||||
BIG(tn ,tn+shft+4) = R2(1) !cov(X''(tn),X(tn))
|
||||
BIG(tn ,tn+shft+1) =-R3(tn) !cov(X''(tn),X'(t1))
|
||||
BIG(tn ,tn+shft+2) = 0.d0 !cov(X''(tn),X'(tn))
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
BIG(i,j) = BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
END PROGRAM sp2mmt
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,498 +0,0 @@
|
||||
PROGRAM sp2tccpdf
|
||||
C***********************************************************************
|
||||
C This program computes: *
|
||||
C *
|
||||
C density of T= T_1+T_2 in a gaussian process i.e. *
|
||||
C *
|
||||
C wavelengthes for crests <h1 and troughs >h2 *
|
||||
C *
|
||||
C Sylvie and Igor 7 dec. 1999 *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
& NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex,CY1,CY2
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h1,h2
|
||||
double precision, dimension(: ),allocatable :: hh1,hh2
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Ntime,tn,ts,speed,ph,seed1,seed_size,Nx1,Nx2,N0
|
||||
integer :: icy,icy2
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! DIGITAL:
|
||||
! f90 -g2 -C -automatic -o ~/WAT/V4/sp2tthpdf1.exe rind49.f sp2tthpdf1.f
|
||||
! SOLARIS:
|
||||
!f90 -g -O -w3 -Bdynamic -fixed -o ../sp2tthpdf.exe rind49.f sp2tthpdf1.f
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
CALL INIT_LEVELS(U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
|
||||
!print *,'U,Ntime,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,Ntime,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!Nx1=1
|
||||
!Nx2=1
|
||||
|
||||
Nx=Nx1*Nx1
|
||||
!print *,'NN',Nx1,Nx2,Nx
|
||||
|
||||
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
|
||||
allocate(h1(1:Nx1))
|
||||
allocate(h2(1:Nx2))
|
||||
CALL INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
|
||||
|
||||
allocate(hh1(1:Nx))
|
||||
allocate(hh2(1:Nx))
|
||||
!h transformation
|
||||
do icy=1,Nx1
|
||||
do icy2=1,Nx2
|
||||
hh1((icy-1)*Nx2+icy2)=h1(icy);
|
||||
hh2((icy-1)*Nx2+icy2)=h2(icy2);
|
||||
enddo
|
||||
enddo
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
!h1(1)=XtInf
|
||||
!h2(1)=XtInf
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
allocate(CY1(1:Nx))
|
||||
allocate(CY2(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY1(icy)=exp(-0.5*hh1(icy)*hh1(icy)/100)/(10*sqrt(twopi))
|
||||
CY2(icy)=exp(-0.5*hh2(icy)*hh2(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
!print *,CY1
|
||||
allocate(ansr(1:Ntime,1:Nx))
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
fxind=0.d0
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
! Y={X(t2)..,X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)} !!
|
||||
! = [Xt Xd Xc] !!
|
||||
! !!
|
||||
! Nt=tn-2, Nd=3, Nc=2+3 !!
|
||||
! !!
|
||||
! Xt= contains Nt time points in the indicator function !!
|
||||
! Xd= " Nd derivatives !!
|
||||
! Xc= " Nc variables to condition on !!
|
||||
! (Y1,Y2) dummy variables ind. of all other v. inputing h1,h2 into rindd !!
|
||||
! !!
|
||||
! There are 6 ( NI=7) regions with constant bariers: !!
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] u<Y(i)<h1 !!
|
||||
! (indI(2)=ts-2); for i\in (indI(2),indI(2)], inf<Y(i)<inf (no restr.) !!
|
||||
! (indI(3)=ts-1); for i\in (indI(3),indI(4)], h2 <Y(i)<u !!
|
||||
! (indI(4)=Nt) ; for i\in (indI(4),indI(5)], Y(i)<0 (deriv. X'(ts)) !!
|
||||
! (indI(5)=Nt+1); for i\in (indI(5),indI(6)], Y(i)>0 (deriv. X'(t1)) !!
|
||||
! (indI(6)=Nt+2); for i\in (indI(6),indI(7)], Y(i)>0 (deriv. X'(tn)) !!
|
||||
! (indI(7)=Nt+3); NI=7. !!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
||||
NI=7; Nd=3
|
||||
Nc=5; Mb=3
|
||||
allocate(a_up(1:Mb,1:(NI-1)))
|
||||
allocate(a_lo(1:Mb,1:(NI-1)))
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
allocate(BIG(1:(Ntime+Nc+1),1:(Ntime+Nc+1)))
|
||||
ALLOCATE(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:(Ntime+Nc+1)))
|
||||
!print *,size(ex),Ntime
|
||||
ex=0.d0
|
||||
!print *,size(ex),ex
|
||||
xc(1,1:Nx)=hh1(1:Nx)
|
||||
xc(2,1:Nx)=hh2(1:Nx)
|
||||
xc(3,1:Nx)=u
|
||||
xc(4,1:Nx)=u
|
||||
xc(5,1:Nx)=u
|
||||
! upp- down- upp-crossings at t1,ts,tn
|
||||
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(1,3)=u
|
||||
|
||||
|
||||
a_lo(1,4)=-XdInf
|
||||
a_up(1,5)= XdInf
|
||||
a_up(1,6)= XdInf
|
||||
|
||||
a_up(2,1)=1.d0
|
||||
a_lo(3,3)=1.d0 !signe a voir!!!!!!
|
||||
! print *,a_up
|
||||
! print *,a_lo
|
||||
do tn=N0,Ntime,1
|
||||
! do tn=Ntime,Ntime,1
|
||||
Ntd=tn+1
|
||||
Nt=Ntd-Nd
|
||||
Ntdc=Ntd+Nc
|
||||
indI(4)=Nt
|
||||
indI(5)=Nt+1
|
||||
indI(6)=Nt+2
|
||||
indI(7)=Ntd
|
||||
if (SCIS.gt.0) then
|
||||
if (SCIS.EQ.2) then
|
||||
Nj=max(Nt,0)
|
||||
else
|
||||
Nj=min(max(Nt-5, 0),0)
|
||||
endif
|
||||
endif
|
||||
do ts=3,tn-2
|
||||
!print *,'ts,tn' ,ts,tn,Ntdc
|
||||
CALL COV_INPUT(Big(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2)!positive wave period
|
||||
indI(2)=ts-2
|
||||
indI(3)=ts-1
|
||||
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
|
||||
ds=dt
|
||||
do icy=1,Nx
|
||||
! ansr(tn,:)=ansr(tn,:)+fxind*CC*ds./(CY1.*CY2)
|
||||
ansr(tn,icy)=ansr(tn,icy)+fxind(icy)*CC*ds/(CY1(icy)*CY2(icy))
|
||||
enddo
|
||||
enddo ! ts
|
||||
print *,'Ready: ',tn,' of ',Ntime
|
||||
|
||||
enddo !tn
|
||||
!print *,'ansr',ansr
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
!print *, ansr
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
!write(11,*) ansr(ts,ph),hh1(ph),hh2(ph)
|
||||
write(11,111) ansr(ts,ph)
|
||||
|
||||
enddo
|
||||
enddo
|
||||
111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
deallocate(h1)
|
||||
deallocate(h2)
|
||||
deallocate(hh1)
|
||||
deallocate(hh2)
|
||||
deallocate(a_up)
|
||||
deallocate(a_lo)
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,N0,NIT,speed,Nx1,Nx2,SCIS,seed1
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
|
||||
READ (14,*) U
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) N0
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
|
||||
|
||||
READ (14,*) Nx1,Nx2
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.5) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h1,h2
|
||||
integer, intent(in) :: Nx1,Nx2
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx1
|
||||
READ (4,*) H1(ix)
|
||||
enddo
|
||||
do ix=1,Nx2
|
||||
READ (4,*) H2(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
!
|
||||
! ||X(t2)..X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)||
|
||||
! = [Xt Xd Xc]
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+Nc
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1) = 0.d0 !cov(X(ti+1),Y1)
|
||||
BIG(i ,Ntd1+2) = 0.d0 !cov(X(ti+1),Y2)
|
||||
BIG(i ,Ntd1+4) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+5) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X'(t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X'(tn))
|
||||
enddo
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1-1) = -R2(ts) !cov(X'(ts),X'(t1))
|
||||
BIG(Ntd1-2,Ntd1-2) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1 ) = -R2(tn+1-ts) !cov(X'(ts),X'(tn))
|
||||
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1,Ntd1+1) = 100.d0 ! cov(Y1 Y1)
|
||||
BIG(Ntd1+1,Ntd1+2) = 0.d0 ! cov(Y1 Y2)
|
||||
BIG(Ntd1+1,Ntd1+3) = 0.d0 ! cov(Y1 X(ts))
|
||||
BIG(Ntd1+1,Ntd1+4) = 0.d0 ! cov(Y1 X(t1))
|
||||
BIG(Ntd1+1,Ntd1+5) = 0.d0 ! cov(Y1 X(tn))
|
||||
BIG(Ntd1+2,Ntd1+2) = 100.d0 ! cov(Y2 Y2)
|
||||
BIG(Ntd1+2,Ntd1+3) = 0.d0 ! cov(Y2 X(ts))
|
||||
BIG(Ntd1+2,Ntd1+4) = 0.d0 ! cov(Y2 X(t1))
|
||||
BIG(Ntd1+2,Ntd1+5) = 0.d0 ! cov(Y2 X(tn))
|
||||
|
||||
BIG(Ntd1+3,Ntd1+3) = R0(1) ! cov(X(ts),X (ts)
|
||||
BIG(Ntd1+3,Ntd1+4) = R0(ts) ! cov(X(ts),X (t1))
|
||||
BIG(Ntd1+3,Ntd1+5) = R0(tn+1-ts) ! cov(X(ts),X (tn))
|
||||
BIG(Ntd1+4,Ntd1+4) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+4,Ntd1+5) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+5,Ntd1+5) = R0(1) ! cov(X(tn),X (tn))
|
||||
|
||||
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1) = 0.d0 !cov(X'(tn),Y1)
|
||||
BIG(Ntd1 ,Ntd1+2) = 0.d0 !cov(X'(tn),Y2)
|
||||
BIG(Ntd1-1 ,Ntd1+1) = 0.d0 !cov(X'(t1),Y1)
|
||||
BIG(Ntd1-1 ,Ntd1+2) = 0.d0 !cov(X'(t1),Y2)
|
||||
BIG(Ntd1-2 ,Ntd1+1) = 0.d0 !cov(X'(ts),Y1)
|
||||
BIG(Ntd1-2 ,Ntd1+2) = 0.d0 !cov(X'(ts),Y2)
|
||||
|
||||
BIG(Ntd1 ,Ntd1+4) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+5) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+4) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+5) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
BIG(Ntd1 ,Ntd1+3) = R1(tn+1-ts) !cov(X'(tn),X (ts))
|
||||
BIG(Ntd1-1,Ntd1+3) =-R1(ts) !cov(X'(t1),X (ts))
|
||||
BIG(Ntd1-2,Ntd1+3) = 0.d0 !cov(X'(ts),X (ts)
|
||||
BIG(Ntd1-2,Ntd1+4) = R1(ts) !cov(X'(ts),X (t1))
|
||||
BIG(Ntd1-2,Ntd1+5) = -R1(tn+1-ts) !cov(X'(ts),X (tn))
|
||||
|
||||
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
!cov(Xt,Xc)
|
||||
BIG(i,Ntd1+3) = R0(j+1) !cov(X(ti+1),X(ts))
|
||||
!Cov(Xt,Xd)
|
||||
if ((i+1-ts).lt.0) then
|
||||
BIG(i,Ntd1-2) = R1(j+1)
|
||||
else !cov(X(ti+1),X'(ts))
|
||||
BIG(i,Ntd1-2) = -R1(j+1)
|
||||
endif
|
||||
enddo
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
enddo
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2tccpdf
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,440 +0,0 @@
|
||||
PROGRAM sp2tcpdf
|
||||
C***********************************************************************
|
||||
C This program computes: *
|
||||
C *
|
||||
C density of T_i, for Ac <=h, in a gaussian process i.e. *
|
||||
C *
|
||||
C half wavelength (up-crossing to downcrossing) for crests <h *
|
||||
C or half wavelength (down-crossing to upcrossing) for trough >h *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
&NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex,CY
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(2,6) :: a_up=0.d0,a_lo=0.d0
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nstart,Ntime,tn,ts,speed,ph,def,seed1,seed_size,icy
|
||||
integer ::it1,it2
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! DIGITAL:
|
||||
! f90 -o ~/WAT/V1/sp2tcpdf.exe rind44.f sp2tcpdf.f
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
CALL INIT_LEVELS(U,def,Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
!print *,'U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
if (abs(def).GT.1) THEN
|
||||
!allocate(h(1:Nx))
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
!CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
endif
|
||||
allocate(h(1:Nx))
|
||||
CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
CALL INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
|
||||
print *,'Nx',Nx
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
print *,'XdInf,XtInf'
|
||||
print *,XdInf,XtInf
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
!fy(h)
|
||||
allocate(CY(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY(icy)=exp(-0.5*h(icy)*h(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
|
||||
allocate(ansr(1:Ntime,1:Nx))
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
fxind=0.d0 !this is not needed
|
||||
|
||||
NI=4; Nd=2
|
||||
Nc=3; Mb=2
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc))
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:Ntime+Nc))
|
||||
ex=0.d0
|
||||
!print *,'nc',Nc,Nx
|
||||
xc(1,1:Nx)=h(1:Nx)
|
||||
print *,'xc',h(1)
|
||||
print *,'test',def;
|
||||
xc(2,1:Nx)=u
|
||||
xc(3,1:Nx)=u
|
||||
if (def.GT.0) then
|
||||
a_up(1,1)=u !+XtInf
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XdInf
|
||||
a_lo(1,3)=-XdInf
|
||||
a_up(2,1)=1.d0
|
||||
else
|
||||
a_up(1,1)=u
|
||||
a_lo(1,1)=u !-XtInf
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)= XdInf
|
||||
a_lo(2,1)=1.d0
|
||||
print *,'a_lo',a_lo(2,1)
|
||||
endif
|
||||
!print *,'Nstart',Nstart
|
||||
Nstart=MAX(3,Nstart)
|
||||
!print *,'Nstart',Nstart
|
||||
if (SCIS.GT.0) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
do Ntd=Nstart,Ntime
|
||||
!CALL COV_INPUT2(BIG,Ntd, R0,R1,R2)
|
||||
Ntdc=Ntd+Nc;
|
||||
CALL COV_INPUT(BIG,Ntd,-1,R0,R1,R2,R3,R4) ! positive wave period
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
!Ntdc=Ntd+Nc;
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
! print *,'test',fxind/CY(1:Nx)
|
||||
do icy=1,Nx
|
||||
ansr(Ntd,icy)=fxind(icy)*CC/CY(icy)
|
||||
enddo
|
||||
if (SCIS.GT.0) then
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
goto 300
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
|
||||
!print *, ansr
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansr(ts,ph)
|
||||
! write(11,111) ansr(ts,ph)
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
|
||||
if (allocated(R3)) then
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
ENDIF
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,def,Ntime,Nstart,NIT,speed,Nx,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: def,Ntime,Nstart,NIT,speed,Nx
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) def
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
print *,'def',def
|
||||
if (abs(def).GT.1) then
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
else
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
endif
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,def,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: def
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
!if (def.LT.0) THEN
|
||||
! H=-H
|
||||
!endif
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime,def
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
|
||||
close(4)
|
||||
close(5)
|
||||
endif
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,shft,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! For ts>1:
|
||||
! X(t2)..X(ts),..X(tn-1) X''(ts) X'(t1) X'(tn) X(ts) X(t1) X(tn) X'(ts)
|
||||
! = [Xt Xd Xc]
|
||||
!
|
||||
! For ts<=1:
|
||||
! X(t2)..,..X(tn-1) X'(t1) X'(tn) Y X(t1) X(tn)
|
||||
! = [Xt Xd Xc]
|
||||
!Add Y Condition : Y=h
|
||||
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
if (ts.LE.1) THEN
|
||||
Ntd1=tn
|
||||
N=Ntd1+Nc;
|
||||
shft=0 ! def=1 want only crest period Tc
|
||||
else
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+4
|
||||
shft=1 ! def=2 or 3 want Tc Ac or Tcf, Ac
|
||||
endif
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1+shft) = 0.d0 !cov(X(ti+1),Y)
|
||||
BIG(i ,Ntd1+2+shft) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+3+shft) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X' (t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X' (tn))
|
||||
enddo
|
||||
!call echo(big(1:tn,1:tn),tn)
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
|
||||
!cov(Xc)
|
||||
!print *,'t'
|
||||
BIG(Ntd1+1+shft,Ntd1+1+shft) = 100.d0!100.d0 ! cov(Y,Y)
|
||||
BIG(Ntd1+1+shft,Ntd1+2+shft) = 0.d0
|
||||
BIG(Ntd1+1+shft,Ntd1+3+shft) = 0.d0
|
||||
BIG(Ntd1+2+shft,Ntd1+2+shft) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+2+shft,Ntd1+3+shft) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+3+shft,Ntd1+3+shft) = R0(1) ! cov(X(tn),X (tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1+shft) = 0.d0 !cov(X'(tn),Y)
|
||||
BIG(Ntd1 ,Ntd1+2+shft) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+3+shft) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+1+shft) = 0.d0 !cov(X'(t1),Y)
|
||||
BIG(Ntd1-1,Ntd1+2+shft) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+3+shft) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
|
||||
|
||||
!call echo(big(1:N,1:N),N)
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
enddo
|
||||
!if (tn.eq.3) then
|
||||
!do j=1,N
|
||||
! do i=j,N
|
||||
! print *,'test',j,i,BIG(j,i)
|
||||
! enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
!enddo
|
||||
!endif
|
||||
!call echo(big(1:N,1:N),N)
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2tcpdf
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,569 +0,0 @@
|
||||
PROGRAM sp2thpdf
|
||||
!***********************************************************************
|
||||
! This program computes: *
|
||||
! *
|
||||
! density of S_i,Hi,T_i in a gaussian process i.e. *
|
||||
! *
|
||||
! quart wavelength (up-crossing to crest) and crest amplitude *
|
||||
!
|
||||
! def = 1, gives half wave period, Tc (default).
|
||||
! -1, gives half wave period, Tt.
|
||||
! 2, gives half wave period and wave crest amplitude (Tc,Ac).
|
||||
! -2, gives half wave period and wave trough amplitude (Tt,At).
|
||||
! 3, gives crest front period and wave crest amplitude (Tcf,Ac).
|
||||
! -3, gives trough back period and wave trough amplitude (Ttb,At).
|
||||
! 4, gives minimum of crest front/back period and wave crest
|
||||
! amplitude (max(Tcf,Tcb),Ac).
|
||||
! -4, gives minimum of trough front/back period and wave trough
|
||||
! amplitude (max(Ttf,Ttb),At).
|
||||
!***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
& NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(2,6) :: a_up=0.d0,a_lo=0.d0
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nstart,Ntime,tn,ts,speed,ph,def,seed1,seed_size
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! DIGITAL:
|
||||
! f90 -g2 -C -automatic -o ../wave/alpha/sp2thpdf.exe rind44.f sp2thpdf.f
|
||||
! SOLARIS:
|
||||
!f90 -g -O -w3 -Bdynamic -fixed -o ../wave/sol2/sp2thpdf.exe rind44.f sp2thpdf.f
|
||||
! linux:
|
||||
! f90 -gline -Nl126 -C -o sp2thpdf.exe rind45.f sp2thpdf.f
|
||||
! HP700
|
||||
!f90 -g -C -o ../exec/hp700/sp2thpdf.exe rind45.f sp2thpdf.f
|
||||
!f90 -g -C +check=all +FPVZID -o ../exec/hp700/sp2thpdf2.exe rind45.f sp2thpdf.f
|
||||
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
CALL INIT_LEVELS(U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT)
|
||||
!print *,'U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT
|
||||
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
if (abs(def).GT.1) THEN
|
||||
allocate(h(1:Nx))
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
|
||||
CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
endif
|
||||
CALL INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
|
||||
!print *,'Nx',Nx
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
!print *,'XdInf,XtInf'
|
||||
!print *,XdInf,XtInf
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
if (abs(def).EQ.4) CC=2.d0*CC
|
||||
allocate(ansr(1:Ntime,1:Nx))
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
!fxind=0.d0 this is not needed
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
GOTO 200
|
||||
endif
|
||||
NI=4; Nd=2
|
||||
Nc=2; Mb=1
|
||||
Nx=1
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc))
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:Ntime+Nc))
|
||||
ex=0.d0
|
||||
xc(1,1)=u
|
||||
xc(2,1)=u
|
||||
|
||||
if (def.GT.0) then
|
||||
a_up(1,1)=u+XtInf
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XdInf
|
||||
a_lo(1,3)=-XdInf
|
||||
else
|
||||
a_up(1,1)=u
|
||||
a_lo(1,1)=u-XtInf
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)= XdInf
|
||||
endif
|
||||
!print *,'Nstart',Nstart
|
||||
Nstart=MAX(2,Nstart)
|
||||
!print *,'Nstart',Nstart
|
||||
if (SCIS.GT.0) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
do Ntd=Nstart,Ntime
|
||||
!CALL COV_INPUT2(BIG,Ntd, R0,R1,R2)
|
||||
CALL COV_INPUT(BIG,Ntd,-1,R0,R1,R2,R3,R4) ! positive wave period
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
Ntdc=Ntd+Nc;
|
||||
!if (SCIS.gt.0) then
|
||||
! if (SCIS.EQ.2) then
|
||||
! Nj=max(Nt,0)
|
||||
! else
|
||||
! Nj=min(max(Nt-5, 0),0)
|
||||
! endif
|
||||
!endif
|
||||
!Ex=0.d0
|
||||
!CALL echo(BIG(1:Ntdc,1:min(7,Ntdc)),Ntdc)
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
ansr(Ntd,1)=fxind(1)*CC
|
||||
if (SCIS.GT.0) then
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
if (SCIS.GT.0) then
|
||||
close(11)
|
||||
endif
|
||||
goto 300
|
||||
200 continue
|
||||
XddInf=10.d0*SQRT(R4(1))
|
||||
NI=7; Nd=3
|
||||
Nc=4; Mb=2
|
||||
allocate(BIG(1:Ntime+Nc+1,1:Ntime+Nc+1))
|
||||
ALLOCATE(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:Ntime+Nc+1))
|
||||
|
||||
ex=0.d0
|
||||
xc(1,1:Nx)=h
|
||||
xc(2,1:Nx)=u
|
||||
xc(3,1:Nx)=u
|
||||
xc(4,1:Nx)=0.d0
|
||||
|
||||
if (def.GT.0) then
|
||||
a_up(2,1)=1.d0 !*h
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(2,2)=1.d0 ! *h
|
||||
a_lo(2,2)=1.d0 ! *h
|
||||
a_up(2,3)=1.d0 !*h
|
||||
a_lo(1,3)=u
|
||||
|
||||
a_lo(1,4)=-XddInf
|
||||
a_up(1,5)= XdInf
|
||||
a_lo(1,6)=-XdInf
|
||||
else !def<0
|
||||
a_up(1,1)=u
|
||||
a_lo(2,1)=1.d0 !*h
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(2,2)=1.d0 ! *h
|
||||
a_lo(2,2)=1.d0 ! *h
|
||||
a_up(1,3)=u
|
||||
a_lo(2,3)=1.d0 !*h
|
||||
|
||||
a_up(1,4)=XddInf
|
||||
a_lo(1,5)=-XdInf
|
||||
a_up(1,6)=XdInf
|
||||
endif
|
||||
|
||||
Nstart=MAX(Nstart,3)
|
||||
do tn=Nstart,Ntime,1
|
||||
Ntd=tn+1
|
||||
Nt=Ntd-Nd
|
||||
Ntdc=Ntd+Nc
|
||||
indI(4)=Nt
|
||||
indI(5)=Nt+1
|
||||
indI(6)=Nt+2
|
||||
indI(7)=Ntd
|
||||
if (SCIS.gt.0) then
|
||||
if (SCIS.EQ.2) then
|
||||
Nj=max(Nt,0)
|
||||
else
|
||||
Nj=min(max(Nt-5, 0),0)
|
||||
endif
|
||||
endif
|
||||
do ts=2,FLOOR(DBLE(tn+1)/2.d0)
|
||||
!print *,'ts,tn' ,ts,tn
|
||||
CALL COV_INPUT(Big(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2,R3,R4) ! positive wave period
|
||||
indI(2)=ts-2
|
||||
indI(3)=ts-1
|
||||
!CALL echo(BIG(1:Ntdc,1:min(7,Ntdc)),Ntdc)
|
||||
!print *,'sp call rind'
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
!CALL echo(BIG(1:Ntdc,1:min(7,Ntdc)),Ntdc)
|
||||
!print *,'sp rind finished',fxind
|
||||
!goto 900
|
||||
if (abs(def).LT.3) THEN
|
||||
if (ts .EQ.tn-ts+1) then
|
||||
ds=dt
|
||||
else
|
||||
ds=2.d0*dt
|
||||
endif
|
||||
ansr(tn,1:Nx)=ansr(tn,1:Nx)+fxind*CC*ds
|
||||
else
|
||||
ansr(ts,1:Nx)=ansr(ts,1:Nx)+fxind*CC*dT
|
||||
if ((ts.LT.tn-ts+1).and. (abs(def).lt.4)) THEN
|
||||
ansr(tn-ts+1,1:Nx)=ansr(tn-ts+1,1:Nx)+fxind*CC*dT ! exploiting the symmetry
|
||||
endif
|
||||
endif
|
||||
enddo ! ts
|
||||
print *,'Ready: ',tn,' of ',Ntime
|
||||
|
||||
enddo !tn
|
||||
!print *,'ansr',ansr
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
!print *, ansr
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansr(ts,ph)
|
||||
! write(11,111) ansr(ts,ph)
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
if (allocated(R3)) then
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
ENDIF
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: def,Ntime,Nstart,NIT,speed,Nx,SCIS,seed1
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) def
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
READ (14,*) Nx
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
else
|
||||
Nx=1
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
endif
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,def,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: def
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
!if (def.LT.0) THEN
|
||||
! H=-H
|
||||
!endif
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime,def
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
|
||||
close(4)
|
||||
close(5)
|
||||
endif
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,shft,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! For ts>1:
|
||||
! ||X(t2)..X(ts),..X(tn-1)||X''(ts) X'(t1) X'(tn)||X(ts) X(t1) X(tn) X'(ts)||
|
||||
! = [Xt Xd Xc]
|
||||
!
|
||||
! For ts<=1:
|
||||
! ||X(t2)..,..X(tn-1)||X'(t1) X'(tn)||X(t1) X(tn)||
|
||||
! = [Xt Xd Xc]
|
||||
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
if (ts.LE.1) THEN
|
||||
Ntd1=tn
|
||||
N=Ntd1+2;
|
||||
shft=0 ! def=1 want only crest period Tc
|
||||
else
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+4
|
||||
shft=1 ! def=2 or 3 want Tc Ac or Tcf, Ac
|
||||
endif
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1+shft) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+2+shft) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X' (t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X' (tn))
|
||||
enddo
|
||||
!call echo(big(1:tn,1:tn),tn)
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1+shft,Ntd1+1+shft) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+1+shft,Ntd1+2+shft) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+2+shft,Ntd1+2+shft) = R0(1) ! cov(X(tn),X (tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1+shft) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+2+shft) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+1+shft) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+2+shft) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
|
||||
|
||||
if (ts.GT.1) then
|
||||
|
||||
!
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1,Ntd1+1) = R0(1) ! cov(X(ts),X (ts)
|
||||
BIG(Ntd1+1,Ntd1+2) = R0(ts) ! cov(X(ts),X (t1))
|
||||
BIG(Ntd1+1,Ntd1+3) = R0(tn+1-ts) ! cov(X(ts),X (tn))
|
||||
BIG(Ntd1+1,Ntd1+4) = 0.d0 ! cov(X(ts),X'(ts))
|
||||
|
||||
BIG(Ntd1+2,Ntd1+4) = R1(ts) ! cov(X(t1),X'(ts))
|
||||
BIG(Ntd1+3,Ntd1+4) = -R1(tn+1-ts) !cov(X(tn),X'(ts))
|
||||
BIG(Ntd1+4,Ntd1+4) = -R2(1) ! cov(X'(ts),X'(ts))
|
||||
|
||||
!cov(Xd)
|
||||
BIG(Ntd1-2,Ntd1-1) = -R3(ts) !cov(X''(ts),X'(t1))
|
||||
BIG(Ntd1-2,Ntd1-2) = R4(1)
|
||||
BIG(Ntd1-2,Ntd1 ) = R3(tn+1-ts) !cov(X''(ts),X'(tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+4) =-R2(tn+1-ts) !cov(X'(tn),X'(ts))
|
||||
BIG(Ntd1 ,Ntd1+1) = R1(tn+1-ts) !cov(X'(tn),X (ts))
|
||||
|
||||
BIG(Ntd1-1,Ntd1+4) =-R2(ts) !cov(X'(t1),X'(ts))
|
||||
BIG(Ntd1-1,Ntd1+1) =-R1(ts) !cov(X'(t1),X (ts))
|
||||
|
||||
BIG(Ntd1-2,Ntd1+1) = R2(1) !cov(X''(ts),X (ts)
|
||||
BIG(Ntd1-2,Ntd1+2) = R2(ts) !cov(X''(ts),X (t1))
|
||||
BIG(Ntd1-2,Ntd1+3) = R2(tn+1-ts) !cov(X''(ts),X (tn))
|
||||
BIG(Ntd1-2,Ntd1+4) = 0.d0 !cov(X''(ts),X'(ts))
|
||||
!cov(Xt,Xc)
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
BIG(i,Ntd1+1) = R0(j+1) !cov(X(ti+1),X(ts))
|
||||
BIG(i,Ntd1+4) = sign(R1(j+1),R1(j+1)*dble(ts-i-1)) !cov(X(ti+1),X'(ts)) ! check this
|
||||
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),X(ts))
|
||||
BIG(i,Ntd1-2) = R2(j+1) !cov(X(ti+1),X''(ts))
|
||||
enddo
|
||||
endif ! ts>1
|
||||
|
||||
!call echo(big(1:N,1:N),N)
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2thpdf
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,632 +0,0 @@
|
||||
PROGRAM sp2thpdf
|
||||
!***********************************************************************
|
||||
! This program computes: *
|
||||
! *
|
||||
! density of S_i,Hi,T_i in a gaussian process i.e. *
|
||||
! *
|
||||
! quart wavelength (up-crossing to crest) and crest amplitude *
|
||||
!
|
||||
! def = 1, gives half wave period, Tc (default).
|
||||
! -1, gives half wave period, Tt.
|
||||
! 2, gives half wave period and wave crest amplitude (Tc,Ac).
|
||||
! -2, gives half wave period and wave trough amplitude (Tt,At).
|
||||
! 3, gives crest front period and wave crest amplitude (Tcf,Ac).
|
||||
! -3, gives trough back period and wave trough amplitude (Ttb,At).
|
||||
! 4, gives minimum of crest front/back period and wave crest
|
||||
! amplitude (min(Tcf,Tcb),Ac).
|
||||
! -4, gives minimum of trough front/back period and wave trough
|
||||
! amplitude (min(Ttf,Ttb),At).
|
||||
!***********************************************************************
|
||||
!History:
|
||||
! revised Per A. Brodtkorb 04.04.2000
|
||||
! -
|
||||
! revised Per A. Brodtkorb 23.11.99
|
||||
! - fixed a bug in calculating pdf for def = +/- 4
|
||||
! revised Per A. Brodtkorb 03.11.99
|
||||
! - added def = +/-4
|
||||
! revised Per A. Brodtkorb 23.09.99
|
||||
! - minor changes to covinput
|
||||
! - removed the calculation of the transformation to spec2thpdf.m
|
||||
! by Igor Rychlik
|
||||
|
||||
|
||||
use GLOBALDATA, only : rateLHD,SCIS,NSIMmax,COV,ABSEPS
|
||||
use globalconst
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansr
|
||||
double precision, dimension(: ),allocatable :: ex
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(: ),allocatable :: fxind,h
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2,R3,R4
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(2,6) :: a_up=0.d0,a_lo=0.d0
|
||||
integer, dimension(6) :: INFIN=2
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Nx,Nt,Nc,Nd,NI,Mb,Ntd, Ntdc
|
||||
integer :: Nstart,Ntime,tn,ts,speed,ph,def,seed1,seed_size
|
||||
double precision :: dT, EPSOLD ! lag spacing for covariances
|
||||
LOGICAL :: init=.TRUE.
|
||||
! DIGITAL:
|
||||
! f90 -g2 -C -automatic -o ../wave/alpha/sp2thpdf.exe rind44.f sp2thpdf.f
|
||||
! SOLARIS:
|
||||
!f90 -g -O -w3 -Bdynamic -fixed -o ../wave/sol2/sp2thpdf.exe rind44.f sp2thpdf.f
|
||||
! linux:
|
||||
! f90 -gline -Nl126 -C -o ../exec/lnx86/sp2thpdf8.exe intmodule.f rind60.f sp2thpdf.f
|
||||
! f90 -gline -Nl126 -C -o sp2thpdf.exe rind45.f sp2thpdf.f
|
||||
! f90 -gline -Nl126 -C -o ../exec/lnx86/sp2thpdf3.exe adaptmodule.f krbvrcmod.f krobovmod.f rcrudemod.f rind55.f sp2thpdf.f
|
||||
! HP700
|
||||
!f90 -g -C -o ../exec/hp700/sp2thpdf.exe rind45.f sp2thpdf.f
|
||||
!f90 -g -C +check=all +FPVZID -o ../exec/hp700/sp2thpdf.exe rind45.f sp2thpdf.f
|
||||
! f90 +gprof +extend_source +Oall +Odataprefetch +Ofastaccess +Oinfo +Oprocelim -C +check=all -o ../exec/hp700/sp2thpdf.exe rind48.f sp2thpdf.f
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
|
||||
CALL INIT_LEVELS(U,def,Ntime,Nstart,speed,SCIS,seed1,
|
||||
& Nx,dT,rateLHD)
|
||||
!print *,'U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,def,Ntime,Nstart,NIT,speed,SCIS,seed1,Nx,dT
|
||||
|
||||
|
||||
if (SCIS.GT.0) then
|
||||
!allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
if (abs(def).GT.1) THEN
|
||||
allocate(h(1:Nx))
|
||||
allocate(R3(1:Ntime+1))
|
||||
allocate(R4(1:Ntime+1))
|
||||
CALL INIT_AMPLITUDES(h,def,Nx)
|
||||
endif
|
||||
CALL INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
|
||||
!print *,'Nx',Nx
|
||||
|
||||
|
||||
indI(1)=0
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
!print *,'XdInf,XtInf'
|
||||
!print *,XdInf,XtInf
|
||||
! normalizing constant
|
||||
CC=TWPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
allocate(ansr(1:Ntime,1:Nx))
|
||||
ansr=0.d0
|
||||
allocate(fxind(1:Nx))
|
||||
!fxind=0.d0 this is not needed
|
||||
|
||||
if (abs(def).GT.1) GOTO 200
|
||||
|
||||
NI=4; Nd=2
|
||||
Nc=2; Mb=1
|
||||
Nx=1
|
||||
allocate(BIG(1:Ntime+Nc,1:Ntime+Nc))
|
||||
allocate(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:Ntime+Nc))
|
||||
ex=0.d0
|
||||
xc(1,1)=u
|
||||
xc(2,1)=u
|
||||
! INFIN = INTEGER, array of integration limits flags: size 1 x Nb (in)
|
||||
! if INFIN(I) < 0, Ith limits are (-infinity, infinity);
|
||||
! if INFIN(I) = 0, Ith limits are (-infinity, Hup(I)];
|
||||
! if INFIN(I) = 1, Ith limits are [Hlo(I), infinity);
|
||||
! if INFIN(I) = 2, Ith limits are [Hlo(I), Hup(I)].
|
||||
|
||||
if (def.GT.0) then
|
||||
INFIN(1:2) = 1
|
||||
INFIN(3) = 0
|
||||
a_up(1,1)= u+XtInf
|
||||
a_lo(1,1)= u
|
||||
a_up(1,2)= XdInf
|
||||
a_lo(1,3)=-XdInf
|
||||
else
|
||||
INFIN(1:2) = 0
|
||||
INFIN(3) = 1
|
||||
a_up(1,1)=u
|
||||
a_lo(1,1)=u-XtInf
|
||||
a_lo(1,2)=-XdInf
|
||||
a_up(1,3)= XdInf
|
||||
endif
|
||||
!print *,'Nstart',Nstart
|
||||
Nstart=MAX(2,Nstart)
|
||||
!print *,'Nstart',Nstart
|
||||
if (ALLOCATED(COV)) then
|
||||
open (unit=11, file='COV.out', STATUS='unknown')
|
||||
write(11,*) 0.d0
|
||||
endif
|
||||
do Ntd=Nstart,Ntime
|
||||
!CALL COV_INPUT2(BIG,Ntd, R0,R1,R2)
|
||||
CALL COV_INPUT(BIG,Ntd,-1,R0,R1,R2,R3,R4) ! positive wave period
|
||||
Nt=Ntd-Nd;
|
||||
indI(2)=Nt;
|
||||
indI(3)=Nt+1;
|
||||
indI(4)=Ntd;
|
||||
Ntdc=Ntd+Nc;
|
||||
! IF (Ntd.GT.5.AND.(INIT)) THEN
|
||||
! INIT=.FALSE.
|
||||
! CALL INITDATA(speed)
|
||||
! ENDIF
|
||||
!if (SCIS.gt.1) Nj=Nt
|
||||
!if (SCIS.gt.0) then
|
||||
! if (SCIS.EQ.2) then
|
||||
! Nj=max(Nt,0)
|
||||
! else
|
||||
! Nj=min(max(Nt-5, 0),0)
|
||||
! endif
|
||||
!endif
|
||||
!Ex=0.d0
|
||||
!CALL echo(BIG(1:Ntdc,1:min(7,Ntdc)),Ntdc)
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),xc,
|
||||
& Nt,indI(1:NI),a_lo(1:Mb,1:NI-1),a_up(1:Mb,1:NI-1),
|
||||
& INFIN(1:NI-1))
|
||||
ansr(Ntd,1)=fxind(1)*CC
|
||||
if (ALLOCATED(COV)) then !SCIS.GT.0
|
||||
write(11,*) COV(1) ! save coefficient of variation
|
||||
endif
|
||||
print *,'Ready: ',Ntd,' of ',Ntime
|
||||
enddo
|
||||
if (ALLOCATED(COV)) then
|
||||
close(11)
|
||||
endif
|
||||
goto 300
|
||||
200 continue
|
||||
XddInf=10.d0*SQRT(R4(1))
|
||||
NI=7; Nd=3
|
||||
Nc=4; Mb=2
|
||||
allocate(BIG(1:Ntime+Nc+1,1:Ntime+Nc+1))
|
||||
ALLOCATE(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:Ntime+Nc+1))
|
||||
|
||||
ex=0.d0
|
||||
xc(1,1:Nx)=h(1:Nx)
|
||||
xc(2,1:Nx)=u
|
||||
xc(3,1:Nx)=u
|
||||
xc(4,1:Nx)=0.d0
|
||||
|
||||
! INFIN = INTEGER, array of integration limits flags: size 1 x Nb (in)
|
||||
! if INFIN(I) < 0, Ith limits are (-infinity, infinity);
|
||||
! if INFIN(I) = 0, Ith limits are (-infinity, Hup(I)];
|
||||
! if INFIN(I) = 1, Ith limits are [Hlo(I), infinity);
|
||||
! if INFIN(I) = 2, Ith limits are [Hlo(I), Hup(I)].
|
||||
if (def.GT.0) then
|
||||
INFIN(2)=-1
|
||||
INFIN(4)=0
|
||||
INFIN(5)=1
|
||||
INFIN(6)=0
|
||||
a_up(2,1)=1.d0 !*h
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(2,2)=1.d0 ! *h
|
||||
a_lo(2,2)=1.d0 ! *h
|
||||
a_up(2,3)=1.d0 !*h
|
||||
a_lo(1,3)=u
|
||||
|
||||
a_lo(1,4)=-XddInf
|
||||
a_up(1,5)= XdInf
|
||||
a_lo(1,6)=-XdInf
|
||||
else !def<0
|
||||
INFIN(2)=-1
|
||||
INFIN(4)=1
|
||||
INFIN(5)=0
|
||||
INFIN(6)=1
|
||||
a_up(1,1)=u
|
||||
a_lo(2,1)=1.d0 !*h
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(2,2)=1.d0 ! *h
|
||||
a_lo(2,2)=1.d0 ! *h
|
||||
a_up(1,3)=u
|
||||
a_lo(2,3)=1.d0 !*h
|
||||
a_up(1,4)=XddInf
|
||||
a_lo(1,5)=-XdInf
|
||||
a_up(1,6)=XdInf
|
||||
endif
|
||||
EPSOLD=ABSEPS
|
||||
Nstart=MAX(Nstart,3)
|
||||
do tn=Nstart,Ntime,1
|
||||
Ntd=tn+1
|
||||
Nt=Ntd-Nd
|
||||
Ntdc=Ntd+Nc
|
||||
indI(4)=Nt
|
||||
indI(5)=Nt+1
|
||||
indI(6)=Nt+2
|
||||
indI(7)=Ntd
|
||||
! IF (Ntd.GT.5.AND.INIT) THEN
|
||||
! INIT=.FALSE.
|
||||
! CALL INITDATA(speed)
|
||||
! ENDIF
|
||||
!if (SCIS.gt.1) Nj=Nt
|
||||
!if (SCIS.gt.0) then
|
||||
! if (SCIS.EQ.2) then
|
||||
! Nj=max(Nt,0)
|
||||
! else
|
||||
! Nj=min(max(Nt-5, 0),0)
|
||||
! endif
|
||||
!endif
|
||||
ABSEPS=MIN(SQRT(DBLE(tn))*EPSOLD*0.5D0,0.1D0)
|
||||
do ts=2,FLOOR(DBLE(tn+1)/2.d0)
|
||||
!print *,'ts,tn' ,ts,tn
|
||||
CALL COV_INPUT(Big(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2,R3,R4) ! positive wave period
|
||||
indI(2)=ts-2
|
||||
indI(3)=ts-1
|
||||
!CALL echo(BIG(1:Ntdc,1:min(7,Ntdc)),Ntdc)
|
||||
!print *,'sp call rind'
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),xc,
|
||||
& Nt,indI(1:NI),a_lo(1:Mb,1:NI-1),a_up(1:Mb,1:NI-1),
|
||||
& INFIN(1:NI-1))
|
||||
!CALL echo(BIG(1:Ntdc,1:min(7,Ntdc)),Ntdc)
|
||||
!print *,'sp rind finished',fxind
|
||||
!goto 900
|
||||
SELECT CASE (ABS(def))
|
||||
CASE (:2)
|
||||
! 2, gives half wave period and wave crest amplitude (Tc,Ac).
|
||||
! -2, gives half wave period and wave trough amplitude (Tt,At).
|
||||
if (ts .EQ.tn-ts+1) then
|
||||
ansr(tn,1:Nx)=ansr(tn,1:Nx)+fxind*CC*dt
|
||||
else
|
||||
ansr(tn,1:Nx)=ansr(tn,1:Nx)+fxind*CC*2.d0*dt
|
||||
endif
|
||||
CASE (3)
|
||||
! 3, gives crest front period and wave crest amplitude (Tcf,Ac).
|
||||
! -3, gives trough back period and wave trough amplitude (Ttb,At).
|
||||
ansr(ts,1:Nx)=ansr(ts,1:Nx)+fxind*CC*dT
|
||||
if ((ts.LT.tn-ts+1)) THEN
|
||||
ansr(tn-ts+1,1:Nx)=ansr(tn-ts+1,1:Nx)+fxind*CC*dT ! exploiting the symmetry
|
||||
endif
|
||||
CASE (4:)
|
||||
! 4, gives minimum of crest front/back period and wave crest amplitude (min(Tcf,Tcb),Ac).
|
||||
! -4, gives minimum of trough front/back period and wave trough amplitude (min(Ttf,Ttb),At).
|
||||
if (ts .EQ.tn-ts+1) then
|
||||
ansr(ts,1:Nx)=ansr(ts,1:Nx)+fxind*CC*dt
|
||||
else
|
||||
ansr(ts,1:Nx)=ansr(ts,1:Nx)+fxind*CC*2.0*dt
|
||||
endif
|
||||
end select
|
||||
enddo ! ts
|
||||
print *,'Ready: ',tn,' of ',Ntime, ' ABSEPS = ', ABSEPS
|
||||
|
||||
enddo !tn
|
||||
!print *,'ansr',ansr
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
!print *, ansr
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansr(ts,ph)
|
||||
! write(11,111) ansr(ts,ph)
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansr)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
if (allocated(R3)) then
|
||||
deallocate(R3)
|
||||
deallocate(R4)
|
||||
deallocate(h)
|
||||
ENDIF
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,def,Ntime,Nstart,speed,SCIS,seed1,Nx,dT,rateLHD)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: def,Ntime,Nstart,speed,Nx,SCIS,seed1,
|
||||
& rateLHD
|
||||
double precision ,intent(out) :: U,dT
|
||||
double precision :: XSPLT
|
||||
integer :: NIT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) def
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) Nstart
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
READ (14,*) Nx
|
||||
READ (14,*) dT
|
||||
READ (14,*) rateLHD
|
||||
READ (14,*) XSPLT
|
||||
if (abs(def).GT.1) then
|
||||
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
else
|
||||
Nx=1
|
||||
if (Ntime.lt.2) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
endif
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h,def,Nx)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h
|
||||
integer, intent(in) :: def
|
||||
integer, intent(in) :: Nx
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx
|
||||
READ (4,*) H(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
!if (def.LT.0) THEN
|
||||
! H=-H
|
||||
!endif
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,def,R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(out) :: R3,R4
|
||||
integer,intent(in) :: Ntime,def
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
if (abs(def).GT.1) then
|
||||
open (unit=4, file='Cd3.in',STATUS='unknown')
|
||||
open (unit=5, file='Cd4.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(4,*) R3(i)
|
||||
read(5,*) R4(i)
|
||||
enddo
|
||||
|
||||
close(4)
|
||||
close(5)
|
||||
endif
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2,R3,R4)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
double precision, dimension(:),intent(in) :: R3,R4
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,shft,Ntd1,N !=Ntdc
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
! For ts>1:
|
||||
! ||X(t2)..X(ts),..X(tn-1)||X''(ts) X'(t1) X'(tn)||X(ts) X(t1) X(tn) X'(ts)||
|
||||
! = [Xt Xd Xc]
|
||||
!
|
||||
! For ts<=1:
|
||||
! ||X(t2)..,..X(tn-1)||X'(t1) X'(tn)||X(t1) X(tn)||
|
||||
! = [Xt Xd Xc]
|
||||
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
if (ts.LE.1) THEN
|
||||
Ntd1=tn
|
||||
N=Ntd1+2;
|
||||
shft=0 ! def=1 want only crest period Tc
|
||||
else
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+4
|
||||
shft=1 ! def=2 or 3 want Tc Ac or Tcf, Ac
|
||||
endif
|
||||
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1+shft) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+2+shft) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X' (t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X' (tn))
|
||||
enddo
|
||||
!call echo(big(1:tn,1:tn),tn)
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1+shft,Ntd1+1+shft) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+1+shft,Ntd1+2+shft) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+2+shft,Ntd1+2+shft) = R0(1) ! cov(X(tn),X (tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1+shft) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+2+shft) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+1+shft) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+2+shft) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
|
||||
|
||||
if (ts.GT.1) then
|
||||
|
||||
!
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1,Ntd1+1) = R0(1) ! cov(X(ts),X (ts)
|
||||
BIG(Ntd1+1,Ntd1+2) = R0(ts) ! cov(X(ts),X (t1))
|
||||
BIG(Ntd1+1,Ntd1+3) = R0(tn+1-ts) ! cov(X(ts),X (tn))
|
||||
BIG(Ntd1+1,Ntd1+4) = 0.d0 ! cov(X(ts),X'(ts))
|
||||
|
||||
BIG(Ntd1+2,Ntd1+4) = R1(ts) ! cov(X(t1),X'(ts))
|
||||
BIG(Ntd1+3,Ntd1+4) = -R1(tn+1-ts) !cov(X(tn),X'(ts))
|
||||
BIG(Ntd1+4,Ntd1+4) = -R2(1) ! cov(X'(ts),X'(ts))
|
||||
|
||||
!cov(Xd)
|
||||
BIG(Ntd1-2,Ntd1-1) = -R3(ts) !cov(X''(ts),X'(t1))
|
||||
BIG(Ntd1-2,Ntd1-2) = R4(1)
|
||||
BIG(Ntd1-2,Ntd1 ) = R3(tn+1-ts) !cov(X''(ts),X'(tn))
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+4) =-R2(tn+1-ts) !cov(X'(tn),X'(ts))
|
||||
BIG(Ntd1 ,Ntd1+1) = R1(tn+1-ts) !cov(X'(tn),X (ts))
|
||||
|
||||
BIG(Ntd1-1,Ntd1+4) =-R2(ts) !cov(X'(t1),X'(ts))
|
||||
BIG(Ntd1-1,Ntd1+1) =-R1(ts) !cov(X'(t1),X (ts))
|
||||
|
||||
BIG(Ntd1-2,Ntd1+1) = R2(1) !cov(X''(ts),X (ts)
|
||||
BIG(Ntd1-2,Ntd1+2) = R2(ts) !cov(X''(ts),X (t1))
|
||||
BIG(Ntd1-2,Ntd1+3) = R2(tn+1-ts) !cov(X''(ts),X (tn))
|
||||
BIG(Ntd1-2,Ntd1+4) = 0.d0 !cov(X''(ts),X'(ts))
|
||||
!cov(Xt,Xc)
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
BIG(i,Ntd1+1) = R0(j+1) !cov(X(ti+1),X(ts))
|
||||
BIG(i,Ntd1+4) = sign(R1(j+1),R1(j+1)*dble(ts-i-1)) !cov(X(ti+1),X'(ts)) ! check this
|
||||
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),X(ts))
|
||||
BIG(i,Ntd1-2) = R2(j+1) !cov(X(ti+1),X''(ts))
|
||||
enddo
|
||||
endif ! ts>1
|
||||
|
||||
!call echo(big(1:N,1:N),N)
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
BIG(i,j) =BIG(j,i)
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
enddo
|
||||
!call echo(big(1:N,1:N),N)
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
END PROGRAM sp2thpdf
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,505 +0,0 @@
|
||||
PROGRAM sp2tthpdf
|
||||
C***********************************************************************
|
||||
C This program computes upper and lower bounds for the: *
|
||||
C *
|
||||
C density of T= T_1+T_2 in a gaussian process i.e. *
|
||||
C *
|
||||
C wavelengthes for crests <h1 and troughs >h2 *
|
||||
C *
|
||||
C Sylvie and Igor 7 dec. 1999 *
|
||||
C***********************************************************************
|
||||
use GLOBALDATA, only : Nt,Nj,Nd,Nc,Ntd,Ntdc,NI,Mb,
|
||||
& NIT,Nx,TWOPI,XSPLT,SCIS,NSIMmax,COV
|
||||
use rind
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),allocatable :: BIG
|
||||
double precision, dimension(:,:),allocatable :: ansrup
|
||||
double precision, dimension(:,:),allocatable :: ansrlo
|
||||
double precision, dimension(: ),allocatable :: ex,CY1,CY2
|
||||
double precision, dimension(:,:),allocatable :: xc
|
||||
double precision, dimension(:,:),allocatable ::fxind
|
||||
double precision, dimension(: ),allocatable :: h1,h2
|
||||
double precision, dimension(: ),allocatable :: hh1,hh2
|
||||
double precision, dimension(: ),allocatable :: R0,R1,R2
|
||||
double precision ::CC,U,XddInf,XdInf,XtInf
|
||||
double precision, dimension(:,:),allocatable :: a_up,a_lo
|
||||
integer , dimension(: ),allocatable :: seed
|
||||
integer ,dimension(7) :: indI
|
||||
integer :: Ntime,N0,tn,ts,speed,ph,seed1,seed_size,Nx1,Nx2
|
||||
integer :: icy,icy2
|
||||
double precision :: ds,dT ! lag spacing for covariances
|
||||
! DIGITAL:
|
||||
! f90 -g2 -C -automatic -o ~/WAT/V4/sp2tthpdf.exe rind48.f sp2tthpdf.f
|
||||
! SOLARIS:
|
||||
!f90 -g -O -w3 -Bdynamic -fixed -o ../sp2tthpdf.exe rind48.f sp2tthpdf.f
|
||||
|
||||
!print *,'enter sp2thpdf'
|
||||
CALL INIT_LEVELS(U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
|
||||
!print *,'U,Ntime,NIT,speed,SCIS,seed1,Nx,dT'
|
||||
!print *,U,Ntime,NIT,speed,SCIS,seed1,Nx,dT
|
||||
!Nx1=1
|
||||
!Nx2=1
|
||||
|
||||
Nx=Nx1*Nx2
|
||||
!print *,'NN',Nx1,Nx2,Nx
|
||||
|
||||
|
||||
!XSPLT=1.5d0
|
||||
if (SCIS.GT.0) then
|
||||
allocate(COV(1:Nx))
|
||||
call random_seed(SIZE=seed_size)
|
||||
allocate(seed(seed_size))
|
||||
call random_seed(GET=seed(1:seed_size)) ! get current seed
|
||||
seed(1)=seed1 ! change seed
|
||||
call random_seed(PUT=seed(1:seed_size))
|
||||
deallocate(seed)
|
||||
endif
|
||||
CALL INITDATA(speed)
|
||||
!print *,ntime,speed,u,NIT
|
||||
allocate(R0(1:Ntime+1))
|
||||
allocate(R1(1:Ntime+1))
|
||||
allocate(R2(1:Ntime+1))
|
||||
|
||||
allocate(h1(1:Nx1))
|
||||
allocate(h2(1:Nx2))
|
||||
CALL INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
CALL INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
|
||||
|
||||
allocate(hh1(1:Nx))
|
||||
allocate(hh2(1:Nx))
|
||||
!h transformation
|
||||
do icy=1,Nx1
|
||||
do icy2=1,Nx2
|
||||
hh1((icy-1)*Nx2+icy2)=h1(icy);
|
||||
hh2((icy-1)*Nx2+icy2)=h2(icy2);
|
||||
enddo
|
||||
enddo
|
||||
|
||||
Nj=0
|
||||
indI(1)=0
|
||||
|
||||
C ***** The bound 'infinity' is set to 10*sigma *****
|
||||
XdInf=10.d0*SQRT(-R2(1))
|
||||
XtInf=10.d0*SQRT(R0(1))
|
||||
!h1(1)=XtInf
|
||||
!h2(1)=XtInf
|
||||
! normalizing constant
|
||||
CC=TWOPI*SQRT(-R0(1)/R2(1))*exp(u*u/(2.d0*R0(1)) )
|
||||
allocate(CY1(1:Nx))
|
||||
allocate(CY2(1:Nx))
|
||||
do icy=1,Nx
|
||||
CY1(icy)=exp(-0.5*hh1(icy)*hh1(icy)/100)/(10*sqrt(twopi))
|
||||
CY2(icy)=exp(-0.5*hh2(icy)*hh2(icy)/100)/(10*sqrt(twopi))
|
||||
enddo
|
||||
!print *,CY1
|
||||
allocate(ansrup(1:Ntime,1:Nx))
|
||||
allocate(ansrlo(1:Ntime,1:Nx))
|
||||
ansrup=0.d0
|
||||
ansrlo=0.d0
|
||||
allocate(fxind(1:Nx,1:2))
|
||||
!fxind=0.d0 this is not needed
|
||||
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
! Y={X(t2)..,X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)} !!
|
||||
! = [Xt Xd Xc] !!
|
||||
! !!
|
||||
! Nt=tn-2, Nd=3, Nc=2+3 !!
|
||||
! !!
|
||||
! Xt= contains Nt time points in the indicator function !!
|
||||
! Xd= " Nd derivatives !!
|
||||
! Xc= " Nc variables to condition on !!
|
||||
! (Y1,Y2) dummy variables ind. of all other v. inputing h1,h2 into rindd !!
|
||||
! !!
|
||||
! There are 6 ( NI=7) regions with constant bariers: !!
|
||||
! (indI(1)=0); for i\in (indI(1),indI(2)] u<Y(i)<h1 !!
|
||||
! (indI(2)=ts-2); for i\in (indI(2),indI(2)], inf<Y(i)<inf (no restr.) !!
|
||||
! (indI(3)=ts-1); for i\in (indI(3),indI(4)], h2 <Y(i)<u !!
|
||||
! (indI(4)=Nt) ; for i\in (indI(4),indI(5)], Y(i)<0 (deriv. X'(ts)) !!
|
||||
! (indI(5)=Nt+1); for i\in (indI(5),indI(6)], Y(i)>0 (deriv. X'(t1)) !!
|
||||
! (indI(6)=Nt+2); for i\in (indI(6),indI(7)], Y(i)>0 (deriv. X'(tn)) !!
|
||||
! (indI(7)=Nt+3); NI=7. !!
|
||||
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
|
||||
NI=7; Nd=3
|
||||
Nc=5; Mb=3
|
||||
allocate(a_up(1:Mb,1:(NI-1)))
|
||||
allocate(a_lo(1:Mb,1:(NI-1)))
|
||||
a_up=0.d0
|
||||
a_lo=0.d0
|
||||
allocate(BIG(1:(Ntime+Nc+1),1:(Ntime+Nc+1)))
|
||||
ALLOCATE(xc(1:Nc,1:Nx))
|
||||
allocate(ex(1:(Ntime+Nc+1)))
|
||||
!print *,size(ex),Ntime
|
||||
ex=0.d0
|
||||
!print *,size(ex),ex
|
||||
xc(1,1:Nx)=hh1(1:Nx)
|
||||
xc(2,1:Nx)=hh2(1:Nx)
|
||||
xc(3,1:Nx)=u
|
||||
xc(4,1:Nx)=u
|
||||
xc(5,1:Nx)=u
|
||||
! upp- down- upp-crossings at t1,ts,tn
|
||||
|
||||
a_lo(1,1)=u
|
||||
a_up(1,2)=XtInf ! X(ts) is redundant
|
||||
a_lo(1,2)=-Xtinf
|
||||
a_up(1,3)=u
|
||||
|
||||
|
||||
a_lo(1,4)=-XdInf
|
||||
a_up(1,5)= XdInf
|
||||
a_up(1,6)= XdInf
|
||||
|
||||
a_up(2,1)=1.d0
|
||||
a_lo(3,3)=1.d0 !signe a voir!!!!!!
|
||||
! print *,a_up
|
||||
! print *,a_lo
|
||||
do tn=N0,Ntime,1
|
||||
! do tn=Ntime,Ntime,1
|
||||
Ntd=tn+1
|
||||
Nt=Ntd-Nd
|
||||
Ntdc=Ntd+Nc
|
||||
indI(4)=Nt
|
||||
indI(5)=Nt+1
|
||||
indI(6)=Nt+2
|
||||
indI(7)=Ntd
|
||||
if (SCIS.gt.0) then
|
||||
if (SCIS.EQ.2) then
|
||||
Nj=max(Nt,0)
|
||||
else
|
||||
Nj=min(max(Nt-5, 0),0)
|
||||
endif
|
||||
endif
|
||||
do ts=3,tn-2
|
||||
!print *,'ts,tn' ,ts,tn,Ntdc
|
||||
CALL COV_INPUT(Big(1:Ntdc,1:Ntdc),tn,ts,R0,R1,R2)!positive wave period
|
||||
indI(2)=ts-2
|
||||
indI(3)=ts-1
|
||||
|
||||
|
||||
CALL RINDD(fxind,Big(1:Ntdc,1:Ntdc),ex(1:Ntdc),
|
||||
& xc,indI,a_lo,a_up)
|
||||
|
||||
ds=dt
|
||||
do icy=1,Nx
|
||||
! ansr(tn,:)=ansr(tn,:)+fxind*CC*ds./(CY1.*CY2)
|
||||
ansrup(tn,icy)=ansrup(tn,icy)+fxind(icy,1)*CC*ds
|
||||
& /(CY1(icy)*CY2(icy))
|
||||
ansrlo(tn,icy)=ansrlo(tn,icy)+fxind(icy,2)*CC*ds
|
||||
& /(CY1(icy)*CY2(icy))
|
||||
enddo
|
||||
enddo ! ts
|
||||
print *,'Ready: ',tn,' of ',Ntime
|
||||
|
||||
enddo !tn
|
||||
|
||||
300 open (unit=11, file='dens.out', STATUS='unknown')
|
||||
|
||||
do ts=1,Ntime
|
||||
do ph=1,Nx
|
||||
write(11,*) ansrup(ts,ph),ansrlo(ts,ph)!,hh1(ph),hh2(ph)
|
||||
! write(11,111) ansrup(ts,ph),ansrlo(ts,ph)
|
||||
|
||||
enddo
|
||||
enddo
|
||||
!111 FORMAT(2x,F12.8)
|
||||
close(11)
|
||||
900 deallocate(big)
|
||||
deallocate(fxind)
|
||||
deallocate(ansrup)
|
||||
deallocate(ansrlo)
|
||||
deallocate(xc)
|
||||
deallocate(ex)
|
||||
deallocate(R0)
|
||||
deallocate(R1)
|
||||
deallocate(R2)
|
||||
if (allocated(COV) ) then
|
||||
deallocate(COV)
|
||||
endif
|
||||
deallocate(h1)
|
||||
deallocate(h2)
|
||||
deallocate(hh1)
|
||||
deallocate(hh2)
|
||||
deallocate(a_up)
|
||||
deallocate(a_lo)
|
||||
stop
|
||||
!return
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
|
||||
SUBROUTINE INIT_LEVELS
|
||||
& (U,Ntime,N0,NIT,speed,SCIS,seed1,Nx1,Nx2,dT)
|
||||
IMPLICIT NONE
|
||||
integer, intent(out):: Ntime,N0,NIT,speed,Nx1,Nx2,SCIS,seed1
|
||||
double precision ,intent(out) :: U,dT
|
||||
|
||||
|
||||
OPEN(UNIT=14,FILE='reflev.in',STATUS= 'UNKNOWN')
|
||||
READ (14,*) U
|
||||
READ (14,*) Ntime
|
||||
READ (14,*) N0
|
||||
READ (14,*) NIT
|
||||
READ (14,*) speed
|
||||
READ (14,*) SCIS
|
||||
READ (14,*) seed1
|
||||
|
||||
|
||||
READ (14,*) Nx1,Nx2
|
||||
READ (14,*) dT
|
||||
if (Ntime.lt.3) then
|
||||
print *,'The number of wavelength points is too small, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
CLOSE(UNIT=14)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_LEVELS
|
||||
|
||||
C******************************************************
|
||||
SUBROUTINE INIT_AMPLITUDES(h1,Nx1,h2,Nx2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:), intent(out) :: h1,h2
|
||||
integer, intent(in) :: Nx1,Nx2
|
||||
integer :: ix
|
||||
|
||||
|
||||
OPEN(UNIT=4,FILE='h.in',STATUS= 'UNKNOWN')
|
||||
|
||||
C
|
||||
C Reading in amplitudes
|
||||
C
|
||||
do ix=1,Nx1
|
||||
READ (4,*) H1(ix)
|
||||
enddo
|
||||
do ix=1,Nx2
|
||||
READ (4,*) H2(ix)
|
||||
enddo
|
||||
CLOSE(UNIT=4)
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INIT_AMPLITUDES
|
||||
|
||||
C**************************************************
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
SUBROUTINE INIT_COVARIANCES(Ntime,R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:),intent(out) :: R0,R1,R2
|
||||
integer,intent(in) :: Ntime
|
||||
integer :: i
|
||||
open (unit=1, file='Cd0.in',STATUS='unknown')
|
||||
open (unit=2, file='Cd1.in',STATUS='unknown')
|
||||
open (unit=3, file='Cd2.in',STATUS='unknown')
|
||||
|
||||
do i=1,Ntime
|
||||
read(1,*) R0(i)
|
||||
read(2,*) R1(i)
|
||||
read(3,*) R2(i)
|
||||
enddo
|
||||
close(1)
|
||||
close(2)
|
||||
close(3)
|
||||
|
||||
return
|
||||
END SUBROUTINE INIT_COVARIANCES
|
||||
|
||||
C***********************************************************************
|
||||
C***********************************************************************
|
||||
|
||||
C**********************************************************************
|
||||
|
||||
SUBROUTINE COV_INPUT(BIG,tn,ts, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:),intent(inout) :: BIG
|
||||
double precision, dimension(:),intent(in) :: R0,R1,R2
|
||||
integer ,intent(in) :: tn,ts
|
||||
integer :: i,j,Ntd1,N !=Ntdc
|
||||
double precision :: tmp
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows:
|
||||
!
|
||||
! ||X(t2)..X(ts),..X(tn-1)||X'(ts) X'(t1) X'(tn)||Y1 Y2 X(ts) X(t1) X(tn)||
|
||||
! = [Xt Xd Xc]
|
||||
! where
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
! Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s),
|
||||
! then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have
|
||||
! a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result).
|
||||
!
|
||||
! Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|)
|
||||
! Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|)
|
||||
! Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|)
|
||||
! Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|)
|
||||
! Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|)
|
||||
|
||||
Ntd1=tn+1
|
||||
N=Ntd1+Nc
|
||||
do i=1,tn-2
|
||||
!cov(Xt)
|
||||
do j=i,tn-2
|
||||
BIG(i,j) = R0(j-i+1) ! cov(X(ti+1),X(tj+1))
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
BIG(i ,Ntd1+1) = 0.d0 !cov(X(ti+1),Y1)
|
||||
BIG(i ,Ntd1+2) = 0.d0 !cov(X(ti+1),Y2)
|
||||
BIG(i ,Ntd1+4) = R0(i+1) !cov(X(ti+1),X(t1))
|
||||
BIG(tn-1-i ,Ntd1+5) = R0(i+1) !cov(X(t.. ),X(tn))
|
||||
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj)
|
||||
BIG(i,Ntd1-1) =-R1(i+1) !cov(X(ti+1),X'(t1))
|
||||
BIG(tn-1-i,Ntd1)= R1(i+1) !cov(X(ti+1),X'(tn))
|
||||
enddo
|
||||
!cov(Xd)
|
||||
BIG(Ntd1 ,Ntd1 ) = -R2(1)
|
||||
BIG(Ntd1-1,Ntd1 ) = -R2(tn) !cov(X'(t1),X'(tn))
|
||||
BIG(Ntd1-1,Ntd1-1) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1-1) = -R2(ts) !cov(X'(ts),X'(t1))
|
||||
BIG(Ntd1-2,Ntd1-2) = -R2(1)
|
||||
BIG(Ntd1-2,Ntd1 ) = -R2(tn+1-ts) !cov(X'(ts),X'(tn))
|
||||
|
||||
!cov(Xc)
|
||||
BIG(Ntd1+1,Ntd1+1) = 100.d0 ! cov(Y1 Y1)
|
||||
BIG(Ntd1+1,Ntd1+2) = 0.d0 ! cov(Y1 Y2)
|
||||
BIG(Ntd1+1,Ntd1+3) = 0.d0 ! cov(Y1 X(ts))
|
||||
BIG(Ntd1+1,Ntd1+4) = 0.d0 ! cov(Y1 X(t1))
|
||||
BIG(Ntd1+1,Ntd1+5) = 0.d0 ! cov(Y1 X(tn))
|
||||
BIG(Ntd1+2,Ntd1+2) = 100.d0 ! cov(Y2 Y2)
|
||||
BIG(Ntd1+2,Ntd1+3) = 0.d0 ! cov(Y2 X(ts))
|
||||
BIG(Ntd1+2,Ntd1+4) = 0.d0 ! cov(Y2 X(t1))
|
||||
BIG(Ntd1+2,Ntd1+5) = 0.d0 ! cov(Y2 X(tn))
|
||||
|
||||
BIG(Ntd1+3,Ntd1+3) = R0(1) ! cov(X(ts),X (ts)
|
||||
BIG(Ntd1+3,Ntd1+4) = R0(ts) ! cov(X(ts),X (t1))
|
||||
BIG(Ntd1+3,Ntd1+5) = R0(tn+1-ts) ! cov(X(ts),X (tn))
|
||||
BIG(Ntd1+4,Ntd1+4) = R0(1) ! cov(X(t1),X (t1))
|
||||
BIG(Ntd1+4,Ntd1+5) = R0(tn) ! cov(X(t1),X (tn))
|
||||
BIG(Ntd1+5,Ntd1+5) = R0(1) ! cov(X(tn),X (tn))
|
||||
|
||||
|
||||
!cov(Xd,Xc)
|
||||
BIG(Ntd1 ,Ntd1+1) = 0.d0 !cov(X'(tn),Y1)
|
||||
BIG(Ntd1 ,Ntd1+2) = 0.d0 !cov(X'(tn),Y2)
|
||||
BIG(Ntd1-1 ,Ntd1+1) = 0.d0 !cov(X'(t1),Y1)
|
||||
BIG(Ntd1-1 ,Ntd1+2) = 0.d0 !cov(X'(t1),Y2)
|
||||
BIG(Ntd1-2 ,Ntd1+1) = 0.d0 !cov(X'(ts),Y1)
|
||||
BIG(Ntd1-2 ,Ntd1+2) = 0.d0 !cov(X'(ts),Y2)
|
||||
|
||||
BIG(Ntd1 ,Ntd1+4) = R1(tn) !cov(X'(tn),X(t1))
|
||||
BIG(Ntd1 ,Ntd1+5) = 0.d0 !cov(X'(tn),X(tn))
|
||||
BIG(Ntd1-1,Ntd1+4) = 0.d0 !cov(X'(t1),X(t1))
|
||||
BIG(Ntd1-1,Ntd1+5) =-R1(tn) !cov(X'(t1),X(tn))
|
||||
BIG(Ntd1 ,Ntd1+3) = R1(tn+1-ts) !cov(X'(tn),X (ts))
|
||||
BIG(Ntd1-1,Ntd1+3) =-R1(ts) !cov(X'(t1),X (ts))
|
||||
BIG(Ntd1-2,Ntd1+3) = 0.d0 !cov(X'(ts),X (ts)
|
||||
BIG(Ntd1-2,Ntd1+4) = R1(ts) !cov(X'(ts),X (t1))
|
||||
BIG(Ntd1-2,Ntd1+5) = -R1(tn+1-ts) !cov(X'(ts),X (tn))
|
||||
|
||||
|
||||
do i=1,tn-2
|
||||
j=abs(i+1-ts)
|
||||
!cov(Xt,Xc)
|
||||
BIG(i,Ntd1+3) = R0(j+1) !cov(X(ti+1),X(ts))
|
||||
!Cov(Xt,Xd)
|
||||
if ((i+1-ts).lt.0) then
|
||||
BIG(i,Ntd1-2) = R1(j+1)
|
||||
else !cov(X(ti+1),X'(ts))
|
||||
BIG(i,Ntd1-2) = -R1(j+1)
|
||||
endif
|
||||
enddo
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,N-1
|
||||
do i=j+1,N
|
||||
tmp =BIG(j,i)
|
||||
|
||||
BIG(i,j)=tmp
|
||||
enddo
|
||||
enddo
|
||||
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT
|
||||
|
||||
SUBROUTINE COV_INPUT2(BIG,pt, R0,R1,R2)
|
||||
IMPLICIT NONE
|
||||
double precision, dimension(:,:), intent(out) :: BIG
|
||||
double precision, dimension(:), intent(in) :: R0,R1,R2
|
||||
integer :: pt,i,j
|
||||
! the order of the variables in the covariance matrix
|
||||
! are organized as follows;
|
||||
! X(t2)...X(tn-1) X'(t1) X'(tn) X(t1) X(tn) = [Xt Xd Xc]
|
||||
!
|
||||
! where Xd is the derivatives
|
||||
!
|
||||
! Xt= time points in the indicator function
|
||||
! Xd= derivatives
|
||||
! Xc=variables to condition on
|
||||
|
||||
!cov(Xc)
|
||||
BIG(pt+2,pt+2) = R0(1)
|
||||
BIG(pt+1,pt+1) = R0(1)
|
||||
BIG(pt+1,pt+2) = R0(pt)
|
||||
!cov(Xd)
|
||||
BIG(pt,pt) = -R2(1)
|
||||
BIG(pt-1,pt-1) = -R2(1)
|
||||
BIG(pt-1,pt) = -R2(pt)
|
||||
!cov(Xd,Xc)
|
||||
BIG(pt,pt+2) = 0.d0
|
||||
BIG(pt,pt+1) = R1(pt)
|
||||
BIG(pt-1,pt+2) = -R1(pt)
|
||||
BIG(pt-1,pt+1) = 0.d0
|
||||
|
||||
if (pt.GT.2) then
|
||||
!cov(Xt)
|
||||
do i=1,pt-2
|
||||
do j=i,pt-2
|
||||
BIG(i,j) = R0(j-i+1)
|
||||
enddo
|
||||
enddo
|
||||
!cov(Xt,Xc)
|
||||
do i=1,pt-2
|
||||
BIG(i,pt+1) = R0(i+1)
|
||||
BIG(pt-1-i,pt+2) = R0(i+1)
|
||||
enddo
|
||||
!Cov(Xt,Xd)=cov(X(ti+1),x(tj))
|
||||
do i=1,pt-2
|
||||
BIG(i,pt-1) = -R1(i+1)
|
||||
BIG(pt-1-i,pt)= R1(i+1)
|
||||
enddo
|
||||
endif
|
||||
|
||||
|
||||
! make lower triangular part equal to upper
|
||||
do j=1,pt+1
|
||||
do i=j+1,pt+2
|
||||
BIG(i,j)=BIG(j,i)
|
||||
enddo
|
||||
enddo
|
||||
C write (*,10) ((BIG(j,i),i=N+1,N+6),j=N+1,N+6)
|
||||
C 10 format(6F8.4)
|
||||
RETURN
|
||||
END SUBROUTINE COV_INPUT2
|
||||
|
||||
|
||||
END PROGRAM sp2tthpdf
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,30 +0,0 @@
|
||||
"""
|
||||
f2py c_library.pyf c_functions.c -c
|
||||
|
||||
gfortran -W -Wall -pedantic-errors -fbounds-check -Werror -c dsvdc.f mregmodule.f
|
||||
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
from wafo.f2py_tools import f2py_call_str
|
||||
|
||||
|
||||
def compile_all():
|
||||
f2py_call = f2py_call_str()
|
||||
print '=' * 75
|
||||
print 'compiling cov2mod'
|
||||
print '=' * 75
|
||||
|
||||
files = ['dsvdc', 'mregmodule', 'intfcmod']
|
||||
compile1_format = 'gfortran -fPIC -c %s.f'
|
||||
format1 = '%s.o ' * len(files)
|
||||
for file_ in files:
|
||||
os.system(compile1_format % file_)
|
||||
file_objects = format1 % tuple(files)
|
||||
|
||||
os.system(f2py_call + ' -m cov2mod -c %s cov2mmpdfreg_intfc.f' %
|
||||
file_objects)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
compile_all()
|
||||
@ -1,651 +0,0 @@
|
||||
C Version 1994-X-18
|
||||
|
||||
C This is a new version of WAMP program computing crest-trough wavelength
|
||||
C and amplitude density.
|
||||
C
|
||||
C revised pab 2007
|
||||
C -moved all common blocks into modules
|
||||
C -renamed from minmax to sp2mmpdfreg + fixed some bugs
|
||||
C revised pab July 2007
|
||||
! -renamed from sp2mmpdfreg to cov2mmpdfreg
|
||||
|
||||
PROGRAM cov2mmpdfreg
|
||||
USE SIZEMOD
|
||||
USE EPSMOD
|
||||
USE CHECKMOD
|
||||
USE MREGMOD
|
||||
IMPLICIT NONE
|
||||
real*8 Q0,SQ0,Q1,SQ1, AA, BB, DAI, AI , U,V,VV, XL0, XL2, XL4
|
||||
REAL*8 VDERI, CDER,SDER, DER, CONST, F, HHHH,FM, VALUE
|
||||
C INTEGER, PARAMETER :: MMAX = 5, NMAX = 101, RDIM = 10201
|
||||
REAL*8, DIMENSION(NMAX) :: HHT,T,Ulev,Vlev,VT,UT,Vdd,Udd
|
||||
REAL*8, DIMENSION(RDIM) :: R,R1,R2,R3
|
||||
REAL*8, DIMENSION(5*NMAX) :: COV
|
||||
REAL*8, DIMENSION(NMAX,NMAX) :: UVdens
|
||||
C DIMENSION UVdens(NMAX,NMAX),HHT(NMAX)
|
||||
C DIMENSION T(NMAX),Ulev(NMAX),Vlev(NMAX)
|
||||
C DIMENSION VT(NMAX),UT(NMAX),Vdd(NMAX),Udd(NMAX)
|
||||
C DIMENSION COV(5*NMAX),R(RDIM),R1(RDIM),R2(RDIM),R3(RDIM)
|
||||
DIMENSION AA(MMAX-2,MMAX-2),BB(MMAX+1),DAI(MMAX),AI((MMAX+1)*NMAX)
|
||||
|
||||
C
|
||||
C The program computes the joint density of maximum the following minimum
|
||||
C and the distance between Max and min for a zero-mean stationary
|
||||
C Gaussian process with covariance function defined explicitely with 4
|
||||
C derivatives. The process should be normalized so that the first and
|
||||
C the second spectral moments are equal to 1. The values of Max are taken
|
||||
C as the nodes at Hermite-Quadrature and then integrated out so that
|
||||
C the output is a joint density of wavelength T and amplitude H=Max-min.
|
||||
C The Max values are defined by subroutine Gauss_M with the accuracy
|
||||
C input epsu. The principle is that the integral of the marginal density
|
||||
C of f_Max is computed with sufficient accuracy.
|
||||
C
|
||||
REAL*8, DIMENSION(NMAX) :: B0,DB0,DDB0,B1,DB1,DDB1,DB2,DDB2
|
||||
REAL*8, DIMENSION(NMAX) :: Q,SQ,VDER,DBI,BI
|
||||
C DIMENSION B0(NMAX),DB0(NMAX),DDB0(NMAX)
|
||||
C DIMENSION B1(NMAX),DB1(NMAX),DDB1(NMAX)
|
||||
C DIMENSION DB2(NMAX),DDB2(NMAX)
|
||||
C DIMENSION Q(NMAX),SQ(NMAX),VDER(NMAX),DBI(NMAX),BI(NMAX)
|
||||
INTEGER :: J,I,I1,I2,I3,IU, IV, NU,NV,NG,N,NIT, NNIT, INF
|
||||
INTEGER :: fffff
|
||||
C REAL*8 EPS0
|
||||
C INTEGER III01,III11,III21,III31,III41,III51
|
||||
C *,III61,III71,III81,III91,III101 , III0
|
||||
C COMMON/CHECK1/III01,III11,III21,III31,III41,III51
|
||||
C *,III61,III71,III81,III91,III101
|
||||
C COMMON/CHECKQ/III0
|
||||
C COMMON /EPS/ EPS,EPSS,CEPSS
|
||||
|
||||
C
|
||||
C Initiation of all constants and integration nodes 'INITINTEG'
|
||||
C
|
||||
CALL INITINTEG(NIT)
|
||||
c
|
||||
c OBS. we are using the variables R,R1,R2 R3 as a temporary storage
|
||||
C for transformation g of the process.
|
||||
|
||||
|
||||
|
||||
c
|
||||
CALL INITLEVELS(Ulev,NU,Vlev,NV,T,HHT,N,R1,R2,NG)
|
||||
IF( R1(1) .gt. R1(ng)) then
|
||||
do 13 I=1,ng
|
||||
R3(I)=R1(I)
|
||||
R(I) =R2(I)
|
||||
13 continue
|
||||
do 17 i=1,ng
|
||||
R1(i) = R3(ng-i+1)
|
||||
R2(i) = R(ng-i+1)
|
||||
17 continue
|
||||
end if
|
||||
if(abs(R1(ng)-R1(1))*abs(R2(ng)-R2(1)).lt.0.01d0) then
|
||||
print *,'The transformation g is singular, stop'
|
||||
stop
|
||||
end if
|
||||
DO 14 IV=1,Nv
|
||||
V=Vlev(IV)
|
||||
CALL TRANSF(NG,V,R2,R1,VALUE,DER)
|
||||
VT(IV)=VALUE
|
||||
Vdd(IV)=DER
|
||||
14 continue
|
||||
DO 16 IU=1,Nu
|
||||
U = Ulev(IU)
|
||||
CALL TRANSF(NG,U,R2,R1,VALUE,DER)
|
||||
UT(IU) = VALUE
|
||||
Udd(IU) = DER
|
||||
do 16 IV=1,Nv
|
||||
UVdens(IU,IV)=0.0d0
|
||||
16 CONTINUE
|
||||
|
||||
|
||||
CALL COVG(XL0,XL2,XL4,COV,R1,R2,R3,T,N)
|
||||
|
||||
|
||||
Q0=XL4
|
||||
IF (Q0.le.1.0D0+EPS) then
|
||||
Print *,'Covariance structure is singular, stop.'
|
||||
stop
|
||||
end if
|
||||
SQ0 = SQRT(Q0)
|
||||
Q1 = XL0-XL2*XL2/XL4
|
||||
IF (Q1.le.eps) then
|
||||
Print *,'Covariance structure is singular, stop.'
|
||||
stop
|
||||
end if
|
||||
SQ1 = SQRT(Q1)
|
||||
DO 10 I=1,N
|
||||
B0(I) =-COV(I+2*N)
|
||||
DB0(I) =-COV(I+3*N)
|
||||
DDB0(I)=-COV(I+4*N)
|
||||
|
||||
B1(I) =COV(I)+COV(I+2*N)*(XL2/XL4)
|
||||
DB1(I) =COV(I+N)+COV(I+3*N)*(XL2/XL4)
|
||||
DDB1(I)=COV(I+2*N)+XL2*(COV(I+4*N)/XL4)
|
||||
C
|
||||
C Q(I) contains Var(X(T(i))|X'(0),X''(0),X(0))
|
||||
C VDER(I) contains Var(X''(T(i))|X'(0),X''(0),X(0))
|
||||
C
|
||||
Q(I)=XL0 - COV(I+N)*(COV(I+N)/XL2) - B0(I)*(B0(I)/Q0)
|
||||
1 -B1(I)*(B1(I)/Q1)
|
||||
VDER(I)=XL4 - (COV(I+3*N)*COV(I+3*N))/XL2 - (DDB0(I)*DDB0(I))/Q0
|
||||
1 - (DDB1(I)*DDB1(I))/Q1
|
||||
|
||||
|
||||
C
|
||||
C DDB2(I) contains Cov(X''(T(i)),X(T(i))|X'(0),X''(0),X(0))
|
||||
C
|
||||
DDB2(I)=-XL2 - (COV(I+N)*COV(I+3*N))/XL2 - DDB0(I)*(B0(I)/Q0)
|
||||
1 -DDB1(I)*(B1(I)/Q1)
|
||||
IF(Q(I).LE.eps) then
|
||||
SQ(i) =0.0d0
|
||||
DDB2(i)=0.0d0
|
||||
else
|
||||
SQ(I)=SQRT(Q(I))
|
||||
C
|
||||
C VDER(I) contains Var(X''(T(i))|X'(0),X''(0),X(0),X(T(i))
|
||||
C
|
||||
|
||||
VDER(I)=VDER(I) - (DDB2(I)*DDB2(I))/Q(I)
|
||||
end if
|
||||
|
||||
10 CONTINUE
|
||||
DO 15 I=1,N
|
||||
DO 15 J=1,N
|
||||
C
|
||||
C R1 contains Cov(X(T(I)),X'(T(J))|X'(0),X''(0),X(0))
|
||||
C
|
||||
R1(J+(I-1)*N)=R1(J+(I-1)*N) - COV(I+N)*(COV(J+2*N)/XL2)
|
||||
1 - (B0(I)*DB0(J)/Q0) - (B1(I)*DB1(J)/Q1)
|
||||
|
||||
C
|
||||
C R2 contains Cov(X'(T(I)),X'(T(J))|X'(0),X''(0),X(0))
|
||||
C
|
||||
R2(J+(I-1)*N) = -R2(J+(I-1)*N) - COV(I+2*N)*(COV(J+2*N)/XL2)
|
||||
1 - DB0(I)*DB0(J)/Q0 - DB1(I)*(DB1(J)/Q1)
|
||||
C
|
||||
C R3 contains Cov(X''(T(I)),X'(T(J))|X'(0),X''(0),X(0))
|
||||
C
|
||||
R3(J+(I-1)*N) = R3(J+(I-1)*N) - COV(I+3*N)*(COV(J+2*N)/XL2)
|
||||
1 - DB0(J)*(DDB0(I)/Q0) - DDB1(I)*(DB1(J)/Q1)
|
||||
15 CONTINUE
|
||||
|
||||
C The initiations are finished and we are beginning with 3 loops
|
||||
C on T=T(I), U=Ulevels(IU), V=Ulevels(IV), U>V.
|
||||
|
||||
DO 20 I=1,N
|
||||
|
||||
NNIT=NIT
|
||||
IF (Q(I).LE.EPS) GO TO 20
|
||||
|
||||
DO 30 I1=1,I
|
||||
DB2(I1)=R1(I1+(I-1)*N)
|
||||
|
||||
C Cov(X'(T(I1)),X(T(i))|X'(0),X''(0),X(0))
|
||||
C DDB2(I) contains Cov(X''(T(i)),X(T(i))|X'(0),X''(0),X(0))
|
||||
|
||||
30 CONTINUE
|
||||
|
||||
DO 50 I3=1,I
|
||||
DBI(I3) = R3(I3+(I-1)*N) - (DDB2(I)*DB2(I3)/Q(I))
|
||||
BI(I3) = R2(I3+(I-1)*N) - (DB2(I)*DB2(I3)/Q(I))
|
||||
50 CONTINUE
|
||||
DO 51 I3=1,I-1
|
||||
AI(I3)=0.0d0
|
||||
AI(I3+I-1)=DB0(I3)/SQ0
|
||||
AI(I3+2*(I-1))=DB1(I3)/SQ1
|
||||
AI(I3+3*(I-1))=DB2(I3)/SQ(I)
|
||||
51 CONTINUE
|
||||
VDERI=VDER(I)
|
||||
DAI(1)=0.0d0
|
||||
DAI(2)=DDB0(I)/SQ0
|
||||
DAI(3)=DDB1(I)/SQ1
|
||||
DAI(4)=DDB2(I)/SQ(I)
|
||||
AA(1,1)=DB0(I)/SQ0
|
||||
AA(1,2)=DB1(I)/SQ1
|
||||
AA(1,3)=DB2(I)/SQ(I)
|
||||
AA(2,1)=XL2/SQ0
|
||||
AA(2,2)=SQ1
|
||||
AA(2,3)=0.0d0
|
||||
AA(3,1)=B0(I)/SQ0
|
||||
AA(3,2)=B1(I)/SQ1
|
||||
AA(3,3)=SQ(I)
|
||||
IF (BI(I).LE.EPS) NNIT=0
|
||||
IF (NNIT.GT.1) THEN
|
||||
IF(I.LT.1) GO TO 41
|
||||
DO 40 I1=1,I-1
|
||||
DO 40 I2=1,I-1
|
||||
|
||||
C R contains Cov(X'(T(I1)),X'(T(I2))|X'(0),X''(0),X(0),X(I))
|
||||
|
||||
R(I2+(I1-1)*(I-1))=R2(I2+(I1-1)*N)-(DB2(I1)*DB2(I2)/Q(I))
|
||||
|
||||
40 CONTINUE
|
||||
41 CONTINUE
|
||||
END IF
|
||||
|
||||
C Here the covariance of the problem would be innitiated
|
||||
|
||||
INF=0
|
||||
Print *,' Laps to go:',N-I+1
|
||||
DO 80 IV=1,Nv
|
||||
V=VT(IV)
|
||||
! IF (ABS(V).GT.5.0D0) GO TO 80
|
||||
IF (Vdd(IV).LT.EPS0) GO TO 80
|
||||
DO 60 IU=1,Nu
|
||||
U=UT(IU)
|
||||
IF (U.LE.V) go to 60
|
||||
! IF (ABS(U).GT.5.0D0) GO TO 60
|
||||
IF (Udd(IU).LT.EPS0) GO TO 60
|
||||
BB(1)=0.0d0
|
||||
BB(2)=U
|
||||
BB(3)=V
|
||||
! if (IV.EQ.2.AND.IU.EQ.1) THEN
|
||||
! fffff = 10
|
||||
! endif
|
||||
|
||||
CALL MREG(F,R,BI,DBI,AA,BB,AI,DAI,VDERI,3,I-1,NNIT,INF)
|
||||
INF=1
|
||||
UVdens(IU,IV) = UVdens(IU,IV) + Udd(IU)*Vdd(IV)*HHT(I)*F
|
||||
! if (F.GT.0.01.AND.U.GT.2.AND.V.LT.-2) THEN
|
||||
! if (N-I+1 .eq. 38.and.IV.EQ.26.AND.IU.EQ.16) THEN
|
||||
! if (IV.EQ.32.AND.IU.EQ.8.and.I.eq.11) THEN
|
||||
! PRINT * ,' R:', R(1:I)
|
||||
! PRINT * ,' BI:', BI(1:I)
|
||||
! PRINT * ,' DBI:', DBI(1:I)
|
||||
! PRINT * ,' DB2:', DB2(1:I)
|
||||
! PRINT * ,' DB0(1):', DB0(1)
|
||||
! PRINT * ,' DB1(1):', DB1(1)
|
||||
! PRINT * ,' DAI:', DAI
|
||||
! PRINT * ,' BB:', BB
|
||||
! PRINT * ,' VDERI:', VDERI
|
||||
! PRINT * ,' F :', F
|
||||
! PRINT * ,' UVDENS :', UVdens(IU,IV)
|
||||
! fffff = 10
|
||||
! endif
|
||||
|
||||
60 CONTINUE
|
||||
80 continue
|
||||
20 CONTINUE
|
||||
hhhh=0.0d0
|
||||
do 90 Iu=1,Nu
|
||||
do 90 Iv=1,Nv
|
||||
WRITE(10,300) Ulev(iu),Vlev(iv),UVdens(iu,iv)
|
||||
hhhh=hhhh+UVdens(iu,iv)
|
||||
90 continue
|
||||
if (nu.gt.1.and.nv.gt.1) then
|
||||
write(11,*) 'SumSum f_uv *du*dv='
|
||||
1,(Ulev(2)-Ulev(1))*(Vlev(2)-Vlev(1))*hhhh
|
||||
end if
|
||||
|
||||
C sder=sqrt(XL4-XL2*XL2/XL0)
|
||||
C cder=-XL2/sqrt(XL0)
|
||||
C const=1/sqrt(XL0*XL4)
|
||||
C DO 95 IU=1,NU
|
||||
C U=UT(IU)
|
||||
C FM=Udd(IU)*const*exp(-0.5*U*U/XL0)*PMEAN(-cder*U,sder)
|
||||
C WRITE(9,300) Ulev(IU),FM
|
||||
C 95 continue
|
||||
C DO 105 IV=1,NV
|
||||
C V=VT(IV)
|
||||
C VV=cder*V
|
||||
C Fm=Vdd(IV)*const*exp(-0.5*V*V/XL0)*PMEAN(VV,sder)
|
||||
C WRITE(8,300) Vlev(IV),Fm
|
||||
C 105 continue
|
||||
if (III0.eq.0) III0=1
|
||||
|
||||
write(11,*) 'Rate of calls RINDT0:',float(iii01)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT1:',float(iii11)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT2:',float(iii21)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT3:',float(iii31)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT4:',float(iii41)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT5:',float(iii51)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT6:',float(iii61)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT7:',float(iii71)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT8:',float(iii81)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT9:',float(iii91)/float(III0)
|
||||
write(11,*) 'Rate of calls RINDT10:',float(iii101)/float(III0)
|
||||
write(11,*) 'Number of calls of RINDT*',III0
|
||||
|
||||
CLOSE(UNIT=8)
|
||||
CLOSE(UNIT=9)
|
||||
CLOSE(UNIT=10)
|
||||
CLOSE(UNIT=11)
|
||||
|
||||
300 FORMAT(4(3X,F10.6))
|
||||
STOP
|
||||
END
|
||||
|
||||
SUBROUTINE INITLEVELS(ULEVELS,NU,Vlevels,Nv,T,HT,N,TG,XG,NG)
|
||||
USE TBRMOD
|
||||
USE SIZEMOD
|
||||
IMPLICIT NONE
|
||||
C INTEGER, PARAMETER:: NMAX = 101, RDIM = 10201
|
||||
C DIMENSION ULEVELS(1),Vlevels(1),T(1),HT(1),TG(1),XG(1),HH(101)
|
||||
REAL*8, DIMENSION(NMAX), intent(inout) :: ULEVELS,Vlevels,T,HT
|
||||
REAL*8, DIMENSION(RDIM), intent(inout) :: TG,XG
|
||||
INTEGER, intent(inout) :: NG
|
||||
REAL*8 :: UMIN,UMAX,VMIN,VMAX, HU,HV
|
||||
integer :: N, I, NU, NV
|
||||
C REAL*8, DIMENSION(NMAX) :: HH
|
||||
C COMMON/TBR/HH
|
||||
OPEN(UNIT=2,FILE='transf.in')
|
||||
OPEN(UNIT=4,FILE='Mm.in')
|
||||
OPEN(UNIT=3,FILE='t.in')
|
||||
|
||||
|
||||
NG=1
|
||||
12 READ (2,*,END=11) TG(NG),XG(NG)
|
||||
NG=NG+1
|
||||
GO TO 12
|
||||
11 CONTINUE
|
||||
NG=NG-1
|
||||
IF (NG.GT.501) THEN
|
||||
PRINT *,'Vector defining transformation of data > 501, stop'
|
||||
STOP
|
||||
END IF
|
||||
|
||||
|
||||
N=1
|
||||
32 READ (3,*,END=31) T(N)
|
||||
N=N+1
|
||||
GO TO 32
|
||||
31 CONTINUE
|
||||
N=N-1
|
||||
|
||||
CLOSE(UNIT=3)
|
||||
|
||||
IF(N.ge.NMAX) then
|
||||
print *,'The number of wavelength points >',NMAX-1, ' stop'
|
||||
stop
|
||||
end if
|
||||
IF(N.lt.2) then
|
||||
print *,'The number of wavelength points < 2, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
HT(1)=0.5d0*(T(2)-T(1))
|
||||
HT(N)=0.5d0*(T(N)-T(N-1))
|
||||
HH(1)=-100.0d0
|
||||
HH(N)=-100.0d0
|
||||
DO 10 I=2,N-1
|
||||
HT(I)=0.5d0*(T(I+1)-T(I-1))
|
||||
HH(I)=-100.0d0
|
||||
10 CONTINUE
|
||||
|
||||
|
||||
|
||||
READ(4,*) Umin,Umax,NU
|
||||
READ(4,*) Vmin,Vmax,NV
|
||||
|
||||
IF(NU.gt.NMAX) then
|
||||
print *,'The number of maxima >',NMAX,' stop'
|
||||
stop
|
||||
end if
|
||||
IF(NV.gt.NMAX) then
|
||||
print *,'The number of minima >',NMAX,' stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
IF(NU.LT.1) Then
|
||||
print *,'The number of maxima < 1, stop'
|
||||
stop
|
||||
end if
|
||||
IF(NV.LT.1) Then
|
||||
print *,'The number of minima < 1, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
Ulevels(1)=Umax
|
||||
IF (NU.lt.2) go to 25
|
||||
HU=(Umax-Umin)/DBLE(NU-1)
|
||||
DO 20 I=1,NU-1
|
||||
ULEVELS(I+1)=Umax-DBLE(I)*HU
|
||||
20 CONTINUE
|
||||
|
||||
25 continue
|
||||
Vlevels(1)=Vmax
|
||||
IF (NV.lt.2) go to 35
|
||||
HV=(Vmax-Vmin)/DBLE(NV-1)
|
||||
DO 30 I=1,Nv-1
|
||||
VLEVELS(I+1)=Vmax-DBLE(I)*HV
|
||||
30 CONTINUE
|
||||
35 continue
|
||||
CLOSE(UNIT=4)
|
||||
RETURN
|
||||
END
|
||||
|
||||
|
||||
SUBROUTINE TRANSF(N,T,A,TIMEV,VALUE,DER)
|
||||
C
|
||||
C N number of data points
|
||||
C TIMEV vector of time points
|
||||
C A a vector of values of a function G(TIME)
|
||||
C T independent time point
|
||||
C VALUE is a value of a function at T, i.e. VALUE=G(T).
|
||||
c DER=G'(t)
|
||||
C
|
||||
USE SIZEMOD
|
||||
IMPLICIT NONE
|
||||
REAL*8, intent(inout):: VALUE, DER,T
|
||||
C INTEGER, PARAMETER :: RDIM = 10201
|
||||
REAL*8, DIMENSION(RDIM), intent(in) :: A,TIMEV
|
||||
integer, intent(in) :: N
|
||||
REAL*8:: T1
|
||||
integer :: I
|
||||
|
||||
IF (T.LT.TIMEV(1)) then
|
||||
der=(A(2)-A(1))/(TIMEV(2)-TIMEV(1))
|
||||
T1=T-TIMEV(1)
|
||||
VALUE=A(1)+T1*DER
|
||||
return
|
||||
end if
|
||||
IF (T.GT.TIMEV(N)) then
|
||||
der = (A(N)-A(N-1))/(TIMEV(N)-TIMEV(N-1))
|
||||
T1 = T-TIMEV(N)
|
||||
VALUE=A(N)+T1*DER
|
||||
return
|
||||
end if
|
||||
DO 5 I=2,N
|
||||
IF (T.LT.TIMEV(I)) GO TO 10
|
||||
5 CONTINUE
|
||||
10 I=I-1
|
||||
T1=T-TIMEV(I)
|
||||
DER=(A(I+1)-A(I))/(TIMEV(i+1)-TIMEV(I))
|
||||
VALUE=A(I)+T1*DER
|
||||
RETURN
|
||||
END
|
||||
|
||||
REAL*8 FUNCTION SPLE(N,T,A,TIMEV)
|
||||
C
|
||||
C N number of data points
|
||||
C TIME vector of time points
|
||||
C A a vector of values of a function G(TIME)
|
||||
C T independent time point
|
||||
C SPLE is a value of a function at T, i.e. SPLE=G(T).
|
||||
C
|
||||
USE SIZEMOD
|
||||
IMPLICIT NONE
|
||||
INTEGER, INTENT(IN):: N
|
||||
|
||||
REAL*8, INTENT(IN) :: T
|
||||
REAL*8, DIMENSION(5*NMAX), INTENT(IN) :: A,TIMEV
|
||||
REAL*8 :: T1
|
||||
INTEGER :: I
|
||||
SPLE=-9.9d0
|
||||
IF (T.LT.TIMEV(1) .OR. T.GT.TIMEV(N)) RETURN
|
||||
DO 5 I=2,N
|
||||
IF (T.LT.TIMEV(I)) GO TO 10
|
||||
5 CONTINUE
|
||||
10 I=I-1
|
||||
T1=T-TIMEV(I)
|
||||
SPLE=A(I)+T1*(A(I+1)-A(I))/(TIMEV(i+1)-TIMEV(I))
|
||||
RETURN
|
||||
END
|
||||
|
||||
|
||||
|
||||
SUBROUTINE COVG(XL0,XL2,XL4,COV,COV1,COV2,COV3,T,N)
|
||||
C
|
||||
C COVG evaluates:
|
||||
C
|
||||
C XL0,XL2,XL4 - spectral moments.
|
||||
C
|
||||
C Covariance function and its four derivatives for a vector T of length N.
|
||||
C It is saved in a vector COV; COV(1,...,N)=r(T), COV(N+1,...,2N)=r'(T), etc.
|
||||
C The vector COV should be of the length 5*N.
|
||||
C
|
||||
C Covariance matrices COV1=r'(T-T), COV2=r''(T-T) and COV3=r'''(T-T)
|
||||
C Dimension of COV1, COV2 should be N*N.
|
||||
C
|
||||
USE SIZEMOD
|
||||
! IMPLICIT NONE
|
||||
C INTEGER, PARAMETER:: NMAX = 101, RDIM = 10201
|
||||
REAL*8, PARAMETER:: ZERO = 0.0d0
|
||||
REAL*8, intent(inout) :: XL0,XL2,XL4
|
||||
REAL*8, DIMENSION(5*NMAX), intent(inout) :: COV
|
||||
REAL*8, DIMENSION(5*NMAX) :: A, TIMEV
|
||||
REAL*8, DIMENSION(RDIM), intent(inout) :: COV1,COV2,COV3
|
||||
REAL*8, DIMENSION(NMAX), intent(in) :: T
|
||||
INTEGER, intent(in) :: N
|
||||
integer :: NT, I, J, II
|
||||
REAL*8 :: TT, T0
|
||||
OPEN(UNIT=32,FILE='Cd0.in')
|
||||
OPEN(UNIT=33,FILE='Cd1.in')
|
||||
OPEN(UNIT=34,FILE='Cd2.in')
|
||||
OPEN(UNIT=35,FILE='Cd3.in')
|
||||
OPEN(UNIT=36,FILE='Cd4.in')
|
||||
C
|
||||
C COV(Y(T),Y(0))
|
||||
C
|
||||
|
||||
NT=1
|
||||
12 READ (32,*,END=11) TIMEV(NT),A(NT)
|
||||
NT=NT+1
|
||||
GO TO 12
|
||||
11 CONTINUE
|
||||
NT=NT-1
|
||||
|
||||
|
||||
XL0=SPLE(NT,ZERO,A,TIMEV)
|
||||
|
||||
DO 10 I=1,N
|
||||
COV(I)=SPLE(NT,T(I),A,TIMEV)
|
||||
10 CONTINUE
|
||||
|
||||
C
|
||||
C DERIVATIVE COV(Y(T),Y(0))
|
||||
C
|
||||
|
||||
NT=1
|
||||
22 READ (33,*,END=21) TIMEV(NT),A(NT)
|
||||
NT=NT+1
|
||||
GO TO 22
|
||||
21 CONTINUE
|
||||
NT=NT-1
|
||||
|
||||
II=0
|
||||
DO 20 I=1,N
|
||||
COV(I+N)=SPLE(NT,T(I),A,TIMEV)
|
||||
DO 20 J=1,N
|
||||
II=II+1
|
||||
T0=T(J)-T(I)
|
||||
TT=ABS(T0)
|
||||
COV1(II)=SPLE(NT,TT,A,TIMEV)
|
||||
IF (T0.LT.0.0d0) COV1(II)=-COV1(II)
|
||||
20 CONTINUE
|
||||
|
||||
C 2-DERIVATIVE COV(Y(T),Y(0))
|
||||
|
||||
NT=1
|
||||
32 READ (34,*,END=31) TIMEV(NT),A(NT)
|
||||
NT=NT+1
|
||||
GO TO 32
|
||||
31 CONTINUE
|
||||
NT=NT-1
|
||||
|
||||
II=0
|
||||
XL2=-SPLE(NT,ZERO,A,TIMEV)
|
||||
|
||||
DO 30 I=1,N
|
||||
COV(I+2*N)=SPLE(NT,T(I),A,TIMEV)
|
||||
DO 30 J=1,N
|
||||
II=II+1
|
||||
TT=ABS(T(J)-T(I))
|
||||
COV2(II)=SPLE(NT,TT,A,TIMEV)
|
||||
30 CONTINUE
|
||||
|
||||
C 3-DERIVATIVE COV(Y(T),Y(0))
|
||||
|
||||
NT=1
|
||||
42 READ (35,*,END=41) TIMEV(NT),A(NT)
|
||||
NT=NT+1
|
||||
GO TO 42
|
||||
41 CONTINUE
|
||||
NT=NT-1
|
||||
|
||||
|
||||
II=0
|
||||
DO 40 I=1,N
|
||||
COV(I+3*N)=SPLE(NT,T(I),A,TIMEV)
|
||||
DO 40 J=1,N
|
||||
II=II+1
|
||||
T0=T(J)-T(I)
|
||||
TT=ABS(T0)
|
||||
COV3(II)=SPLE(NT,TT,A,TIMEV)
|
||||
IF (T0.LT.0.0d0) COV3(II)=-COV3(II)
|
||||
40 CONTINUE
|
||||
|
||||
|
||||
|
||||
C 4-DERIVATIVE COV(Y(T),Y(0))
|
||||
|
||||
NT=1
|
||||
52 READ (36,*,END=51) TIMEV(NT),A(NT)
|
||||
NT=NT+1
|
||||
GO TO 52
|
||||
51 CONTINUE
|
||||
NT=NT-1
|
||||
|
||||
XL4=SPLE(NT,ZERO,A,TIMEV)
|
||||
|
||||
DO 50 I=1,N
|
||||
COV(I+4*N)=SPLE(NT,T(I),A,TIMEV)
|
||||
50 CONTINUE
|
||||
CLOSE(UNIT=32)
|
||||
CLOSE(UNIT=33)
|
||||
CLOSE(UNIT=34)
|
||||
CLOSE(UNIT=35)
|
||||
CLOSE(UNIT=36)
|
||||
RETURN
|
||||
END
|
||||
|
||||
SUBROUTINE INITINTEG(NIT)
|
||||
USE RINTMOD
|
||||
USE EPSMOD
|
||||
USE INFCMOD
|
||||
USE MREGMOD
|
||||
! IMPLICIT NONE
|
||||
INTEGER, intent(inout) :: NIT
|
||||
! INTEGER ISQ1
|
||||
C dimension INF(10),INFO(10)
|
||||
|
||||
C COMMON /RINT/ C,FC
|
||||
C COMMON /EPS/ EPS,EPSS,CEPSS
|
||||
C COMMON /INFC/ ISQ,INF,INFO
|
||||
OPEN(UNIT=1,FILE='accur.in')
|
||||
OPEN(UNIT=8,FILE='min.out')
|
||||
OPEN(UNIT=9,FILE='Max.out')
|
||||
OPEN(UNIT=10,FILE='Maxmin.out')
|
||||
OPEN(UNIT=11,FILE='Maxmin.log')
|
||||
|
||||
READ(1,*) NIT,IAC,ISQ
|
||||
READ(1,*) EPS,EPSS,EPS0
|
||||
|
||||
CLOSE (UNIT=1)
|
||||
|
||||
FC=FI(C)-FI(-C)
|
||||
CEPSS=1.0d0-EPSS
|
||||
|
||||
RETURN
|
||||
END
|
||||
|
||||
@ -1,370 +0,0 @@
|
||||
C Version 1994-X-18
|
||||
|
||||
C This is a new version of WAMP program computing crest-trough wavelength
|
||||
C and amplitude density.
|
||||
C
|
||||
C revised pab 2007
|
||||
C -moved all common blocks into modules
|
||||
C -renamed from minmax to sp2mmpdfreg + fixed some bugs
|
||||
C revised pab July 2007
|
||||
! -renamed from sp2mmpdfreg to cov2mmpdfreg
|
||||
! gfortran -W -Wall -pedantic-errors -fbounds-check -Werror -c dsvdc.f mregmodule.f cov2mmpdfreg.f
|
||||
|
||||
SUBROUTINE INITINTEG(EPS_,EPSS_,EPS0_,C_,IAC_,ISQ_)
|
||||
! Initiation of all constants and integration nodes 'INITINTEG'
|
||||
USE RINTMOD
|
||||
USE EPSMOD
|
||||
USE INFCMOD
|
||||
USE MREGMOD
|
||||
REAL*8 :: EPS_,EPSS_,EPS0_,C_
|
||||
INTEGER :: IAC_,ISQ_
|
||||
Cf2py real*8, optional :: EPS_ = 0.01
|
||||
Cf2py real*8, optional :: EPSS_ = 0.00005
|
||||
Cf2py real*8, optional :: EPS0_ = 0.00005
|
||||
Cf2py real*8, optional :: C_ = 4.5
|
||||
Cf2py integer, optional :: IAC_ = 1
|
||||
Cf2py integer, optional :: ISQ_ = 0
|
||||
! IMPLICIT NONE
|
||||
C COMMON /RINT/ C,FC
|
||||
C COMMON /EPS/ EPS,EPSS,CEPSS
|
||||
C COMMON /INFC/ ISQ,INF,INFO
|
||||
|
||||
IAC = IAC_
|
||||
ISQ = ISQ_
|
||||
EPS = EPS_
|
||||
EPSS = EPSS_
|
||||
EPS0 = EPS0_
|
||||
C = C_
|
||||
|
||||
FC = FI(C)-FI(-C)
|
||||
! CEPSS = 1.0d0-EPSS
|
||||
RETURN
|
||||
END SUBROUTINE INITINTEG
|
||||
|
||||
subroutine cov2mmpdfreg(UVdens,t,COV,ULev,VLev,Tg,Xg,Nt,Nu,Nv,Ng,
|
||||
& NIT)
|
||||
USE SIZEMOD
|
||||
USE EPSMOD
|
||||
USE CHECKMOD
|
||||
USE MREGMOD
|
||||
USE INTFCMOD
|
||||
IMPLICIT NONE
|
||||
INTEGER, INTENT(IN) :: Nt, Nu, Nv, Ng, NIT
|
||||
REAL*8, DIMENSION(Nt,5), intent(in):: COV
|
||||
REAL*8, DIMENSION(Nu,Nv), intent(out):: UVdens
|
||||
REAL*8, DIMENSION(Nu), intent(in):: ULev
|
||||
REAL*8, DIMENSION(Nv), intent(in):: VLev
|
||||
REAL*8, DIMENSION(Ng), intent(in):: Tg, Xg
|
||||
REAL*8, dimension(Nt), intent(in):: T
|
||||
Cf2py integer, intent(hide), depend(t) :: Nt = len(t)
|
||||
Cf2py integer, intent(hide), depend(Ulev) :: Nu = len(Ulev)
|
||||
Cf2py integer, intent(hide), depend(Vlev) :: Nv = len(Vlev)
|
||||
Cf2py integer, intent(hide), depend(Tg) :: Ng = len(Tg)
|
||||
Cf2py integer, optional :: NIT = 2
|
||||
Cf2py real*8, intent(out), depend(Nu,Nv) :: UVdens
|
||||
Cf2py depend(Ng) Xg
|
||||
Cf2py depend(Nt,5) COV
|
||||
real*8 Q0,SQ0,Q1,SQ1, U,V, XL0, XL2, XL4
|
||||
REAL*8 VDERI, DER, F, HHHH, VALUE
|
||||
C REAL*8 VV, CDER,SDER, CONST1, FM
|
||||
C INTEGER, PARAMETER :: MMAX = 5, NMAX = 101, RDIM = 10201
|
||||
REAL*8, DIMENSION(NMAX) :: HHT,VT,UT,Vdd,Udd
|
||||
REAL*8, DIMENSION(RDIM) :: R,R1,R2,R3
|
||||
REAL*8:: AA(MMAX-2,MMAX-2),AI((MMAX+1)*NMAX)
|
||||
REAL*8, DIMENSION(MMAX+1) :: BB, DAI
|
||||
C DIMENSION UVdens(NMAX,NMAX),HHT(NMAX)
|
||||
C DIMENSION T(NMAX),Ulev(NMAX),Vlev(NMAX)
|
||||
C DIMENSION VT(NMAX),UT(NMAX),Vdd(NMAX),Udd(NMAX)
|
||||
C DIMENSION COV(5*NMAX),R(RDIM),R1(RDIM),R2(RDIM),R3(RDIM)
|
||||
|
||||
|
||||
C
|
||||
C The program computes the joint density of maximum the following minimum
|
||||
C and the distance between Max and min for a zero-mean stationary
|
||||
C Gaussian process with covariance function defined explicitely with 4
|
||||
C derivatives. The process should be normalized so that the first and
|
||||
C the second spectral moments are equal to 1. The values of Max are taken
|
||||
C as the nodes at Hermite-Quadrature and then integrated out so that
|
||||
C the output is a joint density of wavelength T and amplitude H=Max-min.
|
||||
C The Max values are defined by subroutine Gauss_M with the accuracy
|
||||
C input epsu. The principle is that the integral of the marginal density
|
||||
C of f_Max is computed with sufficient accuracy.
|
||||
C
|
||||
REAL*8, DIMENSION(NMAX) :: B0,DB0,DDB0,B1,DB1,DDB1,DB2,DDB2
|
||||
REAL*8, DIMENSION(NMAX) :: Q,SQ,VDER,DBI,BI
|
||||
C DIMENSION B0(NMAX),DB0(NMAX),DDB0(NMAX)
|
||||
C DIMENSION B1(NMAX),DB1(NMAX),DDB1(NMAX)
|
||||
C DIMENSION DB2(NMAX),DDB2(NMAX)
|
||||
C DIMENSION Q(NMAX),SQ(NMAX),VDER(NMAX),DBI(NMAX),BI(NMAX)
|
||||
INTEGER :: J,I,I1,I2,I3,IU, IV,N, NNIT, INF
|
||||
C INTEGER :: fffff
|
||||
C REAL*8 EPS0
|
||||
C INTEGER III01,III11,III21,III31,III41,III51
|
||||
C *,III61,III71,III81,III91,III101 , III0
|
||||
C COMMON/CHECK1/III01,III11,III21,III31,III41,III51
|
||||
C *,III61,III71,III81,III91,III101
|
||||
C COMMON/CHECKQ/III0
|
||||
C COMMON /EPS/ EPS,EPSS,CEPSS
|
||||
|
||||
C
|
||||
C Initiation of all constants and integration nodes 'INITINTEG'
|
||||
C
|
||||
! CALL INITINTEG()
|
||||
|
||||
! OPEN(UNIT=8,FILE='min.out')
|
||||
! OPEN(UNIT=9,FILE='Max.out')
|
||||
! OPEN(UNIT=10,FILE='Maxmin.out')
|
||||
! OPEN(UNIT=11,FILE='Maxmin.log')
|
||||
c
|
||||
c OBS. we are using the variables R,R1,R2 R3 as a temporary storage
|
||||
C for transformation g of the process.
|
||||
|
||||
N = Nt
|
||||
CALL INITLEVELS(T,HHT,Nt,NU,Nv)
|
||||
C CALL INITLEVELS(Ulev,NU,Vlev,NV,T,HHT,Nt,R1,R2,NG)
|
||||
IF( Tg(1) .gt. Tg(ng)) then
|
||||
print *,'Error Tg must be strictly increasing'
|
||||
return
|
||||
end if
|
||||
if(abs(Tg(ng)-Tg(1))*abs(Xg(ng)-Xg(1)).lt.0.01d0) then
|
||||
print *,'The transformation g is singular, stop'
|
||||
return
|
||||
end if
|
||||
|
||||
! do IV=1,Nt
|
||||
! print *, 'Cov', COV(IV,:)
|
||||
! end do
|
||||
|
||||
DO IV=1,Nv
|
||||
V=Vlev(IV)
|
||||
CALL TRANSF(NG,V,Xg,Tg,VALUE,DER)
|
||||
VT(IV)=VALUE
|
||||
Vdd(IV)=DER
|
||||
enddo
|
||||
DO IU=1,Nu
|
||||
U = Ulev(IU)
|
||||
CALL TRANSF(NG,U,Xg,Tg,VALUE,DER)
|
||||
UT(IU) = VALUE
|
||||
Udd(IU) = DER
|
||||
do IV=1,Nv
|
||||
UVdens(IU,IV)=0.0d0
|
||||
enddo
|
||||
enddo
|
||||
|
||||
CALL COVG(XL0,XL2,XL4,R1,R2,R3,COV,T,Nt)
|
||||
|
||||
|
||||
Q0=XL4
|
||||
IF (Q0.le.1.0D0+EPS) then
|
||||
Print *,'Covariance structure is singular, stop.'
|
||||
return
|
||||
end if
|
||||
SQ0 = SQRT(Q0)
|
||||
Q1 = XL0-XL2*XL2/XL4
|
||||
IF (Q1.le.EPS) then
|
||||
Print *,'Covariance structure is singular, stop.'
|
||||
return
|
||||
end if
|
||||
SQ1 = SQRT(Q1)
|
||||
DO I=1,Nt
|
||||
B0(I) =-COV(I,3)
|
||||
DB0(I) =-COV(I,4)
|
||||
DDB0(I)=-COV(I,5)
|
||||
|
||||
B1(I) =COV(I,1)+COV(I,3)*(XL2/XL4)
|
||||
DB1(I) =COV(I,2)+COV(I,4)*(XL2/XL4)
|
||||
DDB1(I)=COV(I,3)+XL2*(COV(I,5)/XL4)
|
||||
C
|
||||
C Q(I) contains Var(X(T(i))|X'(0),X''(0),X(0))
|
||||
C VDER(I) contains Var(X''(T(i))|X'(0),X''(0),X(0))
|
||||
C
|
||||
Q(I)=XL0 - COV(I,2)*(COV(I,2)/XL2) - B0(I)*(B0(I)/Q0)
|
||||
1 -B1(I)*(B1(I)/Q1)
|
||||
VDER(I)=XL4 - (COV(I,4)*COV(I,4))/XL2 - (DDB0(I)*DDB0(I))/Q0
|
||||
1 - (DDB1(I)*DDB1(I))/Q1
|
||||
|
||||
|
||||
C
|
||||
C DDB2(I) contains Cov(X''(T(i)),X(T(i))|X'(0),X''(0),X(0))
|
||||
C
|
||||
DDB2(I)=-XL2 - (COV(I,2)*COV(I,4))/XL2 - DDB0(I)*(B0(I)/Q0)
|
||||
1 -DDB1(I)*(B1(I)/Q1)
|
||||
IF(Q(I).LE.eps) then
|
||||
SQ(i) =0.0d0
|
||||
DDB2(i)=0.0d0
|
||||
else
|
||||
SQ(I)=SQRT(Q(I))
|
||||
C
|
||||
C VDER(I) contains Var(X''(T(i))|X'(0),X''(0),X(0),X(T(i))
|
||||
C
|
||||
|
||||
VDER(I)=VDER(I) - (DDB2(I)*DDB2(I))/Q(I)
|
||||
end if
|
||||
|
||||
c10 CONTINUE
|
||||
enddo
|
||||
DO I=1,Nt
|
||||
DO J=1,Nt
|
||||
C
|
||||
C R1 contains Cov(X(T(I)),X'(T(J))|X'(0),X''(0),X(0))
|
||||
C
|
||||
R1(J+(I-1)*N) = R1(J+(I-1)*N) - COV(I,2)*(COV(J,3)/XL2)
|
||||
1 - (B0(I)*DB0(J)/Q0) - (B1(I)*DB1(J)/Q1)
|
||||
|
||||
C
|
||||
C R2 contains Cov(X'(T(I)),X'(T(J))|X'(0),X''(0),X(0))
|
||||
C
|
||||
R2(J+(I-1)*N) = -R2(J+(I-1)*N) - COV(I,3)*(COV(J,3)/XL2)
|
||||
1 - DB0(I)*DB0(J)/Q0 - DB1(I)*(DB1(J)/Q1)
|
||||
C
|
||||
C R3 contains Cov(X''(T(I)),X'(T(J))|X'(0),X''(0),X(0))
|
||||
C
|
||||
R3(J+(I-1)*N) = R3(J+(I-1)*N) - COV(I,4)*(COV(J,3)/XL2)
|
||||
1 - DB0(J)*(DDB0(I)/Q0) - DDB1(I)*(DB1(J)/Q1)
|
||||
c15 CONTINUE
|
||||
enddo
|
||||
enddo
|
||||
|
||||
C The initiations are finished and we are beginning with 3 loops
|
||||
C on T=T(I), U=Ulevels(IU), V=Ulevels(IV), U>V.
|
||||
|
||||
DO I=1,Nt
|
||||
|
||||
NNIT=NIT
|
||||
IF (Q(I).LE.EPS) GO TO 20
|
||||
|
||||
DO I1=1,I
|
||||
DB2(I1)=R1(I1+(I-1)*N)
|
||||
|
||||
C Cov(X'(T(I1)),X(T(i))|X'(0),X''(0),X(0))
|
||||
C DDB2(I) contains Cov(X''(T(i)),X(T(i))|X'(0),X''(0),X(0))
|
||||
|
||||
enddo
|
||||
|
||||
DO I3=1,I
|
||||
DBI(I3) = R3(I3+(I-1)*N) - (DDB2(I)*DB2(I3)/Q(I))
|
||||
BI(I3) = R2(I3+(I-1)*N) - (DB2(I)*DB2(I3)/Q(I))
|
||||
enddo
|
||||
DO I3=1,I-1
|
||||
AI(I3)=0.0d0
|
||||
AI(I3+I-1)=DB0(I3)/SQ0
|
||||
AI(I3+2*(I-1))=DB1(I3)/SQ1
|
||||
AI(I3+3*(I-1))=DB2(I3)/SQ(I)
|
||||
enddo
|
||||
VDERI=VDER(I)
|
||||
DAI(1)=0.0d0
|
||||
DAI(2)=DDB0(I)/SQ0
|
||||
DAI(3)=DDB1(I)/SQ1
|
||||
DAI(4)=DDB2(I)/SQ(I)
|
||||
AA(1,1)=DB0(I)/SQ0
|
||||
AA(1,2)=DB1(I)/SQ1
|
||||
AA(1,3)=DB2(I)/SQ(I)
|
||||
AA(2,1)=XL2/SQ0
|
||||
AA(2,2)=SQ1
|
||||
AA(2,3)=0.0d0
|
||||
AA(3,1)=B0(I)/SQ0
|
||||
AA(3,2)=B1(I)/SQ1
|
||||
AA(3,3)=SQ(I)
|
||||
IF (BI(I).LE.EPS) NNIT=0
|
||||
IF (NNIT.GT.1) THEN
|
||||
IF(I.LT.1) GO TO 41
|
||||
DO I1=1,I-1
|
||||
DO I2=1,I-1
|
||||
C R contains Cov(X'(T(I1)),X'(T(I2))|X'(0),X''(0),X(0),X(I))
|
||||
R(I2+(I1-1)*(I-1))=R2(I2+(I1-1)*N)-(DB2(I1)*DB2(I2)/Q(I))
|
||||
|
||||
enddo
|
||||
enddo
|
||||
41 CONTINUE
|
||||
END IF
|
||||
|
||||
C Here the covariance of the problem would be initiated
|
||||
|
||||
INF=0
|
||||
Print *,' Laps to go:',Nt-I+1
|
||||
DO IV=1,Nv
|
||||
V=VT(IV)
|
||||
! IF (ABS(V).GT.5.0D0) GO TO 80
|
||||
IF (Vdd(IV).LT.EPS0) GO TO 80
|
||||
DO IU=1,Nu
|
||||
U=UT(IU)
|
||||
IF (U.LE.V) go to 60
|
||||
! IF (ABS(U).GT.5.0D0) GO TO 60
|
||||
IF (Udd(IU).LT.EPS0) GO TO 60
|
||||
BB(1)=0.0d0
|
||||
BB(2)=U
|
||||
BB(3)=V
|
||||
! if (IV.EQ.2.AND.IU.EQ.1) THEN
|
||||
! fffff = 10
|
||||
! endif
|
||||
|
||||
CALL MREG(F,R,BI,DBI,AA,BB,AI,DAI,VDERI,3,I-1,NNIT,INF)
|
||||
INF=1
|
||||
UVdens(IU,IV) = UVdens(IU,IV) + Udd(IU)*Vdd(IV)*HHT(I)*F
|
||||
|
||||
! if (F.GT.0.01.AND.U.GT.2.AND.V.LT.-2) THEN
|
||||
! if (N-I+1 .eq. 38.and.IV.EQ.26.AND.IU.EQ.16) THEN
|
||||
! if (IV.EQ.32.AND.IU.EQ.8.and.I.eq.11) THEN
|
||||
! PRINT * ,' R:', R(1:I)
|
||||
! PRINT * ,' BI:', BI(1:I)
|
||||
! PRINT * ,' DBI:', DBI(1:I)
|
||||
! PRINT * ,' DB2:', DB2(1:I)
|
||||
! PRINT * ,' DB0(1):', DB0(1)
|
||||
! PRINT * ,' DB1(1):', DB1(1)
|
||||
! PRINT * ,' DAI:', DAI
|
||||
! PRINT * ,' BB:', BB
|
||||
! PRINT * ,' VDERI:', VDERI
|
||||
! PRINT * ,' F :', F
|
||||
! PRINT * ,' UVDENS :', UVdens(IU,IV)
|
||||
! fffff = 10
|
||||
! endif
|
||||
|
||||
60 CONTINUE
|
||||
enddo
|
||||
80 continue
|
||||
enddo
|
||||
20 CONTINUE
|
||||
enddo
|
||||
|
||||
hhhh=0.0d0
|
||||
do Iu=1,Nu
|
||||
do Iv=1,Nv
|
||||
! WRITE(10,300) Ulev(iu),Vlev(iv),UVdens(iu,iv)
|
||||
hhhh=hhhh+UVdens(iu,iv)
|
||||
enddo
|
||||
enddo
|
||||
if (nu.gt.1.and.nv.gt.1) then
|
||||
VALUE = (Ulev(2)-Ulev(1))*(Vlev(2)-Vlev(1))*hhhh
|
||||
print *,'SumSum f_uv *du*dv=', VALUE
|
||||
end if
|
||||
|
||||
C sder=sqrt(XL4-XL2*XL2/XL0)
|
||||
C cder=-XL2/sqrt(XL0)
|
||||
C const1=1/sqrt(XL0*XL4)
|
||||
C DO 95 IU=1,NU
|
||||
C U=UT(IU)
|
||||
C FM=Udd(IU)*const1*exp(-0.5*U*U/XL0)*PMEAN(-cder*U,sder)
|
||||
C WRITE(9,300) Ulev(IU),FM
|
||||
C 95 continue
|
||||
C DO 105 IV=1,NV
|
||||
C V=VT(IV)
|
||||
C VV=cder*V
|
||||
C Fm=Vdd(IV)*const1*exp(-0.5*V*V/XL0)*PMEAN(VV,sder)
|
||||
C WRITE(8,300) Vlev(IV),Fm
|
||||
C 105 continue
|
||||
if (III0.eq.0) III0=1
|
||||
|
||||
PRINT *, 'Rate of calls RINDT0:',float(iii01)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT1:',float(iii11)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT2:',float(iii21)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT3:',float(iii31)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT4:',float(iii41)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT5:',float(iii51)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT6:',float(iii61)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT7:',float(iii71)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT8:',float(iii81)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT9:',float(iii91)/float(III0)
|
||||
PRINT *, 'Rate of calls RINDT10:',float(iii101)/float(III0)
|
||||
PRINT *, 'Number of calls of RINDT*',III0
|
||||
return
|
||||
END subroutine cov2mmpdfreg
|
||||
@ -1,613 +0,0 @@
|
||||
MODULE SVD
|
||||
IMPLICIT NONE
|
||||
INTEGER, PARAMETER :: dp = SELECTED_REAL_KIND(12, 60)
|
||||
|
||||
! Based upon routines from the NSWC (Naval Surface Warfare Center),
|
||||
! which were based upon LAPACK routines.
|
||||
|
||||
! Code converted using TO_F90 by Alan Miller
|
||||
! Date: 2003-11-11 Time: 17:50:44
|
||||
! Revised pab 2007
|
||||
! Converted to fixed form
|
||||
|
||||
|
||||
CONTAINS
|
||||
|
||||
|
||||
SUBROUTINE drotg(da, db, dc, ds)
|
||||
|
||||
! DESIGNED BY C.L.LAWSON, JPL, 1977 SEPT 08
|
||||
!
|
||||
! CONSTRUCT THE GIVENS TRANSFORMATION
|
||||
!
|
||||
! ( DC DS )
|
||||
! G = ( ) , DC**2 + DS**2 = 1 ,
|
||||
! (-DS DC )
|
||||
!
|
||||
! WHICH ZEROS THE SECOND ENTRY OF THE 2-VECTOR (DA,DB)**T .
|
||||
!
|
||||
! THE QUANTITY R = (+/-)SQRT(DA**2 + DB**2) OVERWRITES DA IN
|
||||
! STORAGE. THE VALUE OF DB IS OVERWRITTEN BY A VALUE Z WHICH
|
||||
! ALLOWS DC AND DS TO BE RECOVERED BY THE FOLLOWING ALGORITHM:
|
||||
! IF Z=1 SET DC=0.D0 AND DS=1.D0
|
||||
! IF DABS(Z) < 1 SET DC=SQRT(1-Z**2) AND DS=Z
|
||||
! IF DABS(Z) > 1 SET DC=1/Z AND DS=SQRT(1-DC**2)
|
||||
!
|
||||
! NORMALLY, THE SUBPROGRAM DROT(N,DX,INCX,DY,INCY,DC,DS) WILL
|
||||
! NEXT BE CALLED TO APPLY THE TRANSFORMATION TO A 2 BY N MATRIX.
|
||||
!
|
||||
! ------------------------------------------------------------------
|
||||
|
||||
REAL (dp), INTENT(IN OUT) :: da
|
||||
REAL (dp), INTENT(IN OUT) :: db
|
||||
REAL (dp), INTENT(OUT) :: dc
|
||||
REAL (dp), INTENT(OUT) :: ds
|
||||
|
||||
REAL (dp) :: u, v, r
|
||||
IF (ABS(da) <= ABS(db)) GO TO 10
|
||||
|
||||
! *** HERE ABS(DA) > ABS(DB) ***
|
||||
|
||||
u = da + da
|
||||
v = db / u
|
||||
|
||||
! NOTE THAT U AND R HAVE THE SIGN OF DA
|
||||
|
||||
r = SQRT(.25D0 + v**2) * u
|
||||
|
||||
! NOTE THAT DC IS POSITIVE
|
||||
|
||||
dc = da / r
|
||||
ds = v * (dc + dc)
|
||||
db = ds
|
||||
da = r
|
||||
RETURN
|
||||
|
||||
! *** HERE ABS(DA) <= ABS(DB) ***
|
||||
|
||||
10 IF (db == 0.d0) GO TO 20
|
||||
u = db + db
|
||||
v = da / u
|
||||
|
||||
! NOTE THAT U AND R HAVE THE SIGN OF DB
|
||||
! (R IS IMMEDIATELY STORED IN DA)
|
||||
|
||||
da = SQRT(.25D0 + v**2) * u
|
||||
|
||||
! NOTE THAT DS IS POSITIVE
|
||||
|
||||
ds = db / da
|
||||
dc = v * (ds + ds)
|
||||
IF (dc == 0.d0) GO TO 15
|
||||
db = 1.d0 / dc
|
||||
RETURN
|
||||
15 db = 1.d0
|
||||
RETURN
|
||||
|
||||
! *** HERE DA = DB = 0.D0 ***
|
||||
|
||||
20 dc = 1.d0
|
||||
ds = 0.d0
|
||||
RETURN
|
||||
|
||||
END SUBROUTINE drotg
|
||||
|
||||
|
||||
SUBROUTINE dswap1 (n, dx, dy)
|
||||
! INTERCHANGES TWO VECTORS.
|
||||
! USES UNROLLED LOOPS FOR INCREMENTS EQUAL ONE.
|
||||
! JACK DONGARRA, LINPACK, 3/11/78.
|
||||
! This version is for increments = 1.
|
||||
|
||||
INTEGER, INTENT(IN) :: n
|
||||
REAL (dp), INTENT(IN OUT) :: dx(*)
|
||||
REAL (dp), INTENT(IN OUT) :: dy(*)
|
||||
|
||||
REAL (dp) :: dtemp
|
||||
INTEGER :: i, m, mp1
|
||||
|
||||
IF(n <= 0) RETURN
|
||||
|
||||
! CODE FOR BOTH INCREMENTS EQUAL TO 1
|
||||
!
|
||||
! CLEAN-UP LOOP
|
||||
|
||||
m = MOD(n,3)
|
||||
IF( m == 0 ) GO TO 40
|
||||
DO i = 1,m
|
||||
dtemp = dx(i)
|
||||
dx(i) = dy(i)
|
||||
dy(i) = dtemp
|
||||
END DO
|
||||
IF( n < 3 ) RETURN
|
||||
40 mp1 = m + 1
|
||||
DO i = mp1,n,3
|
||||
dtemp = dx(i)
|
||||
dx(i) = dy(i)
|
||||
dy(i) = dtemp
|
||||
dtemp = dx(i + 1)
|
||||
dx(i + 1) = dy(i + 1)
|
||||
dy(i + 1) = dtemp
|
||||
dtemp = dx(i + 2)
|
||||
dx(i + 2) = dy(i + 2)
|
||||
dy(i + 2) = dtemp
|
||||
END DO
|
||||
RETURN
|
||||
END SUBROUTINE dswap1
|
||||
|
||||
|
||||
SUBROUTINE drot1 (n, dx, dy, c, s)
|
||||
! APPLIES A PLANE ROTATION.
|
||||
! JACK DONGARRA, LINPACK, 3/11/78.
|
||||
! This version is for increments = 1.
|
||||
|
||||
INTEGER, INTENT(IN) :: n
|
||||
REAL (dp), INTENT(IN OUT) :: dx(*)
|
||||
REAL (dp), INTENT(IN OUT) :: dy(*)
|
||||
REAL (dp), INTENT(IN) :: c
|
||||
REAL (dp), INTENT(IN) :: s
|
||||
|
||||
REAL (dp) :: dtemp
|
||||
INTEGER :: i
|
||||
|
||||
IF(n <= 0) RETURN
|
||||
! CODE FOR BOTH INCREMENTS EQUAL TO 1
|
||||
|
||||
DO i = 1,n
|
||||
dtemp = c*dx(i) + s*dy(i)
|
||||
dy(i) = c*dy(i) - s*dx(i)
|
||||
dx(i) = dtemp
|
||||
END DO
|
||||
RETURN
|
||||
END SUBROUTINE drot1
|
||||
|
||||
|
||||
SUBROUTINE dsvdc(x, n, p, s, e, u, v, job, info)
|
||||
|
||||
INTEGER, INTENT(IN) :: n
|
||||
INTEGER, INTENT(IN) :: p
|
||||
REAL (dp), INTENT(IN OUT) :: x(:,:)
|
||||
REAL (dp), INTENT(OUT) :: s(:)
|
||||
REAL (dp), INTENT(OUT) :: e(:)
|
||||
REAL (dp), INTENT(OUT) :: u(:,:)
|
||||
REAL (dp), INTENT(OUT) :: v(:,:)
|
||||
INTEGER, INTENT(IN) :: job
|
||||
INTEGER, INTENT(OUT) :: info
|
||||
|
||||
! DSVDC IS A SUBROUTINE TO REDUCE A DOUBLE PRECISION NXP MATRIX X
|
||||
! BY ORTHOGONAL TRANSFORMATIONS U AND V TO DIAGONAL FORM. THE
|
||||
! DIAGONAL ELEMENTS S(I) ARE THE SINGULAR VALUES OF X. THE
|
||||
! COLUMNS OF U ARE THE CORRESPONDING LEFT SINGULAR VECTORS,
|
||||
! AND THE COLUMNS OF V THE RIGHT SINGULAR VECTORS.
|
||||
!
|
||||
! ON ENTRY
|
||||
!
|
||||
! X DOUBLE PRECISION(LDX,P), WHERE LDX.GE.N.
|
||||
! X CONTAINS THE MATRIX WHOSE SINGULAR VALUE
|
||||
! DECOMPOSITION IS TO BE COMPUTED. X IS
|
||||
! DESTROYED BY DSVDC.
|
||||
!
|
||||
! LDX INTEGER.
|
||||
! LDX IS THE LEADING DIMENSION OF THE ARRAY X.
|
||||
!
|
||||
! N INTEGER.
|
||||
! N IS THE NUMBER OF ROWS OF THE MATRIX X.
|
||||
!
|
||||
! P INTEGER.
|
||||
! P IS THE NUMBER OF COLUMNS OF THE MATRIX X.
|
||||
!
|
||||
! LDU INTEGER.
|
||||
! LDU IS THE LEADING DIMENSION OF THE ARRAY U.
|
||||
! (SEE BELOW).
|
||||
!
|
||||
! LDV INTEGER.
|
||||
! LDV IS THE LEADING DIMENSION OF THE ARRAY V.
|
||||
! (SEE BELOW).
|
||||
!
|
||||
! JOB INTEGER.
|
||||
! JOB CONTROLS THE COMPUTATION OF THE SINGULAR
|
||||
! VECTORS. IT HAS THE DECIMAL EXPANSION AB
|
||||
! WITH THE FOLLOWING MEANING
|
||||
!
|
||||
! A.EQ.0 DO NOT COMPUTE THE LEFT SINGULAR VECTORS.
|
||||
! A.EQ.1 RETURN THE N LEFT SINGULAR VECTORS IN U.
|
||||
! A.GE.2 RETURN THE FIRST MIN(N,P) SINGULAR
|
||||
! VECTORS IN U.
|
||||
! B.EQ.0 DO NOT COMPUTE THE RIGHT SINGULAR VECTORS.
|
||||
! B.EQ.1 RETURN THE RIGHT SINGULAR VECTORS IN V.
|
||||
!
|
||||
! ON RETURN
|
||||
!
|
||||
! S DOUBLE PRECISION(MM), WHERE MM=MIN(N+1,P).
|
||||
! THE FIRST MIN(N,P) ENTRIES OF S CONTAIN THE SINGULAR
|
||||
! VALUES OF X ARRANGED IN DESCENDING ORDER OF MAGNITUDE.
|
||||
!
|
||||
! E DOUBLE PRECISION(P).
|
||||
! E ORDINARILY CONTAINS ZEROS. HOWEVER SEE THE
|
||||
! DISCUSSION OF INFO FOR EXCEPTIONS.
|
||||
!
|
||||
! U DOUBLE PRECISION(LDU,K), WHERE LDU.GE.N. IF
|
||||
! JOBA.EQ.1 THEN K.EQ.N, IF JOBA.GE.2
|
||||
! THEN K.EQ.MIN(N,P).
|
||||
! U CONTAINS THE MATRIX OF LEFT SINGULAR VECTORS.
|
||||
! U IS NOT REFERENCED IF JOBA.EQ.0. IF N.LE.P
|
||||
! OR IF JOBA.EQ.2, THEN U MAY BE IDENTIFIED WITH X
|
||||
! IN THE SUBROUTINE CALL.
|
||||
!
|
||||
! V DOUBLE PRECISION(LDV,P), WHERE LDV.GE.P.
|
||||
! V CONTAINS THE MATRIX OF RIGHT SINGULAR VECTORS.
|
||||
! V IS NOT REFERENCED IF JOB.EQ.0. IF P.LE.N,
|
||||
! THEN V MAY BE IDENTIFIED WITH X IN THE
|
||||
! SUBROUTINE CALL.
|
||||
!
|
||||
! INFO INTEGER.
|
||||
! THE SINGULAR VALUES (AND THEIR CORRESPONDING SINGULAR
|
||||
! VECTORS) S(INFO+1),S(INFO+2),...,S(M) ARE CORRECT
|
||||
! (HERE M=MIN(N,P)). THUS IF INFO.EQ.0, ALL THE
|
||||
! SINGULAR VALUES AND THEIR VECTORS ARE CORRECT.
|
||||
! IN ANY EVENT, THE MATRIX B = TRANS(U)*X*V IS THE
|
||||
! BIDIAGONAL MATRIX WITH THE ELEMENTS OF S ON ITS DIAGONAL
|
||||
! AND THE ELEMENTS OF E ON ITS SUPER-DIAGONAL (TRANS(U)
|
||||
! IS THE TRANSPOSE OF U). THUS THE SINGULAR VALUES
|
||||
! OF X AND B ARE THE SAME.
|
||||
!
|
||||
! LINPACK. THIS VERSION DATED 03/19/79 .
|
||||
! G.W. STEWART, UNIVERSITY OF MARYLAND, ARGONNE NATIONAL LAB.
|
||||
!
|
||||
! DSVDC USES THE FOLLOWING FUNCTIONS AND SUBPROGRAMS.
|
||||
!
|
||||
! EXTERNAL DROT
|
||||
! BLAS DAXPY,DDOT,DSCAL,DSWAP,DNRM2,DROTG
|
||||
! FORTRAN DABS,DMAX1,MAX0,MIN0,MOD,DSQRT
|
||||
|
||||
! INTERNAL VARIABLES
|
||||
|
||||
INTEGER :: iter, j, jobu, k, kase, kk, l, ll, lls, lm1, lp1, ls,
|
||||
& lu, m, maxit,mm, mm1, mp1, nct, nctp1, ncu, nrt, nrtp1
|
||||
REAL (dp) :: t, work(n)
|
||||
REAL (dp) :: b, c, cs, el, emm1, f, g, scale, shift, sl, sm, sn,
|
||||
& smm1, t1, test, ztest
|
||||
LOGICAL :: wantu, wantv
|
||||
|
||||
! SET THE MAXIMUM NUMBER OF ITERATIONS.
|
||||
|
||||
maxit = 30
|
||||
|
||||
! DETERMINE WHAT IS TO BE COMPUTED.
|
||||
|
||||
wantu = .false.
|
||||
wantv = .false.
|
||||
jobu = MOD(job,100)/10
|
||||
ncu = n
|
||||
IF (jobu > 1) ncu = MIN(n,p)
|
||||
IF (jobu /= 0) wantu = .true.
|
||||
IF (MOD(job,10) /= 0) wantv = .true.
|
||||
|
||||
! REDUCE X TO BIDIAGONAL FORM, STORING THE DIAGONAL ELEMENTS
|
||||
! IN S AND THE SUPER-DIAGONAL ELEMENTS IN E.
|
||||
|
||||
info = 0
|
||||
nct = MIN(n-1, p)
|
||||
s(1:nct+1) = 0.0_dp
|
||||
nrt = MAX(0, MIN(p-2,n))
|
||||
lu = MAX(nct,nrt)
|
||||
IF (lu < 1) GO TO 170
|
||||
DO l = 1, lu
|
||||
lp1 = l + 1
|
||||
IF (l > nct) GO TO 20
|
||||
|
||||
! COMPUTE THE TRANSFORMATION FOR THE L-TH COLUMN AND
|
||||
! PLACE THE L-TH DIAGONAL IN S(L).
|
||||
|
||||
s(l) = SQRT( SUM( x(l:n,l)**2 ) )
|
||||
IF (s(l) == 0.0D0) GO TO 10
|
||||
IF (x(l,l) /= 0.0D0) s(l) = SIGN(s(l), x(l,l))
|
||||
x(l:n,l) = x(l:n,l) / s(l)
|
||||
x(l,l) = 1.0D0 + x(l,l)
|
||||
|
||||
10 s(l) = -s(l)
|
||||
|
||||
20 IF (p < lp1) GO TO 50
|
||||
DO j = lp1, p
|
||||
IF (l > nct) GO TO 30
|
||||
IF (s(l) == 0.0D0) GO TO 30
|
||||
|
||||
! APPLY THE TRANSFORMATION.
|
||||
|
||||
t = -DOT_PRODUCT(x(l:n,l), x(l:n,j)) / x(l,l)
|
||||
x(l:n,j) = x(l:n,j) + t * x(l:n,l)
|
||||
|
||||
! PLACE THE L-TH ROW OF X INTO E FOR THE
|
||||
! SUBSEQUENT CALCULATION OF THE ROW TRANSFORMATION.
|
||||
|
||||
30 e(j) = x(l,j)
|
||||
END DO
|
||||
|
||||
50 IF (.NOT.wantu .OR. l > nct) GO TO 70
|
||||
|
||||
! PLACE THE TRANSFORMATION IN U FOR SUBSEQUENT BACK MULTIPLICATION.
|
||||
|
||||
u(l:n,l) = x(l:n,l)
|
||||
|
||||
70 IF (l > nrt) CYCLE
|
||||
|
||||
! COMPUTE THE L-TH ROW TRANSFORMATION AND PLACE THE
|
||||
! L-TH SUPER-DIAGONAL IN E(L).
|
||||
|
||||
e(l) = SQRT( SUM( e(lp1:p)**2 ) )
|
||||
IF (e(l) == 0.0D0) GO TO 80
|
||||
IF (e(lp1) /= 0.0D0) e(l) = SIGN(e(l), e(lp1))
|
||||
e(lp1:lp1+p-l-1) = e(lp1:p) / e(l)
|
||||
e(lp1) = 1.0D0 + e(lp1)
|
||||
|
||||
80 e(l) = -e(l)
|
||||
IF (lp1 > n .OR. e(l) == 0.0D0) GO TO 120
|
||||
|
||||
! APPLY THE TRANSFORMATION.
|
||||
|
||||
work(lp1:n) = 0.0D0
|
||||
DO j = lp1, p
|
||||
work(lp1:lp1+n-l-1) = work(lp1:lp1+n-l-1) + e(j) *
|
||||
& x(lp1:lp1+n-l-1,j)
|
||||
END DO
|
||||
DO j = lp1, p
|
||||
x(lp1:lp1+n-l-1,j) = x(lp1:lp1+n-l-1,j) - (e(j)/e(lp1)) *
|
||||
& work(lp1:lp1+n-l-1)
|
||||
END DO
|
||||
|
||||
120 IF (.NOT.wantv) CYCLE
|
||||
|
||||
! PLACE THE TRANSFORMATION IN V FOR SUBSEQUENT
|
||||
! BACK MULTIPLICATION.
|
||||
|
||||
v(lp1:p,l) = e(lp1:p)
|
||||
END DO
|
||||
|
||||
! SET UP THE FINAL BIDIAGONAL MATRIX OF ORDER M.
|
||||
|
||||
170 m = MIN(p,n+1)
|
||||
nctp1 = nct + 1
|
||||
nrtp1 = nrt + 1
|
||||
IF (nct < p) s(nctp1) = x(nctp1,nctp1)
|
||||
IF (n < m) s(m) = 0.0D0
|
||||
IF (nrtp1 < m) e(nrtp1) = x(nrtp1,m)
|
||||
e(m) = 0.0D0
|
||||
|
||||
! IF REQUIRED, GENERATE U.
|
||||
|
||||
IF (.NOT.wantu) GO TO 300
|
||||
IF (ncu < nctp1) GO TO 200
|
||||
DO j = nctp1, ncu
|
||||
u(1:n,j) = 0.0_dp
|
||||
u(j,j) = 1.0_dp
|
||||
END DO
|
||||
|
||||
200 DO ll = 1, nct
|
||||
l = nct - ll + 1
|
||||
IF (s(l) == 0.0D0) GO TO 250
|
||||
lp1 = l + 1
|
||||
IF (ncu < lp1) GO TO 220
|
||||
DO j = lp1, ncu
|
||||
t = -DOT_PRODUCT(u(l:n,l), u(l:n,j)) / u(l,l)
|
||||
u(l:n,j) = u(l:n,j) + t * u(l:n,l)
|
||||
END DO
|
||||
|
||||
220 u(l:n,l) = -u(l:n,l)
|
||||
u(l,l) = 1.0D0 + u(l,l)
|
||||
lm1 = l - 1
|
||||
IF (lm1 < 1) CYCLE
|
||||
u(1:lm1,l) = 0.0_dp
|
||||
CYCLE
|
||||
|
||||
250 u(1:n,l) = 0.0_dp
|
||||
u(l,l) = 1.0_dp
|
||||
END DO
|
||||
|
||||
! IF IT IS REQUIRED, GENERATE V.
|
||||
|
||||
300 IF (.NOT.wantv) GO TO 350
|
||||
DO ll = 1, p
|
||||
l = p - ll + 1
|
||||
lp1 = l + 1
|
||||
IF (l > nrt) GO TO 320
|
||||
IF (e(l) == 0.0D0) GO TO 320
|
||||
DO j = lp1, p
|
||||
t = -DOT_PRODUCT(v(lp1:lp1+p-l-1,l),
|
||||
& v(lp1:lp1+p-l-1,j)) / v(lp1,l)
|
||||
v(lp1:lp1+p-l-1,j) = v(lp1:lp1+p-l-1,j) + t * v(lp1:lp1+p-l-1,l)
|
||||
END DO
|
||||
|
||||
320 v(1:p,l) = 0.0D0
|
||||
v(l,l) = 1.0D0
|
||||
END DO
|
||||
|
||||
! MAIN ITERATION LOOP FOR THE SINGULAR VALUES.
|
||||
|
||||
350 mm = m
|
||||
iter = 0
|
||||
|
||||
! QUIT IF ALL THE SINGULAR VALUES HAVE BEEN FOUND.
|
||||
|
||||
! ...EXIT
|
||||
360 IF (m == 0) GO TO 620
|
||||
|
||||
! IF TOO MANY ITERATIONS HAVE BEEN PERFORMED, SET FLAG AND RETURN.
|
||||
|
||||
IF (iter < maxit) GO TO 370
|
||||
info = m
|
||||
! ......EXIT
|
||||
GO TO 620
|
||||
|
||||
! THIS SECTION OF THE PROGRAM INSPECTS FOR NEGLIGIBLE ELEMENTS
|
||||
! IN THE S AND E ARRAYS. ON COMPLETION
|
||||
! THE VARIABLES KASE AND L ARE SET AS FOLLOWS.
|
||||
!
|
||||
! KASE = 1 IF S(M) AND E(L-1) ARE NEGLIGIBLE AND L < M
|
||||
! KASE = 2 IF S(L) IS NEGLIGIBLE AND L < M
|
||||
! KASE = 3 IF E(L-1) IS NEGLIGIBLE, L < M, AND
|
||||
! S(L), ..., S(M) ARE NOT NEGLIGIBLE (QR STEP).
|
||||
! KASE = 4 IF E(M-1) IS NEGLIGIBLE (CONVERGENCE).
|
||||
|
||||
370 DO ll = 1, m
|
||||
l = m - ll
|
||||
! ...EXIT
|
||||
IF (l == 0) EXIT
|
||||
test = ABS(s(l)) + ABS(s(l+1))
|
||||
ztest = test + ABS(e(l))
|
||||
IF (ztest /= test) CYCLE
|
||||
e(l) = 0.0D0
|
||||
! ......EXIT
|
||||
EXIT
|
||||
END DO
|
||||
|
||||
IF (l /= m - 1) GO TO 410
|
||||
kase = 4
|
||||
GO TO 480
|
||||
|
||||
410 lp1 = l + 1
|
||||
mp1 = m + 1
|
||||
DO lls = lp1, mp1
|
||||
ls = m - lls + lp1
|
||||
! ...EXIT
|
||||
IF (ls == l) EXIT
|
||||
test = 0.0D0
|
||||
IF (ls /= m) test = test + ABS(e(ls))
|
||||
IF (ls /= l + 1) test = test + ABS(e(ls-1))
|
||||
ztest = test + ABS(s(ls))
|
||||
IF (ztest /= test) CYCLE
|
||||
s(ls) = 0.0D0
|
||||
! ......EXIT
|
||||
EXIT
|
||||
END DO
|
||||
|
||||
IF (ls /= l) GO TO 450
|
||||
kase = 3
|
||||
GO TO 480
|
||||
|
||||
450 IF (ls /= m) GO TO 460
|
||||
kase = 1
|
||||
GO TO 480
|
||||
|
||||
460 kase = 2
|
||||
l = ls
|
||||
480 l = l + 1
|
||||
|
||||
! PERFORM THE TASK INDICATED BY KASE.
|
||||
|
||||
SELECT CASE ( kase )
|
||||
CASE ( 1)
|
||||
GO TO 490
|
||||
CASE ( 2)
|
||||
GO TO 520
|
||||
CASE ( 3)
|
||||
GO TO 540
|
||||
CASE ( 4)
|
||||
GO TO 570
|
||||
END SELECT
|
||||
|
||||
! DEFLATE NEGLIGIBLE S(M).
|
||||
|
||||
490 mm1 = m - 1
|
||||
f = e(m-1)
|
||||
e(m-1) = 0.0D0
|
||||
DO kk = l, mm1
|
||||
k = mm1 - kk + l
|
||||
t1 = s(k)
|
||||
CALL drotg(t1, f, cs, sn)
|
||||
s(k) = t1
|
||||
IF (k == l) GO TO 500
|
||||
f = -sn*e(k-1)
|
||||
e(k-1) = cs*e(k-1)
|
||||
|
||||
500 IF (wantv) CALL drot1(p, v(1:,k), v(1:,m), cs, sn)
|
||||
END DO
|
||||
GO TO 610
|
||||
|
||||
! SPLIT AT NEGLIGIBLE S(L).
|
||||
|
||||
520 f = e(l-1)
|
||||
e(l-1) = 0.0D0
|
||||
DO k = l, m
|
||||
t1 = s(k)
|
||||
CALL drotg(t1, f, cs, sn)
|
||||
s(k) = t1
|
||||
f = -sn*e(k)
|
||||
e(k) = cs*e(k)
|
||||
IF (wantu) CALL drot1(n, u(1:,k), u(1:,l-1), cs, sn)
|
||||
END DO
|
||||
GO TO 610
|
||||
|
||||
! PERFORM ONE QR STEP.
|
||||
!
|
||||
! CALCULATE THE SHIFT.
|
||||
|
||||
540 scale = MAX(ABS(s(m)),ABS(s(m-1)),ABS(e(m-1)),ABS(s(l)),ABS(e(l)))
|
||||
sm = s(m)/scale
|
||||
smm1 = s(m-1)/scale
|
||||
emm1 = e(m-1)/scale
|
||||
sl = s(l)/scale
|
||||
el = e(l)/scale
|
||||
b = ((smm1 + sm)*(smm1 - sm) + emm1**2)/2.0D0
|
||||
c = (sm*emm1)**2
|
||||
shift = 0.0D0
|
||||
IF (b == 0.0D0 .AND. c == 0.0D0) GO TO 550
|
||||
shift = SQRT(b**2+c)
|
||||
IF (b < 0.0D0) shift = -shift
|
||||
shift = c/(b + shift)
|
||||
|
||||
550 f = (sl + sm)*(sl - sm) - shift
|
||||
g = sl*el
|
||||
|
||||
! CHASE ZEROS.
|
||||
|
||||
mm1 = m - 1
|
||||
DO k = l, mm1
|
||||
CALL drotg(f, g, cs, sn)
|
||||
IF (k /= l) e(k-1) = f
|
||||
f = cs*s(k) + sn*e(k)
|
||||
e(k) = cs*e(k) - sn*s(k)
|
||||
g = sn*s(k+1)
|
||||
s(k+1) = cs*s(k+1)
|
||||
IF (wantv) CALL drot1(p, v(1:,k), v(1:,k+1), cs, sn)
|
||||
CALL drotg(f, g, cs, sn)
|
||||
s(k) = f
|
||||
f = cs*e(k) + sn*s(k+1)
|
||||
s(k+1) = -sn*e(k) + cs*s(k+1)
|
||||
g = sn*e(k+1)
|
||||
e(k+1) = cs*e(k+1)
|
||||
IF (wantu .AND. k < n) CALL drot1(n, u(1:,k), u(1:,k+1), cs, sn)
|
||||
END DO
|
||||
e(m-1) = f
|
||||
iter = iter + 1
|
||||
GO TO 610
|
||||
|
||||
! CONVERGENCE.
|
||||
|
||||
! MAKE THE SINGULAR VALUE POSITIVE.
|
||||
|
||||
570 IF (s(l) >= 0.0D0) GO TO 590
|
||||
s(l) = -s(l)
|
||||
IF (wantv) v(1:p,l) = -v(1:p,l)
|
||||
|
||||
! ORDER THE SINGULAR VALUE.
|
||||
|
||||
590 IF (l == mm) GO TO 600
|
||||
! ...EXIT
|
||||
IF (s(l) >= s(l+1)) GO TO 600
|
||||
t = s(l)
|
||||
s(l) = s(l+1)
|
||||
s(l+1) = t
|
||||
IF (wantv .AND. l < p) CALL dswap1(p, v(1:,l), v(1:,l+1))
|
||||
IF (wantu .AND. l < n) CALL dswap1(n, u(1:,l), u(1:,l+1))
|
||||
l = l + 1
|
||||
GO TO 590
|
||||
|
||||
600 iter = 0
|
||||
m = m - 1
|
||||
|
||||
610 GO TO 360
|
||||
|
||||
620 RETURN
|
||||
END SUBROUTINE dsvdc
|
||||
|
||||
END MODULE SVD
|
||||
@ -1,189 +0,0 @@
|
||||
MODULE INTFCMOD
|
||||
IMPLICIT NONE
|
||||
PUBLIC :: INITLEVELS, TRANSF, COVG
|
||||
|
||||
CONTAINS
|
||||
SUBROUTINE INITLEVELS(T,HT,N,NU,Nv)
|
||||
USE TBRMOD
|
||||
USE SIZEMOD
|
||||
IMPLICIT NONE
|
||||
C INTEGER, PARAMETER:: NMAX = 101, RDIM = 10201
|
||||
C DIMENSION ULEVELS(1),Vlevels(1),T(1),HT(1),TG(1),XG(1),HH(101)
|
||||
REAL*8, DIMENSION(:), intent(in) :: T
|
||||
REAL*8, DIMENSION(:), intent(out) :: HT
|
||||
C INTEGER, intent(in) :: NG
|
||||
REAL*8 :: UMIN,UMAX,VMIN,VMAX, HU,HV
|
||||
integer :: N, I, NU, NV
|
||||
C REAL*8, DIMENSION(NMAX) :: HH
|
||||
C COMMON/TBR/HH
|
||||
|
||||
C IF (NG.GT.501) THEN
|
||||
C PRINT *,'Vector defining transformation of data > 501, stop'
|
||||
C STOP
|
||||
C END IF
|
||||
|
||||
|
||||
IF(N.ge.NMAX) then
|
||||
print *,'The number of wavelength points >',NMAX-1, ' stop'
|
||||
stop
|
||||
end if
|
||||
IF(N.lt.2) then
|
||||
print *,'The number of wavelength points < 2, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
HT(1)=0.5d0*(T(2)-T(1))
|
||||
HT(N)=0.5d0*(T(N)-T(N-1))
|
||||
HH(1)=-100.0d0
|
||||
HH(N)=-100.0d0
|
||||
DO I=2,N-1
|
||||
HT(I)=0.5d0*(T(I+1)-T(I-1))
|
||||
HH(I)=-100.0d0
|
||||
c10 CONTINUE
|
||||
enddo
|
||||
|
||||
|
||||
IF(NU.gt.NMAX) then
|
||||
print *,'The number of maxima >',NMAX,' stop'
|
||||
stop
|
||||
end if
|
||||
IF(NV.gt.NMAX) then
|
||||
print *,'The number of minima >',NMAX,' stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
IF(NU.LT.1) Then
|
||||
print *,'The number of maxima < 1, stop'
|
||||
stop
|
||||
end if
|
||||
IF(NV.LT.1) Then
|
||||
print *,'The number of minima < 1, stop'
|
||||
stop
|
||||
end if
|
||||
|
||||
RETURN
|
||||
END SUBROUTINE INITLEVELS
|
||||
|
||||
|
||||
SUBROUTINE TRANSF(N,T,A,TIMEV,VALUE,DER)
|
||||
C
|
||||
C N number of data points
|
||||
C TIMEV vector of time points
|
||||
C A a vector of values of a function G(TIME)
|
||||
C T independent time point
|
||||
C VALUE is a value of a function at T, i.e. VALUE=G(T).
|
||||
c DER=G'(t)
|
||||
C
|
||||
USE SIZEMOD
|
||||
IMPLICIT NONE
|
||||
REAL*8, intent(inout):: VALUE, DER,T
|
||||
C INTEGER, PARAMETER :: RDIM = 10201
|
||||
REAL*8, DIMENSION(:), intent(in) :: A,TIMEV
|
||||
integer, intent(in) :: N
|
||||
REAL*8:: T1
|
||||
integer :: I
|
||||
|
||||
IF (T.LT.TIMEV(1)) then
|
||||
der=(A(2)-A(1))/(TIMEV(2)-TIMEV(1))
|
||||
T1=T-TIMEV(1)
|
||||
VALUE=A(1)+T1*DER
|
||||
return
|
||||
end if
|
||||
IF (T.GT.TIMEV(N)) then
|
||||
der = (A(N)-A(N-1))/(TIMEV(N)-TIMEV(N-1))
|
||||
T1 = T-TIMEV(N)
|
||||
VALUE=A(N)+T1*DER
|
||||
return
|
||||
end if
|
||||
DO I=2,N
|
||||
IF (T.LT.TIMEV(I)) GO TO 10
|
||||
ENDDO
|
||||
10 I=I-1
|
||||
T1=T-TIMEV(I)
|
||||
DER=(A(I+1)-A(I))/(TIMEV(i+1)-TIMEV(I))
|
||||
VALUE=A(I)+T1*DER
|
||||
RETURN
|
||||
END SUBROUTINE TRANSF
|
||||
|
||||
REAL*8 FUNCTION SPLE(N,T,A,TIMEV)
|
||||
C
|
||||
C N number of data points
|
||||
C TIME vector of time points
|
||||
C A a vector of values of a function G(TIME)
|
||||
C T independent time point
|
||||
C SPLE is a value of a function at T, i.e. SPLE=G(T).
|
||||
C
|
||||
USE SIZEMOD
|
||||
IMPLICIT NONE
|
||||
INTEGER, INTENT(IN):: N
|
||||
|
||||
REAL*8, INTENT(IN) :: T
|
||||
REAL*8, DIMENSION(:), INTENT(IN) :: A,TIMEV
|
||||
REAL*8 :: T1
|
||||
INTEGER :: I
|
||||
SPLE=-9.9d0
|
||||
IF (T.LT.TIMEV(1) .OR. T.GT.TIMEV(N)) RETURN
|
||||
DO I=2,N
|
||||
IF (T.LT.TIMEV(I)) GO TO 10
|
||||
ENDDO
|
||||
10 I=I-1
|
||||
T1=T-TIMEV(I)
|
||||
SPLE=A(I)+T1*(A(I+1)-A(I))/(TIMEV(i+1)-TIMEV(I))
|
||||
RETURN
|
||||
END FUNCTION SPLE
|
||||
|
||||
SUBROUTINE COVG(XL0,XL2,XL4,COV1,COV2,COV3,COV,T,N)
|
||||
C
|
||||
C Covariance function and its four derivatives for a vector T of length N
|
||||
C is assumed in a vector COV; COV(1,...,N,1)=r(T), COV(1,...,N, 2)=r'(T), etc.
|
||||
C The vector COV should be of the shape N x 5.
|
||||
C
|
||||
C COVG Returns:
|
||||
C XL0,XL2,XL4 - spectral moments.
|
||||
C
|
||||
C Covariance matrices COV1=r'(T-T), COV2=r''(T-T) and COV3=r'''(T-T)
|
||||
C Dimension of COV1, COV2 should be atleast N*N.
|
||||
C
|
||||
USE SIZEMOD
|
||||
! IMPLICIT NONE
|
||||
C INTEGER, PARAMETER:: NMAX = 101, RDIM = 10201
|
||||
REAL*8, PARAMETER:: ZERO = 0.0d0
|
||||
REAL*8, intent(inout) :: XL0,XL2,XL4
|
||||
REAL*8, DIMENSION(N,5), intent(in) :: COV
|
||||
REAL*8, DIMENSION(N), intent(in) :: T
|
||||
REAL*8, DIMENSION(RDIM), intent(inout) :: COV1,COV2,COV3
|
||||
INTEGER, intent(in) :: N
|
||||
integer :: I, J, II
|
||||
REAL*8 :: TT, T0
|
||||
C
|
||||
C COV(Y(T),Y(0)) = COV(:,1)
|
||||
C DERIVATIVE COV(Y(T),Y(0)) = COV(:,2)
|
||||
C 2-DERIVATIVE COV(Y(T),Y(0)) = COV(:,3)
|
||||
C 3-DERIVATIVE COV(Y(T),Y(0)) = COV(:,4)
|
||||
C 4-DERIVATIVE COV(Y(T),Y(0)) = COV(:,5)
|
||||
|
||||
XL0 = COV(1,1)
|
||||
XL2 = -COV(1,3)
|
||||
XL4 = COV(1,5)
|
||||
! XL0 = SPLE(NT, ZERO, COV(:,1), T)
|
||||
! XL2 = -SPLE(NT, ZERO, COV(:,3), T)
|
||||
! XL4 = SPLE(NT, ZERO, COV(:,5), T)
|
||||
|
||||
II=0
|
||||
DO I=1,N
|
||||
DO J=1,N
|
||||
II = II+1
|
||||
T0 = T(J)-T(I)
|
||||
TT = ABS(T0)
|
||||
COV1(II) = SPLE(N, TT, COV(:,2), T)
|
||||
COV2(II) = SPLE(N, TT, COV(:,3), T)
|
||||
COV3(II) = SPLE(N, TT, COV(:,4), T)
|
||||
IF (T0.LT.0.0d0) then
|
||||
COV1(II)=-COV1(II)
|
||||
COV3(II)=-COV3(II)
|
||||
endif
|
||||
enddo
|
||||
enddo
|
||||
RETURN
|
||||
END SUBROUTINE COVG
|
||||
END module intfcmod
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,20 +0,0 @@
|
||||
"""
|
||||
builds mvn.pyd
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
|
||||
|
||||
from wafo.f2py_tools import f2py_call_str
|
||||
|
||||
def compile_all():
|
||||
f2py_call = f2py_call_str()
|
||||
print '=' * 75
|
||||
print 'compiling mvn'
|
||||
print '=' * 75
|
||||
|
||||
os.system(f2py_call + ' mvn.pyf mvndst.f -c ')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
compile_all()
|
||||
@ -1,39 +0,0 @@
|
||||
! -*- f90 -*-
|
||||
! Note: the context of this file is case sensitive.
|
||||
|
||||
python module mvn ! in
|
||||
interface ! in :mvn
|
||||
subroutine mvnun(d,n,lower,upper,means,covar,maxpts,abseps,releps,value,inform) ! in :mvn:mvndst.f
|
||||
integer intent(hide) :: d=shape(means,0)
|
||||
integer intent(hide) :: n=shape(means,1)
|
||||
double precision dimension(d) :: lower
|
||||
double precision dimension(d) :: upper
|
||||
double precision dimension(d,n) :: means
|
||||
double precision dimension(d,d) :: covar
|
||||
integer intent(optional) :: maxpts=d*1000
|
||||
double precision intent(optional) :: abseps=1e-6
|
||||
double precision intent(optional) :: releps=1e-6
|
||||
double precision intent(out) :: value
|
||||
integer intent(out) :: inform
|
||||
end subroutine mvnun
|
||||
|
||||
subroutine mvndst(n,lower,upper,infin,correl,maxpts,abseps,releps,error,value,inform) ! in :mvn:mvndst.f
|
||||
integer intent(hide) :: n=len(lower)
|
||||
double precision dimension(n) :: lower
|
||||
double precision dimension(n) :: upper
|
||||
integer dimension(n) :: infin
|
||||
double precision dimension(n*(n-1)/2) :: correl
|
||||
integer intent(optional) :: maxpts=2000
|
||||
double precision intent(optional) :: abseps=1e-6
|
||||
double precision intent(optional) :: releps=1e-6
|
||||
double precision intent(out) :: error
|
||||
double precision intent(out) :: value
|
||||
integer intent(out) :: inform
|
||||
integer :: ivls
|
||||
common /dkblck/ ivls
|
||||
end subroutine mvndst
|
||||
end interface
|
||||
end python module mvn
|
||||
|
||||
! This file was auto-generated with f2py (version:2.39.235_1752).
|
||||
! See http://cens.ioc.ee/projects/f2py2e/
|
||||
File diff suppressed because it is too large
Load Diff
Some files were not shown because too many files have changed in this diff Show More
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Reference in New Issue