Removed the title of the plot from the canvas, as it is now shown above

the canvas as well. Updated the matplotlib axis limits to reflect this and
allow more space for the plot.

LICENSE

@@ -276,3 +276,64 @@ TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
 YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
 PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
 POSSIBILITY OF SUCH DAMAGES.
+
+		     END OF TERMS AND CONDITIONS
+
+	    How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+convey the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software; you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation; either version 2 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License along
+    with this program; if not, write to the Free Software Foundation, Inc.,
+    51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If the program is interactive, make it output a short notice like this
+when it starts in an interactive mode:
+
+    Gnomovision version 69, Copyright (C) year name of author
+    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, the commands you use may
+be called something other than `show w' and `show c'; they could even be
+mouse-clicks or menu items--whatever suits your program.
+
+You should also get your employer (if you work as a programmer) or your
+school, if any, to sign a "copyright disclaimer" for the program, if
+necessary.  Here is a sample; alter the names:
+
+  Yoyodyne, Inc., hereby disclaims all copyright interest in the program
+  `Gnomovision' (which makes passes at compilers) written by James Hacker.
+
+  <signature of Ty Coon>, 1 April 1989
+  Ty Coon, President of Vice
+
+This General Public License does not permit incorporating your program into
+proprietary programs.  If your program is a subroutine library, you may
+consider it more useful to permit linking proprietary applications with the
+library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.

Makefile.m4

@@ -1,30 +0,0 @@
-PREFIX=M4_PREFIX
-BIN_DIR=M4_BINDIR
-DATA_DIR=M4_DATADIR
-PY_DIR=M4_PYDIR
-DOC_DIR=M4_DOCDIR
-
-SCRIPTS_DIR=${DATA_DIR}/laydi/scripts/
-
-install: install_laydi install_doc
-
-install_laydi:
-	## Install binary files		
-	install -m 755 -D bin/laydi ${BIN_DIR}/laydi
-	install -m 755 -D bin/dataset ${BIN_DIR}/dataset
-	
-	## Install library files
-	find laydi/ -type f -name '*.py' -exec install -m 644 -D {} ${PY_DIR}/{} \;
-	find laydi/ -type f -name '*.glade' -exec install -m 644 -D {} ${PY_DIR}/{} \;
-	
-	## Install icons
-	find icons/ -type f -name '*.png' -exec install -m 644 -D {} ${DATA_DIR}/{} \;
-
-install_scripts:
-	find scripts/ -type f -exec install -D {} ${SCRIPTS_DIR}/{} \;
-
-install_doc:
-	DOC_DIR=${DOC_DIR}/ make -C doc install
-	install -m 644 README ${DOC_DIR}/
-	install -m 644 LICENSE ${DOC_DIR}/
-

R/laydi/DESCRIPTION

@@ -1,10 +0,0 @@
-Package: laydi
-Type: Package
-Title: Interface to Laydi
-Version: 0.1.0
-Date: 2011-03-05
-Author: Einar Ryeng <einarr@pvv.ntnu.no>
-Maintainer: Einar Ryeng <einarr@pvv.ntnu.no>
-Description: R interface to Laydi
-License: LGPL
-LazyLoad: yes

R/laydi/R/laydi.R

@@ -1,61 +0,0 @@
-
-write.ftsv <- function(data, con, name="unnamed_dataset", rowdim="rows", coldim="cols") {
-    # If con is a file name, open it 
-    opened.here = FALSE
-    if (is.character(con)){
-        con = file(con, "w")
-        opened.here = TRUE 
-    }
-    
-    # Substitute all whitespace with underscores in identifiers 
-    rows <- paste(gsub("\\s", "_", rownames(data)), collapse=" ")
-    cols <- paste(gsub("\\s", "_", colnames(data)), collapse=" ")
-
-    # Write header 
-    writeLines(c("# type: dataset", 
-                 paste("# dimension:", rowdim, rows, collapse=' '), 
-                 paste("# dimension:", coldim, cols, collapse=' '), 
-                 paste("# name:", name, collapse=' '),
-                 ""),
-               con=con)
-
-    # Write matrix 
-    write.table(data, file=con, col.names=FALSE, row.names=FALSE, sep="\t")
-
-    # If con was a string, close file now 
-    if (opened.here)
-        close(con)
-}
-
-write.laydi.selection <- function(data, con) {
-    # If con is a file name, open it 
-    opened.here = FALSE
-    if (is.character(con)){
-        con = file(con, "w")
-        opened.here = TRUE 
-    }
-    
-    writeLines(gsub("\\s", "_", data), con=con)
-    
-    # If con was a string, close file now 
-    if (opened.here)
-        close(con)
-}
-
-read.laydi.selection <- function(con) {
-    # If con is a file name, open it 
-    opened.here = FALSE
-    if (is.character(con)){
-        con = file(con)
-        opened.here = TRUE 
-    }
-    
-    ids <- readLines(con=con, encoding="UTF-8")
-    
-    # If con was a string, close file now 
-    if (opened.here)
-        close(con)
-
-    ids
-}
-

R/laydi/man/laydi-package.Rd

@@ -1,38 +0,0 @@
-\name{laydi-package}
-\alias{laydi-package}
-\alias{laydi}
-\docType{package}
-\title{Interface to Laydi}
-\description{
-Interface to Laydi files and projects. Allows writing R matrices to laydi data files.
-}
-\details{
-\tabular{ll}{
-Package: \tab laydi\cr
-Type: \tab Package\cr
-Version: \tab 0.1.0\cr
-Date: \tab 2011-03-05\cr
-License: \tab LGPL\cr
-LazyLoad: \tab yes\cr
-}
-
-library(laydi)
-write.ftsv(matrix, file, ...)
-write.laydi.selection(idlist, file)
-
-}
-\author{
-Einar Ryeng <einarr@pvv.org>\cr
-Arnar Flatberg <arnar.flatberg@gmail.com>
-
-Maintainer: Einar Ryeng <einarr@pvv.org>
-}
-\references{
-}
-\keyword{ package }
-\seealso{
-% ~~ Optional links to other man pages, e.g. ~~
-% ~~ \code{\link[<pkg>:<pkg>-package]{<pkg>}} ~~
-}
-\examples{
-}

R/laydi/man/read.laydi.selection.Rd

@@ -1,53 +0,0 @@
-\name{read.laydi.selection}
-\alias{read.laydi.selection}
-\title{read.laydi.selection}
-\description{
-Reads a list of identifiers from a file.
-}
-\usage{
-read.laydi.selection(con)
-}
-\arguments{
-  \item{con}{
-     Connection (or file name) to read from. If con is a character string, it
-     will be treated as a file name, and the file will be opened, read and
-     closed.  If con is an open connection (file descriptor), the file will
-     remain open.
-  }
-}
-
-\details{
-%%  ~~ If necessary, more details than the description above ~~
-}
-\value{
-%%  ~Describe the value returned
-%%  If it is a LIST, use
-%%  \item{comp1 }{Description of 'comp1'}
-%%  \item{comp2 }{Description of 'comp2'}
-%% ...
-}
-\references{
-Laydi and the laydi R package are not published in the litterature. Source code
-can be found at http://dev.pvv.ntnu.no/projects/laydi
-}
-\author{
-Einar Ryeng
-}
-\note{
-%%  ~~further notes~~
-}
-
-%% ~Make other sections like Warning with \section{Warning }{....} ~
-
-\seealso{
-%% ~~objects to See Also as \code{\link{help}}, ~~~
-}
-\examples{
-
-read.laydi.selection("/tmp/selected_samples")
-
-}
-
-\keyword{ IO }
-\keyword{ file }
-

R/laydi/man/write.ftsv.Rd

@@ -1,74 +0,0 @@
-\name{write.ftsv}
-\alias{write.ftsv}
-\title{write.ftsv}
-\description{
-Writes a matrix to a ftsv (laydi dataset) file. The matrix must have rownames
-and colnames. Rownames and colnames must be unique, and whitespace characters
-will be replaced with underscores.
-}
-\usage{
-write.ftsv(data, con, name = "unnamed_dataset", rowdim = "rows", coldim = "cols")
-}
-\arguments{
-  \item{data}{
-     A data matrix. All items in rownames(data) and colnames(data) must be
-     unique, and all whitespace characters will be replaced wity underscores in
-     the output. (Laydi requires unique identifiers along a dimension, and does
-     not allow identifiers to contain spaces.)
-  }
-  \item{con}{
-     Connection (or file name) to write the data to. If con is a character
-     string, it will be treated as a file name, and a file by that name will be
-     created, written to and closed. If con is an open connection (file descriptor),
-     the file will remain open.
-  }
-  \item{name}{
-     Datasets in laydi have a name.
-  }
-  \item{rowdim}{
-     Laydi names all dimensions of matrices. rowdim is the dimension
-     name for rows in the the file. E.g. "samples", if rows denotes samples.
-  }
-  \item{coldim}{
-     Laydi names all dimensions of matrices. coldim is the dimension
-     name for columns in the the file. E.g. "gene-ids" if columns represent
-     genes.
-  }
-}
-
-\details{
-%%  ~~ If necessary, more details than the description above ~~
-}
-\value{
-%%  ~Describe the value returned
-%%  If it is a LIST, use
-%%  \item{comp1 }{Description of 'comp1'}
-%%  \item{comp2 }{Description of 'comp2'}
-%% ...
-}
-\references{
-Laydi and this R package are not published in the litterature. Source code can be found at
-http://dev.pvv.ntnu.no/projects/laydi
-}
-\author{
-Einar Ryeng
-}
-\note{
-%%  ~~further notes~~
-}
-
-%% ~Make other sections like Warning with \section{Warning }{....} ~
-
-\seealso{
-%% ~~objects to See Also as \code{\link{help}}, ~~~
-}
-\examples{
-
-library(datasets)
-write.ftsv(randu, "/tmp/randu.ftsv")
-
-}
-
-\keyword{ IO }
-\keyword{ file }
-

R/laydi/man/write.laydi.selection.Rd

@@ -1,61 +0,0 @@
-\name{write.laydi.selection}
-\alias{write.laydi.selection}
-\title{write.laydi.selection}
-\description{
-Writes a Laydi selection file from a list of identifiers. Identifiers are
-written one per line, with all whitespace characters substituted with
-underscores.
-}
-\usage{
-write.laydi.selection(data, con)
-}
-\arguments{
-  \item{data}{
-     A list of identifiers. All whitespace characters will be replaced wity
-     underscores in the output. (Laydi requires unique identifiers along a
-     dimension, and does not allow identifiers to contain spaces.) This method
-     does not ensure that identifiers are unique.  
-  }
-  \item{con}{
-     Connection (or file name) to write the data to. If con is a character
-     string, it will be treated as a file name, and a file by that name will be
-     created, written to and closed. If con is an open connection (file descriptor),
-     the file will remain open.
-  }
-}
-
-\details{
-%%  ~~ If necessary, more details than the description above ~~
-}
-\value{
-%%  ~Describe the value returned
-%%  If it is a LIST, use
-%%  \item{comp1 }{Description of 'comp1'}
-%%  \item{comp2 }{Description of 'comp2'}
-%% ...
-}
-\references{
-Laydi and the laydi R package are not published in the litterature. Source code
-can be found at http://dev.pvv.ntnu.no/projects/laydi
-}
-\author{
-Einar Ryeng
-}
-\note{
-%%  ~~further notes~~
-}
-
-%% ~Make other sections like Warning with \section{Warning }{....} ~
-
-\seealso{
-%% ~~objects to See Also as \code{\link{help}}, ~~~
-}
-\examples{
-
-write.laydi.selection(c("sample1", "sample2", "sample3"), "/tmp/selected_samples")
-
-}
-
-\keyword{ IO }
-\keyword{ file }
-

README

@@ -0,0 +1,27 @@
+Fluents Data Analysis Software
+
+LICENSE
+-------
+Fluents is relased under the terms of the GNU GPL, included in the LICENSE file
+in this directory.
+
+DOCUMENTATION
+-------------
+The primary and canonical source of documentation is the source code. If a
+keyboard shortcut is listed on the wiki but it does not work in the program,
+the program is right, and the wiki is wrong.
+
+That said, the next best place to look for documentation is the project wiki,
+located at https://dev.pvv.ntnu.no/projects/fluent/help
+
+BUILDING
+--------
+Fluents is a python program, and as such, python will build compiled versions
+of each .py file as it loads them. You do not need to explicitly compile the
+program.
+
+TODO
+----
+The current TODO list can be found on
+https://dev.pvv.ntnu.no/projects/fluent/report/1 
+

README.md

@@ -1,57 +0,0 @@
-![](./wiki/graphics/project_icon.png)
-
-# Laydi
-
-## Look At Your Data Interactively
-
-Laydi is an acronym for look at your data interactively, which is what the program is aimed at. It is a lightweight data analysis program for bilinear modeling (PCA and PLS) with a strong focus on interactive use. Laydi is released under the GNU GPL and the latest development snapshot can be downloaded from https://git.pvv.ntnu.no/Projects/laydi.git
-
-![](./wiki/graphics/screenshot-00.png)
-
-## Features
-
-- Principal Component Analysis (PCA)
-- Partial Least Squares Regression (PLS)
-- L-shaped PLS regression (L-PLS)
-- Easy mapping of variables between plots, selections in one plot propagates to other plots.
-
-## Nonfeatures
-
-- Does not import arbitrary files. Files must be prepared in a (simple) file format prior to import.
-- Saving and loading of projects is not implemented. (Datasets can be saved and loaded, though, and plots can be exported)
-- Not very stable
-
-## Installation requirements
-
-Laydi currently requires the following extra packages, available from apt on Debian and Ubuntu.
-
-- python2.4 or python2.5
-- python-glade2
-- python-gnome2
-- python-gtk2
-- python-matplotlib
-- python-scipy
-- python-numpy
-
-Partially needed
-
-- python-networkx
-- python-pygraphviz
-
-## Download laydi
-
-Laydi is not debianized. To download it, use the clone the git repo.
-
-```console
-git clone https://git.pvv.ntnu.no/Projects/laydi.git
-```
-
-## User documentation
-
-- [Frequently Asked Questions](./wiki/faq.md)
-- [Laydi help](./wiki/help.md) (the same as available through the help menu in the application.)
-- [Terminology](./wiki/Terminology.md)
-
-## Developer documentation
-
-- [Developer tips and tricks](./wiki/development/hints.md)

README.old

@@ -1,41 +0,0 @@
-Laydi Data Analysis Software
-
-LICENSE
--------
-Laydi is relased under the terms of the GNU GPL, included in the LICENSE file
-in this directory.
-
-DOCUMENTATION
--------------
-The primary and canonical source of documentation is the source code. If a
-keyboard shortcut is listed on the wiki but it does not work in the program,
-the program is right, and the wiki is wrong.
-
-That said, the next best place to look for documentation is the project wiki,
-located at https://dev.pvv.ntnu.no/projects/laydi/help
-
-Class documentation is in HTML form in the doc/ directory.
-
-BUILDING
---------
-Laydi is a python program, and as such, python will build compiled versions
-of each .py file as it loads them. You do not need to explicitly compile the
-program.
-
-If you have just checked out the program to a directory named laydi, e.g. with
-the command:
-
-svn co https://dev.pvv.org/svn/laydi/trunk laydi
-
-you can run it by typing:
-
-cd laydi
-./configure --prefix=`pwd`/build
-make
-./run-laydi
-
-TODO
-----
-The current TODO list can be found on
-https://dev.pvv.ntnu.no/projects/laydi/report/1 
-

bin/dataset

@@ -1,116 +0,0 @@
-#!/usr/bin/python
-
-import os,sys
-from laydi import dataset
-import cfgparse, optparse
-import re
-
-PROGRAM_NAME = 'dataset'
-VERSION = '0.1.0'
-
-def read_dataset_header(input):
-    name = ""
-    type = ""
-    dimensions = []
-
-    kv_re = re.compile('^\s*#\s*(\w+)\s*:(.*)$')
-
-    lines = []
-    line = input.readline()
-    while line.startswith('#'):
-        lines.append(line)
-        line = input.readline()
-
-    for line in lines:
-        match = kv_re.match(line)
-        if not match:
-            continue
-        k, v = match.groups()
-        k = k.strip()
-        
-        if k == 'name':
-            name = v
-        elif k == 'type':
-            type = v
-        elif k == 'dimension':
-            values = v.split()
-            dimensions.append((values[0], values[1:]))
-    
-    return (name, type, dimensions)
-
-def show_info(input):
-    name, type, dimensions = read_dataset_header(input)
-
-    print "Name: %s" % name
-    print "Type: %s" % type
-    print "Dimensions:",
-    for i, dim in enumerate(dimensions):
-        dimname = dim[0]
-        length = len(dim[1])
-        print "%s(%i)" % (dimname, length),
-        if i < len(dimensions)-1:
-            print "x",
-    print
-
-def list_dimension_ids(input, dimname):
-    name, type, dimensions = read_dataset_header(input)
-    for i, dim in enumerate(dimensions):
-        name, ids = dim
-        if name == dimname:
-            for id in ids:
-                print id
-            
-def parse_options():
-    conf_files = ['/etc/laydirc',
-                  os.path.join(os.environ['HOME'], '.laydi')]
-
-    cp = cfgparse.ConfigParser()
-    op = optparse.OptionParser()
-
-    op.add_option('-c', '--csv',
-                  action='store_true', default=False,
-                  help='Export as CSV file.')
-
-    op.add_option('-d', '--dimension',
-                  action='store', default=None,
-                  help='Get all identifiers along a dimension.')
-
-    op.add_option('-i', '--info',
-                  action='store_true', default=False,
-                  help='Show dataset information.')
-
-    op.add_option('-l', '--longinfo',
-                  action='store_true', default=False,
-                  help='Display more information than -i.')
-
-    op.add_option('-o', '--output-file',
-                  action='store_true', default=False,
-                  help='Send output to file instead of stdout.')
-
-    op.add_option('-t', '--transpose',
-                  action='store_true', default=False,
-                  help='Transpose dataset.')
-
-    op.add_option('-y', '--change-type',
-                  action='store_true', default=False,
-                  help='Set new dataset type.')
-
-
-    for cf in conf_files:
-        if os.path.isfile(cf):
-            cp.add_file(cf)
-
-    return cp.parse(op)
-
-if __name__ == '__main__':
-    options, params = parse_options()
-    input = sys.stdin
-    output = sys.stdout
-
-    if options.info:
-        show_info(input)
-        sys.exit(0)
-
-    elif options.dimension != None:
-        list_dimension_ids(input, options.dimension)
-    

bin/ftsv2csv

@@ -1,50 +0,0 @@
-#!/usr/bin/python
-
-import sys
-
-from laydi import dataset
-from getopt import getopt
-
-def read_options():
-    short_opts = ""
-    long_opts = []
-
-    options, params = getopt(sys.argv[1:], short_opts, long_opts)
-
-    return params
-
-
-def write_csv(fd, ds):
-    rowdim, coldim = ds.get_dim_name()
-    rowids = ds.get_identifiers(rowdim, sorted=True)
-    colids = ds.get_identifiers(coldim, sorted=True)
-    x = ds.asarray()
-
-    ## Print ID row
-    print >> fd, rowdim,
-    for id in colids:
-        print >> fd, id,
-    print >> fd
-    
-    ## Print column IDs and data
-    for i, row in enumerate(rowids):
-        print >> fd, row,
-        for j in range(len(colids)):
-            print >> fd, x[i,j],
-        print >> fd
-
-if __name__ == "__main__":
-    params = read_options()
-    input_fn = params[0]
-
-    if len(params) == 2:
-	output_fn = params[1]
-    else:
-        name, ext = input_fn.rsplit('.', 1)
-        output_fn = name + '.csv'
-
-    ds = dataset.read_ftsv(input_fn)
-    output_fd = open(output_fn, 'w')
-    write_csv(output_fd, ds)
-    output_fd.close()
-

bin/laydi

@@ -1,145 +0,0 @@
-#!/usr/bin/python
-
-from getopt import getopt
-import os
-import sys
-from laydi import laydi, project, projectview, workflow, main
-#import workflows
-from laydi import cfgparse
-import optparse
-
-PROGRAM_NAME = 'laydi'
-VERSION = '0.1.0'
-
-def list_workflows():
-    print 'laydi %s' % VERSION
-    print
-    print 'Available workflows:'
-
-    wfs = workflow.workflow_list()
-    for wf in wfs:
-        print '    %s (%s)' % (wf.ident, wf.name)
-    print
-
-
-def generate_config():
-    fn = os.path.join(os.environ['HOME'], '.laydi')
-    if not os.path.exists(fn):
-	fd = open(fn, 'w')
-	print >> fd, "home = %s" % os.environ['HOME']
-        print >> fd, "datadir = %%(home)s/laydi/datasets"
-        print >> fd, "workflowdir = %%(home)s/laydi/workflows"
-	fd.close()
-   
-    laydidir = os.path.join(os.environ['HOME'], 'laydi')
-    if not os.path.exists(laydidir):
-	os.mkdir(laydidir, 0755)
-
-    datadir = os.path.join(os.environ['HOME'], 'laydi/datasets')
-    if not os.path.exists(datadir):
-	os.mkdir(datadir, 0755)
-
-    workflowdir = os.path.join(os.environ['HOME'], 'laydi/workflows')
-    if not os.path.exists(workflowdir):
-	os.mkdir(workflowdir, 0755)
-
-
-def parse_options():
-    conf_files = ['/etc/laydirc',
-                  os.path.join(os.environ['HOME'], '.laydi')]
-
-    cp = cfgparse.ConfigParser()
-
-    cp.add_option('home', type='string',
-                  default=os.environ['HOME'])
-
-    cp.add_option('datadir', type='string',
-                  default=os.environ['HOME'])
-    cp.add_option('workflowdir', type='string',
-                  default='workflows')
-    cp.parse()
-    op = optparse.OptionParser()
-
-    op.add_option('-l', '--list-workflows',
-                  action='store_true',
-                  default=False,
-                  help='List available workflows.')
-
-    op.add_option('-w', '--workflow',
-                 default='default',
-                 help='Start with selected workflow')
-
-    op.add_option('-c', '--generate-config',
-                 action='store_true',
-                 help='Generate configuration file ~/.laydi if it does not exist.')
-
-    op.add_option('-n', '--new-project',
-                  action='store_true',
-                  help='Create new project directory.')
-
-    for cf in conf_files:
-        if os.path.isfile(cf):
-            cp.add_file(cf)
-
-    options, params = cp.parse(op)
-    if len(params) != 1:
-        print "error: project directory must be specified."
-        print "notice: to create a new project use -n /path/to/project"
-        sys.exit(1)
-        
-    return options, params
-    
-if __name__ == '__main__':
-    import gtk
-    import gnome
-    
-    gnome.program_init(PROGRAM_NAME, VERSION)
-
-    options, params = parse_options()
-
-    ## Workflow setup
-    main.options = options
-    
-    for dir in main.options.workflowdir.split(';'):
-        if dir.strip() != "" and os.path.exists(dir):
-            sys.path.append(dir)
-
-    if options.list_workflows:
-        list_workflows()
-        sys.exit(0)
-
-    if options.generate_config:
-        generate_config()
-	sys.exit(0)
-
-    selected_wf = workflow.find_workflow(options.workflow) 
-    if selected_wf == None: selected_wf = workflow.EmptyWorkflow
-
-#    workflow_list = workflow.workflow_list()
-#    for wf in workflow_list:
-#        if wf.ident == options.workflow:
-#            selected_wf = wf            
-
-    main.set_workflow(selected_wf())
-    main.set_options(options)
-    app = laydi.LaydiApp()
-
-    ## Project setup
-    prjroot = params[0]
-    if not project.is_project_directory(prjroot):
-        if options.new_project:
-            project.make_project_directory(prjroot)
-        else:
-            print "error: project directory not found: %s" % prjroot
-            print "notice: use the -n option to make a new project"
-            sys.exit(2)
-    proj = project.Project(prjroot)
-    main.project = proj
-
-    main.set_application(app)
-    main.set_projectview(projectview.ProjectView(proj))
-
-    app.set_projectview(main.projectview)
-    app.show()
-    gtk.main()
-

bin/mat2ftsv

@@ -1,37 +0,0 @@
-#!/usr/bin/python
-
-import sys 
-from getopt import getopt
-
-def show_help():
-    print "mat2ftsv - Matlab matrix to laydi dataset converter."
-    print
-    print "Usage: mat2ftsv <mat-file> [<matfile> ...]"
-    print 
-    print "Description: For each mat file given as input, a ftsv file"
-    print "    will be created with the same name, but suffixed with.ftsv"
-    print "    in addition to .mat or any other suffix already on the"
-    print "    file name."
-
-options, params = getopt(sys.argv[1:], 'h', ['help'])
-
-for opt, val in options:
-    if opt in ['-h', '--help']:
-        show_help()
-        sys.exit(0)
-
-if len(params) == 0:
-    show_help()
-    sys.exit(0)
-
-from scipy import io
-from numpy import ndarray
-from laydi import dataset
-
-fn_in = params[0]
-data = io.loadmat(fn_in)
-for key, value in data.items():
-    if isinstance(value, ndarray):
-        ds = dataset.Dataset(value, name=key)
-    dataset.write_ftsv(fn_in + '.ftsv', ds)
-

bin/txt2ftsv

@@ -1,100 +0,0 @@
-#!/usr/bin/env python
-
-import numpy
-import os.path
-import sys
-
-from laydi import dataset
-from getopt import getopt
-
-dimension = 'dim_doe'
-output_fn = '-'
-ds_name = None
-category = False
-sparse = False
-
-def print_help():
-    print
-    print 'options:'
-    print '    -h, --help             Show this help text.'
-    print '    -c, --category         Make category dataset'
-    print '    -d, --dimension=DIM    Make output in dimension DIM'
-    print '    -n, --name=NAME        Set name of output dataset'
-    print '    -o, --output=FILE      Save output dataset in FILE'
-    print '    -s, --sparse           Save output in sparse format'
-    print
-
-def parse_options():
-    global ds_name
-    global output_fn
-
-    short_opts = 'cd:hn:o:'
-    long_opts = ['help', 'category', 'dimension', 'name', 'output', 'sparse']
-    options, params = getopt(sys.argv[1:], short_opts, long_opts)
-
-    for opt, val in options:
-        if opt in ['-h', '--help']:
-            print_help()
-            sys.exit(0)
-        elif opt in ['-c', '--category']:
-            global category
-            category = True
-        elif opt in ['-d', '--dimension']:
-            global dimension
-            dimension = val
-        elif opt in ['-n', '--name']:
-            ds_name = val
-        elif opt in ['-o', '--output']:
-            output_fn = val
-        elif opt in ['-s', '--sparse']:
-            global sparse
-            sparse = True
-    
-    if ds_name == None:
-        if output_fn != None:
-            ds_name = output_fn
-        else:
-            ds_name = 'txt2ftsv'
-
-    if len(params) == 0:
-        print_help()
-        sys.exit(1)
-
-    return params
-
-def read_file(fd):
-    lines = fd.readlines()
-    return [l.strip() for l in lines if l.strip() != '']
-    
-def build_dataset(dimension, id_lists, filenames):
-    all_ids = list(reduce(set.union, [set(x) for x in id_lists]))
-    x = numpy.zeros((len(all_ids), len(id_lists)), 'b')
-    for i, idl in enumerate(id_lists):
-        for id in idl:
-            x[all_ids.index(id),i] = True
-
-    if category:
-        ds = dataset.CategoryDataset(x, [(dimension, all_ids), ('files', filenames)], name=ds_name)
-    else:
-        ds = dataset.Dataset(x, [(dimension, all_ids), ('files', filenames)], name=ds_name)
-    return ds
-
-if __name__ == '__main__':
-    id_lists = []
-
-    filenames = parse_options()
-    for fn in filenames:
-        if os.path.exists(fn):
-            fd = open(fn)
-            id_lists.append(read_file(fd))
-            fd.close()
-        elif fn == '-':
-            id_lists.append(read_file(sys.stdin))
-
-    ds = build_dataset(dimension, id_lists, filenames)
-
-    if output_fn == '-':
-        dataset.write_ftsv(sys.stdout, ds, sp_format=sparse)
-    else:
-        dataset.write_ftsv(output_fn, ds, sp_format=sparse)
-

configure

@@ -1,38 +0,0 @@
-#!/bin/bash
-
-
-TEMP=`getopt -o "" --long prefix:,bindir:,datadir:,pydir:,root: \
-     -n 'configure' -- "$@"`
-
-eval set -- "$TEMP"
-
-while true ; do
-	case "$1" in
-		--prefix) PREFIX=$2 ; shift 2 ;;
-		--bindir) BINDIR=$2 ; shift 2 ;;
-		--datadir) DATADIR=$2 ; shift 2 ;;
-		--pydir) PYDIR=$2 ; shift 2 ;;
-		--root) ROOT=$2 ; shift 2 ;;
-		--) shift ; break ;;
-		*) echo "Internal error!" ; exit 1 ;;
-	esac
-done
-
-#if [[ $PREFIX == "" ]] ; then PREFIX=/usr/local ; fi
-if [[ $BINDIR == "" ]] ; then BINDIR=$PREFIX/bin ; fi
-if [[ $DATADIR == "" ]] ; then DATADIR=$PREFIX/share/laydi ; fi
-if [[ $DOCDIR == "" ]] ; then DOCDIR=$PREFIX/share/doc/laydi; fi
-if [[ $PYDIR == "" ]] ; then PYDIR=$PREFIX/share/pyshared/laydi; fi
-
-m4 -D M4_PREFIX=$ROOT/$PREFIX -D M4_BINDIR=$ROOT/$BINDIR \
-   -D M4_DATADIR=$ROOT/$DATADIR -D M4_DOCDIR=$ROOT/$DOCDIR \
-   -D M4_PYDIR=$ROOT/$PYDIR Makefile.m4 > Makefile
-
-m4 -D M4_PREFIX=$ROOT/$PREFIX -D M4_BINDIR=$ROOT/$BINDIR \
-   -D M4_DATADIR=$ROOT/$DATADIR -D M4_DOCDIR=$ROOT/$DOCDIR \
-   -D M4_PYDIR=$ROOT/$PYDIR doc/Makefile.m4 > doc/Makefile
-
-m4 -D M4_PREFIX=$PREFIX -D M4_BINDIR=$BINDIR \
-   -D M4_DATADIR=$DATADIR -D M4_DOCDIR=$DOCDIR \
-   -D M4_PYDIR=$PYDIR laydi/paths.py.m4 > laydi/paths.py
-

debian/changelog

@@ -1,6 +0,0 @@
-laydi (0.1.0) unstable; urgency=low
-
-  * Initial packaging
-
- -- Einar Ryeng <einarr@pvv.org>  Tue, 11 Dec 2007 16:12:59 +0100
-

debian/compat

@@ -1 +0,0 @@
-5

debian/control

@@ -1,22 +0,0 @@
-Source: laydi
-Section: science
-Priority: optional
-Maintainer: Einar Ryeng <einarr@pvv.org>
-Build-Depends: debhelper (>= 5.0.37.2), python-dateutil, python-all-dev (>= 2.3.5-7), python-central (>= 0.5), python-epydoc, python-setuptools (>=0.6b3-1) 
-Standards-Version: 3.7.2
-
-Package: laydi
-Architecture: any
-Depends: ${python:Depends},  python-numpy (>= 1:1.0.1), python-dev
-Provides: ${python:Provides}
-Description: Python library of bilinear modeling algorithms.
- Bilinear modeling algorithms.
-
-Package: laydi-doc
-Architecture: all
-Enhances: laydi
-Description: Laydi API documentation.
- Bilinear modeling algorithms.
- .
- This package contains documentation for Laydi
-

debian/copyright

@@ -1,32 +0,0 @@
-This package was debianized by Einar Ryeng <einarr@pvv.org> on
-2007-09-11.
-
-It was downloaded from https://dev.pvv.org/projects/laydi/downloads
-
-Upstream Author: Arnar Flatberg <arnar.flatberg@gmail.com>
-
-Copyright: Arnar Flatberg <arnar.flatberg@gmail.com>
-
-License:
-    Redistribution and use in source and binary forms, with or without
-    modification, are permitted under the terms of the BSD License.
-
-    THIS SOFTWARE IS PROVIDED BY THE REGENTS 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 REGENTS 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.
-
-On Debian systems, the complete text of the BSD License can be 
-found in `/usr/share/common-licenses/BSD'.
-
-
-The Debian packaging is (C) 2007, Einar Ryeng <einarr@pvv.org> and
-is licensed under the GPL, see `/usr/share/common-licenses/GPL'.
-

debian/docs

@@ -1 +0,0 @@
-README

debian/rules

@@ -1,74 +0,0 @@
-#!/usr/bin/make -f
-# -*- makefile -*-
-# Sample debian/rules that uses debhelper.
-# This file was originally written by Joey Hess and Craig Small.
-# As a special exception, when this file is copied by dh-make into a
-# dh-make output file, you may use that output file without restriction.
-# This special exception was added by Craig Small in version 0.37 of dh-make.
-
-# Uncomment this to turn on verbose mode.
-#export DH_VERBOSE=1
-
-CFLAGS = -Wall -g
-PYVERS=$(shell pyversions -vs)
-
-configure: configure-stamp
-
-configure-stamp:
-	dh_testdir
-	touch configure-stamp
-
-build: $(PYVERS:%=build-python%)
-
-build-python%:
-	dh_testdir
-#	python$* setup.py build
-	touch $@
-
-clean: 
-	# Add here commands to clean up after the build process.
-	-rm -r build
-	dh_clean 
-
-install: build $(PYVERS:%=install-python%)
-install-python%:
-	dh_testdir
-	dh_testroot
-	dh_clean -k 
-	dh_installdirs
-	./configure --root ${CURDIR}/debian/laydi --prefix /usr --pydir /usr/share/pyshared/
-	make install
-#	python$* setup.py install --root=$(CURDIR)/debian/laydi --install-data=/usr/share/laydi
-
-	# Remove all *.pyc files, created in the postinst
-#	find $(CURDIR)/debian/python-networkx -name "*.pyc" -exec rm {} ';'
-
-
-# Build architecture-independent files here.
-binary-indep: build install
-	make -C doc install
-#	mkdir -p $(CURDIR)/debian/laydi-doc/usr/share/doc/laydi-doc/html
-#	epydoc --html -o $(CURDIR)/debian/laydi-doc/usr/share/doc/laydi-doc/html laydi
-
-# Build architecture-dependent files here.
-binary-arch: build install
-	dh_testdir
-	dh_testroot
-	dh_installchangelogs 
-	dh_installdocs
-	dh_installexamples
-#	dh_install
-	dh_pysupport
-	dh_installman
-	dh_link
-	dh_strip
-	dh_compress
-#	dh_makeshlibs
-	dh_installdeb
-	dh_shlibdeps
-	dh_gencontrol
-	dh_md5sums
-	dh_builddeb
-
-binary: binary-indep binary-arch
-.PHONY:  build clean binary-indep binary-arch binary install configure

doc/Makefile.m4

@@ -1,20 +0,0 @@
-
-DOC_DIR=M4_DOCDIR
-
-all: html
-
-html:
-	@echo ----------------
-	@echo Generating epydoc html code documentation.
-	@echo See epydoc-html.log for epydoc log.
-	@echo ----------------
-	epydoc --html --inheritance listed -o html/ ../laydi 2> epydoc-html.log
-
-install: html
-	find html/ -type f -exec install -m 644 -D '{}' ${DOC_DIR}/'{}' \;
-
-clean:
-	-rm -rf html
-	-rm epydoc-html.log
-
-	

doc/examples/gastrin-ts/VERSION

@@ -1 +0,0 @@
-Laydi project version 1

doc/gui-overview.svg

@@ -1,215 +0,0 @@
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fluents

@@ -0,0 +1,68 @@
+#!/usr/bin/python2.4
+
+from getopt import getopt
+import sys
+from system import fluents, project, workflow
+import workflows
+
+PROGRAM_NAME = 'fluents'
+VERSION = '0.1.0'
+
+parameters = {'workflow': workflow.EmptyWorkflow}
+
+def show_help():
+    print 'fluent %s' % VERSION
+    print 'This software is released under the GNU General Public Licence'
+    print
+    print 'Usage: fluent [options]'
+    print 
+    print 'Description:'
+    print '    Fluent is a lightweight data analysis application for bilinear models.'
+    print
+    print 'Options:'
+    print '    -h --help              Show this help text'
+    print '    -l --list-workflows    Lists available workflows'
+    print '    -w --workflow=<wf>     Generates a new project based on workflow wf.'
+    print
+
+def list_workflows():
+    print 'fluent %s' % VERSION
+    print
+    print 'Workflows:'
+
+    wfs = workflow.workflow_list()
+    for wf in wfs:
+        print '    %s (%s)' % (wf.ident, wf.name)
+    print
+
+def parse_options():
+    short_opts = 'hlw:'
+    long_opts = ['help', 'list-workflows', 'workflow=']
+
+    options, params = getopt(sys.argv[1:], short_opts, long_opts)
+
+    for opt, val in options:
+        if opt in ['-h', '--help']:
+            show_help()
+            sys.exit(0)
+        elif opt in ['-l', '--list-workflows']:
+            list_workflows()
+            sys.exit(0)
+        elif opt in ['-w', '--workflow']:
+            wfs = workflow.workflow_list()
+            for wf in wfs:
+                if wf.ident == val:
+                    parameters['workflow'] = wf
+            parameters['workflow']
+
+if __name__ == '__main__':
+    parse_options()
+    
+    import gtk
+    import gnome
+    
+    gnome.program_init(PROGRAM_NAME, VERSION)
+    app = fluents.FluentApp(parameters['workflow'])
+    app.set_project(project.Project())
+    app.show()
+    gtk.main()

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laydi/__init__.py

@@ -1,3 +0,0 @@
-
-import main
-

laydi/annotations.py

@@ -1,100 +0,0 @@
-
-_dim_annotation_handlers = {}
-
-def get_dim_annotations(dimname, annotation, ids):
-    """Returns a list of annotations corresponding to the given ids in 
-    dimension dimname"""
-    global _dim_annotation_handlers
-
-    if _dim_annotation_handlers.has_key(dimname):
-        return _dim_annotation_handlers[dimname].get_annotations(annotation, ids)
-    return None
-
-def set_dim_handler(dimname, handler):
-    """Set the handler for the given dimension."""
-    global _dim_annotation_handlers
-    _dim_annotation_handlers[dimname] = handler
-    
-def get_dim_handler(dimname):
-    """Get the handler for the given dimension."""
-    global _dim_annotation_handlers
-    return _dim_annotation_handlers.get(dimname, None)
-
-
-class AnnotationHandler:
-    def __init__(self):
-        pass
-
-    def get_annotations(self, annotationname, ids, default=None):
-        return None
-
-    def get_annotation_names(self):
-        return []
-
-
-class DictAnnotationHandler(AnnotationHandler):
-
-    def __init__(self, d=None):
-        if d == None:
-            d = {}
-        self._dict = d
-    
-    def get_annotations(self, annotationname, ids, default=None):
-        d = self._dict
-        retval = []
-        for id in ids:
-            if d[annotationname].has_key(id):
-                retval.append(d[annotationname][id])
-            else:
-                retval.append(default)
-        return retval
-    
-    def add_annotations(self, annotationname, d):
-        self._dict[annotationname] = d
-
-    def get_annotation_names(self):
-        return self._dict.keys()
-        
-
-def read_annotations_file(filename):
-    """Read annotations from file.
-    
-    Reads annotations from a tab delimited file of the format::
-     dimname     annotation_name1    annotation_name2 ...
-     id1         Foo                 0.43
-     id2         Bar                 0.59
-    """
-    
-    ann = DictAnnotationHandler()
-    dimname = None
-    annotation_dicts = []
-    annotation_names = []
-    
-    fd = open(filename)
-
-    ## Read the first line, which contains the dimension name and
-    ## annotation names.
-    line = fd.readline()
-    values = [x.strip() for x in line.split('\t')]
-    dimname = values[0]  
-    annotation_names = values[1:]
-    annotation_dicts = [{} for x in annotation_names]
-
-    ## Read the lines containing the annotations. The first value on
-    ## each line is an id along the dimension.
-    while line:
-        values = [x.strip() for x in line.split('\t')]
-        for i, x in enumerate(values[1:]):
-            annotation_dicts[i][values[0]] = x
-        line = fd.readline()
-
-    fd.close()
-
-    ## Add everything to the annotation object and add the object to
-    ## the specified dimension.
-    for i, a in enumerate(annotation_names):
-        ann.add_annotations(a, annotation_dicts[i])
-
-    _dim_annotation_handlers[dimname] = ann
-    return ann
-

laydi/dataset.py

@@ -1,862 +0,0 @@
-from scipy import ndarray, atleast_2d, asarray, intersect1d, zeros
-from scipy import empty, sparse, where
-from scipy import sort as array_sort
-from itertools import izip
-import shelve
-import copy
-import re
-
-class Universe(object):
-    def __init__(self, name):
-        self.name = name
-        self._ids = {}
-
-    def register(self, dim):
-        """Increase reference count for identifiers in Dimension object dim"""
-        if dim.name != self.name:
-            return
-        for i in dim:
-            self._ids[i] = self._ids.get(i, 0) + 1
-    
-    def unregister(self, dim):
-        """Update reference count for identifiers in Dimension object dim
-           Update reference count for identifiers in Dimension object dim, and remove all
-           identifiers with a reference count of 0, as they do not (by definition) exist 
-           any longer.
-        """
-        if dim.name != self.name:
-            return
-        for i in dim:
-            refcount = self._ids[i]
-            if refcount == 1:
-                self._ids.pop(i)
-            else:
-                self._ids[i] -= 1
-
-    def __str__(self):
-        return "%s: %i elements, %i references" % (self.name, len(self._ids), sum(self._ids.values()))
-
-    def __contains__(self, element):
-        return self._ids.__contains__(element)
-
-    def __len__(self):
-        return len(self._ids)
-
-    def intersection(self, dim):
-        return set(self._ids).intersection(dim.idset)
-
-
-class Dimension(object):
-    """A Dimension represents the set of identifiers an object has along an axis.
-    """
-    def __init__(self, name, ids=[]):
-        self.name = name
-        self.idset = set(ids)
-        self.idlist = list(ids)
-
-    def __getitem__(self, element):
-        return self.idlist[element]
-
-    def __getslice__(self, start, end):
-        return self.idlist[start:end]
-
-    def __contains__(self, element):
-        return self.idset.__contains__(element)
-        
-    def __str__(self):
-        return "%s: %s" % (self.name, str(self.idlist))
-
-    def __len__(self):
-        return len(self.idlist)
-
-    def __iter__(self):
-        return iter(self.idlist)
-
-    def intersection(self, dim):
-        return self.idset.intersection(dim.idset)
-
-
-class Dataset(object):
-    """The Dataset base class.
-    
-    A Dataset is an n-way array with defined string identifiers across
-    all dimensions.
-
-    example of use:
-
-    ---
-    dim_name_rows = 'rows'
-    names_rows = ('row_a','row_b')
-    ids_1 = [dim_name_rows, names_rows]
-
-    dim_name_cols = 'cols'
-    names_cols = ('col_a','col_b','col_c','col_d')
-    ids_2 = [dim_name_cols, names_cols]
-
-    Array_X = rand(2,4)
-    data = Dataset(Array_X,(ids_1,ids_2),name="Testing")
-
-    dim_names = [dim for dim in data]
-
-    column_identifiers = [id for id in data['cols'].keys()]
-    column_index = [index for index in data['cols'].values()]
-
-    'cols' in data -> True
-
-    ---
-
-    data = Dataset(rand(10,20)) (generates dims and ids (no links))
-    """
-    
-    def __init__(self, array, identifiers=None, name='Unnamed dataset'):
-        self._dims = [] #existing dimensions in this dataset
-        self._map = {} # internal mapping for dataset:  identifier <--> index
-        self._name = name
-        self._identifiers = identifiers
-        
-        if not isinstance(array, sparse.spmatrix):
-            array = atleast_2d(asarray(array))
-        # vector are column (array)
-        if array.shape[0] == 1:
-            array = array.T
-        self.shape = array.shape
-        
-        if identifiers != None:
-            self._validate_identifiers(identifiers)
-            self._set_identifiers(identifiers, self._all_dims)
-        else:
-            self._identifiers = self._create_identifiers(self.shape, self._all_dims)
-            self._set_identifiers(self._identifiers, self._all_dims)
-        self._array = array
-
-    def __iter__(self):
-        """Returns an iterator over dimensions of dataset."""
-        return self._dims.__iter__()
-
-    def __contains__(self,dim):
-        """Returns True if dim is a dimension name in dataset."""
-        # return self._dims.__contains__(dim)
-        return self._map.__contains__(dim)
-
-    def __len__(self):
-        """Returns the number of dimensions in the dataset"""
-        return len(self._map)
-
-    def __getitem__(self,dim):
-        """Return the identifers along the dimension dim."""
-        return self._map[dim]
-
-    def _create_identifiers(self, shape, all_dims):
-        """Creates dimension names and identifier names, and returns
-        identifiers."""
-        
-        dim_names = ['rows','cols'] 
-        ids = []
-        for axis, n in enumerate(shape):
-            if axis < 2:
-                dim_suggestion = dim_names[axis]
-            else:
-                dim_suggestion = 'dim'
-            dim_suggestion = self._suggest_dim_name(dim_suggestion, all_dims) 
-            identifier_creation = [str(axis) + "_" + i for i in map(str, range(n))]
-            ids.append((dim_suggestion, identifier_creation))
-            all_dims.add(dim_suggestion)
-        return ids
-
-    def _set_identifiers(self, identifiers, all_dims):
-        """Creates internal mapping of identifiers structure."""
-        for dim, ids in identifiers:
-            pos_map = ReverseDict()
-            if dim not in self._dims:
-                self._dims.append(dim)
-                all_dims.add(dim)
-            else:
-                raise ValueError, "Dimension names must be unique whitin dataset"
-            for pos, id in enumerate(ids):
-                pos_map[id] = pos
-            self._map[dim] = pos_map
-            
-    def _suggest_dim_name(self,dim_name,all_dims):
-        """Suggests a unique name for dim and returns it"""
-        c = 0
-        new_name = dim_name
-        while new_name in all_dims:
-            new_name = dim_name + "_" + str(c)
-            c += 1
-        return new_name
-        
-    def asarray(self):
-        """Returns the numeric array (data) of dataset"""
-        if isinstance(self._array, sparse.spmatrix):
-            return self._array.toarray()
-        return self._array
-
-    def set_array(self, array):
-        """Adds array as an ArrayType object.
-        A one-dim array is transformed to a two-dim array (row-vector)
-        """
-        if not isinstance(array, type(self._array)):
-            raise ValueError("Input array of type: %s does not match existing array type: %s") %(type(array), type(self._array))
-        if self.shape != array.shape:
-            raise ValueError, "Input array must be of similar dimensions as dataset"
-        self._array = atleast_2d(asarray(array))
-
-    def get_name(self):
-        """Returns dataset name"""
-        return self._name
-
-    def get_all_dims(self):
-        """Returns all dimensions in project"""
-        return self._all_dims
-
-    def get_dim_name(self, axis=None):
-        """Returns dim name for an axis, if no axis is provided it
-        returns a list of dims"""
-        if type(axis) == int:
-            return self._dims[axis]
-        else:
-            return [dim for dim in self._dims]
-
-    def common_dims(self, ds):
-        """Returns a list of the common dimensions in the two datasets."""
-        dims = self.get_dim_name()
-        ds_dims = ds.get_dim_name()
-        return [d for d in dims if d in ds_dims]
-        
-    def get_identifiers(self, dim, indices=None, sorted=False):
-        """Returns identifiers along dim, sorted by position (index)
-        is optional.
-	
-        You can optionally provide a list/ndarray of indices to get
-        only the identifiers of a given position.
-
-        Identifiers are the unique names (strings) for a variable in a
-        given dim.  Index (Indices) are the Identifiers position in a
-        matrix in a given dim.
-        """
-        if indices != None:
-            if len(indices) == 0:# if empty list or empty array
-                return []
-        if indices != None:
-            # be sure to match intersection
-            #indices = intersect1d(self.get_indices(dim),indices)
-            ids = [self._map[dim].reverse[i] for i in indices]
-	else:
-            if sorted == True:
-                ids = [self._map[dim].reverse[i] for i in array_sort(self._map[dim].values())]
-            else:
-                ids = self._map[dim].keys()
-
-        return ids
-        
-    def get_indices(self, dim, idents=None):
-        """Returns indices for identifiers along dimension.
-        You can optionally provide a list of identifiers to retrieve a
-        index subset.
-        
-        Identifiers are the unique names (strings) for a variable in a
-        given dim.  Index (Indices) are the Identifiers position in a
-        matrix in a given dim.  If none of the input identifiers are
-        found an empty index is returned
-        """
-        if not isinstance(idents, list) and not isinstance(idents, set):
-            raise ValueError("idents needs to be a list/set got: %s" %type(idents))
-        if idents == None:
-            index = array_sort(self._map[dim].values())
-        else:
-            index = [self._map[dim][key]
-                     for key in idents if self._map[dim].has_key(key)]
-        return asarray(index)        
-
-    def existing_identifiers(self, dim, idents):
-        """Filters a list of identifiers to find those that are present in the
-        dataset.
-
-        The most common use of this function is to get a list of
-        identifiers who correspond one to one with the list of indices produced
-        when get_indices is given an identifier list. That is
-        ds.get_indices(dim, idents) and ds.exisiting_identifiers(dim, idents)
-        will have the same order.
-
-        @param dim: A dimension present in the dataset.
-        @param idents: A list of identifiers along the given dimension.
-        @return: A list of identifiers in the same order as idents, but
-        without elements not present in the dataset.
-        """
-        if not isinstance(idents, list) and not isinstance(idents, set):
-            raise ValueError("idents needs to be a list/set got: %s" %type(idents))
-
-        return [key for key in idents if self._map[dim].has_key(key)]
-
-    def copy(self):
-        """ Returns deepcopy of dataset.
-        """
-        return copy.deepcopy(self)
-
-    def subdata(self, dim, idents):
-        """Returns a new dataset based on dimension and given identifiers.
-        
-        """
-        ds = self.copy()
-        indices = array_sort(ds.get_indices(dim, idents))
-        
-        idents = ds.get_identifiers(dim, indices=indices)
-        if not idents:
-            raise ValueError("No of identifers from: \n%s \nfound in %s" %(str(idents), ds._name))
-        ax = [i for i, name in enumerate(ds._dims) if name == dim][0]
-        subarr = ds._array.take(indices, ax)
-        new_indices = range(len(idents))
-        ds._map[dim] = ReverseDict(zip(idents, new_indices))
-        ds.shape = tuple(len(ds._map[d]) for d in ds._dims)
-        ds.set_array(subarr)
-        return ds
-    
-    def transpose(self):
-        """Returns a copy of transpose of a dataset.
-
-        As for the moment: only support for 2D-arrays.
-        """
-        
-        assert(len(self.shape) == 2)
-        ds = self.copy()
-        ds._array = ds._array.T
-        ds._dims.reverse()
-        ds.shape = ds._array.shape
-        return ds
-    
-    def _validate_identifiers(self, identifiers):
-        for dim_name, ids in identifiers: 
-            if len(set(ids)) != len(ids):
-                raise ValueError("Identifiers not unique in : %s" %dim_name)
-        identifier_shape = [len(i[1]) for i in identifiers]
-        if len(identifier_shape) != len(self.shape):
-            raise ValueError("Identifier list length must equal array dims")
-        for ni, na in zip(identifier_shape, self.shape):
-            if ni != na:
-                raise ValueError, "Identifier-array mismatch: %s: (idents: %s, array: %s)" %(self._name, ni, na)
-
-
-class CategoryDataset(Dataset):
-    """The category dataset class.
-
-    A dataset for representing class information as binary
-    matrices (0/1-matrices).
-
-    There is support for using a less memory demanding, sparse format. The
-    prefered (default) format for a category dataset is the compressed sparse row 
-    format (csr)
-
-    Always has linked dimension in first dim:
-    ex matrix:
-    .        go_term1    go_term2  ...
-    gene_1
-    gene_2
-    gene_3
-    .
-    .
-    .
-    
-    """
-    
-    def __init__(self, array, identifiers=None, name='C'):
-        Dataset.__init__(self, array, identifiers=identifiers, name=name)
-
-    def as_spmatrix(self):
-        if isinstance(self._array, sparse.spmatrix):
-            return self._array
-        else:
-            arr = self.asarray()
-            return sparse.csr_matrix(arr.astype('i'))
-
-    def to_spmatrix(self):
-        if isinstance(self._array, sparse.spmatrix):
-            self._array = self._array.tocsr()
-        else:
-            self._array = sparse.scr_matrix(self._array)
-
-    def as_dictlists(self):
-        """Returns data as dict of identifiers along first dim.
-
-        ex: data['gene_1'] = ['map0030','map0010', ...]
-        
-        fixme: Deprecated?
-        """
-        data = {}
-        for name, ind in self._map[self.get_dim_name(0)].items():
-            if isinstance(self._array, ndarray):
-                indices = self._array[ind,:].nonzero()[0]
-            elif isinstance(self._array, sparse.spmatrix):
-                if not isinstance(self._array, sparse.csr_matrix):
-                    array = self._array.tocsr()
-                else:
-                    array = self._array
-                indices = array[ind,:].indices
-            if len(indices) == 0: # should we allow categories with no members?
-                continue
-            data[name] = self.get_identifiers(self.get_dim_name(1), indices)
-        self._dictlists = data
-        return data
-
-    def as_selections(self):
-        """Returns data as a list of Selection objects.
-
-        The list of selections is not ordered (sorted) by any means.
-        """
-        ret_list = []
-        for cat_name, ind in self._map[self.get_dim_name(1)].items():
-            if isinstance(self._array, sparse.spmatrix):
-                if not isinstance(self._array, sparse.csc_matrix):
-                    self._array = self._array.tocsc()
-                indices = self._array[:,ind].indices
-            else:
-                indices = self._array[:,ind].nonzero()[0]
-            if len(indices) == 0:
-                continue
-            ids = self.get_identifiers(self.get_dim_name(0), indices)
-            selection = Selection(cat_name)
-            selection.select(self.get_dim_name(0), ids)
-            ret_list.append(selection)
-        return ret_list
-    
-
-class GraphDataset(Dataset):
-    """The graph dataset class.
-
-    A dataset class for representing graphs. The constructor may use an 
-    incidence matrix (possibly sparse) or (if networkx installed) a 
-    networkx.(X)Graph structure.
-    
-    If the networkx library is installed, there is support for
-    representing the graph as a networkx.Graph, or networkx.XGraph structure.
-    """
-    
-    def __init__(self, input, identifiers=None, name='A', nodepos = None):      
-        if isinstance(input, sparse.spmatrix):
-            arr = input
-        else:
-            try:
-                arr = asarray(input)
-            except:
-                raise ValueError("Could not identify input")
-        Dataset.__init__(self, array=arr, identifiers=identifiers, name=name)
-        self._graph = None
-        self.nodepos = nodepos
-        
-    def as_spmatrix(self):
-        if isinstance(self._array, sparse.spmatrix):
-            return self._array
-        else:
-            arr = self.asarray()
-            return sparse.csr_matrix(arr.astype('i'))
-
-    def to_spmatrix(self):
-        if isinstance(self._array, sparse.spmatrix):
-            self._array = self._array.tocsr()
-        else:
-            self._array = sparse.scr_matrix(self._array)
-    
-    def asnetworkx(self):
-        if self._graph != None:
-            return self._graph
-        dim0, dim1 = self.get_dim_name()
-        node_ids = self.get_identifiers(dim0, sorted=True)
-        edge_ids = self.get_identifiers(dim1, sorted=True)
-        G, weights = self._graph_from_incidence_matrix(self._array, node_ids=node_ids, edge_ids=edge_ids)
-        self._graph = G
-        return G
-        
-    def from_networkx(cls, G, node_dim, edge_dim, sp_format=True):
-        """Create graph dataset from networkx graph.
-        
-        When G is a Graph/Digraph edge identifiers will be created,
-        else (XGraoh/XDigraph) it is assumed that edge attributes are
-        the edge identifiers.
-        """
-        
-        import networkx as nx
-        n = G.number_of_nodes()
-        m = G.number_of_edges()
-        
-        if isinstance(G, nx.DiGraph):
-            G = nx.XDiGraph(G)
-        elif isinstance(G, nx.Graph):
-            G = nx.XGraph(G)
-        
-        edge_ids = [e[2] for e in G.edges()]
-        node_ids = map(str, G.nodes())
-        n2ind = {}
-        for ind, node in enumerate(node_ids):
-            n2ind[node] = ind
-        
-        if sp_format:
-            I = sparse.lil_matrix((n, m))
-        else:
-            I = zeros((m, n), dtype='i')
-        
-        for i, (h, t, eid) in enumerate(G.edges()):
-            if eid != None:
-                edge_ids[i] = eid
-            else:
-                edge_ids[i] = 'e_' + str(i)
-            hind = n2ind[str(h)]
-            tind = n2ind[str(t)]
-            I[hind, i] = 1
-            if G.is_directed():
-                I[tind, i] = -1
-            else:
-                I[tind, i] = 1
-        idents = [[node_dim, node_ids], [edge_dim, edge_ids]]
-        if G.name != '':
-            name = G.name
-        else:
-            name = 'A'
-        ds = GraphDataset(I, idents, name)
-        return ds
-    
-    from_networkx = classmethod(from_networkx)            
-    
-    def _incidence2adjacency(self, I):
-        """Incidence to adjacency matrix.
-        
-        I*I.T - eye(n)?
-        """
-        raise NotImplementedError
-    
-    def _graph_from_incidence_matrix(self, I, node_ids, edge_ids):
-        """Creates a networkx graph class from incidence
-        (possibly weighted) matrix and ordered labels.
-        
-        labels = None, results in string-numbered labels
-        """
-        try:
-            import networkx as nx
-        except:
-            print "Failed in import of NetworkX"
-            return None
-
-        m, n = I.shape
-        assert(m == len(node_ids))
-        assert(n == len(edge_ids))
-        weights = []
-        directed = False
-        G = nx.XDiGraph(name=self._name)
-        if isinstance(I, sparse.spmatrix):
-            I = I.tocsr()
-        for ename, col in izip(edge_ids, I.T):
-            if isinstance(I, sparse.spmatrix):
-                node_ind = col.indices
-                w1, w2 = col.data
-            else:
-                node_ind = where(col != 0)[0]
-                w1, w2 = col[node_ind]
-            node1 = node_ids[node_ind[0]]
-            node2 = node_ids[node_ind[1]]
-            if w1 < 0: # w1 is tail
-                directed = True
-                assert(w2 > 0 and (w1 + w2) == 0)
-                G.add_edge(node2, node1, ename)
-                weights.append(w2)
-            else: #w2 is tail or graph is undirected
-                assert(w1 > 0)
-                if w2 < 0:
-                    directed = True
-                G.add_edge(node1, node2, ename)
-                weights.append(w1)
-        if not directed:
-            G = G.to_undirected()
-        return G, asarray(weights)
-
-Dataset._all_dims = set()
-
-
-class ReverseDict(dict):
-    """A dictionary which can lookup values by key, and keys by value.
-    
-    All values and keys must be hashable, and unique.
-    
-    example:
-    >>d = ReverseDict((['a',1],['b',2]))
-    >>print d['a'] --> 1
-    >>print d.reverse[1] --> 'a'
-    """
-    def __init__(self, *args, **kw):
-        dict.__init__(self, *args, **kw)
-        self.reverse = dict([[v, k] for k, v in self.items()])
-
-    def __setitem__(self, key, value):
-        dict.__setitem__(self, key, value)
-        try:
-            self.reverse[value] = key
-        except:
-            self.reverse = {value:key}
-
-
-class Selection(dict):
-    """Handles selected identifiers along each dimension of a dataset"""
-
-    def __init__(self, title='Unnamed Selecton'):
-        self.title = title
-        
-    def __getitem__(self, key):
-        if not self.has_key(key):
-            return None
-        return dict.__getitem__(self, key)
-
-    def dims(self):
-        return self.keys()
-        
-    def axis_len(self, axis):
-        if self._selection.has_key(axis):
-            return len(self._selection[axis])
-        return 0
-
-    def select(self, axis, labels):
-        self[axis] = labels
-
-
-def write_ftsv(fd, ds, decimals=7, sep='\t', fmt=None, sp_format=True):
-    """Writes a dataset in laydi tab separated values (ftsv) form.
-    
-    @param fd: An open file descriptor to the output file.
-    @param ds: The dataset to be written. 
-    @param decimals: Number of decimals, only supported for dataset.
-    @param fmt: String formating
-    The function handles datasets of these classes: 
-    Dataset, CategoryDataset and GraphDataset
-    """
-    opened = False
-    if isinstance(fd, str):
-        fd = open(fd, 'w')
-        opened = True
-    
-    # Write header information
-    if isinstance(ds, CategoryDataset):
-        type = 'category'
-        if fmt == None:
-            fmt = '%d'
-    elif isinstance(ds, GraphDataset):
-        type = 'network'
-        if fmt == None:
-            fmt = '%d'
-    elif isinstance(ds, Dataset):
-        type = 'dataset'
-        if fmt == None:
-            fmt = '%%.%df' % decimals
-        else:
-            fmt = '%%.%d' %decimals + fmt
-    else:
-        raise Exception("Unknown object type")
-    fd.write('# type: %s' %type + '\n')
-
-    for dim in ds.get_dim_name():
-        fd.write("# dimension: %s" % dim)
-        for ident in ds.get_identifiers(dim, sorted=True):
-            fd.write(" " + ident)
-        fd.write("\n")
-
-    fd.write("# name: %s" % ds.get_name() + '\n')
-    # xy-node-positions
-    if type == 'network' and ds.nodepos != None:
-        fd.write("# nodepos:")
-        node_dim = ds.get_dim_name(0)
-        for ident in ds.get_identifiers(node_dim, sorted=True):
-            fd.write(" %s,%s" %ds.nodepos[ident])
-        fd.write("\n")
-    
-    # Write data
-    if hasattr(ds, "as_spmatrix") and sp_format == True:
-        m = ds.as_spmatrix()
-    else:
-        m = ds.asarray()
-    if isinstance(m, sparse.spmatrix):
-        _write_sparse_elements(fd, m, fmt, sep)
-    else:
-        _write_elements(fd, m, fmt, sep)
-
-    if opened:
-        fd.close()
-
-def read_ftsv(fd, sep=None):
-    """Read a dataset in laydi tab separated values (ftsv) form and return it.
-    
-    @param fd: An open file descriptor.
-    @return: A Dataset, CategoryDataset or GraphDataset depending on the information
-    read.
-    """
-    opened = False
-    if isinstance(fd, str):
-        fd = open(fd)
-        opened = True
-
-    split_re = re.compile('^#\s*(\w+)\s*:\s*(.+)')
-    dimensions = []
-    identifiers = {}
-    type = 'dataset'
-    name = 'Unnamed dataset'
-    sp_format = False
-    nodepos = None
-    # graphtype = 'graph'
-
-    # Read header lines from file.
-    line = fd.readline()
-    while line:
-        m = split_re.match(line)
-        if m:
-            key, val = m.groups()
-            
-            # The line is on the form;
-            # dimension: dimname id1 id2 id3 ...
-            if key == 'dimension':
-                values = [v.strip() for v in val.split(' ')]
-                dimensions.append(values[0])
-                identifiers[values[0]] = values[1:]
-
-            # Read type of dataset.
-            # Should be dataset, category, or network
-            elif key == 'type':
-                type = val
-            
-            elif key == 'name':
-                name = val
-            
-            # storage format
-            # if sp_format is True then use coordinate triplets
-            elif key == 'sp_format':
-                if val in ['False', 'false', '0', 'F', 'f',]:
-                    sp_format = False
-                elif val in ['True', 'true', '1', 'T', 't']:
-                    sp_format = True
-                else:
-                    raise ValueError("sp_format: %s not valid " %sp_format)
-            
-            elif key == 'nodepos':
-                node_dim = dimensions[0]
-                idents = identifiers[node_dim]
-                nodepos = {}
-                xys = val.split(" ")
-                for node_id, xy in zip(idents, xys):
-                    x, y = map(float, xy.split(","))
-                    nodepos[node_id] = (x, y)
-        
-        else:
-            break
-        line = fd.readline()
-
-    # Dimensions in the form [(dim1, [id1, id2, id3 ..) ...] 
-    dims = [(x, identifiers[x]) for x in dimensions]
-    dim_lengths = [len(identifiers[x]) for x in dimensions]
-
-    # Create matrix and assign element reader
-    if type == 'category':
-        if sp_format:
-            matrix = sparse.lil_matrix(dim_lengths)
-        else:
-            matrix = empty(dim_lengths, dtype='i')
-    else:
-        if sp_format:
-            matrix = sparse.lil_matrix(dim_lengths)
-        else:
-            matrix = empty(dim_lengths)
-
-    if sp_format:
-        matrix = _read_sparse_elements(fd, matrix)
-    else:
-        matrix = _read_elements(fd, matrix)
-    
-
-    # Create dataset of specified type
-    if type == 'category':
-        ds = CategoryDataset(matrix, dims, name)
-    elif type == 'network':
-        ds = GraphDataset(matrix, dims, name=name, nodepos=nodepos)
-    else:
-        ds = Dataset(matrix, dims, name)
-
-    if opened:
-        fd.close()
-
-    return ds
-
-def write_csv(fd, ds, decimals=7, sep='\t'):
-    """Write a dataset as comma/tab/whatever dilimited data.
-    
-    @param fd: An open file descriptor to the output file.
-    @param ds: The dataset to be written. 
-    @param decimals: Number of decimals, only supported for dataset.
-    @param sep: Value separator
-    """
-
-    ## Open file if a string is passed instead of a file descriptor
-    opened = False
-    if isinstance(fd, str):
-        fd = open(fd, 'w')
-        opened = True
-    
-    ## Get data
-    rowdim, coldim = ds.get_dim_name()
-    rowids = ds.get_identifiers(rowdim)
-    colids = ds.get_identifiers(coldim)
-    a = ds.asarray()
-    y, x = a.shape
-    fmt = '%%%if' % decimals
-
-    ## Write header
-    fd.write(rowdim)
-    fd.write(sep)
-    for i, id in enumerate(colids):
-        fd.write(id)
-        fd.write(sep)
-    fd.write('\n')
-
-    ## Write matrix data
-    for j in range(y):
-        fd.write(rowids[j])
-        fd.write(sep)
-        for i in range(x):
-            fd.write(fmt % (a[j, i],))
-            fd.write(sep)
-        fd.write('\n')
-    
-    ## If we opened the stream, close it
-    if opened:
-        fd.close()
-
-def _write_sparse_elements(fd, arr, fmt='%d', sep=None):
-    """ Sparse coordinate format.""" 
-    fd.write('# sp_format: True\n\n')
-    fmt = '%d %d ' + fmt + '\n'
-    csr = arr.tocsr()
-    for ii in xrange(csr.size):
-        ir, ic = csr.rowcol(ii)
-        data = csr.getdata(ii)
-        fd.write(fmt % (ir, ic, data))
-
-def _write_elements(fd, arr, fmt='%f', sep='\t'):
-    """Standard value separated format."""
-    fmt = fmt + sep
-    fd.write('\n')
-    y, x = arr.shape
-    for j in range(y):
-        for i in range(x):
-            fd.write(fmt %arr[j, i])
-        fd.write('\n')
-
-def _read_elements(fd, arr, sep=None):
-    line = fd.readline()
-    i = 0
-    while line:
-        values = line.split(sep)
-        for j, val in enumerate(values):
-            arr[i,j] = float(val)
-        i += 1
-        line = fd.readline()
-    return arr
-
-def _read_sparse_elements(fd, arr, sep=None):
-    line = fd.readline()
-    while line:
-        i, j, val = line.split()
-        arr[int(i),int(j)] = float(val)
-        line = fd.readline()
-    return arr.tocsr()
-

laydi/laydi.py

@@ -1,401 +0,0 @@
-#!/usr/bin/python
-
-import os
-import sys
-
-import pygtk
-pygtk.require('2.0')
-import gobject
-import gtk
-import gtk.gdk
-import gtk.glade
-import gnome
-import gnome.ui
-import scipy
-import pango
-import projectview, workflow, dataset, view, navigator, dialogs, selections, plots, main
-from logger import logger, LogView
-
-
-PROGRAM_NAME = 'laydi'
-VERSION = '0.1.0'
-DATADIR = os.path.join(main.PYDIR, 'laydi')
-#ICONDIR = os.path.join(DATADIR,"..","icons")
-ICONDIR = main.ICONDIR
-GLADEFILENAME = os.path.join(main.PYDIR, 'laydi/laydi.glade')
-_icon_mapper = {dataset.Dataset: 'dataset',
-               dataset.CategoryDataset: 'category_dataset',
-               dataset.GraphDataset: 'graph_dataset',
-               plots.Plot: 'line_plot'}
-
-class IconFactory:
-    """Factory for icons that ensures that each icon is only loaded once."""
-
-    def __init__(self, path):
-        self._path = path
-        self._icons = {}
-
-    def get(self, iconname):
-        """Returns the gdk loaded PixBuf for the given icon.
-        Reads the icon from file if necessary."""
-        
-        # if iconname isnt a string, try to autoconvert
-        if not isinstance(iconname, str):
-            for cls in _icon_mapper.keys():
-                if isinstance(iconname, cls):
-                    iconname = _icon_mapper[cls]
-        
-        if self._icons.has_key(iconname):
-            return self._icons[iconname]
-
-        icon_fname = os.path.join(self._path, '%s.png' % iconname)
-        icon = gtk.gdk.pixbuf_new_from_file(icon_fname)
-        self._icons[iconname] = icon
-        return icon
-
-icon_factory = IconFactory(ICONDIR)
-
-class TableSizeSelection(gtk.Window):
-
-    def __init__(self):
-        self._SIZE = size = 5
-        gtk.Window.__init__(self, gtk.WINDOW_POPUP)
-        self._table = gtk.Table(size, size, True)
-        self._items = []
-
-        ## Create a 3x3 table of EventBox object, doubly stored because 
-        ## gtk.Table does not support indexed retrieval.
-
-        for y in range(size):
-            line = []
-            for x in range(size):
-                ebox = gtk.EventBox()
-                ebox.add(gtk.Frame())
-                ebox.set_size_request(20, 20)
-                ebox.set_visible_window(True)
-                self._table.attach(ebox, x, x+1, y, y+1, gtk.FILL, gtk.FILL)
-                line.append(ebox)
-            self._items.append(line)
-
-        self.set_border_width(5)
-        self.add(self._table)
-        self.connect_signals()
-
-    def _get_child_pos(self, child):
-        size = self._SIZE
-        for x in range(size):
-            for y in range(size):
-                if self._items[y][x] == child:
-                    return (x, y)
-        return None
-
-    def connect_signals(self):
-        size = self._SIZE
-        for x in range(size):
-            for y in range(size):
-                self._items[y][x].add_events(gtk.gdk.ENTER_NOTIFY_MASK)
-                self._items[y][x].connect("enter-notify-event", 
-                                          self._on_enter_notify)
-                self._items[y][x].connect("button-release-event", 
-                                          self._on_button_release)
-
-    def _on_enter_notify(self, widget, event):
-        size = self._SIZE
-        x, y = self._get_child_pos(widget)
-        for i in range(size):
-            for j in range(size):
-                if i <= x and j <= y:
-                    self._items[j][i].set_state(gtk.STATE_SELECTED)
-                else:
-                    self._items[j][i].set_state(gtk.STATE_NORMAL)
-        self.x = x
-        self.y = y
-
-    def _on_button_release(self, widget, event):
-        size = self._SIZE
-        self.emit('table-size-set', self.x+1, self.y+1)
-        self.hide_all()
-
-        for x in range(size):
-            for y in range(size):
-                self._items[y][x].set_state(gtk.STATE_NORMAL)
-
-
-class ViewFrameToolButton (gtk.ToolItem):
-
-    def __init__(self):
-        gtk.ToolItem.__init__(self)
-
-        fname = os.path.join(ICONDIR, "table_size.png")
-        image = gtk.Image()
-        image.set_from_file(fname)
-
-        self._button = gtk.Button()
-        self._button.set_image(image)
-        self._button.set_property("can-focus", False)
-
-        eb = gtk.EventBox()
-        eb.add(self._button)
-        self.add(eb)
-        self._item = TableSizeSelection()
-        self._button.connect("button-press-event", self._on_show_menu)
-        image.show()
-        self._image = image
-
-        self._item.connect("table-size-set", self._on_table_size_set)
-        self._button.set_relief(gtk.RELIEF_NONE)
-        self.show_all()
-
-    def _on_show_menu(self, widget, event):
-        x, y = self._image.window.get_origin()
-        x2, y2, w, h, b = self._image.window.get_geometry()
-        
-        self._item.move(x, y+h)
-        self._item.show_all()
-
-    def _on_table_size_set(self, widget, width, height):
-        main.application['main_view'].resize_table(width, height)
-
-
-class LaydiApp:
-
-    def __init__(self): # Application variables
-        self.current_data = None
-        self._last_view = None
-        self._plot_toolbar = None
-        self._toolbar_state = None
-        
-        gtk.glade.set_custom_handler(self.custom_object_factory)
-        self.widget_tree = gtk.glade.XML(GLADEFILENAME, 'appwindow')
-#        self.workflow = wf
-    
-        self.idlist_crt = selections.IdListController(self['identifier_list'])
-        self.sellist_crt = selections.SelectionListController(self['selection_tree'],
-                                                        self.idlist_crt)
-        self.dimlist_crt = selections.DimListController(self['dim_list'],
-                                                        self.sellist_crt)
-        self.sellist_crt.set_dimlist_controller(self.dimlist_crt)
-
-    def init_gui(self):
-        self['appwindow'].set_size_request(800, 600)
-
-        # Set up workflow
-        self.wf_view = workflow.WorkflowView(main.workflow)
-        self.wf_view.show()
-        self['workflow_vbox'].pack_end(self.wf_view)
-
-        self._wf_menu = workflow.WorkflowMenu(main.workflow)
-        self._wf_menu.show()
-        wf_menuitem = gtk.MenuItem('Fu_nctions')
-        wf_menuitem.set_submenu(self._wf_menu)
-        wf_menuitem.show()
-
-        self['menubar1'].insert(wf_menuitem, 2)
-
-        # Connect signals
-        signals = {'on_quit1_activate' : (gtk.main_quit),
-                   'on_appwindow_delete_event' : (gtk.main_quit),
-                   'on_zoom_in_button_clicked' : (self.on_single_view),
-                   'on_zoom_out_button_clicked' : (self.on_multiple_view),
-                   'on_new1_activate' : (self.on_create_project),
-                   'on_button_new_clicked' : (self.on_create_project),
-                   'on_workflow_refresh_clicked' : (self.on_workflow_refresh_clicked),
-                   'on_index1_activate' : (self.on_help_index),
-                   'on_about1_activate' : (self.on_help_about),
-                   'on_report_bug1_activate' : (self.on_help_report_bug),
-                   'on_small_view1_activate' : (self.on_multiple_view),
-                   'on_large_view1_activate' : (self.on_single_view),
-
-                   'on_left1_activate' : (self.on_left),
-                   'on_right1_activate' : (self.on_right),
-                   'on_up1_activate' : (self.on_up),
-                   'on_down1_activate' : (self.on_down),
-
-                   'on_navigator1_activate' : (self.on_show_navigator),
-                   'on_workflow1_activate' : (self.on_show_workflow),
-                   'on_information1_activate' : (self.on_show_infopane),
-                  }
-        self.widget_tree.signal_autoconnect(signals)
-
-        self['main_view'].connect('view-changed', self.on_view_changed)
-
-        # Log that we've set up the app now
-        logger.debug('Program started')
-
-        # Add ViewFrame table size to toolbar
-        tb = ViewFrameToolButton()
-        self['toolbar'].add(tb)
-
-    def set_projectview(self, proj):
-        logger.notice('Welcome to your new project. Grasp That Data!')
-        self.navigator_view.add_projectview(proj)
-        self.dimlist_crt.set_projectview(proj)
-        self.sellist_crt.set_projectview(proj)
-        
-    def set_workflow(self, workflow):
-        main.workflow = workflow
-        self.wf_view.set_workflow(main.workflow)
-
-    def show(self):
-        self.init_gui()
-
-    def change_plot(self, plot):
-        """Sets the plot in the currently active ViewFrame. If the plot is 
-        already shown in another ViewFrame it will be moved from there."""
-        # Set current selection in the plot before showing it.
-        plot.selection_changed(None, main.projectview.get_selection())
-
-        self['main_view'].insert_view(plot)
-        self._update_toolbar(plot)
-       
-    def change_plots(self, plots):
-        """Changes all plots.""" 
-        self['main_view'].set_all_plots(plots)
-        v = self.get_active_view_frame().get_view()
-        self._update_toolbar(v)
-
-    def get_active_view_frame(self):
-        return self['main_view'].get_active_view_frame()
-
-    def _update_toolbar(self, view):
-        """Set the plot specific toolbar to the toolbar of the currently 
-        active plot."""
-
-        # don't do anything on no change
-        if self._last_view == view:
-            return
-        self._last_view = view
-        
-        logger.debug("view changed to %s" % view)
-
-        window = self['plot_toolbar_dock']
-        if self._plot_toolbar:
-            toolbar_state = self._plot_toolbar.get_mode()
-            window.remove(self._plot_toolbar)
-        else:
-            toolbar_state = "default"
-        
-        if view:    
-            self._plot_toolbar = view.get_toolbar()
-            self._plot_toolbar.set_mode(toolbar_state)
-        else:
-            self._plot_toolbar = None
-
-        if self._plot_toolbar:
-            window.add(self._plot_toolbar)
-
-    # Methods to create GUI widgets from CustomWidgets in the glade file.
-    # The custom_object_factory calls other functions to generate specific
-    # widgets.
-
-    def custom_object_factory(self, glade, fun_name, widget_name, s1, s2, i1, i2):
-        "Called by the glade file reader to create custom GUI widgets."
-        handler = getattr(self, fun_name)
-        return handler(s1, s2, i1, i2)
-        
-    def create_logview(self, str1, str2, int1, int2):
-        self.log_view = LogView(logger)
-        self.log_view.show()
-        return self.log_view
-
-    def create_main_view(self, str1, str2, int1, int2):
-        self.main_view = view.MainView()
-        self.main_view.show()
-        return self.main_view
-
-    def create_navigator_view(self, str1, str2, int1, int2):
-        self.navigator_view = navigator.NavigatorView()
-        self.navigator_view.show()
-        return self.navigator_view
-
-    def create_dim_list(self, str1, str2, int1, int2):
-        self.dim_list = selections.DimList()
-        self.dim_list.show()
-        return self.dim_list
-    
-    def create_selection_tree(self, str1, str2, int1, int2):
-        self.selection_tree = selections.SelectionTree()
-        self.selection_tree.show()
-        return self.selection_tree
-
-    def create_identifier_list(self, str1, str2, int1, int2):
-        self.identifier_list = selections.IdentifierList()
-        self.identifier_list.show()
-        return self.identifier_list
-
-    def __getitem__(self, key):
-        return self.widget_tree.get_widget(key)
-
-    # Event handlers.
-    # These methods are called by the gtk framework in response to events and
-    # should not be called directly.
-
-    def on_single_view(self, *ignored):
-        self['main_view'].goto_large()
-
-    def on_multiple_view(self, *ignored):
-        self['main_view'].goto_small()
-
-    def on_create_project(self, *rest):
-        d = dialogs.CreateProjectDruid(self)
-        d.run()
-        
-    def on_help_about(self, *rest):
-        widget_tree = gtk.glade.XML(GLADEFILENAME, 'aboutdialog')
-        about = widget_tree.get_widget('aboutdialog')
-        about.run()
-
-    def on_help_index(self, *ignored):
-        gnome.help_display_uri('https://dev.pvv.org/projects/laydi/wiki/help')
-
-    def on_help_report_bug(self, *ignored):
-        gnome.help_display_uri('https://dev.pvv.org/projects/laydi/newticket')
-
-    def on_workflow_refresh_clicked(self, *ignored):
-        try:
-            reload(sys.modules[main.workflow.__class__.__module__])
-        except Exception, e:
-            logger.warning('Cannot reload workflow')
-            logger.warning(e)
-        else:
-            logger.notice('Successfully reloaded workflow')
-
-    def on_view_changed(self, widget, vf):
-        self._update_toolbar(vf.get_view())
-
-    def on_show_navigator(self, item):
-        if item.get_active():
-            self['data_vbox'].show()
-        else:
-            self['data_vbox'].hide()
-
-    def on_show_workflow(self, item):
-        if item.get_active():
-            self['workflow_vbox'].show()
-        else:
-            self['workflow_vbox'].hide()
-
-    def on_show_infopane(self, item):
-        if item.get_active():
-            self['bottom_notebook'].show()
-        else:
-            self['bottom_notebook'].hide()
-
-    def on_left(self, item):
-        self.main_view.move_focus_left()
-
-    def on_right(self, item):
-        self.main_view.move_focus_right()
-
-    def on_up(self, item):
-        self.main_view.move_focus_up()
-
-    def on_down(self, item):
-        self.main_view.move_focus_down()
-
-
-gobject.signal_new('table-size-set', TableSizeSelection, 
-                   gobject.SIGNAL_RUN_LAST,
-                   gobject.TYPE_NONE, 
-                   (gobject.TYPE_INT, gobject.TYPE_INT))
-

laydi/lib/R_utils.py

@@ -1,284 +0,0 @@
-"""A collection of functions that use R.
-
-Most functions use libraries from bioconductor
-
-depends on:
-(not updated)
--- bioconductor min. install
--- hgu133a
--- hgu133plus2
-
-"""
-
-import scipy
-import Numeric as N
-import rpy
-silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
-
-def get_locusid(probelist=None,org="hgu133a"):
-    """Returns a dictionary of locus link id for each affy probeset
-    and reverse mapping
-
-    innput:
-    [probelist] -- probelist of affy probesets
-    [org] -- chip type (organism)
-
-    out:
-    aff2loc, loc2aff
-
-    The mapping is one-to-one for affy->locus_id
-    However, there are several affy probesets for one locus_id
-
-    From bioc-mail-archive: BioC takes the GeneBank ids associated
-    with the probes (provided by the manufacture) and then maps them
-    to Entrez Gene ids using data from UniGene, Entrez Gene, and other
-    available data sources we trust. The Entrez Gene id a probe is
-    assigned to is determined by votes from all the sources used. If
-    there is no agreement among the sources, we take the smallest
-    Entrez Gene id.
-    """
-    silent_eval("library("+org+")")
-    silent_eval('locus_ids = as.list('+org+'LOCUSID)')
-    silent_eval('pp<-as.list(locus_ids[!is.na(locus_ids)])')
-    loc_ids = rpy.r("pp")
-    for id in loc_ids:
-        loc_ids[id] = str(loc_ids[id])
-        
-    aff2loc = {}
-    if probelist:
-        for pid in probelist:
-            try:
-                aff2loc[pid]=loc_ids[pid]
-            except:
-                print "Affy probeset: %s has no locus id" %pid
-        print "\nCONVERSION SUMMARY:\n \
-        Number of probesets input %s \n \
-        Number of translated locus ids: %s \n \
-        Number of missings: %s" %(len(probelist),len(aff2loc),len(probelist)-len(aff2loc))
-    else:
-        aff2loc = loc_ids
-    # reverse mapping
-    loc2aff = {}
-    for k,v in aff2loc.items():
-        if loc2aff.has_key(v):
-            loc2aff[v].append(k)
-        else:
-            loc2aff[v]=[k]
-    
-    return aff2loc,loc2aff
-
-def get_kegg_paths(org="hgu133plus2",id_type='aff',probelist=None):
-    """Returns a dictionary of KEGG maps.
-
-    input:
-             org  --  chip_type (see bioconductor.org)
-             id_type -- id ['aff','loc']
-    
-    key: affy_id, value = list of kegg map id
-    example: '65884_at': ['00510', '00513']
-    """
-    silent_eval("library("+org+")")
-    silent_eval('xx<-as.list('+org+'PATH)')
-    silent_eval('xp <- xx[!is.na(xx)]')
-    aff2path = rpy.r("xp")
-    dummy = rpy.r("xx")
-    
-    if id_type=='loc':
-        aff2loc,loc2aff = get_locusid(org=org)
-        loc2path = {}
-        for id,path in aff2path.items():
-            if loc2path.has_key(id):
-                pp = [path.append(i) for i in loc2path[id]]
-                print "Found duplicate in path: %s" %path
-            loc2path[aff2loc[id]]=path
-        aff2path = loc2path
-    out = {}
-
-    if probelist:
-        for pid in probelist:
-            try:
-                out[pid]=aff2path[pid]
-            except:
-                print "Could not find id: %s" %pid
-    else:
-        out = aff2path
-    for k,v in out.items():
-        # if string convert tol list
-        try:
-            v + ''
-            out[k] = [v]
-        except:
-            out[k] = v
-            
-    return out
-
-def get_probe_list(org="hgu133plus2"):
-    rpy.r.library(org)
-    silent_eval('probe_list<-ls('+org+'ACCNUM )')
-    pl = rpy.r("probe_list")
-    return pl
-
-def get_GO_from_aff(org="hgu133plus2",id_type='aff',probelist=None):
-    """Returns a dictionary of GO terms.
-
-    input:
-             org  --  chip_type (see bioconductor.org)
-             id_type -- id ['aff','loc']
-    
-    key: 
-    example: '65884_at': 
-    """
-    silent_eval("library("+org+")")
-    silent_eval('xx<-as.list('+org+'GO)')
-    silent_eval('xp <- xx[!is.na(xx)]')
-    aff2path = rpy.r("xp")
-    dummy = rpy.r("xx")
-    if id_type=='loc':
-        LOC = get_locusid(org=org)
-        loc2path = {}
-        for id,path in aff2path.items():
-            if loc2path.has_key(id):
-                pp = [path.append(i) for i in loc2path[id]]
-                print "Found duplicate in path: %s" %path
-            loc2path[LOC[id]]=path
-        aff2path = loc2path
-    out = {}
-    if probelist:
-        for pid in probelist:
-            try:
-                out[pid]=aff2path[pid]
-            except:
-                print "Could not find id: %s" %pid
-    return aff2path
-
-def get_kegg_as_category(org="hgu133plus2",id_type='aff',probelist=None):
-    """Returns kegg pathway memberships in dummy (1/0) matrix (genes x maps)
-    
-    """
-    kegg = get_kegg_paths(org=org, id_type=id_type, probelist=probelist)
-    maps = set()
-    for kpth in kegg.values():
-        maps.update(kpth)
-    
-    n_maps = len(maps)
-    n_genes = len(kegg)
-    gene2index = dict(zip(kegg.keys(), range(n_genes)))
-    map2index = dict(zip(maps, range(n_maps)))
-    C = scipy.zeros((n_genes, n_maps))
-    for k,v in kegg.items():
-        for m in v:
-            C[gene2index[k], map2index[m]]=1
-        
-    return C, list(maps), kegg.keys()
-
-def impute(X, k=10, rowmax=0.5, colmax=0.8, maxp=1500, seed=362436069):
-    """
-    A function to impute missing expression data, using nearest
-    neighbor averaging. (from bioconductors impute)
-
-    input:
-
-    data: An expression matrix with genes in the rows, samples in the
-          columns
-
-       k: Number of neighbors to be used in the imputation (default=10)
-
-    rowmax: The maximum percent missing data allowed in any row (default
-          50%). For any rows with more than 'rowmax'% missing are
-          imputed using the overall mean per sample.
-
-    colmax: The maximum percent missing data allowed in any column
-          (default 80%). If any column has more than 'colmax'% missing
-          data, the program halts and reports an error.
-
-    maxp: The largest block of genes imputed using the knn algorithm
-          inside 'impute.knn' (default 1500); larger blocks are divided
-          by two-means clustering (recursively) prior to imputation. If
-          'maxp=p', only knn imputation is done
-
-    seed: The seed used for the random number generator (default
-          362436069) for reproducibility.
-
-    
-    call:
-    impute(data ,k = 10, rowmax = 0.5, colmax = 0.8, maxp = 1500, rng.seed=362436069)
-    """
-    
-    rpy.r.library("impute")
-    X = N.asarray(X) # cast as numeric array
-    m, n = scipy.shape(X)
-    if m>n:
-        print "Warning (impute): more samples than variables. running transpose"
-        t_flag = True
-    else:
-        X = N.transpose(X)
-        t_flag = False
-    
-    rpy.r.assign("X", X)
-    rpy.r.assign("k", k)
-    rpy.r.assign("rmax", rowmax)
-    rpy.r.assign("cmax", colmax)
-    rpy.r.assign("maxp", maxp)
-
-    call = "out<-impute.knn(X,k=k,rowmax=rmax,colmax=cmax,maxp=maxp)"
-    silent_eval(call)
-    out = rpy.r("out")
-    if not t_flag:
-        E = out['data']
-        E = scipy.asarray(E)
-        E = E.T
-    else:
-        E =  out['data']
-        E = scipy.asarray(E)
-    return E
-
-
-def get_chip_annotation(org="hgu133a",annot='pmid', id_type='loc',probelist=None):
-    """Returns a dictionary of annoations.
-
-    input:
-             org  --  chip_type (see bioconductor.org)
-             annot -- annotation ['genename', 'pmid', ' symbol']
-             id_type -- id ['aff','loc']
-             
-    
-    key: id, value = list of annoations
-    example: '65884_at': ['15672394', '138402']
-    """
-    _valid_annot = ['genename', 'pmid', 'symbol', 'enzyme', 'chr', 'chrloc']
-    if annot.lower() not in _valid_annot:
-        raise ValueError("Annotation must be one of %s" %_valid_annot)
-    silent_eval("library("+org+")")
-    silent_eval("dummy<-as.list("+org+annot.upper()+")")
-    silent_eval('annotations <- dummy[!is.na(dummy)]')
-    aff2annot = rpy.r("annotations")
-    if id_type=='loc':
-        aff2loc, loc2aff = get_locusid(org=org)
-        loc2annot = {}
-        for geneid, annotation in aff2annot.items():
-            annotation = ensure_list(annotation)
-            print annotation
-            if loc2annot.has_key(geneid):
-                for extra in loc2annot[geneid]:
-                    annotation.append(extra) 
-                print "Found duplicate in gene: %s" %geneid
-            loc2annot[aff2loc[geneid]] = annotation
-        aff2annot = loc2annot
-
-    out = {}
-    if probelist:
-        for pid in probelist:
-            try:
-                out[pid] = aff2annot.get(pid, 'none')
-            except:
-                print "Could not find id: %s" %pid
-    else:
-        out = aff2annot
-            
-    return out
-
-def ensure_list(value):
-    if isinstance(value, list):
-        return value
-    else:
-        return [value]

laydi/lib/blmplots.py

@@ -1,458 +0,0 @@
-"""Specialised plots for functions defined in blmfuncs.py.
-
-fixme:
-        -- If scatterplot is not inited with a colorvector there will be no
-        colorbar, but when adding colors the colorbar shoud be created.
-"""
-
-from matplotlib import cm,patches
-import gtk
-import laydi
-from laydi import plots, main,logger
-import scipy
-from scipy import dot,sum,diag,arange,log,newaxis,sqrt,apply_along_axis,empty
-from numpy import corrcoef
-
-def correlation_loadings(data, T, test=True):
-    """ Returns correlation loadings.
-
-    :input:
-        - D: [nsamps, nvars], data (non-centered data)
-        - T: [nsamps, a_max], Scores
-    :ouput:
-        - R: [nvars, a_max], Correlation loadings
-
-    :notes:
-    
-    """
-    nsamps, nvars = data.shape
-    nsampsT, a_max = T.shape
-    
-    if nsamps!=nsampsT: raise IOError("D/T mismatch")
-    
-    # center
-    data = data - data.mean(0)
-    R = empty((nvars, a_max),'d')
-    for a in range(a_max):
-        for k in range(nvars):
-            R[k,a] = corrcoef(data[:,k], T[:,a])[0,1]
-    
-    return R
-
-class BlmScatterPlot(plots.ScatterPlot):
-    """Scatter plot used for scores and loadings in bilinear models."""
-
-    def __init__(self, title, model, absi=0, ordi=1, part_name='T', color_by=None):
-        self.model = model
-        if model.model.has_key(part_name)!=True:
-            raise ValueError("Model part: %s not found in model" %mod_param)
-        self._T = model.model[part_name]
-        if self._T.shape[1]==1:
-            logger.log('notice', 'Scores have only one component')
-            absi= ordi = 0
-        self._absi = absi
-        self._ordi = ordi
-        self._cmap = cm.summer
-        
-        dataset_1 = model.as_dataset(part_name)
-        id_dim = dataset_1.get_dim_name(0)
-        sel_dim = dataset_1.get_dim_name(1)
-        id_1, = dataset_1.get_identifiers(sel_dim, [absi])
-        id_2, = dataset_1.get_identifiers(sel_dim, [ordi])
-        col = 'b'
-        if model.model.has_key(color_by):
-            col = model.model[color_by].ravel()
-        plots.ScatterPlot.__init__(self, dataset_1, dataset_1, id_dim, sel_dim, id_1, id_2 ,c=col ,s=40 , name=title)
-        self._mappable.set_cmap(self._cmap)
-        self.sc = self._mappable
-        self.add_pc_spin_buttons(self._T.shape[1], absi, ordi)
-        
-    def set_facecolor(self, colors):
-        """Set patch facecolors.
-        """
-        pass
-
-    def set_alphas(self, alphas):
-        """Set alpha channel for all patches."""
-        pass
-
-    def set_sizes(self, sizes):
-        """Set patch sizes."""
-        pass
-
-    def set_expvar_axlabels(self, param=None):
-        if param == None:
-            param = self._expvar_param
-        else:
-            self._expvar_param = param
-        if not self.model.model.has_key(param):
-            self.model.model[param] = None
-        if self.model.model[param]==None:
-            logger.log('notice', 'Param: %s not in model' %param)
-            print self.model.model.keys()
-            print self.model.model[param]
-            pass #fixme: do expvar calc here if not present
-        else:
-            expvar = self.model.model[param]
-            xstr = "Comp: %s , %.1f " %(self._absi, expvar[self._absi+1])
-            ystr = "Comp: %s , %.1f " %(self._ordi, expvar[self._ordi+1])
-            self.axes.set_xlabel(xstr)
-            self.axes.set_ylabel(ystr)
-    
-    def add_pc_spin_buttons(self, amax, absi, ordi):    
-        sb_a = gtk.SpinButton(climb_rate=1)
-        sb_a.set_range(1, amax)
-        sb_a.set_value(absi+1)
-        sb_a.set_increments(1, 5)
-        sb_a.connect('value_changed', self.set_absicca)
-        sb_o = gtk.SpinButton(climb_rate=1)
-        sb_o.set_range(1, amax)
-        sb_o.set_value(ordi+1)
-        sb_o.set_increments(1, 5)
-        sb_o.connect('value_changed', self.set_ordinate)
-        hbox = gtk.HBox()
-        gtk_label_a = gtk.Label("A:")
-        gtk_label_o = gtk.Label(" O:")
-        toolitem = gtk.ToolItem()                          
-        toolitem.set_expand(False)
-        toolitem.set_border_width(2)
-        toolitem.add(hbox)        
-        hbox.pack_start(gtk_label_a)        
-        hbox.pack_start(sb_a)
-        hbox.pack_start(gtk_label_o)        
-        hbox.pack_start(sb_o)
-        self._toolbar.insert(toolitem, -1)
-        toolitem.set_tooltip(self._toolbar.tooltips, "Set Principal component")
-        self._toolbar.show_all() #do i need this?
-
-    def set_absicca(self, sb):
-        self._absi = sb.get_value_as_int() - 1
-        xy = self._T[:,[self._absi, self._ordi]]
-        self.xaxis_data = xy[:,0]
-        self.yaxis_data = xy[:,1]
-        self.sc._offsets = xy
-        self.selection_collection._offsets = xy
-        self.canvas.draw_idle()
-        pad = abs(self.xaxis_data.min()-self.xaxis_data.max())*0.05
-        new_lims = (self.xaxis_data.min() - pad, self.xaxis_data.max() + pad)
-        self.axes.set_xlim(new_lims, emit=True)
-        self.set_expvar_axlabels()
-        self.canvas.draw_idle()
-        
-    def set_ordinate(self, sb):
-        self._ordi = sb.get_value_as_int() - 1
-        xy = self._T[:,[self._absi, self._ordi]]
-        self.xaxis_data = xy[:,0]
-        self.yaxis_data = xy[:,1]
-        self.sc._offsets = xy
-        self.selection_collection._offsets = xy
-        pad = abs(self.yaxis_data.min()-self.yaxis_data.max())*0.05
-        new_lims = (self.yaxis_data.min() - pad, self.yaxis_data.max() + pad)
-        self.axes.set_ylim(new_lims, emit=True)
-        self.set_expvar_axlabels()
-        self.canvas.draw_idle()
-    
-    def show_labels(self, index=None):
-        if self._text_labels == None:
-            x = self.xaxis_data
-            y = self.yaxis_data
-            self._text_labels = {}
-            for name, n in self.dataset_1[self.current_dim].items():
-                txt = self.axes.text(x[n],y[n], name)
-                txt.set_visible(False)
-                self._text_labels[n] = txt
-        if index!=None:
-            self.hide_labels()
-            for indx,txt in self._text_labels.items():
-                if indx in index:
-                    txt.set_visible(True)
-        self.canvas.draw_idle()
-                
-    def hide_labels(self):
-        for txt in self._text_labels.values():
-            txt.set_visible(False)
-        self.canvas.draw_idle()
-
-
-class PcaScreePlot(plots.BarPlot):
-    def __init__(self, model):
-        title = "Pca, (%s) Scree" %model._dataset['X'].get_name()
-        ds = model.as_dataset('eigvals')
-        if ds==None:
-            logger.log('notice', 'Model does not contain eigvals')
-        plots.BarPlot.__init__(self, ds, name=title)
-
-
-class PcaScorePlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Pca scores (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
-        self.set_expvar_axlabels(param="expvarx")
-
-class PcaLoadingPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Pca loadings (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='p_tsq')
-        self.set_expvar_axlabels(param="expvarx")
-
-class PlsScorePlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Pls scores (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
-
-
-class PlsXLoadingPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Pls x-loadings (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='w_tsq')
-        #self.set_expvar_axlabels(self, param="expvarx")
-
-
-class PlsYLoadingPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Pls y-loadings (%s)" %model._dataset['Y'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Q')
-
-
-class PlsCorrelationLoadingPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Pls correlation loadings (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='CP')
-
-
-class LplsScorePlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "L-pls scores (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, 'T')
-        self.set_expvar_axlabels("evx")
-
-
-class LplsXLoadingPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Lpls x-loadings (%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='P', color_by='tsqx')
-        self.set_expvar_axlabels("evx")
-
-
-class LplsZLoadingPlot(BlmScatterPlot, plots.PlotThresholder):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Lpls z-loadings (%s)" %model._dataset['Z'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='L', color_by='tsqz')
-        self.set_expvar_axlabels(param="evz")
-        plots.PlotThresholder.__init__(self, "IC")
-        
-
-    def _update_color_from_dataset(self, ds):
-        BlmScatterPlot._update_color_from_dataset(self, ds)
-        self.set_threshold_dataset(ds)
-        
-
-class LplsXCorrelationPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Lpls x-corr. loads (%s)" %model._dataset['X'].get_name()
-        if not model.model.has_key('Rx'):
-            R = correlation_loadings(model._data['X'], model.model['T'])
-            model.model['Rx'] = R
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Rx')
-        self.set_expvar_axlabels("evx")
-        radius = 1
-        center = (0,0)
-        c100 = patches.Circle(center,radius=radius,
-                              facecolor='gray',
-                              alpha=.1,
-                              zorder=1)
-        c50 = patches.Circle(center, radius= sqrt(radius/2.0),
-                             facecolor='gray',
-                             alpha=.1,
-                             zorder=2)
-        self.axes.add_patch(c100)
-        self.axes.add_patch(c50)
-        self.axes.axhline(lw=1.5,color='k')
-        self.axes.axvline(lw=1.5,color='k')
-        self.axes.set_xlim([-1.05,1.05])
-        self.axes.set_ylim([-1.05, 1.05])
-        self.canvas.show()
-        
-class LplsZCorrelationPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Lpls z-corr. loads (%s)" %model._dataset['Z'].get_name()
-        if not model.model.has_key('Rz'):
-            R = correlation_loadings(model._data['Z'].T, model.model['W'])
-            model.model['Rz'] = R
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='Rz')
-        self.set_expvar_axlabels("evz")
-        radius = 1
-        center = (0,0)
-        c100 = patches.Circle(center,radius=radius,
-                              facecolor='gray',
-                              alpha=.1,
-                              zorder=1)
-        c50 = patches.Circle(center, radius=sqrt(radius/2.0),
-                             facecolor='gray',
-                             alpha=.1,
-                             zorder=2)
-        self.axes.add_patch(c100)
-        self.axes.add_patch(c50)
-        self.axes.axhline(lw=1.5,color='k')
-        self.axes.axvline(lw=1.5,color='k')
-        self.axes.set_xlim([-1.05,1.05])
-        self.axes.set_ylim([-1.05, 1.05])
-        self.canvas.show()
-
-
-class LplsHypoidCorrelationPlot(BlmScatterPlot):
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Hypoid correlations(%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, part_name='W')
-
-
-class LplsExplainedVariancePlot(plots.Plot):
-    def __init__(self, model):
-        self.model = model
-        plots.Plot.__init__(self, "Explained variance")
-        xax = scipy.arange(model.model['evx'].shape[0])
-        self.axes.plot(xax, model.model['evx'], 'b-', label='X', linewidth=1.5)
-        self.axes.plot(xax, model.model['evy'], 'k-', label='Y', linewidth=1.5)
-        self.axes.plot(xax, model.model['evz'], 'g-', label='Z', linewidth=1.5)
-        self.canvas.draw()
-    
-class LineViewXc(plots.LineViewPlot):
-    """A line view of centered raw data
-    """
-    def __init__(self, model, name='Profiles'):
-        dx = model._dataset['X']
-        plots.LineViewPlot.__init__(self, dx, 1, None, False,name)
-        self.add_center_check_button(self.data_is_centered)
-    
-    def add_center_check_button(self, ticked):
-        """Add a checker button for centerd view of data."""
-        cb = gtk.CheckButton("Center")
-        cb.set_active(ticked)
-        cb.connect('toggled', self._toggle_center)
-        toolitem = gtk.ToolItem()   
-        toolitem.set_expand(False)
-        toolitem.set_border_width(2)
-        toolitem.add(cb)
-        self._toolbar.insert(toolitem, -1)
-        toolitem.set_tooltip(self._toolbar.tooltips, "Column center the line view")
-        self._toolbar.show_all() #do i need this?
-
-    def _toggle_center(self, active):
-        if self.data_is_centered:
-            self._data = self._data + self._mn_data
-            self.data_is_centered = False
-        else:
-            self._mn_data = self._data.mean(0)
-            self._data = self._data - self._mn_data
-            self.data_is_centered = True
-        self.make_lines()
-        self.set_background()
-        self.set_current_selection(main.project.get_selection())
-
-
-class ParalellCoordinates(plots.Plot):
-    """Parallell coordinates for score loads with many comp.
-    """
-    def __init__(self, model, p='loads'):
-        pass
-
-
-class PlsQvalScatter(plots.ScatterPlot):
-    """A vulcano like plot of loads vs qvals
-    """
-    def __init__(self, model, pc=0):
-        if not model.model.has_key('w_tsq'):
-            return None
-        self._W = model.model['W']
-        dataset_1 = model.as_dataset('W')
-        dataset_2 = model.as_dataset('w_tsq')
-        id_dim = dataset_1.get_dim_name(0) #genes
-        sel_dim = dataset_1.get_dim_name(1) #_comp
-        sel_dim_2 = dataset_2.get_dim_name(1) #_zero_dim
-        id_1, = dataset_1.get_identifiers(sel_dim, [0])
-        id_2, = dataset_2.get_identifiers(sel_dim_2, [0])
-        if model.model.has_key('w_tsq'):
-            col = model.model['w_tsq'].ravel()
-            #col = normalise(col)
-        else:
-            col = 'g'
-        plots.ScatterPlot.__init__(self, dataset_1, dataset_2,
-                                   id_dim, sel_dim, id_1, id_2,
-                                   c=col, s=20, sel_dim_2=sel_dim_2,
-                                   name='Load Volcano')
-
-
-class PredictionErrorPlot(plots.Plot):
-    """A boxplot of prediction error vs. comp. number.
-    """
-    def __init__(self, model, name="Prediction Error"):
-        if not model.model.has_key('sep'):
-            logger.log('notice', 'Model has no calculations of sep')
-            return None
-        plots.Plot.__init__(self, name)
-        self._frozen = True
-        self.current_dim = 'johndoe'
-        self.axes = self.fig.add_subplot(111)
-        
-        # draw
-        sep = model.model['sep']
-        aopt = model.model['aopt']
-        bx_plot_lines = self.axes.boxplot(sqrt(sep))
-        aopt_marker = self.axes.axvline(aopt, linewidth=10,
-                                      color='r',zorder=0,
-                                      alpha=.5)
-        
-        # add canvas
-        self.add(self.canvas)
-        self.canvas.show()
-
-    def set_current_selection(self, selection):
-        pass
-
-
-class TRBiplot(plots.ScatterPlot):    
-    def __init__(self, model, absi=0, ordi=1):
-        title = "Target rotation biplot(%s)" %model._dataset['X'].get_name()
-        BlmScatterPlot.__init__(self, title, model, absi, ordi, 'B')
-        B = model.model.get('B')
-        # normalize B
-        Bnorm = scipy.apply_along_axis(scipy.linalg.norm, 1, B)
-        x = model._dataset['X'].copy()
-        Xc = x._array - x._array.mean(0)[newaxis]
-        w_rot = B/Bnorm 
-        t_rot = dot(Xc, w_rot)
-    
-
-class InfluencePlot(plots.ScatterPlot):
-    """ Returns a leverage vs resiudal scatter plot.
-    """
-    def __init__(self, model, dim, name="Influence"):
-        if not model.model.has_key('levx'):
-            logger.log('notice', 'Model has no calculations of leverages')
-            return
-        if not model.model.has_key('ssqx'):
-            logger.log('notice', 'Model has no calculations of residuals')
-            return
-        ds1 = model.as_dataset('levx')
-        ds2 = model.as_dataset('ssqx')
-        plots.ScatterPlot.__init__(self, ds1, ds2,
-                                   id_dim, sel_dim, id_1, id_2,
-                                   c=col, s=20, sel_dim_2=sel_dim_2,
-                                   name='Load Volcano')
-        
-
-class RMSEPPlot(plots.BarPlot):
-    def __init__(self, model, name="RMSEP"):
-        if not model.model.has_key('rmsep'):
-            logger.log('notice', 'Model has no calculations of sep')
-            return
-        dataset = model.as_dataset('rmsep')
-        plots.BarPlot.__init__(self, dataset, name=name)
-
-
-def normalise(x):
-    """Scale vector x to [0,1]
-    """
-    x = x - x.min()
-    x = x/x.max()
-    return x

laydi/lib/cv_index.py

@@ -1,66 +0,0 @@
-from numpy import array_split,arange
-
-    
-def cv(n, k, randomise=False, sequential=False):
-    """
-    Generates k (training, validation) index pairs.
-    
-    Each pair is a partition of arange(n), where validation is an iterable
-    of length ~n/k.
-    
-    If randomise is true, a copy of index is shuffled before partitioning,
-    otherwise its order is preserved in training and validation.
-
-    Randomise overrides the sequential argument. If randomise is true,
-    sequential is False
-    
-    If sequential is true the index is partioned in continous blocks,
-    otherwise interleaved ordering is used.
-    """
-    index = xrange(N)
-    if randomise:
-        from random import shuffle
-        index = list(index)
-        shuffle(index)
-        sequential = False
-    if sequential:
-        for validation in array_split(index, K):
-            training = [i for i in index if i not in validation]
-            yield training, validation
-    else:
-        for k in xrange(K):
-            training = [i for i in index if i % K != k]
-            validation = [i for i in index if i % K == k]
-            yield training, validation
-
-def shuffle_diag(shape, K, randomise=False, sequential=False):
-    """
-    Generates k (training, validation) index pairs.
-    """
-    m, n = shape
-    
-    if K>m or K>n:
-        msg = "You may not use more subsets than max(n_rows, n_cols)"
-        raise ValueError, msg
-
-    mon = max(m, n)
-    #index = xrange(n)
-    index = [i for i in range(m*n) if i % m == 0]
-    print index
-    if randomise:
-        from random import shuffle
-        index = list(index)
-        shuffle(index)
-        sequential = False
-        
-    if sequential:
-        start_inds = array_split(index, K)
-    else:
-        for k in xrange(K):
-            start_inds = [index[i] for i in xrange(n) if i % K == k]
-    
-    print start_inds
-    for start in start_inds:
-        ind = arange(start, n*m, mon+1)
-        yield ind
-

laydi/lib/cx_stats.py

@@ -1,438 +0,0 @@
-import time
-import cPickle
-
-from scipy import zeros,zeros_like,sqrt,dot,trace,sign,round_,argmax,\
-     sort,ravel,newaxis,asarray,diag,sum,outer,argsort,arange,ones_like,\
-     all,apply_along_axis,eye,atleast_2d,empty
-from scipy.linalg import svd,inv,norm,det,sqrtm
-from numpy import median
-
-#import plots_lpls
-
-from cx_utils import mat_center
-from validation import pls_jkW, lpls_jk
-from select_generators import shuffle_1d
-from engines import pca, pls, bridge
-from engines import nipals_lpls as lpls
-
-
-
-def hotelling(Pcv, P, p_center='med', cov_center='med',
-              alpha=0.3, crot=True, strict=False):
-    """Returns regularized hotelling T^2.
-    
-    alpha -- regularisation towards pooled cov estimates
-    beta -- regularisation for unstable eigenvalues
-    p_center -- location method for submodels
-    cov_center -- location method for sub coviariances
-    alpha -- regularisation
-    crot -- rotate submodels toward full?
-    strict -- only rotate 90 degree ?
-    
-    """
-    m, n = P.shape
-    n_sets, n, amax = Pcv.shape
-    # allocate
-    T_sq = empty((n, ),dtype='d')
-    Cov_i = zeros((n, amax, amax),dtype='d')
-    
-    # rotate sub_models to full model
-    if crot:
-        for i, Pi in enumerate(Pcv):
-            Pcv[i] = procrustes(P, Pi, strict=strict)
-
-    # center of pnull
-    if p_center=='med':
-        P_ctr = median(Pcv, 0)
-    elif p_center=='mean':
-        # fixme: mean is unstable
-        P_ctr = Pcv.mean(0)
-    else: #use full
-        P_ctr = P
-
-    for i in xrange(n):
-        Pi = Pcv[:,i,:] # (n_sets x amax) 
-        Pi_ctr = P_ctr[i,:] # (1 x amax)
-        Pim = (Pi - Pi_ctr[newaxis])*sqrt(n_sets-1)
-        Cov_i[i] = (1./n_sets)*dot(Pim.T, Pim)
-        
-    if cov_center == 'med':
-        Cov = median(Cov_i, 0)
-    else:
-        Cov = Cov_i.mean(0)
-    
-    reg_cov = (1. - alpha)*Cov_i + alpha*Cov
-    for i in xrange(n):
-        #Pc = P_ctr[i,:][:,newaxis]
-        Pc = P_ctr[i,:]
-        sigma = reg_cov[i]
-        # T_sq[i] = (dot(Pc, inv(sigma) )*Pc).sum() #slow
-        T_sq[i] = dot(dot(Pc, inv(sigma)), Pc) # dont need to care about transposes
-        #T_sq[i] = dot(dot(Pc.T, inv(sigma)), Pc).ravel()
-    return T_sq
-
-def procrustes(A, B, strict=True, center=False, verbose=False):
-    """Rotation of B to A.
-
-    strict -- Only do flipping and shuffling
-    center -- Center before rotation, translate back after
-    verbose -- Print ssq
-    
-    No scaling calculated.
-    Output B_rot = Rotated B
-    """
-    if center:
-        A,mn_A = mat_center(A, ret_mn=True)
-        B,mn_B = mat_center(B, ret_mn=True)
-    u,s,vh = svd(dot(B.T, A))
-    v = vh.T
-    Cm = dot(u, v.T) #orthogonal rotation matrix
-    if strict: # just inverting and flipping
-       Cm = ensure_strict(Cm)
-    b_rot = dot(B, Cm)
-
-    if verbose:
-        print Cm.round()
-        fit = sum(ravel(B - b_rot)**2)
-        print "Sum of squares: %s" %fit
-    if center:
-        return mn_B + b_rot
-    else:
-        return b_rot
-
-def expl_var_x(Xc, T):
-    """Returns explained variance of X.
-    T should carry variance in length, Xc has zero col-mean.
-    """
-    exp_var_x = diag(dot(T.T, T))*100/(sum(Xc**2))
-    return exp_var_x
-
-def expl_var_y(Y, T, Q):
-    """Returns explained variance of Y.
-    """
-    # centered Y
-    exp_var_y = zeros((Q.shape[1], ))
-    for a in range(Q.shape[1]):
-        Ya = outer(T[:,a], Q[:,a])
-        exp_var_y[a] = 100*sum(Ya**2)/sum(Y**2)
-    return exp_var_y
-        
-def pls_qvals(a, b, aopt=None, alpha=.3,
-              n_iter=20, algo='pls',
-              center=True,
-              sim_method='shuffle',
-              p_center='med', cov_center='med',
-              crot=True, strict=False):
-
-    """Returns qvals for pls model.
-
-    input:
-    a -- data matrix
-    b -- data matrix
-    aopt -- scalar, opt. number of components
-    alpha -- [0,1] regularisation parameter for T2-test
-    n_iter -- number of permutations
-    sim_method -- permutation method ['shuffle']
-    p_center -- location estimator for sub models ['med']
-    cov_center -- location estimator for covariance of submodels ['med']
-    crot -- bool, use rotations of sub models?
-    strict -- bool, use stict (rot/flips only) rotations?
-    """
-    
-    m, n = a.shape
-    TSQ = zeros((n, n_iter), dtype='d') # (nvars x n_subsets)
-    n_false = zeros((n, n_iter), dtype='d')
-
-    #full model
-    if center:
-        ac = a - a.mean(0)
-        bc = b - b.mean(0)
-    
-    if algo=='bridge':
-        dat = bridge(ac, bc, aopt, 'loads', 'fast')
-    else:
-        dat = pls(ac, bc, aopt, 'loads', 'fast')
-    Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo,center=True)
-    tsq_full = hotelling(Wcv, dat['W'], p_center=p_center,
-                         alpha=alpha, crot=crot, strict=strict,
-                         cov_center=cov_center)
-    #t0 = time.time()
-    Vs = shuffle_1d(bc, n_iter, axis=0)
-    for i, b_shuff in enumerate(Vs):
-        #t1 = time.time()
-        if algo=='bridge':
-            dat = bridge(ac, b_shuff, aopt, 'loads','fast')
-        else:
-            dat = pls(ac, b_shuff, aopt, 'loads', 'fast')
-        Wcv = pls_jkW(a, b_shuff, aopt, n_blocks=None, algo=algo)
-        TSQ[:,i] = hotelling(Wcv, dat['W'], p_center=p_center,
-                             alpha=alpha, crot=crot, strict=strict,
-                             cov_center=cov_center)
-        #print time.time() - t1
-    
-    return fdr(tsq_full, TSQ, median)
-    
-
-def ensure_strict(C, only_flips=True):
-    """Ensure that a rotation matrix does only 90 degree rotations.
-    In multiplication with pcs this allows flips and reordering.
-
-    if only_flips is True there will onlt be flips allowed
-    """
-    Cm = C
-    S = sign(C) # signs
-    if only_flips==True:
-        C = eye(Cm.shape[0])*S
-        return C
-    Cm = zeros_like(C)
-    Cm.putmask(1.,abs(C)>.6)
-    if det(Cm)>1:
-        raise ValueError,"Implement this!"
-    return Cm*S
-
-def pls_qvals_II(a, b, aopt=None, center=True, alpha=.3,
-                 n_iter=20, algo='pls',
-                 sim_method='shuffle',
-                 p_center='med', cov_center='med',
-                 crot=True, strict=False):
-
-    """Returns qvals for pls model.
-    Shuffling of variables in X.
-    Null model is 'If I put genes randomly on network' ... if they are sign:
-    then this is due to network structure and not covariance with response.
-
-    input:
-    a -- data matrix
-    b -- data matrix
-    aopt -- scalar, opt. number of components
-    alpha -- [0,1] regularisation parameter for T2-test
-    n_iter -- number of permutations
-    sim_method -- permutation method ['shuffle']
-    p_center -- location estimator for sub models ['med']
-    cov_center -- location estimator for covariance of submodels ['med']
-    crot -- bool, use rotations of sub models?
-    strict -- bool, use stict (rot/flips only) rotations?
-    """
-    
-    m, n = a.shape
-    TSQ = zeros((n, n_iter), dtype='<f8') # (nvars x n_subsets)
-    n_false = zeros((n, n_iter), dtype='<f8')
-
-    #full model
-
-    # center?
-    if center==True:
-        ac = a - a.mean(0)
-        bc = b - b.mean(0)
-    
-    if algo=='bridge':
-        dat = bridge(ac, bc, aopt, 'loads', 'fast')
-    else:
-        dat = pls(ac, bc, aopt, 'loads', 'fast')
-    Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo)
-    tsq_full = hotelling(Wcv, dat['W'], p_center=p_center,
-                         alpha=alpha, crot=crot, strict=strict,
-                         cov_center=cov_center)
-    t0 = time.time()
-    Vs = shuffle_1d(a, n_iter, 1)
-    for i, a_shuff in enumerate(Vs):
-        t1 = time.time()
-        a = a_shuff - a_shuff.mean(0)
-        
-        if algo=='bridge':
-            dat = bridge(a, b, aopt, 'loads','fast')
-        else:
-            dat = pls(a, b, aopt, 'loads', 'fast')
-        Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo)
-        TSQ[:,i] = hotelling(Wcv, dat['W'], p_center=p_center,
-                             alpha=alpha, crot=crot, strict=strict,
-                             cov_center=cov_center)
-        print time.time() - t1
-    sort_index = argsort(tsq_full)[::-1]
-    back_sort_index = sort_index.argsort()
-    print time.time() - t0
-
-    # count false positives
-    tsq_full_sorted = tsq_full.take(sort_index)
-    for i in xrange(n_iter):
-        for j in xrange(n):
-            n_false[j,i] = sum(TSQ[:,i]>=tsq_full[j])
-    false_pos = median(n_false, 1)
-    ll = arange(1, len(false_pos)+1, 1)
-    sort_qval = false_pos.take(sort_index)/ll
-    qval = false_pos/ll.take(back_sort_index)
-    print time.time() - t0
-    #return qval, false_pos, TSQ, tsq_full
-
-    return qval
-
-def leverage(aopt=1,*args):
-    """Returns leverages
-    input : aopt, number of components to base leverage calculations on
-            *args, matrices of normed blm-paramters
-    output: leverages
-    
-    For PCA typical inputs are normalised T or normalised P
-    For PLSR typical inputs are normalised T or normalised W
-    """
-    if aopt<1:
-        raise ValueError,"Leverages only make sense for aopt>0"
-    lev  = []
-    for u in args:
-        lev_u = 1./u.shape[0] + dot(u[:,:aopt], u[:,:aopt].T).diagonal()
-        lev.append(lev_u)
-    return lev
-
-def variances(a, t, p):
-    """Returns explained variance and ind. var from blm-params.
-    input:
-          a -- full centered matrix
-          t,p -- parameters from a bilinear approx of the above matrix.
-    output:
-          var -- variance of each component
-          var_exp -- cumulative explained variance in percentage
-
-    Typical inputs are:  X(centered),T,P for PCA or
-                         X(centered),T,P / Y(centered),T,Q for PLSR.
-    """
-    
-    tot_var = sum(a**2)
-    var = 100*(sum(p**2, 0)*sum(t**2, 0))/tot_var
-    var_exp = var.cumsum()
-    return var, var_exp
-
-def residual_diagnostics(Y, Yhat, aopt=1):
-    """Root mean errors and press values. 
-    R2 vals
-    """
-    pass
-    
-
-def ssq(E, axis=0, weights=None):
-    """Sum of squares, supports weights."""
-    n = E.shape[axis]
-    if weights==None:
-        weights = eye(n)
-    else:
-        weigths = diag(weigths)
-    if axis==0:
-        Ew = dot(weights, E)
-    elif axis==1:
-        Ew = dot(E, weights)
-    else:
-        raise NotImplementedError, "Higher order modes not supported"
-    return pow(Ew,2).sum(axis)
-
-
-def vnorm(x):
-    """Returns the euclidian norm of a vector.
-
-    This is considerably faster than linalg.norm
-    """
-    return sqrt(dot(x,x.conj()))
-
-def mahalanobis(a, loc=None, acov=None, invcov=None):
-    """Returns the distance of each observation in a
-    from the location estimate (loc) of the data,
-    relative to the shape of the data.
-    
-    
-    a : data matrix (n observations in rows, p variables in columns)
-    loc : location estimate of the data (p-dimensional vector)
-    covmat or invcov : scatter estimate of the data or the inverse of the scatter estimate (pxp matrix)
-
-    :Returns:
-    A vector containing the distances of all the observations to locvct.
-    
-    """
-    n, p = a.shape
-    if loc==None:
-        loc = a.mean(0)
-    loc = atleast_2d(loc)
-    if loc.shape[1]==1:
-        loc = loc.T; #ensure rowvector
-    assert(loc.shape[1]==p)
-    xc = a - loc
-    if acov==None and invcov==None:
-        acov = dot(xc.T, xc)
-        
-    if invcov != None:
-        covmat = atleast_2d(invcov)
-        if min(covmat.shape)==1:
-            covmat = diag(invcov.ravel())
-    else:
-        covmat = atleast_2d(acov)
-        if min(covmat.shape)==1:
-            covmat = diag(covmat.ravel())
-        covmat = inv(covmat)
-    # mdist = diag(dot(dot(xc, covmat),xc.T))
-    mdist = (dot(xc, covmat)*xc).sum(1)
-    return mdist
-
-def lpls_qvals(a, b, c, aopt=None, alpha=.3, zx_alpha=.5, n_iter=20,
-               sim_method='shuffle',p_center='med', cov_center='med',crot=True,
-               strict=False, mean_ctr=[2,0,2], nsets=None):
-
-    """Returns qvals for l-pls model.
-
-    input:
-    a -- data matrix
-    b -- data matrix
-    c -- data matrix
-    aopt -- scalar, opt. number of components
-    alpha -- [0,1] regularisation parameter for T2-test
-    xz_alpha -- [0,1] how much z info to include
-    n_iter -- number of permutations
-    sim_method -- permutation method ['shuffle']
-    p_center -- location estimator for sub models ['med']
-    cov_center -- location estimator for covariance of submodels ['med']
-    crot -- bool, use rotations of sub models?
-    strict -- bool, use stict (rot/flips only) rotations?
-    """
-    
-    m, n = a.shape
-    p, k = c.shape
-    pert_tsq_x = zeros((n, n_iter), dtype='d') # (nxvars x n_subsets)
-    pert_tsq_z = zeros((p, n_iter), dtype='d') # (nzvars x n_subsets)
-
-    # Full model
-    #print "Full model start"
-    dat = lpls(a, b, c, aopt, scale='loads', mean_ctr=mean_ctr)
-    Wc, Lc = lpls_jk(a, b, c , aopt, nsets=nsets)
-    #print "Full hot"
-    cal_tsq_x = hotelling(Wc, dat['W'], alpha = alpha)
-    cal_tsq_z = hotelling(Lc, dat['L'], alpha = 0)
-    
-    # Perturbations
-    Vs = shuffle_1d(b, n_iter, axis=0)
-    for i, b_shuff in enumerate(Vs):
-        print i
-        dat = lpls(a, b_shuff,c, aopt, scale='loads', mean_ctr=mean_ctr)
-        Wi, Li = lpls_jk(a, b_shuff, c, aopt, nsets=nsets)
-        pert_tsq_x[:,i] = hotelling(Wi, dat['W'], alpha=alpha)
-        pert_tsq_z[:,i] = hotelling(Li, dat['L'], alpha=alpha)
-   
-    return cal_tsq_z, pert_tsq_z, cal_tsq_x, pert_tsq_x
-    
-
-
-def fdr(tsq, tsqp, loc_method='mean'):
-    n, = tsq.shape
-    k, m = tsqp.shape
-    assert(n==k)
-    n_false = empty((n, m), 'd')
-    sort_index = argsort(tsq)[::-1]
-    r_index = argsort(sort_index)
-    for i in xrange(m):
-        for j in xrange(n):
-            n_false[j,i] = (tsqp[:,i]>tsq[j]).sum()
-    #cPickle.dump(n_false, open("/tmp/nfalse.dat_"+str(n), "w"))
-    if loc_method=='mean':
-        fp = n_false.mean(1)
-    elif loc_method == 'median':
-        fp = median(n_false.T)
-    else:
-        raise ValueError
-    n_signif = (arange(n) + 1.0)[r_index]
-    fd_rate = fp/n_signif
-    return fd_rate

laydi/lib/cx_utils.py

@@ -1,115 +0,0 @@
-from scipy import apply_along_axis,newaxis,zeros,\
-     median,round_,nonzero,dot,argmax,any,sqrt,ndarray,\
-     trace,zeros_like,sign,sort,real,argsort,rand,array,\
-     matrix,nan
-from scipy.linalg import norm,svd,inv,eig
-from numpy import median
-
-def normalise(a, axis=0, return_scales=False):
-    s = apply_along_axis(norm, axis, a)
-    if axis==0:
-        s = s[newaxis]
-    else:
-        s = s[:,newaxis]
-	    
-    a_s = a/s
-
-    if return_scales:
-       return a_s, s
-
-    return a_s
-
-def sub2ind(shape, i, j):
-	"""Indices from subscripts. Only support for 2d"""
-	row,col = shape
-	ind = []
-	for k in xrange(len(i)):
-		for m in xrange(len(j)):
-			ind.append(i[k]*col + j[m])
-	return ind
-
-
-def sorted_eig(a, b=None,sort_by='sm'):
-    """
-    Just eig with real part of output sorted:
-    This is for convenience only, not general!
-    
-    sort_by='sm': return the eigenvectors by eigenvalues
-                  of smallest magnitude first. (default)
-            'lm': returns largest eigenvalues first      
-
-    output: just as eig with 2 outputs
-            -- s,v (eigvals,eigenvectors)
-    (This is reversed output compared to matlab)
-    
-    """
-    s,v = eig(a, b)
-    s = real(s) # dont expect any imaginary part
-    v = real(v)
-    ind = argsort(s)
-    if sort_by=='lm':
-        ind = ind[::-1]
-    v = v.take(ind, 1)
-    s = s.take(ind)
-
-    return s,v
-
-def str2num(string_number):
-    """Convert input (string number) into number, if float(string_number) fails, a nan is inserted. 
-    """
-    missings = ['','nan','NaN','NA']
-    try:
-        num = float(string_number)
-    except:
-        if string_number in missings:
-            num = nan
-        else:
-            print "Found strange entry: %s" %string_number
-            raise
-    return num
-
-def randperm(n):
-  r = rand(n)
-  dict={}
-  for i in range(n):
-     dict[r[i]] = i
-  r = sort(r)
-  out = zeros(n)
-  for i in range(n):
-     out[i] = dict[r[i]]
-  return array(out).astype('i')
-
-def mat_center(X,axis=0,ret_mn=False):
-    """Mean center matrix along axis.
-    
-        X -- matrix, data
-        axis -- dim,
-        ret_mn -- bool, return mean
-
-    output:
-            Xc, [mnX]
-            
-    NB: axis = 1 is column-centering, axis=0=row-centering
-    default is row centering (axis=0)
-    """
-
-    try:
-        rows,cols = X.shape
-    except ValueError:
-        print "The X data needs to be two-dimensional"
-        
-    if axis==0:
-        mnX = X.mean(axis)[newaxis]
-        Xs = X - mnX
-    
-    elif axis==1:
-        mnX = X.mean(axis)[newaxis]
-        Xs = (X.T - mnX).T
-    if ret_mn:
-        return Xs,mnX
-    else:
-        return Xs
-
-def m_shape(array):
-	"""Returns the array shape on the form of a numpy.matrix."""
-	return matrix(array).shape

laydi/lib/engines.py

@@ -1,879 +0,0 @@
-"""Module contain algorithms for low-rank models.
-
-There is almost no typechecking of any kind here, just focus on speed
-"""
-
-import math
-import warnings
-from scipy.linalg import svd,inv
-from scipy import dot,empty,eye,newaxis,zeros,sqrt,diag,\
-     apply_along_axis,mean,ones,randn,empty_like,outer,r_,c_,\
-     rand,sum,cumsum,matrix, expand_dims,minimum,where,arange,inner,tile
-has_sym = True
-has_arpack = True
-try:
-    from symeig import symeig
-except:
-    has_sym = False
-try:
-    from scipy.sandbox import arpack
-except:
-    has_arpack = False
-
-
-def pca(a, aopt,scale='scores',mode='normal',center_axis=0):
-    """ Principal Component Analysis.
-
-    Performs PCA on given matrix and returns results in a dictionary.
-
-    :Parameters:
-        a : array
-    Data measurement matrix, (samples x variables)
-        aopt : int
-    Number of components to use, aopt<=min(samples, variables)
-
-    :Returns:
-    results : dict
-        keys -- values,  T -- scores, P -- loadings, E -- residuals,
-        lev --leverages, ssq -- sum of squares, expvar -- cumulative
-        explained variance, aopt -- number of components used
-    
-    :OtherParam eters:
-    mode : str
-        Amount of info retained, ('fast', 'normal', 'detailed')
-    center_axis : int
-        Center along given axis. If neg.: no centering (-inf,..., matrix modes)
-  
-    :SeeAlso:
-        - pcr : other blm
-        - pls : other blm
-        - lpls : other blm
-
-    Notes
-    -----
-    Uses kernel speed-up if m>>n or m<<n.
-
-    If residuals turn rank deficient, a lower number of component than given
-    in input will be used. The number of components used is given in
-    results-dict.
-    
-    Examples
-    --------
-    
-    >>> import scipy,engines
-    >>> a=scipy.asarray([[1,2,3],[2,4,5]])
-    >>> dat=engines.pca(a, 2)
-    >>> dat['expvarx']
-    array([0.,99.8561562,  100.])
-
-    """
-    m, n = a.shape
-    assert(aopt<=min(m,n))
-    if center_axis>=0:
-        a = a - expand_dims(a.mean(center_axis), center_axis)
-    if m>(n+100) or n>(m+100):
-        u, s, v = esvd(a, amax=None) # fixme:amax option need to work with expl.var
-    else:
-        u, s, vt = svd(a, 0)
-        v = vt.T
-    e = s**2
-    tol = 1e-10
-    eff_rank = sum(s>s[0]*tol)
-    aopt = minimum(aopt, eff_rank)
-    T = u*s
-    s = s[:aopt]
-    T = T[:,:aopt]
-    P = v[:,:aopt]
-    
-    if scale=='loads':
-        T = T/s
-        P = P*s
-
-    if mode == 'fast':
-        return {'T':T, 'P':P, 'aopt':aopt}
-    
-    if mode=='detailed':
-        E = empty((aopt, m, n))
-        ssq = []
-        lev = []
-        for ai in range(aopt):
-            E[ai,:,:] = a - dot(T[:,:ai+1], P[:,:ai+1].T)
-            ssq.append([(E[ai,:,:]**2).mean(0), (E[ai,:,:]**2).mean(1)])
-            if scale=='loads':
-                lev.append([((s*T)**2).sum(1), (P**2).sum(1)])
-            else:
-                lev.append([(T**2).sum(1), ((s*P)**2).sum(1)])
-    else:
-        # residuals
-        E = a - dot(T, P.T)
-        #E = a
-        SEP = E**2
-        ssq = [SEP.sum(0), SEP.sum(1)]
-        # leverages
-        if scale=='loads':
-            lev = [(1./m)+(T**2).sum(1), (1./n)+((P/s)**2).sum(1)]
-        else:
-            lev = [(1./m)+((T/s)**2).sum(1), (1./n)+(P**2).sum(1)]
-        # variances
-    expvarx = r_[0, 100*e.cumsum()/e.sum()][:aopt+1]
-    
-    return {'T':T, 'P':P, 'E':E, 'expvarx':expvarx, 'levx':lev, 'ssqx':ssq, 'aopt':aopt, 'eigvals': e[:aopt,newaxis]}
-
-def pcr(a, b, aopt, scale='scores',mode='normal',center_axis=0):
-    """ Principal Component Regression.
-
-    Performs PCR on given matrix and returns results in a dictionary.
-
-    :Parameters:
-        a : array
-    Data measurement matrix, (samples x variables)
-        b : array
-    Data response matrix, (samples x responses)
-        aopt : int
-    Number of components to use, aopt<=min(samples, variables)
-
-    :Returns:
-    results : dict
-        keys -- values,  T -- scores, P -- loadings, E -- residuals,
-        levx -- leverages, ssqx -- sum of squares, expvarx -- cumulative
-        explained variance, aopt -- number of components used
-    
-    :OtherParameters:
-    mode : str
-        Amount of info retained, ('fast', 'normal', 'detailed')
-    center_axis : int
-        Center along given axis. If neg.: no centering (-inf,..., matrix modes)
-  
-    :SeeAlso:
-        - pca : other blm
-        - pls : other blm
-        - lpls : other blm
- 
-    Notes
-    -----
-
-    Uses kernel speed-up if m>>n or m<<n.
-
-    If residuals turn rank deficient, a lower number of component than given
-    in input will be used. The number of components used is given in results-dict. 
-    
-
-    Examples
-    --------
-    
-    >>> import scipy,engines
-    >>> a=scipy.asarray([[1,2,3],[2,4,5]])
-    >>> b=scipy.asarray([[1,1],[2,3]])
-    >>> dat=engines.pcr(a, 2)
-    >>> dat['expvarx']
-    array([0.,99.8561562,  100.])
-
-    """
-    k, l = m_shape(b)
-    if center_axis>=0:
-        b = b - expand_dims(b.mean(center_axis), center_axis)
-    dat = pca(a, aopt=aopt, scale=scale, mode=mode, center_axis=center_axis)
-    T = dat['T']
-    weights = apply_along_axis(vnorm, 0, T)**2
-    if scale=='loads':
-        Q = dot(b.T, T*weights)
-    else:
-        Q = dot(b.T, T/weights)
-
-    if mode=='fast':
-        dat.update({'Q':Q})
-        return dat
-    if mode=='detailed':
-        F = empty((aopt, k, l))
-        for i in range(aopt):
-            F[i,:,:] = b - dot(T[:,:i+1], Q[:,:i+1].T)
-    else:
-        F = b - dot(T, Q.T)
-    expvary = r_[0,  100*((T**2).sum(0)*(Q**2).sum(0)/(b**2).sum()).cumsum()[:aopt]]
-    #fixme: Y-var leverages
-    dat.update({'Q':Q, 'F':F, 'expvary':expvary})
-    return dat
-
-def pls(a, b, aopt=2, scale='scores', mode='normal', center_axis=-1, ab=None):
-    """Partial Least Squares Regression.
-
-    Performs PLS on given matrix and returns results in a dictionary.
-
-    :Parameters:
-        a : array
-    Data measurement matrix, (samples x variables)
-        b : array
-    Data response matrix, (samples x responses)
-        aopt : int
-    Number of components to use, aopt<=min(samples, variables)
-
-    :Returns:
-    results : dict
-        keys -- values,  T -- scores, P -- loadings, E -- residuals,
-        levx -- leverages, ssqx -- sum of squares, expvarx -- cumulative
-        explained variance of descriptors, expvary -- cumulative explained
-        variance of responses, aopt -- number of components used
-    
-    :OtherParameters:
-    mode : str
-        Amount of info retained, ('fast', 'normal', 'detailed')
-    center_axis : int
-        Center along given axis. If neg.: no centering (-inf,..., matrix modes)
-    
-    :SeeAlso:
-        - pca : other blm
-        - pcr : other blm
-        - lpls : other blm
- 
-    Notes
-    -----
-
-    Uses kernel speed-up if m>>n or m<<n.
-
-    If residuals turn rank deficient, a lower number of component than given
-    in input will be used. The number of components used is given in results-dict.
-
-    Examples
-    --------
-    
-    >>> import scipy,engines
-    >>> a=scipy.asarray([[1,2,3],[2,4,5]])
-    >>> b=scipy.asarray([[1,1],[2,3]])
-    >>> dat=engines.pls(a, b, 2)
-    >>> dat['expvarx']
-    array([0.,99.8561562,  100.])
-
-    """
-    
-    m, n = m_shape(a)
-    if ab!=None:
-        mm, l = m_shape(ab)
-        assert(m==mm)
-    else:
-         k, l = m_shape(b)
-
-    if center_axis>=0:
-        a = a - expand_dims(a.mean(center_axis), center_axis)
-        b = b - expand_dims(b.mean(center_axis), center_axis)
-    
-    W = empty((n, aopt))
-    P = empty((n, aopt))
-    R = empty((n, aopt))
-    Q = empty((l, aopt))
-    T = empty((m, aopt))
-    B = empty((aopt, n, l))
-    tt = empty((aopt,))
-
-    if ab==None:
-        ab = dot(a.T, b)
-    for i in range(aopt):
-        if ab.shape[1]==1: #pls 1
-            w = ab.reshape(n, l)
-            w = w/vnorm(w)
-        elif n<l: # more yvars than xvars
-            if has_sym:
-                s, w = symeig(dot(ab, ab.T),range=[n,n],overwrite=True)
-            else:
-                w, s, vh = svd(dot(ab, ab.T))
-            w = w[:,:1]
-        else: # standard wide xdata
-            if has_sym:
-                s, q = symeig(dot(ab.T, ab),range=[l,l],overwrite=True)
-            else:
-                q, s, vh = svd(dot(ab.T, ab))
-                q = q[:,:1]
-            w = dot(ab, q)
-            w = w/vnorm(w)
-        r = w.copy()
-        if i>0:
-            for j in range(0, i, 1):
-                r = r - dot(P[:,j].T, w)*R[:,j][:,newaxis]
-        
-        t = dot(a, r)
-        tt[i] = tti = dot(t.T, t).ravel()
-        p  = dot(a.T, t)/tti
-        q = dot(r.T, ab).T/tti
-        ab = ab - dot(p, q.T)*tti
-        T[:,i] = t.ravel()
-        W[:,i] = w.ravel()
-
-        if mode=='fast' and i==aopt-1:
-            if scale=='loads':
-                tnorm = sqrt(tt)
-                T = T/tnorm
-                W = W*tnorm
-            return {'T':T, 'W':W}
-
-        P[:,i] = p.ravel()
-        R[:,i] = r.ravel()
-        Q[:,i] = q.ravel()
-        #B[i] = dot(R[:,:i+1], Q[:,:i+1].T)
-
-
-        
-    qnorm = apply_along_axis(vnorm, 0, Q)
-    tnorm = sqrt(tt)
-    pp = (P**2).sum(0)
-    if mode=='detailed':
-        E = empty((aopt, m, n))
-        F = empty((aopt, k, l))
-        ssqx, ssqy = [], []
-        leverage = empty((aopt, m))
-        h2x = [] #hotellings T^2
-        h2y = []
-        for ai in range(aopt):
-            E[ai,:,:] = a - dot(T[:,:ai+1], P[:,:ai+1].T)
-            F[i-1] = b - dot(T[:,:i], Q[:,:i].T)
-            ssqx.append([(E[ai,:,:]**2).mean(0), (E[ai,:,:]**2).mean(1)])
-            ssqy.append([(F[ai,:,:]**2).mean(0), (F[ai,:,:]**2).mean(1)])
-            leverage[ai,:] = 1./m + ((T[:,:ai+1]/tnorm[:ai+1])**2).sum(1)
-            h2y.append(1./k + ((Q[:,:ai+1]/qnorm[:ai+1])**2).sum(1))
-    else:
-        # residuals
-        E = a - dot(T, P.T)
-        F = b - dot(T, Q.T)
-        sepx = E**2
-        ssqx = [sepx.sum(0), sepx.sum(1)]
-        sepy = F**2
-        ssqy = [sepy.sum(0), sepy.sum(1)]
-        # leverage
-        leverage = 1./m + ((T/tnorm)**2).sum(1)
-        h2x = []
-        h2y = []
-    # variances
-    tp= tt*pp
-    tq = tt*qnorm*qnorm
-    expvarx = r_[0, 100*tp/(a*a).sum()]
-    expvary = r_[0, 100*tq/(b*b).sum()]
-    
-    if scale=='loads':
-        T = T/tnorm
-        W = W*tnorm
-        Q = Q*tnorm
-        P = P*tnorm
-
-    return {'Q':Q, 'P':P, 'T':T, 'W':W, 'R':R, 'E':E, 'F':F,
-            'expvarx':expvarx, 'expvary':expvary, 'ssqx':ssqx, 'ssqy':ssqy,
-            'leverage':leverage, 'h2':h2x}
-
-def w_simpls(aat, b, aopt):
-    """ Simpls for wide matrices.
-    Fast pls for crossval, used in calc rmsep for wide X
-    There is no P or W.  T is normalised
-    """
-    bb = b.copy()
-    m, m = aat.shape
-    U = empty((m, aopt)) # W
-    T = empty((m, aopt))
-    H = empty((m, aopt)) # R
-    PROJ = empty((m, aopt)) # P?
-    for i in range(aopt):
-        q, s, vh = svd(dot(dot(b.T, aat), b), full_matrices=0)
-        u = dot(b, q[:,:1]) #y-factor scores
-        U[:,i] = u.ravel()
-        t = dot(aat, u)
-        t = t/vnorm(t)
-        T[:,i] = t.ravel()
-        h = dot(aat, t) #score-weights
-        H[:,i] = h.ravel()
-        PROJ[:,:i+1] = dot(T[:,:i+1], inv(dot(T[:,:i+1].T, H[:,:i+1])) )
-        if i<aopt:
-            b = b - dot(PROJ[:,:i+1], dot(H[:,:i+1].T,b) )
-    C = dot(bb.T, T)
-
-    return {'T':T, 'U':U, 'Q':C, 'H':H}
-
-def w_pls(aat, b, aopt):
-    """ Pls for wide matrices.
-    Fast pls for crossval, used in calc rmsep for wide X
-    There is no P or W.  T is normalised
-
-    aat = centered kernel matrix
-    b = centered y
-    """
-    bb = b.copy()
-    k, l = m_shape(b)
-    m, m = m_shape(aat)
-    U = empty((m, aopt)) # W
-    T = empty((m, aopt))
-    R = empty((m, aopt)) # R
-    PROJ = empty((m, aopt)) # P?
-
-    for i in range(aopt):
-        if has_sym:
-            s, q = symeig(dot(dot(b.T, aat), b), range=(l,l),overwrite=True)
-        else:
-            q, s, vh = svd(dot(dot(b.T, aat), b), full_matrices=0)
-            q = q[:,:1]
-        u = dot(b , q) #y-factor scores
-        U[:,i] = u.ravel()
-        t = dot(aat, u)
-        
-        t = t/vnorm(t)
-        T[:,i] = t.ravel()
-        r = dot(aat, t)#score-weights
-        #r = r/vnorm(r)
-        R[:,i] = r.ravel()
-        PROJ[:,: i+1] = dot(T[:,:i+1], inv(dot(T[:,:i+1].T, R[:,:i+1])) )
-        if i<aopt:
-            b = b - dot(PROJ[:,:i+1], dot(R[:,:i+1].T,  b) )
-    C = dot(bb.T, T)
-
-    return {'T':T, 'U':U, 'Q':C, 'R':R}
-
-def bridge(a, b, aopt, scale='scores', mode='normal', r=0):
-    """Undeflated Ridged svd(X'Y)
-    """
-    m, n = m_shape(a)
-    k, l = m_shape(b)
-    u, s, vt = svd(b, full_matrices=0)
-    g0 = dot(u*s, u.T)
-    g = (1 - r)*g0 + r*eye(m)
-    ag = dot(a.T, g)
-    u, s, vt = svd(ag, full_matrices=0)
-    W = u[:,:aopt]
-    K = vt[:aopt,:].T
-    T = dot(a, W)
-    tnorm = apply_along_axis(vnorm, 0, T) # norm of T-columns
-
-    if mode == 'fast':
-        if scale=='loads':
-            T = T/tnorm
-            W = W*tnorm
-        return {'T':T, 'W':W}
-
-    U = dot(g0, K) #fixme check this 
-    Q = dot(b.T, dot(T, inv(dot(T.T, T)) ))
-    B = zeros((aopt, n, l), dtype='f')
-    for i in range(aopt):
-        B[i] = dot(W[:,:i+1], Q[:,:i+1].T)
-
-    if mode == 'detailed':
-        E = empty((aopt, m, n))
-        F = empty((aopt, k, l))
-        for i in range(aopt):
-            E[i] = a - dot(T[:,:i+1], W[:,:i+1].T)
-            F[i] = b - dot(a, B[i])
-    else: #normal
-        F = b - dot(a, B[-1])
-        E = a - dot(T, W.T)
-
-    if scale=='loads':
-        T = T/tnorm
-        W = W*tnorm
-        Q = Q*tnorm
-
-    return {'B':B, 'W':W, 'T':T, 'Q':Q, 'E':E, 'F':F, 'U':U, 'P':W}
-
-
-def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', verbose=False):
-    """ L-shaped Partial Least Sqaures Regression by the nipals algorithm.
-
-    (X!Z)->Y
-    :input:
-        X : data matrix (m, n)
-        Y : data matrix (m, l)
-        Z : data matrix (n, o)
-
-    :output:
-      T : X-scores
-      W : X-weights/Z-weights
-      P : X-loadings
-      Q : Y-loadings
-      U : X-Y relation
-      L : Z-scores
-      K : Z-loads
-      B : Regression coefficients X->Y
-      b0: Regression coefficient intercept
-      evx : X-explained variance
-      evy : Y-explained variance
-      evz : Z-explained variance
-      mnx : X location
-      mny : Y location
-      mnz : Z location
-
-    :Notes:
-    
-    """
-    if mean_ctr!=None:
-        xctr, yctr, zctr = mean_ctr
-        X, mnX = center(X, xctr)
-        Y, mnY = center(Y, yctr)
-        Z, mnZ = center(Z, zctr)
-
-    varX = (X**2).sum()
-    varY = (Y**2).sum()
-    varZ = (Z**2).sum()
-    
-    m, n = X.shape
-    k, l = Y.shape
-    u, o = Z.shape
-
-    # initialize 
-    U = empty((k, a_max))
-    Q = empty((l, a_max))
-    T = empty((m, a_max))
-    W = empty((n, a_max))
-    P = empty((n, a_max))
-    K = empty((o, a_max))
-    L = empty((u, a_max))
-    B = empty((a_max, n, l))
-    #b0 = empty((a_max, 1, l))
-    var_x = empty((a_max,))
-    var_y = empty((a_max,))
-    var_z = empty((a_max,))
-
-    MAX_ITER = 250
-    LIM = 1e-1
-    for a in range(a_max):
-        if verbose:
-            print "\nWorking on comp. %s" %a
-        u = Y[:,:1]
-        diff = 1
-        niter = 0
-        while (diff>LIM and niter<MAX_ITER):
-            niter += 1
-            u1 = u.copy()
-            w = dot(X.T, u)
-            w = w/sqrt(dot(w.T, w))
-            #w = w/dot(w.T, w)
-            l = dot(Z, w)
-            k = dot(Z.T, l)
-            k = k/sqrt(dot(k.T, k))
-            #k = k/dot(k.T, k)
-            w = alpha*k + (1-alpha)*w
-            #print sqrt(dot(w.T, w))
-            w = w/sqrt(dot(w.T, w))
-            t = dot(X, w)
-            c = dot(Y.T, t)
-            c = c/sqrt(dot(c.T, c))
-            u = dot(Y, c)
-            diff = dot((u-u1).T, (u-u1))
-        if verbose:
-            print "Converged after %s iterations" %niter
-            print "Error: %.2E" %diff
-        tt = dot(t.T, t)
-        p = dot(X.T, t)/tt
-        q = dot(Y.T, t)/tt
-        l = dot(Z, w)
-        
-        U[:,a] = u.ravel()
-        W[:,a] = w.ravel()
-        P[:,a] = p.ravel()
-        T[:,a] = t.ravel()
-        Q[:,a] = q.ravel()
-        L[:,a] = l.ravel()
-        K[:,a] = k.ravel()
-
-        X = X - dot(t, p.T)
-        Y = Y - dot(t, q.T)
-        Z = (Z.T - dot(w, l.T)).T
-
-        var_x[a] = pow(X, 2).sum()
-        var_y[a] = pow(Y, 2).sum()
-        var_z[a] = pow(Z, 2).sum()
-    
-        B[a] = dot(dot(W[:,:a+1], inv(dot(P[:,:a+1].T, W[:,:a+1]))), Q[:,:a+1].T)
-        #b0[a] = mnY - dot(mnX, B[a])
-        
-    
-    # variance explained
-    evx = 100.0*(1 - var_x/varX)
-    evy = 100.0*(1 - var_y/varY)
-    evz = 100.0*(1 - var_z/varZ)
-    if scale=='loads':
-        tnorm = apply_along_axis(vnorm, 0, T)
-        T = T/tnorm
-        W = W*tnorm
-        Q = Q*tnorm
-        knorm = apply_along_axis(vnorm, 0, K)
-        L = L*knorm
-        K = K/knorm
-    
-    return {'T':T, 'W':W, 'P':P, 'Q':Q, 'U':U, 'L':L, 'K':K, 'B':B, 'evx':evx, 'evy':evy, 'evz':evz,'mnx': mnX, 'mny': mnY, 'mnz': mnZ}    
-
-
-
-def nipals_pls(X, Y, a_max, alpha=.7, ax_center=0, mode='normal', scale='scores', verbose=False):
-    """Partial Least Sqaures Regression by the nipals algorithm.
-
-    (X!Z)->Y
-    :input:
-        X : data matrix (m, n)
-        Y : data matrix (m, l)
-
-    :output:
-      T : X-scores
-      W : X-weights/Z-weights
-      P : X-loadings
-      Q : Y-loadings
-      U : X-Y relation
-      B : Regression coefficients X->Y
-      b0: Regression coefficient intercept
-      evx : X-explained variance
-      evy : Y-explained variance
-      evz : Z-explained variance
-
-    :Notes:
-    
-    """
-    if ax_center>=0:
-        mn_x = expand_dims(X.mean(ax_center), ax_center)
-        mn_y = expand_dims(Y.mean(ax_center), ax_center)
-        X = X - mn_x
-        Y = Y - mn_y
-
-    varX = pow(X, 2).sum()
-    varY = pow(Y, 2).sum()
-    
-    m, n = X.shape
-    k, l = Y.shape
-
-    # initialize 
-    U = empty((k, a_max))
-    Q = empty((l, a_max))
-    T = empty((m, a_max))
-    W = empty((n, a_max))
-    P = empty((n, a_max))
-    B = empty((a_max, n, l))
-    b0 = empty((a_max, m, l))
-    var_x = empty((a_max,))
-    var_y = empty((a_max,))
-
-    t1 = X[:,:1]
-    for a in range(a_max):
-        if verbose:
-            print "\n Working on comp. %s" %a
-        u = Y[:,:1]
-        diff = 1
-        MAX_ITER = 100
-        lim = 1e-16
-        niter = 0
-        while (diff>lim and niter<MAX_ITER):
-            niter += 1
-            #u1 = u.copy()
-            w = dot(X.T, u)
-            w = w/sqrt(dot(w.T, w))
-            #l = dot(Z, w)
-            #k = dot(Z.T, l)
-            #k = k/sqrt(dot(k.T, k))
-            #w = alpha*k + (1-alpha)*w
-            #w = w/sqrt(dot(w.T, w))
-            t = dot(X, w)
-            q = dot(Y.T, t)
-            q = q/sqrt(dot(q.T, q))
-            u = dot(Y, q)
-            diff = vnorm(t1 - t)
-            t1 = t.copy()
-        if verbose:
-            print "Converged after %s iterations" %niter
-        #tt = dot(t.T, t)
-        #p = dot(X.T, t)/tt
-        #q = dot(Y.T, t)/tt
-        #l = dot(Z, w)
-        p = dot(X.T, t)/dot(t.T, t)
-        p_norm = vnorm(p)
-        t = t*p_norm
-        w = w*p_norm
-        p = p/p_norm
-        
-        U[:,a] = u.ravel()
-        W[:,a] = w.ravel()
-        P[:,a] = p.ravel()
-        T[:,a] = t.ravel()
-        Q[:,a] = q.ravel()
-
-        X = X - dot(t, p.T)
-        Y = Y - dot(t, q.T)
-
-        var_x[a] = pow(X, 2).sum()
-        var_y[a] = pow(Y, 2).sum()
-    
-        B[a] = dot(dot(W[:,:a+1], inv(dot(P[:,:a+1].T, W[:,:a+1]))), Q[:,:a+1].T)
-        b0[a] =  mn_y - dot(mn_x, B[a])
-    
-    # variance explained
-    evx = 100.0*(1 - var_x/varX)
-    evy = 100.0*(1 - var_y/varY)
-    
-    if scale=='loads':
-        tnorm = apply_along_axis(vnorm, 0, T)
-        T = T/tnorm
-        W = W*tnorm
-        Q = Q*tnorm
-    
-    return {'T':T, 'W':W, 'P':P, 'Q':Q, 'U':U, 'B':B, 'b0':b0, 'evx':evx, 'evy':evy,
-            'mnx': mnX, 'mny': mnY, 'xc': X, 'yc': Y}
-
-
-########### Helper routines #########
-
-def m_shape(array):
-    return matrix(array).shape
-
-def esvd(data, amax=None):
-    """SVD with the option of economy sized calculation
-    Calculate subspaces of X'X or XX' depending on the shape
-    of the matrix.
-
-    Good for extreme fat or thin matrices
-
-    :notes:
-    Numpy supports this by setting full_matrices=0
-    """
-    has_arpack = True
-    try:
-        import arpack
-    except:
-        has_arpack = False
-    m, n = data.shape
-    if m>=n:
-        kernel = dot(data.T, data)
-        if has_arpack:
-            if amax==None:
-                amax = n
-                s, v = arpack.eigen_symmetric(kernel,k=amax, which='LM',
-                                              maxiter=200,tol=1e-5)
-        if has_sym:
-            if amax==None:
-                amax = n
-                pcrange = None
-            else:
-                pcrange = [n-amax, n]
-            s, v = symeig(kernel, range=pcrange, overwrite=True)
-            s = s[::-1].real
-            v = v[:,::-1].real
-        else:
-            u, s, vt = svd(kernel)
-            v = vt.T
-        s = sqrt(s)
-        u = dot(data, v)/s
-    else:
-        kernel = dot(data, data.T)
-        if has_sym:
-            if amax==None:
-                amax = m
-                pcrange = None
-            else:
-                pcrange = [m-amax, m]
-            s, u = symeig(kernel, range=pcrange, overwrite=True)
-            s = s[::-1]
-            u = u[:,::-1]
-        else:
-            u, s, vt = svd(kernel)
-        s = sqrt(s)
-        v = dot(data.T, u)/s
-    # some use of symeig returns the 0 imaginary part
-    return u.real, s.real, v.real
-
-def vnorm(x):
-    # assume column arrays (or vectors)
-    return math.sqrt(dot(x.T, x))
-
-def center(a, axis):
-    # 0 = col center, 1 = row center, 2 = double center
-    # -1 = nothing
-
-    # check if we have a vector
-    is_vec = len(a.shape)==1
-    if not is_vec:
-        is_vec = a.shape[0]==1 or a.shape[1]==1
-    if is_vec:
-        if axis==2:
-            warnings.warn("Double centering of vecor ignored, using ordinary centering")
-        if axis==-1:
-            mn = 0
-        else:
-            mn = a.mean()
-        return a - mn, mn
-    # !!!fixme: use broadcasting
-    if axis==-1:
-        mn = zeros((1,a.shape[1],))
-        #mn = tile(mn, (a.shape[0], 1))
-    elif axis==0:
-        mn = a.mean(0)[newaxis]
-        #mn = tile(mn, (a.shape[0], 1)) 
-    elif axis==1:
-        mn = a.mean(1)[:,newaxis]
-        #mn = tile(mn, (1, a.shape[1]))
-    elif axis==2:
-        mn = a.mean(0)[newaxis] + a.mean(1)[:,newaxis] - a.mean()
-        return a - mn , a.mean(0)[newaxis]
-    else:
-        raise IOError("input error: axis must be in [-1,0,1,2]")
-
-    return a - mn, mn
-
-def scale(a, axis):
-    if axis==-1:
-        sc = zeros((a.shape[1],))
-    elif axis==0:
-        sc = a.std(0)
-    elif axis==1:
-        sc = a.std(1)[:,newaxis]
-    else:
-        raise IOError("input error: axis must be in [-1,0,1]")
-
-    return a - sc, sc
-
-
-
-## #PCA CALCS
-
-## %  Calculate Q limit using unused eigenvalues
-## temp = diag(s);
-## if n < m
-##   emod = temp(lv+1:n,:);
-## else
-##   emod = temp(lv+1:m,:);
-## end
-## th1 = sum(emod);
-## th2 = sum(emod.^2);
-## th3 = sum(emod.^3);
-## h0 = 1 - ((2*th1*th3)/(3*th2^2));
-## if h0 <= 0.0
-## h0 = .0001;
-## disp('  ')
-## disp('Warning:  Distribution of unused eigenvalues indicates that')
-## disp('          you should probably retain more PCs in the model.')
-## end
-## q = th1*(((1.65*sqrt(2*th2*h0^2)/th1) + 1 + th2*h0*(h0-1)/th1^2)^(1/h0));
-## disp('  ')
-## disp('The 95% Q limit is')
-## disp(q)
-## if plots >= 1
-##   lim = [q q];
-##   plot(scl,res,scllim,lim,'--b')
-##   str = sprintf('Process Residual Q with 95 Percent Limit Based on %g PC Model',lv);
-##   title(str)
-##   xlabel('Sample Number')
-##   ylabel('Residual')
-##   pause
-## end
-## %  Calculate T^2 limit using ftest routine
-## if lv > 1
-##   if m > 300
-##     tsq = (lv*(m-1)/(m-lv))*ftest(.95,300,lv,2);
-##   else
-##     tsq = (lv*(m-1)/(m-lv))*ftest(.95,m-lv,lv,2);
-##   end
-##   disp('  ')
-##   disp('The 95% T^2 limit is')
-##   disp(tsq)
-## %  Calculate the value of T^2 by normalizing the scores to
-## %  unit variance and summing them up
-##   if plots >= 1.0
-##     temp2 = scores*inv(diag(ssq(1:lv,2).^.5));
-##     tsqvals = sum((temp2.^2)');
-##     tlim = [tsq tsq];
-##     plot(scl,tsqvals,scllim,tlim,'--b')
-##     str = sprintf('Value of T^2 with 95 Percent Limit Based on %g PC Model',lv);
-##     title(str)
-##     xlabel('Sample Number')
-##     ylabel('Value of T^2')
-##   end
-## else
-##   disp('T^2 not calculated when number of latent variables = 1')
-##   tsq = 1.96^2;
-## end
-

laydi/lib/hypergeom.py

@@ -1,95 +0,0 @@
-import scipy
-
-try:
-    # FIXME: remove rpy in a more proper way
-    import rpy_does_not_exist
-    has_rpy = True
-    silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
-except:
-    has_rpy = False
-
-def gene_hypergeo_test(selection, category_dataset):
-    """Returns the pvals from a hypergeometric test of significance.
-
-    input:
-           -- selection: list of selected identifiers along 0 dim of cat.set
-           -- category dataset, categories along dim 1 (cols)
-    """
-    gene_dim_name = category_dataset.get_dim_name(0)
-    category_dim_name = category_dataset.get_dim_name(1)
-
-    #categories
-    all_cats = category_dataset.get_identifiers(category_dim_name, sorted=True)
-
-    # gene_ids universe
-    all_genes = category_dataset.get_identifiers(gene_dim_name)
-    
-    # signifcant genes
-    good_genes_all = list(selection)
-    gg_index = category_dataset.get_indices(gene_dim_name, good_genes_all)
-    
-    # significant genes pr. category
-    good_genes_cat = []
-    for col in category_dataset.asarray().T:
-        index = scipy.where(col==1)[0]
-        index = scipy.intersect1d(index, gg_index)
-        if index.size==0:
-            good_genes_cat.append([])
-        else:
-            good_genes_cat.append(category_dataset.get_identifiers(gene_dim_name, index))
-    count = map(len, good_genes_cat)
-    count = scipy.asarray([max(i, 0) for i in count])
-    cat_count = category_dataset.asarray().sum(0)
-    if has_rpy:
-        rpy.r.assign("x", count - 1) #number of sign. genes in category i
-        rpy.r.assign("m", len(good_genes_all)) # number of sign. genes tot
-        rpy.r.assign("n", len(all_genes)-len(good_genes_all) ) # num. genes not sign.
-        rpy.r.assign("k", cat_count) #num. genes in cat i
-        silent_eval('pvals <- phyper(x, m, n, k, lower.tail=FALSE)')
-        pvals = rpy.r("pvals")
-
-    else:
-        pvals = p_hyper_geom(count, len(good_genes_all),
-                             len(all_genes)-len(good_genes_all),
-                             cat_count)
-    
-    pvals = scipy.where(cat_count==0, 2, pvals)
-    pvals = scipy.where(scipy.isnan(pvals), 2, pvals)
-    out = {}
-    for i in range(pvals.size):
-        out[str(all_cats[i])] = (count[i], cat_count[i], pvals[i])
-    return out
-
-    
-def p_hyper_geom(x, m, n, k):
-    """Distribution function for the hypergeometric distribution.
-
-    Inputs:
-           -- x: vector of quantiles representing the number of white balls
-           drawn without replacement from an urn which contains both
-           black and white balls.
-           -- m: the number of white balls in the urn.
-           -- n: the number of black balls in the urn.
-           -- k: [vector] the number of balls drawn from the urn
-
-    Comments:
-    Similar to R's phyper with lower.tail=FALSE
-
-    """
-    
-    M = m + n
-    multiple_draws = False
-    if isinstance(k, scipy.ndarray) and k.size>1:
-        multiple_draws = True
-        n_draws = k.size
-        if n_draws<x.size:
-            print "n_draws: %d and n_found: %d  Length mismatch, zero padded" %(k.size, x.size)
-    N = k
-    n = m
-    if not multiple_draws:
-        out = scipy.stats.hypergeom.pmf(x, M, n, N).cumsum()
-    else:
-        out = scipy.zeros((max(n_draws, x.size),))
-        for i in xrange(N.size):
-            out[i] = scipy.stats.hypergeom.pmf(x, M, n, N[i]).cumsum()[i]
-    return out

laydi/lib/nx_utils.py

@@ -1,567 +0,0 @@
-import os,sys
-from itertools import izip
-import networkx as NX
-from scipy import shape,diag,dot,asarray,sqrt,real,zeros,eye,exp,maximum,\
-     outer,maximum,sum,diag,real,atleast_2d
-from scipy.linalg import eig,svd,inv,expm,norm
-from cx_utils import sorted_eig
-
-import numpy
-
-
-
-eps = numpy.finfo(float).eps.item()
-feps = numpy.finfo(numpy.single).eps.item()
-_array_precision = {'f': 0, 'd': 1, 'F': 0, 'D': 1,'i': 1}
-
-class NXUTILSException(Exception): pass
-
-def xgraph_to_graph(G):
-    """Convert an Xgraph to an ordinary graph.
-    Edge attributes, mult.edges and self-loops are lost in the process.
-    """
-    
-    GG = NX.convert.from_dict_of_lists(NX.convert.to_dict_of_lists(G))
-    return GG
-
-def get_affinity_matrix(G, data, ids, dist='e', mask=None, weight=None, t=0, out='dist'):
-    """
-    Function for calculating a general affinity matrix, based upon distances.
-    Affiniy = 1 - distance ((10-1) 1 is far apart)
-    INPUT
-
-    data:
-    gene expression data, type dict data[gene] = expression-vector
-
-    G:
-    The network (networkx.base.Graph object)
-    
-    mask:
-    The array mask shows which data are missing. If mask[i][j]==0, then
-    data[i][j] is missing.
-
-    weights:
-    The array weight contains the weights to be used when calculating distances.
-
-    transpose:
-    If transpose==0, then genes are clustered. If transpose==1, microarrays are
-    clustered.
-
-    dist:
-    The character dist defines the distance function to be used:
-    dist=='e': Euclidean distance
-    dist=='b': City Block distance
-    dist=='h': Harmonically summed Euclidean distance
-    dist=='c': Pearson correlation
-    dist=='a': absolute value of the correlation
-    dist=='u': uncentered correlation
-    dist=='x': absolute uncentered correlation
-    dist=='s': Spearman's rank correlation
-    dist=='k': Kendall's tau
-    For other values of dist, the default (Euclidean distance) is used.
-
-    OUTPUT
-    D :
-    Similariy matrix (nGenes x nGenes), symetric, d_ij e in [0,1]
-    Normalized so max weight = 1.0
-    """
-    try:
-        from Bio import Cluster as CLS
-    except:
-        raise NXUTILSError("Import of Biopython failed")
-    n_var = len(data)
-    n_samp = len(data[data.keys()[0]])
-    X = zeros((nVar, nSamp),dtpye='<f8')
-
-    for i, gene in enumerate(ids): #this shuld be right!!
-        X[i,:] = data[gene]
-    
-    
-    #X = transpose(X) # distancematrix needs matrix as (nGenes,nSamples)
-    
-    D_list  = CLS.distancematrix(X, dist=dist)
-    D = zeros((nVar,nVar),dtype='<f8')
-    for i,row in enumerate(D_list):
-        if i>0:
-            D[i,:len(row)]=row
-
-    D = D + D.T
-    MAX = 30.0
-    D_max = max(ravel(D))/MAX
-    D_n = D/D_max #normalised (max = 10.0)
-    D_n = (MAX+1.) - D_n #using correlation (inverse distance for dists)
-    
-    A = NX.adj_matrix(G, nodelist=ids)
-    if out=='dist':
-        return D_n*A
-    elif out=='heat_kernel':
-        t=1.0
-        K = exp(-t*D*A)
-        return K
-    elif out=='complete':
-        return D_n
-    else:
-        return []
-
-def remove_one_degree_nodes(G, iter=True):
-    """Removes all nodes with only one neighbour.  These nodes does
-    not contribute to community structure.
-    input:
-             G -- graph
-             iter -- True/False iteratively remove?
-    """
-    G_copy = G.copy()
-    if iter==True:
-        while 1:
-            bad_nodes=[]
-            for node in G_copy.nodes():
-                if len(G_copy.neighbors(node))==1:
-                    bad_nodes.append(node)
-            if len(bad_nodes)>0:
-                G_copy.delete_nodes_from(bad_nodes)
-            else:
-                break
-    else:
-       bad_nodes=[]
-       for ngb in G_copy.neighbors_iter():
-           if len(G_copy.neighbors(node))==1:
-               bad_nodes.append(node)
-           if len(bad_nodes)>0:
-               G_copy.delete_nodes_from(bad_nodes)
-
-    print "Deleted %s nodes from network" %(len(G)-len(G_copy))
-    return G_copy
-
-def key_players(G, n=1, with_labels=False):
-    """
-    Resilince measure
-    Identification of key nodes by fraction of nodes in
-    disconnected subgraph when the node is removed.
-
-    output:
-           fraction of nodes disconnected when node i is removed
-    """
-    i=0
-    frac=[]
-    labels = {}
-    for node in G.nodes():
-        i+=1
-        print i
-        T = G.copy()
-        T.delete_node(node)
-        n_nodes = T.number_of_nodes()
-        sub_graphs = NX.connected_component_subgraphs(T)
-        n = len(sub_graphs)
-        if n>1:
-            strong_comp = sub_graphs[0]
-            fraction = 1.0 - 1.0*strong_comp.number_of_nodes()/n_nodes
-            frac.append(fraction)
-            labels[node]=fraction
-            
-        else:
-            frac.append(0.0)
-            labels[node]=0.0
-
-    out = 1.0 - array(frac)
-    if with_labels==True:
-        return out,labels
-    else:
-        return out
-
-def node_weighted_adj_matrix(G, weights=None, ave_type='harmonic', with_labels=False):
-    """Return a weighted adjacency matrix of graph. The weights are
-    node weights.
-    input: G -- graph
-           weights -- dict, keys: nodes, values: weights
-           with_labels -- True/False, return labels?
-
-    output: A -- weighted eadjacency matrix
-            [index] -- node labels 
-    
-    """
-    n=G.order()
-    # make an dictionary that maps vertex name to position
-    index={}
-    count=0
-    for node in G.nodes():
-        index[node]=count
-        count = count+1
-  
-    a = zeros((n,n))
-    if type(G)=='networkx.xbase.XGraph':
-        raise
-    for head,tail in G.edges():
-        if ave_type == 'geometric':
-            a[index[head],index[tail]]= sqrt(weights[head]*weights[tail])
-            a[index[tail],index[head]]= a[index[head],index[tail]]
-        elif ave_type == 'harmonic':
-            a[index[head],index[tail]] = mean(weights[head],weights[tail])
-            a[index[tail],index[head]]= mean(weights[head],weights[tail])
-    if with_labels:
-        return a,index
-    else:
-        return a            
-
-def weighted_adj_matrix(G, with_labels=False):
-    """Adjacency matrix of an XGraph whos weights are given in edges.
-    """
-    A, labels = NX.adj_matrix(G, with_labels=True)
-    W = A.astype('<f8')
-    for orf, i in labels.items():
-        for orf2, j in labels.items():
-            if G.has_edge(orf, orf2):
-                edge_weight = G.get_edge(orf, orf2)
-                W[i,j] = edge_weight
-                W[j,i] = edge_weight
-    if with_labels==True:
-        return W, labels
-    else:
-        return W
-
-def assortative_index(G):
-    """Ouputs two vectors: the degree and the neighbor average degree.
-    Used to measure the assortative mixing.  If the average degree is
-    pos. correlated with the degree we know that hubs tend to connect
-    to other hubs.
-
-    input: G, graph connected!!
-    ouput: d,mn_d: degree, and average degree of neighb.
-    (degree sorting from degree(with_labels=True))
-    """
-    d = G.degree(with_labels=True)
-    out=[]
-    for node in G.nodes():
-        nn = G.neighbors(node)
-        if len(nn)>0:
-            nn_d = mean([float(d[i]) for i in nn])
-            out.append((d[node], nn_d))
-    return array(out).T
-
-
-def struct_equivalence(G,n1,n2):
-    """Returns the structural equivalence of a node pair.  Two nodes
-    are structural equal if they share the same neighbors.
-
-    x_s = [ne(n1) union ne(n2) - ne(n1) intersection ne(n2)]/[ne(n1)
-    union ne(n2) + ne(n1) intersection ne(n2)]
-    ref: Brun et.al 2003
-    """
-
-    #[ne(n1) union ne(n2) - ne(n1) intersection ne(n2
-    s1 = set(G.neighbors(n1))
-    s2 = set(G.neighbors(n2))
-    num_union = len(s1.union(s2))
-    num_intersection = len(s1.intersection(s2))
-    if num_union & num_intersection:
-        xs=0
-    else:
-        xs = (num_union - num_intersection)/(num_union + num_intersection)
-    return xs
-
-def struct_equivalence_all(G):
-    """Not finnished.
-    """
-    A,labels = NX.adj_matrix(G,with_labels=True)
-    pass
-
-def hamming_distance(n1,n2):
-    """Not finnsihed.
-    """
-    pass
-
-def graph_corrcoeff(G, vec=None, nodelist=None, sim='corr'):
-    """Returns the correlation coefficient for each node. The
-    correlation coefficient is between the node and its neighbours.
-
-    """
-    if nodelist==None:
-        nodelist=G.nodes()
-    if vec == None:
-        vec = G.degree(nodelist)
-    if len(vec)!=len(nodelist):
-        raise NXUTILSError("The node value vector is not of same length (%s) as the nodelist(%s)") %(len(vec), len(nodelist))
-
-    A = NX.ad_matrix(G, nodelist=nodelist)
-    for i, node in enumerate(nodelist):
-        nei_i = A[i,:]==1
-        vec_i = vec[nei_i]
-
-def weighted_laplacian(G,with_labels=False):
-    """Return standard Laplacian of graph from a weighted adjacency matrix."""
-    n= G.order()
-    I = scipy.eye(n)
-    A = weighted_adj_matrix(G)
-    D = I*scipy.sum(A, 0)
-    L = D-A
-    if with_labels:
-        A,index = weighted_adj_matrix(G, with_labels=True)
-	return L, index
-    else:	
-        return L            
-
-def grow_subnetworks(G, T2):
-    """Return the highest scoring (T2-test) subgraph og G.
-
-    Use simulated annealing to identify highly grow subgraphs.
-
-    ref: -- Ideker et.al (Bioinformatics 18, 2002)
-         -- Patil and Nielsen (PNAS 2006)
-    
-    """
-    N = 1000
-    states = [(node, False) for node in G.nodes()]
-    t2_last = 0.0
-    for i in xrange(N):
-        if i==0: #assign random states
-            states = [(state[0], True) for state in states if rand(1)>.5]
-        sub_nodes = [state[0] for state in states if state[1]]
-        Gsub = NX.subgraph(G, sub_nodes)
-        Gsub = NX.connected_components_subgraphs(Gsub)[0]
-        t2 = [T2[node] for node in Gsub]
-        if t2>t2_last:
-            pass
-        else:
-            p = numpy.exp()
-            
-    
-
-"""Below are methods for calculating graph metrics
-
-Four main decompositions :
-0.) Adjacency diffusion kernel expm(A),
-1.) von neumann kernels (diagonalisation of adjacency matrix)
-
-2.) laplacian kernels (geometric series of adj.)
-
-3.) diffusion kernels (exponential series of adj.)
-
----- Kv
-von_neumann : Kv = (I-alpha*A)^-1 (mod: A(I-alpha*A)^-1)? ,
-geom. series
-
----- Kl
-laplacian: Kl = (I-alpha*L)^-1 , geom. series
-
----- Kd