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
-laplacian_diffusion: Kd = expm(-alpha*L)
-exp. series
-
----- Ke
-Exponential diffusion.
-Ke = expm(A) .... expm(-A)?
-
-"""
-
-# TODO:
-# check for numerical unstable eigenvalues and set to zero
-# othervise some inverses wil explode ->ok ..using pinv for inverses
-#
-# This gives results that look numerical unstable
-#
-# -- divided adj by sum(A[:]), check this one (paper by Lebart scales with number of edges)
-#
-#
-#
-# the neumann kernel is defined in Kandola to be K = A*(I-A)^-1
-# lowest eigenvectors are same as the highest of K = A*A ?
-# this needs clarification
-
-# diffusion is still wrong! ... ok
-# diff needs normalisation?! check the meaning of exp(-s) = exp(1/s) -L = 1/degree ... etc
-# Is it the negative of exp. of adj. metrix in Kandola?
-#
-# Normalised=False returns only nans (no idea why!!) ... fixed ok
-
-# 31.1: diff is ok exp(0)=1 not zero!
-# 07.03.2005: normalisation is ok: -> normalisation will emphasize high degree nodes
-# 10.03.2005: symeig is unstable an returns nans of some eigenvectors? switching back to eig
-# 14.05.2006: diffusion returns negative values, using expm(-LL) instead (FIX)
-# 13.09.2206: update for use in numpy
-
-# 27.04.2007: diffusion now uses pade approximations to matrix exponential. Also the last 
-
-def K_expAdj(W, normalised=True, alpha=1.0):
-    """Matrix exponential of adjacency matrix, mentioned in Kandola as a general diffusion kernel. 
-    """
-    W = asarray(W)
-    t = W.dtype.char
-    if len(W.shape)!=2:
-        raise ValueError, "Non-matrix input to matrix function."
-    m,n = W.shape
-    if t in ['F','D']:
-        raise TypeError, "Complex input!"
-    if normalised==True:
-        T = diag( sqrt( 1./(sum(W,0))) )
-        W = dot(dot(T, W), T)
-    e,vr = eig(W)
-    s = real(e)**2 # from eigenvalues to singularvalues
-    vri = inv(vr)
-    s = maximum.reduce(s) + s
-    cond = {0: feps*1e3, 1: eps*1e6}[_array_precision[t]]
-    cutoff = abs(cond*maximum.reduce(s))
-    psigma = eye(m)
-    for i in range(len(s)):
-        if abs(s[i]) > cutoff:
-            psigma[i,i] = .5*alpha*exp(s[i])
-    
-    return dot(dot(vr,psigma),vri)
-
-def K_vonNeumann(W, normalised=True, alpha=1.0):
-    """ The geometric series of path lengths.
-    Returns matrix square root of pseudo inverse of the adjacency matrix.
-    """
-    W = asarray(W)
-    t = W.dtype.char
-    if len(W.shape)!=2:
-        raise ValueError, "Non-matrix input to matrix function."
-    m,n = W.shape
-    if t in ['F','D']:
-        raise TypeError, "Complex input!"
-    
-    if normalised==True:
-        T = diag(sqrt(1./(sum(W,0))))
-        W = dot(dot(T,W),T)
-    e,vr = eig(W)
-    vri = inv(vr)
-    e = real(e)  # we only work with real pos. eigvals
-    e = maximum.reduce(e) + e
-    cond = {0: feps*1e3, 1: eps*1e6}[_array_precision[t]]
-    cutoff = cond*maximum.reduce(e)
-    psigma = zeros((m,n),t)
-    for i in range(len(e)):
-        if e[i] > cutoff:
-            psigma[i,i] = 1.0/e[i] #these are eig.vals (=sqrt(sing.vals))
-    return dot(dot(vr,psigma),vri).astype(t)
-
-def K_laplacian(W, normalised=True, alpha=1.0):
-    """ This is the matrix pseudo inverse of L.
-    Also known as the average commute time matrix.
-    """
-    W = asarray(W)
-    t = W.dtype.char
-    if len(W.shape)!=2:
-        raise ValueError, "Non-matrix input to matrix function."
-    m,n = W.shape
-    if t in ['F','D']:
-        raise TypeError, "Complex input!"
-    D = diag(sum(W,0))
-    L = D - W
-    if normalised==True:
-        T = diag(sqrt(1./sum(W, 0)))
-        L = dot(dot(T, L), T)
-    e,vr = eig(L)
-    e = real(e)
-    vri = inv(vr)
-    cond = {0: feps*1e3, 1: eps*1e6}[_array_precision[t]]
-    cutoff = cond*maximum.reduce(e)
-    psigma = zeros((m,),t) # if s close to zero -> set 1/s = 0 
-    for i in range(len(e)):
-        if e[i] > cutoff:
-            psigma[i] = 1.0/e[i]
-    K = dot(dot(vr, diag(psigma)), vri).astype(t)
-    K = real(K)
-    I = eye(n)
-    K = (1-alpha)*I + alpha*K
-    return K
-
-
-def K_diffusion(W, normalised=True, alpha=1.0, beta=0.5, use_cut=False):
-    """Returns diffusion kernel.
-    input:
-            -- W, adj. matrix
-            -- normalised [True/False]
-            -- alpha, [0,1] (degree of network influence)
-            -- beta, [0->), (diffusion degree)
-    """
-    W = asarray(W)
-    t = W.dtype.char
-    if len(W.shape)!=2:
-        raise ValueError, "Non-matrix input to matrix function."
-    m, n = W.shape
-    if t in ['F','D']:
-        raise TypeError, "Complex input!"
-    D = diag(W.sum(0))
-    L = D - W
-    if normalised==True:
-        T = diag(sqrt(1./W.sum(0)))
-        L = dot(dot(T, L), T)
-    e, vr = eig(L)
-    vri = inv(vr) #inv
-    cond = 1.0*{0: feps*1e3, 1: eps*1e6}[_array_precision[t]]
-    cutoff = 1.*abs(cond*maximum.reduce(e))
-    psigma = eye(m) # if eigvals are 0 exp(0)=1 (unnecessary)
-    #psigma = zeros((m,n), dtype='<f8')
-    for i in range(len(e)):
-        if abs(e[i]) > cutoff:
-            psigma[i,i] = exp(-beta*e[i])
-        #else:
-        #    psigma[i,i] = 0.0
-    K = real(dot(dot(vr, psigma), vri))
-    I = eye(n, dtype='<f8')
-    K = (1. - alpha)*I + alpha*K
-    return K
-
-def K_diffusion2(W, normalised=True, alpha=1.0, beta=0.5, ncomp=None):
-    """Returns diffusion kernel, using fast pade approximation.
-    input:
-            -- W, adj. matrix
-            -- normalised [True/False]
-            -- beta, [0->), (diffusion degree)
-    """
-    
-    D = diag(W.sum(0))
-    L = D - W
-    if normalised==True:
-        T = diag(sqrt(1./W.sum(0)))
-        L = dot(dot(T, L), T)
-    return expm(-beta*L)
-    
-    
-def K_modularity(W, alpha=1.0):
-    """ Returns the matrix square root of Newmans modularity."""
-    W = asarray(W)
-    t = W.dtype.char
-    m, n = W.shape
-    d = sum(W, 0)
-    m = 1.*sum(d)
-    B = W - (outer(d, d)/m)
-    s,v = sorted_eig(B, sort_by='lm')
-    psigma = zeros( (n, n), dtype='<f8' )
-    for i in range(len(s)):
-        if s[i]>1e-7:
-            psigma[i,i] = sqrt(s[i])
-            #psigma[i,i] = s[i]
-    K = dot(dot(v, psigma), v.T)
-    I = eye(n)
-    K = (1 - alpha)*I + alpha*K
-    return K
-
-def kernel_score(K, W):
-    """Returns the modularity score.
-    K -- (modularity) kernel
-    W -- adjacency matrix (possibly weighted)
-    """
-    # normalize W (: W'W=I)
-    m, n = shape(W)
-    for i in range(n):
-        W[:,i] = W[:,i]/norm(W[:,i])
-    score = diag(dot(W, dot(K, W)) )
-    tot = sum(score)
-    return score, tot
-
-
-def modularity_matrix(G, nodelist=None):
-    if not nodelist:
-        nodelist = G.nodes()
-    else:
-        G = NX.subgraph(G, nodelist) 
-        
-    A = NX.adj_matrix(G, nodelist=nodelist)
-    d = atleast_2d(G.degree(nbunch=nodelist))
-    m = 1.*G.number_of_edges()
-    B = A - dot(d.T, d)/m
-    return B
-
-
-                        
-    

laydi/lib/packer.py

@@ -1,28 +0,0 @@
-class Packer:
-    """A compression object used to speed up model calculations.
-
-    Often used in conjunction with crossvalidation and perturbations
-    analysis. 
-    """
-    def __init__(self,array):
-        self._shape = array.shape
-        self._array = array
-        self._packed_data = None
-        
-    def expand(self,a):
-        if self._inflater!=None:
-            return dot(self._inflater,a)
-        
-    def collapse(self,axis=None,mode='svd'):
-        if not axis:
-            axis = argmin(self._array.shape) # default is the smallest dim
-
-        if axis == 1:
-            self._array = self._array.T
-        u, s, vt = svd(self._array,full_matrices=0)
-        self._inflater = vt.T
-        self._packed_data = u*s
-        return self._packed_data
-
-    def get_packed_data(self):
-        return self._packed_data

laydi/lib/select_generators.py

@@ -1,223 +0,0 @@
-"""Matrix cross validation selection generators
-"""
-from scipy import take,arange,ceil,repeat,newaxis,asarray,dot,ones,\
-     random,array_split,floor,vstack,asarray,minimum
-from cx_utils import randperm
-
-def w_pls_gen(aat,b,n_blocks=None,center=True,index_out=False):
-     """Random block crossvalidation for wide (XX.T) trick in PLS.
-     Leave-one-out is a subset, with n_blocks equals nSamples
-     
-     aat -- outerproduct of X
-     b -- Y
-     n_blocks = 
-     center -- use centering of calibration ,sets (aat_in,b_in) are centered
-
-     Returns:
-         -- aat_in,aat_out,b_in,b_out,[out]
-     """
-     m, n = aat.shape
-     index = randperm(m)
-     if n_blocks==None: n_blocks = m
-     nValuesInBlock = m/n_blocks
-     if n_blocks==m:
-         index = arange(m)
-     out_ind = [index[i*nValuesInBlock:(i+1)*nValuesInBlock] for i in range(n_blocks)]
-     
-     for out in out_ind:
-          inn = [i for i in index if i not in out]
-          aat_in = aat[inn,:][:,inn]
-          aat_out = aat[out,:][:,inn]
-          b_in = b[inn,:]
-          b_out = b[out,:]
-          if center:
-               aat_in, mn = outerprod_centering(aat_in)
-               b_in = b_in - b_in.mean(0) # b_in + b_out/(b_in.shape[0])
-          if index_out:
-               yield aat_in,aat_out,b_in,b_out,out
-          else:
-               yield aat_in,aat_out,b_in,b_out
-
-def pls_gen(a, b, n_blocks=None, center=False, index_out=False,axis=0):
-     """Random block crossvalidation
-    Leave-one-out is a subset, with n_blocks equals a.shape[-1]
-    """
-     index = randperm(a.shape[axis])
-     #index = arange(a.shape[axis])
-     if n_blocks==None:
-          n_blocks = a.shape[axis]
-     n_in_set = ceil(float(a.shape[axis])/n_blocks)
-     out_ind_sets = [index[i*n_in_set:(i+1)*n_in_set] for i in range(n_blocks)]
-     for out in out_ind_sets:
-          inn = [i for i in index if i not in out]
-          acal = a.take(inn, 0)
-          atrue = a.take(out, 0)
-          bcal = b.take(inn, 0)
-          btrue = b.take(out, 0)
-          if center:
-               mn_a = acal.mean(0)[newaxis]
-               acal = acal - mn_a
-               atrue = atrue - mn_a
-               mn_b = bcal.mean(0)[newaxis]
-               bcal = bcal - mn_b
-               btrue = btrue - mn_b
-          
-          if index_out:
-               yield acal, atrue, bcal, btrue, out
-          else:     
-               yield acal, atrue, bcal, btrue
-
-         
-def pca_gen(a, n_sets=None, center=False, index_out=False, axis=0):
-     """Returns a generator of crossvalidation sample segments.
-
-     input:
-           -- a, data matrix (m x n)
-           -- n_sets, number of segments/subsets to generate.
-           -- center, bool, choice of centering each subset
-           -- index_out, bool, return subset index
-           -- axis, int, which axis to get subset from
-
-     ouput:
-           -- V, generator with (n_sets) memebers (subsets)
-     
-     """
-     m = a.shape[axis]
-     index = randperm(m)
-     if n_sets==None:
-          n_sets = m
-     n_in_set = ceil(float(m)/n_sets)
-     out_ind_sets = [index[i*n_in_set:(i+1)*n_in_set] for i in range(n_sets)]
-     for out in out_ind_sets:
-         inn = [i for i in index if i not in out]
-         acal = a.take(inn, 0)
-         atrue = a.take(out, 0)
-         if center:
-              mn_a = acal.mean(0)[newaxis]
-              acal = acal - mn_a
-              atrue = atrue - mn_a
-         
-         if index_out:
-              yield acal, atrue, out
-         else:
-              yield acal, atrue
-
-def w_pls_gen_jk(a, b, n_sets=None, center=True,
-                 index_out=False, axis=0):
-     """Random block crossvalidation for wide X (m>>n)
-     Leave-one-out is a subset, with n_sets equals a.shape[-1]
-
-     Returns : X_m and X_m'Y_m
-     """
-     m = a.shape[axis]
-     ab = dot(a.T, b)
-     index = randperm(m)
-     if n_sets==None:
-          n_sets = m
-     n_in_set = ceil(float(m)/n_sets)
-     out_ind_sets = [index[i*n_in_set:(i+1)*n_in_set] for i in range(n_sets)]
-     for out in out_ind_sets:
-         inn = [i for i in index if i not in out]
-         nin = len(inn)
-         nout = len(out)
-         a_in = a[inn,:]
-         mn_a = 0
-         mAB = 0
-         if center:
-              mn_a = a_in.mean(0)[newaxis]
-              mAin = dot(-ones((1,nout)), a[out,:])/nin
-              mBin = dot(-ones((1,nout)), b[out,:])/nin
-              mAB = dot(mAin.T, (mBin*nin))
-         ab_in = ab - dot(a[out,].T, b[out,:]) - mAB
-         a_in = a_in - mn_a
-
-         if index_out:
-              yield a_in, ab_in, out
-         else:     
-              yield a_in, ab_in
-
-def shuffle_1d_block(a, n_sets=None, blocks=None, index_out=False, axis=0):
-     """Random block shuffling along 1d axis
-     Returns : Shuffled a by axis
-     """
-     m = a.shape[axis]
-     if blocks==None:
-         blocks = m
-     for ii in xrange(n_sets):
-         index = randperm(m)
-         if blocks==m:
-             a_out = a.take(index, axis)
-         else:
-             index = arange(m)
-             dummy = map(random.shuffle, array_split(index, blocks))
-             a_out = a.take(index, axis)
-         
-         if index_out:
-              yield a_out, index
-         else:
-              yield a_out
-
-def shuffle_1d(a, n_sets, axis=0):
-     """Random shuffling along 1d axis.
-
-     Returns : Shuffled a by axis
-     """
-     m = a.shape[axis]
-     for ii in xrange(n_sets):
-         index = randperm(m)
-         a = a.take(index, axis)
-         yield a
-         
-def diag_pert(a, n_sets=10, center=True, index_out=False):
-    """Alter generator returning sets perturbed with means at diagonals.
-
-    input:
-            X -- matrix, data
-            alpha -- scalar, approx. portion of data perturbed  
-    """
-    
-    m, n = a.shape
-    tr=False
-    if m>n:
-         a = a.T
-         m, n = a.shape
-         tr = True
-    if n_sets>m or n_sets>n:
-         msg = "You may not use more subsets than max(n_rows, n_cols)"
-         raise ValueError, msg
-    nm=n*m
-    start_inds = array_split(randperm(m),n_sets) # we use random start diags
-    if center:
-         a = a - a.mean(0)[newaxis]
-    for v in range(n_sets):
-        a_out = a.copy()
-        out = []
-        for start in start_inds[v]: 
-            ind = arange(start+v, nm, n+1)
-            [out.append(i) for i in ind]
-            if center:
-                a_out.put(a.mean(),ind) 
-            else:
-                 a_out.put(0, ind)
-        if tr:
-             a_out = a_out.T
-             
-        if index_out:
-             yield a_out, asarray(out)
-        else:
-             yield a_out
-
- 
-def outerprod_centering(aat, ret_mn=True):
-    """Returns double centered symmetric outerproduct matrix.
-    """
-    h = aat.mean(0)[newaxis]
-    h = h - 0.5*h.mean()
-    mn_a = h + h.T # beauty of broadcasting
-    aatc = aat - mn_a
-    if ret_mn:
-        return aatc, h
-    return aatc
-     
-     
-     

laydi/lib/validation.py

@@ -1,315 +0,0 @@
-"""This module implements some common validation schemes from pca and pls.
-"""
-from scipy import ones,sqrt,dot,newaxis,zeros,sum,empty,\
-     apply_along_axis,eye,kron,array,sort,zeros_like,argmax,atleast_2d
-from numpy import median
-from scipy.linalg import triu,inv,svd,norm
-
-from select_generators import w_pls_gen,w_pls_gen_jk,pls_gen,pca_gen,diag_pert
-from engines import w_simpls,pls,bridge,pca,nipals_lpls
-from cx_utils import m_shape
-
-
-def w_pls_cv_val(X, Y, amax, n_blocks=None):
-    """Returns rmsep and aopt for pls tailored for wide X.
-
-    The root mean square error of cross validation is calculated
-    based on random block cross-validation. With number of blocks equal to
-    number of samples [default] gives leave-one-out cv.
-    The pls model is based on the simpls algorithm for wide X.
-
-    :Parameters:
-    X : ndarray 
-        column centered data matrix of size (samples x variables)
-    Y : ndarray
-        column centered response matrix of size (samples x responses)
-    amax : scalar 
-        Maximum number of components
-    n_blocks : scalar
-        Number of blocks in cross validation
-    
-    :Returns: 
-    rmsep : ndarray
-        Root Mean Square Error of cross-validated Predictions 
-    aopt : scalar
-        Guestimate of the optimal number of components
-
-    :SeeAlso:
-    - pls_cv_val : Same output, not optimised for wide X
-    - w_simpls : Simpls algorithm for wide X
-    
-    Notes
-    -----
-    Based (cowardly translated) on m-files from the Chemoact toolbox
-    X, Y inputs need to be centered (fixme: check)
-    
-
-    Examples
-    --------
-
-    >>> import numpy as n
-    >>> X = n.array([[1., 2., 3.],[]])
-    >>> Y = n.array([[1., 2., 3.],[]])
-    >>> w_pls(X, Y, 1)
-    [4,5,6], 1
-    """
-    
-    k, l = m_shape(Y)
-    PRESS = zeros((l, amax+1), dtype='f')
-    if n_blocks==None:
-        n_blocks = Y.shape[0]
-    XXt = dot(X, X.T)
-    V = w_pls_gen(XXt, Y, n_blocks=n_blocks, center=True)
-    for Din, Doi, Yin, Yout in V:
-        ym = -sum(Yout, 0)[newaxis]/(1.0*Yin.shape[0])
-        PRESS[:,0] = PRESS[:,0] + ((Yout - ym)**2).sum(0)
-        
-        dat = w_simpls(Din, Yin, amax)
-        Q, U, H = dat['Q'], dat['U'], dat['H']
-        That = dot(Doi, dot(U, inv(triu(dot(H.T, U))) ))
-        
-        Yhat = []
-        for j in range(l):
-            TQ = dot(That, triu(dot(Q[j,:][:,newaxis], ones((1,amax)))) )
-            E = Yout[:,j][:,newaxis] - TQ
-            E = E + sum(E, 0)/Din.shape[0] 
-            PRESS[j,1:] = PRESS[j,1:] + sum(E**2, 0)
-    #Yhat = Yin - dot(That,Q.T)
-    msep = PRESS/(Y.shape[0])
-    aopt = find_aopt_from_sep(msep)
-    return sqrt(msep), aopt
-
-def pls_val(X, Y, amax=2, n_blocks=10, algo='pls'):
-    k, l = m_shape(Y)
-    PRESS = zeros((l, amax+1), dtype='<f8')
-    EE = zeros((amax, k, l), dtype='<f8')
-    Yhat = zeros((amax, k, l), dtype='<f8')
-    V = pls_gen(X, Y, n_blocks=n_blocks, center=True, index_out=True)
-    for Xin, Xout, Yin, Yout, out in V:
-        ym = -sum(Yout,0)[newaxis]/Yin.shape[0]
-        Yin = (Yin - ym)
-        PRESS[:,0] = PRESS[:,0] + ((Yout - ym)**2).sum(0)
-
-        if algo=='pls':
-            dat = pls(Xin, Yin, amax, mode='normal')
-        elif algo=='bridge':
-            dat = simpls(Xin, Yin, amax, mode='normal')
-        
-        for a in range(amax):
-            Ba = dat['B'][a,:,:]
-            Yhat[a,out[:],:] = dot(Xout, Ba)
-            E = Yout -  dot(Xout, Ba)
-            EE[a,out,:] = E
-            PRESS[:,a+1] = PRESS[:,a+1] + sum(E**2,0)
-
-    #rmsep = sqrt(PRESS/(k-1.))
-    msep = PRESS
-    aopt = find_aopt_from_sep(msep)
-    return msep, Yhat, aopt
-
-def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2]):
-    """Performs crossvalidation to get generalisation error in lpls"""
-    assert(nsets<=X.shape[0])
-    
-    cv_iter = pls_gen(X, Y, n_blocks=nsets,center=False,index_out=True)
-    k, l = Y.shape
-    Yc = empty((k, l), 'd')
-    Yhat = empty((a_max, k, l), 'd')
-    Yhatc = empty((a_max, k, l), 'd')
-    sep2 = empty((a_max, k, l), 'd')
-    for i, (xcal,xi,ycal,yi,ind) in enumerate(cv_iter):
-        print ind
-        dat = nipals_lpls(xcal,ycal,Z,
-                          a_max=a_max,
-                          alpha=alpha,
-                          mean_ctr=mean_ctr,
-                          verbose=False)
-        
-        B = dat['B']
-        #b0 = dat['b0'] 
-        for a in range(a_max):
-            if mean_ctr[0] in [0, 2]:
-                xi = xi - dat['mnx']
-            else:
-                xi = xi - xi.mean(1)[:,newaxis] #???: cheating?
-            if mean_ctr[1] in [0, 2]:
-                ym = dat['mny']
-            else:
-                ym = yi.mean(1)[:,newaxis] #???: check this
-                
-            Yhat[a,ind,:] = atleast_2d(ym + dot(xi, B[a]))
-            #Yhat[a,ind,:] = atleast_2d(b0[a] + dot(xi, B[a]))
-            
-    # todo: need a better support for class validation
-    y_is_class = Y.dtype.char.lower() in ['i','p', 'b', 'h','?']
-    #print Y.dtype.char
-    if y_is_class:
-        Yhat_class = zeros_like(Yhat)
-        for a in range(a_max):
-            for i in range(k):
-                Yhat_class[a,i,argmax(Yhat[a,i,:])] = 1.0
-        class_err = 100*((Yhat_class+Y)==2).sum(1)/Y.sum(0).astype('d')
-
-    sep = (Y - Yhat)**2
-    rmsep = sqrt(sep.mean(1)).T
-    #rmsep2 = sqrt(sep2.mean(1))
-    
-    aopt = find_aopt_from_sep(rmsep)
-    
-    return rmsep, Yhat, aopt
-
-def pca_alter_val(a, amax, n_sets=10, method='diag'):
-    """Pca validation by altering elements in X.
-
-    comments:
-             -- may do all jk estimates in this loop
-    """
-    
-    V = diag_pert(a, n_sets, center=True, index_out=True)
-    sep = empty((n_sets, amax), dtype='f')
-    for i, (xi, ind) in enumerate(V):
-        dat_i = pca(xi, amax, mode='detailed')
-        Ti, Pi = dat_i['T'],dat_i['P']
-        for j in xrange(amax):
-            Xhat = dot(Ti[:,:j+1], Pi[:,:j+1].T)
-            a_sub = a.ravel().take(ind)
-            EE = a_sub - Xhat.ravel().take(ind)
-            tot = (a_sub**2).sum()
-            sep[i,j] = (EE**2).sum()/tot
-    sep = sqrt(sep)
-    aopt = find_aopt_from_sep(sep)
-    return sep, aopt
-
-def pca_cv_val(a, amax, n_sets):
-    """ Returns PRESS from cross-validated pca using random segments.
-
-    input:
-          -- a, data matrix (m x n)
-          -- amax, maximum nuber of components used
-          -- n_sets, number of segments to calculate
-    output:
-          -- sep, (amax x m x n), squared error of prediction (press)
-          -- aopt, guestimated optimal number of components
-
-    """
-
-    m, n = a.shape
-    E = empty((amax, m, n), dtype='f')
-    xtot = (a**2).sum() # this needs centering
-    V = pca_gen(a, n_sets=7, center=True, index_out=True)
-    for xi, xout, ind in V:
-        dat_i = pca(xi, amax, mode='fast')
-        Pi = dat_i['P']
-        for a in xrange(amax):
-            Pia = Pi[:,:a+1]
-            E[a][ind,:] = (X[ind,:] - dot(xout, dot(Pia,Pia.T) ))**2
-
-    sep = []
-    for a in xrange(amax):
-        sep.append(E[a].sum()/xtot)
-    sep = array(sep)
-    aopt = find_aopt_from_sep(sep)
-
-    return sep, aopt
-
-def pls_jkW(a, b, amax, n_blocks=None, algo='pls', use_pack=True, center=True):
-    """ Returns CV-segments of paramter W for wide X.
-    
-    todo: add support for T,Q and B
-    """
-    if n_blocks == None:
-        n_blocks = b.shape[0]
-
-    Wcv = empty((n_blocks, a.shape[1], amax), dtype='d')
-    if use_pack:
-        u, s, inflater = svd(a, full_matrices=0)
-        a = u*s
-    
-    V = pls_gen(a, b, n_blocks=n_blocks, center=center)
-    for nn,(a_in, a_out, b_in, b_out) in enumerate(V):
-        if algo=='pls':
-            dat = pls(a_in, b_in, amax, 'loads', 'fast')
-
-        elif algo=='bridge':
-            dat = bridge(a_in, b_in, amax, 'loads', 'fast')
-
-        W = dat['W']
-        if use_pack:
-            W = dot(inflater.T, W)
-
-        Wcv[nn,:,:] = W[:,:,]
-        
-    return Wcv
-
-def pca_jkP(a, aopt, n_blocks=None):
-    """Returns loading from PCA on CV-segments.
-    
-    input:
-           -- a, data matrix (n x m)
-           -- aopt, number of components in model.
-           -- nblocks, number of segments
-    output:
-           -- PP, loadings collected in a three way matrix
-           (n_segments, m, aopt)
-
-    comments:
-    * The loadings are scaled with the (1/samples)*eigenvalues.
-    * Crossvalidation method is currently set to random blocks of samples.
-
-    todo: add support for T
-    fixme: more efficient to add this in validation loop
-    """
-    if n_blocks == None:
-        n_blocks = a.shape[0]
-
-    PP = empty((n_blocks, a.shape[1], aopt), dtype='f')
-    V = pca_gen(a, n_sets=n_blocks, center=True)
-    for nn,(a_in, a_out) in enumerate(V):  
-        dat = pca(a_in, aopt, mode='fast', scale='loads')
-        P = dat['P']
-        PP[nn,:,:] = P
-        
-    return PP
-
-
-def lpls_jk(X, Y, Z, a_max, nsets=None, xz_alpha=.5, mean_ctr=[2,0,2]):
-    cv_iter = pls_gen(X, Y, n_blocks=nsets,center=False,index_out=False)
-    m, n = X.shape
-    k, l = Y.shape
-    o, p = Z.shape
-    if nsets==None:
-        nsets = m
-    WWx = empty((nsets, n, a_max), 'd')
-    WWz = empty((nsets, o, a_max), 'd')
-    #WWy = empty((nsets, l, a_max), 'd')
-    for i, (xcal, xi, ycal, yi) in enumerate(cv_iter):
-        dat = nipals_lpls(xcal,ycal,Z,a_max=a_max,alpha=xz_alpha,
-                          mean_ctr=mean_ctr,scale='loads',verbose=False)
-        WWx[i,:,:] = dat['W']
-        WWz[i,:,:] = dat['L']
-        #WWy[i,:,:] = dat['Q']
-
-    return WWx, WWz
-
-def find_aopt_from_sep(sep, method='75perc'):
-    """Returns an estimate of optimal number of components from rmsecv.
-    """
-    sep = sep.copy()
-    if method=='vanilla':
-        # min rmsep
-        rmsecv = sqrt(sep.mean(0))
-        return rmsecv.argmin() + 1
-
-    elif method=='75perc':
-        prct = .75 #percentile
-        ind = 1.*sep.shape[0]*prct
-        med = median(sep)
-        prc_75 = []
-        for col in sep.T:
-            col.sort() #this is inplace -> ruins sep, so we are doing a copy
-            prc_75.append(col[int(ind)])
-        prc_75 = array(prc_75)
-        for i in range(1, sep.shape[1], 1):
-            if med[i-1]<prc_75[i]:
-                return i
-        return len(med)

laydi/main.py

@@ -1,101 +0,0 @@
-    
-import sys
-import os.path
-import paths
-
-# Site specific directories set by configure script.
-PREFIX = paths.PREFIX
-BINDIR = paths.BINDIR
-DATADIR = paths.DATADIR
-DOCDIR = paths.DOCDIR
-PYDIR = paths.PYDIR
-
-ICONDIR = os.path.join(DATADIR, 'icons')
-
-#: Dictionary of observers
-_observers = {}
-
-#: The current Navigator object.
-navigator = None
-
-#: The current application
-application = None
-
-#: The current projectview
-projectview = None
-
-#: The current workflow
-workflow = None
-
-#: A cfgparse/optparse options object.
-options = None
-        
-def notify_observers(name):
-    call = "%s_changed" % name
-    for s in _observers.get(name, []):
-        getattr(s, call)(getattr(sys.modules[__name__], name))
-
-def _add_observer(name, observer):
-    """Adds observer as an observer of the named object."""
-    if not _observers.has_key(name):
-        _observers[name] = []
-    _observers[name].append(observer)
-    
-def _remove_observer(name, observer):
-    """Removes observer as an observer of the named object."""
-    if not _observers.has_key(name):
-        return
-    _observers.remove(observer)
-
-def add_navigator_observer(observer):
-    _add_observer('navigator', observer)
-
-def add_project_observer(observer):
-    _add_observer('project', observer)
-
-def add_workflow_observer(observer):
-    _add_observer('workflow', observer)
-
-def add_application_observer(observer):
-    _add_observer('application', observer)
-
-def remove_navigator_observer(observer):
-    _remove_observer('navigator', observer)
-
-def remove_project_observer(observer):
-    _remove_observer('project', observer)
-
-def remove_workflow_observer(observer):
-    _remove_observer('workflow', observer)
-
-def remove_application_observer(observer):
-    _remove_observer('application', observer)
-
-def remove_options_observer(observer):
-    _remove_observer('options', observer)
-
-def set_navigator(nav):
-    global navigator
-    navigator = nav
-    notify_observers('navigator')
-
-def set_application(app):
-    global application
-    application = app
-    notify_observers('application')
-
-def set_projectview(p):
-    global projectview
-    projectview = p
-    notify_observers('project')
-
-def set_workflow(wf):
-    global workflow
-    workflow = wf
-    notify_observers('workflow')
-
-def set_options(opt):
-    global options 
-    options = opt
-    notify_observers('options')
-

laydi/navigator.py

@@ -1,476 +0,0 @@
-import gtk
-import gobject
-import plots
-import time
-import laydi
-from logger import logger
-import dataset, plots, projectview, workflow, main
-import scipy
-
-class NavigatorView (gtk.TreeView):
-    """The NavigatorView is a tree view of the project.
-
-    There is always one NavigatorView, that shows the functions, plots and
-    datasets in the current project.
-    """
-
-    def __init__(self):
-        if main.projectview:
-            self.data_tree = main.projectview.data_tree
-        else:
-            self.data_tree = None
-        
-        gtk.TreeView.__init__(self) 
-
-        # Various properties
-        self.set_enable_tree_lines(True)
-        self.set_headers_visible(False)
-        self.get_hadjustment().set_value(0)
-
-        # Selection Mode
-        self.get_selection().set_mode(gtk.SELECTION_MULTIPLE)
-        self.get_selection().connect('changed',self.on_selection_changed)
-        self._previous_selection = []
-
-        # Setting up TextRenderers etc
-        self.connect('row_activated', self.on_row_activated)
-        self.connect('cursor_changed', self.on_cursor_changed)
-
-        # Activate context menu
-        self.menu = NavigatorMenu(self)
-        self.connect('popup_menu', self.popup_menu)
-        self.connect('button_press_event', self.on_mouse_event)
-
-        self.textrenderer = textrenderer = gtk.CellRendererText()
-        pixbufrenderer = gtk.CellRendererPixbuf()
-        self.object_col = gtk.TreeViewColumn('Object')
-        self.object_col.pack_start(pixbufrenderer,expand=False)
-        self.object_col.pack_start(textrenderer,expand=False)
-        self.object_col.set_attributes(textrenderer, cell_background=3, 
-                                       foreground=4, text=0)
-        self.object_col.set_attributes(pixbufrenderer, pixbuf=5)
-        self.append_column(self.object_col)
-
-        # send events to plots / itself
-        self.enable_model_drag_source(gtk.gdk.BUTTON1_MASK,
-            [("GTK_TREE_MODEL_ROW", gtk.TARGET_SAME_APP, 7)],
-            gtk.gdk.ACTION_LINK | gtk.gdk.ACTION_MOVE)
-    
-        self.connect("drag-data-get",self.slot_drag_data)
-
-        logger.debug('Initializing navigator window.')
-   
-    def slot_drag_data(self, treeview, context, selection, target_id, etime):
-        """Sets the data for a drag event."""
-        treeselection = treeview.get_selection()
-        model, paths = treeselection.get_selected_rows()
-        if paths:
-            self.data_tree.drag_data_get(paths[0], selection)
-
-    def add_projectview(self, projectview):
-        """Dependency injection."""
-        self.data_tree = projectview.data_tree
-        self.set_model(projectview.data_tree)
-        self.data_tree.connect('row-changed',self.on_row_changed)
-    
-    def on_selection_changed(self, selection):
-        """Update the list of currently selected datasets."""
-
-        # update prev selection right away in case of multiple events
-        model, paths = selection.get_selected_rows()
-        if not paths: # a plot is marked: do nothing
-            return
-
-        tmp = self._previous_selection
-        self._previous_selection = paths
-        tree = self.data_tree
-
-        # set timestamp on newly selected objects
-        [tree.set_value(tree.get_iter(path), 6, time.time()) 
-         for path in paths if path not in tmp]
-
-        objs = [tree.get_iter(path) for path in paths]
-        objs = [(tree[iter][6], tree[iter][2]) for iter in objs]
-
-        objs.sort()
-        objs = [obj for timestamp, obj in objs]
-
-        if objs and isinstance(objs[0], dataset.Dataset):
-            logger.debug('Selecting dataset')
-            main.projectview.current_data = objs
-        else:
-            logger.debug('Deselecting dataset')
-            main.projectview.current_data = []
-
-    def on_row_changed(self, treestore, pos, iter):
-        """Set correct focus and colours when rows have changed."""
-        obj = treestore[iter][2]
-        obj_type = treestore[iter][1]
-
-        if not (obj or obj_type):
-            return
-        self.expand_to_path(pos)        
-
-        if isinstance(obj, dataset.Dataset):
-            self.set_cursor(pos)
-            self.grab_focus()
-        
-    def on_row_activated(self, widget, path, column):
-        tree_iter = self.data_tree.get_iter(path)
-        obj = self.data_tree.get_value(tree_iter, 2)
-        
-        if isinstance(obj, plots.Plot):
-            logger.debug('Activating plot')
-            main.application.change_plot(obj)
-        elif isinstance(obj, dataset.Dataset):
-            pass
-        elif obj == None:
-            children = []
-            i = self.data_tree.iter_children(tree_iter)
-            while i:
-                child = self.data_tree.get(i, 2)[0]
-                if isinstance(child, plots.Plot):
-                    children.append(child)
-                i = self.data_tree.iter_next(i)
-            main.application.change_plots(children)
-        else:
-            t = type(obj)
-            logger.notice('Activated datatype was %s. Don\'t know what to do.' % t)
-
-    def popup_menu(self, *rest):
-        self.menu.popup(None, None, None, 0, 0)
-
-    def on_mouse_event(self, widget, event):
-        path = widget.get_path_at_pos(int(event.x), int(event.y))
-
-        if path:
-            iter = self.data_tree.get_iter(path[0])
-            obj = self.data_tree.get_value(iter, 2)
-        else:
-            iter = None
-            obj = None
-
-        if isinstance(obj, dataset.Dataset):
-            self.menu.set_dataset(obj, iter)
-        else:
-            self.menu.set_dataset(None, iter)
-
-        if event.button == 3:
-            self.menu.popup(None, None, None, event.button, event.time)
-
-    def on_cursor_changed(self, widget):
-        """Update statusbar to contain dataset information.
-
-        Lists the dimensions of a dataset in the statusbar of the program
-        if a dataset is focused in the navigator.
-        """
-        path = widget.get_cursor()[0]
-        tree_iter = self.data_tree.get_iter(path)
-        obj = self.data_tree.get_value(tree_iter, 2)
-
-        if isinstance(obj, dataset.Dataset):
-            dims = zip(obj.get_dim_name(), obj.shape)
-            dim_text = ", ".join(["%s (%d)" % dim for dim in dims])
-        else:
-            dim_text = ""
-        main.application['appbar1'].push(dim_text)
-        
-
-class NavigatorMenu(gtk.Menu):
-    def __init__(self, navigator):
-        gtk.Menu.__init__(self)
-        self.navigator = navigator
-        self.dataset = None
-        self.tree_iter = None        
-
-        # Populate main menu
-        self.load_item = gtk.MenuItem('Load dataset')
-        self.load_item.connect('activate', self.on_load_dataset, navigator)
-        self.append(self.load_item)
-        self.load_item.show()
-
-        self.save_item = gtk.MenuItem('Save dataset')
-        self.save_item.connect('activate', self.on_save_dataset, navigator)
-        self.append(self.save_item)
-        self.save_item.show()
-
-        self.delete_item = gtk.MenuItem('Delete')
-        self.delete_item.connect('activate', self.on_delete, navigator)
-        self.append(self.delete_item)
-        self.delete_item.show()
-
-        self.split_item = gtk.MenuItem('Split on selection')
-        self.split_item.connect('activate', self.on_split, navigator)
-        self.append(self.split_item)
-        self.split_item.show()
-
-        # Build transform sub menu
-        self.trans_menu = gtk.Menu()
-        
-        self.trans_tr_item = gtk.MenuItem('Transpose')
-        self.trans_tr_item.connect('activate', self.on_transpose, navigator)
-        self.trans_menu.append(self.trans_tr_item)
-        self.trans_tr_item.show()
-
-        self.trans_stdr_item = gtk.MenuItem('Std. rows')
-        self.trans_stdr_item.connect('activate', self.on_standardise_rows, navigator)
-        self.trans_menu.append(self.trans_stdr_item)
-        self.trans_stdr_item.show()
-        
-        self.trans_stdc_item = gtk.MenuItem('Std. cols')
-        self.trans_stdc_item.connect('activate', self.on_standardise_cols, navigator)
-        self.trans_menu.append(self.trans_stdc_item)
-        self.trans_stdc_item.show()
-
-        self.trans_log_item = gtk.MenuItem('Log')
-        self.trans_log_item.connect('activate', self.on_log, navigator)
-        self.trans_menu.append(self.trans_log_item)
-        self.trans_log_item.show()
-
-        self.trans_item = gtk.MenuItem("Transformation")
-        self.append(self.trans_item)
-        self.trans_item.set_submenu(self.trans_menu)
-        self.trans_item.show()
-        
-        # Build plot sub menu
-        self.plot_menu = gtk.Menu()
-
-        self.plot_image_item = gtk.MenuItem('Image Plot')
-        self.plot_image_item.connect('activate', self.on_plot_image, navigator)
-        self.plot_menu.append(self.plot_image_item)
-        self.plot_image_item.show()
-
-        self.plot_hist_item = gtk.MenuItem('Histogram')
-        self.plot_hist_item.connect('activate', self.on_plot_hist, navigator)
-        self.plot_menu.append(self.plot_hist_item)
-        self.plot_hist_item.show()
-        
-        self.plot_scatter_item = gtk.MenuItem('Scatter')
-        self.plot_scatter_item.connect('activate', self.on_plot_scatter, navigator)
-        self.plot_menu.append(self.plot_scatter_item)
-        self.plot_scatter_item.show()
-        
-        self.plot_line_item = gtk.MenuItem('Line view')
-        self.plot_line_item.connect('activate', self.on_plot_line, navigator)
-        self.plot_menu.append(self.plot_line_item)
-        self.plot_line_item.show()
-        
-        self.plot_bar_item = gtk.MenuItem('Bar Plot')
-        self.plot_bar_item.connect('activate', self.on_plot_bar, navigator)
-        self.plot_menu.append(self.plot_bar_item)
-        self.plot_bar_item.show()
-        
-        self.plot_box_item = gtk.MenuItem('Box Plot')
-        self.plot_box_item.connect('activate', self.on_plot_box, navigator)
-        self.plot_menu.append(self.plot_box_item)
-        self.plot_box_item.show()
-        
-        self.plot_item = gtk.MenuItem('Plot')
-        self.append(self.plot_item)
-        self.plot_item.set_submenu(self.plot_menu)
-        self.plot_item.show()
-
-    def set_dataset(self, ds, it):
-        self.dataset = ds
-        self.tree_iter = it
-
-        if ds == None:
-            self.save_item.set_property('sensitive', False)
-            self.plot_item.set_property('sensitive', False)
-            self.trans_item.set_property('sensitive', False)
-        else:
-            self.save_item.set_property('sensitive', True)
-            self.plot_item.set_property('sensitive', True)
-            self.trans_item.set_property('sensitive', True)
-
-    def load_dataset(self, filename):
-        """Load the dataset from the given file and add it to the project."""
-        ds = dataset.read_ftsv(filename)
-
-        if isinstance(ds, dataset.GraphDataset):
-            icon = laydi.icon_factory.get("graph_dataset")
-        elif isinstance(ds, dataset.CategoryDataset):
-            icon = laydi.icon_factory.get("category_dataset")
-        else:
-            icon = laydi.icon_factory.get("dataset")
-
-        main.projectview.add_dataset(ds)
-        main.projectview.data_tree_insert(None, ds.get_name(), ds, None, "black", icon)
-
-    def on_load_dataset(self, item, navigator):
-        # Set up file chooser.
-        dialog = gtk.FileChooserDialog('Load dataset')
-        dialog.set_action(gtk.FILE_CHOOSER_ACTION_OPEN)
-        dialog.add_buttons(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL, 
-                           gtk.STOCK_OPEN, gtk.RESPONSE_OK)
-        dialog.set_select_multiple(True)
-        dialog.set_current_folder(main.project.datadir)
-
-        retval = dialog.run()
-        if retval in [gtk.RESPONSE_CANCEL, gtk.RESPONSE_DELETE_EVENT]:
-            pass
-        elif retval == gtk.RESPONSE_OK:
-            for filename in dialog.get_filenames():
-                self.load_dataset(filename)
-        else:
-            print "unknown; ", retval 
-        dialog.destroy()
-
-    def on_save_dataset(self, item, navigator):
-        dialog = gtk.FileChooserDialog('Save dataset')
-        dialog.set_action(gtk.FILE_CHOOSER_ACTION_SAVE)
-        dialog.add_buttons(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL, gtk.STOCK_SAVE, gtk.RESPONSE_OK)
-        dialog.set_current_name("%s.ftsv" % self.dataset.get_name())
-        retval = dialog.run()
-        if retval in [gtk.RESPONSE_CANCEL, gtk.RESPONSE_DELETE_EVENT]:
-            logger.debug("Cancelled save dataset")
-        elif retval == gtk.RESPONSE_OK:
-            logger.debug("Saving dataset as: %s" % dialog.get_filename())
-            fd = open(dialog.get_filename(), 'w')
-            dataset.write_ftsv(fd, self.dataset)
-            fd.close()
-        else:
-            print "unknown; ", retval 
-        dialog.destroy()
-
-    def on_delete(self, item, navigator):
-        tm, rows = navigator.get_selection().get_selected_rows()
-        iters = [tm.get_iter(r) for r in rows]
-        iters.reverse()
-        for i in iters:
-            main.projectview.delete_data(i)
-   #         tm.remove(i)
-    
-    def on_plot_image(self, item, navigator):
-        plot = plots.ImagePlot(self.dataset, name='Image Plot')
-        icon = laydi.icon_factory.get("line_plot")
-        main.projectview.data_tree_insert(self.tree_iter, 'Image Plot', plot, None, "black", icon)
-        # fixme: image plot selections are not well defined
-        #plot.set_selection_listener(projectview.set_selection)
-        #projectview._selection_observers.append(plot)
-
-    def on_plot_hist(self, item, navigator):
-        projectview = main.projectview
-        plot = plots.HistogramPlot(self.dataset, name='Histogram')
-        icon = laydi.icon_factory.get("line_plot")
-        projectview.data_tree_insert(self.tree_iter, 'Histogram', plot, None, "black", icon)
-        plot.set_selection_listener(projectview.set_selection)
-        projectview._selection_observers.append(plot)
-
-    def on_plot_scatter(self, item, navigator):
-        projectview = main.projectview
-        datasets = main.projectview.current_data
-        ds_major = datasets[0]
-        dims_major = ds_major.get_dim_name()
-        ids_major = ds_major.get_identifiers(dims_major[1], sorted=True)
-        if len(datasets) > 1:
-            # If there is more than one active dataset -> try to use the two first
-            ds_minor = datasets[1]
-            dims_minor = ds_minor.get_dim_name()
-            if dims_minor != dims_major or ds_minor.shape[0] != ds_major.shape[0]:
-                # the selected datasets are not matched -> use initial selected
-                ds_minor = ds_major
-        else:
-            #Only one dataset selected
-            ds_minor = ds_major
-        
-        plot = plots.ScatterPlot(ds_major, ds_minor, 
-                                 dims_major[0], dims_major[1], 
-                                 ids_major[0], ids_major[1],
-                                 name='Scatter (%s)' % ds_major.get_name())
-        plot.add_axes_spin_buttons(len(ids_major), 0, 1)
-        icon = laydi.icon_factory.get("line_plot")
-        projectview.data_tree_insert(self.tree_iter, 'Scatter', plot, None, "black", icon)
-        plot.set_selection_listener(projectview.set_selection)
-        projectview._selection_observers.append(plot)
-        
-    def on_plot_line(self, item, navigator):
-        projectview = main.projectview
-        ds = self.dataset
-        dims = ds.get_dim_name()
-        ids = ds.get_identifiers(dims[1])
-        plot = plots.LineViewPlot(ds, name='Line (%s)' % ds.get_name())
-        icon = laydi.icon_factory.get("line_plot")
-        projectview.data_tree_insert(self.tree_iter, 'Line view', plot, None, "black", icon)
-        plot.set_selection_listener(projectview.set_selection)
-        projectview._selection_observers.append(plot)
-        
-    def on_plot_bar(self, item, navigator):
-        projectview = main.projectview
-        ds = self.dataset
-        dims = ds.get_dim_name()
-        ids = ds.get_identifiers(dims[1])
-        plot = plots.BarPlot(ds, name='Bar (%s)' % ds.get_name())
-        icon = laydi.icon_factory.get("line_plot")
-        projectview.data_tree_insert(self.tree_iter, 'Bar plot', plot, None, "black", icon)
-        plot.set_selection_listener(projectview.set_selection)
-        projectview._selection_observers.append(plot)
-
-    def on_plot_box(self, item, navigator):
-        projectview = main.projectview
-        ds = self.dataset
-        dims = ds.get_dim_name()
-        ids = ds.get_identifiers(dims[1])
-        plot = plots.BoxPlot(ds, name='Box (%s)' % ds.get_name())
-        icon = laydi.icon_factory.get("line_plot")
-        projectview.data_tree_insert(self.tree_iter, 'Box plot', plot, None, "black", icon)
-        plot.set_selection_listener(projectview.set_selection)
-        projectview._selection_observers.append(plot)
-
-    def on_transpose(self, item, navigator):
-        projectview = main.projectview
-        ds = self.dataset.transpose()
-        ds._name = ds._name + ".T"
-        icon = laydi.icon_factory.get(ds)
-        projectview.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
-
-    def on_standardise_rows(self, item, navigator):
-        projectview = main.projectview
-        ds = self.dataset.copy()
-        ds._name = self.dataset._name + ".rsc"
-        axis = 1
-        ds._array = ds.asarray()/scipy.expand_dims(ds.asarray().std(axis), axis)
-        icon = laydi.icon_factory.get(ds)
-        projectview.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
-
-    def on_standardise_cols(self, item, navigator):
-        projectview = main.projectview
-        ds = self.dataset.copy()
-        ds._name = self.dataset._name + ".csc"
-        axis = 0
-        ds._array = ds.asarray()/scipy.expand_dims(ds.asarray().std(axis), axis)
-        icon = laydi.icon_factory.get(ds)
-        projectview.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
-
-    def on_log(self, item, navigator):
-        projectview = main.projectview
-        try:
-            if not scipy.all(self.dataset.asarray()>0):
-                raise ValueError
-        except:
-            logger.log('warning', 'Datasets needs to be strictly positive for a log transform')
-            return
-        
-        ds = self.dataset.copy()
-        ds._array = scipy.log(ds.asarray())
-        icon = laydi.icon_factory.get(ds)
-        ds._name = ds._name + ".log"
-        projectview.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
-
-    def on_split(self, item, navigator):
-        if self.dataset is None:
-            logger.warn("Only datasets can be split.")
-            return
-
-        dim = self.dataset.get_dim_name(0)
-
-        projectview = main.projectview
-        sel_ids = set(projectview.get_selection()[dim])
-        sel_ds = self.dataset.subdata(dim, sel_ids)
-
-        unsel_ids = set(self.dataset.get_identifiers(dim)) - set(sel_ids)
-        unsel_ds = self.dataset.subdata(dim, unsel_ids)
-
-        icon = laydi.icon_factory.get(self.dataset)
-        projectview.data_tree_insert(self.tree_iter, 'Selected', sel_ds, None, "black", icon)
-        projectview.data_tree_insert(self.tree_iter, 'Unselected', unsel_ds, None, "black", icon)

laydi/paths.py.m4

@@ -1,7 +0,0 @@
-
-PREFIX = "M4_PREFIX"
-BINDIR = "M4_BINDIR"
-DATADIR = "M4_DATADIR"
-DOCDIR = "M4_DOCDIR"
-PYDIR = "M4_PYDIR"
-

laydi/project.py

@@ -1,434 +0,0 @@
-import os, os.path
-import sys
-import time
-
-import dataset
-import annotations
-
-NAME = "laydi-cmd"
-VERSION = "0.1.0"
-PROJECT_VERSION_STRING = "Laydi project version 1"
-
-def is_project_directory(dirname):
-    """Verifies that a directory is a laydi project"""
-
-    if not os.path.isdir(dirname):
-        return False
-    
-    ## Verify that the version is correct.
-    version_fn = os.path.join(dirname, "VERSION")
-    if not os.path.exists(version_fn):
-        return False
-    fd = open(version_fn)
-    line = fd.readline()
-    fd.close()
-
-    if line.strip() != PROJECT_VERSION_STRING:
-        return False
-
-    ## Require directories to be present.
-    if not os.path.isdir(os.path.join(dirname, "annotations")):
-        return False
-    if not os.path.isdir(os.path.join(dirname, "data")):
-        return False
-    if not os.path.isdir(os.path.join(dirname, "selections")):
-        return False
-    if not os.path.isdir(os.path.join(dirname, "exports")):
-        return False
-
-    ## If no tests failed, return True
-    return True
-
-
-def make_project_directory(dirname, force=False):
-    """Creates a project directory
-
-    force: ignore that directory exists and proceed anyway.
-    """
-    if os.path.exists(dirname) and not force:
-        return False
-
-    rootdir = dirname
-    anndir = os.path.join(dirname, "annotations")
-    seldir = os.path.join(dirname, "selections")
-    datadir = os.path.join(dirname, "data")
-    exportdir = os.path.join(dirname, "exports")
-    version_file_path = os.path.join(dirname, "VERSION")
-        
-    os.makedirs(rootdir)
-    for d in [anndir, seldir, datadir, exportdir]:
-        os.mkdir(d)
-
-    fd = open(version_file_path, "w")
-    print >> fd, PROJECT_VERSION_STRING
-    fd.close()
-
-
-
-class Universe(object):
-    """A Universe is a collection of all existing identifiers in a set of datasets"""
-
-    def __init__(self):
-        self.refcount = {}
-
-    def register_dim(self, dim):
-        """Increase reference count for identifiers in Dimension object dim"""
-        d = self.refcount.get(dim.name, None)
-        if d == None:
-            d = {}
-            self.refcount[dim.name] = d
-        for i in dim:
-            d[i] = d.get(i, 0) + 1
-        
-    def register_ds(self, ds):
-        """Increase reference count for identifiers in all Dimensions of dataset ds"""
-        for dim in ds.dims:
-            self.register_dim(dim)
-
-    def unregister_dim(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.
-        """
-        ids = self.refcount[dim.name]
-        for i in dim:
-            refcount = ids[i]
-            if refcount == 1:
-                ids.pop(i)
-            else:
-                ids[i] -= 1
-        if len(ids) == 0:
-            self.refcount.pop(dim.name)
-
-
-    def unregister_ds(self, ds):
-        """Update reference count for identifiers along Dimensions in Dataset ds.
-	   Update reference count for identifiers along all Dimensions in
-	   Dataset ds, and remove all identifiers with a reference count of 0,
-           as they do not (by definition) exist any longer.
-        """
-        for dim in ds:
-            self.register_dim(dim)
-
-    def register(self, obj):
-        if isinstance(obj, Dataset):
-            self.register_ds(obj)
-        else:
-            self.register_dim(obj)
-
-    def unregister(self, obj):
-        if isinstance(obj, Dataset):
-            self.unregister_ds(obj)
-        else:
-            self.unregister_dim(obj)
-
-    def __getitem___(self, dimname):
-        return set(self.references[dimname].keys())
-
-    def __iter__(self):
-        return self.references.keys().__iter__()
-
-
-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)
-
-        if len(self.idset) != len(self.idlist):
-            raise Exception("Duplicate identifiers are not allowed")
-
-    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):
-        if self.name != dim.name:
-            return None
-        return Dimension(self.name, self.idset.intersection(dim.idset))
-
-    def as_tuple(self):
-        return (self.name, self.idlist)
-
-    def verify(self):
-        for i in self.idlist:
-            if " " in i or "\t" in i:
-                raise Exception("Invalid identifier: %s" % i)
-
-
-class Directory(object):
-    def __init__(self, path):
-        self.path = path
-        self.files = set()
-        self.timestamp = -1
-        self.update()
-
-    def update(self):
-        now = time.time()
-        newfiles = set(os.listdir(self.path))
-        for fn in newfiles - self.files:
-            if os.path.isdir(os.path.join(self.path, fn)):
-                self.dir_created(fn)
-            else:
-                self.file_created(fn)
-        for fn in self.files - newfiles:
-            if os.path.isdir(os.path.join(self.path, fn)):
-                self.dir_deleted(fn)
-            else:
-                self.file_removed(fn)
-        for fn in self.files.intersection(newfiles):
-            filepath = os.path.join(self.path, fn)
-            if os.path.getctime(filepath) >= self.timestamp:
-                if os.path.isdir(filepath):
-                    self.dir_changed(fn)
-                else:
-                    self.file_changed(fn)
-        self.files = newfiles
-        self.timestamp = now
-            
-    def file_created(self, fn):
-        print "file created: %s" % fn
-        pass
-
-    def file_changed(self, fn):
-        print "file changed: %s" % fn
-        pass
-
-    def file_removed(self, fn):
-        print "file removed: %s" % fn
-        pass
-
-    def dir_created(self, fn):
-        print "directory created: %s" % fn
-        pass
-
-    def dir_changed(self, fn):
-        print "directory changed: %s" % fn
-        pass
-
-    def dir_removed(self, fn):
-        print "directory removed: %s" % fn
-        pass
-
-
-class DataDirectory(Directory):
-    def __init__(self, dirname, project):
-        self.project = project
-        self.datasets= []
-        self.dsfiles = {}
-        Directory.__init__(self, dirname)
-
-    def file_created(self, fn):
-        """Called from update() when new files are created.
-
-           Load new datasets that have appeared since last update.
-        """
-        filepath = os.path.join(self.path, fn)
-        name, ext = os.path.splitext(fn)
-        if ext == ".ftsv":
-            ds = dataset.read_ftsv(filepath)
-            self.datasets.append(ds)
-            self.dsfiles[fn] = ds
-
-    def file_changed(self, fn):
-        """Called from update() when files are changed.
-
-           Delete old dataset and load the new one when dataset files 
-           have been changed.
-        """
-        filepath = os.path.join(self.path, fn)
-        name, ext = os.path.splitext(fn)
-        if ext == ".ftsv":
-            oldds = self.dsfiles[fn]
-            self.datasets.remove(oldds)
-            
-            ds = dataset.read_ftsv(filepath)
-            self.datasets.append(ds)
-
-            self.dsfiles[fn] = ds
-
-    def file_removed(self, fn):
-        """Called from update() when a file is deleted
-
-           Removes the associated dataset if a dataset file is removed.
-        """
-        filepath = os.path.join(self.path, fn)
-        name, ext = os.path.splitext(fn)
-        if ext == ".ftsv":
-            ds = self.dsfiles[fn]
-            self.datasets.remove(ds)
-            self.dsfiles.pop(fn)
-
-    def dir_created(self, fn):
-        """Called from update() when a subdirectory is created.
-
-           Instantiate new handlers for the directory if possible.
-        """
-        filepath = os.path.join(self.path, fn)
-        
-
-class SelectionParentDirectory(Directory):
-    def __init__(self, dirname, project):
-        self.project = project
-        self.handlers = {}
-        Directory.__init__(self, dirname)
-
-    def dimensions(self):
-        return self.handlers.keys()
-
-    def __getitem__(self, key):
-        return self.handlers[key]
-
-    def file_created(self, fn):
-        pass
-
-    def file_changed(self, fn):
-        pass
-
-    def file_removed(self, fn):
-        pass
-
-    def dir_created(self, fn):
-        print("dir_created: %s" % fn)
-        dimname = os.path.split(fn)[-1]
-        self.handlers[dimname] = SelectionDirectory(os.path.join(self.path, fn), dimname, self.project)
-
-    def dir_removed(self, fn):
-        print("dir_removed: %s" % fn)
-        dimname = os.path.split(fn)[-1]
-        removed = self.handlers.pop(dimname)
-
-    def update(self):
-        Directory.update(self)
-        for e in self.handlers.values():
-            e.update()
-
-
-class SelectionDirectory(Directory):
-    def __init__(self, fn, dimname, project):
-        self.project = project
-        self.dimension = dimname
-        self.selections = {}
-        Directory.__init__(self, fn)
-
-    def read_selection_file(self, fn):
-        """Reads a selection file and returns the corresponding Dimension object.
-
-        Warnings are printed to terminal on duplicated ids and invalid ids.
-        """
-        print "read_selection_file(%s)" % (fn,)
-        fd = open(fn)
-        ids = []
-        for line in fd.readlines():
-            e = line.strip()
-            if e.startswith("#") or e == "":
-                continue
-            ids.append(e)
-        fd.close()
-        return Dimension(self.dimname, ids)
-
-    def file_created(self, fn):
-        """Called from update() when new files are created.
-
-           Load new datasets that have appeared since last update.
-        """
-        print "loading selection: %s [%s]" % (fn, self.dimension)
-        filepath = os.path.join(self.path, fn)
-        name, ext = os.path.splitext(fn)
-        if ext == ".sel":
-            sel = read_selection_files(fn)
-            self.dsfiles[fn] = ds
-
-    def file_changed(self, fn):
-        """Called from update() when files are changed.
-
-           Delete old dataset and load the new one when dataset files 
-           have been changed.
-        """
-        filepath = os.path.join(self.path, fn)
-        name, ext = os.path.splitext(fn)
-        if ext == ".ftsv":
-            oldds = self.dsfiles[fn]
-            self.datasets.remove(oldds)
-            
-            ds = dataset.read_ftsv(filepath)
-            self.datasets.append(ds)
-
-            self.dsfiles[fn] = ds
-
-    def file_removed(self, fn):
-        """Called from update() when a file is deleted
-
-           Removes the associated dataset if a dataset file is removed.
-        """
-        filepath = os.path.join(self.path, fn)
-        name, ext = os.path.splitext(fn)
-        if ext == ".ftsv":
-            ds = self.dsfiles[fn]
-            self.datasets.remove(ds)
-            self.dsfiles.pop(fn)
-
-    def dir_created(self, fn):
-        """Called from update() when a subdirectory is created.
-
-           Instantiate new handlers for the directory if possible.
-        """
-        filepath = os.path.join(self.path, fn)
-
-
-class AnnotationDirectory(Directory):
-    def __init__(self, dirname, project):
-        self.project = project
-        self.dirname = dirname
-        Directory.__init__(self, dirname)
-
-    def file_created(self, fn):
-        annotations.read_annotations_file(os.path.join(self.dirname, fn))
-
-    def file_changed(self, fn):
-        annotations.read_annotations_file(os.path.join(self.dirname, fn))
-
-    def file_removed(self, fn):
-        print "File removed: %s" % fn
-
-
-class Project(object):
-    def __init__(self, dirname):
-        """Opens a project directory. The directory must exist and be a valid project."""
-        
-        ## Set path names.
-        self.rootdir = dirname
-        self.anndir = os.path.join(dirname, "annotations")
-        self.seldir = os.path.join(dirname, "selections")
-        self.datadir = os.path.join(dirname, "data")
-        self.exportdir = os.path.join(dirname, "exports")
-        version_file_path = os.path.join(dirname, "VERSION")
-
-        self.universe = Universe()
-        
-        self.data = DataDirectory(self.datadir, self)
-        self.annotations = AnnotationDirectory(self.anndir, self)
-        self.selections = SelectionParentDirectory(self.seldir, self)
-
-    def update(self):
-        print "updating project"
-        self.data.update()
-        self.selections.update()
-

laydi/projectview.py

@@ -1,169 +0,0 @@
-import os
-import scipy
-import gobject
-import gtk
-import laydi
-import logger, dataset, plots, main, project
-
-class ProjectView:
-    """A Project contains datasets, selections etc.
-    The project, of which the application has only one at any given time,
-    is the container for all datasets, plots and selections in use. The data
-    in the project is organized in a gtk.TreeStrore that is displayed in the
-    navigator.
-    """
-
-    def __init__(self, proj):
-        self.data_tree = gtk.TreeStore(str,
-                                       str,
-                                       object, 
-                                       str,
-                                       str,
-                                       gobject.TYPE_OBJECT, 
-                                       float)
-
-        self.project = proj
-        self.dim_names = []
-        self._selection_observers = []
-        self._dataset_observers = []
-        self.current_data = []
-        self.datasets = []
-        self.sel_obj = dataset.Selection('Current Selection')
-        self.selections = []
-        self._last_selection = None
-        self._dataset_iter_map = {}
-        self._load_datasets()
-
-
-    def _load_datasets(self):
-        print "load datasets from project..."
-        print "datasets: ", self.project.data.datasets
-        for ds in self.project.data.datasets:
-            if isinstance(ds, dataset.GraphDataset):
-                icon = laydi.icon_factory.get("graph_dataset")
-            elif isinstance(ds, dataset.CategoryDataset):
-                icon = laydi.icon_factory.get("category_dataset")
-            else:
-                icon = laydi.icon_factory.get("dataset")
-
-            self.add_dataset(ds)
-            self.data_tree_insert(None, ds.get_name(), ds, None, "black", icon)
-        print "...loaded"
-        
-    def add_selection_observer(self, observer):
-        self._selection_observers.append(observer)
-        observer.selection_changed(None, self.get_selection())
-
-    def notify_selection_listeners(self, dim_name):
-        """Notifies observers"""
-        for observer in self._selection_observers:
-            observer.selection_changed(dim_name, self.get_selection())
-
-    def add_dataset_observer(self, observer):
-        self._dataset_observers.append(observer)
-        observer.dataset_changed()
-
-    def notify_dataset_listeners(self):
-        """Notifies observers when new datasets are added"""
-        for observer in self._dataset_observers:
-            observer.dataset_changed()
-        
-    def set_selection(self, dim_name, selection):
-        """Sets a current selection and notify observers"""
-        self.sel_obj[dim_name] = set(selection)
-        self.notify_selection_listeners(dim_name)
-        self._last_selection = selection
-
-    def get_selection(self):
-        """Returns the current selection object"""
-        return self.sel_obj
-
-    def delete_data(self, it):
-        """Delete elements from the project."""
-        child = self.data_tree.iter_children(it)
-        while child != None:
-            c = self.data_tree.iter_next(child)
-            self.delete_data(child)
-            child = c
-        main.application.main_view.remove_view(self.data_tree.get(it, 2)[0])
-        self.data_tree.remove(it)
-
-    def add_data(self, parents, data, fun='Function'):
-        """Adds a set of data and plots to the navigator.
-
-        This method is usually called after a Function in a workflow
-        has finished and returns its output."""
-        
-        if len(parents) > 0: 
-            parent_iter = self._dataset_iter_map[parents[0]]
-        else:
-            parent_iter = None
-
-        # Add the function node to the tree
-        icon = laydi.icon_factory.get("folder_grey")
-        it = self.data_tree_insert(parent_iter, fun, None, None, "black", icon)
-        
-        # Add all returned datasets/plots/selections
-        for d in data:
-            # Any kind of dataset
-            if isinstance(d, dataset.Dataset):
-                if isinstance(d, dataset.GraphDataset):
-                    icon = laydi.icon_factory.get("graph_dataset")
-                elif isinstance(d, dataset.CategoryDataset):
-                    icon = laydi.icon_factory.get("category_dataset")
-                else:
-                    icon = laydi.icon_factory.get("dataset")
-
-                self.add_dataset(d)
-                self.data_tree_insert(it, d.get_name(), d, None, "black", icon) 
-
-            # Any kind of plot
-            elif isinstance(d, plots.Plot):
-                icon = laydi.icon_factory.get("line_plot")
-                self.data_tree_insert(it, d.get_title(), d, None, "black", icon)
-                d.set_selection_listener(self.set_selection)
-                self._selection_observers.append(d)
-
-            # Selections are not added to the data tree
-            elif isinstance(d, dataset.Selection):
-                self.add_selection(d)
-
-    def data_tree_insert(self, parent, text, data, bg, fg, icon, selected = 0):
-        """Inserts data into the tree view.
-        @param text: The title of the object.
-        @param data: A dataset, plot or function object.
-        @param bg: Background color.
-        @param fg: Foreground (font) color.
-        @param icon: Pixmap icon.
-        """
-        tree = self.data_tree
-        it = tree.append(parent)
-        tree[it] = [text, type(data), data, bg, fg, icon, selected]
-        self._dataset_iter_map[data] = it
-        return it
-
-    def add_dataset(self, dataset):
-        """Appends a new Dataset to the project."""
-        logger.log('debug','Adding dataset: %s' %dataset.get_name())
-        self.datasets.append(dataset)
-        for dim_name in dataset.get_all_dims():
-            if dim_name not in self.dim_names:
-                self.dim_names.append(dim_name)
-                self.sel_obj[dim_name] = set()
-                self.notify_selection_listeners(dim_name)
-        self.notify_dataset_listeners()
-
-    def add_selection(self, selection):
-        """Adds a new selection to the project."""
-        self.selections.append(selection)
-        self.notify_dataset_listeners()
-        
-    def object_at(self, path):
-        """Returns the object at a given path in the tree."""
-        it = self.get_iter(path)
-        obj = self[it][2]
-        if obj:
-            obj.show()
-        return obj
-
-

laydi/selections.py

@@ -1,659 +0,0 @@
-import pygtk
-import gtk
-import gtk.gdk
-import gtk.glade
-import gnome
-import gnome.ui
-import gobject
-import scipy
-
-import logger, dataset, main
-import annotations
-from lib import hypergeom
-
-
-class SimpleMenu(gtk.Menu):
-    def __init__(self):
-        gtk.Menu.__init__(self)
-        
-    def add_simple_item(self, title, function, *args):
-        item = gtk.MenuItem(title)
-        item.connect('activate', function, *args)
-        self.append(item)
-        item.show()
-
-
-class IdListController:
-    """Controller class for the identifier list."""
-
-    def __init__(self, idlist):
-        self._idlist = idlist
-        self._idlist.get_selection().set_mode(gtk.SELECTION_MULTIPLE)
-        self._idlist.set_rubber_banding(True)
-
-        # dimname: current_annotation_name
-        self._annotation = {}
-
-        # current dimension
-        self._dimension = None
-
-        # id, annotation
-        self._idstore = gtk.ListStore(gobject.TYPE_STRING,
-                                      gobject.TYPE_STRING)
-        self._idstore.set_sort_func(0, self._numeric_compare)
-        
-        # Annotation tree column
-        self._annotation_column = None
-
-        ## Set up identifier list
-        idlist.set_model(self._idstore)
-        
-        renderer = gtk.CellRendererText()
-        dim_column = gtk.TreeViewColumn('Identifiers', renderer, text=0)
-        dim_column.set_sort_indicator(True)
-        dim_column.set_sort_column_id(0)
-        dim_column.set_sort_order(gtk.SORT_ASCENDING)
-        idlist.insert_column(dim_column, 0)
-        idlist.connect('button-press-event', self._button_pressed)
-
-        ## Enable dropping
-        idlist.drag_dest_set(gtk.DEST_DEFAULT_ALL,
-                              [("GTK_TREE_MODEL_ROW", gtk.TARGET_SAME_APP, 7)],
-                              gtk.gdk.ACTION_LINK)
-        idlist.connect('drag-data-received', self._drag_data_received)
-
-        ## Set up identifier list context menu
-        menu = self._menu = SimpleMenu()
-        menu.add_simple_item('Import...', self._on_import_list)
-        menu.add_simple_item('Export...', self._on_export_list)
-        menu.add_simple_item('Add to selection', self._on_make_selection)
-        item = gtk.MenuItem('Show annotations')
-        menu.append(item)
-        item.show()
-        self._menu_ann = item
-       
-    ##
-    ## Public interface
-    ##
-    def set_dimension(self, dimname):
-        """Set dimension"""
-        if dimname == self._dimension:
-            return
-
-        self._dimension = dimname 
-        self.set_annotation(self._annotation.get(dimname, None))
-
-        if not self._annotation.has_key(dimname):
-            self._annotation[dimname] = None
-
-    def set_annotation(self, annotation):
-        """Set the displayed annotation to annotation.  If annotation is None,
-        the annotation column is hidden. Otherwise the annotation column is
-        shown and filled with values from the given annotation field."""
-
-        if annotation == None:
-            if self._annotation_column != None:
-                self._idlist.remove_column(self._annotation_column)
-            self._annotation_column = None
-        else:
- 
-            idlist = [x[0] for x in self._idstore]
-            annlist = annotations.get_dim_annotations(self._dimension, 
-                          annotation,
-                          idlist)
-
-            for i, x in enumerate(self._idstore):
-                x[1] = annlist[i]
-
-            if self._annotation_column == None:
-                renderer = gtk.CellRendererText()
-                col = gtk.TreeViewColumn(annotation, renderer, text=1)
-                col.set_sort_indicator(True)
-                col.set_sort_column_id(1)
-                col.set_sort_order(gtk.SORT_ASCENDING)
-                self._idlist.append_column(col)
-                self._annotation_column = col
-            self._annotation_column.set_title(annotation)
-
-        self._annotation[self._dimension] = annotation
-
-    def set_selection(self, selection):
-        """Set the selection to be displayed.
-        The selection is not stored, the values are copied into the TreeStore"""
-        self._idstore.clear()
-
-        # Return if no selection
-        if selection == None:
-            return
-
-        # Otherwise show selection, possibly with annotations.
-        #id_list = list(selection[self._dimension])
-        idlist = list(selection[self._dimension])
-        if self._annotation[self._dimension] != None:
-            annlist = annotations.get_dim_annotations(self._dimension, 
-                          self._annotation[self._dimension],
-                          idlist)
-            for id, ann in zip(idlist, annlist):
-                self._idstore.append((id, ann))
-        else:
-            for e in idlist:
-                self._idstore.append((e, None))
-
-    ##
-    ## Private interface
-    ##
-    def _update_annotations_menu(self):
-        """Updates the annotations menu with the available annotations for the
-        current dim."""
-
-        dim_h = annotations.get_dim_handler(self._dimension)
-        if not dim_h:
-            self._menu_ann.set_sensitive(False)
-        else:
-            annotations_menu = gtk.Menu()
-            self._menu_ann.set_sensitive(True)
-            dh = annotations.get_dim_handler(self._dimension)
-            ann_names = dh.get_annotation_names()
-
-            for ann in ann_names:
-                item = gtk.MenuItem(ann)
-                item.connect('activate', self._on_annotation_activated, ann)
-                annotations_menu.append(item)
-                item.show()
-
-            self._menu_ann.set_submenu(annotations_menu)
-            
-
-    def import_annotation_file(self):
-        """Pops up a file dialog and ask the user to select the annotation
-        file to be loaded. Only one file can be selected. The file is loaded
-        into a annotations.AnnotationDictHandler object"""
-
-        dialog = gtk.FileChooserDialog('Load annotations')
-        dialog.set_action(gtk.FILE_CHOOSER_ACTION_OPEN)
-        dialog.add_buttons(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL, 
-                           gtk.STOCK_OPEN, gtk.RESPONSE_OK)
-        dialog.set_select_multiple(True)
-        dialog.set_current_folder(main.project.anndir)
-        retval = dialog.run()
-        if retval in [gtk.RESPONSE_CANCEL, gtk.RESPONSE_DELETE_EVENT]:
-            pass
-        elif retval == gtk.RESPONSE_OK:
-            for filename in dialog.get_filenames():
-                annotations.read_annotations_file(filename)
-        else:
-            print "unknown; ", retval 
-        dialog.destroy()
-
-    def export_annotations(self):
-        """Pops up a file dialog and ask the user to select a file to save
-        the currently displayed annotations to.
-        """
-
-        dialog = gtk.FileChooserDialog('Load annotations')
-        dialog.set_current_folder(main.project.exportdir)
-        dialog.set_action(gtk.FILE_CHOOSER_ACTION_SAVE)
-        dialog.add_buttons(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL, 
-                           gtk.STOCK_SAVE, gtk.RESPONSE_OK)
-        retval = dialog.run()
-        if retval in [gtk.RESPONSE_CANCEL, gtk.RESPONSE_DELETE_EVENT]:
-            pass
-        elif retval == gtk.RESPONSE_OK:
-            filename = dialog.get_filename()
-            fd = open(filename, 'w')
-            dim = self._dimension
-            print >> fd, "%s\t%s" % (dim, self._annotation[dim])
-            for id, value in self._idstore:
-                print >> fd, "%s\t%s" % (id, value)
-            fd.close()
-        else:
-            print "unknown; ", retval 
-        dialog.destroy()
-
-    def set_rank(self, ds):
-        print "Set rank."
-
-        ra = scipy.sum(ds.asarray(), 1)
-        ranks = {}
-        dim = ds.get_dim_name()[0]
-        for key, value in ds[dim].items():
-            ranks[key] = ra[value]
-
-        ann_h = annotations.get_dim_handler(self._dimension)
-        if ann_h is None:
-            ann_h = annotations.DictAnnotationHandler()
-            annotations.set_dim_handler(self._dimension, ann_h)
-
-        ann_h.add_annotations('Rank', ranks)
-
-    ##
-    ## GTK Callbacks
-    ##
-
-    def _numeric_compare(self, treemodel, iter1, iter2):
-        column = treemodel.get_sort_column_id()[0]
-        
-        item1 = treemodel.get_value(iter1, column)
-        item2 = treemodel.get_value(iter2, column)
-        
-        try:
-            item1 = float(item1)
-            item2 = float(item2)
-        except:
-            logger.log("notice", "Could not convert to float: %s, %s" %(item1, item2))
-
-        return cmp(item1, item2)
-    
-    def _popup_menu(self, *rest):
-        self._update_annotations_menu()
-        self._menu.popup(None, None, None, 0, 0)
-
-    def _on_annotation_activated(self, menuitem, annotation):
-        self.set_annotation(annotation)
-
-    def _button_pressed(self, widget, event):
-        if event.button == 3:
-            self._update_annotations_menu()
-            self._menu.popup(None, None, None, event.button, event.time)
-            return True
-        
-    def _on_export_list(self, menuitem):
-        self.export_annotations()
-
-    def _on_import_list(self, menuitem):
-        self.import_annotation_file()
-
-    def _on_make_selection(self, menuitem):
-        selection = self._idlist.get_selection()
-        model, paths = selection.get_selected_rows()
-        if paths==None: return
-        iters = [self._idstore.get_iter(p) for p in paths]
-        ids = [self._idstore.get_value(i, 0) for i in iters]
-        main.projectview.set_selection(self._dimension, ids)
-        
-    def _drag_data_received(self, widget, drag_context, x, y,
-                              selection, info, timestamp):
-        treestore, path = selection.tree_get_row_drag_data()
-        i = treestore.get_iter(path)
-        obj = treestore.get_value(i, 2)
-        if isinstance(obj, dataset.Dataset):
-            if self._dimension in obj.get_dim_name():
-                self.set_rank(obj)
-        widget.emit_stop_by_name('drag-data-received')
-
-
-class SelectionListController:
-    def __init__(self, seltree, idlist_controller):
-        self._seltree = seltree
-        self._sel_stores = {}
-        self._detail_cols = []
-        self._dimension = None
-        self._idlist_controller = idlist_controller
-        self._details_on = False
-        
-        # Selection column
-        renderer = gtk.CellRendererText()
-        sel_column = gtk.TreeViewColumn('Selection', renderer, text=0)
-        sel_column.set_resizable(True)
-        sel_column.set_max_width(200)
-        seltree.insert_column(sel_column, 0)
-
-        # Detail columns
-        cols = [('In CS', 3), ('All', 4), ('Rank', 5)]
-        for name, store_col_num in cols:
-            col = gtk.TreeViewColumn(name, renderer, text=store_col_num)
-            col.set_sort_indicator(True)
-            col.set_sort_column_id(store_col_num)
-            col.set_sort_order(gtk.SORT_ASCENDING)
-            
-            self._detail_cols.append(col)
-        # Signals
-        seltree.connect('row-activated', self._on_row_activated)
-        seltree.connect('cursor-changed', self._on_cursor_changed)
-        seltree.connect('button-press-event', self._on_button_pressed)
-        seltree.drag_dest_set(gtk.DEST_DEFAULT_ALL,
-                              [("GTK_TREE_MODEL_ROW", gtk.TARGET_SAME_APP, 7)],
-                              gtk.gdk.ACTION_LINK)
-        
-        seltree.connect('drag-data-received', self._drag_data_received)
-
-        # Selections context menu
-        self._seltree_menu = SimpleMenu()
-        self._seltree_menu.add_simple_item('Sort by selection', 
-                                           self._on_seltree_sort)
-        self._seltree_menu.add_simple_item('Show details',
-                                           self._enable_details, True)
-        self._seltree_menu.add_simple_item('Hide details', 
-                                           self._enable_details, False)
-
-    #
-    # Public interface
-    #
-    def activate(self):
-        self._seltree.set_cursor((0,))
-
-    def set_projectview(self, projectview):
-        """Dependency injection."""
-        main.projectview.add_selection_observer(self)
-        
-    def set_dimlist_controller(self, dimlist_controller):
-        """Dependency injection of the dimension list controller."""
-        self._dimlist_controller = dimlist_controller
-
-    def set_dimension(self, dim):
-        """Set the current dimension, changing the model of the treeview
-        to match dim. After this the current dimension of the identifier list
-        is updated."""
-        self._ensure_selection_store(dim)
-        self._seltree.set_model(self._sel_stores[dim])
-        self._idlist_controller.set_dimension(dim)
-        self._dimension = dim
-
-    def selection_changed(self, dimname, selection):
-        """Callback function from Project."""
-        for dim in selection.dims():
-            self._ensure_selection_store(dim)
-            store = self._sel_stores[dim]
-
-            if not self._get_current_selection_iter(selection, dim):
-                n = len(selection[dim])
-                values = (selection.title, selection, dim, n, n, 0)
-                store.insert_after(None, None, values)
-            else:
-                # update size of current selection
-                for row in store:
-                    if row[1]==selection:
-                        row[3] = row[4] = len(selection[dim])
-
-        path = self._seltree.get_cursor()
-        if path and self._sel_stores.has_key(self._dimension):
-            it = self._sel_stores[self._dimension].get_iter(path[0])
-            sel = self._sel_stores[self._dimension].get_value(it, 1)
-            self._idlist_controller.set_selection(sel)
-
-    def add_dataset(self, dataset):
-        """Converts a CategoryDataset to Selection objects and adds it to
-        the selection tree. The name of the dataset will be the parent 
-        node in the tree, and the identifers along the first axis will
-        be added as the names of the subselections."""
-        dim_name = dataset.get_dim_name(0)
-        self._ensure_selection_store(dim_name)
-        store = self._sel_stores[dim_name]
-        di = self._get_dataset_iter(dataset)
-        if not di:
-            n_tot = dataset.shape[0]
-            selection = main.projectview.get_selection().get(dim_name)
-            ds_idents = dataset.get_identifiers(dim_name)
-            n_cs = len(selection.intersection(ds_idents))
-            values = (dataset.get_name(), dataset, dim_name, n_cs, n_tot, 2)
-
-            i = store.insert_after(None, None, values)
-            for selection in dataset.as_selections():
-                n_sel = len(selection[dim_name])
-                values = (selection.title, selection, dim_name, 0, n_sel, 0)
-                store.insert_after(i, None, values)
-        
-    #
-    # Private interface
-    #
-    def _add_selection_store(self, dim):
-        """Add a new gtk.TreeStore for the selections on a dimension."""
-        # Create new store
-        # Two types of lines, one for CategoryDatasets and one for
-        # Selections. The elements are title, link to dataset or selection,
-        # name of dimension, num. members in selection, num. in
-        # intersection with current selection and the rank of selection.
-        store = gtk.TreeStore(gobject.TYPE_STRING,
-                              gobject.TYPE_PYOBJECT,
-                              gobject.TYPE_STRING,
-                              gobject.TYPE_INT,
-                              gobject.TYPE_INT,
-                              gobject.TYPE_FLOAT)
-
-        # Set selection store for this dimension
-        self._sel_stores[dim] = store
-
-    def _ensure_selection_store(self, dim):
-        """Ensure that the object has a gtk.TreeStore for the given dimension"""
-        # Do not overwrite existing stores
-        if self._sel_stores.has_key(dim):    
-            return
-        self._add_selection_store(dim)
-
-    def _get_dataset_iter(self, ds):
-        """Returns the iterator to the selection tree row containing a
-        given dataset."""
-
-        store = self._sel_stores[ds.get_dim_name(0)]
-
-        i = store.get_iter_first()
-        while i:
-            if store.get_value(i, 1) == ds:
-                return i
-            i = store.iter_next(i)
-        return None
-
-    def _get_current_selection_iter(self, selection, dimension):
-        if not self._sel_stores.has_key(dimension):
-            return None
-
-        store = self._sel_stores[dimension]
-
-        i = store.get_iter_first()
-        while i:
-            if store.get_value(i, 1) == selection:
-                if store.get_value(i, 2) == dimension:
-                    return i
-            i = store.iter_next(i)
-        return None
-
-    def _sort_selections(self, dataset):
-        """Ranks selections by intersection with current selection.
-        Ranks determined by the hypergeometric distribution.
-        """
-        dim_name = dataset.get_dim_name(0)
-        sel_store = self._sel_stores[dim_name]
-        selection_obj = main.projectview.get_selection()
-        current_selection = selection_obj.get(dim_name)
-        if current_selection==None: return
-
-        pvals = hypergeom.gene_hypergeo_test(current_selection, dataset)
-
-        for row in sel_store:
-            if row[1]==dataset:
-                for child in row.iterchildren():
-                    name = child[0]
-                    child[3] = pvals[name][0]
-                    child[4] = pvals[name][1]
-                    child[5] = pvals[name][2]
-        
-        sel_store.set_sort_column_id(5, gtk.SORT_ASCENDING)
-
-    #
-    # GTK callbacks
-    #
-    def _enable_details(self, widget, bool):
-        if self._details_on == bool : return
-        self._details_on = bool
-        if bool==True:
-            for col in self._detail_cols:
-                self._seltree.insert_column(col, -1)
-        else:
-            for col in self._detail_cols:
-                self._seltree.remove_column(col)
-    
-    def _drag_data_received(self, widget, drag_context, x, y,
-                              selection, info, timestamp):
-
-        treestore, path = selection.tree_get_row_drag_data()
-        i = treestore.get_iter(path)
-        obj = treestore.get_value(i, 2)
-        if isinstance(obj, dataset.CategoryDataset):
-            self.add_dataset(obj)
-            self._dimlist_controller.set_dimension(obj.get_dim_name(0))
-        widget.emit_stop_by_name('drag-data-received')
-
-    def _on_cursor_changed(self, widget):
-        "Show the list of identifier strings."
-        store = self._sel_stores[self._dimension]
-
-        p = self._seltree.get_cursor()[0]
-        i = store.get_iter(p)
-        obj = store.get_value(i, 1)
-
-        if isinstance(obj, dataset.Selection):
-            self._idlist_controller.set_selection(obj)
-        else:
-            self._idlist_controller.set_selection(None)
-        
-    def _on_row_activated(self, widget, path, column):
-        store = self._sel_stores[self._dimension]
-        i = store.get_iter(path)
-        obj = store.get_value(i, 1)
-        if isinstance(obj, dataset.Dataset):
-            seltree = self._seltree
-            if seltree.row_expanded(path):
-                seltree.collapse_row(path)
-            else:
-                seltree.expand_row(path, True)
-        elif isinstance(obj, dataset.Selection):
-            main.projectview.set_selection(self._dimension,
-                                       obj[self._dimension])
-
-    def _on_button_pressed(self, widget, event):
-        """Button press callbak."""
-        if event.button == 3:
-            self._seltree_menu.popup(None, None, None, event.button, event.time)
-
-    def _on_seltree_sort(self, menuitem):
-        """Sort selection tree if row is category dataset."""
-        store = self._sel_stores[self._dimension]
-        p = self._seltree.get_cursor()[0]
-        i = store.get_iter(p)
-        obj = store.get_value(i, 1)
-        if isinstance(obj, dataset.CategoryDataset):
-            self._sort_selections(obj)
-
-
-class DimListController:
-    def __init__(self, dimlist, seltree_controller):
-        
-        self._current_dim = None
-        self._seltree_controller = seltree_controller
-        
-        self.show_hidden = False
-
-        ## dimstore is a list of all dimensions in the application
-        self.dimstore = gtk.ListStore(gobject.TYPE_STRING)
-
-        # filter for hiding dims prefixed with underscore
-        self.dimstore_filter = self.dimstore.filter_new()
-        self.dimstore_filter.set_visible_func(self._dimension_filter)
-        
-        ## The widgets we are controlling
-        self.dimlist = dimlist
-
-        ## Set up dimensions list
-        dimlist.set_model(self.dimstore_filter)
-        
-        renderer = gtk.CellRendererText()
-        dim_column = gtk.TreeViewColumn('Dimension', renderer, text=0)
-        dimlist.insert_column(dim_column, 0)
-
-        # Signals
-        dimlist.connect('row-activated', self._dim_row_activated)
-        dimlist.connect('cursor-changed', self._dim_cursor_changed)
-        dimlist.connect('button-press-event', self._dimlist_button_pressed)
-        
-        # Set up  dimension context menu
-        self._dimlist_menu = SimpleMenu()
-        self._dimlist_menu.add_simple_item('Hide', self._on_dim_hide)
-        self._dimlist_menu.add_simple_item('Show all', self._on_dim_show)
-
-
-    ## 
-    ## Public interface
-    ##
-    def set_projectview(self, projectview):
-        """Dependency injection."""
-        self.dim_names = projectview.dim_names
-        self.update_dims()
-        projectview.add_dataset_observer(self)
-
-    def get_dimension(self, dim):
-        """Returns the iterator to the dimension with the given name, or
-        None if not found."""
-        
-        i = self.dimstore_filter.get_iter_first()
-        while i:
-            if self.dimstore_filter.get_value(i, 0) == dim:
-                return i
-            i = self.dimstore_filter.iter_next(i)
-        return None
-
-    def set_dimension(self, dimname):
-        """Sets the current dimension."""
-        self._current_dim = dimname
-        
-        dim = self.get_dimension(self._current_dim)
-        path = self.dimstore_filter.get_path(dim)
-        
-        if self.dimlist.get_cursor()[0] != path:
-            self.dimlist.set_cursor(self.dimstore_filter.get_path(dim))
-        self._seltree_controller.set_dimension(dimname)
-
-    def dataset_changed(self):
-        """Callback function from Project."""
-        self.update_dims()
-    
-    def update_dims(self):
-        """Update the list of dimensions shown"""
-        for dim in self.dim_names:
-            if not self.get_dimension(dim):
-                self.dimstore.insert_after(None, (dim,))
-        self.dimstore_filter.refilter()
-
-    #
-    # Private interface
-    #
-    def _dimension_filter(self, store, row):
-        """Filters out dimensions with underscore prefix."""
-        if self.show_hidden:
-            return True
-
-        visible = False
-        name = store.get_value(row, 0)
-        if name != None:
-            visible = name[0]!="_"
-        return visible
-
-    #
-    # GTK Callbacks.
-    #
-    def _on_dim_hide(self, menuitem):
-        """Menu item callback function which hides underscore prefixed
-        dimensions."""
-        self.show_hidden = False
-        self.dimstore_filter.refilter()
-
-    def _on_dim_show(self, menuitem):
-        """Menu item callback function that shows underscore prefixed
-        dimension names."""
-        self.show_hidden = True
-        self.dimstore_filter.refilter()
-    
-    def _dim_cursor_changed(self, widget):
-        cursor = self.dimlist.get_cursor()[0]
-        i = self.dimstore_filter.get_iter(cursor)
-        row = self.dimstore_filter.get_value(i, 0)
-        self.set_dimension(row)
-        self._seltree_controller.activate()
-
-    def _dim_row_activated(self, widget, path, column):
-        #self._seltree_controller.set_dimension(dim)
-        pass
-        
-    def _dimlist_button_pressed(self, widget, event):
-        if event.button == 3:
-            self._dimlist_menu.popup(None, None, None, event.button, event.time)
-

laydi/workflow.py

@@ -1,476 +0,0 @@
-import gtk, gobject
-import sys
-import os
-import inspect
-import logger
-import laydi
-import main
-
-def _workflow_classes(dir, modname):
-    """Returns a list of all subclasses of Workflow in a given module"""
-    workflow_classes = []
-
-    module = __import__('%s' % (modname,))
-
-    d = module.__dict__
-    for wf in d.values():
-        try:
-            if issubclass(wf, Workflow):
-                workflow_classes.append(wf)
-        except TypeError, e:
-            pass
-    return workflow_classes
-        
-def workflow_list():
-    """Returns a list containing all new workflows"""
-    retval = []
-
-    # List all .py files that can contain workflow classes
-    wf_path = sys.modules['workflows'].__path__
-    wf_files = []
-
-    for dir in wf_path:
-        for fn in os.listdir(dir):
-            if fn.endswith('.py') and ('#' not in fn):
-                wf_files.append(fn[:-3])
-
-    # Try to load each file and look for Workflow derived classes
-    for fn in wf_files:
-        try:
-            for wf in _workflow_classes(fn):
-                retval.append(wf)
-        except Exception, e:
-            logger.log('warning', 'Cannot load workflow: %s' % fn)
-            logger.log('warning', e)
-        
-    return retval
-
-def find_workflow(basename):
-    """Searches for a workflow with a given filename."""
-    
-    # List all .py files that can contain workflow classes
-    wf_path = main.options.workflowdir.split(':')
-    wf_file = None
-
-    for dir in wf_path:
-        fn = os.path.join(dir, "%s.py" % basename)
-        if os.path.isfile(fn):
-            wf_file = fn
-            return _workflow_classes(dir, basename)[0]
-
-    return None
-
-class Workflow:
-    """Defines a workflow that contains a set of analysis stages.
-
-    A Workflow is a set of analysis stages for a certain type of analysis.
-    Each stage contains some possible operations to do accomplish that 
-    task.
-    """
-
-    name = "Workflow"
-    ident = None
-    description = "Workflow Description"
-
-    def __init__(self):
-        self.stages = []
-        self.stages_by_id = {}
-
-    def get_data_file_name(self, filename):
-        """Checks if a file with the given name exists in the data directory.
-        Returns the file name if the file exists in the data directory, which
-        is defined as datadir/workflowname. If the file does not exist, or the
-        workflow does not have an identificator, this method returns None."""
-        if self.ident == None:
-            return None
-        fn = os.path.join(main.options.datadir, self.ident, filename)
-        if os.path.isfile(fn):
-            return fn
-        return None
-
-    def add_stage(self, stage):
-        self.stages.append(stage)
-        self.stages_by_id[stage.id] = stage
-
-    def print_tree(self):
-        print "Workflow:", self.name
-        for stage in self.stages:
-            print '   %s' % stage.name
-            for fun in stage.functions:
-                print '        %s' % fun.name
-
-#    def add_project(self,project):
-#        if project == None:
-#            logger.log('notice','Proejct is empty')
-#        logger.log('notice','Project added in : %s' %self.name)
-#        self.project = project
-
-
-class EmptyWorkflow(Workflow):
-    name = 'Empty Workflow'
-
-    def __init__(self):
-        Workflow.__init__(self)
-    
-
-class Stage:
-    """A stage is a part of the data analysis process.
-
-    Each stage contains a set of functions that can be used to
-    accomplish the task. A typical early stage is 'preprocessing', which
-    can be done in several ways, each represented by a function.
-    """
-
-    def __init__(self, id, name):
-        self.id = id
-        self.name = name
-        self.functions = []
-        self.functions_by_id = {}
-
-    def add_function(self, fun):
-        self.functions.append(fun)
-        self.functions_by_id[fun.id] = fun
-
-
-class Function:
-    """A Function object encapsulates a function on a data set.
-
-    Each Function instance encapsulates some function that can be applied
-    to one or more types of data.
-    """
-
-    def __init__(self, id, name):
-        self.id = id
-        self.name = name
-
-    # just return a Validation object
-    def validate_input(input):
-        return Validation(True,"Validation Not Implemented")
-
-    def run(self):
-        pass
-
-
-class Validation:
-    def __init__(self,result, reason):
-        self.succeeded = result
-        self.reason = reason
-
-
-class WorkflowView (gtk.VBox):
-   
-    def __init__(self, wf):
-        gtk.VBox.__init__(self)
-        self.workflow = wf
-        self.setup_workflow(wf)
-
-    def setup_workflow(self, wf):
-        # Add stage in the process
-        for stage in wf.stages:
-            exp = gtk.Expander(stage.name)
-            btn_align = gtk.Alignment(xscale=0.9)
-            btn_align.set_padding(0,4,20,0)
-            btn_align.show()
-            btn_box = gtk.VBox()
-            btn_align.add(btn_box)
-            btn_box.show()
-            exp.add(btn_align)
-            
-            # Add functions in each stage 
-            for fun in stage.functions:
-                btn = gtk.Button(fun.name)
-                btn.connect('clicked',
-                            lambda button, f=fun : run_function(f))
-
-                btn_box.add(btn)
-                btn.show()
-            
-            exp.show()
-            self.pack_start(exp, expand=False, fill=False)
-
-    def remove_workflow(self):
-        for c in self.get_children():
-            c.hide()
-            self.remove(c)
-
-    def set_workflow(self, workflow):
-        self.workflow = workflow
-        self.remove_workflow()
-        self.setup_workflow(workflow)
-
-
-class Options(dict):
-    """Options base class.
-    """
-    def __init__(self, *args,**kw):
-        dict.__init__(self, *args, **kw)
-        self['out_plots'] = []
-        self['out_data'] = []
-	self['all_plots'] = []
-	self['all_data'] = []
-
-    def _copy_from_list(self, key_list):
-        """Returns suboptions (dictionary) from a list of keys. 
-        """
-        d = {}
-        for key in key_list:
-            d[key] = self.get(key, None)
-        return d
-
-
-class OptionsDialog(gtk.Dialog):
-    """The basic input/output dialog box.
-    
-    This defines the first page of the function options-gui.
-    Any function that invokes a option-gui will inherit from this class.
-    """
-    def __init__(self, data, options, input_names=['X','Y']):
-        gtk.Dialog.__init__(self, 'Input-Output dialog',
-                            None,
-                            gtk.DIALOG_DESTROY_WITH_PARENT,
-                            (gtk.STOCK_OK, gtk.RESPONSE_OK,
-                             gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL))
-
-        self._options = options
-        self._data = data
-        self._editable = True
-        self.set_size_request(550,450)
-        
-        # create notebook
-        self.nb = nb = gtk.Notebook()
-
-        # 1. page: input/output
-        
-        #inputs
-        input_frame = gtk.Frame("Input")
-        hbox = gtk.HBox(True, 8)
-        align = gtk.Alignment(1, 1, 1, 1)
-        align.set_padding(8, 8, 8, 8)
-        align.add(hbox)
-        input_frame.add(align)
-        for i, name in enumerate(input_names):
-            frame = gtk.Frame(name)
-            frame.set_label_align(0.5, 0.5)
-            label = gtk.Label(data[i]._name + "\n" + str(data[i]._array.shape))
-            frame.add(label)
-            hbox.add(frame)
-        
-        #outputs
-        output_frame = gtk.Frame("Output")
-        output_hbox = gtk.HBox(True,4)
-        output_align = gtk.Alignment(1, 1, 1, 1)
-        output_align.set_padding(8, 8, 8, 8) #left padding:8
-        output_align.add(output_hbox)
-        output_frame.add(output_align)
-        
-        # plots
-        plot_list = gtk.ListStore(str, 'gboolean', gtk.gdk.Pixbuf)
-        plot_treeview = gtk.TreeView(plot_list)
-
-        # Add plots
-        plot_icon = laydi.icon_factory.get('line_plot')
-        for plt, name, use in self._options['all_plots']:
-            plot_list.append((name, use, plot_icon))
-
-        # Renderer for icon
-        plot_icon = laydi.icon_factory.get('line_plot')
-        icon_renderer = gtk.CellRendererPixbuf()
-        icon_renderer.set_property('pixbuf', plot_icon)
-
-        # Renderer for active toggle.
-        active_renderer = gtk.CellRendererToggle()
-        active_renderer.set_property('mode', gtk.CELL_RENDERER_MODE_ACTIVATABLE)
-        active_renderer.connect('toggled', toggled, plot_list)
-        active_column = gtk.TreeViewColumn('Use', active_renderer, active=1)
-
-        # Renderer for plot title.
-        title_renderer = gtk.CellRendererText()
-        title_renderer.set_property('mode', gtk.CELL_RENDERER_MODE_EDITABLE)
-        title_column = gtk.TreeViewColumn('Plot', title_renderer, text=0)
-        title_column.pack_start(icon_renderer, expand=False)
-        
-        # Add columns to tree view.
-        plot_treeview.append_column(active_column)
-        plot_treeview.append_column(title_column)
-
-        ## datasets
-        dataset_list = gtk.ListStore(str, 'gboolean', gtk.gdk.Pixbuf)
-        dataset_treeview = gtk.TreeView(dataset_list)
-
-        # Add datasets
-        data_icon = laydi.icon_factory.get('dataset')
-        for dat, name, use in self._options['all_data']:
-            dataset_list.append((name, use, data_icon))
-
-        # Renderer for icon
-        icon_renderer = gtk.CellRendererPixbuf()
-        icon_renderer.set_property('pixbuf', data_icon)
-        
-        # Renderer for active toggle.
-        active_renderer = gtk.CellRendererToggle()
-        active_renderer.set_property('mode', gtk.CELL_RENDERER_MODE_ACTIVATABLE)
-        active_renderer.connect('toggled', toggled, dataset_list)
-        active_column = gtk.TreeViewColumn('Use', active_renderer, active=1)
-        
-        # Renderer for dataset title.
-        title_renderer = gtk.CellRendererText()
-        title_renderer.set_property('mode', gtk.CELL_RENDERER_MODE_EDITABLE)
-        title_column = gtk.TreeViewColumn('Dataset', title_renderer, text=0)
-        title_column.pack_start(icon_renderer, expand=False)
-
-        # Add columns to tree view.
-        dataset_treeview.append_column(active_column)
-        dataset_treeview.append_column(title_column)
-        
-        # add treeviews to output frame
-        output_hbox.add(plot_treeview)
-        output_hbox.add(dataset_treeview)
-        
-        # vbox for input/spacer/output
-        vbox1 = gtk.VBox()
-        vbox1.add(input_frame)
-        vbox1.add(gtk.HSeparator())
-        vbox1.add(output_frame)
-        
-        # add vbox to notebook
-        nb.insert_page(vbox1, gtk.Label("Input/Output"), 0)
-        self.vbox.add(nb)
-
-        #keep ref to liststores
-        self.dataset_list = dataset_list
-        self.plot_list = plot_list
-
-    def run(self):
-        self.vbox.show_all()
-        return gtk.Dialog.run(self)
-        
-    def set_options(self, options):
-        self._options = options
-        
-    def update_options(self, options):
-        self._options.update(options)
-
-    def set_output(self):
-        # get toggled output data
-        out_data = [item[0] for name, mark, ic in self.dataset_list for item in self._options['all_data'] if mark==True and name==item[1]]
-        # get toggled plots
-        out_plots = [item[0] for name, mark, ic in self.plot_list for item in self._options['all_plots'] if mark==True and name==item[1]]
-        # update options
-        self._options['out_data'] = out_data
-        self._options['out_plots'] = out_plots
-        
-    def set_editable(self, editable):
-        self._editable = True
-                
-    def set_data(self, data):
-        self._data = data
-
-    def get_data(self):
-        return self._data
-
-    def get_options(self):
-        return self._options
-
-    def add_page_from_glade(self, glade_file, widget_name, page_title):
-        """Adds a new page(s) to the existing notebook.
-        The input widget (added as a page in notebook) is defined
-        in the glade file.
-
-        input:
-        glade_file -- path to glade file
-        widget_name -- name of widget from glade file
-        """
-        
-        try:
-            self.wTree = gtk.glade.XML(glade_file)
-        except:
-            logger.log('notice', 'Could not find glade file: %s' %glade_file)
-
-        widget = self.wTree.get_widget(widget_name)
-        win = widget.get_parent()
-        win.hide()
-        widget.unparent()
-        self.nb.insert_page(widget, gtk.Label(page_title), -1)
-        self.nb.set_current_page(0)
-
-
-def toggled(renderer, path, store):
-    it = store.get_iter(path)
-    old_value = store.get_value(it, 1)
-    store.set_value(it, 1, not old_value)
-
-
-class WorkflowMenu (gtk.Menu):
-    
-    def __init__(self, workflow):
-        gtk.Menu.__init__(self)
-        self._workflow = workflow
-        for stage in workflow.stages:
-            self.append(self._create_stage_item(stage))
-
-    def _create_stage_item(self, stage):
-        stage_menu_item = gtk.MenuItem(stage.name)
-        stage_menu_item.show()
-        stage_menu = gtk.Menu()
-        stage_menu_item.set_submenu(stage_menu)
-
-        for fun in stage.functions:
-            stage_menu.append(self._create_function_item(fun))
-        return stage_menu_item
-
-    def _create_function_item(self, func):
-        menuitem = gtk.MenuItem(func.name)
-        menuitem.connect('activate',
-                         lambda item, f=func : run_function(f))
-        menuitem.show()
-        return menuitem
-
-def run_function(function):
-    logger.log('debug', 'Starting function: %s' % function.name)
-    parent_data = main.projectview.current_data
-
-    validation  = function.validate_input()
-
-    if not validation.succeeded:
-        logger.log('warning','Invalid Inputdata: ' + str(reason))
-        return
-
-    args, varargs, varkw, defaults = inspect.getargspec(function.run)
-
-    # first argument is 'self' and second should be the selection
-    # and we don't care about those...
-    args.remove('self')
-    if "selection" in args:
-        pass_selection = True
-        args.remove('selection')
-    else:
-        pass_selection = False
-
-    if varargs and len(parent_data) < len(args):
-        logger.log('warning', "Function requires minimum %d datasets selected." % len(args))
-        return
-    elif not varargs and args and len(args) != len(parent_data):
-        # functions requiring datasets have to have the right number
-        logger.log('warning', "Function requires %d datasets, but only %d selected." % (len(args), len(parent_data)))
-        return
-
-    if not args:
-        # we allow functions requiring no data to be run even if a
-        # dataset is is selected
-        data = []
-    else:
-        data = parent_data
-        
-    if pass_selection:
-        # if the function has a 'selection' argument, we pass in
-        # the selection
-        new_data = function.run(selection=main.projectview.get_selection(), *data)
-    else:
-        new_data = function.run(*data)
-        
-    if new_data != None:
-        main.projectview.add_data(parent_data, new_data, function.name)
-    
-    logger.log('debug', 'Function ended: %s' % function.name)
-
-

matplotlibrc

@@ -0,0 +1,256 @@
+### MATPLOTLIBRC FORMAT
+
+# This is a sample matplotlib configuration file.  It should be placed
+# in HOME/.matplotlib/matplotlibrc (unix/linux like systems) and
+# C:\Documents and Settings\yourname\.matplotlib (win32 systems)
+#
+# By default, the installer will overwrite the existing file in the
+# install path, so if you want to preserve your's, please move it to
+# your HOME dir and set the environment variable if necessary.
+#
+# This file is best viewed in a editor which supports python mode
+# syntax highlighting
+#
+# Blank lines, or lines starting with a comment symbol, are ignored,
+# as are trailing comments.  Other lines must have the format
+#
+#   key : val   # optional comment
+#
+# Colors: for the color values below, you can either use
+#  - a matplotlib color string, such as r, k, or b
+#  - an rgb tuple, such as (1.0, 0.5, 0.0)
+#  - a hex string, such as ff00ff  (no '#' symbol)
+#  - a scalar grayscale intensity such as 0.75
+#  - a legal html color name, eg red, blue, darkslategray
+
+#### CONFIGURATION BEGINS HERE
+# the default backend; one of GTK GTKAgg GTKCairo FltkAgg QtAgg TkAgg
+#     Agg Cairo GD GDK Paint PS PDF SVG Template
+backend      : GTKAgg
+numerix      : numpy  # numpy, Numeric or numarray
+interactive  : False      # see http://matplotlib.sourceforge.net/interactive.html
+toolbar      : toolbar2   # None | classic | toolbar2
+timezone     : UTC        # a pytz timezone string, eg US/Central or Europe/Paris
+
+# Where your matplotlib data lives if you installed to a non-default
+# location.  This is where the matplotlib fonts, bitmaps, etc reside
+#datapath : /home/jdhunter/mpldata
+
+
+### LINES
+# See http://matplotlib.sourceforge.net/matplotlib.lines.html for more
+# information on line properties.
+lines.linewidth   : 1.0     # line width in points
+lines.linestyle   : -       # solid line
+lines.color       : blue
+lines.marker      : None    # the default marker
+lines.markerfacecolor  : blue
+lines.markeredgecolor  : black
+lines.markeredgewidth  : 0.5     # the line width around the marker symbol
+lines.markersize  : 6            # markersize, in points
+lines.dash_joinstyle : miter        # miter|round|bevel
+lines.dash_capstyle : butt          # butt|round|projecting
+lines.solid_joinstyle : miter       # miter|round|bevel
+lines.solid_capstyle : projecting   # butt|round|projecting
+lines.antialiased : True         # render lines in antialised (no jaggies)
+
+### PATCHES
+# Patches are graphical objects that fill 2D space, like polygons or
+# circles.  See
+# http://matplotlib.sourceforge.net/matplotlib.patches.html for more
+# information on patch properties
+patch.linewidth   	: 1.0     # edge width in points
+patch.facecolor  	: blue
+patch.edgecolor  	: black
+patch.antialiased 	: True    # render patches in antialised (no jaggies)
+
+### FONT
+#
+# font properties used by text.Text.  See
+# http://matplotlib.sourceforge.net/matplotlib.font_manager.html for more
+# information on font properties.  The 6 font properties used for font
+# matching are given below with their default values.
+#
+# The font.family property has five values: 'serif' (e.g. Times),
+# 'sans-serif' (e.g. Helvetica), 'cursive' (e.g. Zapf-Chancery),
+# 'fantasy' (e.g. Western), and 'monospace' (e.g. Courier).  Each of
+# these font families has a default list of font names in decreasing
+# order of priority associated with them.
+#
+# The font.style property has three values: normal (or roman), italic
+# or oblique.  The oblique style will be used for italic, if it is not
+# present.
+#
+# The font.variant property has two values: normal or small-caps.  For
+# TrueType fonts, which are scalable fonts, small-caps is equivalent
+# to using a font size of 'smaller', or about 83% of the current font
+# size.
+#
+# The font.weight property has effectively 13 values: normal, bold,
+# bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
+# 400, and bold is 700.  bolder and lighter are relative values with
+# respect to the current weight.
+#
+# The font.stretch property has 11 values: ultra-condensed,
+# extra-condensed, condensed, semi-condensed, normal, semi-expanded,
+# expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
+# property is not currently implemented.
+#
+# The font.size property is the default font size for text, given in pts.
+# 12pt is the standard value.
+#
+font.family         : sans-serif
+font.style          : normal
+font.variant        : normal
+font.weight         : medium
+font.stretch        : normal
+# note that font.size controls default text sizes.  To configure
+# special text sizes tick labels, axes, labels, title, etc, see the rc
+# settings for axes and ticks. Special text sizes can be defined
+# relative to font.size, using the following values: xx-small, x-small,
+# small, medium, large, x-large, xx-large, larger, or smaller
+font.size           : 12.0
+font.serif          : Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
+font.sans-serif     : Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
+font.cursive        : Apple Chancery, Textile, Zapf Chancery, Sand, cursive
+font.fantasy        : Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy
+font.monospace      : Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+
+### TEXT
+# text properties used by text.Text.  See
+# http://matplotlib.sourceforge.net/matplotlib.text.html for more
+# information on text properties
+text.color          : black
+text.usetex         : False  # use latex for all text handling.  For more information, see
+                             # http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex
+text.dvipnghack     : False  # some versions of dvipng don't handle
+                             # alpha channel properly.  Use True to correct and flush
+                             # ~/.matplotlib/tex.cache before testing
+
+### AXES
+# default face and edge color, default tick sizes,
+# default fontsizes for ticklabels, and so on.  See
+# http://matplotlib.sourceforge.net/matplotlib.axes.html#Axes
+axes.hold           : True    # whether to clear the axes by default on
+axes.facecolor      : white   # axes background color
+axes.edgecolor      : black   # axes edge color
+axes.linewidth      : 1.0     # edge linewidth
+axes.grid           : True   # display grid or not
+axes.titlesize      : 12      # fontsize of the axes title
+axes.labelsize      : 10     # fontsize of the x any y labels
+axes.labelcolor     : black
+axes.axisbelow      : True # whether axis gridlines and ticks are below
+                              # the axes elements (lines, text, etc)  
+                      
+
+polaraxes.grid      : True    # display grid on polar axes
+
+### TICKS
+# see http://matplotlib.sourceforge.net/matplotlib.axis.html#Ticks
+xtick.major.size     : 4      # major tick size in points
+xtick.minor.size     : 0      # minor tick size in points
+xtick.major.pad      : 2      # distance to major tick label in points
+xtick.minor.pad      : 2      # distance to the minor tick label in points
+xtick.color          : k      # color of the tick labels
+xtick.labelsize      : 8     # fontsize of the tick labels
+xtick.direction      : in     # direction: in or out
+
+ytick.major.size     : 4      # major tick size in points
+ytick.minor.size     : 0      # minor tick size in points
+ytick.major.pad      : 2      # distance to major tick label in points
+ytick.minor.pad      : 2      # distance to the minor tick label in points
+ytick.color          : k      # color of the tick labels
+ytick.labelsize      : 8     # fontsize of the tick labels
+ytick.direction      : in     # direction: in or out
+
+
+### GRIDS
+grid.color       :   0.85   # grid color
+grid.linestyle   :   :      # dotted
+grid.linewidth   :   0.5     # in points
+
+### Legend
+legend.isaxes        : True
+legend.numpoints     : 4      # the number of points in the legend line
+legend.fontsize      : 12
+legend.pad           : 0.2    # the fractional whitespace inside the legend border
+legend.markerscale   : 1.0    # the relative size of legend markers vs. original
+# the following dimensions are in axes coords
+legend.labelsep      : 0.010  # the vertical space between the legend entries
+legend.handlelen     : 0.05   # the length of the legend lines
+legend.handletextsep : 0.02   # the space between the legend line and legend text
+legend.axespad       : 0.02   # the border between the axes and legend edge
+legend.shadow        : False
+
+### FIGURE
+# See http://matplotlib.sourceforge.net/matplotlib.figure.html#Figure
+figure.figsize   : 5, 4    # figure size in inches
+figure.dpi       : 72      # figure dots per inch
+figure.facecolor : white    # figure facecolor; 0.75 is scalar gray
+figure.edgecolor : white   # figure edgecolor
+
+# The figure subplot parameters.  All dimensions are fraction of the
+# figure width or height
+figure.subplot.left  : 0.04  # the left side of the subplots of the figure
+figure.subplot.right : 0.999 # the right side of the subplots of the figure
+figure.subplot.bottom : 0.04 # the bottom of the subplots of the figure
+figure.subplot.top : 0.999   # the top of the subplots of the figure
+figure.subplot.wspace : 0.1  # the amount of width reserved for blank space between subplots
+figure.subplot.hspace : 0.1   # the amount of height reserved for white space between subplots
+
+
+### IMAGES
+image.aspect : equal             # equal | auto | a number
+image.interpolation  : bilinear  # see help(imshow) for options
+image.cmap   : jet               # gray | jet etc...
+image.lut    : 256               # the size of the colormap lookup table
+image.origin : upper             # lower | upper
+
+
+### CONTOUR PLOTS
+contour.negative_linestyle :  6.0, 6.0 # negative contour dashstyle (size in points)
+
+### SAVING FIGURES
+# the default savefig params can be different for the GUI backends.
+# Eg, you may want a higher resolution, or to make the figure
+# background white
+savefig.dpi       : 100      # figure dots per inch
+savefig.facecolor : white    # figure facecolor when saving
+savefig.edgecolor : white    # figure edgecolor when saving
+
+# tk backend params
+tk.window_focus   : False    # Maintain shell focus for TkAgg
+tk.pythoninspect  : False    # tk sets PYTHONINSEPCT
+
+# ps backend params
+ps.papersize      : A4   # auto, letter, legal, ledger, A0-A10, B0-B10
+ps.useafm         : False    # use of afm fonts, results in small files
+ps.usedistiller   : False    # can be: None, ghostscript or xpdf
+                                          # Experimental: may produce smaller files.
+                                          # xpdf intended for production of publication quality files,
+                                          # but requires ghostscript, xpdf and ps2eps
+ps.distiller.res  : 6000      # dpi
+
+# pdf backend params
+pdf.compression   : 6 # integer from 0 to 9
+		      # 0 disables compression (good for debugging)
+
+# Set the verbose flags.  This controls how much information
+# matplotlib gives you at runtime and where it goes.  Ther verbosity
+# levels are: silent, helpful, debug, debug-annoying.  Any level is
+# inclusive of all the levels below it.  If you setting is debug,
+# you'll get all the debug and helpful messages.  When submitting
+# problems to the mailing-list, please set verbose to helpful or debug
+# and paste the output into your report.
+#
+# The fileo gives the destination for any calls to verbose.report.
+# These objects can a filename, or a filehandle like sys.stdout.
+#
+# You can override the rc default verbosity from the command line by
+# giving the flags --verbose-LEVEL where LEVEL is one of the legal
+# levels, eg --verbose-helpful.
+#
+# You can access the verbose instance in your code
+#   from matplotlib import verbose.
+verbose.level  : silent      # one of silent, helpful, debug, debug-annoying
+verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr

run-laydi

@@ -1,8 +0,0 @@
-#!/bin/sh
-
-## To be able to run laydi without installing it, we have to set
-## PYTHONPATH.
-
-export PYTHONPATH=$PYTHONPATH:.:./workflows
-./bin/laydi $@
-

scripts/README

@@ -1,10 +0,0 @@
-
-This directory and its subdirectories are intended for small scripts that are
-not considered parts of laydi proper. They are included because they do
-useful tings in preprocessing data, often for a specific use (e.g. microarray
-analysis with gene ontology background information).
-
-To find out what each script does, try running it with the --help option.
-
-2007-03-15, Einar Ryeng
-

scripts/geneontology/entrez-go-mapping

@@ -1,112 +0,0 @@
-#!/usr/bin/python 
-
-import optparse
-import os
-import sys
-
-probes = {}
-bp = {}
-cc = {}
-mf = {}
-
-
-def split_value(string):
-    """Splits a tab delimited value from affymetrix csv files"""
-    string = string.strip()
-    values = [x.strip() for x in string.split('///')]
-    if len(values) == 1 and values[0] == '---':
-        return []
-    return values
-
-def split_subvalues(string):
-    """Splits a value into smaller components"""
-    string = string.strip()
-    values = [x.strip() for x in string.split('//')]
-    if len(values) == 1 and values[0] == '--':
-        return []
-    return values
-
-def set_probes(probe, entrez):
-    """Set probe values for each entrez value."""
-    for gene_id in split_value(entrez):
-        if not probes.has_key(gene_id):
-            probes[gene_id] = []
-        probes[gene_id].append(probe.strip())
-
-def set_go(d, entrez, terms):
-    genes = split_value(entrez)
-    terms = split_value(terms)
-    for gene in genes:
-        if not d.has_key(gene):
-            d[gene] = []
-        for term in terms:
-            d[gene].append(split_subvalues(term)[0])
-
-
-def parse_options():
-    op = optparse.OptionParser()
-    op.add_option('-b', '--biological-process', dest="bp",
-                  help="Output annotations in the biological process tree.",
-                  action="store_true", default=False)
-    op.add_option('-c', '--cellular-component', dest="cc",
-                  help="Output annotations in the cellular component tree.",
-                  action="store_true", default=False)
-    op.add_option('-d', '--output-dataset',
-                  help="Export as ftsv (Laydi dataset) file.")
-    op.add_option('-m', '--molecular-function', dest="mf",
-                  help="Output annotations in the molecular function tree.",
-                  action="store_true", default=False)
-    op.add_option('-u', '--unique-terms-only', dest="only_terms",
-                  help="Output only a list of all unique GO terms annotated to the genes",
-                  action="store_true", default=False)
-    return op.parse_args()
-
-def read_file(options):
-    fd = open('entrez-go-mapping.cccsv')
-    for line in fd.readlines():
-        values = line.split(':::')
-        
-        probeid = values[0]
-        set_probes(probeid, values[1])
-        if options.bp:
-            set_go(bp, values[1], values[2])
-        if options.cc:
-            set_go(cc, values[1], values[3])
-        if options.mf:
-            set_go(mf, values[1], values[4])
-    fd.close()
-        
-if __name__ == '__main__':
-    options, args = parse_options()
-    read_file(options)
-
-    if options.only_terms:
-        s = set()
-        for gene in args:
-            if options.bp and bp.has_key(gene):
-                for x in bp[gene]:
-                    s.add(x)
-            if options.mf and bp.has_key(gene):
-                for x in mf[gene]:
-                    s.add(x)
-            if options.cc and bp.has_key(gene):
-                for x in cc[gene]:
-                    s.add(x)
-        
-        for term in s:
-            print "GO:%07d" % int(term)
-        sys.exit(0)
-
-    for gene in args:
-        print gene,
-        if options.bp and bp.has_key(gene):
-            for x in bp[gene]:
-                print "GO:%07d" % int(x),
-        if options.cc and bp.has_key(gene):
-            for x in cc[gene]:
-                print "GO:%07d" % int(x),
-        if options.mf and bp.has_key(gene):
-            for x in mf[gene]:
-                print "GO:%07d" % int(x),
-        print
-

scripts/geneontology/go-distance/Makefile

@@ -1,12 +0,0 @@
-
-all: go-distance
-
-godist.o: godist.c godist.h
-	gcc -ggdb -c godist.c
-
-go-distance: godist.o main.o
-	gcc -ggdb -o go-distance godist.o main.o -lm
-
-clean:
-	-rm go-distance godist.o main.o 
-

scripts/geneontology/go-distance/godist.c

@@ -1,359 +0,0 @@
-
-#include <math.h>
-#include <string.h>
-#include <errno.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <search.h>
-#include "godist.h"
-
-void print_terms();
-void add_link(char*, char*);
-struct node* get_bp();
-struct node* get_term(char *);
-void calc_ic(struct node *, unsigned int);
-struct node *common_subsumer(struct node *, struct node *);
-float resnik(struct node *, struct node *);
-
-
-/* initialisation */
-int godist_init() {
-    /* Initialize hash table and array */
-    hcreate(MAX_NODES);
-    term_array_size = 0;
-    link_count = 0;
-    struct node *n;
-
-    /* Read ontology terms from file */
-    printf("Reading GO terms from go-terms.txt...");
-    FILE *term_fd = fopen("go-terms.txt", "r");
-
-    if (term_fd == NULL) {
-        printf("cannot open file: go-terms.txt\n");
-        exit(errno);
-    }
-
-    int i;
-    while((i = godist_read_term(term_fd)) == 13) {
- /*       printf("%d\n", i);*/
-    }
-    fclose(term_fd);
-    printf(" %d terms\n", term_array_size);
-
-    /* Read ontology structure from file */
-    printf("Reading GO structure from go-tree.txt...");
-    FILE *tree_fd = fopen("go-tree.txt", "r");
-
-    if (tree_fd == NULL) {
-        printf("cannot open file: go-tree.txt\n");
-        exit(errno);
-    }
-
-    while((i = godist_read_assoc(tree_fd)) == 2) {
-	link_count++;
-    }
-    fclose(tree_fd);
-    printf(" %d edges\n", link_count);
-
-    printf("Calculating accumulated evidence...");
-    fflush(stdout);
-    for (i=0; i<term_array_size; i++) {
-        clear_flags(get_bp());
-        accumulate_evidence(term_array[i]);
-    }
-    printf("\n");
-
-    evidence = 0xff;
-    total_ann = 0;
-    n = get_bp();
-    for (i=0; i<12; i++)
-	if (evidence & 1<<i)
-	    total_ann += n->acc_evidence[i];
-    printf("Using %d annotations.\n", total_ann);
-
-    print_term(get_term("GO:0006006"));
-    print_term(get_term("GO:0019318"));
-    print_term(get_term("GO:0005996"));
-    print_term(get_bp());
-   /* 
-    print_term(get_term("GO:0040007"));
-    print_term(get_term("GO:0007275"));
-    print_term(get_term("GO:0007582"));
-    print_term(get_term("GO:0043473"));
-    print_term(get_term("GO:0000004"));
-    print_term(get_term("GO:0051704"));
-    print_term(get_term("GO:0000003"));
-    print_term(get_term("GO:0016032"));
-    print_term(get_term("GO:0009987"));
-    print_term(get_term("GO:0050896"));
-    print_term(get_term("GO:0050789"));
-*/
-    printf("Calculation information content...");
-    fflush(stdout);
-    calculate_ics(0xffff);
-    printf("\n");
-/*    calc_ic(get_bp(), 0xffff);*/
-/*    find_multi_parented();*/
-    common_subsumer(get_term("GO:0000003"), get_term("GO:0000004"));
-    /** should return go:0016032 */
-    common_subsumer(get_term("GO:0019081"), get_term("GO:0050434"));
-
-    printf("Resnik: %f\n", resnik(get_term("GO:0000003"), get_term("GO:0000004")));
-
-}
-
-void godist_exit() {
-    int i;
-    for (i=0; i<term_array_size; i++) {
-	free(term_array[i]);
-    }
-}
-
-int godist_read_assoc(FILE *fd) {
-    char term1[11], term2[11];
-    int retval;
-    retval = fscanf(fd, " %10s %10s ", term1, term2);
-    if (retval != EOF) {
-	add_link(term1, term2);
-    }
-    return retval;
-}
-
-int godist_read_term(FILE *fd) {
-    char term[11];
-    int ev[12];
-    int i;
-    ENTRY e, *res;
-    int nread = fscanf(fd, " %10s %d %d %d %d %d %d %d %d %d %d %d %d ",
-                       term, &ev[0], &ev[1], &ev[2], &ev[3], &ev[4], &ev[5],
-                       &ev[6], &ev[7], &ev[8], &ev[9], &ev[10], &ev[11]);
-    if (errno != 0) {
-        printf("errno: %d\n", errno);
-    }
-    if (nread == 13) {
-        struct node *n = (struct node*) malloc(sizeof(struct node));
-	n->parentc = 0;
-	n->childrenc = 0;
-	n->visited = 0;
-	for (i=0; i<12; i++) {
-	    n->evidence[i] = ev[i];
-	    n->acc_evidence[i] = 0;
-	}
-	strcpy(n->term, term);
-
-	/* add to hash table */
-	e.key = n->term;
-	e.data = (void*)n;
-	res = hsearch(e, ENTER);
-
-        term_array[term_array_size++] = n;
-    } 
-
-    return nread;
-}
-
-/* distance functions */
-float go_distance(char *term1, char *term2) {
-    return 0.0;
-}
-
-void clear_flags(struct node *n) {
-    int i, j;
-    for (i=0; i<term_array_size; i++) {
-	term_array[i]->visited = 0;
-	for (j=0; j<12; j++)
-	    term_array[i]->temp_acc[j] = 0;
-    }
-}
-
-void add_link(char *parent_id, char *child_id) {
-    ENTRY *ep, e;
-    struct node *parent, *child;
-
-    char key[11];
-    strcpy(key, parent_id);
-    e.key = key;
-    ep = hsearch(e, FIND);
-    if (!ep) {
-	printf("Cannot find term %s\n", e.key);
-	return;
-    }
-    parent = (struct node*) ep->key;
-
-    strcpy(key, child_id);
-    e.key = key;
-    ep = hsearch(e, FIND);
-    if (!ep) {
-	printf("Cannot find term %s\n", e.key);
-	return;
-    }
-    child = (struct node*) ep->key;
-
-    if (parent->childrenc +1 > MAX_CHILDREN) {
-        printf("FIXME: increase child count");
-	return;
-    }
-    parent->children[parent->childrenc] = child;
-    parent->childrenc++;
-    child->parents[child->parentc] = parent;
-    child->parentc++;
-}
-
-struct node *get_bp() {
-    return get_term("GO:0008150");
-}
-
-struct node *get_term(char *term) {
-    ENTRY e, *ep;
-    e.key = term;
-    ep = hsearch(e, FIND);
- 
-    if (ep) {
-	return ep->data;
-    }
-    return NULL;
-}
-
-void accumulate_evidence(struct node *n) {
-    int i;
-    acc_ev(n);
-    for (i=0; i<12; i++) {
-	n->acc_evidence[i] = n->temp_acc[i];
-    }
-}
-
-void acc_ev(struct node *n) {
-    int i, j;
-    if (n->visited)
-        return;
-    n->visited = 1;
-
-    for (i=0; i<12; i++)
-        n->temp_acc[i] = n->evidence[i];
-
-    for (i=0; i<(n->childrenc); i++) {
-	if (!n->children[i]->visited) {
-            acc_ev(n->children[i]);
-	    for (j=0; j<12; j++)
-                n->temp_acc[j] += n->children[i]->temp_acc[j];
-        }
-    }
-}
-
-void print_terms() {
-    int i;
-    for (i=0; i<term_array_size; i++) {
-	printf("%s\n", term_array[i]->term);
-    }
-}
-
-void print_term(struct node *n) {
-    int i;
-    printf("%s\n", n->term);
-    printf("    children: %d\n", n->childrenc);
-    printf("    parents:  %d\n", n->parentc);
-    printf("    evidence: ");
-    for (i=0; i<12; i++)
-        printf("%d ", n->evidence[i]);
-    printf("\n");
-    printf("    accumulated evidence: ");
-    for (i=0; i<12; i++)
-        printf("%d ", n->acc_evidence[i]);
-    printf("\n");
-}
-
-void find_multi_parented() {
-    int i;
-    for (i=0; i<term_array_size; i++) {
-        if (term_array[i]->parentc > 1)
-            printf("%s  -- %d\n", term_array[i]->term, term_array[i]->parentc);
-    }
-}
-
-void calculate_ics(unsigned int evidence) {
-    int i;
-    for (i=0; i<term_array_size; i++)
-	calc_ic(term_array[i], evidence);
-}
-
-void calc_ic(struct node *n, unsigned int evidence) {
-    int i;
-    float ann=0.0;
-    for (i=0; i<12; i++)
-	if (evidence & 1<<i)
-	    ann += (float) n->acc_evidence[i];
-    n->ic = -log(ann/total_ann);
-    /*    printf("%f\n", n->ic);*/
-}
-
-struct node *common_subsumer(struct node *n1, struct node *n2) {
-    struct node *anc1[MAX_NODES];
-    struct node *anc2[MAX_NODES];
-    int ancc1=0, ancc2=0;
-    int i, j;
-    struct node *retval=NULL;
-
-    add_ancestors(&ancc1, anc1, n1);
-    add_ancestors(&ancc2, anc2, n2);
-    for (i=0; i<ancc1; i++)
-        for (j=0; j<ancc2; j++)
-            if (anc1[i] == anc2[j])
-                if ((!retval) || (anc1[i]->ic > retval->ic))
-                    retval = anc1[i];
-    if (retval)
-	;//        printf("Retval: %s\n", retval->term); 
-    else
-        printf("No value to return");
-    return retval;
-}
-
-void add_ancestors(int *ancc, struct node *anc[], struct node *n) { 
-    int i=0;
-    anc[(*ancc)++] = n;
-    for (i=0; i<n->parentc; i++)
-        add_ancestors(ancc, anc, n->parents[i]);
-}
-
-float resnik(struct node *n1, struct node *n2) {
-    struct node *subsumer = common_subsumer(n1, n2);
-    if (!subsumer)
-	return 20;
-    else
-        return n1->ic + n2->ic - 2.0 * subsumer->ic;
-}
-
-
-int read_terms(FILE *fd, struct node *terms[], int *termc) {
-    char term[11];
-    int retval;
-    printf("read_terms\n");
-    retval = fscanf(fd, " %10s ", term);
-    while (retval != EOF) {
-	printf(".");
-    	fflush(stdout);
-	terms[(*termc)++] = get_term(term);
-	retval = fscanf(fd, " %10s ", term);
-    }
-    return retval;
-}
-
-void build_dataset() {
-    struct node *terms[MAX_NODES];
-    int termc = 0;
-    int i, j;
-
-    FILE *fd = fopen("dimension", "r");
-
-    read_terms(fd, terms, &termc);
-
-    for (i=0; i<termc; i++) {
-	for (j=0; j<termc; j++) {
-            printf("%f ", resnik(terms[i], terms[j]));
-	}
-	printf("\n");
-    }
-       
-
-    fclose(fd);
-}
-

scripts/geneontology/go-distance/godist.h

@@ -1,77 +0,0 @@
-#ifndef GODIST_H
-#define GODIST_H
-
-#include <search.h>
-
-#define MAX_NODES 15000
-#define MAX_PARENTS 100
-#define MAX_CHILDREN 100
-
-enum EVIDENCE { MP = 1, 
-                IGI = 1 << 1, 
-		IPI = 1 << 2, 
-		ISS = 1 << 3, 
-		IDA = 1 << 4, 
-		IEP = 1 << 5,
-		IEA = 1 << 6, 
-		TAS = 1 << 7, 
-		NAS = 1 << 8, 
-		ND =  1 << 9, 
-		RCA = 1 << 10, 
-		IC =  1 << 11 };
-
-struct node;
-struct node {
-    /* GO term id. E.g: "GO:0005180" */
-    char term[11];
-    /* Information content */
-    float ic;
-    /* Depth in tree */
-    int depth;
-    /* Evidence codes */
-    int evidence[12];
-    /* Accumulated evidence codes */
-    int acc_evidence[12];
-
-    /* Working memory */
-    int temp_acc[12];
-
-    /* Parent count and parents */
-    int parentc;
-    struct node *parents[MAX_PARENTS];
-
-    /* Child count and children */
-    int childrenc;
-    struct node *children[MAX_CHILDREN];
-
-    /* Flag to ensure that a node is only visited once in DAG operations */
-    char visited;
-};
-
-struct node* term_array[MAX_NODES];
-long term_array_size;
-int link_count;
-int total_ann;
-int evidence; /* bitvector with one bit per evidence code */
-
-/* Ontology initialisation functions. */
-int godist_init();
-int godist_read_assoc(FILE *fd);
-int godist_read_term(FILE *fd);
-void accumulate_evidence(struct node*);
-
-/* Distance metric functions */
-float resnik_distance(char *term1, char *term2);
-float fussimeg_distance(char *term1, char *term2);
-void calc_ic(struct node *n, unsigned int evidence);
-
-void clear_flags(struct node *n);
-void print_term(struct node *n);
-
-void add_ancestors(int *ancc, struct node *anc[], struct node *n);
-
-void calculate_ics(unsigned int);
-void acc_ev(struct node*);
-
-#endif
-

scripts/geneontology/go-distance/main.c

@@ -1,35 +0,0 @@
-
-#include <stdio.h>
-#include <stdlib.h>
-#include <unistd.h>
-#include "godist.h"
-
-extern char *optarg;
-extern int optind, opterr, optopt;
-
-#define _GNU_SOURCE
-#include <getopt.h>
-
-void print_help() {
-    printf("go-distance 0.1.0\n\n");
-    printf("Usage: go-distance [hr] <go-terms>\n\n");
-}
-
-int main(int argc, char **argv) {
-    int i;
-    char *dimension[MAX_NODES];
-
-    while ((i = getopt(argc, argv, "h")) != -1) {
-        switch(i) {
-            case 104: 
-                print_help(); 
-                exit(0);
-                break;
-	};
-    }
-    godist_init();
-
-    build_dataset();
-    godist_exit();
-}
-

scripts/geneontology/go-gene-matrix

@@ -1,80 +0,0 @@
-#!/usr/bin/python
-
-import os, sys
-import getopt
-sys.path.append('../..')
-from laydi import dataset
-import numpy
-
-max_val = numpy.inf
-no_nan = False
-
-def print_help():
-    print
-    print "Usage: go-gene-matrix <go-dist-matrix.ftsv> <gene-go-mapping.txt>"
-    print
-    print "Description:"
-    print "    Takes a GO term by GO term distance matrix and a file that"
-    print "    maps GO terms to genes as input arguments and produces a"
-    print "    dataset that contains the shortest distances between all"
-    print "    genes and GO terms."
-    print
-    print "Options:"
-    print "    -h, --help       Show this help text."
-    print "    -m, --max-dist   Trunkate all distances to this value."
-    print
-
-def get_parameters():
-    global max_val
-    short_opts = "hm:"
-    long_opts = ["help", "max-dist="]
-
-    options, params = getopt.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 ['-m', '--max-dist']:
-            max_val = int(val)
-
-    if len(params) < 2:
-        print_help()
-        sys.exit(1)
-
-    return params
-
-if __name__ == '__main__':
-    params = get_parameters()
-
-    # Read dataset
-    fd = open(params[0])
-    ds = dataset.read_ftsv(fd)
-    array = ds.asarray()
-    fd.close()
-
-    # Read mapping
-    sorted_keys = []
-    mapping = {}
-    fd = open(params[1])
-    lines = fd.readlines()
-    for line in lines:
-        values = line.split()
-        if len(values) > 0:
-            mapping[values[0]] = values[1:]
-            sorted_keys.append(values[0])
-
-    # Create new dataset
-    matrix = numpy.zeros((len(sorted_keys), ds.shape[0]))
-    dim = ds.get_dim_name(0)
-    for i, gene in enumerate(sorted_keys):
-        for j, go in enumerate(ds[dim]):
-            min = max_val
-            for go2 in mapping[gene]:
-                if ds[dim].has_key(go2) and array[j, ds[dim][go2]] < min:
-                    min = array[j, ds[dim][go2]]
-            matrix[i, j] = min
-    out_ds = dataset.Dataset(matrix, 
-                             (('genes', sorted_keys), ('go-terms', ds[dim])),
-                             "Gene by GO matrix")
-    dataset.write_ftsv(sys.stdout, out_ds)
-

scripts/geneontology/go-to-network-ds

@@ -1,3 +0,0 @@
-#!/usr/bin/env python
-
-

scripts/illumina/illumina2ftsv

@@ -1,92 +0,0 @@
-#!/usr/bin/python
-
-import getopt
-import numpy
-import sys
-
-from laydi import dataset
-
-VERSION = "0.1.0"
-
-dataset_fn = "-"
-
-def print_help():
-    print "illumina2ftsv %s" % VERSION
-    print 
-    print "Usage: illumina2ftsv [options] <illumina_genome_studio_file>"
-    print
-
-
-def parse_options():
-    s_opts = "d:h"
-    l_opts = ["dataset", "help"]
-
-    options, params = getopt.getopt(sys.argv[1:], s_opts, l_opts)
-
-    for opt, val in options:
-        if opt in ["-d", "--dataset"]:
-            global dataset_fn
-            dataset_fn = val
-        elif opt in ["-h", "--help"]:
-            print_help()
-            sys.exit(0)
-
-    if len(params) != 1:
-        print_help()
-        sys.exit(1)
-
-    return params
-
-def read_illumina_file(fn):
-    fd = open(fn)
-    line = fd.readline()
-    if line.strip() != "Illumina Inc. GenomeStudio version 1.7.0":
-        raise Exception("File cannot be recognized as Illumina textual data")
-
-    headers = {}
-    line= fd.readline()
-    while line.strip() != "":
-        key, val = line.split("=", 1)
-        headers[key.strip()] = val.strip()
-        line = fd.readline()
-    
-    col_headers = fd.readline().split('\t')
-    
-    values = []
-    line = fd.readline()
-    while line != "":
-        values.append([x.strip() for x in line.split('\t')])
-        line = fd.readline()
-
-    probe_col = col_headers.index("ProbeID")
-    print "probe id column:"   
-
-    header_cols = []
-    samples = []
-    for i, colname in enumerate(col_headers):
-        if colname.startswith("AVG_Signal-"):
-            header_cols.append(i)
-            samples.append(colname.split("-", 1)[1])
-
-    print header_cols
-    print samples
-
-    a = numpy.array(values)
-    m = numpy.array(a[:,header_cols], dtype='d')
-    print m
-    
-    probe_ids = list(a[:, probe_col])
-
-    print "samples: ", len(samples)
-    print "probe_ids: ", len(probe_ids)
-    print "shape: ", m.shape
-
-    ds = dataset.Dataset(m.transpose(), [('samples', samples), ('probe-ids', probe_ids)], name="Average Expr.")
-    dataset.write_ftsv("test.ftsv", ds)
-
-if __name__ == '__main__':
-    
-    fn = parse_options()[0]
-    read_illumina_file(fn)
-
-

scripts/illumina/laydi-annot-illumina

@@ -1,72 +0,0 @@
-#!/usr/bin/python
-
-import getopt
-import os, os.path
-import sys
-
-#OUTPUT_COLS = ["Array_Address_Id", "Entrez_Gene_ID", "Accession", "Chromosome", "Definition", "Ontology_Component", "Ontology_Process", "Ontology_Function", "ILMN_Gene"]
-OUTPUT_COLS = ["Array_Address_Id", "Entrez_Gene_ID", "Accession", "ILMN_Gene", "Definition", ]
-
-def print_help(): 
-    print "laydi-annot-illumina"
-    print
-    print "Usage: laydi-annot-illumina <illumina-annotation-file.txt>"
-    print
-    print "Description:"
-    print "    Produce laydi annotation files from Illumina text annotation files"
-    print "    Illumina files can be downloaded from:"
-    print "    http://www.switchtoi.com/annotationfiles.ilmn"
-    print
-
-def parse_cmdline():
-    short_opts = "h"
-    long_opts = ["help"]
-    options, params = getopt.getopt(sys.argv[1:], short_opts, long_opts)
-
-    for key, val in options:
-        if key in ["-h", "--help"]:
-            print_help()
-            sys.exit(0)
-    
-    if len(params) != 1:
-        print_help()
-        sys.exit(1)
-
-    return params[0]
-
-def convert_annotations(fn_in, fn_out):
-    fd_in = open(fn_in)
-    fd_out = open(fn_out, "w")
-
-    # Skip headers
-    line = fd_in.readline()
-    while not line.startswith("[Probes]"):
-        line = fd_in.readline()
-
-    colnames = fd_in.readline().split("\t")
-    export_colnums = [colnames.index(x) for x in OUTPUT_COLS]
-
-    # Print output column headers
-    export_colnames = ["probe-id"] + OUTPUT_COLS[1:]
-    print >> fd_out, "\t".join(OUTPUT_COLS)
-
-    line = fd_in.readline()
-    while not line == "" and not line.startswith("["):
-        values = line.split("\t")
-        output_values = [values[x] for x in export_colnums]
-        print >> fd_out, "\t".join(output_values)
-        line = fd_in.readline()
-
-if __name__ == "__main__":
-    fn_in = parse_cmdline()
-    fn_out = os.path.split(fn_in)[1]
-    fn_out = os.path.splitext(fn_out)[0] + ".annot"
-    
-    print "Reading: %s" % (fn_in,)
-    print "Writing: %s" % (fn_out,)
-    print
-    print "Annotations:"
-    print ", ".join(OUTPUT_COLS)
-
-    convert_annotations(fn_in, fn_out)
-    

scripts/illumina/laydi-mapping-illumina

@@ -1,93 +0,0 @@
-#!/usr/bin/python
-
-import getopt
-import os, os.path
-import sys
-
-OUTPUT_COLS = ["Array_Address_Id", "Entrez_Gene_ID", "Accession", "ILMN_Gene", "Definition", ]
-
-def print_help(): 
-    print "laydi-mapping-illumina"
-    print
-    print "Usage: laydi-mapping-illumina <illumina-annotation-file.txt> <from_dim> <to_dim>"
-    print
-    print "Description:"
-    print "    Produce mapping files from Illumina text annotation files"
-    print "    Illumina files can be downloaded from:"
-    print "    http://www.switchtoi.com/annotationfiles.ilmn"
-    print
-    print "    NOTE: <from_dim> and <to_dim> are the column names in the illumina text file,"
-    print "    not laydi dimensions."
-    print
-
-
-def parse_cmdline():
-    short_opts = "h"
-    long_opts = ["help"]
-    options, params = getopt.getopt(sys.argv[1:], short_opts, long_opts)
-
-    for key, val in options:
-        if key in ["-h", "--help"]:
-            print_help()
-            sys.exit(0)
-    
-    if len(params) != 3:
-        print_help()
-        sys.exit(1)
-
-    return params
-
-
-def build_map(fn, from_dim, to_dim):
-    retval = {}
-    fd = open(fn)
-    line = fd.readline()
-    while line != "" and line.strip() != "[Probes]":
-        line = fd.readline()
-    if line == "":
-        return None
-
-    line = fd.readline()
-    cols = [x.strip() for x in line.split("\t")]
-    from_col = cols.index(from_dim)
-    to_col = cols.index(to_dim)
-
-    line = fd.readline()
-    while line != "" and not line.strip().startswith("["):
-        key = line.split("\t")[from_col]
-        val = line.split("\t")[to_col]
-        if not retval.has_key(key):
-            retval[key] = [val]
-        else:
-            retval[key].append(val)
-
-        line = fd.readline()
-
-    return retval
-
-
-def write_map(fd, d, from_dim, to_dim):
-    opened_here = False
-    if isinstance(fd, str):
-        fd = open(fd, "w")
-        opened_here = True
-
-    print >> fd, "# from: %s" % from_dim
-    print >> fd, "# to: %s" % to_dim
-    print >> fd, "# description: "
-    print >> fd
-
-    for k, v in d.items():
-        print >> fd, k,
-        for e in v:
-            print >> fd, e,
-        print >> fd
-    if opened_here:
-        fd.close()
-
-
-if __name__ == '__main__':
-    fn, from_dim, to_dim = parse_cmdline()
-    m = build_map(fn, from_dim, to_dim)
-    write_map(sys.stdout, m, from_dim, to_dim)
-

scripts/laydi-project/project.py

@@ -1,281 +0,0 @@
-import os, os.path
-import sys
-import configobj
-
-from laydi import dataset
-
-NAME = "laydi-cmd"
-VERSION = "0.1.0"
-PROJECT_VERSION_STRING = "Laydi project version 1"
-
-def is_project_directory(dirname):
-    """Verifies that a directory is a laydi project"""
-
-    if not os.path.isdir(dirname):
-        return False
-    
-    ## Verify that the version is correct.
-    version_fn = os.path.join(dirname, "VERSION")
-    if not os.path.exists(version_fn):
-        return False
-    fd = open(version_fn)
-    line = fd.readline()
-    fd.close()
-
-    if fd.strip() != PROJECT_VERSION_STRING:
-        return False
-
-    ## Require directories to be present.
-    if not os.path.isdir(os.path.join(dirname, "annotations")):
-        return False
-    if not os.path.isdir(os.path.join(dirname, "data")):
-        return False
-    if not os.path.isdir(os.path.join(dirname, "selections")):
-        return False
-    if not os.path.isdir(os.path.join(dirname, "exports")):
-        return False
-
-    ## If no tests failed, return True
-    return True
-
-
-def make_project_directory(dirname, force=False):
-    """Creates a project directory
-
-    force: ignore that directory exists and proceed anyway.
-    """
-    if os.path.exists(dirname) and not force:
-        return False
-
-    rootdir = dirname
-    anndir = os.path.join(dirname, "annotations")
-    seldir = os.path.join(dirname, "selections")
-    datadir = os.path.join(dirname, "data")
-    exportdir = os.path.join(dirname, "exports")
-    version_file_path = os.path.join(dirname, "VERSION")
-        
-    os.makedirs(rootdir)
-    for d in [anndir, seldir, datadir, exportdir]:
-        os.mkdir(d)
-
-    fd = open(version_file_path, "w")
-    print >> fd, PROJECT_VERSION_STRING
-    fd.close()
-
-
-
-class Universe(object):
-    """A Universe is a collection of all existing identifiers in a set of datasets"""
-
-    def __init__(self):
-        self.refcount = {}
-
-    def register_dim(self, dim):
-        """Increase reference count for identifiers in Dimension object dim"""
-        d = self.refcount.get(dim.name, None)
-        if d == None:
-            d = {}
-            self.refcount[dim.name] = d
-        for i in dim:
-            d[i] = d.get(i, 0) + 1
-        
-    def register_ds(self, ds):
-        """Increase reference count for identifiers in all Dimensions of dataset ds"""
-        for dim in ds.dims:
-            self.register_dim(dim)
-
-    def unregister_dim(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.
-        """
-        ids = self.refcount[dim.name]
-        for i in dim:
-            refcount = ids[i]
-            if refcount == 1:
-                ids.pop(i)
-            else:
-                ids[i] -= 1
-        if len(ids) == 0:
-            self.refcount.pop(dim.name)
-
-
-    def unregister_ds(self, ds):
-        """Update reference count for identifiers along Dimensions in Dataset ds.
-	   Update reference count for identifiers along all Dimensions in
-	   Dataset ds, and remove all identifiers with a reference count of 0,
-           as they do not (by definition) exist any longer.
-        """
-        for dim in ds:
-            self.register_dim(dim)
-
-    def register(self, obj):
-        if isinstance(obj, Dataset):
-            self.register_ds(obj)
-        else:
-            self.register_dim(obj)
-
-    def unregister(self, obj):
-        if isinstance(obj, Dataset):
-            self.unregister_ds(obj)
-        else:
-            self.unregister_dim(obj)
-
-    def __getent___(self, dimname):
-        return set(self.references[dimname].keys())
-
-    def __iter__(self):
-        return self.references.keys().__iter__()
-
-
-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)
-
-        if len(self.idset) != len(self.idlist):
-            raise Exception("Duplicate identifiers are not allowed")
-
-    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)
-
-    def as_tuple(self):
-        return (self.name, self.idlist)
-
-
-class DirectoryNotifier(object):
-    def __init__(self, path):
-        self.path = path
-        self.files = {}
-        self.subdirs = {}
-        self.timestamp = -1
-        self.file_listeners = {}
-        self.dir_listeners = {}
-        self.update()
-
-    def update(self):
-        now = time.time()
-        for fn in os.listdir(self.path):
-            if os.getctime(fn) > self.timestamp:
-                ext = os.path.splitext(fn)[1]
-                
-
-    def listen_files(self, obj, ext=None):
-        listeners = self.file_listeners
-        if listeners.has_key(ext):
-            listeners[ext].append(obj)
-        else:
-            listeners[ext] = [obj]
-
-
-    def listen_dirs(self, obj, ext=None):
-        listeners = self.dir_listeners
-        if listeners.has_key(ext):
-            listeners[ext].append(obj)
-        else:
-            listeners[ext] = [obj]
-
-
-class DataDirectory(object):
-    def __init__(self, dirname, recursive=False, universe=None):
-        self.dirname = dirname
-
-        ## Read datasets, plots and optionally subdirectories
-        datasets = []
-        ds_fn = {}
-        plots = []
-        plot_fn = {}
-        subdirs = []
-        subdir_fn = {}
-        update_time = 0
-
-        self.update()
-
-    def update(self):
-        ## Remember new timestamp.
-        now = time.time()
-
-        ## Read configuration
-        ini_fn = os.path.join(dirname, "directory.ini")
-        if os.path.isfile(ini_fn) and os.getctime(ini_fn) > self.update_time:
-            self.config = configobj(ini_fn, unrepr=True)
-
-        for fn in os.listdir(self.dirname):
-            ext = os.path.splitext(fn)[1]
-            if ext == "ftsv":
-                ds = dataset.read_ftsv(fn)
-                if universe is not None:
-                    universe.register_ds(ds)
-            elif ext == "plot":
-                plot = configobj(fn, unrepr=True)
-                plots.append(plot)
-            elif os.path.isdir(fn) and recursive:
-                subdirs.append(DataDirectory(fn, recursive=True, universe=universe))
-        
-        ## Set new update time
-        self.update_time = now
-
-
-def SelectionDirectory(object):
-    def __init__(self, dirname):
-        pass
-
-class Project(object):
-    def __init__(self, dirname):
-        """Opens a project directory. The directory must exist and be a valid project."""
-        
-        ## Set path names.
-        self.rootdir = dirname
-        self.anndir = os.path.join(dirname, "annotations")
-        self.seldir = os.path.join(dirname, "selections")
-        self.datadir = os.path.join(dirname, "data")
-        self.exportdir = os.path.join(dirname, "exports")
-        version_file_path = os.path.join(dirname, "VERSION")
-
-        self.universe = Universe()
-        
-        self.data = DataDirectory(self.datadir, universe=self.universe, recursive=True)
-
-    def update(self):
-        for datadir in self.data:
-            datadir.update()
-
-## class Dataset
-## 
-## 
-## class Plot
-## 
-## 
-## class Selection
-## 
-## 
-## class Annotation
-## 
-## 
-## class DataDirectory()
-## 
-## 
-
-

scripts/lpls/lpls.py

@@ -1,438 +0,0 @@
-import sys
-from pylab import *
-import matplotlib
-from scipy import *
-from scipy.linalg import inv,norm
-
-sys.path.append("../../laydi/lib")
-import select_generators
-
-def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', verbose=True):
-    """ 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)
-        alpha : how much z influence (1=max, 0=none)
-
-    :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
-
-    :Notes:
-    
-    """
-    if mean_ctr:
-        xctr, yctr, zctr = mean_ctr
-        X, mnX = center(X, xctr)
-        Y, mnY = center(Y, yctr)
-        Z, mnZ = center(Z, zctr)
-
-    varX = pow(X, 2).sum()
-    varY = pow(Y, 2).sum()
-    varZ = pow(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, m, l))
-    var_x = empty((a_max,))
-    var_y = empty((a_max,))
-    var_z = empty((a_max,))
-
-    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-7
-        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)
-            c = dot(Y.T, t)
-            c = c/sqrt(dot(c.T, c))
-            u = dot(Y, c)
-            diff = abs(u1 - u).max()
-        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)
-        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(norm, 0, T)
-        T = T/tnorm
-        Q = Q*tnorm
-        W = W*tnorm
-    return T, W, P, Q, U, L, K, B, b0, evx, evy, evz, mnX, mnY, mnZ
-
-def svd_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
-    """
-    NB: In the works ...
-    L-shaped Partial Least Sqaures Regression by the svd 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
-
-    :Notes:
-        Not quite there ,,,,,,,,,,,,,,
-    
-    """
-    if mean_ctr:
-        xctr, yctr, zctr = mean_ctr
-        X, mnX = center(X, xctr)
-        Y, mnY = center(Y, xctr)
-        Z, mnZ = center(Z, zctr)
-
-    varX = pow(X, 2).sum()
-    varY = pow(Y, 2).sum()
-    varZ = pow(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))
-    var_x = empty((a_max,))
-    var_y = empty((a_max,))
-    var_z = empty((a_max,))
-    
-    for a in range(a_max):
-        if verbose:
-            print "\n Working on comp. %s" %a
-        xyz = dot(dot(Z,X.T),Y)
-        u,s,vt = linalg.svd(xyz, 0)
-        w = u[:,o]
-        t = dot(X, w)
-        tt = dot(t.T, t)
-        p = dot(X.T, t)/tt
-        q = dot(Y.T, t)/tt
-        l = dot(Z.T, w)
-        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 = dot(dot(W, inv(dot(P.T, W))), Q.T)
-    b0 = mnY - dot(mnX, B)
-    # variance explained
-    evx = 100.0*(1 - var_x/varX)
-    evy = 100.0*(1 - var_y/varY)
-    evz = 100.0*(1 - var_z/varZ)
-    return T, W, P, Q, U, L, K, B, b0, evx, evy, evz
-
-
-def lplsr(X, Y, Z, a_max, mean_ctr=[2,0,1]):
-    """ Haralds LPLS.
-    """
-    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 = pow(X, 2).sum()
-    varY = pow(Y, 2).sum()
-    varZ = pow(Z, 2).sum()
-    m, n = X.shape
-    k, l = Y.shape
-    u, o = Z.shape
-
-    # initialize
-    Wy = empty((l, a_max))
-    Py = empty((l, a_max))
-    Ty = empty((m, a_max))
-    Tz = empty((o, a_max))
-    Wz = empty((u, a_max))
-    Pz = empty((u, a_max))
-    var_x = empty((a_max,))
-    var_y = empty((a_max,))
-    var_z = empty((a_max,))
-
-    # residuals
-    Ey = Y.copy()
-    Ez = Z.copy()
-    Ex = X.copy()
-    for i in range(a_max):
-        YtXZ = dot(Ey.T, dot(X, Ez.T))
-        U, S, V = linalg.svd(YtXZ)
-        wy = U[:,0]
-        print wy
-        wz = V[0,:]
-        ty = dot(Ey, wy)
-        tz = dot(Ez.T, wz)
-        py = dot(Ey.T, ty)/dot(ty.T,ty)
-        pz = dot(Ez, tz)/dot(tz.T,tz)
-        Wy[:,i] = wy
-        Wz[:,i] = wz
-        Ty[:,i] = ty
-        Tz[:,i] = tz
-        Py[:,i] = py
-        Pz[:,i] = pz
-        Ey = Ey - outer(ty, py.T)
-        Ez = (Ez.T - outer(tz, pz.T)).T
-        var_y[i] = pow(Ey, 2).sum()
-        var_z[i] = pow(Ez, 2).sum()
-
-    tyd = apply_along_axis(norm, 0, Ty)
-    tzd = apply_along_axis(norm, 0, Tz)
-    Tyu = Ty/tyd
-    Tzu = Tz/tzd
-    C = dot(dot(Tyu.T, X), Tzu)
-    for i in range(a_max):
-        Ex = Ex - dot(dot(Ty[:,:i+1],C[:i+1,:i+1]), Tz[:,:i+1].T)
-        var_x[i] = pow(Ex,2).sum()
-    # variance explained
-    print "var_x:"
-    print var_x
-    print "varX total:"
-    print varX
-
-    evx = 100.0*(1 - var_x/varX)
-    evy = 100.0*(1 - var_y/varY)
-    evz = 100.0*(1 - var_z/varZ)
-
-    return Ty, Tz, Wy, Wz, Py, Pz, C, Ey, Ez, Ex, evx, evy, evz
-
-def bifpls(X, Y, Z, a_max, alpha):
-    """Swedssihsh LPLS by nipals.
-    """
-    u = X[:,0]
-    Ey = Y.copy()
-    Ez = Z.copy()
-    for i in range(100):
-        w = dot(X.T,u)
-        w = w/vnorm(w)
-        t = dot(X, w)
-        q = dot(Ey, t.T)/dot(t.T,t)
-        qnorm = vnorm(q)
-        q = q/qnorm
-        v = dot(Ez, q)
-        s = dot(Ez.T, v)/dot(v.T,v)
-        v = v*vnorm(s)
-        s = s/vnorm(s)
-        c = qnorm*(alpha*q + (1-alpha)*s)
-        u = dot(Ey, c)/dot(s.T,s)
-        p = dot(X.T, t)/dot(t.T,t)
-        v2 = dot(Ez, s)/dot(s.T,s)
-    Ey = Ey - dot(t, p.T)
-    Ez = Ez - dot(v2, c.T)
-    # variance explained
-    evx = 100.0*(1 - var_x/varX)
-    evy = 100.0*(1 - var_y/varY)
-    evz = 100.0*(1 - var_z/varZ)
-
-def center(a, axis):
-    # 0 = col center, 1 = row center, 2 = double center
-    # -1 = nothing
-    if len(a.shape)==1:
-        mn = a.mean()
-        return a - mn, mn
-    if a.shape[0]==1 or a.shape[1]==1:
-        mn = a.mean()
-        return a - mn, mn
-    if axis==-1:
-        mn = zeros((a.shape[1],))
-        return a - mn, mn
-    elif axis==0:
-        mn = a.mean(0)
-        return a - mn, mn
-    elif axis==1:
-        mn = a.mean(1)[:,newaxis]
-        return a - mn , mn
-    elif axis==2:
-        mn = a.mean(1)[:,newaxis] + a.mean(0) - a.mean()
-        return a - mn, mn
-    else:
-        raise IOError("input error: axis must be in [-1,0,1,2]")
-
-def correlation_loadings(D, T, P, test=True):
-    """ Returns correlation loadings.
-
-    :input:
-        - D: [nsamps, nvars], data (non-centered data)
-        - T: [nsamps, a_max], Scores
-        - P: [nvars, a_max], Loadings
-    :ouput:
-        - Rloads: [nvars, a_max], Correlation loadings
-        - rmseVars: [nvars], scaling coeff. for each var in D
-
-    :notes:
-        - FIXME: Calculation is not valid .... using corrceof instead 
-    """
-    nsamps, nvars = D.shape
-    nsampsT, a_max = T.shape
-    nvarsP, a_maxP = P.shape
-    if nsamps!=nsampsT: raise IOError("D/T mismatch")
-    if a_max!=a_maxP: raise IOError("a_max mismatch")
-    if nvars!=nvarsP: raise IOError("D/P mismatch")
-
-    #init
-    Rloads = empty((nvars, a_max), 'd')
-    stdvar = stats.std(D, 0)
-    rmseVars = sqrt(nsamps-1)*stdvar
-
-    # center
-    D = D - D.mean(0)
-    TT = diag(dot(T.T, T))
-    sTT = sqrt(TT)
-    for a in range(a_max):
-        Rloads[:,a] = sTT[a]*P[:,a]/rmseVars
-    R = empty_like(Rloads)
-    for a in range(a_max):
-        for k in range(nvars):
-            r = corrcoef(D[:,k], T[:,a])
-            R[k,a] = r[0,1]
-    #Rloads = R
-    return Rloads, R, rmseVars
-
-
-
-def cv_lpls(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,1]):
-    """Performs crossvalidation to get generalisation error in lpls"""
-
-    # if double centering of x or y:
-    #     row-center prior to cross validation (as this is independent of subsets)
-    if mean_ctr[0]==2:
-        mnx_row = X.mean(1)[:,newaxis]
-        X = X - mnx_row
-        mean_ctr[0] = 0
-    else:
-        mnx_row = 0
-    if mean_ctr[1]==2:
-        if Y.shape[1]!=1:
-            mny_row = Y.mean(1)[:,newaxis]
-            Y = Y - mny_row
-    else:
-        mny_row = 0
-    
-    cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=True)
-    k, l = Y.shape
-    Yhat = empty((a_max,k,l), 'd')
-    for i, (xcal,xi,ycal,yi,ind) in enumerate(cv_iter):
-        T, W, P, Q, U, L, K, B, b0, evx, evy, evz, mnx, mny, mnz = nipals_lpls(xcal,ycal,Z,
-                                                                               a_max=a_max,
-                                                                               alpha=alpha,
-                                                                               mean_ctr=mean_ctr,
-                                                                               verbose=False)
-        
-        for a in range(a_max):
-            xc = xi - mnx
-            Yhat[a,ind,:] = mny + dot(xc, B[a])
-        
-    Yhat_class = zeros_like(Yhat)
-    for a in range(a_max):
-        for i in range(k):
-            Yhat_class[a,i,argmax(Yhat[a,i,:])] = 1.0
-    class_err = 100*((Yhat_class+Y)==2).sum(1)/Y.sum(0).astype('d')
-    
-    sep = (Y - Yhat)**2
-    rmsep = sqrt(sep.mean(1))
-    return rmsep, Yhat, class_err
-
-def jk_lpls(X, Y, Z, a_max, nsets=None, xz_alpha=.5, mean_ctr=[2,0,1]):
-    cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=False)
-    m, n = X.shape
-    k, l = Y.shape
-    o, p = Z.shape
-    if nsets==None:
-        nsets = m
-    WWx = empty((nsets, n, a_max), 'd')
-    WWz = empty((nsets, o, a_max), 'd')
-    WWy = empty((nsets, l, a_max), 'd')
-    for i, (xcal,xi,ycal,yi) in enumerate(cv_iter):
-        T, W, P, Q, U, L, K, B, b0, evx, evy, evz,mnx,mny,mnz = nipals_lpls(xcal,ycal,Z,
-                                                                            a_max=a_max,
-                                                                            alpha=xz_alpha,
-                                                                            mean_ctr=mean_ctr,
-                                                                            scale='loads',
-                                                                            verbose=False)
-        WWx[i,:,:] = W
-        WWz[i,:,:] = L
-        WWy[i,:,:] = Q
-
-    return WWx, WWz, WWy

scripts/lpls/plots_lpls.py

@@ -1,148 +0,0 @@
-import pylab
-import matplotlib
-import networkx as nx
-import scipy
-import rpy
-
-def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None, **kwds):
-    """ Correlation loading plot."""
-
-    # background
-    if ax==None or drawback==True:
-        radius = 1
-        center = (0,0)
-        c100 = matplotlib.patches.Circle(center,
-                                         radius=radius,
-                                         facecolor=(0.97, .97, .97),
-                                         zorder=1,
-                                         linewidth=1,
-                                         edgecolor=(0,0,0))
-        
-        c50 = matplotlib.patches.Circle(center,
-                                        radius=radius/2.0,
-                                        facecolor=(.85,.85,.85),
-                                        zorder=1,
-                                        linewidth=1,
-                                        edgecolor=(0,0,0))
-        
-        ax = pylab.gca()
-        ax.add_patch(c100)
-        ax.add_patch(c50)
-        ax.axhline(lw=1.5,color='k', zorder=4)
-        ax.axvline(lw=1.5,color='k', zorder=4)
-
-    # corrloads
-    ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder, **kwds)
-    ax.set_xlim([-1.1,1.1])
-    ax.set_ylim([-1.1,1.1])
-    if expvar!=None:
-        xstring = "Comp: %d   expl.var:  %.1f " %(pc1+1, expvar[pc1])
-        pylab.xlabel(xstring)
-        ystring = "Comp: %d   expl.var.:  %.1f " %(pc2+1, expvar[pc2])
-        pylab.ylabel(ystring)
-    if labels!=None:
-        assert(len(labels)==R.shape[0])
-        for name, r in zip(labels, R):
-            pylab.text(r[pc1], r[pc2], "  " + name)
-    #pylab.show()
-
-
-def dag(terms, ontology):
-    rpy.r.library("GOstats")
-    __parents = {'bp' : rpy.r.GOBPPARENTS,
-                 'mf' : rpy.r.GOMFPARENTS,
-                 'cc' : rpy.r.GOCCPARENTS}
-    gograph = rpy.r.GOGraph(terms, __parents.get(ontology.lower()))
-    dag = rpy.r.edges(gograph)
-    #setattr(dag, "_ontology", ontology)
-    return dag
-
-def plot_dag(dag, node_color='b', node_size=30,with_labels=False,nodelist=None,pos=None,**kwd):
-
-    rpy.r.library("GOstats")
-    
-    dag_name = "GO-bp" 
-    # networkx does not play well with colon in node names
-    clean_edges = {}
-    for head, neigb in dag.items():
-        head = head.replace(":", "_")
-        nei = [i.replace(":", "_") for i in neigb]
-        clean_edges[head] = nei
-    if pos==None:
-        G = nx.from_dict_of_lists(clean_edges, nx.DiGraph(name=dag_name))
-        pos = nx.pydot_layout(G, prog='dot')
-        pos_new = {}
-        for k, v in pos.items():
-            x,y = v
-            k = k.replace("_", ":")
-            pos_new[k] = (x, -y) 
-        pos = pos_new
-    
-    G = nx.from_dict_of_lists(dag, nx.Graph(name=dag_name))
-    if len(node_color)>1:
-        assert(len(node_color)==len(nodelist))
-        
-    nx.draw_networkx(G,pos, with_labels=with_labels, node_size=node_size, node_color=node_color, nodelist=nodelist, **kwd)
-    return pos
-
-
-def plot_ZXcorr(gene_ids, term_ids, gene2go, X, D, scale=True):
-    """ Plot correlation/covariance between genes as a function of
-    semantic difference.
-
-    input: X (n, p) data matrix
-           D (p, p) gene-gene sematic similarity matrix
-    """
-    D = scipy.corrcoef(X)
-    term2ind = dict(enumerate(term_ids))
-    for i, gene_i in enumerate(gene_ids):
-        for j, gene_j in enumerate(gene_ids):
-            if j<i:
-                r2 = D[i,j]
-                terms_i = gene2go[gene_i]
-                terms_j = gene2go[gene_j]
-                for ti, term in enumerate(term_ids):
-                    if term in terms_i:
-                        pass
-
-
-def clustering_index(T, Yg):
-    pass
-
-def draw_gene(gid, gene_ids, gene2go, Z, tmat, terms, G, pos):
-    """Draw dags with marked go terms and distance to all terms.
-    """
-    sub_terms = gene2go[gid]
-    sub_index = [i for i, tid in enumerate(terms) if tid in sub_terms]
-    node_size = 70.*scipy.ones((len(terms),))
-    node_size[sub_index] = 500
-    gene_index = [i for i, gene_id in enumerate(gene_ids) if gene_id==gid]
-    node_color = Z[:,gene_index].ravel()
-    #1/0
-    #node_size=200*node_color
-    #node_color='g'
-    pylab.figure()
-    nx.draw_networkx(G, pos, node_color=node_color, node_size=node_size, with_labels=False, nodelist=terms)
-    ax = pylab.gca()
-    pylab.colorbar(ax.collections[0])
-
-    for tid in sub_index:
-        pylab.figure()
-        node_color = tmat[tid,:]
-        #node_size = 70*scipy.ones((len(terms),))
-        node_size = 170*node_color
-        node_size[tid] = 500
-        nx.draw_networkx(G, pos, node_color=node_color, node_size=node_size, with_labels=False, nodelist=terms)
-        pylab.title(terms[tid])
-        ax = pylab.gca()
-        pylab.colorbar(ax.collections[0])
-    pylab.show()
-    #nx.show()
-    
-    
-    
-                
-                
-                
-                
-    

scripts/lpls/rpy_go.py

@@ -1,263 +0,0 @@
-""" Module for Gene ontology related functions called in R"""
-import scipy
-import rpy
-silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
-import collections
-
-def goterms_from_gene(genelist, ontology='BP', garbage=['IEA'], ic_cutoff=2.0, verbose=False):
-    """ Returns the go-terms from a specified genelist (Entrez id).
-
-    Recalculates the information content if needed based on selected evidence codes.
-    
-    """
-    rpy.r.library("GOSim")
-    _CODES = {"IMP" : "inferred from mutant phenotype",
-              "IGI" : "inferred from genetic interaction",
-              "IPI" :"inferred from physical interaction",
-              "ISS" : "inferred from sequence similarity",
-              "IDA" : "inferred from direct assay",
-              "IEP" : "inferred from expression pattern",
-              "IEA" : "inferred from electronic annotation",
-               "TAS" : "traceable author statement",
-              "NAS" : "non-traceable author statement",
-              "ND" : "no biological data available",
-              "IC" : "inferred by curator"
-              }
-    _ONTOLOGIES = ['BP', 'CC', 'MF']
-    #assert(scipy.all([(code in _CODES) for code in garbage]) or garbage==None)
-    assert(ontology in _ONTOLOGIES)
-    dummy = rpy.r.setOntology(ontology)
-    ddef = False
-    if ontology=='BP' and garbage!=None:
-        # This is for ont=BP and garbage =['IEA', 'ISS', 'ND']
-        rpy.r.load("ICsBP_small.rda") # Excludes IEA
-        ic = rpy.r.assign("IC",rpy.r.IC, envir=rpy.r.GOSimEnv)
-        max_val = 0
-        for key, val in ic.items():
-            if val != scipy.inf:
-                if val>max_val:
-                    max_val = val
-        for key, val in ic.items():
-            ic[key] = val/max_val
-    else:
-        # NB! this IC is just for BP
-        ic = rpy.r('get("IC", envir=GOSimEnv)')
-    print "loading GO definitions environment"
-    
-    gene2terms = collections.defaultdict(list)
-    cc = 0
-    dd = 0
-    ii = 0
-    jj = 0
-    kk = 0
-    all = rpy.r.mget(genelist, rpy.r.GOENTREZID2GO,ifnotfound="NA")
-    n_ic = len(ic)
-    print "Number of terms with IC: %d" %n_ic
-    stopp = False
-    for gene, terms in all.items():
-        if verbose:
-            print "\n\n ======ITEM========\n"
-            print "Gene: " + str(gene)
-            print "Number of terms: %d " %len(terms)
-            print terms
-            print "---\n"
-            if stopp:
-                1/0
-        if terms!="NA":
-            for term, desc in terms.items():
-                if verbose:
-                    print "\nChecking term: " + str(term)
-                    print "With description: " + str(desc)
-                if desc['Ontology'].lower() == ontology.lower() and term in ic:
-                    if ic[term]>ic_cutoff:
-                        #print ic[term]
-                        jj+=1
-                        if verbose:
-                            print "too high" + str((gene, term))
-                            stopp = True
-                        continue
-                    cc += 1
-                    if verbose:
-                        print "accepted" + str((gene, term))
-                    gene2terms[gene].append(term)
-                else:
-                    if verbose:
-                        print "Not accepted: " + str((gene, term))
-                    if term not in ic:
-                        if verbose:
-                            print "Not in IC: "  + str((gene, term))
-                        kk+=1
-                    if desc['Ontology'].lower() != ontology:
-                        if verbose:
-                            print "Not in Ontology" + str((gene, term))
-                        dd+=1
-        else:
-            ii+=1
-                    
-    print "Number of genes total: %d" %len(all)
-    print "\nNumber of genes without annotation: (%d (NA))" %ii
-    print "\nNumber of terms with annoation but no IC: %d" %kk
-    print "\nNumber of terms not in %s : %d " %(ontology, dd)
-    print "\nNumber of terms with too high IC : %d " %jj
-    print "\n Number of accepted terms: %d" %cc
-    
-    return gene2terms
-
-def genego_matrix(goterms, tmat, gene_ids, term_ids, func=max):
-    ngenes = len(gene_ids)
-    nterms = len(term_ids)
-    gene2indx = {}
-    for i,id in enumerate(gene_ids):
-        gene2indx[id]=i
-    term2indx = {}
-    for i,id in enumerate(term_ids):
-        term2indx[id]=i
-    #G = scipy.empty((nterms, ngenes),'d')
-    G = []
-    new_gene_index = []
-    for gene, terms in goterms.items():
-        g_ind = gene2indx[gene]
-        if len(terms)>0:
-            t_ind = []
-            new_gene_index.append(g_ind)
-            for term in terms:
-                if term2indx.has_key(term): t_ind.append(term2indx[term])
-            subsim = tmat[t_ind, :]
-            gene_vec = scipy.apply_along_axis(func, 0, subsim)
-            G.append(gene_vec)
-
-    return scipy.asarray(G), new_gene_index
-
-def genego_sim(gene2go, gene_ids, all_go_terms, STerm, go_term_sim="OA", term_sim="Lin", verbose=False):
-    """Returns go-terms x genes similarity matrix.
-
-    :input:
-           - gene2go: dict: keys: gene_id, values: go_terms
-           - gene_ids: list of gene ids (entrez ids)
-           - STerm: (go_terms x go_terms) similarity matrix
-           - go_terms_sim: similarity measure between a gene and multiple go terms (max, mean, OA)
-           - term_sim: similarity measure between two go-terms
-           - verbose
-    """
-    rpy.r.library("GOSim")
-
-    #gene_ids = gene2go.keys()
-    GG = scipy.empty((len(all_go_terms), len(gene_ids)), 'd')
-    for j,gene in enumerate(gene_ids):
-        for i,go_term in enumerate(all_go_terms):
-            if verbose:
-                print "\nAssigning similarity from %s to terms(gene): %s" %(go_term,gene)
-            GG_ij = rpy.r.getGSim(go_term, gene2go[gene], similarity=go_term_sim,
-                                  similarityTerm=term_sim, STerm=STerm, verbose=verbose)
-            GG[i,j] = GG_ij
-    return GG
-
-def goterm2desc(gotermlist):
-    """Returns the go-terms description keyed by go-term.
-    """
-    rpy.r.library("GO")
-    term2desc = {}
-    for term in gotermlist:
-        try:
-            desc = rpy.r('Term(GOTERM[["' +str(term)+ '"]])')
-            term2desc[str(term)] = desc
-        except:
-            raise Warning("Description not found for %s\n Mapping incomplete" %term)
-    return term2desc
-
-def parents_dag(go_terms, ontology=['BP']):
-    """ Returns a list of lists representation of a GO DAG parents of goterms.
-
-    make the networkx graph by:
-        G = networkx.Digraph()
-        G = networkx.from_dict_of_lists(edge_dict, G)
-    """
-    try:
-        rpy.r.library("GOstats")
-    except:
-        raise ImportError, "Gostats"
-    assert(go_terms[0][:3]=='GO:')
-
-    # go valid namespace
-    go_env = {'BP':rpy.r.GOBPPARENTS, 'MF':rpy.r.GOMFPARENTS, 'CC': rpy.r.GOCCPARENTS}
-    graph = rpy.r.GOGraph(go_terms, go_env[ontology[0]])
-    edges = rpy.r.edges(graph)
-    edges.pop('all')
-    edge_dict = {}
-    for head, neighbours in edges.items():
-        for nn in neighbours.values():
-            if edge_dict.has_key(nn):
-                edge_dict[nn].append(head)
-            else:
-                edge_dict[nn] = [head]
-    return edge_dict
-
-def gene_GO_hypergeo_test(genelist,universe="entrezUniverse",ontology="BP",chip = "hgu133a",pval_cutoff=0.01,cond=False,test_direction="over"):
-    
-    #assert(scipy.alltrue([True for i in genelist if i in universe]))
-    universeGeneIds = universe
-    params = rpy.r.new("GOHyperGParams",
-                       geneIds=genelist,
-                       annotation="hgu133a",
-                       ontology=ontology,
-                       pvalueCutoff=pval_cutoff,
-                       conditional=cond,
-                       testDirection=test_direction
-                       )
-    result = rpy.r.summary(rpy.r.hyperGTest(params))
-    
-    return result, params
-
-def data_aff2loc_hgu133a(X, aff_ids, verbose=False):
-    aff_ids = scipy.asarray(aff_ids)
-    if verbose:
-        print "\nNumber of probesets in affy list: %s" %len(aff_ids)
-    import rpy
-    rpy.r.library("hgu133a")
-    trans_table = rpy.r.as_list(rpy.r.hgu133aENTREZID)
-    if verbose:
-        print "Number of entrez ids: %d" %(scipy.asarray(trans_table.values())>0).sum()
-    enz2aff = collections.defaultdict(list)
-    #aff2enz = collections.defaultdict(list)
-    for aff, enz in trans_table.items():
-        if int(enz)>0 and (aff in aff_ids):
-            enz2aff[enz].append(aff)
-            #aff2enz[aff].append(enz)
-    if verbose:
-        print "\nNumber of translated entrez ids: %d" %len(enz2aff)
-    aff2ind = dict(zip(aff_ids, scipy.arange(len(aff_ids))))
-    var_x = X.var(0)
-    new_data = []
-    new_ids = []
-    m = 0
-    s = 0
-    for enz, aff_id_list in enz2aff.items():
-        index = [aff2ind[aff_id] for aff_id in aff_id_list]
-        if len(index)>1:
-            m+=1
-            if verbose:
-                pass
-                #print "\nEntrez id: %s has %d probesets" %(enz, len(index))
-                #print index
-            xsub = X[:,index]
-            choose_this = scipy.argmax(xsub.var(0))
-            new_data.append(xsub[:,choose_this].ravel())
-        else:
-            s+=1
-            new_data.append(X[:,index].ravel())
-        new_ids.append(enz)
-    if verbose:
-        print "Ids with multiple probesets: %d" %m
-        print "Ids with unique probeset: %d" %s
-    X = scipy.asarray(new_data).T
-    return X, new_ids
-
-def R_PLS(x,y,ncomp=3, validation='"LOO"'):
-    rpy.r.library("pls")
-    rpy.r.assign("X", x)
-    rpy.r.assign("Y", y)
-    callstr = "plsr(Y~X, ncomp=" + str(ncomp) + ", validation=" + validation + ")"
-    print callstr
-    result = rpy.r(callstr)
-    return result
-

scripts/lpls/run_smoker.py

@@ -1,654 +0,0 @@
-import sys,time,cPickle
-import rpy
-from pylab import gca, figure, subplot,plot
-from scipy import *
-from scipy.linalg import norm
-from lpls import correlation_loadings
-
-import rpy_go
-sys.path.append("../../laydi") # home of dataset
-sys.path.append("../../laydi/lib") # home of cx_stats
-sys.path.append("/home/flatberg/laydi/scripts/lpls")
-sys.path.append("/home/flatberg/pyblm/")
-import dataset
-import cx_stats
-import pyblm
-from pyblm.engines import nipals_lpls, pls
-from pyblm.crossvalidation import lpls_val, lpls_jk
-from pyblm.statistics import pls_qvals
-from plots_lpls import plot_corrloads, plot_dag
-import plots_lpls
-
-
-def iqr(X, axis=0):
-    """Interquartile range filtering."""
-    def _iqr(c):
-        return stats.scoreatpercentile(c, 75) - stats.scoreatpercentile(c, 25)
-    return apply_along_axis(_iqr, axis, X)
-
-
-
-# Possible outliers
-# http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=16817967
-sample_outliers = ['OV:NCI_ADR_RES', 'CNS:SF_295', 'CNS:SF_539', 'RE:SN12C', 'LC:NCI_H226', 'LC:NCI_H522', 'PR:PC_3', 'PR:DU_145']
-
-outlier = 'ME:LOXIMVI' # 19
-
-####### OPTIONS ###########
-# data
-
-chip = "hgu133a"
-use_data = 'uma'
-#use_data = 'scherf'
-#use_data = 'uma'
-
-if use_data == 'scherf':
-    data_cached = False
-    use_saved_plsr_result = False
-    subset = 'plsr'
-    small_test = False
-    use_sbg_subset = True # the sandberg nci-Ygroups subset
-    std_y = False
-    std_z = False
-    # go
-    ontology = "bp"
-    min_genes = 5
-    similarities = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
-    meth = similarities[2]
-    go_term_sim = "OA"
-    # lpls
-    a_max = 10
-    aopt = 4
-    aopt = 2 # doubling-time
-    xz_alpha = .5
-    w_alpha = .3
-    center_axis = [2, 0, 2]
-    zorth = True
-    nsets = None
-    qval_cutoff = 0.1
-    n_iter = 50
-    
-    alpha_check = True
-    calc_rmsep = True
-    
-    bevel_check = False
-    
-    save_calc = True
-
-elif use_data == 'uma':
-    data_cached = False
-    use_saved_plsr_result = False
-    subset = 'iqr'
-    small_test = False
-    use_sbg_subset = True # the sandberg nci-Ygroups subset
-    std_y = False
-    std_z = False
-    # go
-    ontology = "bp"
-    min_genes = 5
-    similarities = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
-    meth = similarities[2]
-    go_term_sim = "OA"
-    # lpls
-    a_max = 10
-    aopt = 5
-    xz_alpha = .5
-    w_alpha = .3
-    center_axis = [2, 0, 2]
-    zorth = True
-    nsets = None
-    qval_cutoff = 0.01
-    n_iter = 50
-    
-    alpha_check = True
-    calc_rmsep = True
-    
-    bevel_check = False
-    
-    save_calc = True
-
-elif use_data == 'smoker':
-    data_cached = False
-    use_saved_plsr_result = False
-    #subset = 'plsr'
-    subset = 'plsr'
-    small_test = False
-    use_sbg_subset = False # the sandberg nci-Ygroups subset
-    std_y = False
-    std_z = False
-    # go
-    ontology = "bp"
-    min_genes = 5
-    similarities = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
-    meth = similarities[2]
-    go_term_sim = "OA"
-    # lpls
-    a_max = 5
-    aopt = 2
-    xz_alpha = .5
-    w_alpha = .3
-    center_axis = [2, 0, 2]
-    zorth = True
-    nsets = None
-    qval_cutoff = 0.01
-    n_iter = 50
-    
-    alpha_check = True
-    calc_rmsep = True
-    
-    bevel_check = False
-    
-    save_calc = True
-else:
-    raise ValueError
-
-print "Using options for : " + use_data
-
-######## DATA ##########
-if use_data=='smoker':
-    # full smoker data
-    DX = dataset.read_ftsv(open("/home/flatberg/datasets/smokers/full/Smokers.ftsv"))
-    DY = dataset.read_ftsv(open("/home/flatberg/datasets/smokers/full/Yg.ftsv"))
-    DYr = dataset.read_ftsv(open("/home/flatberg/datasets/smokers/full/Ypy.ftsv"))
-    Y = DYr.asarray().astype('d')
-    gene_ids = DX.get_identifiers('gene_ids', sorted=True)
-    sample_ids = DY.get_identifiers('_patient', sorted=True)
-
-elif use_data=='scherf':
-    print "hepp"
-    #DX = dataset.read_ftsv(open("../../data/scherf/old_data/scherfX.ftsv"))
-    #DY = dataset.read_ftsv(open("../../data/scherf/old_data/scherfY.ftsv"))
-    DX = dataset.read_ftsv(open("nci60/X5964.ftsv", "r"))
-    DYg = dataset.read_ftsv(open("../../data/uma/Yg133.ftsv"))
-    DYr = dataset.read_ftsv(open("../../data/uma/Yd.ftsv"))
-    Y = DYg.asarray().astype('d')
-    DY = DYg.copy()
-    Yg = Y
-    Yr = DYr.asarray().astype('d')
-    X = DX.asarray()
-    gene_ids = DX.get_identifiers('gene_ids', sorted=True)
-    sample_ids = DY.get_identifiers('cline', sorted=True)
-
-elif use_data=='staunton':
-    pass
-
-elif use_data=='uma':
-    DX = dataset.read_ftsv(open("/home/flatberg/datasets/uma/X133.ftsv"))
-    DYg = dataset.read_ftsv(open("/home/flatberg/datasets/uma/Yg133.ftsv"))
-    DYr = dataset.read_ftsv(open("/home/flatberg/datasets/uma/Yd.ftsv"))
-    X = DX.asarray()
-    Y = DYg.asarray().astype('d')
-    DY = DYg.copy()
-    Yg = Y
-    Yr = DYr.asarray().astype('d')
-    gene_ids = DX.get_identifiers('gene_ids', sorted=True)
-    sample_ids = DY.get_identifiers('cline', sorted=True)
-
-else:
-    print "use_data argument: (%s) not valid" %use_method
-
-if use_sbg_subset and use_data in ['uma', 'scherf', 'staunton']:
-    print "Using sbg subset of cancers"
-    Y = Yg
-    Y_old = Y.copy()
-    Yr_old = Yr.copy()
-    X_old = X.copy() 
-    keep_samples = ['CN', 'ME', 'LE', 'CO', 'RE']
-    #keep_samples = ['CN', 'ME', 'LE', 'CO', 'RE']
-    sample_ids_original = DY.get_identifiers('cline', sorted=True)
-    sample_ids= [i for i in sample_ids if i[:2] in keep_samples]
-    rows_ind = [i for i,name in enumerate(sample_ids_original) if name[:2] in keep_samples]
-    # take out rows in X,Y
-    X = X[rows_ind,:]
-    Y = Y[rows_ind,:]
-    Yr = Yr[rows_ind,:]
-
-    #  identify redundant columns in Y
-    cols_ind = where(Y.sum(0)>1)[0]
-    Y = Y[:, cols_ind]
-
-    # create new datasets with updated idents
-    cat_ids = [name for i,name in enumerate(DYg.get_identifiers('_cancer', sorted=True)) if i in cols_ind]
-    DX = dataset.Dataset(X, [['cline', sample_ids], ['gene_ids', gene_ids]], name='Dxr')
-    DYg = dataset.CategoryDataset(Y, [['cline', sample_ids], ['_cancer', cat_ids]], name='Dyr')
-    DYr = dataset.Dataset(Yr, [['cline', sample_ids], ['_time', ['doubling_time']]], name='Dyrr')
-    DY_old = DY.copy()
-    DY = DYg
-    print "Now there are %d samples in X" %X.shape[0]
-
-# use subset of genes with defined GO-terms
-ic_all = 2026006.0 # sum of all ic in BP
-max_ic = -log(1/ic_all)
-ic_cutoff = -log(min_genes/ic_all)/max_ic
-print "Information cutoff for min %d genes: %.2f" %(min_genes, ic_cutoff)
-
-gene2goterms = rpy_go.goterms_from_gene(gene_ids, ic_cutoff=ic_cutoff)
-all_terms = set()
-for t in gene2goterms.values():
-    all_terms.update(t)
-terms = list(all_terms)
-print "\nNumber of go-terms: %s" %len(terms)
-
-# update genelist
-gene_ids = gene2goterms.keys()
-print "\nNumber of genes: %s" %len(gene_ids)
-
-X = DX.asarray()
-index = DX.get_indices('gene_ids', gene_ids)
-X = X[:,index]
-
-# Use only subset defined on GO
-ontology = 'BP'
-print "\n\nFiltering genes by Go terms "
-
-# use subset based on SAM,PLSR or (IQR)
-
-if subset=='plsr':
-    print "plsr filter on genes"
-    if use_saved_plsr_result:
-        index = cPickle.load(open('plsr_index.pkl'))
-         # Subset data
-        X = X[:,index]
-
-        gene_ids = [gid for i, gid in enumerate(gene_ids) if i in index]
-        print "\nNumber of genes: %s" %len(gene_ids)
-        print "\nWorking on subset with %s genes " %len(gene_ids)
-
-        # update valid go-terms
-        gene2goterms = rpy_go.goterms_from_gene(gene_ids, ic_cutoff=ic_cutoff)
-        all_terms = set()
-        for t in gene2goterms.values():
-            all_terms.update(t)
-        terms = list(all_terms)
-        print "\nNumber of go-terms: %s" %len(terms)
-        # update genelist
-        gene_ids = gene2goterms.keys()
-    else:
-        
-        print "Initial plsr qvals"
-        
-        xcal_tsq_x, xpert_tsq_x = pyblm.pls_qvals(X, Y, aopt=aopt, n_iter=n_iter, center_axis=[0,0], nsets=None)
-        qvals = pyblm.statistics._fdr(xcal_tsq_x, xpert_tsq_x, median)
-        
-        # cut off
-        #sort_index = qvals.argsort()
-        #index = sort_index[:800]
-        #qval_cutoff = qvals[sort_index[500]]
-        print "Using cuf off: %.2f" %qval_cutoff
-        index = where(qvals<qval_cutoff)[0]
-        if small_test:
-            index = index[:20]
-        # Subset data
-        X = X[:,index]
-
-        gene_ids = [gid for i, gid in enumerate(gene_ids) if i in index]
-        print "\nNumber of genes: %s" %len(gene_ids)
-        print "\nWorking on subset with %s genes " %len(gene_ids)
-
-        # update valid go-terms
-        gene2goterms = rpy_go.goterms_from_gene(gene_ids, ic_cutoff=ic_cutoff)
-        all_terms = set()
-        for t in gene2goterms.values():
-            all_terms.update(t)
-        terms = list(all_terms)
-        print "\nNumber of go-terms: %s" %len(terms)
-        # update genelist
-        gene_ids = gene2goterms.keys()
-    print "\nNumber of genes: %s" %len(gene_ids)
-
-elif subset == 'iqr':
-    iqr_vals = iqr(X)
-    index = where(iqr_vals>1)[0]
-    X = X[:,index]
-    gene_ids = [gid for i, gid in enumerate(gene_ids) if i in index]
-    print "\nNumber of genes: %s" %len(gene_ids)
-    print "\nWorking on subset with %s genes " %len(gene_ids)
-    
-    # update valid go-terms
-    gene2goterms = rpy_go.goterms_from_gene(gene_ids, ic_cutoff=ic_cutoff)
-    all_terms = set()
-    for t in gene2goterms.values():
-        all_terms.update(t)
-    terms = list(all_terms)
-    print "\nNumber of go-terms: %s" %len(terms)
-    # update genelist
-    gene_ids = gene2goterms.keys()
-else:
-    # noimp (smoker data is prefiltered)
-    print "No prefiltering on data used"
-    pass 
-
-
-rpy.r.library("GOSim")
-# Go-term similarity matrix
-
-print "Term-term similarity matrix (method = %s)" %meth
-print "\nCalculating term-term similarity matrix"
-
-if meth=="CoutoEnriched":
-    aa = 0
-    ba = 0
-    rpy.r.setEnrichmentFactors(alpha = aa, beta =ba)
-if not data_cached:
-    rpytmat = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.getTermSim)(terms, method=meth,verbose=False)
-    tmat = rpy.r.assign("haha", rpytmat)
-    print "\n Calculating Z matrix"
-    Z = rpy_go.genego_sim(gene2goterms,gene_ids,terms,rpytmat,go_term_sim=go_term_sim,term_sim=meth)
-
-    DZ = dataset.Dataset(Z, [['go-terms', terms], ['gene_ids', gene_ids]], name='Dz_'+str(meth))
-    # update data (X) matrix
-    newind = DX.get_indices('gene_ids', gene_ids)
-    Xr = DX.asarray()[:,newind]
-    DXr = dataset.Dataset(Xr, [['cline', sample_ids], ['gene_ids', gene_ids]], name='Dxr')
-else:
-    #DXr = dataset.read_ftsv(open('Xr.ftsv', 'r'))
-    newind = DX.get_indices('gene_ids', gene_ids)
-    Xr = DX.asarray()[:,newind]
-    DXr = dataset.Dataset(Xr, [['cline', sample_ids], ['gene_ids', gene_ids]], name='Dxr')
-    DY = dataset.read_ftsv(open('Y.ftsv', 'r'))
-    DZ = dataset.read_ftsv(open('Z.ftsv', 'r')) 
-    Xr = DXr.asarray()
-    Y = DY.asarray()
-    Z = DZ.asarray()
-    sample_ids = DX.get_identifiers('cline', sorted=True)
-
-# standardize Z?
-sdtz = False
-if sdtz:
-    DZ._array = DZ._array/Dz._array.std(0)
-sdty = False
-if sdty:
-    DY._array = DY._array/DY._array.std(0)
-
-
-# ##### PLS ONLY, CHECK FOR SIMILARITY BETWEEN W and Z #######
-if bevel_check:
-    Xr = DXr.asarray()
-    Y = DY.asarray()
-    from pylab import figure, scatter, xlabel, subplot,xticks,yticks
-    Xrcc = Xr - Xr.mean(0) - Xr.mean(1)[:,newaxis] + Xr.mean()
-    Zcc = Z - Z.mean(0) - Z.mean(1)[:,newaxis] + Z.mean()
-    Yc = Y - Y.mean(0)
-    xy_pls_result = pls(Xrcc, Yc, a_max)
-    xz_pls_result = pls(Xrcc.T, Zcc.T, a_max)
-    # check for linearity between scores of xz-result and W of xy-result
-    Wxy = xy_pls_result['W']
-    Txz = xz_pls_result['T']
-    figure()
-    n = 0
-    for i in range(a_max):
-        w = Wxy[:,i]
-        for j in range(a_max):
-            n += 1
-            t = Txz[:,j]
-            r2 = stats.corrcoef(w, t)[0,-1]
-            subplot(a_max, a_max, n)
-            scatter(w, t)
-            xticks([])
-            yticks([])
-            xlabel('(Wxy(%d), Tzx(%d)), r2: %.1f ' %(i+1,j+1,r2))
-# ####### LPLSR ######## 
-
-if save_calc and not data_cached:
-    print "Saving calculations"
-    import cPickle
-    fh =  open("g2go_s.pkl", "w")
-    cPickle.dump(gene2goterms, fh)
-    fh.close()
-    dataset.write_ftsv(open('Xs.ftsv', 'w'), DXr, decimals=7)
-    dataset.write_ftsv(open('Ysg.ftsv', 'w'), DY, decimals=7)
-    dataset.write_ftsv(open('Yspy.ftsv', 'w'), DYr, decimals=7)
-    dataset.write_ftsv(open('Zs.ftsv', 'w'), DZ, decimals=7)
-
-def read_calc():
-    import cPickle
-    fh =  open("g2go_s.pkl")
-    gene2goterms = cPickle.load(fh)
-    fh.close()
-    DXr = dataset.read_ftsv('Xu.ftsv')
-    DY = dataset.read_ftsv('Yu.ftsv')
-    DYr = dataset.read_ftsv('Ydu.ftsv')
-    DZ = dataset.read_ftsv('Zu.ftsv')
-    return DXr, DY, DYr, DZ, gene2goterms
-
-    
-print "LPLSR ..."
-lpls_result = nipals_lpls(Xr,Y,Z, a_max,alpha=xz_alpha, center_axis=center_axis, zorth=zorth)
-globals().update(lpls_result)
-
-# Correlation loadings
-dx,Rx,rssx = correlation_loadings(Xr, T, P)
-dx,Ry,rssy = correlation_loadings(Y, T, Q)
-cadz,Rz,rssz = correlation_loadings(Z.T, W, L)
-
-# Prediction error
-if calc_rmsep:
-    rmsep , yhat, class_error = pyblm.crossvalidation.lpls_val(Xr, Y, Z, a_max, alpha=xz_alpha,center_axis=center_axis, nsets=nsets,zorth=zorth)
-
-Alpha = arange(0.0, 1.01, .05)
-if alpha_check:
-    Rmsep,Yhat, CE = [],[],[]
-    for a in Alpha:
-        print "alpha %f" %a
-        rmsep_a , yhat, ce = pyblm.lpls_val(Xr, Y, Z, a_max, alpha=a,
-                                          center_axis=center_axis,nsets=nsets,
-                                          zorth=zorth)
-        Rmsep.append(rmsep_a.copy())
-        Yhat.append(yhat.copy())
-        CE.append(ce.copy())
-    Rmsep = asarray(Rmsep)
-    Yhat = asarray(Yhat)
-    #CE = asarray(CE)
-
-random_alpha_check = True
-if random_alpha_check:
-    n_zrand = 100
-    RMS,YHAT, CEE = [],[],[]
-    zindex = arange(Z.shape[1])
-    for ii in range(n_zrand):
-        zind_rand = zindex.copy()
-        random.shuffle(zind_rand)
-        Zrand = Z[:,zind_rand]
-        #Alpha = arange(0.0, 1.1, .25)
-        Rmsep_r,Yhat_r, CE_r = [],[],[]
-        for a in Alpha:
-            print "Iter: %d  alpha %.2f" %(ii, a)
-            rmsep , yhat, ce = pyblm.lpls_val(Xr, Y, Zrand, a_max, alpha=a,center_axis=center_axis,nsets=nsets, zorth=zorth)
-            Rmsep_r.append(rmsep.copy())
-            Yhat_r.append(yhat.copy())
-            CE_r.append(ce.copy())
-        RMS.append(Rmsep_r)
-        YHAT.append(Yhat_r)
-        CEE.append(CE_r)
-    RMS = asarray(RMS)
-    YHAT = asarray(YHAT)
-    CEE = asarray(CEE)
-
-# Significance Hotellings T
-calc_qvals = True
-if not calc_qvals:
-    Wx, Wz = pyblm.crossvalidation.lpls_jk(Xr, Y, Z, aopt, center_axis=center_axis, xz_alpha=xz_alpha, nsets=nsets)
-    Ws = W*apply_along_axis(norm, 0, T)
-    Ws = Ws[:,:aopt]
-    cal_tsq_x = pyblm.statistics.hotelling(Wx, Ws[:,:aopt], alpha=w_alpha)
-    Ls = L*apply_along_axis(norm, 0, K) 
-    cal_tsq_z = pyblm.statistics.hotelling(Wz, Ls[:,:aopt], alpha=0.01)
-
-# qvals
-
-if calc_qvals:
-    cal_tsq_z, pert_tsq_z, cal_tsq_x, pert_tsq_x = pyblm.lpls_qvals(Xr, Y, Z, aopt=aopt, zx_alpha=xz_alpha, n_iter=n_iter, nsets=nsets)
-
-    qvalz = pyblm.statistics._fdr(cal_tsq_z, pert_tsq_z, median)
-    qvalx = pyblm.statistics._fdr(cal_tsq_x, pert_tsq_x, median)
-
-
-    # p-values, set-enrichment analysis
-    active_genes_ids = where(qvalx < qval_cutoff)[0]
-    active_genes = [name for i,name in enumerate(gene_ids) if i in active_genes_ids]
-    active_universe = gene_ids
-    gsea_result, gsea_params= rpy_go.gene_GO_hypergeo_test(genelist=active_genes,universe=active_universe,chip=chip,pval_cutoff=1.0,cond=False,test_direction="over")
-    active_goterms_ids = where(qvalz < qval_cutoff)[0]
-    active_goterms = [name for i,name in enumerate(terms) if i in active_goterms_ids]
-    
-    gsea_t2p = dict(zip(gsea_result['GOBPID'], gsea_result['Pvalue']))
-
-
-
-#### PLOTS ####
-
-from pylab import *
-from scipy import where
-dg = plots_lpls.dag(terms, "bp")
-pos = None
-
-if calc_qvals:
-    figure(300)
-    subplot(2,1,1)
-    pos = plots_lpls.plot_dag(dg, node_color=cal_tsq_z, pos=pos, nodelist=terms)
-    ax = gca()
-    colorbar(ax.collections[0])
-    xlabel('q values')
-    xticks([])
-    yticks([])
-    subplot(2,1,2)
-    pos = plot_dag(dg, node_color=qvalz, pos=pos, nodelist=terms)
-    ax = gca()
-    colorbar(ax.collections[0])
-    xlabel('T2 values')
-else:
-    figure(300)
-    subplot(2,1,1)
-    pos = plots_lpls.plot_dag(dg, pos=pos, nodelist=terms)
-
-if calc_rmsep:
-    figure(190) #rmsep
-    
-    bar_col = 'rgbcmyk'*2
-    m = Y.shape[1]
-    bar_w = 1./(m + 2.)
-    for a in range(m):
-        bar(arange(a_max)+a*bar_w+.1, rmsep[a,:], width=bar_w, color=bar_col[a])
-    ylim([rmsep.min()-.05, rmsep.max()+.05]) 
-    title('RMSEP: Y(%s)' %DY.get_name())
-
-#figure(2)
-#for a in range(m):
-#    bar(arange(a_max)+a*bar_w+.1, class_error[:,a], width=bar_w, color=bar_col[a])
-#ylim([class_error.min()-.05, class_error.max()+.05])
-#title('Classification accuracy')
-
-figure(5) # Hyploid correlations
-pc1 = 2
-pc2 = 3
-tsqz = cal_tsq_z
-tsqx = cal_tsq_x
-tsqz_s = 550*tsqz/tsqz.max()
-td = rpy_go.goterm2desc(terms)
-tlabels = [td[i] for i in terms]
-#keep = tsqz.argsort()[:100]
-#k_Rz = Rz[keep,:]
-#k_tsqz_s = tsqz_s[keep]
-#k_tsq = tsqz[keep]
-#k_tlabels = [name for i,name in enumerate(tlabels) if i in keep]
-plot_corrloads(Rz, pc1=pc1, pc2=pc2, s=tsqz_s, c=tsqz, zorder=6, expvar=evz, ax=None,alpha=.9,labels=None)
-#plot_corrloads(k_Rz, pc1=0, pc2=1, s=k_tsqz_s, c=k_tsqz, zorder=5, expvar=evz, ax=None,alpha=.5,labels=None)
-ax = gca()
-ylabels = DYg.get_identifiers(DYg.get_dim_name()[1], sorted=True)
-#ylabels = DYr.get_identifiers(DYr.get_dim_name()[1], sorted=True)
-#blabels = yglabels[:]
-#blabels.append(ylabels[0])
-plot_corrloads(Ry, pc1=pc1, pc2=pc2, s=350, c='g', marker='s', zorder=7, expvar=evy, ax=ax,labels=ylabels,alpha=1.0, drawback=False)
-plot_corrloads(Rx, pc1=pc1, pc2=pc2, s=3, c=(.6,.6,.6), alpha=1, zorder=4, expvar=evx, ax=ax, drawback=False, faceted=False)
-
-
-figure(4)
-subplot(221)
-ax = gca()
-plot_corrloads(Rx, pc1=0, pc2=1, s=tsqx/2.0, c='b', zorder=5, expvar=evx, ax=ax)
-# title('X correlation')
-subplot(222)
-ax = gca()
-plot_corrloads(Ry, pc1=0, pc2=1, s=250, c='g', zorder=5, expvar=evy, ax=ax)
-#title('Y correlation')
-subplot(223)
-ax = gca()
-plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='r', zorder=5, expvar=evz, ax=ax)
-#title('Z correlation')
-subplot(224)
-plot(arange(len(evx)), evx, 'b', label='X', linewidth=2)
-plot(evy, 'g', label='Y', linewidth=2)
-plot(evz, 'r', label='Z', linewidth=2)
-legend(loc=2)
-ylabel('Explained variance')
-xlabel('Component')
-xticks((arange(len(evx))), [str(int(i+1)) for i in arange(len(evx))])
-show()
-
-
-figure(19)
-#subplot(1,2,1)
-# RMS : (n_rand_iter, n_alpha, nvarY, a_max)
-# Rmsep : (n_alpha, nvarY, a_max)
-
-rms = RMS[:,:,:,aopt] # looking at solution at aopt
-m_rms = rms.mean(2) # mean over all y-variables
-mm_rms = m_rms.mean(0) # mean over iterations
-std_mrms = m_rms.std(0) # standard deviation over iterations
-
-rms_t = Rmsep[:,:,aopt]
-m_rms_t = rms_t.mean(1)
-xax = arange(mm_rms.shape[0])
-std2_lim_down =  mm_rms - 1.*std_mrms
-std2_lim_up =  mm_rms + 1.*std_mrms
-xx = r_[xax, xax[::-1]] 
-yy = r_[std2_lim_down, std2_lim_up[::-1]]
-fill(xx, yy, fc='.9')
-plot(mm_rms, '--r', lw=1.5, label='Perm. mean')
-plot(std2_lim_down, 'b--')
-plot(std2_lim_up, 'b--', label='Perm. 2*std')
-plot(m_rms_t, 'g', lw=1.5, label='True')
-#c_ylim = ylim()
-#ylim(c_ylim[0], c_ylim[1]-1)
-alpha_ind = linspace(0, Alpha.shape[0]-1, 11)
-xticks(alpha_ind, ['%.1f' %a for a in arange(0,1.01, .1)])
-xlabel(r'$\alpha$')
-ylabel('mean error')
-leg = legend(loc=2)
-# delete fill from legend
-del leg.texts[-1]
-del leg.legendHandles[-1]
-# delete one of the std legends
-del leg.texts[1]
-del leg.legendHandles[1]
-
-klass = True
-
-if klass:
-    figure(20)
-    # subplot(1,2,1)
-    # RMS : (n_rand_iter, n_alpha, nvarY, a_max)
-    # Rmsep : (n_alpha, nvarY, a_max)
-
-    cee = CEE[:,:,aopt,:] # looking at solution at aopt
-    m_cee = cee.mean(-1) # mean over all y-variables
-    mm_cee = m_cee.mean(0) # mean over iterations
-    std_cee = m_cee.std(0) # standard deviation over iterations
-    CE = asarray(CE)
-    cee_t = CE[:,:,aopt]
-    m_cee_t = cee_t.mean(1)
-    xax = arange(mm_cee.shape[0])
-    std2_lim_down =  mm_cee - 2*std_cee
-    std2_lim_up =  mm_cee + 2*std_cee
-    xx = r_[xax, xax[::-1]] 
-    yy = r_[std2_lim_down, std2_lim_up[::-1]]
-    fill(xx, yy, fc='.9')
-    plot(mm_cee, '--r', lw=1.5)
-    plot(std2_lim_down, 'b--')
-    plot(std2_lim_up, 'b--')
-    plot(m_cee_t, 'g', lw=1.5)
-    c_ylim = ylim()
-    ylim = ylim(c_ylim[0], .2)
-    xticks(xax, [str(a)[:3] for a in Alpha])
-    xlabel(r'$\alpha$')
-    ylabel('mean error')

setup.py

@@ -1,48 +0,0 @@
-#!/usr/bin/env python
-
-#from distutils.core import setup
-
-short_description=\
-"""Look at your data interactively.
-"""
-
-long_description=\
-"""Look at your data interactively (Laydi) is a lightweight data analysis
-application to do bilinear analysis.
-"""
-
-classifiers = """\
-Development Status :: 4 - Beta
-Environment :: Console
-Intended Audience :: Science/Research
-License :: OSI Approved :: GPL
-Operating System :: OS Independent
-Programming Language :: Python
-Topic :: Scientific/Engineering
-"""
-
-from laydi.laydi import VERSION
-
-def configuration(parent_package='', top_path=None):
-    from numpy.distutils.misc_util import Configuration
-    config = Configuration('', parent_package, top_path)
-    config.add_data_dir('tests')
-    config.add_scripts(['bin/laydi', 'bin/dataset'])
-    config.add_data_dir('icons')
-    config.add_subpackage('laydi')
-    return config
-
-if __name__ == "__main__":
-    from numpy.distutils.core import setup
-    config = configuration(top_path='').todict() 
-    config['author'] = 'Einar Ryeng'
-    config['author_email'] = 'einarr@pvv.org'
-#    config['short_description'] = short_description
-    config['long_description'] = long_description
-    config['url'] = 'https://dev.pvv.org/projects/laydi'
-    config['version'] = VERSION
-    config['license'] = 'GPL'
-    config['platforms'] = ['Linux']
-    config['classifiers'] = filter(None, classifiers.split('\n'))
-    setup(**config)
-

system/dataset.py

@@ -0,0 +1,384 @@
+from scipy import ndarray,atleast_2d,asarray,intersect1d
+from scipy import sort as array_sort
+from itertools import izip
+import shelve
+import copy
+
+class Dataset:
+    """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
+        self._type = 'n'
+        try:
+            array = atleast_2d(asarray(array))
+        except:
+            print "Cant cast array as numpy-array"
+            return
+        # vectors are column vectors 
+        if array.shape[0]==1:
+            array = array.T
+        self.shape = array.shape
+
+        if identifiers!=None:
+            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"""
+        return self._array
+
+    def add_array(self,array):
+        """Adds array as an ArrayType object.
+        A one-dim array is transformed to a two-dim array (row-vector)
+        """
+        
+        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]
+
+    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 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 copy(self):
+        """ Returns deepcopy of dataset.
+        """
+        return copy.deepcopy(self)
+
+
+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, and
+    fast intersection look-ups by representing the binary matrix as a
+    dictionary in each dimension.
+
+    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)
+        self.has_dictlists = False
+        self._type = 'c'
+            
+    def as_dict_lists(self):
+        """Returns data as dict of indices along first dim.
+
+        ex: data['gene_id'] = ['map0030','map0010', ...]
+        """
+        data={}
+        for name,ind in self._map[self.get_dim_name(0)].items():
+            data[name] = self.get_identifiers(self.get_dim_name(1),
+                                              list(self._array[ind,:].nonzero()))
+        self._dictlists = data
+        self.has_dictlists = True
+        return data
+
+    def as_selections(self):
+        """Returns data as a list of Selection objects.
+        """
+        ret_list = []
+        for cat_name,ind in self._map[self.get_dim_name(1)].items():
+            ids = self.get_identifiers(self.get_dim_name(0),
+                                       self._array[:,ind].nonzero()[0])
+            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 using an (weighted)
+    adjacency matrix
+    (aka. restricted to square symmetric matrices)
+    
+    If the library NetworkX is installed, there is support for
+    representing the graph as a NetworkX.Graph, or NetworkX.XGraph structure.
+    """
+    
+    def __init__(self,array=None,identifiers=None,shape=None,all_dims=[],**kwds):
+        Dataset.__init__(self,array=array,identifiers=identifiers,name='A')
+        self._graph = None
+        self._type = 'g'
+        self._pos = None
+        
+    def asnetworkx(self,nx_type='graph'):
+        dim = self.get_dim_name()[0]
+        ids = self.get_identifiers(dim,sorted=True)
+        adj_mat = self.asarray()
+        G = self._graph_from_adj_matrix(adj_mat,labels=ids)
+        self._graph = G
+        return G
+    
+    def _graph_from_adj_matrix(self,A,labels=None):
+        """Creates a networkx graph class from adjacency
+        (possibly weighted) matrix and ordered labels.
+        
+        nx_type = ['graph',['xgraph']]
+        labels = None, results in string-numbered labels
+        """
+        
+        try:
+            import networkx as nx
+        except:
+            print "Failed in import of NetworkX"
+            return
+        m,n = A.shape# adjacency matrix must be of type that evals to true/false for neigbours
+        if m!=n:
+            raise IOError, "Adjacency matrix must be square"
+
+        if A[A[:,0].nonzero()[0][0],0]==1: #unweighted graph
+            G = nx.Graph()
+        else:
+            G = nx.XGraph()
+
+        if labels==None: # if labels not provided mark vertices with numbers
+            labels = [str(i) for i in range(m)]
+
+        for nbrs,head in izip(A,labels):
+            for i,nbr in enumerate(nbrs):
+                if nbr:
+                    tail = labels[i]
+                    if type(G)==nx.XGraph:
+                        G.add_edge(head,tail,nbr)
+                    else:
+                        G.add_edge(head,tail)
+        return G
+        
+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.
+
+    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}
+
+def to_file(filepath,dataset,name=None):
+    """Write dataset to file. A file may contain multiple datasets.
+    append to file by using option mode='a'
+    """
+    if not name:
+        name = dataset._name
+    data = shelve.open(filepath,flag='c',protocol=2)
+    if data: #we have an append 
+        names = data.keys()
+        if name in names:
+            print "Data with name: %s overwritten" %dataset._name
+    sub_data = {'array':dataset._array,'idents':dataset._identifiers,'type':dataset._type}
+    data[name] = sub_data 
+    data.close()
+
+def from_file(filepath):
+    """Read dataset from file """
+    data = shelve.open(filepath,flag='r')
+    out_data = []
+    for name in data.keys():
+        sub_data = data[name]
+        if sub_data['type']=='c':
+            out_data.append(CategoryDataset(sub_data['array'],identifiers=sub_data['idents'],name=name))
+        elif sub_data['type']=='g':
+            out_data.append(GraphDataset(sub_data['array'],identifiers=sub_data['idents'],name=name))
+        else:
+            out_data.append(Dataset(sub_data['array'],identifiers=sub_data['idents'],name=name)) 
+            
+    return out_data
+    
+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
+

system/dialogs.py

@@ -4,10 +4,11 @@ import gtk
 import sys
 import os
 import gobject
-import logger, projectview, workflow
+from system import logger, project, workflow
+import workflows
 
-DATADIR = os.path.dirname(sys.modules['laydi'].__file__)
+DATADIR = os.path.dirname(sys.modules['system'].__file__)
-GLADEFILENAME = os.path.join(DATADIR, 'laydi.glade')
+GLADEFILENAME = os.path.join(DATADIR, 'fluents.glade')
 
 class CreateProjectDruid(gtk.Window):
     """A druid for creating a new project.
@@ -16,8 +17,9 @@ class CreateProjectDruid(gtk.Window):
     Workflow, and asks the user to select one of these. A new project of
     the selected class is added to the application."""
     
-    def __init__(self):
+    def __init__(self, app):
         gtk.Window.__init__(self)
+        self.app = app
         self.widget_tree = gtk.glade.XML(GLADEFILENAME, 'new_project_druid')
         self.workflows = self.make_workflow_list()
         self.selected = None
@@ -61,12 +63,10 @@ class CreateProjectDruid(gtk.Window):
         
     def finish(self, *rest):
         tree, it = self['workflow_list'].get_selection().get_selected()
-        wf_class = self.workflows.get_value(it, 1)
+        wf = self.workflows.get_value(it, 1)
-        proj = projectview.ProjectView()
+        proj = project.Project()
-        main.set_workflow(wf_class())
+        self.app.set_workflow(wf(self.app))
-#        self.app.set_workflow(wf(self.app))
+        self.app.set_project(proj)
-#        self.app.set_project(proj)
-        main.set_projectview(proj)
         self.hide()
         self.destroy()
 

system/fluents.glade

@@ -7,7 +7,7 @@
 
 <widget class="GnomeApp" id="appwindow">
   <property name="visible">True</property>
-  <property name="title" translatable="yes">Laydi</property>
+  <property name="title" translatable="yes">Fluent</property>
   <property name="type">GTK_WINDOW_TOPLEVEL</property>
   <property name="window_position">GTK_WIN_POS_NONE</property>
   <property name="modal">False</property>
@@ -101,6 +101,78 @@
 		</widget>
 	      </child>
 
+	      <child>
+		<widget class="GtkMenuItem" id="edit1">
+		  <property name="visible">True</property>
+		  <property name="stock_item">GNOMEUIINFO_MENU_EDIT_TREE</property>
+
+		  <child>
+		    <widget class="GtkMenu" id="edit1_menu">
+
+		      <child>
+			<widget class="GtkImageMenuItem" id="cut1">
+			  <property name="visible">True</property>
+			  <property name="stock_item">GNOMEUIINFO_MENU_CUT_ITEM</property>
+			  <signal name="activate" handler="on_cut1_activate" last_modification_time="Thu, 13 Apr 2006 11:24:18 GMT"/>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkImageMenuItem" id="copy1">
+			  <property name="visible">True</property>
+			  <property name="stock_item">GNOMEUIINFO_MENU_COPY_ITEM</property>
+			  <signal name="activate" handler="on_copy1_activate" last_modification_time="Thu, 13 Apr 2006 11:24:18 GMT"/>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkImageMenuItem" id="paste1">
+			  <property name="visible">True</property>
+			  <property name="stock_item">GNOMEUIINFO_MENU_PASTE_ITEM</property>
+			  <signal name="activate" handler="on_paste1_activate" last_modification_time="Thu, 13 Apr 2006 11:24:18 GMT"/>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkImageMenuItem" id="clear1">
+			  <property name="visible">True</property>
+			  <property name="stock_item">GNOMEUIINFO_MENU_CLEAR_ITEM</property>
+			  <signal name="activate" handler="on_clear1_activate" last_modification_time="Thu, 13 Apr 2006 11:24:18 GMT"/>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkSeparatorMenuItem" id="separator2">
+			  <property name="visible">True</property>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkImageMenuItem" id="properties1">
+			  <property name="visible">True</property>
+			  <property name="stock_item">GNOMEUIINFO_MENU_PROPERTIES_ITEM</property>
+			  <signal name="activate" handler="on_properties1_activate" last_modification_time="Thu, 13 Apr 2006 11:24:18 GMT"/>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkSeparatorMenuItem" id="separator3">
+			  <property name="visible">True</property>
+			</widget>
+		      </child>
+
+		      <child>
+			<widget class="GtkImageMenuItem" id="preferences1">
+			  <property name="visible">True</property>
+			  <property name="stock_item">GNOMEUIINFO_MENU_PREFERENCES_ITEM</property>
+			  <signal name="activate" handler="on_preferences1_activate" last_modification_time="Thu, 13 Apr 2006 11:24:18 GMT"/>
+			</widget>
+		      </child>
+		    </widget>
+		  </child>
+		</widget>
+	      </child>
+
 	      <child>
 		<widget class="GtkMenuItem" id="view1">
 		  <property name="visible">True</property>
@@ -109,46 +181,10 @@
 		  <child>
 		    <widget class="GtkMenu" id="view1_menu">
 
-		      <child>
-			<widget class="GtkCheckMenuItem" id="navigator1">
-			  <property name="visible">True</property>
-			  <property name="label" translatable="yes">_Navigator</property>
-			  <property name="use_underline">True</property>
-			  <property name="active">True</property>
-			  <signal name="activate" handler="on_navigator1_activate" last_modification_time="Thu, 06 Dec 2007 00:03:35 GMT"/>
-			</widget>
-		      </child>
-
-		      <child>
-			<widget class="GtkCheckMenuItem" id="workflow1">
-			  <property name="visible">True</property>
-			  <property name="label" translatable="yes">_Workflow</property>
-			  <property name="use_underline">True</property>
-			  <property name="active">True</property>
-			  <signal name="activate" handler="on_workflow1_activate" last_modification_time="Thu, 06 Dec 2007 00:03:35 GMT"/>
-			</widget>
-		      </child>
-
-		      <child>
-			<widget class="GtkCheckMenuItem" id="information1">
-			  <property name="visible">True</property>
-			  <property name="label" translatable="yes">_Information</property>
-			  <property name="use_underline">True</property>
-			  <property name="active">True</property>
-			  <signal name="activate" handler="on_information1_activate" last_modification_time="Thu, 06 Dec 2007 00:03:35 GMT"/>
-			</widget>
-		      </child>
-
-		      <child>
-			<widget class="GtkSeparatorMenuItem" id="separator5">
-			  <property name="visible">True</property>
-			</widget>
-		      </child>
-
 		      <child>
 			<widget class="GtkMenuItem" id="large_view1">
 			  <property name="visible">True</property>
-			  <property name="label" translatable="yes">One plot</property>
+			  <property name="label" translatable="yes">Large View</property>
 			  <property name="use_underline">True</property>
 			  <signal name="activate" handler="on_large_view1_activate" last_modification_time="Fri, 26 May 2006 12:15:59 GMT"/>
 			  <accelerator key="plus" modifiers="GDK_CONTROL_MASK" signal="activate"/>
@@ -158,7 +194,7 @@
 		      <child>
 			<widget class="GtkMenuItem" id="small_view1">
 			  <property name="visible">True</property>
-			  <property name="label" translatable="yes">All plots</property>
+			  <property name="label" translatable="yes">Small View</property>
 			  <property name="use_underline">True</property>
 			  <signal name="activate" handler="on_small_view1_activate" last_modification_time="Fri, 26 May 2006 12:15:59 GMT"/>
 			  <accelerator key="minus" modifiers="GDK_CONTROL_MASK" signal="activate"/>
@@ -234,7 +270,7 @@
 		      <child>
 			<widget class="GtkImageMenuItem" id="index1">
 			  <property name="visible">True</property>
-			  <property name="tooltip" translatable="yes">Open the laydi documentation.</property>
+			  <property name="tooltip" translatable="yes">Open the fluents documentation.</property>
 			  <property name="label" translatable="yes">_Index</property>
 			  <property name="use_underline">True</property>
 			  <signal name="activate" handler="on_index1_activate" last_modification_time="Thu, 27 Apr 2006 09:21:48 GMT"/>
@@ -284,13 +320,59 @@
 	  <property name="shadow_type">GTK_SHADOW_OUT</property>
 
 	  <child>
-	    <widget class="GtkToolbar" id="toolbar">
+	    <widget class="GtkToolbar" id="toolbar1">
 	      <property name="visible">True</property>
 	      <property name="orientation">GTK_ORIENTATION_HORIZONTAL</property>
 	      <property name="toolbar_style">GTK_TOOLBAR_ICONS</property>
 	      <property name="tooltips">True</property>
 	      <property name="show_arrow">False</property>
 
+	      <child>
+		<widget class="GtkToolButton" id="button_new">
+		  <property name="visible">True</property>
+		  <property name="tooltip" translatable="yes">Ny fil</property>
+		  <property name="stock_id">gtk-new</property>
+		  <property name="visible_horizontal">True</property>
+		  <property name="visible_vertical">True</property>
+		  <property name="is_important">False</property>
+		  <signal name="clicked" handler="on_button_new_clicked" last_modification_time="Fri, 21 Apr 2006 13:46:38 GMT"/>
+		</widget>
+		<packing>
+		  <property name="expand">False</property>
+		  <property name="homogeneous">True</property>
+		</packing>
+	      </child>
+
+	      <child>
+		<widget class="GtkToolButton" id="toolbutton2">
+		  <property name="visible">True</property>
+		  <property name="tooltip" translatable="yes">Åpne fil</property>
+		  <property name="stock_id">gtk-open</property>
+		  <property name="visible_horizontal">True</property>
+		  <property name="visible_vertical">True</property>
+		  <property name="is_important">False</property>
+		</widget>
+		<packing>
+		  <property name="expand">False</property>
+		  <property name="homogeneous">True</property>
+		</packing>
+	      </child>
+
+	      <child>
+		<widget class="GtkToolButton" id="toolbutton3">
+		  <property name="visible">True</property>
+		  <property name="tooltip" translatable="yes">Lagre fil</property>
+		  <property name="stock_id">gtk-save</property>
+		  <property name="visible_horizontal">True</property>
+		  <property name="visible_vertical">True</property>
+		  <property name="is_important">False</property>
+		</widget>
+		<packing>
+		  <property name="expand">False</property>
+		  <property name="homogeneous">True</property>
+		</packing>
+	      </child>
+
 	      <child>
 		<widget class="GtkToolButton" id="zoom_in_button">
 		  <property name="visible">True</property>
@@ -414,15 +496,6 @@
 			</packing>
 		      </child>
 
-		      <child>
-			<widget class="GtkScrolledWindow" id="scrolledwindow11">
-			  <property name="visible">True</property>
-			  <property name="can_focus">True</property>
-			  <property name="hscrollbar_policy">GTK_POLICY_AUTOMATIC</property>
-			  <property name="vscrollbar_policy">GTK_POLICY_AUTOMATIC</property>
-			  <property name="shadow_type">GTK_SHADOW_IN</property>
-			  <property name="window_placement">GTK_CORNER_TOP_LEFT</property>
-
 		      <child>
 			<widget class="Custom" id="navigator_view">
 			  <property name="visible">True</property>
@@ -431,8 +504,6 @@
 			  <property name="int2">0</property>
 			  <property name="last_modification_time">Sat, 15 Apr 2006 12:34:23 GMT</property>
 			</widget>
-			  </child>
-			</widget>
 			<packing>
 			  <property name="padding">0</property>
 			  <property name="expand">True</property>
@@ -896,16 +967,15 @@ The functions of the workflow you select will be available on the right part of
 </widget>
 
 <widget class="GtkAboutDialog" id="aboutdialog">
-  <property name="border_width">5</property>
   <property name="visible">True</property>
   <property name="destroy_with_parent">True</property>
-  <property name="name" translatable="yes">Laydi</property>
+  <property name="name" translatable="yes">Fluents</property>
-  <property name="copyright" translatable="yes">Copyright (C) 2006 the Laydi Team
+  <property name="copyright" translatable="yes">Copyright (C) 2006 the Fluents Team
 Released under the GNU General Public Licence
 </property>
-  <property name="comments" translatable="yes">From WordNet (r) 2.0 [wn]: laydi (adj)
+  <property name="comments" translatable="yes">From WordNet (r) 2.0 [wn]: fluent (adj)
        
-1: easy and graceful in shape; &quot;a yacht with long, laydi curves&quot;
+1: easy and graceful in shape; &quot;a yacht with long, fluent curves&quot;
 
 2: smooth and unconstrained in movement; &quot;a long, smooth stride&quot;; &quot;the fluid motion of a cat&quot;; &quot;the liquid grace of a ballerina&quot;; &quot;liquid prose&quot; [syn:{flowing}, {fluid}, {liquid}, {smooth}]
 
@@ -913,9 +983,9 @@ Released under the GNU General Public Licence
 
 
 
-From the Laydi team: laydi (sw)
+From the Fluents team: fluents (sw)
 
-1: laydi software for lightweight data analysis.</property>
+1: fluent software for lightweight data analysis.</property>
   <property name="license" translatable="yes">GNU GENERAL PUBLIC LICENSE
 
 
@@ -1212,8 +1282,8 @@ NO WARRANTY
 END OF TERMS AND CONDITIONS
 </property>
   <property name="wrap_license">False</property>
-  <property name="website">https://dev.pvv.ntnu.no/projects/laydi</property>
+  <property name="website">https://dev.pvv.ntnu.no/projects/fluent</property>
-  <property name="website_label" translatable="yes">The Laydi project website</property>
+  <property name="website_label" translatable="yes">The Fluent project website</property>
   <property name="authors">Arnar Flatberg
 Einar Ryeng
 Truls A. Tangstad</property>

system/fluents.py

@@ -0,0 +1,212 @@
+#!/usr/bin/python
+
+import os
+import sys
+
+import pygtk
+pygtk.require('2.0')
+import gtk
+import gtk.gdk
+import gtk.glade
+import gnome
+import gnome.ui
+import scipy
+import pango
+from system import project, workflow, dataset, logger, plots, navigator, dialogs, selections
+
+
+PROGRAM_NAME = 'fluents'
+VERSION = '0.1.0'
+DATADIR = os.path.dirname(sys.modules['system'].__file__)
+ICONDIR = os.path.join(DATADIR,"..","icons")
+GLADEFILENAME = os.path.join(DATADIR, 'fluents.glade')
+
+class FluentApp:
+
+    def __init__(self, wf): # Application variables
+        self.project = None
+        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)
+#        self['selection_tree'].set_identifier_list(self['identifier_list'])
+        self.dimlistcontroller = selections.DimListController(self['dim_list'],
+                                                              self['selection_tree'],
+                                                              self['identifier_list'])
+#        self['dim_list'].set_selection_tree(self['selection_tree'])
+
+    def init_gui(self):
+        self['appwindow'].set_size_request(800, 600)
+
+        # Set up workflow
+        self.wf_menu = workflow.WorkflowView(self.workflow)
+        self.wf_menu.show()
+        self['workflow_vbox'].pack_end(self.wf_menu)
+
+        # Set up plot toolbar
+#        self['plot_toolbar_dock'].add(plot.get_toolbar(self.app))
+
+        # 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),
+                  }
+        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.log('debug', 'Program started')
+
+    def set_project(self, project):
+        logger.log('notice', 'Creating a new project')
+        self.project = project
+        self.workflow.add_project(self.project)
+        self.navigator_view.add_project(self.project)
+        self.dimlistcontroller.set_project(project)
+#        self['selection_tree'].set_project(self.project)
+#        self['dim_list'].set_project(self.project)
+
+    def set_workflow(self, workflow):
+        self.workflow = workflow
+        self.wf_menu.set_workflow(self.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(self.project.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)
+
+    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.log("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:
+            "Setting DEAULT in fluents"
+            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 = logger.LogView(logger.logger)
+        self.log_view.show()
+        return self.log_view
+
+    def create_main_view(self, str1, str2, int1, int2):
+        self.main_view = plots.MainView()
+        self.main_view.show()
+        return self.main_view
+
+    def create_navigator_view(self, str1, str2, int1, int2):
+        self.navigator_view = navigator.NavigatorView(None, self)
+        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/fluent/wiki/help')
+
+    def on_help_report_bug(self, *ignored):
+        gnome.help_display_uri('https://dev.pvv.org/projects/fluent/newticket')
+
+    def on_workflow_refresh_clicked(self, *ignored):
+        try:
+            reload(sys.modules[self.workflow.__class__.__module__])
+        except Exception, e:
+            logger.log('warning', 'Cannot reload workflow')
+            logger.log('warning', e)
+        else:
+            logger.log('notice', 'Successfully reloaded workflow')
+
+    def on_view_changed(self, widget, vf):
+        self._update_toolbar(vf.get_view())
+

system/logger.py

@@ -28,19 +28,6 @@ class Logger:
     def level_number(self, level):
         return self.levels.index(level)
 
-    def debug(self, message):
-        self.log('debug', message)
-
-    def notice(self, message):
-        self.log('notice', message)
-
-    def warning(self, message):
-        self.log('warning', message)
-
-    def error(self, message):
-        self.log('error', message)
-
-
 class LogView(gtk.TreeView):
 
     def __init__(self, logger=None, level='notice'):
@@ -165,4 +152,3 @@ class LogMenu(gtk.Menu):
 
 logger = Logger()
 log = logger.log
-

system/navigator.py

@@ -0,0 +1,151 @@
+
+import gtk
+import gobject
+import plots
+import time
+from system import dataset, logger, plots, project, workflow
+
+class NavigatorView (gtk.TreeView):
+    def __init__(self, project, app):
+        self.project = project
+        self.app = app
+        if project:
+            self.data_tree = project.data_tree
+        else:
+            self.data_tree = None
+        
+        gtk.TreeView.__init__(self) 
+
+        # various properties
+        self.set_headers_visible(False)
+        self.get_hadjustment().set_value(0)
+        # Selection Mode
+        self.get_selection().set_mode(gtk.SELECTION_MULTIPLE)
+        self.get_selection().set_select_function(self.is_selectable)
+        self.get_selection().connect('changed',self.selection_changed_handler)
+        self._previous_selection = []
+
+        # Setting up TextRenderers etc
+        #        self.connect('cursor_changed', self.cursor_changed_handler)
+        self.connect('row_activated', self.row_activated_handler)
+
+        self.textrenderer = textrenderer = gtk.CellRendererText()
+        pixbufrenderer = gtk.CellRendererPixbuf()
+        self.object_col = gtk.TreeViewColumn('Object')
+        self.object_col.pack_start(pixbufrenderer,expand=False)
+        self.object_col.pack_start(textrenderer,expand=False)
+        self.object_col.set_attributes(textrenderer, cell_background=3, foreground=4, text=0)
+        self.object_col.set_attributes(pixbufrenderer, pixbuf=5)
+        self.append_column(self.object_col)
+
+        # send events to plots / itself
+        self.enable_model_drag_source(gtk.gdk.BUTTON1_MASK,
+            [("GTK_TREE_MODEL_ROW", gtk.TARGET_SAME_APP, 7)],
+            gtk.gdk.ACTION_LINK | gtk.gdk.ACTION_MOVE)
+    
+        self.connect("drag-data-get",self.slot_drag_data)
+
+        logger.log('debug', 'Initializing navigator window.')
+
+   
+    # sets data for drag event.
+    def slot_drag_data(self,treeview,context,selection,target_id,etime):
+        treeselection = treeview.get_selection()
+        model, paths = treeselection.get_selected_rows()
+        if paths:
+            self.data_tree.drag_data_get(paths[0], selection)
+
+    def add_project(self, project):
+        self.project = project
+        self.data_tree = project.data_tree
+        self.set_model(project.data_tree)
+        self.data_tree.connect('row-changed',self.row_changed_handler)
+    
+    def is_selectable(self,path):
+        if self.data_tree:
+            obj = self.data_tree.get_value(self.data_tree.get_iter(path),2)
+            if not obj:
+                return False
+            if not isinstance(obj, dataset.Dataset):
+                return False
+        return True
+
+    # selection changed, setting current_data ojbects
+    def selection_changed_handler(self, selection):
+        # update prev selection right away in case of multiple events
+        model, paths = selection.get_selected_rows()
+        if not paths: # a plot is marked: do nothing
+            return
+
+        tmp = self._previous_selection
+        self._previous_selection = paths
+        # set timestamp on newly selected objects
+        [self.data_tree.set_value(self.data_tree.get_iter(path), 6, time.time()) 
+            for path in paths if path not in tmp]
+
+        objs = [self.data_tree.get_iter(path) for path in paths]
+        objs = [(self.data_tree.get_value(iter,6), self.data_tree.get_value(iter,2)) 
+            for iter in objs]
+        objs.sort()
+        objs = [obj for timestamp, obj in objs]
+        # order dataset
+
+        if objs and isinstance(objs[0], dataset.Dataset):
+            logger.log('debug', 'Selecting dataset')
+            self.project.set_current_data(objs)
+        else:
+            logger.log('debug', 'Deselecting dataset')
+            self.project.set_current_data([])
+
+
+    # TreeView changed. Set correct focus and colours
+    def row_changed_handler(self, treestore, pos, iter):
+        obj  = treestore.get_value(iter,2)
+        type_= treestore.get_value(iter,1)
+
+        if not (treestore.get_value(iter,2) or treestore.get_value(iter,1)):
+            return
+        self.expand_to_path(pos)        
+
+        if isinstance(obj,dataset.Dataset):
+            self.set_cursor(pos)
+            self.grab_focus()
+        
+    def display_data_info(self, data):
+        dims = zip(data.get_dim_name(), data.shape)
+        
+        dim_text = ", ".join(["%s (%d)" % dim for dim in dims])
+
+        text = """<span weight="bold">Data:</span> %s
+
+<span weight="bold">Dimensions:</span> %s""" % (data.get_name(), dim_text)
+
+        d = gtk.MessageDialog(flags=(gtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT),
+                              buttons=gtk.BUTTONS_OK)
+        d.set_markup(text)
+        d.set_default_response(gtk.BUTTONS_OK)
+        d.run()
+        d.destroy()        
+        
+    def row_activated_handler(self, widget, path, column):
+        tree_iter = self.data_tree.get_iter(path)
+        obj = self.data_tree.get_value(tree_iter, 2)
+        
+        if isinstance(obj, plots.Plot):
+            logger.log('debug', 'Activating plot')
+            self.app.change_plot(obj)
+        elif isinstance(obj, dataset.Dataset):
+            self.display_data_info(obj)
+        elif obj == None:
+            children = []
+            i = self.data_tree.iter_children(tree_iter)
+            while i:
+                child = self.data_tree.get(i, 2)[0]
+                if isinstance(child, plots.Plot):
+                    children.append(child)
+                i = self.data_tree.iter_next(i)
+            self.app.change_plots(children)
+        else:
+            t = type(obj)
+            logger.log('debug', 'Activated datatype was %s. Don\'t know what to do.' % t)
+