#! /usr/bin/python
#
#   Python script that communicates with the GlobPlotter web service at EMBL
#   WSDL: http://globplot.embl.de/webservice/globplot.wsdl
#   By Kate Gould (gould@embl.de).
#
#   save the contents of this file to your computer and save with the .py extension
#   in order for this client script to run you need the following modules installed as part of your python package:
#   SOAPpy (see webpage: http://pywebsvcs.sourceforge.net/)
#
#   pre-requirements for SOAPpy are the following two packages(as specified in SOAPpy package README file):
#   1. fpconst 0.6.0 or later,
#      <http://pypi.python.org/pypi/fpconst/0.7.2>
#   2. pyXML 0.8.3 or later, <http://pyxml.sourceforge.net>   
#

import SOAPpy, sys


def main():

    """Command line interface to the GlobPlotter Web service """
   
    endpoint = 'http://luna.embl.de:8080/axis2/services/GlobPlotter'
    namespace = 'http://globplot.embl.de/GlobPlotter'
    globplot = SOAPpy.SOAPProxy(endpoint)
    
    globplot.namespace = namespace
    globplot.noroot = 1

    try:

        # the following three input fields are mandatory
        sequenceId = 'src_human'
        sequenceIdentifier = SOAPpy.Types.untypedType(sequenceId)
        sequenceIdentifier._name = 'sequenceIdentifier'

        seq = 'MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL'
        sequence = SOAPpy.Types.untypedType(seq)
        sequence._name = 'querySequence'

        # see the WSDL file for all possible valid values for this input field.
        propensityValue = 'Russell/Linding'
        propensity = SOAPpy.Types.untypedType(propensityValue)
        propensity._name = 'propensity'

        
        # all the following input values are optional.

        # if this value is left with the default value of "no" all subsequent values are effectively ignored
        _includeMainGraphics = 'yes'
        includeMainGraphics = SOAPpy.Types.untypedType(_includeMainGraphics)
        includeMainGraphics._name = 'includeMainGraphics'

        # the includeMainGraphics element must be set to "yes" for all the following  element settings to affect the output details
        _disorderFrames_minimumPeakWidth = '4'
        disorderFrames_minimumPeakWidth = SOAPpy.Types.untypedType(_disorderFrames_minimumPeakWidth)
        disorderFrames_minimumPeakWidth._name = 'disorderFrames_minimumPeakWidth'
                        
        _disorderFrames_maximumJoinDistance = '5'
        disorderFrames_maximumJoinDistance = SOAPpy.Types.untypedType(_disorderFrames_maximumJoinDistance)
        disorderFrames_maximumJoinDistance._name = 'disorderFrames_maximumJoinDistance'
                
        _globularityFrames_minimumDomainLength = '74'
        globularityFrames_minimumDomainLength = SOAPpy.Types.untypedType(_globularityFrames_minimumDomainLength)
        globularityFrames_minimumDomainLength._name = 'globularityFrames_minimumDomainLength'
                
        _globularity_maximumLinkerLength = '15'
        globularity_maximumLinkerLength = SOAPpy.Types.untypedType(_globularity_maximumLinkerLength)
        globularity_maximumLinkerLength._name = 'globularity_maximumLinkerLength'

        _smoothingSettings_firstDerivativeSavitzkyGolayFrame = '10'
        smoothingSettings_firstDerivativeSavitzkyGolayFrame = SOAPpy.Types.untypedType(_smoothingSettings_firstDerivativeSavitzkyGolayFrame)
        smoothingSettings_firstDerivativeSavitzkyGolayFrame._name = 'smoothingSettings_firstDerivativeSavitzkyGolayFrame'
                
        _smoothingSettings_secondDerivativeSavitzkyGolayFrame = '10'
        smoothingSettings_secondDerivativeSavitzkyGolayFrame = SOAPpy.Types.untypedType(_smoothingSettings_secondDerivativeSavitzkyGolayFrame)
        smoothingSettings_secondDerivativeSavitzkyGolayFrame._name = 'smoothingSettings_secondDerivativeSavitzkyGolayFrame'

        _titleOfGraphics = 'test'
        titleOfGraphics = SOAPpy.Types.untypedType(_titleOfGraphics)
        titleOfGraphics._name = 'titleOfGraphics'

        _performSmartPfamDomainPredictionInGraphics = 'yes'
        performSmartPfamDomainPredictionInGraphics = SOAPpy.Types.untypedType(_performSmartPfamDomainPredictionInGraphics)
        performSmartPfamDomainPredictionInGraphics._name = 'performSmartPfamDomainPredictionInGraphics'

        _includeRawPlotInMainGraphics = 'yes'
        includeRawPlotInMainGraphics = SOAPpy.Types.untypedType(_includeRawPlotInMainGraphics)
        includeRawPlotInMainGraphics._name = 'includeRawPlotInMainGraphics'
       
        _includeDydxPlotGraphics = 'yes'
        includeDydxPlotGraphics = SOAPpy.Types.untypedType(_includeDydxPlotGraphics)
        includeDydxPlotGraphics._name = 'includeDydxPlotGraphics'
 
        _includeSmoothedResultFile = 'yes'
        includeSmoothedResultFile = SOAPpy.Types.untypedType(_includeSmoothedResultFile)
        includeSmoothedResultFile._name = 'includeSmoothedResultFile'

        _includeDisorderGlobularDomainRangesFile = 'yes'
        includeDisorderGlobularDomainRangesFile = SOAPpy.Types.untypedType(_includeDisorderGlobularDomainRangesFile)
        includeDisorderGlobularDomainRangesFile._name = 'includeDisorderGlobularDomainRangesFile'
        
        _includeDydxPlotFile = 'yes'
        includeDydxPlotFile = SOAPpy.Types.untypedType(_includeDydxPlotFile)
        includeDydxPlotFile._name = 'includeDydxPlotFile'
                        
        # the following is a bare-bones call to the globplot webservice
        #predictions = globplot.runGlobPlotter(sequenceIdentifier, sequence, propensity)

        # the following is a call with optional input elements included
        predictions = globplot.runGlobPlotter(sequenceIdentifier, sequence, propensity, includeMainGraphics, disorderFrames_minimumPeakWidth, disorderFrames_maximumJoinDistance, globularityFrames_minimumDomainLength, globularity_maximumLinkerLength, smoothingSettings_firstDerivativeSavitzkyGolayFrame, smoothingSettings_secondDerivativeSavitzkyGolayFrame, titleOfGraphics, performSmartPfamDomainPredictionInGraphics, includeRawPlotInMainGraphics, includeDydxPlotGraphics, includeSmoothedResultFile, includeDisorderGlobularDomainRangesFile, includeDydxPlotFile)

        identifier = predictions.sequence.identifier

        basicResultsFileName = identifier + "_basic_results.txt"
        basicResultsFile = open(basicResultsFileName, 'w')
        
        basicResultsFile.write("identifier: " + identifier + "\n")
        basicResultsFile.write("sequence: " + predictions.sequence.sequence + "\n\n")

        for gff_record in predictions.prediction.gff_record:
            basicResultsFile.write("seqname: " + gff_record.seqname + "\n")
            basicResultsFile.write("source: " +   gff_record.source + "\n")
            basicResultsFile.write("feature: " + gff_record.feature + "\n")
            basicResultsFile.write("start: " +  gff_record.start + "\n")
            basicResultsFile.write("end: " + gff_record.end + "\n")
            basicResultsFile.write("score: " + gff_record.score + "\n\n")
        
        print "basic results stored in text file: " +  basicResultsFileName + "\n"
        basicResultsFile.close()
        import base64


        if (predictions.__dict__.has_key('file')):
            # need to determine if we are processing one file object or a list of file objects
            if isinstance(predictions.file, list):
                for file in predictions.file:
                    resultsFileName = identifier + "_" + file.comment + ".txt"
                    f = open(resultsFileName, 'w')
                    f.write(base64.decodestring(file.content))
                    f.close()
                    print resultsFileName + " text file stored in current directory\n"
            else:
                resultsFileName = identifier + "_" + predictions.file.comment + ".txt"
                f = open(resultsFileName, 'w')
                f.write(base64.decodestring(predictions.file.content))
                f.close()
                print resultsFileName + " text file stored in current directory\n"

        if (predictions.__dict__.has_key('image')):
            # need to determine if we are processing one image object or a list of image objects
            if isinstance(predictions.image, list):
                for image in predictions.image:
                    resultsFileName = identifier + "_" + image.comment + ".png"
                    f = open(resultsFileName, 'w')
                    f.write(base64.decodestring(image.content))
                    f.close()
                    print resultsFileName + " image file stored in current directory\n"
            else:
                resultsFileName = identifier + "_" + predictions.image.comment + ".png"
                f = open(resultsFileName, 'w')
                f.write(base64.decodestring(predictions.image.content))
                f.close()
                print resultsFileName + " image file stored in current directory\n"
        
    except Exception, e:
            print 'Exception:', e            


if __name__ == '__main__':
 main ()