def linkToStylesheet(self, _root=None):
"""Link the settings within this Settings to a stylesheet.
_root is an internal parameter as this function is recursive."""
# build up root part of pathname to reference
if _root is None:
path = []
obj = self
while not obj.parent.isWidget():
path.insert(0, obj.name)
obj = obj.parent
path = ['', 'StyleSheet', obj.parent.typename] + path + ['']
_root = '/'.join(path)
# iterate over subsettings
for name, setn in self.setdict.iteritems():
thispath = _root + name
if isinstance(setn, Settings):
# call recursively if this is a Settings
setn.linkToStylesheet(_root=thispath + '/')
else:
ref = Reference(thispath)
try:
# if the reference resolves, then set it
ref.resolve(setn)
setn.set(ref)
setn.default = ref
except Reference.ResolveException:
pass
def surv_data_2_ref(surv_data):
# instantiate an empty reference
ref = Reference()
# add meta data to reference
# at present just a timestamp
from time import gmtime, strftime
ref.set_meta({'timestamp':strftime("%Y-%m-%d %H:%M:%S", gmtime())})
for channel_code in objectives_channel_codes:
# add reference objective corresponding to each model objective
if channel_code == 'prevalence':
#debug_p('surv_data ' + str(surv_data['prevalence']))
d_points = prevalence_surv_data_2_d_points(surv_data['prevalence'])
else:
msg = "Channel " + channel_code + " not implemeneted yet!\nSetting reference data to None."
warn_p(msg)
d_points = None
#debug_p('adding objective ' + channel_code)
#debug_p('num d_points ' + str(len(d_points)))
ref.add_objective(channel_code, d_points)
return ref
def linkToStylesheet(self, _root=None):
"""Link the settings within this Settings to a stylesheet.
_root is an internal parameter as this function is recursive."""
# build up root part of pathname to reference
if _root is None:
path = []
obj = self
while not obj.parent.isWidget():
path.insert(0, obj.name)
obj = obj.parent
path = ['', 'StyleSheet', obj.parent.typename] + path + ['']
_root = '/'.join(path)
# iterate over subsettings
for name, setn in self.setdict.iteritems():
thispath = _root + name
if isinstance(setn, Settings):
# call recursively if this is a Settings
setn.linkToStylesheet(_root=thispath+'/')
else:
ref = Reference(thispath)
try:
# if the reference resolves, then set it
ref.resolve(setn)
setn.set(ref)
setn.default = ref
except Reference.ResolveException:
pass
def __init__(self, reference_tokens, negative_value=0.0):
"""
@param reference the reference translation that hypotheses shall be
scored against. Must be an iterable of tokens (any
type).
"""
Reference.__init__(self, reference_tokens)
self.negative_value = negative_value
def __init__(self):
Reference.__init__(self, 'German Wiktionary',
'http://de.wiktionary.org/w/api.php', 'wiktionary.pickle')
#TODO(PM) Add a text interface to login
# Open dictionary of noun categories
with open('reference/kategorien.json', 'r') as store:
self.category_dict = json.load(store)
def __init__(self, reference_tokens, n=4):
"""
@param reference the reference translation that hypotheses shall be
scored against. Must be an iterable of tokens (any
type).
@param n maximum n-gram order to consider.
"""
Reference.__init__(self, reference_tokens)
self.n = n
# preprocess reference
self._reference_length = len(self._reference_tokens)
self._reference_ngrams = self._get_ngrams(self._reference_tokens, self.n)
def __init__(self, reference_tokens, n=4):
"""
@param reference the reference translation that hypotheses shall be
scored against. Must be an iterable of tokens (any
type).
@param n maximum n-gram order to consider.
"""
Reference.__init__(self, reference_tokens)
self.n = n
# preprocess reference
self._reference_length = len(self._reference_tokens)
self._reference_ngrams = self._get_ngrams(self._reference_tokens,
self.n)
def _get_reference(self):
""" Returns a reference to this object suitable for binding. """
reference = Reference(
class_name=self.__class__.__name__,
addresses=[Address(type='py_context', content=self.namespace)])
return reference
def decode(cls, dat):
if len(dat) == 1:
dat = dat[0]
n = len(dat)
if n != len(cls.fields):
return None
if n == 2:
return list(dat)
return Reference.decode(tuple(dat))
def _get_reference(self):
""" Returns a reference to this object suitable for binding. """
abspath = os.path.abspath(self.path)
reference = Reference(
class_name=self.__class__.__name__,
addresses=[Address(type='pyfs_context', content=abspath)])
return reference
def __init__(self, method, template):
self.__method = method
self.__iohelper = IOHelper()
self.__iohelper.set_cache_directory(Benchmark.REFERENCES)
self.__iohelper.set_output_directory(Benchmark.OUTPUT_DIR)
if template:
self.__iohelper.select_template(template)
self.__annotator = Annotator(self.__iohelper)
self.__analyzer = Analyzer(self.__annotator, self.__iohelper)
self.__precision = None
self.__recall = None
self.__references = Reference.load_from_folder(Benchmark.REFERENCES)
def __init__(self, method, template):
self.__method = method
self.__iohelper = IOHelper()
self.__iohelper.set_cache_directory(Benchmark.REFERENCES)
self.__iohelper.set_output_directory(Benchmark.OUTPUT_DIR)
if template:
self.__iohelper.select_template(template)
self.__annotator = Annotator(self.__iohelper)
self.__analyzer = Analyzer(self.__annotator, self.__iohelper)
self.__precision = None
self.__recall = None
self.__references = Reference.load_from_folder(Benchmark.REFERENCES)
def run(options):
if options.input is None:
sys.exit('\nInput RefSeq database file not specified.')
if options.reference is None:
sys.exit('\nReference genome file not specified.')
# Print reference genome name
print 'RefSeq transcript db: ' + options.input
print 'Reference genome: ' + options.reference
print ''
# Initialize output file
outfile = open(options.output, 'w')
outfile.write(
'#Created: ' + datetime.datetime.now().strftime("%d-%m-%Y") + '; Reference genome: ' + options.reference + '\n')
outfile.write('#' + '\t'.join(['ID', 'VERSION', 'DIFFS']) + '\n')
# Transcript database
tdb = TranscriptDB(options.input)
# Initialize Reference object
reference = Reference(options.reference)
sys.stdout.write('Processing transcripts ... ')
sys.stdout.flush()
# Iterate through transcripts
counter = 0
for transcript in tdb.generator():
# Retrieve reference sequence corresponding to transcript
reference_sequence = get_reference_sequence(transcript, reference)
# Compare transcript sequence with reference sequence
diffs = compare_sequences(reference_sequence, transcript.sequence, transcript.cdna_coding_start,
transcript.cdna_coding_end)
# Write results to output file
outfile.write('\t'.join([transcript.id, transcript.version, diffs]) + '\n')
counter += 1
print '- Done'
# Close output files
outfile.close()
print '\nA total of {} transcripts have been processed.'.format(counter)
# Print output file name
print '\nOutput file: {}'.format(options.output)
def __init__(self, reference_tokens, additional_flags=''):
"""
Computes the TER of a sentence.
:param reference_tokens: the reference translation that hypotheses shall be
scored against. Must be an iterable of tokens (any
/tmp/3420971.ref type).
:param additional_flags: additional TERCOM flags.
"""
self.d = dict(os.environ.copy())
self.d['LANG'] = 'C'
self.extension = str(random.randint(0, 10000000))
self.hyp_filename = "/tmp/" + self.extension + ".hyp"
self.ref_filename = "/tmp/" + self.extension + ".ref"
self.ter_cmd = "bash " + TER_JAR + " -r " + self.ref_filename + " -h " + self.hyp_filename \
+ additional_flags + "| grep TER | awk '{print $3}'"
self.clean_cmd = "rm -f " + self.ref_filename + " " + self.hyp_filename
# Used to guarantee thread safety
self.lock = threading.Lock()
Reference.__init__(self, reference_tokens)
self._gts_ter = ' '.join(reference_tokens) + '\t(sentence%d)\n' % 0
with open(self.ref_filename, 'w') as f:
f.write(self._gts_ter)
def __init__(self, reference_tokens, n=6, beta=1):
"""
@param reference the reference translation that hypotheses shall be
scored against.
@param n maximum character n-gram order to consider.
@param beta algorithm paramater beta (interpolation weight, needs to be > 0).
"""
if beta <= 0:
raise ValueError("Value of beta needs to be larger than zero!")
Reference.__init__(self, reference_tokens)
self.n = n
self.max_order = n
self.beta_squared = beta ** 2
# The paper specifies that whitespace is ignored, but for a training objective,
#it's perhaps better to leave it in. According to the paper, it makes no
#difference in practise for scoring.
self._reference_string = " ".join(reference_tokens).strip()
# Get n-grams from reference:
self._reference_ngrams = self._get_ngrams(self._reference_string, self.n)
def get_state_to_bind(self, obj, name, context):
""" Returns the state of an object for binding. """
state = None
if isinstance(obj, File):
# If the file is not actually in the directory represented by the
# context then we create and bind a reference to it.
if obj.parent.path != context.path:
state = Reference(
class_name=obj.__class__.__name__,
addresses=[Address(type='file', content=obj.path)])
return state
def create(self):
"""Create a VLArray for containing frames as variable-length sequences of t,width, pixels..."""
f = Reference.create(self)
if not f:
return False
self.outfile.create_vlarray(where=f,
name=self.handle,
atom=tables.UInt8Atom(shape=()),
title=self.name,
filters=cfilter,
createparents=True,
reference_class=self.__class__.__name__)
self.path = self.folder + self.handle
return True
def create(self):
self.makePPMScale()
self.newPlot()
self.dataSource = ColumnDataSource(
data=dict(ppm=self.ppmScale, data=self.pdata))
self.reference = Reference(self.logger, self.dataSource)
self.reference.create()
self.reference.draw(self.plot)
self.peakPicking = PeakPicking(self.logger, self.id, self.dic,
self.udic, self.pdata, self.dataSource,
self.reference)
self.peakPicking.create()
self.peakPicking.draw(self.plot)
self.integration = Integration(self.logger, self.id, self.pdata,
self.dataSource, self.reference)
self.integration.create()
self.integration.draw(self.plot)
self.multipletAnalysis = MultipletAnalysis(self.logger, self.id,
self.dic, self.udic,
self.pdata, self.dataSource,
self.peakPicking,
self.integration,
self.reference)
self.multipletAnalysis.create()
self.multipletAnalysis.draw(self.plot)
self.createMeasureJTool()
self.plot.line('ppm', 'data', source=self.dataSource, line_width=2)
def create(self):
"""Create the output array"""
f = Reference.create(self)
if not f:
return False
print 'creating vlarray', self.folder, self.handle, self.name
self.outfile.create_vlarray(where=f,
name=self.handle,
atom=tables.UInt8Atom(shape=()),
title=self.name,
filters=cfilter,
createparents=True,
reference_class=self.__class__.__name__)
print 'updating path'
self.path = self.folder + self.handle
print 'ref created', self.path
return True
def create(self):
"""Create a Table instance configured for Log storage"""
f = Reference.create(self)
if not f:
return False
# print 'creating
# table',self.outfile,self.path,f,self.handle,self.name,self.outfile.get_path()
self.outfile.create_table(where=f,
name=self.handle,
description=np.dtype(self.fields),
title=self.name,
filters=cfilter, createparents=True,
reference_class=self.__class__.__name__)
# print 'setting path'
self.path = self.folder + self.handle
# print 'done',self.path
self.outfile.flush()
return True
def interpolate(self, step=1, kind=1):
"""Array interpolation for summary synchronization."""
vt = Reference.interpolate(self, step)
if vt is False:
return False
# Value sequence
# starting from the oldest time minus step
oldi = self.get_time(vt[0] - step)
# Check if we have enough points to interpolate
if len(self) - oldi < 5:
return False
# If possible, go back one more point, for interpolation safety
if oldi > 1:
oldi -= 1
# Decode values and separate time and value vectors
dat = self[oldi:]
# print 'Getting data',self.path,dat,vt
dat = np.array(dat)
dat = dat.transpose()
# Build a linear spline using vt points as knots
#f=LSQUnivariateSpline(dat[0],dat[1],vt, k=kind)
# Do a linear fitting
(slope, const), res, rank, sing, rcond = np.polyfit(
dat[0], dat[1], kind, full=True)
# Build a vectorized evaluator
f = np.vectorize(lambda x: slope * x + const)
while vt[0] < dat[0][0] and len(vt) > 1:
vt = vt[1:]
while vt[-1] > dat[0][-1] and len(vt) > 1:
vt = vt[:-1]
if len(vt) <= 1:
return False
try:
# Interpret time series
out = f(vt)
except:
print 'Array.interpolate', self.path, vt, dat
raise
# Encode in (t,v) append-able list
out = np.array([vt, out]).transpose()
self.summary.commit(out)
return True
def __init__(self, filepath):
with io.open(filepath, 'r', encoding='utf-8') as f:
self.raw = json.load(f)
self.compounds = [
Compound(x['name'], x['xrefs']) for x in self.raw['compounds']
]
self.remedies = [
Remedy(x['name'], x['xrefs']) for x in self.raw['remedies']
]
self.enzymes = [
Enzyme(x['name'], x['xrefs']) for x in self.raw['enzymes']
]
self.transporter = [
Transporter(x['name'], x['xrefs']) for x in self.raw['transporter']
]
self.drugs = [Drug(x['name'], x['xrefs']) for x in self.raw['drugs']]
publication = self.raw['publication']
doi = publication['doi'] if 'doi' in publication else None
self.publication = Reference(publication['pmid'], doi,
publication['citation'])
def create(self, fields=False):
"""Create an EArray (enlargeable array) as data storage"""
f = Reference.create(self)
if not f:
return False
if fields:
self.fields = fields
self.outfile.create_table(where=f,
name=self.handle,
description=np.dtype(self.fields),
title=self.name,
filters=cfilter,
createparents=True,
reference_class=self.__class__.__name__)
# print 'created',f,self.folder,self.handle
self.path = self.folder + self.handle
# print 'done',self.path
self.outfile.flush()
# Create the summary mirror
if (not self.path.startswith('/summary')) and len(self.fields) == 2 and self.with_summary:
# print 'Creating summary',self.path
self.summary = Array(
self.outfile, '/summary' + self.folder, opt=self.opt)
return True
def __getattr__(self, name):
if name[0:2] != "__":
return Reference(name)
else:
raise AttributeError
def getter(self):
from reference import Reference
return Reference.by_ids(self.lib.reference_lib.values())
def getReferences(self, json=False):
references = self.soup.find_all('ref')
referenceList = []
for reference in references:
extractedReference = Reference()
try:
extractedReference.id = reference.attrs['id']
except AttributeError:
print('id not present')
continue
try:
authors = reference.find_all('string-name')
extractedReference.authors[:] = []
for author in authors:
surname = author.find('surname').get_text().encode("utf-8")
givenName = author.find('given-names').get_text().encode(
"utf-8")
authorObject = Author()
authorObject.surname = surname
authorObject.givenName = givenName
extractedReference.authors.append(authorObject)
except AttributeError:
print('author not present')
try:
extractedReference.year = reference.find(
'year').get_text().encode("utf-8")
except AttributeError:
print('year not present')
try:
extractedReference.articleTitle = reference.find_all(
'article-title')[-1].get_text().encode("utf-8")
print(extractedReference.articleTitle)
except AttributeError:
print('article title not present')
try:
extractedReference.source = reference.find(
'source').get_text().encode("utf-8")
except AttributeError:
print('source not present')
try:
extractedReference.volume = reference.find(
'volume').get_text().encode("utf-8")
except AttributeError:
print('volume not present')
try:
extractedReference.fromPage = reference.find(
'fpage').get_text().encode("utf-8")
except AttributeError:
print('fromPage not present')
try:
extractedReference.toPage = reference.find(
'lpage').get_text().encode("utf-8")
except AttributeError:
print('toPage not present')
referenceList.append(extractedReference)
if json is True:
referenceListJsons = []
for reference in referenceList:
referenceListJsons.append(self.convertToJson(reference))
return self.convertToJson(referenceList)
return referenceList
def __init__(self, reference_tokens, beer_scorer):
Reference.__init__(self, reference_tokens)
#Construct reference string from tokens
self._reference_string = " ".join(reference_tokens)
self._beer_scorer = beer_scorer
def __init__(self, name, source):
"""
Constructor called either by PhysicsObject or Tree.
Parse the source text block and collect all information on this object.
"""
self.name = name
self.includes = []
self.constants = []
self.enums = []
self.objbranches = []
self.branches = []
self.references = []
self.functions = []
while True:
line = source.readline()
line = line.strip()
if line == '':
break
try:
self.includes.append(Include(line))
continue
except Definition.NoMatch:
pass
try:
self.enums.append(Enum(line, source))
continue
except Definition.NoMatch:
pass
try:
self.constants.append(Constant(line, source))
continue
except Definition.NoMatch:
pass
try:
self.branches.append(RefBranch(line))
continue
except Definition.NoMatch:
pass
try:
self.branches.append(RefVectorBranch(line))
continue
except Definition.NoMatch:
pass
try:
self.objbranches.append(ObjBranch(line))
continue
except Definition.NoMatch:
pass
try:
self.branches.append(Branch(line))
continue
except Definition.NoMatch:
pass
try:
self.branches.append(GenericBranch(line))
continue
except Definition.NoMatch:
pass
try:
self.references.append(Reference(line))
continue
except Definition.NoMatch:
pass
try:
self.functions.append(Function(line, source))
continue
except Definition.NoMatch:
pass
break
class Plot:
WIDTH = 800
HEIGHT = 600
def __init__(self, logger, id, path, compound):
self.logger = logger
self.logger.info("Parsing experiment data")
self.dic, _ = ng.bruker.read(path)
_, self.pdata = ng.bruker.read_pdata("{}/pdata/1/".format(path))
self.logger.info("Experiment data parsed successfully")
self.compound = compound
self.id = SpectrumDB.Create(
id
) # SpectrumDB.Create(hashlib.sha256(self.pdata.tostring()).hexdigest())
def createReferenceLayout(self):
return column(
row(column(self.reference.old), column(self.reference.new)),
row(self.reference.button))
def createPeakPickingLayout(self):
return column(
CustomRow(column(self.peakPicking.manual),
column(self.peakPicking.peak),
hide=True), row(self.peakPicking.dataTable),
row(column(self.peakPicking.deselectButton),
column(self.peakPicking.deleteButton)),
row(self.peakPicking.chemicalShiftReportTitle),
row(self.peakPicking.chemicalShiftReport))
def createIntegrationLayout(self):
return column(
CustomRow(column(self.integration.manual), hide=True),
row(self.integration.dataTable),
row(column(self.integration.deselectButton),
column(self.integration.deleteButton)))
def createMultipletManagerLayout(self):
return column(
CustomRow(column(self.multipletAnalysis.manual), hide=True),
row(self.multipletAnalysis.dataTable),
row(self.multipletAnalysis.title),
row(column(self.multipletAnalysis.classes),
column(self.multipletAnalysis.integral),
column(self.multipletAnalysis.j)),
row(self.multipletAnalysis.delete),
row(self.multipletAnalysis.reportTitle),
row(self.multipletAnalysis.report))
def createTabs(self, tabs):
callback = CustomJS(args=dict(
referenceTool=self.reference.tool,
peakPickingManualTool=self.peakPicking.manualTool,
peakByPeakTool=self.peakPicking.peakTool,
integrationTool=self.integration.tool,
multipletAnalysisTool=self.multipletAnalysis.tool),
code="""
switch(this.active) {
case 0:
referenceTool.active = true;
break;
case 1:
if (!peakByPeakTool.active) {
peakPickingManualTool.active = true;
}
break;
case 2:
integrationTool.active = true;
break;
case 3:
multipletAnalysisTool.active = true;
break;
}
""")
return Tabs(tabs=tabs, width=500, callback=callback, id="tabs")
def draw(self):
try:
referenceLayout = self.createReferenceLayout()
peakPickingLayout = self.createPeakPickingLayout()
integrationLayout = self.createIntegrationLayout()
multipletManagerLayout = self.createMultipletManagerLayout()
referenceTab = Panel(child=referenceLayout, title="Reference")
peakPickingTab = Panel(child=peakPickingLayout,
title="Peak Picking")
integrationTab = Panel(child=integrationLayout,
title="Integration")
multipletAnalysisTab = Panel(child=multipletManagerLayout,
title="Multiplet Analysis")
tabs = self.createTabs([
referenceTab, peakPickingTab, integrationTab,
multipletAnalysisTab
])
curdoc().add_root(
row(
column(
row(self.plot),
row(Div(text=self.compound, id="compoundContainer"))),
column(row(tabs))))
curdoc().title = "NMR Analysis Tool - " + str(self.id)
except NameError:
print("Please create plot first")
def create(self):
self.makePPMScale()
self.newPlot()
self.dataSource = ColumnDataSource(
data=dict(ppm=self.ppmScale, data=self.pdata))
self.reference = Reference(self.logger, self.dataSource)
self.reference.create()
self.reference.draw(self.plot)
self.peakPicking = PeakPicking(self.logger, self.id, self.dic,
self.udic, self.pdata, self.dataSource,
self.reference)
self.peakPicking.create()
self.peakPicking.draw(self.plot)
self.integration = Integration(self.logger, self.id, self.pdata,
self.dataSource, self.reference)
self.integration.create()
self.integration.draw(self.plot)
self.multipletAnalysis = MultipletAnalysis(self.logger, self.id,
self.dic, self.udic,
self.pdata, self.dataSource,
self.peakPicking,
self.integration,
self.reference)
self.multipletAnalysis.create()
self.multipletAnalysis.draw(self.plot)
self.createMeasureJTool()
self.plot.line('ppm', 'data', source=self.dataSource, line_width=2)
# make ppm scale
def makePPMScale(self):
self.udic = ng.bruker.guess_udic(self.dic, self.pdata)
uc = ng.fileiobase.uc_from_udic(self.udic)
self.ppmScale = uc.ppm_scale()
# create a new plot with a title and axis labels
def newPlot(self):
#Constants
xr = Range1d(start=int(max(self.ppmScale) + 1),
end=int(min(self.ppmScale) - 1))
self.plot = figure(x_axis_label='ppm',
x_range=xr,
toolbar=CustomToolbar(),
tools="pan,save,reset",
plot_width=self.WIDTH,
plot_height=self.HEIGHT)
# Remove grid from plot
self.plot.xgrid.grid_line_color = None
self.plot.ygrid.grid_line_color = None
horizontalBoxZoomTool = HorizontalBoxZoomTool()
self.plot.add_tools(horizontalBoxZoomTool)
self.plot.toolbar.active_drag = horizontalBoxZoomTool
fixedWheelZoomTool = FixedWheelZoomTool(dimensions="height")
self.plot.add_tools(fixedWheelZoomTool)
self.plot.toolbar.active_scroll = fixedWheelZoomTool
fixedZoomOutTool = FixedZoomOutTool(factor=0.4)
self.plot.add_tools(fixedZoomOutTool)
hoverTool = HoverTool(tooltips="($x, $y)")
self.plot.add_tools(hoverTool)
def createMeasureJTool(self):
source = ColumnDataSource(data=dict(x=[], y=[]))
label = Label(x=0,
y=0,
text="",
text_color="#000000",
render_mode="css")
self.plot.add_layout(label)
measureJTool = MeasureJTool(label=label,
frequency=getFrequency(self.udic),
id="measureJTool")
self.plot.add_tools(measureJTool)
def __init__(self, reference_tokens, meteor_scorer):
Reference.__init__(self, reference_tokens)
#Construct reference string from tokens
self._reference_string = " ".join(reference_tokens)
self._meteor_scorer = meteor_scorer
def getter(self):
from reference import Reference
return Reference.by_ids(self.lib.reference_lib.values())
def main():
#Get arguments
args = argParse()
print("- ARGS :")
print(args, "\n")
#create indexedReference
print("- REFERENCE :")
reference = Reference()
#check if already indexed
fileName = path.splitext(args.ref)[0]
# retrieve from .gz
if (path.exists(fileName + ".dumped.gz")):
reference.load(fileName)
print("reference loaded from dumped.gz")
#index sequence
else:
sequence = getSequence(args.ref)
reference.createIndex(sequence)
reference.save(fileName)
print("reference indexed and saved")
print("DEBUG", reference.N)
print("Reference : ", "\n", reference.text[:10], "...", "\n")
#open Reads
print("- READS :")
readStream = openReads(args.reads)
readName, readContent = getNextRead(readStream)
print("First read :", readName, readContent)
print("RevCompl :", reverseCompl(readContent), "\n")
#outputStream
outputStream = open(args.out, "w")
#Start the timer
print("- FINDING ALIGNEMENT :")
with Timer() as total_time:
while (readContent != -1):
print(readName, "(processing)")
isRevCompl = False
bestScore, bestPos = getBestSemiGlobalAlg(readContent, reference,
args.k, args.dmax)
bestScoreRev, bestPosRev = getBestSemiGlobalAlg(
reverseCompl(readContent), reference, args.k, args.dmax)
#if the reverse search is better we take it
if (bestScoreRev < bestScore):
bestScore, bestPos = bestScoreRev, bestPosRev
isRevCompl = True
#Found a result
if (bestScore != len(readContent)):
if bestScore <= args.dmax:
appendResults(outputStream, readName, bestPos, isRevCompl,
bestScore) #with tabs
readName, readContent = getNextRead(readStream)
total_time.print('\nIt tooks {} secondes.', 5)
print()
readStream.close()
outputStream.close()
#print out file
print("- RESULTS :")
f = open(args.out, "r")
print(f.read())
f.close()
print("exported in:", args.out)
def main():
global lr, outputFile, opts
opts, args = parseOptions()
lr = args[1]
animalId = args[0] + '_' + lr
male = animalId[0] == 'M'
# Open our files...
ref = Reference(opts.reference + '_ordered.fa')
indels = open(opts.indels)
snps = open(opts.snps)
dir = opts.dir
if opts.output:
outputFile = open(os.path.join(dir, opts.output), 'w')
else:
outputFile = sys.stdout
# Prepare for processing the indels file
# Skip the indels vcf header cruft
iHeaders = skipHeaders(indels)
iStrainTracker = StrainTracker(opts.control, iHeaders, 'I')
Indel.setStrainTracker(iStrainTracker)
# From here on out all lines in the indels file will be indel records.
# Flag that we need to refresh the indel
needIndel = True
# Similarly set up the snps file
sHeaders = skipHeaders(snps)
sStrainTracker = StrainTracker(opts.control, sHeaders, 'S')
Snp.setStrainTracker(sStrainTracker)
# From here on out all lines in the snps file will be snp records.
# Flag that we need to refresh the snp
needSnp = True
# Main processing loop...
while True:
if needIndel:
indel = Indel(indels)
if indel.chr == 'EOF':
# We exhausted the indels file. We'll clean up the snps
# after the loop
break
needIndel = False
if needSnp:
snp = Snp(snps)
if snp.chr == 'EOF':
# We exhausted the snps file. We'll clean up the indels
# after the loop
break
needSnp = False
# Now we have an indel and a snp. Process whichever is first.
# This function will return True if it processed the indel,
# False if it processed the snp.
processedIndel = processNextEvent(ref, indel, snp, animalId)
if processedIndel:
needIndel = True
else:
needSnp = True
# End of the main loop. We have exhausted one or the other input
# file. Now clean up the remainder of the other file.
if indel.chr == 'EOF':
# Last parameter False indicates processing snps
processRemaining(ref, snps, animalId, False)
elif snp.chr == 'EOF':
processRemaining(ref, indels, animalId, True)
# That's about it!
finishUp(ref, opts.reference, male, lr)
def open(self, folder):
"""Open an existing Array in `folder` with its summary Array"""
Reference.open(self, folder)
# Open the summary mirror
if not self.path.startswith('/summary') and len(self.fields) == 2:
self.summary = Array(self.outfile, '/summary' + self.path)
def __init__(self, outfile, folder=False, opt=False, write_current=False, with_summary=True):
self.with_summary = with_summary
Reference.__init__(self, outfile, folder=folder, opt=opt, write_current=write_current)
