def kineticsTest():
"""Tests kinetics data for paper"""
p = Pipeline()
colheaderlabels = 'wt 5,wt 3,wt 2,68 5,68 3,68 2,138 5,138 3,138 2,248 5,248 3,248 2'
rowheaderlabels = '3.2,1.6,0.8,0.4,0.2,0.1,0.05,0.025'
conf = {
'format': 'kineticsdata',
'delimeter': 'tab',
'rowstart': 3,
'colend': 12,
'rowrepeat': 9,
'colheaderlabels': colheaderlabels,
'rowheaderlabels': rowheaderlabels,
'decimalsymbol': ',',
'xformat': '%M:%S',
'groupbyname': 1,
'parsenamesindex': 2,
'parsemethod': 'numeric',
'model1': 'linear',
'model2': 'Michaelis-Menten',
'model3': '1 pKa 2 Chemical shifts',
'variable1': 'a',
'variable2': 'Km',
'variable3': 'pKa', #'xerror':.1,'yerror':0.05,
}
p = Pipeline()
p.createConfig('temp.conf', **conf)
path = 'testfiles/kineticsdata/jan/setF/MM/rep1'
p.addFolder(path)
p.run()
return
def peakDetectionTest(path=None, noise=0.08):
"""Use pre-processing funcs to detect peaks"""
if path == None:
path = "testfiles"
names = Utilities.createRandomStrings(8, 6)
fname = os.path.join(path, 'spectraldata.txt')
peaks = Utilities.createSimulatedSpectralData(fname, names, noise=noise)
conf = {
'format': 'databycolumn',
'saveplots': 1,
'marker': '-',
'markers': '-,x',
'alpha': 0.7,
'normalise': 1,
'function1': 'smooth',
'function2': 'baselinecorrection',
'function3': 'detectpeaks'
}
p = Pipeline()
p.createConfig('temp.conf', **conf)
p.openRaw(fname)
results = p.run()
#compare predicted peaks
successrates = []
res = results[results.keys()[0]]
for name in peaks:
#print name, sorted(peaks[name]), results[name][0]
orig = set(peaks[name])
pred = set(res[name][0])
s = float(len(orig.intersection(pred))) / (len(orig))
successrates.append(s)
return np.mean(successrates)
def replicatesTest():
"""Tests handling of replicates"""
p = Pipeline()
conf = {
'format': 'databycolumn',
'groupbyname': 1,
'parsenamesindex': 1,
'parsemethod': 'numeric',
'replicates': 1,
'model1': 'linear',
'variable1': 'a',
'model2': 'sigmoid',
'variable2': 'tm'
}
p.createConfig('temp.conf', **conf)
reps = ['rep1', 'rep2', 'rep3']
path = 'testfiles/replicates'
Utilities.createDirectory(path)
names = Utilities.createRandomStrings(3, 6)
for r in reps:
rpath = os.path.join(path, r)
Utilities.createGroupedData(rpath, names=names)
p.addFolder(path)
p.run()
return
def fitPropagationTest():
"""Tests the propagation of fit data direct from a dict - no importing"""
start = time.time()
p = Pipeline()
conf = {
'model1': 'linear',
'model2': 'Michaelis-Menten',
'model3': 'sigmoid',
'variable1': 'a',
'variable2': 'Km',
'variable3': 'tm', #'xerror':.1,'yerror':0.05,
}
p.createConfig('temp.conf', **conf)
data = Utilities.createNestedData()
Em = EkinProject()
E, fits = p.processFits(data, Em=Em)
print 'final fits', fits
fname = os.path.join(p.workingdir, 'results')
Em.saveProject(fname)
p.saveEkinPlotstoImages(Em, fname)
print 'completed fit propagation test'
print 'took %s seconds' % round((time.time() - start), 2)
print '-------------------'
return
def multiFolderTest():
"""Handling of multiple folders in a hierarchy with replicates"""
p = Pipeline()
conf = {
'format': 'databycolumn',
'groupbyname': 1,
'parsenamesindex': 0,
'parsemethod': 'numeric',
'replicates': 1, #'saveplots':1,
'model1': 'linear',
'variable1': 'a',
'model2': 'sigmoid',
'variable2': 'tm'
}
p.createConfig('temp.conf', **conf)
path = 'testfiles/multifolders'
Utilities.createDirectory(path)
phs = range(2, 10)
reps = range(1, 4) #replicates
names = Utilities.createRandomStrings(3, 6)
today = str(datetime.date.today())
for i in phs:
#sigmoid dependence of the slopes on 'ph'
#so we know we are getting the right results
val = 1 / (1 + exp((i - 4) / 1.04))
folder = os.path.join(path, 'ph' + str(i))
Utilities.createDirectory(folder)
for r in reps:
fname = os.path.join(folder, 'r' + str(r) + '_' + today + '.txt')
Utilities.createTempData(fname, names, val)
p.addFolder(path)
p.run()
return
def doTest(info, name='test', path='testfiles'):
print 'running test %s' % name
p = Pipeline()
conf = info[0]
filename = info[1]
confpath = os.path.join(p.defaultpath, 'temp.conf')
p.createConfig(confpath, **conf)
lines = p.openRaw(os.path.join(path, filename))
data = p.doImport(lines)
if p.model1 != '':
p.run()
return
def groupbyFieldsTest():
"""Tests grouping by fields function using NMRdata"""
conf = {
'format': 'databycolumn',
'colheaderlabels': '15N,1H',
'parsenamesindex': 0,
'parsemethod': 'numeric',
'delimeter': ' ',
'groupbyfields': 1,
'extension': '.inp'
}
path = 'testfiles/NMRdata'
p = Pipeline()
p.createConfig('temp.conf', **conf)
p.addFolder(path)
p.run()
return
def multiFileTest():
"""Test handling of single datasets per file with grouping per filename"""
path = 'testfiles/singlefiles'
Utilities.createSingleFileData(path)
conf = {
'format': 'databycolumn',
'groupbyname': 1,
'parsenamesindex': '0,1',
'parsemethod': 'both',
'model1': 'linear',
'variable1': 'a',
'model2': 'sigmoid',
'variable2': 'tm'
}
p = Pipeline()
p.createConfig('temp.conf', **conf)
p.addFolder(path)
p.run()
return
def preProcessingTest():
"""Test processing steps like differentation of the data"""
path = "testfiles"
names = Utilities.createRandomStrings(8, 6)
fname = os.path.join(path, 'preprocessingtest.txt')
Utilities.createCDData(fname, names, 300, .5)
conf = {
'format': 'databycolumn',
'model1': 'gaussian',
'function1': 'differentiate',
'function2': 'gaussiansmooth',
'iterations': 100,
'variable1': 'a',
'saveplots': 1
}
p = Pipeline()
p.createConfig('temp.conf', **conf)
p.openRaw(fname)
p.run()
return
def groupedFilesTest():
"""Tests the processing and grouping of multiple files with the same
sets of datasets in all files"""
path = 'testfiles/grouped'
Utilities.createGroupedData(path)
conf = {
'format': 'databycolumn',
'groupbyname': 1,
'parsenamesindex': 0,
'parsemethod': 'numeric',
'model1': 'linear',
'variable1': 'a',
'model2': 'sigmoid',
'variable2': 'tm'
}
p = Pipeline()
p.createConfig('temp.conf', **conf)
p.addFolder(path)
p.run()
return
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-c", "--conf", dest="conf",
help="Provide a conf file", metavar="FILE")
parser.add_option("-f", "--file", dest="file",
help="Raw file", metavar="FILE")
parser.add_option("-d", "--dir", dest="directory",
help="Folder of raw files")
parser.add_option("-p", "--project", dest="project",
help="Project file", metavar="FILE")
opts, remainder = parser.parse_args()
P = Pipeline()
if opts.project != None:
P = loadProject(opts.project)
else:
if opts.conf != None:
P.parseConfig(opts.conf)
if opts.file != None:
P.openRaw(opts.file)
if opts.directory != None:
P.addFolder(opts.directory)
P.run()
def setUp(self):
self.p = Pipeline()
modulepath = os.path.dirname(DataPipeline.__file__)
self.confpath = os.path.join(self.p.defaultpath, 'temp.conf')
self.filepath = os.path.join(modulepath, 'testfiles')
