def analyseHill(ekindicts):
"""make hist of n coefficents for hill fits"""
import pylab
pylab.rc('text', usetex=True)
f=pylab.figure()
f.suptitle('n distributions- No linear (case 3)')
i=1
for e in ekindicts:
ekindata = ekindicts[e]
proteins = ekindata.keys()
nvals = []
for prot in proteins:
edata = ekindata[prot]
E = EkinProject(data=edata)
for d in E.datasets:
fdata = E.getMetaData(d)
if fdata != None and fdata.has_key('model'):
if fdata['model'] == 'Modified Hill':
n=fdata['n']
if n<5 and n>-5:
nvals.append(n)
print 'n=', n
ax = f.add_subplot(2,2,i)
n, b, patches = pylab.hist(nvals, 30, histtype='bar', alpha=0.8)
std = round(numpy.std(nvals), 2)
ave = round(numpy.mean(nvals), 2)
ax.set_title(e +' mean= '+str(ave)+r' $\sigma$= '+str(std))
i+=1
f.subplots_adjust(hspace=0.4)
f.savefig('n_hist.png')
return
def analyseHill(ekindicts):
"""make hist of n coefficents for hill fits"""
import pylab
pylab.rc('text', usetex=True)
f = pylab.figure()
f.suptitle('n distributions- No linear (case 3)')
i = 1
for e in ekindicts:
ekindata = ekindicts[e]
proteins = ekindata.keys()
nvals = []
for prot in proteins:
edata = ekindata[prot]
E = EkinProject(data=edata)
for d in E.datasets:
fdata = E.getMetaData(d)
if fdata != None and fdata.has_key('model'):
if fdata['model'] == 'Modified Hill':
n = fdata['n']
if n < 5 and n > -5:
nvals.append(n)
print 'n=', n
ax = f.add_subplot(2, 2, i)
n, b, patches = pylab.hist(nvals, 30, histtype='bar', alpha=0.8)
std = round(numpy.std(nvals), 2)
ave = round(numpy.mean(nvals), 2)
ax.set_title(e + ' mean= ' + str(ave) + r' $\sigma$= ' + str(std))
i += 1
f.subplots_adjust(hspace=0.4)
f.savefig('n_hist.png')
return
def showMetaData(self,
ekinproj=None,
ekindata=None,
dataset=None,
fdata=None,
silent=False):
"""Print html of fit and metadata for the given dataset"""
if fdata == None:
if ekinproj == None and ekindata != None:
E = EkinProject(data=ekindata)
else:
E = ekinproj
fdata = E.getMetaData(dataset)
fsock = None
if silent == True:
saveout = sys.stdout
sys.stdout = fsock = StringIO.StringIO()
print '<table id="mytable">'
print '<tr>'
print '<td class="alt" style="bold" colspan=2>%s</td><tr>' % dataset
kys = fdata.keys()
ignore = ['error']
for k in sorted(fdata):
if k in ignore:
continue
if fdata[k] == None:
continue
elif type(fdata[k]) is types.DictType:
print '<td>%s</td>' % k
print '<td> <table id="mytable">'
for n in fdata[k]:
val = fdata[k][n][1]
print '<td class="alt">%s</td><td>%.2f</td><tr>' % (n, val)
print '</table></td><tr>'
elif type(fdata[k]) is types.StringType:
print '<td class="alt">%s</td><td>%s</td><tr>' % (k, fdata[k])
else:
print '<td class="alt">%s</td><td>%.2f</td><tr>' % (k,
fdata[k])
print '</table>'
if silent == True:
sys.stdout = saveout
if fsock == None:
return ''
else:
return fsock.getvalue()
return
def showMetaData(self, ekinproj=None, ekindata=None, dataset=None, fdata=None, silent=False):
"""Print html of fit and metadata for the given dataset"""
if fdata == None:
if ekinproj == None and ekindata!=None:
E = EkinProject(data=ekindata)
else:
E = ekinproj
fdata = E.getMetaData(dataset)
fsock = None
if silent == True:
saveout = sys.stdout
sys.stdout = fsock = StringIO.StringIO()
print '<table id="mytable">'
print '<tr>'
print '<td class="alt" style="bold" colspan=2>%s</td><tr>' % dataset
kys = fdata.keys()
ignore = ['error']
for k in sorted(fdata):
if k in ignore:
continue
if fdata[k] == None:
continue
elif type(fdata[k]) is types.DictType:
print '<td>%s</td>' %k
print '<td> <table id="mytable">'
for n in fdata[k]:
val = fdata[k][n][1]
print '<td class="alt">%s</td><td>%.2f</td><tr>' %(n, val)
print '</table></td><tr>'
elif type(fdata[k]) is types.StringType:
print '<td class="alt">%s</td><td>%s</td><tr>' %(k, fdata[k])
else:
print '<td class="alt">%s</td><td>%.2f</td><tr>' %(k, fdata[k])
print '</table>'
if silent == True:
sys.stdout = saveout
if fsock == None:
return ''
else:
return fsock.getvalue()
return
