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