def rebin2d(self, bin_size):
"""
Rebin 2D plot along x dimension
"""
if self.dim != 2:
raise GE('This rebins 2D histograms only')
if int(bin_size)<1:
raise GE('binsize < 1, using 1 instead')
intbinsize=1
else:
intbinsize=int(bin_size)
excess=len(self.weights[:,0])%intbinsize
numyslices=self.weights.shape[1]
if excess != 0:
currweights=self.weights[0:-excess,:]
currxaxis=self.x_axis[0:-excess]
else:
currweights=self.weights[:,:]
currxaxis=self.x_axis[:]
rebinned=[]
for ii in range(numyslices):
currslice=currweights[:,ii]
rebinned.append(currslice.reshape((-1,intbinsize)).sum(axis=1))
currweights=np.asarray(rebinned)
currxaxis=currxaxis.reshape((-1,intbinsize)).mean(axis=1)
currerrors=np.sqrt(abs(currweights))
currtitle='{}, bin {}'.format(self.title,intbinsize)
currhistogram=Histogram(dim=self.dim,weights=currweights,x_axis=currxaxis,y_axis=self.y_axis,title=currtitle,errors=currerrors)
return currhistogram
def rebin1d(self, bin_size=1):
"""
Bin 1D histogram, binsize must be larger than 1
"""
if self.dim != 1:
raise GE('This Rebins 1D histograms only.')
if int(bin_size)<1:
raise GE('binsize < 1, using 1 instead')
intbinsize=1
else:
intbinsize=int(bin_size)
excess=len(self.weights)%intbinsize
if excess != 0:
currweights=self.weights[0:-excess]
currxaxis=self.x_axis[0:-excess]
else:
currweights=self.weights[:]
currxaxis=self.x_axis[:]
currweights=currweights.reshape((-1,intbinsize)).sum(axis=1)
currxaxis=currxaxis.reshape((-1,intbinsize)).mean(axis=1)
currerrors=np.sqrt(abs(currweights))
currtitle='{}, bin {}'.format(self.title,intbinsize)
currhistogram=Histogram(dim=self.dim,weights=currweights,x_axis=currxaxis,title=currtitle,errors=currerrors)
return currhistogram
def __set_rounding(self, rounding):
if not isinstance(rounding, str):
raise GE('rounding must be \'low\',\'mid\',\'high\'')
else:
if rounding.lower() == 'low':
self.dx = 0.0
elif rounding.lower() == 'mid':
self.dx = 0.5
elif rounding.lower() == 'high':
self.dx = 1.0
else:
raise GE('unknown rounding ---> {}'.format(rounding))
self.__rounding = rounding
def __set_bin_size(self,bin_size):
if self.histogram.dim!=1:
raise GE('Currently only 1D histograms can be binned')
if isinstance(bin_size,int):
if bin_size > self.bin_size:
self.__bin_size=bin_size
self.histogram=self.histogram.rebin1d(bin_size)
elif bin_size <= self.bin_size:
pass
else:
raise GE('Attempt to set bin size to {}'.format(bin_size))
else:
raise GE('Attempt to set bin size to {}'.format(bin_size))
def __init__(self,size=11,max_2d_bin=1024,font_size=20,legend=True,cmap=color_maps[0]):
"""
size:
0 -------------> None
1 -------------> 8x6
11 (default)---> 12x8
2 -------------> 2 figs of 8x8
12 ------------> 2 figs of 12x8
"""
self.max_2d_bin=max_2d_bin
self.font_size=font_size
self.legend=legend
self.cmap=cmap
if size==0:
pass
elif size==1:
plt.figure(1,(8,6))
elif size==11:
plt.figure(1,(12,8))
elif size==2:
plt.figure(1,(8,6))
plt.figure(2,(8,6))
elif size==12:
plt.figure(1,(12,8))
plt.figure(2,(12,8))
else:
raise GE('Unknown size {}, must be 1,2,11,12'.format(size))
if size != 0:
plt.tick_params(axis='both',labelsize=self.font_size)
plt.grid()
plt.ion()
plt.show()
def load_histogram(self, hisid):
if self.histograms.get(hisid) is None:
raise GE('Histogram {} not found'.format(hisid))
offset = self.histograms[hisid]['offset']
hisname = '{}{}'.format(self.base_name, '.his')
with open(hisname, 'rb') as hisfile:
length = 1
dim = self.histograms[hisid]['dimension']
if dim > 2:
raise GE('Histograms with dimensions > 2 not supported')
for d in range(self.histograms[hisid]['dimension']):
length *= self.histograms[hisid]['scaled'][d]
if self.histograms[hisid]['half_words_per_ch'] == 1:
data = array('h')
elif self.histograms[hisid]['half_words_per_ch'] == 2:
data = array('i')
else:
msg = 'half-words per channel histograms not supported'
raise GE(
'{} {}'.format(
self.histograms[hisid]['half_words_per_ch']), msg)
hisfile.seek(offset * 2)
data.fromfile(hisfile, length)
xaxis=np.arange(self.histograms[hisid]['minc'][0]+self.dx,\
self.histograms[hisid]['maxc'][0]+self.dx+1.0)
if self.histograms[hisid]['dimension'] == 2:
yaxis=np.arange(self.histograms[hisid]['minc'][1]+self.dx,\
self.histograms[hisid]['maxc'][1]+self.dx+1.0)
data=np.reshape(data,(self.histograms[hisid]['scaled'][1],\
self.histograms[hisid]['scaled'][0]))
data = np.transpose(data)
if self.histograms[hisid]['dimension'] == 1:
return [1, xaxis, None, np.array(data)]
else:
return [2, xaxis, yaxis, data]
def normalize1d(self,norm='area',bin_size=1,xmin=None,xmax=None):
"""
Normalize 1D spectra by norm default to area so that it integrates to 1
n_i=n_i/(norm*binsize)
"""
if self.dim != 1:
raise GE('This Normalizes 1D histograms only.')
if int(bin_size)<1:
raise GE('binsize < 1, using 1 instead')
intbinsize=1
else:
intbinsize=int(bin_size)
currhistogram=Histogram(dim=self.dim,x_axis=self.x_axis,weights=self.weights,errors=self.errors)
if isinstance(norm,str):
if norm.lower()=='area':
normalization=currhistogram.weights[xmin:xmax].sum()
if normalization==0:
print('Normalization came out to 0, using 1 instead')
normalization=1
else:
raise GE('norm must be int, float, or \'area\'')
elif isinstance(norm,float) or isinstance(norm,int):
if norm == 0:
print('norm set to 0, using 1 instead')
normalization=1
else:
normalization=norm
currhistogram.title='{},/{:.2e}'.format(self.title,normalization*intbinsize)
currhistogram.weights=currhistogram.weights/(normalization*intbinsize)
currhistogram.errors=currhistogram.errors/(normalization*intbinsize)
return currhistogram
def __untar(self, tarball):
sys.stderr.write('{:.<30}'.format('Checking tarball'))
sys.stderr.flush()
archive = []
exstensions = [".drr", ".his", ".list", ".log"]
for tarinfo in tarball:
if os.path.splitext(tarinfo.name)[1] not in exstensions:
raise GE(
' File {} does not belong to tar.gz HISS/DRR histograms'.
format(tarinfo.name))
if os.path.exists(tarinfo.name):
raise GE('File {} alread exists'.format(tarinfo.name))
archive.append(tarinfo.name)
sys.stderr.write('[Done]\n')
self.tmp_files += archive
sys.stderr.write('{:.<30}'.format('Extracting tarball'))
sys.stderr.flush()
tarball.extractall()
sys.stderr.write('[Done]\n')
tarball.close()
def plot1d(self,plot,xlim=None,ylim=None):
hist=plot.histogram
if hist.dim!=1:
raise GE('1d plotter not {}d'.format(hist.dim))
if plot.mode=='histogram':
plt.plot(hist.x_axis,hist.weights,ls='steps-mid',label=hist.title)
elif plot.mode=='function':
plt.plot(hist.x_axis,hist.weights,ls='-',label=hist.title)
elif plot.mode=='errorbar':
plt.errorbar(hist.x_axis,hist.weights,yerr=hist.errors,\
marker='o',ls='None',label=hist.title)
else:
raise GE('Unknown plot mode {}'.format(plot.mode))
if xlim is not None:
plt.xlim(xlim)
if ylim is not None:
plt.ylim(ylim)
if self.legend:
plt.legend(loc=0,numpoints=1,fontsize='small')
def __set_norm(self,norm):
if self.histogram.dim != 1:
raise GE('Currently only 1D histograms can be normalized')
self.__norm=norm
self.histogram=self.histogram.normalize1d(norm,self.bin_size)
def plot2d(self,plot,xc=None,yc=None,logz=True):
if plot.histogram.dim!=2:
raise GE('2d plotter not {}d'.format(plot.histogram.dim))
x=plot.histogram.x_axis
y=plot.histogram.y_axis
w=plot.histogram.weights
if xc is not None:
x=x[xc[0]:xc[1]]
w=w[xc[0]:xc[1],:]
if yc is not None:
y=y[yc[0]:yc[1]]
w=w[:,yc[0]:yc[1]]
init_nx=len(x)
init_ny=len(y)
nx=len(x)
ny=len(y)
binx=1
biny=1
if nx > self.max_2d_bin:
binx=math.ceil(nx/self.max_2d_bin)
missing=binx*self.max_2d_bin-nx
if missing > 0:
addx=np.arange(plot.histogram.x_axis[-1]+1,plot.histogram.x_axis[-1]+missing+1)
x=np.concatenate((x,addx))
nx=len(x)
z=np.zeros((missing,ny))
w=np.concatenate((w,z),axis=0)
x=np.reshape(x,(-1,binx))
x=x.mean(axis=1)
if ny > self.max_2d_bin:
biny=math.ceil(ny/self.max_2d_bin)
missing=biny*self.max_2d_bin-ny
if missing > 0:
addy=np.arange(plot.histogram.y_axis[-1]+1,plot.histogram.y_axis[-1]+missing+1)
y=np.concatenate((y,addy))
ny=len(y)
z=np.zeros((nx,missing))
w=np.concatenate((w,z),axis=1)
y=np.reshape(y,(-1,biny))
y=y.mean(axis=1)
nx=len(x)
ny=len(y)
if nx!=init_nx or ny!=init_ny:
w=np.reshape(w,(nx,binx,ny,biny)).mean(3).mean(1)
w=np.transpose(w)
title=plot.histogram.title
if logz:
w=np.ma.masked_where(w<=0,np.log10(w))
title+='(log10'
plt.title(title)
CS=plt.pcolormesh(x,y,w,cmap=self.cmap)
plt.xlim(xc)
plt.ylim(yc)
plt.colorbar()