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formatting improved

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Fabian Joswig 2021-09-24 16:49:00 +01:00
parent 63037fd98f
commit caa1330ab8
2 changed files with 24 additions and 24 deletions
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38
pyerrors/correlators.py
View file

@ -20,14 +20,14 @@ class Corr:
"""
def __init__(self, data_input,padding_front=0,padding_back=0):
def __init__(self, data_input, padding_front=0, padding_back=0):
#All data_input should be a list of things at different timeslices. This needs to be verified
if not (isinstance(data_input,list)):
if not (isinstance(data_input, list)):
raise TypeError('Corr__init__ expects a list of timeslices.')
# data_input can have multiple shapes. The simplest one is a list of Obs.
#We check, if this is the case
if all([isinstance(item,Obs) for item in data_input]):
if all([isinstance(item, Obs) for item in data_input]):
self.content=[np.asarray([item]) for item in data_input]
#Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices.
self.N = 1 # number of smearings
@ -70,7 +70,7 @@ class Corr:
#The method can use one or two vectors.
#If two are specified it returns v1@G@v2 (the order might be very important.)
#By default it will return the lowest source, which usually means unsmeared-unsmeared (0,0), but it does not have to
def projected(self,vector_l=None,vector_r=None):
def projected(self, vector_l=None, vector_r=None):
if self.N == 1:
raise Exception("Trying to project a Corr, that already has N=1.")
#This Exception is in no way necessary. One could just return self
@ -224,7 +224,7 @@ class Corr:
#We want to apply a pe.standard_fit directly to the Corr using an arbitrary function and range.
def fit(self, function, fitrange=None):
def fit(self, function, fitrange=None, silent=False):
if self.N != 1:
raise Exception("Correlator must be projected before fitting")
@ -233,7 +233,7 @@ class Corr:
xs = [x for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
result = standard_fit(xs, ys, function, silent=True)
result = standard_fit(xs, ys, function, silent=silent)
[item.gamma_method() for item in result if isinstance(item,Obs)]
return result
@ -245,10 +245,10 @@ class Corr:
raise Exception("plateau is undefined at all timeslices in plateaurange.")
if method == "fit":
def const_func(a, t):
return a[0] + a[1] * 0 # At some point pe.standard fit had an issue with single parameter fits. Being careful does not hurt
return a[0] # At some point pe.standard fit had an issue with single parameter fits. Being careful does not hurt
return self.fit(const_func,plateau_range)[0]
elif method in ["avg","average","mean"]:
returnvalue= np.mean([item[0] for item in self.content if not item is None])
returnvalue= np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1]+1] if not item is None])
returnvalue.gamma_method()
return returnvalue
@ -258,28 +258,28 @@ class Corr:
#quick and dirty plotting function to view Correlator inside Jupyter
#If one would not want to import pyplot, this could easily be replaced by a call to pe.plot_corrs
#This might be a bit more flexible later
def show(self,xrange=None,logscale=False):
def show(self, x_range=None, logscale=False):
if self.N!=1:
raise Exception("Correlator must be projected before plotting")
if xrange is None:
xrange=[0,self.T]
if x_range is None:
x_range=[0, self.T]
x,y,y_err=self.plottable()
plt.errorbar(x,y,y_err)
if logscale:
plt.yscale("log")
plt.yscale('log')
else:
# we generate ylim instead of using autoscaling.
y_min=min([ (x[0].value-x[0].dvalue) for x in self.content[xrange[0]:xrange[1]] if(not x is None)])
y_max=max([ (x[0].value+x[0].dvalue) for x in self.content[xrange[0]:xrange[1]] if(not x is None)])
plt.ylim([y_min-0.1*(y_max-y_min),y_max+0.1*(y_max-y_min)])
y_min=min([(x[0].value - x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if(not x is None)])
y_max=max([(x[0].value + x[0].dvalue) for x in self.content[x_range[0]:x_range[1]] if(not x is None)])
plt.ylim([y_min - 0.1 * (y_max - y_min), y_max + 0.1 * (y_max - y_min)])
plt.xlabel(r"$an_t$")
plt.xlim([xrange[0] - 0.5, xrange[1] + 0.5])
plt.title("Quickplot")
plt.xlabel(r'$x_0 / a$')
plt.xlim([x_range[0] - 0.5, x_range[1] + 0.5])
#plt.title("Quickplot")
plt.show()
plt.clf()
#plt.clf()
return
def dump(self,filename):

10
pyerrors/pyerrors.py
View file

@ -356,7 +356,7 @@ class Obs:
plt.ylabel('tauint')
length = int(len(self.e_n_tauint[e_name]))
plt.errorbar(np.arange(length), self.e_n_tauint[e_name][:], yerr=self.e_n_dtauint[e_name][:], linewidth=1, capsize=2)
plt.axvline(x=self.e_windowsize[e_name], color='r', alpha=0.25)
plt.axvline(x=self.e_windowsize[e_name], color='r', alpha=0.25, marker=',')
if self.tau_exp[e_name] > 0:
base = self.e_n_tauint[e_name][self.e_windowsize[e_name]]
x_help = np.arange(2 * self.tau_exp[e_name])
@ -1174,17 +1174,17 @@ def plot_corrs(observables, **kwargs):
if 'prange' in kwargs:
prange = kwargs.get('prange')
plt.axvline(x=prange[0] - 0.5, ls='--', c='k', lw=1, alpha=0.5)
plt.axvline(x=prange[1] + 0.5, ls='--', c='k', lw=1, alpha=0.5)
plt.axvline(x=prange[0] - 0.5, ls='--', c='k', lw=1, alpha=0.5, marker=',')
plt.axvline(x=prange[1] + 0.5, ls='--', c='k', lw=1, alpha=0.5, marker=',')
if 'plateau' in kwargs:
plateau = kwargs.get('plateau')
if isinstance(plateau, Obs):
plt.axhline(y=plateau.value, linewidth=2, color='k', alpha=0.6, label='Plateau')
plt.axhline(y=plateau.value, linewidth=2, color='k', alpha=0.6, label='Plateau', marker=',')
plt.axhspan(plateau.value - plateau.dvalue, plateau.value + plateau.dvalue, alpha=0.25, color='k')
elif isinstance(plateau, list):
for i in range(len(plateau)):
plt.axhline(y=plateau[i].value, linewidth=2, color='C' + str(i), alpha=0.6, label='Plateau' + str(i + 1))
plt.axhline(y=plateau[i].value, linewidth=2, color='C' + str(i), alpha=0.6, label='Plateau' + str(i + 1), marker=',')
plt.axhspan(plateau[i].value - plateau[i].dvalue, plateau[i].value + plateau[i].dvalue,
color='C' + str(i), alpha=0.25)
else: