def PlotEffMass_VarMeth(self, plot_plane, **kwargs):
if hasattr(self,
'booted_proj_corr') and self.booted_proj_corr is not None:
return self.PlotEffMass_VarMeth_Boot(plot_plane, **kwargs)
if not hasattr(self, 'eff_mass'):
print('effective mass not computed, computing now')
self.PlotEffMass_VarMeth()
hold_series = null_series
if 'color' in kwargs.keys():
hold_series['color'] = kwargs['color']
del kwargs['color']
if 'shift' in kwargs.keys():
hold_series['shift'] = kwargs['shift']
del kwargs['shift']
try:
data_plot = self.proj_eff_mass.unstack().sel(**kwargs)
except Exception as err:
data_plot = self.proj_eff_mass.sel(**kwargs).unstack()
data_plot = data_plot.stack(all=self.proj_eff_mass.dims).to_pandas()
this_index = pa.MultiIndex.from_tuples(list(map_str(data_plot.index)),
names=data_plot.index.names)
data_plot = pa.Series(data_plot.values, index=this_index)
hold_series['x_data'] = 'from_keys'
hold_series['key_select'] = data_plot.index[0]
hold_series['type'] = 'plot'
if isinstance(data_plot.index[0], (tuple, list, np.ndarray)):
if len(data_plot.index[0]) > 1:
hold_series['key_select'] = data_plot.index[0]
hold_series['key_select'] = list(
hold_series['key_select'])[:-1] + [slice(None)]
hold_series['type'] += '_vary'
hold_series['y_data'] = data_plot
hold_series['label'] = self.name + ' VarMeth'
plot_plane.AppendData(hold_series)
return plot_plane
def PlotEffMass_Prony_Boot(self, plot_plane, **kwargs):
if not hasattr(self, 'booted_pro_proj_eff_mass'
) or self.booted_pro_proj_eff_mass is None:
print('effective mass not computed, computing now')
self.MakeEffMass()
hold_series = null_series
if 'color' in kwargs.keys():
hold_series['color'] = kwargs['color']
del kwargs['color']
if 'shift' in kwargs.keys():
hold_series['shift'] = kwargs['shift']
del kwargs['shift']
try:
data_plot = self.booted_pro_proj_eff_mass.unstack().sel(**kwargs)
except Exception as err:
data_plot = self.booted_pro_proj_eff_mass.sel(**kwargs).unstack()
data_plot = data_plot.stack(
all=self.booted_pro_proj_eff_mass.dims).to_pandas()
this_index = pa.MultiIndex.from_tuples(list(map_str(data_plot.index)),
names=data_plot.index.names)
data_plot = pa.Series(data_plot.values, index=this_index)
data_plot.apply(lambda x: x.Stats())
data_avg = data_plot.apply(lambda x: x.Avg)
data_std = data_plot.apply(lambda x: x.Std)
hold_series['x_data'] = 'from_keys'
hold_series['key_select'] = data_plot.index[0]
hold_series['type'] = 'error_bar'
if isinstance(data_plot.index[0], (tuple, list, np.ndarray)):
if len(data_plot.index[0]) > 1:
hold_series['key_select'] = data_plot.index[0]
hold_series['key_select'] = list(
hold_series['key_select'])[:-1] + [slice(None)]
hold_series['type'] += '_vary'
hold_series['y_data'] = data_avg
hold_series['yerr_data'] = data_std
hold_series['label'] = self.name + ' Prony'
plot_plane.AppendData(hold_series)
return plot_plane