def hatchplot_fit(xp: XPS_experiment,
region: str,
fitRes: ModelResult,
lb: str = None,
marker='o',
ls: str = 'solid',
colc: str = None,
ax=None,
plot_comps: bool = True,
flag_fill: bool = False):
""""Plot fit result with predefined hatch patterns for each component (up to three components)"""
if ax == None: ax = plt.gca()
if lb == None: lb = xp.name
if colc == None: colc = xp.color
p1 = ax.scatter(xp.dfx[region].energy,
xp.dfx[region].counts,
marker=marker,
label=lb,
zorder=1)
p1.set_color(colc)
x = xp.dfx[region].dropna().energy
ax.plot(x,
fitRes.best_fit,
linestyle=ls,
color=colc,
lw=1.5,
label='Fit, $\chi^2_N$ = %i' % fitRes.redchi)
hatch = ['//', 'ox', '+']
if plot_comps:
comps = fitRes.eval_components(x=x)
for i, compo in enumerate(comps):
posx = fitRes.best_values[compo + 'center']
ax.text(x=posx,
y=comps[compo].max() * 1.02,
s='%.1f' % posx,
fontsize=12)
ax.fill_between(x,
y1=0,
y2=comps[compo],
alpha=1,
label='Component @ %.1f eV' % posx,
facecolor='w',
hatch=hatch[i],
edgecolor=colc,
zorder=-1)
ax.legend(loc='best') #, bbox_to_anchor=(1.12, 0.5), fontsize=16)
cosmetics_plot()
return ax
def plot_fit_result(xp: XPS_experiment,
region: str,
fitRes: ModelResult,
lb: str = None,
ax=None,
plot_comps: bool = True,
flag_fill: bool = False):
if ax == None: ax = plt.gca()
col = plot_region(xp, region, ax=ax, lb=lb).get_color()
x = xp.dfx[region].dropna().energy
ax.plot(x,
fitRes.best_fit,
'--',
color=col,
lw=1.5,
label='best fit, $\chi^2_N$ = %i' % fitRes.redchi)
ax.legend()
if plot_comps:
comps = fitRes.eval_components(x=x)
for compo in comps:
posx = fitRes.best_values[compo + 'center']
colc = ax.plot(x,
comps[compo],
ls='dotted',
lw=1.5,
color=col,
label='__nolabel__')[0].get_color()
ax.vlines(x=posx,
ymin=0,
ymax=comps[compo].max(),
linestyle='dotted',
colors=col)
ax.text(x=posx,
y=comps[compo].max() * 0.9,
s='%.1f' % posx,
fontsize=12)
if flag_fill:
ax.fill_between(x,
y1=0,
y2=comps[compo],
alpha=0.3,
color=colc)
return ax