diff --git a/benchmarks/zdc_lambda/analysis/lambda_plots.py b/benchmarks/zdc_lambda/analysis/lambda_plots.py
index bf855d9da24ae04e424fceca11a49eea4963d831..931a44b3bb37ee98918ae61bcde7ad47ed98390a 100644
--- a/benchmarks/zdc_lambda/analysis/lambda_plots.py
+++ b/benchmarks/zdc_lambda/analysis/lambda_plots.py
@@ -191,7 +191,7 @@ slc=abs(bc)<0.3
fnc=gauss
p0=[100, 0, 0.05]
coeff, var_matrix = curve_fit(fnc, bc[slc], y[slc], p0=p0,
- sigma=np.sqrt(y[slc])+(y[slc]==0))
+ sigma=np.sqrt(y[slc])+(y[slc]==0), maxfev=10000)
x=np.linspace(-1, 1)
plt.plot(x, gauss(x, *coeff), color='tab:orange')
plt.xlabel("$\\theta^{*\\rm recon}_{\\Lambda}-\\theta^{*\\rm truth}_{\\Lambda}$ [mrad]")
@@ -214,7 +214,7 @@ for p in momenta:
#print(bc[slc],y[slc])
sigma=np.sqrt(y[slc])+(y[slc]==0)
try:
- coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,sigma=list(sigma))
+ coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0, sigma=list(sigma), maxfev=10000)
sigmas.append(coeff[2])
dsigmas.append(np.sqrt(var_matrix[2][2]))
xvals.append(p)
@@ -259,7 +259,7 @@ slc=abs(bc)<5
fnc=gauss
p0=[100, 0, 1]
coeff, var_matrix = curve_fit(fnc, bc[slc], y[slc], p0=p0,
- sigma=np.sqrt(y[slc])+(y[slc]==0))
+ sigma=np.sqrt(y[slc])+(y[slc]==0), maxfev=10000)
x=np.linspace(-5, 5)
plt.plot(x, gauss(x, *coeff), color='tab:orange')
print(coeff[2], np.sqrt(var_matrix[2][2]))
@@ -284,7 +284,7 @@ for p in momenta:
#print(bc[slc],y[slc])
sigma=np.sqrt(y[slc])+(y[slc]==0)
try:
- coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,sigma=list(sigma))
+ coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0, sigma=list(sigma), maxfev=10000)
sigmas.append(coeff[2])
dsigmas.append(np.sqrt(var_matrix[2][2]))
xvals.append(p)
@@ -327,7 +327,7 @@ slc=abs(bc-lambda_mass)<0.07
fnc=gauss
p0=[100, lambda_mass, 0.04]
coeff, var_matrix = curve_fit(fnc, bc[slc], y[slc], p0=p0,
- sigma=np.sqrt(y[slc])+(y[slc]==0))
+ sigma=np.sqrt(y[slc])+(y[slc]==0), maxfev=10000)
x=np.linspace(0.8, 1.3, 200)
plt.plot(x, gauss(x, *coeff), color='tab:orange')
print(coeff[2], np.sqrt(var_matrix[2][2]))
@@ -350,7 +350,7 @@ for p in momenta:
p0=[100, lambda_mass, 0.05]
try:
coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
- sigma=list(np.sqrt(y[slc])+(y[slc]==0)))
+ sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
x=np.linspace(0.8, 1.3, 200)
sigmas.append(coeff[2])
dsigmas.append(np.sqrt(var_matrix[2][2]))
diff --git a/benchmarks/zdc_photon/analysis/zdc_photon_plots.py b/benchmarks/zdc_photon/analysis/zdc_photon_plots.py
index 94a931019a205ddbb7ed235cdb1a5716c8818f5a..a2cf53397159e871c2b202840dfeb33201218721 100644
--- a/benchmarks/zdc_photon/analysis/zdc_photon_plots.py
+++ b/benchmarks/zdc_photon/analysis/zdc_photon_plots.py
@@ -57,7 +57,7 @@ for p in momenta:
p0=[100, p, 10]
#print(list(y), list(x))
coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
- sigma=list(np.sqrt(y[slc])+(y[slc]==0)))
+ sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
if p==100:
xx=np.linspace(p*0.75,p*1.25, 100)
plt.plot(xx, fnc(xx,*coeff))
@@ -78,7 +78,7 @@ xx=np.linspace(15, 275, 100)
fnc=lambda E,a: a/np.sqrt(E)
#pvals, resvals, dresvals
coeff, var_matrix = curve_fit(fnc, pvals, resvals, p0=(1,),
- sigma=dresvals)
+ sigma=dresvals, maxfev=10000)
xx=np.linspace(15, 275, 100)
plt.plot(xx, fnc(xx, *coeff), label=f'fit: $\\frac{{{coeff[0]*100:.0f}\\%}}{{\\sqrt{{E}}}}$')
@@ -129,7 +129,7 @@ for p in momenta:
p0=[100, 0, 0.1]
#print(list(y), list(x))
coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
- sigma=list(np.sqrt(y[slc])+(y[slc]==0)))
+ sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
if p==100:
xx=np.linspace(-0.5,0.5, 100)
plt.plot(xx, fnc(xx,*coeff))
@@ -143,7 +143,7 @@ plt.errorbar(pvals, resvals, dresvals, ls='', marker='o')
fnc=lambda E,a, b: np.hypot(a/np.sqrt(E), b)
#pvals, resvals, dresvals
coeff, var_matrix = curve_fit(fnc, pvals, resvals, p0=(1,.1),
- sigma=dresvals)
+ sigma=dresvals, maxfev=10000)
xx=np.linspace(15, 275, 100)
diff --git a/benchmarks/zdc_pi0/analysis/zdc_pi0_plots.py b/benchmarks/zdc_pi0/analysis/zdc_pi0_plots.py
index 1b54430ea5c9d5b60e980b14a5ee9a7486ed367c..6d0cdddd0c1b8692b382f924131267dc78258a29 100644
--- a/benchmarks/zdc_pi0/analysis/zdc_pi0_plots.py
+++ b/benchmarks/zdc_pi0/analysis/zdc_pi0_plots.py
@@ -57,7 +57,7 @@ for p in momenta:
p0=[100, p, 10]
#print(list(y), list(x))
coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
- sigma=list(np.sqrt(y[slc])+(y[slc]==0)))
+ sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
if p==100:
xx=np.linspace(p*0.5,p*1.5, 100)
plt.plot(xx, fnc(xx,*coeff))
@@ -76,7 +76,7 @@ plt.xlabel("$p_{\\pi^0}$ [GeV]")
fnc=lambda E,a: a/np.sqrt(E)
#pvals, resvals, dresvals
coeff, var_matrix = curve_fit(fnc, pvals, resvals, p0=(1,),
- sigma=dresvals)
+ sigma=dresvals, maxfev=10000)
xx=np.linspace(55, 200, 100)
plt.plot(xx, fnc(xx, *coeff), label=f'fit: $\\frac{{{coeff[0]:.2f}\\%}}{{\\sqrt{{E}}}}$')
plt.legend()
@@ -133,7 +133,7 @@ for p in momenta:
p0=[100, 0, 0.1]
#print(list(y), list(x))
coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
- sigma=list(np.sqrt(y[slc])+(y[slc]==0)))
+ sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
if p==100:
xx=np.linspace(-0.5,0.5, 100)
plt.plot(xx, fnc(xx,*coeff))
@@ -148,7 +148,7 @@ plt.errorbar(pvals, resvals, dresvals, ls='', marker='o')
fnc=lambda E,a: a/np.sqrt(E)
#pvals, resvals, dresvals
coeff, var_matrix = curve_fit(fnc, pvals, resvals, p0=(1,),
- sigma=dresvals)
+ sigma=dresvals, maxfev=10000)
xx=np.linspace(55, 200, 100)
@@ -201,7 +201,7 @@ for p in momenta:
p0=[100, .135, 0.2]
#print(list(y), list(x))
coeff, var_matrix = curve_fit(fnc, list(bc[slc]), list(y[slc]), p0=p0,
- sigma=list(np.sqrt(y[slc])+(y[slc]==0)))
+ sigma=list(np.sqrt(y[slc])+(y[slc]==0)), maxfev=10000)
if p==100:
xx=np.linspace(0,0.2)
plt.plot(xx, fnc(xx,*coeff))
@@ -218,7 +218,7 @@ plt.xlabel("$p_{\\pi^0}$ [GeV]")
fnc=lambda E,a,b: a+b*E
#pvals, resvals, dresvals
coeff, var_matrix = curve_fit(fnc, pvals, resvals, p0=(1,1),
- sigma=dresvals)
+ sigma=dresvals, maxfev=10000)
xx=np.linspace(55, 200, 100)
#plt.plot(xx, fnc(xx, *coeff), label=f'fit: ${coeff[0]*1000:.1f}+{coeff[1]*1000:.4f}\\times E$ MeV')
plt.plot(xx, fnc(xx, *coeff), label=f'fit: $({coeff[0]*1000:.1f}+{coeff[1]*1000:.4f}\\times [E\,in\,GeV])$ MeV')