diff --git a/benchmarks/insert_muon/analysis/muon_plots.py b/benchmarks/insert_muon/analysis/muon_plots.py index 5c81b52dccb918484991e88e35b308623b68601e..ac2aa100971c1595394b0d32ce79ccf2c4edfd83 100644 --- a/benchmarks/insert_muon/analysis/muon_plots.py +++ b/benchmarks/insert_muon/analysis/muon_plots.py @@ -55,7 +55,7 @@ for p in 50,: p0=[100, .5, .05] #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) print(coeff) xx=np.linspace(0,.7, 100) MIP=coeff[1]/1000 diff --git a/benchmarks/insert_neutron/analysis/neutron_plots.py b/benchmarks/insert_neutron/analysis/neutron_plots.py index b9d2002a38372b9799c7bb5d90f9904c9d22e31d..424295cf211a5c0657a9df4433b7bae758e008be 100644 --- a/benchmarks/insert_neutron/analysis/neutron_plots.py +++ b/benchmarks/insert_neutron/analysis/neutron_plots.py @@ -79,7 +79,7 @@ slc=abs(bc)<3 fnc=gauss sigma=np.sqrt(y[slc])+(y[slc]==0) p0=(100, 0, 5) -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) xx=np.linspace(-5,5,100) plt.plot(xx,fnc(xx,*coeff)) # except: @@ -104,7 +104,7 @@ for eta_min, eta_max in zip(r[:-1],r[1:]): #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(np.abs(coeff[2])) dsigmas.append(np.sqrt(var_matrix[2][2])) xvals.append(p) @@ -151,7 +151,7 @@ for p in 20, 30,40,50,60,70, 80: sigma=np.sqrt(y[slc])+0.5*(y[slc]==0) p0=(100, np.mean(r), np.std(r)) try: - coeff, var_matrix = curve_fit(fnc, list(bcs[slc]), list(y[slc]), p0=p0,sigma=list(sigma)) + coeff, var_matrix = curve_fit(fnc, list(bcs[slc]), list(y[slc]), p0=p0, sigma=list(sigma), maxfev=10000) res=np.abs(coeff[2]/coeff[1]) if res<best_res: @@ -234,7 +234,7 @@ for eta_min, eta_max in zip(partitions[:-1],partitions[1:]): sigma=np.sqrt(y[slc])+0.5*(y[slc]==0) p0=(100, np.mean(r), np.std(r)) try: - coeff, var_matrix = curve_fit(fnc, list(bcs[slc]), list(y[slc]), p0=p0,sigma=list(sigma)) + coeff, var_matrix = curve_fit(fnc, list(bcs[slc]), list(y[slc]), p0=p0, sigma=list(sigma), maxfev=10000) res=np.abs(coeff[2]/coeff[1]) if res<best_res: