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Unverified Commit fee8b16a authored by Dmitry Kalinkin's avatar Dmitry Kalinkin Committed by GitHub
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zdc_sigma: use maxfev=10000 (#79)

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Pipeline #104524 passed with warnings
Stage: status-pending
Stage: config
Stage: data_init
Stage: simulate
Stage: calibrate
Stage: benchmarks
Stage: collect
Stage: deploy
Stage: status-report
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benchmarks/zdc_sigma/analysis/sigma_plots.py
+ 8
− 8
View file @ fee8b16a
......@@ -236,7 +236,7 @@ slc=abs(bc)<0.6
fnc=gauss
p0=[100, 0, 0.5]
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("$\\theta^{*\\rm recon}_{\\Sigma}-\\theta^{*\\rm truth}_{\\Sigma}$ [mrad]")
......@@ -259,7 +259,7 @@ 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(np.abs(coeff[2]))
dsigmas.append(np.sqrt(var_matrix[2][2]))
xvals.append(p)
......@@ -307,7 +307,7 @@ 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')
plt.xlabel("$z^{*\\rm recon}_{\\rm vtx}-z^{*\\rm truth}_{\\rm vtx}$ [m]")
......@@ -331,7 +331,7 @@ 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(abs(coeff[2]))
dsigmas.append(np.sqrt(var_matrix[2][2]))
xvals.append(p)
......@@ -373,7 +373,7 @@ slc=abs(bc-lambda_mass)<0.05
fnc=gauss
p0=[100, lambda_mass, 0.03]
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]))
......@@ -396,7 +396,7 @@ for p in momenta:
p0=[100, lambda_mass, 0.03]
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]))
......@@ -437,7 +437,7 @@ slc=abs(bc-sigma_mass)<0.02
fnc=gauss
p0=[100, sigma_mass, 0.03]
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]))
......@@ -460,7 +460,7 @@ for p in momenta:
p0=[100, sigma_mass, 0.03]
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)
sigmas.append(abs(coeff[2]))
dsigmas.append(np.sqrt(var_matrix[2][2]))
xvals.append(p)
......
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