$$\theta(t) = A_{B_z}\cos(\omega t + \phi) + A_{\mathrm{EDM}}\sin(\omega t + \phi) + c$$
where
[0] $A_{\mathrm{B_z}}$ is the $B_z$ amplitude
[1] $A_{\mathrm{EDM}}$ is the EDM amplitude
[2] $c$ is the offset in the central angle
[3] $\omega$ is the anomalous precision frequency (unblinded, for now)
'''
par, pcov = optimize.curve_fit(cu.thetaY_unblinded_phase,
x,
y,
sigma=y_err,
p0=[0.0, 0.17, 0.0, 1.4],
absolute_sigma=False,
method='lm')
par_e = np.sqrt(np.diag(pcov))
chi2_ndf, chi2, ndf = cu.chi2_ndf(x, y, y_err, cu.thetaY_unblinded_phase,
par)
print("Params:", par)
print("Errors:", par_e)
print(r"Fit $\frac{\chi^2}{\rm{DoF}}$=" + str(round(chi2_ndf, 2)))
### Plot
fig, ax = plt.subplots()
ax.errorbar(x,
y,
xerr=x_err,
yerr=y_err,
linewidth=0,
elinewidth=2,
color="green",
marker="o",
def iter_plots(n_prof_bins=15,
extraLabel="",
outdir="profFits",
gauss=False,
best=False,
vertex=False,
df=None):
#loop over common plots
# fileLabel=("LargeEDM", "noEDM")
# fileName=("DATA/VLEDM.root", "DATA/noEDM.root")
fileLabel = [("LargeEDM")]
fileName = [("../DATA/VLEDM.root")]
plotLabel = ("Tracks", "Vertices")
plotName = ["TrackFit", "VertexExt"]
if (best):
plotLabel = [("Vertices")]
plotName = [("VertexExt")]
# qLabel=("QT", "NQT")
# qName=("AllStations", "AllStationsNoTQ")\
qLabel = [("NQT")]
qName = [("AllStationsNoTQ")]
cutLabel = ("0_p_3600", "400_p_2700", "700_p_2400", "1500_p_3600",
"1600_p_3600", "1700_p_3600", "1800_p_3600")
cutName = ("t>0/0<p<3600", "t>0/400<p<2700", "t>0/700<p<2400",
"t>0/1500<p<3600", "t>0/1600<p<3600", "t>0/1700<p<3600",
"t>0/1800<p<3600")
if (best):
cutLabel = [("700_p_2400")]
cutName = [("t>0/700<p<2400")]
for i_file, i_fileName in enumerate(fileName):
# change of name on noEDM
if (i_file == 1):
plotName[1] = "VertexExtap" #old art code had a typo...
for i_cut, i_cutName in enumerate(cutName):
for i_plot, i_plotName in enumerate(plotName):
for i_q, i_qName in enumerate(qName):
# form full paths and labels
fullPath = i_qName + "/" + i_plotName + "/" + i_cutName + "/thetay_vs_time_modg2"
fullLabel = extraLabel + fileLabel[
i_file] + "_" + plotLabel[i_plot] + "_" + qLabel[
i_q] + "_" + cutLabel[i_cut]
print("Plotting a profile for", fullPath)
#extract data from the histogram
dataXY, n_binsXY, dBinsXY = ru.hist2np(
file_path=i_fileName, hist_path=fullPath)
#bin data into a profile
if (gauss):
df_data = cu.Profile(dataXY[0],
dataXY[1],
False,
nbins=n_prof_bins,
xmin=np.min(dataXY[0]),
xmax=np.max(dataXY[0]),
full_y=True,
only_binned=True)
y = df_data['y']
else:
df_data = cu.Profile(dataXY[0],
dataXY[1],
False,
nbins=n_prof_bins,
xmin=np.min(dataXY[0]),
xmax=np.max(dataXY[0]),
mean=True,
only_binned=True)
y = df_data['ymean']
x = df_data['bincenters']
x_err = df_data['xerr']
y_err = df_data['yerr']
y = y * 1e3 # rad -> mrad
y_err = y_err * 1e3 # rad -> mrad
# extract info
edm_setting = fullPath.split("/")[1].split(".")[0]
Q_cut = fullPath.split("/")[0]
data_type = fullPath.split("/")[1]
time_cut = fullPath.split("/")[2] + r" $\mathrm{\mu}$s"
p_cut = fullPath.split("/")[3] + " MeV"
y_label = fullPath.split("/")[4].split("_")[0]
x_label = fullPath.split("/")[4].split("_")[2:]
N = len(dataXY[0])
#some specific string transforms
if (edm_setting == "VLEDM"):
edm_setting = r"$d_{\mu} = 5.4\times10^{-18} \ e\cdot{\mathrm{cm}}$"
if (edm_setting == "noEDM"):
edm_setting = r"$d_{\mu} = 0 \ e\cdot{\mathrm{cm}}$"
if (y_label == "thetay"):
y_label = r"$\langle\theta_y\rangle$ [mrad]"
if (x_label[0] == "time" and x_label[1] == "modg2"):
x_label = r"$t^{mod}_{g-2} \ \mathrm{[\mu}$s]"
# if extracting y and delta(y) from a Gaussian fit
if (gauss):
means = []
means_errors = []
for i_point in range(0, len(df_data)):
# fit for a range of data
n_bins = 25
y_min, y_max = -25, +25
# all data y_hist, range is y
y_hist = y[i_point]
y_select = y_hist[np.logical_and(
y_hist >= y_min, y_hist <= y_max)]
#bin the data in range
hist, bin_edges = np.histogram(y_select,
bins=n_bins,
density=False)
bin_centres = (bin_edges[:-1] + bin_edges[1:]) / 2
bin_width = bin_edges[1] - bin_edges[0]
#find the right number of bins for. all data
n_bins_hist = int(
(np.max(y_hist) - np.min(y_hist)) / bin_width)
y_err = np.sqrt(hist) # sqrt(N) per bin
# fit in range
p0 = [1, 1, 1]
par, pcov = optimize.curve_fit(
cu.gauss,
bin_centres,
hist,
p0=p0,
sigma=y_err,
absolute_sigma=False,
method='trf')
par_e = np.sqrt(np.diag(pcov))
chi2ndf = cu.chi2_ndf(bin_centres, hist, y_err,
cu.gauss, par)
#append the fit parameters
means.append(par[1])
means_errors.append(par_e[1])
#plot and stats + legend
units = "mrad"
legend_fit = cu.legend4_fit(chi2ndf[0],
par[1],
par_e[1],
par[2],
par_e[2],
units,
prec=2)
ax, legend = cu.plotHist(y_hist,
n_bins=n_bins_hist,
units=units,
prec=2)
ax.plot(bin_centres,
cu.gauss(bin_centres, *par),
color="red",
linewidth=2,
label='Fit')
cu.textL(ax,
0.8,
0.78,
r"$\theta_y$:" + "\n" + str(legend),
font_size=15)
cu.textL(ax,
0.2,
0.78,
"Fit:" + "\n" + str(legend_fit),
font_size=15,
color="red")
ax.set_xlabel(r"$\theta_y$ [mrad]", fontsize=18)
ax.set_ylabel(r"$\theta_y$ / " +
str(round(bin_width)) + " mrad",
fontsize=18)
plt.tight_layout()
plt.savefig("../fig/Gauss/Gauss_" + fullLabel +
"_" + str(i_point) + ".png",
dpi=300)
plt.clf()
#done looping over y bins
#reassign data "pointer names"
y = means
y_err = means_errors
#fit a function and get pars
par, pcov = optimize.curve_fit(cu.thetaY_unblinded,
x,
y,
sigma=y_err,
p0=[0.0, 0.18, -0.06, 1.4],
absolute_sigma=False,
method='lm')
par_e = np.sqrt(np.diag(pcov))
chi2_n = cu.chi2_ndf(x, y, y_err, cu.thetaY_unblinded, par)
# plot the fit and data
fig, ax = plt.subplots()
# data
ax.errorbar(x,
y,
xerr=x_err,
yerr=y_err,
linewidth=0,
elinewidth=2,
color="green",
marker="o",
label="Sim.")
# fit
ax.plot(x,
cu.thetaY_unblinded(x, par[0], par[1], par[2],
par[3]),
color="red",
label='Fit')
# deal with fitted parameters (to display nicely)
parNames = [
r"$ A_{\mu}$", r"$ A_{\rm{EDM}}$", "c", r"$\omega$"
]
units = ["mrad", "mrad", "mrad", "MhZ"]
prec = 2 # set custom precision
#form complex legends
legend1_chi2 = cu.legend1_fit(chi2_n[0])
legned1_par = ""
legned1_par = cu.legend_par(legned1_par, parNames, par,
par_e, units)
legend1 = legend1_chi2 + "\n" + legned1_par
print(legend1)
legend2 = data_type + "\n" + p_cut + "\n N=" + cu.sci_notation(
N)
#place on the plot and save
y1, y2, x1, x2 = 0.15, 0.85, 0.25, 0.70
cu.textL(ax, x1, y1, legend1, font_size=16, color="red")
cu.textL(ax, x2, y2, legend2, font_size=16)
ax.legend(loc='center right', fontsize=16)
ax.set_ylabel(y_label, fontsize=18)
ax.set_xlabel(x_label, fontsize=18)
plt.tight_layout()
plt.savefig("../fig/" + outdir + "/" + fullLabel + ".png")
plt.clf()
