diff --git a/PeakOTron.py b/PeakOTron.py
index 445084ae53a8e20fd962e33d8b896dc73beb747b..0e3d8576b9a662b71ab94a7619b393d5315dd2e8 100644
--- a/PeakOTron.py
+++ b/PeakOTron.py
@@ -1139,8 +1139,100 @@ class PeakOTron:
else:
plt.close(fig)
-
-
+ def PlotFitSumlab(self,figsize=(10.0,10.0),labelsize=None,ticksize=None,titlesize=None,
+ legendsize=None,title=None,xlabel = None,scaled=False,save_directory=None,
+ label=None,y_limits = [1, None],residual_limits = [-5, 5],residualticksize=None,
+ linewidth_main = 5, x_limits = [None, None],display=True,prefit=False,plot_in_bins=False):
+ large = 25
+ med = 20
+ small = 12
+ if labelsize is None: labelsize = large
+ if ticksize is None: ticksize = large
+ if titlesize is None: titlesize = large
+ if legendsize is None: legendsize = med
+ if residualticksize is None: residualticksize = med
+ if(plot_in_bins):
+ x_all = self._hist_data["bin_numbers"]
+ bw = 1
+ else:
+ x_all = self._hist_data["bin_centres"]
+ bw = self._hist_data["bw"]
+ y_all = self._hist_data["counts"]
+ y_err_all = self._hist_data["counts_error"]
+ Q_0_prefit = self._prefit_values_bin["Q_0"]
+ sigma_0_prefit = self._prefit_values_bin["sigma_0"]
+ if(self._hist_data["truncate_nsigma0_do_min"] is not None):
+ lim_nsigma0 = Q_0_prefit - self._hist_data["truncate_nsigma0_do_min"]*sigma_0_prefit
+ cond_sel_nsigma0 = (self._hist_data["bin_numbers"]>lim_nsigma0)
+ else:
+ cond_sel_nsigma0 = np.full(len(x_all), True).astype(bool)
+ x = x_all[cond_sel_nsigma0]
+ y = y_all[cond_sel_nsigma0]
+ y_err = y_err_all[cond_sel_nsigma0]
+ y_hat = self.GetModel(x, prefit=prefit, bin_units=plot_in_bins)*bw
+ y_hat_err = np.sqrt(y_hat)
+ chi2, ndf = self.GetChi2(prefit=prefit)
+
+ if(xlabel is None):
+ if(plot_in_bins): xlabel="Bin"
+ else: xlabel = "Q"
+ fig = plt.figure(figsize=figsize)
+ gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1])
+ ax0 = plt.subplot(gs[0])
+ ax0.tick_params(axis="x", direction='in', top=True, labeltop=False, length=small, width=1)
+ ax0.tick_params(axis="y", direction='in', right=True, labelright=False, length=small, width=1)
+ if(label is None): label = "Histogram"
+ label = "{:s}".format(label)
+ dusty = (0.3, 0.6, 1.0)
+ brown = (0.35, 0, 0)
+ ax0.step(x_all, y_all, label=label, lw=0.8, color="grey", where="mid")
+ ax0.plot(x, y_hat, linestyle="--", label="Fit", lw=2, color='red')
+ ax0.plot([], [], ' ', label="$\\chi^{{2}}$/NDF = {:s}".format(LatexFormat(chi2/ndf)))
+ ax0.set_ylabel("Entries".format(xlabel, xlabel), fontsize=labelsize, loc='top')
+ ax0.tick_params(axis="y", labelsize=ticksize)
+ if(y_limits[0] is None): y_limits[0] = np.min(y[y>0])
+ ax0.set_ylim(y_limits)
+ ax0.set_xlim(x_limits)
+ if(title is not None): ax0.set_title(title, fontsize=titlesize)
+ ax0.grid(which="both", axis="both", linewidth=0.2, color="grey", linestyle=":")
+ ax0.locator_params(axis='y', nbins=8)
+ #ax0.set_yscale("log")
+ ax0.legend(fontsize=legendsize, loc='upper right', frameon=True, edgecolor='None')
+
+ ax1 = plt.subplot(gs[1], sharex = ax0)
+ ax1.tick_params(axis="x", direction='in', top=True, labeltop=False, length=small, width=1)
+ ax1.tick_params(axis="y", direction='in', right=True, labelright=False, length=small, width=1)
+ ax1.scatter(x, (y - y_hat)/(y_hat_err), color="black", s=1)
+ ax1.tick_params(axis="x", labelsize=ticksize)
+ ax1.tick_params(axis="y", labelsize=ticksize)
+ ax1.set_ylabel("Pull", fontsize=labelsize, loc='top')
+ ax1.set_xlabel(xlabel, fontsize=labelsize, loc='right')
+ ax1.set_ylim(residual_limits)
+ ax1.set_yticks(np.arange(int(np.floor(np.min(residual_limits))), int(np.ceil(np.max(residual_limits)))))
+ ax1.axhline(y=-1.0, color=dusty, lw=2, linestyle="--")
+ ax1.axhline(y=1.0, color=dusty, lw=2, linestyle="--")
+ ax1.tick_params(axis="x", labelsize=ticksize)
+ ax1.tick_params(axis="y", labelsize=ticksize)
+ ax1.grid(which="both", axis="both", linewidth=0.2, color="grey", linestyle=":")
+ ax1.locator_params(axis='y', nbins=6)
+
+ fig.tight_layout()
+ if(not plot_in_bins):
+ mf = ScalarFormatter(useMathText=True)
+ mf.set_powerlimits((-2,2))
+ plt.gca().xaxis.set_major_formatter(mf)
+ offset = ax1.xaxis.get_offset_text()
+ offset.set_size(int(0.8*ticksize))
+
+ fig.subplots_adjust(hspace=.0)
+ if(save_directory is not None):
+ print("Saving figure to {0}...".format(save_directory))
+ fig.savefig(save_directory)
+ if(display):
+ plt.pause(0.01)
+ fig.show()
+ else:
+ plt.close(fig)
########################################################################################################
## Fit(prefit=False)
diff --git a/user/sumlab_auto.py b/user/sumlab_auto.py
index 1db86beeb75273412ae1459185c11507db4a7459..2f71407ed10e9c8f3bbb2c73fcc5a95cee243802 100644
--- a/user/sumlab_auto.py
+++ b/user/sumlab_auto.py
@@ -121,6 +121,8 @@ for i, (file, path) in enumerate(files_to_fit):
fit_out["d_{:s}".format(key)] = value
f_data.PlotFit(plot_in_bins=True, display=False,
save_directory=f"{folder}/{file[:-4]}_fit.png")
+ f_data.PlotFitSumlab(plot_in_bins=True, display=False,
+ save_directory=f"{folder}/{file[:-4]}_fit_sumlab.png")
df = pd.DataFrame.from_dict([fit_out])
df.to_csv("{}/fit_results_{:s}.csv".format(folder, file[:-4]))