added optional local baseline correction to integrate peak

metabolabpy/nmr/nmrConfig.py

@@ -47,6 +47,7 @@ def __init__(self):
         self.print_nmr_spectrum_aspect_ratio = 'auto'
         self.print_hsqc_peak_aspect_ratio = 1.33
         self.print_hsqc_multiplet_aspect_ratio = 1.33
+        self.local_baseline_correction = False
 
     def make_config(self):
         config = configparser.ConfigParser()
@@ -65,6 +66,7 @@ def make_config(self):
         print_stacked_plot_repeat_axes = 'yes' if self.print_stacked_plot_repeat_axes is True else 'no'
         print_auto_scale = 'yes' if self.print_auto_scale is True else 'no'
         print_label = 'yes' if self.print_label is True else 'no'
+        local_baseline_correction = 'yes' if self.local_baseline_correction is True else 'no'
         config['GUI'] = {'auto_plot': auto_plot,
                          'keep_zoom': keep_zoom,
                          'font_size': str(self.font_size),
@@ -83,7 +85,8 @@ def make_config(self):
                           'neg_col21': self.neg_col21,
                           'neg_col22': self.neg_col22,
                           'plot_legend': plot_legend}
-        config['System'] = {'current_directory': self.current_directory}
+        config['System'] = {'current_directory': self.current_directory,
+                            'local_baseline_correction': self.local_baseline_correction}
         config['Print'] = {'print_top_axis': print_top_axis,
                            'print_right_axis': print_right_axis,
                            'print_left_axis': print_left_axis,
@@ -130,6 +133,7 @@ def read_config(self):
                 local_var = local_var.replace("phasereferencecolour", "phase_reference_colour")
                 local_var = local_var.replace("poscol", "pos_col")
                 local_var = local_var.replace("negcol", "neg_col")
+                local_var = local_var.replace("localbaselinecorrection", "local_baseline_correction")
                 self.set_values(local_var, config[k][l])
 
     def set_auto_plot(self, value):
@@ -255,6 +259,9 @@ def set_neg_col22(self, value):
     def set_mode(self, value):
         self.mode = value
 
+    def set_local_baseline_correction(self, value):
+        self.local_baseline_correction = True if value == "True" else False
+
     def set_values(self, key, value):
         m_name = "self.set_" + key
         eval(m_name)(value)

metabolabpy/nmr/nmrData.py

@@ -3,6 +3,7 @@
 from metabolabpy.nmr import procPars
 from metabolabpy.nmr import dispPars
 from metabolabpy.nmr import splineBaseline
+from metabolabpy.nmr import nmrConfig
 import os
 from scipy.fftpack import fft, ifft, fftshift
 from scipy.interpolate import CubicSpline
@@ -121,6 +122,8 @@ def __init__(self):
         self.plot_legend = False
         self.legend_label = ''
         self.legend_handle = []
+        self.cf = nmrConfig.NmrConfig()
+        self.cf.read_config()
         if 'pygamma' in sys.modules:
             self.has_pg = True
         else:
@@ -228,10 +231,19 @@ def add_peak(self, start_end=np.array([], dtype='float64'), peak_label='', n_pro
             npts = len(self.spc[0])
             spc = self.spc[0][npts - start_peak_points:npts - end_peak_points].real
             max_pos = np.where(spc == np.max(spc))[0][0] + 1
-            self.peak_max = np.append(self.peak_max, np.max(spc))
+            self.cf.read_config()
+            if self.cf.local_baseline_correction:
+                self.peak_max = np.append(self.peak_max, np.max(spc) - np.mean([spc[0], spc[-1]]))
+            else:
+                self.peak_max = np.append(self.peak_max, np.max(spc))
+
             self.peak_max_points = np.append(self.peak_max_points, start_peak_points - max_pos)
             self.peak_max_ppm = np.append(self.peak_max_ppm, self.points2ppm(start_peak_points - max_pos))
-            self.peak_int = np.append(self.peak_int, sum(spc - np.mean([spc[0], spc[-1]])))
+            if self.cf.local_baseline_correction:
+                self.peak_int = np.append(self.peak_int, sum(spc - np.mean([spc[0], spc[-1]])))
+            else:
+                self.peak_int = np.append(self.peak_int, sum(spc))
+
             sort_idx = np.argsort(self.start_peak)[::-1]
             self.start_peak = self.start_peak[sort_idx]
             self.end_peak = self.end_peak[sort_idx]