feat: change csv columns header

ComPlat · Oct 23, 2023 · 9ebb0d6 · 9ebb0d6
1 parent 7ce580c
commit 9ebb0d6
Showing 1 changed file with 21 additions and 19 deletions.
diff --git a/chem_spectra/lib/composer/ni.py b/chem_spectra/lib/composer/ni.py
@@ -486,26 +486,26 @@ def tf_img(self):
 
     def __prepare_metadata_info_for_csv(self, csv_writer: csv.DictWriter):
         csv_writer.writerow({
-            'Max x': 'Measurement type',
-            'Max y': 'Cyclic Voltammetry',
+            'Ox E(V)': 'Measurement type',
+            'Red E(V)': 'Cyclic Voltammetry',
         })
         csv_writer.writerow({
-            'Max x': 'Measurement type ID',
+            'Ox E(V)': 'Measurement type ID',
         })
         csv_writer.writerow({
-            'Max x': 'Sample ID',
+            'Ox E(V)': 'Sample ID',
         })
         csv_writer.writerow({
-            'Max x': 'Analysis ID',
+            'Ox E(V)': 'Analysis ID',
         })
         csv_writer.writerow({
-            'Max x': 'Dataset ID',
+            'Ox E(V)': 'Dataset ID',
         })
         csv_writer.writerow({
-            'Max x': 'Dataset name',
+            'Ox E(V)': 'Dataset name',
         })
         csv_writer.writerow({
-            'Max x': 'Link to sample',
+            'Ox E(V)': 'Link to sample',
         })
         csv_writer.writerow({
         })
@@ -523,8 +523,7 @@ def tf_csv(self):
             listMaxMinPeaks = self.core.params['list_max_min_peaks']
 
         with open(tf_csv.name, 'w', newline='', encoding='utf-8') as csvfile:
-            # fieldnames = ['Max', 'Min', 'I λ0', 'I ratio', 'Pecker']
-            fieldnames = ['Max x', 'Max y', 'Min x', 'Min y', 'Delta Ep', 'I lambda0', 'I ratio']
+            fieldnames = ['Ox E(V)', 'Ox I(mA)', 'Red E(V)', 'Red I(mA)', 'I lambda0(mA)', 'I ratio', 'E1/2(V)', 'Delta Ep(mV)']
             writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
 
             self.__prepare_metadata_info_for_csv(writer)
@@ -539,11 +538,13 @@ def tf_csv(self):
                 y_pecker = self.core.ys[idx]
 
             for peak in listMaxMinPeaks:
-                max_peak, min_peak = None, None
+                max_peak, min_peak, e12 = None, None, None
                 if 'max' in peak:
                     max_peak = peak['max']
                 if 'min' in peak:
                     min_peak = peak['min']
+                if 'e12' in peak:
+                    e12 = peak['e12']
                 x_max_peak, y_max_peak = self.__get_xy_of_peak(max_peak)
                 x_min_peak, y_min_peak = self.__get_xy_of_peak(min_peak)
 
@@ -554,7 +555,7 @@ def tf_csv(self):
                 if (x_max_peak == '' or x_min_peak == ''):
                     delta = ''
                 else:
-                    delta = abs(x_max_peak - x_min_peak)
+                    delta = abs(x_max_peak - x_min_peak) * 1000
 
                 x_pecker = ''
                 # calculate ratio
@@ -572,13 +573,14 @@ def tf_csv(self):
                         y_pecker = ''
 
                 writer.writerow({
-                    'Max x': '{x_max}'.format(x_max=x_max_peak),
-                    'Max y': '{y_max}'.format(y_max=y_max_peak),
-                    'Min x': '{x_min}'.format(x_min=x_min_peak),
-                    'Min y': '{y_min}'.format(y_min=y_min_peak),
-                    'Delta Ep': '{y_pecker}'.format(y_pecker=y_pecker),
-                    'I lambda0': '{ratio}'.format(ratio=ratio),
-                    'I ratio': '{delta}'.format(delta=delta)
+                    'Ox E(V)': '{x_max}'.format(x_max=x_max_peak),
+                    'Ox I(mA)': '{y_max}'.format(y_max=y_max_peak),
+                    'Red E(V)': '{x_min}'.format(x_min=x_min_peak),
+                    'Red I(mA)': '{y_min}'.format(y_min=y_min_peak),
+                    'I lambda0(mA)': '{y_pecker}'.format(y_pecker=y_pecker),
+                    'I ratio': '{ratio}'.format(ratio=ratio),
+                    'E1/2(V)': '{e12}'.format(e12=e12),
+                    'Delta Ep(mV)': '{delta}'.format(delta=delta)
                 })
         return tf_csv