feat: refactor code

chem_spectra/lib/composer/ni.py

@@ -413,7 +413,7 @@ def tf_img(self):
                 plt.plot([xL, xU], [itg_h, itg_h], color='#228B22')
                 plt.plot([xL, xL], [itg_h + h * 0.01, itg_h - h * 0.01], color='#228B22')   # noqa: E501
                 plt.plot([xU, xU], [itg_h + h * 0.01, itg_h - h * 0.01], color='#228B22')   # noqa: E501
-                plt.text((xL + xU) / 2, itg_h + h * 0.015, '{:0.2f}'.format(area), color='#228B22', ha='center', size=12)   # noqa: E501
+                plt.text((xL + xU) / 2, itg_h + h * 0.015, '{:0.2f}'.format(area), color='#228B22', ha='center', size=10, rotation=-90.)   # noqa: E501
                 # cys = (ks[iL:iU] - ref) * 1.5 + (y_max - h * 0.4)
                 cys = (ks[iL:iU] - ref) * 1.5 + itg_h + itg_h * 0.1
                 plt.plot(cxs, cys, color='#228B22')
@@ -495,7 +495,17 @@ def tf_img(self):
         plt.locator_params(nbins=self.__plt_nbins())
         plt.grid(False)
 
-
+        self.__draw_peaks(plt, x_peaks, y_peaks, h, w)
+
+        # Save
+        tf_img = tempfile.NamedTemporaryFile(suffix='.png')
+        plt.savefig(tf_img, format='png')
+        tf_img.seek(0)
+        plt.clf()
+        plt.cla()
+        return tf_img
+
+    def __draw_peaks(self, plt, x_peaks, y_peaks, h, w):
         # TODO: Need to be refactor
         ax = plt.gca()
         differences = np.diff(x_peaks)
@@ -523,53 +533,50 @@ def tf_img(self):
         max_values = [np.max(items) for items in groups_y]
         diff_max_values = np.diff(max_values)
 
+        x_boundary_min, x_boundary_max = np.min(x_peaks), np.max(x_peaks)
+
         for i in range(len(groups_x)):
             mygroup_x = groups_x[i]
             mygroup_y = groups_y[i]
             max_current_group = max_values[i] + h * 0.2
+            if (i > 0):
+                prev_max_group = max_values[i-1] + h * 0.2
+                my_gap = abs(max_current_group - prev_max_group)
+                if my_gap < h*0.1:
+                    max_current_group = max_current_group + h * 0.45
             middle_idx = int(len(mygroup_x)/2)
             gap_value = np.mean(mygroup_x)
             x_text = 0
             y_text = 20
             for j in range(len(mygroup_x)):
-              x_pos = mygroup_x[j]
-              y_pos = mygroup_y[j] + h * 0.5
-              x_float = '{:.2f}'.format(x_pos)
-              peak_label = '{x}'.format(x=x_float)
-              if j > middle_idx:
-                  x_text = (-w * 0.02) * (j-middle_idx)
-              elif j < middle_idx:
-                  x_text = w * 0.02 * j
-
-              ax.add_patch(FancyArrowPatch((x_pos, max_current_group), (x_pos, max_current_group  + h * 0.05)))
-              ax.add_patch(FancyArrowPatch((x_pos, max_current_group  + h * 0.05), (gap_value + x_text, max_current_group  + h * 0.2)))
-              # ax.add_patch(FancyArrowPatch((gap_value + x_text, max_current_group  + h * 0.2), (gap_value + x_text, max_current_group  + h * 0.25)))
-
-              ax.annotate(peak_label,
-                  xy=(gap_value + x_text, max_current_group  + h * 0.2), xycoords='data',
-                  xytext=(0, 20), textcoords='offset points',
-                  arrowprops=dict(arrowstyle="-"),
-                  rotation=90, size=10)
-
-        # for i in range(len(x_peaks)):
-        #     x_pos = x_peaks[i]
-        #     y_pos = y_peaks[i] + h * 0.1
-        #     x_float = '{:.2e}'.format(x_pos)
-        #     y_float = '{:.2e}'.format(y_peaks[i])
-        #     peak_label = '{x}'.format(x=x_float)
-        #     ax.annotate(peak_label,
-        #         xy=(x_pos, y_boundary_max - y_boundary_max * 0.05), xycoords='data',
-        #         xytext=(0, 20), textcoords='offset points',
-        #         arrowprops=dict(arrowstyle="-", connectionstyle="arc3"),
-        #         rotation=90, size=10)
-
-        # Save
-        tf_img = tempfile.NamedTemporaryFile(suffix='.png')
-        plt.savefig(tf_img, format='png')
-        tf_img.seek(0)
-        plt.clf()
-        plt.cla()
-        return tf_img
+                x_pos = mygroup_x[j]
+                y_pos = mygroup_y[j] + h * 0.5
+                x_float = '{:.2f}'.format(x_pos)
+                peak_label = '{x}'.format(x=x_float)
+                if j >= middle_idx:
+                    x_text = -(w * 0.005) * (middle_idx - j)
+                elif j < middle_idx:
+                    x_text = w * 0.005 * (j-middle_idx)
+
+                ax.add_patch(FancyArrowPatch((x_pos, max_current_group), (x_pos, max_current_group  + h * 0.05), linewidth=0.2))
+                ax.add_patch(FancyArrowPatch((x_pos, max_current_group  + h * 0.05), (gap_value + x_text, max_current_group  + h * 0.2), linewidth=0.2))
+
+                x_boundary_min = min(gap_value + x_text, x_boundary_min)
+                x_boundary_max = max(gap_value + x_text, x_boundary_max)
+
+                ax.annotate(peak_label,
+                    xy=(gap_value + x_text, max_current_group  + h * 0.2), xycoords='data',
+                    xytext=(0, 20), textcoords='offset points',
+                    arrowprops=dict(arrowstyle="-", linewidth=0.2),
+                    rotation=90, size=10)
+
+        x_boundary_min = x_boundary_min - w * 0.02
+        x_boundary_max = x_boundary_max + w * 0.02
+        plt.xlim(
+            x_boundary_max,
+            x_boundary_min,
+        )
+        return plt
 
     def __prepare_metadata_info_for_csv(self, csv_writer: csv.DictWriter):
         csv_writer.writerow({