added bud data to g2a_dict

dataloader.py

@@ -89,13 +89,11 @@ class dataloader:
             "extraction/general/None/volume": "volume",
             "extraction/Flavin_bgsub/max/mean": "flavin",
             "extraction/GFP/max/nuc_est_conv": "GFP_nuc_est_conv",
+            "postprocessing/buddings/extraction_general_None_volume": "buddings",
+            "postprocessing/bud_metric/extraction_general_None_volume": "bud_volume",
+            "postprocessing/bud_metric/extraction_mCherry_max_median": "bud_median_mCherry",
+            "postprocessing/bud_metric/extraction_mCherry_max_median": "bud_median_GFP",
         }
-        self.buddings_path = (
-            "postprocessing/buddings/extraction_general_None_volume"
-        )
-        self.bud_volume_path = (
-            "postprocessing/bud_metric/extraction_general_None_volume"
-        )
         # from abbreviations to grouper.siglist
         self.a2g_dict = {v: k for (k, v) in self.g2a_dict.items()}
         # establish paths
@@ -229,10 +227,7 @@ class dataloader:
             # find time interval between images
             self.dt = grouper.tintervals
             # get key index for choosing cells
-            if key_index == "buddings":
-                key_index_path = self.buddings_path
-            else:
-                key_index_path = self.a2g_dict[key_index]
+            key_index_path = self.a2g_dict[key_index]
             key_index = self.get_key_index(grouper, key_index_path, cutoff)
             # load from h5 files
             r_df = self.load_h5(grouper, key_index, cutoff, interpolate_list)
@@ -267,7 +262,8 @@ class dataloader:
         else:
             bud_interpolate = False
         # load and correct buddings and bud_volume
-        r_df = self.load_buddings_bud_volume(
+        print(" bud data")
+        r_df = self.load_bud_data(
             grouper,
             cutoff,
             figs=False,
@@ -276,7 +272,9 @@ class dataloader:
         )
         # load other signals
         for i, char in enumerate(self.g2a_dict):
-            if char in grouper.available:
+            if char in grouper.available and not (
+                "buddings" in char or "bud_metric" in char
+            ):
                 print(" " + char)
                 # load all cells
                 record = grouper.concat_signal(char, cutoff=0)
@@ -292,8 +290,6 @@ class dataloader:
                 tdf = self.long_df_with_id(new_record, self.g2a_dict[char])
                 # merge into one data set
                 r_df = pd.merge(r_df, tdf, how="left")
-            else:
-                print(" Warning: " + char, "not found")
         return r_df
 
     def get_key_index(self, grouper, key_index_path, cutoff):
@@ -311,17 +307,19 @@ class dataloader:
         sdf = df.loc[df.index.intersection(key_index)]
         return sdf
 
-    def load_buddings_bud_volume(
-        self, grouper, cutoff, figs, key_index, interpolate
-    ):
+    def load_bud_data(self, grouper, cutoff, figs, key_index, interpolate):
         """
         Load buddings and bud_volume.
 
         Drop adjacent buddings and interpolate over NaNs.
         """
         # load buddings and all bud_volumes
-        buddings = grouper.concat_signal(self.buddings_path, cutoff=cutoff)
-        bud_volume = grouper.concat_signal(self.bud_volume_path, cutoff=0)
+        buddings = grouper.concat_signal(
+            self.a2g_dict["buddings"], cutoff=cutoff
+        )
+        bud_volume = grouper.concat_signal(
+            self.a2g_dict["bud_volume"], cutoff=0
+        )
         # perform correction
         new_buddings, new_bud_volume = correct_buds(
             buddings, bud_volume, figs, interpolate