float implementation of problematic input values

src/app.py

@@ -4,26 +4,36 @@
 import tempfile
 import anndata as ad
 import json
-import numpy as np
 
 from sliders import slider_ui, slider_server
 from distributions import distributions_server
 from plots import plots_server, plots_ui
 from metadata import metadata_server, metadata_ui
 from helpers import calculate_qc_metrics
 
-
+'''
 def convert_float32_to_float(data):
     if isinstance(data, dict):
         #print('dict')
+        for k, v in data.items():
+            if isinstance(v, np.float32):
+                print(f"Converting 32 {v} to float in dict")
         return {k: convert_float32_to_float(v) for k, v in data.items()}
     elif isinstance(data, list):
-        #print('list')
+        for v in data:
+            if isinstance(v, np.float32):
+                print(f"Converting 32 {v} to float in list")
+    
         return [convert_float32_to_float(v) for v in data]
-    elif isinstance(data, np.float32) or isinstance(data, np.float64):
+    elif isinstance(data, np.float32):
+        if isinstance(data, np.float32):
+            print(f"Converting 32 {data} to float")
         #print(f"Converting {data} to float")
         return float(data)
     elif isinstance(data, np.ndarray):
+        for v in data:
+            if isinstance(v, np.float32):
+                print(f"Converting 32 {v} to float in ndarray")
         #print('ndarray')
         return data.tolist()
     return data
@@ -37,6 +47,8 @@ def custom_json_dumps(data, *args, **kwargs):
 
 json.dumps = custom_json_dumps
 
+'''
+
 _pretty_names = reactive.value({
     'total_counts': 'Total counts',
     'n_genes_by_counts': 'Number of genes with counts',

src/sliders.py

@@ -155,12 +155,12 @@ def update_absolute_by_mads():
                 if mads is None or absolute is None:
                     continue
 
-                min_val = (
+                min_val = float((
                     distributions[col]["median"] - mads * distributions[col]["std"]
-                )
-                max_val = (
+                ))
+                max_val = float((
                     distributions[col]["median"] + mads * distributions[col]["std"]
-                )
+                ))
 
                 ui.update_slider(absolute_name, value=[min_val, max_val])