diff --git a/public/Basics_of_H3_in_Fused/collection.json b/public/Basics_of_H3_in_Fused/collection.json
index 52c2ec86..0e6617a6 100644
--- a/public/Basics_of_H3_in_Fused/collection.json
+++ b/public/Basics_of_H3_in_Fused/collection.json
@@ -1,5 +1,5 @@
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(auto)\",\"visible\":true,\"componentType\":\"udf\",\"renderType\":\"iframe\",\"canSend\":true,\"canReceive\":true,\"messageTypes\":[\"data\",\"filter\"],\"supportedFilters\":[\"spatial\",\"categorical\"],\"udfUniqueId\":\"1775207655744-114702500\",\"frameType\":null},\"isOverlapping\":true,\"isExternalDragActive\":false},\"style\":{\"width\":702,\"height\":531},\"measured\":{\"width\":702,\"height\":531}}],\"edges\":[{\"id\":\"edge-1772101586213\",\"source\":\"udf-node-1772101459313-17798961\",\"target\":\"udf-node-1772101508916-561881723\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-left\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772101459313-17798961\",\"targetUdfUniqueId\":\"1772101508916-561881723\",\"sourceUdfName\":\"Widget_2\",\"targetUdfName\":\"aquamarine_shrew\"},\"selected\":false},{\"id\":\"edge-1772103330336\",\"source\":\"udf-node-1772103321577-776162561\",\"target\":\"udf-node-1772101508916-561881723\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-left\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772103321577-776162561\",\"targetUdfUniqueId\":\"1772102429928-206743880\",\"sourceUdfName\":\"Widget_3\",\"targetUdfName\":\"change_h3_resolution\"},\"selected\":false},{\"id\":\"edge-1772103372651\",\"source\":\"udf-node-1772103363736-129385581\",\"target\":\"udf-node-1772101508916-561881723\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-left\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772103363736-129385581\",\"targetUdfUniqueId\":\"1772102429928-206743880\",\"sourceUdfName\":\"Widget_4\",\"targetUdfName\":\"change_h3_resolution\"},\"selected\":false},{\"id\":\"edge-1772103400185\",\"source\":\"udf-node-1772103377994-365836492\",\"target\":\"udf-node-1772101508916-561881723\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-left\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772103377994-365836492\",\"targetUdfUniqueId\":\"1772102429928-206743880\",\"sourceUdfName\":\"Widget_5\",\"targetUdfName\":\"change_h3_resolution\"},\"selected\":false},{\"id\":\"edge-1772114186166\",\"source\":\"udf-node-1772104862677-337090349\",\"target\":\"udf-node-1772104894282-677353689\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-left\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772110890839-210750081\",\"targetUdfUniqueId\":\"1772110890839-75502871\",\"sourceUdfName\":\"era5_temp_month_hex4\",\"targetUdfName\":\"era5_temp_map_hex4\"},\"selected\":false},{\"id\":\"edge-1772184557788\",\"source\":\"udf-node-1772184130046-575735140\",\"target\":\"udf-node-1772184249314-435306225\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-left\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772184130046-575735140\",\"targetUdfUniqueId\":\"1772184249314-435306225\",\"sourceUdfName\":\"copdem_elevation\",\"targetUdfName\":\"era5_temp_map_hex4_3\"},\"selected\":false},{\"id\":\"edge-1772529971025\",\"source\":\"udf-node-1772184680514-244547589\",\"target\":\"udf-node-1772479780372-618281400\",\"sourceHandle\":\"source\",\"targetHandle\":\"target-top\",\"type\":\"studio\",\"data\":{\"managedBy\":\"user\",\"edgeType\":\"user\",\"animated\":true,\"isDirectional\":true,\"sourceUdfUniqueId\":\"1772529580452-455161585\",\"targetUdfUniqueId\":\"1772529580451-641377434\",\"sourceUdfName\":\"temp_elev_corr\",\"targetUdfName\":\"temp_elevation_corr_chart\"},\"selected\":false}],\"viewport\":{\"x\":703.284480268641,\"y\":487.57826529747643,\"zoom\":0.1515716566510398},\"selectedNodes\":[],\"selectedEdges\":[],\"annotations\":[],\"outlineItems\":[],\"isToolbarOpen\":true,\"isSidebarOpen\":true,\"sidebarWidth\":280,\"currentTool\":\"select\",\"isPresentationMode\":false,\"isCanvasLocked\":false,\"edgeDisplayMode\":\"floating\",\"lastUpdated\":1775207846273,\"viewportBounds\":{\"minX\":-4639.9471761913765,\"minY\":-3216.8168909047254,\"maxX\":6107.444757053848,\"maxY\":4891.559220794709},\"suppressedAutoEdges\":[]}}"
 }
\ No newline at end of file
diff --git a/public/Basics_of_H3_in_Fused/copdem_elevation/README.MD b/public/Basics_of_H3_in_Fused/copdem_elevation/README.MD
index 85c797ab..479b398a 100644
--- a/public/Basics_of_H3_in_Fused/copdem_elevation/README.MD
+++ b/public/Basics_of_H3_in_Fused/copdem_elevation/README.MD
@@ -1,5 +1,2 @@
-<!--fused:preview-->
-<p align="center"><img src="fused_uploaded_preview" width="600" alt="UDF preview image"></p>
-
 <!--fused:readme-->
 Reading H3 Ingested Copernicus 30m DEM dataset
diff --git a/public/Basics_of_H3_in_Fused/era5_full_year/era5_full_year.py b/public/Basics_of_H3_in_Fused/era5_full_year/era5_full_year.py
index c3db933a..d94ee7cb 100644
--- a/public/Basics_of_H3_in_Fused/era5_full_year/era5_full_year.py
+++ b/public/Basics_of_H3_in_Fused/era5_full_year/era5_full_year.py
@@ -10,7 +10,7 @@ def udf(bounds: fused.types.Bounds = [-125, 32, -114, 42]):
     # Run all months in parallel
     pool = monthly_udf.map([{
         'month': m,
-        'bounds': list(bounds)
+        'bounds': bounds
     } for m in months])
     df = pool.df()
 
diff --git a/public/Basics_of_H3_in_Fused/era5_full_year/meta.json b/public/Basics_of_H3_in_Fused/era5_full_year/meta.json
index 90935462..084d2dd6 100644
--- a/public/Basics_of_H3_in_Fused/era5_full_year/meta.json
+++ b/public/Basics_of_H3_in_Fused/era5_full_year/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf\ndef udf(bounds: fused.types.Bounds = [-125, 32, -114, 42]):\n    import pandas as pd\n\n    monthly_udf = fused.load('era5_temp_monthly_average')\n\n    # All months from 2019 to 2024\n    months = [f'{y}-{m:02d}' for y in range(2019, 2025) for m in range(1, 13)]\n\n    # Run all months in parallel\n    pool = monthly_udf.map([{\n        'month': m,\n        'bounds': list(bounds)\n    } for m in months])\n    df = pool.df()\n\n    df = df.reset_index(drop=True).sort_values('date').reset_index(drop=True)\n\n    print(f\"{df.shape=}\")\n    print(df.head())\n\n    return df",
+          "code": "@fused.udf\ndef udf(bounds: fused.types.Bounds = [-125, 32, -114, 42]):\n    import pandas as pd\n\n    monthly_udf = fused.load('era5_temp_monthly_average')\n\n    # All months from 2019 to 2024\n    months = [f'{y}-{m:02d}' for y in range(2019, 2025) for m in range(1, 13)]\n\n    # Run all months in parallel\n    pool = monthly_udf.map([{\n        'month': m,\n        'bounds': bounds\n    } for m in months])\n    df = pool.df()\n\n    df = df.reset_index(drop=True).sort_values('date').reset_index(drop=True)\n\n    print(f\"{df.shape=}\")\n    print(df.head())\n\n    return df",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/era5_hex_temp/era5_hex_temp.py b/public/Basics_of_H3_in_Fused/era5_hex_temp/era5_hex_temp.py
index 7738ffe8..90277518 100644
--- a/public/Basics_of_H3_in_Fused/era5_hex_temp/era5_hex_temp.py
+++ b/public/Basics_of_H3_in_Fused/era5_hex_temp/era5_hex_temp.py
@@ -2,14 +2,12 @@
 def udf():
     map_utils = fused.load("https://github.com/fusedio/udfs/tree/3eb82cb/community/milind/map_utils/")
 
-    import h3
     import geopandas as gpd
-    from shapely.geometry import Point
 
     hex_data = fused.load("era5_monthly_mean")
     data = hex_data()
 
-    # We can't visualize hex 15 hexagons, so turning to small points to visualixe
+    # We can't visualize hex 15 hexagons, so turning to small points to visualise
     gdf = data.copy()
     common = fused.load("https://github.com/fusedio/udfs/tree/9bad664/public/common/")
     con = common.duckdb_connect()
diff --git a/public/Basics_of_H3_in_Fused/era5_hex_temp/meta.json b/public/Basics_of_H3_in_Fused/era5_hex_temp/meta.json
index 1619586a..5e9e110f 100644
--- a/public/Basics_of_H3_in_Fused/era5_hex_temp/meta.json
+++ b/public/Basics_of_H3_in_Fused/era5_hex_temp/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf\ndef udf():\n    map_utils = fused.load(\"https://github.com/fusedio/udfs/tree/3eb82cb/community/milind/map_utils/\")\n\n    import h3\n    import geopandas as gpd\n    from shapely.geometry import Point\n\n    hex_data = fused.load(\"era5_monthly_mean\")\n    data = hex_data()\n\n    # We can't visualize hex 15 hexagons, so turning to small points to visualixe\n    gdf = data.copy()\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/9bad664/public/common/\")\n    con = common.duckdb_connect()\n    gdf = con.sql(\"\"\"\n        SELECT *, \n            h3_cell_to_latlng(CAST(hex AS UBIGINT)) AS latlng\n        FROM gdf\n    \"\"\").df()\n    gdf['geometry'] = gpd.points_from_xy(\n        gdf['latlng'].apply(lambda x: x[1]),\n        gdf['latlng'].apply(lambda x: x[0])\n    )\n    gdf = gpd.GeoDataFrame(gdf.drop(columns=['latlng']), geometry='geometry', crs='EPSG:4326')\n    \n    print(f\"{data.T=}\")\n\n    val_min = data['monthly_mean_temp'].min()\n    val_max = data['monthly_mean_temp'].max()\n\n    # Layer config for Census population data\n    config_census = {\n        \"style\": {\n            \"fillColor\": {\n                \"type\": \"continuous\",\n                \"attr\": \"monthly_mean_temp\",\n                \"domain\": [val_min, val_max],\n                \"steps\": 20,\n                \"palette\": \"DarkMint\",\n            },\n            \"filled\": True,\n            \"stroked\": False,\n            \"opacity\": 0.9,\n            \"pointRadius\": 1,\n        },\n        \"tooltip\": [\"hex\", \"monthly_mean_temp\"],\n    }\n\n    widgets = {\n        \"controls\": \"bottom-right\",\n        \"scale\": \"bottom-left\",\n        \"basemap\": \"bottom-right\",\n        \"layers\": {\"position\": \"top-right\", \"expanded\": False},\n        \"legend\": {\"position\": \"top-right\", \"expanded\": True},\n    }\n\n    html = map_utils.deckgl_layers(\n        layers=[\n            {\n                \"type\": \"vector\",\n                \"data\": gdf,\n                \"config\": config_census,\n                \"visible\": True,\n                \"name\": \"Hex 15 Temperature\",\n            }\n        ],\n        basemap=\"dark\",\n        theme=\"dark\",\n        initialViewState=None,\n        widgets=widgets,\n    \n    )\n    return html",
+          "code": "@fused.udf\ndef udf():\n    map_utils = fused.load(\"https://github.com/fusedio/udfs/tree/3eb82cb/community/milind/map_utils/\")\n\n    import geopandas as gpd\n\n    hex_data = fused.load(\"era5_monthly_mean\")\n    data = hex_data()\n\n    # We can't visualize hex 15 hexagons, so turning to small points to visualise\n    gdf = data.copy()\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/9bad664/public/common/\")\n    con = common.duckdb_connect()\n    gdf = con.sql(\"\"\"\n        SELECT *, \n            h3_cell_to_latlng(CAST(hex AS UBIGINT)) AS latlng\n        FROM gdf\n    \"\"\").df()\n    gdf['geometry'] = gpd.points_from_xy(\n        gdf['latlng'].apply(lambda x: x[1]),\n        gdf['latlng'].apply(lambda x: x[0])\n    )\n    gdf = gpd.GeoDataFrame(gdf.drop(columns=['latlng']), geometry='geometry', crs='EPSG:4326')\n    \n    print(f\"{data.T=}\")\n\n    val_min = data['monthly_mean_temp'].min()\n    val_max = data['monthly_mean_temp'].max()\n\n    # Layer config for Census population data\n    config_census = {\n        \"style\": {\n            \"fillColor\": {\n                \"type\": \"continuous\",\n                \"attr\": \"monthly_mean_temp\",\n                \"domain\": [val_min, val_max],\n                \"steps\": 20,\n                \"palette\": \"DarkMint\",\n            },\n            \"filled\": True,\n            \"stroked\": False,\n            \"opacity\": 0.9,\n            \"pointRadius\": 1,\n        },\n        \"tooltip\": [\"hex\", \"monthly_mean_temp\"],\n    }\n\n    widgets = {\n        \"controls\": \"bottom-right\",\n        \"scale\": \"bottom-left\",\n        \"basemap\": \"bottom-right\",\n        \"layers\": {\"position\": \"top-right\", \"expanded\": False},\n        \"legend\": {\"position\": \"top-right\", \"expanded\": True},\n    }\n\n    html = map_utils.deckgl_layers(\n        layers=[\n            {\n                \"type\": \"vector\",\n                \"data\": gdf,\n                \"config\": config_census,\n                \"visible\": True,\n                \"name\": \"Hex 15 Temperature\",\n            }\n        ],\n        basemap=\"dark\",\n        theme=\"dark\",\n        initialViewState=None,\n        widgets=widgets,\n    \n    )\n    return html",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/era5_monthly_mean/era5_monthly_mean.py b/public/Basics_of_H3_in_Fused/era5_monthly_mean/era5_monthly_mean.py
index 51b77ff1..36bf5fb1 100644
--- a/public/Basics_of_H3_in_Fused/era5_monthly_mean/era5_monthly_mean.py
+++ b/public/Basics_of_H3_in_Fused/era5_monthly_mean/era5_monthly_mean.py
@@ -1,6 +1,6 @@
 @fused.udf
 def udf(month='2024-05',
-    bounds:list=[-130, 25, -60, 50]
+    bounds:fused.types.Bounds=[-130, 25, -60, 50]
 ):
     path = f's3://fused-asset/data/era5/t2m_daily_mean_v4_1000/month={month}/0.parquet'
     
diff --git a/public/Basics_of_H3_in_Fused/era5_monthly_mean/meta.json b/public/Basics_of_H3_in_Fused/era5_monthly_mean/meta.json
index 68617307..b99ab7bf 100644
--- a/public/Basics_of_H3_in_Fused/era5_monthly_mean/meta.json
+++ b/public/Basics_of_H3_in_Fused/era5_monthly_mean/meta.json
@@ -50,7 +50,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf\ndef udf(month='2024-05',\n    bounds:list=[-130, 25, -60, 50]\n):\n    path = f's3://fused-asset/data/era5/t2m_daily_mean_v4_1000/month={month}/0.parquet'\n    \n    common = fused.load(\"https://github.com/fusedio/udfs/tree/56ec615/public/common/\")\n    con = common.duckdb_connect() \n\n    query = f\"\"\"\n        SELECT  \n            hex, \n            avg(daily_mean) - 273.15 as monthly_mean_temp\n        FROM read_parquet('{path}')\n        WHERE 1=1\n        AND h3_cell_to_lng(hex) between {bounds[0]} and {bounds[2]} \n        AND h3_cell_to_lat(hex) between {bounds[1]} and {bounds[3]} \n        GROUP BY 1\n    \"\"\" \n\n    data = con.execute(query).df()\n\n    print(f\"{data.T=}\")\n\n    return data",
+          "code": "@fused.udf\ndef udf(month='2024-05',\n    bounds:fused.types.Bounds=[-130, 25, -60, 50]\n):\n    path = f's3://fused-asset/data/era5/t2m_daily_mean_v4_1000/month={month}/0.parquet'\n    \n    common = fused.load(\"https://github.com/fusedio/udfs/tree/56ec615/public/common/\")\n    con = common.duckdb_connect() \n\n    query = f\"\"\"\n        SELECT  \n            hex, \n            avg(daily_mean) - 273.15 as monthly_mean_temp\n        FROM read_parquet('{path}')\n        WHERE 1=1\n        AND h3_cell_to_lng(hex) between {bounds[0]} and {bounds[2]} \n        AND h3_cell_to_lat(hex) between {bounds[1]} and {bounds[3]} \n        GROUP BY 1\n    \"\"\" \n\n    data = con.execute(query).df()\n\n    print(f\"{data.T=}\")\n\n    return data",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/era5_temp_map_hex4.py b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/era5_temp_map_hex4.py
index fa43f719..929ed1bf 100644
--- a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/era5_temp_map_hex4.py
+++ b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/era5_temp_map_hex4.py
@@ -1,5 +1,5 @@
 @fused.udf(cache_max_age=0)
-def udf(bounds:list=[-130,-50,-35,50]):
+def udf(bounds:fused.types.Bounds=[-130,-50,-35,50]):
     map_utils = fused.load("https://github.com/fusedio/udfs/tree/5c526cc/community/milind/map_utils/")
 
     era5_udf = fused.load("era5_temp_month_hex4")
diff --git a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/meta.json b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/meta.json
index ae41f1eb..54665c48 100644
--- a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/meta.json
+++ b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf(cache_max_age=0)\ndef udf(bounds:list=[-130,-50,-35,50]):\n    map_utils = fused.load(\"https://github.com/fusedio/udfs/tree/5c526cc/community/milind/map_utils/\")\n\n    era5_udf = fused.load(\"era5_temp_month_hex4\")\n    data = era5_udf(month='2025-03', hex_res=4)\n    \n    val_min = float(data[\"monthly_mean_temp\"].min())\n    val_max = float(data[\"monthly_mean_temp\"].max())\n\n    # Layer config for Census population data\n    config_census = {\n        \"style\": {\n            \"fillColor\": {\n                \"type\": \"continuous\",\n                \"attr\": \"monthly_mean_temp\",\n                \"domain\": [val_min, val_max],\n                \"steps\": 20,\n                \"palette\": \"DarkMint\",\n            },\n            \"filled\": True,\n            \"stroked\": False,\n            \"opacity\": 0.9,\n        },\n        \"tooltip\": [\"hex\", \"monthly_mean_temp\"],\n    }\n\n    widgets = {\n        \"controls\": \"bottom-right\",\n        \"scale\": \"bottom-left\",\n        \"basemap\": \"bottom-right\",\n        \"layers\": {\"position\": \"top-right\", \"expanded\": False},\n        \"legend\": {\"position\": \"top-right\", \"expanded\": True},\n    }\n\n    html = map_utils.deckgl_layers(\n        layers=[\n            {\n                \"type\": \"hex\",\n                \"data\": data,\n                \"config\": config_census,\n                \"visible\": True,\n                \"name\": \"Hex 4 Temperature\",\n            }\n        ],\n        basemap=\"dark\",\n        theme=\"dark\",\n        initialViewState=None,\n        widgets=widgets,\n        debug=False,\n    )\n    return html",
+          "code": "@fused.udf(cache_max_age=0)\ndef udf(bounds:fused.types.Bounds=[-130,-50,-35,50]):\n    map_utils = fused.load(\"https://github.com/fusedio/udfs/tree/5c526cc/community/milind/map_utils/\")\n\n    era5_udf = fused.load(\"era5_temp_month_hex4\")\n    data = era5_udf(month='2025-03', hex_res=4)\n    \n    val_min = float(data[\"monthly_mean_temp\"].min())\n    val_max = float(data[\"monthly_mean_temp\"].max())\n\n    # Layer config for Census population data\n    config_census = {\n        \"style\": {\n            \"fillColor\": {\n                \"type\": \"continuous\",\n                \"attr\": \"monthly_mean_temp\",\n                \"domain\": [val_min, val_max],\n                \"steps\": 20,\n                \"palette\": \"DarkMint\",\n            },\n            \"filled\": True,\n            \"stroked\": False,\n            \"opacity\": 0.9,\n        },\n        \"tooltip\": [\"hex\", \"monthly_mean_temp\"],\n    }\n\n    widgets = {\n        \"controls\": \"bottom-right\",\n        \"scale\": \"bottom-left\",\n        \"basemap\": \"bottom-right\",\n        \"layers\": {\"position\": \"top-right\", \"expanded\": False},\n        \"legend\": {\"position\": \"top-right\", \"expanded\": True},\n    }\n\n    html = map_utils.deckgl_layers(\n        layers=[\n            {\n                \"type\": \"hex\",\n                \"data\": data,\n                \"config\": config_census,\n                \"visible\": True,\n                \"name\": \"Hex 4 Temperature\",\n            }\n        ],\n        basemap=\"dark\",\n        theme=\"dark\",\n        initialViewState=None,\n        widgets=widgets,\n        debug=False,\n    )\n    return html",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/era5_temp_map_hex4_3.py b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/era5_temp_map_hex4_3.py
index cc808242..a807e84f 100644
--- a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/era5_temp_map_hex4_3.py
+++ b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/era5_temp_map_hex4_3.py
@@ -1,5 +1,5 @@
 @fused.udf(cache_max_age=0)
-def udf(bounds:list=[-130,-50,-35,50]):
+def udf(bounds:fused.types.Bounds=[-130,-50,-35,50]):
     map_utils = fused.load("https://github.com/fusedio/udfs/tree/5c526cc/community/milind/map_utils/")
 
     era5_udf = fused.load("era5_temp_month_hex4")
diff --git a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/meta.json b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/meta.json
index 08ca6839..e362c42e 100644
--- a/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/meta.json
+++ b/public/Basics_of_H3_in_Fused/era5_temp_map_hex4_3/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf(cache_max_age=0)\ndef udf(bounds:list=[-130,-50,-35,50]):\n    map_utils = fused.load(\"https://github.com/fusedio/udfs/tree/5c526cc/community/milind/map_utils/\")\n\n    era5_udf = fused.load(\"era5_temp_month_hex4\")\n    data = era5_udf(month='2025-03', hex_res=2)\n    \n    val_min = float(data[\"monthly_mean_temp\"].min())\n    val_max = float(data[\"monthly_mean_temp\"].max())\n\n    # Layer config for Census population data\n    config_census = {\n        \"style\": {\n            \"fillColor\": {\n                \"type\": \"continuous\",\n                \"attr\": \"monthly_mean_temp\",\n                \"domain\": [val_min, val_max],\n                \"steps\": 20,\n                \"palette\": \"DarkMint\",\n            },\n            \"filled\": True,\n            \"stroked\": False,\n            \"opacity\": 0.9,\n        },\n        \"tooltip\": [\"hex\", \"monthly_mean_temp\"],\n    }\n\n    widgets = {\n        \"controls\": \"bottom-right\",\n        \"scale\": \"bottom-left\",\n        \"basemap\": \"bottom-right\",\n        \"layers\": {\"position\": \"top-right\", \"expanded\": False},\n        \"legend\": {\"position\": \"top-right\", \"expanded\": True},\n    }\n\n    html = map_utils.deckgl_layers(\n        layers=[\n            {\n                \"type\": \"hex\",\n                \"data\": data,\n                \"config\": config_census,\n                \"visible\": True,\n                \"name\": \"Hex 4 Temperature\",\n            }\n        ],\n        basemap=\"dark\",\n        theme=\"dark\",\n        initialViewState=None,\n        widgets=widgets,\n        debug=False,\n    )\n    return html",
+          "code": "@fused.udf(cache_max_age=0)\ndef udf(bounds:fused.types.Bounds=[-130,-50,-35,50]):\n    map_utils = fused.load(\"https://github.com/fusedio/udfs/tree/5c526cc/community/milind/map_utils/\")\n\n    era5_udf = fused.load(\"era5_temp_month_hex4\")\n    data = era5_udf(month='2025-03', hex_res=2)\n    \n    val_min = float(data[\"monthly_mean_temp\"].min())\n    val_max = float(data[\"monthly_mean_temp\"].max())\n\n    # Layer config for Census population data\n    config_census = {\n        \"style\": {\n            \"fillColor\": {\n                \"type\": \"continuous\",\n                \"attr\": \"monthly_mean_temp\",\n                \"domain\": [val_min, val_max],\n                \"steps\": 20,\n                \"palette\": \"DarkMint\",\n            },\n            \"filled\": True,\n            \"stroked\": False,\n            \"opacity\": 0.9,\n        },\n        \"tooltip\": [\"hex\", \"monthly_mean_temp\"],\n    }\n\n    widgets = {\n        \"controls\": \"bottom-right\",\n        \"scale\": \"bottom-left\",\n        \"basemap\": \"bottom-right\",\n        \"layers\": {\"position\": \"top-right\", \"expanded\": False},\n        \"legend\": {\"position\": \"top-right\", \"expanded\": True},\n    }\n\n    html = map_utils.deckgl_layers(\n        layers=[\n            {\n                \"type\": \"hex\",\n                \"data\": data,\n                \"config\": config_census,\n                \"visible\": True,\n                \"name\": \"Hex 4 Temperature\",\n            }\n        ],\n        basemap=\"dark\",\n        theme=\"dark\",\n        initialViewState=None,\n        widgets=widgets,\n        debug=False,\n    )\n    return html",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/era5_temp_month_hex4.py b/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/era5_temp_month_hex4.py
index b5e284e7..8982ca92 100644
--- a/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/era5_temp_month_hex4.py
+++ b/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/era5_temp_month_hex4.py
@@ -1,6 +1,6 @@
 @fused.udf
 def udf(month='2024-05',
-    bounds:list=[-130, 25, -60, 50],
+    bounds:fused.types.Bounds=[-130, 25, -60, 50],
     hex_res: int = 4
 ):
     path = f's3://fused-asset/data/era5/t2m_daily_mean_v4_1000/month={month}/0.parquet'
diff --git a/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/meta.json b/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/meta.json
index 7264d1d5..7632c66c 100644
--- a/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/meta.json
+++ b/public/Basics_of_H3_in_Fused/era5_temp_month_hex4/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf\ndef udf(month='2024-05',\n    bounds:list=[-130, 25, -60, 50],\n    hex_res: int = 4\n):\n    path = f's3://fused-asset/data/era5/t2m_daily_mean_v4_1000/month={month}/0.parquet'\n\n    # This loads DuckDB with the spatial & H3 extensions directly\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/56ec615/public/common/\")\n    con = common.duckdb_connect() \n\n    query = f\"\"\"\n        SELECT  \n            h3_cell_to_parent(hex, {hex_res}) as hex,\n            round(avg(daily_mean) - 273.15, 2) as monthly_mean_temp\n        FROM read_parquet('{path}')\n        WHERE 1=1\n        AND h3_cell_to_lng(hex) between {bounds[0]} and {bounds[2]}\n        AND h3_cell_to_lat(hex) between {bounds[1]} and {bounds[3]} \n        GROUP BY 1\n    \"\"\" \n\n    data = con.execute(query).df()\n\n    print(f\"{data.T=}\")\n\n    return data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    ",
+          "code": "@fused.udf\ndef udf(month='2024-05',\n    bounds:fused.types.Bounds=[-130, 25, -60, 50],\n    hex_res: int = 4\n):\n    path = f's3://fused-asset/data/era5/t2m_daily_mean_v4_1000/month={month}/0.parquet'\n\n    # This loads DuckDB with the spatial & H3 extensions directly\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/56ec615/public/common/\")\n    con = common.duckdb_connect() \n\n    query = f\"\"\"\n        SELECT  \n            h3_cell_to_parent(hex, {hex_res}) as hex,\n            round(avg(daily_mean) - 273.15, 2) as monthly_mean_temp\n        FROM read_parquet('{path}')\n        WHERE 1=1\n        AND h3_cell_to_lng(hex) between {bounds[0]} and {bounds[2]}\n        AND h3_cell_to_lat(hex) between {bounds[1]} and {bounds[3]} \n        GROUP BY 1\n    \"\"\" \n\n    data = con.execute(query).df()\n\n    print(f\"{data.T=}\")\n\n    return data\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    ",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/ny411_noise_h3/meta.json b/public/Basics_of_H3_in_Fused/ny411_noise_h3/meta.json
index 600fb26d..ef5d57be 100644
--- a/public/Basics_of_H3_in_Fused/ny411_noise_h3/meta.json
+++ b/public/Basics_of_H3_in_Fused/ny411_noise_h3/meta.json
@@ -62,7 +62,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf(cache_max_age=0)\ndef udf(\n    noise_311_link: str = \"https://gist.githubusercontent.com/kashuk/670a350ea1f9fc543c3f6916ab392f62/raw/4c5ced45cc94d5b00e3699dd211ad7125ee6c4d3/NYC311_noise.csv\",\n    res: int = 9\n):\n    # 1. Load points from above UDF \n    points_udf = fused.load(\"ny411_noise_points\")\n    data = points_udf()\n    \n    # Load common utilities (loads DuckDB with spatial & H3 addons)\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/9bad664/public/common/\")\n    con = common.duckdb_connect()\n\n    # Remove geometry to prevent error with DuckDB\n    del data['geometry']\n\n    # 2. Convert points to H3, \n    # 3. count total number of duplicated H3 cells (assign to `cnt`) & groupby unique H3 cells\n    qr = f\"\"\"\n    SELECT\n      h3_latlng_to_cell(lat, lng, {res}) AS hex,\n      COUNT(*) AS cnt,\n    FROM (SELECT lat, lng FROM data) AS data\n    GROUP BY 1\n    \"\"\"\n\n    df = con.sql(qr).df()\n    print(df.T)\n\n    return df",
+          "code": "@fused.udf(cache_max_age=0)\ndef udf(\n    res: int = 9\n):\n    # 1. Load points from above UDF \n    points_udf = fused.load(\"ny411_noise_points\")\n    data = points_udf()\n    \n    # Load common utilities (loads DuckDB with spatial & H3 addons)\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/9bad664/public/common/\")\n    con = common.duckdb_connect()\n\n    # Remove geometry to prevent error with DuckDB\n    del data['geometry']\n\n    # 2. Convert points to H3, \n    # 3. count total number of duplicated H3 cells (assign to `cnt`) & groupby unique H3 cells\n    qr = f\"\"\"\n    SELECT\n      h3_latlng_to_cell(lat, lng, {res}) AS hex,\n      COUNT(*) AS cnt,\n    FROM (SELECT lat, lng FROM data) AS data\n    GROUP BY 1\n    \"\"\"\n\n    df = con.sql(qr).df()\n    print(df.T)\n\n    return df",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/ny411_noise_h3/ny411_noise_h3.py b/public/Basics_of_H3_in_Fused/ny411_noise_h3/ny411_noise_h3.py
index 4c730d3a..3ee63b39 100644
--- a/public/Basics_of_H3_in_Fused/ny411_noise_h3/ny411_noise_h3.py
+++ b/public/Basics_of_H3_in_Fused/ny411_noise_h3/ny411_noise_h3.py
@@ -1,6 +1,5 @@
 @fused.udf(cache_max_age=0)
 def udf(
-    noise_311_link: str = "https://gist.githubusercontent.com/kashuk/670a350ea1f9fc543c3f6916ab392f62/raw/4c5ced45cc94d5b00e3699dd211ad7125ee6c4d3/NYC311_noise.csv",
     res: int = 9
 ):
     # 1. Load points from above UDF 
diff --git a/public/Basics_of_H3_in_Fused/temp_elev_corr/meta.json b/public/Basics_of_H3_in_Fused/temp_elev_corr/meta.json
index 03432f3b..c1d3dcb0 100644
--- a/public/Basics_of_H3_in_Fused/temp_elev_corr/meta.json
+++ b/public/Basics_of_H3_in_Fused/temp_elev_corr/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf\ndef udf(\n    # whole US\n    bounds: fused.types.Bounds = [-124.784, 24.396, -66.885, 49.384],\n    res: int = 4,\n    month: str = '2024-05',\n):\n    import pandas as pd\n\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/f661b6d/public/common/\")\n\n    # Load both UDFs\n    elev_udf = fused.load(\"copdem_elevation\")\n    temp_udf = fused.load(\"era5_monthly_mean\")\n\n    # Call both with the same bounds\n    df_elev = elev_udf(bounds=bounds, res=res)\n    df_temp = temp_udf(month=month, bounds=list(bounds))\n\n    print(\"Elev columns:\", df_elev.columns.tolist())\n    print(\"Temp columns:\", df_temp.columns.tolist())\n    print(f\"Elev rows: {len(df_elev)}, Temp rows: {len(df_temp)}\")\n\n    # Aggregate temp to res 4 then join on hex\n    con = common.duckdb_connect()\n    qr = f\"\"\"\n        WITH temp_agg AS (\n            SELECT\n                h3_cell_to_parent(hex, {res}) AS hex,\n                avg(monthly_mean_temp) AS monthly_mean_temp\n            FROM df_temp\n            GROUP BY 1\n        )\n        SELECT\n            e.hex,\n            e.data_avg AS elevation_avg,\n            t.monthly_mean_temp\n        FROM df_elev e\n        JOIN temp_agg t ON e.hex = t.hex\n    \"\"\"\n    df_joined = con.query(qr).df()\n\n    print(f\"Joined rows: {len(df_joined)}\")\n    print(df_joined.T)\n\n    return df_joined\n",
+          "code": "@fused.udf\ndef udf(\n    # whole US\n    bounds: fused.types.Bounds = [-124.784, 24.396, -66.885, 49.384],\n    res: int = 4,\n    month: str = '2024-05',\n):\n    import pandas as pd\n\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/f661b6d/public/common/\")\n\n    # Load both UDFs\n    elev_udf = fused.load(\"copdem_elevation\")\n    temp_udf = fused.load(\"era5_monthly_mean\")\n\n    # Call both with the same bounds\n    df_elev = elev_udf(bounds=bounds, res=res)\n    df_temp = temp_udf(month=month, bounds=bounds)\n\n    print(\"Elev columns:\", df_elev.columns.tolist())\n    print(\"Temp columns:\", df_temp.columns.tolist())\n    print(f\"Elev rows: {len(df_elev)}, Temp rows: {len(df_temp)}\")\n\n    # Aggregate temp to res 4 then join on hex\n    con = common.duckdb_connect()\n    qr = f\"\"\"\n        WITH temp_agg AS (\n            SELECT\n                h3_cell_to_parent(hex, {res}) AS hex,\n                avg(monthly_mean_temp) AS monthly_mean_temp\n            FROM df_temp\n            GROUP BY 1\n        )\n        SELECT\n            e.hex,\n            e.data_avg AS elevation_avg,\n            t.monthly_mean_temp\n        FROM df_elev e\n        JOIN temp_agg t ON e.hex = t.hex\n    \"\"\"\n    df_joined = con.query(qr).df()\n\n    print(f\"Joined rows: {len(df_joined)}\")\n    print(df_joined.T)\n\n    return df_joined\n",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/temp_elev_corr/temp_elev_corr.py b/public/Basics_of_H3_in_Fused/temp_elev_corr/temp_elev_corr.py
index 80e5926c..4fc88b34 100644
--- a/public/Basics_of_H3_in_Fused/temp_elev_corr/temp_elev_corr.py
+++ b/public/Basics_of_H3_in_Fused/temp_elev_corr/temp_elev_corr.py
@@ -15,7 +15,7 @@ def udf(
 
     # Call both with the same bounds
     df_elev = elev_udf(bounds=bounds, res=res)
-    df_temp = temp_udf(month=month, bounds=list(bounds))
+    df_temp = temp_udf(month=month, bounds=bounds)
 
     print("Elev columns:", df_elev.columns.tolist())
     print("Temp columns:", df_temp.columns.tolist())
diff --git a/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/meta.json b/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/meta.json
index fc2f6701..0cc579ac 100644
--- a/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/meta.json
+++ b/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/meta.json
@@ -57,7 +57,7 @@
             },
             "fused:udfType": "auto"
           },
-          "code": "@fused.udf(cache_max_age=0)\ndef udf(\n    bounds: fused.types.Bounds = [-125, 32, -114, 42],\n    res: int = 4,\n    month: str = '2024-05',\n):\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/9bad664/public/common/\")\n    import pandas as pd\n    import altair as alt\n    alt.data_transformers.enable('default')\n\n    # Load joined elevation + temperature data\n    parent_udf = fused.load('temp_elev_corr')\n    df = parent_udf(bounds=bounds, res=res, month=month)\n\n    print(f\"{df.T=}\")\n\n    # Compute Pearson correlation\n    corr_val = df['monthly_mean_temp'].corr(df['elevation_avg'])\n    print(f\"Pearson r = {corr_val:.4f}\")\n\n    # Stratified sample for chart (bin by elevation, sample within each bin)\n    df['elev_bin'] = pd.cut(df['elevation_avg'], bins=50)\n    df_sample = df.groupby('elev_bin', observed=True).apply(\n        lambda g: g.sample(min(len(g), 80), random_state=42)\n    ).reset_index(drop=True).drop(columns='elev_bin')\n    print(f\"Sampled {len(df_sample)} rows from {len(df)} for chart\")\n\n    # Scatter plot: Temperature vs Elevation\n    scatter = (\n        alt.Chart(df_sample)\n        .mark_circle(size=40, opacity=0.5)\n        .encode(\n            x=alt.X('elevation_avg:Q', title='Elevation (m)'),\n            y=alt.Y('monthly_mean_temp:Q', title='Temperature (&deg;K)'),\n            tooltip=[\n                alt.Tooltip('elevation_avg:Q', title='Elevation', format='.1f'),\n                alt.Tooltip('monthly_mean_temp:Q', title='Temp (K)', format='.2f'),\n            ]\n        )\n    )\n\n    # Add regression line\n    regression = scatter.transform_regression(\n        'elevation_avg', 'monthly_mean_temp'\n    ).mark_line(color='red', strokeWidth=2)\n\n    chart = (\n        (scatter + regression)\n        .properties(\n            width=\"container\",\n            height=450,\n            title=f'Temperature vs Elevation (Pearson r = {corr_val:.3f})'\n        )\n        .configure(background='white')\n    )\n\n    return chart.to_html(embed_options={\"renderer\": \"svg\"})\n",
+          "code": "@fused.udf(cache_max_age=0)\ndef udf(\n    bounds: fused.types.Bounds = [-125, 32, -114, 42],\n    res: int = 4,\n    month: str = '2024-05',\n):\n    common = fused.load(\"https://github.com/fusedio/udfs/tree/9bad664/public/common/\")\n    import pandas as pd\n    import altair as alt\n    alt.data_transformers.enable('default')\n\n    # Load joined elevation + temperature data\n    parent_udf = fused.load('temp_elev_corr')\n    df = parent_udf(bounds=bounds, res=res, month=month)\n\n    print(f\"{df.T=}\")\n\n    # Compute Pearson correlation\n    corr_val = df['monthly_mean_temp'].corr(df['elevation_avg'])\n    print(f\"Pearson r = {corr_val:.4f}\")\n\n    # Stratified sample for chart (bin by elevation, sample within each bin)\n    df['elev_bin'] = pd.cut(df['elevation_avg'], bins=50)\n    df_sample = df.groupby('elev_bin', observed=True).apply(\n        lambda g: g.sample(min(len(g), 80), random_state=42)\n    ).reset_index(drop=True).drop(columns='elev_bin')\n    print(f\"Sampled {len(df_sample)} rows from {len(df)} for chart\")\n\n    # Scatter plot: Temperature vs Elevation\n    scatter = (\n        alt.Chart(df_sample)\n        .mark_circle(size=40, opacity=0.5)\n        .encode(\n            x=alt.X('elevation_avg:Q', title='Elevation (m)'),\n            y=alt.Y('monthly_mean_temp:Q', title='Temperature (\u00b0C)'),\n            tooltip=[\n                alt.Tooltip('elevation_avg:Q', title='Elevation', format='.1f'),\n                alt.Tooltip('monthly_mean_temp:Q', title='Temp (\u00b0C)', format='.2f'),\n            ]\n        )\n    )\n\n    # Add regression line\n    regression = scatter.transform_regression(\n        'elevation_avg', 'monthly_mean_temp'\n    ).mark_line(color='red', strokeWidth=2)\n\n    chart = (\n        (scatter + regression)\n        .properties(\n            width=\"container\",\n            height=450,\n            title=f'Temperature vs Elevation (Pearson r = {corr_val:.3f})'\n        )\n        .configure(background='white')\n    )\n\n    return chart.to_html(embed_options={\"renderer\": \"svg\"})\n",
           "headers": []
         }
       }
diff --git a/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/temp_elevation_corr_chart.py b/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/temp_elevation_corr_chart.py
index e62aca08..90335fdb 100644
--- a/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/temp_elevation_corr_chart.py
+++ b/public/Basics_of_H3_in_Fused/temp_elevation_corr_chart/temp_elevation_corr_chart.py
@@ -32,10 +32,10 @@ def udf(
         .mark_circle(size=40, opacity=0.5)
         .encode(
             x=alt.X('elevation_avg:Q', title='Elevation (m)'),
-            y=alt.Y('monthly_mean_temp:Q', title='Temperature (&deg;K)'),
+            y=alt.Y('monthly_mean_temp:Q', title='Temperature (°C)'),
             tooltip=[
                 alt.Tooltip('elevation_avg:Q', title='Elevation', format='.1f'),
-                alt.Tooltip('monthly_mean_temp:Q', title='Temp (K)', format='.2f'),
+                alt.Tooltip('monthly_mean_temp:Q', title='Temp (°C)', format='.2f'),
             ]
         )
     )