diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..684b139
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,5 @@
+__pycache__/
+*.pyc
+.venv/
+.streamlit/
+*.png
diff --git a/__pycache__/era5_data.cpython-312.pyc b/__pycache__/era5_data.cpython-312.pyc
new file mode 100644
index 0000000..838b9c6
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diff --git a/__pycache__/nasa_data.cpython-312.pyc b/__pycache__/nasa_data.cpython-312.pyc
index 5bf9b2e..43c23a9 100644
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diff --git a/era5_data.py b/era5_data.py
index 7cf793c..e673ca8 100644
--- a/era5_data.py
+++ b/era5_data.py
@@ -455,29 +455,24 @@ def get_era5_extreme_heat(lat, lon, year, percentile=95):
             np.exp((17.625 * df['Temperature (°C)']) / (243.04 + df['Temperature (°C)']))
         )
         
-        # Calculate heat index
-        def calculate_heat_index(row):
-            t = row['Temperature (°C)']
-            rh = row['Relative Humidity (%)']
-            
-            # Convert to Fahrenheit for the formula
-            t_f = t * 9/5 + 32
-            
-            # Simple formula for lower temperatures
-            if t_f < 80:
-                return t
-            
-            # Full formula for higher temperatures
-            hi = -42.379 + 2.04901523 * t_f + 10.14333127 * rh
-            hi = hi - 0.22475541 * t_f * rh - 0.00683783 * t_f**2
-            hi = hi - 0.05481717 * rh**2 + 0.00122874 * t_f**2 * rh
-            hi = hi + 0.00085282 * t_f * rh**2 - 0.00000199 * t_f**2 * rh**2
-            
-            # Convert back to Celsius
-            hi_c = (hi - 32) * 5/9
-            return hi_c
-        
-        df['Heat Index (°C)'] = df.apply(calculate_heat_index, axis=1)
+        # Calculate heat index using vectorized operations
+        t = df['Temperature (°C)']
+        rh = df['Relative Humidity (%)']
+        
+        # Convert to Fahrenheit for the formula
+        t_f = t * 9/5 + 32
+
+        # Full formula calculation for all points
+        hi = -42.379 + 2.04901523 * t_f + 10.14333127 * rh
+        hi = hi - 0.22475541 * t_f * rh - 0.00683783 * t_f**2
+        hi = hi - 0.05481717 * rh**2 + 0.00122874 * t_f**2 * rh
+        hi = hi + 0.00085282 * t_f * rh**2 - 0.00000199 * t_f**2 * rh**2
+
+        # Convert back to Celsius
+        hi_c = (hi - 32) * 5/9
+
+        # Use simple formula (just temperature) where t_f < 80, else full formula
+        df['Heat Index (°C)'] = np.where(t_f < 80, t, hi_c)
         
         # Get daily maximum heat index
         daily_heat_index = df.groupby('Date')['Heat Index (°C)'].max().reset_index()
diff --git a/nasa_data.py b/nasa_data.py
index 419477f..567f243 100644
--- a/nasa_data.py
+++ b/nasa_data.py
@@ -350,30 +350,24 @@ def _get_extreme_heat_days_cached(lat, lon, year, percentile):
     df = fetch_nasa_power_data(lat, lon, start_date, end_date, 
                             parameters=["T2M_MAX", "RH2M"])
     
-    # Calculate heat index
-    def calculate_heat_index(row):
-        t = row['T2M_MAX']  # Temperature in Celsius
-        rh = row['RH2M']    # Relative humidity in %
-        
-        # Simple formula for heat index
-        if t < 26:
-            return t  # Below this temperature, heat index equals temperature
-        
-        # Full formula
-        hi = -8.78469475556 + \
-             1.61139411 * t + \
-             2.33854883889 * rh + \
-             -0.14611605 * t * rh + \
-             -0.012308094 * t**2 + \
-             -0.0164248277778 * rh**2 + \
-             0.002211732 * t**2 * rh + \
-             0.00072546 * t * rh**2 + \
-             -0.000003582 * t**2 * rh**2
-        
-        return hi
-    
-    # Apply heat index calculation
-    df['Heat Index (°C)'] = df.apply(calculate_heat_index, axis=1)
+    # Calculate heat index using vectorized operations for performance
+    t = df['T2M_MAX']  # Temperature in Celsius
+    rh = df['RH2M']    # Relative humidity in %
+
+    # Full formula calculation for all points
+    # This is much faster than applying a function row by row
+    hi_full = -8.78469475556 + \
+              1.61139411 * t + \
+              2.33854883889 * rh + \
+              -0.14611605 * t * rh + \
+              -0.012308094 * t**2 + \
+              -0.0164248277778 * rh**2 + \
+              0.002211732 * t**2 * rh + \
+              0.00072546 * t * rh**2 + \
+              -0.000003582 * t**2 * rh**2
+    
+    # Use simple formula (just temperature) where t < 26
+    df['Heat Index (°C)'] = np.where(t < 26, t, hi_full)
     
     # Determine thresholds
     temp_threshold = np.percentile(df['T2M_MAX'], percentile)
diff --git a/test_heat_index_performance.py b/test_heat_index_performance.py
new file mode 100644
index 0000000..3b6244c
--- /dev/null
+++ b/test_heat_index_performance.py
@@ -0,0 +1,166 @@
+
+import unittest
+import pandas as pd
+import numpy as np
+from unittest.mock import MagicMock, patch
+import nasa_data
+import era5_data
+
+class TestHeatIndexLogic(unittest.TestCase):
+
+    def test_nasa_heat_index_logic(self):
+        # Create a mock DataFrame with known values
+        # Case 1: Low temp (Heat Index = Temp)
+        # Case 2: High temp, Low RH
+        # Case 3: High temp, High RH
+
+        # NASA data uses T2M_MAX (C) and RH2M (%)
+        mock_df = pd.DataFrame({
+            'Date': pd.date_range(start='2023-01-01', periods=3),
+            'T2M_MAX': [20.0, 30.0, 35.0],
+            'RH2M': [50.0, 40.0, 80.0],
+            'T2M': [20.0, 30.0, 35.0], # Needed for other parts but not heat index
+            'T2M_MIN': [15.0, 25.0, 30.0] # Needed for other parts but not heat index
+        })
+
+        # Mock fetch_nasa_power_data to return our mock_df
+        with patch('nasa_data.fetch_nasa_power_data', return_value=mock_df):
+            # We call the internal cached function directly or the wrapper.
+            # The wrapper calls the cached function.
+            # However, since the function is cached, we might hit the cache if not careful.
+            # But in a fresh test run, cache is empty.
+
+            # The function calculates heat index internally
+            result_df, _, _ = nasa_data.get_extreme_heat_days(0, 0, 2023)
+
+            # Verify calculations
+
+            # Row 0: T=20 (<26), so HI should be 20
+            self.assertAlmostEqual(result_df.loc[0, 'Heat Index (°C)'], 20.0)
+
+            # Row 1: T=30, RH=40.
+            # Formula check:
+            # HI = -8.78469475556 + 1.61139411*30 + 2.33854883889*40 + ...
+            # Let's verify against the known formula value.
+            # I will use the old scalar implementation to verify the new vectorized one
+            # matches the expected logic.
+
+            t = 30.0
+            rh = 40.0
+            hi_expected_1 = -8.78469475556 + \
+                 1.61139411 * t + \
+                 2.33854883889 * rh + \
+                 -0.14611605 * t * rh + \
+                 -0.012308094 * t**2 + \
+                 -0.0164248277778 * rh**2 + \
+                 0.002211732 * t**2 * rh + \
+                 0.00072546 * t * rh**2 + \
+                 -0.000003582 * t**2 * rh**2
+
+            self.assertAlmostEqual(result_df.loc[1, 'Heat Index (°C)'], hi_expected_1)
+
+            # Row 2: T=35, RH=80
+            t = 35.0
+            rh = 80.0
+            hi_expected_2 = -8.78469475556 + \
+                 1.61139411 * t + \
+                 2.33854883889 * rh + \
+                 -0.14611605 * t * rh + \
+                 -0.012308094 * t**2 + \
+                 -0.0164248277778 * rh**2 + \
+                 0.002211732 * t**2 * rh + \
+                 0.00072546 * t * rh**2 + \
+                 -0.000003582 * t**2 * rh**2
+
+            self.assertAlmostEqual(result_df.loc[2, 'Heat Index (°C)'], hi_expected_2)
+
+    def test_era5_heat_index_logic(self):
+        # Mock ERA5 data
+        # It expects '2m_temperature' (K) and '2m_dewpoint_temperature' (K)
+        # or 'Temperature (°C)' and 'Dewpoint (°C)' if processed?
+        # fetch_era5_data returns processed data.
+
+        # Let's see what get_era5_extreme_heat does.
+        # It calls fetch_era5_data, which returns a DF with 'Temperature (°C)' and 'Dewpoint (°C)' (if computed)
+        # But wait, fetch_era5_data processes raw data.
+        # So if we mock fetch_era5_data, we should return the processed format that get_era5_extreme_heat expects.
+
+        # get_era5_extreme_heat calls:
+        # df = fetch_era5_data(..., variables=['2m_temperature', '2m_dewpoint_temperature'])
+
+        # And expects:
+        # 'Temperature (°C)'
+        # '2m_dewpoint_temperature' (raw?) or 'd2m'?
+
+        # Let's check era5_data.py again.
+        # inside get_era5_extreme_heat:
+        # if '2m_dewpoint_temperature' in df.columns:
+        #     df['Dewpoint (°C)'] = df['2m_dewpoint_temperature'] - 273.15
+
+        # So fetch_era5_data returns whatever process_era5_data returns.
+        # process_era5_data converts '2m_temperature' to 'Temperature (°C)'.
+        # It does NOT seem to touch '2m_dewpoint_temperature' explicitly in the renames dict?
+        # Renames: {'t2m': 'Temperature (K)', '2m_temperature': 'Temperature (K)', ...}
+        # And then converts 'Temperature (K)' to 'Temperature (°C)'.
+
+        # So '2m_dewpoint_temperature' likely remains as is in the dataframe returned by fetch_era5_data.
+
+        # Let's verify correct mocking.
+
+        # Create mock data with Temperature in C (already processed) and Dewpoint in K (raw)
+        # T = 30°C
+        # RH needs to be calculated to verify heat index.
+        # RH = 100 * (EXP((17.625 * TD)/(243.04 + TD)) / EXP((17.625 * T)/(243.04 + T)))
+
+        # Let's just mock 'Temperature (°C)' and '2m_dewpoint_temperature' (K)
+        # Case 1: Low temp (20°C) -> 293.15 K
+        # Case 2: High temp (35°C) -> 308.15 K
+
+        # To get a specific RH, we need to set Dewpoint appropriately.
+        # Approx T=35, RH=50% -> TD approx 23°C -> 296.15 K
+
+        mock_df = pd.DataFrame({
+            'time': pd.to_datetime(['2023-01-01 12:00:00', '2023-01-02 12:00:00']),
+            'Temperature (°C)': [20.0, 35.0],
+            '2m_dewpoint_temperature': [293.15 - 10, 296.15] # 283.15K (10°C), 296.15K (23°C)
+        })
+
+        with patch('era5_data.fetch_era5_data', return_value=mock_df):
+            result_df, _, _ = era5_data.get_era5_extreme_heat(0, 0, 2023)
+
+            # Verify results
+            # Row 0: T=20C. F = 68F < 80F. Heat Index should be T.
+            # Note: get_era5_extreme_heat groups by Date and takes MAX.
+            # We have 2 dates, so 2 rows.
+
+            row0 = result_df[result_df['Date'] == pd.to_datetime('2023-01-01').date()].iloc[0]
+            self.assertAlmostEqual(row0['Heat Index (°C)'], 20.0)
+
+            row1 = result_df[result_df['Date'] == pd.to_datetime('2023-01-02').date()].iloc[0]
+
+            # Verify calculation for row 1
+            # Re-calculate inputs locally
+            t = 35.0
+            td = 296.15 - 273.15 # 23.0
+
+            # RH calculation used in code
+            rh = 100 * (
+                np.exp((17.625 * td) / (243.04 + td)) /
+                np.exp((17.625 * t) / (243.04 + t))
+            )
+
+            # Heat Index calculation
+            t_f = t * 9/5 + 32 # 95F
+
+            # Full formula
+            hi = -42.379 + 2.04901523 * t_f + 10.14333127 * rh
+            hi = hi - 0.22475541 * t_f * rh - 0.00683783 * t_f**2
+            hi = hi - 0.05481717 * rh**2 + 0.00122874 * t_f**2 * rh
+            hi = hi + 0.00085282 * t_f * rh**2 - 0.00000199 * t_f**2 * rh**2
+
+            hi_c = (hi - 32) * 5/9
+
+            self.assertAlmostEqual(row1['Heat Index (°C)'], hi_c)
+
+if __name__ == '__main__':
+    unittest.main()