Update main.py

main.py

@@ -1,117 +1,103 @@
 from fastapi import FastAPI
 import uvicorn
 
-from geopy.extra.rate_limiter import RateLimiter
-from geopy.geocoders import Nominatim
 import pandas as pd
 import numpy as np
 import pickle
 import rasterio
 import h5py
-from skimage.morphology import disk 
+from skimage.morphology import disk
 
 app = FastAPI()
 
 
-#Endpoints 
+#Endpoints
 #Root endpoints
 @app.get("/")
 def root():
     return {"API": "Hail Docker Data"}
 
 
-def geocode_address(address):
-
-    try:
-        address2 = address.replace(' ', '+').replace(',', '%2C')
-        df = pd.read_json(
-            f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
-        results = df.iloc[:1, 0][0][0]['coordinates']
-        lat, lon = results['y'], results['x']
-    except:
-        geolocator = Nominatim(user_agent='GTA Lookup')
-        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=2)
-        location = geolocator.geocode(address)
-        lat, lon = location.latitude, location.longitude
-        
-    return lat, lon
-
-
-def get_hail_data(address, start_date,end_date,radius_miles):
+def get_hail_data(lat, lon, start_date, end_date, radius_miles, get_max):
 
     start_date = pd.Timestamp(str(start_date)).strftime('%Y%m%d')
     end_date = pd.Timestamp(str(end_date)).strftime('%Y%m%d')
     date_years = pd.date_range(start=start_date, end=end_date, freq='M')
+    date_range_days = pd.date_range(start_date, end_date)
     years = list(set([d.year for d in date_years]))
-    
-    
-    if len(years)==0:
-        years=[pd.Timestamp(start_date).year]
-    
-    # Geocode Address
-    lat, lon= geocode_address(address)
-    
+
+    if len(years) == 0:
+        years = [pd.Timestamp(start_date).year]
+
     # Convert Lat Lon to row & col on Array
-    transform = pickle.load(open('Data/transform_mrms.pkl', 'rb')) 
+    transform = pickle.load(open('transform_mrms.pkl', 'rb'))
     row, col = rasterio.transform.rowcol(transform, lon, lat)
-    
+
     files = [
-        'Data/2023_hail.h5',
-        'Data/2022_hail.h5',
-        'Data/2021_hail.h5',
-        'Data/2020_hail.h5'
-        ]
-    
-    files_choosen=[i for i in files if any(i for j in years if str(j) in i)]
-    
-    # Query and Collect H5 Data  
-    all_data=[]
-    all_dates=[]
+        'C:/Users/mritchey/Documents/Desktop 2/Inforce with Geocodes/Hail/Hail Mesh/raw data 1440/2023_hail.h5',
+        'C:/Users/mritchey/Documents/Desktop 2/Inforce with Geocodes/Hail/Hail Mesh/raw data 1440/2022_hail.h5',
+        'C:/Users/mritchey/Documents/Desktop 2/Inforce with Geocodes/Hail/Hail Mesh/raw data 1440/2021_hail.h5',
+        'C:/Users/mritchey/Documents/Desktop 2/Inforce with Geocodes/Hail/Hail Mesh/raw data 1440/2020_hail.h5'
+    ]
+
+    files_choosen = [i for i in files if any(i for j in years if str(j) in i)]
+
+    # Query and Collect H5 Data
+    all_data = []
+    all_dates = []
     for file in files_choosen:
         with h5py.File(file, 'r') as f:
             # Get Dates from H5
             dates = f['dates'][:]
-            date_idx=np.where((dates>=int(start_date)) & (dates<=int(end_date)) )[0]
-            
+            date_idx = np.where((dates >= int(start_date))
+                                & (dates <= int(end_date)))[0]
+
             # Select Data by Date and Radius
-            dates=dates[date_idx]
+            dates = dates[date_idx]
             data = f['hail'][date_idx, row-radius_miles:row +
-                              radius_miles+1, col-radius_miles:col+radius_miles+1]
-        
+                             radius_miles+1, col-radius_miles:col+radius_miles+1]
+
             all_data.append(data)
             all_dates.append(dates)
-    
-    data_all=np.vstack(all_data)
-    dates_all=np.concatenate(all_dates)
-    
+
+    data_all = np.vstack(all_data)
+    dates_all = np.concatenate(all_dates)
+
     # Convert to Inches
-    data_mat = np.where(data_all < 0, 0, data_all)*0.0393701 
-    
+    data_mat = np.where(data_all < 0, 0, data_all)*0.0393701
+
     # Get Radius of Data
-    disk_mask = np.where(disk(radius_miles)==1,True, False)
-    data_mat = np.where(disk_mask, data_mat, -1)
-    
-    # Find Max of Data
-    data_max = np.max(data_mat, axis=(1, 2)).round(3)
-    
+    disk_mask = np.where(disk(radius_miles) == 1, True, False)
+    data_mat = np.where(disk_mask, data_mat, -1).round(3)
+
     # Process to DataFrame
-    date_range_days = pd.date_range(start_date,end_date)
-    df_data=pd.DataFrame({'Date':dates_all,
-                          'Hail_max':data_max})
-    df_data['Date']=pd.to_datetime(df_data['Date'],format='%Y%m%d')
-    df_data=df_data.set_index('Date')
-    
-    df_data = df_data.reindex(date_range_days, fill_value=0).reset_index().rename(columns={'index': 'Date'})
-    df_data['Date']=df_data['Date'].dt.strftime('%Y-%m-%d')
+    # Find Max of Data
+    if get_max == True:
+        data_max = np.max(data_mat, axis=(1, 2))
+        df_data = pd.DataFrame({'Date': dates_all,
+                               'Hail_max': data_max})
+    else:
+        data_all = list(data_mat)
+        df_data = pd.DataFrame({'Date': dates_all,
+                               'Hail_all': data_all})
+
+    df_data['Date'] = pd.to_datetime(df_data['Date'], format='%Y%m%d')
+    df_data = df_data.set_index('Date')
+
+    df_data = df_data.reindex(date_range_days, fill_value=0).reset_index().rename(
+        columns={'index': 'Date'})
+    df_data['Date'] = df_data['Date'].dt.strftime('%Y-%m-%d')
+
     return df_data
 
-    
+
 @app.get('/Hail_Docker_Data')
-async def predict(address: str, start_date: int,end_date: int, radius_miles:int ):
-    
+async def predict(lat: float, lon: float, start_date: int, end_date: int, radius_miles: int, get_max: bool):
+
     try:
-        results = get_hail_data(address, start_date,end_date, radius_miles)
+        results = get_hail_data(lat, lon, start_date,
+                                end_date, radius_miles, get_max)
     except:
-        results=pd.DataFrame({'Date':['error'],'df_data':['error']})
-        
+        results = pd.DataFrame({'Date': ['error'], 'Hail_max': ['error']})
+
     return results.to_json()