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from fastapi import FastAPI
import uvicorn
import pandas as pd
import numpy as np
import pickle
import rasterio
import h5py
from skimage.morphology import disk
from geopy.extra.rate_limiter import RateLimiter
from geopy.geocoders import Nominatim
app = FastAPI()
#Endpoints
#Root endpoints
@app.get("/")
def root():
return {"API": "ACPC HRRR"}
def lat_lon_to_row_col(lat, lon):
crs_dic = pickle.load(open('Data/hrrr_crs.pkl', 'rb'))
transform = crs_dic['affine']
trans_rtma = crs_dic['proj_4326']
lon_rtma, lat_rtma = trans_rtma.transform(lon, lat)
row, col = rasterio.transform.rowcol(transform, lon_rtma, lat_rtma)
row, col = int(row), int(col)
return row, col
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_data(address, start_date, end_date, radius_miles, get_max):
start_date = pd.Timestamp(str(start_date)).strftime('%Y%m%d%H')
end_date = pd.Timestamp(str(end_date)).strftime('%Y%m%d%H')
date_years = pd.date_range(start=start_date[:-2], end=end_date[:-2], freq='M')
date_range_days = pd.date_range(start_date[:-2], end_date[:-2], freq='H')
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)
# Convert Lat Lon to row & col on Array
try:
row, col = lat_lon_to_row_col(lat, lon)
except:
row=col=None
# files = [
# # 'Data/APCP_2024_hrrr_v2.h5',
# 'Data/APCP_2020_hrrr_v3.h5',
# 'Data/APCP_2021_hrrr_3.h5',
# 'Data/APCP_2022_hrrr_v2.h5',
# # 'Data/APCP_2023_hrrr_v2c.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['date_time_hr'][:]
# date_idx = np.where((dates >= int(start_date))
# & (dates <= int(end_date)))[0]
# # Select Data by Date and Radius
# dates = dates[date_idx]
# data = f['APCP'][date_idx, row-radius_miles:row +
# 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)
# # Convert to Inches
# 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).round(3)
# # Process to DataFrame
# # Find Max of Data
# if get_max == True:
# data_max = np.max(data_mat, axis=(1, 2))
# df_data = pd.DataFrame({'Date': dates_all,
# 'APCP_max': data_max})
# # Get all Data
# else:
# data_all = list(data_mat)
# df_data = pd.DataFrame({'Date': dates_all,
# 'APCP_all': data_all})
# df_data['Date'] = pd.to_datetime(df_data['Date'], format='%Y%m%d%H')
# 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:%H')
# return df_data
return lat, lon, crs_dic, row, col
@app.get('/APCP_Docker_Data')
async def predict(address: str, start_date: str, end_date: str, radius_miles: int, get_max: bool):
try:
results = get_data(address, start_date,
end_date, radius_miles, get_max)
except:
results = pd.DataFrame({'Date': ['error'], 'APCP_max': ['error']})
# return results.to_json()
return results
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