Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 14 10:35:25 2022
+
+@author: mritchey
+"""
+# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\ERA\ERA2.py"
+import datetime
+import glob
+import os
+import branca.colormap as cm
+import folium
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+from matplotlib import colors as colors
+from streamlit_folium import st_folium
+import xarray as xr
+import cdsapi
+import os
+
+
+
+def mapvalue2color(value, cmap):
+    if np.isnan(value):
+        return (1, 0, 0, 0)
+    else:
+        return colors.to_rgba(cmap(value), 0.7)
+
+
+def geocode(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=1)
+        location = geolocator.geocode(address)
+        lat, lon = location.latitude, location.longitude
+    return lat, lon
+
+
+def graph_within_date_range(d, number_days_range):
+    year, month, day = d[:4], d[4:6], d[6:8]
+    date = pd.Timestamp(d)
+    start_date, end_date = date - \
+        pd.Timedelta(days=number_days_range), date + \
+        pd.Timedelta(days=number_days_range+1)
+    start_date = start_date.strftime("%Y-%m-%d")
+    end_date = end_date.strftime("%Y-%m-%d")
+    url = f'https://archive-api.open-meteo.com/v1/archive?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,precipitation,windspeed_10m,windgusts_10m&models=best_match&temperature_unit=fahrenheit&windspeed_unit=mph&precipitation_unit=inch'
+    df = pd.read_json(url).reset_index()
+    data = pd.DataFrame({c['index']: c['hourly'] for r, c in df.iterrows()})
+    data['time'] = pd.to_datetime(data['time'])
+    data['date'] = pd.to_datetime(data['time'].dt.date)
+    data = data.query("temperature_2m==temperature_2m")
+
+    data_agg = data.groupby(['date']).agg({'temperature_2m': ['min', 'mean', 'max'],
+                                           'precipitation': ['sum'],
+                                           'windspeed_10m': ['min', 'mean', 'max'],
+                                           'windgusts_10m': ['min', 'mean', 'max']
+                                           })
+    data_agg.columns = data_agg.columns.to_series().str.join('_')
+    data_agg = data_agg.query("temperature_2m_min==temperature_2m_min")
+    return data.drop(columns=['date']), data_agg
+
+
+@st.cache(allow_output_mutation=True)
+def get_era5_data(year, month, day):
+    c = cdsapi.Client(key=os.environ['key'],
+                      url="https://cds.climate.copernicus.eu/api/v2")
+
+    c.retrieve(
+        'reanalysis-era5-single-levels',
+        {
+            'product_type': 'reanalysis',
+            'variable': ['10m_u_component_of_wind', '10m_v_component_of_wind',
+                            'instantaneous_10m_wind_gust',
+                            '2m_temperature', 'total_precipitation'],
+            'year': year,
+            'month': [month],
+            'day': [day],
+            'time': ['00:00', '06:00', '12:00', '18:00'],
+            'area': [49.5, -125, 24.5, -66.5, ],
+            'format': 'netcdf',
+        },
+        'data.nc')
+
+
+@st.cache
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+
+try:
+    for i in glob.glob('*.grib2'):
+        os.remove(i)
+except:
+    pass
+
+st.set_page_config(layout="wide")
+col1, col2 = st.columns((2))
+
+address = st.sidebar.text_input(
+    "Address", "123 Main Street, Columbus, OH 43215")
+date = st.sidebar.date_input(
+    "Date",  pd.Timestamp(2022, 9, 28))
+d = date.strftime('%Y%m%d')
+date = date.strftime('%Y-%m-%d')
+time = st.sidebar.selectbox('Time (UTC):', ('12 AM', '6 AM', '12 PM', '6 PM',))
+type_var = st.sidebar.selectbox(
+    'Type:', ('Gust', 'Wind', 'Temp', 'Precipitation'))
+number_days_range = st.sidebar.selectbox(
+    'Within Day Range:', (5, 10, 30, 90, 180))
+hourly_daily = st.sidebar.radio('Aggregate Data', ('Hourly', 'Daily'))
+
+# Keys
+var_key = {'Gust': 'i10fg', 'Wind': 'wind10',
+           'Temp': 't2m', 'Precipitation': 'tp'}
+
+variable = var_key[type_var]
+
+unit_key = {'Gust': 'MPH', 'Wind': 'MPH',
+            'Temp': 'F', 'Precipitation': 'In.'}
+unit = unit_key[type_var]
+
+cols_key = {'Gust': ['windgusts_10m'], 'Wind': ['windspeed_10m'], 'Temp': ['temperature_2m'],
+            'Precipitation': ['precipitation']}
+
+cols_key_agg = {'Gust': ['windgusts_10m_min', 'windgusts_10m_mean',
+                         'windgusts_10m_max'],
+                'Wind': ['windspeed_10m_min', 'windspeed_10m_mean',
+                         'windspeed_10m_max'],
+                'Temp': ['temperature_2m_min', 'temperature_2m_mean', 'temperature_2m_max'],
+                'Precipitation': ['precipitation_sum']}
+
+if hourly_daily == 'Hourly':
+    cols = cols_key[type_var]
+else:
+    cols = cols_key_agg[type_var]
+
+
+if time[-2:] == 'PM' and int(time[:2].strip()) < 12:
+    t = datetime.time(int(time[:2].strip())+12, 00).strftime('%H')+'00'
+elif time[-2:] == 'AM' and int(time[:2].strip()) == 12:
+    t = '00:00'
+else:
+    t = datetime.time(int(time[:2].strip()), 00).strftime('%H')+'00'
+
+year, month, day = d[:4], d[4:6], d[6:8]
+
+get_era5_data(year, month, day)
+ds = xr.open_dataset('data.nc')
+ds = ds.sel(time=f'{date}T{t}').drop('time')
+
+#Convert Units
+ds = ds.assign(t2m=(ds.t2m - 273.15) * 9/5 + 32)
+ds = ds.assign(i10fg=(ds.i10fg*2.237))
+ds = ds.assign(tp=(ds.tp/24.5))
+ds = ds.assign(wind10=((ds.v10**2+ds.u10**2)**.5)*2.237)
+
+lat, lon = geocode(address)
+
+var_value = ds[variable].sel(
+    longitude=lon, latitude=lat, method="nearest").values.item()
+var_value = round(var_value, 1)
+
+img = ds[variable].values
+boundary = ds.rio.bounds()
+left, bottom, right, top = boundary
+
+img[img < 0.0] = np.nan
+
+clat = (bottom + top)/2
+clon = (left + right)/2
+
+vmin = np.floor(np.nanmin(img))
+vmax = np.ceil(np.nanmax(img))
+
+colormap = cm.LinearColormap(
+    colors=['blue', 'lightblue', 'red'], vmin=vmin, vmax=vmax)
+
+m = folium.Map(location=[lat, lon],  zoom_start=5, height=500)
+
+folium.Marker(
+    location=[lat, lon],
+    popup=f"{var_value} {unit}"
+).add_to(m)
+
+folium.raster_layers.ImageOverlay(
+    image=img,
+    name='Wind Speed Map',
+    opacity=.8,
+    bounds=[[bottom, left], [top, right]],
+    colormap=lambda value: mapvalue2color(value, colormap)
+).add_to(m)
+
+
+folium.LayerControl().add_to(m)
+colormap.caption = 'Wind Speed: MPH'
+m.add_child(colormap)
+
+with col1:
+    st.title('ERA5 Model')
+    # st.write(
+    #     f"{type_wind.title()} Speed: {wind_mph[0].round(2)} MPH at {time} UTC")
+    st_folium(m, height=500)
+    df_all, df_all_agg = graph_within_date_range(d, number_days_range)
+
+    if hourly_daily == 'Hourly':
+        fig = px.line(df_all, x="time", y=cols)
+        df_downloald = df_all
+    else:
+        fig = px.line(df_all_agg.reset_index(), x="date", y=cols)
+        df_downloald = df_all_agg.reset_index()
+
+with col2:
+    st.title('Analysis')
+    st.plotly_chart(fig)
+
+    csv = convert_df(df_downloald)
+
+    st.download_button(
+        label="Download data as CSV",
+        data=csv,
+        file_name=f'{d}.csv',
+        mime='text/csv')
+
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)