Delete ERA2.py

ERA2.py

@@ -1,237 +0,0 @@
-# -*- 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
-
-
-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='82849:2dc2d4ad-9e03-4a42-8914-d7faf8c4a44d',
-                      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)