Delete app.py.py

app.py.py

@@ -1,257 +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\hrrr\windtrial2_streamlit_hrrr 2.py"
-import base64
-import datetime
-import itertools
-
-import branca.colormap as cm
-import folium
-import numpy as np
-import pandas as pd
-import plotly.express as px
-import rasterio
-import rioxarray
-import s3fs
-
-import streamlit as st
-import xarray as xr
-from geogif import gif
-from geopy.extra.rate_limiter import RateLimiter
-from geopy.geocoders import Nominatim
-from joblib import Parallel, delayed
-from matplotlib import colors as colors
-from pyproj import CRS, Transformer
-from streamlit_folium import st_folium
-
-
-@st.cache
-def convert_df(df):
-    # IMPORTANT: Cache the conversion to prevent computation on every rerun
-    return df.to_csv(index=0).encode('utf-8')
-
-@st.cache
-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
-
-@st.cache
-def get_data_date_time(date_time, lat, lon):
-    date, time = date_time
-    path = f"hrrrzarr/sfc/{date}/{date}_{time[-2:]}z_{analysis_forecast}.zarr/{vertical_level}/{variable}"
-    try:
-        ds = xr.open_mfdataset([lookup(path), lookup(f"{path}/{vertical_level}")],
-                               engine="zarr")
-        result = ds.sel(projection_x_coordinate=lon, projection_y_coordinate=lat, method="nearest")[
-            variable].values*2.23694
-    except:
-        result = None
-    date2 = pd.to_datetime(str(date)+str(time), format='%Y%m%d%H')
-    df = pd.DataFrame({'Date': date2, 'MPH': result}, index=[0])
-    return df
-
-def mapvalue2color(value, cmap):
-
-    if np.isnan(value):
-        return (1, 0, 0, 0)
-    else:
-        return colors.to_rgba(cmap(value), 0.7)
-
-
-def lookup(path):
-    return s3fs.S3Map(path, s3=s3)
-
-
-
-s3 = s3fs.S3FileSystem(anon=True)
-
-st.set_page_config(layout="wide")
-col1, col2 = st.columns((2))
-
-address = st.sidebar.text_input(
-    "Address", "123 Main Street, Columbus, OH 43215")
-d = st.sidebar.date_input(
-"Date",  pd.Timestamp(2022, 9, 28)).strftime('%Y%m%d')
-
-time = st.sidebar.selectbox('Time:', ('12 AM', '6 AM', '12 PM', '6 PM',))
-type_wind = st.sidebar.selectbox('Type:', ('Gust', 'Wind'))
-analysis_forecast1 = st.sidebar.radio(
-    'Analysis or Forecast:', ('Analysis', 'Forecast'))
-entire_day = st.sidebar.radio(
-    'Graph Entire Day/Forecast (Takes a Bit):', ('No', 'Yes'))
-animate_forecast = st.sidebar.radio(
-    'Animate Forecast:', ('No', 'Yes'))
-
-
-if analysis_forecast1 == 'Analysis':
-    analysis_forecast = 'anl'
-else:
-    analysis_forecast = 'fcst'
-
-
-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 = '0000'
-else:
-    t = datetime.time(int(time[:2].strip()), 00).strftime('%H')+'00'
-
-year, month, day = d[:4], d[4:6], d[6:8]
-
-if type_wind == 'Gust':
-    variable = type_wind.upper()
-    vertical_level = 'surface'
-else:
-    vertical_level = '10m_above_ground'
-    if analysis_forecast == 'anl':
-        variable = 'WIND_max_fcst'
-    else:
-        variable = 'WIND_1hr_max_fcst'
-
-path = f"hrrrzarr/sfc/{d}/{d}_{t[:2]}z_{analysis_forecast}.zarr/{vertical_level}/{variable}"
-ds = xr.open_mfdataset([lookup(path), lookup(f"{path}/{vertical_level}")],
-                       engine="zarr")
-
-lat, lon = geocode(address)
-
-ds = ds.rename(projection_x_coordinate="x", projection_y_coordinate="y")
-crs = CRS.from_cf({"grid_mapping_name": "lambert_conformal_conic",
-                   "longitude_of_central_meridian": -97.5,
-                   "latitude_of_projection_origin": 38.5,
-                   "standard_parallel": 38.5})
-ds = ds.rio.write_crs(crs, inplace=True)
-
-ds[variable] = ds[variable].astype("float64")
-projected = ds.rio.reproject("EPSG:4326")
-
-if analysis_forecast == 'fcst':
-    projected_org = projected
-    projected = projected.sel(time=projected.time.values[0])
-else:
-    pass
-
-wind_mph = projected.sel(x=lon, y=lat, method="nearest")[
-    variable].values*2.23694
-
-
-affine = projected.rio.transform()
-
-rows, columns = rasterio.transform.rowcol(affine, lon, lat)
-
-size = 40
-
-projected2 = projected[variable][rows-size:rows+size, columns-size:columns+size]
-img = projected2.values*2.23694
-
-boundary = projected2.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=9, height=500)
-
-folium.Marker(
-    location=[lat, lon],
-    popup=f"{wind_mph.round(2)} MPH").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('HRRR Model')
-    st.write(f"{type_wind.title()} Speed: {wind_mph.round(2)} MPH at {time} UTC")
-    st_folium(m, height=500)
-
-
-proj = Transformer.from_crs(4326, crs, always_xy=True)
-lon2, lat2 = proj.transform(lon, lat)
-
-
-if entire_day == 'Yes':
-    if analysis_forecast == 'anl':
-        times = [f'0{str(i)}'[-2:] for i in range(0, 24)]
-        dates_times = list(itertools.product([d], times))
-        
-        results = Parallel(n_jobs=-1, prefer="threads")(
-            delayed(get_data_date_time)(i, lat2, lon2) for i in dates_times)
-
-        df_all = pd.concat(results)
-        df_all['MPH'] = df_all['MPH'].round(2)
-
-    else:
-        result = ds[variable].sel(x=lon2, y=lat2, method="nearest")*2.23694
-        df_all = result.to_dataframe()[variable].reset_index()
-        df_all.columns = ['Date', 'MPH']
-        df_all['MPH'] = df_all['MPH'].round(2)
-        if animate_forecast == 'Yes':
-            # Not working
-            gif(projected_org[variable], to='ds.gif',
-                date_format='%m-%d-%Y: %I%p', cmap="RdBu_r", vmax=35)
-            st.download_button(
-                label="Download Gif",
-                data='ds.gif',
-                file_name='ds.gif',
-            )
-            file_ = open("ds.gif", "rb")
-            contents = file_.read()
-            data_url = base64.b64encode(contents).decode("utf-8")
-            file_.close()
-
-            st.markdown(
-                f'<img src="data:image/gif;base64,{data_url}" alt="ds gif" width="1200">',
-                unsafe_allow_html=True,
-            )
-
-    fig = px.line(df_all, x="Date", y="MPH")
-    with col2:
-        st.title(f'{analysis_forecast1}')
-        st.plotly_chart(fig)
-
-        csv = convert_df(df_all)
-
-        st.download_button(
-            label="Download data as CSV",
-            data=csv,
-            file_name='data.csv',
-            mime='text/csv')
-
-else:
-    pass
-
-st.markdown(""" <style>
-#MainMenu {visibility: hidden;}
-footer {visibility: hidden;}
-</style> """, unsafe_allow_html=True)