Duplicate from mattritchey/multipage

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Multipage
+emoji: 😻
+colorFrom: indigo
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+duplicated_from: mattritchey/multipage
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 30 08:44:14 2023
+
+@author: mritchey
+"""
+# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\multipage_app\Homepage.py"
+import streamlit as st
+
+st.set_page_config(
+    page_title="Multipage App",
+    # page_icon="👋",
+)   
+
+
+
+st.title("Select A Page in Upper Left")
+st.header("Hail")
+# url_hail='https://mattritchey-multipage.hf.space/Hail'
+# link = f'[Hail]({url_hail})'
+# st.markdown(link, unsafe_allow_html=True)
+
+st.header("Hail Plus")
+st.header("Wind (High Resolution): RTMA")
+st.header("Wind (Low Resolution): ERA")
+st.sidebar.success("Select a page above.")
+
+# if "my_input" not in st.session_state:
+#     st.session_state["my_input"] = ""
+
+# my_input = st.text_input("Input a text here", st.session_state["my_input"])
+# submit = st.button("Submit")
+# if submit:
+#     st.session_state["my_input"] = my_input
+#     st.write("You have entered: ", my_input)

hail scale.csv

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+c_code,occurence,RGB,R,G,B,Hail Scale,Hail Scale In,R+G
+#01A0F6,408,"(1, 160, 246)",1,160,246,2,0.08,78546
+#BE55DC,408,"(190, 85, 220)",190,85,220,100,3.94,3545724
+#FF9000,408,"(255, 144, 0)",255,144,0,30,1.18,36608
+#00C800,408,"(0, 200, 0)",0,200,0,8,0.31,199
+#FF00FF,396,"(255, 0, 255)",255,0,255,75,2.95,-65536
+#C00000,396,"(192, 0, 0)",192,0,0,50,1.97,-193
+#FF0000,396,"(255, 0, 0)",255,0,0,40,1.57,-256
+#E7C000,396,"(231, 192, 0)",231,192,0,20,0.79,44312
+#FFFF00,396,"(255, 255, 0)",255,255,0,15,0.59,65024
+#009000,396,"(0, 144, 0)",0,144,0,10,0.39,143
+#00FF00,396,"(0, 255, 0)",0,255,0,6,0.24,254
+#0000F6,396,"(0, 0, 246)",0,0,246,4,0.16,-247
+#00ECEC,336,"(0, 236, 236)",0,236,236,1,0.04,55695
+#7E32A7,60,"(126, 50, 167)",126,50,167,200,7.87,1045464
+#7F7F7F,,"(127, 127, 127)",127,127,127,0,0,2064384

pages/Hail Plus.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  6 09:56:29 2022
+
+@author: mritchey
+"""
+#streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\mrms\mrms_all buffer.py"
+
+import plotly.express as px
+
+from joblib import Parallel, delayed
+import pandas as pd
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+import folium
+from streamlit_folium import st_folium
+import math
+import geopandas as gpd
+from skimage.io import imread
+from streamlit_plotly_events import plotly_events
+import requests
+import rasterio
+import rioxarray
+import numpy as np
+import base64
+import re
+
+
+@st.cache
+def geocode(address, buffer_size):
+    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
+
+    df = pd.DataFrame({'Lat': [lat], 'Lon': [lon]})
+    gdf = gpd.GeoDataFrame(
+        df, geometry=gpd.points_from_xy(df.Lon, df.Lat, crs=4326))
+    gdf['buffer'] = gdf['geometry'].to_crs(
+        3857).buffer(buffer_size/2*2580).to_crs(4326)
+    return gdf
+
+
+def get_pngs(date):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    data = imread(url)[:, :, :3]
+    data2 = data.reshape(630*920, 3)
+    data2_df = pd.DataFrame(data2, columns=['R', 'G', 'B'])
+    data2_df2 = pd.merge(data2_df, lut[['R', 'G', 'B', 'Value', ]], on=['R', 'G', 'B'],
+                         how='left')[['Value', ]]
+    data2_df2['Date'] = date
+    return data2_df2.reset_index()
+
+
+@st.cache
+def get_pngs_parallel(dates):
+    results1 = Parallel(n_jobs=32, prefer="threads")(
+        delayed(get_pngs)(i) for i in dates)
+    return results1
+
+
+def png_data(date):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    data = imread(url)
+    return data
+
+
+@st.cache(allow_output_mutation=True)
+def map_folium(data, gdf):
+    m = folium.Map(location=[lat, lon],  zoom_start=zoom, height=300)
+    folium.Marker(
+        location=[lat, lon],
+        popup=address).add_to(m)
+
+    folium.GeoJson(gdf['buffer']).add_to(m)
+    folium.raster_layers.ImageOverlay(
+        data, opacity=0.8, bounds=bounds).add_to(m)
+    return m
+
+
+def to_radians(degrees):
+  return degrees * math.pi / 180
+
+
+def lat_lon_to_bounds(lat, lng, zoom, width, height):
+    earth_cir_m = 40075016.686
+    degreesPerMeter = 360 / earth_cir_m
+    m_pixel_ew = earth_cir_m / math.pow(2, zoom + 8)
+    m_pixel_ns = earth_cir_m / \
+        math.pow(2, zoom + 8) * math.cos(to_radians(lat))
+
+    shift_m_ew = width/2 * m_pixel_ew
+    shift_m_ns = height/2 * m_pixel_ns
+
+    shift_deg_ew = shift_m_ew * degreesPerMeter
+    shift_deg_ns = shift_m_ns * degreesPerMeter
+
+    return [[lat-shift_deg_ns, lng-shift_deg_ew], [lat+shift_deg_ns, lng+shift_deg_ew]]
+
+
+def image_to_geotiff(bounds, input_file_path, output_file_path='template.tiff'):
+    south, west, north, east = tuple(
+        [item for sublist in bounds for item in sublist])
+    dataset = rasterio.open(input_file_path, 'r')
+    bands = [1, 2, 3]
+    data = dataset.read(bands)
+    transform = rasterio.transform.from_bounds(west, south, east, north,
+                                               height=data.shape[1],
+                                               width=data.shape[2])
+    crs = {'init': 'epsg:4326'}
+
+    with rasterio.open(output_file_path, 'w', driver='GTiff',
+                       height=data.shape[1],
+                       width=data.shape[2],
+                       count=3, dtype=data.dtype, nodata=0,
+                       transform=transform, crs=crs,
+                       compress='lzw') as dst:
+        dst.write(data, indexes=bands)
+
+
+def get_mask(bounds, buffer_size):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    img_data = requests.get(url, verify=False).content
+    input_file_path = f'image_name_{date}_{var}.png'
+    output_file_path = 'template.tiff'
+    with open(input_file_path, 'wb') as handler:
+        handler.write(img_data)
+
+    image_to_geotiff(bounds, input_file_path, output_file_path)
+    rds = rioxarray.open_rasterio(output_file_path)
+    # rds.plot.imshow()
+    rds = rds.assign_coords(distance=(haversine(rds.x, rds.y, lon, lat)))
+    mask = rds['distance'].values <= buffer_size
+    mask = np.transpose(np.stack([mask, mask, mask]), (1, 2, 0))
+    return mask
+
+
+def haversine(lon1, lat1, lon2, lat2):
+    # convert decimal degrees to radians
+    lon1 = np.deg2rad(lon1)
+    lon2 = np.deg2rad(lon2)
+    lat1 = np.deg2rad(lat1)
+    lat2 = np.deg2rad(lat2)
+
+    # haversine formula
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
+    c = 2 * np.arcsin(np.sqrt(a))
+    r = 6371
+    return c * r
+
+
+def render_svg(svg):
+    """Renders the given svg string."""
+    b64 = base64.b64encode(svg.encode('utf-8')).decode("utf-8")
+    html = r'<img src="data:image/svg+xml;base64,%s"/>' % b64
+    st.write(html, unsafe_allow_html=True)
+
+
+def rgb_to_hex(rgb):
+    return '#'+'%02x%02x%02x' % rgb
+
+
+def get_legend_lut(prod_root):
+    url_legend = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/shared/fetch_svg_legend_via_config.php?web_resources_dir=/var/www/html/qvs/product_viewer/resources/&config_name=title_and_legend_config.txt&product={prod_root}'
+    r = requests.get(url_legend)  # Get the webpage
+    svg = r.content.decode()  # Decoded response content with the svg string
+
+    if svg.find('size="16">mm</text>') > 0:
+        svg = svg.replace('size="16">mm</text>', 'size="16">in</text>')
+        beg_string = '"13">'
+        end_string = '</text>'
+        res = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+        for mm in res:
+            inc = round(float(mm)*0.0393701, 2)
+            svg = svg.replace(f'{beg_string}{mm}{end_string}',
+                              f'{beg_string}{str(inc)}{end_string}')
+
+    elif svg.find('font-size="12">') > 0:
+        beg_string = '"12">'
+        end_string = '</text>'
+
+    else:
+        beg_string = '"13">'
+        end_string = '</text>'
+
+    #Make LUT
+    values = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+
+    beg_string, end_string = 'fill="rgb(', ')" />'
+    rgb = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+    rgb = [eval(i[0]) for i in rgb]
+
+    beg_string, end_string = 'style="fill:rgb(', ');" />'
+    rgb2 = re.findall('%s(.*)%s' % (beg_string, end_string), svg)
+    rgb2 = [eval(i[0]) for i in rgb2]
+
+    rgb = rgb2+rgb
+
+    lut = pd.DataFrame({'Value': values,
+                       'RGB': rgb})
+    lut['R'], lut['G'], lut['B'] = lut['RGB'].str
+    lut[['R', 'G', 'B']] = lut[['R', 'G', 'B']].astype('uint8')
+    lut['Value'] = lut['Value'].astype(float)
+    lut['hex'] = lut['RGB'].apply(rgb_to_hex)
+    return svg, lut
+
+
+#Set Columns
+st.set_page_config(layout="wide")
+
+
+#Input Data
+zoom = 10
+address = st.sidebar.text_input(
+    "Address", "123 Main Street, Columbus, OH 43215")
+var = st.sidebar.selectbox(
+    'Product:', ('Hail', 'Flooding', 'Rain: Radar', 'Rain: Multi Sensor', 'Tornado'))
+
+date = st.sidebar.date_input("Date",  pd.Timestamp(
+    2022, 9, 8), key='date').strftime('%Y%m%d')
+d = pd.Timestamp(date)
+days_within = st.sidebar.selectbox('Within Days:', (5, 30, 60))
+
+mask_select = st.sidebar.radio('Only Show Buffer Data:', ("No", "Yes"))
+buffer_size = st.sidebar.radio('Buffer Size (miles):', (5, 10, 15))
+
+year, month, day = date[:4], date[4:6], date[6:]
+hour = 23
+minute = 0
+
+
+#Select Variable
+if var == 'Hail':
+    var_input = 'hails&product=MESHMAX1440M'
+elif var == 'Flooding':
+    var_input = 'flash&product=FL_ARI24H'
+elif var == 'Rain: Radar':
+    var_input = 'q3rads&product=Q3EVAP24H'
+elif var == 'Rain: Multi Sensor':
+    var_input = 'q3mss&product=P1_Q3MS24H'
+elif var == 'Tornado':
+    var_input = 'azsh&product=RT1440M'
+
+prod_root = var_input[var_input.find('=')+1:]
+
+#Geocode
+gdf = geocode(address, buffer_size)
+lat, lon = tuple(gdf[['Lat', 'Lon']].values[0])
+
+#Get Value
+url = 'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/get_multi_domain_rect_binary_value.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/'\
+    + f'&prod_root={prod_root}&lon={lon}&lat={lat}&year={year}&month={month}&day={day}&hour={hour}&minute={minute}'
+
+response = requests.get(url, verify=False).json()
+qvs_values = pd.DataFrame(response, index=[0])[
+    ['qvs_value', 'qvs_units']].values[0]
+qvs_value = qvs_values[0]
+qvs_unit = qvs_values[1]
+
+#Get PNG Focus
+data = png_data(date)
+
+#Get PNG Max
+start_date, end_date = d - \
+    pd.Timedelta(days=days_within), d+pd.Timedelta(days=days_within)
+dates = pd.date_range(start_date,
+                      end_date).strftime('%Y%m%d')
+#Get SVG and Lut
+svg, lut = get_legend_lut(prod_root)
+
+bounds = lat_lon_to_bounds(lat, lon, zoom, 920, 630)
+
+results1 = get_pngs_parallel(dates)
+# results1 = Parallel(n_jobs=32, prefer="threads")(delayed(get_pngs)(i) for i in dates)
+results = pd.concat(results1).fillna(0)
+max_data = results.groupby('index')[['Value']].max()
+
+max_data2 = pd.merge(max_data,
+                     lut[['R', 'G', 'B', 'Value']],
+                     on=['Value'],
+                     how='left')[['R', 'G', 'B']]
+
+data_max = max_data2.values.reshape(630, 920, 3)
+
+#Masked Data
+if mask_select == "Yes":
+    mask = get_mask(bounds, buffer_size)
+    mask1 = mask[:, :, 0].reshape(630*920)
+    results = pd.concat([i[mask1] for i in results1])
+    data_max = data_max*mask
+else:
+    pass
+
+
+#Bar
+if var == 'Tornado':
+    bar = results.query("Value>.006").groupby(
+        ['Date', 'Value'])['index'].count().reset_index()
+else:
+    bar = results.query("Value>.2").groupby(['Date', 'Value'])[
+        'index'].count().reset_index()
+
+bar['Date'] = pd.to_datetime(bar['Date'])
+
+bar = bar.reset_index()
+bar.columns = ['level_0', 'Date', 'Value', 'count']
+bar = bar.sort_values('Value', ascending=True)
+bar['Value'] = bar['Value'].astype(str)
+
+
+color_discrete_map = lut[['Value', 'hex']].sort_values(
+    'Value', ascending=True).astype(str)
+color_discrete_map = color_discrete_map.set_index(
+    'Value').to_dict()['hex']
+
+fig = px.bar(bar, x="Date", y="count", color="Value",
+             barmode='stack',
+             color_discrete_map=color_discrete_map)
+
+#Submit Url to New Tab
+url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/index.php?web_exec_mode=run&menu=menu_config.txt&year={year}&month={month}&day={day}&hour=23&minute=30&time_mode=static&zoom=9&clon={lon}&clat={lat}&base=0&overlays=1&mping_mode=0&product_type={var_input}&qpe_pal_option=0&opacity=.75&looping_active=off&num_frames=6&frame_step=200&seconds_step=600'
+
+
+#Map Focus
+m = map_folium(data, gdf)
+#Map Max
+m_max = map_folium(data_max, gdf)
+
+with st.container():
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.header(f'{var} on {pd.Timestamp(date).strftime("%D")}')
+        st_folium(m, height=300)
+    with col2:
+        st.header(
+            f'Max from {start_date.strftime("%D")} to {end_date.strftime("%D")}')
+        st_folium(m_max, height=300)
+
+with st.container():
+    col1, col2, col3 = st.columns((1, 10, 6))
+    with col1:
+        render_svg(svg)
+    with col2:
+        link = f'[Go To MRMS Site]({url})'
+        st.markdown(link, unsafe_allow_html=True)
+        selected_points = plotly_events(
+            fig, click_event=True, hover_event=False)
+    with col3:
+        try:
+            date2 = pd.Timestamp(selected_points[0]['x']).strftime('%Y%m%d')
+            data2 = png_data(date2)
+            m3 = map_folium(data2, gdf)
+            st.header(f'{var} on {pd.Timestamp(date2).strftime("%D")}')
+            st_folium(m3, height=300)
+        except:
+            pass
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)

pages/Hail.py

@@ -0,0 +1,306 @@
+
+
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  6 09:56:29 2022
+
+@author: mritchey
+"""
+#streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\mrms\mrms_hail2 buffer.py"
+
+import plotly.express as px
+import os
+from PIL import Image
+from joblib import Parallel, delayed
+import pandas as pd
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+import folium
+from streamlit_folium import st_folium
+import math
+import geopandas as gpd
+from skimage.io import imread
+from streamlit_plotly_events import plotly_events
+import requests
+from requests.packages.urllib3.exceptions import InsecureRequestWarning
+import rasterio
+import rioxarray
+import numpy as np
+
+
+@st.cache
+def geocode(address, buffer_size):
+    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
+
+    df = pd.DataFrame({'Lat': [lat], 'Lon': [lon]})
+    gdf = gpd.GeoDataFrame(
+        df, geometry=gpd.points_from_xy(df.Lon, df.Lat, crs=4326))
+    gdf['buffer'] = gdf['geometry'].to_crs(
+        3857).buffer(buffer_size/2*2580).to_crs(4326)
+    return gdf
+
+
+@st.cache
+def get_pngs(date):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    data = imread(url)[:, :, :3]
+    data2 = data.reshape(630*920, 3)
+    data2_df = pd.DataFrame(data2, columns=['R', 'G', 'B'])
+    data2_df2 = pd.merge(data2_df, lut[['R', 'G', 'B', 'Hail Scale', 'Hail Scale In']], on=['R', 'G', 'B'],
+                         how='left')[['Hail Scale', 'Hail Scale In']]
+    data2_df2['Date'] = date
+    return data2_df2.reset_index()
+
+
+@st.cache
+def get_pngs_parallel(dates):
+    results1 = Parallel(n_jobs=32, prefer="threads")(
+        delayed(get_pngs)(i) for i in dates)
+    return results1
+
+
+@st.cache
+def png_data(date):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    data = imread(url)
+    return data
+
+
+@st.cache(allow_output_mutation=True)
+def map_folium(data, gdf):
+    m = folium.Map(location=[lat, lon],  zoom_start=zoom, height=300)
+    folium.Marker(
+        location=[lat, lon],
+        popup=address).add_to(m)
+
+    folium.GeoJson(gdf['buffer']).add_to(m)
+    folium.raster_layers.ImageOverlay(
+        data, opacity=0.8, bounds=bounds).add_to(m)
+    return m
+
+
+def to_radians(degrees):
+  return degrees * math.pi / 180
+
+
+def lat_lon_to_bounds(lat, lng, zoom, width, height):
+    earth_cir_m = 40075016.686
+    degreesPerMeter = 360 / earth_cir_m
+    m_pixel_ew = earth_cir_m / math.pow(2, zoom + 8)
+    m_pixel_ns = earth_cir_m / \
+        math.pow(2, zoom + 8) * math.cos(to_radians(lat))
+
+    shift_m_ew = width/2 * m_pixel_ew
+    shift_m_ns = height/2 * m_pixel_ns
+
+    shift_deg_ew = shift_m_ew * degreesPerMeter
+    shift_deg_ns = shift_m_ns * degreesPerMeter
+
+    return [[lat-shift_deg_ns, lng-shift_deg_ew], [lat+shift_deg_ns, lng+shift_deg_ew]]
+
+
+def image_to_geotiff(bounds, input_file_path, output_file_path='template.tiff'):
+    south, west, north, east = tuple(
+        [item for sublist in bounds for item in sublist])
+    dataset = rasterio.open(input_file_path, 'r')
+    bands = [1, 2, 3]
+    data = dataset.read(bands)
+    transform = rasterio.transform.from_bounds(west, south, east, north,
+                                               height=data.shape[1],
+                                               width=data.shape[2])
+    crs = {'init': 'epsg:4326'}
+
+    with rasterio.open(output_file_path, 'w', driver='GTiff',
+                       height=data.shape[1],
+                       width=data.shape[2],
+                       count=3, dtype=data.dtype, nodata=0,
+                       transform=transform, crs=crs,
+                       compress='lzw') as dst:
+        dst.write(data, indexes=bands)
+
+
+def get_mask(bounds, buffer_size):
+    year, month, day = date[:4], date[4:6], date[6:]
+    url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/render_multi_domain_product_layer.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/&prod_root={prod_root}&qperate_pal_option=0&qpe_pal_option=0&year={year}&month={month}&day={day}&hour={hour}&minute={minute}&clon={lon}&clat={lat}&zoom={zoom}&width=920&height=630'
+    img_data = requests.get(url, verify=False).content
+    input_file_path = f'image_name_{date}_{var}.png'
+    output_file_path = 'template.tiff'
+    with open(input_file_path, 'wb') as handler:
+        handler.write(img_data)
+
+    image_to_geotiff(bounds, input_file_path, output_file_path)
+    rds = rioxarray.open_rasterio(output_file_path)
+    # rds.plot.imshow()
+
+    rds = rds.assign_coords(distance=(haversine(rds.x, rds.y, lon, lat)))
+    mask = rds['distance'].values <= buffer_size
+    mask = np.transpose(np.stack([mask, mask, mask]), (1, 2, 0))
+    return mask
+
+
+def haversine(lon1, lat1, lon2, lat2):
+    # convert decimal degrees to radians
+    lon1 = np.deg2rad(lon1)
+    lon2 = np.deg2rad(lon2)
+    lat1 = np.deg2rad(lat1)
+    lat2 = np.deg2rad(lat2)
+
+    # haversine formula
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
+    c = 2 * np.arcsin(np.sqrt(a))
+    r = 6371
+    return c * r
+
+
+#Set Columns
+st.set_page_config(layout="wide")
+col1, col2, col3 = st.columns((3))
+col1, col2, col3 = st.columns((3, 3, 1))
+
+#Input Data
+zoom = 10
+_ = st.sidebar.text_input(
+    "Claim Number", "836-xxxxxxx")
+address = st.sidebar.text_input(
+    "Address", "123 Main Street, Cincinnati, OH 43215")
+
+date = st.sidebar.date_input("Date",  pd.Timestamp(
+    2022, 7, 6), key='date').strftime('%Y%m%d')
+d = pd.Timestamp(date)
+days_within = st.sidebar.selectbox('Within Days:', (5, 30, 60, 90, 180))
+var = 'Hail'
+var_input = 'hails&product=MESHMAX1440M'
+mask_select = st.sidebar.radio('Only Show Buffer Data:', ("No", "Yes"))
+buffer_size = st.sidebar.radio('Buffer Size (miles):', (5, 10, 15))
+
+year, month, day = date[:4], date[4:6], date[6:]
+hour = 23
+minute = 30
+
+prod_root = var_input[var_input.find('=')+1:]
+
+#Geocode
+gdf = geocode(address, buffer_size)
+lat, lon = tuple(gdf[['Lat', 'Lon']].values[0])
+
+#Get Value
+url = 'https://mrms.nssl.noaa.gov/qvs/product_viewer/local/get_multi_domain_rect_binary_value.php?mode=run&cpp_exec_dir=/home/metop/web/specific/opv/&web_resources_dir=/var/www/html/qvs/product_viewer/resources/'\
+    + f'&prod_root={prod_root}&lon={lon}&lat={lat}&year={year}&month={month}&day={day}&hour={hour}&minute={minute}'
+
+response = requests.get(url, verify=False).json()
+qvs_values = pd.DataFrame(response, index=[0])[
+    ['qvs_value', 'qvs_units']].values[0]
+qvs_value = qvs_values[0]
+qvs_unit = qvs_values[1]
+
+#Get PNG Focus
+data = png_data(date)
+
+#Legend
+legend = Image.open('hail scale3b.png')
+
+#Get PNG Max
+start_date, end_date = d - \
+    pd.Timedelta(days=days_within), d+pd.Timedelta(days=days_within)
+dates = pd.date_range(start_date,
+                      end_date).strftime('%Y%m%d')
+lut = pd.read_csv('hail scale.csv')
+bounds = lat_lon_to_bounds(lat, lon, zoom, 920, 630)
+
+
+results1 = get_pngs_parallel(dates)
+# results1 = Parallel(n_jobs=32, prefer="threads")(delayed(get_pngs)(i) for i in dates)
+results = pd.concat(results1)
+max_data = results.groupby('index')[['Hail Scale']].max()
+
+max_data2 = pd.merge(max_data,
+                     lut[['R', 'G', 'B', 'Hail Scale']],
+                     on=['Hail Scale'],
+                     how='left')[['R', 'G', 'B']]
+
+data_max = max_data2.values.reshape(630, 920, 3)
+
+#Masked Data
+if mask_select == "Yes":
+    mask = get_mask(bounds, buffer_size)
+    mask1 = mask[:, :, 0].reshape(630*920)
+    results = pd.concat([i[mask1] for i in results1])
+    data_max = data_max*mask
+else:
+    pass
+
+
+#Bar
+bar = results.query("`Hail Scale`>4").groupby(
+    ['Date', 'Hail Scale In'])['index'].count().reset_index()
+bar['Date'] = pd.to_datetime(bar['Date'])
+
+bar = bar.reset_index()
+bar.columns = ['level_0', 'Date', 'Hail Scale In', 'count']
+bar['Hail Scale In'] = bar['Hail Scale In'].astype(str)
+bar = bar.sort_values('Hail Scale In', ascending=True)
+
+color_discrete_map = lut[['Hail Scale In', 'c_code']].sort_values(
+    'Hail Scale In', ascending=True).astype(str)
+color_discrete_map = color_discrete_map.set_index(
+    'Hail Scale In').to_dict()['c_code']
+
+fig = px.bar(bar, x="Date", y="count", color="Hail Scale In",
+             barmode='stack',
+             color_discrete_map=color_discrete_map)
+
+#Submit Url to New Tab
+url = f'https://mrms.nssl.noaa.gov/qvs/product_viewer/index.php?web_exec_mode=run&menu=menu_config.txt&year={year}&month={month}&day={day}&hour=23&minute=30&time_mode=static&zoom=9&clon={lon}&clat={lat}&base=0&overlays=1&mping_mode=0&product_type={var_input}&qpe_pal_option=0&opacity=.75&looping_active=off&num_frames=6&frame_step=200&seconds_step=600'
+
+#Map Focus
+m = map_folium(data, gdf)
+#Map Max
+m_max = map_folium(data_max, gdf)
+
+with st.container():
+    col1, col2, col3 = st.columns((1, 2, 2))
+    with col1:
+        link = f'[Go To MRMS Site]({url})'
+        st.markdown(link, unsafe_allow_html=True)
+        st.image(legend)
+    with col2:
+        st.header(f'{var} on {pd.Timestamp(date).strftime("%D")}')
+        st_folium(m, height=300)
+    with col3:
+        st.header(
+            f'Max from {start_date.strftime("%D")} to {end_date.strftime("%D")}')
+        st_folium(m_max, height=300)
+
+try:
+    selected_points = plotly_events(fig, click_event=True, hover_event=False)
+    date2 = pd.Timestamp(selected_points[0]['x']).strftime('%Y%m%d')
+    data2 = png_data(date2)
+    m3 = map_folium(data2, gdf)
+    st.header(f'{var} on {pd.Timestamp(date2).strftime("%D")}')
+    st_folium(m3, height=300)
+except:
+    pass
+
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)
+
+
+

pages/Wind (High Resolution): RTMA.py

@@ -0,0 +1,221 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 14 10:35:25 2022
+@author: mritchey
+"""
+
+import datetime
+import glob
+import os
+import urllib.request
+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 streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+from joblib import Parallel, delayed
+from matplotlib import colors as colors
+from streamlit_folium import st_folium
+from threading import Thread
+
+
+def download_file_get_data(url, rows, columns):
+    file = urllib.request.urlretrieve(url, url[-23:])[0]
+    rds = rioxarray.open_rasterio(file)
+    wind_mph = rds.rio.reproject("EPSG:4326")[0, rows, columns].values*2.23694
+    time = url[-15:-11]
+    return [wind_mph, time]
+
+
+def threading(df_input, func_input):
+    starttime = time.time()
+    tasks_thread = df_input
+    results_thread = []
+
+    def thread_func(value_input):
+        response = func_input(value_input)
+        results_thread.append(response)
+        return True
+
+    threads = []
+    for i in range(len(tasks_thread)):
+        process = Thread(target=thread_func, args=[tasks_thread[i]])
+        process.start()
+        threads.append(process)
+
+    for process in threads:
+        process.join()
+    print(f'Time: {str(round((time.time()-starttime)/60,5))} Minutes')
+    return results_thread
+
+
+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
+
+
+@st.cache
+def get_grib_data(url, d, t):
+    file = urllib.request.urlretrieve(url, f'{d}{t}{type_wind}.grib2')[0]
+    return file
+
+
+# @st.cache
+def graph_entire_day(d, rows, columns):
+    year, month, day = d[:4], d[4:6], d[6:8]
+    times = [f'0{str(i)}'[-2:] for i in range(0, 24)]
+    urls = [
+        f'https://mtarchive.geol.iastate.edu/{year}/{month}/{day}/grib2/ncep/RTMA/{d}{t}00_{type_wind.upper()}.grib2' for t in times]
+
+    results = Parallel(n_jobs=4)(
+        delayed(download_file_get_data)(i, rows, columns) for i in urls)
+
+    df_all = pd.DataFrame(results, columns=['MPH', 'Time'])
+    df_all['MPH'] = df_all['MPH'].round(2)
+    df_all['Time'] = pd.to_datetime(d+df_all['Time'], format='%Y%m%d%H%M')
+    return df_all
+
+
+@st.cache
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+# try:
+#     for i in glob.glob('*.grib2'):
+#         try:
+#             os.remove(i)
+#         except:
+#             pass
+# 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")
+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'))
+entire_day = st.sidebar.radio(
+    'Graph Entire Day (Takes a Bit):', ('No', 'Yes'))
+
+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]
+
+url = f'https://mtarchive.geol.iastate.edu/{year}/{month}/{day}/grib2/ncep/RTMA/{d}{t}_{type_wind.upper()}.grib2'
+file = get_grib_data(url, d, t)
+
+lat, lon = geocode(address)
+
+rds = rioxarray.open_rasterio(file)
+projected = rds.rio.reproject("EPSG:4326")
+wind_mph = projected.sel(x=lon, y=lat, method="nearest").values*2.23694
+
+affine = projected.rio.transform()
+
+rows, columns = rasterio.transform.rowcol(affine, lon, lat)
+
+size = 40
+
+projected2 = projected[0, 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[0].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('RTMA Model')
+    url_error='https://mattritchey-rtma.hf.space/'
+    link = f'[If RTMA not working click here]({url_error})'
+    st.markdown(link, unsafe_allow_html=True)
+
+    st.write(
+        f"{type_wind.title()} Speed: {wind_mph[0].round(2)} MPH at {time} UTC")
+    st_folium(m, height=500)
+
+
+if entire_day == 'Yes':
+    df_all = graph_entire_day(d, rows, columns)
+    fig = px.line(df_all, x="Time", y="MPH")
+    with col2:
+        st.title('Analysis')
+        st.plotly_chart(fig)
+
+        csv = convert_df(df_all)
+
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name=f'{d}.csv',
+            mime='text/csv')
+else:
+    pass
+
+
+    
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)

pages/Wind (Low Resolution): ERA.py

@@ -0,0 +1,240 @@
+# -*- 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 rioxarray
+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)

pages/hail scale.csv

@@ -0,0 +1,16 @@
+c_code,occurence,RGB,R,G,B,Hail Scale,Hail Scale In,R+G
+#01A0F6,408,"(1, 160, 246)",1,160,246,2,0.08,78546
+#BE55DC,408,"(190, 85, 220)",190,85,220,100,3.94,3545724
+#FF9000,408,"(255, 144, 0)",255,144,0,30,1.18,36608
+#00C800,408,"(0, 200, 0)",0,200,0,8,0.31,199
+#FF00FF,396,"(255, 0, 255)",255,0,255,75,2.95,-65536
+#C00000,396,"(192, 0, 0)",192,0,0,50,1.97,-193
+#FF0000,396,"(255, 0, 0)",255,0,0,40,1.57,-256
+#E7C000,396,"(231, 192, 0)",231,192,0,20,0.79,44312
+#FFFF00,396,"(255, 255, 0)",255,255,0,15,0.59,65024
+#009000,396,"(0, 144, 0)",0,144,0,10,0.39,143
+#00FF00,396,"(0, 255, 0)",0,255,0,6,0.24,254
+#0000F6,396,"(0, 0, 246)",0,0,246,4,0.16,-247
+#00ECEC,336,"(0, 236, 236)",0,236,236,1,0.04,55695
+#7E32A7,60,"(126, 50, 167)",126,50,167,200,7.87,1045464
+#7F7F7F,,"(127, 127, 127)",127,127,127,0,0,2064384

requirements.txt

@@ -0,0 +1,20 @@
+folium==0.12.1
+geopandas==0.10.2
+geopy==2.2.0
+joblib==1.1.0
+numpy==1.21.5
+pandas==1.4.2
+Pillow==9.4.0
+plotly==5.7.0
+rasterio==1.2.10
+requests==2.27.1
+rioxarray==0.12.2
+scikit_image==0.19.2
+streamlit==1.4.0
+streamlit_folium==0.6.15
+streamlit_plotly_events==0.0.6
+branca==0.4.2
+matplotlib==3.6.2
+scikit_image==0.19.2
+xarray[complete]
+cdsapi