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from cmath import pi
from json import load, tool
from os import stat
#from telnetlib import RCP
import streamlit as st
import pandas as pd
import numpy as np
import pydeck as pdk
from typing import Dict, Union
import streamlit.components.v1 as components
#import streamlit_shared_funcs as my
st.title("Live 3D Map")
location = st.checkbox('Location Filter')
queried_zip_code = None
queried_city = None
queried_state = None
queried_age = None
if location:
queried_zip_code = st.text_input('Zip Code:')
queried_city = st.text_input('City')
queried_state = st.selectbox('State:', ('AL', 'AK', 'AZ', 'AR', 'AS','CA','CO','CT','DE','DC','FL','GA','GU','HI','ID','IL',
'IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT','NE','NV','NH','NJ','NM','NY','NC','ND','CM','OH',
'OK','OR','PA','PR','RI','SC','SD','TN','TX','UT','VT','VA','VI','WA','WV','WI','WY'))
ageBox = st.checkbox("Age Filter")
if ageBox:
queried_age = st.slider("Age",0,200,(0,200))
queried_male = st.checkbox("Male",value=True)
queried_female = st.checkbox("Female",value=True)
@st.cache(allow_output_mutation=True)
def gen_load() -> pd.DataFrame:
#df = my.get_data()
df = pd.read_csv('US.txt')
return df
#AI Emotional State Score: Anxiety, Confusion, Trepidation, Fear, Guilt
@st.cache(allow_output_mutation=True)
def gen_load_old() -> pd.DataFrame:
# file = pd.read_csv('us-zip-code-latitude-and-longitude.txt',names = ['Zip','City','State','lat','lon','Timezone',"Daylight Savings", "Geo point"], skiprows=1, delimiter=";")
filename = "US.txt"
file = pd.read_csv(filename,names = ['Country','Zip','City','State','Abb','Name1','Code1','Name2','Code2','lat','lon','Accuracy'], delimiter="\t",usecols=["Zip","City","State","Abb","lat","lon"])
file['Zip'] = file['Zip'].astype(str).str.zfill(5)
w = lambda x: (x[-5:])
filename = "NPIProviders.xlsx"
df = pd.read_csv('Locations.csv',header = None,names = ['Zip','TIN','Name', 'Services','Age','Gender','StartDate','Questions','Entity'], skiprows=1,converters={'Zip':w },usecols="A,B,C,D,E,F,G,H,I")
'Zip','TIN','Name', 'Services','Age','Gender','StartDate','Questions','Entity'
df = df.merge(file, left_on = 'Zip', right_on = 'Zip', how='inner')
df = df.fillna(" ")
return df
#queried_ctss = st.multiselect("Ctss",options=gen_load()["Ctss"].value_counts().reset_index())
@st.cache(allow_output_mutation=True)
def load_data(state = None,zip_code = None,city = None,age = None,male = None,female = None,selected_cohorts = None) -> pd.DataFrame:
df = gen_load()
try:
if (male or female) and not (male and female):
df = df.loc[df['Gender'] == ("Male" if male else ("Female" if female else ""))]
elif not (male or female):
df = df.loc[df['Gender'] == "(null)"]
except:
pass
try:
df = df[(df['Age'].gt(age[0]) & df['Age'].lt(age[1]))]
except:
pass
try:
if zip_code or state or city:
val = "Zip" if zip_code != "" else "City" if city != "" else "Abb"
df = df.loc[df[val] == (zip_code or city or state)]
except:
pass
try:
if len(selected_cohorts) > 0:
df = df.loc[df['Cohort'].isin(selected_ctss)]
except:
pass
#value_counts = df["FCName"].value_counts()
#df2 = pd.DataFrame(value_counts)
#df2 = df2.reset_index()
#df2.columns = ['FCName', 'Count']
#df = df.merge(df2, left_on = 'FCName', right_on = 'FCName',how='left')
return df
def mapF(data):
print(data)
geo = data.iloc[0]
print(geo)
#print(geo[7], geo[8])
#lat = float(geo['lat'])
#lon = float(geo['lon'])
lat = float(geo[7])
lon = float(geo[8])
max = data['Count'].max()
view_state = pdk.ViewState(
pitch = 40.5,
bearing = -27.36,
latitude = lat,
longitude = lon,
zoom = 4,
)
layer = pdk.Layer(
'ColumnLayer',
data=data,
get_position='[lon, lat]',
pickable=True,
extruded=True,
auto_highlight=True,
get_elevation="Count",
# cell_size=200,
radius = 1000,
elevation_scale=1000000/ max,
get_fill_color='[255, 255 - (Count/3500) * 255, 0,100]',
coverage=10
)
r = pdk.Deck(
# map_style = x,
layers=[layer],
initial_view_state= view_state,
tooltip = {
"html": "<b>City,State:</b> {City} </br> <b>Services:</b> {Count} </br> <b>F:</b> {FCName}",
"style": {
"backgroundColor": "steelblue",
"color": "white"
}
},
)
# st.pydeck_chart(r)
components.html(r.to_html(as_string=True), height=600)
data = load_data(state=queried_state,zip_code=queried_zip_code,city=queried_city,age=queried_age,male=queried_male,female=queried_female)
if st.checkbox('Display data ?'):
data
mapF(data)
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