def display_state_deaths(clickData, date_choice, state_dropdown):
# Get states historic data
states_historic_df = pd.read_csv(config['historic_loc'])
states_historic_df['dates_dt'] = pd.to_datetime(states_historic_df['date'], format='%Y%m%d')
states_historic_df = utils.date_filter(states_historic_df, date_choice)
changed_id = [p['prop_id'] for p in dash.callback_context.triggered][0]
# Show the California plot as the default plot if there is no click is recorded on the US map
if 'main-choro.clickData' not in changed_id and 'states-dropdown.value' not in changed_id :
perday_state = states_historic_df[states_historic_df['state'] == 'CA']
title = "Daily Death counts for {}".format("California")
deaths_fig = px.bar(perday_state,
x='dates_dt',
y='deathIncrease',
labels={"deathIncrease":"Total Number of new deaths",
"dates_dt" : "Time"},
title=title)
deaths_fig.update_xaxes(nticks=20)
deaths_fig.update_layout(dict(paper_bgcolor=config['paper_color'],
plot_bgcolor=config['plot_color'],
font_color="white" ))
deaths_fig.update_traces(marker_color=config['bar_color'], marker_line_color=config['marker_line_color'])
deaths_fig.update_xaxes(nticks=20, showgrid=False, gridcolor='grey', mirror=True, gridwidth=1, linecolor='grey', linewidth=3, zeroline= True)
deaths_fig.update_yaxes(showgrid=False, gridcolor='grey', mirror=True, gridwidth=0.1, linecolor='grey', linewidth=3, zeroline= True)
state_fig = deaths_fig
else:
if 'main-choro.clickData' not in changed_id:
single_state = us.states.lookup(state_dropdown).abbr
else:
single_state = clickData['points'][0]['location']
states_historic_df = pd.read_csv(config['historic_loc'])
states_historic_df['dates_dt'] = pd.to_datetime(states_historic_df['date'], format='%Y%m%d')
states_historic_df = utils.date_filter(states_historic_df, date_choice)
perday_state = states_historic_df[states_historic_df['state'] == single_state]
title = "Death counts for {}".format(str(us.states.lookup(single_state)))
state_fig = px.bar(perday_state,
x='dates_dt',
y='deathIncrease',
labels={"deathIncrease":"Total Number of new deaths",
"dates_dt" : "Time"},
title=title)
state_fig.update_xaxes(nticks=20)
state_fig.update_layout(dict(paper_bgcolor=config['paper_color'],
plot_bgcolor=config['plot_color'],
font_color="white" ))
state_fig.update_traces(marker_color=config['bar_color'], marker_line_color=config['marker_line_color'])
state_fig.update_xaxes(nticks=20, showgrid=False, gridcolor='grey', mirror=True, gridwidth=1, linecolor='grey', linewidth=3, zeroline= True)
state_fig.update_yaxes(showgrid=False, gridcolor='grey', mirror=True, gridwidth=0.1, linecolor='grey', linewidth=3, zeroline= True)
return state_fig
def county_deaths(county_click, state_click, date_choice, county_dropdown):
changed_id = [p['prop_id'] for p in dash.callback_context.triggered][0]
if 'counties-dropdown.value' in changed_id:
single_county = county_dropdown
else:
single_county = county_click['points'][0]['location']
if state_click['points']:
single_state = state_click['points'][0]['location']
county_cum_cases, county_day_cases = utils.county_cases_deaths(single_county, single_state, cases=False)
county_day_cases = utils.date_filter(county_day_cases, date_choice)
figure = utils.plot_county_data(county_day_cases, single_county, single_state, cases=False, cumulative=False)
return figure
def render_confirmed(tab, date_choice):
overall_us_df = pd.read_csv(config['overall_loc'])
overall_us_df['dates_dt'] = pd.to_datetime(overall_us_df['date'], format='%Y%m%d')
overall_us_df = utils.date_filter(overall_us_df, date_choice)
if tab == 'tab-1':
dc_fig = px.bar(overall_us_df,
x='dates_dt',
y='positiveIncrease',
labels={"positiveIncrease":"Total Number of new cases",
"dates_dt" : "Time"},
title='New cases per day for US')
dc_fig.update_xaxes(nticks=20, showgrid=False, gridcolor='grey', mirror=True, gridwidth=1, linecolor='grey', linewidth=3, zeroline= True)
dc_fig.update_yaxes(showgrid=False, gridcolor='grey', mirror=True, gridwidth=0.1, linecolor='grey', linewidth=3, zeroline= True)
dc_fig.update_layout(dict(paper_bgcolor=config['paper_color'],
plot_bgcolor=config['plot_color'],
font_color="white" ))
dc_fig.update_traces(marker_color=config['bar_color'], marker_line_color=config['marker_line_color'])
return dc_fig
elif tab == 'tab-2':
dc_fig = px.scatter(overall_us_df,
x='dates_dt',
y='positive',
labels={"positive":"Cumulative total number of cases",
"dates_dt" : "Time"},
title='New cases per day for US')
dc_fig.update_xaxes(nticks=20)
dc_fig.update_traces(marker_color=config['bar_color'],
marker_line_color=config['marker_line_color'], marker_size=config['marker_size'])
dc_fig.update_xaxes(nticks=20, showgrid=True, gridcolor='grey', mirror=True, gridwidth=1, linecolor='grey', linewidth=3, zeroline= True)
dc_fig.update_yaxes(showgrid=True, gridcolor='grey', mirror=True, gridwidth=0.1, linecolor='grey', linewidth=3, zeroline= True)
dc_fig.update_layout(dict(paper_bgcolor=config['paper_color'],
plot_bgcolor=config['plot_color'],
font_color="white" ))
return dc_fig
incr_days = 30
diter = utils.date_iterator(first=start_date,
last=end_date,
incr_days=incr_days)
fulldf = pd.DataFrame()
prevfull = pd.DataFrame(
columns=['name', 'gempermid', 'pctchouthld', 'state', 'pct'])
prev_date = start_date - datetime.timedelta(incr_days)
evlist = []
for date in diter:
print(date)
datedf = pd.DataFrame()
newev = dict()
ev = utils.date_filter(df=events,
date_col="date",
start=prev_date,
end=date)
if ev.shape[0] == 0:
continue
ev.sort_values(by="date", inplace=True)
ev.drop_duplicates(subset=['name', 'gempermid'], inplace=True)
for idx, ser in ev.iterrows():
name = ser['name']
cid = ser['gempermid']
pct = ser['pctshouthld']
key = (name, cid)
newev = dict(name=name, gempermid=cid, pct=pct, date=date)
try:
temp = prevfull[np.logical_and(prevfull.name == name,
prevfull.gempermid == cid)]
lastev = (dict() if temp.shape[0] == 0 else
end_date = datetime.date(2018, 1, 1)
incr_days = 30
diter = utils.month_iterator(first=start_date, last=end_date)
lastdf = pd.DataFrame(columns= names + ["end_date"],
index = gids)
for name in names:
for gid in gids:
lastdf[name].ix[gid] = ("none_init", 0.0)
dflist = []
for start_date in diter:
print (start_date)
end_date = utils.add_months(start_date, n=1)
ev = utils.date_filter(df=events, date_col="date", start=start_date,
end=end_date)
if ev.shape[0] == 0:
continue
ev.sort_values(by="date", inplace=True)
ev.drop_duplicates(subset=['name', 'gempermid'], inplace=True)
thisdf = pd.DataFrame(columns=names + ["end_date"],
index=gids)
thisdf["end_date"] = end_date
for name in names:
for gid in gids:
prev_state, prev_pct = lastdf[name].ix[gid]
new_state = prev_state.split("_")[0]
new_state = new_state + "_unch"
thisdf[name].ix[gid] = (new_state, prev_pct)
