def build_transfers_timeline(df_opt_pre, df_opt_post, p1, p2, p3):
df_opt_post.loc[:, 'Date'] = pd.to_datetime(df_opt_post['Date'],
format='y%m%d').dt.date
timeline_cols = ["Date", "Shortage"]
df_opt_pre = df_opt_pre.loc[df_opt_pre.State == 'US']
df_opt_pre = df_opt_pre[timeline_cols]
no_model_visual = get_no_model_visual()
model_visual = get_model_visual()
df_opt_pre.columns = ["Date", no_model_visual["Shortage"]]
df_opt_post = df_opt_post.loc[
(df_opt_post.State == 'US') & \
(df_opt_post.Param1==float(p1)) & \
(df_opt_post.Param2==float(p2)) & \
(df_opt_post.Param3==float(p3))
]
df_opt_post = df_opt_post[timeline_cols]
df_opt_post.columns = ["Date", model_visual["Shortage"]]
df_opt_effect = pd.merge(df_opt_pre, df_opt_post, on='Date', how='inner')
return us_timeline(df_opt_effect, "Optimization Effect on Shortage", True)
def build_transfers_timeline(chosen_model,p1,p2,p3):
if chosen_model == "Washington IHME":
df_opt_pre, _ = get_df_mod1_projections()
df_opt_post = pd.read_csv('data/predicted_ventilator/state_supplies_table-ihme.csv', sep=",", parse_dates = ['Date'])
else:
df_opt_pre = get_df_mod2_projections()
df_opt_post = pd.read_csv('data/predicted_ventilator/state_supplies_table-ode.csv', sep=",", parse_dates = ['Date'])
df_opt_post.loc[:,'Date'] = pd.to_datetime(df_opt_post['Date'], format='y%m%d').dt.date
timeline_cols = ["Date","Shortage"]
df_opt_pre = df_opt_pre.loc[df_opt_pre.State == 'US']
df_opt_pre = df_opt_pre[timeline_cols]
no_model_visual = get_no_model_visual()
model_visual = get_model_visual()
df_opt_pre.columns = ["Date",no_model_visual["Shortage"]]
df_opt_post = df_opt_post.loc[
(df_opt_post.State == 'US') & \
(df_opt_post.Param1==float(p1)) & \
(df_opt_post.Param2==float(p2)) & \
(df_opt_post.Param3==float(p3))
]
df_opt_post = df_opt_post[timeline_cols]
df_opt_post.columns = ["Date",model_visual["Shortage"]]
df_opt_effect = pd.merge(df_opt_pre,df_opt_post,on='Date',how='inner')
return us_timeline(df_opt_effect,"Optimization Effect on Shortage",True)
def build_shortage_map(chosen_model, chosen_date, val):
if chosen_model == "Washington IHME":
df_map, _ = get_df_mod1_projections()
else:
df_map = get_df_mod2_projections()
no_model_visual = get_no_model_visual()
return us_map(df_map, chosen_date, val, no_model_visual)
def update_shortage_map(chosen_model,chosen_date,val):
if chosen_model == "Washington IHME":
df_map = df_mod1_projections
else:
df_map = df_mod2_projections
no_model_visual = get_no_model_visual()
return us_map(df_map,chosen_date,val,no_model_visual)
def get_shortage():
firstDate = get_first_date()
_, params = get_df_mod1_projections(True)
state_cols = ["Shortage", "Supply", "Demand"]
no_model_visual = get_no_model_visual()
# Defines the top of the page, where the current situation is displayed (no optimization yet)
shortage = \
[dbc.Row(
[
dbc.Col(
[
dbc.Alert("The total ventilator supply in the US exceeds projected demand, \
but imbalanced demand from local 'hot spots' leads to shortages." , color="danger"),
]
)
]
)] + \
[dbc.Row(
[
dbc.Col(
[
html.Div(
id = 'us_ventilator_graph',
children = [],
style={
'width': '100%',
'display': 'inline-block',
}
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
dbc.Col(
[
html.Div(
id = 'us_map_projections_vent',
children = [],
style={
'width': '100%',
'display': 'inline-block',
}
),
dbc.Row(
[
dbc.Col(
[
html.H6('Date:',id="date-projections"),
html.Div(
dcc.DatePickerSingle(
id='us-map-date-picker-range-vent',
min_date_allowed=params[0],
max_date_allowed=params[1],
date=firstDate,
initial_visible_month=firstDate,
style={'marginBottom':20}
),
id="date-projections-picker-div"
),
],
xs=12,
sm=4,
md='auto',
lg='auto',
),
dbc.Col(
[
html.H6('Plotted Value:',id="date-projections"),
html.Div(
dcc.Dropdown(
id = 'us_map_dropdown-vent',
options = [{'label': no_model_visual[x], 'value': x} for x in state_cols],
value = 'Shortage',
style={'marginBottom':20}
),
),
],
xs=12,
sm=8,
md=True,
lg=True,
)
]
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
]
)
]
return shortage
def get_shortage():
df = pd.read_csv(
'data/predicted_ventilator/state_supplies_table_baseline-ihme.csv',
sep=",",
parse_dates=['Date'])
df.loc[:, 'Date'] = pd.to_datetime(df['Date'], format='y%m%d').dt.date
min_shortage_date = min(df.Date.values)
max_shortage_date = max(df.Date.values)
del df
firstDate = get_first_date()
state_cols = ["Shortage", "Supply", "Demand"]
no_model_visual = get_no_model_visual()
# Defines the top of the page, where the current situation is displayed (no optimization yet)
shortage = \
[dbc.Row(
[
dbc.Col(
[
dbc.Alert("The total ventilator supply in the US exceeds projected demand, \
but imbalanced demand from local 'hot spots' leads to shortages." , color="danger"),
]
)
]
)] + \
[dbc.Row(
[
dbc.Col(
[
html.Div(
id = 'us_ventilator_graph',
children = [],
style={
'width': '100%',
'display': 'inline-block',
}
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
dbc.Col(
[
html.Div(
id = 'us_map_projections_vent',
children = [],
style={
'width': '100%',
'display': 'inline-block',
}
),
dbc.Row(
[
dbc.Col(
[
html.H6('Date:',id="date-projections"),
html.Div(
dcc.DatePickerSingle(
id='us-map-date-picker-range-vent',
min_date_allowed=min_shortage_date,
max_date_allowed=max_shortage_date,
date=firstDate,
initial_visible_month=firstDate,
style={'marginBottom':20}
),
id="date-projections-picker-div"
),
],
xs=12,
sm=4,
md='auto',
lg='auto',
),
dbc.Col(
[
html.H6('Plotted Value:',id="date-projections"),
html.Div(
dcc.Dropdown(
id = 'us_map_dropdown-vent',
options = [{'label': no_model_visual[x], 'value': x} for x in state_cols],
value = 'Shortage',
style={'marginBottom':20}
),
),
],
xs=12,
sm=8,
md=True,
lg=True,
)
]
),
],
xs=12,
sm=12,
md=6,
lg=6,
),
]
)
]
return shortage