def build_graph(df, y_title, x_title, survivor_vals):
if y_title not in df.columns or x_title not in df.columns:
return None
cols = [x_title, y_title] + ["Survivors", "Country"]
pre_cols = cols + ["Study Pop Size (N)"]
post_cols = cols + ["Population"]
df = df[pre_cols]
df = df.dropna()
max_pop = max(df["Study Pop Size (N)"].values)
#round up the maximum number to the nearest hundred
max_pop = int(max_pop) + 100 - int(max_pop) % 100
buckets = [100, 500, 1000, 2000, max_pop]
df["Population"] = df["Study Pop Size (N)"].apply(
lambda x: graph_bucket(x, buckets))
df = df[post_cols]
df = df[df['Survivors'].isin(survivor_vals)]
colors = get_colors()
fig = go.Figure()
color_ind = {'Non-survivors only': 1, 'Survivors only': 4, 'Both': 2}
sizes = [5, 10, 20, 40, 60]
for i in df.Survivors.unique():
s = 0
lb = 0
for ind, j in enumerate(buckets):
pop_size = df[(df['Survivors'] == i)
& (df['Population'] == j)].shape[0]
if pop_size > 0:
fig.add_trace(
go.Scatter(
x=df[(df['Survivors'] == i)
& (df['Population'] == j)][x_title],
y=df[(df['Survivors'] == i)
& (df['Population'] == j)][y_title],
legendgroup=i,
name='{} <br> {} < Pop. Size ≤ {}'.format(
i, lb, str(int(j))),
mode="markers",
marker=dict(color=colors[color_ind[i]],
size=sizes[ind]),
text=df['Country'],
))
lb = j
s += 1
fig.update_layout(height=550,
title={
'text':
'<br>'.join(
wrap('<b> {} vs. {} </b>'.format(
x_title, y_title),
width=26)),
'y':
0.97,
'x':
0.5,
'xanchor':
'center',
'yanchor':
'top'
},
title_font_size=20,
xaxis={
'title': x_title,
'linecolor': 'lightgrey'
},
yaxis={
'title': "Percentage with " + y_title,
'linecolor': 'lightgrey'
},
legend_title='<b> Survivors <br> Population Size </b>',
margin={
'l': 40,
'b': 40,
't': 40,
'r': 10
},
hovermode='closest',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
legend={
"orientation": "h",
"xanchor": "center",
"y": -0.2,
"x": 0.5,
"itemsizing": "trace"
},
modebar={
'orientation': 'v',
'bgcolor': 'rgba(0,0,0,0)',
'color': 'lightgray',
'activecolor': 'gray'
})
graph = dcc.Graph(id='interactive-graph', figure=fig)
return graph
def build_state_projection(df_projections, state, country, continent, vals):
location = find_smallest_scope(state, country, continent)
df_projections_sub = df_projections.loc[ (df_projections.Province == state) & (df_projections.Country == country)]
if continent not in ['US', 'World']:
df_projections_sub = df_projections_sub.loc[(df_projections_sub.Continent == continent)]
if continent == 'US':
df_projections_sub = df_projections.loc[(df_projections.Country == 'US') & (df_projections.Province == state)]
if continent == 'World':
if country =='None':
df_projections_sub = df_projections.loc[(df_projections.Continent == 'None')] #include only global world data
fig = go.Figure()
cols = get_cols()
if (vals is not None) and (set(vals).issubset(set(cols))):
colors = get_colors()
for val in vals:
i = cols[val]
fig.add_trace(go.Scatter(
name=val,
showlegend=True,
x=df_projections_sub['Day'],
y=df_projections_sub[val].values,
mode="lines+markers",
marker=dict(color=colors[i]),
line=dict(color=colors[i])
))
title = '<br>'.join(wrap('<b> Projections for {} </b>'.format(location), width=26))
fig.update_layout(
height=550,
title={
'text': title,
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'},
title_font_size=25,
xaxis={'title': "Date",'linecolor': 'lightgrey'},
yaxis={'title': "Count",'linecolor': 'lightgrey'},
legend_title='<b> Values Predicted </b>',
margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
hovermode='closest',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
legend={
"orientation": "h",
"xanchor": "center",
"y": -0.2,
"x": 0.5
},
modebar={
'orientation': 'v',
'bgcolor': 'rgba(0,0,0,0)',
'color': 'lightgray',
'activecolor': 'gray'
}
)
graph = dcc.Graph(
id='projection-graph',
figure=fig
)
return graph
def us_timeline(df, title, with_opt):
fig = go.Figure()
colors = get_colors()
if with_opt:
# we want to keep the baseline shortage the same color as the prev. graph
# and add a new color for the new shortage
col_ind = [0, 3]
c = 0
i = 0
for val in df.columns:
if val != "Date":
fig.add_trace(
go.Scatter(x=df['Date'],
y=df[val].values,
legendgroup=i,
name=val.replace(' ', '<br>'),
mode="lines+markers",
marker=dict(color=colors[col_ind[c]]),
line=dict(color=colors[col_ind[c]])))
c += 1
i += 1
else:
state_cols = ["Shortage", "Supply", "Demand"]
no_model_visual = get_no_model_visual()
for i, val in enumerate(state_cols):
fig.add_trace(
go.Scatter(x=df['Date'],
y=df[val].values,
legendgroup=i,
name=no_model_visual[val].replace(' ', '<br>'),
mode="lines+markers",
marker=dict(color=colors[i]),
line=dict(color=colors[i])))
i += 1
fig.update_layout(height=550,
title={
'text':
'<br>'.join(
wrap(''.join(['<b> ', title, ' </b>']),
width=30)),
'y':
0.96,
'x':
0.5,
'xanchor':
'center',
'yanchor':
'top'
},
title_font_size=20,
xaxis={
'title': "Date",
'linecolor': 'lightgrey'
},
yaxis={
'title': "Count",
'linecolor': 'lightgrey'
},
legend_title='<b> Values Predicted </b>',
margin={
'l': 40,
'b': 40,
't': 40,
'r': 10
},
hovermode='closest',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
legend={
"orientation": "h",
"xanchor": "center",
"y": -0.2,
"x": 0.5
},
modebar={
'orientation': 'v',
'bgcolor': 'rgba(0,0,0,0)',
'color': 'lightgray',
'activecolor': 'gray'
})
graph = dcc.Graph(id='projection-graph-vent', figure=fig)
return graph