if 'DEBUG' in os.environ:
debug = os.environ['DEBUG'] == 'True'
print(f"DEBUG environment variable present, DEBUG set to {debug}")
else:
print("No DEBUG environment variable: defaulting to debug mode")
debug = True
# -------- Data --------------------------
df, df_prediction = get_all_data()
df_tidy = tidy_most_recent(
df) # most recent date, tidy format (one column for countries)
df_tidy_table = df_tidy[['country_region',
'value']] # keep only two columns for Dash DataTable
# ----------- Figures ---------------------
fig1 = make_map(df_tidy)
fig2 = make_timeplot(df, df_prediction)
# ------------ Markdown text ---------------
# maybe later we can break the text in several parts
with open("text_block.md", "r") as f:
intro_md = f.read()
# -----------App definition-----------------------
app = dash.Dash(
__name__,
external_stylesheets=[
{
'href': 'https://unpkg.com/[email protected]/build/pure-min.css',
'rel': 'stylesheet',
'integrity':
# most recent date, tidy format (one column for countries)
df_tidy = tidy_most_recent(df)
df_tidy_fatalities = tidy_most_recent(df, 'death')
df_tidy_recovered = tidy_most_recent(df, 'recovered')
# keep only two columns for Dash DataTable
df_tidy_table = df_tidy[['country_region', 'value']]
df_tidy_table = df_tidy_table.reset_index()
# The indices initially displayed
initial_indices = list(df_tidy_table['value'].nlargest(2).index)
# We hardcode the second index shown as being China, to give a message of
# hope
initial_indices[-1] = np.where(df_tidy['iso'] == 'CHN')[0][0]
# ----------- Figures ---------------------
fig1 = make_map(df_tidy, df_tidy_fatalities, df_tidy_recovered)
fig2 = make_timeplot(df, df_prediction)
# ------------ Markdown text ---------------
# maybe later we can break the text in several parts
with open("text_block.md", "r") as f:
intro_md = f.read()
# -----------App definition-----------------------
app = dash.Dash(__name__,
external_stylesheets = [
{
'href': 'https://unpkg.com/[email protected]/build/pure-min.css',
'rel': 'stylesheet',
'integrity': 'sha384-oAOxQR6DkCoMliIh8yFnu25d7Eq/PHS21PClpwjOTeU2jRSq11vu66rf90/cZr47',
import dash
from dash.dependencies import Input, Output, State, ClientsideFunction
import dash_table
import dash_html_components as html
import dash_core_components as dcc
from make_figures import make_map, make_timeplot
from data_input import get_data, get_mapping, tidy_most_recent
# Data
df = get_data()
mapping = get_mapping()
df_tidy = tidy_most_recent(df)
df_tidy_table = df_tidy[['country_region', 'value']]
# Figures
fig1 = make_map(df_tidy, mapping)
fig2 = make_timeplot(df)
# Markdown text
with open("text_block.md", "r") as f:
intro_md = f.read()
app = dash.Dash(__name__)
server = app.server
app.layout = html.Div(
[
html.Div(
[ #row
html.Div([dcc.Graph(id='map', figure=fig1)],
className="seven columns"),
df_tidy_fatalities = tidy_most_recent(df, 'death')
# keep only two columns for Dash DataTable
df_tidy_table = df_tidy[['country_region', 'value']]
df_tidy_table = df_tidy_table.reset_index()
# The indices initially displayed
initial_indices = list(df_tidy_table['value'].nlargest(3).index)
# We hardcode the second and third index shown as being China, and Korea
# to give a message of hope
# Not China so far, as it is still the top on in terms of numbers of
# total confirmed cases
#initial_indices[-1] = np.where(df_tidy['iso'] == 'CHN')[0][0]
initial_indices[-2] = np.where(df_tidy['iso'] == 'KOR')[0][0]
# ----------- Figures ---------------------
fig1 = make_map(df_tidy, df_tidy_fatalities)
fig2 = make_timeplot(df, df_prediction, countries=['France', 'Italy', 'Spain'])
fig_store = make_timeplot(df, df_prediction)
# ------------ Markdown text ---------------
# maybe later we can break the text in several parts
with open("text_block.md", "r") as f:
intro_md = f.read()
# -----------App definition-----------------------
app = dash.Dash(
__name__,
external_stylesheets=[
{
'href': 'https://unpkg.com/[email protected]/build/pure-min.css',
'rel': 'stylesheet',