def test_graph_stacked(self):
date = datetime.datetime(2020, 1, 1)
reg1 = 1
reg2 = 3
d = {
'dates': [],
'region1': [],
'region2': [],
}
for i in range(15):
date += datetime.timedelta(days=1)
d['dates'].append(date)
reg1 += 1
reg2 += 2
d['region1'].append(reg1)
d['region2'].append(reg2)
p = common.graph_stacked(data=d,
start=1,
plot_height=450,
line_width=10)
def test_graph_stacked_date(self):
date = datetime.datetime(2020, 1, 1)
reg1 = 1
reg2 = 3
d = {
'dates': [],
'region1': [],
'region2': [],
}
for i in range(15):
date += datetime.timedelta(days=1)
d['dates'].append(date)
reg1 += 1
reg2 += 2
d['region1'].append(reg1)
d['region2'].append(reg2)
d['dates'] = [x.date() for x in d['dates']]
p = common.graph_stacked(data=d,
start=1,
plot_height=450,
line_width=10,
colors=['Crimson', 'LightGreen', 'Salmon'])
def make_state_graphs(verbose = False, plot_height = 400, plot_width = 400,
window = None):
state_pop = get_state_pop()
if not window:
window = int(variables.values['by_state_window'])
if not os.path.isdir('html_temp'):
os.mkdir('html_temp')
df_day = get_state_data_day()
change_dict = change_with_sig(df_day, state_pop)
df_deaths = get_data_deaths()
df_cases = get_data_cases()
df_day = get_state_data_day()
df_poisson = get_poisson_data()
dir_path = make_territories_dir('state')
df_hospital = pd.read_csv('data/hospital.csv')
df_hospital['date'] = pd.to_datetime(df_hospital['date'])
for state in set(df_deaths['state']):
if verbose:
print('working on {state}'.format(state = state))
ps = []
for the_info in [
(df_deaths, 'deaths', df_day, 'deaths',
'Deaths by Week'.format(w = window),
'Deaths by Day ({w} day mean)'.format(w = window)),
(df_cases, 'cases', df_day, 'cases',
'Cases by Week',
'Cases by Day ({w} day mean)'.format(w = window))]:
df = the_info[0]
df_day_ = the_info[2]
rt_death, rt_death2 = common.get_rt(df_day_[df_day_['state'] == state]['deaths'], 7, 7)
rt_cases, rt_cases2 = common.get_rt(df_day_[df_day_['state'] == state]['cases'], 7, 7)
the_dict = {'date': sorted(list(set(df['date'].tolist())))}
for i in range(1,5):
shape_data(df, state, i, the_dict, key = the_info[1],
)
if state in ['Northern Mariana Islands', 'Virgin Islands']:
continue
p_poisson = make_state_graph(df = df_poisson, df2 = df_day_, state = state)
p_hospital = make_hospital_graph(df_hospital, state = state)
the_dict = _trim_data(the_dict)
y = df_day_[df_day_['state'] == state][the_info[1]].rolling(window).mean()
first = _get_first_nonzero(y)
y = y[first:]
x = df_day_[df_day_['state'] == state]['date']
x = x[first:]
p = common.graph_stacked(data = the_dict, start = 0,
plot_height = plot_height,plot_width = plot_width ,
line_width = 10, title = the_info[4] )
p_day = common.incidents_over_time_bar2(
x = x,
y = y,
plot_height = plot_height,
line_width = 3,
plot_width = plot_width, title = the_info[5])
ps.append(p)
ps.append(p_day)
if the_info[1] == 'cases':
ps.append(p_poisson)
ps.append(p_hospital)
grid = gridplot(ps, ncols = 2)
script, div = components(grid)
html = get_html(territory = state, script = script, div = div,
last_week_deaths = change_dict[state]['last_week_mean'],
curr_death = change_dict[state]['current_week_mean'],
p_curr_week = change_dict[state]['p_value_last_week'],
p_last_week = change_dict[state]['p_value_last_week2'],
cur_week_per_million = change_dict[state]['current_week_per_million'],
last_week_per_million = change_dict[state]['last_week_per_million'],
)
with open(os.path.join(dir_path,
'{territory}'.format(territory = slugify(state))), 'w') as write_obj:
write_obj.write(html)
make_territories_ref_list('state', list(set(df_day['state'])))
make_wa()
def make_state_graphs(verbose=False, plot_height=400, plot_width=400):
if not os.path.isdir('html_temp'):
os.mkdir('html_temp')
date = datetime.datetime.now()
df_deaths = get_data_deaths()
df_cases = get_data_cases()
df_day = get_state_data_day()
dir_path = make_territories_dir('state')
for state in set(df_deaths['state']):
if verbose:
print('working on {state}'.format(state=state))
ps = []
for the_info in [(
df_deaths,
'death',
df_day,
'deaths',
'Deaths by Week',
'Deaths by Day',
), (df_cases, 'cases', df_day, 'cases', 'Cases by Week',
'Cases by Day')]:
df = the_info[0]
df_day_ = the_info[2]
rt_death, rt_death2 = common.get_rt(
df_day_[df_day_['state'] == state]['deaths'], 7, 7)
rt_cases, rt_cases2 = common.get_rt(
df_day_[df_day_['state'] == state]['cases'], 7, 7)
the_dict = {'dates': sorted(list(set(df['dates'].tolist())))}
for i in range(1, 5):
shape_data(
df,
state,
i,
the_dict,
key=the_info[1],
)
the_dict = _trim_data(the_dict)
p = common.graph_stacked(data=the_dict,
start=0,
plot_height=plot_height,
plot_width=plot_width,
line_width=10,
title=the_info[4])
p_day = common.incidents_over_time_bar(
df_day_[df_day_['state'] == state],
key=the_info[3],
window=3,
plot_height=plot_height,
plot_width=plot_width,
title=the_info[5],
line_width=2)
ps.append(p)
ps.append(p_day)
grid = gridplot(ps, ncols=2)
script, div = components(grid)
html = get_html(territory=state,
script=script,
div=div,
date=date,
rt_cases=rt_cases,
rt_death=rt_death)
tt = '{territory}'.format(territory=common.tidy_name(state)) + '.html'
with open(os.path.join(dir_path, tt), 'w') as write_obj:
write_obj.write(html)
make_territories_ref_list('state', list(set(df_day['state'])))