def update_ts_graph(zipcode,
radius,
date_window_option='ALL',
region_of_interest=[]):
print('zipcode: {}'.format(zipcode))
if len(zipcode) == 5:
radius = float(radius)
recs = geoSvr.search_by_zipcode('Confirmed', zipcode,
radius) # list of (county,state)
region_options = ['{}, {}'.format(r[0], r[1]) for r in recs]
# region_defaults = ['{}, {}'.format(r[0],r[1]) for r in recs]
ds = CSBS()
dt_range = ds.date_range_str(date_window_option)
if region_of_interest is None:
region_of_interest = []
# print('len of region_options:{}, of region_of_interest:{}'.format(region_options is None, region_of_interest is None))
if set(region_of_interest).isdisjoint(region_options):
region_of_interest = region_options
confirmed = ds.refresh_county_state_category('Confirmed',
date_window_option,
region_of_interest)
deaths = ds.refresh_county_state_category('Deaths', date_window_option,
region_of_interest)
return plot_ts_figure(confirmed, 'Confirmed', dt_range), \
plot_ts_figure(deaths, 'Deaths', dt_range), \
plot_inc_number(confirmed, 'Confirmed', dt_range), \
plot_inc_number(deaths, 'Deaths', dt_range), \
plot_increase(confirmed, 'Confirmed', dt_range), \
plot_increase(deaths, 'Deaths', dt_range), \
'Time Window:{}'.format(dt_range), \
[{'label': x, 'value': x} for x in region_options]
def load_region_options():
ds = CSBS()
ds.dataSet['Confirmed'] = ds.dataSet['Confirmed'].fillna(0)
ds.dataSet['Deaths'] = ds.dataSet['Deaths'].fillna(0)
# data_list_confirmed, data_list_deaths, data_list_recovered, date_list, region_of_interest = utl.load_data_2()
region_of_interest = ds.regions()
region_options = [{'label': x, 'value': x} for x in region_of_interest]
return region_options
def load_options():
ds = CSBS()
# data_list_confirmed, data_list_deaths, data_list_recovered, date_list, region_of_interest = utl.load_data_2()
region_of_interest = ds.regions()
options = [{'label': x, 'value': x} for x in region_of_interest]
defaults = region_of_interest[:7]
return options, defaults
def __init__(self):
search = SearchEngine()
self.defaultZipcodeInfo = search.by_zipcode('22030')
self.defaultRadius = 70
self.ds = CSBS()
print('.... geoClass Initialized, id(self.ds):{}'.format(id(self.ds)))
def update_graph(date_window_option, region_of_interest):
ds = CSBS()
dt_range = ds.date_range_str(date_window_option)
confirmed = ds.refresh_category('Confirmed', date_window_option,
region_of_interest)
deaths = ds.refresh_category('Deaths', date_window_option,
region_of_interest)
return plot_figure(confirmed, 'Confirmed', dt_range), \
plot_figure(deaths, 'Deaths', dt_range), \
plot_inc_number(confirmed, 'Confirmed', dt_range), \
plot_inc_number(deaths, 'Deaths', dt_range), \
plot_increase(ds, 'Confirmed', region_of_interest), \
plot_increase(ds, 'Deaths', region_of_interest), \
'Time Window:{}'.format(dt_range)
# tmp_y = us_cases[i:i+window_size]
# xs = np.array(tmp_x, dtype=np.float64)
# ys = np.array(tmp_y, dtype=np.float64)
# mean_y = np.average(ys)
# slopes.append(best_fit_slope(xs,ys)/mean_y if mean_y>0 else 0)
# five_days.append(xs[-1])
# values.append(ys)
# # df_5d = pd.DataFrame({'fivedate':five_days,'slope':slopes})
# country_slopes.append({'Country':country, 'five-date':five_days,'slope':slopes,'value':values})
ax = sns.lineplot(x=country_slopes[0]['five-date'],
y=country_slopes[0]['slope'])
plt.show()
ds = CSBS()
df_ds = ds.dataSet['Confirmed']
df_ds['county_state'] = df_ds['County_Name'] + ', ' + df_ds['State_Name']
county_list = df_ds['county_state'].unique().tolist()
county_slopes = get_slopes(df_ds,
column_name='county_state',
patten='2020-',
strp='%Y-%m-%d')
s = 2
ax = sns.lineplot(x=county_slopes[s]['five-date'], y=county_slopes[s]['slope'])
####################################
N = 100000
G = nx.fast_gnp_random_graph(N, 25. / (N - 1))
# they will vary in the rate of leaving exposed class.