def plotdata():
try:
country = data.get()
country = country.strip()
country = country.replace(" ", ",").split(",")
countries = []
for x in country:
countries.append(x)
df = pd.read_csv(
'https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv',
parse_dates=['Date'])
df = df[df['Country'].isin(countries)]
#Creating a Summary Column
df['Cases'] = df[['Confirmed', 'Recovered', 'Deaths']].sum(axis=1)
#Restructuring our Data
df = df.pivot(index='Date', columns='Country', values='Cases')
countries = list(df.columns)
covid = df.reset_index('Date')
covid.set_index(['Date'], inplace=True)
covid.columns = countries
#Calculating Rates per 100,000
populations = {}
for i in countries:
c = CountryInfo(i)
populations[i] = c.population()
percap = covid.copy()
for country in list(percap.columns):
percap[country] = percap[country] / populations[country] * 100000
#Generating Colours
colors = {}
k = 0
col = ['#045275', '#089099', '#DC3977', '#7C1D6F', '#7CCBA2']
for i in countries:
colors[i] = col[k]
k += 1
plt.style.use('fivethirtyeight')
percapplot = percap.plot(figsize=(12, 8),
color=list(colors.values()),
linewidth=5,
legend=False)
percapplot.grid(color='#d4d4d4')
percapplot.set_xlabel('Month', color='brown')
percapplot.set_ylabel('# of Cases per 100,000 People', color='brown')
for country in list(colors.keys()):
percapplot.text(x=percap.index[-1],
y=percap[country].max(),
color=colors[country],
s=country,
weight='bold')
percapplot.text(
x=percap.index[2],
y=percap.max().max() + 45,
s=
"Covid-19 Comparison Graph Between Countries \nIncludes Current Cases, Recoveries, and Deaths",
fontsize=18,
color='brown',
alpha=.75)
plt.show()
except Exception as e:
print("Country data not present ")
def getPopulation(country):
try:
country = CountryInfo(country)
popoulation = country.population()
except:
popoulation = 10000000
return popoulation
def population(message):
message = message.replace('!population ', '')
message = message.replace('!population', '')
if message == '':
return 'Please specify country.'
else:
countryinfo = CountryInfo(message)
message = message.replace(message[0], message[0].upper())
message = message.replace(message[1:], message[1:].lower())
return "The population of " + message + " is " + str(
countryinfo.population() + ".")
def get_country_population_and_continent(country: str, ) -> Tuple[int, str]:
try:
country_translated = (country if country
not in COUNTRIES_TRANSLATE_TO_COUNTRYINFO.keys()
else COUNTRIES_TRANSLATE_TO_COUNTRYINFO[country])
country_translated_info = CountryInfo(country_translated)
population = country_translated_info.population()
continent = country_translated_info.region()
except KeyError:
population, continent = -1, FILLNA
return population, continent
def country_info(country_name):
d = []
country = CountryInfo(country_name)
d.append(["name", country.name().capitalize()])
d.append(["capital", country.capital().capitalize()])
d.append(["region", country.region().capitalize()])
d.append(["currency", country.currencies()])
d.append(["area", country.area()])
d.append(["population", country.population()])
d.append(["languages", country.languages()])
d.append(["borders", country.borders()])
d.append(["calling code", country.calling_codes()])
d.append(["lat/long", country.capital_latlng()])
d.append(["code", country.iso(2)])
return d
def main():
try:
start_date = date(2020, 3, 1)
end_date = date(2020, 10, 2)
delta = timedelta(days=1)
while start_date <= end_date:
day = start_date.strftime("%Y-%m-%d")
print ("Downloading " + day)
url = "https://api.covid19tracking.narrativa.com/api/" + day
r = requests.get(url)
data = r.json()
start_date += delta
for day in data['dates']:
for country in data['dates'][day]['countries']:
try:
country_info = CountryInfo(country)
country_iso_3 = country_info.iso(3)
population = country_info.population()
except Exception as e:
print("Error with " + country)
country_iso_3 = country
population = None
infection_rate=0
print(e)
if population != None:
try:
infection_rate=getInfectionRate(data['dates'][day]['countries'][country]['today_confirmed'], population)
except:
infection_rate=0
result_data = data['dates'][day]['countries'][country]
del result_data['regions']
result_data['timestamp'] = result_data.pop('date')
result_data.update(
timestamp=datetime.strptime(day, "%Y-%m-%d"),
country_iso_3=country_iso_3,
population=population,
infection_rate=infection_rate,
)
save_elasticsearch_es('covid-19-live-global',result_data)
except Exception as e:
print(e)
def getVariableBetaForecast(df_location,
location,
x_days,
forecast_size,
mortality_rate = 0.01,
disease_duration = 14):
location_key = location.lower().replace(' ','_')
country = CountryInfo(location)
population = country.population()
x_num = df_location[location_key].iloc[0]
df_location = df_location[location_key]
num_days = df_location.shape[0]
full_t = np.linspace(0, num_days, num_days)
full_loc = df_location
r0_list = []
t, best_D, best_I, best_R, best_r0 = getBestFit(df_location.head(forecast_size),
x_num,
population,
mortality_rate = mortality_rate,
disease_duration = disease_duration)
full_D = best_D
full_I = best_I
r0_list.append(best_r0)
remain_days = num_days-forecast_size
while remain_days > 2*forecast_size:
df_location = df_location.tail(remain_days+1)
print_bool = False
if (remain_days-forecast_size + 1) < 2*forecast_size:
print ("Best I = ", best_I.iloc[-1])
print ("Best R = ", best_R.iloc[-1])
print_bool = True
t, best_D, best_I, best_R, best_r0 = getBestFit(df_location.head(forecast_size),
x_num,
population,
mortality_rate = mortality_rate,
disease_duration = disease_duration,
first_pass=False,
I_in=best_I.iloc[-1],
R_in=best_R.iloc[-1],
print_bool=print_bool)
full_D = pd.concat([full_D, best_D.tail(forecast_size-1)], ignore_index=True,axis=0)
full_I = pd.concat([full_I, best_I.tail(forecast_size-1)], ignore_index=True,axis=0)
r0_list.append(best_r0)
if (remain_days-forecast_size + 1) < 2*forecast_size:
print (df_location.head(forecast_size))
print (full_D)
print (full_I)
remain_days = remain_days-forecast_size + 1
df_location = df_location.tail(remain_days+1)
t, best_D, best_I, best_R, best_r0 = getBestFit(df_location,
x_num,
population,
mortality_rate = mortality_rate,
disease_duration = disease_duration,
first_pass=False,
I_in=best_I.iloc[-1],
R_in=best_R.iloc[-1],
x_days=x_days)
full_D = pd.concat([full_D, best_D.tail(-1)], ignore_index=True,axis=0)
full_I = pd.concat([full_I, best_I.tail(-1)], ignore_index=True,axis=0)
r0_list.append(best_r0)
return full_D, full_I, r0_list
for x in range(options[category_index]):
country_choice = CountryInfo(country_pool[x])
if category == "area":
print('(' + choices[x] + ') ' + str(country_choice.area()) +
" sq kilometers")
elif category == "subregion":
print('(' + choices[x] + ') ' + subregion_pool[x])
elif category == "calling code":
print('(' + choices[x] + ') ' +
str(country_choice.calling_codes()[0]))
elif category == "population":
print('(' + choices[x] + ') ' + str(country_choice.population()))
elif category == "capital":
print('(' + choices[x] + ') ' + country_choice.capital())
elif category == "currency":
print('(' + choices[x] + ') ' + currencies_pool[x])
elif category == "latitude":
print('(' + choices[x] + ') ' + str(country_choice.latlng()[0]))
elif category == "longitude":
print('(' + choices[x] + ') ' + str(country_choice.latlng()[1]))
answer_user = input("\nPlease choose an option: ")
while answer_user not in choices[0:options[category_index]]:
def get_population_by_country(country):
country = CountryInfo(country)
return country.population()
def getMobilityPlots(locations, data_type, shift_days, smoothing_range,
mobility_type, pop_type, location_num):
timeline = 'Days since X number of deaths/cases'
x_num = 10
if len(locations) <= location_num:
return None
else:
mob_loc_df = getGoogleMobilityReportsFromCountry(
locations[location_num])
#print (mob_reports_df.head(10))
#mob_loc_df = mob_reports_df[mob_reports_df['sub_region_1'].isnull()]
if data_type == 'Deaths':
df = getNewDeaths()
df_total = getTotalDeaths()
elif data_type == 'Cases':
df = getNewCases()
df_total = getTotalCases()
else:
return None
data = []
if not x_num: x_num = 0
location = locations[location_num]
location_key = location.lower().replace(' ', '_')
if pop_type == 'Per Million':
country = CountryInfo(location)
population = country.population()
area = country.area()
pop_density = float(population) / area
print(population)
print(area)
print(pop_density)
df[location_key] = 1000000.0 * df[location_key] / population
else:
population = 1.0
if timeline == 'Days since X number of deaths/cases':
df_total_loc = df_total.fillna(0)
df_total_loc['date'] = df_total_loc['date'] - datetime.timedelta(
days=shift_days)
df_total_loc = df_total_loc.drop(
df_total_loc[(df_total_loc[location_key] < x_num)].index)
df_total_loc = df_total_loc.reset_index()
df_total_loc = df_total_loc.drop([location_key], axis=1)
df[location_key] = df[location_key].fillna(0).rolling(
smoothing_range).mean()
df_loc = df[['date', location_key]].copy()
df_loc['date'] = df_loc['date'] - datetime.timedelta(
days=shift_days)
df_loc = pd.merge(df_total_loc, df_loc, how='left', on='date')
#mob_loc_df = mob_reports_df[mob_reports_df['country_region']==location]
print(mob_loc_df.info())
#print (mob_loc_df['sub_region_1'])
#mob_loc_df = mob_loc_df[mob_loc_df['sub_region_1'].isnull()]
mob_loc_df['date'] = pd.to_datetime(mob_loc_df.date)
mob_loc_df.sort_values(by=['date'])
mob_loc_df[mobility_type] = mob_loc_df[mobility_type].fillna(
0).rolling(smoothing_range).mean()
country = pycountry.countries.get(name=location).alpha_3
stringency_df = getStringencyDataFrame(country)
stringency_df['date'] = pd.to_datetime(stringency_df.date)
stringency_df.sort_values(by=['date'])
df_loc = pd.merge(df_loc, mob_loc_df, how='left', on='date')
df_loc = pd.merge(df_loc, stringency_df, how='left', on='date')
if mobility_type != 'residential_percent_change_from_baseline':
df_loc[mobility_type] = -df_loc[mobility_type]
mobility_name = '% Time less at ' + mobility_dict[mobility_type]
else:
mobility_name = '% Time more at ' + mobility_dict[mobility_type]
#print (df_loc[mobility_type])
data.append(
go.Bar(
x=df_loc.index,
y=df_loc[location_key],
marker=dict(color=colour_palette[location_num], ),
opacity=0.7,
text=location,
#name=location + ' ' + data_type,
yaxis='y2',
showlegend=False))
data.append(
go.Scatter(x=df_loc.index,
y=df_loc['stringency'],
mode='lines',
marker=dict(color='rgb(0,0,0)', ),
line=dict(color='rgb(0,0,0)', dash='dot'),
opacity=1.0,
text=location,
name='Stringency Index',
yaxis='y1',
showlegend=True))
data.append(
go.Scatter(x=df_loc.index,
y=df_loc[mobility_type],
mode='lines',
marker=dict(color='rgb(0,0,0)', ),
line=dict(color='rgb(0,0,0)', dash='solid'),
opacity=1.0,
text=location,
name=mobility_name,
yaxis='y1',
showlegend=True))
else:
return None
if mobility_type == 'residential_percent_change_from_baseline':
y_range = [0, 100]
else:
y_range = [0, 100]
if pop_type == "Per Million":
title_sub = ' (per million)'
else:
title_sub = ''
figure = {
'data':
data,
'layout':
go.Layout(
title={
'text': location,
'y': 1.0,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'
},
showlegend=True, #show_legend,
legend=dict(
orientation="h",
x=0,
y=1.1,
font=dict(family="Arial", size=14, color="#000000"),
),
font=dict(family='Arial', size=12, color='#000000'),
hovermode='closest',
margin=dict(t=50),
xaxis=dict(tickfont=dict(family='Arial',
size=14,
color='#000000'), ),
yaxis2=dict(
#range=[0,1200],
rangemode="tozero",
tickfont=dict(family='Arial', size=14, color='#000000'),
side='left',
title=data_type + title_sub,
showgrid=False),
yaxis=dict(
#autorange='reversed',
range=y_range,
rangemode='tozero',
anchor='x',
overlaying='y',
tickfont=dict(family='Arial', size=14, color='#000000'),
side='right',
title='Mobility/Stringency',
showgrid=False),
height=400,
autosize=True,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return dcc.Graph(figure=figure,
config={'displayModeBar': False},
id='reg-str-graph')
def add_population(country, name=country):
country_info = CountryInfo(name)
countries[country][key_population] = country_info.population()
# coding=utf-8
from countryinfo import CountryInfo
country = CountryInfo('Austria')
print (country.population())
print (country.area())
def updateTotalDeathsTimeline(locations, timeline, data_type, x_num,
smoothing_range, x_days, pop_type):
if locations:
if data_type == 'Deaths':
df = getTotalDeaths()
elif data_type == 'Cases':
df = getTotalCases()
else:
return None
df['sum_data'] = df.apply(lambda x: sumLocations(x, locations), axis=1)
df = df.drop(df[(df['sum_data'] == 0)].index)
data = []
count = 0
for location in locations:
location_key = location.lower().replace(' ', '_')
if pop_type == '% of Population':
country = CountryInfo(location)
population = country.population()
area = country.area()
pop_density = float(population) / area
print(population)
print(area)
print(pop_density)
df[location_key] = 100.0 * df[location_key] / population
else:
population = 1.0
if not x_num: x_num = 0
if timeline == 'Days since X number of deaths/cases':
df_location = df.fillna(0)
df_location = df_location.drop(df_location[(
df_location[location_key] < x_num / population)].index)
df_location = df_location.reset_index()
data.append(
go.Scatter(x=df_location.index,
y=df_location[location_key],
mode='lines',
marker=dict(color=colour_palette[count], ),
opacity=1.0,
text=location,
name=location))
prediction_df = df_location[['date', location_key]]
prediction_df[
'rolling_mean'] = 1.0 + df_location[location_key].fillna(
0).pct_change().rolling(smoothing_range).mean()
prediction_df = prediction_df.iloc[[-1]]
r_index = prediction_df.index.values[0]
r_mean = prediction_df['rolling_mean'].values[0]
r_num = prediction_df[location_key].values[0]
for x in range(1, x_days):
extra_df = pd.DataFrame(
[[float(r_num) * r_mean**x, r_mean]],
columns=[location_key, "rolling_mean"])
prediction_df = pd.concat([prediction_df, extra_df],
ignore_index=True,
axis=0)
prediction_df.index = pd.RangeIndex(start=r_index,
stop=r_index + x_days,
step=1)
data.append(
go.Scatter(x=prediction_df.index,
y=prediction_df[location_key],
mode='lines',
marker=dict(color=colour_palette[count], ),
line=dict(color=colour_palette[count],
dash='dot'),
opacity=1.0,
text=location,
name=location,
showlegend=False))
else:
data.append(
go.Scatter(x=df['date'],
y=df[location_key].fillna(0),
mode='lines',
marker=dict(color=colour_palette[count], ),
opacity=1.0,
text=location,
name=location))
prediction_df = df[['date', location_key]]
prediction_df['rolling_mean'] = 1.0 + df[location_key].fillna(
0).pct_change().rolling(smoothing_range).mean()
prediction_df = prediction_df.iloc[[-1]]
r_date = prediction_df['date'].values[0]
r_mean = prediction_df['rolling_mean'].values[0]
r_num = prediction_df[location_key].values[0]
for x in range(1, x_days):
r_dt = convert_to_datetime(
r_date
) #datetime.datetime.strptime(r_date, '%Y-%m-%d')
extra_df = pd.DataFrame(
[[
r_dt + datetime.timedelta(days=x),
float(r_num) * r_mean**x, r_mean
]],
columns=["date", location_key, "rolling_mean"])
prediction_df = pd.concat([prediction_df, extra_df],
ignore_index=True,
axis=0)
data.append(
go.Scatter(x=prediction_df['date'],
y=prediction_df[location_key],
mode='lines',
marker=dict(color=colour_palette[count], ),
line=dict(color=colour_palette[count],
dash='dot'),
opacity=1.0,
text=location,
name=location,
showlegend=False))
count += 1
if pop_type == "% of Population":
title_sub = ' (% of population)'
else:
title_sub = ''
figure = {
'data':
data,
'layout':
go.Layout(
title='Cumulative ' + data_type + title_sub,
legend=dict(orientation="h", x=0, y=1.1),
font=dict(family='Arial', size=15, color='#000000'),
hovermode='closest',
margin=dict(t=50),
xaxis=dict(tickfont=dict(family='Arial',
size=14,
color='#000000'), ),
yaxis=dict(
#range=y_axis_range,
tickfont=dict(family='Arial', size=14, color='#000000'), ),
height=400,
autosize=True,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return dcc.Graph(figure=figure,
config={'displayModeBar': False},
id='total-deaths-t-graph')
print('\nBorders:')
for border in country_borders:
print(border, end=', ')
calling_codes = country.calling_codes()
print('\n\nCall Code:')
for call_code in calling_codes:
print(call_code)
country_region = country.region()
print(f'\nRegion:\n{country_region}')
sub_region = country.subregion()
print(f'\nSub-Region:\n{sub_region}')
country_population = country.population()
print(f'\nPopulation:\n{country_population}')
country_states = country.provinces()
print('\nStates/Provinces:')
for states in country_states:
print(states, end=', ')
about_country = country.wiki()
country_map = country.capital_latlng()
map = folium.Map(location=(country_map))
print(
f'\n\nCheck your directory for the map of {user}, named: (mylocation.html)'
)
map.save("mylocation.html")
def getVariableBetaForecast(df_location,
location,
x_days,
forecast_size,
r0_shift,
mortality_rate = 0.01,
disease_duration = 14):
location_key = location.lower().replace(' ','_')
country = CountryInfo(location)
population = country.population()
x_num = df_location[location_key].iloc[0]
df_location = df_location[location_key]
num_days = df_location.shape[0]
full_t = np.linspace(0, num_days, num_days)
full_loc = df_location
r0_list = []
t, best_D, best_I, best_R, best_S, best_r0 = getBestFit(df_location.head((forecast_size+1)),
x_num,
population,
mortality_rate = mortality_rate,
disease_duration = disease_duration)
full_D = best_D
full_I = best_I
r0_list.append(best_r0)
print ("Num days = ", num_days)
remain_days = num_days-(forecast_size+1)
print ("0: remain_days = ", remain_days)
debug_count = 1
while remain_days > 2*(forecast_size):
df_location = df_location.tail(remain_days+1)
print (debug_count, ": df_location.shape = ", df_location.shape[0])
print_bool = False
'''if (remain_days-forecast_size + 1) < 2*forecast_size:
print ("Best I = ", best_I.iloc[-1])
print ("Best R = ", best_R.iloc[-1])
print_bool = True'''
t, best_D, best_I, best_R, best_S, best_r0 = getBestFit(df_location.head((forecast_size+1)),
x_num,
population,
mortality_rate = mortality_rate,
disease_duration = disease_duration,
first_pass=False,
I_in=best_I.iloc[-1],
R_in=best_R.iloc[-1],
print_bool=print_bool)
full_D = pd.concat([full_D, best_D.tail(forecast_size)], ignore_index=True,axis=0)
full_I = pd.concat([full_I, best_I.tail(forecast_size)], ignore_index=True,axis=0)
r0_list.append(best_r0)
'''if (remain_days-forecast_size + 1) < 2*forecast_size:
print (df_location.head(forecast_size))
print (full_D)
print (full_I)'''
remain_days = remain_days-forecast_size
print (debug_count,': remain_days = ',remain_days)
df_location = df_location.tail(remain_days+1)
print ("df_location.shape (last)= ", df_location.shape[0])
t, best_D, best_I, best_R, best_S, best_r0 = getBestFit(df_location,
x_num,
population,
mortality_rate = mortality_rate,
disease_duration = disease_duration,
first_pass=False,
I_in=best_I.iloc[-1],
R_in=best_R.iloc[-1],
x_days=x_days,
r0_shift=r0_shift)
full_D = pd.concat([full_D, best_D.tail(-1)], ignore_index=True,axis=0)
full_I = pd.concat([full_I, best_I.tail(-1)], ignore_index=True,axis=0)
r0_list.append(best_r0)
total_susceptible = int(best_S.iloc[-1])
total_infected = population - total_susceptible
print (total_infected, '>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>.' )
return full_D, full_I, r0_list, total_infected, total_susceptible
