import numpy as np
from utils import downloadCovidData
from utils import removeColumn
from utils import plotChart
confirmed, death, latitude = removeColumn(downloadCovidData(),"Hubei")
bins = [np.floor(l/15) for l in latitude]
i = -180
result = {}
x = []
while i < 180:
result[np.floor(float(i)/15)] = np.zeros(confirmed.shape[0])
i+=12
x.append("{}-{}".format(15*np.floor(float(i)/15),15*np.floor(float(i+12)/15)))
for i in range(0,len(confirmed.columns)):
if not np.isnan(bins[i]):
result[bins[i]] += confirmed.iloc[:,i]
finalDf = pd.DataFrame(data=pd.to_datetime(confirmed.index,format='%m/%d/%y'),columns=["Date"])
cols = []
for i in result.keys():
if np.sum(result[i]) > 0:
label = "{}-{}".format(15*i,15*(i+1))
cols.append(label)
finalDf.loc[:, label] = result[i].values
for col in cols:
finalDf[col] = np.log(1+finalDf[col])
print(finalDf)
plotChart(finalDf,title="log(Confirmed cases)",cols=cols)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as mtick
import numpy as np
from collections import Counter
from utils import downloadCovidData
from utils import plotChart
target = "Italy"
confirmed, death, _ = downloadCovidData()
confirmed = confirmed[confirmed.Date >= '2020-03-01']
print(confirmed)
cols = [target]
for col in cols:
confirmed[col] = confirmed[col].diff()
plotChart(confirmed, title="Newly confirmed cases", cols=[target])
import pandas as pd import numpy as np from utils import downloadCovidData from utils import plotChart confirmed, death, _ = downloadCovidData() print(confirmed.columns) death["Spain"] = 100 * death["Spain"] / confirmed["Spain"] death["Germany"] = 100 * death["Germany"] / confirmed["Germany"] death["Italy"] = 100 * death["Italy"] / confirmed["Italy"] death["France"] = 100 * death["France"] / confirmed["France"] death["Hubei"] = 100 * death["Hubei"] / confirmed["Hubei"] plotChart(death,title="Deaths / Confirmed cases", cols=["Spain","Italy","France","Germany","Hubei"], percent=True)
region = "Madrid"
finalDf = None
for nreport in range(32,999):
try:
df,lastModified=downloadSpainData(nreport,region)
print(df)
#df["HospitalizationRate"] = 100 * df["Hospitalizados"] / df["Total casos"]
df = df.loc[:,["Total casos"]].transpose()
df["Date"] = [datetime.datetime(*eut.parsedate(lastModified)[:6])]
#df = df.drop("CCAA",axis=1)
df.index = df["Date"]
if finalDf is None:
finalDf = df
else:
finalDf = pd.concat([finalDf,df],axis=0)
except FileNotFoundError:
break
except Exception as e:
pass
cols = finalDf.columns.tolist()
cols.remove("Date")
for col in cols:
finalDf[col] = np.log(finalDf[col])
last_row = finalDf.ix[finalDf.last_valid_index()].drop("Date")
#cols = finalDf.columns[last_row.argsort()].tolist()[-5:]
plotChart(finalDf,title="Log(Confirmed cases)", cols=cols, percent=False)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
import matplotlib.ticker as mtick
import numpy as np
from collections import Counter
from utils import plotChart
confirmed = pd.read_csv("data/lombardy.csv").iloc[17:,]
print(confirmed)
confirmed["Lombardy"] = confirmed["Cases"].diff()
confirmed["LogGrowth"] = np.log(confirmed["Cases"])
confirmed["LogGrowth"] = 100*confirmed["Cases"].pct_change(periods=1)
confirmed['Date'] = pd.to_datetime(confirmed['Date'], format='%d-%m-%Y')
confirmed.index = confirmed['Date']
def exponential(x, a, k, b):
return a*np.exp(x*k) + b
confirmed = confirmed.iloc[1:,]
country = "LogGrowth"
print(confirmed[country])
plotChart(confirmed,title="Newly confirmed cases", cols=[country], percent=True)
from collections import Counter
from utils import downloadCovidData
from utils import plotChart
from model import forecastRegion
confirmed,death,_ = downloadCovidData()
col = "World w/o China"
confirmed[col] = np.log(1+confirmed.sum(axis=1))
confirmed["Hubei"] = confirmed["Hubei"].astype(float)
cols = ["Italy", "Spain","Hubei", "Germany", "France"]
for col in cols:
confirmed[col] = np.log(1+confirmed[col])
death["World w/o China"] = death.sum(axis=1)
start = "2020-02-25"
daysforecast = 200
target = "Spain"
print(confirmed[target])
projections, model = forecastRegion(confirmed,target,start,daysforecast)
print(projections)
projections["Forecast"] = [math.exp(x) for x in projections["Forecast"]]
projections["Forecast"] = projections["Forecast"].diff()
i = 80
projections = projections.iloc[:i,:]
print(projections)
plotChart(projections,"Newly confirmed cases",cols=["Forecast"],legend=False,labels=["Spain"])
projections["Cumulative"] = projections["Forecast"]
projections["Forecast"] = projections["Cumulative"].diff()
print(projections[["Date","Cumulative","Forecast"]])
print("MAX: ",projections["Forecast"].max())
print(projections[projections["Forecast"] == projections["Forecast"].max()])
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
import matplotlib.ticker as mtick
import numpy as np
from collections import Counter
from utils import plotChart
confirmed = pd.read_csv("data/lombardy.csv").iloc[17:, ]
print(confirmed)
confirmed["Lombardy"] = confirmed["Cases"].diff()
confirmed['Date'] = pd.to_datetime(confirmed['Date'], format='%d-%m-%Y')
confirmed.index = confirmed['Date']
def exponential(x, a, k, b):
return a * np.exp(x * k) + b
confirmed = confirmed.iloc[1:, ]
country = "Lombardy"
print(confirmed[country])
plotChart(confirmed, title="Newly confirmed cases", cols=["Lombardy"])
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as mtick
import numpy as np
from collections import Counter
from utils import downloadCovidData
from utils import plotChart
confirmed, death, _ = downloadCovidData()
confirmed["World w/o China"] = np.log(
confirmed.drop(["Hubei"], axis=1).sum(axis=1))
confirmed["Hubei"] = confirmed["Hubei"].astype(float)
cols = ["Italy", "Spain", "Hubei", "Germany", "France", "United Kingdom"]
for col in cols:
confirmed[col] = np.log(confirmed[col])
death["World w/o China"] = death.sum(axis=1)
plotChart(confirmed,
title="log(Confirmed cases)",
cols=[
"Italy", "Spain", "World w/o China", "Hubei", "France",
"United Kingdom"
])
i = -180
result = {}
x = []
while i < 180:
result[np.floor(float(i) / step)] = np.zeros(confirmed.shape[0])
i += 12
x.append("{}-{}".format(step * np.floor(float(i) / step),
step * np.floor(float(i + 12) / step)))
for i in range(0, len(confirmed.columns)):
if not np.isnan(bins[i]):
result[bins[i]] += confirmed.iloc[:, i]
finalDf = pd.DataFrame(data=pd.to_datetime(confirmed.index, format='%m/%d/%y'),
columns=["Date"])
cols = []
for i in result.keys():
if np.sum(result[i]) > 0:
label = "{}-{}".format(step * i, step * (i + 1))
cols.append(label)
finalDf.loc[:, label] = result[i].values
for col in cols:
finalDf[col] = np.log(1 + finalDf[col])
print(finalDf)
#sys.exit(1)
plotChart(finalDf,
title="log(Confirmed cases)",
cols=cols,
labels=["Southern Hemisphere", "Northern Hemisphere"])
#ax.set(xlabel='Latitude', ylabel='Confirmed cases')
from utils import plotSubchart
from utils import plotChart
from model import forecastRegion
target = "NY"
confirmed = downloadUsaCovidData()
last_row = confirmed.loc[confirmed.last_valid_index(), :].drop("Date")
print(confirmed.columns[last_row.argsort()])
cols = ["NY"]
#cols = confirmed.columns[last_row.argsort()].tolist()[-5:]
print(last_row[cols])
if "Date" in cols:
cols.remove("Date")
for col in cols:
confirmed[col] = np.log(1 + confirmed[col])
plotChart(confirmed, title="log(Confirmed cases)", cols=cols, useLabels=True)
def processState(confirmed, target):
try:
print("Forecasting... {}".format(target))
startDate = "2020-03-01"
confirmed = confirmed[confirmed["Date"] >= startDate]
#confirmed = confirmed[confirmed[target] >= np.log(1.1)]
base = confirmed["Date"][0].strftime("%Y-%m-%d")
if confirmed[target].max() < np.log(1000):
print("TARGET: {} MAX: {}".format(target, confirmed[target].max()))
raise Exception("spam", "eggs")
projections, model = forecastRegion(confirmed, target, base)
f, ax = plt.subplots(1, 1)
plotSubchart(confirmed,