tw[i] /= swk
tw = tw[tw <= 100.0] # only times in the first 100 weeks
tw = tw % 1
tw = tw * 7
nc = len(tw)
# Now get Fourier curve
nw = 336
nb = 84
t = np.linspace(0.0, 1.0, nw)
MyF = Fourier(645.0, 16, 52.1775, 12, 1.0, 6, 1.0/7.0, 8, df, t0)
MyF.compute()
fv = np.empty(nw)
#fv.fill(2700.0)
for i in range(0, nw):
fv[i] = 0.0
for j in range(0, 50):
fv[i] += MyF.f(t[i] + j * 2.0, 'DRUNKENNESS')
fv[i] *= nc + 0.0 # nc - number of crimes
fv[i] /= 50.0 * nb # nb - number of histogram bins
# Got Fourier curve
t = t * 7
plt.hist(tw, bins=nb) # 645 weeks but we only have alternating weeks
plt.plot(t, fv, linewidth=2.5)
plt.title("Drunkenness")
plt.xlabel("Days")
plt.ylabel("50 times average Drunkenness arrests per 2 hours")
plt.savefig('time-5.png')
plt.show()
nd = len(mydate)
print "%d crimes in total" % nd
tw = np.zeros(nd) # array of times in weeks
ni = np.linspace(0, nd-1, nd, dtype=np.int)
mydate.index = ni
for i in range (0, nd):
tw[i] = (mydate[i]-t0).total_seconds()
tw[i] /= swk
# Now get Fourier curve
nw = 323
t = np.linspace(0.5, 644.5, nw)
#MyF = Fourier(645.0, 20, 52.1775, 12, 1.0, 6, 1.0/7.0, 6, df)
MyF = Fourier(645.0, 16, 52.1775, 12, 1.0, 0, 1.0/7.0, 0, df, t0)
MyF.compute()
fv = np.empty(nw)
#fv.fill(2700.0)
for i in range(0, nw):
fv[i] = MyF.f(t[i], 'VEHICLE THEFT')
fv[i] *= nd + 0.0
fv[i] /= 322.0
# Got Fourier curve
plt.hist(tw, bins=322) # 645 weeks but we only have alternating weeks
plt.plot(t, fv, linewidth=2.5)
plt.title("Vehicle Theft History")
plt.xlabel("Weeks")
plt.ylabel("Thefts per week")
plt.savefig('time-3.png')
plt.show()
mydate = vt['Dates']
nd = len(mydate)
print "%d crimes in total" % nd
tw = np.zeros(nd) # array of times in weeks
ni = np.linspace(0, nd - 1, nd, dtype=np.int)
mydate.index = ni
for i in range(0, nd):
tw[i] = (mydate[i] - t0).total_seconds()
tw[i] /= swk
# Now get Fourier curve
nw = 323
t = np.linspace(0.5, 644.5, nw)
#MyF = Fourier(645.0, 20, 52.1775, 12, 1.0, 6, 1.0/7.0, 6, df)
MyF = Fourier(645.0, 16, 52.1775, 12, 1.0, 0, 1.0 / 7.0, 0, df, t0)
MyF.compute()
fv = np.empty(nw)
#fv.fill(2700.0)
for i in range(0, nw):
fv[i] = MyF.f(t[i], 'VEHICLE THEFT')
fv[i] *= nd + 0.0
fv[i] /= 322.0
# Got Fourier curve
plt.hist(tw, bins=322) # 645 weeks but we only have alternating weeks
plt.plot(t, fv, linewidth=2.5)
plt.title("Vehicle Theft History")
plt.xlabel("Weeks")
plt.ylabel("Thefts per week")
plt.savefig('time-3.png')
plt.show()
tw = tw[tw <= 100.0] # only times in the first 100 weeks
tw = tw % 1
tw = tw * 7
nc = len(tw)
# Now get Fourier curve
nw = 336
nb = 84
t = np.linspace(0.0, 1.0, nw)
MyF = Fourier(645.0, 16, 52.1775, 12, 1.0, 6, 1.0/7.0, 8, df, t0)
MyF.compute()
fv = np.empty(nw)
#fv.fill(2700.0)
for i in range(0, nw):
fv[i] = 0.0
for j in range(0, 50):
#fv[i] = MyF.f(t[i], 'DRUNKENNESS')
fv[i] += MyF.f(t[i] + j * 2.0, 'LARCENY/THEFT')
fv[i] *= nc + 0.0 # nc - number of crimes
fv[i] /= 50.0 * nb # nb - number of histogram bins
# Got Fourier curve
t = t * 7
plt.hist(tw, bins=nb) # 645 weeks but we only have alternating weeks
plt.plot(t, fv, linewidth=2.5)
plt.title("Larceny/Theft")
plt.xlabel("Days")
plt.ylabel("50 times average Larceny/Theft arrests per 2 hours")
plt.savefig('time-4.png')
plt.show()
tw[i] = (mydate[i] - t0).total_seconds()
tw[i] /= swk
tw = tw[tw <= 100.0] # only times in the first 100 weeks
tw = tw % 1
tw = tw * 7
nc = len(tw)
# Now get Fourier curve
nw = 336
nb = 84
t = np.linspace(0.0, 1.0, nw)
MyF = Fourier(645.0, 16, 52.1775, 12, 1.0, 6, 1.0 / 7.0, 8, df, t0)
MyF.compute()
fv = np.empty(nw)
#fv.fill(2700.0)
for i in range(0, nw):
fv[i] = 0.0
for j in range(0, 50):
fv[i] += MyF.f(t[i] + j * 2.0, 'DRUNKENNESS')
fv[i] *= nc + 0.0 # nc - number of crimes
fv[i] /= 50.0 * nb # nb - number of histogram bins
# Got Fourier curve
t = t * 7
plt.hist(tw, bins=nb) # 645 weeks but we only have alternating weeks
plt.plot(t, fv, linewidth=2.5)
plt.title("Drunkenness")
plt.xlabel("Days")
plt.ylabel("50 times average Drunkenness arrests per 2 hours")
plt.savefig('time-5.png')
plt.show()
