def show_irs_vs_tenure(irs_threshold,
tenure,
no_banks,
threshold,
autocontinue=False):
if not autocontinue:
a = raw_input("Do you want to see IRS value vs tenure?[Y/n]")
if (autocontinue or a != 'n'):
for t in threshold:
for b in no_banks:
im_data = np.zeros((len(irs_threshold), len(tenure)))
for i, irst in enumerate(irs_threshold):
for j, ten in enumerate(tenure):
agg_id = get_aggregate_id(b, t, irst, ten)
x, y, r, raw = get_aggregate_dist(agg_id)
err, alp = get_hump_error(x, y, raw)
im_data[i, j] = min(alp, 10)
if alp > 10:
print "Large value for alpha"
print alp
#im = get_color_image(im_data)
min_value = np.min(1)
max_value = np.max(10)
fig = pplot.figure()
ax = fig.add_subplot(111)
ax.set_ylabel("IRS Value")
ax.set_yticklabels(np.arange(0, 9))
ax.set_xlabel("Term to maturity")
ax.set_xticklabels([0, 50, 100, 250, 400, 550, 700, 850])
ax.set_title("Heatmap for %d banks, threshold %d" % (b, t))
cax = ax.imshow(im_data,
origin='lower',
interpolation='nearest',
vmin=min_value,
vmax=max_value)
cb = fig.colorbar(cax)
cb.set_ticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
cb.set_ticklabels([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
cb.set_label('Alpha', rotation=270)
# pplot.show()
filename = 'alpha_irs_ten_%d_banks_%d_threshold.png' % (b, t)
path = '/home/xxxxx/Programming/GitRepos/scriptie/figs/heatmaps/alpha/'
fl = '%s%s' % (path, filename)
pplot.savefig(fl, bbox_inches='tight')
pplot.close(fig)
if not autocontinue:
try:
a = raw_input("Do you want to continue [Y/n]")
if (a == 'n'):
return
except KeyboardInterrupt:
exit(0)
def run_svm(threshold, no_banks,irs_threshold,tenure):
for t in threshold:
for b in no_banks:
points = np.dstack(np.meshgrid(irs_threshold, tenure)).reshape(-1, 2)
alpha_class = np.zeros(len(points))
for i,x in enumerate(points):
irst = x[0]
ten = x[1]
agg_id = get_aggregate_id(b,t,irst,ten)
x,y,r,raw = get_aggregate_dist(agg_id)
_,alp = get_hump_error(x,y,raw)
if alp <= 3:
alpha_class[i] = 1
else:
alpha_class[i] = 0
svc = svm.SVC(kernel='poly').fit(points,alpha_class)
xx, yy = np.meshgrid(np.arange(min(irs_threshold),max(irs_threshold), 0.02),
np.arange(min(tenure),max(tenure), 0.02))
Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
pplot.figure()
pplot.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
pplot.scatter(points[:,0],points[:,1], c=alpha_class)
pplot.show()
def show_mulitple(aggregate_ids):
fig = pplot.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
cmap = pplot.get_cmap('YlOrRd')
n = len(aggregate_ids)
for (i,aggregate_id) in enumerate(aggregate_ids):
x,y,run,raw = get_aggregate_dist(aggregate_id)
strpol = (run.no_banks,run.max_tenure,run.max_irs_value,run.threshold)
l = "%d banks, %d tenure, %d IRS Value, %d threshold"%strpol
err,alpha = get_hump_info(aggregate_id)
xn = x#/run.no_banks
yn = gaussian_filter1d([yi*pow(xi,alpha) for xi,yi in zip(x,y)],4)
ax.scatter(xn,yn, color=cmap((i+1)/n),label=l)
# Shrink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([box.x0, box.y0,
box.width*0.75, box.height])
# Put a legend below current axis
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),
fancybox=True, shadow=True, ncol=1)
pplot.show()
def show_irs_vs_threshold(irs_threshold,
tenure,
no_banks,
threshold,
autocontinue=False):
if not autocontinue:
a = raw_input("Do you want to see IRS value vs threshold?[Y/n]")
if (autocontinue or a != 'n'):
for ten in tenure:
for b in no_banks:
im_data = np.zeros((len(irs_threshold), len(threshold)))
for i, irst in enumerate(irs_threshold):
for j, t in enumerate(threshold):
agg_id = get_aggregate_id(b, t, irst, ten)
x, y, r, raw = get_aggregate_dist(agg_id)
err, alp = get_hump_error(x, y, raw)
im_data[i, j] = round(alp, 2)
#im = get_color_image(im_data)
min_value = np.min(1)
max_value = np.max(10)
fig = pplot.figure()
ax = fig.add_subplot(111)
ax.set_ylabel("IRS Value")
ax.set_yticklabels(np.arange(0, 9))
ax.set_xlabel("Threshold")
ax.set_xticklabels([0, 10, 15, 20, 25, 30])
ax.set_title("Heatmap for %d banks, term to maturity %d" %
(b, ten))
cax = ax.imshow(im_data,
origin='lower',
interpolation='nearest',
vmin=min_value,
vmax=max_value)
cb = fig.colorbar(cax)
cb.set_ticks([min_value, max_value])
cb.set_ticklabels([min_value, max_value])
cb.set_label('P(x > Last powerlaw point)', rotation=270)
filename = 'alpha_irs_thresh_%d_banks_%d_tenure.png' % (b, ten)
path = '/home/..../Programming/GitRepos/scriptie/figs/heatmaps/alpha/'
fl = '%s%s' % (path, filename)
pplot.savefig(fl, bbox_inches='tight')
pplot.close(fig)
if not autocontinue:
try:
a = raw_input("Do you want to continue [Y/n]")
if (a == 'n'):
return
except KeyboardInterrupt:
exit(0)
def show_single(aggregate_id):
x,y,run = get_aggregate_dist(aggregate_id)
fig = pplot.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
ax.scatter(x,y)
pplot.show()
def moments_by_type(tpe):
query = """SELECT aggregate_type.aggregate_id FROM aggregate_type
INNER JOIN run ON run.aggregate_id = aggregate_type.aggregate_id
WHERE type_id = %d and run.no_banks = 200
GROUP BY aggregate_type.aggregate_id""" % tpe
session = get_session()
aggregate_ids = session.execute(query).fetchall()
session.close()
moments = np.zeros((len(aggregate_ids)))
for i, aggregate_id in enumerate(aggregate_ids):
_, _, _, d = get_aggregate_dist(aggregate_id[0])
moments[i] = stats.moment(d, 3)
print moments[i], aggregate_id
return moments
def show_irs_vs_threshold(irs_threshold, tenure, no_banks, threshold, moment):
a = raw_input("Do you want to see IRS value vs threshold?[Y/n]")
if (a != 'n'):
for ten in tenure:
for b in no_banks:
im_data = np.zeros((len(irs_threshold), len(threshold)))
for i, irst in enumerate(irs_threshold):
for j, t in enumerate(threshold):
agg_id = get_aggregate_id(b, t, irst, ten)
x, y, r, raw = get_aggregate_dist(agg_id)
m = stats.moment(raw, moment)
im_data[i, j] = np.log(m)
fig = pplot.figure()
ax = fig.add_subplot(111)
ax.set_ylabel("IRS Value")
ax.set_yticklabels(np.arange(0, 9))
ax.set_xlabel("Threshold")
ax.set_xticklabels([0, 10, 15, 20, 25, 30])
ax.set_title("Heatmap for %d banks, tenure %d" % (b, ten))
cax = ax.imshow(im_data,
origin='lower',
interpolation='nearest',
vmin=np.min(im_data),
vmax=np.max(im_data),
cmap=clrs.Reds)
cb = fig.colorbar(cax,
ticks=[np.min(im_data),
np.max(im_data)])
pplot.show()
try:
a = raw_input("Do you want to continue [Y/n]")
if (a == 'n'):
return
except KeyboardInterrupt:
exit(0)
if __name__ == '__main__':
irs_threshold = np.arange(1,9)
tenure = [50,100,250,400,550,700,850]
no_banks = [100, 200]#np.arange(100,600,100)
threshold = np.arange(10,35,5)
a = raw_input("Do you want to see IRS value vs tenure?[Y/n]")
if(a != 'n'):
for t in threshold:
for b in no_banks:
im_data = np.zeros((len(irs_threshold),len(tenure)))
for i,irst in enumerate(irs_threshold):
for j,ten in enumerate(tenure):
agg_id = get_aggregate_id(b,t,irst,ten)
x,y,r,raw = get_aggregate_dist(agg_id)
err,alp = get_hump_error(x,y,raw)
im_data[i,j] = alp
fig = pplot.figure()
ax = fig.add_subplot(111)
ax.set_ylabel("IRS Value")
ax.set_yticklabels(np.arange(0,9))
ax.set_xlabel("Tenure")
ax.set_xticklabels([0,50,100,250,400,550,700,850])
ax.set_title("Heatmap for %d banks, threshold %d:Alpha"%(b,t))
cax = ax.imshow(im_data,origin='lower',interpolation='nearest',
vmin=np.min(im_data),vmax=np.max(im_data))
pplot.colorbar(cax)
def get_frequencies(no_banks, aggregate_id):
x, y, r, raw = get_aggregate_dist(aggregate_id)
_, alp = get_hump_error(x, y, raw)
cumv = get_hump_cumulative_value(x,y)
return [alp, cumv]
def get_weight(aggregate_id):
x, y, _, raw = get_aggregate_dist(aggregate_id)
_, alp = get_hump_error(x, y, raw)
cumv = get_hump_cumulative_value(x,y)
return (aggregate_id, alp, cumv)