# Set constant
num_features = 2
batch_size = 512
epochs = 10000
lr = 1e-2
lr_decay = 0.999
flow_length = 6
true_density_choice = 2
# Generate sample from base distribution
base_dist = D.MultivariateNormal(torch.zeros(num_features), torch.eye(num_features))
z_0 = base_dist.sample([batch_size])
flow = NormalizingFlow(num_features, flow_length)
transformed_z, log_jacobs = flow(z_0)
# Visualize before training
with torch.no_grad():
visualize_flow(transformed_z)
# Set up loss
z_k = transformed_z[-1]
if true_density_choice == 1:
true_density = pot1
if true_density_choice == 2:
true_density = pot2
def KL(base_dist, true_density, z_0, z_k, log_jacobs):
def generate_flows(
start
, end
, period = 6
, duration = ppf.date_time.months
, notional = 10000000
, accrual_basis = ppf.date_time.basis_act_360
, pay_currency = "USD"
, pay_shift_method = ppf.date_time.shift_convention.modified_following
, pay_holiday_centers = None
, accrual_shift_method = ppf.date_time.shift_convention.modified_following
, accrual_holiday_centers = None
, observables = None
, *arguments
, **keywords):
'''
>>> from ppf.date_time import *
>>> flows = generate_flows(
... start = date(2007, Jun, 29)
... , end = date(2017, Jun, 29)
... , period = 6
... , duration = ppf.date_time.months
... , notional = 1000000
... , accrual_basis = basis_30360
... , pay_currency = "JPY"
... , pay_shift_method = shift_convention.modified_following)
>>> for f in flows:
... print f
1000000.000000, JPY, [2007-Jun-29, 2007-Dec-31], basis_30360, 2007-Dec-31,
1000000.000000, JPY, [2007-Dec-31, 2008-Jun-30], basis_30360, 2008-Jun-30,
1000000.000000, JPY, [2008-Jun-30, 2008-Dec-29], basis_30360, 2008-Dec-29,
1000000.000000, JPY, [2008-Dec-29, 2009-Jun-29], basis_30360, 2009-Jun-29,
1000000.000000, JPY, [2009-Jun-29, 2009-Dec-29], basis_30360, 2009-Dec-29,
1000000.000000, JPY, [2009-Dec-29, 2010-Jun-29], basis_30360, 2010-Jun-29,
1000000.000000, JPY, [2010-Jun-29, 2010-Dec-29], basis_30360, 2010-Dec-29,
1000000.000000, JPY, [2010-Dec-29, 2011-Jun-29], basis_30360, 2011-Jun-29,
1000000.000000, JPY, [2011-Jun-29, 2011-Dec-29], basis_30360, 2011-Dec-29,
1000000.000000, JPY, [2011-Dec-29, 2012-Jun-29], basis_30360, 2012-Jun-29,
1000000.000000, JPY, [2012-Jun-29, 2012-Dec-31], basis_30360, 2012-Dec-31,
1000000.000000, JPY, [2012-Dec-31, 2013-Jun-28], basis_30360, 2013-Jun-28,
1000000.000000, JPY, [2013-Jun-28, 2013-Dec-30], basis_30360, 2013-Dec-30,
1000000.000000, JPY, [2013-Dec-30, 2014-Jun-30], basis_30360, 2014-Jun-30,
1000000.000000, JPY, [2014-Jun-30, 2014-Dec-29], basis_30360, 2014-Dec-29,
1000000.000000, JPY, [2014-Dec-29, 2015-Jun-29], basis_30360, 2015-Jun-29,
1000000.000000, JPY, [2015-Jun-29, 2015-Dec-29], basis_30360, 2015-Dec-29,
1000000.000000, JPY, [2015-Dec-29, 2016-Jun-29], basis_30360, 2016-Jun-29,
1000000.000000, JPY, [2016-Jun-29, 2016-Dec-29], basis_30360, 2016-Dec-29,
1000000.000000, JPY, [2016-Dec-29, 2017-Jun-29], basis_30360, 2017-Jun-29,
'''
i, day = 0, start
flows = []
shift = ppf.date_time.shift
while day < end:
roll_start = start + duration(i*period)
roll_end = start + duration((i + 1)*period)
accrual_start = shift(
roll_start
, accrual_shift_method, accrual_holiday_centers)
accrual_end = shift(
roll_end
, accrual_shift_method, accrual_holiday_centers)
pay = shift(
roll_end
, pay_shift_method, pay_holiday_centers)
flows.append(
flow(notional
, pay_currency
, accrual_start
, accrual_end
, accrual_basis
, pay)
)
day = roll_end
i += 1
if observables <> None:
if len(observables) <> len(flows):
raise RuntimeError, "too few or too many observables"
for i in range(len(flows)):
f = flows[i]
obs = observables[i]
f.set_observables(obs)
return flows
/tot_length if rh_type == 4: rh = ones(wcs.x.size) rh[:al_L] = brh rh[al_L:al_L+101] = S_rh rh[al_L+101:] = brh # Set maximum velocity location -- centroid wcs.calcShapeParams() uMx, uMy = wcs.findCentroid() wcs.setUMPoint(uMx, uMy) # Calculate flow parameters wcs.genRL() F = flow(wcs, Q, rh) F.calcChezy(c_rh if rh_type == 4 else False) F.calcFlowParams() ## Get adjusted tau_b phi = arctan2( (wcs.umy - wcs.y), (wcs.umx - wcs.x) ) alpha = arctan2(wcs.yp-wcs.ym, wcs.xp-wcs.xm) F.regenT_b(phi, alpha) # Locate bed-load layer, if not enough capacity set to 0, -1 to garuntee # alluviation q_t = calcLSTC(F.T_b, rho_s, D_s, T_sc) xLs, xRs = findBedLoad(wcs, y_min, q_t, Q_s) # Alluviate by D_s if BLL thickness > 5*D_s
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# Please familiarize yourself with the license of this tool, available
# in the distribution with the filename: /docs/license.txt
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# sample flow_rider test
from flow import *
if __name__ == '__main__':
fl = flow()
# read states, run intersections, and calc global mean
#fl.states.read_states ('../df.csv')
fl.states.read_states('../test/kayak2/cut_states.csv')
fl.intersections.update(fl.states.df)
fl.intersections.write_intersections('../test/kayak2/intersections.csv')
flow_x_mean, flow_y_mean, flow_az, flow_vel = fl.calc_global_mean_flow()
print('flow mean x ' + str(flow_x_mean))
print('flow mean y ' + str(flow_y_mean))
print('flow az ' + str(flow_az))
print('flow vel ' + str(flow_vel))
# assimilate to a grid
print('performing assimilations')
fl.assimilate()
g_h = h # use for next guess
L, R = cs.findLR(h) # get indices for left and right of free-surface
# New wetted cross-section from portion below free-surface
wx = cs.x[L:R+1]
wy = cs.y[L:R+1]
wcs = CrossSection(wx, wy)
widths[ts] = wx[-1]-wx[0]
uMx = 0. # U_{max} ref point in center of f-s
#uMx = (wcs.x[-1]+wcs.x[0])/2
uMy = wcs.y[0]
wcs.setUMPoint(uMx, uMy) # set reference point to center of free-surface
wcs.calcShapeParams()
wcs.genRL()
F = flow(wcs, Q, rh) # flow object to find shear stress
F.setS(S) # we define slope in f-s case
F.calcFlowParams()
# Adjust onto bed-normal vector
phi = fabs( arctan2( (wcs.umy - wcs.y), (wcs.umx - wcs.x) ) )
alpha = arctan2(wcs.yp-wcs.ym, wcs.xp-wcs.xm)
alpha[0] = alpha[1]
alpha[-1] = alpha[-2]
F.regenT_b(phi, alpha) # adjust \tau_b with angles
# Redraw cross-section with shear stress erosion law
rl = zeros(cs.x.size)
rl[L:R+1] = ff*( ( 1*10**(-1*(2*a+2)) )/50. )*F.T_b**(a) # power law erosion
cs.redraw(rl, resample=True) # redraw cross-section w/ spline resample
def generate_flows(
start,
end,
period=6,
duration=ppf.date_time.months,
notional=10000000,
accrual_basis=ppf.date_time.basis_act_360,
pay_currency="USD",
pay_shift_method=ppf.date_time.shift_convention.modified_following,
pay_holiday_centers=None,
accrual_shift_method=ppf.date_time.shift_convention.modified_following,
accrual_holiday_centers=None,
observables=None,
*arguments,
**keywords):
'''
>>> from ppf.date_time import *
>>> flows = generate_flows(
... start = date(2007, Jun, 29)
... , end = date(2017, Jun, 29)
... , period = 6
... , duration = ppf.date_time.months
... , notional = 1000000
... , accrual_basis = basis_30360
... , pay_currency = "JPY"
... , pay_shift_method = shift_convention.modified_following)
>>> for f in flows:
... print f
1000000.000000, JPY, [2007-Jun-29, 2007-Dec-31], basis_30360, 2007-Dec-31,
1000000.000000, JPY, [2007-Dec-31, 2008-Jun-30], basis_30360, 2008-Jun-30,
1000000.000000, JPY, [2008-Jun-30, 2008-Dec-29], basis_30360, 2008-Dec-29,
1000000.000000, JPY, [2008-Dec-29, 2009-Jun-29], basis_30360, 2009-Jun-29,
1000000.000000, JPY, [2009-Jun-29, 2009-Dec-29], basis_30360, 2009-Dec-29,
1000000.000000, JPY, [2009-Dec-29, 2010-Jun-29], basis_30360, 2010-Jun-29,
1000000.000000, JPY, [2010-Jun-29, 2010-Dec-29], basis_30360, 2010-Dec-29,
1000000.000000, JPY, [2010-Dec-29, 2011-Jun-29], basis_30360, 2011-Jun-29,
1000000.000000, JPY, [2011-Jun-29, 2011-Dec-29], basis_30360, 2011-Dec-29,
1000000.000000, JPY, [2011-Dec-29, 2012-Jun-29], basis_30360, 2012-Jun-29,
1000000.000000, JPY, [2012-Jun-29, 2012-Dec-31], basis_30360, 2012-Dec-31,
1000000.000000, JPY, [2012-Dec-31, 2013-Jun-28], basis_30360, 2013-Jun-28,
1000000.000000, JPY, [2013-Jun-28, 2013-Dec-30], basis_30360, 2013-Dec-30,
1000000.000000, JPY, [2013-Dec-30, 2014-Jun-30], basis_30360, 2014-Jun-30,
1000000.000000, JPY, [2014-Jun-30, 2014-Dec-29], basis_30360, 2014-Dec-29,
1000000.000000, JPY, [2014-Dec-29, 2015-Jun-29], basis_30360, 2015-Jun-29,
1000000.000000, JPY, [2015-Jun-29, 2015-Dec-29], basis_30360, 2015-Dec-29,
1000000.000000, JPY, [2015-Dec-29, 2016-Jun-29], basis_30360, 2016-Jun-29,
1000000.000000, JPY, [2016-Jun-29, 2016-Dec-29], basis_30360, 2016-Dec-29,
1000000.000000, JPY, [2016-Dec-29, 2017-Jun-29], basis_30360, 2017-Jun-29,
'''
i, day = 0, start
flows = []
shift = ppf.date_time.shift
while day < end:
roll_start = start + duration(i * period)
roll_end = start + duration((i + 1) * period)
accrual_start = shift(roll_start, accrual_shift_method,
accrual_holiday_centers)
accrual_end = shift(roll_end, accrual_shift_method,
accrual_holiday_centers)
pay = shift(roll_end, pay_shift_method, pay_holiday_centers)
flows.append(
flow(notional, pay_currency, accrual_start, accrual_end,
accrual_basis, pay))
day = roll_end
i += 1
if observables <> None:
if len(observables) <> len(flows):
raise RuntimeError, "too few or too many observables"
for i in range(len(flows)):
f = flows[i]
obs = observables[i]
f.set_observables(obs)
return flows