def calc_Vsh_l(A, lm1_sqrt, sanity_checks=False):
D = A.shape[2]
Dm1 = A.shape[1]
q = A.shape[0]
if q * Dm1 - D <= 0:
return None
L = sp.zeros((D, q, Dm1), dtype=A.dtype, order='C')
for s in xrange(q):
L[:,s,:] = lm1_sqrt.dot(A[s]).conj().T
L = L.reshape((D, q * Dm1))
V = ns.nullspace_qr(L)
if sanity_checks:
if not sp.allclose(L.dot(V), 0):
log.warning("Sanity Fail in calc_Vsh_l!: LV != 0")
if not sp.allclose(V.conj().T.dot(V), sp.eye(V.shape[1])):
log.warning("Sanity Fail in calc_Vsh_l!: V H(V) != eye")
V = V.reshape((q, Dm1, q * Dm1 - D))
Vsh = sp.transpose(V.conj(), axes=(0, 2, 1))
Vsh = sp.asarray(Vsh, order='C')
if sanity_checks:
M = eps_l_noop(lm1_sqrt, A, V)
if not sp.allclose(M, 0):
log.warning("Sanity Fail in calc_Vsh_l!: Bad Vsh")
return Vsh
def test1_read_excel_curve_data(self):
dirs = determine_this_file_path()
excel_file = 'synthetic_data_Flood_2012.xls'
excel_file = os.path.join(dirs, excel_file)
temp = create_vuln_xml.read_excel_curve_data(excel_file)
depths, fab, contents = temp
self.assertTrue(allclose(depths, array([0., 1.0])))
actually_fab = {u'FCM1_INSURED': array([0., 0.1]),
u'FCM2_INSURED': array([0., 0.12]),
u'FCM1_UNINSURED': array([0., 0.5]),
u'FCM2_UNINSURED': array([0., 0.52])}
act_cont = {
u'FCM1_INSURED_SAVE': array([0., 0.2]),
u'FCM1_INSURED_NOACTION': array([0., 0.3]),
u'FCM1_INSURED_EXPOSE': array([0., 0.4]),
u'FCM1_UNINSURED_SAVE': array([0., 0.6]),
u'FCM1_UNINSURED_NOACTION': array([0., 0.7]),
u'FCM1_UNINSURED_EXPOSE': array([0., 0.8]),
u'FCM2_INSURED_SAVE': array([0., 0.22]),
u'FCM2_INSURED_NOACTION': array([0., 0.32]),
u'FCM2_INSURED_EXPOSE': array([0., 0.42]),
u'FCM2_UNINSURED_SAVE': array([0., 0.62]),
u'FCM2_UNINSURED_NOACTION': array([0., 0.72]),
u'FCM2_UNINSURED_EXPOSE': array([0., 0.82])
}
for key in actually_fab:
self.assertTrue(allclose(actually_fab[key], fab[key]))
for key in act_cont:
self.assertTrue(allclose(act_cont[key], contents[key]))
def test_outside_polygon(self):
U = [[0,0], [1,0], [1,1], [0,1]] #Unit square
assert not is_outside_polygon( [0.5, 0.5], U )
#evaluate to False as the point 0.5, 0.5 is inside the unit square
assert is_outside_polygon( [1.5, 0.5], U )
#evaluate to True as the point 1.5, 0.5 is outside the unit square
indices = outside_polygon( [[0.5, 0.5], [1, -0.5], [0.3, 0.2]], U )
assert allclose( indices, [1] )
#One more test of vector formulation returning indices
polygon = [[0,0], [1,0], [0.5,-1], [2, -1], [2,1], [0,1]]
points = [ [0.5, 0.5], [1, -0.5], [1.5, 0], [0.5, 1.5], [0.5, -0.5]]
res = outside_polygon( points, polygon )
assert allclose( res, [3, 4] )
polygon = [[0,0], [1,0], [0.5,-1], [2, -1], [2,1], [0,1]]
points = [ [0.5, 1.4], [0.5, 0.5], [1, -0.5], [1.5, 0], [0.5, 1.5], [0.5, -0.5]]
res = outside_polygon( points, polygon )
assert allclose( res, [0, 4, 5] )
def test_gets_axes_right(self):
Map = tools.set_up_map(self.Data, (0.0, 5.2), (45, 23), (0.4, 0.5))
Map.calc_axes()
self.Data.calc_freq()
self.assertTrue(sp.allclose(Map.freq, self.Data.freq))
self.assertTrue(sp.allclose(Map.long, tools.calc_bins(0.0, 45, 0.4, "middle")))
self.assertTrue(sp.allclose(Map.lat, tools.calc_bins(5.2, 23, 0.5, "middle")))
def test_rigged_pointing(self) :
Data = self.blocks[0]
Data.calc_freq()
map = self.map
# Set all data = (f + cal_ind)*time_ind
Data.data[:,:,:,:] = (sp.arange(-4.5, 5)
[:,sp.newaxis,sp.newaxis,sp.newaxis]
*(Data.freq/100e6))
Data.data[...] -= sp.mean(Data.data, 0)
Data.data[...] += (sp.arange(6,8).reshape((1,1,2,1)) * (Data.freq/100e6)
* sp.arange(-4.5, 5).reshape((10, 1, 1, 1)))
map[:,:,:] = 0.0
# Set 10 pixels to match data (except for cal_ind part).
map[:, range(10), range(10)] = (sp.arange(-4.5, 5)[None,:]
* map.get_axis('freq')[:,None]/100e6)
# We should be completely insensitive to the map mean. Th following
# should have no effect.
map[...] += 0.352*map.get_axis('freq')[:, None, None]/800.0e7
# Rig the pointing to point to those 10 pixels.
def rigged_pointing() :
Data.ra = map.get_axis('ra')[range(10)]
Data.dec = map.get_axis('dec')[range(10)]
Data.calc_pointing = rigged_pointing
smd.sub_map(Data, map)
# Now data should be just f*time_ind*(cal_ind+6), within 2.0 MHz/2.
Data.data /= sp.arange(-4.5, 5)[:,sp.newaxis,sp.newaxis,sp.newaxis]
Data.data /= Data.freq/100e6
# Relative tol of 1/700, is the frequency bin width.
self.assertTrue(sp.allclose(Data.data[:,:,0,:], 6.0, rtol=1.0/700))
self.assertTrue(sp.allclose(Data.data[:,:,1,:], 7.0, rtol=1.0/700))
def test_correlate(self) :
Data = self.blocks[0]
Data.calc_freq()
map = self.map
gain = 3.45
const = 2.14
# Set all data = gain*(cos(time_ind)).
Data.data[:,:,:,:] = gain*sp.cos(sp.arange(1,11)
[:,sp.newaxis,sp.newaxis,sp.newaxis])
# Explicitly set time mean to something known.
Data.data -= ma.mean(Data.data, 0)
Data.data += gain*const*Data.freq/800.0e6
# Now the Map.
map[:,:,:] = 0.0
# Set 10 pixels to match cos part of data.
map[:, range(10), range(10)] = (
sp.cos(sp.arange(1,11)[None, :]))
map[:, range(10), range(10)] -= ma.mean(
map[:, range(10), range(10)], 1)[:, None]
# Give Map a mean to test things out. Should really have no effect.
map[...] += 0.352*map.get_axis('freq')[:, None, None]/800.0e6
# Rig the pointing to point to those 10 pixels.
def rigged_pointing() :
Data.ra = map.get_axis('ra')[range(10)]
Data.dec = map.get_axis('dec')[range(10)]
Data.calc_pointing = rigged_pointing
solved_gains = smd.sub_map(Data, map, correlate=True)
# Now data should be just be gain*const*f, within machine precision.
Data.data /= gain*Data.freq/800.0e6
self.assertTrue(sp.allclose(Data.data[:,:,:,:], const))
self.assertTrue(sp.allclose(solved_gains, gain))
def calc_Vsh(A, r_s, sanity_checks=False):
D = A.shape[2]
Dm1 = A.shape[1]
q = A.shape[0]
if q * D - Dm1 <= 0:
return None
R = sp.zeros((D, q, Dm1), dtype=A.dtype, order='C')
for s in xrange(q):
R[:,s,:] = r_s.dot(A[s].conj().T)
R = R.reshape((q * D, Dm1))
Vconj = ns.nullspace_qr(R.conj().T).T
if sanity_checks:
if not sp.allclose(mm.mmul(Vconj.conj(), R), 0):
log.warning("Sanity Fail in calc_Vsh!: VR != 0")
if not sp.allclose(mm.mmul(Vconj, Vconj.conj().T), sp.eye(Vconj.shape[0])):
log.warning("Sanity Fail in calc_Vsh!: V H(V) != eye")
Vconj = Vconj.reshape((q * D - Dm1, D, q))
Vsh = Vconj.T
Vsh = sp.asarray(Vsh, order='C')
if sanity_checks:
Vs = sp.transpose(Vsh, axes=(0, 2, 1)).conj()
M = eps_r_noop(r_s, Vs, A)
if not sp.allclose(M, 0):
log.warning("Sanity Fail in calc_Vsh!: Bad Vsh")
return Vsh
def test_build_replacement_ratios(self):
#usage_values_per_struct = ['RES1',
# 'RES3', 'COM8', 'GOV1', 'GOV1', 'GOV1']
usage_values_per_struct = [111, 231, 231]
rcp_actual = {'structural':[0.2344, 0.1918, 0.1918],
'nonstructural drift sensitive':[0.5,0.3288, 0.3288],
'nonstructural acceleration sensitive':[0.2656,
0.4795,
0.4795
]}
buildings_usage_classification = 'FCB'
rcp = build_replacement_ratios(usage_values_per_struct,
buildings_usage_classification)
components = ['structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive']
for comp in components:
self.assert_ (allclose(rcp[comp], rcp_actual[comp], 0.001))
usage_values_per_struct = ['RES1', 'COM4', 'COM4']
rcp_actual = {'structural':[0.2344, 0.1918, 0.1918],
'nonstructural drift sensitive':[0.5,0.3288, 0.3288],
'nonstructural acceleration sensitive':[0.2656,
0.4795,
0.4795
]}
buildings_usage_classification = 'HAZUS'
rcp = build_replacement_ratios(usage_values_per_struct,
buildings_usage_classification)
components = ['structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive']
for comp in components:
self.assert_ (allclose(rcp[comp], rcp_actual[comp], 0.001))
def test_wind_v3_template(self):
# Test running an end to end cyclone test based
# on a wind config template.
# The output file
f = tempfile.NamedTemporaryFile(
suffix='.npz',
prefix='HAZIMPt_wind_scenarios_test_const',
delete=False)
wind_dir = os.path.join(misc.EXAMPLE_DIR, 'wind')
exp_filename = os.path.join(wind_dir,
'syn_small_exposure_tcrm.csv')
wind_filename = os.path.join(wind_dir, 'gust01.txt')
a_config = [{TEMPLATE: WINDV3},
{LOADCSVEXPOSURE: {'file_name': exp_filename,
'exposure_latitude': 'LATITUDE',
'exposure_longitude': 'LONGITUDE'}},
{LOADWINDTCRM: [wind_filename]},
{CALCSTRUCTLOSS: {REP_VAL_NAME: 'REPLACEMENT_VALUE'}},
{SAVE: f.name}]
context = hazimp.start(config_list=a_config)
self.assertTrue(allclose(
context.exposure_att['structural_loss'],
context.exposure_att['calced-loss']))
# Only the head node writes a file
if parallel.STATE.rank == 0:
exp_dict = numpy.load(f.name)
self.assertTrue(allclose(exp_dict['structural_loss'],
exp_dict['calced-loss']))
os.remove(f.name)
def test_lowrank_ard(self):
theta = SP.array(SP.random.randn(1+self.n_train)**2)
theta_hat = SP.exp(2*theta)
_K = theta_hat[0]*SP.dot(self.Xtrain,self.Xtrain.T) + SP.diag(theta_hat[1:])
_Kcross = theta_hat[0]*SP.dot(self.Xtrain,self.Xtest.T)
_Kgrad_theta = 2*theta_hat[0]*SP.dot(self.Xtrain,self.Xtrain.T)
cov = lowrank.LowRankArdCF(n_dimensions=self.n_dimensions,n_hyperparameters=self.n_train+1)
cov.X = self.Xtrain
cov.Xcross = self.Xtest
K = cov.K(theta)
Kcross = cov.Kcross(theta)
assert SP.allclose(K,_K), 'ouch, covariance matrix is wrong'
assert SP.allclose(Kcross,_Kcross), 'ouch, cross covariance matrix is wrong'
assert SP.allclose(_Kgrad_theta,cov.Kgrad_theta(theta,0)), 'ouch gradient with respect to theta[0] is wrong'
# gradient with respect to parameters of the diagonal matrix
for i in range(self.n_train):
Kgrad_theta = cov.Kgrad_theta(theta,i+1)
_Kgrad_theta = SP.zeros(Kgrad_theta.shape)
_Kgrad_theta[i,i] = 2*theta_hat[i+1]
assert SP.allclose(Kgrad_theta, _Kgrad_theta), 'ouch gradient with respect to theta[%d] is wrong'%(i+1)
# gradient with respect to latent factors
for i in range(self.n_dimensions):
for j in range(self.n_train):
Xgrad = SP.zeros(self.Xtrain.shape)
Xgrad[j,i] = 1
_Kgrad_x = theta_hat[0]*(SP.dot(Xgrad,self.Xtrain.T) + SP.dot(self.Xtrain,Xgrad.T))
Kgrad_x = cov.Kgrad_x(theta,i,j)
assert SP.allclose(Kgrad_x,_Kgrad_x), 'ouch, gradient with respect to x is wrong for entry [%d,%d]'%(i,j)
def test_calc_activities_Characteristic(self):
## As far as I can tell you can regard this function as generating
## events for testing.
def make_bins(min_mag,max_magnitude,num_bins,
recurrence_model_dist = 'bounded_gutenberg_richter'):
if (recurrence_model_dist == 'characteristic'):
m2=0.5
m_c=max_magnitude-m2
delta_mag = (m_c-min_mag)/(num_bins)
bins = r_[min_mag+delta_mag/2:m_c-delta_mag/2:(num_bins)*1j]
characteristic_bin = array([m_c+(m2/2)])
bins = append(bins,characteristic_bin)
else:
delta_mag = (max_magnitude-min_mag)/num_bins
bins = r_[min_mag+delta_mag/2:max_magnitude-delta_mag/2:num_bins*1j]
#approximate the number of earthquakes in discrete (0.1 unit) bins
return bins
max_magnitude = 7.0
min_magnitude = 4.0
slip_rate_mm = 2.0
area_kms = float(30*10)
b = 1.
prob_number_of_mag_sample_bins = 10
bin_centroids = make_bins(min_magnitude, max_magnitude,
prob_number_of_mag_sample_bins,
'characteristic')
# event_bins = r_[0:10]
# event_bins = sorted(event_bins)
#
A_minCharacteristic = calc_A_min_from_slip_rate_Characteristic(
b, min_magnitude,
max_magnitude,
slip_rate_mm, area_kms)
pdfs = calc_activities_Characteristic(bin_centroids, b,
min_magnitude,
max_magnitude)
# event_activity_source = array(
# [(A_minCharacteristic*pdfs[z]/(sum(where(
# event_bins == z, 1,0)))) for z in event_bins])
event_activity_source = array(A_minCharacteristic*pdfs)
self.assert_(allclose(sum(event_activity_source),A_minCharacteristic))
self.assert_(allclose(event_activity_source,[1.09104980e-02,
6.13542392e-03,
3.45020242e-03,
1.94019140e-03,
1.09104980e-03,
6.13542392e-04,
3.45020242e-04,
1.94019140e-04,
1.09104980e-04,
6.13542392e-05,
4.60091887e-04]))
def test_linear(self):
theta = SP.array([SP.random.randn()**2])
theta_hat = SP.exp(2*theta)
_K = SP.dot(self.Xtrain,self.Xtrain.T)
_Kcross = SP.dot(self.Xtrain,self.Xtest.T)
cov = linear.LinearCF(n_dimensions=self.n_dimensions)
cov.X = self.Xtrain
cov.Xcross = self.Xtest
K = cov.K(theta)
Kcross = cov.Kcross(theta)
Kgrad_x = cov.Kgrad_x(theta,0)
Kgrad_theta = cov.Kgrad_theta(theta,0)
assert SP.allclose(K, theta_hat*_K), 'ouch covariance matrix is wrong'
assert SP.allclose(Kgrad_theta, 2*theta_hat*_K), 'ouch, gradient with respect to theta is wrong'
assert SP.allclose(Kcross, theta_hat*_Kcross), 'ouch, cross covariance is wrong'
# gradient with respect to latent factors
# for each entry
for i in range(self.n_dimensions):
for j in range(self.n_train):
Xgrad = SP.zeros(self.Xtrain.shape)
Xgrad[j,i] = 1
_Kgrad_x = theta_hat*(SP.dot(Xgrad,self.Xtrain.T) + SP.dot(self.Xtrain,Xgrad.T))
Kgrad_x = cov.Kgrad_x(theta,i,j)
assert SP.allclose(Kgrad_x,_Kgrad_x), 'ouch, gradient with respect to x is wrong for entry [%d,%d]'%(i,j)
def test_lowrank_iso(self):
theta = SP.array(SP.random.randn(2)**2)
theta_hat = SP.exp(2*theta)
_K = theta_hat[0]*SP.dot(self.Xtrain,self.Xtrain.T) + theta_hat[1]*SP.eye(self.n_train)
_Kcross = theta_hat[0]*SP.dot(self.Xtrain,self.Xtest.T)
_Kgrad_theta = []
_Kgrad_theta.append(2*theta_hat[0]*SP.dot(self.Xtrain,self.Xtrain.T) )
_Kgrad_theta.append(2*theta_hat[1]*SP.eye(self.n_train))
cov = lowrank.LowRankCF(self.n_dimensions)
cov.X = self.Xtrain
cov.Xcross = self.Xtest
K = cov.K(theta)
Kcross = cov.Kcross(theta)
assert SP.allclose(K,_K), 'ouch, covariance matrix is wrong'
assert SP.allclose(Kcross,_Kcross), 'ouch, cross covariance matrix is wrong'
assert SP.allclose(_Kgrad_theta[0],cov.Kgrad_theta(theta,0))
assert SP.allclose(_Kgrad_theta[1],cov.Kgrad_theta(theta,1))
# gradient with respect to latent factors
for i in range(self.n_dimensions):
for j in range(self.n_train):
Xgrad = SP.zeros(self.Xtrain.shape)
Xgrad[j,i] = 1
_Kgrad_x = theta_hat[0]*(SP.dot(Xgrad,self.Xtrain.T) + SP.dot(self.Xtrain,Xgrad.T))
Kgrad_x = cov.Kgrad_x(theta,i,j)
assert SP.allclose(Kgrad_x,_Kgrad_x), 'ouch, gradient with respect to x is wrong for entry [%d,%d]'%(i,j)
def test_correlated_scatter(self) :
n = 50
r = (sp.arange(n, dtype=float) + 10.0*n)/10.0*n
data = sp.sin(sp.arange(n)) * r
amp = 25.0
theory = data/amp
# Generate correlated matrix.
C = random.rand(n, n) # [0, 1)
# Raise to high power to make values near 1 rare.
C = (C**10) * 0.2
C = (C + C.T)/2.0
C += sp.identity(n)
C *= r[:, None]/2.0
C *= r[None, :]/2.0
# Generate random numbers in diagonal frame.
h, R = linalg.eigh(C)
self.assertTrue(sp.alltrue(h>0))
rand_vals = random.normal(size=n)*sp.sqrt(h)
# Rotate back.
data += sp.dot(R.T, rand_vals)
out = utils.ampfit(data, C, theory)
a, s = out['amp'], out['error']
self.assertTrue(sp.allclose(a, amp, atol=5.0*s, rtol=0))
# Expect the next line to fail 1/100 trials.
self.assertFalse(sp.allclose(a, amp, atol=0.01*s, rtol=0))
def unnfinished_test_quick_convert_csv_to_arrays_lats_longs_file(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
LONGITUDE = [11.5, 11.6, 11.7, 11.8]
LATITUDE = [-3.1, -3.2, -3.3, -3.4]
WALLS = ['Brick veneer', 'Double Brick', 'Fibro', 'Double Brick']
attribute_dic = {'LONGITUDE': LONGITUDE,
'LATITUDE': LATITUDE,
'WALLS': WALLS}
title_index_dic = {'LONGITUDE': 0,
'LATITUDE': 1,
'WALLS': 2}
util.dict2csv(file_name, title_index_dic, attribute_dic)
print "file_name", file_name
lon = csvi.quick_convert_csv_to_arrays(file_name, LONGITUDE=float)
assert lon.keys()[0] == 'LONGITUDE'
assert len(lon.keys()) == 1
assert scipy.allclose(LONGITUDE, lon['LONGITUDE'])
all_conversions = {'LONGITUDE': float,
'LATITUDE': float,
'WALLS': str}
all = csvi.quick_convert_csv_to_arrays(self.dummy_f, **all_conversions)
assert len(all.keys()) == 3
assert scipy.allclose(LATITUDE, all['LATITUDE'])
assert scipy.allclose(LONGITUDE, all['LONGITUDE'])
assert scipy.allclose(self.WALLS == all['WALLS'])
os.remove(file_name)
def restore_LCF(self, use_QR=True, update_r=True, diag_r=True):
"""Use a gauge-transformation to restore left canonical form.
See restore_RCF.
"""
if use_QR:
tm.restore_LCF_l_seq(self.A, self.l, sanity_checks=self.sanity_checks,
sc_data="restore_LCF_l")
else:
G = sp.eye(self.D[0], dtype=self.typ) #This is actually just the number 1
for n in xrange(1, self.N + 1):
self.l[n], G, Gi = tm.restore_LCF_l(self.A[n], self.l[n - 1], G,
zero_tol=self.zero_tol,
sanity_checks=self.sanity_checks)
if self.sanity_checks:
lN = tm.eps_l_noop(self.l[self.N - 1], self.A[self.N], self.A[self.N])
if not sp.allclose(lN, 1, atol=1E-12, rtol=1E-12):
log.warning("Sanity Fail in restore_LCF!: l_N is bad / norm failure")
if diag_r:
tm.restore_LCF_r_seq(self.A, self.r, sanity_checks=self.sanity_checks,
sc_data="restore_LCF_r")
if self.sanity_checks:
if not sp.allclose(self.r[0].A, 1, atol=1E-12, rtol=1E-12):
log.warning("Sanity Fail in restore_LCF!: r_0 is bad / norm failure")
log.warning("r_0 = %s", self.r[0].squeeze().real)
for n in xrange(1, self.N + 1):
l = tm.eps_l_noop(self.l[n - 1], self.A[n], self.A[n])
if not sp.allclose(l, self.l[n], atol=1E-11, rtol=1E-11):
log.warning("Sanity Fail in restore_LCF!: l_%u is bad (off by %g)", n, la.norm(l - self.l[n]))
elif update_r:
self.calc_r()
def check_RCF(self):
"""Tests for right canonical form.
Uses the criteria listed in sub-section 3.1, theorem 1 of arXiv:quant-ph/0608197v2.
This is a consistency check mainly intended for debugging purposes.
FIXME: The tolerances appear to be too tight!
Returns
-------
(rnsOK, ls_trOK, ls_pos, ls_diag, normOK) : tuple of bool
rnsOK: Right orthonormalization is fullfilled (self.r[n] = eye)
ls_trOK: all self.l[n] have trace 1
ls_pos: all self.l[n] are positive-definite
ls_diag: all self.l[n] are diagonal
normOK: the state it normalized
"""
rnsOK = True
ls_trOK = True
ls_herm = True
ls_pos = True
ls_diag = True
for n in xrange(1, self.N + 1):
rnsOK = rnsOK and sp.allclose(self.r[n], sp.eye(self.r[n].shape[0]), atol=self.eps*2, rtol=0)
ls_herm = ls_herm and sp.allclose(self.l[n] - m.H(self.l[n]), 0, atol=self.eps*2)
ls_trOK = ls_trOK and sp.allclose(sp.trace(self.l[n]), 1, atol=self.eps*1000, rtol=0)
ls_pos = ls_pos and all(la.eigvalsh(self.l[n]) > 0)
ls_diag = ls_diag and sp.allclose(self.l[n], sp.diag(self.l[n].diagonal()))
normOK = sp.allclose(self.l[self.N], 1., atol=self.eps*1000, rtol=0)
return (rnsOK, ls_trOK, ls_pos, ls_diag, normOK)
def test_fold_point(self):
self.assertTrue(
scipy.allclose(fold_point([0., -0.5, 0.5], lattice=self.rec_latt),
self.rec_latt.get_cartesian_coords([0., 0.5, 0.5])))
self.assertTrue(
scipy.allclose(fold_point([0.1, -0.6, 0.2], lattice=self.rec_latt),
self.rec_latt.get_cartesian_coords([0.1, 0.4, 0.2])))
def test_apply_threshold_distance_partial(self):
"""
Test apply_threshold_distance function for atten_threshold_distance
scenario where apply_threshold_distance sets some SA figures to zero
"""
# Use the ones array for the initial SA figures
bedrock_SA = self.SA_ones.copy()
soil_SA = self.SA_ones.copy()
# Set a normal threshold distance
# event 0 event 1
# distances [[ 337.69538742 27105.63126916]]
atten_threshold_distance = 400
# Set up SA arrays that match the expected outcome, noting the distances
# in the comment above
site_inds = [0]
event_inds = [1]
bedrock_SA_expected = bedrock_SA.copy()
bedrock_SA_expected[...,site_inds,event_inds,:] = 0
soil_SA_expected = soil_SA.copy()
soil_SA_expected[...,site_inds,event_inds,:] = 0
# Run the threshold distance function
apply_threshold_distance(bedrock_SA,
soil_SA,
self.sites,
atten_threshold_distance,
self.use_amplification,
self.event_set)
assert allclose(bedrock_SA, bedrock_SA_expected)
assert allclose(soil_SA, soil_SA_expected)
def test_calc_annloss_deagg_grid(self):
# See documentation/annualised_loss_calc.xls
# for the calculations of the expected values.
lat = scipy.array([-25, -24])
lon = scipy.array([130, 132])
total_building_loss = scipy.array([[2000.0, 5.0],
[ 10.0, 2001.0]])
total_building_value = scipy.array([2020.0, 2030.0])
event_activity = scipy.array([0.01, 0.001])
bins = (1,2)
percent_ann_loss, lat_lon, _, _ = ca.calc_annloss_deagg_grid(
lat,
lon,
total_building_loss,
total_building_value,
event_activity,
bins=bins)
expected_loss = scipy.array([[0.049233, 0.491135]])
expected_lat_lon = scipy.array([[-24.5,130.5],[-24.5,131.5]])
#print "percent_ann_loss", percent_ann_loss
#print "expected_loss", expected_loss
self.failUnless(scipy.allclose(percent_ann_loss, expected_loss))
self.failUnless(scipy.allclose(lat_lon, expected_lat_lon))
def test_small_checkable(self):
# See documentation/annualised_loss_calc.xls
# for the calculations of the expected values.
saved_ecloss = [[2000.0, 5.0],
[ 10.0, 2001.0]]
saved_ecbval2 = [2020.0, 2030.0]
nu = [0.01, 0.001]
expected_ann_loss = scipy.array([19.95996, 0.4928386])
expected_cum_ann_loss = scipy.array([
[1000, 90.909090],
[19.95996, 0.0],
[0.4928386, 0.0]])
# call function
(ann_loss,
cum_ann_loss) = ca.calc_annloss(saved_ecloss, saved_ecbval2, nu)
#print('expected_ann_loss=%s' % str(expected_ann_loss))
#print('ann_loss=%s' % str(ann_loss))
#print('cum_ann_loss=%s' % str(cum_ann_loss))
# test return values
self.failUnless(scipy.allclose(ann_loss, expected_ann_loss))
self.failUnless(scipy.allclose(cum_ann_loss, expected_cum_ann_loss))
def test_few_masked(self):
Blocks = self.make_blocks()
for Data in Blocks:
Data.data[:,:,:,6] = ma.masked
Data.data[:,:,:,13] = ma.masked
model_name = 'freq_modes_over_f_' + str(3)
parameters = mn.measure_noise_parameters(Blocks, [model_name])
for pol, pol_params in parameters.iteritems():
for ii in range(3):
mode_noise = pol_params[model_name]['over_f_mode_' + str(ii)]
#self.assertTrue(sp.allclose(mode_noise['thermal'], self.dt,
# rtol=0.5))
self.assertTrue(sp.allclose(mode_noise['mode'][6], 0,
atol=1.e-10))
self.assertTrue(sp.allclose(mode_noise['mode'][13], 0,
atol=1.e-10))
expected = sp.ones(self.nf, dtype=float) * self.dt
expected[6] = T_infinity**2
expected[13] = T_infinity**2
thermal = pol_params[model_name]['thermal']
# weak test since the above modes may have favoured a few channels.
#print thermal, expected
self.assertTrue(sp.allclose(thermal, expected, rtol=0.8))
measured_general_ind = pol_params[model_name]['all_channel_index']
measured_corner = pol_params[model_name]['all_channel_corner_f']
self.assertTrue(measured_corner < 4. / self.dt / self.nt / self.nb)
def test_fit_over_f_plus_const(self):
dt = 0.13
n_time = 10000
amp = 0.67 # K**2/Hz
index = -1.3
f_0 = 1.0
thermal = 2.7 # K**2/Hz
BW = 1./dt/2
window = sig.get_window('hanning', n_time)
n_spec = 10
p = 0
for ii in range(n_spec):
time_stream = noise_power.generate_overf_noise(amp, index, f_0,
dt, n_time)
time_stream += rand.normal(size=n_time) * sp.sqrt(thermal * BW * 2)
time_stream -= sp.mean(time_stream)
time_stream *= window
p += noise_power.calculate_power(time_stream)
p /= n_spec
p = noise_power.make_power_physical_units(p, dt)
w = noise_power.calculate_power(window)
w_norm = sp.mean(w).real
#w /= w_norm
p = noise_power.prune_power(p).real
#p /= w_norm
f = noise_power.ps_freq_axis(dt, n_time)
p = p[1:]
f = f[1:]
amp_m, index_m, f0_m, thermal_m = mn.fit_overf_const(p, w, f)
self.assertTrue(sp.allclose(amp_m, amp, atol=0.2))
self.assertTrue(sp.allclose(index_m, index, atol=0.1))
self.assertTrue(sp.allclose(thermal_m, thermal, atol=0.1))
def testing(self):
attributes = {"mo": array(["money", "soup"]), "SITE_CLASS": array(["E", "C"])}
latitude = [10, 20]
longitude = [1, 2]
sites = Sites(latitude, longitude, **attributes)
site_class2Vs30 = {"C": 30, "E": 40}
sites.set_Vs30(site_class2Vs30)
actual = array(latitude)
self.assert_(allclose(sites.latitude, actual, 0.001))
actual = array(longitude)
self.assert_(allclose(sites.longitude, actual, 0.001))
actual = array(["money", "soup"])
for (att, act) in map(None, sites.attributes["mo"], actual):
self.assert_(att == act)
actual = array([40, 30])
self.assert_(allclose(sites.attributes["Vs30"], actual, 0.001))
site_class2Vs30 = {"C": 30}
try:
sites.set_Vs30(site_class2Vs30)
except KeyError:
pass
else:
self.failUnless(False, "KeyError not raised")
def test_gets_thermal_with_correlated(self):
"""Checks that the part of the freq_modes code that compensates the
thermal for mode subtraction works."""
self.data *= sp.sqrt(self.bw * 2) # Makes thermal unity.
# Need to add something correlated so the modes arn't just the
# channels.
correlated_overf = noise_power.generate_overf_noise(1,
-2, 0.5, self.dt, self.data.shape[0])
correlated_overf += (rand.normal(size=(self.data.shape[0],))
* sp.sqrt((self.bw * 2) * 0.3))
self.data += correlated_overf[:,None,None,None] / sp.sqrt(self.nf)
Blocks = self.make_blocks()
# Mask a channel out completly.
for Data in Blocks:
Data.data[:,:,:,3] = ma.masked
model = 'freq_modes_over_f_4' # Take out 20% of the thermal power.
parameters = mn.measure_noise_parameters(Blocks,
[model])
right_ans = sp.ones(self.nf)
right_ans[3] = T_infinity**2
for p in parameters.itervalues():
pars = p[model]
thermal = pars['thermal']
self.assertTrue(sp.allclose(thermal,
right_ans, rtol=0.3))
mean_thermal = sp.mean(thermal[right_ans==1])
self.assertTrue(sp.allclose(mean_thermal, 1, rtol=0.05))
self.assertTrue(sp.allclose(pars['over_f_mode_0']['thermal'],
0.3, atol=0.1))
def test_gaussian_mixture_generator_replicatability():
"Test the GaussianMixtureModel generator"
import tempfile
fname = tempfile.mktemp()
N = 1000
n = 500
D = 10
K = 3
gmm = GaussianMixtureModel.generate(fname, K, D)
gmm.set_seed(100)
gmm.save()
X = gmm.sample(N)
del gmm
gmm = GaussianMixtureModel.from_file(fname)
Y = gmm.sample(N)
assert(sc.allclose(X, Y))
del gmm
gmm = GaussianMixtureModel.from_file(fname)
Y = gmm.sample(N, n)
assert(sc.allclose(X[:n], Y))
def _LML_covar(self,hyperparams,debugging=False):
"""
log marginal likelihood
"""
try:
KV = self.get_covariances(hyperparams,debugging=debugging)
except LA.LinAlgError:
LG.error('linalg exception in _LML_covar')
return 1E6
except ValueError:
LG.error('value error in _LML_covar')
return 1E6
lml_quad = 0.5*(KV['Ytilde']*KV['UYU']).sum()
lml_det = 0.5 * SP.log(KV['S']).sum()
lml_const = 0.5*self.n*self.t*(SP.log(2*SP.pi))
if debugging:
# do calculation without kronecker tricks and compare
_lml_quad = 0.5 * (KV['alpha']*KV['Yvec']).sum()
_lml_det = SP.log(SP.diag(KV['L'])).sum()
assert SP.allclose(_lml_quad,lml_quad), 'ouch, quadratic form is wrong in _LMLcovar'
assert SP.allclose(_lml_det, lml_det), 'ouch, ldet is wrong in _LML_covar'
lml = lml_quad + lml_det + lml_const
return lml
def intersect(self,e,coords=True,actual=True):
#Returns data about the intersection of self and edge e.
#if coords, return the intersection as a point or false if intersection DNE. If not coords, return true/false
#actual is a boolean for whether the intersection must be on both edges or not.
AA = sp.array(e.a-self.a)
proj = sp.dot(self.dir[0],AA)*unit(self.dir[0])
point = sp.array(self.a+proj) #One issue with this method is in case of parallel edges, it returns a rather arbitrary point on self.
if actual:
#Here the parallels are solved because the erroneous intersections won't be on both lines.
if self.containsPoint(point) and e.containsPoint(point):
if coords: return point
return True
return False
else:
#Here we have to check that the point is at least colinear with both.
EAP = sp.array(point-e.a)
EBP = sp.array(point-e.b)
SAP = sp.array(point-self.a)
SBP = sp.array(point-self.b)
s1 = EAP[0]/EBP[0]
s2 = SAP[0]/EBP[0]
if sp.allclose(EAP/EBP,s1,1e-8,0) and sp.allclose(SAP/SBP,s2,1e-8,0):
if coords: return point
return True
return False
def test_eps_r_noop_multi(self):
r0 = tc.eps_r_noop(tc.eps_r_noop(self.r2, self.A2, self.B2), self.A1, self.B1)
r0_ = tc.eps_r_noop_multi(self.r2, [self.A1, self.A2], [self.B1, self.B2])
self.assertTrue(sp.allclose(r0, r0_))
r0__ = tc.eps_r_noop_multi(self.r2, [self.AA12], [self.BB12])
self.assertTrue(sp.allclose(r0, r0__))
r0C = tc.eps_r_op_2s_C12(self.r2, self.C_A12, self.B1, self.B2)
r0C_ = tc.eps_r_noop_multi(self.r2, [self.C_A12], [self.B1, self.B2])
self.assertTrue(sp.allclose(r0C, r0C_))
r0C2 = tc.eps_r_op_2s_C12_AA34(self.r2, self.C_A12, self.BB12)
r0C2_ = tc.eps_r_noop_multi(self.r2, [self.C_A12], [self.BB12])
self.assertTrue(sp.allclose(r0C2, r0C2_))
r0CA2 = tc.eps_r_op_2s_C12(tc.eps_r_noop(self.r2, self.A2, self.B2),
self.C01, self.A0, self.B1)
r0CA2_ = tc.eps_r_noop_multi(self.r2, [self.C01, self.A2], [self.A0, self.BB12])
self.assertTrue(sp.allclose(r0CA2, r0CA2_))
def test_slice_interpolate_linear(self):
# Construct a 3D array that is a linear function.
v = self.vect
a = sp.arange(5)
a.shape = (5, 1, 1)
b = sp.arange(2)
b.shape = (1, 2, 1)
c = sp.arange(3)
c.shape = (1, 1, 3)
v[:, :, :] = a + b + c
v.set_axis_info('freq', 2, 1)
v.set_axis_info('a', 1, 1)
v.set_axis_info('b', 1, 1)
#### First test the weights.
# Test input sanitization.
self.assertRaises(ValueError, v.slice_interpolate_weights, [0, 1], 2.5)
# Test bounds.
self.assertRaises(ValueError, v.slice_interpolate_weights, [1, 2],
[2.5, 1.5])
# Test linear interpolations in 1D.
points, weights = v.slice_interpolate_weights(0, 2.5, 'linear')
self.assertTrue(sp.allclose(weights, 0.5))
self.assertTrue(2 in points)
self.assertTrue(3 in points)
# Test linear interpolations in multi D.
points, weights = v.slice_interpolate_weights([0, 1, 2],
[0.5, 0.5, 1.5],
'linear')
self.assertTrue(sp.allclose(weights, 1.0/8))
self.assertTrue(points.shape == (8, 3))
points, weights = v.slice_interpolate_weights([0, 1, 2],
[3, 1, 2],
'linear')
self.assertTrue(sp.allclose(weights % 1, 0))
#### Test linear interpolation on linear function.
# Test on the grid points.
self.assertEqual(v.slice_interpolate([0, 1, 2], [3.0, 1.0, 1.0]),
3.0 + 1.0 + 1.0)
# Test in 1D interpoation.
out = a + c + 0.347
out.shape = (5, 3)
self.assertTrue(sp.allclose(out, v.slice_interpolate(1, 0.347,
'linear')))
# Test in 2D.
out = b + 3.14159 + 1.4112
out.shape = (2,)
self.assertTrue(sp.allclose(out, v.slice_interpolate([0, 2],
[3.14159, 1.4112], 'linear')))
def test_build_capacityII(self):
"""
Test that the capacity is the same as matlabs
"""
surface_displacement = array([
0,
3.77315416609723,
10.76027112052043,
19.35492219305678,
30.15026521648398,
35.54123657393061,
34.27393320582447,
33.24309574336885,
31.61082700735715,
30.28033350490522,
28.99562491669272,
27.72120498849989,
26.45537611101864,
25.14598796310046,
23.61941589424981,
22.22764650672691,
20.81732741591404,
19.34361135133465,
17.85074552102223,
16.37154426563715,
])
surface_displacement.shape = 1, 1, -1
Ay, Dy, Au, Du = (0.40000000000000, 1.67808144348440, 0.80000000000000,
6.71232577393759)
aa, bb, cc, kappa = (-1.08731273138362, 0.59591863308111,
0.80000000000000, 1.000000000000000e-003)
capacity_parameters = Dy, Ay, Du, Au, aa, bb, cc
capacity_parameters = array(capacity_parameters)[:, newaxis, newaxis,
newaxis]
capacity = calculate_capacity(surface_displacement,
capacity_parameters)
#out
capacity_m = [
0, 0.68522523139676, 0.80000000000000, 0.80000000000000,
0.80000000000000, 0.80000000000000, 0.80000000000000,
0.80000000000000, 0.80000000000000, 0.80000000000000,
0.80000000000000, 0.80000000000000, 0.80000000000000,
0.80000000000000, 0.80000000000000, 0.80000000000000,
0.80000000000000, 0.80000000000000, 0.80000000000000, 0.80000000000
]
assert allclose(capacity[0, 0], capacity_m)
def test_solve_eig_bad_ind(self):
# Set all the information in one pixel to nil.
self.noise_inv[17, 3, 1, ...] = 0
self.noise_inv[..., 17, 3, 1] = 0
self.dirty_map = al.partial_dot(self.noise_inv, self.clean_map)
self.eig()
new_clean_map, noise_diag = clean_map.solve_from_eig(
self.noise_evalsinv,
self.noise_evects,
self.dirty_map,
True,
feedback=0)
self.clean_map[17, 3, 1] = 0
self.assertTrue(sp.allclose(new_clean_map, self.clean_map))
def test_to_from_file(self):
"""Test that vects and mats can be written to and from file and have
all thier properties preserved."""
# For vectors.
file_io.save('temp.npy', self.vect)
new_vect = vector.vect_array(file_io.load('temp.npy'))
self.assertTrue(sp.allclose(self.vect, new_vect))
self.assertEqual(self.vect.axes, new_vect.axes)
# For matricies.
file_io.save('temp.npy', self.mat)
new_mat = matrix.mat_array(file_io.load('temp.npy'))
self.assertTrue(sp.allclose(self.mat, new_mat))
self.assertEqual(self.mat.axes, new_mat.axes)
# Messing with stuf should raise exceptions.
new_mat = file_io.load('temp.npy')
new_mat.info['cols'] = (0, 3)
self.assertRaises(ValueError, matrix.mat_array, new_mat)
# Clean up
os.remove('temp.npy')
os.remove('temp.npy.meta')
def test_partial_dot_mat_vect(self):
self.mat.shape = (4, 6, 5)
self.mat.rows = (0, 1)
self.mat.cols = (2, )
self.mat.axes = ('x', 'y', 'freq')
new_vect = dot_products.partial_dot(self.mat, self.vect)
self.assertEqual(new_vect.shape, (4, 6, 2, 3))
self.assertEqual(new_vect.axes, ('x', 'y', 'a', 'b'))
numerical_result = sp.dot(sp.reshape(self.mat, (4 * 6, 5)),
sp.reshape(self.vect, (5, 2 * 3)))
self.assertTrue(
sp.allclose(numerical_result.flatten(), new_vect.flatten()))
def test_circle_write_read(self):
map_copy = copy.deepcopy(self.test_map)
fits_map.write(map_copy, 'temp_testmap.fits', feedback=0)
read_map = fits_map.read('temp_testmap.fits', feedback=0)
os.remove('temp_testmap.fits')
self.assertTrue(sp.allclose(self.test_map.data, read_map.data))
for field_name in fits_map.fields:
self.assertTrue(read_map.field.has_key(field_name))
self.assertAlmostEqual(self.test_map.field[field_name],
read_map.field[field_name])
# Finally, check the history.
hist = read_map.history
self.assertTrue(hist.has_key('000: Created from scratch.'))
def test_DLN_no_variability(self):
# dimensions (2,1,3,4) = 24 elements
dim = (2,1,3,4)
count_up = arange(1,24,1)
log_mean = resize(count_up*10, dim)
log_sigma = resize(count_up, dim)
var_method = None
dist = Distribution_Log_Normal(var_method)
sample_values = dist.sample_for_eqrm(log_mean,log_sigma)
actual = exp(log_mean)
self.assert_(allclose(sample_values, actual))
self.assert_(actual.shape == dim)
def test_spawning(self):
spawn_bins = 2
dln = GroundMotionDistributionLogNormal(var_method=SPAWN,
atten_spawn_bins=spawn_bins,
n_recurrence_models=1)
log_mean = ones((1, 1, 3,4))
log_mean *= 10
log_sigma = ones((1, 1, 3,4))
sample_values = dln.ground_motion_sample(log_mean,log_sigma)
act_SA_0 = ones((1, 1, 1, 1, 3, 4)) * (10 - 2.5)
act_SA_1 = ones((1, 1, 1, 1, 3, 4)) * (10 + 2.5)
act_SA = exp(concatenate((act_SA_0, act_SA_1)))
self.assert_(allclose(act_SA, sample_values))
def test_load_raster(self):
# Write a file to test
f = tempfile.NamedTemporaryFile(suffix='.txt',
prefix='HAZIMPtest_jobs',
delete=False,
mode='w+t')
f.write('exposure_latitude, exposure_longitude, ID, haz_actual\n')
f.write('8.1, 0.1, 1, 4\n')
f.write('7.9, 1.5, 2, -9999\n')
f.write('8.9, 2.9, 3, 6\n')
f.write('8.9, 3.1, 4, -9999\n')
f.write('9.9, 2.9, 5, -9999\n')
f.close()
inst = JOBS[LOADCSVEXPOSURE]
con_in = context.Context()
con_in.exposure_lat = None
con_in.exposure_long = None
con_in.exposure_att = {}
test_kwargs = {'file_name': f.name}
inst(con_in, **test_kwargs)
os.remove(f.name)
# Write a hazard file
f = tempfile.NamedTemporaryFile(suffix='.aai',
prefix='HAZIMPtest_jobs',
delete=False,
mode='w+t')
f.write('ncols 3 \r\n')
f.write('nrows 2 \r\n')
f.write('xllcorner +0. \r\n')
f.write('yllcorner +8. \r\n')
f.write('cellsize 1 \r\n')
f.write('NODATA_value -9999 \r\n')
f.write('1 2 -9999 \r\n')
f.write('4 5 6 ')
f.close()
haz_v = 'haz_v'
inst = JOBS[LOADRASTER]
test_kwargs = {'file_list': [f.name], 'attribute_label': haz_v}
inst(con_in, **test_kwargs)
the_nans = isnan(con_in.exposure_att[haz_v])
con_in.exposure_att.loc[the_nans, (haz_v, )] = -9999
msg = "con_in.exposure_att[haz_v] " + str(con_in.exposure_att[haz_v])
msg += "\n not = con_in.exposure_att['haz_actual'] " + \
str(con_in.exposure_att['haz_actual'])
self.assertTrue(
allclose(con_in.exposure_att[haz_v],
con_in.exposure_att['haz_actual']), msg)
os.remove(f.name)
def test_separate_points_by_polygon(self):
U = [[0, 0], [1, 0], [1, 1], [0, 1]] #Unit square
indices, count = separate_points_by_polygon(
[[0.5, 0.5], [1, -0.5], [0.3, 0.2]], U)
assert allclose(indices, [0, 2, 1])
assert count == 2
#One more test of vector formulation returning indices
polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]
points = [[0.5, 0.5], [1, -0.5], [1.5, 0], [0.5, 1.5], [0.5, -0.5]]
res, count = separate_points_by_polygon(points, polygon)
assert allclose(res, [0, 1, 2, 4, 3])
assert count == 3
polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]
points = [[0.5, 1.4], [0.5, 0.5], [1, -0.5], [1.5, 0], [0.5, 1.5],
[0.5, -0.5]]
res, count = separate_points_by_polygon(points, polygon)
assert allclose(res, [1, 2, 3, 5, 4, 0])
assert count == 3
def __init__(self, ground_motion_model_names, periods, model_weights):
self.periods = periods
# Should do this just once, when the para values are first verified.
# The -ve value means 'the logic tree is not collapsed'
self.model_weights = asarray(model_weights)
if not allclose(1, self.model_weights.sum()):
print 'model_weights,', -self.model_weights
raise ValueError('abs(self.model_weights) did not sum to 1!')
self.GM_models = []
for GM_model_name in ground_motion_model_names:
self.GM_models.append(
Ground_motion_calculator(GM_model_name, periods))
def test_reassign_phase_regenerate_models(self):
physmods = OpenPNM.Physics.models
net = OpenPNM.Network.Cubic(shape=[5, 5, 5])
geo1 = OpenPNM.Geometry.GenericGeometry(network=net,
pores=net.Ps,
throats=net.Ts)
geo1['pore.diameter'] = 1
geo1['throat.diameter'] = 1
geo1['throat.length'] = 1
phase1 = OpenPNM.Phases.GenericPhase(network=net)
phase1['pore.viscosity'] = 1
phase2 = OpenPNM.Phases.GenericPhase(network=net)
phase2['pore.viscosity'] = 10
phys = OpenPNM.Physics.GenericPhysics(network=net,
phase=phase1,
geometry=geo1)
phys.models.add(propname='throat.hydraulic_conductance',
model=physmods.hydraulic_conductance.hagen_poiseuille)
assert sp.allclose(phys['throat.hydraulic_conductance'], 0.02454369)
phys.parent_phase = phase2
assert sp.allclose(phys['throat.hydraulic_conductance'], 0.02454369)
phys.models.regenerate()
assert sp.allclose(phys['throat.hydraulic_conductance'], 0.00245437)
def test_forecast_fatality(self):
# This test is bad, since I'm writing it based on the code.
MMI = array([1, 2, 4., 10., 11.])
population = ones(MMI.shape)
beta = 1.0
theta = 10.0 * e**-2.0
fatality = forecast_fatality(MMI, population, beta, theta)
expected = array([0, 0, 0, norm.cdf(2.0), norm.cdf(2.0)])
#print "expected", expected
#print "fatality", fatality
self.failUnless(allclose(expected, fatality))
MMI = array([5.])
pop_scaler = 5.0
population = ones(MMI.shape) * pop_scaler
beta = 1.0
theta = 5.0 * e**-2.0
fatality = forecast_fatality(MMI, population, beta, theta)
expected = array([pop_scaler * 1.0 / beta * norm.cdf(2.0)])
#print "expected", expected
#print "fatality", fatality
self.failUnless(allclose(expected, fatality))
def test_data(self):
nf = self.Data.dims[-1]
# Rebin by 3's.
data = sp.arange(10, dtype=float) * 3.0
data = data[...,None] * sp.arange(4)
data = data[...,None] * sp.arange(2)
data = data[...,None] * sp.arange(nf)
new_data = sp.arange(3, dtype=float) * 9.0 + 3.0
new_data = new_data[...,None] * sp.arange(4)
new_data = new_data[...,None] * sp.arange(2)
new_data = new_data[...,None] * sp.arange(nf)
self.Data.data[...] = data
rebin_time.rebin(self.Data, 3)
self.assertTrue(sp.allclose(self.Data.data, new_data))
def test_washburn_throat_values(self):
self.water['throat.surface_tension'] = 0.072
self.water['throat.contact_angle'] = 120
f = OpenPNM.Physics.models.capillary_pressure.washburn
self.phys.models.add(propname='throat.capillary_pressure',
model=f,
surface_tension='throat.surface_tension',
contact_angle='throat.contact_angle',
throat_diameter='throat.diameter')
a = 0.14399999999999993
assert sp.allclose(self.water['throat.capillary_pressure'][0], a)
self.phys.models.remove('throat.capillary_pressure')
del self.water['throat.surface_tension']
del self.water['throat.contact_angle']
def __add__(self, other):
assert self.ells == other.ells
if scipy.allclose(self.sedges,
other.sedges,
rtol=1e-04,
atol=1e-05,
equal_nan=False):
self.counts += other.counts
self.weight_tot += other.weight_tot
else:
raise ValueError('s-edges are not compatible.')
return self
def restore_LCF_r(A, r, Gi, sanity_checks=False):
if Gi is None:
x = r
else:
x = Gi.dot(r.dot(Gi.conj().T))
M = eps_r_noop(x, A, A)
ev, EV = la.eigh(M) #wraps lapack routines, which return eigenvalues in ascending order
if sanity_checks:
assert np.all(ev == np.sort(ev)), "unexpected eigenvalue ordering"
rm1 = mm.simple_diag_matrix(ev, dtype=A.dtype)
Gm1 = EV.conj().T
if Gi is None:
Gi = EV #for left uniform case
r = rm1 #for sanity check
for s in xrange(A.shape[0]):
A[s] = Gm1.dot(A[s].dot(Gi))
if sanity_checks:
rm1_ = eps_r_noop(r, A, A)
if not sp.allclose(rm1_, rm1, atol=1E-12, rtol=1E-12):
log.warning("Sanity Fail in restore_LCF_r!: r is bad!")
log.warning(la.norm(rm1_ - rm1))
Gm1_i = EV
if sanity_checks:
eye = sp.eye(A.shape[1])
if not sp.allclose(sp.dot(Gm1, Gm1_i), eye,
atol=1E-12, rtol=1E-12):
log.warning("Sanity Fail in restore_LCF_r!: Bad GT! (off by %g)", la.norm(sp.dot(Gm1, Gm1_i) - eye))
return rm1, Gm1, Gm1_i
def test_MaximizeLikelihood(self):
"""Tests maximization likelihood.
Make sure it gives the same value for several starting points."""
random.seed(1)
scipy.random.seed(1)
tl = phydmslib.treelikelihood.TreeLikelihood(self.tree, self.alignment,
self.model)
logliks = []
paramsarrays = []
for itest in range(3):
modelparams = self.getModelParams(itest)
tl.updateParams(modelparams)
startloglik = tl.loglik
result = tl.maximizeLikelihood()
self.assertTrue(
tl.loglik > startloglik, "no loglik increase: "
"start = {0}, end = {1}".format(startloglik, tl.loglik))
for (otherloglik, otherparams) in zip(logliks, paramsarrays):
self.assertTrue(
scipy.allclose(tl.loglik,
otherloglik,
atol=1e-3,
rtol=1e-3),
"Large difference in loglik: {0} vs {1}".format(
otherloglik, tl.loglik))
self.assertTrue(
scipy.allclose(tl.paramsarray,
otherparams,
atol=1e-2,
rtol=1e-1),
"Large difference in paramsarray: {0} vs {1}, {2}".format(
otherparams, tl.paramsarray, self.model))
logliks.append(tl.loglik)
paramsarrays.append(tl.paramsarray)
def test_lowrank_iso(self):
theta = SP.array(SP.random.randn(2)**2)
theta_hat = SP.exp(2 * theta)
_K = theta_hat[0] * SP.dot(
self.Xtrain, self.Xtrain.T) + theta_hat[1] * SP.eye(self.n_train)
_Kcross = theta_hat[0] * SP.dot(self.Xtrain, self.Xtest.T)
_Kgrad_theta = []
_Kgrad_theta.append(2 * theta_hat[0] *
SP.dot(self.Xtrain, self.Xtrain.T))
_Kgrad_theta.append(2 * theta_hat[1] * SP.eye(self.n_train))
cov = lowrank.LowRankCF(self.n_dimensions)
cov.X = self.Xtrain
cov.Xcross = self.Xtest
K = cov.K(theta)
Kcross = cov.Kcross(theta)
assert SP.allclose(K, _K), 'ouch, covariance matrix is wrong'
assert SP.allclose(Kcross,
_Kcross), 'ouch, cross covariance matrix is wrong'
assert SP.allclose(_Kgrad_theta[0], cov.Kgrad_theta(theta, 0))
assert SP.allclose(_Kgrad_theta[1], cov.Kgrad_theta(theta, 1))
# gradient with respect to latent factors
for i in range(self.n_dimensions):
for j in range(self.n_train):
Xgrad = SP.zeros(self.Xtrain.shape)
Xgrad[j, i] = 1
_Kgrad_x = theta_hat[0] * (SP.dot(Xgrad, self.Xtrain.T) +
SP.dot(self.Xtrain, Xgrad.T))
Kgrad_x = cov.Kgrad_x(theta, i, j)
assert SP.allclose(
Kgrad_x, _Kgrad_x
), 'ouch, gradient with respect to x is wrong for entry [%d,%d]' % (
i, j)
def test_generate_motion_csv(self):
# Parameters
output_dir = self.dir
site_tag = 'ernabella'
#is_bedrock = False
soil_amp = False
# 1. Create and save analysis objects objects
self.save_analysis_objects(output_dir, site_tag)
# 2. Run through generate_motion_csv
output_filenames = generate_motion_csv(output_dir, site_tag, soil_amp)
expected_ground_motion = asarray([[0, 1, 2], [3, 4, 5], [6, 7, 8],
[9, 10, 11], [12, 13, 14]])
expected_atten_periods = asarray([0, 1.0, 2.0])
# 3. Read in generated files
for gmm_i, filename in enumerate(output_filenames):
file_h = open(filename, 'r')
text = file_h.read().splitlines()
# ditch the comment lines
text.pop(0)
text.pop(0)
text.pop(0)
text.pop(0)
text.pop(0)
# Convert a space separated text line into a numeric float array
periods_f = array([float(ix) for ix in text[0].split(' ')])
self.assert_(allclose(periods_f, array(expected_atten_periods)))
text.pop(0)
motion_f = array([float(ix) for ix in text[0].split(' ')])
self.assert_(allclose(motion_f, expected_ground_motion[gmm_i]))
file_h.close()
def _LML_covar(self, hyperparams, debugging=False):
"""
log marginal likelihood
"""
self._update_inputs(hyperparams)
try:
KV = self.get_covariances(hyperparams, debugging=debugging)
except LA.LinAlgError:
pdb.set_trace()
LG.error('linalg exception in _LML_covar')
return 1E6
Si = 1. / KV['Stilde_rc']
lml_quad = 0.5 * (ravel(KV['UYtildeU_rc'])**2 * Si).sum()
lml_det = 0.5 * (SP.log(KV['S_s']).sum() * self.n +
SP.log(KV['S_o']).sum() * self.t)
lml_det += 0.5 * SP.log(KV['Stilde_rc']).sum()
lml_const = 0.5 * self.nt * (SP.log(2 * SP.pi))
if debugging:
# do calculation without kronecker tricks and compare
_lml_quad = 0.5 * (KV['alpha'] * KV['Yvec']).sum()
_lml_det = SP.log(SP.diag(KV['L'])).sum()
assert SP.allclose(
_lml_quad, lml_quad, atol=1E-2,
rtol=1E-2), 'ouch, quadratic form is wrong: %.2f' % LA.norm(
lml_quad, _lml_quad)
assert SP.allclose(
_lml_det, lml_det, atol=1E-2,
rtol=1E-2), 'ouch, ldet is wrong in _LML_covar' % LA.norm(
lml_det, _lml_det)
lml = lml_quad + lml_det + lml_const
return lml
def test_load(self):
"""Test initial load of Bridges object."""
b = bridges.Bridges.from_csv(self.file_name)
actual = scipy.array(self.lat)
b_lat_str = self.pp.pformat(b.latitude)
actual_str = self.pp.pformat(actual)
msg = ('b.latitude != actual\n'
'(%s !=\n%s)' % (b_lat_str, actual_str))
self.assert_(scipy.allclose(b.latitude, actual, 0.001), msg)
actual = scipy.array(self.lon)
b_lon_str = self.pp.pformat(b.longitude)
actual_str = self.pp.pformat(actual)
msg = ('b.longitude != actual\n'
'(%s !=\n%s)' % (b_lon_str, actual_str))
self.assert_(scipy.allclose(b.longitude, actual, 0.001))
actual = scipy.array(['E', 'F', 'G', 'D', 'E', 'F', 'G', 'C'])
for (att, act) in map(None, b.attributes['SITE_CLASS'], actual):
msg = ('Expected attribute == actual (got %s == %s)' %
(str(att), str(act)))
self.failUnlessEqual(att, act, msg)
actual = scipy.array([0, 32, 20, 4, 0, 0, 12, 0])
for (att, act) in map(None, b.attributes['SKEW'], actual):
msg = ('Expected attribute == actual (got %s == %s)' %
(str(att), str(act)))
self.failUnlessEqual(att, act, msg)
actual = scipy.array([2, 3, 4, 5, 6, 7, 8, 9])
for (att, act) in map(None, b.attributes['BID'], actual):
msg = ('Expected attribute == actual (got %s == %s)' %
(str(att), str(act)))
self.failUnlessEqual(att, act, msg)
def test_purcell_throat_values(self):
self.water['throat.surface_tension'] = 0.072
self.water['throat.contact_angle'] = 120
f = OpenPNM.Physics.models.capillary_pressure.purcell
self.phys.models.add(propname='throat.capillary_pressure',
model=f,
r_toroid=0.1,
surface_tension='throat.surface_tension',
contact_angle='throat.contact_angle',
throat_diameter='throat.diameter')
a = 0.26206427646507374
assert sp.allclose(self.water['throat.capillary_pressure'][0], a)
self.phys.models.remove('throat.capillary_pressure')
del self.water['throat.surface_tension']
del self.water['throat.contact_angle']
def test_Strasser_et_al_2010_interface_rup_width(self):
#
Mw = array([4.5, 6.5, 13., 13.])
area=array([100., 100., 100., 100.])
dip = array([ 0., 0., 90., 90.])
fault_width = array([ 15000. , 15000. , 15000., 2.])
scaling_dic = {'scaling_rule':'Strasser_et_al_2010_interface'}
width = scaling_calc_rup_width(Mw, scaling_dic,
dip, rup_area=area,
max_rup_width=fault_width)
correct = [ 4.983104559705295, 25.089961794654, 4797.334486366892, 2.]
assert allclose(correct,
width)
def test_Strasser_et_al_2010_intraslab_rup_width(self):
#
Mw = array([4.5, 6.5, 13., 13.])
area=array([100., 100., 100., 100.])
dip = array([ 0., 0., 90., 90.])
fault_width = array([ 15000. , 15000. , 15000., 2.])
scaling_dic = {'scaling_rule':'Strasser_et_al_2010_intraslab'}
width = scaling_calc_rup_width(Mw, scaling_dic,
dip, rup_area=area,
max_rup_width=fault_width)
correct = [ 3.499451670283573, 18.03017740859569, 3715.352290971724, 2.]
assert allclose(correct,
width)
def test_linear(self):
theta = SP.array([SP.random.randn()**2])
theta_hat = SP.exp(2 * theta)
_K = SP.dot(self.Xtrain, self.Xtrain.T)
_Kcross = SP.dot(self.Xtrain, self.Xtest.T)
cov = linear.LinearCF(n_dimensions=self.n_dimensions)
cov.X = self.Xtrain
cov.Xcross = self.Xtest
K = cov.K(theta)
Kcross = cov.Kcross(theta)
Kgrad_x = cov.Kgrad_x(theta, 0)
Kgrad_theta = cov.Kgrad_theta(theta, 0)
assert SP.allclose(K,
theta_hat * _K), 'ouch covariance matrix is wrong'
assert SP.allclose(Kgrad_theta, 2 * theta_hat *
_K), 'ouch, gradient with respect to theta is wrong'
assert SP.allclose(Kcross, theta_hat *
_Kcross), 'ouch, cross covariance is wrong'
# gradient with respect to latent factors
# for each entry
for i in range(self.n_dimensions):
for j in range(self.n_train):
Xgrad = SP.zeros(self.Xtrain.shape)
Xgrad[j, i] = 1
_Kgrad_x = theta_hat * (SP.dot(Xgrad, self.Xtrain.T) +
SP.dot(self.Xtrain, Xgrad.T))
Kgrad_x = cov.Kgrad_x(theta, i, j)
assert SP.allclose(
Kgrad_x, _Kgrad_x
), 'ouch, gradient with respect to x is wrong for entry [%d,%d]' % (
i, j)
def test_build_replacement_ratios(self):
#usage_values_per_struct = ['RES1',
# 'RES3', 'COM8', 'GOV1', 'GOV1', 'GOV1']
usage_values_per_struct = [111, 231, 231]
rcp_actual = {
'structural': [0.2344, 0.1918, 0.1918],
'nonstructural drift sensitive': [0.5, 0.3288, 0.3288],
'nonstructural acceleration sensitive': [0.2656, 0.4795, 0.4795]
}
buildings_usage_classification = 'FCB'
rcp = build_replacement_ratios(usage_values_per_struct,
buildings_usage_classification)
components = [
'structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive'
]
for comp in components:
self.assert_(allclose(rcp[comp], rcp_actual[comp], 0.001))
usage_values_per_struct = ['RES1', 'COM4', 'COM4']
rcp_actual = {
'structural': [0.2344, 0.1918, 0.1918],
'nonstructural drift sensitive': [0.5, 0.3288, 0.3288],
'nonstructural acceleration sensitive': [0.2656, 0.4795, 0.4795]
}
buildings_usage_classification = 'HAZUS'
rcp = build_replacement_ratios(usage_values_per_struct,
buildings_usage_classification)
components = [
'structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive'
]
for comp in components:
self.assert_(allclose(rcp[comp], rcp_actual[comp], 0.001))
def check_YNGKP_M0_attributes(self):
"""Make sure `YNGKP_M0` has the expected attribute values."""
self.assertEqual(self.nsites, self.YNGKP_M0.nsites)
# make sure Prxy has rows summing to zero
self.assertFalse(scipy.isnan(self.YNGKP_M0.Pxy).any())
self.assertFalse(scipy.isinf(self.YNGKP_M0.Pxy).any())
diag = scipy.eye(N_CODON, dtype='bool')
self.assertTrue(
scipy.allclose(0, scipy.sum(self.YNGKP_M0.Pxy[0], axis=1)))
self.assertTrue(scipy.allclose(0, self.YNGKP_M0.Pxy[0].sum()))
self.assertTrue((self.YNGKP_M0.Pxy[0][diag] <= 0).all())
self.assertTrue((self.YNGKP_M0.Pxy[0][~diag] >= 0).all())
assert self.YNGKP_M0.Pxy.shape == (1, N_CODON, N_CODON)
for param in self.YNGKP_M0.dPxy:
assert self.YNGKP_M0.dPxy[param].shape == (1, N_CODON, N_CODON)
assert self.YNGKP_M0.B[param].shape == (1, N_CODON, N_CODON)
assert self.YNGKP_M0.Ainv.shape == (1, N_CODON, N_CODON)
assert self.YNGKP_M0.A.shape == (1, N_CODON, N_CODON)
assert self.YNGKP_M0.M(0.2).shape == (self.nsites, N_CODON, N_CODON)
for (pname, value) in self.params.items():
assert self.YNGKP_M0.dM(
0.2, pname,
self.YNGKP_M0.M(0.2)).shape == (self.nsites, N_CODON, N_CODON)
def test_transpose_partial_dot(self):
self.mat.shape = (5, 4, 6)
self.mat.cols = (1, 2)
self.mat.rows = (0, )
self.mat.axes = ('freq', 'x', 'y')
matT = self.mat.mat_transpose()
new_vect = algebra.partial_dot(matT, self.vect)
self.assertEqual(new_vect.shape, (4, 6, 2, 3))
self.assertEqual(new_vect.axes, ('x', 'y', 'a', 'b'))
# Reform origional matrix to get same numerical result.
mat = sp.reshape(self.mat, (5, 4 * 6))
mat = sp.rollaxis(mat, 1, 0)
numerical_result = sp.dot(mat, sp.reshape(self.vect, (5, 2 * 3)))
self.assertTrue(
sp.allclose(numerical_result.flatten(), new_vect.flatten()))
def test_modified_Wells_and_Coppersmith_94_rup_width2(self):
#
Mw = array([4.5, 6.5, 13., 13.])
area=array([100., 100., 100., 100.])
dip = array([ 0., 0., 90., 90.])
fault_width = array([ 15000. , 15000. , 15000., 2.])
scaling_dic = {'scaling_rule':'modified_Wells_and_Coppersmith_94'}
width = scaling_calc_rup_width(Mw, scaling_dic,
dip, rup_area=area,
max_rup_width=fault_width)
correct = [ 10., 10., 5., 2.]
assert allclose(correct,
width)
def compare_attributes(atts1,atts2):
self.assertEqual(len(atts1.keys()),len(atts2.keys()),"{}".format(nameRun))
self.assertListEqual(sorted(atts1.keys()),sorted(atts2.keys()),"{}".format(nameRun))
for item in atts1:
nequal = True
if isinstance(atts1[item],numpy.ndarray):
nequal = sp.logical_not(sp.array_equal(atts1[item],atts2[item]))
else:
nequal = atts1[item]!=atts2[item]
if nequal:
print("WARNING: {}: not exactly equal, using allclose for {}".format(nameRun,item))
print(atts1[item],atts2[item])
allclose = sp.allclose(atts1[item],atts2[item])
self.assertTrue(allclose,"{}".format(nameRun))
return
