def calculate_cubic_value_not_a_knot(t, y):
n = len(t)
h = []
b = []
v = [0]
A = zeros(shape=(n, n))
for i in range(0, n - 1):
h.append(t[i + 1] - t[i])
b.append(6 * (y[i + 1] - y[i]) / h[i])
A[0][0] = h[1]
A[0][1] = -h[1] - h[0]
A[0][2] = h[0]
for i in range(1, n - 1):
A[i][i - 1] = h[i - 1]
A[i][i] = 2 * (h[i - 1] + h[i])
A[i][i + 1] = h[i]
v.append(b[i] - b[i - 1])
v.append(0)
A[n - 1][n - 3] = h[n - 2]
A[n - 1][n - 2] = -h[n - 2] - h[n - 3]
A[n - 1][n - 1] = h[n - 3]
z = linalg.solve(A, v)
return z, h
def deq(self, x):
"""
Differential equation solution
:param x: array of length 6 that contains coordinates and velocities
:return:
"""
r2 = np.dot(x[:3], x[:3])
r3 = r2 * sqrt(r2)
omg2 = self.OMGE_GLO * self.OMGE_GLO
if r2 <= 0:
return zeros(6)
deq_a = 1.5 * self.J2_GLO * self.MU_GLO * self.RE_GLO**2 / r2 / r3 # /* 3/2*J2*mu*Ae^2/r^5 */
deq_b = 5.0 * x[2] * x[2] / r2 # /* 5*z^2/r^2 */
deq_c = -self.MU_GLO / r3 - deq_a * (1.0 - deq_b
) # /* -mu/r^3-a(1-b) */
xdot0_2 = x[3:6]
xdot_3 = (deq_c +
omg2) * x[0] + 2.0 * self.OMGE_GLO * x[4] + self.acc[0]
xdot_4 = (deq_c +
omg2) * x[1] - 2.0 * self.OMGE_GLO * x[3] + self.acc[1]
xdot_5 = (deq_c - 2.0 * deq_a) * x[2] + self.acc[2]
return np.append(xdot0_2, np.array([xdot_3, xdot_4, xdot_5]))
def calculate_cubic_value_not_a_knot(t, y):
n = len(t)
h = []
b = []
v = [0]
A = zeros(shape=(n, n))
for i in range(0, n - 1):
h.append(t[i + 1] - t[i])
b.append(6 * (y[i + 1] - y[i]) / h[i])
A[0][0] = h[1]
A[0][1] = - h[1] - h[0]
A[0][2] = h[0]
for i in range(1, n - 1):
A[i][i - 1] = h[i - 1]
A[i][i] = 2 * (h[i - 1] + h[i])
A[i][i + 1] = h[i]
v.append(b[i] - b[i - 1])
v.append(0)
A[n - 1][n - 3] = h[n - 2]
A[n - 1][n - 2] = - h[n - 2] - h[n - 3]
A[n - 1][n - 1] = h[n - 3]
z = linalg.solve(A, v)
return z, h
def convert(self):
self.g = g = Gcode(safetyheight=self.safetyheight,
tolerance=self.tolerance,
spindle_speed=self.spindle_speed,
units=self.units)
g.begin()
g.continuous(self.tolerance)
g.safety()
if self.roughing_delta and self.roughing_offset:
base_image = self.image
rough = make_tool_shape(ball_tool,
2*self.roughing_offset, self.pixelsize)
w, h = base_image.shape
tw, th = rough.shape
w1 = w + tw
h1 = h + th
nim1 = numarray.zeros((w1, h1), 'Float32') + base_image.min()
nim1[tw/2:tw/2+w, th/2:th/2+h] = base_image
self.image = numarray.zeros((w,h), type="Float32")
for j in range(0, w):
progress(j,w)
for i in range(0, h):
self.image[j,i] = (nim1[j:j+tw,i:i+th] - rough).max()
self.feed = self.roughing_feed
r = -self.roughing_delta
m = self.image.min()
self.ro = self.roughing_offset
while r > m:
self.rd = r
self.one_pass()
r = r - self.roughing_delta
if r < m + epsilon:
self.rd = m
self.one_pass()
self.image = base_image
self.cache.clear()
self.feed = self.base_feed
self.ro = 0
self.rd = self.image.min()
self.one_pass()
g.end()
def convert(self):
self.g = g = Gcode(safetyheight=self.safetyheight,
tolerance=self.tolerance,
spindle_speed=self.spindle_speed,
units=self.units)
g.begin()
g.continuous(self.tolerance)
g.safety()
if self.roughing_delta and self.roughing_offset:
base_image = self.image
rough = make_tool_shape(ball_tool, 2 * self.roughing_offset,
self.pixelsize)
w, h = base_image.shape
tw, th = rough.shape
w1 = w + tw
h1 = h + th
nim1 = numarray.zeros((w1, h1), 'Float32') + base_image.min()
nim1[tw / 2:tw / 2 + w, th / 2:th / 2 + h] = base_image
self.image = numarray.zeros((w, h), type="Float32")
for j in range(0, w):
progress(j, w)
for i in range(0, h):
self.image[j, i] = (nim1[j:j + tw, i:i + th] - rough).max()
self.feed = self.roughing_feed
r = -self.roughing_delta
m = self.image.min()
self.ro = self.roughing_offset
while r > m:
self.rd = r
self.one_pass()
r = r - self.roughing_delta
if r < m + epsilon:
self.rd = m
self.one_pass()
self.image = base_image
self.cache.clear()
self.feed = self.base_feed
self.ro = 0
self.rd = self.image.min()
self.one_pass()
g.end()
def setFromRotatedBasis(self, vecX, vecY, vecZ):
#print "setFromRotatedBases: ",vecX,vecY,vecZ
m = Numeric.zeros([3,3])
normX = vecX.norm()
normY = vecY.norm()
normZ = vecZ.norm()
#print "norms: ",normX, normY, normZ
for i in range(3):
m[i][0] = vecX[i] / normX
m[i][1] = vecY[i] / normY
m[i][2] = vecZ[i] / normZ
self.setFromRotationMatrix(m)
def calculate_quadratic_value_not_a_knot(t, y):
n = len(t)
d = [(y[1] - y[0]) / (t[1] - t[0])]
A = zeros(shape=(n, n))
A[0][0] = 1
for i in range(1, n):
A[i][i - 1] = 1
A[i][i] = 1
d.append(2 * ((y[i] - y[i - 1]) / (t[i] - t[i - 1])))
return linalg.solve(A, d)
def setFromRotatedBasis(self, vecX, vecY, vecZ):
#print "setFromRotatedBases: ",vecX,vecY,vecZ
m = Numeric.zeros([3, 3])
normX = vecX.norm()
normY = vecY.norm()
normZ = vecZ.norm()
#print "norms: ",normX, normY, normZ
for i in range(3):
m[i][0] = vecX[i] / normX
m[i][1] = vecY[i] / normY
m[i][2] = vecZ[i] / normZ
self.setFromRotationMatrix(m)
def calculate_quadratic_value_not_a_knot(t, y):
n = len(t)
d = [(y[1] - y[0]) / (t[1] - t[0])]
A = zeros(shape=(n, n))
A[0][0] = 1
for i in range(1, n):
A[i][i-1] = 1
A[i][i] = 1
d.append(2 * ((y[i] - y[i-1]) / (t[i] - t[i-1])))
return linalg.solve(A, d)
def scale(data):
m = line_len(data)
x_min, x_max = numpy.array([1e10] * m), zeros(m)
for (x, _) in data:
for i in range(m):
x_min[i] = min(x_min[i], x[i])
x_max[i] = max(x_max[i], x[i])
for (x, _) in data:
for i in range(m):
x[i] = (x[i] - x_min[i]) / (x_max[i] - x_min[i]) if x_max[i] != x_min[i] else 1
return data
def scale(data):
m = line_len(data)
x_min, x_max = numpy.array([1e10] * m), zeros(m)
for (x, _) in data:
for i in range(m):
x_min[i] = min(x_min[i], x[i])
x_max[i] = max(x_max[i], x[i])
for (x, _) in data:
for i in range(m):
x[i] = (x[i] - x_min[i]) / (
x_max[i] - x_min[i]) if x_max[i] != x_min[i] else 1
return data
def update(self, arr):
"""Update the accumulators of the StatsCollector given a complete matrix;
assume that all observations have the same weight.
Properly handle missing values.
"""
assert self.width() == arr.shape[1] #shape(arr)[1]
i = 0
## Update number of elements counters
(length, width) = arr.shape #shape(arr)
initial_n = self.n.copy(
) #self.n[:] # Keep old n for argmin/argmax
n = zeros(width) + length
missings = isnan(arr)
nnan = sum(missings, 0)
self.n += n
self.nnan += nnan
self.nnonnan += n - nnan
## Create masked version of arr and update accumulators
ma = masked_array(arr, mask=missings) # Here, mask missings only
arr_nomissings = arr[~normal_sometrue(missings,
1)] # Here, strip missing rows
self.sum = self.sum + sum(ma, 0) # += does not work...
self.sum_ssq = self.sum_ssq + sum(ma * ma, 0) # += does not work...
self.sum_xxt = self.sum_xxt + matrixmultiply(transpose(arr_nomissings),
arr_nomissings)
self.sum_nomi = self.sum_nomi + sum(arr_nomissings, 0)
self.nxxt += arr_nomissings.shape[0] #shape(arr_nomissings)[0]
## Update (arg)min / make sure old argmin is kept if not updated
ma_argmin = argmin(ma, 0)
ma_min = ma[ma_argmin, range(width)]
min_newpos = argmin(array([self.min, ma_min]), 0).astype('Bool')
self.min[min_newpos] = ma_min[min_newpos]
# XXX Argmin computation needs to be revised! Does not work, at least
# when passing array of shape (1,1).
self.argmin[min_newpos] = ma_argmin[min_newpos] + initial_n[min_newpos]
## Update (arg)max / make sure old argmax is kept if not updated
ma_argmax = argmax(ma, 0)
ma_max = ma[ma_argmax, range(width)]
max_newpos = argmax(array([self.max, ma_max]), 0).astype('Bool')
self.max[max_newpos] = ma_max[max_newpos]
# XXX Argmax computation needs to be revised! Does not work, at least
# when passing array of shape (1,1). Also, is the use of min_newpos
# correct?
self.argmax[max_newpos] = ma_argmax[max_newpos] + initial_n[min_newpos]
def update(self, arr):
"""Update the accumulators of the StatsCollector given a complete matrix;
assume that all observations have the same weight.
Properly handle missing values.
"""
assert self.width() == arr.shape[1] #shape(arr)[1]
i = 0
## Update number of elements counters
(length,width)= arr.shape #shape(arr)
initial_n = self.n.copy()#self.n[:] # Keep old n for argmin/argmax
n = zeros(width) + length
missings = isnan(arr)
nnan = sum(missings,0)
self.n += n
self.nnan += nnan
self.nnonnan += n - nnan
## Create masked version of arr and update accumulators
ma = masked_array(arr, mask=missings) # Here, mask missings only
arr_nomissings = arr[~normal_sometrue(missings,1)] # Here, strip missing rows
self.sum = self.sum + sum(ma,0) # += does not work...
self.sum_ssq = self.sum_ssq + sum(ma*ma,0) # += does not work...
self.sum_xxt = self.sum_xxt + matrixmultiply(transpose(arr_nomissings),
arr_nomissings)
self.sum_nomi= self.sum_nomi + sum(arr_nomissings,0)
self.nxxt += arr_nomissings.shape[0] #shape(arr_nomissings)[0]
## Update (arg)min / make sure old argmin is kept if not updated
ma_argmin = argmin(ma,0)
ma_min = ma[ma_argmin, range(width)]
min_newpos = argmin(array([self.min, ma_min]), 0).astype('Bool')
self.min[min_newpos] = ma_min[min_newpos]
# XXX Argmin computation needs to be revised! Does not work, at least
# when passing array of shape (1,1).
self.argmin[min_newpos] = ma_argmin[min_newpos] + initial_n[min_newpos]
## Update (arg)max / make sure old argmax is kept if not updated
ma_argmax = argmax(ma,0)
ma_max = ma[ma_argmax, range(width)]
max_newpos = argmax(array([self.max, ma_max]), 0).astype('Bool')
self.max[max_newpos] = ma_max[max_newpos]
# XXX Argmax computation needs to be revised! Does not work, at least
# when passing array of shape (1,1). Also, is the use of min_newpos
# correct?
self.argmax[max_newpos] = ma_argmax[max_newpos] + initial_n[min_newpos]
def damage_state_position(capacityDisp, demandDisp):
numberAccs = len(demandDisp)
numberLS = len(capacityDisp)
dsPositions = []
for i in range(numberAccs):
dsPositionsAcc = numarray.zeros(4)
dsPositionsAcc[3] = dsPositionsAcc[3] + 1.0
for j in range(numberLS):
if demandDisp[i][j] < capacityDisp[j]:
dsPositionsAcc[j] = dsPositionsAcc[j] + 1.0
dsPositionsAcc[3] = dsPositionsAcc[3] - 1.0
break
dsPositions.append(dsPositionsAcc)
return dsPositions
def scanSequence(mix, bg, seq,scoring='mix'):
"""
Scores all positions of a sequence with the given model and background.
@param mix: MixtureModel object
@param bg: background MixtureModel object
@param seq: sequence as list of nucleotides
@param scoring: flag to determine the scoring scheme used for the mixtures.
'compmax' means maximum density over the components, 'mix' means true mixture density
@return: list of position-wise log-odd scores
"""
# convert sequence to internal representation, alphabet of seq must be DNA
alph = mixture.Alphabet(['A','C','G','T'])
f = lambda x: alph.internal(x)
seq=map(f,seq)
dnr = mix.components[0].dist_nr
# init with dummy value at first position
s = numarray.array([[-1]+ seq[0:dnr-1]])
score = []
for i in range(dnr-1,len(seq),1):
# shift query sequence by one position
s[0] = numarray.concatenate( [s[0][1:],numarray.array([seq[i]])],0)
if scoring == 'compmax':
# score as maximum over components
c_m_l = numarray.zeros(mix.G,numarray.Float)
for i in range(mix.G):
c_m_l[i] = mix.components[i].pdf(s)[0]
m_l = c_m_l.max()
elif scoring == 'mix':
m_l = mix.pdf(s)[0]
bg_l = bg.pdf(s)[0]
score.append(m_l-bg_l)
return score
def get_data():
result = []
file = urlopen("http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data")
for line in file.readlines():
array = line.decode("utf-8").split(',')
result.append(([1.0] + [float(f) for f in array[2:]], 1 if array[1] == "M" else -1))
rlen = llength(result)
x_min, x_max = numpy.array([1e10] * rlen), zeros(rlen)
for (x, _) in result:
for i in range(rlen):
x_min[i] = min(x_min[i], x[i])
x_max[i] = max(x_max[i], x[i])
for (x, _) in result:
for i in range(rlen):
x[i] = (x[i] - x_min[i]) / (x_max[i] - x_min[i]) if x_max[i] != x_min[i] else 1
return result
def scanSequence(mix, bg, seq, scoring='mix'):
"""
Scores all positions of a sequence with the given model and background.
@param mix: MixtureModel object
@param bg: background MixtureModel object
@param seq: sequence as list of nucleotides
@param scoring: flag to determine the scoring scheme used for the mixtures.
'compmax' means maximum density over the components, 'mix' means true mixture density
@return: list of position-wise log-odd scores
"""
# convert sequence to internal representation, alphabet of seq must be DNA
alph = mixture.Alphabet(['A', 'C', 'G', 'T'])
f = lambda x: alph.internal(x)
seq = map(f, seq)
dnr = mix.components[0].dist_nr
# init with dummy value at first position
s = numarray.array([[-1] + seq[0:dnr - 1]])
score = []
for i in range(dnr - 1, len(seq), 1):
# shift query sequence by one position
s[0] = numarray.concatenate([s[0][1:], numarray.array([seq[i]])], 0)
if scoring == 'compmax':
# score as maximum over components
c_m_l = numarray.zeros(mix.G, numarray.Float)
for i in range(mix.G):
c_m_l[i] = mix.components[i].pdf(s)[0]
m_l = c_m_l.max()
elif scoring == 'mix':
m_l = mix.pdf(s)[0]
bg_l = bg.pdf(s)[0]
score.append(m_l - bg_l)
return score
def deq(self, x):
"""
Differential equation solution
:param x: array of length 6 that contains coordinates and velocities
:return:
"""
r2 = np.dot(x[:3], x[:3])
r3 = r2 * sqrt(r2)
omg2 = self.OMGE_GLO * self.OMGE_GLO
if r2 <= 0:
return zeros(6)
deq_a = 1.5 * self.J2_GLO * self.MU_GLO * self.RE_GLO**2 / r2 / r3 # /* 3/2*J2*mu*Ae^2/r^5 */
deq_b = 5.0 * x[2] * x[2] / r2 # /* 5*z^2/r^2 */
deq_c = -self.MU_GLO / r3 - deq_a * (1.0 - deq_b) # /* -mu/r^3-a(1-b) */
xdot0_2 = x[3:6]
xdot_3 = (deq_c + omg2) * x[0] + 2.0 * self.OMGE_GLO * x[4] + self.acc[0]
xdot_4 = (deq_c + omg2) * x[1] - 2.0 * self.OMGE_GLO * x[3] + self.acc[1]
xdot_5 = (deq_c - 2.0 * deq_a) * x[2] + self.acc[2]
return np.append(xdot0_2, np.array([xdot_3, xdot_4, xdot_5]))
#data.fromFiles(["filt_DAT_134.txt","DRD1_134.txt","DRD2_134.txt","DRD3_134.txt","DRD5_134.txt" ,"filt_WISC_WIAT_DISC_134.txt"])
#m = mixture.readMixture("NEC_ADHD_struct_7.mix")
data.internalInit(m)
c = m.classify(data,entropy_cutoff=0.50)
data.printClustering(m.G,c)
m.getClusterEntropy(data)
m.evalStructure(data.headers)
print m.groups
plot = numarray.zeros((m.G, len(m.leaders)))
for i in range(len(m.leaders)):
# check for noise variables
if len(m.leaders[i]) == 1:
l = m.leaders[i][0]
for g in range(m.G):
plot[g, i] = 1
else:
for l in m.leaders[i]:
if len(m.groups[i][l]) == 0:
plot[l, i] = 2
else:
plot[l, i] = l + 3
for g in m.groups[i][l]:
plot[g, i] = l + 3
print
def forget(self, fieldnames):
"""Reset all accumulators to zero.
"""
width = len(fieldnames)
self.fieldnames = fieldnames
self.n = zeros(width) # Sum of nnonnan and nnan
self.nnonnan = zeros(width) # Nb of non-missing elements
self.nnan = zeros(width) # Nb of missing elements
self.nxxt = 0 # Nb of elements in sum_xxt
self.sum = zeros(width) # Sum of elements
self.sum_ssq = zeros(width) # Sum of square of elements
self.sum_xxt = zeros((width, width)) # Accumulator of outer products
self.sum_nomi = zeros(width) # Sum of elements (no missings)
self.min = zeros(width) + PosInf # Minimum element
self.argmin = zeros(width) + -1 # Position of minimum
self.max = zeros(width) + NegInf # Maximum element
self.argmax = zeros(width) + -1 # Position of maximum
struct=1)
data = mix.sampleDataSet(500)
#print mix
mix.updateStructureGlobal(data)
#print mix
#print mix.groups
#print mix.leaders
#writeMixture(mix, "test.mix")
#mix.evalStructure(data.headers)
plot = numarray.zeros((mix.G, mix.dist_nr))
for i in range(mix.dist_nr):
for l in mix.leaders[i]:
plot[l, i] = l + 1
for g in mix.groups[i][l]:
plot[g, i] = l + 1
print "Generating", get_loglikelihood(mix, data.internalData)
#print "G ="
#print range(0,len(plot[0]))
for p in plot:
print p
for k in range(5):
m = MixtureModel(6, [0.1, 0.1, 0.1, 0.2, 0.2, 0.3],
maxPeriod = 10.0
step = 0.04
damping = 0.05
spectraPeriods = demand_calculations.compute_spectra_periods(minPeriod,maxPeriod,step)
spectraDisp = demand_calculations.compute_spectra(minPeriod,maxPeriod,step,damping,ACCELEROGRAMS,SPECTRA)
numberAccs = len(spectraDisp)
print 'Spectra produced'
# READ THE PORFOLIO OF BUILDINGS
lines = portfolio_builder.parse_input(EXPOSURE)
assets_count = portfolio_builder.buildings_counter(lines)
number_categories = int(assets_count[0])
number_assets = assets_count[1]
# CREATE THE VECTOR FOR THE DAMAGE STATES AND BETA FOR INFILLED FRAMES
damageStates= numarray.zeros(4)
IMTs=['PGA','PGV','Sa03','Saelastic']
elasticPeriods = []
betas = [0.52, 0.46, 0.28]
# COMPUTE THE DISPLACAMENT FOR EACH BUILDING
for asset_category in range(number_categories):
for asset in range(number_assets[asset_category]):
data = portfolio_builder.create_asset(lines[asset_category])
code=data[1]
if code == "Low_Code":
ec_ls2 = 0.0035
# ec_ls3 = 0.0075
es_ls2 = 0.0150
print data
for i in range(10):
for j in range(10):
data[i][j] = float(i * j)
print data
#fitsobj=pyfits.HDUList()
#hdu=pyfits.PrimaryHDU()
#hdu.data=data
#fitsobj.append(hdu)
#fitsobj.writeto('tmp.fits')
fitsobj = pyfits.HDUList()
# create Primary HDU with minimal header keywords
hdu = pyfits.PrimaryHDU()
# add a 10x5 array of zeros
hdu.data = numarray.zeros((10, 10), type=numarray.Float32)
hdu.data[5][5] = 10.
hdu.data[4][5] = 5.
hdu.data[6][5] = 5.
hdu.data[5][4] = 5.
hdu.data[5][6] = 5.
print hdu.data
fitsobj.append(hdu)
# save to a file, the writeto method will make sure the required
# keywords are conforming to the data
fitsobj.writeto('tmp.fits')
def compute_disp(accs, accstep, period, damping):
fraction = 1 / 0.02
pNocount = 0
lfine = 0
np = len(accs)
pNocount = pNocount + 1
MaxSteps = (np + 1) * (round(int(accstep * fraction / period)) + 1) + 1
ugh = numarray.zeros(MaxSteps)
fine = round(int(accstep * fraction / period)) + 1
if fine != lfine:
L = 1
i = 1
while (i <= 1 + (np - 1) * fine):
i = i + fine
L = L + 1
dt = accstep / fine
lfine = fine
M = 1 + (np - 1) * fine
pNocount = 0
xie = damping
maxug = max(accs)
lfine = 0
pNocount = 1
fine = round(int(accstep * fraction / period)) + 1
if fine != lfine:
L = -1
i = 0
while (i <= (np - 1) * fine):
i = i + fine
L = L + 1
ugh[i] = accs[L]
for M in range(int(fine)):
ugh[i - M +
1] = ugh[i - fine] + (ugh[i] - ugh[i - fine]) * (fine - M +
1) / fine
lfine = fine
M = 1 + (np - 1) * fine
dt = accstep / fine
ncf = 2.0 * 3.14159265 / period
fraction = 1.0 / 0.02
Gamma_Parm = 0.5
Beta_Parm = 0.25
damp = 0.05
THdsps = []
THaccs = []
U0 = 0.0
U1 = 0.0
V0 = 0.0
V1 = 0.0
A0 = 0.0
A1 = 0.0
xie = damping
for i in range(int(M)):
U1 = (-ugh[i + 1] + U0 / Beta_Parm / (dt * dt) + V0 / Beta_Parm / dt +
(1.0 / 2.0 / Beta_Parm - 1.0) * A0 +
(U0 * Gamma_Parm / Beta_Parm / dt +
(Gamma_Parm / Beta_Parm - 1.0) * V0 +
(Gamma_Parm / 2.0 / Beta_Parm - 1.0) * dt * A0) * 2.0 * xie *
ncf) / (1.0 / Beta_Parm / (dt * dt) +
2.0 * xie * ncf * Gamma_Parm / Beta_Parm / dt +
(ncf * ncf))
V1 = (U1 - U0) * Gamma_Parm / Beta_Parm / dt + (
1.0 - Gamma_Parm / Beta_Parm) * V0 + (
1.0 - Gamma_Parm / 2.0 / Beta_Parm) * dt * A0
A1 = (U1 - U0) / Beta_Parm / (dt * dt) - V0 / Beta_Parm / dt + (
1.0 - 1.0 / 2.0 / Beta_Parm) * A0
U0 = U1
V0 = V1
A0 = A1
THdsps.append(U1)
THaccs.append(A1 + ugh[i + 1])
Sa = max([math.fabs(min(THaccs)), max(THaccs)])
Sd = max([math.fabs(min(THdsps)), max(THdsps)]) * 9.81 * 100
return Sd, Sa
def parse_input(path):
file = open(path)
lines = file.readlines()
file.close
Latitude=[]
Longitude=[]
UniqueLocations=[]
UniqueAreas=[]
NumberUniqueLocations=1
Area=[]
Population=[]
numberAssets = len(lines)
for line in lines:
Longitude.append(line.split(',')[0].strip())
Latitude.append(line.split(',')[1].strip())
Area.append(line.split(',')[2].strip())
Population.append(int(line.split(',')[3].strip()))
UniqueLocations.append(str(Longitude[0]) + ','+ str(Latitude[0]))
UniqueAreas.append(float(Area[0]))
addLocation = 0;
for i in range(numberAssets):
# for i in range(100):
for j in range(NumberUniqueLocations):
if str(Longitude[i]) + ',' + str(Latitude[i]) == UniqueLocations[j]:
addLocation = 0
if addLocation == 1:
UniqueLocations.append(str(Longitude[i]) + ','+ str(Latitude[i]))
UniqueAreas.append(float(Area[i]))
NumberUniqueLocations=NumberUniqueLocations+1
addLocation = 1;
SumPopulation = numarray.zeros(NumberUniqueLocations)
for i in range(numberAssets):
for j in range(NumberUniqueLocations):
if str(Longitude[i]) + ','+ str(Latitude[i]) == UniqueLocations[j]:
SumPopulation[j]=SumPopulation[j]+Population[i]
Ratios=[0.1, 0.1 ,0.2, 0.3, 0.3]
VulnerabilityFunction=['HAZUS_W1_MC','HAZUS_C1L_MC','HAZUS_C1M_MC','HAZUS_C1H_MC','HAZUS_S1H_MC']
Periods=['0.00', '0.30', ' 0.8', '1.50', '2.20']
Results=[]
print SumPopulation
print UniqueLocations
for typoligies in range(5):
for j in range(NumberUniqueLocations):
print typoligies
Results.append(UniqueLocations[j]+','+str(UniqueAreas[j]*float(SumPopulation[j])*Ratios[typoligies]*1000*1000*100)+','+Periods[typoligies])
for j in range(NumberUniqueLocations*5):
log(str(Results[j]+'\n'))
for typoligies in range(5):
for j in range(NumberUniqueLocations):
log1(VulnerabilityFunction[typoligies]+'\n')
print Results
def compute_disp(accs, accstep, period, damping): # interpolator = interpolate.interp1d(times,accs, kind = 'linear') fraction = 1/0.02 pNocount = 0 lfine = 0 np = len(accs) pNocount=pNocount+1 MaxSteps = (np+1) * (round(int(accstep*fraction/period)) + 1) +1 ugh=numarray.zeros(MaxSteps) fine = round(int(accstep * fraction / period)) + 1 if fine != lfine: L = 1 i = 1 while (i <=1 + (np - 1) * fine ): i=i+fine L=L+1 dt = accstep / fine lfine = fine M = 1 + (np - 1) * fine pNocount = 0 xie = damping maxug = max(accs) lfine =0 pNocount = 1 fine = round(int(accstep * fraction / period)) + 1 if fine != lfine: L = -1 i = 0 while (i <= (np - 1) * fine ): i=i+fine L=L+1 ugh[i] = accs[L] for M in range(int(fine)): ugh[i-M+1] = ugh[i - fine] + (ugh[i] - ugh[i - fine]) * (fine - M +1) / fine lfine = fine M = 1 + (np - 1) * fine dt = accstep / fine ncf = 2.0 * 3.14159265 / period fraction = 1.0/0.02 Gamma_Parm = 0.5 Beta_Parm = 0.25 damp = 0.05 THdsps = [] THvels = [] THaccs = [] U0 = 0.0 U1 = 0.0 V0 = 0.0 V1 = 0.0 A0 = 0.0 A1 = 0.0 xie = damping for i in range(int(M)): U1 = (-ugh[i+1] + U0 / Beta_Parm / (dt*dt) + V0 / Beta_Parm / dt + (1.0 / 2.0 / Beta_Parm - 1.0) * A0 + (U0 * Gamma_Parm / Beta_Parm / dt + (Gamma_Parm / Beta_Parm - 1.0) * V0 + (Gamma_Parm / 2.0 / Beta_Parm - 1.0) * dt * A0) * 2.0 * xie * ncf) / (1.0 / Beta_Parm / (dt*dt) + 2.0 * xie * ncf * Gamma_Parm / Beta_Parm / dt + (ncf*ncf)) V1 = (U1 - U0) * Gamma_Parm / Beta_Parm / dt + (1.0 - Gamma_Parm / Beta_Parm) * V0 + (1.0 - Gamma_Parm / 2.0 / Beta_Parm) * dt * A0 A1 = (U1 - U0) / Beta_Parm / (dt*dt) - V0 / Beta_Parm / dt + (1.0 - 1.0 / 2.0 / Beta_Parm) * A0 U0 = U1 V0 = V1 A0 = A1 THdsps.append(U1) # THvels.append(V1) THaccs.append(A1+ugh[i+1]) Sa = max([math.fabs(min(THaccs)),max(THaccs)]) # Sv = max([math.fabs(min(THvels)),max(THvels)])*9.81 Sd = max([math.fabs(min(THdsps)),max(THdsps)])*9.81 return Sd,Sa
def forget(self, fieldnames):
"""Reset all accumulators to zero.
"""
width = len(fieldnames)
self.fieldnames = fieldnames
self.n = zeros(width) # Sum of nnonnan and nnan
self.nnonnan = zeros(width) # Nb of non-missing elements
self.nnan = zeros(width) # Nb of missing elements
self.nxxt = 0 # Nb of elements in sum_xxt
self.sum = zeros(width) # Sum of elements
self.sum_ssq = zeros(width) # Sum of square of elements
self.sum_xxt = zeros((width, width)) # Accumulator of outer products
self.sum_nomi = zeros(width) # Sum of elements (no missings)
self.min = zeros(width) + PosInf # Minimum element
self.argmin = zeros(width) + -1 # Position of minimum
self.max = zeros(width) + NegInf # Maximum element
self.argmax = zeros(width) + -1 # Position of maximum
pix = fitsobj[0].data
fitsobj.close()
try:
xs = float(sys.argv[-2])
ys = float(sys.argv[-1])
except:
print 'error in shift values'
sys.exit()
xf = xs - int(xs)
xs = int(xs)
yf = ys - int(ys)
ys = int(ys)
out = numarray.zeros((ny, nx), 'Float32')
for j in range(ny):
for i in range(nx):
out[j][i] = float('nan')
for j in range(max(0, ys), min(ny, ny + ys)):
for i in range(max(0, xs), min(nx, nx + xs)):
out[j][i] = (1. - xf) * (1. - yf) * pix[j - ys][i - xs]
out[j][i] = out[j][i] + xf * (1. - yf) * pix[j - ys][i - xs - 1]
out[j][i] = out[j][i] + (1. - xf) * yf * pix[j - ys - 1][i - xs]
out[j][i] = out[j][i] + xf * yf * pix[j - ys - 1][i - xs - 1]
fitsobj = pyfits.HDUList()
hdu = pyfits.PrimaryHDU()
hdu.data = out
fitsobj.append(hdu)
data = mix.sampleDataSet(500)
# print mix
mix.updateStructureGlobal(data)
# print mix
# print mix.groups
# print mix.leaders
# writeMixture(mix, "test.mix")
# mix.evalStructure(data.headers)
plot = numarray.zeros((mix.G, mix.dist_nr))
for i in range(mix.dist_nr):
for l in mix.leaders[i]:
plot[l, i] = l + 1
for g in mix.groups[i][l]:
plot[g, i] = l + 1
print "Generating", get_loglikelihood(mix, data.internalData)
# print "G ="
# print range(0,len(plot[0]))
for p in plot:
print p
for k in range(5):
m = MixtureModel(6, [0.1, 0.1, 0.1, 0.2, 0.2, 0.3], [pd, pd2, pd3, pd4, pd5, pd6], struct=1)
#data.fromFiles(["filt_DAT_134.txt","DRD1_134.txt","DRD2_134.txt","DRD3_134.txt","DRD5_134.txt" ,"filt_WISC_WIAT_DISC_134.txt"])
#m = mixture.readMixture("NEC_ADHD_struct_7.mix")
data.internalInit(m)
c = m.classify(data,entropy_cutoff=0.50)
data.printClustering(m.G,c)
m.getClusterEntropy(data)
m.evalStructure(data.headers)
print m.groups
plot = numarray.zeros(( m.G,m.dist_nr ) )
for i in range(m.dist_nr):
# check for noise variables
if len(m.leaders[i]) == 1:
l = m.leaders[i][0]
for g in range(m.G):
plot[g,i] = 1
else:
for l in m.leaders[i]:
if len(m.groups[i][l]) == 0:
plot[l,i] = 2
else:
plot[l,i] = l+3
for g in m.groups[i][l]:
def main():
if len(sys.argv) > 1:
im_name = sys.argv[1]
else:
import tkFileDialog, Tkinter
im_name = tkFileDialog.askopenfilename(
defaultextension=".png", filetypes=((_("Depth images"), ".gif .png .jpg"), (_("All files"), "*"))
)
if not im_name:
raise SystemExit
Tkinter._default_root.destroy()
Tkinter._default_root = None
im = Image.open(im_name)
size = im.size
im = im.convert("L") # grayscale
w, h = im.size
nim = numarray.fromstring(im.tostring(), "UInt8", (h, w)).astype("Float32")
options = ui(im, nim, im_name)
step = options["pixelstep"]
depth = options["depth"]
if options["normalize"]:
a = nim.min()
b = nim.max()
if a != b:
nim = (nim - a) / (b - a)
else:
nim = nim / 255.0
maker = tool_makers[options["tool_type"]]
tool_diameter = options["tool_diameter"]
pixel_size = options["pixel_size"]
tool = make_tool_shape(maker, tool_diameter, pixel_size)
if options["expand"]:
if options["expand"] == 1:
pixel = 1
else:
pixel = 0
w, h = nim.shape
tw, th = tool.shape
w1 = w + 2 * tw
h1 = h + 2 * th
nim1 = numarray.zeros((w1, h1), "Float32") + pixel
nim1[tw : tw + w, th : th + h] = nim
nim = nim1
w, h = w1, h1
nim = nim * depth
if options["invert"]:
nim = -nim
else:
nim = nim - depth
rows = options["pattern"] != 1
columns = options["pattern"] != 0
columns_first = options["pattern"] == 3
spindle_speed = options["spindle_speed"]
if rows:
convert_rows = convert_makers[options["converter"]]()
else:
convert_rows = None
if columns:
convert_cols = convert_makers[options["converter"]]()
else:
convert_cols = None
if options["bounded"] and rows and columns:
slope = tan(options["contact_angle"] * pi / 180)
if columns_first:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step + 1)
else:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step + 1)
if options["bounded"] > 1:
if columns_first:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step + 1)
else:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step + 1)
units = unitcodes[options["units"]]
convert(
nim,
units,
tool,
pixel_size,
step,
options["safety_height"],
options["tolerance"],
options["feed_rate"],
convert_rows,
convert_cols,
columns_first,
ArcEntryCut(options["plunge_feed_rate"], 0.125),
spindle_speed,
options["roughing_offset"],
options["roughing_depth"],
options["feed_rate"],
)
def main():
if len(sys.argv) > 1:
im_name = sys.argv[1]
else:
import tkFileDialog, Tkinter
im_name = tkFileDialog.askopenfilename(defaultextension=".png",
filetypes=((_("Depth images"),
".gif .png .jpg"),
(_("All files"),
"*")))
if not im_name: raise SystemExit
Tkinter._default_root.destroy()
Tkinter._default_root = None
im = Image.open(im_name)
size = im.size
im = im.convert("L") #grayscale
w, h = im.size
nim = numarray.fromstring(im.tostring(), 'UInt8', (h, w)).astype('Float32')
options = ui(im, nim, im_name)
step = options['pixelstep']
depth = options['depth']
if options['normalize']:
a = nim.min()
b = nim.max()
if a != b:
nim = (nim - a) / (b - a)
else:
nim = nim / 255.0
maker = tool_makers[options['tool_type']]
tool_diameter = options['tool_diameter']
pixel_size = options['pixel_size']
tool = make_tool_shape(maker, tool_diameter, pixel_size)
if options['expand']:
if options['expand'] == 1: pixel = 1
else: pixel = 0
w, h = nim.shape
tw, th = tool.shape
w1 = w + 2 * tw
h1 = h + 2 * th
nim1 = numarray.zeros((w1, h1), 'Float32') + pixel
nim1[tw:tw + w, th:th + h] = nim
nim = nim1
w, h = w1, h1
nim = nim * depth
if options['invert']:
nim = -nim
else:
nim = nim - depth
rows = options['pattern'] != 1
columns = options['pattern'] != 0
columns_first = options['pattern'] == 3
spindle_speed = options['spindle_speed']
if rows: convert_rows = convert_makers[options['converter']]()
else: convert_rows = None
if columns: convert_cols = convert_makers[options['converter']]()
else: convert_cols = None
if options['bounded'] and rows and columns:
slope = tan(options['contact_angle'] * pi / 180)
if columns_first:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step + 1)
else:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step + 1)
if options['bounded'] > 1:
if columns_first:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step + 1)
else:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step + 1)
units = unitcodes[options['units']]
convert(nim, units, tool, pixel_size, step, options['safety_height'],
options['tolerance'], options['feed_rate'], convert_rows,
convert_cols, columns_first,
ArcEntryCut(options['plunge_feed_rate'],
.125), spindle_speed, options['roughing_offset'],
options['roughing_depth'], options['feed_rate'])
