def sample(self, density, deviation, add_source_point=True, seed=None):
try: seed = int(seed)
except: seed = None
np_random.seed(seed)
self.sample_seed = seed
points = set()
for k, node in self.K.items():
if node.parent is None: continue
parent = self.K[node.parent]
avg_radius = (node.radius + parent.radius) / 2
axis = node.pos - parent.pos
surf = 2 * pi * avg_radius * linalg.norm(axis)
n = int(round(density * surf))
p1 = parent.pos
p2 = node.pos
p = np_random.randn(3)
r = cross(p-p1, p2-p1)
r /= linalg.norm(r)
s = cross(r, p2-p1)
s /= linalg.norm(s)
for i in range(n):
theta = np_random.uniform(0, radians(360))
d = np_random.uniform() # relative distance of the point, on line between p1 and p2
t = p1 + d * axis
interp_radius = (node.radius - parent.radius) * d + parent.radius
if deviation:
interp_radius += np_random.exponential(deviation)
q = harray([t[0] + interp_radius * cos(theta) * r[0] + interp_radius * sin(theta) * s[0],
t[1] + interp_radius * cos(theta) * r[1] + interp_radius * sin(theta) * s[1],
t[2] + interp_radius * cos(theta) * r[2] + interp_radius * sin(theta) * s[2]])
points.add(q)
if add_source_point:
points.add((0,-0.01,0))
self.P = vstack(points)
def width_uniform(max_width, num_profiles, num_samples):
"""Uniform width distribution
Generates halfwidths in U[0, max_width]
"""
halfwidths = rand.uniform(0, max_width, num_profiles)
return rand.uniform(-halfwidths, halfwidths, (num_samples, num_profiles)).T
def expose(self, widget, event):
cr = widget.window.cairo_create()
environ["GKS_WSTYPE"] = "142"
pc = PyCairoContext.from_address(id(cr))
environ['GKSconid'] = "%lu" % pc.ctx
cr.move_to(15, 15)
cr.set_font_size(14)
cr.show_text("Contour Plot using Gtk ...")
seed(0)
xd = uniform(-2, 2, 100)
yd = uniform(-2, 2, 100)
zd = xd * np.exp(-xd**2 - yd**2)
gr.setviewport(0.15, 0.95, 0.1, 0.9)
gr.setwindow(-2, 2, -2, 2)
gr.setspace(-0.5, 0.5, 0, 90)
gr.setmarkersize(1)
gr.setmarkertype(gr.MARKERTYPE_SOLID_CIRCLE)
gr.setcharheight(0.024)
gr.settextalign(2, 0)
gr.settextfontprec(3, 0)
x, y, z = gr.gridit(xd, yd, zd, 200, 200)
h = np.linspace(-0.5, 0.5, 20)
gr.surface(x, y, z, 5)
gr.contour(x, y, h, z, 0)
gr.polymarker(xd, yd)
gr.axes(0.25, 0.25, -2, -2, 2, 2, 0.01)
gr.updatews()
def make_random_transform():
transform_type = rand.randint(0,3)
if transform_type == 0:
axis = 'freq' # FIXME generalize later
nbins = rand.randint(1, 5)
nt_chunk = 8 * rand.randint(5, 11)
epsilon = rand.uniform(3.0e-4, 1.0e-3)
return rf_pipelines.spline_detrender(nt_chunk, axis, nbins, epsilon)
elif transform_type == 1:
# intensity_clipper
axis = rand.randint(0,2) if (rand.uniform() < 0.66) else None
Df = 2**rand.randint(0,4)
Dt = 2**rand.randint(0,4)
sigma = rand.uniform(1.3, 1.7)
niter = rand.randint(1,5)
iter_sigma = rand.uniform(1.8, 2.0)
nt_chunk = Dt * 8 * rand.randint(1,8)
two_pass = True if rand.randint(0,2) else False
return rf_pipelines.intensity_clipper(nt_chunk, axis, sigma, niter, iter_sigma, Df, Dt, two_pass)
else:
# std_dev_clipper
axis = rand.randint(0,2)
Df = 2**rand.randint(0,4)
Dt = 2**rand.randint(0,4)
sigma = rand.uniform(1.3, 1.7)
nt_chunk = Dt * 8 * rand.randint(1,8)
two_pass = True if rand.randint(0,2) else False
return rf_pipelines.std_dev_clipper(nt_chunk, axis, sigma, Df, Dt, two_pass)
def PSO_global(self):
_list = [rd.uniform(self.low_bound, self.up_bound, self.dimension) for x in range(0, self.particles_count, 1)]
g_best = _list[0]
position = _list
vel = []
for x in position:
if self.function(x) < self.function(g_best):
g_best = x
vel.append(rd.uniform(-(abs(self.up_bound - self.low_bound)), abs(self.up_bound -self. low_bound), self.dimension))
count = 0
while count < self.stop_case:
for x in range(len(_list)):
for y in xrange(self.dimension):
r_g, r_p = rd.random(1), rd.random(1)
vel[x][y] = self.w * vel[x][y] + self.especial_param_p * r_p * (position[x][y] - _list[x][y]) + self.especial_param_g \
* r_g * (
g_best[y] -
_list[x][y])
_list[x] += vel[x]
if self.function(_list[x]) < self.function(position[x]):
position[x] = _list[x]
if self.function(position[x]) < self.function(g_best):
g_best = position[x]
count += 1
return g_best
def _create_plot_component():
# Create a random scattering of XY pairs
x = random.uniform(0.0, 10.0, 50)
y = random.uniform(0.0, 5.0, 50)
pd = ArrayPlotData(x=x, y=y)
plot = Plot(pd, border_visible=True, overlay_border=True)
scatter = plot.plot(("x", "y"), type="scatter", color="lightblue")[0]
# Tweak some of the plot properties
plot.set(title="Scatter Inspector Demo", padding=50)
# Attach some tools to the plot
plot.tools.append(PanTool(plot))
plot.overlays.append(ZoomTool(plot))
# Attach the inspector and its overlay
scatter.tools.append(ScatterInspector(scatter))
overlay = ScatterInspectorOverlay(
scatter,
hover_color="red",
hover_marker_size=6,
selection_marker_size=6,
selection_color="yellow",
selection_outline_color="purple",
selection_line_width=3,
)
scatter.overlays.append(overlay)
return plot
def _build(self):
'''Create populations.'''
if self._open_bc:
ldict_s = {'elements': 'iaf_psc_alpha',
'positions': [[self._x_d, self._y_d]],
'extent': [self._L] * self._dimensions,
'edge_wrap': False}
x = rnd.uniform(-self._L / 2., self._L / 2., self._N)
y = rnd.uniform(-self._L / 2., self._L / 2., self._N)
pos = list(zip(x, y))
ldict_t = {'elements': 'iaf_psc_alpha', 'positions': pos,
'extent': [self._L] * self._dimensions,
'edge_wrap': False}
self._ls = topo.CreateLayer(ldict_s)
self._lt = topo.CreateLayer(ldict_t)
self._driver = nest.GetLeaves(self._ls)[0]
else:
ldict_s = {'elements': 'iaf_psc_alpha',
'positions': [[0.] * self._dimensions],
'extent': [self._L] * self._dimensions,
'edge_wrap': True}
x = rnd.uniform(-self._L / 2., self._L / 2., self._N)
y = rnd.uniform(-self._L / 2., self._L / 2., self._N)
if self._dimensions == 3:
z = rnd.uniform(-self._L / 2., self._L / 2., self._N)
pos = list(zip(x, y, z))
else:
pos = list(zip(x, y))
ldict_t = {'elements': 'iaf_psc_alpha', 'positions': pos,
'extent': [self._L] * self._dimensions,
'edge_wrap': True}
self._ls = topo.CreateLayer(ldict_s)
self._lt = topo.CreateLayer(ldict_t)
self._driver = topo.FindCenterElement(self._ls)
def generateHaarFeatures(number):
prototype = np.array(
[0, 0, 1, 1, 0.25, 0.25, 0.5, 0.5])
scale = random.uniform(0.15, 0.6, (number, 1))
protoypes = np.tile(prototype, (number, 1)) * scale
translation = random.uniform(0, 0.8, (number, 2))
protoypes[:, 0:2] += translation
protoypes[:, 4:6] += translation
# If generated features lies outside of range [0,1] translate it back
# inside
for row in protoypes:
if row[0] + row[2] > 1:
move = row[0] + row[2] - 1
row[0] -= move
row[4] -= move
if row[1] + row[3] > 1:
move = row[1] + row[3] - 1
row[1] -= move
row[5] -= move
protoypes[:,2:4] = protoypes[:,2:4] + protoypes[:,0:2]
protoypes[:,6:8] = protoypes[:,6:8] + protoypes[:,4:6]
for row in protoypes:
for i in range(row.shape[0]):
if(row[i] > 1):
row[i] = 1
return protoypes.astype(np.float32)
def shift_vertices(vertices, s): for i,(x,y) in enumerate(vertices): x += random.uniform(-s/2.,s/2.) y += random.uniform(-s/2.,s/2.) vertices[i,0] = x vertices[i,1] = y return vertices
def randomize(self):
'''Randomize with uniform distribution within bounds.'''
# Iterate over self.pts
for i, (lowerbound, upperbound) in enumerate(self.constraints):
self.pts[i] = uniform(lowerbound, upperbound)
absrange = abs(upperbound-lowerbound)
self.spds[i] = uniform(-absrange, absrange)
def test_random_positions_lim_fraction(self): R = uniform(0,1) N = uniform(0,1000) x = random_positions(R,N) r = dot(x,x) assert_array_less(r,R**2)
def gen_traffic_list(time_serial_num,traffic_num,traffic_data_mean_size):
traffic_list = [0]*time_serial_num
#生成时序包络
mu = int(random.uniform(0, time_serial_num))
sigma = random.uniform(1,6)
#每一时序生成随机的业务数
i = 0
while (i<time_serial_num):
tmp_time_serial = int(random.gauss(mu, sigma))
if tmp_time_serial < time_serial_num and tmp_time_serial >=0:
traffic_list[tmp_time_serial] = traffic_list[tmp_time_serial]+1
i+=1
plotLine(range(0,len(traffic_list)),traffic_list)
#每个业务生成随机的数据块
mu = traffic_data_mean_size
sigma = traffic_data_mean_size*0.1
floor_data_size = mu - 3*sigma
ceil_data_size = mu + 3*sigma
traffic_entity_list=[[]]*time_serial_num
for index,k in enumberate(traffic_list):
i=0
while (i<k):
data_size = int(random.gauss(mu, sigma))
if data_size>floor_data_size and data_size < ceil_data_size :
traffic_entity_list[index]
i=i+1
return traffic_entity_list
def load_linear_dataset(n, d, noise_stdev, xmax):
signs = rand.choice([-1, 1], size=(d,))
B = np.dot(signs, rand.uniform(size=(d,)))
errors = noise_stdev * rand.normal(size=(d,))
X = xmax * rand.uniform(size=(d, n))
y = np.dot(X.T, B) + errors
return X, y, (B, errors)
def test_no_zenith(self):
"""Azimuths are irrelevant when from the Zenith"""
azimuth1 = random.uniform(-pi, pi, 10000)
azimuth2 = random.uniform(-pi, pi, 10000)
angle = utils.angle_between(0, azimuth1, 0, azimuth2)
self.assertTrue(all(angle == 0))
def test_single_values(self):
"""Other tests use arrays, check if single values also work"""
zenith = random.uniform(0, pi / 2)
azimuth = random.uniform(-pi, pi)
angle = utils.angle_between(zenith, azimuth, zenith, azimuth)
self.assertTrue(angle == 0)
def InitiateCataract(self):
key = int((self.Attribute['Age'] -50)/5)
cataractRisk = random.triangular(1.5,2.7,4.9)
if self.medicalRecords['NumberTrabeculectomy'] == 0:
if self.Attribute['Gender'] == 1:
if key < 7:
RateCataract = (cataract_formation[key]/1000)
else:
RateCataract = (cataract_formation[7]/1000)
else:
if key < 7:
RateCataract = (cataract_formation_female[key]/1000)
else:
RateCataract = (cataract_formation_female[7]/1000)
else:
if self.Attribute['Gender'] == 1:
if key < 7:
RateCataract = (cataract_formation[key]/1000)*cataractRisk
else:
RateCataract = (cataract_formation[7]/1000)*cataractRisk
else:
if key < 7:
RateCataract = (cataract_formation_female[key]/1000)*cataractRisk
else:
RateCataract = (cataract_formation_female[7]/1000)*cataractRisk
if random.uniform(0,1) <RateCataract:
self.medicalRecords['Cataract'] = True
if random.uniform(0,1) < random.beta(123,109) and self.medicalRecords['Cataract'] == True:
self.medicalRecords['SurgeryCataract'] += 1
self.medicalRecords['Cataract'] = False
def pcolorRandom():
"Makes a pcolormesh plot of randomly generated data pts."
# make up some randomly distributed data
npts = 100
x = uniform(-3, 3, npts)
y = uniform(-3, 3, npts)
z = x * N.exp(-x ** 2 - y ** 2)
# define grid.
xi = N.arange(-3.1, 3.1, 0.05)
yi = N.arange(-3.1, 3.1, 0.05)
# grid the data.
zi = griddata(x, y, z, xi, yi)
# contour the gridded data, plotting dots at the randomly spaced data points.
plt.pcolormesh(xi, yi, zi)
#CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
#CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x, y, marker='o', c='b', s=5)
plt.xlim(-3, 3)
plt.ylim(-3, 3)
plt.title('griddata test (%d points)' % npts)
plt.show()
def make_init_file(start_lon,start_lat,npts,position_init_file):
npts=100
xyz=zeros((npts,3))
xyz[:,0]= [random.uniform(start_lon-0.5,start_lon+0.5) for _ in xrange(npts)] # x index 100 points
xyz[:,1]= [random.uniform(start_lat-0.5,start_lat+0.5) for _ in xrange(npts)] # y index 100 points
xyz[:,2]=23 # at k=20 level, z level will be overwritten if the target_density in the namelist is larger than 0.
xyz.T.astype('>f8').tofile('particle_init.bin') #the saving sequence should be x[:], y[:], z[:], not [x1,y1,z1],[x2,y2,z2]...
def slice_sampler(px, N = 1, x = None):
"""
Provides samples from a user-defined distribution.
slice_sampler(px, N = 1, x = None)
Inputs:
px = A discrete probability distribution.
N = Number of samples to return, default is 1
x = Optional list/array of observation values to return, where prob(x) = px.
Outputs:
If x=None (default) or if len(x) != len(px), it will return an array of integers
between 0 and len(px)-1. If x is supplied, it will return the
samples from x according to the distribution px.
"""
values = np.zeros(N, dtype=np.int)
samples = np.arange(len(px))
px = np.array(px) / (1.*sum(px))
u = uniform(0, max(px))
for n in xrange(N):
included = px>=u
choice = random.sample(range(np.sum(included)), 1)[0]
values[n] = samples[included][choice]
u = uniform(0, px[included][choice])
if x:
if len(x) == len(px):
x=np.array(x)
values = x[values]
else:
print "px and x are different lengths. Returning index locations for px."
if N == 1:
return values[0]
return values
def astep(self, q0, logp):
"""q0 : current state
logp : log probability function
"""
# Draw from the normal prior by multiplying the Cholesky decomposition
# of the covariance with draws from a standard normal
chol = draw_values([self.prior_chol])
nu = np.dot(chol, nr.randn(chol.shape[0]))
y = logp(q0) - nr.standard_exponential()
# Draw initial proposal and propose a candidate point
theta = nr.uniform(0, 2 * np.pi)
theta_max = theta
theta_min = theta - 2 * np.pi
q_new = q0 * np.cos(theta) + nu * np.sin(theta)
while logp(q_new) <= y:
# Shrink the bracket and propose a new point
if theta < 0:
theta_min = theta
else:
theta_max = theta
theta = nr.uniform(theta_min, theta_max)
q_new = q0 * np.cos(theta) + nu * np.sin(theta)
return q_new
def MakeHexagons(self):
print "Building %i Hexagons"%NumHexagons
# get a list of colors for random colors
wx.lib.colourdb.updateColourDB()
self.colors = wx.lib.colourdb.getColourList()
print "Max colors:", len(self.colors)
Canvas = self.Canvas
D = 1.0
h = D *N.sqrt(3)/2
Hex = N.array(((D , 0),
(D/2 , -h),
(-D/2, -h),
(-D , 0),
(-D/2, h),
(D/2 , h),
))
Centers = uniform(-100, 100, (NumHexagons, 2))
for center in Centers:
# scale the hexagon
Points = Hex * uniform(5,20)
#print Points
# shift the hexagon
Points = Points + center
#print Points
cf = random.randint(0,len(self.colors)-1)
#cf = 55
H = Canvas.AddPolygon(Points, LineColor = None, FillColor = self.colors[cf])
#print "BrushList is: %i long"%len(H.BrushList)
H.Bind(FloatCanvas.EVT_FC_LEFT_DOWN, self.HexHit)
print "BrushList is: %i long"%len(H.BrushList)
def evaluation_function(self, X):
################################################
# write voltages and algorithm parameters into
# the database so that working nodes can access
# this data.
################################################
write_algo_parameters_to_db(X,
self.geom_iteration_counter,
self.geom_particle_counter)
################################################
# wait for Rs to calculate target functions
################################################
dont_have_target_function = True
print 'waiting to recieve target functions from R'
t0 = time.time()
while dont_have_target_function:
target_function = get_target_functions_from_db(len(X))
if len(target_function) == len(X):
dont_have_target_function = False
target_function.reverse()
print target_function
#raw_input()
return target_function
else:
time.sleep(1)
print target_function
print random.uniform(0,1)
def downRp(self,myslice,base=0,noise=1,gradient=0.2, noise_type = 'gauss'):
"""Replace Time series myslice with downward trend control chart pattern
myslice is a tuple (i,j) of the start and end point. Uses Python syntax
i <= x < j
All values edited are those of x
"""
if noise_type == 'gauss':
series = normal(base,noise,myslice[1]-myslice[0])
elif noise_type == 'uni' :
series = uniform(0-noise,noise,myslice[1]-myslice[0]) + base
elif noise_type == 'none' :
series = array([base] * myslice)
series = uniform(0-noise,noise,myslice[1]-myslice[0]) + base
for i in range(len(series)):
series[i] = series[i] - gradient*i
a = self[:myslice[0]]
b = self[myslice[1]:]
self = a.extend(series)
self = self.extend(b)
return self
def pillbox_sunshape_directions(num_rays, ang_range): """ Calculates directions for a ray bundles with ``num_rays`` rays, distributed as a pillbox sunshape shining toward the +Z axis, and deviating from it by at most ang_range, such that if all rays have the same energy, the flux distribution comes out right. Arguments: num_rays - number of rays to generate directions for. ang_range - in radians, the maximum deviation from +Z. Returns: A (3, num_rays) array whose each column is a unit direction vector for one ray, distributed to match a pillbox sunshape. """ # Diffuse divergence from +Z: # development based on eq. 2.12 from [1] xi1 = random.uniform(high=2.*N.pi, size=num_rays) # Phi xi2 = random.uniform(size=num_rays) # Rtheta #theta = N.arcsin(N.sin(ang_range)*N.sqrt(xi2)) #sin_th = N.sin(theta) #a = N.vstack((N.cos(xi1)*sin_th, N.sin(xi1)*sin_th , N.cos(theta))) sinsqrt = N.sin(ang_range)*N.sqrt(xi2) a = N.vstack((N.cos(xi1)*sinsqrt, N.sin(xi1)*sinsqrt , N.sqrt(1.-sinsqrt**2.))) return a
def SimpleMC(f, nIter, nVar, upper, lower):
"""
Description: Finds the Minimum of a function using the Simple Monte-Carlo method (all non-improving moves are discarded)
Inputs :
f : Function to be minimized
nIter : Number of Global iterations to perform the search over
nVar : Number of variables involved in function f
upper : Upper search limit
lower : Lower search limit
Outputs:
Returns a list with 2 elements
i) fist element : The minimum value of f
ii) second element : List of coordinates corresponding to the minimum
Note: Values are obtained after max(100 x nIter) search attempts.
"""
bestVal = [] ; bestCoord = []
for n in range(nIter):
for i in range(100):
if i == 0 :
step = [float(uniform(lower,upper,1)) for m in range(nVar)]; min_Coord = [m for m in step]
minVal = E = f(min_Coord)
else:
if any(m > upper or m < lower for m in step ):break
step = [m + float(uniform(-1,1,1)) for m in step] ; E_new = f(step)
deltaE = E_new - E
if deltaE < 0 : minVal = E = E_new ; min_Coord = [m for m in step]
bestVal.append(minVal); bestCoord.append(min_Coord)
index = min(enumerate(bestVal),key = itemgetter(1))[0]
return [bestVal[index],bestCoord[index]]
def set_interest(self, household, firm):
# Set nominal interest rate as 0,1,2,...,i,i+1,... where i stands for 0.1*i percent and announce to the public
#current_coefs = [self.alp_i, self.alp_p]
current_irate = self.irate
Tremble = uniform() < self.TrblActn
Inertia = uniform() < self.inertia
satisficing_output = self.val_output >= self.sl_output
satisficing_infltn = self.val_infltn >= self.sl_infltn
if not(Inertia):
if Tremble or not(satisficing_output) or not(satisficing_infltn):
self.irate = uniform_range(max(self.min_irate, self.irate-(self.delta)), self.irate+(self.delta))
else:
self.irate = uniform_range(max(self.min_irate, self.irate-(self.delta)), self.irate+(self.delta))
# self.irate = min(self.irate, int(self.max_irate/self.unit))
if current_irate == self.irate:
self.ActionChanged = False
# if not(Inertia):
# if (Tremble or not(satisficing_output) or not(satisficing_infltn)):
# self.alp_i = randint(max(-2, self.alp_i - self.delta), min(5, self.alp_i + self.delta))
# self.alp_p = randint(max(-2, self.alp_p - self.delta), min(5, self.alp_p + self.delta))
# # Rule for random choice randint(max(0, self.irate - self.delta), self.irate + self.delta)
# self.irate = int(min(max(0, self.alp_i*household.c[irate_node]
# + self.alp_p*self.inflation), self.max_irate/self.unit))
# if current_coefs == [self.alp_i, self.alp_p] :
# self.ActionChanged = False
Tremble = uniform() < self.TrbSatLv
lamda = uniform()**self.gamma
if not(Tremble):
self.sl_output += lamda*self.LAMBDA*min(self.val_output - self.sl_output, 0)
self.sl_infltn += lamda*self.LAMBDA*min(self.val_infltn - self.sl_infltn, 0)
else:
self.sl_output += lamda*(self.val_output-self.sl_output)
self.sl_infltn += lamda*(self.val_infltn-self.sl_infltn)
household.irate = firm.irate = self.irate
def edge_rays_bundle(num_rays, center, direction, radius, ang_range, flux=None, radius_in=0.): radius = float(radius) radius_in = float(radius_in) a = edge_rays_directions(num_rays, ang_range) # Rotate to a frame in which <direction> is Z: perp_rot = rotation_to_z(direction) directions = N.sum(perp_rot[...,None] * a[None,...], axis=1) # Locations: # See [1] xi1 = random.uniform(size=num_rays) thetas = random.uniform(high=2.*N.pi, size=num_rays) rs = N.sqrt(radius_in**2.+xi1*(radius**2.-radius_in**2.)) xs = rs * N.cos(thetas) ys = rs * N.sin(thetas) # Rotate locations to the plane defined by <direction>: vertices_local = N.vstack((xs, ys, N.zeros(num_rays))) vertices_global = N.dot(perp_rot, vertices_local) rayb = RayBundle(vertices=vertices_global + center, directions=directions) if flux != None: rayb.set_energy(N.pi*(radius**2.-radius_in**2.)/num_rays*flux*N.ones(num_rays)) return rayb
def __init__(self, **kwargs):
"""
Parameters:
AdaggerA_X -- list of ne expressions for the coordinate dependent product of the dissipator
apply_A -- list of the functions applying the operator A onto the wavefunction
BdaggerB_P -- list of ne expressions for the coordinate dependent product of the dissipator
apply_B -- list of the functions applying the operator B onto the wavefunction
"""
# Extract and save the list of dissipators
AdaggerA_X = kwargs.pop("AdaggerA_X", [])
self.apply_A = kwargs.pop("apply_A", [])
assert len(self.apply_A) == len(AdaggerA_X), "Lengths of AdaggerA_X and apply_A must be equal"
BdaggerB_P = kwargs.pop("BdaggerB_P", [])
self.apply_B = kwargs.pop("apply_B", [])
assert len(self.apply_B) == len(BdaggerB_P), "Lengths of BdaggerB_P and apply_B must be equal"
# Put the operators in the brackets, just to be on safe side for compilation
AdaggerA_X = ["({})".format(_) for _ in AdaggerA_X]
BdaggerB_P = ["({})".format(_) for _ in BdaggerB_P]
# ne codes for calculating lambda_{A_k}(t) = < A_k^\dagger A_k (x) >
self.code_lambda_A = ["sum({} * abs(wavefunction) ** 2)".format(_) for _ in AdaggerA_X]
# ne codes for calculating lambda_{B_k}(t) = < B_k^\dagger B_k (p) >
self.code_lambda_B = ["sum({} * density)".format(_) for _ in BdaggerB_P]
# Set the arrays for P_A and P_B
self.P_A = np.ones(len(AdaggerA_X), dtype=np.float)
self.r_A = uniform(0., 1., len(AdaggerA_X))
self.P_B = np.ones(len(BdaggerB_P), dtype=np.float)
self.r_B = uniform(0., 1., len(BdaggerB_P))
# Modify the potential energy with the contribution \
# from the coordinate dependent dissipators
kwargs["V"] = "{} -0.5j * ({})".format(
kwargs.pop("V"),
("+ ".join(AdaggerA_X) if AdaggerA_X else "0.")
)
# the save for the kinetic energy
kwargs["K"] = "{} -0.5j * ({})".format(
kwargs.pop("K"),
("+ ".join(BdaggerB_P) if BdaggerB_P else "0.")
)
# Call the parent constructor
super().__init__(**kwargs)
if BdaggerB_P:
# Allocate a copy of the wavefunction for storing the wavefunction in the momentum representation
# if the momentum dependent dissipator is given
self.wavefunction_p = np.zeros_like(self.wavefunction)
# and also for density
self.density = np.zeros(self.wavefunction_p.shape, dtype=np.float)
self.AdaggerA_X = AdaggerA_X
self.BdaggerB_P = BdaggerB_P
def set_price(self, bank, household): # set price and announce it to the public
irate_node = self.irate_node(self.irate)
self.current_price = self.price[irate_node]
Tremble = uniform() < self.TrblActn
Inertia = uniform() < self.inertia
Satisficing = self.Val[irate_node] >= self.SatLv[irate_node]
# print 'price range:', self.price[irate_node]*(1-self.delta), self.price[irate_node]*(1+self.delta)
if not(Inertia):
if Tremble or not(Satisficing):
self.price[irate_node] = uniform_range(self.price[irate_node]*(1-self.delta), self.price[irate_node]*(1+self.delta))
if self.current_price != self.price[irate_node]:
# if self.ActionChanged or self.current_price != self.price[irate_node]:
self.ActionChanged = True
# current_markup = self.markup[irate_node]
# Tremble = uniform() < self.TrblActn
# Inertia = uniform() < self.inertia
# Satisficing = self.Val[irate_node] >= self.SatLv[irate_node]
# if not(Inertia):
# if Tremble or not(Satisficing):
# self.markup[irate_node] = uniform_range(max(0, self.markup[irate_node]*(1-self.delta)), self.markup[irate_node]*(1+self.delta))
# if self.ActionChanged or current_markup != self.markup[irate_node]:
# self.ActionChanged = True
# price = (1+self.markup[irate_node])*(self.irate*self.unit)*(self.capital**(1-self.capital_power))/(self.capital_power*self.tech)
household.price = bank.price = max(0.01, self.price[irate_node])
Tremble = uniform() < self.TrbSatLv
lamda = uniform()**self.gamma
if not(Tremble):
self.SatLv[irate_node] += lamda*self.LAMBDA*min(self.Val[irate_node] - self.SatLv[irate_node], 0)
else:
self.SatLv[irate_node] += lamda*(self.Val[irate_node] - self.SatLv[irate_node])
def test_write_output(output_dir):
"""
render the basemap
"""
r = Renderer(bna_star,
output_dir,
image_size=(600, 600),
draw_back_to_fore=True,
formats=['png'])
r.draw_background()
BB = r.map_BB
(min_lon, min_lat) = BB[0]
(max_lon, max_lat) = BB[1]
N = 100
# create some random particle positions:
lon = random.uniform(min_lon, max_lon, (N, ))
lat = random.uniform(min_lat, max_lat, (N, ))
# create a sc
sc = sample_sc_release(num_elements=N)
sc['positions'][:, 0] = lon
sc['positions'][:, 1] = lat
r.cache = FakeCache(sc)
r.write_output(0)
r.save_foreground(os.path.join(output_dir, 'map_and_elements.png'))
r.draw_back_to_fore = False
r.clear_foreground()
r.write_output(1)
r.save_foreground(os.path.join(output_dir, 'just_elements.png'))
def __call__(self, image):
if random.randint(2):
delta = random.uniform(-self.delta, self.delta)
image += delta
return image
def __call__(self, image, boxes=None, labels=None):
height, width, _ = image.shape
while True:
# randomly choose a mode
mode = random.choice(self.sample_options)
if mode is None:
return image, boxes, labels
min_iou, max_iou, min_scale = mode
if min_iou is None:
min_iou = float('-inf')
if max_iou is None:
max_iou = float('inf')
# max trails (50)
for _ in range(50):
current_image = image
w = random.uniform(min_scale * width, width)
h = random.uniform(min_scale * height, height)
# aspect ratio constraint b/t .5 & 2
if h / w < 0.5 or h / w > 1.3:
continue
left = random.uniform(width - w)
top = random.uniform(height - h)
# convert to integer rect x1,y1,x2,y2
rect = np.array(
[int(left),
int(top),
int(left + w),
int(top + h)])
# calculate IoU (jaccard overlap) b/t the cropped and gt boxes
overlap = jaccard_numpy(boxes, rect)
# is min and max overlap constraint satisfied? if not try again
if overlap.min() < min_iou or max_iou < overlap.max(): #TODO
continue
# cut the crop from the image
current_image = current_image[rect[1]:rect[3],
rect[0]:rect[2], :]
# keep overlap with gt box IF center in sampled patch
centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0
# mask in all gt boxes that above and to the left of centers
m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
# mask in all gt boxes that under and to the right of centers
m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
# mask in that both m1 and m2 are true
mask = m1 * m2
# have any valid boxes? try again if not
if not mask.any(): #TODO LP is not work
continue
# take only matching gt boxes
current_boxes = boxes[mask, :].copy()
# take only matching gt labels
current_labels = labels[mask]
# should we use the box left and top corner or the crop's
current_boxes[:, :2] = np.maximum(current_boxes[:, :2],
rect[:2])
# adjust to crop (by substracting crop's left,top)
current_boxes[:, :2] -= rect[:2]
current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],
rect[2:])
# adjust to crop (by substracting crop's left,top)
current_boxes[:, 2:] -= rect[:2]
return current_image, current_boxes, current_labels
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
delta = random.uniform(-self.delta, self.delta)
image += delta
return image, boxes, labels
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
image *= alpha
return image, boxes, labels
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
image[:, :, 0] += random.uniform(-self.delta, self.delta)
image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
return image, boxes, labels
def __call__(self, image, boxes=None, labels=None):
if random.randint(2):
image[:, :, 1] *= random.uniform(self.lower, self.upper)
return image, boxes, labels
def __call__(self, image):
if random.randint(2):
image[:, :, 1] *= random.uniform(self.lower, self.upper)
return image
def next(self):
if self.num < self.num_samples:
self.num = self.num + 1
return random.uniform(0, 1, self.num_parameters)
else:
raise StopIteration()
output.write(insert_template %
((current, ) + tuple(tuples[current][tix])))
tix += 1
#Caclulate progress along the line normalized to the interval [0...1]
progress = (dir_vector[0] / (clusters - history) -
(centers[current][0] - base[0])) / (dir_vector[0] /
(clusters - history))
#Calculate probabilities for querying each cluster
if (workloadtype == "insert_delete"):
probs = mc.calc_prob(current, progress, max_prob, clusters,
current - history)
else:
probs = mc.calc_prob(current, progress, max_prob, clusters, 0)
#Pick target clusters for queries
query_centers = random.choice(clusters, queries_per_step, p=probs)
#Generate queries
queries = []
for i in query_centers:
c = centers[i] + random.normal(0, sigma, (dimension))
rng = random.uniform(0, 3 * sigma, dimension)
low = c - rng
high = c + rng
output.write(query_template % tuple(mc.createBoundsList(low, high)))
output.close()
def __call__(self, image):
if random.randint(2):
alpha = random.uniform(self.lower, self.upper)
image *= alpha
return image
def getSamples():
n = 2000
alpha = 0.1
global dim
#x = [0.]*dim
vec = []
resl = []
#vec.append(x)
iter = 100
#for j in range(iter):
x = np.concatenate(
(nprand.uniform(
10.0, 30.0, [iter, 6]), nprand.uniform(
-0.3, 0.3, [iter, 42]), nprand.uniform(0.0, 30.0, [iter, 1]),
[[-3.5] * 4] * iter, nprand.uniform(
-30.0, 30.0, [iter, 1]), nprand.uniform(
0.0, 0.3, [iter, 6]), nprand.uniform(5.0, 20.0, [iter, 6])),
axis=1)
#x = nprand.uniform(low=0.0, high=10.0, size=[iter,dim])
vec.extend(x)
resl.extend(sdnorm(x))
noise = np.concatenate(
(nprand.uniform(
-0.2, 0.2, [n, 6]), nprand.uniform(
-0.006, 0.006, [n, 42]), nprand.uniform(-0.3, 0.3, [n, 1]),
[[0.0] * 4] * n, nprand.uniform(
-0.6, 0.6, [n, 1]), nprand.uniform(
-0.003, 0.003, [n, 6]), nprand.uniform(-0.15, 0.15, [n, 6])),
axis=1)
for i in xrange(1, n):
can = x + noise[i] #candidate
can[:, 0:6] = np.clip(can[:, 0:6], 10.0, 30.0)
can[:, 6:48] = np.clip(can[:, 6:48], -0.3, 0.3)
can[:, 48:49] = np.clip(can[:, 48:49], 0.0, 30.0)
#can[49:53]=np.clip(can[49:53],-3.0,30.0)
can[:, 53:54] = np.clip(can[:, 53:54], -30.0, -30.0)
can[:, 54:60] = np.clip(can[:, 54:60], 0.0, 0.3)
can[:, 60:66] = np.clip(can[:, 60:66], 5.0, 20.0)
#can=[[max(min(u,10.0),0.0) for u in yy] for yy in can]
#can=map(lambda x: map(lambda y: max(0.0,min(10.0,y)), x), can)
#can=map(lambda x: map(lambda y: max(0.0,y), x), can)
k = sdnorm(can)
k2 = sdnorm(x)
aprob = map(lambda a, b: min([1., a / b]), k2,
k) #acceptance probability
u = nprand.uniform(0, 1, iter)
for j in range(iter):
if u[j] < aprob[j]:
x[j] = can[j]
vec.append(x[j])
resl.append(k[j])
return [vec, resl]
from plot_clone_tool import PlotCloneTool, MPPlotCloneTool
from data_source_button import ButtonController, DataSourceButton
from mp_move_tool import MPMoveTool
from mp_viewport_pan_tool import MPViewportPanTool
#from canvas_grid import CanvasGrid
# Multitouch imports
if MULTITOUCH:
from mptools import MPPanTool, MPDragZoom, MPLegendTool, \
MPPanZoom, MPRangeSelection
#AxisTool = MPAxisTool
PlotCloneTool = MPPlotCloneTool
NUMPOINTS = 250
DATA = {
"GOOG": random.uniform(-2.0, 10.0, NUMPOINTS),
"MSFT": random.uniform(-2.0, 10.0, NUMPOINTS),
"AAPL": random.uniform(-2.0, 10.0, NUMPOINTS),
"YHOO": random.uniform(-2.0, 10.0, NUMPOINTS),
"CSCO": random.uniform(-2.0, 10.0, NUMPOINTS),
"INTC": random.uniform(-2.0, 10.0, NUMPOINTS),
"ORCL": random.uniform(-2.0, 10.0, NUMPOINTS),
"HPQ": random.uniform(-2.0, 10.0, NUMPOINTS),
"DELL": random.uniform(-2.0, 10.0, NUMPOINTS),
}
def add_basic_tools(plot):
plot.tools.append(PanTool(plot))
plot.tools.append(MoveTool(plot, drag_button="right"))
zoom = ZoomTool(component=plot, tool_mode="box", always_on=False)
true_data = sample_Poisson(1, 100)
# initialize algorithm
theta1 = 6
theta2 = 0.5
current_model = 2
# MH algorithm parameters
num_of_iter = 1
total_iter = 5000
# number of times visit M1
visits = 0
while num_of_iter <= total_iter:
u = nprand.uniform(low=0, high=1)
if current_model == 1:
visits += 1
theta2 = nprand.uniform(low=0, high=1)
numerator = like_M2(theta2, true_data) * eval_lognormal(theta1)
denom = like_M1(theta1, true_data) * np.exp(-theta1)
print('likeM1', like_M1(theta1, true_data))
print('theta1', theta1)
print('theta2', theta2)
print('likeM2', like_M2(theta2, true_data))
ratio = float(numerator / denom)
alpha = min(1.0, ratio)
if u < alpha:
current_model = 2
elif current_model == 2:
def get_uniform_mab_env(bounds: Sequence[Tuple[float, float]]) -> 'MabEnv':
return MabEnv(
[lambda c=c, d=d: uniform(c, d, 1)[0] for c, d in bounds])
def uniform_distribution(x, dx):
from numpy.random import uniform
return uniform(x - dx, x + dx)
#def f(x):
# global dim
# a=[5.0]*dim
# z=(x[:,0]-a[0])**2.0
# for i in range(1,len(x[0])):
# z=z+(x[:,i]-a[i])**2.0
# return z
#def f(x):
#return (x[:,0]-5.0)**2.0+(x[:,1]-5.0)**2.0+(x[:,2]-5.0)**2.0+(x[:,3]-5.0)**2.0+(x[:,4]-5.0)**2.0
#X=np.atleast_2d([[8.0,0.0,4.0,6.0,9.0,1.0,7.5,9.8,4.2,0.8],[1.0, 9.0, 2.0,7.0,3.0,6.0,2.3,8.2,3.9,7.0]])
dim = 66
#X=nprand.uniform(low=0.0, high=10.0, size=[2,dim])
X = np.concatenate(
(nprand.uniform(10.0, 30.0, [2, 6]), nprand.uniform(-0.3, 0.3, [2, 42]),
nprand.uniform(0.0, 30.0, [2, 1]), [[-3.5] * 4] * 2,
nprand.uniform(-30.0, 30.0, [2, 1]), nprand.uniform(
0.0, 0.3, [2, 6]), nprand.uniform(5.0, 20.0, [2, 6])),
axis=1)
maxprev = X[0]
y = np.asarray([f1(X[0]), f1(X[1])])
#y=[odefunc(X[0]),odefunc(X[1])]
prevmax1 = y[0]
#prevmax1=f1(X[0])
#y = f(X).ravel()
#x1=[1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]
x1 = np.linspace(0, 10, 100)
#v1,v2,v3,v4,v5= np.meshgrid(x1,x1,x1,x1,x1[0])
gp = gaussian_process.GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1)
#n=10
def init_particle(x_range, y_range, N):
particles = np.empty((N, 2))
particles[:, 0] = uniform(x_range[0], x_range[1], size=N)
particles[:, 1] = uniform(y_range[0], y_range[1], size=N)
return particles
for i in range(N):
word_entropy[i]=H_dir[i]
for k in sorted(word_entropy.keys(),key=lambda x:word_entropy[x]):
print(ref_changes_pruned[k],changes_pruned[k][1],word_entropy[k])
"""
M = [[sum(sound_ind[i, x[0]:x[1]]) for x in s_breaks]
for i in range(len(sound_ind))]
full_prior_ln = []
full_prior_ln_T = []
for t in range(T):
alpha = uniform(0, 100)
while alpha == 0:
alpha = uniform(0, 100)
theta_star = np.array(
[np.random.dirichlet([alpha, alpha]) for l in range(L)])
psi_star = np.array(
[np.random.multivariate_normal([0] * S, Sigma * 10) for k in range(K)])
phi_star = np.array([
np.concatenate([
(softmax([psi_star[k][s_breaks[x][0]:s_breaks[x][1]]])[0]**10) /
np.sum(
softmax([psi_star[k][s_breaks[x][0]:s_breaks[x][1]]])[0]**10)
for x in range(X)
]) for k in range(K)
])
p_z = np.exp(np.dot(lang_ind, log(theta_star)))
#!/usr/bin/env python
import SamplePdf
import numpy as np
import os
from numpy.random import uniform
import pickle as pkl
num_samples = 5000
uni_samples = np.zeros(num_samples)
# TODO: draw uniform samples
uni_samples = uniform(size=num_samples)
# create instance of our new PDF sampler
my_pdf_sampler = SamplePdf.SamplePdf(num_samples)
# feed the uniform samples and create our custom ones
# TODO this function needs to be implemented in SamplePdf.py
new_samples = my_pdf_sampler.sample_pdf(uni_samples)
my_pdf_sampler.plot_result()
# safe the result in a struct
pkl.dump(new_samples, open("results.pkl", 'wb'))
# N vienmieriigi sadaliiti gadiijuma skaitlji
# N uniformly distributed random numbers
from numpy import random
#print(random.__doc__)
#print(random.uniform.__doc__)
N = 10000
a = 0
b = 5
#pseido-gadiijuma skaitlju generatora grauds
#random.seed(1)
x = random.uniform(a, b, N)
#x = random.normal(a,b,N)
k = [0, 0, 0, 0, 0]
for i in range(N):
if x[i] < 1:
k[0] = k[0] + 1
elif x[i] < 2:
k[1] = k[1] + 1
elif x[i] < 3:
k[2] = k[2] + 1
elif x[i] < 4:
k[3] = k[3] + 1
else:
k[4] = k[4] + 1
print(k)
def perturbParticle(params, priors, kernel, kernel_type, special_cases):
np = len(priors)
prior_prob = 1
if special_cases == 1:
# this is the case where kernel is uniform and all priors are uniform
ind = 0
for n in kernel[0]:
lflag = (params[n] + kernel[2][ind][0]) < priors[n][1]
uflag = (params[n] + kernel[2][ind][1]) > priors[n][2]
lower = kernel[2][ind][0]
upper = kernel[2][ind][1]
if lflag == True:
lower = -(params[n] - priors[n][1])
if uflag == True:
upper = priors[n][2] - params[n]
delta = 0
positive = False
if lflag == False and uflag == False:
# proceed as normal
delta = rnd.uniform(low=kernel[2][ind][0],
high=kernel[2][ind][1])
else:
# decide if the particle is to be perturbed positively or negatively
positive = rnd.uniform(0,
1) > abs(lower) / (abs(lower) + upper)
if positive == True:
# theta = theta + U(0, min(prior,kernel) )
delta = rnd.uniform(low=0, high=upper)
else:
# theta = theta + U( max(prior,kernel), 0 )
delta = rnd.uniform(low=lower, high=0)
params[n] = params[n] + delta
ind += 1
# this is not the actaul value of the pdf but we only require it to be non zero
return 1.0
else:
if kernel_type == 1:
ind = 0
# n refers to the index of the parameter (integer between 0 and np-1)
# ind is an integer between 0 and len(kernel[0])-1 which enables to determine the kernel to use
for n in kernel[0]:
params[n] = params[n] + rnd.uniform(low=kernel[2][ind][0],
high=kernel[2][ind][1])
ind += 1
if kernel_type == 2:
ind = 0
# n refers to the index of the parameter (integer between 0 and np-1)
# ind is an integer between 0 and len(kernel[0])-1 which enables to determine the kernel to use
for n in kernel[0]:
params[n] = rnd.normal(params[n], numpy.sqrt(kernel[2][ind]))
ind += 1
if kernel_type == 3:
mean = list()
for n in kernel[0]:
mean.append(params[n])
tmp = statistics.mvnd_gen(mean, kernel[2])
ind = 0
for n in kernel[0]:
params[n] = tmp[ind]
ind = ind + 1
if (kernel_type == 4 or kernel_type == 5):
mean = list()
for n in kernel[0]:
mean.append(params[n])
D = kernel[2]
tmp = statistics.mvnd_gen(mean, D[str(params)])
ind = 0
for n in kernel[0]:
params[n] = tmp[ind]
ind = ind + 1
# compute the likelihood
prior_prob = 1
for n in range(np):
x = 1.0
#if priors[n][0]==1:
# x=statistics.getPdfGauss(priors[n][1], numpy.sqrt(priors[n][2]), params[n])
# if we do not care about the value of prior_prob, then here: x=1.0
if priors[n][0] == 2:
x = statistics.getPdfUniform(priors[n][1], priors[n][2],
params[n])
#if priors[n][0]==3:
# x=statistics.getPdfLognormal(priors[n][1],priors[n][2],params[n])
# if we do not care about the value of prior_prob, then here: x=1.0 if params[n]>=0 and 0 otherwise
prior_prob = prior_prob * x
return prior_prob
testSeq['ctpd'] = condsList[len(condsList)-i-1]['ctpd_prime']
testSeq['dtype'] = cond['dtype_test']
testSeq['dsd'] = cond['dsd_test']
testSeq['seq_type'] = 'probe'
testSeq['set_size'] = cond['test_set_size']
totalTrials+=primeSeq['nextProbe']+testSeq['nextProbe']
conds.append(primeSeq)
conds.append(testSeq)
for blockN, block in enumerate(conds):
block['blockN'] = blockN
block['blockRepN'] = blockRepN
block['totBlockN'] = totBlockN
totBlockN+=1
if block['seq_type']=='prime':
dmean = random.uniform(0, 360)
else:
# we use different bin sizes at different CTPDs so we have a bit more precision closer to the mean, where we actually expect to see differences
if abs(prev_ctpd)==80:
targetOri = prev_dmean+np.sign(prev_ctpd)*random.uniform(70,90)
elif abs(prev_ctpd)==60:
targetOri = prev_dmean+np.sign(prev_ctpd)*random.uniform(50,70)
elif abs(prev_ctpd)==40:
targetOri = prev_dmean+np.sign(prev_ctpd)*random.uniform(35,50)
else:
targetOri = prev_dmean+prev_ctpd+random.uniform(-5,5)
if totBlockN>1:
block['prevDistrMean'] = prev_dmean
block['prevDistrCTPD'] = prev_ctpd
block['prevDistrType'] = prev_dtype
prev_dmean = dmean
def onemove(self, x, u, xp, up):
"""One move of the twalk. This is basically the raw twalk kernel.
It is usefull if the twalk is needed inside a more complex MCMC.
onemove(x, u, xp, up),
x, xp, two points WITHIN the support ***each entry of x0 and xp0 must be different***.
and the value of the objective at x, and xp
u=U(x), up=U(xp).
It returns: [y, yp, ke, A, u_prop, up_prop]
y, yp: the proposed jump
ke: The kernel used, 0=nothing, 1=Walk, 2=Traverse, 3=Blow, 4=Hop
A: the M-H ratio
u_prop, up_prop: The values for the objective func. at the proposed jumps
"""
#### Make local references for less writing
n = self.n
U = self.U
Supp = self.Supp
Fw = self.Fw
ker = uniform() ### To choose the kernel to be used
ke = 1
A = 0
## Kernel nothing exchange x with xp, not used
if ((0.0 <= ker) & (ker < Fw[0])):
ke = 0
y = xp.copy()
up_prop = u
yp = x.copy()
u_prop = up
### A is the MH acceptance ratio
A = 1.0
#always accepted
## The Walk move
if ((Fw[0] <= ker) & (ker < Fw[1])):
ke = 1
dir = uniform()
if ((0 <= dir) & (dir < 0.5)): ## x as pivot
yp = self.SimWalk(xp, x)
y = x.copy()
u_prop = u
if ((Supp(yp)) & (all(abs(yp - y) > 0))):
up_prop = U(yp)
A = exp(up - up_prop)
else:
up_prop = None
A = 0
##out of support, not accepted
else: ## xp as pivot
y = self.SimWalk(x, xp)
yp = xp.copy()
up_prop = up
if ((Supp(y)) & (all(abs(yp - y) > 0))):
u_prop = U(y)
A = exp(u - u_prop)
else:
u_prop = None
A = 0
##out of support, not accepted
#### The Traverse move
if ((Fw[1] <= ker) & (ker < Fw[2])):
ke = 2
dir = uniform()
if ((0 <= dir) & (dir < 0.5)): ## x as pivot
beta = self.Simbeta()
yp = self.SimTraverse(xp, x, beta)
y = x.copy()
u_prop = u
if Supp(yp):
up_prop = U(yp)
if (self.nphi == 0):
A = 1 ###Nothing moved
else:
A = exp((up - up_prop) + (self.nphi - 2) * log(beta))
else:
up_prop = None
A = 0 ##out of support, not accepted
else: ## xp as pivot
beta = self.Simbeta()
y = self.SimTraverse(x, xp, beta)
yp = xp.copy()
up_prop = up
if Supp(y):
u_prop = U(y)
if (self.nphi == 0):
A = 1 ###Nothing moved
else:
A = exp((u - u_prop) + (self.nphi - 2) * log(beta))
else:
u_prop = None
A = 0 ##out of support, not accepted
### The Blow move
if ((Fw[2] <= ker) & (ker < Fw[3])):
ke = 3
dir = uniform()
if ((0 <= dir) & (dir < 0.5)): ## x as pivot
yp = self.SimBlow(xp, x)
y = x.copy()
u_prop = u
if ((Supp(yp)) & all(yp != x)):
up_prop = U(yp)
W1 = self.GBlowU(yp, xp, x)
W2 = self.GBlowU(xp, yp, x)
A = exp((up - up_prop) + (W1 - W2))
else:
up_prop = None
A = 0 ##out of support, not accepted
else: ## xp as pivot
y = self.SimBlow(x, xp)
yp = xp.copy()
up_prop = up
if ((Supp(y)) & all(y != xp)):
u_prop = U(y)
W1 = self.GBlowU(y, x, xp)
W2 = self.GBlowU(x, y, xp)
A = exp((u - u_prop) + (W1 - W2))
else:
u_prop = None
A = 0 ##out of support, not accepted
### The Hop move
if ((Fw[3] <= ker) & (ker < Fw[4])):
ke = 4
dir = uniform()
if ((0 <= dir) & (dir < 0.5)): ## x as pivot
yp = self.SimHop(xp, x)
y = x.copy()
u_prop = u
if ((Supp(yp)) & all(yp != x)):
up_prop = U(yp)
W1 = self.GHopU(yp, xp, x)
W2 = self.GHopU(xp, yp, x)
A = exp((up - up_prop) + (W1 - W2))
else:
up_prop = None
A = 0 ##out of support, not accepted
else: ## xp as pivot
y = self.SimHop(x, xp)
yp = xp.copy()
up_prop = up
if ((Supp(y)) & all(y != xp)):
u_prop = U(y)
W1 = self.GHopU(y, x, xp)
W2 = self.GHopU(x, y, xp)
A = exp((u - u_prop) + (W1 - W2))
else:
u_prop = None
A = 0 ##out of support, not accepted
return [y, yp, ke, A, u_prop, up_prop]
def stablernd(alpha, size=1):
# cf Devroye, 2009, Equation (2)
U = npr.uniform(low=0.0, high=np.pi, size=size)
E = npr.exponential(size=size)
samples = (zolotarev(U, alpha) / E)**((1 - alpha) / alpha)
return samples
def generate_fake_fits_observation(event_list=None, filename=None,
instr='FPMA', gti=None, tstart=None,
tstop=None, mission='NUSTAR',
mjdref=55197.00076601852,
livetime=None, additional_columns={}):
"""Generate fake NuSTAR data.
Takes an event list (as a list of floats)
All inputs are None by default, and can be set during the call.
Parameters
----------
event_list : list-like
:class:`stingray.events.Eventlist` object. If left None, 1000
random events will be generated, for a total length of 1025 s or the
difference between tstop and tstart.
filename : str
Output file name
Returns
-------
hdulist : FITS hdu list
FITS hdu list of the output file
Other Parameters
----------------
mjdref : float
Reference MJD. Default is 55197.00076601852 (NuSTAR)
pi : list-like
The PI channel of each event
tstart : float
Start of the observation (s from mjdref)
tstop : float
End of the observation (s from mjdref)
instr : str
Name of the instrument. Default is 'FPMA'
livetime : float
Total livetime. Default is tstop - tstart
"""
from astropy.io import fits
import numpy.random as ra
if event_list is None:
tstart = assign_value_if_none(tstart, 8e+7)
tstop = assign_value_if_none(tstop, tstart + 1025)
ev_list = sorted(ra.uniform(tstart, tstop, 1000))
else:
ev_list = event_list.time
if hasattr(event_list, 'pi'):
pi = event_list.pi
else:
pi = ra.randint(0, 1024, len(ev_list))
tstart = assign_value_if_none(tstart, np.floor(ev_list[0]))
tstop = assign_value_if_none(tstop, np.ceil(ev_list[-1]))
gti = assign_value_if_none(gti, np.array([[tstart, tstop]]))
filename = assign_value_if_none(filename, 'events.evt')
livetime = assign_value_if_none(livetime, tstop - tstart)
if livetime > tstop - tstart:
raise ValueError('Livetime must be equal or smaller than '
'tstop - tstart')
# Create primary header
prihdr = fits.Header()
prihdr['OBSERVER'] = 'Edwige Bubble'
prihdr['TELESCOP'] = (mission, 'Telescope (mission) name')
prihdr['INSTRUME'] = (instr, 'Instrument name')
prihdu = fits.PrimaryHDU(header=prihdr)
# Write events to table
col1 = fits.Column(name='TIME', format='1D', array=ev_list)
col2 = fits.Column(name='PI', format='1J', array=pi)
allcols = [col1, col2]
if mission.lower().strip() == 'xmm':
ccdnr = np.zeros(len(ev_list)) + 1
ccdnr[1] = 2 # Make it less trivial
ccdnr[10] = 7
allcols.append(fits.Column(name='CCDNR', format='1J',
array=ccdnr))
for c in additional_columns.keys():
col = fits.Column(name=c, array=additional_columns[c]["data"],
format=additional_columns[c]["format"])
allcols.append(col)
cols = fits.ColDefs(allcols)
tbhdu = fits.BinTableHDU.from_columns(cols)
tbhdu.name = 'EVENTS'
# ---- Fake lots of information ----
tbheader = tbhdu.header
tbheader['OBSERVER'] = 'Edwige Bubble'
tbheader['COMMENT'] = ("FITS (Flexible Image Transport System) format is"
" defined in 'Astronomy and Astrophysics', volume"
" 376, page 359; bibcode: 2001A&A...376..359H")
tbheader['TELESCOP'] = (mission, 'Telescope (mission) name')
tbheader['INSTRUME'] = (instr, 'Instrument name')
tbheader['OBS_ID'] = ('00000000001', 'Observation ID')
tbheader['TARG_ID'] = (0, 'Target ID')
tbheader['OBJECT'] = ('Fake X-1', 'Name of observed object')
tbheader['RA_OBJ'] = (0.0, '[deg] R.A. Object')
tbheader['DEC_OBJ'] = (0.0, '[deg] Dec Object')
tbheader['RA_NOM'] = (0.0,
'Right Ascension used for barycenter corrections')
tbheader['DEC_NOM'] = (0.0,
'Declination used for barycenter corrections')
tbheader['RA_PNT'] = (0.0, '[deg] RA pointing')
tbheader['DEC_PNT'] = (0.0, '[deg] Dec pointing')
tbheader['PA_PNT'] = (0.0, '[deg] Position angle (roll)')
tbheader['EQUINOX'] = (2.000E+03, 'Equinox of celestial coord system')
tbheader['RADECSYS'] = ('FK5', 'Coordinate Reference System')
tbheader['TASSIGN'] = ('SATELLITE', 'Time assigned by onboard clock')
tbheader['TIMESYS'] = ('TDB', 'All times in this file are TDB')
tbheader['MJDREFI'] = (int(mjdref),
'TDB time reference; Modified Julian Day (int)')
tbheader['MJDREFF'] = (mjdref - int(mjdref),
'TDB time reference; Modified Julian Day (frac)')
tbheader['TIMEREF'] = ('SOLARSYSTEM',
'Times are pathlength-corrected to barycenter')
tbheader['CLOCKAPP'] = (False, 'TRUE if timestamps corrected by gnd sware')
tbheader['COMMENT'] = ("MJDREFI+MJDREFF = epoch of Jan 1, 2010, in TT "
"time system.")
tbheader['TIMEUNIT'] = ('s', 'unit for time keywords')
tbheader['TSTART'] = (tstart,
'Elapsed seconds since MJDREF at start of file')
tbheader['TSTOP'] = (tstop,
'Elapsed seconds since MJDREF at end of file')
tbheader['LIVETIME'] = (livetime, 'On-source time')
tbheader['TIMEZERO'] = (0.000000E+00, 'Time Zero')
tbheader['COMMENT'] = (
"Generated with HENDRICS by {0}".format(os.getenv('USER')))
# ---- END Fake lots of information ----
# Fake GTIs
start = gti[:, 0]
stop = gti[:, 1]
col1 = fits.Column(name='START', format='1D', array=start)
col2 = fits.Column(name='STOP', format='1D', array=stop)
allcols = [col1, col2]
cols = fits.ColDefs(allcols)
gtinames = ['GTI']
if mission.lower().strip() == 'xmm':
gtinames = ['STDGTI01', 'STDGTI02', 'STDGTI07']
all_new_hdus = [prihdu, tbhdu]
for name in gtinames:
gtihdu = fits.BinTableHDU.from_columns(cols)
gtihdu.name = name
all_new_hdus.append(gtihdu)
thdulist = fits.HDUList(all_new_hdus)
thdulist.writeto(filename, overwrite=True)
return thdulist
def Simbeta(self):
at = self.at
if (uniform() < (at - 1.0) / (2.0 * at)):
return exp(1.0 / (at + 1.0) * log(uniform()))
else:
return exp(1.0 / (1.0 - at) * log(uniform()))
def __call__(self, image, boxes=None, labels=None):
height, width, _ = image.shape
while True:
# randomly choose a mode
mode = random.choice(self.sample_options)
boxes_rect = []
for i in range(len(boxes)):
boxes_rect.append([
min(boxes[i, ::2]),
min(boxes[i, 1::2]),
max(boxes[i, ::2]),
max(boxes[i, 1::2])
])
boxes_rect = np.array(boxes_rect)
if mode is None or len(boxes_rect) == 0:
return image, boxes, labels
min_boxes, max_boxes = mode
if min_boxes is None:
min_boxes = float('-inf')
if max_boxes is None:
max_boxes = float('inf')
# max trails (50)
for _ in range(50):
current_image = image
w = random.uniform(0.1 * width, width)
h = random.uniform(0.1 * height, height)
# aspect ratio constraint b/t .5 & 2
if h / w < 0.5 or h / w > 2:
continue
left = random.uniform(width - w)
top = random.uniform(height - h)
# convert to integer rect x1,y1,x2,y2
rect = np.array(
[int(left),
int(top),
int(left + w),
int(top + h)])
# calculate IoU (jaccard overlap) b/t the cropped and gt boxes
overlap = modified_jaccard_numpy(boxes_rect, rect)
if (overlap > 0.9).sum() <= min_boxes or (
overlap > 0.9).sum() >= max_boxes:
continue
# cut the crop from the image
current_image = current_image[rect[1]:rect[3],
rect[0]:rect[2], :]
""" No Mask """
current_boxes = boxes.copy()
num_pt = int(current_boxes.shape[1] / 2)
current_boxes[:, :2 * num_pt] -= rect[:2].tolist() * num_pt
current_labels = labels
return current_image, current_boxes, current_labels
def Run(self, T, x0, xp0):
"""Run the twalk.
Run( T, x0, xp0),
T = Number of iterations.
x0, xp0, two initial points within the support,
***each entry of x0 and xp0 most be different***.
"""
sec = time()
print "pytwalk: Running the twalk with %d iterations." % (
T, ), strftime("%a, %d %b %Y, %H:%M.", localtime(sec))
### Check x0 and xp0 are in the support
[rt, u, up] = self._SetUpInitialValues(x0, xp0)
if (not (rt)):
return 0
### send an estimation for the duration of the sampling if
### evaluating the ob. func. twice (in self._SetUpInitialValues) takes more than one second
sec2 = time() # last time we sent a message
print " " + Remain(T, 2, sec, sec2)
x = x0 ### Use x and xp by reference, so we can retrive the last values used
xp = xp0
### Set the array to place the iterations and the U's ... we donot save up's
self.Output = zeros((T + 1, self.n + 1))
self.T = T + 1
self.Acc = zeros(6)
kercall = zeros(6) ## Times each kernel is called
#### Make local references for less writing
n = self.n
Output = self.Output
U = self.U
Supp = self.Supp
Acc = self.Acc
Fw = self.Fw
Output[0, 0:n] = x.copy()
Output[0, n] = u
j1 = 1
j = 0
### Sampling
for it in range(T):
y, yp, ke, A, u_prop, up_prop = self.onemove(x, u, xp, up)
kercall[ke] += 1
kercall[5] += 1
if (uniform() < A):
x = y.copy() ### Accept the propolsal y
u = u_prop
xp = yp.copy() ### Accept the propolsal yp
up = up_prop
Acc[ke] += 1
Acc[5] += 1
### To retrive the current values
self.x = x
self.xp = xp
self.u = u
self.up = up
Output[it + 1, 0:n] = x.copy()
Output[it + 1, n] = u
### Estimate the remaing time, every 2**j1 iterations
if ((it % (1 << j1)) == 0):
j1 += 1
j1 = min(
j1,
10) # check the time at least every 2^10=1024 iterations
ax = time()
if ((ax - sec2) > (1 << j) *
self.WAIT): # Print an estimation every WAIT*2**j
print "pytwalk: %10d iterations so far. " % (
it, ) + Remain(T, it, sec, ax)
sec2 = ax
j += 1
j1 -= 1 # check the time as often
if (Acc[5] == 0):
print "pytwalk: WARNING, all propolsals were rejected!"
print strftime("%a, %d %b %Y, %H:%M:%S.", localtime(time()))
return 0
else:
print "pytwalk: finished, " + strftime("%a, %d %b %Y, %H:%M:%S.",
localtime(time()))
for i in range(6):
if kercall[i] != 0:
Acc[i] /= kercall[i]
return 1
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = ((center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) *
(center[:, 0] < patch[2]) * (center[:, 1] < patch[3]))
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
if 'gt_masks' in results:
valid_masks = [
results['gt_masks'][i] for i in range(len(mask))
if mask[i]
]
results['gt_masks'] = np.stack([
gt_mask[patch[1]:patch[3], patch[0]:patch[2]]
for gt_mask in valid_masks
])
# not tested
if 'gt_semantic_seg' in results:
results['gt_semantic_seg'] = results['gt_semantic_seg'][
patch[1]:patch[3], patch[0]:patch[2]]
return results
# First do it with no incentive
expected_payoffs = []
for bid in bids:
ep = (bid[0], utility(bid[1], activation_threshold, 0, 0))
expected_payoffs.append(ep)
pos_expected_payoffs = [payoff for payoff in expected_payoffs if payoff[1] > 0]
count_A = 0
count_B = 0
proportion_A = [0]
count = 0
while len(pos_expected_payoffs) > 0:
rand_pick = int(round(uniform(0, len(pos_expected_payoffs) - 1)))
picked_tuple = pos_expected_payoffs[rand_pick]
picked_agent = picked_tuple[0]
picked_payoff = picked_tuple[1]
picked_bids_list = [bid for bid in bids if bid[0] == picked_agent]
picked_bid = picked_bids_list[0][1]
if agents[picked_agent] == 'A':
count_A += 1
else:
count_B += 1
count = count + 1
proportion_A.append(float(count_A) / float(count_A + count_B))
