def test_convolve2d_king(self):
gfn = lambda r, s: np.power(2*np.pi*s**2,-1)*np.exp(-r**2/(2*s**2))
kfn = lambda r, s, g: np.power(2*np.pi*s**2,-1)*(1.-1./g)* \
np.power(1+0.5/g*(r/s)**2,-g)
kfn0 = lambda x, y, mux, muy, s, g: kfn(np.sqrt((x-mux)**2+(y-muy)**2),s,g)
xaxis = Axis.create(-3,3,501)
yaxis = Axis.create(-3,3,501)
x, y = np.meshgrid(xaxis.center,yaxis.center)
xbin, ybin = np.meshgrid(xaxis.width,yaxis.width)
r = np.sqrt(x**2+y**2)
# Scalar Input
mux = 0.5
muy = -0.2
mur = (mux**2+muy**2)**0.5
gsig = 0.1
ksig = 0.2
kgam = 4.0
fval0 = np.sum(kfn0(x,y,mux,muy,ksig,kgam)*gfn(r,gsig)*xbin*ybin)
fval1 = convolve2d_king(lambda t: gfn(t,gsig),mur,ksig,kgam,3.0,
nstep=10000)
# fval2 = convolve2d_gauss(lambda t: kfn(t,ksig,kgam),mur,gsig,3.0,
# nstep=1000)
# print fval0, fval1, fval2, fval1/fval0
assert_almost_equal(fval0,fval1,4)
def __init__(self, Lx=600.0, Ly=600.0, nx=31, ny=31, dt=0.2, epsilon=0.02, h0=10.0, t0=0.0):
"""Initialization with grid parameters and initial conditions"""
# store the parameters
self.Lx, self.Ly = float(Lx), float(Ly)
self.nx, self.ny = nx, ny
self.dt = float(dt)
self.dx = self.Lx / (self.nx - 1)
self.dy = self.Ly / (self.ny - 1)
self.h0 = h0
self.epsilon = float(epsilon)
self.t0 = float(t0)
self.t = float(t0)
self.i = 0
# make axes
self.xeta = np.linspace(0, self.Lx, self.nx)
self.yeta = np.linspace(0, self.Ly, self.ny)
self.xxeta, self.yyeta = np.meshgrid(self.xeta, self.yeta)
self.xu = self.xeta + self.dx / 2.0
self.yu = self.yeta + self.dy / 2.0
self.xxu, self.yyu = np.meshgrid(self.xu, self.yu)
# initialize the arrays
shape = np.array((nx, ny)) + 2
self.uold = np.zeros(shape)
self.vold = np.zeros(shape)
self.unew = np.zeros(shape)
self.vnew = np.zeros(shape)
self.etaold = np.zeros(shape)
self.etaint = np.zeros(shape)
self.etanew = np.zeros(shape)
self.h = np.ones(shape, dtype=float) * h0
self.gridargs = (self.nx, self.ny, self.dx, self.dy, self.dt)
def allowed_region( V_nj, ave_j ):
# read PCs
PC1 = V_nj[0]
PC2 = V_nj[1]
n_band = len( PC1 )
band_ticks = np.arange( n_band )
x_ticks = np.linspace(-0.4,0.2,RESOLUTION)
y_ticks = np.linspace(-0.2,0.4,RESOLUTION)
x_mesh, y_mesh, band_mesh = np.meshgrid( x_ticks, y_ticks, band_ticks, indexing='ij' )
vec_mesh = x_mesh * PC1[ band_mesh ] + y_mesh * PC2[ band_mesh ] + ave_j[ band_mesh ]
x_grid, y_grid = np.meshgrid( x_ticks, y_ticks, indexing='ij' )
prohibited_grid = np.zeros_like( x_grid )
for ii in xrange( len( x_ticks ) ) :
for jj in xrange( len( y_ticks ) ) :
if np.any( vec_mesh[ii][jj] < 0. ) :
prohibited_grid[ii][jj] = 1
if np.any( vec_mesh[ii][jj] > 1. ) :
prohibited_grid[ii][jj] = 3
elif np.any( vec_mesh[ii][jj] > 1. ) :
prohibited_grid[ii][jj] = 2
else :
prohibited_grid[ii][jj] = 0
return x_grid, y_grid, prohibited_grid
def test_kernel_error():
"""
"""
for xx2d in [np.array(np.meshgrid(np.arange(-100, 100, 5),
np.arange(-100, 100, 5))),
np.array(np.meshgrid(np.arange(-100, 100, 5),
np.arange(-100, 100, 5))).reshape(2, -1)]:
mean1 = [20, 20]
sigma1 = [10, 10]
params1 = np.hstack([mean1, sigma1])
mean2 = [30, 40]
sigma2 = [10, 50]
params2 = np.hstack([mean2, sigma2])
params = [params1, params2]
betas = [0.3, 0.7]
y = ebp.mixture_of_kernels(xx2d, betas, params,
ebp.gaussian_kernel)
err = ebp.kernel_err(y, xx2d, betas, params, ebp.gaussian_kernel)
npt.assert_almost_equal(err, np.zeros(err.shape))
def get_gauss_points(self, ng):
if isinstance(ng, int):
return self.gauss_points.get(ng)
elif isinstance(ng, list):
if len(ng) > 3:
raise Exception('Gauss points for 4 dimensions' +
'and above not supported')
if len(ng) == 2:
Xi1, W1 = self.get_gauss_points(ng[0])
Xi2, W2 = self.get_gauss_points(ng[1])
Xi1g, Xi2g = numpy.meshgrid(Xi1.flatten(), Xi2.flatten())
Xi1 = numpy.array([Xi1g.flatten(), Xi2g.flatten()]).T
W1g, W2g = numpy.meshgrid(W1.flatten(), W2.flatten())
W1 = W1g.flatten() * W2g.flatten()
return Xi1, W1
elif len(ng) == 3:
Xi1, W1 = self.get_gauss_points(ng[0])
Xi2, W2 = self.get_gauss_points(ng[1])
Xi3, W3 = self.get_gauss_points(ng[2])
gindex = numpy.mgrid[0:ng[0], 0:ng[1], 0:ng[2]]
gindex = numpy.array([
gindex[2, :, :].flatten(),
gindex[1, :, :].flatten(),
gindex[0, :, :].flatten()]).T
Xi = numpy.array([
Xi1[gindex[:,0]], Xi2[gindex[:,1]], Xi3[gindex[:,2]]])[:,:,0].T
W = numpy.array([
W1[gindex[:,0]], W2[gindex[:,1]], W3[gindex[:,2,]]]).T.prod(1)
return Xi, W
raise Exception('Invalid number of gauss points')
return None, None
def getBoxbyX(X, grid=50, padding=True):
'''
Get the meshgrid X,Y given data set X
:param X: dataset
:param grid: meshgrid step size
:param padding: if add extra padding around
:return: X,Y
'''
if X.shape[1] > 2:
print 'We can only get the grid in 2-d dimension!'
return None
else:
minx = min(X[:,0])
maxx = max(X[:,0])
miny = min(X[:,1])
maxy = max(X[:,1])
padding_x = 0.05*(maxx-minx)
padding_y = 0.05*(maxy-miny)
if padding:
X,Y = np.meshgrid(np.linspace(minx-padding_x, maxx+padding_x, grid),
np.linspace(miny-padding_y, maxy+padding_y, grid))
else:
X,Y = np.meshgrid(np.linspace(minx, maxx, grid),
np.linspace(miny, maxy, grid))
return (X, Y)
def logLikelihood(self, data): assert self.numImages == data.numImages which = [np.nonzero(data.id == i)[0]\ for i in xrange(0, self.numImages)] # Mean vector m = np.empty(data.t.size) for i in xrange(0, self.numImages): m[which[i]] = self.mag[i] # Covariance matrix [t1, t2] = np.meshgrid(data.t, data.t) lags = np.abs(t2 - t1) plt.imshow(lags) plt.show() ids = np.meshgrid(data.id, data.id) equal = ids[0] == ids[1] ## try: ## L = la.cholesky(C) ## except: ## return -np.inf # y = data.y - m # logDeterminant = 2.0*np.sum(np.log(np.diag(L))) # solution = la.cho_solve((L, True), y) # exponent = np.dot(y, solution) # logL = -0.5*data.t.size*np.log(2.0*np.pi) - 0.5*logDeterminant - 0.5*exponent return 0.
def makePathAndGrid(data, xsteps, ysteps, x_col='x_m', y_col='y_m'):
x = data[x_col]
y = data[y_col]
xmin = min(x)
xmax = max(x)
ymin = min(y)
ymax = max(y)
# make grid
dx = (xmax - xmin)/xsteps
dy = (ymax - ymin)/ysteps
XXraw, YYraw = np.meshgrid(np.arange(xmin, xmax, dx) + dx/2, np.arange(ymin, ymax, dy) + dy/2)
x = ((x - xmin)/(xmax-xmin) * xsteps).astype(int)
y = ((y - ymin)/(ymax-ymin) * ysteps).astype(int)
xx = np.arange(0, xsteps)
yy = np.arange(0, ysteps)
M = np.zeros((len(xx),len(yy)))
N = len(xx) * len(yy)
idx = np.arange(N)
np.random.shuffle(idx)
XX, YY = np.meshgrid(xx, yy)
XX = XX.reshape(N)
YY = YY.reshape(N)
XXraw = XXraw.reshape(N)
YYraw = YYraw.reshape(N)
return idx, np.c_[XXraw, YYraw].T, np.c_[XX, YY].T, M
def check_cross_amplification(self, max_dist):
pop_set = set()
for pos in self.pairs_pos:
p1,p2 = pos
c1,a1,s1 = zip(*self.alignments_dict[p1])
c2,a2,s2 = zip(*self.alignments_dict[p2])
chrs_x, chrs_y = na.meshgrid(c1, c2)
chrs_eq = chrs_x==chrs_y
orient_x,orient_y = na.meshgrid(s1, s2)
orient_fr = orient_x==na.logical_not(orient_y)
pos_x,pos_y = na.meshgrid(a1, a2)
# Filter by minimum distance.
# And if orientation is forward/reverse
# Amps is [Alignments i X Alignments j ]
amps = na.logical_and(na.logical_and(na.abs(pos_x-pos_y)<max_dist,
na.logical_and(orient_fr, pos_x<pos_y)), # Forward reverse if one position is less than the other and is forward, while the other is reverse
chrs_eq)
number_of_amps = na.sum(amps)
#assert number_of_amps!=0
if number_of_amps>1:
pop_set.add(pos)
self.pairs_pos.difference_update(pop_set)
def P1(mode="a"):
if mode=="a":
fig = plt.figure()
for N,p in [(8,221),(16,222),(32,223),(64,224)]:
u = OneD(0.035,2.5,60,20,20,N,0.01,5)
x = [i*2.5/N for i in range(N)]
y = [j*0.01 for j in range(int(5/0.01))]
X,Y = np.meshgrid(x,y)
ax = fig.add_subplot(p, projection='3d')
surf = ax.plot_surface(X,Y,u,rstride=1, cstride=1,linewidth=0, cmap=cm.coolwarm)
ax.set_zlim(20,60.01)
ax.set_xlabel('X Position (cm)')
ax.set_ylabel('Time (s)')
ax.set_zlabel('Temperature (C)')
ax.set_title('N='+str(N))
fig.colorbar(surf,ticks=[t for t in range(20,61,5)])
plt.show()
if mode == "b":
fig = plt.figure()
for t,p in [(0.001,221),(0.01,222),(0.1,223)]:
u = OneD(0.035,2.5,60,20,20,32,t,5)
x = [i*2.5/32 for i in range(32)]
y = [j*t for j in range(int(5/t))]
X,Y = np.meshgrid(x,y)
ax = fig.add_subplot(p, projection='3d')
surf = ax.plot_surface(X,Y,u,rstride=1, cstride=1,linewidth=0, cmap=cm.coolwarm)
if t!=0.1:
ax.set_zlim(20,60.01)
ax.set_xlabel('X Position (cm)')
ax.set_ylabel('Time (s)')
ax.set_zlabel('Temperature (C)')
ax.set_title('Timestep:'+str(t))
if t != 0.1:
fig.colorbar(surf,ticks=[t for t in range(20,61,5)])
plt.show()
def polyval2d_general(c, x, y, function="polynomial", minx=None, maxx=None, miny=None, maxy=None):
if function == "polynomial":
xx, yy = np.meshgrid(x, y)
return np.polynomial.polynomial.polyval2d(xx, yy, c)
elif function in ["legendre", "chebyshev"]:
# Scale x-direction
if minx is None or maxx is None:
if np.size(x) == 1:
xmin, xmax = -1.0, 1.0
else:
xmin, xmax = np.min(x), np.max(x)
else:
xmin, xmax = minx, maxx
xv = 2.0 * (x-xmin)/(xmax-xmin) - 1.0
# Scale y-direction
if miny is None or maxy is None:
if np.size(y) == 1:
ymin, ymax = -1.0, 1.0
else:
ymin, ymax = np.min(y), np.max(y)
else:
ymin, ymax = miny, maxy
yv = 2.0 * (y-ymin)/(ymax-ymin) - 1.0
xx, yy = np.meshgrid(xv, yv)
if function == "legendre":
return np.polynomial.legendre.legval2d(xx, yy, c)
elif function == "chebyshev":
return np.polynomial.chebyshev.chebval2d(xx, yy, c)
else:
msgs.error("Function {0:s} has not yet been implemented".format(function))
return None
def _calc_partials(self):
"""Calculate the partial derivatives
Uses sympy.utilities.lambdify to convert equation into
a function that can be easily computed.
Raises:
MissingEquationError: if method is called before equation is
attached.
"""
if not self.equation:
raise self.MissingEquationError('No equation attached')
x, y = sympy.symbols('x,y')
compute_func = sympy.utilities.lambdify((x, y), self.equation)
X, Y = np.meshgrid(self.xrange, self.yrange)
DX, DY = np.meshgrid(np.zeros(len(self.xrange)),
np.zeros(len(self.yrange)))
# Iterate through grid and compute function value at each point
# If value cannot be computed, default to 0
# If value can be computed, scale by sqrt of the magnitude
for i, a in enumerate(self.xrange):
for j, b in enumerate(self.yrange):
dx = 1
try:
dy = compute_func(a, b)
n = sqrt(dx**2 + dy**2)
dy /= sqrt(n)
dx /= sqrt(n)
DX[j][i] = dx
DY[j][i] = dy
except (ValueError, ZeroDivisionError):
pass
return X, Y, DX, DY
def mask_polar_to_cart(mask, center, min_radius, max_radius, output_shape, zoom_factor=1):
'''Converts a polar binary mask to Cartesian and places in an image of zeros'''
# Account for upsampling
if zoom_factor != 1:
center = (center[0]*zoom_factor + zoom_factor/2, center[1]*zoom_factor + zoom_factor/2)
min_radius = min_radius * zoom_factor
max_radius = max_radius * zoom_factor
output_shape = map(lambda a: a * zoom_factor, output_shape)
# new image
image = np.zeros(output_shape)
# coordinate conversion
theta, r = np.meshgrid(np.linspace(0, 2*np.pi, mask.shape[1]),
np.arange(0, max_radius))
x, y = coord_polar_to_cart(r, theta, center)
x, y = np.round(x), np.round(y)
x, y = x.astype(int), y.astype(int)
x = np.clip(x, 0, image.shape[0]-1)
y = np.clip(y, 0, image.shape[1]-1)
ix,iy = np.meshgrid(np.arange(0,mask.shape[1]), np.arange(0,mask.shape[0]))
image[x,y] = mask
# downsample image
if zoom_factor != 1:
zf = 1/float(zoom_factor)
image = zoom(image, (zf, zf), order=4)
# ensure image remains a filled binary mask
image = (image > 0.5).astype(int)
image = binary_fill_holes(image)
return image
def stmDatToArray(filename):
with open(filename, 'r') as f:
f.readline()
delta = []
for i in range(2):
line = map(float, [entry for entry in f.readline().strip().split(' ') if entry!=''][:2])
delta.append(line)
shape = [entry for entry in f.readline().strip().split(' ') if len(entry)!=0]
shape = map(int, shape[:2])
arrays = []
xm, ym = [delta[i][0] + np.arange(shape[i])*delta[i][1] for i in range(2)]
xo, yo = [delta[i][0] - 0.5*delta[i][1]+ np.arange(shape[i] + 1)*delta[i][1] for i in range(2)]
Xm, Ym = np.meshgrid(xm, ym)
Xo, Yo = np.meshgrid(xo, yo)
collect = []
for line in f:
try:
line = [entry for entry in line.strip().split(' ') if len(entry)>0]
line = map(float, line)
except ValueError:
break
else:
collect.extend(line)
Z = np.reshape(collect, shape)
return Xm, Ym, Z
def test_get_decomposition(self):
# Test a stable entry to ensure that it's zero in the stable region
entry = self.test_data['Zn'][12] # Should correspond to mp-2133
self.assertAlmostEqual(self.pbx.get_decomposition_energy(entry, 10, 1),
0.0, 5, "Decomposition energy of ZnO is not 0.")
# Test an unstable entry to ensure that it's never zero
entry = self.test_data['Zn'][11]
ph, v = np.meshgrid(np.linspace(0, 14), np.linspace(-2, 4))
result = self.pbx_nofilter.get_decomposition_energy(entry, ph, v)
self.assertTrue((result >= 0).all(),
"Unstable energy has hull energy of 0 or less")
# Test an unstable hydride to ensure HER correction works
self.assertAlmostEqual(self.pbx.get_decomposition_energy(entry, -3, -2),
11.093744395)
# Test a list of pHs
self.pbx.get_decomposition_energy(entry, np.linspace(0, 2, 5), 2)
# Test a list of Vs
self.pbx.get_decomposition_energy(entry, 4, np.linspace(-3, 3, 10))
# Test a set of matching arrays
ph, v = np.meshgrid(np.linspace(0, 14), np.linspace(-3, 3))
self.pbx.get_decomposition_energy(entry, ph, v)
def find_intersections(contour1, contour2):
"""
Vectorized code to find intersections between contours. All
successive duplicate vertices along the input contours must be
removed to help avoid division-by-zero errors.
There are cases were no intersection exists (eg. parallel lines)
where division by zero and invalid value exceptions occur. These
exceptions should be caught as warnings: these edge cases are
unavoidable with this algorithm and do not indicate that the
output is erroneous.
"""
amin = lambda x1, x2: np.where(x1<x2, x1, x2) # Elementwise min selection
amax = lambda x1, x2: np.where(x1>x2, x1, x2) # Elementwise max selection
aall = lambda abools: np.dstack(abools).all(axis=2) # dstacks, checks True depthwise
# Uses delta (using np.diff) to find successive slopes along path
slope = lambda line: (lambda d: d[:,1]/d[:,0])(np.diff(line, axis=0))
# Meshgrids between both paths (x and y). One element sliced off end/beginning
x11, x21 = np.meshgrid(contour1[:-1, 0], contour2[:-1, 0])
x12, x22 = np.meshgrid(contour1[1:, 0], contour2[1:, 0])
y11, y21 = np.meshgrid(contour1[:-1, 1], contour2[:-1, 1])
y12, y22 = np.meshgrid(contour1[1:, 1], contour2[1:, 1])
# Meshgrid of all slopes for both paths
m1, m2 = np.meshgrid(slope(contour1), slope(contour2))
m2inv = 1/m2 # m1inv was not used.
yi = (m1*(x21-x11-m2inv*y21) + y11)/(1 - m1*m2inv)
xi = (yi - y21)*m2inv + x21 # (xi, yi) is intersection candidate
# Bounding box type conditions for intersection candidates
xconds = (amin(x11, x12) < xi, xi <= amax(x11, x12),
amin(x21, x22) < xi, xi <= amax(x21, x22) )
yconds = (amin(y11, y12) < yi, yi <= amax(y11, y12),
amin(y21, y22) < yi, yi <= amax(y21, y22) )
return xi[aall(xconds)], yi[aall(yconds)]
def select_master_date(date_list, pbase_list=[]):
"""Select super master date based on input temporal and/or perpendicular baseline info.
Return master date in YYYYMMDD format.
"""
date8_list = ptime.yyyymmdd(date_list)
if not pbase_list:
# Choose date in the middle
m_date8 = date8_list[int(len(date8_list)/2)]
else:
# Get temporal baseline list
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_list)-min(pbase_list)) / (max(tbase_list)-min(tbase_list))
tbase_list = [tbase*temp2perp_scale for tbase in tbase_list]
# Get distance matrix
ttMat1, ttMat2 = np.meshgrid(np.array(tbase_list),
np.array(tbase_list))
ppMat1, ppMat2 = np.meshgrid(np.array(pbase_list),
np.array(pbase_list))
ttMat = np.abs(ttMat1 - ttMat2) # temporal distance matrix
ppMat = np.abs(ppMat1 - ppMat2) # spatial distance matrix
# 2D distance matrix in temp/perp domain
disMat = np.sqrt(np.square(ttMat) + np.square(ppMat))
# Choose date minimize the total distance of temp/perp baseline
disMean = np.mean(disMat, 0)
m_idx = np.argmin(disMean)
m_date8 = date8_list[m_idx]
return m_date8
def increase_grid(x, y, z, increase_y = 0, increase_x = 0, new_y_lims = None, number=100):
#generic use function to re-grid data
x_grid,y_grid = np.meshgrid(x,y)
input_grid = np.ones([len(x_grid.flatten()),2],dtype=float)
input_grid[:,0]=x_grid.flatten()
input_grid[:,1]=y_grid.flatten()
if increase_x:
x_out = np.linspace(min(x),max(x),num=number)
else:
x_out = x
if increase_y:
if new_y_lims == None:
y_out = np.linspace(min(y),max(y),num=number)
else:
y_out = np.linspace(new_y_lims[0],new_y_lims[1],num=number)
else:
y_out = y
x_grid_output, y_grid_output = np.meshgrid(x_out,y_out)
output_grid = np.ones([len(x_grid_output.flatten()),2],dtype=float)
output_grid[:,0]=x_grid_output.flatten()
output_grid[:,1]=y_grid_output.flatten()
z_out = griddata(input_grid,z.flatten(),output_grid)
z_out.resize(len(y_out),len(x_out))
return x_out, y_out, z_out
def pflux(self, p, x):
Egam = x * 1e-9 # in TeV
if self.EG is None:
self.EG, self.EP = np.meshgrid(Egam, self.Ep)
self.Fgam = self.F_gamma(self.EG, self.EP)
elif np.array_equal(Egam, self.EG[0, :]) is False:
print(Egam.shape, self.EG.shape)
print("Warning different internal vectors. Recomputing.")
self.EG, self.EP = np.meshgrid(Egam, self.Ep)
self.Fgam = self.F_gamma(self.EG, self.EP)
# proton_spec = p[3]*np.exp(-self.EP*p[1]*1e-3)*(self.EP/p[0])**(-p[2])
proton_spec = np.exp(-self.EP * p[1] * 1e-3) * (self.EP / p[0]) ** (-p[2])
if self.Fgam is None:
self.Fgam = self.F_gamma(self.EG, self.EP)
res = self.Fgam * proton_spec
integral = 3e10 * res.sum(0) * self.lbsize / np.log(10.)
# Normalize with proton energy content at 1 kpc for n_H = 1 cm^-3
pe_spec = self.Ep ** 2 * np.exp(-self.Ep * p[1] * 1e-3) * (self.Ep / p[0]) ** (-p[2])
pe_spec = pe_spec[(self.Ep >= p[4]) * (self.Ep <= p[5])]
norm = 4 * np.pi * (3.1e21) ** 2 * 1.602e9 * pe_spec.sum() * self.lbsize / np.log(10.) / 1e50
# return 3e10*res.sum(0)*self.lbsize/np.log(10.)*1e-9 # per TeV not keV
return integral / norm * p[3]
def VectorField(ax, VelocityField = None):
if VelocityField is not None:
# Get all velocities
x, y, z, u, v, w = VelocityField.GetVelocity()
#print(x.shape)
#print(y.shape)
#print(z.shape)
#print(u.shape)
#print(v.shape)
#print(w.shape)
#pdb.set_trace()
# Meshgrid the vectors
x_rec, y_rec, z_rec = np.meshgrid(x, y, z)
u_rec, v_rec, w_rec = np.meshgrid(u, v, w)
#pdb.set_trace()
# Plot Vector field of velocity
ax.quiver(x_rec, y_rec, z_rec, u_rec, v_rec, w_rec, length = 0.5)
# Set axis labels
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
# Show photo
plt.show()
def generate_ind_pairs(img_num_per_cam_person, person_inds, cam_inds):
positive_pair_a = numpy.asarray([], dtype='int32')
positive_pair_b = numpy.asarray([], dtype='int32')
negative_pair_a = numpy.asarray([], dtype='int32')
negative_pair_b = numpy.asarray([], dtype='int32')
past_person_inds = numpy.asarray([], dtype='int32')
for i in person_inds:
past_person_inds = numpy.append(past_person_inds, i)
past_cam_inds = numpy.asarray([], dtype='int32')
for j in cam_inds:
past_cam_inds = numpy.append(past_cam_inds, j)
target_abs_inds = get_abs_ind_one(img_num_per_cam_person, i, j)
pos_cand_abs_inds = get_abs_ind_per_cam_person(img_num_per_cam_person,
numpy.asarray([i], dtype='int32'),
numpy.setdiff1d(cam_inds, past_cam_inds))
neg_cand_abs_inds = get_abs_ind_per_cam_person(img_num_per_cam_person,
numpy.setdiff1d(person_inds, past_person_inds),
numpy.setdiff1d(cam_inds, j))
[tmp_a, tmp_b] = numpy.meshgrid(pos_cand_abs_inds, target_abs_inds)
positive_pair_a = numpy.append(positive_pair_a, tmp_b.flatten())
positive_pair_b = numpy.append(positive_pair_b, tmp_a.flatten())
[tmp_a, tmp_b] = numpy.meshgrid(neg_cand_abs_inds, target_abs_inds)
negative_pair_a = numpy.append(negative_pair_a, tmp_b.flatten())
negative_pair_b = numpy.append(negative_pair_b, tmp_a.flatten())
return [positive_pair_a, positive_pair_b, negative_pair_a, negative_pair_b]
def _init_grid(self):
""" Initialize (x,y,z) coordinate grid + (re)draw plot."""
phi_UC = np.linspace(0, 2*pi, 400, endpoint=True) # angles for unit circle
self.xy_UC = np.exp(1j * phi_UC) # x,y coordinates of unity circle
steps = 100 # number of steps for x, y, r, phi
#
self.xmin = -1.5; self.xmax = 1.5 # cartesian range limits
self.ymin = -1.5; self.ymax = 1.5
rmin = 0; rmax = self.xmin # polar range limits
# Calculate grids for 3D-Plots
dr = rmax / steps * 2 # grid size for polar range
dx = (self.xmax - self.xmin) / steps
dy = (self.ymax - self.ymin) / steps # grid size cartesian range
if self.chkPolar.isChecked(): # # Plot circular range in 3D-Plot
[r, phi] = np.meshgrid(np.arange(rmin, rmax, dr),
np.linspace(0, 2 * pi, steps, endpoint=True))
self.x = r * cos(phi)
self.y = r * sin(phi)
else: # cartesian grid
[self.x, self.y] = np.meshgrid(np.arange(self.xmin, self.xmax, dx),
np.arange(self.ymin, self.ymax, dy))
self.z = self.x + 1j*self.y # create coordinate grid for complex plane
self.draw() # initial plot
def __init__(self):
# sift features
self.gS = 8
self.pS = 16
self.nrml_thres = 1.0
self.sigma = 0.8
self.sift_thres = 0.2
self.Nangles = 8
self.Nbins = 4
self.Nsamples = self.Nbins**2
self.alpha = 9.0
self.angles = np.array(range(self.Nangles))*2.0*np.pi/self.Nangles
self.GH, self.GW = self.gen_dgauss(self.sigma)
# compute the weight contribution map
# weights is the contribution of each pixel to the corresponding bin center
sample_res = self.pS / np.double(self.Nbins)
sample_p = np.array(range(self.pS))
sample_ph, sample_pw = np.meshgrid(sample_p,sample_p)
sample_ph.resize(sample_ph.size)
sample_pw.resize(sample_pw.size)
bincenter = np.array(range(1,self.Nbins*2,2)) / 2.0 / self.Nbins * self.pS - 0.5
bincenter_h, bincenter_w = np.meshgrid(bincenter,bincenter)
bincenter_h.resize((bincenter_h.size,1))
bincenter_w.resize((bincenter_w.size,1))
dist_ph = abs(sample_ph - bincenter_h)
dist_pw = abs(sample_pw - bincenter_w)
weights_h = dist_ph / sample_res
weights_w = dist_pw / sample_res
weights_h = (1-weights_h) * (weights_h <= 1)
weights_w = (1-weights_w) * (weights_w <= 1)
self.weights = weights_h * weights_w
def graph(self, x_axis, y_axis, image, params, result = None):
#plot the sample data
from matplotlib import pyplot
pyplot.figure()
pyplot.subplot(2,1,1)
pyplot.contourf(x_axis, y_axis, image, alpha = 0.5)
#plot the fit
#sample the fit with more precision
x_axis_fit = np.linspace(x_axis.min(), x_axis.max(), x_axis.size * 5)
y_axis_fit = np.linspace(y_axis.min(), y_axis.max(), y_axis.size * 5)
xx, yy = np.meshgrid(x_axis_fit, y_axis_fit)
params['background_level'].value = 0
fit_1d = self.ion_model(params, xx, yy, use_1d=True)
xx, yy = np.meshgrid(x_axis, y_axis)
pyplot.subplot(2,1,2)
ximage = image.sum(axis=0)
ximage = ximage - ximage[0]
a1d = np.ones_like(self.allions_1d)
#low_values_indices = self.allions_1d - bg < (0.5)*params['amplitude'].value # Where values are low
low_values_indices = self.allions_1d < self.model_threshold # Where values are low
a1d[low_values_indices] = 0 # All low values set to 0
pyplot.plot(x_axis, a1d * ximage.max())
#pyplot.plot(x_axis, ximage * a1d, 'o', markersize=10)
pyplot.plot(x_axis, ximage)
pyplot.plot(x_axis_fit, fit_1d)
pyplot.tight_layout()
pyplot.show()
def get_patches(data, var, dql, centers, patch_size=5):
"""
Return patched data, etc, at given centers.
"""
buff = (patch_size - 1) / 2
# SExtractor doesnt alway put peak on brightest pixel
# find shifts, up to 1 pixel that correct for this
x, y = np.meshgrid(range(-2, 3),
range(-2, 3))
xcs = (x[None, :, :] + centers[:,0][:, None, None]).astype(np.int)
ycs = (y[None, :, :] + centers[:,1][:, None, None]).astype(np.int)
for i in range(centers.shape[0]):
test = data[xcs[i], ycs[i]]
ind = (test == np.max(test))
centers[i, 0] += x[ind]
centers[i, 1] += y[ind]
# Full patch indices
x, y = np.meshgrid(range(-buff, buff + 1),
range(-buff, buff + 1))
xcs = (x[None, :, :] + centers[:,0][:, None, None]).astype(np.int)
ycs = (y[None, :, :] + centers[:,1][:, None, None]).astype(np.int)
return data[xcs, ycs], var[xcs, ycs], dql[xcs, ycs], xcs, ycs, \
centers
def __init__(self, gridSpacing, patchSize, nrml_thres=1.0, sigma_edge=0.8, sift_thres=0.2):
"""
gridSpacing: the spacing for sampling dense descriptors
patchSize: the size for each sift patch
nrml_thres: low contrast normalization threshold
sigma_edge: the standard deviation for the gaussian smoothing
before computing the gradient
sift_thres: sift thresholding (0.2 works well based on
Lowe's SIFT paper)
"""
self.gS = gridSpacing
self.pS = patchSize
self.nrml_thres = nrml_thres
self.sigma = sigma_edge
self.sift_thres = sift_thres
# compute the weight contribution map
sample_res = self.pS / np.double(Nbins)
sample_p = np.array(range(self.pS))
sample_ph, sample_pw = np.meshgrid(sample_p, sample_p)
sample_ph.resize(sample_ph.size)
sample_pw.resize(sample_pw.size)
bincenter = np.array(range(1, Nbins * 2, 2)) / 2.0 / Nbins * self.pS - 0.5
bincenter_h, bincenter_w = np.meshgrid(bincenter, bincenter)
bincenter_h.resize((bincenter_h.size, 1))
bincenter_w.resize((bincenter_w.size, 1))
dist_ph = abs(sample_ph - bincenter_h)
dist_pw = abs(sample_pw - bincenter_w)
weights_h = dist_ph / sample_res
weights_w = dist_pw / sample_res
weights_h = (1 - weights_h) * (weights_h <= 1)
weights_w = (1 - weights_w) * (weights_w <= 1)
# weights is the contribution of each pixel to the corresponding bin center
self.weights = weights_h * weights_w
def test_label_propagation_closed_form():
n_classes = 2
X, y = make_classification(n_classes=n_classes, n_samples=200,
random_state=0)
y[::3] = -1
Y = np.zeros((len(y), n_classes + 1))
Y[np.arange(len(y)), y] = 1
unlabelled_idx = Y[:, (-1,)].nonzero()[0]
labelled_idx = (Y[:, (-1,)] == 0).nonzero()[0]
clf = label_propagation.LabelPropagation(max_iter=10000,
gamma=0.1)
clf.fit(X, y)
# adopting notation from Zhu et al 2002
T_bar = clf._build_graph()
Tuu = T_bar[tuple(np.meshgrid(unlabelled_idx, unlabelled_idx,
indexing='ij'))]
Tul = T_bar[tuple(np.meshgrid(unlabelled_idx, labelled_idx,
indexing='ij'))]
Y = Y[:, :-1]
Y_l = Y[labelled_idx, :]
Y_u = np.dot(np.dot(np.linalg.inv(np.eye(Tuu.shape[0]) - Tuu), Tul), Y_l)
expected = Y.copy()
expected[unlabelled_idx, :] = Y_u
expected /= expected.sum(axis=1)[:, np.newaxis]
assert_array_almost_equal(expected, clf.label_distributions_, 4)
def find_intersections(self, A, B):
''' (matrix, matrix -> bool'''
amin = lambda x1, x2: np.where(x1<x2, x1, x2)
amax = lambda x1, x2: np.where(x1>x2, x1, x2)
aall = lambda abools: np.dstack(abools).all(axis=2)
slope = lambda line: (lambda d: d[:,1]/d[:,0])(np.diff(line, axis=0))
x11, x21 = np.meshgrid(A[:-1, 0], B[:-1, 0])
x12, x22 = np.meshgrid(A[1:, 0], B[1:, 0])
y11, y21 = np.meshgrid(A[:-1, 1], B[:-1, 1])
y12, y22 = np.meshgrid(A[1:, 1], B[1:, 1])
m1, m2 = np.meshgrid(slope(A), slope(B))
m1inv, m2inv = 1/m1, 1/m2
yi = (m1*(x21-x11-m2inv*y21) + y11)/(1 - m1*m2inv)
xi = (yi - y21)*m2inv + x21
xconds = (amin(x11, x12) < xi, xi <= amax(x11, x12),
amin(x21, x22) < xi, xi <= amax(x21, x22) )
yconds = (amin(y11, y12) < yi, yi <= amax(y11, y12),
amin(y21, y22) < yi, yi <= amax(y21, y22) )
if xi[aall(xconds)] and yi[aall(yconds)]:
return xi[aall(xconds)], yi[aall(yconds)], True # intersection, don't go!
return xi[aall(xconds)], yi[aall(yconds)], False
def _make_carts_dict(self):
""" Return a carts dictionary, distances in meters. """
az_deg, range_km, el_deg = self._calc_geometry()
# simple calculation involving 4/3 earth radius
nsweeps = self.master_header['max_nz']
nrays = self.master_header['max_ny']
ngates = self.master_header['max_nx']
xx = np.empty([nsweeps, nrays, ngates], dtype=np.float32)
yy = np.empty([nsweeps, nrays, ngates], dtype=np.float32)
zz = np.empty([nsweeps, nrays, ngates], dtype=np.float32)
if self.projection == 'rhi':
rg, ele = np.meshgrid(range_km, el_deg)
rg = np.array(rg, dtype=np.float64)
ele = np.array(ele, dtype=np.float64)
for aznum in range(nsweeps):
azg = np.ones(rg.shape, dtype=np.float64) * az_deg[aznum]
x, y, z = antenna_to_cartesian(rg, azg, ele)
zz[aznum, :, :] = z
xx[aznum, :, :] = x
yy[aznum, :, :] = y
elif self.projection == 'ppi':
rg, azg = np.meshgrid(range_km, az_deg)
rg = np.array(rg, dtype=np.float64)
azg = np.array(azg, dtype=np.float64)
for elnum in range(nsweeps):
ele = np.ones(rg.shape, dtype=np.float64) * el_deg[elnum]
x, y, z = antenna_to_cartesian(rg, azg, ele)
zz[elnum, :, :] = z
xx[elnum, :, :] = x
yy[elnum, :, :] = y
return {'x': xx, 'y': yy, 'z': zz}
def plot_beam(dirname, input_beam, Rho, Phi, ref_output_fluence):
filename = lambda name: os.path.join(dirname, name)
if len(Rho) > 1:
vfluence = np.vectorize(input_beam.fluence)
ref_input_fluence = vfluence(*np.meshgrid(Rho, Phi)).T
norm_input_fluence = ref_input_fluence / input_beam.ref_fluence
norm_output_fluence = ref_output_fluence / input_beam.ref_fluence
max_output_fluence = np.amax(norm_output_fluence)
n_ref = -1
for n, phi in enumerate(Phi):
if n_ref < 0 or abs(phi - input_beam.phi_ref) < abs(Phi[n_ref] - input_beam.phi_ref):
n_ref = n
rholim = (Rho[0], Rho[-1])
plot.plot_data(filename("fluences"), "Input and Output Fluence", ((Rho,)*2, None, rholim, rho_label), ((norm_input_fluence[:, n_ref], norm_output_fluence[:, n_ref]), None, None, fluence_rel_label), ("input beam", "output beam"))
plot.plot_data(filename("fluences_norm"), "Normalized Input and Output Fluence", ((Rho,)*2, None, rholim, rho_label), ((norm_input_fluence[:, n_ref], norm_output_fluence[:, n_ref] / max_output_fluence), None, None, fluence_norm_rel_label), ("input beam", "output beam"))
if len(Phi) > 1:
FR, RF = np.meshgrid(Phi, Rho)
XY, YX = RF * np.cos(FR), RF * np.sin(FR)
stride_rho = max(len(Rho) // params.out_count_rho, 1)
stride_phi = max(len(Phi) // params.out_count_phi, 1)
plot.plot_projection(filename("fluence_in"), "Input Fluence", (XY, None, x_label), (YX, None, y_label), (norm_input_fluence, None, fluence_rel_label), (30, -60), (stride_rho, stride_phi))
plot.plot_projection(filename("fluence_out"), "Output Fluence", (XY, None, x_label), (YX, None, y_label), (norm_output_fluence, None, fluence_rel_label), (30, -60), (stride_rho, stride_phi))
if __name__ == '__main__':
# # xor exmaple
# X = np.array([[0,0], [0,1], [1,0], [1,1]])
# y = np.array([[0,1,1,0]]).T
# nn = Neural_Network([2,2,1], learning_rate=1, opt=False, check_gradients=True, epochs=100000, batch_size=4)
# nn.fit(X, y)
# Another example with two random circlular regions as positive class
X = np.random.rand(300, 2)
y = np.atleast_2d(1 * np.logical_or(
(X[:, 0] - .8)**2 + (X[:, 1] - .5)**2 < .03,
(X[:, 0] - .2)**2 + (X[:, 1] - .8)**2 < .03)).T
nn = Neural_Network([2, 10, 1],
learning_rate=1,
C=0,
opt=False,
check_gradients=True,
batch_size=50,
epochs=100000)
nn.fit(X, y)
xx, yy = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, 1, 100))
Z = np.round(nn.predict(np.c_[xx.ravel(), yy.ravel()]))
Z = Z.reshape(xx.shape)
plt.figure(figsize=(10, 8))
plt.contourf(xx, yy, Z, alpha=.8)
plt.scatter(X[:, 0], X[:, 1], c=y, s=60)
plt.title("Neural Net Accuracy is " + str(np.round(nn.score(X, y), 2)))
plt.show()
climo = climoU[i]
elif i > 1:
var = diffZ[i - 2]
pvar = pZ[i - 2]
climo = climoZ[i - 2]
ax1 = plt.subplot(2, 2, i + 1)
m = Basemap(projection='ortho',
lon_0=0,
lat_0=89,
resolution='l',
area_thresh=10000.)
var, lons_cyclic = addcyclic(var, lon)
var, lons_cyclic = shiftgrid(180., var, lons_cyclic, start=False)
lon2d, lat2d = np.meshgrid(lons_cyclic, lat)
x, y = m(lon2d, lat2d)
pvar, lons_cyclic = addcyclic(pvar, lon)
pvar, lons_cyclic = shiftgrid(180., pvar, lons_cyclic, start=False)
climoq, lons_cyclic = addcyclic(climo, lon)
climoq, lons_cyclic = shiftgrid(180., climoq, lons_cyclic, start=False)
m.drawmapboundary(fill_color='white', color='dimgray', linewidth=0.7)
cs = m.contourf(x, y, var, limit, extend='both')
cs1 = m.contourf(x,
y,
pvar,
colors='None',
hatches=['....'],
return surf,
anim = animation.FuncAnimation(fig, update_surf,
frames=len(eta_list), interval=10, blit=False)
mpeg_writer = animation.FFMpegWriter(fps=24, bitrate=10000,
codec="libx264", extra_args=["-pix_fmt", "yuv420p"])
anim.save("{}.mp4".format(filename), writer=mpeg_writer)
return anim
if __name__ == '__main__':
eta_list = []
x = np.asarray([i for i in range(100)])
y = np.asarray([i for i in range(100)])
x, y = np.meshgrid(x, y)
for i in range(0, 10001, 10):
eta = []
file = open(f"result/outputH_{i}.txt")
lines = file.readlines()[1:]
for line in lines:
tmp = [float(x) for x in line.split()]
eta.append(tmp)
eta = np.asarray(eta) - 1
eta_list.append(eta)
if i in [0, 10, 100, 1000, 10000]:
surface_plot3d(x, y, eta, f"img/H{i}.png", i)
eta_animation3d(x, y, eta_list, 10, "h")
d = computeGrad(X, y, theta, reg)
theta[0] -= step_size * d[0]
theta[1] -= step_size * d[1]
# WRITEME: write your update rule(s) here
# TODO: remove this line below once you have correctly implemented/gradient-checked your various sub-routines
# sys.exit(0)
# evaluate training set accuracy
scores, probs = predict(X, theta)
predicted_class = np.argmax(scores, axis=1)
print('training accuracy: %.2f' % (np.mean(predicted_class == y)))
# plot the resulting classifier
h = 0.02
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = np.dot(np.c_[xx.ravel(), yy.ravel()], W) + b
Z = np.argmax(Z, axis=1)
Z = Z.reshape(xx.shape)
fig = plt.figure()
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.savefig(os.getcwd() + '/out/spiral_linear.png')
plt.show()
def grid(self):
grid = [np.linspace(0, 2 * np.pi, num=self.resolution, endpoint=False) for _ in range(self.units.lattice.D)]
return np.meshgrid(*grid)
plt.scatter(x, y)
plt.title("scatter")
plt.subplot(233)
plt.pie(y)
plt.title("pie")
plt.subplot(234)
plt.bar(x, y)
plt.title("bar")
# 2D data
import numpy as np
delta = 0.025
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z = Y**2 + X**2
plt.subplot(235)
plt.contour(X,Y,Z)
plt.colorbar()
plt.title("contour")
# read image
import matplotlib.image as mpimg
img=mpimg.imread('marvin.jpg')
plt.subplot(236)
plt.imshow(img)
plt.title("imshow")
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
from matplotlib import cm
from scipy.stats import norm,chi2,binom,gamma
import seaborn as sns
from math import cos, sin,log,tan,pi,exp,sqrt,cosh,sinh,tanh,atan,atan2,e
from cmath import exp as cexp, log as clog
from mpl_toolkits.mplot3d import Axes3D
for i,t in enumerate(np.linspace(0,2*pi,360)):
fig = plt.figure(figsize=(16,16))
ax = fig.add_subplot(111, projection='3d',facecolor='black')
p = plt.axis('off')
x = np.linspace(-2*pi,2*pi,1000)
y = np.linspace(-2*pi,2*pi,1000)
xx, yy = np.meshgrid(x, y, sparse=True)
z = np.cos(xx*yy +6*t+pi)
ax.view_init(t*180/(pi),t*180/(4*pi))
ax.set_zlim(-3,3)
ax.plot_surface(xx, yy, z, antialiased=True, shade=True,cmap=cm.winter,alpha=0.8)
p = plt.savefig(f'C:/Users/Alejandro/Pictures/RandomPlots/15052020/plot{i}.PNG',facecolor='black')
def readVar(varnames, qbophase, period):
### Call function for surface temperature data from reach run
lat, lon, time, lev, tashit = MO.readExperiAll('%s' % varnames, 'HIT',
'surface')
lat, lon, time, lev, tasfict = MO.readExperiAll('%s' % varnames, 'FICT',
'surface')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
pos_hit = np.append(
np.genfromtxt(filenamehitp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitp2, unpack=True, usecols=[0], dtype='int') +
101)
non_hit = np.append(
np.genfromtxt(filenamehitno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitno2, unpack=True, usecols=[0], dtype='int') +
101)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictno = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
1]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
0]
filenamefictno2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
1]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
2]
pos_fict = np.append(
np.genfromtxt(filenamefictp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictp2, unpack=True, usecols=[0], dtype='int') +
101)
non_fict = np.append(
np.genfromtxt(filenamefictno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictno2, unpack=True, usecols=[0], dtype='int') +
101)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictn2, unpack=True, usecols=[0], dtype='int') +
101)
### Concatonate runs
runs = [tashit, tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'ON':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, 9:11, :, :], axis=1)
elif period == 'DJ':
tas_mo = np.empty(
(3, tashit.shape[0] - 1, tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJan(runs[i], runs[i], lat, lon,
'surface', 1)
elif period == 'FM':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, 1:3, :, :], axis=1)
elif period == 'DJF':
tas_mo = np.empty(
(3, tashit.shape[0] - 1, tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJanFeb(runs[i], runs[i], lat, lon,
'surface', 1)
elif period == 'M':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, 2, :, :]
elif period == 'D':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, -1, :, :]
elif period == 'N':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, -2, :, :]
elif period == 'ND':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, -2:, :, :], axis=1)
else:
ValueError('Wrong period selected! (ON,DJ,FM)')
### Composite by QBO phase
tas_mohitpos = tas_mo[0][pos_hit, :, :]
tas_mofictpos = tas_mo[1][pos_fict, :, :]
tas_mohitneg = tas_mo[0][neg_hit, :, :]
tas_mofictneg = tas_mo[1][neg_fict, :, :]
### Compute climatology
climohitpos = np.nanmean(tas_mohitpos, axis=0)
climohitneg = np.nanmean(tas_mohitneg, axis=0)
climo = [climohitpos, climohitneg]
### Compute comparisons for months - taken ensemble average
ficthitpos = np.nanmean(tas_mofictpos - tas_mohitpos, axis=0)
ficthitneg = np.nanmean(tas_mofictneg - tas_mohitneg, axis=0)
diffruns_mo = [ficthitpos, ficthitneg]
### Calculate significance for ND
stat_FICTHITpos, pvalue_FICTHITpos = UT.calc_indttest(
tas_mo[1][pos_fict, :, :], tas_mo[0][pos_hit, :, :])
stat_FICTHITnon, pvalue_FICTHITnon = UT.calc_indttest(
tas_mo[1][non_fict, :, :], tas_mo[0][non_hit, :, :])
stat_FICTHITneg, pvalue_FICTHITneg = UT.calc_indttest(
tas_mo[1][neg_fict, :, :], tas_mo[0][neg_hit, :, :])
pruns_mo = [pvalue_FICTHITpos, pvalue_FICTHITneg]
return lat, lon, climo, diffruns_mo, pruns_mo
Mxx = globalMatrix1D(pde.a, mesh, fem, 1, fem, 1, ng)
M00 = globalMatrix1D(pde.c, mesh, fem, 0, fem, 0, ng)
f = globalVec1D(pde.f, mesh, fem, 0, ng)
#%% 5 Dirichlet boundary condition
uD = pde.gD(fem.p)
uD[fem.dof] = 0.0
bxx = Mxx @ uD
b00 = M00 @ uD
#%% 6 Neumann boundary condition
bN = np.zeros(np.shape(fem.p))
bN[fem.ptype == 2] = pde.gN(fem.p[fem.ptype == 2])
#%% 7 Extract degree of freedom
ii, jj = np.meshgrid(fem.dof, fem.dof, indexing='ij')
A = Mxx[ii, jj] + M00[ii, jj]
b = f[fem.dof] + bN[fem.dof] - bxx[fem.dof] - b00[fem.dof]
#%% 8 Solve Linear Sysetem
uDof = inv(A) @ b
uh = np.copy(uD)
uh[fem.dof] = uDof
#%%
fig, axs = plt.subplots(1, 1, sharex=True, sharey=True, figsize=(8, 6))
tu = pde.exactu(fem.p)
for k in range(n):
vert = mesh.p[mesh.t[k, :]]
x = np.linspace(vert[0], vert[1], 11)
def putVecMaps(centerA, centerB, accumulate_vec_map, count, params_transform,
thre):
"""Implement Part Affinity Fields
:param centerA: int with shape (2,) or (3,), centerA will pointed by centerB.
:param centerB: int with shape (2,) or (3,), centerB will point to centerA.
:param accumulate_vec_map: one channel of paf.
:param count: store how many pafs overlaped in one coordinate of accumulate_vec_map.
:param params_transform: store the value of stride and crop_szie_y, crop_size_x
"""
centerA = centerA.astype(float)
centerB = centerB.astype(float)
stride = params_transform['stride']
crop_size_y = params_transform['crop_size_y']
crop_size_x = params_transform['crop_size_x']
grid_y = crop_size_y / stride
grid_x = crop_size_x / stride
centerB = centerB / stride
centerA = centerA / stride
limb_vec = centerB - centerA
norm = np.linalg.norm(limb_vec)
if norm < 1.0:
# print 'limb is too short, ignore it...'
return accumulate_vec_map, count
limb_vec_unit = limb_vec / norm
# print 'limb unit vector: {}'.format(limb_vec_unit)
# To make sure not beyond the border of this two points
min_x = max(int(round(min(centerA[0], centerB[0]) - thre)), 0)
max_x = min(int(round(max(centerA[0], centerB[0]) + thre)), grid_x)
min_y = max(int(round(min(centerA[1], centerB[1]) - thre)), 0)
max_y = min(int(round(max(centerA[1], centerB[1]) + thre)), grid_y)
range_x = list(range(int(min_x), int(max_x), 1))
range_y = list(range(int(min_y), int(max_y), 1))
xx, yy = np.meshgrid(range_x, range_y)
ba_x = xx - centerA[0] # the vector from (x,y) to centerA
ba_y = yy - centerA[1]
limb_width = np.abs(ba_x * limb_vec_unit[1] - ba_y * limb_vec_unit[0])
mask = limb_width < thre # mask is 2D
vec_map = np.copy(accumulate_vec_map) * 0.0
vec_map[:, yy, xx] = np.repeat(mask[np.newaxis, :, :], 2, axis=0)
vec_map[:, yy, xx] *= limb_vec_unit[:, np.newaxis, np.newaxis]
mask = np.logical_or.reduce(
(np.abs(vec_map[0, :, :]) > 0, np.abs(vec_map[1, :, :]) > 0))
accumulate_vec_map = np.multiply(accumulate_vec_map,
count[np.newaxis, :, :])
accumulate_vec_map += vec_map
count[mask == True] += 1
mask = count == 0
count[mask == True] = 1
accumulate_vec_map = np.divide(accumulate_vec_map, count[np.newaxis, :, :])
count[mask == True] = 0
return accumulate_vec_map, count
def main():
optimizers = ['L2L', 'L2L_adv', 'Adam', 'Momentum', 'SGD', 'NAG', 'RMSProp']
problem_path = './problems/quadratic.npz'
npzfile = np.load(problem_path)
problems_w, problems_b = npzfile['arr_0'], npzfile['arr_1']
prob_num = len(problems_w)
x = {}
obj = {}
for optimizer in optimizers:
x[optimizer] = np.load(osp.join('./results', optimizer + '.npy'))
print('problem_w', problems_w)
print('problem_b', problems_b)
for prob_idx in range(prob_num):
fig = plt.figure(figsize=(10, 6))
for optimizer in optimizers:
obj[optimizer] = list(
map(lambda x: np.sum((problems_w[prob_idx].dot(x) - problems_b[prob_idx].squeeze()) ** 2),
x[optimizer][prob_idx]))
plt.plot(obj[optimizer], label=optimizer)
print('Plotting: problem {}, optimizer {}, loss {}, x {}'.format(prob_idx, optimizer, obj[optimizer][-1], x[optimizer][prob_idx][-1]))
plt.legend(loc='upper right')
plt.xlabel('number of iterations')
plt.ylabel('objective value')
ax = fig.add_subplot(1, 1, 1)
ax.set_yscale("log")
plt.savefig('./figs/loss_prob_{}.png'.format(prob_idx))
# X = {} # dictionary to store data
# obj = {} # dictionary to store obj value
# W = 0.5 * np.eye(2)
# Y = 0.5 * np.ones(2)
# for name in names:
# X[name] = np.loadtxt(name + '.txt')
# obj[name] = list(map(lambda x: LA.norm(W.dot(x) - Y) ** 2, X[name]))
# plt.plot(obj[name], label=name)
# plt.legend(loc='upper right')
# plt.xlabel('number of iterations')
# plt.ylabel('objective value')
#
# # plot level set and trajectory for SGD
# W = 0.5 * np.eye(2)
# Y = 0.5 * np.ones(2)
n = len(optimizers)
for prob_idx in range(prob_num):
fig = plt.figure(figsize=(12, 8))
for i, optimizer in enumerate(optimizers):
W = problems_w[prob_idx]
Y = problems_b[prob_idx]
minx = np.min(x[optimizer][prob_idx][:, 0])
miny = np.min(x[optimizer][prob_idx][:, 1])
maxx = np.max(x[optimizer][prob_idx][:, 0])
maxy = np.max(x[optimizer][prob_idx][:, 1])
min_plot = min(minx, miny)
max_plot = max(maxx, maxy)
t_min = min_plot - 0.3 * (max_plot - min_plot)
t_max = max_plot + 0.3 * (max_plot - min_plot)
x1 = np.linspace(t_min, t_max, num=50)
x2 = np.linspace(t_min, t_max, num=50)
X1, X2 = np.meshgrid(x1, x2)
Z = f_quad(X1, X2, W, Y.squeeze())
ax = fig.add_subplot(2, (n+1)/2, i+1)
ax.contour(X1, X2, Z, 20)
ax.set_aspect('equal')
ax.axis([t_min, t_max, t_min, t_max])
ax.set_title(optimizer)
ax.scatter(x[optimizer][prob_idx][:, 0], x[optimizer][prob_idx][:, 1], s=5, edgecolors='r', facecolors='none',
marker='o')
ax.plot(x[optimizer][prob_idx][:, 0], x[optimizer][prob_idx][:, 1], color='r')
#plt.colorbar()
plt.savefig('./figs/contour_{}.png'.format(prob_idx))
def _all_rgb():
"""Return all 256**3 valid rgb tuples."""
base = np.arange(256, dtype=np.uint8)
r, g, b = np.meshgrid(base, base, base, indexing='ij')
return np.stack((r, g, b), axis=-1).reshape((-1, 3))
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(
np.arange(start=X_set[:, 0].min() - 1,
stop=X_set[:, 0].max() + 1,
step=0.01),
np.arange(start=X_set[:, 1].min() - 1,
stop=X_set[:, 1].max() + 1,
step=0.01))
plt.contourf(X1,
X2,
classifier.predict(np.array([X1.ravel(),
X2.ravel()]).T).reshape(X1.shape),
alpha=0.75,
cmap=ListedColormap(('red', 'green', 'blue')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0],
X_set[y_set == j, 1],
c=ListedColormap(('red', 'green', 'blue'))(i),
if __name__ == '__main__':
n = 4
V = np.arange(0, n, 1)
E = [(0, 1, 1.0), (0, 2, 1.0), (1, 2, 1.0), (3, 2, 1.0), (3, 1, 1.0)]
G = nx.Graph()
G.add_nodes_from(V)
G.add_weighted_edges_from(E)
step_size = 0.1
a_gamma = np.arange(0, np.pi, step_size)
a_beta = np.arange(0, np.pi, step_size)
a_gamma, a_beta = np.meshgrid(a_gamma, a_beta)
F1 = 3 - (np.sin(2 * a_beta) ** 2 * np.sin(2 * a_gamma) ** 2 - 0.5 * np.sin(4 * a_beta) * np.sin(4 * a_gamma)) * (
1 + np.cos(4 * a_gamma) ** 2)
result = np.where(F1 == np.amax(F1))
a = list(zip(result[0], result[1]))[0]
gamma = a[0] * step_size
beta = a[1] * step_size
prog = make_circuit(4)
sample_shot =3962
writefile = open("../data/startQiskit_noisy414.csv", "w")
# prog.draw('mpl', filename=(kernel + '.png'))
backend = FakeYorktown()
else:
m = Basemap(projection='cyl',
llcrnrlat=llat,
urcrnrlat=ulat,
llcrnrlon=llon,
urcrnrlon=ulon,
resolution='c',
area_thresh=1000.,
ax=ax[mon])
m.drawcoastlines(linewidth=0.5)
m.drawcountries(linewidth=0.5)
m.drawparallels(np.arange(-90., 90., 10.))
m.drawmeridians(np.arange(-180., 180., 10.))
#m.drawlsmask(land_color='lightgrey',ocean_color='grey')
lats = range(-90, 90)
lons = range(-180, 180)
X, Y = np.meshgrid(lons, lats)
m.pcolormesh(X,
Y,
mongrid[mon],
norm=colors.LogNorm(vmin=np.nanmin(mongrid[mon]),
vmax=np.nanmax(mongrid[mon])),
cmap='jet')
ax[mon].title.set_text(names[mon])
plt.tight_layout()
plt.suptitle(year) #+' - surface to '+str(limit)+'m')
plt.savefig(path + '/plots/' + year + '_' + site + '.png')
plt.close()
print('Plot saved')
# ---------------------------------------------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------------------------------------------------
# PLOT ALL BAND STRUCTURES (ONE FOR EACH VALUE OF KZ) ON 3D PLOT
if delta > 0:
fig4 = plt.figure()
ax4 = fig4.gca(projection='3d')
x = range(len(freqss[0]))
kz = kz_list
X, KZ = np.meshgrid(x, kz)
# Plot bands
for index in integer_list:
ax4.plot(x, freqss[index], zs=kz[index], zdir='y', color='red', alpha=0.2)
ax4.set_xlim([x[0], x[-1]]) # x-axis goes from x[0] = 0 to x[-1] = 33; x[-1] = x[33], x[-2] = x[32], etc.
ax4.set_ylim([0.0, max_kz])
upper_z = freqss[-1][-1][-1] + 0.05
ax4.set_zlim([0.0, upper_z]) # z-axis goes from 0 to upper_z
# Plot labels
if block == 'y':
plt.title('Band Structure of Square Photonic Bandgap Fiber; $\epsilon$_core = ' + coreepsilonstring + '; $\epsilon$_PC = ' + pcepsilonstring, size=18)
else:
# Define datasets
blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)
datasets = [
make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5,
**blobs_params)[0],
make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5],
**blobs_params)[0],
make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3],
**blobs_params)[0],
4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0)[0] -
np.array([0.5, 0.25])),
14. * (np.random.RandomState(42).rand(n_samples, 2) - 0.5)]
# Compare given classifiers under given settings
xx, yy = np.meshgrid(np.linspace(-7, 7, 150),
np.linspace(-7, 7, 150))
plt.figure(figsize=(len(anomaly_algorithms) * 2 + 3, 12.5))
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
hspace=.01)
plot_num = 1
rng = np.random.RandomState(42)
for i_dataset, X in enumerate(datasets):
# Add outliers
X = np.concatenate([X, rng.uniform(low=-6, high=6,
size=(n_outliers, 2))], axis=0)
for name, algorithm in anomaly_algorithms:
t0 = time.time()
"""
iris = datasets.load_iris()
X = iris.data[:, :2]
Y = iris.target
# 建模
Linearsvc = svm.LinearSVC(dual=False).fit(X, Y)
Linear_svc = svm.SVC(kernel='linear').fit(X, Y) # Linear kernel
Poly_svc = svm.SVC(kernel='poly').fit(X, Y) # Ploy kernel
Rbf_svc = svm.SVC(C=2.0).fit(X, Y) # Rbf kernel
# 网格
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
np.arange(y_min, y_max, 0.02)) # 生成采样点
# Title
titles = ['LinearSVC (linear kernel)',
'SVC with linear kernel',
'SVC with polynomial kernel',
'SVC with RBF kernel']
for i, clf in enumerate((Linearsvc, Linear_svc, Poly_svc, Rbf_svc)):
plt.subplot(2, 2, i+1)
plt.subplots_adjust(wspace=0.3, hspace=0.6) # 控制图之间的间隙
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=Y, s=30, cmap=plt.cm.coolwarm, alpha=0.8)
def make_meshgrid(x, y, h=.02):
x_min, x_max = x.min() - 1, x.max() + 1
y_min, y_max = y.min() - 1, y.max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
return xx, yy
def __init__(self, use_kmeans_anchors=False):
super(RPN, self).__init__()
if use_kmeans_anchors:
print('using k-means anchors')
self.anchor_scales = self.anchor_scales_kmeans
self.anchor_ratios = self.anchor_ratios_kmeans
self.anchor_scales_region = self.anchor_scales_kmeans_region
self.anchor_ratios_region = self.anchor_ratios_kmeans_region
else:
print('using normal anchors')
self.anchor_scales, self.anchor_ratios = \
np.meshgrid(self.anchor_scales_normal, self.anchor_ratios_normal, indexing='ij')
self.anchor_scales = self.anchor_scales.reshape(-1)
self.anchor_ratios = self.anchor_ratios.reshape(-1)
self.anchor_scales_region, self.anchor_ratios_region = \
np.meshgrid(self.anchor_scales_normal_region, self.anchor_ratios_normal_region, indexing='ij')
self.anchor_scales_region = self.anchor_scales_region.reshape(-1)
self.anchor_ratios_region = self.anchor_ratios_region.reshape(-1)
self.anchor_num = len(self.anchor_scales)
self.anchor_num_region = len(self.anchor_scales_region)
# self.features = VGG16(bn=False)
self.features = models.vgg16(pretrained=True).features
self.features.__delattr__('30') # to delete the max pooling
# by default, fix the first four layers
network.set_trainable_param(list(self.features.parameters())[:8],
requires_grad=False)
# self.features = models.vgg16().features
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512,
self.anchor_num * 2,
1,
relu=False,
same_padding=False)
self.bbox_conv = Conv2d(512,
self.anchor_num * 4,
1,
relu=False,
same_padding=False)
self.conv1_region = Conv2d(512, 512, 3, same_padding=True)
self.score_conv_region = Conv2d(512,
self.anchor_num_region * 2,
1,
relu=False,
same_padding=False)
self.bbox_conv_region = Conv2d(512,
self.anchor_num_region * 4,
1,
relu=False,
same_padding=False)
# loss
self.cross_entropy = None
self.loss_box = None
self.cross_entropy_region = None
self.loss_box_region = None
# initialize the parameters
self.initialize_parameters()
def do_elastic_transform2(image, mask, grid=32, distort=0.2):
borderMode = cv2.BORDER_REFLECT_101
height, width = image.shape[:2]
x_step = int(grid)
xx = np.zeros(width, np.float32)
prev = 0
for x in range(0, width, x_step):
start = x
end = x + x_step
if end > width:
end = width
cur = width
else:
cur = prev + x_step * (1 + random.uniform(-distort, distort))
xx[start:end] = np.linspace(prev, cur, end - start)
prev = cur
y_step = int(grid)
yy = np.zeros(height, np.float32)
prev = 0
for y in range(0, height, y_step):
start = y
end = y + y_step
if end > height:
end = height
cur = height
else:
cur = prev + y_step * (1 + random.uniform(-distort, distort))
yy[start:end] = np.linspace(prev, cur, end - start)
prev = cur
#grid
map_x, map_y = np.meshgrid(xx, yy)
map_x = map_x.astype(np.float32)
map_y = map_y.astype(np.float32)
#image = map_coordinates(image, coords, order=1, mode='reflect').reshape(shape)
image = cv2.remap(image,
map_x,
map_y,
interpolation=cv2.INTER_LINEAR,
borderMode=borderMode,
borderValue=(
0,
0,
0,
))
mask = cv2.remap(mask,
map_x,
map_y,
interpolation=cv2.INTER_NEAREST,
borderMode=borderMode,
borderValue=(
0,
0,
0,
))
mask = (mask > 0.5).astype(np.float32)
return image, mask
def get_radolan_grid(nrows=None, ncols=None, trig=False, wgs84=False):
"""Calculates x/y coordinates of radolan grid of the German Weather Service
.. versionadded:: 0.4.0
Returns the x,y coordinates of the radolan grid positions
(lower left corner of every pixel). The radolan grid is a
polarstereographic projection, the projection information was taken from
RADOLAN-RADVOR-OP Kompositformat_2.2.2 :cite:`DWD2009`
.. table:: Coordinates for 900km x 900km grid
+------------+-----------+------------+-----------+-----------+
| Coordinate | lon | lat | x | y |
+============+===========+============+===========+===========+
| LowerLeft | 3.5889E | 46.9526N | -523.4622 | -4658.645 |
+------------+-----------+------------+-----------+-----------+
| LowerRight | 14.6209E | 47.0705N | 376.5378 | -4658.645 |
+------------+-----------+------------+-----------+-----------+
| UpperRight | 15.7208E | 54.7405N | 376.5378 | -3758.645 |
+------------+-----------+------------+-----------+-----------+
| UpperLeft | 2.0715E | 54.5877N | -523.4622 | -3758.645 |
+------------+-----------+------------+-----------+-----------+
.. table:: Coordinates for 1100km x 900km grid
+------------+-----------+------------+-----------+-----------+
| Coordinate | lon | lat | x | y |
+============+===========+============+===========+===========+
| LowerLeft | 4.6759E | 46.1929N | -443.4622 | -4758.645 |
+------------+-----------+------------+-----------+-----------+
| LowerRight | 15.4801E | 46.1827N | 456.5378 | -4758.645 |
+------------+-----------+------------+-----------+-----------+
| UpperRight | 17.1128E | 55.5342N | 456.5378 | -3658.645 |
+------------+-----------+------------+-----------+-----------+
| UpperLeft | 3.0889E | 55.5482N | -433.4622 | -3658.645 |
+------------+-----------+------------+-----------+-----------+
.. table:: Coordinates for 1500km x 1400km grid
+------------+-----------+------------+-----------+-----------+
| Coordinate | lon | lat | x | y |
+============+===========+============+===========+===========+
| LowerLeft | 2.3419E | 43.9336N | -673.4622 | -5008.645 |
+------------+-----------+------------+-----------+-----------+
Parameters
----------
nrows : int
number of rows (460, 900 by default, 1100, 1500)
ncols : int
number of columns (460, 900 by default, 1400)
trig : boolean
if True, uses trigonometric formulas for calculation
if False, uses osr spatial reference system to transform between
projections
`trig` is recommended to be False, however, the two ways of computation
are expected to be equivalent.
wgs84 : boolean
if True, output coordinates are in wgs84 lonlat format (default: False)
Returns
-------
radolan_grid : :class:`numpy:numpy.ndarray`
Array of shape (rows, cols, 2) xy- or lonlat-grid.
Examples
--------
>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef # noqa
>>> radolan_grid = georef.get_radolan_grid()
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:])) # noqa
(900, 900, 2), (-523.4622, -4658.6447)
>>> # using pure trigonometric transformations
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(trig=True)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:])) # noqa
(900, 900, 2), (-523.4622, -4658.6447)
>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(1500, 1400)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:])) # noqa
(1500, 1400, 2), (-673.4622, -5008.6447)
>>> # using osr spatial reference transformation
>>> import wradlib.georef as georef
>>> radolan_grid = georef.get_radolan_grid(900, 900, wgs84=True)
>>> print("{0}, ({1:.4f}, {2:.4f})".format(radolan_grid.shape, *radolan_grid[0,0,:])) # noqa
(900, 900, 2), (3.5889, 46.9526)
See :ref:`notebooks/radolan/radolan_grid.ipynb#Polar-Stereographic-Projection`. # noqa
Raises
------
TypeError, ValueError
"""
# setup default parameters in dicts
tiny = {'j_0': 450, 'i_0': 450, 'res': 2}
small = {'j_0': 460, 'i_0': 460, 'res': 2}
normal = {'j_0': 450, 'i_0': 450, 'res': 1}
normal_wx = {'j_0': 370, 'i_0': 550, 'res': 1}
extended = {'j_0': 600, 'i_0': 800, 'res': 1}
griddefs = {(450, 450): tiny, (460, 460): small,
(900, 900): normal, (1100, 900): normal_wx,
(1500, 1400): extended}
# type and value checking
if nrows and ncols:
if not (isinstance(nrows, int) and isinstance(ncols, int)):
raise TypeError("wradlib.georef: Parameter *nrows* "
"and *ncols* not integer")
if (nrows, ncols) not in griddefs.keys():
raise ValueError("wradlib.georef: Parameter *nrows* "
"and *ncols* mismatch.")
else:
# fallback for call without parameters
nrows = 900
ncols = 900
# tiny, small, normal or extended grid check
# reference point changes according to radolan composit format
j_0 = griddefs[(nrows, ncols)]['j_0']
i_0 = griddefs[(nrows, ncols)]['i_0']
res = griddefs[(nrows, ncols)]['res']
x_0, y_0 = get_radolan_coords(9.0, 51.0, trig=trig)
x_arr = np.arange(x_0 - j_0, x_0 - j_0 + ncols * res, res)
y_arr = np.arange(y_0 - i_0, y_0 - i_0 + nrows * res, res)
x, y = np.meshgrid(x_arr, y_arr)
radolan_grid = np.dstack((x, y))
if wgs84:
if trig:
# inverse projection
lon0 = 10. # central meridian of projection
lat0 = 60. # standard parallel of projection
sinlat0 = np.sin(np.radians(lat0))
fac = (6370.040 ** 2.) * ((1. + sinlat0) ** 2.)
lon = np.degrees(np.arctan((-x / y))) + lon0
lat = np.degrees(np.arcsin((fac - (x ** 2. + y ** 2.)) /
(fac + (x ** 2. + y ** 2.))))
radolan_grid = np.dstack((lon, lat))
else:
# create radolan projection osr object
proj_stereo = create_osr("dwd-radolan")
# create wgs84 projection osr object
proj_wgs = get_default_projection()
radolan_grid = reproject(radolan_grid,
projection_source=proj_stereo,
projection_target=proj_wgs)
return radolan_grid
xx = np.linspace(4, 7.2)
plt.scatter(X[y == 0, 0], X[y == 0, 1], color=MAROON, label='\emph{Iris setosa}')
plt.scatter(X[y == 1, 0], X[y == 1, 1], color=BLUE, label='\emph{Iris versicolor}')
plt.xlim(xx.min(), xx.max())
plt.ylim(1, 5)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
xx = np.linspace(xlim[0], xlim[1], 30)
yy = np.linspace(ylim[0], ylim[1], 30)
h = 0.002
xx, yy = np.meshgrid(np.arange(xx.min(), xx.max() + h, h),
np.arange(1, 5 + h, h))
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
Z = Z[:, 0] - Z[:, 1]
Z = Z.reshape(xx.shape)
ax.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
linestyles=['--', '-', '--'])
ax.contourf(xx, yy, Z, colors=[MAROON, BLUE], levels=[-1, 0, 1], alpha=0.2)
ax.contourf(xx, yy, Z, colors=[MAROON, ], levels=[-100, -1], alpha=0.5)
ax.contourf(xx, yy, Z, colors=[BLUE, ], levels=[1, 1000], alpha=0.5)
plt.legend(loc=1)
plt.title('MLP')
plt.xlabel('Sepal length')
def read(dictArgs):
"""Function to read in the data. Returns xarray datasets"""
infile = dictArgs["infile"]
# Open ice model output and the static file
ds = xr.open_mfdataset(infile, combine="by_coords")
if "siconc" in ds.variables:
ds["CN"] = ds["siconc"]
if ds["CN"].max() > 1.0:
ds["CN"] = ds["CN"] / 100.0
else:
ds["CN"] = ds["CN"].sum(dim="ct")
# Detect if we are native grid or 1x1
if (ds.CN.shape[-2] == 180) and (ds.CN.shape[-1] == 360):
standard_grid = True
else:
standard_grid = False
if dictArgs["model"] is not None:
# use dataset from catalog, either from command line or default
cat_platform = (
f"catalogs/{dictArgs['model']}_catalog_{dictArgs['platform']}.yml"
)
catfile = pkgr.resource_filename("om4labs", cat_platform)
cat = intake.open_catalog(catfile)
if standard_grid is True:
dstatic = cat["ice_static_1x1"].to_dask()
else:
dstatic = cat["ice_static"].to_dask()
# Override static file if provided
if dictArgs["static"] is not None:
dstatic = xr.open_dataset(dictArgs["static"])
# Append static fields to the return Dataset
if standard_grid is True:
_lon = np.array(dstatic["lon"].to_masked_array())
_lat = np.array(dstatic["lat"].to_masked_array())
X, Y = np.meshgrid(_lon, _lat)
ds["GEOLON"] = xr.DataArray(X, dims=["lat", "lon"])
ds["GEOLAT"] = xr.DataArray(Y, dims=["lat", "lon"])
_AREA = standard_grid_cell_area(_lat, _lon)
_MASK = np.array(dstatic["mask"].fillna(0.0).to_masked_array())
_AREA = _AREA * _MASK
ds["CELL_AREA"] = xr.DataArray(_AREA, dims=["lat", "lon"])
ds["AREA"] = xr.DataArray(_AREA, dims=["lat", "lon"])
ds = ds.rename({"lon": "x", "lat": "y"})
else:
ds["CELL_AREA"] = dstatic["CELL_AREA"]
ds["GEOLON"] = dstatic["GEOLON"]
ds["GEOLAT"] = dstatic["GEOLAT"]
ds["AREA"] = dstatic["CELL_AREA"] * 4.0 * np.pi * (6.378e6 ** 2)
# Get Valid Mask
valid_mask = np.where(ds["CELL_AREA"] == 0.0, True, False)
# Open observed SIC on 25-km EASE grid (coords already named lat and lon)
if dictArgs["obsfile"] is not None:
dobs = xr.open_dataset(dictArgs["obsfile"])
else:
cat_platform = "catalogs/obs_catalog_" + dictArgs["platform"] + ".yml"
catfile = pkgr.resource_filename("om4labs", cat_platform)
cat = intake.open_catalog(catfile)
dobs = cat[dictArgs["dataset"]].to_dask()
# Close the static file (no longer used)
dstatic.close()
return ds, dobs, valid_mask
def dataproc(ruta, path, paleta):
# Open NC file
nc = Dataset(path)
# Load data
data_subset = nc.variables['CMI'][:][:, :]
# Create the projection variables
ori_proj = nc.variables['goes_imager_projection']
sat_h = ori_proj.perspective_point_height
sat_lon = ori_proj.longitude_of_projection_origin
sat_sweep = ori_proj.sweep_angle_axis
# The projection x and y coordinates equals
# the scanning angle (in radians) multiplied by the satellite height (http://proj4.org/projections/geos.html)
X = nc.variables['x'][:] * sat_h
Y = nc.variables['y'][:] * sat_h
p = Proj(proj='geos', h=sat_h, lon_0=sat_lon, sweep=sat_sweep)
# Convert map points to latitude and longitude with the magic provided by Pyproj
XX, YY = np.meshgrid(X, Y)
lons, lats = p(XX, YY, inverse=True)
ii = []
jj = []
for i in range(lons.shape[1]):
for j in range(lats.shape[0]):
if lons[1515, i] >= -100.0 and lats[j, 2325] >= 15.0 and lons[
1515, i] <= -65.0 and lats[j, 2325] <= 40.0:
ii.append(i)
jj.append(j)
jmin, jmax = (int(np.min(ii)), int(np.max(ii)))
imin, imax = (int(np.min(jj)), int(np.max(jj)))
# Create map
DPI = 150
ax = plt.figure(figsize=(2000 / float(DPI), 2000 / float(DPI)),
frameon=True,
dpi=DPI)
bmap = Basemap(projection='cyl',
llcrnrlon=lons[1515, jmin],
llcrnrlat=lats[imax, 2325],
urcrnrlon=lons[1515, jmax],
urcrnrlat=lats[imin, 2325],
resolution='l')
bmap.drawparallels(np.arange(-90.0, 90.0, 10.0),
linewidth=0.1,
color='black',
labels=[True, False, False, True])
bmap.drawmeridians(np.arange(0.0, 360.0, 10.0),
linewidth=0.1,
color='black',
labels=[True, False, False, True])
# Load Shapefile
# bmap.readshapefile('Nueva_DPA','Nueva_DPA',linewidth=0.90,color='darkslategray')
# bmap.readshapefile('ne_10m_admin_0_countries','ne_10m_admin_0_countries',linewidth=0.90,color='darkslategray')
bmap.drawstates(color='gray', linewidth=0.25)
bmap.drawcoastlines(color='k', linewidth=0.9)
bmap.drawcountries(color='k', linewidth=0.9)
# Converts a CPT file to be used in Python
cpt = loadCPT(paleta)
# Makes a linear interpolation
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors
#bmap.pcolormesh(lons[imin:imax,jmin:jmax],lats[imin:imax,jmin:jmax],data_subset[imin:imax,jmin:jmax], cmap=cpt_convert, vmin=170, vmax=378)
bmap.pcolormesh(lons[imin:imax, jmin:jmax],
lats[imin:imax, jmin:jmax],
data_subset[imin:imax, jmin:jmax] - 273.15,
cmap=cpt_convert,
vmin=-103,
vmax=104)
# Search for the GOES-16 channel in the file name
Band = (path[path.find("M3C") + 3:path.find("_G16")])
# Search for the Scan start in the file name
Start = (path[path.find("_s") + 2:path.find("_e")])
Start_Formatted = Start[0:4] + " Day " + Start[4:7] + " - " + Start[
7:9] + ":" + Start[9:11] + ":" + Start[11:13] + "." + Start[
13:14] + " UTC"
# Search for the Scan end in the file name
End = (path[path.find("_e") + 2:path.find("_c")])
End_Formatted = End[0:4] + " Day " + End[4:7] + " - " + End[
7:9] + ":" + End[9:11] + ":" + End[11:13] + "." + End[13:14] + " UTC"
# Add a title to the plot
plt.title("GOES-16 ABI Band " + Band + "\n Scan from " + Start_Formatted +
" to " + End_Formatted,
fontsize=10)
#bmap.colorbar(location='right', label='Brightness Temperature [K]')
bmap.colorbar(location='right', label='Brightness Temperature [C]')
# Save the result
# Converting from julian day to dd-mm-yyyy
year = int(Start[0:4])
dayjulian = int(
Start[4:7]) - 1 # Subtract 1 because the year starts at "0"
dayconventional = datetime.datetime(year, 1, 1) + datetime.timedelta(
dayjulian) # Convert from julian to conventional
date = dayconventional.strftime(
'%d-%b-%Y') # Format the date according to the strftime directives
date_save = dayconventional.strftime('%d%m%Y')
time_save = Start[7:9] + Start[9:11] + Start[11:13]
plt.savefig('G16_C' + str(Band) + '_' + date_save + '_' + time_save +
'_fulldisk.png',
dpi=DPI,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.cla()
from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np map = Basemap(projection='sinu', lat_0=0, lon_0=0) lons = np.arange(30, 390, 30) lats = np.arange(0, 100, 10) data = np.indices((lats.shape[0], lons.shape[0])) data = data[0] + data[1] print data print lons lons, data = map.shiftdata(lons, datain=data, lon_0=0) print lons llons, llats = np.meshgrid(lons, lats) x, y = map(llons, llats) map.contourf(x, y, data) map.drawcoastlines() plt.show()
font = {'family': 'Arial',
'weight': 'bold',
'size': 10,
}
####LOAD DATA ####
obs_data_full = np.genfromtxt('model-data/contour_plot_data.csv', delimiter=',', skip_header=1)
####Interpolation for contours and heatmap
R0 = obs_data_full[:,0]
I0 = np.log10(obs_data_full[:,1])
prob = obs_data_full[:,4]
xi_full, yi_full2 = np.linspace(I0.min(), 2, 300), np.linspace(R0.min(), 1.5, 300)
xi_full, yi_full2 = np.meshgrid(xi_full, yi_full2)
zi_full2 = scipy.interpolate.griddata((I0, R0), prob, (xi_full, yi_full2), method='linear')
#### Make plot
fig3 = plt.subplot(1,1,1)
plt.contourf(xi_full, yi_full2, zi_full2, 9, cmap=plt.cm.jet, alpha = 0.75)
plt.colorbar()
CS = plt.contour(xi_full, yi_full2, zi_full2, 9, colors='black', linewidth=.5)
# for labels but plotted on the log scale
class nf(float):
def __repr__(self):
str = '%.1f' % (self.__float__(),)
if str[-1] == '0':
# =============================================================================
# First file: full-lift-down
# =============================================================================
### Start with uncertainty bars on landline and spaceline
## Load file archive and get data
filename = './../results/sweeps/Neptune_27_3sigLow_0.25_180_0522005334.npz'
data = np.load(filename, allow_pickle=True)
# params = data['params'][0] # array of 1
outsList = data['outsList']
efpaList = data['efpaList']
BCList = data['BCList']
## Create mesh grid for contour plots, reshape result arrays
BCgrid, EFPAgrid = np.meshgrid(BCList, efpaList)
fpafgrid = np.reshape([out.fpaf for out in outsList], BCgrid.shape)
engfgrid = np.reshape([out.engf for out in outsList], BCgrid.shape)
# find line between landing and aerocapture
landline_BC_low = BCList
landline_EFPA_low = []
for rind in range(fpafgrid.shape[1]):
ind = next(ind for ind, val in enumerate(fpafgrid[:,rind])\
if val < 0)
landline_EFPA_low.append(efpaList[ind])
# find line between aerocapture and escape
spaceline_BC_low = []
spaceline_EFPA_low = []
for rind in range(engfgrid.shape[1]):
def tile_anchors_3d(area_extents, anchor_3d_sizes, anchor_stride,
ground_plane):
"""
Tiles anchors over the area extents by using meshgrids to
generate combinations of (x, y, z), (l, w, h) and ry.
Args:
area_extents: [[min_x, max_x], [min_y, max_y], [min_z, max_z]]
anchor_3d_sizes: list of 3d anchor sizes N x (l, w, h)
anchor_stride: stride lengths (x_stride, z_stride)
ground_plane: coefficients of the ground plane e.g. [0, -1, 0, 0]
Returns:
boxes: list of 3D anchors in box_3d format N x [x, y, z, l, w, h, ry]
"""
# Convert sizes to ndarray
anchor_3d_sizes = np.asarray(anchor_3d_sizes)
anchor_stride_x = anchor_stride[0]
anchor_stride_z = anchor_stride[1]
anchor_rotations = np.asarray([0, np.pi / 2.0])
x_start = area_extents[0][0] + anchor_stride[0] / 2.0
x_end = area_extents[0][1]
x_centers = np.array(np.arange(x_start, x_end, step=anchor_stride_x),
dtype=np.float32)
z_start = area_extents[2][1] - anchor_stride[1] / 2.0
z_end = area_extents[2][0]
z_centers = np.array(np.arange(z_start, z_end, step=-anchor_stride_z),
dtype=np.float32)
# Use ranges for substitution
size_indices = np.arange(0, len(anchor_3d_sizes))
rotation_indices = np.arange(0, len(anchor_rotations))
# Generate matrix for substitution
# e.g. for two sizes and two rotations
# [[x0, z0, 0, 0], [x0, z0, 0, 1], [x0, z0, 1, 0], [x0, z0, 1, 1],
# [x1, z0, 0, 0], [x1, z0, 0, 1], [x1, z0, 1, 0], [x1, z0, 1, 1], ...]
before_sub = np.stack(np.meshgrid(x_centers, z_centers, size_indices,
rotation_indices),
axis=4).reshape(-1, 4)
# Place anchors on the ground plane
a, b, c, d = ground_plane
all_x = before_sub[:, 0]
all_z = before_sub[:, 1]
all_y = -(a * all_x + c * all_z + d) / b
# Create empty matrix to return
num_anchors = len(before_sub)
all_anchor_boxes_3d = np.zeros((num_anchors, 7))
# Fill in x, y, z
all_anchor_boxes_3d[:, 0:3] = np.stack((all_x, all_y, all_z), axis=1)
# Fill in shapes
sizes = anchor_3d_sizes[np.asarray(before_sub[:, 2], np.int32)]
all_anchor_boxes_3d[:, 3:6] = sizes
# Fill in rotations
rotations = anchor_rotations[np.asarray(before_sub[:, 3], np.int32)]
all_anchor_boxes_3d[:, 6] = rotations
return all_anchor_boxes_3d
ax = plt.subplot(111)
ZM = np.arange(1, 200, 0.35)
nx = 384
ny = 216
n_iter = 80
for zm in ZM:
zmf = np.exp(-zm/20.)
x1 = x0 - 2 * zmf
x2 = x0 + 2 * zmf
y1 = y0 - 1.13 * zmf
y2 = y0 + 1.13 * zmf
x, y = np.meshgrid(np.linspace(x1, x2, nx), np.linspace(y1, y2, ny))
c = x + 1j*y
z = np.zeros(c.shape)
I = np.zeros(c.shape)
for k in range(n_iter):
z = z ** degree + c
ind = (np.abs(z) > tol) * (I == 0)
I[ind] = 255 - k
ax.imshow(I, extent=(x1, x2, y1, y2)) #, shape=(3840, 2160), interpolation='bilinear')
plt.pause(0.001)
ax.cla()
