def DisplayPyPlot(A,title=''):
isVec = min(A.Height(),A.Width()) == 1
if A.tag == cTag or A.tag == zTag:
AReal = Matrix(Base(A.tag))
AImag = Matrix(Base(A.tag))
RealPart(A,AReal)
ImagPart(A,AImag)
fig, (ax1,ax2) = plt.subplots(1,2)
ax1.set_title('Real part')
ax2.set_title('Imag part')
if isVec:
ax1.plot(np.squeeze(AReal.ToNumPy()),'bo-')
ax2.plot(np.squeeze(AImag.ToNumPy()),'bo-')
else:
imReal = ax1.imshow(AReal.ToNumPy())
cBarReal = fig.colorbar(imReal,ax=ax1)
imImag = ax2.imshow(AImag.ToNumPy())
cBarImag = fig.colorbar(imImag,ax=ax2)
plt.suptitle(title)
plt.tight_layout()
else:
fig = plt.figure()
axis = fig.add_axes([0.1,0.1,0.8,0.8])
if isVec:
axis.plot(np.squeeze(A.ToNumPy()),'bo-')
else:
im = axis.imshow(A.ToNumPy())
fig.colorbar(im,ax=axis)
plt.title(title)
plt.draw()
if not isInlinePyPlot:
plt.show(block=False)
return fig
def getAcc(model,words,f):
f = open(f,'r')
lines = f.readlines()
preds = []
golds = []
seq1 = []
seq2 = []
ct = 0
for i in lines:
i = i.split("\t")
p1 = i[0]; p2 = i[1]; score = i[2]
X1, X2 = getSeqs(p1,p2,words)
seq1.append(X1)
seq2.append(X2)
ct += 1
if ct % 100 == 0:
x1,m1 = utils.prepare_data(seq1)
x2,m2 = utils.prepare_data(seq2)
scores = model.scoring_function(x1,x2,m1,m2)
scores = np.squeeze(scores)
preds.extend(scores.tolist())
seq1 = []
seq2 = []
golds.append(score)
if len(seq1) > 0:
x1,m1 = utils.prepare_data(seq1)
x2,m2 = utils.prepare_data(seq2)
scores = model.scoring_function(x1,x2,m1,m2)
scores = np.squeeze(scores)
preds.extend(scores.tolist())
return acc(preds,golds)
def _plot(self,names,title,style,when=0,showLegend=True):
if isinstance(names,str):
names = [names]
assert isinstance(names,list)
legend = []
for name in names:
assert isinstance(name,str)
legend.append(name)
# if it's a differential state
if name in self.xNames:
index = self.xNames.index(name)
ys = np.squeeze(self._log['x'])[:,index]
ts = np.arange(len(ys))*self.Ts
plt.plot(ts,ys,style)
if name in self.outputNames:
index = self.outputNames.index(name)
ys = np.squeeze(self._log['outputs'][name])
ts = np.arange(len(ys))*self.Ts
plt.plot(ts,ys,style)
if title is not None:
assert isinstance(title,str), "title must be a string"
plt.title(title)
plt.xlabel('time [s]')
if showLegend is True:
plt.legend(legend)
plt.grid()
def surf(z,x=None,y=None,win=None,shade=0,edges=1,edge_color='fg',phi=-45.0,
theta=30.0,zscale=1.0,palette=None,gnomon=0):
'''Plot a three-dimensional wire-frame (surface): z=f(x,y)
'''
if win is None:
pl3d.window3()
else:
pl3d.window3(win)
pl3d.set_draw3_(0)
phi0 = phi*numpy.pi/180.0
theta0 = theta*numpy.pi/180.0
pl3d.orient3(phi=phi0,theta=theta0)
pl3d.light3()
_change_palette(palette)
sz = numpy.shape(z)
if len(sz) != 2:
raise ValueError('Input must be a 2-d array --- a surface.')
N,M = sz
if x is None:
x = numpy.arange(0,N)
if y is None:
y = numpy.arange(0,M)
x = numpy.squeeze(x)
y = numpy.squeeze(y)
if (len(numpy.shape(x)) == 1):
x = x[:,newaxis]*numpy.ones((1,M))
if (len(numpy.shape(y)) == 1):
y = numpy.ones((N,1))*y[newaxis,:]
plwf.plwf(z,y,x,shade=shade,edges=edges,ecolor=edge_color,scale=zscale)
lims = pl3d.draw3(1)
gist.limits(lims[0],lims[1],lims[2],lims[3])
pl3d.gnomon(gnomon)
def log_diff_exp(x, axis=0):
""" Calculates the logarithm of the diffs of e to the power of input 'x'. The method tries to avoid
overflows by using the relationship: log(diff(exp(x))) = alpha + log(diff(exp(x-alpha))).
:Parameter:
x: data.
-type: float or numpy array
axis: Sums along the given axis.
-type: int
:Return:
Logarithm of the sum of exp of x.
-type: float or numpy array.
"""
alpha = x.max(axis) - numx.log(numx.finfo(numx.float64).max)/2.0
if axis == 1:
return numx.squeeze(alpha + numx.log(
numx.diff(
numx.exp(x.T - alpha)
, n=1, axis=0)))
else:
return numx.squeeze(alpha + numx.log(
numx.diff(
numx.exp(x - alpha)
, n=1, axis=0)))
def CoAddFinal(frames, mode='mean', display=True):
# co-add FINSIHED, reduced spectra
# only trick: resample on to wavelength grid of 1st frame
files = np.loadtxt(frames, dtype='string',unpack=True)
# read in first file
wave_0, flux_0 = np.loadtxt(files[0],dtype='float',skiprows=1,
unpack=True,delimiter=',')
for i in range(1,len(files)):
wave_i, flux_i = np.loadtxt(files[i],dtype='float',skiprows=1,
unpack=True,delimiter=',')
# linear interp on to wavelength grid of 1st frame
flux_i0 = np.interp(wave_0, wave_i, flux_i)
flux_0 = np.dstack( (flux_0, flux_i0))
if mode == 'mean':
flux_out = np.squeeze(flux_0.sum(axis=2) / len(files))
if mode == 'median':
flux_out = np.squeeze(np.median(flux_0, axis=2))
if display is True:
plt.figure()
plt.plot(wave_0, flux_out)
plt.xlabel('Wavelength')
plt.ylabel('Co-Added Flux')
plt.show()
return wave_0, flux_out
def plotForce():
figure(size=3,aspect=0.5)
subplot(1,2,1)
from EvalTraj import plotFF
plotFF(vp=351,t=28,f=900,cm=0.6,foffset=8)
subplot_annotate()
subplot(1,2,2)
for i in [1,2,3,4]:
R=np.squeeze(np.load('Rdpse%d.npy'%i))
R=stats.nanmedian(R,axis=2)[:,1:,:]
dps=np.linspace(-1,1,201)[1:]
plt.plot(dps,R[:,:,2].mean(0));
plt.legend([0,0.1,0.2,0.3],loc=3)
i=2
R=np.squeeze(np.load('Rdpse%d.npy'%i))
R=stats.nanmedian(R,axis=2)[:,1:,:]
mn=np.argmin(R,axis=1)
y=np.random.randn(mn.shape[0])*0.00002+0.0438
plt.plot(np.sort(dps[mn[:,2]]),y,'+',mew=1,ms=6,mec=[ 0.39 , 0.76, 0.64])
plt.xlabel('Displacement of Force Origin')
plt.ylabel('Average Net Force Magnitude')
hh=dps[mn[:,2]]
err=np.std(hh)/np.sqrt(hh.shape[0])*stats.t.ppf(0.975,hh.shape[0])
err2=np.std(hh)/np.sqrt(hh.shape[0])*stats.t.ppf(0.75,hh.shape[0])
m=np.mean(hh)
print m, m-err,m+err
np.save('force',[m, m-err,m+err,m-err2,m+err2])
plt.xlim([-0.5,0.5])
plt.ylim([0.0435,0.046])
plt.grid(b=True,axis='x')
subplot_annotate()
def __init__(self, endog, exog, sigma=None):
#TODO: add options igls, for iterative fgls if sigma is None
#TODO: default is sigma is none should be two-step GLS
if sigma is not None:
self.sigma = np.asarray(sigma)
else:
self.sigma = sigma
if self.sigma is not None and not self.sigma.shape == (): #greedy logic
nobs = int(endog.shape[0])
if self.sigma.ndim == 1 or np.squeeze(self.sigma).ndim == 1:
if self.sigma.shape[0] != nobs:
raise ValueError("sigma is not the correct dimension. \
Should be of length %s, if sigma is a 1d array" % nobs)
elif self.sigma.shape[0] != nobs and \
self.sigma.shape[1] != nobs:
raise ValueError("expected an %s x %s array for sigma" % \
(nobs, nobs))
if self.sigma is not None:
nobs = int(endog.shape[0])
if self.sigma.shape == ():
self.sigma = np.diag(np.ones(nobs)*self.sigma)
if np.squeeze(self.sigma).ndim == 1:
self.sigma = np.diag(np.squeeze(self.sigma))
self.cholsigmainv = np.linalg.cholesky(np.linalg.pinv(\
self.sigma)).T
super(GLS, self).__init__(endog, exog)
def downsize(self, coefs, cut=None, verbose=True):
"""
Given a set of coefs, sort the coefs and get rid of the bottom cut
percent of variables with lowest cut coefs. Return the new coefs.
"""
downsized_coefs = np.squeeze(np.array(coefs))
if cut is None:
cut = self.cut
n_trash = int(floor(cut * self.n_features))
if verbose:
print("Downsampling...")
print("Current shape:", self.Xview.shape)
print("Removing {} columns... ".format(n_trash))
self.tail_start -= n_trash
if self.tail_start <= 0:
raise ValueError("Trying to downsize more variables than present")
# get sorted order of coefs
csort = np.squeeze(np.argsort(np.argsort(np.absolute(coefs))))
keep_feature = np.squeeze(csort >= n_trash)
tail_start = self.tail_start
# columns in the tail we want to keep
keep_idx = np.squeeze(
np.where(keep_feature[tail_start:tail_start+n_trash]))
keep_idx += tail_start
# columns we want to move to the tail
trash_idx = np.squeeze(np.where(keep_feature[0:tail_start] == False))
if len(trash_idx) != len(keep_idx):
raise ValueError("trash_idx and keep_idx not the same length")
# swap the columns
for trash, keep in zip(trash_idx, keep_idx):
#print(keep, trash)
keep_col = self.X[:, keep].copy()
self.X[:, keep] = self.X[:, trash]
self.X[:, trash] = keep_col
self.orig_feature_index[trash], self.orig_feature_index[keep] = self.orig_feature_index[keep], self.orig_feature_index[trash]
downsized_coefs[trash], downsized_coefs[keep] = downsized_coefs[keep], downsized_coefs[trash]
if self.test_subj is not None:
self.X_test[:, (trash, keep)] = self.X_test[:, (keep, trash)]
self.n_features -= n_trash
self.Xview = self.X.view()[:, :self.n_features]
if self.test_subj is not None:
self.X_testview = self.X_test.view()[:, :self.n_features]
print("New Xview shape:", self.Xview.shape)
return downsized_coefs[:-n_trash]
def get_contents(self, height, width):
"""Returns the contents (pixel values) of both images of the pair as
one numpy array.
Args:
height: Output height of each image.
width: Output width of each image.
Returns:
Numpy array of shape (2, height, width) with dtype uint8.
"""
img1 = self.image1.get_content()
img2 = self.image2.get_content()
if img1.shape[0] != height or img1.shape[1] != width:
# imresize can only handle (height, width) or (height, width, 3),
# not (height, width, 1), so squeeze away the last channel
if IMAGE_CHANNELS == 1:
img1 = misc.imresize(np.squeeze(img1), (height, width))
img1 = img1[:, :, np.newaxis]
else:
img1 = misc.imresize(img1, (height, width))
if img2.shape[0] != height or img2.shape[1] != width:
if IMAGE_CHANNELS == 1:
img2 = misc.imresize(np.squeeze(img2), (height, width))
img2 = img2[:, :, np.newaxis]
else:
img2 = misc.imresize(img2, (height, width))
return np.array([img1, img2], dtype=np.uint8)
def _field_gradient_jac(ref, target):
"""
Given a reference field ref and a target field target
compute the jacobian of the target with respect to ref
Parameters
----------
ref: Field instance that yields the topology of the space
target array of shape(ref.V,dim)
Results
-------
fgj: array of shape (ref.V) that gives the jacobian
implied by the ref.field->target transformation.
"""
import numpy.linalg as nl
n = ref.V
xyz = ref.field
dim = xyz.shape[1]
fgj = []
ln = ref.list_of_neighbors()
for i in range(n):
j = ln[i]
if np.size(j) > dim - 1:
dx = np.squeeze(xyz[j] - xyz[i])
df = np.squeeze(target[j] - target[i])
FG = np.dot(nl.pinv(dx), df)
fgj.append(nl.det(FG))
else:
fgj.append(1)
fgj = np.array(fgj)
return fgj
def main():
x = np.loadtxt(sys.argv[1], skiprows=1, delimiter=",")
x = np.array(x)
# separating the class 1 rows from class 2 rows
indic = np.where(x[:,1] == 1)
x_one = np.squeeze(x[indic,:])
indic = np.where(x[:,1] == 2)
x_two = np.squeeze(x[indic,:])
m = np.loadtxt(sys.argv[2])
m = np.array(m)
var = np.loadtxt(sys.argv[3])
var = np.array(var)
w = np.loadtxt(sys.argv[4])
w = np.array(w)
its = sys.argv[5]
its = np.int32(its)
ll = gmmest(x_two[:,0], m, var, w, its)
plt.plot(ll[3])
plt.ylabel('log likelihood')
plt.xlabel('iterations')
plt.show()
print "mu: ", ll[0], " sigmasq: ", ll[1], " wt: ", ll[2], " ll: ", ll[3]
def cos_distance(self, strike1, dip1, strike2, dip2):
"""Angular distance betwen the poles of two planes."""
xyz1 = sph2cart(*mplstereonet.pole(strike1, dip1))
xyz2 = sph2cart(*mplstereonet.pole(strike2, dip2))
r1, r2 = np.linalg.norm(xyz1), np.linalg.norm(xyz2)
dot = np.dot(np.squeeze(xyz1), np.squeeze(xyz2)) / r1 / r2
return np.abs(np.degrees(np.arccos(dot)))
def reparametrization_LS_assembler(self):
"""In this function we compute the arclength reparametrization by mean of a Least Square
problem."""
s_array = np.linspace(0, self.points_s[-1,1], self.arcfactor * self.n_dofs)
#self.point_ls = list()
self.point_ls = np.asmatrix(np.zeros([s_array.shape[0],self.dim + 2]))
tval = np.zeros(s_array.shape[0])
sval = np.linspace(0,1,s_array.shape[0])
for i in range(0, s_array.shape[0]):
tval[i] = self.find_s(s_array[i])
#rint tval
# The curve should have a value( or __call__ if prefered) method that we can query to know its value in space
self.point_ls[:,0:self.dim] = np.squeeze(self.curve(tval).transpose())
self.rhsinit = np.squeeze(self.curve(tval).transpose())
#self.point_ls[:,self.dim] = s_array[:,np.newaxis]
self.point_ls[:,self.dim] = tval[:,np.newaxis]
# In point_ls we store the value in space, the s_array and the tval obtained
#self.point_ls.append([cval, s_array[i], tval])
#self.point_ls = np.array(self.point_ls)
# We compute the number of elements in the system rectangular matrix(Bmatrix), it will have dim*s_array.shape[0] rows and dim*nknot columns.
# We want it to be rectangular because we are approximating its resilution so we search for something that solves the reparametrization in a
# least square sense.
#Bmatrixnumelem = self.dim * s_array.shape[0] * self.n_dofs * self.dim
#self.matrixB = np.zeros(Bmatrixnumelem).reshape(self.dim * s_array.shape[0], self.n_dofs * self.dim)
#self.rhsinit = np.zeros(self.dim * s_array.shape[0])
self.matrixB = interpolation_matrix(self.vector_space, sval)
def visualize_ns_old(self, term, points=200):
"""
Use randomly selected coordinates instead of most active
"""
if term in self.no.term:
term_index = self.no._ns['features_df'].columns.get_loc(term)
rand_point_inds = np.random.random_integers(0, len(np.squeeze(zip(self.no._ns['mni_coords'].data))), points)
rand_points = np.squeeze(zip(self.no._ns['mni_coords'].data))[rand_point_inds]
weights = []
inds_of_real_points_with_no_fucking_missing_study_ids = []
for rand_point in range(len(rand_points)):
if len(self.no.coord_to_ns_act(rand_points[rand_point].astype(list))) > 0:
inds_of_real_points_with_no_fucking_missing_study_ids.append(rand_point_inds[rand_point])
weights.append(self.no.coord_to_ns_act(rand_points[rand_point].astype(list))[term_index])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = cm.jet(weights/max(weights))
color_map = cm.ScalarMappable(cmap=cm.jet)
color_map.set_array(weights)
fig.colorbar(color_map)
x = self.no._ns['mni_coords'].data[inds_of_real_points_with_no_fucking_missing_study_ids, 0]
y = self.no._ns['mni_coords'].data[inds_of_real_points_with_no_fucking_missing_study_ids, 1]
z = self.no._ns['mni_coords'].data[inds_of_real_points_with_no_fucking_missing_study_ids, 2]
else:
raise ValueError('Term '+term + ' has not been initialized. '
'Use get_ns_act(' + term + ')')
ax.scatter(x, y, z, c=colors, alpha=0.4)
ax.set_title('Estimation of ' + term)
def getEPSFourierCoeffs(self, wl, n, anisotropic=True):
"""Return the Fourier coefficients of eps and eps**-1, orders [-n,n]."""
nood = 2 * n + 1
hmax = nood - 1
if not anisotropic:
# isotropic
rix1 = self.mat1.n(wl)
rix2 = self.mat2.n(wl)
f = self.dc
h = numpy.arange(-hmax, hmax + 1)
EPS = (rix1 ** 2 - rix2 ** 2) * f * \
numpy.sinc(h * f) + rix2 ** 2 * (h == 0)
EPS1 = (rix1 ** -2 - rix2 ** -2) * f * \
numpy.sinc(h * f) + rix2 ** -2 * (h == 0)
return EPS, EPS1
else:
# anisotropic
EPS = numpy.zeros((3, 3, 2 * hmax + 1), dtype=complex)
EPS1 = numpy.zeros_like(EPS)
eps1 = numpy.squeeze(
self.mat1.epsilonTensor(wl)) / EMpy.constants.eps0
eps2 = numpy.squeeze(
self.mat2.epsilonTensor(wl)) / EMpy.constants.eps0
f = self.dc
h = numpy.arange(-hmax, hmax + 1)
for ih, hh in enumerate(h):
EPS[:, :, ih] = (eps1 - eps2) * f * \
numpy.sinc(hh * f) + eps2 * (hh == 0)
EPS1[:, :, ih] = (
scipy.linalg.inv(eps1) - scipy.linalg.inv(eps2)
) * f * numpy.sinc(hh * f) + scipy.linalg.inv(eps2) * (hh == 0)
return EPS, EPS1
def generate_ic_grid(dR=0.1*u.kpc, dRdot=5.*u.km/u.s):
# spacing between IC's in R and Rdot
dR = dR.decompose(usys).value
dRdot = dRdot.decompose(usys).value
max_Rdot = (50*10*u.km/u.s).decompose(usys).value
max_R = (15*u.kpc).decompose(usys).value
# from the paper
E = (600*100*(u.km/u.s)**2).decompose(usys).value
Lz = (10.*10.*u.km*u.kpc/u.s).decompose(usys).value # typo in paper? km/kpc instead of km*kpc
z = 0.
params = oblate_params
w0s = []
for R in np.arange(0, max_R, dR):
# zero velocity curve
V = zotos_potential(R, z, *params)
ZVC_Rdot = np.squeeze(np.sqrt(2*(E-V) - Lz**2/R**2))
for Rdot in np.arange(0, max_Rdot, dRdot):
if Rdot > ZVC_Rdot or R < 0.2 or R >= 13: continue
zdot = np.squeeze(np.sqrt(2*(E - V) - Lz**2/R**2 - Rdot**2))
w0 = [R,z,Rdot,zdot]
w0s.append(w0)
w0s = np.array(w0s)
return w0s, Lz
def weightMatrix(mtx3d,bgq):
"""
Calculation of weight tensor, which replaces unity values in
mtx3d with the positions relative entropy.
Helper function of Rama Ranganathan MATLAB sca5 function.
"""
nseq,npos,naa = mtx3d.shape
mtx3d_mat = np.reshape(mtx3d.transpose(2,1,0),(naa*npos,nseq),order='F')
f1_v =np.sum(mtx3d_mat.T,axis=0)/nseq
w_v = np.squeeze(np.ones((1,naa*npos)))
q1_v = np.squeeze(np.tile(bgq,(1,npos)))
for x in range(naa*npos):
q = q1_v[x]
f = f1_v[x]
# here I hard coded their metric DerivEntropy
if f > 0 and f < 1:
w_v[x] = np.abs(np.log(f*(1-q)/(q*(1-f))))
else:
w_v[x] = 0.
W = np.zeros((npos,naa))
for i in range(npos):
for j in range(naa):
W[i,j] = w_v[naa*i+j]
Wx = np.tile(np.reshape(W,(1, npos, naa),order='F'),(nseq,1,1))*mtx3d
return Wx,W
def Load_Answer(self, Str_DataName, Int_DataNum):
if Str_DataName == "PPG_KW_long":
Str_AnnoName = "../Data/" + str(Int_DataNum) + "_Anno.txt"
List_Anno = file(Str_AnnoName,'r').read()
List_Anno = List_Anno.split("\n")
List_Anno = [int(x) for x in List_Anno]
Array_Anno = np.array(List_Anno)
Array_Anno = np.unique(Array_Anno)
elif Str_DataName == "PPG_Walk":
Str_AnnoName = "../Data/" + Str_DataName + str(Int_DataNum)+ "_Anno.txt"
List_Anno = file(Str_AnnoName,'r').read()
List_Anno = List_Anno.split("\n")
List_Anno = [int(x) for x in List_Anno]
Array_Anno = np.array(List_Anno)
Array_Anno = np.unique(Array_Anno)
elif Str_DataName == "PPG_Label":
Str_DataPathABP = "../Data/BeatDetection/ABP"
Str_DataPathICP = "../Data/BeatDetection/ICP"
MatFile_ABP = scipy.io.loadmat(Str_DataPathABP)
Int_CutIdx = 125*3600
if Int_DataNum == 1:
Array_Anno = np.squeeze(np.array(MatFile_ABP['dDT1']))
Array_Anno = np.array([int(val) for val in Array_Anno if val < Int_CutIdx])
elif Int_DataNum == 2:
Array_Anno = np.squeeze(np.array(MatFile_ABP['dDT2']))
Array_Anno = np.array([int(val) for val in Array_Anno if val < Int_CutIdx])
return Array_Anno
def ll2ij(self, lon, lat, mask=None, cutoff=None, nei=1, all_nei=False,
return_dist=False):
"""Reproject a lat-lon vector to i-j grid coordinates"""
self.add_ij()
if mask is not None:
self.add_kd(mask)
elif not hasattr(self, 'kd'):
self.add_kd()
dist,ij = self.kd.query(list(np.vstack((lon,lat)).T), nei)
#if cutoff is not None:
# ij[dist>cutoff] = 0
if nei == 1 :
ivec = self.kdijvec[ij[:] - 1][:, 0]
jvec = self.kdijvec[ij[:] - 1][:, 1]
if cutoff is not None:
ivec[dist>cutoff] = -999
jvec[dist>cutoff] = -999
elif all_nei == False:
ivec = np.squeeze(self.kdijvec[ij[:,:]-1])[:, nei-1, 0]
jvec = np.squeeze(self.kdijvec[ij[:,:]-1])[:, nei-1, 1]
dist = np.squeeze(dist[:, nei-1])
else:
ivec = np.squeeze(self.kdijvec[ij[:,:]-1])[:, :, 0]
jvec = np.squeeze(self.kdijvec[ij[:,:]-1])[:, :, 1]
if return_dist == True:
return ivec,jvec,dist
else:
return ivec,jvec
def value_for_all(estimator,N):
from scipy.sparse import csr_matrix
ch_left = estimator.tree_.children_left
ch_right = estimator.tree_.children_right
(cl,) = np.where(ch_left!=-1)
(cr,) = np.where(ch_right!=-1)
cap = estimator.tree_.capacity
dis_node = np.zeros((cap,estimator.tree_.n_classes))
A = np.zeros([cap,cap])
D = A
A = csr_matrix(A)
A[cl,ch_left[cl]] = 1
A[cr,ch_right[cr]] = 1
B = A
C = B
while(C.sum()!=0):
C = A*C
B = B + C
I,J = B.nonzero()
D[I,J] = 1
(I,) = np.where(ch_left==-1)
dis_node[I,:] = np.squeeze(estimator.tree_.value[I])
for i in I:
dis_node[i,:] = dis_node[i,:]/dis_node[i,:].sum()
(remain1,) = np.where(ch_left!=-1)
for i in remain1:
(I,) = np.where(D[i,:]==1)
dis_node[i,:] = np.sum(np.squeeze(estimator.tree_.value[I]),axis = 0)
dis_node[i,:] = dis_node[i,:]/dis_node[i,:].sum()
Dis_node = np.zeros((cap,N))
Dis_node[:,estimator.classes_.astype(int)] = dis_node
return Dis_node
def sdss_source_props_ota(img,ota):
"""
Use photutils to get the elongation of all of the sdss sources
can maybe use for point source filter
Also fit a gaussian along a row and col of pixels passing
through the center of the star
"""
image = odi.reprojpath+'reproj_'+ota+'.'+str(img[16:])
hdulist = odi.fits.open(image)
data = hdulist[0].data
sdss_source_file = odi.coordspath+'reproj_'+ota+'.'+str(img[16:-5])+'.sdssxy'
x,y,ra,dec,g,g_err,r,r_err = np.loadtxt(sdss_source_file,usecols=(0,1,2,3,
6,7,8,9),unpack=True)
box_centers = zip(y,x)
box_centers = np.reshape(box_centers,(len(box_centers),2))
source_dict = {}
total_fwhm = []
for i,center in enumerate(box_centers):
x1 = center[0]-50
x2 = center[0]+50
y1 = center[1]-50
y2 = center[1]+50
#print x1,x2,y1,y2,center
box = data[x1:x2,y1:y2]
col = data[x1:x2,int(center[1]-0.5):int(center[1]+0.5)]
row = data[int(center[0]-0.5):int(center[0]+0.5),y1:y2]
row = np.squeeze(row) - np.median(row)
col = np.squeeze(col) - np.median(col)
g_init = models.Gaussian1D(amplitude=250., mean=50, stddev=2.)
fit_g = fitting.LevMarLSQFitter()
pix = np.linspace(0,100,num=100)
g_row = fit_g(g_init, pix, row)
g_col = fit_g(g_init, pix, col)
mean_fwhm = 0.5*(g_row.stddev*2.355+g_col.stddev*2.355)
total_fwhm.append(mean_fwhm)
#odi.plt.imshow(box)
#odi.plt.plot(row)
#odi.plt.plot(pix,g(pix))
#plt.imshow(row2)
#plt.show()
mean, median, std = odi.sigma_clipped_stats(box, sigma=3.0)
threshold = median + (std * 2.)
segm_img = odi.detect_sources(box, threshold, npixels=20)
source_props = odi.source_properties(box,segm_img)
if len(source_props) > 0:
columns = ['xcentroid', 'ycentroid','elongation','semimajor_axis_sigma','semiminor_axis_sigma']
if i == 0:
source_tbl = odi.properties_table(source_props,columns=columns)
else:
source_tbl.add_row((source_props[0].xcentroid,source_props[0].ycentroid,
source_props[0].elongation,source_props[0].semimajor_axis_sigma,
source_props[0].semiminor_axis_sigma))
elong_med,elong_std = np.median(source_tbl['elongation']),np.std(source_tbl['elongation'])
hdulist.close()
return elong_med,elong_std,np.mean(total_fwhm),np.std(total_fwhm)
def __init__(self, timber_variable_bbq, beam=0):
if not (beam == 1 or beam == 2):
raise ValueError('You need to specify which beam! (1 or 2)')
if type(timber_variable_bbq) is dict:
dict_timber = timber_variable_bbq
self.beam = beam
self.amp_1 = np.squeeze(np.array(
dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_AMPL_1'.format(beam)][1]))
self.amp_2 = np.squeeze(np.array(
dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_AMPL_2'.format(beam)][1]))
self.xamp_1 = np.squeeze(np.array(
dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_X_AMPL_1'.format(beam)][1]))
self.xamp_2 = np.squeeze(np.array(
dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_X_AMPL_2'.format(beam)][1]))
self.qh = dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:TUNE_H'.format(beam)][1]
self.qv = dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:TUNE_V'.format(beam)][1]
self.q1 = dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_FREQ_1'.format(beam)][1]
self.q2 = dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_FREQ_2'.format(beam)][1]
self.t_stamps = np.ravel(np.squeeze(np.array(
dict_timber['LHC.BQBBQ.CONTINUOUS_HS.B{:d}:EIGEN_AMPL_1'.format(beam)][0])))
self.t_str=[datetime.datetime.fromtimestamp(self.t_stamps[ii]) for ii in np.arange(len(self.t_stamps))]
def __init__(self, complete_path):
if complete_path.endswith('.mat.gz'):
temp_filename = complete_path.split('.gz')[0]
with open(temp_filename, "wb") as tmp:
shutil.copyfileobj(gzip.open(complete_path), tmp)
dict_mr = sio.loadmat(temp_filename)
os.remove(temp_filename)
elif complete_path.endswith('.mat'):
dict_mr = sio.loadmat(complete_path)
else:
print('Unknown file extension for MountainRange file. Should be ' +
'.mat or .mat.gz')
self.value = dict_mr['value']
self.trigger_stamp = dict_mr['triggerStamp']
self.SC_numb = np.int(np.squeeze(dict_mr['superCycleNb']))
self.first_trigger_t_stamp_unix = dict_mr['first_trigger_t_stamp_unix']
self.sample_interval = float(np.squeeze(dict_mr['sampleInterval']))
self.first_sample_time = dict_mr['firstSampleTime']
self.sensitivity = dict_mr['sensitivity']
self.offset = dict_mr['offset']
self.SPSuser = dict_mr['SPSuser']
self.t_stamp_unix = dict_mr['t_stamp_unix']
self.time_axis = np.float_(range(self.value.shape[1]))*self.sample_interval-self.value.shape[1]*self.sample_interval/2.
def kalman(x, u, P, A, B, C, W, V, z=np.NaN):
"""
This function returns an optimal expected value of the state and covariance
error matrix given an update and system parameters.
x: Estimate of state at time t-1.
u: Input at time t-1.
P: Estimate of error covariance matrix at time t-1.
A: Discrete time state tranistion matrix at time t-1.
B: Input to state model matrix at time t-1.
C: Observation model matrix at time t.
W: Process noise covariance at time t-1.
V: Measurement noise covariance at time t.
z: Measurement at time t.
returns: (x,P) tuple
x: Updated estimate of state at time t.
P: Updated estimate of error covariance matrix at time t.
"""
x = np.atleast_2d(x)
u = np.atleast_2d(u)
P = np.atleast_2d(P)
A = np.atleast_2d(A)
B = np.atleast_2d(B)
x_p = np.dot(A, x) + np.dot(B, u) # Prediction of estimated state vector
P_p = np.dot(A, np.dot(P, A.T)) + W # Prediction of error covariance matrix
if np.any(np.isnan(z)):
return (x_p, P_p)
else:
C = np.atleast_2d(C)
W = np.atleast_2d(W)
V = np.atleast_2d(V)
z = np.atleast_2d(z)
[M, N] = np.shape(C)
if W.shape[0] == 1 or W.shape[1] == 1:
W = np.diag(np.squeeze(W))
if (V.shape[0] == 1 or V.shape[1] == 1) and not (V.shape[0] == 1 and V.shape[1] == 1):
V = np.diag(np.squeeze(V))
I = np.eye(N) # N x N identity matrix
S = np.dot(C, np.dot(P_p, C.T)) + V # Sum of error variances
S_inv = np.linalg.inv(S) # Inverse of sum of error variances
K = np.dot(P_p, np.dot(C.T, S_inv)) # Kalman gain
r = z - np.dot(C, x_p) # Prediction residual
w = np.dot(-K, r) # Process error
x = x_p - w # Update estimated state vector
# v = z - np.dot(C, x) # Measurement error
if np.any(np.isnan(np.dot(K, V))):
P = P_p
else:
# Updated error covariance matrix
P = np.dot((I - np.dot(K, C)), np.dot(P_p, (I - np.dot(K, C)).T)) + np.dot(K, np.dot(V, K.T))
return (x, P)
def gray2rgb(image):
"""Create an RGB representation of a gray-level image.
Parameters
----------
image : array_like
Input image of shape ``(M, N [, P])``.
Returns
-------
rgb : ndarray
RGB image of shape ``(M, N, [, P], 3)``.
Raises
------
ValueError
If the input is not a 2- or 3-dimensional image.
"""
if np.squeeze(image).ndim == 3 and image.shape[2] in (3, 4):
return image
elif image.ndim != 1 and np.squeeze(image).ndim in (1, 2, 3):
image = image[..., np.newaxis]
return np.concatenate(3 * (image,), axis=-1)
else:
raise ValueError("Input image expected to be RGB, RGBA or gray.")
def testTrainNetwork(self, distribution, optimizer_fn,
use_callable_loss=True):
with distribution.scope():
model_fn, dataset_fn, layer = minimize_loss_example(
optimizer_fn, use_bias=True, use_callable_loss=use_callable_loss)
iterator = distribution.make_input_fn_iterator(lambda _: dataset_fn())
def run_step():
return control_flow_ops.group(
distribution.experimental_local_results(
distribution.extended.call_for_each_replica(
model_fn, args=(iterator.get_next(),))))
if not context.executing_eagerly():
with self.cached_session() as sess:
sess.run(iterator.initialize())
run_step = sess.make_callable(run_step())
self.evaluate(variables.global_variables_initializer())
weights, biases = [], []
for _ in range(10):
run_step()
weights.append(self.evaluate(layer.kernel))
biases.append(self.evaluate(layer.bias))
error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1)
is_not_increasing = all(y <= x for x, y in zip(error, error[1:]))
self.assertTrue(is_not_increasing)
def action_value(self, obs):
# executes call() under the hood
logits, value = self.predict(obs)
action = self.dist.predict(logits)
# a simpler option, will become clear later why we don't use it
# action = tf.random.categorical(logits, 1)
return np.squeeze(action, axis=-1), np.squeeze(value, axis=-1)
def plsurf(z,x=None,y=None,win=None,shade=0,edges=1,edge_color='fg',phi=-45.0,
theta=30.0,zscale=1.0,palette=None,gnomon=0,animate=False,limits=True, ireg=None):
'''Plot a 3-D wire-frame surface z=f(x,y)
'''
if win is None:
pass
#pl3d.window3()
else:
pl3d.window3(win)
pl3d.set_draw3_(0)
phi0 = phi*numpy.pi/180.0
theta0 = theta*numpy.pi/180.0
pl3d.orient3(phi=phi0,theta=theta0)
pl3d.light3()
_change_palette(palette)
sz = numpy.shape(z)
if len(sz) != 2:
raise ValueError('Input must be a 2-d array --- a surface.')
N,M = sz
if x is None:
x = numpy.arange(0,N)
if y is None:
y = numpy.arange(0,M)
x = numpy.squeeze(x)
y = numpy.squeeze(y)
if (len(numpy.shape(x)) == 1):
x = x[:,newaxis]*numpy.ones((1,M))
if (len(numpy.shape(y)) == 1):
y = numpy.ones((N,1))*y[newaxis,:]
plwf.plwf(z,y,x,shade=shade,edges=edges,ecolor=edge_color,scale=zscale, ireg=ireg)
# if animate, the application is responsible to fma
lims = pl3d.draw3(not animate)
if limits:
gist.limits(lims[0],lims[1],lims[2],lims[3])
pl3d.gnomon(gnomon)
def plot_animation_each_neuron(name_s, save_name, print_loss=False):
"""Plot the movie for all the networks in the information plane"""
# If we want to print the loss function also
#The bins that we extened the x axis of the accuracy each time
epochs_bins = [0, 500, 1500, 3000, 6000, 10000, 20000]
data_array = utils.get_data(name_s[0][0])
data = np.squeeze(data_array['information'])
f, (axes) = plt.subplots(1, 1)
axes = [axes]
f.subplots_adjust(left=0.14, bottom=0.1, right=.928, top=0.94, wspace=0.13, hspace=0.55)
colors = LAYERS_COLORS
#new/old version
Ix = np.squeeze(data[0,:, :, :])
Iy = np.squeeze(data[1,:, :, :])
#Interploation of the samplings (because we don't cauclaute the infomration in each epoch)
#interp_data_x = interp1d(epochsInds, Ix, axis=1)
#interp_data_y = interp1d(epochsInds, Iy, axis=1)
#new_x = np.arange(0,epochsInds[-1])
#new_data = np.array([interp_data_x(new_x), interp_data_y(new_x)])
""""
train_data = interp1d(epochsInds, np.squeeze(train_data), axis=1)(new_x)
test_data = interp1d(epochsInds, np.squeeze(test_data), axis=1)(new_x)
if print_loss:
loss_train_data = interp1d(epochsInds, np.squeeze(loss_train_data), axis=1)(new_x)
loss_test_data=interp1d(epochsInds, np.squeeze(loss_test_data), axis=1)(new_x)
"""
line_ani = animation.FuncAnimation(f, update_line_each_neuron, Ix.shape[1], repeat=False,
interval=1, blit=False, fargs=(print_loss, Ix, axes,Iy,train_data,test_data,epochs_bins, loss_train_data,loss_test_data, colors,epochsInds))
Writer = animation.writers['ffmpeg']
writer = Writer(fps=100)
#Save the movie
line_ani.save(save_name+'_movie.mp4',writer=writer,dpi=250)
plt.show()
def normalize_weight_shape(w: np.ndarray, n_samples: int,
n_tasks: int) -> np.ndarray:
"""A utility function to correct the shape of the weight array.
This utility function is used to normalize the shapes of a given
weight array.
Parameters
----------
w: np.ndarray
`w` can be `None` or a scalar or a `np.ndarray` of shape
`(n_samples,)` or of shape `(n_samples, n_tasks)`. If `w` is a
scalar, it's assumed to be the same weight for all samples/tasks.
n_samples: int
The number of samples in the dataset. If `w` is not None, we should
have `n_samples = w.shape[0]` if `w` is a ndarray
n_tasks: int
The number of tasks. If `w` is 2d ndarray, then we should have
`w.shape[1] == n_tasks`.
Examples
--------
>>> import numpy as np
>>> w_out = normalize_weight_shape(None, n_samples=10, n_tasks=1)
>>> (w_out == np.ones((10, 1))).all()
True
Returns
-------
w_out: np.ndarray
Array of shape `(n_samples, n_tasks)`
"""
if w is None:
w_out = np.ones((n_samples, n_tasks))
elif isinstance(w, np.ndarray):
if len(w.shape) == 0:
# scalar case
w_out = w * np.ones((n_samples, n_tasks))
elif len(w.shape) == 1:
if len(w) != n_samples:
raise ValueError("Length of w isn't n_samples")
# per-example case
# This is a little arcane but it repeats w across tasks.
w_out = np.tile(w, (n_tasks, 1)).T
elif len(w.shape) == 2:
if w.shape == (n_samples, 1):
# If w.shape == (n_samples, 1) handle it as 1D
w = np.squeeze(w, axis=1)
w_out = np.tile(w, (n_tasks, 1)).T
elif w.shape != (n_samples, n_tasks):
raise ValueError(
"Shape for w doens't match (n_samples, n_tasks)")
else:
# w.shape == (n_samples, n_tasks)
w_out = w
else:
raise ValueError("w must be of dimension 1, 2, or 3")
else:
# scalar case
w_out = w * np.ones((n_samples, n_tasks))
return w_out
# load packages
import numpy as np
import scipy.stats as st
from matplotlib import pyplot as plt
import matplotlib as mpl
mpl.rc('font', size=16,
weight='bold') #set default font size and weight for plots
# We will load in the NINO3.4 ENSO index from Jan. 1958 - Dec. 2019.
# In[2]:
# load data
filename = 'NINO34_monthly_Jan1958_Dec2019.csv'
ENSO = np.squeeze(np.genfromtxt(filename, delimiter=','))
# let's also reshape ENSO into one long time series
Ny, Nm = ENSO.shape
ENSO = np.reshape(ENSO, Ny * Nm)
# What does this time series look like? Can we see different time scales of variability in the data?
# In[3]:
# plot ENSO time series
plt.figure(figsize=(12, 5))
plt.plot(ENSO)
plt.title('ENSO Time Series (1958-2019)')
plt.xlabel('Months')
plt.xlim(0, len(ENSO))
skiprows=1)
if True:
from lib.pair_matching import RT_transform
print("trans: {}".format(pose_src[:, -1]))
print("euler: {}".format(RT_transform.mat2euler(pose_src[:, :3])))
pose_tgt = np.loadtxt(
"/home/yili/PoseEst/render_pangolin/synthesize/train/002_master_chef_can/{}_pose.txt"
.format(idx2),
skiprows=1)
K = np.array([[1066.778, 0, 312.9869], [0, 1067.487, 241.3109], [0, 0, 1]])
t = time()
flow, visible = calc_flow(depth_src, pose_src, pose_tgt, K, depth_tgt)
print(time() - t)
a = np.where(np.squeeze(visible[:, :]))
print(a[0][:20])
print(a[1][:20])
import matplotlib.pyplot as plt
fig = plt.figure()
plt.axis("off")
fig.add_subplot(2, 4, 1)
plt.imshow(im_src)
fig.add_subplot(2, 4, 2)
plt.imshow(im_tgt)
fig.add_subplot(2, 4, 3)
plt.imshow(depth_src)
fig.add_subplot(2, 4, 4)
plt.imshow(depth_tgt)
st.title('Neural Network Visualizer')
st.sidebar.markdown('# Input Image')
if st.button('Get random predictions'):
response = requests.post(URL, data={})
# print(response.text)
response = json.loads(response.text)
preds = response.get('prediction')
image = response.get('image')
image = np.reshape(image, (28, 28))
st.sidebar.image(image, width=150)
for layer, p in enumerate(preds):
numbers = np.squeeze(np.array(p))
plt.figure(figsize=(32, 4))
if layer == 2:
row = 1
col = 10
else:
row = 2
col = 16
for i, number in enumerate(numbers):
plt.subplot(row, col, i + 1)
plt.imshow((number * np.ones((8, 8, 3))).astype('float32'), cmap='binary')
plt.xticks([])
plt.yticks([])
chkeps=1
#define periodic dof by single cell
dof=np.zeros(ny*nx*numlayers)
for iy in range(ny):
for ix in range(nx):
for il in range(numlayers):
i=il + numlayers*ix + numlayers*nx*iy
if (ix-nx/2)**2 + (iy-ny/2)**2 <= 1000**2:
#dof[i]=round(np.random.rand())
#dof[i]=np.random.rand()
dof[i]=0.5
fid=hp.File('dof_singlecell.h5','w')
tmp=dof.reshape((ny,nx,numlayers))
for il in range(numlayers):
fid.create_dataset('layer'+str(il),data=np.squeeze(tmp[:,:,il]))
fid.close()
tmp=dof
dof=np.array([])
for icy in range(numcells_y):
for icx in range(numcells_x):
if dof.size==0:
dof=tmp
else:
dof=np.concatenate((dof,tmp))
np.savetxt(init_filename,dof)
#############################################################################################################
iz=[0,mpmlz[0]+pml2src+src2stk]
for i in range(numlayers-1):
## ======= Evaluate ======= ##
## ========================= ##
if evaluate:
raw_dataset = load_nodule_raw_dataset(size=size,
res=res,
sample=sample)[DataSubSet]
failed_same, failed_diff = [], []
K = 12
p = np.zeros(K)
r = np.zeros(K)
for t in range(K):
p[t] = precision(labels_test, np.squeeze(pred), 0.1 * t)
r[t] = recall(labels_test, np.squeeze(pred), 0.1 * t)
plt.figure()
plt.title('PR Curve')
plt.plot(r, p)
plt.ylabel('Precision')
plt.xlabel('Recall')
plt.axis([0, 1, 0, 1])
diff_len = np.count_nonzero(labels_test)
same_len = len(labels_test) - diff_len
d, s = [], []
for l, p, m1, m2 in zip(labels_test, pred, meta[0],
meta[1]): # similarity labels
if l:
if __name__ == "__main__":
start = time.time()
# 定义感知机
# n_iter_no_change表示迭代次数，eta0表示学习率，shuffle表示是否打乱数据集
clf = Perceptron(n_iter_no_change=30, eta0=0.0001, shuffle=False)
# 使用训练数据进行训练
train_data_matrix, train_label_matrix = loadData(
"../MnistData/mnist_train.csv")
test_data_matrix, test_label_matrix = loadData(
"../MnistData/mnist_test.csv")
print(train_data_matrix.shape)
print(test_data_matrix.shape)
train_label_matrix = np.squeeze(train_label_matrix)
test_label_matrix = np.squeeze(test_label_matrix)
print(train_label_matrix.shape)
print(test_label_matrix.shape)
# 训练模型
clf.fit(train_data_matrix, train_label_matrix)
# 利用测试集进行验证，得到模型在测试集上的准确率
accuracy = clf.score(test_data_matrix, test_label_matrix)
end = time.time()
print(f"accuracy is {accuracy}.")
print(f"the total time is {end - start}.")
def normalize_labels_shape(y: np.ndarray,
mode: Optional[str] = None,
n_tasks: Optional[int] = None,
n_classes: Optional[int] = None) -> np.ndarray:
"""A utility function to correct the shape of the labels.
Parameters
----------
y: np.ndarray
`y` is an array of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)`.
mode: str, default None
If `mode` is "classification" or "regression", attempts to apply
data transformations.
n_tasks: int, default None
The number of tasks this class is expected to handle.
n_classes: int, default None
If specified use this as the number of classes. Else will try to
impute it as `n_classes = max(y) + 1` for arrays and as
`n_classes=2` for the case of scalars. Note this parameter only
has value if `mode=="classification"`
Returns
-------
y_out: np.ndarray
If `mode=="classification"`, `y_out` is an array of shape `(N,
n_tasks, n_classes)`. If `mode=="regression"`, `y_out` is an array
of shape `(N, n_tasks)`.
"""
if n_tasks is None:
raise ValueError("n_tasks must be specified")
if mode not in ["classification", "regression"]:
raise ValueError("mode must be either classification or regression.")
if mode == "classification" and n_classes is None:
raise ValueError("n_classes must be specified")
if not isinstance(y, np.ndarray):
raise ValueError("y must be a np.ndarray")
# Handle n_classes/n_task shape ambiguity
if mode == "classification" and len(y.shape) == 2:
if n_classes == y.shape[1] and n_tasks != 1 and n_classes != n_tasks:
raise ValueError("Shape of input doesn't match expected n_tasks=1")
elif n_classes == y.shape[1] and n_tasks == 1:
# Add in task dimension
y = np.expand_dims(y, 1)
if len(y.shape) == 1 and n_tasks != 1:
raise ValueError("n_tasks must equal 1 for a 1D set of labels.")
if (len(y.shape) == 2 or len(y.shape) == 3) and n_tasks != y.shape[1]:
raise ValueError("Shape of input doesn't match expected n_tasks=%d" %
n_tasks)
if len(y.shape) >= 4:
raise ValueError(
"Labels y must be a float scalar or a ndarray of shape `(N,)` or "
"`(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems and "
"of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, 1)` for classification problems"
)
if len(y.shape) == 1:
# Insert a task dimension (we know n_tasks=1 from above0
y_out = np.expand_dims(y, 1)
elif len(y.shape) == 2:
y_out = y
elif len(y.shape) == 3:
# If 3D and last dimension isn't 1, assume this is one-hot encoded and return as-is.
if y.shape[-1] != 1:
return y
y_out = np.squeeze(y, axis=-1)
# Handle classification. We need to convert labels into one-hot representation.
if mode == "classification":
all_y_task = []
for task in range(n_tasks):
y_task = y_out[:, task]
# check whether n_classes is int or not
assert isinstance(n_classes, int)
y_hot = to_one_hot(y_task, n_classes=n_classes)
y_hot = np.expand_dims(y_hot, 1)
all_y_task.append(y_hot)
y_out = np.concatenate(all_y_task, axis=1)
return y_out
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1)
# Loading the data (signs)
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Example of a picture
index = 6
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
X_train = X_train_orig/255.
X_test = X_test_orig/255.
Y_train = convert_to_one_hot(Y_train_orig, 6).T
Y_test = convert_to_one_hot(Y_test_orig, 6).T
print ("number of training examples = " + str(X_train.shape[0]))
print ("number of test examples = " + str(X_test.shape[0]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))
conv_layers = {}
# GRADED FUNCTION: create_placeholders
def normalize_prediction_shape(y: np.ndarray,
mode: Optional[str] = None,
n_tasks: Optional[int] = None,
n_classes: Optional[int] = None):
"""A utility function to correct the shape of provided predictions.
The metric computation classes expect that inputs for classification
have the uniform shape `(N, n_tasks, n_classes)` and inputs for
regression have the uniform shape `(N, n_tasks)`. This function
normalizes the provided input array to have the desired shape.
Examples
--------
>>> import numpy as np
>>> y = np.random.rand(10)
>>> y_out = normalize_prediction_shape(y, "regression", n_tasks=1)
>>> y_out.shape
(10, 1)
Parameters
----------
y: np.ndarray
If `mode=="classification"`, `y` is an array of shape `(N,)` or
`(N, n_tasks)` or `(N, n_tasks, n_classes)`. If
`mode=="regression"`, `y` is an array of shape `(N,)` or `(N,
n_tasks)`or `(N, n_tasks, 1)`.
mode: str, default None
If `mode` is "classification" or "regression", attempts to apply
data transformations.
n_tasks: int, default None
The number of tasks this class is expected to handle.
n_classes: int, default None
If specified use this as the number of classes. Else will try to
impute it as `n_classes = max(y) + 1` for arrays and as
`n_classes=2` for the case of scalars. Note this parameter only
has value if `mode=="classification"`
Returns
-------
y_out: np.ndarray
If `mode=="classification"`, `y_out` is an array of shape `(N,
n_tasks, n_classes)`. If `mode=="regression"`, `y_out` is an array
of shape `(N, n_tasks)`.
"""
if n_tasks is None:
raise ValueError("n_tasks must be specified")
if mode == "classification" and n_classes is None:
raise ValueError("n_classes must be specified")
if not isinstance(y, np.ndarray):
raise ValueError("y must be a np.ndarray")
# Handle n_classes/n_task shape ambiguity
if mode == "classification" and len(y.shape) == 2:
if n_classes == y.shape[1] and n_tasks != 1 and n_classes != n_tasks:
raise ValueError("Shape of input doesn't match expected n_tasks=1")
elif n_classes == y.shape[1] and n_tasks == 1:
# Add in task dimension
y = np.expand_dims(y, 1)
if (len(y.shape) == 2 or len(y.shape) == 3) and n_tasks != y.shape[1]:
raise ValueError("Shape of input doesn't match expected n_tasks=%d" %
n_tasks)
if len(y.shape) >= 4:
raise ValueError(
"Predictions y must be a float scalar or a ndarray of shape `(N,)` or "
"`(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems and "
"of shape `(N,)` or `(N, n_tasks)` or `(N, n_tasks, n_classes)` for classification problems"
)
if mode == "classification":
if n_classes is None:
raise ValueError("n_classes must be specified.")
if len(y.shape) == 1 or len(y.shape) == 2:
# Make everything 2D so easy to handle
if len(y.shape) == 1:
y = y[:, np.newaxis]
# Handle each task separately.
all_y_task = []
for task in range(n_tasks):
y_task = y[:, task]
if len(np.unique(y_task)) > n_classes:
# Handle continuous class probabilites of positive class for binary
if n_classes > 2:
raise ValueError(
"Cannot handle continuous probabilities for multiclass problems."
"Need a per-class probability")
# Fill in class 0 probabilities
y_task = np.array([1 - y_task, y_task]).T
# Add a task dimension to concatenate on
y_task = np.expand_dims(y_task, 1)
all_y_task.append(y_task)
else:
# Handle binary labels
# make y_hot of shape (N, n_classes)
y_task = to_one_hot(y_task, n_classes=n_classes)
# Add a task dimension to concatenate on
y_task = np.expand_dims(y_task, 1)
all_y_task.append(y_task)
y_out = np.concatenate(all_y_task, axis=1)
elif len(y.shape) == 3:
y_out = y
elif mode == "regression":
if len(y.shape) == 1:
# Insert a task dimension
y_out = np.expand_dims(y, 1)
elif len(y.shape) == 2:
y_out = y
elif len(y.shape) == 3:
if y.shape[-1] != 1:
raise ValueError(
"y must be a float scalar or a ndarray of shape `(N,)` or "
"`(N, n_tasks)` or `(N, n_tasks, 1)` for regression problems."
)
y_out = np.squeeze(y, axis=-1)
else:
raise ValueError("mode must be either classification or regression.")
return y_out
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs["labels"].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif args.output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
print(result)
accuracy_matrix[train_task_num][current_task_num] = format(result['acc'],".2f")
#for key in sorted(result.keys()):
# logger.info(" %s = %s", key, str(result[key]))
# writer.write("%s = %s\n" % (key, str(result[key])))
return results, accuracy_matrix
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,
num_epochs = 100, minibatch_size = 64, print_cost = True):
"""
Implements a three-layer ConvNet in Tensorflow:
CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED
Arguments:
X_train -- training set, of shape (None, 64, 64, 3)
Y_train -- test set, of shape (None, n_y = 6)
X_test -- training set, of shape (None, 64, 64, 3)
Y_test -- test set, of shape (None, n_y = 6)
learning_rate -- learning rate of the optimization
num_epochs -- number of epochs of the optimization loop
minibatch_size -- size of a minibatch
print_cost -- True to print the cost every 100 epochs
Returns:
train_accuracy -- real number, accuracy on the train set (X_train)
test_accuracy -- real number, testing accuracy on the test set (X_test)
parameters -- parameters learnt by the model. They can then be used to predict.
"""
ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables
tf.set_random_seed(1) # to keep results consistent (tensorflow seed)
seed = 3 # to keep results consistent (numpy seed)
(m, n_H0, n_W0, n_C0) = X_train.shape
n_y = Y_train.shape[1]
costs = [] # To keep track of the cost
# Create Placeholders of the correct shape
### START CODE HERE ### (1 line)
X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)
### END CODE HERE ###
# Initialize parameters
### START CODE HERE ### (1 line)
parameters = initialize_parameters()
### END CODE HERE ###
# Forward propagation: Build the forward propagation in the tensorflow graph
### START CODE HERE ### (1 line)
Z3 = forward_propagation(X, parameters)
### END CODE HERE ###
# Cost function: Add cost function to tensorflow graph
### START CODE HERE ### (1 line)
cost = compute_cost(Z3, Y)
### END CODE HERE ###
# Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.
### START CODE HERE ### (1 line)
optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
### END CODE HERE ###
# Initialize all the variables globally
init = tf.global_variables_initializer()
# Start the session to compute the tensorflow graph
with tf.Session() as sess:
# Run the initialization
sess.run(init)
# Do the training loop
for epoch in range(num_epochs):
minibatch_cost = 0.
num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
seed = seed + 1
minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)
for minibatch in minibatches:
# Select a minibatch
(minibatch_X, minibatch_Y) = minibatch
# IMPORTANT: The line that runs the graph on a minibatch.
# Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).
### START CODE HERE ### (1 line)
_ , temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})
### END CODE HERE ###
minibatch_cost += temp_cost / num_minibatches
# Print the cost every epoch
if print_cost == True and epoch % 5 == 0:
print ("Cost after epoch %i: %f" % (epoch, minibatch_cost))
if print_cost == True and epoch % 1 == 0:
costs.append(minibatch_cost)
# plot the cost
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations (per tens)')
plt.title("Learning rate =" + str(learning_rate))
plt.show()
# Calculate the correct predictions
predict_op = tf.argmax(Z3, 1)
correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))
# Calculate accuracy on the test set
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(accuracy)
train_accuracy = accuracy.eval({X: X_train, Y: Y_train})
test_accuracy = accuracy.eval({X: X_test, Y: Y_test})
print("Train Accuracy:", train_accuracy)
print("Test Accuracy:", test_accuracy)
return train_accuracy, test_accuracy, parameters
def __init__(self, model, params, est_llr, llf_el):
self.model = model
self.params = np.squeeze(params)
self.llr = est_llr
self.llf_el = llf_el
def main(meshfile,file,iexpt=10,iversn=22,yrflag=3,bio_path=None) :
#
# Trim input netcdf file name being appropriate for reading
#
meshfile=str(meshfile)[2:-2]
logger.info("Reading mesh information from %s."%(meshfile))
#
# Read mesh file containing grid and coordinate information.
# Note that for now, we are using T-grid in vertical which may need
# to be improved by utilizing W-point along the vertical axis.
#
hdept,gdept,mbathy,mbathy_u,mbathy_v,mask,e3t,plon,plat=read_grid(meshfile)
logger.warning("Reading grid information from regional.grid.[ab] (not completed)")
#
# Convert from P-point (i.e. NEMO grid) to U and V HYCOM grids
#
mask_u=p2u_2d(mask)
mask_v=p2v_2d(mask)
#
# Read regional.grid.[ab]
# Grid angle is not used for this product because all quantities are
# on regular rectangular grid points.
#
angle=numpy.zeros(plon.shape)
#
# Number vertical layers in T-point.
#
nlev=gdept.size
#
# layer thickness in the absence of layer partial steps.
#
dt = gdept[1:] - gdept[:-1]
#
# Prepare/read input data file (in netcdf format). Reference time is 1950-01-01
#
logger.info("Reading data files.")
file=str(file).strip()[2:-2]
dirname=os.path.dirname(file)
logger.debug("file name is {}".format(file))
logger.debug("dirname is {}".format(dirname))
logger.debug("basename is {}".format(os.path.basename(file)))
m=re.match("(MERCATOR-PHY-24-)(.*\.nc)",os.path.basename(file))
logger.debug("file prefix is {}".format(file_pre))
### m=re.match(file_pre,os.path.basename(file))
if not m:
msg="File %s is not a grid2D file, aborting"%file
logger.error(msg)
raise ValueError(msg)
#fileinput0=os.path.join(dirname+"/"+"MERCATOR-PHY-24-"+m.group(2))
file_date=file[-16:-6]
fileinput0=file
print((file_date,file))
next_day=datetime.datetime.strptime(file_date, '%Y-%m-%d')+datetime.timedelta(days=1)
fileinput1=datetime.datetime.strftime(next_day,'%Y%m%d')
fileinput1=os.path.join(dirname+"/"+file_pre+fileinput1+'.nc')
logger.info("Reading from %s"%(fileinput0))
ncid0=netCDF4.Dataset(fileinput0,"r")
if timeavg_method==1 and os.path.isfile(fileinput1) :
logger.info("timeavg_method=1, Reading from %s"%(fileinput1))
ncid1=netCDF4.Dataset(fileinput1,"r")
#
# Calculate temporal averaged temperature, salinity, and velocity
#
uo = 0.5*(ncid0.variables["uo"][0,:,:,:]+ ncid1.variables["uo"][0,:,:,:])
vo = 0.5*(ncid0.variables["vo"][0,:,:,:]+ ncid1.variables["vo"][0,:,:,:])
salt = 0.5*(ncid0.variables["so"][0,:,:,:]+ ncid1.variables["so"][0,:,:,:])
temp = 0.5*(ncid0.variables["thetao"][0,:,:,:]+ncid1.variables["thetao"][0,:,:,:])
ssh = numpy.squeeze(0.5*(ncid0.variables["zos"][0,:,:]+ncid1.variables["zos"][0,:,:]))
else:
#
# Set variables based on current file when timeavg_method ~=1 or the next netcdf file is not available
logger.debug("time average method set to {}".format(timeavg_method))
uo = ncid0.variables["uo"][0,:,:,:]
vo = ncid0.variables["vo"][0,:,:,:]
salt = ncid0.variables["so"][0,:,:,:]
temp = ncid0.variables["thetao"][0,:,:,:]
ssh = numpy.squeeze(ncid0.variables["zos"][0,:,:])
#
# I will account these values afterward. Because in the current version, I am accounting for missing values using a gap-filling methodology.
#
logger.debug("getting _FillValue")
uofill=ncid0.variables["uo"]._FillValue
vofill=ncid0.variables["vo"]._FillValue
slfill=ncid0.variables["so"]._FillValue
tlfill=ncid0.variables["thetao"]._FillValue
shfill=ncid0.variables["zos"]._FillValue
# Set time
logger.info("Set time.")
time=ncid0.variables["time"][0]
unit=ncid0.variables["time"].units
tmp=cfunits.Units(unit)
refy,refm,refd=(1950,1,1)
tmp2=cfunits.Units("hours since %d-%d-%d 00:00:00"%(refy,refm,refd))
tmp3=int(numpy.round(cfunits.Units.conform(time,tmp,tmp2)))
mydt = datetime.datetime(refy,refm,refd,0,0,0) + datetime.timedelta(hours=tmp3) # Then calculate dt. Phew!
if timeavg_method==1 and os.path.isfile(fileinput1) :
fnametemplate="archv.%Y_%j_%H"
deltat=datetime.datetime(refy,refm,refd,0,0,0) + \
datetime.timedelta(hours=tmp3) + \
datetime.timedelta(hours=12)
oname=deltat.strftime(fnametemplate)
else:
#
# I am assuming that daily mean can be set at 00 instead of 12
# for cases that there is no information of next day.
#
fnametemplate="archv.%Y_%j"
deltat=datetime.datetime(refy,refm,refd,0,0,0) + \
datetime.timedelta(hours=tmp3)
oname=deltat.strftime(fnametemplate) + '_00'
# model day
refy, refm, refd=(1900,12,31)
model_day= deltat-datetime.datetime(refy,refm,refd,0,0,0)
model_day=model_day.days
logger.info("Model day in HYCOM is %s"%str(model_day))
if bio_path:
jdm,idm=numpy.shape(plon)
points = numpy.transpose(((plat.flatten(),plon.flatten())))
delta = mydt.strftime( '%Y-%m-%d')
# filename format MERCATOR-BIO-14-2013-01-05-00
print((bio_path,delta))
idx,biofname=search_biofile(bio_path,delta)
if idx >7:
msg="No available BIO file within a week difference with PHY"
logger.error(msg)
raise ValueError(msg)
logger.info("BIO file %s reading & interpolating to 1/12 deg grid cells ..."%biofname)
ncidb=netCDF4.Dataset(biofname,"r")
blon=ncidb.variables["longitude"][:];
blat=ncidb.variables["latitude"][:]
minblat=blat.min()
no3=ncidb.variables["NO3"][0,:,:,:];
no3[numpy.abs(no3)>1e+10]=numpy.nan
po4=ncidb.variables["PO4"][0,:,:,:]
si=ncidb.variables["Si"][0,:,:,:]
po4[numpy.abs(po4)>1e+10]=numpy.nan
si[numpy.abs(si)>1e+10]=numpy.nan
# TODO: Ineed to improve this part
nz,ny,nx=no3.shape
dummy=numpy.zeros((nz,ny,nx+1))
dummy[:,:,:nx]=no3;dummy[:,:,-1]=no3[:,:,-1]
no3=dummy
dummy=numpy.zeros((nz,ny,nx+1))
dummy[:,:,:nx]=po4;dummy[:,:,-1]=po4[:,:,-1]
po4=dummy
dummy=numpy.zeros((nz,ny,nx+1))
dummy[:,:,:nx]=si;dummy[:,:,-1]=si[:,:,-1]
si=dummy
dummy=numpy.zeros((nx+1))
dummy[:nx]=blon
blon=dummy
blon[-1]=-blon[0]
# TODO: Note that the coordinate files are for global configuration while
# the data file saved for latitude larger than 30. In the case you change your data file coordinate
# configuration you need to modify the following lines
bio_coordfile=bio_path[:-4]+"/GLOBAL_ANALYSIS_FORECAST_BIO_001_014_COORD/GLO-MFC_001_014_mask.nc"
biocrd=netCDF4.Dataset(bio_coordfile,"r")
blat2 = biocrd.variables['latitude'][:]
index=numpy.where(blat2>=minblat)[0]
depth_lev = biocrd.variables['deptho_lev'][index[0]:,:]
#
#
#
dummy=numpy.zeros((ny,nx+1))
dummy[:,:nx]=depth_lev;dummy[:,-1]=depth_lev[:,-1]
depth_lev=dummy
depth_lev[depth_lev>50]=0
depth_lev=depth_lev.astype('i')
dummy_no3=no3
dummy_po4=po4
dummy_si=si
for j in range(ny):
for i in range(nx):
dummy_no3[depth_lev[j,i]:nz-2,j,i]=no3[depth_lev[j,i]-1,j,i]
dummy_po4[depth_lev[j,i]:nz-2,j,i]=po4[depth_lev[j,i]-1,j,i]
dummy_si[depth_lev[j,i]:nz-2,j,i]=si[depth_lev[j,i]-1,j,i]
no3=dummy_no3
po4=dummy_po4
si=dummy_si
#
po4 = po4 * 106.0 * 12.01
si = si * 6.625 * 12.01
no3 = no3 * 6.625 * 12.01
logger.info("Read, trim, rotate NEMO velocities.")
u=numpy.zeros((nlev,mbathy.shape[0],mbathy.shape[1]))
v=numpy.zeros((nlev,mbathy.shape[0],mbathy.shape[1]))
utmp=numpy.zeros((mbathy.shape))
vtmp=numpy.zeros((mbathy.shape))
#
# Metrices to detect carrefully bottom at p-, u-, and v-grid points.While I have used 3D, mask data,following methods are good enough for now.
#
if mbathy_method == 1 :
ip = mbathy == -1
iu = mbathy_u == -1
iv = mbathy_v == -1
else:
ip = mask == 0
iu = mask_u == 0
iv = mask_v == 0
#
# Read 3D velocity field to calculate barotropic velocity
#
# Estimate barotropic velocities using partial steps along the vertical axis. Note that for the early version of this code,
# I used dt = gdept[1:] - gdept[:-1] on NEMO t-grid. Furthermore, you may re-calculate this part on vertical grid cells for future.
#
logger.info("Calculate barotropic velocities.")
ubaro,vbaro=calc_uvbaro(uo,vo,e3t,iu,iv)
#
# Save 2D fields (here only ubaro & vbaro)
#
zeros=numpy.zeros(mbathy.shape)
#flnm = open(oname+'.txt', 'w')
#flnm.write(oname)
#flnm.close()
ssh = numpy.where(numpy.abs(ssh)>1000,0.,ssh*9.81) # NB: HYCOM srfhgt is in geopotential ...
#
outfile = abf.ABFileArchv("./data/"+oname,"w",iexpt=iexpt,iversn=iversn,yrflag=yrflag,)
outfile.write_field(zeros, ip,"montg1" ,0,model_day,1,0)
outfile.write_field(ssh, ip,"srfhgt" ,0,model_day,0,0)
outfile.write_field(zeros, ip,"surflx" ,0,model_day,0,0) # Not used
outfile.write_field(zeros, ip,"salflx" ,0,model_day,0,0) # Not used
outfile.write_field(zeros, ip,"bl_dpth" ,0,model_day,0,0) # Not used
outfile.write_field(zeros, ip,"mix_dpth",0,model_day,0,0) # Not used
outfile.write_field(ubaro, iu,"u_btrop" ,0,model_day,0,0)
outfile.write_field(vbaro, iv,"v_btrop" ,0,model_day,0,0)
#
if bio_path:
logger.info("Calculate baroclinic velocities, temperature, and salinity data as well as BIO field.")
else:
logger.info("Calculate baroclinic velocities, temperature, and salinity data.")
for k in numpy.arange(u.shape[0]) :
if bio_path:
no3k=interpolate2d(blat, blon, no3[k,:,:], points).reshape((jdm,idm))
no3k = maplev(no3k)
po4k=interpolate2d(blat, blon, po4[k,:,:], points).reshape((jdm,idm))
po4k = maplev(po4k)
si_k=interpolate2d(blat, blon, si[k,:,:], points).reshape((jdm,idm))
si_k = maplev(si_k)
if k%10==0 : logger.info("Writing 3D variables including BIO, level %d of %d"%(k+1,u.shape[0]))
else:
if k%10==0 : logger.info("Writing 3D variables, level %d of %d"%(k+1,u.shape[0]))
#
#
uo[k,:,:]=numpy.where(numpy.abs(uo[k,:,:])<10,uo[k,:,:],0)
vo[k,:,:]=numpy.where(numpy.abs(vo[k,:,:])<10,vo[k,:,:],0)
# Baroclinic velocity (in HYCOM U- and V-grid)
ul = p2u_2d(numpy.squeeze(uo[k,:,:])) - ubaro
vl = p2v_2d(numpy.squeeze(vo[k,:,:])) - vbaro
ul[iu]=spval
vl[iv]=spval
# Layer thickness
dtl=numpy.zeros(mbathy.shape)
# Use dt for the water column except the nearest cell to bottom
if thickness_method==1:
if k < u.shape[0]-1 :
J,I = numpy.where(mbathy>k)
e3=(e3t[k,:,:])
dtl[J,I]=dt[k]
J,I = numpy.where(mbathy==k)
dtl[J,I]=e3[J,I]
else:
e3=(e3t[k,:,:])
J,I = numpy.where(mbathy==k)
dtl[J,I]=e3[J,I]
# Use partial cells for the whole water column.
else :
J,I = numpy.where(mbathy>=k)
dtl[J,I]=e3t[k,J,I]
# Salinity
sl = salt[k,:,:]
# Temperature
tl = temp[k,:,:]
# Need to be carefully treated in order to minimize artifacts to the resulting [ab] files.
if fillgap_method==1:
J,I= numpy.where(mbathy<k)
sl = maplev(numpy.where(numpy.abs(sl)<1e2,sl,numpy.nan))
sl[J,I]=spval
J,I= numpy.where(mbathy<k)
tl = maplev(numpy.where(numpy.abs(tl)<1e2,tl,numpy.nan))
tl[J,I]=spval
else:
sl = numpy.where(numpy.abs(sl)<1e2,sl,numpy.nan)
sl = numpy.minimum(numpy.maximum(maplev(sl),25),80.)
tl = numpy.where(numpy.abs(tl)<=5e2,tl,numpy.nan)
tl = numpy.minimum(numpy.maximum(maplev(tl),-5.),50.)
# Thickness
dtl = maplev(dtl)
if k > 0 :
with numpy.errstate(invalid='ignore'):
K= numpy.where(dtl < 1e-4)
sl[K] = sl_above[K]
tl[K] = tl_above[K]
#
sl[ip]=spval
tl[ip]=spval
# Save 3D fields
outfile.write_field(ul ,iu,"u-vel.",0,model_day,k+1,0)
outfile.write_field(vl ,iv,"v-vel.",0,model_day,k+1,0)
outfile.write_field(dtl*onem,ip,"thknss",0,model_day,k+1,0)
outfile.write_field(tl ,ip,"temp" , 0,model_day,k+1,0)
outfile.write_field(sl ,ip,"salin" ,0,model_day,k+1,0)
if bio_path :
outfile.write_field(no3k ,ip,"ECO_no3" ,0,model_day,k+1,0)
outfile.write_field(po4k ,ip,"ECO_pho" ,0,model_day,k+1,0)
outfile.write_field(si_k ,ip,"ECO_sil" ,0,model_day,k+1,0)
tl_above=numpy.copy(tl)
sl_above=numpy.copy(sl)
outfile.close()
ncid0.close()
if timeavg_method==1 and os.path.isfile(fileinput1) :
ncid1.close()
if bio_path :
ncidb.close()
# define species
plasma.b_field = ConstantVector3D(Vector3D(1.0, 1.0, 1.0))
plasma.electron_distribution = e_distribution
plasma.composition = [d0_species, d1_species]
# Add a balmer alpha line for visualisation purposes
d_alpha_excit = ExcitationLine(Line(deuterium, 0, (3, 2)))
plasma.models = [d_alpha_excit]
####################
# Visualise Plasma #
# Run some plots to check the distribution functions and emission profile are as expected
r, _, z, t_samples = sample3d(d1_temperature, (0, 4, 200), (0, 0, 1), (-2, 2, 200))
plt.imshow(np.transpose(np.squeeze(t_samples)), extent=[0, 4, -2, 2])
plt.colorbar()
plt.axis('equal')
plt.xlabel('r axis')
plt.ylabel('z axis')
plt.title("Ion temperature profile in r-z plane")
plt.figure()
r, _, z, t_samples = sample3d(d1_temperature, (-4, 4, 400), (-4, 4, 400), (0, 0, 1))
plt.imshow(np.transpose(np.squeeze(t_samples)), extent=[-4, 4, -4, 4])
plt.colorbar()
plt.axis('equal')
plt.xlabel('x axis')
plt.ylabel('y axis')
plt.title("Ion temperature profile in x-y plane")
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
VideoFileOutput.write(image_np)
cv2.imshow('live_detection',image_np)
if cv2.waitKey(25) & 0xFF==ord('q'):
break
cv2.destroyAllWindows()
cap.release()
def variable_to_numpy(variable):
return np.squeeze(variable.cpu().detach().numpy())
roi_paths = glob.glob(ROI_DIR + '/*.nii')
roi_names = [r.split(os.sep)[-1].split('.nii')[0] for r in roi_paths]
tmp_img = nib.load('colin.nii')
roi_coords = []
for i_roi, roi in enumerate(roi_paths):
roi_nii = nib.load(roi)
roi_th = nib.Nifti1Image(np.array(roi_nii.get_data() > 0, dtype=np.int16),
roi_nii.get_affine(),
header=roi_nii.get_header())
rroi = resample_img(roi_th,
target_affine=tmp_img.get_affine(),
target_shape=tmp_img.shape[:3],
interpolation='nearest')
cur_roi_img = nib.Nifti1Image(np.array(np.squeeze(rroi.get_data()) > 0,
dtype=np.int32),
affine=tmp_img.get_affine())
roi_coords.append(plotting.find_xyz_cut_coords(cur_roi_img))
RES_NAME = 'net_pred_combined_clf_' + ROI_DIR
WRITE_DIR = op.join(os.getcwd(), RES_NAME)
if not op.exists(WRITE_DIR):
os.mkdir(WRITE_DIR)
##############################################################################
# load+preprocess data
##############################################################################
print('Loading ADHD data (1=ADHD)...')
rs_files = glob.glob('/Volumes/porsche/adhd_niak/fmri*/*.nii.gz')
def main():
"""test for Sparse AE"""
os.system('rm -rf log')
T = []
T1 = SparseAE(64,
49,
optimize_method='sgd',
max_iter=10,
debug=0,
verbose=True,
tol=1e-8,
mini_batch=64,
momentum=0,
momen_beta=.95,
alpha=.01,
adastep=1,
logger='sgd_good.csv')
T2 = SparseAE(64,
49,
optimize_method='sgd',
max_iter=10,
debug=0,
verbose=True,
tol=1e-8,
mini_batch=64,
momentum=0,
momen_beta=.95,
alpha=.01,
adastep=1,
logger='sgd_ill.csv')
T3 = SparseAE(64,
49,
optimize_method='sgd',
max_iter=10,
debug=0,
verbose=True,
tol=1e-8,
mini_batch=64,
momentum=0,
momen_beta=.95,
alpha=.01,
adastep=1,
logger='sgd_worse.csv')
T4 = SparseAE(64,
49,
optimize_method='sgd',
max_iter=10,
debug=0,
verbose=True,
tol=1e-8,
mini_batch=64,
momentum=0,
momen_beta=.95,
alpha=.01,
adastep=1,
logger='sgd_worst.csv')
T.append(T1)
T.append(T2)
T.append(T3)
T.append(T4)
X = []
X1 = vs.load_sample('IMAGES.mat', patch_size=8, n_patches=20480)
X2 = vs.load_sample('IMAGES.mat', patch_size=8, n_patches=20480)
X3 = vs.load_sample('IMAGES.mat', patch_size=8, n_patches=20480)
X4 = vs.load_sample('IMAGES.mat', patch_size=8, n_patches=20480)
idx_jitter = np.random.permutation(64)
idx_jitter_pos = idx_jitter[:16]
idx_jitter_neg = idx_jitter[-16:]
for i, j in zip(idx_jitter_pos[:3], idx_jitter_neg[:3]):
X2[i, :] = np.squeeze(np.random.normal(0.89, 0.01, [
20480,
]))
X2[j, :] = np.squeeze(np.random.normal(0.11, 0.01, [
20480,
]))
for i, j in zip(idx_jitter_pos[:8], idx_jitter_neg[:8]):
X3[i, :] = np.squeeze(np.random.normal(0.899, 0.0001, [
20480,
]))
X3[j, :] = np.squeeze(np.random.normal(0.101, 0.0001, [
20480,
]))
for i, j in zip(idx_jitter_pos, idx_jitter_neg):
X4[i, :] = np.squeeze(np.random.normal(0.89999, 0.000001, [
20480,
]))
X4[j, :] = np.squeeze(np.random.normal(0.10001, 0.000001, [
20480,
]))
X.extend([X1, X2, X3, X4])
print "cond(X1): ", np.linalg.cond(X1)
print "cond(X2): ", np.linalg.cond(X2)
print "cond(X3): ", np.linalg.cond(X3)
print "cond(X4): ", np.linalg.cond(X4)
for i in range(len(T)):
try:
T[i].train(X[i])
except:
print 'Training shut down\n'
pass
def test_model(self, path, model_name, type_to_restore="SavedModel", show_incorrect=True):
"""
:param path:
:param model_name:
:param type_to_restore: String, "SavedModel" or "SavedSession"
:param show_incorrect: show incorrectly guessed images
:return:
"""
print("\n{}".format("#" * 50))
# self.test_data = self.create_test_data("dataset/test", "test_data", typ="np")
test_imgs, test_lbls = self.test_data[0], self.test_data[1]
test_batch = 5
length = len(test_lbls)
n_correct = 0
total_n = length
with tf.Session(graph=tf.Graph()) as sess:
graph = tf.get_default_graph()
if type_to_restore == "SavedModel":
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.TRAINING], path + model_name)
else:
saver = tf.train.import_meta_graph(path + model_name)
saver.restore(sess, tf.train.latest_checkpoint(path))
x_img = graph.get_tensor_by_name("x_img:0")
y_lbl = graph.get_tensor_by_name("y_lbl:0")
predictor = graph.get_tensor_by_name("accuracy/prediction:0")
keep_prob = graph.get_tensor_by_name("dropout/keep_prob:0")
i = 0
lngth = len(test_lbls)
while i < lngth:
# ensure that all images in test set are tested, even if final set has size less than `test_batch`
if (lngth - test_batch) >= i:
imgs = test_imgs[i: (i + test_batch)]
lbls = test_lbls[i: (i + test_batch)]
else:
imgs = test_imgs[i:lngth]
lbls = test_lbls[i:lngth]
print("{}\nTest batch {}".format("-" * 15, i / test_batch))
preds = sess.run(predictor, feed_dict={x_img: imgs, keep_prob: 1.0})
truths = np.asarray([t[1] for t in lbls]) # TODO: These indices should match
print("predictions: \t", preds)
print("truths: \t\t", truths)
correct = np.asarray([1 if p == t else 0 for p, t in zip(preds, truths)])
n_correct += sum(correct)
acc = float(sum(correct)) / len(correct)
print("accuracy: {:.3f}".format(acc))
acc_thresh = 0.8
if show_incorrect and acc < acc_thresh:
print("Accuracy less than {}%, displaying incorrect guesses...".format(acc_thresh))
for idx in range(len(correct)):
if correct[idx]:
continue
incorrect_img = imgs[idx]
guess = preds[idx]
# im =
plt.imshow(np.squeeze(incorrect_img, axis=2), cmap='gray')
plt.title(
(TadpoleConvNet.CLASS_ONE, TadpoleConvNet.CLASS_TWO)[guess] + " (false)"
)
plt.show()
i += test_batch # increment i with test_batch step size
accuracy = graph.get_tensor_by_name("accuracy/accuracy:0")
print("test final accuracy: ", sess.run(accuracy, feed_dict={x_img: test_imgs, y_lbl: test_lbls, keep_prob: 1.0}))
print("-" * 30)
print('Final Test Accuracy: {:.6f}'.format(
float(n_correct) / total_n)
)
print("-" * 30)
return float(n_correct) / total_n
def main(args):
sleep(random.random())
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
dataset = facenet.get_dataset(args.input_dir)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
# Add a random key to the filename to allow alignment using multiple processes
random_key = np.random.randint(0, high=99999)
bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
if args.random_order:
random.shuffle(dataset)
for cls in dataset:
output_class_dir = os.path.join(output_dir, cls.name)
if not os.path.exists(output_class_dir):
os.makedirs(output_class_dir)
if args.random_order:
random.shuffle(cls.image_paths)
for image_path in cls.image_paths:
nrof_images_total += 1
filename = os.path.splitext(os.path.split(image_path)[1])[0]
output_filename = os.path.join(output_class_dir, filename + '.png')
print(image_path)
if not os.path.exists(output_filename):
try:
img = imageio.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = facenet.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
img_center = img_size / 2
offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
index = np.argmax(
bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering
det = det[index, :]
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
# print(det[0], det[1], det[2], det[3])
bb[0] = np.maximum(det[0] - args.margin / 2, 0)
bb[1] = np.maximum(det[1] - args.margin / 2, 0)
bb[2] = np.minimum(det[2] + args.margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + args.margin / 2, img_size[0])
# print(bb)
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
# scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
scaled = np.array(Image.fromarray(cropped).resize((args.image_size, args.image_size)))
'''
img = scipy.misc.imresize(myImage, size=(num_px, num_px))
img = np.array(Image.fromarray(myImage).resize((num_px, num_px)))'''
nrof_successfully_aligned += 1
imageio.imsave(output_filename, scaled)
text_file.write('%s %d %d %d %d\n' % (output_filename, bb[0], bb[1], bb[2], bb[3]))
else:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
def model(X, Y, word_to_vec_map, learning_rate=0.01, num_iterations=400):
"""
Model to train word vector representations in numpy.
Arguments:
X -- input data, numpy array of sentences as strings, of shape (m, 1)
Y -- labels, numpy array of integers between 0 and 7, numpy-array of shape (m, 1)
word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation
learning_rate -- learning_rate for the stochastic gradient descent algorithm
num_iterations -- number of iterations
Returns:
pred -- vector of predictions, numpy-array of shape (m, 1)
W -- weight matrix of the softmax layer, of shape (n_y, n_h)
b -- bias of the softmax layer, of shape (n_y,)
"""
np.random.seed(1)
# Define number of training examples
m = Y.shape[0] # number of training examples
n_y = 5 # number of classes
n_h = 50 # dimensions of the GloVe vectors
# Initialize parameters using Xavier initialization
W = np.random.randn(n_y, n_h) / np.sqrt(n_h)
b = np.zeros((n_y,))
# Convert Y to Y_onehot with n_y classes
Y_oh = convert_to_one_hot(Y, C=n_y)
# Optimization loop
for t in range(num_iterations): # Loop over the number of iterations
for i in range(m): # Loop over the training examples
### START CODE HERE ### (≈ 4 lines of code)
# Average the word vectors of the words from the i'th training example
avg = sentence_to_avg(X[i], word_to_vec_map)
# Forward propagate the avg through the softmax layer
z = np.dot(W, avg) + b
a = softmax(z)
# Compute cost using the i'th training label's one hot representation and "A" (the output of the softmax)
cost = -np.squeeze(np.sum(Y_oh[i] * np.log(a)))
### END CODE HERE ###
# Compute gradients
dz = a - Y_oh[i]
dW = np.dot(dz.reshape(n_y, 1), avg.reshape(1, n_h))
db = dz
# Update parameters with Stochastic Gradient Descent
W = W - learning_rate * dW
b = b - learning_rate * db
if t % 100 == 0:
print("Epoch: " + str(t) + " --- cost = " + str(cost))
pred = predict(X, Y, W, b, word_to_vec_map)
return pred, W, b
c_cover = list(np.reshape(cover, np.size(cover)))
tf_cover = np.expand_dims(cover, axis=2)
# f = open(message_file, "rb")
# message = f.read()
# f.close()
# c_message = str2bitlist(message)
covers = tf.placeholder(tf.float32, [None, Height, Width, 1], name="covers")
generator = GeneratorModel(covers, is_training=False)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(gan_savedir))
tf_probmap = sess.run(generator.generator_prediction, {covers: [tf_cover]})
probmap = np.squeeze(tf_probmap)
c_probmap = list(np.reshape(probmap, np.size(probmap)))
for bpp in range(1, 2):
c_message = list(np.random.randint(0, 2, int(512 * 512 * bpp / 100)))
(success, c_stego, c_lsb) = STC.embed(c_cover, c_probmap, c_message)
if success:
stego = np.reshape(c_stego, (Height, Width)).astype(int)
diff = np.abs(cover - stego).astype(int)
scipy.misc.imsave('test/steg10/{0:05d}.png'.format(e.step), diff)
# write_pgm(stego_file, stego)
# np.save('probmap.npy',probmap)
# print(key)
def extent(self):
bc = np.squeeze(self.bin_centers())
bmin = bc.min(axis=0)
bmax = bc.max(axis=0)
return np.asarray([bmin, bmax])
def generate_SeqFile_SpiralDiffusion(gx, gy, tr, n_shots, mg, ms, fA, n_slices,
reps, st, tPlot, tReport, b_values,
n_dirs, fov, Nx):
#%% --- 1 - Create new Sequence Object + Parameters
seq = Sequence()
# =========
# Parameters
# =========
i_raster_time = 100000
assert 1 / i_raster_time == seq.grad_raster_time, "Manualy inputed inverse raster time does not match the actual value."
# =========
# Code parameters
# =========
fatsat_enable = 0 # Fat saturation
kplot = 0
# =========
# Acquisition Parameters
# =========
TR = tr # Spin-Echo parameters - TR in [s]
n_TR = math.ceil(
TR * i_raster_time) # Spin-Echo parameters - number of points TR
bvalue = b_values # b-value [s/mm2]
nbvals = np.shape(bvalue)[0] # b-value parameters
ndirs = n_dirs # b-value parameters
Ny = Nx
slice_thickness = st # Acquisition Parameters in [m]
Nshots = n_shots
# =========
# Gradient Scaling
# =========
gscl = np.zeros(nbvals + 1)
gscl[1:] = np.sqrt(bvalue / np.max(bvalue))
gdir, nb0s = difunc.get_dirs(ndirs)
# =========
# Create system
# =========
system = Opts(max_grad=mg,
grad_unit='mT/m',
max_slew=ms,
slew_unit='T/m/s',
rf_ringdown_time=20e-6,
rf_dead_time=100e-6,
adc_dead_time=10e-6)
#%% --- 2 - Fat saturation
if fatsat_enable:
fatsat_str = "_fatsat"
b0 = 1.494
sat_ppm = -3.45
sat_freq = sat_ppm * 1e-6 * b0 * system.gamma
rf_fs, _, _ = make_gauss_pulse(flip_angle=110 * math.pi / 180,
system=system,
duration=8e-3,
bandwidth=abs(sat_freq),
freq_offset=sat_freq)
gz_fs = make_trapezoid(channel='z',
system=system,
delay=calc_duration(rf_fs),
area=1 / 1e-4)
else:
fatsat_str = ""
#%% --- 3 - Slice Selection
# =========
# Create 90 degree slice selection pulse and gradient
# =========
flip90 = fA * pi / 180
rf, gz, _ = make_sinc_pulse(flip_angle=flip90,
system=system,
duration=3e-3,
slice_thickness=slice_thickness,
apodization=0.5,
time_bw_product=4)
# =========
# Refocusing pulse with spoiling gradients
# =========
rf180, gz180, _ = make_sinc_pulse(flip_angle=math.pi,
system=system,
duration=5e-3,
slice_thickness=slice_thickness,
apodization=0.5,
time_bw_product=4)
rf180.phase_offset = math.pi / 2
gz_spoil = make_trapezoid(channel='z',
system=system,
area=6 / slice_thickness,
duration=3e-3)
#%% --- 4 - Gradients
# =========
# Spiral trajectory
# =========
G = gx + 1J * gy
#%% --- 5 - ADCs / Readouts
delta_k = 1 / fov
adc_samples = math.floor(
len(G) / 4
) * 4 - 2 # Apparently, on Siemens the number of samples needs to be divisible by 4...
adc = make_adc(num_samples=adc_samples,
system=system,
duration=adc_samples / i_raster_time)
# =========
# Pre-phasing gradients
# =========
pre_time = 1e-3
n_pre_time = math.ceil(pre_time * i_raster_time)
gz_reph = make_trapezoid(channel='z',
system=system,
area=-gz.area / 2,
duration=pre_time)
#%% --- 6 - Obtain TE and diffusion-weighting gradient waveform
# For S&T monopolar waveforms
# From an initial TE, check we satisfy all constraints -> otherwise increase TE.
# Once all constraints are okay -> check b-value, if it is lower than the target one -> increase TE
# Looks time-inefficient but it is fast enough to make it user-friendly.
# TODO: Re-scale the waveform to the exact b-value because increasing the TE might produce slightly higher ones.
# Calculate some times constant throughout the process
# We need to compute the exact time sequence. For the normal SE-MONO-EPI sequence micro second differences
# are not important, however, if we wanna import external gradients the allocated time for them needs to
# be the same, and thus exact timing is mandatory. With this in mind, we establish the following rounding rules:
# Duration of RFs + spoiling, and EPI time to the center of the k-space is always math.ceil().
# The time(gy) refers to the number of blips, thus we substract 0.5 since the number of lines is always even.
# The time(gx) refers to the time needed to read each line of the k-space. Thus, if Ny is even, it would take half of the lines plus another half.
n_duration_center = 0 # The spiral starts right in 0 -- or ADC_dead_time??
rf_center_with_delay = rf.delay + calc_rf_center(rf)[0]
n_rf90r = math.ceil((calc_duration(gz) - rf_center_with_delay + pre_time) /
seq.grad_raster_time)
n_rf180r = math.ceil((calc_duration(rf180) + 2 * calc_duration(gz_spoil)) /
2 / seq.grad_raster_time)
n_rf180l = math.floor(
(calc_duration(rf180) + 2 * calc_duration(gz_spoil)) / 2 /
seq.grad_raster_time)
# =========
# Find minimum TE considering the readout times.
# =========
n_TE = math.ceil(20e-3 / seq.grad_raster_time)
n_delay_te1 = -1
while n_delay_te1 <= 0:
n_TE = n_TE + 2
n_tINV = math.floor(n_TE / 2)
n_delay_te1 = n_tINV - n_rf90r - n_rf180l
# =========
# Find minimum TE for the target b-value
# =========
bvalue_tmp = 0
while bvalue_tmp < np.max(bvalue):
n_TE = n_TE + 2
n_tINV = math.floor(n_TE / 2)
n_delay_te1 = n_tINV - n_rf90r - n_rf180l
delay_te1 = n_delay_te1 / i_raster_time
n_delay_te2 = n_tINV - n_rf180r - n_duration_center
delay_te2 = n_delay_te2 / i_raster_time
# Waveform Ramp time
n_gdiff_rt = math.ceil(system.max_grad / system.max_slew /
seq.grad_raster_time)
# Select the shortest available time
n_gdiff_delta = min(n_delay_te1, n_delay_te2)
n_gdiff_Delta = n_delay_te1 + 2 * math.ceil(
calc_duration(gz_spoil) / seq.grad_raster_time) + math.ceil(
calc_duration(gz180) / seq.grad_raster_time)
gdiff = make_trapezoid(channel='x',
system=system,
amplitude=system.max_grad,
duration=n_gdiff_delta / i_raster_time)
# delta only corresponds to the rectangle.
n_gdiff_delta = n_gdiff_delta - 2 * n_gdiff_rt
bv = difunc.calc_bval(system.max_grad, n_gdiff_delta / i_raster_time,
n_gdiff_Delta / i_raster_time,
n_gdiff_rt / i_raster_time)
bvalue_tmp = bv * 1e-6
# =========
# Show final TE and b-values:
# =========
print("TE:", round(n_TE / i_raster_time * 1e3, 2), "ms")
for bv in range(1, nbvals + 1):
print(
round(
difunc.calc_bval(system.max_grad * gscl[bv], n_gdiff_delta /
i_raster_time, n_gdiff_Delta / i_raster_time,
n_gdiff_rt / i_raster_time) * 1e-6, 2),
"s/mm2")
TE = n_TE / i_raster_time
TR = n_TR / i_raster_time
#%% --- 7 - Crusher gradients
gx_crush = make_trapezoid(channel='x',
area=2 * Nx * delta_k,
system=system)
gz_crush = make_trapezoid(channel='z',
area=4 / slice_thickness,
system=system)
# TR delay - Takes everything into account
# Distance between the center of the RF90s must be TR
# The n_pre_time here is the time used to drive the Gx, and Gy spiral gradients to zero.
n_spiral_time = adc_samples
n_tr_per_slice = math.ceil(TR / n_slices * i_raster_time)
if fatsat_enable:
n_tr_delay = n_tr_per_slice - (n_TE - n_duration_center + n_spiral_time) \
- math.ceil(rf_center_with_delay * i_raster_time) \
- n_pre_time \
- math.ceil(calc_duration(gx_crush, gz_crush) * i_raster_time) \
- math.ceil(calc_duration(rf_fs, gz_fs) * i_raster_time)
else:
n_tr_delay = n_tr_per_slice - (n_TE - n_duration_center + n_spiral_time) \
- math.ceil(rf_center_with_delay * i_raster_time) \
- n_pre_time \
- math.ceil(calc_duration(gx_crush, gz_crush) * i_raster_time)
tr_delay = n_tr_delay / i_raster_time
#%% --- 8 - Checks
# =========
# Check TR delay time
# =========
assert n_tr_delay > 0, "Such parameter configuration needs longer TR."
# =========
# Delay time,
# =========
# Time between the gradient and the RF180. This time might be zero some times, although it is not normal.
if n_delay_te1 > n_delay_te2:
n_gap_te1 = n_delay_te1 - n_delay_te2
gap_te1 = n_gap_te1 / i_raster_time
gap_te2 = 0
else:
n_gap_te2 = n_delay_te2 - n_delay_te1
gap_te2 = n_gap_te2 / i_raster_time
gap_te1 = 0
#%% --- 9 - b-zero acquisition
for r in range(reps):
for d in range(nb0s):
for nshot in range(Nshots):
for s in range(n_slices):
# Fat saturation
if fatsat_enable:
seq.add_block(rf_fs, gz_fs)
# RF90
rf.freq_offset = gz.amplitude * slice_thickness * (
s - (n_slices - 1) / 2)
seq.add_block(rf, gz)
seq.add_block(gz_reph)
# Delay for RF180
seq.add_block(make_delay(delay_te1))
# RF180
seq.add_block(gz_spoil)
rf180.freq_offset = gz180.amplitude * slice_thickness * (
s - (n_slices - 1) / 2)
seq.add_block(rf180, gz180)
seq.add_block(gz_spoil)
# Delay for spiral
seq.add_block(make_delay(delay_te2))
# Read k-space
# Imaging Gradient waveforms
gx = make_arbitrary_grad(channel='x',
waveform=np.squeeze(
G[:, nshot].real),
system=system)
gy = make_arbitrary_grad(channel='y',
waveform=np.squeeze(
G[:, nshot].imag),
system=system)
seq.add_block(gx, gy, adc)
# Make the spiral finish in zero - I use pre_time because I know for sure it's long enough.
# Furthermore, this is after readout and TR is supposed to be long.
amp_x = [G[:, nshot].real[-1], 0]
amp_y = [G[:, nshot].imag[-1], 0]
gx_to_zero = make_extended_trapezoid(channel='x',
amplitudes=amp_x,
times=[0, pre_time],
system=system)
gy_to_zero = make_extended_trapezoid(channel='y',
amplitudes=amp_y,
times=[0, pre_time],
system=system)
seq.add_block(gx_to_zero, gy_to_zero)
seq.add_block(gx_crush, gz_crush)
# Wait TR
if tr_delay > 0:
seq.add_block(make_delay(tr_delay))
#%% --- 9 - DWI acquisition
for r in range(reps):
for bv in range(1, nbvals + 1):
for d in range(ndirs):
for nshot in range(Nshots):
for s in range(n_slices):
# Fat saturation
if fatsat_enable:
seq.add_block(rf_fs, gz_fs)
# RF90
rf.freq_offset = gz.amplitude * slice_thickness * (
s - (n_slices - 1) / 2)
seq.add_block(rf, gz)
seq.add_block(gz_reph)
# Diffusion-weighting gradient
gdiffx = make_trapezoid(channel='x',
system=system,
amplitude=system.max_grad *
gscl[bv] * gdir[d, 0],
duration=calc_duration(gdiff))
gdiffy = make_trapezoid(channel='y',
system=system,
amplitude=system.max_grad *
gscl[bv] * gdir[d, 1],
duration=calc_duration(gdiff))
gdiffz = make_trapezoid(channel='z',
system=system,
amplitude=system.max_grad *
gscl[bv] * gdir[d, 2],
duration=calc_duration(gdiff))
seq.add_block(gdiffx, gdiffy, gdiffz)
# Delay for RF180
seq.add_block(make_delay(gap_te1))
# RF180
seq.add_block(gz_spoil)
rf180.freq_offset = gz180.amplitude * slice_thickness * (
s - (n_slices - 1) / 2)
seq.add_block(rf180, gz180)
seq.add_block(gz_spoil)
# Diffusion-weighting gradient
seq.add_block(gdiffx, gdiffy, gdiffz)
# Delay for spiral
seq.add_block(make_delay(gap_te2))
# Read k-space
# Imaging Gradient waveforms
gx = make_arbitrary_grad(channel='x',
waveform=np.squeeze(
G[:, nshot].real),
system=system)
gy = make_arbitrary_grad(channel='y',
waveform=np.squeeze(
G[:, nshot].imag),
system=system)
seq.add_block(gx, gy, adc)
# Make the spiral finish in zero - I use pre_time because I know for sure it's long enough.
# Furthermore, this is after readout and TR is supposed to be long.
amp_x = [G[:, nshot].real[-1], 0]
amp_y = [G[:, nshot].imag[-1], 0]
gx_to_zero = make_extended_trapezoid(
channel='x',
amplitudes=amp_x,
times=[0, pre_time],
system=system)
gy_to_zero = make_extended_trapezoid(
channel='y',
amplitudes=amp_y,
times=[0, pre_time],
system=system)
seq.add_block(gx_to_zero, gy_to_zero)
seq.add_block(gx_crush, gz_crush)
# Wait TR
if tr_delay > 0:
seq.add_block(make_delay(tr_delay))
if tPlot:
seq.plot()
if tReport:
print(seq.test_report())
seq.check_timing()
return seq, TE, TR, fatsat_str
def test_loading_weights_by_name_and_reshape():
"""
test loading model weights by name on:
- sequential model
"""
# test with custom optimizer, loss
custom_opt = optimizers.rmsprop
custom_loss = losses.mse
# sequential model
model = Sequential()
model.add(Conv2D(2, (1, 1), input_shape=(1, 1, 1), name='rick'))
model.add(Flatten())
model.add(Dense(3, name='morty'))
model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc'])
x = np.random.random((1, 1, 1, 1))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
old_weights = [layer.get_weights() for layer in model.layers]
_, fname = tempfile.mkstemp('.h5')
model.save_weights(fname)
# delete and recreate model
del (model)
model = Sequential()
model.add(Conv2D(2, (1, 1), input_shape=(1, 1, 1), name='rick'))
model.add(Conv2D(3, (1, 1), name='morty'))
model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc'])
# load weights from first model
with pytest.raises(ValueError):
model.load_weights(fname, by_name=True, reshape=False)
with pytest.raises(ValueError):
model.load_weights(fname, by_name=False, reshape=False)
model.load_weights(fname, by_name=False, reshape=True)
model.load_weights(fname, by_name=True, reshape=True)
out2 = model.predict(x)
assert_allclose(np.squeeze(out), np.squeeze(out2), atol=1e-05)
for i in range(len(model.layers)):
new_weights = model.layers[i].get_weights()
for j in range(len(new_weights)):
# only compare layers that have weights, skipping Flatten()
if old_weights[i]:
assert_allclose(old_weights[i][j], new_weights[j], atol=1e-05)
# delete and recreate model with `use_bias=False`
del (model)
model = Sequential()
model.add(
Conv2D(2, (1, 1), input_shape=(1, 1, 1), use_bias=False, name='rick'))
model.add(Flatten())
model.add(Dense(3, name='morty'))
with pytest.raises(
ValueError,
match=r'.* expects [0-9]+ .* but the saved .* [0-9]+ .*'):
model.load_weights(fname)
with pytest.raises(
ValueError,
match=r'.* expects [0-9]+ .* but the saved .* [0-9]+ .*'):
model.load_weights(fname, by_name=True)
with pytest.warns(UserWarning,
match=r'Skipping loading .* due to mismatch .*'):
model.load_weights(fname, by_name=True, skip_mismatch=True)
# delete and recreate model with `filters=10`
del (model)
model = Sequential()
model.add(Conv2D(10, (1, 1), input_shape=(1, 1, 1), name='rick'))
with pytest.raises(ValueError,
match=r'.* has shape .* but the saved .* shape .*'):
model.load_weights(fname, by_name=True)
with pytest.raises(
ValueError,
match=r'.* load .* [0-9]+ layers into .* [0-9]+ layers.'):
model.load_weights(fname)
os.remove(fname)
'family': 'Avenir',
'weight': 'normal',
'size': 26
}
math_font = 'stixsans'
plt.rc('font', **font)
plt.rcParams['mathtext.fontset'] = math_font
plt.rcParams['axes.labelsize'] = font['size']
plt.rcParams['xtick.labelsize'] = font['size']-2
plt.rcParams['ytick.labelsize'] = font['size']-2
plt.rcParams['legend.fontsize'] = font['size']-2
i = 2
fig, ax = plt.subplots(1, 2, figsize=(13,5.3))
s0 = ax[0].scatter(train_latent[:,0],train_latent[:,i],s=7,c=np.squeeze(real_y_train_un.iloc[:,1]), cmap=cmap) # real_y_train_un[:,1]
cbar = plt.colorbar(s0, ax=ax[0], ticks=[0.15, 0.85])
cbar.ax.set_yticklabels(['0', '1'])
ax[0].set_xticks([-6,-2,2,6,10])
ax[0].set_yticks([-2, 2, 6, 10, 14])
x, y = 4, 8
ax[0].scatter(x, y, s=150, facecolors='none', edgecolors='#d62728', linewidths=2.5, linestyle='-')
s1 = ax[1].scatter(train_latent[:,0],train_latent[:,i],s=7,c=np.squeeze(real_y_train_un.iloc[:,0])) # real_y_train_un[:,0]
plt.colorbar(s1, ax=ax[1], ticks=[-1, -3, -5, -7])
ax[1].set_xticks([-6,-2,2,6,10])
ax[1].set_yticks([-2, 2, 6, 10, 14])
ax[1].scatter(x, y, s=150, facecolors='none', edgecolors='#d62728', linewidths=2.5, linestyle='-')
plt.tight_layout()
plt.subplots_adjust(wspace=0.2)
def detect_face(self, image, *, save: Any = False):
"""Primary method to detect a face from an image.
Detects faces from an image and draws bounding boxes around them. Images can be
saved to a file by updating the `save` parameter, and the method returns the annotated image.
Usage:
>>> model = FacialDetector()
>>> image = model.detect_face(cv2.imread('image/file/path'), save = 'image/save/path')
Arguments:
- image: A numpy array representing the image, or an image file path.
- save: Either a filepath to save the annotated image to or None.
Returns:
- The annotated image, with bounding boxes around faces.
"""
# Validate image.
if not isinstance(image, np.ndarray):
if isinstance(image, str):
if not os.path.exists(image):
raise FileNotFoundError(f"Received image path string, but the path {image} does not exist.")
image = cv2.imread(image)
else:
raise TypeError(f"Expected a numpy array representing the image, got {type(image)}.")
if len(image.shape) != 3:
if len(image.shape) == 4:
if image.shape[0] == 1 or image.shape[3] == 1:
logging.warning("Received image with 4 dimensions, with either the first or last channel being the batch size."
"The image has been compressed, but it may result in issues with the output. For better "
"performance, only include images with three dimensions. ")
image = np.squeeze(image)
else:
raise ValueError(f"Image should have three dimensions: width, height, and channels, "
f"got {len(image.shape)} dims.")
# Detect faces from image.
image_coords = []
(h, w) = image.shape[:2]
# Set input blob and forward pass through network.
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), swapRB = False, crop = False)
self.net.setInput(blob)
faces = self.net.forward()
# Iterate over faces.
for dim in range(0, faces.shape[2]):
# Determine prediction confidence.
confidence = faces[0, 0, dim, 2]
if confidence < 0.5:
continue
# Determine actual face coordinates.
box = faces[0, 0, dim, 3:7] * np.array([w, h, w, h])
(x, y, xe, ye) = box.astype(int)
image_coords.append((x, y, xe, ye))
# Annotate image with bounding box.
cv2.rectangle(image, (x, y), (xe, ye), (0, 255, 255), 3)
# Save image if requested to.
if save:
if not isinstance(save, str):
raise TypeError(f"The save argument should be a path where the image is going "
f"to be saved, got {type(save)}.")
if not os.path.exists(os.path.dirname(save)):
raise NotADirectoryError("The directory of the save path provided does not exist. Check your paths.")
cv2.imwrite(save, image)
# Return annotated image.
return image
print shape(Magvec)
sm=shape(Magvec)[0]
sy=shape(data)
s=(sm, sy[0], sy[2])
print s
Magcom=Magvec[:,0,:]+1j*Magvec[:,1,:]
Magcom=reshape(Magcom, s, order="F")
freq=linspace(fstart, fstart+fstep*(sm-1), sm)
#
#freq=linspace(4.0e9, 5.0e9, 1001)
#print Magcom.dtype, pwr.dtype, yoko.dtype, freq.dtype
#print rept
#print yoko
#print time
print shape(Magcom)
Magcom=squeeze(Magcom)
# print shape(Magcom)
# print shape(yoko)
# print yoko
#print freq
#Magcom=mean(Magcom, axis=1)
powind=4
print pwr[powind]
Magabs=Magcom[:, :, :]-mean(Magcom[:, 197:200, :], axis=1, keepdims=True)
fridge_att=87.0+20.0+5.0
pwrlin=0*0.001*10.0**((pwr[powind]-fridge_att)/10.0)
if 0:
pcolormesh(dB(Magcom[:, :, powind]))
show()
def nlmeans_proxy(in_file,
settings,
snr=None,
smask=None,
nmask=None,
out_file=None):
"""
Uses non-local means to denoise 4D datasets
"""
from dipy.denoise.nlmeans import nlmeans
from scipy.ndimage.morphology import binary_erosion
from scipy import ndimage
if out_file is None:
fname, fext = op.splitext(op.basename(in_file))
if fext == ".gz":
fname, fext2 = op.splitext(fname)
fext = fext2 + fext
out_file = op.abspath("./%s_denoise%s" % (fname, fext))
img = nb.load(in_file, mmap=NUMPY_MMAP)
hdr = img.header
data = img.get_data()
aff = img.affine
if data.ndim < 4:
data = data[..., np.newaxis]
data = np.nan_to_num(data)
if data.max() < 1.0e-4:
raise RuntimeError("There is no signal in the image")
df = 1.0
if data.max() < 1000.0:
df = 1000.0 / data.max()
data *= df
b0 = data[..., 0]
if smask is None:
smask = np.zeros_like(b0)
smask[b0 > np.percentile(b0, 85.0)] = 1
smask = binary_erosion(smask.astype(np.uint8),
iterations=2).astype(np.uint8)
if nmask is None:
nmask = np.ones_like(b0, dtype=np.uint8)
bmask = settings["mask"]
if bmask is None:
bmask = np.zeros_like(b0)
bmask[b0 > np.percentile(b0[b0 > 0], 10)] = 1
label_im, nb_labels = ndimage.label(bmask)
sizes = ndimage.sum(bmask, label_im, range(nb_labels + 1))
maxidx = np.argmax(sizes)
bmask = np.zeros_like(b0, dtype=np.uint8)
bmask[label_im == maxidx] = 1
nmask[bmask > 0] = 0
else:
nmask = np.squeeze(nmask)
nmask[nmask > 0.0] = 1
nmask[nmask < 1] = 0
nmask = nmask.astype(bool)
nmask = binary_erosion(nmask, iterations=1).astype(np.uint8)
den = np.zeros_like(data)
est_snr = True
if snr is not None:
snr = [snr] * data.shape[-1]
est_snr = False
else:
snr = []
for i in range(data.shape[-1]):
d = data[..., i]
if est_snr:
s = np.mean(d[smask > 0])
n = np.std(d[nmask > 0])
snr.append(s / n)
den[..., i] = nlmeans(d, snr[i], **settings)
den = np.squeeze(den)
den /= df
nb.Nifti1Image(den.astype(hdr.get_data_dtype()), aff,
hdr).to_filename(out_file)
return out_file, snr
