def __init__(self, spline_vals, coeff_vals, scmos_data, verbose=False):
CSplineFit.__init__(self, scmos_data, verbose)
# Initialize spline.
self.py_spline = spline2D.Spline2D(spline_vals, coeff=coeff_vals)
self.c_spline = self.clib.initSpline2D(
numpy.ascontiguousarray(self.py_spline.coeff, dtype=numpy.float64),
self.py_spline.max_i, self.py_spline.max_i)
def __init__(self,
spline_vals,
coeff_vals,
scmos_data,
tolerance=default_tol):
CSplineFit.__init__(self, spline_vals, scmos_data, tolerance)
# Initialize spline.
self.py_spline = spline2D.Spline2D(spline_vals, coeff=coeff_vals)
cubic_fit.initSpline2D(
numpy.ascontiguousarray(self.py_spline.coeff, dtype=numpy.float64),
self.py_spline.max_i, self.py_spline.max_i)
def test_psf_2D_dy():
# Only test for Python3 due to pickle incompatibility issues.
if (sys.version_info < (3, 0)):
return
spline_filename = storm_analysis.getData(
"test/data/test_spliner_psf_2d.spline")
with open(spline_filename, "rb") as fp:
spline_data = pickle.load(fp)
py_spline = spline2D.Spline2D(spline_data["spline"], spline_data["coeff"])
c_spline = cubicSplineC.CSpline2D(py_spline)
size = py_spline.getSize() - 1.0e-6
for i in range(reps):
x = random.uniform(1.0e-6, size)
y = random.uniform(1.0e-6, size)
#print("{0:.3f} {1:.3f}".format(py_spline.dyf(x, y), c_spline.dyf(x, y)))
assert (abs(py_spline.dyf(x, y) - c_spline.dyf(x, y)) < 1.0e-6)
def __init__(self, spline_file = None, **kwds):
super(SplineToPSF2D, self).__init__(**kwds)
spline_data = self.loadSplineFile(spline_file)
# Check that this is not an old spline, which will be transposed.
assert("version" in spline_data), "v0 spline file detected! Please re-measure!"
assert(spline_data["version"] >= 2.0), "v0/v1 spline file detected! Please re-measure!"
# These are used when we check the starting z-value(s)
# provided by the user, if any.
self.zmin = -1.0
self.zmax = 1.0
# The Python representation of the spline.
self.spline = spline2D.Spline2D(spline_data["spline"], spline_data["coeff"])
self.spline_size = self.spline.getSize()
# The C representation of the spline. This class does not use
# this, but it keeps track of it for the C fitting library.
self.c_spline = cubicSplineC.CSpline2D(self.spline)
exit()
psf_data = pickle.load(open(sys.argv[1], 'rb'))
np_psf = psf_data["psf"]
s_size = int(sys.argv[3])
spline = False
start = np_psf.shape[1] / 2.0 - s_size - 0.5
# 2D spline
if (len(np_psf.shape) == 2):
print("Generating 2D spline.")
s_size = 2 * s_size
np_spline = numpy.zeros((s_size, s_size))
#np_psf = np_psf/numpy.max(np_psf)
xy_spline = spline2D.Spline2D(np_psf)
x = start
for i in range(s_size):
y = start
for j in range(s_size):
np_spline[j, i] = xy_spline.f(y, x)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline2D.Spline2D(np_spline)
if 1:
import sa_library.daxwriter as daxwriter
def __init__(self, d, coeff=False, verbose=False):
if (d.shape[0] != d.shape[1]) or (d.shape[0] != d.shape[2]):
assert "input matrix must be square!"
size = d.shape[0]
self.max_i = size - 1
#
# The coefficients have already been calculated, so just use them.
#
if (type(coeff) == type(numpy.array([]))):
self.coeff = coeff
return
if verbose:
print("Calculating spline coefficients.")
#
# Create 2D splines in the "yz-plane".
#
yzs = []
for i in range(size):
yzs.append(spline2D.Spline2D(d[i, :, :]))
#
# Use splines in the "yz-plane" to create splines on the
# "x axis" with sub-integer spacing.
#
xs = []
cx = 0.0
cy = 0.0
while (cx <= (float(self.max_i) + 0.01)):
if (cx > float(self.max_i)):
cx = float(self.max_i)
cy = 0.0
while (cy <= (float(self.max_i) + 0.01)):
if (cy > float(self.max_i)):
cy = float(self.max_i)
xv = numpy.zeros(size)
for i in range(size):
xv[i] = yzs[i].f(cy, cx)
xs.append(spline1D.Spline1D(xv))
cy += 1.0 / 3.0
cx += 1.0 / 3.0
#
# Compute spline coefficients using the "x axis" splines
# to generate 64 values per cell and then solving for
# the coefficients.
#
self.coeff = numpy.zeros((self.max_i, self.max_i, self.max_i, 64))
A = numpy.zeros((64, 64))
for i in range(4):
dx = float(i) / 3.0
for j in range(4):
dy = float(j) / 3.0
for k in range(4):
dz = float(k) / 3.0
for l in range(4):
for m in range(4):
for n in range(4):
A[i * 16 + j * 4 + k, l * 16 +
m * 4 + n] = math.pow(dx, l) * math.pow(
dy, m) * math.pow(dz, n)
b = numpy.zeros(64)
row_size = 3 * self.max_i + 1
for i in range(self.max_i):
for j in range(self.max_i):
for k in range(self.max_i):
for m in range(4):
for n in range(4):
sp = xs[i * 3 * row_size + j * 3 + m * row_size +
n]
for o in range(4):
cx = float(k) + float(o) / 3.0
b[m * 16 + n * 4 + o] = sp.f(cx)
self.coeff[i, j, k, :] = numpy.linalg.solve(A, b)
if verbose:
print("Finished calculating spline coefficients.")
def psfToSpline(psf_name, spline_name, s_size):
psf_data = pickle.load(open(psf_name, 'rb'))
np_psf = psf_data["psf"]
spline = False
start = np_psf.shape[1]/2.0 - s_size - 0.5
# 2D spline
if (len(np_psf.shape) == 2):
print("Generating 2D spline.")
s_size = 2*s_size
np_spline = numpy.zeros((s_size, s_size))
#np_psf = np_psf/numpy.max(np_psf)
xy_spline = spline2D.Spline2D(np_psf)
x = start
for i in range(s_size):
y = start
for j in range(s_size):
np_spline[j,i] = xy_spline.f(y,x)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline2D.Spline2D(np_spline)
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
daxwriter.singleFrameDax(os.path.join(os.path.dirname(spline_name), "spline.dax"), 1000.0*np_spline + 100)
# 3D spline
else:
print("Generating 3D spline.")
s_size = 2*s_size
np_spline = numpy.zeros((s_size, s_size, s_size))
xy_splines = []
print("Generating XY splines.")
for i in range(np_psf.shape[0]):
xy_splines.append(spline2D.Spline2D(np_psf[i,:,:]))
print("Generating fitting spline.")
x = start
for i in range(s_size):
y = start
for j in range(s_size):
zvals = numpy.zeros(np_psf.shape[0])
for k in range(np_psf.shape[0]):
zvals[k] = xy_splines[k].f(y,x)
z_spline = spline1D.Spline1D(zvals)
max_z = float(np_psf.shape[0]) - 1.0
inc = max_z/(float(s_size)-1.0)
for k in range(s_size):
z = float(k)*inc
if (z > max_z):
z = max_z
np_spline[k,j,i] = z_spline.f(z)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline3D.Spline3D(np_spline)
if True:
import storm_analysis.sa_library.daxwriter as daxwriter
dxw = daxwriter.DaxWriter(os.path.join(os.path.dirname(spline_name), "spline.dax"),
np_spline.shape[1],
np_spline.shape[2])
for i in range(s_size):
dxw.addFrame(1000.0*np_spline[i,:,:] + 100)
dxw.close()
del psf_data["psf"]
psf_data["spline"] = np_spline
psf_data["coeff"] = spline.getCoeff()
pickle.dump(psf_data, open(spline_name, 'wb'))
def __init__(self, spline_file):
spline_data = self.loadSplineFile(spline_file)
self.spline = spline2D.Spline2D(spline_data["spline"],
spline_data["coeff"])
self.spline_size = self.spline.getSize()
def psfToSpline(psf_name, spline_name, s_size):
# Load PSF.
with open(psf_name, 'rb') as fp:
psf_data = pickle.load(fp)
np_psf = psf_data["psf"]
spline = False
start = np_psf.shape[1] / 2.0 - s_size - 0.5
# 2D spline
if (len(np_psf.shape) == 2):
print("Generating 2D spline.")
s_size = 2 * s_size
np_spline = numpy.zeros((s_size, s_size))
#np_psf = np_psf/numpy.max(np_psf)
xy_spline = spline2D.Spline2D(np_psf)
x = start
for i in range(s_size):
y = start
for j in range(s_size):
np_spline[j, i] = xy_spline.f(y, x)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline2D.Spline2D(np_spline)
if True:
import tifffile
np_spline = np_spline.astype(numpy.float32)
tiff_name = os.path.splitext(spline_name)[0] + "_sp.tif"
tifffile.imsave(tiff_name, np_spline)
# 3D spline
else:
print("Generating 3D spline.")
s_size = 2 * s_size
np_spline = numpy.zeros((s_size, s_size, s_size))
xy_splines = []
print("Generating XY splines.")
for i in range(np_psf.shape[0]):
xy_splines.append(spline2D.Spline2D(np_psf[i, :, :]))
print("Generating fitting spline.")
x = start
for i in range(s_size):
y = start
for j in range(s_size):
zvals = numpy.zeros(np_psf.shape[0])
for k in range(np_psf.shape[0]):
zvals[k] = xy_splines[k].f(y, x)
z_spline = spline1D.Spline1D(zvals)
max_z = float(np_psf.shape[0]) - 1.0
inc = max_z / (float(s_size) - 1.0)
for k in range(s_size):
z = float(k) * inc
if (z > max_z):
z = max_z
np_spline[k, j, i] = z_spline.f(z)
y += 1.0
x += 1.0
print("Calculating spline coefficients.")
spline = spline3D.Spline3D(np_spline, verbose=True)
if True:
import tifffile
np_spline = np_spline.astype(numpy.float32)
tiff_name = os.path.splitext(spline_name)[0] + "_sp.tif"
with tifffile.TiffWriter(tiff_name) as tf:
for i in range(s_size):
tf.save(np_spline[i, :, :])
del psf_data["psf"]
psf_data["spline"] = np_spline
psf_data["coeff"] = spline.getCoeff()
psf_data["psf_name"] = psf_name
with open(spline_name, 'wb') as fp:
pickle.dump(psf_data, fp)
