def inspect_influence(actmat, measmat, apt_mask, infldat=None, what="all", fignum0=1000, store=False, interactive=True, outdir='./'):
"""
Given influence data, inspect it
"""
# Check matrix size, measmat should be (n_data, n_meas), actmat should
# be (n_act, n_meas). We should have:
# n_data > n_meas
# n_meas > n_act
n_data, n_meas1 = measmat.shape
n_act, n_meas2 = actmat.shape
assert (n_data > n_meas1), "Measurement matrix wrong, transposed?"
assert (n_meas2 > n_act), "Actuation matrix wrong, transposed?"
assert (n_meas2 == n_meas1), "Matrices incompatible, not the same number of measurements"
if infldat == None:
infldat = comp_influence(measmat, actmat)
svd_U, svd_s, svd_Vh = infldat['svdcomps']
offsetmeas = infldat['offsetmeas'].ravel()
dshape = apt_mask.shape
apt_mask_c = im.mk_rad_mask(*dshape) < 0.9
plot_mask = np.ones(apt_mask.shape)
plot_mask[apt_mask==False] = np.nan
if (what in ["all", "singval"]):
plt.figure(fignum0+100); plt.clf()
plt.title("Singvals (n=%d, sum=%.4g, c=%.4g)" % (len(svd_s), svd_s.sum(), svd_s[0]/svd_s[-1]))
plt.xlabel("Mode [#]")
plt.ylabel("Singular value [AU]")
plt.plot(svd_s)
if (store): plt.savefig(pjoin(outdir, "cal_singvals.pdf"))
plt.figure(fignum0+101); plt.clf()
plt.title("Singvals [log] (n=%d, sum=%.4g, c=%.4g)" % (len(svd_s), svd_s.sum(), svd_s[0]/svd_s[-1]))
plt.xlabel("Mode [#]")
plt.ylabel("Singular value [AU]")
plt.semilogy(svd_s)
if (store): plt.savefig(pjoin(outdir, "cal_singvals_logy.pdf"))
if (interactive): raw_input("Continue...")
if (what in ["all", "offset"]):
# Init new empty image
thisvec = np.zeros(dshape)
# Fill image with masked vector data
thisvec[apt_mask] = offsetmeas
imin, imax = offsetmeas.min(), offsetmeas.max()
plt.figure(fignum0+200); plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("Offset phase (system aberration)")
plt.imshow(thisvec*plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_offset_phase.pdf"))
if (interactive): raw_input("Continue...")
if (what in ["all", "actrec"]):
plt.figure(fignum0+250); plt.clf()
plt.title("Actuation matrix reconstruction")
vmin, vmax = actmat.min(), actmat.max()
plt.subplot(131)
plt.xlabel("Actuator [id]")
plt.ylabel("Measurement [#]")
plt.imshow(actmat.T, vmin=vmin, vmax=vmax)
plt.subplot(132)
plt.xlabel("Actuator [id]")
plt.imshow(infldat['actmat_rec'].T, vmin=vmin, vmax=vmax)
plt.colorbar()
plt.subplot(133)
plt.xlabel("Actuator [id]")
plt.imshow((actmat[:-1]-infldat['actmat_rec']).T)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_actmat_rec.pdf"))
if (interactive): raw_input("Continue...")
if (what in ["all", "actmat"]):
# Init new empty image
thisvec = np.zeros(dshape)
for idx, inflvec in enumerate(infldat['inflmat'].T):
thisvec[apt_mask] = inflvec.ravel()
imin, imax = inflvec.min(), inflvec.max()
plt.figure(fignum0+300); plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("Actuator %d influence" % (idx))
plt.imshow(thisvec*plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_infl_vec_%d.pdf" % (idx)))
if (interactive):
if (raw_input("Continue [b=break]...") == 'b'): break
if (what in ["all", "sysmodes"]):
# Init new empty image
thisbase = np.zeros(dshape)
for idx, base in enumerate(svd_U.T):
thisbase[apt_mask] = base.ravel()
imin, imax = base.min(), base.max()
plt.figure(fignum0+400); plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("DM base %d, s=%g (%g)" % (idx, svd_s[idx], svd_s[idx]/svd_s.sum()))
plt.imshow(thisbase*plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_dm_base_vec_%d.pdf" % (idx)))
if (interactive):
if (raw_input("Continue [b=break]...") == 'b'): break
if (what in ["measrec"]):
measmat_rec = np.dot(infldat['inflmat'], actmat[:-1])
thismeasrec = np.zeros(dshape)
for idx, measvec_rec in enumerate(measmat_rec.T):
thismeasrec[apt_mask] = measvec_rec.ravel()
imin, imax = measvec_rec.min(), measvec_rec.max()
plt.figure(fignum0+500); plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("influence meas. %d rec" % (idx))
plt.imshow(thismeasrec*plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_infl_vec_%d_rec.pdf" % (idx)))
if (interactive):
if (raw_input("Continue [b=break]...") == 'b'): break
raw_input("Will now exit and close windows...")
for fnum in [100, 101, 200, 250, 300, 400, 500]:
plt.figure(fignum0 + fnum); plt.close()
def cam_setup(camidx=0, roi=None, flatf=None, darkf=None, maskshape='all', procd=32, usecam=True, outdir='./', verb=0):
"""
Setup IEEE1394 camera using Python's binding for OpenCV. Setup will be
stored in global CAM_CFG dictionary for use by cam_getimage().
@param camidx Camera index
@param roi Region of Interest to use, as (x, y, width, height)
@param flatf Flatfield image to use [file]
@param darkf Darkfield image to use [file]
@param maskshape Shape for processing mask, 'circ' or 'all'
@param procd Bitdepth to process images at (32 or 64)
@param outdir Directory to store stuff to
@param verb Verbosity
"""
global CAM_CFG
if (verb&VERB_M > L_INFO): print "Setting up camera..."
if (procd == 64):
npdtype = np.float64
cvdtype = cv.IPL_DEPTH_64F
else:
npdtype = np.float32
cvdtype = cv.IPL_DEPTH_32F
if (darkf and os.path.isfile(darkf)):
if (verb&VERB_M > L_DEBG): print "Processing dark frames..."
# Hard-link to used files for new cache
newdarkf = pjoin(outdir, CAM_DARKFIELD)
if (os.path.exists(newdarkf)):
os.unlink(newdarkf)
os.link(darkf, newdarkf)
darkim = file.read_file(darkf).astype(npdtype)
CAM_CFG['dark'] = cv.fromarray(darkim)
if (flatf and os.path.isfile(flatf)):
if (verb&VERB_M > L_DEBG): print "Processing flat frame(s)..."
newflatf = pjoin(outdir, CAM_FLATFIELD)
if (os.path.exists(newflatf)):
os.unlink(newflatf)
os.link(flatf, newflatf)
flatim = file.read_file(flatf).astype(npdtype)
CAM_CFG['flat'] = cv.fromarray(flatim)
if (CAM_CFG.has_key('dark')):
cv.Sub(CAM_CFG['flat'], CAM_CFG['dark'], CAM_CFG['flat'])
if (verb&VERB_M > L_XNFO): print "Configuring camera..."
if (not CAM_CFG.has_key('window')):
CAM_CFG['window'] = 'cam_live'
cv.NamedWindow(CAM_CFG['window'], cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("cam_histogram", cv.CV_WINDOW_AUTOSIZE)
CAM_CFG['idx'] = camidx
CAM_CFG['roi'] = roi
if (usecam):
CAM_CFG['handle'] = cv.CaptureFromCAM(camidx)
#cv.GetCaptureProperty(CAM_CFG['handle'], cv.CV_CAP_PROP_FPS)
cv.SetCaptureProperty(CAM_CFG['handle'], cv.CV_CAP_PROP_FPS, 60)
else:
CAM_CFG['handle'] = None
if (roi):
CAM_CFG['dshape'] = (roi[2], roi[3])
elif (usecam):
# GetSize returns (width, h), NumPy arrays expect (height, w)
rawframe = cv.QueryFrame(CAM_CFG['handle'])
CAM_CFG['dshape'] = cv.GetSize(rawframe)[::-1]
else:
raise ValueError("Need ROI or camera to determine data shape.")
CAM_CFG['frame'] = cv.CreateImage(CAM_CFG['dshape'][::-1], cvdtype, 1)
if (maskshape == 'circ'):
CAM_CFG['mask'] = im.mk_rad_mask(*CAM_CFG['dshape']) < 1
else:
CAM_CFG['mask'] = np.ones(CAM_CFG['dshape']).astype(np.bool)
CAM_CFG['imask'] = (CAM_CFG['mask'] == False)
file.store_file(pjoin(outdir, CAM_APTMASK), CAM_CFG['mask'].astype(np.uint8), clobber=True)
if (verb&VERB_M > L_INFO): print "Camera setup complete..."
cam_getimage(show=True)
def cam_setup(camidx=0,
roi=None,
flatf=None,
darkf=None,
maskshape='all',
procd=32,
usecam=True,
outdir='./',
verb=0):
"""
Setup IEEE1394 camera using Python's binding for OpenCV. Setup will be
stored in global CAM_CFG dictionary for use by cam_getimage().
@param camidx Camera index
@param roi Region of Interest to use, as (x, y, width, height)
@param flatf Flatfield image to use [file]
@param darkf Darkfield image to use [file]
@param maskshape Shape for processing mask, 'circ' or 'all'
@param procd Bitdepth to process images at (32 or 64)
@param outdir Directory to store stuff to
@param verb Verbosity
"""
global CAM_CFG
if (verb & VERB_M > L_INFO): print "Setting up camera..."
if (procd == 64):
npdtype = np.float64
cvdtype = cv.IPL_DEPTH_64F
else:
npdtype = np.float32
cvdtype = cv.IPL_DEPTH_32F
if (darkf and os.path.isfile(darkf)):
if (verb & VERB_M > L_DEBG): print "Processing dark frames..."
# Hard-link to used files for new cache
newdarkf = pjoin(outdir, CAM_DARKFIELD)
if (os.path.exists(newdarkf)):
os.unlink(newdarkf)
os.link(darkf, newdarkf)
darkim = file.read_file(darkf).astype(npdtype)
CAM_CFG['dark'] = cv.fromarray(darkim)
if (flatf and os.path.isfile(flatf)):
if (verb & VERB_M > L_DEBG): print "Processing flat frame(s)..."
newflatf = pjoin(outdir, CAM_FLATFIELD)
if (os.path.exists(newflatf)):
os.unlink(newflatf)
os.link(flatf, newflatf)
flatim = file.read_file(flatf).astype(npdtype)
CAM_CFG['flat'] = cv.fromarray(flatim)
if (CAM_CFG.has_key('dark')):
cv.Sub(CAM_CFG['flat'], CAM_CFG['dark'], CAM_CFG['flat'])
if (verb & VERB_M > L_XNFO): print "Configuring camera..."
if (not CAM_CFG.has_key('window')):
CAM_CFG['window'] = 'cam_live'
cv.NamedWindow(CAM_CFG['window'], cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("cam_histogram", cv.CV_WINDOW_AUTOSIZE)
CAM_CFG['idx'] = camidx
CAM_CFG['roi'] = roi
if (usecam):
CAM_CFG['handle'] = cv.CaptureFromCAM(camidx)
#cv.GetCaptureProperty(CAM_CFG['handle'], cv.CV_CAP_PROP_FPS)
cv.SetCaptureProperty(CAM_CFG['handle'], cv.CV_CAP_PROP_FPS, 60)
else:
CAM_CFG['handle'] = None
if (roi):
CAM_CFG['dshape'] = (roi[2], roi[3])
elif (usecam):
# GetSize returns (width, h), NumPy arrays expect (height, w)
rawframe = cv.QueryFrame(CAM_CFG['handle'])
CAM_CFG['dshape'] = cv.GetSize(rawframe)[::-1]
else:
raise ValueError("Need ROI or camera to determine data shape.")
CAM_CFG['frame'] = cv.CreateImage(CAM_CFG['dshape'][::-1], cvdtype, 1)
if (maskshape == 'circ'):
CAM_CFG['mask'] = im.mk_rad_mask(*CAM_CFG['dshape']) < 1
else:
CAM_CFG['mask'] = np.ones(CAM_CFG['dshape']).astype(np.bool)
CAM_CFG['imask'] = (CAM_CFG['mask'] == False)
file.store_file(pjoin(outdir, CAM_APTMASK),
CAM_CFG['mask'].astype(np.uint8),
clobber=True)
if (verb & VERB_M > L_INFO): print "Camera setup complete..."
cam_getimage(show=True)
def inspect_influence(actmat,
measmat,
apt_mask,
infldat=None,
what="all",
fignum0=1000,
store=False,
interactive=True,
outdir='./'):
"""
Given influence data, inspect it
"""
# Check matrix size, measmat should be (n_data, n_meas), actmat should
# be (n_act, n_meas). We should have:
# n_data > n_meas
# n_meas > n_act
n_data, n_meas1 = measmat.shape
n_act, n_meas2 = actmat.shape
assert (n_data > n_meas1), "Measurement matrix wrong, transposed?"
assert (n_meas2 > n_act), "Actuation matrix wrong, transposed?"
assert (n_meas2 == n_meas1
), "Matrices incompatible, not the same number of measurements"
if infldat == None:
infldat = comp_influence(measmat, actmat)
svd_U, svd_s, svd_Vh = infldat['svdcomps']
offsetmeas = infldat['offsetmeas'].ravel()
dshape = apt_mask.shape
apt_mask_c = im.mk_rad_mask(*dshape) < 0.9
plot_mask = np.ones(apt_mask.shape)
plot_mask[apt_mask == False] = np.nan
if (what in ["all", "singval"]):
plt.figure(fignum0 + 100)
plt.clf()
plt.title("Singvals (n=%d, sum=%.4g, c=%.4g)" %
(len(svd_s), svd_s.sum(), svd_s[0] / svd_s[-1]))
plt.xlabel("Mode [#]")
plt.ylabel("Singular value [AU]")
plt.plot(svd_s)
if (store): plt.savefig(pjoin(outdir, "cal_singvals.pdf"))
plt.figure(fignum0 + 101)
plt.clf()
plt.title("Singvals [log] (n=%d, sum=%.4g, c=%.4g)" %
(len(svd_s), svd_s.sum(), svd_s[0] / svd_s[-1]))
plt.xlabel("Mode [#]")
plt.ylabel("Singular value [AU]")
plt.semilogy(svd_s)
if (store): plt.savefig(pjoin(outdir, "cal_singvals_logy.pdf"))
plt.figure(fignum0 + 102)
plt.clf()
plt.title("1-singvals cumsum (n=%d, sum=%.4g, c=%.4g)" %
(len(svd_s), svd_s.sum(), svd_s[0] / svd_s[-1]))
plt.xlabel("Mode [#]")
plt.ylabel("Residual singval [AU]")
plt.semilogy(1 - svd_s.cumsum() / svd_s.sum())
if (store): plt.savefig(pjoin(outdir, "cal_singvals_cumsum_logy.pdf"))
if (interactive): raw_input("Continue...")
if (what in ["all", "offset"]):
# Init new empty image
thisvec = np.zeros(dshape)
# Fill image with masked vector data
thisvec[apt_mask] = offsetmeas
imin, imax = offsetmeas.min(), offsetmeas.max()
plt.figure(fignum0 + 200)
plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("Offset phase (system aberration)")
plt.imshow(thisvec * plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_offset_phase.pdf"))
if (interactive): raw_input("Continue...")
if (what in ["all", "actrec"]):
plt.figure(fignum0 + 250)
plt.clf()
plt.title("Actuation matrix reconstruction")
vmin, vmax = actmat.min(), actmat.max()
plt.subplot(131)
plt.xlabel("Actuator [id]")
plt.ylabel("Measurement [#]")
plt.imshow(actmat.T, vmin=vmin, vmax=vmax)
plt.subplot(132)
plt.xlabel("Actuator [id]")
plt.imshow(infldat['actmat_rec'].T, vmin=vmin, vmax=vmax)
plt.colorbar()
plt.subplot(133)
plt.xlabel("Actuator [id]")
plt.imshow((actmat[:-1] - infldat['actmat_rec']).T)
plt.colorbar()
if (store): plt.savefig(pjoin(outdir, "cal_actmat_rec.pdf"))
if (interactive): raw_input("Continue...")
if (what in ["all", "actmat"]):
# Init new empty image
thisvec = np.zeros(dshape)
for idx, inflvec in enumerate(infldat['inflmat'].T):
thisvec[apt_mask] = inflvec.ravel()
imin, imax = inflvec.min(), inflvec.max()
plt.figure(fignum0 + 300)
plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("Actuator %d influence" % (idx))
plt.imshow(thisvec * plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store):
plt.savefig(pjoin(outdir, "cal_infl_vec_%d.pdf" % (idx)))
if (interactive):
if (raw_input("Continue [b=break]...") == 'b'): break
if (what in ["all", "sysmodes"]):
# Init new empty image
thisbase = np.zeros(dshape)
for idx, base in enumerate(svd_U.T):
thisbase[apt_mask] = base.ravel()
imin, imax = base.min(), base.max()
plt.figure(fignum0 + 400)
plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("DM base %d, s=%g (%g)" %
(idx, svd_s[idx], svd_s[idx] / svd_s.sum()))
plt.imshow(thisbase * plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store):
plt.savefig(pjoin(outdir, "cal_dm_base_vec_%d.pdf" % (idx)))
if (interactive):
if (raw_input("Continue [b=break]...") == 'b'): break
if (what in ["measrec"]):
measmat_rec = np.dot(infldat['inflmat'], actmat[:-1])
thismeasrec = np.zeros(dshape)
for idx, measvec_rec in enumerate(measmat_rec.T):
thismeasrec[apt_mask] = measvec_rec.ravel()
imin, imax = measvec_rec.min(), measvec_rec.max()
plt.figure(fignum0 + 500)
plt.clf()
plt.xlabel("X [pix]")
plt.ylabel("Y [pix]")
plt.title("influence meas. %d rec" % (idx))
plt.imshow(thismeasrec * plot_mask, vmin=imin, vmax=imax)
plt.colorbar()
if (store):
plt.savefig(pjoin(outdir, "cal_infl_vec_%d_rec.pdf" % (idx)))
if (interactive):
if (raw_input("Continue [b=break]...") == 'b'): break
raw_input("Will now exit and close windows...")
for fnum in [100, 101, 102, 200, 250, 300, 400, 500]:
plt.figure(fignum0 + fnum)
plt.close()