def readCylWFS(fn,rotate=np.linspace(.75,1.5,50),interp=None):
"""
Load in data from WFS measurement of cylindrical mirror.
Assumes that data was processed using processHAS, and loaded into
a .fits file.
Scale to microns, remove misalignments,
strip NaNs.
If rotate is set to an array of angles, the rotation angle
which minimizes the number of NaNs in the image after
stripping perimeter nans is selected.
Distortion is bump positive looking at concave surface.
Imshow will present distortion in proper orientation as if
viewing the concave surface.
"""
#Remove NaNs and rescale
d = pyfits.getdata(fn)
d = man.stripnans(d)
d = d - np.nanmean(d)
#Remove cylindrical misalignment terms
d = d - fit.fitCylMisalign(d)[0]
# Negate to make bump positive and rotate to be consistent with looking at the part beamside.
#d = -d
d = -np.fliplr(d) #np.rot90(d,k = 2)
#Interpolate over NaNs
if interp is not None:
d = man.nearestNaN(d,method=interp)
return d
def readCylScript(fn,rotate=np.linspace(.75,1.5,50),interp=None):
"""
Load in data from 4D measurement of cylindrical mirror.
File is assumed to have been saved with Ryan's scripting function.
Scale to microns, remove misalignments,
strip NaNs.
If rotate is set to an array of angles, the rotation angle
which minimizes the number of NaNs in the image after
stripping perimeter nans is selected.
Distortion is bump positive looking at concave surface.
Imshow will present distortion in proper orientation as if
viewing the concave surface.
"""
#Read in values from header
f = open(fn+'.hdr','r')
l = f.readlines()
f.close()
#Wavelength should never change
wave = float(l[0].split()[0])*.001 #in microns
#Ensure wedge factor is 0.5
wedge = float(l[1])
if wedge!=0.5:
print 'Wedge factor is ' + str(wedge)
pdb.set_trace()
#Get pixel scale size
dx = float(l[-1])
#Remove NaNs and rescale
d = np.fromfile(fn+'.bin',dtype=np.float32)
try:
d = d.reshape((1002,981))
except:
d = d.reshape((1003,982))
d[d>1e10] = np.nan
d = man.stripnans(d)
d = d *wave
d = d - np.nanmean(d)
#Remove cylindrical misalignment terms
d = d - fit.fitCylMisalign(d)[0]
#Rotate out CGH roll misalignment?
if rotate is not None:
b = [np.sum(np.isnan(\
man.stripnans(\
nd.rotate(d,a,order=1,cval=np.nan)))) for a in rotate]
d = man.stripnans(\
nd.rotate(d,rotate[np.argmin(b)],order=1,cval=np.nan))
#Interpolate over NaNs
if interp is not None:
d = man.nearestNaN(d,method=interp)
return d,dx
def readCyl4D(fn,rotate=np.linspace(.75,1.5,50),interp=None):
"""
Load in data from 4D measurement of cylindrical mirror.
Scale to microns, remove misalignments,
strip NaNs.
If rotate is set to an array of angles, the rotation angle
which minimizes the number of NaNs in the image after
stripping perimeter nans is selected.
Distortion is bump positive looking at concave surface.
Imshow will present distortion in proper orientation as if
viewing the concave surface.
"""
#Get xpix value in mm
l = getline(fn,9)
dx = float(l.split()[1])*1000.
#Remove NaNs and rescale
d = np.genfromtxt(fn,skip_header=12,delimiter=',')
d = man.stripnans(d)
d = d *.6328
d = d - np.nanmean(d)
#d = np.rot90(d,k = 2)
#Remove cylindrical misalignment terms
d = d - fit.fitCylMisalign(d)[0]
#Rotate out CGH roll misalignment?
if rotate is not None:
b = [np.sum(np.isnan(\
man.stripnans(\
nd.rotate(d,a,order=1,cval=np.nan)))) for a in rotate]
d = man.stripnans(\
nd.rotate(d,rotate[np.argmin(b)],order=1,cval=np.nan))
#Interpolate over NaNs
if interp is not None:
d = man.nearestNaN(d,method=interp)
return d,dx
def readCyl4D_h5(h5_file):
f = h5py.File(h5_file, 'r')
meas = f['measurement0']
#################
# Getting the attributes of the data directly from the .h5 file
wedge = meas['genraw'].attrs['wedge']
height_unit = meas['genraw'].attrs['height_units']
wave = meas['genraw'].attrs['wavelength']
xpix = meas['genraw'].attrs['xpix']
#################
# Processing the data as though it's a cylinder.
# Apply the wedge factor.
raw_data = array(meas['genraw']['data']) #*wedge
# Removing the absurdly large value defaulted to for bad data and replacing it with a NaN.
raw_data[raw_data > 1e10] = NaN
# Then stripping that bad data flagged as nans from the perimeter.
data = man.stripnans(raw_data)
# Set the average surface to zero (i.e., remove piston)
data -= nanmean(data)
# Remove cylindrical misalignment.
data = data - fit.fitCylMisalign(data)[0]
# Apply unit transformation converting data to microns.
if height_unit == 'wv':
wavelength = float(wave[:-3])
data *= wavelength / 1000
if height_unit == 'nm':
data /= 1000
# Apply unit transformation converting pixel size to mm.
pix_unit = xpix[xpix.find(' ') + 1:]
pix_num = float(xpix[:xpix.find(' ')])
if pix_unit == 'inch':
pix_num *= 25.4
return data, pix_num
def plot_data_repeat_leveling(dlist,outfile=None,dis=True,name = ""):
"""Create a multiplot on a grid with plots of all data with
same leveling: raw, conical, cyliindrical, sag removed by line
abused function in old notebooks when type of leveling was uncertain or of interest."""
xs,ys=find_grid_size(len(dlist),3,square=False)
res=[]
r=plot_data_repeat(dlist,name=name,num=1)
res.append(r)
if outfile:
os.makedirs(os.path.dirname(outfile)+'\\raw\\',exist_ok=True)
plt.savefig(fn_add_subfix(outfile,'_raw','.png',pre='raw\\'))
if dis: display(plt.gcf())
fig,axes=plt.subplots(xs,ys,num=2)
axes=axes.flatten()
maximize()
res.append([])
for i,(ll,ax) in enumerate(zip(dlist,axes)):
plt.subplot(xs,ys,i+1,sharex=axes[0],sharey=axes[0])
tmp=ll.copy()
tmp.data=ll.data-fit.fitCylMisalign(ll.data)[0]
tmp.plot(stats=True)
res[-1].append(tmp.std())
#plt.clim([-3,3])
plt.clim(*(tmp.std()*np.array([-1,1])))
for ax in axes[:len(dlist)-1:-1]:
fig.delaxes(ax)
#plt.pause(0.1)
plt.suptitle(name+' CYL corrected')
if outfile:
os.makedirs(os.path.dirname(outfile)+'\\cyl\\',exist_ok=True)
plt.savefig(fn_add_subfix(outfile,'_cyl','.png',pre='cyl\\'))
if dis: display(plt.gcf())
fig,axes=plt.subplots(xs,ys,num=3)
axes=axes.flatten()
maximize()
res.append([])
for i,(ll,ax) in enumerate(zip(dlist,axes)):
plt.subplot(xs,ys,i+1,sharex=axes[0],sharey=axes[0])
tmp=ll.copy()
tmp.data=ll.data-fit.fitConeMisalign(ll.data)[0]
tmp.plot(stats=True)
res[-1].append(tmp.std())
#plt.clim([-3,3])
plt.clim(*(tmp.std()*np.array([-1,1])))
for ax in axes[:len(dlist)-1:-1]:
fig.delaxes(ax)
#plt.pause(0.1)
plt.suptitle(name+' CONE corrected')
if outfile:
os.makedirs(os.path.dirname(outfile)+'\\cone\\',exist_ok=True)
plt.savefig(fn_add_subfix(outfile,'_cone','.png',pre='cone\\'))
if dis: display(plt.gcf())
fig,axes=plt.subplots(xs,ys,num=4)
axes=axes.flatten()
maximize()
res.append([])
for i,(ll,ax) in enumerate(zip(dlist,axes)):
plt.subplot(xs,ys,i+1,sharex=axes[0],sharey=axes[0])
tmp=ll.copy()
#tmp.data=level_data(*ll(),2)[0]
tmp=tmp.level(2,axis=1)
tmp.plot(stats=True)
res[-1].append(tmp.std())
#plt.clim([-1,1])
plt.clim(*(tmp.std()*np.array([-1,1])))
for ax in axes[:len(dlist)-1:-1]:
fig.delaxes(ax)
#plt.pause(0.1)
plt.suptitle(name+' SAG removed by line')
plt.tight_layout(rect=[0,0,1,0.95])
if outfile:
os.makedirs(os.path.dirname(outfile)+'\\leg\\',exist_ok=True)
plt.savefig(fn_add_subfix(outfile,'_leg','.png',pre='leg\\'))
if dis: display(plt.gcf())
return res