def plot_stray_field(scannum, helicities=[0, 90, 180]):
path = '/Users/alec/UCSB/cofeb_analysis_data/ta/'
scan_params_path = path + str(scannum) + '/' + 'scan_parameters.json'
field_path = path + str(scannum) + '/stray_field_sim/'
datapath = path + '1760/'
with open(scan_params_path, 'r') as fread:
scan_params = json.load(fread)
phi = scan_params['phi']
theta = scan_params['theta']
xres = scan_params['xres']
xcenter = scan_params['xcenter']
ycenter = scan_params['ycenter']
scanSize = (1e6) * scan_params['scanSize'] # convert to microns
errnames = ["lower", "mean", "upper"]
ffdata = lscan.load_ff('/Users/alec/UCSB/scan_data/' + str(scannum) +
'-esrdata/fitdata.txt',
xres,
xres,
maxfgrad=20)
scd = np.empty((len(helicities)), dtype=object)
vcd = np.empty_like(scd)
meff = np.empty_like(scd)
bx = np.empty((len(helicities), len(errnames)), dtype=object)
by = np.empty_like(bx)
bz = np.empty_like(bx)
bNV = np.empty_like(bx)
fieldCutNV = np.empty_like(bx)
for j in range(len(helicities)):
scd[j] = np.loadtxt(field_path + 'scd_' + str(helicities[j]) +
str(scannum) + '.txt',
delimiter=',')
vcd[j] = np.loadtxt(field_path + 'vcd_' + str(helicities[j]) +
str(scannum) + '.txt',
delimiter=',')
meff[j] = np.loadtxt(field_path + 'meff_' + str(helicities[j]) +
str(scannum) + '.txt',
delimiter=',')
for i in range(len(errnames)):
bx[j, i] = (1e4) * np.loadtxt(
field_path + 'h' + str(helicities[j]) + '_x_' + errnames[i] +
'_' + str(scannum) + '.txt',
delimiter=',')
by[j, i] = (1e4) * np.loadtxt(
field_path + 'h' + str(helicities[j]) + '_y_' + errnames[i] +
'_' + str(scannum) + '.txt',
delimiter=',')
bz[j, i] = (1e4) * np.loadtxt(
field_path + 'h' + str(helicities[j]) + '_z_' + errnames[i] +
'_' + str(scannum) + '.txt',
delimiter=',')
bNV[j, i] = cNVf.calc_NV_field(bx[j, i], by[j, i], bz[j, i], theta,
phi)
slen = len(bx[0, 0])
cutSize = 2.2
phinum = 8
philist = np.linspace(0, pi, phinum, endpoint=False)
ffdataCut = np.empty((phinum), dtype=object)
ffdataCutErr = np.empty((phinum), dtype=object)
fieldCutNV = np.empty((phinum, len(helicities), len(errnames)),
dtype=object)
for k in range(phinum):
ffdataCut[k] = linecut.linecut(ffdata[0], scanSize, cutSize,
philist[k], xcenter, ycenter)
ffdataCutErr[k] = linecut.linecut(ffdata[1], scanSize, cutSize,
philist[k], xcenter, ycenter)
for j in range(len(helicities)):
for i in range(len(errnames)):
fieldCutNV[k, j, i] = linecut.linecut(bNV[j, i], scanSize,
cutSize, philist[k])
#---------------- PLOTS ------------------------------------------
#-----------------------------------------------------------------
savepath = '/Users/alec/UCSB/papers/tacofeb/figures/'
plt.close('all')
if (len(helicities) == 4):
fig, ax = plt.subplots(figsize=(4, 4))
im = plt.imshow(bNV[3, 1], interpolation='nearest', cmap='bone')
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
plt.subplots_adjust(left=0.0,
bottom=0.0,
right=0.9,
top=1.0,
wspace=0,
hspace=0)
plt.savefig(savepath + 'BNV_bestFitHelicity_' + str(scannum) + '.pdf',
format='pdf')
fig, ax = plt.subplots(figsize=(4, 4))
im = plt.imshow(ffdata[0], interpolation='nearest', cmap='bone')
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
plt.subplots_adjust(left=0.0,
bottom=0.0,
right=0.9,
top=1.0,
wspace=0,
hspace=0)
plt.savefig(savepath + 'BNV_data_' + str(scannum) + '.pdf', format='pdf')
fig, axes = plt.subplots(ncols=2,
nrows=int(phinum / 2),
sharex=True,
sharey=True,
figsize=(6, 7.5))
for j in range(0, 2):
for i in range(0, int(phinum / 2)):
axes[i, j].errorbar(ffdataCut[int(i + (phinum / 2) * j)][0],
ffdataCut[int(i + (phinum / 2) * j)][1],
color='#000000',
fmt='.',
linewidth=1.0,
label='data')
axes[i, j].plot(fieldCutNV[0, 0, 1][0],
fieldCutNV[int(i + (phinum / 2) * j), 0, 1][1],
linewidth=2.0,
label=u'right-handed Néel')
axes[i, j].plot(fieldCutNV[0, 1, 1][0],
fieldCutNV[int(i + (phinum / 2) * j), 1, 1][1],
linewidth=2.0,
label="Bloch")
axes[i, j].plot(fieldCutNV[0, 2, 1][0],
fieldCutNV[int(i + (phinum / 2) * j), 2, 1][1],
linewidth=2.0,
label=u'left-handed Néel')
if (len(fieldCutNV[0, :, 1]) == 4):
axes[i,
j].plot(fieldCutNV[0, 3, 1][0],
fieldCutNV[int(i + (phinum / 2) * j), 3, 1][1],
linewidth=2.0,
label=r'$\psi_h$ = ' + str(helicities[3]) + u'°')
axes[i, j].get_yaxis().set_visible(False)
axes[i, j].get_xaxis().set_visible(False)
axes[i, j].text(
0.04,
0.86,
r'$\phi$ = ' +
'{:d}{}{:d}'.format(int(i +
(phinum / 2) * j), r'$\pi$/', phinum),
horizontalalignment='left',
verticalalignment='center',
transform=axes[i, j].transAxes,
fontsize=10)
axes[int((phinum / 2) - 1), 1].get_yaxis().set_visible(True)
axes[int((phinum / 2) - 1), 1].get_xaxis().set_visible(True)
axes[int((phinum / 2) - 1), 1].yaxis.tick_right()
axes[int((phinum / 2) - 1), 1].yaxis.set_label_position("right")
axes[int((phinum / 2) - 1), 1].set_xlabel(r'r ($\mu$m)')
axes[int((phinum / 2) - 1), 1].set_ylabel(r'B$\mathrm{_{NV}}$ (G)')
axes[int((phinum / 2) - 1), 0].legend(bbox_to_anchor=(0.0, -0.74),
loc=3,
borderaxespad=0.,
frameon=False,
prop={'size': 10})
plt.ylim([0, 32])
plt.subplots_adjust(left=0.0,
bottom=0.15,
right=0.88,
top=1.0,
wspace=0,
hspace=0)
plt.savefig(savepath + 'BNV_linecuts_' + str(scannum) + '.pdf',
format='pdf')
# fp.format_plots(plt, small=False, tight=False)
plt.show()
path = '/Users/alec/UCSB/cofeb_analysis_data/ta/'
filespec = 'Msfixed'
cal_params_path = path + 'cal_parameters_' + filespec + '.json'
with open(cal_params_path, 'r') as fread:
cal_params = json.load(fread)
Ms = cal_params['Ms']
t = cal_params['t']
phi = cal_params['phi']
theta = cal_params['theta']
thetaError = cal_params['thetaError']
height = cal_params['height']
heightError = cal_params['heightError']
data = lscan.load_ff(
'/Users/alec/UCSB/scan_data/' + str(scannum) + '-esrdata/fitdata.txt',
xres, yres, 20)
misc.imsave('/Users/alec/UCSB/scan_images/full-field/ff1760.png', data[0])
ffmask = ndimage.imread(
'/Users/alec/UCSB/scan_images/full-field/ff1760mask.png', flatten=True)
ffmask = np.multiply(np.add(np.multiply(ffmask, 1 / 255), -0.5), -2)
#---------------- FIT FUNCTIONS ----------------------------------
#-----------------------------------------------------------------
def fit_tanh(x, *params):
y = np.zeros_like(x)
c = params[0]
a = params[1]
x0 = params[2]
def vector_m_reconstruction(scannum,
kcutoff=1,
mask=True,
printError=False,
material='ta'):
path = '/Users/alec/UCSB/cofeb_analysis_data/' + material + '/'
scan_path = path + str(scannum) + '/'
scan_params_path = scan_path + 'scan_parameters.json'
material_params_path = path + 'material_parameters.json'
with open(scan_params_path, 'r') as fread:
scan_params = json.load(fread)
with open(material_params_path, 'r') as fread:
material_params = json.load(fread)
Ms = material_params['Ms']
t = material_params['t']
DWWidth = material_params['DWWidth']
phi = scan_params['phi']
theta = scan_params['theta']
thetaError = scan_params['thetaError']
height = scan_params['height']
heightError = scan_params['heightError']
scanSize = scan_params['scanSize']
xres = scan_params['xres']
yres = scan_params['yres']
xcenter = scan_params['xcenter']
ycenter = scan_params['ycenter']
zfield = scan_params['zfield'] * (1e4) # convert to G
data = lscan.load_ff('/Users/alec/UCSB/scan_data/' + str(scannum) +
'-esrdata/fitdata.txt',
xres,
yres,
Dgs=2870,
maxfgrad=20,
fieldangle=True,
printNVCalcError=printError)
misc.imsave(
'/Users/alec/UCSB/scan_images/full-field/ff' + str(scannum) + '.png',
data[0])
np.savetxt(scan_path + '/fail_index.txt', data[7])
#---------------- RECONSTRUCTION ---------------------------------
#-----------------------------------------------------------------
datas = data[0]
if (mask):
ffmask = ndimage.imread('/Users/alec/UCSB/scan_images/full-field/ff' +
str(scannum) + 'mask.png',
flatten=True)
ffmask = np.multiply(np.add(np.multiply(ffmask, 1 / 255), -0.5), -2)
datas = np.multiply(ffmask, datas)
datas0 = np.add(datas, -np.cos(theta) * zfield)
wimdata = fi.window_image(datas0, power=1 / 10)
recon_data = vr.vector_reconstruction(wimdata,
data[1],
theta,
thetaError,
phi,
height,
scanSize,
t,
kcutoff=kcutoff)
bxdata = recon_data[0]
bydata = recon_data[1]
bzdata = recon_data[2] + zfield
mdata = recon_data[3]
Vdata = recon_data[4]
bzdataError = recon_data[5]
np.savetxt(scan_path + 'bNV.txt', data[0], delimiter=',')
np.savetxt(scan_path + 'bNVError.txt', data[1], delimiter=',')
np.savetxt(scan_path + 'bz.txt', bzdata, delimiter=',')
np.savetxt(scan_path + 'bzError.txt', bzdataError, delimiter=',')
np.savetxt(scan_path + 'bx.txt', bxdata, delimiter=',')
np.savetxt(scan_path + 'by.txt', bydata, delimiter=',')
np.savetxt(scan_path + 'mdata.txt', mdata, delimiter=',')
np.savetxt(scan_path + 'V.txt', Vdata, delimiter=',')
#bz0 = 11.0
#phi = 0*pi/180
#file constants
filenum = 1885
xres = 120
yres = 5
scan_size = 0.6*5000
dres = scan_size/xres
hlist = np.zeros(2*yres)
thetalist = np.zeros(2*yres)
mstlist = np.zeros(2*yres)
#bz0list = np.zeros(2*yres)
ffdata = lscan.load_ff('/Users/alec/UCSB/scan_data/'+str(filenum)+'-esrdata/fitdata.txt',xres,yres,maxfgrad=30)
x = np.arange(0,dres*xres,dres)
plt.close('all')
ploth = 1
plt.figure(1,[17,10])
gs = gridspec.GridSpec(5, 2)
gs.update(left=0.05, right=0.97, top=0.97, bottom=0.05, wspace=0.25, hspace=0.25)
guess = [8e5, 70, 1500, 55]
rguess = guess
rguess[2] = guess[2]-100
for j in range(0,yres):
y = ffdata[0][j,:]
nleftnv[i][0] = simx
nleftnv[i][1] = np.multiply(
sfieldpre,
np.abs((nleftcuts[fi] * np.sin(theta) * np.cos(phi)) +
(nleftcuts[fi + rnum * hnum] * np.sin(theta) * np.sin(phi)) +
(np.add(nleftcuts[fi + 2 * rnum * hnum], zfields) *
np.cos(theta))))
nrightnv[i][0] = simx
nrightnv[i][1] = np.multiply(
sfieldpre,
np.abs((nrightcuts[fi] * np.sin(theta) * np.cos(phi)) +
(nrightcuts[fi + rnum * hnum] * np.sin(theta) * np.sin(phi)) +
(np.add(nrightcuts[fi + 2 * rnum * hnum], zfields) *
np.cos(theta))))
ffdata = ls.load_ff('/Users/alec/UCSB/scan_data/760-esrdata/fitdata.txt', dres,
5, 5)
line = 2
ffycut = [
np.add(np.arange(0, dsize, dsize / dres), 0.65), ffdata[0][line, 0:dres],
ffdata[1][line, 0:dres]
]
#ffycut = [np.arange(0,100),ffdata[0][:,3],ffdata[1][:,3]]
plt.close('all')
plt.figure(1, [5, 4])
imgplot = plt.imshow(ffdata[0], cmap='gray', interpolation='nearest')
plt.colorbar(fraction=0.027, pad=0.04)
plt.show()
plt.tight_layout()
font = {'family': 'Arial', 'weight': 'normal', 'size': 14}
matplotlib.rc('font', **font)
scannum = 1739
bscannum = 1740
hnum = 1
rnum = 3
dres = 50
dsize = 0.6 * 5
xres = 50
yres = 50
#ffdata = ls.load_rf_track('/Users/alec/UCSB/scan_data/'+str(scannum)+'/'+str(scannum).zfill(6)+'.scan',xres,yres)
ffdata = ls.load_ff(
'/Users/alec/UCSB/scan_data/' + str(scannum) + '-esrdata/fitdata.txt',
xres, yres, 7)
ffback = ls.load_ff(
'/Users/alec/UCSB/scan_data/' + str(bscannum) + '-esrdata/fitdata.txt',
xres, yres, 7)
ffdatac = ffdata[0][1:50, 0:48]
sub = np.zeros((5, 5))
#for j in range(0,5):
# for i in range(0,5):
# ffbackc = ffback[0][j:j+45,i+1:i+46]
# ffdatasub = np.add(ffdatac,np.multiply(ffbackc,-1))
# ffdatacmask = ffdatasub
# ffdatacmask[0:14,0:11] = np.zeros_like(ffdatacmask[0:14,0:11])
# ffdatacmask[10:35,8:33]= np.zeros_like(ffdatacmask[10:35,8:33])
# sub[i,j] = np.abs(np.sum(ffdatacmask))
#pi = np.pi
#bz0 = 11.0
#phi = 0*pi/180
#file constants
xres = 100
yres = 10
scan_size = 0.8*5000
dres = scan_size/xres
hlist = np.zeros(2*yres)
thetalist = np.zeros(2*yres)
xlist = np.zeros(2*yres)
mstlist = np.zeros(2*yres)
ffdata = ls.load_ff('/Users/alec/UCSB/scan_data/1773-esrdata/fitdata.txt',xres,yres,10)
x = np.arange(0,dres*xres,dres)
plt.close('all')
plt.figure(1,[17,10])
gs = gridspec.GridSpec(5, 4)
gs.update(left=0.05, right=0.97, top=0.97, bottom=0.05, wspace=0.25, hspace=0.25)
for j in range(0,10):
y = ffdata[0][j,:]
ye = ffdata[1][j,:]
ry = np.flipud(ffdata[2][j,:])
rye = np.flipud(ffdata[3][j,:])
bx = (2e-3) * mstnm * h * bdenomlist
bz = -(2e-3) * mstnm * (x - x0) * bdenomlist
bnv = np.abs(bx * np.sin(theta) * np.cos(phi) +
(bz + zfieldCal) * np.cos(theta))
return bnv
hlist = np.zeros(2 * yresCal)
thetalist = np.zeros(2 * yresCal)
# philist = np.zeros(2*yresCal)
dwlist = np.zeros(2 * yresCal)
r2list = np.zeros(2 * yresCal)
ffdata = lscan.load_ff('/Users/alec/UCSB/scan_data/' +
str(calibrationScanNum) + '-esrdata/fitdata.txt',
xresCal,
yresCal,
maxfgrad=20,
printNVCalcError=True)
x = np.arange(0, dres * xresCal, dres)
plt.close('all')
ploth = 1
plt.figure(1, [17, 10])
gs = gridspec.GridSpec(5, 4)
gs.update(left=0.05,
right=0.97,
top=0.97,
bottom=0.05,
wspace=0.25,
hspace=0.25)
from scipy import ndimage
from scipy import signal
import load_scan as lscan
import vector_reconstruction as vr
import fourier_image as fi
import m_reconstruction_Dovzhenko as mr
pi = np.pi
scannum = 1760
xres = 50
yres = 50
zfield = 9.5
scanL = 0.6*5000
data = lscan.load_ff('fitdata_1760.txt',xres,yres,15)
ffmask = ndimage.imread('ff_1760mask.png',flatten=True)
ffmask = np.multiply(np.add(np.multiply(ffmask,1/255),-0.5),-2)
filespec = 'Msnotfixed'
cal_params = np.loadtxt('cal_parameters_'+filespec+'.txt', delimiter=',')
theta = cal_params[2]
phi = cal_params[3]
height = cal_params[4]
#---------------- FIT FUNCTIONS ----------------------------------
#-----------------------------------------------------------------
def fit_arctan(x, *params):
y = np.zeros_like(x)
#file constants
xres = 150
yres = 5
scan_size = 1.2 * 5000
dres = scan_size / xres
mstlist = np.zeros(2 * yres)
hlist = np.zeros(2 * yres)
thetalist = np.zeros(2 * yres)
# philist = np.zeros(2*yres)
r2list = np.zeros(2 * yres)
ffdata = lscan.load_ff('/Users/alec/UCSB/scan_data/1572-esrdata/fitdata.txt',
xres,
yres,
printNVCalcError=True)
x = np.arange(0, dres * xres, dres)
BzMeanEnd = 0
BzStdEnd = 0
BzTails = [0, 1, 2, 3, 4]
BzEnd = np.zeros((len(BzTails), 12))
for i in range(len(BzTails)):
BzEnd[i] = np.concatenate((ffdata[0][BzTails[i],
-6:], ffdata[2][BzTails[i], :6]))
BzMeanEnd = np.mean(BzEnd)
BzStdEnd = np.std(BzEnd)
scanFolder = '/Users/alec/UCSB/scan_data/'
dataPath = scanFolder + str(scannum) + '-esrdata/' + fileName
def plot_point_fit(event):
if event.dblclick:
xp = int(np.round(event.xdata))
yp = int(np.round(event.ydata))
filenum = yp * res * 2 + xp + startFileNum
filePath = dataPath + str(filenum).zfill(6) + '_' + str(
numAvg) + '.txt'
cpsf.cwesr_plot_single_fit(filePath, filenum)
print(filenum)
data = lscan.load_ff(scanFolder + str(scannum) + '-esrdata/fitdata.txt',
res,
res,
fieldangle=True,
printNVCalcError=True)
plt.close('all')
fig, ax = plt.subplots()
im = plt.imshow(data[0])
cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
fp.format_plot(plt, fwidth=500, fheight=550)
plt.show()
cid = fig.canvas.mpl_connect('button_press_event', plot_point_fit)
