def get_shifts(dist,
ref_dist,
max_shift=DEFAULT_MAX_SHIFT,
pixsize=DEFAULT_PIXSIZE):
'''try to return shifts between two distributions, not really working for now
'''
imgs = [ci(ff) for ff in dist.filelist]
cdimgs = [im.get_od_image() / im.s_lambda for im in imgs]
ldimgs = [bp.line_density(cdim, pixsize) for cdim in cdimgs]
ldsnorm = [ld / np.sum(ld) for ld in ldimgs]
ref_img = ci(ref_dist.filelist[-1])
ref_cdimg = ref_img.get_od_image() / ref_img.s_lambda
ref_ldimg = bp.line_density(ref_cdimg, pixsize)
ref_ldnorm = ref_ldimg / np.sum(ref_ldimg)
shifts = []
for nn, ld in enumerate(ldimgs):
shift = ia.optimal_shift(ref_ldnorm, ldsnorm[nn])
if abs(shift) > max_shift:
pass
else:
#aligned.append(np.roll(ld, -shift))
shifts.append(shift)
return shifts
def get_line_densities(dist, pixsize=DEFAULT_PIXSIZE):
'''returns the integrated line densities (lds) and the normalized lds
dist - a cloud distribution object
'''
imgs = [ci(ff) for ff in dist.filelist]
cdimgs = [im.get_od_image() / im.s_lambda for im in imgs]
ldimgs = [bp.line_density(cdim, pixsize) for cdim in cdimgs]
ldsnorm = [ld / np.sum(ld) for ld in ldimgs]
return ldsnorm, ldimgs
def field_dist(dist, unbias=False, **kwargs):
'''Return a list of magnetic field profiles calculated from data in distribution.
Args:
dist: a CloudDistribution
unbias: if True, subtract the mean magnetic field from each field profile
'''
imgs = [ci(ff) for ff in dist.filelist]
cdimgs = [img.get_od_image() / img.s_lambda for img in imgs]
lds = [bp.line_density(cd) for cd in cdimgs]
fas = [bp.field_array(ld, **kwargs) for ld in lds]
if unbias:
fas = [fa - np.mean(fa) for fa in fas]
return fas
def field_dist(dist, unbias=False, **kwargs):
'''Return a list of magnetic field profiles calculated from data in distribution.
Args:
dist: a CloudDistribution
unbias: if True, subtract the mean magnetic field from each field profile
'''
imgs = [ci(ff) for ff in dist.filelist]
cdimgs = [img.get_od_image()/ img.s_lambda for img in imgs]
lds = [bp.line_density(cd) for cd in cdimgs]
fas = [bp.field_array(ld, **kwargs) for ld in lds]
if unbias:
fas = [fa - np.mean(fa) for fa in fas]
return fas
import matplotlib.pyplot as plt
gamma = 8.63e-29 # T/m**3, from Wildermuth thesis p 85
rho0 = 2e-6
z0 = 2e-6
# dnoise = CD(r'C:\Users\Levlab\Documents\becy_stats_data\dragonfly_noise\2014-10-16\small_sample')
dnoise = CD(r'Z:\Data\ACM Data\dragonfly_noise\2014-10-20')
cts_per_px = []
ln_per_px = []
dn_per_px = []
for imgfile in dnoise.filelist:
img = ci(imgfile)
# img.truncate_image(600, 1000, 500, 600)
odimg = img.get_od_image()
# light_noise = img.atom_image_trunc - img.light_image_trunc*img.fluc_cor
light_noise = img.atom_image_trunc - img.light_image_trunc
dark_noise = img.dark_image_trunc
ln_sum = np.sum(light_noise, axis = 0)
dn_sum = np.sum(dark_noise, axis = 0)
odsum = np.sum(odimg, axis = 0) * (img.pixel_size / img.magnification)**2 / img.s_lambda
# plt.plot(odsum); plt.show()
cts_per_px.append(odsum)
ln_per_px.append(ln_sum)
dn_per_px.append(dn_sum)
ct_mu = np.mean(cts_per_px)
delB = gamma * ct_mu / (rho0**2 * z0)
print 'Delta N = %2.2f\n'%ct_mu
import numpy as np
import matplotlib.pyplot as plt
gamma = 8.63e-29 # T/m**3, from Wildermuth thesis p 85
rho0 = 2e-6
z0 = 2e-6
# dnoise = CD(r'C:\Users\Levlab\Documents\becy_stats_data\dragonfly_noise\2014-10-16\small_sample')
dnoise = CD(r'Z:\Data\ACM Data\dragonfly_noise\2014-10-20')
cts_per_px = []
ln_per_px = []
dn_per_px = []
for imgfile in dnoise.filelist:
img = ci(imgfile)
# img.truncate_image(600, 1000, 500, 600)
odimg = img.get_od_image()
# light_noise = img.atom_image_trunc - img.light_image_trunc*img.fluc_cor
light_noise = img.atom_image_trunc - img.light_image_trunc
dark_noise = img.dark_image_trunc
ln_sum = np.sum(light_noise, axis=0)
dn_sum = np.sum(dark_noise, axis=0)
odsum = np.sum(
odimg, axis=0) * (img.pixel_size / img.magnification)**2 / img.s_lambda
# plt.plot(odsum); plt.show()
cts_per_px.append(odsum)
ln_per_px.append(ln_sum)
dn_per_px.append(dn_sum)
ct_mu = np.mean(cts_per_px)
delB = gamma * ct_mu / (rho0**2 * z0)
A_guess = np.max(image)
theta_guess = 2*pi/180
k_guess = 4*pi*np.sin(theta_guess) / WAVELENGTH
p_0 = [A_guess, k_guess, 0, 0] #fit guess for k, phi, offset
coef, covar = curve_fit(sinusoid_1d, xdata, image, p0=p_0)
return coef, covar
if __name__ == "__main__":
# img = ci(r'Z:\Data\ACM Data\MoveMicro_h_from_sample\2014-10-15\40.5\2014-10-15_192124.mat')
dir = r'D:\ACMData\Imaging System\expose_time\2014-10-16\MoveMicro40.5\fringes_fits'
imgs = CD(dir, False).filelist
thetas = []
for f in imgs:
img = ci(f)
limg = img.light_image
plt.imshow(limg)
plt.show()
limg_trunc = limg[:200, :]
plt.imshow(limg_trunc)
plt.show()
light_pattern = np.sum(limg_trunc, axis = 1)
distances = np.cumsum(np.ones(len(light_pattern))*(PIXELSIZE / MAGNIFICATION))
coef, covar = fit_sinusoid_1d(light_pattern, distances)
plt.plot(distances, sinusoid_1d(distances, *coef))
plt.plot(distances, light_pattern)
plt.show()
from cloud_distribution import * from cloud_image import CloudImage as ci import BECphysics as bp import matplotlib.pyplot as plt import numpy as np cdist = CD(r'C:\Users\Levlab\Documents\becy_stats_data\statistics\5umMap\2014-10-17') imgs = [ci(ff) for ff in cdist.filelist] cdimgs = [img.get_od_image / img.s_lambda for img in imgs] lds = [bp.line_density(cd) for cd in cdimgs] plt.plot(lds[0]) plt.show() fas = [bp.field_array(ld) for ld in lds] fasarr = np.array(fas) xaxis = np.cumsum(np.ones(len(fas)) * (3.75 / 6.4)) ylocs = np.cumsum(np.ones(fasarr.shape[1])*2) X, Y = np.meshgrid(xaxis, ylocs) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') plt.contourf(X, Y, fasarr) plt.show()
from cloud_distribution import *
from cloud_image import CloudImage as ci
import BECphysics as bp
import matplotlib.pyplot as plt
import numpy as np
cdist = CD(
r'C:\Users\Levlab\Documents\becy_stats_data\statistics\5umMap\2014-10-17')
imgs = [ci(ff) for ff in cdist.filelist]
cdimgs = [img.get_od_image / img.s_lambda for img in imgs]
lds = [bp.line_density(cd) for cd in cdimgs]
plt.plot(lds[0])
plt.show()
fas = [bp.field_array(ld) for ld in lds]
fasarr = np.array(fas)
xaxis = np.cumsum(np.ones(len(fas)) * (3.75 / 6.4))
ylocs = np.cumsum(np.ones(fasarr.shape[1]) * 2)
X, Y = np.meshgrid(xaxis, ylocs)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plt.contourf(X, Y, fasarr)
plt.show()
from cloud_distribution import CD
from cloud_image import CloudImage as ci
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from scipy import ndimage
img = ci(
r'Z:\Data\ACM Data\MoveMicro_h_from_sample\2014-10-15\40.5\2014-10-15_192124.mat'
)
# img = ci(r'Z:\Data\ACM Data\Statistics\loadmot_number\2014-08-11\2014-08-11_104746.mat')
dimg = img.dark_image
# plt.imshow(dimg)
# plt.show()
# noise_floor = np.mean(dimg) + 50*np.std(dimg)
noise_floor = 2000
print 'mean: %2.2f' % np.mean(dimg)
print 'std: %2.2f' % np.std(dimg)
light_masked = np.ma.masked_less_equal(img.light_image, noise_floor)
od_img = img.get_od_image(trunc_switch=False)
struct = ndimage.generate_binary_structure(2, 10)
Zmask1 = ndimage.morphology.binary_erosion(light_masked.mask, struct)
Zmask2 = light_masked.mask
Zmask3 = np.ma.masked_less_equal(img.atom_image, noise_floor).mask
Zmask = np.logical_or(Zmask1, Zmask2)
Zmask = np.logical_or(Zmask, Zmask3)