gao
/
analyseScript
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Axes can start from non zero now (2d bimodal fit)

master
Joschka 1 year ago
parent
a841f10379
commit
16c6ba7d86
1 changed files with 39 additions and 16 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 55
      Analyser/FitAnalyser.py

55
Analyser/FitAnalyser.py
View File

@ -428,6 +428,8 @@ class DensityProfileBEC2dModel(lmfit.Model):
# reshaping the image to 2D in the form [[a_00, a_01, .., a_0Y], [a_10,.., a_1Y], .., [a_X0, .., a_XY]], with a_xy
x_width = len(np.unique(x))
y_width = len(np.unique(y))
x_1d = np.linspace(x[0], x[-1], x_width)
y_1d = np.linspace(y[0], y[-1], y_width)
data = np.reshape(data, (y_width, x_width))
data = data.T
@ -440,13 +442,15 @@ class DensityProfileBEC2dModel(lmfit.Model):
# binarizing image to guess BEC width and calculate center
thresh = self.calc_thresh(data,thresh_val=0.5)
# calculating center of cloud by statistical distribution of binarized image
center = self.calc_cen(thresh)
center_pix = self.calc_cen_pix(thresh)
center = self.center_pix_conv(center_pix, x_1d, y_1d)
# guessing BEC width, or better of width of center blob if no BEC is present
BEC_width_guess = self.guess_BEC_width(thresh, center)
BEC_width_guess = self.guess_BEC_width(thresh, center_pix)
# plot binarized image and center position for debugging
if self.is_debug:
plt.pcolormesh(thresh.T, cmap='jet')
X, Y = np.meshgrid(x_1d,y_1d)
plt.pcolormesh(X,Y, thresh.T, cmap='jet')
plt.plot(center[0], center[1], marker='x', markersize=25, color='green')
plt.gca().set_aspect('equal')
plt.title(f'Binarized image for guessing BEC width + center position (BEC_width: x={BEC_width_guess[0]:.0f}, y={BEC_width_guess[1]:.0f} pix)')
@ -459,17 +463,19 @@ class DensityProfileBEC2dModel(lmfit.Model):
if self.is_debug:
print(f'x smaller y, 1d fit along x')
s_width_ind = 0
x_fit = x_1d
# slice of the image along the short BEC axis with width of BEC width is taken
X_guess = np.sum(data[:, round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)], 1) / len(data[0,round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)])
X_guess = np.sum(data[:, round(center_pix[1] - BEC_width_guess[1]/2) : round(center_pix[1] + BEC_width_guess[1]/2)], 1) / len(data[0,round(center_pix[1] - BEC_width_guess[1]/2) : round(center_pix[1] + BEC_width_guess[1]/2)])
else:
if self.is_debug:
print(f'y smaller x, 1d fit along y')
s_width_ind = 1
X_guess = np.sum(data[round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2), :], 0) / len(data[0,round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2)])
x_fit = y_1d
X_guess = np.sum(data[round(center_pix[0] - BEC_width_guess[0]/2) : round(center_pix[0] + BEC_width_guess[0]/2), :], 0) / len(data[0,round(center_pix[0] - BEC_width_guess[0]/2) : round(center_pix[0] + BEC_width_guess[0]/2)])
# Creating 1d fit init params + Performing fit
x = np.linspace(0, len(X_guess), len(X_guess))
max_val = np.max(X_guess)
@ -487,7 +493,7 @@ class DensityProfileBEC2dModel(lmfit.Model):
params_1d.add('sigma_th', 3*BEC_width_guess[0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')
res_1d = fitmodel_1d.fit(X_guess, x=x, params=params_1d)
res_1d = fitmodel_1d.fit(X_guess, x=x_fit, params=params_1d)
bval_1d = res_1d.best_values
if self.is_debug:
@ -501,17 +507,16 @@ class DensityProfileBEC2dModel(lmfit.Model):
print('1d fitted values')
self.print_bval(res_1d)
x = np.linspace(0, len(X_guess), len(X_guess))
plt.plot(x, X_guess, label='1d int. data')
plt.plot(x, density_1d(x,**bval_1d), label='bimodal fit')
plt.plot(x, thermal(x,x0=bval_1d['x0_th'], amp=bval_1d['amp_th'], sigma=bval_1d['sigma_th']), label='thermal part')
plt.plot(x_fit, X_guess, label='1d int. data')
plt.plot(x_fit, density_1d(x_fit,**bval_1d), label='bimodal fit')
plt.plot(x_fit, thermal(x_fit,x0=bval_1d['x0_th'], amp=bval_1d['amp_th'], sigma=bval_1d['sigma_th']), label='thermal part')
plt.legend()
if s_width_ind==0:
plt.title('1d fit of data along x-axis')
plt.xlabel('x_axis (pix)')
plt.xlabel('x_')
else:
plt.title('1d fit of data along y-axis')
plt.xlabel('y_axis (pix)')
plt.xlabel('y_axis')
plt.show()
# scaling amplitudes of 1d fit with the maximum value of blurred 2d data
@ -600,6 +605,7 @@ class DensityProfileBEC2dModel(lmfit.Model):
bval = res.best_values
# Do described post_check if enabled
if res.params['amp_bec'].vary and res.params['amp_th'].vary and bval['amp_bec']>0.5*bval['amp_th'] and self.post_check:
# creating image by cutting out region around BEC and counting number of atoms
@ -648,8 +654,8 @@ class DensityProfileBEC2dModel(lmfit.Model):
return thresh
def calc_cen(self, thresh1):
"""Calculating the center of a blob (atom cloud) in a binarized image by first calculating the probability distribution along both axes and afterwards the expectation value
def calc_cen_pix(self, thresh1):
"""Calculating the center (in pixel) of a blob (atom cloud) in a binarized image by first calculating the probability distribution along both axes and afterwards the expectation value
:param thresh1: Binary 2D image in the form [[a_00, a_01, .., a_0Y], [a_10,.., a_1Y], .., [a_X0, .., a_XY]], with a_xy, x_dim=X, y_dim=Y
:type thresh1: 2D numpy array
@ -669,13 +675,30 @@ class DensityProfileBEC2dModel(lmfit.Model):
cen[0] = np.sum(dx * np.arange(X))
cen[1] = np.sum(dy * np.arange(Y))
return cen
def center_pix_conv(self, center_pix, x, y):
"""Converts center in pixel to center in values of x and y
:param center_pix: pixel values of center
:type center_pix: numpy array (shape=(1,2))
:param x: x-axis
:type x: 1d numpy array
:param y: y-axis
:type y: 1d numpy array
:return: center coordinates in form [x_center, y_center] with respect to the axes
:rtype: numpy array (shap=(1,2))
"""
center = np.empty(2)
center[0] = x[round(center_pix[0])]
center[1] = y[round(center_pix[1])]
return center
def guess_BEC_width(self, thresh, center):
""" returns width of blob in binarized image along both axis through the center
:param thresh: Binary 2D image in the form [[a_00, a_01, .., a_0Y], [a_10,.., a_1Y], .., [a_X0, .., a_XY]], with a_xy, x_dim=X, y_dim=Y
:type thresh: 2d numpy array
:param center: center of blob in image in form [x_center, y_center]
:param center: center of blob in image in form [x_center, y_center] in pixel
:type center: 1d numpy array (shape=(1,2))
:return: width of blob in image as [x_width, y_width]
:rtype: 1d numpy array (shape=(1,2))

Write Preview
Loading…
Cancel
Save