Axes can start from non zero now (2d bimodal fit)
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@ -428,6 +428,8 @@ class DensityProfileBEC2dModel(lmfit.Model):
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# 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
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x_width = len(np.unique(x))
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y_width = len(np.unique(y))
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x_1d = np.linspace(x[0], x[-1], x_width)
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y_1d = np.linspace(y[0], y[-1], y_width)
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data = np.reshape(data, (y_width, x_width))
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data = data.T
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@ -440,13 +442,15 @@ class DensityProfileBEC2dModel(lmfit.Model):
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# binarizing image to guess BEC width and calculate center
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thresh = self.calc_thresh(data,thresh_val=0.5)
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# calculating center of cloud by statistical distribution of binarized image
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center = self.calc_cen(thresh)
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center_pix = self.calc_cen_pix(thresh)
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center = self.center_pix_conv(center_pix, x_1d, y_1d)
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# guessing BEC width, or better of width of center blob if no BEC is present
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BEC_width_guess = self.guess_BEC_width(thresh, center)
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BEC_width_guess = self.guess_BEC_width(thresh, center_pix)
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# plot binarized image and center position for debugging
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if self.is_debug:
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plt.pcolormesh(thresh.T, cmap='jet')
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X, Y = np.meshgrid(x_1d,y_1d)
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plt.pcolormesh(X,Y, thresh.T, cmap='jet')
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plt.plot(center[0], center[1], marker='x', markersize=25, color='green')
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plt.gca().set_aspect('equal')
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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)')
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@ -459,17 +463,19 @@ class DensityProfileBEC2dModel(lmfit.Model):
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if self.is_debug:
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print(f'x smaller y, 1d fit along x')
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s_width_ind = 0
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x_fit = x_1d
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# slice of the image along the short BEC axis with width of BEC width is taken
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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)])
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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)])
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else:
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if self.is_debug:
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print(f'y smaller x, 1d fit along y')
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s_width_ind = 1
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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)])
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x_fit = y_1d
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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)])
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# Creating 1d fit init params + Performing fit
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x = np.linspace(0, len(X_guess), len(X_guess))
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max_val = np.max(X_guess)
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@ -487,7 +493,7 @@ class DensityProfileBEC2dModel(lmfit.Model):
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params_1d.add('sigma_th', 3*BEC_width_guess[0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')
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res_1d = fitmodel_1d.fit(X_guess, x=x, params=params_1d)
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res_1d = fitmodel_1d.fit(X_guess, x=x_fit, params=params_1d)
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bval_1d = res_1d.best_values
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if self.is_debug:
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@ -501,17 +507,16 @@ class DensityProfileBEC2dModel(lmfit.Model):
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print('1d fitted values')
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self.print_bval(res_1d)
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x = np.linspace(0, len(X_guess), len(X_guess))
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plt.plot(x, X_guess, label='1d int. data')
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plt.plot(x, density_1d(x,**bval_1d), label='bimodal fit')
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plt.plot(x, thermal(x,x0=bval_1d['x0_th'], amp=bval_1d['amp_th'], sigma=bval_1d['sigma_th']), label='thermal part')
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plt.plot(x_fit, X_guess, label='1d int. data')
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plt.plot(x_fit, density_1d(x_fit,**bval_1d), label='bimodal fit')
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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')
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plt.legend()
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if s_width_ind==0:
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plt.title('1d fit of data along x-axis')
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plt.xlabel('x_axis (pix)')
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plt.xlabel('x_')
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else:
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plt.title('1d fit of data along y-axis')
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plt.xlabel('y_axis (pix)')
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plt.xlabel('y_axis')
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plt.show()
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# scaling amplitudes of 1d fit with the maximum value of blurred 2d data
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@ -600,6 +605,7 @@ class DensityProfileBEC2dModel(lmfit.Model):
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bval = res.best_values
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# Do described post_check if enabled
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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:
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# creating image by cutting out region around BEC and counting number of atoms
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@ -648,8 +654,8 @@ class DensityProfileBEC2dModel(lmfit.Model):
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return thresh
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def calc_cen(self, thresh1):
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"""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
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def calc_cen_pix(self, thresh1):
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"""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
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: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
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:type thresh1: 2D numpy array
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@ -670,12 +676,29 @@ class DensityProfileBEC2dModel(lmfit.Model):
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cen[1] = np.sum(dy * np.arange(Y))
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return cen
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def center_pix_conv(self, center_pix, x, y):
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"""Converts center in pixel to center in values of x and y
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:param center_pix: pixel values of center
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:type center_pix: numpy array (shape=(1,2))
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:param x: x-axis
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:type x: 1d numpy array
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:param y: y-axis
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:type y: 1d numpy array
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:return: center coordinates in form [x_center, y_center] with respect to the axes
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:rtype: numpy array (shap=(1,2))
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"""
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center = np.empty(2)
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center[0] = x[round(center_pix[0])]
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center[1] = y[round(center_pix[1])]
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return center
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def guess_BEC_width(self, thresh, center):
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""" returns width of blob in binarized image along both axis through the center
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: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
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:type thresh: 2d numpy array
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:param center: center of blob in image in form [x_center, y_center]
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:param center: center of blob in image in form [x_center, y_center] in pixel
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:type center: 1d numpy array (shape=(1,2))
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:return: width of blob in image as [x_width, y_width]
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:rtype: 1d numpy array (shape=(1,2))
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