First try with Brittas data
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Joschka/Crop_data.ipynb
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Joschka/Crop_data.ipynb
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Joschka/Not_used_Fitting.ipynb
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Joschka/Not_used_Fitting.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Fitting with constraint\n",
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"result = []\n",
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"times = []\n",
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"x = np.linspace(0,shape[3],150)\n",
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"y = np.linspace(0,shape[2], 150)\n",
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"\n",
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"for i in range(0,shape[0]):\n",
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" temp_res_arr = []\n",
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" for j in range(0,shape[1]):\n",
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" data = cropOD[i,j]\n",
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" fitModel = lmfit.Model(density_profile_BEC_2d, independent_vars=['x','y'])\n",
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" #fitModel.set_param_hint('deltax', value=5)\n",
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"\n",
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" bval = result_x[i][j].best_values\n",
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" S = np.max(blurred[i,j])/(bval['amp_bec'] + bval['amp_th'])\n",
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"\n",
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" params = lmfit.Parameters()\n",
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" #print(bval['sigma_th'])\n",
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"\n",
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" params.add_many(\n",
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" ('amp_bec', S * bval['amp_bec'], True, 0, 1.3 * np.max(data)),\n",
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" ('amp_th',S * bval['amp_th'], True, 0, 1.3 * np.max(data)),\n",
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" ('x0_bec',center[i,j,0], True, 0, 150),\n",
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" ('y0_bec',center[i,j,1], True, 0, 150),\n",
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" ('x0_th',center[i,j,0], True, 0, 150),\n",
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" ('y0_th',center[i,j,1], True, 0, 150),\n",
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" ('sigmax_bec',bval['sigma_bec'], True, 0, 50),\n",
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" ('sigmay_bec',BEC_width_guess[i,j,1], True, 0, 50),\n",
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" ('D_sigX', 1.93*bval['sigma_th'] - 1.22*bval['sigma_bec'], True, 0.1, 50),\n",
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" ('D_sig_th', 0, True, -10, 10)\n",
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" )\n",
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"\n",
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" params.add('sigmax_th',bval['sigma_th'], min=0, expr=f'0.632*sigmax_bec + 0.518*D_sigX')\n",
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" params.add('sigmay_th',bval['sigma_th'], min=0, expr=f'sigmax_th + D_sig_th')\n",
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"\n",
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" X,Y = np.meshgrid(x, y)\n",
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" X_1d = X.flatten()\n",
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" Y_1d = Y.flatten()\n",
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"\n",
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" data1d = data.flatten()\n",
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" start = time.time()\n",
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" res = fitModel.fit(data1d, x=X_1d, y=Y_1d, params=params)\n",
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" stop = time.time()\n",
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" print(stop-start)\n",
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" times.append(stop-start)\n",
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" temp_res_arr.append(res)\n",
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" result.append(temp_res_arr)\n",
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"times = np.array(times)\n",
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"print(f\"fitting time = {np.mean(times)} +- {np.std(times, ddof=1)}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# from opencv import moments\n",
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"shape = np.shape(cropOD)\n",
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"sigma = 0.4\n",
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"blurred = gaussian_filter(cropOD, sigma=sigma)\n",
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"\n",
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"thresh = np.zeros(shape)\n",
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"for i in range(0,shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.5,0,1)\n",
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"\n",
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"center = calc_cen_bulk(thresh)\n",
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"\n",
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"BEC_width_guess = guess_BEC_width(thresh, center)\n",
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"\n",
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"X_guess_og = np.zeros((shape[0], shape[1], shape[3]))\n",
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"# Y_guess_og = np.zeros((shape[0], shape[1], shape[2]))\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" X_guess_og[i,j] = np.sum(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2) , :], 0) / len(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2),0])\n",
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"\n",
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" # Y_guess_og[i,j] = np.sum(cropOD[i,j, :, round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)], 1) / len(cropOD[i,j,0,round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)])\n",
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"\n",
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"#[nr images x, nr images y, X / Y, center / sigma]\n",
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"x = np.linspace(0,149,150)\n",
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"y = np.linspace(0,149, 200)\n",
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"\n",
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"popt = np.zeros((shape[0], shape[1], 6))\n",
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"\n",
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"p0 = np.ones((shape[0], shape[1], 6))\n",
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"\n",
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"max = np.zeros((shape[0], shape[1]))\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" max[i] = np.ndarray.max(X_guess_og[i],axis=1)\n",
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"\n",
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"\n",
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"p0[:, :, 0] = center[:, :, 0] # center BEC\n",
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"p0[:, :, 1] = center[:, :, 0] # center th\n",
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"p0[:, :, 2] = 0.7 * max # amp BEC\n",
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"p0[:, :, 3] = 0.3 * max # amp th\n",
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"p0[:, :, 4] = BEC_width_guess[:, :, 0] # sigma BEC\n",
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"p0[:, :, 5] = BEC_width_guess[:, :, 0] * 3 # sigma th\n",
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"\n",
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"start = time.time()\n",
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"for i in range(0, shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" popt[i,j], pcov = curve_fit(density_1d, x, X_guess_og[i,j] , p0[i, j], nan_policy='omit')\n",
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"stop = time.time()\n",
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"\n",
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"print(f'fitting time: {(stop-start)*1e3} ms')\n",
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"\n",
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"fsize=(15,10)\n",
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"vmax = 1.5\n",
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"fig, ax = plt.subplots(shape[0],shape[1],figsize=fsize)\n",
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"for i in range(0, shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" lab = f\"A$_{{BEC}}$ = {popt[i,j,0]:.1f} \\n A$_{{th}}$ = {popt[i,j,1]:.1f} \"\n",
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" ax[i,j].plot(x, X_guess_og[i,j])\n",
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" ax[i,j].plot(x, density_1d(x, *popt[i,j]), label = lab, zorder=3)\n",
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" ax[i,j].plot(x, thermal(x, popt[i,j,1], popt[i,j, 3], popt[i,j, 5]))\n",
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"\n",
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"\n",
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" #ax[i,j].legend(fontsize=10)\n",
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" ax[i,j].set_facecolor('#FFFFFF')\n",
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"plt.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# from opencv import moments\n",
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"start = time.time()\n",
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"shape = np.shape(cropOD)\n",
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"sigma = 0.4\n",
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"blurred = gaussian_filter(cropOD, sigma=sigma)\n",
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"\n",
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"thresh = np.zeros(shape)\n",
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"for i in range(0,shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.5,0,1)\n",
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"\n",
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"center = calc_cen_bulk(thresh)\n",
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"\n",
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"BEC_width_guess = guess_BEC_width(thresh, center)\n",
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"\n",
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"X_guess_og = np.zeros((shape[0], shape[1], shape[3]))\n",
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"# Y_guess_og = np.zeros((shape[0], shape[1], shape[2]))\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" X_guess_og[i,j] = np.sum(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2) , :], 0) / len(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2),0])\n",
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"\n",
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" # Y_guess_og[i,j] = np.sum(cropOD[i,j, :, round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)], 1) / len(cropOD[i,j,0,round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)])\n",
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"\n",
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"#[nr images x, nr images y, X / Y, center / sigma]\n",
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"x = np.linspace(0,shape[3],150)\n",
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"y = np.linspace(0,shape[2], 150)\n",
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"\n",
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"popt = np.zeros((shape[0], shape[1], 6))\n",
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"\n",
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"p0 = np.ones((shape[0], shape[1], 6))\n",
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"\n",
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"max = np.zeros((shape[0], shape[1]))\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" max[i] = np.ndarray.max(X_guess_og[i],axis=1)\n",
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"\n",
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"# Fitting x without math constr\n",
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"fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
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"\n",
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"result_x = []\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" temp_res = []\n",
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" for j in range(0, shape[1]):\n",
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" t1 = time.time()\n",
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" params = lmfit.Parameters()\n",
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" params.add_many(\n",
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" ('x0_bec', center[i,j,0], True,0, 200),\n",
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" ('x0_th',center[i,j,0], True,0, 200),\n",
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" ('amp_bec', 0.7 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
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" ('amp_th', 0.3 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
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" ('deltax', 0, True, 0,50),\n",
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" ('sigma_bec',BEC_width_guess[i,j,0], True, 0, 50)\n",
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" )\n",
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" params.add('sigma_th', popt[i,j,5], min=0, expr=f'sigma_bec + deltax')\n",
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"\n",
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" t2 = time.time()\n",
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" res = fitmodel.fit(X_guess_og[i,j], x=x, params=params)\n",
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" t3 = time.time()\n",
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" temp_res.append(res)\n",
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" t4 = time.time()\n",
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" print(t2 - t1)\n",
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" print(t3 - t2)\n",
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" print(t4 - t3)\n",
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" print(\"\")\n",
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" result_x.append(temp_res)\n",
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"stop = time.time()\n",
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"\n",
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"print(f'total time: {(stop-start)*1e3} ms')3"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# from opencv import moments\n",
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"start = time.time()\n",
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"shape = np.shape(cropOD)\n",
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"sigma = 0.4\n",
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"blurred = gaussian_filter(cropOD, sigma=sigma)\n",
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"\n",
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"thresh = np.zeros(shape)\n",
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"for i in range(0,shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.5,0,1)\n",
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"\n",
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"center = calc_cen_bulk(thresh)\n",
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"\n",
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"BEC_width_guess = guess_BEC_width(thresh, center)\n",
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"\n",
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"X_guess_og = np.zeros((shape[0], shape[1], shape[3]))\n",
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"# Y_guess_og = np.zeros((shape[0], shape[1], shape[2]))\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" X_guess_og[i,j] = np.sum(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2) , :], 0) / len(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2),0])\n",
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"\n",
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" # Y_guess_og[i,j] = np.sum(cropOD[i,j, :, round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)], 1) / len(cropOD[i,j,0,round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)])\n",
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"\n",
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"#[nr images x, nr images y, X / Y, center / sigma]\n",
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"x = np.linspace(0,shape[3],150)\n",
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"y = np.linspace(0,shape[2], 150)\n",
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"\n",
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"popt = np.zeros((shape[0], shape[1], 6))\n",
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"\n",
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"p0 = np.ones((shape[0], shape[1], 6))\n",
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"\n",
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"max = np.zeros((shape[0], shape[1]))\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" max[i] = np.ndarray.max(X_guess_og[i],axis=1)\n",
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"\n",
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"# Fitting x without math constr\n",
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"fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
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"\n",
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"result_x = []\n",
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"\n",
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"for i in range(0, shape[0]):\n",
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" temp_res = []\n",
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" for j in range(0, shape[1]):\n",
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" t1 = time.time()\n",
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" params = lmfit.Parameters()\n",
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" params.add_many(\n",
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" ('x0_bec', center[i,j,0], True,0, 200),\n",
|
||||||
|
" ('x0_th',center[i,j,0], True,0, 200),\n",
|
||||||
|
" ('amp_bec', 0.7 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
|
||||||
|
" ('amp_th', 0.3 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
|
||||||
|
" ('sigma_bec',BEC_width_guess[i,j,0] / 1.22 , True, 0, 50),\n",
|
||||||
|
" ('sigma_th', 3 * BEC_width_guess[i,j,0], True, 0, 50)\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
" t2 = time.time()\n",
|
||||||
|
" res = fitmodel.fit(X_guess_og[i,j], x=x, params=params)\n",
|
||||||
|
" t3 = time.time()\n",
|
||||||
|
" temp_res.append(res)\n",
|
||||||
|
" t4 = time.time()\n",
|
||||||
|
" # print(t2 - t1)\n",
|
||||||
|
" # print(t3 - t2)\n",
|
||||||
|
" # print(t4 - t3)\n",
|
||||||
|
" # print(\"\")\n",
|
||||||
|
" result_x.append(temp_res)\n",
|
||||||
|
"stop = time.time()\n",
|
||||||
|
"\n",
|
||||||
|
"print(f'total time: {(stop-start)*1e3} ms')"
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"# get center of thresholded image\n",
|
||||||
|
"\n",
|
||||||
|
"def calc_thresh(data):\n",
|
||||||
|
" shape = np.shape(data)\n",
|
||||||
|
" thresh = np.zeros(shape)\n",
|
||||||
|
" sigma = 0.4\n",
|
||||||
|
"\n",
|
||||||
|
" if len(shape) == 4:\n",
|
||||||
|
" blurred = gaussian_filter(data, sigma=sigma)\n",
|
||||||
|
" for i in range(0,shape[0]):\n",
|
||||||
|
" for j in range(0, shape[1]):\n",
|
||||||
|
" thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.5,0,1)\n",
|
||||||
|
"\n",
|
||||||
|
" elif len(shape) == 3:\n",
|
||||||
|
" blurred = gaussian_filter(data, sigma=sigma)\n",
|
||||||
|
" for i in range(0,shape[0]):\n",
|
||||||
|
" thresh[i] = np.where(blurred[i] < np.max(blurred[i])*0.5,0,1)\n",
|
||||||
|
"\n",
|
||||||
|
" else:\n",
|
||||||
|
" print(\"Shape of data is wrong, output is empty\")\n",
|
||||||
|
"\n",
|
||||||
|
" return thresh\n",
|
||||||
|
"\n",
|
||||||
|
"def calc_cen(thresh1):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" returns array: [Y_center,X_center]\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" cen = np.zeros(2)\n",
|
||||||
|
" (Y,X) = np.shape(thresh1)\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
" thresh1 = thresh1 /np.sum(thresh1)\n",
|
||||||
|
"\n",
|
||||||
|
" # marginal distributions\n",
|
||||||
|
" dx = np.sum(thresh1, 0)\n",
|
||||||
|
" dy = np.sum(thresh1, 1)\n",
|
||||||
|
"\n",
|
||||||
|
" # expected values\n",
|
||||||
|
" cen[0] = np.sum(dx * np.arange(X))\n",
|
||||||
|
" cen[1] = np.sum(dy * np.arange(Y))\n",
|
||||||
|
" return cen\n",
|
||||||
|
"\n",
|
||||||
|
"def calc_cen_bulk(thresh):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" returns array in shape of input, containing array with [Y_center,X_center] for each image\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" shape = np.shape(thresh)\n",
|
||||||
|
" cen = np.zeros((shape[0], shape[1], 2))\n",
|
||||||
|
" for i in range(0, shape[0]):\n",
|
||||||
|
" for j in range(0, shape[1]):\n",
|
||||||
|
" cen[i,j] = calc_cen(thresh[i,j])\n",
|
||||||
|
" return cen\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def gaussian(x, x0, sigma, A):\n",
|
||||||
|
" return A * np.exp(-0.5 * (x-x0)**2 / sigma**2)"
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": false
|
||||||
|
}
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 2
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython2",
|
||||||
|
"version": "2.7.6"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 0
|
||||||
|
}
|
||||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user