2023-09-14 14:13:49 +02:00
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import lmfit\n",
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"import xarray as xr\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import copy\n",
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"\n",
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"import glob\n",
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"\n",
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"import xrft\n",
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"import finufft\n",
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"\n",
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"from uncertainties import ufloat\n",
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"from uncertainties import unumpy as unp\n",
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"from uncertainties import umath\n",
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"\n",
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"from datetime import datetime\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"#test\n",
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"plt.rcParams['font.size'] = 18\n",
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"\n",
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"from scipy.ndimage import gaussian_filter, rotate\n",
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"import matplotlib as mpl\n",
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"from scipy.interpolate import CubicSpline\n",
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"from scipy.optimize import curve_fit\n",
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"mpl.rc('xtick', labelsize=8)\n",
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"mpl.rc('ytick', labelsize=8)\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"C:/Users/Jianshun Gao/VisualCodeProjects/analysescript\")\n",
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"sys.path.append(\"C:/Users/Jianshun Gao/PycharmProjects/Foglight4/Data\")\n",
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"\n",
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"from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
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"from Analyser.ImagingAnalyser import ImageAnalyser\n",
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"from Analyser.FitAnalyser import FitAnalyser\n",
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"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
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"from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
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"from ToolFunction.ToolFunction import *\n",
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"\n",
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"import time\n",
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"import logging as log\n",
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"import scipy.constants as const\n",
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"\n",
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"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
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"xr.plot.dataarray_plot.errorbar = errorbar\n",
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"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
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"\n",
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"imageAnalyser = ImageAnalyser()\n",
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"\n",
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"\n",
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"import Analyser.FitAnalyser as dp"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"C:/Users/Jianshun Gao/PycharmProjects/Foglight4/Data/josch/high_cond_1.nc\n"
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]
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}
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],
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"source": [
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"path = \"C:/Users/Jianshun Gao/PycharmProjects/Foglight4/Data\"\n",
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"file = 'high_cond_1'\n",
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"file_path = f'{path}/josch/{file}.nc'\n",
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"print(file_path)\n",
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"with xr.open_dataarray(file_path) as data_x:\n",
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" data_x\n",
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"\n",
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"data = data_x.to_numpy()"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"is_debug = True\n",
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"fitm = DensityProfileBEC2dModel(is_debug=is_debug, atom_n_conv=144/2)\n",
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"\n",
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"def calc_cen_bulk(thresh):\n",
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" \"\"\"\n",
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" returns array in shape of input, containing array with [Y_center,X_center] for each image\n",
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" \"\"\"\n",
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" shape = np.shape(thresh)\n",
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" cen = np.zeros((shape[0], 2))\n",
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" for i in range(0, shape[0]):\n",
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" cen[i] = fitm.calc_cen_pix(thresh[i])\n",
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" return cen"
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]
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},
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{
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"cell_type": "code",
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2023-09-15 11:18:16 +02:00
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"execution_count": 8,
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2023-09-14 14:13:49 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"shape = np.shape(data)\n",
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"data_rot = np.empty(shape)\n",
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"\n",
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"for i in range(0,shape[0]):\n",
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" data_rot[i] = rotate(data[i], 36, reshape=False)"
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]
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},
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{
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"cell_type": "code",
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2023-09-15 11:18:16 +02:00
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"execution_count": 9,
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2023-09-14 14:13:49 +02:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[599.42377261 959.62015504]\n",
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" [599.46599496 959.50629723]\n",
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" [599.47858942 959.49118388]\n",
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" [599.47505938 959.41330166]\n",
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" [599.68149883 959.26697892]\n",
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" [599.4627907 959.58372093]\n",
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" [599.46412556 959.46188341]]\n"
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]
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}
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],
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"source": [
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"# cropping\n",
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"pr_data = data_rot\n",
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"\n",
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"cut_width_x = 200\n",
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"cut_width_y = 150\n",
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"thresh = fitm.calc_thresh(pr_data)\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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"print(center)\n",
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"\n",
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"\n",
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"if len(shape) == 3:\n",
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" cropOD = np.zeros((shape[0], cut_width_y, cut_width_x))\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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" cropOD[i] = pr_data[i, round(center[i,0]-cut_width_y/2):round(center[i,0]+cut_width_y/2), round(center[i,1]-cut_width_x/2):round(center[i,1]+cut_width_x/2)]\n",
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"\n",
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"shape = np.shape(cropOD)"
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]
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},
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{
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"cell_type": "code",
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2023-09-15 11:18:16 +02:00
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"execution_count": 10,
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2023-09-14 14:13:49 +02:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2023-09-15 11:18:16 +02:00
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"image/png": "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2023-09-14 14:13:49 +02:00
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"text/plain": [
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"<Figure size 1750x250 with 7 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig, ax = plt.subplots(1, shape[0], figsize=(2.5 *shape[0],2.5))\n",
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"for i in range(0, shape[0]):\n",
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" ax[i].pcolormesh(cropOD[i], cmap='jet')\n",
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"\n",
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2023-09-15 11:18:16 +02:00
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" ax[i].set_aspect('equal')\n",
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2023-09-14 14:13:49 +02:00
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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2023-09-15 11:18:16 +02:00
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"execution_count": 11,
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2023-09-14 14:13:49 +02:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" \n",
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"file high_cond_1 \n",
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2023-09-15 11:18:16 +02:00
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" image: 0\n"
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2023-09-14 14:13:49 +02:00
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]
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},
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{
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"data": {
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2023-09-15 11:18:16 +02:00
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"image/png": "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
|
2023-09-14 14:13:49 +02:00
|
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|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 640x480 with 1 Axes>"
|
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|
|
|
|
]
|
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|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
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|
},
|
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{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
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|
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|
|
"text": [
|
|
|
|
|
|
"shape: (200, 150)\n"
|
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|
|
]
|
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|
},
|
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|
{
|
|
|
|
|
|
"data": {
|
2023-09-15 11:18:16 +02:00
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"image/png": "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2023-09-14 14:13:49 +02:00
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"y smaller x, 1d fit along y\n",
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"\n",
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"1d fit initialization\n",
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"center = [99.49748744 75.5033557 ]\n",
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2023-09-15 11:18:16 +02:00
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"BEC widths: [23 13]\n",
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"\n",
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"1d init fit values\n",
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"Name Value Min Max Stderr Vary Expr Brute_Step\n",
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2023-09-15 11:18:16 +02:00
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"amp_bec 1.821 0 4.734 None True None None\n",
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"amp_th 1.821 0 4.734 None True None None\n",
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"deltax 39 0 200 None True None None\n",
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"sigma_bec 10.66 0 26 None True None None\n",
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"sigma_th 26.94 0 inf None False 0.632*sigma_bec + 0.518*deltax None\n",
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"x0_bec 75.5 65.5 85.5 None True None None\n",
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"x0_th 75.5 65.5 85.5 None True None None\n",
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"1d fitted values\n",
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2023-09-15 11:18:16 +02:00
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"x0_bec: 75.720, (init = 75.503), bounds = [65.50 : 85.50] \n",
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"x0_th: 75.824, (init = 75.503), bounds = [65.50 : 85.50] \n",
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"amp_bec: 2.943, (init = 1.821), bounds = [0.00 : 4.73] \n",
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"amp_th: 0.742, (init = 1.821), bounds = [0.00 : 4.73] \n",
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"sigma_bec: 10.919, (init = 10.656), bounds = [0.00 : 26.00] \n",
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"sigma_th: 9.846, (init = 26.936), bounds = [0.00 : inf] \n",
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"\n"
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]
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},
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{
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"data": {
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2023-09-15 11:18:16 +02:00
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"image/png": "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2023-09-14 14:13:49 +02:00
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Init Params\n",
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|
"Name Value Min Max Stderr Vary Expr Brute_Step\n",
|
2023-09-15 11:18:16 +02:00
|
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|
"amp_bec 3.5 0 6.577 None True None None\n",
|
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"amp_th 0.8824 0 6.577 None True None None\n",
|
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"atom_number_bec 905.4 -inf inf None False amp_bec / 5 * 2 * 3.14159265359 * sigmax_bec * sigmay_bec None\n",
|
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|
"atom_number_th 393.2 -inf inf None False amp_th * 2 * 3.14159265359 * 1.20206 / 1.643 * sigma_th * sigma_th None\n",
|
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|
"condensate_fraction 0.6972 -inf inf None False atom_number_bec / (atom_number_bec + atom_number_th) None\n",
|
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"rot_angle 40 10 70 None True None None\n",
|
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|
"sigma_th 9.846 0 200 None True None None\n",
|
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"sigmax_bec 18.85 0 46 None True None None\n",
|
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"sigmay_bec 10.92 0 26 None True None None\n",
|
2023-09-14 14:13:49 +02:00
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"x0_bec 99.5 89.5 109.5 None True None None\n",
|
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"x0_th 99.5 89.5 109.5 None True None None\n",
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"y0_bec 75.5 65.5 85.5 None True None None\n",
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|
|
"y0_th 75.5 65.5 85.5 None True None None\n",
|
|
|
|
|
|
"\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"bval first fit\n",
|
|
|
|
|
|
"amp_bec: 3.361, (init = 3.500), bounds = [0.00 : 6.58] \n",
|
|
|
|
|
|
"amp_th: 1.033, (init = 0.882), bounds = [0.00 : 6.58] \n",
|
|
|
|
|
|
"x0_bec: 100.041, (init = 99.497), bounds = [89.50 : 109.50] \n",
|
|
|
|
|
|
"y0_bec: 76.005, (init = 75.503), bounds = [65.50 : 85.50] \n",
|
|
|
|
|
|
"x0_th: 99.794, (init = 99.497), bounds = [89.50 : 109.50] \n",
|
|
|
|
|
|
"y0_th: 75.935, (init = 75.503), bounds = [65.50 : 85.50] \n",
|
|
|
|
|
|
"sigmax_bec: 23.817, (init = 18.852), bounds = [0.00 : 46.00] \n",
|
|
|
|
|
|
"sigmay_bec: 11.257, (init = 10.919), bounds = [0.00 : 26.00] \n",
|
|
|
|
|
|
"sigma_th: 9.384, (init = 9.846), bounds = [0.00 : 200.00] \n",
|
|
|
|
|
|
"rot_angle: 36.040, (init = 40.000), bounds = [10.00 : 70.00] \n",
|
|
|
|
|
|
"\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"Name Value Min Max Stderr Vary Expr Brute_Step\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"amp_bec 3.5 0 6.577 None True None None\n",
|
|
|
|
|
|
"amp_th 0.8824 0 6.577 None True None None\n",
|
|
|
|
|
|
"atom_number_bec 905.4 -inf inf None False amp_bec / 5 * 2 * 3.14159265359 * sigmax_bec * sigmay_bec None\n",
|
|
|
|
|
|
"atom_number_th 393.2 -inf inf None False amp_th * 2 * 3.14159265359 * 1.20206 / 1.643 * sigma_th * sigma_th None\n",
|
|
|
|
|
|
"condensate_fraction 0.6972 -inf inf None False atom_number_bec / (atom_number_bec + atom_number_th) None\n",
|
|
|
|
|
|
"rot_angle 40 10 70 None True None None\n",
|
|
|
|
|
|
"sigma_th 9.846 0 200 None True None None\n",
|
|
|
|
|
|
"sigmax_bec 18.85 0 46 None True None None\n",
|
|
|
|
|
|
"sigmay_bec 10.92 0 26 None True None None\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"x0_bec 99.5 89.5 109.5 None True None None\n",
|
|
|
|
|
|
"x0_th 99.5 89.5 109.5 None True None None\n",
|
|
|
|
|
|
"y0_bec 75.5 65.5 85.5 None True None None\n",
|
|
|
|
|
|
"y0_th 75.5 65.5 85.5 None True None None\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"amp_bec: 3.361, (init = 3.500), bounds = [0.00 : 6.58] \n",
|
|
|
|
|
|
"amp_th: 1.033, (init = 0.882), bounds = [0.00 : 6.58] \n",
|
|
|
|
|
|
"x0_bec: 100.041, (init = 99.497), bounds = [89.50 : 109.50] \n",
|
|
|
|
|
|
"y0_bec: 76.005, (init = 75.503), bounds = [65.50 : 85.50] \n",
|
|
|
|
|
|
"x0_th: 99.794, (init = 99.497), bounds = [89.50 : 109.50] \n",
|
|
|
|
|
|
"y0_th: 75.935, (init = 75.503), bounds = [65.50 : 85.50] \n",
|
|
|
|
|
|
"sigmax_bec: 23.817, (init = 18.852), bounds = [0.00 : 46.00] \n",
|
|
|
|
|
|
"sigmay_bec: 11.257, (init = 10.919), bounds = [0.00 : 26.00] \n",
|
|
|
|
|
|
"sigma_th: 9.384, (init = 9.846), bounds = [0.00 : 200.00] \n",
|
|
|
|
|
|
"rot_angle: 36.040, (init = 40.000), bounds = [10.00 : 70.00] \n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"Atom numbers:\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
" N_bec: 80595\n",
|
|
|
|
|
|
" N_th: 29742\n",
|
|
|
|
|
|
" N_ges: 110338\n",
|
|
|
|
|
|
" Cond. frac: 73.04 %\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"Temperature: 31.40 nK\n"
|
2023-09-14 14:13:49 +02:00
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"times = []\n",
|
|
|
|
|
|
"x = np.linspace(0,shape[2],shape[2])\n",
|
|
|
|
|
|
"y = np.linspace(0,shape[1], shape[1])\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"X,Y = np.meshgrid(x, y)\n",
|
|
|
|
|
|
"X_1d = X.flatten()\n",
|
|
|
|
|
|
"Y_1d = Y.flatten()\n",
|
|
|
|
|
|
"result = []\n",
|
|
|
|
|
|
"for i in range(0, shape[0]):\n",
|
|
|
|
|
|
"# for i in [0]:\n",
|
|
|
|
|
|
" print('\\n ')\n",
|
|
|
|
|
|
" print(f'file {file} \\n image: {i}')\n",
|
|
|
|
|
|
" start = time.time()\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
" \n",
|
|
|
|
|
|
" init = fitm.guess(cropOD[i].flatten(), X_1d, Y_1d, rot_angle=40, vary_rot=True, is_debug=True)\n",
|
|
|
|
|
|
"\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
" res = fitm.fit(cropOD[i].flatten(), x=X_1d, y=Y_1d, params=init)\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
" init.pretty_print()\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
" fitm.print_bval(res)\n",
|
|
|
|
|
|
" stop = time.time()\n",
|
|
|
|
|
|
" fitm.return_atom_number(res, X_1d, Y_1d)\n",
|
|
|
|
|
|
" fitm.return_temperature(res, omg=1036.5, tof=26e-3, is_print=False)\n",
|
|
|
|
|
|
" fitm.return_temperature(res, tof=26e-3)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" # print(f' time = {stop-start:.3f} s')\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" result.append(res)\n",
|
|
|
|
|
|
" times.append(stop-start)\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
" break\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"times = np.array(times)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"execution_count": 11,
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2023-09-15 11:18:16 +02:00
|
|
|
|
"image/png": "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
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 2000x2800 with 42 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"# fig, axs = plt.subplots(shape[0] , 5, figsize=(14, 4 * shape[0] ),dpi = 100)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"fig, axs = plt.subplots(shape[0] , 6, figsize=(20,4 * shape[0] ),dpi = 100)\n",
|
|
|
|
|
|
"fig.suptitle(f'{file}: ν = ({data_x.trap_f[0]}, {data_x.trap_f[1]}, {data_x.trap_f[2]}) Hz, T = {data_x.temp[0]*1e9:.0f}nK, tof = {data_x.tof * 1e3:.0f}ms, a = {data_x.a_fac :.0f}a$_0$, N = {data_x.N:.0e} ', y=-0.005*shape[0]+0.965, fontsize=24)\n",
|
|
|
|
|
|
"# fig.suptitle('test', va='bottom')\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"for i in range(0,shape[0]):\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" axs[i,0].set_title(f'image {i}, cond. frac = {fitm.cond_frac(result[i], X, Y) *1e2:.2f} %', loc='left', fontsize=18)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" lmfit.fit_report(result[i])\n",
|
|
|
|
|
|
" bval = result[i].best_values\n",
|
|
|
|
|
|
" fit = dp.density_profile_BEC_2d(X,Y, **bval)\n",
|
|
|
|
|
|
" vmax = np.max(fit)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" if bval['amp_bec'] > bval['amp_th']:\n",
|
|
|
|
|
|
" cen_str = 'bec'\n",
|
|
|
|
|
|
" else:\n",
|
|
|
|
|
|
" cen_str = 'th'\n",
|
|
|
|
|
|
" cen_x = round(bval[f'x0_{cen_str}'])\n",
|
|
|
|
|
|
" cen_y = round(bval[f'y0_{cen_str}'])\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" ax = axs[i,0]\n",
|
|
|
|
|
|
" ax.pcolormesh(X, Y, cropOD[i], vmin=0, vmax=vmax, cmap='jet', shading='auto')\n",
|
|
|
|
|
|
" #plt.colorbar(art, ax=ax, label='z')\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" # Plot gaussian 2d Fit + legend including Width parameters\n",
|
|
|
|
|
|
" ax = axs[i,1]\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" ax.pcolormesh(X, Y, fit, vmin=0, vmax=vmax, cmap='jet', shading='auto')\n",
|
|
|
|
|
|
" #plt.colorbar(art, ax=ax, label='z')\n",
|
|
|
|
|
|
"\n",
|
|
|
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" ax = axs[i,2]\n",
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"\n",
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" ax.pcolormesh(X, Y, fit-cropOD[i], vmin=0, vmax=0.2, cmap='jet', shading='auto')\n",
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"\n",
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"\n",
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|
|
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" ax = axs[i,3]\n",
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"\n",
|
|
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|
|
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" ax.plot(x, cropOD[i, cen_y, :])\n",
|
|
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|
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" ax.plot(x, fit[cen_y, :], label='bimodal')\n",
|
|
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|
|
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" ax.plot(x, dp.thermal(x, bval['x0_th'], bval['amp_th'], bval['sigma_th']), label='thermal')\n",
|
|
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|
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" ax.legend(fontsize=8)\n",
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"\n",
|
|
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|
|
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" ax = axs[i,4]\n",
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"\n",
|
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" ax.plot(y, cropOD[i, :, cen_x])\n",
|
|
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" ax.plot(y, fit[:, cen_x], label='bimodal')\n",
|
|
|
|
|
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" ax.plot(y, dp.thermal(y, bval['y0_th'], bval['amp_th'], bval['sigma_th']), label='thermal')\n",
|
|
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|
|
|
" ax.legend(fontsize=8)\n",
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"\n",
|
|
|
|
|
|
" ax = axs[i,5]\n",
|
|
|
|
|
|
" Na = fitm.return_atom_number(result[i], X, Y, is_print=False)\n",
|
|
|
|
|
|
" ax.axis('off')\n",
|
|
|
|
|
|
" col_labels = ['Sim', 'Fit']\n",
|
|
|
|
|
|
" row_labels = ['N', f'N$_{{bec}}$', f'N$_{{th}}$','f', 'T (nk)']\n",
|
|
|
|
|
|
" table_vals = [[f'{data_x.N:.2e}',f\"{Na['N']:.2e}\"],\n",
|
|
|
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|
|
" [f'{data_x.cond_frac.data[i] * data_x.N:.2e}',f\"{Na['N_bec']:.2e}\"],\n",
|
|
|
|
|
|
" [f'{(1-data_x.cond_frac.data[i]) * data_x.N:.2e}',f\"{Na['N_th']:.2e}\"],\n",
|
|
|
|
|
|
" [f'{data_x.cond_frac.data[i] *1e2:.1f} %', f\"{Na['cond_f'] *1e2:.2f} %\"],\n",
|
|
|
|
|
|
" [f'{data_x.temp[i] *1e9:.0f} nK', f'{fitm.return_temperature(result[i], tof=data_x.tof, is_print=False)*1e9:.1f} nK']\n",
|
|
|
|
|
|
" ]\n",
|
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|
"\n",
|
|
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|
"\n",
|
|
|
|
|
|
" my_table = ax.table(cellText=table_vals,\n",
|
|
|
|
|
|
" colWidths=[0.4]*2,\n",
|
|
|
|
|
|
" rowLabels=row_labels,\n",
|
|
|
|
|
|
" colLabels=col_labels,\n",
|
|
|
|
|
|
" loc='upper right')\n",
|
|
|
|
|
|
" my_table.scale(1,2)\n",
|
|
|
|
|
|
" ax.text(0.1,0.1,f'fitting time = {times[i]:.2f} s', fontsize=14)\n",
|
2023-09-15 11:18:16 +02:00
|
|
|
|
" ax.text(0.1,0,f'rot angle = {bval[\"rot_angle\"]}°', fontsize=14)\n",
|
2023-09-14 14:13:49 +02:00
|
|
|
|
"\n",
|
|
|
|
|
|
"t_fonts=20\n",
|
|
|
|
|
|
"axs[0,0].set_title(f'Data \\n ', fontsize=t_fonts)\n",
|
|
|
|
|
|
"axs[0,1].set_title('Fit \\n ', fontsize=t_fonts)\n",
|
|
|
|
|
|
"axs[0,2].set_title('Data - Fit \\n ', fontsize=t_fonts)\n",
|
|
|
|
|
|
"axs[0,3].set_title('cut along x \\n ', fontsize=t_fonts)\n",
|
|
|
|
|
|
"axs[0,4].set_title('cut along y \\n ', fontsize=t_fonts)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"# plt.tight_layout()\n",
|
|
|
|
|
|
"# plt.savefig(f'fit_output/final/{file}_{saveprefix}.png')\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 32,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"array([3.e-08, 3.e-08, 3.e-08, 3.e-08, 3.e-08, 3.e-08, 3.e-08])"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 32,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"data_x.temp"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": []
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
|
"display_name": ".venv",
|
|
|
|
|
|
"language": "python",
|
|
|
|
|
|
"name": "python3"
|
|
|
|
|
|
},
|
|
|
|
|
|
"language_info": {
|
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
|
"version": 3
|
|
|
|
|
|
},
|
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
|
"name": "python",
|
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
|
"version": "3.9.13"
|
|
|
|
|
|
},
|
|
|
|
|
|
"orig_nbformat": 4
|
|
|
|
|
|
},
|
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
|
"nbformat_minor": 2
|
|
|
|
|
|
}
|
