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{ "cells": [ { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import mpmath as mp\n", "import matplotlib.pyplot as plt\n", "\n", "def polylog(power, numerator):\n", " \n", " order = 100\n", " \n", " dataShape = numerator.shape\n", " numerator = np.tile(numerator, (order, 1))\n", " numerator = np.power(numerator.T, np.arange(1, order+1)).T\n", "\n", " denominator = np.arange(1, order+1)\n", " denominator = np.tile(denominator, (dataShape[0], 1))\n", " denominator = denominator.T\n", "\n", " data = numerator/ np.power(denominator, power)\n", "\n", " return np.sum(data, axis=0)\n", "\n", "x = np.linspace(0, 1, 51)\n", "y1 = polylog(2, x)\n", "y2 = [float(mp.polylog(2, i).real) for i in x]\n", "\n", "plt.figure()\n", "\n", "plt.plot(x, y1, 'r')\n", "plt.plot(x, y2, 'b')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "from lmfit.lineshapes import (not_zero, breit_wigner, damped_oscillator, dho, doniach,\n", " expgaussian, exponential, gaussian, gaussian2d,\n", " linear, lognormal, lorentzian, moffat, parabolic,\n", " pearson7, powerlaw, pvoigt, rectangle, sine,\n", " skewed_gaussian, skewed_voigt, split_lorentzian, step,\n", " students_t, thermal_distribution, tiny, voigt)\n", "\n", "def polylog(power, numerator):\n", " \n", " order = 100\n", " \n", " dataShape = numerator.shape\n", " numerator = np.tile(numerator, (order, 1))\n", " numerator = np.power(numerator.T, np.arange(1, order+1)).T\n", "\n", " denominator = np.arange(1, order+1)\n", " denominator = np.tile(denominator, (dataShape[0], 1))\n", " denominator = denominator.T\n", "\n", " data = numerator/ np.power(denominator, power)\n", "\n", " return np.sum(data, axis=0)\n", "\n", "def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0): \n", " ## Approximation of the polylog function with 2D gaussian as argument. -> discribes the thermal part of the cloud\n", " return amplitude / 2 / 5.403642092095097 / max(tiny, sigmax * sigmay) * polylog(2, np.exp( -((x-centerx)**2/(2 * (sigmax)**2))-((y-centery)**2/( 2 * (sigmay)**2)) ))\n" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.403642092095097\n" ] } ], "source": [ "from scipy import special\n", "\n", "sum = 0\n", "for i in range(1,20000):\n", " sum += 1/i**4 * special.gamma(1/2/i)**2\n", " \n", "print(sum)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.0" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[1] - x[0] " ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8405962721688879" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.linspace(-100, 100, 101)\n", "y = np.linspace(-100, 100, 101)\n", "\n", "X, Y = np.meshgrid(x, y)\n", "X = X.flatten()\n", "Y = Y.flatten()\n", "Z = polylog2_2d(x=X, y=Y).reshape(101, 101)\n", "\n", "np.sum(Z)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Import supporting package" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "import copy\n", "\n", "import glob\n", "\n", "import xrft\n", "import finufft\n", "\n", "from uncertainties import ufloat\n", "from uncertainties import unumpy as unp\n", "from uncertainties import umath\n", "\n", "from datetime import datetime\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['font.size'] = 18\n", "\n", "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n", "from Analyser.ImagingAnalyser import ImageAnalyser\n", "from Analyser.FitAnalyser import FitAnalyser\n", "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n", "from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n", "from ToolFunction.ToolFunction import *\n", "\n", "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n", "xr.plot.dataarray_plot.errorbar = errorbar\n", "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n", "\n", "imageAnalyser = ImageAnalyser()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Import supporting package" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "\n", "from uncertainties import ufloat\n", "from uncertainties import unumpy as unp\n", "from uncertainties import umath\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from DataContainer.ReadData import read_hdf5_file\n", "from Analyser.ImagingAnalyser import ImageAnalyser\n", "from Analyser.FitAnalyser import FitAnalyser\n", "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n", "from Analyser.FitAnalyser import NewFitModel\n", "from ToolFunction.ToolFunction import *\n", "\n", "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n", "xr.plot.dataarray_plot.errorbar = errorbar\n", "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n", "\n", "imageAnalyser = ImageAnalyser()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Start a client for parallel computing" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", " <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <h3 style=\"margin-bottom: 0px;\">Client</h3>\n", " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-f3431762-0b91-11ee-bc80-80e82ce2fa8e</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", "\n", " <tr>\n", " \n", " <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n", " <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n", " \n", " </tr>\n", "\n", " \n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " \n", "\n", " </table>\n", "\n", " \n", "\n", " \n", " <details>\n", " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n", " <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n", " <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n", " </div>\n", " <div style=\"margin-left: 48px;\">\n", " <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n", " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">6e648e73</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Workers:</strong> 6\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads:</strong> 60\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total memory:</strong> 55.88 GiB\n", " </td>\n", " </tr>\n", " \n", " <tr>\n", " <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n", " <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n", "</tr>\n", "\n", " \n", " </table>\n", "\n", " <details>\n", " <summary style=\"margin-bottom: 20px;\">\n", " <h3 style=\"display: inline;\">Scheduler Info</h3>\n", " </summary>\n", "\n", " <div style=\"\">\n", " <div>\n", " <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n", " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-669a9b65-bae9-4798-96b7-f5d552eb72f9</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm:</strong> tcp://127.0.0.1:51057\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Workers:</strong> 6\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads:</strong> 60\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Started:</strong> Just now\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total memory:</strong> 55.88 GiB\n", " </td>\n", " </tr>\n", " </table>\n", " </div>\n", " </div>\n", "\n", " <details style=\"margin-left: 48px;\">\n", " <summary style=\"margin-bottom: 20px;\">\n", " <h3 style=\"display: inline;\">Workers</h3>\n", " </summary>\n", "\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:51088\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 10\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51093/status\" target=\"_blank\">http://127.0.0.1:51093/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 9.31 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:51060\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-9s507mc2\n", " </td>\n", " </tr>\n", "\n", " \n", "\n", " \n", "\n", " </table>\n", " </details>\n", " </div>\n", " </div>\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:51084\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 10\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51085/status\" target=\"_blank\">http://127.0.0.1:51085/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 9.31 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:51061\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-y5skkt4c\n", " </td>\n", " </tr>\n", "\n", " \n", "\n", " \n", "\n", " </table>\n", " </details>\n", " </div>\n", " </div>\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:51098\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 10\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51100/status\" target=\"_blank\">http://127.0.0.1:51100/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 9.31 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:51062\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-gmddkxg0\n", " </td>\n", " </tr>\n", "\n", " \n", "\n", " \n", "\n", " </table>\n", " </details>\n", " </div>\n", " </div>\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:51095\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 10\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51096/status\" target=\"_blank\">http://127.0.0.1:51096/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 9.31 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:51063\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-oycines6\n", " </td>\n", " </tr>\n", "\n", " \n", "\n", " \n", "\n", " </table>\n", " </details>\n", " </div>\n", " </div>\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:51087\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 10\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51091/status\" target=\"_blank\">http://127.0.0.1:51091/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 9.31 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:51064\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-a8kpxp6o\n", " </td>\n", " </tr>\n", "\n", " \n", "\n", " \n", "\n", " </table>\n", " </details>\n", " </div>\n", " </div>\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:51099\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 10\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51101/status\" target=\"_blank\">http://127.0.0.1:51101/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 9.31 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:51065\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-thoxr07z\n", " </td>\n", " </tr>\n", "\n", " \n", "\n", " \n", "\n", " </table>\n", " </details>\n", " </div>\n", " </div>\n", " \n", "\n", " </details>\n", "</div>\n", "\n", " </details>\n", " </div>\n", "</div>\n", " </details>\n", " \n", "\n", " </div>\n", "</div>" ], "text/plain": [ "<Client: 'tcp://127.0.0.1:51057' processes=6 threads=60, memory=55.88 GiB>" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n", "client" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Set global path for experiment" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/03/0043/*.h5\"\n", "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n", "\n", "# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n", "\n", "# filepath = r\"./testData/0002/*.h5\"\n", "\n", "# filepath = r\"./testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5\"\n", "\n", "# filepath = r'd:/Jianshun Gao/Simulations/analyseScripts/testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5'\n", "\n", "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n", "\n", "filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\"\n", "\n", "# filepath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_13.h5'" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "groupList = [\n", " \"images/MOT_3D_Camera/in_situ_absorption\",\n", " \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n", " \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n", "]\n", "\n", "dskey = {\n", " \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n", " \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n", " \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_3\",\n", "}\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "img_dir = '//DyLabNAS/Data/'\n", "SequenceName = \"Evaporative_Cooling\" + \"/\"\n", "folderPath = img_dir + SequenceName + '2023/05/23'# get_date()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# An example for one experimental run" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "f:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:178: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if not key in datesetOfGlobal.scanAxis\n" ] }, { "data": { 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var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", "Dimensions: (y: 1200, x: 1920)\n", "Dimensions without coordinates: y, x\n", "Data variables:\n", " atoms (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", " background (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", " dark (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", " shotNum <U2 '11'\n", " OD (y, x) float64 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", "Attributes: (12/96)\n", " TOF_free: 0.02\n", " abs_img_freq: 110.858\n", " absorption_imaging_flag: True\n", " backup_data: True\n", " blink_off_time: nan\n", " blink_on_time: nan\n", " ... ...\n", " y_offset: 0\n", " y_offset_img: 0\n", " z_offset: 0.189\n", " z_offset_img: 0.189\n", " scanAxis: []\n", " scanAxisLength: []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3f6a2152-ef01-4819-91f5-a47fe1a85483' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f6a2152-ef01-4819-91f5-a47fe1a85483' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 1200</li><li><span>x</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-71114a49-5445-43f4-a531-d6bdc6ba83e9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71114a49-5445-43f4-a531-d6bdc6ba83e9' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-87511f20-517f-4220-90f4-f268366d6d73' class='xr-section-summary-in' type='checkbox' checked><label for='section-87511f20-517f-4220-90f4-f268366d6d73' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-81526946-1703-4ca5-8e13-3fc13c175996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81526946-1703-4ca5-8e13-3fc13c175996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77f2b9fc-57a9-4fc9-bcb9-7283191c0038' class='xr-var-data-in' type='checkbox'><label for='data-77f2b9fc-57a9-4fc9-bcb9-7283191c0038' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 4.39 MiB </td>\n", " <td> 4.39 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1200, 1920) </td>\n", " <td> (1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> uint16 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n", " <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-cf0352d3-81af-4aa1-8a4b-d510a6d9ed9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf0352d3-81af-4aa1-8a4b-d510a6d9ed9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cebfcb4-0749-41e1-a9df-8a3ca0e9cd57' class='xr-var-data-in' type='checkbox'><label for='data-7cebfcb4-0749-41e1-a9df-8a3ca0e9cd57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 4.39 MiB </td>\n", " <td> 4.39 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1200, 1920) </td>\n", " <td> (1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> uint16 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n", " <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-ea5ca203-117f-4d7f-a382-5ea49ef757fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea5ca203-117f-4d7f-a382-5ea49ef757fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c46121e-396f-4c19-b720-5bfd85d3db08' class='xr-var-data-in' type='checkbox'><label for='data-1c46121e-396f-4c19-b720-5bfd85d3db08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 4.39 MiB </td>\n", " <td> 4.39 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1200, 1920) </td>\n", " <td> (1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> uint16 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n", " <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>'11'</div><input id='attrs-931dff3b-9054-49ae-a9a3-af23a3abe9d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-931dff3b-9054-49ae-a9a3-af23a3abe9d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16b7a2e7-307c-45ef-8fd7-4961a352c6cd' class='xr-var-data-in' type='checkbox'><label for='data-16b7a2e7-307c-45ef-8fd7-4961a352c6cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array('11', dtype='<U2')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-c45c4a1c-0006-43c9-be36-4e3d40196cf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c45c4a1c-0006-43c9-be36-4e3d40196cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81f04315-1fc1-4433-9b5f-8dbcef03d3d4' class='xr-var-data-in' type='checkbox'><label for='data-81f04315-1fc1-4433-9b5f-8dbcef03d3d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 17.58 MiB </td>\n", " <td> 17.58 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1200, 1920) </td>\n", " <td> (1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 16 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n", " <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c33811d7-8cf5-4e35-8e55-5bc202a3ba60' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c33811d7-8cf5-4e35-8e55-5bc202a3ba60' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-47715f26-a2f1-48c1-9cd1-23dbb2fa5488' class='xr-section-summary-in' type='checkbox' ><label for='section-47715f26-a2f1-48c1-9cd1-23dbb2fa5488' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavel ], "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (y: 1200, x: 1920)\n", "Dimensions without coordinates: y, x\n", "Data variables:\n", " atoms (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", " background (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", " dark (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", " shotNum <U2 '11'\n", " OD (y, x) float64 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n", "Attributes: (12/96)\n", " TOF_free: 0.02\n", " abs_img_freq: 110.858\n", " absorption_imaging_flag: True\n", " backup_data: True\n", " blink_off_time: nan\n", " blink_on_time: nan\n", " ... ...\n", " y_offset: 0\n", " y_offset_img: 0\n", " z_offset: 0.189\n", " z_offset_img: 0.189\n", " scanAxis: []\n", " scanAxisLength: []" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shotNum = \"0069\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "# filePath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/12/0065/*.h5\"\n", "filePath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_11.h5'\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0] # range(len(groupList))\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "dataSet = swap_xy(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "dataSet" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate an plot OD images" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# imageAnalyser.center = (960, 1040)\n", "# imageAnalyser.span = (100, 100)\n", "# imageAnalyser.fraction = (0.1, 0.1)\n", "\n", "imageAnalyser.center = (960, 875)\n", "imageAnalyser.span = (300, 300)\n", "imageAnalyser.fraction = (0.1, 0.1)\n", "\n", "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n", "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n", "\n", "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Do a 2D two-peak gaussian fit to the OD images" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Do the fit" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n", " res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n" ] } ], "source": [ "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, polylog2_2d\n", "\n", "fitModel = DensityProfileBEC2dModel()\n", "# fitModel = ThomasFermi2dModel()\n", "\n", "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n", "\n", "# fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "\n", "# dataSet_cropOD = dataSet_cropOD.chunk((1,1,100,100))\n", "\n", "params = fitAnalyser.guess(dataSet_cropOD, guess_kwargs=dict(pureBECThreshold=0.3), dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th><th> expression </th></tr><tr><td> BEC_amplitude </td><td> 366.888620 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_amplitude </td><td> 0.00000000 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_centerx </td><td> 152.087577 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_centery </td><td> 156.309927 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_centerx </td><td> 169.884333 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_centery </td><td> 157.547034 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_sigmax </td><td> 1.63603577 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_sigmay </td><td> 3.42894901 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_sigmax </td><td> 158.415970 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_sigmay </td><td> 190.099163 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> thermalAspectRatio * thermal_sigmax </td></tr><tr><td> thermalAspectRatio </td><td> 1.20000000 </td><td> None </td><td> 0.80000000 </td><td> 1.20000000 </td><td> True </td><td> </td></tr><tr><td> condensate_fraction </td><td> 1.00000000 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> BEC_amplitude / (BEC_amplitude + thermal_amplitude) </td></tr></table>" ], "text/plain": [ "Parameters([('BEC_amplitude', <Parameter 'BEC_amplitude', value=366.8886196688306, bounds=[0:inf]>), ('thermal_amplitude', <Parameter 'thermal_amplitude', value=0, bounds=[0:inf]>), ('BEC_centerx', <Parameter 'BEC_centerx', value=152.08757708603804, bounds=[-inf:inf]>), ('BEC_centery', <Parameter 'BEC_centery', value=156.30992724959418, bounds=[-inf:inf]>), ('thermal_centerx', <Parameter 'thermal_centerx', value=169.88433327212883, bounds=[-inf:inf]>), ('thermal_centery', <Parameter 'thermal_centery', value=157.5470340720574, bounds=[-inf:inf]>), ('BEC_sigmax', <Parameter 'BEC_sigmax', value=1.636035766171183, bounds=[0:inf]>), ('BEC_sigmay', <Parameter 'BEC_sigmay', value=3.4289490138791647, bounds=[0:inf]>), ('thermal_sigmax', <Parameter 'thermal_sigmax', value=158.41596952074627, bounds=[0:inf]>), ('thermal_sigmay', <Parameter 'thermal_sigmay', value=190.09916342489552, bounds=[-inf:inf], expr='thermalAspectRatio * thermal_sigmax'>), ('thermalAspectRatio', <Parameter 'thermalAspectRatio', value=1.2, bounds=[0.8:1.2]>), ('condensate_fraction', <Parameter 'condensate_fraction', value=1.0, bounds=[-inf:inf], expr='BEC_amplitude / (BEC_amplitude + thermal_amplitude)'>)])" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params.compute().item()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0x1e0e84006d0>" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "fitModel2 = Polylog22dModel(prefix='thermal_')\n", "fitAnalyser2 = FitAnalyser(fitModel2, fitDim=2)\n", "fitCurve2 = fitAnalyser2.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n", "\n", "fitModel3 = ThomasFermi2dModel(prefix='BEC_')\n", "fitAnalyser3 = FitAnalyser(fitModel3, fitDim=2)\n", "fitCurve3 = fitAnalyser3.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "dataSet_cropOD.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve2.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve3.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "dataSet_cropOD.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve2.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve3.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n", " outputs = ufunc(*inputs)\n" ] } ], "source": [ "value = fitAnalyser.get_fit_full_result(fitResult)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 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dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", "Dimensions: ()\n", "Data variables:\n", " BEC_amplitude object 0.0+/-nan\n", " thermal_amplitude object 2104.548431645919+/-nan\n", " BEC_centerx object 146.94301032591366+/-nan\n", " BEC_centery object 147.47224593536436+/-nan\n", " thermal_centerx object 146.27287010988167+/-nan\n", " thermal_centery object 148.78153517037947+/-nan\n", " BEC_sigmax object 17.155488681677085+/-nan\n", " BEC_sigmay object 18.315601451967396+/-nan\n", " thermal_sigmax object 42.999686622150065+/-nan\n", " thermal_sigmay object 51.599623946580074+/-nan\n", " thermalAspectRatio object 1.2+/-nan\n", " condensate_fraction object 0.0+/-nan\n", "Attributes:\n", " IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n", " IMAGE_VERSION: 1.2\n", " IMAGE_WHITE_IS_ZERO: 0\n", " x_start: 810\n", " x_end: 1110\n", " y_end: 1025\n", " y_start: 725\n", " x_center: 960\n", " y_center: 875\n", " x_span: 300\n", " y_span: 300</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-53523a01-2c07-4165-91ae-60ce98257240' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-53523a01-2c07-4165-91ae-60ce98257240' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-448573c2-6f7b-479e-ae18-5c693d5ebbdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-448573c2-6f7b-479e-ae18-5c693d5ebbdb' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f81fa7c4-b59b-456c-929b-d5fdf0999ba7' class='xr-section-summary-in' type='checkbox' checked><label for='section-f81fa7c4-b59b-456c-929b-d5fdf0999ba7' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>BEC_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan</div><input id='attrs-ae17e55d-accd-49e8-9a73-373506561892' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae17e55d-accd-49e8-9a73-373506561892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d309f58-8d37-475d-9462-884f4ad37e77' class='xr-var-data-in' type='checkbox'><label for='data-4d309f58-8d37-475d-9462-884f4ad37e77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.0+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2104.548431645919+/-nan</div><input id='attrs-ce8230fc-40ed-4545-8712-ec500a361b66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce8230fc-40ed-4545-8712-ec500a361b66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d45705-76ae-4681-a822-8d9af1dbbece' class='xr-var-data-in' type='checkbox'><label for='data-c4d45705-76ae-4681-a822-8d9af1dbbece' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2104.548431645919+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>146.94301032591366+/-nan</div><input id='attrs-d3679e45-9660-4dc8-9900-6ff9b96700a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3679e45-9660-4dc8-9900-6ff9b96700a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d54cdcf-9b44-4720-b151-cdd4cfffd6be' class='xr-var-data-in' type='checkbox'><label for='data-2d54cdcf-9b44-4720-b151-cdd4cfffd6be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(146.94301032591366+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name ], "text/plain": [ "<xarray.Dataset>\n", "Dimensions: ()\n", "Data variables:\n", " BEC_amplitude object 0.0+/-nan\n", " thermal_amplitude object 2104.548431645919+/-nan\n", " BEC_centerx object 146.94301032591366+/-nan\n", " BEC_centery object 147.47224593536436+/-nan\n", " thermal_centerx object 146.27287010988167+/-nan\n", " thermal_centery object 148.78153517037947+/-nan\n", " BEC_sigmax object 17.155488681677085+/-nan\n", " BEC_sigmay object 18.315601451967396+/-nan\n", " thermal_sigmax object 42.999686622150065+/-nan\n", " thermal_sigmay object 51.599623946580074+/-nan\n", " thermalAspectRatio object 1.2+/-nan\n", " condensate_fraction object 0.0+/-nan\n", "Attributes:\n", " IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n", " IMAGE_VERSION: 1.2\n", " IMAGE_WHITE_IS_ZERO: 0\n", " x_start: 810\n", " x_end: 1110\n", " y_end: 1025\n", " y_start: 725\n", " x_center: 960\n", " y_center: 875\n", " x_span: 300\n", " y_span: 300" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "value" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "unable to infer dtype on variable 'OD'; xarray cannot serialize arbitrary Python objects", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mf:\\Jianshun\\analyseScript\\test.ipynb Cell 25\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test.ipynb#Y216sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m fitResult\u001b[39m.\u001b[39;49mto_netcdf(\u001b[39m\"\u001b[39;49m\u001b[39msaved_on_disk.nc\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\dataarray.py:3959\u001b[0m, in \u001b[0;36mDataArray.to_netcdf\u001b[1;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[0;32m 3955\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 3956\u001b[0m \u001b[39m# No problems with the name - so we're fine!\u001b[39;00m\n\u001b[0;32m 3957\u001b[0m dataset \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mto_dataset()\n\u001b[1;32m-> 3959\u001b[0m \u001b[39mreturn\u001b[39;00m to_netcdf( \u001b[39m# type: ignore # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[0;32m 3960\u001b[0m dataset,\n\u001b[0;32m 3961\u001b[0m path,\n\u001b[0;32m 3962\u001b[0m mode\u001b[39m=\u001b[39;49mmode,\n\u001b[0;32m 3963\u001b[0m \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mformat\u001b[39;49m,\n\u001b[0;32m 3964\u001b[0m group\u001b[39m=\u001b[39;49mgroup,\n\u001b[0;32m 3965\u001b[0m engine\u001b[39m=\u001b[39;49mengine,\n\u001b[0;32m 3966\u001b[0m encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[0;32m 3967\u001b[0m unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims,\n\u001b[0;32m 3968\u001b[0m compute\u001b[39m=\u001b[39;49mcompute,\n\u001b[0;32m 3969\u001b[0m multifile\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m 3970\u001b[0m invalid_netcdf\u001b[39m=\u001b[39;49minvalid_netcdf,\n\u001b[0;32m 3971\u001b[0m )\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1216\u001b[0m, in \u001b[0;36mto_netcdf\u001b[1;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[0;32m 1211\u001b[0m \u001b[39m# TODO: figure out how to refactor this logic (here and in save_mfdataset)\u001b[39;00m\n\u001b[0;32m 1212\u001b[0m \u001b[39m# to avoid this mess of conditionals\u001b[39;00m\n\u001b[0;32m 1213\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1214\u001b[0m \u001b[39m# TODO: allow this work (setting up the file for writing array data)\u001b[39;00m\n\u001b[0;32m 1215\u001b[0m \u001b[39m# to be parallelized with dask\u001b[39;00m\n\u001b[1;32m-> 1216\u001b[0m dump_to_store(\n\u001b[0;32m 1217\u001b[0m dataset, store, writer, encoding\u001b[39m=\u001b[39;49mencoding, unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims\n\u001b[0;32m 1218\u001b[0m )\n\u001b[0;32m 1219\u001b[0m \u001b[39mif\u001b[39;00m autoclose:\n\u001b[0;32m 1220\u001b[0m store\u001b[39m.\u001b[39mclose()\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1263\u001b[0m, in \u001b[0;36mdump_to_store\u001b[1;34m(dataset, store, writer, encoder, encoding, unlimited_dims)\u001b[0m\n\u001b[0;32m 1260\u001b[0m \u001b[39mif\u001b[39;00m encoder:\n\u001b[0;32m 1261\u001b[0m variables, attrs \u001b[39m=\u001b[39m encoder(variables, attrs)\n\u001b[1;32m-> 1263\u001b[0m store\u001b[39m.\u001b[39;49mstore(variables, attrs, check_encoding, writer, unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims)\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\common.py:269\u001b[0m, in \u001b[0;36mAbstractWritableDataStore.store\u001b[1;34m(self, variables, attributes, check_encoding_set, writer, unlimited_dims)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[39mif\u001b[39;00m writer \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 267\u001b[0m writer \u001b[39m=\u001b[39m ArrayWriter()\n\u001b[1;32m--> 269\u001b[0m variables, attributes \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mencode(variables, attributes)\n\u001b[0;32m 271\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_attributes(attributes)\n\u001b[0;32m 272\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_dimensions(variables, unlimited_dims\u001b[39m=\u001b[39munlimited_dims)\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\common.py:358\u001b[0m, in \u001b[0;36mWritableCFDataStore.encode\u001b[1;34m(self, variables, attributes)\u001b[0m\n\u001b[0;32m 355\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mencode\u001b[39m(\u001b[39mself\u001b[39m, variables, attributes):\n\u001b[0;32m 356\u001b[0m \u001b[39m# All NetCDF files get CF encoded by default, without this attempting\u001b[39;00m\n\u001b[0;32m 357\u001b[0m \u001b[39m# to write times, for example, would fail.\u001b[39;00m\n\u001b[1;32m--> 358\u001b[0m variables, attributes \u001b[39m=\u001b[39m cf_encoder(variables, attributes)\n\u001b[0;32m 359\u001b[0m variables \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencode_variable(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 360\u001b[0m attributes \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencode_attribute(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m attributes\u001b[39m.\u001b[39mitems()}\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:775\u001b[0m, in \u001b[0;36mcf_encoder\u001b[1;34m(variables, attributes)\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[39m# add encoding for time bounds variables if present.\u001b[39;00m\n\u001b[0;32m 773\u001b[0m _update_bounds_encoding(variables)\n\u001b[1;32m--> 775\u001b[0m new_vars \u001b[39m=\u001b[39m {k: encode_cf_variable(v, name\u001b[39m=\u001b[39mk) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 777\u001b[0m \u001b[39m# Remove attrs from bounds variables (issue #2921)\u001b[39;00m\n\u001b[0;32m 778\u001b[0m \u001b[39mfor\u001b[39;00m var \u001b[39min\u001b[39;00m new_vars\u001b[39m.\u001b[39mvalues():\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:775\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[39m# add encoding for time bounds variables if present.\u001b[39;00m\n\u001b[0;32m 773\u001b[0m _update_bounds_encoding(variables)\n\u001b[1;32m--> 775\u001b[0m new_vars \u001b[39m=\u001b[39m {k: encode_cf_variable(v, name\u001b[39m=\u001b[39;49mk) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 777\u001b[0m \u001b[39m# Remove attrs from bounds variables (issue #2921)\u001b[39;00m\n\u001b[0;32m 778\u001b[0m \u001b[39mfor\u001b[39;00m var \u001b[39min\u001b[39;00m new_vars\u001b[39m.\u001b[39mvalues():\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:189\u001b[0m, in \u001b[0;36mencode_cf_variable\u001b[1;34m(var, needs_copy, name)\u001b[0m\n\u001b[0;32m 186\u001b[0m var \u001b[39m=\u001b[39m coder\u001b[39m.\u001b[39mencode(var, name\u001b[39m=\u001b[39mname)\n\u001b[0;32m 188\u001b[0m \u001b[39m# TODO(kmuehlbauer): check if ensure_dtype_not_object can be moved to backends:\u001b[39;00m\n\u001b[1;32m--> 189\u001b[0m var \u001b[39m=\u001b[39m ensure_dtype_not_object(var, name\u001b[39m=\u001b[39;49mname)\n\u001b[0;32m 191\u001b[0m \u001b[39mfor\u001b[39;00m attr_name \u001b[39min\u001b[39;00m CF_RELATED_DATA:\n\u001b[0;32m 192\u001b[0m pop_to(var\u001b[39m.\u001b[39mencoding, var\u001b[39m.\u001b[39mattrs, attr_name)\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:145\u001b[0m, in \u001b[0;36mensure_dtype_not_object\u001b[1;34m(var, name)\u001b[0m\n\u001b[0;32m 143\u001b[0m data[missing] \u001b[39m=\u001b[39m fill_value\n\u001b[0;32m 144\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 145\u001b[0m data \u001b[39m=\u001b[39m _copy_with_dtype(data, dtype\u001b[39m=\u001b[39m_infer_dtype(data, name))\n\u001b[0;32m 147\u001b[0m \u001b[39massert\u001b[39;00m data\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mO\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mor\u001b[39;00m data\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mmetadata\n\u001b[0;32m 148\u001b[0m var \u001b[39m=\u001b[39m Variable(dims, data, attrs, encoding, fastpath\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n", "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:77\u001b[0m, in \u001b[0;36m_infer_dtype\u001b[1;34m(array, name)\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[39mif\u001b[39;00m dtype\u001b[39m.\u001b[39mkind \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mO\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 75\u001b[0m \u001b[39mreturn\u001b[39;00m dtype\n\u001b[1;32m---> 77\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 78\u001b[0m \u001b[39m\"\u001b[39m\u001b[39munable to infer dtype on variable \u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m; xarray \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 79\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcannot serialize arbitrary Python objects\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(name)\n\u001b[0;32m 80\u001b[0m )\n", "\u001b[1;31mValueError\u001b[0m: unable to infer dtype on variable 'OD'; xarray cannot serialize arbitrary Python objects" ] } ], "source": [ "fitResult.to_netcdf(\"saved_on_disk.nc\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Get the Ncount" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount = dataSet_crop.OD.sum(dim=(scanAxis[0], 'x', 'y'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount.load()\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount.plot(ax=ax)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitAnalyser = FitAnalyser(\"Lorentzian With Offset\")\n", "params = fitAnalyser.guess(Ncount, x='runs', dask=\"parallelized\", guess_kwargs=dict(negative=True))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitResult = fitAnalyser.fit(Ncount, params, x='runs', dask=\"parallelized\")\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(40), dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure()\n", "ax = fig.gca()\n", "plt.errorbar([1], [1], yerr=[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitCurve.plot.errorbar(yerr=fitCurve)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.ufunc(fitCurve)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Read CSV" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# filePath = 'Z:/Dy_Lab/Data/Measurements/Experiments/DyBEC/BEC Stability Check/20230509-0007/*.csv'\n", "\n", "# filePath = np.sort(glob.glob(filePath))\n", "\n", "# read_csv_file(filePath, maxFileNum=5, csvEngine='pandas', csvKwargs=dict(header=[0,1], na_filter=False, index_col=0))\n", "# read_csv_file(filePath, csvEngine='dask')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "filePath = 'Z:/Dy_Lab/Data/Measurements/Experiments/DyBEC/BEC Stability Check/20230509-0007/*.csv'\n", "\n", "filePath = np.sort(glob.glob(filePath))\n", "\n", "data = np.empty(filePath.shape,dtype=object)\n", "\n", "i = 0\n", "for fp in filePath:\n", " data_single = pd.read_csv(fp)\n", " data_single = xr.Dataset.from_dataframe(data_single)\n", " data_single = data_single.drop_isel(index=0)\n", " # data_single = data_single.expand_dims(dim='runs')\n", " data[i] = data_single\n", " i = i + 1\n", "\n", "data = xr.concat(data, 'runs')\n", "\n", "data = data.assign_coords(dict(index=data.Time.isel(runs=0).astype(float))).rename(dict(index='time')).astype(float)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "arm2_mean = data['Channel A'].mean(dim='runs')\n", "arm2_std = data['Channel A'].std(dim='runs')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "arm2_mean.plot.errorbar(yerr=arm2_std, fmt='ob')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "arm2_std.plot.errorbar(fmt='ob')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data['Channel A'].sel(time=4.55, method='nearest').plot.errorbar(fmt='ob')\n", "\n", "plt.ylim([0, 0.15])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "env", "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": "3.9.12" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650" } } }, "nbformat": 4, "nbformat_minor": 2}
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