{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Centroids\n",
    "Continuous Data "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import loadmat\n",
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import seaborn.objects as so\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import lombscargle\n",
    "from astropy.timeseries import LombScargle\n",
    "from joblib import Parallel, delayed\n",
    "import astropy.units as u\n",
    "from astropy.visualization import quantity_support\n",
    "\n",
    "import re\n",
    "import os\n",
    "import continuousanalysis as ca\n",
    "import importlib as imp\n",
    "import scipy as sci\n",
    "import re\n",
    "import loadcontinuousmatlabfiles as lcm\n",
    "from seaborn import axes_style\n",
    "\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp.reload(lcm)\n",
    "path='/Users/ryanmaloney/Documents/Matlab/' \n",
    "# t, a=lcm.load_24hrmatfiles(path)\n",
    "# sns.scatterplot(x=t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "\n",
    "# from dask.distributed import Client\n",
    "# import xarray as xr\n",
    "\n",
    "# client = Client(n_workers=4, threads_per_worker=2, memory_limit='8GB')\n",
    "# client\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# daydata=glob.glob('/Users/ryanmaloney/Documents/Matlab/*24h*.mat' )\n",
    "# daydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration=24\n",
    "tpaths=glob.glob(path+'*TimeArray'+str(duration)+'h_u.mat')\n",
    "apaths=glob.glob(path+'*AngleArray'+str(duration)+'h.mat')\n",
    "vpaths=glob.glob(path+'*VectorArray'+str(duration)+'h.mat')\n",
    "cpaths=glob.glob(path+'*CentroidArray'+str(duration)+'h.mat')\n",
    "len(tpaths)==len(apaths) & len(tpaths)==len(cpaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/ryanmaloney/Documents/Matlab/2AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/6AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/8AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/3AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/7AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/4AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/1AngleArray24h.mat',\n",
       " '/Users/ryanmaloney/Documents/Matlab/5AngleArray24h.mat']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "testfile=h5py.File(apaths[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 dataset \"angle\": shape (5, 42, 1, 863851), type \"<f8\">"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testfile['angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting all the stuff\n",
      "0\n",
      "(10, 1, 1, 863889)\n",
      "(10, 863889)\n",
      "numtrials:  10\n",
      "numt:  863889\n",
      "/Users/ryanmaloney/Documents/Matlab/1AngleArray24h.mat\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 42, 863889)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    5.2s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    9.5s remaining:    3.8s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   11.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.9s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/astropy/timeseries/periodograms/lombscargle/implementations/main.py:216: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  PLS = METHODS[method](*args, **kwds)\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    7.7s remaining:    3.1s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   10.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.3s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/astropy/timeseries/periodograms/lombscargle/implementations/main.py:216: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  PLS = METHODS[method](*args, **kwds)\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    6.4s remaining:    2.5s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    9.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "(5, 1, 1, 863851)\n",
      "(5, 863851)\n",
      "numtrials:  5\n",
      "numt:  863851\n",
      "/Users/ryanmaloney/Documents/Matlab/2AngleArray24h.mat\n",
      "(5, 42, 863851)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.8s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    4.6s remaining:    1.8s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    6.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.5s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/astropy/timeseries/periodograms/lombscargle/implementations/main.py:216: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  PLS = METHODS[method](*args, **kwds)\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    4.6s remaining:    1.9s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    5.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.5s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/astropy/timeseries/periodograms/lombscargle/implementations/main.py:216: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  PLS = METHODS[method](*args, **kwds)\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    4.8s remaining:    1.9s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    5.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "(1, 863768)\n",
      "(863768,)\n",
      "numtrials:  1\n",
      "numt:  863768\n",
      "/Users/ryanmaloney/Documents/Matlab/3AngleArray24h.mat\n",
      "(42, 863768)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    0.4s remaining:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    0.4s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    0.5s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    0.4s remaining:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "(10, 1, 1, 2590791)\n",
      "(10, 2590791)\n",
      "numtrials:  10\n",
      "numt:  2590791\n",
      "/Users/ryanmaloney/Documents/Matlab/4AngleArray24h.mat\n",
      "(10, 42, 2590791)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   12.7s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   27.5s remaining:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   29.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.4s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   13.6s remaining:    5.5s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   17.0s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.2s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   16.0s remaining:    6.4s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   19.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "(11, 1, 1, 2848845)\n",
      "(11, 2848845)\n",
      "numtrials:  11\n",
      "numt:  2848845\n",
      "/Users/ryanmaloney/Documents/Matlab/5AngleArray24h.mat\n",
      "(11, 42, 2848845)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   10.3s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   24.9s remaining:   10.0s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   27.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:    9.8s remaining:    3.9s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   12.6s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   10.0s remaining:    4.0s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   13.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "(9, 1, 1, 2590483)\n",
      "(9, 2590483)\n",
      "numtrials:  9\n",
      "numt:  2590483\n",
      "/Users/ryanmaloney/Documents/Matlab/6AngleArray24h.mat\n",
      "(9, 42, 2590483)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    4.8s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   14.6s remaining:    5.9s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   17.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   11.3s remaining:    4.5s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   15.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   12.8s remaining:    5.1s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   17.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "(12, 1, 1, 2591646)\n",
      "(12, 2591646)\n",
      "numtrials:  12\n",
      "numt:  2591646\n",
      "/Users/ryanmaloney/Documents/Matlab/7AngleArray24h.mat\n",
      "(12, 42, 2591646)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   10.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   26.3s remaining:   10.5s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   30.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   13.7s remaining:    5.5s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   17.2s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   14.3s remaining:    5.7s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   17.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "(10, 1, 1, 2591634)\n",
      "(10, 2591634)\n",
      "numtrials:  10\n",
      "numt:  2591634\n",
      "/Users/ryanmaloney/Documents/Matlab/8AngleArray24h.mat\n",
      "(10, 42, 2591634)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   10.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   22.8s remaining:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   24.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8_Angles_24h.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   16.0s remaining:    6.4s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   20.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.9s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  42 | elapsed:   16.7s remaining:    6.7s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:   20.5s finished\n"
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "test=lcm.convertmattonc(path, duration=24, vector=False, circumvert=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting all the stuff\n"
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "lcm.convertmattonc(path, duration=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/ryanmaloney/Documents/GitHub/Continuous Turns'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (Freq: 100, Fly: 42, Batch: 6, Shuffled: 2)\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 4.552e-07 5.371e-07 ... 3.592 4.238 5.0\n",
       "  * Fly       (Fly) int64 0 1 2 3 4 5 6 7 8 9 ... 32 33 34 35 36 37 38 39 40 41\n",
       "  * Batch     (Batch) int64 1 2 3 4 5 6\n",
       "  * Shuffled  (Shuffled) object &#x27;Unshuffled&#x27; &#x27;Shuffled&#x27;\n",
       "Data variables:\n",
       "    Power     (Freq, Fly, Batch, Shuffled) float64 dask.array&lt;chunksize=(100, 42, 1, 2), meta=np.ndarray&gt;</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-7de0b18d-cd67-4828-bdf9-2447f6160d4e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7de0b18d-cd67-4828-bdf9-2447f6160d4e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Freq</span>: 100</li><li><span class='xr-has-index'>Fly</span>: 42</li><li><span class='xr-has-index'>Batch</span>: 6</li><li><span class='xr-has-index'>Shuffled</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b27ae383-da27-45bf-a4e8-07856d86d7f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b27ae383-da27-45bf-a4e8-07856d86d7f9' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>Freq</span></div><div class='xr-var-dims'>(Freq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.858e-07 4.552e-07 ... 4.238 5.0</div><input id='attrs-d7f9a19d-af11-4173-97ce-a00e1cbd7bf6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7f9a19d-af11-4173-97ce-a00e1cbd7bf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-516cda98-3aa6-48e3-a0e1-c86b147be93e' class='xr-var-data-in' type='checkbox'><label for='data-516cda98-3aa6-48e3-a0e1-c86b147be93e' 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([3.858025e-07, 4.552076e-07, 5.370986e-07, 6.337216e-07, 7.477269e-07,\n",
       "       8.822415e-07, 1.040955e-06, 1.228221e-06, 1.449175e-06, 1.709879e-06,\n",
       "       2.017483e-06, 2.380425e-06, 2.808659e-06, 3.313931e-06, 3.910101e-06,\n",
       "       4.613520e-06, 5.443484e-06, 6.422756e-06, 7.578197e-06, 8.941501e-06,\n",
       "       1.055006e-05, 1.244799e-05, 1.468737e-05, 1.732959e-05, 2.044715e-05,\n",
       "       2.412556e-05, 2.846570e-05, 3.358663e-05, 3.962879e-05, 4.675794e-05,\n",
       "       5.516960e-05, 6.509451e-05, 7.680488e-05, 9.062194e-05, 1.069246e-04,\n",
       "       1.261602e-04, 1.488562e-04, 1.756351e-04, 2.072315e-04, 2.445121e-04,\n",
       "       2.884993e-04, 3.403998e-04, 4.016371e-04, 4.738908e-04, 5.591429e-04,\n",
       "       6.597316e-04, 7.784160e-04, 9.184516e-04, 1.083679e-03, 1.278631e-03,\n",
       "       1.508654e-03, 1.780058e-03, 2.100287e-03, 2.478125e-03, 2.923935e-03,\n",
       "       3.449945e-03, 4.070584e-03, 4.802874e-03, 5.666902e-03, 6.686367e-03,\n",
       "       7.889232e-03, 9.308489e-03, 1.098307e-02, 1.295890e-02, 1.529018e-02,\n",
       "       1.804086e-02, 2.128637e-02, 2.511575e-02, 2.963403e-02, 3.496513e-02,\n",
       "       4.125529e-02, 4.867704e-02, 5.743394e-02, 6.776620e-02, 7.995721e-02,\n",
       "       9.434136e-02, 1.113132e-01, 1.313382e-01, 1.549657e-01, 1.828437e-01,\n",
       "       2.157370e-01, 2.545477e-01, 3.003403e-01, 3.543709e-01, 4.181216e-01,\n",
       "       4.933408e-01, 5.820919e-01, 6.868091e-01, 8.103648e-01, 9.561479e-01,\n",
       "       1.128157e+00, 1.331110e+00, 1.570574e+00, 1.853118e+00, 2.186490e+00,\n",
       "       2.579836e+00, 3.043943e+00, 3.591543e+00, 4.237654e+00, 5.000000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Fly</span></div><div class='xr-var-dims'>(Fly)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 36 37 38 39 40 41</div><input id='attrs-3df93272-ff29-454f-9ef2-e8c095b2da78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3df93272-ff29-454f-9ef2-e8c095b2da78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca5b1520-bf9a-4508-bf27-0e0194b43e2b' class='xr-var-data-in' type='checkbox'><label for='data-ca5b1520-bf9a-4508-bf27-0e0194b43e2b' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6</div><input id='attrs-87ce8a00-1346-4e9b-bb42-76cf72b87550' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87ce8a00-1346-4e9b-bb42-76cf72b87550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a58b626e-f402-4ac5-a773-a8e2a39678c6' class='xr-var-data-in' type='checkbox'><label for='data-a58b626e-f402-4ac5-a773-a8e2a39678c6' 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([1, 2, 3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Shuffled</span></div><div class='xr-var-dims'>(Shuffled)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Unshuffled&#x27; &#x27;Shuffled&#x27;</div><input id='attrs-71a69a4b-a7e1-42b1-a127-0cfe43af295c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71a69a4b-a7e1-42b1-a127-0cfe43af295c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c186db1e-73e9-46fe-9d68-105809451089' class='xr-var-data-in' type='checkbox'><label for='data-c186db1e-73e9-46fe-9d68-105809451089' 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([&#x27;Unshuffled&#x27;, &#x27;Shuffled&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d68d3e64-e388-4d71-91cf-24ae98ae8f6c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d68d3e64-e388-4d71-91cf-24ae98ae8f6c' class='xr-section-summary' >Data variables: <span>(1)</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>Power</span></div><div class='xr-var-dims'>(Freq, Fly, Batch, Shuffled)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 42, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-98abf9de-9c4d-4675-affb-b2aaacd38ac0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98abf9de-9c4d-4675-affb-b2aaacd38ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc005730-cb4a-4eeb-b26b-9f9b86835d22' class='xr-var-data-in' type='checkbox'><label for='data-dc005730-cb4a-4eeb-b26b-9f9b86835d22' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 393.75 kiB </td>\n",
       "                        <td> 65.62 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 42, 6, 2) </td>\n",
       "                        <td> (100, 42, 1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 18 Tasks </td>\n",
       "                        <td> 6 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8dc2557e-891a-465e-9021-7846b1c192b3' class='xr-section-summary-in' type='checkbox'  ><label for='section-8dc2557e-891a-465e-9021-7846b1c192b3' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>Freq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee132c8b-61c5-412b-a33e-1bb7bd8e66d6' class='xr-index-data-in' type='checkbox'/><label for='index-ee132c8b-61c5-412b-a33e-1bb7bd8e66d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 3.858024691358029e-07, 4.5520759316352364e-07,\n",
       "               5.370985658487016e-07,  6.337215674104629e-07,\n",
       "               7.477268615800098e-07,  8.822414894492029e-07,\n",
       "              1.0409550408030415e-06, 1.2282208555502897e-06,\n",
       "              1.4491754310973268e-06,  1.709879310879473e-06,\n",
       "               2.017483318468778e-06, 2.3804246968789164e-06,\n",
       "               2.808658532954692e-06, 3.3139308145652504e-06,\n",
       "               3.910100610262502e-06,  4.613520208441916e-06,\n",
       "               5.443483642809144e-06, 6.4227559067174645e-06,\n",
       "               7.578197372149323e-06,  8.941500540474626e-06,\n",
       "              1.0550059333257072e-05, 1.2447994766808602e-05,\n",
       "              1.4687365143628707e-05, 1.7329593954961562e-05,\n",
       "               2.044715466028399e-05, 2.4125558555390847e-05,\n",
       "               2.846570025413544e-05,  3.358662511783494e-05,\n",
       "               3.962879453991645e-05,  4.675793864900665e-05,\n",
       "               5.516960210591547e-05,  6.509450768077647e-05,\n",
       "                7.68048847274235e-05,  9.062193613816818e-05,\n",
       "               0.0001069246485894143,  0.0001261601877335573,\n",
       "              0.00014885616346596193, 0.00017563510168993942,\n",
       "               0.0002072315195244782, 0.00024451207230908635,\n",
       "               0.0002884993250161538,  0.0003403998000947918,\n",
       "              0.00040163707106796964, 0.00047389080960428393,\n",
       "               0.0005591428570830279,  0.0006597315843454277,\n",
       "               0.0007784160306608326,  0.0009184515811698771,\n",
       "               0.0010836792585544746,  0.0012786310781079306,\n",
       "               0.0015086543559800622,   0.001780058380237097,\n",
       "                 0.00210028746776388,   0.002478125153770778,\n",
       "               0.0029239351146011045,   0.003449945432089408,\n",
       "                0.004070583996532471,   0.004802874248011252,\n",
       "                0.005666902110817452,   0.006686366928487636,\n",
       "                0.007889231510992878,    0.00930848912417089,\n",
       "                  0.0109830684590853,   0.012958901403636596,\n",
       "                 0.01529018290423738,   0.018040857474185715,\n",
       "                0.021286373122043137,   0.025115750808362983,\n",
       "                 0.02963402619371353,    0.03496513065248621,\n",
       "                 0.04125529057556084,    0.04867703820099797,\n",
       "                 0.05743394398547897,    0.06776620032028792,\n",
       "                  0.0799572097470869,    0.09434135838106995,\n",
       "                 0.11131318775802759,    0.13133821667910306,\n",
       "                  0.1549657098846588,    0.18284374378807225,\n",
       "                 0.21573698250613965,    0.25454765176324656,\n",
       "                  0.3003402859606561,     0.3543709271960876,\n",
       "                 0.41812157713090004,    0.49334084668207806,\n",
       "                  0.5820919185158296,     0.6868091379017538,\n",
       "                  0.8103647841531089,     0.9561478541210875,\n",
       "                  1.1281570186885486,     1.3311103019586454,\n",
       "                  1.5705744915190722,     1.8531178293645474,\n",
       "                  2.1864901716233334,      2.579835558672923,\n",
       "                   3.043943026211684,      3.591542536761141,\n",
       "                   4.237654148677736,      4.999999999999999],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Freq&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Fly</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3086ba1-8d4f-4846-a57f-28a3b7e8b21f' class='xr-index-data-in' type='checkbox'/><label for='index-e3086ba1-8d4f-4846-a57f-28a3b7e8b21f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;Fly&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e70afde-5ec6-4ae0-95f8-8bf5196064fb' class='xr-index-data-in' type='checkbox'/><label for='index-9e70afde-5ec6-4ae0-95f8-8bf5196064fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6], dtype=&#x27;int64&#x27;, name=&#x27;Batch&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Shuffled</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7ccfb895-bcf8-4f6e-98dd-bcfc952ba3f0' class='xr-index-data-in' type='checkbox'/><label for='index-7ccfb895-bcf8-4f6e-98dd-bcfc952ba3f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Unshuffled&#x27;, &#x27;Shuffled&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Shuffled&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0823112-75eb-4caa-8416-8605abbf5268' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0823112-75eb-4caa-8416-8605abbf5268' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (Freq: 100, Fly: 42, Batch: 6, Shuffled: 2)\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 4.552e-07 5.371e-07 ... 3.592 4.238 5.0\n",
       "  * Fly       (Fly) int64 0 1 2 3 4 5 6 7 8 9 ... 32 33 34 35 36 37 38 39 40 41\n",
       "  * Batch     (Batch) int64 1 2 3 4 5 6\n",
       "  * Shuffled  (Shuffled) object 'Unshuffled' 'Shuffled'\n",
       "Data variables:\n",
       "    Power     (Freq, Fly, Batch, Shuffled) float64 dask.array<chunksize=(100, 42, 1, 2), meta=np.ndarray>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "allpower=lcm.loadallpowerdata()\n",
    "allpower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob('*VectorPower*h.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Power Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "allpower2=xr.open_mfdataset('*_Power2h.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "allpower2=xr.open_mfdataset('*_Power2h.nc')\n",
    "allpower2\n",
    "allpower2_df=allpower2.to_dataframe().reset_index()\n",
    "allpower2_df[\"Method\"]=\"2 hr (Angle)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (Freq: 100, Fly: 42, Batch: 6, Shuffled: 2)\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 4.552e-07 5.371e-07 ... 3.592 4.238 5.0\n",
       "  * Fly       (Fly) int64 0 1 2 3 4 5 6 7 8 9 ... 32 33 34 35 36 37 38 39 40 41\n",
       "  * Batch     (Batch) int64 1 2 3 4 5 6\n",
       "  * Shuffled  (Shuffled) object &#x27;Unshuffled&#x27; &#x27;Shuffled&#x27;\n",
       "Data variables:\n",
       "    Power     (Freq, Fly, Batch, Shuffled) float64 dask.array&lt;chunksize=(100, 42, 1, 2), meta=np.ndarray&gt;</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-e45fd855-6212-4fe6-90f3-fcc390c9214f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e45fd855-6212-4fe6-90f3-fcc390c9214f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Freq</span>: 100</li><li><span class='xr-has-index'>Fly</span>: 42</li><li><span class='xr-has-index'>Batch</span>: 6</li><li><span class='xr-has-index'>Shuffled</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74168fd5-479d-427d-91ba-7bfbdd953fa6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74168fd5-479d-427d-91ba-7bfbdd953fa6' class='xr-section-summary' >Coordinates: <span>(4)</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 class='xr-has-index'>Freq</span></div><div class='xr-var-dims'>(Freq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.858e-07 4.552e-07 ... 4.238 5.0</div><input id='attrs-4da0672e-7f09-4968-821d-4773884fd448' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4da0672e-7f09-4968-821d-4773884fd448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60c48de2-dc57-426b-81ff-e0c1f1dc74b6' class='xr-var-data-in' type='checkbox'><label for='data-60c48de2-dc57-426b-81ff-e0c1f1dc74b6' 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([3.858025e-07, 4.552076e-07, 5.370986e-07, 6.337216e-07, 7.477269e-07,\n",
       "       8.822415e-07, 1.040955e-06, 1.228221e-06, 1.449175e-06, 1.709879e-06,\n",
       "       2.017483e-06, 2.380425e-06, 2.808659e-06, 3.313931e-06, 3.910101e-06,\n",
       "       4.613520e-06, 5.443484e-06, 6.422756e-06, 7.578197e-06, 8.941501e-06,\n",
       "       1.055006e-05, 1.244799e-05, 1.468737e-05, 1.732959e-05, 2.044715e-05,\n",
       "       2.412556e-05, 2.846570e-05, 3.358663e-05, 3.962879e-05, 4.675794e-05,\n",
       "       5.516960e-05, 6.509451e-05, 7.680488e-05, 9.062194e-05, 1.069246e-04,\n",
       "       1.261602e-04, 1.488562e-04, 1.756351e-04, 2.072315e-04, 2.445121e-04,\n",
       "       2.884993e-04, 3.403998e-04, 4.016371e-04, 4.738908e-04, 5.591429e-04,\n",
       "       6.597316e-04, 7.784160e-04, 9.184516e-04, 1.083679e-03, 1.278631e-03,\n",
       "       1.508654e-03, 1.780058e-03, 2.100287e-03, 2.478125e-03, 2.923935e-03,\n",
       "       3.449945e-03, 4.070584e-03, 4.802874e-03, 5.666902e-03, 6.686367e-03,\n",
       "       7.889232e-03, 9.308489e-03, 1.098307e-02, 1.295890e-02, 1.529018e-02,\n",
       "       1.804086e-02, 2.128637e-02, 2.511575e-02, 2.963403e-02, 3.496513e-02,\n",
       "       4.125529e-02, 4.867704e-02, 5.743394e-02, 6.776620e-02, 7.995721e-02,\n",
       "       9.434136e-02, 1.113132e-01, 1.313382e-01, 1.549657e-01, 1.828437e-01,\n",
       "       2.157370e-01, 2.545477e-01, 3.003403e-01, 3.543709e-01, 4.181216e-01,\n",
       "       4.933408e-01, 5.820919e-01, 6.868091e-01, 8.103648e-01, 9.561479e-01,\n",
       "       1.128157e+00, 1.331110e+00, 1.570574e+00, 1.853118e+00, 2.186490e+00,\n",
       "       2.579836e+00, 3.043943e+00, 3.591543e+00, 4.237654e+00, 5.000000e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Fly</span></div><div class='xr-var-dims'>(Fly)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 36 37 38 39 40 41</div><input id='attrs-95ae58d0-2f1a-4a6d-a906-f6f9dfb8739f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95ae58d0-2f1a-4a6d-a906-f6f9dfb8739f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-888b0668-ce70-4cd2-a512-c9cb16483f9c' class='xr-var-data-in' type='checkbox'><label for='data-888b0668-ce70-4cd2-a512-c9cb16483f9c' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6</div><input id='attrs-73e57df6-832f-4cde-8f59-ede16cb12d2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73e57df6-832f-4cde-8f59-ede16cb12d2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03eefb9d-00ab-438a-9c2a-1de39025c925' class='xr-var-data-in' type='checkbox'><label for='data-03eefb9d-00ab-438a-9c2a-1de39025c925' 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([1, 2, 3, 4, 5, 6])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Shuffled</span></div><div class='xr-var-dims'>(Shuffled)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Unshuffled&#x27; &#x27;Shuffled&#x27;</div><input id='attrs-3baede40-414e-487c-8f7c-0b9c607b0d9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3baede40-414e-487c-8f7c-0b9c607b0d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-719ad50f-633a-4217-8f46-60623bba12df' class='xr-var-data-in' type='checkbox'><label for='data-719ad50f-633a-4217-8f46-60623bba12df' 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([&#x27;Unshuffled&#x27;, &#x27;Shuffled&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6799ee22-3306-459c-8709-dc4fe5dc436a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6799ee22-3306-459c-8709-dc4fe5dc436a' class='xr-section-summary' >Data variables: <span>(1)</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>Power</span></div><div class='xr-var-dims'>(Freq, Fly, Batch, Shuffled)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 42, 1, 2), meta=np.ndarray&gt;</div><input id='attrs-468eb8b9-45ea-4d7e-9d66-bac5c3d03e82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-468eb8b9-45ea-4d7e-9d66-bac5c3d03e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d93aaf20-06f4-4581-a2f3-02cbbcbc8019' class='xr-var-data-in' type='checkbox'><label for='data-d93aaf20-06f4-4581-a2f3-02cbbcbc8019' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 393.75 kiB </td>\n",
       "                        <td> 65.62 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 42, 6, 2) </td>\n",
       "                        <td> (100, 42, 1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 18 Tasks </td>\n",
       "                        <td> 6 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-df562be4-ade1-47df-89d7-778891fad57e' class='xr-section-summary-in' type='checkbox'  ><label for='section-df562be4-ade1-47df-89d7-778891fad57e' class='xr-section-summary' >Indexes: <span>(4)</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-index-name'><div>Freq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3f7edca0-e8aa-430a-8c17-5bef16a14e91' class='xr-index-data-in' type='checkbox'/><label for='index-3f7edca0-e8aa-430a-8c17-5bef16a14e91' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 3.858024691358029e-07, 4.5520759316352364e-07,\n",
       "               5.370985658487016e-07,  6.337215674104629e-07,\n",
       "               7.477268615800098e-07,  8.822414894492029e-07,\n",
       "              1.0409550408030415e-06, 1.2282208555502897e-06,\n",
       "              1.4491754310973268e-06,  1.709879310879473e-06,\n",
       "               2.017483318468778e-06, 2.3804246968789164e-06,\n",
       "               2.808658532954692e-06, 3.3139308145652504e-06,\n",
       "               3.910100610262502e-06,  4.613520208441916e-06,\n",
       "               5.443483642809144e-06, 6.4227559067174645e-06,\n",
       "               7.578197372149323e-06,  8.941500540474626e-06,\n",
       "              1.0550059333257072e-05, 1.2447994766808602e-05,\n",
       "              1.4687365143628707e-05, 1.7329593954961562e-05,\n",
       "               2.044715466028399e-05, 2.4125558555390847e-05,\n",
       "               2.846570025413544e-05,  3.358662511783494e-05,\n",
       "               3.962879453991645e-05,  4.675793864900665e-05,\n",
       "               5.516960210591547e-05,  6.509450768077647e-05,\n",
       "                7.68048847274235e-05,  9.062193613816818e-05,\n",
       "               0.0001069246485894143,  0.0001261601877335573,\n",
       "              0.00014885616346596193, 0.00017563510168993942,\n",
       "               0.0002072315195244782, 0.00024451207230908635,\n",
       "               0.0002884993250161538,  0.0003403998000947918,\n",
       "              0.00040163707106796964, 0.00047389080960428393,\n",
       "               0.0005591428570830279,  0.0006597315843454277,\n",
       "               0.0007784160306608326,  0.0009184515811698771,\n",
       "               0.0010836792585544746,  0.0012786310781079306,\n",
       "               0.0015086543559800622,   0.001780058380237097,\n",
       "                 0.00210028746776388,   0.002478125153770778,\n",
       "               0.0029239351146011045,   0.003449945432089408,\n",
       "                0.004070583996532471,   0.004802874248011252,\n",
       "                0.005666902110817452,   0.006686366928487636,\n",
       "                0.007889231510992878,    0.00930848912417089,\n",
       "                  0.0109830684590853,   0.012958901403636596,\n",
       "                 0.01529018290423738,   0.018040857474185715,\n",
       "                0.021286373122043137,   0.025115750808362983,\n",
       "                 0.02963402619371353,    0.03496513065248621,\n",
       "                 0.04125529057556084,    0.04867703820099797,\n",
       "                 0.05743394398547897,    0.06776620032028792,\n",
       "                  0.0799572097470869,    0.09434135838106995,\n",
       "                 0.11131318775802759,    0.13133821667910306,\n",
       "                  0.1549657098846588,    0.18284374378807225,\n",
       "                 0.21573698250613965,    0.25454765176324656,\n",
       "                  0.3003402859606561,     0.3543709271960876,\n",
       "                 0.41812157713090004,    0.49334084668207806,\n",
       "                  0.5820919185158296,     0.6868091379017538,\n",
       "                  0.8103647841531089,     0.9561478541210875,\n",
       "                  1.1281570186885486,     1.3311103019586454,\n",
       "                  1.5705744915190722,     1.8531178293645474,\n",
       "                  2.1864901716233334,      2.579835558672923,\n",
       "                   3.043943026211684,      3.591542536761141,\n",
       "                   4.237654148677736,      4.999999999999999],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Freq&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Fly</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ecb9fdb5-a7b3-49b5-8765-d2554d2e20db' class='xr-index-data-in' type='checkbox'/><label for='index-ecb9fdb5-a7b3-49b5-8765-d2554d2e20db' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;Fly&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34fbceeb-d85c-4099-bc47-d94679a104a8' class='xr-index-data-in' type='checkbox'/><label for='index-34fbceeb-d85c-4099-bc47-d94679a104a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6], dtype=&#x27;int64&#x27;, name=&#x27;Batch&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Shuffled</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6762d624-fa2f-43ec-a9a9-0d4d8f125e63' class='xr-index-data-in' type='checkbox'/><label for='index-6762d624-fa2f-43ec-a9a9-0d4d8f125e63' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Unshuffled&#x27;, &#x27;Shuffled&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Shuffled&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8aef6c04-56ad-4d7b-92a3-40708cd406c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8aef6c04-56ad-4d7b-92a3-40708cd406c7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (Freq: 100, Fly: 42, Batch: 6, Shuffled: 2)\n",
       "Coordinates:\n",
       "  * Freq      (Freq) float64 3.858e-07 4.552e-07 5.371e-07 ... 3.592 4.238 5.0\n",
       "  * Fly       (Fly) int64 0 1 2 3 4 5 6 7 8 9 ... 32 33 34 35 36 37 38 39 40 41\n",
       "  * Batch     (Batch) int64 1 2 3 4 5 6\n",
       "  * Shuffled  (Shuffled) object 'Unshuffled' 'Shuffled'\n",
       "Data variables:\n",
       "    Power     (Freq, Fly, Batch, Shuffled) float64 dask.array<chunksize=(100, 42, 1, 2), meta=np.ndarray>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allpower2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allpower2=xr.open_mfdataset('*_VectorPower2h.nc')\n",
    "# allpower2\n",
    "# allpower2_dfv=allpower2.to_dataframe().reset_index()\n",
    "# allpower2_dfv[\"Method\"]=\"2 hr (Vector)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "allpower24=xr.open_mfdataset('*_Power24h.nc')\n",
    "allpower24\n",
    "allpower24_df=allpower24.to_dataframe().reset_index()\n",
    "allpower24_df[\"Method\"]=\"24 hr (Angle)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allpower24v=xr.open_mfdataset('*_VectorPower24h.nc')\n",
    "# allpower24v\n",
    "# allpower24v_df=allpower24v.to_dataframe().reset_index()\n",
    "# allpower24v_df[\"Method\"]=\"24 hr (Vector)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "print('hi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allpower24=xr.open_mfdataset('*_Power24h.nc')\n",
    "# allpower24\n",
    "# allpower24_df=allpower24.to_dataframe().reset_index()\n",
    "# allpower24_df[\"Method\"]=\"24 hr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freq</th>\n",
       "      <th>Fly</th>\n",
       "      <th>Batch</th>\n",
       "      <th>Shuffled</th>\n",
       "      <th>Power</th>\n",
       "      <th>Method</th>\n",
       "      <th>Experiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.858025e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Unshuffled</td>\n",
       "      <td>7.125478e-04</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Unshuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.858025e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>4.203779e-06</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Shuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.858025e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Unshuffled</td>\n",
       "      <td>1.734569e-04</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Unshuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.858025e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>7.819265e-05</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Shuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.858025e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Unshuffled</td>\n",
       "      <td>2.324488e-03</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Unshuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67195</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>3.995921e-07</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Shuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67196</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>Unshuffled</td>\n",
       "      <td>2.758815e-07</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Unshuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67197</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>5.976785e-07</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Shuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67198</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>Unshuffled</td>\n",
       "      <td>2.726698e-08</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Unshuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67199</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>5.207703e-06</td>\n",
       "      <td>24 hr (Angle)</td>\n",
       "      <td>Shuffled24 hr (Angle)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Freq  Fly  Batch    Shuffled         Power         Method  \\\n",
       "0      3.858025e-07    0      1  Unshuffled  7.125478e-04  24 hr (Angle)   \n",
       "1      3.858025e-07    0      1    Shuffled  4.203779e-06  24 hr (Angle)   \n",
       "2      3.858025e-07    0      2  Unshuffled  1.734569e-04  24 hr (Angle)   \n",
       "3      3.858025e-07    0      2    Shuffled  7.819265e-05  24 hr (Angle)   \n",
       "4      3.858025e-07    0      3  Unshuffled  2.324488e-03  24 hr (Angle)   \n",
       "...             ...  ...    ...         ...           ...            ...   \n",
       "67195  5.000000e+00   41      6    Shuffled  3.995921e-07  24 hr (Angle)   \n",
       "67196  5.000000e+00   41      7  Unshuffled  2.758815e-07  24 hr (Angle)   \n",
       "67197  5.000000e+00   41      7    Shuffled  5.976785e-07  24 hr (Angle)   \n",
       "67198  5.000000e+00   41      8  Unshuffled  2.726698e-08  24 hr (Angle)   \n",
       "67199  5.000000e+00   41      8    Shuffled  5.207703e-06  24 hr (Angle)   \n",
       "\n",
       "                    Experiment  \n",
       "0      Unshuffled24 hr (Angle)  \n",
       "1        Shuffled24 hr (Angle)  \n",
       "2      Unshuffled24 hr (Angle)  \n",
       "3        Shuffled24 hr (Angle)  \n",
       "4      Unshuffled24 hr (Angle)  \n",
       "...                        ...  \n",
       "67195    Shuffled24 hr (Angle)  \n",
       "67196  Unshuffled24 hr (Angle)  \n",
       "67197    Shuffled24 hr (Angle)  \n",
       "67198  Unshuffled24 hr (Angle)  \n",
       "67199    Shuffled24 hr (Angle)  \n",
       "\n",
       "[67200 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# allpower_df=pd.concat([allpower24v_df, allpower24_df, allpower2_df, allpower2_dfv])\n",
    "\n",
    "allpower_df=pd.concat([allpower24_df])\n",
    "allpower_df[\"Experiment\"]=allpower_df[\"Shuffled\"]+allpower_df[\"Method\"]\n",
    "allpower_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seaborn import axes_style\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<seaborn._core.plot.Plot at 0x2a3c2d570>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "image/png": {
       "height": 378.25,
       "width": 623.9
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "powerfig=(so.Plot(allpower_df.reset_index(), x=\"Freq\", y=\"Power\",\n",
    "                  #  color=\"Method\", linestyle=\"Shuffled\", \n",
    "                   color=\"Shuffled\",\n",
    "                   group=\"Shuffled\")\n",
    " .add(so.Line(), so.Agg())\n",
    " .add(so.Band(), so.Est())\n",
    " .scale(x='log', y='log')\n",
    " .theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n",
    " .label(x=\"Frequency (Hz)\")\n",
    " )\n",
    "\n",
    "powerfig.save('24hrContinuosdata.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.load_dataset('1_Power24h.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removetrials(tatest1, fly):\n",
    "  return tatest1.sel(Fly=fly).to_dataframe().dropna(subset=[\"Angles\", \"Timestamps\"]).reset_index().drop(columns=[\"t\", \"Trial\"]).set_index([\"Timestamps\", \"Batch\"]).to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=Parallel(n_jobs=-1, verbose=4)(delayed(removetrials)(tatest1, fi) for fi, f in enumerate(tatest1[\"Fly\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acat=xr.concat(a, dim=\"Fly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acat[\"Timestamps\"][0:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(acat[\"Timestamps\"][0:100], acat[\"Angles\"][:,0:100,0].T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tatdf=tatest1.to_dataframe([\"Batch\", \"Fly\", \"Trial\", \"t\"])\n",
    "tatdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tatdftrunc=tatdf.isna()\n",
    "tatdf[~tatdftrunc]\n",
    "tatdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "txr=tatdf.reset_index().drop(columns=[\"t\", \"Trial\"]).to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tatdf=tatest1a.to_dataframe()\n",
    "# tatdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "so.Plot(data=tatest1.to_dataframe(), x=\"Freq\", y=\"Power\", color=\"Fly\").add(so.Line()).scale(x=\"log\", y=\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=xr.load_dataset(\"1_Angles2h.nc\")\n",
    "a[\"Fly\"]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=xr.load_dataset(\"1_Timestamps2h.nc\")\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=xr.load_dataset(\"1_Timestamps2h.nc\")\n",
    "c=b.merge(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c[\"Timestamps\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_b2_2h=c.to_dataframe().dropna(subset=[\"Angles\", \"Timestamps\"]).reset_index().drop(columns=[\"t\", \"Trial\"]).set_index([\"Timestamps\", \"Fly\", \"Batch\"]).to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_b2_2h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd=d.to_dask_dataframe()\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf=d.sel(Fly=0).squeeze().to_dataframe().drop(columns=[\"Batch\", \"Fly\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf.index=pd.to_datetime(ddf.index, origin=\"unix\", unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddfs=np.sin(ddf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf[-1::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(0,5, dtype=float)\n",
    "x[:]=np.nan\n",
    "mp=int(np.floor(x.shape[0]/2))\n",
    "tau=1\n",
    "win=np.exp(-np.abs(np.arange(x.shape[0])-mp)/tau)\n",
    "win*=~np.isnan(x)\n",
    "a=np.nansum(x*win/np.nansum(win))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddfs[\"Angles\"].resample('1s').mean()[0:60*60*24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exponentialnan(x, sigma=24):\n",
    "  mp=int(np.floor(x.shape[0]/2))\n",
    "  # sigma=60*24\n",
    "  win=1/(sigma*np.sqrt(2)*np.pi)*np.exp(-.5*np.abs(np.arange(x.shape[0])-mp)/sigma)**2\n",
    "  win*=~np.isnan(x)\n",
    "  return np.nansum(x*win/np.nansum(win))\n",
    "\n",
    "def nandownsample(x):\n",
    "  return np.nanmean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expmnan_h=(ddfs[\"Angles\"].resample('1h').apply(nandownsample)\n",
    "         .rolling(window=24*4, min_periods=2).apply(exponentialnan, kwargs={\"sigma\":24}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[39m.\u001b[39marange(\u001b[39m2\u001b[39m,\u001b[39m7\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.arange(2,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(expmnan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expmnan2=ddfs[\"Angles\"].resample('1h').apply(nandownsample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expmnan_min=ddfs[\"Angles\"].resample('60s').apply(nandownsample)\n",
    "expmnan_day=ddfs[\"Angles\"].resample('1d').apply(nandownsample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expmnan_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expmnan_h=(ddfs[\"Angles\"].resample('1h').apply(nandownsample)\n",
    "         .rolling(window=24*4, min_periods=2).apply(exponentialnan, kwargs={\"sigma\":24}))\n",
    "expmnan_d=(ddfs[\"Angles\"].resample('1d').apply(nandownsample)\n",
    "         .rolling(window=4, min_periods=2).apply(exponentialnan, kwargs={\"sigma\":1}))\n",
    "expmnan_m=(ddfs[\"Angles\"].resample('60s').apply(nandownsample)\n",
    "         .rolling(window=24*4*60, min_periods=20).apply(exponentialnan, kwargs={\"sigma\":60*24}))\n",
    "expmnan_s=(ddfs[\"Angles\"].resample('1s').apply(nandownsample)\n",
    "         .rolling(window=4*24*60*60, min_periods=120).apply(exponentialnan, kwargs={\"sigma\":60*60*24}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(expmnan.dropna())\n",
    "# plt.plot(expmnan_min.dropna())\n",
    "\n",
    "# plt.plot(expmnan2.dropna())\n",
    "plt.plot(expmnan_day.dropna())\n",
    "plt.plot(expmnan_h.dropna())\n",
    "plt.plot(expmnan_d.dropna())\n",
    "plt.plot(expmnan_m.dropna())\n",
    "plt.plot(expmnan_s.dropna())\n",
    "\n",
    "\n",
    "plt.legend([\n",
    "    # \"Second\", \"Minute\", \n",
    "            # \"Hour\", \n",
    "            \"Day\", \"H_Gauss\", \"D_Gauss\", \"M_Gauss\", \"S_Gauss\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show some raw traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/ryanmaloney/Continuous Turns/1_Angles2h.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/file_manager.py:209\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 209\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/lru_cache.py:55\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 55\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     56\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/Users/ryanmaloney/Continuous Turns/1_Angles2h.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '4a41476d-6b15-4f94-baed-18491ee5a12b']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m a\u001b[38;5;241m=\u001b[39m\u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1_Angles2h.nc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m a\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/api.py:278\u001b[0m, in \u001b[0;36mload_dataset\u001b[0;34m(filename_or_obj, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcache has no effect in this context\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m ds:\n\u001b[1;32m    279\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\u001b[38;5;241m.\u001b[39mload()\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/api.py:539\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    527\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    528\u001b[0m     decode_cf,\n\u001b[1;32m    529\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    535\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    536\u001b[0m )\n\u001b[1;32m    538\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 539\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    541\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    542\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    543\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    544\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    545\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    546\u001b[0m     backend_ds,\n\u001b[1;32m    547\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    556\u001b[0m )\n\u001b[1;32m    557\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:572\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    551\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(\n\u001b[1;32m    552\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    553\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    568\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    569\u001b[0m ):\n\u001b[1;32m    571\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 572\u001b[0m     store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    574\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    575\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    576\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    577\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    578\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    579\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    580\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    581\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    582\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    584\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:376\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    370\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    371\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    372\u001b[0m )\n\u001b[1;32m    373\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    374\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    375\u001b[0m )\n\u001b[0;32m--> 376\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:323\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    322\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 323\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    324\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:385\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    383\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 385\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:379\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 379\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m    380\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/file_manager.py:197\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    195\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    196\u001b[0m     \u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 197\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    198\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    199\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/backends/file_manager.py:215\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    213\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    214\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 215\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2449\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2012\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/ryanmaloney/Continuous Turns/1_Angles2h.nc'"
     ]
    }
   ],
   "source": [
    "a=xr.load_dataset(\"1_Angles2h.nc\")\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'a' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAngles\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
     ]
    }
   ],
   "source": [
    "a[\"Angles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'a' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m onefly\u001b[38;5;241m=\u001b[39m\u001b[43ma\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAngles\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m,:,\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'a' is not defined"
     ]
    }
   ],
   "source": [
    "onefly=a[\"Angles\"][0,:,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'onefly' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m oft\u001b[39m=\u001b[39monefly[\u001b[39m\"\u001b[39m\u001b[39mTimestamps\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m      2\u001b[0m oft\u001b[39m=\u001b[39moft\u001b[39m-\u001b[39moft[\u001b[39m0\u001b[39m]\n\u001b[1;32m      3\u001b[0m oft\u001b[39m=\u001b[39moft\u001b[39m*\u001b[39m(\u001b[39m10\u001b[39m\u001b[39m^\u001b[39m\u001b[39m-\u001b[39m\u001b[39m9\u001b[39m)\u001b[39m/\u001b[39m\u001b[39m60\u001b[39m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'onefly' is not defined"
     ]
    }
   ],
   "source": [
    "oft=onefly[\"Timestamps\"]\n",
    "oft=oft-oft[0]\n",
    "oft=oft*(10^-9)/60\n",
    "oft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'oft' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m onefly[\u001b[39m\"\u001b[39m\u001b[39mTimestamps\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m=\u001b[39moft\u001b[39m.\u001b[39mvalues\n",
      "\u001b[0;31mNameError\u001b[0m: name 'oft' is not defined"
     ]
    }
   ],
   "source": [
    "onefly[\"Timestamps\"]=oft.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'onefly' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m onefly\n",
      "\u001b[0;31mNameError\u001b[0m: name 'onefly' is not defined"
     ]
    }
   ],
   "source": [
    "onefly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate the STD and mean for each fly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: 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'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Trial: 38, t: 179957, Batch: 6, Fly: 42)\n",
       "Coordinates:\n",
       "  * Trial       (Trial) int64 0 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object &#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; &#x27;6&#x27;\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Trial, Fly, t) float64 nan nan nan nan ... nan nan nan\n",
       "    Timestamps  (Batch, Trial, t) float64 1.548e+09 1.548e+09 ... nan nan</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-354fc190-6802-43d9-a5c7-77cf1129c10d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-354fc190-6802-43d9-a5c7-77cf1129c10d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Trial</span>: 38</li><li><span class='xr-has-index'>t</span>: 179957</li><li><span class='xr-has-index'>Batch</span>: 6</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-30f0a9b3-0247-4176-aba6-20d379096de4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30f0a9b3-0247-4176-aba6-20d379096de4' class='xr-section-summary' >Coordinates: <span>(3)</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 class='xr-has-index'>Trial</span></div><div class='xr-var-dims'>(Trial)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 32 33 34 35 36 37</div><input id='attrs-d883c586-2167-41d2-855e-bbebe3aee6ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d883c586-2167-41d2-855e-bbebe3aee6ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a57524cc-fc2f-4955-901f-992c27e5d042' class='xr-var-data-in' type='checkbox'><label for='data-a57524cc-fc2f-4955-901f-992c27e5d042' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 179954 179955 179956</div><input id='attrs-e70a578d-b93f-4f43-9275-66e54b655a58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e70a578d-b93f-4f43-9275-66e54b655a58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-955027c2-1807-4f5e-bd09-c239c607eb36' class='xr-var-data-in' type='checkbox'><label for='data-955027c2-1807-4f5e-bd09-c239c607eb36' 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,      1,      2, ..., 179954, 179955, 179956])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; &#x27;6&#x27;</div><input id='attrs-afc0f2a1-82a2-49d9-8f20-755e803f175d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afc0f2a1-82a2-49d9-8f20-755e803f175d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a0ca289-6b44-440e-a4c5-72c0c0939a9b' class='xr-var-data-in' type='checkbox'><label for='data-5a0ca289-6b44-440e-a4c5-72c0c0939a9b' 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([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8582007d-6784-44f8-bd41-b959d4b2c8ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8582007d-6784-44f8-bd41-b959d4b2c8ee' class='xr-section-summary' >Data variables: <span>(2)</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>Angles</span></div><div class='xr-var-dims'>(Batch, Trial, Fly, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-1fbb78df-29c8-406f-9775-fe5c38f22069' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fbb78df-29c8-406f-9775-fe5c38f22069' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38741317-f00b-4787-821c-db41af75eca7' class='xr-var-data-in' type='checkbox'><label for='data-38741317-f00b-4787-821c-db41af75eca7' 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([[[[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "...\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Timestamps</span></div><div class='xr-var-dims'>(Batch, Trial, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.548e+09 1.548e+09 ... nan nan</div><input id='attrs-0bd95ab7-db93-49d9-b1c0-3a3b46484644' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bd95ab7-db93-49d9-b1c0-3a3b46484644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3522c23f-c3bd-4b25-b7b8-a0e723a66517' class='xr-var-data-in' type='checkbox'><label for='data-3522c23f-c3bd-4b25-b7b8-a0e723a66517' 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([[[1.54774554e+09, 1.54774554e+09, 1.54774554e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.54780939e+09, 1.54780939e+09, 1.54780939e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.54782763e+09, 1.54782763e+09, 1.54782763e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        ...,\n",
       "        [1.55015569e+09, 1.55015569e+09, 1.55015569e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55022353e+09, 1.55022353e+09, 1.55022353e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55024044e+09, 1.55024044e+09, 1.55024044e+09, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[1.55065519e+09, 1.55065519e+09, 1.55065519e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55067461e+09, 1.55067461e+09, 1.55067461e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55076116e+09, 1.55076116e+09, 1.55076116e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "...\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[1.55843667e+09, 1.55843667e+09, 1.55843667e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55845522e+09, 1.55845522e+09, 1.55845522e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55851840e+09, 1.55851840e+09, 1.55851840e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2000c745-18b0-4381-a9c5-9c0f26ec9385' class='xr-section-summary-in' type='checkbox'  ><label for='section-2000c745-18b0-4381-a9c5-9c0f26ec9385' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>Trial</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da85ff0d-cdda-4bb7-95ea-b3841ce10686' class='xr-index-data-in' type='checkbox'/><label for='index-da85ff0d-cdda-4bb7-95ea-b3841ce10686' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;Trial&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a2fc956c-1a3c-485b-bfcc-d0c163dc93f4' class='xr-index-data-in' type='checkbox'/><label for='index-a2fc956c-1a3c-485b-bfcc-d0c163dc93f4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
       "                 8,      9,\n",
       "            ...\n",
       "            179947, 179948, 179949, 179950, 179951, 179952, 179953, 179954,\n",
       "            179955, 179956],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;t&#x27;, length=179957))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c95ae298-58ee-4621-a23a-9082e55f63ef' class='xr-index-data-in' type='checkbox'/><label for='index-c95ae298-58ee-4621-a23a-9082e55f63ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1afcde69-c583-49ab-bc00-89e054b0b3f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1afcde69-c583-49ab-bc00-89e054b0b3f8' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Trial: 38, t: 179957, Batch: 6, Fly: 42)\n",
       "Coordinates:\n",
       "  * Trial       (Trial) int64 0 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object '1' '2' '3' '4' '5' '6'\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Trial, Fly, t) float64 nan nan nan nan ... nan nan nan\n",
       "    Timestamps  (Batch, Trial, t) float64 1.548e+09 1.548e+09 ... nan nan"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tha=xr.load_dataset('Continuous2h_all.nc')\n",
    "tha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1696053277</th>\n",
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       "      <th>1696053278</th>\n",
       "      <td>1</td>\n",
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       "      <td>1.550248e+09</td>\n",
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       "      <th>1696053279</th>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
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       "      <td>1.550248e+09</td>\n",
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       "    <tr>\n",
       "      <th>1696053280</th>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
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       "      <td>1.550248e+09</td>\n",
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       "      <th>1696053281</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.550248e+09</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>511742784 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Batch  Fly    Angles    Timestamps\n",
       "0              1    0       NaN  1.547746e+09\n",
       "1              1    1       NaN  1.547746e+09\n",
       "2              1    2       NaN  1.547746e+09\n",
       "3              1    3       NaN  1.547746e+09\n",
       "4              1    4       NaN  1.547746e+09\n",
       "...          ...  ...       ...           ...\n",
       "1696053277     1   37       NaN  1.550248e+09\n",
       "1696053278     1   38       NaN  1.550248e+09\n",
       "1696053279     1   39  1.115397  1.550248e+09\n",
       "1696053280     1   40       NaN  1.550248e+09\n",
       "1696053281     1   41       NaN  1.550248e+09\n",
       "\n",
       "[511742784 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testtrunc=tha.to_dataframe().reset_index().drop(columns=[\"Trial\",\"t\"]).dropna(subset=\"Timestamps\")\n",
    "testtrunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "testtrunc.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "testtrunc[\"batchxfly\"]=\"f\"+testtrunc[\"Fly\"].astype(str)+\"b\"+testtrunc[\"Batch\"].astype(str)\n",
    "testtrunc=testtrunc.drop(columns=[\"Fly\",\"Batch\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.000000</td>\n",
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       "      <td>f23b1</td>\n",
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       "      <th>1696053267</th>\n",
       "      <td>4.611243</td>\n",
       "      <td>1.550248e+09</td>\n",
       "      <td>f27b1</td>\n",
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       "  </tbody>\n",
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       "<p>172500240 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Angles    Timestamps batchxfly\n",
       "31         -0.000000  1.547746e+09     f31b1\n",
       "35          0.000000  1.547746e+09     f35b1\n",
       "45         -0.000000  1.550655e+09      f3b2\n",
       "54         -0.000000  1.550655e+09     f12b2\n",
       "56          0.000000  1.550655e+09     f14b2\n",
       "...              ...           ...       ...\n",
       "1696053260  1.030697  1.550248e+09     f20b1\n",
       "1696053261  0.000000  1.550248e+09     f21b1\n",
       "1696053263  5.472081  1.550248e+09     f23b1\n",
       "1696053267  4.611243  1.550248e+09     f27b1\n",
       "1696053279  1.115397  1.550248e+09     f39b1\n",
       "\n",
       "[172500240 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testtrunc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "testtrunc['Angles']=np.sin(testtrunc['Angles'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>f9b2</th>\n",
       "      <td>0.036261</td>\n",
       "      <td>0.588903</td>\n",
       "      <td>304705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b3</th>\n",
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       "      <td>0.444193</td>\n",
       "      <td>151828</td>\n",
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       "    <tr>\n",
       "      <th>f9b4</th>\n",
       "      <td>-0.010829</td>\n",
       "      <td>0.584947</td>\n",
       "      <td>776059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b5</th>\n",
       "      <td>-0.031233</td>\n",
       "      <td>0.560642</td>\n",
       "      <td>398343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b6</th>\n",
       "      <td>0.063270</td>\n",
       "      <td>0.553382</td>\n",
       "      <td>267360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean       std    count\n",
       "batchxfly                             \n",
       "f0b1       0.000167  0.381613   107996\n",
       "f0b2      -0.005237  0.398393  1188401\n",
       "f0b3       0.037451  0.397048   639885\n",
       "f0b4      -0.044089  0.422915   899967\n",
       "f0b5      -0.010291  0.458791   224863\n",
       "...             ...       ...      ...\n",
       "f9b2       0.036261  0.588903   304705\n",
       "f9b3       0.005737  0.444193   151828\n",
       "f9b4      -0.010829  0.584947   776059\n",
       "f9b5      -0.031233  0.560642   398343\n",
       "f9b6       0.063270  0.553382   267360\n",
       "\n",
       "[252 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testtrunc_summary=testtrunc.groupby([\"batchxfly\"]).agg([\"mean\",\"std\",\"count\"])\n",
    "testtrunc_summary[\"Angles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tha.rolling(Timestamps=10, min_periods=2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Timestamps&#x27; (t: 300)&gt;\n",
       "array([ 0.        ,  0.52833557,  0.62842417,  0.72842884,  0.82842946,\n",
       "        0.92844248,  1.02844334,  1.12844682,  1.22847629,  1.32848549,\n",
       "        1.42849278,  1.52850032,  1.62851071,  1.72851753,  1.82852006,\n",
       "        1.92875338,  2.02875996,  2.12877369,  2.22878504,  2.3288002 ,\n",
       "        2.42880392,  2.53265095,  2.6326611 ,  2.73267055,  2.83271813,\n",
       "        2.93271828,  3.03272986,  3.13273811,  3.23274446,  3.332757  ,\n",
       "        3.43276525,  3.53277898,  3.6327889 ,  3.73281598,  3.83281636,\n",
       "        3.93282819,  4.03284168,  4.13284826,  4.23286653,  4.33288097,\n",
       "        4.4328928 ,  4.53290391,  4.63291693,  4.73319936,  4.83321452,\n",
       "        4.93462777,  5.03464031,  5.13464451,  5.23464656,  5.33465719,\n",
       "        5.43466043,  5.53466868,  5.6346724 ,  5.73471403,  5.83471894,\n",
       "        5.93472981,  6.03473687,  6.13473964,  6.23474717,  6.33569813,\n",
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       "        7.93583798,  8.03584313,  8.13584924,  8.23585224,  8.33586097,\n",
       "        8.43587565,  8.53588057,  8.63588166,  8.73991942,  8.839926  ,\n",
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       "        9.94004202, 10.04006052, 10.14007401, 10.24007821, 10.34008503,\n",
       "...\n",
       "       20.44351578, 20.54352283, 20.64353037, 20.74353886, 20.8435514 ,\n",
       "       20.94356108, 21.04356956, 21.14357805, 21.24357891, 21.34358501,\n",
       "       21.44359326, 21.54359365, 21.64361048, 21.74361634, 21.84361839,\n",
       "       21.94363165, 22.04363608, 22.14463568, 22.24464417, 22.34465384,\n",
       "       22.44465446, 22.54466701, 22.64467454, 22.74468136, 22.84468484,\n",
       "       22.9447031 , 23.04471803, 23.14471984, 23.24473214, 23.34474397,\n",
       "       23.44474673, 23.54474974, 23.64475274, 23.74475574, 23.84476399,\n",
       "       23.94477367, 24.04477978, 24.14479113, 24.24480081, 24.34481478,\n",
       "       24.44482708, 24.54483199, 24.64483762, 24.74571037, 24.84571624,\n",
       "       24.94578743, 25.04579687, 25.14581537, 25.24581599, 25.34582806,\n",
       "       25.44588017, 25.54589343, 25.64589739, 25.74590302, 25.84591079,\n",
       "       25.94592953, 26.04593968, 26.14594221, 26.24595308, 26.34596038,\n",
       "       26.44596982, 26.54598212, 26.64599538, 26.74599719, 26.84603643,\n",
       "       26.94765687, 27.04766107, 27.14767265, 27.2476728 , 27.34768605,\n",
       "       27.44769311, 27.54863501, 27.64863849, 27.74864125, 27.84865284,\n",
       "       27.94866085, 28.05000162, 28.15000701, 28.25002337, 28.3500421 ,\n",
       "       28.45004702, 28.55006981, 28.65008163, 28.75009274, 28.85010529,\n",
       "       28.95010829, 29.05012822, 29.15012932, 29.25013733, 29.35014582,\n",
       "       29.4501574 , 29.55193377, 29.65193939, 29.75194025, 29.85194349,\n",
       "       29.95196867, 30.05197978, 30.15199256, 30.25200224, 30.35200858])\n",
       "Coordinates:\n",
       "    Trial    int64 0\n",
       "  * t        (t) int64 0 1 2 3 4 5 6 7 8 ... 291 292 293 294 295 296 297 298 299\n",
       "    Batch    &lt;U1 &#x27;1&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Timestamps'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>t</span>: 300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-85d01702-518a-439b-a28e-85bd9896b755' class='xr-array-in' type='checkbox' checked><label for='section-85d01702-518a-439b-a28e-85bd9896b755' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.5283 0.6284 0.7284 0.8284 ... 29.95 30.05 30.15 30.25 30.35</span></div><div class='xr-array-data'><pre>array([ 0.        ,  0.52833557,  0.62842417,  0.72842884,  0.82842946,\n",
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       "        1.42849278,  1.52850032,  1.62851071,  1.72851753,  1.82852006,\n",
       "        1.92875338,  2.02875996,  2.12877369,  2.22878504,  2.3288002 ,\n",
       "        2.42880392,  2.53265095,  2.6326611 ,  2.73267055,  2.83271813,\n",
       "        2.93271828,  3.03272986,  3.13273811,  3.23274446,  3.332757  ,\n",
       "        3.43276525,  3.53277898,  3.6327889 ,  3.73281598,  3.83281636,\n",
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       "        4.4328928 ,  4.53290391,  4.63291693,  4.73319936,  4.83321452,\n",
       "        4.93462777,  5.03464031,  5.13464451,  5.23464656,  5.33465719,\n",
       "        5.43466043,  5.53466868,  5.6346724 ,  5.73471403,  5.83471894,\n",
       "        5.93472981,  6.03473687,  6.13473964,  6.23474717,  6.33569813,\n",
       "        6.43570447,  6.53570604,  6.63570833,  6.73572016,  6.8357327 ,\n",
       "        6.93574333,  7.03574681,  7.13575292,  7.23577237,  7.33577633,\n",
       "        7.43578577,  7.5357995 ,  7.63581181,  7.7358222 ,  7.8358264 ,\n",
       "        7.93583798,  8.03584313,  8.13584924,  8.23585224,  8.33586097,\n",
       "        8.43587565,  8.53588057,  8.63588166,  8.73991942,  8.839926  ,\n",
       "        8.93994331,  9.03994441,  9.13995171,  9.23997259,  9.33997631,\n",
       "        9.43998504,  9.53999305,  9.64000273,  9.74001598,  9.84003401,\n",
       "        9.94004202, 10.04006052, 10.14007401, 10.24007821, 10.34008503,\n",
       "...\n",
       "       20.44351578, 20.54352283, 20.64353037, 20.74353886, 20.8435514 ,\n",
       "       20.94356108, 21.04356956, 21.14357805, 21.24357891, 21.34358501,\n",
       "       21.44359326, 21.54359365, 21.64361048, 21.74361634, 21.84361839,\n",
       "       21.94363165, 22.04363608, 22.14463568, 22.24464417, 22.34465384,\n",
       "       22.44465446, 22.54466701, 22.64467454, 22.74468136, 22.84468484,\n",
       "       22.9447031 , 23.04471803, 23.14471984, 23.24473214, 23.34474397,\n",
       "       23.44474673, 23.54474974, 23.64475274, 23.74475574, 23.84476399,\n",
       "       23.94477367, 24.04477978, 24.14479113, 24.24480081, 24.34481478,\n",
       "       24.44482708, 24.54483199, 24.64483762, 24.74571037, 24.84571624,\n",
       "       24.94578743, 25.04579687, 25.14581537, 25.24581599, 25.34582806,\n",
       "       25.44588017, 25.54589343, 25.64589739, 25.74590302, 25.84591079,\n",
       "       25.94592953, 26.04593968, 26.14594221, 26.24595308, 26.34596038,\n",
       "       26.44596982, 26.54598212, 26.64599538, 26.74599719, 26.84603643,\n",
       "       26.94765687, 27.04766107, 27.14767265, 27.2476728 , 27.34768605,\n",
       "       27.44769311, 27.54863501, 27.64863849, 27.74864125, 27.84865284,\n",
       "       27.94866085, 28.05000162, 28.15000701, 28.25002337, 28.3500421 ,\n",
       "       28.45004702, 28.55006981, 28.65008163, 28.75009274, 28.85010529,\n",
       "       28.95010829, 29.05012822, 29.15012932, 29.25013733, 29.35014582,\n",
       "       29.4501574 , 29.55193377, 29.65193939, 29.75194025, 29.85194349,\n",
       "       29.95196867, 30.05197978, 30.15199256, 30.25200224, 30.35200858])</pre></div></div></li><li class='xr-section-item'><input id='section-82ce48ee-3a42-4d8d-a7a0-58b4963a48a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82ce48ee-3a42-4d8d-a7a0-58b4963a48a7' class='xr-section-summary' >Coordinates: <span>(3)</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>Trial</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-cc309632-ad71-4f52-9209-74ea5ed77172' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc309632-ad71-4f52-9209-74ea5ed77172' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b23c108-07ed-4773-870d-4f7133720ee4' class='xr-var-data-in' type='checkbox'><label for='data-9b23c108-07ed-4773-870d-4f7133720ee4' 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)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-f7b9fd52-c455-4ce1-8c46-43c68399bf2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7b9fd52-c455-4ce1-8c46-43c68399bf2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90324116-f45b-4573-b60e-fa4e80ca3a1a' class='xr-var-data-in' type='checkbox'><label for='data-90324116-f45b-4573-b60e-fa4e80ca3a1a' 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,   1,   2, ..., 297, 298, 299])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Batch</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27;</div><input id='attrs-84f4191e-6194-4acc-8933-a57f4f3d2665' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84f4191e-6194-4acc-8933-a57f4f3d2665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2204793-efca-40a5-9096-5b56e4f18ac5' class='xr-var-data-in' type='checkbox'><label for='data-e2204793-efca-40a5-9096-5b56e4f18ac5' 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(&#x27;1&#x27;, dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-833eed1e-a3a0-4b49-b596-cf29c86d5613' class='xr-section-summary-in' type='checkbox'  ><label for='section-833eed1e-a3a0-4b49-b596-cf29c86d5613' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-16c0bdc5-6a70-4b4e-b218-21da04083281' class='xr-index-data-in' type='checkbox'/><label for='index-16c0bdc5-6a70-4b4e-b218-21da04083281' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;t&#x27;, length=300))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46dca6f6-0528-42e4-9ea6-cae2747ff9c2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46dca6f6-0528-42e4-9ea6-cae2747ff9c2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Timestamps' (t: 300)>\n",
       "array([ 0.        ,  0.52833557,  0.62842417,  0.72842884,  0.82842946,\n",
       "        0.92844248,  1.02844334,  1.12844682,  1.22847629,  1.32848549,\n",
       "        1.42849278,  1.52850032,  1.62851071,  1.72851753,  1.82852006,\n",
       "        1.92875338,  2.02875996,  2.12877369,  2.22878504,  2.3288002 ,\n",
       "        2.42880392,  2.53265095,  2.6326611 ,  2.73267055,  2.83271813,\n",
       "        2.93271828,  3.03272986,  3.13273811,  3.23274446,  3.332757  ,\n",
       "        3.43276525,  3.53277898,  3.6327889 ,  3.73281598,  3.83281636,\n",
       "        3.93282819,  4.03284168,  4.13284826,  4.23286653,  4.33288097,\n",
       "        4.4328928 ,  4.53290391,  4.63291693,  4.73319936,  4.83321452,\n",
       "        4.93462777,  5.03464031,  5.13464451,  5.23464656,  5.33465719,\n",
       "        5.43466043,  5.53466868,  5.6346724 ,  5.73471403,  5.83471894,\n",
       "        5.93472981,  6.03473687,  6.13473964,  6.23474717,  6.33569813,\n",
       "        6.43570447,  6.53570604,  6.63570833,  6.73572016,  6.8357327 ,\n",
       "        6.93574333,  7.03574681,  7.13575292,  7.23577237,  7.33577633,\n",
       "        7.43578577,  7.5357995 ,  7.63581181,  7.7358222 ,  7.8358264 ,\n",
       "        7.93583798,  8.03584313,  8.13584924,  8.23585224,  8.33586097,\n",
       "        8.43587565,  8.53588057,  8.63588166,  8.73991942,  8.839926  ,\n",
       "        8.93994331,  9.03994441,  9.13995171,  9.23997259,  9.33997631,\n",
       "        9.43998504,  9.53999305,  9.64000273,  9.74001598,  9.84003401,\n",
       "        9.94004202, 10.04006052, 10.14007401, 10.24007821, 10.34008503,\n",
       "...\n",
       "       20.44351578, 20.54352283, 20.64353037, 20.74353886, 20.8435514 ,\n",
       "       20.94356108, 21.04356956, 21.14357805, 21.24357891, 21.34358501,\n",
       "       21.44359326, 21.54359365, 21.64361048, 21.74361634, 21.84361839,\n",
       "       21.94363165, 22.04363608, 22.14463568, 22.24464417, 22.34465384,\n",
       "       22.44465446, 22.54466701, 22.64467454, 22.74468136, 22.84468484,\n",
       "       22.9447031 , 23.04471803, 23.14471984, 23.24473214, 23.34474397,\n",
       "       23.44474673, 23.54474974, 23.64475274, 23.74475574, 23.84476399,\n",
       "       23.94477367, 24.04477978, 24.14479113, 24.24480081, 24.34481478,\n",
       "       24.44482708, 24.54483199, 24.64483762, 24.74571037, 24.84571624,\n",
       "       24.94578743, 25.04579687, 25.14581537, 25.24581599, 25.34582806,\n",
       "       25.44588017, 25.54589343, 25.64589739, 25.74590302, 25.84591079,\n",
       "       25.94592953, 26.04593968, 26.14594221, 26.24595308, 26.34596038,\n",
       "       26.44596982, 26.54598212, 26.64599538, 26.74599719, 26.84603643,\n",
       "       26.94765687, 27.04766107, 27.14767265, 27.2476728 , 27.34768605,\n",
       "       27.44769311, 27.54863501, 27.64863849, 27.74864125, 27.84865284,\n",
       "       27.94866085, 28.05000162, 28.15000701, 28.25002337, 28.3500421 ,\n",
       "       28.45004702, 28.55006981, 28.65008163, 28.75009274, 28.85010529,\n",
       "       28.95010829, 29.05012822, 29.15012932, 29.25013733, 29.35014582,\n",
       "       29.4501574 , 29.55193377, 29.65193939, 29.75194025, 29.85194349,\n",
       "       29.95196867, 30.05197978, 30.15199256, 30.25200224, 30.35200858])\n",
       "Coordinates:\n",
       "    Trial    int64 0\n",
       "  * t        (t) int64 0 1 2 3 4 5 6 7 8 ... 291 292 293 294 295 296 297 298 299\n",
       "    Batch    <U1 '1'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmax=300\n",
    "angle200=np.sin(tha[\"Angles\"][0,0,1:4,:tmax])\n",
    "t200=tha[\"Timestamps\"][0,0,:tmax]\n",
    "t200=t200-t200[0]\n",
    "# t200=t200*(10^-9)\n",
    "t200\n",
    "# p=plt.plot(t200, angle200)\n",
    "# p\n",
    "# plt.xlabel(\"time (s)\")\n",
    "# plt.ylabel(\"Heading\")\n",
    "# plt.savefig(\"heading_30s.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>511742784 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "            Trial Batch  Fly    Angles    Timestamps\n",
       "0               0     1    0       NaN  1.547746e+09\n",
       "1               0     1    1       NaN  1.547746e+09\n",
       "2               0     1    2       NaN  1.547746e+09\n",
       "3               0     1    3       NaN  1.547746e+09\n",
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       "...           ...   ...  ...       ...           ...\n",
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       "1696053280     37     1   40       NaN  1.550248e+09\n",
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tha_df=tha.to_dataframe().reset_index().dropna(subset=[\"Timestamps\"]).drop(columns=[\"t\"])\n",
    "tha_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tha_df.rolling(Timestamps=100, min_periods=2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: 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'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Trial: 38, t: 179957, Batch: 6, Fly: 42)\n",
       "Coordinates:\n",
       "  * Trial       (Trial) int64 0 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object &#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; &#x27;6&#x27;\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Trial, Fly, t) float64 nan nan nan nan ... nan nan nan\n",
       "    Timestamps  (Batch, Trial, t) float64 1.548e+09 1.548e+09 ... nan nan</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-5c0a15cf-b22d-4517-aea4-bf9d4fa9bda9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c0a15cf-b22d-4517-aea4-bf9d4fa9bda9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Trial</span>: 38</li><li><span class='xr-has-index'>t</span>: 179957</li><li><span class='xr-has-index'>Batch</span>: 6</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4574b65-86e8-45d5-8ac4-380b2edf35f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4574b65-86e8-45d5-8ac4-380b2edf35f9' class='xr-section-summary' >Coordinates: <span>(3)</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 class='xr-has-index'>Trial</span></div><div class='xr-var-dims'>(Trial)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 32 33 34 35 36 37</div><input id='attrs-8451ad94-ea56-40db-9028-2ee02a69fa7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8451ad94-ea56-40db-9028-2ee02a69fa7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-228781e4-3c2f-426e-8d49-1d7ff73b514c' class='xr-var-data-in' type='checkbox'><label for='data-228781e4-3c2f-426e-8d49-1d7ff73b514c' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 179954 179955 179956</div><input id='attrs-9e94f713-4b9b-45f0-a2dd-f5a00a198b1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e94f713-4b9b-45f0-a2dd-f5a00a198b1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e6a9fe6-3e45-47bc-b290-8c7881a2dd9d' class='xr-var-data-in' type='checkbox'><label for='data-7e6a9fe6-3e45-47bc-b290-8c7881a2dd9d' 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,      1,      2, ..., 179954, 179955, 179956])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; &#x27;6&#x27;</div><input id='attrs-9c268593-ed3a-41de-91c9-14dabdf6dcb3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c268593-ed3a-41de-91c9-14dabdf6dcb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efe3cf29-706e-47a4-b646-41cb1730e798' class='xr-var-data-in' type='checkbox'><label for='data-efe3cf29-706e-47a4-b646-41cb1730e798' 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([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-40f8ad9c-c189-4a76-a068-1df086ac5a2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40f8ad9c-c189-4a76-a068-1df086ac5a2f' class='xr-section-summary' >Data variables: <span>(2)</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>Angles</span></div><div class='xr-var-dims'>(Batch, Trial, Fly, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-54c153aa-5939-449f-95d6-a8e08d018b4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54c153aa-5939-449f-95d6-a8e08d018b4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94c874d8-4231-48a3-a4f2-b951c270380f' class='xr-var-data-in' type='checkbox'><label for='data-94c874d8-4231-48a3-a4f2-b951c270380f' 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([[[[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "...\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Timestamps</span></div><div class='xr-var-dims'>(Batch, Trial, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.548e+09 1.548e+09 ... nan nan</div><input id='attrs-1fc6d430-cb5c-4255-b7e1-387c1c45138f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fc6d430-cb5c-4255-b7e1-387c1c45138f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-139e3445-ae6e-4482-9074-78c754687b39' class='xr-var-data-in' type='checkbox'><label for='data-139e3445-ae6e-4482-9074-78c754687b39' 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([[[1.54774554e+09, 1.54774554e+09, 1.54774554e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.54780939e+09, 1.54780939e+09, 1.54780939e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.54782763e+09, 1.54782763e+09, 1.54782763e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        ...,\n",
       "        [1.55015569e+09, 1.55015569e+09, 1.55015569e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55022353e+09, 1.55022353e+09, 1.55022353e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55024044e+09, 1.55024044e+09, 1.55024044e+09, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[1.55065519e+09, 1.55065519e+09, 1.55065519e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55067461e+09, 1.55067461e+09, 1.55067461e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55076116e+09, 1.55076116e+09, 1.55076116e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "...\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[1.55843667e+09, 1.55843667e+09, 1.55843667e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55845522e+09, 1.55845522e+09, 1.55845522e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55851840e+09, 1.55851840e+09, 1.55851840e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68227316-f5a9-434a-9647-ded5795b129e' class='xr-section-summary-in' type='checkbox'  ><label for='section-68227316-f5a9-434a-9647-ded5795b129e' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>Trial</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a83b8d36-b9dc-4c99-a442-e9d1d30e1481' class='xr-index-data-in' type='checkbox'/><label for='index-a83b8d36-b9dc-4c99-a442-e9d1d30e1481' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;Trial&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4258d34b-6857-4e54-b6c0-29e26bcf4000' class='xr-index-data-in' type='checkbox'/><label for='index-4258d34b-6857-4e54-b6c0-29e26bcf4000' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
       "                 8,      9,\n",
       "            ...\n",
       "            179947, 179948, 179949, 179950, 179951, 179952, 179953, 179954,\n",
       "            179955, 179956],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;t&#x27;, length=179957))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b11a823-ae59-4f95-a4d1-1b2a6d4f4f73' class='xr-index-data-in' type='checkbox'/><label for='index-4b11a823-ae59-4f95-a4d1-1b2a6d4f4f73' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a169c08-4e0e-4471-9f17-ce5b86e93b54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a169c08-4e0e-4471-9f17-ce5b86e93b54' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "Dimensions:     (Trial: 38, t: 179957, Batch: 6, Fly: 42)\n",
       "Coordinates:\n",
       "  * Trial       (Trial) int64 0 1 2 3 4 5 6 7 8 9 ... 29 30 31 32 33 34 35 36 37\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object '1' '2' '3' '4' '5' '6'\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Trial, Fly, t) float64 nan nan nan nan ... nan nan nan\n",
       "    Timestamps  (Batch, Trial, t) float64 1.548e+09 1.548e+09 ... nan nan"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "Data variables:\n",
       "    Angles      (Batch, Fly, t) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "    Timestamps  (Batch, t) float64 1.548e+09 1.548e+09 1.548e+09 ... nan nan nan</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-b98eb4c6-2592-4667-aa52-355af2e9a342' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b98eb4c6-2592-4667-aa52-355af2e9a342' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t</span>: 179957</li><li><span class='xr-has-index'>Batch</span>: 6</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7d76fd3f-821a-4fd2-a44a-dccf8812e84e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d76fd3f-821a-4fd2-a44a-dccf8812e84e' class='xr-section-summary' >Coordinates: <span>(3)</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>Trial</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-cdbfd332-b918-4200-8515-d78c15d01307' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdbfd332-b918-4200-8515-d78c15d01307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76121ee6-6c15-4261-9346-6a0c88c63254' class='xr-var-data-in' type='checkbox'><label for='data-76121ee6-6c15-4261-9346-6a0c88c63254' 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)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 179954 179955 179956</div><input id='attrs-274f4dc7-3ec5-4481-ada4-9c3cb1daf6c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-274f4dc7-3ec5-4481-ada4-9c3cb1daf6c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8539343-4898-4ff2-a922-14e1d8200ea9' class='xr-var-data-in' type='checkbox'><label for='data-c8539343-4898-4ff2-a922-14e1d8200ea9' 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,      1,      2, ..., 179954, 179955, 179956])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; &#x27;6&#x27;</div><input id='attrs-dd26f78f-4b00-4e1a-a502-befadf73c03c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd26f78f-4b00-4e1a-a502-befadf73c03c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01ddb595-950b-4e37-8164-07a9962b9af1' class='xr-var-data-in' type='checkbox'><label for='data-01ddb595-950b-4e37-8164-07a9962b9af1' 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([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2ad92a9-701f-433c-a9e5-b8a6226cffaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2ad92a9-701f-433c-a9e5-b8a6226cffaf' class='xr-section-summary' >Data variables: <span>(2)</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>Angles</span></div><div class='xr-var-dims'>(Batch, Fly, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-f0cada13-e318-40ea-ad05-057c368726a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0cada13-e318-40ea-ad05-057c368726a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0caa53d-4c3e-4049-b009-fac90c856b4a' class='xr-var-data-in' type='checkbox'><label for='data-a0caa53d-4c3e-4049-b009-fac90c856b4a' 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([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 0.        ,  0.        ,  0.        , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 0.        ,  0.        ,  0.        , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-0.        , -0.        , -0.        , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [ 0.        ,  0.        ,  0.        , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-0.        , -0.        , -0.        , ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [-0.        , -0.        , -0.        , ...,         nan,\n",
       "                 nan,         nan]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Timestamps</span></div><div class='xr-var-dims'>(Batch, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.548e+09 1.548e+09 ... nan nan</div><input id='attrs-3323e9f8-b248-4e24-941a-1c18d3e7873c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3323e9f8-b248-4e24-941a-1c18d3e7873c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc5891b2-01c5-4161-b7bc-4e35c8db9625' class='xr-var-data-in' type='checkbox'><label for='data-cc5891b2-01c5-4161-b7bc-4e35c8db9625' 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([[1.54774554e+09, 1.54774554e+09, 1.54774554e+09, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [1.55065519e+09, 1.55065519e+09, 1.55065519e+09, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [1.55238645e+09, 1.55238645e+09, 1.55238645e+09, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [1.55420122e+09, 1.55420122e+09, 1.55420122e+09, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [1.55541388e+09, 1.55541388e+09, 1.55541388e+09, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [1.55843667e+09, 1.55843667e+09, 1.55843667e+09, ...,\n",
       "                   nan,            nan,            nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43b118ad-ceff-419c-8c1f-ff00b68b9fb6' class='xr-section-summary-in' type='checkbox'  ><label for='section-43b118ad-ceff-419c-8c1f-ff00b68b9fb6' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c88df1d3-dc68-42a5-8a1c-feac5cf6c714' class='xr-index-data-in' type='checkbox'/><label for='index-c88df1d3-dc68-42a5-8a1c-feac5cf6c714' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
       "                 8,      9,\n",
       "            ...\n",
       "            179947, 179948, 179949, 179950, 179951, 179952, 179953, 179954,\n",
       "            179955, 179956],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;t&#x27;, length=179957))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b96bc55d-a43b-4995-94c3-83ebc684f35f' class='xr-index-data-in' type='checkbox'/><label for='index-b96bc55d-a43b-4995-94c3-83ebc684f35f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a9941ce-75d9-4bfe-98aa-0ff79e1750cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a9941ce-75d9-4bfe-98aa-0ff79e1750cf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (t: 179957, Batch: 6, Fly: 42)\n",
       "Coordinates:\n",
       "    Trial       int64 0\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object '1' '2' '3' '4' '5' '6'\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Fly, t) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "    Timestamps  (Batch, t) float64 1.548e+09 1.548e+09 1.548e+09 ... nan nan nan"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin=tha.loc[{\"Trial\":0}]\n",
    "# thamin=tha.loc[{\"Trial\":0}]\n",
    "\n",
    "thamin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: 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'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Trial: 3, t: 179957, Batch: 2, Fly: 42)\n",
       "Coordinates:\n",
       "  * Trial       (Trial) int64 0 1 2\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object &#x27;1&#x27; &#x27;2&#x27;\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Trial, Fly, t) float64 nan nan nan nan ... nan nan nan\n",
       "    Timestamps  (Batch, Trial, t) float64 1.548e+09 1.548e+09 ... nan nan</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-a5c2ade7-ab10-4e56-ba98-5cc0d1dc8a82' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a5c2ade7-ab10-4e56-ba98-5cc0d1dc8a82' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Trial</span>: 3</li><li><span class='xr-has-index'>t</span>: 179957</li><li><span class='xr-has-index'>Batch</span>: 2</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-725c9e20-c1c1-4237-843d-da682b462389' class='xr-section-summary-in' type='checkbox'  checked><label for='section-725c9e20-c1c1-4237-843d-da682b462389' class='xr-section-summary' >Coordinates: <span>(3)</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 class='xr-has-index'>Trial</span></div><div class='xr-var-dims'>(Trial)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-b3bdd074-9407-4d69-b588-6f3fd8bb4d07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3bdd074-9407-4d69-b588-6f3fd8bb4d07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b2b7faf-c14c-4bd9-8d18-95e6321e9107' class='xr-var-data-in' type='checkbox'><label for='data-9b2b7faf-c14c-4bd9-8d18-95e6321e9107' 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, 1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 179954 179955 179956</div><input id='attrs-e46cebd3-0ca6-4a82-a7e2-637b85d2f31c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e46cebd3-0ca6-4a82-a7e2-637b85d2f31c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cae985e0-e98e-4734-9073-6a838b4bfe6e' class='xr-var-data-in' type='checkbox'><label for='data-cae985e0-e98e-4734-9073-6a838b4bfe6e' 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,      1,      2, ..., 179954, 179955, 179956])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27;</div><input id='attrs-4db37213-a9f1-4e79-a1be-0883d997741a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4db37213-a9f1-4e79-a1be-0883d997741a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d9f641-f983-4463-8d9c-399a39c8b8c2' class='xr-var-data-in' type='checkbox'><label for='data-36d9f641-f983-4463-8d9c-399a39c8b8c2' 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([&#x27;1&#x27;, &#x27;2&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70c80534-6b7c-4724-95d1-d5f7e078289a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70c80534-6b7c-4724-95d1-d5f7e078289a' class='xr-section-summary' >Data variables: <span>(2)</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>Angles</span></div><div class='xr-var-dims'>(Batch, Trial, Fly, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ce9170a3-0857-427f-8833-747c2694916a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce9170a3-0857-427f-8833-747c2694916a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-773247b8-7591-4f15-8b06-709dcbe94149' class='xr-var-data-in' type='checkbox'><label for='data-773247b8-7591-4f15-8b06-709dcbe94149' 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([[[[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "...\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]],\n",
       "\n",
       "        [[        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [-0.        , -0.        ,  1.1891923 , ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         ...,\n",
       "         [ 0.        ,  5.57902431,  5.21275806, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan],\n",
       "         [        nan,         nan,         nan, ...,         nan,\n",
       "                  nan,         nan]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Timestamps</span></div><div class='xr-var-dims'>(Batch, Trial, t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.548e+09 1.548e+09 ... nan nan</div><input id='attrs-739eb488-7632-48f5-aa99-10432334867d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-739eb488-7632-48f5-aa99-10432334867d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1056b127-9d75-4756-a6f2-56cdb679b27a' class='xr-var-data-in' type='checkbox'><label for='data-1056b127-9d75-4756-a6f2-56cdb679b27a' 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([[[1.54774554e+09, 1.54774554e+09, 1.54774554e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.54780939e+09, 1.54780939e+09, 1.54780939e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.54782763e+09, 1.54782763e+09, 1.54782763e+09, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[1.55065519e+09, 1.55065519e+09, 1.55065519e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55067461e+09, 1.55067461e+09, 1.55067461e+09, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [1.55076116e+09, 1.55076116e+09, 1.55076116e+09, ...,\n",
       "                    nan,            nan,            nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58156a81-136e-4b48-8e8d-f9f913995722' class='xr-section-summary-in' type='checkbox'  ><label for='section-58156a81-136e-4b48-8e8d-f9f913995722' class='xr-section-summary' >Indexes: <span>(3)</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-index-name'><div>Trial</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5eb20f68-35fe-4fbd-bd5b-bef103a943c5' class='xr-index-data-in' type='checkbox'/><label for='index-5eb20f68-35fe-4fbd-bd5b-bef103a943c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([0, 1, 2], dtype=&#x27;int64&#x27;, name=&#x27;Trial&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-616ad71d-1128-4de9-bb75-cf4d531b606b' class='xr-index-data-in' type='checkbox'/><label for='index-616ad71d-1128-4de9-bb75-cf4d531b606b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([     0,      1,      2,      3,      4,      5,      6,      7,\n",
       "                 8,      9,\n",
       "            ...\n",
       "            179947, 179948, 179949, 179950, 179951, 179952, 179953, 179954,\n",
       "            179955, 179956],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;t&#x27;, length=179957))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31e1672f-dde7-4cc9-b850-dcada581ab7b' class='xr-index-data-in' type='checkbox'/><label for='index-31e1672f-dde7-4cc9-b850-dcada581ab7b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;1&#x27;, &#x27;2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-620d1746-2f05-457b-872a-42527559bc07' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-620d1746-2f05-457b-872a-42527559bc07' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Trial: 3, t: 179957, Batch: 2, Fly: 42)\n",
       "Coordinates:\n",
       "  * Trial       (Trial) int64 0 1 2\n",
       "  * t           (t) int64 0 1 2 3 4 5 ... 179952 179953 179954 179955 179956\n",
       "  * Batch       (Batch) object '1' '2'\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Batch, Trial, Fly, t) float64 nan nan nan nan ... nan nan nan\n",
       "    Timestamps  (Batch, Trial, t) float64 1.548e+09 1.548e+09 ... nan nan"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thatrial_test=tha.loc[{\"Trial\":slice(0,2), \"Batch\":slice('1', '2')}]\n",
    "thatrial_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Trial</th>\n",
       "      <th>Angles</th>\n",
       "      <th>Timestamps</th>\n",
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       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">10000</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th>37</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.558438e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.558438e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.558438e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.558438e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.558438e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2520252 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Trial  Angles    Timestamps\n",
       "t     Batch Fly                             \n",
       "0     1     0        0     NaN  1.547746e+09\n",
       "            1        0     NaN  1.547746e+09\n",
       "            2        0     NaN  1.547746e+09\n",
       "            3        0     NaN  1.547746e+09\n",
       "            4        0     NaN  1.547746e+09\n",
       "...                ...     ...           ...\n",
       "10000 6     37       0     NaN  1.558438e+09\n",
       "            38       0     NaN  1.558438e+09\n",
       "            39       0     NaN  1.558438e+09\n",
       "            40       0     NaN  1.558438e+09\n",
       "            41       0     NaN  1.558438e+09\n",
       "\n",
       "[2520252 rows x 3 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanmaloney/Documents/GitHub/Continuous Turns/loadcontinuousmatlabfiles.py:516: FutureWarning: ['Timestamps'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  return xarraydataset, xarraydataset.groupby(\"batchxfly\").agg([\"mean\",\"std\", \"count\"])\n"
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "a,b=lcm.getmeanandstdofalltimestamps(np.sin(thamin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanmaloney/Documents/GitHub/Continuous Turns/loadcontinuousmatlabfiles.py:516: FutureWarning: ['Timestamps'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.\n",
      "  return xarraydataset, xarraydataset.groupby(\"batchxfly\").agg([\"mean\",\"std\", \"count\"])\n"
     ]
    }
   ],
   "source": [
    "# imp.reload(lcm)\n",
    "# a1, b1=lcm.getmeanandstdofalltimestamps(np.sin(thatrial_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mCannot execute code, session has been disposed. Please try restarting the Kernel."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# a0, b0=lcm.getmeanandstdofalltimestamps(np.sin(tha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
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       "             Angles                  \n",
       "               mean       std   count\n",
       "batchxfly                            \n",
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       "f0b2      -0.006607  0.429607   96898\n",
       "f10b1     -0.008371  0.474335  107574\n",
       "f10b2      0.010453  0.557172  148879\n",
       "f11b1      0.005954  0.405944   65520\n",
       "...             ...       ...     ...\n",
       "f7b2      -0.001242  0.441150  121130\n",
       "f8b1       0.003331  0.450965   89592\n",
       "f8b2       0.020737  0.622701  189170\n",
       "f9b1       0.007724  0.447725  111219\n",
       "f9b2      -0.006077  0.530326   92274\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
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       "<p>252 rows × 3 columns</p>\n",
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      "text/plain": [
       "               mean       std  count\n",
       "batchxfly                           \n",
       "f0b1      -0.004312  0.281770   7223\n",
       "f0b2      -0.011072  0.391785   8802\n",
       "f0b3       0.016283  0.353050   8680\n",
       "f0b4      -0.103261  0.445501   9414\n",
       "f0b5       0.003612  0.596397   2734\n",
       "...             ...       ...    ...\n",
       "f9b2      -0.082128  0.575167   9980\n",
       "f9b3       0.013898  0.470166   9830\n",
       "f9b4       0.017030  0.502118   9668\n",
       "f9b5       0.002607  0.547903   9792\n",
       "f9b6       0.016462  0.672820   1098\n",
       "\n",
       "[252 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[\"Angles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_70889/2900983168.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  day1[\"Time\"]=\"Day 1\"\n",
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_70889/2900983168.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  alldays[\"Time\"]=\"All Days\"\n"
     ]
    },
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       "      <td>0.063270</td>\n",
       "      <td>0.553382</td>\n",
       "      <td>267360</td>\n",
       "      <td>All Days</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>504 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean       std   count      Time\n",
       "batchxfly                                      \n",
       "f0b1       0.003032  0.389199   19154     Day 1\n",
       "f0b2      -0.008786  0.393989   23540     Day 1\n",
       "f0b3       0.008000  0.318762   62366     Day 1\n",
       "f0b4      -0.056276  0.516842   43851     Day 1\n",
       "f0b5       0.005760  0.611226    6452     Day 1\n",
       "...             ...       ...     ...       ...\n",
       "f9b2       0.036261  0.588903  304705  All Days\n",
       "f9b3       0.005737  0.444193  151828  All Days\n",
       "f9b4      -0.010829  0.584947  776059  All Days\n",
       "f9b5      -0.031233  0.560642  398343  All Days\n",
       "f9b6       0.063270  0.553382  267360  All Days\n",
       "\n",
       "[504 rows x 4 columns]"
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     },
     "execution_count": 40,
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   "source": [
    "day1=b[\"Angles\"]\n",
    "day1[\"Time\"]=\"Day 1\"\n",
    "alldays=testtrunc_summary[\"Angles\"]\n",
    "alldays[\"Time\"]=\"All Days\"\n",
    "allconds=pd.concat([day1, alldays])\n",
    "allconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8Yn7M1NTUGkd6T5w4UaNHj5YkzZkzRyUlJV73/c4773juKHnggQcUHh5eZXtCQoIefPBBSeUjmRcvXlxl++HDh/XJJ59Iki6++GINGjSo2jEq57im27yb6hokWARyvj/44AO5XC5J0iOPPFLjFCk9e/bULbfcIkk6ePCgVq9eXWV7UVGRdu/eLanln7veCOR8Sz//npX8yxfnd1WBnu+6rFq1yvM7/Mwzz9RVV11VbR/ObwCwDwVnBL2CggKtXLlSUvntrrXNrzhp0iRJ5XP3+XqBIpUXCSdMmKDnnntOLpdLbdu21VNPPeVV25UrVyo3N1eSdOGFF9a63yWXXCJJ2rlzZ7V50SrmlEtPT1dKSkqt7SsKVSfPB9xSNEW+K/YPDw/XhAkTatynb9++6t27tyR5ihXeeuyxx3TixAkZhqFHHnmk2gW1JGVlZXlux/engNlSBEu+KwrOYWFhfi3sx/n9s2DIeUUByuFw6Oyzz671OJXn86yYoqNCqJzj27dv144dOyRJ559/fq0f1FbkMz8/X19//bXX/VfkslOnTho6dGiN+4wePVpxcXGSqp87S5cu9RQga3t/joiI8PycfPPNN573c6nprkGCRaDnu+LcTUxMVP/+/Ws9zumnn+55nJGRUWVbxYJxUss+d70R6PmWfn5/Tk5O9nl6B87vqoIh37UpKyvTo48+KrfbrfDwcP3xj3+scT/ObwCwDwVnBL0NGzZ45usaNmxYrfv16dPHM8eWrwu6SeWr11eMWjjvvPP0wQcfeD2P6tq1az2Phw8fXut+p556qudxxQWuJO3du9dTmKjrNcbHx3uKXf68xmDQFPmuyNfAgQMVERFR635DhgyRJG3atEn5+fle9b106VLPwnLnn39+rRfUDR0x21IES74rRlD16NGjxg8Q6sL5XVUw5HzUqFG66aabdNlllykqKqrW9lalZTKKi4urbAuVc7zy+19d+az4XktV3//qUlJSok2bNtXbt2maSk9Pl1T9Z6UiPqfTWeU9uLb4SktLq7ymproGCRaBnu8LLrhAN954o375y196dUyp+rnb0BGzLUmg51tq2J0knN9VBUO+a/PGG294Pvi98cYb1bVr1xr34/wGAPs4mzsAoKEqPmmXVOvFg1R+gZKcnKyMjAxt377d5+MYhqEhQ4borrvu8ox82bdvn1dtK47ndDrVqVOnWvervNBI5RgrP67rNVb0sW3bNr9eYzBo7HwXFhZ6brXv0qVLnftW5Mvtdmvnzp0aMGBAnfu73W49++yzkspHwtZ0a2aFyiNmIyIi9Pjjj+uLL77wLHLSq1cvXXDBBZo8ebKczpb7qzxY8l3xB0rfvn21bNkyzZ07V+vWrdPx48fVpk0bDR8+XNddd12NH1JxflcVDDkfP358jYtTney7777zPD75d3+onOPe/nwnJCQoJiZGBQUFXudz9+7dKisrk+R9LrOyspSVlaWEhIQq8XXo0KHOD4sqvz9v27bNs8hrU12DBItAz/dll13m1bG8OXeTk5N16NAhPf3001qxYoUOHz6smJgY9evXT5deeqnOP//8atMjtTSBnm/Lsjwj1Hv37q0PP/xQ8+fP1/fff6+CggIlJSVpxIgRmjp1ao13J3F+VxXo+a5NYWGhZ8HX+Ph43XzzzbXuy/kNAPYJ3r9ggJ9kZmZ6Hnfs2LHOfZOSkpSRkVGljbeGDh2qOXPm+NxO+jnG9u3by+Fw1LpfmzZtFBERoeLi4iox+voapfKLq/z8/Fpv/wtWjZ3vyvvW9eGAVJ7PmtrVZsmSJZ4/fC688MI6VzKvPMLi4osvrjIHXklJiVavXq3Vq1fr7bff1owZM6rE0pIEQ76Li4u1a9cuSeVzA8+fP7/aMT788EN9+OGHuuWWW3TvvfdW+SOF87uqYMi5NyzL0ssvv+z5+owzzqiyPVTO8Yrvm2ma9b6GpKQk7dy50+vvdUNyWVGgqOjD35+FproGCRaBnm9vFBUVafbs2ZLKX0fl6TWkn8/d7OxsXXLJJXK73Z5tOTk5+uqrr/TVV19p/vz5+uc//9kif09XCPR879mzRwUFBZLK52I++e6k/fv365133tF7772nhx56SNdee22tMXB+B36+azN37lwdPXpUknTdddepVatWte7L+Q0A9qHgjKB3/Phxz+Po6Og6963YXrHYky+8XSCwJhXzPdYXn1S+WFVxcXGVOSL9eY0Vx21pF0KNne/K3/f6+q98K703x6hYeEYqv52vLhUXvKWlpYqJidGUKVN0xhlnKCYmRhkZGZo5c6a2bt2qzZs3a9q0aZo7d26dt/YHq2DId0ZGhmdUTn5+vtLS0nTttdeqV69eKi4u1ldffaXXX39dhYWFev755xUZGak77rjD057zu6pgyLk3XnnlFc/ijunp6dXugAiVc7zi+x0ZGVnnB67Sz/monKO6+PKzUlsuvX1/rty+oe/P/lyDBItAz7c3nnrqKR04cECS9Itf/ELt2rXzbLMsS1u2bJFU/sFfQkKCrrvuOp166qkKCwvTxo0b9dprr+nAgQNavny57rzzTr300ksNuoYMZIGe78pTF+Xn5ys9PV1XXnmlUlNTlZubq6VLl+qdd95RaWmp/vSnPyk2NtaznoqvMXB+VxUo57fb7fZ8gBQREaGrr7661n05vwHAXhSc0ex27drl04ISFfr3768BAwZUGRVW11yclbe73W6VlpYqLCzM5+P6oyJGb+Z2rYjx5NFuJ2+vr/3J7QJFoOfbl/4jIyNrbFeTLVu2eOaaO/PMM5WWllZnDDt37pRUPkLktddeU/fu3T3bBw8erEsuuUR33XWXPvvsM2VkZGjGjBm655576oyhOYRCviuPVL3gggv0xBNPVDnXR4wYoQsvvFDXXXedjh8/rn/961+aOHGiJ6ct6fyWQiPn9fnss8/0zDPPSCqfMuPhhx+uFkNLOcfr09D3P2/6rty2NrXlsuJxQ9t700dzXYM0pUDPd33mzJnjKU7FxcXpvvvuq7J97969nhGzaWlpeumllzx3nkjlc9deeumlmjp1qjZu3KgVK1Zo3rx5uvzyy72OIZgEer4rvz9PnTpV999/f5U7jM4++2xNmDBB06ZNU2lpqR577DGde+65nsUFOb+rCvR81+Tzzz/X3r17JZUvvlzXaGjObwCwFwVnNLv169fr0Ucf9bndr371Kw0YMKDKp8r1zaVVeQGnpvw0uuJY3sz1VRFj5fh8eY01HTeQBHq+K/fpS//17fv66697Htc3ujksLEyLFy/Wnj171K5duyqFqArh4eH629/+ptGjRysvL09z5szRXXfdVe+Ik6YWCvm++OKLlZ6err1792rkyJE1/iHWu3dvPfDAA/rd734nt9utOXPm6Pe//321WIP9/JZCI+d1+fLLL3X33Xd7Rr3ff//91UY3t6RzvD4Nff/zpm9v+q/tZ8XhcFS5ZdrX4wbDNUhTCvR81+Xdd9/Vn/70J0//TzzxRLVb+1NSUrR48WLt3btX3bp1q1KMqhAXF6dnnnlGEydOlMvl0syZM1tsQSrQ833LLbdowoQJOnTokEaNGlVjP6effrpuueUW/fvf/1ZBQYHeffddz3Ua53dVgZ7vmlS+/r7hhhvq3JfzGwDs1TLfDRFSKt92dfJK4ier+BTc4XA06R/tFTF68yl/TaMHfHmNlbd7MwIh2DR2vitWGfemf2+/1y6XSx9//LEkKTExsdpcriczDEMdO3bUaaedVuMiNhXi4uI8C5fl5uZq8+bNdfYbjIIh3+Hh4erZs6fOPffcOn8OLrzwQs+onG+++cbzPOd3VcGQ89p8+umnmj59uqfdjTfeqClTplTbL5TO8Yp81ve9lnwbPVe5b2/6r7y98sjDiluz63t/Lioq8jz29/25ua5BmlKg57s2s2fP9nwgKEkPP/ywxo4dW20/0zTVuXNnnXHGGXXO6du1a1cNGzZMUvm0S1lZWfXGEIwCPd/R0dHq06ePzjnnnDqLmJULhv6+P3N+VxUI53dWVpbnjquBAwfW+OFuZZzfAGAvRjij2V188cW6+OKL/W5fuXhw4sQJtWnTptZ9CwsLJZX/Ed+UKmKsOH5dTpw4IalqjCe/xrpUPkZTv05vBHq+K/dfX74q56Li9suarF69Wjk5OZKkcePG2TrypXfv3p7HBw8erDaSsrmFYr5rEx4erm7duumHH37wzA96cgzBfn5LoZvz119/XX/+8589BaspU6bowQcf9Dqu2gT6OV6fiu93cXGx3G53nb//fM2nL+dObbmMiYlRbm6uTz8Ldb0/B+I1SFMK9HzX5O9//7uee+45z9cPPfSQrrvuOq9iqkvv3r09xcuDBw/6tHBhsAjGfNekY8eOiouLU25ubp3vz5zfwZXvZcuWyeVySZLOO+88r+LwViic3wDQUIxwRtCrfLvj4cOH69y3YgXkmm6RakwVMWZmZla5Dexk2dnZnk/tK8eYnJzseezta4yNjfVqkcJg09j5bt++vWdkSn0ra1c+fl3HWLp0qeex3Re8lRdOKS0ttbXvQBCM+a5LxQjnyrni/K4q2HJuWZaefvppPfbYY55i82233VZt3mZ/Bfs5XpFPl8ulo0eP1rmvr/n05dypLZcV8fnSvn379tXae9NHc12DNKVAz3dlpaWl+u1vf+spNpumqUcffbTe2+69FeznrjeCKd/1qen9mfO7qmDLN9ffANC8KDgj6FW+HXnPnj217ud2u7V//35JUs+ePRs9rsoqYiwpKanzIqpiUQupaoyVbwGr6zVW7qOpX2NTaex8h4eHq3PnzvX2L/38vTZNU926dat1v88//1yS1KZNG88tePX1u3jxYs2dO7fe2/Qqb2+JoyuCId+rVq3SwoULtXDhwnqPl52dLUlq27at5znO76qCIeeVY/j973+vF154QVL5VBkPPfRQvYv7hdI57m0+s7KyPIs1eZvPlJQUz+JS3uYyMTGxyoi4ivgOHjzomXe7rvaV25z8OFC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      "text/plain": [
       "<seaborn._core.plot.Plot at 0x5c0c8ed10>"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "image/png": {
       "height": 378.25,
       "width": 610.3
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "so.Plot(data=allconds.reset_index(), x=\"mean\", y=\"std\", color=\"Time\").add(so.Dot())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "allconds[\"var\"]=allconds[\"std\"]**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(context=\"paper\", style=\"ticks\", font_scale=1)\n",
    "f=sns.jointplot(data=allconds.reset_index(), x=\"mean\", y=\"std\", hue=\"Time\", height=4)\n",
    "plt.xlabel(\"Mean\")\n",
    "plt.ylabel(\"Standard Deviation\")\n",
    "# f.set_size_inches(1, 1)\n",
    "f.savefig(\"meanstd_continuousdata.pdf\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret value `count` for parameter `size`",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m f\u001b[39m=\u001b[39msns\u001b[39m.\u001b[39;49mjointplot(data\u001b[39m=\u001b[39;49mallconds, x\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mmean\u001b[39;49m\u001b[39m\"\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mstd\u001b[39;49m\u001b[39m\"\u001b[39;49m, size\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcount\u001b[39;49m\u001b[39m\"\u001b[39;49m, hue\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mTime\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m\"\u001b[39m\u001b[39mMean\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m\"\u001b[39m\u001b[39mStd\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/axisgrid.py:2255\u001b[0m, in \u001b[0;36mjointplot\u001b[0;34m(data, x, y, hue, kind, height, ratio, space, dropna, xlim, ylim, color, palette, hue_order, hue_norm, marginal_ticks, joint_kws, marginal_kws, **kwargs)\u001b[0m\n\u001b[1;32m   2252\u001b[0m \u001b[39mif\u001b[39;00m kind\u001b[39m.\u001b[39mstartswith(\u001b[39m\"\u001b[39m\u001b[39mscatter\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m   2254\u001b[0m     joint_kws\u001b[39m.\u001b[39msetdefault(\u001b[39m\"\u001b[39m\u001b[39mcolor\u001b[39m\u001b[39m\"\u001b[39m, color)\n\u001b[0;32m-> 2255\u001b[0m     grid\u001b[39m.\u001b[39;49mplot_joint(scatterplot, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mjoint_kws)\n\u001b[1;32m   2257\u001b[0m     \u001b[39mif\u001b[39;00m grid\u001b[39m.\u001b[39mhue \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   2258\u001b[0m         marg_func \u001b[39m=\u001b[39m histplot\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/axisgrid.py:1826\u001b[0m, in \u001b[0;36mJointGrid.plot_joint\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1823\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inject_kwargs(func, kwargs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_hue_params)\n\u001b[1;32m   1825\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mstr\u001b[39m(func\u001b[39m.\u001b[39m\u001b[39m__module__\u001b[39m)\u001b[39m.\u001b[39mstartswith(\u001b[39m\"\u001b[39m\u001b[39mseaborn\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m-> 1826\u001b[0m     func(x\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mx, y\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49my, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1827\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1828\u001b[0m     func(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mx, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39my, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/relational.py:742\u001b[0m, in \u001b[0;36mscatterplot\u001b[0;34m(data, x, y, hue, size, style, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, style_order, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    732\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mscatterplot\u001b[39m(\n\u001b[1;32m    733\u001b[0m     data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m,\n\u001b[1;32m    734\u001b[0m     x\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, y\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, hue\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, size\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, style\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    738\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs\n\u001b[1;32m    739\u001b[0m ):\n\u001b[1;32m    741\u001b[0m     variables \u001b[39m=\u001b[39m _ScatterPlotter\u001b[39m.\u001b[39mget_semantics(\u001b[39mlocals\u001b[39m())\n\u001b[0;32m--> 742\u001b[0m     p \u001b[39m=\u001b[39m _ScatterPlotter(data\u001b[39m=\u001b[39;49mdata, variables\u001b[39m=\u001b[39;49mvariables, legend\u001b[39m=\u001b[39;49mlegend)\n\u001b[1;32m    744\u001b[0m     p\u001b[39m.\u001b[39mmap_hue(palette\u001b[39m=\u001b[39mpalette, order\u001b[39m=\u001b[39mhue_order, norm\u001b[39m=\u001b[39mhue_norm)\n\u001b[1;32m    745\u001b[0m     p\u001b[39m.\u001b[39mmap_size(sizes\u001b[39m=\u001b[39msizes, order\u001b[39m=\u001b[39msize_order, norm\u001b[39m=\u001b[39msize_norm)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/relational.py:538\u001b[0m, in \u001b[0;36m_ScatterPlotter.__init__\u001b[0;34m(self, data, variables, legend)\u001b[0m\n\u001b[1;32m    529\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, variables\u001b[39m=\u001b[39m{}, legend\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    530\u001b[0m \n\u001b[1;32m    531\u001b[0m     \u001b[39m# TODO this is messy, we want the mapping to be agnostic about\u001b[39;00m\n\u001b[1;32m    532\u001b[0m     \u001b[39m# the kind of plot to draw, but for the time being we need to set\u001b[39;00m\n\u001b[1;32m    533\u001b[0m     \u001b[39m# this information so the SizeMapping can use it\u001b[39;00m\n\u001b[1;32m    534\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_default_size_range \u001b[39m=\u001b[39m (\n\u001b[1;32m    535\u001b[0m         np\u001b[39m.\u001b[39mr_[\u001b[39m.5\u001b[39m, \u001b[39m2\u001b[39m] \u001b[39m*\u001b[39m np\u001b[39m.\u001b[39msquare(mpl\u001b[39m.\u001b[39mrcParams[\u001b[39m\"\u001b[39m\u001b[39mlines.markersize\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m    536\u001b[0m     )\n\u001b[0;32m--> 538\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(data\u001b[39m=\u001b[39;49mdata, variables\u001b[39m=\u001b[39;49mvariables)\n\u001b[1;32m    540\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlegend \u001b[39m=\u001b[39m legend\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/_oldcore.py:640\u001b[0m, in \u001b[0;36mVectorPlotter.__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    635\u001b[0m \u001b[39m# var_ordered is relevant only for categorical axis variables, and may\u001b[39;00m\n\u001b[1;32m    636\u001b[0m \u001b[39m# be better handled by an internal axis information object that tracks\u001b[39;00m\n\u001b[1;32m    637\u001b[0m \u001b[39m# such information and is set up by the scale_* methods. The analogous\u001b[39;00m\n\u001b[1;32m    638\u001b[0m \u001b[39m# information for numeric axes would be information about log scales.\u001b[39;00m\n\u001b[1;32m    639\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_ordered \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mFalse\u001b[39;00m, \u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mFalse\u001b[39;00m}  \u001b[39m# alt., used DefaultDict\u001b[39;00m\n\u001b[0;32m--> 640\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49massign_variables(data, variables)\n\u001b[1;32m    642\u001b[0m \u001b[39mfor\u001b[39;00m var, \u001b[39mcls\u001b[39m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_semantic_mappings\u001b[39m.\u001b[39mitems():\n\u001b[1;32m    643\u001b[0m \n\u001b[1;32m    644\u001b[0m     \u001b[39m# Create the mapping function\u001b[39;00m\n\u001b[1;32m    645\u001b[0m     map_func \u001b[39m=\u001b[39m partial(\u001b[39mcls\u001b[39m\u001b[39m.\u001b[39mmap, plotter\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/_oldcore.py:701\u001b[0m, in \u001b[0;36mVectorPlotter.assign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    699\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    700\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minput_format \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlong\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 701\u001b[0m     plot_data, variables \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_assign_variables_longform(\n\u001b[1;32m    702\u001b[0m         data, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mvariables,\n\u001b[1;32m    703\u001b[0m     )\n\u001b[1;32m    705\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mplot_data \u001b[39m=\u001b[39m plot_data\n\u001b[1;32m    706\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvariables \u001b[39m=\u001b[39m variables\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/_oldcore.py:938\u001b[0m, in \u001b[0;36mVectorPlotter._assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    933\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(val, (\u001b[39mstr\u001b[39m, \u001b[39mbytes\u001b[39m)):\n\u001b[1;32m    934\u001b[0m \n\u001b[1;32m    935\u001b[0m     \u001b[39m# This looks like a column name but we don't know what it means!\u001b[39;00m\n\u001b[1;32m    937\u001b[0m     err \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCould not interpret value `\u001b[39m\u001b[39m{\u001b[39;00mval\u001b[39m}\u001b[39;00m\u001b[39m` for parameter `\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m`\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 938\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(err)\n\u001b[1;32m    940\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    941\u001b[0m \n\u001b[1;32m    942\u001b[0m     \u001b[39m# Otherwise, assume the value is itself data\u001b[39;00m\n\u001b[1;32m    943\u001b[0m \n\u001b[1;32m    944\u001b[0m     \u001b[39m# Raise when data object is present and a vector can't matched\u001b[39;00m\n\u001b[1;32m    945\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(data, pd\u001b[39m.\u001b[39mDataFrame) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(val, pd\u001b[39m.\u001b[39mSeries):\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interpret value `count` for parameter `size`"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=sns.scatterplot(data=allconds, x=\"mean\", y=\"std\", size=\"count\", hue=\"Time\")\n",
    "\n",
    "plt.xlabel(\"Mean\")\n",
    "plt.ylabel(\"Std\")\n",
    "f.axes.set(xlim=(-.2,.2), ylim=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Batch</th>\n",
       "      <th>Fly</th>\n",
       "      <th>Angles</th>\n",
       "      <th>Timestamps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.547746e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349159</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349160</th>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349161</th>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349162</th>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349163</th>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45349164 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Batch  Fly  Angles    Timestamps\n",
       "0            1    0     NaN  1.547746e+09\n",
       "1            1    1     NaN  1.547746e+09\n",
       "2            1    2     NaN  1.547746e+09\n",
       "3            1    3     NaN  1.547746e+09\n",
       "4            1    4     NaN  1.547746e+09\n",
       "...        ...  ...     ...           ...\n",
       "45349159     6   37     NaN           NaN\n",
       "45349160     6   38     NaN           NaN\n",
       "45349161     6   39     NaN           NaN\n",
       "45349162     6   40     NaN           NaN\n",
       "45349163     6   41     NaN           NaN\n",
       "\n",
       "[45349164 rows x 4 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin=thamin.to_dataframe().reset_index().drop(columns=[\"Trial\", \"t\"])\n",
    "thamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Batch</th>\n",
       "      <th>Fly</th>\n",
       "      <th>Angles</th>\n",
       "      <th>Timestamps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-17 17:18:55.100012032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-17 17:18:55.100012032</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-17 17:18:55.100012032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-01-17 17:18:55.100012032</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>2019-01-17 17:18:55.100012032</td>\n",
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       "    <tr>\n",
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       "      <td>6</td>\n",
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       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349160</th>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>45349161</th>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349162</th>\n",
       "      <td>6</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>45349163</th>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45349164 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Batch  Fly  Angles                    Timestamps\n",
       "0            1    0     NaN 2019-01-17 17:18:55.100012032\n",
       "1            1    1     NaN 2019-01-17 17:18:55.100012032\n",
       "2            1    2     NaN 2019-01-17 17:18:55.100012032\n",
       "3            1    3     NaN 2019-01-17 17:18:55.100012032\n",
       "4            1    4     NaN 2019-01-17 17:18:55.100012032\n",
       "...        ...  ...     ...                           ...\n",
       "45349159     6   37     NaN                           NaT\n",
       "45349160     6   38     NaN                           NaT\n",
       "45349161     6   39     NaN                           NaT\n",
       "45349162     6   40     NaN                           NaT\n",
       "45349163     6   41     NaN                           NaT\n",
       "\n",
       "[45349164 rows x 4 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin[\"Timestamps\"]=pd.to_datetime(thamin[\"Timestamps\"], origin=\"unix\", unit=\"s\")\n",
    "thamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>45349164 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Angles                    Timestamps batchxfly\n",
       "0            NaN 2019-01-17 17:18:55.100012032      f0b1\n",
       "1            NaN 2019-01-17 17:18:55.100012032      f1b1\n",
       "2            NaN 2019-01-17 17:18:55.100012032      f2b1\n",
       "3            NaN 2019-01-17 17:18:55.100012032      f3b1\n",
       "4            NaN 2019-01-17 17:18:55.100012032      f4b1\n",
       "...          ...                           ...       ...\n",
       "45349159     NaN                           NaT     f37b6\n",
       "45349160     NaN                           NaT     f38b6\n",
       "45349161     NaN                           NaT     f39b6\n",
       "45349162     NaN                           NaT     f40b6\n",
       "45349163     NaN                           NaT     f41b6\n",
       "\n",
       "[45349164 rows x 3 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin[\"batchxfly\"]=\"f\"+thamin[\"Fly\"].astype(str)+\"b\"+thamin[\"Batch\"]\n",
    "thamin.drop(columns=[\"Fly\", \"Batch\"], inplace=True)\n",
    "thamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
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       "<p>8435040 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "            Angles                    Timestamps batchxfly\n",
       "31       -0.000000 2019-01-17 17:18:55.100012032     f31b1\n",
       "35        0.000000 2019-01-17 17:18:55.100012032     f35b1\n",
       "45       -0.000000 2019-02-20 09:33:13.100002816      f3b2\n",
       "54       -0.000000 2019-02-20 09:33:13.100002816     f12b2\n",
       "56        0.000000 2019-02-20 09:33:13.100002816     f14b2\n",
       "...            ...                           ...       ...\n",
       "18133737  3.243923 2019-02-20 11:33:13.150642432     f27b2\n",
       "18133740  0.000000 2019-02-20 11:33:13.150642432     f30b2\n",
       "18133744  2.612210 2019-02-20 11:33:13.150642432     f34b2\n",
       "18133745  0.000000 2019-02-20 11:33:13.150642432     f35b2\n",
       "18133747 -0.000000 2019-02-20 11:33:13.150642432     f37b2\n",
       "\n",
       "[8435040 rows x 3 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "thamin.dropna(inplace=True)\n",
    "thamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>8435040 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Angles                Timestamps batchxfly\n",
       "31       -0.000000           0 days 00:00:00     f31b1\n",
       "35        0.000000           0 days 00:00:00     f35b1\n",
       "45       -0.000000           0 days 00:00:00      f3b2\n",
       "54       -0.000000           0 days 00:00:00     f12b2\n",
       "56        0.000000           0 days 00:00:00     f14b2\n",
       "...            ...                       ...       ...\n",
       "18133737  3.243923 0 days 01:59:57.602025472     f27b2\n",
       "18133740  0.000000 0 days 01:59:57.602025472     f30b2\n",
       "18133744  2.612210 0 days 01:59:57.602025472     f34b2\n",
       "18133745  0.000000 0 days 01:59:57.602025472     f35b2\n",
       "18133747 -0.000000 0 days 01:59:57.602025472     f37b2\n",
       "\n",
       "[8435040 rows x 3 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find first timestamp for each batchxfly and subtract from all timestamps for that batchxfly\n",
    "thamin[\"Timestamps\"]=thamin.groupby(\"batchxfly\")[\"Timestamps\"].transform(lambda x: x-x.iloc[0])\n",
    "thamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "thamin[\"Heading\"]=np.sin(thamin[\"Angles\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18133737</th>\n",
       "      <td>3.243923</td>\n",
       "      <td>0 days 01:59:57.602025472</td>\n",
       "      <td>f27b2</td>\n",
       "      <td>-0.102152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18133740</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0 days 01:59:57.602025472</td>\n",
       "      <td>f30b2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18133744</th>\n",
       "      <td>2.612210</td>\n",
       "      <td>0 days 01:59:57.602025472</td>\n",
       "      <td>f34b2</td>\n",
       "      <td>0.505001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18133745</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0 days 01:59:57.602025472</td>\n",
       "      <td>f35b2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18133747</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0 days 01:59:57.602025472</td>\n",
       "      <td>f37b2</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8435040 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Angles                Timestamps batchxfly   Heading\n",
       "31       -0.000000           0 days 00:00:00     f31b1 -0.000000\n",
       "35        0.000000           0 days 00:00:00     f35b1  0.000000\n",
       "45       -0.000000           0 days 00:00:00      f3b2 -0.000000\n",
       "54       -0.000000           0 days 00:00:00     f12b2 -0.000000\n",
       "56        0.000000           0 days 00:00:00     f14b2  0.000000\n",
       "...            ...                       ...       ...       ...\n",
       "18133737  3.243923 0 days 01:59:57.602025472     f27b2 -0.102152\n",
       "18133740  0.000000 0 days 01:59:57.602025472     f30b2  0.000000\n",
       "18133744  2.612210 0 days 01:59:57.602025472     f34b2  0.505001\n",
       "18133745  0.000000 0 days 01:59:57.602025472     f35b2  0.000000\n",
       "18133747 -0.000000 0 days 01:59:57.602025472     f37b2 -0.000000\n",
       "\n",
       "[8435040 rows x 4 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_64073/599615904.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  thamin_group=thamin.drop(columns=\"Angles\").groupby(\"batchxfly\").mean()\n",
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_64073/599615904.py:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.std is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  thamin_group[\"Std Heading\"]=thamin.drop(columns=\"Angles\").groupby(\"batchxfly\").std()[\"Heading\"]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Heading</th>\n",
       "      <th>Std Heading</th>\n",
       "      <th>N</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batchxfly</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f0b1</th>\n",
       "      <td>0.003032</td>\n",
       "      <td>0.389199</td>\n",
       "      <td>19154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f0b2</th>\n",
       "      <td>-0.008786</td>\n",
       "      <td>0.393989</td>\n",
       "      <td>23540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f0b3</th>\n",
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       "      <td>0.318762</td>\n",
       "      <td>62366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f0b4</th>\n",
       "      <td>-0.056276</td>\n",
       "      <td>0.516842</td>\n",
       "      <td>43851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f0b5</th>\n",
       "      <td>0.005760</td>\n",
       "      <td>0.611226</td>\n",
       "      <td>6452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b2</th>\n",
       "      <td>-0.004277</td>\n",
       "      <td>0.575729</td>\n",
       "      <td>47458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b3</th>\n",
       "      <td>0.010027</td>\n",
       "      <td>0.457269</td>\n",
       "      <td>71645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b4</th>\n",
       "      <td>-0.003075</td>\n",
       "      <td>0.500350</td>\n",
       "      <td>70765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b5</th>\n",
       "      <td>0.003976</td>\n",
       "      <td>0.388924</td>\n",
       "      <td>24970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f9b6</th>\n",
       "      <td>0.014823</td>\n",
       "      <td>0.632502</td>\n",
       "      <td>4853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Mean Heading  Std Heading      N\n",
       "batchxfly                                  \n",
       "f0b1           0.003032     0.389199  19154\n",
       "f0b2          -0.008786     0.393989  23540\n",
       "f0b3           0.008000     0.318762  62366\n",
       "f0b4          -0.056276     0.516842  43851\n",
       "f0b5           0.005760     0.611226   6452\n",
       "...                 ...          ...    ...\n",
       "f9b2          -0.004277     0.575729  47458\n",
       "f9b3           0.010027     0.457269  71645\n",
       "f9b4          -0.003075     0.500350  70765\n",
       "f9b5           0.003976     0.388924  24970\n",
       "f9b6           0.014823     0.632502   4853\n",
       "\n",
       "[252 rows x 3 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thamin_group=thamin.drop(columns=\"Angles\").groupby(\"batchxfly\").mean()\n",
    "thamin_group.rename(columns={\"Heading\":\"Mean Heading\"}, inplace=True)\n",
    "thamin_group[\"Std Heading\"]=thamin.drop(columns=\"Angles\").groupby(\"batchxfly\").std()[\"Heading\"]\n",
    "thamin_group[\"N\"]=thamin.drop(columns=\"Angles\").groupby(\"batchxfly\").count()[\"Heading\"]\n",
    "thamin_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Mean Heading', ylabel='Std Heading'>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(thamin_group, x=\"Mean Heading\", y=\"Std Heading\", size=\"N\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Timestamps', ylabel='Angles'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=thamin, x=\"Timestamps\", y=\"Angles\", hue=\"batchxfly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Line2D.set() got an unexpected keyword argument 'hue'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[75], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m thamin\u001b[39m.\u001b[39;49mplot(x\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mTimestamps\u001b[39;49m\u001b[39m\"\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mAngles\u001b[39;49m\u001b[39m\"\u001b[39;49m, hue\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mbatchxfly\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_core.py:1000\u001b[0m, in \u001b[0;36mPlotAccessor.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    997\u001b[0m             label_name \u001b[39m=\u001b[39m label_kw \u001b[39mor\u001b[39;00m data\u001b[39m.\u001b[39mcolumns\n\u001b[1;32m    998\u001b[0m             data\u001b[39m.\u001b[39mcolumns \u001b[39m=\u001b[39m label_name\n\u001b[0;32m-> 1000\u001b[0m \u001b[39mreturn\u001b[39;00m plot_backend\u001b[39m.\u001b[39;49mplot(data, kind\u001b[39m=\u001b[39;49mkind, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m         kwargs[\u001b[39m\"\u001b[39m\u001b[39max\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(ax, \u001b[39m\"\u001b[39m\u001b[39mleft_ax\u001b[39m\u001b[39m\"\u001b[39m, ax)\n\u001b[1;32m     70\u001b[0m plot_obj \u001b[39m=\u001b[39m PLOT_CLASSES[kind](data, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m---> 71\u001b[0m plot_obj\u001b[39m.\u001b[39;49mgenerate()\n\u001b[1;32m     72\u001b[0m plot_obj\u001b[39m.\u001b[39mdraw()\n\u001b[1;32m     73\u001b[0m \u001b[39mreturn\u001b[39;00m plot_obj\u001b[39m.\u001b[39mresult\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:452\u001b[0m, in \u001b[0;36mMPLPlot.generate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compute_plot_data()\n\u001b[1;32m    451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setup_subplots()\n\u001b[0;32m--> 452\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_plot()\n\u001b[1;32m    453\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_add_table()\n\u001b[1;32m    454\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_make_legend()\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:1382\u001b[0m, in \u001b[0;36mLinePlot._make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1379\u001b[0m label \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mark_right_label(label, index\u001b[39m=\u001b[39mi)\n\u001b[1;32m   1380\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mlabel\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m label\n\u001b[0;32m-> 1382\u001b[0m newlines \u001b[39m=\u001b[39m plotf(\n\u001b[1;32m   1383\u001b[0m     ax,\n\u001b[1;32m   1384\u001b[0m     x,\n\u001b[1;32m   1385\u001b[0m     y,\n\u001b[1;32m   1386\u001b[0m     style\u001b[39m=\u001b[39;49mstyle,\n\u001b[1;32m   1387\u001b[0m     column_num\u001b[39m=\u001b[39;49mi,\n\u001b[1;32m   1388\u001b[0m     stacking_id\u001b[39m=\u001b[39;49mstacking_id,\n\u001b[1;32m   1389\u001b[0m     is_errorbar\u001b[39m=\u001b[39;49mis_errorbar,\n\u001b[1;32m   1390\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds,\n\u001b[1;32m   1391\u001b[0m )\n\u001b[1;32m   1392\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_append_legend_handles_labels(newlines[\u001b[39m0\u001b[39m], label)\n\u001b[1;32m   1394\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_is_ts_plot():\n\u001b[1;32m   1395\u001b[0m \n\u001b[1;32m   1396\u001b[0m     \u001b[39m# reset of xlim should be used for ts data\u001b[39;00m\n\u001b[1;32m   1397\u001b[0m     \u001b[39m# TODO: GH28021, should find a way to change view limit on xaxis\u001b[39;00m\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:1412\u001b[0m, in \u001b[0;36mLinePlot._plot\u001b[0;34m(cls, ax, x, y, style, column_num, stacking_id, **kwds)\u001b[0m\n\u001b[1;32m   1410\u001b[0m     \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_initialize_stacker(ax, stacking_id, \u001b[39mlen\u001b[39m(y))\n\u001b[1;32m   1411\u001b[0m y_values \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_get_stacked_values(ax, stacking_id, y, kwds[\u001b[39m\"\u001b[39m\u001b[39mlabel\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m-> 1412\u001b[0m lines \u001b[39m=\u001b[39m MPLPlot\u001b[39m.\u001b[39;49m_plot(ax, x, y_values, style\u001b[39m=\u001b[39;49mstyle, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m   1413\u001b[0m \u001b[39mcls\u001b[39m\u001b[39m.\u001b[39m_update_stacker(ax, stacking_id, y)\n\u001b[1;32m   1414\u001b[0m \u001b[39mreturn\u001b[39;00m lines\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_matplotlib/converter.py:96\u001b[0m, in \u001b[0;36mregister_pandas_matplotlib_converters.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m     94\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapper\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m     95\u001b[0m     \u001b[39mwith\u001b[39;00m pandas_converters():\n\u001b[0;32m---> 96\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/plotting/_matplotlib/core.py:887\u001b[0m, in \u001b[0;36mMPLPlot._plot\u001b[0;34m(cls, ax, x, y, style, is_errorbar, **kwds)\u001b[0m\n\u001b[1;32m    884\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    885\u001b[0m     \u001b[39m# prevent style kwarg from going to errorbar, where it is unsupported\u001b[39;00m\n\u001b[1;32m    886\u001b[0m     args \u001b[39m=\u001b[39m (x, y, style) \u001b[39mif\u001b[39;00m style \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m (x, y)\n\u001b[0;32m--> 887\u001b[0m     \u001b[39mreturn\u001b[39;00m ax\u001b[39m.\u001b[39;49mplot(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1688\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1446\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1447\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1685\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1686\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1687\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m   1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m   1690\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/axes/_base.py:311\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m     this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m    310\u001b[0m     args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 311\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m    312\u001b[0m     this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/axes/_base.py:544\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n\u001b[1;32m    543\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 544\u001b[0m     \u001b[39mreturn\u001b[39;00m [l[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m result]\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/axes/_base.py:544\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n\u001b[1;32m    543\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 544\u001b[0m     \u001b[39mreturn\u001b[39;00m [l[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m result]\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/axes/_base.py:537\u001b[0m, in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    534\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    535\u001b[0m     labels \u001b[39m=\u001b[39m [label] \u001b[39m*\u001b[39m n_datasets\n\u001b[0;32m--> 537\u001b[0m result \u001b[39m=\u001b[39m (make_artist(x[:, j \u001b[39m%\u001b[39;49m ncx], y[:, j \u001b[39m%\u001b[39;49m ncy], kw,\n\u001b[1;32m    538\u001b[0m                       {\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs, \u001b[39m'\u001b[39;49m\u001b[39mlabel\u001b[39;49m\u001b[39m'\u001b[39;49m: label})\n\u001b[1;32m    539\u001b[0m           \u001b[39mfor\u001b[39;00m j, label \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(labels))\n\u001b[1;32m    541\u001b[0m \u001b[39mif\u001b[39;00m return_kwargs:\n\u001b[1;32m    542\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mlist\u001b[39m(result)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/axes/_base.py:351\u001b[0m, in \u001b[0;36m_process_plot_var_args._makeline\u001b[0;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m    349\u001b[0m default_dict \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getdefaults(\u001b[39mset\u001b[39m(), kw)\n\u001b[1;32m    350\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setdefaults(default_dict, kw)\n\u001b[0;32m--> 351\u001b[0m seg \u001b[39m=\u001b[39m mlines\u001b[39m.\u001b[39;49mLine2D(x, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkw)\n\u001b[1;32m    352\u001b[0m \u001b[39mreturn\u001b[39;00m seg, kw\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/_api/deprecation.py:454\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m name_idx:\n\u001b[1;32m    449\u001b[0m     warn_deprecated(\n\u001b[1;32m    450\u001b[0m         since, message\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mPassing the \u001b[39m\u001b[39m%(name)s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m%(obj_type)s\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    451\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mpositionally is deprecated since Matplotlib \u001b[39m\u001b[39m%(since)s\u001b[39;00m\u001b[39m; the \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    452\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mparameter will become keyword-only \u001b[39m\u001b[39m%(removal)s\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    453\u001b[0m         name\u001b[39m=\u001b[39mname, obj_type\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m()\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 454\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/lines.py:393\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[1;32m    391\u001b[0m \u001b[39m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[1;32m    392\u001b[0m \u001b[39m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[0;32m--> 393\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_internal_update(kwargs)\n\u001b[1;32m    394\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pickradius \u001b[39m=\u001b[39m pickradius\n\u001b[1;32m    395\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mind_offset \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/artist.py:1223\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m   1216\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_internal_update\u001b[39m(\u001b[39mself\u001b[39m, kwargs):\n\u001b[1;32m   1217\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1218\u001b[0m \u001b[39m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[1;32m   1219\u001b[0m \u001b[39m    errors as if calling `set`.\u001b[39;00m\n\u001b[1;32m   1220\u001b[0m \n\u001b[1;32m   1221\u001b[0m \u001b[39m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[1;32m   1222\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1223\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_props(\n\u001b[1;32m   1224\u001b[0m         kwargs, \u001b[39m\"\u001b[39;49m\u001b[39m{cls.__name__}\u001b[39;49;00m\u001b[39m.set() got an unexpected keyword argument \u001b[39;49m\u001b[39m\"\u001b[39;49m\n\u001b[1;32m   1225\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39m{prop_name!r}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/matplotlib/artist.py:1197\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[0;34m(self, props, errfmt)\u001b[0m\n\u001b[1;32m   1195\u001b[0m             func \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mset_\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m   1196\u001b[0m             \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m callable(func):\n\u001b[0;32m-> 1197\u001b[0m                 \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[1;32m   1198\u001b[0m                     errfmt\u001b[39m.\u001b[39mformat(\u001b[39mcls\u001b[39m\u001b[39m=\u001b[39m\u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m), prop_name\u001b[39m=\u001b[39mk))\n\u001b[1;32m   1199\u001b[0m             ret\u001b[39m.\u001b[39mappend(func(v))\n\u001b[1;32m   1200\u001b[0m \u001b[39mif\u001b[39;00m ret:\n",
      "\u001b[0;31mAttributeError\u001b[0m: Line2D.set() got an unexpected keyword argument 'hue'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thamin.plot(x=\"Timestamps\", y=\"Angles\", hue=\"batchxflya\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>batchxfly</th>\n",
       "      <th>f0b1</th>\n",
       "      <th>f0b2</th>\n",
       "      <th>f0b3</th>\n",
       "      <th>f0b4</th>\n",
       "      <th>f0b5</th>\n",
       "      <th>f0b6</th>\n",
       "      <th>f10b1</th>\n",
       "      <th>f10b2</th>\n",
       "      <th>f10b3</th>\n",
       "      <th>f10b4</th>\n",
       "      <th>...</th>\n",
       "      <th>f8b3</th>\n",
       "      <th>f8b4</th>\n",
       "      <th>f8b5</th>\n",
       "      <th>f8b6</th>\n",
       "      <th>f9b1</th>\n",
       "      <th>f9b2</th>\n",
       "      <th>f9b3</th>\n",
       "      <th>f9b4</th>\n",
       "      <th>f9b5</th>\n",
       "      <th>f9b6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamps</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-17 17:18:55.100012032</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2019-05-21 11:05:07.513736448</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2385 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "batchxfly                      f0b1  f0b2  f0b3  f0b4  f0b5  f0b6  f10b1  \\\n",
       "Timestamps                                                                 \n",
       "2019-01-17 17:18:55.100012032   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.628347648   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.728436224   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.828440832   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.928441600   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "...                             ...   ...   ...   ...   ...   ...    ...   \n",
       "2019-05-21 11:05:07.113716992   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.213725696   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.313731328   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.413736704   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.513736448   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "\n",
       "batchxfly                      f10b2  f10b3  f10b4  ...  f8b3  f8b4  f8b5  \\\n",
       "Timestamps                                          ...                     \n",
       "2019-01-17 17:18:55.100012032    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.628347648    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.728436224    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.828440832    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.928441600    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "...                              ...    ...    ...  ...   ...   ...   ...   \n",
       "2019-05-21 11:05:07.113716992    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.213725696    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.313731328    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.413736704    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.513736448    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "\n",
       "batchxfly                          f8b6  f9b1  f9b2  f9b3  f9b4  f9b5  f9b6  \n",
       "Timestamps                                                                   \n",
       "2019-01-17 17:18:55.100012032       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.628347648       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.728436224       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.828440832       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.928441600       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...                                 ...   ...   ...   ...   ...   ...   ...  \n",
       "2019-05-21 11:05:07.113716992 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.213725696 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.313731328 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.413736704 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.513736448 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "\n",
       "[2385 rows x 251 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc=np.sin(thamin.pivot(index=\"Timestamps\", columns=\"batchxfly\", values=\"Angles\")).rolling(window='60s').mean()\n",
    "abc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21 11:05:07.113716992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.308369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21 11:05:07.213725696</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.308369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21 11:05:07.313731328</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.308369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21 11:05:07.413736704</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.308369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-21 11:05:07.513736448</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.308369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2385 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "batchxfly                      f0b1  f0b2  f0b3  f0b4  f0b5  f0b6  f10b1  \\\n",
       "Timestamps                                                                 \n",
       "2019-01-17 17:18:55.100012032   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.628347648   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.728436224   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.828440832   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "2019-01-17 17:18:55.928441600   NaN   NaN   NaN   NaN   NaN   NaN    NaN   \n",
       "...                             ...   ...   ...   ...   ...   ...    ...   \n",
       "2019-05-21 11:05:07.113716992   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.213725696   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.313731328   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.413736704   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "2019-05-21 11:05:07.513736448   NaN   NaN   NaN   NaN   NaN   0.0    NaN   \n",
       "\n",
       "batchxfly                      f10b2  f10b3  f10b4  ...  f8b3  f8b4  f8b5  \\\n",
       "Timestamps                                          ...                     \n",
       "2019-01-17 17:18:55.100012032    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.628347648    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.728436224    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.828440832    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-01-17 17:18:55.928441600    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "...                              ...    ...    ...  ...   ...   ...   ...   \n",
       "2019-05-21 11:05:07.113716992    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.213725696    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.313731328    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.413736704    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "2019-05-21 11:05:07.513736448    NaN    NaN    NaN  ...   NaN   NaN   NaN   \n",
       "\n",
       "batchxfly                          f8b6  f9b1  f9b2  f9b3  f9b4  f9b5  f9b6  \n",
       "Timestamps                                                                   \n",
       "2019-01-17 17:18:55.100012032       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.628347648       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.728436224       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.828440832       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2019-01-17 17:18:55.928441600       NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "...                                 ...   ...   ...   ...   ...   ...   ...  \n",
       "2019-05-21 11:05:07.113716992 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.213725696 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.313731328 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.413736704 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "2019-05-21 11:05:07.513736448 -0.308369   NaN   NaN   NaN   NaN   NaN   0.0  \n",
       "\n",
       "[2385 rows x 251 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Timestamps'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "abc.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1891940805.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[54], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m    angle200=\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "tmax=300\n",
    "# angle200\n",
    "angle200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">Batch</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Angles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Fly</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trial</th>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.380105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.155695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.589003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th>179952</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179953</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179954</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179955</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179956</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>719828 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Batch                             ... Angles                    \\\n",
       "Fly             0  1  2  3  4  5  6  7  8  9   ...     32  33  34        35   \n",
       "Trial t                                        ...                            \n",
       "0     0          1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN  0.000000   \n",
       "      1          1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN  0.000000   \n",
       "      2          1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN  3.380105   \n",
       "      3          1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN  0.155695   \n",
       "      4          1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN  5.589003   \n",
       "...            ... .. .. .. .. .. .. .. .. ..  ...    ...  ..  ..       ...   \n",
       "3     179952     1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN       NaN   \n",
       "      179953     1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN       NaN   \n",
       "      179954     1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN       NaN   \n",
       "      179955     1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN       NaN   \n",
       "      179956     1  1  1  1  1  1  1  1  1  1  ...    NaN NaN NaN       NaN   \n",
       "\n",
       "                                      \n",
       "Fly           36  37  38  39  40  41  \n",
       "Trial t                               \n",
       "0     0      NaN NaN NaN NaN NaN NaN  \n",
       "      1      NaN NaN NaN NaN NaN NaN  \n",
       "      2      NaN NaN NaN NaN NaN NaN  \n",
       "      3      NaN NaN NaN NaN NaN NaN  \n",
       "      4      NaN NaN NaN NaN NaN NaN  \n",
       "...           ..  ..  ..  ..  ..  ..  \n",
       "3     179952 NaN NaN NaN NaN NaN NaN  \n",
       "      179953 NaN NaN NaN NaN NaN NaN  \n",
       "      179954 NaN NaN NaN NaN NaN NaN  \n",
       "      179955 NaN NaN NaN NaN NaN NaN  \n",
       "      179956 NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[719828 rows x 84 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tha[\"Angles\"][0,0:4,:tmax].to_dataframe().unstack(level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(tha[\"Angles\"][0,0:4,:tmax].to_dataframe(), t200.to_dataframe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Timestamps', ylabel='Angles'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=t200, y=angle200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<defs>\n",
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       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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       ":root {\n",
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
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       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
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       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: 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'>&lt;xarray.DataArray &#x27;Angles&#x27; (Fly: 4, t: 300)&gt;\n",
       "array([[        nan,         nan,         nan, ..., -0.91541274,\n",
       "        -0.90849673, -0.97375784],\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [        nan,         nan,         nan, ..., -0.29363508,\n",
       "         0.04152883,  0.83074334],\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "         0.        ,  0.        ]])\n",
       "Coordinates:\n",
       "    Trial    int64 0\n",
       "  * t        (t) int64 0 1 2 3 4 5 6 7 8 ... 291 292 293 294 295 296 297 298 299\n",
       "    Batch    &lt;U1 &#x27;1&#x27;\n",
       "Dimensions without coordinates: Fly</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Angles'</div><ul class='xr-dim-list'><li><span>Fly</span>: 4</li><li><span class='xr-has-index'>t</span>: 300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6c5edd88-5fbd-4e14-a80d-4cf54456effd' class='xr-array-in' type='checkbox' checked><label for='section-6c5edd88-5fbd-4e14-a80d-4cf54456effd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan -0.9537 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[        nan,         nan,         nan, ..., -0.91541274,\n",
       "        -0.90849673, -0.97375784],\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [        nan,         nan,         nan, ..., -0.29363508,\n",
       "         0.04152883,  0.83074334],\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "         0.        ,  0.        ]])</pre></div></div></li><li class='xr-section-item'><input id='section-33f2c381-cd0d-4817-bea6-72a98941fec1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33f2c381-cd0d-4817-bea6-72a98941fec1' class='xr-section-summary' >Coordinates: <span>(3)</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>Trial</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-aa14b2e7-7e79-46dd-8a16-24eee04a16c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa14b2e7-7e79-46dd-8a16-24eee04a16c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9755b0f-a999-4fc0-bf5a-b4f1bc51fce3' class='xr-var-data-in' type='checkbox'><label for='data-d9755b0f-a999-4fc0-bf5a-b4f1bc51fce3' 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)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-97cdbea2-7884-4ee6-9e7f-12f114938e61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97cdbea2-7884-4ee6-9e7f-12f114938e61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15947951-f3d3-4e0d-abf4-6f4ee0faa5b3' class='xr-var-data-in' type='checkbox'><label for='data-15947951-f3d3-4e0d-abf4-6f4ee0faa5b3' 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,   1,   2, ..., 297, 298, 299])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Batch</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27;</div><input id='attrs-bc39b046-7370-466e-bd50-973d1d0b2ece' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc39b046-7370-466e-bd50-973d1d0b2ece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f71d8c44-4efc-432c-bcd5-bf25551da55f' class='xr-var-data-in' type='checkbox'><label for='data-f71d8c44-4efc-432c-bcd5-bf25551da55f' 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(&#x27;1&#x27;, dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bdb9fab-ac3d-4d05-892f-cf2960410d20' class='xr-section-summary-in' type='checkbox'  ><label for='section-5bdb9fab-ac3d-4d05-892f-cf2960410d20' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a9a040ea-64b0-4803-a881-ef781bd70f09' class='xr-index-data-in' type='checkbox'/><label for='index-a9a040ea-64b0-4803-a881-ef781bd70f09' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            290, 291, 292, 293, 294, 295, 296, 297, 298, 299],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;t&#x27;, length=300))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fbc5330-9ebc-40a4-b387-16f37a5cd184' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fbc5330-9ebc-40a4-b387-16f37a5cd184' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Angles' (Fly: 4, t: 300)>\n",
       "array([[        nan,         nan,         nan, ..., -0.91541274,\n",
       "        -0.90849673, -0.97375784],\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [        nan,         nan,         nan, ..., -0.29363508,\n",
       "         0.04152883,  0.83074334],\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "         0.        ,  0.        ]])\n",
       "Coordinates:\n",
       "    Trial    int64 0\n",
       "  * t        (t) int64 0 1 2 3 4 5 6 7 8 ... 291 292 293 294 295 296 297 298 299\n",
       "    Batch    <U1 '1'\n",
       "Dimensions without coordinates: Fly"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "angle200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x166aaba90>]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.sin(tha[\"Angles\"][0,0,1,:200]).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'onefly' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m onefly[\u001b[39m0\u001b[39m:\u001b[39m1000\u001b[39m]\u001b[39m.\u001b[39mplot()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'onefly' is not defined"
     ]
    }
   ],
   "source": [
    "onefly[0:1000].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Timestamps: 66239868, Batch: 8, Fly: 42)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "  * Batch       (Batch) int64 1 2 3 4 5 6 7 8\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Fly, Timestamps, Batch) float64 dask.array&lt;chunksize=(1, 66239868, 1), meta=np.ndarray&gt;</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-3eee1d90-8f3b-491e-900b-bfd08e011978' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3eee1d90-8f3b-491e-900b-bfd08e011978' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Timestamps</span>: 66239868</li><li><span class='xr-has-index'>Batch</span>: 8</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c2020562-0ad3-4e75-9243-a6d69fcec897' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2020562-0ad3-4e75-9243-a6d69fcec897' class='xr-section-summary' >Coordinates: <span>(2)</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 class='xr-has-index'>Timestamps</span></div><div class='xr-var-dims'>(Timestamps)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.549e+09 1.549e+09 ... 1.56e+09</div><input id='attrs-b8149f61-b3c2-4ede-ab42-b61f74da6b82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8149f61-b3c2-4ede-ab42-b61f74da6b82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a90ea5-9aff-437e-b0ab-39993c09b112' class='xr-var-data-in' type='checkbox'><label for='data-20a90ea5-9aff-437e-b0ab-39993c09b112' 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([1.548929e+09, 1.548929e+09, 1.548929e+09, ..., 1.559567e+09,\n",
       "       1.559567e+09, 1.559567e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8</div><input id='attrs-df2824e7-ecba-4322-abfc-ad139a0a24ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df2824e7-ecba-4322-abfc-ad139a0a24ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-502fe76d-d35f-48e1-98c4-1d6c76b24a57' class='xr-var-data-in' type='checkbox'><label for='data-502fe76d-d35f-48e1-98c4-1d6c76b24a57' 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([1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8afb5f85-776a-45ac-a099-7daa1daa936f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8afb5f85-776a-45ac-a099-7daa1daa936f' class='xr-section-summary' >Data variables: <span>(1)</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>Angles</span></div><div class='xr-var-dims'>(Fly, Timestamps, Batch)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 66239868, 1), meta=np.ndarray&gt;</div><input id='attrs-1c34ca93-b7f9-4cae-b923-ea2b8b726284' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c34ca93-b7f9-4cae-b923-ea2b8b726284' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16391bce-eace-4699-bf7d-6814c3b3d7b9' class='xr-var-data-in' type='checkbox'><label for='data-16391bce-eace-4699-bf7d-6814c3b3d7b9' 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'><table>\n",
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       "                        <td> 505.37 MiB </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ceba6200-de99-466e-9e4a-00abb23e18e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-ceba6200-de99-466e-9e4a-00abb23e18e0' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>Timestamps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-49d481b4-e1fd-414b-bd05-94a5c34ffd53' class='xr-index-data-in' type='checkbox'/><label for='index-49d481b4-e1fd-414b-bd05-94a5c34ffd53' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 1548929360.028388, 1548929360.1283941,  1548929361.831054,\n",
       "               1548929361.931157, 1548929362.0311651, 1548929362.1312022,\n",
       "              1548929362.2312033, 1548929362.3316262, 1548929362.4316359,\n",
       "              1548929362.5316434,\n",
       "              ...\n",
       "              1559567219.9725165, 1559567220.0725179, 1559567220.3725622,\n",
       "               1559567220.472576, 1559567220.5725913, 1559567220.6726038,\n",
       "              1559567220.7726126, 1559567220.9726315, 1559567221.0726895,\n",
       "               1559567221.172705],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Timestamps&#x27;, length=66239868))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-50c1f502-53ba-454d-8bca-7a54c5e3ace6' class='xr-index-data-in' type='checkbox'/><label for='index-50c1f502-53ba-454d-8bca-7a54c5e3ace6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8], dtype=&#x27;int64&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f30abab5-783a-48b9-9353-96d194e7acf2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f30abab5-783a-48b9-9353-96d194e7acf2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Timestamps: 66239868, Batch: 8, Fly: 42)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "  * Batch       (Batch) int64 1 2 3 4 5 6 7 8\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Fly, Timestamps, Batch) float64 dask.array<chunksize=(1, 66239868, 1), meta=np.ndarray>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarrayall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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       ".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'>&lt;xarray.DataArray &#x27;Angles&#x27; (Timestamps: 66239868)&gt;\n",
       "dask.array&lt;getitem, shape=(66239868,), dtype=float64, chunksize=(66239868,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "    Batch       int64 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Angles'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Timestamps</span>: 66239868</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ab9d6586-3f6c-4723-ae9a-ef832e2d6197' class='xr-array-in' type='checkbox' checked><label for='section-ab9d6586-3f6c-4723-ae9a-ef832e2d6197' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(66239868,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 505.37 MiB </td>\n",
       "                        <td> 505.37 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (66239868,) </td>\n",
       "                        <td> (66239868,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2463 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >66239868</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-eb425da9-bc07-47cf-8e1b-20c2fab97d46' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb425da9-bc07-47cf-8e1b-20c2fab97d46' class='xr-section-summary' >Coordinates: <span>(2)</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 class='xr-has-index'>Timestamps</span></div><div class='xr-var-dims'>(Timestamps)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.549e+09 1.549e+09 ... 1.56e+09</div><input id='attrs-51a2fb73-6892-4800-91d4-133ad92a7c4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51a2fb73-6892-4800-91d4-133ad92a7c4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79bc5435-c537-458b-9780-87f87045bb37' class='xr-var-data-in' type='checkbox'><label for='data-79bc5435-c537-458b-9780-87f87045bb37' 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([1.548929e+09, 1.548929e+09, 1.548929e+09, ..., 1.559567e+09,\n",
       "       1.559567e+09, 1.559567e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Batch</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-008d3e94-8aab-4262-9296-9db2385a8e6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-008d3e94-8aab-4262-9296-9db2385a8e6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7be10b74-a197-4318-8ebd-a5ed36c3bb58' class='xr-var-data-in' type='checkbox'><label for='data-7be10b74-a197-4318-8ebd-a5ed36c3bb58' 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(1)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-131462ce-201e-48f6-9abf-46918bbc3a24' class='xr-section-summary-in' type='checkbox'  ><label for='section-131462ce-201e-48f6-9abf-46918bbc3a24' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>Timestamps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9350cec6-36df-4960-b1fb-5b62394db4c7' class='xr-index-data-in' type='checkbox'/><label for='index-9350cec6-36df-4960-b1fb-5b62394db4c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 1548929360.028388, 1548929360.1283941,  1548929361.831054,\n",
       "               1548929361.931157, 1548929362.0311651, 1548929362.1312022,\n",
       "              1548929362.2312033, 1548929362.3316262, 1548929362.4316359,\n",
       "              1548929362.5316434,\n",
       "              ...\n",
       "              1559567219.9725165, 1559567220.0725179, 1559567220.3725622,\n",
       "               1559567220.472576, 1559567220.5725913, 1559567220.6726038,\n",
       "              1559567220.7726126, 1559567220.9726315, 1559567221.0726895,\n",
       "               1559567221.172705],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Timestamps&#x27;, length=66239868))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86fe7e01-57ac-4409-a0b2-e5cb8bd9ce5b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-86fe7e01-57ac-4409-a0b2-e5cb8bd9ce5b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Angles' (Timestamps: 66239868)>\n",
       "dask.array<getitem, shape=(66239868,), dtype=float64, chunksize=(66239868,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "    Batch       int64 1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "onefly=xarrayall[\"Angles\"][0,:,0]\n",
    "onefly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Angles'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/dataarray.py:806\u001b[0m, in \u001b[0;36mDataArray._getitem_coord\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    805\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 806\u001b[0m     var \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_coords[key]\n\u001b[1;32m    807\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Angles'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m onefly[\u001b[39m\"\u001b[39;49m\u001b[39mAngles\u001b[39;49m\u001b[39m\"\u001b[39;49m]\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/dataarray.py:815\u001b[0m, in \u001b[0;36mDataArray.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    813\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m: T_DataArray, key: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m T_DataArray:\n\u001b[1;32m    814\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, \u001b[39mstr\u001b[39m):\n\u001b[0;32m--> 815\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_coord(key)\n\u001b[1;32m    816\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    817\u001b[0m         \u001b[39m# xarray-style array indexing\u001b[39;00m\n\u001b[1;32m    818\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39misel(indexers\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_item_key_to_dict(key))\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/dataarray.py:809\u001b[0m, in \u001b[0;36mDataArray._getitem_coord\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[1;32m    808\u001b[0m     dim_sizes \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\u001b[39mzip\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdims, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshape))\n\u001b[0;32m--> 809\u001b[0m     _, key, var \u001b[39m=\u001b[39m _get_virtual_variable(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_coords, key, dim_sizes)\n\u001b[1;32m    811\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_replace_maybe_drop_dims(var, name\u001b[39m=\u001b[39mkey)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/dataset.py:177\u001b[0m, in \u001b[0;36m_get_virtual_variable\u001b[0;34m(variables, key, dim_sizes)\u001b[0m\n\u001b[1;32m    175\u001b[0m split_key \u001b[39m=\u001b[39m key\u001b[39m.\u001b[39msplit(\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[1;32m    176\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(split_key) \u001b[39m!=\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[0;32m--> 177\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key)\n\u001b[1;32m    179\u001b[0m ref_name, var_name \u001b[39m=\u001b[39m split_key\n\u001b[1;32m    180\u001b[0m ref_var \u001b[39m=\u001b[39m variables[ref_name]\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Angles'"
     ]
    }
   ],
   "source": [
    "onefly[\"Angles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x145319ed0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KTU0tscyxY8eKrP/EiRNFWrkK8vf3V0hIiNMDAGBNTm6elu88LknKzTNerg1QObwWolq2bKnIyEgtW7bM8VxWVpZWrVqlrl27SpJiY2Pl6+vrVCY5OVnbt293lImLi1NaWpo2btzoKLNhwwalpaU5ygAAPCszJ09ffn9UkpSdm+fl2gCVw6NX5509e1Z79+51/JyUlKTExESFhYWpWbNmGjt2rKZMmaI2bdqoTZs2mjJliurVq6dhw4ZJkux2u+6//3498cQTatCggcLCwjR+/Hh16NDBcbVe+/bt1b9/f40cOVJvvPGGJOnBBx/U4MGDuTIPAAB4jEdD1ObNm9WzZ0/Hz+PGjZMkDR8+XPPmzdOTTz6pCxcuaPTo0UpNTVWXLl20dOlSBQcHO17zyiuvqG7durrrrrt04cIF9erVS/PmzZOPj4+jzPz58zVmzBjHVXxDhw4tdm4qAAAAd/BoiOrRo4eMKb5v3GazaeLEiZo4cWKxZQICAjRr1izNmjWr2DJhYWH64IMPKlJVACjVxexcXcjKVV0fm4IDfL1dHQBe5vWr8wDUHFk5edp+JE3bj6R5uyoesXDrEV33wjKN+/h7b1cFQBVAiALgNifPZmrwrDW64/XiJ7qF+8XP3aCbX/pGCQdSSy8MwG0IUQBQzaWkXdTh1AvKzMktvTAAtyFEAQAAWODRgeUAAKB8cnLzdPpclmSTGgUHeLs6KAEtUQAAVCGHUi/ohikr1OvlVd6uCkpBiAIAALCAEAUAAGABIQoAAMACQhQAAIAFhCgAAAALCFEAAAAWME8UgGrnyJkLOp5+UY1CAtSkfqC3qwOglqIlCkC18+7a/fp//1ir99bu93ZVANRitEQBAGqUc5k5+uL7o6pjk+6+vpm3q4MajBAFAG60Zs9Jnc3M0Q0twxQW5Fep2357TZIa2wPVMjyoUrdb1aRfzNaEz7bJz6cOIaqAExmZOn0uS/Xr+SoihNvJuAPdeQDgRhO//FGjPkjQ7mMZZX5N4qEzumHycv369bUV2vbyncf1U3J6hdaBmuvdtfvVb+Zqvf7tPm9XpcagJQq1wszlu/WfxKO6L665fn9TS29XB260/+Q5fbb1iBoG+3u7KpZl5+bpeEamgvw5JVcl2bl5+uan45Kk3u0j5FPH5uUaoarhE4ta4fS5LCWdPKfU89nergrc7MDp83p1xR5d3ThEAb40rldFxhidzcyRJF3hX1c2W/UII5k5eXro/QRJ0k8v9JdPHR8v1whVDWccAIBH5Rmpw8Sl6jBxqdIu8IdMVXfo9HkNnb1G97613ttVqfJoiQIAeE36xWx9vS1Zvj51dMevmnq7OpCUmZOrHw6nqX4933K97vS5LM1cvlt+PnX07OCrPVS7qoWWKABwo73Hz0q6NFbLG4ykvDyjvDzjle2X5udCx+VkRqb+/Ok2PffFj+Vaz/msHJ0+l6Vzl7sJPW3PsbOVsp3q7OzFHL237oA+2njQ21WpNIQoACgnU0w+MQUWTF+6u5Jq4+xI6gW1enqxrnnuv17ZfmnS3dSdN+ubvfrVC8v08tLdMsZo2+E0XczOdcu6XTl5NtNj60b1RYgCADfJzMlz/P98VuW0kEBasOmQhsxeo9+9tcHbVUEtQ4gCAFR5RsV3T+Z3H20+kFpZ1akcVbNHFgUQogBUO2+u/lnSpRsRF8cY49S9VtnOZ5W9a+nM5ak3krw0jqo6+HjzYUnSmfNZRZb9cDjNI9vMyf2lZTGrwP8l6f55m/T//vGdfj7BWKnajBAFoNo6fa7oF6p0aczS3W+u171vbXBrkFq+49ilf3cec9s6JZVrdvPqbt+JigXFL79PdlNNSpdTYHB+dqEQtf1omrYePKMLHhyH5SnZuXn64wcJeue7JG9XpdpjigMANc6JjIvamHRakpR2IVv167nnHnZJp2gpqqiMi8wT5W2LtiXrzPlsfb09hTs4VBAhCkC1lZPLoJGK+uL7o9p7LEO3to/wdlVQSc5w5wa3IUQB1VxObp7q+tTOnvmN+0/rYW9XogrIuGj9SsDFPyRryY8pahgS4MYawSpjjIa/vdHb1UAZ1c4zL1BDvLTkJ7X7vyXalVJ7xtSgqJT0i47/T16804s1cZ+fLr+n91WRgduVdY3CnuNndfD0+crZGCqMEIVa4b11ByRJ3+467uWayK0DnV//dp9y84z+9t+f3LbO6uaZhdskSXnGFDvQHEUlnTyn99ft1zI3D5J3l9W7T0iSlu/03mfWG7dJzq2iM83DNbrzUKt46lLo0lzIytX5rBzNXrlXS7anaMljt8hezvtSwbXDqRcc/5718C1AjpYwpUJ1s+1Imv7vP7/cauX/Pt+uunXcHxv2nTirN1f97Pb1VrZUF1MrAIQooBLcMHm5Mgp8wb+/fr8eubWNF2tU8xQXoCYv2qljGZl6e3jnCo8du5idV3qhaiz/kn53dl0dT8/UvzYfct8KL8u/xUtl3Tsv2wMXMWTm5GrhliO6pW1DRdUPLLI8o5L2DdbRnQdUgpJOhsYYfZpwWJ8mHPbovb8qg7duuluSTxIOa/XuE7ryma9LLHchK1fJaTWnpaki8xcluGnm77Tz2Xrui+1lKpubZzRj2W5t+PlUmcp/sP5SF70nAlplmbVir576bJv6vrLaa3X4aONBpp2oAEIU4GXGSE988r2e+OT7cs1yXdVsO5ymYVXk3mUfrC//XeQvZOcqbuo32rz/tMvlnu4qrGxjPtpa7DJ3TSB5LitHu485Dww/kuo6qJ7PytWrK/bo7jfXu2Xb1cH/9lwa9+XN99aEz7bpyX//UOH1nDqbqUXbKm8i1KqCEIUaYdvhNL341Q6lu/iLqjJv/fHZlsMaMmtNjRo7U1ZLd6R4uwoOecX8zi+UIaSuLObig6++P1qhOuGSwrdPKa+CVyJ6WsEwWVndht7w9Xbnz+7+k5euDizPfFJ7j5/VS0suXeByrhr/MVhehCjUCENmr9Fba5I0dXHRq9Qq8xLpcR9/r21H0jTpyx2Vts2qKCsnT1/9UHLo+PnEWb29JkmLPfDXa3FXOGXn1ewxTe6SW8HjtOXgGUnSTynpbqiN99z80krH/2cs2+34/8mzmapgFnQp6eQ5RzelNz19+YpXlI4QhRpll4uTdlZOxVqi9h7PUP+ZqxU/t+xdVeeyau5frWX1yIdblXDAddeYJO1ITtekr3bo3bX73b7tn4sZm1WWlqiK8Mbl6Z6Y1sFdA+hdTQLqroZhKwO9z2flaPuRtAq1Ts/7LkmdX1yuk2czLa8j3/ajv5yvMi5mq+f0bzV/Q/m7osuqrGOfquP9AL2FEIUa73Cq9Ynr8vKMes9YrZ9SMrTnWNWY9K+qcvW9VNWOmauQU/iZinzJu7oNzb8TDuvDDQeVdsEzg3f3eODmxYXruudYhh6ev8UtN0peX8aB4wUVDiyvrdxbptdl5ebpvrc36sDlex7e8tdvNXjWGn31g/XWz4mFWplX/mR9HquC78frJy+3vJ6yendd2Vq5KjIDfnE2Jp3WyPc2F7mRc3VXo0LUP/7xD7Vs2VIBAQGKjY3V//73P29XCRV0PitHo+cn6IsKjEe5mGP9Q/vfH38ZK+DOsRhnM3Pd8oWUL+vyl/d/f0zRd3tPum295TG7jF9sFZVagZYXV127mwoNJF+7r/xf8vk6Tlpa5LkXvtqhpxduc0vLhSujPkhw+zoLj/8ZNGuNFm1L1uBZayRJB0+d11c/HHVq0cks4+fseIbzcShu/FpBhX/nf/vvrhLL7z3+y+959e4TjkH0+b+DR0sYVF8SV5+t6Ut3uyhZftV9+ozPthxx+rnwZ+2uN9Zp2Y5juv217yqzWh5XY0LUv/71L40dO1bPPPOMtm7dqm7dumnAgAE6eNBzTaM1zbnMHC3ZnqLlO6rODMZvrv5Zi7elOE6CuXlGv39no6Z+7frWFq5Ox9kVCFEnLH7xpZfyl9ycVfvU95XVeuDdzTpg8RYPmTm/NLmv3n1Cq3af0EPvJ+jeKnKFnCR9nnik9EIluJidqz9+kKC5a5IkSR9uOKjrXlhmeX1/KTC5ZL6FhU7+iYfOOP6fl2f0/aEz+v7QGeVW4gUK5ZHqgZvJflromGRd/gzl/3vL31bqkQ+36q8Fwkzh8U9DZq3R2r0lB9KsnDwt3lb6BQnlPfLpF5w/f9+7aZJdb3y2thx0z3QTnlZ4qglX41Ml6cej1XucXGE1ZrLNGTNm6P7779cDDzwgSZo5c6b++9//6vXXX9fUqVO9XLuqqfeMVY6/2PZNGagTGZka9UGCgv3ratvz/Up8bX4zdB2bZLPZ1OKpRY5l+6cNkiRl5+bpeEammriYRO70uSzd/cY63X19tB7o1srlNjb8fEozl+9x/NzvldXadbn1ZuWuE5owoH2R12y9PKDVGKPnv9yheRUcb+PqS7csvi/wRVzSQNHlO485XQ126mymwoL89MiHW5R+MUdT/l+MmobWk3RpDqaMizlqGOyvKYt3atsR5y+Ggjct3XowVdc1C1V2bp58vXhz4vU/Fz8mavP+S18OG5JOq8VTixTbPFSf/rGrU5n/94+12pmcrq+3p+j2TlEVHvCa5GKs1OYS5kQ6cPq8brv8l/P9N7es0LZLUtZWnHwFP28lsXpz6rK2mr3+7T79uf9Vkop2g247kqao+q5vapxfr14zvtWh06VfyVqeeZQK/nFREbNW7Cm9UCUY/8n3+uaJHpIunXdtkup4YGb5khhjlHjojK6KDFGgn0+ZXrO8it5OyN1qRIjKyspSQkKCnnrqKafn+/btq7Vr17p8TWZmpjIzfzlRpKd7Jh1f9X9f62J2nnq3j9BbwzuX+/Wuwom7Xlewybv104sd/8/IzNHpc1kKC/Irdd3+desU+QIo6QT//NBr9NwXvwSTFxft1APdWpXpS2FXoe6vFk8t0v5pg4q8trR15b+upOXFSTiQqtjmoaXWNX89P08ZqGc/L3mywYLjIvoU+rK4+aWVjrr2mP5tmbYrXQofBZXnvVNeWTl5avts8RNZDvj7pW71Lx+5yekLvXDAdTXB487kXz6XsS+6Z8xIi6cWac7vYtU/JlL3vuV6TiJX74H81rB8t7/2ncb0ulK3XhUhSZq53HW3Tv74ol4vryr29/BqOb6wj5WjW/nKZ77W725spt3HzupP/drp+hZhSr+YrWsnFu12LKysQU2SFm9L1uj5W4o8/98fXX+RljbxaXm2XdiMpbv1xuqit5n5aGP5eiVeXuaebrqK+vnEOZfHo6TP9Pvr9uvlZbsdUxT8+Hw/BfmX/HVfXOAuvG1PnkuqoxrRnXfy5Enl5uYqIiLC6fmIiAilpLhuKp46darsdrvjER0d7ZG65fdzf3/4jEfWXxaurkQpbR6jQN+y/bVR3r+gf3bzdAOVPR/Tna+7DuXFOVIL5ovyq1vyaWRncrp2JqeXqUumsub0amy/1ELyXSndTSVJPHTGaR6d8n4WrCrvwNztR9K1Mem0o64V6d4ujqcGzVvxTjGtz0t/rDrzmHna3uNnnd6bOWW4atRm88btlqu/GhGi8hV+Exhjin1jTJgwQWlpaY7HoUOeuXXAn/q1U5eWYfq/wVd7ZP3F6dYm3PF/V99Lhe/T9Lsbmzn9HOBbtrdG/I3Ny1Wv265rUq7ypXF1v6mqJPwKf29XweNKCj5NQwM17Y4OmnZHB9WpQifpdpHBFV7HnN/FqkurBo6fh3aMKlLGE6Gwfj3XLcTFGdu7jWbc1VHXRIVIUqktElZc3yJUD97iulu+OGv+3LPYZdc1q2+5Ls8OKtrNL0m/K+e5qjob2ilKj/du6/i5LOdzb0zPURPUiO688PBw+fj4FGl1On78eJHWqXz+/v7y9/f8F9zDPa/Uwz2vtPx6q02n79/fpdzrfvH2DmVa985J/bXtSJp86tgU2zxUdWy/XDrrqr4Fm4M7Nq2vDU/3kiRFhPwyXuJ/T/ZUt7+uVO/2jbR8Z9FLhvdMHqA2xXQB5G+zIl0Axdk/bZB2pWSo38zVTtsqq0A/H81/oIvlAakrnujuVBdJevjDLVp0+RLtkd1a6plBrgO6J46HKzabTS//pqOe+OT7IsvW/PnWcq+roILHO+nkOfUs0KW5aMzN+tt/d+nbXSfKtY2C63y4Z2u9tnKfy2XSpbF7v7o8kH3zs73VuUCXYv+YSKey7RuHFNlWwf15Mz622Do9PfAqTSlmIG5hV/jX1bg+bTVj2W7ZbKVPydCjXSOnnwN8fVx2g7tS2mfrDzddGid2ZaNgjevTVm+66EYrrHf7Rnpr+PWSpFuvaqRvCk0RUNHP831xLTS0Y5Q6TXK+AKFr61/+sKxISHOn+vV8yzQreHnPO7HNwxTbPEyP9S77Tc6La1Hu2rqB9hw/qxMZFbu6dMv/9ZExptx/BFR1NaIlys/PT7GxsVq2zPlDs2zZMnXt2rWYV8GqQD8f3dAyzDE26PnbYrR83C3aO3mAy/KfP3yTJOkf9/5KPnVsiggJcApQkhQdVk97Jw/QW8Ov145J/fTlIzc7LS/L4OjwK375cNoDfZ2WFRzc/tqwX5W6roJaNQzS+gm9tPFy+Csvq+0vA2Ii1So8qMjzrQs8d32LMItrd687Y5uW+zUz7+4kSapXxoGqLQsdi2ui7Jr3+xvKvd2CbroyvMTlYUF+2j9tkPZPG1SlWhXH9Gqj/dMGKWlqyV+u+d2WlSGgjEMACo61nHpHB0WEuP+42mSTT6HB1wUHRL9+b/GBNl+bRle4vV6FvRlf/nGynlKw1fS3N/zSM/FsMX+klVdYkJ8aXOFf5PdS3dWIECVJ48aN01tvvaW3335bO3fu1OOPP66DBw9q1KhR3q5arXBlo+BirwLqFF1f+6cN0sAOjUtcR/7r6/nVVYemdr027FeKsgdo0ZibS3xdvreGX6/mDerpjfhYzfv99Y7nF4/ppgUP3qhpd3TQAze31MAOkSWspShfnzqKtAeoUUjpX0hvxscWOyC/ON3ahGtMr6J/Mb7+u1iX3dEFr8xp3qBoyCrsxdtjylWfynL7dU20f9ogbZvYT4/1aqMFD95Y5tf+xkJoc6Vunco7BYYUCvYFBQcUv6wierRrWO7X1LFJk267Rv97svjutopo1fCXcBIREqANT/cu9TXBAeXrNLHX89W+KQOLPJ//BV6WnuXSArY7+JcynrAyFTzXFGypuzqqaAsrflEjuvMk6e6779apU6c0adIkJScnKyYmRosXL1bz5rWnH7ymGXRtYw26tmjweqTnlS4nduwUXV+r/nTpxF9wlvIW4fVUz6+u7rmhWZHXuFvfayLVq32Env/yx2Kv4uvdPkLLdx5TTJMQtY0IVtuIYP06tmm5rtDKV9LYnvUTeinx0Bn1vdp1l3ZV4VPHpsf7tC29oC6Nd9m0/7Sm3lG2rueqpEEJ4Tq/1aNwa1tFRYaUf8ygf10f3RfXosLbbmwPUHKaeyaoHXxtVIlX170ZH6snP/1Br1xu3XSHJ/u3q/AUKaUp2BM7sltL/fN/ScWWrUxVKdxVdTUmREnS6NGjNXr0aG9XAx6y9PFblH4hW51bhOn266IUWkLfujcHMfvUsWnSba5bfz58oIvW/3xKy3ceU2yzUD1foNxXj96sC9m5+st/ftRdnSve0hJpD1B/e/la3SripisbVOhqt7J4oFurYucVK4uHyjn42Z0qc76uf9z7K63adaLEwd4jurZwGRLuuaH4K5WvKMeg9HUTeqnr1BU6WihI9W7fqJhXXOo6t6LvNZHqc3VEsRcSPXpr+cel1vOr3K/Hsb3banjXFk43PUbVR9xEtdE2IlidL48BurJRsBqUMEalsT1A/a+J1O2doir9ZFiSriV0EcQ0sev6FmH6+rFu+v1NnpvY0VP+2N36BRQV0TS05NaWLi1/GTfmqmUz381l6L6xMi5qVPfW+u0NzdTCza1M+Tq7aPEc2KGxXvr1tSVOjPirYlpKnxtyTbGvKS3kRBbq8i44WLlTdH2tm3CrrmxU8SsjXXEVoG5pe6k7s0UZur2turuz+6bHaRpaTyO6tnB6rvD4zoooOMZy1m+vK/Prfn9TC0lSr6uKD8CFlffK7eqKEIUayWazaU58rGbeU/YTRb4bW1360nVHa5An/L/L00SUddLPytIx2u6V7eZfuFAcd06DcV/cpS+GO8oxVcdTA67yaPdjecfg5RsYc+mPjLKY/puOahoaqOm/6ShJCr7cItWrUKvSQ90vtXzlj8Uq2E0bfoW/Gtut/S5aNKhX7LKSWtvc2R7971FxTj9//FCc7rk+Wk8PdD2lglUTh16jqAIXBJR36oiSDCgwHrSkMaqFQ+eoW1rr84dv0j9+V/xFOc0v/47ui2uu3S8O0AtVdCymu1WdP9GBKmLBg3E6n5Xjthasq1xc+l4RzRsE6fvn+para6UmK611qKzjO3x9yv6V61/GK9Eqg9VB6XV96mjmPdfp88TSb+7969im+nWBwfz/+3NPHTh1Xh2j6zuVyx+4nX/F5cAOjfXYgkRL9StoZLdWWrX7hJLTLup8Vo6eHxqj11bu1bYjaYorMFeXJ3UudCXsDS3DdENLz1wd+3+Dr9YfL88A784B7rbLsfK+uOYlXiXXMbq+Xv3tdWoWdikY1aljU6dCv+vC/j2qq1buOq4h10aVOgFvTcJZGHDBnV2AYUF+WvvUrQpy4zrd2cSPS66shEvaPeHPA9rp4Olz2rS/8m5UW7+eX6XO91Onjk0fjnS+evM1FxeXuFvT0EAdTq38uw4M6NBY6ybcqsOpF0oNL+UREeKvmCYhLu9nWpiryWNL0jDYX3e5sWuzuqg9cRHwoqj6gbLXq9nBp6rcNmLVn3pYep036x8W5KfbO0WVOOi6OI2CA/TJKOvz4VXWtD1+davG+6M8brvc3TkgpvIu0MjX2B7o9nng4uNa6KtHu+mh7q3dut7ajBAFwC2u8K9brrFCnpLfBVGdtGp4hWbec12xs8970p2/qpyxf78t5xQjwVWguzr/Kt9GwZ6ZaLW881+h6iFEAYCbVLXB/uUVVMbZ48uqbh2bY8xaecdueeIef1VNWbrVULXV/HcpALhJv2si1bxBvWJniu/cIlQJBypvbJI7FJxTzd2zdNtsNsfA/vJ25rWJuEIp6RWbrDM4oK4aBPnJv4w3VAfKixAFoNYoaW6xsmgXGVziLPHV0ZP92+lfmw+5ZV1BfnXVNDRQDYIq3v31QLdW+t+ekxVax+xy3icTKC9CFACPKOuNhd1h0ZibNejVNbqqhIBzVWRwpXafLB7TrdK2VZA3r9y8M7appZtR57u6wHQgfpdneC/uqsnrmtVX/Xq+Ci3HPFnXNrUrL8+obg27CS68hxAFwCMesXCrDauuibJr/7RBJZYZUs5LtiuqvpeuxizrBJpVSefmodp8IFXXRJV9wtbibq1UkoWjS56YtbC7r49W97YN1Si49JuPo3YiRAHwiIdu8d5l1K3Cg/TzyXPlek11b50oLURWZeP6ttXPJ865dU4kd2gaWk9NQ6vf1Z6oPIy2A+ARJc2I7Gl/v3y7n4K3z3ClYBVDmMDUa7q2DtfvbmyumCbeuXUQYBUhCkCtVTA4BVahW7kAqB7ozgMAeW8MU013V+dopV3IVriHJqwEvIkQBQDwmMd6t/F2FVCM4IC6yriYo9/dWL7Z5PELQhQAAF7g51NHS8ZemgojwAvdyVf4XwpR91xPiLKKMVEA3Kb/5Ru1dqxiV1lVlnF92nq7CqhG6tSx6arIEF0VGeLVCzFgHSEKgNvYLt9CpLZ+HfjX9XFMEoma557royVJkSHMG4VL+LQDAFAGg6+9NJEpFyEgHyEKAADAAkIUAACABYQoAJDUuqHrG90CQHGY4gBAjRMdFqi/39NJAb4+WvnT8VLLNwjy45YjAMqNEAWgxqlfz0+3dWoiSWUKUUBZXN8yVBuf7sV0BHAgRAEAUAb+dX3UKIR7LOIXjIkCAACwgBAFAABgASEKAADAAsZEAZWsS6sGGiOpU7P63q4KAKACCFFAJbvpynDddGW4t6sBAKgguvMAAAAsoCUKQK1lD/TVb29opiv8uWwdQPkRogDUWhEhAZp6RwdvVwNANUV3HgAAgAW0RAGAG308Kk7GGDW4ws/bVQHgYYQoAHCjTtH1vV0FAJWE7jwAAAALCFEAAAAWEKIAAAAsIEQBAABY4NEQNXnyZHXt2lX16tVT/fr1XZY5ePCghgwZoqCgIIWHh2vMmDHKyspyKrNt2zZ1795dgYGBatKkiSZNmiRjjFOZVatWKTY2VgEBAWrVqpXmzJnjqd0CAADw7NV5WVlZ+s1vfqO4uDjNnTu3yPLc3FwNGjRIDRs21Jo1a3Tq1CkNHz5cxhjNmjVLkpSenq4+ffqoZ8+e2rRpk3bv3q0RI0YoKChITzzxhCQpKSlJAwcO1MiRI/XBBx/ou+++0+jRo9WwYUPdeeedntxFAABQS3k0RD3//POSpHnz5rlcvnTpUu3YsUOHDh1SVFSUJOnll1/WiBEjNHnyZIWEhGj+/Pm6ePGi5s2bJ39/f8XExGj37t2aMWOGxo0bJ5vNpjlz5qhZs2aaOXOmJKl9+/bavHmzpk+fTogCarmxvdvq9ze1VDjzNrlUv56fHr31Ss36Zq+3qwJUO14dE7Vu3TrFxMQ4ApQk9evXT5mZmUpISHCU6d69u/z9/Z3KHD16VPv373eU6du3r9O6+/Xrp82bNys7O9vltjMzM5Wenu70AFDzRNoD1C4yWA2u8C+9cC3kU8emSHuAJMlm83JlgGrGqyEqJSVFERERTs+FhobKz89PKSkpxZbJ/7m0Mjk5OTp58qTLbU+dOlV2u93xiI6Odss+AQCA2qHcIWrixImy2WwlPjZv3lzm9dlc/OljjHF6vnCZ/EHl5S1T0IQJE5SWluZ4HDp0qMx1BgAAKPeYqEceeUT33HNPiWVatGhRpnVFRkZqw4YNTs+lpqYqOzvb0bIUGRnpaHHKd/z4cUkqtUzdunXVoEEDl9v29/d36iIEAAAoj3KHqPDwcIWHh7tl43FxcZo8ebKSk5PVuHFjSZcGm/v7+ys2NtZR5umnn1ZWVpb8/PwcZaKiohxhLS4uTl9++aXTupcuXarOnTvL19fXLXUFAFSeRiH+uuf6aDUK5o9dT3m8T1udz8xxjIlD+Xl0TNTBgweVmJiogwcPKjc3V4mJiUpMTNTZs2clSX379tXVV1+t+Ph4bd26VStWrND48eM1cuRIhYSESJKGDRsmf39/jRgxQtu3b9fChQs1ZcoUx5V5kjRq1CgdOHBA48aN086dO/X2229r7ty5Gj9+vCd3DwDgIa0bXqFpd16rcX3bebsqNdZdnaM14qaWCueiC8s8OsXBX/7yF7377ruOn6+77jpJ0sqVK9WjRw/5+Pho0aJFGj16tG666SYFBgZq2LBhmj59uuM1drtdy5Yt08MPP6zOnTsrNDRU48aN07hx4xxlWrZsqcWLF+vxxx/Xa6+9pqioKL366qtMbwAAADzGoyFq3rx5xc4Rla9Zs2b66quvSizToUMHrV69usQy3bt315YtW8pbRQAAAEu4dx4AAIAFhCgAAAALCFEAAAAWEKIAAAAsIEQBAABYQIgCAACwgBAFAABgASEKAADAAkIUAACABYQoAAAACwhRAAAAFhCiAAAALCBEAQAAWECIAgAAsIAQBQAAYAEhCgAAwAJCFAAAgAWEKAAAAAsIUQAAABYQogAAACwgRAEAAFhAiAIAALCAEAUAAGABIQoAAMACQhQAAIAFhCgAAAALCFEAAAAWEKIAAAAsIEQBAABYQIgCAACwgBAFAABgASEKAADAAkIUAACABYQoAAAACwhRAAAAFhCiAAAALCBEAQAAWECIAgAAsIAQBQAAYAEhCgAAwAJCFAAAgAUeC1H79+/X/fffr5YtWyowMFCtW7fWc889p6ysLKdyBw8e1JAhQxQUFKTw8HCNGTOmSJlt27ape/fuCgwMVJMmTTRp0iQZY5zKrFq1SrGxsQoICFCrVq00Z84cT+0aAACA6npqxT/99JPy8vL0xhtv6Morr9T27ds1cuRInTt3TtOnT5ck5ebmatCgQWrYsKHWrFmjU6dOafjw4TLGaNasWZKk9PR09enTRz179tSmTZu0e/dujRgxQkFBQXriiSckSUlJSRo4cKBGjhypDz74QN99951Gjx6thg0b6s477/TULgIAgFrMYyGqf//+6t+/v+PnVq1aadeuXXr99dcdIWrp0qXasWOHDh06pKioKEnSyy+/rBEjRmjy5MkKCQnR/PnzdfHiRc2bN0/+/v6KiYnR7t27NWPGDI0bN042m01z5sxRs2bNNHPmTElS+/bttXnzZk2fPp0QBQAAPKJSx0SlpaUpLCzM8fO6desUExPjCFCS1K9fP2VmZiohIcFRpnv37vL393cqc/ToUe3fv99Rpm/fvk7b6tevnzZv3qzs7GyXdcnMzFR6errTAwAAoKwqLUTt27dPs2bN0qhRoxzPpaSkKCIiwqlcaGio/Pz8lJKSUmyZ/J9LK5OTk6OTJ0+6rM/UqVNlt9sdj+jo6IrtIAAAqFXKHaImTpwom81W4mPz5s1Orzl69Kj69++v3/zmN3rggQecltlstiLbMMY4PV+4TP6g8vKWKWjChAlKS0tzPA4dOlTargMAADiUe0zUI488onvuuafEMi1atHD8/+jRo+rZs6fi4uL05ptvOpWLjIzUhg0bnJ5LTU1Vdna2o2UpMjLS0eKU7/jx45JUapm6deuqQYMGLuvo7+/v1EUIAABQHuUOUeHh4QoPDy9T2SNHjqhnz56KjY3VO++8ozp1nBu+4uLiNHnyZCUnJ6tx48aSLg029/f3V2xsrKPM008/raysLPn5+TnKREVFOcJaXFycvvzyS6d1L126VJ07d5avr295dxEAAKBUHhsTdfToUfXo0UPR0dGaPn26Tpw4oZSUFKcWo759++rqq69WfHy8tm7dqhUrVmj8+PEaOXKkQkJCJEnDhg2Tv7+/RowYoe3bt2vhwoWaMmWK48o8SRo1apQOHDigcePGaefOnXr77bc1d+5cjR8/3lO7BwAAajmPTXGwdOlS7d27V3v37lXTpk2dluWPV/Lx8dGiRYs0evRo3XTTTQoMDNSwYcMcUyBIkt1u17Jly/Twww+rc+fOCg0N1bhx4zRu3DhHmZYtW2rx4sV6/PHH9dprrykqKkqvvvoq0xsAAACP8ViIGjFihEaMGFFquWbNmumrr74qsUyHDh20evXqEst0795dW7ZsKU8VAQAALOPeeQAAABYQogAAACwgRAEAAFhAiAIAALCAEAUAAGABIQoAAMACQhQAAIAFhCgAAAALCFEAAAAWEKIAAAAsIEQBAABYQIgCAACwgBAFAABgASEKAADAAkIUAACABYQoAAAACwhRAAAAFhCiAAAALCBEAQAAWECIAgAAsIAQBQAAYAEhCgAAwAJCFAAAgAWEKAAAAAsIUQAAABYQogAAACwgRAEAAFhAiAIAALCAEAUAAGABIQoAAMACQhQAAIAFhCgAAAALCFEAAAAWEKIAAAAsIEQBAABYQIgCAACwgBAFAABgASEKAADAAkIUAACABYQoAAAACwhRAAAAFng0RA0dOlTNmjVTQECAGjdurPj4eB09etSpzMGDBzVkyBAFBQUpPDxcY8aMUVZWllOZbdu2qXv37goMDFSTJk00adIkGWOcyqxatUqxsbEKCAhQq1atNGfOHE/uGgAAqOU8GqJ69uypjz/+WLt27dKnn36qffv26de//rVjeW5urgYNGqRz585pzZo1WrBggT799FM98cQTjjLp6enq06ePoqKitGnTJs2aNUvTp0/XjBkzHGWSkpI0cOBAdevWTVu3btXTTz+tMWPG6NNPP/Xk7gEAgFqsridX/vjjjzv+37x5cz311FO6/fbblZ2dLV9fXy1dulQ7duzQoUOHFBUVJUl6+eWXNWLECE2ePFkhISGaP3++Ll68qHnz5snf318xMTHavXu3ZsyYoXHjxslms2nOnDlq1qyZZs6cKUlq3769Nm/erOnTp+vOO+/05C4CAIBaqtLGRJ0+fVrz589X165d5evrK0lat26dYmJiHAFKkvr166fMzEwlJCQ4ynTv3l3+/v5OZY4ePar9+/c7yvTt29dpe/369dPmzZuVnZ3tsj6ZmZlKT093egAAAJSVx0PUn//8ZwUFBalBgwY6ePCg/vOf/ziWpaSkKCIiwql8aGio/Pz8lJKSUmyZ/J9LK5OTk6OTJ0+6rNfUqVNlt9sdj+jo6IrtKAAAqFXKHaImTpwom81W4mPz5s2O8n/605+0detWLV26VD4+PrrvvvucBoXbbLYi2zDGOD1fuEz+68tbpqAJEyYoLS3N8Th06FBZDwEAAED5x0Q98sgjuueee0os06JFC8f/w8PDFR4errZt26p9+/aKjo7W+vXrFRcXp8jISG3YsMHptampqcrOzna0LEVGRjpanPIdP35ckkotU7duXTVo0MBlHf39/Z26CAEAAMqj3CEqPxRZkd86lJmZKUmKi4vT5MmTlZycrMaNG0uSli5dKn9/f8XGxjrKPP3008rKypKfn5+jTFRUlCOsxcXF6csvv3Ta1tKlS9W5c2fH+CsAAAB38tiYqI0bN2r27NlKTEzUgQMHtHLlSg0bNkytW7dWXFycJKlv3766+uqrFR8fr61bt2rFihUaP368Ro4cqZCQEEnSsGHD5O/vrxEjRmj79u1auHChpkyZ4rgyT5JGjRqlAwcOaNy4cdq5c6fefvttzZ07V+PHj/fU7gEAgFrOYyEqMDBQn332mXr16qV27drpD3/4g2JiYrRq1SpHN5qPj48WLVqkgIAA3XTTTbrrrrt0++23a/r06Y712O12LVu2TIcPH1bnzp01evRojRs3TuPGjXOUadmypRYvXqxvv/1WnTp10gsvvKBXX32V6Q0AAIDHeGyeqA4dOuibb74ptVyzZs301Vdflbqu1atXl1ime/fu2rJlS7nqCAAAYBX3zgMAALDAozOWAwCqvmub1NfjvduqdaMgb1cFqFYIUQBQy3VoaleHpnZvVwOodujOAwAAsIAQBQAAYAHdeQDcJqZJiP5+TyfVr+fn7aoAgMcRogC4TWN7oG7r1MTb1QCASkF3HgAAgAWEKAAAAAsIUQAAABYQogAAACwgRAEAAFhAiAIAALCAEAUAAGABIQoAAMACQhQAAIAFhCgAAAALCFEAAAAWEKIAAAAsIEQBAABYUNfbFagqjDGSpPT0dC/XBAAAlFX+93b+93hlIkRdlpGRIUmKjo72ck0AAEB5ZWRkyG63V+o2bcYb0a0KysvL09GjRxUcHCybzSbpUrqNjo7WoUOHFBIS4uUaVg0cE2ccD2ccD2ccj6I4Js44Hs6sHA9jjDIyMhQVFaU6dSp3lBItUZfVqVNHTZs2dbksJCSEN3chHBNnHA9nHA9nHI+iOCbOOB7Oyns8KrsFKh8DywEAACwgRAEAAFhAiCqBv7+/nnvuOfn7+3u7KlUGx8QZx8MZx8MZx6Mojokzjoez6nY8GFgOAABgAS1RAAAAFhCiAAAALCBEAQAAWECIAgAAsKBahKjVq1dryJAhioqKks1m0+eff15i+W+//VY2m63I46effnJZfsGCBbLZbLr99tudns/JydGzzz6rli1bKjAwUK1atdKkSZOUl5fnKHPs2DGNGDFCUVFRqlevnvr37689e/Y4lp8+fVqPPvqo2rVrp3r16qlZs2YaM2aM0tLSnLbVokWLIvV96qmnatzxkKQePXoUqcs999zjVCY1NVXx8fGy2+2y2+2Kj4/XmTNnatzx2L9/v8u62Gw2ffLJJ45y1eH9kZGRobFjx6p58+YKDAxU165dtWnTJqcyxhhNnDhRUVFRCgwMVI8ePfTjjz86lcnMzNSjjz6q8PBwBQUFaejQoTp8+LBTmfK8P6r7MalJ5xB3vUdqyjnEHcejtp1DPvvsM/Xr10/h4eGy2WxKTEwssn5PnEOKZaqBxYsXm2eeecZ8+umnRpJZuHBhieVXrlxpJJldu3aZ5ORkxyMnJ6dI2f3795smTZqYbt26mdtuu81p2YsvvmgaNGhgvvrqK5OUlGQ++eQTc8UVV5iZM2caY4zJy8szN954o+nWrZvZuHGj+emnn8yDDz5omjVrZs6ePWuMMWbbtm3mjjvuMF988YXZu3evWbFihWnTpo258847nbbVvHlzM2nSJKf6ZmRk1LjjYYwx3bt3NyNHjnSqy5kzZ5y21b9/fxMTE2PWrl1r1q5da2JiYszgwYNr3PHIyclxqkNycrJ5/vnnTVBQkNPvvzq8P+666y5z9dVXm1WrVpk9e/aY5557zoSEhJjDhw87ykybNs0EBwebTz/91Gzbts3cfffdpnHjxiY9Pd1RZtSoUaZJkyZm2bJlZsuWLaZnz56mY8eOTvUpz/ujuh+TmnQOcdd7pKacQ9xxPGrbOeS9994zzz//vPnnP/9pJJmtW7cW2YYnziHFqRYhqqDy/EJTU1NLLJeTk2Nuuukm89Zbb5nhw4cX+YUOGjTI/OEPf3B67o477jC/+93vjDHG7Nq1y0gy27dvd1pnWFiY+ec//1nsdj/++GPj5+dnsrOzHc81b97cvPLKKyXW15XqeDy6d+9uHnvssWLrsWPHDiPJrF+/3vHcunXrjCTz008/lbgP1fF4FNapU6ci663q74/z588bHx8f89VXXzm9pmPHjuaZZ54xxlwKlZGRkWbatGmO5RcvXjR2u93MmTPHGGPMmTNnjK+vr1mwYIGjzJEjR0ydOnXMkiVLjDEVe38YU/2OiSvV8RzizuNRE84hnnx/1NRzSEFJSUkuQ1RlnEMKqhbdeVZdd911aty4sXr16qWVK1cWWT5p0iQ1bNhQ999/v8vX33zzzVqxYoV2794tSfr++++1Zs0aDRw4UNKlJkNJCggIcLzGx8dHfn5+WrNmTbH1SktLU0hIiOrWdb514UsvvaQGDRqoU6dOmjx5srKyssq3w6WoSsdj/vz5Cg8P1zXXXKPx48crIyPDsWzdunWy2+3q0qWL47kbb7xRdrtda9eutbj3RVWl45EvISFBiYmJLrdZld8fOTk5ys3NddpXSQoMDHTsa1JSklJSUtS3b1/Hcn9/f3Xv3t3xe01ISFB2drZTmaioKMXExDjKVNb7Q6oax8SV6ngOcffxqO7nEE+9P2ryOaQsKvscUiNvQNy4cWO9+eabio2NVWZmpt5//3316tVL3377rW655RZJ0nfffae5c+e67E/N9+c//1lpaWm66qqr5OPjo9zcXE2ePFm//e1vJUlXXXWVmjdvrgkTJuiNN95QUFCQZsyYoZSUFCUnJ7tc56lTp/TCCy/ooYcecnr+scce069+9SuFhoZq48aNmjBhgpKSkvTWW2/VuONx7733qmXLloqMjNT27ds1YcIEff/991q2bJkkKSUlRY0aNSqy/UaNGiklJaXGHY+C5s6dq/bt26tr165Oz1f190dwcLDi4uL0wgsvqH379oqIiNBHH32kDRs2qE2bNpLk+N1FREQ4vTYiIkIHDhxwlPHz81NoaGiRMvmv9/T7Q6pax6Sw6noOcefxqAnnEE+9P2ryOaQsKv0cUq52qypAZWhadGXw4MFmyJAhxhhj0tPTTYsWLczixYsdy11113z00UemadOm5qOPPjI//PCDee+990xYWJiZN2+eo8zmzZtNx44djSTj4+Nj+vXrZwYMGGAGDBhQpA5paWmmS5cupn///iYrK6vE+v773/82kszJkydLLFedj0fB10gyCQkJxhhjJk+ebNq2bVuk3JVXXmmmTp1a4n5V5+Nx/vx5Y7fbzfTp00utb1V8f+zdu9fccsstjn29/vrrzb333mvat29vjDHmu+++M5LM0aNHnV73wAMPmH79+hljjJk/f77x8/MrUp/evXubhx56yBhTsfeHMdXvmBRU3c8h7j4e+arrOcTdx6Omn0MKKq47rzLOIQXVmhD14osvmquuusoYY8zWrVsdv6T8h81mMzabzfj4+Ji9e/caY4xp2rSpmT17ttN6XnjhBdOuXbsi6z9z5ow5fvy4McaYG264wYwePdppeXp6uomLizO9evUyFy5cKLW+hw8fLtJn60p1PR4F5eXlOfVhz50719jt9iLl7Ha7efvtt0vcr+p8PN577z3j6+vrKFeSqvj+yHf27FnHSf+uu+4yAwcONMYYs2/fPiPJbNmyxan80KFDzX333WeMMWbFihVGkjl9+rRTmWuvvdb85S9/McZU7P1hTPU7JvlqwjkknzuOR0HV9RySz13Ho6afQwoqLkRVxjmkoBo9JqqgrVu3qnHjxpIudbNs27ZNiYmJjsfQoUPVs2dPJSYmKjo6WpJ0/vx51anjfIh8fHycLmHPZ7fb1bBhQ+3Zs0ebN2/Wbbfd5liWnp6uvn37ys/PT1988UWRPt/i6ivJUWd38+bxKOzHH39Udna2oz5xcXFKS0vTxo0bHWU2bNigtLS0Ik3U7lIVjsfcuXM1dOhQNWzYsEz1larW+yNfUFCQGjdurNTUVP33v/917Gt+90t+l4skZWVladWqVY7fa2xsrHx9fZ3KJCcna/v27Y4y3nh/SN47JlLNOYfkq+jxKKy6nkPyuet41PRzSFlU+jmkXJHLSzIyMszWrVsdaXbGjBlm69at5sCBA8YYY5566ikTHx/vKP/KK6+YhQsXmt27d5vt27ebp556ykgyn376abHbcNW0OHz4cNOkSRPHJeyfffaZCQ8PN08++aSjzMcff2xWrlxp9u3bZz7//HPTvHlzc8cddziWp6enmy5dupgOHTqYvXv3urz8c+3atY59+vnnn82//vUvExUVZYYOHVrjjsfevXvN888/bzZt2mSSkpLMokWLzFVXXWWuu+66IpefXnvttWbdunVm3bp1pkOHDsVeflqdj0e+PXv2GJvNZr7++usiy6rL+2PJkiXm66+/Nj///LNZunSp6dixo7nhhhucup2mTZtm7Ha7+eyzz8y2bdvMb3/7W5dTHDRt2tQsX77cbNmyxdx6660uL08u6/ujuh+TmnQOccfxqEnnEHd9ZoypPeeQU6dOma1bt5pFixYZSWbBggVm69atJjk52VHGE+eQ4lSLEJV/+WThx/Dhw40xl34Z3bt3d5R/6aWXTOvWrU1AQIAJDQ01N998s1m0aFGJ23D1C01PTzePPfaYadasmQkICDCtWrUyzzzzjMnMzHSU+fvf/26aNm1qfH19TbNmzcyzzz7rtLy4uksySUlJxhhjEhISTJcuXYzdbjcBAQGmXbt25rnnnjPnzp2rccfj4MGD5pZbbjFhYWHGz8/PtG7d2owZM8acOnXKaVunTp0y9957rwkODjbBwcHm3nvvLfby2ep8PPJNmDDBNG3a1OTm5hZZVl3eH//6179Mq1atjJ+fn4mMjDQPP/xwkbl78vLyzHPPPWciIyONv7+/ueWWW8y2bducyly4cME88sgjJiwszAQGBprBgwebgwcPOpUpz/ujuh+TmnQOccfxqEnnEHd9ZoypPeeQd955x2XdnnvuOUcZT5xDimMzxpjytV0BAACg1oyJAgAAcCdCFAAAgAWEKAAAAAsIUQAAABYQogAAACwgRAEAAFhAiAIAALCAEAUAADxm9erVGjJkiKKiomSz2fT555+Xex0ff/yxOnXqpHr16ql58+b629/+5v6KWkCIAgAAHnPu3Dl17NhRs2fPtvT6r7/+Wvfee69GjRql7du36x//+IdmzJhheX3uxIzlAACgUthsNi1cuFC3336747msrCw9++yzmj9/vs6cOaOYmBi99NJL6tGjhyRp2LBhys7O1ieffOJ4zcyZM/Xyyy/r4MGDstlslbwXv6AlCgAAeM3vf/97fffdd1qwYIF++OEH/eY3v1H//v21Z88eSVJmZqYCAgKcXhMYGKjDhw/rwIED3qiyAyEKAAB4xb59+/TRRx/pk08+Ubdu3dS6dWuNHz9eN998s9555x1JUr9+/fTZZ59pxYoVysvL0+7duzVz5kxJUnJyshdrL9X16tYBAECttWXLFhlj1LZtW6fnMzMz1aBBA0nSyJEjtW/fPg0ePFjZ2dkKCQnRY489pokTJ8rHx8cb1XYgRAEAAK/Iy8uTj4+PEhISigSiK664QtKlcVQvvfSSpkyZopSUFDVs2FArVqyQJLVo0aKyq+yEEAUAALziuuuuU25uro4fP65u3bqVWNbHx0dNmjSRJH300UeKi4tTo0aNKqOaxSJEAQAAjzl79qz27t3r+DkpKUmJiYkKCwtT27Ztde+99+q+++7Tyy+/rOuuu04nT57UN998ow4dOmjgwIE6efKk/v3vf6tHjx66ePGi3nnnHX3yySdatWqVF/fqEqY4AAAAHvPtt9+qZ8+eRZ4fPny45s2bp+zsbL344ot67733dOTIETVo0EBxcXF6/vnn1aFDB508eVJDhgzRtm3bZIxRXFycJk+erC5dunhhb5wRogAAACxgigMAAAALCFEAAAAWEKIAAAAsIEQBAABYQIgCAACwgBAFAABgASEKAADAAkIUAACABYQoAAAACwhRAAAAFhCiAAAALCBEAQAAWPD/AYLkCbDVleS+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(onefly[\"Timestamps\"], onefly.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
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       "        [        nan,         nan,         nan, ...,         nan,\n",
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       "        [        nan,         nan,         nan, ...,         nan,\n",
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       "        [        nan,         nan,         nan, ...,         nan,\n",
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       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [30.17992732,         nan,         nan, ...,         nan,\n",
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       "        [18.54195326,         nan,         nan, ...,         nan,\n",
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       "        [        nan,         nan,         nan, ...,         nan,\n",
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       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
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       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
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       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [13.30178123,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [ 1.13345171,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "littlesubsetofallflies=xarrayall[\"Angles\"][:,1:10000,:]\n",
    "littlesubsetofallflies.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsoav_pd=littlesubsetofallflies.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fly</th>\n",
       "      <th>Timestamps</th>\n",
       "      <th>Batch</th>\n",
       "      <th>Angles</th>\n",
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       "      <td>2.630816</td>\n",
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       "      <td>2</td>\n",
       "      <td>1.548929e+09</td>\n",
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       "      <td>41</td>\n",
       "      <td>1.548930e+09</td>\n",
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       "      <td>0.772566</td>\n",
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       "      <th>3355536</th>\n",
       "      <td>41</td>\n",
       "      <td>1.548930e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.523044</td>\n",
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       "    <tr>\n",
       "      <th>3355912</th>\n",
       "      <td>41</td>\n",
       "      <td>1.548930e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>0.729545</td>\n",
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      ],
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       "         Fly    Timestamps  Batch    Angles\n",
       "165256     2  1.548929e+09      1  0.991311\n",
       "165264     2  1.548929e+09      1  2.630816\n",
       "165272     2  1.548929e+09      1 -2.124477\n",
       "165304     2  1.548929e+09      1 -0.388789\n",
       "165384     2  1.548929e+09      1  0.642593\n",
       "...      ...           ...    ...       ...\n",
       "3355496   41  1.548930e+09      1 -0.522970\n",
       "3355504   41  1.548930e+09      1  0.772566\n",
       "3355536   41  1.548930e+09      1 -0.523044\n",
       "3355912   41  1.548930e+09      1  0.729545\n",
       "3355952   41  1.548930e+09      1  0.729572\n",
       "\n",
       "[81646 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsoav_pd=lsoav_pd.reset_index().dropna()\n",
    "lsoav_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "165256      2\n",
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       "165384      2\n",
       "           ..\n",
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       "Length: 81646, dtype: int64"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "lsoav_pd[\"Fly\"]+((lsoav_pd[\"Batch\"]-1)*42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "mark must be a Mark instance, not <class 'type'>.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m so\u001b[39m.\u001b[39;49mPlot(data\u001b[39m=\u001b[39;49mlsoav_pd\u001b[39m.\u001b[39;49mreset_index(), x\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mTimestamps\u001b[39;49m\u001b[39m\"\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mAngles\u001b[39;49m\u001b[39m\"\u001b[39;49m, group\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mFly\u001b[39;49m\u001b[39m\"\u001b[39;49m, color\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mBatch\u001b[39;49m\u001b[39m\"\u001b[39;49m)\u001b[39m.\u001b[39;49madd(so\u001b[39m.\u001b[39;49mLine)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/seaborn/_core/plot.py:435\u001b[0m, in \u001b[0;36mPlot.add\u001b[0;34m(self, mark, orient, legend, data, *transforms, **variables)\u001b[0m\n\u001b[1;32m    433\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(mark, Mark):\n\u001b[1;32m    434\u001b[0m     msg \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mmark must be a Mark instance, not \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(mark)\u001b[39m!r}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m--> 435\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(msg)\n\u001b[1;32m    437\u001b[0m \u001b[39m# TODO This API for transforms was a late decision, and previously Plot.add\u001b[39;00m\n\u001b[1;32m    438\u001b[0m \u001b[39m# accepted 0 or 1 Stat instances and 0, 1, or a list of Move instances.\u001b[39;00m\n\u001b[1;32m    439\u001b[0m \u001b[39m# It will take some work to refactor the internals so that Stat and Move are\u001b[39;00m\n\u001b[1;32m    440\u001b[0m \u001b[39m# treated identically, and until then well need to \"unpack\" the transforms\u001b[39;00m\n\u001b[1;32m    441\u001b[0m \u001b[39m# here and enforce limitations on the order / types.\u001b[39;00m\n\u001b[1;32m    443\u001b[0m stat: Optional[Stat]\n",
      "\u001b[0;31mTypeError\u001b[0m: mark must be a Mark instance, not <class 'type'>."
     ]
    }
   ],
   "source": [
    "so.Plot(data=lsoav_pd.reset_index(), x=\"Timestamps\", y=\"Angles\", group=\"Fly\", color=\"Batch\").add(so.Line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Must pass 2-d input. shape=(42, 9999, 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m lsoav_pd\u001b[39m=\u001b[39mpd\u001b[39m.\u001b[39;49mDataFrame(lsoav)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/core/frame.py:722\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    712\u001b[0m         mgr \u001b[39m=\u001b[39m dict_to_mgr(\n\u001b[1;32m    713\u001b[0m             \u001b[39m# error: Item \"ndarray\" of \"Union[ndarray, Series, Index]\" has no\u001b[39;00m\n\u001b[1;32m    714\u001b[0m             \u001b[39m# attribute \"name\"\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    719\u001b[0m             typ\u001b[39m=\u001b[39mmanager,\n\u001b[1;32m    720\u001b[0m         )\n\u001b[1;32m    721\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 722\u001b[0m         mgr \u001b[39m=\u001b[39m ndarray_to_mgr(\n\u001b[1;32m    723\u001b[0m             data,\n\u001b[1;32m    724\u001b[0m             index,\n\u001b[1;32m    725\u001b[0m             columns,\n\u001b[1;32m    726\u001b[0m             dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m    727\u001b[0m             copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m    728\u001b[0m             typ\u001b[39m=\u001b[39;49mmanager,\n\u001b[1;32m    729\u001b[0m         )\n\u001b[1;32m    731\u001b[0m \u001b[39m# For data is list-like, or Iterable (will consume into list)\u001b[39;00m\n\u001b[1;32m    732\u001b[0m \u001b[39melif\u001b[39;00m is_list_like(data):\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/core/internals/construction.py:329\u001b[0m, in \u001b[0;36mndarray_to_mgr\u001b[0;34m(values, index, columns, dtype, copy, typ)\u001b[0m\n\u001b[1;32m    324\u001b[0m         values \u001b[39m=\u001b[39m values\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[1;32m    326\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    327\u001b[0m     \u001b[39m# by definition an array here\u001b[39;00m\n\u001b[1;32m    328\u001b[0m     \u001b[39m# the dtypes will be coerced to a single dtype\u001b[39;00m\n\u001b[0;32m--> 329\u001b[0m     values \u001b[39m=\u001b[39m _prep_ndarraylike(values, copy\u001b[39m=\u001b[39;49mcopy_on_sanitize)\n\u001b[1;32m    331\u001b[0m \u001b[39mif\u001b[39;00m dtype \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m is_dtype_equal(values\u001b[39m.\u001b[39mdtype, dtype):\n\u001b[1;32m    332\u001b[0m     \u001b[39m# GH#40110 see similar check inside sanitize_array\u001b[39;00m\n\u001b[1;32m    333\u001b[0m     rcf \u001b[39m=\u001b[39m \u001b[39mnot\u001b[39;00m (is_integer_dtype(dtype) \u001b[39mand\u001b[39;00m values\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/pandas/core/internals/construction.py:583\u001b[0m, in \u001b[0;36m_prep_ndarraylike\u001b[0;34m(values, copy)\u001b[0m\n\u001b[1;32m    581\u001b[0m     values \u001b[39m=\u001b[39m values\u001b[39m.\u001b[39mreshape((values\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m], \u001b[39m1\u001b[39m))\n\u001b[1;32m    582\u001b[0m \u001b[39melif\u001b[39;00m values\u001b[39m.\u001b[39mndim \u001b[39m!=\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[0;32m--> 583\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mMust pass 2-d input. shape=\u001b[39m\u001b[39m{\u001b[39;00mvalues\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    585\u001b[0m \u001b[39mreturn\u001b[39;00m values\n",
      "\u001b[0;31mValueError\u001b[0m: Must pass 2-d input. shape=(42, 9999, 8)"
     ]
    }
   ],
   "source": [
    "lsoav_pd=xr.DataFrame(lsoav)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Low Pass Filtered Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: 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'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Timestamps: 7564278, Batch: 1, Fly: 42)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.55e+09 1.55e+09\n",
       "  * Batch       (Batch) int64 1\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Fly, Timestamps, Batch) float64 nan nan nan nan ... nan nan nan</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-42861e63-636c-4be0-b599-db728d729511' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-42861e63-636c-4be0-b599-db728d729511' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Timestamps</span>: 7564278</li><li><span class='xr-has-index'>Batch</span>: 1</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b692bc33-1fed-4318-8aba-d446280d16fa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b692bc33-1fed-4318-8aba-d446280d16fa' class='xr-section-summary' >Coordinates: <span>(2)</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 class='xr-has-index'>Timestamps</span></div><div class='xr-var-dims'>(Timestamps)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.549e+09 1.549e+09 ... 1.55e+09</div><input id='attrs-5be98d56-4f5d-4835-b23c-ec32f7f2a159' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5be98d56-4f5d-4835-b23c-ec32f7f2a159' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208194e9-5c4d-40eb-8e06-c771bcfabe02' class='xr-var-data-in' type='checkbox'><label for='data-208194e9-5c4d-40eb-8e06-c771bcfabe02' 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([1.548929e+09, 1.548929e+09, 1.548929e+09, ..., 1.550138e+09,\n",
       "       1.550138e+09, 1.550138e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-eaf9edaa-c815-4a17-aa3b-e3f5e86e7512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eaf9edaa-c815-4a17-aa3b-e3f5e86e7512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e89321d-b31b-46a5-83df-c3aaa907faa2' class='xr-var-data-in' type='checkbox'><label for='data-1e89321d-b31b-46a5-83df-c3aaa907faa2' 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([1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5baec306-ded5-43e0-8a7b-f367e6ca1bdb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5baec306-ded5-43e0-8a7b-f367e6ca1bdb' class='xr-section-summary' >Data variables: <span>(1)</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>Angles</span></div><div class='xr-var-dims'>(Fly, Timestamps, Batch)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5d802e11-0ff1-4375-a9e5-688ea45fbfd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d802e11-0ff1-4375-a9e5-688ea45fbfd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-259eaa13-b740-4b39-95ca-6493ab06d6ae' class='xr-var-data-in' type='checkbox'><label for='data-259eaa13-b740-4b39-95ca-6493ab06d6ae' 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([[[       nan],\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        ...,\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        [       nan]],\n",
       "\n",
       "       [[       nan],\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        ...,\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        [       nan]],\n",
       "\n",
       "       [[       nan],\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        [       nan]],\n",
       "\n",
       "       [[       nan],\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        ...,\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        [       nan]],\n",
       "\n",
       "       [[       nan],\n",
       "        [       nan],\n",
       "        [0.99856426],\n",
       "        ...,\n",
       "        [       nan],\n",
       "        [       nan],\n",
       "        [       nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8ddb1cd-88d1-477c-b0bc-9180221a551c' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8ddb1cd-88d1-477c-b0bc-9180221a551c' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>Timestamps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-034098b4-6bf5-4892-9092-2d4870ef82c1' class='xr-index-data-in' type='checkbox'/><label for='index-034098b4-6bf5-4892-9092-2d4870ef82c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 1548929360.028388, 1548929360.1283941,  1548929361.831054,\n",
       "               1548929361.931157, 1548929362.0311651, 1548929362.1312022,\n",
       "              1548929362.2312033, 1548929362.3316262, 1548929362.4316359,\n",
       "              1548929362.5316434,\n",
       "              ...\n",
       "              1550138090.2619033, 1550138090.3619177, 1550138090.4619207,\n",
       "              1550138090.5619323, 1550138090.6619432, 1550138090.7619467,\n",
       "               1550138090.861952,  1550138090.961966, 1550138091.0619724,\n",
       "              1550138091.1619751],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Timestamps&#x27;, length=7564278))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5a1435b-bf45-4dc4-857b-c7d76666aa08' class='xr-index-data-in' type='checkbox'/><label for='index-e5a1435b-bf45-4dc4-857b-c7d76666aa08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1], dtype=&#x27;int64&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dec924e2-0ef5-4ed2-8e55-180a8e5a11d7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dec924e2-0ef5-4ed2-8e55-180a8e5a11d7' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Timestamps: 7564278, Batch: 1, Fly: 42)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.55e+09 1.55e+09\n",
       "  * Batch       (Batch) int64 1\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Fly, Timestamps, Batch) float64 nan nan nan nan ... nan nan nan"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=xr.load_dataset(\"1_Angles_24h.nc\")\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp.reload(lcm)\n",
    "lcm.nonchunkedmemoryhelper(glob.glob(\"*_Angles24h.nc\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/xarray/core/indexing.py:1374: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n"
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       "Dimensions:     (Timestamps: 66239868, Batch: 8, Fly: 42)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "  * Batch       (Batch) int64 1 2 3 4 5 6 7 8\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Fly, Timestamps, Batch) float64 dask.array&lt;chunksize=(1, 66239868, 1), meta=np.ndarray&gt;</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-d20d86fc-e324-443e-bad9-95281a3fa24d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d20d86fc-e324-443e-bad9-95281a3fa24d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Timestamps</span>: 66239868</li><li><span class='xr-has-index'>Batch</span>: 8</li><li><span>Fly</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-22224afe-2118-4f75-8375-020042dfa697' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22224afe-2118-4f75-8375-020042dfa697' class='xr-section-summary' >Coordinates: <span>(2)</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 class='xr-has-index'>Timestamps</span></div><div class='xr-var-dims'>(Timestamps)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.549e+09 1.549e+09 ... 1.56e+09</div><input id='attrs-24c33195-72c4-4d16-8e7a-492c7cb2852d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-24c33195-72c4-4d16-8e7a-492c7cb2852d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73548e8c-093d-4491-b016-3caa3fd7a6ef' class='xr-var-data-in' type='checkbox'><label for='data-73548e8c-093d-4491-b016-3caa3fd7a6ef' 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([1.548929e+09, 1.548929e+09, 1.548929e+09, ..., 1.559567e+09,\n",
       "       1.559567e+09, 1.559567e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Batch</span></div><div class='xr-var-dims'>(Batch)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8</div><input id='attrs-9bea534d-8f5a-412c-8ac8-6c463183f9cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9bea534d-8f5a-412c-8ac8-6c463183f9cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd4bba14-af6e-47e5-aa62-cd5ac36db993' class='xr-var-data-in' type='checkbox'><label for='data-dd4bba14-af6e-47e5-aa62-cd5ac36db993' 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([1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f5cc3d4-7f99-4d3b-811d-4153f882643d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1f5cc3d4-7f99-4d3b-811d-4153f882643d' class='xr-section-summary' >Data variables: <span>(1)</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>Angles</span></div><div class='xr-var-dims'>(Fly, Timestamps, Batch)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 66239868, 1), meta=np.ndarray&gt;</div><input id='attrs-34531251-16fb-4328-bbf6-942fd3894b16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34531251-16fb-4328-bbf6-942fd3894b16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-873ade8f-7315-413b-8beb-2d50e2d244ba' class='xr-var-data-in' type='checkbox'><label for='data-873ade8f-7315-413b-8beb-2d50e2d244ba' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 165.82 GiB </td>\n",
       "                        <td> 505.37 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (42, 66239868, 8) </td>\n",
       "                        <td> (1, 66239868, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2462 Tasks </td>\n",
       "                        <td> 336 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-eeb26176-93a6-479e-8458-d0bed4c3d268' class='xr-section-summary-in' type='checkbox'  ><label for='section-eeb26176-93a6-479e-8458-d0bed4c3d268' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>Timestamps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-230d0f43-9d58-4e84-a05c-fc50b5eefefb' class='xr-index-data-in' type='checkbox'/><label for='index-230d0f43-9d58-4e84-a05c-fc50b5eefefb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 1548929360.028388, 1548929360.1283941,  1548929361.831054,\n",
       "               1548929361.931157, 1548929362.0311651, 1548929362.1312022,\n",
       "              1548929362.2312033, 1548929362.3316262, 1548929362.4316359,\n",
       "              1548929362.5316434,\n",
       "              ...\n",
       "              1559567219.9725165, 1559567220.0725179, 1559567220.3725622,\n",
       "               1559567220.472576, 1559567220.5725913, 1559567220.6726038,\n",
       "              1559567220.7726126, 1559567220.9726315, 1559567221.0726895,\n",
       "               1559567221.172705],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Timestamps&#x27;, length=66239868))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Batch</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c87b3329-1212-4d6c-be80-c4772bde464e' class='xr-index-data-in' type='checkbox'/><label for='index-c87b3329-1212-4d6c-be80-c4772bde464e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8], dtype=&#x27;int64&#x27;, name=&#x27;Batch&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13c24e7a-9f04-4d23-a9f4-98b07285ccb2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13c24e7a-9f04-4d23-a9f4-98b07285ccb2' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Timestamps: 66239868, Batch: 8, Fly: 42)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "  * Batch       (Batch) int64 1 2 3 4 5 6 7 8\n",
       "Dimensions without coordinates: Fly\n",
       "Data variables:\n",
       "    Angles      (Fly, Timestamps, Batch) float64 dask.array<chunksize=(1, 66239868, 1), meta=np.ndarray>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarrayall=xr.open_mfdataset(\"./*_Angles_24h.nc\", chunks={\"Fly\":1}, combine=\"nested\", concat_dim=\"Batch\")\n",
    "xarrayall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Angles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamps</th>\n",
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       "      <th>0 days 00:00:00</th>\n",
       "      <td>-0.811019</td>\n",
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       "      <th>0 days 00:00:00.200018688</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1 days 23:29:04.089152512</th>\n",
       "      <td>-0.188322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:04.189157120</th>\n",
       "      <td>-0.223853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:06.089363712</th>\n",
       "      <td>-0.152254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:06.689427712</th>\n",
       "      <td>-0.257611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:07.589494528</th>\n",
       "      <td>-0.155959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32885 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Angles\n",
       "Timestamps                         \n",
       "0 days 00:00:00           -0.811019\n",
       "0 days 00:00:00.200018688 -0.723345\n",
       "0 days 00:09:20.161115136 -0.810474\n",
       "0 days 00:09:20.761156864 -0.818860\n",
       "0 days 00:09:20.861157376 -0.832601\n",
       "...                             ...\n",
       "1 days 23:29:04.089152512 -0.188322\n",
       "1 days 23:29:04.189157120 -0.223853\n",
       "1 days 23:29:06.089363712 -0.152254\n",
       "1 days 23:29:06.689427712 -0.257611\n",
       "1 days 23:29:07.589494528 -0.155959\n",
       "\n",
       "[32885 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdf=xarrayall[\"Angles\"].sel(Fly=1, Batch=1).dropna(dim=\"Timestamps\").to_dataframe().drop(columns=\"Batch\")\n",
    "xdf.index=pd.to_datetime(xdf.index, origin=\"unix\", unit='s')\n",
    "xdf=np.sin(xdf)\n",
    "if len(xdf)>0:\n",
    "    xdf.index=xdf.index-xdf.index[0]\n",
    "# xdf.resample('1H').mean()\n",
    "xdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Angles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamps</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0 days 00:00:00</th>\n",
       "      <td>-0.791666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:00:00.200018688</th>\n",
       "      <td>0.798403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:09:20.161115136</th>\n",
       "      <td>0.405328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:09:20.761156864</th>\n",
       "      <td>-0.534380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 00:09:20.861157376</th>\n",
       "      <td>-0.838047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:04.089152512</th>\n",
       "      <td>-0.840692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:04.189157120</th>\n",
       "      <td>-0.585472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:06.089363712</th>\n",
       "      <td>0.626317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:06.689427712</th>\n",
       "      <td>0.723737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:29:07.589494528</th>\n",
       "      <td>0.676157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32885 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Angles\n",
       "Timestamps                         \n",
       "0 days 00:00:00           -0.791666\n",
       "0 days 00:00:00.200018688  0.798403\n",
       "0 days 00:09:20.161115136  0.405328\n",
       "0 days 00:09:20.761156864 -0.534380\n",
       "0 days 00:09:20.861157376 -0.838047\n",
       "...                             ...\n",
       "1 days 23:29:04.089152512 -0.840692\n",
       "1 days 23:29:04.189157120 -0.585472\n",
       "1 days 23:29:06.089363712  0.626317\n",
       "1 days 23:29:06.689427712  0.723737\n",
       "1 days 23:29:07.589494528  0.676157\n",
       "\n",
       "[32885 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=xdf.sample(frac=1).reset_index(drop=True)\n",
    "x.index=xdf.index\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  48 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=-1)]: Done 360 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    4.8s finished\n"
     ]
    },
    {
     "data": {
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       "      <td>-0.065789</td>\n",
       "      <td>-0.113240</td>\n",
       "      <td>-0.055908</td>\n",
       "      <td>-0.030086</td>\n",
       "      <td>-0.045008</td>\n",
       "      <td>-0.048077</td>\n",
       "      <td>-0.007791</td>\n",
       "      <td>-0.083642</td>\n",
       "      <td>-0.044417</td>\n",
       "      <td>-0.074455</td>\n",
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       "      <th>0 days 02:00:00</th>\n",
       "      <td>-0.038374</td>\n",
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       "      <td>-0.038582</td>\n",
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       "      <td>-0.045105</td>\n",
       "      <td>-0.074153</td>\n",
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       "    <tr>\n",
       "      <th>0 days 03:00:00</th>\n",
       "      <td>-0.038514</td>\n",
       "      <td>-0.099690</td>\n",
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       "      <td>-0.038822</td>\n",
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       "      <td>-0.067111</td>\n",
       "      <td>-0.116371</td>\n",
       "      <td>-0.056773</td>\n",
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       "      <td>-0.045878</td>\n",
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       "      <th>0 days 04:00:00</th>\n",
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       "      <td>-0.065466</td>\n",
       "      <td>-0.032425</td>\n",
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       "      <td>-0.017953</td>\n",
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       "      <td>-0.039235</td>\n",
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       "      <td>-0.067384</td>\n",
       "      <td>-0.116618</td>\n",
       "      <td>-0.057218</td>\n",
       "      <td>-0.030866</td>\n",
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       "      <td>-0.009108</td>\n",
       "      <td>-0.084620</td>\n",
       "      <td>-0.046722</td>\n",
       "      <td>-0.072591</td>\n",
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       "      <th>0 days 05:00:00</th>\n",
       "      <td>-0.039702</td>\n",
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       "      <td>-0.059987</td>\n",
       "      <td>-0.039725</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.067404</td>\n",
       "      <td>-0.116155</td>\n",
       "      <td>-0.057601</td>\n",
       "      <td>-0.031677</td>\n",
       "      <td>-0.048067</td>\n",
       "      <td>-0.046839</td>\n",
       "      <td>-0.010293</td>\n",
       "      <td>-0.084073</td>\n",
       "      <td>-0.047614</td>\n",
       "      <td>-0.071468</td>\n",
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       "    <tr>\n",
       "      <th>0 days 06:00:00</th>\n",
       "      <td>-0.040691</td>\n",
       "      <td>-0.095969</td>\n",
       "      <td>-0.037181</td>\n",
       "      <td>-0.065504</td>\n",
       "      <td>-0.035403</td>\n",
       "      <td>-0.082660</td>\n",
       "      <td>-0.071468</td>\n",
       "      <td>-0.022146</td>\n",
       "      <td>-0.059943</td>\n",
       "      <td>-0.040208</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.067172</td>\n",
       "      <td>-0.115118</td>\n",
       "      <td>-0.057882</td>\n",
       "      <td>-0.032630</td>\n",
       "      <td>-0.048845</td>\n",
       "      <td>-0.046714</td>\n",
       "      <td>-0.011717</td>\n",
       "      <td>-0.083290</td>\n",
       "      <td>-0.048527</td>\n",
       "      <td>-0.070187</td>\n",
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       "    <tr>\n",
       "      <th>0 days 07:00:00</th>\n",
       "      <td>-0.041868</td>\n",
       "      <td>-0.094213</td>\n",
       "      <td>-0.037087</td>\n",
       "      <td>-0.065392</td>\n",
       "      <td>-0.037168</td>\n",
       "      <td>-0.080122</td>\n",
       "      <td>-0.070923</td>\n",
       "      <td>-0.024449</td>\n",
       "      <td>-0.059820</td>\n",
       "      <td>-0.040649</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066735</td>\n",
       "      <td>-0.113654</td>\n",
       "      <td>-0.058050</td>\n",
       "      <td>-0.033660</td>\n",
       "      <td>-0.049515</td>\n",
       "      <td>-0.046638</td>\n",
       "      <td>-0.013290</td>\n",
       "      <td>-0.082358</td>\n",
       "      <td>-0.049438</td>\n",
       "      <td>-0.068813</td>\n",
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       "    <tr>\n",
       "      <th>0 days 08:00:00</th>\n",
       "      <td>-0.043179</td>\n",
       "      <td>-0.092337</td>\n",
       "      <td>-0.037162</td>\n",
       "      <td>-0.065233</td>\n",
       "      <td>-0.039026</td>\n",
       "      <td>-0.077449</td>\n",
       "      <td>-0.070215</td>\n",
       "      <td>-0.026770</td>\n",
       "      <td>-0.059631</td>\n",
       "      <td>-0.041019</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066130</td>\n",
       "      <td>-0.111882</td>\n",
       "      <td>-0.058098</td>\n",
       "      <td>-0.034712</td>\n",
       "      <td>-0.050052</td>\n",
       "      <td>-0.046599</td>\n",
       "      <td>-0.014945</td>\n",
       "      <td>-0.081348</td>\n",
       "      <td>-0.050328</td>\n",
       "      <td>-0.067397</td>\n",
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       "    <tr>\n",
       "      <th>0 days 09:00:00</th>\n",
       "      <td>-0.044576</td>\n",
       "      <td>-0.090402</td>\n",
       "      <td>-0.037373</td>\n",
       "      <td>-0.065050</td>\n",
       "      <td>-0.040924</td>\n",
       "      <td>-0.074717</td>\n",
       "      <td>-0.069394</td>\n",
       "      <td>-0.029045</td>\n",
       "      <td>-0.059386</td>\n",
       "      <td>-0.041297</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.065386</td>\n",
       "      <td>-0.109905</td>\n",
       "      <td>-0.058026</td>\n",
       "      <td>-0.035745</td>\n",
       "      <td>-0.050444</td>\n",
       "      <td>-0.046587</td>\n",
       "      <td>-0.016628</td>\n",
       "      <td>-0.080314</td>\n",
       "      <td>-0.051186</td>\n",
       "      <td>-0.065979</td>\n",
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       "    <tr>\n",
       "      <th>0 days 10:00:00</th>\n",
       "      <td>-0.046018</td>\n",
       "      <td>-0.088454</td>\n",
       "      <td>-0.037688</td>\n",
       "      <td>-0.064860</td>\n",
       "      <td>-0.042823</td>\n",
       "      <td>-0.071990</td>\n",
       "      <td>-0.068501</td>\n",
       "      <td>-0.031224</td>\n",
       "      <td>-0.059099</td>\n",
       "      <td>-0.041473</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.064532</td>\n",
       "      <td>-0.107806</td>\n",
       "      <td>-0.057842</td>\n",
       "      <td>-0.036728</td>\n",
       "      <td>-0.050691</td>\n",
       "      <td>-0.046595</td>\n",
       "      <td>-0.018300</td>\n",
       "      <td>-0.079297</td>\n",
       "      <td>-0.052002</td>\n",
       "      <td>-0.064589</td>\n",
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       "    <tr>\n",
       "      <th>0 days 11:00:00</th>\n",
       "      <td>-0.047471</td>\n",
       "      <td>-0.086530</td>\n",
       "      <td>-0.038078</td>\n",
       "      <td>-0.064673</td>\n",
       "      <td>-0.044691</td>\n",
       "      <td>-0.069314</td>\n",
       "      <td>-0.067572</td>\n",
       "      <td>-0.033278</td>\n",
       "      <td>-0.058779</td>\n",
       "      <td>-0.041548</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.063594</td>\n",
       "      <td>-0.105652</td>\n",
       "      <td>-0.057558</td>\n",
       "      <td>-0.037644</td>\n",
       "      <td>-0.050799</td>\n",
       "      <td>-0.046615</td>\n",
       "      <td>-0.019933</td>\n",
       "      <td>-0.078324</td>\n",
       "      <td>-0.052771</td>\n",
       "      <td>-0.063249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 12:00:00</th>\n",
       "      <td>-0.048906</td>\n",
       "      <td>-0.084657</td>\n",
       "      <td>-0.038519</td>\n",
       "      <td>-0.064498</td>\n",
       "      <td>-0.046505</td>\n",
       "      <td>-0.066727</td>\n",
       "      <td>-0.066633</td>\n",
       "      <td>-0.035188</td>\n",
       "      <td>-0.058439</td>\n",
       "      <td>-0.041529</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062598</td>\n",
       "      <td>-0.103495</td>\n",
       "      <td>-0.057191</td>\n",
       "      <td>-0.038481</td>\n",
       "      <td>-0.050783</td>\n",
       "      <td>-0.046640</td>\n",
       "      <td>-0.021505</td>\n",
       "      <td>-0.077412</td>\n",
       "      <td>-0.053489</td>\n",
       "      <td>-0.061973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 13:00:00</th>\n",
       "      <td>-0.050304</td>\n",
       "      <td>-0.082856</td>\n",
       "      <td>-0.038991</td>\n",
       "      <td>-0.064337</td>\n",
       "      <td>-0.048246</td>\n",
       "      <td>-0.064251</td>\n",
       "      <td>-0.065706</td>\n",
       "      <td>-0.036946</td>\n",
       "      <td>-0.058085</td>\n",
       "      <td>-0.041424</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.061566</td>\n",
       "      <td>-0.101377</td>\n",
       "      <td>-0.056756</td>\n",
       "      <td>-0.039237</td>\n",
       "      <td>-0.050659</td>\n",
       "      <td>-0.046668</td>\n",
       "      <td>-0.023003</td>\n",
       "      <td>-0.076571</td>\n",
       "      <td>-0.054157</td>\n",
       "      <td>-0.060772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 14:00:00</th>\n",
       "      <td>-0.051649</td>\n",
       "      <td>-0.081138</td>\n",
       "      <td>-0.039479</td>\n",
       "      <td>-0.064193</td>\n",
       "      <td>-0.049905</td>\n",
       "      <td>-0.061903</td>\n",
       "      <td>-0.064804</td>\n",
       "      <td>-0.038551</td>\n",
       "      <td>-0.057727</td>\n",
       "      <td>-0.041248</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.060516</td>\n",
       "      <td>-0.099323</td>\n",
       "      <td>-0.056270</td>\n",
       "      <td>-0.039911</td>\n",
       "      <td>-0.050445</td>\n",
       "      <td>-0.046694</td>\n",
       "      <td>-0.024420</td>\n",
       "      <td>-0.075804</td>\n",
       "      <td>-0.054774</td>\n",
       "      <td>-0.059648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 15:00:00</th>\n",
       "      <td>-0.052903</td>\n",
       "      <td>-0.079441</td>\n",
       "      <td>-0.039942</td>\n",
       "      <td>-0.064082</td>\n",
       "      <td>-0.051510</td>\n",
       "      <td>-0.059652</td>\n",
       "      <td>-0.063959</td>\n",
       "      <td>-0.040073</td>\n",
       "      <td>-0.057301</td>\n",
       "      <td>-0.041075</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.059477</td>\n",
       "      <td>-0.097281</td>\n",
       "      <td>-0.055726</td>\n",
       "      <td>-0.040512</td>\n",
       "      <td>-0.050144</td>\n",
       "      <td>-0.046771</td>\n",
       "      <td>-0.025738</td>\n",
       "      <td>-0.075167</td>\n",
       "      <td>-0.055358</td>\n",
       "      <td>-0.058613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 16:00:00</th>\n",
       "      <td>-0.054040</td>\n",
       "      <td>-0.077692</td>\n",
       "      <td>-0.040387</td>\n",
       "      <td>-0.064025</td>\n",
       "      <td>-0.053098</td>\n",
       "      <td>-0.057457</td>\n",
       "      <td>-0.063205</td>\n",
       "      <td>-0.041569</td>\n",
       "      <td>-0.056764</td>\n",
       "      <td>-0.040948</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058473</td>\n",
       "      <td>-0.095217</td>\n",
       "      <td>-0.055114</td>\n",
       "      <td>-0.041044</td>\n",
       "      <td>-0.049754</td>\n",
       "      <td>-0.046983</td>\n",
       "      <td>-0.026956</td>\n",
       "      <td>-0.074710</td>\n",
       "      <td>-0.055916</td>\n",
       "      <td>-0.057684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 17:00:00</th>\n",
       "      <td>-0.055060</td>\n",
       "      <td>-0.075882</td>\n",
       "      <td>-0.040858</td>\n",
       "      <td>-0.064013</td>\n",
       "      <td>-0.054666</td>\n",
       "      <td>-0.055314</td>\n",
       "      <td>-0.062544</td>\n",
       "      <td>-0.043029</td>\n",
       "      <td>-0.056126</td>\n",
       "      <td>-0.040852</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.057501</td>\n",
       "      <td>-0.093150</td>\n",
       "      <td>-0.054456</td>\n",
       "      <td>-0.041506</td>\n",
       "      <td>-0.049298</td>\n",
       "      <td>-0.047358</td>\n",
       "      <td>-0.028096</td>\n",
       "      <td>-0.074442</td>\n",
       "      <td>-0.056424</td>\n",
       "      <td>-0.056872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 18:00:00</th>\n",
       "      <td>-0.055962</td>\n",
       "      <td>-0.074001</td>\n",
       "      <td>-0.041355</td>\n",
       "      <td>-0.064028</td>\n",
       "      <td>-0.056211</td>\n",
       "      <td>-0.053222</td>\n",
       "      <td>-0.061964</td>\n",
       "      <td>-0.044451</td>\n",
       "      <td>-0.055381</td>\n",
       "      <td>-0.040796</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056563</td>\n",
       "      <td>-0.091066</td>\n",
       "      <td>-0.053775</td>\n",
       "      <td>-0.041900</td>\n",
       "      <td>-0.048799</td>\n",
       "      <td>-0.047894</td>\n",
       "      <td>-0.029180</td>\n",
       "      <td>-0.074375</td>\n",
       "      <td>-0.056872</td>\n",
       "      <td>-0.056170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 19:00:00</th>\n",
       "      <td>-0.056736</td>\n",
       "      <td>-0.072038</td>\n",
       "      <td>-0.041874</td>\n",
       "      <td>-0.064057</td>\n",
       "      <td>-0.057731</td>\n",
       "      <td>-0.051181</td>\n",
       "      <td>-0.061463</td>\n",
       "      <td>-0.045849</td>\n",
       "      <td>-0.054524</td>\n",
       "      <td>-0.040792</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.055671</td>\n",
       "      <td>-0.088959</td>\n",
       "      <td>-0.053080</td>\n",
       "      <td>-0.042228</td>\n",
       "      <td>-0.048273</td>\n",
       "      <td>-0.048597</td>\n",
       "      <td>-0.030214</td>\n",
       "      <td>-0.074505</td>\n",
       "      <td>-0.057263</td>\n",
       "      <td>-0.055568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 20:00:00</th>\n",
       "      <td>-0.057372</td>\n",
       "      <td>-0.069985</td>\n",
       "      <td>-0.042418</td>\n",
       "      <td>-0.064093</td>\n",
       "      <td>-0.059226</td>\n",
       "      <td>-0.049191</td>\n",
       "      <td>-0.061043</td>\n",
       "      <td>-0.047232</td>\n",
       "      <td>-0.053550</td>\n",
       "      <td>-0.040849</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054831</td>\n",
       "      <td>-0.086825</td>\n",
       "      <td>-0.052381</td>\n",
       "      <td>-0.042497</td>\n",
       "      <td>-0.047734</td>\n",
       "      <td>-0.049473</td>\n",
       "      <td>-0.031202</td>\n",
       "      <td>-0.074827</td>\n",
       "      <td>-0.057595</td>\n",
       "      <td>-0.055065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 21:00:00</th>\n",
       "      <td>-0.057864</td>\n",
       "      <td>-0.067839</td>\n",
       "      <td>-0.042988</td>\n",
       "      <td>-0.064132</td>\n",
       "      <td>-0.060697</td>\n",
       "      <td>-0.047254</td>\n",
       "      <td>-0.060697</td>\n",
       "      <td>-0.048604</td>\n",
       "      <td>-0.052464</td>\n",
       "      <td>-0.040976</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054047</td>\n",
       "      <td>-0.084656</td>\n",
       "      <td>-0.051682</td>\n",
       "      <td>-0.042721</td>\n",
       "      <td>-0.047194</td>\n",
       "      <td>-0.050522</td>\n",
       "      <td>-0.032152</td>\n",
       "      <td>-0.075335</td>\n",
       "      <td>-0.057862</td>\n",
       "      <td>-0.054662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 22:00:00</th>\n",
       "      <td>-0.058214</td>\n",
       "      <td>-0.065600</td>\n",
       "      <td>-0.043587</td>\n",
       "      <td>-0.064171</td>\n",
       "      <td>-0.062149</td>\n",
       "      <td>-0.045369</td>\n",
       "      <td>-0.060420</td>\n",
       "      <td>-0.049960</td>\n",
       "      <td>-0.051266</td>\n",
       "      <td>-0.041179</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053323</td>\n",
       "      <td>-0.082451</td>\n",
       "      <td>-0.050985</td>\n",
       "      <td>-0.042910</td>\n",
       "      <td>-0.046660</td>\n",
       "      <td>-0.051732</td>\n",
       "      <td>-0.033073</td>\n",
       "      <td>-0.076017</td>\n",
       "      <td>-0.058058</td>\n",
       "      <td>-0.054355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0 days 23:00:00</th>\n",
       "      <td>-0.058214</td>\n",
       "      <td>-0.065600</td>\n",
       "      <td>-0.043587</td>\n",
       "      <td>-0.064171</td>\n",
       "      <td>-0.062149</td>\n",
       "      <td>-0.045369</td>\n",
       "      <td>-0.060420</td>\n",
       "      <td>-0.049960</td>\n",
       "      <td>-0.051266</td>\n",
       "      <td>-0.041179</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053323</td>\n",
       "      <td>-0.082451</td>\n",
       "      <td>-0.050985</td>\n",
       "      <td>-0.042910</td>\n",
       "      <td>-0.046660</td>\n",
       "      <td>-0.051732</td>\n",
       "      <td>-0.033073</td>\n",
       "      <td>-0.076017</td>\n",
       "      <td>-0.058058</td>\n",
       "      <td>-0.054355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 00:00:00</th>\n",
       "      <td>-0.058214</td>\n",
       "      <td>-0.065600</td>\n",
       "      <td>-0.043587</td>\n",
       "      <td>-0.064171</td>\n",
       "      <td>-0.062149</td>\n",
       "      <td>-0.045369</td>\n",
       "      <td>-0.060420</td>\n",
       "      <td>-0.049960</td>\n",
       "      <td>-0.051266</td>\n",
       "      <td>-0.041179</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053323</td>\n",
       "      <td>-0.082451</td>\n",
       "      <td>-0.050985</td>\n",
       "      <td>-0.042910</td>\n",
       "      <td>-0.046660</td>\n",
       "      <td>-0.051732</td>\n",
       "      <td>-0.033073</td>\n",
       "      <td>-0.076017</td>\n",
       "      <td>-0.058058</td>\n",
       "      <td>-0.054355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 01:00:00</th>\n",
       "      <td>-0.058214</td>\n",
       "      <td>-0.065600</td>\n",
       "      <td>-0.043587</td>\n",
       "      <td>-0.064171</td>\n",
       "      <td>-0.062149</td>\n",
       "      <td>-0.045369</td>\n",
       "      <td>-0.060420</td>\n",
       "      <td>-0.049960</td>\n",
       "      <td>-0.051266</td>\n",
       "      <td>-0.041179</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053323</td>\n",
       "      <td>-0.082451</td>\n",
       "      <td>-0.050985</td>\n",
       "      <td>-0.042910</td>\n",
       "      <td>-0.046660</td>\n",
       "      <td>-0.051732</td>\n",
       "      <td>-0.033073</td>\n",
       "      <td>-0.076017</td>\n",
       "      <td>-0.058058</td>\n",
       "      <td>-0.054355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 02:00:00</th>\n",
       "      <td>-0.059238</td>\n",
       "      <td>-0.063641</td>\n",
       "      <td>-0.043877</td>\n",
       "      <td>-0.064149</td>\n",
       "      <td>-0.063961</td>\n",
       "      <td>-0.042926</td>\n",
       "      <td>-0.059918</td>\n",
       "      <td>-0.051728</td>\n",
       "      <td>-0.050532</td>\n",
       "      <td>-0.041209</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052422</td>\n",
       "      <td>-0.080499</td>\n",
       "      <td>-0.050471</td>\n",
       "      <td>-0.043530</td>\n",
       "      <td>-0.046354</td>\n",
       "      <td>-0.052201</td>\n",
       "      <td>-0.034250</td>\n",
       "      <td>-0.075972</td>\n",
       "      <td>-0.058755</td>\n",
       "      <td>-0.053439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 03:00:00</th>\n",
       "      <td>-0.060381</td>\n",
       "      <td>-0.061420</td>\n",
       "      <td>-0.044284</td>\n",
       "      <td>-0.064125</td>\n",
       "      <td>-0.065942</td>\n",
       "      <td>-0.040260</td>\n",
       "      <td>-0.059285</td>\n",
       "      <td>-0.053654</td>\n",
       "      <td>-0.049704</td>\n",
       "      <td>-0.041202</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051405</td>\n",
       "      <td>-0.078224</td>\n",
       "      <td>-0.049846</td>\n",
       "      <td>-0.044152</td>\n",
       "      <td>-0.045974</td>\n",
       "      <td>-0.052724</td>\n",
       "      <td>-0.035615</td>\n",
       "      <td>-0.075850</td>\n",
       "      <td>-0.059452</td>\n",
       "      <td>-0.052443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 04:00:00</th>\n",
       "      <td>-0.061651</td>\n",
       "      <td>-0.058920</td>\n",
       "      <td>-0.044829</td>\n",
       "      <td>-0.064128</td>\n",
       "      <td>-0.068083</td>\n",
       "      <td>-0.037359</td>\n",
       "      <td>-0.058526</td>\n",
       "      <td>-0.055714</td>\n",
       "      <td>-0.048786</td>\n",
       "      <td>-0.041158</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050257</td>\n",
       "      <td>-0.075614</td>\n",
       "      <td>-0.049091</td>\n",
       "      <td>-0.044764</td>\n",
       "      <td>-0.045509</td>\n",
       "      <td>-0.053325</td>\n",
       "      <td>-0.037157</td>\n",
       "      <td>-0.075688</td>\n",
       "      <td>-0.060139</td>\n",
       "      <td>-0.051377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 05:00:00</th>\n",
       "      <td>-0.063015</td>\n",
       "      <td>-0.056153</td>\n",
       "      <td>-0.045515</td>\n",
       "      <td>-0.064147</td>\n",
       "      <td>-0.070365</td>\n",
       "      <td>-0.034247</td>\n",
       "      <td>-0.057665</td>\n",
       "      <td>-0.057890</td>\n",
       "      <td>-0.047769</td>\n",
       "      <td>-0.041100</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048965</td>\n",
       "      <td>-0.072664</td>\n",
       "      <td>-0.048207</td>\n",
       "      <td>-0.045380</td>\n",
       "      <td>-0.044940</td>\n",
       "      <td>-0.054039</td>\n",
       "      <td>-0.038838</td>\n",
       "      <td>-0.075525</td>\n",
       "      <td>-0.060823</td>\n",
       "      <td>-0.050261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 06:00:00</th>\n",
       "      <td>-0.064424</td>\n",
       "      <td>-0.053142</td>\n",
       "      <td>-0.046320</td>\n",
       "      <td>-0.064145</td>\n",
       "      <td>-0.072779</td>\n",
       "      <td>-0.030957</td>\n",
       "      <td>-0.056725</td>\n",
       "      <td>-0.060161</td>\n",
       "      <td>-0.046615</td>\n",
       "      <td>-0.041042</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047535</td>\n",
       "      <td>-0.069377</td>\n",
       "      <td>-0.047197</td>\n",
       "      <td>-0.046007</td>\n",
       "      <td>-0.044251</td>\n",
       "      <td>-0.054887</td>\n",
       "      <td>-0.040624</td>\n",
       "      <td>-0.075387</td>\n",
       "      <td>-0.061496</td>\n",
       "      <td>-0.049121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 07:00:00</th>\n",
       "      <td>-0.065857</td>\n",
       "      <td>-0.049896</td>\n",
       "      <td>-0.047248</td>\n",
       "      <td>-0.064129</td>\n",
       "      <td>-0.075311</td>\n",
       "      <td>-0.027516</td>\n",
       "      <td>-0.055724</td>\n",
       "      <td>-0.062498</td>\n",
       "      <td>-0.045310</td>\n",
       "      <td>-0.040977</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.045966</td>\n",
       "      <td>-0.065771</td>\n",
       "      <td>-0.046051</td>\n",
       "      <td>-0.046624</td>\n",
       "      <td>-0.043422</td>\n",
       "      <td>-0.055887</td>\n",
       "      <td>-0.042484</td>\n",
       "      <td>-0.075321</td>\n",
       "      <td>-0.062150</td>\n",
       "      <td>-0.047990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 08:00:00</th>\n",
       "      <td>-0.067281</td>\n",
       "      <td>-0.046432</td>\n",
       "      <td>-0.048296</td>\n",
       "      <td>-0.064112</td>\n",
       "      <td>-0.077945</td>\n",
       "      <td>-0.023954</td>\n",
       "      <td>-0.054687</td>\n",
       "      <td>-0.064863</td>\n",
       "      <td>-0.043837</td>\n",
       "      <td>-0.040893</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044266</td>\n",
       "      <td>-0.061872</td>\n",
       "      <td>-0.044752</td>\n",
       "      <td>-0.047203</td>\n",
       "      <td>-0.042427</td>\n",
       "      <td>-0.057061</td>\n",
       "      <td>-0.044383</td>\n",
       "      <td>-0.075381</td>\n",
       "      <td>-0.062773</td>\n",
       "      <td>-0.046899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 09:00:00</th>\n",
       "      <td>-0.068657</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>-0.049454</td>\n",
       "      <td>-0.064112</td>\n",
       "      <td>-0.080658</td>\n",
       "      <td>-0.020312</td>\n",
       "      <td>-0.053649</td>\n",
       "      <td>-0.067220</td>\n",
       "      <td>-0.042175</td>\n",
       "      <td>-0.040779</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042457</td>\n",
       "      <td>-0.057719</td>\n",
       "      <td>-0.043285</td>\n",
       "      <td>-0.047708</td>\n",
       "      <td>-0.041243</td>\n",
       "      <td>-0.058436</td>\n",
       "      <td>-0.046274</td>\n",
       "      <td>-0.075637</td>\n",
       "      <td>-0.063356</td>\n",
       "      <td>-0.045888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 10:00:00</th>\n",
       "      <td>-0.069950</td>\n",
       "      <td>-0.038927</td>\n",
       "      <td>-0.050705</td>\n",
       "      <td>-0.064157</td>\n",
       "      <td>-0.083427</td>\n",
       "      <td>-0.016632</td>\n",
       "      <td>-0.052662</td>\n",
       "      <td>-0.069534</td>\n",
       "      <td>-0.040288</td>\n",
       "      <td>-0.040625</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040565</td>\n",
       "      <td>-0.053359</td>\n",
       "      <td>-0.041644</td>\n",
       "      <td>-0.048096</td>\n",
       "      <td>-0.039841</td>\n",
       "      <td>-0.060046</td>\n",
       "      <td>-0.048105</td>\n",
       "      <td>-0.076174</td>\n",
       "      <td>-0.063883</td>\n",
       "      <td>-0.045006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 11:00:00</th>\n",
       "      <td>-0.071103</td>\n",
       "      <td>-0.034912</td>\n",
       "      <td>-0.052026</td>\n",
       "      <td>-0.064282</td>\n",
       "      <td>-0.086237</td>\n",
       "      <td>-0.012957</td>\n",
       "      <td>-0.051776</td>\n",
       "      <td>-0.071769</td>\n",
       "      <td>-0.038138</td>\n",
       "      <td>-0.040437</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038613</td>\n",
       "      <td>-0.048848</td>\n",
       "      <td>-0.039819</td>\n",
       "      <td>-0.048325</td>\n",
       "      <td>-0.038189</td>\n",
       "      <td>-0.061932</td>\n",
       "      <td>-0.049825</td>\n",
       "      <td>-0.077089</td>\n",
       "      <td>-0.064329</td>\n",
       "      <td>-0.044309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 12:00:00</th>\n",
       "      <td>-0.072032</td>\n",
       "      <td>-0.030719</td>\n",
       "      <td>-0.053394</td>\n",
       "      <td>-0.064510</td>\n",
       "      <td>-0.089079</td>\n",
       "      <td>-0.009350</td>\n",
       "      <td>-0.051049</td>\n",
       "      <td>-0.073905</td>\n",
       "      <td>-0.035682</td>\n",
       "      <td>-0.040231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036628</td>\n",
       "      <td>-0.044237</td>\n",
       "      <td>-0.037797</td>\n",
       "      <td>-0.048351</td>\n",
       "      <td>-0.036260</td>\n",
       "      <td>-0.064149</td>\n",
       "      <td>-0.051367</td>\n",
       "      <td>-0.078494</td>\n",
       "      <td>-0.064669</td>\n",
       "      <td>-0.043853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 13:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 14:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 15:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 16:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 17:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 18:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 19:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 20:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 21:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 22:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 days 23:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 1000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Angles    Angles    Angles    Angles    Angles    Angles  \\\n",
       "Timestamps                                                                    \n",
       "0 days 00:00:00 -0.039139 -0.099212 -0.043112 -0.063100 -0.030480 -0.089157   \n",
       "0 days 01:00:00 -0.038570 -0.100006 -0.041383 -0.064094 -0.030296 -0.089666   \n",
       "0 days 02:00:00 -0.038374 -0.100124 -0.039963 -0.064785 -0.030622 -0.089370   \n",
       "0 days 03:00:00 -0.038514 -0.099690 -0.038844 -0.065228 -0.031355 -0.088412   \n",
       "0 days 04:00:00 -0.038963 -0.098799 -0.038025 -0.065466 -0.032425 -0.086913   \n",
       "0 days 05:00:00 -0.039702 -0.097530 -0.037481 -0.065541 -0.033795 -0.084964   \n",
       "0 days 06:00:00 -0.040691 -0.095969 -0.037181 -0.065504 -0.035403 -0.082660   \n",
       "0 days 07:00:00 -0.041868 -0.094213 -0.037087 -0.065392 -0.037168 -0.080122   \n",
       "0 days 08:00:00 -0.043179 -0.092337 -0.037162 -0.065233 -0.039026 -0.077449   \n",
       "0 days 09:00:00 -0.044576 -0.090402 -0.037373 -0.065050 -0.040924 -0.074717   \n",
       "0 days 10:00:00 -0.046018 -0.088454 -0.037688 -0.064860 -0.042823 -0.071990   \n",
       "0 days 11:00:00 -0.047471 -0.086530 -0.038078 -0.064673 -0.044691 -0.069314   \n",
       "0 days 12:00:00 -0.048906 -0.084657 -0.038519 -0.064498 -0.046505 -0.066727   \n",
       "0 days 13:00:00 -0.050304 -0.082856 -0.038991 -0.064337 -0.048246 -0.064251   \n",
       "0 days 14:00:00 -0.051649 -0.081138 -0.039479 -0.064193 -0.049905 -0.061903   \n",
       "0 days 15:00:00 -0.052903 -0.079441 -0.039942 -0.064082 -0.051510 -0.059652   \n",
       "0 days 16:00:00 -0.054040 -0.077692 -0.040387 -0.064025 -0.053098 -0.057457   \n",
       "0 days 17:00:00 -0.055060 -0.075882 -0.040858 -0.064013 -0.054666 -0.055314   \n",
       "0 days 18:00:00 -0.055962 -0.074001 -0.041355 -0.064028 -0.056211 -0.053222   \n",
       "0 days 19:00:00 -0.056736 -0.072038 -0.041874 -0.064057 -0.057731 -0.051181   \n",
       "0 days 20:00:00 -0.057372 -0.069985 -0.042418 -0.064093 -0.059226 -0.049191   \n",
       "0 days 21:00:00 -0.057864 -0.067839 -0.042988 -0.064132 -0.060697 -0.047254   \n",
       "0 days 22:00:00 -0.058214 -0.065600 -0.043587 -0.064171 -0.062149 -0.045369   \n",
       "0 days 23:00:00 -0.058214 -0.065600 -0.043587 -0.064171 -0.062149 -0.045369   \n",
       "1 days 00:00:00 -0.058214 -0.065600 -0.043587 -0.064171 -0.062149 -0.045369   \n",
       "1 days 01:00:00 -0.058214 -0.065600 -0.043587 -0.064171 -0.062149 -0.045369   \n",
       "1 days 02:00:00 -0.059238 -0.063641 -0.043877 -0.064149 -0.063961 -0.042926   \n",
       "1 days 03:00:00 -0.060381 -0.061420 -0.044284 -0.064125 -0.065942 -0.040260   \n",
       "1 days 04:00:00 -0.061651 -0.058920 -0.044829 -0.064128 -0.068083 -0.037359   \n",
       "1 days 05:00:00 -0.063015 -0.056153 -0.045515 -0.064147 -0.070365 -0.034247   \n",
       "1 days 06:00:00 -0.064424 -0.053142 -0.046320 -0.064145 -0.072779 -0.030957   \n",
       "1 days 07:00:00 -0.065857 -0.049896 -0.047248 -0.064129 -0.075311 -0.027516   \n",
       "1 days 08:00:00 -0.067281 -0.046432 -0.048296 -0.064112 -0.077945 -0.023954   \n",
       "1 days 09:00:00 -0.068657 -0.042770 -0.049454 -0.064112 -0.080658 -0.020312   \n",
       "1 days 10:00:00 -0.069950 -0.038927 -0.050705 -0.064157 -0.083427 -0.016632   \n",
       "1 days 11:00:00 -0.071103 -0.034912 -0.052026 -0.064282 -0.086237 -0.012957   \n",
       "1 days 12:00:00 -0.072032 -0.030719 -0.053394 -0.064510 -0.089079 -0.009350   \n",
       "1 days 13:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 14:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 15:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 16:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 17:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 18:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 19:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 20:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 21:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 22:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 23:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "                   Angles    Angles    Angles    Angles  ...    Angles  \\\n",
       "Timestamps                                               ...             \n",
       "0 days 00:00:00 -0.068058 -0.014492 -0.058807 -0.038852  ... -0.064643   \n",
       "0 days 01:00:00 -0.069693 -0.014442 -0.059213 -0.038575  ... -0.065789   \n",
       "0 days 02:00:00 -0.070833 -0.015084 -0.059543 -0.038582  ... -0.066595   \n",
       "0 days 03:00:00 -0.071530 -0.016291 -0.059789 -0.038822  ... -0.067111   \n",
       "0 days 04:00:00 -0.071833 -0.017953 -0.059938 -0.039235  ... -0.067384   \n",
       "0 days 05:00:00 -0.071793 -0.019945 -0.059987 -0.039725  ... -0.067404   \n",
       "0 days 06:00:00 -0.071468 -0.022146 -0.059943 -0.040208  ... -0.067172   \n",
       "0 days 07:00:00 -0.070923 -0.024449 -0.059820 -0.040649  ... -0.066735   \n",
       "0 days 08:00:00 -0.070215 -0.026770 -0.059631 -0.041019  ... -0.066130   \n",
       "0 days 09:00:00 -0.069394 -0.029045 -0.059386 -0.041297  ... -0.065386   \n",
       "0 days 10:00:00 -0.068501 -0.031224 -0.059099 -0.041473  ... -0.064532   \n",
       "0 days 11:00:00 -0.067572 -0.033278 -0.058779 -0.041548  ... -0.063594   \n",
       "0 days 12:00:00 -0.066633 -0.035188 -0.058439 -0.041529  ... -0.062598   \n",
       "0 days 13:00:00 -0.065706 -0.036946 -0.058085 -0.041424  ... -0.061566   \n",
       "0 days 14:00:00 -0.064804 -0.038551 -0.057727 -0.041248  ... -0.060516   \n",
       "0 days 15:00:00 -0.063959 -0.040073 -0.057301 -0.041075  ... -0.059477   \n",
       "0 days 16:00:00 -0.063205 -0.041569 -0.056764 -0.040948  ... -0.058473   \n",
       "0 days 17:00:00 -0.062544 -0.043029 -0.056126 -0.040852  ... -0.057501   \n",
       "0 days 18:00:00 -0.061964 -0.044451 -0.055381 -0.040796  ... -0.056563   \n",
       "0 days 19:00:00 -0.061463 -0.045849 -0.054524 -0.040792  ... -0.055671   \n",
       "0 days 20:00:00 -0.061043 -0.047232 -0.053550 -0.040849  ... -0.054831   \n",
       "0 days 21:00:00 -0.060697 -0.048604 -0.052464 -0.040976  ... -0.054047   \n",
       "0 days 22:00:00 -0.060420 -0.049960 -0.051266 -0.041179  ... -0.053323   \n",
       "0 days 23:00:00 -0.060420 -0.049960 -0.051266 -0.041179  ... -0.053323   \n",
       "1 days 00:00:00 -0.060420 -0.049960 -0.051266 -0.041179  ... -0.053323   \n",
       "1 days 01:00:00 -0.060420 -0.049960 -0.051266 -0.041179  ... -0.053323   \n",
       "1 days 02:00:00 -0.059918 -0.051728 -0.050532 -0.041209  ... -0.052422   \n",
       "1 days 03:00:00 -0.059285 -0.053654 -0.049704 -0.041202  ... -0.051405   \n",
       "1 days 04:00:00 -0.058526 -0.055714 -0.048786 -0.041158  ... -0.050257   \n",
       "1 days 05:00:00 -0.057665 -0.057890 -0.047769 -0.041100  ... -0.048965   \n",
       "1 days 06:00:00 -0.056725 -0.060161 -0.046615 -0.041042  ... -0.047535   \n",
       "1 days 07:00:00 -0.055724 -0.062498 -0.045310 -0.040977  ... -0.045966   \n",
       "1 days 08:00:00 -0.054687 -0.064863 -0.043837 -0.040893  ... -0.044266   \n",
       "1 days 09:00:00 -0.053649 -0.067220 -0.042175 -0.040779  ... -0.042457   \n",
       "1 days 10:00:00 -0.052662 -0.069534 -0.040288 -0.040625  ... -0.040565   \n",
       "1 days 11:00:00 -0.051776 -0.071769 -0.038138 -0.040437  ... -0.038613   \n",
       "1 days 12:00:00 -0.051049 -0.073905 -0.035682 -0.040231  ... -0.036628   \n",
       "1 days 13:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 14:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 15:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 16:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 17:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 18:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 19:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 20:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 21:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 22:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "1 days 23:00:00       NaN       NaN       NaN       NaN  ...       NaN   \n",
       "\n",
       "                   Angles    Angles    Angles    Angles    Angles    Angles  \\\n",
       "Timestamps                                                                    \n",
       "0 days 00:00:00 -0.110046 -0.055560 -0.030614 -0.044647 -0.048609 -0.008461   \n",
       "0 days 01:00:00 -0.113240 -0.055908 -0.030086 -0.045008 -0.048077 -0.007791   \n",
       "0 days 02:00:00 -0.115291 -0.056322 -0.029992 -0.045595 -0.047633 -0.007757   \n",
       "0 days 03:00:00 -0.116371 -0.056773 -0.030275 -0.046353 -0.047276 -0.008235   \n",
       "0 days 04:00:00 -0.116618 -0.057218 -0.030866 -0.047211 -0.047017 -0.009108   \n",
       "0 days 05:00:00 -0.116155 -0.057601 -0.031677 -0.048067 -0.046839 -0.010293   \n",
       "0 days 06:00:00 -0.115118 -0.057882 -0.032630 -0.048845 -0.046714 -0.011717   \n",
       "0 days 07:00:00 -0.113654 -0.058050 -0.033660 -0.049515 -0.046638 -0.013290   \n",
       "0 days 08:00:00 -0.111882 -0.058098 -0.034712 -0.050052 -0.046599 -0.014945   \n",
       "0 days 09:00:00 -0.109905 -0.058026 -0.035745 -0.050444 -0.046587 -0.016628   \n",
       "0 days 10:00:00 -0.107806 -0.057842 -0.036728 -0.050691 -0.046595 -0.018300   \n",
       "0 days 11:00:00 -0.105652 -0.057558 -0.037644 -0.050799 -0.046615 -0.019933   \n",
       "0 days 12:00:00 -0.103495 -0.057191 -0.038481 -0.050783 -0.046640 -0.021505   \n",
       "0 days 13:00:00 -0.101377 -0.056756 -0.039237 -0.050659 -0.046668 -0.023003   \n",
       "0 days 14:00:00 -0.099323 -0.056270 -0.039911 -0.050445 -0.046694 -0.024420   \n",
       "0 days 15:00:00 -0.097281 -0.055726 -0.040512 -0.050144 -0.046771 -0.025738   \n",
       "0 days 16:00:00 -0.095217 -0.055114 -0.041044 -0.049754 -0.046983 -0.026956   \n",
       "0 days 17:00:00 -0.093150 -0.054456 -0.041506 -0.049298 -0.047358 -0.028096   \n",
       "0 days 18:00:00 -0.091066 -0.053775 -0.041900 -0.048799 -0.047894 -0.029180   \n",
       "0 days 19:00:00 -0.088959 -0.053080 -0.042228 -0.048273 -0.048597 -0.030214   \n",
       "0 days 20:00:00 -0.086825 -0.052381 -0.042497 -0.047734 -0.049473 -0.031202   \n",
       "0 days 21:00:00 -0.084656 -0.051682 -0.042721 -0.047194 -0.050522 -0.032152   \n",
       "0 days 22:00:00 -0.082451 -0.050985 -0.042910 -0.046660 -0.051732 -0.033073   \n",
       "0 days 23:00:00 -0.082451 -0.050985 -0.042910 -0.046660 -0.051732 -0.033073   \n",
       "1 days 00:00:00 -0.082451 -0.050985 -0.042910 -0.046660 -0.051732 -0.033073   \n",
       "1 days 01:00:00 -0.082451 -0.050985 -0.042910 -0.046660 -0.051732 -0.033073   \n",
       "1 days 02:00:00 -0.080499 -0.050471 -0.043530 -0.046354 -0.052201 -0.034250   \n",
       "1 days 03:00:00 -0.078224 -0.049846 -0.044152 -0.045974 -0.052724 -0.035615   \n",
       "1 days 04:00:00 -0.075614 -0.049091 -0.044764 -0.045509 -0.053325 -0.037157   \n",
       "1 days 05:00:00 -0.072664 -0.048207 -0.045380 -0.044940 -0.054039 -0.038838   \n",
       "1 days 06:00:00 -0.069377 -0.047197 -0.046007 -0.044251 -0.054887 -0.040624   \n",
       "1 days 07:00:00 -0.065771 -0.046051 -0.046624 -0.043422 -0.055887 -0.042484   \n",
       "1 days 08:00:00 -0.061872 -0.044752 -0.047203 -0.042427 -0.057061 -0.044383   \n",
       "1 days 09:00:00 -0.057719 -0.043285 -0.047708 -0.041243 -0.058436 -0.046274   \n",
       "1 days 10:00:00 -0.053359 -0.041644 -0.048096 -0.039841 -0.060046 -0.048105   \n",
       "1 days 11:00:00 -0.048848 -0.039819 -0.048325 -0.038189 -0.061932 -0.049825   \n",
       "1 days 12:00:00 -0.044237 -0.037797 -0.048351 -0.036260 -0.064149 -0.051367   \n",
       "1 days 13:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 14:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 15:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 16:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 17:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 18:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 19:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 20:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 21:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 22:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1 days 23:00:00       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "                   Angles    Angles    Angles  \n",
       "Timestamps                                     \n",
       "0 days 00:00:00 -0.082054 -0.043836 -0.074336  \n",
       "0 days 01:00:00 -0.083642 -0.044417 -0.074455  \n",
       "0 days 02:00:00 -0.084526 -0.045105 -0.074153  \n",
       "0 days 03:00:00 -0.084816 -0.045878 -0.073506  \n",
       "0 days 04:00:00 -0.084620 -0.046722 -0.072591  \n",
       "0 days 05:00:00 -0.084073 -0.047614 -0.071468  \n",
       "0 days 06:00:00 -0.083290 -0.048527 -0.070187  \n",
       "0 days 07:00:00 -0.082358 -0.049438 -0.068813  \n",
       "0 days 08:00:00 -0.081348 -0.050328 -0.067397  \n",
       "0 days 09:00:00 -0.080314 -0.051186 -0.065979  \n",
       "0 days 10:00:00 -0.079297 -0.052002 -0.064589  \n",
       "0 days 11:00:00 -0.078324 -0.052771 -0.063249  \n",
       "0 days 12:00:00 -0.077412 -0.053489 -0.061973  \n",
       "0 days 13:00:00 -0.076571 -0.054157 -0.060772  \n",
       "0 days 14:00:00 -0.075804 -0.054774 -0.059648  \n",
       "0 days 15:00:00 -0.075167 -0.055358 -0.058613  \n",
       "0 days 16:00:00 -0.074710 -0.055916 -0.057684  \n",
       "0 days 17:00:00 -0.074442 -0.056424 -0.056872  \n",
       "0 days 18:00:00 -0.074375 -0.056872 -0.056170  \n",
       "0 days 19:00:00 -0.074505 -0.057263 -0.055568  \n",
       "0 days 20:00:00 -0.074827 -0.057595 -0.055065  \n",
       "0 days 21:00:00 -0.075335 -0.057862 -0.054662  \n",
       "0 days 22:00:00 -0.076017 -0.058058 -0.054355  \n",
       "0 days 23:00:00 -0.076017 -0.058058 -0.054355  \n",
       "1 days 00:00:00 -0.076017 -0.058058 -0.054355  \n",
       "1 days 01:00:00 -0.076017 -0.058058 -0.054355  \n",
       "1 days 02:00:00 -0.075972 -0.058755 -0.053439  \n",
       "1 days 03:00:00 -0.075850 -0.059452 -0.052443  \n",
       "1 days 04:00:00 -0.075688 -0.060139 -0.051377  \n",
       "1 days 05:00:00 -0.075525 -0.060823 -0.050261  \n",
       "1 days 06:00:00 -0.075387 -0.061496 -0.049121  \n",
       "1 days 07:00:00 -0.075321 -0.062150 -0.047990  \n",
       "1 days 08:00:00 -0.075381 -0.062773 -0.046899  \n",
       "1 days 09:00:00 -0.075637 -0.063356 -0.045888  \n",
       "1 days 10:00:00 -0.076174 -0.063883 -0.045006  \n",
       "1 days 11:00:00 -0.077089 -0.064329 -0.044309  \n",
       "1 days 12:00:00 -0.078494 -0.064669 -0.043853  \n",
       "1 days 13:00:00       NaN       NaN       NaN  \n",
       "1 days 14:00:00       NaN       NaN       NaN  \n",
       "1 days 15:00:00       NaN       NaN       NaN  \n",
       "1 days 16:00:00       NaN       NaN       NaN  \n",
       "1 days 17:00:00       NaN       NaN       NaN  \n",
       "1 days 18:00:00       NaN       NaN       NaN  \n",
       "1 days 19:00:00       NaN       NaN       NaN  \n",
       "1 days 20:00:00       NaN       NaN       NaN  \n",
       "1 days 21:00:00       NaN       NaN       NaN  \n",
       "1 days 22:00:00       NaN       NaN       NaN  \n",
       "1 days 23:00:00       NaN       NaN       NaN  \n",
       "\n",
       "[48 rows x 1000 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "xdfs=lcm.parallelshuffleandfilter(xdf)\n",
    "xdfs2=lcm.parallelshuffleandfilter(xdf)\n",
    "\n",
    "list=Parallel(n_jobs=-1, verbose=5)(delayed(lcm.parallelshuffleandfilter)(xdf) for i in range(1000))\n",
    "listruns=pd.concat(list, axis=1)\n",
    "listruns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>lb</th>\n",
       "      <th>ub</th>\n",
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       "      <td>0.001602</td>\n",
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       "      <td>-0.441484</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.052687</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.107363</td>\n",
       "      <td>0.003135</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Shuffled</td>\n",
       "      <td>-0.052754</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.108042</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.517760</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.052772</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.107347</td>\n",
       "      <td>0.002949</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007753</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.052783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>43</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Low Pass Filtered</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>92</th>\n",
       "      <td>44</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.011476</td>\n",
       "      <td>Low Pass Filtered</td>\n",
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       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005666</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023940</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.018496</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours        lb        ub  Chunk       Raw           variable     value\n",
       "0       0 -0.105415  0.001602      0 -0.441484           Shuffled -0.052687\n",
       "1       1 -0.107363  0.003135      0 -0.081189           Shuffled -0.052726\n",
       "2       2 -0.108086  0.003563      0 -0.420087           Shuffled -0.052754\n",
       "3       3 -0.108042  0.003297      0 -0.517760           Shuffled -0.052772\n",
       "4       4 -0.107347  0.002949      0  0.007753           Shuffled -0.052783\n",
       "..    ...       ...       ...    ...       ...                ...       ...\n",
       "91     43       NaN       NaN      1 -0.017532  Low Pass Filtered       NaN\n",
       "92     44       NaN       NaN      1  0.011476  Low Pass Filtered       NaN\n",
       "93     45       NaN       NaN      1  0.005666  Low Pass Filtered       NaN\n",
       "94     46       NaN       NaN      1  0.023940  Low Pass Filtered       NaN\n",
       "95     47       NaN       NaN      1  0.018496  Low Pass Filtered       NaN\n",
       "\n",
       "[96 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcm.getpercentilesfromshuffleddata(listruns, xdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done 336 out of 336 | elapsed: 461.1min finished\n"
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "lcm.getallflies(xarrayall, save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Hours</th>\n",
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       "      <th>2</th>\n",
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       "      <td>-0.051035</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448755</td>\n",
       "      <td>Shuffled</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-0.051438</td>\n",
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       "      <td>-0.077841</td>\n",
       "      <td>-0.051519</td>\n",
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       "      <th>87</th>\n",
       "      <td>41</td>\n",
       "      <td>-0.074558</td>\n",
       "      <td>-0.052470</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.158191</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>-0.086355</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>42</td>\n",
       "      <td>-0.074468</td>\n",
       "      <td>-0.052845</td>\n",
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       "      <th>90</th>\n",
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       "      <td>-0.074147</td>\n",
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       "      <th>91</th>\n",
       "      <td>45</td>\n",
       "      <td>-0.073930</td>\n",
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       "      <td>-0.157133</td>\n",
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       "      <td>-0.076967</td>\n",
       "      <td>0</td>\n",
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       "<p>90 rows × 9 columns</p>\n",
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       "    Hours        lb        ub  Chunk       Raw           variable     value  \\\n",
       "0       0 -0.078369 -0.050853      0 -0.208208           Shuffled -0.064532   \n",
       "1       1 -0.077863 -0.050900      0 -0.017965           Shuffled -0.064490   \n",
       "2       2 -0.077889 -0.051035      0  0.448755           Shuffled -0.064448   \n",
       "3       3 -0.077982 -0.051438      0 -0.245786           Shuffled -0.064407   \n",
       "4       4 -0.077841 -0.051519      0 -0.348904           Shuffled -0.064365   \n",
       "..    ...       ...       ...    ...       ...                ...       ...   \n",
       "87     41 -0.074558 -0.052470      0 -0.158191  Low Pass Filtered -0.086355   \n",
       "88     42 -0.074468 -0.052845      0  0.031485  Low Pass Filtered -0.084554   \n",
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       "91     45 -0.073930 -0.053997      0 -0.157133  Low Pass Filtered -0.076967   \n",
       "\n",
       "    Fly  Batch  \n",
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       "3     0      1  \n",
       "4     0      1  \n",
       "..  ...    ...  \n",
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       "88    0      1  \n",
       "89    0      1  \n",
       "90    0      1  \n",
       "91    0      1  \n",
       "\n",
       "[90 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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    "pbsubset=lcm.getonefly(xarrayall, 0,1, numshuffles=1000)\n",
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       "    Hours        lb        ub  Chunk       Raw           variable     value  \\\n",
       "0       0 -0.078369 -0.050853      0 -0.208208           Shuffled -0.064532   \n",
       "1       1 -0.077863 -0.050900      0 -0.017965           Shuffled -0.064490   \n",
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       "91     45 -0.073930 -0.053997      0 -0.157133  Low Pass Filtered -0.076967   \n",
       "\n",
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       "88    0      1  \n",
       "89    0      1  \n",
       "90    0      1  \n",
       "91    0      1  \n",
       "\n",
       "[90 rows x 9 columns]"
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     "execution_count": 23,
     "metadata": {},
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   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
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       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: 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'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (Timestamps: 66239868)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "    Batch       int64 1\n",
       "Data variables:\n",
       "    Angles      (Timestamps) float64 dask.array&lt;chunksize=(66239868,), meta=np.ndarray&gt;</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-bd2aea4b-2647-4ce3-b5f5-454b496460c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd2aea4b-2647-4ce3-b5f5-454b496460c6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Timestamps</span>: 66239868</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ce7c0827-7485-473c-9736-849f19816d5e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce7c0827-7485-473c-9736-849f19816d5e' class='xr-section-summary' >Coordinates: <span>(2)</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 class='xr-has-index'>Timestamps</span></div><div class='xr-var-dims'>(Timestamps)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.549e+09 1.549e+09 ... 1.56e+09</div><input id='attrs-6aa53581-1e38-45b2-9204-3fd718c707d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6aa53581-1e38-45b2-9204-3fd718c707d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecb1cce5-3bca-4f6f-9f5c-57f44e0ae3f1' class='xr-var-data-in' type='checkbox'><label for='data-ecb1cce5-3bca-4f6f-9f5c-57f44e0ae3f1' 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([1.548929e+09, 1.548929e+09, 1.548929e+09, ..., 1.559567e+09,\n",
       "       1.559567e+09, 1.559567e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Batch</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-b9af5065-853b-4454-a8c5-fcdb066147dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9af5065-853b-4454-a8c5-fcdb066147dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b1cff39-f39c-4743-99f7-eb252ca0c49a' class='xr-var-data-in' type='checkbox'><label for='data-3b1cff39-f39c-4743-99f7-eb252ca0c49a' 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(1)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34909391-daf1-4390-883f-1e133635325c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-34909391-daf1-4390-883f-1e133635325c' class='xr-section-summary' >Data variables: <span>(1)</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>Angles</span></div><div class='xr-var-dims'>(Timestamps)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(66239868,), meta=np.ndarray&gt;</div><input id='attrs-d25bd438-ef18-4ec8-a3b6-23760fae688c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d25bd438-ef18-4ec8-a3b6-23760fae688c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ad764e8-dc7f-40bb-ba21-81aa367e6463' class='xr-var-data-in' type='checkbox'><label for='data-9ad764e8-dc7f-40bb-ba21-81aa367e6463' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 505.37 MiB </td>\n",
       "                        <td> 505.37 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (66239868,) </td>\n",
       "                        <td> (66239868,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2463 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4909ddc-3d7f-478c-adf0-631133c2596f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a4909ddc-3d7f-478c-adf0-631133c2596f' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>Timestamps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd6df0b4-a73d-4e4d-bd52-402d034daa7e' class='xr-index-data-in' type='checkbox'/><label for='index-fd6df0b4-a73d-4e4d-bd52-402d034daa7e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 1548929360.028388, 1548929360.1283941,  1548929361.831054,\n",
       "               1548929361.931157, 1548929362.0311651, 1548929362.1312022,\n",
       "              1548929362.2312033, 1548929362.3316262, 1548929362.4316359,\n",
       "              1548929362.5316434,\n",
       "              ...\n",
       "              1559567219.9725165, 1559567220.0725179, 1559567220.3725622,\n",
       "               1559567220.472576, 1559567220.5725913, 1559567220.6726038,\n",
       "              1559567220.7726126, 1559567220.9726315, 1559567221.0726895,\n",
       "               1559567221.172705],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;Timestamps&#x27;, length=66239868))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a784cc22-88d7-4ce6-8e6a-7437880f8ea6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a784cc22-88d7-4ce6-8e6a-7437880f8ea6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (Timestamps: 66239868)\n",
       "Coordinates:\n",
       "  * Timestamps  (Timestamps) float64 1.549e+09 1.549e+09 ... 1.56e+09 1.56e+09\n",
       "    Batch       int64 1\n",
       "Data variables:\n",
       "    Angles      (Timestamps) float64 dask.array<chunksize=(66239868,), meta=np.ndarray>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarrayall.sel(Fly=1, Batch=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "f=0\n",
    "b=1\n",
    "name=str('F'+str(f)+'B'+str(b)+'.csv')\n",
    "pbsubset.to_csv(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "return\n"
     ]
    }
   ],
   "source": [
    "f=1\n",
    "b=1\n",
    "name=str('F'+str(f)+'B'+str(b)+'.csv')\n",
    "save=True\n",
    "len(glob.glob(name))\n",
    "\n",
    "if save & len(glob.glob(name))>0:\n",
    "  print('return')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.062070</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>41</td>\n",
       "      <td>-0.068341</td>\n",
       "      <td>-0.068341</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.158191</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>-0.086355</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>42</td>\n",
       "      <td>-0.067581</td>\n",
       "      <td>-0.067581</td>\n",
       "      <td>0</td>\n",
       "      <td>0.031485</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>-0.084554</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>43</td>\n",
       "      <td>-0.066838</td>\n",
       "      <td>-0.066838</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.089251</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>-0.082512</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>44</td>\n",
       "      <td>-0.066135</td>\n",
       "      <td>-0.066135</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009785</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>-0.080053</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>45</td>\n",
       "      <td>-0.065506</td>\n",
       "      <td>-0.065506</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.157133</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>-0.076967</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hours        lb        ub  Chunk       Raw           variable     value  \\\n",
       "0       0 -0.055394 -0.055394      0 -0.208208           Shuffled -0.055394   \n",
       "1       1 -0.057140 -0.057140      0 -0.017965           Shuffled -0.057140   \n",
       "2       2 -0.058838 -0.058838      0  0.448755           Shuffled -0.058838   \n",
       "3       3 -0.060490 -0.060490      0 -0.245786           Shuffled -0.060490   \n",
       "4       4 -0.062070 -0.062070      0 -0.348904           Shuffled -0.062070   \n",
       "..    ...       ...       ...    ...       ...                ...       ...   \n",
       "87     41 -0.068341 -0.068341      0 -0.158191  Low Pass Filtered -0.086355   \n",
       "88     42 -0.067581 -0.067581      0  0.031485  Low Pass Filtered -0.084554   \n",
       "89     43 -0.066838 -0.066838      0 -0.089251  Low Pass Filtered -0.082512   \n",
       "90     44 -0.066135 -0.066135      0  0.009785  Low Pass Filtered -0.080053   \n",
       "91     45 -0.065506 -0.065506      0 -0.157133  Low Pass Filtered -0.076967   \n",
       "\n",
       "    Fly  Batch  \n",
       "0     0      1  \n",
       "1     0      1  \n",
       "2     0      1  \n",
       "3     0      1  \n",
       "4     0      1  \n",
       "..  ...    ...  \n",
       "87    0      1  \n",
       "88    0      1  \n",
       "89    0      1  \n",
       "90    0      1  \n",
       "91    0      1  \n",
       "\n",
       "[90 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcm.getonefly(xarrayall[\"Angles\"], 0,1, numshuffles=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>52126 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Batch    Angles\n",
       "Timestamps                   \n",
       "1.548931e+09      1 -0.994065\n",
       "1.548931e+09      1 -0.994065\n",
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       "1.548931e+09      1  0.641327\n",
       "...             ...       ...\n",
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       "1.549096e+09      1  0.960654\n",
       "1.549096e+09      1  0.794808\n",
       "\n",
       "[52126 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sel(Fly=0, Batch=1).squeeze().to_dataframe().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "              Batch    Angles\n",
       "Timestamps                   \n",
       "1.548931e+09      1 -0.994065\n",
       "1.548931e+09      1 -0.994065\n",
       "1.548931e+09      1  0.641327\n",
       "1.548931e+09      1 -0.645485\n",
       "1.548931e+09      1  0.641327\n",
       "...             ...       ...\n",
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       "1.549096e+09      1  0.960654\n",
       "1.549096e+09      1  0.794808\n",
       "\n",
       "[52126 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarrayall.sel(Fly=0, Batch=1).squeeze().to_dataframe().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xarrayall[\"Batch\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(1,  0),\n",
       "            (1,  1),\n",
       "            (1,  2),\n",
       "            (1,  3),\n",
       "            (1,  4),\n",
       "            (1,  5),\n",
       "            (1,  6),\n",
       "            (1,  7),\n",
       "            (1,  8),\n",
       "            (1,  9),\n",
       "            ...\n",
       "            (8, 32),\n",
       "            (8, 33),\n",
       "            (8, 34),\n",
       "            (8, 35),\n",
       "            (8, 36),\n",
       "            (8, 37),\n",
       "            (8, 38),\n",
       "            (8, 39),\n",
       "            (8, 40),\n",
       "            (8, 41)],\n",
       "           length=336)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flyindex=pd.MultiIndex.from_product([xarrayall[\"Batch\"].values, xarrayall[\"Fly\"].values])\n",
    "flyindex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan, nan, ..., nan, nan, nan])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hmm=xarrayall.sel(Fly=23, Batch=1)[\"Angles\"].values\n",
    "hmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>lb</th>\n",
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       "Empty DataFrame\n",
       "Columns: [Hours, lb, ub, Chunk, Raw, variable, value, Fly, Batch]\n",
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     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "lcm.getonefly(xarray=xarrayall, f=23, b=1, numshuffles=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m imp\u001b[39m.\u001b[39mreload(lcm)\n\u001b[0;32m----> 2\u001b[0m test\u001b[39m=\u001b[39mlcm\u001b[39m.\u001b[39;49mgetallflies(xarray\u001b[39m=\u001b[39;49mxarrayall, numshuffles\u001b[39m=\u001b[39;49m\u001b[39m1000\u001b[39;49m)\n",
      "File \u001b[0;32m~/Documents/GitHub/Continuous Turns/loadcontinuousmatlabfiles.py:460\u001b[0m, in \u001b[0;36mgetallflies\u001b[0;34m(xarray, numshuffles, maxflies, save)\u001b[0m\n\u001b[1;32m    458\u001b[0m   \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m    459\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 460\u001b[0m   pdall\u001b[39m=\u001b[39mParallel(n_jobs\u001b[39m=\u001b[39;49m\u001b[39m4\u001b[39;49m, verbose\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m)(delayed(getonefly)(xarray\u001b[39m=\u001b[39;49mxarray, f\u001b[39m=\u001b[39;49mf, b\u001b[39m=\u001b[39;49mb, numshuffles\u001b[39m=\u001b[39;49mnumshuffles, save\u001b[39m=\u001b[39;49msave) \u001b[39mfor\u001b[39;49;00m b, f \u001b[39min\u001b[39;49;00m flyindex)\n\u001b[1;32m    461\u001b[0m   pdall\u001b[39m=\u001b[39mpd\u001b[39m.\u001b[39mconcat(pdall)\n\u001b[1;32m    462\u001b[0m   \u001b[39mreturn\u001b[39;00m pdall\u001b[39m.\u001b[39mreset_index()\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/parallel.py:1061\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1058\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_iterating \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m   1060\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend\u001b[39m.\u001b[39mretrieval_context():\n\u001b[0;32m-> 1061\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mretrieve()\n\u001b[1;32m   1062\u001b[0m \u001b[39m# Make sure that we get a last message telling us we are done\u001b[39;00m\n\u001b[1;32m   1063\u001b[0m elapsed_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_start_time\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/parallel.py:938\u001b[0m, in \u001b[0;36mParallel.retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    937\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backend, \u001b[39m'\u001b[39m\u001b[39msupports_timeout\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m--> 938\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39;49mget(timeout\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout))\n\u001b[1;32m    939\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    940\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_output\u001b[39m.\u001b[39mextend(job\u001b[39m.\u001b[39mget())\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/_parallel_backends.py:542\u001b[0m, in \u001b[0;36mLokyBackend.wrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[39m\"\"\"Wrapper for Future.result to implement the same behaviour as\u001b[39;00m\n\u001b[1;32m    540\u001b[0m \u001b[39mAsyncResults.get from multiprocessing.\"\"\"\u001b[39;00m\n\u001b[1;32m    541\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 542\u001b[0m     \u001b[39mreturn\u001b[39;00m future\u001b[39m.\u001b[39;49mresult(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m    543\u001b[0m \u001b[39mexcept\u001b[39;00m CfTimeoutError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    544\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mTimeoutError\u001b[39;00m \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/concurrent/futures/_base.py:453\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m    451\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 453\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    456\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
      "File \u001b[0;32m~/miniforge3/envs/python_data_analysis/lib/python3.10/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    319\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    321\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "test=lcm.getallflies(xarray=xarrayall, numshuffles=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp.reload(lcm)\n",
    "test=lcm.getonefly(xarrayall, f=1, numshuffles=20)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xarrayall[\"Angles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Angles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamps</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:20.028388096</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:20.128394240</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:21.831054080</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:21.931156992</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:22.031165184</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:50.761946624</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:50.861952000</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:50.961966080</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:51.061972480</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:51.161975040</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7564278 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Angles\n",
       "Timestamps                           \n",
       "2019-01-31 10:09:20.028388096     NaN\n",
       "2019-01-31 10:09:20.128394240     NaN\n",
       "2019-01-31 10:09:21.831054080     NaN\n",
       "2019-01-31 10:09:21.931156992     NaN\n",
       "2019-01-31 10:09:22.031165184     NaN\n",
       "...                               ...\n",
       "2019-02-14 09:54:50.761946624     NaN\n",
       "2019-02-14 09:54:50.861952000     NaN\n",
       "2019-02-14 09:54:50.961966080     NaN\n",
       "2019-02-14 09:54:51.061972480     NaN\n",
       "2019-02-14 09:54:51.161975040     NaN\n",
       "\n",
       "[7564278 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf=a.sel(Fly=3).squeeze().to_dataframe().drop(columns=\"Batch\")\n",
    "ddf.index=pd.to_datetime(ddf.index, origin=\"unix\", unit='s')\n",
    "ddfs=np.sin(ddf)\n",
    "ddfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ddf=d.squeeze().to_dataframe().drop(columns=[\"Batch\", \"Fly\"])a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.54892951e+18, 1.54892951e+18, 1.54892951e+18, ...,\n",
       "       1.55013806e+18, 1.55013806e+18, 1.55013806e+18])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=ddfs.dropna().index.to_numpy()\n",
    "y=ddfs.dropna()[\"Angles\"]\n",
    "min(x)\n",
    "yi=~np.isnan(y)\n",
    "yi\n",
    "imax=x.shape[0]\n",
    "yi=np.arange(0,imax)\n",
    "x=np.array(x,dtype=float)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(673918,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time 13.987954408090422 d\n"
     ]
    }
   ],
   "source": [
    "t=x*10**-9\n",
    "t-=t[0]\n",
    "t=t*u.second\n",
    "t[-1].to(u.day)\n",
    "print(\"Total time\", t[-1].to(u.day))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Angles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamps</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:20.028388096</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:20.128394240</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:21.831054080</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:21.931156992</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31 10:09:22.031165184</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:50.761946624</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:50.861952000</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:50.961966080</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:51.061972480</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-14 09:54:51.161975040</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7564278 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Angles\n",
       "Timestamps                           \n",
       "2019-01-31 10:09:20.028388096     NaN\n",
       "2019-01-31 10:09:20.128394240     NaN\n",
       "2019-01-31 10:09:21.831054080     NaN\n",
       "2019-01-31 10:09:21.931156992     NaN\n",
       "2019-01-31 10:09:22.031165184     NaN\n",
       "...                               ...\n",
       "2019-02-14 09:54:50.761946624     NaN\n",
       "2019-02-14 09:54:50.861952000     NaN\n",
       "2019-02-14 09:54:50.961966080     NaN\n",
       "2019-02-14 09:54:51.061972480     NaN\n",
       "2019-02-14 09:54:51.161975040     NaN\n",
       "\n",
       "[7564278 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddfs0=ddfs\n",
    "ddfs0.index=ddfs.index-ddfs.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ddfs.index=ddfs.index-ddfs.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "673917\n",
      "1208559.260859136 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme()\n",
    "imp.reload(lcm)\n",
    "plot=lcm.lombscarglelowpass(ddfs, maxsamples=-1, lp=60*60*24*2)\n",
    "lsplot=plot.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Method</th>\n",
       "      <th>Turning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>Averaged</td>\n",
       "      <td>0.011536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:20:09.769029889</td>\n",
       "      <td>Averaged</td>\n",
       "      <td>0.011230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 00:40:19.538059778</td>\n",
       "      <td>Averaged</td>\n",
       "      <td>0.010931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 01:00:29.307089667</td>\n",
       "      <td>Averaged</td>\n",
       "      <td>0.010637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 01:20:39.076119556</td>\n",
       "      <td>Averaged</td>\n",
       "      <td>0.010350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>13 days 22:22:00.184739580</td>\n",
       "      <td>Weighted</td>\n",
       "      <td>0.063169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>13 days 22:42:09.953769469</td>\n",
       "      <td>Weighted</td>\n",
       "      <td>0.062919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>13 days 23:02:19.722799358</td>\n",
       "      <td>Weighted</td>\n",
       "      <td>0.062668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>13 days 23:22:29.491829247</td>\n",
       "      <td>Weighted</td>\n",
       "      <td>0.062416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>13 days 23:42:39.260859136</td>\n",
       "      <td>Weighted</td>\n",
       "      <td>0.062163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Time    Method   Turning\n",
       "0               0 days 00:00:00  Averaged  0.011536\n",
       "1     0 days 00:20:09.769029889  Averaged  0.011230\n",
       "2     0 days 00:40:19.538059778  Averaged  0.010931\n",
       "3     0 days 01:00:29.307089667  Averaged  0.010637\n",
       "4     0 days 01:20:39.076119556  Averaged  0.010350\n",
       "...                         ...       ...       ...\n",
       "1995 13 days 22:22:00.184739580  Weighted  0.063169\n",
       "1996 13 days 22:42:09.953769469  Weighted  0.062919\n",
       "1997 13 days 23:02:19.722799358  Weighted  0.062668\n",
       "1998 13 days 23:22:29.491829247  Weighted  0.062416\n",
       "1999 13 days 23:42:39.260859136  Weighted  0.062163\n",
       "\n",
       "[2000 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsmelt=plot.reset_index().melt(id_vars=\"Time\", var_name=\"Method\", value_name=\"Turning\")\n",
    "lsmelt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Method</th>\n",
       "      <th>Turning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:02:25.066161920</td>\n",
       "      <td>Angles</td>\n",
       "      <td>-0.054368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:02:25.166166784</td>\n",
       "      <td>Angles</td>\n",
       "      <td>-0.492395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 00:02:25.266167552</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.310350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 00:02:25.966218496</td>\n",
       "      <td>Angles</td>\n",
       "      <td>-0.284608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 00:02:26.066234880</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.301341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673913</th>\n",
       "      <td>13 days 23:45:01.426793216</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.705446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673914</th>\n",
       "      <td>13 days 23:45:02.426872320</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.629012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673915</th>\n",
       "      <td>13 days 23:45:02.526884864</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.297625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673916</th>\n",
       "      <td>13 days 23:45:03.926982912</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.787571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673917</th>\n",
       "      <td>13 days 23:45:04.327021056</td>\n",
       "      <td>Angles</td>\n",
       "      <td>0.519858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673918 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Time  Method   Turning\n",
       "0       0 days 00:02:25.066161920  Angles -0.054368\n",
       "1       0 days 00:02:25.166166784  Angles -0.492395\n",
       "2       0 days 00:02:25.266167552  Angles  0.310350\n",
       "3       0 days 00:02:25.966218496  Angles -0.284608\n",
       "4       0 days 00:02:26.066234880  Angles  0.301341\n",
       "...                           ...     ...       ...\n",
       "673913 13 days 23:45:01.426793216  Angles  0.705446\n",
       "673914 13 days 23:45:02.426872320  Angles  0.629012\n",
       "673915 13 days 23:45:02.526884864  Angles  0.297625\n",
       "673916 13 days 23:45:03.926982912  Angles  0.787571\n",
       "673917 13 days 23:45:04.327021056  Angles  0.519858\n",
       "\n",
       "[673918 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawdata=ddfs0.dropna().reset_index().melt(id_vars=\"Timestamps\", var_name=\"Method\", value_name=\"Turning\")\n",
    "rawdata.columns=lsmelt.columns\n",
    "rawdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$[0,~1,~2,~3,~4,~5,~6,~7,~8,~9,~10,~11,~12,~13,~14,~15,~16,~17,~18,~19,~20,~21,~22,~23,~24,~25,~26,~27,~28,~29,~30,~31,~32,~33,~34,~35,~36,~37,~38,~39,~40,~41,~42,~43,~44,~45,~46,~47,~48,~49,~50,~51,~52,~53,~54,~55,~56,~57,~58,~59,~60,~61,~62,~63,~64,~65,~66,~67,~68,~69,~70,~71,~72,~73,~74,~75,~76,~77,~78,~79,~80,~81,~82,~83,~84,~85,~86,~87,~88,~89,~90,~91,~92,~93,~94,~95,~96,~97,~98,~99] \\; \\mathrm{s}$"
      ],
      "text/plain": [
       "<Quantity [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
       "           13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
       "           26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,\n",
       "           39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,\n",
       "           52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,\n",
       "           65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77.,\n",
       "           78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90.,\n",
       "           91., 92., 93., 94., 95., 96., 97., 98., 99.] s>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=np.arange(0,100)*u.second\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
       "       13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
       "       26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,\n",
       "       39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,\n",
       "       52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,\n",
       "       65., 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76., 77.,\n",
       "       78., 79., 80., 81., 82., 83., 84., 85., 86., 87., 88., 89., 90.,\n",
       "       91., 92., 93., 94., 95., 96., 97., 98., 99.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n"
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "bp30n15=lcm.ntermslombscargle(ddfs, nterms=15)\n",
    "bp30n10=lcm.ntermslombscargle(ddfs, nterms=10)\n",
    "bp30n5=lcm.ntermslombscargle(ddfs, nterms=5)\n",
    "bp30n30=lcm.ntermslombscargle(ddfs, nterms=30)\n",
    "bp30n60=lcm.ntermslombscargle(ddfs, nterms=60)\n",
    "\n",
    "bp10n5=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/10, nterms=5)\n",
    "bp10n10=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/10, nterms=10)\n",
    "\n",
    "# bp30n10=lcm.ntermslombscargle(ddfs, nterms=10)\n",
    "# bp30n5=lcm.ntermslombscargle(ddfs, nterms=5)\n",
    "# bp30n30=lcm.ntermslombscargle(ddfs, nterms=30)\n",
    "# bp30n60=lcm.ntermslombscargle(ddfs, nterms=60)\n",
    "\n",
    "\n",
    "\n",
    "# fit=ls.model(np.linspace(0,14,1000)*u.day, 1/(30*u.day))\n",
    "# plt.plot(fit)\n",
    "# ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n"
     ]
    }
   ],
   "source": [
    "bp60n5=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/60, nterms=5)\n",
    "bp60n10=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/60, nterms=10)\n",
    "bp60n20=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/60, nterms=20)\n",
    "\n",
    "bp120n10=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/60/2, nterms=10)\n",
    "bp120n20=lcm.ntermslombscargle(ddfs, basefreq=1/60/60/24/60/2, nterms=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# daily2=ddfs0.dropna().resample('1d').apply(nandownsample).reset_index()\n",
    "# daily2=daily2.rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "# daily2[\"Method\"]=\"Daily Mean (nan)\"\n",
    "# daily2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Turning</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:02:25.066161920</td>\n",
       "      <td>-0.106241</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 days 00:02:25.066161920</td>\n",
       "      <td>-0.025563</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 days 00:02:25.066161920</td>\n",
       "      <td>-0.326300</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 days 00:02:25.066161920</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4 days 00:02:25.066161920</td>\n",
       "      <td>0.021083</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5 days 00:02:25.066161920</td>\n",
       "      <td>0.034048</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6 days 00:02:25.066161920</td>\n",
       "      <td>0.016866</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7 days 00:02:25.066161920</td>\n",
       "      <td>0.050338</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8 days 00:02:25.066161920</td>\n",
       "      <td>0.047542</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9 days 00:02:25.066161920</td>\n",
       "      <td>0.181510</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10 days 00:02:25.066161920</td>\n",
       "      <td>0.163862</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11 days 00:02:25.066161920</td>\n",
       "      <td>0.110681</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12 days 00:02:25.066161920</td>\n",
       "      <td>0.093878</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13 days 00:02:25.066161920</td>\n",
       "      <td>0.096301</td>\n",
       "      <td>Daily Mean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Time   Turning      Method\n",
       "0   0 days 00:02:25.066161920 -0.106241  Daily Mean\n",
       "1   1 days 00:02:25.066161920 -0.025563  Daily Mean\n",
       "2   2 days 00:02:25.066161920 -0.326300  Daily Mean\n",
       "3   3 days 00:02:25.066161920  0.015469  Daily Mean\n",
       "4   4 days 00:02:25.066161920  0.021083  Daily Mean\n",
       "5   5 days 00:02:25.066161920  0.034048  Daily Mean\n",
       "6   6 days 00:02:25.066161920  0.016866  Daily Mean\n",
       "7   7 days 00:02:25.066161920  0.050338  Daily Mean\n",
       "8   8 days 00:02:25.066161920  0.047542  Daily Mean\n",
       "9   9 days 00:02:25.066161920  0.181510  Daily Mean\n",
       "10 10 days 00:02:25.066161920  0.163862  Daily Mean\n",
       "11 11 days 00:02:25.066161920  0.110681  Daily Mean\n",
       "12 12 days 00:02:25.066161920  0.093878  Daily Mean\n",
       "13 13 days 00:02:25.066161920  0.096301  Daily Mean"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily=ddfs0.dropna().resample('1d').mean().reset_index()\n",
    "daily=daily.rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "daily[\"Method\"]=\"Daily Mean\"\n",
    "daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Turning</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:02:25.066161920</td>\n",
       "      <td>-0.249368</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 01:02:25.066161920</td>\n",
       "      <td>-0.126522</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 02:02:25.066161920</td>\n",
       "      <td>-0.065274</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 03:02:25.066161920</td>\n",
       "      <td>-0.068238</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 04:02:25.066161920</td>\n",
       "      <td>-0.262246</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>13 days 19:02:25.066161920</td>\n",
       "      <td>0.169884</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>13 days 20:02:25.066161920</td>\n",
       "      <td>0.160197</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>13 days 21:02:25.066161920</td>\n",
       "      <td>0.117457</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>13 days 22:02:25.066161920</td>\n",
       "      <td>0.242679</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13 days 23:02:25.066161920</td>\n",
       "      <td>0.018445</td>\n",
       "      <td>Hourly Mean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Time   Turning       Method\n",
       "0    0 days 00:02:25.066161920 -0.249368  Hourly Mean\n",
       "1    0 days 01:02:25.066161920 -0.126522  Hourly Mean\n",
       "2    0 days 02:02:25.066161920 -0.065274  Hourly Mean\n",
       "3    0 days 03:02:25.066161920 -0.068238  Hourly Mean\n",
       "4    0 days 04:02:25.066161920 -0.262246  Hourly Mean\n",
       "..                         ...       ...          ...\n",
       "331 13 days 19:02:25.066161920  0.169884  Hourly Mean\n",
       "332 13 days 20:02:25.066161920  0.160197  Hourly Mean\n",
       "333 13 days 21:02:25.066161920  0.117457  Hourly Mean\n",
       "334 13 days 22:02:25.066161920  0.242679  Hourly Mean\n",
       "335 13 days 23:02:25.066161920  0.018445  Hourly Mean\n",
       "\n",
       "[336 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourly=ddfs0.dropna().resample('1h').mean().reset_index()\n",
    "hourly=hourly.rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "hourly[\"Method\"]=\"Hourly Mean\"\n",
    "hourly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Turning</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:02:25.066161920</td>\n",
       "      <td>-0.013152</td>\n",
       "      <td>weekly Mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7 days 00:02:25.066161920</td>\n",
       "      <td>0.085992</td>\n",
       "      <td>weekly Mean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Time   Turning       Method\n",
       "0 0 days 00:02:25.066161920 -0.013152  weekly Mean\n",
       "1 7 days 00:02:25.066161920  0.085992  weekly Mean"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly=ddfs0.dropna().resample('7d').mean().reset_index()\n",
    "weekly=weekly.rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "weekly[\"Method\"]=\"weekly Mean\"\n",
    "weekly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x323307190>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=sci.signal.windows.blackman(51)\n",
    "plt.plot(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22278353],\n",
       "       [0.13558555],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.46198053],\n",
       "       [0.35503093],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.12038458],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.26949342],\n",
       "       [       nan],\n",
       "       [0.36564664],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.37567254],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.28840014],\n",
       "       [0.03825739],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.44014395],\n",
       "       [0.16919826],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.0826562 ],\n",
       "       [       nan],\n",
       "       [0.09281711],\n",
       "       [0.41560472],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.16425125],\n",
       "       [       nan],\n",
       "       [0.45704177],\n",
       "       [0.41567414],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [       nan],\n",
       "       [0.45113398],\n",
       "       [0.3111521 ]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisydata=np.random.random([50,1])\n",
    "noisydata[noisydata>.5]=np.nan\n",
    "noisydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blacknan(x, threshold=.1):\n",
    "  if np.nansum(sci.signal.windows.blackman(x.shape[0])*~np.isnan(x).squeeze())>(threshold):\n",
    "    return np.nanmean(sci.signal.windows.blackman(x.shape[0])*x)\n",
    "  else:\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nantest(x):\n",
    "  return np.nansum(sci.signal.windows.blackman(x.shape[0])*~np.isnan(x).squeeze())/np.sum(sci.signal.windows.blackman(x.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blacknanbs(x):\n",
    "  if np.nansum(sci.signal.windows.blackman(x.shape[0])*~np.isnan(x).squeeze())>(threshold):\n",
    "    weights=sci.signal.windows.blackman(x.shape[0])*x\n",
    "    rng = np.random.default_rng()\n",
    "    # rng.choice(weights)\n",
    "    return np.nanmean(rng.choice(weights))\n",
    "  else:\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def blacknanerror(x):\n",
    "#     if np.nansum(sci.signal.windows.blackman(x.shape[0])*~np.isnan(x).squeeze())>(threshold):\n",
    "#     weights=sci.signal.windows.blackman(x.shape[0])*x\n",
    "#     rng=rng\n",
    "#     return np.nanmean(sci.signal.windows.blackman(x.shape[0])*x)\n",
    "#   else:\n",
    "#     return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.561483103534727"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisydata=np.random.random([50,1])\n",
    "noisydata[noisydata>.5]=np.nan\n",
    "nantest(noisydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# blacknan(noisydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [False],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(noisydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "473.34"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(sci.signal.windows.blackman(50)*~np.isnan(noisydata))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.38777878e-17,  1.48858213e-03,  6.05806481e-03,  1.40095693e-02,\n",
       "        2.58120534e-02,  4.20553960e-02,  6.33894512e-02,  9.04534244e-02,\n",
       "        1.23800653e-01,  1.63824257e-01,  2.10689158e-01,  2.64275593e-01,\n",
       "        3.24138580e-01,  3.89486780e-01,  4.59182958e-01,  5.31766851e-01,\n",
       "        6.05499812e-01,  6.78429126e-01,  7.48468603e-01,  8.13490971e-01,\n",
       "        8.71426720e-01,  9.20363618e-01,  9.58640963e-01,  9.84932919e-01,\n",
       "        9.98315897e-01,  9.98315897e-01,  9.84932919e-01,  9.58640963e-01,\n",
       "        9.20363618e-01,  8.71426720e-01,  8.13490971e-01,  7.48468603e-01,\n",
       "        6.78429126e-01,  6.05499812e-01,  5.31766851e-01,  4.59182958e-01,\n",
       "        3.89486780e-01,  3.24138580e-01,  2.64275593e-01,  2.10689158e-01,\n",
       "        1.63824257e-01,  1.23800653e-01,  9.04534244e-02,  6.33894512e-02,\n",
       "        4.20553960e-02,  2.58120534e-02,  1.40095693e-02,  6.05806481e-03,\n",
       "        1.48858213e-03, -1.38777878e-17])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci.signal.windows.blackman(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.02467772925532"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(np.isnan(noisydata).squeeze()*sci.signal.windows.blackman(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sci.signal.windows.blackman(50).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "555.66"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(sci.signal.windows.blackman(50)*[np.isnan(noisydata)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.580000000000002"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nansum(sci.signal.windows.blackman(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146.487831713326"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisydata=np.random.random([50,1])\n",
    "noisydata[noisydata>.5]=np.nan\n",
    "noisydata\n",
    "\n",
    "np.nansum(noisydata*sci.signal.windows.blackman(51))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Turning</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 days 00:00:00</td>\n",
       "      <td>-0.239170</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 days 02:00:00</td>\n",
       "      <td>-0.043039</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 days 03:00:00</td>\n",
       "      <td>-0.037358</td>\n",
       "      <td>Blacknan</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 days 04:00:00</td>\n",
       "      <td>-0.033823</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>13 days 19:00:00</td>\n",
       "      <td>0.019128</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>13 days 20:00:00</td>\n",
       "      <td>0.019313</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>13 days 21:00:00</td>\n",
       "      <td>0.019578</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>13 days 22:00:00</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>13 days 23:00:00</td>\n",
       "      <td>0.020289</td>\n",
       "      <td>Blacknan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time   Turning    Method\n",
       "0    0 days 00:00:00 -0.239170  Blacknan\n",
       "1    0 days 01:00:00       NaN  Blacknan\n",
       "2    0 days 02:00:00 -0.043039  Blacknan\n",
       "3    0 days 03:00:00 -0.037358  Blacknan\n",
       "4    0 days 04:00:00 -0.033823  Blacknan\n",
       "..               ...       ...       ...\n",
       "331 13 days 19:00:00  0.019128  Blacknan\n",
       "332 13 days 20:00:00  0.019313  Blacknan\n",
       "333 13 days 21:00:00  0.019578  Blacknan\n",
       "334 13 days 22:00:00  0.019907  Blacknan\n",
       "335 13 days 23:00:00  0.020289  Blacknan\n",
       "\n",
       "[336 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blacknand=ddfs0.resample('1h').mean().rolling(window=51, min_periods=1).apply(blacknan)\n",
    "\n",
    "blacknand[\"Method\"]=\"Blacknan\"\n",
    "blacknand=blacknand.reset_index().rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "blacknand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A collection of different methods to lowpass filter the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# px.scatter(x=blackman.index.values, y=blackman.values)\n",
    "# blackmana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanmaloney/Continuous Turns/loadcontinuousmatlabfiles.py:321: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(x)\n"
     ]
    }
   ],
   "source": [
    "expmnan_h=(ddfs0[\"Angles\"].resample('1h').apply(lcm.nandownsample)\n",
    "         .rolling(window=24*4, min_periods=2).apply(lcm.exponentialnan, kwargs={\"sigma\":24}))\n",
    "# expmnan_d=(ddfs0[\"Angles\"].resample('1d').apply(nandownsample)\n",
    "#          .rolling(window=4, min_periods=2).apply(exponentialnan, kwargs={\"sigma\":1}))\n",
    "# expmnan_m=(ddfs0[\"Angles\"].resample('60s').apply(nandownsample)\n",
    "#          .rolling(window=24*4*60, min_periods=20).apply(exponentialnan, kwargs={\"sigma\":60*24}))\n",
    "# expmnan_s=(ddfs0[\"Angles\"].resample('1s').apply(nandownsample)\n",
    "#          .rolling(window=4*24*60*60, min_periods=120).apply(exponentialnan, kwargs={\"sigma\":60*60*24}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanmaloney/Continuous Turns/loadcontinuousmatlabfiles.py:321: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(x)\n"
     ]
    }
   ],
   "source": [
    "# pd.DataFrame(expmnan_h).reset_index().rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "expmnan_h=(ddfs0[\"Angles\"].resample('10s').apply(lcm.nandownsample)\n",
    "         .rolling(window=24*60*60*6, min_periods=2).apply(lcm.exponentialnan, kwargs={\"sigma\":60*6}))\n",
    "resample1h=pd.DataFrame(expmnan_h).reset_index().rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "resample1h[\"Method\"]=\"Gaussian 1hr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ryanmaloney/Continuous Turns/loadcontinuousmatlabfiles.py:321: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(x)\n"
     ]
    }
   ],
   "source": [
    "# pd.DataFrame(expmnan_h).reset_index().rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "expmnan_h=(ddfs0[\"Angles\"].resample('60s').apply(lcm.nandownsample)\n",
    "         .rolling(window=24*60*60*4, min_periods=2).apply(lcm.exponentialnan, kwargs={\"sigma\":60*24}))\n",
    "resample1d=pd.DataFrame(expmnan_h).reset_index().rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "resample1d[\"Method\"]=\"Gaussian 1d\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>0 days 00:00:10</td>\n",
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       "      <th>2</th>\n",
       "      <td>0 days 00:00:20</td>\n",
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       "      <th>3</th>\n",
       "      <td>0 days 00:00:30</td>\n",
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       "      <th>4</th>\n",
       "      <td>0 days 00:00:40</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>120869</th>\n",
       "      <td>13 days 23:44:50</td>\n",
       "      <td>0.110380</td>\n",
       "      <td>Gaussian 1hr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120870</th>\n",
       "      <td>13 days 23:45:00</td>\n",
       "      <td>0.110380</td>\n",
       "      <td>Gaussian 1hr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120871</th>\n",
       "      <td>13 days 23:45:10</td>\n",
       "      <td>0.110502</td>\n",
       "      <td>Gaussian 1hr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120872</th>\n",
       "      <td>13 days 23:45:20</td>\n",
       "      <td>0.110502</td>\n",
       "      <td>Gaussian 1hr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120873</th>\n",
       "      <td>13 days 23:45:30</td>\n",
       "      <td>0.110607</td>\n",
       "      <td>Gaussian 1hr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120874 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Time   Turning        Method\n",
       "0       0 days 00:00:00       NaN  Gaussian 1hr\n",
       "1       0 days 00:00:10       NaN  Gaussian 1hr\n",
       "2       0 days 00:00:20       NaN  Gaussian 1hr\n",
       "3       0 days 00:00:30       NaN  Gaussian 1hr\n",
       "4       0 days 00:00:40       NaN  Gaussian 1hr\n",
       "...                 ...       ...           ...\n",
       "120869 13 days 23:44:50  0.110380  Gaussian 1hr\n",
       "120870 13 days 23:45:00  0.110380  Gaussian 1hr\n",
       "120871 13 days 23:45:10  0.110502  Gaussian 1hr\n",
       "120872 13 days 23:45:20  0.110502  Gaussian 1hr\n",
       "120873 13 days 23:45:30  0.110607  Gaussian 1hr\n",
       "\n",
       "[120874 rows x 3 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resample1h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n",
      "Total time is 13.987954408091852 d\n",
      "Processing until 13.987954408091852 d\n"
     ]
    }
   ],
   "source": [
    "bp30n15=lcm.ntermslombscargle(ddfs, nterms=15)\n",
    "bp30n5=lcm.ntermslombscargle(ddfs, nterms=5)\n",
    "bp30n30=lcm.ntermslombscargle(ddfs, nterms=30)\n",
    "bp30n60=lcm.ntermslombscargle(ddfs, nterms=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0 days 00:02:25.066161920</td>\n",
       "      <td>-0.054368</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 days 00:02:25.166166784</td>\n",
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       "      <td>0 days 00:02:25.966218496</td>\n",
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       "      <td>0 days 00:02:26.066234880</td>\n",
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       "    <tr>\n",
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       "      <td>0.629012</td>\n",
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       "    <tr>\n",
       "      <th>673915</th>\n",
       "      <td>13 days 23:45:02.526884864</td>\n",
       "      <td>0.297625</td>\n",
       "      <td>Raw Data</td>\n",
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       "    <tr>\n",
       "      <th>673916</th>\n",
       "      <td>13 days 23:45:03.926982912</td>\n",
       "      <td>0.787571</td>\n",
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       "    <tr>\n",
       "      <th>673917</th>\n",
       "      <td>13 days 23:45:04.327021056</td>\n",
       "      <td>0.519858</td>\n",
       "      <td>Raw Data</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>673918 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Time   Turning    Method\n",
       "0       0 days 00:02:25.066161920 -0.054368  Raw Data\n",
       "1       0 days 00:02:25.166166784 -0.492395  Raw Data\n",
       "2       0 days 00:02:25.266167552  0.310350  Raw Data\n",
       "3       0 days 00:02:25.966218496 -0.284608  Raw Data\n",
       "4       0 days 00:02:26.066234880  0.301341  Raw Data\n",
       "...                           ...       ...       ...\n",
       "673913 13 days 23:45:01.426793216  0.705446  Raw Data\n",
       "673914 13 days 23:45:02.426872320  0.629012  Raw Data\n",
       "673915 13 days 23:45:02.526884864  0.297625  Raw Data\n",
       "673916 13 days 23:45:03.926982912  0.787571  Raw Data\n",
       "673917 13 days 23:45:04.327021056  0.519858  Raw Data\n",
       "\n",
       "[673918 rows x 3 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawdata=ddfs0.dropna().reset_index().rename(columns={\"Timestamps\":\"Time\", \"Angles\":\"Turning\"})\n",
    "rawdata[\"Method\"]=\"Raw Data\"\n",
    "rawdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## shuffle Code (F1B)\n",
    "This part of the code is used to generate Figure 1B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonanraw=ddfs0.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "nonanrawvalues=nonanraw.values\n",
    "nonanindex=nonanraw.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=xr.load_dataset(\"1_Angles_24h.nc\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:   18.8s\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "/Users/ryanmaloney/miniforge3/envs/python_data_analysis/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n",
      "[Parallel(n_jobs=4)]: Done  42 out of  42 | elapsed:  1.2min finished\n"
     ]
    }
   ],
   "source": [
    "imp.reload(lcm)\n",
    "pdall=lcm.getallflies(a, numshuffles=100, maxflies=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>-0.049729</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448755</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.063870</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>-0.050065</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.245786</td>\n",
       "      <td>Shuffled</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>-0.078920</td>\n",
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       "      <th>11339</th>\n",
       "      <td>93</td>\n",
       "      <td>44</td>\n",
       "      <td>0.058479</td>\n",
       "      <td>0.156316</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666656</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.059207</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11340</th>\n",
       "      <td>94</td>\n",
       "      <td>45</td>\n",
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       "      <td>0.149212</td>\n",
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       "      <td>0.067949</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11342</th>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "      <td>0.074695</td>\n",
       "      <td>0.144953</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300116</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.072560</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0.140033</td>\n",
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       "      <td>0.046086</td>\n",
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       "      <td>41</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "<p>11344 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Hours        lb        ub  Chunk       Raw           variable  \\\n",
       "0          0      0 -0.078772 -0.051117      0 -0.208208           Shuffled   \n",
       "1          1      1 -0.078344 -0.050345      0 -0.017965           Shuffled   \n",
       "2          2      2 -0.079384 -0.049729      0  0.448755           Shuffled   \n",
       "3          3      3 -0.079608 -0.050065      0 -0.245786           Shuffled   \n",
       "4          4      4 -0.078920 -0.049994      0 -0.348904           Shuffled   \n",
       "...      ...    ...       ...       ...    ...       ...                ...   \n",
       "11339     93     44  0.058479  0.156316      0  0.666656  Low Pass Filtered   \n",
       "11340     94     45  0.062502  0.153159      0  0.105854  Low Pass Filtered   \n",
       "11341     95     46  0.068851  0.149212      0 -0.003298  Low Pass Filtered   \n",
       "11342     96     47  0.074695  0.144953      0  0.300116  Low Pass Filtered   \n",
       "11343     97     48  0.079638  0.140033      0  0.046086  Low Pass Filtered   \n",
       "\n",
       "          value Fly  Batch  \n",
       "0     -0.064109   0      1  \n",
       "1     -0.063980   0      1  \n",
       "2     -0.063870   0      1  \n",
       "3     -0.063777   0      1  \n",
       "4     -0.063698   0      1  \n",
       "...         ...  ..    ...  \n",
       "11339  0.059207  41      1  \n",
       "11340  0.063396  41      1  \n",
       "11341  0.067949  41      1  \n",
       "11342  0.072560  41      1  \n",
       "11343  0.076755  41      1  \n",
       "\n",
       "[11344 rows x 10 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdall[\"Fly\"]=pd.Categorical(pdall[\"Fly\"])\n",
    "pdall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdallnonan=pdall.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset=pdallnonan[pdallnonan['Chunk']==2][\"Fly\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/3785823828.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/3785823828.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n"
     ]
    }
   ],
   "source": [
    "subset=[1,2]\n",
    "pdsubset=pdall[pdall[\"Fly\"].isin(subset)]\n",
    "pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
    "# pdsubset[\"Fly\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.105854</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.063396</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11341</th>\n",
       "      <td>95</td>\n",
       "      <td>46</td>\n",
       "      <td>0.068851</td>\n",
       "      <td>0.149212</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003298</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.067949</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11342</th>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "      <td>0.074695</td>\n",
       "      <td>0.144953</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300116</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.072560</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11343</th>\n",
       "      <td>97</td>\n",
       "      <td>48</td>\n",
       "      <td>0.079638</td>\n",
       "      <td>0.140033</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046086</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.076755</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11344 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Hours        lb        ub  Chunk       Raw           variable  \\\n",
       "0          0      0 -0.078772 -0.051117      0 -0.208208           Shuffled   \n",
       "1          1      1 -0.078344 -0.050345      0 -0.017965           Shuffled   \n",
       "2          2      2 -0.079384 -0.049729      0  0.448755           Shuffled   \n",
       "3          3      3 -0.079608 -0.050065      0 -0.245786           Shuffled   \n",
       "4          4      4 -0.078920 -0.049994      0 -0.348904           Shuffled   \n",
       "...      ...    ...       ...       ...    ...       ...                ...   \n",
       "11339     93     44  0.058479  0.156316      0  0.666656  Low Pass Filtered   \n",
       "11340     94     45  0.062502  0.153159      0  0.105854  Low Pass Filtered   \n",
       "11341     95     46  0.068851  0.149212      0 -0.003298  Low Pass Filtered   \n",
       "11342     96     47  0.074695  0.144953      0  0.300116  Low Pass Filtered   \n",
       "11343     97     48  0.079638  0.140033      0  0.046086  Low Pass Filtered   \n",
       "\n",
       "          value Fly  Batch  \n",
       "0     -0.064109   0      1  \n",
       "1     -0.063980   0      1  \n",
       "2     -0.063870   0      1  \n",
       "3     -0.063777   0      1  \n",
       "4     -0.063698   0      1  \n",
       "...         ...  ..    ...  \n",
       "11339  0.059207  41      1  \n",
       "11340  0.063396  41      1  \n",
       "11341  0.067949  41      1  \n",
       "11342  0.072560  41      1  \n",
       "11343  0.076755  41      1  \n",
       "\n",
       "[11344 rows x 10 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdallnonan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Hours</th>\n",
       "      <th>lb</th>\n",
       "      <th>ub</th>\n",
       "      <th>Chunk</th>\n",
       "      <th>Raw</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Fly</th>\n",
       "      <th>Batch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.078772</td>\n",
       "      <td>-0.051117</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.208208</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.064109</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.078344</td>\n",
       "      <td>-0.050345</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.017965</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.063980</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.079384</td>\n",
       "      <td>-0.049729</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448755</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.063870</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.079608</td>\n",
       "      <td>-0.050065</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.245786</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.063777</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.078920</td>\n",
       "      <td>-0.049994</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.348904</td>\n",
       "      <td>Shuffled</td>\n",
       "      <td>-0.063698</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>93</td>\n",
       "      <td>44</td>\n",
       "      <td>0.058479</td>\n",
       "      <td>0.156316</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666656</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.059207</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11340</th>\n",
       "      <td>94</td>\n",
       "      <td>45</td>\n",
       "      <td>0.062502</td>\n",
       "      <td>0.153159</td>\n",
       "      <td>0</td>\n",
       "      <td>0.105854</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.063396</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11341</th>\n",
       "      <td>95</td>\n",
       "      <td>46</td>\n",
       "      <td>0.068851</td>\n",
       "      <td>0.149212</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.003298</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.067949</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11342</th>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "      <td>0.074695</td>\n",
       "      <td>0.144953</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300116</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.072560</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11343</th>\n",
       "      <td>97</td>\n",
       "      <td>48</td>\n",
       "      <td>0.079638</td>\n",
       "      <td>0.140033</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046086</td>\n",
       "      <td>Low Pass Filtered</td>\n",
       "      <td>0.076755</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11344 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  Hours        lb        ub  Chunk       Raw           variable  \\\n",
       "0          0      0 -0.078772 -0.051117      0 -0.208208           Shuffled   \n",
       "1          1      1 -0.078344 -0.050345      0 -0.017965           Shuffled   \n",
       "2          2      2 -0.079384 -0.049729      0  0.448755           Shuffled   \n",
       "3          3      3 -0.079608 -0.050065      0 -0.245786           Shuffled   \n",
       "4          4      4 -0.078920 -0.049994      0 -0.348904           Shuffled   \n",
       "...      ...    ...       ...       ...    ...       ...                ...   \n",
       "11339     93     44  0.058479  0.156316      0  0.666656  Low Pass Filtered   \n",
       "11340     94     45  0.062502  0.153159      0  0.105854  Low Pass Filtered   \n",
       "11341     95     46  0.068851  0.149212      0 -0.003298  Low Pass Filtered   \n",
       "11342     96     47  0.074695  0.144953      0  0.300116  Low Pass Filtered   \n",
       "11343     97     48  0.079638  0.140033      0  0.046086  Low Pass Filtered   \n",
       "\n",
       "          value Fly  Batch  \n",
       "0     -0.064109   0      1  \n",
       "1     -0.063980   0      1  \n",
       "2     -0.063870   0      1  \n",
       "3     -0.063777   0      1  \n",
       "4     -0.063698   0      1  \n",
       "...         ...  ..    ...  \n",
       "11339  0.059207  41      1  \n",
       "11340  0.063396  41      1  \n",
       "11341  0.067949  41      1  \n",
       "11342  0.072560  41      1  \n",
       "11343  0.076755  41      1  \n",
       "\n",
       "[11344 rows x 10 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdallnonan['variable']=pd.Categorical(pdallnonan['variable'])\n",
    "pdallnonan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdsubset=pdallnonan[pdallnonan['variable']==\"Low Pass Filtered\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<seaborn._core.plot.Plot at 0x321f095d0>"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "image/png": {
       "height": 836.8249999999999,
       "width": 663.425
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# subset=[3, 9, 10, 11]\n",
    "# subset=[12,13,14,15]\n",
    "# subset=[16, 17, 18, 19, 20]\n",
    "# subset=pdallnonan[[\"Fly\"].unique()\n",
    "# pdsubset=pdall[pdallnonan['variable']==\"Low Pass Filtered\"]\n",
    "# pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
    "\n",
    "fig=(so.Plot(pdsubset, y=\"value\", x=\"Hours\", \n",
    "color=\"Fly\", \n",
    "group=\"Chunk\", \n",
    "linestyle=\"variable\", ymin=\"lb\", ymax=\"ub\"\n",
    ")\n",
    "#  .add(so.Band())\n",
    " .add(so.Line(linewidth=.5),)\n",
    "#  .add(so.Line(linewidth=.2, linestyle=\":\"), y=\"Raw\")\n",
    " .limit(x=(0,None), y=(-.3, .3))\n",
    " .theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n",
    "\n",
    ")\n",
    "# so.Plot(bm1h, y=\"Raw\", x=\"Hours\", group=\"Chunk\").add(so.Line())\n",
    "fig.save('lowpassfilteredflytraces.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/167889081.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/167889081.py:6: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<seaborn._core.plot.Plot at 0x321eb0070>"
      ]
     },
     "execution_count": 98,
     "metadata": {
      "image/png": {
       "height": 582.675,
       "width": 663.425
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# subset=[3, 9, 10, 11]\n",
    "# subset=[12,13,14,15]\n",
    "# subset=[16, 17, 18, 19, 20]\n",
    "subset=pdallnonan[pdallnonan['Chunk']==2][\"Fly\"].unique()\n",
    "pdsubset=pdall[pdall[\"Fly\"].isin(subset)]\n",
    "pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
    "\n",
    "fig=(so.Plot(pdsubset, y=\"value\", x=\"Hours\", \n",
    "color=\"Fly\", \n",
    "group=\"Chunk\", \n",
    "linestyle=\"variable\", ymin=\"lb\", ymax=\"ub\"\n",
    ")\n",
    " .add(so.Band())\n",
    " .add(so.Line(),)\n",
    "#  .add(so.Line(linewidth=.2, linestyle=\":\"), y=\"Raw\")\n",
    " .limit(x=(0,None), y=(-.5, .5))\n",
    " .theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n",
    "\n",
    ")\n",
    "# so.Plot(bm1h, y=\"Raw\", x=\"Hours\", group=\"Chunk\").add(so.Line())\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/3162039544.py:12: DeprecationWarning: This function is deprecated. Please call randint(1, 30 + 1) instead\n",
      "  subset=np.append(subset, np.random.random_integers(1, 30, 1))\n",
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/3162039544.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
      "/var/folders/s_/7_g1r1fs2wdcxn113q39_gzw0000gn/T/ipykernel_31459/3162039544.py:16: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.set_theme(context=\"paper\", style=\"ticks\", font_scale=1)\n",
    "fig, ax=plt.subplots(figsize=(5,5))\n",
    "# subset=[3, 9, 10, 11]\n",
    "# subset=[12,13,14,15]\n",
    "# subset=[16, 17, 18, 19, 20]\n",
    "subset=pdallnonan[pdallnonan['Chunk']==1][\"Fly\"].unique()\n",
    "#Best subset\n",
    "subset=[3, 17, 18, 20,22, 24,29]\n",
    "subset=[9, 10, 14, 18, 24, 27]\n",
    "subset=[9, 30, 18, 24]\n",
    "\n",
    "subset=np.append(subset, np.random.random_integers(1, 30, 1))\n",
    "\n",
    "# subset=range(20, 29)\n",
    "pdsubset=pdall[pdall[\"Fly\"].isin(subset)]\n",
    "pdsubset.loc[:,\"Fly\"]=pd.Categorical(pdsubset.loc[:,\"Fly\"]).remove_unused_categories()\n",
    "\n",
    "(so.Plot(pdsubset, y=\"value\", x=\"Hours\", \n",
    "color=\"Fly\", \n",
    "group=\"Chunk\", \n",
    "linestyle=\"variable\", ymin=\"lb\", ymax=\"ub\",\n",
    ")\n",
    " .add(so.Band())\n",
    " .add(so.Line(linewidth=2),)\n",
    " .add(so.Line(linewidth=1,alpha=.1), y=\"Raw\", color=\"Fly\")\n",
    " .limit(x=(100,200), y=(-.75, .75))\n",
    " .label(x=\"Time (Hours)\", y=\"Turning Direction\")\n",
    "  .theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n",
    "  .on(ax)\n",
    "\n",
    "  .plot()\n",
    "  \n",
    "  # .legend(title=\"Fly\")\n",
    "\n",
    "#  .theme({**axes_style(\"ticks\"), **axes_context(\"paper\"), \"grid.linestyle\": \":\"})\n",
    "\n",
    ")#.theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n",
    "# so.Plot(bm1h, y=\"Raw\", x=\"Hours\", group=\"Chunk\").add(so.Line())\n",
    "# plt.xlabel(\"Time (Hours)\")\n",
    "# plt.ylabel(\"Turning Direction\")\n",
    "# fig.save('lowpassfilteredflytraces.pdf')\n",
    "# fig.theme({**axes_style(\"ticks\"), \"grid.linestyle\": \":\"})\n",
    "# fig.save('lowpassfilteredflytraces.pdf')\n",
    "# fig\n",
    "fig.savefig('lowpassfilteredflytraces.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python_data_analysis",
   "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": "3.10.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "9be465c89b4732e9c881a15d49491786ab7563bceac3cfbcb74ee009242c95a5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}