Plot_temperature_loggers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gentemann\\Miniconda3\\envs\\satenv\\lib\\site-packages\\xarray\\core\\merge.py:17: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  PANDAS_TYPES = (pd.Series, pd.DataFrame, pd.Panel)\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy.ma as ma\n",
    "import numpy\n",
    "import datetime as dt\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.cm as cm\n",
    "#from mpl_toolkits.basemap import Basemap\n",
    "import matplotlib.pyplot as plt\n",
    "from palettable import colorbrewer\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def smooth(x,window_len=11,window='hanning'):\n",
    "    \"\"\"smooth the data using a window with requested size.\n",
    "    \n",
    "    This method is based on the convolution of a scaled window with the signal.\n",
    "    The signal is prepared by introducing reflected copies of the signal \n",
    "    (with the window size) in both ends so that transient parts are minimized\n",
    "    in the begining and end part of the output signal.\n",
    "    \n",
    "    input:\n",
    "        x: the input signal \n",
    "        window_len: the dimension of the smoothing window; should be an odd integer\n",
    "        window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\n",
    "            flat window will produce a moving average smoothing.\n",
    "\n",
    "    output:\n",
    "        the smoothed signal\n",
    "        \n",
    "    example:\n",
    "\n",
    "    t=linspace(-2,2,0.1)\n",
    "    x=sin(t)+randn(len(t))*0.1\n",
    "    y=smooth(x)\n",
    "    \n",
    "    see also: \n",
    "    \n",
    "    numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve\n",
    "    scipy.signal.lfilter\n",
    " \n",
    "    TODO: the window parameter could be the window itself if an array instead of a string\n",
    "    NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y.\n",
    "    \"\"\"\n",
    "\n",
    "    if x.ndim != 1:\n",
    "        raise ValueError( \"smooth only accepts 1 dimension arrays.\")\n",
    "\n",
    "    if x.size < window_len:\n",
    "        raise ValueError(\"Input vector needs to be bigger than window size.\")\n",
    "\n",
    "\n",
    "    if window_len<3:\n",
    "        return x\n",
    "\n",
    "\n",
    "    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:\n",
    "        raise ValueError(\"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'\")\n",
    "\n",
    "\n",
    "    s=numpy.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]\n",
    "    #print(len(s))\n",
    "    if window == 'flat': #moving average\n",
    "        w=numpy.ones(window_len,'d')\n",
    "    else:\n",
    "        w=eval('numpy.'+window+'(window_len)')\n",
    "\n",
    "    y=numpy.convolve(w/w.sum(),s,mode='valid')\n",
    "    return y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "adir='F:/data/cruise_data/saildrone/baja-2018/'\n",
    "#adir='C:/Users/gentemann/Desktop/data/'\n",
    "fname=adir+'saildrone-gen_4-baja_2018-sd1002-20180411T180000-20180611T055959-1_minutes-v1_withloggers.nc'\n",
    "#fname=adir+'saildrone-gen_4-baja_2018-EP-sd1002-ALL-1_min-v1_with_loggers.nc'\n",
    "ds=xr.open_dataset(fname)\n",
    "ds = ds.isel(trajectory=0)\n",
    "lons_usv=ds.longitude\n",
    "lats_usv=ds.latitude\n",
    "time_usv=ds.time\n",
    "ds = ds.swap_dims({'obs':'time'})\n",
    "ds = ds.sel(time=slice('2018-04-12T02','2018-06-10T18'))\n",
    "\n",
    "#dstem.sel(**{'time':slice('2018-05-01','2018-05-02')})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['spd']=np.sqrt(ds.UWND_MEAN**2+ds.VWND_MEAN**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                               (time: 85980)\n",
       "Coordinates:\n",
       "    trajectory                            float32 1002.0\n",
       "  * time                                  (time) datetime64[ns] 2018-04-12T02:00:00 ... 2018-06-10T18:59:00\n",
       "    latitude                              (time) float64 ...\n",
       "    longitude                             (time) float64 ...\n",
       "Data variables:\n",
       "    SOG                                   (time) float64 ...\n",
       "    COG                                   (time) float64 ...\n",
       "    HDG                                   (time) float64 ...\n",
       "    HDG_WING                              (time) float64 ...\n",
       "    ROLL                                  (time) float64 ...\n",
       "    PITCH                                 (time) float64 ...\n",
       "    WING_ANGLE                            (time) float64 ...\n",
       "    BARO_PRES_MEAN                        (time) float64 ...\n",
       "    BARO_PRES_STDDEV                      (time) float64 ...\n",
       "    TEMP_AIR_MEAN                         (time) float64 ...\n",
       "    TEMP_AIR_STDDEV                       (time) float64 ...\n",
       "    RH_MEAN                               (time) float64 ...\n",
       "    RH_STDDEV                             (time) float64 ...\n",
       "    TEMP_IR_UNCOR_MEAN                    (time) float64 ...\n",
       "    TEMP_IR_UNCOR_STDDEV                  (time) float64 ...\n",
       "    UWND_MEAN                             (time) float64 9.31 nan ... nan nan\n",
       "    UWND_STDDEV                           (time) float64 ...\n",
       "    VWND_MEAN                             (time) float64 -6.38 nan ... nan nan\n",
       "    VWND_STDDEV                           (time) float64 ...\n",
       "    WWND_MEAN                             (time) float64 ...\n",
       "    WWND_STDDEV                           (time) float64 ...\n",
       "    GUST_WND_MEAN                         (time) float64 ...\n",
       "    GUST_WND_STDDEV                       (time) float64 ...\n",
       "    TEMP_CTD_MEAN                         (time) float64 ...\n",
       "    TEMP_CTD_STDDEV                       (time) float64 ...\n",
       "    COND_MEAN                             (time) float64 ...\n",
       "    COND_STDDEV                           (time) float64 ...\n",
       "    SAL_MEAN                              (time) float64 ...\n",
       "    SAL_STDDEV                            (time) float64 ...\n",
       "    O2_CONC_UNCOR_MEAN                    (time) float64 ...\n",
       "    O2_CONC_UNCOR_STDDEV                  (time) float64 ...\n",
       "    O2_SAT_MEAN                           (time) float64 ...\n",
       "    O2_SAT_STDDEV                         (time) float64 ...\n",
       "    TEMP_O2_MEAN                          (time) float64 ...\n",
       "    TEMP_O2_STDDEV                        (time) float64 ...\n",
       "    CHLOR_MEAN                            (time) float64 ...\n",
       "    CHLOR_STDDEV                          (time) float64 ...\n",
       "    CDOM_MEAN                             (time) float64 ...\n",
       "    CDOM_STDDEV                           (time) float64 ...\n",
       "    BKSCT_RED_MEAN                        (time) float64 ...\n",
       "    BKSCT_RED_STDDEV                      (time) float64 ...\n",
       "    sea_water_temperature_logger1_mean    (time) float64 ...\n",
       "    sea_water_temperature_logger2_mean    (time) float64 ...\n",
       "    sea_water_temperature_logger3_mean    (time) float64 ...\n",
       "    sea_water_temperature_logger4_mean    (time) float64 ...\n",
       "    sea_water_temperature_logger1_stddev  (time) float64 ...\n",
       "    sea_water_temperature_logger2_stddev  (time) float64 ...\n",
       "    sea_water_temperature_logger3_stddev  (time) float64 ...\n",
       "    sea_water_temperature_logger4_stddev  (time) float64 ...\n",
       "    spd                                   (time) float64 11.29 nan ... nan nan\n",
       "Attributes:\n",
       "    title:                     Baja 2018 Near-Real-Time Mission Data\n",
       "    ncei_template_version:     NCEI_NetCDF_Trajectory_Template_v2.0\n",
       "    Conventions:               CF-1.6, ACDD-1.3\n",
       "    netcdf_version:            4.4.1.1\n",
       "    featureType:               trajectory\n",
       "    cdm_data_type:             Trajectory\n",
       "    standard_name_vocabulary:  CF Standard Name Table v58\n",
       "    description:               Saildrone NetCDF Format\n",
       "    creator_name:              Saildrone\n",
       "    platform:                  Saildrone\n",
       "    institution:               Saildrone\n",
       "    creator_email:             support@saildrone.com\n",
       "    creator_url:               http://saildrone.com/\n",
       "    date_created:              2018-08-29T21:02:49.774718Z\n",
       "    interval:                  60.0\n",
       "    time_coverage_resolution:  PT1M\n",
       "    time_coverage_start:       2018-04-11T18:00:00.000000Z\n",
       "    time_coverage_end:         2018-06-11T05:59:59.000000Z\n",
       "    time_coverage_duration:    P60DT11H59M59S\n",
       "    data_mode:                 realtime\n",
       "    drone_id:                  1002\n",
       "    id:                        3860\n",
       "    naming_authority:          com.saildrone\n",
       "    uuid:                      b98a9a81-d9f0-4751-aad6-8ea5b8cea963\n",
       "    geospatial_lat_min:        28.0176832\n",
       "    geospatial_lat_max:        37.8174912\n",
       "    geospatial_lat_units:      degrees_north\n",
       "    geospatial_lon_min:        -125.55297279999999\n",
       "    geospatial_lon_max:        -115.5226624\n",
       "    geospatial_lon_units:      degrees_east\n",
       "    area:                      Baja California Coast\n",
       "    project:                   baja_2018\n",
       "    product_version:           1\n",
       "    summary:                   The Saildrone unmanned surface vehicle (USV) c..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xlon = ds.longitude.interpolate_na('time')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset = ds.where(((ds.time.dt.hour>12)|(ds.time.dt.hour<6))&(ds.spd<7.5))\n",
    "ds['tdif'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger4_mean\n",
    "ds['tdif1'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger3_mean\n",
    "ds['tdif2'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger2_mean\n",
    "ds['tdif3'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger1_mean\n",
    "ds['tdif_ctd'] = ds.sea_water_temperature_logger1_mean-ds.TEMP_CTD_MEAN\n",
    "ds['tdif_o2'] = ds.sea_water_temperature_logger1_mean-ds.TEMP_O2_MEAN\n",
    "ds['tdif_both'] = ds.TEMP_O2_MEAN-ds.TEMP_CTD_MEAN\n",
    "ds['tdif_ir'] = ds.TEMP_IR_UNCOR_MEAN-ds.TEMP_CTD_MEAN\n",
    "subset = ds.where(np.logical_not((ds.time.dt.hour>12)&(ds.spd<7.5)))\n",
    "subset = subset.where(np.logical_not((ds.time.dt.hour<6)&(ds.spd<7.5)))\n",
    "subset = subset.where(np.logical_not((ds.time>np.datetime64('2018-05-24T12')) & (ds.time<np.datetime64('2018-05-26T12'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'longitude' (time: 10)>\n",
      "array(['2018-04-11T17:49:00.000000000', '2018-04-11T17:50:00.000000000',\n",
      "       '2018-04-11T17:51:00.000000000', '2018-04-11T17:52:00.000000000',\n",
      "       '2018-04-11T17:53:00.000000000', '2018-04-11T17:54:00.000000000',\n",
      "       '2018-04-11T17:55:00.000000000', '2018-04-11T17:56:00.000000000',\n",
      "       '2018-04-11T17:57:00.000000000', '2018-04-11T17:58:00.000000000'],\n",
      "      dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "    trajectory  float32 1002.0\n",
      "  * time        (time) datetime64[ns] 2018-04-12T02:00:00 ... 2018-04-12T02:09:00\n",
      "    latitude    (time) float64 ...\n",
      "    longitude   (time) float64 ...\n",
      "['2018-04-12T02:00:00.000000000' '2018-04-12T02:01:00.000000000'\n",
      " '2018-04-12T02:02:00.000000000' '2018-04-12T02:03:00.000000000'\n",
      " '2018-04-12T02:04:00.000000000' '2018-04-12T02:05:00.000000000'\n",
      " '2018-04-12T02:06:00.000000000' '2018-04-12T02:07:00.000000000'\n",
      " '2018-04-12T02:08:00.000000000' '2018-04-12T02:09:00.000000000']\n",
      "-122.77792\n"
     ]
    }
   ],
   "source": [
    "#calculate local time\n",
    "time_offset_to_lmt=(xlon/360.)*24.*60\n",
    "tlmt= ds.time.data+time_offset_to_lmt*np.timedelta64(1,'m')# dt.timedelta(seconds=1)\n",
    "ds['tlmt']=tlmt\n",
    "print(tlmt[0:10])\n",
    "print(ds.time[0:10].data)\n",
    "print(ds.longitude[0].data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'hour' ()>\n",
       "array(23, dtype=int64)\n",
       "Coordinates:\n",
       "    trajectory  float32 1002.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.tlmt.dt.hour.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) =plt.subplots(1,2)\n",
    "cs=ax1.scatter(ds.spd,ds.tdif,c=ds.tlmt.dt.hour,vmin=0,vmax=24,cmap='brg',s=1)\n",
    "ax1.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "cbar = fig.colorbar(cs,orientation='horizontal',ax=ax1)\n",
    "cbar.set_label('Local Time (hrs)')\n",
    "cs2=ax2.scatter(ds.tlmt.dt.hour,ds.tdif,c=ds.spd,vmin=0,vmax=15,cmap='gist_stern',s=1)\n",
    "ax2.set(xlabel='GMT (hr)')\n",
    "cbar = fig.colorbar(cs2,orientation='horizontal',ax=ax2)\n",
    "cbar.set_label('Wind Speed (ms$^{-1}$)')\n",
    "fig.savefig(adir+'figs/diurnal/temp_loggers_data_localtime.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) =plt.subplots(1,2)\n",
    "cs=ax1.scatter(ds.spd,ds.tdif,c=ds.time.dt.hour)\n",
    "ax1.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "cbar = fig.colorbar(cs,orientation='horizontal',ax=ax1)\n",
    "cbar.set_label('GMT Time (hrs)')\n",
    "cs2=ax2.scatter(ds.time.dt.hour,ds.tdif,c=ds.spd)\n",
    "ax2.set(xlabel='GMT (hr)')\n",
    "cbar = fig.colorbar(cs2,orientation='horizontal',ax=ax2)\n",
    "cbar.set_label('Wind Speed (ms$^{-1}$)')\n",
    "fig.savefig(adir+'figs/diurnal/temp_loggers_data.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.05, 0.05)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.scatter(subset.spd,subset.tdif1,c=subset.time.dt.hour)\n",
    "plt.scatter(ds.spd,ds.tdif1,c=ds.time.dt.hour)\n",
    "plt.ylim(-.05, .05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) =plt.subplots(1,2)\n",
    "cs=ax1.scatter(subset.spd,subset.tdif,c=subset.time.dt.hour)\n",
    "ax1.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "cbar = fig.colorbar(cs,orientation='horizontal',ax=ax1)\n",
    "cbar.set_label('GMT Time (hrs)')\n",
    "cs2=ax2.scatter(subset.time.dt.hour,subset.tdif,c=subset.spd)\n",
    "ax2.set(xlabel='GMT (hr)')\n",
    "cbar = fig.colorbar(cs2,orientation='horizontal',ax=ax2)\n",
    "cbar.set_label('Wind Speed (ms$^{-1}$)')\n",
    "fig.savefig(adir+'figs/diurnal/temp_loggers_masked_data.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array(0.00343223), array(-0.00363333), 0.9992519306679358, array(0.07558568), 0.003706505546263268, array(0.01141388), 77708]\n",
      "[array(0.00193529), array(-0.00321667), 0.9994431303873988, array(0.06521465), 0.003138174695835689, array(0.00898069), 77708]\n",
      "[array(0.0006463), array(-0.00265), 0.9996184403554482, array(0.05398221), 0.002446293660536443, array(0.00646812), 77708]\n",
      "[array(0.), array(0.), 1.0, array(0.), 0.0, array(0.), 77708]\n",
      "[array(0.00190253), array(-0.00085), 0.9997658778027368, array(0.0422911), 0.0037559256202098277, array(0.0061456), 77478]\n",
      "[array(-0.01401686), array(-0.01508333), 0.9997729327472852, array(0.04166219), 0.005114977653840307, array(0.01786088), 77478]\n",
      "[array(-0.21233035), array(-0.178), 0.9933221291697418, array(0.22871279), 0.2594553882384788, array(0.25051958), 76997]\n",
      "0.0034322315591702184 0.07558567747356534 77708\n",
      "0.0019352859422453277 0.06521465431396252 77708\n",
      "0.000646296391619916 0.053982213769273216 77708\n",
      "0.0 0.0 77708\n",
      "0.0019025280294617997 0.042291102401226265 77478\n",
      "-0.014016855537486241 0.041662191901573234 77478\n",
      "-0.2123303505331377 0.2287127893749872 76997\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#subset = ds.where(((ds.time.dt.hour>12)|(ds.time.dt.hour<6))&(ds.spd<7.5))\n",
    "ds['tdif'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger4_mean\n",
    "ds['tdif1'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger3_mean\n",
    "ds['tdif2'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger2_mean\n",
    "ds['tdif3'] = ds.sea_water_temperature_logger1_mean-ds.sea_water_temperature_logger1_mean\n",
    "ds['tdif_ctd'] = ds.sea_water_temperature_logger1_mean-ds.TEMP_CTD_MEAN\n",
    "ds['tdif_o2'] = ds.sea_water_temperature_logger1_mean-ds.TEMP_O2_MEAN\n",
    "ds['tdif_both'] = ds.TEMP_O2_MEAN-ds.TEMP_CTD_MEAN\n",
    "ds['tdif_ir'] = ds.TEMP_IR_UNCOR_MEAN-ds.TEMP_CTD_MEAN\n",
    "subset = ds.where(np.logical_not((ds.time.dt.hour>12)&(ds.spd<7.5)))\n",
    "subset = subset.where(np.logical_not((ds.time.dt.hour<6)&(ds.spd<7.5)))\n",
    "subset = subset.where(np.logical_not((ds.time>np.datetime64('2018-05-24T12')) & (ds.time<np.datetime64('2018-05-26T12'))))\n",
    "\n",
    "sdif,s1,s2 = subset.tdif.dropna('time'),subset.sea_water_temperature_logger1_mean.dropna('time'),subset.sea_water_temperature_logger4_mean.dropna('time')\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "sdif,s1,s2 = subset.tdif1.dropna('time'),subset.sea_water_temperature_logger1_mean.dropna('time'),subset.sea_water_temperature_logger3_mean.dropna('time')\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "sdif,s1,s2 = subset.tdif2.dropna('time'),subset.sea_water_temperature_logger1_mean.dropna('time'),subset.sea_water_temperature_logger2_mean.dropna('time')\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "sdif,s1,s2 = subset.tdif3.dropna('time'),subset.sea_water_temperature_logger1_mean.dropna('time'),subset.sea_water_temperature_logger1_mean.dropna('time')\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "cond = (np.isfinite(subset.sea_water_temperature_logger1_mean)) & (np.isfinite(subset.TEMP_CTD_MEAN))\n",
    "sdif,s1,s2 = subset.tdif_ctd.where(cond,drop=True),subset.sea_water_temperature_logger1_mean.where(cond,drop=True),subset.TEMP_CTD_MEAN.where(cond,drop=True)\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "cond = (np.isfinite(subset.sea_water_temperature_logger1_mean)) & (np.isfinite(subset.TEMP_O2_MEAN))\n",
    "sdif,s1,s2 = subset.tdif_o2.where(cond,drop=True),subset.sea_water_temperature_logger1_mean.where(cond,drop=True),subset.TEMP_O2_MEAN.where(cond,drop=True)\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "\n",
    "cond = (np.isfinite(subset.sea_water_temperature_logger1_mean)) & (np.isfinite(subset.TEMP_IR_UNCOR_MEAN))\n",
    "sdif,s1,s2 = subset.tdif_ir.where(cond,drop=True),subset.sea_water_temperature_logger1_mean.where(cond,drop=True),subset.TEMP_IR_UNCOR_MEAN.where(cond,drop=True)\n",
    "sdifcor = np.corrcoef(s1,s2)[0,1]\n",
    "std_robust,ilen = np.nanmedian(np.abs(sdif - np.nanmedian(sdif))) * 1.482602218505602, sdif.shape[0]\n",
    "print([sdif.mean().data,sdif.median().data,sdifcor,sdif.std().data,std_robust,\n",
    "                    np.abs(sdif).mean().data,sdif.shape[0]])\n",
    "\n",
    "\n",
    "\n",
    "print(subset.tdif.mean().data,subset.tdif.std().data,np.isfinite(subset.tdif).sum().data)\n",
    "print(subset.tdif1.mean().data,subset.tdif1.std().data,np.isfinite(subset.tdif1).sum().data)\n",
    "print(subset.tdif2.mean().data,subset.tdif2.std().data,np.isfinite(subset.tdif2).sum().data)\n",
    "print(subset.tdif3.mean().data,subset.tdif3.std().data,np.isfinite(subset.tdif3).sum().data)\n",
    "print(subset.tdif_ctd.mean().data,subset.tdif_ctd.std().data,np.isfinite(subset.tdif_ctd).sum().data)\n",
    "print(subset.tdif_o2.mean().data,subset.tdif_o2.std().data,np.isfinite(subset.tdif_o2).sum().data)\n",
    "print(subset.tdif_ir.mean().data,subset.tdif_ir.std().data,np.isfinite(subset.tdif_ir).sum().data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = (np.isfinite(subset.TEMP_CTD_MEAN)) & (np.isfinite(subset.sea_water_temperature_logger1_mean))\n",
    "subset.tdif.where(cond,drop=True).size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-6f4a4004dcca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'ds' is not defined"
     ]
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((-0.08, 0.08), (0, 5))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['tdif'] = ds.sea_water_temperature_logger4_mean-ds.sea_water_temperature_logger4_mean\n",
    "ds['tdif1'] = ds.sea_water_temperature_logger4_mean-ds.sea_water_temperature_logger3_mean\n",
    "ds['tdif2'] = ds.sea_water_temperature_logger4_mean-ds.sea_water_temperature_logger2_mean\n",
    "ds['tdif3'] = ds.sea_water_temperature_logger4_mean-ds.sea_water_temperature_logger1_mean\n",
    "ds['tdif_ctd'] = ds.sea_water_temperature_logger1_mean-ds.TEMP_CTD_MEAN\n",
    "ds['tdif_o2'] = ds.sea_water_temperature_logger1_mean-ds.TEMP_O2_MEAN\n",
    "ds['tdif_both'] = ds.TEMP_O2_MEAN-ds.TEMP_CTD_MEAN\n",
    "ds['tdif_ir'] = ds.TEMP_IR_UNCOR_MEAN-ds.TEMP_CTD_MEAN\n",
    "subset = ds.where(np.logical_not((ds.time.dt.hour>12)&(ds.spd<7.5)))\n",
    "subset = subset.where(np.logical_not((ds.time.dt.hour<6)&(ds.spd<7.5)))\n",
    "subset = subset.where(np.logical_not((ds.time>np.datetime64('2018-05-24T12')) & (ds.time<np.datetime64('2018-05-26T12'))))\n",
    "fig,axes =plt.subplots(3,2)\n",
    "ax=axes[0,0]\n",
    "ax.scatter(subset.spd,subset.tdif3,c=subset.time.dt.hour)\n",
    "#ax.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "ax.set_ylim(-.08,0.08), ax.set_xlim(0,5)\n",
    "ax=axes[0,1]\n",
    "ax.scatter(subset.spd,subset.tdif1,c=subset.time.dt.hour)\n",
    "#ax.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "ax.set_ylim(-.08,0.08), ax.set_xlim(0,5)\n",
    "ax=axes[1,0]\n",
    "ax.scatter(subset.spd,subset.tdif2,c=subset.time.dt.hour)\n",
    "ax.set(ylabel='SBE4 minus CTD SST (K)')\n",
    "ax.set_ylim(-.08,0.08), ax.set_xlim(0,5)\n",
    "ax=axes[1,1]\n",
    "ax.scatter(subset.spd,subset.tdif_ctd,c=subset.time.dt.hour)\n",
    "#ax.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "ax.set_ylim(-.08,0.08), ax.set_xlim(0,5)\n",
    "ax=axes[2,0]\n",
    "ax.scatter(subset.spd,subset.tdif_o2,c=subset.time.dt.hour)\n",
    "#ax.set(xlabel='Wind Speed (ms$^{-1}$)', ylabel='SBE4 minus CTD SST (K)')\n",
    "ax.set_ylim(-.08,0.08), ax.set_xlim(0,5)\n",
    "ax=axes[2,1]\n",
    "ax.scatter(subset.spd,subset.tdif_both,c=subset.time.dt.hour)\n",
    "ax.set(xlabel='Wind Speed (ms$^{-1}$)')\n",
    "ax.set_ylim(-.08,0.08), ax.set_xlim(0,5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset.tdif_ir.plot()\n",
    "h,xbin = np.histogram(subset.tdif_ir,bins=100,range=(-1,1))\n",
    "plt.bar(xbin[:-1],h)\n",
    "for i in range(100):\n",
    "    print(i,xbin[i],h[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_warm = subset.where(subset.tdif>.1)\n",
    "plt.scatter(ds_warm.time.dt.day,ds_warm.tdif,c=ds_warm.spd)\n",
    "#plt.scatter(ds_warm.time.dt.hour,ds_warm.tdif,c=ds_warm.spd)\n",
    "#plt.plot(ds_warm.time,ds_warm.tdif2,'r.')\n",
    "#plt.scatter(subset.time.dt.hour,subset.tdif,c=subset.spd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset1 = ds.sel(time=slice('2018-05-24T12','2018-05-26T12'))\n",
    "plt.plot(subset1.time,subset1.tdif)\n",
    "plt.plot(subset1.time,subset1.tdif2)\n",
    "plt.plot(subset1.time,subset1.spd,'.')\n",
    "plt.ylim(-.05,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset1 = subset.where(subset.time=slice('2018-05-24T12','2018-05-26T12'))\n",
    "plt.plot(subset1.time,subset1.tdif)\n",
    "plt.plot(subset1.time,subset1.tdif2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=[-0.295,-0.6,-0.985,-1.42,-1.785]\n",
    "time_offset_to_lmt=(xlon[0,:]/360.)*24.*60.*60.\n",
    "#print(time_offset_to_lmt)\n",
    "tlmt= time_usv+time_offset_to_lmt*dt.timedelta(seconds=1)\n",
    "i1save=[12800,35800,46000,61400,62080]\n",
    "i2save=[13300,36300,46700,62080,63400]\n",
    "file_str=['p1','p2','p3','p4','p5','p6','p7']\n",
    "ilen=len(i1save)\n",
    "#resample data to 30 min averages\n",
    "t1_ave = xr.DataArray(ds.sea_water_temperature_logger1_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='30min').mean()\n",
    "t2_ave = xr.DataArray(ds.TEMP_CTD_MEAN[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='30min').mean()\n",
    "t3_ave = xr.DataArray(ds.sea_water_temperature_logger2_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='30min').mean()\n",
    "t4_ave = xr.DataArray(ds.sea_water_temperature_logger3_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='30min').mean()\n",
    "t5_ave = xr.DataArray(ds.sea_water_temperature_logger4_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='30min').mean()\n",
    "ilen_ave=len(t1_ave)\n",
    "tdata=np.ones([ilen_ave,5])\n",
    "for i in range(0,ilen_ave):\n",
    "    tdata[i,0]=t1_ave[i]-t5_ave[i]\n",
    "    tdata[i,1]=t2_ave[i]-(t5_ave[i]) #-273.15)\n",
    "    tdata[i,2]=t3_ave[i]-t5_ave[i]\n",
    "    tdata[i,3]=t4_ave[i]-t5_ave[i]\n",
    "    tdata[i,4]=t5_ave[i]-t5_ave[i]\n",
    "#create xarray structure\n",
    "profiles = xr.DataArray(tdata, coords={'time': t1_ave.time,'depth':z}, dims=('time','depth'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create profiles at 1 min as well\n",
    "z=[-0.295,-0.6,-0.985,-1.42,-1.785]\n",
    "time_offset_to_lmt=(xlon[0,:]/360.)*24.*60.*60.\n",
    "#print(time_offset_to_lmt)\n",
    "tlmt= time_usv+time_offset_to_lmt*dt.timedelta(seconds=1)\n",
    "i1save=[12800,35800,46000,61400,62080]\n",
    "i2save=[13300,36300,46700,62080,63400]\n",
    "file_str=['p1','p2','p3','p4','p5','p6','p7']\n",
    "ilen=len(i1save)\n",
    "#resample data to 30 min averages\n",
    "t1_ave = xr.DataArray(ds.sea_water_temperature_logger1_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='1min').mean()\n",
    "t2_ave = xr.DataArray(ds.TEMP_CTD_MEAN[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='1min').mean()\n",
    "t3_ave = xr.DataArray(ds.sea_water_temperature_logger2_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='1min').mean()\n",
    "t4_ave = xr.DataArray(ds.sea_water_temperature_logger3_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='1min').mean()\n",
    "t5_ave = xr.DataArray(ds.sea_water_temperature_logger4_mean[0,:], coords={'time': tlmt.values}, dims=('time')).resample(time='1min').mean()\n",
    "ilen_ave=len(t1_ave)\n",
    "tdata=np.ones([ilen_ave,5])\n",
    "for i in range(0,ilen_ave):\n",
    "    tdata[i,0]=t1_ave[i]-t5_ave[i]\n",
    "    tdata[i,1]=t2_ave[i]-(t5_ave[i]) #-273.15)\n",
    "    tdata[i,2]=t3_ave[i]-t5_ave[i]\n",
    "    tdata[i,3]=t4_ave[i]-t5_ave[i]\n",
    "    tdata[i,4]=t5_ave[i]-t5_ave[i]\n",
    "#create xarray structure\n",
    "profiles_1min = xr.DataArray(tdata, coords={'time': t1_ave.time,'depth':z}, dims=('time','depth'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check the NEDT\n",
    "#smooth by 10 minutes using hanning window\n",
    "#calculate STD on temp - smoothed(temp)\n",
    "t=ds.time[0,:]\n",
    "s1=ds.sea_water_temperature_logger1_mean[0,:]\n",
    "s2=smooth(s1,10)\n",
    "d=s1-s2[5:-4]\n",
    "print(d.std(ddof=1))\n",
    "t=ds.time[0,:]\n",
    "s1=ds.sea_water_temperature_logger2_mean[0,:]\n",
    "s2=smooth(s1,10)\n",
    "d=s1-s2[5:-4]\n",
    "print(d.std(ddof=1))\n",
    "t=ds.time[0,:]\n",
    "s1=ds.sea_water_temperature_logger3_mean[0,:]\n",
    "s2=smooth(s1,10)\n",
    "d=s1-s2[5:-4]\n",
    "print(d.std(ddof=1))\n",
    "t=ds.time[0,:]\n",
    "s1=ds.sea_water_temperature_logger4_mean[0,:]\n",
    "s2=smooth(s1,10)\n",
    "d=s1-s2[5:-4]\n",
    "print(d.std(ddof=1))\n",
    "t=ds.time[0,:]\n",
    "s1=ds.TEMP_CTD_MEAN[0,:]\n",
    "s2=smooth(s1,10)\n",
    "d=s1-s2[5:-4]\n",
    "print(d.std(ddof=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make plots of times with diurnal warming, PLOT LOCAL TIME\n",
    "time_offset_to_lmt=(xlon[0,:]/360.)*24.*60.*60.\n",
    "tlmt= ds.time[0,:]+time_offset_to_lmt* dt.timedelta(seconds=1)\n",
    "i1save=[12800,35800,46000,61600,63080]\n",
    "i2save=[13300,36300,46700,63080,63800]\n",
    "file_str=['p1','p2','p3','p4','p5','p6','p7']\n",
    "ilen=len(i1save)\n",
    "for ii in range(0,ilen):\n",
    "    i1=i1save[ii]\n",
    "    i2=i2save[ii]\n",
    "#    t=ds.TIME[0,:].isel(obs=slice(i1,i2)) #UTC time\n",
    "    t=tlmt.isel(obs=slice(i1,i2)) #local time\n",
    "    s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))\n",
    "    s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s0mask = np.isfinite(s0)\n",
    "    s1mask = np.isfinite(s1)\n",
    "    s2mask = np.isfinite(s2)\n",
    "    s3mask = np.isfinite(s3)\n",
    "    s4mask = np.isfinite(s4)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "    ax.set(xlabel='Local Date and Time', ylabel='Temperature (K)')\n",
    "    # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    #ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "    tem=str(ds.TEMP_CTD_MEAN.installed_height)+' m'\n",
    "    ax.legend([ds.sea_water_temperature_logger1_mean.installed_height,tem,ds.sea_water_temperature_logger2_mean.installed_height,ds.sea_water_temperature_logger3_mean.installed_height,ds.sea_water_temperature_logger4_mean.installed_height])\n",
    "    fig.savefig(adir+'figs/diurnal/temp_loggers_'+file_str[ii]+'.png')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make plots of times with diurnal warming\n",
    "#add wind speed to figure\n",
    "i1save=[12800,35800,46000,61600,63080]\n",
    "i2save=[13300,36300,46700,63080,63800]\n",
    "ilen_str=['p1','p2','p3','p4','p5','p6','p7']\n",
    "ilen=len(i1save)\n",
    "for ii in range(0,ilen):\n",
    "    i1=i1save[ii]\n",
    "    i2=i2save[ii]\n",
    "#    t=ds.TIME[0,:].isel(obs=slice(i1,i2)) #UTC time\n",
    "    t=tlmt.isel(obs=slice(i1,i2)) #local time\n",
    "    tem2=(tlmt[i1].values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    tem3=datetime.utcfromtimestamp(tem2)\n",
    "    day_of_year = tem3.timetuple().tm_yday\n",
    "\n",
    "    s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))\n",
    "    s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2)) #-273.15\n",
    "    s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    wnd=(ds.UWND_MEAN[0,:].isel(obs=slice(i1,i2))**2+ds.VWND_MEAN[0,:].isel(obs=slice(i1,i2))**2)**.5\n",
    "    s0mask = np.isfinite(s0)\n",
    "    s1mask = np.isfinite(s1)\n",
    "    s2mask = np.isfinite(s2)\n",
    "    s3mask = np.isfinite(s3)\n",
    "    s4mask = np.isfinite(s4)\n",
    "    wmask = np.isfinite(wnd)\n",
    "    \n",
    "    fig = plt.figure(figsize=(8,6))\n",
    "    ax = fig.add_axes([0.1, 0.5, 0.8, 0.35])\n",
    "#    fig, ax = plt.subplots()\n",
    "    ax2 = ax.twinx()\n",
    "    ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "    ax2.plot_date(t[wmask], wnd[wmask],'k-',markersize=.5,lw=1)\n",
    "    ax.set(xlabel='Date', ylabel='Temperature (K)')\n",
    "    ax2.set(ylabel='Wind Speed (ms$^{-1}$)')\n",
    "    # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    #ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "    tem=str(ds.TEMP_CTD_MEAN.installed_height)+' m'\n",
    "    ax.legend([ds.sea_water_temperature_logger1_mean.installed_height,tem,ds.sea_water_temperature_logger2_mean.installed_height,ds.sea_water_temperature_logger3_mean.installed_height,ds.sea_water_temperature_logger4_mean.installed_height])\n",
    "\n",
    "    \n",
    "    s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))-(ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))-273.15)\n",
    "    s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    #s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    wnd=(ds.UWND_MEAN[0,:].isel(obs=slice(i1,i2))**2+ds.VWND_MEAN[0,:].isel(obs=slice(i1,i2))**2)**.5\n",
    "    s0mask = np.isfinite(s0)\n",
    "    s1mask = np.isfinite(s1)\n",
    "    s2mask = np.isfinite(s2)\n",
    "    s3mask = np.isfinite(s3)\n",
    "    #s4mask = np.isfinite(s4)\n",
    "    wmask = np.isfinite(wnd)\n",
    "    \n",
    "    #fig, ax = plt.subplots()\n",
    "    ax = fig.add_axes([0.1, 0.1, 0.8, 0.35])\n",
    "    ax2 = ax.twinx()\n",
    "    ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "    #ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "    ax2.plot_date(t[wmask], wnd[wmask],'k-',markersize=.5,lw=1)\n",
    "    ax.set(xlabel='Date', ylabel='Temperature (K)')\n",
    "    ax2.set(ylabel='Wind Speed (ms$^{-1}$)')\n",
    "    # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    #ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "    tem1=ds.sea_water_temperature_logger1_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem2=str(ds.TEMP_CTD_MEAN.installed_height)+' m - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem3=ds.sea_water_temperature_logger2_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem4=ds.sea_water_temperature_logger3_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    ax.legend([tem1,tem2,tem3,tem4])\n",
    "    fig.savefig(adir+'figs/diurnal/temp_loggers_sst_Dsst_'+str(day_of_year)+'.png')\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    #fig.savefig(adir+'temp_loggers_sst_'+str(day_of_year)+'.png')\n",
    "    #plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make plots of times with diurnal warming\n",
    "#add wind speed to figure\n",
    "i1save=[12800,35800,46000,61600,63080]\n",
    "i2save=[13300,36300,46700,63080,63800]\n",
    "file_str=['q1','q2','q3','q4','q5','q6','q7']\n",
    "ilen=len(i1save)\n",
    "for ii in range(0,ilen):\n",
    "    i1=i1save[ii]\n",
    "    i2=i2save[ii]\n",
    "#    t=ds.TIME[0,:].isel(obs=slice(i1,i2)) #UTC time\n",
    "    t=tlmt.isel(obs=slice(i1,i2)) #local time\n",
    "    tem2=(tlmt[i1].values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    tem3=datetime.utcfromtimestamp(tem2)\n",
    "    day_of_year = tem3.timetuple().tm_yday\n",
    "\n",
    "    s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))-(ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))) #-273.15)\n",
    "    s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    #s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    wnd=(ds.UWND_MEAN[0,:].isel(obs=slice(i1,i2))**2+ds.VWND_MEAN[0,:].isel(obs=slice(i1,i2))**2)**.5\n",
    "    s0mask = np.isfinite(s0)\n",
    "    s1mask = np.isfinite(s1)\n",
    "    s2mask = np.isfinite(s2)\n",
    "    s3mask = np.isfinite(s3)\n",
    "    #s4mask = np.isfinite(s4)\n",
    "    wmask = np.isfinite(wnd)\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    ax2 = ax.twinx()\n",
    "    ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "    #ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "    ax2.plot_date(t[wmask], wnd[wmask],'k-',markersize=.5,lw=1)\n",
    "    ax.set(xlabel='Date', ylabel='Temperature (K)')\n",
    "    ax2.set(ylabel='Wind Speed (ms$^{-1}$)')\n",
    "    # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    #ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "    tem1=ds.sea_water_temperature_logger1_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem2=str(ds.TEMP_CTD_MEAN.installed_height)+' m - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem3=ds.sea_water_temperature_logger2_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem4=ds.sea_water_temperature_logger3_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    ax.legend([tem1,tem2,tem3,tem4])\n",
    "    fig.savefig(adir+'figs/diurnal/temp_loggers_Dsst_'+str(day_of_year)+'.png')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot a slice \n",
    "#i1save=[12800,35800,46000,61400,62080]\n",
    "#i2save=[13300,36300,46700,62080,63400]\n",
    "i1=62080\n",
    "i2=85592\n",
    "t=ds.time[0,:].isel(obs=slice(i1,i2))\n",
    "s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))\n",
    "s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "wnd=(ds.UWND_MEAN[0,:].isel(obs=slice(i1,i2))**2+ds.VWND_MEAN[0,:].isel(obs=slice(i1,i2))**2)**.5\n",
    "s0mask = np.isfinite(s0)\n",
    "s1mask = np.isfinite(s1)\n",
    "s2mask = np.isfinite(s2)\n",
    "s3mask = np.isfinite(s3)\n",
    "s4mask = np.isfinite(s4)\n",
    "wmask = np.isfinite(wnd)\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "ax2.plot_date(t[wmask], wnd[wmask],'-',markersize=.5,lw=1)\n",
    "ax.set(xlabel='Date', ylabel='Temperature (K)')\n",
    "# rotate and align the tick labels so they look better\n",
    "fig.autofmt_xdate()\n",
    "#ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "fig.savefig(adir+'temp_loggers_p3.png')\n",
    "tem=str(ds.TEMP_CTD_MEAN.installed_height)+' m'\n",
    "ax.legend([ds.sea_water_temperature_logger1_mean.installed_height,tem,ds.sea_water_temperature_logger2_mean.installed_height,ds.sea_water_temperature_logger3_mean.installed_height,ds.sea_water_temperature_logger4_mean.installed_height])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.UWND_MEAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.sea_water_temperature_logger4_mean[0:12000].std(ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig, ax = plt.subplots()\n",
    "#ax.plot_date(t,s1,'-',markersize=.5,lw=1)\n",
    "#ax.plot_date(t,s2[5:-4],'-',markersize=.5,lw=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(tlmt[0:5])\n",
    "ds.time[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fig, ax = plt.subplots()\n",
    "#for i in range(420,450):\n",
    "#    ax.plot(tdata[i,:],z,'-',markersize=.5,lw=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#resample data to 30 min averages\n",
    "#profiles = xr.DataArray(tdata, coords={'time': tdata_time,'depth':z}, dims=('time','depth'))\n",
    "#tlmt[i1].year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "#i1save=[12800,35800,46000,61400,62080]\n",
    "#i2save=[13300,36300,46700,62080,63400]\n",
    "i1save=[12800,35800,46000,61600,63080]\n",
    "i2save=[13300,36300,46700,63080,63800]\n",
    "\n",
    "file_str=['r1','r2','r3','r4','r5','r6','r7']\n",
    "ilen=len(i1save)\n",
    "for ii in range(0,ilen):\n",
    "    i1=i1save[ii]\n",
    "    i2=i2save[ii]\n",
    "\n",
    "    tem2=(tlmt[i1].values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    tem3=datetime.utcfromtimestamp(tem2)\n",
    "    day_of_year = tem3.timetuple().tm_yday\n",
    "   \n",
    "    mn = xr.open_dataset(adir+'sat_data/virs_sst/sst'+str(day_of_year)+'.nc')\n",
    "    profile_subset = profiles_1min.sel(time=slice(tlmt[i1].values,tlmt[i2].values))\n",
    "    #tdata_subset = tdata.sel(time=slice(tlmt[i1].values,tlmt[i2].values))\n",
    "    fig = plt.figure(figsize=(8,3))\n",
    "    ax = fig.add_axes([0.08, 0.3, 0.15, 0.65])\n",
    "    #x = profile_subset.time\n",
    "    #calculate hours from 0:00 on day in LMT\n",
    "    x = profile_subset.time\n",
    "    x2=(x.values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    x3=datetime.utcfromtimestamp(x2[0])\n",
    "    #print(x3.hour+(x2-x2[0])/60/60)\n",
    "    x = x3.hour+(x2-x2[0])/60/60\n",
    "    y = profile_subset.depth\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    c = profile_subset.values.T\n",
    "    s = ax.contourf(X, Y, c, 8, alpha=.75, cmap=cm.YlOrBr, origin='lower', vmax=1, vmin=0)\n",
    "        # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    colorbar_ax = fig.add_axes([0.08, 0.2, 0.15, 0.03])\n",
    "    fig.colorbar(s, cax=colorbar_ax,orientation='horizontal',label='$\\Delta$SST [K]',ticks=[0,.5,1])\n",
    "    #fig.set_label('$\\Delta$SST [K]')\n",
    "    \n",
    "    ax2 = fig.add_axes([.32, 0.15, 0.2, 0.8])\n",
    "    for i in range(0,len(profile_subset.time)):\n",
    "        ax2.plot(profile_subset[i,:].values,z,'-',markersize=.5,lw=1)\n",
    "        ax2.plot(profile_subset[i,:].values,z,'.',markersize=.5,lw=1)\n",
    "    ax2.set_xlabel('$\\Delta$SST (K)')\n",
    "    ax2.set_ylabel('Depth (m)')\n",
    "\n",
    "    #blue marble image of ssts for cruise\n",
    "    ax3 = fig.add_axes([.55, 0.1, 0.2, 0.85])\n",
    "    sub_lons=lons_usv[i1:i2].values\n",
    "    sub_lats=lats_usv[i1:i2].values\n",
    "    cmap = colorbrewer.get_map('Spectral', 'diverging', 11, reverse=True).mpl_colormap\n",
    "    m = Basemap(projection='merc', lat_0 = 27, lon_0 = -120,    resolution = 'l', area_thresh = 0.1,    \n",
    "                                  llcrnrlon=-124, llcrnrlat=27,urcrnrlon=-112, urcrnrlat=38)\n",
    "    m.bluemarble()\n",
    "    #    m.fillcontinents(color='grey',lake_color='white')\n",
    "    lon_grid, lat_grid = np.meshgrid(mn.lon, mn.lat)\n",
    "    x,y = m(lon_grid,lat_grid)\n",
    "    cs = m.pcolormesh(x,y, mn.sea_surface_temperature - 273.15, cmap=cmap, vmin = 12, vmax = 19)\n",
    "    x,y = m(sub_lons,sub_lats)\n",
    "    m.plot(x,y,'w', linewidth=3)\n",
    "    m.plot(x,y,color='magenta', linewidth=1)\n",
    "    m.colorbar(location=\"bottom\",label='SST [C]') # draw colorbar\n",
    "\n",
    "    #blue marble image of ssts for cruise\n",
    "    ax4 = fig.add_axes([.77, 0.1, 0.2, 0.85])\n",
    "    sub_lons=lons_usv[i1:i2].values\n",
    "    sub_lats=lats_usv[i1:i2].values\n",
    "    cmap = colorbrewer.get_map('Spectral', 'diverging', 11, reverse=True).mpl_colormap\n",
    "    m = Basemap(projection='merc', lat_0 = 27, lon_0 = -120,    resolution = 'l', area_thresh = 0.1,    \n",
    "                                  llcrnrlon=min(sub_lons)-.1, llcrnrlat=min(sub_lats)-.1,urcrnrlon=max(sub_lons)+.1, urcrnrlat=max(sub_lats)+.1)\n",
    "    m.bluemarble()\n",
    "    #    m.fillcontinents(color='grey',lake_color='white')\n",
    "    lon_grid, lat_grid = np.meshgrid(mn.lon, mn.lat)\n",
    "    x,y = m(lon_grid,lat_grid)\n",
    "    cs = m.pcolormesh(x,y, mn.sea_surface_temperature - 273.15, cmap=cmap, vmin = 12, vmax = 19)\n",
    "    x,y = m(sub_lons,sub_lats)\n",
    "    m.plot(x,y,'w', linewidth=3)\n",
    "    m.plot(x,y,color='magenta', linewidth=1)\n",
    "    m.colorbar(location=\"bottom\",label='SST [C]') # draw colorbar\n",
    "    fig.savefig(adir+'figs/diurnal/temp_loggers_1min_diurnal'+str(day_of_year)+'.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make plots of times with diurnal warming\n",
    "#add wind speed to figure\n",
    "#i1save=[12800,35800,46000,61400,62000]\n",
    "#i2save=[13300,36300,46700,62080,63400]\n",
    "i1save=[12800,35800,46000,61600,63080]\n",
    "i2save=[13300,36300,46700,63080,63800]\n",
    "\n",
    "file_str=['p1','p2','p3','p4','p5','p6','p7']\n",
    "ilen=len(i1save)\n",
    "for ii in range(0,ilen):\n",
    "    i1=i1save[ii]\n",
    "    i2=i2save[ii]\n",
    "#    t=ds.TIME[0,:].isel(obs=slice(i1,i2)) #UTC time\n",
    "    t=tlmt.isel(obs=slice(i1,i2)) #local time\n",
    "    tem2=(tlmt[i1].values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    tem3=datetime.utcfromtimestamp(tem2)\n",
    "    day_of_year = tem3.timetuple().tm_yday\n",
    "\n",
    "    s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))\n",
    "    s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))#-273.15\n",
    "    wnd=(ds.UWND_MEAN[0,:].isel(obs=slice(i1,i2))**2+ds.VWND_MEAN[0,:].isel(obs=slice(i1,i2))**2)**.5\n",
    "    s0mask = np.isfinite(s0)\n",
    "    s1mask = np.isfinite(s1)\n",
    "    s2mask = np.isfinite(s2)\n",
    "    s3mask = np.isfinite(s3)\n",
    "    s4mask = np.isfinite(s4)\n",
    "    wmask = np.isfinite(wnd)\n",
    "    \n",
    "    fig = plt.figure(figsize=(8,10))\n",
    "    ax = fig.add_axes([0.1, 0.8, 0.8, 0.2])\n",
    "#    fig, ax = plt.subplots()\n",
    "    ax2 = ax.twinx()\n",
    "    ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "    ax2.plot_date(t[wmask], wnd[wmask],'k-',markersize=.5,lw=1)\n",
    "    ax.set(xlabel='Date', ylabel='SST (C)')\n",
    "    ax2.set(ylabel='Wind Speed (ms$^{-1}$)')\n",
    "    # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    #ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "    tem=str(ds.TEMP_CTD_MEAN.installed_height)+' m'\n",
    "    ax.legend([ds.sea_water_temperature_logger1_mean.installed_height,tem,ds.sea_water_temperature_logger2_mean.installed_height,ds.sea_water_temperature_logger3_mean.installed_height,ds.sea_water_temperature_logger4_mean.installed_height])\n",
    "\n",
    "    \n",
    "    s0=ds.TEMP_CTD_MEAN[0,:].isel(obs=slice(i1,i2))-(ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))) #-273.15)\n",
    "    s1=ds.sea_water_temperature_logger1_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    s2=ds.sea_water_temperature_logger2_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    s3=ds.sea_water_temperature_logger3_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    #s4=ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))-ds.sea_water_temperature_logger4_mean[0,:].isel(obs=slice(i1,i2))\n",
    "    wnd=(ds.UWND_MEAN[0,:].isel(obs=slice(i1,i2))**2+ds.VWND_MEAN[0,:].isel(obs=slice(i1,i2))**2)**.5\n",
    "    s0mask = np.isfinite(s0)\n",
    "    s1mask = np.isfinite(s1)\n",
    "    s2mask = np.isfinite(s2)\n",
    "    s3mask = np.isfinite(s3)\n",
    "    #s4mask = np.isfinite(s4)\n",
    "    wmask = np.isfinite(wnd)\n",
    "    \n",
    "    #fig, ax = plt.subplots()\n",
    "    ax = fig.add_axes([0.1, 0.55, 0.8, 0.2])\n",
    "    ax2 = ax.twinx()\n",
    "    ax.plot_date(t[s1mask], s1[s1mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s0mask], s0[s0mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s2mask], s2[s2mask],'-',markersize=.5,lw=1)\n",
    "    ax.plot_date(t[s3mask], s3[s3mask],'-',markersize=.5,lw=1)\n",
    "    #ax.plot_date(t[s4mask], s4[s4mask],'-',markersize=.5,lw=1)\n",
    "    ax2.plot_date(t[wmask], wnd[wmask],'k-',markersize=.5,lw=1)\n",
    "    ax.set(xlabel='Date', ylabel='$\\Delta$SST (K)')\n",
    "    ax2.set(ylabel='Wind Speed (ms$^{-1}$)')\n",
    "    # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    #ax.fmt_xdata = t.DateFormatter('%Y-%m-%d')\n",
    "    tem1=ds.sea_water_temperature_logger1_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem2=str(ds.TEMP_CTD_MEAN.installed_height)+' m - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem3=ds.sea_water_temperature_logger2_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    tem4=ds.sea_water_temperature_logger3_mean.installed_height+' - '+ds.sea_water_temperature_logger4_mean.installed_height\n",
    "    ax.legend([tem1,tem2,tem3,tem4])\n",
    "    \n",
    "    \n",
    "    tem2=(tlmt[i1].values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    tem3=datetime.utcfromtimestamp(tem2)\n",
    "    day_of_year = tem3.timetuple().tm_yday\n",
    "   \n",
    "    mn = xr.open_dataset(adir+'sat_data/virs_sst/sst'+str(day_of_year)+'.nc')\n",
    "    profile_subset = profiles_1min.sel(time=slice(tlmt[i1].values,tlmt[i2].values))\n",
    "    #tdata_subset = tdata.sel(time=slice(tlmt[i1].values,tlmt[i2].values))\n",
    "    #fig = plt.figure(figsize=(8,3))\n",
    "    ax = fig.add_axes([0.1, 0.3, 0.8, 0.2])\n",
    "    #x = profile_subset.time\n",
    "    #calculate hours from 0:00 on day in LMT\n",
    "    x = profile_subset.time\n",
    "    #x2=(x.values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "    #x3=datetime.utcfromtimestamp(x2[0])\n",
    "    #print(x3.hour+(x2-x2[0])/60/60)\n",
    "    #x = x3.hour+(x2-x2[0])/60/60\n",
    "    y = profile_subset.depth\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    c = profile_subset.values.T\n",
    "    s = ax.contourf(X, Y, c, 8, cmap=cm.YlOrBr, origin='lower', vmax=1, vmin=0)\n",
    "        # rotate and align the tick labels so they look better\n",
    "    fig.autofmt_xdate()\n",
    "    m = plt.cm.ScalarMappable(cmap=cm.YlOrBr)\n",
    "    m.set_array(c)\n",
    "    m.set_clim(0., 1.)\n",
    "    colorbar_ax = fig.add_axes([0.1, 0.248, 0.8, 0.015])\n",
    "    fig.colorbar(m, cax=colorbar_ax,orientation='horizontal',label='$\\Delta$SST [K]',ticks=[0,.25,.5,.75,1])\n",
    "    #fig.set_label('$\\Delta$SST [K]')\n",
    "    \n",
    "    ax2 = fig.add_axes([.1, 0.05, 0.4, 0.15])\n",
    "    for i in range(0,len(profile_subset.time)):\n",
    "        ax2.plot(profile_subset[i,:].values,z,'-',markersize=.5,lw=1)\n",
    "        ax2.plot(profile_subset[i,:].values,z,'.',markersize=.5,lw=1)\n",
    "    ax2.set_xlabel('$\\Delta$SST (K)')\n",
    "    ax2.set_ylabel('Depth (m)')\n",
    "\n",
    "    #blue marble image of ssts for cruise\n",
    "    ax3 = fig.add_axes([.55, 0.05, 0.15, 0.15])\n",
    "    sub_lons=lons_usv[i1:i2].values\n",
    "    sub_lats=lats_usv[i1:i2].values\n",
    "    cmap = colorbrewer.get_map('Spectral', 'diverging', 11, reverse=True).mpl_colormap\n",
    "    m = Basemap(projection='merc', lat_0 = 27, lon_0 = -120,    resolution = 'l', area_thresh = 0.1,    \n",
    "                                  llcrnrlon=-124, llcrnrlat=27,urcrnrlon=-112, urcrnrlat=38)\n",
    "    m.bluemarble()\n",
    "    #    m.fillcontinents(color='grey',lake_color='white')\n",
    "    lon_grid, lat_grid = np.meshgrid(mn.lon, mn.lat)\n",
    "    x,y = m(lon_grid,lat_grid)\n",
    "    cs = m.pcolormesh(x,y, mn.sea_surface_temperature - 273.15, cmap=cmap, vmin = 12, vmax = 19)\n",
    " #   cs = m.pcolormesh(x,y, mn.sea_surface_temperature , cmap=cmap, vmin = 12, vmax = 19)\n",
    "    x,y = m(sub_lons,sub_lats)\n",
    "    m.plot(x,y,'w', linewidth=3)\n",
    "    m.plot(x,y,color='magenta', linewidth=1)\n",
    "    m.colorbar(location=\"bottom\",label='SST [C]') # draw colorbar\n",
    "\n",
    "    #blue marble image of ssts for cruise\n",
    "    ax4 = fig.add_axes([.75, 0.05, 0.15, 0.15])\n",
    "    sub_lons=lons_usv[i1:i2].values\n",
    "    sub_lats=lats_usv[i1:i2].values\n",
    "    cmap = colorbrewer.get_map('Spectral', 'diverging', 11, reverse=True).mpl_colormap\n",
    "    m = Basemap(projection='merc', lat_0 = 27, lon_0 = -120,    resolution = 'l', area_thresh = 0.1,    \n",
    "                                  llcrnrlon=min(sub_lons)-.1, llcrnrlat=min(sub_lats)-.1,urcrnrlon=max(sub_lons)+.1, urcrnrlat=max(sub_lats)+.1)\n",
    "    m.bluemarble()\n",
    "    #    m.fillcontinents(color='grey',lake_color='white')\n",
    "    lon_grid, lat_grid = np.meshgrid(mn.lon, mn.lat)\n",
    "    x,y = m(lon_grid,lat_grid)\n",
    "#    cs = m.pcolormesh(x,y, mn.sea_surface_temperature , cmap=cmap, vmin = 12, vmax = 19)\n",
    "    cs = m.pcolormesh(x,y, mn.sea_surface_temperature - 273.15, cmap=cmap, vmin = 12, vmax = 19)\n",
    "    x,y = m(sub_lons,sub_lats)\n",
    "    m.plot(x,y,'w', linewidth=3)\n",
    "    m.plot(x,y,color='magenta', linewidth=1)\n",
    "    m.colorbar(location=\"bottom\",label='SST [C]') # draw colorbar\n",
    "#    fig.savefig(adir+'temp_loggers__diurnal'+str(day_of_year)+'.png')\n",
    "    \n",
    "    \n",
    "    fig.savefig(adir+'figs/diurnal/temp_loggers_1min_all_'+str(day_of_year)+'.png')\n",
    " #   plt.show()\n",
    "    \n",
    "    \n",
    "    #fig.savefig(adir+'temp_loggers_sst_'+str(day_of_year)+'.png')\n",
    "    #plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_subset = profiles.sel(time=slice(tlmt[i1].values,tlmt[i2].values))\n",
    "#tdata_subset = tdata.sel(time=slice(tlmt[i1].values,tlmt[i2].values))\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.1, 0.1, 0.6, 0.8])\n",
    "x = profile_subset.time\n",
    "y = profile_subset.depth\n",
    "X, Y = np.meshgrid(x, y)\n",
    "c = profile_subset.values.T\n",
    "s = ax.contourf(X, Y, c, 8, alpha=.75, cmap=cm.YlOrBr, origin='lower', vmax=1, vmin=0)\n",
    "m = plt.cm.ScalarMappable(cmap=cm.YlOrBr)\n",
    "m.set_array(c)\n",
    "m.set_clim(0., 1.)\n",
    "colorbar_ax = fig.add_axes([0.71, 0.1, 0.05, 0.8])\n",
    "fig.colorbar(m, cax=colorbar_ax)\n",
    "\n",
    "#ax2 = fig.add_axes([.95, 0.1, 0.3, 0.8])\n",
    "#for i in range(0,len(profile_subset.time)):\n",
    "#    ax2.plot(profile_subset[i,:].values,z,'-',markersize=.5,lw=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x = profile_subset.time\n",
    "x2=(x.values-np.datetime64('1970-01-01'))/np.timedelta64(1,'s')\n",
    "x3=datetime.utcfromtimestamp(x2[0])\n",
    "print(x3.hour+(x2-x2[0])/60/60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(8,3))\n",
    "ax = fig.add_axes([0.08, 0.3, 0.15, 0.7])\n",
    "x = profile_subset.time\n",
    "y = profile_subset.depth\n",
    "X, Y = np.meshgrid(x, y)\n",
    "c = profile_subset.values.T\n",
    "s = ax.contourf(X, Y, c, 8, alpha=.75, cmap=cm.YlOrBr, origin='lower', vmax=1, vmin=0)\n",
    "        # rotate and align the tick labels so they look better\n",
    "fig.autofmt_xdate()\n",
    "colorbar_ax = fig.add_axes([0.05, 0.05, 0.15, 0.03])\n",
    "fig.colorbar(s, cax=colorbar_ax,orientation='horizontal',label='$\\Delta$SST [K]',ticks=[0,.5,1])\n",
    "font = {'size'   : 8}\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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