plot_depthfreq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Depth-frequency plots\n",
    "\n",
    "Loads depth-frequency-time data and then plots time-averaged depth-frequency power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "Necessary modules for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-20T00:44:44.829600Z",
     "start_time": "2023-06-20T00:44:43.274426Z"
    },
    "code_folding": [
     0
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   "outputs": [],
   "source": [
    "# import modules\n",
    "\n",
    "import xarray as xr\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.dates as pldates\n",
    "import numpy as np\n",
    "for i in range(2):\n",
    "    %matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-20T00:44:55.588215Z",
     "start_time": "2023-06-20T00:44:55.503600Z"
    },
    "code_folding": [
     7,
     13
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import data\n",
    "\n",
    "adcp = 'Axis75'     # Slope(2013,2014,2017,2018), Axis75(2013,2014), Axis55(2017,2018)\n",
    "year = 2013\n",
    "ds_in = xr.open_dataset(f'../Data/data/spectra/spectra_{adcp}_{year}_0.nc')\n",
    "\n",
    "n_seg = ds_in.n_seg\n",
    "if n_seg > 1:\n",
    "    ds = [ds_in]\n",
    "    for i in range(n_seg):\n",
    "        if i > 0:\n",
    "            ds_temp = xr.open_dataset(f'../Data/data/spectra/spectra_{adcp}_{year}_{i}.nc')\n",
    "            ds.append(ds_temp)\n",
    "elif n_seg == 1:\n",
    "    ds = [ds_in]\n",
    "\n",
    "#print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-20T00:44:56.197930Z",
     "start_time": "2023-06-20T00:44:56.193547Z"
    },
    "code_folding": [
     0
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   },
   "outputs": [],
   "source": [
    "# extract plotting variables\n",
    "\n",
    "t_stamp = int(ds[0].t_stamp)\n",
    "start_date = dt.datetime(t_stamp,1,1)                                   # input start date in YYYY,MM,DD\n",
    "end_date = dt.datetime(t_stamp+1,1,1)                                     # input end date in YYYY,MM,DD\n",
    "depth = ds[0].depth.values\n",
    "d = len(depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Depth-frequency PSD plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
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   },
   "outputs": [],
   "source": [
    "# time-average at each depth\n",
    "\n",
    "t_tot = 0                          # for weighted averaging\n",
    "for i in range(n_seg):\n",
    "    t_tot += len(ds[i].spect)\n",
    "\n",
    "Sxxu_t,Sxxv_t = [],[]\n",
    "for i in range(n_seg):\n",
    "    t_weight = (len(ds[i].spect)) / t_tot\n",
    "    Sxxu_temp = ds[i].Sxxu.mean(dim='spect') * t_weight\n",
    "    Sxxu_t.append(np.asarray(Sxxu_temp.values))\n",
    "    Sxxv_temp = ds[i].Sxxv.mean(dim='spect') * t_weight\n",
    "    Sxxv_t.append(np.asarray(Sxxv_temp.values))\n",
    "    \n",
    "Sxxu_tm,Sxxv_tm = [Sxxu_t[0]],[Sxxv_t[0]]\n",
    "for i in range(1,n_seg):\n",
    "    Sxxu_tm += Sxxu_t[i]\n",
    "    Sxxv_tm += Sxxv_t[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "u_WKB,v_WKB = [],[]\n",
    "\n",
    "for j in range(d):\n",
    "    u_WKB.append(Sxxu_tm[0][j] / ds[0].WKB_scale[j].values)          # [list][depth][frequency]\n",
    "    v_WKB.append(Sxxv_tm[0][j] / ds[0].WKB_scale[j].values)          # [list][depth][frequency]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "band = 'Tidal'           # Tidal, Subdiurnal, Continuum\n",
    "\n",
    "fig,(ax0,ax1) = plt.subplots(2,1,figsize=(7.2,6),sharey=True,sharex=True)\n",
    "fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.5, 0.94, f'{adcp} - {t_stamp}', ha='center', fontsize=14)\n",
    "fig.text(0.005, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.965, 0.5, 'PSD [(m/s)$^2$/Hz]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.02, 'Frequency [Hz]', ha='center',fontsize=14)\n",
    "fig.text(0.21, 0.887, 'WKB-scaled', ha='center',fontsize=14)\n",
    "\n",
    "if band == 'Tidal':\n",
    "    ax1.set_xlim(1e-6,1e-4)\n",
    "    for i in [ax0,ax1]:\n",
    "        if adcp == 'Slope':\n",
    "            pltdepth = -125\n",
    "        elif adcp == 'Axis75' or adcp == 'Axis55':\n",
    "            pltdepth = -720\n",
    "        pltcolor = 'white'\n",
    "        i.axvline(1.161e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8) # constituents\n",
    "        i.axvline(1.732e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)    \n",
    "        i.axvline(2.236e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)\n",
    "        i.text(0.90e-5,pltdepth,'K$_1$',fontsize=15,color=pltcolor)\n",
    "        i.text(1.40e-5,pltdepth,'$f$',fontsize=15,color=pltcolor)\n",
    "        i.text(2.30e-5,pltdepth,'M$_2$',fontsize=15,color=pltcolor)    \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.477, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e1\n",
    "        vmax = 2e2\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.477, 'Along-slope', ha='center', fontsize=14)\n",
    "    \n",
    "im0 = ax0.pcolormesh(ds[0].specf, -depth, u_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "im1 = ax1.pcolormesh(ds[0].specf, -depth, v_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "\n",
    "cbar0 = fig.colorbar(im0, ax=ax0, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar0.ax.tick_params(labelsize=14)\n",
    "cbar1.ax.tick_params(labelsize=14)\n",
    "\n",
    "ax0.patch.set_facecolor('grey')\n",
    "ax1.patch.set_facecolor('grey')\n",
    "ax0.set_xscale('log')\n",
    "ax1.set_xscale('log')\n",
    "\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthfreq_plots/depthfreq_psd_{band}_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# save PSD data to .nc for inter-annual comparison\n",
    "\n",
    "#for i in range(n_seg):\n",
    "ds_out = xr.Dataset( \n",
    "    data_vars=dict(\n",
    "        u_PSD_WKB=(['depth','specf'], u_WKB),        # low-pass data\n",
    "        v_PSD_WKB=(['depth','specf'], v_WKB),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        depth=depth,                        # depth values\n",
    "        specf=ds[0].specf.values,           # datetime values in segments\n",
    "    ),\n",
    "    attrs=dict(\n",
    "        description=f'Depth-freq WKB PSD data for {adcp} {t_stamp}.',\n",
    "        adcp=adcp,                   # adcp\n",
    "        #upidx=upidx,                 # upper depth index\n",
    "        #lowidx=lowidx,               # lower depth index\n",
    "        #dup_stamp=dup_stamp,         # upper depth stamp\n",
    "        #dlow_stamp=dlow_stamp,       # lower depth stamp\n",
    "        t_stamp=t_stamp,             # year stamp\n",
    "        #t=t,                         # length of time series\n",
    "        #d=d,                         # length of depth series\n",
    "        n_seg=n_seg,                 # total number of segments\n",
    "    ),\n",
    ") \n",
    "ds_out.to_netcdf(f'../Data/interannual/depthfreq_psd_{adcp}_{t_stamp}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Depth-frequency rotary plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
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   },
   "outputs": [],
   "source": [
    "# time-average at each depth\n",
    "\n",
    "t_tot = 0                          # for weighted averaging\n",
    "for i in range(n_seg):\n",
    "    t_tot += len(ds[i].spect)\n",
    "\n",
    "cw_t,ccw_t = [],[]\n",
    "for i in range(n_seg):\n",
    "    t_weight = (len(ds[i].spect)) / t_tot\n",
    "    cw_temp = ds[i].cw.mean(dim='spect') * t_weight\n",
    "    cw_t.append(np.asarray(cw_temp.values))\n",
    "    ccw_temp = ds[i].ccw.mean(dim='spect') * t_weight\n",
    "    ccw_t.append(np.asarray(ccw_temp.values))\n",
    "    \n",
    "cw_tm,ccw_tm = [cw_t[0]],[ccw_t[0]]\n",
    "for i in range(1,n_seg):\n",
    "    cw_tm += cw_t[i]\n",
    "    ccw_tm += ccw_t[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "cw_WKB,ccw_WKB = [],[]\n",
    "\n",
    "for j in range(d):\n",
    "    cw_WKB.append(cw_tm[0][j] / ds[0].WKB_scale[j].values)          # [list][depth][frequency]\n",
    "    ccw_WKB.append(ccw_tm[0][j] / ds[0].WKB_scale[j].values)          # [list][depth][frequency]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     11,
     14,
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     46
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    "editable": false,
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    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\n",
    "\n",
    "fig,(ax0,ax1) = plt.subplots(2,1,figsize=(7,6),sharey=True,sharex=True)\n",
    "fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.5, 0.94, f'{adcp} - {t_stamp}', ha='center', fontsize=14)\n",
    "fig.text(0.005, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.965, 0.5, 'Rotary PSD [(m/s)$^2$/Hz]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.02, 'Frequency [Hz]', ha='center',fontsize=14)\n",
    "fig.text(0.21, 0.887, 'WKB-scaled', ha='center',fontsize=14)\n",
    "\n",
    "if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "    vmin = 1e1\n",
    "    vmax = 1e3\n",
    "elif adcp == 'Slope':\n",
    "    vmin = 1e1\n",
    "    vmax = 2e2\n",
    "\n",
    "if band == 'Tidal':\n",
    "    ax1.set_xlim(1e-6,1e-4)\n",
    "    for i in [ax0,ax1]:\n",
    "        if adcp == 'Slope':\n",
    "            pltdepth = -125\n",
    "        elif adcp == 'Axis75' or adcp == 'Axis55':\n",
    "            pltdepth = -720\n",
    "        pltcolor = 'white'\n",
    "        i.axvline(1.161e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8) # constituents\n",
    "        i.axvline(1.732e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)    \n",
    "        i.axvline(2.236e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)\n",
    "        i.text(0.90e-5,pltdepth,'K$_1$',fontsize=15,color=pltcolor)\n",
    "        i.text(1.40e-5,pltdepth,'$f$',fontsize=15,color=pltcolor)\n",
    "        i.text(2.30e-5,pltdepth,'M$_2$',fontsize=15,color=pltcolor)    \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e1\n",
    "        vmax = 2e2\n",
    "elif band == 'Subdiurnal':\n",
    "    ax1.set_xlim(1e-6,1e-5)\n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e2\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e3\n",
    "elif band == 'Continuum':\n",
    "    ax1.set_xlim(7e-5,2e-4)\n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-1\n",
    "        vmax = 5e0\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-1\n",
    "        vmax = 5e0 \n",
    "        \n",
    "fig.text(0.5, 0.887, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.477, 'CCW', ha='center', fontsize=14)    \n",
    "    \n",
    "\n",
    "im0 = ax0.pcolormesh(ds[0].specf, -depth, cw_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "im1 = ax1.pcolormesh(ds[0].specf, -depth, ccw_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "\n",
    "cbar0 = fig.colorbar(im0, ax=ax0, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar0.ax.tick_params(labelsize=14)\n",
    "cbar1.ax.tick_params(labelsize=14)\n",
    "\n",
    "ax0.patch.set_facecolor('grey')\n",
    "ax1.patch.set_facecolor('grey')\n",
    "ax0.set_xscale('log')\n",
    "ax1.set_xscale('log')\n",
    "\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthfreq_plots/depthfreq_rot_{band}_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# save rotary data to .nc for inter-annual comparison\n",
    "\n",
    "#for i in range(n_seg):\n",
    "ds_out = xr.Dataset( \n",
    "    data_vars=dict(\n",
    "        cw_rot_WKB=(['depth','specf'], cw_WKB),        # low-pass data\n",
    "        ccw_rot_WKB=(['depth','specf'], ccw_WKB),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        depth=depth,                        # depth values\n",
    "        specf=ds[0].specf.values,           # datetime values in segments\n",
    "    ),\n",
    "    attrs=dict(\n",
    "        description=f'Depth-freq WKB rotary data for {adcp} {t_stamp}.',\n",
    "        adcp=adcp,                   # adcp\n",
    "        #upidx=upidx,                 # upper depth index\n",
    "        #lowidx=lowidx,               # lower depth index\n",
    "        #dup_stamp=dup_stamp,         # upper depth stamp\n",
    "        #dlow_stamp=dlow_stamp,       # lower depth stamp\n",
    "        t_stamp=t_stamp,             # year stamp\n",
    "        #t=t,                         # length of time series\n",
    "        #d=d,                         # length of depth series\n",
    "        n_seg=n_seg,                 # total number of segments\n",
    "    ),\n",
    ") \n",
    "ds_out.to_netcdf(f'../Data/interannual/depthfreq_rot_{adcp}_{t_stamp}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inter-annual comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-20T00:45:15.204337Z",
     "start_time": "2023-06-20T00:45:14.300545Z"
    },
    "code_folding": [
     5,
     10
    ]
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1300\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import and plot PSD data\n",
    "\n",
    "site = 'Axis'     # Slope or Axis\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_{site}_2013.nc')\n",
    "    ds_2014 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_{site}_2014.nc')\n",
    "    ds_2017 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_{site}_2017.nc')\n",
    "    ds_2018 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_{site}_2018.nc')\n",
    "elif site == 'Axis':\n",
    "    ds_2013 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_Axis75_2013.nc')\n",
    "    ds_2014 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_Axis75_2014.nc')\n",
    "    ds_2017 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_Axis55_2017.nc')\n",
    "    ds_2018 = xr.open_dataset(f'../Data/interannual/depthfreq_psd_Axis55_2018.nc')\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot inter-annual PSD data\n",
    "\n",
    "fig, (ax0,ax1) = plt.subplots(1,2, figsize=(13,4), sharex = True, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.1)\n",
    "\n",
    "#fig.text(0.51, 0.92, f'Depth-frequency - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.06, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.94, 0.5, 'PSD [(m/s)$^2$/Hz]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.008, 'Frequency [Hz]', ha='center',fontsize=14)\n",
    "fig.text(0.835, 0.886, 'WKB-scaled', ha='center',fontsize=14)\n",
    "\n",
    "for ax in fig.get_axes():\n",
    "    ax.label_outer()\n",
    "    \n",
    "axs = [ax0,ax1]\n",
    "    \n",
    "band = 'Tidal'           # Tidal\n",
    "if band == 'Tidal':\n",
    "    ax1.set_xlim(1e-6,1e-4)\n",
    "    for i in axs:\n",
    "        if site == 'Slope':\n",
    "            pltdepth = -125\n",
    "        elif site == 'Axis':\n",
    "            pltdepth = -650\n",
    "        pltcolor = 'white'\n",
    "        i.axvline(1.161e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8) # constituents\n",
    "        i.axvline(1.732e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)    \n",
    "        i.axvline(2.236e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)\n",
    "        i.text(0.90e-5,pltdepth,'K$_1$',fontsize=15,color=pltcolor)\n",
    "        i.text(1.40e-5,pltdepth,'$f$',fontsize=15,color=pltcolor)\n",
    "        i.text(2.30e-5,pltdepth,'M$_2$',fontsize=15,color=pltcolor)    \n",
    "    if site == 'Axis':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e3\n",
    "        fig.text(0.31, 0.886, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.71, 0.886, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif site == 'Slope':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e3\n",
    "        fig.text(0.31, 0.886, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.71, 0.886, 'Along-slope', ha='center', fontsize=14)\n",
    "        \n",
    "years = [ds_2013]  \n",
    "for k,i in enumerate(years):\n",
    "    if k == 0:\n",
    "        im0 = ax0.pcolormesh(i.specf, -i.depth, i.u_PSD_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "        im1 = ax1.pcolormesh(i.specf, -i.depth, i.v_PSD_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto') \n",
    "        ax0.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "        ax1.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, fraction=0.05, pad=0.01, aspect=20, extend='both')\n",
    "cbar1.ax.tick_params(labelsize=14)\n",
    "\n",
    "if site == 'Axis':\n",
    "    ax1.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax1.set_ylim(-ds_2013.depth[-1],-ds_2013.depth[0])\n",
    "    \n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "    i.set_xscale('log')\n",
    "\n",
    "if site == 'Slope':\n",
    "    ax0.set_yticks([-300,-200,-100,0])\n",
    "    ax0.set_yticklabels(['300','200','100','0'])\n",
    "    fig.text(0.05,0.88,'(a)',fontweight='bold',fontsize=14)\n",
    "else:\n",
    "    ax0.set_yticks([-900,-800,-700,-600])\n",
    "    ax0.set_yticklabels(['900','800','700','600'])\n",
    "    fig.text(0.05,0.88,'(b)',fontweight='bold',fontsize=14)\n",
    "    \n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthfreq_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# import and plot rotary data\n",
    "\n",
    "site = 'Axis'     # Slope or Axis\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_{site}_2013.nc')\n",
    "    ds_2014 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_{site}_2014.nc')\n",
    "    ds_2017 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_{site}_2017.nc')\n",
    "    ds_2018 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_{site}_2018.nc')\n",
    "elif site == 'Axis':\n",
    "    ds_2013 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_Axis75_2013.nc')\n",
    "    ds_2014 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_Axis75_2014.nc')\n",
    "    ds_2017 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_Axis55_2017.nc')\n",
    "    ds_2018 = xr.open_dataset(f'../Data/interannual/depthfreq_rot_Axis55_2018.nc')\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot inter-annual rotary data\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = True, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.1)\n",
    "\n",
    "#fig.text(0.51, 0.92, f'Depth-frequency - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.94, 0.5, 'Rotary PSD [(m/s)$^2$/Hz]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Frequency [Hz]', ha='center',fontsize=14)\n",
    "fig.text(0.835, 0.886, 'WKB-scaled', ha='center',fontsize=14)\n",
    "fig.text(0.142, 0.886, '2013', ha='center',fontsize=14)\n",
    "fig.text(0.142, 0.683, '2014', ha='center',fontsize=14)\n",
    "fig.text(0.142, 0.482, '2017', ha='center',fontsize=14)\n",
    "fig.text(0.142, 0.281, '2018', ha='center',fontsize=14)\n",
    "\n",
    "for ax in fig.get_axes():\n",
    "    ax.label_outer()\n",
    "    \n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "    \n",
    "band = 'Tidal'           # Tidal\n",
    "if band == 'Tidal':\n",
    "    ax1.set_xlim(1e-6,1e-4)\n",
    "    for i in axs:\n",
    "        if site == 'Slope':\n",
    "            pltdepth = -125\n",
    "        elif site == 'Axis':\n",
    "            pltdepth = -650\n",
    "        pltcolor = 'white'\n",
    "        i.axvline(1.161e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8) # constituents\n",
    "        i.axvline(1.732e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)    \n",
    "        i.axvline(2.236e-5, color =pltcolor,lw=2,ls=(0,(1,3)),alpha=0.8)\n",
    "        i.text(0.90e-5,pltdepth,'K$_1$',fontsize=15,color=pltcolor)\n",
    "        i.text(1.40e-5,pltdepth,'$f$',fontsize=15,color=pltcolor)\n",
    "        i.text(2.30e-5,pltdepth,'M$_2$',fontsize=15,color=pltcolor)    \n",
    "    if site == 'Axis':\n",
    "        vmin = 1e1\n",
    "        vmax = 1e3\n",
    "        fig.text(0.31, 0.886, 'CW', ha='center', fontsize=14)\n",
    "        fig.text(0.71, 0.886, 'CCW', ha='center', fontsize=14)\n",
    "    elif site == 'Slope':\n",
    "        vmin = 1e1\n",
    "        vmax = 3e2\n",
    "        fig.text(0.31, 0.886, 'CW', ha='center', fontsize=14)\n",
    "        fig.text(0.71, 0.886, 'CCW', ha='center', fontsize=14)\n",
    "        \n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018]  \n",
    "for k,i in enumerate(years):\n",
    "    if k == 0:\n",
    "        im0 = ax0.pcolormesh(i.specf, -i.depth, i.cw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "        im1 = ax1.pcolormesh(i.specf, -i.depth, i.ccw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto') \n",
    "        ax0.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "        ax1.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "    elif k == 1:\n",
    "        im2 = ax2.pcolormesh(i.specf, -i.depth, i.cw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "        im3 = ax3.pcolormesh(i.specf, -i.depth, i.ccw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto') \n",
    "        ax2.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "        ax3.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "    elif k == 2:\n",
    "        im4 = ax4.pcolormesh(i.specf, -i.depth, i.cw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "        im5 = ax5.pcolormesh(i.specf, -i.depth, i.ccw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto') \n",
    "        ax4.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "        ax5.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "    elif k == 3:\n",
    "        im6 = ax6.pcolormesh(i.specf, -i.depth, i.cw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "        im7 = ax7.pcolormesh(i.specf, -i.depth, i.ccw_rot_WKB, rasterized=True, \\\n",
    "                        norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto') \n",
    "        ax6.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "        ax7.hlines(y=-700, xmin=i.specf[0],xmax=i.specf[-1], linewidth=1, color='white', ls='--')\n",
    "\n",
    "cbar1 = fig.colorbar(im1, ax=ax1, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar3 = fig.colorbar(im3, ax=ax3, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar5 = fig.colorbar(im5, ax=ax5, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar7 = fig.colorbar(im7, ax=ax7, fraction=0.05, pad=0.01, aspect=15, extend='both')\n",
    "cbar1.ax.tick_params(labelsize=14)\n",
    "cbar3.ax.tick_params(labelsize=14)\n",
    "cbar5.ax.tick_params(labelsize=14)\n",
    "cbar7.ax.tick_params(labelsize=14)\n",
    "\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013.depth[-1],-ds_2013.depth[0])\n",
    "\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "    i.set_xscale('log')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthfreq_rot_{site}.pdf',format='pdf')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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