plot_depthband.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Depth-band plots\n",
    "\n",
    "Loads depth-frequency-time data and then plots integrated depth-band power for various circumstances."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "Necessary modules for analysis."
   ]
  },
  {
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   "execution_count": 2,
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   "source": [
    "# import modules\n",
    "\n",
    "import xarray as xr\n",
    "import datetime as dt\n",
    "import cmocean\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.dates as pldates\n",
    "import matplotlib.lines as lines\n",
    "import scipy.signal as sig\n",
    "import numpy as np\n",
    "for i in range(2):\n",
    "    %matplotlib notebook"
   ]
  },
  {
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   "execution_count": 3,
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   "source": [
    "# import data\n",
    "\n",
    "adcp = 'Shoulder'     # Slope(2013,2014,2017,2018), Axis75(2013,2014), Axis55(2017,2018)\n",
    "year = 2013\n",
    "\n",
    "if adcp == 'Shoulder':\n",
    "    adcp2 = 'Axis75'\n",
    "    ds_in = xr.open_dataset(f'../Data/data/spectra/spectra_{adcp2}_1min_{year}_0.nc')\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_{adcp2}_1min_{year}_{i}.nc')\n",
    "                ds.append(ds_temp)\n",
    "    elif n_seg == 1:\n",
    "        ds = [ds_in]\n",
    "\n",
    "else:\n",
    "    ds_in = xr.open_dataset(f'../Data/data/spectra/spectra_{adcp}_{year}_0.nc')\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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-23T17:30:54.088357Z",
     "start_time": "2024-04-23T17:30:54.084491Z"
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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": [
    "## Subdiurnal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# select frequency band\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx      # returns index of nearest value\n",
    "\n",
    "f_array = np.asarray(ds[0].specf_s)    # input array to process \n",
    "\n",
    "f_name = 'Subdiurnal'          # Subdiurnal, Diurnal, Inertial, Semidiurnal, Continuum\n",
    "if f_name == 'Diurnal':\n",
    "    f_freq = 1.16e-5         # desired frequency constituent\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx \n",
    "elif f_name == 'Inertial':\n",
    "    f_freq = 1.73e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx - 1\n",
    "elif f_name == 'Semidiurnal':\n",
    "    f_freq = 2.24e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx\n",
    "elif f_name == 'Continuum':\n",
    "    f_freq = [7.00e-5,1.20e-4]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Subdiurnal':\n",
    "    f_freq = [2.00e-6,9.00e-6]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "    \n",
    "    \n",
    "print(\"Desired frequency band:\",f_name,'at', f_freq, \"Hz\")\n",
    "print(\"---\")\n",
    "print(\"Index at upper freq cutoff:\", up_f_idx)\n",
    "print(\"Value at upper freq cutoff:\", f_array[up_f_idx], \"Hz\" )\n",
    "print('---')\n",
    "print(\"Index at lower freq cutoff:\", low_f_idx)\n",
    "print(\"Value at lower freq cutoff:\", f_array[low_f_idx], \"Hz\" )\n",
    "\n",
    "f_band = f_array[low_f_idx:up_f_idx+1]              # remove unwanted frequencies\n",
    "fup_stamp = f_band[-1]                              # freq stamps for use in output\n",
    "flow_stamp = f_band[0]\n",
    "\n",
    "print('---')\n",
    "print(\"Length of new freq array: \", len(f_band))   # new freq interval\n",
    "print(f'Upper limit at',fup_stamp,'Hz')            # freq upper limit\n",
    "print(f'Lower limit at',flow_stamp,'Hz')           # freq lower limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band PSD spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "u_power, v_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    u_depth_power_temp,v_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_time_power_temp,v_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            u_PSD_temp = ds[i].Sxxu_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            u_power_temp = np.trapz(y=u_PSD_temp,x=f_band)\n",
    "            u_time_power_temp.append(u_power_temp)\n",
    "            v_PSD_temp = ds[i].Sxxv_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            v_power_temp = np.trapz(y=v_PSD_temp,x=f_band)\n",
    "            v_time_power_temp.append(v_power_temp)\n",
    "        u_depth_power_temp.append(u_time_power_temp)\n",
    "        v_depth_power_temp.append(v_time_power_temp)\n",
    "    u_np = np.asarray(u_depth_power_temp)\n",
    "    v_np = np.asarray(v_depth_power_temp)\n",
    "    u_power.append(u_np)            # n_seg, [depth, time]\n",
    "    v_power.append(v_np)            # n_seg, [depth, time]"
   ]
  },
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   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "u_WKB,v_WKB = [],[]\n",
    "for i in range(n_seg):\n",
    "    u_WKB_temp,v_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_WKB_temp.append(u_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        v_WKB_temp.append(v_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    u_WKB.append(u_WKB_temp)\n",
    "    v_WKB.append(v_WKB_temp)"
   ]
  },
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   "source": [
    "# plot PSD frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)  \n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, u_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, v_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
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   "outputs": [],
   "source": [
    "# save PSD data to .nc for inter-annual comparison\n",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            u_WKB=(['depth','spect_s'], u_WKB[i]),        # low-pass data\n",
    "            v_WKB=(['depth','spect_s'], v_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB PSD {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_psd_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
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   },
   "source": [
    "### Depth-band rotary spectra"
   ]
  },
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   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "cw_power, ccw_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    cw_depth_power_temp,ccw_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_time_power_temp,ccw_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            cw_PSD_temp = ds[i].cw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            cw_power_temp = np.trapz(y=cw_PSD_temp,x=f_band)\n",
    "            cw_time_power_temp.append(cw_power_temp)\n",
    "            ccw_PSD_temp = ds[i].ccw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            ccw_power_temp = np.trapz(y=ccw_PSD_temp,x=f_band)\n",
    "            ccw_time_power_temp.append(ccw_power_temp)\n",
    "        cw_depth_power_temp.append(cw_time_power_temp)\n",
    "        ccw_depth_power_temp.append(ccw_time_power_temp)\n",
    "    cw_np = np.asarray(cw_depth_power_temp)\n",
    "    ccw_np = np.asarray(ccw_depth_power_temp)\n",
    "    cw_power.append(cw_np)            # n_seg, [depth, time]\n",
    "    ccw_power.append(ccw_np)            # n_seg, [depth, time]"
   ]
  },
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   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "cw_WKB,ccw_WKB = [],[]\n",
    "for i in range(n_seg):\n",
    "    cw_WKB_temp,ccw_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_WKB_temp.append(cw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        ccw_WKB_temp.append(ccw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    cw_WKB.append(cw_WKB_temp)\n",
    "    ccw_WKB.append(ccw_WKB_temp)"
   ]
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     "frozen": true
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   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "\n",
    "fig.text(0.5, 0.884, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.473, 'CCW', ha='center', fontsize=14)\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, cw_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, ccw_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{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": [
    "# depth average for each time at upper (lower) depths and save .nc\n",
    "\n",
    "if f_name == 'Inertial':\n",
    "    # select depths\n",
    "    if adcp == 'Slope':\n",
    "        d_up_idx = 4     # 35\n",
    "        d_low_idx = 20   # 155\n",
    "    elif adcp == 'Axis75':\n",
    "        d_up_idx = 58\n",
    "        d_low_idx = 79\n",
    "    elif adcp == 'Axis55':\n",
    "        d_up_idx = 40\n",
    "        d_low_idx = 48\n",
    "\n",
    "    # save to .nc file for wind\n",
    "\n",
    "    for i in range(n_seg):\n",
    "        ds_out = xr.Dataset( \n",
    "            data_vars=dict(\n",
    "                cw=(['depth','time'], cw_WKB[i]),        # integrated near-inertial power [(m/s)^2]\n",
    "            ),\n",
    "            coords=dict(\n",
    "                depth=depth,               # m\n",
    "                time=ds[i].spect_s.values,                     # np.datetime64    \n",
    "            ),\n",
    "            attrs=dict(\n",
    "                description=f'WKB-scaled and integrated near-inertial power for wind comparison, {adcp}, {t_stamp}. Segment {i} of {n_seg}.',\n",
    "                units=['(m/s)^2, m, numpy.datetime64'],\n",
    "                n_seg=n_seg,\n",
    "                d_up_idx = d_up_idx,\n",
    "                d_low_idx = d_low_idx,\n",
    "                depth_up=depth[d_up_idx],\n",
    "                depth_low=depth[d_low_idx],\n",
    "            ),\n",
    "        ) \n",
    "        ds_out.to_netcdf(f'../Data/wind/cw_inertial_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            cw_WKB=(['depth','spect_s'], cw_WKB[i]),        # low-pass data\n",
    "            ccw_WKB=(['depth','spect_s'], ccw_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB rotary {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_rot_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Diurnal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     2,
     10,
     15,
     20,
     25,
     29
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# select frequency band\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx      # returns index of nearest value\n",
    "\n",
    "f_array = np.asarray(ds[0].specf_s)    # input array to process \n",
    "\n",
    "f_name = 'Diurnal'          # Subdiurnal, Diurnal, Inertial, Semidiurnal, Continuum\n",
    "if f_name == 'Diurnal':\n",
    "    f_freq = 1.16e-5         # desired frequency constituent\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx \n",
    "elif f_name == 'Inertial':\n",
    "    f_freq = 1.73e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx - 1\n",
    "elif f_name == 'Semidiurnal':\n",
    "    f_freq = 2.24e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx\n",
    "elif f_name == 'Continuum':\n",
    "    f_freq = [7.00e-5,1.20e-4]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Subdiurnal':\n",
    "    f_freq = [2.00e-6,1.00e-5]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "    \n",
    "    \n",
    "print(\"Desired frequency band:\",f_name,'at', f_freq, \"Hz\")\n",
    "print(\"---\")\n",
    "print(\"Index at upper freq cutoff:\", up_f_idx)\n",
    "print(\"Value at upper freq cutoff:\", f_array[up_f_idx], \"Hz\" )\n",
    "print('---')\n",
    "print(\"Index at lower freq cutoff:\", low_f_idx)\n",
    "print(\"Value at lower freq cutoff:\", f_array[low_f_idx], \"Hz\" )\n",
    "\n",
    "f_band = f_array[low_f_idx:up_f_idx+1]              # remove unwanted frequencies\n",
    "fup_stamp = f_band[-1]                              # freq stamps for use in output\n",
    "flow_stamp = f_band[0]\n",
    "\n",
    "print('---')\n",
    "print(\"Length of new freq array: \", len(f_band))   # new freq interval\n",
    "print(f'Upper limit at',fup_stamp,'Hz')            # freq upper limit\n",
    "print(f'Lower limit at',flow_stamp,'Hz')           # freq lower limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band PSD spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "u_power, v_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    u_depth_power_temp,v_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_time_power_temp,v_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            u_PSD_temp = ds[i].Sxxu_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            u_power_temp = np.trapz(y=u_PSD_temp,x=f_band)\n",
    "            u_time_power_temp.append(u_power_temp)\n",
    "            v_PSD_temp = ds[i].Sxxv_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            v_power_temp = np.trapz(y=v_PSD_temp,x=f_band)\n",
    "            v_time_power_temp.append(v_power_temp)\n",
    "        u_depth_power_temp.append(u_time_power_temp)\n",
    "        v_depth_power_temp.append(v_time_power_temp)\n",
    "    u_np = np.asarray(u_depth_power_temp)\n",
    "    v_np = np.asarray(v_depth_power_temp)\n",
    "    u_power.append(u_np)            # n_seg, [depth, time]\n",
    "    v_power.append(v_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    u_WKB_temp,v_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_WKB_temp.append(u_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        v_WKB_temp.append(v_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    u_WKB.append(u_WKB_temp)\n",
    "    v_WKB.append(v_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     22,
     33,
     44,
     60
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# plot PSD frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)  \n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, u_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, v_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            u_WKB=(['depth','spect_s'], u_WKB[i]),        # low-pass data\n",
    "            v_WKB=(['depth','spect_s'], v_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB PSD {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_psd_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band rotary spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "cw_power, ccw_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    cw_depth_power_temp,ccw_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_time_power_temp,ccw_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            cw_PSD_temp = ds[i].cw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            cw_power_temp = np.trapz(y=cw_PSD_temp,x=f_band)\n",
    "            cw_time_power_temp.append(cw_power_temp)\n",
    "            ccw_PSD_temp = ds[i].ccw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            ccw_power_temp = np.trapz(y=ccw_PSD_temp,x=f_band)\n",
    "            ccw_time_power_temp.append(ccw_power_temp)\n",
    "        cw_depth_power_temp.append(cw_time_power_temp)\n",
    "        ccw_depth_power_temp.append(ccw_time_power_temp)\n",
    "    cw_np = np.asarray(cw_depth_power_temp)\n",
    "    ccw_np = np.asarray(ccw_depth_power_temp)\n",
    "    cw_power.append(cw_np)            # n_seg, [depth, time]\n",
    "    ccw_power.append(ccw_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    cw_WKB_temp,ccw_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_WKB_temp.append(cw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        ccw_WKB_temp.append(ccw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    cw_WKB.append(cw_WKB_temp)\n",
    "    ccw_WKB.append(ccw_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     18,
     25,
     32,
     42
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "\n",
    "fig.text(0.5, 0.884, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.473, 'CCW', ha='center', fontsize=14)\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, cw_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, ccw_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{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": [
    "# depth average for each time at upper (lower) depths and save .nc\n",
    "\n",
    "if f_name == 'Inertial':\n",
    "    # select depths\n",
    "    if adcp == 'Slope':\n",
    "        d_up_idx = 4     # 35\n",
    "        d_low_idx = 20   # 155\n",
    "    elif adcp == 'Axis75':\n",
    "        d_up_idx = 58\n",
    "        d_low_idx = 79\n",
    "    elif adcp == 'Axis55':\n",
    "        d_up_idx = 40\n",
    "        d_low_idx = 48\n",
    "\n",
    "    # save to .nc file for wind\n",
    "\n",
    "    for i in range(n_seg):\n",
    "        ds_out = xr.Dataset( \n",
    "            data_vars=dict(\n",
    "                cw=(['depth','time'], cw_WKB[i]),        # integrated near-inertial power [(m/s)^2]\n",
    "            ),\n",
    "            coords=dict(\n",
    "                depth=depth,               # m\n",
    "                time=ds[i].spect_s.values,                     # np.datetime64    \n",
    "            ),\n",
    "            attrs=dict(\n",
    "                description=f'WKB-scaled and integrated near-inertial power for wind comparison, {adcp}, {t_stamp}. Segment {i} of {n_seg}.',\n",
    "                units=['(m/s)^2, m, numpy.datetime64'],\n",
    "                n_seg=n_seg,\n",
    "                d_up_idx = d_up_idx,\n",
    "                d_low_idx = d_low_idx,\n",
    "                depth_up=depth[d_up_idx],\n",
    "                depth_low=depth[d_low_idx],\n",
    "            ),\n",
    "        ) \n",
    "        ds_out.to_netcdf(f'../Data/wind/cw_inertial_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            cw_WKB=(['depth','spect_s'], cw_WKB[i]),        # low-pass data\n",
    "            ccw_WKB=(['depth','spect_s'], ccw_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB rotary {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_rot_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Inertial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     2,
     10,
     15,
     20,
     25,
     29
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# select frequency band\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx      # returns index of nearest value\n",
    "\n",
    "f_array = np.asarray(ds[0].specf_s)    # input array to process \n",
    "\n",
    "f_name = 'Inertial'          # Subdiurnal, Diurnal, Inertial, Semidiurnal, Continuum\n",
    "if f_name == 'Diurnal':\n",
    "    f_freq = 1.16e-5         # desired frequency constituent\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx \n",
    "elif f_name == 'Inertial':\n",
    "    f_freq = 1.73e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx - 1\n",
    "elif f_name == 'Semidiurnal':\n",
    "    f_freq = 2.24e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx\n",
    "elif f_name == 'Continuum':\n",
    "    f_freq = [7.00e-5,1.20e-4]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Subdiurnal':\n",
    "    f_freq = [2.00e-6,1.00e-5]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "    \n",
    "    \n",
    "print(\"Desired frequency band:\",f_name,'at', f_freq, \"Hz\")\n",
    "print(\"---\")\n",
    "print(\"Index at upper freq cutoff:\", up_f_idx)\n",
    "print(\"Value at upper freq cutoff:\", f_array[up_f_idx], \"Hz\" )\n",
    "print('---')\n",
    "print(\"Index at lower freq cutoff:\", low_f_idx)\n",
    "print(\"Value at lower freq cutoff:\", f_array[low_f_idx], \"Hz\" )\n",
    "\n",
    "f_band = f_array[low_f_idx:up_f_idx+1]              # remove unwanted frequencies\n",
    "fup_stamp = f_band[-1]                              # freq stamps for use in output\n",
    "flow_stamp = f_band[0]\n",
    "\n",
    "print('---')\n",
    "print(\"Length of new freq array: \", len(f_band))   # new freq interval\n",
    "print(f'Upper limit at',fup_stamp,'Hz')            # freq upper limit\n",
    "print(f'Lower limit at',flow_stamp,'Hz')           # freq lower limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Depth-band PSD spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "u_power, v_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    u_depth_power_temp,v_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_time_power_temp,v_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            u_PSD_temp = ds[i].Sxxu_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            u_power_temp = np.trapz(y=u_PSD_temp,x=f_band)\n",
    "            u_time_power_temp.append(u_power_temp)\n",
    "            v_PSD_temp = ds[i].Sxxv_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            v_power_temp = np.trapz(y=v_PSD_temp,x=f_band)\n",
    "            v_time_power_temp.append(v_power_temp)\n",
    "        u_depth_power_temp.append(u_time_power_temp)\n",
    "        v_depth_power_temp.append(v_time_power_temp)\n",
    "    u_np = np.asarray(u_depth_power_temp)\n",
    "    v_np = np.asarray(v_depth_power_temp)\n",
    "    u_power.append(u_np)            # n_seg, [depth, time]\n",
    "    v_power.append(v_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "u_WKB,v_WKB = [],[]\n",
    "for i in range(n_seg):\n",
    "    u_WKB_temp,v_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_WKB_temp.append(u_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        v_WKB_temp.append(v_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    u_WKB.append(u_WKB_temp)\n",
    "    v_WKB.append(v_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     33,
     44,
     60
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# plot PSD frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)  \n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, u_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, v_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax1.set_ylim(-depth[-1],-600)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# save PSD data to .nc for inter-annual comparison\n",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            u_WKB=(['depth','spect_s'], u_WKB[i]),        # low-pass data\n",
    "            v_WKB=(['depth','spect_s'], v_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB PSD {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_psd_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Depth-band rotary spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "cw_power, ccw_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    cw_depth_power_temp,ccw_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_time_power_temp,ccw_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            cw_PSD_temp = ds[i].cw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            cw_power_temp = np.trapz(y=cw_PSD_temp,x=f_band)\n",
    "            cw_time_power_temp.append(cw_power_temp)\n",
    "            ccw_PSD_temp = ds[i].ccw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            ccw_power_temp = np.trapz(y=ccw_PSD_temp,x=f_band)\n",
    "            ccw_time_power_temp.append(ccw_power_temp)\n",
    "        cw_depth_power_temp.append(cw_time_power_temp)\n",
    "        ccw_depth_power_temp.append(ccw_time_power_temp)\n",
    "    cw_np = np.asarray(cw_depth_power_temp)\n",
    "    ccw_np = np.asarray(ccw_depth_power_temp)\n",
    "    cw_power.append(cw_np)            # n_seg, [depth, time]\n",
    "    ccw_power.append(ccw_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "cw_WKB,ccw_WKB = [],[]\n",
    "for i in range(n_seg):\n",
    "    cw_WKB_temp,ccw_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_WKB_temp.append(cw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        ccw_WKB_temp.append(ccw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    cw_WKB.append(cw_WKB_temp)\n",
    "    ccw_WKB.append(ccw_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     18,
     19,
     22,
     25,
     32,
     42,
     56
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\n",
    "\n",
    "fig,(ax0,ax1) = plt.subplots(2,1,figsize=(8,6),sharey=True,sharex=True)\n",
    "fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.5, 0.93, f'{f_name} - {adcp} - {t_stamp}', ha='center', fontsize=14)\n",
    "fig.text(0.02, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.965, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.02, 'Time [mm/dd]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.885, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "\n",
    "fig.text(0.5, 0.884, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.473, 'CCW', ha='center', fontsize=14)\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, cw_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, ccw_WKB[i], 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",
    "\n",
    "# draw vertical lines\n",
    "#plt.annotate(\"\", xy=(210, 330), xycoords='figure points', xytext=(115, 375), textcoords='figure points',\\\n",
    "#              arrowprops=dict(arrowstyle=\"-\", edgecolor = \"white\", linewidth=3, alpha=1, connectionstyle=\"arc3,rad=0.\"))\n",
    "#plt.annotate(\"\", xy=(208, 332), xycoords='figure points', xytext=(208, 230), textcoords='figure points',\\\n",
    "#              arrowprops=dict(arrowstyle=\"-\", edgecolor = \"white\", linewidth=3, alpha=1, connectionstyle=\"arc3,rad=0.\"))\n",
    "#fig.text(0.2,0.755,r'$\\sim$8 days',fontsize=14,color='white')\n",
    "\n",
    "date_form = pldates.DateFormatter(\"%m/%d\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "#ax1.set_xlim(np.datetime64('2014-09-01'),np.datetime64('2014-10-01'))\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "ax1.xaxis.set_major_locator(plt.MaxNLocator(6))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# depth average for each time at upper (lower) depths and save .nc\n",
    "\n",
    "if f_name == 'Inertial':\n",
    "    # select depths\n",
    "    if adcp == 'Slope':\n",
    "        d_up_idx = 4     # 35\n",
    "        d_low_idx = -1   # lower\n",
    "    elif adcp == 'Axis75':\n",
    "        d_up_idx = 58\n",
    "        d_low_idx = 79\n",
    "    elif adcp == 'Axis55':\n",
    "        d_up_idx = 40\n",
    "        d_low_idx = 48\n",
    "\n",
    "    # save to .nc file for wind\n",
    "\n",
    "    for i in range(n_seg):\n",
    "        ds_out = xr.Dataset( \n",
    "            data_vars=dict(\n",
    "                cw=(['depth','time'], cw_WKB[i]),        # integrated near-inertial power [(m/s)^2]\n",
    "            ),\n",
    "            coords=dict(\n",
    "                depth=depth,               # m\n",
    "                time=ds[i].spect_s.values,                     # np.datetime64    \n",
    "            ),\n",
    "            attrs=dict(\n",
    "                description=f'WKB-scaled and integrated near-inertial power for wind comparison, {adcp}, {t_stamp}. Segment {i} of {n_seg}.',\n",
    "                units=['(m/s)^2, m, numpy.datetime64'],\n",
    "                n_seg=n_seg,\n",
    "                d_up_idx = d_up_idx,\n",
    "                d_low_idx = d_low_idx,\n",
    "                depth_up=depth[d_up_idx],\n",
    "                depth_low=depth[d_low_idx],\n",
    "            ),\n",
    "        ) \n",
    "        ds_out.to_netcdf(f'../Data/wind/cw_inertial_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# save rotary data to .nc for inter-annual comparison\n",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            cw_WKB=(['depth','spect_s'], cw_WKB[i]),        # low-pass data\n",
    "            ccw_WKB=(['depth','spect_s'], ccw_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB rotary {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_rot_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Semidiurnal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     2,
     10,
     15,
     20,
     25,
     29
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# select frequency band\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx      # returns index of nearest value\n",
    "\n",
    "f_array = np.asarray(ds[0].specf_s)    # input array to process \n",
    "\n",
    "f_name = 'Semidiurnal'          # Subdiurnal, Diurnal, Inertial, Semidiurnal, Continuum\n",
    "if f_name == 'Diurnal':\n",
    "    f_freq = 1.16e-5         # desired frequency constituent\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx \n",
    "elif f_name == 'Inertial':\n",
    "    f_freq = 1.73e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx - 1\n",
    "elif f_name == 'Semidiurnal':\n",
    "    f_freq = 2.24e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx\n",
    "elif f_name == 'Continuum':\n",
    "    f_freq = [7.00e-5,1.20e-4]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Subdiurnal':\n",
    "    f_freq = [2.00e-6,1.00e-5]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "    \n",
    "    \n",
    "print(\"Desired frequency band:\",f_name,'at', f_freq, \"Hz\")\n",
    "print(\"---\")\n",
    "print(\"Index at upper freq cutoff:\", up_f_idx)\n",
    "print(\"Value at upper freq cutoff:\", f_array[up_f_idx], \"Hz\" )\n",
    "print('---')\n",
    "print(\"Index at lower freq cutoff:\", low_f_idx)\n",
    "print(\"Value at lower freq cutoff:\", f_array[low_f_idx], \"Hz\" )\n",
    "\n",
    "f_band = f_array[low_f_idx:up_f_idx+1]              # remove unwanted frequencies\n",
    "fup_stamp = f_band[-1]                              # freq stamps for use in output\n",
    "flow_stamp = f_band[0]\n",
    "\n",
    "print('---')\n",
    "print(\"Length of new freq array: \", len(f_band))   # new freq interval\n",
    "print(f'Upper limit at',fup_stamp,'Hz')            # freq upper limit\n",
    "print(f'Lower limit at',flow_stamp,'Hz')           # freq lower limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band PSD spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "u_power, v_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    u_depth_power_temp,v_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_time_power_temp,v_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            u_PSD_temp = ds[i].Sxxu_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            u_power_temp = np.trapz(y=u_PSD_temp,x=f_band)\n",
    "            u_time_power_temp.append(u_power_temp)\n",
    "            v_PSD_temp = ds[i].Sxxv_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            v_power_temp = np.trapz(y=v_PSD_temp,x=f_band)\n",
    "            v_time_power_temp.append(v_power_temp)\n",
    "        u_depth_power_temp.append(u_time_power_temp)\n",
    "        v_depth_power_temp.append(v_time_power_temp)\n",
    "    u_np = np.asarray(u_depth_power_temp)\n",
    "    v_np = np.asarray(v_depth_power_temp)\n",
    "    u_power.append(u_np)            # n_seg, [depth, time]\n",
    "    v_power.append(v_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    u_WKB_temp,v_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_WKB_temp.append(u_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        v_WKB_temp.append(v_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    u_WKB.append(u_WKB_temp)\n",
    "    v_WKB.append(v_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     22,
     33,
     44,
     60
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# plot PSD frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)  \n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, u_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, v_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            u_WKB=(['depth','spect_s'], u_WKB[i]),        # low-pass data\n",
    "            v_WKB=(['depth','spect_s'], v_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB PSD {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_psd_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band rotary spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "cw_power, ccw_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    cw_depth_power_temp,ccw_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_time_power_temp,ccw_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            cw_PSD_temp = ds[i].cw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            cw_power_temp = np.trapz(y=cw_PSD_temp,x=f_band)\n",
    "            cw_time_power_temp.append(cw_power_temp)\n",
    "            ccw_PSD_temp = ds[i].ccw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            ccw_power_temp = np.trapz(y=ccw_PSD_temp,x=f_band)\n",
    "            ccw_time_power_temp.append(ccw_power_temp)\n",
    "        cw_depth_power_temp.append(cw_time_power_temp)\n",
    "        ccw_depth_power_temp.append(ccw_time_power_temp)\n",
    "    cw_np = np.asarray(cw_depth_power_temp)\n",
    "    ccw_np = np.asarray(ccw_depth_power_temp)\n",
    "    cw_power.append(cw_np)            # n_seg, [depth, time]\n",
    "    ccw_power.append(ccw_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    cw_WKB_temp,ccw_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_WKB_temp.append(cw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        ccw_WKB_temp.append(ccw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    cw_WKB.append(cw_WKB_temp)\n",
    "    ccw_WKB.append(ccw_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     18,
     25,
     32,
     42
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "\n",
    "fig.text(0.5, 0.884, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.473, 'CCW', ha='center', fontsize=14)\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, cw_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, ccw_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            cw_WKB=(['depth','spect_s'], cw_WKB[i]),        # low-pass data\n",
    "            ccw_WKB=(['depth','spect_s'], ccw_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB rotary {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_rot_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Continuum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     2,
     10,
     15,
     20,
     25,
     29
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# select frequency band\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx      # returns index of nearest value\n",
    "\n",
    "f_array = np.asarray(ds[0].specf_s)    # input array to process \n",
    "\n",
    "f_name = 'Continuum'          # Subdiurnal, Diurnal, Inertial, Semidiurnal, Continuum\n",
    "if f_name == 'Diurnal':\n",
    "    f_freq = 1.16e-5         # desired frequency constituent\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx \n",
    "elif f_name == 'Inertial':\n",
    "    f_freq = 1.73e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx - 1\n",
    "elif f_name == 'Semidiurnal':\n",
    "    f_freq = 2.24e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx\n",
    "elif f_name == 'Continuum':\n",
    "    f_freq = [7.00e-5,1.20e-4]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Subdiurnal':\n",
    "    f_freq = [2.00e-6,1.00e-5]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "    \n",
    "    \n",
    "print(\"Desired frequency band:\",f_name,'at', f_freq, \"Hz\")\n",
    "print(\"---\")\n",
    "print(\"Index at upper freq cutoff:\", up_f_idx)\n",
    "print(\"Value at upper freq cutoff:\", f_array[up_f_idx], \"Hz\" )\n",
    "print('---')\n",
    "print(\"Index at lower freq cutoff:\", low_f_idx)\n",
    "print(\"Value at lower freq cutoff:\", f_array[low_f_idx], \"Hz\" )\n",
    "\n",
    "f_band = f_array[low_f_idx:up_f_idx+1]              # remove unwanted frequencies\n",
    "fup_stamp = f_band[-1]                              # freq stamps for use in output\n",
    "flow_stamp = f_band[0]\n",
    "\n",
    "print('---')\n",
    "print(\"Length of new freq array: \", len(f_band))   # new freq interval\n",
    "print(f'Upper limit at',fup_stamp,'Hz')            # freq upper limit\n",
    "print(f'Lower limit at',flow_stamp,'Hz')           # freq lower limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band PSD spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "u_power, v_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    u_depth_power_temp,v_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_time_power_temp,v_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            u_PSD_temp = ds[i].Sxxu_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            u_power_temp = np.trapz(y=u_PSD_temp,x=f_band)\n",
    "            u_time_power_temp.append(u_power_temp)\n",
    "            v_PSD_temp = ds[i].Sxxv_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            v_power_temp = np.trapz(y=v_PSD_temp,x=f_band)\n",
    "            v_time_power_temp.append(v_power_temp)\n",
    "        u_depth_power_temp.append(u_time_power_temp)\n",
    "        v_depth_power_temp.append(v_time_power_temp)\n",
    "    u_np = np.asarray(u_depth_power_temp)\n",
    "    v_np = np.asarray(v_depth_power_temp)\n",
    "    u_power.append(u_np)            # n_seg, [depth, time]\n",
    "    v_power.append(v_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    u_WKB_temp,v_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_WKB_temp.append(u_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        v_WKB_temp.append(v_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    u_WKB.append(u_WKB_temp)\n",
    "    v_WKB.append(v_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     22,
     33,
     44,
     60
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# plot PSD frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)  \n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, u_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, v_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            u_WKB=(['depth','spect_s'], u_WKB[i]),        # low-pass data\n",
    "            v_WKB=(['depth','spect_s'], v_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB PSD {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_psd_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Depth-band rotary spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "cw_power, ccw_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    cw_depth_power_temp,ccw_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_time_power_temp,ccw_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            cw_PSD_temp = ds[i].cw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            cw_power_temp = np.trapz(y=cw_PSD_temp,x=f_band)\n",
    "            cw_time_power_temp.append(cw_power_temp)\n",
    "            ccw_PSD_temp = ds[i].ccw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            ccw_power_temp = np.trapz(y=ccw_PSD_temp,x=f_band)\n",
    "            ccw_time_power_temp.append(ccw_power_temp)\n",
    "        cw_depth_power_temp.append(cw_time_power_temp)\n",
    "        ccw_depth_power_temp.append(ccw_time_power_temp)\n",
    "    cw_np = np.asarray(cw_depth_power_temp)\n",
    "    ccw_np = np.asarray(ccw_depth_power_temp)\n",
    "    cw_power.append(cw_np)            # n_seg, [depth, time]\n",
    "    ccw_power.append(ccw_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "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",
    "for i in range(n_seg):\n",
    "    cw_WKB_temp,ccw_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_WKB_temp.append(cw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        ccw_WKB_temp.append(ccw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    cw_WKB.append(cw_WKB_temp)\n",
    "    ccw_WKB.append(ccw_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     11,
     18,
     25,
     32,
     42
    ],
    "deletable": false,
    "editable": false,
    "hidden": true,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\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'{f_name} - {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(0.97, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "\n",
    "fig.text(0.5, 0.884, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.473, 'CCW', ha='center', fontsize=14)\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, cw_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, ccw_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{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",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            cw_WKB=(['depth','spect_s'], cw_WKB[i]),        # low-pass data\n",
    "            ccw_WKB=(['depth','spect_s'], ccw_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB rotary {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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/depthband_rot_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shoulder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-23T17:30:58.175114Z",
     "start_time": "2024-04-23T17:30:58.162959Z"
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     2,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Desired frequency band: Shoulder at [0.0002, 0.001] Hz\n",
      "---\n",
      "Index at upper freq cutoff: 492\n",
      "Value at upper freq cutoff: 0.0010009965820312501 Hz\n",
      "---\n",
      "Index at lower freq cutoff: 98\n",
      "Value at lower freq cutoff: 0.000199385498046875 Hz\n",
      "---\n",
      "Length of new freq array:  395\n",
      "Upper limit at 0.0010009965820312501 Hz\n",
      "Lower limit at 0.000199385498046875 Hz\n"
     ]
    }
   ],
   "source": [
    "# select frequency band\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx      # returns index of nearest value\n",
    "\n",
    "f_array = np.asarray(ds[0].specf_s)    # input array to process \n",
    "\n",
    "f_name = 'Shoulder'          # Subdiurnal, Diurnal, Inertial, Semidiurnal, Continuum\n",
    "if f_name == 'Diurnal':\n",
    "    f_freq = 1.16e-5         # desired frequency constituent\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx \n",
    "elif f_name == 'Inertial':\n",
    "    f_freq = 1.73e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)                 # index of desired frequency\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx - 1\n",
    "elif f_name == 'Semidiurnal':\n",
    "    f_freq = 2.24e-5\n",
    "    f_idx = find_nearest(f_array, f_freq)\n",
    "    up_f_idx = f_idx + 1\n",
    "    low_f_idx = f_idx\n",
    "elif f_name == 'Continuum':\n",
    "    f_freq = [7.00e-5,1.20e-4]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Subdiurnal':\n",
    "    f_freq = [2.00e-6,1.00e-5]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])\n",
    "elif f_name == 'Shoulder':\n",
    "    f_freq = [2.00e-4,1.00e-3]\n",
    "    low_f_idx = find_nearest(f_array, f_freq[0])\n",
    "    up_f_idx = find_nearest(f_array, f_freq[1])    \n",
    "    \n",
    "print(\"Desired frequency band:\",f_name,'at', f_freq, \"Hz\")\n",
    "print(\"---\")\n",
    "print(\"Index at upper freq cutoff:\", up_f_idx)\n",
    "print(\"Value at upper freq cutoff:\", f_array[up_f_idx], \"Hz\" )\n",
    "print('---')\n",
    "print(\"Index at lower freq cutoff:\", low_f_idx)\n",
    "print(\"Value at lower freq cutoff:\", f_array[low_f_idx], \"Hz\" )\n",
    "\n",
    "f_band = f_array[low_f_idx:up_f_idx+1]              # remove unwanted frequencies\n",
    "fup_stamp = f_band[-1]                              # freq stamps for use in output\n",
    "flow_stamp = f_band[0]\n",
    "\n",
    "print('---')\n",
    "print(\"Length of new freq array: \", len(f_band))   # new freq interval\n",
    "print(f'Upper limit at',fup_stamp,'Hz')            # freq upper limit\n",
    "print(f'Lower limit at',flow_stamp,'Hz')           # freq lower limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depth-band PSD spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-23T17:31:15.077856Z",
     "start_time": "2024-04-23T17:31:03.450749Z"
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   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "u_power, v_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    u_depth_power_temp,v_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_time_power_temp,v_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            u_PSD_temp = ds[i].Sxxu_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            u_power_temp = np.trapz(y=u_PSD_temp,x=f_band)\n",
    "            u_time_power_temp.append(u_power_temp)\n",
    "            v_PSD_temp = ds[i].Sxxv_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            v_power_temp = np.trapz(y=v_PSD_temp,x=f_band)\n",
    "            v_time_power_temp.append(v_power_temp)\n",
    "        u_depth_power_temp.append(u_time_power_temp)\n",
    "        v_depth_power_temp.append(v_time_power_temp)\n",
    "    u_np = np.asarray(u_depth_power_temp)\n",
    "    v_np = np.asarray(v_depth_power_temp)\n",
    "    u_power.append(u_np)            # n_seg, [depth, time]\n",
    "    v_power.append(v_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-23T17:31:26.042618Z",
     "start_time": "2024-04-23T17:31:25.922008Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "u_WKB,v_WKB = [],[]\n",
    "for i in range(n_seg):\n",
    "    u_WKB_temp,v_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        u_WKB_temp.append(u_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        v_WKB_temp.append(v_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    u_WKB.append(u_WKB_temp)\n",
    "    v_WKB.append(v_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-23T17:33:40.336688Z",
     "start_time": "2024-04-23T17:33:39.946578Z"
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     14,
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     36,
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     63
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    "scrolled": false
   },
   "outputs": [
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       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
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       "    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",
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       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
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       "        canvas_div.focus();\n",
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       "\n",
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       "};\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"
    },
    {
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width=\"1300\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig,(ax0,ax1) = plt.subplots(2,1,figsize=(13,5),sharey=True,sharex=True)\n",
    "fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "# if f_name == 'Shoulder':\n",
    "#     fig.text(0.5, 0.94, f'{f_name} - {adcp2} - {t_stamp}', ha='center', fontsize=14)\n",
    "# else:\n",
    "#     fig.text(0.5, 0.94, f'{f_name} - {adcp} - {t_stamp}', ha='center', fontsize=14)\n",
    "fig.text(0.07, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.97, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.02, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.9,0.886, 'WKB-scaled', fontsize=12, ha='right')\n",
    "fig.text(0.125,0.886, f'{year}', fontsize=12, ha='left')\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14)\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14)\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14)  \n",
    "elif f_name == 'Shoulder':\n",
    "    if adcp2 == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 1e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-slope', ha='center', fontsize=14, fontweight='bold')\n",
    "        fig.text(0.5, 0.479, 'Along-slope', ha='center', fontsize=14, fontweight='bold')\n",
    "        fig.text(0.06, 0.855, '(a)', ha='left', fontsize=14, fontweight='bold')\n",
    "    elif adcp2 == 'Axis75' or adcp2 == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 1e-3\n",
    "        fig.text(0.5, 0.887, 'Cross-canyon', ha='center', fontsize=14, fontweight='bold')\n",
    "        fig.text(0.5, 0.479, 'Along-canyon', ha='center', fontsize=14, fontweight='bold')\n",
    "        fig.text(0.06, 0.855, '(b)', ha='left', fontsize=14, fontweight='bold')\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, u_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, v_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "\n",
    "cbar = fig.add_axes([0.905, 0.11, 0.009, 0.77])\n",
    "cbar = fig.colorbar(im1, cax=cbar, fraction=0.05, pad=0.04, aspect=15, extend='both', ticks=[1e-4,1e-3])\n",
    "cbar.ax.tick_params(labelsize=14)\n",
    "\n",
    "ax0.patch.set_facecolor('grey')\n",
    "ax1.patch.set_facecolor('grey')\n",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "if adcp == 'Shoulder':\n",
    "    if adcp2 == 'Slope':\n",
    "        ax0.set_yticks([-300,-200,-100,0])\n",
    "        ax0.set_yticklabels(['300','200','100','0'])\n",
    "    elif adcp2 == 'Axis75' or adcp2 == 'Axis55':\n",
    "        ax0.set_yticks([-900,-800,-700,-600])\n",
    "        ax0.set_yticklabels(['900','800','700','600'])\n",
    "        ax0.set_xlim(start_dates[2],end_dates[2])\n",
    "        ax0.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "        ax1.set_xlim(start_dates[2],end_dates[2])\n",
    "        ax1.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if adcp == 'Shoulder':\n",
    "    plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{adcp2}_{t_stamp}.pdf',format='pdf')\n",
    "else:\n",
    "    plt.savefig(fname=f'./plots/depthband_plots/{f_name}_psd_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# save PSD data to .nc for inter-annual comparison\n",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            u_WKB=(['depth','spect_s'], u_WKB[i]),        # low-pass data\n",
    "            v_WKB=(['depth','spect_s'], v_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB PSD {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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",
    "    if f_name == 'Shoulder':\n",
    "        ds_out.to_netcdf(f'../Data/interannual/depthband_psd_{f_name}_{adcp2}_{t_stamp}_{i}.nc')\n",
    "    else:\n",
    "        ds_out.to_netcdf(f'../Data/interannual/depthband_psd_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Depth-band rotary spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# integrate over band for each time step\n",
    "\n",
    "cw_power, ccw_power = [],[]\n",
    "\n",
    "for i in range(n_seg):\n",
    "    t = len(ds[i].spect_s)\n",
    "    cw_depth_power_temp,ccw_depth_power_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_time_power_temp,ccw_time_power_temp = [],[]\n",
    "        for k in range(t):\n",
    "            cw_PSD_temp = ds[i].cw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            cw_power_temp = np.trapz(y=cw_PSD_temp,x=f_band)\n",
    "            cw_time_power_temp.append(cw_power_temp)\n",
    "            ccw_PSD_temp = ds[i].ccw_d[j,low_f_idx:up_f_idx+1,k]\n",
    "            ccw_power_temp = np.trapz(y=ccw_PSD_temp,x=f_band)\n",
    "            ccw_time_power_temp.append(ccw_power_temp)\n",
    "        cw_depth_power_temp.append(cw_time_power_temp)\n",
    "        ccw_depth_power_temp.append(ccw_time_power_temp)\n",
    "    cw_np = np.asarray(cw_depth_power_temp)\n",
    "    ccw_np = np.asarray(ccw_depth_power_temp)\n",
    "    cw_power.append(cw_np)            # n_seg, [depth, time]\n",
    "    ccw_power.append(ccw_np)            # n_seg, [depth, time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# WKB scale at each depth\n",
    "\n",
    "cw_WKB,ccw_WKB = [],[]\n",
    "for i in range(n_seg):\n",
    "    cw_WKB_temp,ccw_WKB_temp = [],[]\n",
    "    for j in range(d):\n",
    "        cw_WKB_temp.append(cw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "        ccw_WKB_temp.append(ccw_power[i][j] / ds[0].WKB_scale[j].values)          # list[depth][frequency]\n",
    "    cw_WKB.append(cw_WKB_temp)\n",
    "    ccw_WKB.append(ccw_WKB_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     14,
     21,
     28,
     35,
     45
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# plot rotary frequency band power versus depth\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",
    "if f_name == 'Shoulder':\n",
    "    fig.text(0.5, 0.94, f'{f_name} - {adcp2} - {t_stamp}', ha='center', fontsize=14)\n",
    "else:\n",
    "    fig.text(0.5, 0.94, f'{f_name} - {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(0.97, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.5, 0.01, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.125,0.886, 'WKB-scaled',fontsize=14)\n",
    "\n",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\n",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\n",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\n",
    "elif f_name == 'Shoulder':\n",
    "    if adcp2 == 'Slope':\n",
    "        vmin = 8e-5\n",
    "        vmax = 5e-4\n",
    "    elif adcp2 == 'Axis75' or adcp2 == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 8e-4\n",
    "        \n",
    "fig.text(0.5, 0.884, 'CW', ha='center', fontsize=14)\n",
    "fig.text(0.5, 0.473, 'CCW', ha='center', fontsize=14)\n",
    "\n",
    "for i in range(n_seg):\n",
    "    im0 = ax0.pcolormesh(ds[i].spect_s, -depth, cw_WKB[i], rasterized=True, \\\n",
    "                            norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma',shading='auto')  \n",
    "    im1 = ax1.pcolormesh(ds[i].spect_s, -depth, ccw_WKB[i], 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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "ax1.xaxis.set_major_formatter(date_form)\n",
    "ax1.set_xlim(start_date,end_date)\n",
    "ax0.tick_params(labelsize=14)\n",
    "ax1.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if adcp == 'Shoulder':\n",
    "    plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{adcp2}_{t_stamp}.pdf',format='pdf')\n",
    "else:\n",
    "    plt.savefig(fname=f'./plots/depthband_plots/{f_name}_rot_{adcp}_{t_stamp}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# save rotary data to .nc for inter-annual comparison\n",
    "for i in range(n_seg):\n",
    "    ds_out = xr.Dataset( \n",
    "        data_vars=dict(\n",
    "            cw_WKB=(['depth','spect_s'], cw_WKB[i]),        # low-pass data\n",
    "            ccw_WKB=(['depth','spect_s'], ccw_WKB[i]),\n",
    "        ),\n",
    "        coords=dict(\n",
    "            depth=depth,                        # depth values\n",
    "            spect_s=ds[i].spect_s.values,           # datetime values in segments\n",
    "        ),\n",
    "        attrs=dict(\n",
    "            description=f'Depth-band WKB rotary {f_name} data for {adcp} {t_stamp} segment {i} (from 0 to {n_seg - 1}).',\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",
    "            start_date=f'{start_date}',  # start date\n",
    "            end_date=f'{end_date}',      # end date\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",
    "    if f_name == 'Shoulder':\n",
    "        ds_out.to_netcdf(f'../Data/interannual/depthband_rot_{f_name}_{adcp2}_{t_stamp}_{i}.nc')\n",
    "    else:\n",
    "        ds_out.to_netcdf(f'../Data/interannual/depthband_rot_{f_name}_{adcp}_{t_stamp}_{i}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "## Inter-annual comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "site = 'Axis'     # Slope or Axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sub- and tidal 2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sl_sub, sl_di, sl_semi = [],[],[]\n",
    "\n",
    "sl_sub.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Subdiurnal_Slope_2013_0.nc'))\n",
    "sl_di.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Diurnal_Slope_2013_0.nc'))\n",
    "sl_semi.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Semidiurnal_Slope_2013_0.nc'))\n",
    "\n",
    "ax_sub, ax_di, ax_semi = [],[],[]\n",
    "\n",
    "ax_sub.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Subdiurnal_Axis75_2013_0.nc'))\n",
    "ax_sub.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Subdiurnal_Axis75_2013_1.nc'))\n",
    "ax_di.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Diurnal_Axis75_2013_0.nc'))\n",
    "ax_di.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Diurnal_Axis75_2013_1.nc'))\n",
    "ax_semi.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Semidiurnal_Axis75_2013_0.nc'))\n",
    "ax_semi.append(xr.open_dataset(f'../Data/interannual/depthband_psd_Semidiurnal_Axis75_2013_1.nc'))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     30,
     47,
     77,
     81,
     89,
     93,
     99
    ],
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5)) = plt.subplots(3,2, figsize=(13,7), sharex = False, sharey = False)\n",
    "fig.subplots_adjust(wspace=0.13, hspace=0.25)\n",
    "\n",
    "#fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.065, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.96, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.901,0.883, 'WKB-scaled',ha='right',fontsize=10)\n",
    "fig.text(0.126, 0.855, 'Sub-diurnal', ha='left',fontsize=14, color='white')\n",
    "fig.text(0.126, 0.580, 'Diurnal', ha='left',fontsize=14, color='white')\n",
    "fig.text(0.126, 0.305, 'Semidiurnal', ha='left',fontsize=14, color='white')\n",
    "fig.text(0.537, 0.855, 'Sub-diurnal', ha='left',fontsize=14, color='white')\n",
    "fig.text(0.537, 0.580, 'Diurnal', ha='left',fontsize=14, color='white')\n",
    "fig.text(0.537, 0.305, 'Semidiurnal', ha='left',fontsize=14, color='white')\n",
    "fig.text(0.100, 0.855, '(a)', ha='left', fontsize=14, fontweight='bold')\n",
    "fig.text(0.100, 0.580, '(b)', ha='left', fontsize=14, fontweight='bold')\n",
    "fig.text(0.100, 0.305, '(c)', ha='left', fontsize=14, fontweight='bold')\n",
    "\n",
    "#for ax in fig.get_axes():\n",
    "#    ax.label_outer()\n",
    "\n",
    "vmin = 1e-5\n",
    "vmax = 5e-3\n",
    "ax0.set_title('Along-slope', fontsize=14, fontweight='bold')\n",
    "ax1.set_title('Along-canyon', fontsize=14, fontweight='bold')\n",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "band = [sl_sub, sl_di, sl_semi] \n",
    "for k,i in enumerate(band):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "\n",
    "band = [ax_sub, ax_di, ax_semi] \n",
    "for k,i in enumerate(band):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2: \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax5.xaxis.set_major_formatter(date_form)\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",
    "#cbar1.ax.tick_params(labelsize=14)\n",
    "#cbar3.ax.tick_params(labelsize=14)\n",
    "#cbar5.ax.tick_params(labelsize=14)\n",
    "\n",
    "cbar = fig.add_axes([0.905, 0.11, 0.009, 0.77])\n",
    "cbar = fig.colorbar(im5, cax=cbar, fraction=0.05, pad=0.04, aspect=15, extend='both')\n",
    "cbar.ax.tick_params(labelsize=14) \n",
    "\n",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2013-01-01'),np.datetime64('2013-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2014-01-01'),np.datetime64('2014-01-01')]\n",
    "\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0], xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "    i.set_xticklabels('')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "    i.set_xticklabels('')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "\n",
    "for i in [ax0,ax2,ax4]:\n",
    "    i.set_ylim(-sl_sub[0].depth[-1],-sl_sub[0].depth[0])\n",
    "    i.set_yticks([-100,-200,-300])\n",
    "    i.set_yticklabels(['100', '200', '300'])\n",
    "for i in [ax1,ax3,ax5]:\n",
    "    i.set_ylim(-950,-600)\n",
    "    i.set_yticks([-600,-700,-800,-900],)\n",
    "    i.set_yticklabels(['600', '700', '800','900'])\n",
    "    \n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5]   \n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "    \n",
    "fig.add_artist(lines.Line2D([0.126, 0.900], [0.633, 0.633],color='black',lw=2.2))    \n",
    "fig.add_artist(lines.Line2D([0.126, 0.900], [0.357, 0.357],color='black',lw=2.2))    \n",
    "    \n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_sub_tidal_psd.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Sub-diurnal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     47,
     58,
     69,
     80,
     96,
     110,
     153,
     156,
     159,
     162,
     167,
     169,
     172
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import PSD data and plot\n",
    "\n",
    "f_name = 'Subdiurnal'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-3\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 = 1e-4\n",
    "        vmax = 4e-3\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "    \n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     44,
     47,
     58,
     69,
     80,
     96,
     110,
     135,
     171
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    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import rotary data and plot\n",
    "\n",
    "f_name = 'Subdiurnal'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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 f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-3\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 = 1e-4\n",
    "        vmax = 4e-3\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_rot_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Diurnal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     47,
     58,
     69,
     80,
     91,
     96,
     115
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import PSD data and plot\n",
    "\n",
    "f_name = 'Diurnal'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 = 1e-5\n",
    "        vmax = 5e-4\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",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     44,
     47,
     58,
     69,
     80,
     96,
     115,
     140
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import rotary data and plot\n",
    "\n",
    "f_name = 'Diurnal'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 = 1e-5\n",
    "        vmax = 5e-4\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 f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_rot_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Inertial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     5,
     11,
     47,
     58,
     69,
     80,
     91,
     96,
     115
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import PSD data and plot\n",
    "\n",
    "f_name = 'Inertial'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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",
    "elif f_name == 'Inertial':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 6e-5\n",
    "        vmax = 5e-3\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 = 6e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     44,
     47,
     58,
     69,
     80,
     96,
     115,
     140
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# import rotary data and plot\n",
    "\n",
    "f_name = 'Inertial'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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 f_name == 'Inertial':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 6e-5\n",
    "        vmax = 5e-3\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 = 6e-5\n",
    "        vmax = 5e-3\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 f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_rot_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Semidiurnal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     47,
     58,
     69,
     80,
     91,
     96,
     115
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import PSD data and plot\n",
    "\n",
    "f_name = 'Semidiurnal'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Semidiurnal':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     44,
     47,
     58,
     69,
     80,
     96,
     115,
     140
    ],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# import rotary data and plot\n",
    "\n",
    "f_name = 'Semidiurnal'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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 f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Semidiurnal':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Continuum':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 3e-4\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 4e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_rot_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "heading_collapsed": true,
    "run_control": {
     "frozen": true
    }
   },
   "source": [
    "### Continuum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     47,
     58,
     69,
     80,
     91,
     110
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import PSD data and plot\n",
    "\n",
    "f_name = 'Continuum'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Continuum':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 0.9e-4\n",
    "        vmax = 4e-4\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 = 5e-5\n",
    "        vmax = 3e-4\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",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "\n",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     44,
     47,
     58,
     69,
     80,
     91,
     110
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import rotary data and plot\n",
    "\n",
    "f_name = 'Continuum'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_2.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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 f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Continuum':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 0.9e-4\n",
    "        vmax = 4e-4\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 = 5e-5\n",
    "        vmax = 3e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_rot_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Shoulder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0,
     5,
     11,
     47,
     58,
     69,
     80,
     91,
     121,
     164,
     167,
     170,
     173,
     178,
     180,
     182,
     184
    ],
    "hidden": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import PSD data and plot\n",
    "\n",
    "f_name = 'Shoulder'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_0.nc'))\n",
    "    #ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_2.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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",
    "elif f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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",
    "elif f_name == 'Continuum':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 0.9e-4\n",
    "        vmax = 4e-4\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 = 5e-5\n",
    "        vmax = 3e-4\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",
    "elif f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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",
    "    elif adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 f_name == 'Shoulder':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 1.5e-4\n",
    "        vmax = 6e-4\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 = 7e-5\n",
    "        vmax = 5e-4\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].u_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].v_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if f_name == 'Shoulder' and site == 'Slope':\n",
    "    ax7.set_ylim(-ds_2013[0].depth[0],0)\n",
    "elif f_name == 'Shoulder' and site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_psd_{site}.pdf',format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# import rotary data and plot\n",
    "\n",
    "f_name = 'Shoulder'     # do not change\n",
    "\n",
    "ds_2013,ds_2014,ds_2017,ds_2018 = [],[],[],[]\n",
    "if site == 'Slope':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2013_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2014_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_0.nc'))\n",
    "    #ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_{site}_2018_0.nc'))\n",
    "elif site == 'Axis':\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_0.nc'))\n",
    "    ds_2013.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2013_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_0.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_1.nc'))\n",
    "    ds_2014.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis75_2014_2.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_0.nc'))\n",
    "    ds_2017.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2017_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_0.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_1.nc'))\n",
    "    ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_2.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_rot_{f_name}_Axis55_2018_3.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_4.nc'))\n",
    "    #ds_2018.append(xr.open_dataset(f'../Data/interannual/depthband_psd_{f_name}_Axis55_2018_5.nc'))\n",
    "    \n",
    "#print(ds_2018[0])\n",
    "\n",
    "# plot PSD frequency band power versus depth\n",
    "\n",
    "fig, ((ax0,ax1),(ax2,ax3),(ax4,ax5),(ax6,ax7)) = plt.subplots(4,2, figsize=(13,9), sharex = False, sharey = True)\n",
    "fig.subplots_adjust(wspace = 0.08)\n",
    "#fig.subplots_adjust(hspace = 0.15)\n",
    "\n",
    "fig.text(0.51, 0.92, f'{f_name} - {site}', ha='center', fontsize=14)\n",
    "fig.text(0.05, 0.5, 'Depth [m]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.95, 0.5, 'Rotary band power [(m/s)$^2$]', va='center', rotation='vertical',fontsize=14)\n",
    "fig.text(0.51, 0.04, 'Time [months]', ha='center',fontsize=14)\n",
    "fig.text(0.79,0.886, 'WKB-scaled',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",
    "if f_name == 'Diurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-5\n",
    "        vmax = 5e-4\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 f_name == 'Inertial':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Semidiurnal':  \n",
    "    if adcp == 'Axis55' or adcp == 'Axis75':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 5e-5\n",
    "        vmax = 5e-3\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 f_name == 'Continuum':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 0.9e-4\n",
    "        vmax = 4e-4\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 = 5e-5\n",
    "        vmax = 3e-4\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 f_name == 'Subdiurnal':  \n",
    "    if adcp == 'Axis75':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Slope':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 adcp == 'Axis55':\n",
    "        vmin = 1e-4\n",
    "        vmax = 5e-3\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 f_name == 'Shoulder':  \n",
    "    if site == 'Axis':\n",
    "        vmin = 1.5e-4\n",
    "        vmax = 6e-4\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 = 7e-5\n",
    "        vmax = 5e-4\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",
    "date_form = pldates.DateFormatter(\"%m\")\n",
    "years = [ds_2013,ds_2014,ds_2017,ds_2018] \n",
    "for k,i in enumerate(years):\n",
    "    n_seg_temp = i[0].n_seg\n",
    "    for j in range(n_seg_temp):\n",
    "        if k == 0:\n",
    "            im0 = ax0.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im1 = ax1.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax0.xaxis.set_major_formatter(date_form)\n",
    "            ax1.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 1:\n",
    "            im2 = ax2.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im3 = ax3.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax2.xaxis.set_major_formatter(date_form)\n",
    "            ax3.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 2:\n",
    "            im4 = ax4.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im5 = ax5.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax4.xaxis.set_major_formatter(date_form)\n",
    "            ax5.xaxis.set_major_formatter(date_form)\n",
    "        elif k == 3:\n",
    "            im6 = ax6.pcolormesh(i[j].spect_s, -i[j].depth, i[j].cw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto')  \n",
    "            im7 = ax7.pcolormesh(i[j].spect_s, -i[j].depth, i[j].ccw_WKB, rasterized=True, \\\n",
    "                norm=colors.LogNorm(vmin=vmin, vmax=vmax), cmap='plasma', shading='auto') \n",
    "            ax6.xaxis.set_major_formatter(date_form)\n",
    "            ax7.xaxis.set_major_formatter(date_form)\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",
    "start_dates = [np.datetime64('2013-01-01'),np.datetime64('2014-01-01'),np.datetime64('2017-01-01'),np.datetime64('2018-01-01')]\n",
    "end_dates = [np.datetime64('2014-01-01'),np.datetime64('2015-01-01'),np.datetime64('2018-01-01'),np.datetime64('2019-01-01')]\n",
    "for i in [ax0,ax1]:\n",
    "    i.set_xlim(start_dates[0],end_dates[0])\n",
    "    i.hlines(y=-700, xmin=start_dates[0],xmax=end_dates[0], linewidth=1, color='white', ls='--')\n",
    "for i in [ax2,ax3]:\n",
    "    i.set_xlim(start_dates[1],end_dates[1])\n",
    "    i.hlines(y=-700, xmin=start_dates[1],xmax=end_dates[1], linewidth=1, color='white', ls='--')\n",
    "for i in [ax4,ax5]:\n",
    "    i.set_xlim(start_dates[2],end_dates[2])\n",
    "    i.hlines(y=-700, xmin=start_dates[2],xmax=end_dates[2], linewidth=1, color='white', ls='--')\n",
    "for i in [ax6,ax7]:\n",
    "    i.set_xlim(start_dates[3],end_dates[3])\n",
    "    i.hlines(y=-700, xmin=start_dates[3],xmax=end_dates[3], linewidth=1, color='white', ls='--')\n",
    "\n",
    "axs = [ax0,ax1,ax2,ax3,ax4,ax5,ax6,ax7]\n",
    "if f_name == 'Shoulder' and site == 'Slope':\n",
    "    ax7.set_ylim(-ds_2013[0].depth[0],0)\n",
    "elif f_name == 'Shoulder' and site == 'Axis':\n",
    "    ax7.set_ylim(-950,-600)\n",
    "else:\n",
    "    ax7.set_ylim(-ds_2013[0].depth[-1],-ds_2013[0].depth[0])\n",
    "for i in axs:\n",
    "    i.patch.set_facecolor('grey')\n",
    "    i.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.savefig(fname=f'./plots/interannual_plots/depthband_{f_name}_rot_{site}.pdf',format='pdf')"
   ]
  },
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