data_exploration/figure/Supplemental/klmsupplemental.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "464a2be2",
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   "outputs": [],
   "source": [
    "#dataframes\n",
    "import pandas as pd\n",
    "import h5py\n",
    "\n",
    "#speedup\n",
    "import multiprocessing as mp\n",
    "\n",
    "#suppress warnings\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "pd.TimeSeries = pd.Series \n",
    "\n",
    "#math\n",
    "import numpy as np\n",
    "from scipy.spatial.distance import cdist\n",
    "from scipy import signal\n",
    "import math\n",
    "import copy\n",
    "\n",
    "\n",
    "#plots\n",
    "import pylab as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "import matplotlib\n",
    "from figurefirst import FigureLayout,mpl_functions\n",
    "import figurefirst\n",
    "import matplotlib.image as mpimg\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable # for colorbar\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_style(\"whitegrid\")\n",
    "import figurefirst as fifi\n",
    "from IPython.display import display,SVG\n",
    "\n",
    "#misc\n",
    "import cairosvg\n",
    "import itertools\n",
    "import time\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "FS=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94ad04ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def __kalman_forward_update__(xhat_fm, P_fm, y, u, A, B, C, R, Q):\n",
    "    \"\"\"\n",
    "    :param xhat_fm:\n",
    "    :param P_fm:\n",
    "    :param y:\n",
    "    :param u:\n",
    "    :param A:\n",
    "    :param B:\n",
    "    :param C:\n",
    "    :param R:\n",
    "    :param Q:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    I = np.array(np.eye(A.shape[0]))\n",
    "    gammaW = np.array(np.eye(A.shape[0]))\n",
    "\n",
    "    K_f = P_fm@C.T@np.linalg.pinv(C@P_fm@C.T + R)\n",
    "\n",
    "    if y is not None:\n",
    "        new_info = (y - C@xhat_fm)\n",
    "        new_info[np.isnan(new_info)] = 0\n",
    "        xhat_fp = xhat_fm + K_f@new_info\n",
    "        \n",
    "        P_fp = (I - K_f@C)@P_fm\n",
    "        xhat_fm = A@xhat_fp + B@u\n",
    "        P_fm = A@P_fp@A.T + gammaW@Q@gammaW.T\n",
    "        \n",
    "    return xhat_fp, xhat_fm, P_fp, P_fm\n",
    "\n",
    "\n",
    "def __kalman_forward_filter__(xhat_fm, P_fm, y, u, A, B, C, R, Q):\n",
    "    \"\"\"\n",
    "    :param xhat_fm:\n",
    "    :param P_fm:\n",
    "    :param y:\n",
    "    :param u:\n",
    "    :param A:\n",
    "    :param B:\n",
    "    :param C:\n",
    "    :param R:\n",
    "    :param Q:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    if u is None:\n",
    "        u = np.array(np.zeros([B.shape[1], y.shape[1]]))\n",
    "\n",
    "    xhat_fp = None\n",
    "    P_fp = []\n",
    "    P_fm = [P_fm]\n",
    "\n",
    "    for i in range(y.shape[1]):\n",
    "        _xhat_fp, _xhat_fm, _P_fp, _P_fm = __kalman_forward_update__(xhat_fm[:, [-1]], P_fm[-1], y[:, [i]], u[:, [i]],\n",
    "                                                                     A, B, C, R, Q)\n",
    "        \n",
    "        if xhat_fp is None:\n",
    "            xhat_fp = _xhat_fp\n",
    "        else:\n",
    "            xhat_fp = np.hstack((xhat_fp, _xhat_fp))\n",
    "        xhat_fm = np.hstack((xhat_fm, _xhat_fm))\n",
    "\n",
    "        P_fp.append(_P_fp)\n",
    "        P_fm.append(_P_fm)\n",
    "\n",
    "    return xhat_fp, xhat_fm, P_fp, P_fm\n",
    "\n",
    "\n",
    "def __kalman_backward_smooth__(xhat_fp, xhat_fm, P_fp, P_fm, A):\n",
    "    \"\"\"\n",
    "    :param xhat_fp:\n",
    "    :param xhat_fm:\n",
    "    :param P_fp:\n",
    "    :param P_fm:\n",
    "    :param A:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    N = xhat_fp.shape[1]\n",
    "\n",
    "    xhat_smooth = copy.copy(xhat_fp)\n",
    "    P_smooth = copy.copy(P_fp)\n",
    "    for t in range(N-2, -1, -1):\n",
    "        L = P_fp[t]@A.T@np.linalg.pinv(P_fm[t])\n",
    "        xhat_smooth[:, [t]] = xhat_fp[:, [t]] + L@(xhat_smooth[:, [t+1]] - xhat_fm[:, [t+1]])\n",
    "        P_smooth[t] = P_fp[t] - L@(P_smooth[t+1] - P_fm[t+1])\n",
    "\n",
    "    return xhat_smooth, P_smooth\n",
    "\n",
    "\n",
    "def klm_cal(data1,data2):\n",
    "    wind_vector = np.vstack((data_2.U.values, data_2.V.values)).T\n",
    "    wind_magnitude = np.linalg.norm(wind_vector, axis=1)\n",
    "    unit_wind_vector = wind_vector / np.vstack((wind_magnitude, wind_magnitude)).T\n",
    "\n",
    "\n",
    "    velocity_vector = np.vstack((data_2.ground_speed_x.values, data_2.ground_speed_y.values)).T\n",
    "\n",
    "    # There is probably a more efficient syntax\n",
    "    velocity_ground_relative_to_wind = [np.dot(velocity_vector[i,:], unit_wind_vector[i,:]) for i in range(len(velocity_vector))]\n",
    "\n",
    "    data_for_kf_original = pd.DataFrame({'time': data_2.time, \n",
    "                                    'velocity_ground_relative_to_wind': velocity_ground_relative_to_wind,\n",
    "                                    'distance_true': data_2.distance_from_source,\n",
    "                                    'distance_odor_observed': np.nan*np.ones_like(velocity_ground_relative_to_wind)})\n",
    "    # kalman filter is slow, let's subsample the data\n",
    "    data_for_kf = copy.copy( data_for_kf_original[::20] )\n",
    "    # put distance from odor predictions in the right places\n",
    "    for ix_odor in data_1.index:\n",
    "        ix_kf = np.argmin( np.abs(data_1.mean_whiff_time[ix_odor] - data_for_kf.time))\n",
    "        data_for_kf.distance_odor_observed.iloc[ix_kf] = data_1.predicted_distance.loc[ix_odor]\n",
    "    #     print(data_for_kf.distance_odor_observed[ix_kf])\n",
    "\n",
    "    dt = np.median(np.diff(data_for_kf.time.values))\n",
    "    y = np.array([data_for_kf.distance_odor_observed.values, \n",
    "                  data_for_kf.velocity_ground_relative_to_wind.values])\n",
    "\n",
    "    ### Define dynamics: assume constant velocity\n",
    "\n",
    "    # constant velocity\n",
    "    A = np.array([[1, dt], [0, 1]])\n",
    "\n",
    "    # no explicit control inputs\n",
    "    B = np.array([[0], [0]])\n",
    "\n",
    "    # we have measurements of distance and velocity\n",
    "    C = np.array([[1, 0], [0, 1]])\n",
    "\n",
    "\n",
    "    ### Define covariances\n",
    "    # measurement covariances: the measurements of the observed distance are worse than the velocity measurements\n",
    "    R = np.array([[1e-1, 0], [0, 1e-3]])\n",
    "    # R = np.array([[1e-2, 0], [0, 1e-3]]) #Forest\n",
    "\n",
    "    # model covariances: the model for constant velocity is probably worse than the model for distance = integral of velocity\n",
    "    Q = np.array([[1e-3, 0], [0, 1e-1]])\n",
    "    # Q = np.array([[1e-2, 0], [0, 1]]) #Forest\n",
    "\n",
    "    ### Define initial conditions\n",
    "    x0 = np.array([[0], [0]])\n",
    "    P0 = np.array(100*np.eye(2))\n",
    "    u = None # control input -- technically this could be the acceleration, but not needed\n",
    "\n",
    "    ### Choose a subset of the measurements for testing\n",
    "    ix_start = 0\n",
    "    ix_end = 100000\n",
    "\n",
    "    y_subset = copy.copy( y[:, ix_start:ix_end] )\n",
    "    y_subset[0,0] = 5 # start with a guess for initial distance (can't be nan)\n",
    "\n",
    "    time = data_for_kf.time[ix_start:ix_end].values\n",
    "\n",
    "    ### Run the filter and smoother\n",
    "    xhat_fp, xhat_fm, P_fp, P_fm = __kalman_forward_filter__(x0, P0, y_subset, u, A, B, C, R, Q)\n",
    "    xhat_smooth, _ = __kalman_backward_smooth__(xhat_fp, xhat_fm, P_fp, P_fm, A)\n",
    "    return xhat_smooth, time, data_for_kf\n",
    "\n",
    "def klm_cal_forest(data1,data2):\n",
    "    wind_vector = np.vstack((data_2.U.values, data_2.V.values)).T\n",
    "    wind_magnitude = np.linalg.norm(wind_vector, axis=1)\n",
    "    unit_wind_vector = wind_vector / np.vstack((wind_magnitude, wind_magnitude)).T\n",
    "\n",
    "\n",
    "    velocity_vector = np.vstack((data_2.ground_speed_x.values, data_2.ground_speed_y.values)).T\n",
    "\n",
    "    # There is probably a more efficient syntax\n",
    "    velocity_ground_relative_to_wind = [np.dot(velocity_vector[i,:], unit_wind_vector[i,:]) for i in range(len(velocity_vector))]\n",
    "\n",
    "    data_for_kf_original = pd.DataFrame({'time': data_2.time, \n",
    "                                    'velocity_ground_relative_to_wind': velocity_ground_relative_to_wind,\n",
    "                                    'distance_true': data_2.distance_from_source,\n",
    "                                    'distance_odor_observed': np.nan*np.ones_like(velocity_ground_relative_to_wind)})\n",
    "    # kalman filter is slow, let's subsample the data\n",
    "    data_for_kf = copy.copy( data_for_kf_original[::20] )\n",
    "    # put distance from odor predictions in the right places\n",
    "    for ix_odor in data_1.index:\n",
    "        ix_kf = np.argmin( np.abs(data_1.mean_whiff_time[ix_odor] - data_for_kf.time))\n",
    "        data_for_kf.distance_odor_observed.iloc[ix_kf] = data_1.predicted_distance.loc[ix_odor]\n",
    "    #     print(data_for_kf.distance_odor_observed[ix_kf])\n",
    "\n",
    "    dt = np.median(np.diff(data_for_kf.time.values))\n",
    "    y = np.array([data_for_kf.distance_odor_observed.values, \n",
    "                  data_for_kf.velocity_ground_relative_to_wind.values])\n",
    "\n",
    "    ### Define dynamics: assume constant velocity\n",
    "\n",
    "    # constant velocity\n",
    "    A = np.array([[1, dt], [0, 1]])\n",
    "\n",
    "    # no explicit control inputs\n",
    "    B = np.array([[0], [0]])\n",
    "\n",
    "    # we have measurements of distance and velocity\n",
    "    C = np.array([[1, 0], [0, 1]])\n",
    "\n",
    "\n",
    "    ### Define covariances\n",
    "    # measurement covariances: the measurements of the observed distance are worse than the velocity measurements\n",
    "    R = np.array([[1e-2, 0], [0, 1e-3]]) #Forest\n",
    "\n",
    "    # model covariances: the model for constant velocity is probably worse than the model for distance = integral of velocity\n",
    "    Q = np.array([[1e-2, 0], [0, 1]]) #Forest\n",
    "\n",
    "    ### Define initial conditions\n",
    "    x0 = np.array([[0], [0]])\n",
    "    P0 = np.array(100*np.eye(2))\n",
    "    u = None # control input -- technically this could be the acceleration, but not needed\n",
    "\n",
    "    ### Choose a subset of the measurements for testing\n",
    "    ix_start = 0\n",
    "    ix_end = 100000\n",
    "\n",
    "    y_subset = copy.copy( y[:, ix_start:ix_end] )\n",
    "    y_subset[0,0] = 5 # start with a guess for initial distance (can't be nan)\n",
    "\n",
    "    time = data_for_kf.time[ix_start:ix_end].values\n",
    "\n",
    "    ### Run the filter and smoother\n",
    "    xhat_fp, xhat_fm, P_fp, P_fm = __kalman_forward_filter__(x0, P0, y_subset, u, A, B, C, R, Q)\n",
    "    xhat_smooth, _ = __kalman_backward_smooth__(xhat_fp, xhat_fm, P_fp, P_fm, A)\n",
    "    return xhat_smooth, time, data_for_kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01953d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 595.276x841.89 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout = fifi.svg_to_axes.FigureLayout('../../../../Figure/klmsupplmental.svg',\n",
    "                                           autogenlayers=True, make_mplfigures=True, hide_layers=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721280ab",
   "metadata": {},
   "source": [
    "## HWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4758a501",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir='~/DataAnalysis/data/Sprints/HighRes/KF/'\n",
    "data_1=pd.read_hdf(dir+'AllPredictions.h5')\n",
    "data_2=pd.read_hdf(dir+'WindyMASigned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7375aa0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xhat_smooth,time,data_for_kf = klm_cal(data_1,data_2)\n",
    "\n",
    "ax=layout.axes[('fig','a1')]\n",
    "ax.grid(False)\n",
    "line_width=0.5\n",
    "marker_size=0.5\n",
    "ax.plot(data_1.mean_whiff_time, data_1.predicted_distance, '.', \n",
    "        color='black', label='Predicted Distance',linewidth=line_width,\n",
    "        markersize=marker_size*2,\n",
    "        rasterized= True)\n",
    "ax.plot(time, xhat_smooth[0,:], color='black', label='Smoothed Distance', \n",
    "        linewidth=line_width,markersize=marker_size,\n",
    "        rasterized= True)\n",
    "ax.plot(data_for_kf.time, data_for_kf.distance_true, '--', color='red',\n",
    "        label='True Distance',linewidth=line_width, markersize=marker_size,rasterized= True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.set_xlim(1000, 2000)\n",
    "\n",
    "mpl_functions.adjust_spines(ax,['left'],\n",
    "                            spine_locations={'left':4,'bottom':4},\n",
    "                            smart_bounds=True,\n",
    "                            yticks=[-10,70],\n",
    "                            xticks=[1000,2000],\n",
    "                            linewidth=0.5)\n",
    "\n",
    "ax.set_xticklabels([])\n",
    "ax.set_ylabel('Distance, m')\n",
    "ax.yaxis.set_label_coords(-0.04, 0.5)\n",
    "\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig'], 'fig', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/klmsupplmental.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eeae2dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir='~/DataAnalysis/data/Sprints/HighRes/KF/'\n",
    "data_1=pd.read_hdf(dir+'WindyPredictions.h5')\n",
    "data_2=pd.read_hdf(dir+'WindyMASigned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58b29dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "xhat_smooth,time,data_for_kf = klm_cal(data_1,data_2)\n",
    "\n",
    "ax=layout.axes[('fig','a1')]\n",
    "ax.grid(False)\n",
    "line_width=0.8\n",
    "marker_size=0.5\n",
    "\n",
    "ax.plot(time, xhat_smooth[0,:], color='g', label='Smoothed Distance', \n",
    "        linewidth=line_width,markersize=marker_size,\n",
    "        rasterized= True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.set_xlim(1000, 2000)\n",
    "\n",
    "mpl_functions.adjust_spines(ax,['left'],\n",
    "                            spine_locations={'left':4,'bottom':4},\n",
    "                            smart_bounds=True,\n",
    "                            yticks=[-10,70],\n",
    "                            xticks=[1000,2000],\n",
    "                            linewidth=0.5)\n",
    "\n",
    "ax.set_xticklabels([])\n",
    "ax.set_ylabel('Distance, m')\n",
    "ax.yaxis.set_label_coords(-0.04, 0.5)\n",
    "\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig'], 'fig', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/klmsupplmental.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c54bd98",
   "metadata": {},
   "source": [
    "## LWS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b6c02c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir='~/DataAnalysis/data/Sprints/HighRes/KF/'\n",
    "data_1=pd.read_hdf(dir+'AllPredictions.h5')\n",
    "data_2=pd.read_hdf(dir+'NotWindyMASigned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "afb65d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "xhat_smooth,time,data_for_kf = klm_cal(data_1,data_2)\n",
    "\n",
    "ax=layout.axes[('fig','a2')]\n",
    "ax.grid(False)\n",
    "line_width=0.5\n",
    "marker_size=0.5\n",
    "ax.plot(data_1.mean_whiff_time, data_1.predicted_distance, '.', \n",
    "        color='black', label='Predicted Distance',linewidth=line_width,\n",
    "        markersize=marker_size*2,\n",
    "        rasterized= True)\n",
    "ax.plot(time, xhat_smooth[0,:], color='black', label='Smoothed Distance', \n",
    "        linewidth=line_width,markersize=marker_size,\n",
    "        rasterized= True)\n",
    "ax.plot(data_for_kf.time, data_for_kf.distance_true, '--', color='red',\n",
    "        label='True Distance',linewidth=line_width, markersize=marker_size,rasterized= True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.set_xlim(1500,2500)\n",
    "ax.set_ylim(-10,70)\n",
    "\n",
    "mpl_functions.adjust_spines(ax,['left'],\n",
    "                            spine_locations={'left':4},\n",
    "                            smart_bounds=True,\n",
    "                            yticks=[-10,70],\n",
    "                            xticks=[2000,3000],\n",
    "                            linewidth=0.5)\n",
    "ax.set_xticklabels([], rotation=90)\n",
    "ax.set_ylabel('Distance, m')\n",
    "ax.yaxis.set_label_coords(-0.04, 0.5)\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig'], 'fig', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/klmsupplmental.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3fdb3b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir='~/DataAnalysis/data/Sprints/HighRes/KF/'\n",
    "data_1=pd.read_hdf(dir+'NotWindyPredictions.h5')\n",
    "data_2=pd.read_hdf(dir+'NotWindyMASigned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "995d2c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "xhat_smooth,time,data_for_kf = klm_cal(data_1,data_2)\n",
    "\n",
    "\n",
    "ax=layout.axes[('fig','a2')]\n",
    "ax.grid(False)\n",
    "line_width=0.8\n",
    "marker_size=0.5\n",
    "\n",
    "ax.plot(time, xhat_smooth[0,:], color='g', label='Smoothed Distance', \n",
    "        linewidth=line_width,markersize=marker_size,\n",
    "        rasterized= True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.set_xlim(1500,2500)\n",
    "ax.set_ylim(-10,70)\n",
    "\n",
    "mpl_functions.adjust_spines(ax,['left'],\n",
    "                            spine_locations={'left':4},\n",
    "                            smart_bounds=True,\n",
    "                            yticks=[-10,70],\n",
    "                            xticks=[2000,3000],\n",
    "                            linewidth=0.5)\n",
    "ax.set_xticklabels([], rotation=90)\n",
    "ax.set_ylabel('Distance, m')\n",
    "ax.yaxis.set_label_coords(-0.04, 0.5)\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig'], 'fig', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/klmsupplmental.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e2c8b2",
   "metadata": {},
   "source": [
    "## Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05f76e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir='~/DataAnalysis/data/Sprints/HighRes/KF/'\n",
    "data_1=pd.read_hdf(dir+'AllPredictions.h5')\n",
    "data_2=pd.read_hdf(dir+'ForestMASigned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "602842d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "xhat_smooth,time,data_for_kf = klm_cal_forest(data_1,data_2)\n",
    "\n",
    "ax=layout.axes[('fig','a3')]\n",
    "ax.grid(False)\n",
    "line_width=0.5\n",
    "marker_size=0.5\n",
    "ax.plot(data_1.mean_whiff_time, data_1.predicted_distance, '.', \n",
    "        color='black', label='Predicted Distance',linewidth=line_width,\n",
    "        markersize=marker_size*2,\n",
    "        rasterized= True)\n",
    "ax.plot(time, xhat_smooth[0,:], color='black', label='Smoothed Distance', \n",
    "        linewidth=line_width,markersize=marker_size,\n",
    "        rasterized= True)\n",
    "ax.plot(data_for_kf.time, data_for_kf.distance_true, '--', color='red',\n",
    "        label='True Distance',linewidth=line_width, markersize=marker_size,rasterized= True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.set_ylim(-10,70)\n",
    "ax.set_xlim(1000,2000)\n",
    "mpl_functions.adjust_spines(ax,['left','bottom'],\n",
    "                            spine_locations={'left':4,'bottom':6},\n",
    "                            smart_bounds=True,\n",
    "                            yticks=[-10,70],\n",
    "                            xticks=[1000,2000],\n",
    "                            linewidth=0.5)\n",
    "ax.set_xticklabels([], rotation=90)\n",
    "\n",
    "\n",
    "ax.set_xlabel('1000 secs')\n",
    "ax.xaxis.set_label_coords(0.5, -0.1)\n",
    "ax.set_ylabel('Distance, m')\n",
    "ax.yaxis.set_label_coords(-0.04, 0.5)\n",
    "\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig'], 'fig', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/klmsupplmental.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "493dd1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir='~/DataAnalysis/data/Sprints/HighRes/KF/'\n",
    "data_1=pd.read_hdf(dir+'ForestPredictions.h5')\n",
    "data_2=pd.read_hdf(dir+'ForestMASigned.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81c0cbf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "xhat_smooth,time,data_for_kf = klm_cal_forest(data_1,data_2)\n",
    "\n",
    "\n",
    "ax=layout.axes[('fig','a3')]\n",
    "ax.grid(False)\n",
    "line_width=0.8\n",
    "\n",
    "ax.plot(time, xhat_smooth[0,:], color='g', label='Smoothed Distance', \n",
    "        linewidth=line_width,markersize=marker_size,\n",
    "        rasterized= True)\n",
    "\n",
    "ax.set_rasterization_zorder(-20)\n",
    "ax.set_ylim(-10,70)\n",
    "ax.set_xlim(1000,2000)\n",
    "mpl_functions.adjust_spines(ax,['left','bottom'],\n",
    "                            spine_locations={'left':4,'bottom':6},\n",
    "                            smart_bounds=True,\n",
    "                            yticks=[-10,70],\n",
    "                            xticks=[1000,2000],\n",
    "                            linewidth=0.5)\n",
    "ax.set_xticklabels([], rotation=90)\n",
    "\n",
    "\n",
    "ax.set_xlabel('1000 secs')\n",
    "ax.xaxis.set_label_coords(0.5, -0.1)\n",
    "ax.set_ylabel('Distance, m')\n",
    "ax.yaxis.set_label_coords(-0.04, 0.5)\n",
    "\n",
    "\n",
    "figurefirst.mpl_functions.set_fontsize(ax, FS)\n",
    "layout.append_figure_to_layer(layout.figures['fig'], 'fig', cleartarget=True)\n",
    "layout.write_svg('../../../../Figure/klmsupplmental.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a86e5e72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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