make_odom.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "SOURCE = \"../bbb3_glass_eval_111716.log\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Install instructions\\nAlways run in jupyterlab\\n\\n- sudo apt install nodejs # didn't work\\n- curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.38.0/install.sh | bash\\n- nvm install --lts\\n- sudo chmod a+rwx /usr/local/share/jupyter/lab/\\n- mkdir /usr/local/share/jupyter/lab/extensions\\n- jupyter labextension install @jupyter-widgets/jupyterlab-manager --debug # will need to chmod some stouff to make work\\n\\n- jupyter nbextension enable --py widgetsnbextension\\n\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" Install instructions\n",
    "Always run in jupyterlab\n",
    "\n",
    "- sudo apt install nodejs # didn't work\n",
    "- curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.38.0/install.sh | bash\n",
    "- nvm install --lts\n",
    "- sudo chmod a+rwx /usr/local/share/jupyter/lab/\n",
    "- mkdir /usr/local/share/jupyter/lab/extensions\n",
    "- jupyter labextension install @jupyter-widgets/jupyterlab-manager --debug # will need to chmod some stouff to make work\n",
    "\n",
    "- jupyter nbextension enable --py widgetsnbextension\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "_= \"\"\" \n",
    "To use this file:\n",
    "For vulcan, set the moving_laser_scan rayPose to pose_t(0,0,0) so the dumped scans are always in robot frame.\n",
    "If you have the mapper.cpp return false right after dumping, it also speeds up the dump drastically.\n",
    "This file requires also decent dumped odoe\n",
    "\n",
    "Then the dumped \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Known bugs:\n",
    " - Code is inconsistent on whether scan period is the whole period or just for the active part of the scan (hack corrected)\n",
    "\n",
    "\n",
    "In 112016 the gyro(or odom rotation? not sure) readings need to be multiplied by  0.78(+-0.02) to be accurate(not tested on other runs)\n",
    "\n",
    "The intensity seems to fall off at a rate of 218.3 (unknown uncert) units per meter for the Hokuyo. Perhaps it reports in a log scale? Not normalized in any case.\n",
    "Below two meters, the intensity is pretty weird. 2-10 m is where the intensity seems to be sane. Somewhere between 0.5 and 1.0 m seems to be where most things report peak reflectivity\n",
    "\n",
    "Localization seems to be accurate to +-10 mrad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib widget\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rayfile = os.path.join(SOURCE, \"raw_lidar_dumps\",\"rays\",\"rays.rays\")\n",
    "# rayfile = os.path.join(SOURCE, \"raw_lidar_dumps\",\"rays\",\"rays_moving_unfixed.rays\")\n",
    "# rayfile = os.path.join(SOURCE, \"raw_lidar_dumps\",\"rays\",\"rays_moving_earlyts.rays\")\n",
    "# rayfile = os.path.join(SOURCE, \"raw_lidar_dumps\",\"rays\",\"rays_moving_hiqual.rays\")\n",
    "# rayfile = os.path.join(SOURCE, \"raw_lidar_dumps\",\"rays\",\"rays_manyparticle_highrecall.rays\")\n",
    "\n",
    "rayfile = \"/home/anuran/repos/Vulcan/bin/rayDump.rays\" # HACK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rays_struct = np.dtype(\n",
    "    [\n",
    "        ('sx', np.float32),\n",
    "        ('sy', np.float32),\n",
    "        ('ex', np.float32),\n",
    "        ('ey', np.float32),\n",
    "        ('r' , np.float32),\n",
    "        ('th', np.float32),\n",
    "        ('i',  np.uint16),\n",
    "        ('pad',  np.uint16),\n",
    "        ('idx',  np.int32),\n",
    "        ('ts', np.float64), #remove for old ray file formats\n",
    "      ])\n",
    "\n",
    "# rays = np.memmap(rayfile,rays_struct, 'r',1)\n",
    "rays0 = np.fromfile(rayfile,rays_struct)\n",
    "# Clip rays0 to a multiple of 1081 rays, under the assumption that a scan is 1081 rays long\n",
    "if len(rays0)%1081 != 0:\n",
    "    print(\"WARNING: file is not made of complete scans!\")\n",
    "    print(\"Remainder:\", len(rays0)%1081)\n",
    "# For the new sensors, we will need to find a different way to split scans\n",
    "rays0 = rays0[:int(len(rays0)//1081*1081)]\n",
    "\n",
    "polar_rays_struct = np.dtype(\n",
    "    [\n",
    "        ('r' , np.float32),\n",
    "        ('th', np.float32),\n",
    "        ('i',  np.uint16),\n",
    "        ('pad',  np.uint16),\n",
    "        ('idx',  np.int32),\n",
    "        ('ts', np.float64), #remove for old ray file formats\n",
    "      ])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rays1=rays0[rays0['idx']==0]\n",
    "rays2=rays0[rays0['idx']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-e50cb4296749>:6: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  nan_th=rays1['th']/(rays1['r']>1.0)/(rays1['r']<30.0)\n",
      "<ipython-input-8-e50cb4296749>:6: RuntimeWarning: invalid value encountered in true_divide\n",
      "  nan_th=rays1['th']/(rays1['r']>1.0)/(rays1['r']<30.0)\n",
      "/usr/lib/python3/dist-packages/numpy/lib/function_base.py:1273: RuntimeWarning: invalid value encountered in subtract\n",
      "  a = op(a[slice1], a[slice2])\n",
      "<ipython-input-8-e50cb4296749>:12: RuntimeWarning: invalid value encountered in less\n",
      "  slope=slope[np.abs(dth)<np.pi/2]\n",
      "<ipython-input-8-e50cb4296749>:30: RuntimeWarning: invalid value encountered in true_divide\n",
      "  _=plt.hist(np.diff(np.diff(flat_rangle)/np.diff(rays1['ts'][::1081])),1000)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ddf826bd24d429e86eeebab1fc2d3fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/usr/lib/python3/dist-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "# plt.imshow(np.diff(rays0['ts'].reshape(-1,1081)[:1000]-rays0['ts'][0],axis=0))\n",
    "# d=(d+np.pi/2)%\n",
    "# plt.plot(np.diff(rays0['th'].reshape(-1,1081)[:1000],axis=0)[:,100],'b,')\n",
    "# plt.plot(rays1['ts'],rays1['th'],'b,')\n",
    "nan_th=rays1['th']/(rays1['r']>1.0)/(rays1['r']<30.0)\n",
    "woof=np.diff(rays1['th'].reshape(-1,1081))\n",
    "dts=np.diff(rays1['ts'].reshape(-1,1081))\n",
    "dth=np.diff(nan_th.reshape(-1,1081))\n",
    "\n",
    "slope=dth/dts\n",
    "slope=slope[np.abs(dth)<np.pi/2]\n",
    "stopped=[]\n",
    "rangle=[]\n",
    "for row,rts in zip(nan_th.reshape(-1,1081),rays1['ts'].reshape(-1,1081)):\n",
    "    use = np.unwrap(row[np.isfinite(row)])\n",
    "    use_ts = rts[np.isfinite(row)]\n",
    "    if len(use)==0:\n",
    "        print('err')\n",
    "        print(row)\n",
    "        break\n",
    "    slope = (use[-1]-use[0])/(use_ts[-1]-use_ts[0])\n",
    "    off=np.mean(use-(use_ts-rts[0])*slope)\n",
    "    stopped.append(row-(rts-rts[0])*slope-off)\n",
    "    rangle.append((rts-rts[0])*slope+off)\n",
    "stopped = np.vstack(stopped)\n",
    "rangle = np.vstack(rangle)\n",
    "flat_rangle = np.unwrap(rangle[:,0].ravel()*2)/2\n",
    "# plt.plot(rays1['ts'][::1081],flat_rangle,'b,')\n",
    "_=plt.hist(np.diff(np.diff(flat_rangle)/np.diff(rays1['ts'][::1081])),1000)\n",
    "plt.gca().set_yscale(\"log\")\n",
    "# plt.plot((stopped.ravel()+np.pi)%(2*np.pi)-np.pi,'b,')\n",
    "# expect_th = (rays1['ts']-rays1['ts'][0])*np.float64(np.median(slope[slope>0]))%(2*np.pi)\n",
    "# plt.plot(rays1['ts'][rays1['r']>1],(expect_th-rays1['th'])[rays1['r']>1]%(2*np.pi),'b,')\n",
    "# plt.plot(np.diff(flat_rangle)/np.diff(rays1['ts'][::1081]))\n",
    "# plt.hist(slope[np.isfinite(np.abs(slope))],1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Odo loader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "# TODO: transition to floating point timestamps, not microseconds\n",
    "odofile = \"/home/anuran/repos/Vulcan/bin/odoDump.rays\" # HACK\n",
    "odo_struct = np.dtype(\n",
    "    [\n",
    "        ('ts', np.float64),\n",
    "        ('x', np.float32),\n",
    "        ('y', np.float32),\n",
    "        ('th', np.float32),\n",
    "        ('pad' , np.float32),\n",
    "        ('lx', np.float32),\n",
    "        ('ly', np.float32),\n",
    "      ])\n",
    "def odosub(odo):\n",
    "    fields = ['ts','x','y','th']\n",
    "    for f in fields:\n",
    "        odo[f]-=odo[0][f]\n",
    "    \n",
    "\n",
    "# rays = np.memmap(rayfile,rays_struct, 'r',1)\n",
    "poses = np.fromfile(odofile,odo_struct)\n",
    "imu= poses[0::3]\n",
    "odo= poses[1::3]\n",
    "lp = poses[2::3]\n",
    "assert(np.all(np.diff(odo['ts'])>=0))\n",
    "assert(np.all(np.diff(imu['ts'])>=0))\n",
    "assert(np.all(np.diff(lp['ts'])>=0))\n",
    "odo['th']=np.unwrap(odo['th'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Differentiator Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# A nice differentiator function that works way better than diff\n",
    "def d_dt(t,x,dt,order=1):\n",
    "    if order>1:\n",
    "        x=d_dt(t,x,dt,order-1)\n",
    "    if order==0:\n",
    "        return x\n",
    "    dt = dt/2\n",
    "    lot = np.clip(t-dt, t[0], t[-1])\n",
    "    hit = np.clip(t+dt, t[0], t[-1])\n",
    "    assert(np.all(hit-lot > 0))\n",
    "    lox = np.interp(lot,t,x)\n",
    "    hix = np.interp(hit,t,x)\n",
    "    return (hix-lox)/(hit-lot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "th_step 0.004363323129985824\n",
      "0.11017027127612611\n",
      "8.969295515952949\n",
      "(2, 3, 540500)\n",
      "(2, 540500)\n"
     ]
    }
   ],
   "source": [
    "th_step = (np.pi/180/4)\n",
    "print('th_step',th_step)\n",
    "\n",
    "raysx = rays1.reshape(-1,1081)[500:1000].ravel()\n",
    "def derivatives(rays):\n",
    "    th_step = (np.pi/180/4)\n",
    "    x = rays['ex']\n",
    "    y = rays['ey']\n",
    "    th = rays['th']\n",
    "    dr_dx = (np.sqrt((rays['ex']-(rays['sx']+0.01))**2+(rays['ey']-(rays['sy']))**2) - rays['r'])*100\n",
    "    dr_dy = (np.sqrt((rays['ex']-(rays['sx']+0.01))**2+(rays['ey']-(rays['sy']+0.01))**2) - rays['r'])*100\n",
    "    dth_dx = (np.arctan2((rays['ey']-(rays['sy'])),(rays['ex']-(rays['sx']+0.01))) - rays['th'])*100/th_step # from radians to steps on the output\n",
    "    dth_dy = (np.arctan2((rays['ey']-(rays['sy']+0.01)),(rays['ex']-(rays['sx']))) - rays['th'])*100/th_step # from radians to steps on the output\n",
    "    x2_dth = rays['ex']*np.cos(th_step) + rays['ey']*np.sin(th_step) \n",
    "    y2_dth = -rays['ex']*np.sin(th_step) + rays['ey']*np.cos(th_step) \n",
    "    dth_dth = (np.arctan2(y2_dth-rays['sy'], x2_dth-rays['sx']) - rays['th'])/th_step/th_step # from radians to steps on the output\n",
    "    dr_dth = (np.sqrt((y2_dth-rays['sy'])**2 + (x2_dth-rays['sx'])**2)-rays['r'])/th_step\n",
    "    D = np.vstack((dr_dx.ravel(), dr_dy.ravel(), dr_dth.ravel(),\n",
    "                   dth_dx.ravel(), dth_dy.ravel(), dth_dth.ravel()))\n",
    "    D[:,rays['r'] < 0.05] = 0\n",
    "    D[:,rays['r'] > 50] = 0\n",
    "    D = D.reshape(2,3,-1)\n",
    "    return D\n",
    "\n",
    "D_r_th__x_y_th=derivatives(raysx) # dims: NxOutxIn\n",
    "\n",
    "# Test it \n",
    "dx = np.interp(raysx['ts'], odo['ts'], d_dt(odo['ts'],odo['x'],1/40))/40\n",
    "dy = np.interp(raysx['ts'], odo['ts'], d_dt(odo['ts'],odo['y'],1/40))/40\n",
    "ds = dx*np.cos(np.interp(raysx['ts'], odo['ts'],odo['th']))+dy*np.sin(np.interp(raysx['ts'], odo['ts'],odo['th']))\n",
    "\n",
    "dth = np.interp(raysx['ts'], odo['ts'], d_dt(odo['ts'],odo['th'],1/40))/40\n",
    "# print(np.min(ds))\n",
    "print(2*np.max(ds))\n",
    "print(2*np.max(dth)/th_step)\n",
    "\n",
    "print(D_r_th__x_y_th.shape)\n",
    "motions = np.vstack((ds.ravel(),np.zeros_like(ds),dth.ravel()))\n",
    "D_r_th=np.sum(D_r_th__x_y_th*motions[np.newaxis, :, :],axis=1)\n",
    "\n",
    "# D_r_th = np.dot(D_r_th__x_y_th, np.vstack((dx.ravel(),dy.ravel(),dth.ravel())))\n",
    "# D_r_th = np.tensordot(D_r_th__x_y_th,  np.vstack((dx.ravel(),dy.ravel(),dth.ravel())), 1)\n",
    "# D_r_th = np.einsum('Noi,iN->No',D_r_th__x_y_th,  np.vstack((dx.ravel(),dy.ravel(),dth.ravel())))\n",
    "print(D_r_th.shape)\n",
    "%matplotlib widget\n",
    "raysx=raysx.reshape(-1,1081)\n",
    "# plt.plot(raysx['r'][1:].ravel()[7907531:7907531+2162],'b.')\n",
    "# plt.plot(raysx['r'].ravel()[7907531:7907531+2162],'r.')\n",
    "# plt.plot(np.arange(2162)+D_r_th[1][7907531:7907531+2162],raysx['r'].ravel()[7907531:7907531+2162]+D_r_th[0][7907531:7907531+2162],'g.')\n",
    "# plt.plot(np.arange(2162)+D_r_th[1][7907531:7907531+2162]*2,raysx['r'].ravel()[7907531:7907531+2162]+D_r_th[0][7907531:7907531+2162]*2,'y.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "
     ]
    }
   ],
   "source": [
    "# D_r_th = D_r_th.reshape(2,-1,1081)[:,:384].copy()\n",
    "r=raysx['r'].copy().reshape(-1,1081)[:,:384].copy()\n",
    "D_r_th__x_y_th_b=D_r_th__x_y_th.reshape(2,3,-1,1081)[...,:384].copy()\n",
    "def score_motions(r, D_r_th__x_y_th_b, smin, smax, ssteps, thmin, thmax, thsteps):\n",
    "    N=r.shape[1]\n",
    "    moxies=[]\n",
    "    for sinc in np.linspace(0,.15,16,dtype=np.float32):\n",
    "        for thinc in np.linspace(-10,10,41,dtype=np.float32)*th_step:\n",
    "            print('.',end=' ')\n",
    "            d_r  = D_r_th__x_y_th_b[0,0]*sinc+D_r_th__x_y_th_b[0,2]*thinc\n",
    "            d_th = D_r_th__x_y_th_b[1,0]*sinc+D_r_th__x_y_th_b[1,2]*thinc\n",
    "            thoff = np.clip(d_th+np.arange(N)[np.newaxis,:],0,N-1)[:-1]\n",
    "            thlo = thoff.astype('int16')\n",
    "            thhi = np.ceil(thoff).astype('int16')\n",
    "            rlo = r[1:][np.arange(len(r)-1)[:, np.newaxis], thlo]\n",
    "            rhi = r[1:][np.arange(len(r)-1)[:, np.newaxis], thhi]\n",
    "            rlin = (1-thoff%1)*rlo+(thoff%1)*rhi\n",
    "    #         print(r.shape)\n",
    "            moxies.append(np.sum(np.clip(np.abs(rlin - (r[:-1]+d_r[:-1])),-0.05,0.05),axis=1))\n",
    "    return moxies\n",
    "\n",
    "r = raysx['r'].copy().reshape(-1,1081)[:, :384].copy()\n",
    "D_r_th__x_y_th_b = D_r_th__x_y_th.reshape(2,3,-1,1081)[..., :384].copy()\n",
    "ma = score_motions(r, D_r_th__x_y_th_b, smin=0, smax=0.15, ssteps=16, thmin=-10, thmax=10, thsteps=41)\n",
    "\n",
    "r = raysx['r'].copy().reshape(-1,1081)[:, 384:768].copy()\n",
    "D_r_th__x_y_th_b = D_r_th__x_y_th.reshape(2,3,-1,1081)[..., 384:768].copy()\n",
    "mb = score_motions(r, D_r_th__x_y_th_b, smin=0, smax=0.15, ssteps=16, thmin=-10, thmax=10, thsteps=41)\n",
    "\n",
    "r = raysx['r'].copy().reshape(-1,1081)[:, 768:].copy()\n",
    "D_r_th__x_y_th_b = D_r_th__x_y_th.reshape(2,3,-1,1081)[..., 768:].copy()\n",
    "mc = score_motions(r, D_r_th__x_y_th_b, smin=0, smax=0.15, ssteps=16, thmin=-10, thmax=10, thsteps=41)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18d64ecd3c3e4607ae6aec57ecbbd02b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4d7112b6a0>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "readout=np.hstack((np.array(ma).T.reshape(-1,41),np.array(mb).T.reshape(-1,41),np.array(mc).T.reshape(-1,41)))\n",
    "A=np.array(ma).T.reshape(-1,16,41)\n",
    "plat=np.argmin(A,axis=2).ravel()\n",
    "B=np.array(mb).T.reshape(-1,16,41)\n",
    "platb=np.argmin(B,axis=2).ravel()\n",
    "C=np.array(mc).T.reshape(-1,16,41)\n",
    "platc=np.argmin(C,axis=2).ravel()\n",
    "\n",
    "# readout = np.array(mb).T.reshape(-1,41)\n",
    "# np.rollaxis(readout)\n",
    "# help(np.swapaxes)\n",
    "# print(np.array(moxies).shape)\n",
    "plt.imshow(readout,aspect='auto')\n",
    "plt.plot(plat,np.arange(len(plat)),'r')\n",
    "plt.plot(platb,np.arange(len(plat)),'k')\n",
    "plt.plot(platc,np.arange(len(plat)),'c')\n",
    "\n",
    "# plt.plot(np.diff(np.interp(raysx['ts'][:,540],odo['ts'],odo['th']))/th_step)\n",
    "#         rhi\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.5367431640625e-07 9.5367431640625e-07\n"
     ]
    }
   ],
   "source": [
    "a=1.0\n",
    "while(np.float32(a/2+8.0)!=8.0):\n",
    "    a/=2\n",
    "print(a, 1/2**20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 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     ]
    }
   ],
   "source": [
    "# Plan: \n",
    "# 1. Project scans back by one to solve for best time offset from the surrounding ones\n",
    "# 2. Average offsets within a block to get new pseudo times for each block\n",
    "# 3. Assign pseudotimes\n",
    "# 4. Repeat\n",
    "cos=np.cos; sin = np.sin\n",
    "odo['th']=np.unwrap(odo['th'])\n",
    "def in_base_frame(s, odo):\n",
    "    out = s.copy()\n",
    "    xo=np.interp(s['ts'],odo['ts'],odo['x'])\n",
    "    yo=np.interp(s['ts'],odo['ts'],odo['y'])\n",
    "    tho=np.interp(s['ts'],odo['ts'],odo['th'])\n",
    "    x=s['ex']\n",
    "    y=s['ey']\n",
    "    xp = cos(tho)*x - sin(tho)*y + xo\n",
    "    yp = sin(tho)*x + cos(tho)*y + yo\n",
    "    out['ex'] = xp\n",
    "    out['ey'] = yp\n",
    "    x=s['sx']\n",
    "    y=s['sy']\n",
    "    xp0 = cos(tho)*x - sin(tho)*y + xo\n",
    "    yp0 = sin(tho)*x + cos(tho)*y + yo\n",
    "    out['sx'] = xp0\n",
    "    out['sy'] = yp0\n",
    "    gts = np.arange(len(out['ts']))[out['r']>0]\n",
    "    ang = np.arctan2(yp-yp0,xp-xp0)\n",
    "    gang = np.unwrap(ang[out['r']>0])\n",
    "    out['th'] = np.interp(np.arange(len(out['ts'])), gts, gang)\n",
    "    out['r'][out['r']>0]= np.sqrt((xp-xp0)**2+(yp-yp0)**2)[out['r']>0]\n",
    "    return out\n",
    "    \n",
    "def get_err(s1,s2):#result is at sample points of s2\n",
    "    MAX_ERR=0.2\n",
    "    good = (s1['r']>0 )& (s1['r']<40)\n",
    "    xc=s1['sx'][540]\n",
    "    yc=s1['sy'][540]\n",
    "    th = np.arctan2(s2['ey']-yc, s2['ex']-xc)%(2*np.pi) # TODO: figure out which sx and sy are the right ones to pair!\n",
    "    r = np.sqrt((s2['ey']-yc)**2+(s2['ex']-xc)**2)\n",
    "    \n",
    "    th1= np.arctan2(s1['ey']-yc, s1['ex']-xc)%(2*np.pi)\n",
    "#     r1 = np.sqrt((s2['ey']-yc)**2+(s2['ex']-xc)**2) #not used, but could prob simplify lookup\n",
    "    \n",
    "#     plt.plot(th%(2*np.pi),r)\n",
    "    \n",
    "    errx = np.interp(th, th1, s1['ex'], period=2*np.pi)-xc\n",
    "    erry = np.interp(th, th1, s1['ey'], period=2*np.pi)-yc\n",
    "    r_from_s1=np.sqrt(errx**2+erry**2)\n",
    "    err=r_from_s1-r # TODO: dot product with central direction instead of sqrt a square, so we have sign of error\n",
    "#     plt.plot(th, np.sqrt(errx**2+erry**2),'.')\n",
    "#     err = errx*np.cos(th)+erry*np.sin(th)\n",
    "    \n",
    "    err*=(s2['r']>0)&(s2['r']<40)\n",
    "    err = np.clip(err, -MAX_ERR, MAX_ERR)\n",
    "    return err\n",
    "\n",
    "# def project(ss, ns, odo):\n",
    "#     # this version is pretty bad\n",
    "#     ssb=in_base_frame(ss, odo)\n",
    "#     nsb=in_base_frame(ns, odo)\n",
    "#     nsmin = ns.copy(); nsmin['ts']-=4/40\n",
    "#     nsbmin = in_base_frame(nsmin, odo)\n",
    "#     nsmax = ns.copy(); nsmax['ts']+=4/40\n",
    "#     nsbmax = in_base_frame(nsmax, odo)\n",
    "#     derr_dst = (get_err(ssb,nsbmax)-get_err(ssb,nsbmin))/8 #error per scan displaced\n",
    "#     err = get_err(ssb,nsb)\n",
    "#     offset = (derr_dst.dot(err))/derr_dst.dot(derr_dst) \n",
    "#     return offset, np.clip(err/derr_dst,-4,4)*err\n",
    "\n",
    "def project(ss, ns, odo, noffsets=17):\n",
    "    ssb=in_base_frame(ss, odo)\n",
    "    nsb=in_base_frame(ns, odo)\n",
    "    \n",
    "    offsets = np.linspace(-4/40,4/40,noffsets) if noffsets>1 else np.array([0])\n",
    "#     plt.plot(ssb['ex'],ssb['ey'],'.')\n",
    "#     plt.plot(ssb['th'][ssb['r']<40],ssb['r'][ssb['r']<40],'.')\n",
    "    stack=[]\n",
    "    for off in offsets:\n",
    "        nsoff = ns.copy(); nsoff['ts']+=off\n",
    "        nsboff = in_base_frame(nsoff, odo)\n",
    "#         plt.plot(nsboff['th'][nsboff['r']<40],nsboff['r'][nsboff['r']<40],)\n",
    "        err = get_err(ssb,nsboff)\n",
    "#         plt.plot(nsboff['th'],nsboff['r']+err,'x')\n",
    "#         plt.plot(nsboff['th'], err)\n",
    "        stack.append(err)\n",
    "    \n",
    "    stack = np.array(stack)\n",
    "    minerridx = np.argmin(np.abs(stack),axis=0)\n",
    "    minerr = stack[minerridx,np.arange(len(ns))]\n",
    "    \n",
    "    sols=offsets[minerridx] # sols is the best offsets\n",
    "    residual=np.min(np.abs(stack),axis=0) #qual is the abs err at the min offset\n",
    "    \n",
    "    nsoff = ns.copy(); nsoff['ts']+=sols+1/40\n",
    "    nsboff = in_base_frame(nsoff, odo)\n",
    "    errplus = get_err(ssb,nsboff)\n",
    "    nsoff['ts']-=2/40\n",
    "    nsboff = in_base_frame(nsoff, odo)\n",
    "    errminus = get_err(ssb,nsboff)\n",
    "    sensitivity = np.minimum(np.abs(errminus-minerr),np.abs(errplus-minerr))\n",
    "\n",
    "#     qual=np.min(np.abs(stack),axis=0)\n",
    "    return sols*40, sensitivity, minerr\n",
    "\n",
    "def resolve_times(pseudotime):\n",
    "    offset=tsj[0]*40\n",
    "    resolved = []\n",
    "    for ps in pseudotime:\n",
    "        apptime = ps-offset\n",
    "        offset+= ((apptime+0.5)%1-0.5)/10\n",
    "        resolved.append(int(np.round(apptime)))\n",
    "        \n",
    "    return resolved\n",
    "\n",
    "                             \n",
    "                             \n",
    "# %matplotlib widget\n",
    "# raysx=rays2.copy().reshape(-1,1081)\n",
    "raysx=raysy.copy()#HACK! remove for original style\n",
    "# raysx[356:371,768:]['ts']-=1/40\n",
    "\n",
    "# raysx[150:298,:384]['ts']-=1/40\n",
    "# raysx[320:323,:384]['ts']-=1/40\n",
    "# raysx[450:540,:384]['ts']-=1/40\n",
    "# raysx[454:502,:384]['ts']+=1/40\n",
    "# raysx[463:478,:384]['ts']-=1/40\n",
    "# raysx[474,:384]['ts']+=1/40\n",
    "# raysx[488,:384]['ts']-=1/40\n",
    "# raysx[491,:384]['ts']-=1/40\n",
    "# raysx[521:531,:384]['ts']+=1/40\n",
    "# raysx[523,:384]['ts']-=1/40\n",
    "# raysx[536:539,:384]['ts']-=1/40\n",
    "# raysx[710:740,:384]['ts']+=1/40\n",
    "# raysx[732,:384]['ts']-=1/40\n",
    "# raysx[737:740,:384]['ts']-=1/40\n",
    "# raysx[745:750,:384]['ts']+=1/40\n",
    "# raysx[746,:384]['ts']-=1/40\n",
    "# raysx[808:847,:384]['ts']+=1/40\n",
    "# raysx[927:939,:384]['ts']+=1/40\n",
    "# raysx[951:954,:384]['ts']+=1/40\n",
    "# raysx[955:960,:384]['ts']+=1/40\n",
    "# raysx[992:1000,:384]['ts']-=1/40\n",
    "\n",
    "# raysx[668:992,:768]['ts']-=1/40\n",
    "\n",
    "# raysx[643:649,384:768]['ts']-=1/40\n",
    "# raysx[643:646,384:768]['ts']+=1/40\n",
    "\n",
    "# raysx[646,384:768]['ts']+=1/40\n",
    "# raysx[647:649,384:768]['ts']+=1/40\n",
    "\n",
    "# raysx[739:750,384:768]['ts']+=1/40\n",
    "\n",
    "\n",
    "# raysx[520,384:]['ts']-=1/40\n",
    "\n",
    "# raysx[137:141,768:]['ts']+=1/40\n",
    "# raysx[146:356,768:]['ts']+=1/40\n",
    "# raysx[356:371,768:]['ts']-=1/40\n",
    "# raysx[382:413,768:]['ts']+=1/40\n",
    "# raysx[453,768:]['ts']+=1/40\n",
    "# raysx[491:497,768:]['ts']+=1/40\n",
    "# raysx[504:506,768:]['ts']+=1/40\n",
    "# raysx[520:531,768:]['ts']+=1/40\n",
    "# raysx[633:635,768:]['ts']+=1/40\n",
    "# raysx[630:684,768:]['ts']=rays2.reshape(-1,1081)[630:684,768:]['ts']\n",
    "# raysx[638,768:]['ts']+=1/40\n",
    "# raysx[641,768:]['ts']+=1/40\n",
    "# raysx[645,768:]['ts']+=1/40\n",
    "# raysx[684:708,768:]['ts']-=1/40\n",
    "# raysx[708:740,768:]['ts']+=1/40\n",
    "# raysx[738:739,768:]['ts']-=1/40\n",
    "# raysx[750:756,768:]['ts']-=1/40\n",
    "# raysx[890:1000,768:]['ts']+=1/40\n",
    "# raysx[903:922,768:]['ts']-=1/40\n",
    "# raysx[959:961,768:]['ts']-=1/40\n",
    "# raysx[973:977,768:]['ts']-=1/40\n",
    "# raysx[984:993,768:]['ts']-=1/40\n",
    "\n",
    "\n",
    "\n",
    "tsj = raysx[:,0]['ts'].copy()\n",
    "pseudotime=tsj*40\n",
    "resolved = resolve_times(pseudotime)\n",
    "\n",
    "solvate = []\n",
    "qual = []\n",
    "residual = []\n",
    "ans = []\n",
    "for scannum in range(0,len(raysx)):\n",
    "#     print(raysx[0]['r'][raysx[0]['r']>0])\n",
    "    sol, q, resid = project(raysx[scannum-1], raysx[scannum], odo,41)\n",
    "#     assert np.all(sol==0)\n",
    "    s=sol\n",
    "    qf = np.abs(q/(np.abs(resid)+0.000001))\n",
    "    slc=slice(0,384)\n",
    "    A = round(np.median(s[slc][(np.abs(q[slc])>0.01)&(qf[slc]>4)]),2)\n",
    "    slc=slice(384,768)\n",
    "    B = round(np.median(s[slc][(np.abs(q[slc])>0.01)&(qf[slc]>4)]),2)\n",
    "    slc=slice(768, None)\n",
    "    C = round(np.median(s[slc][(np.abs(q[slc])>0.01)&(qf[slc]>4)]),2)\n",
    "#     Y = resolved[scannum]-resolved[scannum-1]\n",
    "#     print(A,B,C,Y)\n",
    "    ans.append((A,B,C))\n",
    "#     print(np.mean(q[(q<0.05)&(qf>4)]))\n",
    "#     assert scannum<=2 or 0 < Y <= 2\n",
    "#     buff=[]\n",
    "#     for j in range(5):\n",
    "#         q, sol = project(raysx[scannum-j], raysx[scannum], odo)\n",
    "#         buff.append(sol)\n",
    "#     sol=np.mean(buff,axis=0)\n",
    "    solvate.append(sol)\n",
    "    qual.append(q)\n",
    "    residual.append(resid)\n",
    "    print(scannum, end=' ')\n",
    "\n",
    "\n",
    "# # # plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "model_id": "775f8291a1c341cd9cd2dee23d906066",
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "ans = np.array(ans)\n",
    "solvate=np.array(solvate)\n",
    "# solvate[:,16:30]=np.median(solvate[:,0:384],axis=1)[:,np.newaxis]\n",
    "mask = (np.abs(qual)>0.02)&(np.abs(solvate)<=3.5)\n",
    "qual_frac = qual/(np.abs(residual)+.00001)\n",
    "solve2=(solvate+.2)*(qual_frac>4)*(np.abs(qual)>0.01)-0.2#*np.clip(qual,-.01,.01)*100\n",
    "# solve2[:,260:312]=ans[:,1:2]\n",
    "# solve2[np.abs(solve2)>3] = -4\n",
    "# solve2[:,9:16]= (np.mean(np.abs(solve2),axis=1)/(np.mean(mask,axis=1)+.001))[:,np.newaxis]\n",
    "blockA=np.array([np.median(s[0:384][np.abs(q[:384])>0.02])for s,q in zip(solvate,qual)])\n",
    "blockA[np.isnan(blockA)]=0\n",
    "# solve2[:,16:30]=blockA[:,np.newaxis]\n",
    "cm=tuple(np.linspace(0,1,3000).reshape(-1,1)*[1,1,1])\n",
    "cm=ListedColormap(cm)\n",
    "plt.imshow(np.clip(1/raysx[500:1000]['r'],0,3),aspect = 'auto', cmap=cm)\n",
    "plt.figure()\n",
    "plt.imshow(solve2, aspect = 'auto', interpolation='nearest', cmap='bwr')\n",
    "plt.figure()\n",
    "plt.imshow((raysx['ts']-rays2['ts'].reshape(-1,1081)[:len(raysx)])[:len(solve2)], aspect = 'auto', interpolation='nearest', cmap='bwr')\n",
    "plt.figure()\n",
    "plt.imshow(np.clip(qual/(np.abs(residual)+.00001),0,2), aspect = 'auto',cmap='bwr')\n",
    "plt.figure()\n",
    "plt.imshow(residual, aspect = 'auto',cmap='bwr')\n",
    "\n",
    "plt.imsave(\"Corrected_residuals.png\", solve2,cmap='bwr')\n",
    "# plt.plot([np.median(s[0:384][np.abs(s[0:384])<2])for s in solvate])\n",
    "\n",
    "#notes: Block A likes to go -1 or 0. Block B likes to go 0 or -1. Block C likes to go 0 or +1\n",
    "\n",
    "# First, subtract off one scan period from ts on A and B blocks for their base value. Set their starting offset to 1 scan.\n",
    "# For block C just set the starting offset to 0. When trying out options, just make sure to keep the offset in %2 and add the appropriate time to the base\n",
    "\n",
    "# If chose a wrong block while spinning, will see a diagonal across the 384 point(or other block crossing!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "tsj = raysx[:,0]['ts'].copy()\n",
    "plt.plot(np.diff(tsj)*40,'b.')\n",
    "\n",
    "pseudotime=tsj*40\n",
    "def resolve_times(pseudotime):\n",
    "    offset=tsj[0]*40\n",
    "    resolved = []\n",
    "    for ps in pseudotime:\n",
    "        apptime = ps-offset\n",
    "        offset+= ((apptime+0.5)%1-0.5)/10\n",
    "        resolved.append(int(np.round(apptime)))\n",
    "        \n",
    "    return resolved\n",
    "resolved = resolve_times(pseudotime)\n",
    "plt.plot(np.diff(resolved))\n",
    "\n",
    "newview = np.zeros((resolved[-1]+2, 1081))-3\n",
    "for outidx, inidx in zip(resolved, range(len(solvate))):\n",
    "    newview[outidx] = solve2[inidx]\n",
    "plt.imshow(newview[:outidx+1], aspect = 'auto', cmap='bwr')\n",
    "\n",
    "\n",
    "\n",
    "# Energy: For every pair of blocks\n",
    "# F(a_block, b_block, excess_jump) is the energy function\n",
    "# for a trial between two scans:\n",
    "# off1, off2, off3\n",
    "# off4, off5, off6\n",
    "# we get \n",
    "\n",
    "stub=np.zeros(1081)\n",
    "def score(s1, s2, offsets):\n",
    "    # returns the cost for a given choice of scan offsets\n",
    "    if np.any(offsets)<-1 or np.any(offsets)>1:\n",
    "        return -100, stub\n",
    "    if np.any(np.abs(offsets[3:]-offsets[:3])>1):\n",
    "        return -100, stub\n",
    "    if np.any(offsets*[-1,-1,1,-1,-1,1] < 0): #we know what signs are allowed now, so suppress the others\n",
    "        return -100, stub\n",
    "    s1[0:384]['ts']+=offsets[0]/40\n",
    "    s1[384:768]['ts']+=offsets[1]/40\n",
    "    s1[768:]['ts']+=offsets[2]/40\n",
    "    s2[0:384]['ts']+=offsets[3]/40\n",
    "    s2[384:768]['ts']+=offsets[4]/40\n",
    "    s2[768:]['ts']+=offsets[5]/40\n",
    "    sol, q, resid = project(s1, s2, odo,9)\n",
    "    assert(len(resid)==1081)\n",
    "    qf = np.abs(q/(np.abs(resid)+0.000001))\n",
    "    good = (q>0.01)&(qf>4)&(np.abs(sol)<=1)\n",
    "    \n",
    "    scores= good*(sol==0)*np.clip(q,-0.05,0.05)\n",
    "#     print(np.sum(scores>=0))\n",
    "#     scores=(sol[good]==0)*np.clip(q[good],-0.05,0.05) # minimize the residual on reliable samples # HIGH GOOD\n",
    "#     scores=(sol==0)*np.clip(resid,-0.05,0.05) # minimize the residual on reliable samples # LOW GOOD\n",
    "    score = np.sum(np.abs(scores))\n",
    "    s1[0:384]['ts']-=offsets[0]/40\n",
    "    s1[384:768]['ts']-=offsets[1]/40\n",
    "    s1[768:]['ts']-=offsets[2]/40\n",
    "    s2[0:384]['ts']-=offsets[3]/40\n",
    "    s2[384:768]['ts']-=offsets[4]/40\n",
    "    s2[768:]['ts']-=offsets[5]/40\n",
    "    return score, scores\n",
    "\n",
    "def solve(s1,s2):\n",
    "    lsc=np.zeros(1081)\n",
    "    off=np.zeros(1081)\n",
    "    loff = -.02*np.zeros(1081)\n",
    "    \n",
    "    sols = {}\n",
    "    for o1 in range(-1,2):\n",
    "        for o2 in range(-1,2):\n",
    "            for o3 in range(-1,2):\n",
    "                for o4 in range(-1,2):\n",
    "                    for o5 in range(-1,2):\n",
    "                        for o6 in range(-1,2):\n",
    "                            offsets = (o1,o2,o3,o4,o5,o6)\n",
    "                            sols[offsets], scores = score(s1, s2, np.array(offsets))\n",
    "                            \n",
    "                            #debug\n",
    "                            better=scores>lsc\n",
    "                            off[0:384] = o4-o1\n",
    "                            off[384:768] = o5-o2\n",
    "                            off[768:] = o6-o3\n",
    "                            lsc[better]=scores[better]\n",
    "                            loff[better]=off[better]\n",
    "    return sols, lsc, loff\n",
    "\n",
    "def vitterbi(sn,bps,bcs,img):\n",
    "    states=[]\n",
    "    for o1 in range(-1,2):\n",
    "        for o2 in range(-1,2):\n",
    "            for o3 in range(-1,2):\n",
    "                states.append((o1,o2,o3))\n",
    "                \n",
    "    curr_cost = bcs[-1] if len(bcs) else [0]*len(states)\n",
    "    startstep = sn[-1]+1 if sn else 0\n",
    "    for stepnum in range(sn[-1]+1,len(raysx)-1):\n",
    "        print(stepnum)\n",
    "        print(curr_cost)\n",
    "        next_cost = [-1]*len(states)\n",
    "        best_prior = [0]*len(states)\n",
    "        s1 = raysx[stepnum]\n",
    "        s2 = raysx[stepnum+1]\n",
    "        sols, lsc, loff= solve(s1,s2)\n",
    "#         print(\"Best:\", list(sols.items())[np.argmax(list(sols.values()))])\n",
    "#         print(sols)\n",
    "#         print(sum(lsc>0))\n",
    "        img.append(lsc*loff-0.1*(lsc==0))\n",
    "        for startidx in range(len(states)):\n",
    "            for endidx in range(len(states)):\n",
    "                offsets = states[startidx]+states[endidx]\n",
    "                newcost = curr_cost[startidx]+sols[offsets]\n",
    "                oldcost = next_cost[endidx]\n",
    "                if newcost>oldcost:\n",
    "                    best_prior[endidx] = startidx\n",
    "                    next_cost[endidx] = newcost\n",
    "                    \n",
    "                if newcost==oldcost:\n",
    "                    if np.sum(np.abs(states[startidx]))<=np.sum(np.abs(states[best_prior[endidx]])):\n",
    "                        best_prior[endidx] = startidx\n",
    "                        next_cost[endidx] = newcost\n",
    "        print(best_prior)\n",
    "        sn.append(stepnum)\n",
    "        bps.append(best_prior)\n",
    "        bcs.append(next_cost)\n",
    "        curr_cost = next_cost\n",
    "    return bps, bcs, img\n",
    "            \n",
    "# bps = []\n",
    "# bcs = []\n",
    "# sn = [-1]\n",
    "# img = []\n",
    "# bcs = list(bcs)\n",
    "bps,bcs,img = vitterbi(sn,bps,bcs,img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# np.savez('vitterbi_intermediates_2state.npz', original_rays = raysx, retimed_rays = raysy, bcs=bcs, bps=bps, img=img, sn = sn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-1, -1, 0)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "457f239314fd4ef7971cfb1375f7f6aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7138b74821e4423d8599cd69480ffc8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (1,) and (8642,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-419-56d141157870>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraysx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ts'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0modo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ts'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdth_dt\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelta_cost\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdelta_cost\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'b.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2785\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2786\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2787\u001b[0;31m     return gca().plot(\n\u001b[0m\u001b[1;32m   2788\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[1;32m   2789\u001b[0m         is not None else {}), **kwargs)\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \"\"\"\n\u001b[1;32m   1664\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1665\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1666\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    270\u001b[0m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[1;32m    271\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (1,) and (8642,)"
     ]
    }
   ],
   "source": [
    "# Decode the vitterbi outpus\n",
    "states=[]\n",
    "for o1 in range(-1,2):\n",
    "    for o2 in range(-1,2):\n",
    "        for o3 in range(-1,2):\n",
    "            states.append((o1,o2,o3))\n",
    "%matplotlib widget\n",
    "# print(bps)\n",
    "# plt.imshow(bcs)\n",
    "# solve(raysx[177],raysx[178])\n",
    "start=np.argmax(bcs[-1])\n",
    "print(states[start])\n",
    "solution=[]\n",
    "sol_idx=[]\n",
    "for line in reversed(bps):\n",
    "#     print(states[start])\n",
    "    sol_idx.append(start)\n",
    "    solution.append(states[start])\n",
    "    start = line[start]\n",
    "#last iteration is needed since we got from t to t+1, so the first state is pointed to by the initial bps\n",
    "sol_idx.append(start) \n",
    "solution.append(states[start]) # the last solution is needed\n",
    "\n",
    "\n",
    "sol_idx = np.array(list(reversed(sol_idx)))\n",
    "solution = np.array(list(reversed(solution)))\n",
    "# bcs=np.array(bcs)\n",
    "# cost_path = bcs[np.arange(len(sol_idx)),sol_idx]\n",
    "delta_cost = np.diff(cost_path, prepend=0)\n",
    "pre= np.diff(solution,axis=0,prepend=0)\n",
    "# help(np.diff)\n",
    "# print(solution)\n",
    "dimg=np.vstack((np.zeros(1081),np.array(img).copy()))# zeros because first row not availale\n",
    "bkg=dimg.copy()\n",
    "bkg[:,:384]=pre[:,0:1]*0.05\n",
    "bkg[:,384:768]=pre[:,1:2]*0.05\n",
    "bkg[:,768:]=pre[:,2:3]*0.05\n",
    "dimg[dimg==-0.1]=bkg[dimg==-0.1]*0.5\n",
    "plt.imshow(dimg,aspect='auto')\n",
    "plt.figure()\n",
    "plt.imshow(solution,aspect='auto')\n",
    "dth_dt=d_dt(odo['ts'],odo['th'],1/20)\n",
    "w = np.interp(raysx['ts'][:,-1], odo['ts'], dth_dt )\n",
    "plt.plot(w[:len(solution)],np.arange(len(solution)))\n",
    "plt.plot(delta_cost/10,np.arange(len(solution)))\n",
    "plt.figure()\n",
    "plt.plot(w[:len(solution)],delta_cost,'b.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "108409eddc4d4262b8ce2789400f2ee6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88f7546b50>]"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "# bcs=np.array(bcs)\n",
    "# bcs[sn]=bcs\n",
    "# bps=np.array(bps)\n",
    "# bps[sn]=bps\n",
    "# sn=np.array(sn)\n",
    "# sn[sn]=sn\n",
    "raysy=raysx[:len(solution)].copy()\n",
    "raysy[:,:384]['ts']+=solution[:,0:1]*1/40\n",
    "raysy[:,384:768]['ts']+=solution[:,1:2]*1/40\n",
    "raysy[:,768:]['ts']+=solution[:,2:3]*1/40\n",
    "# plt.plot(np.diff(sn))\n",
    "# plt.imshow(raysy['ts']-raysx[:len(solution)]['ts'])\n",
    "# plt.plot(rays0[::1081]['ts'],rays0[::1081]['idx']==1,'b.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bd85196cec44bcaa67f823c7f743e71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=7280, description='scn', max=8641), Output()), _dom_classes=('widget-int…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# inspect the quality of our fix\n",
    "# Project the scan to the base frame, and then to the times they should have been\n",
    "def out_base_frame(s,ts,odo):\n",
    "    # does the opposite of in_base_frame (sorry! couldn't think of good name)\n",
    "    # but uses the specified times instead of the ones in the scan to define the local frame\n",
    "    out = s.copy()\n",
    "    xo=np.interp(ts,odo['ts'],odo['x'])\n",
    "    yo=np.interp(ts,odo['ts'],odo['y'])\n",
    "    tho=np.interp(ts,odo['ts'],odo['th'])\n",
    "    x=s['ex']\n",
    "    y=s['ey']\n",
    "    xp = cos(tho)*(x-xo) + sin(tho)*(y-yo)\n",
    "    yp = -sin(tho)*(x-xo) + cos(tho)*(y-yo)\n",
    "    out['ex'] = xp\n",
    "    out['ey'] = yp\n",
    "    x=s['sx']\n",
    "    y=s['sy']\n",
    "    xp0 = cos(tho)*(x-xo) + sin(tho)*(y-yo)\n",
    "    yp0 = -sin(tho)*(x-xo) + cos(tho)*(y-yo)\n",
    "    out['sx'] = xp0\n",
    "    out['sy'] = yp0\n",
    "    gts = out['ts'][out['r']>0]\n",
    "    ang = np.arctan2(yp-yp0,xp-xp0)\n",
    "    gang = np.unwrap(ang[out['r']>0])\n",
    "    out['th'] = np.interp(out['ts'], gts, gang)\n",
    "    out['r'][out['r']>0]= np.sqrt((xp-xp0)**2+(yp-yp0)**2)[out['r']>0]\n",
    "    return out\n",
    "    \n",
    "# %matplotlib widget\n",
    "\n",
    "scn = 136\n",
    "rx_bf = in_base_frame(raysx[scn],odo)\n",
    "ry_bf = in_base_frame(raysy[scn],odo)\n",
    "ts = raysx[scn]['ts'] #use the expected time\n",
    "s1 = out_base_frame(rx_bf,raysx[scn]['ts'],odo) #should be identical up to roundoff from the original\n",
    "s2 = out_base_frame(ry_bf,raysx[scn]['ts'],odo)\n",
    "plt.plot(s1['ex'],s1['ey'], 'b.')\n",
    "plt.plot(s2['ex'],s2['ey'], 'r.', markersize=1)\n",
    "\n",
    "# raysy=raysx #HACK\n",
    "# raysx=rays2.copy().reshape(-1,1081)#end HACK\n",
    "\n",
    "from ipywidgets import interact, interact_manual\n",
    "plt.plot([1],[1])\n",
    "@interact(scn=(0,len(raysy)-1,1))\n",
    "def draw(scn=7280):\n",
    "    for artist in plt.gca().lines + plt.gca().collections:\n",
    "            artist.remove()\n",
    "    rm_bf = in_base_frame(raysx[scn-1],odo)\n",
    "    rx_bf = in_base_frame(raysx[scn],odo)\n",
    "    ry_bf = in_base_frame(raysy[scn],odo)\n",
    "    ts = raysx[scn]['ts'] #use the expected time\n",
    "    s1 = out_base_frame(rx_bf,raysx[scn]['ts'],odo) #should be identical up to roundoff from the original\n",
    "    s2 = out_base_frame(ry_bf,raysx[scn]['ts'],odo)\n",
    "#     plt.plot(s1['ex'],s1['ey'], 'b.')\n",
    "#     plt.plot(s2['ex'],s2['ey'], 'r.', markersize=2)\n",
    "#     plt.plot(rm_bf['ex'],rm_bf['ey'], 'kx')\n",
    "    plt.plot(rx_bf['ex'][:384],rx_bf['ey'][:384], 'gx',markersize=10)\n",
    "    plt.plot(rx_bf['ex'][384:768],rx_bf['ey'][384:768], 'go')\n",
    "    plt.plot(rx_bf['ex'][768:],rx_bf['ey'][768:], 'g.')\n",
    "#     plt.plot(ry_bf['ex'],ry_bf['ey'], 'r.', markersize=2)\n",
    "#     plt.clf()\n",
    "    tmp1=raysx[scn].copy();tmp2=raysx[scn].copy()\n",
    "    tmp1['ts']-=1/40;tmp2['ts']+=1/40\n",
    "    tmp1=in_base_frame(tmp1,odo);tmp2=in_base_frame(tmp2,odo)\n",
    "    qx=np.vstack((tmp1['ex'],rx_bf['ex'],rx_bf['ex']*np.nan))\n",
    "    qy=np.vstack((tmp1['ey'],rx_bf['ey'],rx_bf['ey']*np.nan))\n",
    "    plt.plot(qx.T.ravel(),qy.T.ravel(),'b')\n",
    "    qx=np.vstack((tmp2['ex'],rx_bf['ex'],rx_bf['ex']*np.nan))\n",
    "    qy=np.vstack((tmp2['ey'],rx_bf['ey'],rx_bf['ey']*np.nan))\n",
    "    plt.plot(qx.T.ravel(),qy.T.ravel(),'r')\n",
    "    for i in range(-9,1):\n",
    "        ry_bf = in_base_frame(raysy[scn+i],odo)\n",
    "        plt.plot(ry_bf['ex'],ry_bf['ey'], '.',mew=0, markersize=10+i, mfc=[(10+i)/10,0,(-i)/10])\n",
    "    plt.title(str(solution[scn]))\n",
    "    \n",
    "# np.savez(\"hand_retimed_1000\", rays2=rays2, retimed=raysy, odometry=odo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "637fb5ee3c1a431492a923adae4b8af6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88dca47190>]"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(#np.unwrap(raysy['th'].ravel()),\n",
    "         rays2['ts'].ravel(),'r,')\n",
    "plt.plot(#np.unwrap(raysy['th'].ravel()),\n",
    "         raysy['ts'].ravel(),'b,')\n",
    "# plt.plot(#np.unwrap(raysy['th'].ravel()),\n",
    "#          raysy['ts'].ravel()+1/40,'r,')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3686.6302083333335\n",
      "8642 25926\n",
      "9075\n",
      "max motion 1.0631881171052249\n",
      "Help on function timeit in module timeit:\n",
      "\n",
      "timeit(stmt='pass', setup='pass', timer=<built-in function perf_counter>, number=1000000, globals=None)\n",
      "    Convenience function to create Timer object and call timeit method.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89c7334f7e86422d85072ff91d0a0aa6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# faster way to do this crap: \n",
    "# 1. Project everything to the base frame and save it\n",
    "# 2. Project everything -1 and +1 timestep to the base frame and also save that\n",
    "# 3. Take the points in 2 and measure them against the linear interpolant of 1. You now have 3 numbers per point, and associated block couplings\n",
    "# 3.5 Discard couplings that are worse than a certain threshold\n",
    "# 4. Now you have evidence for hi, lo, or same as last time\n",
    "# 5. Put those in a big matrix equation for the time of every block and solve it? You can have the target be the solution picked for that point, and the gradient be the quality\n",
    "# 6. \n",
    "%matplotlib widget\n",
    "# plt.plot(raysx[500:600]['th'].T,raysx[500:600]['r'].T,',')\n",
    "# plt.hist(rays0['r'],500, weights=np.abs(rays0['r']))\n",
    "# help(plt.hist)\n",
    "plt.imshow\n",
    "rsteps = 50\n",
    "roto = np.zeros((rsteps*2,1081), np.int64)\n",
    "r = raysx['r']\n",
    "stripes=np.abs((r/0.99*255).astype(np.int8)).astype(np.uint8)*2\n",
    "# plt.imshow(np.clip(np.diff(r,axis=0), -0.05,0.05\n",
    "#                   ),aspect='auto',cmap='gray')\n",
    "print(np.sum(np.diff(r,axis=0)==0)/384)\n",
    "# rotation rate up to 50 spots per scan\n",
    "print(len(r), 3*len(r))\n",
    "print(np.sum(solution!=0))\n",
    "\n",
    "\n",
    "def build_differentials(rays,odo):\n",
    "    # requires 0.2sec per invocation on 8k scans\n",
    "    # probabably mostly in the giant interp calls\n",
    "    \n",
    "    # returns the velocity of the ray endpoints wrt the coordinate frame of the bot\n",
    "    x = rays['ex']*np.sign(rays['r'])\n",
    "    x.reshape(-1)[np.sign(rays['r'].ravel())<0] = (-np.cos(rays['th'])*rays['r']).reshape(-1)[np.sign(rays['r'].ravel())<0]\n",
    "    y = rays['ey']*np.sign(rays['r'])\n",
    "    y.reshape(-1)[np.sign(rays['r'].ravel())<0] = (-np.sin(rays['th'])*rays['r']).reshape(-1)[np.sign(rays['r'].ravel())<0]\n",
    "#     print(odo.dtype)\n",
    "    dx = d_dt(odo['ts'],odo['x'],1/10)\n",
    "    dy = d_dt(odo['ts'],odo['y'],1/10)\n",
    "    dx_dt = dx*np.cos(odo['th'])+dy*np.sin(odo['th']) # change to x from bot coords instead of x from world coords\n",
    "    dth_dt = d_dt(odo['ts'],odo['th'],1/10)\n",
    "    dx_dt = np.interp(rays['ts'],odo['ts'],dx_dt)\n",
    "    dth_dt = np.interp(rays['ts'],odo['ts'],dth_dt)\n",
    "    dx = dx_dt*np.sign(rays['r'])-dth_dt*y\n",
    "    dy = dth_dt*x\n",
    "    return dx, dy\n",
    "\n",
    "# motion model: \n",
    "rinv = 1/raysx['r']\n",
    "def build_derivatives(r,th):\n",
    "    #       fwd        turn    slip\n",
    "    # dr    sin/r        0      -sin\n",
    "    # dth   -cos        -1      -cos/r\n",
    "    \n",
    "    # [1/r 1]  sin      0       0        fwd\n",
    "    #           0       0       -sin  @  turn\n",
    "    #                                    slip\n",
    "    \n",
    "    # dr  =  1/r* (fwd*sin) + (-sin*slip)\n",
    "    3\n",
    "    \n",
    "    dr_dfwd = -cos(th)\n",
    "    dth_dfwd = sin(th)/r\n",
    "    dr_dturn = 0\n",
    "    dth_dturn = -1\n",
    "    return \n",
    "    \n",
    "# we will need +-12 angle bins to cover +- 2m/s at 40hz\n",
    "# assuming we only use things >1m away and max speed is 4m/s, we can see up to 0.1m of motion at 40 hz, or an additional +- 23 bins\n",
    "# so true max is +-35 bins at 40 hz, or +-70 bins at 20hz when we miss some scans\n",
    "# for those offsets, we can have up to 0.2m of range change\n",
    "# Use scans 2891 and 2892 as examples for testing\n",
    "\n",
    "print(\"max motion\",np.max(d_dt(odo['ts'],odo['th'],1/20)))\n",
    "\n",
    "import timeit\n",
    "help(timeit.timeit)\n",
    "dx,dy = build_differentials(raysx,odo)\n",
    "# print(timeit.timeit(l,number=100))\n",
    "img=(np.diff(np.abs(r),axis=0))\n",
    "expect=np.sqrt(dx**2+dy**2)[:-1]/40\n",
    "# plt.imshow(expect,aspect='auto',cmap='gray')\n",
    "# plt.imshow(np.clip(img,0,0.1),aspect='auto',cmap='gray')\n",
    "plt.imshow(np.clip(img/np.maximum(expect,0.005),-2.5,2.5),aspect='auto',cmap='gray') #normalized to expected motion magnitude\n",
    "\n",
    "# plt.imshow(np.clip(1/np.abs(raysx['r']),0,1),aspect='auto',cmap='gray')\n",
    "\n",
    "aug = np.hstack((np.clip(np.abs(np.diff(r,axis=0)), -0.05,0.05), np.sqrt(dx**2+dy**2)[:-1]/40))\n",
    "# plt.imshow(np.clip(img/np.maximum(expect,0.005),0,2.5),aspect='auto',cmap='gray')\n",
    "            \n",
    "# plt.imsave(\"dr_dt image normalized by odom.png\", np.clip(img/np.maximum(expect,0.005),0,2.5),cmap='gray')\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "inputs:\n",
    "dx_bot - bot fwd displace\n",
    "dy_bot - bot sideways displace\n",
    "dth_bot - bot spin displacement\n",
    "\n",
    "outputs:\n",
    "veth\n",
    "ver\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc6e12bb9ce54456b766367555cdd6bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='delt', max=4.0, min=-4.0), IntSlider(value=500, desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib widget\n",
    "# fig = plt.figure()\n",
    "from ipywidgets import interact, interact_manual\n",
    "plt.plot([1],[1])\n",
    "@interact(delt=(-4,4,.1),sc1=(500,len(raysx),1),sc2=(500,len(raysx),1))\n",
    "def draw(delt=0, sc1 = 10, sc2 = 20):\n",
    "    for artist in plt.gca().lines + plt.gca().collections:\n",
    "        artist.remove()\n",
    "    delt/=40\n",
    "    sin=np.sin\n",
    "    cos=np.cos\n",
    "    ss=list(range(7888,7889))\n",
    "    ss= [sc1]\n",
    "    for scn in ss:\n",
    "#         ax=plt.gca()\n",
    "#         ax.set_xlim(-5,30)\n",
    "#         ax.relim()\n",
    "        x=raysx['ex'][scn]\n",
    "        y=raysx['ey'][scn]\n",
    "        delt+=1/40*1\n",
    "        xo=np.interp(raysx['ts'][scn],odo['ts'],odo['x'])\n",
    "        yo=np.interp(raysx['ts'][scn],odo['ts'],odo['y'])\n",
    "        tho=np.interp(raysx['ts'][scn],odo['ts'],np.unwrap(odo['th']))\n",
    "        delt -= 1/40*1\n",
    "        xp = cos(tho)*x - sin(tho)*y + xo\n",
    "        yp = sin(tho)*x + cos(tho)*y + yo\n",
    "        good = raysx[scn]['r']>0\n",
    "#         plt.plot(xp[good],yp[good],',')\n",
    "        \n",
    "        for scn2 in [sc2]:\n",
    "#             xo=np.interp(raysx['ts'][scn2]+delt-1/40,odo['ts'],odo['x'])\n",
    "#             yo=np.interp(raysx['ts'][scn2]+delt-1/40,odo['ts'],odo['y'])\n",
    "#             tho=np.interp(raysx['ts'][scn2]+delt-1/40,odo['ts'],np.unwrap(odo['th']))\n",
    "#             x2 = cos(tho)*(xp-xo) + sin(tho)*(yp-yo)\n",
    "#             y2 = -sin(tho)*(xp-xo) + cos(tho)*(yp-yo) \n",
    "#             good = raysx[scn]['r']>0\n",
    "#             #now we have scn in scn2\n",
    "#             plt.plot(x2[good],y2[good],',')\n",
    "            xo=np.interp(raysx['ts'][scn2]+delt,odo['ts'],odo['x'])\n",
    "            yo=np.interp(raysx['ts'][scn2]+delt,odo['ts'],odo['y'])\n",
    "            tho=np.interp(raysx['ts'][scn2]+delt,odo['ts'],np.unwrap(odo['th']))\n",
    "            x2 = (cos(tho)*(xp-xo) + sin(tho)*(yp-yo))\n",
    "            y2 = (-sin(tho)*(xp-xo) + cos(tho)*(yp-yo)) \n",
    "            good = (raysx[scn]['r']>0)\n",
    "            #now we have scn in scn2\n",
    "            plt.plot(x2[good],y2[good],'.')\n",
    "#             xo=np.interp(raysx['ts'][scn2]+delt+1/40,odo['ts'],odo['x'])[:384]\n",
    "#             yo=np.interp(raysx['ts'][scn2]+delt+1/40,odo['ts'],odo['y'])[:384]\n",
    "#             tho=np.interp(raysx['ts'][scn2]+delt+1/40,odo['ts'],np.unwrap(odo['th']))[:384]\n",
    "#             x2 = cos(tho)*(xp-xo) + sin(tho)*(yp-yo)\n",
    "#             y2 = -sin(tho)*(xp-xo) + cos(tho)*(yp-yo) \n",
    "#             good = raysx[scn]['r']>0\n",
    "#             #now we have scn in scn2\n",
    "#             plt.plot(x2[good],y2[good],'o')\n",
    "            odotmp=odo.copy()\n",
    "            odotmp['ts']-=np.interp(raysx['ts'][scn2][-1]+delt,odo['ts'],odo['x'])\n",
    "            t\n",
    "            plt.plot(raysx['ex'][scn2][384:768],raysx['ey'][scn2][384:768],'x')\n",
    "#     plt.ylim(-4,-10)\n",
    "#     plt.xlim(-4,4)\n",
    "#     plt.xlim(-40,-36)\n",
    "#     plt.ylim(2.0,5.5)\n",
    "      \n",
    "    s=plt.gcf().canvas\n",
    "    _, _, w, h = s.figure.bbox.bounds\n",
    "\n",
    "    s.manager.resize(w, h)\n",
    "# plt.plot\n",
    "\n",
    "# plt.plot(raysx['ts'][7580:7995].ravel(), np.interp(raysx['ts'][7580:7995].ravel()+delt,odo['ts'],np.unwrap(odo['th'])),',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "221948b17951416397fae0890720f214",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "0.0 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-216-f208ab9bbf55>:128: RuntimeWarning: invalid value encountered in true_divide\n",
      "  dth = -dx*sin(th)/r + dy*cos(th)/r - dth0\n",
      "<ipython-input-216-f208ab9bbf55>:149: RuntimeWarning: invalid value encountered in floor_divide\n",
      "  blockjump = (np.arange(len(scan1))+0.5)//384 != (pseudoindex+0.5)//384\n",
      "<ipython-input-216-f208ab9bbf55>:153: RuntimeWarning: invalid value encountered in less_equal\n",
      "  use1=np.abs(nextr1-pb1)<=np.abs(nextr2-pb2)\n",
      "<ipython-input-216-f208ab9bbf55>:222: RuntimeWarning: invalid value encountered in greater\n",
      "  plt.imshow(acc>95)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e075c442d5b48d3a7d775abe9390e77",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca0414ac173647e980f7e07d77aa4469",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-216-f208ab9bbf55>:225: RuntimeWarning: invalid value encountered in less\n",
      "  plt.imshow(jumps*(np.abs(np.clip(acc,-.1,0.1))<0.05))\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cafe71c7bb4f4066ba02247b46b28931",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-216-f208ab9bbf55>:229: RuntimeWarning: invalid value encountered in less\n",
      "  tes=jumps*(np.abs(np.clip(acc,-.1,0.1))<0.05) # best solution is within 5 cm\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "592def99b6634b62bdc3d3e0a9d0dc60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "10d4fa5caaa44fd287da1c1a9e9b8897",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nan nan nan ... nan nan nan]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-216-f208ab9bbf55>:241: RuntimeWarning: invalid value encountered in less\n",
      "  plt.imshow(np.abs(np.clip(acc,-.1,0.1))<0.05)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "091d03d451304199bc663923f94a2959",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plan\n",
    "\"\"\"\n",
    "1. Read laser and odometry\n",
    "2. Project local group of laser scans with odometry to one and two time steps forward\n",
    "3. By block, select best time offset among both choices\n",
    "4. Reproject using only best choices\n",
    "5. Reselect by block from both choices\n",
    "6. Repeat until convergence\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "val=[]\n",
    "for k1 in range(-3,3,0.1):\n",
    "    for k2 in range(-3,3,0.1):\n",
    "        for fwd in range(0,6.28,0.3):\n",
    "            \n",
    "\n",
    "def offset(rays, dx0, dy0, dth(0)\n",
    "\n",
    "def get_next_roto(rays,odo,interval):\n",
    "    # gives the expected next positon of ray endpoints, given only rotation\n",
    "    # takes in rays rotated so that the direction of motion is always +x\n",
    "    derivative_width = 1/20\n",
    "    dx = d_dt(ots, odo['x'], derivative_width)\n",
    "    dy = d_dt(ots, odo['y'], derivative_width)\n",
    "    ds = np.sqrt(dx**2 + dy**2)\n",
    "    dth = d_dt(ots, np.unwrap(odo['th']), derivative_width)\n",
    "    th_step = 1/4*np.pi/180\n",
    "    raysout = rays.copy()\n",
    "    for scan, scanout,dth2 in zip(rays.reshape(-1,1081), raysout.reshape(-1,1081), dth):\n",
    "        thp = np.arange(len(scan))*th_step\n",
    "        good = (scan['r']>0) & (scan['r']<40)\n",
    "        nextth = np.clip(thp-dth2*interval, thp[0], thp[-1])\n",
    "        nextx = np.interp(nextth,thp[good],scan['ex'][good])\n",
    "        nexty = np.interp(nextth,thp[good],scan['ey'][good])\n",
    "        scanout['ex'] = nextx\n",
    "        scanout['ey'] = nexty\n",
    "    return raysout\n",
    "raysx=rays2\n",
    "next_raysx = get_next_roto(raysx,odo,1/40)\n",
    "dx = d_dt(ots, odo['x'], 1/40)\n",
    "dy = d_dt(ots, odo['y'], 1/40)\n",
    "ds = np.sqrt(dx**2 + dy**2)\n",
    "@interact(fwddir=(0,6.28,0.01))\n",
    "def tr(fwddir=0):\n",
    "    plt.clf()\n",
    "    scannum=235\n",
    "\n",
    "    next_raysx = get_next_roto(raysx,odo,1/40)\n",
    "    pred_scan = next_raysx.reshape(-1,1081)[scannum]\n",
    "    nscan = raysx.reshape(-1,1081)[scannum+1]\n",
    "    dss = ds[scannum]*1/40\n",
    "    plt.plot(pred_scan['ex'],pred_scan['ey'],'g.')\n",
    "    pred_scan['ex']+=dss*np.cos(fwddir)\n",
    "    pred_scan['ey']+=dss*np.sin(fwddir)\n",
    "#     print(dss)\n",
    "    plt.plot(nscan['ex'],nscan['ey'],'b.')\n",
    "    plt.plot(pred_scan['ex'],pred_scan['ey'],'r.')\n",
    "    plt.xlim(-3,3)\n",
    "    plt.ylim(-3,3)\n",
    "\n",
    "# raysx=raysx[1081:]\n",
    "# good=(raysx['r']>0)&(raysx['r']<40)&(next_raysx['r']>0)&(next_raysx['r']<30)\n",
    "# err= np.sqrt((next_raysx['ey']-raysx['ey'])**2+(next_raysx['ey']-raysx['ey'])**2)\n",
    "# plt.plot(raysx['th'][good],(err)[good],'b,')\n",
    "\"\"\"\n",
    "        \n",
    "\"\"\"\n",
    "def fwdize(rays, th):\n",
    "    #rotates ray endpoints by th\n",
    "    raysfwd=rays.copy()\n",
    "    raysfwd['ex']=rays['ex']*np.cos(th)-rays['ey']*sin(th)\n",
    "    raysfwd['ey']=rays['ex']*np.sin(th)+rays['ey']*cos(th)\n",
    "    return raysfwd\n",
    "sols=[]\n",
    "for th in np.linspace(2*np.pi):\n",
    "    rf1m=fwdize(rays1, th).reshape(-1,1081)[1:]\n",
    "    rf1p=fwdize(rays1, th).reshape(-1,1081)[:-1]\n",
    "    good = (rf1m['r']>0) & (rf1m['r']<40) & (rf1p['r']>0) & (rf1p['r']<40)\n",
    "    plt.plot((rf1p['ex']-rf1m['ex'])\n",
    "    rays1['ex']-rays[next_raysx = get_next_roto(raysx,odo,1/40)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def get_next(rays,odo,interval):\n",
    "    # takes in rays rotated so that the direction of motion is always +x\n",
    "    derivative_width = 1/20\n",
    "    dx = d_dt(ots, odo['x'], derivative_width)\n",
    "    dy = d_dt(ots, odo['y'], derivative_width)\n",
    "    ds = np.sqrt(dx**2 + dy**2)\n",
    "    dth = d_dt(ots, np.unwrap(odo['th']), derivative_width)\n",
    "    th_step = 1/4*np.pi/180\n",
    "    for scan in rays.reshape(-1,1081):\n",
    "        thp = np.arange(len(scan))*th_step\n",
    "        good = (scan['r']>0) & (scan['r']<40)\n",
    "        nextth = np.clip(dth*interval, thp[0], thp[-1])\n",
    "        nextx = np.interp(nextth,thp[good],scan['ex'][good]) + ds*interval\n",
    "        nexty = np.interp(nextth,thp[good],scan['ey'][good])\n",
    "        \n",
    "\"\"\"\n",
    "%matplotlib widget\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "dx0=-ds*cos(fwd)\n",
    "dy0=-ds*sin(fwd)\n",
    "dx = dx0 + k1*dth0\n",
    "dy = dy0 + k2*dth0\n",
    "dth = dth0\n",
    "dr = dx*cos(th) + dy*sin(th)\n",
    "dth = -dx*sin(th)/r + dy*cos(th) - dth0\n",
    "\"\"\"\n",
    "\n",
    "def differentials(rays,ds,dth0,fwd,k1,k2):#k1 = y offset, k2 = x offset\n",
    "#     th_step = 1/4*np.pi/180\n",
    "#     th = np.arange(len(scan0))*th_step\n",
    "    th=rays['th']\n",
    "    r=rays['r']\n",
    "    sin=np.sin\n",
    "    cos=np.cos\n",
    "    dx0=-ds*cos(fwd)\n",
    "    dy0=-ds*sin(fwd)\n",
    "    dx = dx0 + k1*dth0\n",
    "    dy = dy0 - k2*dth0\n",
    "    dth = dth0\n",
    "    dr = dx*cos(th) + dy*sin(th)\n",
    "    dth = -dx*sin(th)/r + dy*cos(th)/r - dth0\n",
    "    return dr,dth\n",
    "\n",
    "def mismatch(scan0, scan1, dr, dth):\n",
    "    def get_nextr(interval):\n",
    "        th_step = 1/4*np.pi/180\n",
    "        assert len(scan0)==1081\n",
    "        assert len(dth)==1081\n",
    "#         thp = np.arange(len(scan0))*th_step\n",
    "        thp = scan0['th']\n",
    "        good = (scan0['r']>0.2) & (scan0['r']<40)\n",
    "        nextth = np.clip(thp+dth*interval, thp[good][0], thp[good][-1])\n",
    "        nextr = scan0['r']+dr*interval\n",
    "        return nextth,nextr\n",
    "    nextth1, nextr1=get_nextr(1)\n",
    "    nextth2, nextr2=get_nextr(2)\n",
    "    thp = scan1['th']\n",
    "    good = (scan1['r']>0.2) & (scan1['r']<40)\n",
    "    pb1=np.interp(nextth1,thp[good],scan1['r'][good])\n",
    "    pb2=np.interp(nextth2,thp[good],scan1['r'][good])\n",
    "    pseudoindex = np.interp(nextth2,thp[good], np.arange(len(scan1))[good])\n",
    "    blockjump = (np.arange(len(scan1))+0.5)//384 != (pseudoindex+0.5)//384\n",
    "    \n",
    "    \n",
    "    good = (scan0['r']>0) & (scan0['r']<40)&(scan1['r']>0) & (scan1['r']<40)\n",
    "    use1=np.abs(nextr1-pb1)<=np.abs(nextr2-pb2)\n",
    "    nextr=nextr2.copy()\n",
    "    nextr[use1]=nextr1[use1]\n",
    "    nextth=nextth2.copy()\n",
    "    nextth[use1]=nextth1[use1]\n",
    "    pb=pb2.copy()\n",
    "    pb[use1]=pb1[use1]\n",
    "#     th_step = 1/4*np.pi/180\n",
    "#     thp=np.arange(len(scan0))*th_step\n",
    "    thp=scan0['th']\n",
    "#     plt.plot(thp,scan0['r'],'.')\n",
    "#     plt.plot(scan1['th'],scan1['r'],'x')\n",
    "    \n",
    "#     plt.plot((nextth1)[good],nextr1[good],'o')\n",
    "#     plt.plot((nextth2)[good],nextr2[good],'o')\n",
    "#     plt.plot(pb2-nextr2)\n",
    "#     plt.plot(pb1-nextr1)\n",
    "#     plt.plot(np.arange(len(scan0))[good],use1[good],'.')\n",
    "#     plt.plot(nextth[good],pb[good])\n",
    "#     plt.plot(nextth[good],nextr[good])\n",
    "#     plt.plot((pb-nextr)[good])\n",
    "    indic = use1*2-1\n",
    "    indic *= good\n",
    "    indic = np.clip(np.abs(nextr1-pb1)-np.abs(nextr2-pb2),-0.05,0.05)\n",
    "    return indic,(pb-nextr)+100*blockjump\n",
    "#     plt.plot(thp,dr)\n",
    "#     plt.plot(thp,nextr2)\n",
    "ots = (odo['ts']-odo['ts'][0])\n",
    "raysx=rays1.copy().reshape(-1,1081)\n",
    "raysx['th']=np.arctan2(raysx['ey'],raysx['ex'])  # redefine angle\n",
    "raysx['r'] = np.sqrt(raysx['ey']**2+raysx['ex']**2)*(raysx['r']>0) #redefine range\n",
    "dx = d_dt(ots, odo['x'], 1/40)\n",
    "dy = d_dt(ots, odo['y'], 1/40)\n",
    "ds = dx*np.cos(odo['th'])+dy*np.sin(odo['th'])\n",
    "dth = d_dt(ots, np.unwrap(odo['th']), 1/40)\n",
    "# plt.plot(ds)\n",
    "# plt.plot(np.cos(odo['th']))\n",
    "# plt.plot(dy)\n",
    "# plt.plot(odo['th'])\n",
    "# plt.plot(np.arctan2(dy,dx))\n",
    "scannum=325\n",
    "# ds=ds[scannum]\n",
    "# dth=dth[scannum]\n",
    "# scan= rays1.reshape(-1,1081)[scannum]\n",
    "ds=np.interp(raysx[:,-1]['ts'], odo['ts'],ds)\n",
    "dth00=np.interp(raysx[:,-1]['ts'], odo['ts'],dth)\n",
    "\n",
    "ds2=ds.reshape(-1,1)*np.ones_like(raysx['th'])\n",
    "dth0=dth00.reshape(-1,1)*np.ones_like(raysx['th'])\n",
    "print(ds2)\n",
    "# assert False\n",
    "v=-0.1\n",
    "for fwd in [.0]:#np.linspace(0.5,0.8,10):\n",
    "    print(fwd, end=' ')\n",
    "    dr,dth = differentials(raysx,ds=ds2,dth0=dth0,fwd=fwd,k1=0,k2=0)\n",
    "    \n",
    "    jumps = []\n",
    "    acc=[]\n",
    "    for scannum in range(0,1000):#range(325,326): # 5138 has high turn rate\n",
    "#         plt.title(str(dth0[scannum]/40))\n",
    "# #         print(dr[scannum][0])\n",
    "        scan0= raysx.reshape(-1,1081)[scannum]\n",
    "        scan1= raysx.reshape(-1,1081)[scannum+1]\n",
    "        jump, err = mismatch(scan0,scan1,dr[scannum]/40, dth[scannum]/40)\n",
    "        jumps.append(jump)\n",
    "        acc.append(err)\n",
    "    jumps = np.array(jumps)\n",
    "    jumps[np.isnan(jumps)]=0\n",
    "    acc = np.array(acc)\n",
    "    plt.imshow(acc>95)\n",
    "    plt.figure()\n",
    "    plt.title(\"Yellow = Long jump btwn scans. Blue = Short Jump. Turquoise = No Evidence\")\n",
    "    plt.imshow(jumps*(np.abs(np.clip(acc,-.1,0.1))<0.05))\n",
    "    plt.ylabel(\"Scan Number\")\n",
    "    plt.xlabel(\"Sample Number\")\n",
    "    plt.figure()\n",
    "    tes=jumps*(np.abs(np.clip(acc,-.1,0.1))<0.05) # best solution is within 5 cm\n",
    "    tes0 = tes.copy()\n",
    "    tes[:,:384]=np.roll(np.sort(tes[:,:384],axis=1),192,axis=1)\n",
    "    tes[:,384:768]=np.roll(np.sort(tes[:,384:768],axis=1),192,axis=1)\n",
    "    tes[:,768:]=np.roll(np.sort(tes[:,768:],axis=1),156,axis=1)\n",
    "    tes[:,:384]=np.mean(tes[:,:384],axis=1).reshape(-1,1)\n",
    "    tes[:,384:768]=np.mean(tes[:,384:768],axis=1).reshape(-1,1)\n",
    "    tes[:,768:]=np.mean(tes[:,768:],axis=1).reshape(-1,1)\n",
    "    plt.imshow(raysx[:2000,:]['i'])\n",
    "    plt.figure()\n",
    "    plt.imshow(np.hstack((tes*60,tes0*20)))\n",
    "    plt.figure()\n",
    "    plt.imshow(np.abs(np.clip(acc,-.1,0.1))<0.05)\n",
    "#     acc=np.sort(np.hstack(acc))\n",
    "#     plt.plot(acc)\n",
    "    print(acc[100])\n",
    "#     # plt.plot(np.arctan2(scan1['ey'],scan1['ex']))\n",
    "#     plt.ylim(-1,1)\n",
    "# #1.28 almost even\n",
    "#0 big\n",
    "    plt.figure()\n",
    "    plt.plot(dth00/40)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "802ebf17b543432d80fe3cb283fb65d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7faeaa49b1c0>]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "plt.plot(raysx[1000]['sx'])\n",
    "plt.plot(raysx[0]['sy'])\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-44-ca9e79a57758>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-ca9e79a57758>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    ure 1\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "ure 1\n",
    "# This cell checks odometry for smoothness\n",
    "%matplotlib widget\n",
    "def d_dt(t,x,dt,order=1):\n",
    "    if order>1:\n",
    "        x=d_dt(t,x,dt,order-1)\n",
    "    if order==0:\n",
    "        return x\n",
    "    dt = dt/2\n",
    "    lot = np.clip(t-0.001*dt, t[0], t[-1])\n",
    "    hit = np.clip(t+dt, t[0], t[-1])\n",
    "    assert(np.all(hit-lot > 0))\n",
    "    lox = np.interp(lot,t,x)\n",
    "    hix = np.interp(hit,t,x)\n",
    "    return (hix-lox)/(hit-lot)\n",
    "# first, resample to 100hz\n",
    "odo_struct_rt = np.dtype(\n",
    "    [\n",
    "        ('ts', np.float64),\n",
    "        ('x', np.float32),\n",
    "        ('y', np.float32),\n",
    "        ('th', np.float32),\n",
    "        ('pad' , np.float32),\n",
    "        ('lx', np.float32),\n",
    "        ('ly', np.float32),\n",
    "      ])\n",
    "ots = (odo['ts']-odo['ts'][0])\n",
    "hz=2\n",
    "assert(np.all(np.diff(ots)>=0))\n",
    "tsr = np.max(ots)-np.min(ots)\n",
    "ts100 = np.linspace(ots[0],ots[-1],np.maximum(int(tsr*hz),2))\n",
    "odo100 = np.zeros_like(ts100,dtype=odo_struct_rt)\n",
    "odo100['ts'] = ts100\n",
    "for key in ['x','y','th','lx','ly']:\n",
    "    odo100[key]=np.interp(ts100,ots,odo[key])\n",
    "\n",
    "for hz in [40,20,10,5,2]:\n",
    "    # now check smoothness\n",
    "#     plt.plot(np.unwrap(odo['th']))\n",
    "    dk_dt = d_dt(ots,np.unwrap(odo['y']),1/hz,2)#/(d_dt(ots,np.unwrap(odo['th']),1/(2*hz))+0.1)\n",
    "    plt.plot(ots,(dk_dt),)\n",
    "    print(np.max(dk_dt))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b46587c921524c66bbd761f24fe7ca71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.         0.         0.        ... -0.7624614 -0.7518224 -0.7309431]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "# odosub(odo)\n",
    "# plt.plot(lp['ts'],np.sqrt(lp['x']**2+lp['y']**2), ',')\n",
    "# plt.plot(lp['ts'],lp['th'], ',')\n",
    "\n",
    "# plt.plot(rays0['ts'],np.sqrt(rays0['sx']**2+rays0['sy']**2), ',')\n",
    "# plt.plot(rays0['ts'][647::1081],rays0['th'][647::1081], ',')\n",
    "# plt.plot(np.arange(1081*20)%1081,rays0['r'][1081*200:1081*200+1081*20],',')\n",
    "\n",
    "plt.plot(odo['ts'],odo['x'],'b')\n",
    "\n",
    "\n",
    "# plt.plot(odo['x'],odo['y'], ',')\n",
    "# ax1 = plt.subplot(211)\n",
    "# ax2 = plt.subplot(212,sharex=ax1)\n",
    "# ax1.plot(odo['ts'][:-1],np.diff(np.unwrap(odo['th'])), ',')\n",
    "# ax2.plot(rays0['ts'][500::1081][:-1],(np.diff(rays0['th'][500::1081])+np.pi/4)%(np.pi/2)-np.pi/4, ',')\n",
    "\n",
    "# ax2.plot(odo['ts'],odo['x'], ',')\n",
    "\n",
    "# plt.plot(odo['ts'],np.unwrap(odo['th']),',')\n",
    "# plt.plot(imu['ts'],np.unwrap((imu['th'])))\n",
    "print(lp['th'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7983d4020be46ef9202c5df30ab5df9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As a hack to split scans up by sensor, we will look for sudden large jumps in the ray \n",
    "# starting position. As long as the localization doesn't jump too much, this hack should work.\n",
    "# Plot the cdf of jumps to find a good cutoff.\n",
    "# (as of this writing a 0.5m jump looks like a good cutoff)\n",
    "%matplotlib widget\n",
    "\n",
    "tmp=np.sqrt(np.diff(rays0['sx'],append=[0])**2+np.diff(rays0['sy'],append=[0])**2)\n",
    "plt.plot(np.sort(tmp),',')\n",
    "\n",
    "SENSOR_JUMP_THRESHOLD=0.5 # by inspection of the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eba6837434174d278476071edd5b0d14",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "94bc81bf5ed240538e809993e4f8be91",
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      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-f0d3a60601ae>:71: RuntimeWarning: invalid value encountered in true_divide\n",
      "  d1y=np.diff(c1y)/np.diff(c1x)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe2345522b0>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use index to read sensor (uncomment block after if you want to use position hack instead\n",
    "rays1=rays0[rays0['idx']==0]\n",
    "rays2=rays0[rays0['idx']==1]\n",
    "\n",
    "# # Use the jump threshold to detect changes of sensor\n",
    "# restarts=np.sqrt(np.diff(rays0['sx'],append=[0])**2+np.diff(rays0['sy'],append=[0])**2)>SENSOR_JUMP_THRESHOLD\n",
    "\n",
    "# # scans=rays0[:len(rays0)-len(rays0)%2162].reshape(-1,2162)\n",
    "# raysr = rays0[restarts]# HACK to separate scans, gives the ray right before a restart\n",
    "# idx=np.arange(len(restarts))[restarts]\n",
    "# print(\"GCD of scan restarts:\", np.gcd.reduce(idx[:-1]+1), \n",
    "#       \"\\nThis should be a multiple of the scan size if it's constant\")\n",
    "# rs=[]\n",
    "# sn=[]\n",
    "# n=0\n",
    "# for s,e in zip(idx[:-1:2],idx[1::2]):\n",
    "#     rays0[s+1:e+1]['idx']=0\n",
    "#     rs.append(rays0[s+1:e+1])\n",
    "#     sn.append(np.zeros(e-s)+n)\n",
    "#     n+=1\n",
    "# rays1=np.hstack(rs)\n",
    "# scannum1=np.hstack(sn)\n",
    "# # scannum1=scannum1[rays1['r']>=0]\n",
    "# # rays1=rays1[rays1['r']>=0]\n",
    "\n",
    "# rs=[rays0[:idx[0]+1]]\n",
    "# sn=[]\n",
    "# n=0\n",
    "# for s,e in zip(idx[1::2],idx[2::2]):\n",
    "#     rays0[s+1:e+1]['idx']=1\n",
    "#     rs.append(rays0[s+1:e+1])\n",
    "#     sn.append(np.zeros(e-s)+n)\n",
    "#     assert(len(rays0[s+1:e+1])%1081==0)\n",
    "#     n+=1\n",
    "\n",
    "# rays2=np.hstack(rs)\n",
    "# scannum2=np.hstack(sn)\n",
    "\n",
    "# print(\"Timestamp of first ray:\",rays1['ts'][0])\n",
    "\n",
    "%matplotlib widget\n",
    "# plt.plot(rays1['sx'],',')\n",
    "# plt.plot(rays0['ts']-rays0['ts'][0],rays0['sx'],',')\n",
    "# t=rays1['ts']-rays1['ts'][0]\n",
    "# plt.plot(t,rays0['th']-300*t,',')\n",
    "# plt.plot(rays1['ts'],np.mod(rays1['th']-2*np.pi*t/0.025025,2*np.pi),',')\n",
    "# plt.plot(rays1['ts'],rays1['th'],',')\n",
    "# plt.plot(rays1['ts'][540::1081],rays1['th'][540::1081],',')\n",
    "# plt.plot(rays0['r']>0,',')\n",
    "# _=plt.hist(np.diff(rays0['sx']),1000,range=[-.001,.001])\n",
    "# plt.plot(np.arange(len(rays0))[restarts]%2162,',')\n",
    "# plt.plot(np.diff(rays0[restarts]['ts'][::2]),',')\n",
    "\n",
    "thtmp = rays1['th'].reshape(-1,1081)\n",
    "rtmp = rays1['r'].reshape(-1,1081)\n",
    "\n",
    "\n",
    "\n",
    "# print(rays2['th'].reshape(-1,1081)[:,540][0]-rays1['th'].reshape(-1,1081)[:,540][0])\n",
    "c1x=rays1['ts'].reshape(-1,1081)[:,540]-rays1['ts'][0]\n",
    "c1y=np.unwrap(thtmp[:,540])\n",
    "c2x=rays2['ts'].reshape(-1,1081)[:,540]-rays2['ts'][0]\n",
    "c2y=np.unwrap(np.mod(rays2['th'].reshape(-1,1081)[:,540]+3.145+np.pi,2*np.pi)-np.pi)\n",
    "plt.plot((lp['ts']-lp['ts'][0]),lp['th']+c1y[0], 's')\n",
    "plt.plot(c1x, c1y,'k.')\n",
    "plt.plot(c2x, c2y,'r.')\n",
    "plt.title(\"Plot of reference ray angle over time for both sensors\")\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "d1y=np.diff(c1y)/np.diff(c1x)\n",
    "d1x=(c1x[:-1]+c1x[1:])/2\n",
    "plt.plot(np.diff(d1y)/np.diff(d1x),'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "072c48e6b2e542cb8e06b53003e72d6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe25e8dd280>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "# plt.plot(rays1['ts'],rays1['sx'],',')\n",
    "# plt.plot(rays2['ts'],rays2['sx'],',')\n",
    "\n",
    "dx=np.diff(rays1['sx'])\n",
    "dy=np.diff(rays1['sy'])\n",
    "dd=np.sqrt(dx**2+dy**2)\n",
    "dth=np.diff(rays1['th'])\n",
    "dt=np.diff(rays1['ts'])\n",
    "intertime=0.5*rays1['ts'][:-1]+0.5 * rays1['ts'][1:]\n",
    "\n",
    "\n",
    "# plt.plot(intertime, dd/dt,',')\n",
    "plt.plot( np.sort(dth/dt),',')\n",
    "# plt.plot(rays1['ts'],rays1['th'],',')\n",
    "\n",
    "# plt.plot(np.unwrap(rays1['th'])/np.pi,rays1['sx'],',')\n",
    "# plt.plot(np.sort(np.diff(rays0['sx'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1479662727.0105062\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a02417d4c394793849f4e0d22738942",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb6bc15e3c1e4457a7266c864b441481",
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       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59442b9805254a12bc672fe7b157f802",
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      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "67df4f562200475db2e899dd418fb926",
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "f451f3fca3c441a1807e89fafd40c425",
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      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe23e1b5d00>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## This cell tests for gyro scale error. \n",
    "#  If the plot shows a clear linear trend when GYRO_SCALE=1.0,\n",
    "#  there is scale error. Adjust GYRO_SCALE until there is no \n",
    "#  clear linear trend. This corresponds to the amount the gyro \n",
    "#  needs to be multipled by.\n",
    "#\n",
    "# Input is \"rays1\" which is the rays from a single scanner.\n",
    "# Right now it is hardcoded that a scan is 1081 rays.\n",
    "# First scan is assumed to be stationary. If it's not, things get a little weird.\n",
    "\n",
    "GYRO_SCALE=1.0 #0.95\n",
    "GYRO_OFFSET = 0.0005#0.0014 # this number is 0.0005 even for perfect odometry! I wonder why?\n",
    "\n",
    "%matplotlib widget\n",
    "scans=rays1.reshape(-1,1081) #organize into scans\n",
    "\n",
    "# Fill in the angles of the first scan to find offset angles of each ray\n",
    "offth = scans['th']\n",
    "tmp=scans['th'][0] # first scan angles\n",
    "idxtmp=np.arange(len(tmp))[tmp!=0]\n",
    "tmp2=tmp[tmp!=0] # first scan angles that are validish\n",
    "    # Add in endpoints\n",
    "slope=(tmp2[-1]-tmp2[0])/(idxtmp[-1]-idxtmp[0]) # Use the average slope to extrapolate endpoints\n",
    "intercept=tmp2[0]-idxtmp[0]*slope\n",
    "idxtmp=np.hstack((0,idxtmp,10000))\n",
    "tmp2=np.hstack((intercept,tmp2,10000*slope+intercept))\n",
    "offsets=np.interp(np.arange(len(tmp)),idxtmp,tmp2,left=0,right=0)\n",
    "\n",
    "# Subtract the offsets to find the robot angle over tme\n",
    "rotor=scans['th']-offsets\n",
    "decent_angles=np.unwrap(rotor.ravel()[scans['r'].ravel()>0])\n",
    "print(scans['ts'][0][0])\n",
    "decent_ts=scans['ts'][scans['r']>0].ravel()\n",
    "delt=np.diff(decent_angles,prepend=0)\n",
    "slop=np.diff(decent_angles)/np.diff(decent_ts)\n",
    "dupls=np.arange(len(slop))[np.diff(decent_ts)>.005] # jumps in timestamp indicating edge of scan\n",
    "slop[dupls]=(slop[dupls+1]+slop[dupls-1])/2 # smooth out derivative at jumps\n",
    "slop_rescale = (slop*GYRO_SCALE-GYRO_OFFSET)\n",
    "incrs=slop_rescale*np.diff(decent_ts) # factor for correcting gyro scale error\n",
    "regen_ang=np.cumsum(np.hstack((decent_angles[0],incrs)))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(offsets,'.')\n",
    "plt.figure()\n",
    "plt.plot(decent_ts,decent_angles,',')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(decent_angles,decent_angles-regen_ang)\n",
    "plt.title(\"Adjust GYRO_SCALE to remove slope from this line\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(decent_ts,decent_angles)\n",
    "plt.plot(decent_ts,regen_ang)\n",
    "plt.title(\"Adjust GYRO_OFFSET to make flat areas in both lines flat\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(decent_ts,decent_angles-regen_ang)\n",
    "# At end of cell:\n",
    "# \"decent_angles\" is the angles from SLAM, using original odometry/IMU to do the slope within the scan\n",
    "# \"regen_ang\" is the angles using only odometry/IMU, corrected for scale and bias\n",
    "# \"decent_ts\" is the timestamps shared by both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16b0334d86db401b9db29d5689754dd3",
       "version_major": 2,
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      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timestep 0.024839401245117188\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27e5e94a913440d4bf76c2090cf20239",
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       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe23e11bd00>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "# plt.plot(decent_ts,decent_angles)\n",
    "plt.plot(decent_ts,regen_ang,',')\n",
    "\n",
    "its = np.linspace(decent_ts[0],decent_ts[-1], 1.29*len(scans))\n",
    "print(\"timestep\",np.diff(its)[0])\n",
    "iang = np.interp(its,decent_ts,decent_angles)\n",
    "plt.plot(its,iang,'.')\n",
    "     \n",
    "kernel_size = 10\n",
    "kernel = np.ones(kernel_size) / kernel_size\n",
    "smoothed = np.convolve(np.hstack((iang,iang[::-1])), kernel, mode='same')[:len(iang)]#poor man's 'mirror' end conditions\n",
    "smoothed2 = np.convolve(smoothed, kernel, mode='same')\n",
    "smoothed3 = np.convolve(smoothed2, kernel, mode='same')\n",
    "comp_smooth=smoothed2+(smoothed2-smoothed3)*2\n",
    "\n",
    "# help(plt.hist)\n",
    "plt.plot(its,smoothed)\n",
    "# plt.plot(its,smoothed2)\n",
    "# plt.plot(its,smoothed3)\n",
    "# plt.plot(its,comp_smooth,'.')\n",
    "# plt.plot(its,iang-comp_smooth)\n",
    "\n",
    "# plt.hist(np.diff(smoothed)*10,1000,(-0.07,0.07))\n",
    "# plt.hist(iang-smoothed,1000,(-0.07,0.07))\n",
    "ira=np.interp(its,decent_ts,regen_ang)\n",
    "# plt.plot(its,(ira-comp_smooth)*100)\n",
    "# plt.plot(its,(ira-iang)*100)\n",
    "plt.plot(lp['ts'],np.unwrap(lp['th'])-np.mean(np.unwrap(lp['th'])), markersize=2)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "tmp=(np.diff(smoothed,prepend=0)+np.diff(smoothed,append=smoothed[-1]))*10\n",
    "# plt.plot(its,(np.diff(smoothed,prepend=0)+np.diff(smoothed,append=smoothed[-1]))*10)\n",
    "plt.plot(its,iang-ira,'.')\n",
    "plt.plot(its,np.diff(iang,prepend=0),'.')\n",
    "# plt.plot(its,smoothed-ira)\n",
    "\n",
    "# plt.plot(its,smoothed-ira -tmp*.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No idea what this cell did. Disabling.\n",
    "\n",
    "# %matplotlib widget\n",
    "# # plt.plot(decent_ts,decent_angles-regen_ang,',')\n",
    "\n",
    "# slop_rate=.030/100\n",
    "# tosum = np.hstack((decent_angles[0],incrs))\n",
    "# dts = np.hstack((0,np.diff(decent_ts)))\n",
    "# appr = regen_ang\n",
    "\n",
    "# ang=0\n",
    "# cors=[]\n",
    "# angs=regen_ang.copy()\n",
    "# for i in range(len(tosum)):\n",
    "#     ang+=tosum[i]\n",
    "#     corr=np.sign(decent_angles[i]-ang)*(dts[i]*slop_rate+0.1*np.abs(tosum[i]))\n",
    "# #     print(decent_angles[i],ang,end=':')\n",
    "#     ang+=corr\n",
    "#     angs[i]=ang\n",
    "# #     print(ang)\n",
    "# fig, axs = plt.subplots(2,sharex=True)\n",
    "# axs[0].plot(decent_ts,angs,',')\n",
    "# axs[0].plot(decent_ts,decent_angles,',')\n",
    "# axs[1].plot(decent_ts,np.cumsum(np.sign(decent_angles-angs)))\n",
    "    \n",
    "    \n",
    "# # for i in range(10):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No idea what this cell did. Disabling.\n",
    "\n",
    "# %matplotlib widget\n",
    "# for i in range(11):\n",
    "#     corr = np.sign(decent_angles-appr)*dts*slop_rate\n",
    "\n",
    "#     appr=np.cumsum(tosum+corr)\n",
    "#     print(np.sum(np.abs(decent_angles-appr)))\n",
    "\n",
    "\n",
    "# plt.plot(decent_ts,decent_angles-appr,',')\n",
    "# print(np.sum(np.abs(decent_angles-appr)))\n",
    "# # plt.plot(corr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No idea what this cell did. Disabling.\n",
    "\n",
    "# %matplotlib widget\n",
    "\n",
    "# plt.plot(decent_ts,np.hstack((0,slop_rescale)),',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orininal rays:\t (14716734,)\n",
      "kept rays:\t (3545511,)\n"
     ]
    }
   ],
   "source": [
    "rays=rays0\n",
    "print(\"orininal rays:\\t\",rays.shape)\n",
    "r=rays['r']\n",
    "keep=(r<10) & (r>0)\n",
    "rays=rays[keep]\n",
    "print(\"kept rays:\\t\",rays.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "f0dffe0939b840a688d8a6e749d81c84",
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      },
      "text/plain": [
       "interactive(children=(IntSlider(value=127, description='number', max=1000, min=2), Output()), _dom_classes=('w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets\n",
    "%matplotlib widget\n",
    "from ipywidgets import interact, interact_manual\n",
    "from IPython.display import display,clear_output\n",
    "# rays2=rays1\n",
    "# https://towardsdatascience.com/interactive-controls-for-jupyter-notebooks-f5c94829aee6\n",
    "# reveals issues: https://stackoverflow.com/questions/61715937/how-to-enable-matplotlib-blitting-to-create-fast-realtime-graph-in-jupyterlab\n",
    "# \n",
    "number = 300\n",
    "# plt.plot(rays['ex'][number*1081:(number+1)*1081],rays['ey'][number*1081:(number+1)*1081],'r,',c=[.5,.5,.5,.5])\n",
    "\n",
    "@interact(number=(2,1000,1))\n",
    "def display_scan(number=127):\n",
    "    xlim=plt.gca().get_xlim()\n",
    "    ylim=plt.gca().get_ylim()\n",
    "    plt.gcf().clear()\n",
    "    \n",
    "#     plt.plot(raysy['ex'][number*1081:(number+1)*1081],raysy['ey'][number*1081:(number+1)*1081],'r,',c=[.5,.5,.5,.5])\n",
    "    number -= 1\n",
    "    plt.plot(raysy['ex'][number],raysy['ey'][number],'r.')\n",
    "    number += 1\n",
    "    plt.plot(raysy['ex'][number],raysy['ey'][number],'g.')\n",
    "    number +=1 \n",
    "    plt.plot(raysy['ex'][number],raysy['ey'][number],'b.')\n",
    "    plt.plot(raysy['ex'][number][:768],raysy['ey'][number][:768],'c.')\n",
    "    if raysy['ts'][number][-1]-raysy['ts'][number-1][-1]>1/30:\n",
    "        plt.plot(raysy['ex'][number][:384],raysy['ey'][number][:384],'k')\n",
    "        plt.title('Timestamp jump = Long (1)')\n",
    "    else:\n",
    "        plt.plot(raysy['ex'][number][:384],raysy['ey'][number][:384],'k')\n",
    "        plt.title('Timestamp jump = Short (0)')\n",
    "    number -=1\n",
    "    plt.plot(raysy['sx'][number],raysy['sy'][number],'b.')\n",
    "\n",
    "    if xlim[1]!=1:\n",
    "        plt.xlim(xlim)\n",
    "        plt.ylim(ylim)\n",
    "#     plt.gcf().canvas.draw()\n",
    "    s=plt.gcf().canvas\n",
    "    _, _, w, h = s.figure.bbox.bounds\n",
    "#     s._png_is_old = True\n",
    "#     s.handle_refresh(1)\n",
    "#     print(s._png_is_old)\n",
    "    s.manager.resize(w, h)\n",
    "    \n",
    "#     plt.gcf().canvas.set_image_mode('full')\n",
    "    \n",
    "\n",
    "# plt.figure()\n",
    "# for n in range(166,200):\n",
    "#     plt.plot(raysy['ey'][n*1081:(n+1)*1081])\n",
    "# plt.plot(raysy['ey'][198*1081:(198+1)*1081]) # weird jump at 383 to 384. Appears to repeat several data points (13?)\n",
    "\n",
    "\n",
    "# DISCOVERY: The hokuyo has a glitch where sometimes the points [384:] are from one scan in the future relative to the early points!\n",
    "# DISCOVERY2: Sometimes its the first points that go to the future!\n",
    "# DISCOVERY3: It happens to both sensors\n",
    "# DISCOVERY4: Actually there are at least 3 blocks of points [0:384][384:768][768:] that can jump to the future, as well as the timestamp. None of the 4 have a clear relation\n",
    "# I wonder if the possibility of this is detectable from the timestamps? Like does it always happen before or after a missing scan? A: seems unpredictable :-(\n",
    "# Also I notice that it can persist for a couple of scans, so that several in a row are split.\n",
    "\n",
    "# Laser id 0\n",
    "# Observations start at 2245\n",
    "#                             a      a|\n",
    "# # 5678901234567890123456789012345678|901234567890123456789\n",
    "# S 0001100100110110011011010!00!00!!0|!!0000110110010011\n",
    "# C 011001001000100010010?100100100010|0100110010010001001\n",
    "# B 0101000100100100010010010010010001|001010100100010010 // Higher rays jumped (not sure, but mostly [384:768]\n",
    "# A 1100010010001001000100100100100100|100010001001001000 // [0:384] jumped\n",
    "# Y 0101001001001000100100100010010010|010011001000100100 // timestamp jumped\n",
    "\n",
    "# Laser id 0\n",
    "#  2590\n",
    "#  90123456789012345678901234567890\n",
    "#Y 101000010000100000100001000001\n",
    "#C 101000010001000001000001000001\n",
    "#B 100100001000001000010000010000\n",
    "#A 100000100010000010000010000010\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# utility to find the ray nearest a certain point\n",
    "def nearray(rays, x,y):\n",
    "    return np.argmin((rays['ex']-x)**2+(rays['ey']-y)**2)\n",
    "\n",
    "def plotcolored(x,y,phi):\n",
    "    # plots multicolored data with phi indicating color angle\n",
    "    rgb_cycle = np.vstack((            # Three sinusoids\n",
    "    .5*(1.+np.cos(phi          )), # scaled to [0,1]\n",
    "    .5*(1.+np.cos(phi+2*np.pi/3)), # 120° phase shifted.\n",
    "    .5*(1.+np.cos(phi-2*np.pi/3)))).T\n",
    "    idx = np.round(phi%(2*np.pi)/np.pi*30) #30 divisions for now\n",
    "    for i in np.unique(idx):\n",
    "        plt.plot(x[idx==i],y[idx==i],',',c=rgb_cycle[idx==i][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22e2f8d474bb4c849a7caeb6aabe1d1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a692918f9f04faeb3e6bb5eb6f37528",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11829627\n"
     ]
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "cut=0\n",
    "raysd=rays[(rays['i']>=cut)]#&(rays['idx']==1)]\n",
    "span=540\n",
    "# plt.plot(raysd['ex'][5925215-span:5925215+span],raysd['ey'][5925215-span:5925215+span],'r,')\n",
    "# print(raysd['ex'][11706031])\n",
    "plt.gca().set_facecolor('xkcd:black')\n",
    "cycle_time = 10.0\n",
    "phi=(raysd['ts']-raysd['ts'][0])*np.pi*2/cycle_time\n",
    "rgb_cycle = np.vstack((            # Three sinusoids\n",
    "    .5*(1.+np.cos(phi          )), # scaled to [0,1]\n",
    "    .5*(1.+np.cos(phi+2*np.pi/3)), # 120° phase shifted.\n",
    "    .5*(1.+np.cos(phi-2*np.pi/3)))).T\n",
    "plotcolored(raysd['ex'],raysd['ey'],phi)\n",
    "# line=plt.plot(raysd['ex'],raysd['ey'],',')#s=1,linewidth=0,c=rgb_cycle,antialiased=False)\n",
    "# help(line[0])\n",
    "\n",
    "\n",
    "# # Plot Odometry\n",
    "# plt.plot(rays['sx'][::10],rays['sy'][::10],'r,')\n",
    "# plt.plot(odo['x'],odo['y'], ',')\n",
    "# plt.plot(lp['lx'],lp['ly'], 'c.')\n",
    "# plt.gca().grid('on')\n",
    "plt.gca().set_aspect('equal','box')\n",
    "\n",
    "# #Plot a slice of interest\n",
    "# s1=533456\n",
    "# s2=534456\n",
    "# plt.plot(rays1['ex'][s1:s2],rays1['ey'][s1:s2],'g.')\n",
    "# plt.plot(rays1['sx'][s1:s2],rays1['sy'][s1:s2],'b.')\n",
    "\n",
    "\n",
    "# plt.plot(raysd['ex'][slc:10000000],raysd['ey'][slc:10000000],'b,')\n",
    "\n",
    "\n",
    "# plt.plot(rays1['sx'],rays1['sy'],'r,')\n",
    "# plt.plot(raysd['sx'][1::2],raysd['sy'][1::2],'k,')\n",
    "# plt.plot(raysr['sx'][::2],raysr['sy'][::2],'r.-')\n",
    "# plt.plot(raysr['sx'][1::2],raysr['sy'][1::2],'k.-')\n",
    "# plt.show()\n",
    "# plt.figure()\n",
    "# plt.plot(raysd['th'],'b,')\n",
    "\n",
    "plt.figure()\n",
    "ags=np.argsort(odo['ts'])\n",
    "\n",
    "# plt.plot(odo['ts'][:-1],1/40*np.diff(odo['x'])/(np.diff(odo['ts'])),',')\n",
    "plt.plot(lp['ts'][:],lp['th'],',')\n",
    "# plt.plot(odo['ts'][:-1],1/40*np.diff(lp['th'])/(np.diff(lp['ts'])),'r,')\n",
    "# plt.plot(odo['ts'],odo['th'])\n",
    "\n",
    "print(nearray(raysd,16.08,-2.72))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a87cb1f521ae4809a040b74a48db00c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b6046b820>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Debug Odometry directly\n",
    "\n",
    "%matplotlib widget\n",
    "\n",
    "t=odo['ts']\n",
    "x=odo['x']\n",
    "y=odo['y']\n",
    "th=odo['th']\n",
    "ex = x - np.sin(th)*4\n",
    "ey = y + np.cos(th)*4\n",
    "\n",
    "plt.plot(ex,ey,'.')\n",
    "\n",
    "ex = x + np.cos(th)*4\n",
    "ey = y + np.sin(th)*4\n",
    "\n",
    "plt.plot(ex,ey,'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ex -12.918765 29.284042 42.20281\n",
      "ey -56.17813 7.3319387 63.51007\n"
     ]
    }
   ],
   "source": [
    "## Find the max extent of the map\n",
    "np.max(rays['ex'])\n",
    "for c in ['ex','ey']:\n",
    "    print(c, np.min(rays[c]),np.max(rays[c]), np.max(rays[c])-np.min(rays[c]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fcorns\n",
      " [[     -12.92      -56.18]\n",
      " [      29.28        7.33]]\n",
      "pxc_corns\n",
      " [[   -1291.88     5617.81]\n",
      " [    2928.4      -733.19]]\n",
      "pxc_topleft\n",
      " [   -1292        -734   ]\n",
      "pxc_btmright\n",
      " [    2929        5618   ]\n",
      "pxc_extent\n",
      " [    4221        6352   ]\n",
      "px_corns\n",
      " [[       0.12     6351.81]\n",
      " [    4220.4         0.81]]\n",
      "px_topleft\n",
      " [       0           0   ]\n",
      "px_btmright\n",
      " [    4221        6352   ]\n",
      "px_extent\n",
      " [    4221        6352   ]\n"
     ]
    }
   ],
   "source": [
    "## Figure out rasterization\n",
    "# Floor of float coords is integer coords\n",
    "# Images are y flipped\n",
    "\n",
    "def homp(T,pts):\n",
    "    tmppts = pts @ T[:-1,:-1].T + T[:-1,-1]\n",
    "    denom = pts @ T[-1:,:-1].T + T[-1,-1]\n",
    "    return tmppts/denom\n",
    "\n",
    "\n",
    "CELLS_PER_M = 100.0\n",
    "\n",
    "formatter = {'float':lambda x:np.format_float_positional(x,precision=2,fractional=True,trim='-',pad_left=8,pad_right=2)}\n",
    "\n",
    "def get_T_px_f_with_extent(rays, debug=False):\n",
    "    # The corners in world space\n",
    "    f_corns = np.array(\n",
    "        [[np.min(rays['ex']),np.min(rays['ey'])],\n",
    "         [np.max(rays['ex']),np.max(rays['ey'])]])\n",
    "    T_pxc_f = np.array(\n",
    "        [[1*CELLS_PER_M, 0             , 0],\n",
    "         [0            , -1*CELLS_PER_M, 0],\n",
    "         [0            , 0             , 1]])\n",
    "    pxc_corns=homp(T_pxc_f,f_corns)\n",
    "    pxc_topleft = np.array([  np.floor(np.min(pxc_corns[:,0])), np.floor(np.min(pxc_corns[:,1]))  ]) #inclusive\n",
    "    pxc_btmright= np.array([  np.floor(np.max(pxc_corns[:,0])), np.floor(np.max(pxc_corns[:,1]))  ]) + 1 #exclusive\n",
    "    pxc_extent = pxc_btmright-pxc_topleft\n",
    "\n",
    "    T_px_f = T_pxc_f.copy()\n",
    "    T_px_f[:-1,-1] = -pxc_topleft\n",
    "\n",
    "    px_corns=homp(T_px_f,f_corns)\n",
    "    px_topleft = np.array([  np.floor(np.min(px_corns[:,0])), np.floor(np.min(px_corns[:,1]))  ]) #inclusive\n",
    "    px_btmright= np.array([  np.floor(np.max(px_corns[:,0])), np.floor(np.max(px_corns[:,1]))  ]) + 1 #exclusive\n",
    "    px_extent = px_btmright-px_topleft\n",
    "\n",
    "    assert (np.all(pxc_extent == px_extent))\n",
    "\n",
    "    if debug:\n",
    "        print(\"fcorns\\n\",f_corns)\n",
    "\n",
    "        for var in ['pxc_corns',\n",
    "                    'pxc_topleft',\n",
    "                    'pxc_btmright',\n",
    "                    'pxc_extent',\n",
    "                    'px_corns',\n",
    "                    'px_topleft',\n",
    "                    'px_btmright',\n",
    "                    'px_extent',\n",
    "                    ]:\n",
    "            print(var+'\\n', eval(var))\n",
    "    return T_px_f, px_extent.astype('int64')\n",
    "\n",
    "with np.printoptions(formatter=formatter):\n",
    "    T_px_f, px_extent = get_T_px_f_with_extent(rays, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c86fdefc0044a6e9b8ff36c3f13a856",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxhits= 931.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a260098547f4685b17bd6958421bbbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f52b4263d60>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rasterize hits\n",
    "\n",
    "def get_raster_coords(rays, T_px_f):\n",
    "    # implicit inputs: T_px_f\n",
    "    f_ept = np.vstack((rays['ex'].ravel(),rays['ey'].ravel())).T.copy()\n",
    "    px_coords = homp(T_px_f, f_ept)\n",
    "    px_coords = px_coords.reshape(rays['ex'].shape+(2,))\n",
    "    return px_coords\n",
    "\n",
    "px_coords = get_raster_coords(rays0, T_px_f)\n",
    "\n",
    "bad0=rays0['r']<=0\n",
    "bad0|=rays0['r']>59\n",
    "\n",
    "img = np.zeros(px_extent[::-1])\n",
    "\n",
    "# clip out bad pixels\n",
    "bad = np.any(px_coords<0,axis=1) + np.any(px_coords >= px_extent,axis=1) + bad0\n",
    "px_coords = px_coords[bad==0,:]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "np.add.at(img, tuple(np.floor(px_coords.T)[::-1].astype('int32')), 1)\n",
    "%matplotlib widget\n",
    "\n",
    "imsc=img.copy()\n",
    "imsc[img!=0]=np.log10(img[img!=0])+0.301\n",
    "\n",
    "\n",
    "\n",
    "plt.imshow(img)\n",
    "print('maxhits=',np.max(img))\n",
    "plt.figure()\n",
    "plt.plot(img[900,:],'.')\n",
    "# plt.hist(rays0['r'],100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14bc9c3b5984401ca398cd00426ffe34",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f930c9c9b80>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It turns out that gaussian blurring and selecting where >1 produces a very good approximation of where the walls are\n",
    "# without much reflection. This cell does that calculation\n",
    "%matplotlib widget\n",
    "\n",
    "def produce_wall_mask(rays0):\n",
    "    import cv2\n",
    "    T_px_f, px_extent = get_T_px_f_with_extent(rays) # extents and transform\n",
    "    px_coords = get_raster_coords(rays0, T_px_f) # pixel coordinates\n",
    "    img = np.zeros(px_extent[::-1]) #image to fill\n",
    "\n",
    "    # clip out bad rays\n",
    "    bad0=rays0['r']<=0\n",
    "    bad0|=rays0['r']>59\n",
    "    # clip out bad pixels\n",
    "    bad = np.any(px_coords<0,axis=1) + np.any(px_coords >= px_extent,axis=1) + bad0\n",
    "    px_coords = px_coords[bad==0,:]\n",
    "\n",
    "    np.add.at(img, tuple(np.floor(px_coords.T)[::-1].astype('int32')), 1)\n",
    "    \n",
    "    gaussian_func = lambda img:cv2.GaussianBlur(img,(61,61),sigmaX=3,borderType=cv2.BORDER_DEFAULT)\n",
    "\n",
    "    local_mean = gaussian_func(img)\n",
    "    return local_mean>1.0\n",
    "wall_mask=produce_wall_mask(rays0)\n",
    "plt.imshow(wall_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97b9509563074b46912880232847aca4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "def produce_fwhm_mask(rays0):\n",
    "    import cv2\n",
    "    T_px_f, px_extent = get_T_px_f_with_extent(rays) # extents and transform\n",
    "    px_coords = get_raster_coords(rays0, T_px_f) # pixel coordinates\n",
    "    img = np.zeros(px_extent[::-1]) #image to fill\n",
    "\n",
    "    # clip out bad rays\n",
    "    bad0=rays0['r']<=0\n",
    "    bad0|=rays0['r']>59\n",
    "    # clip out bad pixels\n",
    "    bad = np.any(px_coords<0,axis=1) + np.any(px_coords >= px_extent,axis=1) + bad0\n",
    "    px_coords = px_coords[bad==0,:]\n",
    "\n",
    "    np.add.at(img, tuple(np.floor(px_coords.T)[::-1].astype('int32')), 1)\n",
    "    \n",
    "    kernel = np.ones((10,10), np.uint8)\n",
    "    dil=cv2.dilate(img, kernel, borderType=cv2.BORDER_DEFAULT)\n",
    "    plt.imshow((img>=dil/2)*(img>3))\n",
    "\n",
    "    return (img>dil/2)\n",
    "fwhm_mask=produce_fwhm_mask(rays0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf293a368b8b47808274bf2407bb4e1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6cedca9af16e49759468d62a8d0dddae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f515aa93be0>]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load gt data and estimate normals\n",
    "%matplotlib widget\n",
    "import cv2\n",
    "\n",
    "gtrgb=cv2.imread(\"gtmap.png\")\n",
    "# plt.imshow((gtrgb[:,:,1]>0)+0.5*fwhm_mask)\n",
    "gtwall = gtrgb[:,:,1]>0\n",
    "gtwall_coords = np.array(np.nonzero(gtwall))[::-1].T+0.5\n",
    "\n",
    "\n",
    "# see the distribution of coordinates by distance\n",
    "from scipy.spatial import KDTree\n",
    "tree= KDTree(gtwall_coords)\n",
    "d,nn=tree.query(px_coords[::360],distance_upper_bound=10.0)\n",
    "# d,nn=tree.query(px_coords[:108100],distance_upper_bound=10.0)\n",
    "\n",
    "# histogram out to +- 0.5 cell spacing is linear ramp, because it is an expanding circle. Beyond that it should approach gaussian in absense of localization error\n",
    "# arclen=arcsin(CELL_SPACING/2/x)*4/(2*pi)*(2*pi*r)\n",
    "# density = exp(-2*(x/SD)**2) # where x points in the normal direction\n",
    "REGRESSION_SD=2.0\n",
    "plt.hist(d[d<10],100)\n",
    "x=np.linspace(0.01,10,1000)\n",
    "plt.plot(x,np.arcsin(np.clip(0.5/x,0,1))*4*x*np.exp(-(x/REGRESSION_SD)**2)*1000)#1000 scale factor for visualization, adjust as needed\n",
    "\n",
    "# plt.plot(px_coords[:108100,0],px_coords[:108100,1],',')\n",
    "plt.figure()\n",
    "plt.plot(gtwall_coords[:,0],gtwall_coords[:,1],',')\n",
    "# x=np.linspace(0,10)\n",
    "# plt.plot(x,np.exp(-(x/4)**2)*10000)\n",
    "# print(nn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14929\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2a8f6da939348759425ae883e7258a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-218.34532374]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0451ec9cd3244c82b01d2dab4b25a586",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f930c8fda90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "def produce_max_inten_image(rays0):\n",
    "    import cv2\n",
    "    T_px_f, px_extent = get_T_px_f_with_extent(rays) # extents and transform\n",
    "    px_coords = get_raster_coords(rays0, T_px_f) # pixel coordinates\n",
    "    img = np.zeros(px_extent[::-1]) #image to fill\n",
    "\n",
    "    # clip out bad rays\n",
    "    bad0=rays0['r']<=0\n",
    "    bad0|=rays0['r']>59\n",
    "    bad0|=np.abs(rays0['th']+1.627-np.pi*3/2)>.0305\n",
    "    # clip out bad pixels\n",
    "    bad = np.any(px_coords<0,axis=1) + np.any(px_coords >= px_extent,axis=1) + bad0\n",
    "    px_coords = px_coords[bad==0,:]\n",
    "    \n",
    "    inten = rays0['i'][bad==0]\n",
    "\n",
    "    np.maximum.at(img, tuple(np.floor(px_coords.T)[::-1].astype('int32')), inten)\n",
    "#     plt.imshow(img)\n",
    "    f2=np.all(np.abs(np.floor(px_coords)-[2101,4168])<1000,axis=1) #top right:[3773,960] , mid left:[363,3871], alcove:[2018,2032], inner pillar:[2483,713]\n",
    "    print(np.sum(f2))\n",
    "    \n",
    "    angs = np.interp(rays0['ts'],decent_ts,decent_angles)\n",
    "#     plt.plot(rays0['ts'][bad==0][f2],(rays0['th'])[bad==0][f2],'.')\n",
    "#     plt.plot(rays0['r'][bad==0][f2],rays0['i'][bad==0][f2],'.')\n",
    "    plt.plot(rays0['th'][bad==0][f2],rays0['i'][bad==0][f2]+rays0['r'][bad==0][f2]*0,',')\n",
    "#     plt.plot(rays0['r'][bad==0][f2],rays0['i'][bad==0][f2]+rays0['r'][bad==0][f2]*0,',')\n",
    "\n",
    "#     plt.plot(rays0['ts'][bad==0][f2],rays0['i'][bad==0][f2],'.')\n",
    "#     plt.plot(rays0['ts'][bad==0][f2],rays0['r'][bad==0][f2]*100,'.')\n",
    "#     plt.plot(rays0['ts'][bad==0][f2],rays0['th'][bad==0][f2]*1000,'.')\n",
    "\n",
    "\n",
    "#     plt.plot(rays0['i'][bad==0][f2],'.')\n",
    "\n",
    "    plt.plot([2.37,7.93],[3105,1891])\n",
    "    print(np.diff([3105,1891])/np.diff([2.37,7.93]))\n",
    "    \n",
    "    return img\n",
    "maxinten=produce_max_inten_image(rays0)\n",
    "plt.figure()\n",
    "plt.imshow(maxinten>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected best for line: 7.519884823893004\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "900c79b2db524b089c8471c45bcd820c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "730015816d624078aedf7d84145b8f0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(858, 1257)\n",
      "best observed: 3.6316730282930405\n"
     ]
    }
   ],
   "source": [
    "#try some local enhancement:\n",
    "%matplotlib widget\n",
    "\n",
    "import cv2\n",
    "gaussian_func = lambda img:cv2.GaussianBlur(img,(61,61),sigmaX=3,borderType=cv2.BORDER_DEFAULT)\n",
    "DECENT_LINE_FRACTION = 0.7\n",
    "\n",
    "local_mean = gaussian_func(img)\n",
    "\n",
    "line_img = np.zeros((300,300))\n",
    "line_img[150]=100\n",
    "print(\"expected best for line:\",100/np.max(gaussian_func(line_img)))\n",
    "# plt.imshow(np.clip(img/(local_mean+1),0,10)>.8)\n",
    "kernel_max = 1/np.max(img/(local_mean+0.0000001)) # appoximation of the center value of the gaussian kernel\n",
    "gaussian_without_center=(local_mean-img*kernel_max)/(1-kernel_max)#mean estimate ignoring center pixel\n",
    "denom=np.clip(local_mean,.0001,1e9) # considered gaussian_without_center, but nope \n",
    "qual_analog=np.clip(np.clip(img-np.sqrt(img),0,1e9)/denom,0,50)#lower bound of conf interval scales with sqrt\n",
    "determiner = (qual_analog>0.7)*(img>0)\n",
    "# plt.imshow((qual_analog>0.7) +(img>0))  \n",
    "plt.imshow(img>0)\n",
    "plt.figure()\n",
    "plt.plot(img[630:645,1257],'.')\n",
    "print(np.unravel_index(np.argmax(img),img.shape))\n",
    "# plt.imshow(qual_analog>.7)\n",
    "# plt.plot(np.sort(qual_analog.ravel()))\n",
    "print(\"best observed:\",np.max(img)/local_mean.ravel()[np.argmax(img)])\n",
    "# plt.imshow((img>2)*0.5+(np.clip(img/(local_mean+1),0,10)>.8)*0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find local maxima for realigning scans\n",
    "%matplotlib widget\n",
    "\n",
    "kernels=np.array([\n",
    "        [\n",
    "            [0,0,1],\n",
    "            [0,1,0],\n",
    "            [1,0,0.0],\n",
    "        ],\n",
    "        [\n",
    "            [0,1,0],\n",
    "            [0,1,0],\n",
    "            [0,1,0],\n",
    "        ],\n",
    "        [\n",
    "            [1,0,0],\n",
    "            [0,1,0],\n",
    "            [0,0,1],\n",
    "        ],\n",
    "        [\n",
    "            [0,0,0],\n",
    "            [1,1,1],\n",
    "            [0,0,0],\n",
    "        ],\n",
    "    ]).astype(np.uint8)\n",
    "sol=np.zeros_like(local_mean)\n",
    "gaussian_func_s = lambda img:cv2.GaussianBlur(img,(61,61),sigmaX=3,borderType=cv2.BORDER_DEFAULT)\n",
    "ridgeimg=gaussian_func_s(img)\n",
    "for kernel in kernels:\n",
    "    sol+=(ridgeimg==cv2.dilate(ridgeimg, kernel, iterations=1))\n",
    "plt.imshow(np.clip(sol,0,8)*(local_mean>0.5)+(img>20))\n",
    "print(\"nonmax\",np.sum(img>0),np.sum((img>0)*(sol>0)),np.sum(img>1),np.sum((img>1)*(sol>0)))\n",
    "print(np.sum((sol>0)*(local_mean>0.5)))\n",
    "\n",
    "cores=np.array(np.nonzero((sol>0)*(local_mean>0.5))).T+[0.5,0.5]\n",
    "\n",
    "from scipy.spatial import KDTree\n",
    "\n",
    "tree=KDTree(cores)\n",
    "T_px_f, px_extent = get_T_px_f_with_extent(rays) # extents and transform\n",
    "px_coords = get_raster_coords(rays0, T_px_f) # pixel coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18135937\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 126014 is out of bounds for axis 0 with size 126014",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-4494db87119b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmatchidx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpx_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m100000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdistance_upper_bound\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpx_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmatchidx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrays0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 126014 is out of bounds for axis 0 with size 126014"
     ]
    }
   ],
   "source": [
    "print(len(px_coords))\n",
    "\n",
    "d,matchidx = tree.query(px_coords[:,::-1][:100000], eps=2,distance_upper_bound=20) # can only query 10k pts per second :-(\n",
    "error = px_coords[:,::-1]-cores[matchidx]\n",
    "plt.figure()\n",
    "plt.hist(d[rays0['r']>0], )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cimshow(img):\n",
    "    dpi = 200 # Affects only tick and text sizes\n",
    "    margin = 0.05/(np.max(img.shape)/100) # (5% of the width/height of the figure...)\n",
    "    xpixels, ypixels = img.shape[1], img.shape[0]\n",
    "\n",
    "    # Make a figure big enough to accomodate an axis of xpixels by ypixels\n",
    "    # as well as the ticklabels, etc...\n",
    "    figsize = (1 + margin) * ypixels / dpi, (1 + margin) * xpixels / dpi\n",
    "\n",
    "    fig = plt.figure(figsize=figsize, dpi=dpi)\n",
    "    # Make the axis the right size...\n",
    "    ax = fig.add_axes([margin, margin, 1 - 2*margin, 1 - 2*margin])\n",
    "\n",
    "    ax.imshow(img, interpolation='none')\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8f158adc2194c6f9c093c0fa29cd8e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1676189\n",
      "1692366\n",
      "1698652\n",
      "1698657\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "160640ac83ef498fb719c9209e8983f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c12d4f2e2fa45aab2e43b4c601d747a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.10840484271694317\n",
      "3.861872264348472\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "import scipy.signal as signal\n",
    "\n",
    "inten=rays1['i'].reshape(-1,1081)\n",
    "plt.imshow(inten[:3000])\n",
    "\n",
    "#(img>2)*0.5+(np.clip(img/(local_mean+1),0,10)>.8)*0.5\n",
    "px_xy = get_raster_coords(rays2.reshape(-1,1081), T_px_f)\n",
    "bad=rays2['r'].reshape(-1,1081)<=0\n",
    "bad|=rays2['r'].reshape(-1,1081)>59\n",
    "print(np.sum(bad))\n",
    "bad|=px_xy[:,:,0]<0\n",
    "bad|=px_xy[:,:,1]<0\n",
    "# bad|=np.any(px_xy,axis=2)<0\n",
    "print(np.sum(bad))\n",
    "bad|=px_xy[:,:,0]>img.shape[1]\n",
    "print(np.sum(bad))\n",
    "bad|=px_xy[:,:,1]>img.shape[0]\n",
    "print(np.sum(bad))\n",
    "\n",
    "\n",
    "px_inds = tuple(np.rollaxis(np.floor(px_xy)[...,::-1].astype('int32'),-1))\n",
    "# print(px_inds.shape)\n",
    "flatinds = np.ravel_multi_index(px_inds,img.shape,mode='clip') # need to hand linearize to specify what to do with bad inds\n",
    "img[0,:]=0\n",
    "img[:,0]=0\n",
    "img_samp = img.ravel()[flatinds].reshape(-1,1081)\n",
    "mean_samp = local_mean.ravel()[flatinds].reshape(-1,1081)\n",
    "detr_samp = determiner.ravel()[flatinds].reshape(-1,1081)\n",
    "\n",
    "qual=np.clip(np.clip(img_samp-np.sqrt(img_samp),0,1e9)/(mean_samp+0.00001),0,10)\n",
    "metric = np.sum(qual>1.0,axis=1)/np.sum(bad==False,axis=1)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(metric)\n",
    "# plt.plot(np.sum(img_samp>10,axis=1))\n",
    "# plt.plot(metric,'.')\n",
    "\n",
    "cutoff=signal.order_filter(metric,np.ones(61),45)\n",
    "metric_flattened = metric-cutoff\n",
    "metric_flattened[:31]=metric_flattened[31]\n",
    "metric_flattened[-31:]=metric_flattened[-31]\n",
    "to_keep=metric_flattened\n",
    "to_keep=to_keep.reshape(-1,1)+np.zeros(1081)\n",
    "# print(to_keep[100:120,100:110])\n",
    "plt.plot(cutoff)\n",
    "# plt.plot(metric_flattened)\n",
    "plt.plot(np.sort(metric))\n",
    "\n",
    "# display filtered points\n",
    "imf1=np.zeros_like(img)\n",
    "# px_inds=np.array(px_inds)\n",
    "# np.maximum.at(imf.ravel(), flatinds, (metric-signal.order_filter(metric,np.ones(21),5)).ravel())\n",
    "# np.maximum.at(imf1.ravel(), flatinds[bad==False], -to_keep[bad==False])\n",
    "np.maximum.at(imf1.ravel(), flatinds[bad==False], to_keep[bad==False]+1)\n",
    "\n",
    "# np.maximum.at(imf1.ravel(), flatinds, to_keep==51)\n",
    "\n",
    "# np.add.at(imf1.ravel(), flatinds, (-1.0*(to_keep<0)))\n",
    "# np.add.at(imf1.ravel(), flatinds[bad==False], (1.0*(to_keep>0))[bad==False])\n",
    "# np.add.at(imf1.ravel(), flatinds, 1.0)\n",
    "\n",
    "im=np.clip(imf1,-100,1)+determiner*0.2\n",
    "plt.figure()\n",
    "plt.imshow(img/(local_mean+10))\n",
    "ce=5112\n",
    "plt.plot(px_xy[ce-1,:,0],px_xy[ce-1,:,1],'r.')\n",
    "plt.plot(px_xy[ce,:,0],px_xy[ce,:,1],'.')\n",
    "plt.plot(px_xy[ce+1,:,0],px_xy[ce+1,:,1],'.')\n",
    "\n",
    "# cimshow(im[:2000,:5000])\n",
    "print(metric_flattened[2198])\n",
    "print(np.max(img/(local_mean+10)))\n",
    "goodgrad=img/(local_mean+10)\n",
    "cv2.imwrite('tmp.png',goodgrad/3.8*256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rasterize hits by time\n",
    "\n",
    "%matplotlib widget\n",
    "colorang=rays0['ts']\n",
    "colorvec=2*np.vstack([np.cos(colorang),np.sin(colorang)])\n",
    "rv=[1,0.0]\n",
    "gv=[np.cos(2/3*np.pi),np.sin(2/3*np.pi)]\n",
    "bv=[np.cos(4/3*np.pi),np.sin(4/3*np.pi)]\n",
    "th=np.linspace(0,7)\n",
    "r=np.clip(np.dot(rv,colorvec),-1,1)*0.5+0.5\n",
    "g=np.clip(np.dot(gv,colorvec),-1,1)*0.5+0.5\n",
    "b=np.clip(np.dot(bv,colorvec),-1,1)*0.5+0.5\n",
    "# plt.plot(r)\n",
    "# plt.plot(g)\n",
    "# plt.plot(b)\n",
    "\n",
    "\n",
    "f_ept = np.vstack((rays0['ex'],rays0['ey'])).T.copy()\n",
    "bad0=rays0['r']<=0\n",
    "bad0|=rays0['r']>5\n",
    "\n",
    "im3=np.zeros(list(px_extent[::-1])+[3])\n",
    "px_coords = homp(T_px_f, f_ept)\n",
    "\n",
    "px_xyrgb = np.hstack((px_coords,r[:,np.newaxis],g[:,np.newaxis],b[:,np.newaxis]))\n",
    "bad = np.any(px_coords<0,axis=1) + np.any(px_coords >= px_extent,axis=1) + bad0\n",
    "del px_coords\n",
    "\n",
    "px_xyrgb = px_xyrgb[bad==0]\n",
    "px_xy=np.floor(px_xyrgb[:,0:2]).astype('int64')\n",
    "\n",
    "lindex = np.ravel_multi_index((px_xy[:,1],px_xy[:,0]), px_extent[::-1],mode='raise')\n",
    "\n",
    "im3.ravel()[3*lindex]=px_xyrgb[:,2]\n",
    "im3.ravel()[3*lindex+1]=px_xyrgb[:,3]\n",
    "im3.ravel()[3*lindex+2]=px_xyrgb[:,4]\n",
    "\n",
    "# plt.imshow(im3)\n",
    "import cv2\n",
    "cv2.imwrite('color_by_time.png',\n",
    "            (np.concatenate(\n",
    "                (im3[:,:,::-1]*255.9,255*np.ones(list(px_extent[::-1])+[1])),axis=2\n",
    "            )*255).astype('uint16'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "# dump every second of data into a file\n",
    "timedir='time_imgs'\n",
    "start_time = np.min(rays0['ts'])\n",
    "duration = np.ceil(np.max(rays0['ts'])-np.min(rays0['ts']))\n",
    "import cv2\n",
    "\n",
    "def produce_basic_coords(rays0):\n",
    "    import cv2\n",
    "    T_px_f, px_extent = get_T_px_f_with_extent(rays) # extents and transform\n",
    "    px_coords = get_raster_coords(rays0, T_px_f) # pixel coordinates\n",
    "    img = np.zeros(px_extent[::-1]) #image to fill\n",
    "\n",
    "    # clip out bad rays\n",
    "    bad0=rays0['r']<=0\n",
    "    bad0|=rays0['r']>59\n",
    "    # clip out bad pixels\n",
    "    bad = np.any(px_coords<0,axis=1) + np.any(px_coords >= px_extent,axis=1) + bad0\n",
    "    px_coords = px_coords[bad==0,:]\n",
    "    return px_coords\n",
    "\n",
    "img = np.zeros(px_extent[::-1],np.uint8) #image to fill\n",
    "    \n",
    "   \n",
    "for i in range(int(duration)):\n",
    "    name = '{}.png'.format(i)\n",
    "    select=rays0['ts']>=start_time+i\n",
    "    select&= rays0['ts']<start_time+i+1\n",
    "    px_coords = produce_basic_coords(rays0[select])\n",
    "    np.maximum.at(img, tuple(np.floor(px_coords.T)[::-1].astype('int32')), 255)\n",
    "    print(name)\n",
    "    cv2.imwrite(name, img)\n",
    "    np.minimum.at(img, tuple(np.floor(px_coords.T)[::-1].astype('int32')), 0)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# BELOW HERE IS ALL EXPERIMENTAL STUFF\n",
    "# TODO Cell: Display only the points that have a closer start point than any other start point \n",
    "# i.e. the point which is when the robot was closest to that spot.\n",
    "\n",
    "\n",
    "%matplotlib widget\n",
    "#as a simple approximation, lets see if using a certain offset foward and backward in time is okay\n",
    "offset = 1000\n",
    "\n",
    "def dist(pt,pts):\n",
    "    return np.sqrt(np.sum((pt-pts)**2,axis=1))\n",
    "\n",
    "sx = rays1['sx']#use rays only from one laser\n",
    "sy = rays1['sy']\n",
    "spt= np.vstack((sx,sy)).T\n",
    "\n",
    "ex = rays1['ex']\n",
    "ey = rays1['ey']\n",
    "ept=np.vstack((ex,ey)).T\n",
    "\n",
    "#remove far rays\n",
    "rays11=rays1[dist(ept,spt)<10]\n",
    "scannum11=scannum1[dist(ept,spt)<10]\n",
    "\n",
    "\n",
    "\n",
    "#reunpack the ray that remain\n",
    "\n",
    "sx = rays11['sx']#use rays only from one laser\n",
    "sy = rays11['sy']\n",
    "spt= np.vstack((sx,sy)).T\n",
    "\n",
    "ex = rays11['ex']\n",
    "ey = rays11['ey']\n",
    "ept=np.vstack((ex,ey)).T\n",
    "\n",
    "# # check that we are closer that\n",
    "# fsx = sx[offset:]\n",
    "# fsy = sy[offset:]\n",
    "# bsx = sx[:-offset]\n",
    "# bsy = sy[:-offset]\n",
    "# cex = ex[offset//2:-offset//2]\n",
    "# cey = ex[offset//2:-offset//2]\n",
    "\n",
    "\n",
    "rsx=raysr['sx']\n",
    "rsy= raysr['sy']\n",
    "rpt= np.vstack((rsx,rsy)).T\n",
    "\n",
    "\n",
    "probes = np.arange(0,len(sx),1000) # some points where we can get the true answer\n",
    "# probes = [5025083]\n",
    "\n",
    "def dist(pt,pts):\n",
    "    return np.sqrt(np.sum((pt-pts)**2,axis=1))\n",
    "sols=[] #the ones with a good closest approach\n",
    "bsols=[] # the ones that never seem to have a good closest approach\n",
    "stats=[]\n",
    "for p in probes:\n",
    "    pex=ex[p]\n",
    "    pey=ey[p]\n",
    "    ppt=np.array([pex,pey])\n",
    "    d=dist(ppt, rpt)\n",
    "    d=dist(ppt, spt)\n",
    "    timecost=np.abs(scannum11[p]-scannum11)/300\n",
    "#     plt.clf()\n",
    "#     plt.plot(timecost)\n",
    "#     plt.plot(d)\n",
    "#     plt.plot(d+timecost)\n",
    "    d3=dist(ppt,ept)#distance to original endpoint\n",
    "\n",
    "#     break\n",
    "    closest = np.argmin(d+timecost)\n",
    "#     plt.plot(d)\n",
    "\n",
    "#     print(closest,pex,pey)\n",
    "    cpt=spt[closest]\n",
    "    d2=dist(cpt,spt)#distance to best center\n",
    "#     plt.plot(d2)\n",
    "#     d3=dist(ppt,ept)#distance to original endpoint\n",
    "#     plt.plot(d3+d2)\n",
    "    cost=d3+d2\n",
    "    remin=np.argmin(cost)\n",
    "    print(p,remin,cost[remin])\n",
    "    if cost[remin]<0.1:\n",
    "        sols.append(remin)\n",
    "    else:\n",
    "#         plt.plot(d)\n",
    "#         plt.plot(d3)\n",
    "        bsols.append(remin)\n",
    "#         break\n",
    "    stats.append(cost[remin])\n",
    "plt.plot(ex[sols],ey[sols],\"b.\")\n",
    "plt.plot(ex[bsols],ey[bsols],\"r.\")\n",
    "\n",
    "# rsx[closest],rsy[closest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "%matplotlib widget\n",
    "plt.close('all')\n",
    "\n",
    "# plt.plot(rays11['ex'],rays11['ey'],'b,')\n",
    "\n",
    "# tpti = 1000000\n",
    "# tpt=np.array([rays11['ex'][tpti],rays11['ey'][tpti]])\n",
    "# stpt=np.array([rays11['sx'][tpti],rays11['sy'][tpti]])\n",
    "# # plt.plot(raysr['sx'][::2],raysr['sy'][::2],'r.-')\n",
    "# plt.plot(raysr['sx'][1::2],raysr['sy'][1::2],'k.-')\n",
    "\n",
    "# plt.plot(rays11['ex'][tpti],rays11['ey'][tpti],'r.')\n",
    "# plt.plot(rays11['sx'][tpti],rays11['sy'][tpti],'g.')\n",
    "\n",
    "# print(len(raysr),np.max(scannum11))\n",
    "# dr=dist(stpt, rpt)\n",
    "# iraysr = np.argmin(dr)\n",
    "# print(iraysr,scannum11[tpti])\n",
    "# plt.figure()\n",
    "# plt.plot(dr)\n",
    "\n",
    "# plt.figure()\n",
    "dr=dist(spt,ept)\n",
    "plt.plot(dr,'-')\n",
    "\n",
    "def advance(line, marker, mask):\n",
    "    left=line[:-1]\n",
    "    line[1:]=np.minimum(left,line[1:])\n",
    "    line[1:]=np.maximum(line[1:],mask[1:])\n",
    "#     left[left<line[1:]]left[left<line[1:]]\n",
    "    return line\n",
    "# advance(dr,dr,dr-0.1)\n",
    "line=dr.copy()\n",
    "marker=dr\n",
    "mask=dr-0.05\n",
    "for i in range(100):\n",
    "    line=advance(line, marker, mask)\n",
    "line=line[::-1]\n",
    "marker = marker[::-1]\n",
    "mask = mask[::-1]\n",
    "for i in range(100):\n",
    "    line=advance(line, marker, mask)\n",
    "line=line[::-1]\n",
    "marker = marker[::-1]\n",
    "mask = mask[::-1]\n",
    "plt.plot(line,'-')\n",
    "localmins = (line==dr)\n",
    "plt.figure()\n",
    "plt.plot(rays11['ex'],rays11['ey'],'b,')\n",
    "plt.plot(rays11['ex'][localmins],rays11['ey'][localmins],'r.')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "plt.plot(ex[sols],ey[sols],\"b.\")\n",
    "plt.plot(ex[bsols],ey[bsols],\"r.\")\n",
    "plt.plot(ex[probes],ey[probes],\"g,\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "plt.hist(stats,500)"
   ]
  }
 ],
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