jointlikelihood.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the most distinguished parameter that distinghuishs the ASD vs control.\n",
    "\n",
    "define a midpoint\n",
    "vary by d1,d2, where d1,d2 is delta theta in the interested direction\n",
    "calcualte the logll for ASD and healthy seperatly, add together.\n",
    "final result is a heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from cmaes import CMA\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pickle\n",
    "import numpy as np\n",
    "import torch\n",
    "import heapq\n",
    "from torch.distributions.multivariate_normal import MultivariateNormal\n",
    "from matplotlib import pyplot as plt\n",
    "import time\n",
    "from stable_baselines3 import TD3\n",
    "torch.manual_seed(0)\n",
    "from numpy import linspace, pi\n",
    "from InverseFuncs import *\n",
    "from monkey_functions import *\n",
    "from firefly_task import ffacc_real\n",
    "from env_config import Config\n",
    "# from cma_mpi_helper import run\n",
    "import ray\n",
    "from pathlib import Path\n",
    "arg = Config()\n",
    "import os\n",
    "from timeit import default_timer as timer\n",
    "from plot_ult import *\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "\n",
    "import configparser\n",
    "config = configparser.ConfigParser()\n",
    "config.read_file(open('privateconfig'))\n",
    "datafolder=config['Datafolder']['data']\n",
    "datapath=Path(datafolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# load agent and task \n",
    "env=ffacc_real.FireFlyPaper(arg)\n",
    "env.episode_len=50\n",
    "env.debug=1\n",
    "env.terminal_vel=0.2\n",
    "phi=torch.tensor([[1],\n",
    "            [pi/2],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.13],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "    ])\n",
    "agent_=TD3.load('trained_agent/paper.zip')\n",
    "agent=agent_.actor.mu.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/yc/Documents/lab_data/human/fixragroup\n",
      "using ind:  60 final logll :  10.684259755270821\n",
      "/Users/yc/Documents/lab_data/human/clusterpaperhgroup\n",
      "using ind:  60 final logll :  8.739409999999998\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# define the midpoint between ASD and healthy \n",
    "logls=[datapath/'human/fixragroup',datapath/'human/clusterpaperhgroup']\n",
    "monkeynames=['ASD', 'Ctrl' ]\n",
    "\n",
    "mus,covs,errs=[],[],[]\n",
    "thetas=[]\n",
    "for inv in logls:\n",
    "    finaltheta,finalcov, err=process_inv(inv,ind=60,removegr=False)\n",
    "    mus.append(np.array(finaltheta).reshape(-1))\n",
    "    covs.append(finalcov)\n",
    "    errs.append(err)\n",
    "    thetas.append(finaltheta)\n",
    "thetas=torch.tensor(mus)\n",
    "theta_init=thetas[0]\n",
    "theta_final=thetas[1]\n",
    "\n",
    "# load theta distribution\n",
    "alltag=[]\n",
    "alltheta=[]\n",
    "loglls=[]\n",
    "with open(logls[0],'rb') as f:\n",
    "    log=pickle.load(f)\n",
    "    res=[l[2] for l in log[19:99]]\n",
    "    for r in res:\n",
    "        for point in r:\n",
    "            alltheta.append(point[0]) # theta\n",
    "            loglls.append(point[1])\n",
    "            alltag.append(0)\n",
    "with open(logls[1],'rb') as f:\n",
    "    log=pickle.load(f)\n",
    "    res=[l[2] for l in log[19:99]]\n",
    "    for r in res:\n",
    "        for point in r:\n",
    "            alltheta.append(point[0]) # theta\n",
    "            loglls.append(point[1])\n",
    "            alltag.append(1)\n",
    "\n",
    "alltheta=np.array(alltheta)\n",
    "alltag=np.array(alltag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute svm\n",
    "clf = svm.SVC(kernel=\"linear\", C=1000)\n",
    "clf.fit(alltheta, alltag)\n",
    "w = clf.coef_[0] # the normal vector\n",
    "midpoint=(mus[0]+mus[1])/2\n",
    "lb=np.array([0,0,0,0,0,0,0.129,0,0,0,0])\n",
    "hb=np.array([1,2,1,1,1,1,0.131,2,2,1,1])\n",
    "\n",
    "theta_init=np.min(midpoint-lb)/w[np.argmin(hb-midpoint)]*-w*3+midpoint\n",
    "theta_final=np.min(midpoint-lb)/w[np.argmin(midpoint-lb)]*w*3+midpoint\n",
    "theta_init,theta_final=torch.tensor(theta_init).view(-1,1).float(),torch.tensor(theta_final).view(-1,1).float()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "name": "stdout",
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      " 3545 3556 3571 3573 3576 3596 3600 3604 3624 3625 3626 3639]\n",
      "[   4    5    7    8   11   12   15   19   20   21   22   25   29   39\n",
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     "text": [
      "2024-07-18 14:28:04,391\tINFO worker.py:1788 -- Started a local Ray instance.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\ncomplexity:\\ngridreso**2 *(ntrialT * nsample) * 2\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to eval the logll of the delta theta\n",
    "gridreso=11\n",
    "num_sample=200\n",
    "# load the data\n",
    "thisdatapath=datapath/(\"human/hgroup\")\n",
    "with open(thisdatapath, 'rb') as f:\n",
    "    hstates, hactions, htasks = pickle.load(f)\n",
    "thisdatapath=Path(datapath/\"human/agroup\")\n",
    "with open(thisdatapath, 'rb') as f:\n",
    "    astates, aactions, atasks = pickle.load(f)\n",
    "# select the side tasks\n",
    "hstates, hactions, htasks=select_tar(hstates, hactions, htasks)\n",
    "astates, aactions, atasks=select_tar(astates, aactions, atasks)\n",
    "# randomly select some data\n",
    "hstates, hactions, htasks=hstates[:num_sample], hactions[:num_sample], htasks[:num_sample].astype('float32')\n",
    "astates, aactions, atasks=astates[:num_sample], aactions[:num_sample], atasks[:num_sample].astype('float32')\n",
    "\n",
    "ray.init(log_to_driver=False,ignore_reinit_error=True)\n",
    "@ray.remote\n",
    "def getlogll(x):\n",
    "    atheta,htheta=x[0],x[1]\n",
    "    atheta.clamp_(1e-3,3)\n",
    "    htheta.clamp_(1e-3,3)\n",
    "    with torch.no_grad():\n",
    "        lla=monkeyloss_(agent, aactions, atasks, phi, atheta, env, action_var=0.001,num_iteration=1, states=astates, samples=5,gpu=False,debug=False)\n",
    "        llh=monkeyloss_(agent, hactions, htasks, phi, htheta, env, action_var=0.001,num_iteration=1, states=hstates, samples=5,gpu=False,debug=False)\n",
    "        return -lla-llh\n",
    "    \n",
    "'''\n",
    "complexity:\n",
    "gridreso**2 *(ntrialT * nsample) * 2\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# vary noise and gain -------------------------------\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[5]=1 # noise\n",
    "dy[1]=1 # gain\n",
    "X,Y=np.linspace(-0.7,0.7,gridreso), np.linspace(-0.45,0.45,gridreso)\n",
    "paramls=[]\n",
    "# Z=np.zeros((gridreso,gridreso))\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        atheta=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        htheta=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        # eval the logll\n",
    "        # Z[i,j]=atheta,htheta\n",
    "        paramls.append([atheta,htheta])\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZnoisecost3finer19', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# vary noise and cost -------------------------------\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[5]=1 # noise\n",
    "dy[8]=1 # cost\n",
    "X,Y=np.linspace(-0.7,0.7,gridreso), np.linspace(-0.45,0.45,gridreso)\n",
    "paramls=[]\n",
    "# Z=np.zeros((gridreso,gridreso))\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        atheta=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        htheta=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        # eval the logll\n",
    "        # Z[i,j]=atheta,htheta\n",
    "        paramls.append([atheta,htheta])\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZnoisecost3finer19', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(datapath/'human/jointlikelihood/distinguishparamZnoisecost3finer19', 'rb') as f:\n",
    "    paramls,Z= pickle.load(f)\n",
    "formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "truedelta=mus[1]-mus[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.filters import gaussian_filter\n",
    "sigma = 0.5\n",
    "formatedZ = gaussian_filter(formatedZ, sigma)\n",
    "\n",
    "with initiate_plot(5,5,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    # im=ax.imshow(formatedZ[1:-1],origin='lower', extent=(X[0],X[-2],Y[2],Y[-2]),aspect=(X[-2]-X[0])/(Y[-2]-Y[0]),vmin=-79, vmax=-73, cmap='jet')    \n",
    "    im=ax.contourf(formatedZ[1:-1],origin='lower', extent=(X[0],X[-2],Y[2],Y[-2]),aspect=(X[-2]-X[0])/(Y[-2]-Y[0]),vmin=-79, vmax=-73, cmap='Greys')    \n",
    "    ax.set_aspect((X[-2]-X[0])/(Y[-2]-Y[0]))\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes(\"right\", size=\"3%\", pad=0.005)\n",
    "    plt.colorbar(im, cax=cax,label='joint log likelihood')\n",
    "    ax.scatter(0,0,label='midpoint', color='r',s=99) # midpoint, 0,0\n",
    "    ax.set_xlabel('delta noise')\n",
    "    ax.set_ylabel('delta cost')\n",
    "    ax.scatter(truedelta[5]/2,truedelta[8]/2,label='inferred delta', color='green',s=99) # inferred delta\n",
    "    quickleg(ax)\n",
    "    quickspine(ax)\n",
    "    # quicksave('joint likelihood noise and cost')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# prediciton over obs 1d ---------------------------------\n",
    "# vary obs noise, while try to keep simlar uncertainty and same bias\n",
    "'''\n",
    "constaints:\n",
    "1, total uncertainty same. po/(p+o)=c\n",
    "2, biased degree same. (1-k)*g, \n",
    "where k=p/(p+o), p and o are uncertainty variance \n",
    "'''\n",
    "def totalbias(g,p,o):\n",
    "    return g*(p**2/(p**2+o**2))\n",
    "def totalgivenop(a,b):\n",
    "    return (a**2)*(b**2)/((a**2)+(b**2))\n",
    "def pogiventotal(a, c):\n",
    "    return ((a**2)*c/((a**2)-c))**0.5\n",
    "\n",
    "\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[5]=0.3 # noise\n",
    "# dy[8]=0.45 # \n",
    "auncertainty=mus[0][5]**2*mus[0][3]**2/(mus[0][5]**2+mus[0][3]**2)\n",
    "huncertainty=(mus[1][5]**2*mus[1][3]**2)/(mus[1][5]**2+mus[1][3]**2)\n",
    "abias=totalbias(mus[0][1],mus[0][3],mus[0][5])\n",
    "hbias=totalbias(mus[1][1],mus[1][3],mus[1][5])\n",
    "\n",
    "c=totalgivenop(mus[0][5],mus[0][3])\n",
    "# pogiventotal(mus[0][5],auncertainty)\n",
    "\n",
    "X,Y=np.linspace(-1,1,gridreso), np.linspace(-1,1,gridreso)\n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        # calcualte the dy, keep uncertainty same\n",
    "        anewparam_=torch.tensor(-dx*X[i]+midpoint).float().view(-1,1)\n",
    "        anewpnoise=pogiventotal(anewparam_[5],auncertainty)\n",
    "        anewk = anewpnoise**2 / (anewparam_[5]**2+anewpnoise**2)\n",
    "        anewg=abias/anewk\n",
    "        anewparam_[3]=anewpnoise\n",
    "        anewparam_[1]=anewg\n",
    "\n",
    "\n",
    "        hnewparam_=torch.tensor(dx*X[i]+midpoint).float().view(-1,1)\n",
    "        hnewpnoise=pogiventotal(hnewparam_[5],huncertainty)\n",
    "        hnewk = hnewpnoise**2 / (hnewparam_[5]**2+hnewpnoise**2)\n",
    "        hnewg=hbias/hnewk\n",
    "        hnewparam_[3]=hnewpnoise\n",
    "        hnewparam_[1]=hnewg\n",
    "\n",
    "        atheta=anewparam_.clone()\n",
    "        htheta=hnewparam_.clone()\n",
    "        paramls.append([atheta,htheta])\n",
    "\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZobspre', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "\n",
    "# # vary obs, while keep uncertainty and kalman gain the same.\n",
    "# with open(datapath/'human/distinguishparamZobspre', 'rb') as f:\n",
    "#     paramls,Z= pickle.load(f)\n",
    "# formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "# truedelta=mus[1]-mus[0]\n",
    "# X=np.linspace(-1,1,gridreso)\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     # ax.errorbar(X,np.mean(formatedZ,axis=0),yerr=np.array([np.mean(formatedZ,axis=0)-np.percentile(formatedZ,20,axis=0),np.percentile(formatedZ,80,axis=0)-np.mean(formatedZ,axis=0)]))\n",
    "#     ax.errorbar(X,np.mean(formatedZ,axis=0),yerr=np.std(formatedZ,axis=0))\n",
    "#     ax.set_xlabel('delta observation')\n",
    "#     ax.set_ylabel('log likelihood')\n",
    "#     ax.vlines(truedelta[5],np.min(formatedZ),np.max(formatedZ),'orange',label='inferred delta') # inferred delta\n",
    "#     ax.vlines(0,np.min(formatedZ),np.max(formatedZ),label='zero') # inferred delta\n",
    "#     ax.legend()\n",
    "#     quickspine(ax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# vary two noise -----------------------------------------------\n",
    "'''\n",
    "        # vary process noise and obs noise.\n",
    "        # calcualte the gains while keep bias same\n",
    "'''\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[5]=1\n",
    "dy[3]=1\n",
    "\n",
    "auncertainty=mus[0][5]**2*mus[0][3]**2/(mus[0][5]**2+mus[0][3]**2)\n",
    "huncertainty=(mus[1][5]**2*mus[1][3]**2)/(mus[1][5]**2+mus[1][3]**2)\n",
    "abias=totalbias(mus[0][1],mus[0][3],mus[0][5])\n",
    "hbias=totalbias(mus[1][1],mus[1][3],mus[1][5])\n",
    "\n",
    "c=totalgivenop(mus[0][5],mus[0][3])\n",
    "pogiventotal(mus[0][5],auncertainty)\n",
    "\n",
    "X,Y=np.linspace(-0.4,0.4,gridreso),np.linspace(-0.2,0.2,gridreso) # on and pn\n",
    "paramls=[]\n",
    "z=np.zeros((gridreso,gridreso))\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        # vary process noise and obs noise.\n",
    "        # calcualte the gains while keep bias same\n",
    "        anewparam_=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        hnewparam_=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "\n",
    "        anewpnoise=anewparam_[3]\n",
    "        anewk = anewpnoise**2 / (anewparam_[5]**2+anewpnoise**2)\n",
    "        anewg=abias/anewk\n",
    "        anewparam_[1]=anewg\n",
    "\n",
    "\n",
    "        hnewpnoise=hnewparam_[3]\n",
    "        hnewk = hnewpnoise**2 / (hnewparam_[5]**2+hnewpnoise**2)\n",
    "        hnewg=hbias/hnewk\n",
    "        hnewparam_[1]=hnewg\n",
    "\n",
    "        atheta=anewparam_.clone()\n",
    "        htheta=hnewparam_.clone()\n",
    "        paramls.append([atheta,htheta])\n",
    "        z[i,j]=(atheta-htheta)[5]\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZtwonoisessmaller2finer19', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# vary obs and process noise, while keep bias the same (so we vary gains), result in vary uncertainties, smaller range\n",
    "with open(datapath/'human/jointlikelihood/2noise2', 'rb') as f:\n",
    "    paramls,Z= pickle.load(f)\n",
    "formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "truedelta=mus[1]-mus[0]\n",
    "\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     im=ax.imshow(formatedZ,origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),aspect='auto',vmin=-88, vmax=-73)\n",
    "#     # im=ax.imshow(formatedZ[:-1,:],origin='lower', extent=(X[0],X[-2],Y[0],Y[-1]),aspect='auto',vmin=-103, vmax=-73)\n",
    "#     # im=ax.contourf(formatedZ,origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),vmin=-103, vmax=-73)\n",
    "#     ax.set_aspect('equal')\n",
    "#     plt.colorbar(im,label='joint log likelihood') \n",
    "#     ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "#     ax.set_xlabel('delta obs noise')\n",
    "#     ax.set_ylabel('delta pro noise')\n",
    "#     ax.scatter(truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "#     # ax.legend()\n",
    "#     quickspine(ax)\n",
    "\n",
    "def center_rotation(mat):\n",
    "    new=np.zeros_like(mat)\n",
    "    m,n=mat.shape\n",
    "    for i in range(mat.shape[0]):\n",
    "        for j in range(mat.shape[1]):\n",
    "            new[i,j]=mat[m-1-i, n-1-j]\n",
    "    return new\n",
    "sigma = 0.5     \n",
    "formatedZ=center_rotation(gaussian_filter(formatedZ, sigma))\n",
    "\n",
    "with initiate_plot(3,3,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    im=ax.contourf(formatedZ,origin='lower', extent=(X[0],X[-1],Y[-1],Y[0]),cmap='Greys')\n",
    "    # ax.set_aspect('equal')\n",
    "    plt.colorbar(im,label='joint log likelihood') \n",
    "    ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "    ax.set_xlabel('delta obs noise')\n",
    "    ax.set_ylabel('delta prediction')\n",
    "    ax.scatter(-truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "    # ax.legend()\n",
    "    # plt.plot([theta_init[5], theta_final[5]],[theta_init[3],theta_final[3]])\n",
    "    # plt.plot(ax.get_xlim(),-np.array(ax.get_xlim())*(theta_init[3]-theta_final[3]/(theta_init[5]-theta_final[5])).item())\n",
    "    # plt.plot(ax.get_xlim(),np.array(ax.get_xlim())*-1)\n",
    "    # plt.plot(theta_init[[5,3]],theta_final[[5,3]])\n",
    "    quickspine(ax)\n",
    "    # quicksave('vary 2 noise joint')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# kalman gain vs uncertainty ------------------------------------------\n",
    "\n",
    "def getk(midpoint):\n",
    "    return midpoint[5]**2/(midpoint[3]**2+midpoint[5]**2)\n",
    "\n",
    "# ak=getk(mus[0])\n",
    "# hk=getk(mus[1])\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[5]=1\n",
    "dy[3]=1\n",
    "\n",
    "X,Y=np.linspace(-0.2,0.2,gridreso),np.linspace(-0.7,0.7,gridreso) # k, uncertainty\n",
    "\n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "\n",
    "        anewparam_=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        hnewparam_=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "\n",
    "        anewpnoise=anewparam_[3]\n",
    "        anewk = anewpnoise**2 / (anewparam_[5]**2+anewpnoise**2)\n",
    "        anewg=abias/anewk\n",
    "        anewparam_[1]=anewg\n",
    "\n",
    "\n",
    "        hnewpnoise=hnewparam_[3]\n",
    "        hnewk = hnewpnoise**2 / (hnewparam_[5]**2+hnewpnoise**2)\n",
    "        hnewg=hbias/hnewk\n",
    "        hnewparam_[1]=hnewg\n",
    "\n",
    "        atheta=anewparam_.clone()\n",
    "        htheta=hnewparam_.clone()\n",
    "        paramls.append([atheta,htheta])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# bias vs uncertainty -----------------------------------------------------------\n",
    "# vary obs noise and gain\n",
    "\n",
    "'''\n",
    "to vary the uncertainty, we vary po/(p+o)\n",
    "the range is 0, 1/2\n",
    "to vary the bias, we vary (1-k)*g, \n",
    "    where k=p/(p+o), thus pg/(p+o)\n",
    "this is equvalent to vary o and g, while solve for p that fit the wantted uncertainty.\n",
    "'''\n",
    "\n",
    "X,Y=np.linspace(-0.7,0.7,gridreso),np.linspace(-1,1,gridreso) # o, gain\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[5]=1\n",
    "dy[1]=1\n",
    "\n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        anewparam_=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        hnewparam_=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        # print(anewparam_[1],anewparam_[5])\n",
    "        # print(hnewparam_[1],hnewparam_[5])\n",
    "        paramls.append([anewparam_,hnewparam_])\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZgainonoisefiner19', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# vary gain and noise (vary bias vs uncertainty)\n",
    "with open(datapath/'human/jointlikelihood/distinguishparamZgainonoisefiner19', 'rb') as f:\n",
    "    paramls,Z= pickle.load(f)\n",
    "formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "truedelta=mus[1]-mus[0]\n",
    "X,Y=np.linspace(-0.7,0.7,gridreso),np.linspace(-1,1,gridreso) # o, gain\n",
    "\n",
    "sigma = 0.5\n",
    "formatedZ = gaussian_filter(formatedZ, sigma)\n",
    "\n",
    "with initiate_plot(3,3,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    # im=ax.imshow(formatedZ,origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]), aspect='auto')\n",
    "    # im=ax.imshow(formatedZ,origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),aspect='auto',vmin=-84, vmax=-73)\n",
    "    im=ax.contourf(-np.log(-formatedZ[2:-3]),origin='lower', extent=(X[0],X[-1],Y[2],Y[-3]))\n",
    "    ax.set_aspect('equal')\n",
    "    # ax.set_ylim(-0.5,0.5)\n",
    "    plt.colorbar(im,label='joint log likelihood') \n",
    "    ax.scatter(0,0,label='delta zero') # midpoint, 0,0\n",
    "    ax.set_xlabel('delta obs noise')\n",
    "    ax.set_ylabel('delta assumed control gain')\n",
    "    ax.scatter(truedelta[5]/2,truedelta[1]/2,label='inferred delta') # inferred delta\n",
    "    ax.legend(loc='center left', bbox_to_anchor=(2, 0.5))\n",
    "    quickspine(ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "with open(datapath/'human/jointlikelihood/noisegain', 'rb') as f:\n",
    "    paramls,Z= pickle.load(f)\n",
    "Z=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5))\n",
    "truedelta=mus[1]-mus[0]\n",
    "X,Y=np.linspace(-0.7,0.7,gridreso),np.linspace(-0.3,0.3,gridreso) # o, gain\n",
    "sigma = 0.5\n",
    "formatedZ=center_rotation(gaussian_filter(Z, sigma))\n",
    "plt.contourf(formatedZ, origin='image',)\n",
    "\n",
    "with initiate_plot(3,3,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    im=ax.contourf((formatedZ),origin='image', extent=(X[0],X[-1],Y[0],Y[-1]),cmap='Greys')\n",
    "    # ax.set_aspect('equal')\n",
    "    plt.colorbar(im,label='joint log likelihood') \n",
    "    ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "    ax.set_xlabel('delta obs noise')\n",
    "    ax.set_ylabel('delta gain')\n",
    "    ax.scatter(-truedelta[5]/2,-truedelta[1]/2,label='inferred delta') # inferred delta\n",
    "    ax.set_ylim(ax.get_ylim())\n",
    "    plt.plot(ax.get_xlim(),np.array(ax.get_xlim())*(theta_init[1]-theta_final[1]/(theta_init[5]-theta_final[5])).item())\n",
    "    quickspine(ax)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     # im=ax.imshow(formatedZ[2:-2],origin='lower', extent=(X[0],X[-1],Y[2],Y[-3]), aspect='auto')\n",
    "#     im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0],X[-1],Y[0],Y[-3]),aspect='auto',vmin=-103, vmax=-73)\n",
    "#     # im=ax.contourf(formatedZ[2:-2],origin='lower', extent=(X[0],X[-1],Y[2],Y[-3]),vmin=-103, vmax=-73)\n",
    "#     ax.set_aspect('equal')\n",
    "#     plt.colorbar(im,label='joint log likelihood') \n",
    "#     ax.scatter(0,0,label='delta zero') # midpoint, 0,0\n",
    "#     ax.set_xlabel('delta uncertainty')\n",
    "#     ax.set_ylabel('delta prediction bias')\n",
    "#     ax.scatter(truedelta[5]/2,truedelta[1]/2,label='inferred delta') # inferred delta\n",
    "#     ax.legend(loc='center left', bbox_to_anchor=(2, 0.5))\n",
    "#     quickspine(ax)\n",
    "\n",
    "# ks,ss=transform2bs(paramls)\n",
    "# plt.tricontourf(ks,ss,formatedZ.flatten(),3)\n",
    "\n",
    "# # plot the bias instead of gain.\n",
    "# # bias=assumed gain * (1-kalman gain)\n",
    "\n",
    "# bs,ss=transform2bs(paramls)\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     im=ax.imshow(formatedZ,origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]), aspect='auto')\n",
    "#     ax.set_aspect('equal')\n",
    "#     plt.colorbar(im,label='joint log likelihood') \n",
    "#     ax.scatter(0,0,label='delta zero') # midpoint, 0,0\n",
    "#     ax.set_xlabel('delta obs noise')\n",
    "#     ax.set_ylabel('delta assumed control gain')\n",
    "#     ax.scatter(truedelta[5]/2,truedelta[1]/2,label='inferred delta') # inferred delta\n",
    "#     ax.legend(loc='center left', bbox_to_anchor=(2, 0.5))\n",
    "#     quickspine(ax)\n",
    "\n",
    "# plt.tricontourf(bs,ss,formatedZ.flatten(),3)\n",
    "\n",
    "# transform2bs([thetas.view(2,-1,1)])\n",
    "# thetas.view(2,-1,1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "'''\n",
    "vary gain, and vary some other param. vary cost here.\n",
    "'''\n",
    "# vary gain and cost, show bias matters ------------------------\n",
    "\n",
    "gridreso=17\n",
    "X,Y=np.linspace(-0.45,0.45,gridreso),np.linspace(-1,1,gridreso) # cost, gain\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[8]=1 # cost\n",
    "dy[1]=1 # gain\n",
    "\n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        anewparam_=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        hnewparam_=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        # print(anewparam_[1],anewparam_[5])\n",
    "        # print(hnewparam_[1],hnewparam_[5])\n",
    "        paramls.append([anewparam_,hnewparam_])\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZcostgain2', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# with open('distinguishparamZcostgain2', 'rb+') as f:\n",
    "#     paramls,Z= pickle.load(f)\n",
    "# formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "# truedelta=mus[1]-mus[0]\n",
    "\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     # im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),aspect='auto')\n",
    "#     im=ax.imshow(formatedZ[:,1:-1],origin='lower', extent=(X[0],X[-1],Y[1],Y[-2]),aspect='auto',vmin=-103, vmax=-73)\n",
    "#     # im=ax.contourf(formatedZ,origin='lower', extent=(X[2],X[-1],Y[0],Y[-1]),vmin=-103, vmax=-73)\n",
    "#     ax.set_aspect('equal')\n",
    "#     plt.colorbar(im,label='joint log likelihood') \n",
    "#     ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "#     ax.set_xlabel('delta w cost')\n",
    "#     ax.set_ylabel('delta assumed control gain')\n",
    "#     ax.scatter(truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "#     # ax.legend()\n",
    "#     quickspine(ax)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# show v does not matter\n",
    "# vary v and w cost ----------------------------------------------------------------------\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[8]=1 \n",
    "dy[7]=1 \n",
    "X,Y=np.linspace(-0.45,0.3,gridreso), np.linspace(-0.9,0.9,gridreso)\n",
    "paramls=[]\n",
    "# Z=np.zeros((gridreso,gridreso))\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        atheta=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        htheta=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        # eval the logll\n",
    "        # Z[i,j]=atheta,htheta\n",
    "        paramls.append([atheta,htheta])\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('distinguishparamZvwcost2', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "# [p[0][8] for p in paramls]\n",
    "# [p[1][8] for p in paramls]\n",
    "\n",
    "# # vary v and w cost\n",
    "# with open('distinguishparamZvwcost2', 'rb+') as f:\n",
    "#     paramls,Z= pickle.load(f)\n",
    "# formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "# truedelta=mus[1]-mus[0]\n",
    "\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     # im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),aspect='auto')\n",
    "#     im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),aspect='equal',vmin=-103, vmax=-73)\n",
    "#     plt.colorbar(im,label='joint log likelihood') \n",
    "#     ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "#     ax.set_xlabel('delta w cost')\n",
    "#     ax.set_ylabel('delta v cost')\n",
    "#     ax.scatter(truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "#     # ax.legend()\n",
    "#     quickspine(ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# vary v and w gain ----------------------------------------------------------------------\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[1]=1 \n",
    "dy[0]=1 \n",
    "X,Y=np.linspace(-1,1,gridreso) , np.linspace(-1,1,gridreso) \n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        atheta=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        htheta=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        paramls.append([atheta,htheta])\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "\n",
    "# with open('distinguishparamZvwgain', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z:/bruno_pert/cmafull_b_pert\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Z:/bruno_pert/cmafull_b_pert'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# load the data\u001b[39;00m\n\u001b[1;32m      9\u001b[0m num_sample\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m\n\u001b[0;32m---> 10\u001b[0m theta,_,_\u001b[38;5;241m=\u001b[39m\u001b[43mprocess_inv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mZ:/bruno_pert/cmafull_b_pert\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mremovegr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZ:/victor_pert/packed\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m     12\u001b[0m     df \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n",
      "File \u001b[0;32m~/repo/firefly_plots/plot_ult.py:5774\u001b[0m, in \u001b[0;36mprocess_inv\u001b[0;34m(res, removegr, ci, ind, usingbest)\u001b[0m\n\u001b[1;32m   5772\u001b[0m     res\u001b[38;5;241m=\u001b[39mPath(res)\n\u001b[1;32m   5773\u001b[0m \u001b[38;5;28mprint\u001b[39m(res)\n\u001b[0;32m-> 5774\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mres\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m   5775\u001b[0m     log \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m   5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ind\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(log): ind\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Z:/bruno_pert/cmafull_b_pert'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# vary two noises w for burno pert data ----------------------------------------------\n",
    "\n",
    "'''\n",
    "we found that process noise w and obs noise w are possitively correlated,\n",
    "test if this is true\n",
    "by varying the two parameters\n",
    "'''\n",
    "# load the data\n",
    "num_sample=300\n",
    "theta,_,_=process_inv(\"Z:/bruno_pert/cmafull_b_pert\", removegr=False)\n",
    "with open('Z:/victor_pert/packed','rb') as f:\n",
    "    df = pickle.load(f)\n",
    "df=datawash(df)\n",
    "df=df[df.category=='normal']\n",
    "df=df[df.target_r>200]\n",
    "states, actions, tasks=monkey_data_downsampled(df,factor=0.0025)\n",
    "states, actions, tasks=states[:num_sample], actions[:num_sample], tasks[:num_sample]\n",
    "# vary the two params\n",
    "base=np.array(theta).reshape(-1)\n",
    "base[3]=0\n",
    "base[5]=0\n",
    "gridreso=15\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[3]=1 # pro noise\n",
    "dy[5]=1 # obs noise\n",
    "X,Y=np.linspace(0,2,gridreso), np.linspace(0,2,gridreso)\n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        htheta=torch.tensor(dx*X[i]+dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        paramls.append(htheta)\n",
    "\n",
    "# logll function\n",
    "env=ffacc_real.FireFlyPaper(arg)\n",
    "phi=torch.tensor([[0.5],\n",
    "            [pi/2],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.13],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "            [0.001],\n",
    "    ])\n",
    "agent_=TD3.load('trained_agent/paper.zip')\n",
    "agent=agent_.actor.mu.cpu()\n",
    "@ray.remote\n",
    "def getlogll(x):\n",
    "    if torch.any(x>2) or torch.any(x<=0):\n",
    "        return None\n",
    "    with torch.no_grad():\n",
    "        return  monkeyloss_(agent, actions, tasks, phi, torch.tensor(x).view(-1,1), env, action_var=0.01,num_iteration=1, states=states, samples=5,gpu=False).item()\n",
    "\n",
    "# Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "# with open('varytwonoiseburnopert', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "# with open('varytwonoiseburnopert', 'rb') as f:\n",
    "#     paramls,Z= pickle.load(f) \n",
    "\n",
    "# formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "# with initiate_plot(3,3,300) as f:\n",
    "#     ax=f.add_subplot(111)\n",
    "#     im=ax.imshow(-formatedZ,origin='lower', extent=(X[0],X[-1],Y[0],Y[-1]),aspect='equal')\n",
    "#     plt.colorbar(im,label='log likelihood') \n",
    "#     ax.set_xlabel('process noise w')\n",
    "#     ax.set_ylabel('obs noise w')\n",
    "#     quickspine(ax)\n",
    "    # quicksave('two noise log likelihood burno pert')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Z:\\\\bruno_pert\\\\packed'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 6\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# vary along pc direction ----------------------------------------------\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m# background ---------------------\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# load data\u001b[39;00m\n\u001b[1;32m      5\u001b[0m datapath\u001b[38;5;241m=\u001b[39mPath(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZ:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mbruno_pert\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mpacked\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdatapath\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m      7\u001b[0m     df \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m      8\u001b[0m df\u001b[38;5;241m=\u001b[39mdatawash(df)\n",
      "File \u001b[0;32m~/miniconda3/envs/lab/lib/python3.11/site-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    304\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    305\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    306\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    307\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    308\u001b[0m     )\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Z:\\\\bruno_pert\\\\packed'"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# vary along pc direction ----------------------------------------------\n",
    "# background ---------------------\n",
    "\n",
    "# load data\n",
    "datapath=Path(\"Z:\\\\bruno_pert\\\\packed\")\n",
    "with open(datapath,'rb') as f:\n",
    "    df = pickle.load(f)\n",
    "df=datawash(df)\n",
    "df=df[df.category=='normal']\n",
    "df=df[df.target_r>200]\n",
    "states, actions, tasks=monkey_data_downsampled(df[:100],factor=0.0025)\n",
    "\n",
    "# from cov, find the eig axis we are most sure about\n",
    "theta,cov,err=process_inv(\"Z:/bruno_pert/cmafull_packed_bruno_pert\", removegr=False)\n",
    "ev, evector=torch.linalg.eig(torch.tensor(cov),eigenvectors=True)\n",
    "ev=ev[:,0]\n",
    "ev,esortinds=ev.sort(descending=False)\n",
    "evector=evector[:,esortinds]\n",
    "axis1=evector[:,0].reshape(-1,1)\n",
    "axis2=evector[:,1].reshape(-1,1)\n",
    "\n",
    "# vary the two axis to cover the possible range, calculate the backgrond and plot\n",
    "# X=np.linspace(-1.2,0.085,15)\n",
    "# Y=np.linspace(-0.3,0.195,15)\n",
    "# vary by eig value\n",
    "gridreso=11\n",
    "numev=6\n",
    "X,Y=np.linspace(-1,1,gridreso)*ev[0].item()*numev,np.linspace(-1,1,gridreso)*ev[1].item()*numev\n",
    "\n",
    "paramls=[]\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        htheta=torch.tensor(axis1*X[i]+axis2*Y[j]+theta).float().view(-1,1)\n",
    "        paramls.append(htheta)\n",
    "[1  for pp  in paramls if torch.any(pp<0)]\n",
    "\n",
    "print('starting background')\n",
    "Z=ray.get([getlogll.remote(each) for each in paramls])\n",
    "with open('background bruno pert', 'wb+') as f:\n",
    "        pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "with open('background bruno pert', 'rb') as f:\n",
    "    paramls,Z= pickle.load(f) \n",
    "# Z=normalizematrix(Z)\n",
    "formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "\n",
    "masked_array = np.ma.masked_invalid(formatedZ.astype('float'))\n",
    "cmap = matplotlib.cm.get_cmap()\n",
    "cmap.set_bad('black',1.)\n",
    "im=plt.contourf(masked_array[:-2,:-2]*-1, levels=[-30,-20,-15,-14,-13,-12],interpolation='nearest', cmap=cmap)\n",
    "plt.colorbar(im)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# project the inverse trajectory onto the background\n",
    "\n",
    "sys.exit()\n",
    "for i in range(11):\n",
    "    print(i)\n",
    "    print(min([pp[i] for pp in paramls]),(max([pp[i] for pp in paramls])))\n",
    "    \n",
    "with initiate_plot(3,3,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0]+midpoint[8],X[-1]+midpoint[8],Y[0]+midpoint[7],Y[-1]+midpoint[7]),aspect='auto')\n",
    "    plt.colorbar(im,label='curvature') \n",
    "    ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "    ax.set_xlabel('delta w cost')\n",
    "    ax.set_ylabel('delta v cost')\n",
    "    # ax.scatter(truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "    # ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def tmp(name):\n",
    "    with open(name, 'rb') as f:\n",
    "        slog = pickle.load(f)\n",
    "    log=slog\n",
    "    res=[l[2] for l in log]\n",
    "    allsamples=[]\n",
    "    alltheta=[]\n",
    "    loglls=[]\n",
    "    for r in res:\n",
    "        for point in r:\n",
    "            alltheta.append(torch.tensor(point[0]))\n",
    "            loglls.append(torch.tensor(point[1]))\n",
    "            allsamples.append([point[1],point[0]])\n",
    "    alltheta=torch.stack(alltheta)\n",
    "    logllsall=torch.tensor(loglls).flatten()\n",
    "    allsamples.sort(key=lambda x: x[0])\n",
    "    allthetameans=np.array([l[0]._mean for l in log])\n",
    "    alltheta=alltheta[50:]\n",
    "    logllsall=logllsall[50:]\n",
    "    mu=np.mean(np.asfarray(alltheta),0).astype('float32')\n",
    "    score, evectors, evals = pca(np.asfarray(alltheta))\n",
    "    x=score[:,0] # pc1\n",
    "    y=score[:,1] # pc2\n",
    "    z=logllsall\n",
    "    npixel=9\n",
    "\n",
    "    finalcov=log[-1][0]._C\n",
    "    realfinalcov=np.cov(np.array([l[0] for l in log[-1][2]]).T)\n",
    "\n",
    "    finaltheta=log[-1][0]._mean\n",
    "    pcfinaltheta=((finaltheta.reshape(-1)-mu)@evectors).astype('float32')\n",
    "    finalcovpc=(evectors.T@finalcov@evectors).astype('float32')\n",
    "    realfinalcovpc=(evectors.T@realfinalcov@evectors).astype('float32')\n",
    "    pccov=evectors.T@(np.cov(alltheta.T))@evectors\n",
    "    allthetameanspc=((allthetameans-mu)@evectors).astype('float32')\n",
    "    with initiate_plot(3, 3.5, 300) as fig, warnings.catch_warnings():\n",
    "        warnings.simplefilter('ignore')\n",
    "        ax = fig.add_subplot(111)\n",
    "        plot_cov_ellipse(pccov[:2,:2], pcfinaltheta[:2], alpha=0.3,nstd=1,ax=ax)\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "        s = ax.scatter(x,y, c=z,alpha=0.5,edgecolors='None', cmap='jet')\n",
    "        ax.set_xlabel('projected parameters')\n",
    "        ax.set_ylabel('projected parameters')\n",
    "        c = fig.colorbar(s, ax=ax)\n",
    "        ax.locator_params(nbins=3, axis='y')\n",
    "        ax.locator_params(nbins=3, axis='x')\n",
    "        # c.clim(min(np.log(loglls)), max(np.log(loglls))) \n",
    "        c.set_label('- log likelihood')\n",
    "        # c.set_ticks([min((loglls)),max((loglls))])\n",
    "        c.ax.locator_params(nbins=4)\n",
    "        # ax.set_xlim(xlow,xhigh)\n",
    "        # ax.set_ylim(ylow,yhigh)\n",
    "        ax.plot(allthetameanspc[:,0],allthetameanspc[:,1])\n",
    "\n",
    "# name='Z:/bruno_pert/testtemp3packed_bruno_pert'\n",
    "# process_inv(name)\n",
    "# tmp(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'distinguishparamZvwcostcurvaturesingle'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 43\u001b[0m\n\u001b[1;32m     35\u001b[0m         paramls\u001b[38;5;241m.\u001b[39mappend(theta)\n\u001b[1;32m     37\u001b[0m \u001b[38;5;66;03m# Z=ray.get([getcur.remote(each) for each in paramls])\u001b[39;00m\n\u001b[1;32m     38\u001b[0m \n\u001b[1;32m     39\u001b[0m \u001b[38;5;66;03m# with open('distinguishparamZvwcostcurvaturesingle', 'wb+') as f:\u001b[39;00m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\u001b[39;00m\n\u001b[1;32m     41\u001b[0m \n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# vary v and w cost, heatmap of curvature\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdistinguishparamZvwcostcurvaturesingle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m     44\u001b[0m     paramls,Z\u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m     45\u001b[0m formatedZ\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray(Z)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(Z)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m0.5\u001b[39m),\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(Z)\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m0.5\u001b[39m))\u001b[38;5;241m.\u001b[39mT\n",
      "File \u001b[0;32m~/miniconda3/envs/lab/lib/python3.11/site-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    304\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    305\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    306\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    307\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    308\u001b[0m     )\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'distinguishparamZvwcostcurvaturesingle'"
     ]
    }
   ],
   "source": [
    "\n",
    "# vary v and w cost, eval the curvature   ----------------------------------------------\n",
    "# ASD-health\n",
    "\n",
    "# curvature 1, total angle turned---------------------------------------------------\n",
    "datapath=Path(datafolder)/\"human/hgroup\"\n",
    "with open(datapath, 'rb') as f:\n",
    "    hstates, hactions, htasks = pickle.load(f)\n",
    "res=[]\n",
    "for task in htasks:\n",
    "    d,a=xy2pol(task, rotation=False)\n",
    "    # if  env.min_angle/2<=a<env.max_angle/2:\n",
    "    if a<=-pi/5*0.7 or a>=pi/5*0.7:\n",
    "        res.append(task)\n",
    "sidetasks=np.array(res)\n",
    "\n",
    "sidetasks=sidetasks[:100]\n",
    "\n",
    "@ray.remote\n",
    "def getcur(x):\n",
    "    x.clamp_(1e-3,3)\n",
    "    with torch.no_grad():\n",
    "        curvature=eval_curvature(agent, env, phi, x,sidetasks,vctrl=0.,wctrl=0.,ntrials=50)\n",
    "        return  curvature\n",
    "\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[8]=1 \n",
    "dy[7]=1 \n",
    "\n",
    "X,Y=np.linspace(-0.45,1,gridreso), np.linspace(-1.1,0.4,gridreso)\n",
    "paramls=[]\n",
    "# Z=np.zeros((gridreso,gridreso))\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        theta=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        paramls.append(theta)\n",
    "\n",
    "# Z=ray.get([getcur.remote(each) for each in paramls])\n",
    "\n",
    "# with open('distinguishparamZvwcostcurvaturesingle', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "# vary v and w cost, heatmap of curvature\n",
    "with open('distinguishparamZvwcostcurvaturesingle', 'rb+') as f:\n",
    "    paramls,Z= pickle.load(f)\n",
    "formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "X,Y=np.linspace(-0.45,1,gridreso), np.linspace(-1.1,0.4,gridreso)\n",
    "\n",
    "with initiate_plot(3,3,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0]+midpoint[8],X[-1]+midpoint[8],Y[0]+midpoint[7],Y[-1]+midpoint[7]),aspect='auto')\n",
    "    plt.colorbar(im,label='curvature') \n",
    "    ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "    ax.set_xlabel('delta w cost')\n",
    "    ax.set_ylabel('delta v cost')\n",
    "    # ax.scatter(truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "    # ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# using curvature 2, the furthest point -----------------------------------------------------\n",
    "def eval_curvature2(agent, env, phi, theta, tasks,ntrials=10):\n",
    "    costs=[]\n",
    "    states=[]\n",
    "    with suppress():\n",
    "        for task in tasks:\n",
    "            while len(states)<ntrials:\n",
    "                env.reset(phi=phi, theta=theta, goal_position=task, pro_traj=None,vctrl=0.,wctrl=0. )\n",
    "                _,_,_,epstates=run_trial(agent,env,given_action=None, given_state=None, action_noise=0.1)\n",
    "                if len(epstates)>5:\n",
    "                    states.append(torch.stack(epstates)[:,:,0])\n",
    "    for s in states:\n",
    "        end=s[-1][:2]\n",
    "        rotation=xy2pol(end,rotation=False)[1].item()\n",
    "        R=np.array([[np.cos(rotation),np.sin(rotation)],[-np.sin(rotation),np.cos(rotation)]])\n",
    "        rotatedxy=R@np.array(astates[i][:,:2].T)\n",
    "        epcost=np.max(rotatedxy[1])\n",
    "        costs.append(epcost)    \n",
    "    return sum(costs)\n",
    "\n",
    "@ray.remote\n",
    "def getcur(x):\n",
    "    x.clamp_(1e-3,3)\n",
    "    with torch.no_grad():\n",
    "        curvature=eval_curvature2(agent, env, phi, theta, [None]*50, ntrials=50)\n",
    "        return  curvature\n",
    "\n",
    "dx,dy=np.zeros((11)),np.zeros((11))\n",
    "dx[8]=1 \n",
    "dy[7]=1 \n",
    "\n",
    "X,Y=np.linspace(-0.45,1,gridreso), np.linspace(-1.1,0.4,gridreso)\n",
    "paramls=[]\n",
    "# Z=np.zeros((gridreso,gridreso))\n",
    "for i in range(gridreso):\n",
    "    for j in range(gridreso):\n",
    "        theta=torch.tensor(-dx*X[i]-dy*Y[j]+midpoint).float().view(-1,1)\n",
    "        paramls.append(theta)\n",
    "\n",
    "# Z=ray.get([getcur.remote(each) for each in paramls])\n",
    "\n",
    "# with open('distinguishparamZvwcostcurvature2', 'wb+') as f:\n",
    "#         pickle.dump((paramls,Z), f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# vary v and w cost, heatmap of curvature\n",
    "with open('distinguishparamZvwcostcurvature2', 'rb+') as f:\n",
    "    paramls,Z= pickle.load(f)\n",
    "formatedZ=np.array(Z).reshape(int(len(Z)**0.5),int(len(Z)**0.5)).T\n",
    "X,Y=np.linspace(-0.45,1,gridreso), np.linspace(-1.1,0.4,gridreso)\n",
    "\n",
    "with initiate_plot(3,3,300) as f:\n",
    "    ax=f.add_subplot(111)\n",
    "    im=ax.imshow(formatedZ[:,2:],origin='lower', extent=(X[0]+midpoint[8],X[-1]+midpoint[8],Y[0]+midpoint[7],Y[-1]+midpoint[7]),aspect='auto')\n",
    "    plt.colorbar(im,label='curvature') \n",
    "    ax.scatter(0,0,label='zero') # midpoint, 0,0\n",
    "    ax.set_xlabel('delta w cost')\n",
    "    ax.set_ylabel('delta v cost')\n",
    "    # ax.scatter(truedelta[5]/2,truedelta[3]/2,label='inferred delta') # inferred delta\n",
    "    # ax.legend()\n",
    "\n",
    "\n",
    "\n",
    "def transform2ks(paramls):\n",
    "    # transform the param list to kalman gain vs total uncertainty \n",
    "    ks=[] # kalman gain diffs\n",
    "    for ta, th in paramls:\n",
    "        ks.append((getk(ta)-getk(th)).item())\n",
    "    ss=[] # uncertainty diffs\n",
    "    for ta, th in paramls:\n",
    "        ss.append((totalgivenop(ta[3],ta[5])-totalgivenop(th[3],th[5])).item())\n",
    "\n",
    "    plt.scatter(ks,ss)\n",
    "    return ks,ss\n",
    "\n",
    "def transform2bs(paramls):\n",
    "    # transform the param list to controlgain*(1-kalman gain) vs total uncertainty \n",
    "    ks=[] # kalman gain diffs\n",
    "    for ta, th in paramls:\n",
    "        ks.append((ta[1]*(1-getk(ta))-th[1]*(1-getk(th))).item())\n",
    "    ss=[] # uncertainty diffs\n",
    "    for ta, th in paramls:\n",
    "        ss.append((totalgivenop(ta[3],ta[5])-totalgivenop(th[3],th[5])).item())\n",
    "\n",
    "    plt.scatter(ks,ss)\n",
    "    return ks,ss\n",
    "\n"
   ]
  }
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