calculate synthetic ramps given a series of time points and coefficients of unit steps at each time. \n convolution ramps

synthetic_ramp_method.ipynb

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   "cell_type": "markdown",
   "id": "324422b2",
   "metadata": {},
   "source": [
    "## synthetic ramp method\n",
    "\n",
    "calculate synthetic ramps using convolution for a given series of time points:\n",
    "* define matrix $A$ with the coefficients $a_{i,j}$ of the unit steps in $n$ different series $i$ for each time $t_j$.\n",
    "* define matrix $\\sigma(t-t_j)*\\sigma(t)=(t-t_j)\\cdot \\sigma(t-t_j)$ which is lower triangular with permutations of $(t-t_j)\\cdot \\sigma(t-t_j)$\n",
    "    * example with $t_0<t_1$: $(t_0-t_1)\\cdot \\sigma(t_0-t_1)=0$, and $(t_1-t_0)\\cdot \\sigma(t_1-t_0)=(t_1-t_0)$ due to step definition\n",
    "* the matrix $\\sigma(t-t_j)\\cdot A$ has then the slopes $\\frac{dy_i}{dt}$ at times $t_j$ and $(t-t_j)\\cdot \\sigma(t-t_j)\\cdot A$ has the synthetic ramps by convolution.\n",
    "$$\n",
    "\\underbrace{\\left[\\begin{array}{cccc}\n",
    "1 & 0 & 0 & ...\\\\\n",
    "1 & 1 & 0 & ...\\\\\n",
    "1 & 1 & 1 & ...\\\\\n",
    "... & ... & ... & ...\n",
    "\\end{array}\\right]}_{\\sigma(t-t_j)}\\cdot \n",
    "\\underbrace{\\left[\\begin{array}{ccc}\n",
    "a_{1,0} & a_{2,0} & ...\\\\\n",
    "a_{1,1} & a_{2,1} & ...\\\\\n",
    "a_{1,2} & a_{2,2} & ...\\\\\n",
    "... & ... & ...\\\\\n",
    "\\end{array}\\right]}_{A} = \n",
    "\\underbrace{\\left[\\begin{array}{ccc}\n",
    "a_{1,0} & a_{2,0} & ...\\\\\n",
    "a_{1,0}+a_{1,1} & a_{2,0}+a_{2,1} & ...\\\\\n",
    "a_{1,0}+a_{1,1}+a_{1,2} & a_{2,0}+a_{2,1}a_{2,2} & ...\\\\\n",
    "... & ... & ...\\\\\n",
    "\\end{array}\\right]}_{\\mathrm{slopes}}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\underbrace{\\left[\\begin{array}{cccc}\n",
    "t_0-t_0 & 0 & 0 & ...\\\\\n",
    "t_1-t_0 & t_1-t_1 & 0 & ...\\\\\n",
    "t_2-t_0 & t_2-t_1 & t_2-t_2 & ...\\\\\n",
    "... & ... & ... & ...\n",
    "\\end{array}\\right]}_{(t-t_j)\\cdot \\sigma(t-t_j)}\\cdot \n",
    "\\underbrace{\\left[\\begin{array}{ccc}\n",
    "a_{1,0} & a_{2,0} & ...\\\\\n",
    "a_{1,1} & a_{2,1} & ...\\\\\n",
    "a_{1,2} & a_{2,2} & ...\\\\\n",
    "... & ... & ...\\\\\n",
    "\\end{array}\\right]}_{A} = \n",
    "\\underbrace{\\left[\\begin{array}{ccc}\n",
    "a_{1,0}\\cdot(t_0-t_0) & a_{2,0}\\cdot(t_0-t_0) & ...\\\\\n",
    "a_{1,0}\\cdot(t_1-t_0)+a_{1,1}\\cdot(t_1-t_1) & a_{2,0}\\cdot(t_1-t_0)+a_{2,1}\\cdot(t_1-t_1) & ...\\\\\n",
    "a_{1,0}\\cdot(t_2-t_0)+a_{1,1}\\cdot(t_2-t_1)+a_{1,2}\\cdot(t_2-t_2) & a_{2,0}\\cdot(t_2-t_0)+a_{2,1}\\cdot(t_2-t_1)+a_{2,2}\\cdot(t_2-t_2) & ...\\\\\n",
    "... & ... & ...\\\\\n",
    "\\end{array}\\right]}_{\\mathrm{synth\\ curves}}\n",
    "$$\n",
    "\n",
    "example below with three series $i=\\{1,2,3\\}$"
   ]
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   "cell_type": "markdown",
   "id": "799252eb",
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   "source": [
    "\n",
    "### filtering to delay/advance signals according to one of the signals\n",
    "\n",
    "problem description: \n",
    "* based on one of the signals as key signal: $y_a$\n",
    "* when $y_a$ increases, other signals should be delayed by a fixed time $\\delta t$\n",
    "* when $y_a$ decreases, all other signals should be advanced by a fixed time $\\delta t$\n",
    "\n",
    "solution: use filtering to obtain adapted $\\sigma(t-t_j\\pm\\delta t)*\\sigma(t)=\\sigma(t-t_j\\pm\\delta t)\\cdot (t-t_j\\pm\\delta t)$ , where \n",
    "* $\\pm\\delta t$ is negative (delaying $\\sigma$) whenever the slope (and delayed slope) of $y_a$ is positive\n",
    "* $\\pm\\delta t$ is positive (advancing $\\sigma$) whenever the slope (and advanced slope) of $y_a$ is negative\n",
    "\n",
    "This ensures positive steady states are reached including delay, or negative steady states are reached including advancement.\n",
    "\n",
    "Method:\n",
    "1. extend times vector in every time $t_j$ by the next time offsets by time-shift $\\pm \\delta t$, thereby triplicating its size\n",
    ">(TODO: save space by extending only based on actual derivatives of $y_a$)\n",
    "2. calculate slopes for extended times vector as $\\sigma(t-t_j)\\cdot A$, delayed slopes as $\\sigma(t-t_j-\\delta t)\\cdot A$ and advanced slopes $\\sigma(t-t_j+\\delta t)\\cdot A$\n",
    "3. based on slopes obtained, calculate elementwise the matrices $\\sigma(t-t_j\\pm\\delta t)$ and $(t-t_j\\pm\\delta t)\\cdot\\sigma(t-t_j\\pm\\delta t)$, determining the sign of $\\pm \\delta t$ by each corresponding slope\n",
    "4. filtered slopes are $\\sigma(t-t_j\\pm\\delta t)\\cdot A$ and filtered ramps are $(t-t_j\\pm\\delta t)\\cdot\\sigma(t-t_j\\pm\\delta t)\\cdot A$ as above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28d6547c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lower triangular (t-tj)sigma(t-tj)) \n",
      " [[ 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 1.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 2.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 3.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 4.   1.   0.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 5.   2.   1.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 6.   3.   2.   0.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 7.   4.   3.   1.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 7.5  4.5  3.5  1.5  0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 8.   5.   4.   2.   0.   0.   0.   0.   0.   0.   0. ]\n",
      " [ 8.5  5.5  4.5  2.5  0.5  0.   0.   0.   0.   0.   0. ]\n",
      " [ 9.   6.   5.   3.   1.   0.5  0.   0.   0.   0.   0. ]\n",
      " [ 9.5  6.5  5.5  3.5  1.5  1.   0.   0.   0.   0.   0. ]\n",
      " [10.   7.   6.   4.   2.   1.5  0.   0.   0.   0.   0. ]\n",
      " [11.   8.   7.   5.   3.   2.5  0.   0.   0.   0.   0. ]\n",
      " [12.   9.   8.   6.   4.   3.5  1.   0.   0.   0.   0. ]\n",
      " [13.  10.   9.   7.   5.   4.5  2.   0.   0.   0.   0. ]\n",
      " [14.  11.  10.   8.   6.   5.5  3.   0.   0.   0.   0. ]\n",
      " [15.  12.  11.   9.   7.   6.5  4.   1.   0.   0.   0. ]\n",
      " [16.  13.  12.  10.   8.   7.5  5.   2.   0.   0.   0. ]\n",
      " [17.  14.  13.  11.   9.   8.5  6.   3.   1.   0.   0. ]\n",
      " [18.  15.  14.  12.  10.   9.5  7.   4.   2.   1.   0. ]\n",
      " [20.  17.  16.  14.  12.  11.5  9.   6.   4.   3.   0. ]\n",
      " [21.  18.  17.  15.  13.  12.5 10.   7.   5.   4.   0. ]\n",
      " [22.  19.  18.  16.  14.  13.5 11.   8.   6.   5.   1. ]]\n"
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",
      "text/plain": [
       "<Figure size 900x900 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy import array,linspace,zeros,pi,exp\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "steps_times=array([\n",
    "    [0,0,0,0],\n",
    "    [3,0,-1,0],\n",
    "    [4,0,1,0],\n",
    "    [6,3/5,0,0],\n",
    "    [8,0,2,3/5],\n",
    "    [8.5,0,-2,0],\n",
    "    [11,-3/5,0,0],\n",
    "    [14,-1.5/2,0,-3/5],\n",
    "    [16,-1.5/2,0.5,-3/4],\n",
    "    [17,1.5,-0.5,-3/4],\n",
    "    [21,0,0,+3/2],\n",
    "])\n",
    "t_shift=1\n",
    "times=steps_times[:,0]\n",
    "steps=steps_times[:,1:]\n",
    "initial_vals=array([2,1,2])\n",
    "\n",
    "eye=array([[1 if times[i]-times[j]==0 else 0 for j in range(times.shape[0])] for i in range(times.shape[0])])\n",
    "sigma=array([[1 if times[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times.shape[0])])\n",
    "sigma_t=array([[(times[i]-times[j]) if times[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times.shape[0])])\n",
    "\n",
    "slopes_min=sigma.dot(steps)\n",
    "synth_ramps_min=initial_vals+sigma_t.dot(steps)\n",
    "\n",
    "times_extended_min=array(list(set(list(times)+[x for x in list(times-t_shift)+list(times+t_shift)]))) # include all unique points shifted by +/- t_shift\n",
    "times_extended_min.sort()\n",
    "eye=array([[1 if times_extended_min[i]-times[j]==0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "eye_shifted=array([[1 if times_extended_min[i]-times[j]-t_shift==0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "sigma=array([[1 if times_extended_min[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "sigma_t=array([[(times_extended_min[i]-times[j]) if times_extended_min[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "sigma_shifted=array([[1 if times_extended_min[i]-times[j]-t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "\n",
    "slopes=sigma.dot(steps)\n",
    "synth_ramps=initial_vals+sigma_t.dot(steps)\n",
    "shifted_steps=eye_shifted.dot(steps)\n",
    "shifted_slopes=sigma_shifted.dot(steps)\n",
    "\n",
    "eye_shifted_neg=array([[1 if times_extended_min[i]-times[j]+t_shift==0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "sigma_shifted_neg=array([[1 if times_extended_min[i]-times[j]+t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "shifted_steps_neg=eye_shifted_neg.dot(steps)\n",
    "shifted_slopes_neg=sigma_shifted_neg.dot(steps)\n",
    "\n",
    "sigma_shifted_func=zeros([times_extended_min.shape[0],times.shape[0]])\n",
    "sigma_t_shifted_func=zeros([times_extended_min.shape[0],times.shape[0]])\n",
    "eye_shifted_func=zeros([times_extended_min.shape[0],times.shape[0]])\n",
    "for i in range(times_extended_min.shape[0]):\n",
    "    for j in range(times.shape[0]):\n",
    "        t_shift_func=0\n",
    "        if (shifted_slopes[i,0]>0) | (slopes[i,0]>0):\n",
    "            t_shift_func=+t_shift\n",
    "        elif (slopes[i,0]<0) | (shifted_slopes_neg[i,0]<0):\n",
    "            t_shift_func=-t_shift\n",
    "        sigma_shifted_func[i,j]=1 if (times_extended_min[i]-times[j]-t_shift_func>=0) else 0\n",
    "        eye_shifted_func[i,j]=1 if (times_extended_min[i]-times[j]-t_shift_func==0) else 0 # doesn't work to produce steps to integrate, some duplication of steps\n",
    "        sigma_t_shifted_func[i,j]=(times_extended_min[i]-times[j]-t_shift_func) if (times_extended_min[i]-times[j]-t_shift_func>=0) else 0\n",
    "\n",
    "#shifted_steps_func=eye_shifted_func.dot(steps) # doesn't work to produce steps to integrate, some duplication of steps\n",
    "shifted_slopes_func=sigma_shifted_func.dot(steps)\n",
    "shifted_synth_ramps=initial_vals+sigma_t_shifted_func.dot(steps)\n",
    "\n",
    "eye_ext=array([[1 if times_extended_min[i]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "eye_ext_shift1=array([[1 if times_extended_min[i-1]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "#eye_ext_shift1=concatenate([[zeros(times_extended_min.shape[0])],eye_ext_shift1])\n",
    "sigma_ext=array([[1 if times_extended_min[i]-times_extended_min[j]>=0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "sigma_t_ext=array([[(times_extended_min[i]-times_extended_min[j]) if times_extended_min[i]-times_extended_min[j]>=0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])\n",
    "\n",
    "shifted_steps_func=(eye_ext-eye_ext_shift1).dot(sigma_shifted_func.dot(steps))\n",
    "reintegrated_ramps_from_steps=initial_vals+sigma_t_ext.dot((eye_ext-eye_ext_shift1).dot(sigma_shifted_func.dot(steps)))\n",
    "\n",
    "idx_output = (shifted_steps_func!=0).any(axis=1)\n",
    "\n",
    "times_extended=array(list(set(linspace(times.min(),times.max(),1000).tolist()+times_extended_min.tolist())))\n",
    "times_extended.sort()\n",
    "\n",
    "sigma_extended=array([[1 if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "sigma_t_extended=array([[(times_extended[i]-times[j]) if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "slopes_extended=sigma_extended.dot(steps)\n",
    "\n",
    "sigma_extended_shifted=array([[1 if times_extended[i]-times[j]-t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "sigma_extended_shifted_neg=array([[1 if times_extended[i]-times[j]+t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "\n",
    "slopes_extended_shifted=sigma_extended_shifted.dot(steps)\n",
    "slopes_extended_shifted_neg=sigma_extended_shifted_neg.dot(steps)\n",
    "\n",
    "eye=array([[1 if times_extended[i]-times[j]==0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "sigma=array([[1 if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "slopes_extended_shifted_func=sigma.dot(steps)\n",
    "\n",
    "\n",
    "# increase size of obtained arrays from times_extended_min to times_extended\n",
    "eye_ext=array([[1 if times_extended[i]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])\n",
    "eye_ext_shift1=array([[1 if times_extended[i]-times_extended_min[j-1]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])\n",
    "sigma_ext=array([[1 if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])\n",
    "sigma_t_ext=array([[(times_extended[i]-times_extended[j]) if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])\n",
    "\n",
    "shifted_steps_ext_func=(eye_ext_shift1-eye_ext).dot(sigma_shifted_func.dot(steps))\n",
    "shifted_slopes_ext_func=sigma_ext.dot(shifted_steps_ext_func)\n",
    "synth_ramps_ext_func=initial_vals+sigma_t_ext.dot(shifted_steps_ext_func)\n",
    "\n",
    "print('lower triangular (t-tj)sigma(t-tj)) \\n',sigma_t)\n",
    "fig,ax_list=plt.subplots(nrows=5,ncols=1,height_ratios=[1,1/2,1/2,1/2,1/2],constrained_layout=True,sharex=True,figsize=[9,9])\n",
    "for j in range(steps.shape[1]):\n",
    "    l1,=ax_list[0].plot(times,synth_ramps_min[:,j],marker=['o','<','s'][j],label=f'series i={j+1}')\n",
    "    ax_list[0].plot(times_extended_min[idx_output],shifted_synth_ramps[idx_output,j],'-.',marker=['o','<','s'][j],color=l1.get_color(),fillstyle='none',label=f'series i={j+1} (filtered i=1)')\n",
    "    markerline, stemlines, baseline=ax_list[1].stem(times,steps[:,j],markerfmt=['o','<','s'][j],linefmt=['blue','orange','green'][j],label=f'series i={j+1}')\n",
    "    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')\n",
    "    markerline, stemlines, baseline=ax_list[3].stem(times_extended_min[idx_output],shifted_steps_func[idx_output,j],markerfmt=['o','<','s'][j],linefmt=['blue','orange','green'][j],label=f'series i={j+1}')\n",
    "    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')\n",
    "    ax_list[2].plot(times_extended,slopes_extended[:,j],linestyle=['-','--','-.'][j],label=f'series i={j+1}')\n",
    "\n",
    "ax_list[0].plot(times_extended_min[idx_output],reintegrated_ramps_from_steps[idx_output,0],':',marker='none',color='red',fillstyle='none',label=f'series i={0+1} reconstructed')\n",
    "ax_list[4].plot(times_extended,slopes_extended[:,0],['-','--','-.'][0],label=f'series i={0+1}, slope')\n",
    "ax_list[4].plot(times_extended,slopes_extended_shifted[:,0],['-','--','-.'][1],label=f'series i={0+1}, delay')\n",
    "ax_list[4].plot(times_extended,slopes_extended_shifted_neg[:,0],['-','--','-.'][2],label=f'series i={0+1}, adv')\n",
    "ax_list[4].plot(times_extended+t_shift,shifted_slopes_ext_func[:,0],['-','--','-.',':'][3],label=f'series i={0+1}, func')\n",
    "for j in range(len(times)):\n",
    "    ax_list[0].axvline(times[j],linestyle='--',color=[0.85,0.85,0.85],zorder=-1)\n",
    "    ax_list[1].axvline(times[j],linestyle='--',color=[0.85,0.85,0.85],zorder=-1)\n",
    "    ax_list[2].axvline(times[j],linestyle='--',color=[0.85,0.85,0.85],zorder=-1)\n",
    "for j in times_extended_min[idx_output]:\n",
    "    ax_list[4].axvline(j,linestyle='--',color=[0.6,0.6,0.6],zorder=-1)\n",
    "    ax_list[3].axvline(j,linestyle='--',color=[0.6,0.6,0.6],zorder=-1)\n",
    "    ax_list[0].axvline(j,linestyle='--',color=[0.6,0.6,0.6],zorder=-1)\n",
    "\n",
    "ax_list[1].plot()\n",
    "\n",
    "ax_list[-1].set_xlabel('time $t_j$')\n",
    "ax_list[0].set_ylabel('$y_i$')\n",
    "ax_list[1].set_ylabel('$a_{i,j}$')\n",
    "ax_list[2].set_ylabel(r'$\\frac{dy_i}{dt}$')\n",
    "ax_list[3].set_ylabel('$a_{i,j}$ filtered')\n",
    "ax_list[0].legend(loc='center left',bbox_to_anchor=[1.01,0.5]);\n",
    "ax_list[1].legend(loc='center left',bbox_to_anchor=[1.01,0.5])\n",
    "ax_list[2].legend(loc='center left',bbox_to_anchor=[1.01,0.5])\n",
    "ax_list[3].legend(loc='center left',bbox_to_anchor=[1.01,0.5])\n",
    "ax_list[4].legend(loc='center left',bbox_to_anchor=[1.01,0.5]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3807056e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c796c051420c44108a4c4db4b19bf2e5",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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d0lADiPw3ubtH5pjSRp+p2jlJnNy1dv+f2xuYDL/muLPvnjHo2bPkgZQbqAgBfQAQTgeNGoqeXPb1E0amrYpf017JL+dpeUNkZfWaKC/tk62dimjcv32l0OgBQhAAJwvE0r9ulAdUiZWX5N+9oou8tJK36/T5NvqpAkfbK0Rq0t7fYWBCAlCIAAHK3xaIvWLNkpSZpy63Dl9Q3aXFH6GXX5QBUOyFZLE11AwCsIgAAcyzRNrVy0TW3hiEqGFWjstEF2l5SWfH6fJn+lUlJHF/AkXUAg3REAATjWrvWHtHvDYfl8hmbcM0aGj8u9JcvIyQPVZ2COws3t2rCMLiCQ7giAABwpfLJdqxZtkyRdeuMQ9RuUZ3NF6c3nMzT55gpJ0vq3axSmCwikNQIgAEdas2SnmhtaVTggW5M6FikguUZMHKi+JTkKn2jXhne41jmQzgiAABynfmeDNq2MbUw8454xCmSwN10qxLqAsbmA69+uVfhEm80VAUgWAiAAR4m0R2OXezOlMVNKNHh0X7tL8pQRlw1QUVmuWk+2a/2f6AIC6YoACMBRPvljjY7WNSsrL0NX3TbS7nI8x/AZuryjC/jpn2rV0kwXEEhHBEAAjnH8wAl99MZuSdLVXx2prLwMewvyqGGX9Fe/QXlqa4lo/ds1dpcDIAkIgAAcwTRNLX9hiyLtUZVfVKRRlw+0uyTPMnyGLr8l1gXc8M5enWxqtbkiAFYjAAJwhC2r92vf1uMKZPh0zV2jZRjs+WenygnFKi7PU1s4ovVLmQsIpBsCIADbnQi16r3fbpckTb65UoX9s22uCIZh6PJbhkmSNizfq5ONdAGBdEIABGC7917ernBzu/oNztOE68vtLgcdKsb304Ch+WoPR/TJH5kLCKQTAiAAW9VsPqJtHx6QDGnm18fI7+dlySkM49S+gBtX7NWJEF1AIF3wSgvANm2tEa14cask6eKZgzWwosDminC2oeP6aWBlgdpbo/r4j3vsLgeARQiAAGyz9rVqhQ63KK9vUFf82TC7y8E5GMapfQE3rdin5oawzRUBsAIBEIAtDtU2av3bsdWl0+8arcysgM0V4XzKLypSybACRdqi+vRtVgQD6YAACCDlolFTy5/bIjNqavhl/VV5cbHdJeECDMPQhOuGSJJ2bzpiczUArEAABJByG5ft1cE9jcrMDmjaHaPsLgddMHhMX8mQjtU3sxgESAMEQAAp1Xi0RWte3SVJmnLrcOUWBm2uCF2RlZuhfoPyJEn7th2zuRoAvUUABJAypmlq5Ytb1R6OqHR4ocZeXWZ3SeiGwaP6SpL2bTtubyEAeo0ACCBldn58SLs3HpHPb2jGPWNk+Ljcm5sMGt1HkrRvKx1AwO0IgABSInyiTate2iZJuuzGoSoqy7W5InRX6Yg+kiEdP3CC7WAAlyMAAkiJ1a/s1IlQq/oMzNHE2UPtLgc9kJWbof7l+ZKYBwi4HQEQQNLV7TiuzavqJEkz7hmtQIbf5orQU4NG9ZEk7dt63NY6APQOARBAUkXaolr+3BZJ0peuKtWgjoUEcKdBozsWgjAPEHA1AiCApPr4j3t0bP8JZednaOq8EXaXg14qHdFHhiE1HDqppmMtdpcDoIcIgACS5tj+Zn305m5J0tVfG6ms3Ax7C0KvBbMD6j8kPg/wuL3FAOgxAiCApDCjppY/v1XRdlNDxhZp5KSBdpcEizAMDLgfARBAUny+ul51248rkOnTNXeNlmGw51+6GJTYEJoACLgVARCA5U6EWvX+b3dIki6/ZZgKirNtrghWKh1RKMNnKHS4RY1HmQcIuBEBEIDl3v3v7QqfaFdxeZ4mXDvY7nJgscysgAYM7ZgHyDAw4EoEQACW2rPpiLavPSDDkGZ+fYx8fl5m0hHzAAF345UZgGXawhGteGGrJOnia8s1YGiBzRUhWRIbQrMSGHAlAiAAy3z4+11qPNqivKKgLr+l0u5ykESlw/vI5zfUeLRFocMn7S4HQDcRAAFY4lBNoz79U60k6Zq7RiszK2BzRUimjKBfAytiHd69DAMDrkMABNBr0UhUy57bItOURkwaoIrxxXaXhBQoSwwDEwABtyEAAui1Dcv26lBNo4I5AV391ZF2l4MUiS8Eqdt2XKZp2lwNgO7wbAB8+umndfHFF6ugoEAFBQWaMmWK3nzzTbvLAlwndPikPnh1lyRpyq3DlVsYtLkipErpsEL5AoaajoXVcIh5gICbeDYADh48WD/5yU/00Ucf6aOPPtK1116rOXPmaPPmzXaXBriGaZpa8eI2tbdGVTqiUBddVWZ3SUihQKZfJZWFktgOBnAbzwbAW265RTfddJNGjRqlUaNG6cc//rHy8vK0Zs0au0sDXGPHuoOq2XxEvoChGfeMkeHjcm9eU8Z2MIAreTYAni4SiWjRokVqbm7WlClT7C4HcIWW5jatemmbJGnilytUVJprc0Www+DTrgvMPEDAPTy9T8PGjRs1ZcoUtbS0KC8vT6+88oouuuii894/HA4rHA4nvg6FQqkoE3Ck1a/s1MnGNvUtydHEG4faXQ5sMnBYgfwBn040tOr4gRPqW8IbAcANPN0BHD16tNavX681a9bo29/+tu6991599tln571/VVWVCgsLEx/l5eUprBZwjrrtx/TZu3WSpBn3jJE/w9MvJZ4WyPCrZHhsP0CGgQH38PSrdmZmpkaMGKFJkyapqqpKEyZM0D/90z+d9/4LFixQQ0ND4qO2tjaF1QLOEGmLatlzscu9XXR1mcpG9rG3INhu0GnDwADcwdNDwGczTfOMId6zBYNBBYNscQFvW/fWbh0/cELZBZmacutwu8uBA8QCYLX2dewHaBgsBgKczrMB8Ac/+IFmz56t8vJyNTY2atGiRVq+fLneeustu0sDHOtofbPWvbVHkjTtayOVlZthc0VwgoEVBQpk+HQy1Kpj9SdUVMY8QMDpPBsADxw4oG984xuqr69XYWGhLr74Yr311lu64YYb7C4NcCQzamr581sUjZgaOr6fRkwcYHdJcAh/hk8lwwu1d8sx7dt2jAAIuIBnA+B//ud/2l0C4CqfvVen+h0NCmT6NP3OUQzz4QyDRvVNBMDxMwbbXQ6ATnh6EQiArmluCOv9xTslSVf82TAV9Mu2uSI4Tfy6wPu2HZcZZT9AwOkIgAA69e5vtqv1ZLv6D8nXxTPp7uCLBlTkK5DpU0tTm47WN9tdDoBOEAABXNDuDYe1Y91BGT5DM78+Rj4/Lxv4Ir/fp9IRfSRJe7kuMOB4vJIDOK/WlnatWBTb82/CdeXqPyTf5orgZIM6rgtcx4bQgOMRAAGc14e/r1bT0bDy+2Xp8psr7S4HDpeYB7j9GPMAAYcjAAI4p4N7QtrwTuxqN9fcPVoZQb/NFcHp+g/JV0bQr3Bzuw7va7K7HAAXQAAE8AXRSFTLntsi05RGTh6ooWP72V0SXOD0eYAMAwPORgAE8AWf/mmvDtc2KZgT0NVfHWl3OXCRQaP7SGIhCOB0BEAAZwgdPqkPf79LkjT1thHKKci0uSK4yeCOeYB1248ryjxAwLEIgAASTNPUihe2qr0tqkGj+uhLU0vtLgkuUzw4T5lZfrWebNfh2ka7ywFwHgRAAAnbPzqgms+OyhcwdM3do7ncG7rN5/epbGQfSbGrggBwJgIgAElSS3Ob3v3NdknSpNkV6luSa3NFcKtTl4VjHiDgVARAAJKk93+7Qycb29S3NFeX3TjU7nLgYoNGnTYPMBK1uRoA50IABKC9W4/p8/frJUkz7xktf4CXBvRcv8F5CuYE1NYS0aEa9gMEnIhXecDj2tsiWv78FknS2OmDEvu4AT3l8xmnzQNkGBhwIgIg4HHr3tyjhoMnlVOQqSlzh9ldDtJEfBiYAAg4EwEQ8LAjdU36+A97JEnT7hilYE6GzRUhXcQ3hK7b0aAI8wABxyEAAh5lRk0tf26rohFTFRcXa/hl/e0uCWmkX1mesnIz1B6O6NAe9gMEnIYACHjU5nfrtH9XgzKCfk2/cxR7/sFShs9Q2ag+khgGBpyIAAh4UPPxsFYv3iFJumLOMOUXZdlcEdJRYh4g1wUGHIcACHjQqpe2qbUlogFD8zV+xmC7y0GaGtTRAazf2aBIO/MAASchAAIeU/3pIe385JAMn6GZ3xgjn4+hXyRHUVmusvMz1N4a1cHdIbvLAXAaAiDgIa0t7Vq5aJsk6ZLry1U8ON/mipDODMNQ2Ui2gwGciAAIeMgHv9ulpmNhFRRnafLNlXaXAw+IDwPv3Xrc1joAnIkACHjEgeqQNizfK0m65u7Rysj021wRvGDQ6FgHcP+uBkXamAcIOAUBEPCASCSqZc9tkUxp1BUDNeSifnaXBI/oW5KjnIJMRdqiOrC7we5yAHQgAAIe8OnbtTqyr0nB3ICuvn2k3eXAQwzDYBgYcCACIJDmGg6d0NrXqiVJV902Utn5mTZXBK8pYz9AwHEIgEAaM01TK17Yqva2qAaN7qMxU0rsLgkeNLhjHuCB6pDa2yI2VwNAIgACaW3bhwdU+/kx+QM+zbh7DJd7gy0KB2QrtzBTkfao9u9iP0DACQiAQJo62dSqd/97uyRp0lcq1Gdgjs0VwasMw2AYGHAYAiCQpt5/eYdamtpUVJarS28YYnc58Lj4MDAbQgPOQAAE0tDeLUe1Zc1+yZBmfn2M/AGe6rDXoNF9JMXmAba1Mg8QsBtnBSDNtLdGtPz5rZKkcdMHqWRYoc0VAVJBcbby+gYVjZjav5P9AAG7EQCBNPPRG7vVcOikcgszdeXc4XaXA0iK7wfIPEDAKQiAQBo5sq9Jn/yxRpI0/c7RCmYHbK4IOCU+DMw8QMB+BEAgTUSjppY9t0XRqKnKCcUadml/u0sCzhDvAB7c3ajWlnabqwG8jQAIpInNK/fpQHVIGVl+Tb9zlN3lAF9QUJyt/KIsRaPMAwTsRgAE0kDTsbBWL9kpSbpyznDl9c2yuSLg3BgGBpyBAAikgVUvbVNbS0QDKws07ppBdpcDnNegjv0A9249bm8hgMcRAAGX2/XJIe1af0g+n6EZ94yRz8fl3uBc8XmAh2oa1XqSeYCAXTwbAKuqqjR58mTl5+drwIABmjt3rrZu3Wp3WUC3tJ5s18pFsd/bS2YNUfHgPJsrAi4svyhLBcVZMqOm6nYct7scwLM8GwBXrFih+++/X2vWrNHSpUvV3t6uWbNmqbm52e7SgC5bs2SnmhtaVdA/W5NvqrC7HKBLBiUuC3fc3kIAD/PsJmFvvfXWGV//6le/0oABA7Ru3TpNnz7dpqqArtu/q0EbV+6TJM24e7QCmX6bKwK6ZtCovvr8vXrVsRAEsI1nA+DZGhpiWxIUFRWd9z7hcFjhcDjxdSgUSkotezYfUc2mI0k5NtLHnk1HJFMafWWJyr90/t9bW2xeItWs7vx+/gxp4l9I/bhiiZecPg9w5UvbxKxVXMiQcf00dGw/u8tIOwRASaZpav78+br66qs1bty4896vqqpKP/rRj5Jez4HqkDYs25v0fwful5WboatuH2F3GWdqOyn99ltStK1r9286KM37eXJrgqPk9Q2qb0mOju0/oY281qETwdwMAmASEAAlPfDAA9qwYYPefffdC95vwYIFmj9/fuLrUCik8vJyy+spG1GoiV8eavlxkWYMadgl/ZWdl2l3JWeKtJ4Kf1d9R/Kd52Wm/lNpx9tSK/NuvWjWt8Zq58eHZEZNu0uBw5WNKLS7hLTk+QD44IMP6tVXX9XKlSs1ePDgC943GAwqGAwmvabBY4o0eIzDhvSArjJPO6HP/F9S4DwB9aNfxQIgPKl4cL6KB+fbXQbgWZ4NgKZp6sEHH9Qrr7yi5cuXq7Ky0u6SgDTRzY6OSQcIAFLNswHw/vvv1wsvvKDf/e53ys/P1/79+yVJhYWFys7Otrk6IE0YTO8HACfy7D6ATz/9tBoaGjRjxgyVlpYmPl566SW7SwPc7YyO3gUCYCIc0gEEgFTzbAfQZNgJAAB4lGc7gABS4IJDwAwPA4BdCIAA7EU3HgBSjgAIwFpdnQMIALANARCAxbrY0WOFMADYhgAIIHm6FPIYAgaAVCMAArDW6UPAdPkAwJEIgABs0hEOWQQCAClHAARgMQIdADgdARCAPRgeBgDbEAABWCsxpNvVgEfHEABSjQAIwGIdgY4OHwA4FgEQgE0IiABgFwIgAGt1dwiYVcAAkHIEQAAAAI8J2F0AgHTTxTmAzBH0rEhEWrVKqq+XSkuladMkv9/uqgBvoQMIIElYBYwvWrxYGjFCmjlTuvvu2OcRI2K3A0gdAiAAazGnD+exeLF0++3S+PHS6tVSY2Ps8/jxsdsJgUDqEAABWKyr28BwKTgviUSkhx+Wbr5ZWrJEuvJKKS8v9nnJktjtjzwSux+A5CMAAgCSbtUqafdu6Qc/kHxnnXl8PmnBAqm6OnY/AMlHAARgra5uA8MiEE+pr499HjdOam6OPfyGEftz/PbT7wcguQiAAGzGELAXlJbGPm/adO7vx2+P3w9AchEAAViMS8Hhi6ZNkyoqpIULpWj0zO9Fo1JVlVRZGbsfgOQjAAKwVpevBEJA9BK/X3r8cem116Q77zx1+wcfSHPnxm7/6U/ZDxBIFQIgAHuxCtgz5s2TXn5Z2rz51G3XXRcb/n355dj3AaQGVwIBYDGGgHF+8+ZJ118vFRbGvn7jDWnWLDp/QKrRAQRgj0RApAPoNaeHvenTCX+AHQiAAKzV5TmAAAC7EAABJEdXrwQCAEg5AiAAi3VzSJdFIACQcgRAANZiCBgAHI8ACMAerBIGANsQAAEkR5cDHkPAAJBqBEAAAACPIQACsFZ35wCyCAQAUo4ACCA5mOIHAI5FAARgsS529FgEAgC2IQACsBbbwACA4xEAAQAAPIYACMBiHR1ALgUHAI5FAASQJKwCBgCnIgACsBaBDgAcz9MBcOXKlbrllltUVlYmwzC0ZMkSu0sC0kdnQ8CJ7xMYASDVPB0Am5ubNWHCBD355JN2lwKkEQIdADhdwO4C7DR79mzNnj3b7jKA9NLlbWBYBAIAdvF0AOyucDiscDic+DoUCtlYDeBwXd3omTmDAJBynh4C7q6qqioVFhYmPsrLy+0uCXAgAh0AOB0BsBsWLFighoaGxEdtba3dJQEO1tVFIACAVGMIuBuCwaCCwaDdZQDO1u0hXTqGAJBqdAABWKyrVwIBANjF0x3ApqYm7dixI/F1dXW11q9fr6KiIg0ZMsTGygAvICACgF08HQA/+ugjzZw5M/H1/PnzJUn33nuvnnnmGZuqAlyuy9vAnH1/AECqeDoAzpgxQyYnHyA5GAIGAMdiDiAAi3XxTRWXggMA2xAAASQJHUAAcCoCIABrdXlaBQERAOxCAARgsW5uA8M8XABIOQIggCShwwcATkUABGCtrnb0WCUMALYhAAJIji4HPIaAASDVCIAALEagAwCnIwACSJLOOoAd32cRCACknKevBAK4VSQirVol1ddLpaXStGmS3293VR3IcwDgeHQAAZdZvFgaMUKaOVO6++7Y5xEjYrc7Q3wbmE7uxiIQALANARBwkcWLpdtvl8aPl1avlhobY5/Hj4/d7pwQKHV9GxhahgCQagwBAy4RiUgPPyzdfLO0ZIl0qKZOu9fWKk/SYw9JfUJZ+uf5fo0obJbPxrd2waNbVHwsQ7VtEe1/7TfnvV/W4U0qbsxWuK5Zhy5wP6czMgMqmzBSRsApY/DJMyBngIqzi+0uA4AFDNNkBnZPhUIhFRYWqqGhQQUFBXaXgzS3fHlsuHf1aqk0f4dCt89RwIzaXRYkvTHJ0DM3pH8ADPgCenPemyrJLenVcZqbpby82J+bmqTcXAuKA7qB8zcdQMA16utjn8eNk9a9sk0DzKjazIBCuXkyJJmmodZQUIGcNvkzIrbVGVCbWv3NHV+dvxVpyJRPpkxJUZfORslqM5XXYqr8cEADcwbYXU5SHTl5RO3RdtWEanodAAHYjwAIuERpaezzpk2SorHG/Y7wSN348WLl5sY6g9dNlZYtk6bPsK1MNex6R1ev+o4k6ZNvfKKA7zwvM1vfkl68Qyq7TPrrZSms0DrvPPlr5T1ZpbzoSL391d/aXU5SzXt1nrYf266Iad+bCwDWcefbbsCDpk2TKiqkhQulSCQ29BvtWGgRjUpVVVJlZex+dopGTw1L+4wLvMQkVgG7eBZKOvwMXRQwYkGeAAikBzqAgEv4/dLjj8dW+w44mKOHJZky9MEH0hNPSK+9Jr38sv37AUZ1KgAaXV4J7FKGdzazjof5qFPnnUajUrTNuuP5MmTraiogyQiAgIvMmxcLeb9+yJByYvP+rrsu1vl7+eXY9+0WX1dmmJJxwb3+3B8OL/zzpRe/EXtn0R5tt7mSc2gJSU9fJTXUWHfMomHSfe9JmTnWHRNwEAIg4DLz5kkFhxqk/yOZ8umNN6RZs+zv/MWZHR2iLvdO0qF7lgY/Qmf8vtgvmCM7gIe2Whv+JOnoLunoTqlkvLXHBRyCAAi4kNGROExJ06c7J/xJUrSjNk/0xjo6gIYHEmB8CNiZcwA7/v8Lh0jffrf3h3vycqlpvxR14s8KWIMACLhRR9fMidunxDuAnQbANBg+NTw0BzCxCMTJocjnk7IKLThOx6nRkWEXsIbzzh4AOhWfZ2c6sM8W7wB2/cXFxeEpDUJsVzm6A5gI4BY9HvHFH1EHDncDFiEAAm7UcWIyTec9hePbwDivMuvFO4CGBzqAPp+DA6DVbyI6FrzQAUQ688JrNJB+zFNzAJ3GVBeHgJUGw6eJbQBd/DN0UXwI2JGLQOKs6sh2LHhhDiDSGQEQcKFo1MFDwGZ3h4Bd7EIbXacZdwwBW4QOIDzAO69eQFpx7iKQaFcXgaQBTy0C8blgEYhlcwDpACL9Oe/sAaBTZmIOoPNiVnwIuNMXl0TpLg5PRvyTi3+GLnJ0BzD+/2/VEDAdQHgAARBwIyevAu7uRtAuZsSHgNM//yWuBOLIOYBWd2BZBQwP8MJrNJB2Eh1ABwdAo9NzsvNq765Tl4JL/wQYD4CeGAJOdAAJgEhfBEDAlZw7B9BMLALp4snYzdkpPgTsgTmAnhoC9jEEjPTnvLMHgE6ZEQd3ALs6BzANGD7vdAATi0C8EIoMFoEg/XnhNRpIO4mGkwMXgcS3gfHCpeDiL6GdD3e7n6M7gJZfCYQOINIfARBwo44TU9SBHUCz24tAXJyeEj+ki3+GLnL0IhDLrwQSXwRCAET6IgACbtRxDnbmELB3NoL20j6Afp8LFoFYPgfQiWEXsIYXXqOBtBPvsjl5EYjRaTh1f3iKbwPjvBhuPU8NAdMBhAc47+wBoHPxE54D5wB2fwjYxTx4LWBHdwCtwkbQ8ABPvEYD6SbeZXPiHMAuDwGnwSIQIw1+hq5ydAcwWdvAeCHswrMIgIAbuWEj6C7/Dfd2zxJDwB7oAMbnADpyEYjV//90AOEBBEDAjUwHbwTtoUUgaXE94y5KXAnE0aHIqg5g/BJ/Dgy7gEUCdhdgt6eeekr/+I//qPr6eo0dO1ZPPPGEpk2bZmtNkYi0apVUXy+VlkrTpkl+f8+P9cc/Sj/9qfT559KJE7Fj+Xyx0ZL29lgzyeeLfe7se/HbW1tjfw4Gu/b3zvW9SCT2Z9OUAoFT9+9qLWd/zzBidUmx4wWDp75nd32RyKmPQEDKyurZ/3/8e7ONAn2/ryQZKis7VZ8Vj2lWVu/+P1pbv67Qybu0KepX3oOx+57z8YpOUeuJGsnwK7DgzMeru79zbW2x75umNT/D6X/vQv9/l2YW6KkiqflAtoqKevf7a/X3rP6da2z5plra7tL2QIb+qZfHams79RrV299fn09S+xSdbNytiDLl/975f+e6ckyfT4qceErRyL/I/PtsBYI9f2ysfs20+nsXes3syTFNU8rNlcaMkf7n/5Suv7535y6rzoM4N08HwJdeekkPPfSQnnrqKV111VX693//d82ePVufffaZhgwZYktNixdLDz8s7d596raKCunxx6V587p/rK9/XTp50soKz3TiRPKOnc6amnr399v7xDoUpmkoFLKgoNP0/jHNS/ypuflC9wt2fEjqxs/QlfpS9Xt5ItsvFUlGVDp2LDX/Zk/19ndOypGUo7CkcK+PdYo1v79ZHR+SLvg711UFsU/tklqsON4p6f6aefy4tG+f9Kc/STk50n/9V8/OXVadB3F+nhilOZ+f/exn+su//Et961vf0pe+9CU98cQTKi8v19NPP21LPYsXS7ffLo0fL61eLTU2xj6PHx+7ffHi7h3rttuSG/5gH5+cuxF0bDg0/mHF/Zzr1OYj7v0Zus7Jj5fVtTn5Z3WPEydi56HunrusOg/iwjzbAWxtbdW6dev0/e9//4zbZ82apffffz/l9UQisXc8N98s/f0PV+jwto/1SXXsew/fIeUfGq8nv5OnohOrO22DRyLSP31visYHs5JfOGxRlrFPkuRTVNmGJS2PJLlQQHX/yTXTCEuSgkZY44PrLTlmXVuZTpi5lhzLyXyKqjKzWkZ8V3PLWPGmyP2/m07yz/9vS5fPXf/y/Sm664om/fUdG9VWLX1SLfUfc5mWLLlGc+dKjzwizZnDcLAVDNP0wPK1c6irq9OgQYP03nvvaerUqYnbFy5cqGeffVZbt279wt8Jh8MKh8OJr0OhkMrLy9XQ0KCCgoJe1bN8uTRzZuydTs2/Xa3xa4706ngAAKSDjVf209eeeVerV0tTp0rLlkkzZvTumKFQSIWFhZacv93K00PA0hf38TJN87x7e1VVVamwsDDxUV5eblkd9fWxz+PGSWYw07LjAgDgZvFz4rhxsa/j50v0jmeHgIuLi+X3+7V///4zbj948KAGDhx4zr+zYMECzZ8/P/F1vANohdLS2OdNm6Sv/dufZJ41ee+DD6XrrpXeeFOa3ski5ZWrpJtmW1IWgBRaPeJKZfraNHvXGzrYfu7XoXTx0tCvaURwp+6rfVprT15udzlIsu6cu95ZJl0++dTtY7KzJcXOj9Kp8yV6x7MBMDMzUxMnTtTSpUt16623Jm5funSp5syZc86/EwwGFQwGk1LPtGmxVU4LF0pLlhjy5eQkvheNSlU/k0oqpGnXn9qk/rzHul4qHizV1ialVAc7fTaD1fOAnLjYAukmvq9jm5mpk2ZOJ/dONVPJeB6ElWXBz8pz3xnO/f9WUdH1c9fAodLCx6UlS05txyh1nAerpMrK2PkSvefpIeD58+frP/7jP/TLX/5Sn3/+ub773e+qpqZG9913X8pr8ftjS9xfe02aO/fM1U9z58Zu/+lPuzbx1e+XnngiyQUDsFxUsSe4z0j/DYhPXUaZgJXuHn+86+cuq86D6JxnO4CSdMcdd+jIkSP6u7/7O9XX12vcuHF64403NHToUFvqmTdPevnl2Grg09alqLIydnt39j+aN0/67W+Tvw+gs1h9IuHEhNSKmLH35H458WobyXk+WHM5Q577znDm/1tP9gG08jyIC/PsKmArJGsVEVcC6eGu9u0n1drSKvn8CmTn9f5KIC0nFG0Ly/RlKpCd66grgVj5PauvBCLF3nREIrHbznslkC5ehcDJVwLx+U5tAN2nT+y4vbkqw5vFV6rQ36C7j72hfWalo64EciTUqmg4Nhm/X7/Y7b2p7/mir6gisEv/z5FntSF6ea9+t1sjYYWaWiQzU7nB7F5fCSR0ok2RdlMZAZ9ysgJcCaSLx2yPtOmE/7AKB9XphZ9OdPSVQFgF7PEOoFP5/b1f4n76sWbPjn2kvU1vSC//D6limvTN13p/vOX/Ii1fKE38C+mWJ3p/PKSd5mYpr+PCJ3v3xi6D1ZtjfToh1kFZ9nZEfcZbUKCF5jzxsV797pWSpD17evezStLO2aZaq6XXXpdye7kGZHXdx/rrpX+tUX1H6bd/9tveHUzSQ4s2acn6Ov3NTV/SX00f1uvjecUnBzfpz9/8cw3JH6Ibb3y9V8ey8jyIc/P0HECkGaPj15kLuMOlIh1zAM2I836H/efZHqvHOgafzrftVo8OafEGzlb/yOnOTMJjiuQhACKNdLzoWDaroeM4vJghReKrgJ34JsZn9dnCdO7zi3lRPRPt+L01mEPpCgRApA86gHC5iNkxySnivEUgvmQFNQuOG+84WTWl/VQ2Jch0R7wDy/+bOxAAkT4SLzoWvX9PnEx4MUNqxFfEmlHnvYnx+6x9HpgO7rDzzO8dOoDuQABE+qADCJdLdACjzusAWt7VsXCc1erAcWoum6WHTXvxIWCfQbRwAx4lpBHmAMLdEnMAo86bhWZxA9DRHXbnVuZsVi/CQXIRAJE+rO4AOvgEhfQU7wCaDuwAWj0EnODEpxdzAHuEVcDuQgBE+rB6DiCQYmb8JdmB28BYvgjEwrAQHwK2bBGIGALuifj/v49o4Qo8Skgfls8BZAgYqRVJDAE7sAOYpADoxOdXojR7y3AdVgG7CwEQacTqOYBAakUTQ8De6QBawerAcWr2B0GmOxIBkOjsCgRApI/ECDDbwMCdovHfNScGwKTNAXTelUBOBRl0R2IjaIKzKxAAkT4SWw9Y3AHkxQwp4uSNoP2W7wLjgiFg55XmCnQA3YEAiDSSpG1ggBSJbwNj1WIGK1k/BBz/gxM7gDEEme7hUnDuQgBE+mAbGLhcRM7tAFo+BOzAkBtHB7BnEquA2QjaFXiUkD7YBgYuZ5rxVcAOnAOYrI2gHZmymAPYE6eG9e2tA11DAET6YBsYuFx8GxjTCx3AOAsOa/k+gDz1e4R9AN2FRwlpJFnbwHAWQGrEt4Fx4vWsk9UBdOKKUeYA9gz7ALoLARDpI2lzAIHUiDj4SiCWXwrOwjab9fsAMpTZE+wD6C4EQKSPZM0B5N0sUiSxCtiBVwKxehWwVSt2k4HlXz3DPoDuQgBE+kjWHEDgPE6fqrdyZe8X70YdvQjE+RtBW+VUc9J5tTkZHUB3IQAijVg8B5AhYFzA4sXSxRef+vqmm6QRI2K391RiGxgHBkC/1WcLCy+3llgEYvk+gOgWgrOrEACRPhIdQIaAkVyLF0u33y6NHXvqtnfekcaPj93e0xDo6G1gkrYPoPOeX2ZigYrNhbhMfAiYVcDuwKOE9JF4seZKIEieSER6+GHp5pulRYtO3X755dKSJbHbH3mkZ8PBjt4GxvIrgVi30CJZHScCYPewD6C7EACRRuJDwFZ3T3g1wymrVkm7d0s/+IHkO+sV1OeTFiyQqqtj9+uuxDYwDuwA+pMVAC09pMX7APLc7xbmALpLwO4CAMtYPQTMHECcQ3197PO4cVJu7hd/TcaNO/N+3ZHYBsaBATB5XTbnhYVT+9nZXIjLcCk4d+FRQvpgGxikQGlp7POmTef+fvz2+P26w3TwNjBJ6wA6cREI7/16hA6guxAAkT4s3wYmcWCLjwc3mzZNqqiQFi78YqMuGpWqqqTKytj9uitiOncjaC9dC5htYHomvgiEl0x3IAAijbANDJLP75cef1x67TVp7lxp9WqpsTH2ee7c2O0//Wnsft0RiUjRjm1gdu2M9npPQcuZp87qVux56ORn16lOFrqDawG7C48S0keyOoB0AXCWefOkl1+WNm6Upk6VCgpinzdtit0+b173jhffUzDeAXz2V9Fe7ylopcWLpaq/GpL42oo9DxMsfH5ZvQgEPUPn1B0IgEgfls8B5CyA85s3T9qxQ1q2THrhhdjn7dt7Fv7iewrGLwX3rf8R6fWeglaJ11dW0Zq4zYo9Dx29D2DHZ3JM97APoLuwChjpgzmASDG/X5oxo+d///Q9BZ9/XvrpyNjvcP9jn+pXd76ofz8uvfM96ZqTX9xyJhWiUelPfys9drU0ZvoR/WzjJXrvxNXaqRrd9n1Te4/1130PZqhxYJ183RzyvqQtooCk322oU/hg7364upMHJEmh1ka9tOWlXh1Lko75dsmfkytDl/X6WF7CPoDuQgBEGmEOINwlvqfgiy/GAl5LNFuS1PLuCrW8u0K3SlJAOvi/7avxgQxJByW9LP2iXPqz6lf1d7/fKV9mROGKPjr07lX6//61TllDjnbruC93BMAn3t6uurzjvarRF9yn3GFSqLVBj33wWK+OJUnKlLKHGGpomyGpB8u5PYpVwO5CAET64FJwcJmz9xT83x/eocNPH1P05ElJUnu7tOR30hWXS+Xlqa+vtlb64ENp7hyp5YP3ZDY365v//xrtrbhIktQ63KdfPieN6ztQI8dlduvYgTcNqV2aPqq/GopKelXnoaZCbTg8Q30KGnT1yOJeHUuS3t69TDLadbI91OtjeQn7ALoLARDpI2lzAAmASI7T9xS88kops6JCZX//k8T3V6+Wvvu0tOw70uAZqa9vx3LpuzOlK/9cGrB3jsLbtumha4crd+rERH2/lPS/vjqs20PhW/+PT9Gw9Hdzxilz6NBe1bly2yH9+S+/rOJAgX42owf775zlkl9NU8Q4LhmMAnRH4hrKvGa6AjEd6SMeAK2aA8gQMJIsmXsKWuH0+uKT/MyOPQp7XV8S9gG06hmb2Fia14BuOXUFFQKgGxAAkUYsngOYOCwvZkiOZO0pmIz6dlbHThcnmqOW1Jd4ljry+dURAOkAdkt8FTAdQHcgACJ9WN0BZBsYpIDVewomq77mk7HTxdduj1hTn5WXgku897OuBxg7oPOuyOJkdADdhTmASB+JicdWBzdezJBc8+ZJc+bEVgXX18fmBk6bZl/n72zz5knVL/nU8qn0Px+O6tGZzqrPesYZn9A1zAF0FwIg0kiStoHh3SxSoLd7Ciab0ZH2pl0VUcEMCw5o4UbQ1geO+BxAOoDdQQfQXRgCRvpI2kbQAIz4TtRRa99gOTErJAIlcwC7hQ6gu3g2AP74xz/W1KlTlZOToz59+thdDqzANjBA8iQCYMSa4zm5w27GT428meyOeAeQfQDdwbOPUmtrq7761a/q29/+tt2lwCpWdwDZAgI4xR97fsW3gXESw/INANgGpifoALqLZ+cA/uhHP5IkPfPMM/YWAguxDQyQLEb8Yr9e6ACyDUyPMAfQXTwbAJGGTl8FvOIfen+8uk/iB+79sQC36xgCNi2aA2jlPoDWP0PZBqYn2AfQXQiA3RAOhxUOhxNfh0JcJ9JRAsFYCDSj0rIfW3fcjGzrjgW4lOGCOYCmVfN/TbaB6Qk6gO6SVgHw0UcfTQztns/atWs1adKkHh2/qqqq0+PDRlkF0pynpNoPrD3mJXdbdzzArfzxS8FZFAATnBgWmAPYG3QA3SGtAuADDzygO++884L3qaio6PHxFyxYoPnz5ye+DoVCKi8v7/HxkASX3BX7AGApwx/vADpwkVWSFoHIYAi4O+JDwKwCdoe0CoDFxcUqLi5O2vGDwaCCwWDSjg8AjmXE5wBaHAAd2SyKJ0p7q3AbOqbuklYBsDtqamp09OhR1dTUKBKJaP369ZKkESNGKC8vz97iAMBp4h1Aq7aBMa2bL5a0K4GwD2C3MAfQXTwbAP/2b/9Wzz77bOLrSy+9VJK0bNkyzXDy9ZgAwAZu2AbGsv5TfBEILcBuiXcAfd7dYthVPPsoPfPMMzJN8wsfhD8AOAeLt4FxtlgAjNIB7BY6gO7i2QAIAOg6y7eBSRzYgiHgxCIQq8IpHcCeiLJvoqsQAAEAnUtsA9P7k/wZQc2R3aL4HEACYHdwLWB34VECAHTO1xHUrOgAWhwALY+Q8TmAdLS6hWsBuwsBEADQqfgiEMu2gUkC6/p1XAu4J+gAuguPEgCgc1ZuA+P4/eJOu644uox9AN2FAAgA6NSpDqC1Q8CW7ANoWLxxc8dxTIaAu4VVwO5CAAQAdM6XpA6gI8NCx5Y3dAC7hX0A3YVHCQDQqcQ2MFZ0xaxeBGJ5hmQVcE/QAXQXAiAAoHMWbgNzBgdeCcQkAPYIq4DdhQAIAOiUEd8GJtL7OYCOj1VcCq5H4ldOoQPoDgRAAEDnrNwGJkn7AFp9JRAWgXQPHUB3IQACADoX3wbG6gDoxLBgMgTcG3QA3YEACADolNGxua8l28CccVxnHOOsI3Z8JgB2R/xawHQA3YEACADonAs2grb8SiBiCLg7WAXsLgRAAECnjI5VwE7cBsZqZnwImCtbdEu8A8il4NyBRwkA0DmfhdvAWB4A44HNgkOddjyGgHuGIWB3IAACADpl5TYwZx7YgWGhowMYZQi4W1gF7C4EQABA5yzcBubMRcDOuxKISQewR9gH0F0CdhcAAHA+o2MRSOj3v+/1scz2tl4f41wON4X10KJPen2ctnZTfknv7V+q76/a0/vCPGLjoY2S6AC6BQEQANApf1FR4s9WhEBJ8uXlnVpc0gt9czIlSSdaI1qyvq7XxwsOyJFf0u7G7drduL3Xx/Oavll97S4BXUAABAB0Kv/GG1UWjar92DHLjplz2WUyAr0/DVUW5+o/752k6sPNFlQltUTKddx3mYb0y7DkeF6Sn5mv2ZWz7S4DXWCYrHPvsVAopMLCQjU0NKigoMDucgAAQBdw/mYRCAAAgOcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4TMDuAtzMNE1JUigUsrkSAADQVfHzdvw87kUEwF5obGyUJJWXl9tcCQAA6K7GxkYVFhbaXYYtDNPL8beXotGo6urqlJ+fL8Mw7C7nvEKhkMrLy1VbW6uCggK7y/EsHgdn4HFwBh4HZ/Dq42CaphobG1VWViafz5uz4egA9oLP59PgwYPtLqPLCgoKPPUEdyoeB2fgcXAGHgdn8OLj4NXOX5w3Yy8AAICHEQABAAA8hgDoAcFgUD/84Q8VDAbtLsXTeBycgcfBGXgcnIHHwbtYBAIAAOAxdAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwB0AOeeuopVVZWKisrSxMnTtSqVavsLslTHn30URmGccZHSUmJ3WWlvZUrV+qWW25RWVmZDMPQkiVLzvi+aZp69NFHVVZWpuzsbM2YMUObN2+2p9g01tnj8M1vfvMLz48rr7zSnmLTVFVVlSZPnqz8/HwNGDBAc+fO1datW8+4D88H7yEAprmXXnpJDz30kP7mb/5Gn3zyiaZNm6bZs2erpqbG7tI8ZezYsaqvr098bNy40e6S0l5zc7MmTJigJ5988pzf/4d/+Af97Gc/05NPPqm1a9eqpKREN9xwQ+Ia37BGZ4+DJH35y18+4/nxxhtvpLDC9LdixQrdf//9WrNmjZYuXar29nbNmjVLzc3NifvwfPAgE2nt8ssvN++7774zbhszZoz5/e9/36aKvOeHP/yhOWHCBLvL8DRJ5iuvvJL4OhqNmiUlJeZPfvKTxG0tLS1mYWGh+W//9m82VOgNZz8Opmma9957rzlnzhxb6vGqgwcPmpLMFStWmKbJ88Gr6ACmsdbWVq1bt06zZs064/ZZs2bp/ffft6kqb9q+fbvKyspUWVmpO++8U7t27bK7JE+rrq7W/v37z3huBINBXXPNNTw3bLB8+XINGDBAo0aN0l/91V/p4MGDdpeU1hoaGiRJRUVFkng+eBUBMI0dPnxYkUhEAwcOPOP2gQMHav/+/TZV5T1XXHGFfv3rX+sPf/iDfvGLX2j//v2aOnWqjhw5YndpnhX//ee5Yb/Zs2fr+eef1zvvvKPHH39ca9eu1bXXXqtwOGx3aWnJNE3Nnz9fV199tcaNGyeJ54NXBewuAMlnGMYZX5um+YXbkDyzZ89O/Hn8+PGaMmWKhg8frmeffVbz58+3sTLw3LDfHXfckfjzuHHjNGnSJA0dOlSvv/665s2bZ2Nl6emBBx7Qhg0b9O67737hezwfvIUOYBorLi6W3+//wju4gwcPfuGdHlInNzdX48eP1/bt2+0uxbPiq7B5bjhPaWmphg4dyvMjCR588EG9+uqrWrZsmQYPHpy4neeDNxEA01hmZqYmTpyopUuXnnH70qVLNXXqVJuqQjgc1ueff67S0lK7S/GsyspKlZSUnPHcaG1t1YoVK3hu2OzIkSOqra3l+WEh0zT1wAMPaPHixXrnnXdUWVl5xvd5PngTQ8Bpbv78+frGN76hSZMmacqUKfr5z3+umpoa3XfffXaX5hmPPPKIbrnlFg0ZMkQHDx7UY489plAopHvvvdfu0tJaU1OTduzYkfi6urpa69evV1FRkYYMGaKHHnpICxcu1MiRIzVy5EgtXLhQOTk5uvvuu22sOv1c6HEoKirSo48+qttuu02lpaXavXu3fvCDH6i4uFi33nqrjVWnl/vvv18vvPCCfve73yk/Pz/R6SssLFR2drYMw+D54EW2rkFGSvzrv/6rOXToUDMzM9O87LLLEkv/kRp33HGHWVpaamZkZJhlZWXmvHnzzM2bN9tdVtpbtmyZKekLH/fee69pmrGtL374wx+aJSUlZjAYNKdPn25u3LjR3qLT0IUehxMnTpizZs0y+/fvb2ZkZJhDhgwx7733XrOmpsbustPKuf7/JZm/+tWvEvfh+eA9hmmaZupjJwAAAOzCHEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAEAADzm/wIvtJDOE88TpgAAAABJRU5ErkJggg==' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "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",
    "sigma=array([[1 if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])\n",
    "eye=array([[1 if times_extended[i]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])\n",
    "eye_shifted1=array([[1 if times_extended[i]-times_extended_min[j-1]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])\n",
    "#eye_shifted1=concatenate([[zeros(times_extended.shape[0])],eye_shifted1.T]).T\n",
    "\n",
    "(eye_shifted1-eye)\n",
    "plt.figure();plt.plot(times_extended,sigma.dot(steps))\n",
    "#plt.plot(times_extended,eye.dot(sigma_shifted_func.dot(steps)))\n",
    "#plt.plot(times_extended_min,sigma_shifted_func.dot(steps))\n",
    "#plt.plot(times_extended,eye.dot(sigma_shifted_func.dot(steps)))\n",
    "sigma=array([[1 if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])\n",
    "sigma_t=array([[(times_extended[i]-times_extended[j]) if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])\n",
    "\n",
    "shifted_steps_ext_func=(eye_shifted1-eye).dot(sigma_shifted_func.dot(steps)) # concatenate([[[0,0,0]],(eye_shifted1-eye).dot(sigma_shifted_func.dot(steps))[:-1,:]])#\n",
    "shifted_slopes_ext_func=sigma.dot(shifted_steps_ext_func)\n",
    "synth_ramps_ext_func=initial_vals+sigma_t.dot(shifted_steps_ext_func)\n",
    "for j in [0]:\n",
    "    markerline, stemlines, baseline=plt.stem(times_extended,shifted_steps_ext_func[:,j],linefmt=['blue','orange','green'][j])\n",
    "    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')\n",
    "    plt.plot(times_extended,shifted_slopes_ext_func[:,j])\n",
    "    plt.plot(times_extended,synth_ramps_ext_func[:,j])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66907c34",
   "metadata": {},
   "source": [
    "## low-pass filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1ff5efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7361b384ecd0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0631bce4b09143019d4322006586b4b7",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=700.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_max=120*2\n",
    "t=linspace(0,2*pi*5*4,n_max)\n",
    "T=2*pi*5*4/(n_max-1)\n",
    "t_shift=T\n",
    "omega0=1/(30*T)\n",
    "beta=exp(-omega0*T)\n",
    "\n",
    "steps=array([[-1 if j==30 or j==50 else 0] for j in range(n_max)])-array([[-1 if j==30+1 or j==50+1 else 0] for j in range(n_max)])\n",
    "sigma=array([[1 if t[i]-t[j]>=0 else 0 for j in range(t.shape[0])] for i in range(t.shape[0])])\n",
    "sigma_t=array([[(t[i]-t[j]) if t[i]-t[j]>=0 else 0 for j in range(t.shape[0])] for i in range(t.shape[0])])\n",
    "\n",
    "synth_ramps=sigma_t.dot(steps)\n",
    "slopes=sigma.dot(steps)\n",
    "\n",
    "\n",
    "sigma_t_shifted=array([[(t[i]-t[j]-t_shift) if t[i]-t[j]-t_shift>=0 else 0 for j in range(t.shape[0])] for i in range(t.shape[0])])\n",
    "synth_ramps_shifted=sigma_t_shifted.dot(steps)\n",
    "\n",
    "synth_ramps_filtered=beta*synth_ramps_shifted+(1-beta)*synth_ramps\n",
    "#synth_ramps_filtered=synth_ramps*(1-exp(-omega0*t.reshape([n_max,1])))\n",
    "\n",
    "\n",
    "fig,ax_list=plt.subplots(nrows=3,ncols=1,constrained_layout=True,sharex=True,figsize=(7,7))\n",
    "for j in range(steps.shape[1]):\n",
    "    l1,=ax_list[0].plot(t,synth_ramps[:,j],marker=['o','<','s'][j],label=f'series i={j+1}')\n",
    "    ax_list[0].plot(t,synth_ramps_shifted[:,j],'-.',marker=['o','<','s'][j],fillstyle='none',label=f'series i={j+1} (shifted i=1)')\n",
    "    ax_list[0].plot(t,synth_ramps_filtered[:,j],'-.',marker=['o','<','s'][j],fillstyle='none',label=f'series i={j+1} (filtered i=1)')\n",
    "    markerline, stemlines, baseline=ax_list[1].stem(t,steps[:,j],markerfmt=['o','<','s'][j],linefmt=['blue','orange','green'][j],label=f'series i={j+1}')\n",
    "    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')\n",
    "    ax_list[2].plot(t,slopes[:,j],linestyle=['-','--','-.'][j],label=f'series i={j+1}')\n",
    "\n",
    "\n",
    "ax_list[-1].set_xlabel('time $t_j$')\n",
    "ax_list[0].set_ylabel('$y_i$')\n",
    "ax_list[1].set_ylabel('$a_{i,j}$')\n",
    "ax_list[2].set_ylabel(r'$\\frac{dy_i}{dt}$')\n",
    "ax_list[0].legend(loc='center left',bbox_to_anchor=[1.01,0.5]);\n",
    "ax_list[1].legend(loc='center left',bbox_to_anchor=[1.01,0.5])\n",
    "ax_list[2].legend(loc='center left',bbox_to_anchor=[1.01,0.5])\n"
   ]
  }
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synthetic_ramp_method.py

# ---
# jupyter:
#   jupytext:
#     cell_metadata_filter: -all
#     formats: ipynb,py:percent
#     text_representation:
#       extension: .py
#       format_name: percent
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#       jupytext_version: 1.16.7
#   kernelspec:
#     display_name: main
#     language: python
#     name: python3
# ---

# %% [markdown]
# ## synthetic ramp method
#
# calculate synthetic ramps using convolution for a given series of time points:
# * define matrix $A$ with the coefficients $a_{i,j}$ of the unit steps in $n$ different series $i$ for each time $t_j$.
# * define matrix $\sigma(t-t_j)*\sigma(t)=(t-t_j)\cdot \sigma(t-t_j)$ which is lower triangular with permutations of $(t-t_j)\cdot \sigma(t-t_j)$
#     * example with $t_0<t_1$: $(t_0-t_1)\cdot \sigma(t_0-t_1)=0$, and $(t_1-t_0)\cdot \sigma(t_1-t_0)=(t_1-t_0)$ due to step definition
# * the matrix $\sigma(t-t_j)\cdot A$ has then the slopes $\frac{dy_i}{dt}$ at times $t_j$ and $(t-t_j)\cdot \sigma(t-t_j)\cdot A$ has the synthetic ramps by convolution.
# $$
# \underbrace{\left[\begin{array}{cccc}
# 1 & 0 & 0 & ...\\
# 1 & 1 & 0 & ...\\
# 1 & 1 & 1 & ...\\
# ... & ... & ... & ...
# \end{array}\right]}_{\sigma(t-t_j)}\cdot 
# \underbrace{\left[\begin{array}{ccc}
# a_{1,0} & a_{2,0} & ...\\
# a_{1,1} & a_{2,1} & ...\\
# a_{1,2} & a_{2,2} & ...\\
# ... & ... & ...\\
# \end{array}\right]}_{A} = 
# \underbrace{\left[\begin{array}{ccc}
# a_{1,0} & a_{2,0} & ...\\
# a_{1,0}+a_{1,1} & a_{2,0}+a_{2,1} & ...\\
# a_{1,0}+a_{1,1}+a_{1,2} & a_{2,0}+a_{2,1}a_{2,2} & ...\\
# ... & ... & ...\\
# \end{array}\right]}_{\mathrm{slopes}}
# $$
#
# $$
# \underbrace{\left[\begin{array}{cccc}
# t_0-t_0 & 0 & 0 & ...\\
# t_1-t_0 & t_1-t_1 & 0 & ...\\
# t_2-t_0 & t_2-t_1 & t_2-t_2 & ...\\
# ... & ... & ... & ...
# \end{array}\right]}_{(t-t_j)\cdot \sigma(t-t_j)}\cdot 
# \underbrace{\left[\begin{array}{ccc}
# a_{1,0} & a_{2,0} & ...\\
# a_{1,1} & a_{2,1} & ...\\
# a_{1,2} & a_{2,2} & ...\\
# ... & ... & ...\\
# \end{array}\right]}_{A} = 
# \underbrace{\left[\begin{array}{ccc}
# a_{1,0}\cdot(t_0-t_0) & a_{2,0}\cdot(t_0-t_0) & ...\\
# a_{1,0}\cdot(t_1-t_0)+a_{1,1}\cdot(t_1-t_1) & a_{2,0}\cdot(t_1-t_0)+a_{2,1}\cdot(t_1-t_1) & ...\\
# a_{1,0}\cdot(t_2-t_0)+a_{1,1}\cdot(t_2-t_1)+a_{1,2}\cdot(t_2-t_2) & a_{2,0}\cdot(t_2-t_0)+a_{2,1}\cdot(t_2-t_1)+a_{2,2}\cdot(t_2-t_2) & ...\\
# ... & ... & ...\\
# \end{array}\right]}_{\mathrm{synth\ curves}}
# $$
#
# example below with three series $i=\{1,2,3\}$

# %% [markdown]
#
# ### filtering to delay/advance signals according to one of the signals
#
# problem description: 
# * based on one of the signals as key signal: $y_a$
# * when $y_a$ increases, other signals should be delayed by a fixed time $\delta t$
# * when $y_a$ decreases, all other signals should be advanced by a fixed time $\delta t$
#
# solution: use filtering to obtain adapted $\sigma(t-t_j\pm\delta t)*\sigma(t)=\sigma(t-t_j\pm\delta t)\cdot (t-t_j\pm\delta t)$ , where 
# * $\pm\delta t$ is negative (delaying $\sigma$) whenever the slope (and delayed slope) of $y_a$ is positive
# * $\pm\delta t$ is positive (advancing $\sigma$) whenever the slope (and advanced slope) of $y_a$ is negative
#
# This ensures positive steady states are reached including delay, or negative steady states are reached including advancement.
#
# Method:
# 1. extend times vector in every time $t_j$ by the next time offsets by time-shift $\pm \delta t$, thereby triplicating its size
# >(TODO: save space by extending only based on actual derivatives of $y_a$)
# 2. calculate slopes for extended times vector as $\sigma(t-t_j)\cdot A$, delayed slopes as $\sigma(t-t_j-\delta t)\cdot A$ and advanced slopes $\sigma(t-t_j+\delta t)\cdot A$
# 3. based on slopes obtained, calculate elementwise the matrices $\sigma(t-t_j\pm\delta t)$ and $(t-t_j\pm\delta t)\cdot\sigma(t-t_j\pm\delta t)$, determining the sign of $\pm \delta t$ by each corresponding slope
# 4. filtered slopes are $\sigma(t-t_j\pm\delta t)\cdot A$ and filtered ramps are $(t-t_j\pm\delta t)\cdot\sigma(t-t_j\pm\delta t)\cdot A$ as above

# %%
from numpy import array,linspace,zeros,pi,exp
from matplotlib import pyplot as plt

steps_times=array([
    [0,0,0,0],
    [3,0,-1,0],
    [4,0,1,0],
    [6,3/5,0,0],
    [8,0,2,3/5],
    [8.5,0,-2,0],
    [11,-3/5,0,0],
    [14,-1.5/2,0,-3/5],
    [16,-1.5/2,0.5,-3/4],
    [17,1.5,-0.5,-3/4],
    [21,0,0,+3/2],
])
t_shift=1
times=steps_times[:,0]
steps=steps_times[:,1:]
initial_vals=array([2,1,2])

eye=array([[1 if times[i]-times[j]==0 else 0 for j in range(times.shape[0])] for i in range(times.shape[0])])
sigma=array([[1 if times[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times.shape[0])])
sigma_t=array([[(times[i]-times[j]) if times[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times.shape[0])])

slopes_min=sigma.dot(steps)
synth_ramps_min=initial_vals+sigma_t.dot(steps)

times_extended_min=array(list(set(list(times)+[x for x in list(times-t_shift)+list(times+t_shift)]))) # include all unique points shifted by +/- t_shift
times_extended_min.sort()
eye=array([[1 if times_extended_min[i]-times[j]==0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])
eye_shifted=array([[1 if times_extended_min[i]-times[j]-t_shift==0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])
sigma=array([[1 if times_extended_min[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])
sigma_t=array([[(times_extended_min[i]-times[j]) if times_extended_min[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])
sigma_shifted=array([[1 if times_extended_min[i]-times[j]-t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])

slopes=sigma.dot(steps)
synth_ramps=initial_vals+sigma_t.dot(steps)
shifted_steps=eye_shifted.dot(steps)
shifted_slopes=sigma_shifted.dot(steps)

eye_shifted_neg=array([[1 if times_extended_min[i]-times[j]+t_shift==0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])
sigma_shifted_neg=array([[1 if times_extended_min[i]-times[j]+t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended_min.shape[0])])
shifted_steps_neg=eye_shifted_neg.dot(steps)
shifted_slopes_neg=sigma_shifted_neg.dot(steps)

sigma_shifted_func=zeros([times_extended_min.shape[0],times.shape[0]])
sigma_t_shifted_func=zeros([times_extended_min.shape[0],times.shape[0]])
eye_shifted_func=zeros([times_extended_min.shape[0],times.shape[0]])
for i in range(times_extended_min.shape[0]):
    for j in range(times.shape[0]):
        t_shift_func=0
        if (shifted_slopes[i,0]>0) | (slopes[i,0]>0):
            t_shift_func=+t_shift
        elif (slopes[i,0]<0) | (shifted_slopes_neg[i,0]<0):
            t_shift_func=-t_shift
        sigma_shifted_func[i,j]=1 if (times_extended_min[i]-times[j]-t_shift_func>=0) else 0
        eye_shifted_func[i,j]=1 if (times_extended_min[i]-times[j]-t_shift_func==0) else 0 # doesn't work to produce steps to integrate, some duplication of steps
        sigma_t_shifted_func[i,j]=(times_extended_min[i]-times[j]-t_shift_func) if (times_extended_min[i]-times[j]-t_shift_func>=0) else 0

#shifted_steps_func=eye_shifted_func.dot(steps) # doesn't work to produce steps to integrate, some duplication of steps
shifted_slopes_func=sigma_shifted_func.dot(steps)
shifted_synth_ramps=initial_vals+sigma_t_shifted_func.dot(steps)

eye_ext=array([[1 if times_extended_min[i]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])
eye_ext_shift1=array([[1 if times_extended_min[i-1]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])
#eye_ext_shift1=concatenate([[zeros(times_extended_min.shape[0])],eye_ext_shift1])
sigma_ext=array([[1 if times_extended_min[i]-times_extended_min[j]>=0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])
sigma_t_ext=array([[(times_extended_min[i]-times_extended_min[j]) if times_extended_min[i]-times_extended_min[j]>=0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended_min.shape[0])])

shifted_steps_func=(eye_ext-eye_ext_shift1).dot(sigma_shifted_func.dot(steps))
reintegrated_ramps_from_steps=initial_vals+sigma_t_ext.dot((eye_ext-eye_ext_shift1).dot(sigma_shifted_func.dot(steps)))

idx_output = (shifted_steps_func!=0).any(axis=1)

times_extended=array(list(set(linspace(times.min(),times.max(),1000).tolist()+times_extended_min.tolist())))
times_extended.sort()

sigma_extended=array([[1 if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])
sigma_t_extended=array([[(times_extended[i]-times[j]) if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])
slopes_extended=sigma_extended.dot(steps)

sigma_extended_shifted=array([[1 if times_extended[i]-times[j]-t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])
sigma_extended_shifted_neg=array([[1 if times_extended[i]-times[j]+t_shift>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])

slopes_extended_shifted=sigma_extended_shifted.dot(steps)
slopes_extended_shifted_neg=sigma_extended_shifted_neg.dot(steps)

eye=array([[1 if times_extended[i]-times[j]==0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])
sigma=array([[1 if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])
slopes_extended_shifted_func=sigma.dot(steps)


# increase size of obtained arrays from times_extended_min to times_extended
eye_ext=array([[1 if times_extended[i]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])
eye_ext_shift1=array([[1 if times_extended[i]-times_extended_min[j-1]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])
sigma_ext=array([[1 if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])
sigma_t_ext=array([[(times_extended[i]-times_extended[j]) if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])

shifted_steps_ext_func=(eye_ext_shift1-eye_ext).dot(sigma_shifted_func.dot(steps))
shifted_slopes_ext_func=sigma_ext.dot(shifted_steps_ext_func)
synth_ramps_ext_func=initial_vals+sigma_t_ext.dot(shifted_steps_ext_func)

print('lower triangular (t-tj)sigma(t-tj)) \n',sigma_t)
fig,ax_list=plt.subplots(nrows=5,ncols=1,height_ratios=[1,1/2,1/2,1/2,1/2],constrained_layout=True,sharex=True,figsize=[9,9])
for j in range(steps.shape[1]):
    l1,=ax_list[0].plot(times,synth_ramps_min[:,j],marker=['o','<','s'][j],label=f'series i={j+1}')
    ax_list[0].plot(times_extended_min[idx_output],shifted_synth_ramps[idx_output,j],'-.',marker=['o','<','s'][j],color=l1.get_color(),fillstyle='none',label=f'series i={j+1} (filtered i=1)')
    markerline, stemlines, baseline=ax_list[1].stem(times,steps[:,j],markerfmt=['o','<','s'][j],linefmt=['blue','orange','green'][j],label=f'series i={j+1}')
    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')
    markerline, stemlines, baseline=ax_list[3].stem(times_extended_min[idx_output],shifted_steps_func[idx_output,j],markerfmt=['o','<','s'][j],linefmt=['blue','orange','green'][j],label=f'series i={j+1}')
    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')
    ax_list[2].plot(times_extended,slopes_extended[:,j],linestyle=['-','--','-.'][j],label=f'series i={j+1}')

ax_list[0].plot(times_extended_min[idx_output],reintegrated_ramps_from_steps[idx_output,0],':',marker='none',color='red',fillstyle='none',label=f'series i={0+1} reconstructed')
ax_list[4].plot(times_extended,slopes_extended[:,0],['-','--','-.'][0],label=f'series i={0+1}, slope')
ax_list[4].plot(times_extended,slopes_extended_shifted[:,0],['-','--','-.'][1],label=f'series i={0+1}, delay')
ax_list[4].plot(times_extended,slopes_extended_shifted_neg[:,0],['-','--','-.'][2],label=f'series i={0+1}, adv')
ax_list[4].plot(times_extended+t_shift,shifted_slopes_ext_func[:,0],['-','--','-.',':'][3],label=f'series i={0+1}, func')
for j in range(len(times)):
    ax_list[0].axvline(times[j],linestyle='--',color=[0.85,0.85,0.85],zorder=-1)
    ax_list[1].axvline(times[j],linestyle='--',color=[0.85,0.85,0.85],zorder=-1)
    ax_list[2].axvline(times[j],linestyle='--',color=[0.85,0.85,0.85],zorder=-1)
for j in times_extended_min[idx_output]:
    ax_list[4].axvline(j,linestyle='--',color=[0.6,0.6,0.6],zorder=-1)
    ax_list[3].axvline(j,linestyle='--',color=[0.6,0.6,0.6],zorder=-1)
    ax_list[0].axvline(j,linestyle='--',color=[0.6,0.6,0.6],zorder=-1)

ax_list[1].plot()

ax_list[-1].set_xlabel('time $t_j$')
ax_list[0].set_ylabel('$y_i$')
ax_list[1].set_ylabel('$a_{i,j}$')
ax_list[2].set_ylabel(r'$\frac{dy_i}{dt}$')
ax_list[3].set_ylabel('$a_{i,j}$ filtered')
ax_list[0].legend(loc='center left',bbox_to_anchor=[1.01,0.5]);
ax_list[1].legend(loc='center left',bbox_to_anchor=[1.01,0.5])
ax_list[2].legend(loc='center left',bbox_to_anchor=[1.01,0.5])
ax_list[3].legend(loc='center left',bbox_to_anchor=[1.01,0.5])
ax_list[4].legend(loc='center left',bbox_to_anchor=[1.01,0.5]);

# %%
# %matplotlib widget
sigma=array([[1 if times_extended[i]-times[j]>=0 else 0 for j in range(times.shape[0])] for i in range(times_extended.shape[0])])
eye=array([[1 if times_extended[i]-times_extended_min[j]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])
eye_shifted1=array([[1 if times_extended[i]-times_extended_min[j-1]==0 else 0 for j in range(times_extended_min.shape[0])] for i in range(times_extended.shape[0])])
#eye_shifted1=concatenate([[zeros(times_extended.shape[0])],eye_shifted1.T]).T

(eye_shifted1-eye)
plt.figure();plt.plot(times_extended,sigma.dot(steps))
#plt.plot(times_extended,eye.dot(sigma_shifted_func.dot(steps)))
#plt.plot(times_extended_min,sigma_shifted_func.dot(steps))
#plt.plot(times_extended,eye.dot(sigma_shifted_func.dot(steps)))
sigma=array([[1 if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])
sigma_t=array([[(times_extended[i]-times_extended[j]) if times_extended[i]-times_extended[j]>=0 else 0 for j in range(times_extended.shape[0])] for i in range(times_extended.shape[0])])

shifted_steps_ext_func=(eye_shifted1-eye).dot(sigma_shifted_func.dot(steps)) # concatenate([[[0,0,0]],(eye_shifted1-eye).dot(sigma_shifted_func.dot(steps))[:-1,:]])#
shifted_slopes_ext_func=sigma.dot(shifted_steps_ext_func)
synth_ramps_ext_func=initial_vals+sigma_t.dot(shifted_steps_ext_func)
for j in [0]:
    markerline, stemlines, baseline=plt.stem(times_extended,shifted_steps_ext_func[:,j],linefmt=['blue','orange','green'][j])
    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')
    plt.plot(times_extended,shifted_slopes_ext_func[:,j])
    plt.plot(times_extended,synth_ramps_ext_func[:,j])

# %% [markdown]
# ## low-pass filter

# %%
n_max=120*2
t=linspace(0,2*pi*5*4,n_max)
T=2*pi*5*4/(n_max-1)
t_shift=T
omega0=1/(30*T)
beta=exp(-omega0*T)

steps=array([[-1 if j==30 or j==50 else 0] for j in range(n_max)])-array([[-1 if j==30+1 or j==50+1 else 0] for j in range(n_max)])
sigma=array([[1 if t[i]-t[j]>=0 else 0 for j in range(t.shape[0])] for i in range(t.shape[0])])
sigma_t=array([[(t[i]-t[j]) if t[i]-t[j]>=0 else 0 for j in range(t.shape[0])] for i in range(t.shape[0])])

synth_ramps=sigma_t.dot(steps)
slopes=sigma.dot(steps)


sigma_t_shifted=array([[(t[i]-t[j]-t_shift) if t[i]-t[j]-t_shift>=0 else 0 for j in range(t.shape[0])] for i in range(t.shape[0])])
synth_ramps_shifted=sigma_t_shifted.dot(steps)

synth_ramps_filtered=beta*synth_ramps_shifted+(1-beta)*synth_ramps
#synth_ramps_filtered=synth_ramps*(1-exp(-omega0*t.reshape([n_max,1])))


fig,ax_list=plt.subplots(nrows=3,ncols=1,constrained_layout=True,sharex=True,figsize=(7,7))
for j in range(steps.shape[1]):
    l1,=ax_list[0].plot(t,synth_ramps[:,j],marker=['o','<','s'][j],label=f'series i={j+1}')
    ax_list[0].plot(t,synth_ramps_shifted[:,j],'-.',marker=['o','<','s'][j],fillstyle='none',label=f'series i={j+1} (shifted i=1)')
    ax_list[0].plot(t,synth_ramps_filtered[:,j],'-.',marker=['o','<','s'][j],fillstyle='none',label=f'series i={j+1} (filtered i=1)')
    markerline, stemlines, baseline=ax_list[1].stem(t,steps[:,j],markerfmt=['o','<','s'][j],linefmt=['blue','orange','green'][j],label=f'series i={j+1}')
    stemlines.set_linestyle('-');markerline.set_markerfacecolor('none')
    ax_list[2].plot(t,slopes[:,j],linestyle=['-','--','-.'][j],label=f'series i={j+1}')


ax_list[-1].set_xlabel('time $t_j$')
ax_list[0].set_ylabel('$y_i$')
ax_list[1].set_ylabel('$a_{i,j}$')
ax_list[2].set_ylabel(r'$\frac{dy_i}{dt}$')
ax_list[0].legend(loc='center left',bbox_to_anchor=[1.01,0.5]);
ax_list[1].legend(loc='center left',bbox_to_anchor=[1.01,0.5])
ax_list[2].legend(loc='center left',bbox_to_anchor=[1.01,0.5])