{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "0caed09d-661f-46a0-bc5e-74c69a72fcb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "import scipy.io as sio\n",
    "import scipy.stats.qmc as qmc\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f27b25e5-b907-4922-bff0-ee13ed60f246",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b2f9d1c8-fc8f-4829-b27e-84d9ed014593",
   "metadata": {},
   "outputs": [],
   "source": [
    "## DEFINE INPUT PARAMETERS\n",
    "# defining velocity, density, and viscocity for fluid\n",
    "u0 = 1 # lid velocity\n",
    "rho0 = 1\n",
    "nu0 = 0.01\n",
    "N = 4400 # number of collocation data points [x,y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "afa29b5a-5efc-449f-b0e7-1cdfc9089c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Uniform_with_Cylinder(u0, N_col=4000, N_BC=500, N_circ=400, r=0.5):\n",
    "    \"\"\"\n",
    "    Create collocation points inside a 1by1 box with a cylinder in the middle\n",
    "\n",
    "    Args:\n",
    "        u0 : Inlet velocity\n",
    "        N_col : number of collocation points # note the acc number is slightly less as we are removing points in the cylinder\n",
    "        N_circ : Number of points along cylinder boundary\n",
    "        r : Cylinder radius \n",
    "    \n",
    "    Returns: \n",
    "        Tensors of:\n",
    "        X_col : Collocation points [x, y]\n",
    "        BC_X : Boundary points [x, y]\n",
    "        BC_Y : Boundary condition values [u, v]\n",
    "    \"\"\"\n",
    "\n",
    "    # setting up domain size\n",
    "    xc, yc = 0, 0  # cylinder center\n",
    "\n",
    "    # CREATING collocation points\n",
    "    # set random points across the domain using the random normal distribution function hence *3 or *10 to change standard dev and get distribution centred round cylinder\n",
    "    y = 2*np.random.randn(N_col,1)\n",
    "    x = 4*np.random.randn(N_col,1)\n",
    "    \n",
    "    xy = np.hstack((x, y))  \n",
    "    \n",
    "\n",
    "    # Distance from cylinder center\n",
    "    dist = np.sqrt((xy[:, 0] - xc)**2 + (xy[:, 1] - yc)**2)\n",
    "\n",
    "    # Keep only points outside the cylinder. Boolean mask for cylinder and BCs\n",
    "    valid_col = (dist > r) & (xy[:,0]>-10) & (xy[:,1]>-7) & (xy[:,1]< 7)\n",
    "    xy_outside = xy[valid_col]\n",
    "\n",
    "    X_col = torch.tensor(xy_outside, dtype=torch.float32)\n",
    "\n",
    "    # CREATING Boundary condition locations\n",
    "    # domain currently set y = -7 to 7 : x = -10 to 25\n",
    "\n",
    "    # evenly distribute BC points along the sides\n",
    "    # N_BC = int(np.sqrt(N_col) // 2)\n",
    "    y = np.linspace(-7, 7, N_BC)\n",
    "    x = np.linspace(-10, 35, N_BC)\n",
    "\n",
    "    # turn those points into x,y co-ords\n",
    "    b_left   = np.column_stack([(np.ones_like(y)*-10), y])\n",
    "    b_right  = np.column_stack([np.ones_like(y)*35, y])\n",
    "    b_bottom = np.column_stack([x, np.ones_like(x)*-7])\n",
    "    b_top    = np.column_stack([x, np.ones_like(x)*7])\n",
    "\n",
    "    # cylinder BCs\n",
    "    theta = np.linspace(0, 2*np.pi, N_circ)\n",
    "    cylinder_x = (r * np.cos(theta) + xc)\n",
    "    cylinder_y = (r * np.sin(theta) + yc)\n",
    "    cylinder_xy = np.column_stack([cylinder_x, cylinder_y])\n",
    "\n",
    "    # Add all BC points\n",
    "    BC_X_np = np.vstack([b_left, cylinder_xy])\n",
    "    BC_X = torch.tensor(BC_X_np, dtype=torch.float32)\n",
    "\n",
    "    # CREATING the BC_Y essentially 0s everywhere except inlet\n",
    "    # tensor of zeros for all walls except inlet\n",
    "    # walls_Y = torch.zeros_like(torch.tensor(np.vstack([b_top, b_bottom, b_right]), dtype=torch.float32))\n",
    "\n",
    "    # inlet velocity\n",
    "    inlet = torch.zeros_like(torch.tensor(b_left, dtype=torch.float32))\n",
    "    inlet[:, 0] = u0\n",
    "\n",
    "    # Cylinder no slip so 0\n",
    "    cylinder_uv = torch.zeros_like(torch.tensor(cylinder_xy, dtype=torch.float32))\n",
    "\n",
    "    # Combine boundary velocities\n",
    "    BC_Y = torch.cat([inlet, cylinder_uv], dim=0)\n",
    "\n",
    "    return X_col, BC_X, BC_Y\n",
    "\n",
    "def Data(N=1000):\n",
    "    engine = qmc.LatinHypercube(d=1)\n",
    "    \"\"\"\n",
    "    loads data from benchmark file\n",
    "\n",
    "    Args:\n",
    "        \n",
    "    Returns: As Tensors (dim = Nx2)\n",
    "        X_D   : coordinates of data points [x,y] for u,v, dim = [N*2]\n",
    "        U_D   : training output [u*, v*] at data points X_D, dim = [N*2] \n",
    "        PXY_D : coordinates of data points [x,y] for p, dim = [N*2]\n",
    "        P_D   : training output [p*] at data points PXY_D\n",
    "    Notes about data:\n",
    "        - flow around a cylinder\n",
    "        - Re = 100 , all data has been non-dimensionalised?? , inlet velocity u = 1\n",
    "    \"\"\"\n",
    "    \n",
    "\n",
    "    ## Read file holding training data\n",
    "    xy_data = sio.loadmat(\"Cyl100_Sean/xstar.mat\")\n",
    "    uv_data = sio.loadmat(\"Cyl100_Sean/ustar_Aug24.mat\") \n",
    "    # pressure is calculated at different data points\n",
    "    p_data  = sio.loadmat(\"Cyl100_Sean/pstar.mat\")\n",
    "    pxy_data  = sio.loadmat(\"Cyl100_Sean/xpstar.mat\")\n",
    "    \n",
    "    uv_data_np = np.array(uv_data[\"ustar\"][:,:,0])\n",
    "    u_np = uv_data_np[:,0]\n",
    "    v_np = uv_data_np[:,1]\n",
    "    \n",
    "    xy_data_np = np.array(xy_data[\"xstar\"])\n",
    "    x_np = xy_data_np[:,0]\n",
    "    y_np = xy_data_np[:,1]\n",
    "\n",
    "    p_data_np = np.array(p_data[\"pstar\"])\n",
    "    pxy_data_np = np.array(pxy_data[\"xpstar\"])\n",
    "\n",
    "    # Generate indicies of random points in dataset\n",
    "    idx = engine.integers(l_bounds=0, u_bounds=len(x_np), n=N)\n",
    "    p_idx = engine.integers(l_bounds=0, u_bounds=len(pxy_data_np))\n",
    "    \n",
    "    x = torch.tensor(x_np[idx], dtype=torch.float32)\n",
    "    y = torch.tensor(y_np[idx], dtype=torch.float32)\n",
    "    u = torch.tensor(u_np[idx], dtype=torch.float32)\n",
    "    v = torch.tensor(v_np[idx], dtype=torch.float32)\n",
    "    \n",
    "\n",
    "    X_D = torch.column_stack((x, y))\n",
    "    U_D = torch.column_stack((u,v))\n",
    "    PXY_D = torch.tensor(pxy_data_np[p_idx], dtype=torch.float32)\n",
    "    P_D   = torch.tensor(p_data_np[:,0:1][p_idx], dtype=torch.float32)\n",
    "\n",
    "    #print(X_D.size(), U_D.size(),PXY_D.shape(), P_D.shape())\n",
    "    #print(PXY_D.size(), P_D.size())\n",
    "    return X_D, U_D, PXY_D, P_D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8adfc689-3737-4439-9c96-5e83f62f0451",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Uniform_with_Cylinder' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m X_col, BC_X, BC_Y\u001b[38;5;241m=\u001b[39m \u001b[43mUniform_with_Cylinder\u001b[49m(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m      2\u001b[0m X_D, U_D, PXY_D, P_D \u001b[38;5;241m=\u001b[39m Data()\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(PXY_D)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Uniform_with_Cylinder' is not defined"
     ]
    }
   ],
   "source": [
    "X_col, BC_X, BC_Y= Uniform_with_Cylinder(1)\n",
    "X_D, U_D, PXY_D, P_D = Data()\n",
    "plt.scatter(X_col[:,0].numpy(),X_col[:,1].numpy(),marker='.')\n",
    "plt.scatter(BC_X[:,0].numpy(),BC_X[:,1].numpy(),marker='x')\n",
    "plt.scatter(X_D[:,0].numpy(), X_D[:,1].numpy(),marker='.', color='r')\n",
    "#plt.xlim(-2,5)\n",
    "#plt.ylim(-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "97417112-23ad-4ca5-85d4-639a02e083a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Compute gradients\n",
    "def grad(outputs, inputs):\n",
    "    return torch.autograd.grad(\n",
    "        outputs, inputs,\n",
    "        grad_outputs=torch.ones_like(outputs),\n",
    "        create_graph=True,\n",
    "        retain_graph=True\n",
    "    )[0]\n",
    "def compute_gradients(model, xy):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "        model : PyTorch neural network, with three models inside it (u,v and p) for input xy\n",
    "        xy    : tensor of shape (N, 2), # when setting up model set requires_grad=True\n",
    "\n",
    "    Returns:\n",
    "        p_grads : (p, p_x, p_y) as a tuple\n",
    "        u_grads : (u, u_x, u_y, u_xx, u_yy) as a tuple\n",
    "        v_grads : (v, v_x, v_y, v_xx, v_yy) as a tuple\n",
    "    \"\"\"\n",
    "    # create a clone so we dont change the original tensor\n",
    "    # detatch the xy data from previous gradients so it can be evaluated in isolation\n",
    "    xy = xy.clone().detach().requires_grad_(True)  \n",
    "    x = xy[:, 0:1]\n",
    "    y = xy[:, 1:2]\n",
    "\n",
    "    # Make a fwd pass that goes through the combined mini models collecting all coefficients needed for derivatives\n",
    "    u_v_p = model(torch.cat([x, y], dim=1))\n",
    "    u = u_v_p[:,0:1]\n",
    "    v = u_v_p[:,1:2]\n",
    "    p = u_v_p[:,2:3]\n",
    "\n",
    "    \n",
    "    # First-order derivatives\n",
    "    p_x = grad(p, x)\n",
    "    p_y = grad(p, y)\n",
    "\n",
    "    # Second-order derivatives\n",
    "    u_x = grad(u, x)\n",
    "    u_y = grad(u, y)\n",
    "    v_x = grad(v, x)\n",
    "    v_y = grad(v, y)\n",
    "\n",
    "    # Third-order derivatives\n",
    "    u_xx = grad(u_x, x)\n",
    "    u_yy = grad(u_y, y)\n",
    "    v_xx = grad(v_x, x)\n",
    "    v_yy = grad(v_y, y)\n",
    "    \n",
    "    p_grads = (p, p_x, p_y)\n",
    "    u_grads = (u, u_x, u_y, u_xx, u_yy)\n",
    "    v_grads = (v, v_x, v_y, v_xx, v_yy)\n",
    "\n",
    "    return p_grads, u_grads, v_grads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fb1aef-ab4e-424f-9937-e87a719e9d88",
   "metadata": {},
   "source": [
    "# Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "dc8538d5-9dfa-44a1-8e38-7009b7b8a3fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## LOSS EQUATIONS\n",
    "def navier_stokes_loss(model,X):\n",
    "    \"\"\"\n",
    "    calculates steady navier stokes residuals at collocation points\n",
    "\n",
    "    Args: \n",
    "        model : whatever model calls this function\n",
    "        X : input collocation [x,y] coords as defined in models data creation\n",
    "\n",
    "    Returns: \n",
    "        tensor of all collocation point residuals\n",
    "    \"\"\"\n",
    "    p_grads, u_grads, v_grads = compute_gradients(model, X)\n",
    "    _, p_x, p_y = p_grads\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "\n",
    "    #compute PDE residuals\n",
    "    u_eqn = u*u_x + v*u_y + p_x/rho0 - nu0*(u_xx + u_yy)\n",
    "    v_eqn = u*v_x + v*v_y + p_y/rho0 - nu0*(v_xx + v_yy)\n",
    "    \n",
    "\n",
    "    # combine into one tensor\n",
    "    # [u_residual, v_residual, continuity]\n",
    "    return torch.cat([u_eqn, v_eqn,(u_x+v_y)], dim=1)\n",
    "    \n",
    "def BC_loss(model,BC_X):\n",
    "    \"\"\"\n",
    "    calculates u and v at boundary conditions\n",
    "\n",
    "    Args: \n",
    "        model : whatever model calls this function\n",
    "        BC_X : Input Boundary conditions [x,y] coords as defined in models data creation\n",
    "        \n",
    "    Returns: \n",
    "        tensor of u,v at all boundary condition coords\n",
    "    \"\"\"\n",
    "    _, u_grads, v_grads = compute_gradients(model,BC_X)\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "    return torch.cat([u, v], dim=1)\n",
    "def UV_Data_loss(model, X_D):\n",
    "    p_grads, u_grads, v_grads = compute_gradients(model,X_D)\n",
    "    p, p_x, p_y = p_grads\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "    \n",
    "    return torch.cat([u, v], dim=1)\n",
    "def P_Data_loss(model, PXY_D):\n",
    "    p_grads, u_grads, v_grads = compute_gradients(model,PXY_D)\n",
    "    p, p_x, p_y = p_grads\n",
    "    u, u_x, u_y, u_xx, u_yy = u_grads\n",
    "    v, v_x, v_y, v_xx, v_yy = v_grads\n",
    "\n",
    "    # solution need to use torch.cat rather then torch.tensor to create p data loss as it retains the gradient computation graph whilst torch.tensor performs a .detach()\n",
    "    pval = torch.cat([p], dim=1)\n",
    "    \n",
    "    return pval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7661146-5baf-4d1f-bf77-1c08c047291b",
   "metadata": {},
   "source": [
    "## Define sub models and combine into 1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "4a15f79e-0281-40e5-881c-ddd351dccc1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## CREATING THE MINI MODEL FOR u,v AND p AND THEN COMBINE THEM INTO ONE MODEL\n",
    "\n",
    "# sub model for u,v and p \n",
    "class submodel(nn.Module):\n",
    "    def __init__(\n",
    "            self,\n",
    "            N_input,\n",
    "            N_hidden_arr,\n",
    "            N_output,\n",
    "            activation = nn.Tanh\n",
    "    ):\n",
    "        super(submodel, self).__init__()  # Create network\n",
    "\n",
    "        # Create input layer w/ activation function\n",
    "        layers = [('Input', nn.Linear(N_input, N_hidden_arr[0]))]\n",
    "        layers.append(('Input activation', activation()))\n",
    "\n",
    "        # Create hidden layers\n",
    "        for i in range(len(N_hidden_arr)-1):\n",
    "            layers.append(\n",
    "                (\"Hidden %d\" % (i+1), nn.Linear(N_hidden_arr[i], N_hidden_arr[i+1]))\n",
    "            )\n",
    "            layers.append(('Hidden activation %d' % (i+1), activation()))\n",
    "        layers.append(('Output', nn.Linear(N_hidden_arr[-1], N_output)))\n",
    "        layerdict = OrderedDict(layers)\n",
    "        self.layers = nn.Sequential(layerdict)\n",
    "\n",
    "    def forward(self, x):\n",
    "        y = self.layers(x)\n",
    "        return y\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self, N_input, N_hidden_arr, N_output, activation = nn.Tanh):\n",
    "        \n",
    "        super(Net, self).__init__()  # Create network\n",
    "        # creates three models using submodel as the blueprint\n",
    "        self.model_u = submodel(N_input, N_hidden_arr ,N_output, activation)\n",
    "        self.model_v = submodel(N_input, N_hidden_arr, N_output, activation)\n",
    "        self.model_p = submodel(N_input, N_hidden_arr, N_output, activation)\n",
    "\n",
    "\n",
    "    # combine the outputs of all the models into a single output\n",
    "    def forward(self, xy):\n",
    "        out_u = self.model_u(xy)\n",
    "        out_v = self.model_v(xy)\n",
    "        out_p = self.model_p(xy)\n",
    "        combined = torch.cat((out_u, out_v, out_p), dim=1)\n",
    "        return combined\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c185db0a-8994-4f80-87bf-4715cee2d15c",
   "metadata": {},
   "source": [
    "## Defining the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "435525da-d488-4ccf-b370-a140cf1a538a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## create neural network\n",
    "\n",
    "class PINN:\n",
    "    def __init__(self):\n",
    "        ### Need to change this to check if gpu available as well\n",
    "        device = torch.device(\"cpu\")\n",
    "        print(\"Using CPU\")\n",
    "        self.model = Net(\n",
    "            N_input=2,\n",
    "            N_hidden_arr=[32,16,16,32],\n",
    "            N_output = 1\n",
    "        ).to(device)\n",
    "    \n",
    "        # DATA CREATION \n",
    "        self.X, self.BC_X, self.BC_Y =  Uniform_with_Cylinder(u0)\n",
    "        self.X_D, self.U_D, self.PXY_D, self.P_D = Data()\n",
    "\n",
    "        # copy and seperate tensors into format for loss calculations\n",
    "        self.X    = self.X.clone().detach().requires_grad_(True)\n",
    "        self.BC_X = self.BC_X.clone().detach().requires_grad_(True)\n",
    "\n",
    "        self.X_D   = self.X_D.clone().detach().requires_grad_(True)\n",
    "        self.PXY_D = self.PXY_D.clone().detach().requires_grad_(True)\n",
    "        \n",
    "        # OPTIMISERS\n",
    "        self.optimiser = torch.optim.LBFGS(\n",
    "            params=self.model.parameters(),\n",
    "            lr=1.0,\n",
    "            max_iter = 25*10**3,\n",
    "            max_eval = 25*10**3,\n",
    "            history_size=50,\n",
    "            tolerance_change=1e-7,\n",
    "            tolerance_grad=1e-7,\n",
    "            line_search_fn=\"strong_wolfe\"\n",
    "        )\n",
    "        self.adam = torch.optim.Adam(self.model.parameters())\n",
    "\n",
    "        \n",
    "        self.loss_fn = nn.MSELoss()\n",
    "\n",
    "        # Counter for printing loss\n",
    "        self.iter =1\n",
    "        \n",
    "    # Loss\n",
    "    def compute_loss(self, epoch_split=500):\n",
    "\n",
    "\n",
    "        # Data Loss\n",
    "        uv_data_loss = UV_Data_loss(self.model, self.X_D)\n",
    "        uv_data_loss = self.loss_fn(uv_data_loss, self.U_D)\n",
    "        \n",
    "        p_data_loss  = P_Data_loss(self.model, self.PXY_D)\n",
    "        p_data_loss = self.loss_fn(p_data_loss, self.P_D)\n",
    "\n",
    "        data_loss = p_data_loss+uv_data_loss\n",
    "        pde_loss = bc_loss = 0\n",
    "        if self.iter >= epoch_split:\n",
    "            residuals = navier_stokes_loss(self.model, self.X)\n",
    "            ru, rv, conservation  = residuals[:, 0:1], residuals[:, 1:2], residuals[:, 2:3]\n",
    "\n",
    "            # PDE loss (residuals against tensor of zeros)\n",
    "            pde_loss = self.loss_fn(ru, torch.zeros_like(ru)) + self.loss_fn(rv, torch.zeros_like(rv))+self.loss_fn(conservation, torch.zeros_like(conservation))\n",
    "\n",
    "            # BC loss\n",
    "            bc_loss = BC_loss(self.model,self.BC_X)\n",
    "            bc_loss = self.loss_fn(bc_loss,self.BC_Y)\n",
    "            # Evaluate losses only on steps when they are necessary to save computational load\n",
    "            # Compute PDE residuals at collocation points\n",
    "            \n",
    "        total_loss = data_loss+pde_loss+bc_loss\n",
    "        \n",
    "        if self.iter >= epoch_split:\n",
    "            alt_hist = [data_loss.item(), uv_data_loss.item(), p_data_loss.item(), pde_loss.item(), bc_loss.item()]\n",
    "            if self.iter % 100 == 0:\n",
    "                print(f\"Iteration {self.iter:5}, Total Loss {total_loss:.9f}, Data loss {data_loss:.9f} = uv data loss: {uv_data_loss} + p data loss: {p_data_loss} ,pde loss {pde_loss:.9f}, BC loss {bc_loss:.9f}\")                \n",
    "        else:\n",
    "            alt_hist = [data_loss.item(), uv_data_loss.item(), p_data_loss.item()]\n",
    "            if self.iter % 100 == 0:\n",
    "                print(f\"Iteration: {self.iter:5}, Total Loss {total_loss:.9f}, Data loss {data_loss:.9f} = uv data loss: {uv_data_loss} + p data loss: {p_data_loss}\")\n",
    "            \n",
    "        self.iter+= 1\n",
    "        \n",
    "        return total_loss, alt_hist\n",
    "    \n",
    "    def train(self, adam_epochs=300, lbfgs_epochs=10, epoch_split = 500):\n",
    "        self.model.train()\n",
    "\n",
    "        loss_history = []\n",
    "        data_loss_history = []\n",
    "        uv_loss_history = []\n",
    "        p_loss_history = []\n",
    "        pde_loss_history = []\n",
    "        bc_loss_history = []\n",
    "\n",
    "        def record_history(total_loss, loss_components):\n",
    "            loss_history.append(total_loss.item())\n",
    "            data_loss_history.append(loss_components[0])\n",
    "            uv_loss_history.append(loss_components[1])\n",
    "            p_loss_history.append(loss_components[2])\n",
    "            if self.iter > epoch_split:\n",
    "                pde_loss_history.append(loss_components[3])\n",
    "                bc_loss_history.append(loss_components[4])\n",
    "\n",
    "        for epoch in range(adam_epochs):\n",
    "            self.adam.zero_grad()\n",
    "            loss, components = self.compute_loss()\n",
    "            record_history(loss, components)\n",
    "            loss.backward()\n",
    "            self.adam.step()\n",
    "\n",
    "            # extra printing of loss for when adam optimiser in use\n",
    "            \"\"\" if epoch % 50 == 0:\n",
    "                with torch.inference_mode():  \n",
    "                    print(f\"[Adam] Step {epoch:4}, Loss = {loss.item():.6e}\") \"\"\"\n",
    "        \n",
    "        for epoch in range(lbfgs_epochs):\n",
    "            def closure():\n",
    "                self.optimiser.zero_grad()\n",
    "                loss, components = self.compute_loss()\n",
    "                record_history(loss, components)\n",
    "                loss.backward()\n",
    "                return loss\n",
    "\n",
    "            self.optimiser.step(closure) \n",
    "        \n",
    "        # return [loss_history, data_loss_history, pde_loss_history, bc_loss_history], self.iter\n",
    "        return [loss_history, data_loss_history, uv_loss_history, p_loss_history, pde_loss_history, bc_loss_history], self.iter\n",
    "            \n",
    "    def eval(self):\n",
    "        self.model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8baeb469-b77c-48cc-b8af-f1bcaa59b1d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "test = PINN()\n",
    "#print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d2f57190-7a40-43ca-b5c5-6f042448c11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:   100, Total Loss 0.045927983, Data loss 0.045927983 = uv data loss: 0.04592796415090561 + p data loss: 1.7844753230633614e-08\n",
      "Iteration:   200, Total Loss 0.037159499, Data loss 0.037159499 = uv data loss: 0.037159498780965805 + p data loss: 8.313150515193435e-13\n",
      "Iteration:   300, Total Loss 0.030522797, Data loss 0.030522797 = uv data loss: 0.030522797256708145 + p data loss: 1.0495077029659683e-16\n",
      "Iteration:   400, Total Loss 0.020199118, Data loss 0.020199118 = uv data loss: 0.02019909769296646 + p data loss: 2.0088863905698418e-08\n",
      "Iteration   500, Total Loss 0.117899790, Data loss 0.013951022 = uv data loss: 0.013950376771390438 + p data loss: 6.452971774706384e-07 ,pde loss 0.083328329, BC loss 0.020620435\n",
      "Iteration   600, Total Loss 0.029591577, Data loss 0.023168521 = uv data loss: 0.02316720224916935 + p data loss: 1.318617023571278e-06 ,pde loss 0.005364744, BC loss 0.001058311\n",
      "Iteration   700, Total Loss 0.025785029, Data loss 0.019650765 = uv data loss: 0.019650477916002274 + p data loss: 2.872933748676587e-07 ,pde loss 0.005325524, BC loss 0.000808740\n",
      "Iteration   800, Total Loss 0.024302704, Data loss 0.018241521 = uv data loss: 0.018240978941321373 + p data loss: 5.41151905508741e-07 ,pde loss 0.005502027, BC loss 0.000559157\n",
      "Iteration   900, Total Loss 0.023463365, Data loss 0.017466631 = uv data loss: 0.017466621473431587 + p data loss: 9.74006919562953e-09 ,pde loss 0.005393743, BC loss 0.000602990\n",
      "Iteration  1000, Total Loss 0.022869909, Data loss 0.016989246 = uv data loss: 0.016989046707749367 + p data loss: 1.9888288704805746e-07 ,pde loss 0.005312125, BC loss 0.000568537\n",
      "Iteration  1100, Total Loss 0.022370987, Data loss 0.016496427 = uv data loss: 0.01649577170610428 + p data loss: 6.552012905558513e-07 ,pde loss 0.005374992, BC loss 0.000499567\n",
      "Iteration  1200, Total Loss 0.021853024, Data loss 0.016078951 = uv data loss: 0.016078902408480644 + p data loss: 4.9104830424084867e-08 ,pde loss 0.005284355, BC loss 0.000489718\n",
      "Iteration  1300, Total Loss 0.021291500, Data loss 0.015496790 = uv data loss: 0.015496565029025078 + p data loss: 2.2509414066007594e-07 ,pde loss 0.005377878, BC loss 0.000416833\n",
      "Iteration  1400, Total Loss 0.020796403, Data loss 0.015105194 = uv data loss: 0.015105089172720909 + p data loss: 1.0562676067138455e-07 ,pde loss 0.005339199, BC loss 0.000352010\n",
      "Iteration  1500, Total Loss 0.020243166, Data loss 0.014604749 = uv data loss: 0.014604741707444191 + p data loss: 7.704010762665803e-09 ,pde loss 0.005302404, BC loss 0.000336014\n",
      "Iteration  1600, Total Loss 0.019665362, Data loss 0.014101448 = uv data loss: 0.014101321808993816 + p data loss: 1.2710820840311499e-07 ,pde loss 0.005231112, BC loss 0.000332800\n",
      "Iteration  1700, Total Loss 0.019073563, Data loss 0.013432159 = uv data loss: 0.013432159088551998 + p data loss: 1.038971079309059e-11 ,pde loss 0.005300402, BC loss 0.000341003\n",
      "Iteration  1800, Total Loss 0.018414119, Data loss 0.013002499 = uv data loss: 0.013002481311559677 + p data loss: 1.7346657443795266e-08 ,pde loss 0.005105743, BC loss 0.000305878\n",
      "Iteration  1900, Total Loss 0.017893925, Data loss 0.012289150 = uv data loss: 0.012289077043533325 + p data loss: 7.30795051140376e-08 ,pde loss 0.005306183, BC loss 0.000298593\n",
      "Iteration  2000, Total Loss 0.017518029, Data loss 0.012037038 = uv data loss: 0.012036947533488274 + p data loss: 9.001813339182263e-08 ,pde loss 0.005184581, BC loss 0.000296410\n",
      "Iteration  2100, Total Loss 0.017137973, Data loss 0.011811241 = uv data loss: 0.011811223812401295 + p data loss: 1.6634345456623123e-08 ,pde loss 0.005083246, BC loss 0.000243487\n",
      "Iteration  2200, Total Loss 0.016669605, Data loss 0.011542061 = uv data loss: 0.011541965417563915 + p data loss: 9.619763119417257e-08 ,pde loss 0.004888691, BC loss 0.000238852\n",
      "Iteration  2300, Total Loss 0.016260684, Data loss 0.011112647 = uv data loss: 0.011111782863736153 + p data loss: 8.643026490062766e-07 ,pde loss 0.004913169, BC loss 0.000234868\n",
      "Iteration  2400, Total Loss 0.015952185, Data loss 0.010830793 = uv data loss: 0.010830470360815525 + p data loss: 3.2263207572214014e-07 ,pde loss 0.004880610, BC loss 0.000240783\n",
      "Iteration  2500, Total Loss 0.015711810, Data loss 0.010679477 = uv data loss: 0.010679395869374275 + p data loss: 8.059481615418917e-08 ,pde loss 0.004798001, BC loss 0.000234332\n",
      "Iteration  2600, Total Loss 0.015486739, Data loss 0.010489302 = uv data loss: 0.010489052161574364 + p data loss: 2.495067121799366e-07 ,pde loss 0.004769313, BC loss 0.000228124\n",
      "Iteration  2700, Total Loss 0.015260535, Data loss 0.010382426 = uv data loss: 0.010382270440459251 + p data loss: 1.5591096769185242e-07 ,pde loss 0.004658565, BC loss 0.000219544\n",
      "Iteration  2800, Total Loss 0.014993215, Data loss 0.010088434 = uv data loss: 0.0100880591198802 + p data loss: 3.749761674498586e-07 ,pde loss 0.004682654, BC loss 0.000222127\n",
      "Iteration  2900, Total Loss 0.014667706, Data loss 0.009692563 = uv data loss: 0.009692506864666939 + p data loss: 5.574521466655824e-08 ,pde loss 0.004764854, BC loss 0.000210289\n",
      "Iteration  3000, Total Loss 0.014324337, Data loss 0.009555818 = uv data loss: 0.009555800817906857 + p data loss: 1.7090998838398264e-08 ,pde loss 0.004581413, BC loss 0.000187107\n",
      "Iteration  3100, Total Loss 0.014075288, Data loss 0.009293452 = uv data loss: 0.00929343979805708 + p data loss: 1.1663775723036451e-08 ,pde loss 0.004612015, BC loss 0.000169821\n",
      "Iteration  3200, Total Loss 0.013842049, Data loss 0.009147852 = uv data loss: 0.009147797711193562 + p data loss: 5.372331557396137e-08 ,pde loss 0.004521673, BC loss 0.000172524\n",
      "Iteration  3300, Total Loss 0.013574828, Data loss 0.009000096 = uv data loss: 0.009000052697956562 + p data loss: 4.3394546622721464e-08 ,pde loss 0.004440644, BC loss 0.000134088\n",
      "Iteration  3400, Total Loss 0.013362161, Data loss 0.008743490 = uv data loss: 0.008743313141167164 + p data loss: 1.7741488989031495e-07 ,pde loss 0.004481899, BC loss 0.000136772\n",
      "Iteration  3500, Total Loss 0.013148567, Data loss 0.008678345 = uv data loss: 0.008678304962813854 + p data loss: 4.0441779702860003e-08 ,pde loss 0.004346221, BC loss 0.000124001\n",
      "Iteration  3600, Total Loss 0.012915271, Data loss 0.008563722 = uv data loss: 0.00856371596455574 + p data loss: 5.526024526858464e-09 ,pde loss 0.004227429, BC loss 0.000124121\n",
      "Iteration  3700, Total Loss 0.012730751, Data loss 0.008298533 = uv data loss: 0.008298501372337341 + p data loss: 3.13397805484783e-08 ,pde loss 0.004307959, BC loss 0.000124259\n",
      "Iteration  3800, Total Loss 0.012522487, Data loss 0.008234036 = uv data loss: 0.00823402963578701 + p data loss: 6.755220383070082e-09 ,pde loss 0.004172136, BC loss 0.000116315\n",
      "Iteration  3900, Total Loss 0.012323627, Data loss 0.008104070 = uv data loss: 0.008104069158434868 + p data loss: 1.119795922654987e-09 ,pde loss 0.004103824, BC loss 0.000115734\n",
      "Iteration  4000, Total Loss 0.012072957, Data loss 0.007816755 = uv data loss: 0.00781673938035965 + p data loss: 1.59158126677994e-08 ,pde loss 0.004139806, BC loss 0.000116396\n",
      "Iteration  4100, Total Loss 0.011836114, Data loss 0.007700417 = uv data loss: 0.007700361777096987 + p data loss: 5.563971683386626e-08 ,pde loss 0.004024220, BC loss 0.000111478\n",
      "Iteration  4200, Total Loss 0.011637643, Data loss 0.007532123 = uv data loss: 0.007532114628702402 + p data loss: 8.005100582408886e-09 ,pde loss 0.004000984, BC loss 0.000104537\n",
      "Iteration  4300, Total Loss 0.011489835, Data loss 0.007367848 = uv data loss: 0.007367802783846855 + p data loss: 4.5036344431537145e-08 ,pde loss 0.004019402, BC loss 0.000102585\n",
      "Iteration  4400, Total Loss 0.011360529, Data loss 0.007343479 = uv data loss: 0.007343469653278589 + p data loss: 9.883519780373717e-09 ,pde loss 0.003910365, BC loss 0.000106684\n",
      "Iteration  4500, Total Loss 0.011245582, Data loss 0.007311540 = uv data loss: 0.0073115346021950245 + p data loss: 5.043027773155018e-09 ,pde loss 0.003844673, BC loss 0.000089370\n",
      "Iteration  4600, Total Loss 0.011146552, Data loss 0.007272494 = uv data loss: 0.007272488437592983 + p data loss: 5.62309532270433e-09 ,pde loss 0.003792502, BC loss 0.000081557\n",
      "Iteration  4700, Total Loss 0.011054712, Data loss 0.007161810 = uv data loss: 0.007161769550293684 + p data loss: 4.0094914055543995e-08 ,pde loss 0.003817043, BC loss 0.000075859\n",
      "Iteration  4800, Total Loss 0.010973007, Data loss 0.007127416 = uv data loss: 0.007127393968403339 + p data loss: 2.2479252237417313e-08 ,pde loss 0.003771441, BC loss 0.000074150\n",
      "Iteration  4900, Total Loss 0.010876582, Data loss 0.007102818 = uv data loss: 0.007102816365659237 + p data loss: 1.4351526633760159e-09 ,pde loss 0.003714105, BC loss 0.000059660\n",
      "Iteration  5000, Total Loss 0.010791674, Data loss 0.007071549 = uv data loss: 0.007071548607200384 + p data loss: 4.918452312097088e-10 ,pde loss 0.003664491, BC loss 0.000055634\n",
      "Iteration  5100, Total Loss 0.010712794, Data loss 0.006989080 = uv data loss: 0.006989074405282736 + p data loss: 5.792804458337741e-09 ,pde loss 0.003671554, BC loss 0.000052160\n",
      "Iteration  5200, Total Loss 0.010638122, Data loss 0.006904812 = uv data loss: 0.006904786918312311 + p data loss: 2.4985132185406655e-08 ,pde loss 0.003682652, BC loss 0.000050659\n",
      "Iteration  5300, Total Loss 0.010569130, Data loss 0.006891231 = uv data loss: 0.006891224998980761 + p data loss: 6.176919420397553e-09 ,pde loss 0.003633061, BC loss 0.000044838\n",
      "Iteration  5400, Total Loss 0.010494540, Data loss 0.006784219 = uv data loss: 0.006784199271351099 + p data loss: 1.9429604591891803e-08 ,pde loss 0.003668536, BC loss 0.000041785\n",
      "Iteration  5500, Total Loss 0.010420607, Data loss 0.006750584 = uv data loss: 0.006750580854713917 + p data loss: 2.8750009040123814e-09 ,pde loss 0.003634294, BC loss 0.000035729\n",
      "Iteration  5600, Total Loss 0.010352074, Data loss 0.006712297 = uv data loss: 0.006712296511977911 + p data loss: 3.4822958405733573e-10 ,pde loss 0.003605844, BC loss 0.000033933\n",
      "Iteration  5700, Total Loss 0.010284328, Data loss 0.006672790 = uv data loss: 0.0066727823577821255 + p data loss: 7.707607885265588e-09 ,pde loss 0.003579050, BC loss 0.000032488\n",
      "Iteration  5800, Total Loss 0.010228328, Data loss 0.006567743 = uv data loss: 0.006567725446075201 + p data loss: 1.758148471253662e-08 ,pde loss 0.003628485, BC loss 0.000032101\n",
      "Iteration  5900, Total Loss 0.010164431, Data loss 0.006529207 = uv data loss: 0.006529207341372967 + p data loss: 2.2307275471256105e-10 ,pde loss 0.003603375, BC loss 0.000031849\n",
      "Iteration  6000, Total Loss 0.010100608, Data loss 0.006437430 = uv data loss: 0.006437415257096291 + p data loss: 1.4863668518216855e-08 ,pde loss 0.003631582, BC loss 0.000031596\n",
      "Iteration  6100, Total Loss 0.010047495, Data loss 0.006383758 = uv data loss: 0.006383757572621107 + p data loss: 8.654545829989146e-11 ,pde loss 0.003634997, BC loss 0.000028741\n",
      "Iteration  6200, Total Loss 0.010003131, Data loss 0.006355798 = uv data loss: 0.006355797406286001 + p data loss: 2.55021365136443e-10 ,pde loss 0.003617241, BC loss 0.000030093\n",
      "Iteration  6300, Total Loss 0.009967125, Data loss 0.006320353 = uv data loss: 0.006320318207144737 + p data loss: 3.4283178962368765e-08 ,pde loss 0.003615563, BC loss 0.000031210\n",
      "Iteration  6400, Total Loss 0.009926305, Data loss 0.006308268 = uv data loss: 0.006308266427367926 + p data loss: 1.7486878611805423e-09 ,pde loss 0.003586392, BC loss 0.000031644\n",
      "Iteration  6500, Total Loss 0.009895511, Data loss 0.006289897 = uv data loss: 0.006289894692599773 + p data loss: 2.195288617556912e-09 ,pde loss 0.003576949, BC loss 0.000028664\n"
     ]
    }
   ],
   "source": [
    "histories, epochs = test.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83234dda-bea2-4038-bc04-157290073412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from matplotlib.patches import Circle\n",
    "\n",
    "## Try saving model state??\n",
    "\n",
    "torch.save(test.model.state_dict(), \"model_data_lim_new_BC.txt\")\n",
    "def pressure_distribution(r, x0, y0, model):\n",
    "    d = 0.001\n",
    "    theta_u = np.arange(0, np.pi+d, d)\n",
    "    theta_l = np.arange(np.pi, 2*np.pi+d, d)\n",
    "    x_u = (r * np.cos(theta_u) + x0)\n",
    "    y_u = (r * np.sin(theta_u) + y0)\n",
    "\n",
    "    x_l = (r * np.cos(theta_l) + x0)\n",
    "    y_l = (r * np.sin(theta_l) + y0)\n",
    "\n",
    "    xy_u = np.column_stack([x_u, y_u])\n",
    "    xy_l = np.column_stack([x_l, y_l])\n",
    "    X_u = torch.Tensor(xy_u)\n",
    "    X_l = torch.Tensor(xy_l)\n",
    "    preds_u = model(X_u)\n",
    "    preds_l = model(X_l)\n",
    "\n",
    "    p_u = preds_u[:, 2:3]     #pressure\n",
    "    p_np_u   = p_u.detach().numpy()\n",
    "\n",
    "    p_l = preds_l[:, 2:3]\n",
    "    p_np_l = p_l.detach().numpy()\n",
    "\n",
    "\n",
    "    cp_u = p_np_u/(0.5*rho0*u0**2)\n",
    "    cp_l = p_np_l/(0.5*rho0*u0**2)\n",
    "\n",
    "\n",
    "    return cp_u, cp_l, xy_u, xy_l\n",
    "\n",
    "X, BC_X, BC_Y = Uniform_with_Cylinder(u0)\n",
    "    \n",
    "X = X.clone().detach().requires_grad_(True)      \n",
    "\n",
    "# Test: Create new PINN instance and load the model\n",
    "new = PINN()\n",
    "new.model.load_state_dict(torch.load(\"model_data_lim_new_BC.txt\")) # This is the model that has been trained in the above sections\n",
    "\n",
    "\n",
    "# forward pass of the model !!! fwd pass outside eval/inference is okay as long as you dont .backward()\n",
    "# we have to call .model() because our model is nested inside class PINN\n",
    "# y_preds = test.model(X)\n",
    "y_preds = new.model(X)\n",
    "\n",
    "# Trying to save model state to a text file --- it works!\n",
    "\n",
    "\n",
    "u = y_preds[:, 0:1]   # u velocity\n",
    "v = y_preds[:, 1:2]\n",
    "p = y_preds[:, 2:3]     #pressure\n",
    "\n",
    "\n",
    "# convert to array for postprocessing\n",
    "p_np   = p.detach().numpy()\n",
    "u_np   = u.detach().numpy()\n",
    "v_np   = v.detach().numpy()\n",
    "x_np   = X[:, 0:1].detach().numpy()\n",
    "y_np   = X[:, 1:2].detach().numpy()\n",
    "\n",
    "# create data points to plot a circle on the plots\n",
    "xc,yc, r = 0,0,0.5\n",
    "theta = np.linspace(0, 2*np.pi, 400)\n",
    "cylinder_x = (r * np.cos(theta) + xc)\n",
    "cylinder_y = (r * np.sin(theta) + yc)\n",
    "\n",
    "\n",
    "def tricontour(gs, x, y, z, title):\n",
    "    \"\"\"\n",
    "    this is an explanation for tripcontour() and tripplot() which are two different methods for plotting contour type plots\n",
    "\n",
    "    Args:\n",
    "        grid: plot position in subplot\n",
    "        x: x-array (x coords)\n",
    "        y: y-array  (y coords)\n",
    "        z: z-array (engineering value at [x,y])\n",
    "        title: title \n",
    "    \"\"\"\n",
    "    plt.subplot(gs)\n",
    "    tcf = plt.tricontourf(x, y, z, levels=50, cmap='rainbow')\n",
    "    # Add the circle\n",
    "    plt.fill(cylinder_x,cylinder_y, color='blue', alpha=0.75)\n",
    "    plt.colorbar(tcf)\n",
    "    plt.gca().set_aspect('equal')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    \n",
    "\n",
    "# note we have to squeeze the arrays as they are in column vector format(ML format)\n",
    "gs = GridSpec(2,2)\n",
    "tricontour(gs[0,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(p_np), 'Pressure contour')\n",
    "tricontour(gs[0,1], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(u_np), 'u velocity contour')\n",
    "tricontour(gs[1,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(v_np), 'v velocity contour')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "def tripplot(gs, x, y, z, title):\n",
    "    plt.subplot(gs)\n",
    "    plt.tripcolor(x, y, z, cmap='rainbow')\n",
    "    \n",
    "    # Add the circle\n",
    "    plt.fill(cylinder_x,cylinder_y, color='blue', alpha=0.75)\n",
    "    \n",
    "    plt.ylim(-2.5,2.5)\n",
    "    plt.xlim(-2.5,5)\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "\n",
    "gs2 = GridSpec(2,2)\n",
    "tripplot(gs2[0,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(p_np), 'Pressure contour')\n",
    "tripplot(gs2[0,1], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(u_np), 'u velocity contour')\n",
    "tripplot(gs2[1,0], np.squeeze(x_np), np.squeeze(y_np), np.squeeze(v_np), 'v velocity contour')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Get pressure distribution for cylinder\n",
    "cp_u, cp_l, xy_u, xy_l = pressure_distribution(0.5, 0, 0, new.model)\n",
    "# Plot stuff\n",
    "x_u = xy_u[:,0]\n",
    "y_u = xy_u[:,1]\n",
    "\n",
    "x_l, y_l = xy_l[:,0], xy_l[:,1]\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(x_u, cp_u, '-b', label=\"Upper Surface\")\n",
    "plt.plot(x_l, cp_l, '-r', label=\"Lower Surface\")\n",
    "plt.xlabel(\"x [m]\")\n",
    "plt.ylabel(\"Cp\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "214c5b6d-493f-440e-815e-573500df97a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "6522 6522\n"
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KIbEdPnwYMTExNr0tvXv3RkFBAc6cOYPIyEj069cPXbp0wfDhw7Fo0SJcvnwZANCoUSOMGzcO8fHxGDJkCN555x2bcUe1hT03RETkEry9vVFQUKDYvpUybtw4XLhwAW+//TZatmwJnU6HmJgYlJSU1Mn+1Wo10tPTsW3bNqxfvx5z5szBSy+9hB07dqBly5ZYsmQJnnzySaxbtw4rVqzAyy+/jPT0dNx22221FhOLGyIicgmSJNX41JBS/Pz80LRpU2zduhV9+vSxzt+6dSt69eoFwPbRBGVt27YNM2fOxKBBg6BSqXD69GlcvHjRYbF17NgRX375JYQQ1t6brVu3wtfXF82bNwdgzn3v3r3Ru3dvJCcnIzw8HKtWrUJiYiIA8yDo7t27IykpCTExMfj0009Z3BAREbm65557DlOnTkXr1q3RrVs3LFmyBHv37sWyZcsAAEFBQfDy8sK6devQvHlzeHp6wt/fH23btsXnn3+OO++8EwUFBXjuuefg5eVV5f3n5uba3UOncePGmDRpEtLS0vDEE08gISEBmZmZmDp1KhITE6FSqbBjxw5kZGQgLi4OQUFB2LFjBy5cuICOHTvir7/+wsKFC3HvvfeiadOmyMzMxNGjR2v9al4WN0RERE7gySefRG5uLp555hmcP38enTp1wurVq9G2bVsAgIeHB9599128+uqrSE5Oxp133olNmzZh0aJFmDBhAqKiohAWFobXX38dzz77bJX3v2nTJnTv3t1m3r/+9S988MEHWLNmDZ577jlERkaiUaNG+Ne//oWXX34ZgLnX6aeffkJaWhry8vIQHh6OWbNmYeDAgcjJycGRI0fw0Ucf4e+//0ZoaCgmT56M//znPzVP2A2wuCEiIlLAuHHjMG7cOOtnlUqFqVOnYurUqRWu89hjj+Gxxx6zmde9e3ds3LjRerUUADz44IM2bYQQN4xl6dKlWLp0aYXL+/Tpg507d5a7rGPHjjb34ykrODgYq1atuuG+awOvliIiIiKXwuKGiIiIXAqLGyIiInIpLG6IiIjIpbC4ISIiIpfC4oaIiIhcCosbIiIiciksboiIiMilsLghIiIil8LihoiIiFwKixsiIqI6NG7cOEiSBEmSoNFoEBwcjP79+2Px4sWQZblK21q6dCkaNWrkkLj69u2Lp556yiHbUhqLGyIiojo2YMAAZGVl4cSJE1i7di3uvvtuTJkyBYMHD4bRaFQ6vHqPxQ0REVEd0+l0CAkJQbNmzXDrrbfixRdfxDfffIO1a9faPMBy9uzZ6NKlCxo0aICwsDBMmjQJBQUFAMxP8R4/fjxyc3PRsGFDqNVqpKSkAAA+/vhjREVFwdfXFyEhIXjkkUdw/vz5GsX85ZdfonPnztDpdIiIiMCsWbNsls+bNw9t27aFp6cngoODbR7euXLlSnTp0gVeXl5o3LgxYmNjUVhYWKN4boRPBSciItcgBFBUpMy+vb0BSarRJu655x5ERkbiq6++sj75W6VS4d1330XLli1x/PhxTJo0Cf/3f/+HefPm4fbbb0daWhqSk5Oxc+dO+Pr6ws/PDwBgMBgwffp0tG/fHufPn0diYiLGjRuHNWvWVCu23bt346GHHkJKSgpGjBiBbdu2YdKkSWjcuDHGjRuHXbt24cknn8THH3+M22+/HZcuXcKWLVsAAFlZWRg5ciTefPNNDBs2DPn5+diyZctNn1ReEyxuiIjINRQVAT4+yuy7oABo0KDGm+nQoQP27dtn/Vx2DExERAT++9//YuLEiZg3bx60Wi38/f0hSRKCg4Ph5+cHlcp8QubRRx+1rteqVSu8++676NmzJwoKCuBTjRzNnj0b/fr1wyuvvAIAaNeuHQ4dOoSZM2di3LhxOHXqFBo0aIDBgwfD19cX4eHh6N69OwBzcWM0GnH//fcjPDwcANClS5cqx1AVPC1FRETkJIQQkMr0AG3YsAH9+vVDs2bN4Ovri9GjR+Pvv/9G0U16qHbv3o0hQ4agRYsW8PX1RZ8+fQAAp06dqlZchw8fRu/evW3m9e7dG0ePHoXJZEL//v0RHh6OVq1aYfTo0Vi2bJk1xsjISPTr1w9dunTB8OHDsWjRIly+fLlacVQWixsiInIN3t7mHhQlJm9vhxzC4cOH0bJlSwDAiRMnMHjwYHTt2hVffvkldu/ejblz5wIASkpKKtxGYWEh4uPj4efnh2XLluHXX3/FqlWrbrpeTfj6+mLPnj347LPPEBoaiuTkZERGRuLKlStQq9VIT0/H2rVr0alTJ8yZMwft27fHX3/9VSuxADwtRURErkKSHHJqSCkbN27E/v378fTTTwMw977IsoxZs2ZZTzd9/vnnNutotVqYTCabeUeOHMHff/+NGTNmICwsDACwa9euGsXWsWNHbN261Wbe1q1b0a5dO6jVagCAh4cHYmNjERsbi6lTpyIgIAAbN27E/fffD0mS0Lt3b/Tu3RvJyckIDw/HqlWrkJiYWKO4KsLihoiIqI7p9XpkZ2fDZDIhJycH69atQ2pqKgYPHowxY8YAANq0aQODwYA5c+ZgyJAh2Lp1KxYsWGCznYiICBQUFGDz5s2IiYmBj48PWrRoAa1Wizlz5mDixIk4cOAApk+fXqm4Lly4gL1799rMCw0NxTPPPIOePXti+vTpGDFiBLZv34733nsP8+bNAwB89913OH78OO666y40bNgQa9asgSzLaN++PXbs2IGMjAzExcUhKCgIO3bswIULF9CxY8eaJ7Iiws3k5uYKACI3N9fh2y4pKRFff/21KCkpcfi2q+Kxxx4TAIQz/HidJSfOhDmxx5zYY07slc3J1atXxaFDh8TVq1eVDqvKxo4da/0b7eHhIZo0aSJiY2PF4sWLhclksmk7e/ZsERoaKry8vER8fLz43//+JwCIy5cvW9v85z//EY0aNRIAxNSpU4UQQnz66aciIiJC6HQ6ERMTI1avXi0AiN9++63CuPr06WONq+w0ffp0IYQQK1euFJ06dRIajUa0aNFCzJw507ruli1bRJ8+fUTDhg2Fl5eX6Nq1q1ixYoUQQohDhw6J+Ph40aRJE6HT6US7du3EnDlzyo3hRj/Xqnx/s+eGiIioDi1dutTmXjY38vTTT1tPU1mMHj3a5vO8efMwY8YMm6ulRo4ciZEjR9q0Eze59HrTpk03XP7AAw/ggQceKHfZHXfcUeH6HTt2xLp16264bUfjgGIiIiJyKSxuiIiIyKWwuCEiIiKXwuKGiIiIXIrixc3cuXMREREBT09PREdHY+fOnTdsf+XKFUyePBmhoaHQ6XRo165dtZ+VQURERK5H0aulVqxYgcTERCxYsADR0dFIS0tDfHw8MjMzERQUZNe+pKQE/fv3R1BQEFauXIlmzZrh5MmTCAgIqPvgiYiIyCkpWtzMnj0bEyZMwPjx4wEACxYswPfff4/FixfjhRdesGu/ePFiXLp0Cdu2bYNGowFgvoERERERkYVixU1JSQl2796NpKQk6zyVSoXY2Fhs37693HVWr16NmJgYTJ48Gd988w2aNGmCRx55BM8//7z19s/X0+v10Ov11s95eXkAzI+DNxgMDjwiWLfn6O1WlSzL1vdKx+IsOXEmzIk95sQec2KvbE5MJhOEEJBl2eZvnjuy3L/Gko/6TJZlCCFgMBjsvter8v+CYsXNxYsXYTKZEBwcbDM/ODgYR44cKXed48ePY+PGjRg1ahTWrFmDY8eOYdKkSTAYDJg6dWq566SmpmLatGl289evXw9vBz3o7Hrp6em1st3KOn36tPW9s4xHUjonzog5scec2GNO7KWnp8PDwwMhISEoKCiotYdB1jf5+flKh1BjJSUluHr1Kn766ScYjUabZTd7EnpZ9eoOxbIsIygoCAsXLoRarUaPHj1w9uxZzJw5s8LiJikpyebBXHl5eQgLC0NcXBz8/PwcGp/BYEB6ejr69+9vPW2mhNWrV1vfDxo0SLE4AOfJiTNhTuwxJ/aYE3tlc2IymXD69Gn4+PjA09NT6dAUJYRAfn4+fH19IUnSTdtPmzYN33zzDfbs2VMH0VVNcXExvLy8cNddd9n9XC1nXipDseImMDAQarUaOTk5NvNzcnIQEhJS7jqhoaHQaDQ2XVUdO3ZEdnY2SkpKoNVq7dbR6XTQ6XR28zUaTa39wajNbVeG5fbbllicgdI5cUbMiT3mxB5zYk+j0UClUkGSJKhUKpu/efXBuHHj8NFHHwEwH0uLFi0wZswYvPjii/Dw8MCmTZtw9913AwAkSYKvry9atWqF/v374+mnn0ZoaKh1WykpKeWenWjfvn2FZ0EsBZAz5s3ycy3v974q/x8odmRarRY9evRARkaGdZ4sy8jIyEBMTEy56/Tu3RvHjh2zOaf4xx9/IDQ0tNzChoiIyBkNGDAAWVlZOHr0KJ555hmkpKRg5syZNm0yMzNx7tw5/Prrr3j++eexYcMG3HLLLdi/f79Nu86dO+PIkSM4e/YssrKykJWVhZ9//rkuD8fpKFq2JSYmYtGiRfjoo49w+PBhPP744ygsLLRePTVmzBibAcePP/44Ll26hClTpuCPP/7A999/j9dffx2TJ09W6hCIiIiqTKfTISQkBOHh4Xj88ccRGxtrM6QAAIKCghASEoJ27drh4YcfxtatW9GkSRM8/vjjNu08PDwQHByMkJAQ6xQYGFjpWGRZxquvvormzZtDp9OhW7duNg+6LCkpQUJCAkJDQ+Hp6Ynw8HCkpqYCMJ8SS0lJQYsWLaDT6dC0aVM8+eSTNciMYyg65mbEiBG4cOECkpOTkZ2dbU2oZZDxqVOnbLrNwsLC8MMPP+Dpp59G165d0axZM0yZMgXPP/+8UodAREROQgigCmNOHcrbG6jEcJcKeXl54e+//75pm4kTJ+Lpp5/G+fPny70fXHW88847mDVrFt5//310794dixcvxr333ouDBw+ibdu2ePfdd7F69Wp8/vnnaNGiBU6fPm29cOXLL7/E22+/jeXLl6Nz587Izs7G77//7pC4akLxAcUJCQlISEgod1l5j0+PiYnBL7/8UstRERFRfVNUBPj4KLPvggKgQYOqryeEQEZGBn744Qc88cQTN23foUMHAMCJEyesxc3+/fvRvHlzm3b//Oc/sWDBgkrF8NZbb+H555/Hww8/DAB444038OOPPyItLQ1z587FqVOn0LZtW9xxxx2QJAnh4eHWdU+dOoWQkBDExsZaxw/16tWrUvutTYoXN0RERO7mu+++g4+PDwwGA2RZxiOPPIKUlJSbrme5p03Zq6Lat2+PTz75BD4+PtazHZW9GjgvLw/nzp1D7969beb37t3b2gMzbtw49O/fH+3bt8eAAQMwePBgxMXFAQCGDx+OtLQ0tGrVCgMGDMCgQYMwZMgQeHgoW16wuCEiIpfg7W3uQVFq31Vx9913Y/78+dBqtWjatGmli4HDhw8DsL07v1arRatWreDn51crV0Ddeuut+Ouvv7B27Vps2LABDz30EGJjY7Fy5UqEhYUhMzMTGzZsQHp6OiZNmoSZM2di8+bNil7lx+KGiIhcgiRV79SQEho0aIA2bdpUaZ2rV69i4cKFuOuuu9CkSROHxOHn54emTZti69at6NOnj3X+1q1bbU4v+fn5YcSIERgxYgQefPBBDBgwAJcuXUKjRo3g5eWFIUOGYMiQIZg8eTI6dOiA/fv349Zbb3VIjNXB4sYFVeYmTkRE5NzOnz+P4uJi5OfnY/fu3XjzzTdx8eJFfPXVVzbtjEYjcnJyUFRUZO25kSTJ7gkAFXnuuecwdepUtG7dGt26dcOSJUuwd+9eLFu2DID5OZChoaHo3r07VCoVvvjiC4SEhCAgIABLly6FyWRCdHQ0vL298cknn8DLy8tmXI4SWNy4IMs5WSIiqr/at28PSZLg4+ODVq1aIS4uDomJiXY3uj148KB1oLGFTqdDcXFxpfbz5JNPIjc3F8888wzOnz+PTp06YfXq1Wjbti0AwNfXF2+++SaOHj0KtVqNnj17Ys2aNVCpVAgICMCMGTOQmJgIk8mELl264Ntvv0Xjxo0dk4RqYnFDRERUh5YuXXrD5X379q30P1JTUlKQnJyMvLy8So+5SUlJsRm8rFKpMHXq1AofYzRhwgRMmDCh3GVDhw7F0KFDKxVrXXK+ey8TERER1QCLGyIiInIpLG6IiIjIpbC4ISIiIpfC4oaIiIhcCosbIiIiciksboiIiMilsLghIiIil8LihoiIiFwKixsiIiIXsWnTJkiShCtXrigdiqJY3BAREdWhcePGQZIk69S4cWMMGDAA+/bts2knhMDChQsRHR0NHx8fBAQEICoqCmlpaSgqKlIo+vqBxQ0REVEdGzBgALKyspCVlYWMjAx4eHhg8ODBNm1Gjx6Np556Cvfddx9+/PFH7N27F6+88gq++eYbrF+/XqHI6wcWN0RERHVMp9MhJCQEISEh6NatG1544QWcPn0aFy5cAAB8/vnnWLZsGT777DO8+OKL6NmzJyIiInDfffdh48aNuPvuuyu9ry+//BKdO3eGTqdDREQEZs2aZbN83rx5aNu2LTw9PREcHIwHH3zQumzlypXo0qULvLy80LhxY8TGxqKwsNAxSahFfCq4C5IkSekQiIjqnBACRQZlTtd4a7yr/be3oKAAn3zyCdq0aYPGjRsDAJYtW4b27dvjvvvus2svSRL8/f0rte3du3fjoYceQkpKCkaMGIFt27Zh0qRJaNy4McaNG4ddu3bhySefxMcff4zbb78dly5dwpYtWwAAWVlZGDlyJN58800MGzYM+fn52LJlS6WfWK4kFjdEROQSigxF8En1UWTfBUkFaKBtUOn23333HXx8zLEWFhYiNDQU3333HVQq8wmVo0ePon379jWOa/bs2ejXrx9eeeUVAEC7du1w6NAhzJw5E+PGjcOpU6fQoEEDDB48GL6+vggPD0f37t0BmIsbo9GI+++/H+Hh4QCALl261DimusDTUi6oPlTVRETu7O6778bevXuxd+9e7Ny5E/Hx8Rg4cCBOnjwJwHF/xw8fPozevXvbzOvduzeOHj0Kk8mE/v37Izw8HK1atcLo0aOxbNky62DlyMhI9OvXD126dMHw4cOxaNEiXL582SFx1Tb23BARkUvw1nijIKlAsX1XRYMGDdCmTRvr5w8++AD+/v5YtGgR/vvf/6Jdu3Y4cuSIo8O04+vriz179mDTpk1Yv349kpOTkZKSgl9//RUBAQFIT0/Htm3bsH79esyZMwcvvfQSduzYgZYtW9Z6bDXBnhsiInIJkiShgbaBIlNNxzpKkgSVSoWrV68CAB555BH88ccf+Oabb+zaCiGQm5tbqe127NgRW7dutZm3detWtGvXDmq1GgDg4eGB2NhYvPnmm9i3bx9OnDiBjRs3WuPq3bs3pk2bht9++w1arRarVq2qyaHWCfbcEBER1TG9Xo/s7GwAwOXLl/Hee++hoKAAQ4YMAQA89NBDWLVqFUaOHImXX34ZcXFxaNKkCfbv34+3334bTzzxBIYOHXrT/TzzzDPo2bMnpk+fjhEjRmD79u147733MG/ePADmsT/Hjx/HXXfdhYYNG2LNmjWQZRnt27fHjh07kJGRgbi4OAQFBWHHjh24cOECOnbsWGt5cRQWN0RERHVs3bp1CA0NBWA+NdShQwd88cUX6Nu3LwBzj8mnn36KhQsXYvHixXjttdfg4eGBtm3bYsyYMYiPj6/Ufm699VZ8/vnnSE5OxvTp0xEaGopXX30V48aNAwAEBATgq6++QkpKCoqLi9G2bVt89tln6Ny5Mw4fPoyffvoJaWlpyMvLQ3h4OGbNmoWBAwfWRkocisUNERFRHVq6dCmWLl1603YqlQoTJ07ExIkTK73tvn372g1GfuCBB/DAAw+U2/6OO+7Apk2byl3WsWNHrFu3rtL7diYcc0NEREQuhcUNERERuRQWN0RERORSWNwQERGRS2FxQ0RE9RbvyO5aHPXzZHFDRET1jkajAQDrowLINZSUlACA9QaD1cVLwYmIqN5Rq9UICAjA+fPnAQDe3tV/Knd9J8sySkpKUFxcbH3wZn0kyzIuXLgAb29veHjUrDxhceOC3PV/cCJyLyEhIQBgLXDclRACV69ehZeXV73/+69SqdCiRYsaHweLGyIiqpckSUJoaCiCgoJgMBiUDkcxBoMBP/30E+666y7r6br6SqvVOqT3icUNERHVa2q1usZjNOoztVoNo9EIT0/Pel/cOEr9PTlHFeLVA0RE5M5Y3BAREZFLcYriZu7cuYiIiICnpyeio6Oxc+fOCtsuXboUkiTZTJ6ennUYLRERETkzxYubFStWIDExEVOnTsWePXsQGRmJ+Pj4G45+9/PzQ1ZWlnU6efJkHUZMREREzkzx4mb27NmYMGECxo8fj06dOmHBggXw9vbG4sWLK1xHkiSEhIRYp+Dg4DqMuHwHlyxBkVaLtsOHKx0KERGRW1P0aqmSkhLs3r0bSUlJ1nkqlQqxsbHYvn17hesVFBQgPDwcsizj1ltvxeuvv47OnTuX21av10Ov11s/5+XlATBfOufISweNJSXwB3BFlhW/JFGWZet7pWOx7F/pOJwJc2KPObHHnNhjTsrnLnmpyvEpWtxcvHgRJpPJruclODgYR44cKXed9u3bY/HixejatStyc3Px1ltv4fbbb8fBgwfRvHlzu/apqamYNm2a3fz169fD29vbMQcCIP/AAUQCkIRAenq6w7ZbHadPn7a+X7NmjYKRXKN0TpwRc2KPObHHnNhjTsrn6nmpyqM26t19bmJiYhATE2P9fPvtt6Njx454//33MX36dLv2SUlJSExMtH7Oy8tDWFgY4uLi4Ofn57C4Dl+8CACQAPTv31/Rew2sXr3a+n7QoEGKxQGYK+309HTFc+JMmBN7zIk95sQec1I+d8mL5cxLZSha3AQGBkKtViMnJ8dmfk5OjvW22jej0WjQvXt3HDt2rNzlOp0OOp2u3PUc+UugKd2Hqha2XVVl7+7oLL/oSufEGTEn9pgTe8yJPeakfK6el6ocm6IDirVaLXr06IGMjAzrPFmWkZGRYdM7cyMmkwn79+9HaGhobYVZOaXPwVDxBnpERESKUvy0VGJiIsaOHYuoqCj06tULaWlpKCwsxPjx4wEAY8aMQbNmzZCamgoAePXVV3HbbbehTZs2uHLlCmbOnImTJ0/iscceU/IwoKrhE0yJiIjIMRT/Rh4xYgQuXLiA5ORkZGdno1u3bli3bp11kPGpU6dsTrNcvnwZEyZMQHZ2Nho2bIgePXpg27Zt6NSpk1KHAACQSp9rovi19URERG5O8eIGABISEpCQkFDusk2bNtl8fvvtt/H222/XQVRVZDktpXAYRERE7o7fxQ5iOS0lKRwHYL7JIRERkbticeMg1tNSHFBMRESkKBY3DmIpbthnQkREpCwWNw4ilQ56dobiRrD3iIiI3BiLGwexjLlhQomIiJTF72IH4aXgREREzoHfxQ7iTKeliIiI3BmLGwdhzw0REZFz4Hexg3DMDRERkXPgd7GD8LQUERGRc2Bx4yA8LUVEROQc+F3sIDwtRURE5Bz4XewgltNSTCgREZGy+F3sIJbTUgAgZFnBSIiIiNwbixsHsZyWAgDZaFQwEiIiIvfG4sZBLKelAPbcEBERKYnFjYM4U8+NJPGCdCIicl8sbhyk7JgbpYsbIiIid8bixkF4WoqIiMg5sLhxEGc6LUVEROTOWNw4iDMVN0IIRfdPRESkJBY3DlL2tBRYXBARESmGxY2DOFPPDRERkTtjceMgLG6IiIicA4sbB+HVUkRERM6BxY2DlO25ESaTgpEQERG5NxY3DlK250bp01K5ubmK7p+IiEhJLG4cyNJfo/RpqQ0bNii6fyIiIiWxuHEgywXgPC1FRESkHBY3DmTpr1H6tBQREZE7Y3HjQJbiRunTUkRERO6MxY0DOctpKUmSFN0/ERGRkljcOBBPSxERESmPxY0DWU9G8dlSREREimFx40CWkoY9N0RERMphceNAzjLmRrDniIiI3BiLGweSSwfy8mopIiIi5bC4cSBn6bkhIiJyZyxuHMh6nxsWN0RERIpxiuJm7ty5iIiIgKenJ6Kjo7Fz585Krbd8+XJIkoShQ4fWboCVZO254WkpIiIixShe3KxYsQKJiYmYOnUq9uzZg8jISMTHx+P8+fM3XO/EiRN49tlnceedd9ZRpDcnLGNu2HNDRESkGMWLm9mzZ2PChAkYP348OnXqhAULFsDb2xuLFy+ucB2TyYRRo0Zh2rRpaNWqVR1Ge2PWm/ixuCEiIlKMh5I7Lykpwe7du5GUlGSdp1KpEBsbi+3bt1e43quvvoqgoCD861//wpYtW264D71eD71eb/2cl5cHADAYDDAYDDU8AluW01KmWth2dSkdh2X/SsfhTJgTe8yJPebEHnNSPnfJS1WOT9Hi5uLFizCZTAgODraZHxwcjCNHjpS7zs8//4wPP/wQe/furdQ+UlNTMW3aNLv569evh7e3d5VjvpGo0td9v/+OEw0bOnTbVVH2F2DNmjWKxVFWenq60iE4HebEHnNijzmxx5yUz9XzUlRUVOm2ihY3VZWfn4/Ro0dj0aJFCAwMrNQ6SUlJSExMtH7Oy8tDWFgY4uLi4Ofn59D4ckrH3NzSsSNuGTTIoduuCg+Paz/WQQrGAZgLrfT0dPTv3x8ajUbRWJwFc2KPObHHnNhjTsrnLnmxnHmpDEWLm8DAQKjVauTk5NjMz8nJQUhIiF37P//8EydOnMCQIUOs8+TSK5M8PDyQmZmJ1q1b26yj0+mg0+nstqXRaBz+S2AZUKxWqZzmF8yZ4nCWWJwFc2KPObHHnNhjTsrn6nmpyrEpOqBYq9WiR48eyMjIsM6TZRkZGRmIiYmxa9+hQwfs378fe/futU733nsv7r77buzduxdhYWF1Gb4dZ7mJn1RaZBEREbkjxU9LJSYmYuzYsYiKikKvXr2QlpaGwsJCjB8/HgAwZswYNGvWDKmpqfD09MQtt9xis35AQAAA2M1XAosbIiIi5Sle3IwYMQIXLlxAcnIysrOz0a1bN6xbt846yPjUqVNQqRS/Yr1S+GwpIiIi5Sle3ABAQkICEhISyl22adOmG667dOlSxwdUTc7Sc0NEROTO6keXSD3BOxQTEREpj8WNAwnrG3GjZkRERFSLWNw4kLP03AgWV0RE5MZY3DiQs4y5YXFDRETujMWNAznL1VJCLYDBANoqGgYREZEinOJqKVfhLKel9D305gddRd20KRERkcthz01tULjnRvbjfXaIiMh9sbhxIGc5LUVEROTOWNw4kLOcliIiInJnLG5qgxP13PDKKSIicjcsbhxIOMlpKYPBYH1/+vRpBSMhIiKqeyxuHMgZx9yw54aIiNxNtYqb06dP48yZM9bPO3fuxFNPPYWFCxc6LLB6qbS4AcfcEBERKaZaxc0jjzyCH3/8EQCQnZ2N/v37Y+fOnXjppZfw6quvOjTA+sR6h2L2lhARESmmWsXNgQMH0KtXLwDA559/jltuuQXbtm3DsmXLsHTpUkfGV68I9twQEREprlrFjcFggE6nAwBs2LAB9957LwCgQ4cOyMrKclx09QwvBSciIlJetYqbzp07Y8GCBdiyZQvS09MxYMAAAMC5c+fQuHFjhwZYn1h7bpxoQDEREZG7qVZx88Ybb+D9999H3759MXLkSERGRgIAVq9ebT1d5Y6EypxOYTQqHAkREZH7qtaDM/v27YuLFy8iLy8PDRs2tM7/97//DW9vb4cFV99wzA0REZHyqtVzc/XqVej1emthc/LkSaSlpSEzMxNBQUEODbA+4ZgbIiIi5VWruLnvvvvwv//9DwBw5coVREdHY9asWRg6dCjmz5/v0ADrE7n0tBR7boiIiJRTreJmz549uPPOOwEAK1euRHBwME6ePIn//e9/ePfddx0aYH3iNGNueJsdIiJyY9UqboqKiuDr6wsAWL9+Pe6//36oVCrcdtttOHnypEMDrE8sxQ2vliIiIlJOtYqbNm3a4Ouvv8bp06fxww8/IC4uDgBw/vx5+Pn5OTTAesUy5kbpnhsiIiI3Vq3iJjk5Gc8++ywiIiLQq1cvxMTEADD34nTv3t2hAdYngmNuiIiIFFetS8EffPBB3HHHHcjKyrLe4wYA+vXrh2HDhjksuPqGxQ0REZHyqlXcAEBISAhCQkKsTwdv3ry5W9/ADygzoFjpMTeSsrsnIiJSUrVOS8myjFdffRX+/v4IDw9HeHg4AgICMH36dMhKf7EryNpzwzE3REREiqlWz81LL72EDz/8EDNmzEDv3r0BAD///DNSUlJQXFyM1157zaFB1hs8LUVERKS4ahU3H330ET744APr08ABoGvXrmjWrBkmTZrktsUNx9wQEREpr1qnpS5duoQOHTrYze/QoQMuXbpU46DqLbXa/OrGp+aIiIiUVq3iJjIyEu+9957d/Pfeew9du3atcVD1FXtuiIiIlFet01Jvvvkm/vGPf2DDhg3We9xs374dp0+fxpo1axwaYL3COxQTEREprlo9N3369MEff/yBYcOG4cqVK7hy5Qruv/9+HDx4EB9//LGjY6w3ROlpKcmJem4kideFExGRe6n2fW6aNm1qN3D4999/x4cffoiFCxfWOLB6yQl7boTgUzSJiMi9VKvnhspnGXMjKV3csJ4hIiI3xuLGkSxXSyl9WopnooiIyI2xuHEkJzwtdeTIEaVDICIiqlNVGnNz//3333D5lStXqhXE3LlzMXPmTGRnZyMyMhJz5syp8DlVX331FV5//XUcO3YMBoMBbdu2xTPPPIPRo0dXa98OZRlQ7ETFzZYtWxAfH690GERERHWmSsWNv7//TZePGTOmSgGsWLECiYmJWLBgAaKjo5GWlob4+HhkZmYiKCjIrn2jRo3w0ksvoUOHDtBqtfjuu+8wfvx4BAUFKf8l7iw9NxxzQ0REbqxKxc2SJUscHsDs2bMxYcIEjB8/HgCwYMECfP/991i8eDFeeOEFu/Z9+/a1+TxlyhR89NFH+Pnnn5UvbpzwUnAiIiJ3o+iYm5KSEuzevRuxsbHWeSqVCrGxsdi+fftN1xdCICMjA5mZmbjrrrtqM9TK8TDXis50Wor3uSEiIndT7fvcOMLFixdhMpkQHBxsMz84OPiGA2Fzc3PRrFkz6PV6qNVqzJs3D/379y+3rV6vh16vt37Oy8sDABgMBhgMBgccxTWitJAQsuzwbVeXyWRSNBbLvp0lH86AObHHnNhjTuwxJ+Vzl7xU5fgULW6qy9fXF3v37kVBQQEyMjKQmJiIVq1a2Z2yAoDU1FRMmzbNbv769evh7e3t0Liu/v03AEBfWOg0j6E4duyYU8SSnp6udAhOhzmxx5zYY07sMSflc/W8FBUVVbqtosVNYGAg1Go1cnJybObn5OQgJCSkwvVUKhXatGkDAOjWrRsOHz6M1NTUcoubpKQkJCYmWj/n5eUhLCwMcXFx8PPzc8yBlPpp8WIAgJdOh76DBjl021VSZmhU69atMUjBWAwGA9LT09G/f39oNBrF4nAmzIk95sQec2KPOSmfu+TFcualMhQtbrRaLXr06IGMjAwMHToUACDLMjIyMpCQkFDp7ciybHPqqSydTgedTmc3X6PROPyXQFW6PZUsO80vmFqtdopYaiPf9R1zYo85scec2GNOyufqeanKsSl+WioxMRFjx45FVFQUevXqhbS0NBQWFlqvnhozZgyaNWuG1NRUAObTTFFRUWjdujX0ej3WrFmDjz/+GPPnz1fyMAAAkuXxC3yeExERkWIUL25GjBiBCxcuIDk5GdnZ2ejWrRvWrVtnHWR86tQpqFTXLuoqLCzEpEmTcObMGXh5eaFDhw745JNPMGLECKUO4RonvIkfERGRu1G8uAGAhISECk9Dbdq0yebzf//7X/z3v/+tg6iqTnLCS8GJiIjcDZ8t5UiW4saJTkvxPjdERORuWNw4Umkh4UzFDRERkbthceNAltNSKqVPS5WprQQLLSIicjMsbhyIY26IiIiUx+LGgSRnGXPDYTZEROTGWNw4kuVScKWLG56JIiIiN8bixoGsY26ULm6IiIjcGIsbB1I5y4DiMngpOBERuRsWNw7EMTdERETKY3HjQFLpmBueliIiIlIOixsHcpqemzJ4nxsiInI3LG4ciAOKiYiIlMfixoFUGo35lcUNERGRYljcOBDH3BARESmPxY0DWU9LKRxHWbwUnIiI3I0zfQ/XezwtRUREpDwWNw7kNAOKWVsREZEbY3HjQM54WoqIiMjd8HvYgZym56YM3ueGiIjcDYsbB1JrteZXhQsKwfNSRETkxljcOJDa0xMA4KFwHERERO6MxY0DqXU6AIAHTwUREREphsWNA3l4eZlfFY6DiIjInbG4cSBLz41G4TgkXLtxH2/iR0RE7obFjQOV7bkRsqxsMERERG6KxY0DWXpuAMBYXKxYHLz8m4iI3BmLGwey9NwAyhY3RERE7ozFjQN5lF4KDgCGoiIFIyEiInJfLG4cSOPtbX1vvHpVwUiu4SkqIiJyNyxuHEjlce0icJNer1wgvECKiIjcGIsbB5JUKpSUvneWnhteCk5ERO6GxY2DGUtfFe25KXOjnXzkKxcHERGRAljcOJih9FXRnpsynTUmmJSLg4iISAEsbhzMUkoo2nNDRETkxljcOJihdIyLScn73PACKSIicmMsbhzMZCluFOy5CSr7gYUOERG5GRY3DmYdUKxgz024YnsmIiJSHosbBzOW9tzIJSU3aUlERES1gcWNg1mLG2cZUMzb3BARkZtxiuJm7ty5iIiIgKenJ6Kjo7Fz584K2y5atAh33nknGjZsiIYNGyI2NvaG7euayQl6bkSZgoY38SMiInejeHGzYsUKJCYmYurUqdizZw8iIyMRHx+P8+fPl9t+06ZNGDlyJH788Uds374dYWFhiIuLw9mzZ+s48vKZnOFqqTL4bCkiInI3ihc3s2fPxoQJEzB+/Hh06tQJCxYsgLe3NxYvXlxu+2XLlmHSpEno1q0bOnTogA8++ACyLCMjI6OOIy+fpbgRBsNNWtYe9tUQEZE787h5k9pTUlKC3bt3IykpyTpPpVIhNjYW27dvr9Q2ioqKYDAY0KhRo3KX6/V66MuMf8nLywMAGAwGGBxcgBgMBhhV5nrRUBqX0mSTrGgcln07Qy6cBXNijzmxx5zYY07K5y55qcrxKVrcXLx4ESaTCcHBwTbzg4ODceTIkUpt4/nnn0fTpk0RGxtb7vLU1FRMmzbNbv769evh7e1d9aBvomlpz81ff/yBi2vWOHz7lVLmTNSJkyewRqk4ykhPT1c6BKfDnNhjTuwxJ/aYk/K5el6Kiooq3VbR4qamZsyYgeXLl2PTpk3w9PQst01SUhISExOtn/Py8qzjdPz8/Bwaj8FgwL7SnpsWTZsiZtAgh26/sl77+Nr7iPAIDFIoDsCck/T0dPTv3x8ajebmK7gB5sQec2KPObHHnJTPXfJiOfNSGYoWN4GBgVCr1cjJybGZn5OTg5CQkBuu+9Zbb2HGjBnYsGEDunbtWmE7nU4HnU5nN1+j0dTKL4GptLhRmUxO8Uum9lA7RRy1le/6jDmxx5zYY07sMSflc/W8VOXYFB1QrNVq0aNHD5vBwJbBwTExMRWu9+abb2L69OlYt24doqKi6iLUSrMUN0oOKC57fRSvliIiInej+GmpxMREjB07FlFRUejVqxfS0tJQWFiI8ePHAwDGjBmDZs2aITU1FQDwxhtvIDk5GZ9++ikiIiKQnZ0NAPDx8YGPj49ix2EhW66WUvA+N7kIBZCl2P6JiIiUpHhxM2LECFy4cAHJycnIzs5Gt27dsG7dOusg41OnTkGlutbBNH/+fJSUlODBBx+02c7UqVORkpJSl6GXyxl6bmSoFds3ERGR0hQvbgAgISEBCQkJ5S7btGmTzecTJ07UfkA1IDtBcUNEROTOFL+Jn6sxqUt7TZQsbngXPyIicmMsbhxMWE6hseeGiIhIESxuHEy29NwYjcoGQkRE5KZY3DiYzJ4bIiIiRbG4cTCn6LnhrW2IiMiNsbhxMGvPjYLFDccTExGRO2Nx42DCw3x1vaRgccOOGyIicmcsbhzMclpKyeKGiIjInbG4cTDhDGNuypAknqQiIiL3wuLGwSzFjWQyKRyJGR+cSURE7obFjYNZTkupnKTnhoiIyN2wuHEwZ+u5ISIicjcsbhxMaDQAABWLGyIiIkWwuHEwa3HD01JERESKYHHjYJb73DhLzw2vliIiInfD4sbRSntu1E5S3PBqKSIicjcsbhzNclpKlhULQeIDGIiIyI2xuHEw4QQ9N958AAMREbkxFjeOZiluFOy58VJsz0RERMpjceNoWi0AwEPB4qZsv82a79coFgcREZESWNw4WmnPjZLFTdkhN8ePH1cuDiIiIgWwuHE0y2kpXqVERESkCBY3jqbTAQA8WNwQEREpgsWNg6lKx9xolLwUnHUVERG5MRY3jmYZUKxwGERERO6KxY2DSZaeGwVPSwnew4+IiNwYixsHk0rH3GgUjoOIiMhdsbhxMFWZ4kYoeTk4ERGRm2Jx42iWZ0sBMJWUKBsLERGRG2Jx42Bqr2sPPzAUFSkYCRERkXticeNglgHFAFBSUKBIDDZDmbUVtSIiInJNLG4crGxxY7x6VZEYRNkb3dyiSAhERESKYXHjYGqtFqbS90oVNzZ4WTgREbkZFje1wDKM2FBYqMj+Jd7ohoiI3BiLm1pgKH01FRcrGgcREZE7YnFTC4ySuedEqZ4bG3zOFBERuRkWN7XAUFrcOEXPDc9QERGRm2FxUwuMChc3Za+WaqBIBERERMphcVMLrMWNQldLSWW6a9oqEgEREZFyFC9u5s6di4iICHh6eiI6Oho7d+6ssO3BgwfxwAMPICIiApIkIS0tre4CrQKjypxWpzgtRURE5GYULW5WrFiBxMRETJ06FXv27EFkZCTi4+Nx/vz5ctsXFRWhVatWmDFjBkJCQuo42sqzFjfOcJ8bIiIiN6NocTN79mxMmDAB48ePR6dOnbBgwQJ4e3tj8eLF5bbv2bMnZs6ciYcffhi60qdvOyNTaXEj6/UKRwJeLUVERG7HQ6kdl5SUYPfu3UhKSrLOU6lUiI2Nxfbt2x22H71eD32ZIiMvLw8AYDAYYDAYKlqtWizbM5W5FNzR+6gMm3pGgiIxWFj2rWQMzoY5scec2GNO7DEn5XOXvFTl+BQrbi5evAiTyYTg4GCb+cHBwThy5IjD9pOamopp06bZzV+/fj28vb0dtp+yioW5vPgzMxMX1qyplX1UxRoniCE9PV3pEJwOc2KPObHHnNhjTsrn6nkpKiqqdFvFipu6kpSUhMTEROvnvLw8hIWFIS4uDn5+fg7dl8FgQHp6OlRaLVBUhIimTXHboEEO3Udl/HeZ7bmoQQrEYGHJSf/+/aHRaBSLw5kwJ/aYE3vMiT3mpHzukhfLmZfKUKy4CQwMhFqtRk5Ojs38nJwchw4W1ul05Y7P0Wg0tfZLIHuY0yoZDE7xi+YsMThDHM6EObHHnNhjTuwxJ+Vz9bxU5dgUG1Cs1WrRo0cPZGRkWOfJsoyMjAzExMQoFZZDmNRqAIBcUnKTlkRERORoip6WSkxMxNixYxEVFYVevXohLS0NhYWFGD9+PABgzJgxaNasGVJTUwGYByEfOnTI+v7s2bPYu3cvfHx80KZNG8WO43qWnhvhDPe54dVSRETkZhQtbkaMGIELFy4gOTkZ2dnZ6NatG9atW2cdZHzq1CmoVNc6l86dO4fu3btbP7/11lt466230KdPH2zatKmuw6+QSasFAAiF7nNj9JCvfeCzpYiIyM0oPqA4ISEBCQkJ5S67vmCJiIiAEM7fFSFbxvhUYWS3I+1rm219r/gtqImIiOoYv/tqgfDyMr9RqLiRVdcKQF9FIiAiIlIOi5taIJcWN5ICp6XqQccWERFRrWJxUxssxY0zDCgmIiJyMyxuaoHUoAEAQK1AcSNdP4CYPTlERORmWNzUAqn0sQ5qZ7jPDa+WIiIiN8PiphaofHwAABoneCp4IQKVDoGIiKhOsbipBWpf8zVKGid4QmsemisdAhERUZ1icVMLPEofyKkxGhWOxKwqj4knIiKq71jc1AJLcaMzmRSOxGzevHlKh0BERFRnWNzUAo2/PwAnKW4EcODAAaWjICIiqjMsbmqBZ5MmAAAfZyhueLUUERG5GRY3tcC/9Anl/gAMCj2C4Rre6IaIiNwLi5taENCyJSx9Npf++EPRWABg8+bNSodARERUZ1jc1AKVhwf+VplTe/G33xSNpRh+OHr0qKIxEBER1SUWN7XkRKD55nkFSUkQsqxYHKdwl2L7JiIiUgKLm1ril5YGPYDonBzsfftt5QIRHFFMRETuhcVNLekwciR2dOgAACh4/33lApE4oJiIiNwLi5ta5D95MgAg8uhRXFJw3EuvXr0U2zcREVFdY3FTi7pMnIg/tVr4ATgeE6PYZeG5ubmK7JeIiEgJLG5qkcrDA8b//Q+FAKL+/hu/REYqMrg4MzOzzvdJRESkFBY3taz9iBE4NHUqTADuPHYMP3fogJKCgroLgAOKiYjIzbC4qQM9U1Kwbfx4c4Fz9Cj+CAnB0VWrlA6LiIjIJbG4qSN3Ll6MPdOmIRfALYWFiLj/fmxp1w7H166t3R2rDQCA/Pz82t0PERGRk2BxU4d6JiejaOdO/BIaCg3MvTgRgwbhl6ZNsb+2LhePfhcAcOutt9bO9omIiJwMi5s6FtqzJ247dw4HFi3CjpAQqADclpWFLhMnYp+fH3a8+CJko9FxO9RcBQAcO3YM58+fd9x2iYiInBSLG4Xc8thjiM7Kwp/ffost7dpBD6Brfj6iU1Nx1NcX2597zmFFTmzp69133+2Q7RERETkzFjcKaz14MO7MzMSV337DpttuQx6A9sXFiHnrLRz38cH2sLAa3wDw8dLXQ4cO4bPPPoMQvGsxERG5LhY3TiK4Wzf03b4dpmPHsOmOO5ALoI1ej5gzZ+DZrh30eXnV3vb9Zd4/8sgjiIyMxJIlS1BcXFzjuImIiJwNixsn07B1a/TdsgXi+HFsijWfUPIGsOM//6nRdkvat4e3pycAYP/+/Xj00UfRqFEj9O3bF0lJSfjmm2+QnZ1d0/CJiIgU56F0AFS+gJYt0Tc9HZsffBB9vvwSQd98U+1tCQCazEwU3nknrsydi4Vr1+K9997D6dOnsXnzZmzevNnatlGjRmjbti2aNWuGkJAQhIaGIjQ0FE2bNkVoaCgaN24Mf39/+Pj4QKVibUxERM6HxY2T6zx9OkxffokOV6/ip86dEfTyy2g/YgSkGxQWLa4ApwKufTZAAy0MwJYtCOjaFf/XtSueW7UKh728sH37dvzyyy/45ZdfcPDgQVy6dAk7duy4aVySJMHX1xf+/v7WydfXFz4+Pvjtt9/g7++P6OhoNGrUCKdOncLx48fh4+MDb29veHl5wcvLy/q+vFeNRgNJ4t2ViYio6ljcOLnAjh2xqUcP9N29G3cdOgQ88gj+HDcOp++8E+1eew1No6Pt1rklW41TASbr53VLc3Bv2G/A5MnAkSPAvn2QoqLQKTQUnW6/Hf8aOBB45BEU3XILjmVl4ejRo8jKykJ2djaysrKQlZWFc+fOISsrC5cvX4bBYIAQAnl5ecjLy8Pp06fLjX3Pnj3W9x9//HGVjlulUsHb29ta6AghrL1GDRo0sC4r+/5G88qb7+Xlxd4nIiIXxOKmHuizcyd2vf46DO+/j+5nzqB1SQlaZ2RAzsjAr4GBkKZMQdTLLwMAjLIRazqYbNYfM6Uhrly5Bzh8GFi/HpgxA9iyBcjKAr780jwB8Far0bVzZ3Tt2BFo1Qpo1Aho2RJo3Ng8+ftD+Pmh2NMTeUIgNz8fubm5yMvLQ25uLvLz85Gfn48jR45g7dq1GDZsGAoLC3H06FE0atQIer0eRUVFuHr1aoWvcumDRWVZRkFBAQrKPIfr3LlzDs+tp6dnhcWQj48PfH19bSYfH58K219fOOXm5sLb2xv+/v4Oj5uIiCrG4qYekFQqc/Hy8svIO3MGu1JS4LNyJbrl5qLnxYvAK69g99tvw/udd9Dxn/9E7O9+2BB57eqq3NwyG4uLM08XLgC7dgHbtwM//wwcPQqcOQPs22eeKooFgBcAL0lCsK8vEBAAeHubJz8/wMcHMBiAW24BLlyALEn4y8sLEY0bQ713LzBsGODra26vVgNFRUCDBkDDhhAADB4e0Gu1uKpWo1ClQpFKhUIA+w8dQmhoKIqKiqxTYWGhzefKzit7lVhxcTGKi4vx999/O/aHVoaPjw+0Wi0uXbqEhg0bIiQkBHq9HjNmzICXlxd0Oh08PT0r/VrRMp1OB41GU+7k4eFhfa9Wq3nKj4hcGoubesaveXPc8cEHwAcf4GRGBk48+yxi9u5Fj0uXgNGj8dsTT+BezzxsiLzJhpo0AQYONE8WZ84AP/0EHDsG/P03cPmy+fXSJfNrbi5w5QpQUgIIAeTlmacbUAFoXXbGL79U2FYCoC2dfK9b1kunMxdBlsnHx/yq0QA6nXlq0MBcODVpYn61LPfwADw9zW18fGDy9ESJWo2rHh4o8vREoUqFQiGQbzSiqLjYWgRZeo4sPVL5+fkoKCiwK56un/R6vU3sZXufLl++jMuXLwMAjh8/fsPc1aaKiiBLIWQphsp7rcwyjUYDrVYLrVZrLbxu9F6SJBw+fBhNmjSBp6endTtardauQCv7qlarFcshETkvFjf1WHi/fgj/7Tec/uknnBw3Drf/9Re6X7kCKcS23fjxldxg8+bAI4/cvF1xsbnQsRQ7V68ChYVAfr55On0a+PZbYPhwmEwm/Pn772jj7w9VQYG5t+bqVXOPjdFoLkDy881FkiyblxUUmKf8fMBUeopNrzdPly5VJUXlUqO09wlAo+sXenmZY/L2Lv/Vxwdo2NBcNJlM5oLJzw/w9zcXVJ6eMHl7Q6/VQg/AIAT0APSyjMuXL6NYCMgaDX757Te069oVBpUKRQYD9AYDiouLodfrq/2q1+thMBjspvLcaFl9IknSTYsttVptfV92qmh+dda50bauLwgrOx8ALly4gKysLHh5eZV7XOyBIyofixsXEHbXXQg7fhxnt2/H0SefRN75fQBKrMt/+ONHAA589IKnp3kKDq64zbRpAADZYMDhNWvQctAgqDSaqu1HCHNBk59vLoYKC6+95uebC6GSEnOb4mJzQVRcfO19YaH5FJnBcK1tYeG1dnl55mKpqOjaPq9eNU81oIb53kTeN2jT5/oZKpW58JNl86tlnoeHuYDy8DAXWF5e5s+enubPDRte65WyvGq15m14eECo1RAaDUweHjCp1ZAlCSYhrJOx7HtZhgmw+Wy0tJHla58tryYTDDD/ppUA0AthfS2WZVw1mXDVZEKR0WidiktKoNfrUVL6anlvNBqRm5sLnU4Hg8EAo9FoLcD0ej2MRqN1PFZZQgiXKdSqQ61WV2pSqVSVbld2kiSpVueV/Vze++tfZVnG0aNHsW/fPnh4eNi1qWi9yra50fvyPlemTVXWAWDXtuxU0XKj0YizZ8/ijz/+sPaEVnZdRywvb5lKpYJWq63r/yWsWNy4kGYxMWj26684dukYMKetdf65/vfAZBKodz34knStkKpNlh6jssWT5fX6eQUF5rYGg7n40OuvnZ7LyzMXTYWF5l6tkhJzO8srAOj1EHo9pJIS+xgsX94m2wHhKHNaq6qk0kkFoIqlZe1Qq83FV9lJo4Hw8ECRLMNbrYbk42Mu0jQac3uVClCpIFQqCABCkiAkCbIkQQCQS+fJkgSThwdklQomlcr6ap0kCbJKBaMkwQTAqFKZizkAJlmGLMvXCr/S90YhIJcWdmXnG64r9AxlX2UZhusnkwkGWUZJ2VeTCXqjESUmk3UylL4WGwwoMZlwtaTEvK4Q12KF+d5VAGAymWC6/veFyAnExMRg27Ztiu2fxY0LatOojd28V1+1dqbQ9VSqa2N56oDRYMCa777DoNhYaGTZXPgYjeZJkszFjSSZi52yxVFRkbl4svRUWV6vf280mrdh2WZJybUeLksRZTKV//5Gy8prZ4nv+sky//ovXpOp3N4xCUADAMjJqTBvlkKNzERpb5/lFZJk7qmzFINqtbkQtBSGlslSHJb5LJeZB5iLJ8tU9jOEgJAk8+t17eTSgtJacJauK5dtY9lGmVcBcw+czbzrtm+ZLwuBgoICNGjQwHwclu1blgPmYxHCul+5TAym0rYou851+5TLbMu6ndJ1rp9ns//r1ysbU5nlZfcjl7NPlNm+zbbKHBfKWddgMpl7t66Pp5y4yrax/nzKLqvg51/eVNHyloWF1//K1imnKG7mzp2LmTNnIjs7G5GRkZgzZw569epVYfsvvvgCr7zyCk6cOIG2bdvijTfewKBBg+owYuf355N/ovW714byvvpaCRYv1qJnT/MV4V5e5n8Ye3ubOxn8/c3fO5bhLyrVtYufZNl8hkiIa+/LzpMk87ZycsyveXnmv7VhYYAkqXH58h34v//zgFZrvrhKkszfwSUl5v2rVOZ5Fpb3lo4MDw/zfoBr389lVfQcUA8PczyW7Vu2a4nfZDK/12rNHQWWZTeaLErHJlvPJJXNx/XtPTzMU3GxpSNKjXPneuDr1Q3g4aGydExYz0yV/Vx2Mpls2wlhrl0qpMK1EdplZ1/7LrTJt6NfZRnQX5UhG2VIsgkqYYRKNkENE9TCaP2sko2AqQQnT59A61YtoREmqGQDJJMRkM2J9FCZoIYMD5UMFWT7X8qyBZfRCMgmwGgq8yqXzi/zajJBMhmhEiaoJBkayQiNZISHMEIjGaCBARoYoYYJQjbvRwhAmMz7ErIwzzeZlwuTOSbLckk2QS0boBYGSCbzcUqyCSqTAZKQzZ9NxmuvwgiYzHEZoYYBGkgQ1kkF2fa9LKCS5YqXW98brVuRoYIoLRGl0q+msuuUN6lhsr4vjwT7/wFvNI8Fqnsw1XaP+00oXtysWLECiYmJWLBgAaKjo5GWlob4+HhkZmYiKCjIrv22bdswcuRIpKamYvDgwfj0008xdOhQ7NmzB7fccosCR+CcWjVsBY1KA4NcejrkFR3OvH0SZ1a1qLMYzpwBzN+wjetsn/WDCkDd/RyUpSqdPADobtK2Xe2HQ05BkkrLLUtBXPpeJZlLL0kyf5YgzPMkSxEGyLLxxlfJmbt6rv2DSeD6N9fiQMXLbNvdeLllE5Ik7OaVt/3KbK/CNsJ2e5Z2sizf8KakNtu7YU5utN9rb252DFFnTuPrG7aoXZIQFf27t25ER0ejZ8+eeO+99wCYf0BhYWF44okn8MILL9i1HzFiBAoLC/Hdd99Z5912223o1q0bFixYcNP95eXlwd/fH7m5ufDz83PcgcB8BcqaNWswaNAgaKo6eLYW5Ovz4TfD9hhDTdHoGNgJGtnffBYEEnwaSJBM3oBkgrdOCz8vL5iEjJJiNYRRCw+VBhqVFmpJDUiy+Y+GBKgk8yAyIQMmkwRZBs6eA5qGSLhwEQgJNv88T5w4iebNI3DurBoRLQEICSYTcPGCGmHNzN3YkvXPjFT6Xir9gyZBNl07IaHVSlCrJGt7qTQYCRJUktq6Lkr/QW/5RzsEru2nNHaVyvwPf5MMGEpQZp/mTVjeS5JlsJw1DBhKzD0xsmxeprL0DJXpvZBK38gmc4fCwYPm3p6gIBNOnjyNsOYtIElqyMK298f8as6rLGB9lVDa5Subc62SAEllfr3mRv8uNi8rO7zH+n+/kGw+2/5VkOznWz9LZT+WmQ+oJAkaLaBWWTtgzB0mRvN6luM1mQCTScbFixfRsGEghFBDlOkkEDB3wJhkQDZJqPgv1g2OXZS/zJrPMmfxyk4mk2Q9W1i2Z0qSzNu0+5lbv6xtj88Sc9mfrRDX8mL5GVviVKkAjcY8WFqt1pjzLCTbdUrXM7+XSte37Vm1OfbS33nL7xFKj/2G3wAV5M26Qadar5p9Qjfc541UY71q76v+6dFNh12bQm7esAqq8v2taM9NSUkJdu/ejaSkJOs8lUqF2NhYbN++vdx1tm/fjsTERJt58fHx+Prrr8ttb7kiwyKv9L4stXGFhWV7znLlhqfKE/okPXSp1/7FnKXegazL1z07ytGnRi0PFz9b+qoDcAHm0yNnr2tb/pMbXFNY6asBQFPArpff8o+u+jbw21Fa37wJ1Uz5J5aIHM/Y6DYYDD85dJtV+W5VtLi5ePEiTCYTgq+7pDg4OBhHjhwpd53s7Oxy22dnZ5fbPjU1FdPKGUm7fv16eHvf6GLd6ktPT6+V7VbX192+hkE24Iz+DE5dPYWz+rMwCRNEmf9KZPPVO7KQoZf1UEkqyEKGCSYYZSOMwgi59E+jpddElNMtWXZeeZ2CluUClgFvwnad0vk2nyuxvLxtVRSX3bJqdl7ecJuV6cauyvacJcYbxOHofd10m9XIidvGWM2f2w23WY//X6spR/+/44pMJQVYs2aNQ7dZVPa2HTeh+Jib2paUlGTT05OXl4ewsDDExcXVymmp9PR09O/f3ylOSzkD5sQec2KPObHHnNhjTsrnLnnJu8kd8ctStLgJDAyEWq1GznWXf+bk5CAkpPxzdSEhIVVqb7nF+/Ust3SvDbW57fqKObHHnNhjTuwxJ/aYk/K5el6qcmwVD62uA1qtFj169EBGRoZ1nizLyMjIQExMTLnrxMTE2LQHzKeBKmpPRERE7kXx01KJiYkYO3YsoqKi0KtXL6SlpaGwsBDjSx+INGbMGDRr1gypqakAgClTpqBPnz6YNWsW/vGPf2D58uXYtWsXFi5cqORhEBERkZNQvLgZMWIELly4gOTkZGRnZ6Nbt25Yt26dddDwqVOnbK7dv/322/Hpp5/i5Zdfxosvvoi2bdvi66+/5j1uiIiICIATFDcAkJCQgISEhHKXbdq0yW7e8OHDMXz48FqOioiIiOojRcfcEBERETkaixsiIiJyKSxuiIiIyKWwuCEiIiKXwuKGiIiIXAqLGyIiInIpLG6IiIjIpbC4ISIiIpfC4oaIiIhcilPcobguCSEAVO3R6ZVlMBhQVFSEvLw8l34ya1UwJ/aYE3vMiT3mxB5zUj53yYvle9vyPX4jblfc5OfnAwDCwsIUjoSIiIiqKj8/H/7+/jdsI4nKlEAuRJZlnDt3Dr6+vpAkyaHbzsvLQ1hYGE6fPg0/Pz+Hbru+Yk7sMSf2mBN7zIk95qR87pIXIQTy8/PRtGlTmwdql8ftem5UKhWaN29eq/vw8/Nz6V+w6mBO7DEn9pgTe8yJPeakfO6Ql5v12FhwQDERERG5FBY3RERE5FJY3DiQTqfD1KlTodPplA7FaTAn9pgTe8yJPebEHnNSPubFntsNKCYiIiLXxp4bIiIiciksboiIiMilsLghIiIil8LihoiIiFwKixsHmTt3LiIiIuDp6Yno6Gjs3LlT6ZAc5qeffsKQIUPQtGlTSJKEr7/+2ma5EALJyckIDQ2Fl5cXYmNjcfToUZs2ly5dwqhRo+Dn54eAgAD861//QkFBgU2bffv24c4774SnpyfCwsLw5ptv1vahVVtqaip69uwJX19fBAUFYejQocjMzLRpU1xcjMmTJ6Nx48bw8fHBAw88gJycHJs2p06dwj/+8Q94e3sjKCgIzz33HIxGo02bTZs24dZbb4VOp0ObNm2wdOnS2j68apk/fz66du1qvZFYTEwM1q5da13ubvkoz4wZMyBJEp566inrPHfLS0pKCiRJspk6dOhgXe5u+bA4e/Ys/vnPf6Jx48bw8vJCly5dsGvXLutyd/w7WyOCamz58uVCq9WKxYsXi4MHD4oJEyaIgIAAkZOTo3RoDrFmzRrx0ksvia+++koAEKtWrbJZPmPGDOHv7y++/vpr8fvvv4t7771XtGzZUly9etXaZsCAASIyMlL88ssvYsuWLaJNmzZi5MiR1uW5ubkiODhYjBo1Shw4cEB89tlnwsvLS7z//vt1dZhVEh8fL5YsWSIOHDgg9u7dKwYNGiRatGghCgoKrG0mTpwowsLCREZGhti1a5e47bbbxO23325dbjQaxS233CJiY2PFb7/9JtasWSMCAwNFUlKStc3x48eFt7e3SExMFIcOHRJz5swRarVarFu3rk6PtzJWr14tvv/+e/HHH3+IzMxM8eKLLwqNRiMOHDgghHC/fFxv586dIiIiQnTt2lVMmTLFOt/d8jJ16lTRuXNnkZWVZZ0uXLhgXe5u+RBCiEuXLonw8HAxbtw4sWPHDnH8+HHxww8/iGPHjlnbuOPf2ZpgceMAvXr1EpMnT7Z+NplMomnTpiI1NVXBqGrH9cWNLMsiJCREzJw50zrvypUrQqfTic8++0wIIcShQ4cEAPHrr79a26xdu1ZIkiTOnj0rhBBi3rx5omHDhkKv11vbPP/886J9+/a1fESOcf78eQFAbN68WQhhzoFGoxFffPGFtc3hw4cFALF9+3YhhLloVKlUIjs729pm/vz5ws/Pz5qH//u//xOdO3e22deIESNEfHx8bR+SQzRs2FB88MEHbp+P/Px80bZtW5Geni769OljLW7cMS9Tp04VkZGR5S5zx3wIYf5bd8cdd1S4nH9nq46npWqopKQEu3fvRmxsrHWeSqVCbGwstm/frmBkdeOvv/5Cdna2zfH7+/sjOjraevzbt29HQEAAoqKirG1iY2OhUqmwY8cOa5u77roLWq3W2iY+Ph6ZmZm4fPlyHR1N9eXm5gIAGjVqBADYvXs3DAaDTV46dOiAFi1a2OSlS5cuCA4OtraJj49HXl4eDh48aG1TdhuWNs7+u2UymbB8+XIUFhYiJibG7fMxefJk/OMf/7CL3V3zcvToUTRt2hStWrXCqFGjcOrUKQDum4/Vq1cjKioKw4cPR1BQELp3745FixZZl/PvbNWxuKmhixcvwmQy2fyPBgDBwcHIzs5WKKq6YznGGx1/dnY2goKCbJZ7eHigUaNGNm3K20bZfTgrWZbx1FNPoXfv3rjlllsAmGPWarUICAiwaXt9Xm52zBW1ycvLw9WrV2vjcGpk//798PHxgU6nw8SJE7Fq1Sp06tTJbfMBAMuXL8eePXuQmppqt8wd8xIdHY2lS5di3bp1mD9/Pv766y/ceeedyM/Pd8t8AMDx48cxf/58tG3bFj/88AMef/xxPPnkk/joo48A8O9sdbjdU8GJHG3y5Mk4cOAAfv75Z6VDUVz79u2xd+9e5ObmYuXKlRg7diw2b96sdFiKOX36NKZMmYL09HR4enoqHY5TGDhwoPV9165dER0djfDwcHz++efw8vJSMDLlyLKMqKgovP766wCA7t2748CBA1iwYAHGjh2rcHT1E3tuaigwMBBqtdpuNH9OTg5CQkIUiqruWI7xRscfEhKC8+fP2yw3Go24dOmSTZvytlF2H84oISEB3333HX788Uc0b97cOj8kJAQlJSW4cuWKTfvr83KzY66ojZ+fn1N+EWi1WrRp0wY9evRAamoqIiMj8c4777htPnbv3o3z58/j1ltvhYeHBzw8PLB582a8++678PDwQHBwsFvmpayAgAC0a9cOx44dc9vfk9DQUHTq1MlmXseOHa2n69z972x1sLipIa1Wix49eiAjI8M6T5ZlZGRkICYmRsHI6kbLli0REhJic/x5eXnYsWOH9fhjYmJw5coV7N6929pm48aNkGUZ0dHR1jY//fQTDAaDtU16ejrat2+Phg0b1tHRVJ4QAgkJCVi1ahU2btyIli1b2izv0aMHNBqNTV4yMzNx6tQpm7zs37/f5g9Seno6/Pz8rH/oYmJibLZhaVNffrdkWYZer3fbfPTr1w/79+/H3r17rVNUVBRGjRplfe+OeSmroKAAf/75J0JDQ93296R37952t5L4448/EB4eDsB9/87WiNIjml3B8uXLhU6nE0uXLhWHDh0S//73v0VAQIDNaP76LD8/X/z222/it99+EwDE7NmzxW+//SZOnjwphDBfohgQECC++eYbsW/fPnHfffeVe4li9+7dxY4dO8TPP/8s2rZta3OJ4pUrV0RwcLAYPXq0OHDggFi+fLnw9vZ22ksUH3/8ceHv7y82bdpkc0lrUVGRtc3EiRNFixYtxMaNG8WuXbtETEyMiImJsS63XNIaFxcn9u7dK9atWyeaNGlS7iWtzz33nDh8+LCYO3eu017S+sILL4jNmzeLv/76S+zbt0+88MILQpIksX79eiGE++WjImWvlhLC/fLyzDPPiE2bNom//vpLbN26VcTGxorAwEBx/vx5IYT75UMI820CPDw8xGuvvSaOHj0qli1bJry9vcUnn3xibeOOf2drgsWNg8yZM0e0aNFCaLVa0atXL/HLL78oHZLD/PjjjwKA3TR27FghhPkyxVdeeUUEBwcLnU4n+vXrJzIzM2228ffff4uRI0cKHx8f4efnJ8aPHy/y8/Nt2vz+++/ijjvuEDqdTjRr1kzMmDGjrg6xysrLBwCxZMkSa5urV6+KSZMmiYYNGwpvb28xbNgwkZWVZbOdEydOiIEDBwovLy8RGBgonnnmGWEwGGza/Pjjj6Jbt25Cq9WKVq1a2ezDmTz66KMiPDxcaLVa0aRJE9GvXz9rYSOE++WjItcXN+6WlxEjRojQ0FCh1WpFs2bNxIgRI2zu5+Ju+bD49ttvxS233CJ0Op3o0KGDWLhwoc1yd/w7WxOSEEIo02dERERE5Hgcc0NEREQuhcUNERERuRQWN0RERORSWNwQERGRS2FxQ0RERC6FxQ0RERG5FBY3RERE5FJY3BCRW5IkCV9//bXSYRBRLWBxQ0R1bty4cZAkyW4aMGCA0qERkQvwUDoAInJPAwYMwJIlS2zm6XQ6haIhIlfCnhsiUoROp0NISIjNZHkysSRJmD9/PgYOHAgvLy+0atUKK1eutFl///79uOeee+Dl5YXGjRvj3//+NwoKCmzaLF68GJ07d4ZOp0NoaCgSEhJsll+8eBHDhg2Dt7c32rZti9WrV1uXXb58GaNGjUKTJk3g5eWFtm3b2hVjROScWNwQkVN65ZVX8MADD+D333/HqFGj8PDDD+Pw4cMAgMLCQsTHx6Nhw4b49ddf8cUXX2DDhg02xcv8+fMxefJk/Pvf/8b+/fuxevVqtGnTxmYf06ZNw0MPPYR9+/Zh0KBBGDVqFC5dumTd/6FDh7B27VocPnwY8+fPR2BgYN0lgIiqT+kndxKR+xk7dqxQq9WiQYMGNtNrr70mhDA/dX3ixIk260RHR4vHH39cCCHEwoULRcOGDUVBQYF1+ffffy9UKpXIzs4WQgjRtGlT8dJLL1UYAwDx8ssvWz8XFBQIAGLt2rVCCCGGDBkixo8f75gDJqI6xTE3RKSIu+++G/Pnz7eZ16hRI+v7mJgYm2UxMTHYu3cvAODw4cOIjIxEgwYNrMt79+4NWZaRmZkJSZJw7tw59OvX74YxdO3a1fq+QYMG8PPzw/nz5wEAjz/+OB544AHs2bMHcXFxGDp0KG6//fZqHSsR1S0WN0SkiAYNGtidJnIULy+vSrXTaDQ2nyVJgizLAICBAwfi5MmTWLNmDdLT09GvXz9MnjwZb731lsPjJSLH4pgbInJKv/zyi93njh07AgA6duyI33//HYWFhdblW7duhUqlQvv27eHr64uIiAhkZGTUKIYmTZpg7Nix+OSTT5CWloaFCxfWaHtEVDfYc0NEitDr9cjOzraZ5+HhYR20+8UXXyAqKgp33HEHli1bhp07d+LDDz8EAIwaNQpTp07F2LFjkZKSggsXLuCJJ57A6NGjERwcDABISUnBxIkTERQUhIEDByI/Px9bt27FE088Uan4kpOT0aNHD3Tu3Bl6vR7fffedtbgiIufG4oaIFLFu3TqEhobazGvfvj2OHDkCwHwl0/LlyzFp0iSEhobis88+Q6dOnQAA3t7e+OGHHzBlyhT07NkT3t7eeOCBBzB79mzrtsaOHYvi4mK8/fbbePbZZxEYGIgHH3yw0vFptVokJSXhxIkT8PLywp133only5c74MiJqLZJQgihdBBERGVJkoRVq1Zh6NChSodCRPUQx9wQERGRS2FxQ0RERC6FY26IyOnwbDkR1QR7boiIiMilsLghIiIil8LihoiIiFwKixsiIiJyKSxuiIiIyKWwuCEiIiKXwuKGiIiIXAqLGyIiInIpLG6IiIjIpfw/gtF778/dANkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Loss plots\n",
    "epoch_split = 500\n",
    "\n",
    "tot_loss = histories[0]\n",
    "data_loss = histories[1]\n",
    "uv_loss = histories[2]\n",
    "p_loss = histories[3]\n",
    "print(p_loss)\n",
    "pde_loss = histories[4]\n",
    "bc_loss = histories[5]\n",
    "print(pde_loss)\n",
    "\n",
    "\"\"\" print(data_loss)\n",
    "print(data_loss[0].item()) \"\"\"\n",
    "epoch_total = epochs\n",
    "x = np.arange(1, epoch_total, 1)\n",
    "print(len(x), len(tot_loss))\n",
    "x_split = np.where(x >= epoch_split)[0]\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"Data loss\")\n",
    "plt.plot(x, tot_loss, '-k', label=\"Total Loss\")\n",
    "plt.plot(x, data_loss, '-r', label=\"Data Loss\")\n",
    "\"\"\" plt.plot(x, uv_loss, '--r', label=\"uv Loss\")\n",
    "plt.plot(x, p_loss, '-.r', label=\"p Loss\") \"\"\"\n",
    "plt.plot(x[x_split], pde_loss, '-b', label=\"PDE loss\")\n",
    "plt.plot(x[x_split], bc_loss, '-g', label=\"BC loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Plot data loss breakdown\n",
    "plt.figure()\n",
    "plt.title(\"Data Loss Components\")\n",
    "plt.plot(x, data_loss, '-r', label=\"Data Loss\")\n",
    "plt.plot(x, uv_loss, '-b', label=\"uv Loss\")\n",
    "plt.plot(x, p_loss, '-g', label=\"p Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"Pressure Data Loss\")\n",
    "plt.plot(x, p_loss, '-g', label=\"p Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22b3029c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch_cpu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}