{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjust AP-DV Plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata as ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix='10dpa2'\n",
    "adata  = ad.read_h5ad(f\"D:/wochong_2023/body_axis/h5ad_SPC/SPC.{prefix}.h5ad\")\n",
    "radata = ad.read_h5ad(f'D:/wochong_2023/body_axis/APML/{prefix}.apadj.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.linalg as sl\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import io as skio\n",
    "\n",
    "def polyfix(x, y, xfix, yfix, n=3):\n",
    "    \"\"\"\n",
    "    This code is copy from https://github.com/jbae11/polyfix.py with minor changes \n",
    "    Fits polynomial p with degree n to data with constrains\n",
    "    \n",
    "    param (x,y): 2D coordinates of all training data.\n",
    "    param n: degree of polynomial.\n",
    "    param (xfix,yfix): 2D coordinates of all constrain points.\n",
    "    \"\"\"\n",
    "    nfit = len(x)\n",
    "    if len(y) != nfit:\n",
    "        raise ValueError('x and y must have the same size')\n",
    "    nfix = len(xfix)\n",
    "    if len(yfix) != nfix:\n",
    "        raise ValueError('xfit adn yfit must have the same size')\n",
    "    x = np.vstack(x)\n",
    "    y = np.vstack(y)\n",
    "    if nfix > 1 :\n",
    "        xfix = np.vstack(xfix)\n",
    "        yfix = np.vstack(yfix)\n",
    "\n",
    "    nspec = nfix\n",
    "    specval = yfix\n",
    "    # first find A and pc such that A*pc = specval\n",
    "    A = np.zeros((nspec, n+1))\n",
    "    # specified y values\n",
    "    for i in range(n+1):\n",
    "        A[:nfix, i] = np.hstack(np.ones((nfix, 1)) * xfix**(n+1-(i+1)))\n",
    "\n",
    "    if nfix > 0:\n",
    "        lastcol = n+1\n",
    "        nmin = nspec - 1\n",
    "    else:\n",
    "        lastcol = n\n",
    "        nmin = nspec\n",
    "\n",
    "    if n < nmin:\n",
    "        raise ValueError('Polynomial degree too low, cannot match all constraints')\n",
    "    # find unique polynomial of degree nmin that fits the constraints\n",
    "    firstcol = n-nmin\n",
    "    pc0 = np.linalg.solve(A[:, firstcol:lastcol], specval)\n",
    "    pc = np.zeros((n+1, 1))\n",
    "    pc[firstcol:lastcol] = pc0\n",
    "    \n",
    "    X = np.zeros((nfit, n+1))\n",
    "    for i in range(n+1):\n",
    "        X[:, i] = (np.ones((nfit, 1)) * x**(n+1-(i+1))).flatten()\n",
    "\n",
    "    yfit = y - np.polyval(pc, x)\n",
    "\n",
    "    B = sl.null_space(A)\n",
    "    #z = np.linalg.lstsq(X @ B, yfit,rcond=None)[0]\n",
    "    z = np.linalg.lstsq(X @ B, yfit)[0]\n",
    "    if len(z) == 0:\n",
    "        z = z[0]\n",
    "        p0 = B*z\n",
    "    else:\n",
    "        p0 = B@z\n",
    "    p = np.transpose(p0) + np.transpose(pc)\n",
    "    return p\n",
    "\n",
    "def getMidline(f1, x_max, x_min, y_max, y_min,w):\n",
    "    xvals = np.array(range(0,w))\n",
    "    yvals = np.polyval(f1[0], xvals)\n",
    "    line_df = pd.DataFrame(columns=['x','y'])\n",
    "    line_df['x'] = xvals\n",
    "    # half adjust\n",
    "    line_df['x'] = (line_df['x']+0.5).astype('int')\n",
    "    line_df['y'] = yvals\n",
    "    # half adjust  \n",
    "    line_df['y'] = (line_df['y']+0.5).astype('int')\n",
    "    line_df = line_df[line_df['y']<y_max]\n",
    "    line_df = line_df[line_df['x']<x_max]\n",
    "    line_df = line_df[line_df['y']>y_min]\n",
    "    line_df = line_df[line_df['x']>x_min]\n",
    "    return line_df\n",
    "\n",
    "def drawImageWithLine(x,y,line_df,w,h,prefix='draw01'):\n",
    "    line = np.zeros((h,w))\n",
    "    line[y,x]=200\n",
    "    line[line_df['y'],line_df['x']]=255\n",
    "    skio.imsave(f'{prefix}.png',line)\n",
    "\n",
    "def scatterWithLine(x,y,line_df):\n",
    "    plt.figure()\n",
    "    plt.scatter(x,y,s=2)\n",
    "    plt.plot(line_df['x'],line_df['y'],c='r')\n",
    "    plt.gca().set_aspect('equal')\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obsm['spatial']\n",
    "adata.obs['new_x'] = adata.obsm['spatial'][:,0]\n",
    "adata.obs['new_y'] = adata.obsm['spatial'][:,1]\n",
    "adata.obs['new_z'] = adata.obsm['spatial'][:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 285 × 21579\n",
       "    obs: 'seurat_clusters.r1.6', 'cell_number', 'hood', 'x', 'y', 'z', 'min_radius', 'max_radius', 'Batch', 'nCount_RNA', 'nFeature_RNA', 'nCount_SCT', 'nFeature_SCT', 'seurat_clusters', 'SPC_cluster', 'SPC_name', 'sample', 'new_x', 'new_y', 'new_z'\n",
       "    obsm: 'spatial'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c33 = adata[adata.obs['SPC_cluster']=='c33'].copy()\n",
    "c33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_np_APML(the_raw_pos, the_midline, f1):\n",
    "    def Yinline(f1,xvals):\n",
    "        return np.polyval(f1[0], xvals)\n",
    "    the_result = pd.DataFrame()\n",
    "    the_result['raw_x'] = the_raw_pos[:,0]\n",
    "    the_result['raw_y'] = the_raw_pos[:,1]\n",
    "    the_result['raw_yline'] =  Yinline(f1,the_raw_pos[:,0])\n",
    "\n",
    "    def Ytag(item):\n",
    "        if item['raw_y']>item['raw_yline']:\n",
    "            return 1;\n",
    "        elif item['raw_y']<item['raw_yline']:\n",
    "            return -1;\n",
    "        else:\n",
    "            return 1\n",
    "    the_result['ytag'] = the_result.apply(lambda row: Ytag(row),axis=1)\n",
    "    from scipy.spatial import KDTree\n",
    "    from scipy.spatial import distance\n",
    "    the_line_new_points = the_midline.to_numpy()\n",
    "    length = np.zeros(len(the_line_new_points))\n",
    "    for i in range(1,len(the_line_new_points)):\n",
    "        length[i] = distance.euclidean(the_line_new_points[i,:],the_line_new_points[i-1,:])\n",
    "    length = np.cumsum(length)\n",
    "    the_kdtree = KDTree(np.array(the_line_new_points))\n",
    "\n",
    "    darray, iarray = the_kdtree.query(the_raw_pos)\n",
    "\n",
    "    the_result['ny'] = darray\n",
    "    the_result['ny'] = the_result['ny']*the_result['ytag']\n",
    "    the_result['nx'] = length[np.array(iarray).astype(int)]\n",
    "    return the_result\n",
    "\n",
    "def update_adata_APML(the_data, the_midline, f1):\n",
    "    the_raw_pos = the_data.obs[['new_x','new_y']].to_numpy()\n",
    "    the_result = update_np_APML(the_raw_pos, the_midline, f1)\n",
    "    the_data.obs['AP_adj_x'] = the_result['nx'].to_numpy()\n",
    "    the_data.obs['AP_adj_y'] = the_result['ny'].to_numpy()\n",
    "    return the_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(radata.obs['new_x'],radata.obs['new_y'],c='gray',s=1)\n",
    "plt.scatter(c33.obs['new_x'],c33.obs['new_y'],c='green',s=1)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(radata.obs['new_x'],radata.obs['new_z'],c='gray',s=1)\n",
    "plt.scatter(c33.obs['new_x'],c33.obs['new_z'],c='green',s=1)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 285 × 21579\n",
       "    obs: 'seurat_clusters.r1.6', 'cell_number', 'hood', 'x', 'y', 'z', 'min_radius', 'max_radius', 'Batch', 'nCount_RNA', 'nFeature_RNA', 'nCount_SCT', 'nFeature_SCT', 'seurat_clusters', 'SPC_cluster', 'SPC_name', 'sample', 'new_x', 'new_y', 'new_z', 'AP_adj_x', 'AP_adj_y'\n",
       "    obsm: 'spatial'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "APML_f1 = np.array([[-1.40596268e-12,  9.81619394e-09,  4.52222132e-05,        -4.61221827e-01,  2.83054135e+03]])\n",
    "APML_midline = getMidline(APML_f1,7500, 1500, 3500, 500,8000)\n",
    "update_adata_APML(c33,APML_midline,APML_f1)\n",
    "c33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(radata.obs['AP_adj_x'],radata.obs['AP_adj_y'],c='gray',s=1)\n",
    "plt.scatter(c33.obs['AP_adj_x'],c33.obs['AP_adj_y'],c='green',s=1)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(radata.obs['AP_adj_x'],radata.obs['new_z'],c='gray',s=1)\n",
    "plt.scatter(c33.obs['AP_adj_x'],c33.obs['new_z'],c='green',s=1)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\guolidong\\AppData\\Local\\Temp\\ipykernel_19712\\727444766.py:59: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  z = np.linalg.lstsq(X @ B, yfit)[0]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "fixx = [100 ,6000]\n",
    "fixy = [550,700]\n",
    "#f1 = polyfix(used_adata.obs['new_x'].to_list(),used_adata.obs['new_y'].to_list(),fixx,fixy,n=4)\n",
    "f1 =  polyfix(c33.obs['AP_adj_x'].to_list(),c33.obs['new_z'].to_list(),fixx,fixy,n=2)\n",
    "#f1 = polyfix(slit1_data.obs['new_x'].to_list(),slit1_data.obs['new_y'].to_list(),fixx,fixy,n=4)\n",
    "midline = getMidline(f1, 6500, 0, 850, 0,6500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.scatter(radata.obs['AP_adj_x'],radata.obs['new_z'],c='gray',s=1)\n",
    "plt.scatter(c33.obs['AP_adj_x'],c33.obs['new_z'],c='green',s=1)\n",
    "plt.plot(midline['x'],midline['y'],c='r')\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.40816034e-05,  1.11321510e-01,  5.39008665e+02]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_np(the_raw_pos, the_midline, f1):\n",
    "    def Yinline(f1,xvals):\n",
    "        return np.polyval(f1[0], xvals)\n",
    "    the_result = pd.DataFrame()\n",
    "    the_result['raw_x'] = the_raw_pos[:,0]\n",
    "    the_result['raw_z'] = the_raw_pos[:,1]\n",
    "    the_result['raw_yline'] =  Yinline(f1,the_raw_pos[:,0])\n",
    "\n",
    "    def Ytag(item):\n",
    "        if item['raw_z']>item['raw_yline']:\n",
    "            return 1;\n",
    "        elif item['raw_z']<item['raw_yline']:\n",
    "            return -1;\n",
    "        else:\n",
    "            return 1\n",
    "    the_result['ytag'] = the_result.apply(lambda row: Ytag(row),axis=1)\n",
    "    from scipy.spatial import KDTree\n",
    "    from scipy.spatial import distance\n",
    "    the_line_new_points = the_midline.to_numpy()\n",
    "    length = np.zeros(len(the_line_new_points))\n",
    "    for i in range(1,len(the_line_new_points)):\n",
    "        length[i] = distance.euclidean(the_line_new_points[i,:],the_line_new_points[i-1,:])\n",
    "    length = np.cumsum(length)\n",
    "    the_kdtree = KDTree(np.array(the_line_new_points))\n",
    "\n",
    "    darray, iarray = the_kdtree.query(the_raw_pos)\n",
    "\n",
    "    the_result['ny'] = darray\n",
    "    the_result['ny'] = the_result['ny']*the_result['ytag']\n",
    "    the_result['nx'] = length[np.array(iarray).astype(int)]\n",
    "    return the_result\n",
    "\n",
    "def update_adata(the_data, the_midline, f1):\n",
    "    the_raw_pos = the_data.obs[['AP_adj_x','new_z']].to_numpy()\n",
    "    the_result = update_np(the_raw_pos, the_midline, f1)\n",
    "    the_data.obs['DV_adj_x'] = the_result['nx'].to_numpy()\n",
    "    the_data.obs['DV_adj_z'] = the_result['ny'].to_numpy()\n",
    "    return the_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 225385 × 19574\n",
       "    obs: 'x', 'y', 'nSpots', 'nGenes', 'nUMI', 'sample', 'slice', 'cell_id', 'seurat_cluster_res0.8', 'new_x', 'new_y', 'z', 'seurat_cluster_res0.8_nofilter', 'seurat_cluster_res1.6', 'seurat_cluster_res1.6_nofilter', 'new_z', 'AP_adj_x', 'AP_adj_y', 'DV_adj_x', 'DV_adj_z'\n",
       "    obsm: 'spatial', 'spatial_3d', 'spatial_3d_all17', 'spatial_APadj'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radata = update_adata(radata,midline,f1)\n",
    "radata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "c33 = update_adata(c33,midline,f1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_ret = update_np(np.array([[fixx[0],fixy[0]],[fixx[1], fixy[1]]]), midline, f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mid_ret = update_np(midline.to_numpy(),midline, f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.scatter(radata.obs['AP_adj_x'],radata.obs['new_z'],c='gray',s=3)\n",
    "plt.scatter(c33.obs['AP_adj_x'],c33.obs['new_z'],c='green',s=3)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.scatter(fixx,fixy,s=25,c='cyan' ,marker='^')\n",
    "#plt.plot(line_df['x'],line_df['y'])\n",
    "plt.plot(midline['x'],midline['y'],c='cyan')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.scatter(radata.obs['DV_adj_x'],radata.obs['DV_adj_z'],c='gray',s=3)\n",
    "plt.scatter(c33.obs['DV_adj_x'],c33.obs['DV_adj_z'],c='green',s=3)\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.scatter(fixed_ret['nx'],fixed_ret['ny'],s=25,c='cyan' ,marker='^')\n",
    "plt.plot(mid_ret['nx'],mid_ret['ny'],c='cyan')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "radata.obsm['spatial_DVadj'] = radata.obs[['DV_adj_x','AP_adj_y','DV_adj_z']].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "radata.write(f'{prefix}.apdvadj.h5ad',compression='gzip')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "stereopy1",
   "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": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}