nbs/feature_finding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Finding\n",
    "\n",
    "## Unit Tests\n",
    "\n",
    "### Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpharaw.feature.centroids import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00,  5.72it/s]\n"
     ]
    }
   ],
   "source": [
    "def test_find_centroid_connections():\n",
    "    row_borders = np.array([3, 6, 9])\n",
    "    rowwise_peaks = np.array([3, 3, 3])\n",
    "    max_gap = 2\n",
    "\n",
    "    score = np.full((3,3, max_gap), np.inf)\n",
    "    connections = np.full((3,3, max_gap), -1)\n",
    "\n",
    "    centroids = np.array([10, 20, 30, 10.2, 40.1, 40, 50, 60])\n",
    "    centroid_tol = 0.5*1e6\n",
    "\n",
    "    from_r, from_c, to_r, to_c, score_median, score_std = find_centroid_connections(rowwise_peaks, row_borders, centroids, max_gap, centroid_tol)\n",
    "\n",
    "    assert np.allclose(from_r, np.array([0, 0, 1, 1])) #e.g. 0,0 is connected to 0,1 -> 10 to 10.2\n",
    "    assert np.allclose(from_c, np.array([0, 2, 1, 2]))\n",
    "    assert np.allclose(to_r, np.array([1, 1, 2, 2]))\n",
    "    assert np.allclose(to_c, np.array([0, 1, 0, 0]))\n",
    "\n",
    "test_find_centroid_connections()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 91180.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Centroid Connections from [0 1 2 3] to [3 4 6 5]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAckAAAHWCAYAAAAcv3I/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxyklEQVR4nO3deXhTZd7/8U+6pXspUFtqy1oE2aoiYEWwQGUZVLYH1wdbHXGGKSriRp1LHWbkgZERlUVlHERQGVQUUXxAQVlkfqgsgqgjFi1QUDaRFlraQnP//uAhYygHaJr2kPT9uq5zkZ6cJN+bL+mHs+SOwxhjBAAAqgiyuwAAAM5XhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJOBDOTk5io6OtrsMv/byyy/L4XBo+/btdpcCEJIITCd/0Z5cwsPDddFFF2n06NHau3ev3eVB0v/8z//onXfesbsM4IwISQS0P//5z3rllVc0ffp0XXnllXr++eeVkZGh0tJSu0ur96xCcsSIETp69KiaNWtW90UBpwixuwCgNg0YMECXX365JOnOO+9Uo0aNNGXKFC1atEg333yzzdXhdIKDgxUcHGx3GYAk9iRRz/Tu3VuSVFBQ4F736quvqnPnzoqIiFDDhg110003qbCw0ONxn3zyiYYPH66mTZvK6XQqNTVV9913n44ePXrW19y0aZMSEhKUmZmpI0eOnHHbb7/9VjfccIMSEhIUERGhNm3a6I9//KPHNl988YUGDBig2NhYRUdHq0+fPvr00089tjl5uPlf//qXxo4dq4SEBEVFRWnIkCHav3+/x7bNmzfXtddeqzVr1qhr164KDw9Xy5YtNXfu3Cr1HTp0SGPGjFFqaqqcTqfS0tL017/+VS6Xy2M7l8ulZ599Vh07dlR4eLgSEhLUv39/rV+/XpLkcDhUUlKiOXPmuA+J5+TkeNR+6jnJ5557Tu3bt5fT6VRycrJyc3N16NAhj20yMzPVoUMHffPNN+rVq5ciIyN14YUX6sknn6wylmnTpql9+/aKjIxUfHy8Lr/8cs2bN8+yN6ifCEnUK99//70kqVGjRpKkCRMm6LbbblPr1q01ZcoUjRkzRh999JF69uzp8Qv4zTffVGlpqUaNGqVp06apX79+mjZtmm677bYzvt66devUu3dvXXrppVqyZMkZL+r58ssv1a1bN3388ccaOXKknn32WQ0ePFjvvfeee5uvv/5aPXr00ObNm/XQQw/p0UcfVUFBgTIzM/XZZ59Vec67775bmzdv1uOPP65Ro0bpvffe0+jRo6tst23bNv3Xf/2XrrnmGj311FOKj49XTk6Ovv76a/c2paWluvrqq/Xqq6/qtttu09SpU9W9e3fl5eVp7NixHs/329/+1h2mf/3rXzVu3DiFh4e7w/yVV16R0+lUjx499Morr+iVV17R7373O8u/mz/96U/Kzc1VcnKynnrqKQ0bNkwzZ85U3759dezYMY9tf/nlF/Xv31/p6el66qmn1LZtWz388MNasmSJe5sXX3xR99xzj9q1a6dnnnlG48eP1yWXXHLav0PUcwYIQLNnzzaSzPLly83+/ftNYWGhmT9/vmnUqJGJiIgwu3btMtu3bzfBwcFmwoQJHo/dsmWLCQkJ8VhfWlpa5TUmTpxoHA6H2bFjh3tddna2iYqKMsYYs2bNGhMbG2sGDhxoysrKzlpzz549TUxMjMfzGWOMy+Vy3x48eLAJCwsz33//vXvdjz/+aGJiYkzPnj2rjD8rK8vj8ffdd58JDg42hw4dcq9r1qyZkWRWr17tXrdv3z7jdDrN/fff7173l7/8xURFRZnvvvvOo75x48aZ4OBgs3PnTmOMMR9//LGRZO65554qY/x1LVFRUSY7O7vKNidrLygocNcSFhZm+vbtayorK93bTZ8+3UgyL730knvd1VdfbSSZuXPnuteVl5ebpKQkM2zYMPe6QYMGmfbt21d5beBU7EkioGVlZSkhIUGpqam66aabFB0drYULF+rCCy/U22+/LZfLpRtuuEEHDhxwL0lJSWrdurVWrFjhfp6IiAj37ZKSEh04cEBXXnmljDH64osvqrzuihUr1K9fP/Xp00dvv/22nE7nGevcv3+/Vq9erTvuuENNmzb1uM/hcEiSKisr9eGHH2rw4MFq2bKl+/4mTZrolltu0Zo1a1RcXOzx2Lvuusv9eEnq0aOHKisrtWPHDo/t2rVrpx49erh/TkhIUJs2bfTDDz+417355pvq0aOH4uPjPf6+srKyVFlZqdWrV0uS3nrrLTkcDj3++ONVxvnrWs7V8uXLVVFRoTFjxigo6D+/skaOHKnY2Fi9//77HttHR0frv//7v90/h4WFqWvXrh5jadCggXbt2qV169ZVux7UL1y4g4A2Y8YMXXTRRQoJCVFiYqLatGnj/kWbn58vY4xat2592seGhoa6b+/cuVOPPfaY3n33Xf3yyy8e2xUVFXn8XFZWpoEDB6pz58564403FBJy9rfZyV/gHTp0sNxm//79Ki0tVZs2barcd/HFF8vlcqmwsFDt27d3rz81cOPj4yWpyhhO3e7ktr/eLj8/X19++aUSEhJOW9++ffsknTiknZycrIYNG1qOpTpOBvqp4w4LC1PLli2rBH5KSkqVMI6Pj9eXX37p/vnhhx/W8uXL1bVrV6Wlpalv37665ZZb1L17d5/UjMBBSCKgde3a1X1166lcLpccDoeWLFly2qspT54/rKys1DXXXKODBw/q4YcfVtu2bRUVFaXdu3crJyenykUrTqdTv/nNb7Ro0SItXbpU1157re8Hdo6srhI1xlR7O5fLpWuuuUYPPfTQabe96KKLvKzSt85lLBdffLG2bt2qxYsXa+nSpXrrrbf03HPP6bHHHtP48ePrqlT4AUIS9VarVq1kjFGLFi3O+At+y5Yt+u677zRnzhyPC3WWLVt22u0dDodee+01DRo0SMOHD9eSJUuUmZl5xlpOHj796quvLLdJSEhQZGSktm7dWuW+b7/9VkFBQUpNTT3j69REq1atdOTIEWVlZZ11uw8++EAHDx48497kuR56Pfl5ya1bt3ocZq6oqFBBQcFZ67ESFRWlG2+8UTfeeKMqKio0dOhQTZgwQXl5eQoPD/fqORF4OCeJemvo0KEKDg7W+PHjq+xZGWP0888/S/rPnsmvtzHG6Nlnn7V87rCwML399tvq0qWLrrvuOn3++ednrCUhIUE9e/bUSy+9pJ07d1ap5WQdffv21aJFizw+HrF3717NmzdPV111lWJjY88+cC/dcMMNWrt2rT744IMq9x06dEjHjx+XJA0bNkzGmNPukf367zAqKqrKRzhOJysrS2FhYZo6darH42fNmqWioiINHDiw2mM52duTwsLC1K5dOxljqlwti/qNPUnUW61atdITTzyhvLw8bd++XYMHD1ZMTIwKCgq0cOFC3XXXXXrggQfUtm1btWrVSg888IB2796t2NhYvfXWW1XO650qIiJCixcvVu/evTVgwACtWrXqjOccp06dqquuukqXXXaZ7rrrLrVo0ULbt2/X+++/r02bNkmSnnjiCS1btkxXXXWV/vCHPygkJEQzZ85UeXn5aT8L6EsPPvig3n33XV177bXKyclR586dVVJSoi1btmjBggXavn27GjdurF69emnEiBGaOnWq8vPz1b9/f7lcLn3yySfq1auX+yMonTt31vLlyzVlyhQlJyerRYsW6tatW5XXTUhIUF5ensaPH6/+/fvr+uuv19atW/Xcc8+pS5cuHhfpnKu+ffsqKSlJ3bt3V2Jiov79739r+vTpGjhwoGJiYmr8d4UAYsMVtUCtO/kxgnXr1p1127feestcddVVJioqykRFRZm2bdua3Nxcs3XrVvc233zzjcnKyjLR0dGmcePGZuTIkWbz5s1Gkpk9e7Z7u19/BOSkAwcOmHbt2pmkpCSTn59/xlq++uorM2TIENOgQQMTHh5u2rRpYx599FGPbTZu3Gj69etnoqOjTWRkpOnVq5f5f//v/53T+FesWGEkmRUrVrjXNWvWzAwcOLBKLVdffbW5+uqrPdYdPnzY5OXlmbS0NBMWFmYaN25srrzySvO3v/3NVFRUuLc7fvy4mTx5smnbtq0JCwszCQkJZsCAAWbDhg3ubb799lvTs2dPExERYSS5Pw5y6kdATpo+fbpp27atCQ0NNYmJiWbUqFHml19+qVLz6T7akZ2dbZo1a+b+eebMmaZnz56mUaNGxul0mlatWpkHH3zQFBUVVXks6jeHMaccZwIAAJI4JwkAgCVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwUK8mE3C5XPrxxx8VExPj1bcRAAD8nzFGhw8fVnJyssc3y5xOvQrJH3/8sVbntgQA+I/CwkKlpKSccZt6FZInp5sqLCys1TkuAQDnr+LiYqWmpp7TFIT1KiRPHmKNjY0lJAGgnjuX025cuAMAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAW/CckJEyboyiuvVGRkpBo0aGB3OQCAesBvQrKiokLDhw/XqFGj7C4FAFBPhNhdwLkaP368JOnll1+2txAAQL3hNyHpjfLycpWXl7t/Li4utrEaAIC/8ZvDrd6YOHGi4uLi3EtqaqrdJQEA/IitITlu3Dg5HI4zLt9++63Xz5+Xl6eioiL3UlhY6MPqAQCBztbDrffff79ycnLOuE3Lli29fn6n0ymn0+n14wEA9ZutIZmQkKCEhAQ7SwAAwJLfXLizc+dOHTx4UDt37lRlZaU2bdokSUpLS1N0dLS9xQEAApLfhORjjz2mOXPmuH++9NJLJUkrVqxQZmamTVUBAAKZwxhj7C6irhQXFysuLk5FRUWKjY21uxwAgA2qkwUB/REQAABqgpAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAW/C4kZ8yYoebNmys8PFzdunXT559/bndJAIAA5Vch+frrr2vs2LF6/PHHtXHjRqWnp6tfv37at29f3RWxa5e0YsWJPxE46GvgoafwAb8KySlTpmjkyJG6/fbb1a5dO73wwguKjIzUSy+9VDcFzJolNWsm9e594s9Zs+rmdVG76GvgoafwEYcxxthdxLmoqKhQZGSkFixYoMGDB7vXZ2dn69ChQ1q0aFGVx5SXl6u8vNz9c3FxsVJTU1VUVKTY2NjqFbBrl9SsmYzLpTKd+N+FMzhY2r5dSknxakw4D/xfX+Vy/WcdffVv9BRnUVxcrLi4uHPKAr/Zkzxw4IAqKyuVmJjosT4xMVF79uw57WMmTpyouLg495Kamup9Afn5ksulbEmRkqZJUmWltG2b988J++Xnq8TlkkOSQ1KJRF/9HT2FD/lNSHojLy9PRUVF7qWwsND7J2vdWgoKUsT//XhUOvG/07Q0H1QK27RuLTkcnuvoq3+jp/AhvwnJxo0bKzg4WHv37vVYv3fvXiUlJZ32MU6nU7GxsR6L11JSpL//XX8LClKxpLygIGnmTA7f+LuUFEX+/e/aFxSkfZIi6av/o6fwIb85JylJ3bp1U9euXTVt2jRJksvlUtOmTTV69GiNGzfurI+vznFoS7t2nThsk5bGmy6Q0NfAQ09hoTpZEFJHNfnE2LFjlZ2drcsvv1xdu3bVM888o5KSEt1+++11V0RKCm+4QERfAw89hQ/4VUjeeOON2r9/vx577DHt2bNHl1xyiZYuXVrlYp7a9Nlnn2nx4sVq166dbr755jp7XdSeiooKTZ48WZL04IMPKiwszOaKUFP0FL7iV4dba8oXh1ufe+455ebmatiwYVqwYIGPK4QdSkpKFB0dLUk6cuSIoqKibK4INUVPcSYBe7j1fJCenq7Ro0fr0ksvtbsU+EhISIjuvPNO9234P3oKX2FPEgBQrwTkZAIAANQ1QtILxhgdP37c7jIAALWMkKymtWvXKiQkRBdffLHdpcBHSkpKFBUVpaioKJWUlNhdDnyAnsJXOKNdTU6nUy6XS0ePHrW7FPhQaWmp3SXAx+gpfIGQrKYOHTpo9+7dioyMtLsU+EhERIQKCgrct+H/6Cl8hZCsprCwMCUnJ9tdBnwoKChIzZs3t7sM+BA9ha9wThIAAAvsSVbTsWPH9PTTT6u0tFR5eXlyOp12l4QaOnbsmGbMmCFJys3NVWhoqM0VoaboKXyFyQSq6fjx4+433IEDB9SoUSNflggbMIVZ4KGnOBOmpatFISEhuuOOOxQWFsZ0VwEiODhYt9xyi/s2/B89ha+wJwkAqFeYlg4AAB8gJAEAsEBIeiEjI0NOp1Mff/yx3aXAB0pKSpSQkKCEhASmMAsQ9BS+wpUnXjh+/LgqKiqYmi6AHDhwwO4S4GP0FL5ASHrh7bffliQlJCTYXAl8ISIiQl999ZX7NvwfPYWvcHUrAKBe4epWAAB8gMOtXli+fLk2b96sq666St26dbO7HNTQsWPH9PLLL0uScnJymMIsANBT+AqHW71w5513atasWZowYYIeeeQRH1YIOzCFWeChpzgTpqWrZRkZGSovL1e7du3sLgU+EBwcrEGDBrlvw//RU/gKe5IAgHqFC3cAAPABQhIAAAuEpBdmz56tRo0a6bbbbrO7FPhAaWmpmjdvrubNm6u0tNTucuAD9BS+woU7XqisrNTBgwd16NAhu0uBDxhjtGPHDvdt+D96Cl8hJL0wdOhQde/eXfHx8XaXAh8IDw/X559/7r4N/0dP4Stc3QoAqFe4uhUAAB/gcKsX9u7dq3feeUfh4eHKzs62uxzU0PHjx/X6669Lkm688UaFhPC28Hf0FL7C4VYvrFu3Tl27dlVqaqp27tzpwwphB6YwCzz0FGfCtHS1LCEhQYMGDVJiYqLdpcAHgoKClJWV5b4N/0dP4SvsSQIA6hUu3AEAwAcISQAALBCSXjh27JiaN2+uxMREFRUV2V0Oaqi0tFTt27dX+/btmcIsQNBT+AoX7nghJCREhYWFcrlcKi0tVVxcnN0loQaMMfrmm2/ct+H/6Cl8hZD0gsPh0Keffqrw8HA1btzY7nJQQ+Hh4VqxYoX7NvwfPYWvcHUrAKBe4epWAAB8gMOtXnr//fe1Z88e9e/fXxdeeKHd5aAGjh8/rsWLF0uSrr32WqYwCwD0FL7C4VYvXXbZZfriiy+0ZMkS9e/f30cVwg5MYRZ46CnOhGnp6kCPHj104YUXqmHDhnaXghoKCgrSlVde6b4N/0dP4SvsSQIA6hUu3AEAwAcISQAALBCSXnr00UfVqlUrzZgxw+5SUENHjx5Vly5d1KVLFx09etTucuAD9BS+woU7Xvr555/1ww8/aN++fXaXghpyuVxav369+zb8Hz2FrxCSXho7dqxGjBihpk2b2l0KasjpdLo/U+d0Om2uBr5AT+ErXN0KAKhXuLoVAAAf8KuQXL16ta677jolJyfL4XDonXfesa2WH374Qa+//rpWr15tWw3wjcrKSi1btkzLli1TZWWl3eXAB+gpfMWvQrKkpETp6ennxRWly5Yt00033aRHJjyiXcW77C4HNVBWVqa+ffuqb9+++n7f93aXAx/4dU/LysrsLgd+zK9CcsCAAXriiSc0ZMgQu0vRvyv+LTWX/nX0X2r2TDPN2jjL7pLgpbmb50pJkhKlts+1pZcBICgoSOnp6UpPT2daOtSI316443A4tHDhQg0ePNhym/LycpWXl7t/Li4uVmpqao0v3NlVvEvNnmkml/nPpeXBjmBtH7NdKbEpXj8v6h69BOofLtz5PxMnTlRcXJx7SU1N9cnz5v+cL9d3LulpSW+dWFdpKrXt4DafPD/qTv7P+R4BKdFLAP8R0CGZl5enoqIi91JYWOiT523dqLUcZQ6pSFLJiXXBjmClNUzzyfOj7rRu1FpBDs+3Ab0EcFJAh6TT6VRsbKzH4gspsSl6+g9PK2hkkNTvxC/VmdfO5PCcH0qJTdHfr/27gh3BkuhloDh69KgyMzOVmZnJtHSokYA+J3kqX08msKt4l7Yd3Ka0hmn8UvVz9DKw8KXLOJOA/dLlI0eOaNu2/5wrKigo0KZNm9SwYUNbpodLiU3hF2qAoJeBxel06o033nDfBrzlV3uSK1euVK9evaqsz87O1ssvv3zWx/tyT3Ljxo3aunWr2rVrp/T09Bo9FwCg7gTs1a2ZmZkyxlRZziUgfW3+/Pm65ZZbNHfu3Dp/bQBA3fCrw63nk1atWql3795q06aN3aUAOEVlZaU+/fRTSdIVV1yh4OBgmyuCv/Krw601xbeAAPUDF+7gTAL2wh0AOBcOh0NpaWnu24C3CEkAAScyMlL5+fl2l4EA4FcX7pxP7rjjDnXs2FHvv/++3aUAAGoJIemlbdu26auvvmI2DwAIYBxu9dK0adO0f/9+dezY0e5SAJyirKxMw4YNkyS99dZbCg8Pt7ki+CtC0ktMIACcvyorK/W///u/7tuAtwhJAAEnLCxMs2fPdt8GvEVIeundd9+Vw+FQZmamYmJi7C4HwK+EhoYqJyfH7jIQAAhJL40YMULFxcX67rvvCEkACFCEpJe6du2q4uJiZu4BzkOVlZXasmWLJKljx45MSwevEZJeWrZsmd0lALBQVlamSy+9VBLT0qFmvPqc5N69ezVixAglJycrJCREwcHBHgsA2MnhcCg5OVnJyclMS4ca8WpPMicnRzt37tSjjz6qJk2a8I8QwHklMjJSu3fvtrsMBACvQnLNmjX65JNPdMkll/i4HP+we/duDRw4UA0bNtTHH39sdzkAgFriVUimpqaqHn3DVhXFxcXavHmzGjZsaHcpAIBa5NU5yWeeeUbjxo3T9u3bfVyOf0hNTdUHH3yg1157ze5SAJxGWVmZhg8fruHDh6usrMzucuDHvPrS5fj4eJWWlur48eOKjIxUaGiox/0HDx70WYG+xJcuA/UDX7qMM6n1L11+5plnvHkYANSJsLAwTZ8+3X0b8JZXe5L+yld7kj/++KM2bdqkpKQkXXbZZT6sEABQ22p9T/LXysrKVFFR4bEu0A9lrl69WjfffLN69erF1a0AEMC8unCnpKREo0eP1gUXXKCoqCjFx8d7LIEuJiZGl112mdq0aWN3KQBOw+VyKT8/X/n5+XK5XHaXAz/m1Z7kQw89pBUrVuj555/XiBEjNGPGDO3evVszZ87UpEmTfF3jeWfgwIEaOHCg3WUAsHD06FFddNFFkrhwBzXjVUi+9957mjt3rjIzM3X77berR48eSktLU7NmzfTaa6/p1ltv9XWdAFAtcXFxdpeAAODV4daDBw+qZcuWkk6cfzz5kY+rrrpKq1ev9l11AOCFqKgoHTp0SIcOHWIvEjXiVUi2bNlSBQUFkqS2bdvqjTfekHRiD7NBgwY+K+58NWPGDPXo0UPPP/+83aUAAGqRVyF5++23a/PmzZKkcePGacaMGQoPD9d9992nBx980KcFno/y8/O1Zs0aFRYW2l0KAKAW+eRzkjt27NCGDRuUlpamTp06+aKuWuGrz0lu2bJF3333nVq3bn1ejxeor8rLy/W73/1OkjRz5kw5nU6bK8L5pDpZwGQCAAIO09LhTGp9MoF77rlHaWlpuueeezzWT58+Xdu2bWPaOgC2Cg0N1ZNPPum+DXjLqz3JCy+8UO+++646d+7ssX7jxo26/vrrtWvXLp8V6Eu+2pPcuHGjSkpKdPHFF6tx48Y+rBAAUNuqkwVeXbjz888/n/YzSLGxsTpw4IA3T+lX7r//fvXs2VPLly+3uxQAQC3yKiTT0tK0dOnSKuuXLFni/vxkIEtNTdVFF12kRo0a2V0KgNNwuVzavXu3du/ezbR0qBGvzkmOHTtWo0eP1v79+9W7d29J0kcffaS//e1vevbZZ31a4Plo7ty5dpcA4AyOHj2qlJQUSVy4g5rxKiTvuOMOlZeXa8KECfrLX/4iSWrRooVeeOEF3XbbbT4tEAC8ERJS4y85ArwLyaNHjyo7O1ujRo3S/v37tXfvXi1btkyJiYm+rg8Aqi0qKkrHjh2zuwwEAK/OSQ4aNMh9yDE0NFRZWVmaMmWKBg8eXC+marv++us1YMAA7d692+5SAAC1yKuQ3Lhxo3r06CFJWrBggRITE7Vjxw7NnTtXU6dO9WmB56Nly5Zp6dKlOn78uN2lAABqkVeHW0tLSxUTEyNJ+vDDDzV06FAFBQXpiiuu0I4dO3xa4Plozpw5Ki0tVUJCgt2lADiN8vJyjR07VpI0ZcoUpqWD17yaTKBTp0668847NWTIEHXo0EFLly5VRkaGNmzYoIEDB2rPnj21UWuNMS0dUD8wLR3OpNanpXvsscd0yy236L777lOfPn2UkZEh6cRe5aWXXurNUwKAz4SGhurxxx933wa85fUE53v27NFPP/2k9PR0BQWdOLX5+eefKzY2Vm3btvVpkb7iiz3JsrIybdy4UVFRUUpPT/dxhQCA2lbre5KSlJSUpKSkJI91Xbt29fbp/MbOnTvVvXt3xcXF6dChQ3aXAwCoRXzatpqMMWrVqpX7fAeA848xRkVFRZKkuLg4ORwOmyuCvyIkq6lNmzbatm2b3WUAOIPS0lLFx8dL4sId1IxXn5MEAKA+YE8SQMCJjIxURUWFJOZwRc2wJ1lNq1ev1nXXXac//elPdpcCwILD4VBoaKhCQ0M5H4ka4b9Y1bR9+3YtXryYyZMBoB4gJKspIyND//jHP9SkSRO7SwFgoaKiQn/84x8lSRMmTFBYWJjNFcFfeT2ZgD9iWjqgfmBaOpxJnUwmAADnq9DQUD3wwAPu24C3/ObCnYkTJ6pLly6KiYnRBRdcoMGDB2vr1q11XseuXbu0ZcsW7d+/v85fG8C5CQsL0+TJkzV58mQOtaJG/CYkV61apdzcXH366adatmyZjh07pr59+6qkpKRO6/jzn59Wp06d9Pjjf6vT1wVQPbt2SStWnPgTgaOu++o3Ibl06VLl5OSoffv2Sk9P18svv6ydO3dqw4YNdVbDrFnSiy9GSLpAL7zQQLNm1dlLA6iGf/zDqGnTY+rd+5iaNjW8VwPErFlSs2ZS794n/qyLvvrthTvbtm1T69attWXLFnXo0OG025SXl6u8vNz9c3FxsVJTU726cGfXrhNNcbn+sy44WNq+XUpJ8WYEAGrDrl1S06YlMubk/MpHFBwcxXvVz/nyd3B1Ltzxmz3JX3O5XBozZoy6d+9uGZDSifOYcXFx7iU1NdXr18zP92yOJFVWSkzjCpxf8vMlY6Ikmf9bonivBgC7fgf7ZUjm5ubqq6++0vz588+4XV5enoqKitxLYWGh16/ZurUUdMrfVnCwlJbm9VMCqAW8VwOTXX31u5AcPXq0Fi9erBUrVijlLPvYTqdTsbGxHou3UlKkv//9RFOkE3/OnMnhG+B8w3s1MNnVV785J2mM0d13362FCxdq5cqVat26dbWfwxeTCezadWL3Pi2NNx1wPuO9Gph80deAnEwgNzdX8+bN06JFixQTE6M9e/ZIOvGFqhEREXVWR0oKbzjAH/BeDUx13Ve/2ZO0msl/9uzZysnJOafnYFo6AEBA7kn6SZYDAAKI3124AwBAXSEkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALfhOSzz//vDp16qTY2FjFxsYqIyNDS5YssbssAEAA85uQTElJ0aRJk7RhwwatX79evXv31qBBg/T111/bXRoAIEA5jDHG7iK81bBhQ02ePFm//e1vT3t/eXm5ysvL3T8XFxcrNTVVRUVFio2NrasyAQDnkeLiYsXFxZ1TFvjNnuSvVVZWav78+SopKVFGRobldhMnTlRcXJx7SU1NrcMqAQD+zq/2JLds2aKMjAyVlZUpOjpa8+bN029+8xvL7dmTBACcqjp7kiF1VJNPtGnTRps2bVJRUZEWLFig7OxsrVq1Su3atTvt9k6nU06ns46rBAAECr/akzxVVlaWWrVqpZkzZ57T9tX53wMAIDAF/DnJk1wul8fhVAAAfMlvDrfm5eVpwIABatq0qQ4fPqx58+Zp5cqV+uCDD+wuDQAQoPwmJPft26fbbrtNP/30k+Li4tSpUyd98MEHuuaaa+wuDQAQoPwmJGfNmmV3CQCAesavz0kCAFCbCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFghJAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGDBb0Ny0qRJcjgcGjNmjN2lAAAClF+G5Lp16zRz5kx16tTJ7lIAAAHM70LyyJEjuvXWW/Xiiy8qPj7e7nIAAAHM70IyNzdXAwcOVFZW1lm3LS8vV3FxsccCAMC5CrG7gOqYP3++Nm7cqHXr1p3T9hMnTtT48eNruSoAQKDymz3JwsJC3XvvvXrttdcUHh5+To/Jy8tTUVGReyksLKzlKgEAgcRhjDF2F3Eu3nnnHQ0ZMkTBwcHudZWVlXI4HAoKClJ5ebnHfadTXFysuLg4FRUVKTY2trZLBgCch6qTBX5zuLVPnz7asmWLx7rbb79dbdu21cMPP3zWgAQAoLr8JiRjYmLUoUMHj3VRUVFq1KhRlfUAAPiC35yTBACgrvnNnuTprFy50u4SAAABjD1JAAAsEJIAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWCAkAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAIAYIGQBADAAiEJAIAFQhIAAAuEJAAAFkLsLqAuGWMkScXFxTZXAgCwy8kMOJkJZ1KvQvLw4cOSpNTUVJsrAQDY7fDhw4qLizvjNg5zLlEaIFwul3788UfFxMTI4XB49RzFxcVKTU1VYWGhYmNjfVzh+YWxBqb6Mtb6Mk6JsVaXMUaHDx9WcnKygoLOfNaxXu1JBgUFKSUlxSfPFRsbG/D/GE9irIGpvoy1voxTYqzVcbY9yJO4cAcAAAuEJAAAFgjJanI6nXr88cfldDrtLqXWMdbAVF/GWl/GKTHW2lSvLtwBAKA62JMEAMACIQkAgAVCEgAAC4QkAAAWCEkLq1ev1nXXXafk5GQ5HA698847HvcbY/TYY4+pSZMmioiIUFZWlvLz8+0ptobONtacnBw5HA6PpX///vYUWwMTJ05Uly5dFBMTowsuuECDBw/W1q1bPbYpKytTbm6uGjVqpOjoaA0bNkx79+61qWLvnctYMzMzq/T197//vU0Ve+/5559Xp06d3B8uz8jI0JIlS9z3B0pPzzbOQOnn6UyaNEkOh0Njxoxxr6urvhKSFkpKSpSenq4ZM2ac9v4nn3xSU6dO1QsvvKDPPvtMUVFR6tevn8rKyuq40po721glqX///vrpp5/cyz//+c86rNA3Vq1apdzcXH366adatmyZjh07pr59+6qkpMS9zX333af33ntPb775platWqUff/xRQ4cOtbFq75zLWCVp5MiRHn198sknbarYeykpKZo0aZI2bNig9evXq3fv3ho0aJC+/vprSYHT07ONUwqMfp5q3bp1mjlzpjp16uSxvs76anBWkszChQvdP7tcLpOUlGQmT57sXnfo0CHjdDrNP//5Txsq9J1Tx2qMMdnZ2WbQoEG21FOb9u3bZySZVatWGWNO9DA0NNS8+eab7m3+/e9/G0lm7dq1dpXpE6eO1Rhjrr76anPvvffaV1Qtio+PN//4xz8CuqfG/GecxgRmPw8fPmxat25tli1b5jG+uuwre5JeKCgo0J49e5SVleVeFxcXp27dumnt2rU2VlZ7Vq5cqQsuuEBt2rTRqFGj9PPPP9tdUo0VFRVJkho2bChJ2rBhg44dO+bR17Zt26pp06Z+39dTx3rSa6+9psaNG6tDhw7Ky8tTaWmpHeX5TGVlpebPn6+SkhJlZGQEbE9PHedJgdbP3NxcDRw40KN/Ut2+V+vVBOe+smfPHklSYmKix/rExET3fYGkf//+Gjp0qFq0aKHvv/9ejzzyiAYMGKC1a9cqODjY7vK84nK5NGbMGHXv3l0dOnSQdKKvYWFhatCggce2/t7X041Vkm655RY1a9ZMycnJ+vLLL/Xwww9r69atevvtt22s1jtbtmxRRkaGysrKFB0drYULF6pdu3batGlTQPXUapxSYPVTkubPn6+NGzdq3bp1Ve6ry/cqIYmzuummm9y3O3bsqE6dOqlVq1ZauXKl+vTpY2Nl3svNzdVXX32lNWvW2F1KrbMa61133eW+3bFjRzVp0kR9+vTR999/r1atWtV1mTXSpk0bbdq0SUVFRVqwYIGys7O1atUqu8vyOatxtmvXLqD6WVhYqHvvvVfLli1TeHi4rbVwuNULSUlJklTlSqq9e/e67wtkLVu2VOPGjbVt2za7S/HK6NGjtXjxYq1YscLjq9OSkpJUUVGhQ4cOeWzvz321GuvpdOvWTZL8sq9hYWFKS0tT586dNXHiRKWnp+vZZ58NuJ5ajfN0/LmfGzZs0L59+3TZZZcpJCREISEhWrVqlaZOnaqQkBAlJibWWV8JSS+0aNFCSUlJ+uijj9zriouL9dlnn3mcHwhUu3bt0s8//6wmTZrYXUq1GGM0evRoLVy4UB9//LFatGjhcX/nzp0VGhrq0detW7dq586dftfXs431dDZt2iRJftfX03G5XCovLw+onp7OyXGejj/3s0+fPtqyZYs2bdrkXi6//HLdeuut7tt11lefXgYUQA4fPmy++OIL88UXXxhJZsqUKeaLL74wO3bsMMYYM2nSJNOgQQOzaNEi8+WXX5pBgwaZFi1amKNHj9pcefWdaayHDx82DzzwgFm7dq0pKCgwy5cvN5dddplp3bq1KSsrs7v0ahk1apSJi4szK1euND/99JN7KS0tdW/z+9//3jRt2tR8/PHHZv369SYjI8NkZGTYWLV3zjbWbdu2mT//+c9m/fr1pqCgwCxatMi0bNnS9OzZ0+bKq2/cuHFm1apVpqCgwHz55Zdm3LhxxuFwmA8//NAYEzg9PdM4A6mfVk69ereu+kpIWlixYoWRVGXJzs42xpz4GMijjz5qEhMTjdPpNH369DFbt261t2gvnWmspaWlpm/fviYhIcGEhoaaZs2amZEjR5o9e/bYXXa1nW6Mkszs2bPd2xw9etT84Q9/MPHx8SYyMtIMGTLE/PTTT/YV7aWzjXXnzp2mZ8+epmHDhsbpdJq0tDTz4IMPmqKiInsL98Idd9xhmjVrZsLCwkxCQoLp06ePOyCNCZyenmmcgdRPK6eGZF31la/KAgDAAuckAQCwQEgCAGCBkAQAwAIhCQCABUISAAALhCQAABYISQAALBCSAABYICQBALBASAL12I4dOxQREaEjR47YXQpwXiIkgXps0aJF6tWrl6Kjo+0uBTgvEZJAAMjMzNTdd9+tMWPGKD4+XomJiXrxxRdVUlKi22+/XTExMUpLS9OSJUs8Hrdo0SJdf/31kiSHw1Flad68uQ2jAc4fhCQQIObMmaPGjRvr888/1913361Ro0Zp+PDhuvLKK7Vx40b17dtXI0aMUGlpqSTp0KFDWrNmjTskf/rpJ/eybds2paWlqWfPnnYOCbAd3wICBIDMzExVVlbqk08+kSRVVlYqLi5OQ4cO1dy5cyVJe/bsUZMmTbR27VpdccUVmjdvnp5++mmtW7fO47mMMRo2bJh27typTz75RBEREXU+HuB8EWJ3AQB8o1OnTu7bwcHBatSokTp27Ohel5iYKEnat2+fJM9Drb/2yCOPaO3atVq/fj0BiXqPw61AgAgNDfX42eFweKxzOBySJJfLpYqKCi1durRKSL766qt6+umntXDhQl144YW1XzRwniMkgXpo5cqVio+PV3p6unvd2rVrdeedd2rmzJm64oorbKwOOH9wuBWoh959912Pvcg9e/ZoyJAhuummm9SvXz/t2bNH0onDtgkJCXaVCdiOPUmgHjo1JL/99lvt3btXc+bMUZMmTdxLly5dbKwSsB9XtwL1zMaNG9W7d2/t37+/ynlMAJ7YkwTqmePHj2vatGkEJHAO2JMEAMACe5IAAFggJAEAsEBIAgBggZAEAMACIQkAgAVCEgAAC4QkAAAWCEkAACwQkgAAWPj/bQEu8xrfewUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sample snippet to show centroid conncetions\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "row_borders = np.array([3, 5, 7])\n",
    "rowwise_peaks = np.array([3, 2, 3])\n",
    "max_gap = 2\n",
    "\n",
    "score = np.full((3,3, max_gap), np.inf)\n",
    "connections = np.full((3,3, max_gap), -1)\n",
    "\n",
    "centroids = np.array([10, 20, 30,\n",
    "                      10.2, 20,\n",
    "                      10, 30, 40])\n",
    "\n",
    "centroid_tol = 0.5*1e5\n",
    "\n",
    "from_idx, to_idx, score_median, score_std = connect_centroids(rowwise_peaks, row_borders, centroids, max_gap, centroid_tol)\n",
    "\n",
    "scan_no = np.array([0, 0, 0,\n",
    "                    1, 1,\n",
    "                    2, 2, 2])\n",
    "\n",
    "print(f'Centroid Connections from {from_idx} to {to_idx}')\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "\n",
    "colors = ['r','g','b']\n",
    "\n",
    "for x,y in zip(centroids, scan_no):\n",
    "    plt.plot(x, y, '.', color=colors[y])\n",
    "\n",
    "for i, _ in enumerate(from_idx):\n",
    "    from_ = _\n",
    "    to_ = to_idx[i]\n",
    "    plt.plot([centroids[from_], centroids[to_]], [scan_no[from_], scan_no[to_]], 'k:')\n",
    "    \n",
    "plt.ylabel('scan')\n",
    "plt.xlabel('m/z')\n",
    "plt.ylim(len(row_borders)+1.5, -1.5)\n",
    "plt.title('Peak connections')\n",
    "plt.show()\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 41665.27it/s]\n"
     ]
    }
   ],
   "source": [
    "def test_connect_centroids():\n",
    "    row_borders = np.array([3, 6, 9])\n",
    "    rowwise_peaks = np.array([3, 3, 3])\n",
    "    max_gap = 2\n",
    "\n",
    "    score = np.full((3,3, max_gap), np.inf)\n",
    "    connections = np.full((3,3, max_gap), -1)\n",
    "\n",
    "    centroids = np.array([10, 20, 30, 10.2, 20, 10, 30, 40])\n",
    "    centroid_tol = 0.5*1e5\n",
    "\n",
    "    from_idx, to_idx, score_median, score_std = connect_centroids(rowwise_peaks, row_borders, centroids, max_gap, centroid_tol)\n",
    "\n",
    "    assert np.allclose(from_idx, np.array([0, 1, 2]))\n",
    "    assert np.allclose(to_idx, np.array([3, 4, 6]))\n",
    "\n",
    "test_connect_centroids()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hills "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpharaw.feature.hills import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_fast_minima():\n",
    "    assert fast_minima(np.array([3,2,1,0,1,2,3])) == 3\n",
    "    assert fast_minima(np.array([4,3,2,1,0,1,2])) == 4\n",
    "    assert len(fast_minima(np.array([5,4,3,2,1,0,1]))) == 0\n",
    "    assert len(fast_minima(np.array([6,5,4,3,2,1,0]))) == 0\n",
    "\n",
    "test_fast_minima()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isotope Patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from alpharaw.feature.isotope_pattern import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_check_isotope_pattern():\n",
    "    charge = 1\n",
    "\n",
    "    mass1, delta_mass1 = 100, 0.1\n",
    "    mass2, delta_mass2 = 101.1, 0.05\n",
    "\n",
    "    assert check_isotope_pattern(mass1, mass2, delta_mass1, delta_mass2, charge) == True\n",
    "\n",
    "    mass2, delta_mass2 = 102.1, 0.05\n",
    "\n",
    "    assert check_isotope_pattern(mass1, mass2, delta_mass1, delta_mass2, charge) == False\n",
    "\n",
    "test_check_isotope_pattern()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:FF started\n",
      "INFO:root:Hill extraction with centroid_tol 8 and max_gap 2\n",
      "100%|██████████| 133455/133455 [00:02<00:00, 45832.49it/s]\n",
      "INFO:root:Number of hills 1,416,067, len = 13.33\n",
      "INFO:root:Repeating hill extraction with centroid_tol 7.27\n",
      "100%|██████████| 133455/133455 [00:02<00:00, 47720.58it/s]\n",
      "INFO:root:Number of hills 1,409,772, len = 13.93\n",
      "INFO:root:After duplicate removal of hills 1,261,123\n",
      "INFO:root:After split hill_ptrs 1,744,461\n",
      "INFO:root:After filter hill_ptrs 1,729,965\n",
      "100%|██████████| 1729964/1729964 [01:27<00:00, 19832.46it/s]  \n",
      "INFO:root:Extracting hill stats complete\n",
      "INFO:root:Found 92,981 pre isotope patterns.\n",
      "INFO:root:Extracted 82,593 isotope patterns.\n",
      "100%|██████████| 82593/82593 [00:03<00:00, 26195.42it/s]\n",
      "INFO:root:FF finished.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mz</th>\n",
       "      <th>mz_std</th>\n",
       "      <th>mz_most_abundant</th>\n",
       "      <th>charge</th>\n",
       "      <th>rt_start</th>\n",
       "      <th>rt_apex</th>\n",
       "      <th>rt_end</th>\n",
       "      <th>fwhm</th>\n",
       "      <th>n_isotopes</th>\n",
       "      <th>mass</th>\n",
       "      <th>ms1_int_sum_apex</th>\n",
       "      <th>ms1_int_sum_area</th>\n",
       "      <th>ms1_int_max_apex</th>\n",
       "      <th>ms1_int_max_area</th>\n",
       "      <th>last_isotope_mz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>341.304766</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>341.304766</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.760553</td>\n",
       "      <td>109.800357</td>\n",
       "      <td>109.803620</td>\n",
       "      <td>0.046329</td>\n",
       "      <td>6.0</td>\n",
       "      <td>340.297490</td>\n",
       "      <td>8.069034e+07</td>\n",
       "      <td>2.967918e+06</td>\n",
       "      <td>6.155280e+07</td>\n",
       "      <td>3.184880e+06</td>\n",
       "      <td>346.310479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>336.237270</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>336.237270</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.762010</td>\n",
       "      <td>109.785547</td>\n",
       "      <td>109.790591</td>\n",
       "      <td>0.028581</td>\n",
       "      <td>6.0</td>\n",
       "      <td>335.229993</td>\n",
       "      <td>5.544771e+06</td>\n",
       "      <td>1.410078e+05</td>\n",
       "      <td>3.035509e+06</td>\n",
       "      <td>1.125075e+05</td>\n",
       "      <td>341.250832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>339.271210</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>339.271210</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.790170</td>\n",
       "      <td>109.799837</td>\n",
       "      <td>109.803620</td>\n",
       "      <td>0.013450</td>\n",
       "      <td>5.0</td>\n",
       "      <td>338.263934</td>\n",
       "      <td>4.267049e+07</td>\n",
       "      <td>4.940910e+05</td>\n",
       "      <td>2.337529e+07</td>\n",
       "      <td>8.312475e+05</td>\n",
       "      <td>343.283518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>313.236995</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>313.236995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.777561</td>\n",
       "      <td>109.785547</td>\n",
       "      <td>109.790170</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>5.0</td>\n",
       "      <td>312.229719</td>\n",
       "      <td>1.555356e+07</td>\n",
       "      <td>1.692728e+05</td>\n",
       "      <td>1.176555e+07</td>\n",
       "      <td>4.407118e+05</td>\n",
       "      <td>317.247686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>311.221366</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>311.221366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>109.765372</td>\n",
       "      <td>109.769575</td>\n",
       "      <td>109.790170</td>\n",
       "      <td>0.024798</td>\n",
       "      <td>6.0</td>\n",
       "      <td>310.214090</td>\n",
       "      <td>2.112038e+07</td>\n",
       "      <td>4.707363e+05</td>\n",
       "      <td>1.615747e+07</td>\n",
       "      <td>6.071729e+05</td>\n",
       "      <td>316.238068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82588</th>\n",
       "      <td>390.203040</td>\n",
       "      <td>0.000489</td>\n",
       "      <td>390.203040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>119.976250</td>\n",
       "      <td>119.985541</td>\n",
       "      <td>119.998150</td>\n",
       "      <td>0.014158</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1167.587289</td>\n",
       "      <td>3.197829e+05</td>\n",
       "      <td>4.467089e+03</td>\n",
       "      <td>1.860377e+05</td>\n",
       "      <td>2.628440e+03</td>\n",
       "      <td>390.533501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82589</th>\n",
       "      <td>435.271265</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>435.271265</td>\n",
       "      <td>1.0</td>\n",
       "      <td>119.989068</td>\n",
       "      <td>119.993600</td>\n",
       "      <td>119.993600</td>\n",
       "      <td>0.004532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>434.263989</td>\n",
       "      <td>2.967681e+05</td>\n",
       "      <td>1.078078e+03</td>\n",
       "      <td>1.740591e+05</td>\n",
       "      <td>1.740201e+03</td>\n",
       "      <td>436.272981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82590</th>\n",
       "      <td>653.665320</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>653.997842</td>\n",
       "      <td>3.0</td>\n",
       "      <td>119.980780</td>\n",
       "      <td>119.985517</td>\n",
       "      <td>119.998150</td>\n",
       "      <td>0.011755</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1957.974131</td>\n",
       "      <td>2.854187e+05</td>\n",
       "      <td>3.299227e+03</td>\n",
       "      <td>1.597124e+05</td>\n",
       "      <td>2.400466e+03</td>\n",
       "      <td>653.997842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82591</th>\n",
       "      <td>882.405923</td>\n",
       "      <td>0.001198</td>\n",
       "      <td>882.405923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>119.980780</td>\n",
       "      <td>119.993588</td>\n",
       "      <td>119.998150</td>\n",
       "      <td>0.008597</td>\n",
       "      <td>2.0</td>\n",
       "      <td>881.398646</td>\n",
       "      <td>3.914491e+05</td>\n",
       "      <td>4.331695e+03</td>\n",
       "      <td>2.279465e+05</td>\n",
       "      <td>2.413449e+03</td>\n",
       "      <td>883.409983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82592</th>\n",
       "      <td>409.236718</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>409.236718</td>\n",
       "      <td>1.0</td>\n",
       "      <td>119.985360</td>\n",
       "      <td>119.989107</td>\n",
       "      <td>119.998150</td>\n",
       "      <td>0.009431</td>\n",
       "      <td>2.0</td>\n",
       "      <td>408.229441</td>\n",
       "      <td>2.111856e+05</td>\n",
       "      <td>2.106312e+03</td>\n",
       "      <td>1.078767e+05</td>\n",
       "      <td>1.247658e+03</td>\n",
       "      <td>410.239365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82593 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mz    mz_std  mz_most_abundant  charge    rt_start     rt_apex  \\\n",
       "0      341.304766  0.000056        341.304766     1.0  109.760553  109.800357   \n",
       "1      336.237270  0.000161        336.237270     1.0  109.762010  109.785547   \n",
       "2      339.271210  0.000044        339.271210     1.0  109.790170  109.799837   \n",
       "3      313.236995  0.000086        313.236995     1.0  109.777561  109.785547   \n",
       "4      311.221366  0.000133        311.221366     1.0  109.765372  109.769575   \n",
       "...           ...       ...               ...     ...         ...         ...   \n",
       "82588  390.203040  0.000489        390.203040     3.0  119.976250  119.985541   \n",
       "82589  435.271265  0.000367        435.271265     1.0  119.989068  119.993600   \n",
       "82590  653.665320  0.000381        653.997842     3.0  119.980780  119.985517   \n",
       "82591  882.405923  0.001198        882.405923     1.0  119.980780  119.993588   \n",
       "82592  409.236718  0.000461        409.236718     1.0  119.985360  119.989107   \n",
       "\n",
       "           rt_end      fwhm  n_isotopes         mass  ms1_int_sum_apex  \\\n",
       "0      109.803620  0.046329         6.0   340.297490      8.069034e+07   \n",
       "1      109.790591  0.028581         6.0   335.229993      5.544771e+06   \n",
       "2      109.803620  0.013450         5.0   338.263934      4.267049e+07   \n",
       "3      109.790170  0.012609         5.0   312.229719      1.555356e+07   \n",
       "4      109.790170  0.024798         6.0   310.214090      2.112038e+07   \n",
       "...           ...       ...         ...          ...               ...   \n",
       "82588  119.998150  0.014158         2.0  1167.587289      3.197829e+05   \n",
       "82589  119.993600  0.004532         2.0   434.263989      2.967681e+05   \n",
       "82590  119.998150  0.011755         2.0  1957.974131      2.854187e+05   \n",
       "82591  119.998150  0.008597         2.0   881.398646      3.914491e+05   \n",
       "82592  119.998150  0.009431         2.0   408.229441      2.111856e+05   \n",
       "\n",
       "       ms1_int_sum_area  ms1_int_max_apex  ms1_int_max_area  last_isotope_mz  \n",
       "0          2.967918e+06      6.155280e+07      3.184880e+06       346.310479  \n",
       "1          1.410078e+05      3.035509e+06      1.125075e+05       341.250832  \n",
       "2          4.940910e+05      2.337529e+07      8.312475e+05       343.283518  \n",
       "3          1.692728e+05      1.176555e+07      4.407118e+05       317.247686  \n",
       "4          4.707363e+05      1.615747e+07      6.071729e+05       316.238068  \n",
       "...                 ...               ...               ...              ...  \n",
       "82588      4.467089e+03      1.860377e+05      2.628440e+03       390.533501  \n",
       "82589      1.078078e+03      1.740591e+05      1.740201e+03       436.272981  \n",
       "82590      3.299227e+03      1.597124e+05      2.400466e+03       653.997842  \n",
       "82591      4.331695e+03      2.279465e+05      2.413449e+03       883.409983  \n",
       "82592      2.106312e+03      1.078767e+05      1.247658e+03       410.239365  \n",
       "\n",
       "[82593 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alpharaw.feature.finding import find\n",
    "from alpharaw.ms_data_base import MSData_Base\n",
    "import logging\n",
    "\n",
    "file_path = './HeLa_500ng.raw.hdf' #using file from https://datashare.biochem.mpg.de/s/PQl8nyqfHVZZHte\n",
    "\n",
    "msdata = MSData_Base()\n",
    "msdata.load_hdf(file_path)\n",
    "\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "\n",
    "spectrum_df = msdata.spectrum_df\n",
    "peak_df = msdata.peak_df\n",
    "\n",
    "preview = -1\n",
    "spectrum_df_ = spectrum_df.iloc[:preview]\n",
    "msdata_df_ = msdata.peak_df.iloc[:spectrum_df.iloc[:preview]['peak_stop_idx'].iloc[-1]]\n",
    "\n",
    "feature_df = find(spectrum_df_, msdata_df_)\n",
    "\n",
    "display(feature_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "alphabase",
   "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": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}