From 852856ea35271215e68547d015fe5fa0bfa8e33e Mon Sep 17 00:00:00 2001 From: Zargham Ahmad Date: Fri, 3 Nov 2023 08:57:50 +0100 Subject: [PATCH 1/4] updated boxplot --- .../chemical_composition_boxplot.ipynb | 147 +++++++++++++++--- .../chemical_composition_boxplot.png | Bin 152169 -> 200269 bytes .../chemical_composition_boxplot2.png | Bin 0 -> 196568 bytes analysis/Python_scripts/utils.py | 29 +++- 4 files changed, 156 insertions(+), 20 deletions(-) create mode 100644 analysis/Python_scripts/chemical_composition_boxplot2.png diff --git a/analysis/Python_scripts/chemical_composition_boxplot.ipynb b/analysis/Python_scripts/chemical_composition_boxplot.ipynb index 162da33..58f8697 100644 --- a/analysis/Python_scripts/chemical_composition_boxplot.ipynb +++ b/analysis/Python_scripts/chemical_composition_boxplot.ipynb @@ -33,19 +33,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:58] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:58] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:58] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:58] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[14:03:58] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n" + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:07] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:07] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[16:50:07] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n" ] } ], @@ -65,7 +66,7 @@ "outputs": [ { "data": { - "image/png": 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kolcSERGBiIgIAIBIJELbtm3xxhtvYPbs2Uqrj+siTY0FN2/eVLgwYWRkhJ49e2LNmjWYOnXqq50M0X95eXnhxIkTuHLlCmJiYpCfnw9DQ0NYW1vDx8cHHh4e0NP73w8xZa9hCwsLTYXcZNW0L3RVQ4/FY8aMQXFxMWJjY7Fv3z7k5OSgWbNmsLS0hKenJ+bMmQNzc/M6Obf6os3fZWsypujye6SwsBAikahar7X169fj/PnziIuLw9GjR1FUVIS2bduiT58++Oyzz+RTPOg6tmnNafNYoo20/fNKl/pCV4kEVb91ISLSgD179mDTpk0IDw/H66+/rulwiEhDOBZQY7dgwQKkpqbi2LFjaN68uabDIXolHItrrr7ajGNK1aRSKZydnTF8+HBs2rRJ0+E0CWxTzeH4WzP12V7si6avaV4qJqJG6cKFC3B3d+cHDpGO41hAjdnLly8RFxeHTz/9lAksatQ4FtdcfbQZx5Tq+f333/H8+XN4e3trOpQmg22qORx/a6Y+24t90fTxjnEiIiIiIiIiIiIi0im8Y5yIiIiIiIiIiIiIdAoT40RERERERERERESkU5gYJyIiIiIiIiIiIiKdwsQ4EREREREREREREekUJsaJiIiIiIiIiIiISKcwMU5EREREREREREREOoWJcSIiIiIiIiIiIiLSKUyMExEREREREREREZFOYWKciIiIiIiIiIiIiHQKE+NEREREREREREREpFOYGCciIiIiIiIiIiIincLEOBERERERERERERHpFCbGiYiIiIiIiIiIiEinMDFORERERERERERERDqFiXEiIiIiIiIiIiIi0ilMjBMRERHVoQcPHsDGxgY2NjYICQnRdDgNRtvPW9vjo7p36dIleZ9funSpVsfy8fGBjY0NRo4cWUfREREREZGmNdN0AERERETapqSkBJGRkTh//jwSExORm5sLiUSC1q1bw9LSEvb29hgzZgycnJygp8f7DIiIiIiIiBobJsaJiIiIyjl9+jQ2btyI9PR0pX15eXnIy8tDUlISDhw4ACsrK6xYsQLDhw9v+ECJqFa2b9+O77//HgBw8+ZNDUdDRERERA2NiXEiIiKi/9qxYwe2bNkCQRAAAIMGDYKrqyusra1hbGyM/Px83L17F1FRUYiNjUVaWhq2bNnCxDiAzp07M7lIWmXgwIF19prcuHEjNm7cWCfHIiIiIiLtwMQ4EREREYDDhw/j22+/BQCYmppiy5YtcHJyUio3aNAgeHh44ObNm1i/fj3y8vIaOFIiIiIiIiKqLSbGiYiISOdlZmbiq6++AgAYGhpi79696NGjh9o6NjY28PPzQ1hYWEOESERERERERHWIiXEiIiLSebt370ZRUREAYOHChVUmxWX09PTw7rvvVlkuJiYGe/fuxY0bN5Cfn48OHTpgyJAh8PLyQseOHausn5iYiKCgIFy5cgVZWVkQBAEdO3aEs7MzZs2aBSsrK5X1QkJCsGLFCgDAmTNn0KFDBwQGBiIsLAz37t2Dvr4+bGxsMGfOHIXpYCQSCfbv349jx47h/v370NPTg729PebOnQtnZ2eVz/XgwQO4uroCADZs2ID333+/0vO5du0aQkJCcPXqVWRnZ0MQBFhYWKB79+4YPXo0Ro0ahdatWyvUycrKwqlTp3Dp0iWkpKQgKysLL1++hImJCezs7DBhwgSMHTu2wRZDjY6ORlhYGBISEpCTkwN9fX107NgRYrEYY8eOxbBhw2BgYKBUTyqVIiwsDOHh4fjjjz+Qn5+P1q1bo2fPnhg7diymTJmCFi1aqHzOinNiSyQS+Pn5ITIyEg8ePIChoSFsbW3h5eWF/v37y+vl5ORgz549OH36NDIyMtCyZUv0798fCxcuRJ8+fVQ+l6rXzt69exEeHo779+9DEARYW1tj4sSJcHd3h76+vtr2ys3Nhb+/P6Kjo/HgwQO8ePEC5ubmcHBwwLRp0+Do6Ki2/sWLFxEcHIzffvsNjx8/hkgkgpmZGdq3bw8HBwcMHTpU6bV56dIlzJw5EwCwZ88eDBw4UOncZGxsbJSe88yZM+jcuTMAwMfHB6GhobC0tERUVFSlcd68eRMBAQG4dOkSMjMzoaenh9deew2DBw/GzJkz5cerSNX7p7bjBhERERGpx8Q4ERER6TRBEHD48GEAgJGREaZNm1anx9+8eTN++uknhW0ZGRk4cOAAIiMjERAQAGtra5V1X758ia+//hr79+9X2nf37l3cvXsXwcHBWL16NaZOnao2DolEgqVLl+L69esK2y9fvozLly/Dx8cHf//73/Hw4UPMnTsXt27dUigXGxuLixcvYtOmTdW6GKDK8+fPsXLlSoSHh1d6PmfOnMGCBQuwcOFC+b7S0lIMGzYMUqlUqV5WVhaioqIQFRWFgwcPYvv27WjVqtUrxVcdT548gbe3Ny5evKi07/bt27h9+zYiIiJUXhzIy8uDl5cX4uPjlY4p64fAwED89NNPsLS0VBvHX3/9BU9PT6Slpcm3FRUV4fz584iJicG//vUvuLm5ISUlBXPnzkVmZqa83LNnzxAVFYULFy5g586dlV7skHn69CkWLVqEpKQkhe2JiYlITEzEsWPHsHPnTqWLGTIXLlzA4sWLIZFIFLZnZGQgIyMDR48ehYeHBz7//HOVFzY2bNiA3bt3K22X1b9+/TpCQkJw6dIltedR33bs2IHvvvtO6XUqe13s378fa9euxcSJE6s8Vm3GDSIiIiKqHibGiYiISKfdvn0bubm5AAAHB4dKk3uvIigoCAkJCRgwYACmTZsGKysrFBQU4PDhwzh8+DByc3Px2Wef4ZdfflFZf+XKlfKk/dChQzFhwgRYWVlBJBIhJSUF/v7+uHXrFlatWoX27dtj5MiRlcayevVqJCUlYcaMGXj77bdhbGyM5ORkbN26FdnZ2fjmm28wePBg+Pj4ID09HXPnzsWQIUNgaGiI+Ph4bN++HQUFBfjqq6/g4uICMzOzGrWFVCrFJ598gpiYGACAlZUVpk+fDjs7OxgaGiI7OxsJCQk4fvy4Ul3ZYqhOTk4YOnQoxGIxTE1NUVhYiPT0dAQHByMhIQExMTFYs2YNNm3aVKPYquvZs2eYOXMmUlNTAQC2traYNm0aevbsiRYtWuDRo0e4cuUKIiIilOqWlpZi3rx5SEhIAAAMGDAAHh4e6Ny5M7KysnDo0CGcPn0ad+7cgaenJw4fPqw2wb948WJkZmbi448/xpAhQ2BgYIBr165h+/btkEgkWLlyJezs7DBv3jy8ePEC3t7eeOutt9C8eXOcP38evr6+KC4uxmeffYaTJ09Wepc68L/Xzrhx4zBx4kSYmZkhLS0Nu3fvxo0bN3Dt2jX83//9H3x9fZXqJicnY968eSgpKUHz5s0xY8YMuLq6wtDQEMnJydi5cycePHiAwMBAGBoaYtmyZQr1z549K0+K29jYYPr06bC2tkabNm1QUFCAP//8E7GxsfJ2rY5Ro0bBzs4O+/btk190UjUlkoWFRbWPGRgYqLBGwUcffYT+/fujtLQUFy9exK5du1BUVAQfHx+YmJhg2LBhlR6rtuMGEREREVWTQERERKTDjh49KojFYkEsFgvffvttrY+Xnp4uP55YLBY+//xzQSqVKpVbuXKlvExSUpLS/hMnTsj3BwUFqXyu58+fCzNnzhTEYrEwYsQIoaSkRGH/oUOH5MewsbERTp06pXSMlJQUoXfv3oJYLBacnJwEOzs74bffflMqd+7cOfmx/Pz81J73oUOHlPbv3r1bvn/+/PnCixcvVJ5TaWmp8OjRI4VtUqlUSEtLU1leZuvWrfLzvHv3bo3jq45169bJj/HVV1+p7FdBEIQXL14I2dnZCtsCAgLkdf/xj3+orPvtt9/Ky3zzzTdK+7dt2ybfX51+cnJyEgYOHCjcu3dPqVz5eCIjI5X2l3/tiMViwdfXV6lMSUmJMHv2bHmZs2fPKpWZNGmSIBaLhd69ewu//vqr0v68vDxh3LhxglgsFnr16iWkpqYq7F+2bJn89S2RSJTqyzx58kRpW1xcnDy2uLg4pf3l27Mqy5cvl8dRUU5OjtC3b19BLBYLLi4uwsOHD5XKJCUlCW+++aYgFouFIUOGCMXFxQr762rcICIiIqLqa5hJGImIiIi01JMnT+SPa3oXdFXMzc2xatUqiEQipX2zZ8+WP7569arS/h07dgAA3n77bUyZMkXl8Vu2bInVq1cDKJtm4fLly5XG4ubmhlGjRiltt7GxgYODA4CyeaA9PT3Rt29fpXLDhg2TT++hKl51pFIpdu3aBaDsLtxNmzZVeoeynp6e0p26IpEI3bp1U/sc8+fPh4mJCQRBUDsH9KvKz89HUFAQgLI7xVeuXKmyXwGgRYsWaN++vcK2wMBAAICJiUmlr4lFixahe/fuAIDg4GAUFxdXGs+sWbOq7Kfc3FwsWbIEXbt2VSo3adIktGzZEkDV/WljY4OPPvpIaXuzZs2wbt06NG/eHACwb98+hf2JiYm4ceMGAGDy5MlwcXFROkbbtm2xZs0aAGWvk4rHePz4MQCgT58+au+gb9eundpzqE+HDh3Cs2fPAJTNRd6pUyelMn369MHcuXMBlC32e/r06UqPV5txg4iIiIiqj4lxIiIi0mmFhYXyx4aGhnV67LFjx1aaAO7evTuMjIwAAOnp6Qr7MjMz5fM5u7m5qX0Oa2trmJiYAIDa6STeeeedSveVX3hw3LhxlZYTi8Uq461KcnKyfI7rqVOn1noOcKlUiszMTPz5559ITU1Famoq7ty5I1+QMCUlpVbHV+XSpUvy5OeHH35Y5WKT5WVmZuLOnTsAyvqzsul69PX15fOS5+fnK83pXZ66/pT1k0gkqvT1Y2BgIL/YUFV/vvfee5UuatqxY0cMHjwYQNl89aWlpfJ9sbGx8seTJ0+u9PgODg7y+bLL1wHKksQAcOXKFdy/f19tnJoim2/e2NgYo0ePrrRc+Qtcquaol3nVcYOIiIiIaoZzjBMREZFOK5+klSU+68rrr7+udn/btm1RVFSkkJwHIL/LFgCWLl2KpUuXVuv5ZHfXqmJlZVXpvjZt2lSrnLGxMQAoxVuVP/74Q/7Y0dGxRnVlBEHA0aNHcfDgQSQmJuL58+eVli3/K4C6UptzKL+Qqaq7vMsrv//WrVvo16+fynLV6ScTExO0bdu2ynJV9ae9vX2V+8+dO4dnz54hPT1dHpvsvJs3b44+ffqoPUbfvn1x584d3Lt3D8XFxfLE8MSJE3H48GHk5eVh/PjxcHV1hYuLCxwdHav8FUFDkc0537t3b/nd86q0b98elpaWyMjIkNdR5VXHDSIiIiKqGSbGiYiISKfJ7rYGgJycnDo9dlV3oMvuwpVKpQrbZYuB1pS6ZLGBgUGVcQDqY64s3qqUT1TL7gCuiRcvXmDBggU4f/58tcqra4dXVf4cOnToUKO6+fn58sdVTddTfgqWvLy8SstVp59e9fVXkampqdr95WMuf66y+Nu1a4dmzdT/2yE7hiAIePr0qfxvZ2dnrF69Gt988w2eP3+OiIgI+eKmFhYWGD58OGbMmIFevXqpPX59kp1zxelzVDE3N0dGRoZCO1VUV/1GREREROoxMU5EREQ6rXxCTd3UFQ2p/HQUmzdvVpjqRB11dwdri8rm5Vbnxx9/lCfFBwwYgBkzZsDW1hbt27eHgYGBPFHo4eGh9fMuV3X+giA0UCTV1xAxqzuGh4cHxo4di7CwMMTGxiI+Ph4FBQXIzMzEL7/8gqCgIHz88cfw9vaudRy1UZ3Xtjb2LxEREZGuYmKciIiIdFqPHj1gYmKCJ0+e4Nq1a5BIJJXOAd1Qyi8kKBKJ5HNGN1bl78rPysqSLzBZHYIg4ODBgwDK5qL29/evdL7rp0+f1i5QNSqeQ5cuXapdt/wFC3XT3QCKv1rQ5IKS5eXk5Kid3qN8zOXPVRZ/Xl4eXr58qfaucdkxRCKRfIqX8szMzODp6QlPT09IpVIkJycjMjIS+/btw9OnT+Hr6wt7e3uVC8zWt7Zt2yI7OxvZ2dlVlpX1f2O4iEVERETU1HHxTSIiItJpIpEI7733HgCgqKgIwcHBGo4ICvMxx8TEaDCSumFrayt/XNM7uvPy8uQJRzc3t0qT4oWFhbh79+6rB1mF2pxDz5495Y+vX7+utmxiYqLKeppUfs57VX7//XcAZVOAlL9gIIu/pKREYY52VWTn3a1bt0oXnpTR09ODra0tvL29sXv3bvn248ePq61X0av8ekEV2YWr5ORklJSUVFouJycHDx8+VKhDRERERJrDxDgRERHpPE9PT/m8vtu2bcOdO3eqVU8qleLIkSN1Hk+3bt3Qo0cPAMCxY8fkybTGqlevXujUqRMAIDg4uEaLBpafVkbd4qgHDx5Um5SsrYEDB8LIyAgAsHfvXoW4qmJhYQFra2sAwIkTJyCRSFSWKy0tRWhoKICyO4rLJ+M16ciRI5VOAZKZmYkLFy4AKJvmRl9fX75v0KBB8seyu/5VSUhIwO3bt5XqVIetra387mt1c7KrUj4BX1xcXKO65Tk7OwMo+8VCZGRkpeUOHjwob0dZHSIiIiLSHCbGiYiISOdZWFhg1apVAMruGv/www9x+fJltXVu376NOXPm4Oeff66XmLy8vAD8b+FJdQtyFhcXIzAwEC9evKiXWGpLT08Pc+bMAQA8evQIy5cvrzQRKZVKkZmZKf/b1NRUPrVGRESEynqJiYn47rvv6j7wcoyNjTFt2jQAZXPRr1+/vtJkcUlJidJCrh4eHgDKFlb9+uuvVdb9/vvv5QniKVOmVHnndENJTk7Gf/7zH6XtL1++xOeffy6/IDF9+nSF/W+88Qbs7e0BlCWFVf36oaCgAKtXrwZQ9jqpeIyIiAi1i6neuHFDvpClpaVlDc5KcRHV+/fv16hueZMmTZJfWNu4cSP++usvpTIpKSnw9fUFUDbeaGLKFyIiIiJSxDnGiYiIiFCW3Hr06BG2bduGnJwcfPjhh3BxccHIkSNhbW0NY2Nj5Ofn4+7du4iOjsavv/6K0tJShcU769L48eNx4cIFhIaGIikpCePGjcO0adMwYMAAmJqaoqioCOnp6bh69SpOnTqFvLw8TJw4ES1btqyXeGrLw8MDZ8+eRUxMDE6dOoUJEyZgxowZsLOzg6GhIbKzs3H9+nUcO3YM48ePx8KFCwGUJUsnTJiAwMBAJCcnw8PDA56enujatSskEgmio6Oxb98+GBkZoUOHDkhLS6u3c1i8eDFiYmKQmpqKgIAAJCQkwN3dHWKxGM2bN8ejR49w7do1hIeHY8mSJXj//ffldd3d3REWFoaEhASEhoYiIyMDHh4e6NKlC7Kzs3Ho0CH53cZdu3bFJ598Um/nUVN2dnbYvHkzUlJS8O6778LMzAz37t2Dn5+ffAqUESNGYMSIEUp1165diylTpqCkpAQff/wxPvjgA4wYMQJGRkZITk7Gzp07kZ6eDgCYPXu20hQjmzdvxhdffAFXV1c4OjrCysoKRkZGyMvLw7Vr1xAQEAAA0NfXx9SpU2t0Xv369ZM/3rBhA+bNmwdzc3P5FCuWlpZq50WXMTU1xbJly7BmzRpkZWVh0qRJmDt3Lvr164fS0lLExsZi165dKCoqgkgkwtq1a9G8efMaxUpEREREdY+JcSIiIqL/mj9/Pnr27ImNGzciIyMDFy5ckE8ToUrPnj2xbNmyeotn3bp1MDMzg5+fH548eQJfX1/5XacVGRkZKUxjoW309PTw73//G8uXL8fJkyeRlpaG9evXV6uut7c34uPjkZycjMTERCxdulRhf7t27bB9+3Zs27atXhPjhoaG8Pf3x6JFi3DlyhUkJSXJf2lQFX19ffj6+sLLywvx8fG4fPmyyl8lWFtb46effkKrVq3qOvxXtnbtWqxcuRLh4eEIDw9X2t+/f39s3rxZZd3evXvD19cXixcvhkQigZ+fH/z8/JTKeXh44NNPP1V5jKdPnyI0NFQ+zUxFLVu2xJo1a2BnZ1eDsyqbssjNzQ3Hjx9X+V4/c+YMOnfuXK1jeXh4oKCgAFu3bkVOTg42bNigVKZFixZYu3Ythg0bVqM4iYiIiKh+MDFOREREVM7o0aMxfPhwnDx5EufPn8eNGzeQm5uLwsJCtG7dGpaWlujbty9Gjx4NJyenOlvATxV9fX0sW7YMkydPRlBQEOLi4pCRkQGJRAIDAwO89tpr6NWrF1xcXDBq1CgYGBjUWyx1wdDQENu2bUNcXBxCQkJw7do1PH78GPr6+rCwsECPHj0wZswYjBw5UqFemzZtsH//fvj5+eH48eO4d+8e9PX10alTJwwbNgyzZs1Cx44dG+QcTE1NERAQgFOnTiEsLAzXr19Hbm4uDA0NYWFhARsbG7i5uWHIkCFKddu1a4fAwECEhYUhLCwMycnJyM/PR6tWrSAWizF27FitmkJFxtjYGAcOHIC/vz8iIiJw//59CIIAa2trTJw4EdOnT1d7UcbFxQWnTp2Cv78/oqOjkZ6ejuLiYrRv3x4ODg5wd3eHo6OjyroBAQGIiYlBbGwsbt++jcePH+Pp06cwMDBAt27d4OTkhOnTpyss+lkT//znP2FnZ4eTJ0/i7t27KCwshFQqfaVjzZs3D8OHD0dgYCDi4uKQlZUFPT09dOrUCYMHD8asWbOqnWgnIiIiovonEiqbHJGIiIiIiHRSSEgIVqxYAaBmd04TERERETUWXHyTiIiIiIiIiIiIiHQKE+NEREREREREREREpFOYGCciIiIiIiIiIiIincLEOBERERERERERERHpFCbGiYiIiIiIiIiIiEiniARBEDQdBBERERERERERERFRQ+Ed40RERERERERERESkU5gYJyIiIiIiIiIiIiKdwsQ4EREREREREREREekUJsaJiIiIiIiIiIiISKcwMU5EREREREREREREOoWJcSIiIiIiIiIiIiLSKUyMExEREREREREREZFOYWKciIiIiIiIiIiIiHTK/wN5D0KV3n4CqAAAAABJRU5ErkJggg==", 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", 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" ] @@ -88,23 +89,29 @@ "df_cat = pd.concat([df1, df2])\n", "mdf = pd.melt(df_cat, id_vars=['query', 'reference', 'true_names'], var_name=['Number']) # MELT\n", "\n", + "mdf_v1 = split_and_add_rows(mdf, 'true_names', split_by=', C,O,N,H')\n", + "mdf_v1['true_names'] = mdf_v1['true_names'].replace('', np.nan)\n", + "mdf_v1 = mdf_v1.dropna(subset=['value', 'true_names'])\n", + "mdf_v2 = split_and_add_rows(mdf_v1, 'true_names', split_by=', ')\n", + "mdf_v2 = mdf_v2.sort_values(by=['true_names'])\n", + "\n", "# Set the color palette\n", "colors = ['yellow', 'deepskyblue']\n", "sns.set_palette(sns.color_palette(colors))\n", "\n", "ax = sns.boxplot(x=\"true_names\", y=\"value\", hue=\"Number\", \n", - " data=mdf, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan],\n", + " data=mdf_v2, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan],\n", " medianprops={'color': 'darkgreen', 'linewidth': 4.0},\n", " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT \n", "ax2 = ax.twinx()\n", "\n", "sns.boxplot(ax=ax2,x='true_names', y='value', hue='Number',\n", - " data=mdf, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], \n", + " data=mdf_v2, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], \n", " medianprops={'color': 'b', 'linewidth': 4.0}, \n", " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'})\n", "\n", "ax.legend_.remove()\n", - "ax.set_ylim([0, 6]) # Set y-axis limits\n", + "ax.set_ylim([0, 5]) # Set y-axis limits\n", "ax.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks\n", "ax.set_ylabel('Match values') # Set y-axis label\n", "ax.yaxis.label.set_size(20) # Set font size of y-axis label\n", @@ -119,8 +126,21 @@ "#xticklabels = [f\"{group} ({len(x)})\" for group, x in grouped_df.values]\n", "#ax.set_xticklabels(xticklabels, rotation=90, ha='right')\n", "\n", + "# Create a count for each x-axis label\n", + "count_data = mdf_v2['true_names'].value_counts().reset_index()\n", + "count_data.columns = ['true_names', 'count']\n", + "count_data = count_data.sort_values(by=['true_names'])\n", + "count_data['count'] = count_data['count'] // 2\n", + "\n", + "# Remove the original x-axis labels\n", + "ax.set_xticklabels([])\n", + "\n", + "# Add the count labels to the x-axis\n", + "ax.set_xticks(np.arange(len(count_data)))\n", + "ax.set_xticklabels(count_data['true_names'] + ' (' + count_data['count'].astype(str) + ')', rotation=45, ha='right')\n", + "\n", "ax2.legend_.remove()\n", - "ax2.set_ylim([0, 1.2]) # Set y-axis limits\n", + "ax2.set_ylim([0, 1]) # Set y-axis limits\n", "ax2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks\n", "ax2.set_ylabel('Score values') # Set y-axis label\n", "ax2.yaxis.label.set_size(20) # Set font size of y-axis label\n", @@ -138,6 +158,97 @@ "plt.clf()\n", "plt.close()" ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style(style='white')\n", + "plt.figure(figsize=(17, 5))\n", + "\n", + "mdf_combination_v1 = split_and_add_rows(mdf, 'true_names', split_by=', C,O,N,H')\n", + "mdf_combination_v1['true_names'] = mdf_combination_v1['true_names'].replace('', np.nan)\n", + "mdf_combination_v1 = mdf_combination_v1.dropna(subset=['value', 'true_names'])\n", + "mdf_combination_v2 = generate_combinations(mdf_combination_v1, 'true_names')\n", + "#mdf_combination = generate_combinations(mdf, 'true_names')\n", + "mdf_combination_v2 = mdf_combination_v2.sort_values(by=['true_names'])\n", + "\n", + "# Set the color palette\n", + "colors = ['yellow', 'deepskyblue']\n", + "sns.set_palette(sns.color_palette(colors))\n", + "\n", + "ax_p = sns.boxplot(x=\"true_names\", y=\"value\", hue=\"Number\", \n", + " data=mdf_combination_v2, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan],\n", + " medianprops={'color': 'darkgreen', 'linewidth': 4.0},\n", + " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT \n", + "ax_p2 = ax_p.twinx()\n", + "\n", + "sns.boxplot(ax=ax_p2,x='true_names', y='value', hue='Number',\n", + " data=mdf_combination_v2, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], \n", + " medianprops={'color': 'b', 'linewidth': 4.0}, \n", + " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'})\n", + "\n", + "ax_p.legend_.remove()\n", + "ax_p.set_ylim([0, 5]) # Set y-axis limits\n", + "ax_p.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks\n", + "ax_p.set_ylabel('Match values') # Set y-axis label\n", + "ax_p.yaxis.label.set_size(20) # Set font size of y-axis label\n", + "ax_p.set_xlabel('Chemical composition', fontsize=20) # Set x-axis label and font size\n", + "ax_p.tick_params(axis='x', labelsize=13) # Set font size of x-axis tick labels\n", + "ax_p.tick_params(axis='y', labelsize=13) # Set font size of y-axis tick labels\n", + "ax_p.yaxis.labelpad = 10\n", + "ax_p.xaxis.labelpad = 10\n", + "\n", + "# Add x-axis labels with group sizes\n", + "#grouped_df = merged_top5_same.groupby('true_names')['query'].apply(list).reset_index(name='queries')\n", + "#xticklabels = [f\"{group} ({len(x)})\" for group, x in grouped_df.values]\n", + "#ax.set_xticklabels(xticklabels, rotation=90, ha='right')\n", + "\n", + "# Create a count for each x-axis label\n", + "count_data_p2 = mdf_combination_v2['true_names'].value_counts().reset_index()\n", + "count_data_p2.columns = ['true_names', 'count']\n", + "count_data_p2 = count_data_p2.sort_values(by=['true_names'])\n", + "count_data_p2['count'] = count_data_p2['count'] // 2\n", + "\n", + "# Remove the original x-axis labels\n", + "ax_p.set_xticklabels([])\n", + "\n", + "# Add the count labels to the x-axis\n", + "ax_p.set_xticks(np.arange(len(count_data_p2)))\n", + "ax_p.set_xticklabels(count_data_p2['true_names'] + ' (' + count_data_p2['count'].astype(str) + ')', rotation=45, ha='right')\n", + "\n", + "ax_p2.legend_.remove()\n", + "ax_p2.set_ylim([0, 1]) # Set y-axis limits\n", + "ax_p2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks\n", + "ax_p2.set_ylabel('Score values') # Set y-axis label\n", + "ax_p2.yaxis.label.set_size(20) # Set font size of y-axis label\n", + "ax_p2.tick_params(axis='y', labelsize=12) # Set font size of y-axis tick labels\n", + "ax_p2.yaxis.labelpad = 10\n", + "\n", + "# Change the legend labels\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "labels[0] = 'Matches'\n", + "labels[1] = 'Scores'\n", + "ax.legend(handles, labels, loc='upper right', fontsize=14)\n", + "\n", + "plt.savefig('../Python_scripts/chemical_composition_boxplot2.png', dpi=300, bbox_inches='tight') # SAVE PLOT AS PNG FILE\n", + "plt.show()\n", + "plt.clf()\n", + "plt.close()" + ] } ], "metadata": { diff --git a/analysis/Python_scripts/chemical_composition_boxplot.png 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zyUE0PPt<4r-C690<1XhYIu{+-;ssjYf9fr?2yu8?#Xf#w=`EgnUQ5pF z<6AsD<6%oQ_op{+zg&`OeR_}QsrnNA`033HKL1&YyZHX0(IqbU(+7D5gg;&X(%U;L zm)c-2y@NmhpIziD@NFhf-!IJZ{g23 e+vWDlses7FgIn6ue_rSls=L= Date: Mon, 6 Nov 2023 13:13:49 +0100 Subject: [PATCH 2/4] updated boxplots --- .../chemical_composition_boxplot.ipynb | 244 ++++++------------ .../chemical_composition_boxplot.png | Bin 200269 -> 187690 bytes .../chemical_composition_boxplot2.png | Bin 196568 -> 168148 bytes .../chemical_composition_boxplot2_N.png | Bin 0 -> 211966 bytes .../chemical_composition_boxplot_N.png | Bin 0 -> 200269 bytes analysis/Python_scripts/utils.py | 87 ++++++- 6 files changed, 160 insertions(+), 171 deletions(-) create mode 100644 analysis/Python_scripts/chemical_composition_boxplot2_N.png create mode 100644 analysis/Python_scripts/chemical_composition_boxplot_N.png diff --git a/analysis/Python_scripts/chemical_composition_boxplot.ipynb b/analysis/Python_scripts/chemical_composition_boxplot.ipynb index 58f8697..6c539e8 100644 --- a/analysis/Python_scripts/chemical_composition_boxplot.ipynb +++ b/analysis/Python_scripts/chemical_composition_boxplot.ipynb @@ -6,12 +6,6 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import seaborn as sns\n", - "from matplotlib import pyplot as plt\n", - "from rdkit import Chem\n", - "import numpy as np\n", - "\n", "from utils import *" ] }, @@ -33,36 +27,57 @@ "name": "stderr", "output_type": "stream", "text": [ - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:06] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:07] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:07] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[16:50:07] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n" + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:55] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:55] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:55] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:55] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:10:55] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n" ] } ], "source": [ "top5_same = get_matching_rows(top5, 'query', 'reference')\n", "merged_top5_same = append_classes(top5_same, 'query')\n", + "\n", + "merged_top5_same_N = merged_top5_same.copy()\n", + "\n", + "# exclude nitrigen\n", + "merged_top5_same = merged_top5_same[merged_top5_same['N'] != True]\n", + "\n", "# Apply the function to each row of the DataFrame\n", + "merged_top5_same_N['true_names'] = merged_top5_same_N.apply(lambda row: get_true_names(row, merged_top5_same_N), axis=1)\n", "merged_top5_same['true_names'] = merged_top5_same.apply(lambda row: get_true_names(row, merged_top5_same), axis=1)\n", + "\n", "# Convert the list of names to a comma-separated string\n", - "merged_top5_same['true_names'] = merged_top5_same['true_names'].apply(lambda lst: ', '.join(lst))" + "merged_top5_same_N['true_names'] = merged_top5_same_N['true_names'].apply(lambda lst: ', '.join(lst))\n", + "merged_top5_same['true_names'] = merged_top5_same['true_names'].apply(lambda lst: ', '.join(lst))\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "mdf = preprocess_data(merged_top5_same)\n", + "\n", + "mdf_N = preprocess_data(merged_top5_same_N)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -73,100 +88,50 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "sns.set_style(style='white')\n", - "plt.figure(figsize=(17, 5))\n", - "\n", - "# Concatenate the DataFrames in df1_list and add a 'value' column with the value 'matches'.\n", - "df1 = merged_top5_same[['query', 'reference', 'true_names', 'CosineHungarian_0.01_0.0_1.0_matches']].copy()\n", - "\n", - "# Concatenate the DataFrames in df2_list and add a 'value' column with the value 'scores'.\n", - "df2 = merged_top5_same[['query', 'reference', 'true_names', 'CosineHungarian_0.01_0.0_1.0_scores']].copy()\n", - "\n", - "# Concatenate df1 and df2 into a single DataFrame.\n", - "df_cat = pd.concat([df1, df2])\n", - "mdf = pd.melt(df_cat, id_vars=['query', 'reference', 'true_names'], var_name=['Number']) # MELT\n", - "\n", - "mdf_v1 = split_and_add_rows(mdf, 'true_names', split_by=', C,O,N,H')\n", - "mdf_v1['true_names'] = mdf_v1['true_names'].replace('', np.nan)\n", - "mdf_v1 = mdf_v1.dropna(subset=['value', 'true_names'])\n", - "mdf_v2 = split_and_add_rows(mdf_v1, 'true_names', split_by=', ')\n", - "mdf_v2 = mdf_v2.sort_values(by=['true_names'])\n", + "# with Nitrogen plot 1\n", + "mdf_N_p1 = split_and_add_rows(mdf_N, 'true_names', split_by=', ')\n", + "mdf_N_p1 = mdf_N_p1.sort_values(by=['true_names'])\n", "\n", - "# Set the color palette\n", - "colors = ['yellow', 'deepskyblue']\n", - "sns.set_palette(sns.color_palette(colors))\n", + "create_plot(mdf_N_p1, '../Python_scripts/chemical_composition_boxplot_N.png')\n", "\n", - "ax = sns.boxplot(x=\"true_names\", y=\"value\", hue=\"Number\", \n", - " data=mdf_v2, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan],\n", - " medianprops={'color': 'darkgreen', 'linewidth': 4.0},\n", - " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT \n", - "ax2 = ax.twinx()\n", + "# without Nitrogen plot 1\n", + "mdf_p1 = split_and_add_rows(mdf, 'true_names', split_by=', ')\n", + "mdf_p1 = mdf_p1.sort_values(by=['true_names'])\n", "\n", - "sns.boxplot(ax=ax2,x='true_names', y='value', hue='Number',\n", - " data=mdf_v2, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], \n", - " medianprops={'color': 'b', 'linewidth': 4.0}, \n", - " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'})\n", - "\n", - "ax.legend_.remove()\n", - "ax.set_ylim([0, 5]) # Set y-axis limits\n", - "ax.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks\n", - "ax.set_ylabel('Match values') # Set y-axis label\n", - "ax.yaxis.label.set_size(20) # Set font size of y-axis label\n", - "ax.set_xlabel('Chemical composition', fontsize=20) # Set x-axis label and font size\n", - "ax.tick_params(axis='x', labelsize=13) # Set font size of x-axis tick labels\n", - "ax.tick_params(axis='y', labelsize=13) # Set font size of y-axis tick labels\n", - "ax.yaxis.labelpad = 10\n", - "ax.xaxis.labelpad = 10\n", - "\n", - "# Add x-axis labels with group sizes\n", - "#grouped_df = merged_top5_same.groupby('true_names')['query'].apply(list).reset_index(name='queries')\n", - "#xticklabels = [f\"{group} ({len(x)})\" for group, x in grouped_df.values]\n", - "#ax.set_xticklabels(xticklabels, rotation=90, ha='right')\n", - "\n", - "# Create a count for each x-axis label\n", - "count_data = mdf_v2['true_names'].value_counts().reset_index()\n", - "count_data.columns = ['true_names', 'count']\n", - "count_data = count_data.sort_values(by=['true_names'])\n", - "count_data['count'] = count_data['count'] // 2\n", - "\n", - "# Remove the original x-axis labels\n", - "ax.set_xticklabels([])\n", - "\n", - "# Add the count labels to the x-axis\n", - "ax.set_xticks(np.arange(len(count_data)))\n", - "ax.set_xticklabels(count_data['true_names'] + ' (' + count_data['count'].astype(str) + ')', rotation=45, ha='right')\n", - "\n", - "ax2.legend_.remove()\n", - "ax2.set_ylim([0, 1]) # Set y-axis limits\n", - "ax2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks\n", - "ax2.set_ylabel('Score values') # Set y-axis label\n", - "ax2.yaxis.label.set_size(20) # Set font size of y-axis label\n", - "ax2.tick_params(axis='y', labelsize=12) # Set font size of y-axis tick labels\n", - "ax2.yaxis.labelpad = 10\n", - "\n", - "# Change the legend labels\n", - "handles, labels = ax.get_legend_handles_labels()\n", - "labels[0] = 'Matches'\n", - "labels[1] = 'Scores'\n", - "ax.legend(handles, labels, loc='upper right', fontsize=14)\n", - "\n", - "plt.savefig('../Python_scripts/chemical_composition_boxplot.png', dpi=300, bbox_inches='tight') # SAVE PLOT AS PNG FILE\n", - "plt.show()\n", - "plt.clf()\n", - "plt.close()" + "create_plot(mdf_p1, '../Python_scripts/chemical_composition_boxplot.png')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -176,78 +141,17 @@ } ], "source": [ - "sns.set_style(style='white')\n", - "plt.figure(figsize=(17, 5))\n", - "\n", - "mdf_combination_v1 = split_and_add_rows(mdf, 'true_names', split_by=', C,O,N,H')\n", - "mdf_combination_v1['true_names'] = mdf_combination_v1['true_names'].replace('', np.nan)\n", - "mdf_combination_v1 = mdf_combination_v1.dropna(subset=['value', 'true_names'])\n", - "mdf_combination_v2 = generate_combinations(mdf_combination_v1, 'true_names')\n", - "#mdf_combination = generate_combinations(mdf, 'true_names')\n", - "mdf_combination_v2 = mdf_combination_v2.sort_values(by=['true_names'])\n", - "\n", - "# Set the color palette\n", - "colors = ['yellow', 'deepskyblue']\n", - "sns.set_palette(sns.color_palette(colors))\n", - "\n", - "ax_p = sns.boxplot(x=\"true_names\", y=\"value\", hue=\"Number\", \n", - " data=mdf_combination_v2, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan],\n", - " medianprops={'color': 'darkgreen', 'linewidth': 4.0},\n", - " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT \n", - "ax_p2 = ax_p.twinx()\n", - "\n", - "sns.boxplot(ax=ax_p2,x='true_names', y='value', hue='Number',\n", - " data=mdf_combination_v2, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], \n", - " medianprops={'color': 'b', 'linewidth': 4.0}, \n", - " flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'})\n", - "\n", - "ax_p.legend_.remove()\n", - "ax_p.set_ylim([0, 5]) # Set y-axis limits\n", - "ax_p.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks\n", - "ax_p.set_ylabel('Match values') # Set y-axis label\n", - "ax_p.yaxis.label.set_size(20) # Set font size of y-axis label\n", - "ax_p.set_xlabel('Chemical composition', fontsize=20) # Set x-axis label and font size\n", - "ax_p.tick_params(axis='x', labelsize=13) # Set font size of x-axis tick labels\n", - "ax_p.tick_params(axis='y', labelsize=13) # Set font size of y-axis tick labels\n", - "ax_p.yaxis.labelpad = 10\n", - "ax_p.xaxis.labelpad = 10\n", - "\n", - "# Add x-axis labels with group sizes\n", - "#grouped_df = merged_top5_same.groupby('true_names')['query'].apply(list).reset_index(name='queries')\n", - "#xticklabels = [f\"{group} ({len(x)})\" for group, x in grouped_df.values]\n", - "#ax.set_xticklabels(xticklabels, rotation=90, ha='right')\n", - "\n", - "# Create a count for each x-axis label\n", - "count_data_p2 = mdf_combination_v2['true_names'].value_counts().reset_index()\n", - "count_data_p2.columns = ['true_names', 'count']\n", - "count_data_p2 = count_data_p2.sort_values(by=['true_names'])\n", - "count_data_p2['count'] = count_data_p2['count'] // 2\n", - "\n", - "# Remove the original x-axis labels\n", - "ax_p.set_xticklabels([])\n", - "\n", - "# Add the count labels to the x-axis\n", - "ax_p.set_xticks(np.arange(len(count_data_p2)))\n", - "ax_p.set_xticklabels(count_data_p2['true_names'] + ' (' + count_data_p2['count'].astype(str) + ')', rotation=45, ha='right')\n", + "# without Nitrogen plot 2\n", + "mdf_p2 = generate_combinations(mdf, 'true_names')\n", + "mdf_p2 = mdf_p2.sort_values(by=['true_names'])\n", "\n", - "ax_p2.legend_.remove()\n", - "ax_p2.set_ylim([0, 1]) # Set y-axis limits\n", - "ax_p2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks\n", - "ax_p2.set_ylabel('Score values') # Set y-axis label\n", - "ax_p2.yaxis.label.set_size(20) # Set font size of y-axis label\n", - "ax_p2.tick_params(axis='y', labelsize=12) # Set font size of y-axis tick labels\n", - "ax_p2.yaxis.labelpad = 10\n", + "create_plot(mdf_p2, '../Python_scripts/chemical_composition_boxplot2.png')\n", "\n", - "# Change the legend labels\n", - "handles, labels = ax.get_legend_handles_labels()\n", - "labels[0] = 'Matches'\n", - "labels[1] = 'Scores'\n", - "ax.legend(handles, labels, loc='upper right', fontsize=14)\n", + "# with Nitrogen plot 2\n", + "mdf_N_p2 = generate_combinations(mdf_N, 'true_names')\n", + "mdf_N_p2 = mdf_N_p2.sort_values(by=['true_names'])\n", "\n", - "plt.savefig('../Python_scripts/chemical_composition_boxplot2.png', dpi=300, bbox_inches='tight') # SAVE PLOT AS PNG FILE\n", - "plt.show()\n", - "plt.clf()\n", - "plt.close()" + "create_plot(mdf_N_p2, '../Python_scripts/chemical_composition_boxplot2_N.png')" ] } ], diff --git a/analysis/Python_scripts/chemical_composition_boxplot.png b/analysis/Python_scripts/chemical_composition_boxplot.png index 70527a2a0bcd6069e53b19190f97a35846487020..b5c238cbd3527d480c745a52c894bfefbbfb3137 100644 GIT binary patch literal 187690 zcmeEvd05VQ`}S>S)Fbl)06yxryFtdxO(9 zCRUb$TgA5iyk)hqt?elr@on2I{{DuoR;P`&2|rA}k00{=DR~VW24gD^{co1zZE1VP zECxeCX1AJSXm_K7W7R;G^rZR*p6{PNkU16k&ox*&4p%^6zR% zcg~TD80zZJ+@ol08h&T}q6;#3l&`;p)^yyP|MhR-zYeH-t4{w-&kb_cv`}Aty?VP* z887nnSMaJlzOR3W!PpfTB{qBdZyKLo5;Ohx7)pLEOQ!$k%@+qlznT7z&%eTBWD@rCT%mOMGzoEV4WwES!1NI^Vojk@mf? zm^U9kR6qaX%7>@^_+3pd_WFxJ-U8w3V~c&3@z=f0+IMZsg^b>8iEzi33&V}Q?#m1C 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[has_organic_atoms(m) for m in molecules], + "N": [has_atom(m, 'N') for m in molecules], }) merged_df = pd.merge(df, class_names, left_on=left_on, right_on='molname') return merged_df @@ -77,4 +81,85 @@ def generate_combinations(df, column_name): new_row[column_name] = combo new_rows.append(new_row) - return pd.DataFrame(new_rows).reset_index(drop=True) \ No newline at end of file + return pd.DataFrame(new_rows).reset_index(drop=True) + +def create_plot(df, path): + sns.set_style(style='white') + plt.figure(figsize=(17, 5)) + + # Set the color palette + colors = ['yellow', 'deepskyblue'] + sns.set_palette(sns.color_palette(colors)) + + ax = sns.boxplot(x="true_names", y="value", hue="Number", + data=df, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan], + medianprops={'color': 'darkgreen', 'linewidth': 4.0}, + flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT + ax2 = ax.twinx() + + sns.boxplot(ax=ax2,x='true_names', y='value', hue='Number', + data=df, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], + medianprops={'color': 'b', 'linewidth': 4.0}, + flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) + + ax.legend_.remove() + ax.set_ylim([0, 5]) # Set y-axis limits + ax.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks + ax.set_ylabel('Match values') # Set y-axis label + ax.yaxis.label.set_size(20) # Set font size of y-axis label + ax.set_xlabel('Chemical composition', fontsize=20) # Set x-axis label and font size + ax.tick_params(axis='x', labelsize=13) # Set font size of x-axis tick labels + ax.tick_params(axis='y', labelsize=13) # Set font size of y-axis tick labels + ax.yaxis.labelpad = 10 + ax.xaxis.labelpad = 10 + + # Create a count for each x-axis label + count_data = df['true_names'].value_counts().reset_index() + count_data.columns = ['true_names', 'count'] + count_data = count_data.sort_values(by=['true_names']) + count_data['count'] = count_data['count'] // 2 + + # Remove the original x-axis labels + ax.set_xticklabels([]) + + # Add the count labels to the x-axis + ax.set_xticks(np.arange(len(count_data))) + ax.set_xticklabels(count_data['true_names'] + ' (' + count_data['count'].astype(str) + ')', rotation=45, ha='right') + + ax2.legend_.remove() + ax2.set_ylim([0, 1]) # Set y-axis limits + ax2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks + ax2.set_ylabel('Score values') # Set y-axis label + ax2.yaxis.label.set_size(20) # Set font size of y-axis label + ax2.tick_params(axis='y', labelsize=12) # Set font size of y-axis tick labels + ax2.yaxis.labelpad = 10 + + # Change the legend labels + handles, labels = ax.get_legend_handles_labels() + labels[0] = 'Matches' + labels[1] = 'Scores' + ax.legend(handles, labels, loc='upper right', fontsize=14) + + plt.savefig(path, dpi=300, bbox_inches='tight') # SAVE PLOT AS PNG FILE + plt.show() + plt.clf() + plt.close() + +def preprocess_data(merged_top5_same): + # Concatenate the DataFrames in df1_list and add a 'value' column with the value 'matches'. + df1 = merged_top5_same[['query', 'reference', 'true_names', 'CosineHungarian_0.01_0.0_1.0_matches']].copy() + + # Concatenate the DataFrames in df2_list and add a 'value' column with the value 'scores'. + df2 = merged_top5_same[['query', 'reference', 'true_names', 'CosineHungarian_0.01_0.0_1.0_scores']].copy() + + # Concatenate df1 and df2 into a single DataFrame. + df_cat = pd.concat([df1, df2]) + + mdf = pd.melt(df_cat, id_vars=['query', 'reference', 'true_names'], var_name=['Number']) # MELT + + #cleaning data + mdf = split_and_add_rows(mdf, 'true_names', split_by=', C,O,N,H') + mdf['true_names'] = mdf['true_names'].replace('', np.nan) + mdf = mdf.dropna(subset=['value', 'true_names']) + + return mdf \ No newline at end of file From d7080a3a16bbc2653a6bb828e7d506ef1fd78fc5 Mon Sep 17 00:00:00 2001 From: Zargham Ahmad Date: Mon, 6 Nov 2023 13:16:22 +0100 Subject: [PATCH 3/4] updated utils.py --- analysis/Python_scripts/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/analysis/Python_scripts/utils.py b/analysis/Python_scripts/utils.py index 112bf13..2ba766c 100644 --- a/analysis/Python_scripts/utils.py +++ b/analysis/Python_scripts/utils.py @@ -162,4 +162,4 @@ def preprocess_data(merged_top5_same): mdf['true_names'] = mdf['true_names'].replace('', np.nan) mdf = mdf.dropna(subset=['value', 'true_names']) - return mdf \ No newline at end of file + return mdf From 188e4384c6e5e1307617f010661ae6be6b713818 Mon Sep 17 00:00:00 2001 From: Zargham Ahmad Date: Mon, 6 Nov 2023 13:38:54 +0100 Subject: [PATCH 4/4] moved func to plotting.py --- .../chemical_composition_boxplot.ipynb | 33 +++++----- analysis/Python_scripts/plotting.py | 66 ++++++++++++++++++- analysis/Python_scripts/utils.py | 64 ------------------ 3 files changed, 82 insertions(+), 81 deletions(-) diff --git a/analysis/Python_scripts/chemical_composition_boxplot.ipynb b/analysis/Python_scripts/chemical_composition_boxplot.ipynb index 6c539e8..1f83e2d 100644 --- a/analysis/Python_scripts/chemical_composition_boxplot.ipynb +++ b/analysis/Python_scripts/chemical_composition_boxplot.ipynb @@ -6,7 +6,8 @@ "metadata": {}, "outputs": [], "source": [ - "from utils import *" + "from utils import *\n", + "from plotting import create_plot" ] }, { @@ -27,21 +28,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[13:10:54] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", - "[13:10:54] Skipping unrecognized collection type at line 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"[13:36:56] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:36:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:36:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n", + "[13:36:57] Skipping unrecognized collection type at line 48576: MDLV30/STERAC1 BONDS=(1 7)\n" ] } ], diff --git a/analysis/Python_scripts/plotting.py b/analysis/Python_scripts/plotting.py index 4eed2a4..f46806e 100644 --- a/analysis/Python_scripts/plotting.py +++ b/analysis/Python_scripts/plotting.py @@ -1,6 +1,8 @@ import pandas as pd from matplotlib import pyplot as plt +import seaborn as sns import plotly.graph_objs as go +import numpy as np def make_boxplot(grouped_df: pd.DataFrame, colname: str, legend: str): #Create the plot with a width of 10 inches @@ -79,4 +81,66 @@ def plot_histogram(x, xaxis_title='', title=''): fig = go.Figure(data=[trace1], layout=layout) # Display the plot - fig.show() \ No newline at end of file + fig.show() + +def create_plot(df, path): + sns.set_style(style='white') + plt.figure(figsize=(17, 5)) + + # Set the color palette + colors = ['yellow', 'deepskyblue'] + sns.set_palette(sns.color_palette(colors)) + + ax = sns.boxplot(x="true_names", y="value", hue="Number", + data=df, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan], + medianprops={'color': 'darkgreen', 'linewidth': 4.0}, + flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT + ax2 = ax.twinx() + + sns.boxplot(ax=ax2,x='true_names', y='value', hue='Number', + data=df, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], + medianprops={'color': 'b', 'linewidth': 4.0}, + flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) + + ax.legend_.remove() + ax.set_ylim([0, 5]) # Set y-axis limits + ax.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks + ax.set_ylabel('Match values') # Set y-axis label + ax.yaxis.label.set_size(20) # Set font size of y-axis label + ax.set_xlabel('Chemical composition', fontsize=20) # Set x-axis label and font size + ax.tick_params(axis='x', labelsize=13) # Set font size of x-axis tick labels + ax.tick_params(axis='y', labelsize=13) # Set font size of y-axis tick labels + ax.yaxis.labelpad = 10 + ax.xaxis.labelpad = 10 + + # Create a count for each x-axis label + count_data = df['true_names'].value_counts().reset_index() + count_data.columns = ['true_names', 'count'] + count_data = count_data.sort_values(by=['true_names']) + count_data['count'] = count_data['count'] // 2 + + # Remove the original x-axis labels + ax.set_xticklabels([]) + + # Add the count labels to the x-axis + ax.set_xticks(np.arange(len(count_data))) + ax.set_xticklabels(count_data['true_names'] + ' (' + count_data['count'].astype(str) + ')', rotation=45, ha='right') + + ax2.legend_.remove() + ax2.set_ylim([0, 1]) # Set y-axis limits + ax2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks + ax2.set_ylabel('Score values') # Set y-axis label + ax2.yaxis.label.set_size(20) # Set font size of y-axis label + ax2.tick_params(axis='y', labelsize=12) # Set font size of y-axis tick labels + ax2.yaxis.labelpad = 10 + + # Change the legend labels + handles, labels = ax.get_legend_handles_labels() + labels[0] = 'Matches' + labels[1] = 'Scores' + ax.legend(handles, labels, loc='upper right', fontsize=14) + + plt.savefig(path, dpi=300, bbox_inches='tight') # SAVE PLOT AS PNG FILE + plt.show() + plt.clf() + plt.close() diff --git a/analysis/Python_scripts/utils.py b/analysis/Python_scripts/utils.py index 2ba766c..2519015 100644 --- a/analysis/Python_scripts/utils.py +++ b/analysis/Python_scripts/utils.py @@ -1,7 +1,5 @@ import pandas as pd -import seaborn as sns import numpy as np -from matplotlib import pyplot as plt from rdkit import Chem from itertools import combinations @@ -83,68 +81,6 @@ def generate_combinations(df, column_name): return pd.DataFrame(new_rows).reset_index(drop=True) -def create_plot(df, path): - sns.set_style(style='white') - plt.figure(figsize=(17, 5)) - - # Set the color palette - colors = ['yellow', 'deepskyblue'] - sns.set_palette(sns.color_palette(colors)) - - ax = sns.boxplot(x="true_names", y="value", hue="Number", - data=df, hue_order=['CosineHungarian_0.01_0.0_1.0_matches',np.nan], - medianprops={'color': 'darkgreen', 'linewidth': 4.0}, - flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) # RUN PLOT - ax2 = ax.twinx() - - sns.boxplot(ax=ax2,x='true_names', y='value', hue='Number', - data=df, hue_order=[np.nan, 'CosineHungarian_0.01_0.0_1.0_scores'], - medianprops={'color': 'b', 'linewidth': 4.0}, - flierprops={'marker': 'o', 'markersize': 10, 'markerfacecolor': 'none'}) - - ax.legend_.remove() - ax.set_ylim([0, 5]) # Set y-axis limits - ax.yaxis.set_major_locator(plt.MultipleLocator(1)) # Set major tick marks - ax.set_ylabel('Match values') # Set y-axis label - ax.yaxis.label.set_size(20) # Set font size of y-axis label - ax.set_xlabel('Chemical composition', fontsize=20) # Set x-axis label and font size - ax.tick_params(axis='x', labelsize=13) # Set font size of x-axis tick labels - ax.tick_params(axis='y', labelsize=13) # Set font size of y-axis tick labels - ax.yaxis.labelpad = 10 - ax.xaxis.labelpad = 10 - - # Create a count for each x-axis label - count_data = df['true_names'].value_counts().reset_index() - count_data.columns = ['true_names', 'count'] - count_data = count_data.sort_values(by=['true_names']) - count_data['count'] = count_data['count'] // 2 - - # Remove the original x-axis labels - ax.set_xticklabels([]) - - # Add the count labels to the x-axis - ax.set_xticks(np.arange(len(count_data))) - ax.set_xticklabels(count_data['true_names'] + ' (' + count_data['count'].astype(str) + ')', rotation=45, ha='right') - - ax2.legend_.remove() - ax2.set_ylim([0, 1]) # Set y-axis limits - ax2.yaxis.set_major_locator(plt.MultipleLocator(0.2)) # Set major tick marks - ax2.set_ylabel('Score values') # Set y-axis label - ax2.yaxis.label.set_size(20) # Set font size of y-axis label - ax2.tick_params(axis='y', labelsize=12) # Set font size of y-axis tick labels - ax2.yaxis.labelpad = 10 - - # Change the legend labels - handles, labels = ax.get_legend_handles_labels() - labels[0] = 'Matches' - labels[1] = 'Scores' - ax.legend(handles, labels, loc='upper right', fontsize=14) - - plt.savefig(path, dpi=300, bbox_inches='tight') # SAVE PLOT AS PNG FILE - plt.show() - plt.clf() - plt.close() - def preprocess_data(merged_top5_same): # Concatenate the DataFrames in df1_list and add a 'value' column with the value 'matches'. df1 = merged_top5_same[['query', 'reference', 'true_names', 'CosineHungarian_0.01_0.0_1.0_matches']].copy()