normalize affinities

combine_dbs.ipynb

@@ -1368,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "id": "4e2d89f7-f6ea-41de-a13b-4a184b4fd580",
    "metadata": {},
    "outputs": [],
@@ -1388,7 +1388,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
+   "id": "8f949038-d07d-4d3a-a47e-b825cc9018ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0c027988-0b44-4010-ad61-7d70eead1654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6aeba020-b6ff-4633-902e-4df74463eb2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['affinity'] = scaler.fit_transform(df['neg_log10_affinity_M'].values.reshape(-1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "91196eee-5fd0-4aa4-927a-5c1a3f436ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([6.49685099]), array([2.43570803]))"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler.mean_, scaler.var_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9be91c11-1c58-47de-8ebb-99c25cfc3c55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop(columns=['level_0','index'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "id": "c6c64066-4032-4247-a8b9-00388176cc7b",
    "metadata": {},
    "outputs": [],
@@ -1398,13 +1459,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 28,
    "id": "d91c0d91-474c-4ab2-9a5e-3b7861f7a832",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1416,8 +1477,8 @@
     }
    ],
    "source": [
-    "ax = df['neg_log10_affinity_M'].hist(bins=100,density=True)\n",
-    "ax.set_xlabel('-$\\log_{10}$ affinity[M]',fontsize=16)\n",
+    "ax = df['affinity'].hist(bins=100,density=True)\n",
+    "ax.set_xlabel('affinity[M]',fontsize=16)\n",
     "ax.set_ylabel('probability',fontsize=16)\n",
     "ax.figure.savefig('affinity.pdf')"
    ]

data/all.parquet

@@ -1,3 +1,3 @@
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-size 216004661
+oid sha256:d22b80e9d2f1c3db65b792395b10f84dfd367dbe908cfd798aea5a10f1c62cb9
+size 204641362