From f26cf087ab2158dff5bbdc663414803f4ed7123b Mon Sep 17 00:00:00 2001
From: rstiskalek <richard.stiskalek@protonmail.com>
Date: Sat, 1 Apr 2023 07:56:52 +0100
Subject: [PATCH] Update nb

---
 notebooks/knn.ipynb | 73 +++++++++------------------------------------
 1 file changed, 14 insertions(+), 59 deletions(-)

diff --git a/notebooks/knn.ipynb b/notebooks/knn.ipynb
index c99dbb6..59a005c 100644
--- a/notebooks/knn.ipynb
+++ b/notebooks/knn.ipynb
@@ -6,8 +6,8 @@
    "id": "5a38ed25",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2023-04-01T06:10:36.618752Z",
-     "start_time": "2023-04-01T06:10:10.251616Z"
+     "end_time": "2023-04-01T06:20:33.195162Z",
+     "start_time": "2023-04-01T06:20:29.474122Z"
     },
     "scrolled": true
    },
@@ -43,12 +43,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "4218b673",
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2023-04-01T06:10:44.171672Z",
-     "start_time": "2023-04-01T06:10:42.109733Z"
+     "end_time": "2023-04-01T06:20:35.273662Z",
+     "start_time": "2023-04-01T06:20:33.196875Z"
     }
    },
    "outputs": [],
@@ -58,11 +58,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "5ff7a1b6",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2023-04-01T06:12:15.998Z"
+     "end_time": "2023-04-01T06:55:34.643955Z",
+     "start_time": "2023-04-01T06:55:28.334204Z"
     }
    },
    "outputs": [
@@ -70,8 +71,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      "  0%|          | 0/1 [00:00<?, ?it/s]"
+      "100%|██████████| 1/1 [00:02<00:00,  2.95s/it]\n"
      ]
     }
    ],
@@ -82,61 +82,16 @@
     "knncdf = csiborgtools.match.kNN_CDF()\n",
     "\n",
     "rs, cdf = knncdf(knn, nneighbours=2, Rmax=155 / 0.705, rmin=0.01, rmax=100,\n",
-    "                  nsamples=int(1e8), neval=int(1e4), random_state=42, batch_size=int(1e7))"
+    "                  nsamples=int(1e6), neval=int(1e4), random_state=42, batch_size=int(1e6))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "2b85c1c6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-04-01T06:11:13.560595Z",
-     "start_time": "2023-04-01T06:11:12.888821Z"
-    }
-   },
+   "execution_count": null,
+   "id": "0d5f3d02",
+   "metadata": {},
    "outputs": [],
-   "source": [
-    "X = knncdf.rvs_in_sphere(nsamples=int(1e7), R=1.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3a80cb5b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-04-01T06:11:15.024753Z",
-     "start_time": "2023-04-01T06:11:14.973967Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Variable           Type                Data/Info\n",
-      "------------------------------------------------\n",
-      "NearestNeighbors   ABCMeta             <class 'sklearn.neighbors<...>rvised.NearestNeighbors'>\n",
-      "X                  ndarray             10000000x3: 30000000 elems, type `float32`, 120000000 bytes (114.44091796875 Mb)\n",
-      "cat                HaloCatalogue       <csiborgtools.read.make_c<...>object at 0x7fbbc6073fa0>\n",
-      "cdf                ndarray             2x9999: 19998 elems, type `float32`, 79992 bytes\n",
-      "csiborgtools       module              <module 'csiborgtools' fr<...>siborgtools/__init__.py'>\n",
-      "joblib             module              <module 'joblib' from '/m<...>ages/joblib/__init__.py'>\n",
-      "knn                NearestNeighbors    NearestNeighbors()\n",
-      "knncdf             kNN_CDF             <csiborgtools.match.knn.k<...>object at 0x7fbbc68bb5b0>\n",
-      "matplotlib         module              <module 'matplotlib' from<...>/matplotlib/__init__.py'>\n",
-      "np                 module              <module 'numpy' from '/mn<...>kages/numpy/__init__.py'>\n",
-      "plt                module              <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>\n",
-      "rs                 ndarray             9999: 9999 elems, type `float64`, 79992 bytes\n",
-      "sys                module              <module 'sys' (built-in)>\n",
-      "tqdm               type                <class 'tqdm.std.tqdm'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "%whos"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",