diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 79a5c2db..e709b873 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,7 +29,8 @@ repos:
 #      - id: black
 #        args: [ --line-length=120 ]
 #
-  - repo: https://github.com/roy-ht/pre-commit-jupyter
-    rev: v1.2.1
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.6.1
     hooks:
-      - id: jupyter-notebook-cleanup
+      - id: nbstripout
+
diff --git a/advanced-python/10Basics.ipynb b/advanced-python/10Basics.ipynb
index 93be8a95..9301d4bd 100644
--- a/advanced-python/10Basics.ipynb
+++ b/advanced-python/10Basics.ipynb
@@ -52,12 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.162938280Z",
-     "start_time": "2023-11-09T18:21:54.591214759Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = ['a', 'b', 'c']\n",
@@ -71,12 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.164206324Z",
-     "start_time": "2023-11-09T18:21:54.591634409Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = {'a': '0', 'b': '1', 'c': '2'}\n",
@@ -90,12 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.165379338Z",
-     "start_time": "2023-11-09T18:21:54.592201956Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = 'foo'\n",
@@ -117,12 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.174086165Z",
-     "start_time": "2023-11-09T18:21:54.592801793Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "N = 10\n",
@@ -143,12 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.174961405Z",
-     "start_time": "2023-11-09T18:21:54.639862013Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "squares = {i: i**2 for i in range(10)}\n",
@@ -158,12 +133,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.177083872Z",
-     "start_time": "2023-11-09T18:21:54.640208754Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "N = 5\n",
@@ -232,12 +202,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.178900927Z",
-     "start_time": "2023-11-09T18:21:54.640684978Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pt_cut = 1789.234567890987654\n",
@@ -267,12 +232,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.179761160Z",
-     "start_time": "2023-11-09T18:21:54.683193413Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\"Hello starterkitters\""
@@ -281,12 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.180074403Z",
-     "start_time": "2023-11-09T18:21:54.683507163Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "None"
@@ -302,12 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:55.708097724Z",
-     "start_time": "2023-11-09T18:21:54.683700759Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "!ls"
@@ -316,12 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:57.188240198Z",
-     "start_time": "2023-11-09T18:21:55.712286379Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "!wget https://example.com/index.html"
@@ -337,12 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:57.209109687Z",
-     "start_time": "2023-11-09T18:21:57.194328587Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%time sum([i**2 for i in range(10000)])"
@@ -358,12 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:57.268821159Z",
-     "start_time": "2023-11-09T18:21:57.208320012Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%time\n",
@@ -402,12 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:57.269768775Z",
-     "start_time": "2023-11-09T18:21:57.263045210Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def my_print(my_string):\n",
@@ -417,12 +347,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.320620623Z",
-     "start_time": "2023-11-09T18:21:57.263292639Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_print?"
@@ -438,12 +363,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.324026004Z",
-     "start_time": "2023-11-09T18:21:57.541042260Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_print??"
@@ -452,12 +372,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.357805971Z",
-     "start_time": "2023-11-09T18:21:57.552034854Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "range?"
@@ -473,26 +388,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.396066090Z",
-     "start_time": "2023-11-09T18:21:57.647040419Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-   "{'a': 'b'}.get?"
+    "{'a': 'b'}.get?"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.398964659Z",
-     "start_time": "2023-11-09T18:21:57.695017046Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "{'a': 'b'}.get"
@@ -501,12 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.401053224Z",
-     "start_time": "2023-11-09T18:21:57.695273312Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "junk = {'a': 'b'}.get\n",
@@ -532,12 +432,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.404008533Z",
-     "start_time": "2023-11-09T18:21:57.695446136Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "max(10, 15)"
@@ -549,11 +444,7 @@
    "metadata": {
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:59.040701073Z",
-     "start_time": "2023-11-09T18:21:57.695622001Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -572,11 +463,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.564441476Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -610,10 +497,7 @@
    "metadata": {
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.606952947Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
diff --git a/advanced-python/11AdvancedPython.ipynb b/advanced-python/11AdvancedPython.ipynb
index ea1344cb..2a1c253b 100644
--- a/advanced-python/11AdvancedPython.ipynb
+++ b/advanced-python/11AdvancedPython.ipynb
@@ -17,12 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.287777986Z",
-     "start_time": "2023-11-09T18:21:57.475130307Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import time"
@@ -42,12 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.288125493Z",
-     "start_time": "2023-11-09T18:21:57.475439183Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a, c, *b = [3, 4, 4.5, 5, 6]"
@@ -56,12 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.288659478Z",
-     "start_time": "2023-11-09T18:21:57.475730863Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "b"
@@ -77,12 +62,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.289024121Z",
-     "start_time": "2023-11-09T18:21:57.476020895Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "d1, d2, *d3, d4 = [1, 2, 3]  # nothing left for d3"
@@ -91,12 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.289770343Z",
-     "start_time": "2023-11-09T18:21:57.476279441Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "d3"
@@ -112,12 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.289972173Z",
-     "start_time": "2023-11-09T18:21:57.476626713Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = [3, 4, 5]"
@@ -133,12 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.290148105Z",
-     "start_time": "2023-11-09T18:21:57.519751245Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "d, e, f, g, h, i = *a, *b"
@@ -154,12 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.290328424Z",
-     "start_time": "2023-11-09T18:21:57.519943991Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def func(*args, **kwargs):\n",
@@ -170,12 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.291657968Z",
-     "start_time": "2023-11-09T18:21:57.520119691Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mykwargs = {'a': 5, 'b': 3}\n",
@@ -186,12 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.297387283Z",
-     "start_time": "2023-11-09T18:21:57.520366676Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "func(5, a=4)"
@@ -200,12 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.297741024Z",
-     "start_time": "2023-11-09T18:21:57.520883499Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# play around with it!"
@@ -248,12 +193,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.324668018Z",
-     "start_time": "2023-11-09T18:21:57.563095740Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import contextlib\n",
@@ -269,12 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.326374361Z",
-     "start_time": "2023-11-09T18:21:57.563347017Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with printer(5) as number:\n",
@@ -294,12 +229,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.354444565Z",
-     "start_time": "2023-11-09T18:21:57.563714577Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with open('tmp.txt', 'w') as textfile:\n",
@@ -316,12 +246,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.355126940Z",
-     "start_time": "2023-11-09T18:21:57.563857095Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import contextlib\n",
@@ -344,12 +269,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.358277200Z",
-     "start_time": "2023-11-09T18:21:57.569931680Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "testdict = {'value': 11, 'name': 'the answer'}"
@@ -371,12 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.359563889Z",
-     "start_time": "2023-11-09T18:21:57.611213702Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# SOLUTION\n",
@@ -393,12 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.359882492Z",
-     "start_time": "2023-11-09T18:21:57.611718752Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@contextlib.contextmanager\n",
@@ -421,12 +331,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.394698482Z",
-     "start_time": "2023-11-09T18:21:57.611948597Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class MyContext:\n",
@@ -447,12 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.397073702Z",
-     "start_time": "2023-11-09T18:21:57.612144179Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with MyContext(5) as x:\n",
@@ -478,12 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.397374445Z",
-     "start_time": "2023-11-09T18:21:57.659281779Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_power_func(power):\n",
@@ -495,12 +390,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.397643363Z",
-     "start_time": "2023-11-09T18:21:57.659526538Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pow3 = make_power_func(3)"
@@ -509,12 +399,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.398133567Z",
-     "start_time": "2023-11-09T18:21:57.659712907Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pow3(2)"
@@ -523,12 +408,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.398357818Z",
-     "start_time": "2023-11-09T18:21:57.659911070Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def make_power_func(power):\n",
@@ -541,12 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.404364977Z",
-     "start_time": "2023-11-09T18:21:57.660055943Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pow3 = make_power_func(3)"
@@ -555,12 +430,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.406575801Z",
-     "start_time": "2023-11-09T18:21:57.707103066Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pow3(2)"
@@ -569,12 +439,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.419570582Z",
-     "start_time": "2023-11-09T18:21:57.707383203Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Exercise: test it here"
@@ -610,12 +475,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.421942708Z",
-     "start_time": "2023-11-09T18:21:57.714163427Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# SOLUTION\n",
@@ -633,12 +493,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.422417608Z",
-     "start_time": "2023-11-09T18:21:57.755158988Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def add_notime(x, y):\n",
@@ -648,12 +503,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.422731709Z",
-     "start_time": "2023-11-09T18:21:57.755389720Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "add_timed = timed_func(add_notime)"
@@ -662,12 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.422948361Z",
-     "start_time": "2023-11-09T18:21:57.755554232Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import time"
@@ -676,12 +521,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.425073110Z",
-     "start_time": "2023-11-09T18:21:57.755705542Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "add_timed(y=4, x=5)"
@@ -690,12 +530,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.425479931Z",
-     "start_time": "2023-11-09T18:21:57.756004166Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# test it here"
@@ -713,12 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:58.425804868Z",
-     "start_time": "2023-11-09T18:21:57.756157832Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "@timed_func\n",
@@ -757,11 +587,7 @@
    "metadata": {
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:21:59.048528979Z",
-     "start_time": "2023-11-09T18:21:57.803005602Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -781,10 +607,7 @@
    "metadata": {
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.751370683Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -809,11 +632,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795021637Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class MyError(Exception):\n",
@@ -826,10 +645,7 @@
    "metadata": {
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795152334Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -850,11 +666,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795308728Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class NegativeValueError(ValueError):\n",
@@ -881,11 +693,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795410621Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "try:\n",
@@ -919,11 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795520523Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "try:\n",
@@ -939,10 +743,7 @@
    "metadata": {
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795603420Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -972,9 +773,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795680122Z"
     }
    },
    "outputs": [],
@@ -1010,10 +808,7 @@
     },
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.795985110Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -1048,10 +843,7 @@
     },
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.796186073Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -1076,9 +868,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.796374250Z"
     }
    },
    "outputs": [],
@@ -1104,9 +893,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.796560001Z"
     }
    },
    "outputs": [],
@@ -1134,9 +920,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.796721888Z"
     }
    },
    "outputs": [],
@@ -1156,10 +939,7 @@
     },
     "tags": [
      "raises-exception"
-    ],
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.796792565Z"
-    }
+    ]
    },
    "outputs": [],
    "source": [
@@ -1172,9 +952,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "start_time": "2023-11-09T18:21:58.796936035Z"
     }
    },
    "outputs": [],
diff --git a/advanced-python/12AdvancedClasses.ipynb b/advanced-python/12AdvancedClasses.ipynb
index cd96c254..41185a08 100644
--- a/advanced-python/12AdvancedClasses.ipynb
+++ b/advanced-python/12AdvancedClasses.ipynb
@@ -20,12 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.090086512Z",
-     "start_time": "2023-11-09T18:22:01.447375824Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class NamedValue:\n",
@@ -59,12 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.096491782Z",
-     "start_time": "2023-11-09T18:22:01.447622989Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "valleft = ValueLeft('val left')\n",
@@ -74,12 +64,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.097446715Z",
-     "start_time": "2023-11-09T18:22:01.447833842Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "valleft + valleft2"
@@ -108,12 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.097894774Z",
-     "start_time": "2023-11-09T18:22:01.448096154Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Name:\n",
@@ -154,12 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.098105946Z",
-     "start_time": "2023-11-09T18:22:01.491308102Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Callable:\n",
@@ -174,12 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.098275992Z",
-     "start_time": "2023-11-09T18:22:01.491619526Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "call = Callable()\n",
@@ -189,12 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.098786414Z",
-     "start_time": "2023-11-09T18:22:01.491851791Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "call()"
@@ -203,12 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.099302008Z",
-     "start_time": "2023-11-09T18:22:01.492017010Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "try:\n",
@@ -236,12 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.099478261Z",
-     "start_time": "2023-11-09T18:22:01.498433654Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class Storage:\n",
@@ -261,12 +216,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.099727280Z",
-     "start_time": "2023-11-09T18:22:01.503944286Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "storage = Storage('one')"
@@ -275,12 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.100369605Z",
-     "start_time": "2023-11-09T18:22:01.513503176Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "storage[2]"
@@ -289,12 +234,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.100965196Z",
-     "start_time": "2023-11-09T18:22:01.555278832Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "storage[2] = 3"
@@ -318,12 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.101169939Z",
-     "start_time": "2023-11-09T18:22:01.555519700Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class A:\n",
@@ -337,12 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.101370435Z",
-     "start_time": "2023-11-09T18:22:01.555728122Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = A(4)\n",
@@ -352,12 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.101792662Z",
-     "start_time": "2023-11-09T18:22:01.599103966Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a.add(b)"
@@ -366,12 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.104487019Z",
-     "start_time": "2023-11-09T18:22:01.599306619Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "A.add(a, b)"
@@ -418,12 +338,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.104939450Z",
-     "start_time": "2023-11-09T18:22:01.599476172Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a.__dict__"
@@ -439,12 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.106823366Z",
-     "start_time": "2023-11-09T18:22:01.599607114Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a.__class__.__dict__"
@@ -453,12 +363,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.107525732Z",
-     "start_time": "2023-11-09T18:22:01.643176331Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "A.__dict__"
@@ -481,12 +386,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.107832669Z",
-     "start_time": "2023-11-09T18:22:01.643423445Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class GetAndSet:\n",
@@ -509,12 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.108062078Z",
-     "start_time": "2023-11-09T18:22:01.643613141Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "get = GetAndSet()"
@@ -523,12 +418,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.108503703Z",
-     "start_time": "2023-11-09T18:22:01.643815676Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "get.add(get)"
@@ -537,12 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.108799427Z",
-     "start_time": "2023-11-09T18:22:01.644071731Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "get.addition(get)"
@@ -551,12 +436,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.109426819Z",
-     "start_time": "2023-11-09T18:22:01.655190658Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "get.hello"
@@ -572,12 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.110263772Z",
-     "start_time": "2023-11-09T18:22:01.662211437Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "getattr(get, 'hello')"
@@ -586,12 +461,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.110508234Z",
-     "start_time": "2023-11-09T18:22:01.679854902Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "get.hi"
@@ -614,12 +484,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:02.111369769Z",
-     "start_time": "2023-11-09T18:22:01.685831397Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import this"
diff --git a/advanced-python/20DataAndPlotting.ipynb b/advanced-python/20DataAndPlotting.ipynb
index f2d611df..df8e83ee 100644
--- a/advanced-python/20DataAndPlotting.ipynb
+++ b/advanced-python/20DataAndPlotting.ipynb
@@ -32,12 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:11.209901401Z",
-     "start_time": "2023-11-09T18:22:10.715117611Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import mplhep\n",
@@ -115,12 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:11.213168925Z",
-     "start_time": "2023-11-09T18:22:10.715393509Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "my_file = uproot.open('https://cern.ch/starterkit/data/advanced-python-2018/real_data.root',\n",
@@ -139,12 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:11.577169Z",
-     "start_time": "2023-11-09T18:22:10.996475027Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "tree = my_file['DecayTree']\n",
@@ -155,12 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:24.452463022Z",
-     "start_time": "2023-11-09T18:22:11.577968658Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Load data as a pandas DataFrame\n",
@@ -174,12 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:24.470597938Z",
-     "start_time": "2023-11-09T18:22:24.447634455Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_df.columns"
@@ -196,12 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:24.855253849Z",
-     "start_time": "2023-11-09T18:22:24.453234659Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Start with a basic histogram\n",
@@ -234,10 +204,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:24.855582435Z",
-     "start_time": "2023-11-09T18:22:24.819219840Z"
     }
    },
    "outputs": [],
@@ -253,10 +219,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:25.965614384Z",
-     "start_time": "2023-11-09T18:22:24.819392199Z"
     }
    },
    "outputs": [],
@@ -272,10 +234,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:26.500198095Z",
-     "start_time": "2023-11-09T18:22:25.966183784Z"
     }
    },
    "outputs": [],
@@ -291,10 +249,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:27.468758686Z",
-     "start_time": "2023-11-09T18:22:26.499064264Z"
     }
    },
    "outputs": [],
@@ -314,12 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:29.632389925Z",
-     "start_time": "2023-11-09T18:22:27.444982487Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_mass(df):\n",
@@ -343,12 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:29.667859406Z",
-     "start_time": "2023-11-09T18:22:29.626043515Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# When making the ROOT file we forgot to add some variables, no bother lets add them now!\n",
@@ -366,12 +310,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:29.978476627Z",
-     "start_time": "2023-11-09T18:22:29.648794140Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_df.eval('mup_P = sqrt(mup_PX**2 + mup_PY**2 + mup_PZ**2)', inplace=True)\n",
@@ -399,12 +338,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:30.368613646Z",
-     "start_time": "2023-11-09T18:22:29.678392856Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_mass(data_df)\n",
@@ -415,12 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:31.531723191Z",
-     "start_time": "2023-11-09T18:22:30.368802764Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_mass(data_df)\n",
@@ -442,12 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:32.129701102Z",
-     "start_time": "2023-11-09T18:22:31.166183480Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_mass(df, **kwargs):\n",
@@ -476,12 +400,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:32.156206263Z",
-     "start_time": "2023-11-09T18:22:32.129165982Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_df.columns"
@@ -490,12 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:22:33.885426832Z",
-     "start_time": "2023-11-09T18:22:32.134501210Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from python_lesson import check_truth\n",
@@ -526,12 +440,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:21.141300103Z",
-     "start_time": "2023-11-09T18:22:33.874056558Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with uproot.open('https://starterkit.web.cern.ch/starterkit/data/advanced-python-2018/simulated_data.root') as mc_file:\n",
@@ -558,12 +467,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:21.631231066Z",
-     "start_time": "2023-11-09T18:23:21.182938710Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bkg_df = data_df.query('~(3.0 < Jpsi_M < 3.2)')\n",
@@ -589,12 +493,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:22.134866766Z",
-     "start_time": "2023-11-09T18:23:21.630331279Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "var = 'Jpsi_PT'\n",
@@ -612,12 +511,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:22.642315719Z",
-     "start_time": "2023-11-09T18:23:22.130434993Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Those are hard to compare!\n",
@@ -640,12 +534,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:22.648142526Z",
-     "start_time": "2023-11-09T18:23:22.640877567Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_comparision(var, mc_df, bkg_df):\n",
@@ -663,12 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:22.657440397Z",
-     "start_time": "2023-11-09T18:23:22.644203876Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -682,12 +566,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:39.984761182Z",
-     "start_time": "2023-11-09T18:23:22.649403635Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for var in data_df.columns:\n",
@@ -717,12 +596,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:39.987782824Z",
-     "start_time": "2023-11-09T18:23:39.981983050Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -739,10 +613,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:40.835372688Z",
-     "start_time": "2023-11-09T18:23:39.986976807Z"
     }
    },
    "outputs": [],
@@ -758,10 +628,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:40.835551238Z",
-     "start_time": "2023-11-09T18:23:40.835014982Z"
     }
    },
    "outputs": [],
@@ -773,10 +639,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:23:40.835677817Z",
-     "start_time": "2023-11-09T18:23:40.835124429Z"
     }
    },
    "outputs": [],
diff --git a/advanced-python/30Classification.ipynb b/advanced-python/30Classification.ipynb
index 7679df59..9c07d9aa 100644
--- a/advanced-python/30Classification.ipynb
+++ b/advanced-python/30Classification.ipynb
@@ -19,12 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:09.454693438Z",
-     "start_time": "2023-11-09T18:25:07.925906320Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%store -r bkg_df\n",
@@ -35,12 +30,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:09.455020356Z",
-     "start_time": "2023-11-09T18:25:08.698996144Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import mplhep\n",
@@ -71,12 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:11.748577377Z",
-     "start_time": "2023-11-09T18:25:09.007997107Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.scatter(mc_df['mup_PT'], mc_df['mum_PT'], s=1, marker=',', label='Signal')\n",
@@ -110,12 +95,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:11.762307655Z",
-     "start_time": "2023-11-09T18:25:11.747609552Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "training_columns = [\n",
@@ -130,12 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:11.882528359Z",
-     "start_time": "2023-11-09T18:25:11.752531431Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We then define the classifier we want to use\n",
@@ -155,12 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:11.882858520Z",
-     "start_time": "2023-11-09T18:25:11.795008544Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bkg_df = bkg_df.copy()\n",
@@ -171,12 +141,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:12.365576579Z",
-     "start_time": "2023-11-09T18:25:11.795121407Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Now merge the data together\n",
@@ -188,12 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:13.766805931Z",
-     "start_time": "2023-11-09T18:25:12.375467218Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We can now fit the BDT\n",
@@ -210,12 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:13.798097938Z",
-     "start_time": "2023-11-09T18:25:13.764223021Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bdt.predict_proba(data_df[training_columns].head())"
@@ -233,12 +188,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:13.866038246Z",
-     "start_time": "2023-11-09T18:25:13.787561597Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We can now use slicing to select column 1 in the array from for all rows\n",
@@ -256,12 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:14.147837572Z",
-     "start_time": "2023-11-09T18:25:13.865725416Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc_df['BDT'] = bdt.predict_proba(mc_df[training_columns])[:,1]\n",
@@ -280,12 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:14.352377276Z",
-     "start_time": "2023-11-09T18:25:14.147685274Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for df in [mc_df, bkg_df, data_df, training_data]:\n",
@@ -304,12 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:14.364834431Z",
-     "start_time": "2023-11-09T18:25:14.351502685Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_comparision(var, mc_df, bkg_df):\n",
@@ -335,12 +270,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:14.602892746Z",
-     "start_time": "2023-11-09T18:25:14.359199993Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_comparision('BDT', mc_df, bkg_df)"
@@ -356,12 +286,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:16.410750494Z",
-     "start_time": "2023-11-09T18:25:14.601766515Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_mass(data_df, label='No cuts', density=1)\n",
@@ -388,12 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:17.390975490Z",
-     "start_time": "2023-11-09T18:25:16.409914497Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# That would be too nice to use in analysis\n",
@@ -417,12 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:17.576076450Z",
-     "start_time": "2023-11-09T18:25:17.380970864Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_comparision('BDT', mc_df, bkg_df)"
@@ -442,12 +357,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:17.674307301Z",
-     "start_time": "2023-11-09T18:25:17.573465928Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "y_score = bdt.predict_proba(training_data[training_columns])[:,1]\n",
@@ -469,12 +379,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:17.825716688Z",
-     "start_time": "2023-11-09T18:25:17.695016918Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([0, 1], [0, 1], color='grey', linestyle='--', label='Randomly guess')\n",
@@ -498,12 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:17.883519883Z",
-     "start_time": "2023-11-09T18:25:17.824214208Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "area = auc(fpr, tpr)"
@@ -512,12 +412,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:17.996072891Z",
-     "start_time": "2023-11-09T18:25:17.871252944Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot([0, 1], [0, 1], color='grey', linestyle='--')\n",
@@ -543,12 +438,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.048225663Z",
-     "start_time": "2023-11-09T18:25:17.994427329Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n_sig = 1200\n",
@@ -565,12 +455,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.048735130Z",
-     "start_time": "2023-11-09T18:25:18.035311051Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "S = n_sig*tpr\n",
@@ -588,12 +473,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.256753265Z",
-     "start_time": "2023-11-09T18:25:18.035655070Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot(thresholds, metric)\n",
@@ -612,12 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.257317434Z",
-     "start_time": "2023-11-09T18:25:18.243137106Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "optimal_index = np.argmax(metric)\n",
@@ -629,12 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.635953003Z",
-     "start_time": "2023-11-09T18:25:18.243349315Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_mass(data_df, label='No cuts', density=1)\n",
@@ -648,12 +518,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.742133399Z",
-     "start_time": "2023-11-09T18:25:18.551392406Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_mass(data_with_cuts_df, label='Using BDT only')"
@@ -671,12 +536,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.813621527Z",
-     "start_time": "2023-11-09T18:25:18.743820685Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_roc(bdt, training_data, training_columns, label=None):\n",
@@ -701,12 +561,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:18.813858248Z",
-     "start_time": "2023-11-09T18:25:18.791163354Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_significance(bdt, training_data, training_columns, label=None):\n",
@@ -731,12 +586,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:30:20.710903847Z",
-     "start_time": "2023-11-09T18:25:18.791369479Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with uproot.open('https://cern.ch/starterkit/data/advanced-python-2018/real_data.root') as datafile:\n",
@@ -783,12 +633,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:30:21.685114217Z",
-     "start_time": "2023-11-09T18:30:20.708314393Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%store bkg_df\n",
@@ -799,12 +644,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:30:21.727449098Z",
-     "start_time": "2023-11-09T18:30:21.679809042Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/advanced-python/31ClassificationExtension.ipynb b/advanced-python/31ClassificationExtension.ipynb
index 2b9421ea..3213818e 100644
--- a/advanced-python/31ClassificationExtension.ipynb
+++ b/advanced-python/31ClassificationExtension.ipynb
@@ -25,12 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:00.013914071Z",
-     "start_time": "2023-11-09T18:39:58.305187065Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%store -r training_data\n",
@@ -43,12 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:00.401140920Z",
-     "start_time": "2023-11-09T18:40:00.001945534Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import mplhep\n",
@@ -62,12 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:00.424165539Z",
-     "start_time": "2023-11-09T18:40:00.375142416Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_comparision(var, mc_df, bkg_df):\n",
@@ -123,12 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:22.730604251Z",
-     "start_time": "2023-11-09T18:40:00.386255547Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Train the Gradient Booster\n",
@@ -141,12 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:32.407167754Z",
-     "start_time": "2023-11-09T18:40:22.738816458Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Train the Adaptive Booster\n",
@@ -159,12 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:33.896052239Z",
-     "start_time": "2023-11-09T18:40:32.406995877Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Train XGBoost Classifier\n",
@@ -177,12 +147,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:37.959441690Z",
-     "start_time": "2023-11-09T18:40:33.890325623Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure()\n",
@@ -231,12 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:38.867894240Z",
-     "start_time": "2023-11-09T18:40:37.963705203Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "for df in [mc_df, bkg_df, data_df, training_data]:\n",
@@ -268,12 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.395771938Z",
-     "start_time": "2023-11-09T18:40:38.878807747Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# bdtclass = GradientBoostingClassifier()\n",
@@ -375,12 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.395898908Z",
-     "start_time": "2023-11-09T18:40:44.896492177Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/advanced-python/32BoostingToUniformity.ipynb b/advanced-python/32BoostingToUniformity.ipynb
index e1b81ff5..ae62b845 100644
--- a/advanced-python/32BoostingToUniformity.ipynb
+++ b/advanced-python/32BoostingToUniformity.ipynb
@@ -25,12 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:39.380065511Z",
-     "start_time": "2023-11-09T18:25:38.723060607Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import uproot\n",
@@ -53,12 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:26:25.983039940Z",
-     "start_time": "2023-11-09T18:25:39.055897465Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "used_columns = [\"Y1\", \"Y2\", \"Y3\", \"M2AB\", \"M2AC\"]\n",
@@ -80,12 +70,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:26:26.722476388Z",
-     "start_time": "2023-11-09T18:26:26.022942250Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_distribution(data_frame, var_name1='M2AB', var_name2='M2AC', bins=40):\n",
@@ -111,12 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:26:26.738984299Z",
-     "start_time": "2023-11-09T18:26:26.722774930Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "trainX, testX, trainY, testY = train_test_split(data, labels, random_state=42, test_size=0.5)"
@@ -132,12 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:26:26.847926700Z",
-     "start_time": "2023-11-09T18:26:26.737604646Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "uniform_features  = [\"M2AB\", \"M2AC\"]\n",
@@ -156,12 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:30:50.832483520Z",
-     "start_time": "2023-11-09T18:26:26.744692640Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "classifiers = {}\n",
@@ -176,7 +146,7 @@
     "classifiers['uBoost'] = uboost.uBoostClassifier(uniform_features=uniform_features, uniform_label=1,\n",
     "                                     base_estimator=base_estimator,\n",
     "                                     n_estimators=n_estimators, train_features=train_features,\n",
-    "                                     efficiency_steps=12, n_threads=4)\n",
+    "                                     efficiency_steps=12, n_jobs=4)\n",
     "\n",
     "flatnessloss = ugb.KnnFlatnessLossFunction(uniform_features, fl_coefficient=3., power=1.3, uniform_label=1)\n",
     "classifiers['uGB+FL'] = ugb.UGradientBoostingClassifier(loss=flatnessloss, max_depth=4,\n",
@@ -202,12 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:30:57.270917786Z",
-     "start_time": "2023-11-09T18:30:50.874988212Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import roc_auc_score\n",
@@ -230,12 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:31:04.075394547Z",
-     "start_time": "2023-11-09T18:30:57.271060612Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.metrics import roc_curve\n",
@@ -271,12 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:31:04.079427234Z",
-     "start_time": "2023-11-09T18:31:04.071407036Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/advanced-python/33ModelTuning.ipynb b/advanced-python/33ModelTuning.ipynb
index 0ea4d465..be6ea321 100644
--- a/advanced-python/33ModelTuning.ipynb
+++ b/advanced-python/33ModelTuning.ipynb
@@ -190,7 +190,7 @@
     "LR = 0.3 # the coefficient of step size decay, eta, has alias 'learning_rate' with default 0.3\n",
     "\n",
     "stime = time.time()\n",
-    "bdt = XGBClassifier(learning_rate = LR, n_estimators=100, seed=123, n_threads=-1)\n",
+    "bdt = XGBClassifier(learning_rate = LR, n_estimators=100, seed=123, n_jobs=-1)\n",
     "bdt.fit(training_data[training_columns], training_data['catagory'])\n",
     "print(\"XGBoost --- %s seconds ---\" % (time.time() - stime))\n",
     "\n",
@@ -405,7 +405,7 @@
     "                       #colsample_bytree=0.8, scale_pos_weight=1,\n",
     "                       #objective='binary:logistic', # default for binary classification\n",
     "                       #objective='mutli:softprob', num_class=3, # for multiclassifiers\n",
-    "                       seed=123, n_threads=-1)\n",
+    "                       seed=123, n_jobs=-1)\n",
     "\n",
     "# Timing the CV using early stopping\n",
     "stime = time.time()\n",
@@ -512,7 +512,7 @@
    "source": [
     "# Running with estimators maximum for shortened training\n",
     "bdt_st = XGBClassifier( learning_rate = LR, n_estimators=estimators,\n",
-    "                        seed=123,           n_threads=-1)\n",
+    "                        seed=123,           n_jobs=-1)\n",
     "\n",
     "# Running timed hyperparameter gridscan\n",
     "stime = time.time()\n",
@@ -542,7 +542,7 @@
     "#bdt_opt = XGBClassifier( learning_rate = LR, n_estimators=1000,\n",
     "#                         max_depth=gs.best_params_['max_depth'],\n",
     "#                         min_child_weight=gs.best_params_['min_child_weight'],\n",
-    "#                         seed=123, n_threads=-1 )\n",
+    "#                         seed=123, n_jobs=-1 )\n",
     "\n",
     "## Run with CV early stopping\n",
     "#stime = time.time()\n",
diff --git a/advanced-python/40Histograms.ipynb b/advanced-python/40Histograms.ipynb
index 808cd467..2827a6a9 100644
--- a/advanced-python/40Histograms.ipynb
+++ b/advanced-python/40Histograms.ipynb
@@ -3,7 +3,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true,
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -34,10 +33,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.090763040Z",
-     "start_time": "2023-11-09T18:40:41.774097506Z"
     }
    },
    "outputs": [],
@@ -59,10 +54,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.092581529Z",
-     "start_time": "2023-11-09T18:40:42.719110990Z"
     }
    },
    "outputs": [],
@@ -129,24 +120,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.092804229Z",
-     "start_time": "2023-11-09T18:40:42.910621980Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.092969634Z",
-     "start_time": "2023-11-09T18:40:42.916739161Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "start, stop = data_df['Jpsi_M'].min(), data_df['Jpsi_M'].max()\n",
@@ -166,10 +147,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.093124685Z",
-     "start_time": "2023-11-09T18:40:42.927046922Z"
     }
    },
    "outputs": [],
@@ -180,12 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.094256975Z",
-     "start_time": "2023-11-09T18:40:42.937126378Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h.fill(data_df['Jpsi_M'])"
@@ -194,12 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.094471841Z",
-     "start_time": "2023-11-09T18:40:42.949363041Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc_h = hist.Hist(axis1).fill(mc_df['Jpsi_M'])  # we can also chain the commands"
@@ -219,12 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.095025556Z",
-     "start_time": "2023-11-09T18:40:42.955171365Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mplhep.histplot(data_h)"
@@ -243,12 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.095656170Z",
-     "start_time": "2023-11-09T18:40:43.211187900Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h.plot1d()"
@@ -257,12 +214,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.096292049Z",
-     "start_time": "2023-11-09T18:40:43.415174250Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h.plot1d()\n",
@@ -272,12 +224,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.096697901Z",
-     "start_time": "2023-11-09T18:40:43.622364563Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc_df.columns"
@@ -295,12 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.097017931Z",
-     "start_time": "2023-11-09T18:40:43.629433139Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "start, stop = data_df['BDT'].min(), data_df['BDT'].max()\n",
@@ -310,12 +252,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.097244707Z",
-     "start_time": "2023-11-09T18:40:43.671235206Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc_h2d = hist.Hist(axis1, axis_bdt).fill(BDT=mc_df['BDT'], mass=mc_df['Jpsi_M']) # using names"
@@ -324,12 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.136940250Z",
-     "start_time": "2023-11-09T18:40:43.671469880Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d = hist.Hist(axis1, axis_bdt)\n",
@@ -339,12 +271,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.138342837Z",
-     "start_time": "2023-11-09T18:40:43.712937233Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mplhep.hist2dplot(data_h2d)"
@@ -362,12 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.138747800Z",
-     "start_time": "2023-11-09T18:40:44.022960001Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Access by bin number\n",
@@ -388,12 +310,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.140841528Z",
-     "start_time": "2023-11-09T18:40:44.029760988Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.density()"
@@ -411,12 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.142148636Z",
-     "start_time": "2023-11-09T18:40:44.062252938Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.project(\"mass\")  # we will here retain the 1D histogram"
@@ -434,12 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.142629942Z",
-     "start_time": "2023-11-09T18:40:44.071307572Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.axes"
@@ -448,12 +355,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.143016256Z",
-     "start_time": "2023-11-09T18:40:44.076331708Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.axes['mass']"
@@ -462,12 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.143633118Z",
-     "start_time": "2023-11-09T18:40:44.087011298Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.axes['mass'].edges"
@@ -476,12 +373,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.144130758Z",
-     "start_time": "2023-11-09T18:40:44.098073547Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.axes['mass'].centers  # bin centers"
@@ -490,12 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.144584441Z",
-     "start_time": "2023-11-09T18:40:44.102908180Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.axes['mass'].widths  # bin widths"
@@ -513,12 +400,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.145653144Z",
-     "start_time": "2023-11-09T18:40:44.111345363Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_h2d.axes.edges\n",
@@ -540,12 +422,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.145851826Z",
-     "start_time": "2023-11-09T18:40:44.159338713Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -563,12 +440,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.172428378Z",
-     "start_time": "2023-11-09T18:40:44.159650878Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_df_bdt = data_df.query(\"BDT > 0.9\")\n",
@@ -580,12 +452,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.173168648Z",
-     "start_time": "2023-11-09T18:40:44.190637837Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ratio = data_bdt_h2d.project(\"mass\") / data_h2d.project(\"mass\")"
@@ -594,12 +461,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.200611311Z",
-     "start_time": "2023-11-09T18:40:44.231188307Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ratio.plot1d()"
@@ -608,12 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.213022780Z",
-     "start_time": "2023-11-09T18:40:44.419136722Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ratio_large = ratio * 10\n",
@@ -630,12 +487,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.213326313Z",
-     "start_time": "2023-11-09T18:40:44.667191807Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -653,12 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.213535481Z",
-     "start_time": "2023-11-09T18:40:44.667447153Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "weight = np.random.normal(1., 0.1, size=mc_df.shape[0])\n",
@@ -669,12 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.231990095Z",
-     "start_time": "2023-11-09T18:40:44.667709042Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc_h2d"
@@ -683,12 +525,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.395015033Z",
-     "start_time": "2023-11-09T18:40:44.698968002Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc_h2d.variances()"
@@ -704,12 +541,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:45.395215588Z",
-     "start_time": "2023-11-09T18:40:44.743359631Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/advanced-python/45DemoReweighting.ipynb b/advanced-python/45DemoReweighting.ipynb
index abcf3a2f..02dd9a47 100644
--- a/advanced-python/45DemoReweighting.ipynb
+++ b/advanced-python/45DemoReweighting.ipynb
@@ -25,12 +25,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:25:53.648661700Z",
-     "start_time": "2023-11-09T18:25:53.142978500Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -52,12 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:27:45.758389346Z",
-     "start_time": "2023-11-09T18:25:53.489875451Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "columns = ['hSPD', 'pt_b', 'pt_phi', 'vchi2_b', 'mu_pt_sum']\n",
@@ -90,12 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:27:45.959472949Z",
-     "start_time": "2023-11-09T18:27:45.799133696Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.model_selection import train_test_split\n",
@@ -112,12 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:27:45.975481005Z",
-     "start_time": "2023-11-09T18:27:45.965651595Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from hep_ml.metrics_utils import ks_2samp_weighted\n",
@@ -148,12 +128,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:27:45.990266338Z",
-     "start_time": "2023-11-09T18:27:45.971355495Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# pay attention, actually we have very few data\n",
@@ -163,12 +138,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:27:52.291018726Z",
-     "start_time": "2023-11-09T18:27:45.983111062Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "draw_distributions(original, target, original_weights)"
@@ -184,12 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:27:56.786090353Z",
-     "start_time": "2023-11-09T18:27:52.282166289Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "draw_distributions(original_train, target_train, original_weights_train)"
@@ -205,12 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:28:00.210019779Z",
-     "start_time": "2023-11-09T18:27:56.779869734Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "draw_distributions(original_test, target_test, original_weights_test)"
@@ -241,12 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:28:21.654914654Z",
-     "start_time": "2023-11-09T18:28:00.206358826Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bins_reweighter = reweight.BinsReweighter(n_bins=20, n_neighs=1.)\n",
@@ -275,12 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:22.286971428Z",
-     "start_time": "2023-11-09T18:28:21.649791476Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "reweighter = reweight.GBReweighter(n_estimators=250, learning_rate=0.1, max_depth=3, min_samples_leaf=1000,\n",
@@ -304,12 +254,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:22.445738489Z",
-     "start_time": "2023-11-09T18:33:22.284510361Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def check_ks_of_expression(expression):\n",
@@ -327,12 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:22.447484398Z",
-     "start_time": "2023-11-09T18:33:22.327051319Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "check_ks_of_expression('hSPD')"
@@ -341,12 +281,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:22.658006592Z",
-     "start_time": "2023-11-09T18:33:22.415016102Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "check_ks_of_expression('hSPD * pt_phi')"
@@ -355,12 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:22.903957662Z",
-     "start_time": "2023-11-09T18:33:22.656149470Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "check_ks_of_expression('hSPD * pt_phi * vchi2_b')"
@@ -369,12 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:23.155559842Z",
-     "start_time": "2023-11-09T18:33:22.899381940Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "check_ks_of_expression('pt_b * pt_phi / hSPD ')"
@@ -383,12 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:23.345847337Z",
-     "start_time": "2023-11-09T18:33:23.155057276Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "check_ks_of_expression('hSPD * pt_b * vchi2_b / pt_phi')"
@@ -410,12 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:42.202790691Z",
-     "start_time": "2023-11-09T18:33:23.345111007Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.ensemble import GradientBoostingClassifier\n",
@@ -463,12 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:43.726731060Z",
-     "start_time": "2023-11-09T18:33:42.207967134Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.hist(weights['gb_weights'], bins=50)\n",
@@ -479,12 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:33:43.730253817Z",
-     "start_time": "2023-11-09T18:33:43.723035309Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.max(weights['gb_weights']), np.sum(weights['gb_weights'])"
@@ -521,12 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:38:48.740721947Z",
-     "start_time": "2023-11-09T18:33:43.728815796Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# define base reweighter\n",
@@ -555,12 +455,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:00.647769707Z",
-     "start_time": "2023-11-09T18:38:48.738767636Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = np.concatenate([original, target])\n",
@@ -582,12 +477,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:40:01.473145845Z",
-     "start_time": "2023-11-09T18:40:00.647093226Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.hist(weights['2-folding'], bins=50)\n",
diff --git a/advanced-python/50LikelihoodInference.ipynb b/advanced-python/50LikelihoodInference.ipynb
index 1f70ce61..0ec7c601 100644
--- a/advanced-python/50LikelihoodInference.ipynb
+++ b/advanced-python/50LikelihoodInference.ipynb
@@ -41,12 +41,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.548448189Z",
-     "start_time": "2023-11-09T18:41:03.508890856Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%store -r bkg_df\n",
@@ -57,12 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.555465994Z",
-     "start_time": "2023-11-09T18:41:04.545771470Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import hepstats\n",
@@ -75,12 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.607549796Z",
-     "start_time": "2023-11-09T18:41:04.552563192Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# apply cuts\n",
@@ -103,12 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.643172087Z",
-     "start_time": "2023-11-09T18:41:04.628270711Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "obs = zfit.Space('Jpsi_M', limits=(2.8, 3.5))  # defining the observable"
@@ -117,12 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.643396920Z",
-     "start_time": "2023-11-09T18:41:04.636406001Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# bkg = zfit.Data.from_pandas(bkg_df['Jpsi_M'], obs=obs)\n",
@@ -134,12 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.866062850Z",
-     "start_time": "2023-11-09T18:41:04.643452582Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mc = zfit.Data.from_pandas(mc_df['Jpsi_M'], obs=obs)\n",
@@ -160,12 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.959244148Z",
-     "start_time": "2023-11-09T18:41:04.910984590Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "lambd = zfit.Parameter('lambda', -0.1, -2, 2)\n",
@@ -179,12 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.959518485Z",
-     "start_time": "2023-11-09T18:41:04.959061502Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "bkg_pdf = zfit.pdf.Exponential(lambd, obs=obs)\n",
@@ -194,12 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.959692598Z",
-     "start_time": "2023-11-09T18:41:04.959203182Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "sig_pdf = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma)\n",
@@ -209,12 +164,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.965883067Z",
-     "start_time": "2023-11-09T18:41:04.959353512Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = zfit.pdf.SumPDF([bkg_pdf, sig_pdf])"
@@ -232,12 +182,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:04.966167348Z",
-     "start_time": "2023-11-09T18:41:04.959441956Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_fit(model, data, nbins=30, ax=None):\n",
@@ -273,12 +218,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:07.019191120Z",
-     "start_time": "2023-11-09T18:41:04.959591758Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_fit(model, data)  # before the fit"
@@ -296,12 +236,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:07.303510860Z",
-     "start_time": "2023-11-09T18:41:07.018115082Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "sig_nll = zfit.loss.UnbinnedNLL(sig_pdf, mc)"
@@ -317,12 +252,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:07.315026018Z",
-     "start_time": "2023-11-09T18:41:07.282948708Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "minimizer = zfit.minimize.Minuit()\n",
@@ -333,12 +263,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:08.077766311Z",
-     "start_time": "2023-11-09T18:41:07.283066840Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "minimizer.minimize(sig_nll)"
@@ -356,12 +281,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:08.081842858Z",
-     "start_time": "2023-11-09T18:41:08.067454337Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "sigma.floating = False"
@@ -370,12 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:08.677325245Z",
-     "start_time": "2023-11-09T18:41:08.071212416Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "nll = zfit.loss.ExtendedUnbinnedNLL(model, data)"
@@ -384,12 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:09.481473179Z",
-     "start_time": "2023-11-09T18:41:08.679126453Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "result = minimizer.minimize(nll)"
@@ -398,12 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:09.501631838Z",
-     "start_time": "2023-11-09T18:41:09.483882912Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "result"
@@ -412,12 +317,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:10.998164125Z",
-     "start_time": "2023-11-09T18:41:09.499282781Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "result.hesse()  # calculate hessian error\n",
@@ -428,12 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:11.291271535Z",
-     "start_time": "2023-11-09T18:41:10.991252129Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_fit(model, data)"
@@ -449,12 +344,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:11.471911896Z",
-     "start_time": "2023-11-09T18:41:11.293166815Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from hepstats.hypotests.calculators import AsymptoticCalculator\n",
@@ -481,12 +371,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:13.160970054Z",
-     "start_time": "2023-11-09T18:41:11.474037243Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from hepstats.hypotests import Discovery\n",
diff --git a/advanced-python/60sPlot.ipynb b/advanced-python/60sPlot.ipynb
index 2b713810..cfac4125 100644
--- a/advanced-python/60sPlot.ipynb
+++ b/advanced-python/60sPlot.ipynb
@@ -15,12 +15,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:33.690188079Z",
-     "start_time": "2023-11-09T18:41:33.174962617Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import mplhep\n",
@@ -32,12 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:33.690535258Z",
-     "start_time": "2023-11-09T18:41:33.175105562Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "size = 10000\n",
@@ -60,12 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:35.114704154Z",
-     "start_time": "2023-11-09T18:41:33.175205312Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.hist(sig_data, color='b', alpha=0.5, bins=30, label='electron')\n",
@@ -92,12 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:35.169658981Z",
-     "start_time": "2023-11-09T18:41:35.114998403Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "n_sig1, n_bck1 = 8000, 2000\n",
@@ -109,12 +89,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:35.640708376Z",
-     "start_time": "2023-11-09T18:41:35.159083078Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(figsize=[15, 6])\n",
@@ -165,12 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:35.761702659Z",
-     "start_time": "2023-11-09T18:41:35.641363489Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_with_weights(datas, weights, **kargs):\n",
@@ -183,12 +153,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:35.974742303Z",
-     "start_time": "2023-11-09T18:41:35.682988823Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_with_weights([first_bin, second_bin], [n_bck2, -n_bck1], density=True, label='reconstructed electron')\n",
@@ -209,12 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:36.278752546Z",
-     "start_time": "2023-11-09T18:41:35.973225726Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plot_with_weights([first_bin, second_bin], [n_bck2, -n_bck1], density=True, label='reconstructed electons', edgecolor='none')\n",
@@ -234,12 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:36.370668105Z",
-     "start_time": "2023-11-09T18:41:36.281084677Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.bar([0, 2, 4], [3, 2, 1], width=1, color='b', alpha=0.5)\n",
@@ -282,12 +237,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:36.463010940Z",
-     "start_time": "2023-11-09T18:41:36.367032133Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "mu = zfit.Parameter('mu', 5279, 5100, 5400)\n",
@@ -299,12 +249,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:36.474956577Z",
-     "start_time": "2023-11-09T18:41:36.420048637Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "obs = zfit.Space('mass', (5000, 6000))\n",
@@ -327,12 +272,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:39.707390072Z",
-     "start_time": "2023-11-09T18:41:36.467019213Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Signal distributions.\n",
@@ -376,12 +316,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:41.699026635Z",
-     "start_time": "2023-11-09T18:41:39.708082190Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Builds the loss.\n",
@@ -400,12 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:41.701583440Z",
-     "start_time": "2023-11-09T18:41:41.690949558Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def plot_fit_projection(model, data, nbins=30, ax=None):\n",
@@ -440,12 +370,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:42.049462266Z",
-     "start_time": "2023-11-09T18:41:41.695055246Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Visualization of the result.\n",
@@ -478,13 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:42.306789925Z",
-     "start_time": "2023-11-09T18:41:42.047242018Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from hepstats.splot import compute_sweights\n",
@@ -497,12 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:42.308477707Z",
-     "start_time": "2023-11-09T18:41:42.255058309Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(\"Sum of signal sWeights: \", np.sum(weights[sig_yield]))\n",
@@ -519,12 +433,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:43.917695568Z",
-     "start_time": "2023-11-09T18:41:42.302977717Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n",
@@ -556,12 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:43.935640680Z",
-     "start_time": "2023-11-09T18:41:43.914957342Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(f\"Correlation between m and t: {np.corrcoef(np_m_sw, np_t_sw)[0, 1]}\")"
@@ -577,12 +481,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:44.217521866Z",
-     "start_time": "2023-11-09T18:41:43.919368369Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.hist(np_m_sw, bins=100, range=(5000, 6000), weights=weights[sig_yield]);"
@@ -604,12 +503,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:44.222398796Z",
-     "start_time": "2023-11-09T18:41:44.214787904Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from scipy.stats import expon, norm"
@@ -618,12 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:44.615692777Z",
-     "start_time": "2023-11-09T18:41:44.221274727Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(figsize=[15, 6])\n",
@@ -662,12 +551,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:44.950482870Z",
-     "start_time": "2023-11-09T18:41:44.614171146Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(figsize=[15, 6])\n",
@@ -701,12 +585,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:45.182653107Z",
-     "start_time": "2023-11-09T18:41:44.945907133Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = np.linspace(0, 10)\n",
@@ -731,12 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:45.200613740Z",
-     "start_time": "2023-11-09T18:41:45.185498912Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas\n",
@@ -748,12 +622,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:45.535033034Z",
-     "start_time": "2023-11-09T18:41:45.195431060Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot(mass, probs.sig, label='sig probability')\n",
@@ -773,12 +642,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:45.636452372Z",
-     "start_time": "2023-11-09T18:41:45.536975210Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from hep_ml import splot\n",
@@ -796,12 +660,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:46.023070838Z",
-     "start_time": "2023-11-09T18:41:45.649725765Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.plot(mass, sWeights.sig, label='sig sWeight')\n",
@@ -821,12 +680,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:46.636288872Z",
-     "start_time": "2023-11-09T18:41:46.023629125Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "plt.figure(figsize=[15, 7])\n",
@@ -857,12 +711,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:46.670488310Z",
-     "start_time": "2023-11-09T18:41:46.636425810Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "np.corrcoef(abs(mass - 4), p) [0, 1]"
@@ -878,12 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:46.808545533Z",
-     "start_time": "2023-11-09T18:41:46.644686446Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print(np.corrcoef(abs(sig_mass - 4), sig_p)[0, 1])\n",
@@ -900,12 +744,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:47.029394075Z",
-     "start_time": "2023-11-09T18:41:46.669469133Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "hist_conf = dict(bins=30, alpha=0.5, range=[-1, 7])\n",
diff --git a/advanced-python/70ScikitHEPUniverse.ipynb b/advanced-python/70ScikitHEPUniverse.ipynb
index d311cbf3..98710b73 100644
--- a/advanced-python/70ScikitHEPUniverse.ipynb
+++ b/advanced-python/70ScikitHEPUniverse.ipynb
@@ -28,12 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.509856814Z",
-     "start_time": "2023-11-09T18:41:36.790957906Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import formulate\n",
@@ -45,12 +40,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.519217132Z",
-     "start_time": "2023-11-09T18:41:37.505747578Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "momentum.to_numexpr()  # as used in Pandas eval/query"
@@ -59,12 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.569995559Z",
-     "start_time": "2023-11-09T18:41:37.510628475Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "momentum.to_root()  # as used in ROOT"
@@ -82,12 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.733545504Z",
-     "start_time": "2023-11-09T18:41:37.522736067Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Particle\n",
@@ -107,12 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.779734080Z",
-     "start_time": "2023-11-09T18:41:37.779040900Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "piplus.mass"
@@ -121,12 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.780222164Z",
-     "start_time": "2023-11-09T18:41:37.779268397Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "piplus.charge"
@@ -135,12 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.785865315Z",
-     "start_time": "2023-11-09T18:41:37.779388976Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "piplus.width"
@@ -149,12 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.786318570Z",
-     "start_time": "2023-11-09T18:41:37.779605715Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "piplus.name"
@@ -171,12 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.903977630Z",
-     "start_time": "2023-11-09T18:41:37.779707095Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Particle.findall(lambda p: p.pdgid.has_bottom and p.charge==0)"
@@ -197,12 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.905992034Z",
-     "start_time": "2023-11-09T18:41:37.835409659Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from hepunits.constants import c_light\n",
@@ -213,12 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.907634350Z",
-     "start_time": "2023-11-09T18:41:37.841358263Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import hepunits as u  # u for units"
@@ -227,12 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:37.910230835Z",
-     "start_time": "2023-11-09T18:41:37.883077241Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "150 * u.MeV + 1.1 * u.GeV  # result in MeV"
@@ -255,12 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:38.058044969Z",
-     "start_time": "2023-11-09T18:41:37.883233940Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import vector"
@@ -272,10 +207,6 @@
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
-    },
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:38.087743681Z",
-     "start_time": "2023-11-09T18:41:38.000640796Z"
     }
    },
    "outputs": [],
@@ -316,12 +247,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:38.088074302Z",
-     "start_time": "2023-11-09T18:41:38.047094192Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "vector.obj(x=1, y=2, z=3).theta\n",
@@ -331,12 +257,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:38.088361257Z",
-     "start_time": "2023-11-09T18:41:38.047282206Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "vector.obj(x=3, y=4, z=-2, t=10)   # Cartesian 4D vector"
@@ -345,12 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T18:41:38.088472813Z",
-     "start_time": "2023-11-09T18:41:38.047400199Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/environment.yml b/environment.yml
index 0e4a96ef..a10aefbc 100644
--- a/environment.yml
+++ b/environment.yml
@@ -19,7 +19,7 @@ dependencies:
   - pip
   - scikit-learn
   - scipy
-  - uproot
+  - uproot <5.0.0  # 5.0.0 breaks the httpsource argument with open, TODO upgrade (what's the equivalent?) in the "get_truth" function
   - uproot3
   - vector
   - wget