From 861be23e583792b6d753cd80fe6c6cdf77165b28 Mon Sep 17 00:00:00 2001
From: ThijsLyon <tvaneeden@km3net.de>
Date: Thu, 27 Jan 2022 12:56:44 +0100
Subject: [PATCH] make plot for maarten
---
.ipynb_checkpoints/pmt_gain-checkpoint.ipynb | 48 ++++++++++++++++++--
pmt_gain.ipynb | 48 ++++++++++++++++++--
2 files changed, 86 insertions(+), 10 deletions(-)
diff --git a/.ipynb_checkpoints/pmt_gain-checkpoint.ipynb b/.ipynb_checkpoints/pmt_gain-checkpoint.ipynb
index c786539..1674cc1 100644
--- a/.ipynb_checkpoints/pmt_gain-checkpoint.ipynb
+++ b/.ipynb_checkpoints/pmt_gain-checkpoint.ipynb
@@ -196,7 +196,7 @@
},
{
"cell_type": "code",
- "execution_count": 201,
+ "execution_count": 215,
"id": "4eaed37c-3cae-44ef-88db-fac0ce64acbf",
"metadata": {},
"outputs": [
@@ -206,7 +206,7 @@
"Text(0.5, 1.0, 'Location: Amsterdam')"
]
},
- "execution_count": 201,
+ "execution_count": 215,
"metadata": {},
"output_type": "execute_result"
},
@@ -260,11 +260,49 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 273,
"id": "ca1f27e5-fecb-42d5-8e17-dd58da923f08",
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Mean: 1.299, std_dev: 0.247')"
+ ]
+ },
+ "execution_count": 273,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "###\n",
+ "### Maarten asked in the group meeting (27-01-2022) what the distribution of all PMTs is\n",
+ "###\n",
+ "bins = np.arange( -0.1, 2.1, 0.1 )\n",
+ "\n",
+ "df_gains = df_pmt[gain_keys].stack().reset_index()\n",
+ "df_gains = df_gains[ df_gains[0] < 2.5 ]\n",
+ "df_gains.hist(bins=bins)\n",
+ "\n",
+ "plt.plot([0.3,0.3], [0,1500], color='red')\n",
+ "plt.plot([1.7,1.7], [0,1500], color='red')\n",
+ "plt.xlabel(\"Fitted gain in acceptance test\")\n",
+ "plt.ylabel(\"Counts\")\n",
+ "plt.title(\"Mean: {}, std_dev: {}\".format( round(df_gains[0].mean(),3), round(df_gains[0].std(),3) ) )\n"
+ ]
}
],
"metadata": {
diff --git a/pmt_gain.ipynb b/pmt_gain.ipynb
index c786539..1674cc1 100644
--- a/pmt_gain.ipynb
+++ b/pmt_gain.ipynb
@@ -196,7 +196,7 @@
},
{
"cell_type": "code",
- "execution_count": 201,
+ "execution_count": 215,
"id": "4eaed37c-3cae-44ef-88db-fac0ce64acbf",
"metadata": {},
"outputs": [
@@ -206,7 +206,7 @@
"Text(0.5, 1.0, 'Location: Amsterdam')"
]
},
- "execution_count": 201,
+ "execution_count": 215,
"metadata": {},
"output_type": "execute_result"
},
@@ -260,11 +260,49 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 273,
"id": "ca1f27e5-fecb-42d5-8e17-dd58da923f08",
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Mean: 1.299, std_dev: 0.247')"
+ ]
+ },
+ "execution_count": 273,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "###\n",
+ "### Maarten asked in the group meeting (27-01-2022) what the distribution of all PMTs is\n",
+ "###\n",
+ "bins = np.arange( -0.1, 2.1, 0.1 )\n",
+ "\n",
+ "df_gains = df_pmt[gain_keys].stack().reset_index()\n",
+ "df_gains = df_gains[ df_gains[0] < 2.5 ]\n",
+ "df_gains.hist(bins=bins)\n",
+ "\n",
+ "plt.plot([0.3,0.3], [0,1500], color='red')\n",
+ "plt.plot([1.7,1.7], [0,1500], color='red')\n",
+ "plt.xlabel(\"Fitted gain in acceptance test\")\n",
+ "plt.ylabel(\"Counts\")\n",
+ "plt.title(\"Mean: {}, std_dev: {}\".format( round(df_gains[0].mean(),3), round(df_gains[0].std(),3) ) )\n"
+ ]
}
],
"metadata": {
--
GitLab