2023-09-14 12:07:57 +02:00
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{
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"cells": [
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{
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"cell_type": "code",
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2023-09-14 16:33:27 +02:00
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"execution_count": 7,
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2023-09-14 12:07:57 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\n",
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"import numpy as np\n",
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"import sys\n",
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"import os\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\n",
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"import itertools\n",
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"import awkward as ak\n",
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2023-09-14 16:33:27 +02:00
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"import seaborn as sns\n",
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2023-09-14 12:07:57 +02:00
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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2023-09-14 16:33:27 +02:00
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"execution_count": 31,
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2023-09-14 12:07:57 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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2023-09-14 16:33:27 +02:00
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"file = uproot.open(\"tracking_losses_ntuple_Bd2KstEE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
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"#file = uproot.open(\"tracking_losses_ntuple_Dst0ToD0EE.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\")\n",
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2023-09-14 12:07:57 +02:00
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"\n",
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2023-09-14 16:33:27 +02:00
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"#look at particles only from Signal\n",
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2023-09-14 12:07:57 +02:00
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"allcolumns = file.arrays()\n",
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2023-09-14 16:33:27 +02:00
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"tracked = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromSignal)]\n",
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"lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromSignal)] \n",
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"\n",
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"#ak.num(tracked, axis=0)\n",
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"\n"
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2023-09-14 12:07:57 +02:00
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]
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},
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{
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"cell_type": "code",
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2023-09-14 16:33:27 +02:00
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"execution_count": 143,
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2023-09-14 12:07:57 +02:00
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"metadata": {},
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2023-09-14 16:33:27 +02:00
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre>[[-676, -685, -694, -703, -764, -773, ..., -792, -854, -864, -864, -873, -882],\n",
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" [-1.4e+03, -1.45e+03, -1.5e+03, -1.54e+03, ..., -2.4e+03, -2.45e+03, -2.5e+03],\n",
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" [-148, -148, -149, -150, -156, -156, ..., -157, -158, -164, -164, -165, -166],\n",
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" [21.7, 23, 24.2, 25.4, 34, 35.3, 36.6, 37.9, 46.9, 48.2, 49.6, 50.9],\n",
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" [249, 250, 250, 250, 252, 252, 252, 252, 253, 253, 253, 254],\n",
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" [-134, -139, -145, -151, -191, -197, -203, -209, -251, -258, -264, -270],\n",
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" [213, 217, 220, 223, 244, 247, 250, 253, 275, 278, 282, 285],\n",
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" [1.17e+03, 1.19e+03, 1.21e+03, 1.23e+03, ..., 1.53e+03, 1.55e+03, 1.57e+03],\n",
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" [-135, -142, -149, -156, -207, -214, ..., -229, -229, -281, -289, -297, -304],\n",
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" [456, 460, 465, 469, 500, 504, 509, 514, 544, 549, 553, 558, 558],\n",
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" ...,\n",
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" [616, 629, 642, 655, 745, 758, 772, 785, 785, 878, 891, 905, 919],\n",
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" [-266, -267, -268, -269, -277, -277, ..., -279, -280, -287, -288, -289, -290],\n",
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" [-76.4, -73.2, -69.9, -66.6, -43.8, ..., -36.8, -33.3, -8.97, -5.34, -1.68],\n",
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" [-92, -90.5, -88.9, -87.3, -75.7, ..., -70.3, -57.5, -55.5, -53.6, -51.6],\n",
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" [-518, -527, -536, -545, -608, -617, -627, -636, -700, -710, -719, -729],\n",
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" [510, 517, 523, 576, 583, 583, 590, 597, 643, 650, 657],\n",
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" [1.2e+03, 1.21e+03, 1.21e+03, 1.22e+03, ..., 1.35e+03, 1.36e+03, 1.37e+03],\n",
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" [-1.33e+03, -1.33e+03, -1.34e+03, ..., -1.39e+03, -1.39e+03, -1.4e+03],\n",
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" [-1.27e+03, -1.29e+03, -1.32e+03, ..., -1.72e+03, -1.74e+03, -1.76e+03]]\n",
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"--------------------------------------------------------------------------------\n",
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"type: 9489 * var * float32</pre>"
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],
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"text/plain": [
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"<Array [[-676, -685, -694, ..., -873, -882], ...] type='9489 * var * float32'>"
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]
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},
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"execution_count": 143,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#lost\n",
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"l_eph = lost[\"brem_photons_pe\"]\n",
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"ak.nan_to_num(l_eph)\n",
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"l_pT = lost[\"pt\"]\n",
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"l_sci_x = lost[\"scifi_hit_pos_x\"]\n",
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"ak.nan_to_num(l_sci_x)\n",
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"\n",
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"#found\n",
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"f_eph = tracked[\"brem_photons_pe\"]\n",
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"ak.nan_to_num(f_eph)\n",
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"f_pT = tracked[\"pt\"]\n",
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"f_sci_x = tracked[\"scifi_hit_pos_x\"]\n",
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"ak.nan_to_num(f_sci_x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 151,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 2000x600 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"l_sci_x, l_pT = ak.broadcast_arrays(l_sci_x, l_pT)\n",
|
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"f_sci_x, f_pT = ak.broadcast_arrays(f_sci_x, f_pT)\n",
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"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n",
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"\n",
|
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|
"ax0.hist2d(ak.to_numpy(ak.flatten(l_sci_x)), ak.to_numpy(ak.flatten(l_pT)), bins=100, cmap=plt.cm.jet)\n",
|
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|
|
|
"ax0.set_xlabel(\"scifi x\")\n",
|
|
|
|
|
"ax0.set_ylabel(r\"$p_T$\")\n",
|
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|
|
|
"ax0.set_title(\"lost electron positions in the scifi in regard to their transverse momentum\")\n",
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"\n",
|
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|
|
"ax1.hist2d(ak.to_numpy(ak.flatten(f_sci_x)), ak.to_numpy(ak.flatten(f_pT)), bins=100, cmap=plt.cm.jet)\n",
|
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"ax1.set_xlabel(\"scifi x\")\n",
|
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|
|
"ax1.set_ylabel(r\"$p_T$\")\n",
|
|
|
|
|
"ax1.set_title(\"found electron positions in the scifi in regard to their transverse momentum\")\n",
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|
"\n",
|
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|
|
"plt.show()"
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]
|
2023-09-14 12:07:57 +02:00
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
|
2023-09-14 16:33:27 +02:00
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{
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"cell_type": "code",
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"execution_count": 113,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"x = np.random.normal(size=50000)\n",
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"y = x * 3 + np.random.normal(size=50000)\n",
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" \n",
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"# Big bins\n",
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"plt.hist2d(x, y, bins=(50, 50), cmap=plt.cm.jet)\n",
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"plt.show()\n",
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" \n",
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"# Small bins\n",
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"plt.hist2d(x, y, bins=(300, 300), cmap=plt.cm.jet)\n",
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"plt.show()\n",
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" \n",
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"# If you do not set the same values for X and Y, the bins won't be a square!\n",
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"plt.hist2d(x, y, bins=(300, 30), cmap=plt.cm.jet)\n",
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"plt.show()"
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]
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},
|
2023-09-14 12:07:57 +02:00
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
|
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"cell_type": "code",
|
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"execution_count": null,
|
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
|
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"kernelspec": {
|
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"display_name": "env1",
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"language": "python",
|
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|
|
|
"name": "python3"
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|
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},
|
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|
|
"language_info": {
|
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|
|
|
"codemirror_mode": {
|
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"name": "ipython",
|
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|
|
|
"version": 3
|
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},
|
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|
|
"file_extension": ".py",
|
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|
|
"mimetype": "text/x-python",
|
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|
"name": "python",
|
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|
|
"nbconvert_exporter": "python",
|
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|
|
"pygments_lexer": "ipython3",
|
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|
|
"version": "3.11.5"
|
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},
|
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|
"orig_nbformat": 4
|
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|
|
},
|
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|
"nbformat": 4,
|
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|
|
"nbformat_minor": 2
|
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|
}
|
