B tasks
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@ -15,7 +15,6 @@
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"from mpl_toolkits import mplot3d\n",
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"from mpl_toolkits import mplot3d\n",
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"import itertools\n",
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"import itertools\n",
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"import awkward as ak\n",
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"import awkward as ak\n",
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"import seaborn as sns\n",
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"from scipy.optimize import curve_fit\n",
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"from scipy.optimize import curve_fit\n",
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"%matplotlib inline"
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"%matplotlib inline"
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]
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]
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@ -155,7 +154,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 17,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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@ -172,7 +171,7 @@
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"source": [
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"source": [
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"plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
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"plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n",
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"plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
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"plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n",
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"plt.xticks(np.arange(0,1,0.1), minor=True,)\n",
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"plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n",
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"plt.yticks(np.arange(0,10,1), minor=True)\n",
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"plt.yticks(np.arange(0,10,1), minor=True)\n",
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"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
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"plt.xlabel(r\"$E_\\gamma/E_0$\")\n",
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"plt.ylabel(\"counts (normed)\")\n",
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"plt.ylabel(\"counts (normed)\")\n",
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@ -194,7 +193,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 76,
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"execution_count": 8,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -223,7 +222,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 77,
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"execution_count": 9,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -254,7 +253,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 78,
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"execution_count": 10,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -263,7 +262,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 79,
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"execution_count": 11,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -308,7 +307,7 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 80,
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"execution_count": 12,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 81,
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"execution_count": 13,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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@ -375,7 +374,7 @@
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"-4.6785491318157854e-07"
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"-4.6785491318157854e-07"
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]
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]
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},
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},
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"execution_count": 81,
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"execution_count": 13,
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"metadata": {},
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"metadata": {},
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"output_type": "execute_result"
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"output_type": "execute_result"
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}
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}
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 82,
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"execution_count": 14,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 83,
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"execution_count": 15,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 84,
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"execution_count": 16,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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