atlas-outreach-data-tools · Soap2G · Oct 8, 2024 · Sep 4, 2024 · Sep 4, 2024 · Sep 4, 2024
@@ -7,7 +7,7 @@ on:
       - 'master'
     paths:
       - 'Dockerfile'
-      - 'Dockerfile.conda'
+      - 'binder/environment.yml'
       - '.github/workflows/docker_push.yml'
 
 # Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
@@ -67,4 +67,4 @@ jobs:
           subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
           subject-digest: ${{ steps.push.outputs.digest }}
           push-to-registry: true
-
+
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -136,9 +136,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('data_A.exactly2lep.root', <http.client.HTTPMessage at 0x7f41f7c0cd00>)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "filename = 'data_A.exactly2lep.root'\n",
     "url = 'https://atlas-opendata.web.cern.ch/atlas-opendata/samples/2020/exactly2lep/Data/'+filename\n",
@@ -188,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +210,7 @@
     "    # get the energy of 0th lepton by lep_E[0]\n",
     "\n",
     "    # sumE = sum of energy\n",
-    "    sumE = \n",
+    "    sumE = 0 # Replace the 0 with your solution\n",
     "    \n",
     "    px_0 = lep_pt[0]*np.cos(lep_phi[0]) # x-momentum of 0th lepton\n",
     "    px_1 = lep_pt[1]*np.cos(lep_phi[1]) # x-momentum of 1st lepton\n",
@@ -209,19 +220,19 @@
     "    pz_1 = lep_pt[1]*np.sinh(lep_eta[1]) # z-momentum of 1st lepton\n",
     "    \n",
     "    # sumpx = sum of x-momenta\n",
-    "    sumpx = \n",
+    "    sumpx = 0 # Replace the 0 with your solution\n",
     "    \n",
     "    # sumpy = sum of y-momenta\n",
-    "    sumpy = \n",
+    "    sumpy = 0 # Replace the 0 with your solution\n",
     "    \n",
     "    # sumpz = sum of z-momenta\n",
-    "    sumpz = \n",
+    "    sumpz = 0 # Replace the 0 with your solution\n",
     "    \n",
     "    # sump = magnitude of total momentum vector. Remember it's a vector!\n",
-    "    sump = \n",
+    "    sump = 0 # Replace the 0 with your solution\n",
     "    \n",
     "    # Mll = invariant mass from M^2 = E^2 - p^2\n",
-    "    Mll = \n",
+    "    Mll = 0 # Replace the 0 with your solution\n",
     "    \n",
     "    return Mll/1000 # divide by 1000 to go from MeV to GeV"
    ]
@@ -242,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,7 +268,7 @@
     "    # calculation of 2-lepton invariant mass                                                                                                                                      \n",
     "    data['mll'] = np.vectorize(calc_mll)(data.lep_pt, data.lep_eta, data.lep_phi, data.lep_E)\n",
     "\n",
-    "    data_all = data_all.append(data) # append dataframe from this batch to the dataframe for the whole sample\n"
+    "    data_all = pd.concat([data_all, data], ignore_index=True) # append dataframe from this batch to the dataframe for the whole sample\n"
    ]
   },
   {
@@ -283,11 +294,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2523/3936450928.py:25: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
+      "  plt.yscale('log')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "bin_edges = np.arange(start=35, # The interval includes this value\n",
     "                      stop=110, # The interval doesn't include this value\n",
@@ -405,7 +435,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -419,7 +449,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,