{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## TOC\n\n* **[What is Neutrino?](#What-is-Neutrino?)**\n\n* **[How to measure](#How-to-measure)**\n    * [Cherenkov light](#Cherenkov-light)\n    * [Photomultiplier](#Photomultiplier)\n    * [IceCube](#IceCube)\n\n* **[EDA](#EDA)**\n    * [Import](#Import)\n    * [train_meta.parquet](#train_meta.parquet)\n    * [sensor_gemetry.csv](#sensor_gemetry.csv)\n    * [/train/batch_1.parquet](#/train/batch_1.parquet)\n    * [Plot](#Plot)\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"# What is Neutrino?\n[back to TOC](#TOC)\n### -> Rough answer:\nParticles that have taken the charge and most of their weight from electrons\n\n### -> Detail answer\n(ref. https://sciencenotes.org/what-is-a-neutrino-neutrino-facts/)\n- A neutrino has a neutral electrical charge and very small mass. Its mass is estimated as at least six orders of magnitude smaller than that of the electron, which has a mass of ${9.1×10^{-31}}$ kilograms. The exact mass of a neutrino has yet to be measured.\n- Neutrinos travel at speeds approaching the speed of light.\n- A neutrino only reacts to gravity and the weak nuclear force (weak interaction). Because of this, it very rarely interacts with matter.\n- For example, billions of neutrinos pass through your body every day. Despite this, scientists estimate only one solar neutrino (from our Sun) interacts with a person throughout their entire lifetime.\n- At present, there are three known “flavors” of neutrinos: electron, muon, and tau. A neutrino oscillates between these three flavors. There are also antimatter particles: anti-electron (antineutrino), anti-muon, and anti-tau.\n- There may be other neutrino flavors. For example, scientists predict the existence of the sterile neutrino. A sterile neutrino interacts only with gravity, not the weak nuclear force.\n- Neutrinos are extremely common. They come from nuclear reactions. Sources include the Sun and other stars, supernovae, nuclear decay, fission, and fusion.\n- Like neutrons, neutrinos induce nuclear fission of heavy nuclei. Only neutrino fission of deuterium has been observed in labs, but the process likely occurs within stars and influences the isotope abundance of elements.\n- Scientists estimate between 2% to 3% of the Sun’s radiation takes the form of neutrinos. About 99% of a supernova’s energy gets released as neutrinos.\n- Researcher see the Sun, day or night, using neutrinos. They pass through the Earth when it is night time. Based on neutrino images, astronomers know nuclear reaction only occur in the Sun’s core, which is its inner 20-25%.\n- Neutrinos may be hot dark matter. That is, they neither emit nor absorb light, so they appear dark. Yet, they have energy, so they are hot.\n\n<img src=\"https://biblicalscienceinstitute.com/wp-content/uploads/2022/06/leptons.png\" width=800>","metadata":{}},{"cell_type":"markdown","source":"### High energy (>TeV) neutrino\nHigh-energy neutrinos leave a large signal on the detector indicating their origin, and the direction from which the neutrino came. Ice Cube detects high-energy neutrinos from ${10^{11}}$ eV to ${10^{21}}$ eV with high sensitivity, and it is estimated that once completed, it will be able to detect neutrinos once every 20 minutes.\n\n![image.png](attachment:3074888c-9292-4676-8823-c2c264b01594.png) <br>\n(ref. https://arxiv.org/pdf/1910.11878.pdf)","metadata":{},"attachments":{"3074888c-9292-4676-8823-c2c264b01594.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# How to measure\n[back to TOC](#TOC)<br>","metadata":{}},{"cell_type":"markdown","source":"Neutrinos have no electrical charge, rarely interact (collide) with other matter, and cannot be detected directly by detectors. However, with a very low probability, they collide with water molecules in the ice, producing leptons (electrons, muons, and tau particles) with corresponding charges. If these particles travel faster than the speed of light in ice, they produce [Cherenkov light](#Cherenkov-light) (a cone of light) that can be detected with a [photomultiplier](#Photomultiplier) tube. <br>\n\nTau particles are difficult to detect due to their short decay lifetimes, and the generated Cherenkov light also has a cascading pattern similar to that of electrons. This is due to the successive generation and decay of tau particles, each generating a 'shower' of hadrons. However, this is only possible if the tau particles have sufficiently high energy (velocity is high). Since the DOMs are placed at 17m intervals vertically, it must fly about 17m from the first \"bang\" to the second \"bang\" in order to be detected as a \"double bang\". Since the tau particle has a lifetime of only ${2.9x10^{-13}}$ seconds, the required tau energy must be several PeV to tens of PeV. This \"double bang\" has not been detected at this time.<br>\n\nIt is also important to remove the background (noise) of muons. In addition to muons produced by neutrinos from celestial bodies, which are the main detection targets of the facility, muons generated by cosmic rays colliding with the atmosphere are also detected as noise. The latter is ${10^6}$ times the former. First of all, the rays that fall downward from the sky are regarded as noise and removed, but most of the remaining (the ones that pass through the earth and rise upward) fall on the opposite side of the earth (North Pole side). comes from neutrinos (=noise) generated by colliding with the earth. Ultimately, the signal originating from the target celestial body is found by analyzing the energy of the particles. <br>\n\nWhen completed, it is estimated to detect around 75 upward neutrinos per day. In order to statistically distinguish noise from these, we investigate the correlation between the direction from which the neutrinos came and the energy of the charged particles generated by the neutrinos. If the energy is very high, or if the energy is high relative to the direction from which it came, it is considered to be from a celestial body.\n\n","metadata":{"execution":{"iopub.status.busy":"2023-01-26T23:42:26.514392Z","iopub.execute_input":"2023-01-26T23:42:26.515014Z","iopub.status.idle":"2023-01-26T23:42:26.553328Z","shell.execute_reply.started":"2023-01-26T23:42:26.514899Z","shell.execute_reply":"2023-01-26T23:42:26.551627Z"}}},{"cell_type":"markdown","source":"### Cherenkov light\n#### -> Rough answer: Shock wave of light\n\nIn media such as air and water, the speed of light traveling there is slower than c (=300,000 km/s). For example, the propagation velocity in ice block (refractive index n=1.309) is only c/n=0.76c. Particles are accelerated by nuclear reactions and can exceed the propagation velocity in the medium (although never exceeding the speed of light c). Cerenkov light is emitted when charged particles (mostly electrons) pass through an (insulated) dielectric material at a velocity faster than the velocity of light in that medium.<br>\n\n[IceCube](#IceCube) sensor is most sensitive to mu particles (muons), which are highly penetrating and cross IceCube sensor over long distances. Therefore, IceCube is the most sensitive detectors of muon neutrinos. On the other hand, it means that the direction from which the electron neutrino came cannot be determined because the electron will be scattered several times until it slows down and stops emitting Cherenkov light. Of course, the data will be used for research. Cherenkov light from electrons is observed in clusters such as spheres and waterfalls, while Cherenkov light from muons is ring-shaped.<br>\n<img src=\"https://www.researchgate.net/profile/Thorsten-Gluesenkamp/publication/215457896/figure/fig5/AS:654046566293511@1532948261466/Illustration-of-the-Cherenkov-effect-A-charged-particle-with-relative-velocity-b-v-c.png\" width=\"500\"> <br>\n<iframe width=\"800\" height=\"600\" src=\"https://www.youtube.com/embed/3PZgfPHULHw\" title=\"A Cherenkov neutrino telescope\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>","metadata":{}},{"cell_type":"markdown","source":"#### Photomultiplier\nA photomultiplier tube is roughly divided into a \"photocathode\" that generates primary electrons, called a photocathode, and a \"focusing electrode\" that amplifies the generated primary electrons.<br>\n\nThe photocathode generates electrons when exposed to a small amount of light (Photoelectric effect). In the electron multiplier section following the photocathode, a voltage is generated between several dynode electrodes, accelerating the electrons generated on the photocathode and causing them to collide with the dynodes. Due to this collision, many secondary electrons-Electrons are knocked out from the pole plate and the electrons are amplified. By repeating this process several times, a small amount of light produces a detectable electrical signal.<br>\n<img src=\"https://static1.olympus-lifescience.com/data/olympusmicro/primer/digitalimaging/concepts/images/photomultiplier.jpg?rev=630F\" width=\"600\">","metadata":{"execution":{"iopub.status.busy":"2023-01-23T02:13:15.672167Z","iopub.execute_input":"2023-01-23T02:13:15.672563Z","iopub.status.idle":"2023-01-23T02:13:15.677142Z","shell.execute_reply.started":"2023-01-23T02:13:15.672522Z","shell.execute_reply":"2023-01-23T02:13:15.676294Z"}}},{"cell_type":"markdown","source":"### IceCube\nHigh-energy cosmic rays are not bound to the Milky Way (the particles' velocity is greater than the Galactic escape velocity), so they are thought to come from extragalactic sources. Any intense astronomical event that produces such high-energy cosmic rays will also produce high-energy neutrinos. And neutrinos fly directly to the earth without interacting with other matter.\n\nIce Cube can detect these high-energy (from 100 GeV to several PeV) neutrinos. The more intense the astronomical event, the more likely it is to be detected by Ice Cube. Ice Cube can detect neutrinos coming from the northern hemisphere with high sensitivity. Neutrinos from the Southern Hemisphere can be detected from any direction, but neutrinos from the Southern Hemisphere are drowned out by the background of cosmic ray-derived muons. The Ice Cube search will initially focus on the northern hemisphere, with an ad hoc expansion to the southern hemisphere.\n\nThe neutrinos detected by Ice Cube are tiny compared to the light captured by telescopes, but they have high resolution. In a few years, it may map the northern hemisphere, similar to the cosmic microwave background and gamma-ray telescopes.","metadata":{"execution":{"iopub.status.busy":"2023-01-23T23:43:27.144989Z","iopub.execute_input":"2023-01-23T23:43:27.145447Z","iopub.status.idle":"2023-01-23T23:43:27.151181Z","shell.execute_reply.started":"2023-01-23T23:43:27.145413Z","shell.execute_reply":"2023-01-23T23:43:27.150067Z"}}},{"cell_type":"markdown","source":"# EDA\n[back to TOC](#TOC)","metadata":{"execution":{"iopub.status.busy":"2023-01-23T04:34:02.957151Z","iopub.execute_input":"2023-01-23T04:34:02.957522Z","iopub.status.idle":"2023-01-23T04:34:02.96316Z","shell.execute_reply.started":"2023-01-23T04:34:02.957494Z","shell.execute_reply":"2023-01-23T04:34:02.961483Z"}}},{"cell_type":"markdown","source":"## Import\n[back to TOC](#TOC)","metadata":{}},{"cell_type":"code","source":"import math\nimport glob\nfrom ipywidgets import (interact, interactive, fixed,\n                        Dropdown, RadioButtons)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nimport seaborn as sns\n\n\n# Global\nROOT = '/kaggle/input/icecube-neutrinos-in-deep-ice'\nBATCH_NUM = 1","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:01.247631Z","iopub.execute_input":"2023-01-27T05:33:01.248329Z","iopub.status.idle":"2023-01-27T05:33:01.257792Z","shell.execute_reply.started":"2023-01-27T05:33:01.248275Z","shell.execute_reply":"2023-01-27T05:33:01.256461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## train_meta.parquet\n[back to TOC](#TOC)","metadata":{}},{"cell_type":"code","source":"train_meta = pd.read_parquet(ROOT + '/train_meta.parquet')\nprint(f'# of records: {len(train_meta)}')\ntrain_meta.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:01.260193Z","iopub.execute_input":"2023-01-27T05:33:01.260672Z","iopub.status.idle":"2023-01-27T05:33:18.010998Z","shell.execute_reply.started":"2023-01-27T05:33:01.260627Z","shell.execute_reply":"2023-01-27T05:33:18.010175Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_angle_distribusion(df=train_meta):\n    \n    fig, ax = plt.subplots(1, 2, figsize=(16,6))\n    for i, angle in enumerate(['azimuth', 'zenith']):\n        ax[i].hist(df[angle], bins=50, color='red', alpha=0.5, label=angle)\n        ax[i].set_xlabel(angle)  \n        ax[i].set_ylabel('Frequency')\n   \n    plt.show()\n\ndef show_particle_directions(azimuths, zeniths, title):\n    \n    fig = go.Figure()\n    for azimuth, zenith in zip(azimuths, zeniths):\n        x = math.cos(azimuth) * math.sin(zenith)\n        y = math.sin(azimuth) * math.sin(zenith)\n        z = math.cos(zenith)\n        fig.add_trace(\n            go.Scatter3d(x=[0, x], y=[0, y], z=[0, z], mode='lines', opacity=0.5)\n        )\n        \n    fig.update_layout(\n        height=600, width=600, showlegend=False,\n        title_text=title,\n        title_x=0.5\n    )\n    fig.show()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-01-27T05:33:18.012363Z","iopub.execute_input":"2023-01-27T05:33:18.012878Z","iopub.status.idle":"2023-01-27T05:33:18.022948Z","shell.execute_reply.started":"2023-01-27T05:33:18.012845Z","shell.execute_reply":"2023-01-27T05:33:18.021644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_angle_distribusion(df=train_meta)\n\nshow_particle_directions(\n    azimuths = train_meta['azimuth'][:200],\n    zeniths = train_meta['zenith'][:200],\n    title = 'Directions of the first 200 events',\n)","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:18.025806Z","iopub.execute_input":"2023-01-27T05:33:18.026192Z","iopub.status.idle":"2023-01-27T05:33:24.429166Z","shell.execute_reply.started":"2023-01-27T05:33:18.026119Z","shell.execute_reply":"2023-01-27T05:33:24.427813Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## sensor_gemetry.csv\n[back to TOC](#TOC)","metadata":{}},{"cell_type":"code","source":"sensor_geometry = pd.read_csv(ROOT + '/sensor_geometry.csv')\nprint(f'# of records: {len(sensor_geometry)}')\nsensor_geometry.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:24.430555Z","iopub.execute_input":"2023-01-27T05:33:24.430936Z","iopub.status.idle":"2023-01-27T05:33:24.451936Z","shell.execute_reply.started":"2023-01-27T05:33:24.430900Z","shell.execute_reply":"2023-01-27T05:33:24.451034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_sensor_locations(df):\n    \n    fig = go.Figure()\n    fig.add_trace(\n        go.Scatter3d(x=df['x'], y=df['y'], z=df['z'],\n                     mode='markers', opacity=0.5, marker_size=1)\n    )\n    fig.update_layout(\n        height=600, width=600, showlegend=False,\n        title_text='Sensor locations',\n        title_x=0.5\n    )\n    fig.show()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-01-27T05:33:24.453297Z","iopub.execute_input":"2023-01-27T05:33:24.454284Z","iopub.status.idle":"2023-01-27T05:33:24.462025Z","shell.execute_reply.started":"2023-01-27T05:33:24.454248Z","shell.execute_reply":"2023-01-27T05:33:24.460798Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_sensor_locations(df=sensor_geometry)","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:24.463923Z","iopub.execute_input":"2023-01-27T05:33:24.464299Z","iopub.status.idle":"2023-01-27T05:33:24.489731Z","shell.execute_reply.started":"2023-01-27T05:33:24.464265Z","shell.execute_reply":"2023-01-27T05:33:24.488854Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### /train/batch_1.parquet\n[back to TOC](#TOC)","metadata":{}},{"cell_type":"code","source":"train_batch = pd.read_parquet(ROOT + f'/train/batch_{BATCH_NUM}.parquet')\nevent_ids = train_batch.index.unique()\nprint(f'Batch number: {BATCH_NUM}')\nprint(f'# of records: {len(train_batch)}')\nprint(f'# of events: {len(event_ids)}')\ntrain_batch.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:24.491217Z","iopub.execute_input":"2023-01-27T05:33:24.491835Z","iopub.status.idle":"2023-01-27T05:33:26.732975Z","shell.execute_reply.started":"2023-01-27T05:33:24.491799Z","shell.execute_reply":"2023-01-27T05:33:26.731891Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_number_of_records_per_event(df):\n    \n    fig = plt.figure(figsize=(15, 6))\n    \n    num_records_w_auxiliary  = train_batch[train_batch['auxiliary']].groupby(level=0).size()\n    num_records_wo_auxiliary = train_batch[~train_batch['auxiliary']].groupby(level=0).size()\n    print(f'# of records w/ auxiliary==true: {num_records_w_auxiliary.sum()}')\n    print(f'# of records w/o auxiliary==false: {num_records_wo_auxiliary.sum()}')\n    plt.hist(num_records_w_auxiliary, bins=100, log=True, color='red', alpha=0.5, label='auxiliary==True')\n    plt.hist(num_records_wo_auxiliary, bins=100, log=True, color='gray', alpha=0.5, label='auxiliary==False')\n    plt.xlabel('# of records per a event')  \n    plt.ylabel('Frequency')\n    plt.xlim(-5000, 60000)\n    plt.legend()\n    plt.title(f'Number_of_records_per_event in batch_{BATCH_NUM}')\n    plt.show()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-01-27T05:33:26.734296Z","iopub.execute_input":"2023-01-27T05:33:26.734635Z","iopub.status.idle":"2023-01-27T05:33:26.743596Z","shell.execute_reply.started":"2023-01-27T05:33:26.734604Z","shell.execute_reply":"2023-01-27T05:33:26.742568Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_number_of_records_per_event(df=train_batch)","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:26.747053Z","iopub.execute_input":"2023-01-27T05:33:26.747820Z","iopub.status.idle":"2023-01-27T05:33:29.928409Z","shell.execute_reply.started":"2023-01-27T05:33:26.747779Z","shell.execute_reply":"2023-01-27T05:33:29.927210Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_charge_to_time(df, sampling_size=1000):\n    \n    df_ = df[:sampling_size].copy()\n    event_ids_ = df_.index.unique()[:-1]\n#     print(f'# of events: {len(event_ids_)}')\n    fig = plt.figure(figsize=(12,6))\n    for event_id in event_ids_:\n        charge = df_[df_.index==event_id]['charge']\n        time = df_[df_.index==event_id]['time']\n        plt.plot(time, charge, label=f'event_id:{event_id}', lw=0.8)\n    \n    plt.legend()\n    plt.title('Time - Charge')\n    plt.xlabel('time')\n    plt.ylabel('charge')\n    plt.ylim(0,8)\n    plt.show()\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-01-27T05:33:29.929848Z","iopub.execute_input":"2023-01-27T05:33:29.930220Z","iopub.status.idle":"2023-01-27T05:33:29.939776Z","shell.execute_reply.started":"2023-01-27T05:33:29.930184Z","shell.execute_reply":"2023-01-27T05:33:29.938850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_charge_to_time(df=train_batch, sampling_size=1000) # sampling the first 1000 records","metadata":{"execution":{"iopub.status.busy":"2023-01-27T05:33:29.941351Z","iopub.execute_input":"2023-01-27T05:33:29.941716Z","iopub.status.idle":"2023-01-27T05:33:30.276851Z","shell.execute_reply.started":"2023-01-27T05:33:29.941683Z","shell.execute_reply":"2023-01-27T05:33:30.275542Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Plot\n[back to TOC](#TOC)","metadata":{}},{"cell_type":"code","source":"def show_event_track(event_id, auxiliary, batch_data=train_batch, meta_data=train_meta, sensor_geometry=sensor_geometry):\n    '''\n    event_id: int\n    auxiliary: boolean\n    batch_data: pd.DataFrame\n        pd.read_parquet('/train/batch_{batch_num}.parquet')\n    meta_data: pd.DataFrame\n        pd.read_parquet('train_meta.parquet' or 'train_meta.parquet')\n    sensor_geometry: pd.DataFrame\n        pd.read_csv('sensor_geometry.csv')\n    '''\n    \n    # Data\n    df_event = batch_data[(batch_data.index==event_id) & (batch_data.auxiliary==auxiliary)]\n    df_event = df_event.merge(sensor_geometry, on='sensor_id', how='left')\n    meta_event = meta_data[meta_data['event_id']==event_id]\n    azimuth = meta_event['azimuth']\n    zenith = meta_event['zenith']\n    x = math.cos(azimuth) * math.sin(zenith)\n    y = math.sin(azimuth) * math.sin(zenith)\n    z = math.cos(zenith)\n    \n    # Data (initial plot)\n    data = [\n        # Sensors\n        go.Scatter3d(x=sensor_geometry['x'], y=sensor_geometry['y'], z=sensor_geometry['z'],\n                     mode='markers', opacity=0.5, marker=dict(color='gray', size=1)),\n        # Particle direction\n        go.Scatter3d(x=[-x * 500, x * 500], y=[-y * 500, y * 500], z=[-z * 500, z * 500],\n                     opacity=0.5, mode='lines', line=dict(width=2, color=\"red\")),\n        go.Scatter3d(x=sensor_geometry['x'], y=sensor_geometry['y'], z=sensor_geometry['z'],\n                     mode='markers', opacity=0.5, marker=dict(color='gray', size=1))\n        ]\n \n    # Frames\n    frames = []\n    for i, value in df_event.iterrows():\n        frame_data = [\n            go.Scatter3d(x=df_event.loc[:i, 'x'],\n                         y=df_event.loc[:i, 'y'],\n                         z=df_event.loc[:i, 'z'],\n                         mode='markers',\n                         opacity=0.5,\n                         marker=dict(size=df_event['charge']*10,\n                                     color=df_event['time'],\n                                     colorscale='Plotly3',\n                                     colorbar_title ='Time',\n                                     showscale=True,\n                                     line=dict(color='black', width=20)\n                                    )\n                      )\n        ]\n        frame = go.Frame(data=frame_data)\n        frames.append(frame)           \n\n   # Buttons\n    play_button = [dict(label='Play',\n                        method='animate',\n                        args=[None,\n                              {'frame': {'duration': 50}, \n                               'transition': {'duration': 50}}])]\n    # Layout\n    layout = go.Layout(\n        title=f'event_id: {event_id} with auxiliary=={auxiliary}',\n        updatemenus=[dict(active=0,\n                          type='buttons',\n                          buttons=play_button,\n                          x=-0.08,\n                          y=1.18,\n                          borderwidth=2)]\n            \n    )\n    \n    fig = go.Figure(data=data, layout=layout, frames=frames)\n    fig.update_layout(\n        scene = dict(\n            xaxis = dict(nticks=10, range=[-600,600],),\n            yaxis = dict(nticks=10, range=[-600,600],),\n            zaxis = dict(nticks=10, range=[-600,600],),\n        ),\n        height=600,\n        width=750,\n        showlegend=False,\n        title_x=0.1,\n    )\n    fig.update_layout(scene_aspectmode='cube')\n    fig.show()\n    \n# show_event_track(event_id=41, auxiliary=False, batch_data=train_batch, meta_data=train_meta, sensor_geometry=sensor_geometry)    ","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-01-27T05:46:04.373088Z","iopub.execute_input":"2023-01-27T05:46:04.373563Z","iopub.status.idle":"2023-01-27T05:46:04.393364Z","shell.execute_reply.started":"2023-01-27T05:46:04.373525Z","shell.execute_reply":"2023-01-27T05:46:04.392206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"num_sampling_events = 3\nprint(f'Batch Number: {BATCH_NUM} (number of samplings: {num_sampling_events})')\n\nfor event_id in event_ids[:num_sampling_events]:\n    for auxiliary in [True, False]:\n        show_event_track(event_id=event_id,\n                         auxiliary=auxiliary,\n                         batch_data=train_batch,\n                         meta_data=train_meta,\n                         sensor_geometry=sensor_geometry)  ","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2023-01-27T05:46:04.536954Z","iopub.execute_input":"2023-01-27T05:46:04.537405Z","iopub.status.idle":"2023-01-27T05:46:05.580283Z","shell.execute_reply.started":"2023-01-27T05:46:04.537367Z","shell.execute_reply":"2023-01-27T05:46:05.579034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}