Chapter 2: Log Everything! Like K.I.T.T.’s Mission Recorder

Great Scott! In Chapter 1: Great Scott! Making Animations Easy with animate_activity_log, we met animate_activity_log, our trusty DeLorean control panel for creating process animations. We saw how it takes the key ingredients and produces a visual masterpiece. But what’s the most crucial ingredient? It’s the script, the minute-by-minute breakdown of the action – the Event Log.

Imagine trying to understand what K.I.T.T. did all day just by looking at his parking spot. Impossible! You need his internal logs, telling you when he scanned for trouble, when he deployed the smokescreen, and when he used Turbo Boost. That’s exactly what the Event Log does for vidigi.

The Mission: Recording the Action

Think back to our clinic simulation. We need to tell vidigi precisely when each patient arrived, when they started waiting, when they began treatment (and with which nurse!), when they finished, and when they left. It’s like writing a detailed diary entry for every single patient (or maybe for every droid like R2-D2 on a mission).

Without this detailed log, animate_activity_log is like a director with no script – it doesn’t know what story to tell! The Event Log provides the raw sequence of events and times that vidigi needs to reconstruct the system’s dynamics visually.

The Blueprint: Anatomy of an Event Log

So, what does this “diary” or “log file” look like? It’s simply a table, usually a pandas DataFrame, with specific columns that vidigi understands. Think of it as the standard report format requested by Devon Miles at FLAG.

Here are the essential columns:

  1. patient: This is the unique identifier for the entity moving through the system. It could be a patient ID, a serial number for a component (like Johnny 5!), or even a callsign (like ‘Maverick’ or ‘Goose’). Each row in the log belongs to one specific patient.
  2. event_type: This tells vidigi the kind of activity being recorded. It’s like classifying K.I.T.T.’s actions: ‘Scanning’, ‘Driving’, ‘Special Maneuver’. vidigi recognizes specific types:
    • arrival_departure: Marks when the entity enters or leaves the system. These are the opening and closing scenes!
    • queue: Indicates the entity has started waiting for something (like waiting for E.T. to phone home, or waiting for a treatment cubicle).
    • resource_use: Signals that the entity has started using a specific resource (like a nurse seeing a patient, or Ripley getting into a specific Power Loader).
    • resource_use_end: Signals that the entity has finished using that specific resource.
  3. event: This is a more specific name for the event that occurred. You get to define most of these! Examples: ‘Arrival_Clinic’, ‘Wait_for_Nurse’, ‘Start_Treatment’, ‘End_Treatment’, ‘Depart_Clinic’.
    • Crucial Note: For arrival_departure event types, vidigi specifically expects the event names to be exactly 'arrival' and 'depart'. Don’t get creative here, or the system won’t know when things start and end! Think of these as the fixed “Power On” and “Shutdown” commands.
  4. time: This is the timestamp when the event occurred, measured in your simulation’s time units (e.g., minutes, hours, parsecs!). It’s the “when” for every “what”.

Bonus Column for Resources:

  1. resource_id (Required for resource_use and resource_use_end): When an entity uses a resource (like one of the Ghostbusters using a specific Proton Pack), we need to know which one. This column holds the unique ID of the specific resource unit being used or released. This is essential for tracking which specific nurse (Nurse Jackie 1, Nurse Jackie 2, etc.) is busy. We’ll talk more about how these IDs come from the Simpy Resource Enhancement (CustomResource, Store, populate_store).

Creating Your Own Log: Let’s Get Coding!

Okay, let’s build an Event Log for our Top Gun trainees, Maverick and Goose. We’ll use pandas, the go-to library for data tables in Python.

First, make sure you have pandas imported:

import pandas as pd

Now, let’s create the data as a list of dictionaries. Each dictionary represents one event (one row in our log):

# Define the events for Maverick and Goose
event_data = [
    # Maverick's Journey
    {'patient': 'Maverick', 'event_type': 'arrival_departure', 'event': 'arrival', 'time': 0},
    {'patient': 'Maverick', 'event_type': 'queue', 'event': 'wait_for_simulator', 'time': 1}, # Starts waiting
    {'patient': 'Maverick', 'event_type': 'resource_use', 'event': 'start_simulator', 'time': 10, 'resource_id': 'Sim_1'}, # Got Simulator 1
    {'patient': 'Maverick', 'event_type': 'resource_use_end', 'event': 'end_simulator', 'time': 50, 'resource_id': 'Sim_1'}, # Finished Sim 1
    {'patient': 'Maverick', 'event_type': 'arrival_departure', 'event': 'depart', 'time': 55},

    # Goose's Journey
    {'patient': 'Goose', 'event_type': 'arrival_departure', 'event': 'arrival', 'time': 5},
    {'patient': 'Goose', 'event_type': 'queue', 'event': 'wait_for_simulator', 'time': 6}, # Starts waiting
    {'patient': 'Goose', 'event_type': 'resource_use', 'event': 'start_simulator', 'time': 15, 'resource_id': 'Sim_2'}, # Got Simulator 2
    {'patient': 'Goose', 'event_type': 'resource_use_end', 'event': 'end_simulator', 'time': 65, 'resource_id': 'Sim_2'}, # Finished Sim 2
    {'patient': 'Goose', 'event_type': 'arrival_departure', 'event': 'depart', 'time': 70}
]

# Convert the list of dictionaries into a pandas DataFrame
event_log_df = pd.DataFrame(event_data)

# Let's see what we made! Print the first few rows.
print("Our Awesome Event Log:")
print(event_log_df.head(10)) # Show first 10 rows

This code creates our list of event records and then uses pd.DataFrame() to turn it into the table format vidigi loves. We print the first 10 rows using .head(10) to check our work.

Notice how: * Each row has the required columns (patient, event_type, event, time). * The resource_use and resource_use_end events also have resource_id (‘Sim_1’ or ‘Sim_2’). * The entry and exit points use the mandatory event names 'arrival' and 'depart' with the event_type ‘arrival_departure’. * Events are recorded chronologically per patient, but the overall log doesn’t strictly need to be sorted by time (though it often helps for readability). vidigi will figure out the sequence.

This event_log_df is exactly what you’d pass into the animate_activity_log function we learned about in Chapter 1!

Under the Hood: How vidigi Uses the Log

The Event Log itself is just data – it’s like the script lying on the table. It doesn’t do anything on its own. Other parts of vidigi read this script to understand the plot.

The main consumer of the event_log is the reshape_for_animations function (which we’ll explore in detail later). Think of reshape_for_animations as the assistant director reading the script and figuring out who needs to be where for every single “snapshot” in time.

Here’s a simplified idea of what happens when reshape_for_animations gets the event_log:

sequenceDiagram
    participant EL as Event Log (Data)
    participant RFA as reshape_for_animations
    participant Output as Snapshots (Who is where, when)

    RFA->>EL: Read all event records
    Note over RFA: For each time point (e.g., every minute):
    RFA->>EL: Find events that happened *before* this time point for each patient
    RFA->>EL: Identify the *most recent* event for each patient still in the system
    RFA-->>Output: Record patient's current state (event name) at this time point

Essentially, reshape_for_animations scans through time. At each step (say, every minute), it looks at the event_log to determine the last thing each active patient did. This tells it whether Maverick is currently ‘waiting_for_simulator’ or ‘start_simulator’ at that specific minute.

Internally (looking at vidigi/prep.py), reshape_for_animations might do things like:

# --- Inside reshape_for_animations (Simplified Concept) ---
# (Input: event_log_df)

# Pivot the log to easily find start/end times (like 'arrival', 'depart')
pivoted_log = event_log_df.pivot_table(...)

# Loop through time points (e.g., minute 0, 1, 2...)
for minute in range(simulation_duration):
    # Find patients who arrived before 'minute' and haven't departed yet
    active_patients = find_active_patients(pivoted_log, minute)

    # Filter the original log for these active patients up to 'minute'
    relevant_events = event_log_df[
        (event_log_df['patient'].isin(active_patients)) &
        (event_log_df['time'] <= minute)
    ]

    # For each active patient, find their *very last* event in relevant_events
    latest_states = relevant_events.sort_values('time').groupby('patient').tail(1)

    # Store these latest states as the snapshot for 'minute'
    # ... add to list of snapshots ...

# (Output: A DataFrame where each row is a patient's state at a specific minute)

This process transforms the sequential, event-based log into a state-based log, telling us where everyone is at every moment needed for the animation frames.

Conclusion: Fueling the Flux Capacitor!

You’ve now mastered the concept of the Event Log – the vital, detailed diary that fuels vidigi’s visualizations. It’s the raw data stream, the script detailing every significant moment for every entity, captured with specific columns like patient, event_type, event, time, and sometimes resource_id. Without a good Event Log, our animation DeLorean can’t even reach 88 miles per hour!

You learned the required structure and how to create a basic one using pandas. This log is the fundamental input passed to animate_activity_log, which then uses helpers like reshape_for_animations to process it.

But knowing what happened and when isn’t the whole story. We also need to tell vidigi where on the screen these events should take place. How do we map ‘wait_for_simulator’ or ‘start_simulator’ to actual coordinates? That’s the job of the Layout Configuration!

Engage! Let’s warp to Chapter 3: Layout Configuration (event_position_df).

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