Gist fd0201fe95dfb4390a53

Gist 2ad07f5c9a84e6823e08

This example visualizes the class inheritance structure of a Django model, showing how `HasEvents` connects to `HasAdminUrl`, `TimeStampedModel`, and its custom manager requirement. The code uses Python class definitions and a runtime check with `issubclass` to enforce that subclasses inherit from `HasEventsManager`. The visualization renders the relationships between these classes, highlighting the inheritance chain and the manager constraint through a graph layout. The rendering approach relies on the class metadata visible in the source, with the `HasEvents` class at the center of the diagram.

68% match

Gist 11191339

This example shows a Django model definition for an inventory event system, mapping various event types—bookings, purchases, cross-hires, repairs, and missing items—to related database records via foreign keys. The code uses Django’s `models.ForeignKey` to link each event to its corresponding office, inventory, or booking entity, with nullable and blankable fields allowing flexible associations. It is a data-model snippet, not a visualization, and does not employ any charting or rendering libraries.

66% match

Gist f27e286a30902848d6c4

This example visualizes the relationships between Django model instances and their associated notification rules. It shows how filter conditions map to database query sets, using a static code-based data structure. The visualization relies on Django’s ORM for defining model fields and query filters, while the rendering approach appears to use a custom layout for displaying the rule hierarchy. The code defines a `Something` model with `name` and `type` fields, then constructs a `NOTIFY_RULE` dictionary that filters bookings by a pre-filtered `Location` queryset flagged as `our_base`. No interactive or animated elements are present.

64% match

Gist 32e4ba8d51a4da505005

This example shows a stack trace from a Python Django application, rendered as a visual tree or graph. It displays the call chain leading to an error, with each function call represented as a node and the relationships between calls shown as edges. The visualization uses `d3.forceSimulation` to position the nodes and links, likely with `d3.select` and SVG elements for rendering. The data originates from the traceback text, parsed to extract file paths, line numbers, and function names. The graph highlights the nested structure of the error, from the top-level module through model definitions to the final lambda function.

60% match

Gist 2c4e1c0d19a9894cfe93

This example visualizes the inheritance chain of Python classes as a small, static diagram. It shows the relationship between a base class `Bob` and its subclass `Sid`, with the inherited attribute `hello` displayed on both. The visualization uses a simple tree-like layout, with class names as nodes and connecting lines to indicate the parent-child relationship. The rendering is done through a custom Python script that likely uses a basic drawing library, but the code itself primarily demonstrates the class definitions and attribute access, rather than any specific visualization API.

Gist afdfda5777213cb4fe90

A radial tree visualizes Python class inheritance and notifier type associations. The layout uses d3.forceSimulation to position nodes, with links drawn between parent and child classes. The data originates from recursively traversing `__subclasses__()` and collecting `NOTIFIER_TYPES` attributes, as defined in `r.py`. Each leaf or cluster represents a class or a set of notifier types, with the visualization showing the hierarchy and grouping of notification-related subclasses. The rendering approach relies on D3’s force-directed graph to arrange the interconnected nodes.

Gist 9839784

This visualization displays Django model query results for hotel bookings, distinguishing arrivals and departures for today and tomorrow through grouped bar charts. The code defines a custom `BookingManager` that filters `Booking.objects` using date-based lookups, combining departure and arrival querysets with union operations (`|`). The rendering likely uses d3.js for bar chart layout, while the data pipeline relies on Python’s `datetime` and `timedelta` calculations to segment time periods. The manager properties expose aggregated counts for each category, feeding directly into the visual representation.

Gist 9839271

This visualization shows the daily flow of bookings through a Django-based booking system, tracking arrivals and departures on the current day. The code defines a `BookingManager` that filters booking records by timestamps falling within the day’s start and end boundaries, producing two properties: `departures_today` for bookings starting today and `arrivals_today` for bookings ending today. The example demonstrates how to query and aggregate temporal data using Django’s ORM, with the `Booking` model and `models.Manager` class providing the data structure. The visualization itself is not shown in the snippet; only the data-management logic is presented, implying a chart built separately from these queried results.