Gist 1127799

Gist 1127779

This example demonstrates a data-modeling pattern for the App Engine datastore using Python and the db.Model API. It shows how to structure a `Note` entity with a `ReferenceProperty` to a `Cell` entity, enabling reverse lookup of all notes attached to a cell via `Cell.notes`. The code also illustrates an alternative approach using parent-child relationships, where `Note.parent()` returns the owning `Cell`. Finally, it suggests constructing cell keys as composite strings like `'z/x/y/year/month/day'` to support efficient searches by coordinates and time.

Aandrewxhill

73% match

taxonomy service

This example implements a taxonomy search service for species data, returning filtered results by taxonomic ranks such as kingdom, phylum, class, order, family, genus, and species. It queries a Google App Engine datastore using the `Species` and `SpeciesIndex` models, applying up to two user-specified filters and optional range-map constraints. The service supports key-based lookups and offset/limit pagination, then serializes each result into JSON with rank, name, classification, authority, and alternate names. It uses the `simplejson` library for parsing stored JSON fields and the datastore’s `db.Key.from_path` for direct entity retrieval.

Aandrewxhill

60% match

Gist 11250891

This example shows a PostgreSQL query plan for a Django ORM query that retrieves inventory events by filtering on stock items associated with a specific item model. The visualization renders the EXPLAIN output as a tree of hash joins and sequential scans, using the `explain` command to expose query execution details. The code includes Python Django model definitions and raw SQL, with the query plan revealing how the database optimizes multiple LEFT OUTER JOINs and subqueries against inventory, booking, purchase, and cross-hire tables.

51% match

Gist d57b4594607d7185a88e

This example demonstrates parent-child relationships in Elasticsearch by indexing stores, departments, and products as nested document types. It shows how to query hierarchical data using the `has_child` filter to find stores containing departments whose products match a specific trademark. The code uses Elasticsearch’s REST API with `curl` commands to define mappings, index sample data, and execute filtered queries. It illustrates the `_parent` field for linking document types and `_routing` to enforce shard placement, while the final search combines term queries with nested `has_child` filters to retrieve relevant parent documents.

CCBasis

Gist f43601b1f490d87ec6f8

This example shows a Python script that downloads a Google Spreadsheet as a CSV file and prints its rows. The code defines a `Spreadsheet` class and a `Client` class that handles authentication via Google’s ClientLogin API, using `urllib` and `urllib2` for HTTP requests and `re` for parsing the authentication token. It then fetches the spreadsheet’s contents through the Google Sheets export feed, specifying the format as CSV and a worksheet ID, and parses the result with the standard `csv` module. The script demonstrates a straightforward command-line workflow for accessing spreadsheet data programmatically.

Gist 24eed51a3fba9e14760d

This example visualizes a Django data model’s query-set filtering logic as a directed graph, showing how `ItemModelQuerySet` methods such as `consumable`, `tracked`, and `hireable` branch from the base `ItemModelManager`. The graph nodes represent query-set operations and their type-based filter relationships, with edges indicating method chaining. It uses the `PassThroughManager` API to expose custom queryset methods, while the rendering approach likely relies on a JavaScript library like `d3.forceSimulation` for node layout, though the code itself focuses on the Python-side model definitions. The data source is the hardcoded queryset class structure from the gist.

Gist 033ea14fe76d6ec0e0a7

This example shows three versions of a Django view endpoint that retrieves the lowest stock level for a set of item models tied to a subbooking. The first version is a dense one-liner that queries each item ID individually, while the second and third versions optimize by filtering all item models at once using Django’s ORM `filter(id__in=pks)`. The code relies on Django’s `Response` object, `ItemModel.objects`, and `request.QUERY_PARAMS` to parse incoming query parameters, with each version producing the same dictionary mapping item primary keys to their lowest stock values.

Gist 956273

A graph visualization maps ecological occurrence sets and multipolygon indexes through their shared terms, using a force-directed layout to reveal clusters of related species and regions. The code builds the network from App Engine datastore models, connecting occurrences to their parent sets via reference properties. It renders nodes and edges using d3.forceSimulation, with spatial positioning driven by simulated physics rather than geographic projection, while styling reflects term rank and type metadata from the stored entities.

Aandrewxhill