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Showing 20 of 2143 components

  • function is_param_method

    Checks whether an object is a method on a Parameterized object, with optional verification of parameter dependencies.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1529-1547

    param parameterized introspection method-checking dependencies
  • function get_param_values

    Extracts parameter values from a data object, including key dimensions (kdims), value dimensions (vdims), label, and optionally the group if it differs from the default.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1520-1526

    metadata-extraction parameter-extraction holoviews param object-cloning
  • function is_ibis_expr

    Checks whether a given data object is an Ibis column expression by verifying if the ibis module is loaded and if the data is an instance of ibis.expr.types.ColumnExpr.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1513-1517

    type-checking ibis validation optional-dependency lazy-import
  • function is_cupy_array

    Checks whether the provided data object is a CuPy ndarray by conditionally importing CuPy and performing an isinstance check.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1506-1510

    type-checking cupy array gpu validation
  • function is_dask_array

    Checks whether the provided data object is a Dask array by conditionally importing dask.array and performing an isinstance check.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1499-1503

    type-checking dask array validation conditional-import
  • function is_series

    Checks whether the supplied data object is a pandas Series or dask Series type, with lazy loading support for dask.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1488-1496

    type-checking validation pandas dask series
  • function is_dataframe

    Checks whether the supplied data object is a pandas DataFrame or a Dask DataFrame, with support for lazy imports of both libraries.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1477-1485

    dataframe type-checking validation pandas dask
  • function get_spec

    Extracts a specification tuple from a labeled data object, consisting of the class name, group, and label attributes.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1469-1474

    metadata specification data-object identification holoviews
  • function iterative_select

    Recursively selects subgroups from a hierarchical object structure by iterating through dimensions and applying select operations, avoiding duplication of selections.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1451-1466

    hierarchical-selection recursive data-selection dimension-traversal holoviews
  • function group_select

    Recursively groups a list of key tuples into a nested dictionary structure to optimize indexing operations by avoiding duplicate key lookups.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1433-1448

    data-structures optimization indexing grouping recursion
  • function layer_groups

    Groups elements from an ordering list into a dictionary based on a slice of each element's specification, using the first 'length' items as the grouping key.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1421-1430

    grouping data-organization layer-management hierarchical-structure dictionary
  • function layer_sort

    Computes a global topological ordering of layers from a HoloMap containing CompositeOverlay objects by analyzing layer dependencies and sorting them.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1406-1418

    holoviews visualization layer-ordering topological-sort composite-overlay
  • function get_overlay_spec

    Constructs a specification tuple for an Element within an Overlay by combining the element's type name, group, label, and key dimensions.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1397-1403

    overlay specification element-identification data-structure tuple-construction
  • function one_to_one

    Validates whether a directed graph represents a one-to-one mapping by checking if each node maps to exactly one unique edge and vice versa.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1387-1394

    graph-theory validation mapping bijection one-to-one
  • function is_cyclic

    Detects whether a directed graph contains a cycle using depth-first search with path tracking.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1369-1384

    graph-algorithms cycle-detection directed-graph depth-first-search dfs
  • function sort_topologically

    Performs stackless topological sorting on a directed acyclic graph (DAG), organizing nodes into levels based on their dependencies.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1316-1366

    topological-sort graph-algorithms dependency-resolution dag directed-acyclic-graph
  • class ProgressIndicator

    Abstract base class for progress indicators that display task completion as a percentage, providing a framework for implementing custom progress tracking mechanisms.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1297-1313

    progress-tracking base-class abstract ui-component percentage
  • function is_int

    Checks if an object is an integer type, supporting native Python integers, NumPy integer types, and optionally float types with integer values.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1280-1294

    type-checking validation integer numpy dtype
  • function is_float

    A type-checking utility function that determines whether a given object is a floating-point scalar value, supporting both Python's native float type and NumPy floating-point types.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1273-1277

    type-checking validation float numpy scalar
  • function is_number

    Determines whether an object is a number or behaves like a number, with special handling for numpy types and numeric-like classes.

    File: /tf/active/vicechatdev/patches/util.py | Lines: 1262-1270

    type-checking validation numeric numpy duck-typing