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Found 50 matching component(s)

• class Config_v2

  Configuration class that manages environment-based settings for a SharePoint to FileCloud synchronization application.

  File: /tf/active/vicechatdev/SPFCsync/config.py

  configuration environment-variables sharepoint filecloud sync

• function test_european_csv

  A test function that validates the ability to read and parse European-formatted CSV files (semicolon delimiters, comma decimal separators) and convert them to proper numeric types.

  File: /tf/active/vicechatdev/vice_ai/test_regional_formats.py

  testing csv european-format data-parsing unit-test

• function test_us_csv

  A unit test function that validates the smart_read_csv function's ability to correctly parse US-formatted CSV files with comma delimiters and point decimal separators.

  File: /tf/active/vicechatdev/vice_ai/test_regional_formats.py

  testing unit-test csv data-parsing us-format

• function test_european_with_thousands

  A unit test function that validates the smart_read_csv function's ability to correctly parse European-formatted CSV files with thousand separators (dots) and decimal commas.

  File: /tf/active/vicechatdev/vice_ai/test_regional_formats.py

  testing unit-test csv-parsing european-format number-formatting

• function test_us_with_thousands

  A unit test function that validates the smart_read_csv function's ability to correctly parse US-formatted CSV files containing numbers with thousand separators (commas) and decimal points.

  File: /tf/active/vicechatdev/vice_ai/test_regional_formats.py

  testing unit-test csv-parsing data-validation number-formatting

• function test_tab_delimited_european

  A unit test function that validates the smart_read_csv function's ability to correctly parse tab-delimited CSV files containing European-style decimal numbers (using commas instead of periods).

  File: /tf/active/vicechatdev/vice_ai/test_regional_formats.py

  testing unit-test csv-parsing european-decimals tab-delimited

• class AgentExecutor

  Agent-based script executor that generates standalone Python files, manages dependencies, and provides iterative debugging capabilities

  File: /tf/active/vicechatdev/vice_ai/agent_executor.py

  class agentexecutor

• function convert_european_decimals

  Detects and converts numeric data with European decimal format (comma as decimal separator) to standard format (dot as decimal separator) in a pandas DataFrame, handling mixed formats and missing data patterns.

  File: /tf/active/vicechatdev/vice_ai/smartstat_service.py

  data-processing data-cleaning decimal-conversion european-format locale-handling

• class SmartStatSession

  A session management class that encapsulates a SmartStat statistical analysis session, tracking data, analysis history, plots, and reports for a specific data section.

  File: /tf/active/vicechatdev/vice_ai/smartstat_service.py

  session-management data-analysis statistics dataframe multi-dataset

• class SmartStatService

  Service for running SmartStat analysis sessions in Vice AI

  File: /tf/active/vicechatdev/vice_ai/smartstat_service.py

  class smartstatservice

• class DataAnalysisService

  Service class for managing data analysis operations within document sections, integrating with SmartStat components for statistical analysis, dataset processing, and visualization generation.

  File: /tf/active/vicechatdev/vice_ai/data_analysis_service.py

  data-analysis statistical-analysis session-management dataset-processing visualization

• function clean_nan_for_json

  Recursively traverses nested data structures (dicts, lists) and converts NaN, null, and invalid numeric values to None for safe JSON serialization.

  File: /tf/active/vicechatdev/vice_ai/data_analysis_service.py

  json-serialization data-cleaning nan-handling recursive data-preprocessing

• class StatisticalAgent

  LLM-powered statistical analysis agent

  File: /tf/active/vicechatdev/vice_ai/statistical_agent.py

  class statisticalagent

• function remove_outliers_iqr

  Removes outliers from a pandas DataFrame column using the Interquartile Range (IQR) method with a conservative 3*IQR threshold.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/42b81361-ba7e-4d79-9598-3090af68384b/analysis_2.py

  data-cleaning outlier-detection IQR interquartile-range data-preprocessing

• function clean_for_json_v4

  Recursively traverses nested data structures (dicts, lists, arrays) and converts NaN and Inf float values to None for safe JSON serialization, while also converting NumPy types to native Python types.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/7372154d-807e-4723-a769-4668761944b5/analysis_2.py

  json serialization data-cleaning numpy nan-handling

• function clean_for_json_v5

  Recursively traverses nested data structures (dictionaries, lists) and sanitizes numeric values by converting NaN and Inf to None, and normalizing NumPy numeric types to native Python types for JSON serialization.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e4e8cb00-c17d-4282-aa80-5af67f32952f/analysis_1.py

  data-cleaning json-serialization numpy data-preprocessing nan-handling

• function clean_for_json_v3

  Recursively converts Python objects (including NumPy and Pandas types) into JSON-serializable formats by handling special numeric types, NaN/Inf values, and nested data structures.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f0a78968-1d2b-4fbe-a0c6-a372da2ce2a4/project_1/analysis.py

  json serialization data-conversion numpy pandas

• function clean_for_json_v6

  Recursively traverses nested data structures (dicts, lists) and sanitizes floating-point values by replacing NaN and Inf with None, while also converting NumPy numeric types to native Python types.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_4.py

  json serialization data-cleaning numpy nan-handling

• function correlation_significance

  Calculates Pearson correlation coefficient and statistical significance (p-value) between two numeric arrays, handling NaN values automatically.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_7.py

  statistics correlation pearson p-value significance-testing

• function clean_for_json_v7

  Recursively traverses and sanitizes data structures (dicts, lists, numpy types) to ensure JSON serialization compatibility by converting numpy types to native Python types and handling NaN/Inf values.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_1.py

  json serialization data-cleaning numpy pandas

• function clean_for_json_v2

  Recursively traverses nested data structures (dicts, lists) and sanitizes numeric values by converting NaN and Inf to None, and numpy types to native Python types for JSON serialization.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e9b7c942-87b5-4a6f-865e-e7a0d62fb0a1/analysis_2.py

  json serialization data-cleaning numpy nan-handling

• function clean_for_json

  Recursively traverses and sanitizes Python data structures (dicts, lists, tuples, numpy arrays) to ensure all values are JSON-serializable by converting numpy types, handling NaN/Inf values, and normalizing data types.

  File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f0b81d95-24d9-418a-8d9f-1b241684e64c/project_1/analysis.py

  json serialization data-cleaning numpy pandas

• function initialize_document_counters

  Initializes document counters in Neo4j by analyzing existing ControlledDocument nodes and creating DocumentCounter nodes with values higher than the maximum existing document numbers for each department/type combination.

  File: /tf/active/vicechatdev/CDocs/db/schema_manager.py

  neo4j database-initialization document-management counter-initialization graph-database

• class DocumentDetail_v2

  Document detail view component

  File: /tf/active/vicechatdev/CDocs/ui/document_detail.py

  class documentdetail

• class DynamicSchemaDiscovery

  Discovers database schema from live database connection

  File: /tf/active/vicechatdev/full_smartstat/dynamic_schema_discovery.py

  class dynamicschemadiscovery

• class AgentExecutor_v1

  Agent-based script executor that generates standalone Python files, manages dependencies, and provides iterative debugging capabilities

  File: /tf/active/vicechatdev/full_smartstat/agent_executor.py

  class agentexecutor

• function get_database_tables_columns

  Flask route handler that retrieves database schema information including tables, columns, and relationships, filtered and sorted by relevance for data analysis workflows.

  File: /tf/active/vicechatdev/full_smartstat/app.py

  flask database schema metadata api-endpoint

• function get_database_schema_viewer

  Flask route handler that retrieves and formats detailed database schema information from a discovered schema stored in the Flask app object, returning it as JSON for visualization purposes.

  File: /tf/active/vicechatdev/full_smartstat/app.py

  flask api-endpoint database-schema metadata visualization

• class EnhancedSQLWorkflow

  Enhanced SQL workflow with iterative optimization

  File: /tf/active/vicechatdev/full_smartstat/enhanced_sql_workflow.py

  class enhancedsqlworkflow

• function demo_analysis_workflow

  Demonstrates a complete end-to-end statistical analysis workflow using the SmartStat system, including session creation, data loading, natural language query processing, analysis execution, and result interpretation.

  File: /tf/active/vicechatdev/full_smartstat/demo.py

  demo workflow statistical-analysis natural-language-processing data-analysis

• class DataProcessor

  Handles data loading, validation, and preprocessing

  File: /tf/active/vicechatdev/full_smartstat/data_processor.py

  class dataprocessor

• class DataProcessor_v1

  Handles data loading, validation, and preprocessing

  File: /tf/active/vicechatdev/smartstat/data_processor.py

  class dataprocessor

• function isnumeric

  Determines whether a given value can be converted to a numeric type (int or float), excluding strings and boolean types.

  File: /tf/active/vicechatdev/patches/util.py

  validation type-checking numeric data-validation type-conversion

• function find_minmax

  Computes the minimum of the first elements and maximum of the second elements from two tuples of numeric values, handling NaN values gracefully.

  File: /tf/active/vicechatdev/patches/util.py

  numeric-range min-max bounds-calculation nan-handling data-aggregation

• function max_range

  Computes the maximal lower and upper bounds from a list of range tuples, handling various data types including numeric, datetime, and string values.

  File: /tf/active/vicechatdev/patches/util.py

  data-processing range-computation bounds aggregation datetime-handling

• function range_pad

  Pads a numeric or datetime range by a specified fraction of the interval, with optional logarithmic scaling for positive numeric values.

  File: /tf/active/vicechatdev/patches/util.py

  range padding numeric datetime logarithmic

• function max_extents

  Computes the maximal extent (bounding box) from a list of extent tuples, supporting both 2D (4-tuples) and 3D (6-tuples) coordinate systems with special handling for datetime and string values.

  File: /tf/active/vicechatdev/patches/util.py

  spatial-analysis bounding-box extent-calculation 2D 3D

• 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

  type-checking validation numeric numpy duck-typing

• 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

  type-checking validation float numpy scalar

• 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

  type-checking validation integer numpy dtype

• function is_nan

  A type-safe utility function that checks whether a given value is NaN (Not a Number), handling arbitrary types without raising exceptions.

  File: /tf/active/vicechatdev/patches/util.py

  validation nan-check type-safe data-validation numpy

• function compute_density

  Computes the density (samples per unit) of a grid given start and end boundaries and the number of samples, with special handling for datetime/timedelta types.

  File: /tf/active/vicechatdev/patches/util.py

  density grid datetime timedelta sampling

• function numpy_scalar_to_python

  Converts NumPy scalar types to their equivalent native Python types (float or int), returning the original value if it's not a NumPy numeric scalar.

  File: /tf/active/vicechatdev/patches/util.py

  numpy type-conversion scalar data-processing utility

• function closest_match

  Recursively finds the closest matching specification from a list of specs by comparing hierarchical keys (type, group, label, overlay) with a target match pattern.

  File: /tf/active/vicechatdev/patches/util.py

  matching recursive hierarchical specification pattern-matching

• class GridSpace

  GridSpace is a container class for organizing elements in a 1D or 2D grid structure with floating-point keys, ensuring all contained elements are of the same type.

  File: /tf/active/vicechatdev/patches/spaces.py

  grid layout container mapping 2D-grid

• class AUValidator

  Australia-specific invoice data validator that extends BaseValidator to implement validation rules for Australian invoices including ABN validation, GST calculations, and Australian tax invoice requirements.

  File: /tf/active/vicechatdev/invoice_extraction/validators/au_validator.py

  validation invoice australia abn gst

• class BaseValidator

  Abstract base class for validating extracted invoice data with entity-specific validation rules. Provides common validation functionality for required fields, field types, date consistency, and amount calculations.

  File: /tf/active/vicechatdev/invoice_extraction/validators/base_validator.py

  validation invoice abstract-base-class data-validation entity-validation

• class BEValidator

  Belgium-specific invoice data validator that extends BaseValidator to implement Belgian invoice validation rules including VAT number format, address verification, IBAN validation, and legal requirements.

  File: /tf/active/vicechatdev/invoice_extraction/validators/be_validator.py

  validation invoice belgium vat iban

• class UKValidator

  UK-specific invoice data validator that extends BaseValidator to implement validation rules specific to UK invoices including VAT number format, UK addresses, VAT rates, and banking details.

  File: /tf/active/vicechatdev/invoice_extraction/validators/uk_validator.py

  validation invoice UK VAT tax

• class FormatNormalizer

  Normalizes extracted data formats to ensure consistency. Handles: - Date format standardization - Number/currency normalization - VAT/tax number formatting - Field name standardization - Address formatting - Field value cleaning

  File: /tf/active/vicechatdev/invoice_extraction/utils/format_normalizer.py

  class formatnormalizer

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