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  • function get_document_type

    Retrieves configuration details for a specific document type by looking it up in DOCUMENT_CONFIG or DOCUMENT_TYPES, returning a dictionary with document metadata or an empty dict if not found.

    File: /tf/active/vicechatdev/CDocs/settings_prod.py | Lines: 513-538

    document-management configuration lookup metadata document-type

  • function get_config_value

    Retrieves a configuration value from a nested dictionary structure in the global scope, with fallback to a default value if not found.

    File: /tf/active/vicechatdev/CDocs/settings_prod.py | Lines: 493-510

    configuration settings config-management dictionary-access error-handling

  • function get_department_code

    Retrieves a department code by looking up a department's full name in a DEPARTMENTS dictionary, returning the original name if not found.

    File: /tf/active/vicechatdev/CDocs/settings_prod.py | Lines: 363-365

    lookup dictionary mapping department code-conversion

  • function get_department_name

    Looks up and returns the full department name corresponding to a given department code by searching through a DEPARTMENTS dictionary.

    File: /tf/active/vicechatdev/CDocs/settings_prod.py | Lines: 356-361

    lookup dictionary mapping department reverse-lookup

  • function get_document_type_code

    Retrieves a document type code from a dictionary lookup using the provided document type name, returning the name itself if no mapping exists.

    File: /tf/active/vicechatdev/CDocs/settings_prod.py | Lines: 352-354

    lookup mapping document-type code-conversion dictionary-lookup

  • function get_document_type_name

    Looks up and returns the full document type name corresponding to a given document type code by searching through a DOCUMENT_TYPES dictionary.

    File: /tf/active/vicechatdev/CDocs/settings_prod.py | Lines: 345-350

    lookup dictionary reverse-mapping document-types code-to-name

  • function detect_outliers_iqr_v2

    Detects statistical outliers in a dataset using the Interquartile Range (IQR) method with a conservative 3×IQR threshold.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/84b9ac09-e646-4422-9d3a-e9f96529a553/analysis_1.py | Lines: 82-91

    outlier-detection statistics data-cleaning IQR interquartile-range

  • 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 | Lines: 472-494

    json serialization data-cleaning numpy pandas

  • function clean_for_json_v1

    Recursively traverses nested data structures (dicts, lists) and replaces NaN and Infinity float values with None to ensure JSON serialization compatibility.

    File: /tf/active/vicechatdev/vice_ai/new_app.py | Lines: 290-304

    json serialization data-cleaning nan-handling infinity-handling

  • function clean_for_json_v12

    Recursively sanitizes Python objects to make them JSON-serializable by converting non-serializable types (NumPy types, pandas objects, tuples, NaN/Inf values) into JSON-compatible formats.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/290a39ea-3ae0-4301-8e2f-9d5c3bf80e6e/project_3/analysis.py | Lines: 409-428

    json serialization data-cleaning numpy pandas

  • function clean_for_json_v15

    Recursively sanitizes Python objects to make them JSON-serializable by converting NumPy types to native Python types and handling NaN/Inf float values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/290a39ea-3ae0-4301-8e2f-9d5c3bf80e6e/analysis_3.py | Lines: 392-407

    json serialization data-cleaning numpy type-conversion

  • function clean_for_json_v14

    Recursively sanitizes Python objects to make them JSON-serializable by converting NumPy types to native Python types and handling NaN/Inf values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/42b81361-ba7e-4d79-9598-3090af68384b/analysis_2.py | Lines: 612-629

    json serialization numpy data-conversion type-conversion

  • function clean_for_json_v13

    Recursively sanitizes Python objects to make them JSON-serializable by converting NumPy types to native Python types and handling NaN/Inf values.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d48d7789-9627-4e96-9f48-f90b687cd07d/analysis_1.py | Lines: 356-371

    json serialization numpy data-cleaning type-conversion

  • function clean_for_json_v11

    Recursively sanitizes Python objects (dicts, lists, floats) to make them JSON-serializable by replacing NaN and infinity float values with None.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f5da873e-41e6-4f34-b3e4-f7443d4d213b/analysis_4.py | Lines: 403-412

    json serialization data-cleaning nan-handling infinity-handling

  • function clean_for_json_v10

    Recursively converts Python objects containing NumPy and Pandas data types into JSON-serializable native Python types.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/c385e1f5-fbf6-4832-8fd4-78ef8b72fc53/project_1/analysis.py | Lines: 630-651

    json serialization data-conversion numpy pandas

  • function clean_for_json_v9

    Recursively sanitizes Python objects (dicts, lists, floats) to ensure they are JSON-serializable by converting NaN and infinity values to None and ensuring all dictionary keys are strings.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/c385e1f5-fbf6-4832-8fd4-78ef8b72fc53/project_2/analysis.py | Lines: 107-116

    json serialization data-cleaning sanitization nan-handling

  • function detect_outliers_zscore

    Detects outliers in numerical data using the Z-score statistical method, identifying data points that deviate significantly from the mean.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/328d2f87-3367-495e-89f7-e633ff8c5b3d/analysis_2.py | Lines: 86-93

    outlier-detection statistics data-cleaning anomaly-detection z-score

  • function detect_outliers_iqr_v1

    Detects outliers in a dataset using the Interquartile Range (IQR) method, returning boolean indices of outliers and the calculated bounds.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/328d2f87-3367-495e-89f7-e633ff8c5b3d/analysis_2.py | Lines: 72-83

    outlier-detection IQR interquartile-range statistics data-cleaning

  • function export_results

    Exports correlation analysis results to multiple CSV files, including overall correlations, grouped correlations, and significant findings.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/5a059cb7-3903-4020-8519-14198d1f39c9/analysis_1.py | Lines: 464-483

    data-export csv file-io correlation-analysis results-persistence

  • function generate_conclusions

    Generates and prints comprehensive statistical conclusions from correlation analysis between Eimeria infection variables and broiler performance measures, including overall and group-specific findings.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/5a059cb7-3903-4020-8519-14198d1f39c9/analysis_1.py | Lines: 356-458

    statistical-analysis correlation reporting data-analysis veterinary-research