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  • 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 | Lines: 461-481

    json serialization data-cleaning 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 | Lines: 307-323

    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 | Lines: 367-383

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

  • 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 | Lines: 431-449

    json serialization data-cleaning numpy 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 | Lines: 613-630

    json serialization data-conversion numpy pandas

  • function calculate_sample_size_v1

    Calculates the required sample size per group for a two-group statistical comparison using Cohen's d effect size, significance level, statistical power, and standard deviation.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e9b7c942-87b5-4a6f-865e-e7a0d62fb0a1/analysis_2.py | Lines: 133-149

    statistics power-analysis sample-size experimental-design hypothesis-testing

  • function calculate_sample_size_v2

    Calculates the required sample size per group for a two-sample t-test given standard deviation, effect size (Cohen's d), significance level, and statistical power.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f0a78968-1d2b-4fbe-a0c6-a372da2ce2a4/project_1/analysis.py | Lines: 433-442

    statistics power-analysis sample-size t-test experimental-design

  • function calculate_sample_size

    Calculates the required sample size per group for a two-sample t-test using Cohen's d effect size, significance level, and statistical power.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/1315733d-fb14-4740-a1a4-021696492d5e/analysis_1.py | Lines: 43-65

    statistics power-analysis sample-size t-test experimental-design

  • 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 | Lines: 348-359

    statistics correlation pearson p-value significance-testing

  • function calculate_cv_v1

    Calculates the Coefficient of Variation (CV) for a dataset, expressed as a percentage. CV measures relative variability by dividing standard deviation by mean.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d1e252f5-950c-4ad7-b425-86b4b02c3c62/analysis_4.py | Lines: 46-56

    statistics coefficient-of-variation variability dispersion data-analysis

  • function calculate_cv_v2

    Calculates the coefficient of variation (CV) for a group of numerical values, expressed as a percentage.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f5da873e-41e6-4f34-b3e4-f7443d4d213b/analysis_5.py | Lines: 42-43

    statistics coefficient-of-variation cv variability dispersion

  • function perform_analysis

    Performs comprehensive statistical analysis on grouped biological/experimental data, including descriptive statistics, correlation analysis, ANOVA testing, and visualization of infection levels and growth performance across different groups.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e1ecec5f-4ea5-49c5-b4f5-d051ce851294/project_1/analysis.py | Lines: 23-83

    statistical-analysis data-analysis ANOVA correlation visualization

  • function load_dataset

    Loads a CSV dataset from a specified file path using pandas and returns it as a DataFrame with error handling for file not found and general exceptions.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e1ecec5f-4ea5-49c5-b4f5-d051ce851294/project_1/analysis.py | Lines: 10-20

    data-loading csv pandas file-io error-handling

  • function calculate_cv

    Calculates the coefficient of variation (CV) for a dataset, expressed as a percentage of the standard deviation relative to the mean.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/d48d7789-9627-4e96-9f48-f90b687cd07d/analysis_1.py | Lines: 42-51

    statistics coefficient-of-variation data-analysis variability dispersion

  • function clean_for_json_v1

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

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/e4e8cb00-c17d-4282-aa80-5af67f32952f/project_1/analysis.py | Lines: 364-382

    json serialization data-cleaning numpy nan-handling

  • function remove_outliers

    Removes outliers from a pandas DataFrame based on the Interquartile Range (IQR) method for a specified column.

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/f5da873e-41e6-4f34-b3e4-f7443d4d213b/analysis_5.py | Lines: 26-32

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

  • function remove_outliers_iqr_v1

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

    File: /tf/active/vicechatdev/vice_ai/smartstat_scripts/42b81361-ba7e-4d79-9598-3090af68384b/project_1/analysis.py | Lines: 74-85

    data-cleaning outlier-detection IQR interquartile-range statistics

  • 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 | Lines: 63-74

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

  • class AzureSSO_v1

    A class that handles Azure Active Directory (Azure AD) Single Sign-On (SSO) authentication using OAuth 2.0 authorization code flow.

    File: /tf/active/vicechatdev/vice_ai/auth/azure_auth.py | Lines: 16-175

    azure authentication sso oauth2 azure-ad

  • class DatabaseManager_v1

    SQLite database manager for persistent storage

    File: /tf/active/vicechatdev/vice_ai/models.py | Lines: 603-1684

    class databasemanager