> ## Documentation Index > Fetch the complete documentation index at: https://demo.agenticlabs.com/llms.txt > Use this file to discover all available pages before exploring further. # size_matched_model.py ## High-level description The code defines a `SizeMatchedModel` class that represents a statistical model where parameters vary based on the size of the input data. It provides methods for loading and saving the model, calculating p-values, and determining model means based on input size. ## Code Structure The `SizeMatchedModel` class is the central component. It uses a list of bins to partition the input size range and associates a set of parameters with each bin. The `distribution` attribute holds a statistical distribution object (not defined in this file) used for calculations. ## Symbols ### `SizeMatchedModel` #### Description This class represents a statistical model where parameters are determined by the size of the input data. It uses bins to divide the size range and associates a set of parameters with each bin. #### Inputs | Name | Type | Description | | :----------- | :----- | :------------------------------------------------------------------------ | | bins | list | List of bin edges defining the size ranges. | | params | list | List of parameter sets, one for each bin. | | distribution | object | An instance of a statistical distribution class (e.g., from scipy.stats). | | name | str | Optional name for the model. | #### Outputs N/A - This is a class definition, not a function. #### Internal Logic The class stores the bins, parameters, distribution, and name. It provides methods for: * Loading and saving the model from/to JSON files. * Finding the appropriate parameters for a given size using `_params_for_size`. * Calculating p-values using the provided distribution and parameters. * Determining the model mean for a given size. ### `SizeMatchedModel.from_json` #### Description This class method loads a `SizeMatchedModel` instance from a JSON file. #### Inputs | Name | Type | Description | | :------- | :--- | :----------------------------------------------- | | filename | str | Path to the JSON file containing the model data. | #### Outputs | Name | Type | Description | | :---- | :--------------- | :------------------------------------------------------ | | model | SizeMatchedModel | A new `SizeMatchedModel` instance loaded from the file. | #### Internal Logic * Opens the JSON file and loads the data. * Converts string representations of lists and the distribution name to their respective Python objects. * Instantiates a new `SizeMatchedModel` with the loaded data. ### `SizeMatchedModel.to_json` #### Description This method saves the `SizeMatchedModel` instance to a JSON file. #### Inputs | Name | Type | Description | | :------ | :--- | :---------------------------- | | outfile | str | Path to the output JSON file. | #### Outputs N/A - The method writes data to a file as a side effect. #### Internal Logic * Creates a dictionary containing the model's attributes. * Converts lists to JSON-serializable strings. * Extracts the distribution's class name for serialization. * Writes the dictionary to the specified JSON file. ### `SizeMatchedModel._params_for_size` #### Description This private method retrieves the appropriate parameters for a given size based on the defined bins. #### Inputs | Name | Type | Description | | :------------- | :---- | :------------------------------------------------------------------------------------------------------------------ | | size | float | The size value for which to find the parameters. | | strict\_bounds | bool | If True, raises an error if the size is outside the defined bins. If False, uses the parameters of the nearest bin. | #### Outputs | Name | Type | Description | | :------------ | :---- | :---------------------------------------------------------------- | | params\_match | tuple | The parameters associated with the bin containing the given size. | #### Internal Logic * Uses `np.digitize` to find the index of the bin corresponding to the input size. * Handles cases where the size is outside the defined bins based on `strict_bounds`. * Adjusts the bin index to match the zero-based indexing of the `params` list. * Returns the parameters for the selected bin. ### `SizeMatchedModel.pvalue` #### Description This method calculates the p-value of a given value `x` under the model for a specific size. #### Inputs | Name | Type | Description | | :------------- | :---- | :----------------------------------------------------------------------------------- | | x | float | The value for which to calculate the p-value. | | size | float | The size value used to determine the model parameters. | | invert\_cdf | bool | If True, calculates 1 - CDF(x) instead of CDF(x). | | strict\_bounds | bool | Passed to `_params_for_size` to control behavior for sizes outside the defined bins. | #### Outputs | Name | Type | Description | | :--- | :---- | :---------------------- | | p | float | The calculated p-value. | #### Internal Logic * Retrieves the model parameters for the given size using `_params_for_size`. * Calculates the cumulative distribution function (CDF) of `x` using the model's distribution and parameters. * Inverts the CDF if `invert_cdf` is True. * Returns the calculated p-value. ### `SizeMatchedModel.model_mean` #### Description This method calculates the mean of the model for a given size. #### Inputs | Name | Type | Description | | :------------- | :---- | :----------------------------------------------------------------------------------- | | size | float | The size value used to determine the model parameters. | | strict\_bounds | bool | Passed to `_params_for_size` to control behavior for sizes outside the defined bins. | #### Outputs | Name | Type | Description | | :--- | :---- | :-------------------------------- | | mean | float | The calculated mean of the model. | #### Internal Logic * Retrieves the model parameters for the given size using `_params_for_size`. * Calculates the mean of the distribution using the retrieved parameters. * Returns the calculated mean. ## TODOs * Evaluate distribution in global namespace, so that import is not necessary here