gapnet.scripts.test_model

module documentation
(source)

Undocumented

Function append_dataframe_with_psychometric_fit_threshold Undocumented
Function correct_func Undocumented
Function model_output_plot Undocumented
Function percentage_correct_plot Undocumented
Function threshold_for_impairment Undocumented
Constant N Undocumented
Constant S Undocumented
Constant X Undocumented
Variable an_model_basepath Undocumented
Variable args Undocumented
Variable at_lvl Undocumented
Variable bandwidth Undocumented
Variable data_folder Undocumented
Variable df_at_lvl Undocumented
Variable df_dprime Undocumented
Variable df_impair Undocumented
Variable df_mo Undocumented
Variable df_pct Undocumented
Variable df_pct_ideal Undocumented
Variable experiment_folders Undocumented
Variable experiment_parameter Undocumented
Variable experiment_parameters Undocumented
Variable experiment_parent_regexps Undocumented
Variable external_results_loaders Undocumented
Variable external_results_path Undocumented
Variable gap_lengths Undocumented
Variable history_path Undocumented
Variable impairments Undocumented
Variable in_channels Undocumented
Variable input_length Undocumented
Variable input_mode_input_lengths Undocumented
Variable job_list Undocumented
Variable levels Undocumented
Variable levels_impairment Undocumented
Variable line Undocumented
Variable load_model_path Undocumented
Variable local_process_parameter Undocumented
Variable mode_str Undocumented
Variable model Undocumented
Variable model_output_path Undocumented
Variable output_folder Undocumented
Variable parallel_argument_list Undocumented
Variable params Undocumented
Variable parent_regexp Undocumented
Variable parser Undocumented
Variable regexp_r Undocumented
Variable thres_df Undocumented
Variable thres_df_path Undocumented
Variable threshold Undocumented
Variable threshold_plot_path Undocumented
Variable _data_input_path Undocumented
def append_dataframe_with_psychometric_fit_threshold(df, psychometric_params, impairment, experiment_parameter, method, pct=0.707): (source)

Undocumented

def correct_func(x, p): (source)

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def model_output_plot(impairment): (source)

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def percentage_correct_plot(df, threshold, impairment): (source)

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def threshold_for_impairment(impairment): (source)

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N = (source)

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Value
df_at_lvl[df_at_lvl['gap_length'] == 0]['model_output'].to_numpy()
S = (source)

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Value
df_at_lvl[df_at_lvl['gap_length'] == gap]['model_output'].to_numpy()
X = (source)

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Value
df_at_lvl[df_at_lvl['gap_length'] == gap]['model_output'].to_numpy()
an_model_basepath = (source)

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args = (source)

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at_lvl = (source)

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bandwidth = (source)

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data_folder = (source)

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df_at_lvl = (source)

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df_dprime = (source)

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df_impair = (source)

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df_mo = (source)

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df_pct = (source)

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df_pct_ideal = (source)

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experiment_folders: dict[str, str] = (source)

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experiment_parameter = (source)

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experiment_parameters: dict[str, str] = (source)

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experiment_parent_regexps: dict[str, str] = (source)

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external_results_loaders = (source)

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external_results_path = (source)

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gap_lengths = (source)

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history_path = (source)

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impairments: list[str] = (source)

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in_channels = (source)

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input_length = (source)

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input_mode_input_lengths: dict[str, int] = (source)

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job_list = (source)

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levels = (source)

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levels_impairment = (source)

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line = (source)

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load_model_path = (source)

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local_process_parameter = (source)

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mode_str = (source)

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model = (source)

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model_output_path = (source)

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output_folder = (source)

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parallel_argument_list = (source)

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params = (source)

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parent_regexp = (source)

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parser = (source)

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regexp_r = (source)

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thres_df = (source)

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thres_df_path = (source)

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threshold = (source)

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threshold_plot_path = (source)

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_data_input_path = (source)

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