lynguine.assess

The assess module provides functionality for processing and analysing data, including custom DataFrame implementations and computation capabilities.

Data Module

lynguine.assess.data.iskeyword()

x.__contains__(y) <==> y in x.

class lynguine.assess.data.Accessor(data)

Bases: object

class lynguine.assess.data.DataObject(data=None, colspecs=None, index=None, column=None, selector=None, subindex=None)

Bases: object

get_subseries()

Get the subseries that is the focus for the DataFrame.

Returns:

The subseries that is the focus for the DataFrame.

Return type:

DataFrame

get_subindices()

Get the subindices that are the focus for the DataFrame.

Returns:

The subindices that are the focus for the DataFrame.

Return type:

Index

get_subseries_values()

Return a pd.Series containing all the values in a column.

get_index()

Get the index that is the focus for the DataFrame.

Returns:

The index that is the focus for the DataFrame.

Return type:

int

set_index(index)

Set the index to be the focus for the DataFrame.

Parameters:

index (int) – The index to set.

Raises:

KeyError – If the index is not in the DataFrame.

Return type:

None

get_column()

Get the column that is the focus for the DataFrame.

Returns:

The column that is the focus for the DataFrame.

set_column(column)

Set the column to be the focus for the DataFrame.

Parameters:

column – The column to set.

Raises:

KeyError – If the column is not in the DataFrame.

set_value_column(value, column)

Set a value to a column in the write data frame

Parameters:

• value (object) – The value to be set.

• column (str) – The column to set the value in.

Returns:

None

get_value_column(column)

Get a value from a column in the data frame(s)

Parameters:

column (str) – The column to get the value from.

Returns:

The value from the column.

Return type:

object

ismutable(column)

Is a given column mutable?

Parameters:

column – The column to check.

Returns:

True if the column is mutable, False otherwise.

property autocache

Whether the data structure will automatically cache values.

:return True if the data structure will automatically cache values, False otherwise.

property interface

Return the interface object. :return: The interface object.

property mutable

Is the data structure mutable.

:return True if the data structure is mutable, False otherwise.

isparameter(column)

Test if the column is a given column a parameter (i.e. applicable for all rows)?

Parameters:

column – The column to check.

Returns:

True if the column is a parameter, False otherwise.

isseries(column)

Test if a given column is a series column (i.e. one where the index can be repeated and have multiple rows associated with it).

Parameters:

column – The column to check.

Returns:

True if the column is a series column, False otherwise.

get_selector()

Get the selector that is the focus for the DataFrame.

The selector is the column used to disambiguate a series in the focus.

Returns:

The selector that is the focus for the DataFrame.

set_selector(column)

Set the selector that is the focus for the DataFrame.

The selector

Parameters:

column – The column to set the selector to.

Raises:

KeyError – If the column is not in the DataFrame.

get_subindex()

Get the subindex that is the focus for the DataFrame. The subindex is the index used to disambiguate a series in the focus.

Returns:

The subindex that is the focus for the DataFrame.

Return type:

int

set_subindex(index)

Set the subindex that is the focus for the DataFrame.

If the subindex is set to None, the last row of the subseries is returned. The last row is assumed to be the most recent.

Parameters:

index (int) – The index to set the subindex to.

Raises:

KeyError – If the index is not in the DataFrame.

get_selectors()

Return valid selectors, these are columns that are present in the type “series” :return: The valid selectors.

get_value()

Get the value that is in the cell defined as focus for the DataFrame.

Returns:

The value that is the focus for the DataFrame.

set_value(value)

Set the value that is in the cell defined as focus for the DataFrame.

Parameters:

value – The value to set.

add_row(index, typ=None, values=None)

Add a new row to the CustomDataFrame for the current column in focus.

Parameters:

• index – The index of the new row to be added.

• typ – The type of row being added.

• values – A dictionary of column:value pairs for the new row. If None, adds NaN values.

Raises:

ValueError – If the index already exists or if it’s not possible to add the row.

head(n=5)

Return the first n rows of the DataFrame.

Parameters:

n – Number of rows to select.

Returns:

The first n rows of the DataFrame.

tail(n=5)

Return the last n rows of the DataFrame.

Parameters:

n – Number of rows to select.

Returns:

The last n rows of the DataFrame.

add_column(column_name, data)

Add a new column to the DataFrame.

Parameters:

• column_name – The name of the new column.

• data – The data for the new column.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

drop_column(column_name)

Drop a column from the DataFrame.

Parameters:

column_name – The name of the column to drop.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

filter_rows(condition)

Filter rows based on a specified condition.

Parameters:

condition – The condition to filter rows.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

get_shape()

Get the shape of the DataFrame.

Returns:

A tuple representing the shape of the DataFrame.

describe()

Generate descriptive statistics.

Returns:

Descriptive statistics for the DataFrame.

to_pandas()

Convert the custom DataFrame to a Pandas DataFrame.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

to_clipboard(*args, **kwargs)

Copy the DataFrame to the system clipboard.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_clipboard.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_clipboard.

Returns:

Output of Pandas DataFrame to_clipboard method.

to_feather(*args, **kwargs)

Write the DataFrame to a Feather file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_feather.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_feather.

Returns:

Output of Pandas DataFrame to_feather method.

to_json(*args, **kwargs)

Convert the DataFrame to a JSON string.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_json.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_json.

Returns:

Output of Pandas DataFrame to_json method.

to_orc(*args, **kwargs)

Write the DataFrame to an ORC file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_orc.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_orc.

Returns:

Output of Pandas DataFrame to_orc method.

to_records(*args, **kwargs)

Convert the DataFrame to a NumPy record array.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_records.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_records.

Returns:

Output of Pandas DataFrame to_records method.

to_timestamp(*args, **kwargs)

Cast to DatetimeIndex of timestamps.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_timestamp.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_timestamp.

Returns:

Output of Pandas DataFrame to_timestamp method.

to_csv(*args, **kwargs)

Write the DataFrame to a comma-separated values (csv) file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_csv.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_csv.

Returns:

Output of Pandas DataFrame to_csv method.

to_gbq(*args, **kwargs)

Write the DataFrame to a Google BigQuery table.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_gbq.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_gbq.

Returns:

Output of Pandas DataFrame to_gbq method.

to_latex(*args, **kwargs)

Render the DataFrame as a LaTeX tabular environment table.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_latex.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_latex.

Returns:

Output of Pandas DataFrame to_latex method.

to_parquet(*args, **kwargs)

Write the DataFrame to a Parquet file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_parquet.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_parquet.

Returns:

Output of Pandas DataFrame to_parquet method.

to_sql(*args, **kwargs)

Write records stored in the DataFrame to a SQL database.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_sql.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_sql.

Returns:

Output of Pandas DataFrame to_sql method.

to_xarray(*args, **kwargs)

Convert the DataFrame to an xarray Dataset.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_xarray.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_xarray.

Returns:

Output of Pandas DataFrame to_xarray method.

to_dict(*args, **kwargs)

Convert the DataFrame to a dictionary.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_dict.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_dict.

Returns:

Output of Pandas DataFrame to_dict method.

to_hdf(*args, **kwargs)

Write the DataFrame to a Hierarchical Data Format (HDF) file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_hdf.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_hdf.

Returns:

Output of Pandas DataFrame to_hdf method.

to_markdown(*args, **kwargs)

Convert the DataFrame to a Markdown string.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_markdown.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_markdown.

Returns:

Output of Pandas DataFrame to_markdown method.

to_period(*args, **kwargs)

Convert DataFrame from DatetimeIndex to PeriodIndex.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_period.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_period.

Returns:

Output of Pandas DataFrame to_period method.

to_stata(*args, **kwargs)

Export the DataFrame to Stata data format.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_stata.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_stata.

Returns:

Output of Pandas DataFrame to_stata method.

to_xml(*args, **kwargs)

Convert the DataFrame to an XML string.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_xml.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_xml.

Returns:

Output of Pandas DataFrame to_xml method.

to_excel(*args, **kwargs)

Write the DataFrame to an Excel file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_excel.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_excel.

Returns:

Output of Pandas DataFrame to_excel method.

to_html(*args, **kwargs)

Render the DataFrame as an HTML table.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_html.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_html.

Returns:

Output of Pandas DataFrame to_html method.

to_numpy(*args, **kwargs)

Convert the DataFrame to a NumPy array.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_numpy.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_numpy.

Returns:

Output of Pandas DataFrame to_numpy method.

to_string(*args, **kwargs)

Render the DataFrame to a console-friendly tabular output.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_string.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_string.

Returns:

Output of Pandas DataFrame to_string method.

class property valid_data_types

classmethod from_pandas(df, colspecs)

Create a CustomDataFrame from a pandas DataFrame.

Parameters:

• df (DataFrame) – DataFrame to create from.

• colspecs (dict) – dictionary of the column specifications.

Returns:

A CustomDataFrame object.

Return type:

CustomDataFrame

classmethod from_csv(*args, **kwargs)

Read a comma-separated values (csv) file into a CustomDataFrame.

Parameters:

• args – Positional arguments to be passed to pandas.read_csv.

• kwargs – Keyword arguments to be passed to pandas.read_csv.

Returns:

A CustomDataFrame object.

classmethod from_dict(data, *args, **kwargs)

Construct a CustomDataFrame from a dict of array-like or dicts.

Parameters:

• data – Dictionary of data.

• args – Positional arguments to be passed to pandas.DataFrame.from_dict.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.from_dict.

Returns:

A CustomDataFrame object.

to_flow(interface)

Writes the output data to the flows specified in the interface.

Parameters:

interface – Interface object.

classmethod compute_from_flow(interface)

Construct a compute object from an interface object.

Parameters:

interface (lynguine.config.interface.Interface or dict.) – Interface object.

Returns:

A compute object.

Return type:

Compute

classmethod from_flow(interface)

Construct a CustomDataFrame from a interface object.

Parameters:

interface (lynguine.config.interface.Interface or dict.) – Interface object.

Returns:

A CustomDataFrame object.

augment_with_df(df, colspecs)

This method is used to augment the CustomDataFrame with a new DataFrame.

Parameters:

• df (DataFrame) – The DataFrame to augment with.

• colspecs (dict) – The colspecs to use.

Returns:

None

Return type:

None

save_flows()

Save the output flows.

sort_values(*args, inplace=False, **kwargs)

Sort by the values along either axis.

Parameters:

• by – str or list of str

• axis – {0 or ‘index’, 1 or ‘columns’}, default 0

• ascending – bool or list of bool, default True

• inplace – bool, default False

• kind – {‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

• na_position – {‘first’, ‘last’}, default ‘last’

• ignore_index – bool, default False

• key – callable, optional

Returns:

sorted_obj : CustomDataFrame

Raises:

ValueError – If any of the keys are not in the index

sort_index(*args, inplace=False, **kwargs)

Sort object by labels (along an axis).

Parameters:

• axis – {0 or ‘index’, 1 or ‘columns’}, default 0

• level – int or level name or list of ints or list of level names

• ascending – bool or list of bool, default True

• inplace – bool, default False

• kind – {‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

• na_position – {‘first’, ‘last’}, default ‘last’

• sort_remaining – bool, default True

• ignore_index – bool, default False

• key – callable, optional.

Returns:

sorted_obj : CustomDataFrame

convert(other)

Convert various data types to a CustomDataFrame.

This method handles conversion from different data types like Pandas DataFrame, Pandas Series, NumPy array, list, and dictionary to the CustomDataFrame format.

Parameters:

other – The data to be converted.

Returns:

A CustomDataFrame object.

Raises:

ValueError – If the data type of other cannot be converted.

sum(axis=0)

mean(axis=0)

add(other)

subtract(other)

multiply(other)

equals(other)

transpose()

dot(other)

isna()

isnull()

notna()

fillna(*args, **kwargs)

dropna()

drop_duplicates(*args, **kwargs)

groupby(*args, **kwargs)

Group DataFrame using a mapper or by a Series of columns.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.groupby.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.groupby.

Returns:

A pd.DataFrameGroupBy object.

pivot_table(*args, **kwargs)

Create a pivot table as a CustomDataFrame.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.pivot_table.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.pivot_table.

Returns:

A pivoted CustomDataFrame object.

property dtypes: Series

Property to return the data types of the columns across all sub-DataFrames. :return: A Series containing the data types of the columns.

property log

Access the system log.

Returns:

Reference to the system log.

property T

Transpose the DataFrame.

Returns:

Transposed CustomDataFrame.

property shape

Return the shape of the DataFrame.

Returns:

A tuple representing the DataFrame’s dimensions.

property columns

Return the column labels of the DataFrame.

Returns:

Index object containing the column labels.

property index

Return the index (row labels) of the DataFrame.

Returns:

Index object containing the row labels.

property empty

Return True if DataFrame is empty.

property values

Return the values of the DataFrame as a NumPy array.

Returns:

A NumPy array of the DataFrame’s values.

property colspecs

Return the column specifications.

Returns:

Column specifications.

apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

Apply a function along an axis of the DataFrame.

Parameters:

• func – Function to apply to each column or row.

• axis – Axis along which the function is applied. 0 or ‘index’: apply function to each column. 1 or ‘columns’: apply function to each row.

• raw – Determines if row/column labels are passed to the function.

• result_type – Type of the return value. Can be ‘expand’, ‘reduce’, or None.

• args – Additional positional arguments to pass to func.

• kwargs – Additional keyword arguments to pass to func.

Returns:

A new CustomDataFrame with the results of the applied function.

merge(right, how='inner', on=None, left_on=None, right_on=None, suffixes=('_x', '_y'), *args, **kwargs)

Merge CustomDataFrame with another DataFrame or CustomDataFrame.

Parameters:

• right – Another DataFrame or CustomDataFrame to merge with.

• kwargs (how, on, left_on, right_on, args,) – Parameters for pandas.DataFrame.merge.

• suffixes – A tuple of string suffixes to apply to overlapping column names.

Returns:

A new CustomDataFrame resulting from the merge operation.

join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False, validate=None, *args, **kwargs)

Join CustomDataFrame with another DataFrame or CustomDataFrame.

Parameters:

• other – Another DataFrame or CustomDataFrame to merge with.

• on (str, list, or array-like) – Columns to join on.

• how (str) – How to join the DataFrames.

• lsuffix (str) – Suffix to apply to overlapping column names from the left DataFrame.

• rsuffix (str) – Suffix to apply to overlapping column names from the right DataFrame.

• sort (bool) – Sort the join keys lexicographically in the result DataFrame.

• args – Positional arguments to be passed to pandas.DataFrame.join.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.join.

Returns:

A new CustomDataFrame resulting from the join operation.

class lynguine.assess.data.CustomDataFrame(data, colspecs=None, index=None, column=None, selector=None, subindex=None, compute=None, interface=None)

Bases: DataObject

types = {'cache': ['cache', 'series_cache', 'parameter_cache', 'global_cache'], 'input': ['input', 'data', 'constants', 'global_consts'], 'output': ['output', 'series', 'writedata', 'writeseries', 'parameters', 'globals'], 'parameters': ['constants', 'global_consts', 'parameters', 'globals', 'parameter_cache', 'global_cache'], 'series': ['series', 'writeseries', 'series_cache']}

property autocache

Whether the data structure will automatically cache values.

:return True if the data structure will automatically cache values, False otherwise.

property interface

Return the interface object. :return: The interface object.

get_type_columns(col_type)

Return the columns in the CustomDataFrame that are of a specified type.

Parameters:

col_type – The type of columns to return.

Returns:

A list of columns of the specified type.

get_input_columns()

Return the columns in the CustomDataFrame that are immutable.

Returns:

A list of immutable (input) columns.

get_series_columns()

Return the columns in the CustomDataFrame that are of type “series”.

Returns:

A list of series columns.

get_parameters_columns()

Return the columns in the CustomDataFrame that are of type “parameters”.

Returns:

A list of parameters columns.

get_output_columns()

Return the columns in the CustomDataFrame that are of type “output”.

Returns:

A list of output columns.

get_column_type(col)

Return the type of a given column.

Returns:

Column type

property colspecs

Return the column specifications.

Returns:

Column specifications.

coltype(col)

Return the type of the column.

Parameters:

col – The column name.

Returns:

The type of the column.

property compute

Return the compute object.

Returns:

The compute object.

Return type:

Compute

to_pandas()

Convert the CustomDataFrame to a pandas DataFrame.

Returns:

A pandas DataFrame representation of the CustomDataFrame.

Return type:

pandas.DataFrame

update_from_pandas(df, colspecs=None)

Convert from a pandas data frame to a CustomDataFrame.

Parameters:

• df (DataFrame) – A pandas DataFrame to update an existing data frame with.

• colspecs (dict) – The column specifications to use.

Returns:

None

Return type:

None

filter(*args, **kwargs)

update_name_column_map(name, column)

Update the map from valid variable names to columns in the data frame. Valid variable names are needed e.g. for Liquid filters.

Parameters:

• name (str) – The name of the variable.

• column (str) – The column in the data frame.

Return type:

None

mapping(mapping=None, series=None)

Generate dictionary of mapping between variable names and column values.

Parameters:

• mapping – mapping to use, if None use default mapping

• series – series to use, if None use current series

Returns:

dictionary of mapping between variable names and column values

Return type:

dict

viewer_to_value(viewer, kwargs=None)

Convert a viewer structure to populated values.

Parameters:

• viewer (dict or list of dicts) – The viewer to create the text of.

• kwargs (dict) – The mapping to use to populate the viewer.

Returns:

The text of the viewer.

view_to_value(view, kwargs=None, local={})

Create the text of the view.

Parameters:

• view (dict) – The view to create the text of.

• kwargs (dict) – The mapping to use to populate the view.

Returns:

The text of the view.

summary_viewer_to_value(viewer, kwargs=None)

Convert a summary viewer structure to populated values.

Parameters:

• viewer (dict or list of dicts) – The summary viewer to create the text of.

• kwargs (dict) – The mapping to use to populate the summary viewer.

Returns:

The text of the summary viewer.

property T

Transpose the DataFrame.

Returns:

Transposed CustomDataFrame.

add(other)

add_column(column_name, data)

Add a new column to the DataFrame.

Parameters:

• column_name – The name of the new column.

• data – The data for the new column.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

add_row(index, typ=None, values=None)

Add a new row to the CustomDataFrame for the current column in focus.

Parameters:

• index – The index of the new row to be added.

• typ – The type of row being added.

• values – A dictionary of column:value pairs for the new row. If None, adds NaN values.

Raises:

ValueError – If the index already exists or if it’s not possible to add the row.

apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)

Apply a function along an axis of the DataFrame.

Parameters:

• func – Function to apply to each column or row.

• axis – Axis along which the function is applied. 0 or ‘index’: apply function to each column. 1 or ‘columns’: apply function to each row.

• raw – Determines if row/column labels are passed to the function.

• result_type – Type of the return value. Can be ‘expand’, ‘reduce’, or None.

• args – Additional positional arguments to pass to func.

• kwargs – Additional keyword arguments to pass to func.

Returns:

A new CustomDataFrame with the results of the applied function.

augment_with_df(df, colspecs)

This method is used to augment the CustomDataFrame with a new DataFrame.

Parameters:

• df (DataFrame) – The DataFrame to augment with.

• colspecs (dict) – The colspecs to use.

Returns:

None

Return type:

None

property columns

Return the column labels of the DataFrame.

Returns:

Index object containing the column labels.

classmethod compute_from_flow(interface)

Construct a compute object from an interface object.

Parameters:

interface (lynguine.config.interface.Interface or dict.) – Interface object.

Returns:

A compute object.

Return type:

Compute

convert(other)

Convert various data types to a CustomDataFrame.

This method handles conversion from different data types like Pandas DataFrame, Pandas Series, NumPy array, list, and dictionary to the CustomDataFrame format.

Parameters:

other – The data to be converted.

Returns:

A CustomDataFrame object.

Raises:

ValueError – If the data type of other cannot be converted.

describe()

Generate descriptive statistics.

Returns:

Descriptive statistics for the DataFrame.

dot(other)

drop_column(column_name)

Drop a column from the DataFrame.

Parameters:

column_name – The name of the column to drop.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

drop_duplicates(*args, **kwargs)

dropna()

property dtypes: Series

Property to return the data types of the columns across all sub-DataFrames. :return: A Series containing the data types of the columns.

property empty

Return True if DataFrame is empty.

equals(other)

fillna(*args, **kwargs)

filter_rows(condition)

Filter rows based on a specified condition.

Parameters:

condition – The condition to filter rows.

Raises:

NotImplementedError – Indicates the method needs to be implemented in a subclass.

classmethod from_csv(*args, **kwargs)

Read a comma-separated values (csv) file into a CustomDataFrame.

Parameters:

• args – Positional arguments to be passed to pandas.read_csv.

• kwargs – Keyword arguments to be passed to pandas.read_csv.

Returns:

A CustomDataFrame object.

classmethod from_dict(data, *args, **kwargs)

Construct a CustomDataFrame from a dict of array-like or dicts.

Parameters:

• data – Dictionary of data.

• args – Positional arguments to be passed to pandas.DataFrame.from_dict.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.from_dict.

Returns:

A CustomDataFrame object.

classmethod from_flow(interface)

Construct a CustomDataFrame from a interface object.

Parameters:

interface (lynguine.config.interface.Interface or dict.) – Interface object.

Returns:

A CustomDataFrame object.

classmethod from_pandas(df, colspecs)

Create a CustomDataFrame from a pandas DataFrame.

Parameters:

• df (DataFrame) – DataFrame to create from.

• colspecs (dict) – dictionary of the column specifications.

Returns:

A CustomDataFrame object.

Return type:

CustomDataFrame

get_column()

Get the column that is the focus for the DataFrame.

Returns:

The column that is the focus for the DataFrame.

get_index()

Get the index that is the focus for the DataFrame.

Returns:

The index that is the focus for the DataFrame.

Return type:

int

get_selector()

Get the selector that is the focus for the DataFrame.

The selector is the column used to disambiguate a series in the focus.

Returns:

The selector that is the focus for the DataFrame.

get_selectors()

Return valid selectors, these are columns that are present in the type “series” :return: The valid selectors.

get_shape()

Get the shape of the DataFrame.

Returns:

A tuple representing the shape of the DataFrame.

get_subindex()

Get the subindex that is the focus for the DataFrame. The subindex is the index used to disambiguate a series in the focus.

Returns:

The subindex that is the focus for the DataFrame.

Return type:

int

get_subindices()

Get the subindices that are the focus for the DataFrame.

Returns:

The subindices that are the focus for the DataFrame.

Return type:

Index

get_subseries()

Get the subseries that is the focus for the DataFrame.

Returns:

The subseries that is the focus for the DataFrame.

Return type:

DataFrame

get_subseries_values()

Return a pd.Series containing all the values in a column.

get_value()

Get the value that is in the cell defined as focus for the DataFrame.

Returns:

The value that is the focus for the DataFrame.

get_value_column(column)

Get a value from a column in the data frame(s)

Parameters:

column (str) – The column to get the value from.

Returns:

The value from the column.

Return type:

object

groupby(*args, **kwargs)

Group DataFrame using a mapper or by a Series of columns.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.groupby.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.groupby.

Returns:

A pd.DataFrameGroupBy object.

head(n=5)

Return the first n rows of the DataFrame.

Parameters:

n – Number of rows to select.

Returns:

The first n rows of the DataFrame.

property index

Return the index (row labels) of the DataFrame.

Returns:

Index object containing the row labels.

ismutable(column)

Is a given column mutable?

Parameters:

column – The column to check.

Returns:

True if the column is mutable, False otherwise.

isna()

isnull()

isparameter(column)

Test if the column is a given column a parameter (i.e. applicable for all rows)?

Parameters:

column – The column to check.

Returns:

True if the column is a parameter, False otherwise.

isseries(column)

Test if a given column is a series column (i.e. one where the index can be repeated and have multiple rows associated with it).

Parameters:

column – The column to check.

Returns:

True if the column is a series column, False otherwise.

join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False, validate=None, *args, **kwargs)

Join CustomDataFrame with another DataFrame or CustomDataFrame.

Parameters:

• other – Another DataFrame or CustomDataFrame to merge with.

• on (str, list, or array-like) – Columns to join on.

• how (str) – How to join the DataFrames.

• lsuffix (str) – Suffix to apply to overlapping column names from the left DataFrame.

• rsuffix (str) – Suffix to apply to overlapping column names from the right DataFrame.

• sort (bool) – Sort the join keys lexicographically in the result DataFrame.

• args – Positional arguments to be passed to pandas.DataFrame.join.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.join.

Returns:

A new CustomDataFrame resulting from the join operation.

property log

Access the system log.

Returns:

Reference to the system log.

mean(axis=0)

merge(right, how='inner', on=None, left_on=None, right_on=None, suffixes=('_x', '_y'), *args, **kwargs)

Merge CustomDataFrame with another DataFrame or CustomDataFrame.

Parameters:

• right – Another DataFrame or CustomDataFrame to merge with.

• kwargs (how, on, left_on, right_on, args,) – Parameters for pandas.DataFrame.merge.

• suffixes – A tuple of string suffixes to apply to overlapping column names.

Returns:

A new CustomDataFrame resulting from the merge operation.

multiply(other)

property mutable

Is the data structure mutable.

:return True if the data structure is mutable, False otherwise.

notna()

pivot_table(*args, **kwargs)

Create a pivot table as a CustomDataFrame.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.pivot_table.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.pivot_table.

Returns:

A pivoted CustomDataFrame object.

save_flows()

Save the output flows.

set_column(column)

Set the column to be the focus for the DataFrame.

Parameters:

column – The column to set.

Raises:

KeyError – If the column is not in the DataFrame.

set_index(index)

Set the index to be the focus for the DataFrame.

Parameters:

index (int) – The index to set.

Raises:

KeyError – If the index is not in the DataFrame.

Return type:

None

set_selector(column)

Set the selector that is the focus for the DataFrame.

The selector

Parameters:

column – The column to set the selector to.

Raises:

KeyError – If the column is not in the DataFrame.

set_subindex(index)

Set the subindex that is the focus for the DataFrame.

If the subindex is set to None, the last row of the subseries is returned. The last row is assumed to be the most recent.

Parameters:

index (int) – The index to set the subindex to.

Raises:

KeyError – If the index is not in the DataFrame.

set_value(value)

Set the value that is in the cell defined as focus for the DataFrame.

Parameters:

value – The value to set.

set_value_column(value, column)

Set a value to a column in the write data frame

Parameters:

• value (object) – The value to be set.

• column (str) – The column to set the value in.

Returns:

None

property shape

Return the shape of the DataFrame.

Returns:

A tuple representing the DataFrame’s dimensions.

sort_index(*args, inplace=False, **kwargs)

Sort object by labels (along an axis).

Parameters:

• axis – {0 or ‘index’, 1 or ‘columns’}, default 0

• level – int or level name or list of ints or list of level names

• ascending – bool or list of bool, default True

• inplace – bool, default False

• kind – {‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

• na_position – {‘first’, ‘last’}, default ‘last’

• sort_remaining – bool, default True

• ignore_index – bool, default False

• key – callable, optional.

Returns:

sorted_obj : CustomDataFrame

sort_values(*args, inplace=False, **kwargs)

Sort by the values along either axis.

Parameters:

• by – str or list of str

• axis – {0 or ‘index’, 1 or ‘columns’}, default 0

• ascending – bool or list of bool, default True

• inplace – bool, default False

• kind – {‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

• na_position – {‘first’, ‘last’}, default ‘last’

• ignore_index – bool, default False

• key – callable, optional

Returns:

sorted_obj : CustomDataFrame

Raises:

ValueError – If any of the keys are not in the index

subtract(other)

sum(axis=0)

summary_view_to_value(view, kwargs=None, local={})

Create the text of the summary view.

Parameters:

• view (dict) – The summary view to create the text of.

• kwargs (dict) – The mapping to use to populate the summary view.

Returns:

The text of the summary view.

tail(n=5)

Return the last n rows of the DataFrame.

Parameters:

n – Number of rows to select.

Returns:

The last n rows of the DataFrame.

to_clipboard(*args, **kwargs)

Copy the DataFrame to the system clipboard.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_clipboard.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_clipboard.

Returns:

Output of Pandas DataFrame to_clipboard method.

to_csv(*args, **kwargs)

Write the DataFrame to a comma-separated values (csv) file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_csv.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_csv.

Returns:

Output of Pandas DataFrame to_csv method.

to_dict(*args, **kwargs)

Convert the DataFrame to a dictionary.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_dict.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_dict.

Returns:

Output of Pandas DataFrame to_dict method.

to_excel(*args, **kwargs)

Write the DataFrame to an Excel file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_excel.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_excel.

Returns:

Output of Pandas DataFrame to_excel method.

to_feather(*args, **kwargs)

Write the DataFrame to a Feather file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_feather.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_feather.

Returns:

Output of Pandas DataFrame to_feather method.

to_flow(interface)

Writes the output data to the flows specified in the interface.

Parameters:

interface – Interface object.

to_gbq(*args, **kwargs)

Write the DataFrame to a Google BigQuery table.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_gbq.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_gbq.

Returns:

Output of Pandas DataFrame to_gbq method.

to_hdf(*args, **kwargs)

Write the DataFrame to a Hierarchical Data Format (HDF) file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_hdf.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_hdf.

Returns:

Output of Pandas DataFrame to_hdf method.

to_html(*args, **kwargs)

Render the DataFrame as an HTML table.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_html.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_html.

Returns:

Output of Pandas DataFrame to_html method.

to_json(*args, **kwargs)

Convert the DataFrame to a JSON string.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_json.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_json.

Returns:

Output of Pandas DataFrame to_json method.

to_latex(*args, **kwargs)

Render the DataFrame as a LaTeX tabular environment table.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_latex.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_latex.

Returns:

Output of Pandas DataFrame to_latex method.

to_markdown(*args, **kwargs)

Convert the DataFrame to a Markdown string.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_markdown.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_markdown.

Returns:

Output of Pandas DataFrame to_markdown method.

to_numpy(*args, **kwargs)

Convert the DataFrame to a NumPy array.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_numpy.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_numpy.

Returns:

Output of Pandas DataFrame to_numpy method.

to_orc(*args, **kwargs)

Write the DataFrame to an ORC file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_orc.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_orc.

Returns:

Output of Pandas DataFrame to_orc method.

to_parquet(*args, **kwargs)

Write the DataFrame to a Parquet file.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_parquet.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_parquet.

Returns:

Output of Pandas DataFrame to_parquet method.

to_period(*args, **kwargs)

Convert DataFrame from DatetimeIndex to PeriodIndex.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_period.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_period.

Returns:

Output of Pandas DataFrame to_period method.

to_records(*args, **kwargs)

Convert the DataFrame to a NumPy record array.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_records.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_records.

Returns:

Output of Pandas DataFrame to_records method.

to_sql(*args, **kwargs)

Write records stored in the DataFrame to a SQL database.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_sql.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_sql.

Returns:

Output of Pandas DataFrame to_sql method.

to_stata(*args, **kwargs)

Export the DataFrame to Stata data format.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_stata.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_stata.

Returns:

Output of Pandas DataFrame to_stata method.

to_string(*args, **kwargs)

Render the DataFrame to a console-friendly tabular output.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_string.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_string.

Returns:

Output of Pandas DataFrame to_string method.

to_timestamp(*args, **kwargs)

Cast to DatetimeIndex of timestamps.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_timestamp.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_timestamp.

Returns:

Output of Pandas DataFrame to_timestamp method.

to_xarray(*args, **kwargs)

Convert the DataFrame to an xarray Dataset.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_xarray.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_xarray.

Returns:

Output of Pandas DataFrame to_xarray method.

to_xml(*args, **kwargs)

Convert the DataFrame to an XML string.

Parameters:

• args – Positional arguments to be passed to pandas.DataFrame.to_xml.

• kwargs – Keyword arguments to be passed to pandas.DataFrame.to_xml.

Returns:

Output of Pandas DataFrame to_xml method.

transpose()

valid_data_types = {'cache', 'constants', 'data', 'global_cache', 'global_consts', 'globals', 'input', 'output', 'parameter_cache', 'parameters', 'series', 'series_cache', 'writedata', 'writeseries'}

property values

Return the values of the DataFrame as a NumPy array.

Returns:

A NumPy array of the DataFrame’s values.

view_to_tmpname(view)

Convert a view to a name

Parameters:

view (dict) – The view to convert to a name.

Returns:

A name derived from the view./

Return type:

str

tally_to_value(tally, kwargs=None, local={})

Create the text of the view.

Parameters:

• tally (dict) – The tally to create the text of.

• kwargs (dict) – The mapping to use to populate the tally.

Returns:

The text of the tally.

conditions(view)

Check if a given data viewer should be displayed.

Parameters:

view (dict or Interface) – The data viewer to check.

Returns:

True if the data viewer should be displayed, False otherwise.

Return type:

bool

display_to_tmpname(display)

Convert a display string to a temp name

Parameters:

display (str) – The display string to convert.

Returns:

A name derived from the display string.

Return type:

str

display_to_value(display, kwargs=None, local={})

Convert a display string to a string.

Parameters:

• display (str) – The display string to convert.

• kwargs (dict, optional) – The mapping to use for the display string, defaults to None

• local (dict, optional) – Local overrides to use on top of the kwargs for substitution in the display string, defaults to {}

Returns:

The string extracted from the display string.

Return type:

str

Raises:

KeyError – If the mapping doesn’t contain the key requested in the display string.

compute_to_value(compute)

Extract a value from a computation

Parameters:

compute (dict or Interface) – The interface details containing the computation to extract the value from.

Returns:

The value extracted from the computation.

compute_to_tmpname(compute)

Convert a compute specification to a descriptive name

Parameters:

compute (dict or Interface) – The compute specification to convert to a name.

Returns:

A name derived from the compute specification.

Return type:

str

liquid_to_tmpname(display)

Convert a liquid template specification to a decriptive name.

Parameters:

display (str) – The liquid template specification to convert to a name.

Returns:

A name derived from the liquid template specification.

Return type:

str

liquid_to_value(display, kwargs=None, local={})

Convert a liquid template to a string.

Parameters:

• display (str) – The liquid template to convert.

• kwargs (dict, optional) – The mapping to use for the liquid template, defaults to None

• local (dict, optional) – Local overrides to use on top of the kwargs for substitution in the liquid template, defaults to {}

tally_to_tmpname(tally)

Convert a tally to a temporary name

tally_values(tally, kwargs=None, local={})

Create the text of the tally. A tally has a “begin” field, and an “end” field and is used for summarising a series.

Parameters:

• tally (dict) – The tally to create the text of.

• kwargs (dict) – The mapping to use to populate the tally.

Returns:

The text of the tally.

tally_series(tally)

Return the series to be used for a tally.

Parameters:

tally (dict) – The tally to create the text of.

Returns:

The series to be used for a tally.

Return type:

pd.Series

lynguine.assess.data.concat(objs, *args, **kwargs)

Concatenate a sequence of CustomDataFrame objects into a single CustomDataFrame.

Parameters:

• objs – A sequence of CustomDataFrame objects to concatenate.

• args – Positional arguments to be passed to pandas.concat.

• kwargs – Keyword arguments to be passed to pandas.concat.

Returns:

A new CustomDataFrame representing the concatenation of the input dataframes.

Raises:

ValueError – If objs is empty or contains non-CustomDataFrame objects.

Compute Module

class lynguine.assess.compute.Compute(interface)

Bases: object

The Compute class handles computation operations on data frames.

This class is responsible for executing various computation operations specified in an interface configuration. It supports precomputation, computation, and post-computation operations, along with liquid template rendering.

The computation operations are specified in the interface using a dictionary or list of dictionaries with fields like ‘function’, ‘field’, ‘args’, etc.

_computes

Dictionary of computation operations divided by type (‘precompute’, ‘compute’, ‘postcompute’).

Type:

dict

_liquid_env

The liquid template environment for rendering templates.

logger

Logger instance for logging messages.

Initialize the compute object.

Creates a new Compute object with the specified interface, setting up computation operations, liquid environment, and logger.

Parameters:

interface (lynguine.config.interface.Interface or dict) – The interface to be used for computation operations.

Returns:

None

setup_logger()

Setup the logger for the Compute class.

Initializes the logger with the appropriate name, log level, and output file based on the context configuration.

Returns:

None

property computes

Return the compute operations.

Returns:

The list of compute operations defined in the interface.

Return type:

list

property precomputes

Return the precompute operations.

These operations are executed before the main compute operations.

Returns:

The list of precompute operations defined in the interface.

Return type:

list

property postcomputes

Return the postcompute operations.

These operations are executed after the main compute operations.

Returns:

The list of postcompute operations defined in the interface.

Return type:

list

prep(settings, data)

Prepare a compute entry for use.

Processes the settings dictionary to create a standardized compute entry with function, arguments, and refresh flag.

Parameters:

• settings (dict) – The settings to be used for the computation.

• data (lynguine.assess.data.CustomDataFrame or pandas.DataFrame) – The data to be used in the computation.

Returns:

The prepared compute entry.

Return type:

dict

gca_(function, field=None, refresh=False, args={}, row_args={}, view_args={}, function_args={}, subseries_args={}, column_args={})

Args generator for compute functions.

Parameters:

• function (str) – The name of the function to be used.

• field (str) – The field to be used.

• refresh (bool) – Whether to refresh the field.

• args (dict) – The arguments to be used.

• row_args (dict) – The row arguments to be used.

• view_args (dict) – The view arguments to be used.

• function_args (dict) – The function arguments to be used.

• subseries_args (dict) – The subseries arguments to be used.

• column_args (dict) – The column arguments to be used.

Returns:

The arguments to be used.

Return type:

dict

gcf_(function, data)

Function generator for compute functions.

Creates and returns a function to be used for computation based on the function name specified. The returned function will access values from the data object according to the argument specifications.

Parameters:

• function (str) – The name of the function to be used.

• data (lynguine.assess.data.CustomDataFrame) – The data object to be used in the function.

Returns:

The compute function that will be executed.

Return type:

function

Raises:

ValueError – If the specified function is not found in the list of available functions.

run(data, interface)

Run the compute operations on the data.

This method executes the compute operations specified in the interface on the data. First it runs any precompute operations, then the main compute operations, and finally any postcompute operations.

For each compute operation, it: 1. Prepares the operation with the settings and data 2. Gets the current value for the target field 3. Determines whether to compute (based on refresh flag and existing value) 4. Computes the new value and sets it in the data frame

Parameters:

• data (lynguine.assess.data.CustomDataFrame) – The data to be processed.

• interface (lynguine.config.interface.Interface or dict) – The interface defining the compute operations.

Returns:

None

Raises:

ValueError – If a compute operation is missing required fields.

Return type:

None

preprocess(data, interface)

Run preprocessing operations on the data.

Executes the precompute operations specified in the interface on the data. These operations are run before the main compute operations.

Similar to run(), but only processes the ‘precompute’ operations.

Parameters:

• data (lynguine.assess.data.CustomDataFrame) – The data to be preprocessed.

• interface (lynguine.config.interface.Interface or dict) – The interface defining the precompute operations.

Returns:

None

Raises:

ValueError – If a precompute operation is missing required fields.

Return type:

None

run_onchange(data, index, column)

Run compute operations when a specific cell changes.

This method is used to run compute operations when a specific cell in the data frame changes. It checks if the changed cell is used as an argument in any compute operation and runs those operations if needed.

Parameters:

• data (lynguine.assess.data.CustomDataFrame) – The data frame containing the changed cell.

• index (object) – The index of the changed cell.

• column (str) – The column of the changed cell.

Returns:

None

Return type:

None

run_all(data, interface)

Run compute operations on all indices in the data frame.

Iterates through all indices in the data frame and runs the compute operations for each index. This is useful for batch processing the entire data set.

Parameters:

• data (lynguine.assess.data.CustomDataFrame) – The data to process.

• interface (lynguine.config.interface.Interface or dict) – The interface defining the compute operations.

Returns:

None

Return type:

None

filter(data, interface)

Apply filters to the data based on the interface.

This method applies filters to the data frame based on filter specifications in the interface. It can select or exclude rows based on column values.

Parameters:

• data (lynguine.assess.data.CustomDataFrame) – The data to filter.

• interface (lynguine.config.interface.Interface or dict) – The interface defining the filters.

Returns:

None

Raises:

ValueError – If a filter operation is invalid.

Return type:

None

load_liquid(interface)

Initialize the liquid template environment.

Sets up the liquid template rendering environment with custom filters and configurations from the interface.

Parameters:

interface (lynguine.config.interface.Interface or dict) – The interface containing liquid configuration.

Returns:

None

add_liquid_filters()

Add custom filters to the liquid environment.

This method can be extended to add additional custom filters to the liquid template environment.

Returns:

None

classmethod from_flow(interface)

Create a Compute instance from an interface.

Factory method to create a Compute instance from an interface specification, which can be either an Interface object or a dictionary.

Parameters:

interface (lynguine.config.interface.Interface or dict) – The interface to create the Compute from.

Returns:

A new Compute instance.

Return type:

Compute

Display Module

class lynguine.assess.display.WidgetCluster(name, parent, viewer=False, **kwargs)

Bases: object

A class to hold and manage a cluster of widgets.

Initialize a WidgetCluster.

Parameters:

• name (str) – The name of the widget cluster

• parent (object) – The parent object of this cluster

• viewer (bool, optional) – Whether this cluster is a viewer, defaults to False

• kwargs – Additional widgets to add to the cluster

clear_children()

Clear all child widgets from the cluster.

close()

Close all widgets in the cluster.

has(key)

Check if the widget cluster contains a specific widget.

Parameters:

key (str) – The key of the widget to check for

Returns:

True if the widget exists in the cluster, False otherwise

Return type:

bool

get(key)

Get a specific widget from the cluster.

Parameters:

key (str) – The key of the widget to retrieve

Returns:

The requested widget

Return type:

object

refresh()

Refresh all widgets in the cluster.

add(cluster=None, **kwargs)

Add widgets or a sub-cluster to this cluster.

Parameters:

• cluster (WidgetCluster, optional) – A WidgetCluster to add as a sub-cluster, defaults to None

• kwargs – Widgets to add to the cluster

update(**kwargs)

Update existing widgets in the cluster.

Parameters:

kwargs – The widgets to update, with their new values

Raises:

ValueError – If attempting to update a non-existent widget

to_markdown(skip=[])

Convert the widget cluster to markdown format.

Parameters:

skip (list, optional) – List of widget keys to skip in the conversion, defaults to []

Returns:

Markdown representation of the widget cluster

Return type:

str

to_dict()

Convert the widget cluster to a dictionary.

Returns:

Dictionary representation of the widget cluster

Return type:

dict

display()

Display all widgets in the cluster.

class lynguine.assess.display.DisplaySystem(index=None, data=None, interface=None, system=None)

Bases: object

A class to manage the display system for data visualization and interaction.

Initialize a DisplaySystem.

Parameters:

• index (str, optional) – The index to set for the data, defaults to None

• data (object, optional) – The data to display, defaults to None

• interface (object, optional) – The interface to use, defaults to None

• system (object, optional) – The system to use, defaults to None

property index

Get the current index of the data.

Returns:

The current index

Return type:

pd.Index or None

get_index()

Get the current index of the data.

Returns:

The current index

Return type:

object or None

set_index(value)

Set the index of the data.

Parameters:

value (str) – The new index value

Return type:

None

get_value()

Get the current value of the data.

Returns:

The current value

Return type:

object or None

get_value_by_element(element)

Get a specific element’s value from the data.

Parameters:

element (object) – The element to retrieve

Returns:

The value of the specified element

Return type:

object or None

set_value_by_element(value, element, trigger_update=True)

Set the value of a specific element in the data.

Parameters:

• value (object) – The new value to set

• element (object) – The element to update

• trigger_update (bool, optional) – Whether to trigger an update after setting the value, defaults to True

set_value(value, trigger_update=True)

Set the current value of the data.

Parameters:

• value (object) – The new value to set

• trigger_update (bool, optional) – Whether to trigger an update after setting the value, defaults to True

get_column()

Get the current column of the data.

Returns:

The current column

Return type:

str or None

set_column(column)

Set the current column of the data.

Parameters:

column (str) – The new column to set

Return type:

None

get_indices()

Get all indices of the data.

Returns:

All indices

Return type:

pd.Index or None

add_downstream_display(display)

Add a downstream display to update when this display updates.

Parameters:

display (DisplaySystem) – The downstream display to add

load_flows(reload=False)

Load data flows.

Parameters:

reload (bool, optional) – Whether to force a reload, defaults to False

Return type:

None

save_flows()

Save data flows and update downstream displays.

Return type:

None

load_input_flows()

Load input data flows.

Return type:

None

load_output_flows()

Load output data flows.

Return type:

None

populate_display()

Populate the display with current data.

Return type:

None

value_updated()

Handle updates when a value changes.