Fitting Data¶

class eddington.fitting_data.FittingData(data: Union[collections.OrderedDict, Dict[str, <sphinx.ext.autodoc.importer._MockObject object at 0x7fb3a8116110>]], x_column: Union[str, int, None] = None, xerr_column: Union[str, int, None] = None, y_column: Union[str, int, None] = None, yerr_column: Union[str, int, None] = None, search: bool = True)¶

Fitting data class.

Constructor.

Parameters:

data (dict or OrderedDict from str to numpy.ndarray) – Dictionary from a column name to its values
x_column (str or numpy.ndarray) – Indicates which column should be used as the x parameter
xerr_column (str or numpy.ndarray) – Indicates which column should be used as the x error parameter
y_column (str or numpy.ndarray) – Indicates which column should be used as the y parameter
yerr_column (str or numpy.ndarray) – Indicates which column should be used as the y error parameter
search (bool) – Search for a column if it wasn’t explicitly provided in the constructor.

Raises:

FittingDataColumnsLengthError – Raised if not all columns have the same length

all_columns¶

Property of columns list.

Returns:	list of all columns
Return type:	List[str]

all_records¶

Get all records in data as a list.

Returns:	List of all records
Return type:	List[List[Any]]

all_selected() → bool¶

Checks whether all records are selected.

Returns:	True if all records are selected, False otherwise.
Return type:	bool

cell_data(column_name: str, index: int) → float¶

Get the data of a column.

Parameters:	column_name (str) – The header name of the desired cell. index (int) – The index of the desired cell.
Returns:	The value of the cell
Return type:	float

column_data(column_name: Optional[str], only_selected: bool = True) → Optional[<sphinx.ext.autodoc.importer._MockObject object at 0x7fb3a8127150>]¶

Get the data of a column.

Parameters:	column_name (str) – The header name of the desired column. if None, return None only_selected (bool) – If true, return only values selected records. otherwise, Return values of all records.
Returns:	The data of the given column
Return type:	numpy.ndarray or None

column_domain(column_name: Optional[str], only_selected: bool = True) → eddington.interval.Interval¶

Get the smallest interval containing the values in a column.

Parameters:	column_name (str) – The desired column name. only_selected (bool) – If true, return only values selected records. otherwise, Return values of all records.
Returns:	Minimal interval containing column values
Return type:	Interval

copy(only_selected_columns: bool = False, only_selected_records: bool = False)¶

Make a copy of self.

Parameters:	only_selected_columns (bool) – If true, copy only columns which are used as x, x error, y and y error columns. Otherwise, copy all columns only_selected_records (bool) – If true, copy only selected records. Otherwise, copy all records.
Returns:	a copy of this fitting data.
Return type:	Fitting Data

data¶

Data matrix.

Returns:	The actual raw data
Return type:	OrderedDict

is_selected(index)¶

Checks if a record is selected or not.

Parameters:	index (int) – index of the desired record starting from 1.
Returns:	True if record is selected, otherwise False.
Return type:	bool

non_selected() → bool¶

Checks whether no record has been selected.

Returns:	True if no record has been selected, False otherwise.
Return type:	bool

number_of_columns¶

Number of columns.

Returns:	Number of columns
Return type:	int

number_of_records¶

Number of records.

Returns:	Number of records
Return type:	int

classmethod read_from_csv(filepath: Union[str, pathlib.Path], x_column: Union[str, int, None] = None, xerr_column: Union[str, int, None] = None, y_column: Union[str, int, None] = None, yerr_column: Union[str, int, None] = None, search: bool = True)¶

Read FittingData from csv file.

Parameters:

filepath – str or Path. Path to location of csv file
x_column (str or numpy.ndarray) – Indicates which column should be used as the x parameter
xerr_column (str or numpy.ndarray) – Indicates which column should be used as the x error parameter
y_column (str or numpy.ndarray) – Indicates which column should be used as the x parameter
yerr_column (str or numpy.ndarray) – Indicates which column should be used as the y error parameter
search (bool) – Search for a column if it wasn’t explicitly provided.

Returns:

FittingData read from the csv file.

classmethod read_from_excel(filepath: Union[str, pathlib.Path], sheet: str, x_column: Union[str, int, None] = None, xerr_column: Union[str, int, None] = None, y_column: Union[str, int, None] = None, yerr_column: Union[str, int, None] = None, search: bool = True)¶

Read FittingData from excel file.

Parameters:	filepath – str or Path. Path to location of excel file sheet – str. The name of the sheet to extract the data from. x_column (`str` or `numpy.ndarray`) – Indicates which column should be used as the x parameter xerr_column (`str` or `numpy.ndarray`) – Indicates which column should be used as the x error parameter y_column (`str` or `numpy.ndarray`) – Indicates which column should be used as the x parameter yerr_column (`str` or `numpy.ndarray`) – Indicates which column should be used as the y error parameter search (bool) – Search for a column if it wasn’t explicitly provided.
Returns:	`FittingData` read from the excel file.
Raises:	FittingDataError – Raised when the given sheet do not exist in excel file.

classmethod read_from_json(filepath: Union[str, pathlib.Path], x_column: Union[str, int, None] = None, xerr_column: Union[str, int, None] = None, y_column: Union[str, int, None] = None, yerr_column: Union[str, int, None] = None, search: bool = True)¶

Read FittingData from json file.

Parameters:

filepath – str or Path. Path to location of csv file
x_column (str or numpy.ndarray) – Indicates which column should be used as the x parameter
xerr_column (str or numpy.ndarray) – Indicates which column should be used as the x error parameter
y_column (str or numpy.ndarray) – Indicates which column should be used as the x parameter
yerr_column (str or numpy.ndarray) – Indicates which column should be used as the y error parameter
search (bool) – Search for a column if it wasn’t explicitly provided.

Returns:

FittingData read from the json file.

record_data(index: int) → <sphinx.ext.autodoc.importer._MockObject object at 0x7fb3a8127250>¶

Get the data of a column.

Parameters:	index (int) – The index of the desired record.
Returns:	The record
Return type:	numpy.ndarray

records¶

Get all selected records in data as a list.

Returns:	records list

records_indices¶

Property of selected indices.

Returns:	List of booleans indicating which records are selected.
Return type:	List[bool]

residuals(fit_func, a: Union[List[float], <sphinx.ext.autodoc.importer._MockObject object at 0x7fb3a8116f90>]) → eddington.fitting_data.FittingData¶

Creates residuals FittingData objects.

Parameters:	fit_func (`FittingFunction`) – `FittingFunction` to evaluate with the fit data a (`numpy.ndarray`) – the parameters of the given fitting function
Returns:	residuals `FittingData`
Raises:	FittingDataError – Raised when missing x or y column

save_csv(output_directory: Union[str, pathlib.Path], name: str = 'fitting_data')¶

Save FittingData to csv file.

Parameters:	output_directory (`Path` or `str`) – Path to the directory for the new excel file to be saved. name (str) – Optional. The name of the file, without the .csv suffix. “fitting_data” by default.

save_excel(output_directory: Union[str, pathlib.Path], name: str = 'fitting_data', sheet: Optional[str] = None)¶

Save FittingData to xlsx file.

Parameters:	output_directory (`Path` or `str`) – Path to the directory for the new excel file to be saved. name (str) – Optional. The name of the file, without the .xlsx suffix. “fitting_data” by default. sheet (str) – Optional. Name of the sheet that the data will be saved to.

select_all_records()¶: Select all records to be used in fitting.

select_by_domains(xmin: Optional[float] = None, xmax: Optional[float] = None, ymin: Optional[float] = None, ymax: Optional[float] = None, update_selected: bool = False) → None¶

Select records by limiting y values.

Parameters:

xmin (float) – Optional. Minimum value for x. If none, will not consider lower bound for x values
xmax (float) – Optional. Maximum value for x. If none, will not consider upper bound for x values
ymin (float) – Optional. Minimum value for y. If none, will not consider lower bound for y values
ymax (float) – Optional. Maximum value for y. If none, will not consider upper bound for y values
update_selected (bool) – If true, combine with records which have already been selected. If false, select from all records

select_by_x_domain(xmin: Optional[float] = None, xmax: Optional[float] = None, update_selected: bool = False) → None¶

Select records by limiting x values.

Parameters:	xmin (float) – Optional. Minimum value for x. If none, will not consider lower bound for x values xmax (float) – Optional. Maximum value for x. If none, will not consider upper bound for x values update_selected (bool) – If true, combine with records which have already been selected. If false, select from all records

select_by_y_domain(ymin: Optional[float] = None, ymax: Optional[float] = None, update_selected: bool = False) → None¶

Select records by limiting y values.

Parameters:	ymin (float) – Optional. Minimum value for y. If none, will not consider lower bound for y values ymax (float) – Optional. Maximum value for y. If none, will not consider upper bound for y values update_selected (bool) – If true, combine with records which have already been selected. If false, select from all records

select_record(index: int)¶

Select a record to be used in fitting.

Parameters:	index (int) – index of the desired record starting from 1.

set_cell(column_name: str, index: int, value: float)¶

Set new value to a cell.

Parameters:	column_name (str) – The column name index (int) – The number of the record to set, starting from 1 value (float) – The new value to set for the cell
Raises:	FittingDataSetError – Raised when trying to set a cell with non number value

set_header(old_column, new_column)¶

Rename header.

Parameters:	old_column (str) – The old columns name new_column (str) – The new value to set for the header
Raises:	FittingDataSetError – Raised when trying to set a header which is empty or already been set.

statistics(column_name: str) → Optional[eddington.statistics.Statistics]¶

Get statistics of the values in a column.

Parameters:	column_name (str) – The column name to get statistics of
Returns:	Statistics of the given column
Return type:	Statistics
Raises:	FittingDataColumnExistenceError – Raised when unknown column name is given.

statistics_map¶

Return updated statistics map.

Returns:	Statistics map of the data
Return type:	Statistics

unselect_all_records()¶: Unselect all records from being used in fitting.

unselect_record(index: int)¶

Unselect a record to be used in fitting.

Parameters:	index (int) – index of the desired record starting from 1.

used_columns¶

Dictionary of columns in use.

Returns:	columns used dictionary
Return type:	Columns

x¶

Property of the x values.

Returns:	values of the x column
Return type:	np.ndarray

x_column¶

Name of the x column.

Returns:	The name of the x error column
Return type:	str

x_domain¶

Minimal interval containing the values of the x column.

Returns:	x values domain.
Return type:	Interval

x_index¶

Index of the x column.

Returns:	Index of the x column
Return type:	int

xerr¶

Property of the x error values.

Returns:	values of the x error column
Return type:	np.ndarray

xerr_column¶

Name of the x error column.

Returns:	The name of the x error column
Return type:	str

xerr_index¶

Index of the x error column.

Returns:	Index of the x error column
Return type:	int

y¶

Property of the y values.

Returns:	values of the y column
Return type:	np.ndarray

y_column¶

Name of the y column.

Returns:	The name of the y column
Return type:	str

y_domain¶

Minimal interval containing the values of the y column.

Returns:	y values domain.
Return type:	Interval

y_index¶

Index of the y column.

Returns:	Index of the y column
Return type:	int

yerr¶

Property of the y error values.

Returns:	values of the y error column
Return type:	np.ndarray

yerr_column¶

Name of the y error column.

Returns:	The name of the y error column
Return type:	str

yerr_index¶

Index of the y error column.

Returns:	Index of the y error column
Return type:	int