Stoner.Core.DataFile

class Stoner.Core.DataFile(*args)

Base class object that represents a matrix of data, associated metadata and column headers.

column_headers

list of strings of the column names of the data.

Type:

list

data

The attribute that stores the nuermical data for each DataFile. This is a DataArray instance - which is itself a subclass of numpy.ma.MaskedArray.

Type:

2D numpy masked array

title

The title of the measurement.

Type:

string

filename

The current filename of the data if loaded from or already saved to disc. This is the default filename used by the Stoner.Core.DataFile.load() and Stoner.Core.DataFile.save().

Type:

string

header

A readonly property that returns a pretty formatted string giving the header of tabular representation.

Type:

string

mask

Returns the current mask applied to the numerical data equivalent to self.data.mask.

Type:

array of booleans

mime_type

The possible mime-types of data files represented by each matching filename pattern in Datafile.pattern.

Type:

list of str

patterns

A list of filename extension glob patterns that matrches the expected filename patterns for a DataFile (*.txt and *.dat”)

Type:

list

priority

Used to indicathe order in which subclasses of DataFile are tried when loading data. A higher number means a lower priority (!)

Type:

int

setas

Defines certain columns to contain X, Y, Z or errors in X,Y,Z data.

Type:

_stas

shape

Returns the shape of the data (rows,columns) - equivalent to self.data.shape.

Type:

tuple of integers

records

Returns the data in the form of a list of yuples where each tuple maps to the columns names.

Type:

numpy record array

clone

Creates a deep copy of the :py:class`DataFile` object.

Type:

DataFile

dict_records

View the data as an array or dictionaries where each dictionary represents one row with keys derived from column headers.

Type:

array of dictionaries

dims

When data columns are set as x,y,z etc. returns the number of dimensions implied in the data set

Type:

int

dtype

Returns the datatype stored in the DataFile.data attribute.

Type:

numpoy dtype

T

Transposed version of the data.

Type:

DataArray

subclasses

Returns a list of all the subclasses of DataFile currently in memory, sorted by their py:attr:Stoner.Core.DataFile.priority. Each entry in the list consists of the string name of the subclass and the class object.

Type:

list

xcol

If a column has been designated as containing x values, this will return the index of that column

Type:

int

xerr

Similarly to DataFile.xcol but for the x-error value column.

Type:

int

ycol

Similarly to DataFile.xcol but for the y value columns.

Type:

list of int

yerr

Similarly to DataFile.xcol but for the y error value columns.

Type:

list of int

zcol

Similarly to DataFile.xcol but for the z value columns.

Type:

list of int

zerr

Similarly to DataFile.xcol but for the z error value columns.

Type:

list of int

ucol

Similarly to DataFile.xcol but for the u (x-axis direction cosine) columns.

Type:

list of int

vcol

Similarly to DataFile.xcol but for the v (y-axis direction cosine) columns.

Type:

list of int

wcol

Similarly to DataFile.xcol but for the w (z-axis direction cosine) columns.

Type:

list of int

Methods

__init__(*args, **kargs)	Initialise the DataFile from arrays, dictionaries and filenames.
add_column(column_data[, header, index, ...])	Append a column of data or inserts a column to a datafile instance.
append(value)	S.append(value) -- append value to the end of the sequence
asarray()	Provide a consistent way to get at the underlying array data.
clear()
closest(value[, xcol])	Return the row in a data file which has an x-column value closest to the given value.
column(col)	Extract one or more columns of data from the datafile.
columns([not_masked, reset])	Iterate over the columns of data int he datafile.
count([value, axis, col])	Count the number of un-masked elements in the DataFile.
del_column([col, duplicates])	Delete a column from the current DataFile object.
del_nan([col, clone])	Remove rows that have nan in them.
del_rows([col, val, invert])	Search in the numerica data for the lines that match and deletes the corresponding rows.
dir([pattern])	Return a list of keys in the metadata, filtering with a regular expression if necessary.
extend(values)	S.extend(iterable) -- extend sequence by appending elements from the iterable
filter([func, cols, reset])	Set the mask on rows of data by evaluating a function for each row.
find_col(col[, force_list])	Indexes the column headers in order to locate a column of data.shape.
find_duplicates([xcol, delta])	Find rows with duplicated values of the search column(s).
get(k[,d])
get_filename(mode)	Force the user to choose a new filename using a system dialog box.
index(value, [start, [stop]])	Raises ValueError if the value is not present.
insert(index, obj)	Implement the insert method.
insert_rows(row, new_data)	Insert new_data into the data array at position row.
items()	Make sure we implement an items that doesn't just iterate over self.
keys()	Return the keys of the metadata dictionary.
load(*args, **kargs)	Create a new DataFile from a file on disc guessing a better subclass if necessary.
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
remove(value)	S.remove(value) -- remove first occurrence of value.
remove_duplicates([xcol, delta, strategy, ...])	Find and remove rows with duplicated values of the search column(s).
rename(old_col, new_col)	Rename columns without changing the underlying data.
reorder_columns(cols[, headers_too, setas_too])	Construct a new data array from the original data by assembling the columns in the order given.
reverse()	S.reverse() -- reverse IN PLACE
rolling_window([window, wrap, exclude_centre])	Iterate with a rolling window section of the data.
rows([not_masked, reset])	Iterate over rows of data.
save([filename])	Save a string representation of the current DataFile object into the file 'filename'.
search([xcol, value, columns, accuracy])	Search the numerica data part of the file for lines that match and returns the corresponding rows.
section(**kargs)	Assuming data has x,y or x,y,z coordinates, return data from a section of the parameter space.
select(*args, **kargs)	Produce a copy of the DataFile with only data rows that match a criteria.
setdefault(k[,d])
sort(*order, **kargs)	Sort the data by column name.
split(*args[, final])	Recursively splits the current DataFile into a Stoner.Folders.DataFolder.
swap_column(*swp, **kargs)	Swap pairs of columns in the data.
to_pandas()	Create a pandas DataFrame from a Stoner.Data object.
unique(col[, return_index, return_inverse])	Return the unique values from the specified column - pass through for numpy.unique.
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
values()	Return the values of the metadata dictionary.

Attributes

T	Get the current data transposed.
basename	Return the basename of the current filename.
clone	Get a deep copy of the current DataFile.
column_headers	Pass through to the setas attribute.
data	Property Accessors for the main numerical data.
dict_records	Return the data as a dictionary of single columns with column headers for the keys.
dims	Alias for self.data.axes.
dtype	Return the np dtype attribute of the data.
filename	Return DataFile filename, or make one up.
filepath	Return DataFile filename, or make one up, returning as a pathlib.Path.
header	Make a pretty header string that looks like the tabular representation.
mask	Return the mask of the data array.
metadata	Read the metadata dictionary.
mime_type
patterns
priority
records	Return the data as a np structured data array.
setas	Get the list of column assignments.
shape	Pass through the numpy shape attribute of the data.