flatspin.data

Data management

Module Contents

Classes

Dataset

Functions

col_group(x)
match_column(pattern, columns)	match column pattern from available columns
match_columns(patterns, columns)	match column patterns
csv_columns(filename)	Parse column names at the top of a CSV file
save_csv(df, filename)
read_csv(filename, index_col=None)
save_hdf(df, filename)
read_hdf(filename)
hdf_columns(filename)
read_npy(filename)
save_npy(df, filename)
npy_columns(filename)
read_npz(filename)
save_npz(df, filename)
npz_columns(filename)
archive_key(filename)
get_format(filename)
is_archive_format(fmt)
is_archive(filename)
is_tablefile(filename)
list_dir(filename)
list_npz(filename)
list_hdf(filename)
listfiles(filename)
to_table(data)
match_any(name, patterns)
read_table(filename, index_col=None, **kwargs)
save_table(data, filename)
table_columns(filename)
read_tables(filenames, index_col=None)
read_geometry(filename='geometry.csv')
read_vectors(filenames, quantity='mag', t=None)
crop(X, crop, axis=-1)	Crop array X along one or more axes
rolling_window(X, win_shape, step=1, method='sum')	Apply a function over a rolling window of X
filter_df(df, **kwargs)	Filter dataframe by key=value or range key=start:stop
digitize(X, threshold=0)
bit_array(x, n_bits)	Convert number x to array of bits
array_bit(a)	Convert bit array a to number
vector_grid(pos, vectors, grid_size=None, crop_width=None, win_shape=None, win_step=None, normalize=True, return_grid=False)	Process vectors on a grid
load_output(dataset, quantity, t=None, grid_size=None, crop_width=None, win_shape=None, win_step=None, flatten=True)	Load output vectors from dataset, with optional post-processing

Attributes

table_formats
table_extensions
archive_formats
table_patterns

flatspin.data.col_group(x)

flatspin.data.match_column(pattern, columns)

match column pattern from available columns m -> (mx, my, mz) m.region*x

flatspin.data.match_columns(patterns, columns)

match column patterns

flatspin.data.csv_columns(filename)

Parse column names at the top of a CSV file

flatspin.data.save_csv(df, filename)

flatspin.data.read_csv(filename, index_col=None)

flatspin.data.save_hdf(df, filename)

flatspin.data.read_hdf(filename)

flatspin.data.hdf_columns(filename)

flatspin.data.read_npy(filename)

flatspin.data.save_npy(df, filename)

flatspin.data.npy_columns(filename)

flatspin.data.read_npz(filename)

flatspin.data.save_npz(df, filename)

flatspin.data.npz_columns(filename)

flatspin.data.table_formats

flatspin.data.table_extensions

flatspin.data.archive_formats = ['npz', 'hdf']

flatspin.data.table_patterns

flatspin.data.archive_key(filename)

flatspin.data.get_format(filename)

flatspin.data.is_archive_format(fmt)

flatspin.data.is_archive(filename)

flatspin.data.is_tablefile(filename)

flatspin.data.list_dir(filename)

flatspin.data.list_npz(filename)

flatspin.data.list_hdf(filename)

flatspin.data.listfiles(filename)

flatspin.data.to_table(data)

flatspin.data.match_any(name, patterns)

flatspin.data.read_table(filename, index_col=None, **kwargs)

flatspin.data.save_table(data, filename)

flatspin.data.table_columns(filename)

flatspin.data.read_tables(filenames, index_col=None)

flatspin.data.read_geometry(filename='geometry.csv')

flatspin.data.read_vectors(filenames, quantity='mag', t=None)

flatspin.data.crop(X, crop, axis=- 1)

Crop array X along one or more axes crop is a tuple (before, after) for each axis to crop axis specifies which axis to start cropping from

flatspin.data.rolling_window(X, win_shape, step=1, method='sum')

Apply a function over a rolling window of X

flatspin.data.filter_df(df, **kwargs)

Filter dataframe by key=value or range key=start:stop

class flatspin.data.Dataset(index=None, params={}, info={}, basepath=None)

Bases: object

property name(self)

__getitem__(self, i)

__repr__(self)

Return repr(self).

__str__(self)

Return str(self).

keys(self)

items(self)

iterrows(self)

__iter__(self)

__len__(self)

__eq__(self, other)

Return self==value.

subset(self, i)

filter(self, **kwargs)

groupby(self, key)

sort_values(self, column)

row(self, row=0)

id(self, row=0)

static read(basepath)

save(self, basepath=None)

file(self, filename)

files(self, patterns=None, squash=True)

tablefile(self, tablename, squash=True)

tablefiles(self, patterns=None, squash=True)

flatspin.data.digitize(X, threshold=0)

flatspin.data.bit_array(x, n_bits)

Convert number x to array of bits

flatspin.data.array_bit(a)

Convert bit array a to number

flatspin.data.vector_grid(pos, vectors, grid_size=None, crop_width=None, win_shape=None, win_step=None, normalize=True, return_grid=False)

Process vectors on a grid

flatspin.data.load_output(dataset, quantity, t=None, grid_size=None, crop_width=None, win_shape=None, win_step=None, flatten=True)

Load output vectors from dataset, with optional post-processing