:py:mod:`flatspin.data`
=======================

.. py:module:: flatspin.data

.. autoapi-nested-parse::

   Data management



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   flatspin.data.Dataset



Functions
~~~~~~~~~

.. autoapisummary::

   flatspin.data.col_group
   flatspin.data.match_column
   flatspin.data.match_columns
   flatspin.data.csv_columns
   flatspin.data.save_csv
   flatspin.data.read_csv
   flatspin.data.save_hdf
   flatspin.data.read_hdf
   flatspin.data.hdf_columns
   flatspin.data.read_npy
   flatspin.data.save_npy
   flatspin.data.npy_columns
   flatspin.data.read_npz
   flatspin.data.save_npz
   flatspin.data.npz_columns
   flatspin.data.archive_key
   flatspin.data.get_format
   flatspin.data.is_archive_format
   flatspin.data.is_archive
   flatspin.data.is_tablefile
   flatspin.data.list_dir
   flatspin.data.list_npz
   flatspin.data.list_hdf
   flatspin.data.listfiles
   flatspin.data.to_table
   flatspin.data.match_any
   flatspin.data.read_table
   flatspin.data.save_table
   flatspin.data.table_columns
   flatspin.data.read_tables
   flatspin.data.read_geometry
   flatspin.data.read_vectors
   flatspin.data.crop
   flatspin.data.rolling_window
   flatspin.data.filter_df
   flatspin.data.digitize
   flatspin.data.bit_array
   flatspin.data.array_bit
   flatspin.data.vector_grid
   flatspin.data.load_output



Attributes
~~~~~~~~~~

.. autoapisummary::

   flatspin.data.table_formats
   flatspin.data.table_extensions
   flatspin.data.archive_formats
   flatspin.data.table_patterns


.. py:function:: col_group(x)


.. py:function:: match_column(pattern, columns)

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


.. py:function:: match_columns(patterns, columns)

   match column patterns


.. py:function:: csv_columns(filename)

   Parse column names at the top of a CSV file


.. py:function:: save_csv(df, filename)


.. py:function:: read_csv(filename, index_col=None)


.. py:function:: save_hdf(df, filename)


.. py:function:: read_hdf(filename)


.. py:function:: hdf_columns(filename)


.. py:function:: read_npy(filename)


.. py:function:: save_npy(df, filename)


.. py:function:: npy_columns(filename)


.. py:function:: read_npz(filename)


.. py:function:: save_npz(df, filename)


.. py:function:: npz_columns(filename)


.. py:data:: table_formats
   

   

.. py:data:: table_extensions
   

   

.. py:data:: archive_formats
   :annotation: = ['npz', 'hdf']

   

.. py:data:: table_patterns
   

   

.. py:function:: archive_key(filename)


.. py:function:: get_format(filename)


.. py:function:: is_archive_format(fmt)


.. py:function:: is_archive(filename)


.. py:function:: is_tablefile(filename)


.. py:function:: list_dir(filename)


.. py:function:: list_npz(filename)


.. py:function:: list_hdf(filename)


.. py:function:: listfiles(filename)


.. py:function:: to_table(data)


.. py:function:: match_any(name, patterns)


.. py:function:: read_table(filename, index_col=None, **kwargs)


.. py:function:: save_table(data, filename)


.. py:function:: table_columns(filename)


.. py:function:: read_tables(filenames, index_col=None)


.. py:function:: read_geometry(filename='geometry.csv')


.. py:function:: read_vectors(filenames, quantity='mag', t=None)


.. py:function:: 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


.. py:function:: rolling_window(X, win_shape, step=1, method='sum')

   Apply a function over a rolling window of X


.. py:function:: filter_df(df, **kwargs)

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


.. py:class:: Dataset(index=None, params={}, info={}, basepath=None)

   Bases: :py:obj:`object`

   .. py:method:: name(self)
      :property:


   .. py:method:: __getitem__(self, i)


   .. py:method:: __repr__(self)

      Return repr(self).


   .. py:method:: __str__(self)

      Return str(self).


   .. py:method:: keys(self)


   .. py:method:: items(self)


   .. py:method:: iterrows(self)


   .. py:method:: __iter__(self)


   .. py:method:: __len__(self)


   .. py:method:: __eq__(self, other)

      Return self==value.


   .. py:method:: subset(self, i)


   .. py:method:: filter(self, **kwargs)


   .. py:method:: groupby(self, key)


   .. py:method:: sort_values(self, column)


   .. py:method:: row(self, row=0)


   .. py:method:: id(self, row=0)


   .. py:method:: read(basepath)
      :staticmethod:


   .. py:method:: save(self, basepath=None)


   .. py:method:: file(self, filename)


   .. py:method:: files(self, patterns=None, squash=True)


   .. py:method:: tablefile(self, tablename, squash=True)


   .. py:method:: tablefiles(self, patterns=None, squash=True)



.. py:function:: digitize(X, threshold=0)


.. py:function:: bit_array(x, n_bits)

   Convert number x to array of bits


.. py:function:: array_bit(a)

   Convert bit array a to number


.. py:function:: 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


.. py:function:: 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