galaxy.util
===========

.. py:module:: galaxy.util

.. autoapi-nested-parse::

   Utility functions and enumerations for handling datasets and legacy survey data.



Attributes
----------

.. autoapisummary::

   galaxy.util.required_columns
   galaxy.util.optional_columns


Classes
-------

.. autoapisummary::

   galaxy.util.DataSource
   galaxy.util.IsCluster


Functions
---------

.. autoapisummary::

   galaxy.util.inherit_columns
   galaxy.util.read_vizier
   galaxy.util.bar_progress
   galaxy.util.to_hms_format
   galaxy.util.to_dms_format
   galaxy.util.divide_chunks
   galaxy.util.fits_to_rgb_image


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

.. py:class:: DataSource

   Bases: :py:obj:`str`, :py:obj:`enum.Enum`


   Enumeration of data sources.


   .. py:attribute:: MAP_ACT
      :value: 'map_act'



   .. py:attribute:: DR5
      :value: 'dr5'



   .. py:attribute:: MC
      :value: 'mc'



   .. py:attribute:: SGA
      :value: 'sga'



   .. py:attribute:: TYC2
      :value: 'tyc2'



   .. py:attribute:: GAIA
      :value: 'gaia'



   .. py:attribute:: UPC_SZ
      :value: 'upc_sz'



   .. py:attribute:: SPT_SZ
      :value: 'spt_sz'



   .. py:attribute:: PSZSPT
      :value: 'pszspt'



   .. py:attribute:: CCOMPRASS
      :value: 'comprass'



   .. py:attribute:: SPT2500D
      :value: 'spt2500d'



   .. py:attribute:: SPTECS
      :value: 'sptecs'



   .. py:attribute:: SPT100
      :value: 'spt100'



   .. py:attribute:: ACT_MCMF
      :value: 'act_mcmf'



   .. py:attribute:: TEST_SAMPLE
      :value: 'test_sample'



   .. py:attribute:: RANDOM
      :value: 'rand'



.. py:class:: IsCluster

   Bases: :py:obj:`int`, :py:obj:`enum.Enum`


   Enumeration for cluster classification.


   .. py:attribute:: IS_CLUSTER
      :value: 1



   .. py:attribute:: NOT_CLUSTER
      :value: 0



.. py:data:: required_columns

.. py:data:: optional_columns

.. py:function:: inherit_columns(frame: pandas.DataFrame) -> pandas.DataFrame

   Ensures the DataFrame has required and optional columns.

   Args:
       frame (pd.DataFrame): Input DataFrame.

   Returns:
       pd.DataFrame: DataFrame with required and optional columns ensured.


.. py:function:: read_vizier(catalogue: str) -> pandas.DataFrame

   Fetches a catalogue from Vizier and converts it to a pandas DataFrame.

   Args:
       catalogue (str): Name or identifier of the catalogue.

   Returns:
       pd.DataFrame: DataFrame containing the catalogue data.


.. py:function:: bar_progress(current: int, total: int, width: int = 80) -> None

   Displays a progress bar for downloads.

   Args:
       current (int): Current number of bytes downloaded.
       total (int): Total number of bytes to download.
       width (int, optional): Width of the progress bar. Defaults to 80.


.. py:function:: to_hms_format(time_str: str) -> str

   Converts a time string to HMS format.

   Args:
       time_str (str): Time string in space-separated format (e.g., "12 34 56").

   Returns:
       str: Time string in HMS format (e.g., "12h34m56s").


.. py:function:: to_dms_format(time_str: str) -> str

   Converts a time string to DMS format.

   Args:
       time_str (str): Time string in space-separated format (e.g., "12 34 56").

   Returns:
       str: Time string in DMS format (e.g., "12d34m56s").


.. py:function:: divide_chunks(data_list: List[Any], chunk_size: int) -> Generator[List[Any], None, None]

   Divides a list into chunks of a specified size.

   Args:
       data_list (List[Any]): Input list to divide.
       chunk_size (int): Size of each chunk.

   Yields:
       Generator[List[Any], None, None]: Generator yielding list chunks.


.. py:function:: fits_to_rgb_image(tensor: torch.Tensor) -> torch.Tensor

   Converts a 2-channel tensor to an RGB image tensor.

   Args:
       tensor (torch.Tensor): Input tensor of shape (2, H, W).

   Returns:
       torch.Tensor: RGB image tensor of shape (3, H, W).

   Raises:
       ValueError: If the input tensor does not have 2 channels.