galaxy.util

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

Attributes

PICS_SIZE
objects_naming
sample_sizes
plot_radius
sample_sources
required_columns
optional_columns

Classes

DataPart	str(object='') -> str
DataSource	Enumeration of data sources.
DataSurvey	str(object='') -> str
IsCluster	Enumeration for cluster classification.
IsObject	Enumeration for cluster classification.
RedShiftType	str(object='') -> str
SampleName	Enumeration for sample names.
SampleName2	Enumeration for sample names.
MapType	Enumeration for map types.

Functions

inherit_columns(→ pandas.DataFrame)	Ensures the DataFrame has required and optional columns.
read_vizier(→ pandas.DataFrame)	Fetches a catalogue from Vizier and converts it to a pandas DataFrame.
read_vizier_updated(→ pandas.DataFrame)	Fetches a catalogue from Vizier and converts it to a pandas DataFrame.
bar_progress(→ None)	Displays a progress bar for downloads.
to_hms_format(→ str)	Converts a time string to HMS format.
to_dms_format(→ str)	Converts a time string to DMS format.
divide_chunks(→ Generator[List[Any], None, None])	Divides a list into chunks of a specified size.
fits_to_rgb_image(→ torch.Tensor)	Converts a 2-channel tensor to an RGB image tensor.

Module Contents

galaxy.util.PICS_SIZE = 224

class galaxy.util.DataPart

Bases: str, enum.Enum

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to ‘utf-8’. errors defaults to ‘strict’.

TRAIN = 'train'

VALIDATE = 'validate'

TEST = 'test'

MC = 'mc'

TEST_SAMPLE = 'test_sample'

OTHERS = 'other'

TO_NORMALIZE = 'normalization_sample'

WISE_TO_NORMALIZE = 'normalize_wise'

ACT_TO_NORMALIZE = 'normalize_act'

class galaxy.util.DataSource

Bases: str, enum.Enum

Enumeration of data sources.

MAP_ACT = 'map_act'

DR5 = 'dr5'

MC = 'mc'

SGA = 'sga'

TYC2 = 'tyc2'

GAIA = 'gaia'

UPC_SZ = 'upc_sz'

SPT_SZ = 'spt_sz'

PSZSPT = 'pszspt'

CCOMPRASS = 'comprass'

SPT2500D = 'spt2500d'

SPTECS = 'sptecs'

SPT100 = 'spt100'

ACT_MCMF = 'act_mcmf'

TEST_SAMPLE = 'test_sample'

RANDOM = 'rand'

RANDOM_BASED = 'rand_based'

class galaxy.util.DataSurvey

Bases: str, enum.Enum

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to ‘utf-8’. errors defaults to ‘strict’.

WISE = 'wise'

ACT = 'act'

ARTIFICIAL = 'artificial'

class galaxy.util.IsCluster

Bases: int, enum.Enum

Enumeration for cluster classification.

IS_CLUSTER = 1

NOT_CLUSTER = 0

ANY_OBJECT = 2

class galaxy.util.IsObject

Bases: int, enum.Enum

Enumeration for cluster classification.

UNKNOWN = -2

IS_POINT = -1

IS_CLUSTER = 0

IS_GALAXY = 1

IS_STAR = 2

IS_RANDOM = 3

ANY_OBJECT = 4

galaxy.util.objects_naming: dict

class galaxy.util.RedShiftType

Bases: str, enum.Enum

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to ‘utf-8’. errors defaults to ‘strict’.

PHOT = 'phot'

SPEC = 'spec'

class galaxy.util.SampleName

Bases: str, enum.Enum

Enumeration for sample names.

CLUSTER_SMALL = 'cluster_small'

NEGATIVE_SMALL = 'random_small'

TYC2_SMALL = 'tyc2_small'

OTHER = 'other_small'

class galaxy.util.SampleName2

Bases: str, enum.Enum

Enumeration for sample names.

CLUSTER_SMALL = '0_small'

NEGATIVE_SMALL = '1_small'

OTHER_SMALL = '2_small'

galaxy.util.sample_sizes: dict

class galaxy.util.MapType

Bases: str, enum.Enum

Enumeration for map types.

SMALL = 0

BIG = 1

galaxy.util.plot_radius

galaxy.util.sample_sources

galaxy.util.required_columns

galaxy.util.optional_columns

galaxy.util.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.

galaxy.util.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.

galaxy.util.read_vizier_updated(catalogue: str, source: DataSource, target: IsCluster, survey: DataSurvey, object_type: IsObject, red_shift_type: RedShiftType | None = None, rename_dict: dict | None = None, row_limit=1000) → 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.

galaxy.util.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.

galaxy.util.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”).

galaxy.util.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”).

galaxy.util.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.

galaxy.util.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.