candl.io#
IO module that handles reading in data and providing feedback to the user.
Overview:#
- candl.io.expand_transformation_block(data_set_dict, block_file)#
Read a .yaml file containing a list of transformation entries.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- block_filestr
Full path of the .yaml block file containing the transformations to be read.
- Returns:
- list
List of transformations form the block file.
- candl.io.like_init_output(like)#
Print details after successful initialisation of the likelihood.
- Parameters:
- like:
candl.Like or candl.LensLike
- Returns:
- None
- candl.io.load_info_yaml(yaml_file, variant='default')#
Read the data set yaml file that contains all the information needed to instantiate the likelihood. Navigates through index files if necessary.
- Parameters:
- yaml_filestr
Path of the data set yaml file or index file
- variantstr
Optional, to be used for index files. Indicates the variant of the data set to be read in.
- Returns:
- dict
Data set dictionary.
- str
Path of the data set yaml file.
- candl.io.read_effective_frequencies_from_yaml(data_set_dict)#
Read in effective frequency information from data set dictionary.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- Returns:
- dict
Dictionary containing keys for all source types given in the effective frequency yaml file. Each entry is a dictionary with frequency identifiers as keys and effective frequencies as values.
- candl.io.read_file_from_path(full_path)#
Read in a file (array) from a path. Method used to read in band powers and covariance matrix. Can read four types: (1) Text files. Ending must be “.txt” or “.dat”. (2) Binary files. These must end in “.bin” and be stored as float64s. Will be turned into a square array if possible. (3) Numpy files. These must end in “.npy” and will be read in as a numpy array. (4) FITS files. These must end in “.fits”.
- Parameters:
- full_pathstr
The absolute path of the file.
- Returns:
- array
File read into array format.
- candl.io.read_file_from_yaml(data_set_dict, file_kw)#
Read in a file from the data_set dict.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- file_kwstr
A string that corresponds to a keyword in data_set_dict that specifies the file location relative to the base path.
- Returns:
- array
File read into array format.
- candl.io.read_lensing_M_matrices_from_yaml(full_path, Mtype='pp')#
Read lensing M matrices. This function assumes that window functions are saved in a specific format: window{n}.dat, where n runs over the bin numbers. Each file specifies the response function at all L values. All bins are read in, any cropping is performed later.
- Parameters:
- full_pathstr
The absolute path of the folder.
- Mtypestr
Which M matrices to load, “TT”, “TE”, “EE”, “BB”, “pp”, or “kk”.
- Returns:
- array
M matrices as a (number_of_ells, N_files_total) array.
- candl.io.read_meta_info_from_yaml(data_set_dict)#
Read any meta information.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- Returns:
- str
Name of the likelihood.
- candl.io.read_spectrum_info_from_yaml(data_set_dict, lensing=False)#
Read spectrum info. If lensing == True only returns first, second, and fourth item from usual returns.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- Returns:
- list
List of strings of spectrum identifiers.
- list
List of strings of spectrum types.
- list
List of lists with two entries giving frequencies for each spectrum.
- int
Number of total spectra.
- list
List of ints giving number of bins for each spectrum.
- candl.io.read_transformation_info_from_yaml(data_set_dict, i_tr)#
Read in information about a specific transformation from data set yaml file.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- i_trint
Index of the transformation to be read.
- Returns:
- str
Name of the transformation class.
- dict
Dictionary of information passed by the user, required as instantiation Parameters for the specified class.
- candl.io.read_window_functions_from_yaml(data_set_dict, spec_order, N_bins)#
Read band power window functions using data set dictionary. There are two allowed formats:
Window functions are saved by spectrum as “{spec}_window_functions.txt” The files are arrays of (ell, N_bins+1) size, where the first column gives the theory ell.
Window functions are saved by bin as “window_{i}.txt” starting at i=0. The files are arrays of (ell, N_specs+1) size, where the first column gives the theory ell.
Generally, the first format is preferred as it allows for spectra of different length.
- Parameters:
- data_set_dictdict
The data set dictionary containing all the information from the input yaml file.
- spec_orderlist
List specifying the order of spectra.
- N_binslist (int)
Number of bins for each spectrum.
- Returns:
- list
Band power window functions as a list of N_spectra with (N_ell_theory, N_bins) arrays. Start at ell=2.