dm-dt API

light_curve.DmDt

dm-dt map producer

Each pair of observations is mapped to dm-dt plane bringing unity value. dmdt-map is a rectangle on this plane consisted of dt_size x dm_size cells, and limited by [min_dt; max_dt) and [min_dm; max_dm) intervals. .points*() methods assigns unity value of each observation to a single cell, while .gausses*() methods smears this unity value over all cells with given dt value using normal distribution N(m2 - m1, sigma1^2 + sigma2^2), where (t1, m1, sigma1) and (t2, m2, sigma2) are a pair of observations including uncertainties. Optionally, after the map is built, normalisation is performed ("norm" parameter): "dt" means divide each dt = const column by the total number of all observations corresponded to given dt (in this case gausses() output can be interpreted as conditional probability p(dm|dt)); "max" means divide all values by the maximum value; both options can be combined, then "max" is performed after "dt".

Parameters:

Name	Type	Description	Default
dt	np.array of float64	Ascending array of dt grid edges	required
dm	np.array of float64	Ascending array of dm grid edges	required
dt_type	str	Type of dt grid, one of: - 'auto' (default) means check if grid is linear or logarithmic one, which allows some speed-up - 'linear' says to build a linear grid from the first and last values of dt, using the same number of edges - 'log' is the same as 'linear' but for building logarithmic grid - 'asis' means using the given array as a grid	required
dm_type	str	Type of dm grid, see dt_type for details	required
norm	list of str	Types of normalisation, cab be any combination of "dt" and "max", default is an empty list [] which means no normalisation	required
n_jobs	int	Number of parallel threads to run bulk methods such as points_many() or gausses_batches() default is -1 which means to use as many threads as CPU cores	required
approx_erf	bool	Use approximation normal CDF in gausses* methods, reduces accuracy, but has better performance, default is False	required

Attributes:

Name	Type	Description
n_jobs	int
shape	(int, int)	Shape of a single dmdt map, (dt_size, dm_size)
dt_grid	np.array of float64
min_dt	float
max_dt	float
dm_grid	np.array of float64
min_dm	float
max_dm	float

Methods:

Name	Description
from_borders	Construct DmDt with logarithmic dt grid [10^min_lgdt, 10^max_lgdt) and linear dm grid [-max_abs_dm, max_abs_dm), kwargs are passed to __new__()
points	Produces dmdt-maps from light curve
gausses	Produces smeared dmdt-map from noisy light curve
count_dt	Total number of observations per each dt interval
points_many	Produces dmdt-maps from a list of light curves
gausses_many	Produces smeared dmdt-maps from a list of light curves
count_dt_many	Number of observations in each dt for a list of arrays
points_batches	Gives a reusable iterable which yields dmdt-maps
gausses_batches	Gives a reusable iterable which yields smeared dmdt-maps

dm_grid property

dt_grid property

max_dm property

max_dt property

min_dm property

min_dt property

n_jobs property

shape property

count_dt method descriptor

Total number of observations per each dt interval

Output takes into account all observation pairs within [min_dt; max_dt), even if they are not in [min_dm; max_dm)

Parameters:

Name	Type	Description	Default
t	1d-ndarray of float	Time moments, must be sorted	required
sorted	bool or None	True guarantees that t is sorted	None
cast	bool	If False allow np.ndarray input only, True allows casting. Casting provides more flexibility with input types at the cost of	False
Returns			required

count_dt_many method descriptor

Total number of observations per each dt interval

Output takes into account all observation pairs within [min_dt; max_dt), even if they are not in [min_dm; max_dm)

Parameters:

Name	Type	Description	Default
t_	list of 1d-ndarray of float	List of arrays, each represents time moments, must be sorted	required
sorted	bool or None	True guarantees that t is sorted	None
Returns			required

gausses method descriptor

Produces smeared dmdt-map from light curve

Parameters:

Name	Type	Description	Default
t	1d-ndarray of float	Time moments, must be sorted	required
m	1d-ndarray of float	Magnitudes	required
sigma	1d-ndarray of float	Uncertainties	required
sorted	bool or None	True guarantees that the light curve is sorted	None
cast	bool	If False allow np.ndarray input only, True allows casting. Casting provides more flexibility with input types at the cost of	False

Returns:

Type	Description
2d-array of float

gausses_batches method descriptor

Reusable iterable yielding dmdt-maps

The dmdt-maps are produced in parallel using n_jobs threads, batches are being generated in background, so the next batch is started to generate just after the previous one is yielded. Note that light curves data are copied, so from the performance point of view it is better to use gausses_many if you don't need observation dropping

Parameters:

Name	Type	Description	Default
lcs	list of (ndarray, ndarray, ndarray)	List or tuple of tuple pairs (t, m, sigma) represented individual light curves. All arrays must have the same dtype	required
sorted	bool or None	True guarantees that all light curves is sorted, default is None	None
batch_size	int	The number of dmdt-maps to yield. The last batch can be smaller. Default is 1	1
yield_index	bool	Yield a tuple of (indexes, maps) instead of just maps. Could be useful when shuffle is True. Default is False	False
shuffle	bool	If True, shuffle light curves (not individual observations) on each creating of new iterator. Default is False	False
drop_nobs	int or float	Drop observations from every light curve. If it is a positive integer, it is a number of observations to drop. If it is a floating point between 0 and 1, it is a part of observation to drop. Default is 0, which means usage of the original data	0
random_seed	int or None	Random seed for shuffling and dropping. Default is None which means random seed	None

gausses_many method descriptor

Produces smeared dmdt-map from a collection of light curves

The method is performed in parallel using n_jobs threads

Parameters:

Name	Type	Description	Default
lcs	list of (ndarray, ndarray, ndarray)	List or tuple of tuple pairs (t, m, sigma) represented individual light curves. All arrays must have the same dtype	required
sorted	bool or None	True guarantees that all light curves are sorted	None

Returns:

Type	Description
3d-ndarray of float

points method descriptor

Produces dmdt-map from light curve

Parameters:

Name	Type	Description	Default
t	1d-ndarray of float	Time moments, must be sorted	required
m	1d-ndarray of float	Magnitudes	required
sorted	bool or None	True guarantees that the light curve is sorted	None
cast	bool	If False allow np.ndarray input only, True allows casting. Casting provides more flexibility with input types at the cost of	False

Returns:

Type	Description
2d-ndarray of float

points_batches method descriptor

Reusable iterable yielding dmdt-maps

The dmdt-maps are produced in parallel using n_jobs threads, batches are being generated in background, so the next batch is started to generate just after the previous one is yielded. Note that light curves data are copied, so from the performance point of view it is better to use points_many if you don't need observation dropping

Parameters:

Name	Type	Description	Default
lcs	list of (ndarray, ndarray)	List or tuple of tuple pairs (t, m) represented individual light curves. All arrays must have the same dtype	required
sorted	bool or None	True guarantees that all light curves is sorted, default is None	None
batch_size	int	The number of dmdt-maps to yield. The last batch can be smaller. Default is 1	1
yield_index	bool	Yield a tuple of (indexes, maps) instead of just maps. Could be useful when shuffle is True. Default is False	False
shuffle	bool	If True, shuffle light curves (not individual observations) on each creating of new iterator. Default is False	False
drop_nobs	int or float	Drop observations from every light curve. If it is a positive integer, it is a number of observations to drop. If it is a floating point between 0 and 1, it is a part of observation to drop. Default is 0, which means usage of the original data	0
random_seed	int or None	Random seed for shuffling and dropping. Default is None which means random seed	None

Returns:

Type	Description
Iterable of 3d-ndarray or (1d-ndarray, 3d-ndarray)

points_many method descriptor

Produces dmdt-map from a collection of light curves

The method is performed in parallel using n_jobs threads

Parameters:

Name	Type	Description	Default
lcs	list of (ndarray, ndarray)	List or tuple of tuple pairs (t, m) represented individual light curves. All arrays must have the same dtype	required
sorted	bool or None	True guarantees that all light curves is sorted	None

Returns:

Type	Description
3d-ndarray of float