lcc.utils package¶
Submodules¶
lcc.utils.commons module¶
There are common functions and decorators mainly for query classes
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lcc.utils.commons.
accepts
(*accepted_arg_types)[source]¶ A decorator to validate the parameter types of a given function. It is passed a tuple of types. eg. (<type ‘tuple’>, <type ‘int’>)
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lcc.utils.commons.
args_type
(**decls)[source]¶ Decorator to check argument types
Examples
@args_type(name=”str”,age=(int,float)) def func(...):
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lcc.utils.commons.
check_attribute
(attribute, cond, if_not=False)[source]¶ The class decorator checks if some class attributes has certain value
Parameters: attribute : str
Name of the inspected class attribute
cond : optional
Condition to test for the inspected attribute
id_not : optional
Variable which will be returned if condition is not satisfied. If it is ‘raise’ exception will be raised
Returns: Original output of the function if condition is satisfied
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lcc.utils.commons.
default_values
(**decls)[source]¶ Decorator to add default values to certain arguments if missing
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lcc.utils.commons.
mandatory_args
(*args_options)[source]¶ Decorator to check presence of mandatory arguments
Examples
@default_values((“name”,”age”),(“nick_name”,”sex”)) def func(...):
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lcc.utils.commons.
returns
(*accepted_return_type_tuple)[source]¶ Validates the return type. Since there’s only ever one return type, this makes life simpler. Along with the accepts() decorator, this also only does a check for the top argument. For example you couldn’t check (<type ‘tuple’>, <type ‘int’>, <type ‘str’>). In that case you could only check if it was a tuple.
lcc.utils.data_analysis module¶
There are functions for processing data series
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lcc.utils.data_analysis.
abbe
(x, n)[source]¶ Calculation of Abbe value
Parameters: x : numpy.array
Input data serie
n : int
Dimension of original data (before dimension reduction)
Returns: float
Abbe value
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lcc.utils.data_analysis.
compute_bins
(x_time, days_per_bin, set_min=5)[source]¶ Compute number of bins for given time series according to given ratio of number of days per one bin
Parameters: x_time : numpy.array, list
List of times
days_per_bin : float
Transformation rate for dimension reduction
set_min
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lcc.utils.data_analysis.
histogram
(xx, yy, bins_num=None, centred=True, normed=True)[source]¶ Parameters: xx : numpy.array
Input x data
yy : numpy.array
Input y data
bins_num : int
Number of values in histogram
centred : bool
If True values will be shifted (mean value into the zero)
normed : bool
If True values will be normed (according to standard deviation)
Returns: numpy.array
Number of values in particular ranges
numpy.array
Ranges
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lcc.utils.data_analysis.
normalize
(x, eps=1e-06)[source]¶ Function will normalize an array (give it a mean of 0, and a standard deviation of 1) unless it’s standard deviation is below epsilon, in which case it returns an array of zeros the length of the original array.
Parameters: x : numpy.array, list, iterable
Input data serie
Returns: numpy.arrray
Normalized data serie
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lcc.utils.data_analysis.
sort_pairs
(x, y, rev=False)[source]¶ Sort two numpy arrays according to the first
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lcc.utils.data_analysis.
to_PAA
(x, bins)[source]¶ Function performs Piecewise Aggregate Approximation on data set, reducing the dimension of the dataset x to w discrete levels. returns the reduced dimension data set, as well as the indicies corresponding to the original data for each reduced dimension
Parameters: x : list, array, iterable
1D serie of values
bins : int
Dimension of reduced data
Returns: numpy.array
Approximated data serie
list
Indices
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lcc.utils.data_analysis.
to_ekvi_PAA
(x, y, bins=None, days_per_bin=None)[source]¶ This method perform PAA (see above) on y data set, but it will consider different time steps between values (in x data set) and return corrected data set.
Parameters: x : list, numpy.array, iterable
Times which is treated as template for transformation y values
y : list, numpy.array, iterable
List of values
bins : int
Dimension of result data
days_per_bin : float
This value can be used for calculating bins
Returns: list
Reduced x data
list
Reduced y data
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lcc.utils.data_analysis.
variogram
(x, y, bins=None, log_opt=True)[source]¶ Variogram of function shows variability of function in various time steps
Parameters: x : list, numpy.array, iterable
Time values
y : list, numpy.array
Measured values
bins : int
Number of values in a variogram
log_opt : bool
Option if variogram values return in logarithm values
Returns: tuple
Variogram as two numpy arrays
lcc.utils.helpers module¶
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lcc.utils.helpers.
checkDepth
(a, deep_level, ifnotraise=True)[source]¶ Check if input list has desired level of nested lists
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lcc.utils.helpers.
get_arguments
(insp_classes)[source]¶ Get args and kwargs of the class methods
Parameters: insp_classes : list
Classes to inspect
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lcc.utils.helpers.
get_combinations
(keys, *lists)[source]¶ Make combinations from given lists
Parameters: keys : list
Name of consequent columns (lists)
lists : list of lists
Values to combine
Returns: list
All combinations
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lcc.utils.helpers.
progressbar
(it, prefix='', size=60)[source]¶ Parameters: it : list
List of values
prefix : str
Text which is displayed before progressbar
size : int
Number of items in progressbar
Returns: None
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lcc.utils.helpers.
subDictInDict
(sub_dict, dict_list, remove_keys=[])[source]¶ Parameters: sub_dict : dict
Single dictionary
dict_list : list
List of dictionaries
remove_keys : list
List of keys which are removed from dictionaries
Example
——
subDictInDict({“x”:1},[{“x”:2,”y”:5,..},{“x”:1,”z”:2,..}, ..} –> [{“x”:1, “z”:2, ..},..]
In this example list of dictionaries which contain x = 1 is returned
Returns: list
List of dictionaries which contain condition in sub_dict
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lcc.utils.helpers.
verbose
(txt, verbosity, verb_level=2)[source]¶ Parameters: txt : str
Message which will be showed
verb_level : int
Level of verbosity:
0 - All messages will be showed 1 - Just messages witch verbosity 1 an 2 will be showed 2 - Just messages witch verbosity 2 will be showed
verb_level : int
Verbosity level
Returns: None
lcc.utils.output_process_modules module¶
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lcc.utils.output_process_modules.
loadFromFile
(file_name='saved_object.pickle')[source]¶ Open object from file
Parameters: file_name : str
Name of the serialized file
Returns: object
Loaded object
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lcc.utils.output_process_modules.
saveIntoFile
(obj, path='.', file_name='saved_object.pickle', folder_name=None)[source]¶ This method serialize object (save it into file)
- obj : object
- Object to serialize
- path : str
- Path to the folder
- file_name : str
- Name of result file
- folder_name : str
- Name of folder
Returns: None
lcc.utils.stars module¶
There are common functions for list of star objects (evaluation, plotting...)
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lcc.utils.stars.
plotStarsPicture
(stars, option='show', hist_bins=10, vario_bins=10, center=True, save_loc=None, num_plots=None, abbe_bins=20)[source]¶ This function plot three graphs for all stars: Light curve, histogram and variogram. Additionally Abbe value will be displayed.
Parameters: stars : list of `Star`s
List of star objects to be plot
option : str
Option whether plots will be saved or just showed
hist_bins : int
Dimension of histogram
vario_bins : int
Dimension of variogram
center : bool
Centering of histogram
save_loc : str, NoneType
Location where images will be saved
num_plots : int, NoneType
Number of plots
abbe_bins : int
Dimension of reduced light curve for calculating Abbe value
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lcc.utils.stars.
saveStars
(stars, path='.', clobber=True)[source]¶ Save Star objects into fits files
Parameters: stars : list, iterable
Star objects to be saved
path : str
Relative path to the file where fits are stored
clobber : bool
Files are overwritten if True
Returns: list
List of names of star files