In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.rcParams["figure.figsize"] = 9, 4.51
import expectexception
from tutorial import *
A time series is a sequence of observations, or data points, that is arranged based on the times of their occurrence. The hourly measurement of wind speeds in meteorology, the minute by minute recording of electrical activity along the scalp in electroencephalography, and the weekly changes of stock prices in finances are just some examples of time series, among many others. Some of the following properties may be observed in time series data [gutsequential]:
The study and analysis of time series can have multiple ends: to gain a better understanding of the mechanism generating the data, to predict future outcomes and behaviors, to classify and characterize events, or more.
In [2]:
ts_anim()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/proyectos/feets/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
~/proyectos/feets/src/doc/source/JSAnimation/IPython_display.py in anim_to_html(anim, fps, embed_frames, default_mode)
74 anim.save(f.name, writer=HTMLWriter(fps=fps,
75 embed_frames=embed_frames,
---> 76 default_mode=default_mode))
77 html = open(f.name).read()
78
~/proyectos/feets/lib/python3.6/site-packages/matplotlib/animation.py in save(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)
1154 progress_callback(frame_number, total_frames)
1155 frame_number += 1
-> 1156 writer.grab_frame(**savefig_kwargs)
1157
1158 # Reconnect signal for first draw if necessary
/usr/lib/python3.6/contextlib.py in __exit__(self, type, value, traceback)
86 if type is None:
87 try:
---> 88 next(self.gen)
89 except StopIteration:
90 return False
~/proyectos/feets/lib/python3.6/site-packages/matplotlib/animation.py in saving(self, fig, outfile, dpi, *args, **kwargs)
230 yield self
231 finally:
--> 232 self.finish()
233
234
~/proyectos/feets/lib/python3.6/site-packages/matplotlib/animation.py in finish(self)
526 # are available to be assembled.
527 self._run()
--> 528 MovieWriter.finish(self) # Will call clean-up
529
530 def cleanup(self):
~/proyectos/feets/lib/python3.6/site-packages/matplotlib/animation.py in finish(self)
365 def finish(self):
366 '''Finish any processing for writing the movie.'''
--> 367 self.cleanup()
368
369 def grab_frame(self, **savefig_kwargs):
~/proyectos/feets/lib/python3.6/site-packages/matplotlib/animation.py in cleanup(self)
529
530 def cleanup(self):
--> 531 MovieWriter.cleanup(self)
532
533 # Delete temporary files
~/proyectos/feets/lib/python3.6/site-packages/matplotlib/animation.py in cleanup(self)
397 self._frame_sink().close()
398 # Use the encoding/errors that universal_newlines would use.
--> 399 out = TextIOWrapper(BytesIO(out)).read()
400 err = TextIOWrapper(BytesIO(err)).read()
401 if out:
TypeError: a bytes-like object is required, not 'str'
Out[2]:
<matplotlib.animation.FuncAnimation at 0x7fa3f775ed30>
In time-domain astronomy, data gathered from the telescopes is usually represented in the form of light-curves which are time series that show the brightness variation of an object through a period of time (for a visual representation see video below). Based on the variability characteristics of the light-curves, celestial objects can be classified into different groups (quasars, long period variables, eclipsing binaries, etc.) and consequently can be studied in depth independently.
Classification of data into groups can be performed in several ways given light curve data: primarily, existing methods found in the literature use machine learning algorithms that group light-curves into categories through feature extraction from the light-curve data. These light-curve features, the topic of this work, are numerical or categorical properties of the light-curves which can be used to characterize and distinguish the different variability classes. Features can range from basic statistical properties such as the mean or the standard deviation to more complex time series characteristics such as the autocorrelation function. These features should ideally be informative and discriminative, thus allowing for machine learning or other algorithms to use them to distinguish between classes of light-curves.
In this document, which allows for the fast and efficient calculation of a compilation of many existing light-curve features. The main goal is to create a collaborative and open tool where users can characterize or analyze an astronomical photometric database while also contributing to the library by adding new features. However, it is important to highlight that this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.
Our vision is to be capable of analyzing and comparing light curves from any available astronomical catalog in a standard and universal way. This would facilitate and make more efficient tasks such as modeling, classification, data cleaning, outlier detection, and data analysis in general. Consequently, when studying light curves, astronomers and data analysts using our library would be able to compare and match different features in a standardized way. In order to achieve this goal, the library should be run and features generated for every existent survey (MACHO, EROS, OGLE, Catalina, Pan-STARRS, VVV, etc.), as well as for future surveys (LSST), and the results shared openly, as is this library.
In the remainder of this document, we provide an overview of the features developed so far and explain how users can contribute to the library. A Readme file is also available in case of needing extra information.
The video below shows how data from the brightness intensity of a star through time results on a light-curve. In this particular case we are observing a complex triple system in which three stars have mutual eclipses as each of the stars gets behind or in front of the others.
In [3]:
macho_video()
Out[3]:
The following figure presents example light-curves of each class in the MACHO survey. The x-axis is the modified Julian Date (MJD), and the y-axis is the MACHO B-magnitude.
In [4]:
macho_example11()
Out[4]:
The library is coded in python and can be downloaded from the Github repository https://github.com/carpyncho/feets. New features may be added by issuing pull requests via the Github version control system. For a quick guide on how to use github visit https://guides.github.com/activities/hello-world/.
It is also possible to obtain the library by downloading the python package from https://pypi.python.org/pypi/feets or by directly installing it from the terminal as follows:
$ pip install feets
The library receives as input the time series data and returns as output an array with the calculated features. Depending on the available input the user can calculate different features. For example, if the user has only the vectors magnitude and time, just the features that need this data will be able to be computed.
In order to calculate all the possible features the following vectors (also termed as raw data) are needed per light curve:
where 2 refers to a different observation band. It is worth pointing out that the magnitude vector is the only input strictly required by the library given that it is necessary for the calculation of all the features. The remaining vectors are optional since they are needed just by some features. In other words, if the user does not have this additional data or he is analyzing time series other than light curves, it is still possible to calculate some of the features. More details are presented in the next section.
This is an example of how the input could look like if you have only magnitude and time as input vectors:
In [5]:
lc_example = np.array([time_ex, magnitude_ex])
lc_example
Out[5]:
array([[ 0. , 1. , 2. , 3. , 4. ,
5. , 6. , 7. , 8. , 9. ,
10. , 11. , 12. , 13. , 14. ,
15. , 16. , 17. , 18. , 19. ,
20. , 21. , 22. , 23. , 24. ,
25. , 26. , 27. , 28. , 29. ],
[ 0.20970975, 0.80204085, 0.28820015, 0.37039011, 0.48115464,
0.86820313, 0.48979622, 0.22301646, 0.4935109 , 0.79350497,
0.97494152, 0.81064321, 0.32671638, 0.20660825, 0.37420108,
0.72971381, 0.15585522, 0.72447252, 0.34039641, 0.71799426,
0.81918733, 0.94291775, 0.6184779 , 0.2298462 , 0.41711845,
0.36057235, 0.23657102, 0.41109734, 0.72880648, 0.9590252 ]])
When observed in different bands, light curves of a same object are not always monitored for the same time length and at the same precise times. For some features, it is important to align the light curves and to only consider the simultaneous measurements from both bands. The aligned vectors refer to the arrays obtained by synchronizing the raw data.
Thus, the actual input needed by the library is an array containing the following vectors and in the following order:
The library structure is divided into two main parts.
The following code is an example of a class in extractors package that calculates the slope of a linear fit to the light-curve:
import feets
from scipy import stats
class LinearTrend(feets.Extractor): # must inherit from Extractor
data = ['magnitude', 'time'] # Which data is needed
# to calculate this feature
features = ["LinearTrend"] # The names of the expected
# feature
# This method receives the data specified in the
# previous line with the same name
def fit(self, magnitude, time):
regression_slope = stats.linregress(time, magnitude)[0]
# The return value must be a dict with the same values
# defined in features
return {"LinearTrend": regression_slope}
If the user wants to use their features after the declaration of the extractor they must register the class with the register function. For example:
feets.register_extractor(LinearTrend)
Feets comes with a MACHO Example light-curve,with all the 9 parameters needed to calculate all the posible features.
In [6]:
from feets.datasets import macho
lc = macho.load_MACHO_example()
print("ID:", print(lc.id))
print("Bands:", lc.bands)
lc_1.3444.614
ID: None
Bands: ('R', 'B')
It is sometimes helpful to visualize the data before processing it. For a representation of the light curve, we can plot it as follows:
In [7]:
p = plt.plot(lc.data.B.time, lc.data.B.magnitude, '*-', alpha = 0.6)
plt.xlabel("Time")
plt.ylabel("Magnitude")
plt.gca().invert_yaxis()
Besides opening the file, the data we noww need to:
aligned_time, aligned_magnitude, aligned_magnitude2, aligned_error and aligned_error2.
In [8]:
import feets.preprocess
# removing noise of the data
time, mag, error = feets.preprocess.remove_noise(**lc.data.B)
time2, mag2, error2 = feets.preprocess.remove_noise(**lc.data.R)
# We synchronize the data
atime, amag, amag2, aerror, aerror2 = feets.preprocess.align(
time, time2, mag, mag2, error, error2)
lc = [time, mag, error,
mag2, atime, amag, amag2,
aerror, aerror2]
plt.plot(lc[0], lc[1], '*-', alpha = 0.6)
plt.xlabel("Time")
plt.ylabel("Magnitude")
plt.gca().invert_yaxis()
The library allows the user to either choose the specific features of interest to be calculated or to calculate them all simultaneously. Nevertheless, as already mentioned, the features are divided depending on the input data needed for their computation (magnitude, time, error, second data, etc.). If unspecified, this will be used as an automatic selection parameter. For example, if the user wants to calculate all the available features but only has the vectors magnitude and time, only the features that need magnitude and/or time as an input will be computed.
The list of all the possible features with their corresponding input data, additional parameters and literature source is presented in the following table:
In [9]:
features_table()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-9-0fd58bd566b0> in <module>
----> 1 features_table()
~/proyectos/feets/src/doc/source/tutorial.py in features_table()
97 rows.append(row)
98
---> 99 FourierComponents = feets.extractor_of("Freq2_harmonics_rel_phase_0")
100 rows.append((
101 "Freq{i}_harmonics_amplitude_{j}",
~/proyectos/feets/src/feets/extractors/__init__.py in extractor_of(feature)
105 def extractor_of(feature):
106 """Retrieve the current register extractor class for the given feature."""
--> 107 return _extractors[feature]
108
109
KeyError: 'Freq2_harmonics_rel_phase_0'
The possible ways of how an user can choose the features from the library to be calculated are presented next.
The user can specify a list of features as input by specifying the features as a list for the parameter only. In the following example, we aim to calculate the standard deviation and Stetson L of the data:
In [11]:
fs = feets.FeatureSpace(only=['Std','StetsonL'])
features, values = fs.extract(*lc).as_arrays()
as_table(features, values)
Out[11]:
Feature
Value
Std
0.14157317495929828
StetsonL
0.5823703637198997
You can provide the same parameters one by one or by keyword intead of use the unpacking *lc way. So the following examples will work:
lc(time, mag, error,
mag2, atime, amag, amag2,
aerror, aerror2)
or
lc(time=time, magnitude=mag, error=error,
magnitude2=mag2, aligned_time=atime,
aligned_magnitude=amag, aligned_magnitude2=amag2,
aligned_error=aerror, aligned_error2=aerror2)
In case the user does not have all the input vectors mentioned above, it is necessary to specify the available data by specifying the list of vectors using the parameter data. In the example below, we calculate all the features that can be computed with the magnitude and time as an input.
In [12]:
fs = feets.FeatureSpace(data=['magnitude','time'])
features, values = fs.extract(*lc).as_arrays()
as_table(features, values)
Out[12]:
Feature
Value
Amplitude
0.26500000000000057
AndersonDarling
1.0
Autocor_length
1.0
Con
0.0
DMDT_0_0
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DMDT_0_1
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DMDT_12_5
0.0
DMDT_12_6
1.0
DMDT_12_7
1.0
DMDT_12_8
1.0
DMDT_12_9
1.0
DMDT_12_10
1.0
DMDT_12_11
1.0
DMDT_12_12
1.0
DMDT_12_13
1.0
DMDT_12_14
1.0
DMDT_12_15
1.0
DMDT_12_16
1.0
DMDT_12_17
1.0
DMDT_12_18
0.0
DMDT_12_19
0.0
DMDT_12_20
0.0
DMDT_12_21
0.0
DMDT_12_22
0.0
DMDT_12_23
0.0
DMDT_13_0
0.0
DMDT_13_1
0.0
DMDT_13_2
0.0
DMDT_13_3
0.0
DMDT_13_4
0.0
DMDT_13_5
0.0
DMDT_13_6
1.0
DMDT_13_7
1.0
DMDT_13_8
1.0
DMDT_13_9
1.0
DMDT_13_10
1.0
DMDT_13_11
1.0
DMDT_13_12
1.0
DMDT_13_13
1.0
DMDT_13_14
1.0
DMDT_13_15
1.0
DMDT_13_16
1.0
DMDT_13_17
1.0
DMDT_13_18
0.0
DMDT_13_19
0.0
DMDT_13_20
0.0
DMDT_13_21
0.0
DMDT_13_22
0.0
DMDT_13_23
0.0
DMDT_14_0
0.0
DMDT_14_1
0.0
DMDT_14_2
0.0
DMDT_14_3
0.0
DMDT_14_4
0.0
DMDT_14_5
0.0
DMDT_14_6
1.0
DMDT_14_7
1.0
DMDT_14_8
1.0
DMDT_14_9
1.0
DMDT_14_10
1.0
DMDT_14_11
1.0
DMDT_14_12
1.0
DMDT_14_13
1.0
DMDT_14_14
1.0
DMDT_14_15
1.0
DMDT_14_16
1.0
DMDT_14_17
1.0
DMDT_14_18
0.0
DMDT_14_19
0.0
DMDT_14_20
0.0
DMDT_14_21
0.0
DMDT_14_22
0.0
DMDT_14_23
0.0
DMDT_15_0
0.0
DMDT_15_1
0.0
DMDT_15_2
0.0
DMDT_15_3
0.0
DMDT_15_4
0.0
DMDT_15_5
1.0
DMDT_15_6
1.0
DMDT_15_7
1.0
DMDT_15_8
1.0
DMDT_15_9
1.0
DMDT_15_10
1.0
DMDT_15_11
1.0
DMDT_15_12
1.0
DMDT_15_13
1.0
DMDT_15_14
1.0
DMDT_15_15
1.0
DMDT_15_16
1.0
DMDT_15_17
1.0
DMDT_15_18
0.0
DMDT_15_19
0.0
DMDT_15_20
0.0
DMDT_15_21
0.0
DMDT_15_22
0.0
DMDT_15_23
0.0
DMDT_16_0
0.0
DMDT_16_1
0.0
DMDT_16_2
0.0
DMDT_16_3
0.0
DMDT_16_4
0.0
DMDT_16_5
1.0
DMDT_16_6
1.0
DMDT_16_7
1.0
DMDT_16_8
1.0
DMDT_16_9
1.0
DMDT_16_10
1.0
DMDT_16_11
2.0
DMDT_16_12
2.0
DMDT_16_13
1.0
DMDT_16_14
1.0
DMDT_16_15
1.0
DMDT_16_16
1.0
DMDT_16_17
1.0
DMDT_16_18
1.0
DMDT_16_19
0.0
DMDT_16_20
0.0
DMDT_16_21
0.0
DMDT_16_22
0.0
DMDT_16_23
0.0
DMDT_17_0
0.0
DMDT_17_1
0.0
DMDT_17_2
0.0
DMDT_17_3
0.0
DMDT_17_4
0.0
DMDT_17_5
1.0
DMDT_17_6
1.0
DMDT_17_7
1.0
DMDT_17_8
1.0
DMDT_17_9
1.0
DMDT_17_10
1.0
DMDT_17_11
4.0
DMDT_17_12
4.0
DMDT_17_13
1.0
DMDT_17_14
1.0
DMDT_17_15
1.0
DMDT_17_16
1.0
DMDT_17_17
1.0
DMDT_17_18
1.0
DMDT_17_19
0.0
DMDT_17_20
0.0
DMDT_17_21
0.0
DMDT_17_22
0.0
DMDT_17_23
0.0
DMDT_18_0
0.0
DMDT_18_1
0.0
DMDT_18_2
0.0
DMDT_18_3
0.0
DMDT_18_4
0.0
DMDT_18_5
1.0
DMDT_18_6
1.0
DMDT_18_7
1.0
DMDT_18_8
2.0
DMDT_18_9
2.0
DMDT_18_10
2.0
DMDT_18_11
6.0
DMDT_18_12
6.0
DMDT_18_13
2.0
DMDT_18_14
2.0
DMDT_18_15
2.0
DMDT_18_16
2.0
DMDT_18_17
1.0
DMDT_18_18
1.0
DMDT_18_19
0.0
DMDT_18_20
0.0
DMDT_18_21
0.0
DMDT_18_22
0.0
DMDT_18_23
0.0
DMDT_19_0
0.0
DMDT_19_1
0.0
DMDT_19_2
0.0
DMDT_19_3
0.0
DMDT_19_4
0.0
DMDT_19_5
1.0
DMDT_19_6
1.0
DMDT_19_7
2.0
DMDT_19_8
3.0
DMDT_19_9
3.0
DMDT_19_10
3.0
DMDT_19_11
10.0
DMDT_19_12
10.0
DMDT_19_13
3.0
DMDT_19_14
3.0
DMDT_19_15
3.0
DMDT_19_16
3.0
DMDT_19_17
1.0
DMDT_19_18
1.0
DMDT_19_19
0.0
DMDT_19_20
0.0
DMDT_19_21
0.0
DMDT_19_22
0.0
DMDT_19_23
0.0
DMDT_20_0
0.0
DMDT_20_1
0.0
DMDT_20_2
0.0
DMDT_20_3
0.0
DMDT_20_4
0.0
DMDT_20_5
1.0
DMDT_20_6
1.0
DMDT_20_7
3.0
DMDT_20_8
5.0
DMDT_20_9
5.0
DMDT_20_10
5.0
DMDT_20_11
16.0
DMDT_20_12
18.0
DMDT_20_13
5.0
DMDT_20_14
5.0
DMDT_20_15
5.0
DMDT_20_16
4.0
DMDT_20_17
1.0
DMDT_20_18
1.0
DMDT_20_19
0.0
DMDT_20_20
0.0
DMDT_20_21
0.0
DMDT_20_22
0.0
DMDT_20_23
0.0
DMDT_21_0
0.0
DMDT_21_1
0.0
DMDT_21_2
0.0
DMDT_21_3
0.0
DMDT_21_4
0.0
DMDT_21_5
1.0
DMDT_21_6
1.0
DMDT_21_7
3.0
DMDT_21_8
5.0
DMDT_21_9
5.0
DMDT_21_10
5.0
DMDT_21_11
17.0
DMDT_21_12
20.0
DMDT_21_13
6.0
DMDT_21_14
5.0
DMDT_21_15
6.0
DMDT_21_16
4.0
DMDT_21_17
1.0
DMDT_21_18
1.0
DMDT_21_19
0.0
DMDT_21_20
0.0
DMDT_21_21
0.0
DMDT_21_22
0.0
DMDT_21_23
0.0
DMDT_22_0
0.0
DMDT_22_1
0.0
DMDT_22_2
0.0
DMDT_22_3
0.0
DMDT_22_4
0.0
DMDT_22_5
1.0
DMDT_22_6
1.0
DMDT_22_7
2.0
DMDT_22_8
2.0
DMDT_22_9
2.0
DMDT_22_10
2.0
DMDT_22_11
7.0
DMDT_22_12
9.0
DMDT_22_13
2.0
DMDT_22_14
2.0
DMDT_22_15
2.0
DMDT_22_16
2.0
DMDT_22_17
1.0
DMDT_22_18
0.0
DMDT_22_19
0.0
DMDT_22_20
0.0
DMDT_22_21
0.0
DMDT_22_22
0.0
DMDT_22_23
0.0
Eta_e
905.636200812288
FluxPercentileRatioMid20
0.09131403118040174
FluxPercentileRatioMid35
0.1781737193763922
FluxPercentileRatioMid50
0.3162583518930947
FluxPercentileRatioMid65
0.5233853006681504
FluxPercentileRatioMid80
0.7995545657015593
Freq1_harmonics_amplitude_0
0.13297191886665682
Freq1_harmonics_rel_phase_0
0.0
Freq1_harmonics_amplitude_1
0.07708190071937732
Freq1_harmonics_rel_phase_1
0.11506771848541875
Freq1_harmonics_amplitude_2
0.049703893823420386
Freq1_harmonics_rel_phase_2
0.3342992671936593
Freq1_harmonics_amplitude_3
0.025328725816726485
Freq1_harmonics_rel_phase_3
0.5308555764740739
Freq2_harmonics_amplitude_0
0.016357295575401373
Freq2_harmonics_rel_phase_0
0.0
Freq2_harmonics_amplitude_1
0.00116609801518309
Freq2_harmonics_rel_phase_1
-1.2826352924868387
Freq2_harmonics_amplitude_2
0.006229687748569223
Freq2_harmonics_rel_phase_2
-0.2727495960699948
Freq2_harmonics_amplitude_3
0.003237582323722657
Freq2_harmonics_rel_phase_3
-1.304331620349317
Freq3_harmonics_amplitude_0
0.01765091985997523
Freq3_harmonics_rel_phase_0
0.0
Freq3_harmonics_amplitude_1
0.0072490280397960365
Freq3_harmonics_rel_phase_1
0.35289198840867064
Freq3_harmonics_amplitude_2
0.002865392183527512
Freq3_harmonics_rel_phase_2
-1.555634635546236
Freq3_harmonics_amplitude_3
0.004961723047279988
Freq3_harmonics_rel_phase_3
0.9896319782640918
Gskew
0.24549999999999983
LinearTrend
6.173658576812162e-06
MaxSlope
54.72525836116783
Mean
-5.917989112227805
Meanvariance
-0.023922513589418142
MedianAbsDev
0.05449999999999999
MedianBRP
0.7453936348408711
PairSlopeTrend
0.03333333333333333
PercentAmplitude
-0.11308575739793782
PercentDifferenceFluxPercentile
-0.07527873250062869
PeriodLS_0
0.9369422174047677
Period_fit_0
0.0
Psi_CS_0
0.18807703843435905
Psi_eta_0
0.7078450866241952
Q31
0.14100000000000001
Rcs
0.03917145077266578
SignaturePhMag_0_0
0.0373540748638581
SignaturePhMag_0_1
0.0
SignaturePhMag_0_2
0.0
SignaturePhMag_0_3
0.0
SignaturePhMag_0_4
0.48560297323015567
SignaturePhMag_0_5
1.6062252191458974
SignaturePhMag_0_6
0.29883259891086467
SignaturePhMag_0_7
0.0
SignaturePhMag_0_8
0.0
SignaturePhMag_0_9
0.0
SignaturePhMag_0_10
0.0
SignaturePhMag_0_11
0.0
SignaturePhMag_1_0
0.0
SignaturePhMag_1_1
0.0373540748638581
SignaturePhMag_1_2
0.0
SignaturePhMag_1_3
0.0373540748638581
SignaturePhMag_1_4
0.5603111229578718
SignaturePhMag_1_5
1.905057818056762
SignaturePhMag_1_6
0.07470814972771617
SignaturePhMag_1_7
0.07470814972771625
SignaturePhMag_1_8
0.0
SignaturePhMag_1_9
0.0
SignaturePhMag_1_10
0.0
SignaturePhMag_1_11
0.0
SignaturePhMag_2_0
0.0
SignaturePhMag_2_1
0.0
SignaturePhMag_2_2
0.0
SignaturePhMag_2_3
0.037354074863858104
SignaturePhMag_2_4
0.6350192726855882
SignaturePhMag_2_5
1.75564151860133
SignaturePhMag_2_6
0.14941629945543236
SignaturePhMag_2_7
0.0
SignaturePhMag_2_8
0.0
SignaturePhMag_2_9
0.0
SignaturePhMag_2_10
0.0
SignaturePhMag_2_11
0.0
SignaturePhMag_3_0
0.0
SignaturePhMag_3_1
0.0
SignaturePhMag_3_2
0.0
SignaturePhMag_3_3
0.0
SignaturePhMag_3_4
0.44824889836629744
SignaturePhMag_3_5
1.4941629945543229
SignaturePhMag_3_6
0.29883259891086456
SignaturePhMag_3_7
0.03735407486385811
SignaturePhMag_3_8
0.0
SignaturePhMag_3_9
0.0
SignaturePhMag_3_10
0.0
SignaturePhMag_3_11
0.0
SignaturePhMag_4_0
0.0
SignaturePhMag_4_1
0.0
SignaturePhMag_4_2
0.0373540748638581
SignaturePhMag_4_3
0.0
SignaturePhMag_4_4
0.26147852404700683
SignaturePhMag_4_5
2.3906607912869164
SignaturePhMag_4_6
0.22412444918314847
SignaturePhMag_4_7
0.0
SignaturePhMag_4_8
0.0
SignaturePhMag_4_9
0.0
SignaturePhMag_4_10
0.0
SignaturePhMag_4_11
0.0
SignaturePhMag_5_0
0.0
SignaturePhMag_5_1
0.0
SignaturePhMag_5_2
0.0
SignaturePhMag_5_3
0.0
SignaturePhMag_5_4
0.2241244491831488
SignaturePhMag_5_5
1.4194548448266076
SignaturePhMag_5_6
0.3361866737747229
SignaturePhMag_5_7
0.03735407486385814
SignaturePhMag_5_8
0.0
SignaturePhMag_5_9
0.0
SignaturePhMag_5_10
0.0
SignaturePhMag_5_11
0.0
SignaturePhMag_6_0
0.0
SignaturePhMag_6_1
0.0
SignaturePhMag_6_2
0.0
SignaturePhMag_6_3
0.0
SignaturePhMag_6_4
0.1494162994554324
SignaturePhMag_6_5
1.979765967784477
SignaturePhMag_6_6
0.3361866737747225
SignaturePhMag_6_7
0.0747081497277162
SignaturePhMag_6_8
0.0
SignaturePhMag_6_9
0.0
SignaturePhMag_6_10
0.0
SignaturePhMag_6_11
0.0
SignaturePhMag_7_0
0.0
SignaturePhMag_7_1
0.0
SignaturePhMag_7_2
0.0
SignaturePhMag_7_3
0.07470814972771624
SignaturePhMag_7_4
0.0
SignaturePhMag_7_5
1.9050578180567628
SignaturePhMag_7_6
0.3361866737747229
SignaturePhMag_7_7
0.1120622245915744
SignaturePhMag_7_8
0.0
SignaturePhMag_7_9
0.0
SignaturePhMag_7_10
0.0
SignaturePhMag_7_11
0.0
SignaturePhMag_8_0
0.0
SignaturePhMag_8_1
0.0
SignaturePhMag_8_2
0.0
SignaturePhMag_8_3
0.07470814972771624
SignaturePhMag_8_4
0.1120622245915744
SignaturePhMag_8_5
1.2326844705073172
SignaturePhMag_8_6
0.8964977967325943
SignaturePhMag_8_7
0.18677037431929067
SignaturePhMag_8_8
0.14941629945543256
SignaturePhMag_8_9
0.0
SignaturePhMag_8_10
0.0
SignaturePhMag_8_11
0.0
SignaturePhMag_9_0
0.0
SignaturePhMag_9_1
0.0
SignaturePhMag_9_2
0.0
SignaturePhMag_9_3
0.0
SignaturePhMag_9_4
0.0
SignaturePhMag_9_5
0.037354074863858056
SignaturePhMag_9_6
0.9338518715964516
SignaturePhMag_9_7
0.7844355721410201
SignaturePhMag_9_8
0.2988325989108648
SignaturePhMag_9_9
0.0747081497277162
SignaturePhMag_9_10
0.0
SignaturePhMag_9_11
0.0
SignaturePhMag_10_0
0.0
SignaturePhMag_10_1
0.0
SignaturePhMag_10_2
0.037354074863858076
SignaturePhMag_10_3
0.0
SignaturePhMag_10_4
0.0
SignaturePhMag_10_5
0.0
SignaturePhMag_10_6
0.14941629945543222
SignaturePhMag_10_7
0.4856029732301553
SignaturePhMag_10_8
1.083268171051885
SignaturePhMag_10_9
0.5229570480940133
SignaturePhMag_10_10
0.2241244491831481
SignaturePhMag_10_11
0.0
SignaturePhMag_11_0
0.0
SignaturePhMag_11_1
0.0
SignaturePhMag_11_2
0.0
SignaturePhMag_11_3
0.0
SignaturePhMag_11_4
0.0
SignaturePhMag_11_5
0.03735407486385813
SignaturePhMag_11_6
0.03735407486385813
SignaturePhMag_11_7
0.4482488983662981
SignaturePhMag_11_8
1.2326844705073197
SignaturePhMag_11_9
0.6723733475494471
SignaturePhMag_11_10
0.03735407486385809
SignaturePhMag_11_11
0.037354074863858173
SignaturePhMag_12_0
0.0
SignaturePhMag_12_1
0.0
SignaturePhMag_12_2
0.0
SignaturePhMag_12_3
0.0
SignaturePhMag_12_4
0.0
SignaturePhMag_12_5
0.14941629945543222
SignaturePhMag_12_6
0.8964977967325933
SignaturePhMag_12_7
0.6350192726855877
SignaturePhMag_12_8
0.7844355721410201
SignaturePhMag_12_9
0.41089482350243905
SignaturePhMag_12_10
0.037354074863858014
SignaturePhMag_12_11
0.0
SignaturePhMag_13_0
0.0
SignaturePhMag_13_1
0.0
SignaturePhMag_13_2
0.0
SignaturePhMag_13_3
0.037354074863858076
SignaturePhMag_13_4
0.0747081497277162
SignaturePhMag_13_5
0.8591437218687353
SignaturePhMag_13_6
0.9712059464603094
SignaturePhMag_13_7
0.26147852404700667
SignaturePhMag_13_8
0.0747081497277162
SignaturePhMag_13_9
0.0
SignaturePhMag_13_10
0.0
SignaturePhMag_13_11
0.0
SignaturePhMag_14_0
0.0
SignaturePhMag_14_1
0.0
SignaturePhMag_14_2
0.0
SignaturePhMag_14_3
0.0
SignaturePhMag_14_4
0.1494162994554327
SignaturePhMag_14_5
1.3073926202350346
SignaturePhMag_14_6
0.5976651978217301
SignaturePhMag_14_7
0.22412444918314905
SignaturePhMag_14_8
0.0
SignaturePhMag_14_9
0.0
SignaturePhMag_14_10
0.0
SignaturePhMag_14_11
0.0
SignaturePhMag_15_0
0.0
SignaturePhMag_15_1
0.0
SignaturePhMag_15_2
0.0
SignaturePhMag_15_3
0.037354074863858076
SignaturePhMag_15_4
0.11206222459157429
SignaturePhMag_15_5
1.53151706941818
SignaturePhMag_15_6
0.4856029732301547
SignaturePhMag_15_7
0.0373540748638581
SignaturePhMag_15_8
0.0373540748638581
SignaturePhMag_15_9
0.0
SignaturePhMag_15_10
0.0
SignaturePhMag_15_11
0.0
SignaturePhMag_16_0
0.0
SignaturePhMag_16_1
0.0
SignaturePhMag_16_2
0.037354074863858076
SignaturePhMag_16_3
0.0
SignaturePhMag_16_4
0.373540748638581
SignaturePhMag_16_5
1.6809333688736128
SignaturePhMag_16_6
0.3735407486385806
SignaturePhMag_16_7
0.0747081497277162
SignaturePhMag_16_8
0.0
SignaturePhMag_16_9
0.0
SignaturePhMag_16_10
0.0
SignaturePhMag_16_11
0.0
SignaturePhMag_17_0
0.0
SignaturePhMag_17_1
0.0
SignaturePhMag_17_2
0.0
SignaturePhMag_17_3
0.11206222459157446
SignaturePhMag_17_4
0.4108948235024399
SignaturePhMag_17_5
1.7929955934651902
SignaturePhMag_17_6
0.37354074863858133
SignaturePhMag_17_7
0.0
SignaturePhMag_17_8
0.0
SignaturePhMag_17_9
0.0
SignaturePhMag_17_10
0.0
SignaturePhMag_17_11
0.0
Skew
0.956469867559379
SlottedA_length
1.0
SmallKurtosis
1.3794786801255068
Std
0.14157317495929828
StructureFunction_index_21
2.04757219898926
StructureFunction_index_31
3.1276618569316184
StructureFunction_index_32
1.6990646290639937
In [13]:
fs = feets.FeatureSpace(
only=['Mean','Beyond1Std','CAR_sigma','Color','SlottedA_length'],
data=['magnitude', 'error'])
features, values = fs.extract(*lc).as_arrays()
as_table(features, values)
Out[13]:
Feature
Value
Beyond1Std
0.22278056951423786
Mean
-5.917989112227805
In [14]:
fs = feets.FeatureSpace(
only=['Mean','Beyond1Std','CAR_sigma','Color','SlottedA_length'],
data=['magnitude', 'error'],
exclude=["Beyond1Std"])
features, values = fs.extract(*lc).as_arrays()
as_table(features, values)
Out[14]:
Feature
Value
Mean
-5.917989112227805
In [15]:
fs = feets.FeatureSpace()
features, values = fs.extract(*lc).as_arrays()
as_table(features, values)
Out[15]:
Feature
Value
Amplitude
0.26500000000000057
AndersonDarling
1.0
Autocor_length
1.0
Beyond1Std
0.22278056951423786
CAR_mean
-9.230698873903961
CAR_sigma
-0.2192804929884251
CAR_tau
0.6411203737734862
Color
-0.33325502453332145
Con
0.0
DMDT_0_0
0.0
DMDT_0_1
0.0
DMDT_0_2
0.0
DMDT_0_3
0.0
DMDT_0_4
0.0
DMDT_0_5
0.0
DMDT_0_6
0.0
DMDT_0_7
0.0
DMDT_0_8
0.0
DMDT_0_9
0.0
DMDT_0_10
0.0
DMDT_0_11
0.0
DMDT_0_12
0.0
DMDT_0_13
0.0
DMDT_0_14
0.0
DMDT_0_15
0.0
DMDT_0_16
0.0
DMDT_0_17
0.0
DMDT_0_18
0.0
DMDT_0_19
0.0
DMDT_0_20
0.0
DMDT_0_21
0.0
DMDT_0_22
0.0
DMDT_0_23
0.0
DMDT_1_0
0.0
DMDT_1_1
0.0
DMDT_1_2
0.0
DMDT_1_3
0.0
DMDT_1_4
0.0
DMDT_1_5
0.0
DMDT_1_6
0.0
DMDT_1_7
0.0
DMDT_1_8
0.0
DMDT_1_9
0.0
DMDT_1_10
0.0
DMDT_1_11
0.0
DMDT_1_12
0.0
DMDT_1_13
0.0
DMDT_1_14
0.0
DMDT_1_15
0.0
DMDT_1_16
0.0
DMDT_1_17
0.0
DMDT_1_18
0.0
DMDT_1_19
0.0
DMDT_1_20
0.0
DMDT_1_21
0.0
DMDT_1_22
0.0
DMDT_1_23
0.0
DMDT_2_0
0.0
DMDT_2_1
0.0
DMDT_2_2
0.0
DMDT_2_3
0.0
DMDT_2_4
0.0
DMDT_2_5
0.0
DMDT_2_6
0.0
DMDT_2_7
0.0
DMDT_2_8
0.0
DMDT_2_9
0.0
DMDT_2_10
0.0
DMDT_2_11
0.0
DMDT_2_12
0.0
DMDT_2_13
0.0
DMDT_2_14
0.0
DMDT_2_15
0.0
DMDT_2_16
0.0
DMDT_2_17
0.0
DMDT_2_18
0.0
DMDT_2_19
0.0
DMDT_2_20
0.0
DMDT_2_21
0.0
DMDT_2_22
0.0
DMDT_2_23
0.0
DMDT_3_0
0.0
DMDT_3_1
0.0
DMDT_3_2
0.0
DMDT_3_3
0.0
DMDT_3_4
0.0
DMDT_3_5
0.0
DMDT_3_6
0.0
DMDT_3_7
0.0
DMDT_3_8
0.0
DMDT_3_9
0.0
DMDT_3_10
0.0
DMDT_3_11
0.0
DMDT_3_12
0.0
DMDT_3_13
0.0
DMDT_3_14
0.0
DMDT_3_15
0.0
DMDT_3_16
0.0
DMDT_3_17
0.0
DMDT_3_18
0.0
DMDT_3_19
0.0
DMDT_3_20
0.0
DMDT_3_21
0.0
DMDT_3_22
0.0
DMDT_3_23
0.0
DMDT_4_0
0.0
DMDT_4_1
0.0
DMDT_4_2
0.0
DMDT_4_3
0.0
DMDT_4_4
0.0
DMDT_4_5
0.0
DMDT_4_6
0.0
DMDT_4_7
0.0
DMDT_4_8
0.0
DMDT_4_9
0.0
DMDT_4_10
0.0
DMDT_4_11
1.0
DMDT_4_12
1.0
DMDT_4_13
0.0
DMDT_4_14
0.0
DMDT_4_15
0.0
DMDT_4_16
0.0
DMDT_4_17
0.0
DMDT_4_18
0.0
DMDT_4_19
0.0
DMDT_4_20
0.0
DMDT_4_21
0.0
DMDT_4_22
0.0
DMDT_4_23
0.0
DMDT_5_0
0.0
DMDT_5_1
0.0
DMDT_5_2
0.0
DMDT_5_3
0.0
DMDT_5_4
0.0
DMDT_5_5
0.0
DMDT_5_6
0.0
DMDT_5_7
0.0
DMDT_5_8
0.0
DMDT_5_9
0.0
DMDT_5_10
0.0
DMDT_5_11
1.0
DMDT_5_12
0.0
DMDT_5_13
0.0
DMDT_5_14
0.0
DMDT_5_15
0.0
DMDT_5_16
0.0
DMDT_5_17
0.0
DMDT_5_18
0.0
DMDT_5_19
0.0
DMDT_5_20
0.0
DMDT_5_21
0.0
DMDT_5_22
0.0
DMDT_5_23
0.0
DMDT_6_0
0.0
DMDT_6_1
0.0
DMDT_6_2
0.0
DMDT_6_3
0.0
DMDT_6_4
0.0
DMDT_6_5
0.0
DMDT_6_6
0.0
DMDT_6_7
0.0
DMDT_6_8
0.0
DMDT_6_9
0.0
DMDT_6_10
0.0
DMDT_6_11
1.0
DMDT_6_12
1.0
DMDT_6_13
0.0
DMDT_6_14
0.0
DMDT_6_15
0.0
DMDT_6_16
0.0
DMDT_6_17
0.0
DMDT_6_18
0.0
DMDT_6_19
0.0
DMDT_6_20
0.0
DMDT_6_21
0.0
DMDT_6_22
0.0
DMDT_6_23
0.0
DMDT_7_0
0.0
DMDT_7_1
0.0
DMDT_7_2
0.0
DMDT_7_3
0.0
DMDT_7_4
0.0
DMDT_7_5
0.0
DMDT_7_6
0.0
DMDT_7_7
0.0
DMDT_7_8
1.0
DMDT_7_9
1.0
DMDT_7_10
0.0
DMDT_7_11
1.0
DMDT_7_12
1.0
DMDT_7_13
0.0
DMDT_7_14
0.0
DMDT_7_15
1.0
DMDT_7_16
0.0
DMDT_7_17
0.0
DMDT_7_18
0.0
DMDT_7_19
0.0
DMDT_7_20
0.0
DMDT_7_21
0.0
DMDT_7_22
0.0
DMDT_7_23
0.0
DMDT_8_0
0.0
DMDT_8_1
0.0
DMDT_8_2
0.0
DMDT_8_3
0.0
DMDT_8_4
0.0
DMDT_8_5
0.0
DMDT_8_6
0.0
DMDT_8_7
1.0
DMDT_8_8
1.0
DMDT_8_9
0.0
DMDT_8_10
1.0
DMDT_8_11
1.0
DMDT_8_12
1.0
DMDT_8_13
0.0
DMDT_8_14
1.0
DMDT_8_15
1.0
DMDT_8_16
1.0
DMDT_8_17
0.0
DMDT_8_18
0.0
DMDT_8_19
0.0
DMDT_8_20
0.0
DMDT_8_21
0.0
DMDT_8_22
0.0
DMDT_8_23
0.0
DMDT_9_0
0.0
DMDT_9_1
0.0
DMDT_9_2
0.0
DMDT_9_3
0.0
DMDT_9_4
0.0
DMDT_9_5
0.0
DMDT_9_6
0.0
DMDT_9_7
0.0
DMDT_9_8
1.0
DMDT_9_9
1.0
DMDT_9_10
1.0
DMDT_9_11
1.0
DMDT_9_12
1.0
DMDT_9_13
0.0
DMDT_9_14
0.0
DMDT_9_15
1.0
DMDT_9_16
0.0
DMDT_9_17
0.0
DMDT_9_18
0.0
DMDT_9_19
0.0
DMDT_9_20
0.0
DMDT_9_21
0.0
DMDT_9_22
0.0
DMDT_9_23
0.0
DMDT_10_0
0.0
DMDT_10_1
0.0
DMDT_10_2
0.0
DMDT_10_3
0.0
DMDT_10_4
0.0
DMDT_10_5
0.0
DMDT_10_6
0.0
DMDT_10_7
1.0
DMDT_10_8
1.0
DMDT_10_9
1.0
DMDT_10_10
1.0
DMDT_10_11
1.0
DMDT_10_12
1.0
DMDT_10_13
1.0
DMDT_10_14
1.0
DMDT_10_15
1.0
DMDT_10_16
1.0
DMDT_10_17
0.0
DMDT_10_18
0.0
DMDT_10_19
0.0
DMDT_10_20
0.0
DMDT_10_21
0.0
DMDT_10_22
0.0
DMDT_10_23
0.0
DMDT_11_0
0.0
DMDT_11_1
0.0
DMDT_11_2
0.0
DMDT_11_3
0.0
DMDT_11_4
0.0
DMDT_11_5
0.0
DMDT_11_6
1.0
DMDT_11_7
1.0
DMDT_11_8
1.0
DMDT_11_9
1.0
DMDT_11_10
1.0
DMDT_11_11
1.0
DMDT_11_12
1.0
DMDT_11_13
1.0
DMDT_11_14
1.0
DMDT_11_15
1.0
DMDT_11_16
1.0
DMDT_11_17
0.0
DMDT_11_18
0.0
DMDT_11_19
0.0
DMDT_11_20
0.0
DMDT_11_21
0.0
DMDT_11_22
0.0
DMDT_11_23
0.0
DMDT_12_0
0.0
DMDT_12_1
0.0
DMDT_12_2
0.0
DMDT_12_3
0.0
DMDT_12_4
0.0
DMDT_12_5
0.0
DMDT_12_6
1.0
DMDT_12_7
1.0
DMDT_12_8
1.0
DMDT_12_9
1.0
DMDT_12_10
1.0
DMDT_12_11
1.0
DMDT_12_12
1.0
DMDT_12_13
1.0
DMDT_12_14
1.0
DMDT_12_15
1.0
DMDT_12_16
1.0
DMDT_12_17
1.0
DMDT_12_18
0.0
DMDT_12_19
0.0
DMDT_12_20
0.0
DMDT_12_21
0.0
DMDT_12_22
0.0
DMDT_12_23
0.0
DMDT_13_0
0.0
DMDT_13_1
0.0
DMDT_13_2
0.0
DMDT_13_3
0.0
DMDT_13_4
0.0
DMDT_13_5
0.0
DMDT_13_6
1.0
DMDT_13_7
1.0
DMDT_13_8
1.0
DMDT_13_9
1.0
DMDT_13_10
1.0
DMDT_13_11
1.0
DMDT_13_12
1.0
DMDT_13_13
1.0
DMDT_13_14
1.0
DMDT_13_15
1.0
DMDT_13_16
1.0
DMDT_13_17
1.0
DMDT_13_18
0.0
DMDT_13_19
0.0
DMDT_13_20
0.0
DMDT_13_21
0.0
DMDT_13_22
0.0
DMDT_13_23
0.0
DMDT_14_0
0.0
DMDT_14_1
0.0
DMDT_14_2
0.0
DMDT_14_3
0.0
DMDT_14_4
0.0
DMDT_14_5
0.0
DMDT_14_6
1.0
DMDT_14_7
1.0
DMDT_14_8
1.0
DMDT_14_9
1.0
DMDT_14_10
1.0
DMDT_14_11
1.0
DMDT_14_12
1.0
DMDT_14_13
1.0
DMDT_14_14
1.0
DMDT_14_15
1.0
DMDT_14_16
1.0
DMDT_14_17
1.0
DMDT_14_18
0.0
DMDT_14_19
0.0
DMDT_14_20
0.0
DMDT_14_21
0.0
DMDT_14_22
0.0
DMDT_14_23
0.0
DMDT_15_0
0.0
DMDT_15_1
0.0
DMDT_15_2
0.0
DMDT_15_3
0.0
DMDT_15_4
0.0
DMDT_15_5
1.0
DMDT_15_6
1.0
DMDT_15_7
1.0
DMDT_15_8
1.0
DMDT_15_9
1.0
DMDT_15_10
1.0
DMDT_15_11
1.0
DMDT_15_12
1.0
DMDT_15_13
1.0
DMDT_15_14
1.0
DMDT_15_15
1.0
DMDT_15_16
1.0
DMDT_15_17
1.0
DMDT_15_18
0.0
DMDT_15_19
0.0
DMDT_15_20
0.0
DMDT_15_21
0.0
DMDT_15_22
0.0
DMDT_15_23
0.0
DMDT_16_0
0.0
DMDT_16_1
0.0
DMDT_16_2
0.0
DMDT_16_3
0.0
DMDT_16_4
0.0
DMDT_16_5
1.0
DMDT_16_6
1.0
DMDT_16_7
1.0
DMDT_16_8
1.0
DMDT_16_9
1.0
DMDT_16_10
1.0
DMDT_16_11
2.0
DMDT_16_12
2.0
DMDT_16_13
1.0
DMDT_16_14
1.0
DMDT_16_15
1.0
DMDT_16_16
1.0
DMDT_16_17
1.0
DMDT_16_18
1.0
DMDT_16_19
0.0
DMDT_16_20
0.0
DMDT_16_21
0.0
DMDT_16_22
0.0
DMDT_16_23
0.0
DMDT_17_0
0.0
DMDT_17_1
0.0
DMDT_17_2
0.0
DMDT_17_3
0.0
DMDT_17_4
0.0
DMDT_17_5
1.0
DMDT_17_6
1.0
DMDT_17_7
1.0
DMDT_17_8
1.0
DMDT_17_9
1.0
DMDT_17_10
1.0
DMDT_17_11
4.0
DMDT_17_12
4.0
DMDT_17_13
1.0
DMDT_17_14
1.0
DMDT_17_15
1.0
DMDT_17_16
1.0
DMDT_17_17
1.0
DMDT_17_18
1.0
DMDT_17_19
0.0
DMDT_17_20
0.0
DMDT_17_21
0.0
DMDT_17_22
0.0
DMDT_17_23
0.0
DMDT_18_0
0.0
DMDT_18_1
0.0
DMDT_18_2
0.0
DMDT_18_3
0.0
DMDT_18_4
0.0
DMDT_18_5
1.0
DMDT_18_6
1.0
DMDT_18_7
1.0
DMDT_18_8
2.0
DMDT_18_9
2.0
DMDT_18_10
2.0
DMDT_18_11
6.0
DMDT_18_12
6.0
DMDT_18_13
2.0
DMDT_18_14
2.0
DMDT_18_15
2.0
DMDT_18_16
2.0
DMDT_18_17
1.0
DMDT_18_18
1.0
DMDT_18_19
0.0
DMDT_18_20
0.0
DMDT_18_21
0.0
DMDT_18_22
0.0
DMDT_18_23
0.0
DMDT_19_0
0.0
DMDT_19_1
0.0
DMDT_19_2
0.0
DMDT_19_3
0.0
DMDT_19_4
0.0
DMDT_19_5
1.0
DMDT_19_6
1.0
DMDT_19_7
2.0
DMDT_19_8
3.0
DMDT_19_9
3.0
DMDT_19_10
3.0
DMDT_19_11
10.0
DMDT_19_12
10.0
DMDT_19_13
3.0
DMDT_19_14
3.0
DMDT_19_15
3.0
DMDT_19_16
3.0
DMDT_19_17
1.0
DMDT_19_18
1.0
DMDT_19_19
0.0
DMDT_19_20
0.0
DMDT_19_21
0.0
DMDT_19_22
0.0
DMDT_19_23
0.0
DMDT_20_0
0.0
DMDT_20_1
0.0
DMDT_20_2
0.0
DMDT_20_3
0.0
DMDT_20_4
0.0
DMDT_20_5
1.0
DMDT_20_6
1.0
DMDT_20_7
3.0
DMDT_20_8
5.0
DMDT_20_9
5.0
DMDT_20_10
5.0
DMDT_20_11
16.0
DMDT_20_12
18.0
DMDT_20_13
5.0
DMDT_20_14
5.0
DMDT_20_15
5.0
DMDT_20_16
4.0
DMDT_20_17
1.0
DMDT_20_18
1.0
DMDT_20_19
0.0
DMDT_20_20
0.0
DMDT_20_21
0.0
DMDT_20_22
0.0
DMDT_20_23
0.0
DMDT_21_0
0.0
DMDT_21_1
0.0
DMDT_21_2
0.0
DMDT_21_3
0.0
DMDT_21_4
0.0
DMDT_21_5
1.0
DMDT_21_6
1.0
DMDT_21_7
3.0
DMDT_21_8
5.0
DMDT_21_9
5.0
DMDT_21_10
5.0
DMDT_21_11
17.0
DMDT_21_12
20.0
DMDT_21_13
6.0
DMDT_21_14
5.0
DMDT_21_15
6.0
DMDT_21_16
4.0
DMDT_21_17
1.0
DMDT_21_18
1.0
DMDT_21_19
0.0
DMDT_21_20
0.0
DMDT_21_21
0.0
DMDT_21_22
0.0
DMDT_21_23
0.0
DMDT_22_0
0.0
DMDT_22_1
0.0
DMDT_22_2
0.0
DMDT_22_3
0.0
DMDT_22_4
0.0
DMDT_22_5
1.0
DMDT_22_6
1.0
DMDT_22_7
2.0
DMDT_22_8
2.0
DMDT_22_9
2.0
DMDT_22_10
2.0
DMDT_22_11
7.0
DMDT_22_12
9.0
DMDT_22_13
2.0
DMDT_22_14
2.0
DMDT_22_15
2.0
DMDT_22_16
2.0
DMDT_22_17
1.0
DMDT_22_18
0.0
DMDT_22_19
0.0
DMDT_22_20
0.0
DMDT_22_21
0.0
DMDT_22_22
0.0
DMDT_22_23
0.0
Eta_color
12930.685257570141
Eta_e
905.636200812288
FluxPercentileRatioMid20
0.09131403118040174
FluxPercentileRatioMid35
0.1781737193763922
FluxPercentileRatioMid50
0.3162583518930947
FluxPercentileRatioMid65
0.5233853006681504
FluxPercentileRatioMid80
0.7995545657015593
Freq1_harmonics_amplitude_0
0.13297191886665682
Freq1_harmonics_rel_phase_0
0.0
Freq1_harmonics_amplitude_1
0.07708190071937732
Freq1_harmonics_rel_phase_1
0.11506771848541875
Freq1_harmonics_amplitude_2
0.049703893823420386
Freq1_harmonics_rel_phase_2
0.3342992671936593
Freq1_harmonics_amplitude_3
0.025328725816726485
Freq1_harmonics_rel_phase_3
0.5308555764740739
Freq2_harmonics_amplitude_0
0.016357295575401373
Freq2_harmonics_rel_phase_0
0.0
Freq2_harmonics_amplitude_1
0.00116609801518309
Freq2_harmonics_rel_phase_1
-1.2826352924868387
Freq2_harmonics_amplitude_2
0.006229687748569223
Freq2_harmonics_rel_phase_2
-0.2727495960699948
Freq2_harmonics_amplitude_3
0.003237582323722657
Freq2_harmonics_rel_phase_3
-1.304331620349317
Freq3_harmonics_amplitude_0
0.01765091985997523
Freq3_harmonics_rel_phase_0
0.0
Freq3_harmonics_amplitude_1
0.0072490280397960365
Freq3_harmonics_rel_phase_1
0.35289198840867064
Freq3_harmonics_amplitude_2
0.002865392183527512
Freq3_harmonics_rel_phase_2
-1.555634635546236
Freq3_harmonics_amplitude_3
0.004961723047279988
Freq3_harmonics_rel_phase_3
0.9896319782640918
Gskew
0.24549999999999983
LinearTrend
6.173658576812162e-06
MaxSlope
54.72525836116783
Mean
-5.917989112227805
Meanvariance
-0.023922513589418142
MedianAbsDev
0.05449999999999999
MedianBRP
0.7453936348408711
PairSlopeTrend
0.03333333333333333
PercentAmplitude
-0.11308575739793782
PercentDifferenceFluxPercentile
-0.07527873250062869
PeriodLS_0
0.9369422174047677
Period_fit_0
0.0
Psi_CS_0
0.18807703843435905
Psi_eta_0
0.7078450866241952
Q31
0.14100000000000001
Q31_color
0.10600000000000076
Rcs
0.03917145077266578
SignaturePhMag_0_0
0.0373540748638581
SignaturePhMag_0_1
0.0
SignaturePhMag_0_2
0.0
SignaturePhMag_0_3
0.0
SignaturePhMag_0_4
0.48560297323015567
SignaturePhMag_0_5
1.6062252191458974
SignaturePhMag_0_6
0.29883259891086467
SignaturePhMag_0_7
0.0
SignaturePhMag_0_8
0.0
SignaturePhMag_0_9
0.0
SignaturePhMag_0_10
0.0
SignaturePhMag_0_11
0.0
SignaturePhMag_1_0
0.0
SignaturePhMag_1_1
0.0373540748638581
SignaturePhMag_1_2
0.0
SignaturePhMag_1_3
0.0373540748638581
SignaturePhMag_1_4
0.5603111229578718
SignaturePhMag_1_5
1.905057818056762
SignaturePhMag_1_6
0.07470814972771617
SignaturePhMag_1_7
0.07470814972771625
SignaturePhMag_1_8
0.0
SignaturePhMag_1_9
0.0
SignaturePhMag_1_10
0.0
SignaturePhMag_1_11
0.0
SignaturePhMag_2_0
0.0
SignaturePhMag_2_1
0.0
SignaturePhMag_2_2
0.0
SignaturePhMag_2_3
0.037354074863858104
SignaturePhMag_2_4
0.6350192726855882
SignaturePhMag_2_5
1.75564151860133
SignaturePhMag_2_6
0.14941629945543236
SignaturePhMag_2_7
0.0
SignaturePhMag_2_8
0.0
SignaturePhMag_2_9
0.0
SignaturePhMag_2_10
0.0
SignaturePhMag_2_11
0.0
SignaturePhMag_3_0
0.0
SignaturePhMag_3_1
0.0
SignaturePhMag_3_2
0.0
SignaturePhMag_3_3
0.0
SignaturePhMag_3_4
0.44824889836629744
SignaturePhMag_3_5
1.4941629945543229
SignaturePhMag_3_6
0.29883259891086456
SignaturePhMag_3_7
0.03735407486385811
SignaturePhMag_3_8
0.0
SignaturePhMag_3_9
0.0
SignaturePhMag_3_10
0.0
SignaturePhMag_3_11
0.0
SignaturePhMag_4_0
0.0
SignaturePhMag_4_1
0.0
SignaturePhMag_4_2
0.0373540748638581
SignaturePhMag_4_3
0.0
SignaturePhMag_4_4
0.26147852404700683
SignaturePhMag_4_5
2.3906607912869164
SignaturePhMag_4_6
0.22412444918314847
SignaturePhMag_4_7
0.0
SignaturePhMag_4_8
0.0
SignaturePhMag_4_9
0.0
SignaturePhMag_4_10
0.0
SignaturePhMag_4_11
0.0
SignaturePhMag_5_0
0.0
SignaturePhMag_5_1
0.0
SignaturePhMag_5_2
0.0
SignaturePhMag_5_3
0.0
SignaturePhMag_5_4
0.2241244491831488
SignaturePhMag_5_5
1.4194548448266076
SignaturePhMag_5_6
0.3361866737747229
SignaturePhMag_5_7
0.03735407486385814
SignaturePhMag_5_8
0.0
SignaturePhMag_5_9
0.0
SignaturePhMag_5_10
0.0
SignaturePhMag_5_11
0.0
SignaturePhMag_6_0
0.0
SignaturePhMag_6_1
0.0
SignaturePhMag_6_2
0.0
SignaturePhMag_6_3
0.0
SignaturePhMag_6_4
0.1494162994554324
SignaturePhMag_6_5
1.979765967784477
SignaturePhMag_6_6
0.3361866737747225
SignaturePhMag_6_7
0.0747081497277162
SignaturePhMag_6_8
0.0
SignaturePhMag_6_9
0.0
SignaturePhMag_6_10
0.0
SignaturePhMag_6_11
0.0
SignaturePhMag_7_0
0.0
SignaturePhMag_7_1
0.0
SignaturePhMag_7_2
0.0
SignaturePhMag_7_3
0.07470814972771624
SignaturePhMag_7_4
0.0
SignaturePhMag_7_5
1.9050578180567628
SignaturePhMag_7_6
0.3361866737747229
SignaturePhMag_7_7
0.1120622245915744
SignaturePhMag_7_8
0.0
SignaturePhMag_7_9
0.0
SignaturePhMag_7_10
0.0
SignaturePhMag_7_11
0.0
SignaturePhMag_8_0
0.0
SignaturePhMag_8_1
0.0
SignaturePhMag_8_2
0.0
SignaturePhMag_8_3
0.07470814972771624
SignaturePhMag_8_4
0.1120622245915744
SignaturePhMag_8_5
1.2326844705073172
SignaturePhMag_8_6
0.8964977967325943
SignaturePhMag_8_7
0.18677037431929067
SignaturePhMag_8_8
0.14941629945543256
SignaturePhMag_8_9
0.0
SignaturePhMag_8_10
0.0
SignaturePhMag_8_11
0.0
SignaturePhMag_9_0
0.0
SignaturePhMag_9_1
0.0
SignaturePhMag_9_2
0.0
SignaturePhMag_9_3
0.0
SignaturePhMag_9_4
0.0
SignaturePhMag_9_5
0.037354074863858056
SignaturePhMag_9_6
0.9338518715964516
SignaturePhMag_9_7
0.7844355721410201
SignaturePhMag_9_8
0.2988325989108648
SignaturePhMag_9_9
0.0747081497277162
SignaturePhMag_9_10
0.0
SignaturePhMag_9_11
0.0
SignaturePhMag_10_0
0.0
SignaturePhMag_10_1
0.0
SignaturePhMag_10_2
0.037354074863858076
SignaturePhMag_10_3
0.0
SignaturePhMag_10_4
0.0
SignaturePhMag_10_5
0.0
SignaturePhMag_10_6
0.14941629945543222
SignaturePhMag_10_7
0.4856029732301553
SignaturePhMag_10_8
1.083268171051885
SignaturePhMag_10_9
0.5229570480940133
SignaturePhMag_10_10
0.2241244491831481
SignaturePhMag_10_11
0.0
SignaturePhMag_11_0
0.0
SignaturePhMag_11_1
0.0
SignaturePhMag_11_2
0.0
SignaturePhMag_11_3
0.0
SignaturePhMag_11_4
0.0
SignaturePhMag_11_5
0.03735407486385813
SignaturePhMag_11_6
0.03735407486385813
SignaturePhMag_11_7
0.4482488983662981
SignaturePhMag_11_8
1.2326844705073197
SignaturePhMag_11_9
0.6723733475494471
SignaturePhMag_11_10
0.03735407486385809
SignaturePhMag_11_11
0.037354074863858173
SignaturePhMag_12_0
0.0
SignaturePhMag_12_1
0.0
SignaturePhMag_12_2
0.0
SignaturePhMag_12_3
0.0
SignaturePhMag_12_4
0.0
SignaturePhMag_12_5
0.14941629945543222
SignaturePhMag_12_6
0.8964977967325933
SignaturePhMag_12_7
0.6350192726855877
SignaturePhMag_12_8
0.7844355721410201
SignaturePhMag_12_9
0.41089482350243905
SignaturePhMag_12_10
0.037354074863858014
SignaturePhMag_12_11
0.0
SignaturePhMag_13_0
0.0
SignaturePhMag_13_1
0.0
SignaturePhMag_13_2
0.0
SignaturePhMag_13_3
0.037354074863858076
SignaturePhMag_13_4
0.0747081497277162
SignaturePhMag_13_5
0.8591437218687353
SignaturePhMag_13_6
0.9712059464603094
SignaturePhMag_13_7
0.26147852404700667
SignaturePhMag_13_8
0.0747081497277162
SignaturePhMag_13_9
0.0
SignaturePhMag_13_10
0.0
SignaturePhMag_13_11
0.0
SignaturePhMag_14_0
0.0
SignaturePhMag_14_1
0.0
SignaturePhMag_14_2
0.0
SignaturePhMag_14_3
0.0
SignaturePhMag_14_4
0.1494162994554327
SignaturePhMag_14_5
1.3073926202350346
SignaturePhMag_14_6
0.5976651978217301
SignaturePhMag_14_7
0.22412444918314905
SignaturePhMag_14_8
0.0
SignaturePhMag_14_9
0.0
SignaturePhMag_14_10
0.0
SignaturePhMag_14_11
0.0
SignaturePhMag_15_0
0.0
SignaturePhMag_15_1
0.0
SignaturePhMag_15_2
0.0
SignaturePhMag_15_3
0.037354074863858076
SignaturePhMag_15_4
0.11206222459157429
SignaturePhMag_15_5
1.53151706941818
SignaturePhMag_15_6
0.4856029732301547
SignaturePhMag_15_7
0.0373540748638581
SignaturePhMag_15_8
0.0373540748638581
SignaturePhMag_15_9
0.0
SignaturePhMag_15_10
0.0
SignaturePhMag_15_11
0.0
SignaturePhMag_16_0
0.0
SignaturePhMag_16_1
0.0
SignaturePhMag_16_2
0.037354074863858076
SignaturePhMag_16_3
0.0
SignaturePhMag_16_4
0.373540748638581
SignaturePhMag_16_5
1.6809333688736128
SignaturePhMag_16_6
0.3735407486385806
SignaturePhMag_16_7
0.0747081497277162
SignaturePhMag_16_8
0.0
SignaturePhMag_16_9
0.0
SignaturePhMag_16_10
0.0
SignaturePhMag_16_11
0.0
SignaturePhMag_17_0
0.0
SignaturePhMag_17_1
0.0
SignaturePhMag_17_2
0.0
SignaturePhMag_17_3
0.11206222459157446
SignaturePhMag_17_4
0.4108948235024399
SignaturePhMag_17_5
1.7929955934651902
SignaturePhMag_17_6
0.37354074863858133
SignaturePhMag_17_7
0.0
SignaturePhMag_17_8
0.0
SignaturePhMag_17_9
0.0
SignaturePhMag_17_10
0.0
SignaturePhMag_17_11
0.0
Skew
0.956469867559379
SlottedA_length
1.0
SmallKurtosis
1.3794786801255068
Std
0.14157317495929828
StetsonJ
1.3984111401436403
StetsonK
0.6906266262889181
StetsonK_AC
0.8125631614577979
StetsonL
0.5823703637198997
StructureFunction_index_21
2.04757219898926
StructureFunction_index_31
3.1276618569316184
StructureFunction_index_32
1.6990646290639937
The FeatureSpace object auto generates the set of required data (stored in FeatureSpace.required_data_) based on the data, only and exclude params. If you not provide the required data when you excecute the extract() method, an exception is raised. For example
In [16]:
fs = feets.FeatureSpace(only=["PeriodLS"])
fs.required_data_
Out[16]:
frozenset({'magnitude', 'time'})
In [17]:
%%expect_exception feets.DataRequiredError
fs.extract(time=time)
---------------------------------------------------------------------------
DataRequiredError Traceback (most recent call last)
<ipython-input-17-b1d41c0916d5> in <module>
1
----> 2 fs.extract(time=time)
~/proyectos/feets/src/feets/core.py in extract(self, time, magnitude, error, magnitude2, aligned_time, aligned_magnitude, aligned_magnitude2, aligned_error, aligned_error2)
383 DATA_ALIGNED_MAGNITUDE2: aligned_magnitude2,
384 DATA_ALIGNED_ERROR: aligned_error,
--> 385 DATA_ALIGNED_ERROR2: aligned_error2})
386
387 features, extractors = {}, {}
~/proyectos/feets/src/feets/core.py in preprocess_timeserie(self, d)
343 for k, v in d.items():
344 if k in self._required_data and v is None:
--> 345 raise DataRequiredError(k)
346 array_data[k] = v if v is None else np.asarray(v)
347 return array_data
DataRequiredError: magnitude
As showed the parameters data, only and exclude can be combinded and the selected features will be calculated in three steps:
FeatureSpace select all the features available for the available data, otherwise all the features are selected.only parameter is not None then the selected features from the step 1 are filtered only if they exist in the only list.exclude is not None, then removes the features selected by the step 1 and 2 that are also in exludeWhen calculating the features of a light-curve, the output are two different array.
So for example if we want to print the name and the value of the 3rd feature:
In [20]:
fs = feets.FeatureSpace()
rs = fs.extract(*lc)
features, values = rs.as_arrays()
print(features[2], "=", values[2])
Autocor_length = 1.0
Inother hand this format can be easily converted into a dictionary with the next code:
In [21]:
fdict = rs.as_dict()
fdict
Out[21]:
{'Amplitude': 0.26500000000000057,
'AndersonDarling': 1.0,
'Autocor_length': 1,
'Beyond1Std': 0.22278056951423786,
'CAR_mean': -9.230698873903961,
'CAR_sigma': -0.2192804929884251,
'CAR_tau': 0.6411203737734862,
'Color': -0.33325502453332145,
'Con': 0.0,
'DMDT': array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 6, 6, 2, 2, 2,
2, 1, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 2, 3, 3, 3, 10, 10, 3, 3, 3,
3, 1, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 3, 5, 5, 5, 16, 18, 5, 5, 5,
4, 1, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 3, 5, 5, 5, 17, 20, 6, 5, 6,
4, 1, 1, 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 7, 9, 2, 2, 2,
2, 1, 0, 0, 0, 0, 0, 0]]),
'Eta_color': 12930.685257570141,
'Eta_e': 905.636200812288,
'FluxPercentileRatioMid20': 0.09131403118040174,
'FluxPercentileRatioMid35': 0.1781737193763922,
'FluxPercentileRatioMid50': 0.3162583518930947,
'FluxPercentileRatioMid65': 0.5233853006681504,
'FluxPercentileRatioMid80': 0.7995545657015593,
'Freq1_harmonics': ([0.13297191886665682,
0.07708190071937732,
0.049703893823420386,
0.025328725816726485],
array([0. , 0.11506772, 0.33429927, 0.53085558])),
'Freq2_harmonics': ([0.016357295575401373,
0.00116609801518309,
0.006229687748569223,
0.003237582323722657],
array([ 0. , -1.28263529, -0.2727496 , -1.30433162])),
'Freq3_harmonics': ([0.01765091985997523,
0.0072490280397960365,
0.002865392183527512,
0.004961723047279988],
array([ 0. , 0.35289199, -1.55563464, 0.98963198])),
'Gskew': 0.24549999999999983,
'LinearTrend': 6.173658576812162e-06,
'MaxSlope': 54.72525836116783,
'Mean': -5.917989112227805,
'Meanvariance': -0.023922513589418142,
'MedianAbsDev': 0.05449999999999999,
'MedianBRP': 0.7453936348408711,
'PairSlopeTrend': 0.03333333333333333,
'PercentAmplitude': -0.11308575739793782,
'PercentDifferenceFluxPercentile': -0.07527873250062869,
'PeriodLS': array([0.93694222]),
'Period_fit': array([0.]),
'Psi_CS': array([0.18807704]),
'Psi_eta': array([0.70784509]),
'Q31': 0.14100000000000001,
'Q31_color': 0.10600000000000076,
'Rcs': 0.03917145077266578,
'SignaturePhMag': array([[0.03735407, 0. , 0. , 0. , 0.48560297,
1.60622522, 0.2988326 , 0. , 0. , 0. ,
0. , 0. ],
[0. , 0.03735407, 0. , 0.03735407, 0.56031112,
1.90505782, 0.07470815, 0.07470815, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0.03735407, 0.63501927,
1.75564152, 0.1494163 , 0. , 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0. , 0.4482489 ,
1.49416299, 0.2988326 , 0.03735407, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0.03735407, 0. , 0.26147852,
2.39066079, 0.22412445, 0. , 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0. , 0.22412445,
1.41945484, 0.33618667, 0.03735407, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0. , 0.1494163 ,
1.97976597, 0.33618667, 0.07470815, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0.07470815, 0. ,
1.90505782, 0.33618667, 0.11206222, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0.07470815, 0.11206222,
1.23268447, 0.8964978 , 0.18677037, 0.1494163 , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0.03735407, 0.93385187, 0.78443557, 0.2988326 , 0.07470815,
0. , 0. ],
[0. , 0. , 0.03735407, 0. , 0. ,
0. , 0.1494163 , 0.48560297, 1.08326817, 0.52295705,
0.22412445, 0. ],
[0. , 0. , 0. , 0. , 0. ,
0.03735407, 0.03735407, 0.4482489 , 1.23268447, 0.67237335,
0.03735407, 0.03735407],
[0. , 0. , 0. , 0. , 0. ,
0.1494163 , 0.8964978 , 0.63501927, 0.78443557, 0.41089482,
0.03735407, 0. ],
[0. , 0. , 0. , 0.03735407, 0.07470815,
0.85914372, 0.97120595, 0.26147852, 0.07470815, 0. ,
0. , 0. ],
[0. , 0. , 0. , 0. , 0.1494163 ,
1.30739262, 0.5976652 , 0.22412445, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0.03735407, 0.11206222,
1.53151707, 0.48560297, 0.03735407, 0.03735407, 0. ,
0. , 0. ],
[0. , 0. , 0.03735407, 0. , 0.37354075,
1.68093337, 0.37354075, 0.07470815, 0. , 0. ,
0. , 0. ],
[0. , 0. , 0. , 0.11206222, 0.41089482,
1.79299559, 0.37354075, 0. , 0. , 0. ,
0. , 0. ]]),
'Skew': 0.956469867559379,
'SlottedA_length': 1,
'SmallKurtosis': 1.3794786801255068,
'Std': 0.14157317495929828,
'StetsonJ': 1.3984111401436403,
'StetsonK': 0.6906266262889181,
'StetsonK_AC': 0.8125631614577979,
'StetsonL': 0.5823703637198997,
'StructureFunction_index_21': 2.04757219898926,
'StructureFunction_index_31': 3.1276618569316184,
'StructureFunction_index_32': 1.6990646290639937}
Note: for periodic light-curves we are able to transform the photometric time series into a single light-curve in which each period is mapped onto the same time axis as follows:
$$ t'=\{\frac{t-t_0}{T}\} $$where $T$ is the period, $t_0$ is an arbitrary starting point and the symbol $\{\}$ represents the non-integer part of the fraction. This process produces a folded light-curve on an x-axis of folded time that ranges from 0 to 1. The corresponding folded light-curve of the previous example is shown next:
In [22]:
T = 2 * fdict["PeriodLS"]
new_b = np.mod(lc[0], T) / T;
idx = np.argsort(2 * new_b)
plt.plot(new_b, lc[1], '*')
plt.xlabel("Phase")
plt.ylabel("Magnitude")
plt.gca().invert_yaxis()
The next section details the features that we have developed in order to represent light curves. For each feature, we also describe a benchmark test performed in order to test the feature's correctness.
Let's first assume the existence of some synthetic light-curves in order to explain each one of the features extractors along the library:
lc_normal : its magnitude follows a Gaussian distributionlc_periodic : its magnitude has a periodic variabilitylc_uniform : its magnitude follows a uniform distribution
In [25]:
features_doc()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-25-9085c9996f54> in <module>
----> 1 features_doc()
~/proyectos/feets/src/doc/source/tutorial.py in features_doc()
246 rows = []
247 extractors = sorted({
--> 248 e for e in feets.registered_extractors().values()})
249 for idx, ext in enumerate(extractors):
250 name = ext.__name__
TypeError: '<' not supported between instances of 'ExtractorMeta' and 'ExtractorMeta'
In [ ]:
Content source: carpyncho/feets
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