Merge pull request #177 from COINtoolbox/176-add-malanchev-feature-do…

…cs-to-readthedoc

176 add malanchev feature docs to readthedoc

docs/pre_processing.rst

@@ -9,13 +9,13 @@ as input to the learning algorithm.
 ``resspect`` can handle FITS format data from the RESSPECT project, csv data from the Photometric LSST Astronomical Classification Challenge (`PLAsTiCC <https://zenodo.org/record/2539456#.Xrsk33UzZuQ>`_)  and text-like data from the SuperNova Photometric Classification Challenge (`SNPCC <https://arxiv.org/abs/1008.1024>`_).
 
 
-Before starting any analysis, you need to choose a feature extraction method, all light curves will then be handdled by this method. In the examples below we used the Bazin feature extraction method  (`Bazin et al., 2009 <https://arxiv.org/abs/0904.1066>`_ ).
+Before starting any analysis, you need to choose a feature extraction method, all light curves will then be handdled by this method. In the examples below we used the Bazin feature extraction method  (`Bazin et al., 2009 <https://arxiv.org/abs/0904.1066>`_ ) or the Malanchev feature extraction method (`Malanchev et al., 2021 <https://arxiv.org/abs/2012.01419>`_ ).
 
 Load 1 light curve:
 -------------------
 
 
-For SNPCC:
+For SNPCC using Bazin features:
 ^^^^^^^^^^
 
 The raw data looks like this:
@@ -48,11 +48,42 @@ This allows you to visually inspect the content of the light curve:
     75	56317.051	i	173.200	7.661	22.60	21.904	0.049
     76	56318.035	z	141.000	13.720	10.28	22.127	0.111
 
+For SNPCC using Malanchev features:
+^^^^^^^^^^
+
+You can load the data using:
+
+.. code-block:: python
+   :linenos:
+
+   >>> from resspect import MalanchevFeatureExtractor
+
+   >>> path_to_lc = 'data/SIMGEN_PUBLIC_DES/DES_SN729076.DAT'
+
+   >>> lc = MalanchevFeatureExtractor()         # create light curve instance
+   >>> lc.load_snpcc_lc(path_to_lc)             # read data
+
+This allows you to visually inspect the content of the light curve:
+
+.. code-block:: python
+   :linenos:
+
+   >>> lc.photometry                            # check structure of photometry
+             mjd band     flux  fluxerr   SNR
+    0	56194.012	g	13.090	6.204	2.11	99.000	5.000
+    1	56194.016	r	-4.680	3.585	-1.31	99.000	5.000
+    ...	...	...	...	...	...	...	...
+    75	56317.051	i	173.200	7.661	22.60	21.904	0.049
+    76	56318.035	z	141.000	13.720	10.28	22.127	0.111
 
 
 Fit 1 light curve:
 ------------------
 
+For SNPCC using Bazin features:
+^^^^^^^^^^
+
+
 In order to feature extraction in one specific filter, you can do:
 
 .. code-block:: python
@@ -115,7 +146,7 @@ Before deploying  large batches for pre-processing, you might want to visualize
     >>> max_mjd = max(lc.photometry['mjd']) - min(lc.photometry['mjd'])
 
     >>> lc.plot_fit(save=False, show=True, extrapolate=True,
-                          time_flux_pred=[max_mjd+3, max_mjd+5, max_mjd+10])
+    >>>                   time_flux_pred=[max_mjd+3, max_mjd+5, max_mjd+10])
 
 
 .. figure:: images/SN729076_flux_extrap.png
@@ -127,6 +158,39 @@ Before deploying  large batches for pre-processing, you might want to visualize
     Example of extrapolated light from SNPCC data.
 
 
+For SNPCC using Malanchev features:
+^^^^^^^^^^
+
+In order to feature extraction in one specific filter, you can do:
+
+.. code-block:: python
+   :linenos:
+
+   >>> lc.fit('r')
+   [1.03403418e+00, 3.60823443e+02, 7.24896424e+02, 8.86255944e-01,
+    6.03107809e+01, 1.23027000e+02, 2.50709726e+02, 6.38344483e+01,
+    5.19719109e+01, 6.31578947e-01, 1.22756021e+00, 2.41334828e-02,
+    6.15343688e+02]
+
+The designation for each parameter are stored in:
+
+.. code-block:: python
+   :linenos:
+
+   >>> lc.features_names
+   ['anderson_darling_normal', 'inter_percentile_range_5', 'chi2',
+    'stetson_K', 'weighted_mean', 'duration', 'otsu_mean_diff',
+    'otsu_std_lower', 'otsu_std_upper', 'otsu_lower_to_all_ratio',
+    'linear_fit_slope', 'linear_fit_slope_sigma', 'linear_fit_reduced_chi2']
+
+It is possible to perform the fit in all filters at once:
+
+.. code-block:: python
+   :linenos:
+
+   >>> lc.fit_all()                            # perform fit in all filters
+
+
 For PLAsTiCC:
 ^^^^^^^^^^^^^
 
@@ -167,7 +231,7 @@ Processing all light curves in the data set
 
 There are 2 way to perform the Bazin fits for all three data sets. Using a python interpreter,
 
-For SNPCC:
+For SNPCC using Bazin features:
 ^^^^^^^^^^
 
 .. code-block:: python
@@ -176,11 +240,25 @@ For SNPCC:
    >>> from resspect import fit_snpcc
 
    >>> path_to_data_dir = 'data/SIMGEN_PUBLIC_DES/'            # raw data directory
-   >>> features_file = 'results/Bazin.csv'                       # output file
+   >>> features_file = 'results/Bazin.csv'                     # output file
    >>> feature_extractor = 'bazin'
 
    >>> fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file)
 
+For SNPCC using Malanchev features:
+^^^^^^^^^^
+
+.. code-block:: python
+   :linenos:
+
+   >>> from resspect import fit_snpcc
+
+   >>> path_to_data_dir = 'data/SIMGEN_PUBLIC_DES/'            # raw data directory
+   >>> features_file = 'results/Malanchev.csv'                 # output file
+   >>> feature_extractor = 'malanchev'
+
+   >>> fit_snpcc(path_to_data_dir=path_to_data_dir, features_file=features_file)
+
 
 For PLAsTiCC:
 ^^^^^^^^^^^^^
@@ -198,10 +276,10 @@ For PLAsTiCC:
    >>> sample = 'train'
 
    >>> fit_plasticc(path_photo_file=path_photo_file,
-                    path_header_file=path_header_file,
-                    output_file=output_file,
-                    feature_extractor=feature_extractor,
-                    sample=sample)
+   >>>              path_header_file=path_header_file,
+   >>>              output_file=output_file,
+   >>>              feature_extractor=feature_extractor,
+   >>>              sample=sample)
 
 
 The same result can be achieved using the command line:

resspect/__init__.py

@@ -43,6 +43,7 @@
 from .batch_functions import *
 from .query_budget_strategies import *
 from .bump import *
+from .feature_extractors.malanchev import *
 
 import importlib.metadata