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add functionality for roid fitting using polynomial function
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,168 @@ | ||
| import numpy as np | ||
| import pandas as pd | ||
| import sys | ||
| import doctest | ||
| import fink_fat.roid_fitting.utils_roid_fit as uf | ||
| import warnings | ||
|
|
||
|
|
||
| def fit_polyast(gb_input): | ||
| """ | ||
| GroupBy input | ||
| Parameters | ||
| ---------- | ||
| gb_input : pd.DataFrame | ||
| contains a group from a groupby. | ||
| Examples | ||
| -------- | ||
| >>> input = pd.DataFrame({'ra': [11, 12, 29, 21, 15], 'dec': [20, 4, 0, 17, 23], 'jd': [10, 12, 16, 24, 28], 'magpsf': [16, 12, 9, 8, 24], 'fid': [16, 27, 1, 7, 23], 'trajectory_id': [6, 6, 6, 6, 6]}) | ||
| >>> fit_polyast(input) | ||
| ra_0 11 | ||
| dec_0 20 | ||
| ra_1 12 | ||
| dec_1 4 | ||
| jd_0 10 | ||
| jd_1 12 | ||
| mag_1 12 | ||
| fid_1 27 | ||
| xp [0.0003689548973376653, -0.017083339224524503,... | ||
| yp [-0.003374983061298368, 0.13154198411573034, -... | ||
| zp [0.003889943130855235, -0.13737266595516961, 1... | ||
| dtype: object | ||
| >>> input = pd.DataFrame({'ra': [18, 19], 'dec': [1, 16], 'jd': [3, 24], 'magpsf': [13, 2], 'fid': [18, 7], 'trajectory_id': [7, 7]}) | ||
| >>> fit_polyast(input) | ||
| ra_0 18 | ||
| dec_0 1 | ||
| ra_1 19 | ||
| dec_1 16 | ||
| jd_0 3 | ||
| jd_1 24 | ||
| mag_1 2 | ||
| fid_1 7 | ||
| xp [-0.00022037675949518603, 0.003949178398779884... | ||
| yp [-5.387272454402794e-05, 0.0016443857868339133... | ||
| zp [0.0002562672237379922, 0.0053753063581082955,... | ||
| dtype: object | ||
| """ | ||
|
|
||
| ra, dec, jd, mag, fid = ( | ||
| gb_input["ra"].values, | ||
| gb_input["dec"].values, | ||
| gb_input["jd"].values, | ||
| gb_input["magpsf"].values, | ||
| gb_input["fid"].values, | ||
| ) | ||
|
|
||
| ra_0, ra_1 = ra[0], ra[1] | ||
| dec_0, dec_1 = dec[0], dec[1] | ||
| jd_0, jd_1 = jd[0], jd[1] | ||
| mag_1 = mag[1] | ||
| fid_1 = fid[1] | ||
|
|
||
| with warnings.catch_warnings(): | ||
| warnings.simplefilter("ignore", np.RankWarning) | ||
| xp, yp, zp = uf.fit_traj(ra, dec, jd) | ||
|
|
||
| return pd.Series( | ||
| [ra_0, dec_0, ra_1, dec_1, jd_0, jd_1, mag_1, fid_1, xp, yp, zp], | ||
| index=[ | ||
| "ra_0", | ||
| "dec_0", | ||
| "ra_1", | ||
| "dec_1", | ||
| "jd_0", | ||
| "jd_1", | ||
| "mag_1", | ||
| "fid_1", | ||
| "xp", | ||
| "yp", | ||
| "zp", | ||
| ], | ||
| ) | ||
|
|
||
|
|
||
| def init_polyast( | ||
| night_pdf: pd.DataFrame, | ||
| ) -> pd.DataFrame: | ||
| """ | ||
| Initialize kalman filters based on the seeds or tracklets. | ||
| required columns: ra, dec, jd, magpsf, fid, trajectory_id | ||
| Parameters | ||
| ---------- | ||
| night_pdf : pd.DataFrame | ||
| alerts of one night | ||
| Returns | ||
| ------- | ||
| pd.DataFrame | ||
| a dataframe containing the kalman filters | ||
| Examples | ||
| -------- | ||
| >>> np.random.seed(3) | ||
| >>> df = pd.DataFrame( | ||
| ... np.random.randint(0, 30, size=(30, 5)), | ||
| ... columns=["ra", "dec", "jd", "magpsf", "fid"], | ||
| ... ) | ||
| >>> tr_id = np.repeat([0, 1, 2, 3, 4, 5, 6, 7], [2, 2, 5, 4, 8, 2, 5, 2]) | ||
| >>> df["trajectory_id"] = tr_id | ||
| >>> init_polyast(df) | ||
| trajectory_id ra_0 ... yp zp | ||
| 0 0 8 ... [5.8886683912253095e-05, 0.0021568132839048845... [0.002130983101599165, 0.0036589380953885127, ... | ||
| 1 1 21 ... [-0.0002667547833632723, -0.000121215680172234... [-0.0003281002792150129, -0.000351108876583499... | ||
| 2 2 2 ... [3.564698412338013e-05, 0.007580031292933427, ... [0.0012283807329896033, -0.04112918641543257, ... | ||
| 3 3 5 ... [-0.005899809113439577, 0.27815784005379157, -... [-0.012428484041437362, 0.5283431168170123, -5... | ||
| 4 4 24 ... [0.00033732318423005867, -0.020080309986114004... [0.001367714730073901, -0.06476655293412147, 0... | ||
| 5 5 22 ... [-0.0008599104450328825, -0.001465385641499352... [-0.0003768027364769278, 0.0018922493129352458... | ||
| 6 6 11 ... [-0.003374983061298368, 0.13154198411573034, -... [0.003889943130855235, -0.13737266595516961, 1... | ||
| 7 7 18 ... [-5.387272454402794e-05, 0.0016443857868339133... [0.0002562672237379922, 0.0053753063581082955,... | ||
| <BLANKLINE> | ||
| [8 rows x 12 columns] | ||
| >>> df = pd.DataFrame(columns=["ra", "dec", "jd", "magpsf", "fid", "trajectory_id"]) | ||
| >>> init_polyast(df) | ||
| Empty DataFrame | ||
| Columns: [ra_0, dec_0, ra_1, dec_1, jd_0, jd_1, mag_1, fid_1, xp, yp, zp] | ||
| Index: [] | ||
| """ | ||
| if len(night_pdf) == 0: | ||
| return pd.DataFrame( | ||
| columns=[ | ||
| "ra_0", | ||
| "dec_0", | ||
| "ra_1", | ||
| "dec_1", | ||
| "jd_0", | ||
| "jd_1", | ||
| "mag_1", | ||
| "fid_1", | ||
| "xp", | ||
| "yp", | ||
| "zp", | ||
| ] | ||
| ) | ||
|
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||
| # TODO | ||
| # detecter les jd dupliqués et appliquer une fonction spécial dans le cas ou dt = 0 | ||
|
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||
| # remove dbscan noise | ||
| night_pdf = night_pdf[night_pdf["trajectory_id"] != -1.0] | ||
| return ( | ||
| night_pdf.sort_values(["trajectory_id", "jd"]) | ||
| .groupby("trajectory_id") | ||
| .apply(fit_polyast) | ||
| .reset_index() | ||
| ) | ||
|
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||
|
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||
| if __name__ == "__main__": # pragma: no cover | ||
| if "unittest.util" in __import__("sys").modules: | ||
| # Show full diff in self.assertEqual. | ||
| __import__("sys").modules["unittest.util"]._MAX_LENGTH = 999999999 | ||
|
|
||
| sys.exit(doctest.testmod()[0]) |
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