From 9d8b7562fa9313f3b87e65dbac739852a1023812 Mon Sep 17 00:00:00 2001 From: Jakob van Santen Date: Thu, 28 Nov 2024 13:49:39 +0100 Subject: [PATCH] New forced-photometry loaders (#170) * chore(deps): upgrade fastavro to <1.7 || >=1.9.2 1.9.2 can read pre-4.0 ZTF alert schemas again https://github.com/fastavro/fastavro/pull/738 https://github.com/fastavro/fastavro/issues/723 * ci: switch to setup-cache-python-poetry action * chore(deps): roll back lockfile * chore(deps): upgrade fastavro to ^1.9.2 * mypy: HTTPError.response may be None * mypy: use tuples in various matplotlib apis * mypy: fixup for fastavro 1.9 * chore(deps): exclude ampel >=0.9 * Make deltaT an explicit unit parameter. * Bump version * DecentFilter: check whether drb exists in alert (#132) * Bump version to 0.8.12 * deps: bump requests-toolbelt to 1.0 * ZTFAlertStreamController: shiny for mypy * [no ci] configure renovate * chore: use centralized workflow NB: this skips tests that depend on arbitrary secrets like ARCHIVE_TOPIC_TOKEN * tests: remove stray fields from channel def * tests: pass ttl to compiler * chore(deps): update minor updates (stable/v0.8) (#138) * chore(deps): update minor updates * chore(deps): bump ampel-core --------- Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> Co-authored-by: Jakob van Santen * chore: disable lockfile maintenance * chore(deps): pin ampelproject/ampel-interface action to e880b6e * ZTFCutoutImages: correctly forward init args * Bump version to 0.8.14 * request output parsing updated with new endpoint. (#148) * Bump version to 0.8.15 * Allow floating (JD) time offsets. (#150) * chore(deps): update minor updates (#152) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> * SkyPortalPublisher: raise a more informative error when compiler_opts was not set * SkyPortalPublisher: make cutout posting configurable * Bump version to 0.8.16 * Units for supplying different kinds of forced photometry. * Units for supplying different kinds of forced photometry, cont * ruff: pyupgrade * ruff: if-stmt-min-max * ruff: simplify * ruff: avoid quadratic list summation * refresh locks * tests: close mongo clients * Bump version to 0.10.3a0 --------- Co-authored-by: wombaugh Co-authored-by: andimatter <81298394+andi-matter@users.noreply.github.com> Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com> Co-authored-by: Jakob Nordin <16082999+wombaugh@users.noreply.github.com> --- .../alert/BTSForcedPhotometryAlertSupplier.py | 289 + .../ZTFFPbotForcedPhotometryAlertSupplier.py | 17 +- ampel/ztf/alert/cal_data/chi_lookup.csv | 5002 +++++++++++++++++ .../alert/cal_data/zp_thresholds_quadID.txt | 72 + ampel/ztf/alert/calibrate_fps_fork.py | 1091 ++++ ampel/ztf/ingest/ZiDataPointShaper.py | 32 +- ampel/ztf/view/ZTFFPTabulator.py | 124 + conf/ampel-ztf/ampel.yml | 5 + poetry.lock | 2283 ++++---- pyproject.toml | 2 +- tests/fixtures.py | 4 +- 11 files changed, 7797 insertions(+), 1124 deletions(-) create mode 100644 ampel/ztf/alert/BTSForcedPhotometryAlertSupplier.py create mode 100644 ampel/ztf/alert/cal_data/chi_lookup.csv create mode 100644 ampel/ztf/alert/cal_data/zp_thresholds_quadID.txt create mode 100644 ampel/ztf/alert/calibrate_fps_fork.py create mode 100644 ampel/ztf/view/ZTFFPTabulator.py diff --git a/ampel/ztf/alert/BTSForcedPhotometryAlertSupplier.py b/ampel/ztf/alert/BTSForcedPhotometryAlertSupplier.py new file mode 100644 index 00000000..f5fef6c7 --- /dev/null +++ b/ampel/ztf/alert/BTSForcedPhotometryAlertSupplier.py @@ -0,0 +1,289 @@ +#!/usr/bin/env python +# File: Ampel-ZTF/ampel/ztf/alert/BTSForcedPhotometryAlertSupplier.py +# License: BSD-3-Clause +# Author: valery brinnel +# Date: 25.10.2021 +# Last Modified Date: 15.11.2024 +# Last Modified By: jno + +import sys +from hashlib import blake2b + +import numpy as np +import pandas as pd +from astropy.coordinates import SkyCoord +from astropy.time import Time +from bson import encode + +from ampel.alert.AmpelAlert import AmpelAlert +from ampel.alert.BaseAlertSupplier import BaseAlertSupplier +from ampel.protocol.AmpelAlertProtocol import AmpelAlertProtocol +from ampel.view.ReadOnlyDict import ReadOnlyDict +from ampel.ztf.alert.calibrate_fps_fork import search_peak +from ampel.ztf.util.ZTFIdMapper import to_ampel_id + +dcast = { + "jd": float, + "fnu_microJy": float, + "fnu_microJy_unc": float, + "passband": str, + "programid": int, + "fcqfid": int, + "zpdiff": float, + "sys_unc_factor": float, + "flags": int, +} + +ZTF_FILTER_MAP = {"ZTF_g": 1, "ZTF_r": 2, "ZTF_i": 3} + + +class BTSForcedPhotometryAlertSupplier(BaseAlertSupplier): + """ + Returns an AmpelAlert instance for each file path provided by the underlying alert loader. + + This unit assuming that input files are *baseline corrected* IPAC + forced photometry files, of the kind produced by the BTS group. + + These are assumed to be named as: + {ZTFID}_fnu.csv + with columns ['jd', 'fnu_microJy', 'fnu_microJy_unc', 'passband', 'fcqfid', 'zpdiff', 'sys_unc_factor', 'flags'] + + + """ + + # FP read and baseline correction + flux_key: str = "fnu_microJy" + flux_unc_key: str = "fnu_microJy_unc" + flux_unc_scale: dict[str, float] = {"ZTF_g": 1.0, "ZTF_r": 1.0, "ZTF_i": 1.0} + flux_unc_floor: float = 0.02 + baseline_flag_cut: int = ( + 512 # This will allow also cases with underestimated scaled unc + ) + days_prepeak: None | float = ( + None # Cut epochs earlier than this relative to peak. Assumes this can be found! + ) + days_postpeak: None | float = ( + None # Cut epochs later than this relative to peak. Assumes this can be found! + ) + allow_iband_peak: bool = ( + False # Include I-band in peak calculation (Warning: I-band incomplete) + ) + + # Candidate coordinates + # BTS IPAC FP files are named according to a transient, but do not contain coordinates. + # You can provide a file containing {ZTFid} to {ra,dec} maps, otherwise the file names will be used. + name_file: str | None = None + file_keys: dict[str, str] = { + "id": "ZTFID", + "ra": "RA", + "dec": "Dec", + "raunit": "hourangle", + "decunit": "deg", + } # Name column in file + name_coordinates = None # Loaded at first run + name_values = None # Loaded at first run + + # Run mode (both can be used) + alert_history: bool = False # Return an "alert" for each significant detection + full_history: bool = True # Return the full lightcurve, after alerts. + detection_threshold: float = ( + 5.0 # S/N above this considered a significant detection + ) + + # Loaded transient datapoints (for alert mode) + # Dynamically updated during + transient_name: str = "" + transient_tags: list = [] + transient_pps: list = [] + transient_baselineinfo: dict = {} + transient_hashid: list = [] + alert_counter: int = 0 + + def __init__(self, **kwargs) -> None: + kwargs["deserialize"] = None + super().__init__(**kwargs) + + def _load_pps(self, fpath: str) -> bool: + """ + Load BTS IPAC FP datapoints from input file. + Will set the transient id, tags and pps variables. + Return False in case nothing was found. + """ + + # post_init did not work for Supplier kind of units, loading name maps here. + if self.name_file and not self.name_coordinates: + # Move to init when configured correctly + df = pd.read_csv(self.name_file) + self.name_coordinates = SkyCoord( + ra=df[self.file_keys["ra"]], + dec=df[self.file_keys["dec"]], + unit=(self.file_keys["raunit"], self.file_keys["decunit"]), + ) + self.name_values = df[self.file_keys["id"]] + + # Assuming that filenames follow the BTS convention + ztfname = fpath.split("/")[-1].split("_")[0] + + # Find coordinates - if name file exists + ra, dec = None, None # Allowing events with no coordinate info + if self.name_coordinates: + myc = self.name_coordinates[self.name_values == ztfname] + if len(myc) > 0: + ra = myc.ra.deg[0] + dec = myc.dec.deg[0] + + # Read file + df = pd.read_csv(fpath) + # Reject data with flags above threshold + df = df[df["flags"] <= self.baseline_flag_cut] + tags: list[str] = [] + + # Search each unique filter/quadrant combo for a peak, in order to cut outliers + if self.days_prepeak or self.days_postpeak: + # Reset index to allow mediam time-search + # obs_jd = Time(df.jd.values, format="jd") + df = df.set_index(pd.to_datetime(Time(df.jd.values, format="jd").datetime)) + allpeaks = {} + for obs_group, df_group in df.groupby("fcqfid"): + # Skip secondary grid + if obs_group > 10000000: # type: ignore + continue + if not self.allow_iband_peak and str(obs_group)[-1] == "3": + continue + allpeaks[str(obs_group)] = search_peak( + df_group.fnu_microJy, + df_group.fnu_microJy_unc, + df_group.jd, + window="14D", + ) + peaktimes = [v["t_fcqfid_max"] for k, v in allpeaks.items() if v["det_sn"]] + if len(peaktimes) == 0: + print( + "No peaks found in lightcurve, but a cut around this was required - skip object." + ) + return False + if np.std(peaktimes, ddof=1) > 50: + print("Warning! Large scatter in time of maximum") + df["flags"] += 1 + t_peak = np.mean(peaktimes) + # Could alternatively have weighted with the detection S/N + # snrs = [v["det_snr"] for k, v in allpeaks.items() if v["det_sn"]] + # wt_peak = np.average(peaktimes, weights=snrs) + # print(f"weighted peak time {wt_peak}") + if self.days_prepeak: + df = df[(df["jd"] - t_peak) >= -self.days_prepeak] + if self.days_postpeak: + df = df[(df["jd"] - t_peak) <= self.days_postpeak] + + pps = [] + alert_ids: list[bytes] = [] + for _, row in df.iterrows(): + pp = { + str(k): dcast[str(k)](v) if (k in dcast and v is not None) else v + for k, v in row.items() + } + + pp["fid"] = ZTF_FILTER_MAP[pp["passband"]] + pp["ra"] = ra + pp["dec"] = dec + pp["rcid"] = ( + (int(str(pp["fcqfid"])[-3]) - 1) * 4 + int(str(pp["fcqfid"])[-2]) - 1 + ) + + # Some entries have flux values exactly at 0. Suspicious - we will skip these. + if pp[self.flux_key] == 0: + continue + + # Convert jansky to flux + pp["flux"] = pp[self.flux_key] * 2.75406 + + # Opionally scale uncertainties + pp["flux_unc"] = ( + pp[self.flux_unc_key] * 2.75406 * self.flux_unc_scale[pp["passband"]] + ) + + # Enforce error floor + if pp["flux_unc"] / abs(pp["flux"]) < self.flux_unc_floor: + if tags is None: + tags = ["FLOOR"] + else: + tags.append("FLOOR") + pp["flux_unc"] = pp["flux"] * self.flux_unc_floor + + pp_hash = blake2b(encode(pp), digest_size=7).digest() + # pp["candid"] = int.from_bytes(pp_hash, byteorder=sys.byteorder) + pps.append( + ReadOnlyDict( + dict( + sorted(pp.items()) + ) # Ensure ordered keys for duplication search - necessary here? + ) + ) + # Create a running list of hash ids + if len(alert_ids) == 0: + alert_ids = [pp_hash] + else: + alert_ids.append(alert_ids[-1] + pp_hash) + + self.transient_name = ztfname + self.transient_tags = tags + self.transient_pps = pps + self.transient_hashid = alert_ids + self.alert_counter = 0 + return True + + def _build_alert(self, datapoints: int) -> AmpelAlertProtocol: + return AmpelAlert( + id=int.from_bytes( # alert id + blake2b(self.transient_hashid[datapoints - 1], digest_size=7).digest(), + byteorder=sys.byteorder, + ), + stock=to_ampel_id(self.transient_name), # internal ampel id + # stock=self.transient_name, # internal ampel id - for forced photometry we still have not associated to unique ZTF ... do we need to do that later? + # Ampel alert structure assumes most recent detection to come first + datapoints=tuple( + [self.transient_pps[datapoints - 1]] + + self.transient_pps[0 : datapoints - 1] + ), + extra=ReadOnlyDict( + { + "name": self.transient_name, + } + ), + tag=self.transient_tags, + ) + + def __next__(self) -> AmpelAlertProtocol: + """ + :raises StopIteration: when alert_loader dries out. + :raises AttributeError: if alert_loader was not set properly before this method is called + """ + + # Load next lightcurve if we eighter do not have one or already generated an alert with the full + while len(self.transient_pps) == 0 or self.alert_counter >= len( + self.transient_pps + ): + # Load next lightcurve from file + # If no lightcurves found, alert loader should raise Stop Iteration + self._load_pps( + str(next(self.alert_loader)) + ) # Supplier assumings IO comes as path to file + + if self.alert_history and self.alert_counter < len(self.transient_pps): + # Increase counter until a significant detection is found. + # Set counter to this, return alert with at lightcurve to this point. + for dp in self.transient_pps[self.alert_counter :]: + if abs(dp["flux"]) / dp["flux_unc"] > self.detection_threshold: + # Detection, stop here + self.alert_counter += 1 # still need to nudge this + return self._build_alert(self.alert_counter - 1) + break + self.alert_counter += 1 + + # Here we either have not generated piecewise alerts at all, or want to make sure we get one final alert with all data + if self.full_history and self.alert_counter < len(self.transient_pps): + # Make sure we return full history + self.alert_counter = len(self.transient_pps) + return self._build_alert(self.alert_counter) + + return self.__next__() diff --git a/ampel/ztf/alert/ZTFFPbotForcedPhotometryAlertSupplier.py b/ampel/ztf/alert/ZTFFPbotForcedPhotometryAlertSupplier.py index efe41b1f..f8654fbb 100644 --- a/ampel/ztf/alert/ZTFFPbotForcedPhotometryAlertSupplier.py +++ b/ampel/ztf/alert/ZTFFPbotForcedPhotometryAlertSupplier.py @@ -15,8 +15,6 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd - -# from appdirs import user_cache_dir from astropy.time import Time from bson import encode from scipy.stats import median_abs_deviation @@ -103,7 +101,7 @@ def get_fpbot_baseline( min_det_per_field_band: int = 10, zp_max_deviation_from_median: float = 0.5, reference_days_before_peak: None | float = 50.0, -) -> pd.DataFrame: +) -> tuple[pd.DataFrame, dict[str, Any]]: """ For each unique baseline combination, estimate and store baseline. Partially taken from @@ -126,10 +124,7 @@ def get_fpbot_baseline( """ df["fcqfid"] = np.array( - df.fieldid.values * 10000 - + df.ccdid.values * 100 - + df.qid.values * 10 - + df.filterid.values + df["fieldid"] * 10000 + df["ccdid"] * 100 + df["qid"] * 10 + df["filterid"] ) if primary_grid_only: @@ -157,7 +152,7 @@ def get_fpbot_baseline( df["zp_median_deviation"] = np.abs(np.log10(median_zp / df.magzp)) df.query("zp_median_deviation < @zp_max_deviation_from_median", inplace=True) - unique_fid = np.unique(df.fcqfid.values).astype(int) + unique_fid = np.unique(df.fcqfid).astype(int) # Time index for use for rolling window df = df.sort_values("obsmjd") @@ -176,13 +171,13 @@ def get_fpbot_baseline( fcqf_df = df.iloc[this_fcqfid].copy() # Use the pulls from mean to find largest deviation - pull_series = fcqf_df.ampl / fcqf_df["ampl.err"] + pull_series = fcqf_df["ampl"] / fcqf_df["ampl.err"] roll_med = pull_series.rolling(window, center=True).median().values # Only use medians with a min nbr of values (otherwise we get edge results) t_max = fcqf_df.obsmjd.values[np.argmax(roll_med)] # flux_max = np.max(roll_med) - flux_max = fcqf_df.ampl.values[np.argmax(roll_med)] - flux_scatt = median_abs_deviation(fcqf_df.ampl.values, scale="normal") + flux_max = fcqf_df["ampl"].values[np.argmax(roll_med)] + flux_scatt = median_abs_deviation(fcqf_df["ampl"].values, scale="normal") peak_snr = flux_max / flux_scatt if (peak_snr > min_peak_snr) and (ufid < 10000000): fcqfid_dict[str(ufid)]["det_sn"] = True diff --git a/ampel/ztf/alert/cal_data/chi_lookup.csv b/ampel/ztf/alert/cal_data/chi_lookup.csv new file mode 100644 index 00000000..801b48e9 --- /dev/null +++ b/ampel/ztf/alert/cal_data/chi_lookup.csv @@ -0,0 +1,5002 @@ +diffflux_grid,g_chi_interp,r_chi_interp,i_chi_interp +-398107.1705534969,6.60466495,30.37645495,4.02009655 +-395329.0997899852,6.60466495,30.37645495,4.02009655 +-392570.4149550315,6.60466495,30.37645495,4.02009655 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+389830.9807697845,8.740925,30.824073,4.507931 +392570.4149550315,8.740925,30.824073,4.507931 +395329.0997899852,8.740925,30.824073,4.507931 +398107.1705534969,8.740925,30.824073,4.507931 diff --git a/ampel/ztf/alert/cal_data/zp_thresholds_quadID.txt b/ampel/ztf/alert/cal_data/zp_thresholds_quadID.txt new file mode 100644 index 00000000..ddf07731 --- /dev/null +++ b/ampel/ztf/alert/cal_data/zp_thresholds_quadID.txt @@ -0,0 +1,72 @@ +# +# CCD-quadrant-based ZP thresholds to identify bad quality images. +# See Section 13.4 of the Explanatory Supplement: https://zwicky.tf/ykv +# Updated November 26, 2019 +# +# Columns below: +# CCDquadID (rcid); ZPthres for g; ZPthres for r; ZPthres for i +# +0 25.7675 25.7175 25.1026 +1 25.8339 25.7957 25.191 +2 25.7688 25.7242 25.1141 +3 25.6712 25.6199 25.0117 +4 25.8835 25.8633 25.2648 +5 25.8958 25.8735 25.2782 +6 25.8691 25.8268 25.2397 +7 25.8524 25.8053 25.2042 +8 25.9042 25.852 25.2667 +9 25.8898 25.8284 25.2471 +10 25.8612 25.7819 25.1963 +11 25.8832 25.8061 25.2187 +12 25.8818 25.8232 25.2262 +13 25.8289 25.7602 25.163 +14 25.6988 25.6278 25.0073 +15 25.8278 25.7609 25.1454 +16 25.8153 25.8326 25.3432 +17 25.8115 25.8443 25.3554 +18 25.833 25.8739 25.3877 +19 25.8146 25.8342 25.3535 +20 25.8539 25.8894 25.3902 +21 25.8265 25.8695 25.3844 +22 25.8791 25.9274 25.4526 +23 25.8868 25.9274 25.4403 +24 25.8556 25.9045 25.4256 +25 25.870 25.9077 25.4163 +26 25.8505 25.8823 25.3748 +27 25.8173 25.8624 25.3749 +28 25.8749 25.8992 25.4193 +29 25.8638 25.8681 25.3851 +30 25.8254 25.8292 25.3393 +31 25.819 25.8436 25.3606 +32 25.8035 25.8183 25.3292 +33 25.835 25.8719 25.3952 +34 25.8767 25.9269 25.4519 +35 25.855 25.8799 25.4029 +36 25.8378 25.8692 25.3794 +37 25.8155 25.8517 25.3726 +38 25.8394 25.8929 25.4197 +39 25.8504 25.8928 25.4107 +40 25.9206 25.9759 25.4924 +41 25.9088 25.9622 25.4845 +42 25.9081 25.9648 25.4869 +43 25.8961 25.956 25.4801 +44 25.8949 25.9125 25.4359 +45 25.8771 25.8676 25.3722 +46 25.871 25.856 25.3556 +47 25.881 25.8954 25.4175 +48 25.7302 25.6596 25.045 +49 25.828 25.7647 25.163 +50 25.9027 25.8528 25.2692 +51 25.8247 25.7618 25.1551 +52 25.8719 25.7965 25.2082 +53 25.8787 25.8087 25.2096 +54 25.8749 25.8325 25.2399 +55 25.8841 25.8325 25.2411 +56 25.8857 25.806 25.2079 +57 25.861 25.7724 25.1724 +58 25.9225 25.855 25.282 +59 25.9072 25.8471 25.2801 +60 25.7986 25.7188 25.1254 +61 25.7038 25.6252 25.0348 +62 25.7883 25.7195 25.1277 +63 25.8293 25.7648 25.174 \ No newline at end of file diff --git a/ampel/ztf/alert/calibrate_fps_fork.py b/ampel/ztf/alert/calibrate_fps_fork.py new file mode 100644 index 00000000..25a85512 --- /dev/null +++ b/ampel/ztf/alert/calibrate_fps_fork.py @@ -0,0 +1,1091 @@ +import gc + +import astropy.units as u +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pkg_resources # type: ignore +from astropy.coordinates import AltAz, EarthLocation, SkyCoord +from astropy.time import Time +from matplotlib.pyplot import Figure +from scipy.stats import median_abs_deviation + +##### NOTE ##### +# Methods copied from https://github.com/BrightTransientSurvey/ztf_forced_phot/ +# as these are not pip available (yet). +################ + +pkg_resources.require("pandas>=1.3") + + +def read_ipac_fps(fps_file): + """Read IPAC fps file, manipulate results into pandas DataFrame + + The IPAC forced photometry service (fps; + https://ztfweb.ipac.caltech.edu/cgi-bin/requestForcedPhotometry.cgi) + produces fixed-position, PSF-model flux measurements from ZTF + subtraction images. This function reads fps results, an IPAC table, + calculates the airmass to determine the 'cloudy' parameter, then + returns a pandas DataFrame and an array (fcqfid) of the unique + field+chip+quadrant+filter combo for each observation. + + Parameters + ---------- + fps_file : str + File path to an IPAC table text file with fps results + + Returns + ------- + fp_det : (pandas) DataFrame + Data frame with fps flux measurements and updated bit mask + """ + + ipac_version = pd.read_csv(fps_file, skiprows=1, nrows=1).columns[1][2] + if ipac_version == "2": + fp = pd.read_csv( + fps_file, + sep=r"\s+", + comment="#", + skiprows=70, + names=[ + "ipac_index", + "field", + "ccdid", + "qid", + "filter", + "pid", + "infobitssci", + "airmass", + "moonalt", + "moonillf", + "moonra", + "moondec", + "sciinpseeing", + "scibckgnd", + "scisigpix", + "scimaglim", + "zpmaginpsci", + "zpmaginpsciunc", + "zpmaginpscirms", + "clrcoeff", + "clrcoeffunc", + "ncalmatches", + "exptime", + "diffmaglim", + "zpdiff", + "programid", + "obsdate", + "jd", + "scifilename", + "diffilename", + "rfid", + "refmaglim", + "refbckgnd", + "refsigpix", + "zpref", + "refcreated", + "refjdstart", + "refjdend", + "reffilename", + "forcediffimflux", + "forcediffimfluxunc", + "forcediffimsnr", + "forcediffimchisq", + "forcediffimfluxap", + "forcediffimfluxuncap", + "forcediffimsnrap", + "aperturecorr", + "dnearestrefsrc", + "nearestrefmag", + "nearestrefmagunc", + "nearestrefchi", + "nearestrefsharp", + "procstatus", + ], + ) + + elif int(ipac_version) >= 3: + fp = pd.read_csv( + fps_file, + sep=r"\s+", + comment="#", + header=0, + names=[ + "sindex", + "field", + "ccdid", + "qid", + "filter", + "pid", + "infobitssci", + "sciinpseeing", + "scibckgnd", + "scisigpix", + "zpmaginpsci", + "zpmaginpsciunc", + "zpmaginpscirms", + "clrcoeff", + "clrcoeffunc", + "ncalmatches", + "exptime", + "adpctdif1", + "adpctdif2", + "diffmaglim", + "zpdiff", + "programid", + "jd", + "rfid", + "forcediffimflux", + "forcediffimfluxunc", + "forcediffimsnr", + "forcediffimchisq", + "forcediffimfluxap", + "forcediffimfluxuncap", + "forcediffimsnrap", + "aperturecorr", + "dnearestrefsrc", + "nearestrefmag", + "nearestrefmagunc", + "nearestrefchi", + "nearestrefsharp", + "refjdstart", + "refjdend", + "procstatus", + ], + ) + + palomar = EarthLocation.of_site("Palomar") + ha = pd.read_csv( + fps_file, + skiprows=3, + nrows=2, + sep=r"\s+", + names=["dum", "dum1", "dum2", "coords", "dum4"], + ) + ra, dec = ha.coords.values + targ = SkyCoord(ra * u.deg, dec * u.deg) + time = Time(fp.jd.values, format="jd") + targaltaz = targ.transform_to(AltAz(obstime=time, location=palomar)) + airmass = targaltaz.secz.value + fp["airmass"] = airmass + + union_list = [ + "field", + "ccdid", + "qid", + "filter", + "pid", + "infobitssci", + "airmass", + "sciinpseeing", + "scibckgnd", + "scisigpix", + "zpmaginpsci", + "zpmaginpsciunc", + "zpmaginpscirms", + "clrcoeff", + "clrcoeffunc", + "ncalmatches", + "exptime", + "diffmaglim", + "zpdiff", + "programid", + "jd", + "rfid", + "forcediffimflux", + "forcediffimfluxunc", + "forcediffimsnr", + "forcediffimchisq", + "forcediffimfluxap", + "forcediffimfluxuncap", + "forcediffimsnrap", + "aperturecorr", + "dnearestrefsrc", + "nearestrefmag", + "nearestrefmagunc", + "nearestrefchi", + "nearestrefsharp", + "refjdstart", + "refjdend", + "procstatus", + ] + + fp_det = fp[union_list].dropna(subset=["forcediffimflux"]).copy() + + cloudy = np.zeros_like(fp_det.infobitssci.values) + read_opts = { + "sep": r"\s+", + "names": ["zp_rcid_g", "zp_rcid_r", "zp_rcid_i"], + "comment": "#", + } + + rcid_df = pd.read_csv( + pkg_resources.resource_stream(__name__, "cal_data/zp_thresholds_quadID.txt"), + **read_opts, + ) + + rcid = (fp_det.ccdid.values - 1) * 4 + fp_det.qid.values - 1 + zp_rcid_g = rcid_df.zp_rcid_g.iloc[rcid] + zp_rcid_r = rcid_df.zp_rcid_r.iloc[rcid] + zp_rcid_i = rcid_df.zp_rcid_i.iloc[rcid] + flag_clouds = np.where( + ( + (fp_det["filter"].values == "ZTF_g") + & ( + (fp_det.zpmaginpsci.values > 26.7 - 0.2 * fp_det.airmass.values) + | (fp_det.zpmaginpscirms.values > 0.06) + | (fp_det.ncalmatches.values < 80) + | (fp_det.zpmaginpsci.values < zp_rcid_g - 0.2 * fp_det.airmass.values) + ) + ) + | ( + (fp_det["filter"].values == "ZTF_r") + & ( + (fp_det.zpmaginpsci.values > 26.65 - 0.15 * fp_det.airmass.values) + | (fp_det.zpmaginpscirms.values > 0.05) + | (fp_det.ncalmatches.values < 120) + | (fp_det.zpmaginpsci.values < zp_rcid_r - 0.15 * fp_det.airmass.values) + ) + ) + | ( + (fp_det["filter"].values == "ZTF_i") + & ( + (fp_det.zpmaginpsci.values > 26.0 - 0.07 * fp_det.airmass.values) + | (fp_det.zpmaginpscirms.values > 0.06) + | (fp_det.ncalmatches.values < 100) + | (fp_det.zpmaginpsci.values < zp_rcid_i - 0.07 * fp_det.airmass.values) + ) + ) + ) + cloudy[flag_clouds] = int(2**25) + fp_det["infobitssci"] = fp_det.infobitssci.values + cloudy + + obs_jd = Time(fp_det.jd.values, format="jd") + + fp_det = fp_det.set_index(pd.to_datetime(obs_jd.datetime)) + + fid_dict = {"ZTF_g": 1, "ZTF_r": 2, "ZTF_i": 3} + fcqfid = np.array( + fp_det.field.values * 10000 + + fp_det.ccdid.values * 100 + + fp_det.qid.values * 10 + + np.array([fid_dict[x] for x in fp_det["filter"].values]) + ) + fp_det["fcqfid"] = fcqfid + + return fp_det + + +def search_peak( + flux, + fluxerr, + time, + window="14D", + single_significance=5, + combined_significance=10, +): + """ + Search for a peak in the provided dataframe, if found + also determine significance. + + flux, fluxerr, time: pd.DataFrame. + Input data arrays for peak search + These are assumed to have Datetime like indices. + + + window : int, offset, or BaseIndexer subclass (optional, default = '10D') + Size of the moving window. This is the number of observations used + for calculating the rolling median. + If its an offset then this will be the time period of each window. + Each window will be a variable sized based on the observations + included in the time-period. This is only valid for datetimelike + indexes. + + single_significance: int (optional, default=5). + Min sigma significance required for *both* S/N and peak/scatter to + register a peak as individually detected. + + combined_significance: int (optional, default=10). + Min sigma significance required for combined S/N + peak/scatter to + register a peak as combined detecter. + (allows for a very high S/N peak to be detected if when scatter + has not reached single_significance). + + """ + sumdict = {} + + if len(flux) < 2: + sumdict["det_sn"] = False + return sumdict + + roll_med = flux.rolling(window, center=True).median().values + roll_peak = np.argmax(roll_med) + t_max = time.values[roll_peak] + flux_max = np.max(roll_med) + flux_scatt = median_abs_deviation(flux.values, scale="normal") + max_over_scatt = flux_max / flux_scatt + peak_snr = flux_max / fluxerr.values[roll_peak] + # print( + # f"peaksearch max over scatt {max_over_scatt} peaksnr {peak_snr} fluxmax {flux_max} fluxscatt {flux_scatt}" + # ) + if ( + (max_over_scatt > single_significance and peak_snr > single_significance) + or (max_over_scatt + peak_snr) > combined_significance + and peak_snr > 3 + ): + sumdict["det_sn"] = True + sumdict["t_fcqfid_max"] = t_max + sumdict["det_snr"] = peak_snr + else: + sumdict["det_sn"] = False + sumdict["det_snr"] = peak_snr + return sumdict + + +def get_baseline( + fps_file, + window="14D", + write_lc=False, + make_plot=False, + save_fig=False, + talk_plot=False, + save_path="default", + deprecated=False, +): + """ + calculate the baseline region for an IPAC fps light curve and produce + flux calibrated photometry. + + JN: Also accepting peak estimates when the combined peak and signal/cut + exceed 10 sigma. Old setting missed some clear peaks as one metric was just below + the threshold. + + Parameters + ---------- + fps_file : str or list of str + File path to an IPAC table text file with fps results. If a list is + provided forced photometry is read from each file and the results + appended before producing the final output. + + window : int, offset, or BaseIndexer subclass (optional, default = '10D') + Size of the moving window. This is the number of observations used + for calculating the rolling median. + If its an offset then this will be the time period of each window. + Each window will be a variable sized based on the observations + included in the time-period. This is only valid for datetimelike + indexes. + + write_lc : bool or DataFrame (optional, default = 'False') + If True, the resulting calibrated photometry is written to a text file + If a DataFrame is provided, it will be updated with the resulting + calibrated photometry + + make_plot : bool or matplotlib Figure (optional, default = 'False') + If True, a plot of the calibrated light curve is created + If a matplotlib figure is provided, it will be used for plotting + the calibrated lightcurve + + save_fig : bool (optional, default = 'False') + If True, the resulting light curve plot is saved as a png + + talk_plot : bool (optional, default = 'False') + If True, the saved plot has a transparent background and white + axes/axis labels for projection on a dark background + + save_path : str (optional, default = 'default') + Path for writing light curve figures and ascii files + + deprecated : bool (optional, default = 'False') + Return "old" outputs from the original version of the software circa + 01 Jan 2022. This will be removed eventually. + + Returns + ------- + fcqfid_dict : dict + Dictionary with summary statistics for the baseline region for each + fcqfid + """ + + # # Summary of flags >> to be removed later once flags are locked + # flag | bit value | meaning + # 1 | 1 | large scatter in max + # 2 | 2 | possible emission in pre-SN baseline + # 3 | 4 | possible emission in post-SN baseline + # 4 | 8 | outliers in the baseline region + # 5 | 16 | large scatter in baseline region + # 6 | 32 | N_baseline < 10 + # 7 | 64 | scisigpix > 25 + # 8 | 128 | sciinpseeing > 5 + # 9 | 256 | uncertainties underestimated + # 10 | 512 | infobits > 0 + # 11 | 1024 | no flux detected in forced phot + + bad_obs_fl = 512 + + if isinstance(fps_file, str): + if save_fig is True or write_lc is not False or make_plot is True: + ipac_name = fps_file.split("batchfp_")[1][0:13] + fp_df = read_ipac_fps(fps_file) + if save_path == "default": + save_path = fps_file[0 : -len(fps_file.split("/")[-1])] + elif isinstance(fps_file, list): + ztf_names = [n.split("forcedphotometry_")[1].split("_")[0] for n in fps_file] + if ztf_names.count(ztf_names[0]) == len(ztf_names): + # ztf_name = ztf_names[0] + fp_df = read_ipac_fps(fps_file[0]) + for n in fps_file[1:]: + extra_df = read_ipac_fps(n) + fp_df = fp_df.append(extra_df) + if save_path == "default": + save_path = fps_file[-1].split("forced")[0] + else: + raise AssertionError( + """Photometric observations can only be + combined for a single SN""" + ) + fp_df.drop_duplicates( + ["pid", "forcediffimflux", "forcediffimfluxunc"], inplace=True + ) + + unique_fid = np.unique(fp_df.fcqfid.values).astype(int) + + fcqfid_dict = {} + t_peak_list = [] + + fp_df["flags"] = np.zeros(len(fp_df)).astype(int) + fp_df.loc[fp_df.scisigpix.values > 25, "flags"] += 64 + fp_df.loc[fp_df.sciinpseeing.values > 5, "flags"] += 128 + fp_df.loc[fp_df.infobitssci.values > 0, "flags"] += 512 + fp_df.loc[fp_df.forcediffimfluxunc == -99999, "flags"] += 1024 + bad_obs = np.where(fp_df["flags"].values >= bad_obs_fl, 1, 0) + + for ufid in unique_fid: + fcqfid_dict[str(ufid)] = {} + fcqfid_dict[str(ufid)]["N_bl"] = 0 + # bad_obs = np.zeros_like(fp_df.ccdid.values) + # if deprecated: + # bad_obs[np.where((fp_df.infobitssci.values > 0) | + # (fp_df.scisigpix.values > 25) | + # (fp_df.sciinpseeing.values > 5) + # )] = 1 + # else: + # # AW: added additional filter + # bad_obs[np.where((fp_df.infobitssci.values > 0) | + # (fp_df.forcediffimfluxunc == -99999) + # )] = 1 + + this_fcqfid = np.where( + (fp_df.fcqfid.values == ufid) & (fp_df["flags"].values < bad_obs_fl) + ) + + # For high-z objects, I-band might provide cleanest det. Allow this. + # if (ufid % 10 == 3) or (len(this_fcqfid[0]) < 2): + if len(this_fcqfid[0]) < 2: + continue + + fcqf_df = fp_df.iloc[this_fcqfid].copy() + # flux_series = fcqf_df.forcediffimflux + + # roll_med = flux_series.rolling(window, center=True).median().values + # roll_peak = np.argmax(roll_med) + # t_max = fcqf_df.jd.values[roll_peak] + # flux_max = np.max(roll_med) + # flux_scatt = median_abs_deviation( + # fcqf_df.forcediffimflux.values, scale="normal" + # ) + # max_over_scatt = flux_max / flux_scatt + # peak_snr = flux_max / fcqf_df.forcediffimfluxunc.values[roll_peak] + # print( + # f"peaksearch fid {ufid} max over scatt {max_over_scatt} peaksnr {peak_snr} fluxmax {flux_max} fluxscatt {flux_scatt}" + # ) + # if (max_over_scatt > 5 and peak_snr > 5) or (max_over_scatt + peak_snr) > 10: + # fcqfid_dict[str(ufid)]["det_sn"] = True + # fcqfid_dict[str(ufid)]["t_fcqfid_max"] = t_max + # if ufid < 10000000: + # t_peak_list.append(t_max) + # else: + # fcqfid_dict[str(ufid)]["det_sn"] = False + + peakdict = search_peak( + fcqf_df.forcediffimflux, + fcqf_df.forcediffimfluxunc, + fcqf_df.jd, + window=window, + ) + if peakdict["det_sn"] and ufid < 10000000: + t_peak_list.append(peakdict["t_fcqfid_max"]) + fcqfid_dict[str(ufid)].update(peakdict) + + if len(t_peak_list) > 0: + t_peak = np.mean(t_peak_list) + for ufid in unique_fid: + fcqfid_dict[str(ufid)]["t_peak"] = t_peak + if len(t_peak_list) > 1: + fcqfid_dict[str(ufid)]["peak_scatter"] = np.std(t_peak_list, ddof=1) + if np.std(t_peak_list, ddof=1) > 50: + print("Warning! Large scatter in time of maximum") + fcqfid_dict[str(ufid)]["peak_warning"] = "large scat in t_peak" + fp_df["flags"] += 1 + + around_max = np.where( + (fp_df.jd.values - t_peak > -10) + & (fp_df.jd.values - t_peak < 10) + & (fp_df.forcediffimflux.values > 0) + ) + if len(around_max[0]) > 0: + diff_flux_around_max = fp_df.forcediffimflux.values[around_max] + mag_min = np.nanmin( + fp_df.zpdiff.values[around_max] - 2.5 * np.log10(diff_flux_around_max) + ) + # calculate time when SN signal is "gone" via Co56 decay at z ~ 0.09 + t_faded = t_peak + (22.5 - mag_min) / 0.009 + else: + t_faded = t_peak + 611 # catch strange cases where t_gmax != t_rmax + for ufid in unique_fid: + if ( + ufid % 10 == 4 + ): # check if this ever happens - should not be a filter id = 4 + continue + + this_fcqfid = np.where(fp_df.fcqfid.values == ufid) + fcqf_df = fp_df.iloc[this_fcqfid].copy() + + # measure the baseline + bl = np.where( + ((t_peak - fcqf_df.jd.values > 100) | (fcqf_df.jd.values > t_faded)) + & (fcqf_df["flags"].values < bad_obs_fl) + ) + + if len(bl[0]) > 1: + base_flux = fcqf_df.forcediffimflux.values[bl] + base_flux_unc = fcqf_df.forcediffimfluxunc.values[bl] + mask = np.where( + np.abs((base_flux - np.median(base_flux)) / base_flux_unc) <= 5 + ) + outl = np.where( + np.abs((base_flux - np.median(base_flux)) / base_flux_unc) > 5 + ) + # little kludgy but avoids warning + outl_bool = np.zeros(len(fp_df)).astype(bool) + outl_bool[this_fcqfid[0][bl[0]][outl]] = True + if sum(outl[0]) > 0: + fp_df.loc[outl_bool, "flags"] += 8 + if len(mask[0]) > 2: + non_det = base_flux[mask] + Cmean = np.average(non_det, weights=1 / base_flux_unc[mask] ** 2) + sum_diff_sq = np.sum( + ((non_det - Cmean) / (base_flux_unc[mask])) ** 2 + ) + chi = 1 / (len(mask[0]) - 1) * sum_diff_sq + mean = np.mean(non_det) + median = np.median(non_det) + # using bootstrapping to place unc on median + medians = np.empty(1000) + for i in range(1000): + medians[i] = np.median( + np.random.choice(non_det, size=len(non_det), replace=True) + ) + median_unc = np.diff(np.percentile(medians, (16, 84)))[0] / 2 + + low_limit = np.percentile(non_det, 10) + high_limit = np.percentile(non_det, 90) + trim = np.where((non_det > low_limit) & (non_det < high_limit)) + trim_mean = np.mean(non_det[trim]) + # is this warning needed? + if len(non_det[trim]) == 0: + print(f"{ipac_name} {ufid} bl empty") + scatter = np.diff(np.percentile(non_det, (16, 84)))[0] / 2 + + fcqfid_dict[str(ufid)]["C_bl"] = Cmean + fcqfid_dict[str(ufid)]["chi_bl"] = chi + fcqfid_dict[str(ufid)]["mean_bl"] = mean + fcqfid_dict[str(ufid)]["median_bl"] = median + fcqfid_dict[str(ufid)]["median_unc_bl"] = median_unc + fcqfid_dict[str(ufid)]["trim_mean_bl"] = trim_mean + fcqfid_dict[str(ufid)]["scatter_bl"] = scatter + fcqfid_dict[str(ufid)]["N_bl"] = len(mask[0]) + if fcqfid_dict[str(ufid)]["N_bl"] < 10: + fp_df.loc[fp_df.fcqfid == ufid, "flags"] += 32 + else: + fcqfid_dict[str(ufid)]["N_bl"] = 0 + fp_df.loc[fp_df.fcqfid == ufid, "flags"] += 32 + + # measure the baseline pre-peak + if deprecated: + pre_bl = np.where( + (t_peak - fcqf_df.jd.values > 100) + & (fcqf_df.infobitssci.values == 0) + & (fcqf_df.scisigpix.values < 25) + & (fcqf_df.sciinpseeing.values < 5) + ) + else: + pre_bl = np.where( + (t_peak - fcqf_df.jd.values > 100) + & (fcqf_df["flags"].values < bad_obs_fl) + ) + if len(pre_bl[0]) > 1: + base_flux = fcqf_df.forcediffimflux.values[pre_bl] + base_flux_unc = fcqf_df.forcediffimfluxunc.values[pre_bl] + mask = np.where( + np.abs((base_flux - np.median(base_flux)) / base_flux_unc) <= 5 + ) + if len(mask[0]) > 2: + non_det = base_flux[mask] + Cmean = np.average(non_det, weights=1 / base_flux_unc[mask] ** 2) + sum_diff_sq = np.sum( + ((non_det - Cmean) / (base_flux_unc[mask])) ** 2 + ) + chi = 1 / (len(mask[0]) - 1) * sum_diff_sq + mean = np.mean(non_det) + median = np.median(non_det) + + medians = np.empty(1000) + for i in range(1000): + medians[i] = np.median( + np.random.choice(non_det, size=len(non_det), replace=True) + ) + median_unc = np.diff(np.percentile(medians, (16, 84)))[0] / 2 + + low_limit = np.percentile(non_det, 10) + high_limit = np.percentile(non_det, 90) + trim = np.where((non_det > low_limit) & (non_det < high_limit)) + trim_mean = np.mean(non_det[trim]) + if len(non_det[trim]) == 0: + print(f"{ipac_name} {ufid} pre empty") + scatter = np.diff(np.percentile(non_det, (16, 84)))[0] / 2 + + fcqfid_dict[str(ufid)]["C_pre"] = Cmean + fcqfid_dict[str(ufid)]["chi_pre"] = chi + fcqfid_dict[str(ufid)]["mean_pre"] = mean + fcqfid_dict[str(ufid)]["median_pre"] = median + fcqfid_dict[str(ufid)]["median_unc_pre"] = median_unc + fcqfid_dict[str(ufid)]["trim_mean_pre"] = trim_mean + fcqfid_dict[str(ufid)]["scatter_pre"] = scatter + fcqfid_dict[str(ufid)]["N_pre_peak"] = len(mask[0]) + else: + fcqfid_dict[str(ufid)]["N_pre_peak"] = 0 + + # measure the baseline post-peak + if deprecated: + post_bl = np.where( + (fcqf_df.jd.values > t_faded) + & (fcqf_df.infobitssci.values == 0) + & (fcqf_df.scisigpix.values < 25) + & (fcqf_df.sciinpseeing.values < 5) + ) + else: + post_bl = np.where( + (fcqf_df.jd.values > t_faded) + & (fcqf_df["flags"].values < bad_obs_fl) + ) + + if len(post_bl[0]) > 1: + base_flux = fcqf_df.forcediffimflux.values[post_bl] + base_flux_unc = fcqf_df.forcediffimfluxunc.values[post_bl] + + mask = np.where( + np.abs((base_flux - np.median(base_flux)) / base_flux_unc) <= 5 + ) + if len(mask[0]) > 2: + non_det = base_flux[mask] + Cmean = np.average(non_det, weights=1 / base_flux_unc[mask] ** 2) + sum_diff_sq = np.sum( + ((base_flux[mask] - Cmean) / (base_flux_unc[mask])) ** 2 + ) + chi = 1 / (len(mask[0]) - 1) * sum_diff_sq + mean = np.mean(non_det) + median = np.median(non_det) + medians = np.empty(1000) + for i in range(1000): + medians[i] = np.median( + np.random.choice(non_det, size=len(non_det), replace=True) + ) + median_unc = 0.5 * np.diff(np.percentile(medians, (16, 84)))[0] + low_limit = np.percentile(non_det, 10) + high_limit = np.percentile(non_det, 90) + trim = np.where((non_det > low_limit) & (non_det < high_limit)) + trim_mean = np.mean(non_det[trim]) + if len(non_det[trim]) == 0: + print(f"{ipac_name} {ufid} post empty") + scatter = np.diff(np.percentile(non_det, (16, 84)))[0] / 2 + + fcqfid_dict[str(ufid)]["C_post"] = Cmean + fcqfid_dict[str(ufid)]["chi_post"] = chi + fcqfid_dict[str(ufid)]["mean_post"] = mean + fcqfid_dict[str(ufid)]["median_post"] = median + fcqfid_dict[str(ufid)]["median_unc_post"] = median_unc + fcqfid_dict[str(ufid)]["trim_mean_post"] = trim_mean + fcqfid_dict[str(ufid)]["scatter_post"] = scatter + fcqfid_dict[str(ufid)]["N_post_peak"] = len(mask[0]) + else: + fcqfid_dict[str(ufid)]["N_post_peak"] = 0 + + if make_plot is not False or write_lc is not False: + fnu_microJy = -999.0 * np.ones_like(fp_df.forcediffimflux.values) + fnu_microJy_unc = -999.0 * np.ones_like(fp_df.forcediffimflux.values) + + sys_sigma = np.zeros_like(fp_df.forcediffimflux.values) + if deprecated: + n_base_obs = np.zeros_like(fp_df.forcediffimflux.values).astype(int) + which_base = np.zeros_like(fp_df.forcediffimflux.values).astype(int) + C_baseline = np.zeros_like(fp_df.forcediffimflux.values) + + for key in fcqfid_dict: + if fcqfid_dict[key]["N_bl"] > 1: + ufid = int(key) + this_fcqfid = np.where(fp_df.fcqfid.values == ufid) + if deprecated: + sys_unc = max(fcqfid_dict[key]["chi_pre"] ** 0.5, 1) + else: + good_fcqfid = np.where( + (fp_df.fcqfid.values == ufid) + & (fp_df["flags"].values < bad_obs_fl) + ) + # chi_ser = fp_df.forcediffimchisq.iloc[good_fcqfid].copy() + # good_diffl = fp_df.forcediffimflux.iloc[good_fcqfid].values + this_diffl = fp_df.forcediffimflux.iloc[this_fcqfid].values + chi_df = pd.read_csv( + pkg_resources.resource_stream( + __name__, "cal_data/chi_lookup.csv" + ) + ) + if ufid % 10 == 1: + sys_unc = ( + np.interp( + this_diffl, + chi_df.diffflux_grid.values, + chi_df.g_chi_interp.values, + ) + ** 0.5 + ) + elif ufid % 10 == 2: + sys_unc = ( + np.interp( + this_diffl, + chi_df.diffflux_grid.values, + chi_df.r_chi_interp.values, + ) + ** 0.5 + ) + elif ufid % 10 == 3: + sys_unc = ( + np.interp( + this_diffl, + chi_df.diffflux_grid.values, + chi_df.i_chi_interp.values, + ) + ** 0.5 + ) + + # med_chi = np.median(chi_ser.values) + # if med_chi < 1.5: + # sys_unc = med_chi**0.5 * np.ones_like(this_diffl) + # elif len(good_fcqfid[0]) > 0: + # try: + # model = SuperSmoother() + # model.fit(fp_df.forcediffimflux.iloc[good_fcqfid], + # fp_df.forcediffimchisq.iloc[good_fcqfid]) + # # find the smoothed fit to the data + # # interpolate to non-good obs + # yfit = model.predict(np.sort(good_diffl)) + # chi_interp = np.interp(this_diffl, + # np.sort(good_diffl), + # yfit) + # chi_interp = np.where(chi_interp < 1, 1, + # chi_interp) + # sys_unc = chi_interp**0.5 + # # is this warning necessary? + # if np.isnan(sys_unc).any(): + # print(f'{ztf_name} {ufid} bad interp') + # except: + # chi_ser.index = pd.to_datetime(good_diffl, + # unit='s', + # origin='unix') + # chi_ser.sort_index(inplace=True) + # runmed = chi_ser.rolling('1000s', + # center=True).median() + # sys_unc = np.interp(this_diffl, + # np.sort(good_diffl), + # runmed.values)**0.5 + # else: + # continue + sys_unc = np.where(sys_unc < 1, 1, sys_unc) + + if deprecated: + if (fcqfid_dict[key]["N_pre_peak"] >= 25) or ( + (fcqfid_dict[key]["N_pre_peak"] >= 10) + and (fcqfid_dict[key]["N_post_peak"] < 25) + ): + baseline = fcqfid_dict[key]["C_pre"] + n_baseline = fcqfid_dict[key]["N_pre_peak"] + pre_or_post = -1 + elif (fcqfid_dict[key]["N_post_peak"] >= 25) or ( + (fcqfid_dict[key]["N_pre_peak"] < 10) + and (fcqfid_dict[key]["N_post_peak"] >= 10) + ): + baseline = fcqfid_dict[key]["C_post"] + n_baseline = fcqfid_dict[key]["N_post_peak"] + pre_or_post = 1 + else: + n_base_obs[this_fcqfid] = fcqfid_dict[key]["N_pre_peak"] + which_base[this_fcqfid] = -1 + continue + else: + # determine if there is emission in pre/post SN baseline + if "C_bl" not in fcqfid_dict[key]: + print(f"{ipac_name}, {key} no C_bl") + continue + baseline = fcqfid_dict[key]["median_bl"] + baseline_unc = fcqfid_dict[key]["median_unc_bl"] + base_scat = fcqfid_dict[key]["scatter_bl"] + fcqfid_dict[key]["which_bl"] = "pre+post SN" + + good_df = fp_df.iloc[good_fcqfid].copy() + flux_series = good_df.forcediffimflux + roll_med = flux_series.rolling("14D", center=True).median().values + this_fl = fp_df["flags"].iloc[this_fcqfid].values + scale = sys_unc[np.where(this_fl < bad_obs_fl)] + scale_unc = scale * good_df.forcediffimfluxunc.values + + pre_bl = np.where(t_peak - good_df.jd.values > 100) + pre_em = (roll_med[pre_bl] - baseline) / scale_unc[pre_bl] + + post_bl = np.where(good_df.jd.values > t_faded) + post_em = (roll_med[post_bl] - baseline) / scale_unc[post_bl] + + # test scatter after systematic correction + bl = np.where( + ( + (t_peak - good_df.jd.values > 100) + | (good_df.jd.values > t_faded) + ) + & (good_df["flags"].values & 8 == 0) + ) + chi2nu = np.sum( + ( + (good_df.iloc[bl].forcediffimflux.values - baseline) + / scale_unc[bl] + ) + ** 2 + ) / len(good_df.iloc[bl]) + fcqfid_dict[key]["chi2nu"] = chi2nu + if chi2nu > 2: + print("Warning! scaled unc are underestimated") + print(f"{ipac_name} {key} has chi2nu = {chi2nu:.3f}") + fp_df.loc[fp_df.fcqfid == ufid, "flags"] += 256 + + #### So many nested ifs - cover your eye's Guido + if len(pre_em) < 10 and len(post_em) < 10: + continue + + pre_rise_em = False + if len(pre_em) == 0: + fcqfid_dict[key]["which_bl"] = "post SN" + if len(pre_em) >= 1 and len(pre_bl[0]) < 5 or sum(pre_em >= 7) >= 2: + if (len(post_em) >= 10) and ( + fcqfid_dict[key]["N_post_peak"] > 2 + ): + baseline = fcqfid_dict[key]["median_post"] + baseline_unc = fcqfid_dict[key]["median_unc_post"] + base_scat = fcqfid_dict[key]["scatter_post"] + fcqfid_dict[key]["which_bl"] = "post SN" + if sum(pre_em >= 7) >= 2: + print(f"Warning {ipac_name} {ufid} pre-SN") + fcqfid_dict[key]["Warning"] = "pre-SN emission" + pre_rise_em = True + fp_df.loc[fp_df.fcqfid == ufid, "flags"] += 2 + + post_rise_em = False + if len(post_em) == 0: + fcqfid_dict[key]["which_bl"] = "pre SN" + if len(post_em) >= 1 and ( + len(post_em) < 5 or sum(post_em >= 7) >= 2 + ): + if (len(pre_em) >= 10) and (fcqfid_dict[key]["N_pre_peak"] > 2): + baseline = fcqfid_dict[key]["median_pre"] + baseline_unc = fcqfid_dict[key]["median_unc_pre"] + base_scat = fcqfid_dict[key]["scatter_pre"] + fcqfid_dict[key]["which_bl"] = "pre SN" + if sum(post_em >= 7) >= 2: + print(f"Warning {ipac_name} {ufid} post-SN") + fcqfid_dict[key]["Warning"] = "post-SN emission" + post_rise_em = True + fp_df.loc[fp_df.fcqfid == ufid, "flags"] += 4 + + if pre_rise_em + post_rise_em == 2: + baseline = fcqfid_dict[key]["median_bl"] + baseline_unc = fcqfid_dict[key]["median_unc_bl"] + base_scat = fcqfid_dict[key]["scatter_bl"] + print(f"Warning {ipac_name} {ufid} bad baseline") + fcqfid_dict[key]["Warning"] = "bad baseline" + fcqfid_dict[key]["which_bl"] = "pre+post SN" + # fp_df.loc[fp_df.fcqfid == ufid, 'flags'] += 512 + if base_scat > 100: + fp_df.loc[fp_df.fcqfid == ufid, "flags"] += 16 + flux_dn = fp_df.forcediffimflux.values[this_fcqfid] - baseline + unc_fcqfid = fp_df.forcediffimfluxunc.values[this_fcqfid] + + # AW: fixed bug, add deprecated version + if deprecated: + flux_dn_unc = np.sqrt(unc_fcqfid**2) * sys_unc + else: + flux_dn_unc = np.sqrt(unc_fcqfid**2 + baseline_unc**2) * sys_unc + + zp_fcqfid = fp_df.zpdiff.values[this_fcqfid] + fnu_microJy[this_fcqfid] = flux_dn * 10 ** ( + 29 - 48.6 / 2.5 - 0.4 * zp_fcqfid + ) + + fnu_microJy_unc[this_fcqfid] = flux_dn_unc * 10 ** ( + 29 - 48.6 / 2.5 - 0.4 * zp_fcqfid + ) + if deprecated: + n_base_obs[this_fcqfid] = n_baseline + which_base[this_fcqfid] = pre_or_post + C_baseline[this_fcqfid] = baseline + else: + sys_sigma[this_fcqfid] = sys_unc + + if write_lc is not False: + if isinstance(write_lc, pd.DataFrame): + write_df = write_lc + write_df["jd"] = fp_df.jd.values + write_df["forcediffimflux"] = fp_df.forcediffimflux.values + write_df["forcediffimchisq"] = fp_df.forcediffimchisq.values + else: + write_df = pd.DataFrame(fp_df.jd.values, columns=["jd"]) + write_df["fnu_microJy"] = fnu_microJy + write_df["fnu_microJy_unc"] = fnu_microJy_unc + write_df["passband"] = fp_df["filter"].values + write_df["programid"] = fp_df.programid.values + write_df["fcqfid"] = fp_df.fcqfid.values + write_df["zpdiff"] = fp_df.zpdiff.values + write_df["ccdid"] = fp_df.ccdid.values # JN: Propagate CCD info + write_df["qid"] = fp_df.qid.values # JN: Propagate quadrant info + write_df["sys_unc_factor"] = sys_sigma + write_df["flags"] = fp_df["flags"].values + if deprecated: + write_df["C"] = C_baseline + write_df["N_baseline"] = n_base_obs + write_df["pre_or_post"] = which_base + + gr_obs = np.where(write_df.fcqfid.values % 10 != 4) + if not isinstance(write_lc, pd.DataFrame): + fname = save_path + ipac_name + "_fnu.csv" + write_df.iloc[gr_obs].to_csv(fname, index=False) + + storehdf = pd.HDFStore(save_path + ipac_name + "_fnu.h5") + storehdf.put("light_curve", write_df) + storehdf.get_storer("light_curve").attrs.metadata = fcqfid_dict + storehdf.close() + + if make_plot is not False: + color_dict = {1: "MediumAquaMarine", 2: "Crimson", 3: "Goldenrod"} + nplots = 0 + jdstart = 2458119.5 + + for key in fcqfid_dict: + if fcqfid_dict[key]["N_bl"] > 1: + this_fcqfid_good = np.where( + (fp_df.fcqfid.values == int(key)) & (bad_obs == 0) + ) + plot_flux = fnu_microJy[this_fcqfid_good] + + if (plot_flux == -999).sum() != len(plot_flux) and ( + (fcqfid_dict[key]["N_pre_peak"] > 2) + or (fcqfid_dict[key]["N_post_peak"] > 2) + ): + nplots += 1 + + if nplots > 0: + nplots = 3 # group by filter instead (3 for g,r,i) + marker_dict = {0: "o", 1: "s", 2: "^", 3: "<", 4: ">", 5: "*"} + gri_marker_counters = [0, 0, 0] + + fig = plt.figure() if make_plot is True else make_plot + fig.set_size_inches(8, nplots * 3 + 0.5) + axes = fig.subplots(nplots, 1, sharex=True) + + for key in fcqfid_dict: + if fcqfid_dict[str(key)]["N_bl"] > 1: + ufid = int(key) + this_fcqfid_good = np.where( + (fp_df.fcqfid.values == ufid) & (bad_obs == 0) + ) + + plot_jd = fp_df.jd.values[this_fcqfid_good] - jdstart + plot_flux = fnu_microJy[this_fcqfid_good] + plot_flux_unc = fnu_microJy_unc[this_fcqfid_good] + + if (plot_flux == -999).sum() == len(plot_flux): + continue + + # select subplot based on current filter + plot_num = int(str(key)[-1]) - 1 + + # select marker based on FCQ(F)ID + this_marker = marker_dict[gri_marker_counters[plot_num]] + gri_marker_counters[plot_num] += 1 + + ax = axes[plot_num] + ax.errorbar( + plot_jd, + plot_flux, + plot_flux_unc, + fmt=this_marker, + mec=color_dict[ufid % 10], + ecolor=color_dict[ufid % 10], + mfc="None", + label=str(key), + ) + ax.legend() + + # if roll_med_plot == True: + # jd_time = Time(plot_jd + jdstart, format='jd') + # f_ser = pd.Series(plot_flux, + # index= + # pd.to_datetime(jd_time.datetime)) + # plot_roll = f_ser.rolling(window, + # center=True).median().values + # ax.plot(plot_jd, plot_roll, + # color='lightgrey', zorder=2) + + ax.axvline(x=t_peak - jdstart, color="0.5", ls="--") + ax.axhline( + y=0, color="0.5", ls=(0, [8, 1.5, 1, 1.5]), lw=0.5, alpha=0.75 + ) + ax.axvspan( + 0, t_peak - jdstart - 100, color="Cornsilk", alpha=0.6, lw=0 + ) + ax.axvspan( + t_faded - jdstart, 1e6, color="Cornsilk", alpha=0.6, lw=0 + ) + ax.set_ylabel(r"flux ($\mu$Jy)", fontsize=14) + ax.set_xlim( + np.min(fp_df.jd.values - jdstart) - 10, + np.max(fp_df.jd.values - jdstart) + 10, + ) + + if talk_plot: + ax.tick_params(axis="both", colors="white") + for spine in ["top", "bottom", "left", "right"]: + ax.spines[spine].set_color("white") + ax.yaxis.label.set_color("white") + ax.xaxis.label.set_color("white") + + axes[0].set_title(f"{ipac_name}, ZTF_g") + axes[1].set_title(f"{ipac_name}, ZTF_r") + axes[2].set_title(f"{ipac_name}, ZTF_i") + axes[2].set_xlabel("Time (JD - 2018 Jan 01)", fontsize=14) + + fig.tight_layout() + if save_fig: + pname = save_path + ipac_name + "_fnu.png" + fig.savefig(pname) + if not isinstance(make_plot, Figure): + plt.close(fig) + plt.close("all") + del fig + gc.collect() + + return fcqfid_dict diff --git a/ampel/ztf/ingest/ZiDataPointShaper.py b/ampel/ztf/ingest/ZiDataPointShaper.py index 58f3ce32..a7bf53b9 100755 --- a/ampel/ztf/ingest/ZiDataPointShaper.py +++ b/ampel/ztf/ingest/ZiDataPointShaper.py @@ -6,13 +6,16 @@ # Last Modified Date: 10.05.2021 # Last Modified By: valery brinnel -from collections.abc import Iterable +from collections.abc import Iterable, Sequence from typing import Any +from bson import encode + from ampel.abstract.AbsT0Unit import AbsT0Unit from ampel.base.AmpelUnit import AmpelUnit from ampel.content.DataPoint import DataPoint -from ampel.types import StockId +from ampel.types import StockId, Tag +from ampel.util.hash import hash_payload from ampel.ztf.ingest.tags import tags @@ -24,6 +27,8 @@ class ZiDataPointShaperBase(AmpelUnit): # JD2017 is used to define upper limits primary IDs JD2017: float = 2457754.5 + #: Byte width of datapoint ids + digest_size: int = 8 # Mandatory implementation def process(self, arg: Iterable[dict[str, Any]], stock: StockId) -> list[DataPoint]: # type: ignore[override] @@ -61,7 +66,12 @@ def process(self, arg: Iterable[dict[str, Any]], stock: StockId) -> list[DataPoi popitem(photo_dict, "candid", None) popitem(photo_dict, "programpi", None) - + elif "forcediffimflux" in photo_dict: + ret_list.append(self._create_datapoint(stock, ["ZTF_FP"], photo_dict)) + elif "fcqfid" in photo_dict: + ret_list.append( + self._create_datapoint(stock, ["ZTF_FP", "BTS_PHOT"], photo_dict) + ) else: ret_list.append( { # type: ignore[typeddict-item] @@ -86,6 +96,22 @@ def process(self, arg: Iterable[dict[str, Any]], stock: StockId) -> list[DataPoi return ret_list + def _create_datapoint( + self, stock: StockId, tag: Sequence[Tag], body: dict[str, Any] + ) -> DataPoint: + """ + Create a Datapoint from stock, body, and tags, using the hash of the body as id + """ + # ensure that keys are ordered + sorted_body = dict(sorted(body.items())) + # This is not a complete DataPoint as (channel,meta) is missing, set later. Should these be optional? or added default? + return { # type: ignore + "id": hash_payload(encode(sorted_body), size=-self.digest_size * 8), + "stock": stock, + "tag": [*tags[body["programid"]][body["fid"]], *tag], + "body": sorted_body, + } + def ul_identity(self, uld: dict[str, Any]) -> int: """ Calculate a unique ID for an upper limit from: diff --git a/ampel/ztf/view/ZTFFPTabulator.py b/ampel/ztf/view/ZTFFPTabulator.py new file mode 100644 index 00000000..f504c3cb --- /dev/null +++ b/ampel/ztf/view/ZTFFPTabulator.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python +# File : Ampel-ZTF/ampel/ztf/view/ZTFFPTabulator.py +# License : BSD-3-Clause +# Author : jno +# Date : 26.05.2021 +# Last Modified Date: 05.05.2022 +# Last Modified By : jno + +from collections.abc import Iterable, Sequence +from typing import Any + +import numpy as np +from astropy.table import Table + +from ampel.abstract.AbsT2Tabulator import AbsT2Tabulator +from ampel.content.DataPoint import DataPoint +from ampel.types import StockId +from ampel.util.collections import ampel_iter + +# from ampel.ztf.util.ZTFIdMapper import ZTFIdMapper + +ZTF_BANDPASSES = { + 1: {"name": "ztfg"}, + 2: {"name": "ztfr"}, + 3: {"name": "ztfi"}, +} + + +class ZTFFPTabulator(AbsT2Tabulator): + reject_outlier_sigma: float = ( + 10**30 + ) # Immediately reject flux outliers beyond. 0 means none + flux_max: float = 10**30 # Cut flux above this 0 means none + inclusion_sigma: float = ( + 0 # Only include datapoints with pull detection above this limit + ) + + def filter_detections(self, dps: Iterable[DataPoint]) -> Iterable[DataPoint]: + return [dp for dp in dps if "ZTF_FP" in dp["tag"] and "flux" in dp["body"]] + + def get_flux_table( + self, + dps: Iterable[DataPoint], + ) -> Table: + flux, fluxerr, jd, fids = self.get_values( + self.filter_detections(dps), ["flux", "flux_unc", "jd", "fid"] + ) + filter_names = [ZTF_BANDPASSES[fid]["name"] for fid in fids] + + # Mask data + bMask = ( + ((np.abs(flux) / fluxerr) < self.reject_outlier_sigma) + & ((np.abs(flux) / fluxerr) > self.inclusion_sigma) + & (np.abs(flux) < self.flux_max) + ) + + return Table( + { + "time": jd, + "flux": flux, + "fluxerr": fluxerr, + "band": filter_names, + "zp": [25] * len(filter_names), + "zpsys": ["ab"] * len(filter_names), + # FP also contain the zpdiff field - should this be used here, as value or to scale flux values? + }, + dtype=("float64", "float64", "float64", "str", "int64", "str"), + )[bMask] + + def get_positions( + self, dps: Iterable[DataPoint] + ) -> Sequence[tuple[float, float, float]]: + det_dps = self.filter_detections(dps) + return tuple( + zip( + self.get_jd(det_dps), + *self.get_values(det_dps, ["ra", "dec"]), + strict=False, + ) + ) + + def get_jd( + self, + dps: Iterable[DataPoint], + ) -> Sequence[Any]: + return self.get_values(dps, ["jd"])[0] + + def get_stock_id(self, dps: Iterable[DataPoint]) -> set[StockId]: + return set( + stockid + for el in dps + if "ZTF" in el["tag"] + for stockid in ampel_iter(el["stock"]) + ) + + def get_stock_name(self, dps: Iterable[DataPoint]) -> list[str]: + """ + Methodology for relating FP request ID to official ZTF names not implemented. + Could be here? + Now just converting to strings ("name") + """ + # return [ZTFIdMapper.to_ext_id(el) for el in self.get_stock_id(dps)] + return [str(v) for v in self.get_stock_id(dps)] + + @staticmethod + def get_values( + dps: Iterable[DataPoint], params: Sequence[str] + ) -> tuple[Sequence[Any], ...]: + if tup := tuple( + map( + list, + zip( + *( + [el["body"][param] for param in params] + for el in dps + if "ZTF_FP" in el["tag"] + ), + strict=False, + ), + ) + ): + return tup + + return tuple([[]] * len(params)) diff --git a/conf/ampel-ztf/ampel.yml b/conf/ampel-ztf/ampel.yml index 24eb04dc..fdf16ea0 100644 --- a/conf/ampel-ztf/ampel.yml +++ b/conf/ampel-ztf/ampel.yml @@ -35,8 +35,12 @@ unit: - ampel.ztf.alert.HealpixPathSupplier - ampel.ztf.alert.ZiAlertSupplier - ampel.ztf.alert.ZiTaggedAlertSupplier + - ampel.ztf.alert.ZTFForcedPhotometryAlertSupplier - ampel.ztf.alert.ZTFFPbotForcedPhotometryAlertSupplier +- ampel.ztf.alert.ZTFIPACForcedPhotometryAlertSupplier +- ampel.ztf.alert.BTSForcedPhotometryAlertSupplier + - ampel.ztf.t0.load.UWAlertLoader - ampel.ztf.t0.load.ZTFArchiveAlertLoader - ampel.ztf.util.ZTFIdMapper @@ -46,6 +50,7 @@ unit: - ampel.ztf.alert.ZiHealpixAlertSupplier - ampel.ztf.view.ZTFT2Tabulator +- ampel.ztf.view.ZTFFPTabulator alias: t2: diff --git a/poetry.lock b/poetry.lock index eef81dba..a7a05e97 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,99 +1,99 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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