def _on_query(self, eruption_jd) -> DataFrame:
# Make sure we work with a copy - we don't want to modify the underlying data
df = self._data.copy()
df = df.query("rate_err == rate_err").query("rate_err > 0")
print(
f"\tafter filtering out rate_err is NaN or 0, {len(df)} rows left")
# We create the standard day and day_err fields, relative to passed eruption jd
if "jd" in df.columns:
df["day"] = tm.delta_t_from_jd(df["jd"], eruption_jd)
df["day_err"] = df["jd_plus_err"]
elif "mjd" in df.columns:
df["day"] = tm.delta_t_from_jd(tm.jd_from_mjd(df["mjd"]),
eruption_jd)
df["day_err"] = df["mjd_plus_err"]
return df
def _calculate_shift_factor(self, ix: int, spectrum: Spectrum1DEx) -> float:
shift_factor = 0
if self.y_shift != 0:
if self.y_shift_by_delta_t:
mjd = spectrum.mjd
delta_t = tm.delta_t_from_jd(tm.jd_from_mjd(mjd), self.eruption_jd)
shift_factor = self.y_shift * delta_t
else:
shift_factor = self.y_shift * ix
return shift_factor
def _draw_plot_data(self, ax: Axes, **kwargs):
# The payload should be spectral line fitted models containing Gaussian fits to lines. It's a dictionary keyed
# on spec_name (b_e_20190828_3 etc.). Each item is an array of astropy CompoundModels for each spectral line.
# Each model will have 1+ Gaussian1D models for the line and 1 Polynomial1D for the continuum.
# Compound models named H$\\alpha$ etc. Each Gaussian fit$_{1}$ (wide), fit$_{2}$ (narrow)
spectra = kwargs["spectra"]
all_line_fits = kwargs["line_fits"]
reference_jd = self.eruption_jd
# The data is in a slightly awkward form, dicts keyed on spectrum with each item an array of line_fits (as we
# would generally be interested in one spectrum & associated data at a time). Best to transform into a
# more usable form; an array of dictionaries which can be loaded into a DataFrame / tabular data
rows = list()
columns = ["line", "fit", "delta_t", "velocity", "velocity_err"]
for spec_key, line_fits in all_line_fits.items():
mjd = spectra[spec_key].mjd if spec_key in spectra else None
delta_t = tm.delta_t_from_jd(tm.jd_from_mjd(mjd), reference_jd=reference_jd)
for line_fit in line_fits:
if line_fit.name in self.lines:
for sub_fit in line_fit:
if sub_fit.name in self.lines[line_fit.name] and isinstance(sub_fit, Gaussian1D):
lambda_0 = sub_fit.mean.quantity
v = fu.calculate_velocity_from_sigma(lambda_0, sub_fit.fwhm).to("km / s")
v_err = 0 * v.unit # TODO: uncertainty
rows.append({
"line": line_fit.name.replace("\\", "_"),
"fit": sub_fit.name.replace("\\", "_"),
"delta_t": delta_t,
"velocity": v.value,
"velocity_err": v_err.value
})
df = DataFrame.from_records(rows, columns=columns)
# The line name / fit name will be used to look up the corresponding columns
for line_name, line in self.lines.items():
line_field = line_name.replace("\\", "_")
for fit_name in line:
fit_plot_params = line[fit_name]
color = fit_plot_params["color"] if "color" in fit_plot_params else "k"
label = f"{line_name} {fit_plot_params['label'] if 'label' in fit_plot_params else fit_name}"
fit_field = fit_name.replace("\\", "_")
df_line = df.query(f"line == '{line_field}' and fit == '{fit_field}'").sort_values(by="delta_t")
if len(df_line) > 0:
self._plot_points_to_error_bars_on_ax(ax, x_points=df_line["delta_t"],
y_points=df_line["velocity"],
y_err_points=df_line["velocity_err"],
color=color, label=label)
self._plot_points_to_lines_on_ax(ax, x_points=df_line["delta_t"],
y_points=df_line["velocity"],
color=color, line_style="--", alpha=0.3)
return
def _on_query(self, eruption_jd: float) -> DataFrame:
"""
Return standard magnitude fields for use by a magnitude query.
"""
# Make sure we are working with a copy of the underlying data - we don't want to change the source
df = self._data.copy()
# Apply any filters; the two on mag_err exclude those rows where mag_err is NaN and 0 (both no use to us)
df = df.query("mag_err == mag_err").query("mag_err > 0").query(
"is_null_obs == False")
print(
f"\tafter filtering on mag_err is NaN or 0 and is_null_obs == True, {len(df)} rows left"
)
# We create day and log(day) field, relative to passed eruption jd
df['day'] = tm.delta_t_from_jd(df['jd'], eruption_jd)
df['log_day'] = np.log10(df.query("day>0")['day'])
return df
for plot_config in settings["plots"][plot_group_config]:
spectra = {}
spectral_lines = {}
plot_line_fits = {}
delta_t = None
# Each plot will generally have 2 spectra (blue arm and red arm)
for spec_match in plot_config["spectra"]:
# Get all the data source whose name start with the key value - mostly each individually specified
filtered_data_sources = {
k: v
for k, v in data_sources.items() if k.startswith(spec_match)
}
for spec_name, ds in filtered_data_sources.items():
delta_t = tm.delta_t_from_jd(tm.jd_from_mjd(ds.header["MJD"]),
eruption_jd)
spectrum = ds.query()
print(
f"\tUsing spectrum '{spec_name}': Delta-t={delta_t:.2f} & max_flux={spectrum.max_flux}"
)
spectra[spec_name] = spectrum
flux_units = spectrum.flux.unit
if "line_fits" in plot_config:
# Pick up any fitted spectral lines configured
for fit_match in plot_config["line_fits"]:
plot_line_fits.update({
k: v
for k, v in line_fit_sets.items()
if k.startswith(fit_match)
})
def _get_spectrum_delta_t(self, spectrum: Spectrum1DEx) -> float:
mjd = spectrum.mjd
return tm.delta_t_from_jd(tm.jd_from_mjd(mjd), self.eruption_jd)
def get_spectra_epochs(eruption_jd: float) -> Dict[str, float]:
"""
Gets the timing of the LT Spectra for the 2019 eruption as an epochs dictionary.
"""
return {
"b_e_20190828_11": tm.delta_t_from_jd(2458724.408415, eruption_jd),
"b_e_20190828_3": tm.delta_t_from_jd(2458724.358298, eruption_jd),
"b_e_20190830_5": tm.delta_t_from_jd(2458726.358392, eruption_jd),
"b_e_20190831_11": tm.delta_t_from_jd(2458727.433088, eruption_jd),
"b_e_20190831_5": tm.delta_t_from_jd(2458727.354917, eruption_jd),
"b_e_20190901_11": tm.delta_t_from_jd(2458728.430003, eruption_jd),
"b_e_20190901_5": tm.delta_t_from_jd(2458728.356852, eruption_jd),
"b_e_20190902_5": tm.delta_t_from_jd(2458729.353593, eruption_jd),
"b_e_20190902_7": tm.delta_t_from_jd(2458729.419832, eruption_jd),
"b_e_20190903_5": tm.delta_t_from_jd(2458730.363364, eruption_jd),
"b_e_20190903_7": tm.delta_t_from_jd(2458730.42483, eruption_jd),
"b_e_20190904_4": tm.delta_t_from_jd(2458731.363214, eruption_jd),
"b_e_20190905_5": tm.delta_t_from_jd(2458732.351202, eruption_jd),
"b_e_20190905_7": tm.delta_t_from_jd(2458732.431063, eruption_jd),
"b_e_20190910_1": tm.delta_t_from_jd(2458737.355612, eruption_jd),
"b_e_20190911_5": tm.delta_t_from_jd(2458738.346323, eruption_jd),
"b_e_20190911_7": tm.delta_t_from_jd(2458738.410921, eruption_jd),
"b_e_20190913_5": tm.delta_t_from_jd(2458740.345966, eruption_jd),
"b_e_20190913_7": tm.delta_t_from_jd(2458740.406875, eruption_jd),
"b_e_20190915_5": tm.delta_t_from_jd(2458742.344409, eruption_jd),
"r_e_20190828_11": tm.delta_t_from_jd(2458724.408344, eruption_jd),
"r_e_20190828_3": tm.delta_t_from_jd(2458724.358227, eruption_jd),
"r_e_20190830_5": tm.delta_t_from_jd(2458726.358327, eruption_jd),
"r_e_20190831_11": tm.delta_t_from_jd(2458727.433159, eruption_jd),
"r_e_20190831_5": tm.delta_t_from_jd(2458727.354847, eruption_jd),
"r_e_20190901_11": tm.delta_t_from_jd(2458728.430132, eruption_jd),
"r_e_20190901_5": tm.delta_t_from_jd(2458728.356769, eruption_jd),
"r_e_20190902_5": tm.delta_t_from_jd(2458729.353522, eruption_jd),
"r_e_20190902_7": tm.delta_t_from_jd(2458729.419905, eruption_jd),
"r_e_20190903_5": tm.delta_t_from_jd(2458730.363294, eruption_jd),
"r_e_20190903_7": tm.delta_t_from_jd(2458730.42476, eruption_jd),
"r_e_20190904_4": tm.delta_t_from_jd(2458731.363149, eruption_jd),
"r_e_20190905_5": tm.delta_t_from_jd(2458732.351137, eruption_jd),
"r_e_20190905_7": tm.delta_t_from_jd(2458732.431134, eruption_jd),
"r_e_20190910_1": tm.delta_t_from_jd(2458737.355542, eruption_jd),
"r_e_20190911_5": tm.delta_t_from_jd(2458738.346253, eruption_jd),
"r_e_20190911_7": tm.delta_t_from_jd(2458738.410851, eruption_jd),
"r_e_20190913_5": tm.delta_t_from_jd(2458740.345901, eruption_jd),
"r_e_20190913_7": tm.delta_t_from_jd(2458740.406804, eruption_jd),
"r_e_20190915_5": tm.delta_t_from_jd(2458742.344338, eruption_jd),
}