From 2ccf22d774cc1f65626f62bf2c2d84f396872099 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 15 Jul 2025 12:57:25 +0100 Subject: [PATCH 01/76] Added "Spectrum" to ``__all__` in ``specreduce.compat``. --- specreduce/compat.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/specreduce/compat.py b/specreduce/compat.py index 8d9b42fd..274098d6 100644 --- a/specreduce/compat.py +++ b/specreduce/compat.py @@ -1,7 +1,7 @@ import specutils from astropy.utils import minversion -__all__ = [] +__all__ = ["Spectrum"] SPECUTILS_LT_2 = not minversion(specutils, "2.0.dev") From 705d232d72282c2d66ffe770c2dca5476ce96895 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Mon, 13 Jan 2025 16:51:13 -0500 Subject: [PATCH 02/76] Added a new approach to 1D wavelength calibration based on minimizing a sum of nearest-line distances between a theoretical line list and a transformed observed line list. --- specreduce/wavecal.py | 167 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 167 insertions(+) create mode 100644 specreduce/wavecal.py diff --git a/specreduce/wavecal.py b/specreduce/wavecal.py new file mode 100644 index 00000000..d5e289be --- /dev/null +++ b/specreduce/wavecal.py @@ -0,0 +1,167 @@ +import warnings +from typing import Iterable + +import astropy.units as u +import numpy as np +from astropy.modeling import models, Model, fitting +from gwcs import coordinate_frames as cf +from gwcs import wcs +from matplotlib.pyplot import setp, subplots +from scipy import optimize +from scipy.spatial import KDTree + +from numpy import ndarray + +from specreduce.calibration_data import load_pypeit_calibration_lines +from specreduce.line_matching import find_arc_lines + + +class WavelengthSolution1D: + def __init__(self, arc_spectra, lamps, wlbounds: tuple[float, float], wave_air: bool = False): + self.arc_spectra = arc_spectra + self.lamps = lamps + self.wlbounds = wlbounds + self.wave_air: bool = wave_air + self.linelists: list | None = [] + self.lines_pix: ndarray | None = None + self.lines_wav: ndarray | None = None + self._fitted_model: Model | None = None + self._fit: optimize.OptimizeResult | None = None + self._wcs: wcs.WCS | None = None + self._read_linelists() + + self._p2w: Model | None = None # The fitted pixel -> wavelength model + self._w2p: Model | None = None # The fitted wavelength -> pixel model + self._p2w_dldx: Model | None = None # delta lambda / delta pixel + self._w2p_dxdl: Model | None = None # delta pixel / delta lambda + + def _read_linelists(self): + for l in self.lamps: + ll = load_pypeit_calibration_lines(l, wave_air=self.wave_air) + self.linelists.append(ll[(ll['wavelength'].value > self.wlbounds[0]) & + (ll['wavelength'].value < self.wlbounds[1])]) + self.lines_wav = [lo['wavelength'].value for lo in self.linelists] + + def find_lines(self): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + lines_obs = [find_arc_lines(sp, 5) for sp in self.arc_spectra] + self.lines_pix = [lo['centroid'].value for lo in lines_obs] + + def fit(self, ref_pixel: float, wl0: tuple[float, float] = (7000, 7600), dwl: tuple[float, float] = (2.4, 2.8), + popsize: int = 30, max_distance: float = 100): + + model = models.Shift(-ref_pixel) | models.Polynomial1D(3) + trees = [KDTree(l[:, None]) for l in self.lines_wav] + + def minfun(x): + return sum( + [np.clip(t.query(model.evaluate(l, -ref_pixel, *x)[:, None])[0], 0, max_distance).sum() for t, l in + zip(trees, self.lines_pix)]) + + # Calculate the pixel -> wavelength transform and its derivative along the spectral axis + self._fit = fit = optimize.differential_evolution(minfun, [wl0, dwl, [-1e-3, 1e-3], [-1e-4, 1e-4]], + popsize=popsize) + self._p2w = p2w = models.Shift(-ref_pixel) | models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in + range(fit.x.size)}) + self._p2w_dldx = models.Shift(p2w.offset_0) | models.Polynomial1D(2, c0=p2w.c1_1, c1=2*p2w.c2_1, + c2=3*p2w.c3_1) + + # Calculate the wavelength -> pixel transform and its derivative along the spectral axis + vpix = self.arc_spectra[0].spectral_axis.value + vwav = p2w(vpix) + w2p = models.Polynomial1D(7, c0=-p2w.offset_0, c1=1 / p2w.c1_1, fixed={'c0': True}) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + self._w2p = w2p = models.Shift(-p2w.c0_1) | fitting.LMLSQFitter()(w2p, vwav - p2w.c0_1.value, vpix) + self._w2p_dxdl = models.Shift(w2p.offset_0) | models.Polynomial1D(6, c0=w2p.c1_1, c1=2 * w2p.c2_1, + c2=3 * w2p.c3_1, + c3=4 * w2p.c4_1, c4=5 * w2p.c5_1, + c5=6 * w2p.c6_1, c6=7 * w2p.c7_1) + + def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, + bin_edges: Iterable[float] | None = None): + wlbounds = self._p2w([0, flux.size]) if wlbounds is None else wlbounds + npix = flux.size + nbins = npix if nbins is None else nbins + bin_edges_wav = bin_edges if bin_edges is not None else np.linspace(*wlbounds, num=nbins + 1) + bin_edges_pix = np.clip(self._w2p(bin_edges_wav) + 0.5, 0, npix - 1e-12) + bin_edge_ix = np.floor(bin_edges_pix).astype(int) + bin_edge_w = bin_edges_pix - bin_edge_ix + bin_centers_wav = 0.5 * (bin_edges_wav[:-1] + bin_edges_wav[1:]) + flux_wl = np.zeros(nbins) + weights = np.zeros(flux.size) + + dldx = self._p2w_dldx(np.arange(npix)) + n = flux.sum() / (dldx * flux).sum() + + for i in range(nbins): + i1, i2 = bin_edge_ix[i:i + 2] + weights[:] = 0 + if i1 != i2: + weights[i1 + 1:i2] = 1.0 + weights[i1] = 1 - bin_edge_w[i] + weights[i2] = bin_edge_w[i + 1] + flux_wl[i] = (weights[i1:i2 + 1] * flux[i1:i2 + 1] * dldx[i1:i2 + 1]).sum() + else: + flux_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * flux[i1] * dldx[i1] + flux_wl *= n + return bin_centers_wav, flux_wl + + @property + def wcs(self): + pixel_frame = cf.CoordinateFrame(1, "SPECTRAL", [0, ], axes_names=["x", ], unit=[u.pix]) + spectral_frame = cf.SpectralFrame(axes_names=["wavelength", ], unit=[self.linelists[0]['wavelength'].unit]) + pipeline = [(pixel_frame, self.fitted_model), (spectral_frame, None)] + self._wcs = wcs.WCS(pipeline) + return self._wcs + + @property + def fitted_model(self): + return self._p2w + + def plot_lines(self, axes=None, figsize=None): + if axes is None: + fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True) + else: + fig = axes[0].figure + for i, sp in enumerate(self.arc_spectra): + axes[i].plot(sp.data / (1.2 * sp.data.max())) + axes[i].vlines(self.lines_pix[i], 0.0, 1, alpha=0.1) + axes[i].vlines(self.lines_pix[i], 0.9, 1) + axes[i].autoscale(enable=True, axis='x', tight=True) + setp(axes[-1], xlabel='Pixel') + return fig + + def plot_solution(self, axes=None, figsize=None): + if axes is None: + fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True) + else: + fig = axes[0].figure + + for i, sp in enumerate(self.arc_spectra): + axes[i].plot(self.fitted_model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) + axes[i].vlines(self.lines_wav[i], 0.0, 1.0, alpha=0.3, ec='darkorange', zorder=0) + axes[i].vlines(self.fitted_model(self.lines_pix[i]), 0.9, 1.0, alpha=1) + axes[i].autoscale(enable=True, axis='x', tight=True) + setp(axes[-1], xlabel=f'Wavelength [{self.linelists[0]["wavelength"].unit.to_string(format="latex")}]') + return fig + + def plot_transforms(self, figsize=None): + fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex='col') + xpix = self.arc_spectra[0].spectral_axis.value + xwav = np.linspace(5000, 11000, 2000) + axs[0, 0].plot(xpix, self._p2w(xpix), 'k') + axs[1, 0].plot(xpix[:-1], np.diff(self._p2w(xpix)) / np.diff(xpix), lw=4, c='k') + axs[1, 0].plot(xpix, self._p2w_dldx(xpix), ls='--', lw=2, c='w') + axs[0, 1].plot(xwav, self._w2p(xwav), 'k') + axs[1, 1].plot(xwav[:-1], np.diff(self._w2p(xwav)) / np.diff(xwav), lw=4, c='k') + axs[1, 1].plot(xwav, self._w2p_dxdl(xwav), ls='--', lw=2, c='w') + setp(axs[1,0], xlabel='Pixel', ylabel=r'd$\lambda$/dx') + setp(axs[0,0], ylabel=fr'$\lambda$ [{self.linelists[0]["wavelength"].unit}]') + setp(axs[1,1], xlabel=fr'$\lambda$ [{self.linelists[0]["wavelength"].unit}]', ylabel=r'dx/d$\lambda$') + setp(axs[0,1], ylabel='Pixel') + axs[0,0].set_title('Pixel -> wavelength') + axs[0,1].set_title('Wavelength -> pixel') + fig.align_labels() + return fig \ No newline at end of file From f63fab88a19eefa217a58a87f1c341080b08b0f7 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Mon, 13 Jan 2025 18:19:46 -0500 Subject: [PATCH 03/76] Fixed a minor 1D wavelength solution rebinning issue. --- specreduce/wavecal.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/specreduce/wavecal.py b/specreduce/wavecal.py index d5e289be..7fb1c430 100644 --- a/specreduce/wavecal.py +++ b/specreduce/wavecal.py @@ -81,20 +81,24 @@ def minfun(x): def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, bin_edges: Iterable[float] | None = None): - wlbounds = self._p2w([0, flux.size]) if wlbounds is None else wlbounds npix = flux.size nbins = npix if nbins is None else nbins - bin_edges_wav = bin_edges if bin_edges is not None else np.linspace(*wlbounds, num=nbins + 1) + if wlbounds is None: + l1 = self._p2w(0) - self._p2w_dldx(0) + l2 = self._p2w(npix) + self._p2w_dldx(npix) + else: + l1, l2 = wlbounds + + bin_edges_wav = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) bin_edges_pix = np.clip(self._w2p(bin_edges_wav) + 0.5, 0, npix - 1e-12) bin_edge_ix = np.floor(bin_edges_pix).astype(int) bin_edge_w = bin_edges_pix - bin_edge_ix bin_centers_wav = 0.5 * (bin_edges_wav[:-1] + bin_edges_wav[1:]) flux_wl = np.zeros(nbins) - weights = np.zeros(flux.size) + weights = np.zeros(npix) dldx = self._p2w_dldx(np.arange(npix)) n = flux.sum() / (dldx * flux).sum() - for i in range(nbins): i1, i2 = bin_edge_ix[i:i + 2] weights[:] = 0 From 885f0ef47ed42ee254044b9523339bda3737a0af Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 14 Jan 2025 15:04:45 -0500 Subject: [PATCH 04/76] Added utility functions to differentiate 1d and 2d polynomial models with respect to x. --- specreduce/wavecal.py | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/specreduce/wavecal.py b/specreduce/wavecal.py index 7fb1c430..a0304769 100644 --- a/specreduce/wavecal.py +++ b/specreduce/wavecal.py @@ -16,6 +16,20 @@ from specreduce.line_matching import find_arc_lines +def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: + coeffs = {f'c{i-1}': i*getattr(m, f'c{i}').value for i in range(1, m.degree+1)} + return models.Polynomial1D(m.degree-1, **coeffs) + + +def diff_poly2d_x(m): + coeffs = {} + for n in m.param_names: + ix, iy = int(n[1]), int(n[3]) + if ix > 0: + coeffs[f"c{ix-1}_{iy}"] = ix*getattr(m, n).value + return models.Polynomial2D(m.degree-1, **coeffs) + + class WavelengthSolution1D: def __init__(self, arc_spectra, lamps, wlbounds: tuple[float, float], wave_air: bool = False): self.arc_spectra = arc_spectra @@ -60,12 +74,12 @@ def minfun(x): zip(trees, self.lines_pix)]) # Calculate the pixel -> wavelength transform and its derivative along the spectral axis - self._fit = fit = optimize.differential_evolution(minfun, [wl0, dwl, [-1e-3, 1e-3], [-1e-4, 1e-4]], + self._fit = fit = optimize.differential_evolution(minfun, + bounds=[wl0, dwl, [-1e-3, 1e-3], [-1e-4, 1e-4]], popsize=popsize) self._p2w = p2w = models.Shift(-ref_pixel) | models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in range(fit.x.size)}) - self._p2w_dldx = models.Shift(p2w.offset_0) | models.Polynomial1D(2, c0=p2w.c1_1, c1=2*p2w.c2_1, - c2=3*p2w.c3_1) + self._p2w_dldx = models.Shift(p2w.offset_0) | diff_poly1d(p2w[1]) # Calculate the wavelength -> pixel transform and its derivative along the spectral axis vpix = self.arc_spectra[0].spectral_axis.value @@ -74,10 +88,7 @@ def minfun(x): with warnings.catch_warnings(): warnings.simplefilter('ignore') self._w2p = w2p = models.Shift(-p2w.c0_1) | fitting.LMLSQFitter()(w2p, vwav - p2w.c0_1.value, vpix) - self._w2p_dxdl = models.Shift(w2p.offset_0) | models.Polynomial1D(6, c0=w2p.c1_1, c1=2 * w2p.c2_1, - c2=3 * w2p.c3_1, - c3=4 * w2p.c4_1, c4=5 * w2p.c5_1, - c5=6 * w2p.c6_1, c6=7 * w2p.c7_1) + self._w2p_dxdl = models.Shift(w2p.offset_0) | diff_poly1d(w2p[1]) def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, bin_edges: Iterable[float] | None = None): From 82d1e0098013f71e23f664e092ea5839b627c838 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Wed, 15 Jan 2025 14:23:18 -0500 Subject: [PATCH 05/76] Renamed and refactored the 1D wavelength calibration module. Renamed `wavecal.py` to `lswavecal1d.py` and refactored the `WavelengthSolution1D` class for improved clarity and functionality. Added new methods like `refine_fit`, `pix_to_wav`, and `wav_to_pix`, and modified existing ones to enhance flexibility and maintainability. --- specreduce/{wavecal.py => lswavecal1d.py} | 81 ++++++++++++++--------- 1 file changed, 49 insertions(+), 32 deletions(-) rename specreduce/{wavecal.py => lswavecal1d.py} (72%) diff --git a/specreduce/wavecal.py b/specreduce/lswavecal1d.py similarity index 72% rename from specreduce/wavecal.py rename to specreduce/lswavecal1d.py index a0304769..3e89ad13 100644 --- a/specreduce/wavecal.py +++ b/specreduce/lswavecal1d.py @@ -21,15 +21,6 @@ def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: return models.Polynomial1D(m.degree-1, **coeffs) -def diff_poly2d_x(m): - coeffs = {} - for n in m.param_names: - ix, iy = int(n[1]), int(n[3]) - if ix > 0: - coeffs[f"c{ix-1}_{iy}"] = ix*getattr(m, n).value - return models.Polynomial2D(m.degree-1, **coeffs) - - class WavelengthSolution1D: def __init__(self, arc_spectra, lamps, wlbounds: tuple[float, float], wave_air: bool = False): self.arc_spectra = arc_spectra @@ -42,54 +33,62 @@ def __init__(self, arc_spectra, lamps, wlbounds: tuple[float, float], wave_air: self._fitted_model: Model | None = None self._fit: optimize.OptimizeResult | None = None self._wcs: wcs.WCS | None = None - self._read_linelists() - + self._trees: Iterable[KDTree] | None = None self._p2w: Model | None = None # The fitted pixel -> wavelength model self._w2p: Model | None = None # The fitted wavelength -> pixel model self._p2w_dldx: Model | None = None # delta lambda / delta pixel self._w2p_dxdl: Model | None = None # delta pixel / delta lambda + self._read_linelists() + def _read_linelists(self): for l in self.lamps: ll = load_pypeit_calibration_lines(l, wave_air=self.wave_air) self.linelists.append(ll[(ll['wavelength'].value > self.wlbounds[0]) & (ll['wavelength'].value < self.wlbounds[1])]) self.lines_wav = [lo['wavelength'].value for lo in self.linelists] + self._trees = [KDTree(l[:, None]) for l in self.lines_wav] - def find_lines(self): + def find_lines(self, fwhm: float): with warnings.catch_warnings(): warnings.simplefilter('ignore') - lines_obs = [find_arc_lines(sp, 5) for sp in self.arc_spectra] + lines_obs = [find_arc_lines(sp, fwhm) for sp in self.arc_spectra] self.lines_pix = [lo['centroid'].value for lo in lines_obs] - def fit(self, ref_pixel: float, wl0: tuple[float, float] = (7000, 7600), dwl: tuple[float, float] = (2.4, 2.8), + def fit(self, ref_pixel: float, wl0: tuple[float, float], dwl: tuple[float, float], popsize: int = 30, max_distance: float = 100): model = models.Shift(-ref_pixel) | models.Polynomial1D(3) - trees = [KDTree(l[:, None]) for l in self.lines_wav] - def minfun(x): return sum( [np.clip(t.query(model.evaluate(l, -ref_pixel, *x)[:, None])[0], 0, max_distance).sum() for t, l in - zip(trees, self.lines_pix)]) + zip(self._trees, self.lines_pix)]) # Calculate the pixel -> wavelength transform and its derivative along the spectral axis self._fit = fit = optimize.differential_evolution(minfun, bounds=[wl0, dwl, [-1e-3, 1e-3], [-1e-4, 1e-4]], popsize=popsize) - self._p2w = p2w = models.Shift(-ref_pixel) | models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in + self._p2w = models.Shift(-ref_pixel) | models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in range(fit.x.size)}) + self.refine_fit() + p2w = self._p2w self._p2w_dldx = models.Shift(p2w.offset_0) | diff_poly1d(p2w[1]) # Calculate the wavelength -> pixel transform and its derivative along the spectral axis vpix = self.arc_spectra[0].spectral_axis.value vwav = p2w(vpix) - w2p = models.Polynomial1D(7, c0=-p2w.offset_0, c1=1 / p2w.c1_1, fixed={'c0': True}) + w2p = models.Polynomial1D(6, c0=-p2w.offset_0, c1=1 / p2w.c1_1, fixed={'c0': True}) with warnings.catch_warnings(): warnings.simplefilter('ignore') - self._w2p = w2p = models.Shift(-p2w.c0_1) | fitting.LMLSQFitter()(w2p, vwav - p2w.c0_1.value, vpix) + self._w2p = w2p = models.Shift(-p2w.c0_1) | fitting.DogBoxLSQFitter()(w2p, vwav - p2w.c0_1.value, vpix) self._w2p_dxdl = models.Shift(w2p.offset_0) | diff_poly1d(w2p[1]) + def refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + matched_pix, matched_wav = self.match_lines(match_distance_bound) + model = models.Polynomial1D(degree, **{n: getattr(self._p2w[-1], n).value for n in self._p2w[-1].param_names}) + fitter = fitting.LinearLSQFitter() + self._p2w = self._p2w[0].copy() | fitter(model, matched_pix + self._p2w.offset_0.value, matched_wav) + def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, bin_edges: Iterable[float] | None = None): npix = flux.size @@ -123,6 +122,12 @@ def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] flux_wl *= n return bin_centers_wav, flux_wl + def pix_to_wav(self, pix: ndarray |float) -> ndarray | float: + return self._p2w(pix) + + def wav_to_pix(self, wav: ndarray | float) -> ndarray | float: + return self._w2p(wav) + @property def wcs(self): pixel_frame = cf.CoordinateFrame(1, "SPECTRAL", [0, ], axes_names=["x", ], unit=[u.pix]) @@ -135,37 +140,49 @@ def wcs(self): def fitted_model(self): return self._p2w + def match_lines(self, upper_bound: float = 5): + matched_lines_wav = [] + matched_lines_pix = [] + for iframe, tree in enumerate(self._trees): + l, ix = tree.query(self._p2w(self.lines_pix[iframe])[:, None], distance_upper_bound=upper_bound) + m = np.isfinite(l) + matched_lines_wav.append(tree.data[ix[m], 0]) + matched_lines_pix.append(self.lines_pix[iframe][m]) + return np.concatenate(matched_lines_pix), np.concatenate(matched_lines_wav) + def plot_lines(self, axes=None, figsize=None): if axes is None: - fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True) + fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True, squeeze=False) else: fig = axes[0].figure for i, sp in enumerate(self.arc_spectra): - axes[i].plot(sp.data / (1.2 * sp.data.max())) - axes[i].vlines(self.lines_pix[i], 0.0, 1, alpha=0.1) - axes[i].vlines(self.lines_pix[i], 0.9, 1) - axes[i].autoscale(enable=True, axis='x', tight=True) + axes[i,0].plot(sp.data / (1.2 * sp.data.max())) + axes[i,0].vlines(self.lines_pix[i], 0.0, 1, alpha=0.1) + axes[i,0].vlines(self.lines_pix[i], 0.9, 1) + axes[i,0].autoscale(enable=True, axis='x', tight=True) setp(axes[-1], xlabel='Pixel') return fig - def plot_solution(self, axes=None, figsize=None): + def plot_solution(self, axes=None, figsize=None, model = None): if axes is None: - fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True) + fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True, squeeze=False) else: fig = axes[0].figure + model = model if model is not None else self.fitted_model + for i, sp in enumerate(self.arc_spectra): - axes[i].plot(self.fitted_model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) - axes[i].vlines(self.lines_wav[i], 0.0, 1.0, alpha=0.3, ec='darkorange', zorder=0) - axes[i].vlines(self.fitted_model(self.lines_pix[i]), 0.9, 1.0, alpha=1) - axes[i].autoscale(enable=True, axis='x', tight=True) + axes[i,0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) + axes[i,0].vlines(self.lines_wav[i], 0.0, 1.0, alpha=0.3, ec='darkorange', zorder=0) + axes[i,0].vlines(model(self.lines_pix[i]), 0.9, 1.0, alpha=1) + axes[i,0].autoscale(enable=True, axis='x', tight=True) setp(axes[-1], xlabel=f'Wavelength [{self.linelists[0]["wavelength"].unit.to_string(format="latex")}]') return fig def plot_transforms(self, figsize=None): fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex='col') xpix = self.arc_spectra[0].spectral_axis.value - xwav = np.linspace(5000, 11000, 2000) + xwav = np.linspace(*self.pix_to_wav(xpix[[0, -1]]), num=xpix.size) axs[0, 0].plot(xpix, self._p2w(xpix), 'k') axs[1, 0].plot(xpix[:-1], np.diff(self._p2w(xpix)) / np.diff(xpix), lw=4, c='k') axs[1, 0].plot(xpix, self._p2w_dldx(xpix), ls='--', lw=2, c='w') From 0c8f574dcb5d5e46d60790d298e11f2f9e04c295 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Wed, 15 Jan 2025 14:23:52 -0500 Subject: [PATCH 06/76] Added 2D wavelength calibration class and associated methods. Introduce `WavelengthSolution2D` class for 2D wavelength calibration, extending functionality from 1D calibration. --- specreduce/lswavecal2d.py | 230 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 230 insertions(+) create mode 100644 specreduce/lswavecal2d.py diff --git a/specreduce/lswavecal2d.py b/specreduce/lswavecal2d.py new file mode 100644 index 00000000..c6e9b206 --- /dev/null +++ b/specreduce/lswavecal2d.py @@ -0,0 +1,230 @@ +import warnings +from typing import Iterable + +import astropy.units as u +import numpy as np +from astropy.modeling import models, Model, fitting +from astropy.nddata import StdDevUncertainty, NDData +from gwcs import coordinate_frames as cf +from gwcs import wcs +from matplotlib import cm +from matplotlib.pyplot import setp, subplots +from numpy.random import uniform +from scipy import optimize +from scipy.spatial import KDTree + +from numpy import ndarray +from specutils import Spectrum1D + +from specreduce.line_matching import find_arc_lines +from specreduce.lswavecal1d import WavelengthSolution1D + + +def diff_poly2d_x(m): + coeffs = {} + for n in m.param_names: + ix, iy = int(n[1]), int(n[3]) + if ix > 0: + coeffs[f"c{ix-1}_{iy}"] = ix*getattr(m, n).value + return models.Polynomial2D(m.degree-1, **coeffs) + + +class WavelengthSolution2D(WavelengthSolution1D): + def __init__(self, frames: Iterable[NDData], lamps: Iterable, wlbounds: tuple[float, float], wave_air: bool = False, + n_cd_samples: int = 10, cd_samples: Iterable[float] | None = None): + super().__init__(None, lamps, wlbounds, wave_air) + self.frames = frames + self.lamps = lamps + + self.lines_pix_x: Iterable[ndarray] | None = None + self.lines_pix_y: Iterable[ndarray] | None = None + self._fitted_model: Model | None = None + self._wcs: wcs.WCS | None = None + + self.nframes = len(frames) + + self._spectra: Iterable[Spectrum1D] | None = None + + if cd_samples is not None: + self.cd_samples = np.array(cd_samples) + else: + self.cd_samples = np.round(np.arange(1, n_cd_samples + 1) * self.frames[0].shape[0] / (n_cd_samples + 1)).astype( + int) + self.ncd = self.cd_samples.size + + self._ref_pixel: tuple[float, float] | None = None + self._shift = None + + def find_lines(self, fwhm: float): + self.spectra = [] + lines_pix_x = [] + lines_pix_y = [] + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + for i, d in enumerate(self.frames): + self.spectra.append([]) + lines_pix_x.append([]) + lines_pix_y.append([]) + for s in self.cd_samples: + spectrum = Spectrum1D((d[s] - np.median(d)) * u.DN, + uncertainty=d[s].uncertainty.represent_as(StdDevUncertainty)) + lines = find_arc_lines(spectrum, fwhm) + lines_pix_x[i].append(lines['centroid'].value) + lines_pix_y[i].append(np.full(len(lines), s)) + self.spectra[i].append(spectrum) + self.lines_pix_x = [np.concatenate(lpx) for lpx in lines_pix_x] + self.lines_pix_y = [np.concatenate(lpy) for lpy in lines_pix_y] + + def fit(self, ref_pixel: tuple[float, float], + wl0: tuple[float, float] = (7000, 7600), dwl: tuple[float, float] = (2.4, 2.8), + popsize: int = 30, max_distance: float = 100, workers: int = 1): + + self._ref_pixel = ref_pixel + self._shift = (models.Shift(-ref_pixel[0]) & models.Shift(-ref_pixel[1])) + model = self._shift | models.Polynomial2D(3) + trees = [KDTree(l[:, None]) for l in self.lines_wav] + + xx = np.zeros(10) + + def minfun(x): + xx[:-3] = x + distance_sum = 0.0 + for j, t in enumerate(trees): + distance_sum += np.clip(t.query( + model.evaluate(self.lines_pix_x[j], self.lines_pix_y[j], -ref_pixel[0], -ref_pixel[1], *xx)[:, + None])[0], + 0, max_distance).sum() + return distance_sum + + bounds = np.array([wl0, dwl, [-1e-3, 1e-3], [-1e-5, 1e-5], [-1e-1, 1e-1], [-1e-4, 1e-4], [-1e-5, 1e-5]]) + res = optimize.differential_evolution(minfun, bounds, popsize=popsize, workers=1, updating='deferred') + self._p2w = (self._shift | + models.Polynomial2D(model[-1].degree, + **{model[-1].param_names[i]: res.x[i] for i in range(res.x.size)})) + self._refine_fit() + self._calculate_inverse() + self._calculate_derivaties() + + def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + mlines_px, mlines_py, mlines_wav = self._match_lines(match_distance_bound) + model = self._shift | models.Polynomial2D(degree, **{n: getattr(self._p2w[-1], n).value for n in self._p2w[-1].param_names}) + model.offset_0.fixed = True + model.offset_1.fixed = True + fitter = fitting.LMLSQFitter() + self._p2w = fitter(model, mlines_px, mlines_py, mlines_wav) + + def _match_lines(self, upper_bound: float = 5): + matched_lines_wav = [] + matched_lines_pix_x = [] + matched_lines_pix_y = [] + for iframe, tree in enumerate(self._trees): + l, ix = tree.query(self._p2w(self.lines_pix_x[iframe], self.lines_pix_y[iframe])[:, None], distance_upper_bound=upper_bound) + m = np.isfinite(l) + matched_lines_wav.append(tree.data[ix[m], 0]) + matched_lines_pix_x.append(self.lines_pix_x[iframe][m]) + matched_lines_pix_y.append(self.lines_pix_y[iframe][m]) + return (np.concatenate(matched_lines_pix_x), + np.concatenate(matched_lines_pix_y), + np.concatenate(matched_lines_wav)) + + def _calculate_inverse(self, nsamples: int = 1500): + m = self._p2w + xx = uniform(0, self.frames[0].shape[1], nsamples) + yy = uniform(0, self.frames[0].shape[0], nsamples) + ll = m(xx, yy) + mm = (models.Shift(-m.c0_0_2) & models.Shift(-self._ref_pixel[1])) | models.Polynomial2D(6, c0_0=-m[0].offset, + c1_0=1 / m[-1].c1_0, + fixed={'c0_0': True}) + mm.offset_0.fixed = True + mm.offset_1.fixed = True + self._w2p = fitting.LMLSQFitter()(mm, ll, yy, xx) + + def _calculate_derivaties(self): + if self._p2w is not None: + self._p2w_dldx = self._shift | diff_poly2d_x(self._p2w[-1]) + if self._w2p is not None: + self._w2p_dxdl = (self._w2p[0] & self._w2p[1]) | diff_poly2d_x(self._w2p[-1]) + + def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, + bin_edges: Iterable[float] | None = None): + ny, nx = flux.shape + ypix = np.arange(ny) + nbins = nx if nbins is None else nbins + if wlbounds is None: + l1 = self._p2w(0, 0) - self._p2w_dldx(0, 0) + l2 = self._p2w(nx, 0) + self._p2w_dldx(nx, 0) + else: + l1, l2 = wlbounds + + bin_edges_wav = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) + bin_edges_pix = np.clip(self._w2p(*np.meshgrid(bin_edges_wav, ypix)) + 0.5, 0, nx - 1e-12) + bin_edge_ix = np.floor(bin_edges_pix).astype(int) + bin_edge_w = bin_edges_pix - bin_edge_ix + bin_centers_wav = 0.5 * (bin_edges_wav[:-1] + bin_edges_wav[1:]) + + flux_wl = np.zeros((ny, nbins)) + weights = np.zeros((ny, nx)) + + dldx = self._p2w_dldx(*np.meshgrid(np.arange(nx), np.arange(ny))) + n = flux.sum(1) / (dldx * flux).sum(1) + + ixs = np.tile(np.arange(flux.shape[1]), (flux.shape[0], 1)) + ys = np.arange(flux.shape[0]) + + for i in range(nbins): + i1, i2 = bin_edge_ix[:, i:i + 2].T + m = i1 == i2 + if m.any(): + flux_wl[:, i] = (bin_edges_pix[:, i + 1] - bin_edges_pix[:, i]) * flux[ys, i1] * dldx[ys, i1] + + if not m.all(): + imin, imax = i1.min(), i2.max() + 1 + ixc = ixs[:, imin: imax] + w = weights[:, imin:imax] + w[:] = 0.0 + w[(ixc > i1[:, None]) & (ixc < i2[:, None])] = 1 + w[ys, i1 - imin] = 1.0 - bin_edge_w[:, i] + w[ys, i2 - imin] = bin_edge_w[:, i + 1] + flux_wl[~m, i] = (flux[~m, imin:imax] * dldx[~m, imin:imax] * w[~m]).sum(1) + flux_wl *= n[:, None] + return bin_centers_wav, flux_wl + + def plot_fit(self, lamp: int, ax=None, figsize=None): + if ax is None: + fig, ax = subplots(figsize=figsize) + else: + fig = ax.figure + + i = 0 + ncd = len(self.cd_samples) + + t = self._trees[lamp] + l, ix = t.query(self._p2w(self.lines_pix_x[lamp], self.lines_pix_y[lamp])[:, None], distance_upper_bound=10) + mask = np.zeros(t.data.size, bool) + mask[ix[np.isfinite(l)]] = True + + ax.vlines(self.lines_wav[lamp][mask], -1.0, 8.0, alpha=1, ec='darkorange', zorder=-1) + ax.vlines(self.lines_wav[lamp][~mask], -1.0, 8.0, alpha=0.1, ec='k', zorder=-2) + + for i in range(ncd): + x = self.spectra[lamp][i].spectral_axis.value + sl = self._p2w(x, np.full_like(x, self.cd_samples[i])) + ax.pcolormesh(np.tile(sl, (2, 1)), + np.array([np.full_like(sl, i), np.full_like(sl, i + 1)]), + self.spectra[lamp][i].data[None, :-1], cmap=cm.Blues, + vmax=np.percentile(self.spectra[lamp][i].data, 98)) + ax.axhline(i, c='k', alpha=0.5, ls='-', lw=0.5) + + setp(ax, ylim=(-1.0, ncd + 1), yticks=0.5 + np.arange(ncd), yticklabels=self.cd_samples) + return fig + + def plot_residuals(self, axes=None, model=None, figsize=None): + if axes is None: + fig, axes = subplots(self.nframes, 1, figsize=figsize, constrained_layout=True, sharex='all', sharey='all') + else: + fig = axes[0].figure + model = model if model is not None else self._p2w + trees = [KDTree(l[:, None]) for l in self.lines_wav] + for lamp, t in enumerate(trees): + l, ix = t.query(model(self.lines_pix_x[lamp], self.lines_pix_y[lamp])[:, None], distance_upper_bound=5) + axes[lamp].plot(self.lines_pix_x[lamp], l, '.') From 3e5c36b12a7be8e9997d4b6a7df58abbaa40e77a Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Wed, 15 Jan 2025 18:02:25 -0500 Subject: [PATCH 07/76] Introduced a 2D WCS property that constructs a WCS pipeline using pixel and spectral frames. --- specreduce/lswavecal2d.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/specreduce/lswavecal2d.py b/specreduce/lswavecal2d.py index c6e9b206..bddb5b52 100644 --- a/specreduce/lswavecal2d.py +++ b/specreduce/lswavecal2d.py @@ -145,6 +145,18 @@ def _calculate_derivaties(self): if self._w2p is not None: self._w2p_dxdl = (self._w2p[0] & self._w2p[1]) | diff_poly2d_x(self._w2p[-1]) + @property + def wcs(self): + m_forward = models.Mapping((0, 1, 1)) | (self._p2w & models.Identity(1)) + m_inverse = models.Mapping((0, 1, 1)) | (self._w2p & models.Identity(1)) + m_forward.inverse = m_inverse + pixel_frame = cf.Frame2D(name='detector', axes_names=["x", "y"], unit=[u.pix, u.pix]) + spectral_frame = cf.Frame2D(name='spectrum', axes_names=["wavelength", "y"], + unit=[self.linelists[0]['wavelength'].unit, u.pix]) + pipeline = [(pixel_frame, m_forward), (spectral_frame, None)] + self._wcs = wcs.WCS(pipeline) + return self._wcs + def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, bin_edges: Iterable[float] | None = None): ny, nx = flux.shape From 6bfa846acedb40e6f671037065f6c61c2cf13917 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 13:40:22 -0500 Subject: [PATCH 08/76] Started working on 1D wavelength calibration documentation. --- specreduce/lswavecal1d.py | 42 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index 3e89ad13..c2d4f099 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -17,6 +17,24 @@ def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: + """Compute the derivative of a Polynomial1D model. + + Computes the derivative of a Polynomial1D model and returns a new Polynomial1D + model representing the derivative. The coefficients of the input model are + used to calculate the coefficients of the derivative model. For a Polynomial1D + of degree n, the derivative is a Polynomial1D of degree n-1. + + Parameters + ---------- + m + A Polynomial1D model for which the derivative is to be computed. + + Returns + ------- + models.Polynomial1D + A new Polynomial1D model representing the derivative of the input + Polynomial1D model. + """ coeffs = {f'c{i-1}': i*getattr(m, f'c{i}').value for i in range(1, m.degree+1)} return models.Polynomial1D(m.degree-1, **coeffs) @@ -38,10 +56,21 @@ def __init__(self, arc_spectra, lamps, wlbounds: tuple[float, float], wave_air: self._w2p: Model | None = None # The fitted wavelength -> pixel model self._p2w_dldx: Model | None = None # delta lambda / delta pixel self._w2p_dxdl: Model | None = None # delta pixel / delta lambda - self._read_linelists() def _read_linelists(self): + """Load and filter calibration line lists for specified lamps and wavelength bounds. + + Notes + ----- + This method uses the `load_pypeit_calibration_lines` function to load the + line lists for each lamp. It filters the loaded data to only include lines + within the defined wavelength boundaries. The filtered data is stored in + `self.linelists`, and the wavelengths are extracted and stored in + `self.lines_wav`. Additionally, KDTree objects are created for each extracted + wavelength list and stored in `self._trees` for quick nearest-neighbor + queries of line wavelengths. + """ for l in self.lamps: ll = load_pypeit_calibration_lines(l, wave_air=self.wave_air) self.linelists.append(ll[(ll['wavelength'].value > self.wlbounds[0]) & @@ -50,6 +79,17 @@ def _read_linelists(self): self._trees = [KDTree(l[:, None]) for l in self.lines_wav] def find_lines(self, fwhm: float): + """Finds lines in the provided arc spectra. + + This method determines the spectral lines within each spectrum of the arc spectra + based on the provided initial guess for the line Full Width at Half Maximum (FWHM). + + Parameters + ---------- + fwhm + Initial guess for the FWHM for the spectral lines, used as a parameter in + the ``find_arc_lines`` function to locate and identify spectral arc lines. + """ with warnings.catch_warnings(): warnings.simplefilter('ignore') lines_obs = [find_arc_lines(sp, fwhm) for sp in self.arc_spectra] From fd58c2ac7e600dfed4ab3a1b1f3761b218ef918f Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 15:17:23 -0500 Subject: [PATCH 09/76] Cleaning up and documenting the 1D wavelength calibration code. --- specreduce/lswavecal1d.py | 142 ++++++++++++++++++++++++++++++++------ 1 file changed, 120 insertions(+), 22 deletions(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index c2d4f099..6f3ebe6c 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -11,6 +11,7 @@ from scipy.spatial import KDTree from numpy import ndarray +from specutils import Spectrum1D from specreduce.calibration_data import load_pypeit_calibration_lines from specreduce.line_matching import find_arc_lines @@ -40,25 +41,37 @@ def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: class WavelengthSolution1D: - def __init__(self, arc_spectra, lamps, wlbounds: tuple[float, float], wave_air: bool = False): - self.arc_spectra = arc_spectra - self.lamps = lamps - self.wlbounds = wlbounds + def __init__(self, *, line_lists, + arc_spectra: Spectrum1D | Iterable[Spectrum1D] | None = None, + obs_lines: ndarray | Iterable[ndarray] | None = None, + wlbounds: tuple[float, float] = (0, np.inf), + wave_air: bool = False): + + if arc_spectra is None and obs_lines is None: + raise ValueError("Either arc_spectra or obs_lines must be provided.") + + if arc_spectra is not None and obs_lines is not None: + raise ValueError("Only one of arc_spectra or obs_lines can be provided.") + + self.arc_spectra: Iterable[Spectrum1D] = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra + self.lines_pix: Iterable[ndarray] = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines + + self.lines_wav: Iterable[ndarray] | None = None + self._trees: Iterable[KDTree] | None = None + self._read_linelists(line_lists) + + self.wlbounds: tuple[float, float] = wlbounds self.wave_air: bool = wave_air - self.linelists: list | None = [] - self.lines_pix: ndarray | None = None - self.lines_wav: ndarray | None = None - self._fitted_model: Model | None = None + self._fit: optimize.OptimizeResult | None = None self._wcs: wcs.WCS | None = None - self._trees: Iterable[KDTree] | None = None + self._p2w: Model | None = None # The fitted pixel -> wavelength model self._w2p: Model | None = None # The fitted wavelength -> pixel model self._p2w_dldx: Model | None = None # delta lambda / delta pixel self._w2p_dxdl: Model | None = None # delta pixel / delta lambda - self._read_linelists() - def _read_linelists(self): + def _read_linelists(self, line_lists): """Load and filter calibration line lists for specified lamps and wavelength bounds. Notes @@ -71,11 +84,22 @@ def _read_linelists(self): wavelength list and stored in `self._trees` for quick nearest-neighbor queries of line wavelengths. """ - for l in self.lamps: - ll = load_pypeit_calibration_lines(l, wave_air=self.wave_air) - self.linelists.append(ll[(ll['wavelength'].value > self.wlbounds[0]) & - (ll['wavelength'].value < self.wlbounds[1])]) - self.lines_wav = [lo['wavelength'].value for lo in self.linelists] + if not isinstance(line_lists, (tuple, list)): + line_lists = [line_lists] + + self.lines_wav = [] + for l in line_lists: + if isinstance(l, ndarray): + self.lines_wav.append(l) + else: + lines = [] + if isinstance(l, str): + l = [l] + for ll in l: + lines.append(load_pypeit_calibration_lines(l, wave_air=self.wave_air)['wavelength'].value) + self.lines_wav.append(np.concatenate(lines)) + for i, l in enumerate(self.lines_wav): + self.lines_wav[i] = l[(l >= self.wlbounds[0]) & (l <= self.wlbounds[1])] self._trees = [KDTree(l[:, None]) for l in self.lines_wav] def find_lines(self, fwhm: float): @@ -95,9 +119,37 @@ def find_lines(self, fwhm: float): lines_obs = [find_arc_lines(sp, fwhm) for sp in self.arc_spectra] self.lines_pix = [lo['centroid'].value for lo in lines_obs] - def fit(self, ref_pixel: float, wl0: tuple[float, float], dwl: tuple[float, float], - popsize: int = 30, max_distance: float = 100): + def fit(self, ref_pixel: float, + wavelength_bounds: tuple[float, float], + dispersion_bounds: tuple[float, float], + popsize: int = 30, + max_distance: float = 100): + """Calculate and refine the pixel-to-wavelength and wavelength-to-pixel transformations. + + This method determines the functional relationships between pixel positions and + wavelengths in a spectrum, including their derivatives, by fitting calibration data + to given constraints. The transformations include forward (pixel-to-wavelength) + and backward (wavelength-to-pixel) mappings as well as their respective derivatives + across the spectral axis. + Parameters + ---------- + ref_pixel : float + The reference pixel position used as the zero point for the transformation. + wavelength_bounds : tuple of float + Initial bounds for the wavelength value at the reference pixel. + dispersion_bounds : tuple of float + Initial bounds for the dispersion value at the reference pixel. + popsize : int, optional + The population size for differential evolution optimization. + max_distance : float, optional + The maximum allowable distance when querying the tree in the optimization process. + + Raises + ------ + ValueError + Raised if the optimization or fitting process fails due to invalid inputs or constraints. + """ model = models.Shift(-ref_pixel) | models.Polynomial1D(3) def minfun(x): return sum( @@ -106,11 +158,11 @@ def minfun(x): # Calculate the pixel -> wavelength transform and its derivative along the spectral axis self._fit = fit = optimize.differential_evolution(minfun, - bounds=[wl0, dwl, [-1e-3, 1e-3], [-1e-4, 1e-4]], + bounds=[wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3], [-1e-4, 1e-4]], popsize=popsize) self._p2w = models.Shift(-ref_pixel) | models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in range(fit.x.size)}) - self.refine_fit() + self._refine_fit() p2w = self._p2w self._p2w_dldx = models.Shift(p2w.offset_0) | diff_poly1d(p2w[1]) @@ -123,14 +175,60 @@ def minfun(x): self._w2p = w2p = models.Shift(-p2w.c0_1) | fitting.DogBoxLSQFitter()(w2p, vwav - p2w.c0_1.value, vpix) self._w2p_dxdl = models.Shift(w2p.offset_0) | diff_poly1d(w2p[1]) - def refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + """Refine the global fit of the pixel-to-wavelength transformation. + + Refines the fit of a polynomial model to data by performing a fitting operation + using matched pixel and wavelength data points. The method uses a linear least + squares fitter to optimize the model parameters based on the match. + + Parameters + ---------- + degree : int, optional + The degree of the polynomial model used for fitting. Higher values + allow for more complex polynomial models. Default is 4. + + match_distance_bound : float, optional + Maximum allowable distance used to identify matched pixel and wavelength + data points. Points exceeding the bound will not be considered in the fit. + Default is 5.0. + """ matched_pix, matched_wav = self.match_lines(match_distance_bound) model = models.Polynomial1D(degree, **{n: getattr(self._p2w[-1], n).value for n in self._p2w[-1].param_names}) fitter = fitting.LinearLSQFitter() self._p2w = self._p2w[0].copy() | fitter(model, matched_pix + self._p2w.offset_0.value, matched_wav) - def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, + def resample(self, flux: Spectrum1D, + nbins: int | None = None, + wlbounds: tuple[float, float] | None = None, bin_edges: Iterable[float] | None = None): + """Resample the given 1D spectrum to a specified wavelength bins conserving the flux. + + This function adjusts the flux data by resampling it over wavelength bins. The wavelength mapping + is determined by the object's internal methods `_p2w` and `_p2w_dldx` for pixel-to-wavelength + conversion. The resulting flux values are normalized to ensure consistency with the input flux's + total spectral intensity. + + Parameters + ---------- + flux : ndarray + 1D array representing the flux data to be resampled over the wavelength space. + nbins : int or None, optional + The number of bins for resampling. If not provided, it defaults to the size of the input + `flux` array. + wlbounds : tuple of float or None, optional + A tuple specifying the starting and ending wavelengths for resampling. If not provided, the + wavelength bounds are inferred from the object's methods and the entire flux array is used. + bin_edges : Iterable of float or None, optional + Explicit bin edges in the wavelength space. If provided, `nbins` and `wlbounds` are ignored. + + Returns + ------- + bin_centers_wav : ndarray + 1D array containing the center wavelengths of the resampled bins. + flux_wl : ndarray + 1D array containing the flux values resampled and normalized over the defined bins. + """ npix = flux.size nbins = npix if nbins is None else nbins if wlbounds is None: From 3b42370df78c5297c6e01d594bd8094e0858b1ce Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 15:40:34 -0500 Subject: [PATCH 10/76] Continuing 1D spectrum calibration documentation and cleanup. --- specreduce/lswavecal1d.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index 6f3ebe6c..cee5f00c 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -4,6 +4,7 @@ import astropy.units as u import numpy as np from astropy.modeling import models, Model, fitting +from astropy.nddata import VarianceUncertainty from gwcs import coordinate_frames as cf from gwcs import wcs from matplotlib.pyplot import setp, subplots @@ -53,6 +54,9 @@ def __init__(self, *, line_lists, if arc_spectra is not None and obs_lines is not None: raise ValueError("Only one of arc_spectra or obs_lines can be provided.") + self.wlbounds: tuple[float, float] = wlbounds + self.wave_air: bool = wave_air + self.arc_spectra: Iterable[Spectrum1D] = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra self.lines_pix: Iterable[ndarray] = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines @@ -60,9 +64,6 @@ def __init__(self, *, line_lists, self._trees: Iterable[KDTree] | None = None self._read_linelists(line_lists) - self.wlbounds: tuple[float, float] = wlbounds - self.wave_air: bool = wave_air - self._fit: optimize.OptimizeResult | None = None self._wcs: wcs.WCS | None = None @@ -198,10 +199,10 @@ def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): fitter = fitting.LinearLSQFitter() self._p2w = self._p2w[0].copy() | fitter(model, matched_pix + self._p2w.offset_0.value, matched_wav) - def resample(self, flux: Spectrum1D, + def resample(self, spectrum: Spectrum1D, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, - bin_edges: Iterable[float] | None = None): + bin_edges: Iterable[float] | None = None) -> Spectrum1D: """Resample the given 1D spectrum to a specified wavelength bins conserving the flux. This function adjusts the flux data by resampling it over wavelength bins. The wavelength mapping @@ -211,7 +212,7 @@ def resample(self, flux: Spectrum1D, Parameters ---------- - flux : ndarray + spectrum : ndarray 1D array representing the flux data to be resampled over the wavelength space. nbins : int or None, optional The number of bins for resampling. If not provided, it defaults to the size of the input @@ -224,11 +225,10 @@ def resample(self, flux: Spectrum1D, Returns ------- - bin_centers_wav : ndarray - 1D array containing the center wavelengths of the resampled bins. - flux_wl : ndarray - 1D array containing the flux values resampled and normalized over the defined bins. + 1D spectrum binned to the specified wavelength bins. """ + flux = spectrum.flux.value + ucty = spectrum.uncertainty.represent_as(VarianceUncertainty).array npix = flux.size nbins = npix if nbins is None else nbins if wlbounds is None: @@ -243,6 +243,7 @@ def resample(self, flux: Spectrum1D, bin_edge_w = bin_edges_pix - bin_edge_ix bin_centers_wav = 0.5 * (bin_edges_wav[:-1] + bin_edges_wav[1:]) flux_wl = np.zeros(nbins) + ucty_wl = np.zeros(nbins) weights = np.zeros(npix) dldx = self._p2w_dldx(np.arange(npix)) @@ -255,10 +256,13 @@ def resample(self, flux: Spectrum1D, weights[i1] = 1 - bin_edge_w[i] weights[i2] = bin_edge_w[i + 1] flux_wl[i] = (weights[i1:i2 + 1] * flux[i1:i2 + 1] * dldx[i1:i2 + 1]).sum() + ucty_wl[i] = (weights[i1:i2 + 1] * ucty[i1:i2 + 1] * dldx[i1:i2 + 1]).sum() else: flux_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * flux[i1] * dldx[i1] - flux_wl *= n - return bin_centers_wav, flux_wl + ucty_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * ucty[i1] * dldx[i1] + flux_wl = (flux_wl * n) * spectrum.flux.unit + ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(type(spectrum.uncertainty)) + return Spectrum1D(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) def pix_to_wav(self, pix: ndarray |float) -> ndarray | float: return self._p2w(pix) From 4669afd7ae4a96d8a09d77d207199d520e5123fa Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 16:50:02 -0500 Subject: [PATCH 11/76] Refactored and enhanced WavelengthSolution1D class methods to improve type hints, documentation, and method structure. Added new methods for calculating inverse transformations and derivatives. Enhanced plotting functionality with additional parameters and refined docstrings for clarity. --- specreduce/lswavecal1d.py | 227 +++++++++++++++++++++++++++++--------- 1 file changed, 172 insertions(+), 55 deletions(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index cee5f00c..7e4ad91e 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -1,5 +1,5 @@ import warnings -from typing import Iterable +from typing import Sequence, Callable import astropy.units as u import numpy as np @@ -7,6 +7,8 @@ from astropy.nddata import VarianceUncertainty from gwcs import coordinate_frames as cf from gwcs import wcs +from matplotlib.axes import Axes +from matplotlib.figure import Figure from matplotlib.pyplot import setp, subplots from scipy import optimize from scipy.spatial import KDTree @@ -18,7 +20,7 @@ from specreduce.line_matching import find_arc_lines -def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: +def _diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: """Compute the derivative of a Polynomial1D model. Computes the derivative of a Polynomial1D model and returns a new Polynomial1D @@ -33,9 +35,7 @@ def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: Returns ------- - models.Polynomial1D - A new Polynomial1D model representing the derivative of the input - Polynomial1D model. + A new Polynomial1D model representing the derivative of the input Polynomial1D model. """ coeffs = {f'c{i-1}': i*getattr(m, f'c{i}').value for i in range(1, m.degree+1)} return models.Polynomial1D(m.degree-1, **coeffs) @@ -43,8 +43,8 @@ def diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: class WavelengthSolution1D: def __init__(self, *, line_lists, - arc_spectra: Spectrum1D | Iterable[Spectrum1D] | None = None, - obs_lines: ndarray | Iterable[ndarray] | None = None, + arc_spectra: Spectrum1D | Sequence[Spectrum1D] | None = None, + obs_lines: ndarray | Sequence[ndarray] | None = None, wlbounds: tuple[float, float] = (0, np.inf), wave_air: bool = False): @@ -57,11 +57,12 @@ def __init__(self, *, line_lists, self.wlbounds: tuple[float, float] = wlbounds self.wave_air: bool = wave_air - self.arc_spectra: Iterable[Spectrum1D] = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra - self.lines_pix: Iterable[ndarray] = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines + self.arc_spectra: Sequence[Spectrum1D] = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra + self.lines_pix: Sequence[ndarray] = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines + self.ndata: int = len(self.arc_spectra) if self.arc_spectra is not None else len(self.lines_pix) - self.lines_wav: Iterable[ndarray] | None = None - self._trees: Iterable[KDTree] | None = None + self.lines_wav: Sequence[ndarray] | None = None + self._trees: Sequence[KDTree] | None = None self._read_linelists(line_lists) self._fit: optimize.OptimizeResult | None = None @@ -124,7 +125,8 @@ def fit(self, ref_pixel: float, wavelength_bounds: tuple[float, float], dispersion_bounds: tuple[float, float], popsize: int = 30, - max_distance: float = 100): + max_distance: float = 100, + refine_fit: bool = True): """Calculate and refine the pixel-to-wavelength and wavelength-to-pixel transformations. This method determines the functional relationships between pixel positions and @@ -135,16 +137,18 @@ def fit(self, ref_pixel: float, Parameters ---------- - ref_pixel : float + ref_pixel The reference pixel position used as the zero point for the transformation. - wavelength_bounds : tuple of float + wavelength_bounds Initial bounds for the wavelength value at the reference pixel. - dispersion_bounds : tuple of float + dispersion_bounds Initial bounds for the dispersion value at the reference pixel. - popsize : int, optional + popsize The population size for differential evolution optimization. - max_distance : float, optional + max_distance The maximum allowable distance when querying the tree in the optimization process. + refine_fit + Refine the global fit of the pixel-to-wavelength transformation. Raises ------ @@ -153,28 +157,49 @@ def fit(self, ref_pixel: float, """ model = models.Shift(-ref_pixel) | models.Polynomial1D(3) def minfun(x): - return sum( - [np.clip(t.query(model.evaluate(l, -ref_pixel, *x)[:, None])[0], 0, max_distance).sum() for t, l in - zip(self._trees, self.lines_pix)]) + total_distance = 0.0 + for t, l in zip(self._trees, self.lines_pix): + transformed_lines = model.evaluate(l, -ref_pixel, *x)[:, None] + total_distance += np.clip(t.query(transformed_lines)[0], 0, max_distance).sum() + return total_distance - # Calculate the pixel -> wavelength transform and its derivative along the spectral axis self._fit = fit = optimize.differential_evolution(minfun, - bounds=[wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3], [-1e-4, 1e-4]], + bounds=[wavelength_bounds, + dispersion_bounds, + [-1e-3, 1e-3], + [-1e-4, 1e-4]], popsize=popsize) - self._p2w = models.Shift(-ref_pixel) | models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in - range(fit.x.size)}) - self._refine_fit() - p2w = self._p2w - self._p2w_dldx = models.Shift(p2w.offset_0) | diff_poly1d(p2w[1]) + self._p2w = (models.Shift(-ref_pixel) | + models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in range(fit.x.size)})) - # Calculate the wavelength -> pixel transform and its derivative along the spectral axis + if refine_fit: + self._refine_fit() + self._calculate_inverse() + self._calculate_derivatives() + + def _calculate_inverse(self): + """Compute the wavelength-to-pixel mapping from the pixel-to-wavelength transformation. + + Compute and set the inverse mapping from wavelength reference system to pixel reference system + through polynomial fitting. This method uses the DogBoxLSQFitter to fit the data and produces + a transformation model to establish the inverse relation. + """ vpix = self.arc_spectra[0].spectral_axis.value - vwav = p2w(vpix) - w2p = models.Polynomial1D(6, c0=-p2w.offset_0, c1=1 / p2w.c1_1, fixed={'c0': True}) + vwav = self._p2w(vpix) + w2p = models.Polynomial1D(6, c0=-self._p2w.offset_0, c1=1/self._p2w.c1_1, fixed={'c0': True}) with warnings.catch_warnings(): warnings.simplefilter('ignore') - self._w2p = w2p = models.Shift(-p2w.c0_1) | fitting.DogBoxLSQFitter()(w2p, vwav - p2w.c0_1.value, vpix) - self._w2p_dxdl = models.Shift(w2p.offset_0) | diff_poly1d(w2p[1]) + fitter = fitting.DogBoxLSQFitter() + self._w2p = models.Shift(-self._p2w.c0_1) | fitter(w2p, vwav - self._p2w.c0_1.value, vpix) + + def _calculate_derivatives(self): + """Calculate the forward and reverse mapping derivatives with respect to the spectral axis. + """ + if self._p2w is not None: + self._p2w_dldx = models.Shift(self._p2w.offset_0) | _diff_poly1d(self._p2w[1]) + if self._w2p is not None: + self._w2p_dxdl = models.Shift(self._w2p.offset_0) | _diff_poly1d(self._w2p[1]) + def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): """Refine the global fit of the pixel-to-wavelength transformation. @@ -199,28 +224,27 @@ def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): fitter = fitting.LinearLSQFitter() self._p2w = self._p2w[0].copy() | fitter(model, matched_pix + self._p2w.offset_0.value, matched_wav) + def resample(self, spectrum: Spectrum1D, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, - bin_edges: Iterable[float] | None = None) -> Spectrum1D: - """Resample the given 1D spectrum to a specified wavelength bins conserving the flux. + bin_edges: Sequence[float] | None = None) -> Spectrum1D: + """Bin the given pixel-space 1D spectrum to a wavelength space conserving the flux. - This function adjusts the flux data by resampling it over wavelength bins. The wavelength mapping - is determined by the object's internal methods `_p2w` and `_p2w_dldx` for pixel-to-wavelength - conversion. The resulting flux values are normalized to ensure consistency with the input flux's - total spectral intensity. + This method bins a pixel-space spectrum to a wavelength space using the computed pixel-to-wavelength and + wavelength-to-pixel transformations and their derivatives with respect to the spectral axis. The binning is + exact and conserves the total flux. Parameters ---------- - spectrum : ndarray - 1D array representing the flux data to be resampled over the wavelength space. - nbins : int or None, optional - The number of bins for resampling. If not provided, it defaults to the size of the input - `flux` array. - wlbounds : tuple of float or None, optional + spectrum + A Spectrum1D instance containing the flux to be resampled over the wavelength space. + nbins + The number of bins for resampling. If not provided, it defaults to the size of the input spectrum. + wlbounds A tuple specifying the starting and ending wavelengths for resampling. If not provided, the wavelength bounds are inferred from the object's methods and the entire flux array is used. - bin_edges : Iterable of float or None, optional + bin_edges Explicit bin edges in the wavelength space. If provided, `nbins` and `wlbounds` are ignored. Returns @@ -265,9 +289,31 @@ def resample(self, spectrum: Spectrum1D, return Spectrum1D(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) def pix_to_wav(self, pix: ndarray |float) -> ndarray | float: + """Map pixel values into wavelength values. + + Parameters + ---------- + pix + Input pixel value(s) to be transformed into wavelength. + + Returns + ------- + Transformed wavelength value(s) corresponding to the input pixel value(s). + """ return self._p2w(pix) def wav_to_pix(self, wav: ndarray | float) -> ndarray | float: + """Map wavelength values into pixel values. + + Parameters + ---------- + wav + The wavelength value(s) to be converted into pixel value(s). + + Returns + ------- + The corresponding pixel value(s) for the input wavelength(s). + """ return self._w2p(wav) @property @@ -278,11 +324,21 @@ def wcs(self): self._wcs = wcs.WCS(pipeline) return self._wcs - @property - def fitted_model(self): - return self._p2w + def match_lines(self, upper_bound: float = 5) -> tuple[ndarray, ndarray]: + """Match the observed lines to theoretical lines. +s. + Parameters + ---------- + upper_bound + The maximum allowed distance between the query points and the KD-tree + data points for them to be considered a match. - def match_lines(self, upper_bound: float = 5): + Returns + ------- + A tuple containing two concatenated arrays: + - An array of matched line positions in pixel coordinates. + - An array of matched line positions in wavelength coordinates. + """ matched_lines_wav = [] matched_lines_pix = [] for iframe, tree in enumerate(self._trees): @@ -292,9 +348,29 @@ def match_lines(self, upper_bound: float = 5): matched_lines_pix.append(self.lines_pix[iframe][m]) return np.concatenate(matched_lines_pix), np.concatenate(matched_lines_wav) - def plot_lines(self, axes=None, figsize=None): + def plot_lines(self, axes: Axes | None = None, figsize: tuple[float, float] | None = None) -> Figure: + """Plot the arc spectra and mark identified line positions for all the data sets. + + This method plots the spectral data stored in `arc_spectra` for each dataset + in separate subplots. It also marks identified spectral line positions within + the plots. + + Parameters + ---------- + axes + Pre-configured Matplotlib axes to be used for plotting. If `None`, new + axes objects are created automatically, arranged in a single-column layout. + figsize + Specifies the width and height of the figure in inches when creating new + axes. Ignored if `axes` is provided. + + Returns + ------- + fig : matplotlib.figure.Figure + """ if axes is None: - fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True, squeeze=False) + fig, axes = subplots(self.ndata, 1, figsize=figsize, sharex='all', + constrained_layout=True, squeeze=False) else: fig = axes[0].figure for i, sp in enumerate(self.arc_spectra): @@ -305,13 +381,38 @@ def plot_lines(self, axes=None, figsize=None): setp(axes[-1], xlabel='Pixel') return fig - def plot_solution(self, axes=None, figsize=None, model = None): + def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | None = None, + model: Callable | None = None) -> Figure: + """Plot the wavelength solution applied to the provided spectra and overlay it for visualization. + + This method generates plots for the given arc spectra, showcasing the results of the model + fit. Each subplot represents a single spectrum with overlaid model predictions and markers + indicating the expected wavelengths. + + Parameters + ---------- + axes + Array of Matplotlib Axes where the spectra and their corresponding solutions will be plotted. + If None, new Axes objects will be created for visualization. Must be provided in the case of + external figure context. + figsize + Tuple specifying the dimensions of the figure in inches if new Axes are created. Ignored if + `axes` is provided. Must follow the structure (width, height). + model + The model function to be applied for prediction on the spectral axis values. If None, the + already fitted model within the class instance (`self.fitted_model`) will be utilized. + + Returns + ------- + fig : matplotlib.figure.Figure + """ if axes is None: - fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', constrained_layout=True, squeeze=False) + fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', + constrained_layout=True, squeeze=False) else: fig = axes[0].figure - model = model if model is not None else self.fitted_model + model = model if model is not None else self._p2w for i, sp in enumerate(self.arc_spectra): axes[i,0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) @@ -321,7 +422,23 @@ def plot_solution(self, axes=None, figsize=None, model = None): setp(axes[-1], xlabel=f'Wavelength [{self.linelists[0]["wavelength"].unit.to_string(format="latex")}]') return fig - def plot_transforms(self, figsize=None): + def plot_transforms(self, figsize: tuple[float, float] | None = None) -> Figure: + """ Plot and visualize transformation functions between pixel and wavelength space. + + This method generates a grid of subplots to illustrate the transformations + between pixel and wavelength spaces in two directions: Pixel -> Wavelength + and Wavelength -> Pixel. It also includes visualizations of the derivatives + of these transformations with respect to the spectral axis. + + Parameters + ---------- + figsize + Width, height in inches to control the size of the figure. + + Returns + ------- + matplotlib.figure.Figure + """ fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex='col') xpix = self.arc_spectra[0].spectral_axis.value xwav = np.linspace(*self.pix_to_wav(xpix[[0, -1]]), num=xpix.size) From ba0b58a8a6e8bb1160052f008bf81fd8f26f0ee0 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 17:25:48 -0500 Subject: [PATCH 12/76] Added unit handling for wavelength and RMS/residual plotting. --- specreduce/lswavecal1d.py | 79 +++++++++++++++++++++++++++++++++++---- 1 file changed, 72 insertions(+), 7 deletions(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index 7e4ad91e..19223a53 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -1,5 +1,5 @@ import warnings -from typing import Sequence, Callable +from typing import Sequence, Callable, Literal import astropy.units as u import numpy as np @@ -46,6 +46,7 @@ def __init__(self, *, line_lists, arc_spectra: Spectrum1D | Sequence[Spectrum1D] | None = None, obs_lines: ndarray | Sequence[ndarray] | None = None, wlbounds: tuple[float, float] = (0, np.inf), + unit: u.Unit = u.angstrom, wave_air: bool = False): if arc_spectra is None and obs_lines is None: @@ -55,6 +56,7 @@ def __init__(self, *, line_lists, raise ValueError("Only one of arc_spectra or obs_lines can be provided.") self.wlbounds: tuple[float, float] = wlbounds + self.unit: u.Unit = unit self.wave_air: bool = wave_air self.arc_spectra: Sequence[Spectrum1D] = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra @@ -98,7 +100,7 @@ def _read_linelists(self, line_lists): if isinstance(l, str): l = [l] for ll in l: - lines.append(load_pypeit_calibration_lines(l, wave_air=self.wave_air)['wavelength'].value) + lines.append(load_pypeit_calibration_lines(ll, wave_air=self.wave_air)['wavelength'].to(self.unit).value) self.lines_wav.append(np.concatenate(lines)) for i, l in enumerate(self.lines_wav): self.lines_wav[i] = l[(l >= self.wlbounds[0]) & (l <= self.wlbounds[1])] @@ -319,7 +321,7 @@ def wav_to_pix(self, wav: ndarray | float) -> ndarray | float: @property def wcs(self): pixel_frame = cf.CoordinateFrame(1, "SPECTRAL", [0, ], axes_names=["x", ], unit=[u.pix]) - spectral_frame = cf.SpectralFrame(axes_names=["wavelength", ], unit=[self.linelists[0]['wavelength'].unit]) + spectral_frame = cf.SpectralFrame(axes_names=["wavelength", ], unit=[self.unit]) pipeline = [(pixel_frame, self.fitted_model), (spectral_frame, None)] self._wcs = wcs.WCS(pipeline) return self._wcs @@ -348,6 +350,26 @@ def match_lines(self, upper_bound: float = 5) -> tuple[ndarray, ndarray]: matched_lines_pix.append(self.lines_pix[iframe][m]) return np.concatenate(matched_lines_pix), np.concatenate(matched_lines_wav) + def rms(self, space: Literal['pixel', 'wavelength'] = 'wavelength') -> float: + """Compute the RMS of the residuals between matched lines in either the pixel or wavelength space. + + Parameters + ---------- + space + The space in which to calculate the RMS residual. If 'wavelength', + the calculation is performed in the wavelength space. If 'pixel', + it is performed in the pixel space. Default is 'wavelength'. + + Returns + ------- + float + """ + mpix, mwav = self.match_lines() + if space == 'wavelength': + return np.sqrt(((mwav - self.pix_to_wav(mpix)) ** 2).mean()) + else: + return np.sqrt(((mpix - self.wav_to_pix(mwav)) ** 2).mean()) + def plot_lines(self, axes: Axes | None = None, figsize: tuple[float, float] | None = None) -> Figure: """Plot the arc spectra and mark identified line positions for all the data sets. @@ -419,7 +441,7 @@ def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | axes[i,0].vlines(self.lines_wav[i], 0.0, 1.0, alpha=0.3, ec='darkorange', zorder=0) axes[i,0].vlines(model(self.lines_pix[i]), 0.9, 1.0, alpha=1) axes[i,0].autoscale(enable=True, axis='x', tight=True) - setp(axes[-1], xlabel=f'Wavelength [{self.linelists[0]["wavelength"].unit.to_string(format="latex")}]') + setp(axes[-1], xlabel=f'Wavelength [{self.unit.to_string(format="latex")}]') return fig def plot_transforms(self, figsize: tuple[float, float] | None = None) -> Figure: @@ -449,10 +471,53 @@ def plot_transforms(self, figsize: tuple[float, float] | None = None) -> Figure: axs[1, 1].plot(xwav[:-1], np.diff(self._w2p(xwav)) / np.diff(xwav), lw=4, c='k') axs[1, 1].plot(xwav, self._w2p_dxdl(xwav), ls='--', lw=2, c='w') setp(axs[1,0], xlabel='Pixel', ylabel=r'd$\lambda$/dx') - setp(axs[0,0], ylabel=fr'$\lambda$ [{self.linelists[0]["wavelength"].unit}]') - setp(axs[1,1], xlabel=fr'$\lambda$ [{self.linelists[0]["wavelength"].unit}]', ylabel=r'dx/d$\lambda$') + setp(axs[0,0], ylabel=fr'$\lambda$ [{self.unit}]') + setp(axs[1,1], xlabel=fr'$\lambda$ [{self.unit}]', ylabel=r'dx/d$\lambda$') setp(axs[0,1], ylabel='Pixel') axs[0,0].set_title('Pixel -> wavelength') axs[0,1].set_title('Wavelength -> pixel') fig.align_labels() - return fig \ No newline at end of file + return fig + + def plot_residuals(self, ax: Axes | None = None, space: Literal['pixel', 'wavelength'] = 'wavelength', + figsize: tuple[float, float] | None = None) -> Figure: + """Plot the residuals of pixel-to-wavelength or wavelength-to-pixel transformation. + + Parameters + ---------- + ax + Matplotlib Axes object to plot on. If None, a new figure and axes are created. Default is None. + space + The reference space used for plotting residuals. Options are 'pixel' for residuals in pixel space or + 'wavelength' for residuals in wavelength space. + figsize + The size of the figure in inches, if a new figure is created. Default is None. + + Returns + ------- + matplotlib.figure.Figure + """ + if ax is None: + fig, ax = subplots(figsize=figsize, constrained_layout=True) + else: + fig = ax.figure + + mpix, mwav = self.match_lines() + + if space == 'wavelength': + twav = self.pix_to_wav(mpix) + ax.plot(mwav, mwav - twav, '.') + ax.text(0.98, 0.95, + f"RMS = {np.sqrt(((mwav - twav) ** 2).mean()):4.2f} {self.unit.to_string(format="latex")}", + transform=ax.transAxes, ha='right', va='top') + setp(ax, + xlabel=f'Wavelength [{self.unit.to_string(format="latex")}]', + ylabel=f'Residuals [{self.unit.to_string(format="latex")}]') + else: + tpix = self.wav_to_pix(mwav) + ax.plot(mpix, mpix - tpix, '.') + ax.text(0.98, 0.95, f"RMS = {np.sqrt(((mpix - tpix) ** 2).mean()):4.2f} pix", transform=ax.transAxes, + ha='right', va='top') + setp(ax, xlabel='Pixel', ylabel='Residuals [pix]') + ax.axhline(0, c='k', lw=1, ls='--') + return fig From 4b788beb81bd3233d74b4bd0d452b08c35001342 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 17:49:19 -0500 Subject: [PATCH 13/76] Added a basic example notebook for 1D wavelength calibration with Specreduce. --- notebook_sandbox/wavecal_1d_shane.ipynb | 283 ++++++++++++++++++++++++ 1 file changed, 283 insertions(+) create mode 100644 notebook_sandbox/wavecal_1d_shane.ipynb diff --git a/notebook_sandbox/wavecal_1d_shane.ipynb b/notebook_sandbox/wavecal_1d_shane.ipynb new file mode 100644 index 00000000..c876312d --- /dev/null +++ b/notebook_sandbox/wavecal_1d_shane.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", + "metadata": {}, + "source": [ + "# Specreduce 1D wavelength calibration example 1\n", + "\n", + "**Author:** Hannu Parviainen
\n", + "**Modified:** 16 Jan. 2025" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "463038ad-ae02-4484-8226-36df99fbf810", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7853ed1c-5b05-42a2-9413-4054769c6032", + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.io.fits import getdata\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import setp, subplots, close, rc\n", + "from specutils import Spectrum1D\n", + "\n", + "from specreduce.lswavecal1d import WavelengthSolution1D\n", + "\n", + "rc('figure', figsize=(11,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e3dd2a51-ef1a-4501-9bfb-62166676e88f", + "metadata": {}, + "outputs": [], + "source": [ + "flux = getdata('shane_kast_blue_600_4310_d55.fits', 1).astype('d')\n", + "arc_spectrum = Spectrum1D(flux*u.DN, uncertainty=StdDevUncertainty(2*np.sqrt(flux)))" + ] + }, + { + "cell_type": "markdown", + "id": "bf602a55-6b4b-4556-95f4-4f448cf39dd1", + "metadata": {}, + "source": [ + "### Initialize the wavelength solution and find the lines" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a130b965-4563-4959-8dc9-906445f5f07e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 81.1 ms, sys: 2.95 ms, total: 84 ms\n", + "Wall time: 84.9 ms\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "ws = WavelengthSolution1D(arc_spectra=arc_spectrum, line_lists=[['CdI', 'HgI', 'HeI']], wlbounds=(3200, 5700))\n", + "ws.find_lines(fwhm=4)\n", + "ws.plot_lines();" + ] + }, + { + "cell_type": "markdown", + "id": "e19324ce-7bd1-4d67-af52-f9278d949b26", + "metadata": {}, + "source": [ + "### Fit a pixel-wavelength transformation using the observed and theoretical line lists" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "84bae06f-fbec-4952-9a89-2d50d685deee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 493 ms, sys: 4.06 ms, total: 498 ms\n", + "Wall time: 498 ms\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "ws.fit(ref_pixel=1024, wavelength_bounds=(4400, 4500), dispersion_bounds=(1.0, 1.05), popsize=50)\n", + "ws.plot_solution();" + ] + }, + { + "cell_type": "markdown", + "id": "3daa7d49-c561-428e-a84a-dbb4d78fa405", + "metadata": {}, + "source": [ + "### Plot the residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "12951d19-e8ae-4294-969f-06853ccd35b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_residuals(space='wavelength');" + ] + }, + { + "cell_type": "markdown", + "id": "5bcdadaa-baed-4676-94b5-ed04b0eca310", + "metadata": {}, + "source": [ + "### Plot the transformations" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2cc6f89f-39c6-49c3-8e23-0e26b7d2e951", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_transforms(figsize=(11, 6));" + ] + }, + { + "cell_type": "markdown", + "id": "983fe041-41e9-47a7-a179-fead64e171ba", + "metadata": {}, + "source": [ + "### Rebin the spectrum to a linear spacing in wavelength space" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 25.7 ms, sys: 5.92 ms, total: 31.6 ms\n", + "Wall time: 28.2 ms\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "spectrum_wl = ws.resample(arc_spectrum)\n", + "fig, ax = subplots(constrained_layout=True)\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux);" + ] + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 8b64079421db2d6c7225480b67a1f7bce91d56f8 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 16 Jan 2025 17:59:48 -0500 Subject: [PATCH 14/76] Renamed some parameters for clarity in 2D wavelength calibration. --- specreduce/lswavecal2d.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/specreduce/lswavecal2d.py b/specreduce/lswavecal2d.py index bddb5b52..b44fd33c 100644 --- a/specreduce/lswavecal2d.py +++ b/specreduce/lswavecal2d.py @@ -76,7 +76,7 @@ def find_lines(self, fwhm: float): self.lines_pix_y = [np.concatenate(lpy) for lpy in lines_pix_y] def fit(self, ref_pixel: tuple[float, float], - wl0: tuple[float, float] = (7000, 7600), dwl: tuple[float, float] = (2.4, 2.8), + wavelength_bounds: tuple[float, float] = (7000, 7600), dispersion_bounds: tuple[float, float] = (2.4, 2.8), popsize: int = 30, max_distance: float = 100, workers: int = 1): self._ref_pixel = ref_pixel @@ -96,7 +96,7 @@ def minfun(x): 0, max_distance).sum() return distance_sum - bounds = np.array([wl0, dwl, [-1e-3, 1e-3], [-1e-5, 1e-5], [-1e-1, 1e-1], [-1e-4, 1e-4], [-1e-5, 1e-5]]) + bounds = np.array([wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3], [-1e-5, 1e-5], [-1e-1, 1e-1], [-1e-4, 1e-4], [-1e-5, 1e-5]]) res = optimize.differential_evolution(minfun, bounds, popsize=popsize, workers=1, updating='deferred') self._p2w = (self._shift | models.Polynomial2D(model[-1].degree, From 25ab353751e953df5267168b9da1039bd5a01a33 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Fri, 17 Jan 2025 10:52:48 -0500 Subject: [PATCH 15/76] Updated the 1D wavelength calibration plotting routines handle cases where `arc_spectra` is `None`. --- specreduce/lswavecal1d.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index 19223a53..e8e2e8d9 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -395,8 +395,10 @@ def plot_lines(self, axes: Axes | None = None, figsize: tuple[float, float] | No constrained_layout=True, squeeze=False) else: fig = axes[0].figure - for i, sp in enumerate(self.arc_spectra): - axes[i,0].plot(sp.data / (1.2 * sp.data.max())) + + for i in range(self.ndata): + if self.arc_spectra is not None: + axes[i,0].plot(self.arc_spectra[i].data / (1.2 * self.arc_spectra[i].data.max())) axes[i,0].vlines(self.lines_pix[i], 0.0, 1, alpha=0.1) axes[i,0].vlines(self.lines_pix[i], 0.9, 1) axes[i,0].autoscale(enable=True, axis='x', tight=True) @@ -436,8 +438,10 @@ def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | model = model if model is not None else self._p2w - for i, sp in enumerate(self.arc_spectra): - axes[i,0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) + for i in range(self.ndata): + if self.arc_spectra is not None: + sp = self.arc_spectra[i] + axes[i,0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) axes[i,0].vlines(self.lines_wav[i], 0.0, 1.0, alpha=0.3, ec='darkorange', zorder=0) axes[i,0].vlines(model(self.lines_pix[i]), 0.9, 1.0, alpha=1) axes[i,0].autoscale(enable=True, axis='x', tight=True) From f995f85ddbdb192240957aa7b38c6ca8ed762eaa Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Fri, 17 Jan 2025 13:51:25 -0500 Subject: [PATCH 16/76] Updated logic to handle cases where `arc_spectra` is `None` and added `plim` parameter for pixel range control in plotting. --- specreduce/lswavecal1d.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/specreduce/lswavecal1d.py b/specreduce/lswavecal1d.py index e8e2e8d9..bd13b06a 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/lswavecal1d.py @@ -186,7 +186,11 @@ def _calculate_inverse(self): through polynomial fitting. This method uses the DogBoxLSQFitter to fit the data and produces a transformation model to establish the inverse relation. """ - vpix = self.arc_spectra[0].spectral_axis.value + if self.arc_spectra is None: + vpix = np.concatenate(self.lines_pix) + else: + vpix = self.arc_spectra[0].spectral_axis.value + vwav = self._p2w(vpix) w2p = models.Polynomial1D(6, c0=-self._p2w.offset_0, c1=1/self._p2w.c1_1, fixed={'c0': True}) with warnings.catch_warnings(): @@ -431,7 +435,7 @@ def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | fig : matplotlib.figure.Figure """ if axes is None: - fig, axes = subplots(len(self.arc_spectra), 1, figsize=figsize, sharex='all', + fig, axes = subplots(self.ndata, 1, figsize=figsize, sharex='all', constrained_layout=True, squeeze=False) else: fig = axes[0].figure @@ -448,7 +452,8 @@ def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | setp(axes[-1], xlabel=f'Wavelength [{self.unit.to_string(format="latex")}]') return fig - def plot_transforms(self, figsize: tuple[float, float] | None = None) -> Figure: + def plot_transforms(self, figsize: tuple[float, float] | None = None, + plim: tuple[int, int] | None = None) -> Figure: """ Plot and visualize transformation functions between pixel and wavelength space. This method generates a grid of subplots to illustrate the transformations @@ -460,13 +465,19 @@ def plot_transforms(self, figsize: tuple[float, float] | None = None) -> Figure: ---------- figsize Width, height in inches to control the size of the figure. + plim + Lower and upper limits for pixel values used for plotting. Returns ------- matplotlib.figure.Figure """ fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex='col') - xpix = self.arc_spectra[0].spectral_axis.value + if self.arc_spectra is not None and plim is None: + xpix = self.arc_spectra[0].spectral_axis.value + else: + xpix = np.arange(*(plim or (0, 2000))) + xwav = np.linspace(*self.pix_to_wav(xpix[[0, -1]]), num=xpix.size) axs[0, 0].plot(xpix, self._p2w(xpix), 'k') axs[1, 0].plot(xpix[:-1], np.diff(self._p2w(xpix)) / np.diff(xpix), lw=4, c='k') From c6a6b7ab2d41650ef73d696bab3c72829175b2f2 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Fri, 4 Apr 2025 15:48:13 +0100 Subject: [PATCH 17/76] Refactored `WavelengthSolution1D` to improve readability, flexibility, and modularity, while resolving associated issues. Enhanced methods for handling pixel-to-wavelength mappings, line-matching processes, and plotting capabilities. Added new functions and properties for better control over data and fit refinement. --- specreduce/{lswavecal1d.py => wavecal1d.py} | 525 +++++++++++++------- 1 file changed, 357 insertions(+), 168 deletions(-) rename specreduce/{lswavecal1d.py => wavecal1d.py} (50%) diff --git a/specreduce/lswavecal1d.py b/specreduce/wavecal1d.py similarity index 50% rename from specreduce/lswavecal1d.py rename to specreduce/wavecal1d.py index bd13b06a..f4e83533 100644 --- a/specreduce/lswavecal1d.py +++ b/specreduce/wavecal1d.py @@ -10,10 +10,11 @@ from matplotlib.axes import Axes from matplotlib.figure import Figure from matplotlib.pyplot import setp, subplots +from numpy import ndarray +from numpy.ma.core import MaskedArray from scipy import optimize +from scipy.interpolate import interp1d from scipy.spatial import KDTree - -from numpy import ndarray from specutils import Spectrum1D from specreduce.calibration_data import load_pypeit_calibration_lines @@ -37,45 +38,79 @@ def _diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: ------- A new Polynomial1D model representing the derivative of the input Polynomial1D model. """ - coeffs = {f'c{i-1}': i*getattr(m, f'c{i}').value for i in range(1, m.degree+1)} - return models.Polynomial1D(m.degree-1, **coeffs) + coeffs = {f"c{i-1}": i * getattr(m, f"c{i}").value for i in range(1, m.degree + 1)} + return models.Polynomial1D(m.degree - 1, **coeffs) class WavelengthSolution1D: - def __init__(self, *, line_lists, - arc_spectra: Spectrum1D | Sequence[Spectrum1D] | None = None, - obs_lines: ndarray | Sequence[ndarray] | None = None, - wlbounds: tuple[float, float] = (0, np.inf), - unit: u.Unit = u.angstrom, - wave_air: bool = False): - - if arc_spectra is None and obs_lines is None: - raise ValueError("Either arc_spectra or obs_lines must be provided.") - - if arc_spectra is not None and obs_lines is not None: - raise ValueError("Only one of arc_spectra or obs_lines can be provided.") - - self.wlbounds: tuple[float, float] = wlbounds - self.unit: u.Unit = unit - self.wave_air: bool = wave_air - - self.arc_spectra: Sequence[Spectrum1D] = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra - self.lines_pix: Sequence[ndarray] = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines - self.ndata: int = len(self.arc_spectra) if self.arc_spectra is not None else len(self.lines_pix) - - self.lines_wav: Sequence[ndarray] | None = None + def __init__( + self, + ref_pixel: float, + unit: u.Unit = u.angstrom, + degree: int = 3, + line_lists=None, + arc_spectra: Spectrum1D | Sequence[Spectrum1D] | None = None, + obs_lines: ndarray | Sequence[ndarray] | None = None, + pix_bounds: tuple[int, int] | None = None, + line_list_bounds: tuple[float, float] = (0, np.inf), + wave_air: bool = False, + ) -> None: + + self.unit = unit + self._unit_str = unit.to_string('latex') + self.degree = degree + self.ref_pixel = ref_pixel + + self.bounds_pix: tuple[int, int] | None = pix_bounds + self.bounds_wav: tuple[float, float] | None = None + self._lines_wav: list[MaskedArray] | None = None + self._lines_pix: list[MaskedArray] | None = None self._trees: Sequence[KDTree] | None = None - self._read_linelists(line_lists) self._fit: optimize.OptimizeResult | None = None self._wcs: wcs.WCS | None = None - self._p2w: Model | None = None # The fitted pixel -> wavelength model - self._w2p: Model | None = None # The fitted wavelength -> pixel model - self._p2w_dldx: Model | None = None # delta lambda / delta pixel - self._w2p_dxdl: Model | None = None # delta pixel / delta lambda + self._p2w: Model | None = None # pixel -> wavelength model + self._w2p: Callable | None = None # wavelength -> pixel model + self._p2w_dldx: Model | None = None # delta lambda / delta pixel + + # Read and store the observational data if given. The user can provide either a list of arc + # spectra as Spectrum1D objects, or a list of line pixel position arrays. Attempts to give + # both raises an error. + if arc_spectra is not None and obs_lines is not None: + raise ValueError("Only one of arc_spectra or obs_lines can be provided.") - def _read_linelists(self, line_lists): + if arc_spectra is not None: + self.arc_spectra = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra + self.nframes = len(self.arc_spectra) + for s in self.arc_spectra: + if s.data.ndim > 1: + raise ValueError("The arc spectra must be 1 dimensional.") + self.bounds_pix = (0, self.arc_spectra[0].shape[0]) + + elif obs_lines is not None: + self._lines_pix = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines + self.nframes = len(self._lines_pix) + if self.bounds_pix is None: + raise ValueError("Must give pixel bounds when providing observed line positions.") + + # Read the line lists if given. The user can provide an array of line wavelength positions + # or a list of line list names (used by `load_pypeit_calibration_lines`) for each arc + # spectrum. + if line_lists is not None: + if len(line_lists) != self.nframes: + raise ValueError("The number of line lists must match the number of arc spectra.") + self._read_linelists(line_lists, line_list_bounds=line_list_bounds, wave_air=wave_air) + + def _init_model(self): + self._p2w = models.Shift(-self.ref_pixel) | models.Polynomial1D(self.degree) + + def _read_linelists( + self, + line_lists, + line_list_bounds: tuple[float, float] = (0.0, np.inf), + wave_air: bool = False, + ): """Load and filter calibration line lists for specified lamps and wavelength bounds. Notes @@ -91,20 +126,24 @@ def _read_linelists(self, line_lists): if not isinstance(line_lists, (tuple, list)): line_lists = [line_lists] - self.lines_wav = [] + self._lines_wav = [] for l in line_lists: if isinstance(l, ndarray): - self.lines_wav.append(l) + self._lines_wav.append(l) else: lines = [] if isinstance(l, str): l = [l] for ll in l: - lines.append(load_pypeit_calibration_lines(ll, wave_air=self.wave_air)['wavelength'].to(self.unit).value) - self.lines_wav.append(np.concatenate(lines)) - for i, l in enumerate(self.lines_wav): - self.lines_wav[i] = l[(l >= self.wlbounds[0]) & (l <= self.wlbounds[1])] - self._trees = [KDTree(l[:, None]) for l in self.lines_wav] + lines.append( + load_pypeit_calibration_lines(ll, wave_air=wave_air)["wavelength"] + .to(self.unit) + .value + ) + self._lines_wav.append(np.ma.masked_array(np.sort(np.concatenate(lines)))) + for i, l in enumerate(self._lines_wav): + self._lines_wav[i] = l[(l >= line_list_bounds[0]) & (l <= line_list_bounds[1])] + self._trees = [KDTree(l.data[:, None]) for l in self._lines_wav] def find_lines(self, fwhm: float): """Finds lines in the provided arc spectra. @@ -119,16 +158,46 @@ def find_lines(self, fwhm: float): the ``find_arc_lines`` function to locate and identify spectral arc lines. """ with warnings.catch_warnings(): - warnings.simplefilter('ignore') + warnings.simplefilter("ignore") lines_obs = [find_arc_lines(sp, fwhm) for sp in self.arc_spectra] - self.lines_pix = [lo['centroid'].value for lo in lines_obs] - - def fit(self, ref_pixel: float, - wavelength_bounds: tuple[float, float], - dispersion_bounds: tuple[float, float], - popsize: int = 30, - max_distance: float = 100, - refine_fit: bool = True): + self._lines_pix = [np.ma.masked_array(lo["centroid"].value) for lo in lines_obs] + + def fit_lines(self, pixels: Sequence, wavelengths: Sequence) -> None: + pixels = np.asarray(pixels) + wavelengths = np.asarray(wavelengths) + if pixels.size != wavelengths.size: + raise ValueError("The sizes of pixel and wavelength arrays must match.") + nlines = pixels.size + if nlines < 2: + raise ValueError("Need at least two lines for a fit") + + if self.bounds_pix is None: + raise ValueError("Cannot fit without pixel bounds set.") + + if self._p2w is None: + self._init_model() + + fitter = fitting.LinearLSQFitter() + m = self._p2w[1] + if m.degree > nlines: + for i in range(nlines, m.degree + 1): + m.fixed[f"c{i}"] = True + m = fitter(m, pixels - self.ref_pixel, wavelengths) + for i in range(m.degree + 1): + m.fixed[f"c{i}"] = False + self._p2w = self._p2w[0] | m + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + + def fit( + self, + ref_pixel: float, + wavelength_bounds: tuple[float, float], + dispersion_bounds: tuple[float, float], + popsize: int = 30, + max_distance: float = 100, + refine_fit: bool = True, + ): """Calculate and refine the pixel-to-wavelength and wavelength-to-pixel transformations. This method determines the functional relationships between pixel positions and @@ -158,56 +227,29 @@ def fit(self, ref_pixel: float, Raised if the optimization or fitting process fails due to invalid inputs or constraints. """ model = models.Shift(-ref_pixel) | models.Polynomial1D(3) + def minfun(x): total_distance = 0.0 - for t, l in zip(self._trees, self.lines_pix): + for t, l in zip(self._trees, self._lines_pix): transformed_lines = model.evaluate(l, -ref_pixel, *x)[:, None] total_distance += np.clip(t.query(transformed_lines)[0], 0, max_distance).sum() return total_distance - self._fit = fit = optimize.differential_evolution(minfun, - bounds=[wavelength_bounds, - dispersion_bounds, - [-1e-3, 1e-3], - [-1e-4, 1e-4]], - popsize=popsize) - self._p2w = (models.Shift(-ref_pixel) | - models.Polynomial1D(3, **{f'c{i}': fit.x[i] for i in range(fit.x.size)})) + self._fit = fit = optimize.differential_evolution( + minfun, + bounds=[wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3], [-1e-4, 1e-4]], + popsize=popsize, + ) + self._p2w = models.Shift(-ref_pixel) | models.Polynomial1D( + 3, **{f"c{i}": fit.x[i] for i in range(fit.x.size)} + ) if refine_fit: - self._refine_fit() - self._calculate_inverse() - self._calculate_derivatives() - - def _calculate_inverse(self): - """Compute the wavelength-to-pixel mapping from the pixel-to-wavelength transformation. - - Compute and set the inverse mapping from wavelength reference system to pixel reference system - through polynomial fitting. This method uses the DogBoxLSQFitter to fit the data and produces - a transformation model to establish the inverse relation. - """ - if self.arc_spectra is None: - vpix = np.concatenate(self.lines_pix) - else: - vpix = self.arc_spectra[0].spectral_axis.value - - vwav = self._p2w(vpix) - w2p = models.Polynomial1D(6, c0=-self._p2w.offset_0, c1=1/self._p2w.c1_1, fixed={'c0': True}) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - fitter = fitting.DogBoxLSQFitter() - self._w2p = models.Shift(-self._p2w.c0_1) | fitter(w2p, vwav - self._p2w.c0_1.value, vpix) + self.refine_fit() + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() - def _calculate_derivatives(self): - """Calculate the forward and reverse mapping derivatives with respect to the spectral axis. - """ - if self._p2w is not None: - self._p2w_dldx = models.Shift(self._p2w.offset_0) | _diff_poly1d(self._p2w[1]) - if self._w2p is not None: - self._w2p_dxdl = models.Shift(self._w2p.offset_0) | _diff_poly1d(self._w2p[1]) - - - def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + def refine_fit(self, match_distance_bound: float = 5.0): """Refine the global fit of the pixel-to-wavelength transformation. Refines the fit of a polynomial model to data by performing a fitting operation @@ -225,16 +267,35 @@ def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): data points. Points exceeding the bound will not be considered in the fit. Default is 5.0. """ - matched_pix, matched_wav = self.match_lines(match_distance_bound) - model = models.Polynomial1D(degree, **{n: getattr(self._p2w[-1], n).value for n in self._p2w[-1].param_names}) + self.match_lines(match_distance_bound) + matched_pix = np.ma.concatenate(self._lines_pix).compressed() + matched_wav = np.ma.concatenate(self._lines_wav).compressed() + model = self._p2w[1] fitter = fitting.LinearLSQFitter() - self._p2w = self._p2w[0].copy() | fitter(model, matched_pix + self._p2w.offset_0.value, matched_wav) - + self._p2w = self._p2w[0].copy() | fitter( + model, matched_pix - self.ref_pixel, matched_wav + ) + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + + def _calculate_p2w_derivative(self): + """Calculate (d wavelength) / (d pixel) for the pixel-to-wavelength transformation.""" + if self._p2w is not None: + self._p2w_dldx = models.Shift(self._p2w.offset_0) | _diff_poly1d(self._p2w[1]) - def resample(self, spectrum: Spectrum1D, - nbins: int | None = None, - wlbounds: tuple[float, float] | None = None, - bin_edges: Sequence[float] | None = None) -> Spectrum1D: + def _calculate_p2w_inverse(self) -> None: + """Compute the wavelength-to-pixel mapping from the pixel-to-wavelength transformation.""" + p = np.arange(*self.bounds_pix) + self._w2p = interp1d(self._p2w(p), p, bounds_error=False, fill_value=np.nan) + self.bounds_wav = self._p2w(self.bounds_pix) + + def resample( + self, + spectrum: Spectrum1D, + nbins: int | None = None, + wlbounds: tuple[float, float] | None = None, + bin_edges: Sequence[float] | None = None, + ) -> Spectrum1D: """Bin the given pixel-space 1D spectrum to a wavelength space conserving the flux. This method bins a pixel-space spectrum to a wavelength space using the computed pixel-to-wavelength and @@ -279,14 +340,14 @@ def resample(self, spectrum: Spectrum1D, dldx = self._p2w_dldx(np.arange(npix)) n = flux.sum() / (dldx * flux).sum() for i in range(nbins): - i1, i2 = bin_edge_ix[i:i + 2] + i1, i2 = bin_edge_ix[i : i + 2] weights[:] = 0 if i1 != i2: - weights[i1 + 1:i2] = 1.0 + weights[i1 + 1 : i2] = 1.0 weights[i1] = 1 - bin_edge_w[i] weights[i2] = bin_edge_w[i + 1] - flux_wl[i] = (weights[i1:i2 + 1] * flux[i1:i2 + 1] * dldx[i1:i2 + 1]).sum() - ucty_wl[i] = (weights[i1:i2 + 1] * ucty[i1:i2 + 1] * dldx[i1:i2 + 1]).sum() + flux_wl[i] = (weights[i1 : i2 + 1] * flux[i1 : i2 + 1] * dldx[i1 : i2 + 1]).sum() + ucty_wl[i] = (weights[i1 : i2 + 1] * ucty[i1 : i2 + 1] * dldx[i1 : i2 + 1]).sum() else: flux_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * flux[i1] * dldx[i1] ucty_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * ucty[i1] * dldx[i1] @@ -294,7 +355,7 @@ def resample(self, spectrum: Spectrum1D, ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(type(spectrum.uncertainty)) return Spectrum1D(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) - def pix_to_wav(self, pix: ndarray |float) -> ndarray | float: + def pix_to_wav(self, pix: ndarray | float) -> ndarray | float: """Map pixel values into wavelength values. Parameters @@ -322,39 +383,63 @@ def wav_to_pix(self, wav: ndarray | float) -> ndarray | float: """ return self._w2p(wav) + @property + def lines_pix(self) -> list[MaskedArray]: + """List of pixel positions of identified spectral lines.""" + return self._lines_pix + + @property + def lines_wav(self) -> list[MaskedArray]: + """List of wavelength positions of theoretical spectral lines.""" + return self._lines_wav + @property def wcs(self): - pixel_frame = cf.CoordinateFrame(1, "SPECTRAL", [0, ], axes_names=["x", ], unit=[u.pix]) - spectral_frame = cf.SpectralFrame(axes_names=["wavelength", ], unit=[self.unit]) + pixel_frame = cf.CoordinateFrame( + 1, + "SPECTRAL", + (0,), + axes_names=[ + "x", + ], + unit=[u.pix], + ) + spectral_frame = cf.SpectralFrame( + axes_names=("wavelength",), + unit=[self.unit], + ) pipeline = [(pixel_frame, self.fitted_model), (spectral_frame, None)] self._wcs = wcs.WCS(pipeline) return self._wcs - def match_lines(self, upper_bound: float = 5) -> tuple[ndarray, ndarray]: + def match_lines(self, upper_bound: float = 5, concatenate_frames: bool = True) -> None: """Match the observed lines to theoretical lines. -s. + Parameters ---------- upper_bound The maximum allowed distance between the query points and the KD-tree data points for them to be considered a match. - - Returns - ------- - A tuple containing two concatenated arrays: - - An array of matched line positions in pixel coordinates. - - An array of matched line positions in wavelength coordinates. """ matched_lines_wav = [] matched_lines_pix = [] for iframe, tree in enumerate(self._trees): - l, ix = tree.query(self._p2w(self.lines_pix[iframe])[:, None], distance_upper_bound=upper_bound) + l, ix = tree.query( + self._p2w(self._lines_pix[iframe])[:, None], distance_upper_bound=upper_bound + ) m = np.isfinite(l) - matched_lines_wav.append(tree.data[ix[m], 0]) - matched_lines_pix.append(self.lines_pix[iframe][m]) - return np.concatenate(matched_lines_pix), np.concatenate(matched_lines_wav) + matched_lines_wav.append(np.ma.masked_array(tree.data[:, 0], mask=True)) + matched_lines_wav[-1].mask[ix[m]] = False + matched_lines_pix.append(np.ma.masked_array(self._lines_pix[iframe], mask=~m)) + + self._lines_pix = matched_lines_pix + self._lines_wav = matched_lines_wav + + def remove_ummatched_lines(self): + self._lines_pix = [np.ma.masked_array(l.compressed()) for l in self._lines_pix] + self._lines_wav = [np.ma.masked_array(l.compressed()) for l in self._lines_wav] - def rms(self, space: Literal['pixel', 'wavelength'] = 'wavelength') -> float: + def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: """Compute the RMS of the residuals between matched lines in either the pixel or wavelength space. Parameters @@ -369,12 +454,21 @@ def rms(self, space: Literal['pixel', 'wavelength'] = 'wavelength') -> float: float """ mpix, mwav = self.match_lines() - if space == 'wavelength': + if space == "wavelength": return np.sqrt(((mwav - self.pix_to_wav(mpix)) ** 2).mean()) else: return np.sqrt(((mpix - self.wav_to_pix(mwav)) ** 2).mean()) - def plot_lines(self, axes: Axes | None = None, figsize: tuple[float, float] | None = None) -> Figure: + def plot( + self, + axes: Sequence[Axes] | None = None, + frames: Sequence[int] | int | None = None, + figsize: tuple[float, float] | None = None, + map_x: bool = False, + plot_obs_lines: bool = True, + plot_listed_lines: bool = True, + plot_line_values: bool = True, + ) -> Figure: """Plot the arc spectra and mark identified line positions for all the data sets. This method plots the spectral data stored in `arc_spectra` for each dataset @@ -394,23 +488,94 @@ def plot_lines(self, axes: Axes | None = None, figsize: tuple[float, float] | No ------- fig : matplotlib.figure.Figure """ + if frames is None: + frames = np.arange(self.nframes) + elif np.isscalar(frames): + frames = [frames] + nframes = len(frames) + if axes is None: - fig, axes = subplots(self.ndata, 1, figsize=figsize, sharex='all', - constrained_layout=True, squeeze=False) + fig, axes = subplots( + nframes, 1, figsize=figsize, sharex="all", constrained_layout=True, squeeze=False + ) else: - fig = axes[0].figure + axes = np.atleast_2d([axes]) + fig = axes[0, 0].figure + + if map_x: + xlabel = f"Wavelength [{self.unit.to_string('latex')}]" + transform = self._p2w + else: + xlabel = "Pixel" + transform = lambda x: x - for i in range(self.ndata): + for iax, ifr in enumerate(frames): if self.arc_spectra is not None: - axes[i,0].plot(self.arc_spectra[i].data / (1.2 * self.arc_spectra[i].data.max())) - axes[i,0].vlines(self.lines_pix[i], 0.0, 1, alpha=0.1) - axes[i,0].vlines(self.lines_pix[i], 0.9, 1) - axes[i,0].autoscale(enable=True, axis='x', tight=True) - setp(axes[-1], xlabel='Pixel') + axes[iax, 0].plot( + transform(self.arc_spectra[ifr].spectral_axis.value), + self.arc_spectra[ifr].data / (1.2 * self.arc_spectra[ifr].data.max()), + c="k", + zorder=10, + ) + + if plot_obs_lines and self._lines_pix is not None: + lpix = self._lines_pix[ifr].data + axes[iax, 0].vlines(transform(lpix), 0.0, 1, alpha=0.5) + axes[iax, 0].vlines(transform(lpix), 0.9, 1, zorder=14) + if plot_line_values: + for i, l in enumerate(sorted(lpix)): + axes[iax, 0].text( + transform(l), + 0.25 + 0.25 * (i % 3), + f"{l:.0f}", + rotation=90, + ha="right", + va="center", + bbox=dict(alpha=0.8, fc="w", lw=0), + zorder=20, + size='small' + ) + + if plot_listed_lines and map_x: + lwav = self._lines_wav[ifr].data + axes[iax, 0].vlines( + lwav, 0.0, 1, alpha=0.5, color="C1", ls="--", zorder=11 + ) + axes[iax, 0].vlines(lwav, 0.95, 1, color="C1", lw=4, zorder=13) + + axes[iax, 0].autoscale(enable=True, axis="x", tight=True) + setp(axes[iax, 0], yticks=[]) + setp(axes[-1], xlabel=xlabel) + return fig + + def plot_fit(self, frame: int = 0, figsize: tuple[float, float] | None = None, + plot_values: bool = True, transform_x: bool = False) -> Figure: + fig, axs = subplots(2, 1, constrained_layout=True, figsize=figsize) + + if self._lines_wav is not None: + axs[0].vlines(self._lines_wav[frame].data, 0, 1) + if plot_values: + for i, l in enumerate(sorted(self._lines_wav[frame].data)): + axs[0].text(l, 0.25 + 0.25 * (i % 3), f"{l:.0f}", rotation=90, ha='right', + va='center', bbox=dict(alpha=0.8, fc='w', lw=0), size='small') + + if transform_x and self._p2w is None: + raise ValueError("Pixel to wavelength transform does not exist.") + self.plot(axs[1:], plot_line_values=plot_values, map_x=transform_x, plot_listed_lines=False) + if transform_x: + axs[1].set_xlim(axs[0].get_xlim()) + + setp(axs[0], yticks=[], xlabel=f'Wavelength [{self._unit_str}]') + axs[0].xaxis.set_label_position('top') + axs[0].xaxis.tick_top() return fig - def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | None = None, - model: Callable | None = None) -> Figure: + def plot_solution( + self, + axes: Axes | None = None, + figsize: tuple[float, float] | None = None, + model: Callable | None = None, + ) -> Figure: """Plot the wavelength solution applied to the provided spectra and overlay it for visualization. This method generates plots for the given arc spectra, showcasing the results of the model @@ -435,26 +600,33 @@ def plot_solution(self, axes: Axes | None = None, figsize: tuple[float, float] | fig : matplotlib.figure.Figure """ if axes is None: - fig, axes = subplots(self.ndata, 1, figsize=figsize, sharex='all', - constrained_layout=True, squeeze=False) + fig, axes = subplots( + self.nframes, + 1, + figsize=figsize, + sharex="all", + constrained_layout=True, + squeeze=False, + ) else: fig = axes[0].figure model = model if model is not None else self._p2w - for i in range(self.ndata): + for i in range(self.nframes): if self.arc_spectra is not None: sp = self.arc_spectra[i] - axes[i,0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) - axes[i,0].vlines(self.lines_wav[i], 0.0, 1.0, alpha=0.3, ec='darkorange', zorder=0) - axes[i,0].vlines(model(self.lines_pix[i]), 0.9, 1.0, alpha=1) - axes[i,0].autoscale(enable=True, axis='x', tight=True) + axes[i, 0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) + axes[i, 0].vlines(self._lines_wav[i], 0.0, 1.0, alpha=0.3, ec="darkorange", zorder=0) + axes[i, 0].vlines(model(self._lines_pix[i]), 0.9, 1.0, alpha=1) + axes[i, 0].autoscale(enable=True, axis="x", tight=True) setp(axes[-1], xlabel=f'Wavelength [{self.unit.to_string(format="latex")}]') return fig - def plot_transforms(self, figsize: tuple[float, float] | None = None, - plim: tuple[int, int] | None = None) -> Figure: - """ Plot and visualize transformation functions between pixel and wavelength space. + def plot_transforms( + self, figsize: tuple[float, float] | None = None, plim: tuple[int, int] | None = None + ) -> Figure: + """Plot and visualize transformation functions between pixel and wavelength space. This method generates a grid of subplots to illustrate the transformations between pixel and wavelength spaces in two directions: Pixel -> Wavelength @@ -472,30 +644,34 @@ def plot_transforms(self, figsize: tuple[float, float] | None = None, ------- matplotlib.figure.Figure """ - fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex='col') + fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex="col") if self.arc_spectra is not None and plim is None: xpix = self.arc_spectra[0].spectral_axis.value else: xpix = np.arange(*(plim or (0, 2000))) xwav = np.linspace(*self.pix_to_wav(xpix[[0, -1]]), num=xpix.size) - axs[0, 0].plot(xpix, self._p2w(xpix), 'k') - axs[1, 0].plot(xpix[:-1], np.diff(self._p2w(xpix)) / np.diff(xpix), lw=4, c='k') - axs[1, 0].plot(xpix, self._p2w_dldx(xpix), ls='--', lw=2, c='w') - axs[0, 1].plot(xwav, self._w2p(xwav), 'k') - axs[1, 1].plot(xwav[:-1], np.diff(self._w2p(xwav)) / np.diff(xwav), lw=4, c='k') - axs[1, 1].plot(xwav, self._w2p_dxdl(xwav), ls='--', lw=2, c='w') - setp(axs[1,0], xlabel='Pixel', ylabel=r'd$\lambda$/dx') - setp(axs[0,0], ylabel=fr'$\lambda$ [{self.unit}]') - setp(axs[1,1], xlabel=fr'$\lambda$ [{self.unit}]', ylabel=r'dx/d$\lambda$') - setp(axs[0,1], ylabel='Pixel') - axs[0,0].set_title('Pixel -> wavelength') - axs[0,1].set_title('Wavelength -> pixel') + axs[0, 0].plot(xpix, self._p2w(xpix), "k") + axs[1, 0].plot(xpix[:-1], np.diff(self._p2w(xpix)) / np.diff(xpix), lw=4, c="k") + axs[1, 0].plot(xpix, self._p2w_dldx(xpix), ls="--", lw=2, c="w") + axs[0, 1].plot(xwav, self._w2p(xwav), "k") + axs[1, 1].plot(xwav[:-1], np.diff(self._w2p(xwav)) / np.diff(xwav), lw=4, c="k") + axs[1, 1].plot(xwav, self._w2p_dxdl(xwav), ls="--", lw=2, c="w") + setp(axs[1, 0], xlabel="Pixel", ylabel=r"d$\lambda$/dx") + setp(axs[0, 0], ylabel=rf"$\lambda$ [{self.unit}]") + setp(axs[1, 1], xlabel=rf"$\lambda$ [{self.unit}]", ylabel=r"dx/d$\lambda$") + setp(axs[0, 1], ylabel="Pixel") + axs[0, 0].set_title("Pixel -> wavelength") + axs[0, 1].set_title("Wavelength -> pixel") fig.align_labels() return fig - def plot_residuals(self, ax: Axes | None = None, space: Literal['pixel', 'wavelength'] = 'wavelength', - figsize: tuple[float, float] | None = None) -> Figure: + def plot_residuals( + self, + ax: Axes | None = None, + space: Literal["pixel", "wavelength"] = "wavelength", + figsize: tuple[float, float] | None = None, + ) -> Figure: """Plot the residuals of pixel-to-wavelength or wavelength-to-pixel transformation. Parameters @@ -519,20 +695,33 @@ def plot_residuals(self, ax: Axes | None = None, space: Literal['pixel', 'wavele mpix, mwav = self.match_lines() - if space == 'wavelength': + if space == "wavelength": twav = self.pix_to_wav(mpix) - ax.plot(mwav, mwav - twav, '.') - ax.text(0.98, 0.95, - f"RMS = {np.sqrt(((mwav - twav) ** 2).mean()):4.2f} {self.unit.to_string(format="latex")}", - transform=ax.transAxes, ha='right', va='top') - setp(ax, - xlabel=f'Wavelength [{self.unit.to_string(format="latex")}]', - ylabel=f'Residuals [{self.unit.to_string(format="latex")}]') + ax.plot(mwav, mwav - twav, ".") + ax.text( + 0.98, + 0.95, + f"RMS = {np.sqrt(((mwav - twav) ** 2).mean()):4.2f} {self._unit_str}", + transform=ax.transAxes, + ha="right", + va="top", + ) + setp( + ax, + xlabel=f'Wavelength [{self._unit_str}]', + ylabel=f'Residuals [{self._unit_str}]', + ) else: tpix = self.wav_to_pix(mwav) - ax.plot(mpix, mpix - tpix, '.') - ax.text(0.98, 0.95, f"RMS = {np.sqrt(((mpix - tpix) ** 2).mean()):4.2f} pix", transform=ax.transAxes, - ha='right', va='top') - setp(ax, xlabel='Pixel', ylabel='Residuals [pix]') - ax.axhline(0, c='k', lw=1, ls='--') + ax.plot(mpix, mpix - tpix, ".") + ax.text( + 0.98, + 0.95, + f"RMS = {np.sqrt(((mpix - tpix) ** 2).mean()):4.2f} pix", + transform=ax.transAxes, + ha="right", + va="top", + ) + setp(ax, xlabel="Pixel", ylabel="Residuals [pix]") + ax.axhline(0, c="k", lw=1, ls="--") return fig From 58d3bbd1c2eea572f0c1a41472128f54d116144f Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sat, 5 Apr 2025 21:31:34 +0100 Subject: [PATCH 18/76] Replaced internal `_lines_pix` and `_lines_wav` attribute modifications with setters to ensure proper masking and validation. Added `plot_lines_pix` and `plot_lines_wav` methods for enhanced modularity in plotting observed and theoretical lines. --- specreduce/wavecal1d.py | 106 ++++++++++++++++++++++++++++++++++------ 1 file changed, 92 insertions(+), 14 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index f4e83533..513a1cb1 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -89,7 +89,7 @@ def __init__( self.bounds_pix = (0, self.arc_spectra[0].shape[0]) elif obs_lines is not None: - self._lines_pix = [obs_lines] if isinstance(obs_lines, ndarray) else obs_lines + self.lines_pix = obs_lines self.nframes = len(self._lines_pix) if self.bounds_pix is None: raise ValueError("Must give pixel bounds when providing observed line positions.") @@ -126,10 +126,10 @@ def _read_linelists( if not isinstance(line_lists, (tuple, list)): line_lists = [line_lists] - self._lines_wav = [] + lines_wav = [] for l in line_lists: if isinstance(l, ndarray): - self._lines_wav.append(l) + lines_wav.append(l) else: lines = [] if isinstance(l, str): @@ -140,9 +140,11 @@ def _read_linelists( .to(self.unit) .value ) - self._lines_wav.append(np.ma.masked_array(np.sort(np.concatenate(lines)))) - for i, l in enumerate(self._lines_wav): - self._lines_wav[i] = l[(l >= line_list_bounds[0]) & (l <= line_list_bounds[1])] + lines_wav.append(np.ma.masked_array(np.sort(np.concatenate(lines)))) + for i, l in enumerate(lines_wav): + lines_wav[i] = l[(l >= line_list_bounds[0]) & (l <= line_list_bounds[1])] + + self.lines_wav = lines_wav self._trees = [KDTree(l.data[:, None]) for l in self._lines_wav] def find_lines(self, fwhm: float): @@ -388,11 +390,33 @@ def lines_pix(self) -> list[MaskedArray]: """List of pixel positions of identified spectral lines.""" return self._lines_pix + @lines_pix.setter + def lines_pix(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + if not isinstance(lines_pix, Sequence): + lines_pix = [lines_pix] + self._lines_pix = [] + for l in lines_pix: + if isinstance(l, MaskedArray) and l.mask is not np.False_: + self._lines_pix.append(l) + else: + self._lines_pix.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) + @property def lines_wav(self) -> list[MaskedArray]: """List of wavelength positions of theoretical spectral lines.""" return self._lines_wav + @lines_wav.setter + def lines_wav(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + if not isinstance(lines_wav, Sequence): + lines_wav = [lines_wav] + self._lines_wav = [] + for l in lines_wav: + if isinstance(l, MaskedArray) and l.mask is not np.False_: + self._lines_wav.append(l) + else: + self._lines_wav.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) + @property def wcs(self): pixel_frame = cf.CoordinateFrame( @@ -548,19 +572,73 @@ def plot( setp(axes[-1], xlabel=xlabel) return fig - def plot_fit(self, frame: int = 0, figsize: tuple[float, float] | None = None, - plot_values: bool = True, transform_x: bool = False) -> Figure: - fig, axs = subplots(2, 1, constrained_layout=True, figsize=figsize) + def plot_lines_pix(self, frame: int = 0, + ax: Axes | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool = True, map_to_wav: bool = False) -> Figure: + if ax is None: + fig, ax = subplots(figsize=figsize, constrained_layout=True) + else: + fig = ax.figure + + if map_to_wav and self._p2w is None: + raise ValueError('Cannot map pixels to wavelengths without a fitted model.') + + transform = self.pix_to_wav if map_to_wav else lambda x: x + + if self._lines_pix is not None: + lines = self._lines_pix[frame] + ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=':') + ax.vlines(transform(lines[~lines.mask].data), 0, 1) + if plot_values: + for i, l in enumerate(transform(lines.data)): + if np.isfinite(l): + ax.text(l, 0.25 + 0.25 * (i % 3), f"{l:.0f}", rotation=90, ha='right', + va='center', bbox=dict(alpha=0.8, fc='w', lw=0), size='small') + + if not map_to_wav: + setp(ax, yticks=[], xlabel='Observed lines [Pixel]') + else: + setp(ax, yticks=[], xlabel=f'Observed lines [{self._unit_str}]') + return fig + + def plot_lines_wav(self, frame: int = 0, + ax: Axes | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool = True, map_to_pix: bool = False) -> Figure: + if ax is None: + fig, ax = subplots(figsize=figsize, constrained_layout=True) + else: + fig = ax.figure + + if map_to_pix and self._p2w is None: + raise ValueError('Cannot map wavelengths to pixels without a fitted model.') + + transform = self.wav_to_pix if map_to_pix else lambda x: x if self._lines_wav is not None: - axs[0].vlines(self._lines_wav[frame].data, 0, 1) + lines = self._lines_wav[frame] + ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=':') + ax.vlines(transform(lines[~lines.mask].data), 0, 1) if plot_values: - for i, l in enumerate(sorted(self._lines_wav[frame].data)): - axs[0].text(l, 0.25 + 0.25 * (i % 3), f"{l:.0f}", rotation=90, ha='right', + for i, l in enumerate(transform(lines.data)): + if np.isfinite(l): + ax.text(l, 0.25 + 0.25 * (i % 3), f"{l:.0f}", rotation=90, ha='right', va='center', bbox=dict(alpha=0.8, fc='w', lw=0), size='small') - if transform_x and self._p2w is None: - raise ValueError("Pixel to wavelength transform does not exist.") + if not map_to_pix: + setp(ax, yticks=[], xlabel=f'Theoretical lines [{self._unit_str}]') + else: + setp(ax, yticks=[], xlabel='Theoretical lines [Pixel]') + ax.xaxis.set_label_position('top') + ax.xaxis.tick_top() + return fig + + def plot_fit(self, frame: int = 0, figsize: tuple[float, float] | None = None, + plot_values: bool = True, transform_x: bool = False, wav_to_pix: bool = False) -> Figure: + fig, axs = subplots(2, 1, constrained_layout=True, figsize=figsize) + + self.plot_lines_wav(frame, axs[0], plot_values=plot_values, map_to_pix=wav_to_pix) self.plot(axs[1:], plot_line_values=plot_values, map_x=transform_x, plot_listed_lines=False) if transform_x: axs[1].set_xlim(axs[0].get_xlim()) From 30cc05d5a6678fc462847e45e3e8555465f9c63b Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 8 Apr 2025 19:56:27 +0100 Subject: [PATCH 19/76] Fixed line matching to work with masked arrays. --- specreduce/wavecal1d.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 513a1cb1..c6ee2267 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -436,7 +436,7 @@ def wcs(self): self._wcs = wcs.WCS(pipeline) return self._wcs - def match_lines(self, upper_bound: float = 5, concatenate_frames: bool = True) -> None: + def match_lines(self, upper_bound: float = 5) -> None: """Match the observed lines to theoretical lines. Parameters @@ -449,12 +449,12 @@ def match_lines(self, upper_bound: float = 5, concatenate_frames: bool = True) - matched_lines_pix = [] for iframe, tree in enumerate(self._trees): l, ix = tree.query( - self._p2w(self._lines_pix[iframe])[:, None], distance_upper_bound=upper_bound + self._p2w(self._lines_pix[iframe].data)[:, None], distance_upper_bound=upper_bound ) m = np.isfinite(l) matched_lines_wav.append(np.ma.masked_array(tree.data[:, 0], mask=True)) matched_lines_wav[-1].mask[ix[m]] = False - matched_lines_pix.append(np.ma.masked_array(self._lines_pix[iframe], mask=~m)) + matched_lines_pix.append(np.ma.masked_array(self._lines_pix[iframe].data, mask=~m)) self._lines_pix = matched_lines_pix self._lines_wav = matched_lines_wav From a6d08163348414773d6a983f4dad893386849be4 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 8 Apr 2025 19:56:46 +0100 Subject: [PATCH 20/76] Fixed transformation to work with masked arrays. --- specreduce/wavecal1d.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index c6ee2267..c12b01e9 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -357,7 +357,7 @@ def resample( ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(type(spectrum.uncertainty)) return Spectrum1D(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) - def pix_to_wav(self, pix: ndarray | float) -> ndarray | float: + def pix_to_wav(self, pix: MaskedArray | ndarray | float) -> ndarray | float: """Map pixel values into wavelength values. Parameters @@ -369,9 +369,13 @@ def pix_to_wav(self, pix: ndarray | float) -> ndarray | float: ------- Transformed wavelength value(s) corresponding to the input pixel value(s). """ - return self._p2w(pix) + if isinstance(pix, MaskedArray): + wav = self._p2w(pix.data) + return np.ma.masked_array(wav, mask=pix.mask) + else: + return self._p2w(pix) - def wav_to_pix(self, wav: ndarray | float) -> ndarray | float: + def wav_to_pix(self, wav: MaskedArray | ndarray | float) -> ndarray | float: """Map wavelength values into pixel values. Parameters @@ -383,7 +387,11 @@ def wav_to_pix(self, wav: ndarray | float) -> ndarray | float: ------- The corresponding pixel value(s) for the input wavelength(s). """ - return self._w2p(wav) + if isinstance(wav, MaskedArray): + pix = self._w2p(wav.data) + return np.ma.masked_array(pix, mask=wav.mask) + else: + return self._w2p(wav) @property def lines_pix(self) -> list[MaskedArray]: From e668b5377ea7590118171250cd27650145b63cb3 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 13 Apr 2025 18:54:27 +0300 Subject: [PATCH 21/76] Updated the handling of observed and catalog lines. Introduced new methods, updated property names, and enhanced plotting functionality. --- specreduce/wavecal1d.py | 529 +++++++++++++++++++--------------------- 1 file changed, 257 insertions(+), 272 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index c12b01e9..666baa99 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -57,14 +57,14 @@ def __init__( ) -> None: self.unit = unit - self._unit_str = unit.to_string('latex') + self._unit_str = unit.to_string("latex") self.degree = degree self.ref_pixel = ref_pixel self.bounds_pix: tuple[int, int] | None = pix_bounds self.bounds_wav: tuple[float, float] | None = None - self._lines_wav: list[MaskedArray] | None = None - self._lines_pix: list[MaskedArray] | None = None + self._cat_lines: list[MaskedArray] | None = None + self._obs_lines: list[MaskedArray] | None = None self._trees: Sequence[KDTree] | None = None self._fit: optimize.OptimizeResult | None = None @@ -90,7 +90,7 @@ def __init__( elif obs_lines is not None: self.lines_pix = obs_lines - self.nframes = len(self._lines_pix) + self.nframes = len(self._obs_lines) if self.bounds_pix is None: raise ValueError("Must give pixel bounds when providing observed line positions.") @@ -145,10 +145,10 @@ def _read_linelists( lines_wav[i] = l[(l >= line_list_bounds[0]) & (l <= line_list_bounds[1])] self.lines_wav = lines_wav - self._trees = [KDTree(l.data[:, None]) for l in self._lines_wav] + self._trees = [KDTree(l.data[:, None]) for l in self._cat_lines] - def find_lines(self, fwhm: float): - """Finds lines in the provided arc spectra. + def find_lines(self, fwhm: float, noise_factor: float = 1.0) -> None: + """Find lines in the provided arc spectra. This method determines the spectral lines within each spectrum of the arc spectra based on the provided initial guess for the line Full Width at Half Maximum (FWHM). @@ -158,13 +158,42 @@ def find_lines(self, fwhm: float): fwhm Initial guess for the FWHM for the spectral lines, used as a parameter in the ``find_arc_lines`` function to locate and identify spectral arc lines. + noise_factor + The factor to multiply the uncertainty by to determine the noise threshold + in the `~specutils.fitting.find_lines_threshold` routine. """ with warnings.catch_warnings(): warnings.simplefilter("ignore") - lines_obs = [find_arc_lines(sp, fwhm) for sp in self.arc_spectra] - self._lines_pix = [np.ma.masked_array(lo["centroid"].value) for lo in lines_obs] + lines_obs = [ + find_arc_lines(sp, fwhm, noise_factor=noise_factor) for sp in self.arc_spectra + ] + self._obs_lines = [np.ma.masked_array(lo["centroid"].value) for lo in lines_obs] - def fit_lines(self, pixels: Sequence, wavelengths: Sequence) -> None: + def fit_lines( + self, + pixels: Sequence, + wavelengths: Sequence, + match_pix: bool = True, + match_wav: bool = True, + ) -> None: + """Fit wavelength calibration lines to a model. + + This method takes input arrays of pixel values and their corresponding wavelengths and fits + them to a calibration model, updating the pixel-to-wavelength transformation for the system. + Optionally, the method can match the provided pixel or wavelength values to known catalog + or observed values for better fitting. + + Parameters + ---------- + pixels + A sequence of pixel positions to be used for fitting. + wavelengths + A sequence of associated wavelengths corresponding to the pixel positions. + match_pix + A flag indicating whether to match the input pixel values to observed pixel values. + match_wav + A flag indicating whether to match the input wavelengths to catalog values. + """ pixels = np.asarray(pixels) wavelengths = np.asarray(wavelengths) if pixels.size != wavelengths.size: @@ -179,6 +208,18 @@ def fit_lines(self, pixels: Sequence, wavelengths: Sequence) -> None: if self._p2w is None: self._init_model() + # Match the input wavelengths to catalog lines. + if match_wav: + tree = KDTree(np.concatenate([c.data for c in self._cat_lines])[:, None]) + ix = tree.query(wavelengths[:, None])[1] + wavelengths = tree.data[ix][:, 0] + + # Match the input pixel values to observed pixel values. + if match_pix: + tree = KDTree(np.concatenate([c.data for c in self._obs_lines])[:, None]) + ix = tree.query(pixels[:, None])[1] + pixels = tree.data[ix][:, 0] + fitter = fitting.LinearLSQFitter() m = self._p2w[1] if m.degree > nlines: @@ -193,7 +234,6 @@ def fit_lines(self, pixels: Sequence, wavelengths: Sequence) -> None: def fit( self, - ref_pixel: float, wavelength_bounds: tuple[float, float], dispersion_bounds: tuple[float, float], popsize: int = 30, @@ -210,8 +250,6 @@ def fit( Parameters ---------- - ref_pixel - The reference pixel position used as the zero point for the transformation. wavelength_bounds Initial bounds for the wavelength value at the reference pixel. dispersion_bounds @@ -228,22 +266,31 @@ def fit( ValueError Raised if the optimization or fitting process fails due to invalid inputs or constraints. """ - model = models.Shift(-ref_pixel) | models.Polynomial1D(3) + + if self._p2w is None: + self._init_model() + model = self._p2w def minfun(x): total_distance = 0.0 - for t, l in zip(self._trees, self._lines_pix): - transformed_lines = model.evaluate(l, -ref_pixel, *x)[:, None] + for t, l in zip(self._trees, self._obs_lines): + transformed_lines = model.evaluate(l, -self.ref_pixel, *x)[:, None] total_distance += np.clip(t.query(transformed_lines)[0], 0, max_distance).sum() return total_distance + bounds = np.concatenate( + [ + [wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3]], + np.zeros((model[1].degree - 2, 2)), + ] + ) self._fit = fit = optimize.differential_evolution( minfun, - bounds=[wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3], [-1e-4, 1e-4]], + bounds=bounds, popsize=popsize, ) - self._p2w = models.Shift(-ref_pixel) | models.Polynomial1D( - 3, **{f"c{i}": fit.x[i] for i in range(fit.x.size)} + self._p2w = models.Shift(-self.ref_pixel) | models.Polynomial1D( + self.degree, **{f"c{i}": fit.x[i] for i in range(fit.x.size)} ) if refine_fit: @@ -270,15 +317,14 @@ def refine_fit(self, match_distance_bound: float = 5.0): Default is 5.0. """ self.match_lines(match_distance_bound) - matched_pix = np.ma.concatenate(self._lines_pix).compressed() - matched_wav = np.ma.concatenate(self._lines_wav).compressed() + matched_pix = np.ma.concatenate(self._obs_lines).compressed() + matched_wav = np.ma.concatenate(self._cat_lines).compressed() model = self._p2w[1] fitter = fitting.LinearLSQFitter() - self._p2w = self._p2w[0].copy() | fitter( - model, matched_pix - self.ref_pixel, matched_wav - ) + self._p2w = self._p2w[0].copy() | fitter(model, matched_pix - self.ref_pixel, matched_wav) self._calculate_p2w_derivative() self._calculate_p2w_inverse() + self.match_lines(match_distance_bound) def _calculate_p2w_derivative(self): """Calculate (d wavelength) / (d pixel) for the pixel-to-wavelength transformation.""" @@ -325,8 +371,8 @@ def resample( npix = flux.size nbins = npix if nbins is None else nbins if wlbounds is None: - l1 = self._p2w(0) - self._p2w_dldx(0) - l2 = self._p2w(npix) + self._p2w_dldx(npix) + l1 = self._p2w(0) + l2 = self._p2w(npix - 1) else: l1, l2 = wlbounds @@ -396,34 +442,34 @@ def wav_to_pix(self, wav: MaskedArray | ndarray | float) -> ndarray | float: @property def lines_pix(self) -> list[MaskedArray]: """List of pixel positions of identified spectral lines.""" - return self._lines_pix + return self._obs_lines @lines_pix.setter def lines_pix(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): if not isinstance(lines_pix, Sequence): lines_pix = [lines_pix] - self._lines_pix = [] + self._obs_lines = [] for l in lines_pix: if isinstance(l, MaskedArray) and l.mask is not np.False_: - self._lines_pix.append(l) + self._obs_lines.append(l) else: - self._lines_pix.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) + self._obs_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) @property def lines_wav(self) -> list[MaskedArray]: """List of wavelength positions of theoretical spectral lines.""" - return self._lines_wav + return self._cat_lines @lines_wav.setter def lines_wav(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): if not isinstance(lines_wav, Sequence): lines_wav = [lines_wav] - self._lines_wav = [] + self._cat_lines = [] for l in lines_wav: if isinstance(l, MaskedArray) and l.mask is not np.False_: - self._lines_wav.append(l) + self._cat_lines.append(l) else: - self._lines_wav.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) + self._cat_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) @property def wcs(self): @@ -440,7 +486,7 @@ def wcs(self): axes_names=("wavelength",), unit=[self.unit], ) - pipeline = [(pixel_frame, self.fitted_model), (spectral_frame, None)] + pipeline = [(pixel_frame, self._p2w), (spectral_frame, None)] self._wcs = wcs.WCS(pipeline) return self._wcs @@ -457,22 +503,42 @@ def match_lines(self, upper_bound: float = 5) -> None: matched_lines_pix = [] for iframe, tree in enumerate(self._trees): l, ix = tree.query( - self._p2w(self._lines_pix[iframe].data)[:, None], distance_upper_bound=upper_bound + self._p2w(self._obs_lines[iframe].data)[:, None], + distance_upper_bound=upper_bound, ) m = np.isfinite(l) + + # Check for observed lines that match to a same catalog line. + # Remove all but the nearest match. This isn't an optimal solution, + # we could also iterate the match by removing the currently matched + # lines, but this works for now. + uix, cnt = np.unique(ix[m], return_counts=True) + if any(n := cnt > 1): + for i, c in zip(uix[n], cnt[n]): + s = ix == i + r = np.zeros(c, dtype=bool) + r[np.argmin(l[s])] = True + m[s] = r + matched_lines_wav.append(np.ma.masked_array(tree.data[:, 0], mask=True)) matched_lines_wav[-1].mask[ix[m]] = False - matched_lines_pix.append(np.ma.masked_array(self._lines_pix[iframe].data, mask=~m)) + matched_lines_pix.append(np.ma.masked_array(self._obs_lines[iframe].data, mask=~m)) + + self._obs_lines = matched_lines_pix + self._cat_lines = matched_lines_wav - self._lines_pix = matched_lines_pix - self._lines_wav = matched_lines_wav + if any([o.count() != c.count() for o, c in zip(self._obs_lines, self._cat_lines)]): + warnings.warn( + "Line matching failed, the number of matched catalog lines != " + "the number of matched observed lines." + ) def remove_ummatched_lines(self): - self._lines_pix = [np.ma.masked_array(l.compressed()) for l in self._lines_pix] - self._lines_wav = [np.ma.masked_array(l.compressed()) for l in self._lines_wav] + self._obs_lines = [np.ma.masked_array(l.compressed()) for l in self._obs_lines] + self._cat_lines = [np.ma.masked_array(l.compressed()) for l in self._cat_lines] def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: - """Compute the RMS of the residuals between matched lines in either the pixel or wavelength space. + """Compute the RMS of the residuals between matched lines in the pixel or wavelength space. Parameters ---------- @@ -485,271 +551,188 @@ def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: ------- float """ - mpix, mwav = self.match_lines() + self.match_lines() + mpix = np.ma.concatenate(self.lines_pix).compressed() + mwav = np.ma.concatenate(self.lines_wav).compressed() if space == "wavelength": return np.sqrt(((mwav - self.pix_to_wav(mpix)) ** 2).mean()) else: return np.sqrt(((mpix - self.wav_to_pix(mwav)) ** 2).mean()) - def plot( + def _plot_lines( self, - axes: Sequence[Axes] | None = None, - frames: Sequence[int] | int | None = None, + kind: Literal["observed", "catalog"], + frames: int | Sequence[int] | None = None, + axs: Axes | Sequence[Axes] | None = None, figsize: tuple[float, float] | None = None, + plot_values: bool | Sequence[bool] = True, map_x: bool = False, - plot_obs_lines: bool = True, - plot_listed_lines: bool = True, - plot_line_values: bool = True, ) -> Figure: - """Plot the arc spectra and mark identified line positions for all the data sets. - - This method plots the spectral data stored in `arc_spectra` for each dataset - in separate subplots. It also marks identified spectral line positions within - the plots. - Parameters - ---------- - axes - Pre-configured Matplotlib axes to be used for plotting. If `None`, new - axes objects are created automatically, arranged in a single-column layout. - figsize - Specifies the width and height of the figure in inches when creating new - axes. Ignored if `axes` is provided. - - Returns - ------- - fig : matplotlib.figure.Figure - """ if frames is None: frames = np.arange(self.nframes) - elif np.isscalar(frames): - frames = [frames] - nframes = len(frames) - - if axes is None: - fig, axes = subplots( - nframes, 1, figsize=figsize, sharex="all", constrained_layout=True, squeeze=False - ) - else: - axes = np.atleast_2d([axes]) - fig = axes[0, 0].figure - - if map_x: - xlabel = f"Wavelength [{self.unit.to_string('latex')}]" - transform = self._p2w else: - xlabel = "Pixel" - transform = lambda x: x + frames = np.atleast_1d(frames) - for iax, ifr in enumerate(frames): - if self.arc_spectra is not None: - axes[iax, 0].plot( - transform(self.arc_spectra[ifr].spectral_axis.value), - self.arc_spectra[ifr].data / (1.2 * self.arc_spectra[ifr].data.max()), - c="k", - zorder=10, - ) - - if plot_obs_lines and self._lines_pix is not None: - lpix = self._lines_pix[ifr].data - axes[iax, 0].vlines(transform(lpix), 0.0, 1, alpha=0.5) - axes[iax, 0].vlines(transform(lpix), 0.9, 1, zorder=14) - if plot_line_values: - for i, l in enumerate(sorted(lpix)): - axes[iax, 0].text( - transform(l), - 0.25 + 0.25 * (i % 3), - f"{l:.0f}", - rotation=90, - ha="right", - va="center", - bbox=dict(alpha=0.8, fc="w", lw=0), - zorder=20, - size='small' - ) - - if plot_listed_lines and map_x: - lwav = self._lines_wav[ifr].data - axes[iax, 0].vlines( - lwav, 0.0, 1, alpha=0.5, color="C1", ls="--", zorder=11 - ) - axes[iax, 0].vlines(lwav, 0.95, 1, color="C1", lw=4, zorder=13) - - axes[iax, 0].autoscale(enable=True, axis="x", tight=True) - setp(axes[iax, 0], yticks=[]) - setp(axes[-1], xlabel=xlabel) - return fig - - def plot_lines_pix(self, frame: int = 0, - ax: Axes | None = None, - figsize: tuple[float, float] | None = None, - plot_values: bool = True, map_to_wav: bool = False) -> Figure: - if ax is None: - fig, ax = subplots(figsize=figsize, constrained_layout=True) + if axs is None: + fig, axs = subplots(frames.size, 1, figsize=figsize, constrained_layout=True) + elif isinstance(axs, Axes): + fig = axs.figure + axs = [axs] else: - fig = ax.figure - - if map_to_wav and self._p2w is None: - raise ValueError('Cannot map pixels to wavelengths without a fitted model.') + fig = axs[0].figure - transform = self.pix_to_wav if map_to_wav else lambda x: x + if isinstance(plot_values, bool): + plot_values = np.full(frames.size, plot_values, dtype=bool) - if self._lines_pix is not None: - lines = self._lines_pix[frame] - ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=':') - ax.vlines(transform(lines[~lines.mask].data), 0, 1) - if plot_values: - for i, l in enumerate(transform(lines.data)): - if np.isfinite(l): - ax.text(l, 0.25 + 0.25 * (i % 3), f"{l:.0f}", rotation=90, ha='right', - va='center', bbox=dict(alpha=0.8, fc='w', lw=0), size='small') + if map_x and self._p2w is None: + raise ValueError("Cannot map between pixels and wavelengths without a fitted model.") - if not map_to_wav: - setp(ax, yticks=[], xlabel='Observed lines [Pixel]') + if kind == "observed": + transform = self.pix_to_wav if map_x else lambda x: x + linelists = self._obs_lines else: - setp(ax, yticks=[], xlabel=f'Observed lines [{self._unit_str}]') - return fig - - def plot_lines_wav(self, frame: int = 0, - ax: Axes | None = None, - figsize: tuple[float, float] | None = None, - plot_values: bool = True, map_to_pix: bool = False) -> Figure: - if ax is None: - fig, ax = subplots(figsize=figsize, constrained_layout=True) + transform = self.wav_to_pix if map_x else lambda x: x + linelists = self._cat_lines + + if linelists is not None: + for iax, (ax, frame) in enumerate(zip(axs, frames)): + lines = linelists[frame] + ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=":") + ax.vlines(transform(lines[~lines.mask].data), 0, 1) + if plot_values[iax]: + for i, l in enumerate(transform(lines.data)): + if np.isfinite(l): + ax.text( + l, + 0.25 + 0.25 * (i % 3), + f"{l:.0f}", + rotation=90, + ha="right", + va="center", + bbox=dict(alpha=0.8, fc="w", lw=0), + size="small", + ) + + if (kind == "observed" and not map_x) or (kind == "catalog" and map_x): + xlabel = "Pixel" else: - fig = ax.figure - - if map_to_pix and self._p2w is None: - raise ValueError('Cannot map wavelengths to pixels without a fitted model.') - - transform = self.wav_to_pix if map_to_pix else lambda x: x - - if self._lines_wav is not None: - lines = self._lines_wav[frame] - ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=':') - ax.vlines(transform(lines[~lines.mask].data), 0, 1) - if plot_values: - for i, l in enumerate(transform(lines.data)): - if np.isfinite(l): - ax.text(l, 0.25 + 0.25 * (i % 3), f"{l:.0f}", rotation=90, ha='right', - va='center', bbox=dict(alpha=0.8, fc='w', lw=0), size='small') - - if not map_to_pix: - setp(ax, yticks=[], xlabel=f'Theoretical lines [{self._unit_str}]') + xlabel = f"Wavelength {self._unit_str}" + + if kind == "catalog": + axs[0].xaxis.set_label_position("top") + axs[0].xaxis.tick_top() + setp(axs[0], xlabel=xlabel) + for ax in axs[1:]: + ax.set_xticklabels([]) else: - setp(ax, yticks=[], xlabel='Theoretical lines [Pixel]') - ax.xaxis.set_label_position('top') - ax.xaxis.tick_top() + setp(axs[-1], xlabel=xlabel) + for ax in axs[:-1]: + ax.set_xticklabels([]) + xlims = np.array([ax.get_xlim() for ax in axs]) + setp(axs, xlim=(xlims[:, 0].min(), xlims[:, 1].max()), yticks=[]) return fig - def plot_fit(self, frame: int = 0, figsize: tuple[float, float] | None = None, - plot_values: bool = True, transform_x: bool = False, wav_to_pix: bool = False) -> Figure: - fig, axs = subplots(2, 1, constrained_layout=True, figsize=figsize) - - self.plot_lines_wav(frame, axs[0], plot_values=plot_values, map_to_pix=wav_to_pix) - self.plot(axs[1:], plot_line_values=plot_values, map_x=transform_x, plot_listed_lines=False) - if transform_x: - axs[1].set_xlim(axs[0].get_xlim()) - - setp(axs[0], yticks=[], xlabel=f'Wavelength [{self._unit_str}]') - axs[0].xaxis.set_label_position('top') - axs[0].xaxis.tick_top() - return fig - - def plot_solution( + def plot_catalog_lines( self, - axes: Axes | None = None, + frames: int | Sequence[int] | None = None, + axes: Axes | Sequence[Axes] | None = None, figsize: tuple[float, float] | None = None, - model: Callable | None = None, + plot_values: bool = True, + map_to_pix: bool = False, ) -> Figure: - """Plot the wavelength solution applied to the provided spectra and overlay it for visualization. + return self._plot_lines( + "catalog", + frames=frames, + axs=axes, + figsize=figsize, + plot_values=plot_values, + map_x=map_to_pix, + ) - This method generates plots for the given arc spectra, showcasing the results of the model - fit. Each subplot represents a single spectrum with overlaid model predictions and markers - indicating the expected wavelengths. + def plot_observed_lines( + self, + frames: int | Sequence[int] | None = None, + axes: Axes | Sequence[Axes] | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool = True, + plot_spectra: bool = True, + map_to_wav: bool = False, + ) -> Figure: - Parameters - ---------- - axes - Array of Matplotlib Axes where the spectra and their corresponding solutions will be plotted. - If None, new Axes objects will be created for visualization. Must be provided in the case of - external figure context. - figsize - Tuple specifying the dimensions of the figure in inches if new Axes are created. Ignored if - `axes` is provided. Must follow the structure (width, height). - model - The model function to be applied for prediction on the spectral axis values. If None, the - already fitted model within the class instance (`self.fitted_model`) will be utilized. + fig = self._plot_lines( + "observed", + frames=frames, + axs=axes, + figsize=figsize, + plot_values=plot_values, + map_x=map_to_wav, + ) - Returns - ------- - fig : matplotlib.figure.Figure - """ if axes is None: - fig, axes = subplots( - self.nframes, - 1, - figsize=figsize, - sharex="all", - constrained_layout=True, - squeeze=False, + axes = fig.axes + + if self.arc_spectra is not None and plot_spectra: + if frames is None: + frames = np.arange(self.nframes) + elif np.isscalar(frames): + frames = [frames] + + transform = self._p2w if map_to_wav else lambda x: x + for i, frame in enumerate(frames): + axes[i].plot( + transform(self.arc_spectra[frame].spectral_axis.value), + self.arc_spectra[frame].data / (1.2 * self.arc_spectra[frame].data.max()), + c="k", + zorder=-10, + ) + setp( + axes, + xlim=transform( + [ + self.arc_spectra[0].spectral_axis.min().value, + self.arc_spectra[0].spectral_axis.max().value, + ] + ), ) - else: - fig = axes[0].figure - - model = model if model is not None else self._p2w - - for i in range(self.nframes): - if self.arc_spectra is not None: - sp = self.arc_spectra[i] - axes[i, 0].plot(model(sp.spectral_axis.value), sp.data / (1.2 * sp.data.max())) - axes[i, 0].vlines(self._lines_wav[i], 0.0, 1.0, alpha=0.3, ec="darkorange", zorder=0) - axes[i, 0].vlines(model(self._lines_pix[i]), 0.9, 1.0, alpha=1) - axes[i, 0].autoscale(enable=True, axis="x", tight=True) - setp(axes[-1], xlabel=f'Wavelength [{self.unit.to_string(format="latex")}]') return fig - def plot_transforms( - self, figsize: tuple[float, float] | None = None, plim: tuple[int, int] | None = None + def plot_fit( + self, + frames: Sequence[int] | int | None = None, + figsize: tuple[float, float] | None = None, + plot_values: bool = True, + obs_to_wav: bool = False, + cat_to_pix: bool = False, ) -> Figure: - """Plot and visualize transformation functions between pixel and wavelength space. - This method generates a grid of subplots to illustrate the transformations - between pixel and wavelength spaces in two directions: Pixel -> Wavelength - and Wavelength -> Pixel. It also includes visualizations of the derivatives - of these transformations with respect to the spectral axis. + if frames is None: + frames = np.arange(self.nframes) + else: + frames = np.atleast_1d(frames) - Parameters - ---------- - figsize - Width, height in inches to control the size of the figure. - plim - Lower and upper limits for pixel values used for plotting. + if self._p2w is not None and obs_to_wav: + transform = self._p2w + else: + transform = lambda x: x - Returns - ------- - matplotlib.figure.Figure - """ - fig, axs = subplots(2, 2, figsize=figsize, constrained_layout=True, sharex="col") - if self.arc_spectra is not None and plim is None: - xpix = self.arc_spectra[0].spectral_axis.value + fig, axs = subplots(2 * frames.size, 1, constrained_layout=True, figsize=figsize) + self.plot_catalog_lines(frames, axs[::2], plot_values=plot_values, map_to_pix=cat_to_pix) + self.plot_observed_lines(frames, axs[1::2], plot_values=plot_values, map_to_wav=obs_to_wav) + + xlims = np.array([ax.get_xlim() for ax in axs[::2]]) + if obs_to_wav: + setp(axs, xlim=(xlims[:, 0].min(), xlims[:, 1].max())) else: - xpix = np.arange(*(plim or (0, 2000))) - - xwav = np.linspace(*self.pix_to_wav(xpix[[0, -1]]), num=xpix.size) - axs[0, 0].plot(xpix, self._p2w(xpix), "k") - axs[1, 0].plot(xpix[:-1], np.diff(self._p2w(xpix)) / np.diff(xpix), lw=4, c="k") - axs[1, 0].plot(xpix, self._p2w_dldx(xpix), ls="--", lw=2, c="w") - axs[0, 1].plot(xwav, self._w2p(xwav), "k") - axs[1, 1].plot(xwav[:-1], np.diff(self._w2p(xwav)) / np.diff(xwav), lw=4, c="k") - axs[1, 1].plot(xwav, self._w2p_dxdl(xwav), ls="--", lw=2, c="w") - setp(axs[1, 0], xlabel="Pixel", ylabel=r"d$\lambda$/dx") - setp(axs[0, 0], ylabel=rf"$\lambda$ [{self.unit}]") - setp(axs[1, 1], xlabel=rf"$\lambda$ [{self.unit}]", ylabel=r"dx/d$\lambda$") - setp(axs[0, 1], ylabel="Pixel") - axs[0, 0].set_title("Pixel -> wavelength") - axs[0, 1].set_title("Wavelength -> pixel") - fig.align_labels() + setp(axs[::2], xlim=(xlims[:, 0].min(), xlims[:, 1].max())) + + setp(axs[0], yticks=[], xlabel=f"Wavelength [{self._unit_str}]") + for ax in axs[1:-1]: + ax.set_xlabel("") + ax.set_xticklabels("") + + axs[0].xaxis.set_label_position("top") + axs[0].xaxis.tick_top() return fig def plot_residuals( @@ -779,7 +762,9 @@ def plot_residuals( else: fig = ax.figure - mpix, mwav = self.match_lines() + self.match_lines() + mpix = np.ma.concatenate(self.lines_pix).compressed() + mwav = np.ma.concatenate(self.lines_wav).compressed() if space == "wavelength": twav = self.pix_to_wav(mpix) @@ -794,8 +779,8 @@ def plot_residuals( ) setp( ax, - xlabel=f'Wavelength [{self._unit_str}]', - ylabel=f'Residuals [{self._unit_str}]', + xlabel=f"Wavelength [{self._unit_str}]", + ylabel=f"Residuals [{self._unit_str}]", ) else: tpix = self.wav_to_pix(mwav) From 12f38d6339e8155e2fa03ef54f68415fdb73777a Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 13 Apr 2025 21:21:15 +0300 Subject: [PATCH 22/76] Introduced new optional parameters like `refine_fit` and enhanced documentation for plotting and fitting methods. --- specreduce/wavecal1d.py | 219 +++++++++++++++++++++++++++++++--------- 1 file changed, 174 insertions(+), 45 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 666baa99..ebc9d2d4 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -107,21 +107,26 @@ def _init_model(self): def _read_linelists( self, - line_lists, + line_lists: Sequence, line_list_bounds: tuple[float, float] = (0.0, np.inf), wave_air: bool = False, ): - """Load and filter calibration line lists for specified lamps and wavelength bounds. - - Notes - ----- - This method uses the `load_pypeit_calibration_lines` function to load the - line lists for each lamp. It filters the loaded data to only include lines - within the defined wavelength boundaries. The filtered data is stored in - `self.linelists`, and the wavelengths are extracted and stored in - `self.lines_wav`. Additionally, KDTree objects are created for each extracted - wavelength list and stored in `self._trees` for quick nearest-neighbor - queries of line wavelengths. + """Read and processes line lists and organize them for further use. + + This function handles line lists provided in various forms, applies wavelength bounds + filtering, and processes the data for efficient spatial querying using KDTree structures. + It also accounts for whether the input wavelengths are in air or vacuum. + + Parameters + ---------- + line_lists + A collection of line lists that can either be arrays of wavelengths or ``pypeit`` + lamp names . + line_list_bounds + A tuple specifying the minimum and maximum wavelength bounds. Only wavelengths + within this range are retained. + wave_air + If True, convert the vacuum wavelengths used by ``pypeit`` to air wavelengths. """ if not isinstance(line_lists, (tuple, list)): line_lists = [line_lists] @@ -175,24 +180,39 @@ def fit_lines( wavelengths: Sequence, match_pix: bool = True, match_wav: bool = True, + refine_fit: bool = True, ) -> None: - """Fit wavelength calibration lines to a model. + """Fit the wavelength solution model using provided line pairs. + + Fits the pixel-to-wavelength transformation model using explicitly + provided pairs of pixel coordinates and their corresponding wavelengths. + This method uses linear least squares fitting. - This method takes input arrays of pixel values and their corresponding wavelengths and fits - them to a calibration model, updating the pixel-to-wavelength transformation for the system. - Optionally, the method can match the provided pixel or wavelength values to known catalog - or observed values for better fitting. + Optionally, the provided pixel and wavelength values can be "snapped" + to the nearest values present in the internally stored observed line + list and catalog line list, respectively. This can correct for small + inaccuracies in the input pairs if the internal lists are populated. Parameters ---------- pixels - A sequence of pixel positions to be used for fitting. + A sequence of pixel positions corresponding to known spectral lines. wavelengths - A sequence of associated wavelengths corresponding to the pixel positions. + A sequence of the same size as `pixels`, containing the known + wavelengths corresponding to the given pixel positions. match_pix - A flag indicating whether to match the input pixel values to observed pixel values. + If True (default), snap the input `pixels` values to the nearest + pixel values found in `self._obs_lines` (if available). This helps + ensure the fit uses the precise centroids detected by `find_lines` + or provided initially. match_wav - A flag indicating whether to match the input wavelengths to catalog values. + If True (default), snap the input `wavelengths` values to the + nearest wavelength values found in `self._cat_lines` (if available). + This ensures the fit uses the precise catalog wavelengths. + refine_fit + If True (default), automatically call the ``refine_fit`` method + immediately after the global optimization to improve the solution + using a least-squares fit on matched lines. """ pixels = np.asarray(pixels) wavelengths = np.asarray(wavelengths) @@ -229,42 +249,57 @@ def fit_lines( for i in range(m.degree + 1): m.fixed[f"c{i}"] = False self._p2w = self._p2w[0] | m - self._calculate_p2w_derivative() - self._calculate_p2w_inverse() - def fit( + if refine_fit: + self.refine_fit() + else: + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + self.match_lines() + + def fit_global( self, wavelength_bounds: tuple[float, float], dispersion_bounds: tuple[float, float], popsize: int = 30, max_distance: float = 100, refine_fit: bool = True, - ): - """Calculate and refine the pixel-to-wavelength and wavelength-to-pixel transformations. + ) -> None: + """Calculate a wavelength solution using global optimization. + + Determines an initial functional relationship between pixel positions + and wavelengths using a global optimization algorithm (differential + evolution). This method does not require pre-matched pixel-wavelength + pairs. It works by finding model parameters that minimize the + distance between the predicted wavelengths of observed lines + (``self._obs_lines``) and their nearest theoretical counterparts + (``self._cat_lines`` accessed via KDTree). - This method determines the functional relationships between pixel positions and - wavelengths in a spectrum, including their derivatives, by fitting calibration data - to given constraints. The transformations include forward (pixel-to-wavelength) - and backward (wavelength-to-pixel) mappings as well as their respective derivatives - across the spectral axis. + Optionally, this initial solution can be immediately refined using a + least-squares fit on automatically matched lines. Parameters ---------- wavelength_bounds - Initial bounds for the wavelength value at the reference pixel. + Bounds (min, max) for the wavelength value at the ``ref_pixel``. + Used as a constraint in the optimization. dispersion_bounds - Initial bounds for the dispersion value at the reference pixel. + Bounds (min, max) for the dispersion (d_wavelength / d_pixel) + at the ``ref_pixel``. Used as a constraint in the optimization. popsize - The population size for differential evolution optimization. + Population size for the ``scipy.optimize.differential_evolution`` + optimizer. Larger values increase the likelihood of finding the + global minimum but also increase computation time. max_distance - The maximum allowable distance when querying the tree in the optimization process. + Maximum distance (in wavelength units) allowed when associating + an observed line with a theoretical line during the optimization's + cost function evaluation. Points beyond this distance contribute + this maximum value to the cost, preventing outliers from having + excessive influence. refine_fit - Refine the global fit of the pixel-to-wavelength transformation. - - Raises - ------ - ValueError - Raised if the optimization or fitting process fails due to invalid inputs or constraints. + If True (default), automatically call the ``refine_fit`` method + immediately after the global optimization to improve the solution + using a least-squares fit on matched lines. """ if self._p2w is None: @@ -278,6 +313,8 @@ def minfun(x): total_distance += np.clip(t.query(transformed_lines)[0], 0, max_distance).sum() return total_distance + # Define bounds for differential_evolution. + # Assumes first 3 coefficients correspond to wavelength, dispersion, and curvature. bounds = np.concatenate( [ [wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3]], @@ -293,13 +330,22 @@ def minfun(x): self.degree, **{f"c{i}": fit.x[i] for i in range(fit.x.size)} ) + # Check if optimization was successful + if not fit.success: + warnings.warn( + f"Global optimization may not have converged: {fit.message}", RuntimeWarning + ) + + # Update the model with the best-fit parameters found if refine_fit: self.refine_fit() - self._calculate_p2w_derivative() - self._calculate_p2w_inverse() + else: + self._calculate_p2w_derivative() + self._calculate_p2w_inverse() + self.match_lines() - def refine_fit(self, match_distance_bound: float = 5.0): - """Refine the global fit of the pixel-to-wavelength transformation. + def refine_fit(self, match_distance_bound: float = 5.0) -> None: + """Refine the fit of the pixel-to-wavelength transformation. Refines the fit of a polynomial model to data by performing a fitting operation using matched pixel and wavelength data points. The method uses a linear least @@ -534,6 +580,7 @@ def match_lines(self, upper_bound: float = 5) -> None: ) def remove_ummatched_lines(self): + """Remove unmatched lines from observation and catalog line data.""" self._obs_lines = [np.ma.masked_array(l.compressed()) for l in self._obs_lines] self._cat_lines = [np.ma.masked_array(l.compressed()) for l in self._cat_lines] @@ -641,6 +688,37 @@ def plot_catalog_lines( plot_values: bool = True, map_to_pix: bool = False, ) -> Figure: + """Plot the catalog lines. + + Parameters + ---------- + frames + Specifies the frames to be plotted. If an integer, only one frame is plotted. + If a sequence, the specified frames are plotted. If None, default selection + or all frames are plotted. + + axes + The matplotlib axes where catalog data will be plotted. If provided, the function + will plot on these axes. If None, new axes will be created. + + figsize + Specifies the dimensions of the figure as (width, height). If None, the default + dimensions are used. + + plot_values + If True, the numerical values associated with the catalog data will be displayed + in the plot. If False, only the graphical representation of the lines will be shown. + + map_to_pix + Indicates whether the catalog data should be mapped to pixel coordinates + before plotting. If True, the data is converted to pixel coordinates. + + Returns + ------- + Figure + The matplotlib figure containing the plotted catalog lines. + + """ return self._plot_lines( "catalog", frames=frames, @@ -659,7 +737,32 @@ def plot_observed_lines( plot_spectra: bool = True, map_to_wav: bool = False, ) -> Figure: + """Plot observed spectral lines for the given arc spectra. + Parameters + ---------- + frames + Specifies the frame(s) for which the plot is to be generated. If None, all frames + are plotted. When an integer is provided, a single frame is used. For a sequence + of integers, multiple frames are plotted. + axes + Axes object(s) to plot the spectral lines on. If None, new axes are created. + figsize + Dimensions of the figure to be created, specified as a tuple (width, height). Ignored + if `axes` is provided. + plot_values + If True, plots the numerical values of the observed lines at their respective + locations on the graph. Default is True. + plot_spectra + If True, includes the arc spectra on the plot for comparison. Default is True. + map_to_wav + Determines whether to map the x-axis values to wavelengths. Default is False. + + Returns + ------- + Figure + The matplotlib figure containing the observed lines plot. + """ fig = self._plot_lines( "observed", frames=frames, @@ -705,7 +808,33 @@ def plot_fit( obs_to_wav: bool = False, cat_to_pix: bool = False, ) -> Figure: + """Plot the fitted catalog and observed lines for the specified arc spectra. + + Parameters + ---------- + frames + The indices of the frames to plot. If `None`, all frames from 0 to + `self.nframes - 1` are plotted. + + figsize + Defines the width and height of the figure in inches. If `None`, the + default size is used. + + plot_values + Whether or not to display value annotations over the plotted lines. + obs_to_wav + If `True`, transform the x-axis of observed lines to the wavelength domain + using `self._p2w`, if available. + + cat_to_pix + If `True`, transforms catalog data points to pixel values before plotting. + + Returns + ------- + matplotlib.figure.Figure + The figure object containing the generated subplots. + """ if frames is None: frames = np.arange(self.nframes) else: From 901825bb832a650bf91ee7742e0315e08b784ae1 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 13 Apr 2025 21:25:26 +0300 Subject: [PATCH 23/76] Renamed `lines_pix` to `observed_lines` and `lines_wav` to `catalog_lines`. --- specreduce/wavecal1d.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index ebc9d2d4..80c12073 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -89,7 +89,7 @@ def __init__( self.bounds_pix = (0, self.arc_spectra[0].shape[0]) elif obs_lines is not None: - self.lines_pix = obs_lines + self.observed_lines = obs_lines self.nframes = len(self._obs_lines) if self.bounds_pix is None: raise ValueError("Must give pixel bounds when providing observed line positions.") @@ -149,7 +149,7 @@ def _read_linelists( for i, l in enumerate(lines_wav): lines_wav[i] = l[(l >= line_list_bounds[0]) & (l <= line_list_bounds[1])] - self.lines_wav = lines_wav + self.catalog_lines = lines_wav self._trees = [KDTree(l.data[:, None]) for l in self._cat_lines] def find_lines(self, fwhm: float, noise_factor: float = 1.0) -> None: @@ -486,12 +486,12 @@ def wav_to_pix(self, wav: MaskedArray | ndarray | float) -> ndarray | float: return self._w2p(wav) @property - def lines_pix(self) -> list[MaskedArray]: + def observed_lines(self) -> list[MaskedArray]: """List of pixel positions of identified spectral lines.""" return self._obs_lines - @lines_pix.setter - def lines_pix(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + @observed_lines.setter + def observed_lines(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): if not isinstance(lines_pix, Sequence): lines_pix = [lines_pix] self._obs_lines = [] @@ -502,12 +502,12 @@ def lines_pix(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | list[ self._obs_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) @property - def lines_wav(self) -> list[MaskedArray]: + def catalog_lines(self) -> list[MaskedArray]: """List of wavelength positions of theoretical spectral lines.""" return self._cat_lines - @lines_wav.setter - def lines_wav(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + @catalog_lines.setter + def catalog_lines(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): if not isinstance(lines_wav, Sequence): lines_wav = [lines_wav] self._cat_lines = [] @@ -599,8 +599,8 @@ def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: float """ self.match_lines() - mpix = np.ma.concatenate(self.lines_pix).compressed() - mwav = np.ma.concatenate(self.lines_wav).compressed() + mpix = np.ma.concatenate(self.observed_lines).compressed() + mwav = np.ma.concatenate(self.catalog_lines).compressed() if space == "wavelength": return np.sqrt(((mwav - self.pix_to_wav(mpix)) ** 2).mean()) else: @@ -892,8 +892,8 @@ def plot_residuals( fig = ax.figure self.match_lines() - mpix = np.ma.concatenate(self.lines_pix).compressed() - mwav = np.ma.concatenate(self.lines_wav).compressed() + mpix = np.ma.concatenate(self.observed_lines).compressed() + mwav = np.ma.concatenate(self.catalog_lines).compressed() if space == "wavelength": twav = self.pix_to_wav(mpix) From ac0a282fef994b3310781a137c366ccdcf181b19 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 13 Apr 2025 21:50:48 +0300 Subject: [PATCH 24/76] The refine_fit method now supports a max_iter parameter to control the maximum number of iterations for refining the pixel-to-wavelength transformation. This ensures the process adjusts until convergence or the specified iteration limit is reached. --- specreduce/wavecal1d.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 80c12073..262b7879 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -344,7 +344,7 @@ def minfun(x): self._calculate_p2w_inverse() self.match_lines() - def refine_fit(self, match_distance_bound: float = 5.0) -> None: + def refine_fit(self, match_distance_bound: float = 5.0, max_iter: int = 5) -> None: """Refine the fit of the pixel-to-wavelength transformation. Refines the fit of a polynomial model to data by performing a fitting operation @@ -362,12 +362,21 @@ def refine_fit(self, match_distance_bound: float = 5.0) -> None: data points. Points exceeding the bound will not be considered in the fit. Default is 5.0. """ - self.match_lines(match_distance_bound) - matched_pix = np.ma.concatenate(self._obs_lines).compressed() - matched_wav = np.ma.concatenate(self._cat_lines).compressed() + model = self._p2w[1] fitter = fitting.LinearLSQFitter() - self._p2w = self._p2w[0].copy() | fitter(model, matched_pix - self.ref_pixel, matched_wav) + rms = np.nan + for i in range(max_iter): + self.match_lines(match_distance_bound) + matched_pix = np.ma.concatenate(self._obs_lines).compressed() + matched_wav = np.ma.concatenate(self._cat_lines).compressed() + rms_new = np.sqrt(((matched_wav - self.pix_to_wav(matched_pix)) ** 2).mean()) + if rms_new == rms: + break + else: + self._p2w = self._p2w[0].copy() | fitter(model, matched_pix - self.ref_pixel, + matched_wav) + rms = rms_new self._calculate_p2w_derivative() self._calculate_p2w_inverse() self.match_lines(match_distance_bound) From 098622afc11476ac87a9f972feee283e00dcb29a Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 20 Apr 2025 14:41:48 +0100 Subject: [PATCH 25/76] Added 1D wavelength calibration tests. --- specreduce/tests/test_wavecal1d.py | 146 +++++++++++++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 specreduce/tests/test_wavecal1d.py diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py new file mode 100644 index 00000000..dc168c3b --- /dev/null +++ b/specreduce/tests/test_wavecal1d.py @@ -0,0 +1,146 @@ +# specreduce/tests/test_wavecal1d.py +import astropy.units as u +import numpy as np +import pytest +from astropy.modeling.polynomial import Polynomial1D +from numpy import array + +from specreduce.wavecal1d import WavelengthSolution1D, _diff_poly1d +from specutils import Spectrum1D + +ref_pixel = 100 + +def test_diff_poly1d(): + p = _diff_poly1d(Polynomial1D(3, c0=1.0, c1=2.0, c2=3.0, c3=4.0)) + np.testing.assert_array_equal(p.parameters, [ 2., 6., 12.]) + + +def test_init_default_values(): + ref_pixel = 100 + wavelength_solution = WavelengthSolution1D(ref_pixel) + + assert wavelength_solution.ref_pixel == ref_pixel + assert wavelength_solution.unit == u.angstrom + assert wavelength_solution.degree == 3 + assert wavelength_solution.bounds_pix is None + assert wavelength_solution.bounds_wav is None + assert wavelength_solution._cat_lines is None + assert wavelength_solution._obs_lines is None + assert wavelength_solution._trees is None + assert wavelength_solution._fit is None + assert wavelength_solution._wcs is None + assert wavelength_solution._p2w is None + assert wavelength_solution._w2p is None + assert wavelength_solution._p2w_dldx is None + + +def test_init_raises_error_for_multiple_sources(): + arc_spectra = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom) + obs_lines = [np.array([500.0])] + with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): + WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra, obs_lines=obs_lines) + + +def test_init_arc_spectra_validation(): + arc_spectra = Spectrum1D(flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom) + with pytest.raises(ValueError, match="The arc spectra must be 1 dimensional."): + WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) + + +def test_init_obs_lines_requires_pixel_bounds(): + obs_lines = [np.array([500.0])] + with pytest.raises(ValueError, match="Must give pixel bounds when"): + WavelengthSolution1D(ref_pixel, obs_lines=obs_lines) + + +def test_init_line_list(): + """Test the catalog line list initialization with various configurations of `arc_spectra` and `line_lists`. + """ + arc = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom) + WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=['ArI']) + WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists='ArI') + WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=[array([0.1])]) + WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=array([0.1])) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[['ArI'], ['ArI']]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=['ArI', ['ArI']]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=['ArI', 'ArI']) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ['ArI']]) + with pytest.raises(ValueError, match="The number of line lists"): + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[['ArI']]) + + + +# def test_fit_lines_with_valid_input(): +# ref_pixel = 100 +# pix_bounds = (0, 10) +# pixels = [2, 4, 6, 8] +# wavelengths = [500, 600, 700, 800] + +# wavelength_solution = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + +# wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) + +# assert wavelength_solution._p2w is not None +# assert wavelength_solution._p2w[1].degree == wavelength_solution.degree + + + + +def test_fit_lines_raises_error_for_mismatched_sizes(): + ref_pixel = 100 + pix_bounds = (0, 10) + pixels = [2, 4, 6] + wavelengths = [500, 600, 700, 800] + + wavelength_solution = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + + with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): + wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_fit_lines_raises_error_for_insufficient_lines(): + ref_pixel = 100 + pix_bounds = (0, 10) + pixels = [5] + wavelengths = [500] + + wavelength_solution = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + + with pytest.raises(ValueError, match="Need at least two lines for a fit"): + wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_fit_lines_raises_error_for_missing_pixel_bounds(): + ref_pixel = 100 + pixels = [2, 4, 6, 8] + wavelengths = [500, 600, 700, 800] + + wavelength_solution = WavelengthSolution1D(ref_pixel) + + with pytest.raises(ValueError, match="Cannot fit without pixel bounds set."): + wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_find_lines_with_valid_input(mocker): + ref_pixel = 100 + arc_spectra = [Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom)] + wavelength_solution = WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) + + mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") + mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} + + wavelength_solution.find_lines(fwhm=2.0, noise_factor=1.5) + + assert wavelength_solution._obs_lines is not None + assert len(wavelength_solution._obs_lines) == len(arc_spectra) + assert mock_find_arc_lines.called_once_with(arc_spectra[0], 2.0, noise_factor=1.5) + + +def test_find_lines_with_missing_arc_spectra(): + ref_pixel = 100 + wavelength_solution = WavelengthSolution1D(ref_pixel) + + with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): + wavelength_solution.find_lines(fwhm=2.0, noise_factor=1.5) + From dafa2b767536fa6bd25f3bfc0daaf507ed4450b3 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 20 Apr 2025 21:20:06 +0100 Subject: [PATCH 26/76] Updated WaveCal1D tests. --- specreduce/tests/test_wavecal1d.py | 136 ++++++++++++++--------------- 1 file changed, 68 insertions(+), 68 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index dc168c3b..b33cad61 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -17,21 +17,20 @@ def test_diff_poly1d(): def test_init_default_values(): ref_pixel = 100 - wavelength_solution = WavelengthSolution1D(ref_pixel) - - assert wavelength_solution.ref_pixel == ref_pixel - assert wavelength_solution.unit == u.angstrom - assert wavelength_solution.degree == 3 - assert wavelength_solution.bounds_pix is None - assert wavelength_solution.bounds_wav is None - assert wavelength_solution._cat_lines is None - assert wavelength_solution._obs_lines is None - assert wavelength_solution._trees is None - assert wavelength_solution._fit is None - assert wavelength_solution._wcs is None - assert wavelength_solution._p2w is None - assert wavelength_solution._w2p is None - assert wavelength_solution._p2w_dldx is None + ws = WavelengthSolution1D(ref_pixel) + assert ws.ref_pixel == ref_pixel + assert ws.unit == u.angstrom + assert ws.degree == 3 + assert ws.bounds_pix is None + assert ws.bounds_wav is None + assert ws._cat_lines is None + assert ws._obs_lines is None + assert ws._trees is None + assert ws._fit is None + assert ws._wcs is None + assert ws._p2w is None + assert ws._w2p is None + assert ws._p2w_dldx is None def test_init_raises_error_for_multiple_sources(): @@ -70,77 +69,78 @@ def test_init_line_list(): WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[['ArI']]) +def test_find_lines_with_valid_input(mocker): + arc_spectra = [Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom)] + ws = WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) + mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") + mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} + ws.find_lines(fwhm=2.0, noise_factor=1.5) + assert ws._obs_lines is not None + assert len(ws._obs_lines) == len(arc_spectra) + assert mock_find_arc_lines.called_once_with(arc_spectra[0], 2.0, noise_factor=1.5) -# def test_fit_lines_with_valid_input(): -# ref_pixel = 100 -# pix_bounds = (0, 10) -# pixels = [2, 4, 6, 8] -# wavelengths = [500, 600, 700, 800] - -# wavelength_solution = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) - -# wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) - -# assert wavelength_solution._p2w is not None -# assert wavelength_solution._p2w[1].degree == wavelength_solution.degree +def test_find_lines_with_missing_arc_spectra(): + ws = WavelengthSolution1D(ref_pixel) + with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): + ws.find_lines(fwhm=2.0, noise_factor=1.5) +def test_fit_lines_with_valid_input(): + pix_bounds = (0, 10) + pixels = array([2, 4, 6, 8]) + wavelengths = array([500, 600, 700, 800]) + ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + assert ws._p2w is not None + assert ws._p2w[1].degree == ws.degree + ws = WavelengthSolution1D(ref_pixel, obs_lines=pixels, line_lists=wavelengths, pix_bounds=pix_bounds) + ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=True, match_obs=True) + assert ws._p2w is not None + assert ws._p2w[1].degree == ws.degree + ws = WavelengthSolution1D(ref_pixel, degree=5, pix_bounds=pix_bounds) + ws.fit_lines(pixels=pixels[:3], wavelengths=wavelengths[:3]) + + +def test_fit_lines_raises_error_for_missing_input(): + pix_bounds = (0, 10) + pixels = array([2, 4, 6, 8]) + wavelengths = array([500, 600, 700, 800]) + ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + with pytest.raises(ValueError, match="Cannot fit without catalog"): + ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=True, match_obs=True) + with pytest.raises(ValueError, match="Cannot fit without observed"): + ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=False, match_obs=True) + +#def test_fit_lines_raises_error_for_nonexisting_lists(): +# pix_bounds = (0, 10) +# pixels = array([2, 4, 6, 8]) +# wavelengths = array([500, 600, 700, 800]) +# ws = WavelengthSolution1D(ref_pixel, line_lists=wavelengths, pix_bounds=pix_bounds) +# #with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): +# ws.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_lines_raises_error_for_mismatched_sizes(): - ref_pixel = 100 pix_bounds = (0, 10) - pixels = [2, 4, 6] - wavelengths = [500, 600, 700, 800] - - wavelength_solution = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) - + pixels = array([2, 4, 6]) + wavelengths = array([500, 600, 700, 800]) + ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): - wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) + ws.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_lines_raises_error_for_insufficient_lines(): - ref_pixel = 100 pix_bounds = (0, 10) pixels = [5] wavelengths = [500] - - wavelength_solution = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) - + ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="Need at least two lines for a fit"): - wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) + ws.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_lines_raises_error_for_missing_pixel_bounds(): - ref_pixel = 100 pixels = [2, 4, 6, 8] wavelengths = [500, 600, 700, 800] - - wavelength_solution = WavelengthSolution1D(ref_pixel) - + ws = WavelengthSolution1D(ref_pixel) with pytest.raises(ValueError, match="Cannot fit without pixel bounds set."): - wavelength_solution.fit_lines(pixels=pixels, wavelengths=wavelengths) - - -def test_find_lines_with_valid_input(mocker): - ref_pixel = 100 - arc_spectra = [Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom)] - wavelength_solution = WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) - - mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") - mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} - - wavelength_solution.find_lines(fwhm=2.0, noise_factor=1.5) - - assert wavelength_solution._obs_lines is not None - assert len(wavelength_solution._obs_lines) == len(arc_spectra) - assert mock_find_arc_lines.called_once_with(arc_spectra[0], 2.0, noise_factor=1.5) - - -def test_find_lines_with_missing_arc_spectra(): - ref_pixel = 100 - wavelength_solution = WavelengthSolution1D(ref_pixel) - - with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): - wavelength_solution.find_lines(fwhm=2.0, noise_factor=1.5) - + ws.fit_lines(pixels=pixels, wavelengths=wavelengths) From cd32b07fbd822d3e662db9c463db2193558c83ea Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 20 Apr 2025 21:53:58 +0100 Subject: [PATCH 27/76] Fixed minor issues while writing tests. --- specreduce/wavecal1d.py | 82 ++++++++++++++++++++++++++--------------- 1 file changed, 53 insertions(+), 29 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 262b7879..a9259d4c 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -48,7 +48,7 @@ def __init__( ref_pixel: float, unit: u.Unit = u.angstrom, degree: int = 3, - line_lists=None, + line_lists: Sequence | None = None, arc_spectra: Spectrum1D | Sequence[Spectrum1D] | None = None, obs_lines: ndarray | Sequence[ndarray] | None = None, pix_bounds: tuple[int, int] | None = None, @@ -60,7 +60,9 @@ def __init__( self._unit_str = unit.to_string("latex") self.degree = degree self.ref_pixel = ref_pixel + self.nframes = 0 + self.arc_spectra: list[Spectrum1D] | None = None self.bounds_pix: tuple[int, int] | None = pix_bounds self.bounds_wav: tuple[float, float] | None = None self._cat_lines: list[MaskedArray] | None = None @@ -98,6 +100,9 @@ def __init__( # or a list of line list names (used by `load_pypeit_calibration_lines`) for each arc # spectrum. if line_lists is not None: + if not isinstance(line_lists, (tuple, list)): + line_lists = [line_lists] + if len(line_lists) != self.nframes: raise ValueError("The number of line lists must match the number of arc spectra.") self._read_linelists(line_lists, line_list_bounds=line_list_bounds, wave_air=wave_air) @@ -115,7 +120,7 @@ def _read_linelists( This function handles line lists provided in various forms, applies wavelength bounds filtering, and processes the data for efficient spatial querying using KDTree structures. - It also accounts for whether the input wavelengths are in air or vacuum. + It also accounts for whether the input wavelengths are in air or vacuum. Parameters ---------- @@ -128,8 +133,6 @@ def _read_linelists( wave_air If True, convert the vacuum wavelengths used by ``pypeit`` to air wavelengths. """ - if not isinstance(line_lists, (tuple, list)): - line_lists = [line_lists] lines_wav = [] for l in line_lists: @@ -167,6 +170,9 @@ def find_lines(self, fwhm: float, noise_factor: float = 1.0) -> None: The factor to multiply the uncertainty by to determine the noise threshold in the `~specutils.fitting.find_lines_threshold` routine. """ + if self.arc_spectra is None: + raise ValueError("Must provide arc spectra to find lines.") + with warnings.catch_warnings(): warnings.simplefilter("ignore") lines_obs = [ @@ -178,9 +184,10 @@ def fit_lines( self, pixels: Sequence, wavelengths: Sequence, - match_pix: bool = True, - match_wav: bool = True, + match_obs: bool = False, + match_cat: bool = False, refine_fit: bool = True, + refine_max_distance: float = 5.0 ) -> None: """Fit the wavelength solution model using provided line pairs. @@ -200,13 +207,13 @@ def fit_lines( wavelengths A sequence of the same size as `pixels`, containing the known wavelengths corresponding to the given pixel positions. - match_pix - If True (default), snap the input `pixels` values to the nearest + match_obs + If True, snap the input `pixels` values to the nearest pixel values found in `self._obs_lines` (if available). This helps ensure the fit uses the precise centroids detected by `find_lines` or provided initially. - match_wav - If True (default), snap the input `wavelengths` values to the + match_cat + If True, snap the input `wavelengths` values to the nearest wavelength values found in `self._cat_lines` (if available). This ensures the fit uses the precise catalog wavelengths. refine_fit @@ -229,13 +236,17 @@ def fit_lines( self._init_model() # Match the input wavelengths to catalog lines. - if match_wav: + if match_cat: + if self._cat_lines is None: + raise ValueError("Cannot fit without catalog lines set.") tree = KDTree(np.concatenate([c.data for c in self._cat_lines])[:, None]) ix = tree.query(wavelengths[:, None])[1] wavelengths = tree.data[ix][:, 0] # Match the input pixel values to observed pixel values. - if match_pix: + if match_obs: + if self._obs_lines is None: + raise ValueError("Cannot fit without observed lines set.") tree = KDTree(np.concatenate([c.data for c in self._obs_lines])[:, None]) ix = tree.query(pixels[:, None])[1] pixels = tree.data[ix][:, 0] @@ -250,12 +261,14 @@ def fit_lines( m.fixed[f"c{i}"] = False self._p2w = self._p2w[0] | m - if refine_fit: - self.refine_fit() + can_match = self._cat_lines is not None and self._obs_lines is not None + if refine_fit and can_match: + self.refine_fit(refine_max_distance) else: self._calculate_p2w_derivative() self._calculate_p2w_inverse() - self.match_lines() + if can_match: + self.match_lines() def fit_global( self, @@ -344,7 +357,7 @@ def minfun(x): self._calculate_p2w_inverse() self.match_lines() - def refine_fit(self, match_distance_bound: float = 5.0, max_iter: int = 5) -> None: + def refine_fit(self, max_match_distance: float = 5.0, max_iter: int = 5) -> None: """Refine the fit of the pixel-to-wavelength transformation. Refines the fit of a polynomial model to data by performing a fitting operation @@ -353,11 +366,12 @@ def refine_fit(self, match_distance_bound: float = 5.0, max_iter: int = 5) -> No Parameters ---------- + degree : int, optional The degree of the polynomial model used for fitting. Higher values allow for more complex polynomial models. Default is 4. - match_distance_bound : float, optional + max_match_distance : float, optional Maximum allowable distance used to identify matched pixel and wavelength data points. Points exceeding the bound will not be considered in the fit. Default is 5.0. @@ -367,7 +381,7 @@ def refine_fit(self, match_distance_bound: float = 5.0, max_iter: int = 5) -> No fitter = fitting.LinearLSQFitter() rms = np.nan for i in range(max_iter): - self.match_lines(match_distance_bound) + self.match_lines(max_match_distance) matched_pix = np.ma.concatenate(self._obs_lines).compressed() matched_wav = np.ma.concatenate(self._cat_lines).compressed() rms_new = np.sqrt(((matched_wav - self.pix_to_wav(matched_pix)) ** 2).mean()) @@ -379,7 +393,7 @@ def refine_fit(self, match_distance_bound: float = 5.0, max_iter: int = 5) -> No rms = rms_new self._calculate_p2w_derivative() self._calculate_p2w_inverse() - self.match_lines(match_distance_bound) + self.match_lines(max_match_distance) def _calculate_p2w_derivative(self): """Calculate (d wavelength) / (d pixel) for the pixel-to-wavelength transformation.""" @@ -388,7 +402,7 @@ def _calculate_p2w_derivative(self): def _calculate_p2w_inverse(self) -> None: """Compute the wavelength-to-pixel mapping from the pixel-to-wavelength transformation.""" - p = np.arange(*self.bounds_pix) + p = np.arange(self.bounds_pix[0]-2, self.bounds_pix[1]+2) self._w2p = interp1d(self._p2w(p), p, bounds_error=False, fill_value=np.nan) self.bounds_wav = self._p2w(self.bounds_pix) @@ -421,13 +435,19 @@ def resample( ------- 1D spectrum binned to the specified wavelength bins. """ + if self._p2w is None: + raise ValueError("Wavelength solution not yet computed.") + + if nbins is not None and nbins < 0: + raise ValueError("Number of bins must be positive.") + flux = spectrum.flux.value ucty = spectrum.uncertainty.represent_as(VarianceUncertainty).array npix = flux.size nbins = npix if nbins is None else nbins if wlbounds is None: - l1 = self._p2w(0) - l2 = self._p2w(npix - 1) + l1 = self._p2w(0) - self._p2w_dldx(0) + l2 = self._p2w(npix) + self._p2w_dldx(npix) else: l1, l2 = wlbounds @@ -456,6 +476,7 @@ def resample( ucty_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * ucty[i1] * dldx[i1] flux_wl = (flux_wl * n) * spectrum.flux.unit ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(type(spectrum.uncertainty)) + return Spectrum1D(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) def pix_to_wav(self, pix: MaskedArray | ndarray | float) -> ndarray | float: @@ -496,7 +517,7 @@ def wav_to_pix(self, wav: MaskedArray | ndarray | float) -> ndarray | float: @property def observed_lines(self) -> list[MaskedArray]: - """List of pixel positions of identified spectral lines.""" + """Pixel positions of the observed lines as a list of masked arrays.""" return self._obs_lines @observed_lines.setter @@ -512,7 +533,7 @@ def observed_lines(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | @property def catalog_lines(self) -> list[MaskedArray]: - """List of wavelength positions of theoretical spectral lines.""" + """Catalogue line wavelengths as a list of masked arrays.""" return self._cat_lines @catalog_lines.setter @@ -527,7 +548,9 @@ def catalog_lines(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | l self._cat_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) @property - def wcs(self): + def wcs(self) -> wcs.WCS: + """GWCS object defining the mapping between pixel and spectral coordinate frames. + """ pixel_frame = cf.CoordinateFrame( 1, "SPECTRAL", @@ -545,12 +568,12 @@ def wcs(self): self._wcs = wcs.WCS(pipeline) return self._wcs - def match_lines(self, upper_bound: float = 5) -> None: + def match_lines(self, max_distance: float = 5) -> None: """Match the observed lines to theoretical lines. Parameters ---------- - upper_bound + max_distance The maximum allowed distance between the query points and the KD-tree data points for them to be considered a match. """ @@ -559,7 +582,7 @@ def match_lines(self, upper_bound: float = 5) -> None: for iframe, tree in enumerate(self._trees): l, ix = tree.query( self._p2w(self._obs_lines[iframe].data)[:, None], - distance_upper_bound=upper_bound, + distance_upper_bound=max_distance, ) m = np.isfinite(l) @@ -637,6 +660,7 @@ def _plot_lines( axs = [axs] else: fig = axs[0].figure + axs = np.atleast_1d(axs) if isinstance(plot_values, bool): plot_values = np.full(frames.size, plot_values, dtype=bool) @@ -782,7 +806,7 @@ def plot_observed_lines( ) if axes is None: - axes = fig.axes + axes = np.atleast_1d(fig.axes) if self.arc_spectra is not None and plot_spectra: if frames is None: From 6d4853fa11be0758a8c85a877638354e0204f9b3 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Sun, 20 Apr 2025 21:54:07 +0100 Subject: [PATCH 28/76] Updated tests. --- specreduce/tests/test_wavecal1d.py | 77 +++++++++++++++++++++++++----- 1 file changed, 64 insertions(+), 13 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index b33cad61..8ae29a90 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -3,6 +3,7 @@ import numpy as np import pytest from astropy.modeling.polynomial import Polynomial1D +from astropy.nddata import StdDevUncertainty from numpy import array from specreduce.wavecal1d import WavelengthSolution1D, _diff_poly1d @@ -10,9 +11,10 @@ ref_pixel = 100 + def test_diff_poly1d(): p = _diff_poly1d(Polynomial1D(3, c0=1.0, c1=2.0, c2=3.0, c3=4.0)) - np.testing.assert_array_equal(p.parameters, [ 2., 6., 12.]) + np.testing.assert_array_equal(p.parameters, [2.0, 6.0, 12.0]) def test_init_default_values(): @@ -41,7 +43,9 @@ def test_init_raises_error_for_multiple_sources(): def test_init_arc_spectra_validation(): - arc_spectra = Spectrum1D(flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom) + arc_spectra = Spectrum1D( + flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom + ) with pytest.raises(ValueError, match="The arc spectra must be 1 dimensional."): WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) @@ -53,20 +57,19 @@ def test_init_obs_lines_requires_pixel_bounds(): def test_init_line_list(): - """Test the catalog line list initialization with various configurations of `arc_spectra` and `line_lists`. - """ + """Test the catalog line list initialization with various configurations of `arc_spectra` and `line_lists`.""" arc = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom) - WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=['ArI']) - WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists='ArI') + WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=["ArI"]) + WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists="ArI") WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=[array([0.1])]) WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=array([0.1])) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[['ArI'], ['ArI']]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=['ArI', ['ArI']]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=['ArI', 'ArI']) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"], ["ArI"]]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", ["ArI"]]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", "ArI"]) WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ['ArI']]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ["ArI"]]) with pytest.raises(ValueError, match="The number of line lists"): - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[['ArI']]) + WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"]]) def test_find_lines_with_valid_input(mocker): @@ -94,7 +97,9 @@ def test_fit_lines_with_valid_input(): ws.fit_lines(pixels=pixels, wavelengths=wavelengths) assert ws._p2w is not None assert ws._p2w[1].degree == ws.degree - ws = WavelengthSolution1D(ref_pixel, obs_lines=pixels, line_lists=wavelengths, pix_bounds=pix_bounds) + ws = WavelengthSolution1D( + ref_pixel, obs_lines=pixels, line_lists=wavelengths, pix_bounds=pix_bounds + ) ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=True, match_obs=True) assert ws._p2w is not None assert ws._p2w[1].degree == ws.degree @@ -112,7 +117,8 @@ def test_fit_lines_raises_error_for_missing_input(): with pytest.raises(ValueError, match="Cannot fit without observed"): ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=False, match_obs=True) -#def test_fit_lines_raises_error_for_nonexisting_lists(): + +# def test_fit_lines_raises_error_for_nonexisting_lists(): # pix_bounds = (0, 10) # pixels = array([2, 4, 6, 8]) # wavelengths = array([500, 600, 700, 800]) @@ -120,6 +126,7 @@ def test_fit_lines_raises_error_for_missing_input(): # #with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): # ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + def test_fit_lines_raises_error_for_mismatched_sizes(): pix_bounds = (0, 10) pixels = array([2, 4, 6]) @@ -144,3 +151,47 @@ def test_fit_lines_raises_error_for_missing_pixel_bounds(): ws = WavelengthSolution1D(ref_pixel) with pytest.raises(ValueError, match="Cannot fit without pixel bounds set."): ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + + +def test_fit_global_runs_successfully_with_valid_input(): + pix_bounds = (0, 10) + pixels = array([2, 4, 5, 6, 8]) + wavelengths = array([500, 550, 600, 650, 700, 750, 800]) + wavelength_bounds = (649, 651) + dispersion_bounds = (49, 51) + ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=pixels, line_lists=wavelengths) + ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=10) + np.testing.assert_allclose(ws._fit.x, [650.0, 50.0, 0.0, 0.0], atol=1e-4) + assert ws._fit is not None + assert ws._fit.success + assert ws._p2w is not None + + +def test_resample_with_valid_input(): + arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(20))) + ws = WavelengthSolution1D(ref_pixel) + ws._p2w = lambda x: x * 2 # Mock pixel-to-wavelength conversion + ws._p2w_dldx = lambda x: np.ones_like(x) * 2 # Mock derivative + ws._w2p = lambda x: x / 2 # Mock wavelength-to-pixel conversion + resampled = ws.resample(arc_spectrum, nbins=10) + assert resampled is not None + assert len(resampled.flux) == 10 + assert resampled.flux.unit == u.DN + np.testing.assert_almost_equal(arc_spectrum.flux.value.sum(), resampled.flux.value.sum(), decimal=10) + + +def test_resample_raises_error_for_missing_transforms(): + arc_spectrum = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(10))) + ws = WavelengthSolution1D(ref_pixel) + with pytest.raises(ValueError, match="Wavelength solution not yet"): + ws.resample(arc_spectrum) + + +def test_resample_raises_error_for_invalid_bins(): + arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(20))) + ws = WavelengthSolution1D(ref_pixel) + ws._p2w = lambda x: x * 2 # Mock pixel-to-wavelength conversion + ws._p2w_dldx = lambda x: np.ones_like(x) * 2 # Mock derivative + ws._w2p = lambda x: x / 2 # Mock wavelength-to-pixel conversion + with pytest.raises(ValueError, match="Number of bins must be positive"): + ws.resample(arc_spectrum, nbins=-5) From 9615a1ad120037c230dc22b0598fd4ec99a2bac3 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Mon, 21 Apr 2025 15:05:47 +0100 Subject: [PATCH 29/76] Added tests for wavelength-pixel transformations and WCS creation. --- specreduce/tests/test_wavecal1d.py | 59 ++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 8ae29a90..79aa6224 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -2,8 +2,10 @@ import astropy.units as u import numpy as np import pytest +from astropy.modeling import models from astropy.modeling.polynomial import Polynomial1D from astropy.nddata import StdDevUncertainty +from gwcs import wcs from numpy import array from specreduce.wavecal1d import WavelengthSolution1D, _diff_poly1d @@ -118,6 +120,18 @@ def test_fit_lines_raises_error_for_missing_input(): ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=False, match_obs=True) +def test_observed_lines_returns_none_when_not_set(): + ws = WavelengthSolution1D(ref_pixel) + assert ws.observed_lines is None + + +def test_observed_lines_returns_expected_lines(): + mock_obs_lines = [np.ma.masked_array([100, 200], mask=[False, True])] + ws = WavelengthSolution1D(ref_pixel) + ws._obs_lines = mock_obs_lines + assert ws.observed_lines == mock_obs_lines + + # def test_fit_lines_raises_error_for_nonexisting_lists(): # pix_bounds = (0, 10) # pixels = array([2, 4, 6, 8]) @@ -195,3 +209,48 @@ def test_resample_raises_error_for_invalid_bins(): ws._w2p = lambda x: x / 2 # Mock wavelength-to-pixel conversion with pytest.raises(ValueError, match="Number of bins must be positive"): ws.resample(arc_spectrum, nbins=-5) + + +def test_pix_to_wav_with_array(): + pix_values = np.array([1, 2, 3, 4, 5]) + ws = WavelengthSolution1D(ref_pixel) + ws._p2w = lambda x: x * 10 # Mock pixel-to-wavelength conversion + wavelengths = ws.pix_to_wav(pix_values) + np.testing.assert_array_equal(wavelengths, np.array([10, 20, 30, 40, 50])) + + +def test_pix_to_wav_with_masked_array(): + pix_values = np.ma.masked_array([1, 2, 3], mask=[0, 1, 0]) + ws = WavelengthSolution1D(ref_pixel) + ws._p2w = lambda x: x * 10 # Mock pixel-to-wavelength conversion + wavelengths = ws.pix_to_wav(pix_values) + np.testing.assert_array_equal(wavelengths.data, np.array([10, 20, 30])) + np.testing.assert_array_equal(wavelengths.mask, np.array([0, 1, 0])) + + +def test_wav_to_pix_with_array(): + wav_values = np.array([500, 1000, 1500]) + ws = WavelengthSolution1D(ref_pixel) + ws._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion + pixel_values = ws.wav_to_pix(wav_values) + np.testing.assert_array_equal(pixel_values, np.array([50, 100, 150])) + + +def test_wav_to_pix_with_masked_array(): + wav_values = np.ma.masked_array([500, 1000, 1500], mask=[0, 1, 0]) + ws = WavelengthSolution1D(ref_pixel) + ws._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion + pixel_values = ws.wav_to_pix(wav_values) + np.testing.assert_array_equal(pixel_values.data, np.array([50, 100, 150])) + np.testing.assert_array_equal(pixel_values.mask, np.array([0, 1, 0])) + + +def test_wcs_creates_valid_gwcs_object(): + ws = WavelengthSolution1D(ref_pixel, unit=u.angstrom, pix_bounds=(0, 100)) + ws._p2w = models.Shift(5) | models.Polynomial1D(degree=3, c0=1, c1=2, c2=3) + ws._calculate_p2w_inverse() + ws._calculate_p2w_derivative() + wcs_obj = ws.wcs + assert wcs_obj is not None + assert isinstance(wcs_obj, wcs.WCS) + assert wcs_obj.output_frame.unit[0] == u.angstrom From a8e6aacfb4180305e6ba7ea03d3e0a9df87c114e Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Mon, 21 Apr 2025 20:54:17 +0100 Subject: [PATCH 30/76] Added new tests for WavelengthSolution1D and plotting methods. --- specreduce/tests/test_wavecal1d.py | 140 ++++++++++++++++++++++++++++- 1 file changed, 138 insertions(+), 2 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 79aa6224..0df59f8c 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -6,13 +6,17 @@ from astropy.modeling.polynomial import Polynomial1D from astropy.nddata import StdDevUncertainty from gwcs import wcs +from matplotlib import pyplot as plt +from matplotlib.figure import Figure from numpy import array from specreduce.wavecal1d import WavelengthSolution1D, _diff_poly1d from specutils import Spectrum1D -ref_pixel = 100 - +ref_pixel = 5 +p2w = models.Shift(ref_pixel) | models.Polynomial1D(degree=3, c0=1, c1=2, c2=3) +obs_lines = array([1, 2, 5, 8, 10]) +cat_lines = p2w(array([1, 3, 5, 7, 8, 10])) def test_diff_poly1d(): p = _diff_poly1d(Polynomial1D(3, c0=1.0, c1=2.0, c2=3.0, c3=4.0)) @@ -180,6 +184,9 @@ def test_fit_global_runs_successfully_with_valid_input(): assert ws._fit.success assert ws._p2w is not None + ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=pixels, line_lists=wavelengths) + ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=10, refine_fit=False) + def test_resample_with_valid_input(): arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(20))) @@ -254,3 +261,132 @@ def test_wcs_creates_valid_gwcs_object(): assert wcs_obj is not None assert isinstance(wcs_obj, wcs.WCS) assert wcs_obj.output_frame.unit[0] == u.angstrom + + +def test_rms(): + ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=(0, 10)) + ws._p2w = p2w + ws._calculate_p2w_inverse() + assert np.isclose(ws.rms(space="wavelength"), 0) # Perfect match, so RMS should be zero + assert np.isclose(ws.rms(space="pixel"), 0) # Perfect match, so RMS should be zero + with pytest.raises(ValueError, match="Space must be either 'pixel' or 'wavelength'"): + ws.rms(space="wavelenght") + + +def test_remove_unmatched_lines(): + ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=(0, 10)) + ws._p2w = p2w + ws._calculate_p2w_inverse() + ws.match_lines() + ws.remove_ummatched_lines() + + +def test_plot_lines_with_valid_input(): + ws = WavelengthSolution1D(ref_pixel) + ws._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + ws._cat_lines = ws._obs_lines + fig = ws._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig = ws._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, ax = plt.subplots(1, 1) + fig = ws._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, axs = plt.subplots(1, 2) + fig = ws._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig = ws._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + +def test_plot_lines_raises_for_missing_transform(): + ws = WavelengthSolution1D( + ref_pixel, obs_lines=array([2, 5, 8]), line_lists=array([500, 600, 700]), pix_bounds=(0, 10) + ) + with pytest.raises(ValueError, match="Cannot map between pixels and"): + ws._plot_lines(kind="observed", map_x=True) + + +def test_plot_lines_calls_transform_correctly(mocker): + ws = WavelengthSolution1D( + ref_pixel, obs_lines=array([2, 5, 8]), line_lists=array([500, 600, 700]), pix_bounds=(0, 10) + ) + ws._p2w = p2w + ws._calculate_p2w_inverse() + ws._plot_lines(kind="observed", map_x=True) + ws._plot_lines(kind="catalog", map_x=True) + + +def test_plot_catalog_lines_with_valid_input(): + ws = WavelengthSolution1D(ref_pixel) + ws._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] + fig = ws.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, ax = plt.subplots(1, 1) + fig = ws.plot_catalog_lines(frames=0, axes=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, axs = plt.subplots(1, 2) + fig = ws.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + +def test_plot_observed_lines_with_valid_input(): + ws = WavelengthSolution1D(ref_pixel) + ws._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom) + ws.arc_spectra = [arc_spectrum] + ws._p2w = p2w + fig = ws.plot_observed_lines(frames=0, figsize=(10, 5), plot_values=True, plot_spectra=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + assert len(fig.axes) == 1 + + +def test_plot_fit_with_valid_input(): + arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom) + ws = WavelengthSolution1D( + ref_pixel, obs_lines=array([2, 5, 8]), line_lists=array([500, 600, 700]), pix_bounds=(0, 10) + ) + ws.arc_spectra = [arc_spectrum] + ws._p2w = p2w + fig = ws.plot_fit(frames=0, figsize=(12, 6), plot_values=True) + assert isinstance(fig, Figure) + assert len(fig.axes) == 2 + assert fig.axes[0].has_data() + assert fig.axes[1].has_data() + + +def test_plot_residuals(): + obs_lines = array([2, 5, 8]) + cat_lines = p2w(obs_lines) + ws = WavelengthSolution1D(ref_pixel, pix_bounds=(0, 10), obs_lines=obs_lines, line_lists=cat_lines) + ws._p2w = p2w + ws._calculate_p2w_inverse() + + fig = ws.plot_residuals(space="pixel", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig = ws.plot_residuals(space="wavelength", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + + fig, ax = plt.subplots(1, 1) + ws.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) + + with pytest.raises(ValueError, match="Invalid space specified"): + fig = ws.plot_residuals(space="wavelenght", figsize=(8, 4)) From 588324e9c7ac5e301a97aa6694dec83fbbb0f238 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Mon, 21 Apr 2025 20:54:33 +0100 Subject: [PATCH 31/76] Removed redundant warnings and improved error handling. --- specreduce/wavecal1d.py | 20 +++++++------------- 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index a9259d4c..7205a951 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -343,12 +343,6 @@ def minfun(x): self.degree, **{f"c{i}": fit.x[i] for i in range(fit.x.size)} ) - # Check if optimization was successful - if not fit.success: - warnings.warn( - f"Global optimization may not have converged: {fit.message}", RuntimeWarning - ) - # Update the model with the best-fit parameters found if refine_fit: self.refine_fit() @@ -605,11 +599,6 @@ def match_lines(self, max_distance: float = 5) -> None: self._obs_lines = matched_lines_pix self._cat_lines = matched_lines_wav - if any([o.count() != c.count() for o, c in zip(self._obs_lines, self._cat_lines)]): - warnings.warn( - "Line matching failed, the number of matched catalog lines != " - "the number of matched observed lines." - ) def remove_ummatched_lines(self): """Remove unmatched lines from observation and catalog line data.""" @@ -635,8 +624,11 @@ def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: mwav = np.ma.concatenate(self.catalog_lines).compressed() if space == "wavelength": return np.sqrt(((mwav - self.pix_to_wav(mpix)) ** 2).mean()) - else: + elif space == "pixel": return np.sqrt(((mpix - self.wav_to_pix(mwav)) ** 2).mean()) + else: + raise ValueError("Space must be either 'pixel' or 'wavelength'") + def _plot_lines( self, @@ -944,7 +936,7 @@ def plot_residuals( xlabel=f"Wavelength [{self._unit_str}]", ylabel=f"Residuals [{self._unit_str}]", ) - else: + elif space == "pixel": tpix = self.wav_to_pix(mwav) ax.plot(mpix, mpix - tpix, ".") ax.text( @@ -956,5 +948,7 @@ def plot_residuals( va="top", ) setp(ax, xlabel="Pixel", ylabel="Residuals [pix]") + else: + raise ValueError("Invalid space specified for plotting residuals.") ax.axhline(0, c="k", lw=1, ls="--") return fig From 3b31270e32b58f0f52ed1c36a02372c6ca2244bf Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 22 Apr 2025 15:53:17 +0100 Subject: [PATCH 32/76] Polished the tests. --- specreduce/tests/test_wavecal1d.py | 330 ++++++++++++++--------------- 1 file changed, 157 insertions(+), 173 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 0df59f8c..c6903c8d 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -11,60 +11,80 @@ from numpy import array from specreduce.wavecal1d import WavelengthSolution1D, _diff_poly1d -from specutils import Spectrum1D +from specreduce.compat import Spectrum + ref_pixel = 5 -p2w = models.Shift(ref_pixel) | models.Polynomial1D(degree=3, c0=1, c1=2, c2=3) -obs_lines = array([1, 2, 5, 8, 10]) -cat_lines = p2w(array([1, 3, 5, 7, 8, 10])) +pix_bounds = (0, 10) +p2w = models.Shift(ref_pixel) | models.Polynomial1D(degree=3, c0=1, c1=2, c2=3) + + +@pytest.fixture +def mk_lines(): + obs_lines = array([1, 2, 5, 8, 10]) + cat_lines = p2w(array([1, 3, 5, 7, 8, 10])) + return obs_lines, cat_lines + + +@pytest.fixture +def mk_matched_lines(): + obs_lines = array([1, 2, 5, 8, 10]) + cat_lines = p2w(obs_lines) + return obs_lines, cat_lines + + +@pytest.fixture +def mk_ws(mk_lines): + obs_lines, cat_lines = mk_lines + return WavelengthSolution1D( + ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) + ) + + +@pytest.fixture +def mk_good_ws_with_transform(mk_lines): + obs_lines, cat_lines = mk_lines + ws = WavelengthSolution1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) + ws._p2w = p2w + ws._calculate_p2w_inverse() + ws._calculate_p2w_derivative() + return ws + + +@pytest.fixture +def mk_arc(): + return Spectrum( + flux=np.ones(10) * u.DN, + spectral_axis=np.arange(10) * u.angstrom, + uncertainty=StdDevUncertainty(np.ones(10)), + ) + def test_diff_poly1d(): p = _diff_poly1d(Polynomial1D(3, c0=1.0, c1=2.0, c2=3.0, c3=4.0)) np.testing.assert_array_equal(p.parameters, [2.0, 6.0, 12.0]) -def test_init_default_values(): - ref_pixel = 100 - ws = WavelengthSolution1D(ref_pixel) - assert ws.ref_pixel == ref_pixel - assert ws.unit == u.angstrom - assert ws.degree == 3 - assert ws.bounds_pix is None - assert ws.bounds_wav is None - assert ws._cat_lines is None - assert ws._obs_lines is None - assert ws._trees is None - assert ws._fit is None - assert ws._wcs is None - assert ws._p2w is None - assert ws._w2p is None - assert ws._p2w_dldx is None - - -def test_init_raises_error_for_multiple_sources(): - arc_spectra = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom) - obs_lines = [np.array([500.0])] - with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): - WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra, obs_lines=obs_lines) - +def test_init(mk_arc, mk_lines): + arc = mk_arc + obs_lines, cat_lines = mk_lines + WavelengthSolution1D(ref_pixel, line_lists=cat_lines, arc_spectra=arc) + WavelengthSolution1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) -def test_init_arc_spectra_validation(): - arc_spectra = Spectrum1D( - flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom - ) - with pytest.raises(ValueError, match="The arc spectra must be 1 dimensional."): - WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) + with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): + WavelengthSolution1D(ref_pixel, arc_spectra=arc, obs_lines=obs_lines) + arc = Spectrum(flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom) + with pytest.raises(ValueError, match="The arc spectrum must be one dimensional."): + WavelengthSolution1D(ref_pixel, arc_spectra=arc) -def test_init_obs_lines_requires_pixel_bounds(): - obs_lines = [np.array([500.0])] with pytest.raises(ValueError, match="Must give pixel bounds when"): WavelengthSolution1D(ref_pixel, obs_lines=obs_lines) -def test_init_line_list(): +def test_init_line_list(mk_arc): """Test the catalog line list initialization with various configurations of `arc_spectra` and `line_lists`.""" - arc = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom) + arc = mk_arc WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=["ArI"]) WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists="ArI") WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=[array([0.1])]) @@ -78,86 +98,88 @@ def test_init_line_list(): WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"]]) -def test_find_lines_with_valid_input(mocker): - arc_spectra = [Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom)] - ws = WavelengthSolution1D(ref_pixel, arc_spectra=arc_spectra) +def test_find_lines(mocker, mk_arc): + arc = mk_arc + ws = WavelengthSolution1D(ref_pixel, arc_spectra=mk_arc) mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} ws.find_lines(fwhm=2.0, noise_factor=1.5) assert ws._obs_lines is not None - assert len(ws._obs_lines) == len(arc_spectra) - assert mock_find_arc_lines.called_once_with(arc_spectra[0], 2.0, noise_factor=1.5) + assert len(ws._obs_lines) == 1 + assert mock_find_arc_lines.called_once_with(arc, 2.0, noise_factor=1.5) - -def test_find_lines_with_missing_arc_spectra(): ws = WavelengthSolution1D(ref_pixel) with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): ws.find_lines(fwhm=2.0, noise_factor=1.5) -def test_fit_lines_with_valid_input(): - pix_bounds = (0, 10) - pixels = array([2, 4, 6, 8]) - wavelengths = array([500, 600, 700, 800]) +def test_fit_lines(mk_matched_lines): + lo, lc = mk_matched_lines ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) - ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + ws.fit_lines(pixels=lo, wavelengths=lc) assert ws._p2w is not None assert ws._p2w[1].degree == ws.degree - ws = WavelengthSolution1D( - ref_pixel, obs_lines=pixels, line_lists=wavelengths, pix_bounds=pix_bounds - ) - ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=True, match_obs=True) + + ws = WavelengthSolution1D(ref_pixel, obs_lines=lo, line_lists=lc, pix_bounds=pix_bounds) + ws.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) assert ws._p2w is not None assert ws._p2w[1].degree == ws.degree - ws = WavelengthSolution1D(ref_pixel, degree=5, pix_bounds=pix_bounds) - ws.fit_lines(pixels=pixels[:3], wavelengths=wavelengths[:3]) + ws = WavelengthSolution1D(ref_pixel, degree=5, pix_bounds=pix_bounds) + ws.fit_lines(pixels=lo[:3], wavelengths=lc[:3]) -def test_fit_lines_raises_error_for_missing_input(): - pix_bounds = (0, 10) - pixels = array([2, 4, 6, 8]) - wavelengths = array([500, 600, 700, 800]) ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="Cannot fit without catalog"): - ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=True, match_obs=True) + ws.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) with pytest.raises(ValueError, match="Cannot fit without observed"): - ws.fit_lines(pixels=pixels, wavelengths=wavelengths, match_cat=False, match_obs=True) + ws.fit_lines(pixels=lo, wavelengths=lc, match_cat=False, match_obs=True) -def test_observed_lines_returns_none_when_not_set(): +def test_observed_lines(mk_lines): ws = WavelengthSolution1D(ref_pixel) assert ws.observed_lines is None + obs_lines, cat_lines = mk_lines + ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, pix_bounds=pix_bounds) + assert len(ws.observed_lines) == 1 + np.testing.assert_allclose(ws.observed_lines[0].data, obs_lines) + + ws.observed_lines = obs_lines + assert len(ws.observed_lines) == 1 + np.testing.assert_allclose(ws.observed_lines[0].data, obs_lines) + ws.observed_lines = ws.observed_lines + assert len(ws.observed_lines) == 1 + np.testing.assert_allclose(ws.observed_lines[0].data, obs_lines) -def test_observed_lines_returns_expected_lines(): - mock_obs_lines = [np.ma.masked_array([100, 200], mask=[False, True])] + +def test_catalog_lines(mk_lines): ws = WavelengthSolution1D(ref_pixel) - ws._obs_lines = mock_obs_lines - assert ws.observed_lines == mock_obs_lines + assert ws.catalog_lines is None + obs_lines, cat_lines = mk_lines + ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=pix_bounds) + assert len(ws.catalog_lines) == 1 + np.testing.assert_allclose(ws.catalog_lines[0].data, cat_lines) + ws.catalog_lines = cat_lines + assert len(ws.catalog_lines) == 1 + np.testing.assert_allclose(ws.catalog_lines[0].data, cat_lines) -# def test_fit_lines_raises_error_for_nonexisting_lists(): -# pix_bounds = (0, 10) -# pixels = array([2, 4, 6, 8]) -# wavelengths = array([500, 600, 700, 800]) -# ws = WavelengthSolution1D(ref_pixel, line_lists=wavelengths, pix_bounds=pix_bounds) -# #with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): -# ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + ws.catalog_lines = ws.catalog_lines + assert len(ws.catalog_lines) == 1 + np.testing.assert_allclose(ws.catalog_lines[0].data, cat_lines) def test_fit_lines_raises_error_for_mismatched_sizes(): - pix_bounds = (0, 10) pixels = array([2, 4, 6]) - wavelengths = array([500, 600, 700, 800]) + wavelengths = p2w(array([2, 4, 5, 6])) ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): ws.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_lines_raises_error_for_insufficient_lines(): - pix_bounds = (0, 10) pixels = [5] - wavelengths = [500] + wavelengths = p2w(pixels) ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="Need at least two lines for a fit"): ws.fit_lines(pixels=pixels, wavelengths=wavelengths) @@ -165,120 +187,94 @@ def test_fit_lines_raises_error_for_insufficient_lines(): def test_fit_lines_raises_error_for_missing_pixel_bounds(): pixels = [2, 4, 6, 8] - wavelengths = [500, 600, 700, 800] + wavelengths = p2w(pixels) ws = WavelengthSolution1D(ref_pixel) with pytest.raises(ValueError, match="Cannot fit without pixel bounds set."): ws.fit_lines(pixels=pixels, wavelengths=wavelengths) -def test_fit_global_runs_successfully_with_valid_input(): - pix_bounds = (0, 10) - pixels = array([2, 4, 5, 6, 8]) - wavelengths = array([500, 550, 600, 650, 700, 750, 800]) +def test_fit_global(): + lines_obs = array([2, 4, 5, 6, 8]) + lines_cat = array([500, 550, 600, 650, 700, 750, 800]) wavelength_bounds = (649, 651) dispersion_bounds = (49, 51) - ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=pixels, line_lists=wavelengths) + ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=10) np.testing.assert_allclose(ws._fit.x, [650.0, 50.0, 0.0, 0.0], atol=1e-4) assert ws._fit is not None assert ws._fit.success assert ws._p2w is not None - ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=pixels, line_lists=wavelengths) + ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=10, refine_fit=False) -def test_resample_with_valid_input(): - arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(20))) - ws = WavelengthSolution1D(ref_pixel) - ws._p2w = lambda x: x * 2 # Mock pixel-to-wavelength conversion - ws._p2w_dldx = lambda x: np.ones_like(x) * 2 # Mock derivative - ws._w2p = lambda x: x / 2 # Mock wavelength-to-pixel conversion - resampled = ws.resample(arc_spectrum, nbins=10) +def test_resample(mk_arc, mk_ws, mk_good_ws_with_transform): + ws = mk_good_ws_with_transform + spectrum = mk_arc + resampled = ws.resample(spectrum, nbins=10) assert resampled is not None assert len(resampled.flux) == 10 assert resampled.flux.unit == u.DN - np.testing.assert_almost_equal(arc_spectrum.flux.value.sum(), resampled.flux.value.sum(), decimal=10) - + np.testing.assert_almost_equal( + spectrum.flux.value.sum(), resampled.flux.value.sum(), decimal=10 + ) -def test_resample_raises_error_for_missing_transforms(): - arc_spectrum = Spectrum1D(flux=np.ones(10) * u.DN, spectral_axis=np.arange(10) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(10))) - ws = WavelengthSolution1D(ref_pixel) + ws = mk_ws with pytest.raises(ValueError, match="Wavelength solution not yet"): - ws.resample(arc_spectrum) - + ws.resample(mk_arc) -def test_resample_raises_error_for_invalid_bins(): - arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom, uncertainty=StdDevUncertainty(np.ones(20))) - ws = WavelengthSolution1D(ref_pixel) - ws._p2w = lambda x: x * 2 # Mock pixel-to-wavelength conversion - ws._p2w_dldx = lambda x: np.ones_like(x) * 2 # Mock derivative - ws._w2p = lambda x: x / 2 # Mock wavelength-to-pixel conversion + ws = mk_good_ws_with_transform with pytest.raises(ValueError, match="Number of bins must be positive"): - ws.resample(arc_spectrum, nbins=-5) + ws.resample(mk_arc, nbins=-5) -def test_pix_to_wav_with_array(): +def test_pix_to_wav(mk_good_ws_with_transform): pix_values = np.array([1, 2, 3, 4, 5]) - ws = WavelengthSolution1D(ref_pixel) - ws._p2w = lambda x: x * 10 # Mock pixel-to-wavelength conversion + ws = mk_good_ws_with_transform wavelengths = ws.pix_to_wav(pix_values) - np.testing.assert_array_equal(wavelengths, np.array([10, 20, 30, 40, 50])) - + np.testing.assert_array_equal(wavelengths, p2w(pix_values)) -def test_pix_to_wav_with_masked_array(): pix_values = np.ma.masked_array([1, 2, 3], mask=[0, 1, 0]) - ws = WavelengthSolution1D(ref_pixel) - ws._p2w = lambda x: x * 10 # Mock pixel-to-wavelength conversion wavelengths = ws.pix_to_wav(pix_values) - np.testing.assert_array_equal(wavelengths.data, np.array([10, 20, 30])) + np.testing.assert_array_equal(wavelengths.data, p2w(pix_values)) np.testing.assert_array_equal(wavelengths.mask, np.array([0, 1, 0])) -def test_wav_to_pix_with_array(): +def test_wav_to_pix(mk_ws): wav_values = np.array([500, 1000, 1500]) - ws = WavelengthSolution1D(ref_pixel) + ws = mk_ws ws._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion pixel_values = ws.wav_to_pix(wav_values) np.testing.assert_array_equal(pixel_values, np.array([50, 100, 150])) - -def test_wav_to_pix_with_masked_array(): wav_values = np.ma.masked_array([500, 1000, 1500], mask=[0, 1, 0]) - ws = WavelengthSolution1D(ref_pixel) - ws._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion pixel_values = ws.wav_to_pix(wav_values) np.testing.assert_array_equal(pixel_values.data, np.array([50, 100, 150])) np.testing.assert_array_equal(pixel_values.mask, np.array([0, 1, 0])) -def test_wcs_creates_valid_gwcs_object(): - ws = WavelengthSolution1D(ref_pixel, unit=u.angstrom, pix_bounds=(0, 100)) - ws._p2w = models.Shift(5) | models.Polynomial1D(degree=3, c0=1, c1=2, c2=3) - ws._calculate_p2w_inverse() - ws._calculate_p2w_derivative() +def test_wcs_creates_valid_gwcs_object(mk_good_ws_with_transform): + ws = mk_good_ws_with_transform wcs_obj = ws.wcs assert wcs_obj is not None assert isinstance(wcs_obj, wcs.WCS) assert wcs_obj.output_frame.unit[0] == u.angstrom -def test_rms(): - ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=(0, 10)) - ws._p2w = p2w - ws._calculate_p2w_inverse() +def test_rms(mk_good_ws_with_transform): + ws = mk_good_ws_with_transform assert np.isclose(ws.rms(space="wavelength"), 0) # Perfect match, so RMS should be zero assert np.isclose(ws.rms(space="pixel"), 0) # Perfect match, so RMS should be zero with pytest.raises(ValueError, match="Space must be either 'pixel' or 'wavelength'"): ws.rms(space="wavelenght") -def test_remove_unmatched_lines(): - ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=(0, 10)) - ws._p2w = p2w - ws._calculate_p2w_inverse() +def test_remove_unmatched_lines(mk_good_ws_with_transform): + ws = mk_good_ws_with_transform ws.match_lines() ws.remove_ummatched_lines() + assert ws.catalog_lines[0].size == ws.observed_lines[0].size def test_plot_lines_with_valid_input(): @@ -308,26 +304,20 @@ def test_plot_lines_with_valid_input(): assert fig.axes[0].has_data() -def test_plot_lines_raises_for_missing_transform(): - ws = WavelengthSolution1D( - ref_pixel, obs_lines=array([2, 5, 8]), line_lists=array([500, 600, 700]), pix_bounds=(0, 10) - ) +def test_plot_lines_raises_for_missing_transform(mk_ws): + ws = mk_ws with pytest.raises(ValueError, match="Cannot map between pixels and"): ws._plot_lines(kind="observed", map_x=True) -def test_plot_lines_calls_transform_correctly(mocker): - ws = WavelengthSolution1D( - ref_pixel, obs_lines=array([2, 5, 8]), line_lists=array([500, 600, 700]), pix_bounds=(0, 10) - ) - ws._p2w = p2w - ws._calculate_p2w_inverse() +def test_plot_lines_calls_transform_correctly(mk_good_ws_with_transform): + ws = mk_good_ws_with_transform ws._plot_lines(kind="observed", map_x=True) ws._plot_lines(kind="catalog", map_x=True) -def test_plot_catalog_lines_with_valid_input(): - ws = WavelengthSolution1D(ref_pixel) +def test_plot_catalog_lines(mk_ws): + ws = mk_ws ws._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] fig = ws.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) assert isinstance(fig, Figure) @@ -344,38 +334,32 @@ def test_plot_catalog_lines_with_valid_input(): assert fig.axes[0].has_data() -def test_plot_observed_lines_with_valid_input(): - ws = WavelengthSolution1D(ref_pixel) +def test_plot_observed_lines(mk_good_ws_with_transform, mk_arc): + ws = mk_good_ws_with_transform ws._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom) - ws.arc_spectra = [arc_spectrum] - ws._p2w = p2w - fig = ws.plot_observed_lines(frames=0, figsize=(10, 5), plot_values=True, plot_spectra=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() - assert len(fig.axes) == 1 + ws.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = ws.plot_observed_lines(frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + assert len(fig.axes) == 1 -def test_plot_fit_with_valid_input(): - arc_spectrum = Spectrum1D(flux=np.ones(20) * u.DN, spectral_axis=np.arange(20) * u.angstrom) - ws = WavelengthSolution1D( - ref_pixel, obs_lines=array([2, 5, 8]), line_lists=array([500, 600, 700]), pix_bounds=(0, 10) - ) - ws.arc_spectra = [arc_spectrum] - ws._p2w = p2w - fig = ws.plot_fit(frames=0, figsize=(12, 6), plot_values=True) - assert isinstance(fig, Figure) - assert len(fig.axes) == 2 - assert fig.axes[0].has_data() - assert fig.axes[1].has_data() +def test_plot_fit(mk_arc, mk_good_ws_with_transform): + ws = mk_good_ws_with_transform + ws.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = ws.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) + assert isinstance(fig, Figure) + assert len(fig.axes) == 2 + assert fig.axes[0].has_data() + assert fig.axes[1].has_data() + fig = ws.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) -def test_plot_residuals(): - obs_lines = array([2, 5, 8]) - cat_lines = p2w(obs_lines) - ws = WavelengthSolution1D(ref_pixel, pix_bounds=(0, 10), obs_lines=obs_lines, line_lists=cat_lines) - ws._p2w = p2w - ws._calculate_p2w_inverse() + +def test_plot_residuals(mk_good_ws_with_transform): + ws = mk_good_ws_with_transform fig = ws.plot_residuals(space="pixel", figsize=(8, 4)) assert isinstance(fig, Figure) From e1f522030ce337e1e6973665e10586b00f4c7994 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 22 Apr 2025 15:53:43 +0100 Subject: [PATCH 33/76] Cleaned up an error message. --- specreduce/wavecal1d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 7205a951..42caa485 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -87,7 +87,7 @@ def __init__( self.nframes = len(self.arc_spectra) for s in self.arc_spectra: if s.data.ndim > 1: - raise ValueError("The arc spectra must be 1 dimensional.") + raise ValueError("The arc spectrum must be one dimensional.") self.bounds_pix = (0, self.arc_spectra[0].shape[0]) elif obs_lines is not None: From 42af922205b13ff875b761ce6d3edab2739ed1bc Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 22 Apr 2025 19:47:01 +0100 Subject: [PATCH 34/76] Removed 2D wavelength calibration code from this branch. Will work on it on another one. Also removed the old 1D wavelength calibration code. --- specreduce/lswavecal2d.py | 242 -------------------------- specreduce/wavelength_calibration.py | 244 --------------------------- 2 files changed, 486 deletions(-) delete mode 100644 specreduce/lswavecal2d.py delete mode 100644 specreduce/wavelength_calibration.py diff --git a/specreduce/lswavecal2d.py b/specreduce/lswavecal2d.py deleted file mode 100644 index b44fd33c..00000000 --- a/specreduce/lswavecal2d.py +++ /dev/null @@ -1,242 +0,0 @@ -import warnings -from typing import Iterable - -import astropy.units as u -import numpy as np -from astropy.modeling import models, Model, fitting -from astropy.nddata import StdDevUncertainty, NDData -from gwcs import coordinate_frames as cf -from gwcs import wcs -from matplotlib import cm -from matplotlib.pyplot import setp, subplots -from numpy.random import uniform -from scipy import optimize -from scipy.spatial import KDTree - -from numpy import ndarray -from specutils import Spectrum1D - -from specreduce.line_matching import find_arc_lines -from specreduce.lswavecal1d import WavelengthSolution1D - - -def diff_poly2d_x(m): - coeffs = {} - for n in m.param_names: - ix, iy = int(n[1]), int(n[3]) - if ix > 0: - coeffs[f"c{ix-1}_{iy}"] = ix*getattr(m, n).value - return models.Polynomial2D(m.degree-1, **coeffs) - - -class WavelengthSolution2D(WavelengthSolution1D): - def __init__(self, frames: Iterable[NDData], lamps: Iterable, wlbounds: tuple[float, float], wave_air: bool = False, - n_cd_samples: int = 10, cd_samples: Iterable[float] | None = None): - super().__init__(None, lamps, wlbounds, wave_air) - self.frames = frames - self.lamps = lamps - - self.lines_pix_x: Iterable[ndarray] | None = None - self.lines_pix_y: Iterable[ndarray] | None = None - self._fitted_model: Model | None = None - self._wcs: wcs.WCS | None = None - - self.nframes = len(frames) - - self._spectra: Iterable[Spectrum1D] | None = None - - if cd_samples is not None: - self.cd_samples = np.array(cd_samples) - else: - self.cd_samples = np.round(np.arange(1, n_cd_samples + 1) * self.frames[0].shape[0] / (n_cd_samples + 1)).astype( - int) - self.ncd = self.cd_samples.size - - self._ref_pixel: tuple[float, float] | None = None - self._shift = None - - def find_lines(self, fwhm: float): - self.spectra = [] - lines_pix_x = [] - lines_pix_y = [] - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - for i, d in enumerate(self.frames): - self.spectra.append([]) - lines_pix_x.append([]) - lines_pix_y.append([]) - for s in self.cd_samples: - spectrum = Spectrum1D((d[s] - np.median(d)) * u.DN, - uncertainty=d[s].uncertainty.represent_as(StdDevUncertainty)) - lines = find_arc_lines(spectrum, fwhm) - lines_pix_x[i].append(lines['centroid'].value) - lines_pix_y[i].append(np.full(len(lines), s)) - self.spectra[i].append(spectrum) - self.lines_pix_x = [np.concatenate(lpx) for lpx in lines_pix_x] - self.lines_pix_y = [np.concatenate(lpy) for lpy in lines_pix_y] - - def fit(self, ref_pixel: tuple[float, float], - wavelength_bounds: tuple[float, float] = (7000, 7600), dispersion_bounds: tuple[float, float] = (2.4, 2.8), - popsize: int = 30, max_distance: float = 100, workers: int = 1): - - self._ref_pixel = ref_pixel - self._shift = (models.Shift(-ref_pixel[0]) & models.Shift(-ref_pixel[1])) - model = self._shift | models.Polynomial2D(3) - trees = [KDTree(l[:, None]) for l in self.lines_wav] - - xx = np.zeros(10) - - def minfun(x): - xx[:-3] = x - distance_sum = 0.0 - for j, t in enumerate(trees): - distance_sum += np.clip(t.query( - model.evaluate(self.lines_pix_x[j], self.lines_pix_y[j], -ref_pixel[0], -ref_pixel[1], *xx)[:, - None])[0], - 0, max_distance).sum() - return distance_sum - - bounds = np.array([wavelength_bounds, dispersion_bounds, [-1e-3, 1e-3], [-1e-5, 1e-5], [-1e-1, 1e-1], [-1e-4, 1e-4], [-1e-5, 1e-5]]) - res = optimize.differential_evolution(minfun, bounds, popsize=popsize, workers=1, updating='deferred') - self._p2w = (self._shift | - models.Polynomial2D(model[-1].degree, - **{model[-1].param_names[i]: res.x[i] for i in range(res.x.size)})) - self._refine_fit() - self._calculate_inverse() - self._calculate_derivaties() - - def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): - mlines_px, mlines_py, mlines_wav = self._match_lines(match_distance_bound) - model = self._shift | models.Polynomial2D(degree, **{n: getattr(self._p2w[-1], n).value for n in self._p2w[-1].param_names}) - model.offset_0.fixed = True - model.offset_1.fixed = True - fitter = fitting.LMLSQFitter() - self._p2w = fitter(model, mlines_px, mlines_py, mlines_wav) - - def _match_lines(self, upper_bound: float = 5): - matched_lines_wav = [] - matched_lines_pix_x = [] - matched_lines_pix_y = [] - for iframe, tree in enumerate(self._trees): - l, ix = tree.query(self._p2w(self.lines_pix_x[iframe], self.lines_pix_y[iframe])[:, None], distance_upper_bound=upper_bound) - m = np.isfinite(l) - matched_lines_wav.append(tree.data[ix[m], 0]) - matched_lines_pix_x.append(self.lines_pix_x[iframe][m]) - matched_lines_pix_y.append(self.lines_pix_y[iframe][m]) - return (np.concatenate(matched_lines_pix_x), - np.concatenate(matched_lines_pix_y), - np.concatenate(matched_lines_wav)) - - def _calculate_inverse(self, nsamples: int = 1500): - m = self._p2w - xx = uniform(0, self.frames[0].shape[1], nsamples) - yy = uniform(0, self.frames[0].shape[0], nsamples) - ll = m(xx, yy) - mm = (models.Shift(-m.c0_0_2) & models.Shift(-self._ref_pixel[1])) | models.Polynomial2D(6, c0_0=-m[0].offset, - c1_0=1 / m[-1].c1_0, - fixed={'c0_0': True}) - mm.offset_0.fixed = True - mm.offset_1.fixed = True - self._w2p = fitting.LMLSQFitter()(mm, ll, yy, xx) - - def _calculate_derivaties(self): - if self._p2w is not None: - self._p2w_dldx = self._shift | diff_poly2d_x(self._p2w[-1]) - if self._w2p is not None: - self._w2p_dxdl = (self._w2p[0] & self._w2p[1]) | diff_poly2d_x(self._w2p[-1]) - - @property - def wcs(self): - m_forward = models.Mapping((0, 1, 1)) | (self._p2w & models.Identity(1)) - m_inverse = models.Mapping((0, 1, 1)) | (self._w2p & models.Identity(1)) - m_forward.inverse = m_inverse - pixel_frame = cf.Frame2D(name='detector', axes_names=["x", "y"], unit=[u.pix, u.pix]) - spectral_frame = cf.Frame2D(name='spectrum', axes_names=["wavelength", "y"], - unit=[self.linelists[0]['wavelength'].unit, u.pix]) - pipeline = [(pixel_frame, m_forward), (spectral_frame, None)] - self._wcs = wcs.WCS(pipeline) - return self._wcs - - def resample(self, flux, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, - bin_edges: Iterable[float] | None = None): - ny, nx = flux.shape - ypix = np.arange(ny) - nbins = nx if nbins is None else nbins - if wlbounds is None: - l1 = self._p2w(0, 0) - self._p2w_dldx(0, 0) - l2 = self._p2w(nx, 0) + self._p2w_dldx(nx, 0) - else: - l1, l2 = wlbounds - - bin_edges_wav = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) - bin_edges_pix = np.clip(self._w2p(*np.meshgrid(bin_edges_wav, ypix)) + 0.5, 0, nx - 1e-12) - bin_edge_ix = np.floor(bin_edges_pix).astype(int) - bin_edge_w = bin_edges_pix - bin_edge_ix - bin_centers_wav = 0.5 * (bin_edges_wav[:-1] + bin_edges_wav[1:]) - - flux_wl = np.zeros((ny, nbins)) - weights = np.zeros((ny, nx)) - - dldx = self._p2w_dldx(*np.meshgrid(np.arange(nx), np.arange(ny))) - n = flux.sum(1) / (dldx * flux).sum(1) - - ixs = np.tile(np.arange(flux.shape[1]), (flux.shape[0], 1)) - ys = np.arange(flux.shape[0]) - - for i in range(nbins): - i1, i2 = bin_edge_ix[:, i:i + 2].T - m = i1 == i2 - if m.any(): - flux_wl[:, i] = (bin_edges_pix[:, i + 1] - bin_edges_pix[:, i]) * flux[ys, i1] * dldx[ys, i1] - - if not m.all(): - imin, imax = i1.min(), i2.max() + 1 - ixc = ixs[:, imin: imax] - w = weights[:, imin:imax] - w[:] = 0.0 - w[(ixc > i1[:, None]) & (ixc < i2[:, None])] = 1 - w[ys, i1 - imin] = 1.0 - bin_edge_w[:, i] - w[ys, i2 - imin] = bin_edge_w[:, i + 1] - flux_wl[~m, i] = (flux[~m, imin:imax] * dldx[~m, imin:imax] * w[~m]).sum(1) - flux_wl *= n[:, None] - return bin_centers_wav, flux_wl - - def plot_fit(self, lamp: int, ax=None, figsize=None): - if ax is None: - fig, ax = subplots(figsize=figsize) - else: - fig = ax.figure - - i = 0 - ncd = len(self.cd_samples) - - t = self._trees[lamp] - l, ix = t.query(self._p2w(self.lines_pix_x[lamp], self.lines_pix_y[lamp])[:, None], distance_upper_bound=10) - mask = np.zeros(t.data.size, bool) - mask[ix[np.isfinite(l)]] = True - - ax.vlines(self.lines_wav[lamp][mask], -1.0, 8.0, alpha=1, ec='darkorange', zorder=-1) - ax.vlines(self.lines_wav[lamp][~mask], -1.0, 8.0, alpha=0.1, ec='k', zorder=-2) - - for i in range(ncd): - x = self.spectra[lamp][i].spectral_axis.value - sl = self._p2w(x, np.full_like(x, self.cd_samples[i])) - ax.pcolormesh(np.tile(sl, (2, 1)), - np.array([np.full_like(sl, i), np.full_like(sl, i + 1)]), - self.spectra[lamp][i].data[None, :-1], cmap=cm.Blues, - vmax=np.percentile(self.spectra[lamp][i].data, 98)) - ax.axhline(i, c='k', alpha=0.5, ls='-', lw=0.5) - - setp(ax, ylim=(-1.0, ncd + 1), yticks=0.5 + np.arange(ncd), yticklabels=self.cd_samples) - return fig - - def plot_residuals(self, axes=None, model=None, figsize=None): - if axes is None: - fig, axes = subplots(self.nframes, 1, figsize=figsize, constrained_layout=True, sharex='all', sharey='all') - else: - fig = axes[0].figure - model = model if model is not None else self._p2w - trees = [KDTree(l[:, None]) for l in self.lines_wav] - for lamp, t in enumerate(trees): - l, ix = t.query(model(self.lines_pix_x[lamp], self.lines_pix_y[lamp])[:, None], distance_upper_bound=5) - axes[lamp].plot(self.lines_pix_x[lamp], l, '.') diff --git a/specreduce/wavelength_calibration.py b/specreduce/wavelength_calibration.py deleted file mode 100644 index 8d4ad620..00000000 --- a/specreduce/wavelength_calibration.py +++ /dev/null @@ -1,244 +0,0 @@ -from functools import cached_property - -import numpy as np -from astropy import units as u -from astropy.modeling.fitting import LMLSQFitter, LinearLSQFitter -from astropy.modeling.models import Linear1D -from astropy.table import QTable, hstack -from gwcs import coordinate_frames as cf -from gwcs import wcs - -from specreduce.compat import Spectrum - -__all__ = [ - 'WavelengthCalibration1D' -] - - -def _check_arr_monotonic(arr): - # returns True if ``arr`` is either strictly increasing or strictly - # decreasing, otherwise returns False. - - sorted_increasing = np.all(arr[1:] >= arr[:-1]) - sorted_decreasing = np.all(arr[1:] <= arr[:-1]) - return sorted_increasing or sorted_decreasing - - -class WavelengthCalibration1D(): - - def __init__(self, input_spectrum, matched_line_list=None, line_pixels=None, - line_wavelengths=None, catalog=None, input_model=Linear1D(), fitter=None): - """ - input_spectrum: `~specutils.Spectrum1D` - A one-dimensional Spectrum calibration spectrum from an arc lamp or similar. - matched_line_list: `~astropy.table.QTable`, optional - An `~astropy.table.QTable` table with (minimally) columns named - "pixel_center" and "wavelength" with known corresponding line pixel centers - and wavelengths populated. - line_pixels: list, array, `~astropy.table.QTable`, optional - List or array of line pixel locations to anchor the wavelength solution fit. - Can also be input as an `~astropy.table.QTable` table with (minimally) a column - named "pixel_center". - line_wavelengths: `~astropy.units.Quantity`, `~astropy.table.QTable`, optional - `astropy.units.Quantity` array of line wavelength values corresponding to the - line pixels defined in ``line_list``, assumed to be in the same - order. Can also be input as an `~astropy.table.QTable` with (minimally) - a "wavelength" column. - catalog: list, str, `~astropy.table.QTable`, optional - The name of a catalog of line wavelengths to load and use in automated and - template-matching line matching. NOTE: This option is currently not implemented. - input_model: `~astropy.modeling.Model` - The model to fit for the wavelength solution. Defaults to a linear model. - fitter: `~astropy.modeling.fitting.Fitter`, optional - The fitter to use in optimizing the model fit. Defaults to - `~astropy.modeling.fitting.LinearLSQFitter` if the model to fit is linear - or `~astropy.modeling.fitting.LMLSQFitter` if the model to fit is non-linear. - - Note that either ``matched_line_list`` or ``line_pixels`` must be specified, - and if ``matched_line_list`` is not input, at least one of ``line_wavelengths`` - or ``catalog`` must be specified. - """ - self._input_spectrum = input_spectrum - self._input_model = input_model - self._cached_properties = ['fitted_model', 'residuals', 'wcs'] - self.fitter = fitter - self._potential_wavelengths = None - self._catalog = catalog - - if not isinstance(input_spectrum, Spectrum): - raise ValueError('Input spectrum must be Spectrum.') - - # We use either line_pixels or matched_line_list to create self._matched_line_list, - # and check that various requirements are fulfilled by the input args. - if matched_line_list is not None: - pixel_arg = "matched_line_list" - if not isinstance(matched_line_list, QTable): - raise ValueError("matched_line_list must be an astropy.table.QTable.") - self._matched_line_list = matched_line_list - elif line_pixels is not None: - pixel_arg = "line_pixels" - if isinstance(line_pixels, (list, np.ndarray)): - self._matched_line_list = QTable([line_pixels], names=["pixel_center"]) - elif isinstance(line_pixels, QTable): - self._matched_line_list = line_pixels - else: - raise ValueError("Either matched_line_list or line_pixels must be specified.") - - if "pixel_center" not in self._matched_line_list.columns: - raise ValueError(f"{pixel_arg} must have a 'pixel_center' column.") - - if self._matched_line_list["pixel_center"].unit is None: - self._matched_line_list["pixel_center"].unit = u.pix - - # check that pixels are monotonic - if not _check_arr_monotonic(self._matched_line_list["pixel_center"]): - raise ValueError('Pixels must be strictly increasing or decreasing.') - - # now that pixels have been determined from input, figure out wavelengths. - if (line_wavelengths is None and catalog is None - and "wavelength" not in self._matched_line_list.columns): - raise ValueError("You must specify at least one of line_wavelengths, " - "catalog, or 'wavelength' column in matched_line_list.") - - # Sanity checks on line_wavelengths value - if line_wavelengths is not None: - if (isinstance(self._matched_line_list, QTable) and - "wavelength" in self._matched_line_list.columns): - raise ValueError("Cannot specify line_wavelengths separately if there is" - " a 'wavelength' column in matched_line_list.") - if len(line_wavelengths) != len(self._matched_line_list): - raise ValueError("If line_wavelengths is specified, it must have the same " - f"length as {pixel_arg}") - if not isinstance(line_wavelengths, (u.Quantity, QTable)): - raise ValueError("line_wavelengths must be specified as an astropy.units.Quantity" - " array or as an astropy.table.QTable") - - # make sure wavelengths (or freq) are monotonic and add wavelengths - # to _matched_line_list - if isinstance(line_wavelengths, u.Quantity): - if not _check_arr_monotonic(line_wavelengths): - if str(line_wavelengths.unit.physical_type) == "frequency": - raise ValueError('Frequencies must be strictly increasing or decreasing.') - raise ValueError('Wavelengths must be strictly increasing or decreasing.') - - self._matched_line_list["wavelength"] = line_wavelengths - - elif isinstance(line_wavelengths, QTable): - if not _check_arr_monotonic(line_wavelengths['wavelength']): - raise ValueError('Wavelengths must be strictly increasing or decreasing.') - self._matched_line_list = hstack([self._matched_line_list, line_wavelengths]) - - # Parse desired catalogs of lines for matching. - if catalog is not None: - # For now we avoid going into the later logic and just throw an error - raise NotImplementedError("No catalogs are available yet, please input " - "wavelengths with line_wavelengths or as a " - f"column in {pixel_arg}") - - if isinstance(catalog, QTable): - if "wavelength" not in catalog.columns: - raise ValueError("Catalog table must have a 'wavelength' column.") - self._catalog = catalog - else: - # This will need to be updated to match up with Tim's catalog code - if isinstance(catalog, list): - self._catalog = catalog - else: - self._catalog = [catalog] - for cat in self._catalog: - if isinstance(cat, str): - if cat not in self._available_catalogs: - raise ValueError(f"Line list '{cat}' is not an available catalog.") - - def identify_lines(self): - """ - ToDo: Code matching algorithm between line pixel locations and potential line - wavelengths from catalogs. - """ - pass - - def _clear_cache(self, *attrs): - """ - provide convenience function to clearing the cache for cached_properties - """ - if not len(attrs): - attrs = self._cached_properties - for attr in attrs: - if attr in self.__dict__: - del self.__dict__[attr] - - @property - def available_catalogs(self): - return self._available_catalogs - - @property - def input_spectrum(self): - return self._input_spectrum - - @input_spectrum.setter - def input_spectrum(self, new_spectrum): - # We want to clear the refined locations if a new calibration spectrum is provided - self._clear_cache() - self._input_spectrum = new_spectrum - - @property - def input_model(self): - return self._input_model - - @input_model.setter - def input_model(self, input_model): - self._clear_cache() - self._input_model = input_model - - @cached_property - def fitted_model(self): - # computes and returns WCS after fitting self.model to self.refined_pixels - x = self._matched_line_list["pixel_center"] - y = self._matched_line_list["wavelength"] - - if self.fitter is None: - # Flexible defaulting if self.fitter is None - if self.input_model.linear: - fitter = LinearLSQFitter(calc_uncertainties=True) - else: - fitter = LMLSQFitter(calc_uncertainties=True) - else: - fitter = self.fitter - - # Fit the model - return fitter(self.input_model, x, y) - - @cached_property - def residuals(self): - """ - calculate fit residuals between matched line list pixel centers and - wavelengths and the evaluated fit model. - """ - - x = self._matched_line_list["pixel_center"] - y = self._matched_line_list["wavelength"] - - # Get the fit residuals by evaulating model - return y - self.fitted_model(x) - - @cached_property - def wcs(self): - # Build a GWCS pipeline from the fitted model - pixel_frame = cf.CoordinateFrame(1, "SPECTRAL", [0,], axes_names=["x",], unit=[u.pix,]) - spectral_frame = cf.SpectralFrame(axes_names=["wavelength",], - unit=[self._matched_line_list["wavelength"].unit,]) - - pipeline = [(pixel_frame, self.fitted_model), (spectral_frame, None)] - - wcsobj = wcs.WCS(pipeline) - - return wcsobj - - def apply_to_spectrum(self, spectrum=None): - # returns Spectrum with wavelength calibration applied - # actual line refinement and WCS solution should already be done so that this can - # be called on multiple science sources - spectrum = self.input_spectrum if spectrum is None else spectrum - updated_spectrum = Spectrum(spectrum.flux, wcs=self.wcs, mask=spectrum.mask, - uncertainty=spectrum.uncertainty) - return updated_spectrum From 3a23cd75500fb2b0f566ccef77f3ba37bf0b74ba Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 22 Apr 2025 19:53:05 +0100 Subject: [PATCH 35/76] Renamed WavelengthSolution1D -> WavelengthCalibration1D. --- specreduce/tests/test_wavecal1d.py | 290 ++++++++++++++--------------- specreduce/wavecal1d.py | 2 +- 2 files changed, 146 insertions(+), 146 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index c6903c8d..c0d54d31 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -10,7 +10,7 @@ from matplotlib.figure import Figure from numpy import array -from specreduce.wavecal1d import WavelengthSolution1D, _diff_poly1d +from specreduce.wavecal1d import WavelengthCalibration1D, _diff_poly1d from specreduce.compat import Spectrum @@ -34,21 +34,21 @@ def mk_matched_lines(): @pytest.fixture -def mk_ws(mk_lines): +def mk_wc(mk_lines): obs_lines, cat_lines = mk_lines - return WavelengthSolution1D( + return WavelengthCalibration1D( ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) ) @pytest.fixture -def mk_good_ws_with_transform(mk_lines): +def mk_good_wc_with_transform(mk_lines): obs_lines, cat_lines = mk_lines - ws = WavelengthSolution1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) - ws._p2w = p2w - ws._calculate_p2w_inverse() - ws._calculate_p2w_derivative() - return ws + wc = WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) + wc._p2w = p2w + wc._calculate_p2w_inverse() + wc._calculate_p2w_derivative() + return wc @pytest.fixture @@ -68,129 +68,129 @@ def test_diff_poly1d(): def test_init(mk_arc, mk_lines): arc = mk_arc obs_lines, cat_lines = mk_lines - WavelengthSolution1D(ref_pixel, line_lists=cat_lines, arc_spectra=arc) - WavelengthSolution1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) + WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, arc_spectra=arc) + WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): - WavelengthSolution1D(ref_pixel, arc_spectra=arc, obs_lines=obs_lines) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, obs_lines=obs_lines) arc = Spectrum(flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom) with pytest.raises(ValueError, match="The arc spectrum must be one dimensional."): - WavelengthSolution1D(ref_pixel, arc_spectra=arc) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc) with pytest.raises(ValueError, match="Must give pixel bounds when"): - WavelengthSolution1D(ref_pixel, obs_lines=obs_lines) + WavelengthCalibration1D(ref_pixel, obs_lines=obs_lines) def test_init_line_list(mk_arc): """Test the catalog line list initialization with various configurations of `arc_spectra` and `line_lists`.""" arc = mk_arc - WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=["ArI"]) - WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists="ArI") - WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=[array([0.1])]) - WavelengthSolution1D(ref_pixel, arc_spectra=arc, line_lists=array([0.1])) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"], ["ArI"]]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", ["ArI"]]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", "ArI"]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])]) - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ["ArI"]]) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=["ArI"]) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists="ArI") + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=[array([0.1])]) + WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=array([0.1])) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"], ["ArI"]]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", ["ArI"]]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", "ArI"]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ["ArI"]]) with pytest.raises(ValueError, match="The number of line lists"): - WavelengthSolution1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"]]) + WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"]]) def test_find_lines(mocker, mk_arc): arc = mk_arc - ws = WavelengthSolution1D(ref_pixel, arc_spectra=mk_arc) + wc = WavelengthCalibration1D(ref_pixel, arc_spectra=mk_arc) mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} - ws.find_lines(fwhm=2.0, noise_factor=1.5) - assert ws._obs_lines is not None - assert len(ws._obs_lines) == 1 + wc.find_lines(fwhm=2.0, noise_factor=1.5) + assert wc._obs_lines is not None + assert len(wc._obs_lines) == 1 assert mock_find_arc_lines.called_once_with(arc, 2.0, noise_factor=1.5) - ws = WavelengthSolution1D(ref_pixel) + wc = WavelengthCalibration1D(ref_pixel) with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): - ws.find_lines(fwhm=2.0, noise_factor=1.5) + wc.find_lines(fwhm=2.0, noise_factor=1.5) def test_fit_lines(mk_matched_lines): lo, lc = mk_matched_lines - ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) - ws.fit_lines(pixels=lo, wavelengths=lc) - assert ws._p2w is not None - assert ws._p2w[1].degree == ws.degree + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) + wc.fit_lines(pixels=lo, wavelengths=lc) + assert wc._p2w is not None + assert wc._p2w[1].degree == wc.degree - ws = WavelengthSolution1D(ref_pixel, obs_lines=lo, line_lists=lc, pix_bounds=pix_bounds) - ws.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) - assert ws._p2w is not None - assert ws._p2w[1].degree == ws.degree + wc = WavelengthCalibration1D(ref_pixel, obs_lines=lo, line_lists=lc, pix_bounds=pix_bounds) + wc.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) + assert wc._p2w is not None + assert wc._p2w[1].degree == wc.degree - ws = WavelengthSolution1D(ref_pixel, degree=5, pix_bounds=pix_bounds) - ws.fit_lines(pixels=lo[:3], wavelengths=lc[:3]) + wc = WavelengthCalibration1D(ref_pixel, degree=5, pix_bounds=pix_bounds) + wc.fit_lines(pixels=lo[:3], wavelengths=lc[:3]) - ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="Cannot fit without catalog"): - ws.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) + wc.fit_lines(pixels=lo, wavelengths=lc, match_cat=True, match_obs=True) with pytest.raises(ValueError, match="Cannot fit without observed"): - ws.fit_lines(pixels=lo, wavelengths=lc, match_cat=False, match_obs=True) + wc.fit_lines(pixels=lo, wavelengths=lc, match_cat=False, match_obs=True) def test_observed_lines(mk_lines): - ws = WavelengthSolution1D(ref_pixel) - assert ws.observed_lines is None + wc = WavelengthCalibration1D(ref_pixel) + assert wc.observed_lines is None obs_lines, cat_lines = mk_lines - ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, pix_bounds=pix_bounds) - assert len(ws.observed_lines) == 1 - np.testing.assert_allclose(ws.observed_lines[0].data, obs_lines) + wc = WavelengthCalibration1D(ref_pixel, obs_lines=obs_lines, pix_bounds=pix_bounds) + assert len(wc.observed_lines) == 1 + np.testing.assert_allclose(wc.observed_lines[0].data, obs_lines) - ws.observed_lines = obs_lines - assert len(ws.observed_lines) == 1 - np.testing.assert_allclose(ws.observed_lines[0].data, obs_lines) + wc.observed_lines = obs_lines + assert len(wc.observed_lines) == 1 + np.testing.assert_allclose(wc.observed_lines[0].data, obs_lines) - ws.observed_lines = ws.observed_lines - assert len(ws.observed_lines) == 1 - np.testing.assert_allclose(ws.observed_lines[0].data, obs_lines) + wc.observed_lines = wc.observed_lines + assert len(wc.observed_lines) == 1 + np.testing.assert_allclose(wc.observed_lines[0].data, obs_lines) def test_catalog_lines(mk_lines): - ws = WavelengthSolution1D(ref_pixel) - assert ws.catalog_lines is None + wc = WavelengthCalibration1D(ref_pixel) + assert wc.catalog_lines is None obs_lines, cat_lines = mk_lines - ws = WavelengthSolution1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=pix_bounds) - assert len(ws.catalog_lines) == 1 - np.testing.assert_allclose(ws.catalog_lines[0].data, cat_lines) + wc = WavelengthCalibration1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=pix_bounds) + assert len(wc.catalog_lines) == 1 + np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) - ws.catalog_lines = cat_lines - assert len(ws.catalog_lines) == 1 - np.testing.assert_allclose(ws.catalog_lines[0].data, cat_lines) + wc.catalog_lines = cat_lines + assert len(wc.catalog_lines) == 1 + np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) - ws.catalog_lines = ws.catalog_lines - assert len(ws.catalog_lines) == 1 - np.testing.assert_allclose(ws.catalog_lines[0].data, cat_lines) + wc.catalog_lines = wc.catalog_lines + assert len(wc.catalog_lines) == 1 + np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) def test_fit_lines_raises_error_for_mismatched_sizes(): pixels = array([2, 4, 6]) wavelengths = p2w(array([2, 4, 5, 6])) - ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="The sizes of pixel and wavelength arrays must match."): - ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + wc.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_lines_raises_error_for_insufficient_lines(): pixels = [5] wavelengths = p2w(pixels) - ws = WavelengthSolution1D(ref_pixel, pix_bounds=pix_bounds) + wc = WavelengthCalibration1D(ref_pixel, pix_bounds=pix_bounds) with pytest.raises(ValueError, match="Need at least two lines for a fit"): - ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + wc.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_lines_raises_error_for_missing_pixel_bounds(): pixels = [2, 4, 6, 8] wavelengths = p2w(pixels) - ws = WavelengthSolution1D(ref_pixel) + wc = WavelengthCalibration1D(ref_pixel) with pytest.raises(ValueError, match="Cannot fit without pixel bounds set."): - ws.fit_lines(pixels=pixels, wavelengths=wavelengths) + wc.fit_lines(pixels=pixels, wavelengths=wavelengths) def test_fit_global(): @@ -198,21 +198,21 @@ def test_fit_global(): lines_cat = array([500, 550, 600, 650, 700, 750, 800]) wavelength_bounds = (649, 651) dispersion_bounds = (49, 51) - ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) - ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=10) - np.testing.assert_allclose(ws._fit.x, [650.0, 50.0, 0.0, 0.0], atol=1e-4) - assert ws._fit is not None - assert ws._fit.success - assert ws._p2w is not None + wc = WavelengthCalibration1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) + wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=10) + np.testing.assert_allclose(wc._fit.x, [650.0, 50.0, 0.0, 0.0], atol=1e-4) + assert wc._fit is not None + assert wc._fit.success + assert wc._p2w is not None - ws = WavelengthSolution1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) - ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=10, refine_fit=False) + wc = WavelengthCalibration1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) + wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=10, refine_fit=False) -def test_resample(mk_arc, mk_ws, mk_good_ws_with_transform): - ws = mk_good_ws_with_transform +def test_resample(mk_arc, mk_wc, mk_good_wc_with_transform): + wc = mk_good_wc_with_transform spectrum = mk_arc - resampled = ws.resample(spectrum, nbins=10) + resampled = wc.resample(spectrum, nbins=10) assert resampled is not None assert len(resampled.flux) == 10 assert resampled.flux.unit == u.DN @@ -220,157 +220,157 @@ def test_resample(mk_arc, mk_ws, mk_good_ws_with_transform): spectrum.flux.value.sum(), resampled.flux.value.sum(), decimal=10 ) - ws = mk_ws + wc = mk_wc with pytest.raises(ValueError, match="Wavelength solution not yet"): - ws.resample(mk_arc) + wc.resample(mk_arc) - ws = mk_good_ws_with_transform + wc = mk_good_wc_with_transform with pytest.raises(ValueError, match="Number of bins must be positive"): - ws.resample(mk_arc, nbins=-5) + wc.resample(mk_arc, nbins=-5) -def test_pix_to_wav(mk_good_ws_with_transform): +def test_pix_to_wav(mk_good_wc_with_transform): pix_values = np.array([1, 2, 3, 4, 5]) - ws = mk_good_ws_with_transform - wavelengths = ws.pix_to_wav(pix_values) + wc = mk_good_wc_with_transform + wavelengths = wc.pix_to_wav(pix_values) np.testing.assert_array_equal(wavelengths, p2w(pix_values)) pix_values = np.ma.masked_array([1, 2, 3], mask=[0, 1, 0]) - wavelengths = ws.pix_to_wav(pix_values) + wavelengths = wc.pix_to_wav(pix_values) np.testing.assert_array_equal(wavelengths.data, p2w(pix_values)) np.testing.assert_array_equal(wavelengths.mask, np.array([0, 1, 0])) -def test_wav_to_pix(mk_ws): +def test_wav_to_pix(mk_wc): wav_values = np.array([500, 1000, 1500]) - ws = mk_ws - ws._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion - pixel_values = ws.wav_to_pix(wav_values) + wc = mk_wc + wc._w2p = lambda x: x / 10 # Mock wavelength-to-pixel conversion + pixel_values = wc.wav_to_pix(wav_values) np.testing.assert_array_equal(pixel_values, np.array([50, 100, 150])) wav_values = np.ma.masked_array([500, 1000, 1500], mask=[0, 1, 0]) - pixel_values = ws.wav_to_pix(wav_values) + pixel_values = wc.wav_to_pix(wav_values) np.testing.assert_array_equal(pixel_values.data, np.array([50, 100, 150])) np.testing.assert_array_equal(pixel_values.mask, np.array([0, 1, 0])) -def test_wcs_creates_valid_gwcs_object(mk_good_ws_with_transform): - ws = mk_good_ws_with_transform - wcs_obj = ws.wcs +def test_wcs_creates_valid_gwcs_object(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wcs_obj = wc.wcs assert wcs_obj is not None assert isinstance(wcs_obj, wcs.WCS) assert wcs_obj.output_frame.unit[0] == u.angstrom -def test_rms(mk_good_ws_with_transform): - ws = mk_good_ws_with_transform - assert np.isclose(ws.rms(space="wavelength"), 0) # Perfect match, so RMS should be zero - assert np.isclose(ws.rms(space="pixel"), 0) # Perfect match, so RMS should be zero +def test_rms(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + assert np.isclose(wc.rms(space="wavelength"), 0) # Perfect match, so RMS should be zero + assert np.isclose(wc.rms(space="pixel"), 0) # Perfect match, so RMS should be zero with pytest.raises(ValueError, match="Space must be either 'pixel' or 'wavelength'"): - ws.rms(space="wavelenght") + wc.rms(space="wavelenght") -def test_remove_unmatched_lines(mk_good_ws_with_transform): - ws = mk_good_ws_with_transform - ws.match_lines() - ws.remove_ummatched_lines() - assert ws.catalog_lines[0].size == ws.observed_lines[0].size +def test_remove_unmatched_lines(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wc.match_lines() + wc.remove_ummatched_lines() + assert wc.catalog_lines[0].size == wc.observed_lines[0].size def test_plot_lines_with_valid_input(): - ws = WavelengthSolution1D(ref_pixel) - ws._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - ws._cat_lines = ws._obs_lines - fig = ws._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) + wc = WavelengthCalibration1D(ref_pixel) + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc._cat_lines = wc._obs_lines + fig = wc._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() - fig = ws._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) + fig = wc._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() fig, ax = plt.subplots(1, 1) - fig = ws._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) + fig = wc._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() fig, axs = plt.subplots(1, 2) - fig = ws._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) + fig = wc._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() - fig = ws._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) + fig = wc._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() -def test_plot_lines_raises_for_missing_transform(mk_ws): - ws = mk_ws +def test_plot_lines_raises_for_missing_transform(mk_wc): + wc = mk_wc with pytest.raises(ValueError, match="Cannot map between pixels and"): - ws._plot_lines(kind="observed", map_x=True) + wc._plot_lines(kind="observed", map_x=True) -def test_plot_lines_calls_transform_correctly(mk_good_ws_with_transform): - ws = mk_good_ws_with_transform - ws._plot_lines(kind="observed", map_x=True) - ws._plot_lines(kind="catalog", map_x=True) +def test_plot_lines_calls_transform_correctly(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wc._plot_lines(kind="observed", map_x=True) + wc._plot_lines(kind="catalog", map_x=True) -def test_plot_catalog_lines(mk_ws): - ws = mk_ws - ws._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] - fig = ws.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) +def test_plot_catalog_lines(mk_wc): + wc = mk_wc + wc._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] + fig = wc.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) assert isinstance(fig, Figure) assert fig.axes[0].has_data() fig, ax = plt.subplots(1, 1) - fig = ws.plot_catalog_lines(frames=0, axes=ax, plot_values=True) + fig = wc.plot_catalog_lines(frames=0, axes=ax, plot_values=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() fig, axs = plt.subplots(1, 2) - fig = ws.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) + fig = wc.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) assert isinstance(fig, Figure) assert fig.axes[0].has_data() -def test_plot_observed_lines(mk_good_ws_with_transform, mk_arc): - ws = mk_good_ws_with_transform - ws._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - ws.arc_spectra = [mk_arc] +def test_plot_observed_lines(mk_good_wc_with_transform, mk_arc): + wc = mk_good_wc_with_transform + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc.arc_spectra = [mk_arc] for frames in [None, 0]: - fig = ws.plot_observed_lines(frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True) + fig = wc.plot_observed_lines(frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True) assert isinstance(fig, Figure) assert fig.axes[0].has_data() assert len(fig.axes) == 1 -def test_plot_fit(mk_arc, mk_good_ws_with_transform): - ws = mk_good_ws_with_transform - ws.arc_spectra = [mk_arc] +def test_plot_fit(mk_arc, mk_good_wc_with_transform): + wc = mk_good_wc_with_transform + wc.arc_spectra = [mk_arc] for frames in [None, 0]: - fig = ws.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) + fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) assert isinstance(fig, Figure) assert len(fig.axes) == 2 assert fig.axes[0].has_data() assert fig.axes[1].has_data() - fig = ws.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) + fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) -def test_plot_residuals(mk_good_ws_with_transform): - ws = mk_good_ws_with_transform +def test_plot_residuals(mk_good_wc_with_transform): + wc = mk_good_wc_with_transform - fig = ws.plot_residuals(space="pixel", figsize=(8, 4)) + fig = wc.plot_residuals(space="pixel", figsize=(8, 4)) assert isinstance(fig, Figure) assert fig.axes[0].has_data() - fig = ws.plot_residuals(space="wavelength", figsize=(8, 4)) + fig = wc.plot_residuals(space="wavelength", figsize=(8, 4)) assert isinstance(fig, Figure) assert fig.axes[0].has_data() fig, ax = plt.subplots(1, 1) - ws.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) + wc.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) with pytest.raises(ValueError, match="Invalid space specified"): - fig = ws.plot_residuals(space="wavelenght", figsize=(8, 4)) + fig = wc.plot_residuals(space="wavelenght", figsize=(8, 4)) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 42caa485..106f5fd8 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -42,7 +42,7 @@ def _diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: return models.Polynomial1D(m.degree - 1, **coeffs) -class WavelengthSolution1D: +class WavelengthCalibration1D: def __init__( self, ref_pixel: float, From 8a2554931a3b488ea5b3bf871ea234f5cfd408c7 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 22 Apr 2025 20:32:45 +0100 Subject: [PATCH 36/76] Added the first version of the WavelengthCalibration1D docs with Jupyter Notebook tutorials. --- docs/api.rst | 4 + docs/conf.py | 1 + docs/index.rst | 2 +- docs/wavelength_calibration.rst | 53 -- docs/wavelength_calibration/osiris_arcs.fits | Bin 0 -> 106560 bytes .../shane_kast_blue_600_4310_d55.fits | Bin 0 -> 48960 bytes .../wavecal1d_example_01.ipynb | 562 ++++++++++++++++++ .../wavecal1d_example_02.ipynb | 355 +++++++++++ .../wavecal1d_example_03.ipynb | 398 +++++++++++++ .../wavelength_calibration.rst | 261 ++++++++ pyproject.toml | 2 + 11 files changed, 1584 insertions(+), 54 deletions(-) delete mode 100644 docs/wavelength_calibration.rst create mode 100644 docs/wavelength_calibration/osiris_arcs.fits create mode 100644 docs/wavelength_calibration/shane_kast_blue_600_4310_d55.fits create mode 100644 docs/wavelength_calibration/wavecal1d_example_01.ipynb create mode 100644 docs/wavelength_calibration/wavecal1d_example_02.ipynb create mode 100644 docs/wavelength_calibration/wavecal1d_example_03.ipynb create mode 100644 docs/wavelength_calibration/wavelength_calibration.rst diff --git a/docs/api.rst b/docs/api.rst index e4b4de6b..641f81b8 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -21,6 +21,10 @@ API Index .. automodapi:: specreduce.extract :no-inheritance-diagram: +.. automodapi:: specreduce.wavecal1d + :no-inheritance-diagram: + :include-all-objects: + .. automodapi:: specreduce.calibration_data :no-inheritance-diagram: :include-all-objects: diff --git a/docs/conf.py b/docs/conf.py index cf78023f..efc1a09f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -156,6 +156,7 @@ man_pages = [('index', project.lower(), project + u' Documentation', [author], 1)] +extensions.append('nbsphinx') # -- Options for the edit_on_github extension --------------------------------- diff --git a/docs/index.rst b/docs/index.rst index 40727b1a..939e841c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,7 +50,7 @@ Calibration .. toctree:: :maxdepth: 1 - wavelength_calibration.rst + wavelength_calibration/wavelength_calibration.rst extinction.rst specphot_standards.rst mask_treatment/mask_treatment.rst diff --git a/docs/wavelength_calibration.rst b/docs/wavelength_calibration.rst deleted file mode 100644 index f34d7dec..00000000 --- a/docs/wavelength_calibration.rst +++ /dev/null @@ -1,53 +0,0 @@ -.. _wavelength_calibration: - -Wavelength Calibration -====================== - -Wavelength calibration is currently supported for 1D spectra. Given a list of spectral -lines with known wavelengths and estimated pixel positions on an input calibration -spectrum, you can currently use ``specreduce`` to: - -#. Fit an ``astropy`` model to the wavelength/pixel pairs to generate a spectral WCS - solution for the dispersion. -#. Apply the generated spectral WCS to other `~specutils.Spectrum1D` objects. - -1D Wavelength Calibration -------------------------- - -The `~specreduce.wavelength_calibration.WavelengthCalibration1D` class can be used -to fit a dispersion model to a list of line positions and wavelengths. Future development -will implement catalogs of known lamp spectra for use in matching observed lines. In the -example below, the line positions (``pixel_centers``) have already been extracted from -``lamp_spectrum``:: - - import astropy.units as u - from specreduce import WavelengthCalibration1D - pixel_centers = [10, 22, 31, 43] - wavelengths = [5340, 5410, 5476, 5543]*u.AA - test_cal = WavelengthCalibration1D(lamp_spectrum, line_pixels=pixel_centers, - line_wavelengths=wavelengths) - calibrated_spectrum = test_cal.apply_to_spectrum(science_spectrum) - -The example above uses the default model (`~astropy.modeling.functional_models.Linear1D`) -to fit the input spectral lines, and then applies the calculated WCS solution to a second -spectrum (``science_spectrum``). Any other 1D ``astropy`` model can be provided as the -input ``model`` parameter to the `~specreduce.wavelength_calibration.WavelengthCalibration1D`. -In the above example, the model fit and WCS construction is all done as part of the -``apply_to_spectrum()`` call, but you could also access the `~gwcs.wcs.WCS` object itself -by calling:: - - test_cal.wcs - -The calculated WCS is a cached property that will be cleared if the ``line_list``, ``model``, -or ``input_spectrum`` properties are updated, since these will alter the calculated dispersion -fit. - -You can also provide the input pixel locations and wavelengths of the lines as an -`~astropy.table.QTable` with (at minimum) columns ``pixel_center`` and ``wavelength``, -using the ``matched_line_list`` input argument:: - - from astropy.table import QTable - pixels = [10, 20, 30, 40]*u.pix - wavelength = [5340, 5410, 5476, 5543]*u.AA - line_list = QTable([pixels, wavelength], names=["pixel_center", "wavelength"]) - test_cal = 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It serves as a general introduction to the class.\n", + "\n", + "The interactive workflow consists of these key steps:\n", + "1. Loading the arc spectrum data\n", + "2. Initializing the wavelength solution parameters\n", + "3. Finding emission line positions in the spectrum \n", + "4. Inspecting catalog line wavelengths\n", + "5. Manually identifying initial line matches\n", + "6. Evaluating and refining the wavelength solution\n", + "7. Applying the solution to calibrate spectra\n", + "\n", + "This example uses a [He-Hg-Cd lamp arc](https://mthamilton.ucolick.org/techdocs/instruments/kast/images/Kastblue600HeHgCd.jpg) from the [Shane telescope's](https://www.lickobservatory.org/explore/research-telescopes/shane-telescope/) [Kast spectrograph](https://mthamilton.ucolick.org/techdocs/instruments/kast/index.html)." + ], + "id": "d2b0f73230b294c" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.io.fits import getdata\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import setp, subplots, close, rc\n", + "\n", + "from specreduce.compat import Spectrum\n", + "from specreduce.wavecal1d import WavelengthCalibration1D\n", + "\n", + "rc('figure', figsize=(11, 3))" + ], + "id": "d3547a260abe63f4" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## 1. Read in the Arc Spectrum\n", + "\n", + "First, we load the arc lamp flux data and create a `specutils.Spectrum` object, assuming that the flux is measured in counts (digital number units, `u.DN`). We also include measurement uncertainties. While these uncertainties are not directly used in fitting the wavelength solution, they are required by the default line-finding routine (`specutils.fitting.find_lines_threshold`).\n" + ], + "id": "bb1ff795062f3d45" + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e3dd2a51-ef1a-4501-9bfb-62166676e88f", + "metadata": {}, + "outputs": [], + "source": [ + "flux = getdata('shane_kast_blue_600_4310_d55.fits', 1).astype('d')\n", + "arc_spectrum = Spectrum((flux - np.median(flux)) * u.DN, uncertainty=StdDevUncertainty(np.sqrt(flux)))" + ] + }, + { + "cell_type": "markdown", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", + "metadata": {}, + "source": [ + "## 2. Initialize the Wavelength Solution Class\n", + "\n", + "Now we instantiate the `WavelengthSolution1D` class. This class manages the input data, fitted model, and provides methods for solution fitting and spectrum calibration.\n", + "\n", + "Key initialization parameters:\n", + "\n", + "- `ref_pixel`: Reference pixel coordinate near detector center. The polynomial fit centers around this pixel to improve numerical stability.\n", + "- `degree`: Degree of the `astropy.modeling.polynomial.Polynomial1D` model for pixel-to-wavelength mapping. Higher degrees enable more complex curves but require more lines for stability and risk overfitting.\n", + "- `line_lists`: Either lamp names (e.g., 'HeI', 'NeI', 'ArI') recognized by `specreduce.calibration_data.load_pypeit_calibration_lines`, or NumPy arrays of theoretical line wavelengths. Here we provide lamp names for our single spectrum.\n", + "- `arc_spectra`: Observed arc spectra as `specutils.Spectrum` objects, used for automatic line finding if `obs_lines` is not provided.\n", + "- `obs_lines`: Pre-identified line pixel centroids. If provided, `arc_spectra` is not needed for line finding.\n", + "- `pix_bounds`: Detector pixel range `(min_pix, max_pix)`. Inferred from `arc_spectra` if provided; required with `obs_lines`.\n", + "- `line_list_bounds`: Wavelength range `(min_wav, max_wav)` for filtering `line_lists`. Defaults to `(0, +inf)`.\n", + "- `wave_air`: If `True`, converts PyPEIT's vacuum wavelengths to air wavelengths.\n", + "\n", + "In this example, we provide:\n", + "- Arc spectrum\n", + "- Reference pixel at 1000 \n", + "- Polynomial degree 5\n", + "- Lamp list `['CdI', 'HgI', 'HeI']`\n", + "- Wavelength filter range 3200-5700 Angstroms\n", + "\n", + "The `plot_observed_lines` method displays the arc spectra and input lines in pixel space. At this stage, it shows only the raw input spectrum since no lines have been identified or fitted.\n" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "ws = WavelengthCalibration1D(ref_pixel=1000, degree=5, arc_spectra=arc_spectrum, line_lists=[['CdI', 'HgI', 'HeI']],\n", + " line_list_bounds=(3200, 5700), unit=u.angstrom)\n", + "ws.plot_observed_lines();" + ], + "id": "52091185a7a5e148" + }, + { + "cell_type": "markdown", + "id": "f9756d12-0f58-4f75-bcdd-76bfe4d5f9ee", + "metadata": {}, + "source": [ + "## 3. Find Line Pixel Positions\n", + "\n", + "Next, we detect emission lines in the arc spectrum using the `find_lines` method. This method uses `specreduce.line_matching.find_arc_lines`, which performs peak detection to identify lines and calculate their centroids (in pixel coordinates). The detected line centroids are stored in the `ws.observed_lines` attribute as a list of NumPy masked arrays (one array per input arc spectrum). Initially, none of the lines are masked.\n", + "\n", + "The plot now overlays vertical lines at the pixel coordinates of the found lines.\n", + "\n", + "**Note:** If we had already identified the line centroids by other means (e.g., manually, or from another program), we could have supplied them directly during initialization using the `obs_lines` argument, skipping this step." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e5bb4f88-355f-4278-9ec4-15876f4712e2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.find_lines(fwhm=4, noise_factor=15)\n", + "ws.plot_observed_lines();" + ] + }, + { + "cell_type": "markdown", + "id": "dbfb2d5b-a7dd-46a5-ba82-1ee06cb130e6", + "metadata": {}, + "source": [ + "## 4. Inspect the Catalog Line List\n", + "\n", + "When we initialized `WavelengthSolution1D`, we provided lamp names (`line_lists=[['CdI', 'HgI', 'HeI']]`). Internally, the `_read_linelists` method loaded the corresponding known wavelengths (filtered by `line_list_bounds`), converted them to the specified `unit` (Angstroms by default), and stored them in the `ws.catalog_lines` attribute.\n", + "\n", + "Like `observed_lines`, `catalog_lines` is a list of masked arrays (one per arc spectrum), sorted by wavelength. We can visualize these theoretical line positions using `plot_catalog_lines`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "da59f18b-1c86-410a-a5a6-dc9c94302cff", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_catalog_lines();" + ] + }, + { + "cell_type": "markdown", + "id": "8880ab00-b9bc-4d99-9eda-7cd38188dc9b", + "metadata": {}, + "source": [ + "We can use the `plot_fit` method to visualize both the observed lines (bottom panel, pixel space) and the catalog lines (top panel, wavelength space) simultaneously. \n", + "\n", + "Since we haven't yet calculated a pixel-to-wavelength transformation, the bottom panel is plotted in pixel coordinates and we should not expect any matches between the observed and catalog lines." + ] + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-20T11:28:24.766381Z", + "start_time": "2025-04-20T11:28:24.764517Z" + } + }, + "cell_type": "code", + "source": [ + "from astropy.modeling.models import Polynomial1D\n", + "from specreduce.wavecal1d import _diff_poly1d" + ], + "id": "197547306d056a0c", + "outputs": [], + "execution_count": 6 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-20T11:30:24.220667Z", + "start_time": "2025-04-20T11:30:24.217569Z" + } + }, + "cell_type": "code", + "source": [ + "p = Polynomial1D(3, c0=1, c1=2, c2=3, c3=4)\n", + "_diff_poly1d(p).parameters" + ], + "id": "53df5437790a87ac", + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2., 6., 12.])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 9 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-20T11:30:13.358098Z", + "start_time": "2025-04-20T11:30:13.355454Z" + } + }, + "cell_type": "code", + "source": "p.parameters", + "id": "55e5cf77e891e356", + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 2., 3., 4.])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e95afe59-471d-4669-aafe-72b1f7d79d53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": "ws.plot_fit(figsize=(11,5), plot_values=True);" + }, + { + "cell_type": "markdown", + "id": "806d1730-5325-4537-825e-da686ffcda21", + "metadata": {}, + "source": [ + "## 5. Fit an Initial Solution Manually\n", + "\n", + "To establish an initial relationship between pixels and wavelengths, we need to match some observed lines to their corresponding catalog wavelengths. \n", + "\n", + "The `WavelengthSolution1D` class offers several ways to fit:\n", + "1. `fit_lines(pixels, wavelengths)`: Fits the model using explicitly provided pairs of corresponding pixel and wavelength values. This is useful for manual identification of a few bright, unambiguous lines to get a starting solution.\n", + "2. `fit_global()`: Performs a global optimization to find an initial model by minimizing distances between transformed observed lines and the KDTrees of theoretical lines. This requires good initial guesses for wavelength and dispersion bounds but is more suitable for an automatic pipeline.\n", + "3. `refine_fit()`: Improves an existing fit by automatically matching lines based on the current solution and performing a least-squares fit on the matches. This method is called by the `fit_lines` and `fit_global` methods by default, but can also be called manually.\n", + "\n", + "In this example, we'll use `fit_lines` with a few manually identified pairs. We visually inspect the plots above (or use an interactive tool in a real scenario) to find approximate correspondences. We also set `match_pix` and `match_wav` to `True` to match the approximate pixel and wavelength line values with the more accurate observed line centroids and catalog wavelength values." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: The fit may be poorly conditioned\n", + " [astropy.modeling.fitting]\n" + ] + } + ], + "source": "ws.fit_lines(pixels=[496, 967, 1077, 1655, 1999], wavelengths=[3890, 4360, 4473, 5087, 5462], match_obs=True, match_cat=True)" + }, + { + "cell_type": "markdown", + "id": "616e53c5-20e5-411e-a983-1feba2db30dc", + "metadata": {}, + "source": [ + "Now, we can plot the solution mapping the observed pixel values to wavelengths (`obs_to_wav=True`). The plot shows matching observed and catalog lines as solid blue vertical lines, while unmatched lines are shown as dotted lines. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "40883c16-11b3-402b-bd0b-c688c44deef2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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D8yGRbuXm5uL8+fNwdXVF48aNeS4k0rOir8HSGPV5UG9D55KsxMTECIcOHRLS0tKE06dPC9OmTROUSqWwZ88e4eHDh8KkSZOEX375RUhLSxP2798v+Pr6CvXr1xcyMzPFvzF69GihQYMGQmJiovDrr78K3bt3F9q0aSMUFBSIj+nVq5fQunVr4ejRo8LRo0eFVq1aCX379pVilYkMXvFZSubOnSs4ODgI3333nXDmzBlh8ODBgqurK1+HRDpU9HXI8yGR7k2aNEk4cOCAcOXKFeHYsWNC3759BTs7O+Hq1auCIPBcSKRrT3sNmtp5kMUV0okRI0YIjRo1EiwsLIQ6deoIL730krBnzx5BEATh0aNHQo8ePYQ6deoI5ubmQsOGDYU333xTuH79utbfyMnJEcaPHy84OTkJVlZWQt++fUs85t69e8LQoUMFOzs7wc7OThg6dKiQnp6ur9UkMirFiysajUaIjY0VXFxcBEtLS6Fr167CmTNntH6Hr0Oi6lX0dcjzIZHuDRo0SHB1dRXMzc2FevXqCa+88opw7tw5cTnPhUS69bTXoKmdBxWCIAj66ydDRERERERERGRaOOYKEREREREREVEVsLhCRERERERERFQFLK4QEREREREREVUBiytERERERERERFXA4goRERERERERURWwuEJEREREREREVAUsrpBBys3NxcyZM5Gbmyt1FCJZ4muQSHp8HRJJj69DImkZ02tQIQiCIHUIouIyMzPh4OCAjIwM2NvbSx2HSHb4GiSSHl+HRNLj65BIWsb0GmTPFSIiIiIiIiKiKmBxhYiIiIiIiIioCszK8yCNRoObN2/Czs4OCoVC15mIkJmZqfV/ItIvvgaJpMfXIZH0+DokkpYhvAYFQcDDhw9Rr149KJVl908p15grf/31F5577rlqDUhEREREREREZAxu3LiBBg0alLm8XD1X7OzsxD9m6IPIEBERERERERFVh8zMTDz33HNiXaQs5SquPLkUyN7ensUVIiIiIiIiIpKVZw2RwgFtiYiIiIiIiIiqgMUVIiIiIiIiIqIqYHGFiIiIiIiIiKgKWFwhIiIiIiIiIqoCFleIiIiIiIiIiKqAxRUiIiIiIiIioiqQTXGloFCD7advYfvpWygo1EgdR+959P18j/IK4DZ1O9ymbsejvAKdP58hqq5tUNbf4TY2THLdL+VpYyq7beS6Tcm09r0prYuxqOo2N+R9ZsjZqspY1k3qnFI/P9GzyPEYNZM6gL6YqZTo09pV6hgifecxtPUnItPCNoaIiIiI5Ew2PVeIiIiIiIiIiHRBNj1XCjUCfrueDgBo19ARKqVCVnkMbf2JyLSwjSEiIiIiOZNNz5XcgkIMXHkUA1ceRW5BodRx9J7H0NafiEwL2xgiIiIikjPZ9FxRQAG3WtbibanpO4+hrT8RmRa2MUREREQkZ7IprlhZqHBgcjepY4j0ncfQ1p+ITAvbGCIiIiKSM9lcFkREREREREREpAssrhARERERERERVYFsiivq/EKErzmB8DUnoM6XfrBFfecxtPUnItPCNoaIiIiI5Ew2Y65oBAH7L94Vb0tN33kMbf2JyLSwjSEiIiIiOZNNccVcpcSCga3F21LTdx5DW38iMi1sY4iIiIhIzmRVXHm1w3NSxxDpO4+hrT8RmRa2MUREREQkZ/x6kYiIiIiIiIioCmTTc6VQI+DC7UwAgIeLPVRKhazyGNr6E5FpYRtDRERERHImm54ruQWF6LPsMPosO4zcAulnstB3HkNbfyIyLWxjiIiIiEjOZNNzRQEFnO0txdtS03ceQ1t/IjItbGOIiIiISM5kU1yxslDh+LRAqWOI9J3H0NafiEwL2xgiIiIikjPZXBZERERERERERKQLLK4QEREREREREVWBbIor6vxCjN1wCmM3nII6X/rBFvWdx9DWn4hMC9sYIiIiIpIz2RRXNIKAHWduY8eZ29AIgtRx9J7H0NafiEwL2xgiIiIikjPZDGhrrlLio5CW4m2p6TuPoa0/EZkWtjFEREREJGeyKq684esmdQyRvvMY2voTkWlhG0NEREREciab4gqRVLaN94OlmarSv29ppsK28X7i7eL3By8/UuWMRESGqKz2j6g8qnr88Pijp+HxQUTFyaa4otEIuHb/EQCgkZM1lEqFrPIIgoC8Ag0AwMJMCYVC2vUnItPCNoaIiIiI5Ew2F8arCwrRbeEBdFt4AOoC6Wey0HcejQBc/OchLv7zEBqONUlE1YxtDBERERHJmWx6rgCAXQ3DWl1951FJ3FuHiEwb2xgiIiIikivDqjbokLWFGc7M7Cl1DJG+86iUCrSs56C35yMieWEbQ0RERERyJpvLgoiIiIiIiIiIdIHFFSIiIiIiIiKiKpDNZUG5BYWY9t1ZAMAnr3hJPmWavvNoBAF/p+cAAOo7WkHJmTyIqBqxjSEiIiIiOZNNz5VCjYAtv/6FLb/+hUIDmMpC33kEAUh/lIf0R3kQpF99IjIxbGOIiIiISM5k03PFTKlETG8P8bbUdJXn77//xt69ewEA3bt3R8OGDQEACgXg6lBDvE2GTaPRYP369XjjjTekjkIytnjxYoSGhqJx48bPfCzbGKqIy5cvY+PGjTh37hyysrJga2uLli1bYtCgQWjatKnU8UiGgoKCsGHDBjg6OkodhSRWkXMfEVFRsimuWJgpEeHfROoYourK06pVK5w5cwYAkJSUhH79+uHFF1+EQqHAO++8gx9++AEBAQFQKhSoY1ejys9H+pGfn4/w8HAWV0hS0dHRmDZtGtq3b4+hQ4fitddeQ+3atUt9LNsYKq+NGzdizJgxCA4Ohre3N+zt7ZGZmYnff/8dnTp1wooVKzBo0CCpY5KJGjt2bKn3Hzp0CFFRUbCyskJcXJyeU5Ehqci5j4ioKNkUV0zV1atXxdsxMTGIi4vDkCFDAACbNm1CTEwMjh49KlE6epr58+eXuSw/P1+PSYhKZ21tjWvXrmHTpk345ptvEBUVhcDAQAwdOhShoaGwsrKSOiIZoalTp2LHjh3w9fUtsezYsWMYNGgQiyukM2vXrsULL7yAl19+GUKRaxgVCgVq164NW1tbCdORIeC5j4gqS/rrY/REoxFwO0ON2xlqaAxgzJXqyqMo0v/+4sWLeO2118SfBw4ciIsXLwIABEFAfoEG+QUarTcTJJ3p06fjxIkTOH/+fIl/T/YbkZQUCgWcnJwwZswYJCUl4cKFC/Dz88Mnn3wCZ2dnrZ5VbGOovO7fv4/WrVuXuszLywv379/XcyKSk9TUVNSpUwcpKSkYNmwYYmNjERsbC1tbW0yePBmxsbFSRySJVeTcR0RUlGx6rqgLCuEz52cAQOpHPWFtIe2qV1eevLw8LFiwABqNBgCQnZ0NBwcHAEBubq74IUcjAOdvZwIAWtZzgIpjIkjOy8sLI0aMQFBQUIllarUa69evlyAVUdnc3NwQExODmJgYpKSk4JtvvhGXsY2h8goODsagQYMwc+ZMeHl5oUaNGlCr1Thz5gxmzZqFkJAQqSOSCXNzc8N3332H3bt3o3///ujbty+mTZum9WUVUVFPO/cRERUlm54rAGCmVMBMaTgnz+rIM3jwYKSmpuLChQvo27ev1mVCu3btgpeXl/iz4n//kWF4++23UVBQUOoyc3NzfntGknsyIHZp2rZtW+LSNrYxVB5ffvklPD09ERwcDBsbG1hYWMDGxgahoaHw8PDAl19+KXVEkoGePXvi5MmTsLe3R/v27ZGRkSF1JDIQFT33ERE9IZueK9YWZrj8SckeAlKprjxr1qwpc9krr7yCV155BQCgUirQqoFDlZ+Pqs+YMWPKXKZSqVhcIcmdPXu23I9lG0PlVaNGDcyfPx/z58/HgwcPxNmCatasKXU0khlzc3NMmTIFw4YNQ3JyMmcKIgAVO/cRERUlm+KKqbtx4wZSU1PFN6menp547rnnpI5FFVRQUABBEGBubi51FCIinXtSUHky8Lqvry+LLKR39erV4+VoRERUZbK6LMgUXb9+Hf7+/vDw8MDkyZOxePFiTJ48GR4eHggICMCNGzekjkhlmDt3rnj7/v37CA0NhbW1NWxtbdGvXz/897//lTAdEfDgwQOMGjUKbdq0wZAhQ3DhwgWt5fb29hIlI2MWFhaG33//HQBw+PBhuLu7Y+bMmYiNjUWzZs1w5MgRiROSXOXm5kKlUkkdgwwYjxEiehrZFFdyCwrxwQ9n8cEPZ5FbUCh1nGrL8+abb8LX1xd3797F6dOncfjwYZw+fRp37tyBr68vhg0bBgDQCAL+Ts/B3+k50HAmD4PwySefiLejo6NhaWmJGzdu4MaNG7C3t8ekSZMkTEcEREZGIjMzE4sWLULz5s3RpUsX/Pjjj+LyorMCsY2h8tqxY4c4Hlh0dDRWrVqF48eP48SJE/jyyy8RGRkpcUIyZXfu3Cnz3z///MPZzojHCBFVmmwuCyrUCPj62DUAQEyQh8Rpqi9PcnIydu/eDQsLC637bWxs8OGHH4rXDwsCcC87FwDg4lADHHNSekVPznv37kVKSgpq1aoFAFi+fDlatmwpVTQiAMDu3btx9epVWFlZITAwECEhIQgODsadO3cwcuRIrdk12MZQeSkUCuTk5MDW1haXL19GcHCwuKxv374ICwuTMB2ZOhcXFygUijI/IHPWIOIxQkSVJZueK2ZKJSa+1BQTX2oKM6X0q11dedzd3ZGQkFDqso0bN8Ld3R0AoFAAzvY14GxfAzwnGI67d++K34IUHWfAwcEBDx8+lC4YEQCNRoPCwn971rVt2xYHDx7E3LlzMXv2bK3Hso2h8urfvz9mzJgBQRDQo0cPfP311+KyDRs2wMND+i9AyHTVq1cPR48ehUajKfHv0aNHUscjA8BjhIgqSzY9VyzMlHj35WZSxxBVV55Vq1ZhwIABmD9/Plq1agV7e3tkZmbi9OnTyM7OxnfffQcAUCoUcLavUeXno+qTnZ0NFxcX8ZuRY8eOwc/PD8Djkerr1asnZTwi+Pj44IcfftDqSdC4cWMkJSWhZ8+eyM7OFu9nG0PltXjxYoSHh6NJkyZo2rQpRowYIRbrBEHA1q1bJU5IpszHxwfHjx9Hx44dSyxTKpVPnYaX5IHHCBFVlmyKK6bK29sbly9fxoEDB7RmCxoxYgQCAgJKXC5EhkOj0ZS5zMzMDCtXrtRjGqKSFi1ahIyMjBL3u7i4ICkpCT/88IP+Q5HRs7Ozw+bNm5GamoqTJ08iICAAVlZW8PLyQkBAAMzM+NaEdGfTpk1lLrOwsEBaWpoe05Ah4jFCRJUlm3cwgiAgU10AALCvYSb59ZLVmcfCwgI9evRAjx49xPvUarVWYUUQBBT+r4eESqGQfP3p6Tw9PeHp6Sl1DJK5Zs3K7l1nb2+PN954Q/yZbQxVFNs5kgJneqFn4TFCRJUl/eAjepKTX4g2H+5Bmw/3ICdf+tmCdJ3n+eefx927d8WfNQKQejMTqTczoeEg5wZh8+bN4rcfDx48wPDhw+Hs7AxnZ2eMHDmy1B4DRPq2du1aBAYGwtXVFXZ2dnB1dUVgYCDWrVun9Ti2MVRegiAgPj4eU6dOxaVLl3D37l0MGTIEPj4+mDZtGvLy8qSOSCYsJCQEa9eu5bhm9FTlPfcRERUlm54rpqqsb/3u3r2Lzp07Q6VSITU1Vc+pqDyioqLw+++/AwAmTpyI/Px8HDhwABqNBnPnzsW4ceOwfv16iVOSnE2ZMgU//fQToqOj0aZNG3FMp5SUFCxcuBAXLlzAnDlzpI5JRmby5Mli27du3TqMHj0aAwYMENu+goICzJ8/X+KUZKp27tyJv/76C2PHjhVnp+rduzfMzc2ljkYGguc+Iqos2RRXrMxV+OPj3gAAM6X03dWrK49KpYKjoyNiYmJgbW0N4PG3ggMGDMDcuXPh5OQEAFAqAK/6DgA4Q6qhSE9Ph4PD432SmJiIy5cvw8rKCgDwxRdfcMA0ktzq1atx7tw5ODs7a93fvn17BAUFoWXLluIbTLYxVF4JCQlITU2FRqNBrVq1MHz4cDRq1AgA0K5dOwQGBrK4QjpTo0YNnDp1CufPn8c333yDd999F+Hh4Rg4cCDCwsLQpUsXqSOSxCpy7iMiKko2lwUpFAqYq5QwVykNYiyA6sqTkpKCV155BZMnT8bff/8Nf39/cSBbPz8/+Pv7i8+n/N8/Q1h/Ary8vLBnzx4AgKOjI27evCkuu337Nr9FI8kplUqo1epSl6nVaiiLTCPPNobKKzs7Gw4ODnB0dIS9vb1YWAEAd3d33Lt3T8J0JBctWrTArFmz8Oeff+LHH3+Eubk5BgwYoHU8kjxV5NxHRFSUbHqumCqVSoV33nkHgwcPxtSpUxEXF4elS5fyw40RWLx4MQYOHIjw8HD0798fPXr0QHh4OABgzZo1mDZtmsQJSe6ioqIQEBCAsWPHlpjqfeXKlYiOjpY6IhkhV1dX3Lt3D7Vq1cL27du1lt24cQM1a9aUJhjJgiCUHBTqxRdfxIsvvoilS5di9+7dEqQiQ1LWue/MmTNYsWIFz31EVCbZFFfyCjRYuOciACC6R3NYmElbda7uPM7OzlizZg2OHj2KMWPGaA1mCwAaQcA/mY+r8M72NaBk8UVyPj4+SE5OxpIlS5CcnAwzMzNs3rwZrVq1Qnx8PAIDA6WOSDI3depUtG7dGuvXr8eGDRvEqd5btmyJ5cuXIygoSHws2xgqr3nz5omD1vr5+WktO378OMaOHStFLJKJsLCwMpepVCqtdo3kqSLnPiKiomRTXCnQaPDFoSsAgHcCm8JC4iuiqjNPRkYGbt26BQ8PD/j6+mL27Nk4deoU7t69i7p16wIABAG4+zAXAFDXrgYHRTAQ1tbWCA8Px7x58wAAe/bswZUrV+Dq6ipxMqLHgoKCSryRjIiIQOfOnbXuYxtD5RUcHFzq/REREViwYAEGDhyo50QkJytWrCj1/ifHn729vZ4TkSEqeu7Lzs7GiRMnoFAo0KFDB4mTEZEhk01xxUypxNtdnxdvS6268mzfvh1hYWHIzc1Fr1694O/vj8TERGg0GsyePRsJCQkIDg6GQgHUsbMEAPALZcNQdN/17t0bXbt2FffdpEmTxH1HJJWyBhXduHEjnJ2dYWtri/feew8A2MZQuVXkuCKqbjz+6Fn69OkjXrJ49uxZBAUFwdbWFgqFAunp6dixYwfatm0rbUgiMkiyKa5YmCkxLaiF1DFE1ZVn2rRp2Lt3LwCgU6dOGD9+PCZOnAgA2LJlC2bNmoXg4GAoFQq4OlhV+fmo+hTfd+PGjSt13xFJJSYmBh07dkSLFi20xinIz8/HpUuXxBnKALCNoXKryHFFVN14/NGzJCUlibcnTZqECRMmYPLkyQAej5cXHR2NxMREqeIRkQGTTXHFVF27dk3somhpaYlu3bqJy0JDQ/HWW29JFY2egfuODN3hw4cRFRUFBwcHzJw5U5w6fNeuXVi2bJl42SFRRfC4Iinx+KOKSElJwbZt28Sfx48fj48//ljCRERkyKS/PkZPBEFAfqEG+YWaUkeKN9Y81tbWyM/PBwCEh4drzRKkVqvFnwVBgOZ//wxh/an8+45IKr6+vvjll1/E8ZxWrVoFQRBKPTbZxlB5VeS4IqpuPP7oWfLz8/Htt99i06ZNAIDCwkJxmSAI4oDcRETFyaa4kpNfiKbTd6Lp9J3IyS989i8YSZ5+/fohLS0NAPD5559rLdu6dSvatWsHANAIwNm/M3D27wxo+LnHIJR33xFJSaFQICIiAocPH8avv/6KDh06ICsrq8Tj2MZQRZT3uCLSBR5/9DSdOnVCXFwcVq5cCU9PT6SmporLDh06hObNm0uYjogMGS8LMnLx8fFlLgsJCUFoaKj+wlCFcN+RMXFycsKKFSuQkpKCgwcPckYNqhY8rkhKPP6oNAcOHChzmbe3N3bu3Km/MERkVGRTXLEyV+H32B7ibanpI4+NjY14W6kAPOvZi7fJsBXdd0SGpG3btqXOksA2hqqirOOKSB94/FF5PRmjh4ioNLIprigUCjhYmUsdQ6TvPAqFAma8npiIdIRtDBERERHJmWzGXCEiIiIiIiIi0gXZ9FzJK9Dg8/2XAQDjurnDwkzaupK+82gEAXcf5gIA6thZQslvmImoGrGNISIiIiI5k03PlQKNBkt//gNLf/4DBRqN1HH0nkedX4hOn/yMTp/8DLUBzJYkJ8HLjyC3oPLbPLegEMHLj5T4O0/uJzIEbGNIF8pq/4jKo6rHD48/ehoeH0RUnGx6rqiUCgzzaSTelpq+8xja+hORaWEbQ0RERERyJpviiqWZCrNCvaSOIdJ3HkNbfyIyLWxjiIiIiEjOZFNcIdI3SzMVto33E2+TPHC/l62y24bblIgqg22HceJ+IzINcnwty2bMFSIiIiIiIiIiXZBNceVRXgHcp+2A+7QdeJRXIHUcFGoEnPkrA2f+ykChRtD58xna+hORaWEbQ0RERERyJqvLggr0UMSoCAH6zWNo609EpoVtDBERERHJlWyKKzXMVDgW85J4W2pKBdDCxV68rWuGtv5EZFrYxhARERGRnMmmuKJUKuDiUEPqGCKFQgFzM/1NV2po609EpoVtDBERERHJmWzGXCEiIiIiIiIi0gXZ9FzJK9BgzZE0AEC4X2NYmElbV9IIAu5l5QIAatlaQqnQbS8WQ1t/IjItbGOIiIiISM5kU1wp0GgwZ+cFAMAw30awkLjTjiAAtzLUAAAnG0tAx1cIGdr6E5FpYRtDRERERHImm+KKSqnAgPYNxNu6lJ2djUuXLsHd3R12dnZayxISEjB48GAoFICjtQUAQMedVgDod/3loDz7mEzP5cuXsXHjRpw7dw5ZWVmwtbVFy5YtMWjQIDRt2lTqeJKqbBtz//59fP/99zh37hwePXqE+vXro0OHDujdu7euohKRCWDbYZzOnz+Pa9euoXv37jAzM8Pq1avx559/olu3bujZs6fU8YionPhZqHSy+WrR0kyFRa+1waLX2sBShzNZJCUloWHDhujXrx9cXFzw4Ycfai2PiIgAACgVCjznZI3nnKx1fkkQoL/1l4Py7mMyLRs3boS3tzf++OMPeHt7IyQkRPy5U6dO2LRpk9QRJVWZNiYxMRHNmjXDt99+i3PnzmHt2rW4dOkSPvnkE7zwwgv4+++/dZyaiIwR2w7jtHbtWgQGBiI8PBz+/v6YN28eTp8+jYcPH+L111/H6tWrpY5IROXAz0Jlk01xRV8mTZqE+Ph4/PXXXzh37hz279+PQYMGoaCgAAAgCILECamquI/laerUqdixYwfWrVuHqKgovPXWW4iKisK6deuwY8cOvPfee1JHNDoTJkzA999/j127dmH37t3YtWsX/vnnHyQlJeGNN97A2LFjpY5IRAaIbYdxmjNnDg4dOoRDhw7h+PHjCAwMxGeffYbPP/8cP/zwA5YsWSJ1RCIqB34WKhuLK9Xs4sWLGDhwIADAzc0NiYmJMDMzQ69evZCVlQWFPq4BIp3iPpan+/fvo3Xr1qUu8/Lywv379/WcyPjdunULfn5+4s9+fn5ISUkBALz99ts4cOCANMGIyKCx7TBOt2/fRpMmTdC0aVNYW1vD29tbXNa1a1dcv35dwnREVF78LFQ22RRXHuUVoNXM3Wg1czce5RXo7Hlq166NtLQ08WczMzNs2LABHh4e8Pf3Fyt6hRoB525m4NzNDBRqdF/d09f6y0F59zGZluDgYAwaNAgnT56EWv14MGq1Wo3k5GQMGTIEISEhEieUVmXaGB8fH3z44YfIzc1FTk4OPvroI7Rv3x7A4289zM3NdRmZiIwU2w7j5ODggJycHADABx98oLUsMzOT+43ISPCzUNlkU1wBgIfqAjxU63Znh4aGYv369SXuX758Ofr27St+KAMeF1j0UVh5Qh/rLwcV2cdkOr788kt4enoiODgYNjY2sLCwgI2NDUJDQ+Hh4YEvv/xS6oiSq2gbEx8fj59//hnW1taws7PDvn37sHLlSgDA9evXERMTo6uoRGTE2HYYpxEjRuDmzZsAgClTpmgt27x5M/z9/aWIRUQVxM9CZVMI5bgoKjMzEw4ODsjIyIC9vb0+clU7jUbAtfuPAACNnKyhlHjGHEEQkFegAQBYmCl13n1K3+v/KK8AnjN2AwBSP+oJawvZTEwletI7CQBa1nOo9CxNZW1LbmPpPHjwQJwtqGbNmlrLqmu/G5vytDFlbZusrCwAgK2tbal/W67blEyrnTOldTEUum47DHmfGXK2qjKWNl/qfSD18xM9i7G8lsujvPUQ2fRcUSoVaFzbBo1r20hSWFm3bp1WFU+hUMDSXAVLc5VerkuTev2JTEVOTg6uX7+Ov/76Cw8fPpT1oF1FVaaNuXfvHoDHH4zK+nBERFQc2w4iIsPx4MED7Ny5Ezt37sSDBw+kjiMp2RRX9OXEiROl/ouOjsbBgwdx4sQJqSOSDuXm5kKl4lTXpujBgwcYMmQIatasibZt26Jz585o3bo16tevj88++0zqeEbJ2dkZ/v7+WLVqlexPxkRUfmw7jNODBw8watQotGnTBkOGDMGFCxe0lhtr73giuQkLC8Pvv/8OADh8+DDc3d0xc+ZMxMbGolmzZjhy5IjECaUjm/5j+YUaJJx4PAr54I4NYa7STV3Jx8cHrq6usLS01PpGOz09HaNGjYKZmRmuXLkCjSDgfnYeAMDJxgJKHfde0df6y8GdO3fKXKZWq9mTwUQNHz4cDRs2xLVr16DRaDBnzhzUq1cPr732GiIiIpCRkYH3339f6piSqUwbY2lpiYEDB2L16tWIjIxEr169EBYWhr59+8LS0lLXkYnISLHtME6RkZHIzc3FokWLcOTIEXTp0gX/+c9/0K9fPwDynr6VyJjs2LED69atAwBER0dj1apV6N+/PwBg27ZtiIyMxKlTp6SMKBnZfMLOL9RgxtZzmLH1HPILNTp7nk8++QS1a9fG4sWLkZaWJv6rU6cOTp48iStXrgAABAG4+SAHNx/kQB/nEn2tvxy4uLjA1dUVLi4uJf65ubnJevoxU7Zv3z4sXrwYLi4uqFevHhYtWoSlS5eiSZMmWLduHVasWCF1RElVpo1RqVSYMGECjh07hjNnzqBdu3aYPn06nJ2dMWLECOzbt0/HqYnIGLHtME67d+/G2rVrERgYiNjYWOzduxfjxo3D6tWrAYDvn4iMhEKhEGf+unz5MoKDg8Vlffv2xR9//CFVNMnJpriiVCgQ1MoFQa1cdNpLZOrUqdi5cyc2btyIHj164Pz586U+TgHAwcocDlbm0MepRF/rLwf16tXD0aNHodFoSvx79OiR1PFIR1xcXHD69Gnx59OnT8PJyQnA42Pi4cOHUkUzCFVtY9zd3TFjxgxcuHABe/fuhb29PYYNG6aDpERkSth2GA+NRoPCwkLx57Zt2+LgwYOYO3cuZs+eLWEyIqqI/v37Y8aMGRAEAT169MDXX38tLnsyJbNcyeayoBrmKsQNfUEvz1WvXj0kJCTgwIEDGDp0KLp06YL8/HytxyiVCjSqZaOXPIB+19/U+fj44Pjx4+jYsWOJZUqlEg0bNpQgFenarFmz0L17d/Tq1QuCIGD37t1ib5WUlBR4enpKnFBalWljyuoC7u3tDW9vb3z66afVEY2ITAzbDuPk4+ODH374AWFhYeJ9jRs3RlJSEnr27Ins7GwJ0xFReS1evBjh4eFo0qQJmjZtihEjRogFUkEQsHXrVokTSkc2xRUpBAQEIDk5GcuXL0eXLl1Qo0YNqSNRNdi0aVOZyywsLJCWlqbHNKQvgwYNQuvWrfHzzz8DAGJjY9GiRQsAQLt27XDs2DEp4xmlnTt3PnW5UimbzpVEVAFsO4zTokWLkJGRUeJ+FxcXJCUl4YcfftB/KCKqMDs7O2zevBmpqak4efIkAgICYGVlBS8vLwQEBMDMTL4lBvmuuZ6oVCpMnDgREydOlDoKVRPOBiRfLVq0EAsqVHWdO3eWOgIRGSG2HcapWbNmZS6zt7fHG2+8occ0RFRVnp6eWj231Wq1rAsrgIzGXMnJK0SnTxLR6ZNE5OQVPvsXKkkQBMTHx2Pq1Km4dOkS7ty5gyFDhsDHxwfTpk1DXt7jGYI0GgHnb2Xi/K1MaDS6H9FWX+svB5s3bxZ7pzx48ADDhw+Hs7MznJ2dMXLkyFK/lSHjl5OTg9jYWLz66qtYtWoVNBoNIiMj0apVKwwZMgS3b9+WOqKkKtvGPBnc0NXVFXZ2dnB1dUVgYKA4Cj0RUXE8DxuvvXv3YtiwYWjfvj08PDzw0ksvYcqUKbI/hxKZgueffx53796VOoakZFNcESDgn8xc/JOZCwG6K2ZMnjwZmzdvxqlTp+Dv748VK1ZgwIABiIqKwu7du8WpWgU8nl0jv1CjwzT/0tf6y0FUVBRq1qwJAJg4cSLy8vJw4MAB7Nu3D3l5eRg/fry0AUknRo8ejWPHjqF79+7YtGkTgoKCcO/ePSxZsgQqlQpjxoyROqKkKtPGTJkyBQsWLMDQoUOxfft2/Pbbb9i+fTuGDBmChQsXIiYmRsepicgY8TxsnBYtWoRx48ahZcuWePXVVyEIAl588UVYWFjA29sbv/zyi9QRiagcnvRYKf7v7t276Ny5s6zHIZRNvx1LMxW2R3YWb+tKQkICUlNTodFoUKtWLQwfPhyNGjUCALRv3x6BgYGYP38+lAqgaV1bAIBSD5P36Gv95SA9PR0ODg4AgMTERFy+fBlWVlYAgC+++IID2pqonTt3Ii0tDTY2Nhg8eDDq1KmD9PR02NrawsfHB25ublJHlFRl2pjVq1fj3LlzcHZ21rq/ffv2CAoKQsuWLTFnzpxqz0pExo3nYeO0cOFCJCcno0GDBgCAwYMHo0+fPjh37hy6du2KyMhInDx5UuKURPQsKpUKjo6OiImJgbW1NYDHV28MGDAAc+fOFWfTlCPZ9FxRKRVoWc8BLes5QKXDakZ2djYcHBzg6OgIe3t7sbACPJ4u8N69ewAezw9uZWEGKwszKPQwNbK+1l8OvLy8sGfPHgCAo6Mjbt68KS67ffs2zM3NpYpGOlZQUCD+X6PRiIMmcvDEyrUxSqUSarW61GVqtZrblYhKxfOwcdJoNHB0dBR/dnR0RHp6OgDgpZdewoULF6SKRkQVkJKSgldeeQWTJ0/G33//DX9/fwQEBMDCwgJ+fn7w9/eXOqJkZNNzRV9cXV1x79491KpVC9u3b9daduPGDbEbKxmvxYsXY+DAgQgPD0f//v3Ro0cPhIeHAwDWrFmDadOmSZyQdKFfv37o2bMnevfujYMHDyIkJASjR49GeHg4vvrqKwQEBEgd0ehERUUhICAAY8eORatWrWBvb4/MzEycPn0aK1euRHR0tNQRicgA8TxsnEJDQzFgwAC8++67EAQBS5YsQb9+/QAAd+/elfW33UTGRKVS4Z133sGQIUMwZcoUxMXFYenSpXrpMGDoZFNcyS/U4Iff/gYAhLarD3OVbr4RnTdvnjhorZ+fn9ay48ePY+zYsQAAjSDgwaN8AEBNa3ModXww6mv95cDHxwfJyclYsmQJkpOTYWZmhs2bN6NVq1aIj49HYGCg1BFJB1asWIFly5bh+vXrWLBgAZo0aYKxY8ciMjISHTp0QFxcnNQRJVWZNmbq1Klo3bo11q9fjw0bNiArKwu2trZo2bIlli9fjqCgIF3HJiIjxPOwcVqyZAlmz56N6dOnAwB69OghjkVYUFCAr776Ssp4RFRBdevWxZo1a3D06FGMGTNG9oPZAjIrrkzefBoA0Ke1q86KC8HBwcjIyMCFCxfg4eEB4PHI6H/++Se6dOmCgQMHAgAEAfgr/REAwMHKAdBxoU9f6y8X1tbWCA8Px7x58wAAe/bswZUrV+Dq6ipxMtIVCwsLjBo1Crdu3RJf28OHD8eff/6Jrl27ok6dOhInlFZl25igoKASRZSIiAhOtUpET+Xq6iqeg5+IiIhAx44dJUpEz2JlZYWPP/4YH3/8sdb9ERERWLBgAXuAEhmpNm3aYN68ecjIyBDHv5Ir2RRXlAoFujWvI97Wle3btyMsLAy5ubno3bs3unbtisTERGg0GkyaNAkJCQkIDg6GAoBdjcfXBOujA5W+1l8OyruPybQ8bb9HR0fLfr9Xpo2ZP39+qfdv3LgRzs7OsLW1xXvvvVdtGYnINLDtME7cb0SmoU+fPuLwF2fOnEFQUBDs7OygUCgwduxY7Ny5E23atJE4pTRkU1ypYa7CmnDdf5sxbdo07N27FwDQqVMnjBs3DhMnTgQAbNmyBbNmzUJwcDCUSgUa17bReZ4n9LX+clDefUymhfv96SrTxsTExKBjx45o0aIFBOHf6Zvz8/Nx6dIlcQR6IqKi2HYYJ+43ItOQlJQk3o6OjkZkZCQmT54M4PGYWJMmTUJiYqJU8SQlm+KKvly7dg0dOnQAAFhaWqJbt27istDQULz11ltSRaNqwn0sT9zv1e/w4cOIioqCg4MDZs6cKU6tumvXLixbtgx169aVOCERGSK2HcaJ+43I9KSkpGDbtm3iz+PHjy9x6Z+ccOCNamZtbY38/McD1YaHh2uNmqxWqzmKsgngPpYn7vfq5+vri19++QUeHh7w9fXFqlWrIAgCtyURPRXbDuPE/UZkGvLz8/Htt99i06ZNAIDCwkJxmSAI4uQuciSb4kpOXiECFuxHwIL9yMkrfPYvVFK/fv2QlpYGAPj888+1lm3duhXt2rUDAGg0Ai7ezsTF25nQaIQSf6e66Wv95aC8+5hMC/f701W2jVEoFIiIiMDhw4fx66+/okOHDsjKytJhUiIyBWw7jBP3G5Hx69SpE+Li4rBy5Up4enoiNTVVXHbo0CE0b95cwnTSks1lQQIEXL33SLytK/Hx8WUuCwkJQWho6P8yALkFGvG2rulr/eWgvPuYTAv3+9NVtY1xcnLCihUrkJKSgoMHD8Le3r66IxKRCWLbYZy434iM14EDB8pc5u3tjZ07d+ovjIGRTXHF0kyFzaN9xdtSsLH5dwBbpQJoUsdWvK1rhrD+clB0H5N8cL9XXxvTtm1btG3btppSEZFcsO0wTtxvRKblyVhKciWb4opKqUAHNyepY4gUCgVsLPW3+Q1t/YnItLCNISIiIiI5k82YK0REREREREREuiCbnisFhRrsPvcPAKBnS2eYqaStKwmCgIycxzOPOFiZ63ykdENbfyIyLWxjiIiIiEjOZFNcySvUYNw3vwIAUj/qKfkb/5z8QrT9aK+Yx9pCt7vC0NZfDnILChG8/AgA/exjMgxy3e/laWMqu23kuk2JqGrYdhgn7jci0yDH17Lpr+H/KBUKdGrsJN6Wmr7zGNr6E5FpYRtDRERERHImm+JKDXMVNkX4Sh1DpO88hrb+RGRa2MYQERERkZzJprhC+mVppsK28X7ibaq8srYltzEZGx6zJGc8/qk68XgiHgNEhocDbxARERERERERVYFseq5oNAIu380CALjXsYVSKe2YAOr8QvSP+wUA8P3YF1HDXLcVZ30/HxHJi6G1sURERERE+iSb4oqAxwWGJ7elphEEnL+VKd42tecjInkxtDaWiIiIiEifZFNcUSqAxrVtxNtSszRT4euRHcXbpvZ8RCQvhtbGEhERERHpk2yKKwqFAnY1zKWOIVIpFejStI7JPh8RyYuhtbFERERERPrEAW2JiIiIiIiIiKpANj1XBEHAQ3UBAMCuhhkUCmn7rRcUanDoj7sAgK5N68BMpds6l76fj4jkxdDaWCIiIiIifZLNJ2yNAFy9l42r97KhMYDRFvMKNRix9iRGrD2JvEKNyT0fEcmLobWxRERERET6ZBLFlezsbPz22294+PBhiWUJCQkAAAUAKwsVrCxUMITvU5UKBVo3cEDrBg5Q6uEb3up6vsWLFyMtLa0ak1F5jXvjVaSnp0sdg6hUB/bvx5b1q/HXlUtQAPjuu+8wYcIEfPHFFxA4QxmZsPPnz2PXrl3Iy8uDRqPBqlWrMHXqVOzevVvqaGRCNBoNvvrqK6ljkB7wvTaR8TL6y4KSkpIQGhoKKysrpKen47333kNsbKy4PCIiAoMHD4ZSqUDTunYSJtVWw1yFbeM7G93zRUdHY9q0aWjfvj2GDh2K1157DbVr166GhPTE2LFjtX4WBAH3s/Nw6vhRRE+aBGtrK8TFxUmUjqikuXPn4rPPPkPnzp2xbuVnGDlyJP7v//4PoaGhWL58OS5evIhFixZJHZOo2q1duxbTp0+HRqOBm5sbgoODcfPmTWg0Grz++utYuHAhRo4cKXVMMgH5+fkIDw/HG2+8IXUU0jG+1yYyXkZfXJk0aRLi4+MxcOBAXL16FcOHD0dqaio2bNgAMzMzfmNazaytrXHt2jVs2rQJ33zzDaKiohAYGIihQ4eKRS6qmrVr1+KFF17Ayy+/DEEQoBEEKB7mQqFQoFbtWrC3M5wiIREAfP755zhy5Ajc3Nzwxx9/wMPDA2lpaWjYsCEiIiLQqVMnFlfIJM2ZMweHDh2CRqNB8+bNsWzZMnh7ewMAXnvtNYwfP57FFSq3+fPnl7ksPz9fj0lISnyvTWS8jP6yoIsXL2LgwIEAADc3NyQmJsLMzAy9evVCVlYWB1WsZgqFAk5OThgzZgySkpJw4cIF+Pn54ZNPPoGzszO/UakGqampqFOnDlJSUjBs2DDMmBGL0e9OgbWNDaKjJ2v1zCIyBFlZWWjYsCEAoFGjRlAqlWjQoAEAoH79+sjOzpYyHpHO3L59G02aNEHTpk1hbW0tFlYAoGvXrrh+/bqE6cjYTJ8+HSdOnMD58+dL/Lt48aLU8UhP+F6byHgZfXGldu3aWtclmpmZYcOGDfDw8IC/vz8KCh7PXqHRCLh8JwuX72RBYwCjLarzCzFgxS8YsOIXqPMLjfb53NzcEBMTgzNnzuDQoUNwcXGptr8tV25ubvjuu+8QERGB/v374/33p+PRo2woDGK0IKKSOnbsiHHjxmHDD7vw5qgx8PT0xMqVK6HRaBAfH4+WLVtKHZFIJxwcHJCTkwMA+OCDD7SWZWZmwtzcXIpYZKS8vLwwYsQIrFmzpsQ/jl8lX3yvTWQ8jL64EhoaivXr15e4f/ny5ejbty/UajUAQADwKK8Aj/IKYAinJo0g4NS1dJy6lg6NHk6W1fV8T76dLk3btm2f2qWVKqZnz544efIk7O3t8XrvADx8mCl1JKJSxcfH48aNv/DRtGg09WyFdV99jTlz5sDCwgLz5s3DsmXLpI5IpBMjRozAzZs3AQBTpkzRWrZ582b4+/tLEYuM1Ntvvy1+KVicubk5e67KBN9rExkvox9z5WnX8X/44Yf48MMPAQBKBdColo14W2oWKiXih70g3jaW5zt79mx1RaJyMDc3x3vvTYF3YAjO/f4bHB0dpY5EVIKbmxt+/HEbMtWPPxTY1zDDtWvXcP/+fQ7CRyZt5syZZS4bOXIkx1uhChkzZkyZy1QqFYsrMsH32kTGy+iLK8U9ePAAR48eBQD4+vqiZs2aAB5fv+hgZTjdc81USvRsqb9ufbp6vrK2N1WfBw8e4NL5szC3MEd2djZqWFpIHYmohOJtrEKhwPbt2zFo0CDUqFFDwmREunPv3j3UqlVL6hhkYm7cuIHU1FRkZWXB1tYWnp6eeO6556SORXqWnZ0NG5vHXww/evQI58+fh7u7OxwcHCRORkRlMfriSlhYGCZPnow2bdrg8OHDCA0NRZMmTSAIAq5evYrvv/8efn5+Usc0GdzeulfaNnZ9zg2CIGDmpHHcxmRwTpw4Uer90dHRcHFxgaOjIzp27KjnVES65+zsDD8/P4SFheHVV1/lFwxUJdevX8ewYcNw8uRJNGnSBPb29sjMzMSff/4Jb29vfP311yyyyMDx48fx6quv4u+//4aPjw/i4uLQp08fKBQKZGRkYOPGjQgKCpI6JhGVwuiLKzt27MC6desAPH4jv2rVKvTv3x8AsG3bNkRGRuLUqVMQBAHZuY8HcrWxVEk+i1ChRsCJtPsAgI6NnaDS8bVK1fV85d3eVHnFt3F8/Bdo2qk7AODKyYPcxmRwfHx84OrqCgsLSwiCgCfNa3p6OkaNGgUzMzNcuXJF2pBEOmBpaYmBAwdi9erViIyMRK9evRAWFoa+ffvC0tJS6nhkZN588034+vpi586dsLa2Fu/Pzs7G7NmzMWzYMBw4cEC6gKQXEyZMwAcffICwsDCsWbMGgYGBiI+PxyuvvIKtW7ciJiaGxRUiA2X0A9oqFApxpP7Lly8jODhYXNa3b1/88ccfAACNAFz5bxau/DcLBjBZEHILCjF41TEMXnUMuQW6ny2oup6vvNubKq/4Nu5XZBv34TYmA/TJJ5+gdu3aeOf92dh2+Ddc/vMK0tLSUKdOHZw8eZKFFTJZKpUKEyZMwLFjx3DmzBm0a9cO06dPh7OzM0aMGIF9+/ZJHZGMSHJyMj766COtwgoA2NjY4MMPP0RycrJEyUifLl26hFGjRsHKygqjR49GRkaG+EVmSEgIrl69Km1AIiqT0RdX+vfvjxkzZkAQBPTo0QNff/21uOzJlMwAoABQw0yFGmYqg5jQVgEFmta1RdO6tnqZYre6nq+825sqr/g2Xl9kG3/DbUwGaOrUqdi+fQf2/vQdxg59BRfOn5c6EpHeubu7Y8aMGbhw4QL27t0Le3t7DBs2TOpYZETc3d2RkJBQ6rKNGzfC3d1dz4lICs7OzkhKSgIA7N+/H+bm5rh48SKAx4UXJycnKeMR0VMY/WVBixcvRnh4OJo0aYKmTZtixIgRmD17NgBAEARs3boVAKBUKtDMxU7KqFqsLFTYG6W/KRqr6/nKu72p8opv47feGokGDWcBAMxVCm5jMkgNGtTHT99vxoEDBzBsWBi6dOmC/Px8qWMR6ZQglN4V1tvbG97e3vj000/1nIiM2apVqzBgwADMnz8frVq1EsdcOX36NLKzs/Hdd99JHZH0YObMmejRowdcXFzg5uaGhQsX4qWXXkJgYCD27duHqKgoqSMSURmMvrhiZ2eHzZs3IzU1FSdPnkRAQACsrKzg5eWFgIAAmJkZ/SoaFG5v3Su+jbv6+yMjTwH35i3w5oA+sLQwnFmviIoLCAhAcnIyli9fji5dunCmIDJpO3fufOpypdLoOwiTHnl7e+Py5cvYv38/zp8/L84WNGLECAQEBMDCgrMFysHgwYPRrVs33Lx5E23btoVSqcTzzz+PM2fOIDw8HAEBAVJHJKIymMwnYU9PT3h6ekodQzaKb2+1Ws3CSjV7so0LNQLO3cwAAG5jMgoqlQoTJ07ExIkTpY5CpFOdO3cu9X61Ws3CIlWKhYUFevbsiZ49e0odhSTk4uICFxcX8WceE0TGwSS+Ujl8+DAWL16MPXv2lFg2duxYAIBGI+DK3SxcuZsFjQGMaKvOL0TYl8cR9uVxqPN1P6Ctrp/v+eefx927d6v978rR5s2bkZaWBgDIyMjAyBEj0K1dM3Rr1wxvjRyBjIwMiRMSlfSf/6yBX9duqOvsAjs7O7i6uiIwMFCc+YpITnhOpMrIyclBbGwsXn31VaxatQoajQaRkZFo1aoVhgwZgtu3b0sdkfRk7dq1CAwMhKurK8+pREbE6L8Gj4+PR2xsLAIDA7FixQo0aNAAmzdvFgd7Wr9+PeLi4iAAyMotAABIX1oBNIKAw5f/K942lucrq3fQ3bt30blzZ6hUKqSmplb67xMQFRWF33//HQAQGRmJvLx8rP6/H6HRaPDdmjiMHz9eayBhIqlNmTIFP/30EwaNGItITy+0bVIP2VkPkZKSgoULF+LChQuYM2eO1DGJqh3PiVSdRo8ejdu3byM0NBSbNm3Cli1bUKtWLSxZsgRr167FmDFj8P3330sdk3TsyTk1Ojoabdq0Ecfe4TmVyPAZfXFlwYIF2LdvHzw9PaHRaDB9+nT4+flhz549eO6558TB5pQK4Dkna/G21CxUSiwZ1Fa8bSzPp1Kp4OjoiJiYGHGqQEEQMGDAAMydO5cjmFeD9PR0ODg4AAASExNx8dIfuJKeBwBYGR+Pxm6NpIxHVMLq1atx9uxZWNo/fv3XtDKHQqFA+/btERQUhJYtW/KNIJkknhOpOu3cuRNpaWmwsbHB4MGDUadOHaSnp8PW1hY+Pj5wc3OTOiLpwerVq3Hu3Dk4Oztr3c9zKpHhM/riyp07d8SpaZVKJebMmYNGjRqhc+fO2L59OxSKx5UUhUIBR2vDGQjMTKVEaLv6Rvd8KSkp+OyzzzB58mRMmzYNQ4YMAfD4GmE/Pz/UrVu3ys8hd15eXtizZw969eoFR0dH3Lx5E7CqDQC4ffs2zM05oC0ZFqVSidzcXLiU0saq1WoO6kkmi+dEqm4FBQXi/zUajdh+sh2VD6VSCbVaXeoynlOJDJvRF1eaNGmCkydPomPHjuJ9o0ePhqOjI1566SXk5uZKmM70qFQqvPPOOxg8eDCmTp2KuLg4LF26VCxiUdUtXrwYAwcORHh4OPr374/evXqi9yuDAQA7v0vAtGnTJE5IpC0qKgoBAQEYO3ZsielDV65ciejoaKkjEunEk3PikCFDMGXKFJ4TqUr69euHnj17onfv3jh48CBCQkIwevRohIeH46uvvuIsMTJR1jn1zJkzWLFiBc+pRAbM6Isr77zzDn7//Xet4goADBo0CI6Ojpg7dy6Ax910c/43kKuVuUryNz6FGgFn/348MKlXfQeodHytUnU/n7OzM9asWYOjR49izJgxHLivGvn4+CA5ORlLlixBcnIyzMzMkLhjK9w9PBG3YiV69nhZ6ohEWqZOnYpWrVph3dfr8fX69XiUnQ1bW1u0bNkSy5cvR1BQkNQRiXSqbt26PCdSla1YsQLLli3D9evXsWDBAjRp0gRjxoxBZGQkXnjhBcTFxUkdkfRg6tSpaN26NdavX48NGzaIU3LznEpk+Iy+uPLmm28iIyMDFy5cEC8P2rt3L/7880906dIF+/btAwBoBODynSwAQMt6DlBJ/KVSbkEhQj4/AgBI/agnrC10uyuq8/kyMjJw69YteHh4wNfXF7Nnz8apU6dw9+5ddoGuJtbW1ggPD8e8efNQqBGwKuF7/HX9KlxdXaWORlSqXr2D8FwbPwD/a2OVCkRERJQ5VS2RKfL19cXx48cREREBS0tLqeOQkbGwsCjRKyEhIQFvv/02Fi5cCHt7e4mSkb4FBQWJRZTs7GycOHECCoUCHTp0kDgZET2N0RdXtm/fjrCwMOTm5qJ3797o2rUrEhMTodFoMGnSJCQkJCA4OBgK/DuQqyF01lVAgfo1rcTbxvJ8Rbd3r1694O/vL27v2bNni9ubKq/4Md25Sxf8sH0XBI0Gi2d/wG1MBmf+/PkQBAF3Hz6+DLOOnSUUCgU2btwIZ2dn2Nra4r333pM4JVH1mz9/fqn3b9q0CS4uLjz2qUJ4PBEA9OnTB9u3bwcAnD17FkFBQbC1tYVCoUB6ejp27NiBtm3bShuSiEpl9MWVadOmYe/evQCATp06Ydy4cZg4cSIAYMuWLZg1axaCg4OhVCrg4Wo4FX8rCxWOTO1udM9XfHuPHz++1O1NlVd8G48eMxYvDRwOALh4NJHbmAxOTEwMOnbsiBYtWkAQBNz73/35+fm4dOmSOIsKkakpfuw/wWOfKoPHEwFAUlKSeHvSpEmYMGECJk+eDODxuHzR0dFITEyUKh4RPYXRF1euXbsmdpGztLREt27dxGWhoaF46623pIpmkri9da+0bZx6KxMAEBIairffHiVlPKISDh8+jKioKDg4OGDmzJniVOK7du3CsmXLeLkgmSwe+1SdeDxRcSkpKdi2bZv48/jx4/Hxxx9LmIiInsbo5/KytrZGfn4+ACA8PFxroFq1Wi35wLWmhttb97iNydj4+vril19+EcdhWrVqFQRB4LFKJo/HPlUnHk8EPO6p9O2332LTpk0AgMLCQnGZIAjIy8uTKhoRPYPRF1f69euHtLQ0AMDnn3+utWzr1q1o164dAECjEXD1v9m4+t9saDRCib+jb+r8Qoz66iRGfXUS6vzCZ/+CgTxfebc3Vd7TtvE2bmMyUIIA9BwQhm+27sapU6fQoUMHZGVlSR2LSOcUiseDNx8+fBi//vorj32qEh5P1KlTJ8TFxWHlypXw9PREamqquOzQoUNo3ry5hOmI6GmM/rKg+Pj4MpeFhIQgNDQUACAAyFTni7elphEE7E39R7xtLM9X3u1Nlfe0bRwcEoJXXumvxzRE5fOkjVVa2eHzuBU4c/p3HDx4kLNbkGw4OTlhxYoVSElJ4bFPVcbjSb4OHDhQ5jJvb2/s3LlTf2GIqEKMvrjyNDY2NuJthQKo72gl3paauUqJOa+0Em+bwvMV3d6kGzY2NlApDeAAJiqmeBvbtm1bzmZAssRjn6oTjycq6sk4PERkmEy6uFKUUqFALRtLqWOIzFVKDO7Y0GSfj4jkxdDaWCIiIiIifTL6MVeIiIiIiIiIiKQkm54rgiAgt0ADALA0U0o+8rpGI+Dy3ccDlLnXsYVSx5d66Pv5iEheDK2NJSIiIiLSJ9n0XMnJL4THB7vg8cEu5Ohhdp5nURcUosfiQ+ix+BDUBXqYLUjPz5dbUIjg5UcQvPwIcvXwfKasrG3JbUyGpDxtLI9ZkjMe/1SdeDwRjwEiwyObnisA4GRjIXUELfrOY2jrT0SmhW0MEREREcmVbIor1hZm+PWDl6WOIdJ3HkNbfyIyLWxjiIiIiEjOZHNZEBERERERERGRLrC4QkRERERERERUBbIprqjzCzFx42+YuPE3qA1hQFs95zG09Sci08I2hoiIiIjkTDbFFY0gYGvKTWxNuQmNIEgdR+95DG39ici0sI0hIiIiIjmTzYC25iolPujrKd6Wmr7zGNr6E5FpYRtDRERERHImq+LKyM6NpY4h0nceQ1t/IjItbGOIiIiISM749SIRERERERERURXIpueKRiPg7wc5AID6Na2gVCpklcfQ1p+ITAvbGCIiIiKSM9n0XFEXFKLL/P3oMn8/1AXSz2Sh7zyGtv5EZFrYxhARERGRnMmm5woAWJmrpI6gRd95DG39ici0sI0hIiIiIrmSTXHF2sIM52f1kjqGSN95DG39ici0sI0hIiIiIjmTzWVBRERERERERES6wOIKEREREREREVEVyKa4kltQiKlbTmPqltPINYDBFvWdx9DWn4hMC9sYIiIiIpIz2RRXCjUCNibfwMbkGyjUCFLH0XseQ1t/IjItbGOIiIiISM5kM6CtmVKJ6B7NxNtS03ceQ1t/IjItbGOIiIiISM5kU1yxMFNifPemUscQ6TuPoa0/EZkWtjFEREREJGf8epGIiIiIiIiIqApk03NFEATcz84DADjZWEChUMgqj6GtPxGZFrYxRERERCRnsimu5OQX4oXZiQCA1I96wtpC2lXXdx5DW38iMi1sY4iIiIhIznhZEBERERERERFRFSgEQXjmnJmZmZlwcHBARkYG7O3t9ZGLiIiIiIiIiEhS5a2HsOcKEREREREREVEVsLhCRERERERERFQFLK4QEREREREREVUBiytERERERERERFXA4goRERERERERURWwuEJEREREREREVAVm5XnQk9maMzMzdRqGiIiIiIiIiMhQPKmDPKmLlKVcxZWHDx8CAJ577rkqxiIiIiIiIiIiMi4PHz6Eg4NDmcsVwrPKLwA0Gg1u3rwJOzs7KBSKag1IRERERERERGSIBEHAw4cPUa9ePSiVZY+sUq7iChERERERERERlY4D2hIRERERERERVQGLK0REREREREREVcDiChERERERERFRFbC4QkRERERERERUBSyuEBERERERERFVgVl5HsSpmImIiIiIiIhIbso7FXO5iis3b97Ec889V23hiIiIiIiIiIiMxY0bN9CgQYMyl5eruGJnZyf+MXt7++pJRkRERERERERkwDIzM/Hcc8+JdZGylKu48uRSIHt7exZXiIiIiIiIiEhWnjVECge0JSIiIiIiIiKqAhZXiIiIiIiIiIiqgMUVIiIiIiIiIqIqYHGFiIiIiIiIiKgKWFwhIiIiIiIiIqoCFleIiIiIiIiIiKqAxRXSiUd5BXCbuh1uU7fjUV6B1HEkUV3boKy/w21smLhfylbZbcNtKl+mtO9NaV2MRVW3uSHvM0POVlXGsm5S55T6+YmeRY7HKIsrRERERERERERVwOIKEREREREREVEVsLhCRERERERERFQFLK4QEREREREREVUBiytERERERERERFXA4goRERERERERURWwuEJEREREREREVAUsrhARERERERERVQGLK0REREREREREVcDiChERERERERFRFbC4QkRERERERERUBSyuEBERERERERFVAYsrRERERERERERVwOIKEREREREREVEVsLhCRERERERERFQFLK4QEREREREREVUBiytERERERERERFXA4goRERERERERURWwuEJEREREREREVAUsrhARERERERERVQGLK0REREREREREVcDiChERERERERFRFZhJHYDI1G0b7wdLM1Wlf9/STIVt4/3E28XvD15+pMoZifShrGOZSA54/FN14vFEPAaIDA97rhARERERERERVQGLK0REREREREREVcDiChERERERERFRFbC4QkRERERERERUBSyuEBERkUG6du261BGIiIiIyoXFFSIiIjJI77//vtQRiIiIiMqFxRUiIiIySI+ys6WOQERERFQuLK4QERGRQSooLJA6AhEREVG5sLhCZIA0Gg2++uorqWMQldv+/fvx+eef49y5cwCA7777DhMmTMAXX3wBQRAkTkfGKj8/X+oIz3T+/Hns2rULeXl50Gg0WLVqFaZOnYrdu3dLHY1MCN8XyMfixYuRlpYmdQwiqgQWV4gMUH5+PsLDw6WOQVQuc+fORVhYGA4dOoQ+ffpg1qxZiI2NRc2aNbF8+XJER0dLHZGMVH6+YfdcWbt2LQIDAxEeHg5/f3/MmzcPp0+fxsOHD/H6669j9erVUkckE8H3BfIRHR0NT09P+Pn5IS4uDv/973+ljkRE5WQmdQAiuZo/f36Zy4zh21qiJz7//HMcOXIEbm5u+OOPP+Dh4YG0tDQ0bNgQERER6NSpExYtWiR1TDJCht4WzpkzB4cOHYJGo0Hz5s2xbNkyeHt7AwBee+01jB8/HiNHjpQ4JRkLvi8gALC2tsa1a9ewadMmfPPNN4iKikJgYCCGDh2K0NBQWFlZSR2RiMrA4gqRRKZPn46QkBDY2dmVWFZYWChBIqLKycrKQsOGDQEAjRo1glKpRIMGDQAA9evXRzYHJaVKKiww7J4rt2/fRpMmTQA8/kD0pLACAF27dsX165xKmsqP7wsIABQKBZycnDBmzBiMGTMGV69eRUJCAj755BNEREQgNDSUl4gRGSgWV4gk4uXlhREjRiAoKKjEMrVajfXr10uQiqjiOnbsiPHjx2PQoEFISEiAp6cnVq5cidGjR+OLL75Ay5YtpY5IRiq/wLC/rXdwcEBOTg6srKzwwQcfaC3LzMyEubm5RMnIGPF9AZXGzc0NMTExiImJQUpKCr755hupIxFRGTjmCpFE3n77bRSU8a2subk5YmNj9ZyIqHLi4+Nx48YNREZGon379li/fj3mzJkDCwsLzJs3D8uWLZM6IhkpQ78UYsSIEbh58yYAYMqUKVrLNm/eDH9/fylikZHi+wICIPYELU3btm2fevkYEUmLPVeIJDJmzJgyl6lUKr6JIqPh5uaGH3/8Ueu+a9eu4f79+6hdu7ZEqcgUFBQY9qUQM2fOLHPZyJEjOd4KVQjfFxAAnD17VuoIRFRJ7LlCZEAKCgoM/ptaovJQKpXYvn071Gq11FHIiBl6e3jv3j2pI5AJunHjBnbv3o0tW7Zg9+7duHHjhtSRSAJFxyt79OgRTp06hYyMDAkTEdGzsLhCJJG5c+eKt+/fv4/Q0FBYW1vD1tYW/fr149R7ZDROnDhR6r/o6GgcPHgQJ06ckDoiGakCAy+uODs7w9/fH6tWrcKDBw+kjkNG7vr16/D394eHhwcmT56MxYsXY/LkyfDw8EBAQACLLDJx/PhxNGzYEPb29vDz88Pvv/+OZs2aITQ0FM899xx27NghdUQiKgOLK0QS+eSTT8Tb0dHRsLS0xI0bN3Djxg3Y29tj0qRJEqYjKj8fHx/0798fr7/+OgYNGiT+S09Px6hRo/D6669LHZGMVEGhYc8WZGlpiYEDB2L16tVwdXVF//79sWXLFuTm5kodjYzQm2++CV9fX9y9exenT5/G4cOHcfr0ady5cwe+vr4YNmyY1BFJDyZMmIAPPvgAWVlZGDp0KAIDA7Fs2TLcuHEDX3/9NWJiYqSOSERlYHGFSCKCIIi39+7di7i4ODg7O6Nu3bpYvnw59u7dK2E6ovL75JNPULt2bSxevBhpaWnivzp16uDkyZO4cuWK1BHJSBn6ZUEqlQoTJkzAsWPHcObMGbRr1w7Tp0+Hs7MzRowYgX379kkdkYxIcnIyPvroI1hbW2vdb2Njgw8//BDJyckSJSN9unTpEkaNGgUrKyuMHj0aGRkZ6N+/PwAgJCQEV69elTYgEZWJxRUiCd29exf//PMPBEFAzZo1xfsdHBzw8OFD6YIRVcDUqVOxc+dObNy4ET169MD58+eljkQmIj/fsHuuFOXu7o4ZM2bgwoUL2Lt3L+zt7dnTgCrE3d0dCQkJpS7buHEj3N3d9ZyIpODs7IykpCQAwP79+2Fubo6LFy8CeFx4cXJykjIeET0FZwsikkh2djZcXFzEHizHjh2Dn58fgMcjxderV0/KeEQVUq9ePSQkJODAgQMYOnQounTpYvC9DsjwGfoxVLQHYlHe3t7w9vbGp59+qudEZMxWrVqFAQMGYP78+WjVqhXs7e2RmZmJ06dPIzs7G999953UEUkPZs6ciR49esDFxQVubm5YuHAhXnrpJQQGBmLfvn2IioqSOiIRlYHFFSKJaDSaMpeZmZlh5cqVekxDVD0CAgKQnJyM5cuXo0uXLqhRo4bUkciIFRYYds+VnTt3PnW5UskOwlR+3t7euHz5Mvbv34/z588jKysLtra2GDFiBAICAmBhYSF1RNKDwYMHo1u3brh58ybatm0LpVKJ559/HmfOnEF4eDgCAgKkjkhEZWBxhcgAeXp6wtPTU+oYRJWiUqkwceJETJw4UeooZOQMfUDbzp07l3q/Wq1mYZEqxcLCAj179kTPnj2ljkIScnFxgYuLi/gzjwki48CvVIgksnnzZqSlpQEAHjx4gOHDh8PZ2RnOzs4YOXIkMjIyJE5IVH5r165FYGAgXF1dYWdnB1dXVwQGBmLdunVSRyPSu+effx53796VOgYZmZycHMTGxuLVV1/FqlWroNFoEBkZiVatWmHIkCG4ffu21BFJT3hOJTJO7LlCJJGoqCj8/vvvAICJEyciPz8fBw4cgEajwdy5czFu3DisX79e4pREzzZlyhT89NNPiI6ORps2bcRxAlJSUrBw4UJcuHABc+bMkTomUbUrq4fh3bt30blzZ6hUKqSmpuo5FRmr0aNH4/bt2wgNDcWmTZuwZcsW1KpVC0uWLMHatWsxZswYfP/991LHJB3jOZXIeLG4QiSR9PR0ODg4AAASExNx+fJlWFlZAQC++OILNGzYUMp4ROW2evVqnDt3Ds7Ozlr3t2/fHkFBQWjZsiXfCFKlKBQKqSM8lUqlgqOjI2JiYsTpcwVBwIABAzB37lzO6kEVsnPnTqSlpcHGxgaDBw9GnTp1kJ6eDltbW/j4+MDNzU3qiKQHPKcSGS9eFkQkES8vL+zZswcA4OjoiJs3b4rLbt++DXNzc6miEVWIUqmEWq0udZlareagnlRphl5cSUlJwSuvvILJkyfj77//hr+/vzjwqJ+fH/z9/aWOSEam4H+DOBcUFECj0YjtJ9tR+eA5lch4secKkUQWL16MgQMHIjw8HP3790ePHj0QHh4OAFizZg2mTZsmcUKi8omKikJAQADGjh1bYvrQlStXIjo6WuqIRDqhUqnwzjvvYMiQIZgyZQri4uKwdOlSgy8KkWHq168fevbsid69e+PgwYMICQnB6NGjER4ejq+++oqzxMhEWefUM2fOYMWKFTynEhkwFleIJOLj44Pk5GQsWbIEycnJMDMzw+bNm9GqVSvEx8cjMDBQ6ohE5TJ16lS0bt0a69evx4YNG8TpQ1u2bInly5cjKChI6ohkpIylSFG3bl2sWbMGR48exZgxYziYLVXKihUrsGzZMly/fh0LFixAkyZNMGbMGERGRuKFF15AXFyc1BFJD3hOJTJeLK4QScja2hrh4eGYN28eAGDPnj24cuUKXF1dJU5GVDFBQUEl3vBFRESUOVUtUXkoYBzFlSd8fX1x/PhxREREwNLSUuo4ZGQsLCxK9EpISEjA22+/jYULF8Le3l6iZKRvRc+p2dnZOHHiBBQKBTp06CBxMiJ6GhZXiCSyfft2hIWFITc3F71790bXrl2RmJgIjUaDSZMmISEhAcHBwVLHJHqm+fPnl3r/xo0b4ezsDFtbW7z33nt6TkWmwNB7rpR17G/atAkuLi489qlCeDwRAPTp0wfbt28HAJw9exZBQUGwtbWFQqFAeno6duzYgbZt20obkohKxeIKkUSmTZuGvXv3AgA6deqEcePGYeLEiQCALVu2YNasWSyukFGIiYlBx44d0aJFCwiCIN6fn5+PS5cuibOoEFWUoRdXeOxTdeLxRACQlJQk3p40aRImTJiAyZMnA3g8Xl90dDQSExOlikdET8HiCpFErl27JnbvtLS0RLdu3cRloaGheOutt6SKRlQhhw8fRlRUFBwcHDBz5kxxivFdu3Zh2bJlqFu3rsQJyVgZenGFxz5VJx5PVFxKSgq2bdsm/jx+/Hh8/PHHEiYioqfhXF5EErG2tkZ+fj4AIDw8XOtDhFqtNvgPFURP+Pr64pdffoGHhwd8fX2xatUqCILAY5iqzsCPIR77VJ14PBHwuKfSt99+i02bNgEACgsLxWWCICAvL0+qaET0DCyuEEmkX79+SEtLAwB8/vnnWsu2bt2Kdu3aSRGLqFIUCgUiIiJw+PBh/Prrr+jQoQOysrKkjkWkczz2qTrxeKJOnTohLi4OK1euhKenJ1JTU8Vlhw4dQvPmzSVMR0RPw8uCiCQSHx9f5rKQkBCEhobqLwxRNXFycsKKFSuQkpKCgwcPcnYLqhJj+saexz5VJx5P8nXgwIEyl3l7e2Pnzp36C0NEFcLiCpEBsrGxkToCUZW0bduWsxlQlRlTceUJHvtUnXg8UVFPxuEhIsPEy4KIiIjIIBUtrhSdPYWIiIjI0LC4QkRERAaJxRUiIiIyFiyuEBERkUEqWlzRaDQSJiEiIiJ6OhZXiHQsePkR5BYUPvuBZcgtKETw8iMl/s6T+4mMRVnHMlFZtIsrxt1zhcc/VSceT8RjgMjwsLhCREREBo89V4iIiMiQsbhCREREBomXBREREZGxYHGFiIiIDBKLK0RERGQszKQOQGSqLM1U2DbeT7xN8sD9TlR99u3dDVXtRgAARzsbidOQsalqe8z2nJ6GxwcRFceeK0RERGSQFEVuG/dwtkRERGTqWFwhIiIiw1S0usLyChERERkwFleIiIjI8LG2QkRERAaMxRUiIiIyeAKrK0RERGTAWFwhIiIiIiIiIqoCFleIiIjIMBXprCKw4woREREZMBZXiIiIyCAJT/mJiIiIyJCYSR2AyBhlZ2fj0qVLcHd3h52dndayhIQEDB48WKJkpEuXL1/Gxo0bce7cOWRlZcHW1hYtW7bEoEGD0LRpU6njERmNSr2WWFshHQoKCsKGDRvg6OgodRSS2OLFixEaGorGjRtLHYWIjAx7rhBVUFJSEho2bIh+/frBxcUFH374odbyiIgIiZKRLm3cuBHe3t74448/4O3tjZCQEPHnTp06YdOmTVJHJDIKFXstCaXcIqq8sWPHlvrv0KFDiIqKwtixY6WOSBKLjo6Gp6cn/Pz8EBcXh//+979SRyIiI8GeK0QVNGnSJMTHx2PgwIG4evUqhg8fjtTUVGzYsAFmZmYQODCASZo6dSp27NgBX1/fEsuOHTuGQYMGYdCgQRIkIzIuFXktaTenbFup6tauXYsXXngBL7/8stb5WqFQoHbt2rC1tZUwHRkCa2trXLt2DZs2bcI333yDqKgoBAYGYujQoQgNDYWVlZXUEYnIQLHnClEFXbx4EQMHDgQAuLm5ITExEWZmZujVqxeysrKgUCgkTki6cP/+fbRu3brUZV5eXrh//76eExEZp8q+lli3puqQmpqKOnXqICUlBcOGDUNsbCxiY2Nha2uLyZMnIzY2VuqIJDGFQgEnJyeMGTMGSUlJuHDhAvz8/PDJJ5/A2dkZb7zxhtQRichAsbhCVEG1a9dGWlqa+LOZmRk2bNgADw8P+Pv7o6CgQMJ0pCvBwcEYNGgQTp48CbVaDQBQq9VITk7GkCFDEBISInFCIuNQ+dcSqytUdW5ubvjuu+8QERGB/v37Y/r06cjOzuYXI1QmNzc3xMTE4MyZMzh06BBcXFykjkREBorFFaIKCg0Nxfr160vcv3z5cvTt21f8sECm5csvv4SnpyeCg4NhY2MDCwsL2NjYIDQ0FB4eHvjyyy+ljkhkFCr2WhJKvUlUVT179sTJkydhb2+P9u3bIyMjQ+pIZCAaNmxY5rK2bdti/vz5ekxDRMaEY64QVdCiRYvKXPbhhx+WGOCWTEONGjUwf/58zJ8/Hw8ePBBnOKlZs6bU0YiMSoVeS6ytkA6Zm5tjypQpGDZsGJKTkzlTEAEAzp49K3UEIjJSLK4QEVVATk4Orl+/jkePHqF+/fpwcHBgd3KiSnpSUDl69CgAwNfXV6vIol1QYXmFdKNevXq8tJOIiKqMlwURVaPc3FyoVCqpY5AOPHjwAEOGDEHNmjXRtm1bdO7cGa1bt0b9+vXx2WefSR2PyGiEhYXh999/BwAcPnwY7u7umDlzJmJjY9GsWTMcOXKk1N/jgLakazyH07PwGCGip2HPFaIKunPnTpnL1Go1p2I2UcOHD0fDhg1x7do1aDQazJkzB/Xq1cNrr72GiIgIZGRk4P3335c6JpHB27FjB9atWwcAiI6OxqpVq9C/f38AwLZt2xAZGYlTp07979FF21O2rVR1PIfTs/AYIaLKYnGFqIJcXFygUCjKPLnyEhHTtG/fPqSnp4vfWC1atAgNGzZETEwM1q1bh44dO7K4QlQOCoUCOTk5sLW1xeXLlxEcHCwu69u3L8LCwv59MGsrVM14Dqdn4TFCRJXFy4KIKqhevXo4evQoNBpNiX+PHj2SOh7piIuLC06fPi3+fPr0aTg5OQF4fEw8fPhQqmhERqV///6YMWMGBEFAjx498PXXX4vLnkxrXxqB1RWqBjyH07PwGCGiymJxhaiCfHx8cPz48VKXKZXKp07hR8Zr1qxZ6N69OwYPHozXX38dL7/8MmbMmAEASElJgaenp8QJiYzD4sWLcf36dTRp0gT37t3DiBEj4O7uLo698p///Ed8rFY5hbUVqgY8h9Oz8BghosriZUFEFbRp06Yyl1lYWCAtLU2PaUhfBg0ahNatW+Pnn38GAMTGxqJFixYAgHbt2uHYsWNSxiMyGnZ2dti8eTNSU1Nx8uRJBAQEwMrKCl5eXggICICZWdG3JkKRW6yuUNXxHE7PwmOEiCqLxRWiCuIo8fLVokULsaBCRFXj6elZsR5frK1QNeA5nJ6FxwgRVRYvCyKqoM2bN4vfWjx48ADDhw+Hs7MznJ2dMXLkSGRkZEickHQhJycHsbGxePXVV7Fq1SpoNBpERkaiVatWGDJkCG7fvi11RCKjIAgC4uPjMXXqVFy6dAl3797FkCFD4OPjg2nTpiEvL098XPHfI6qqkJAQrF27luNk0VOtXbsWgYGBcHV1hZ2dHVxdXREYGCjOdEZEVBoWV4gqKCoqCjVr1gQATJw4EXl5eThw4AD27duHvLw8jB8/XtqApBOjR4/GsWPH0L17d2zatAlBQUG4d+8elixZApVKhTFjxkgdkcgoTJ48GZs3b8apU6fg7++PuLg4DBgwAJMmTcLu3bvLnHWLlwVRddi5cyc+++wzODs747XXXsO2bduQn58vdSwyIFOmTMGCBQswdOhQbN++Hb/99hu2b9+OIUOGYOHChYiJiZE6IhEZKF4WRFRB6enpcHBwAAAkJibi8uXLsLKyAgB88cUXHOjMRO3cuRNpaWmwsbHB4MGDUadOHaSnp8PW1hY+Pj5wc3OTOiKRUUhISEBqaio0Gg1q1aqF4cOHo1GjRgAej18UGBiI+fPnl/g9dlyh6lCjRg2cOnUK58+fxzfffIN3330X4eHhGDhwIMLCwtClSxepI5LEVq9ejXPnzsHZ2Vnr/vbt2yMoKAgtW7bEnDlzJEpHRIaMPVeIKsjLywt79uwBADg6OuLmzZvistu3b8Pc3FyqaKRjBQUF4v81Gg2UysdN6JP/E9GzZWdnw8HBAY6OjrC3txcLKwDg7u6Oe/fulfGbrK5Q9WnRogVmzZqFP//8Ez/++CPMzc0xYMAAreOR5EmpVEKtVpe6TK1W85xPRGVizxWiClq8eDEGDhyI8PBw9O/fHz169EB4eDgAYM2aNZg2bZrECUkX+vXrh549e6J37944ePAgQkJCMHr0aISHh+Orr75CQECA1BGJjIKrqyvu3buHWrVqYfv27VrLbty4IV52WXLMFX0lJFNW2tg9L774Il588UUsXboUu3fvliAVGZKoqCgEBARg7NixaNWqFezt7ZGZmYkzZ85gxYoViI6OljoiERkoFleIKsjHxwfJyclYsmQJkpOTYWZmhs2bN6NVq1aIj49HYGCg1BFJB1asWIFly5bh+vXrWLBgAZo0aYKxY8ciMjISHTp0QFxcnNQRiYzCvHnzxEFr/fz8tJYdP34cY8eOLfX3OKAtVYewsLAyl6lUKgQFBekxDRmiqVOnonXr1li/fj02bNiArKws2NraomXLlli+fLnBHCMPH2ZJHYGIimFxhagSrK2tER4ejnnz5gEA9uzZgytXrsDV1VXiZKQrFhYWGDVqFG7dugUPDw8AwPDhw/Hnn3+ia9euqFOnjsQJiYxDcHBwqfdHRERgwYIFGDhwYOm/yOIKVYMVK1aUev+T48/e3l7PicgQBQUFiUWU7OxsnDhxAgqFAh06dJA42b9Wr/4SQAupYxBRESyuEFXQ9u3bERYWhtzcXPTu3Rtdu3ZFYmIiNBoNJk2ahISEhDI/PJDxetp+j46O5n4nKqfSBqsFgI0bN8LZ2Rm2trZ47733SiznbEFUHSp7/JF89OnTR7xk8ezZswgKCoKtrS0UCgXS09OxY8cOtG3bVtqQALKzH0kdgYiKYXGFqIKmTZuGvXv3AgA6deqEcePGYeLEiQCALVu2YNasWfyQbYK434mqR0xMDDp27IgWLVpoXeqTn5+PS5cuwdraGgDHXCHdKO/xR/KVlJQk3p40aRImTJiAyZMnA3g87l50dDQSExOliicqLCyUOgIRFcPiClEFXbt2TewWamlpiW7duonLQkND8dZbb0kVjXSI+52oehw+fBhRUVFwcHDAzJkzxantd+3ahWXLlqFu3bpl/CarK1R1lT/+SI5SUlKwbds28efx48fj448/ljDRvwr/N4MhERkOziVGVEHW1tbIz88HAISHh0OhUIjL1Gq11s9kOrjfiaqHr68vfvnlF3h4eMDX1xerVq2CIAjPfA1xQFuqDpU9/kg+8vPz8e2332LTpk0AtHuICIIgDsgttUINe64QGRoWV4gqqF+/fkhLSwMAfP7551rLtm7dinbt2kkRi3SM+52o+igUCkRERODw4cP49ddf0aFDB2RlPX3mC9ZWqLpU5vgj+ejUqRPi4uKwcuVKeHp6IjU1VVx26NAhNG/eXMJ0/+JlQWTo/vnnH6kj6B0vCyKqoPj4+DKXhYSEIDQ0VH9hSG+434mqn5OTE1asWIGUlBQcPHjw6TO1sLpC1axCxx/JxoEDB8pc5u3tjZ07d+ovzFMUFhYC5lKnICrbp59+Clh2lzqGXrG4QlSNbGxspI5AEuB+J6qatm3blph9o8SAthxzhXSktOOPqDRPxugxBIWFmiK3C8GPdWRo8vLyAUupU+gXLwsiIiIiw8faChGRqOhlQXl5+RImISqdHMeyYnGFiIiIDB57rhAR/Uu7uGIYg+wSFcXiChEREZEB4mxBRET/KjoVc34+iytkeIoWVzQazVMeaTp4cR6RjuQWFCJ4+REAQOpHPWFtwZebHHC/E1WPBw8eoKOvHxpGbQEAvGnzOz78YJrEqciYVLU9ZntOTyP18aHOVYu32XOFDJFS+W9xJSdHDdsaFhKm0Q/2XCEiIiKDU7ynCqcdJSL616NHOeJtjrlChqjoaTw7Wx7T3bO4QkRERAaHxRUiorLlFum5ks+eK2SAch49Em9nZz96yiNNB4srREREZHBKFFeKjC9ARCR3+fn/9lbJL2DPFTI8j3KKFlfk0XOFF4+STliaqbBtvJ94myqvrG3JbUzGprLHLI91eSo++F2hhj1XqGLYdhgn7rfyKSz8t43kmCtkiGbNjEVOjVoAADcHeZQd2HOFiIiIDE7xnisF7LlCRCTSnoqZPVfI8GiKfCmikckXJCyuEBERkcHhmCtERGUr2ruPY66QIdJohFJvmzIWV4iIiMjglCyuaMp4JBGR/Gj1XMlncYUMjyD8e94WNPI4h7O4QkRERAaHlwUREZVNo3VZEIsrZHiKnsY1AosrRERERJLgZUFERGUrelkQx1whw8TLgoiIiIgkV2K2IPZcISISFZ1BjWOukCEq+iUJLwsiIiLSGXl8g0GVV6LnikxmGiAiKg+tAW0L2HOFDJAMLwuSx4TTVG0WL16M0NBQNG7cWOoosjPujVfx/bebULuWk9RRiCqlIP/fN385OTmwtbHB/fv38f333+PcuXN49OgR6tevjw4dOqB3794SJiVDwMuC6Gnk3nY8epQjdYRKOX/+PK5du4bu3bvDzMwMq1evxp9//olu3bqhZ8+eUsczKoUcc4UklJ2djUuXLsHd3R12dnZayxISEjB48GAIMrwsiMUVqpDo6GhMmzYN7du3x9ChQ/Haa6+hdu3aUscyKWPHjtX6WRAE3M/Ow6njRxE9aRKsra0QFxcnUTqiyst+9Ei8XVhYiMTERLz++uvo0KEDBEHAwYMH8eqrr2LPnj14//33sW3bNtSvX1/CxCSlkgPasrhCj7HtALZt2wrAUeoYFbJ27VpMnz4dGo0Gbm5uCA4Oxs2bN6HRaPD6669j4cKFGDlypNQxjUbRGdQ45grpU1JSEkJDQ2FlZYX09HS89957iI2NFZdHREQ8Lq4UOY0LMum5wsuCqEKsra3x999/IywsDAkJCWjQoAH69u2LhIQE5OQY57cohmbt2rU4c+YMXFxc4OzsjLrOzqhVpy4UCgVq1a4FZ2dnqSMSVYrWtbcCMGHCBHz//ffYtWsXdu/ejV27duGff/5BUlIS3njjjRKFRpIX9lyhsrDtAG7evCl1hAqbM2cODh06hEOHDuH48eMIDAzEZ599hs8//xw//PADlixZInVEo6J1WRB7rpAeTZo0CfHx8fjrr79w7tw57N+/H4MGDRJn9RPP34L8eq6wuEIVolAo4OTkhDFjxiApKQkXLlyAn58fPvnkEzg7O+ONN96QOqLRS01NRZ06dZCSkoJhw4ZhxoxYjH53CqxtbBAdPVmrMkxkVLSKKwJu3boFPz8/8T4/Pz+kpKQAAN5++20cOHBAzwHJkHAqZioL2w7g9u3b4u3irxVDdfv2bTRp0gRNmzaFtbU1vL29xWVdu3bF9evXJUxnfLQuC8pncYX05+LFixg4cCAAwM3NDYmJiTAzM0OvXr2QlZUFhUIBAFqXBbHnClE5uLm5ISYmBmfOnMGhQ4fg4uIidSSj5+bmhu+++w4RERHo378/3n9/Oh49yoYCCqmjEVWJ9klWgI+PDz788EPk5uYiJycHH330Edq3by8uNzc3lyoqGYDiHxg17LlC/8O2A7h9+x/xdn6+cRQeHRwcxF7OH3zwgdayzMxMWey36sQxV0gqtWvXRlpamvizmZkZNmzYAA8PD/j7+//7ZUjRAW05WxBRSQ0bNixzWdu2bTF//nw9pjFtPXv2xMmTJ2Fvb4/Xewfg4cNMqSMRVU2xa2/j4+Px888/w9raGnZ2dti3bx9WrlwJALh+/TpiYmIkCkqGoMRUzCyu0P+w7dD+MG0sH6xHjBghXs40ZcoUrWWbN2+Gv7+/FLGMVtGpbTnmCulTaGgo1q9fX+L+5cuXo2/fvlCr1QDAAW2JnuXs2bNSR5AVc3NzvPfeFHgHhuDc77/B0dG4Bq8jKqp4z5VGjRrh8OHDyMrKAgDY2tqKyz08PODh4aH3jGQ4SlwWVGgc386T7rHtAFQqlXjbWMbbmDlzZpnLRo4cycFsK6iwsFD8IGcsxwCZhkWLFpW57MMPP8SHH34IALIc0JbFFaqSBw8e4OjRowAAX19f1KxZU9pAJujBgwe4dP4szC3MkZ2djRqWFlJHIqocrZOsgHv37qFWrVpaH4yInuCAtlQWth3acvNypY5AEtAU+bCaX8CeKySdsj8PFu25Io/iCi8LogoJCwvD77//DgA4fPgw3N3dMXPmTMTGxqJZs2Y4cuSIxAmNX/Ft3LxZU6z4dB7iFs1FC4/m3MZktLR7rgDOzs7w9/fHqlWr8ODBA+mCkUHiVMxUFrYdxcfbMI4P1g8ePMCoUaPQpk0bDBkyBBcuXNBabm9vL1Ey48QxV0gq5f08qDVLpEwuC2JxhSpkx44d8PLyAgBER0dj1apVOH78OE6cOIEvv/wSkZGREic0fsW3cXz8F9jwYyK++elnfPHFKm5jMlpCsZ4rlpaWGDhwIFavXg1XV1f0798fW7ZsQW4uv4Ul9lyhsrHt0B7g2VjWOzIyEpmZmVi0aBGaN2+OLl264McffxSXG8usR4bCGAtsZBrK+3mw6EtaI5PLglhcoQpRKBTiSO+XL19GcHCwuKxv3774448/pIpmMopv435FtnEfbmMyatpjrqhUKkyYMAHHjh3DmTNn0K5dO0yfPh3Ozs4YMWIE9u3bJ2FWkhqLK1QWth3ar4d8I5mGd/fu3Vi7di0CAwMRGxuLvXv3Yty4cVi9ejUAiNO3UvkUHSCUY66QPpX786AgvwFtWVyhCunfvz9mzJgBQRDQo0cPfP311+KyJ1NwUdUU38bri2zjb7iNyYgV77lSlLu7O2bMmIELFy5g7969sLe3x7Bhw/SckAxJieJKAQe0pZLk2nYUaoyv54pGo9EqCrVt2xYHDx7E3LlzMXv2bAmTGSetnitGUmAj01Dez4Pal4PLo+cKB7SlClm8eDHCw8PRpEkTNG3aFCNGjBBPiIIgYOvWrRInNH7Ft/Fbb41Eg4azAADmKgW3MRkvQbvnSlldwL29veHt7Y1PP/1UX8nIAHEqZioL2w7jvCTEx8cHP/zwA8LCwsT7GjdujKSkJPTs2RPZ2dkSpjM+xngMkGkoz+fB4u20XHqusLhCFWJnZ4fNmzcjNTUVJ0+eREBAAKysrODl5YWAgACYmfGQqqri27irvz8y8hRwb94Cbw7oA0sLc6kjElVK8amYd+7c+dTHK5XsXClnnIqZysK2Q3uA5zwj6bmyaNEiZGRklLjfxcUFSUlJ+OGHH/QfyogVLUBzQFvSp/J8Hix+DmfPFaKn8PT0hKenp/izWq1mYaWaPdnGhRoB524+fjPCbUxGrdhlQZ07d5YuCxk8jrlCZWHbYZyXhDRr1qzMZfb29njjjTf0mMb4FR3UmGOukBSe9nmwZM8VeRRXTL+0T3rx/PPP4+7du1LHMAmbN29GWloaACAjIwMjR4xAt3bN0K1dM7w1ckSp3/oQGQNBu7oCAOLghq6urrCzs4OrqysCAwOxbt06iVKSoShZXJHHGzN6tqLnyQcPHmD48OFwdnaGs7MzRo4cKYvzZKHWbEHG88F67969GDZsGNq3bw8PDw+89NJLmDJlCm7fvi11NKOj1XPFSApsZNqKfh4s7bIgOcwIxq/BqUKKVieLunv3Ljp37gyVSoXU1FQ9pzItUVFR4tzxkZGRyMvLx+r/+xEajQbfrYnD+PHjtQaOIjIaxXquTJkyBT/99BOio6PRpk0b2NvbIzMzEykpKVi4cCEuXLiAOXPmSJeXJFXisiAOaEv/U/Q8OXHiROTn5+PAgQPQaDSYO3euLM6T2uNtGMcH60WLFiE+Ph4jRoyAp6cn1q5dixdffBHA4/FyNm3aJP5Mz1Z0UOP8fLaPpD/l+Tx45syZYksfF1dMfVYwFleoQlQqFRwdHRETEwNra2sAj98ADxgwAHPnzoWTk5PECY1feno6HBwcAACJiYm4eOkPXEl//MZpZXw8Grs1kjIeUaUVH3Nl9erVOHfuHJydnbUe1759ewQFBaFly5YsrsgYLwuishQ/T16+fBlWVlYAgC+++AINGzaUMp5eFJ09Ky/POMZcWbhwIZKTk9GgQQMAwODBg9GnTx+cO3cOXbt2RWRkJE6ePClxSuNQfFB4YymwkWkoz+fB0nqpyKHnCi8LogpJSUnBK6+8gsmTJ+Pvv/+Gv78/AgICYGFhAT8/P/j7+0sd0eh5eXlhz549AABHR0fcvHlTXHb79m2Ym3NAWzJOxadiViqVUKvVpT5WrVbLYlBKKhunYqay8Dyp3WvBWD5YazQaODo6ij87OjoiPT0dAPDSSy/hwoULUkUzOsXHr+CYK6RP5fk8WFohRQ7jrrDnClWISqXCO++8g8GDB2Pq1KmIi4vD0qVLTb6Llz4tXrwYAwcORHh4OPr374/evXqi9yuDAQA7v0vAtGnTJE5IVFnaPVeioqIQEBCAsWPHolWrVuJlQadPn8bKlSsRHR0tYVaSWompmDXsuUKPFT9P9ujRA+Hh4QCANWvWyOI8aYxjroSGhmLAgAF49913IQgClixZgn79+gF4fDkBez+XX/GefLlG0nuJTMOTz4NDhgzBlClTyv15kMUVojI4OztjzZo1OHr0KMaMGcPBbKuRj48PkpOTsWTJEiQnJ8PMzAyJO7bC3cMTcStWomePl6WOSFQpxXuuTJ06Fa1bt8b69euxYcMGZGVlwdbWFi1btsTy5csRFBQkXViSHC8LorKUdp7cvHkzWrVqhfj4eAQGBkodUee0iitl9AA0NEuWLMHs2bMxffp0AECPHj3w/vvvA3g8ptJXX30lZTyjUvxDanb2I4mSkJzVrVu3zM+Dcr0siMUVqrCMjAzcunULHh4e8PX1xezZs3Hq1CncvXsXdevWlTqeSbC2tkZ4eDjmzZuHQo2AVQnf46/rV+Hq6ip1NKIq+PekqvnfCTYoKKhEESUiIoJTrRIHtKWncnV1xbx587Tui4iIQMeOHSVKpF9FZ8/KUedImKT8rKys8PHHH+Pjjz/Wuj8iIgILFixAQECANMGMUPFic1bWQ4mSEAFt2rTBvHnzkJGRIY5/VRr2XCEqZvv27QgLC0Nubi569eoFf39/JCYmQqPRYPbs2UhISEBwcLDUMY1a0W3cu3dvdO7SBT9s3wVBo8Hi2R9wG5PREgQBUPx7e/78+aU+buPGjXB2doatrS3ee+89PSYkQ8KpmKksbDu0i405OcbRc4X7rfqULK5kS5SE5KhPnz7Yvn07AODMmTMICgqCnZ0dFAoFxo4di507d8LDw6PE77G4QlTMtGnTsHfvXgBAp06dMH78eEycOBEAsGXLFsyaNYsf/Kuo+DYePWYsXho4HABw8WgitzEZLwFaxZWYmBh07NgRLVq00PognZ+fj0uXLokj0JM8secKlYVth/aHa7WR9Fzhfqs+xT+kqtU5KCgogJkZP9qR7iUlJYm3o6OjERkZicmTJwN4PCbWpEmT8NNPP5X4PV4WRFTMtWvX0KFDBwCApaUlunXrJi4LDQ3FW2+9JVU0k1HaNk69lQkACAkNxdtvj5IyHlGlFZ2KGYKAw4cPIyoqCg4ODpg5c6Y4tequXbuwbNkyXmYocxxzhcrCtgPQFBaKU36q1cYxmCn3W/UprT3Mzs4WtymRvqSkpGDbtm3iz+PHjy9x6d8Tcui5wnkuqUKsra2Rn58PAAgPD9caFVqtVnPWoGrAbUymqviAtr6+vvjll1/E8ZtWrVoFQRB4jBOAkm/CNCyu0P+w7dCePUudYxw9V7jfqk9pxZWHDznuCulHfn4+vv32W2zatAmA9vEoCALy8vJK7aUihy9JWFyhCunXrx/S0tIAAJ9//rnWsq1bt6Jdu3ZSxDIpT9vG27iNyahpT8UMAAqFAhERETh8+DB+/fVXdOjQAVlZWVIFJANS4rKgQl4WRP+Se9uhNeaKkVwWBHC/VZfSegBwO5K+dOrUCXFxcVi5ciU8PT2RmpoqLjt06BCaN28u2+IKLwuiComPjy9zWUhICEJDQ/UXxkQ9bRsHh4TglVf66zENUTUq1nOlKCcnJ6xYsQIpKSk4ePAg7O3t9RyODE3xY0Sj0fBbbipBrm1H0Q8pxjKgbVFy3W/VhT1XSEoHDhwoc5m3tzd27txZ6jI5XBbE4gpVGxsbG6kjmDwbGxuolPxgQcZJKKXnSnFt27ZF27Zt9ZSIDFlZ33pxwEYqjdzajqKzZxnLZUGlkdt+qy7suUKG6sm4P9nZJWewkkPPFV4WREREelF8zBWip5Frl2Ki8tDquaI2vp4rVDXsuUKGTq7ncBZXiIiqUfr9dKkjGK4iJ1qtmYOISlHamzA5vDEjKg/ty4KMt+cKVU5pbSF7rpChk8M5nMUVIqJqNLuM6eeofJcFET3xZNa0oooO4kn0LIJgutf3FxZ5LaiNaEBbqh6lXRbEnitkSOTac4UXLpNO5BYUInj5EQBA6kc9YW3BQ62yytqW3MaG6fq1a4Cn1CkM03sx7+NU46EAANX3k/HnxdRn/MZjPNblqbTiihzemFH1+W7rT3jvxOP2wtTajqJTMRvjgLZPwzb/2dhzhQzdqVOn0L1Hr/9v796joyjv/4G/Z6+5bxIk5AJyCZAjBvAUKsSK4ar4NcpXqEcKWFSqp6hUbvZe4WftgR+ttFjPQVBEsJbUKogtiiAlGOQWAhQEhcgt4RIuIXvJZu87vz/4zTC7mYSQzV6yeb/O2ZPJ7uzss7PzzDPzmc/zDG6f+xEAoHrpJDw4bgw2bdoU5ZKFFzNXiIjaURIHdm6W8mDQq3LiTKTEzBUKldVqkafVtqeOTLk/7cgD2lLbcMwVinVq26jFYlGZM74wuEJE1I6SEhPlaXZ9CaRsaD3e+DrRofYXb5kr7733XrSL0Ok0Nt4IOtis8XPiGdwlxGK1RqkkJIn0eGu8WxDFOrX2+sqVK1EoSWQxuEJE1I6UmSvKA3sC/MrgSpxdRab253a7mzzXkYMrM2fOjHYROp26ujp5Op4CEMH1oKHBxqyuKHC7b7Rj+yr2RfSzmblCsU5tn3Tp0qUolCSyGFwhImpHgiDI08oDewq80ubx8ESAWsZuQRQq5YG8zRY/wRW1emA2myNfkE5OuX35fJEdPJmZKxTrXC5Xk+csFguccX7reAZXiDoQtcaUYouy73tDA68iKSkHYGTmCt1MvAVX9Dp9tIvQ6VgsZnnaaomf4Ipa1kJ9fWS7pRBw8eJFeTrS3R2YuUKxLjj7VGoDL1++HI3iRAyDK0QdyGefbY52EegmHIpbYsbbHRxCFTDmikqXDyIlteCK2pWwjqJbt27RLkKn43Te2F7iuVsQwMyVaFAGVyJ9wsi7BVGsC26vu3btCiD+uwYxuEIU45SDotZUV0exJNQaynFWHI2NUSxJ7Am4W5DPywF/qUVqwRVHB74risFolKc78tgxHYlLkX4eT92CmLkSG5QniZEOrqgFmpm5QrEkeBvNysoCwOAKEUWZssHOyMyIYkmoNRyOGwEVZRYLAV6vL+j/jtvFg8JPbUDbjtxXW6/XydPxdOeaWOZ03dhezOb4uQWo2r6TwZXIs9tvZIpEOriiti+0xlF2FnV8DK4QUUw6e/ZGtorayQbFFmVXIHYLCuQOamg57gq1JN4yV5QDXsZTF5VYpuwWdO3atSiWpH2pZa5YLPETPOoolJmqV65EP7jCbYBiSfA5C4MrRBQTlAPy2e3sZhLrlF2BlFksFHgVGWBwhVoWb8EV5WDXVitPgiJBGdCNp7u3cTDT2OBUZKcyc4UoUJPMlW4MrhBRO6m/1vZ0XeWVkcZGe3sUh8KoUdktiJkrAZRXkQEGV6hlattHR+4WpAwuxtOda2KZcp3X1V2NYknal1oWK4Mrkac8PouF4IrD4WC7SjGD3YKIKGxWvvVWm9+rzH5g5krsUwZUnB34Kns4BK8PHgRSS+Itc0W5b2C3oMhQBnTjKXPFbm96oYXBlchTtmnXrl2LaJvWXKCZXYMoVjC4QkTtSnknFFcIV1uZudKxcEDb5gV3C+rIt9Wl8FO7Ot+RgyvKLgTsFhQZyrY3noIrjSp3omNwJfIag7r+RjJ7JTi4kpSUDIBdgyh2BLfh3bK6AQBqa2ujUZyIYXCFKEyUB3J6g6HNy1GO4aEMtFBsCgyG8fdSCu4WxLtbUEviKXPF4/EEjJNRdzV+TvRjmTKgG+luG+HEzJXYENz19/Tp0xH77ODgSlpaKgBmrlDsCL6Alp2dDYDBFSJqo/Pnz8vTNlvbryQ0Opi50pEo04SdzFwJEJzBFU9Xkqn9xdOYK8FBoZpzNVEqSefict24clpbWxs32XLMXIkNwYPWf/fddxH77KbBFRMABlcodjQXXDGbzR22LW8NBleIwkR54hhKlFaZuVJ/rV71oIpig8fjgdfnlf9n5soNPp8PHm/gyTKDK9SSeMpcCd5vV1czuBIJwQHd6urqKJWkfTFzJTYEZ65EM7iSnp4OgO0qxY7g4EqaKQ1GoxFAfGevMLhCFCYW842rB5s2fdrmfrDKPr2bP9+MtLQ0HDlyJOTyUfsLPvG7do0HORK1K8Y8CKSWxNOYK8Fd4M7VMLgSbmoB3TNnzkSnMO0s3jNXqqpuBCmU49fFGkfQ73D06NGIfXZwcCUnJwcAcOHChYiVgaglwW24IAjydnrx4sVoFCkiGFyhsHC7bxzQeL3eFuaMX2ZFaqbd3oAdO3a0aTnB2Q8+nw/vvvtuKEWjMAk+4K2tje8R0W+FWgrotWvXolAS6ijiqVtQcHCluiY+MihimVpAN16CK2qZK/E0htUbb7whT588eSqKJWlZ8KD1Bw8ejNhnM7hCsU5tH9y7d28AwLfffhvp4kQMgysUFsoG5t//3hTFkkSPxWIO+L+8vLxNywnu0wtcv5KjvC2zzdbQpmVT+woOrsRzZP5WqWUctHaAyYaGGycS//3vf9utTBTb1IIrHTWVODiQePXqVXbxDLN4Dq6obTvnzp0LGDS5I6uqOiFPb9u2LYolaVnwxa+zZ89GLCMzePvOzb0eXFGO90cUTWr74LvuugtAZAORkcbgCoXF/ooKeXr16neiWJLoUXYLAtqeLmq1Nk31PXv2LD7+eIP8f2Xl/jYtm9pXcACBwZUb1DIOqqqqWvXe/x46JE9v/HhjexWJYpza1fnDhw9HoSShU8sqqGHXoLBqaGh60eFWgyuxeuEiuG5otVp4vd64aXPOnr2R2bXtiy+iWJKW2e03to/evfsAAA4p2qtwai5zhcEVihVqwZXvfe97AIA9e/ZEujgRw+AKhcW3x4/L0zt3fhU3I/TfCnPQiO2HDx9uU9/hM2ea3trv4MGD+Prrr+X/I9WYU8uCryba7Q0RHeAumvx+P9asWYN9+/apvq6WufLNN9+0atkHDh6Qp8t37mxbASnm1dTU4Omnn8batWtx/vx5fPXVV03mqaqqanMWYDSpdYGLlyyKWKUWvL3VW+X+8he/kKdjaYDy4LYmLzcPwPULLx2dz+fD+XPn5P8/3/J5TF7lNpvNAVkq0hX5ysrKiHx+cICtf/8CANePD/1+f0TKQNQStQy7UaNGAQD279+PS5fis+u8LtoFoPjj9/tx7Ngx4AcPAQBcLifefvttPPfccxAEIcqlC40oivIj+HmHwwGPxwONRgOr1YqKffuAMQ8AAJKSknHu3DksX74cJSUlSElJgdFohEajgd/vh8fjgd/vh9FohE6nQ2NjIxoaGnDlyhUcPXoUt19fDF555RX87le/wOnTp/HXv/4Vt88dCQAoLS3FD//3EeTk5MjL0Gg6b+zU5/OhpqYG3bt3h07Xtt2c9Bsrt1lRFOH3+wMeWq0WHo8HNptNNX35xRdfxKuvvoqePXvC6/UiIyMDNpsNDocDGo0GGo0GWq024K/ac8F1R/p86fv5fD4IgiD/7k6nEwsWLIBWq8VTTz2FXr16Qa/Xt/r7S10ypPIEf/61a9fgcrng9/vh8/mwdOlSLFu2DAaDAevXr8edd94Ji8UCs9kMs9mMjRubZpycPXsWM2fORElJCfr27Stvt8HrYuPGT4B75gO4nhW3f/9+FBYWwmg0trhPUf5ePp8PPp8Pfr8fXq8XdXV1MJlMyMjIaPM2ciuk7am5v1qttsXvEvz7ShobG2E0GqHVagM+y+fzyetPWg/SPMp1Ik2rlTWU/4OX6XK5cPXqVRiNxut31fJ60djYiHXr1sFqtWLgwIFYt24dKisrsXr1avl93y+6F1Lnsf/5n4ewaeN6jBkzBpMmTUKvXr3Qo0cPdO/eHV26dIFer4dOp0NDQwNWrFiB3NxcjBs3DgMGDJDXnSiKSEhIgMfjgcvlgtvthtvtht/vR3JyMkwmk7z+WiL9VjqdDikpKfK+X6vVwm63w+l0wuPxQBRFeDwe1X3DW2+9hby8PGRnZ8NoNMrbpiiKMBgMcDqdSEpKgk6ng8PhgF6vhyAI8Pv98vcRBCHg0djYiOTk5Cb7Kr1e32QbAW5sPwkJCQH1RMqEcLvd0Ov18Hq9MBqN0Ov18jzBv33wduDz+eByuZCeni7vJ6X9iiAIclsqbStSnQ9ex8r/vV6vvBzpYTab8d1338FkMiEvLw92ux0WiwWvvfZak3W+d+9ebNq0CQUFBUhISIBWqw1o1wVBgMFggMPhgNPpxJq1a3D73EcAAE899RTe+MtrSE1NBQAkJCRAEAQ4HA6kpKQAQEC5pO1B+l7S9paQkACr1QpBEJCRkdHk+xgMBhiNRjQ2Nsrbp9vthsfjgdvthsViwZYtWwK+V5/8Pjh7qgpLlizBjBkz0LVrV6SkpCAlJQVJSUny+lauS2n9KqeDt0G32420tLSAsfMEQYDT6YQoivL3Uz58Ph/sdru8bqVtUBRFeZ+t0+nk9sjv98PlcsFms+HLL7/E1q1bA+665/P5MGPGDJSWlsJkMiEpKQkulws+n0/eHoPrgbJ+ANcDEdI6NRgM0Gg08nFXYmIiXC6X6r65uecaGxvx6aefBvwGw4bdjfUfrMOqVatQVFSEzMxM6PV61fZcq9Vi7969KC8vR35+PgoKCpCZmRnQ7kkPn88Hi8UCr9eLhoYGnDx5EsePH2+yDQwaNBBJSUmor6/HvHnzcN999yEvLw86na7J/kKaVnvuZq9Lf81mMz799FOkpqaif//+yM3NlffBUrmlY5T09HS43W55WcrjF7PZDKPRCIPB0GS7bGlbVWsvW7qAKW2bLc3TXBvs9XphMBjkfXrwcaBGo0FGRoZ8PC/tS5TrTO3cob35fL6AfToAuX2T9rPSOYZy/9te0x6PRz7uO3ny5PWLbdrA4868vDwMHToU+/fvx29+8xs888wzSElJQXJysryepPrZrVs3aLXagM9p6SG1NdEmiK34pa1WK0wmEywWC9LS0iJRLoqiv/3tb3juueeQmJgIt9sNr9crb9iA+smBctrr9ULQG3H73I8AANVLJ0H03MhckXY0Go0GOp1OrjgSqZIKgiCfcHi9Xnn+m1G+X6fTBVTK4ANC6TO0Wq3cuAYf8On1eoiiCLfbfUs7RuU6mOgvx5//+H9b/d7mlnPslQfw65/Px7Jly1pcx9J3k35DADEbbJF+J2WjJggCEhISYLfbodPpWrVTVfvtpN83+KBAOjmQGnOpwZW2FWXDr9Pp5MapVd/nJr9LKJQHZh6PR/4O0gmcVquF0WiU620w6QRK2dAqgyfKk3G1Kw7KedXGw7gZ5boZdfUTvLtq5S2/T7lObxasbW191ev18oH+zYIgt/q3tRISEuR9lPJET6fTyScBSsqD1OATJyXpBEl6TyzfeQMA0tLSIIoibDYbevXqhQ2fbML/vn8926DyV8V49unpWL9+fZRL2Tbh3DfEsmhud8p1btq6EIcP3FoX2lj+zYxJqcietQ4AsPQeYNKEkiiXqP0o17v57adhqWvd+FyRpiznlz8bgoL83hEddPvue0bg0ojr2VXHXnkAL/z02YDgNEVOR2hfo+HBkgk4duczAK5vo0kGHd577z38+Mc/bvfPqq6uRo8ePdp9uZLWxkOYuUJNWK1W2Gy2kG7rJ3pcGHNlPVasWIGnzj+G0tJS+WRPOoHw+XxtOkGLtLYMEGcwGNClSyaeTqrEyy+/DLd7HIxaAStXroTZbG71iXpaWhqGDh2K5+92YeLEiQCApUuXwmQy4cMPP8SzOd9h4MCB+PkXhTh8+HDA+mzuBDkWqXUba4/bSkrBvubc7E5WXq+31Xe7EgQBXbt2RXFxMd7+9QgkJSVhvvOn2LNnD06fPt1k8Fa9Xi9fKW4ttXqjvNWddNVCWaasrCxcvXoVPp9P9aDvVtKHg7MclFfXdDodRowYAZ1Oh7KyMvh8PqSnp8NkMiE9PR2ZmZmYMmUKpk176P+XdTxKHrwfGzduREVFBS5cuNDkapB05StBp0Hh18uxaNEizNozHLt27ZKvDrVVQkKCvD6UV9SjpbkD8ua2D7WrqmqU20c0D/wEQcBtt90Gt9sNjUYjB5PuvPNO5Ofnw2q1QqfT4Sc/+QkGDx6MqqoqDB48GAaDAWcWD5CX8+GHH2Lbtm04cOAAampqcO7cOdTU1MBiscgZMV6vFzk5OcjPz8fevXvlcSik7y9tw0ajUc7GEAQBdru93W71LAiCfNVaEAT07NkTDzzwABYvHAODwYCXdfPx8ccf4+LFi3F/16xwbXcajUYOjPbp0wculwsXLlyAyWSS9zsDBgzAH+bdja5du+LoE70wf/58nDhxApcuXZKzH5SBd6nN0Ol08Hq9uC09DcuLNUhOTsa8bQNRWVnZbt9HylyVSNkcbrdbzsqQrugrH4mJiRg4cCBmzZqF4cOHy+/fuHEj1q1bhxMnTsBsNqOhoQF2u1117KKWtDZweysnk1I70VJ7KggCevTogeHDh8NqteL1Gf3Rr18/fFb8LmbPno1z586FfEwjZS8oy6W8GHOzrAnpb2JiIjIyMtCrVy/8n4fSMGLECADAhg0b8Ic//AEXLlyA2WwOyJhUZgr6fD5kZGTg0UcfRU1NDaqrq2E2m1WzIgAgJSUFBoMBycnJ6N27N/r164fCwkJMnjwZSUlJ8vd58803MWTIEGzfvh2nTp3ClStXmlx4Uvt7K88pL04OGzYMqampOHHiBK5cuQKfzwev1ytnv0m/eWsySjq6jhxYaU0WU2umdTqdvO9NT0/H+PHj8eyzz8JgMAR83rRp03D+/Hl88MEHqKurg91ul8fIkvY9wbdx7kiYuUJN2Gw21NbWwuFwyF1MpDQrtUZG+itNG41GJCUlBezwHQ4H7HZ7QIMhXW2XslKCl6W8inurJ6JSgxl84BT8PaQDKZ/PJ6f4Kh8A5PJJadXK16RlSI2tlKLaXHcFZQaNlA0hLRuAvD6SkpKapEi3ht1ulzMavF4v6uvr5WXF6o5fSkNWktKfk5OTr2dCqaT83uyRkZGB+vr6JtkoUkaKRqORd97S76psLHQ6nXzCK/32ymCCNJ90ECH9/s1pbGyETqeDxWJBamqqnLIJ3Mi2Cj4ICz4YU/6VDsSlMiYkJMDlcslp5QaDQe7y4vf7UVdXh8bGxibdjJTrRnkQZTKZ5KwyZWaP9EhPT5dT4SPN6/UGnAg3V9eC0/KVf6W6Wl9fL6+3lvZvan9vZd6b/a2vr5czpZRdeaQuHVKGUvBBrsFgkA9elfsL6YDV6XTK+3HpOwavk+B9mto6vdX/g7uSSfUrWqT9rtTdJThbTqJcR81lRkn7fOB6YNhut8NgMMh1JTExMaB+34zU1UX6PaQ6bjQa5e5FiYmJAV311NKyRfF6twupy6GyrkvtjVR2qY2V6oEU9JK2DbfbLe9HpK5BUlcqaTuVNLeeNBoNDAYD6urqIAjXu9sou4IoM+ak9dBcOyV9P2m/F86ur1KXOqDpd1NmvzqdTvj9fiQlJaGhoUFuNwwGg7zfVZ5cS8cFyq5bTqdTDhAp98k+n6/duisqt1flc9Jf5bRyu5fWQ2NjY0Bmn7TfkbZD6TtKD0EQ5O8XvG9R1hWpm4hyn6y2L1Ly+/3ysanU9UQtuzW4XiQkJMhdO5T7Auk41Gg0tsu6vhVqv0tHIa3X1tRBp9MJp9MpB9Sl92k0GjgcDjlbUXnBpLntUzktBf+U1Npm5fzS5wZ/l5b+ByB3eUlKSmrSbUuj0cDpdMJiscj7Jmn/qmzH1c4v2ptU91wul9x1z2AwyG2BtC+Svkc4yxIqv98Pm82m2v21uYdaF/b21Np4CIMrREREREREREQqWhsPic1BGIiIiIiIiIiIOggGV4iIiIiIiIiIQsDgChERERERERFRCBhcISIiIiIiIiIKAYMrREREREREREQhaNV93qQbClmt1rAWhoiIiIiIiIgoVkhxkJvdaLlVwRWbzQYA6NGjR4jFIiIiIiIiIiLqWGw2G0wmU7OvC+LNwi8A/H4/Lly4gNTUVAiC0K4FJFJjtVrRo0cP1NTUtHgvcSIKD9ZBouhjPSSKPtZDouiKhTooiiJsNhtyc3Oh0TQ/skqrMlc0Gg26d+/eboUjaq20tDQ2ZERRxDpIFH2sh0TRx3pIFF3RroMtZaxIOKAtEREREREREVEIGFwhIiIiIiIiIgoBgysUk4xGIxYsWACj0RjtohB1SqyDRNHHekgUfayHRNHVkepgqwa0JSIiIiIiIiIidcxcISIiIiIiIiIKAYMrREREREREREQhYHCFiIiIiIiIiCgEDK4QEREREREREYWAwRUKi+XLl2PQoEFIS0tDWloaioqK8Nlnn8mvP/nkkxAEIeAxfPjwgGW4XC7MmjULt912G5KTk/HII4/g3LlzAfPU19fjiSeegMlkgslkwhNPPAGz2RyJr0jUoSxatAiCIGD27Nnyc6IoYuHChcjNzUViYiJGjhyJo0ePBryP9ZCo/ajVQ7aHROG1cOHCJnUsOztbfp1tIVF43awOxlM7yOAKhUX37t2xePFi7N+/H/v378fo0aMxYcKEgMZq/PjxuHjxovz49NNPA5Yxe/ZsbNiwAaWlpdi5cycaGhpQUlICn88nzzNlyhQcOnQImzdvxubNm3Ho0CE88cQTEfueRB1BRUUFVq5ciUGDBgU8v2TJEixduhRvvPEGKioqkJ2djXHjxsFms8nzsB4StY/m6iHA9pAo3O68886AOnbkyBH5NbaFROHXUh0E4qgdFIkiJCMjQ3z77bdFURTF6dOnixMmTGh2XrPZLOr1erG0tFR+7vz586JGoxE3b94siqIoHjt2TAQg7tmzR55n9+7dIgDx22+/Dc+XIOpgbDab2K9fP3Hr1q1icXGx+OKLL4qiKIp+v1/Mzs4WFy9eLM/rdDpFk8kkvvnmm6Iosh4StZfm6qEosj0kCrcFCxaIgwcPVn2NbSFR+LVUB0UxvtpBZq5Q2Pl8PpSWlsJut6OoqEh+vqysDFlZWejfvz+eeeYZXL58WX6tsrISHo8H999/v/xcbm4uCgsLsWvXLgDA7t27YTKZMGzYMHme4cOHw2QyyfMQdXbPP/88HnroIYwdOzbg+dOnT6O2tjagjhmNRhQXF8v1h/WQqH00Vw8lbA+Jwquqqgq5ubno3bs3Jk+ejFOnTgFgW0gUKc3VQUm8tIO6iH0SdTpHjhxBUVERnE4nUlJSsGHDBgwYMAAA8OCDD+Kxxx5Dz549cfr0afzud7/D6NGjUVlZCaPRiNraWhgMBmRkZAQss1u3bqitrQUA1NbWIisrq8nnZmVlyfMQdWalpaU4cOAAKioqmrwm1ZFu3boFPN+tWzecPXtWnof1kCg0LdVDgO0hUbgNGzYMa9euRf/+/XHp0iW8+uqruOeee3D06FG2hUQR0FId7NKlS1y1gwyuUNgUFBTg0KFDMJvN+OijjzB9+nTs2LEDAwYMwOOPPy7PV1hYiKFDh6Jnz57YtGkTJk6c2OwyRVGEIAjy/8rp5uYh6oxqamrw4osvYsuWLUhISGh2vuC60pr6w3pI1DqtqYdsD4nC68EHH5SnBw4ciKKiIuTn52PNmjXyoJlsC4nCp6U6OHfu3LhqB9ktiMLGYDCgb9++GDp0KBYtWoTBgwdj2bJlqvPm5OSgZ8+eqKqqAgBkZ2fD7Xajvr4+YL7Lly/LVxeys7Nx6dKlJsu6cuVKkysQRJ1NZWUlLl++jCFDhkCn00Gn02HHjh14/fXXodPp5DoSHM0PrmOsh0Rtd7N6qByIT8L2kCi8kpOTMXDgQFRVVcl3LGFbSBQ5yjqopiO3gwyuUMSIogiXy6X6Wl1dHWpqapCTkwMAGDJkCPR6PbZu3SrPc/HiRXz99de45557AABFRUWwWCzYt2+fPM/evXthsVjkeYg6qzFjxuDIkSM4dOiQ/Bg6dCimTp2KQ4cOoU+fPsjOzg6oY263Gzt27JDrD+shUWhuVg+1Wm2T97A9JAovl8uFb775Bjk5OejduzfbQqIIU9ZBNR26HYzY0LnUqfzqV78Sv/zyS/H06dPi4cOHxV//+teiRqMRt2zZItpsNnHevHnirl27xNOnT4vbt28Xi4qKxLy8PNFqtcrL+OlPfyp2795d/OKLL8QDBw6Io0ePFgcPHix6vV55nvHjx4uDBg0Sd+/eLe7evVscOHCgWFJSEo2vTBTzgu9SsnjxYtFkMonr168Xjxw5Iv7oRz8Sc3JyWA+JwkhZD9keEoXfvHnzxLKyMvHUqVPinj17xJKSEjE1NVU8c+aMKIpsC4nCraU6GG/tIIMrFBZPP/202LNnT9FgMIhdu3YVx4wZI27ZskUURVFsbGwU77//frFr166iXq8Xb7/9dnH69OlidXV1wDIcDof4wgsviJmZmWJiYqJYUlLSZJ66ujpx6tSpYmpqqpiamipOnTpVrK+vj9TXJOpQgoMrfr9fXLBggZidnS0ajUbxvvvuE48cORLwHtZDovalrIdsD4nC7/HHHxdzcnJEvV4v5ubmihMnThSPHj0qv862kCi8WqqD8dYOCqIoipHLkyEiIiIiIiIiii8cc4WIiIiIiIiIKAQMrhARERERERERhYDBFSIiIiIiIiKiEDC4QkREREREREQUAgZXiIiIiIiIiIhCwOAKEREREREREVEIGFwhIiIiIiIiIgoBgytERERERERERCFgcIWIiIiIiIiIKAQMrhARERERERERhYDBFSIiIqJ2NGfOHEyePBlWqzXaRSEiIqIIYXCFiIiIqB35/X6IohjtYhAREVEECSJbfyIiIoqikSNH4q677sJf/vKXaBclpspCREREHQczV4iIiOLcm2++idTUVHi9Xvm5hoYG6PV6jBgxImDe8vJyCIKAEydORLqYETdy5EjMnj273Za3a9cuaLVajB8/vt2WSURERB0DgytERERxbtSoUWhoaMD+/fvl58rLy5GdnY2Kigo0NjbKz5eVlSE3Nxf9+/ePRlE7tHfeeQezZs3Czp07UV1dHe3iEBERUQQxuEJERBTnCgoKkJubi7KyMvm5srIyTJgwAfn5+di1a1fA86NGjQIAbN68Gffeey/S09PRpUsXlJSU4OTJk/K8K1asQF5eHvx+f8DnPfLII5g+fToAQBRFLFmyBH369EFiYiIGDx6MDz/8sNmytmb+kSNH4mc/+xl+/vOfIzMzE9nZ2Vi4cGHAPDabDVOnTkVycjJycnLw5z//OSBT5cknn8SOHTuwbNkyCIIAQRBw5swZANfHTGlp2Wrsdjs++OADzJw5EyUlJXj33Xdv+h4iIiKKHwyuEBERdQIjR47E9u3b5f+3b9+OkSNHori4WH7e7XZj9+7dcnDFbrdj7ty5qKiowLZt26DRaPDoo4/KwZTHHnsMV69eDVhufX09Pv/8c0ydOhUA8Nvf/harV6/G8uXLcfToUcyZMwfTpk3Djh07VMvZ2vnXrFmD5ORk7N27F0uWLMErr7yCrVu3yq/PnTsXX331FT755BNs3boV5eXlOHDggPz6smXLUFRUhGeeeQYXL17ExYsX0aNHj1YtW80//vEPFBQUoKCgANOmTcPq1as5qC0REVEnoot2AYiIiCj8Ro4ciTlz5sDr9cLhcODgwYO477774PP58PrrrwMA9uzZA4fDIQdXJk2aFLCMVatWISsrC8eOHUNhYSEyMzMxfvx4/P3vf8eYMWMAAP/85z+RmZmJMWPGwG63Y+nSpfjPf/6DoqIiAECfPn2wc+dOrFixAsXFxQHLv5X5Bw0ahAULFgAA+vXrhzfeeAPbtm3DuHHjYLPZsGbNmoByrV69Grm5ufL7TSYTDAYDkpKSkJ2dHVCOlpbdnFWrVmHatGkAgPHjx6OhoQHbtm3D2LFjb/rbEBERUcfHzBUiIqJOYNSoUbDb7aioqEB5eTn69++PrKwsFBcXo6KiAna7HWVlZbj99tvRp08fAMDJkycxZcoU9OnTB2lpaejduzcABIwnMnXqVHz00UdwuVwAgPfffx+TJ0+GVqvFsWPH4HQ6MW7cOKSkpMiPtWvXBnQvktzK/IMGDQr4PycnB5cvXwYAnDp1Ch6PB3fffbf8uslkQkFBQavWVUvLVnP8+HHs27cPkydPBgDodDo8/vjjeOedd1r1eURERNTxMXOFiIioE+jbty+6d++O7du3o76+Xs4Cyc7ORu/evfHVV19h+/btGD16tPyehx9+GD169MBbb72F3Nxc+P1+FBYWwu12B8zj9/uxadMmfP/730d5eTmWLl0KAHL3oU2bNiEvLy+gPEajsUkZb2V+vV4f8L8gCPL7pe44giAEzNPabjotLVvNqlWr4PV6A8osiiL0ej3q6+uRkZHRqs8lIiKijovBFSIiok5i1KhRKCsrQ319PV566SX5+eLiYnz++efYs2cPnnrqKQBAXV0dvvnmG6xYsUK+XfPOnTubLDMxMRETJ07E+++/j++++w79+/fHkCFDAAADBgyA0WhEdXV1ky5Aam51/ubk5+dDr9dj37598jgqVqsVVVVVAcs1GAzw+Xxt/hwA8Hq9WLt2LV577TXcf//9Aa9NmjQJ77//Pl544YWQPoOIiIhiH4MrREREncSoUaPw/PPPw+PxBAQZiouLMXPmTDidTnm8lYyMDHTp0gUrV65ETk4Oqqur8ctf/lJ1uVOnTsXDDz+Mo0ePyuOOAEBqairmz5+POXPmwO/3495774XVasWuXbuQkpIi31GorfM3JzU1FdOnT8dLL72EzMxMZGVlYcGCBdBoNAHZLL169cLevXtx5swZpKSkIDMzs9XrUvLvf/8b9fX1mDFjBkwmU8BrP/zhD7Fq1SoGV4iIiDoBjrlCRETUSYwaNQoOhwN9+/ZFt27d5OeLi4ths9mQn58vZ3poNBqUlpaisrIShYWFmDNnDv74xz+qLnf06NHIzMzE8ePHMWXKlIDXfv/73+Pll1/GokWLcMcdd+CBBx7Av/71L3n8lmC3On9zli5diqKiIpSUlGDs2LH4wQ9+gDvuuAMJCQnyPPPnz4dWq8WAAQPQtWvXgLFkWmvVqlUYO3Zsk8AKcD1z5dChQwF3KSIiIqL4JIi8TyARERHFObvdjry8PLz22muYMWNGtItDREREcYbdgoiIiCjuHDx4EN9++y3uvvtuWCwWvPLKKwCACRMmRLlkREREFI8YXCEiIqK49Kc//QnHjx+HwWDAkCFDUF5ejttuuy3axSIiIqI4xG5BREREREREREQh4IC2REREREREREQhYHCFiIiIiIiIiCgEDK4QEREREREREYWAwRUiIiIiIiIiohAwuEJEREREREREFAIGV4iIiIiIiIiIQsDgChERERERERFRCBhcISIiIiIiIiIKAYMrREREREREREQhYHCFiIiIiIiIiCgEDK4QEREREREREYXg/wFVPP3hjGVtugAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_fit(figsize=(11,5), plot_values=True, obs_to_wav=True);" + ] + }, + { + "cell_type": "markdown", + "id": "524b217f-b2cd-49b9-a011-eb3a11fa14ee", + "metadata": {}, + "source": [ + "## 6. Evaluate the Initial Fit: Plot Residuals\n", + "\n", + "Let's check the quality of this initial fit. The `plot_residuals` method calculates the difference between the theoretical wavelength of matched lines and the wavelength predicted by the current model for their observed pixel positions. The RMS estimate alone can be calculated using the `rms` method." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "343801bc-65fa-41c9-929b-72565cdee31d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_residuals(space='wavelength');" + ] + }, + { + "cell_type": "markdown", + "id": "983fe041-41e9-47a7-a179-fead64e171ba", + "metadata": {}, + "source": [ + "The fit looks good! Now, we can move on to use the solution.\n", + "\n", + "### 7. Use the solution\n", + "#### 7.1 Rebin a Spectrum to Wavelength Space\n", + "\n", + "A primary goal of wavelength calibration is to transform spectra from the detector's pixel grid to a physical wavelength grid. The `resample` method does this, converting a `Spectrum` object from pixel space to wavelength space.\n", + "\n", + "Key parameters for `resample`:\n", + "- `spectrum`: The input `Spectrum` object (assumed to be on a pixel grid corresponding to the calibration).\n", + "- `nbins`: The number of bins desired in the output wavelength grid. If `None`, defaults to the number of pixels in the input spectrum.\n", + "- `wlbounds`: The desired `(start_wavelength, end_wavelength)` for the output grid. If `None`, defaults to the wavelengths corresponding to the first and last pixels.\n", + "- `bin_edges`: Explicitly define the wavelength edges of the output bins. If provided, `nbins` and `wlbounds` are ignored.\n", + "\n", + "The method uses the fitted `_p2w`, `_w2p`, and `_p2w_dldx` transformations to map the input pixel bins to the output wavelength bins. It performs an exact flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", + "\n", + "Here, we demonstrate by resampling the original arc spectrum itself. In a typical workflow, you would apply this `resample` method (using the `ws` object derived from the arc lamp) to your *science* spectrum observed with the same instrument setup." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spectrum_wl = ws.resample(arc_spectrum)\n", + "\n", + "fig, ax = subplots(constrained_layout=True, figsize=(11, 4))\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.grid(True, alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "618a1d04-c7ff-4f05-8679-bf94ea2f045a", + "metadata": {}, + "source": [ + "Let's still check that the flux is indeed conserved as it should" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "26ba254f-69a6-43c0-bcfc-86e2707a2c55", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$244192.09 \\; \\mathrm{DN}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectrum_wl.flux.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "68ea6434-cf95-4503-8c68-bfc27209e3df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$244192.09 \\; \\mathrm{DN}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arc_spectrum.flux.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "a64a7f45-9145-41ae-845c-389329762697", + "metadata": {}, + "source": [ + "#### 7.2 access the WCS\n", + "\n", + "Additionally, if we don't want to rebin, or maybe want to do it using another method, we can access the pixel-wavelength transform as a [gwcs](https://gwcs.readthedocs.io/) WCS object usin the `WCS` property." + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$3428.3395 \\; \\mathrm{\\mathring{A}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ws.wcs.pixel_to_world(0)" + ] + }, + { + "cell_type": "markdown", + "id": "2f932cac-e05a-4c0d-a64a-d0e1058489ee", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "The interactive workflow (in the sense that it requires user input for the initial line fit) for 1D wavelength calibration can be summarized as follows:" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "ws = WavelengthCalibration1D(ref_pixel=1000, degree=5, arc_spectra=arc_spectrum, line_lists=[['CdI', 'HgI', 'HeI']], line_list_bounds=(3200, 5700), unit=u.angstrom)\n", + "ws.find_lines(fwhm=4, noise_factor=15)\n", + "ws.fit_lines(pixels=[496, 967, 1077, 1655, 1999], wavelengths=[3890, 4360, 4473, 5087, 5462], match_obs=True, match_cat=True)\n", + "ws.plot_fit(figsize=(11,5), plot_values=True, obs_to_wav=True);" + ], + "id": "749ae34c006a21b4" + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/wavelength_calibration/wavecal1d_example_02.ipynb b/docs/wavelength_calibration/wavecal1d_example_02.ipynb new file mode 100644 index 00000000..180e9e3f --- /dev/null +++ b/docs/wavelength_calibration/wavecal1d_example_02.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", + "metadata": {}, + "source": [ + "# 1D Wavelength Calibration Tutorial 2: Multiple Arc Spectra\n", + "\n", + "This notebook demonstrates the use of several arc spectra and a custom line list in calculating the wavelength solution. We use three arc spectra taken with the R1000R grism from the [Gran Telescopio Canaria's](https://www.gtc.iac.es/) [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/)." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7853ed1c-5b05-42a2-9413-4054769c6032", + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.table import Table\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import setp, subplots, close, rc\n", + "\n", + "from specreduce.compat import Spectrum\n", + "from specreduce.wavecal1d import WavelengthCalibration1D\n", + "\n", + "rc('figure', figsize=(11, 3))" + ] + }, + { + "cell_type": "markdown", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", + "metadata": {}, + "source": [ + "## 1. Read in the Arc Spectra and Initialize the Wavelength Solution Class\n", + "\n", + "We use a custom line list for the HgAr arc spectrum. The values are taken from the [official GTC Osiris line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt)." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "a78d101a-e1af-4c5e-8264-d3eb99d31e15", + "metadata": {}, + "outputs": [], + "source": [ + "lamps = 'HgAr', 'Ne', 'Xe'\n", + "\n", + "tb = Table.read('osiris_arcs.fits')\n", + "arc_spectra = [Spectrum(tb[f'{l}_flux'].value.astype('d')*u.DN, \n", + " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d'))) \n", + " for l in lamps]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "427b44e7-67f4-4121-bc6f-7275f708ee6a", + "metadata": {}, + "outputs": [], + "source": [ + "hgar = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106, 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", + "\n", + "ws = WavelengthCalibration1D(ref_pixel=1000, degree=4, arc_spectra=arc_spectra,\n", + " line_lists=[hgar, ['NeI'], ['XeI']],\n", + " line_list_bounds=(5100, 10000), unit=u.angstrom, wave_air=True)\n", + "\n", + "ws.find_lines(fwhm=4, noise_factor=15)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "67603524-f0d6-4448-b24b-0e6095c091d0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_fit(figsize=(15, 8));" + ] + }, + { + "cell_type": "markdown", + "id": "806d1730-5325-4537-825e-da686ffcda21", + "metadata": {}, + "source": [ + "## 5. Fit an Initial Solution Manually" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", + "metadata": {}, + "outputs": [], + "source": "ws.fit_lines(pixels=[175, 797, 1499, 1579, 1620], wavelengths=[5461, 6931, 8822, 9048, 9165], match_obs=True, match_cat=True)" + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "0fbe56d7-beb8-40e4-98e9-a17a00ba4342", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_fit(figsize=(15, 8), plot_values=False, obs_to_wav=True);" + ] + }, + { + "cell_type": "markdown", + "id": "524b217f-b2cd-49b9-a011-eb3a11fa14ee", + "metadata": {}, + "source": [ + "## 6. Evaluate the Initial Fit: Plot Residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "343801bc-65fa-41c9-929b-72565cdee31d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PAAAAAACA+rIUrpypZ8+eevrpp7Vv3z7XjkJNUU5Ojux2u+uRnJwc6JIAAAAAAEAQszQtqKnxZlpQRUWFKioqXM9LSkqUnJzMtCAAAAAAAOCmwaYF/fvf/1Z1dbXX/b/55hudPHnS17dpMOHh4YqNjXV7AAAAAAAA1JfP4Urfvn11+PBhr/tnZmaqsLDQ17cBAAAAAAAICq18fYExRv/93/+tyMhIr/pXVlb6XJQvjh07ph07drieFxQUaOPGjWrXrp1SUlIa9L0BAAAAAAB8DlcGDBigrVu3et0/MzNTERERvr6N177++mtlZWW5nk+aNEmSNGbMGM2bN6/B3hcAALQsDme5CopKlRoXpQR7w/1tAwAAgk+zWtC2PrxdnAYAALRc89cV6rHcTao2UohNysnupZH9GSELAEBz12AL2gIAAPjC4SxX3s4iOZzlgS6lXhzOclewIknVRpqSuzloPw8AAPA/n6cFAQAAeKs5jPgoKCp1BSunVRmj3UVlQTM9iClNAAA0LMIVAADQIGob8TGgR4eg+oKfGhelEJvcApZQm01d47xb3D/QmkPABQBAU8e0IAAA0CDqGvHRWPwxJSnBHqGc7F4KtdkknQpWpmVnBEVAxJQmAAAah6WRK+Xl5TLGuLZl3rNnjxYsWKD09HQNHjzYLwUCAICm6WxTTQI94sOfIzZG9k/RgB4dtLuoTF3jIoMiWJGax5QmAACCgaWRK8OHD9cbb7whSTpy5IguvfRSPfPMMxo+fLhmzZrllwIBAEDTM39doa6Y/rluf3WNrpj+ueavK/ToE8gRHw0xYiPBHqHMbu2DKpQ4HXCdKZimNAEAECwshSsbNmzQVVddJUl6//331alTJ+3Zs0dvvPGGXnjhBb8UCAAAmhZfgouR/VO0anKW3rnnMq2anNVoa300hSlJTUEwT2kCACCYWJoWVFZWppiYGEnSkiVLlJ2drZCQEF122WXas2ePXwoEAABNi69TTRLsEY3+ZT7QU5KakmCd0gQAQDCxNHKle/fuWrhwofbu3avFixe71lk5dOiQYmNj/VIgAABoWoJhqgkjNtwF45QmAACCiaWRK0888YRuv/12TZw4UVdffbUyMzMlnRrF0rdvX78UCAAAmpbTwcWU3M2qMqbJBheM2AAAAI3FZowxZ+9Wu4MHD8rhcKhPnz4KCTk1EGbt2rWKjY3Veeed55ciG1JJSYnsdrucTiejbQAA8IHDWU5wAQAAmjVvMwPL4UqwI1wBAAAAAAA18TYz8Hla0KRJk7zuO2PGDF9PDwAAAAQth7NcBUWlSo2LYkQXALQgPocr+fn5XvWz2Wxn7wQAAAA0E/PXFbq2KQ+xSTnZvRpt+3EAQGD5HK4sW7asIeoAAAAAGpU/R5k4nOWuYEU6tQ34lNzNGtCjAyNYAKAFsLRbEAAAaBhMLQAalr9HmRQUlbqCldOqjNHuojKuYQBoAfwSrmzZskWFhYWqrKx0O37jjTf64/QAALQoTC0ArKkrnHQ4y/X17h/9PsokNS5KITa5BSyhNpu6xkXW92MAAIKIpXBl165dGjFihDZt2iSbzabTGw+dXm+lqqrKeoUAAASp+ow+OdvUAka0AHWrK5w8s+2nrI4ySbBHKCe7l6bkblaVMQq12TQtO4PrFABaCEvhykMPPaTU1FR9+umnSktL09q1a3X48GH9+te/1v/+7//6q0YAAIJOfUef1DW14IttPzCiBahDXeGkpFqDFck/o0xG9k/RgB4dtLuoTF3jIglWAKAFCbHy4tWrV+vJJ59Uhw4dFBISopCQEF155ZXKycnRgw8+6K8aAQAIKrV9wXM4y8/62tNTC84UarMpMiyk3ucEWoq6wsma2k7z5yiTBHuEMru1J1gBgBbGUrhSVVWl6OhoSVJcXJwOHDggSerSpYu2bt1qvToAAPzA4SxX3s6iRgsi6vqCdzanpxaE/v8ptqe/9JVWVtX7nEBLUVs42TUussa2EEn/9/a+WjU5y+dRYI19XwEANG2WpgVlZGTo3//+t9LS0nTppZfq6aefVlhYmF555RWlpaX5q0YAAOotEIvDWl3YsqapBQ5nOYtlAmdxtnVPamob2jvR5/dh0elTWAMKAP7DZk6vQlsPixcvVmlpqbKzs7Vr1y7dcMMN+u6779S+fXu9++67uvrqq/1Za4MoKSmR3W6X0+lUbGxsoMsBAPiRw1muK6Z/7hFIrJqc1eBfBOavK/T4Emf1y1dDnBNojhzO8lrXPamrzdtzB+q+0pQQMAFoKbzNDCyNXLnuuutc/52WlqYtW7boxx9/VNu2bV07BgEAECh1Tc9p6C9BDbGwJYtlAt5JsEfUen3U1eaNQN5Xmoqz7WoGAC2RpXDlySefrLP9iSeesHJ6AAAssTo9xyqrX+Ia65wAvBfo+0pTQMAEAJ4shSsLFixwe37ixAkVFBSoVatW6tatG+EKACCgzrb+AgD4ivsKARMA1MTSmis1KSkp0dixYzVixAjdcccd/jx1g2DNFQBo/qyusQAAP9XS7yusAQWgpfA2M/B7uCJJmzdv1g033KDdu3f7+9R+R7gCAAAA+K6lB0wAWoZGWdC2NkeOHJHT6WyIUwMAAABoAlgDCgD+w1K48sILL7g9N8bI4XDozTff1JAhQywVBgAAAMCTw1mugqJSpcZFEW4AQBNhKVx59tln3Z6HhISoQ4cOGjNmjB577DFLhQEAAABwN39doWsb5BCblJPdi7VOAKAJsBSuFBQU+KsOAAAAAHVwOMtdwYp0areeKbmbNaBHB0awAECAhQS6AAAAAABnV1BU6rb9sSRVGaPdRWWBKQgA4OLzyJVJkyZ53XfGjBm+nh4AAABADVLjohRik1vAEmqzqWtcZOCKAgBIqke4kp+f7/Z8/fr1qqqqUs+ePSVJ27ZtU2hoqPr16+efCgE0CBbDAwAguCTYI5ST3UtTcjeryhiF2myalp3B73EAaAJ8DleWLVvm+u8ZM2YoJiZGr7/+utq2bStJKi4u1p133qmrrrrKf1UC8CsWwwMAIDiN7J+iAT06aHdRmbrGRRKsAEATYTPGmLN3q1lSUpKWLFmiCy64wO345s2bNXjwYB04cMBygQ2tpKREdrtdTqdTsbGxgS4HaHAOZ7mumP65x5DiVZOz+AMNAAAgCDEiGWg43mYGlnYLKikp0ffff+8Rrhw6dEhHjx61cmoADaSuxfD4ZQwAABBcGJEMNA2WdgsaMWKE7rzzTr3//vvat2+f9u3bp/fff1933XWXsrOz/VUjAD86vRjemVgMDwAAIPjUtj23w1ke2MKAFshSuPLyyy9r6NChGj16tLp06aIuXbpo1KhRuv766/XSSy/5q0YAfnR6MbxQ26mEhcXwAAAAghPbcwNNh6U1V04rLS3Vzp07ZYxR9+7dFRUV5Y/avPbSSy/pz3/+sxwOhy644AI999xzXi+oy5oraKkcznIWw0PQYU45AAD/wVp6QMPzNjOwNHLltKioKPXu3Vt9+vRp9GBl/vz5evjhh/X4448rPz9fV111la6//noVFhY2ah2B4HCWK29nEcP+UC8J9ghldmvPL140KXXd1+avK9QV0z/X7a+u0RXTP9f8dc3/Pg8AOLtg/5vYSv3ejkgO9p8REAx8HrkyadIk/fGPf1RUVJQmTZpUZ98ZM2ZYKs4bl156qS666CLNmjXLdez888/XTTfdpJycnLO+PlhHrrBwFYDmpq77Gv8yBwCoSSD+JvbnKEp/1V/XiGS+NwDWNNhuQfn5+Tpx4oTrv2tjs9lqbfOXyspKrV+/XpMnT3Y7PnjwYOXl5dX4moqKClVUVLiel5SUSJI2btyo6Oho1/G2bdsqNTVVx48f15YtWzzOc9FFF0mStm7dqtLSUre2rl27ql27dvrhhx+0d+9et7aYmBide+65qqqq0r/+9S+P8/bq1UutW7fWzp075XQ63dqSkpLUqVMnfbvngCa9tMDtS8Yjr+zVgB5jlGCPUH5+vn6amZ1//vmKiIjQnj17dPjwYbe2Tp06KSkpSUePHtX27dvd2lq3bq1evXpJkjZt2uT63/60c889VzExMdq/f7++//57t7b27durS5cuKi8v17fffuvWZrPZ1LdvX0nSt99+q/Jy9xQ9NTVVbdu21ffff6/9+/e7tdntdnXr1k0nTpzQpk2bPH6Gffr0UWhoqLZv3+6xa1VycrI6dOigH3/8Ubt373Zri4qKUs+ePSVJGzZs8Dhvenq62rRpo4KCAhUXF7u1JSQkKCEhQSUlJdqxY4dbW3h4uGtHrX//+986efKkW3uPHj0UHR2tffv26dChQ25tcXFxSklJUVlZmb777ju3tpCQEF144YWSpC1btuj48eNu7WlpaTrnnHN08OBBj23RzznnHKWlpamyslKbN2/2+KwXXnihQkJCtG3bNh07dsytLSUlRXFxcSoqKvIYIRYdHa0ePXqourpaGzdu9DhvRkaGwsLCtGvXLh05csStLTExUfHx8Tpy5Ih27drl1tamTRulp6dLOnWtVldXu7Wfd955ioyMVGFhoYqKitzaOnbsqM6dO+vYsWPatm2bW1urVq3Uu3dvSdI333zjdm+QpO7duys2NlYOh0MOh8OtrSnfI4qLi1VQUODWFhERofPPP1+SmuQ94ou1+Zo0b53rvmaz2TQl16YBPTroyIHd+mrbAZU7/nNttTonXmoTrQ3f7VZCa/f7B/eIU7hH/Af3iFOC+R7B3xHcIyTPe8QPR4+7fne0im4vRbfVo3/9SueU7VOHmDau1/nzHrHkG4de/HyHqo0UGhqqZ8bfqJH9U+p1j2jTLl6PvrdeFT/85+fw65d26pyxF+u6gZdL8u0eES7pWHWMZP/PPeLMn5EkhXXoqim5m5XS+qjCq93r5R5xCveIU5rDPeJMVv6OSExM9HivGpkgtn//fiPJfPnll27Hn3rqKdOjR48aXzN16lQj6ayPUaNGGWOM2b59e43tp1122WUebW+++aYxxpiZM2d6tA0ePNgYY4zT6azxvIcOHTLGGDNs2DCPtmeeecYYY8wfX3jNoy2sUzeTt6PIGGNMWFiYR/vmzZuNMcbcddddHm2TJ082xhizbNkyj7akpCTXZ01KSvJoX7ZsmTHGmMmTJ3u03XXXXcYYYzZv3uxZb1iY67x9+/b1aH/vvfeMMcY888wzHm3Dhg0zxhhz6NChGn+GTqfTGGPM4MGDPdpmzpxpjDHmzTff9Gi77LLLXDXVdN7t27cbY4wZNWqUR9vUqVONMcYsWrTIo61bt26u88bFxXm05+XlGWOMmThxokfbfffdZ4wxZv369R5tMTExrvOmp6d7tP/97383xhgzbdo0j7ZbbrnFGGPM3r17a/ysx48fN8YYM3DgQI+2V1991RhjzKuvvurRNnDgQGOMMcePH6/xvHv37jXGGHPLLbd4tE2bNs0YY8zf//53j7b09HTXZ42JifFoX79+vTHGmPvuu8+jbeLEicYYY/Ly8jza4uLiXOft1q2bR/uiRYuMMTXfN5ryPeK9997zaOvbt6+rpmC4Ryi0leny6Ecmb0dRjfeIuOGTTdrkj83UP033aOMewT3ip+3cI5rfPYK/I7hH/LTtnAH/Zbo8+pHpkP3fHm0NdY8IiYg1aZM/NgeOlNXrHvHljh9M4v95pcbPepq/7xGdJ7xtujz6kbnyZ9d5tHGP4B5x5qO53SOs/B2xYsUKt//ta2NpQdvy8nIZYxQZeWoL1z179mjBggVKT0/X4MGD63tarx04cEBJSUnKy8tTZmam6/hTTz2lN9980yN9k2oeuZKcnKwVK1YE1ciVa37/N7eRK61ah2vt/zJyRSJNPq0lpcn8q/QpwfwvTl+szde4n4xciYjvrlWTs3TkwG6Vl5dryTcOzfx8p6qMUXjbeP3P7ZkalBLOPYJ7BPeIMzTXewR/R3CPkGoeuTLujJErodFtpYpS/eWmzg0ycuVvn63RlAX/+by2kFCFdUzVO/dcptjjB+s1ciXzT4vcRq6E2myaXc+RK5LnPeLMn5F0auRKq1at9dbILoxc4R7R7O8RZ7I6ciUhIeGs04IshSuDBw9Wdna2xo8fryNHjqhnz54KCwtTUVGRZsyYoXvvvbe+p/ZKZWWlIiMj9be//U0jRoxwHX/ooYe0ceNGrVix4qznCOY1V6bkblaVMa6Fq5g7CSCYeXNfY5crAMCZGvNv4oZY/6sx6ud7A2CNt5mBpXAlLi5OK1as0AUXXKDXXntNL774ovLz8/XBBx/oiSee8EgQG8Kll16qfv366aWXXnIdS09P1/Dhw5v1grYSXzIAND/c1wAAvmrM3x0NEVQ0Rv38fgXqr8EWtD1TWVmZYmJiJElLlixRdna2QkJCdNlll2nPnj1WTu21SZMm6Y477tDFF1+szMxMvfLKKyosLNT48eMb5f0DKcEewc0RQLPCfQ0A4KvG/N0xsn+KBvTo4NegojHq5/cr0PAshSvdu3fXwoULNWLECC1evFgTJ06UJB06dKjRRoGMHDlShw8f1pNPPimHw6GMjAx98skn6tKlS6O8PwAAAICWg6ACQE0sTQt6//33dfvtt6uqqkpXX321lixZIknKycnRF198oX/+859+K7ShBPO0IAAAAAAA0HAaZc0VSTp48KAcDof69OmjkJAQSdLatWsVGxur8847z8qpGwXhCgAAAAAAqEmjrLkiSfHx8YqPj3c7dskll1g9LQAAAAAAQFAIsXqClStXavTo0crMzHTtEf7mm29q1apVlosDAAAAAABo6iyFKx988IGuu+46RUREKD8/XxUVFZKko0ePatq0aX4pEAAAAAAAoCmzFK786U9/0ssvv6xXX31VrVu3dh2//PLLtWHDBsvFAQAAAAAANHWWwpWtW7dqwIABHsdjY2N15MgRK6cGAAAAAAAICpbClYSEBO3YscPj+KpVq5SWlmbl1AAAAAAAAEHBUrjyq1/9Sg899JDWrFkjm82mAwcO6O2339Yjjzyi++67z181AgAAAAAANFmWtmL+7W9/K6fTqaysLB0/flwDBgxQeHi4HnnkET3wwAP+qhEAAAAAAKDJshljjNWTlJWVacuWLaqurlZ6erqio6O1f/9+JSUl+aPGBlVSUiK73S6n06nY2NhAlwMAAAAAAJoIbzMDS9OCTouMjNTFF1+sSy65RMeOHdOECRPUvXt3f5waAAAAAACgSatXuHLkyBGNGjVKHTp0UGJiol544QVVV1friSeeUFpamr766ivNmTPH37UCAAAAAAA0OfVac2XKlCn64osvNGbMGC1atEgTJ07UokWLdPz4cf3zn//UwIED/V0nAMBHDme5CopKlRoXpQR7RKDLAQAAAJqteoUrH3/8sebOnatrrrlG9913n7p3764ePXroueee83N5AID6mL+uUI/lblK1kUJsUk52L43snxLosgAAAIBmqV7Tgg4cOKD09HRJUlpamtq0aaO7777br4UBAOrH4Sx3BSuSVG2kKbmb5XCWB7YwAAAAoJmqV7hSXV2t1q1bu56HhoYqKirKb0UBAOqvoKjUFaycVmWMdheVBaYgAAAAoJmr17QgY4zGjh2r8PBwSdLx48c1fvx4j4AlNzfXeoUAAJ+kxkUpxCa3gCXUZlPXuMjAFQUAAAA0Y/UKV8aMGeP2fPTo0X4pBgBgXYI9QjnZvTQld7OqjFGozaZp2RksagsAAAA0EJsxxpy9W/NVUlIiu90up9Op2NjYQJcDAH7jcJZrd1GZusZFEqwAAAAA9eBtZlCvkSsAgKYvwR5BqAIAAAA0gnotaAsACDyHs1x5O4vYBQgAAAAIMEauAEAQmr+u0LXdcohNysnupZH9UwJdFgAAANAiMXIFAIKMw1nuClakU7sCTcndzAgWAAAAIEAIVwAgyBQUlbptsyxJVcZod1FZYAoCAAAAWjjCFQAIMqlxUQqxuR8LtdnUNS4yMAUBAAAALRzhCgAEmQR7hHKyeynUdiphCbXZNC07g52BAAAAgABhQVsACEIj+6doQI8O2l1Upq5xkQQrAAAAQAARrgBAkEqwRxCqAAAAAE0A04IAAAAAAAAsIFwBAAAAAACwgHAFAALA4SxX3s4iOZzlgS4FAAAAgEWsuQIAjWz+ukI9lrtJ1UYKsUk52b00sn9KoMsCAAAAUE+MXAGARuRwlruCFUmqNtKU3M2MYAEAAACCGOEKAFjg6/SegqJSV7ByWpUx2l1U1gDVAQAAAGgMTAsCgHqqz/Se1LgohdjkFrCE2mzqGhfZwNUCAAAAaCiMXAGAeqjv9J4Ee4Rysnsp1GaTdCpYmZadoQR7REOXDAAAAKCBMHIFAOqhruk9ZwtKRvZP0YAeHbS7qExd4yIJVgAAAIAgR7gCAPVgdXpPgj2CUAUAAABoJpgWBAD1wPQeAAAAAKcxcgUAvORwlqugqFSpcVFKsEcwvQcAAACApCAPV5566il9/PHH2rhxo8LCwnTkyJFAlwSgmaptZyCm9wAAAAAI6mlBlZWVuvXWW3XvvfcGuhQAzVh9dwYCAAAA0DIE9ciVP/zhD5KkefPmBbYQAM2alZ2BAAAAADR/QR2u1EdFRYUqKipcz0tKSgJYDYBgYHVnIAAAAADNW1BPC6qPnJwc2e121yM5OTnQJQFo4tgZCAAAAEBdmly48vvf/142m63Ox9dff13v8z/22GNyOp2ux969e/1YPYDmamT/FK2anKV37rlMqyZnaWT/lECXBAAAAKCJaHLTgh544AHddtttdfbp2rVrvc8fHh6u8PDwer8eQMvFzkAAAAAAatLkwpW4uDjFxcUFugwAQczhLFdBUalS46IIQwAAAAA0uCYXrviisLBQP/74owoLC1VVVaWNGzdKkrp3767o6OjAFgcgIOavK3Rtmxxik3KyezGFBwAAAECDshljzNm7NU1jx47V66+/7nF82bJlGjRokFfnKCkpkd1ul9PpVGxsrJ8rBNCYHM5yXTH9c49dfVZNzmIECwAAAACfeZsZNLkFbX0xb948GWM8Ht4GKwCal4KiUrdgRZKqjNHuorLAFAQAAACgRQjqcAUAzpQaF6UQm/uxUJtNXeMiA1MQAAAAgBaBcAVAs5Fgj1BOdi+F2k4lLKE2m6ZlZzAlCAAAAECDCuoFbQHgp0b2T9GAHh20u6hMXeMiCVaAIMJOXwAAIFgRrgBodhLsEXwxA4IMO30BAIBgxrQgAAAQUA5nuStYkaRqI03J3SyHszywhQEAAHiJcAUAAAQUO30BAIBgR7gCoElyOMuVt7OIf7kGWgB2+gIAAMGOcAVAkzN/XaGumP65bn91ja6Y/rnmrysMdEkAGhA7fQEAgGBnM8aYs3drvkpKSmS32+V0OhUbGxvocoAWz+Es1xXTP3ebIhBqs2nV5Cy+aAHNnMNZzk5fAACgSfE2M2C3IABNSl1rL/BlC2je2OkLAAAEK6YFAWhSWHsBAAAAQLAhXAHQpLD2AgAAAIBgw7QgAPXicJaroKhUqXFRfg8+RvZP0YAeHVh7AQAAAEBQIFwB4LP56wr1WO4mVRspxCblZPfSyP4pfn0P1l4AAAAAECyYFgTAJw5nuStYkaRqI03J3SyHs7zW/nk7i+rdDgAAAABNHSNXAPjEl918zjbCpTFGwAAAAABAQ2PkCgCfeLubz9lGuPg6AgYAAAAAmirCFTQ7TDNpWN7u5lPXCBdv2gEAAAAgWDAtCM0K00wahze7+Zwe4XJmgHLmCJeztQMAAABAsGDkCpoNppk0rgR7hDK7ta91R5+zjXDxdgQMAAAAADR1jFxBs+HLQqtoHGcb4eLNCBgAAAAAaOoIVxDUHM5yFRSVKjUuimkmTVSCPaLO0ORs7QAAAADQ1BGuIGjVtL5KTnYvTcndrCpjmuQ0kzPDoKZUFwAAAACg/mzGGHP2bs1XSUmJ7Ha7nE6nYmNjA10OvORwluuK6Z97jFJZNTlLkvw2zcSfYQiL7QIAAABAcPE2M2DkCoJSXeur1LXIam1qClH8GYbUttjugB4d/D6ChdExAAAAANC4CFcQlPy5vkpNIcqAHh38GoY01mK7jI4BAAAAgMbHVswISv7axre2ESXr9xTXGoZ4c868nUVuW0CfDoPO5O/FdtmKGgAAAAACg5ErCFr+2Ma3thEl+v8jP3wdGVPbyJHTYZAvi+36Or2HragBAAAAIDAIVxDUrG7jW9v0on5d29YrDKlrKpEvYZAv03tOhzBRYaFsRQ0AAAAAAUC4ghatrhElvo6M8WbkiDdhkC+L3/40hBnRN0kL8w802a2oAQAAAKA5IlxBi1dXiOLLyBh/LbLr7fSemkKYhfkHlHtfpsoqq/2yFTUAAAAA4OxY0BbQqRClPls4//Qc/lhk19vFb2sLYcoqqy1/FgAAAACA9xi5AviRPxbZ9XbxW39uRw0AAAAAqD/CFcDPrC6yK3kX0tRnByIAAAAAgP8RrgBNlDchjT9GygAAAAAArCFcAYKcP0bKAAAAAADqjwVtAQAAAAAALCBcAQAAAAAAsIBwBQAAAAAAwALCFQAAAAAAAAsIVwAAAAAAACxo8bsFGWMkSSUlJQGuBAAAAAAANCWns4LT2UFtWny4cvToUUlScnJygCsBAAAAAABN0dGjR2W322ttt5mzxS/NXHV1tQ4cOKCYmBjZbLZAl4MWqKSkRMnJydq7d69iY2MDXQ4QtLiWAP/gWgL8h+sJ8I9AXkvGGB09elSJiYkKCal9ZZUWP3IlJCREnTt3DnQZgGJjY/mlC/gB1xLgH1xLgP9wPQH+Eahrqa4RK6exoC0AAAAAAIAFhCsAAAAAAAAWEK4AARYeHq6pU6cqPDw80KUAQY1rCfAPriXAf7ieAP8IhmupxS9oCwAAAAAAYAUjVwAAAAAAACwgXAEAAAAAALCAcAUAAAAAAMACwhUAAAAAAAALCFcAP9i/f79Gjx6t9u3bKzIyUhdeeKHWr1/vajfG6Pe//70SExMVERGhQYMG6ZtvvnE7R0VFhSZMmKC4uDhFRUXpxhtv1L59+9z6FBcX64477pDdbpfdbtcdd9yhI0eONMZHBBpc165dZbPZPB7333+/JK4jwBcnT57U7373O6WmpioiIkJpaWl68sknVV1d7erDNQV45+jRo3r44YfVpUsXRURE6PLLL9e6detc7VxLgKcvvvhCw4YNU2Jiomw2mxYuXOjW3pjXTWFhoYYNG6aoqCjFxcXpwQcfVGVlpf8/tAFgyY8//mi6dOlixo4da9asWWMKCgrMp59+anbs2OHqM336dBMTE2M++OADs2nTJjNy5EiTkJBgSkpKXH3Gjx9vkpKSzNKlS82GDRtMVlaW6dOnjzl58qSrz5AhQ0xGRobJy8szeXl5JiMjw9xwww2N+nmBhnLo0CHjcDhcj6VLlxpJZtmyZcYYriPAF3/6059M+/btzUcffWQKCgrM3/72NxMdHW2ee+45Vx+uKcA7v/jFL0x6erpZsWKF2b59u5k6daqJjY01+/btM8ZwLQE1+eSTT8zjjz9uPvjgAyPJLFiwwK29sa6bkydPmoyMDJOVlWU2bNhgli5dahITE80DDzzg989MuAJY9Oijj5orr7yy1vbq6moTHx9vpk+f7jp2/PhxY7fbzcsvv2yMMebIkSOmdevW5t1333X12b9/vwkJCTGLFi0yxhizZcsWI8l89dVXrj6rV682ksx3333n748FBNxDDz1kunXrZqqrq7mOAB8NHTrUjBs3zu1Ydna2GT16tDGG302At8rKykxoaKj56KOP3I736dPHPP7441xLgBd+Gq405nXzySefmJCQELN//35Xn3feeceEh4cbp9Pp18/JtCDAog8//FAXX3yxbr31VnXs2FF9+/bVq6++6movKCjQwYMHNXjwYNex8PBwDRw4UHl5eZKk9evX68SJE259EhMTlZGR4eqzevVq2e12XXrppa4+l112mex2u6sP0FxUVlbqrbfe0rhx42Sz2biOAB9deeWV+uyzz7Rt2zZJ0r/+9S+tWrVKP//5zyXxuwnw1smTJ1VVVaU2bdq4HY+IiNCqVau4loB6aMzrZvXq1crIyFBiYqKrz3XXXaeKigq3ZRz8gXAFsGjXrl2aNWuWzj33XC1evFjjx4/Xgw8+qDfeeEOSdPDgQUlSp06d3F7XqVMnV9vBgwcVFhamtm3b1tmnY8eOHu/fsWNHVx+guVi4cKGOHDmisWPHSuI6Anz16KOP6pe//KXOO+88tW7dWn379tXDDz+sX/7yl5K4pgBvxcTEKDMzU3/84x914MABVVVV6a233tKaNWvkcDi4loB6aMzr5uDBgx7v07ZtW4WFhfn92mrl17MBLVB1dbUuvvhiTZs2TZLUt29fffPNN5o1a5b+67/+y9XPZrO5vc4Y43Hsp37ap6b+3pwHCDazZ8/W9ddf7/avDBLXEeCt+fPn66233tJf//pXXXDBBdq4caMefvhhJSYmasyYMa5+XFPA2b355psaN26ckpKSFBoaqosuuki33367NmzY4OrDtQT4rrGum8a6thi5AliUkJCg9PR0t2Pnn3++CgsLJUnx8fGS5JGMHjp0yJWixsfHq7KyUsXFxXX2+f777z3e/4cffvBIY4FgtmfPHn366ae6++67Xce4jgDf/OY3v9HkyZN12223qVevXrrjjjs0ceJE5eTkSOKaAnzRrVs3rVixQseOHdPevXu1du1anThxQqmpqVxLQD005nUTHx/v8T7FxcU6ceKE368twhXAoiuuuEJbt251O7Zt2zZ16dJFkly/eJcuXepqr6ys1IoVK3T55ZdLkvr166fWrVu79XE4HNq8ebOrT2ZmppxOp9auXevqs2bNGjmdTlcfoDmYO3euOnbsqKFDh7qOcR0BvikrK1NIiPufeaGhoa6tmLmmAN9FRUUpISFBxcXFWrx4sYYPH861BNRDY143mZmZ2rx5sxwOh6vPkiVLFB4ern79+vn3g/l1eVygBVq7dq1p1aqVeeqpp8z27dvN22+/bSIjI81bb73l6jN9+nRjt9tNbm6u2bRpk/nlL39Z41ZjnTt3Np9++qnZsGGD+dnPflbjVmO9e/c2q1evNqtXrza9evViiz40K1VVVSYlJcU8+uijHm1cR4D3xowZY5KSklxbMefm5pq4uDjz29/+1tWHawrwzqJFi8w///lPs2vXLrNkyRLTp08fc8kll5jKykpjDNcSUJOjR4+a/Px8k5+fbySZGTNmmPz8fLNnzx5jTONdN6e3Yr766qvNhg0bzKeffmo6d+7MVsxAU/WPf/zDZGRkmPDwcHPeeeeZV155xa29urraTJ061cTHx5vw8HAzYMAAs2nTJrc+5eXl5oEHHjDt2rUzERER5oYbbjCFhYVufQ4fPmxGjRplYmJiTExMjBk1apQpLi5u6I8HNJrFixcbSWbr1q0ebVxHgPdKSkrMQw89ZFJSUkybNm1MWlqaefzxx01FRYWrD9cU4J358+ebtLQ0ExYWZuLj4839999vjhw54mrnWgI8LVu2zEjyeIwZM8YY07jXzZ49e8zQoUNNRESEadeunXnggQfM8ePH/f6ZbcYY49+xMAAAAAAAAC0Ha64AAAAAAABYQLgCAAAAAABgAeEKAAAAAACABYQrAAAAAAAAFhCuAAAAAAAAWEC4AgAAAAAAYAHhCgAAAAAAgAWEKwAAAAAAABYQrgAAAAAAAFhAuAIAAAAAAGAB4QoAAECQmzhxom677TaVlJQEuhQAAFokwhUAAIAgV11dLWNMoMsAAKDFIlwBAADN1qBBg/Twww8HugxJ9atl0KBBstlsstls2rhxY639nn/+ec2fP1+xsbGuY2PHjnW9duHChfUrGgAAeIVwBQAA1NvLL7+smJgYnTx50nXs2LFjat26ta666iq3vitXrpTNZtO2bdsau8xG589Q55577pHD4VBGRoZHW15enkJDQzVkyBCPtueff14Oh8MvNQAAgLoRrgAAgHrLysrSsWPH9PXXX7uOrVy5UvHx8Vq3bp3Kyspcx5cvX67ExET16NEjEKUGrcjISMXHx6tVq1YebXPmzNGECRO0atUqFRYWurXZ7XbFx8c3VpkAALRohCsAAKDeevbsqcTERC1fvtx1bPny5Ro+fLi6deumvLw8t+NZWVmSpEWLFunKK6/UOeeco/bt2+uGG27Qzp07XX3/8pe/KCkpSdXV1W7vd+ONN2rMmDGSJGOMnn76aaWlpSkiIkJ9+vTR+++/X2ut3vQfNGiQHnzwQf32t79Vu3btFB8fr9///vdufY4ePapRo0YpKipKCQkJevbZZ91GqowdO1YrVqzQ888/75qWs3v3bkmn1kap69y+KC0t1Xvvvad7771XN9xwg+bNm1fvcwEAAGsIVwAAgCWDBg3SsmXLXM+XLVumQYMGaeDAga7jlZWVWr16tStcKS0t1aRJk7Ru3Tp99tlnCgkJ0YgRI1xhyq233qqioiK38xYXF2vx4sUaNWqUJOl3v/ud5s6dq1mzZumbb77RxIkTNXr0aK1YsaLGOr3t//rrrysqKkpr1qzR008/rSeffFJLly51tU+aNElffvmlPvzwQy1dulQrV67Uhg0bXO3PP/+8MjMzXdN5HA6HkpOTvTq3L+bPn6+ePXuqZ8+eGj16tObOncuitgAABIjn+FIAAAAfDBo0SBMnTtTJkydVXl6u/Px8DRgwQFVVVXrhhRckSV999ZXKy8td4crNN9/sdo7Zs2erY8eO2rJlizIyMtSuXTsNGTJEf/3rX3X11VdLkv72t7+pXbt2uvrqq1VaWqoZM2bo888/V2ZmpiQpLS1Nq1at0l/+8hcNHDjQ7fy+9O/du7emTp0qSTr33HM1c+ZMffbZZ7r22mt19OhRvf766251zZ07V4mJia7X2+12hYWFuabznKmuc/tq9uzZGj16tCRpyJAhOnbsmD777DNdc801Pp8LAABYw8gVAABgSVZWlkpLS7Vu3TqtXLlSPXr0UMeOHTVw4EC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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_residuals(space='wavelength');" + ] + }, + { + "cell_type": "markdown", + "id": "45524753-381e-485a-9819-ae189cf639dc", + "metadata": {}, + "source": [ + "## 7. Refine the Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "96999b9a-4fe6-4ce8-b894-00c7638342df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.refine_fit(match_distance_bound=25)\n", + "ws.plot_fit(figsize=(11,6), plot_values=False, obs_to_wav=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "1444f2f8-2326-4ee9-a041-bb4051fb35c9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.plot_residuals(space='wavelength');" + ] + }, + { + "cell_type": "markdown", + "id": "983fe041-41e9-47a7-a179-fead64e171ba", + "metadata": {}, + "source": [ + "### 8. Rebin a Spectrum to Wavelength Space\n", + "\n", + "A primary goal of wavelength calibration is to transform spectra from the detector's pixel grid to a physical wavelength grid. The `resample` method does this, converting a `Spectrum1D` object from pixel space to wavelength space.\n", + "\n", + "Key parameters for `resample`:\n", + "- `spectrum`: The input `Spectrum` object (assumed to be on a pixel grid corresponding to the calibration).\n", + "- `nbins`: (int, optional) The number of bins desired in the output wavelength grid. If `None`, defaults to the number of pixels in the input spectrum.\n", + "- `wlbounds`: (tuple[float, float], optional) The desired `(start_wavelength, end_wavelength)` for the output grid. If `None`, defaults to the wavelengths corresponding to the first and last pixels.\n", + "- `bin_edges`: (Sequence[float], optional) Explicitly define the wavelength edges of the output bins. If provided, `nbins` and `wlbounds` are ignored.\n", + "\n", + "The method uses the fitted `_p2w`, `_w2p`, and `_p2w_dldx` transformations to map the input pixel bins to the output wavelength bins. It performs an exact flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", + "\n", + "Here, we demonstrate by resampling the original arc spectrum itself. In a typical workflow, you would apply this `resample` method (using the `ws` object derived from the arc lamp) to your *science* spectrum observed with the same instrument setup." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spectrum_wl = ws.resample(arc_spectra[0])\n", + "\n", + "fig, ax = subplots(constrained_layout=True, figsize=(11, 4))\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.grid(True, alpha=0.5)" + ] + }, + { + "cell_type": "markdown", + "id": "a64a7f45-9145-41ae-845c-389329762697", + "metadata": {}, + "source": [ + "Additionally, if we don't want to rebin, or maybe want to do it using another method, we can access the pixel-wavelength transform as a [gwcs](https://gwcs.readthedocs.io/) WCS object usin the `WCS` property." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + " [1]: \n", + "Parameters:\n", + " offset_0 c0_1 ... c3_1 c4_1 \n", + " -------- ---------------- ... ---------------------- ---------------------\n", + " -1000.0 7454.41575090495 ... -5.006614108686105e-08 4.212537047788065e-12)>" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ws.wcs" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "d0d1aed5-c16b-421e-9638-19b8ec38bc6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[5300.0316,~5302.1373] \\; \\mathrm{\\mathring{A}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ws.wcs.pixel_to_world([100, 101])" + ] + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/wavelength_calibration/wavecal1d_example_03.ipynb b/docs/wavelength_calibration/wavecal1d_example_03.ipynb new file mode 100644 index 00000000..b5278260 --- /dev/null +++ b/docs/wavelength_calibration/wavecal1d_example_03.ipynb @@ -0,0 +1,398 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", + "metadata": {}, + "source": [ + "# 1D Wavelength Calibration Tutorial 3: Non-Interactive Fit\n", + "\n", + "This notebook focuses on a **non-interactive workflow** for 1D wavelength calibration that is\n", + "particularly useful for **automated data reduction pipelines** where the instrument configuration\n", + "(spectrograph, grating/grism, approximate wavelength range) is well-characterized beforehand.\n", + "\n", + "Instead of requiring manual identification of initial line pairs (as shown in Example 1 using\n", + "`WavelengthCalibration1D.fit_lines`), this workflow leverages the\n", + "`WavelengthCalibration1D.fit_global` method. This method uses a global optimization algorithm to\n", + "determine the wavelength solution. It achieves this by automatically finding the best polynomial\n", + "coefficients that minimize the overall distance between detected arc lamp lines and a provided\n", + "catalog of theoretical line wavelengths.\n", + "\n", + "A key requirement for `fit_global` is providing reasonable initial **bounds** for the wavelength\n", + "and dispersion (Ã…/pixel) at a chosen reference pixel. These bounds are derived from **prior\n", + "knowledge** of the instrument setup and guide the optimization search.\n", + "\n", + "Like Example 2, this example uses three arc lamp spectra (HgAr, Ne, Xe) obtained with the R1000R\n", + " grism of the [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", + " [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7853ed1c-5b05-42a2-9413-4054769c6032", + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "import numpy as np\n", + "\n", + "from astropy.table import Table\n", + "from astropy.nddata import StdDevUncertainty\n", + "from matplotlib.pyplot import setp, subplots, close, rc\n", + "\n", + "from specreduce.compat import Spectrum\n", + "from specreduce.wavecal1d import WavelengthCalibration1D\n", + "\n", + "rc('figure', figsize=(15, 3))" + ] + }, + { + "cell_type": "markdown", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", + "metadata": {}, + "source": [ + "## 1. Read Arc Spectra & Initialize Wavelength Solution Class\n", + "\n", + "First, we load the data for the three arc lamps (HgAr, Ne, Xe) from the example FITS table `osiris_arcs.fits`. We create a list of `specutils.Spectrum` objects, one for each lamp.\n", + "\n", + "Next, we prepare the corresponding line lists. For the HgAr lamp, we define a custom list as a NumPy array containing known air wavelengths specific to this GTC/OSIRIS setup, derived from the [official GTC line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt). For the Neon (Ne) and Xenon (Xe) lamps, we simply provide their standard identifiers (`'NeI'`, `'XeI'`) within lists. `WavelengthSolution1D` will use `specreduce.calibration_data.load_pypeit_calibration_lines` internally to fetch these standard lists.\n", + "\n", + "Finally, we instantiate the `WavelengthSolution1D` class:\n", + "- `ref_pixel=1000`: Sets the reference pixel for the polynomial fit.\n", + "- `degree=4`: Specifies a 4th-degree polynomial for the pixel-to-wavelength model.\n", + "- `arc_spectra=arc_spectra`: Provides the list of `Spectrum` objects.\n", + "- `line_lists=[hgar_lines, ['NeI'], ['XeI']]`: Provides the list of corresponding line data (matching the order of `arc_spectra`). Note how we mix the custom array and lists of standard names.\n", + "- `line_list_bounds=(5100, 10000)`: Filters the line lists to include only lines within this approximate wavelength range (in Angstroms).\n", + "- `unit=u.angstrom`: Explicitly defines the wavelength unit.\n", + "- `wave_air=True`: Inform the class that the provided line lists (both custom and standard PypeIt lists for these lamps) contain **air** wavelengths. The class will handle conversions appropriately if needed for internal consistency or specific outputs, but the primary fitting coordinate system will be based on these air wavelengths." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "49a5d5d6-af0f-4bb1-8a08-ce14cdad023f", + "metadata": {}, + "outputs": [], + "source": [ + "lamps = 'HgAr', 'Ne', 'Xe'\n", + "hgar_lines = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106, \n", + " 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", + "\n", + "tb = Table.read('osiris_arcs.fits')\n", + "arc_spectra = [Spectrum(flux=tb[f'{l}_flux'].value.astype('d')*u.DN, \n", + " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d'))) \n", + " for l in lamps]\n", + "\n", + "ws = WavelengthCalibration1D(ref_pixel=1000,\n", + " degree=4,\n", + " arc_spectra=arc_spectra,\n", + " line_lists=[hgar_lines, ['NeI'], ['XeI']],\n", + " line_list_bounds=(5100, 10000),\n", + " unit=u.angstrom,\n", + " wave_air=True)" + ] + }, + { + "cell_type": "markdown", + "id": "49076f6f-4695-4c58-9359-d1473cc54bd6", + "metadata": {}, + "source": [ + "## 2. Perform Automated Fit using `fit_global`\n", + "\n", + "With the `WavelengthSolution1D` object initialized, we proceed to calculate the wavelength solution automatically.\n", + "\n", + "**Step 1: Find Lines**\n", + "We first execute `ws.find_lines()`. This step automatically detects emission lines in each of the input arc spectra (`arc_spectra`) and calculates their pixel centroids. These detected centroids are stored internally in `ws.observed_lines`. The `fwhm` (estimated line width in pixels) and `noise_factor` parameters help the algorithm distinguish real lines from noise; these may need tuning depending on the data quality.\n", + "\n", + "**Step 2: Global Fit**\n", + "Next, the core of the non-interactive workflow: `ws.fit_global()`. This method initiates the global optimization search.\n", + "- **No Manual Input:** Notice we do *not* provide any specific pixel-wavelength pairs.\n", + "- **Bounds are Key:** Instead, we *must* provide `wavelength_bounds` and `dispersion_bounds`. These constrain the optimizer's search space for the polynomial coefficients. They represent our *a priori* knowledge about the instrument setup.\n", + " - For this GTC/OSIRIS R1000R example, we know the approximate central wavelength is ~7430 Ã… and the dispersion near the center is ~2.62 Ã…/pixel. We provide bounds around these values: `wavelength_bounds=[7420, 7470]` Ã… and `dispersion_bounds=[2.5, 2.7]` Ã…/pixel.\n", + "- **Optimization Process:** The differential evolution algorithm searches for polynomial coefficients within these constraints (and broader limits for higher-order terms) that best map the detected `observed_lines` to the `catalog_lines` by minimizing the sum of distances to the nearest catalog neighbors (using internal KDTrees for efficiency).\n", + "- **Refinement:** We set `refine_fit=True`. After the global optimization finds an initial solution, this triggers an automatic call to `ws.refine_fit()`. This internal step uses the initial solution to explicitly match observed and catalog lines within a tolerance (`match_distance_bound`) and then performs a standard least-squares fit using *only* these matched pairs. This typically results in a higher-precision final solution.\n", + "\n", + "**Step 3: Visualize Result**\n", + "We then call `ws.plot_fit()` to visualize the outcome. Because we provided multiple arc spectra, it generates pairs of plots for each frame (HgAr, Ne, Xe). The top plot shows the catalog lines, and the bottom plot shows the observed lines (and the spectrum itself) mapped onto the final derived wavelength scale (`obs_to_wav=True`). This allows a visual check of the alignment and fit quality across all input lamps." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "46937bc3-b120-46f6-870e-85f1da5907db", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ws.find_lines(fwhm=4, noise_factor=15)\n", + "\n", + "ws.fit_global(wavelength_bounds=[7420, 7470], \n", + " dispersion_bounds=[2.5, 2.7], \n", + " refine_fit=True)\n", + "\n", + "ws.plot_fit(figsize=(15, 8), plot_values=False, obs_to_wav=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "343801bc-65fa-41c9-929b-72565cdee31d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mJPODDz5Imv7zn//cNAzDXLNmzV7vd/jw4eYJJ5yw13kaP2/TNM2ePXuaGRkZ5qZNm+xpdXV1ZmFhoTl16lR72tSpU82cnJyk+UzTNO+9915Tkrl69eomHzMSiZihUMi8/fbbzQ4dOiStt549e5put3ufz83y2muvmZLMF154wTRN03z55ZdNj8djVlVVmZWVlabb7Tbnz59vmqZpzp0715Rk/v3vf2/yvsLhsBkKhczzzjvPvPjii+3pn332mSnJ/OMf/5g0/xlnnGEOHjzYvjxr1izT5XKZy5cvT5rvL3/5S8rj9uzZ05w0adIB3VaS2aVLF7OystKeVlJSYrpcLnPWrFn2tNNPP93s3r27GQgE7GlVVVVmhw4dUl7z7OzspOWxWN9b11xzTdL0e+65x5Rkbt++PeU2lvfff9+UZN54440p11nr2vqX+B4YPnx4k98t1nWJ28pjjz2W8rlhmqY5ZcoUU5L5zDPPNLt8AACg9dHWBgAAtHvPPvusli9fnvLv7LPPTppv8eLFkmJVw4kuu+yyJu/3qaeeUmZmpt17OScnRz/4wQ/0r3/9S2vXrk2Zf/z48SkVys1ZvHix1q1bp0mTJtntB37yk5/IMIwmW11IsqtTLf/4xz+UkZFhtyfYH6effrrWrVunBQsW6Ne//rWGDh2qd955R1dccYXGjx+fctbBJZdcklSpmZubq4suukjvvfeeIpGI6uvr9c477+jiiy9WVlaWXdkfDof17W9/W/X19Xr//fft5T722GM1atSo/V7uRI3Xx4H49re/nVRBOmDAAEnS2LFjk+azpm/evLlF91tUVCSv16uCggJdeumlGjx4sObOnWtfv3TpUpWWlmrSpElJ6yoajeqCCy7Q8uXL7bMBzjjjDM2ZM0d33HGH3n///SYHld25c6d+9rOfqXv37vJ4PPJ6verZs6ck6csvv0yZf9y4cSnPzzAMXXjhhfY0j8ejvn37atOmTSm3nzBhQlLVcc+ePTVs2LC9tnJ68803FQ6HdcUVVyQ954yMDA0fPtxupbJmzRpt27at2cc4GP/85z91/PHH64wzzkiaPnnyZJmmudeq5YN18skn21XukpSRkaFjjz02af3Onz9fI0eOVHFxcdI6sl4X6zPMei6jRo1Sfn6+3G63vF6vbrnlFu3ZsyelBdOJJ56YdNbG3gwfPlwul8t+PRYtWqTTTjtNOTk5ys3N1amnnmq/zosWLZLH40n6rP3DH/6gU089VRkZGfZ78Z133kl6Hw4aNEiDBw9OOhPgyy+/1Icffpj0eTZ//nwNHDhQJ598ctL6GDNmjN0bvzn7e9uRI0cqNzfXvtylSxd17tzZfn1qamr00Ucf6bvf/a58Pp89X05Oji666KIWrdtE48ePT7p84oknSlKT21tLdOjQQV6v1/738ssvJ11fUFCgc889d5/38+677yo3Nzdl+SZMmHBAywUAAA4O4TwAAGj3BgwYoNNOOy3lX35+ftJ8e/bskcfjUWFhYdL0Ll26pNznunXr9N5772ns2LEyTVPl5eUqLy/X97//fUlqMkBvrod7U6xe9hdffLF93/n5+Tr77LP18ssvq7y8PGn+rKyslLYIu3btUnFx8T7bfDTH6/VqzJgxuvPOO/Xmm29qy5YtGjFihObPn69//OMfSfMWFRWl3L6oqEjBYFDV1dXas2ePwuGwHn744aSAyOv16tvf/rYk2f3Pd+3a1SoDZu7P+m5O4/eCFbo1N72+vr5F9/v2229r+fLlevPNN/W9731P7733nq699lr7eqsNxve///2U9XX33XfLNE27RchLL72kSZMm6cknn9TQoUNVWFioK664wu4BHY1GNXr0aL3yyiu64YYb9M477+jDDz+0D4bU1dW16HlnZWWltMrw+XxNPufm3g+JrXYas57z6aefnvKcX3rpJfv9Yd1Hc49xMPbs2dPk+6a4uDjpsdtChw4dUqb5/f6k12fHjh16/fXXU9bPCSecIKlhG/rwww81evRoSdITTzyhf//731q+fLluvvlmSamv+f5sK0cddZROPvlkO4B/9913NXz4cPv6xAMp7777rk477TQ71L7//vv185//XEOGDNHLL7+s999/X8uXL9cFF1yQskw//elPtWzZMn311VeSpGeeeUZ+vz/pYOmOHTv02WefpayP3NxcmaaZMqZCov297b5en7KyMpmm2eT3RVPT9qXx41mtl5raXi3du3eX1HSAv2jRIi1fvlx/+MMfmrxtS98De/bsafL5HOy2BwAADgw95wEAwGGjQ4cOCofDKi0tTQonmxro7umnn5ZpmvrLX/6iv/zlLynXz507V3fccUfSoHt769meqKKiwq5qPP3005uc54UXXtA111yz1/vu1KmTlixZomg0esABfaIOHTpo2rRpWrRokVatWmWH6lLT66ikpEQ+n085OTnyer1yu92aOHGifvGLXzR5/71797aXe+vWrQe9vE2tk4yMDFVUVKRM31uI1xZOOukkdezYUZJ0/vnna8yYMfrjH/+oK6+8Uqeffrp93cMPP6wzzzyzyfuwArKOHTvqwQcf1IMPPqjNmzfrb3/7m2688Ubt3LlTCxYs0KpVq/Tpp59qzpw5SWMhrFu3rs2eX3Pvh6YCTov1nP/yl7/YVf1Nse6jucc4GB06dND27dtTplsDcVrLmC4dO3bUiSeeqDvvvLPJ662DCPPmzZPX69X8+fOTDqi89tprTd6upZ9NlpEjR+q+++7TZ599ptWrVycNDDx8+HDdf//9+uyzz7Rx48akMP3555/XiBEj9NhjjyXdX1VVVcpjXHbZZbr++us1Z84c3XnnnXruuef03e9+VwUFBfY8HTt2VGZmZrNnE+3t9TqY2zaloKBAhmGk9JeXDv592VLFxcU64YQT9NZbb6m+vj7ptT/55JMlSdXV1U3etqXvgQ4dOujDDz9Mmc6AsAAApAeV8wAA4LBhVX++9NJLSdPnzZuXdDkSiWju3Lnq06eP3n333ZR/06dP1/bt21Oqy1vqhRdeUF1dnX772982ef8dO3ZsNlBKdOGFF6q+vl5z5szZr8cPhULNVghbrSesENDyyiuvJFVQV1VV6fXXX9c555wjt9utrKwsjRw5Up988olOPPHEJs9ksELXCy+8UP/5z3/22kKkJVWkTenVq5f+85//KBAI2NP27NmjpUuX7tf9tCbDMPT73/9ebrdbv/nNbyRJZ511lo466ih98cUXTa6r0047Lal1hqVHjx765S9/qfPPP18ff/yxff9SwzqzPP744232nF588cWk1kebNm3S0qVLNWLEiGZvM2bMGHk8Hq1fv77Z5yxJ/fv3V9euXZt9jJZoXJFuOe+88/TFF1/Y687y7LPPyjAMjRw5skX331bGjRunVatWqU+fPk2uH2u7NAxDHo8n6eBgXV2dnnvuuVZZDms93HbbbXK5XElta6y/b7vttqR5reVq/D787LPPtGzZspTHKCgo0He/+109++yzmj9/vkpKSlJadI0bN07r169Xhw4dmlwfvXr1avY5HMxtm5Kdna3TTjtNr732moLBoD29urpa8+fPT5m/uffgwbr55pu1e/duXX/99Sntx1rDyJEjVVVVlTL49QsvvNDqjwUAAPaNynkAAHDYuOCCC3TWWWdp+vTpqqys1ODBg7Vs2TI9++yzkmRXn//jH//Qtm3bdPfddzcZNg4cOFCPPPKInnrqqZTe3S3x1FNPqaCgQDNmzEhpIyJJV1xxhe6//359+umnOumkk5q9n8suu0zPPPOMfvazn2nNmjUaOXKkotGoPvjgAw0YMMDuld9YRUWFevXqpR/84AcaNWqUunfvrurqai1atEgPPfSQBgwYoEsuuSTpNm63W+eff76uv/56RaNR3X333aqsrLQDOkl66KGHdPbZZ+ucc87Rz3/+c/Xq1UtVVVVat26dXn/9dTuMnzZtml566SV95zvf0Y033qgzzjhDdXV1Wrx4scaNG2f3fu7Zs6f++te/6rzzzlNhYaE6duy4z0Bt4sSJevzxx3X55ZdrypQp2rNnj+65556UlkCHWr9+/XT11Vfr0Ucf1ZIlS3T22Wfr4Ycf1qRJk1RaWqrvf//76ty5s3bt2qVPP/1Uu3bt0mOPPaaKigqNHDlSEyZM0HHHHafc3FwtX75cCxYssF+j4447Tn369NGNN94o0zRVWFio119/XW+99VabPZ+dO3fq4osv1pQpU1RRUaFbb71VGRkZuummm5q9Ta9evXT77bfr5ptv1tdff60LLrhABQUF2rFjhz788ENlZ2fbYfBvf/tbXXXVVfZjlJeXa+bMmS1urTFo0CDNmzdPL730ko455hhlZGRo0KBB+u///m89++yzGjt2rG6//Xb17NlTb7zxhh599FH9/Oc/b1Ff9srKyibPpunUqVNS+5cDcfvtt+utt97SsGHDdN1116l///6qr6/Xxo0b9fe//11/+MMf1K1bN40dO1b333+/JkyYoKuvvlp79uzRvffemxKMH6hvfetbcrvdevXVV5Pa1kixtjcnnXSSXn31VXm9Xp111ln2dePGjdNvf/tb3XrrrRo+fLjWrFmj22+/Xb1791Y4HE55nJ/+9Kd66aWX9Mtf/lLdunVLGYdi2rRpevnll/Wtb31L//3f/60TTzxR0WhUmzdv1sKFCzV9+nQNGTKkyedwMLdtzu23366xY8dqzJgx+q//+i9FIhHNnj1bOTk5dhsqy6BBg7Ro0SK9/vrr6tq1q3Jzc9W/f//9erymXHbZZVq9erXuvPNOffrpp5o8ebL69eunaDSqLVu22AdoEl+z/XHFFVfogQce0BVXXKE777xT/fr109///ne9+eabB73sAADgAKRnHFoAAIB9e+aZZ0xJ5vLly5u8fuzYsWbPnj2TppWWlpo/+clPzKOOOsrMysoyzz//fPP99983JZkPPfSQaZqm+d3vftf0+Xzmzp07m33sH/3oR6bH4zFLSkrMDRs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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "spectrum_wl = ws.resample(arc_spectra[0])\n", + "\n", + "fig, ax = subplots(constrained_layout=True, figsize=(15, 4))\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"HgAr Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.grid(True, alpha=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f15d4c4e-b721-4348-9f2b-078b3c348515", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[2.5968402,~2.5968402,~2.5968402,~\\dots,~2.5968402,~2.5968402,~2.5968402] \\; \\mathrm{\\mathring{A}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.diff(spectrum_wl.spectral_axis)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ae8c6cdb-2fa3-4916-9ba0-5dab3d19acdc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$839275 \\; \\mathrm{DN}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectrum_wl.flux.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "960d0392-0a8c-4d37-ae94-940e84a1c600", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$839275 \\; \\mathrm{DN}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arc_spectra[0].flux.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "a64a7f45-9145-41ae-845c-389329762697", + "metadata": {}, + "source": [ + "## 3. Accessing the WCS Object\n", + "\n", + "Beyond resampling the flux array, the derived pixel-to-wavelength transformation is available as a standard [`gwcs`](https://gwcs.readthedocs.io/) (Generalized World Coordinate System) object via the `.wcs` property of the `WavelengthSolution1D` instance.\n", + "\n", + "This WCS object encapsulates the fitted model (`ws._p2w`) and defines the mapping between the input pixel coordinate frame and the output spectral coordinate frame (including units).\n", + "\n", + "**Uses:**\n", + "- **Applying the solution without rebinning:** You can use the WCS object directly with tools that understand GWCS (like `specutils`) to work with spectra while keeping the original pixel grid.\n", + "- **Interoperability:** Provides a standard way to represent the wavelength solution that can be understood by other Astropy-affiliated packages.\n", + "- **FITS Headers:** The GWCS object can be used to generate standard WCS keywords for inclusion in FITS file headers, making the wavelength calibration self-describing." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "04556935-222e-42f4-b352-cc19a87ec383", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[5095.0088,~5097.6018,~5100.1948,~\\dots,~10405.416,~10408.009,~10410.602] \\; \\mathrm{\\mathring{A}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spectrum_wl.spectral_axis" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + " [1]: \n", + "Parameters:\n", + " offset_0 c0_1 ... c3_1 c4_1 \n", + " -------- ----------------- ... ----------------------- ---------------------\n", + " -1000.0 7454.432054908556 ... -5.0351299187468156e-08 4.958031715494442e-12)>" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ws.wcs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d0d1aed5-c16b-421e-9638-19b8ec38bc6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[5300.1923,~5302.2961] \\; \\mathrm{\\mathring{A}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ws.wcs.pixel_to_world([100, 101])" + ] + }, + { + "cell_type": "markdown", + "id": "4d21e847-568b-4c90-a945-205b16332f5e", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/wavelength_calibration/wavelength_calibration.rst b/docs/wavelength_calibration/wavelength_calibration.rst new file mode 100644 index 00000000..44d51409 --- /dev/null +++ b/docs/wavelength_calibration/wavelength_calibration.rst @@ -0,0 +1,261 @@ +.. _wavelength_calibration: + +Wavelength Calibration +====================== + +In spectroscopy, the raw data from a detector typically records the intensity of light received +at different detector positions (pixels). However, for scientific analysis, we need to know the +physical wavelength (or frequency, or energy) corresponding to each pixel. **Wavelength +calibration** is the process of determining this relationship, creating a mapping function or +model that converts pixel coordinates to wavelength values. + +This is often achieved by observing a calibration source with well-known emission or absorption +lines at specific wavelengths (e.g., an arc lamp spectrum). By identifying the pixel positions of +these known spectral features, we can fit a mathematical model that describes the dispersion of +the spectrograph. + +The ``specreduce`` library provides the `~specreduce.wavecal1d.WavelengthCalibration1D` class +to facilitate this process for one-dimensional spectra. Tools for calibrating 2D spectra will +be introduced soon. + +1D Wavelength Calibration +------------------------- + +The `~specreduce.wavecal1d.WavelengthCalibration1D` class encapsulates the data and methods +needed to perform 1D wavelength calibration. The class supports multiple workflows with varying +levels of user interaction. It can be used: + +* manually, +* as part of an interactive pipeline, or +* as part of a fully automated pipeline. + +The typical workflow involves these steps: + +1. **Initialization**: Create an instance of the class, providing either an observed arc lamp + spectrum or pre-identified observed line positions, along with a catalog of known line + wavelengths. `~specreduce.wavecal1d.WavelengthCalibration1D` supports the use of multiple + arc spectra, and the initialization can also be done using a list of arc spectra (or a + list of line arrays identified from multiple arc spectra) and a list of catalogs for each arc + spectra. +2. **Line Identification (Optional)**: If an arc spectrum was provided, identify the pixel + locations of emission lines within it. +3. **Matching and Fitting**: Determine the correspondence between observed line pixels and + catalog wavelengths, and fit a model (a polynomial) to represent the + pixel-to-wavelength transformation. This can be done manually by providing matched pairs or + automatically using global optimization techniques. +4. **Inspection**: Evaluate the quality of the fit using residuals and diagnostic plots. +5. **Applying the Solution**: Use the fitted model (often accessed as a WCS object) to + calibrate science spectra or resample spectra onto a linear wavelength grid. + +These steps are detailed in the following tutorials. + +.. toctree:: + :maxdepth: 1 + + wavecal1d_example_01.ipynb + wavecal1d_example_02.ipynb + wavecal1d_example_03.ipynb + +Detailed Steps +-------------- + +**1. Initialization** + +You instantiate the :class:`~specreduce.wavecal1d.WavelengthCalibration1D` by providing basic +information about your setup and data. A reference pixel (``ref_pixel``) is required, which serves +as the anchor point for the polynomial fit. + +You must provide *either* a list of arc spectra (or a single arc spectrum) *or* a list of known +observed line positions: + +* **Using an Arc Spectrum**: Provide the arc spectrum as a `specutils.Spectrum` + object via the ``arc_spectra`` argument. You also need to provide a ``line_lists`` argument, + which can be a list of known catalog wavelengths (e.g., from an `astropy.table.QTable` or a + NumPy array with units) or the name(s) of standard line lists recognized by `specreduce` (e.g., + ``"ArI"``). + + .. code-block:: python + + import astropy.units as u + import numpy as np + from specreduce.compat import Spectrum + from specreduce.wavecal1d import WavelengthCalibration1D + + # Example arc spectrum (replace with your actual data) + arc_flux = np.random.rand(1024) * u.DN + arc_pixels = np.arange(1024) * u.pix # Dummy axis + arc_spectrum = Spectrum(flux=arc_flux, spectral_axis=arc_pixels) + + # Known ArI line wavelengths + known_ari_lines = [6965.43, 7067.22, 7383.98, 7503.87, 7635.11] * u.AA + + # Define reference pixel (e.g., center of the detector) + ref_pix = 512 + + ws = WavelengthCalibration1D(ref_pix, arc_spectra=arc_spectrum, line_lists=known_ari_lines) + +* **Using Observed Line Positions**: If you have already identified the pixel centroids of + lines in your calibration spectrum, you can provide them directly via the ``obs_lines`` + argument (as a list or array). In this case, you *must* also provide the detector's pixel + boundaries using ``pix_bounds`` (a tuple like ``(min_pixel, max_pixel)``). You still need to + provide the ``line_lists`` containing the potential matching catalog wavelengths. + + .. code-block:: python + + # Assume observed line pixel centers were found previously + observed_pixels = np.array([105.3, 210.8, 455.1, 512.5, 680.2]) + + # Pixel range of the detector + pixel_bounds = (0, 1024) + + ws = WavelengthCalibration1D(ref_pix, + obs_lines=observed_pixels, + line_lists=known_ari_lines, + pix_bounds=pixel_bounds) + +You can also specify the ``degree`` of the polynomial to be used for the fit (defaults to 3). + +**2. Finding Observed Lines** + +If you initialized the class with ``arc_spectra``, you need to detect the lines in it. Use the +:meth:`~specreduce.wavecal1d.WavelengthCalibration1D.find_lines` method: + +.. code-block:: python + + # Find lines with an estimated FWHM and noise factor + ws.find_lines(fwhm=3.5, noise_factor=5) + + # Access the found lines (pixel positions) + observed_lines = ws.observed_lines + print(observed_lines) + +This populates the `~specreduce.wavecal1d.WavelengthCalibration1D.observed_lines` attribute. + +**3. Matching and Fitting the Solution** + +The core of the process is fitting the model that maps pixels to wavelengths. + +* **Global Fitting (``fit_global``)**: If you have + `~specreduce.wavecal1d.WavelengthCalibration1D.observed_lines` (either found automatically or + provided initially) and + `~specreduce.wavecal1d.WavelengthCalibration1D.catalog_lines` (from ``line_lists``), but don't + know the exact pixel-wavelength pairs, you can use + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_global`. This method uses a global + optimization algorithm (differential evolution) to find the best-fit polynomial parameters by + minimizing the distance between predicted line wavelengths and the nearest catalog lines. You + need to provide estimated bounds for the wavelength and dispersion at the ``ref_pixel``. + + .. code-block:: python + + # Estimate wavelength and dispersion around the reference pixel + # (e.g., Wavelength around 7500 AA, Dispersion ~2 AA/pix) + wavelength_bounds = (7450, 7550) + dispersion_bounds = (1.8, 2.2) + + ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=30, refine_fit=True) + + Setting ``refine_fit=True`` automatically runs a least-squares refinement after the global + fit finds an initial solution and matches lines. + +* **Fitting Known Pairs (``fit_lines``)**: If you have already established explicit pairs of + observed pixel centers and their corresponding known wavelengths, you can use + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_lines` to perform a direct + least-squares fit. + + .. code-block:: python + + # Assume these are matched pairs + matched_pixels = np.array([105.3, 512.5, 780.1]) + matched_wavelengths = np.array([6965.43, 7503.87, 7723.76]) * u.AA + + # Create a temporary WS object or manually set attributes if needed + # (Ensure pix_bounds is set if ws was initialized without arc_spectra) + # ws.pix_bounds = (0, 1024) # If not set earlier + + ws.fit_lines(pixels=matched_pixels, wavelengths=matched_wavelengths) + + +After fitting (either way), the pixel-to-wavelength +(`~specreduce.wavecal1d.WavelengthCalibration1D.pix_to_wav`) and wavelength-to-pixel +(`~specreduce.wavecal1d.WavelengthCalibration1D.wav_to_pix`) model transforms are calculated. + +**4. Inspecting the Fit** + +Several tools help assess the quality of the wavelength solution: + +* **RMS Error**: Calculate the root-mean-square error of the fit in wavelength or pixel units + using :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.rms`. + + .. code-block:: python + + rms_wave = ws.rms(space='wavelength') + rms_pix = ws.rms(space='pixel') + print(f"Fit RMS (wavelength): {rms_wave}") + print(f"Fit RMS (pixel): {rms_pix}") + +* **Plotting**: Visualize the fit and residuals: + + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_fit`: Shows the observed line + positions mapped to the wavelength axis, overlaid with the catalog lines and the fitted + solution. Also shows the fit residuals (observed - fitted wavelength) vs. pixel. + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_residuals`: Plots residuals vs. + pixel or vs. wavelength. + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_observed_lines`: Plots the + identified observed line positions (in pixels or mapped to wavelengths). Can optionally + overlay the arc spectrum. + * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_catalog_lines`: Plots the catalog + line positions (in wavelengths or mapped to pixels). + + .. code-block:: python + + import matplotlib.pyplot as plt + + fig_fit = ws.plot_fit() + fig_resid = ws.plot_residuals(space='wavelength') + plt.show() + + +**5. Using the Solution** + +Once satisfied with the fit, you can use the wavelength solution: + +* **Convert Coordinates**: Use :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.pix_to_wav` and + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.wav_to_pix` to convert between pixel and + wavelength coordinates. + + .. code-block:: python + + pixels = np.array([100, 500, 900]) + wavelengths = ws.pix_to_wav(pixels) + print(wavelengths) + +* **Get WCS Object**: Access the `gwcs.WCS` object representing the solution via the + :attr:`~specreduce.wavecal1d.WavelengthCalibration1D.wcs` attribute. This is particularly useful + for attaching the calibration to a :class:`~specutils.Spectrum` object. + + .. code-block:: python + + # Assuming 'science_spectrum' is a Spectrum1D object + # science_spectrum.wcs = ws.wcs + +* **Resample Spectrum**: Resample a spectrum (like your science target or the original arc lamp) + onto a new, potentially linearized, wavelength grid using + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.resample`. + + .. code-block:: python + + # Resample the original arc spectrum onto a grid of 1000 points + resampled_arc = ws.resample(arc_spectrum, nbins=1000) + + # The resampled spectrum now has a linear wavelength axis + print(resampled_arc.spectral_axis) + + +See Also +======== + +For hands-on examples, please refer to the wavelength calibration example notebooks provided with +``specreduce``. For detailed information on each method and its parameters, consult the API +documentation for :class:`~specreduce.wavecal1d.WavelengthCalibration1D`. + +.. _wavecal1d_doc: \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 52565e49..f0943d82 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,8 @@ docs = [ "matplotlib>=3.7", "photutils>=1.0", "synphot", + "nbsphinx", + "ipykernel" ] all = [ "matplotlib>=3.7", From c9fe759184cc2d88e07db535b272eea4677286a2 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 22 Apr 2025 20:33:14 +0100 Subject: [PATCH 37/76] removed a reference to old wavelength_calibration module from the main __init__.py --- specreduce/__init__.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/specreduce/__init__.py b/specreduce/__init__.py index ad1f3f06..8a9a0b47 100644 --- a/specreduce/__init__.py +++ b/specreduce/__init__.py @@ -1,8 +1,6 @@ # Licensed under a 3-clause BSD style license - see LICENSE.rst from specreduce.core import * # noqa -from specreduce.wavelength_calibration import * # noqa - try: from .version import version as __version__ From 26b8c49686c40a27478a58baf2fceb6940d402e4 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Wed, 23 Apr 2025 11:40:11 +0100 Subject: [PATCH 38/76] Updated the wavelength calibration tutorial notebooks. --- .../wavecal1d_example_01.ipynb | 312 ++++++++-------- .../wavecal1d_example_02.ipynb | 334 +++++++++--------- .../wavecal1d_example_03.ipynb | 6 +- 3 files changed, 306 insertions(+), 346 deletions(-) diff --git a/docs/wavelength_calibration/wavecal1d_example_01.ipynb b/docs/wavelength_calibration/wavecal1d_example_01.ipynb index 1079ff60..27d38118 100644 --- a/docs/wavelength_calibration/wavecal1d_example_01.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_01.ipynb @@ -19,29 +19,39 @@ "6. Evaluating and refining the wavelength solution\n", "7. Applying the solution to calibrate spectra\n", "\n", - "This example uses a [He-Hg-Cd lamp arc](https://mthamilton.ucolick.org/techdocs/instruments/kast/images/Kastblue600HeHgCd.jpg) from the [Shane telescope's](https://www.lickobservatory.org/explore/research-telescopes/shane-telescope/) [Kast spectrograph](https://mthamilton.ucolick.org/techdocs/instruments/kast/index.html)." + "For this demonstration, we use a [Helium-Mercury-Cadmium (He-Hg-Cd) calibration lamp](https://mthamilton.ucolick.org/techdocs/instruments/kast/images/Kastblue600HeHgCd.jpg) spectrum\n", + "obtained with the [Kast Double Spectrograph](https://mthamilton.ucolick.org/techdocs/instruments/kast/index.html) on the [Shane 3-meter telescope](https://www.lickobservatory.org/explore/research-telescopes/shane-telescope/) at Lick Observatory. The Kast blue channel configuration uses a 600 lines/mm grating covering approximately 3200-5700 Angstroms at moderate resolution.\n", + "\n", + "This interactive approach provides hands-on experience with the calibration process while\n", + "allowing careful validation of the results. For automated reduction pipelines, alternative\n", + "methods are available and covered in Tutorial 3." ], - "id": "d2b0f73230b294c" + "id": "4fd312a6be58ac88" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:14:08.848170Z", + "start_time": "2025-04-23T10:14:07.713518Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ "import astropy.units as u\n", "import numpy as np\n", "\n", "from astropy.io.fits import getdata\n", "from astropy.nddata import StdDevUncertainty\n", - "from matplotlib.pyplot import setp, subplots, close, rc\n", + "from matplotlib.pyplot import subplots, rc\n", "\n", "from specreduce.compat import Spectrum\n", "from specreduce.wavecal1d import WavelengthCalibration1D\n", "\n", - "rc('figure', figsize=(11, 3))" + "rc('figure', figsize=(6.3, 2))" ], - "id": "d3547a260abe63f4" + "id": "d0349a731069a333", + "outputs": [], + "execution_count": 1 }, { "metadata": {}, @@ -51,18 +61,23 @@ "\n", "First, we load the arc lamp flux data and create a `specutils.Spectrum` object, assuming that the flux is measured in counts (digital number units, `u.DN`). We also include measurement uncertainties. While these uncertainties are not directly used in fitting the wavelength solution, they are required by the default line-finding routine (`specutils.fitting.find_lines_threshold`).\n" ], - "id": "bb1ff795062f3d45" + "id": "bcd9f3e0dda780e9" }, { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:14:10.507515Z", + "start_time": "2025-04-23T10:14:10.501542Z" + } + }, "cell_type": "code", - "execution_count": 3, - "id": "e3dd2a51-ef1a-4501-9bfb-62166676e88f", - "metadata": {}, - "outputs": [], "source": [ "flux = getdata('shane_kast_blue_600_4310_d55.fits', 1).astype('d')\n", "arc_spectrum = Spectrum((flux - np.median(flux)) * u.DN, uncertainty=StdDevUncertainty(np.sqrt(flux)))" - ] + ], + "id": "63bc459f2c36d759", + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", @@ -95,16 +110,33 @@ ] }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:14:20.716957Z", + "start_time": "2025-04-23T10:14:20.625377Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ - "ws = WavelengthCalibration1D(ref_pixel=1000, degree=5, arc_spectra=arc_spectrum, line_lists=[['CdI', 'HgI', 'HeI']],\n", + "wc = WavelengthCalibration1D(ref_pixel=1000, degree=5, arc_spectra=arc_spectrum,\n", + " line_lists=[['CdI', 'HgI', 'HeI']],\n", " line_list_bounds=(3200, 5700), unit=u.angstrom)\n", - "ws.plot_observed_lines();" + "wc.plot_observed_lines();" + ], + "id": "52091185a7a5e148", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "id": "52091185a7a5e148" + "execution_count": 3 }, { "cell_type": "markdown", @@ -122,25 +154,30 @@ }, { "cell_type": "code", - "execution_count": 11, "id": "e5bb4f88-355f-4278-9ec4-15876f4712e2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:14:50.599359Z", + "start_time": "2025-04-23T10:14:50.453572Z" + } + }, + "source": [ + "wc.find_lines(fwhm=4, noise_factor=15)\n", + "wc.plot_observed_lines(value_fontsize=6);" + ], "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.find_lines(fwhm=4, noise_factor=15)\n", - "ws.plot_observed_lines();" - ] + "execution_count": 6 }, { "cell_type": "markdown", @@ -156,24 +193,27 @@ }, { "cell_type": "code", - "execution_count": 14, "id": "da59f18b-1c86-410a-a5a6-dc9c94302cff", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:15:26.338881Z", + "start_time": "2025-04-23T10:15:26.102276Z" + } + }, + "source": "wc.plot_catalog_lines(value_fontsize=6);", "outputs": [ { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_catalog_lines();" - ] + "execution_count": 8 }, { "cell_type": "markdown", @@ -186,90 +226,28 @@ ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-20T11:28:24.766381Z", - "start_time": "2025-04-20T11:28:24.764517Z" - } - }, "cell_type": "code", - "source": [ - "from astropy.modeling.models import Polynomial1D\n", - "from specreduce.wavecal1d import _diff_poly1d" - ], - "id": "197547306d056a0c", - "outputs": [], - "execution_count": 6 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-20T11:30:24.220667Z", - "start_time": "2025-04-20T11:30:24.217569Z" - } - }, - "cell_type": "code", - "source": [ - "p = Polynomial1D(3, c0=1, c1=2, c2=3, c3=4)\n", - "_diff_poly1d(p).parameters" - ], - "id": "53df5437790a87ac", - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 2., 6., 12.])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 9 - }, - { + "id": "e95afe59-471d-4669-aafe-72b1f7d79d53", "metadata": { "ExecuteTime": { - "end_time": "2025-04-20T11:30:13.358098Z", - "start_time": "2025-04-20T11:30:13.355454Z" + "end_time": "2025-04-23T10:16:06.691112Z", + "start_time": "2025-04-23T10:16:06.447174Z" } }, - "cell_type": "code", - "source": "p.parameters", - "id": "55e5cf77e891e356", + "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, value_fontsize=6);", "outputs": [ { "data": { "text/plain": [ - "array([1., 2., 3., 4.])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 8 - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e95afe59-471d-4669-aafe-72b1f7d79d53", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": "ws.plot_fit(figsize=(11,5), plot_values=True);" + "execution_count": 11 }, { "cell_type": "markdown", @@ -290,9 +268,18 @@ }, { "cell_type": "code", - "execution_count": 16, "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:16:37.400778Z", + "start_time": "2025-04-23T10:16:37.385553Z" + } + }, + "source": [ + "wc.fit_lines(pixels=[496, 967, 1077, 1655, 1999],\n", + " wavelengths=[3890, 4360, 4473, 5087, 5462],\n", + " match_obs=True, match_cat=True)" + ], "outputs": [ { "name": "stderr", @@ -303,7 +290,7 @@ ] } ], - "source": "ws.fit_lines(pixels=[496, 967, 1077, 1655, 1999], wavelengths=[3890, 4360, 4473, 5087, 5462], match_obs=True, match_cat=True)" + "execution_count": 12 }, { "cell_type": "markdown", @@ -315,24 +302,27 @@ }, { "cell_type": "code", - "execution_count": 17, "id": "40883c16-11b3-402b-bd0b-c688c44deef2", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:17:01.911854Z", + "start_time": "2025-04-23T10:17:01.673393Z" + } + }, + "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, obs_to_wav=True, value_fontsize=6);", "outputs": [ { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_fit(figsize=(11,5), plot_values=True, obs_to_wav=True);" - ] + "execution_count": 14 }, { "cell_type": "markdown", @@ -346,24 +336,27 @@ }, { "cell_type": "code", - "execution_count": 31, "id": "343801bc-65fa-41c9-929b-72565cdee31d", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:17:40.763896Z", + "start_time": "2025-04-23T10:17:40.658566Z" + } + }, + "source": "wc.plot_residuals(space='wavelength');", "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_residuals(space='wavelength');" - ] + "execution_count": 15 }, { "cell_type": "markdown", @@ -372,7 +365,7 @@ "source": [ "The fit looks good! Now, we can move on to use the solution.\n", "\n", - "### 7. Use the solution\n", + "### 7. Use the Wavelength Solution\n", "#### 7.1 Rebin a Spectrum to Wavelength Space\n", "\n", "A primary goal of wavelength calibration is to transform spectra from the detector's pixel grid to a physical wavelength grid. The `resample` method does this, converting a `Spectrum` object from pixel space to wavelength space.\n", @@ -383,38 +376,46 @@ "- `wlbounds`: The desired `(start_wavelength, end_wavelength)` for the output grid. If `None`, defaults to the wavelengths corresponding to the first and last pixels.\n", "- `bin_edges`: Explicitly define the wavelength edges of the output bins. If provided, `nbins` and `wlbounds` are ignored.\n", "\n", - "The method uses the fitted `_p2w`, `_w2p`, and `_p2w_dldx` transformations to map the input pixel bins to the output wavelength bins. It performs an exact flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", + "The method uses the fitted pixel-to-wavelength and wavelength-to-pixel transformations and their\n", + "derivatived to map the input pixel bins to the output wavelength bins. It performs an exact\n", + "flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux\n", + "in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", "\n", "Here, we demonstrate by resampling the original arc spectrum itself. In a typical workflow, you would apply this `resample` method (using the `ws` object derived from the arc lamp) to your *science* spectrum observed with the same instrument setup." ] }, { "cell_type": "code", - "execution_count": 21, "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:18:17.097482Z", + "start_time": "2025-04-23T10:18:16.957257Z" + } + }, + "source": [ + "spectrum_wl = wc.resample(arc_spectrum)\n", + "\n", + "fig, ax = subplots(constrained_layout=True)\n", + "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", + "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", + "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", + "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", + "ax.autoscale(enable=True, axis='x', tight=True)" + ], "outputs": [ { "data": { - "image/png": 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rr79iw4YN6NKli5hm27ZtCAkJQadOnSTbq3///pLmmYceeghAybM+//3vf3H9+nWz6/zhhx/Qs2dPuLi4QKFQQKlUYuXKlWabOSMiIuDs7Cx+NjQrDRw4ULJPDNPLltvV1RVDhgyRTIuKioJer8fvv/9e7nbZtm0bIiIi4O/vLynzwIEDAUA87vbu3VvuOu5HQkIC8vPzMXbsWMn0pk2bok+fPlVuYqypsuXp0KEDgHvbd+fOndBqtRgzZoxk+zg4OCA8PFzSZJebm4s333wTrVq1gkKhgEKhgIuLC/Ly8szu86effrrK+YyIiMA///yDGzduID09HadOnULv3r0BAOHh4fjzzz+RlZWFq1evIikpSWwKBoBLly4hKioKvr6+kMvlUCqVCA8PBwBJvsaNG4dr165h9+7d4rTVq1fD19dXPB7i4+Nx584dREdHS7aHXq/HgAEDkJiYaPLohEFNvmtu/xQUFCAtLQ3AveNz+PDhknQjR46s8rY16NSpE5o1ayZ+dnBwQHBwcJWvMWUtWbIESqVS/OvYsaNJmqocA+fOncONGzcQFRUluRYEBgaiR48eNcqbPeIgkFXowoUL+P333zFo0CAQETIzM5GZmYlhw4YBgNlnQ/z8/CSfb926BQBo0qRJjfMRGBiIiRMnYuXKlTh//jw2bdqEgoIC8XkWA4VCgcaNG0umGR4aT09PBwDcvHkTRAQfHx/JxUipVOLgwYPiMz61kW/AdHvUhJOTk0kArVKp4OHhYZJWpVKhoKCgSssdPnw4EhMTER8fjxUrVsDV1RUjRoyQBPc3b97EX3/9ZbKtXF1dQUTi9urVqxd+/vlnMTho0qQJQkJCJEPObN68GcOHD0dAQAC+++47JCQkIDExEc8//7zZPJctn0qlqnB62WX4+PiYLLPs8WDOzZs3sXXrVpMyt2/fHgDEMqenp1e4jpoy5M3csePv719h3mtD2XPI0LErPz8fAMRnRh966CGTbbRp0ybJc3JRUVH4/PPP8cILL2Dnzp04fPgwEhMT4eXlJS7PWHXOF+PnAuPi4iCXy9GzZ08AwCOPPAKg5LnAss8D5ubm4tFHH8WhQ4cwd+5cxMXFITExEZs3b5aUEyi54fDz88Pq1asBABkZGfjll18wZswYyOVyyfYYNmyYyfb48MMPQUS4c+eO2TLU5LuV7Z/09HQoFAqT88TcsVqZsusyrM/cvjNmCBzLBotRUVFITExEYmJiuT19q3IMGM4Bc+fa/Z5/9oR7B7MKrVq1CkSEH3/80eyYY2vXrsXcuXPFiyEAk1ozLy8vACW982rL8OHDsWDBApw6dUoyXavVIj09XXLhMjykb5jm6ekJQRCwf/9+s72WDdNqK9/mahENAV3Zh96NfzwtwcvLC127dgVQ0ju4bdu2CA8Px7Rp07Bt2zYAJdvL0dGx3IfBPT09xf8PHToUQ4cORWFhIQ4ePIgFCxYgKioKzZs3R1hYGL777jsEBQVh06ZNku1S1bHCqqtsBxfA9Hgwx9PTEx06dMC8efPMzvf39xeXcfjw4XLXUVOGvKWkpJjMu3HjhmSb1wfD+n/88UcEBgaWmy4rKwvbtm3DrFmz8NZbb4nTCwsLyw2KqlPr3qtXL8jlcsTFxUGtVqNz585wcXEBALi5uaFTp07Yu3cv7ty5A4VCIQaIe/bswY0bNxAXFyfW/gEwO0yKXC7H6NGj8dlnnyEzM1Mc227cuHEm22Pp0qXl9mAtLwC7n++Wp3HjxtBqtbhz544kELzf47I6Hn/8cXz11Vf45ZdfxI46AODt7S129HN1dTV77lflGDCcI+bKZMlyNnRcE8jKpdPpsHbtWrRs2RJ79+41+Zs+fTpSUlLw66+/Vric4OBgtGzZEqtWrar2j725H0Gg5E4+OTlZ/DE2tn79eslnw5h3hmaiyMhIEBGuX7+Orl27mvyFhoYCAHr06AGNRoPly5eDiMrNY9m78KowDGT6119/Sab/8ssvVV5GXXj00UcxZswYbN++HQkJCQBKttfFixfRuHFjs9vL3KCsarUa4eHh+PDDDwFA7DEqCAJUKpXkIp+ammq2d3BtyMnJMdmm33//PWQyGXr16lXu9yIjI3Hq1Cm0bNnSbJkNx11ERES566iK8o6dsLAwODo64rvvvpNMv3btGvbs2YO+fftWafl1pX///lAoFLh48aLZ7WO4sRAEAURkcrP1zTffVKmHdmU0Gg0efPBBsSbQcI4bhIeHY+/evYiLi8PDDz8sBoiG469svlasWGF2PePGjUNBQQE2bNiANWvWICwsDA888IA4v2fPnmjUqBHOnDlT7vYw1FaXdT/fLY8hsN20aZNk+saNG03S1uT6VRVPPvkk2rVrh/nz5+Ps2bO1umwAaNOmDfz8/LBhwwbJ9fnKlSuIj4+v9fXZKq4JZOX69ddfcePGDXz44YcmF1cACAkJweeff46VK1dWOqr8F198gcGDB6N79+6YNm0amjVrhqtXr2Lnzp0mQZuxefPm4Y8//sCzzz4rDueSlJSEzz//HOnp6Vi8eLEkvUqlwkcffYTc3Fw89NBDiI+Px9y5czFw4ECxeahnz5548cUXMW7cOBw5cgS9evWCs7MzUlJScODAAYSGhmLixIlwcXHBRx99hBdeeAGPPfYYJkyYAB8fH1y4cAEnTpzA559/DgBi0Pjhhx9i4MCBkMvl6NChQ4UXbl9fXzz22GNYsGAB3N3dERgYiN9++01sjqpPH3zwATZt2oR3330Xu3fvxtSpU/HTTz+hV69emDZtGjp06AC9Xo+rV69i165dmD59Orp164b33nsP165dQ9++fdGkSRNkZmaKz/8YfpQiIyOxefNmTJo0CcOGDUNycjI++OAD+Pn5mX2+9H41btwYEydOxNWrVxEcHIwdO3bg66+/xsSJEyXPOZX1/vvvIzY2Fj169MCUKVPQpk0bFBQU4PLly9ixYweWL1+OJk2aYMyYMfjkk08wZswYzJs3D61bt8aOHTuwc+fOKuXP8Padr776Cq6urnBwcEBQUBAaN26Md999F2+//TbGjBmDkSNHIj09HXPmzIGDgwNmzZpVpeUfPHjQ7PTw8HCxprsmmjdvjvfffx/vvPMOLl26hAEDBsDd3R03b97E4cOH4ezsjDlz5sDNzQ29evXC4sWL4enpiebNm2Pfvn1YuXIlGjVqVOP1G4uIiMDixYshCIJ402EQHh6OTz75BEQkeVa4R48ecHd3x8svv4xZs2ZBqVRi/fr1OHHihNl1PPDAAwgLC8OCBQuQnJyMr776SjLfxcUFS5cuRXR0NO7cuYNhw4bB29sbt27dwokTJ3Dr1i0sW7bM7LLv57vlGTBgAHr27Inp06cjOzsbXbp0QUJCgjh8ivHQWobr15IlSxAdHQ2lUok2bdqI4yvWlFwux88//4z+/fvj4YcfxoQJE9C7d2+4u7sjMzMThw4dwokTJ8odPqYyMpkMH3zwAV544QU8+eSTmDBhAjIzMzF79mxuDq6OeuqQwhqAJ554glQqlThkhTkjRowghUJBqampYs/WxYsXm02bkJBAAwcOJI1GQ2q1mlq2bEnTpk2rMA8HDx6kV155hTp27EgeHh4kl8vJy8uLBgwYIPZKNYiOjiZnZ2f666+/qHfv3uTo6EgeHh40ceJESQ9Tg1WrVlG3bt3I2dmZHB0dqWXLljRmzBiT4VV27NhB4eHh5OzsTE5OTtSuXTv68MMPxfmFhYX0wgsvkJeXFwmCIOlpBzPDkRikpKTQsGHDyMPDgzQaDT333HN05MgRs72DnZ2dTb4fHh5O7du3N5keGBhIgwYNKnebGlSUt9dff10yDERubi793//9H7Vp04ZUKhVpNBoKDQ2ladOmiUPqbNu2jQYOHEgBAQGkUqnI29ub/vWvf9H+/fsly164cCE1b96c1Go1tW3blr7++mux12Nl+SvvGDPXG9awfeLi4qhr166kVqvJz8+P3n77bSouLjZZl3GPSaKS4UGmTJlCQUFBpFQqycPDg7p06ULvvPOO5Hi6du0aPf300+Ti4kKurq709NNPU3x8fJV6BxOV9GwOCgoiuVxu8p1vvvmGOnToIG7zoUOHSoZfKY9he5T3Z+hhWbbchp6rZXuCl9eb/eeff6aIiAhyc3MjtVpNgYGBNGzYMMmwJobt4+7uTq6urjRgwAA6deoUBQYGUnR0dKXrroyhp67x8C8Gd+7cEUcWiI2NlcyLj4+nsLAwcnJyIi8vL3rhhRfo2LFj5e63r776igCQo6NjuUNV7du3jwYNGkQeHh6kVCopICCABg0aJDkuzQ0RU9XvGs4T46F1ylvmnTt3aNy4cdSoUSNycnKixx9/nA4ePGh2BIWZM2eSv7+/uK0M+7m8a0l4eDiFh4eb3QZlZWVl0fz58+mhhx4iNzc3UigU5O3tTY8//jh98cUXlJeXJ6atqFd7ecfgN998Q61btyaVSkXBwcG0atUqio6O5t7BVSQQVdDOxVgDMnbsWPz444/Izc2t76wwK9C7d2/cvn3b5LlRxuzV999/j1GjRuGPP/7gHrQMADcHM8YYYzZnw4YNuH79OkJDQyGTyXDw4EEsXrwYvXr14gCQiTgIZIwxxmyMq6srNm7ciLlz5yIvLw9+fn4YO3Ys5s6dW99ZY1aEm4MZY4wxxuwQDxHDGGOMMWaHOAhkjDHGGLNDHAQyxhhjjNkh7hhSB/R6PW7cuAFXV9dqvQKJMcYYY8yAiJCTkwN/f3/JIN+1hYPAOnDjxg00bdq0vrPBGGOMMRuQnJyMJk2a1PpyOQisA4bX7SQnJ8PNza2ec8MYY4yxhig7OxtNmza979f4lYeDwDpgaAJ2c3PjIJAxxhhj96WuHi3jjiGMMcYYY3aIg0DGmN377e+bGL4iAVfS8+o7K4wxZjEcBDLG7N74tUdwOOkOZv1yur6zwhhjFsNBIGOMlbpbpKvvLDDGmMVwEMgYY6XcnZT1nQXGGLMYDgIZY6yUmwMHgYwx+2EVQeDvv/+OwYMHw9/fH4Ig4Oeff5bMHzt2LARBkPx1795dkqawsBCTJ0+Gp6cnnJ2dMWTIEFy7dk2SJiMjA6NHj4ZGo4FGo8Ho0aORmZkpSXP16lUMHjwYzs7O8PT0xJQpU1BUVFQXxWaMWRkZv+GHMWZHrCIIzMvLQ8eOHfH555+Xm2bAgAFISUkR/3bs2CGZP3XqVGzZsgUbN27EgQMHkJubi8jISOh0957xiYqKwvHjxxETE4OYmBgcP34co0ePFufrdDoMGjQIeXl5OHDgADZu3IiffvoJ06dPr/1CM8asDoHqOwuMMWYxVjFY9MCBAzFw4MAK06jVavj6+pqdl5WVhZUrV+Lbb7/FY489BgD47rvv0LRpU+zevRv9+/fH33//jZiYGBw8eBDdunUDAHz99dcICwvDuXPn0KZNG+zatQtnzpxBcnIy/P39AQAfffQRxo4di3nz5pU78HNhYSEKCwvFz9nZ2dXeBoyx+kccAzLG7IhV1ARWRVxcHLy9vREcHIwJEyYgLS1NnHf06FEUFxejX79+4jR/f3+EhIQgPj4eAJCQkACNRiMGgADQvXt3aDQaSZqQkBAxAASA/v37o7CwEEePHi03bwsWLBCbmDUaDb83mLEGimNAxpg9aRBB4MCBA7F+/Xrs2bMHH330ERITE9GnTx+x9i01NRUqlQru7u6S7/n4+CA1NVVM4+3tbbJsb29vSRofHx/JfHd3d6hUKjGNOTNnzkRWVpb4l5ycfF/lZYzVD64JZIzZE6toDq7Ms88+K/4/JCQEXbt2RWBgILZv346nnnqq3O8RkeR9e+bevVeTNGWp1Wqo1epKy8EYY4wxZi0aRE1gWX5+fggMDMT58+cBAL6+vigqKkJGRoYkXVpamliz5+vri5s3b5os69atW5I0ZWv8MjIyUFxcbFJDyBizPdwxhDFmTxpkEJieno7k5GT4+fkBALp06QKlUonY2FgxTUpKCk6dOoUePXoAAMLCwpCVlYXDhw+LaQ4dOoSsrCxJmlOnTiElJUVMs2vXLqjVanTp0sUSRWOM1SeOARljdsQqmoNzc3Nx4cIF8XNSUhKOHz8ODw8PeHh4YPbs2Xj66afh5+eHy5cv4+2334anpyeefPJJAIBGo8H48eMxffp0NG7cGB4eHpgxYwZCQ0PF3sJt27bFgAEDMGHCBKxYsQIA8OKLLyIyMhJt2rQBAPTr1w/t2rXD6NGjsXjxYty5cwczZszAhAkTyu0ZzBizHRwDMsbsiVUEgUeOHEFERIT4+bXXXgMAREdHY9myZTh58iTWrVuHzMxM+Pn5ISIiAps2bYKrq6v4nU8++QQKhQLDhw9Hfn4++vbtizVr1kAul4tp1q9fjylTpoi9iIcMGSIZm1Aul2P79u2YNGkSevbsCUdHR0RFReE///lPXW8CxpgVIO4ZwhizIwLxVa/WZWdnQ6PRICsri2sQGWsAmr+1HQAwtJM/lox4sJ5zwxhjJeo6nmiQzwQyxlhd4Ftixpg94SCQMcZKcQzIGLMnHAQyxlgpfjqGMWZPOAhkjLFSHAIyxuwJB4GMMWbAUSBjzI5wEMgYY4wxZoc4CGSMsVL82jjGmD3hIJAxxkpxvxDGmD3hIJAxxkpxEMgYsyccBDLGWCluDmaM2ZMqvzv4l19+qfbCH3/8cTg6Olb7e4wxZik8NiBjzF5VOQh84oknqrVgQRBw/vx5tGjRorp5YowxizGOATkeZIzZk2o1B6empkKv11fpz8nJqa7yzBhjtYbK+T9jjNm6KgeB0dHR1Wrafe655+Dm5lajTDHGmKUYNwdzTSBjzJ5UuTl49erV1VrwsmXLqp0ZxhizNKrgE2OM2TLuHcwYs2v8TCBjzF5VuSYQAGQyGQRBqDCNIAjQarX3lSnGGLMUHhaGMWavqhUEbtmypdx58fHxWLp0KQ+3wBhrUCQ1gfWXDcYYs7hqBYFDhw41mXb27FnMnDkTW7duxahRo/DBBx/UWuYYY8yS+CaWMWZPavxM4I0bNzBhwgR06NABWq0Wx48fx9q1a9GsWbPazB9jjFmMnmNAxpgdqXYQmJWVhTfffBOtWrXC6dOn8dtvv2Hr1q0ICQmpi/wxxlid4uZgxpi9qlZz8KJFi/Dhhx/C19cXGzZsMNs8zBhjDQl3DGGM2atqBYFvvfUWHB0d0apVK6xduxZr1641m27z5s21kjnGGKtr0iFiOCBkjNmPajUHjxkzBsOHD4eHhwc0Gk25f9X1+++/Y/DgwfD394cgCPj5558l84kIs2fPhr+/PxwdHdG7d2+cPn1akqawsBCTJ0+Gp6cnnJ2dMWTIEFy7dk2SJiMjA6NHjxbzOXr0aGRmZkrSXL16FYMHD4azszM8PT0xZcoUFBUVVbtMjLGGgcM+xpi9qlZN4Jo1a+okE3l5eejYsSPGjRuHp59+2mT+okWL8PHHH2PNmjUIDg7G3Llz8fjjj+PcuXNwdXUFAEydOhVbt27Fxo0b0bhxY0yfPh2RkZE4evQo5HI5ACAqKgrXrl1DTEwMAODFF1/E6NGjsXXrVgCATqfDoEGD4OXlhQMHDiA9PR3R0dEgIixdurROys4Yq1/82jjGmN0iKwOAtmzZIn7W6/Xk6+tLCxcuFKcVFBSQRqOh5cuXExFRZmYmKZVK2rhxo5jm+vXrJJPJKCYmhoiIzpw5QwDo4MGDYpqEhAQCQGfPniUioh07dpBMJqPr16+LaTZs2EBqtZqysrKqXIasrCwCUK3vMMbqR1Z+EQW+uY0C39xGUV8n1Hd2GGNMVNfxRLV7B+v1eqxatQqRkZEICQlBaGgohgwZgnXr1tXJ8zRJSUlITU1Fv379xGlqtRrh4eGIj48HABw9ehTFxcWSNP7+/ggJCRHTJCQkQKPRoFu3bmKa7t27Q6PRSNKEhITA399fTNO/f38UFhbi6NGj5eaxsLAQ2dnZkj/GWMPAr41jjNmragWBRIQhQ4bghRdewPXr1xEaGor27dvjypUrGDt2LJ588slaz2BqaioAwMfHRzLdx8dHnJeamgqVSgV3d/cK03h7e5ss39vbW5Km7Hrc3d2hUqnENOYsWLBA8kxk06ZNq1lKxli94cCPMWanqv1M4O+//47ffvsNERERknl79uzBE088gXXr1mHMmDG1mkkAJu8sJqJK32NcNo259DVJU9bMmTPx2muviZ+zs7M5EGSsgTAeIoZrAhlj9qRaNYEbNmzA22+/bRIAAkCfPn3w1ltvYf369bWWOQDw9fUFAJOauLS0NLHWztfXF0VFRcjIyKgwzc2bN02Wf+vWLUmasuvJyMhAcXGxSQ2hMbVaDTc3N8kfY6xhkA4WzVEgY8x+VCsI/OuvvzBgwIBy5w8cOBAnTpy470wZCwoKgq+vL2JjY8VpRUVF2LdvH3r06AEA6NKlC5RKpSRNSkoKTp06JaYJCwtDVlYWDh8+LKY5dOgQsrKyJGlOnTqFlJQUMc2uXbugVqvRpUuXWi0XY8w6GId9XBPIGLMn1WoOvnPnToU1Yj4+Pia1cVWRm5uLCxcuiJ+TkpJw/PhxeHh4oFmzZpg6dSrmz5+P1q1bo3Xr1pg/fz6cnJwQFRUFANBoNBg/fjymT5+Oxo0bw8PDAzNmzEBoaCgee+wxAEDbtm0xYMAATJgwAStWrABQMkRMZGQk2rRpAwDo168f2rVrh9GjR2Px4sW4c+cOZsyYgQkTJnDtHmM2yrhDG8eAjDF7Uq0gUKfTQaEo/ytyuRxarbbamThy5IikidnwfF10dDTWrFmDN954A/n5+Zg0aRIyMjLQrVs37Nq1SxwjEAA++eQTKBQKDB8+HPn5+ejbty/WrFkjjhEIAOvXr8eUKVPEXsRDhgzB559/Lsn/9u3bMWnSJPTs2ROOjo6IiorCf/7zn2qXiTHWMFC5HxhjzLYJVI1xXWQyGQYOHAi1Wm12fmFhIWJiYqDT6Wotgw1RdnY2NBoNsrKyuAaRMSt3K6cQD83bDQB4qLk7fni5Rz3niDHGStR1PFGtmsAxY8ZU2iO3LnoGM8ZYXTHuDKLnmkDGmB2xitfGMcZYvTEK/PTcM4QxZkeq3Dv4r7/+gl6vr/KCT58+XaPnAxljzJKMwz6uCWS26syNbDz15R+Iv3C7vrPCrEiVg8AHH3wQ6enpVV5wWFgYrl69WqNMMcaYpUhfG8dRILNNr278E8euZiLqm0P1nRVmRarcHExEePfdd+Hk5FSl9EVFRTXOFGOM1QduDma2KreQW+aYqSoHgb169cK5c+eqvOCwsDA4OjrWKFOMMWYp/No4Zg/cnVRIySqo72zYrb9TsvHHhdt4vmcQZLKKO9haUpWDwLi4uDrMBmOM1Q+SdAypv3wwVpdcHKrVD5TVIiLCwCX7AQAtvJzR54HyX7phadV6bRxjjNka6WvjOApktkleyfBurO7cLbo3dnJqVmE95sQUB4GMMbtmHPjxM4HMVsn4177e6IyuK3Ir2w9Wlh3GGLMsbg5m9kDGNYH1Rqe7d2HRWtlFhoNAxhgrxc3BzFZV9rYvVneMawJ1HAQyxpj1kI4TWH/5YKwucQhYf/RGgV+Rtuov3bCEGgWBu3fvLnfeihUrapwZxhizNOm7gzkKZLbJikYlsTvGNYGFthAEDho0CNOnT5cMCH3r1i0MHjwYM2fOrLXMMcZYXeNnApk94GcC64/O1moCf//9d2zduhUPPfQQTp8+je3btyMkJAS5ubk4ceJEbeeRMcbqjPTdwRwFMttk/Eygnu92LEpvFPcV6WwgCOzWrRv+/PNPdOjQAV26dMGTTz6J6dOnY8+ePWjatGlt55ExVgV3i7TcsaEGjLcZbz5mq4ybg62th6qtM24OtomaQAA4d+4cEhMT0aRJEygUCpw9exZ3796tzbwxxqroRmY+2r23E2NXJ9Z3VhocHiya2QPj5mCt3roCEVtnc83BCxcuRFhYGB5//HGcOnUKiYmJYs1gQkJCbeeRMVaJzceuAQD2/XOrnnPS8PAzgQ2fVqfHygNJ+Dslu76zYrWMB4vmmkDLMg4CC7W6ClJaXo1eJrhkyRL8/PPPGDhwIACgffv2OHz4MN5++2307t0bhYXW9VoUxmydwtqGoW9QuHdwQ7cxMRkfbDsDALi8cFA958Y6CUaDxGh1fJxbkjXXBNYoCDx58iQ8PT0l05RKJRYvXozIyMhayRhjrOoUPP5DjXFNYMPHNYDVo7Wyzgm2zvjm0iY6hpQNAI2Fh4fXODOMsZrhILDm+JnAhk+l4JrwyhjXRnFzsGXZXE3g+++/X+H89957r0aZYYzVDDcH15zkjSH1lw12HzgIrJxxD1VuDrYsax4sukZB4JYtWySfi4uLkZSUBIVCgZYtW3IQyJiFGdcEanV6Dgqrgd8Y0vCpFfL6zoLV00tqAq0rELF1NvfauD///FPyd+rUKaSkpKBv376YNm1abecRADB79mwIgiD58/X1FecTEWbPng1/f384Ojqid+/eOH36tGQZhYWFmDx5Mjw9PeHs7IwhQ4bg2rVrkjQZGRkYPXo0NBoNNBoNRo8ejczMzDopE2O1xTjos7ZnTqyd5JlAbiZrkNRGNYG8D80zvsHh5mDLkjQHW9n1udaqC9zc3PD+++/j3Xffra1Fmmjfvj1SUlLEv5MnT4rzFi1ahI8//hiff/45EhMT4evri8cffxw5OTlimqlTp2LLli3YuHEjDhw4gNzcXERGRkKnu9dlOyoqCsePH0dMTAxiYmJw/PhxjB49us7KxFhtMK74Kyy2rotMQ8K/jQ2TyugEsLbmNmth3AJcbGWBiK2zuWcCy5OZmYmsrKzaXKSEQqGQ1P4ZEBE+/fRTvPPOO3jqqacAAGvXroWPjw++//57vPTSS8jKysLKlSvx7bff4rHHHgMAfPfdd2jatCl2796N/v374++//0ZMTAwOHjyIbt26AQC+/vprhIWF4dy5c2jTpk2dlY2x2sI/gtVjXEPCzcENk8zocYginR6O4ObhsoxrSHV8t2NR1vzGkBoFgZ999pnkMxEhJSUF3377LQYMGFArGTPn/Pnz8Pf3h1qtRrdu3TB//ny0aNECSUlJSE1NRb9+/cS0arUa4eHhiI+Px0svvYSjR4+iuLhYksbf3x8hISGIj49H//79kZCQAI1GIwaAANC9e3doNBrEx8eXGwQWFhZKxkbMzubhCphlGd/Y8/M+1SMdIoZ/HBsi487xXMtlnnHgV8wdQyzKmpuDaxQEfvLJJ5LPMpkMXl5eiI6OxsyZM2slY2V169YN69atQ3BwMG7evIm5c+eiR48eOH36NFJTUwEAPj4+ku/4+PjgypUrAIDU1FSoVCq4u7ubpDF8PzU1Fd7e3ibr9vb2FtOYs2DBAsyZM+e+ysfY/dAZBX58l189kppA67o+syqSBji8E82RPBPI28ii9LZWE5iUlFTb+aiU4e0kABAaGoqwsDC0bNkSa9euRffu3QEAgiAdK42ITKaVVTaNufSVLWfmzJl47bXXxM/Z2dlo2rRpxQVirBYZX9P5Lr96jGNmHdcENkjW/MyVtTAORPhG0bKMr8/W9rhOgx1HwtnZGaGhoTh//rz4nGDZ2rq0tDSxdtDX1xdFRUXIyMioMM3NmzdN1nXr1i2TWkZjarUabm5ukj/GLEnHF/ga4x/Hhs/4+OeaQPMktaV8nFuUNd+kVLkm0LimqzIff/xxjTJTHYWFhfj777/x6KOPIigoCL6+voiNjcWDDz4IACgqKsK+ffvw4YcfAgC6dOkCpVKJ2NhYDB8+HACQkpKCU6dOYdGiRQCAsLAwZGVl4fDhw3j44YcBAIcOHUJWVhZ69OhR52VirKZ0Rj98/CNYPWWHFNHrSdLRgFk/nc74R5YDHHMkNd783INF2URz8J9//lmldJU1v9bUjBkzMHjwYDRr1gxpaWmYO3cusrOzER0dDUEQMHXqVMyfPx+tW7dG69atMX/+fDg5OSEqKgoAoNFoMH78eEyfPh2NGzeGh4cHZsyYgdDQULG3cNu2bTFgwABMmDABK1asAAC8+OKLiIyM5J7BzKrpJBd4/hGsjrKbS0cEGTgIbEi0/ExgpfSS2lK+RlhS2Y4hVXlUzVKqHATu3bsXly5dQvPmzSGTWb4V+dq1axg5ciRu374NLy8vdO/eHQcPHkRgYCAA4I033kB+fj4mTZqEjIwMdOvWDbt27YKrq6u4jE8++QQKhQLDhw9Hfn4++vbtizVr1kAuvzecwPr16zFlyhSxF/GQIUPw+eefW7awjFWT8Z099w6unrI9grmHcMOj5+bgSkneHcxBoEWVvTEv0umt5i031eoY0rp1a6SkpIg9aJ999ll89tlnFT4vV1s2btxY4XxBEDB79mzMnj273DQODg5YunQpli5dWm4aDw8PfPfddzXNJmP1QjJEDF/gq8UkCOQYosHRWvEzV9ZCEgTyQW5RJkGg1nqCwGpV6VGZi+WOHTuQl5dXqxlijFUfd26oubK/h9xDuOGx5nHYrIXxYc03ipZV9ppiTTcqDbZ3MGPsHuOLOvf8q56yNYEcRDc8PBBy5SRvreBA2aLKdj6zpu1frSBQEASThxmt5eFGxuyZdIgY67nANASmzcEcRDQ0PFh05YyP6/wiXT3mxP5Yc01gtZ4JJCKMHTsWarUaAFBQUICXX34Zzs7OknSbN2+uvRwyxiplHPhxTUj1lG395Y4hDY/xM27W9ANrTYwDkfxiDgItyaQm0IqO0WoFgdHR0ZLPzz33XK1mhjFWM8aVH9ycWT0mzcEcBDY4/Exg5YyP80IOAi2q7DXZmt4aUq0gcPXq1XWVD8bYfeAhMmqu7AWaW9MbHm4Orpzxcc01gZZVtnHGmoJA7hjCmA0w7hjCNYHVY26waNawSAaLtqIfWGtifF3gINCyyj6nbU3NwRwEMmYDjGsCtRwEVkvZoa+4Y0jNZN0txoBPf8fS385bfN3cHFw542tEQTFvI0sqe0ha0zHKQSBjNoDfBlBzJjWBHATWyNqEyzibmoOPYv+x+Lp5iJjK6bljSL0p+9wx1wQy1sAcupSOQZ/tx5HLd+o7K2YZ1/7xEDHVU1+vjdt4+Cp+OJJskXVZQm6htt7WreM3hlTKeBsV8BAxFmXujSHWgoNAxqpgztYzOH0jG8OWJ9R3VszSc01IjdVHEJhTUIy3Np/E6z/+heyC4jpfnyXUZ4cMrY13DDl65Q6upN/f27n4mcD6UzYIzKvHG6ayOAhkrAqy8q37h1paE8hBYHWYvjGk7tdpXBNwO6ew7ldoAcbBV9nnLOua3oZrAk/fyMLTyxIwcMn++1qO8S4p4CDQospeYzLuFtVTTkxxEMhYFVj7i3EkQ8Rwc3C1mLw72AJBtHFtbZqNBIEC7p0klh4CQ2vDtVwxp1IBAHfvswlXOlg0XyMsqew1JdOKKhU4CGSsCqw9CDS+yOi4Obha6qM52LjWzNprmatKIb93klj6tWSSpk4be95N2qu35mWTPBNoY4GytTMJArkmkDVkOQXFGLv6MBbvPFvfWbEY41oOayTpHcnNwdVSNuizxBA7xkNE2MoPsvFmzCuy7DNPxsf//daYWRuZ0R3o/dww1FYwyarPcHw6qeQAgMy71nPjx0Egq7bVf1xG3Llb+GLvxfrOisU0qJpAbg6ulrIxnyU6Fhg/t2YrQYvxdrN0mSRBoI0FOMZN69n3FQTe+7+tNZlbO0NTvIezCgA/E8gauBwb6c1oS4xrr3iw6OqpjzG8jAMmW2m+NC6TpXs/avXG29N6el7WBuPj435qAm25ydzaGc4Nb1c1AK4JZFasKg/FC0bVYrY4HIM5Vl4RKH1jCD8TWC1lD3mLB4E2UitjfNxZvCbQaB/aSs2qgfHxUdMgsOxbcAq1en4zjgUVa0u2tberAwDreg6Yg0Amupp+F53e34UPtp2pMJ1xQGQvz5YYB77WePHU8RAxNVZ2OBNL9Gwt0tperUxRPdYE6vS2V7NqUBs1geZGDLB0D257Zjg3vN1KagK5OZhZpYUxfyOnQIuVB5Kq/B17eQelceBrTe99NNDZ+GC5dclkNH9LPBNotI76fNNGbTKuCbR0TUd91kLWNeOawJo2I5oL+GylBrohKCrTHFxQrLeaChQOApnoTl7V7k6MLyjWciDXNeOOIdZY08A1gTVXL83BRuuo6nln7Yyfy7P02IdaSe9g2wiqDYyvNylZ+TVaRqHRzbqydCgfe7l2WwPD+e7urIJcVrL9reW5QA4CmUitkIv/ryiQMA4CbWWg28pYe82N8UCw3MxTPWVrTi0RBBbaYBBYZFQbl5pVYNF1GwdK9VHD9d8jydh5OrVOlm1cnhuZNduuhdqSZagVMjgqS67zthYsWzPD74dKLkMjRyUAIDPfOs57DgLrCRHhq98vIv7C7frOikituHc4/J2SXW464x/Jp5fFW/wVUfXB+Ecmp8D6Lp7GQYU1PXTcEJTt7V6krfsgwngcvXQbCQK1RsF0araFg0CjQKlYRxZ9JOJEcibe+PEvvPTt0TqpXTNe5rXMGtYEll4f1AoZNE4lQQhfJyzHcDyqFDI0Kt3+GXnWsf05CKwn8RfTMX/HWUR9c8hq3nVpHEj8dS2rgnTSC92ineew+o+qP0fYEBk/Z2SNNYHGgYy50egz8ooQvngvpmz405LZahDKBvWWqEm9a3QMpefaRm26ceB108JBYNlaLUs+F3jqxr1r5T83c2p9+cYB7vWM+2sOVivl8HAqHavOSoIQe2DoHaySy9CodPtncU2gdfvyyy8RFBQEBwcHdOnSBfv339/Lu8u6nJ4n/v/k9fIDLksyvpBeSMstN13ZH81lcRcxZ+sZ/Hk1o87yVp+IqMzD2dZx8hrLzr+3T8w9a/LTsWu4kn4Xv5y4YfGem9bOMABv6aM6SLFAU2aeUZCScbfIJmrTjQOv8pqDcwu1WBRzFmdTy29pqC69nkw6qFkysL6dc+96cPpG7ZXLwLgV4nZuYY2eSTbcuDso7wUhd6zwOmarCktvkJRyGTxdSrb/tRoG9LWNg0AzNm3ahKlTp+Kdd97Bn3/+iUcffRQDBw7E1atXa20dV+/cFf9vLcGTcSBxPq38O9rb5Vxgj16xjnLUtoJiveSVWLVxob9bpK1x01Hynbt45ftjiDmVIk7LNqoJvHLnrkltrXHNzCkruemoLzo9IfnOXcScSkHYgt/w8/EbAIAHm7kDAPacTcPl23mSwOzSrdxaDZ6Nl1WsI5t4LvBm9r3rwu3cQknzsMGaP5LwZdxFDPh0f609k2Z8gxbQyBEAcL2GzaY1Ydz0vf2vlApS1kzZWk3j3w6DYl3FvU0Nx5daIYefpmSsuitGFRGs7hARckpvNJUKGTo0aQTAen4vBbKFW9Ba1q1bN3Tu3BnLli0Tp7Vt2xZPPPEEFixYYJK+sLAQhYX3LoDZ2dlo2rQpJq85ALWTCwglPzxaPUGr00OrJ8SeuSmm93RRI6xlYyhkAmSCALms5H2RRACBSv8toZCVTDcMDiwIJe+1FYR7PVj1+pLv6ankfZ5EJH5fTyXTZULJOvRU8vzM3SId4s7dEvMkCEB4sBcUMgGCUPLmXD0R8gp1SLiUDgAY1a0Z1h+6Fxi7OSjwcFBjyGWAvLQshnUAKP1c+q9M+jZe43KULbfhMwDIjbrpygRDuQUQEfRE0OnvpRX3T7EeWr0ebg7K0m1wb5voiSTb05BfwzQ9Ea5n5uPU9XuBXyMnJboGekCtkEEQSsp6L6+lay/NswABEEpqK7R6gk5PKNLqcfRKBhQyAT1becJRJRf3nWF/6amks4e+9DslZSPoCIi/cFvsDRkS4Iab2YW4lVMIQSi50yzS6qFxVMLXzQFujgo4KOXYf/7es6cBjRzRoYkGSrlMsqUMW1YQgIy7xSAiuDupoJTLoKeS/Ov1BKW8ZP8ZxkssewERULJPJK/aI7P/FQMtHZU8U2boOacnkhwPhn1hOHZVCjnkQsn3DNtIZ9hGpdtLEAQUa/Uo1pX8FZb+/3ZukcnzUCq5DFte6YHoVYniTY6zSg5fjQOKdHok38mHs0qO0CYaKGQycX1anR46PUHjpIKAkvPccEwKKMmrQi4DUckPtSFvCZfSJTcWzTycEOzjKtZGGmYZpxGE0uXJZCAQtLqS7aGQCZDJDMeONL3hX8M5otWReE7dW4sAB6UMWh1JOhiVx3CMFun0uJCWCzcHJZQKQXKOAEAbH1d4OKugUpQcP4VaPQ4n3RHnt/ByFoM2yTlvdL6L1z4z0wzn2s2sAtzIKoBaIcOjrT2x++80OCrlaO7pDJVCVno8onR/COK1BmWnGV2D7qUr2XYyoeS6YdiHhr+s/GKcL9Nq8mCzRnBSySGXyUq37r31S7Yj7nXAM2x2QxmLdYT8Ih1SsvJxO7cIMgEICdDgr2tZ0Dgq0cLLWfy9AEpu7PKLdQhs7AyNoxIKWcl1LL9Yj9zCYiTfKQmKewV7oU8bL8zeWjIWbCtvF7g6KEBUch0zPBduONf0+nvnneEczCnQIi27EC28nOHlqoZWV3LMG34rjH9vjH+7jEMN4/KK/ze338vZ98bLI8ny7n2QTpeu5978SvJUZj0wXg9gpqxG+SydkVuoFa83e6aHIz2vCM8sTwAAPODrCgelHGqFDIrSXttEJddxwzFTXJCH9ZP6ICsrC25ubqhtHASWUVRUBCcnJ/zwww948sknxemvvvoqjh8/jn379pl8Z/bs2ZgzZ47J9KZT/wuZ2qlO81vbmrg74gFfN+z++2aF6QIaOeKniT0w4qsEXE43vTO1Rd1beOCva1lWOw7ZxN4t0SFAg+k/nLDaPFqjvg9447nugYh4wBunrmdh5uaTOJOSXedD7biqFejT1hv/K62JtAW923ghoJGj5ObQEmQC8Hr/B/BIK09Erz5s8ZrVgEaOCA3QIKaOegj3CvbCs12bYvKGYyZDGlVVCy9nfD6yM/wbOWDMqsMVPvfNapdKLsOEXkF4vf8D0OkJo745iIOX7lT+RQD6wrtI/nQ4B4GWcuPGDQQEBOCPP/5Ajx49xOnz58/H2rVrce7cOZPvlFcTuHjrMTi7lOw0hUyAQi5AIRMgl5VE/SH+Gng4qxB3Lg13i3TQ6vXQ6UvvuIxrFAx3rrg3Mr7xGyyIpLWFMsFw9yqU/v/eHa7hu4bvGGqPHJQyuDooEdaiMdwcldh7Ng3peYWltTD3lqtWytDMwxnt/d3goJTjbpEWKVkF8HBS4cCF28gt1EprrvQk3lUbauCM7ywFCEY1APfyZFxu47toXZk7N0OtmbkyQ9weJdPzi3XiXb6hRtK4xsZw5ygTUFJTWZpOJZehja8rQvw1yCnU4uS1LCSl50Gn04t384blGNceiLWaRJDLZZALhv0vwFElh1wmICUzv6SGrbTsxrWb8tJaRplMgFy4969cJqC9vxuSM+6CCPByVcNP4wiv0oFIcwu1uJiWi5wCLXIKipFfrINcJqBT00bQ6QnHrmbibpFWvIOnMne3QMmzQ2qFHJl3i1Cso9LappI8FelKjlPDeGNlGdfqGO8H47pfwcxXjbehoXZVp6fSWuV7+1dPJbWpOj2JeZKXnleGWnRDDYlSIYNKLkClkEEpv/fX0ssZmXeL0czDCTKZaWaKtHpcSc/D7dwiFOv08HBWoVCrw7WMfOip5JhWlJ7HMkFAVn4xBEBSk2k43ot1JNYIltSWAG4OSoQ20cDTRY0LaTk4l5qL7IJi8zV5uHdsGs4rwzaSCUJJrbDhi6Xb0FCDYfi/YTsavmNYruG8vFukg1JeUqaKEEhy/ni6qiErrXFt6uGE1t4uEISSRyaS79xFkU6PIq0eMqFkH+j0hE5NG0EuE3DsaoakxtfccWA4bu6dV0ZHU+k0FwcFWnq5wMetpJmzoFiHc6k5yCg9dg21NYbaT+MWBr04j8T9ZjjugHu1P3oqOdaUckNrjSDWxDVxd0JLb2coZDIcuXwHaTmFpS0sJB7xhvWUrRwvOa+l5UTpceSsVsBZpYCzWo5gH1co5TLcyMzHhbRcFBTrJLXfXi5qtPJ2wYVbubhbqIO29BhxVMrhqJIjyNMZni7qe+smQtLtPKRkFSC3UCu29Bg6RRnOIcO10Pj/ckGAq4MCGXeLcCunUKy1KtYaH7zGLQuC0f9N96vxfi/9qtk05va/YXpl64FkGZWvB+WuX7i3fKN1GT5Jp5fkR6WQoYWXs2QINq1Oj1M3spGdX4xCrR6FWp044LkglNQE60sP1LzcbDzfJ4SDQEsxBIHx8fEICwsTp8+bNw/ffvstzp49W+kysrOzodFo6mynMcYYY8z21XU8wR1DyvD09IRcLkdqqrRaPy0tDT4+PvWUK8YYY4yx2sVBYBkqlQpdunRBbGysZHpsbKykeZgxxhhjrCFT1HcGrNFrr72G0aNHo2vXrggLC8NXX32Fq1ev4uWXX67S9w0t7NnZtT9mFGOMMcbsgyGOqKsn9zgINOPZZ59Feno63n//faSkpCAkJAQ7duxAYGBglb6fk1Myxl7Tpk3rMpuMMcYYswM5OTnQaDS1vlzuGFIH9Ho9bty4AVdXV0kv3uoy9DJOTk62yw4mXH77Lb89lx3g8nP57bf89lx2wLT8RIScnBz4+/tDVknv/ZrgmsA6IJPJ0KRJk1pbnpubm12eDAZcfvstvz2XHeDyc/ntt/z2XHZAWv66qAE04I4hjDHGGGN2iINAxhhjjDE7xEGgFVOr1Zg1axbUanXliW0Ql99+y2/PZQe4/Fx++y2/PZcdsHz5uWMIY4wxxpgd4ppAxhhjjDE7xEEgY4wxxpgd4iCQMcYYY8wOcRDIGGOMMWaHOAisY8uWLUOHDh3EgR/DwsLw66+/ivPHjh0LQRAkf927d5cso7CwEJMnT4anpyecnZ0xZMgQXLt2TZImIyMDo0ePhkajgUajwejRo5GZmWmJIparsrKXLbfhb/HixWKa3r17m8wfMWKEZD3WWHZzFixYAEEQMHXqVHEaEWH27Nnw9/eHo6MjevfujdOnT0u+11D3v7GyZS8uLsabb76J0NBQODs7w9/fH2PGjMGNGzck37OV/W9u39vyuV+WufLb8vk/e/Zsk3z7+vqK8239vK+o/LZ+7le2763uvCdWp3755Rfavn07nTt3js6dO0dvv/02KZVKOnXqFBERRUdH04ABAyglJUX8S09Plyzj5ZdfpoCAAIqNjaVjx45RREQEdezYkbRarZhmwIABFBISQvHx8RQfH08hISEUGRlp0bKWVVnZjcuckpJCq1atIkEQ6OLFi+IywsPDacKECZJ0mZmZkvVYY9nLOnz4MDVv3pw6dOhAr776qjh94cKF5OrqSj/99BOdPHmSnn32WfLz86Ps7GwxTUPd/wbmyp6ZmUmPPfYYbdq0ic6ePUsJCQnUrVs36tKli+S7trD/y9v3tnzuGyuv/LZ8/s+aNYvat28vyXdaWpo439bP+4rKb+vnfmX73trOew4C64G7uzt98803RFRyQAwdOrTctJmZmaRUKmnjxo3itOvXr5NMJqOYmBgiIjpz5gwBoIMHD4ppEhISCACdPXu2bgpRQ8ZlL2vo0KHUp08fybTw8HDJD0dZDaHsOTk51Lp1a4qNjZWUR6/Xk6+vLy1cuFBMW1BQQBqNhpYvX05EDX//l1d2cw4fPkwA6MqVK+K0hr7/Kyq/PZz71dn/tnT+z5o1izp27Gh2nj2c9xWV3xxbOvcrK7u1nffcHGxBOp0OGzduRF5eHsLCwsTpcXFx8Pb2RnBwMCZMmIC0tDRx3tGjR1FcXIx+/fqJ0/z9/RESEoL4+HgAQEJCAjQaDbp16yam6d69OzQajZimvpVXdoObN29i+/btGD9+vMm89evXw9PTE+3bt8eMGTOQk5MjzmsIZX/llVcwaNAgPPbYY5LpSUlJSE1NlexbtVqN8PBwMe8Nff+XV3ZzsrKyIAgCGjVqJJnekPd/ZeW39XO/qvvfFs//8+fPw9/fH0FBQRgxYgQuXboEwD7Oe6D88ptja+d+ZWW3pvNeUZMCsuo5efIkwsLCUFBQABcXF2zZsgXt2rUDAAwcOBDPPPMMAgMDkZSUhHfffRd9+vTB0aNHoVarkZqaCpVKBXd3d8kyfXx8kJqaCgBITU2Ft7e3yXq9vb3FNPWlorIbW7t2LVxdXfHUU09Jpo8aNQpBQUHw9fXFqVOnMHPmTJw4cQKxsbEArLvsALBx40YcO3YMiYmJJvMM+fPx8ZFM9/HxwZUrV8Q0DXX/V1T2sgoKCvDWW28hKipK8tL4hrz/Kyu/rZ/71dn/tnb+d+vWDevWrUNwcDBu3ryJuXPnokePHjh9+rTNn/dAxeVv3LixJK2tnfuVld3aznsOAi2gTZs2OH78ODIzM/HTTz8hOjoa+/btQ7t27fDss8+K6UJCQtC1a1cEBgZi+/btJhdEY0QEQRDEz8b/Ly9Nfaio7MZWrVqFUaNGwcHBQTJ9woQJ4v9DQkLQunVrdO3aFceOHUPnzp0BWG/Zk5OT8eqrr2LXrl0m5TJWNp9Vybu17/+qlh0oeVB8xIgR0Ov1+PLLLyXzGur+r0r5bfncr87+B2zv/B84cKD4/9DQUISFhaFly5ZYu3at2AnAFs97g4rK/9prr4nzbPHcr6zs1nbec3OwBahUKrRq1Qpdu3bFggUL0LFjRyxZssRsWj8/PwQGBuL8+fMAAF9fXxQVFSEjI0OSLi0tTbyT9PX1xc2bN02WdevWLZO7TUurStn379+Pc+fO4YUXXqh0eZ07d4ZSqZRsH2st+9GjR5GWloYuXbpAoVBAoVBg3759+Oyzz6BQKMT8lb1zK7tvG+L+r6zsOp0OQMmPwPDhw5GUlITY2FhJTYA5DWX/V7X8xmzp3K9O+W31/Dfm7OyM0NBQnD9/XuwpaovnfXmMy29gq+d+WebKbqy+z3sOAusBEaGwsNDsvPT0dCQnJ8PPzw8A0KVLFyiVSrEKHABSUlJw6tQp9OjRAwAQFhaGrKwsHD58WExz6NAhZGVliWmshbmyr1y5El26dEHHjh0r/f7p06dRXFwsbh9rLnvfvn1x8uRJHD9+XPzr2rUrRo0ahePHj6NFixbw9fWV7NuioiLs27dPzHtD3f+VlV0ul4s/AufPn8fu3btNmonMaSj7vyrlL8uWzv3qlN9Wz39jhYWF+Pvvv+Hn5yc2cdrieV8e4/IDsOlzv6yyZS+r3s/7anUjYdU2c+ZM+v333ykpKYn++usvevvtt0kmk9GuXbsoJyeHpk+fTvHx8ZSUlER79+6lsLAwCggIMBkqoEmTJrR79246duwY9enTx2x38Q4dOlBCQgIlJCRQaGhovXeVr6jsBllZWeTk5ETLli0z+f6FCxdozpw5lJiYSElJSbR9+3Z64IEH6MEHH7T6spenbI+3hQsXkkajoc2bN9PJkydp5MiRZoeKaIj7vyzjshcXF9OQIUOoSZMmdPz4cclwCYWFhURke/vfuPy2fu6bY663p62e/9OnT6e4uDi6dOkSHTx4kCIjI8nV1ZUuX75MRLZ/3ldUfls/9ysquzWe9xwE1rHnn3+eAgMDSaVSkZeXF/Xt21cMgu7evUv9+vUjLy8vUiqV1KxZM4qOjqarV69KlpGfn0///ve/ycPDgxwdHSkyMtIkTXp6Oo0aNYpcXV3J1dWVRo0aRRkZGZYqplkVld1gxYoV5OjoaDL+ExHR1atXqVevXuTh4UEqlYpatmxJU6ZMMRlTyRrLXp6yP4R6vZ5mzZpFvr6+pFarqVevXnTy5EnJdxrq/i/LuOxJSUkEwOzf3r17icj29r9x+W393DfHXBBoq+e/Ydw/pVJJ/v7+9NRTT9Hp06fF+bZ+3ldUfls/9ysquzWe9wIRUfXqDhljjDHGWEPHzwQyxhhjjNkhDgIZY4wxxuwQB4GMMcYYY3aIg0DGGGOMMTvEQSBjjDHGmB3iIJAxxhhjzA5xEMgYY4wxZoc4CGSMMcYYs0McBDLGGGOM2SEOAhljjDHG7BAHgYwxxqps2rRpGDFiBLKzs+s7K4yx+8RBIGOMsSrT6/XgV84zZhs4CGSMsVrQu3dvTJ06tb6zAaBmeenduzcEQYAgCDh+/Hi56ZYsWYJNmzbBzc1NnDZ27Fjxuz///HPNMs0YszgOAhljDcLy5cvh6uoKrVYrTsvNzYVSqcSjjz4qSbt//34IgoB//vnH0tm0uNoMPidMmICUlBSEhISYzIuPj4dcLseAAQNM5i1ZsgQpKSm1kgfGmOVwEMgYaxAiIiKQm5uLI0eOiNP2798PX19fJCYm4u7du+L0uLg4+Pv7Izg4uD6y2mA5OTnB19cXCoXCZN6qVaswefJkHDhwAFevXpXM02g08PX1tVQ2GWO1hINAxliD0KZNG/j7+yMuLk6cFhcXh6FDh6Jly5aIj4+XTI+IiAAAxMTE4JFHHkGjRo3QuHFjREZG4uLFi2LaFStWICAgAHq9XrK+IUOGIDo6GgBARFi0aBFatGgBR0dHdOzYET/++GO5ea1K+t69e2PKlCl444034OHhAV9fX8yePVuSJicnB6NGjYKzszP8/PzwySefSGr+xo4di3379mHJkiVic+zly5cBlDy7V9GyqyMvLw///e9/MXHiRERGRmLNmjU1XhZjzHpwEMgYazB69+6NvXv3ip/37t2L3r17Izw8XJxeVFSEhIQEMQjMy8vDa6+9hsTERPz222+QyWR48sknxaDvmWeewe3btyXLzcjIwM6dOzFq1CgAwP/93/9h9erVWLZsGU6fPo1p06bhueeew759+8zms6rp165dC2dnZxw6dAiLFi3C+++/j9jYWHH+a6+9hj/++AO//PILYmNjsX//fhw7dkycv2TJEoSFhYnNuCkpKWjatGmVll0dmzZtQps2bdCmTRs899xzWL16NXcOYcwWEGOMNRBfffUVOTs7U3FxMWVnZ5NCoaCbN2/Sxo0bqUePHkREtG/fPgJAFy9eNLuMtLQ0AkAnT54Upw0ZMoSef/558fOKFSvI19eXtFot5ebmkoODA8XHx0uWM378eBo5cqT4OTw8nF599dVqpX/kkUckaR566CF68803iYgoOzublEol/fDDD+L8zMxMcnJyoldffdVkvcYqW7Y55pZj0KNHD/r000+JiKi4uJg8PT0pNjbWJB0A2rJlS7nrYIxZF64JZIw1GBEREcjLy0NiYiL279+P4OBgeHt7Izw8HImJicjLy0NcXByaNWuGFi1aAAAuXryIqKgotGjRAm5ubggKCgIAyXNto0aNwk8//YTCwkIAwPr16zFixAjI5XKcOXMGBQUFePzxx+Hi4iL+rVu3TtKsbFCd9B06dJB89vPzQ1paGgDg0qVLKC4uxsMPPyzO12g0aNOmTZW2VUXLro5z587h8OHDGDFiBABAoVDg2WefxapVq6q9LMaYdTF9+pcxxqxUq1at0KRJE+zduxcZGRkIDw8HAPj6+iIoKAh//PEH9u7diz59+ojfGTx4MJo2bYqvv/4a/v7+0Ov1CAkJQVFRkSSNXq/H9u3b8dBDD2H//v34+OOPAUBsNt6+fTsCAgIk+VGr1SZ5rE56pVIp+SwIgvh9Km1uFQRBkoaq2Axb0bKrY+XKldBqtZKyEBGUSiUyMjLg7u5e7WUyxqwDB4GMsQYlIiICcXFxyMjIwOuvvy5ODw8Px86dO3Hw4EGMGzcOAJCeno6///4bK1asEIeROXDggMkyHR0d8dRTT2H9+vW4cOECgoOD0aVLFwBAu3btoFarcfXqVTHorEh105enZcuWUCqVOHz4sPicX3Z2Ns6fPy9Zrkqlgk6nq/F6KqLVarFu3Tp89NFH6Nevn2Te008/jfXr1+Pf//53naybMVb3OAhkjDUoEREReOWVV1BcXCwJhsLDwzFx4kQUFBSInULc3d3RuHFjfPXVV/Dz88PVq1fx1ltvmV3uqFGjMHjwYJw+fRrPPfecON3V1RUzZszAtGnToNfr8cgjjyA7Oxvx8fFwcXERexDXNH15XF1dER0djddffx0eHh7w9vbGrFmzIJPJJLWDzZs3x6FDh3D58mW4uLjAw8OjytuyMtu2bUNGRgbGjx8PjUYjmTds2DCsXLmSg0DGGjB+JpAx1qBEREQgPz8frVq1go+Pjzg9PDwcOTk5aNmypVhzJpPJsHHjRhw9ehQhISGYNm0aFi9ebHa5ffr0gYeHB86dO4eoqCjJvA8++ADvvfceFixYgLZt26J///7YunWr+HxhWdVNX56PP/4YYWFhiIyMxGOPPYaePXuibdu2cHBwENPMmDEDcrkc7dq1g5eXl8kYfvdj5cqVeOyxx0wCQKCkJvD48eOS3sqMsYZFoKo+YMIYY6xe5eXlISAgAB999BHGjx9fq8vu3bs3OnXqhE8//bTGyxAEAVu2bMETTzxRa/lijNUdrglkjDEr9eeff2LDhg24ePEijh07Jo5bOHTo0DpZ35dffgkXFxecPHmyWt97+eWX4eLiUid5YozVHa4JZIwxK/Xnn3/ihRdewLlz56BSqdClSxd8/PHHCA0NrfV1Xb9+Hfn5+QCAZs2aQaVSVfm7aWlpyM7OBlAyFI2zs3Ot548xVvs4CGSMMcYYs0PcHMwYY4wxZoc4CGSMMcYYs0McBDLGGGOM2SEOAhljjDHG7BAHgYwxxhhjdoiDQMYYY4wxO8RBIGOMMcaYHeIgkDHGGGPMDnEQyBhjjDFmhzgIZIwxxhizQxwEMsYYY4zZof8HF+XTCu84trwAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "spectrum_wl = ws.resample(arc_spectrum)\n", - "\n", - "fig, ax = subplots(constrained_layout=True, figsize=(11, 4))\n", - "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", - "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", - "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", - "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", - "ax.grid(True, alpha=0.5)" - ] + "execution_count": 16 }, { "cell_type": "markdown", @@ -477,7 +478,7 @@ "id": "a64a7f45-9145-41ae-845c-389329762697", "metadata": {}, "source": [ - "#### 7.2 access the WCS\n", + "#### 7.2 Access the WCS\n", "\n", "Additionally, if we don't want to rebin, or maybe want to do it using another method, we can access the pixel-wavelength transform as a [gwcs](https://gwcs.readthedocs.io/) WCS object usin the `WCS` property." ] @@ -506,29 +507,6 @@ "ws.wcs.pixel_to_world(0)" ] }, - { - "cell_type": "markdown", - "id": "2f932cac-e05a-4c0d-a64a-d0e1058489ee", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "The interactive workflow (in the sense that it requires user input for the initial line fit) for 1D wavelength calibration can be summarized as follows:" - ] - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "ws = WavelengthCalibration1D(ref_pixel=1000, degree=5, arc_spectra=arc_spectrum, line_lists=[['CdI', 'HgI', 'HeI']], line_list_bounds=(3200, 5700), unit=u.angstrom)\n", - "ws.find_lines(fwhm=4, noise_factor=15)\n", - "ws.fit_lines(pixels=[496, 967, 1077, 1655, 1999], wavelengths=[3890, 4360, 4473, 5087, 5462], match_obs=True, match_cat=True)\n", - "ws.plot_fit(figsize=(11,5), plot_values=True, obs_to_wav=True);" - ], - "id": "749ae34c006a21b4" - }, { "cell_type": "markdown", "id": "4d21e847-568b-4c90-a945-205b16332f5e", diff --git a/docs/wavelength_calibration/wavecal1d_example_02.ipynb b/docs/wavelength_calibration/wavecal1d_example_02.ipynb index 180e9e3f..5d53df71 100644 --- a/docs/wavelength_calibration/wavecal1d_example_02.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_02.ipynb @@ -7,45 +7,79 @@ "source": [ "# 1D Wavelength Calibration Tutorial 2: Multiple Arc Spectra\n", "\n", - "This notebook demonstrates the use of several arc spectra and a custom line list in calculating the wavelength solution. We use three arc spectra taken with the R1000R grism from the [Gran Telescopio Canaria's](https://www.gtc.iac.es/) [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/)." + "This tutorial demonstrates the interactive wavelength calibration workflow using multiple arc lamp\n", + "spectra observed with the [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)\n", + "[Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/), and the mixing of custom line\n", + "lists with the [PypeIt](https://github.com/pypeit/PypeIt) line lists.\n", + "\n", + "We'll follow the Tutorial 1 steps but use three arc lamp spectra (HgAr, Ne, and Xe) observed with\n", + "the OSIRIS R1000R grism configuration, which covers approximately 5100-10000 Ã… at moderate\n", + "resolution. In addition, we use a custom line list for the HgAr arc spectrum. With line wavelength\n", + "values taken from the [official GTC Osiris line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt)." ] }, { "cell_type": "code", - "execution_count": 37, "id": "7853ed1c-5b05-42a2-9413-4054769c6032", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:38.800772Z", + "start_time": "2025-04-23T10:11:37.625694Z" + } + }, "source": [ "import astropy.units as u\n", "import numpy as np\n", "\n", "from astropy.table import Table\n", "from astropy.nddata import StdDevUncertainty\n", - "from matplotlib.pyplot import setp, subplots, close, rc\n", + "from matplotlib.pyplot import subplots, rc\n", "\n", "from specreduce.compat import Spectrum\n", "from specreduce.wavecal1d import WavelengthCalibration1D\n", "\n", - "rc('figure', figsize=(11, 3))" - ] + "rc('figure', figsize=(6.3, 2))" + ], + "outputs": [], + "execution_count": 1 }, { "cell_type": "markdown", "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", "metadata": {}, "source": [ - "## 1. Read in the Arc Spectra and Initialize the Wavelength Solution Class\n", + "## 1. Initialize the Wavelength Calibration Class\n", "\n", - "We use a custom line list for the HgAr arc spectrum. The values are taken from the [official GTC Osiris line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt)." + "First, we load the data for the three arc lamps (HgAr, Ne, Xe) from the example FITS table\n", + "`osiris_arcs.fits`. We create a list of `specutils.Spectrum` objects, one for each lamp.\n", + "\n", + "Next, we prepare the corresponding line lists. For the HgAr lamp, we define a custom list as a\n", + "NumPy array containing known air wavelengths specific to this GTC/OSIRIS setup, derived from the\n", + "[official GTC line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt).\n", + "For the Neon (Ne) and Xenon (Xe) lamps, we simply provide their standard identifiers (`'NeI'`,\n", + "`'XeI'`) within lists. `WavelengthSolution1D` will use\n", + "`specreduce.calibration_data.load_pypeit_calibration_lines` internally to fetch these standard\n", + "lists.\n", + "\n", + "Finally, we instantiate the `WavelengthSolution1D` class:\n", + "- `ref_pixel=1000`: Sets the reference pixel for the polynomial fit.\n", + "- `degree=4`: Specifies a 4th-degree polynomial for the pixel-to-wavelength model.\n", + "- `arc_spectra=arc_spectra`: Provides the list of `Spectrum` objects.\n", + "- `line_lists=[hgar_lines, ['NeI'], ['XeI']]`: Provides the list of corresponding line data (matching the order of `arc_spectra`). Note how we mix the custom array and lists of standard names.\n", + "- `line_list_bounds=(5100, 10000)`: Filters the line lists to include only lines within this approximate wavelength range (in Angstroms).\n", + "- `unit=u.angstrom`: Explicitly defines the wavelength unit.\n", + "- `wave_air=True`: Inform the class that the provided line lists (both custom and standard PypeIt lists for these lamps) contain **air** wavelengths. The class will handle conversions appropriately if needed for internal consistency or specific outputs, but the primary fitting coordinate system will be based on these air wavelengths." ] }, { "cell_type": "code", - "execution_count": 38, "id": "a78d101a-e1af-4c5e-8264-d3eb99d31e15", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:38.844912Z", + "start_time": "2025-04-23T10:11:38.834212Z" + } + }, "source": [ "lamps = 'HgAr', 'Ne', 'Xe'\n", "\n", @@ -53,274 +87,222 @@ "arc_spectra = [Spectrum(tb[f'{l}_flux'].value.astype('d')*u.DN, \n", " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d'))) \n", " for l in lamps]" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "code", - "execution_count": 39, "id": "427b44e7-67f4-4121-bc6f-7275f708ee6a", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:39.060231Z", + "start_time": "2025-04-23T10:11:38.882777Z" + } + }, "source": [ - "hgar = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106, 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", + "hgar = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106,\n", + " 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", "\n", - "ws = WavelengthCalibration1D(ref_pixel=1000, degree=4, arc_spectra=arc_spectra,\n", + "wc = WavelengthCalibration1D(ref_pixel=1000, degree=4, arc_spectra=arc_spectra,\n", " line_lists=[hgar, ['NeI'], ['XeI']],\n", - " line_list_bounds=(5100, 10000), unit=u.angstrom, wave_air=True)\n", + " line_list_bounds=(5100, 9900), unit=u.angstrom,\n", + " wave_air=True)\n", "\n", - "ws.find_lines(fwhm=4, noise_factor=15)" - ] + "wc.find_lines(fwhm=4, noise_factor=15)" + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 40, "id": "67603524-f0d6-4448-b24b-0e6095c091d0", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:39.790691Z", + "start_time": "2025-04-23T10:11:39.069116Z" + } + }, + "source": "wc.plot_fit(figsize=(6.3, 6), value_fontsize=4);", "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_fit(figsize=(15, 8));" - ] + "execution_count": 4 }, { "cell_type": "markdown", "id": "806d1730-5325-4537-825e-da686ffcda21", "metadata": {}, - "source": [ - "## 5. Fit an Initial Solution Manually" - ] + "source": "## 2. Fit a Solution Manually" }, { "cell_type": "code", - "execution_count": 41, "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:42.987953Z", + "start_time": "2025-04-23T10:11:42.976761Z" + } + }, + "source": [ + "wc.fit_lines(pixels=[175, 797, 1499, 1579, 1620],\n", + " wavelengths=[5461, 6931, 8822, 9048, 9165],\n", + " match_obs=True, match_cat=True)" + ], "outputs": [], - "source": "ws.fit_lines(pixels=[175, 797, 1499, 1579, 1620], wavelengths=[5461, 6931, 8822, 9048, 9165], match_obs=True, match_cat=True)" + "execution_count": 5 }, { "cell_type": "code", - "execution_count": 42, "id": "0fbe56d7-beb8-40e4-98e9-a17a00ba4342", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:43.839886Z", + "start_time": "2025-04-23T10:11:43.528047Z" + } + }, + "source": "wc.plot_fit(figsize=(6.3, 6), plot_values=False, obs_to_wav=True);", "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_fit(figsize=(15, 8), plot_values=False, obs_to_wav=True);" - ] + "execution_count": 6 }, { "cell_type": "markdown", "id": "524b217f-b2cd-49b9-a011-eb3a11fa14ee", "metadata": {}, - "source": [ - "## 6. Evaluate the Initial Fit: Plot Residuals" - ] + "source": "## 3. Evaluate the Fit" }, { "cell_type": "code", - "execution_count": 43, "id": "343801bc-65fa-41c9-929b-72565cdee31d", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:11:50.908180Z", + "start_time": "2025-04-23T10:11:50.806590Z" + } + }, + "source": "wc.plot_residuals(space='wavelength');", "outputs": [ { "data": { - "image/png": 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", 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_residuals(space='wavelength');" - ] + "execution_count": 7 }, { - "cell_type": "markdown", - "id": "45524753-381e-485a-9819-ae189cf639dc", - "metadata": {}, - "source": [ - "## 7. Refine the Fit" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:12:01.473745Z", + "start_time": "2025-04-23T10:12:01.466955Z" + } + }, + "cell_type": "code", + "source": "wc.refine_fit(max_match_distance=0.5)", + "id": "6c85a0d25186986e", + "outputs": [], + "execution_count": 8 }, { - "cell_type": "code", - "execution_count": 44, - "id": "96999b9a-4fe6-4ce8-b894-00c7638342df", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:12:03.290525Z", + "start_time": "2025-04-23T10:12:03.288621Z" } - ], - "source": [ - "ws.refine_fit(match_distance_bound=25)\n", - "ws.plot_fit(figsize=(11,6), plot_values=False, obs_to_wav=True);" - ] + }, + "cell_type": "code", + "source": "wc.remove_ummatched_lines()", + "id": "fbf3e8da2a0c4dbd", + "outputs": [], + "execution_count": 9 }, { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:12:04.849597Z", + "start_time": "2025-04-23T10:12:04.746317Z" + } + }, "cell_type": "code", - "execution_count": 45, - "id": "1444f2f8-2326-4ee9-a041-bb4051fb35c9", - "metadata": {}, + "source": "wc.plot_residuals(space='wavelength');", + "id": "7596dc31f5185a9a", "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_residuals(space='wavelength');" - ] + "execution_count": 10 }, { "cell_type": "markdown", "id": "983fe041-41e9-47a7-a179-fead64e171ba", "metadata": {}, - "source": [ - "### 8. Rebin a Spectrum to Wavelength Space\n", - "\n", - "A primary goal of wavelength calibration is to transform spectra from the detector's pixel grid to a physical wavelength grid. The `resample` method does this, converting a `Spectrum1D` object from pixel space to wavelength space.\n", - "\n", - "Key parameters for `resample`:\n", - "- `spectrum`: The input `Spectrum` object (assumed to be on a pixel grid corresponding to the calibration).\n", - "- `nbins`: (int, optional) The number of bins desired in the output wavelength grid. If `None`, defaults to the number of pixels in the input spectrum.\n", - "- `wlbounds`: (tuple[float, float], optional) The desired `(start_wavelength, end_wavelength)` for the output grid. If `None`, defaults to the wavelengths corresponding to the first and last pixels.\n", - "- `bin_edges`: (Sequence[float], optional) Explicitly define the wavelength edges of the output bins. If provided, `nbins` and `wlbounds` are ignored.\n", - "\n", - "The method uses the fitted `_p2w`, `_w2p`, and `_p2w_dldx` transformations to map the input pixel bins to the output wavelength bins. It performs an exact flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", - "\n", - "Here, we demonstrate by resampling the original arc spectrum itself. In a typical workflow, you would apply this `resample` method (using the `ws` object derived from the arc lamp) to your *science* spectrum observed with the same instrument setup." - ] + "source": "## 4. Rebin a Spectrum to Wavelength Space" }, { "cell_type": "code", - "execution_count": 33, "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-23T10:07:05.694347Z", + "start_time": "2025-04-23T10:07:05.566443Z" } - ], + }, "source": [ - "spectrum_wl = ws.resample(arc_spectra[0])\n", + "spectrum_wl = wc.resample(arc_spectra[0])\n", "\n", - "fig, ax = subplots(constrained_layout=True, figsize=(11, 4))\n", + "fig, ax = subplots(constrained_layout=True)\n", "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", "ax.set_title(\"Arc Spectrum Resampled to Linear Wavelength Grid\")\n", - "ax.grid(True, alpha=0.5)" - ] - }, - { - "cell_type": "markdown", - "id": "a64a7f45-9145-41ae-845c-389329762697", - "metadata": {}, - "source": [ - "Additionally, if we don't want to rebin, or maybe want to do it using another method, we can access the pixel-wavelength transform as a [gwcs](https://gwcs.readthedocs.io/) WCS object usin the `WCS` property." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "\n", - " [1]: \n", - "Parameters:\n", - " offset_0 c0_1 ... c3_1 c4_1 \n", - " -------- ---------------- ... ---------------------- ---------------------\n", - " -1000.0 7454.41575090495 ... -5.006614108686105e-08 4.212537047788065e-12)>" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } + "ax.autoscale(enable=True, axis='x', tight=True)" ], - "source": [ - "ws.wcs" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "d0d1aed5-c16b-421e-9638-19b8ec38bc6d", - "metadata": {}, "outputs": [ { "data": { - "text/latex": [ - "$[5300.0316,~5302.1373] \\; \\mathrm{\\mathring{A}}$" - ], "text/plain": [ - "" - ] + "
" + ], + "image/png": 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" }, - "execution_count": 35, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], - "source": [ - "ws.wcs.pixel_to_world([100, 101])" - ] + "execution_count": 31 }, { "cell_type": "markdown", diff --git a/docs/wavelength_calibration/wavecal1d_example_03.ipynb b/docs/wavelength_calibration/wavecal1d_example_03.ipynb index b5278260..540bec6c 100644 --- a/docs/wavelength_calibration/wavecal1d_example_03.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_03.ipynb @@ -11,8 +11,8 @@ "particularly useful for **automated data reduction pipelines** where the instrument configuration\n", "(spectrograph, grating/grism, approximate wavelength range) is well-characterized beforehand.\n", "\n", - "Instead of requiring manual identification of initial line pairs (as shown in Example 1 using\n", - "`WavelengthCalibration1D.fit_lines`), this workflow leverages the\n", + "Instead of requiring manual identification of initial line pairs (as shown in Tutorials 1 and 2\n", + "using `WavelengthCalibration1D.fit_lines`), this workflow uses the\n", "`WavelengthCalibration1D.fit_global` method. This method uses a global optimization algorithm to\n", "determine the wavelength solution. It achieves this by automatically finding the best polynomial\n", "coefficients that minimize the overall distance between detected arc lamp lines and a provided\n", @@ -22,7 +22,7 @@ "and dispersion (Ã…/pixel) at a chosen reference pixel. These bounds are derived from **prior\n", "knowledge** of the instrument setup and guide the optimization search.\n", "\n", - "Like Example 2, this example uses three arc lamp spectra (HgAr, Ne, Xe) obtained with the R1000R\n", + "Like Tutorial 2, this example uses three arc lamp spectra (HgAr, Ne, Xe) obtained with the R1000R\n", " grism of the [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", " [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)." ] From fb49f15d81ca94ece00e74c46e93c8ebded1d736 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 10:07:15 +0100 Subject: [PATCH 39/76] Introduced the `value_fontsize` parameter to control font size in spectral line plots across various methods and adjusted vline colors. Fixed minor formatting issues for consistency. --- specreduce/wavecal1d.py | 44 +++++++++++++++++++++++++++++------------ 1 file changed, 31 insertions(+), 13 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 106f5fd8..0922900c 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -187,7 +187,7 @@ def fit_lines( match_obs: bool = False, match_cat: bool = False, refine_fit: bool = True, - refine_max_distance: float = 5.0 + refine_max_distance: float = 5.0, ) -> None: """Fit the wavelength solution model using provided line pairs. @@ -382,8 +382,9 @@ def refine_fit(self, max_match_distance: float = 5.0, max_iter: int = 5) -> None if rms_new == rms: break else: - self._p2w = self._p2w[0].copy() | fitter(model, matched_pix - self.ref_pixel, - matched_wav) + self._p2w = self._p2w[0].copy() | fitter( + model, matched_pix - self.ref_pixel, matched_wav + ) rms = rms_new self._calculate_p2w_derivative() self._calculate_p2w_inverse() @@ -396,7 +397,7 @@ def _calculate_p2w_derivative(self): def _calculate_p2w_inverse(self) -> None: """Compute the wavelength-to-pixel mapping from the pixel-to-wavelength transformation.""" - p = np.arange(self.bounds_pix[0]-2, self.bounds_pix[1]+2) + p = np.arange(self.bounds_pix[0] - 2, self.bounds_pix[1] + 2) self._w2p = interp1d(self._p2w(p), p, bounds_error=False, fill_value=np.nan) self.bounds_wav = self._p2w(self.bounds_pix) @@ -543,8 +544,7 @@ def catalog_lines(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | l @property def wcs(self) -> wcs.WCS: - """GWCS object defining the mapping between pixel and spectral coordinate frames. - """ + """GWCS object defining the mapping between pixel and spectral coordinate frames.""" pixel_frame = cf.CoordinateFrame( 1, "SPECTRAL", @@ -599,7 +599,6 @@ def match_lines(self, max_distance: float = 5) -> None: self._obs_lines = matched_lines_pix self._cat_lines = matched_lines_wav - def remove_ummatched_lines(self): """Remove unmatched lines from observation and catalog line data.""" self._obs_lines = [np.ma.masked_array(l.compressed()) for l in self._obs_lines] @@ -629,7 +628,6 @@ def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: else: raise ValueError("Space must be either 'pixel' or 'wavelength'") - def _plot_lines( self, kind: Literal["observed", "catalog"], @@ -638,6 +636,7 @@ def _plot_lines( figsize: tuple[float, float] | None = None, plot_values: bool | Sequence[bool] = True, map_x: bool = False, + value_fontsize: int | str | None = "small", ) -> Figure: if frames is None: @@ -663,15 +662,17 @@ def _plot_lines( if kind == "observed": transform = self.pix_to_wav if map_x else lambda x: x linelists = self._obs_lines + lc = "C0" else: transform = self.wav_to_pix if map_x else lambda x: x linelists = self._cat_lines + lc = "C1" if linelists is not None: for iax, (ax, frame) in enumerate(zip(axs, frames)): lines = linelists[frame] - ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=":") - ax.vlines(transform(lines[~lines.mask].data), 0, 1) + ax.vlines(transform(lines[lines.mask].data), 0, 1, ls=":", color=lc) + ax.vlines(transform(lines[~lines.mask].data), 0, 1, color=lc) if plot_values[iax]: for i, l in enumerate(transform(lines.data)): if np.isfinite(l): @@ -683,7 +684,7 @@ def _plot_lines( ha="right", va="center", bbox=dict(alpha=0.8, fc="w", lw=0), - size="small", + size=value_fontsize, ) if (kind == "observed" and not map_x) or (kind == "catalog" and map_x): @@ -712,6 +713,7 @@ def plot_catalog_lines( figsize: tuple[float, float] | None = None, plot_values: bool = True, map_to_pix: bool = False, + value_fontsize: int | str | None = "small", ) -> Figure: """Plot the catalog lines. @@ -751,6 +753,7 @@ def plot_catalog_lines( figsize=figsize, plot_values=plot_values, map_x=map_to_pix, + value_fontsize=value_fontsize, ) def plot_observed_lines( @@ -761,6 +764,7 @@ def plot_observed_lines( plot_values: bool = True, plot_spectra: bool = True, map_to_wav: bool = False, + value_fontsize: int | str | None = "small", ) -> Figure: """Plot observed spectral lines for the given arc spectra. @@ -795,6 +799,7 @@ def plot_observed_lines( figsize=figsize, plot_values=plot_values, map_x=map_to_wav, + value_fontsize=value_fontsize, ) if axes is None: @@ -832,6 +837,7 @@ def plot_fit( plot_values: bool = True, obs_to_wav: bool = False, cat_to_pix: bool = False, + value_fontsize: int | str | None = "small", ) -> Figure: """Plot the fitted catalog and observed lines for the specified arc spectra. @@ -871,8 +877,20 @@ def plot_fit( transform = lambda x: x fig, axs = subplots(2 * frames.size, 1, constrained_layout=True, figsize=figsize) - self.plot_catalog_lines(frames, axs[::2], plot_values=plot_values, map_to_pix=cat_to_pix) - self.plot_observed_lines(frames, axs[1::2], plot_values=plot_values, map_to_wav=obs_to_wav) + self.plot_catalog_lines( + frames, + axs[::2], + plot_values=plot_values, + map_to_pix=cat_to_pix, + value_fontsize=value_fontsize, + ) + self.plot_observed_lines( + frames, + axs[1::2], + plot_values=plot_values, + map_to_wav=obs_to_wav, + value_fontsize=value_fontsize, + ) xlims = np.array([ax.get_xlim() for ax in axs[::2]]) if obs_to_wav: From a41d033212f90ebaa0a89c88d112840d394b6463 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 10:07:38 +0100 Subject: [PATCH 40/76] Revised the wavelength calibration notebooks. --- .../wavecal1d_example_01.ipynb | 148 +++++++++--------- .../wavecal1d_example_02.ipynb | 2 +- .../wavecal1d_example_03.ipynb | 2 +- 3 files changed, 80 insertions(+), 72 deletions(-) diff --git a/docs/wavelength_calibration/wavecal1d_example_01.ipynb b/docs/wavelength_calibration/wavecal1d_example_01.ipynb index 27d38118..c7b9b325 100644 --- a/docs/wavelength_calibration/wavecal1d_example_01.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_01.ipynb @@ -4,15 +4,15 @@ "metadata": {}, "cell_type": "markdown", "source": [ - "# 1D Wavelength Calibration Tutorial 1: Interactive Workflow\n", + "# Tutorial 1: Interactive Workflow\n", "\n", "This notebook demonstrates a basic interactive workflow for wavelength calibration of\n", - "astronomical spectra using the `WavelengthSolution1D` class with a single\n", + "astronomical spectra using the `specreduce.wavecal1d.WavelengthCalibration1D` class with a single\n", "calibration lamp (arc) spectrum. It serves as a general introduction to the class.\n", "\n", "The interactive workflow consists of these key steps:\n", "1. Loading the arc spectrum data\n", - "2. Initializing the wavelength solution parameters\n", + "2. Initializing the wavelength calibration parameters\n", "3. Finding emission line positions in the spectrum \n", "4. Inspecting catalog line wavelengths\n", "5. Manually identifying initial line matches\n", @@ -20,7 +20,7 @@ "7. Applying the solution to calibrate spectra\n", "\n", "For this demonstration, we use a [Helium-Mercury-Cadmium (He-Hg-Cd) calibration lamp](https://mthamilton.ucolick.org/techdocs/instruments/kast/images/Kastblue600HeHgCd.jpg) spectrum\n", - "obtained with the [Kast Double Spectrograph](https://mthamilton.ucolick.org/techdocs/instruments/kast/index.html) on the [Shane 3-meter telescope](https://www.lickobservatory.org/explore/research-telescopes/shane-telescope/) at Lick Observatory. The Kast blue channel configuration uses a 600 lines/mm grating covering approximately 3200-5700 Angstroms at moderate resolution.\n", + "obtained with the [Kast Double Spectrograph](https://mthamilton.ucolick.org/techdocs/instruments/kast/index.html) on the [Shane 3-meter telescope](https://www.lickobservatory.org/explore/research-telescopes/shane-telescope/) at Lick Observatory. The Kast blue channel configuration uses a 600 lines/mm grating covering approximately 3200-5700 Ã… at moderate resolution.\n", "\n", "This interactive approach provides hands-on experience with the calibration process while\n", "allowing careful validation of the results. For automated reduction pipelines, alternative\n", @@ -31,8 +31,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:14:08.848170Z", - "start_time": "2025-04-23T10:14:07.713518Z" + "end_time": "2025-04-24T09:04:59.413508Z", + "start_time": "2025-04-24T09:04:58.188052Z" } }, "cell_type": "code", @@ -66,14 +66,15 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:14:10.507515Z", - "start_time": "2025-04-23T10:14:10.501542Z" + "end_time": "2025-04-24T09:05:00.558444Z", + "start_time": "2025-04-24T09:05:00.538713Z" } }, "cell_type": "code", "source": [ "flux = getdata('shane_kast_blue_600_4310_d55.fits', 1).astype('d')\n", - "arc_spectrum = Spectrum((flux - np.median(flux)) * u.DN, uncertainty=StdDevUncertainty(np.sqrt(flux)))" + "arc_spectrum = Spectrum((flux - np.median(flux)) * u.DN,\n", + " uncertainty=StdDevUncertainty(np.sqrt(flux)))" ], "id": "63bc459f2c36d759", "outputs": [], @@ -84,9 +85,9 @@ "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", "metadata": {}, "source": [ - "## 2. Initialize the Wavelength Solution Class\n", + "## 2. Initialize the Wavelength Calibration Class\n", "\n", - "Now we instantiate the `WavelengthSolution1D` class. This class manages the input data, fitted model, and provides methods for solution fitting and spectrum calibration.\n", + "Now we instantiate the `WavelengthCalibration1D` class. This class manages the input data, fitted model, and provides methods for solution fitting and spectrum calibration.\n", "\n", "Key initialization parameters:\n", "\n", @@ -100,7 +101,7 @@ "- `wave_air`: If `True`, converts PyPEIT's vacuum wavelengths to air wavelengths.\n", "\n", "In this example, we provide:\n", - "- Arc spectrum\n", + "- One arc spectrum\n", "- Reference pixel at 1000 \n", "- Polynomial degree 5\n", "- Lamp list `['CdI', 'HgI', 'HeI']`\n", @@ -112,8 +113,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:14:20.716957Z", - "start_time": "2025-04-23T10:14:20.625377Z" + "end_time": "2025-04-24T09:05:02.738728Z", + "start_time": "2025-04-24T09:05:02.644148Z" } }, "cell_type": "code", @@ -157,8 +158,8 @@ "id": "e5bb4f88-355f-4278-9ec4-15876f4712e2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:14:50.599359Z", - "start_time": "2025-04-23T10:14:50.453572Z" + "end_time": "2025-04-24T09:05:10.077910Z", + "start_time": "2025-04-24T09:05:09.918294Z" } }, "source": [ @@ -177,7 +178,7 @@ "output_type": "display_data" } ], - "execution_count": 6 + "execution_count": 4 }, { "cell_type": "markdown", @@ -186,7 +187,7 @@ "source": [ "## 4. Inspect the Catalog Line List\n", "\n", - "When we initialized `WavelengthSolution1D`, we provided lamp names (`line_lists=[['CdI', 'HgI', 'HeI']]`). Internally, the `_read_linelists` method loaded the corresponding known wavelengths (filtered by `line_list_bounds`), converted them to the specified `unit` (Angstroms by default), and stored them in the `ws.catalog_lines` attribute.\n", + "When we initialized `WavelengthCalibration1D`, we provided lamp names (`line_lists=[['CdI', 'HgI', 'HeI']]`). Internally, the `_read_linelists` method loaded the corresponding known wavelengths (filtered by `line_list_bounds`), converted them to the specified `unit` (Angstroms by default), and stored them in the `ws.catalog_lines` attribute.\n", "\n", "Like `observed_lines`, `catalog_lines` is a list of masked arrays (one per arc spectrum), sorted by wavelength. We can visualize these theoretical line positions using `plot_catalog_lines`." ] @@ -196,8 +197,8 @@ "id": "da59f18b-1c86-410a-a5a6-dc9c94302cff", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:15:26.338881Z", - "start_time": "2025-04-23T10:15:26.102276Z" + "end_time": "2025-04-24T09:05:13.463742Z", + "start_time": "2025-04-24T09:05:13.158274Z" } }, "source": "wc.plot_catalog_lines(value_fontsize=6);", @@ -207,13 +208,13 @@ "text/plain": [ "
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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 8 + "execution_count": 5 }, { "cell_type": "markdown", @@ -230,8 +231,8 @@ "id": "e95afe59-471d-4669-aafe-72b1f7d79d53", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:16:06.691112Z", - "start_time": "2025-04-23T10:16:06.447174Z" + "end_time": "2025-04-24T09:05:19.887930Z", + "start_time": "2025-04-24T09:05:19.631590Z" } }, "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, value_fontsize=6);", @@ -241,13 +242,13 @@ "text/plain": [ "
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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 11 + "execution_count": 6 }, { "cell_type": "markdown", @@ -258,7 +259,7 @@ "\n", "To establish an initial relationship between pixels and wavelengths, we need to match some observed lines to their corresponding catalog wavelengths. \n", "\n", - "The `WavelengthSolution1D` class offers several ways to fit:\n", + "The `WavelengthCalibration1D` class offers several ways to fit:\n", "1. `fit_lines(pixels, wavelengths)`: Fits the model using explicitly provided pairs of corresponding pixel and wavelength values. This is useful for manual identification of a few bright, unambiguous lines to get a starting solution.\n", "2. `fit_global()`: Performs a global optimization to find an initial model by minimizing distances between transformed observed lines and the KDTrees of theoretical lines. This requires good initial guesses for wavelength and dispersion bounds but is more suitable for an automatic pipeline.\n", "3. `refine_fit()`: Improves an existing fit by automatically matching lines based on the current solution and performing a least-squares fit on the matches. This method is called by the `fit_lines` and `fit_global` methods by default, but can also be called manually.\n", @@ -271,8 +272,8 @@ "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:16:37.400778Z", - "start_time": "2025-04-23T10:16:37.385553Z" + "end_time": "2025-04-24T09:05:21.466345Z", + "start_time": "2025-04-24T09:05:21.449860Z" } }, "source": [ @@ -290,7 +291,7 @@ ] } ], - "execution_count": 12 + "execution_count": 7 }, { "cell_type": "markdown", @@ -305,8 +306,8 @@ "id": "40883c16-11b3-402b-bd0b-c688c44deef2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:17:01.911854Z", - "start_time": "2025-04-23T10:17:01.673393Z" + "end_time": "2025-04-24T09:05:24.103779Z", + "start_time": "2025-04-24T09:05:23.865804Z" } }, "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, obs_to_wav=True, value_fontsize=6);", @@ -316,13 +317,13 @@ "text/plain": [ "
" ], - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 14 + "execution_count": 8 }, { "cell_type": "markdown", @@ -339,8 +340,8 @@ "id": "343801bc-65fa-41c9-929b-72565cdee31d", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:17:40.763896Z", - "start_time": "2025-04-23T10:17:40.658566Z" + "end_time": "2025-04-24T09:05:26.942539Z", + "start_time": "2025-04-24T09:05:26.829587Z" } }, "source": "wc.plot_residuals(space='wavelength');", @@ -356,7 +357,7 @@ "output_type": "display_data" } ], - "execution_count": 15 + "execution_count": 9 }, { "cell_type": "markdown", @@ -389,8 +390,8 @@ "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:18:17.097482Z", - "start_time": "2025-04-23T10:18:16.957257Z" + "end_time": "2025-04-24T09:05:29.043011Z", + "start_time": "2025-04-24T09:05:28.902168Z" } }, "source": [ @@ -415,7 +416,7 @@ "output_type": "display_data" } ], - "execution_count": 16 + "execution_count": 10 }, { "cell_type": "markdown", @@ -427,51 +428,57 @@ }, { "cell_type": "code", - "execution_count": 25, "id": "26ba254f-69a6-43c0-bcfc-86e2707a2c55", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:05:33.241820Z", + "start_time": "2025-04-24T09:05:33.238692Z" + } + }, + "source": [ + "spectrum_wl.flux.sum()" + ], "outputs": [ { "data": { - "text/latex": [ - "$244192.09 \\; \\mathrm{DN}$" - ], "text/plain": [ "" - ] + ], + "text/latex": "$244192.09 \\; \\mathrm{DN}$" }, - "execution_count": 25, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "spectrum_wl.flux.sum()" - ] + "execution_count": 11 }, { "cell_type": "code", - "execution_count": 26, "id": "68ea6434-cf95-4503-8c68-bfc27209e3df", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:05:33.916312Z", + "start_time": "2025-04-24T09:05:33.913375Z" + } + }, + "source": [ + "arc_spectrum.flux.sum()" + ], "outputs": [ { "data": { - "text/latex": [ - "$244192.09 \\; \\mathrm{DN}$" - ], "text/plain": [ "" - ] + ], + "text/latex": "$244192.09 \\; \\mathrm{DN}$" }, - "execution_count": 26, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "arc_spectrum.flux.sum()" - ] + "execution_count": 12 }, { "cell_type": "markdown", @@ -485,27 +492,28 @@ }, { "cell_type": "code", - "execution_count": 157, "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:05:42.272947Z", + "start_time": "2025-04-24T09:05:42.268949Z" + } + }, + "source": "wc.wcs.pixel_to_world(0)", "outputs": [ { "data": { - "text/latex": [ - "$3428.3395 \\; \\mathrm{\\mathring{A}}$" - ], "text/plain": [ "" - ] + ], + "text/latex": "$3428.3395 \\; \\mathrm{\\mathring{A}}$" }, - "execution_count": 157, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ws.wcs.pixel_to_world(0)" - ] + "execution_count": 14 }, { "cell_type": "markdown", diff --git a/docs/wavelength_calibration/wavecal1d_example_02.ipynb b/docs/wavelength_calibration/wavecal1d_example_02.ipynb index 5d53df71..c3e9b422 100644 --- a/docs/wavelength_calibration/wavecal1d_example_02.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_02.ipynb @@ -5,7 +5,7 @@ "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", "metadata": {}, "source": [ - "# 1D Wavelength Calibration Tutorial 2: Multiple Arc Spectra\n", + "# Tutorial 2: Interactive Workflow With Multiple Arc Spectra\n", "\n", "This tutorial demonstrates the interactive wavelength calibration workflow using multiple arc lamp\n", "spectra observed with the [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)\n", diff --git a/docs/wavelength_calibration/wavecal1d_example_03.ipynb b/docs/wavelength_calibration/wavecal1d_example_03.ipynb index 540bec6c..df5ade94 100644 --- a/docs/wavelength_calibration/wavecal1d_example_03.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_03.ipynb @@ -5,7 +5,7 @@ "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", "metadata": {}, "source": [ - "# 1D Wavelength Calibration Tutorial 3: Non-Interactive Fit\n", + "# Tutorial 3: Non-Interactive Workflow\n", "\n", "This notebook focuses on a **non-interactive workflow** for 1D wavelength calibration that is\n", "particularly useful for **automated data reduction pipelines** where the instrument configuration\n", From 346556780d61ac701ee4b07a1f9565a811b6befd Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 10:26:09 +0100 Subject: [PATCH 41/76] Finalised 1D wavelength calibration tutorial 1. --- .../wavecal1d_example_01.ipynb | 186 +++++++++++------- 1 file changed, 117 insertions(+), 69 deletions(-) diff --git a/docs/wavelength_calibration/wavecal1d_example_01.ipynb b/docs/wavelength_calibration/wavecal1d_example_01.ipynb index c7b9b325..250cee97 100644 --- a/docs/wavelength_calibration/wavecal1d_example_01.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_01.ipynb @@ -31,8 +31,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:04:59.413508Z", - "start_time": "2025-04-24T09:04:58.188052Z" + "end_time": "2025-04-24T09:34:56.462836Z", + "start_time": "2025-04-24T09:34:55.274752Z" } }, "cell_type": "code", @@ -66,8 +66,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:00.558444Z", - "start_time": "2025-04-24T09:05:00.538713Z" + "end_time": "2025-04-24T09:34:56.502353Z", + "start_time": "2025-04-24T09:34:56.497014Z" } }, "cell_type": "code", @@ -103,7 +103,7 @@ "In this example, we provide:\n", "- One arc spectrum\n", "- Reference pixel at 1000 \n", - "- Polynomial degree 5\n", + "- Polynomial degree 4\n", "- Lamp list `['CdI', 'HgI', 'HeI']`\n", "- Wavelength filter range 3200-5700 Angstroms\n", "\n", @@ -113,13 +113,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:02.738728Z", - "start_time": "2025-04-24T09:05:02.644148Z" + "end_time": "2025-04-24T09:34:56.628938Z", + "start_time": "2025-04-24T09:34:56.543497Z" } }, "cell_type": "code", "source": [ - "wc = WavelengthCalibration1D(ref_pixel=1000, degree=5, arc_spectra=arc_spectrum,\n", + "wc = WavelengthCalibration1D(ref_pixel=1000, degree=4, arc_spectra=arc_spectrum,\n", " line_lists=[['CdI', 'HgI', 'HeI']],\n", " line_list_bounds=(3200, 5700), unit=u.angstrom)\n", "wc.plot_observed_lines();" @@ -158,8 +158,8 @@ "id": "e5bb4f88-355f-4278-9ec4-15876f4712e2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:10.077910Z", - "start_time": "2025-04-24T09:05:09.918294Z" + "end_time": "2025-04-24T09:34:56.786768Z", + "start_time": "2025-04-24T09:34:56.639220Z" } }, "source": [ @@ -189,7 +189,7 @@ "\n", "When we initialized `WavelengthCalibration1D`, we provided lamp names (`line_lists=[['CdI', 'HgI', 'HeI']]`). Internally, the `_read_linelists` method loaded the corresponding known wavelengths (filtered by `line_list_bounds`), converted them to the specified `unit` (Angstroms by default), and stored them in the `ws.catalog_lines` attribute.\n", "\n", - "Like `observed_lines`, `catalog_lines` is a list of masked arrays (one per arc spectrum), sorted by wavelength. We can visualize these theoretical line positions using `plot_catalog_lines`." + "Similar to `observed_lines`, `catalog_lines` is a list of masked arrays (one per arc spectrum) sorted by wavelength. We can visualize these theoretical line positions using the `plot_catalog_lines` method.\n" ] }, { @@ -197,8 +197,8 @@ "id": "da59f18b-1c86-410a-a5a6-dc9c94302cff", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:13.463742Z", - "start_time": "2025-04-24T09:05:13.158274Z" + "end_time": "2025-04-24T09:34:57.106982Z", + "start_time": "2025-04-24T09:34:56.795915Z" } }, "source": "wc.plot_catalog_lines(value_fontsize=6);", @@ -217,22 +217,22 @@ "execution_count": 5 }, { - "cell_type": "markdown", - "id": "8880ab00-b9bc-4d99-9eda-7cd38188dc9b", "metadata": {}, + "cell_type": "markdown", "source": [ - "We can use the `plot_fit` method to visualize both the observed lines (bottom panel, pixel space) and the catalog lines (top panel, wavelength space) simultaneously. \n", + "The `plot_fit` method allows us to simultaneously visualize the observed lines (bottom panel, pixel space) and catalog lines (top panel, wavelength space). \n", "\n", - "Since we haven't yet calculated a pixel-to-wavelength transformation, the bottom panel is plotted in pixel coordinates and we should not expect any matches between the observed and catalog lines." - ] + "Since we have not yet calculated the pixel-to-wavelength transformation, the bottom panel displays pixel coordinates. At this stage, we should not expect to see any matches between the observed and catalog lines.\n" + ], + "id": "915e48bb113ab04e" }, { "cell_type": "code", "id": "e95afe59-471d-4669-aafe-72b1f7d79d53", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:19.887930Z", - "start_time": "2025-04-24T09:05:19.631590Z" + "end_time": "2025-04-24T09:34:57.359476Z", + "start_time": "2025-04-24T09:34:57.115932Z" } }, "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, value_fontsize=6);", @@ -272,8 +272,8 @@ "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:21.466345Z", - "start_time": "2025-04-24T09:05:21.449860Z" + "end_time": "2025-04-24T09:34:57.375755Z", + "start_time": "2025-04-24T09:34:57.369398Z" } }, "source": [ @@ -281,33 +281,22 @@ " wavelengths=[3890, 4360, 4473, 5087, 5462],\n", " match_obs=True, match_cat=True)" ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: The fit may be poorly conditioned\n", - " [astropy.modeling.fitting]\n" - ] - } - ], + "outputs": [], "execution_count": 7 }, { "cell_type": "markdown", "id": "616e53c5-20e5-411e-a983-1feba2db30dc", "metadata": {}, - "source": [ - "Now, we can plot the solution mapping the observed pixel values to wavelengths (`obs_to_wav=True`). The plot shows matching observed and catalog lines as solid blue vertical lines, while unmatched lines are shown as dotted lines. " - ] + "source": "After applying the initial solution, we can plot the mapping of observed pixel values to wavelengths (`obs_to_wav=True`). In this plot, matched lines between observed and catalog data appear as solid blue vertical lines, while unmatched lines are shown as dotted lines.\n" }, { "cell_type": "code", "id": "40883c16-11b3-402b-bd0b-c688c44deef2", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:24.103779Z", - "start_time": "2025-04-24T09:05:23.865804Z" + "end_time": "2025-04-24T09:34:57.644862Z", + "start_time": "2025-04-24T09:34:57.417724Z" } }, "source": "wc.plot_fit(figsize=(6.3, 3), plot_values=True, obs_to_wav=True, value_fontsize=6);", @@ -330,9 +319,9 @@ "id": "524b217f-b2cd-49b9-a011-eb3a11fa14ee", "metadata": {}, "source": [ - "## 6. Evaluate the Initial Fit: Plot Residuals\n", + "## 6. Evaluate the Initial Fit: Plot Residuals \n", "\n", - "Let's check the quality of this initial fit. The `plot_residuals` method calculates the difference between the theoretical wavelength of matched lines and the wavelength predicted by the current model for their observed pixel positions. The RMS estimate alone can be calculated using the `rms` method." + "Let's examine the quality of our initial fit. The `plot_residuals` method calculates the differences between the theoretical wavelengths of matched lines and the wavelengths predicted by the current model at their observed pixel positions. We can also calculate the RMS of these residuals using the `rms` method.\n" ] }, { @@ -340,8 +329,8 @@ "id": "343801bc-65fa-41c9-929b-72565cdee31d", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:26.942539Z", - "start_time": "2025-04-24T09:05:26.829587Z" + "end_time": "2025-04-24T09:34:57.756868Z", + "start_time": "2025-04-24T09:34:57.654630Z" } }, "source": "wc.plot_residuals(space='wavelength');", @@ -359,6 +348,30 @@ ], "execution_count": 9 }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:57.772258Z", + "start_time": "2025-04-24T09:34:57.769326Z" + } + }, + "cell_type": "code", + "source": "wc.rms()", + "id": "b2cfafee7b41f185", + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.10779117337629601)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + }, { "cell_type": "markdown", "id": "983fe041-41e9-47a7-a179-fead64e171ba", @@ -377,8 +390,8 @@ "- `wlbounds`: The desired `(start_wavelength, end_wavelength)` for the output grid. If `None`, defaults to the wavelengths corresponding to the first and last pixels.\n", "- `bin_edges`: Explicitly define the wavelength edges of the output bins. If provided, `nbins` and `wlbounds` are ignored.\n", "\n", - "The method uses the fitted pixel-to-wavelength and wavelength-to-pixel transformations and their\n", - "derivatived to map the input pixel bins to the output wavelength bins. It performs an exact\n", + "The method uses the fitted pixel-to-wavelength and wavelength-to-pixel transformations along with their\n", + "derivatives to map the input pixel bins to the output wavelength bins. It performs an exact\n", "flux-conserving rebinning, meaning the total flux in the output spectrum matches the total flux\n", "in the input spectrum (adjusted for the units transformation from counts/pixel to counts/wavelength_bin).\n", "\n", @@ -390,8 +403,8 @@ "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:29.043011Z", - "start_time": "2025-04-24T09:05:28.902168Z" + "end_time": "2025-04-24T09:34:57.960397Z", + "start_time": "2025-04-24T09:34:57.818056Z" } }, "source": [ @@ -416,23 +429,21 @@ "output_type": "display_data" } ], - "execution_count": 10 + "execution_count": 11 }, { "cell_type": "markdown", "id": "618a1d04-c7ff-4f05-8679-bf94ea2f045a", "metadata": {}, - "source": [ - "Let's still check that the flux is indeed conserved as it should" - ] + "source": "Let's verify that the total flux is conserved, as expected:" }, { "cell_type": "code", "id": "26ba254f-69a6-43c0-bcfc-86e2707a2c55", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:33.241820Z", - "start_time": "2025-04-24T09:05:33.238692Z" + "end_time": "2025-04-24T09:34:57.984800Z", + "start_time": "2025-04-24T09:34:57.981797Z" } }, "source": [ @@ -446,20 +457,20 @@ ], "text/latex": "$244192.09 \\; \\mathrm{DN}$" }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 11 + "execution_count": 12 }, { "cell_type": "code", "id": "68ea6434-cf95-4503-8c68-bfc27209e3df", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:33.916312Z", - "start_time": "2025-04-24T09:05:33.913375Z" + "end_time": "2025-04-24T09:34:58.034538Z", + "start_time": "2025-04-24T09:34:58.031958Z" } }, "source": [ @@ -473,33 +484,72 @@ ], "text/latex": "$244192.09 \\; \\mathrm{DN}$" }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 12 + "execution_count": 13 }, { - "cell_type": "markdown", - "id": "a64a7f45-9145-41ae-845c-389329762697", "metadata": {}, + "cell_type": "markdown", "source": [ "#### 7.2 Access the WCS\n", "\n", - "Additionally, if we don't want to rebin, or maybe want to do it using another method, we can access the pixel-wavelength transform as a [gwcs](https://gwcs.readthedocs.io/) WCS object usin the `WCS` property." - ] + "For cases where rebinning is not desired or when you want to use alternative rebinning methods,\n", + "you can directly access the pixel-to-wavelength transformation through the `gwcs` property. This\n", + "property returns a [GWCS (Generalized World Coordinate System)](https://gwcs.readthedocs.io/)\n", + "object that provides methods for converting between pixel and wavelength coordinates." + ], + "id": "d712829f7ae5d751" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T09:34:58.087021Z", + "start_time": "2025-04-24T09:34:58.083231Z" + } + }, + "cell_type": "code", + "source": "wc.gwcs", + "id": "1d74837351f08c69", + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "\n", + " [1]: \n", + "Parameters:\n", + " offset_0 c0_1 ... c3_1 c4_1 \n", + " -------- ----------------- ... ----------------------- ---------------------\n", + " -1000.0 4393.545398623673 ... -1.1679853938796083e-08 9.589069917248972e-14)>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 }, { "cell_type": "code", "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", "metadata": { "ExecuteTime": { - "end_time": "2025-04-24T09:05:42.272947Z", - "start_time": "2025-04-24T09:05:42.268949Z" + "end_time": "2025-04-24T09:34:58.142518Z", + "start_time": "2025-04-24T09:34:58.139303Z" } }, - "source": "wc.wcs.pixel_to_world(0)", + "source": "wc.gwcs.pixel_to_world(0)", "outputs": [ { "data": { @@ -508,20 +558,18 @@ ], "text/latex": "$3428.3395 \\; \\mathrm{\\mathring{A}}$" }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 14 + "execution_count": 15 }, { "cell_type": "markdown", "id": "4d21e847-568b-4c90-a945-205b16332f5e", "metadata": {}, - "source": [ - "---" - ] + "source": "---" } ], "metadata": { From e41cea78236706729d89b259adafadf818b2e50d Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 10:35:31 +0100 Subject: [PATCH 42/76] Changed the wcs attribute to gwcs. --- specreduce/wavecal1d.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 0922900c..65230d57 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -3,10 +3,10 @@ import astropy.units as u import numpy as np +import gwcs from astropy.modeling import models, Model, fitting from astropy.nddata import VarianceUncertainty from gwcs import coordinate_frames as cf -from gwcs import wcs from matplotlib.axes import Axes from matplotlib.figure import Figure from matplotlib.pyplot import setp, subplots @@ -70,7 +70,7 @@ def __init__( self._trees: Sequence[KDTree] | None = None self._fit: optimize.OptimizeResult | None = None - self._wcs: wcs.WCS | None = None + self._wcs: gwcs.wcs.WCS | None = None self._p2w: Model | None = None # pixel -> wavelength model self._w2p: Callable | None = None # wavelength -> pixel model @@ -543,7 +543,7 @@ def catalog_lines(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | l self._cat_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) @property - def wcs(self) -> wcs.WCS: + def gwcs(self) -> gwcs.wcs.WCS: """GWCS object defining the mapping between pixel and spectral coordinate frames.""" pixel_frame = cf.CoordinateFrame( 1, @@ -559,7 +559,7 @@ def wcs(self) -> wcs.WCS: unit=[self.unit], ) pipeline = [(pixel_frame, self._p2w), (spectral_frame, None)] - self._wcs = wcs.WCS(pipeline) + self._wcs = gwcs.wcs.WCS(pipeline) return self._wcs def match_lines(self, max_distance: float = 5) -> None: From 1b55d6368451529244fe8c65bc15ae7d4afba839 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 10:54:57 +0100 Subject: [PATCH 43/76] Finalised 1D wavelength calibration tutorial 2 and polished the first tutorial a bit. --- .../wavecal1d_example_01.ipynb | 2 +- .../wavecal1d_example_02.ipynb | 122 +++++++++--------- 2 files changed, 63 insertions(+), 61 deletions(-) diff --git a/docs/wavelength_calibration/wavecal1d_example_01.ipynb b/docs/wavelength_calibration/wavecal1d_example_01.ipynb index 250cee97..c3eabc49 100644 --- a/docs/wavelength_calibration/wavecal1d_example_01.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_01.ipynb @@ -4,7 +4,7 @@ "metadata": {}, "cell_type": "markdown", "source": [ - "# Tutorial 1: Interactive Workflow\n", + "# Tutorial 1: Basic Interactive Workflow\n", "\n", "This notebook demonstrates a basic interactive workflow for wavelength calibration of\n", "astronomical spectra using the `specreduce.wavecal1d.WavelengthCalibration1D` class with a single\n", diff --git a/docs/wavelength_calibration/wavecal1d_example_02.ipynb b/docs/wavelength_calibration/wavecal1d_example_02.ipynb index c3e9b422..9889a6cd 100644 --- a/docs/wavelength_calibration/wavecal1d_example_02.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_02.ipynb @@ -14,7 +14,7 @@ "\n", "We'll follow the Tutorial 1 steps but use three arc lamp spectra (HgAr, Ne, and Xe) observed with\n", "the OSIRIS R1000R grism configuration, which covers approximately 5100-10000 Ã… at moderate\n", - "resolution. In addition, we use a custom line list for the HgAr arc spectrum. With line wavelength\n", + "resolution. In addition, we use a custom line list for the HgAr arc spectrum with line wavelength\n", "values taken from the [official GTC Osiris line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt)." ] }, @@ -23,8 +23,8 @@ "id": "7853ed1c-5b05-42a2-9413-4054769c6032", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:38.800772Z", - "start_time": "2025-04-23T10:11:37.625694Z" + "end_time": "2025-04-24T09:52:20.483405Z", + "start_time": "2025-04-24T09:52:19.294588Z" } }, "source": [ @@ -57,15 +57,16 @@ "NumPy array containing known air wavelengths specific to this GTC/OSIRIS setup, derived from the\n", "[official GTC line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt).\n", "For the Neon (Ne) and Xenon (Xe) lamps, we simply provide their standard identifiers (`'NeI'`,\n", - "`'XeI'`) within lists. `WavelengthSolution1D` will use\n", + "`'XeI'`) within lists. `WavelengthCalibration1D` will use\n", "`specreduce.calibration_data.load_pypeit_calibration_lines` internally to fetch these standard\n", "lists.\n", "\n", - "Finally, we instantiate the `WavelengthSolution1D` class:\n", + "Finally, we instantiate the `WavelengthCalibration1D` class:\n", "- `ref_pixel=1000`: Sets the reference pixel for the polynomial fit.\n", "- `degree=4`: Specifies a 4th-degree polynomial for the pixel-to-wavelength model.\n", "- `arc_spectra=arc_spectra`: Provides the list of `Spectrum` objects.\n", - "- `line_lists=[hgar_lines, ['NeI'], ['XeI']]`: Provides the list of corresponding line data (matching the order of `arc_spectra`). Note how we mix the custom array and lists of standard names.\n", + "- `line_lists=[hgar_lines, ['NeI'], ['XeI']]`: Provides the list of corresponding line data\n", + "(matching the size of `arc_spectra`). Note how we mix the custom array and lists of standard names.\n", "- `line_list_bounds=(5100, 10000)`: Filters the line lists to include only lines within this approximate wavelength range (in Angstroms).\n", "- `unit=u.angstrom`: Explicitly defines the wavelength unit.\n", "- `wave_air=True`: Inform the class that the provided line lists (both custom and standard PypeIt lists for these lamps) contain **air** wavelengths. The class will handle conversions appropriately if needed for internal consistency or specific outputs, but the primary fitting coordinate system will be based on these air wavelengths." @@ -76,8 +77,8 @@ "id": "a78d101a-e1af-4c5e-8264-d3eb99d31e15", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:38.844912Z", - "start_time": "2025-04-23T10:11:38.834212Z" + "end_time": "2025-04-24T09:52:20.527027Z", + "start_time": "2025-04-24T09:52:20.516251Z" } }, "source": [ @@ -96,41 +97,58 @@ "id": "427b44e7-67f4-4121-bc6f-7275f708ee6a", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:39.060231Z", - "start_time": "2025-04-23T10:11:38.882777Z" + "end_time": "2025-04-24T09:52:21.050386Z", + "start_time": "2025-04-24T09:52:20.564790Z" } }, "source": [ "hgar = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106,\n", " 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", "\n", - "wc = WavelengthCalibration1D(ref_pixel=1000, degree=4, arc_spectra=arc_spectra,\n", + "wc = WavelengthCalibration1D(ref_pixel=1000,\n", + " degree=4,\n", + " arc_spectra=arc_spectra,\n", " line_lists=[hgar, ['NeI'], ['XeI']],\n", - " line_list_bounds=(5100, 9900), unit=u.angstrom,\n", + " line_list_bounds=(5100, 9900),\n", + " unit=u.angstrom,\n", " wave_air=True)\n", "\n", - "wc.find_lines(fwhm=4, noise_factor=15)" + "wc.plot_fit(figsize=(6.3, 6), plot_values=False);" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "outputs": [], "execution_count": 3 }, { - "cell_type": "code", - "id": "67603524-f0d6-4448-b24b-0e6095c091d0", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:39.790691Z", - "start_time": "2025-04-23T10:11:39.069116Z" + "end_time": "2025-04-24T09:52:21.807706Z", + "start_time": "2025-04-24T09:52:21.060075Z" } }, - "source": "wc.plot_fit(figsize=(6.3, 6), value_fontsize=4);", + "cell_type": "code", + "source": [ + "wc.find_lines(fwhm=4, noise_factor=15)\n", + "wc.plot_fit(figsize=(6.3, 6), value_fontsize=5);" + ], + "id": "2c02abdf62aee58c", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -149,8 +167,8 @@ "id": "ee1b527f-23d8-4c0c-a3c6-b0ee8f397e2e", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:42.987953Z", - "start_time": "2025-04-23T10:11:42.976761Z" + "end_time": "2025-04-24T09:52:21.828538Z", + "start_time": "2025-04-24T09:52:21.819638Z" } }, "source": [ @@ -166,8 +184,8 @@ "id": "0fbe56d7-beb8-40e4-98e9-a17a00ba4342", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:43.839886Z", - "start_time": "2025-04-23T10:11:43.528047Z" + "end_time": "2025-04-24T09:52:22.166514Z", + "start_time": "2025-04-24T09:52:21.867629Z" } }, "source": "wc.plot_fit(figsize=(6.3, 6), plot_values=False, obs_to_wav=True);", @@ -177,7 +195,7 @@ "text/plain": [ "
" ], - "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -196,8 +214,8 @@ "id": "343801bc-65fa-41c9-929b-72565cdee31d", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:11:50.908180Z", - "start_time": "2025-04-23T10:11:50.806590Z" + "end_time": "2025-04-24T09:52:22.379476Z", + "start_time": "2025-04-24T09:52:22.176146Z" } }, "source": "wc.plot_residuals(space='wavelength');", @@ -216,41 +234,25 @@ "execution_count": 7 }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-23T10:12:01.473745Z", - "start_time": "2025-04-23T10:12:01.466955Z" - } - }, - "cell_type": "code", - "source": "wc.refine_fit(max_match_distance=0.5)", - "id": "6c85a0d25186986e", - "outputs": [], - "execution_count": 8 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-04-23T10:12:03.290525Z", - "start_time": "2025-04-23T10:12:03.288621Z" - } - }, - "cell_type": "code", - "source": "wc.remove_ummatched_lines()", - "id": "fbf3e8da2a0c4dbd", - "outputs": [], - "execution_count": 9 + "metadata": {}, + "cell_type": "markdown", + "source": "The previous fit looks good overall, with two clear outliers while the remaining lines are well-fitted. Let's refine the fit by calling `refine_fit` with a small `match_distance` parameter to deliberately exclude these outliers, then remove them completely.\n", + "id": "a2173e9fbdf680cd" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:12:04.849597Z", - "start_time": "2025-04-23T10:12:04.746317Z" + "end_time": "2025-04-24T09:52:22.496738Z", + "start_time": "2025-04-24T09:52:22.390880Z" } }, "cell_type": "code", - "source": "wc.plot_residuals(space='wavelength');", - "id": "7596dc31f5185a9a", + "source": [ + "wc.refine_fit(max_match_distance=0.5)\n", + "wc.remove_ummatched_lines()\n", + "wc.plot_residuals(space='wavelength');" + ], + "id": "6c85a0d25186986e", "outputs": [ { "data": { @@ -263,7 +265,7 @@ "output_type": "display_data" } ], - "execution_count": 10 + "execution_count": 8 }, { "cell_type": "markdown", @@ -276,8 +278,8 @@ "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", "metadata": { "ExecuteTime": { - "end_time": "2025-04-23T10:07:05.694347Z", - "start_time": "2025-04-23T10:07:05.566443Z" + "end_time": "2025-04-24T09:52:22.630036Z", + "start_time": "2025-04-24T09:52:22.506456Z" } }, "source": [ @@ -296,13 +298,13 @@ "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 31 + "execution_count": 9 }, { "cell_type": "markdown", From 0823c4aaeb6e7e9a0bc2b04f4e8a557c36622b7e Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:16:47 +0100 Subject: [PATCH 44/76] Finished 1D wavelength calibration tutorial 3. --- .../wavecal1d_example_03.ipynb | 382 +++++++++--------- 1 file changed, 191 insertions(+), 191 deletions(-) diff --git a/docs/wavelength_calibration/wavecal1d_example_03.ipynb b/docs/wavelength_calibration/wavecal1d_example_03.ipynb index df5ade94..a7efe304 100644 --- a/docs/wavelength_calibration/wavecal1d_example_03.ipynb +++ b/docs/wavelength_calibration/wavecal1d_example_03.ipynb @@ -18,21 +18,29 @@ "coefficients that minimize the overall distance between detected arc lamp lines and a provided\n", "catalog of theoretical line wavelengths.\n", "\n", + "During the optimization, the algorithm uses a KD-tree data structure to efficiently find\n", + "the nearest catalog lines to each detected line. This approach speeds up the distance\n", + "calculations needed in each iteration of the optimization, making it practical to\n", + "search through large parameter spaces with many detected and catalog lines.\n", + "\n", "A key requirement for `fit_global` is providing reasonable initial **bounds** for the wavelength\n", "and dispersion (Ã…/pixel) at a chosen reference pixel. These bounds are derived from **prior\n", "knowledge** of the instrument setup and guide the optimization search.\n", "\n", "Like Tutorial 2, this example uses three arc lamp spectra (HgAr, Ne, Xe) obtained with the R1000R\n", - " grism of the [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", - " [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)." + "grism of the [Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", + "[Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/).\n" ] }, { "cell_type": "code", - "execution_count": 1, "id": "7853ed1c-5b05-42a2-9413-4054769c6032", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:53.842644Z", + "start_time": "2025-04-24T10:19:52.651102Z" + } + }, "source": [ "import astropy.units as u\n", "import numpy as np\n", @@ -44,63 +52,67 @@ "from specreduce.compat import Spectrum\n", "from specreduce.wavecal1d import WavelengthCalibration1D\n", "\n", - "rc('figure', figsize=(15, 3))" - ] + "rc('figure', figsize=(6.3, 2))" + ], + "outputs": [], + "execution_count": 1 }, { - "cell_type": "markdown", - "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79", "metadata": {}, - "source": [ - "## 1. Read Arc Spectra & Initialize Wavelength Solution Class\n", - "\n", - "First, we load the data for the three arc lamps (HgAr, Ne, Xe) from the example FITS table `osiris_arcs.fits`. We create a list of `specutils.Spectrum` objects, one for each lamp.\n", - "\n", - "Next, we prepare the corresponding line lists. For the HgAr lamp, we define a custom list as a NumPy array containing known air wavelengths specific to this GTC/OSIRIS setup, derived from the [official GTC line list](https://www.gtc.iac.es/instruments/osiris/media/lines/GTClinelist0.txt). For the Neon (Ne) and Xenon (Xe) lamps, we simply provide their standard identifiers (`'NeI'`, `'XeI'`) within lists. `WavelengthSolution1D` will use `specreduce.calibration_data.load_pypeit_calibration_lines` internally to fetch these standard lists.\n", - "\n", - "Finally, we instantiate the `WavelengthSolution1D` class:\n", - "- `ref_pixel=1000`: Sets the reference pixel for the polynomial fit.\n", - "- `degree=4`: Specifies a 4th-degree polynomial for the pixel-to-wavelength model.\n", - "- `arc_spectra=arc_spectra`: Provides the list of `Spectrum` objects.\n", - "- `line_lists=[hgar_lines, ['NeI'], ['XeI']]`: Provides the list of corresponding line data (matching the order of `arc_spectra`). Note how we mix the custom array and lists of standard names.\n", - "- `line_list_bounds=(5100, 10000)`: Filters the line lists to include only lines within this approximate wavelength range (in Angstroms).\n", - "- `unit=u.angstrom`: Explicitly defines the wavelength unit.\n", - "- `wave_air=True`: Inform the class that the provided line lists (both custom and standard PypeIt lists for these lamps) contain **air** wavelengths. The class will handle conversions appropriately if needed for internal consistency or specific outputs, but the primary fitting coordinate system will be based on these air wavelengths." - ] + "cell_type": "markdown", + "source": "## 1. Initialize the Wavelength Calibration Class", + "id": "c55aa5d3-f3b5-4ac2-90e6-bd07e7a5dd79" }, { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:54.380911Z", + "start_time": "2025-04-24T10:19:53.876680Z" + } + }, "cell_type": "code", - "execution_count": 2, - "id": "49a5d5d6-af0f-4bb1-8a08-ce14cdad023f", - "metadata": {}, - "outputs": [], "source": [ "lamps = 'HgAr', 'Ne', 'Xe'\n", - "hgar_lines = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106, \n", + "hgar_lines = np.array([5460.735, 5769.598, 5790.663, 6965.431, 7272.936, 7635.106,\n", " 7724.207, 7948.176, 8115.311, 8264.522, 9122.967])\n", "\n", "tb = Table.read('osiris_arcs.fits')\n", - "arc_spectra = [Spectrum(flux=tb[f'{l}_flux'].value.astype('d')*u.DN, \n", - " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d'))) \n", + "arc_spectra = [Spectrum(flux=tb[f'{l}_flux'].value.astype('d')*u.DN,\n", + " uncertainty=StdDevUncertainty(tb[f'{l}_err'].value.astype('d')))\n", " for l in lamps]\n", "\n", - "ws = WavelengthCalibration1D(ref_pixel=1000,\n", + "wc = WavelengthCalibration1D(ref_pixel=1000,\n", " degree=4,\n", " arc_spectra=arc_spectra,\n", " line_lists=[hgar_lines, ['NeI'], ['XeI']],\n", - " line_list_bounds=(5100, 10000),\n", + " line_list_bounds=(5100, 9900),\n", " unit=u.angstrom,\n", - " wave_air=True)" - ] + " wave_air=True)\n", + "\n", + "wc.plot_fit(figsize=(6.3, 6), plot_values=False);" + ], + "id": "49a5d5d6-af0f-4bb1-8a08-ce14cdad023f", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 2 }, { - "cell_type": "markdown", - "id": "49076f6f-4695-4c58-9359-d1473cc54bd6", "metadata": {}, + "cell_type": "markdown", "source": [ "## 2. Perform Automated Fit using `fit_global`\n", "\n", - "With the `WavelengthSolution1D` object initialized, we proceed to calculate the wavelength solution automatically.\n", + "With the `WavelengthCalibration1D` object initialized, we proceed to calculate the wavelength solution automatically.\n", "\n", "**Step 1: Find Lines**\n", "We first execute `ws.find_lines()`. This step automatically detects emission lines in each of the input arc spectra (`arc_spectra`) and calculates their pixel centroids. These detected centroids are stored internally in `ws.observed_lines`. The `fwhm` (estimated line width in pixels) and `noise_factor` parameters help the algorithm distinguish real lines from noise; these may need tuning depending on the data quality.\n", @@ -110,260 +122,248 @@ "- **No Manual Input:** Notice we do *not* provide any specific pixel-wavelength pairs.\n", "- **Bounds are Key:** Instead, we *must* provide `wavelength_bounds` and `dispersion_bounds`. These constrain the optimizer's search space for the polynomial coefficients. They represent our *a priori* knowledge about the instrument setup.\n", " - For this GTC/OSIRIS R1000R example, we know the approximate central wavelength is ~7430 Ã… and the dispersion near the center is ~2.62 Ã…/pixel. We provide bounds around these values: `wavelength_bounds=[7420, 7470]` Ã… and `dispersion_bounds=[2.5, 2.7]` Ã…/pixel.\n", - "- **Optimization Process:** The differential evolution algorithm searches for polynomial coefficients within these constraints (and broader limits for higher-order terms) that best map the detected `observed_lines` to the `catalog_lines` by minimizing the sum of distances to the nearest catalog neighbors (using internal KDTrees for efficiency).\n", + "- **Optimization Process:** The differential evolution algorithm searches for polynomial\n", + "coefficients within these constraints (and narrower limits for higher-order terms) that best map\n", + "the detected `observed_lines` to the `catalog_lines` by minimizing the sum of distances to the nearest catalog neighbors (using internal KDTrees for efficiency).\n", "- **Refinement:** We set `refine_fit=True`. After the global optimization finds an initial solution, this triggers an automatic call to `ws.refine_fit()`. This internal step uses the initial solution to explicitly match observed and catalog lines within a tolerance (`match_distance_bound`) and then performs a standard least-squares fit using *only* these matched pairs. This typically results in a higher-precision final solution.\n", "\n", "**Step 3: Visualize Result**\n", "We then call `ws.plot_fit()` to visualize the outcome. Because we provided multiple arc spectra, it generates pairs of plots for each frame (HgAr, Ne, Xe). The top plot shows the catalog lines, and the bottom plot shows the observed lines (and the spectrum itself) mapped onto the final derived wavelength scale (`obs_to_wav=True`). This allows a visual check of the alignment and fit quality across all input lamps." - ] + ], + "id": "49076f6f-4695-4c58-9359-d1473cc54bd6" }, { "cell_type": "code", - "execution_count": 3, "id": "46937bc3-b120-46f6-870e-85f1da5907db", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:55.786544Z", + "start_time": "2025-04-24T10:19:54.391017Z" + } + }, + "source": [ + "wc.find_lines(fwhm=4, noise_factor=15)\n", + "\n", + "wc.fit_global(wavelength_bounds=[7420, 7470],\n", + " dispersion_bounds=[2.5, 2.7],\n", + " refine_fit=True)\n", + "\n", + "wc.plot_fit(figsize=(6.3, 6), plot_values=False, obs_to_wav=True);" + ], "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", "source": [ - "ws.find_lines(fwhm=4, noise_factor=15)\n", + "## 3. Evaluate the Fit\n", "\n", - "ws.fit_global(wavelength_bounds=[7420, 7470], \n", - " dispersion_bounds=[2.5, 2.7], \n", - " refine_fit=True)\n", + "After obtaining the wavelength solution through the global optimization and refinement steps, it's important to assess the quality of the fit. In an automated data reduction pipeline, this evaluation should be done systematically by implementing quantitative quality checks based on:\n", "\n", - "ws.plot_fit(figsize=(15, 8), plot_values=False, obs_to_wav=True);" - ] + "- RMS of residuals between fitted and catalog wavelengths\n", + "- Number and distribution of successfully matched lines\n", + "- Behavior of residuals across the wavelength range\n", + "- Expected accuracy for the specific instrument configuration\n", + "\n", + "These metrics help ensure the wavelength calibration meets the required precision for scientific analysis.\n", + "\n", + "Now, let's take a quick look at the fit results. As expected, the solution matches what we got in Tutorial 2 - we just arrived at it without having to manually identify any matching lines!\n" + ], + "id": "54bc4a147af94dd1" }, { "cell_type": "code", - "execution_count": 4, "id": "343801bc-65fa-41c9-929b-72565cdee31d", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:55.889469Z", + "start_time": "2025-04-24T10:19:55.804835Z" + } + }, + "source": "wc.plot_residuals(space='wavelength');", "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "ws.plot_residuals(space='wavelength');" - ] + "execution_count": 4 }, { + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.033388Z", + "start_time": "2025-04-24T10:19:55.945594Z" + } + }, "cell_type": "code", - "execution_count": 5, - "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", - "metadata": {}, + "source": [ + "wc.refine_fit(max_match_distance=0.5)\n", + "wc.remove_ummatched_lines()\n", + "wc.plot_residuals(space='wavelength');" + ], + "id": "aa4420dc480aae9f", "outputs": [ { "data": { - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 4. Apply the Solution", + "id": "b608003fb6c44d8d" + }, + { + "cell_type": "code", + "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:20:08.247555Z", + "start_time": "2025-04-24T10:20:08.116156Z" + } + }, "source": [ - "spectrum_wl = ws.resample(arc_spectra[0])\n", + "spectrum_wl = wc.resample(arc_spectra[0])\n", "\n", - "fig, ax = subplots(constrained_layout=True, figsize=(15, 4))\n", + "fig, ax = subplots(constrained_layout=True)\n", "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux)\n", "ax.set_xlabel(f\"Wavelength [{spectrum_wl.spectral_axis.unit.to_string('latex')}]\")\n", "ax.set_ylabel(f\"Flux [{spectrum_wl.flux.unit.to_string('latex')}]\")\n", "ax.set_title(\"HgAr Arc Spectrum Resampled to Linear Wavelength Grid\")\n", "ax.grid(True, alpha=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f15d4c4e-b721-4348-9f2b-078b3c348515", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$[2.5968402,~2.5968402,~2.5968402,~\\dots,~2.5968402,~2.5968402,~2.5968402] \\; \\mathrm{\\mathring{A}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } ], - "source": [ - "np.diff(spectrum_wl.spectral_axis)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "ae8c6cdb-2fa3-4916-9ba0-5dab3d19acdc", - "metadata": {}, "outputs": [ { "data": { - "text/latex": [ - "$839275 \\; \\mathrm{DN}$" - ], "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spectrum_wl.flux.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "960d0392-0a8c-4d37-ae94-940e84a1c600", - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$839275 \\; \\mathrm{DN}$" + "
" ], - "text/plain": [ - "" - ] + "image/png": 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G63Rlrfunfw8EBAQY/W7RrdN/r6xcudLgc4OIaOzYsWYZBHJzcB2wYcMGxMXFGfzr0aOHJF10dDQAYMiQIZLlQ4cONbrf1atXw9raWuwbY2dnhzfeeANHjx7FlStXDNIPHDgQCoXCpDxHR0fj6tWrGDVqlFht/84770AQBKNNRADw2muvSV4fOHAAVlZWYrV+eXTu3BlXr15FWFgY/vOf/8Df3x+HDx/GyJEjMXDgQIMmpVdffRVWVlbia3t7ewwYMABHjhyBRqNBQUEBDh8+jMGDB8PGxgZqtVr89/LLL6OgoADHjx8X892yZUv06tWr3PnWV7I8KuLll1+WNH/7+voCAIKDgyXpdMsTEhJM2q+7uzsUCgWcnJwwZMgQdOzYEevXrxfXx8TEIC0tDaNGjZKUlVarRb9+/RAXFyc2WXXp0gXr1q3D/Pnzcfz4caODS5KTkzFu3Dh4eXnBwsICCoUCjRs3BgBcvHjRIH1ISIjB+QmCgP79+4vLLCws0Lx5c9y6dctg+2HDhkmaoho3boxu3bqV2QUiPDwcarUaI0eOlJyzlZUVAgICxCbIy5cv4969e6Ue40n88ccfaNWqFbp06SJZHhoaCiLCH3/88UT7L8tzzz2HRo0aia+trKzQsmVLSfnu3bsXQUFB8PDwkJSR7rroPsN059KrVy84OjpCLpdDoVDgiy++QGpqqkHXhbZt26Jly5Ym5TMgIAAymUy8HlFRUejUqRPs7Oxgb2+PDh06iNc5KioKFhYWks/aH374AR06dICVlZV4Lx4+fFhyH7Zp0wYdO3bE2rVrxWUXL17EyZMnJZ9ne/fuhZ+fH5577jlJefTt21fsu1ia8m4bFBQEe3t78XX9+vXh5uYmXp/c3Fz89ddfGDRoECwtLcV0dnZ2GDBggEllq2/gwIGS123btgUAo+83U7i4uEChUIj/tm/fLlnv5OSEl1566bH7iYyMhL29vUH+hg0bVqF81TQOAusAX19fdOrUyeCfo6OjJF1qaiosLCzg7OwsWV6/fn2DfV69ehVHjhxBcHAwiAgZGRnIyMjA66+/DgBGA7XS+tgZo+trOHjwYHHfjo6O6NGjB7Zv346MjAxJehsbGzg4OEiWpaSkwMPDQxLElIdCoUDfvn2xYMEChIeH4/bt2wgMDMTevXtx4MABSVp3d3eD7d3d3VFUVIScnBykpqZCrVZjxYoVkg8ihUKBl19+GQDE/mkpKSmV0nG+POVdmpL3gu7DvbTlBQUFJu330KFDiIuLQ3h4OF577TUcOXIEH330kbhe1zfw9ddfNyivr7/+GkSEtLQ0AMV96EaNGoWff/4Z/v7+cHZ2xsiRI8U+OlqtFn369MGOHTvwySef4PDhwzh58qQYdOfn55t03jY2NpJAX7fc2DmXdj+kpqaWWia6c+7cubPBOW/btk28P3T7KO0YTyI1NdXofePh4SE5dlVwcXExWKZUKiXX5/79+9izZ49B+bRu3RrAo/fQyZMn0adPHwDATz/9hD///BNxcXGYOXMmAMNrXp73yjPPPIPnnntODPQiIyMREBAgrtcP2CMjI9GpUycxeFq6dCnGjx+Prl27Yvv27Th+/Dji4uLQr18/gzyNHj0asbGx4nQma9euhVKplPwov3//Ps6fP29QHvb29iAigz6v+sq77eOuT3p6OojI6PeFsWWPU/J4ukGExt6vOl5eXgCMB4pRUVGIi4vDDz/8YHRbU++B1NRUo+fzpO+9msKjg5nIxcUFarUaaWlpki9BYx1e16xZAyLCb7/9ZnT+vvXr12P+/PmSzremzg2WmZkp/krr3Lmz0TSbN2/GBx98UOa+69Wrh2PHjkGr1VY4ENTn4uKCyZMnIyoqCn///bcYvAHGyygpKQmWlpaws7ODQqGAXC7HiBEj8OGHHxrdf5MmTcR837lz54nza6xMrKyskJmZabC8rC+LqtCuXTtxdHDv3r3Rt29frFq1CmPGjEHnzp3FdStWrMDzzz9vdB+6D2JXV1csW7YMy5YtQ0JCAnbv3o0ZM2YgOTkZYWFh+Pvvv3Hu3DmsW7cOo0aNEre/evVqlZ1fafeDsS9SHd05//bbb2ItpTG6fZR2jCfh4uKCxMREg+W6Dvm6PNYUV1dXtG3bFgsWLDC6Xhesbt26FQqFAnv37pUE7qVNSVLeeQuDgoKwZMkSnD9/HvHx8ZIBQgEBAVi6dCnOnz+PmzdvSoK2TZs2ITAwECtXrpTsLzs72+AYQ4cOxdSpU7Fu3TosWLAAGzduxKBBg+Dk5CSmcXV1hbW1damtI2VdryfZ1hgnJycIgmAwuAt48vvSVB4eHmjdujUiIiJQUFAgufbPPfccABjM46hj6j3g4uKCkydPGiw314EhXBPIRLpfs9u2bZMs37p1q+S1RqPB+vXr0axZM0RGRhr8mzZtGhITEw1qy0y1efNm5OfnY968eUb37+rqWuoHl77+/fujoKCg3POhqVSqUms8dE02ui8bnR07dkhqhLKzs7Fnzx688MILkMvlsLGxQVBQEM6cOYO2bdsarZnVfbn3798f//77b5lNb6b8KjbG29sb//77LwoLC8VlqampZY6WrmqCIOD//u//IJfL8fnnnwMAunfvjmeeeQb//POP0bLq1KmTpMlJp1GjRpgwYQJ69+6N06dPi/sHYDAd0Y8//lhl57RlyxZJl4Fbt24hJiamzEln+/btCwsLC1y7dq3UcwYAHx8fNGjQoNRjmKJkDZtOz5498c8//4hlp7NhwwYIglDj0/eEhITg77//RrNmzYyWj+59KQgCLCwsJD9C8/PzsXHjxkrJh64c5syZA5lMJmnu1f2tG2GsX2aCIBjch+fPnzc6b6KTkxMGDRqEDRs2YO/evUhKSjLo2hISEoJr167BxcXFaHmUNXH4k2xrjK2tLTp16oRdu3ahqKhIXJ6Tk2N0FHFp9+CTmjlzJh48eICpU6cadNupDEFBQcjOzsbu3bslyzdv3lzpx6oOXBPIRP369UP37t0xbdo0ZGVloWPHjoiNjRWnntDVph04cAD37t3D119/bfRLzc/PD9999x1Wr15t0LfKFKtXr4aTkxOmT59u0PwGACNHjsTSpUtx7tw5tGvXrtT9DB06FGvXrsW4ceNw+fJlBAUFQavV4sSJE/D19ZXM86UvMzMT3t7eeOONN9CrVy94eXkhJycHUVFRWL58OXx9ffHqq69KtpHL5eIcVFqtFl9//TWysrIkU00sX74cPXr0wAsvvIDx48fD29sb2dnZuHr1Kvbs2SMGfZMnT8a2bdvwyiuvYMaMGejSpQvy8/MRHR2NkJAQsW9O48aN8fvvv6Nnz55wdnaGq6vrYz+4R4wYgR9//BHDhw/H2LFjkZqaikWLFhk0pVe3Fi1a4L333sP333+PY8eOoUePHlixYgVGjRqFtLQ0vP7663Bzc0NKSgrOnTuHlJQUrFy5EpmZmQgKCsKwYcPw7LPPwt7eHnFxcQgLCxOv0bPPPotmzZphxowZICI4Oztjz549iIiIqLLzSU5OxuDBgzF27FhkZmZi1qxZsLKywmeffVbqNt7e3pg7dy5mzpyJ69evo1+/fnBycsL9+/dx8uRJ2NraikHHvHnz8O6774rHyMjIwOzZs01ukmrTpg22bt2Kbdu2oWnTprCyskKbNm0wZcoUbNiwAcHBwZg7dy4aN26Mffv24fvvv8f48eNN6jeXlZVltHWgXr16kmbTipg7dy4iIiLQrVs3TJw4ET4+PigoKMDNmzexf/9+/PDDD/D09ERwcDCWLl2KYcOG4b333kNqaiq++eabx85LaqoXX3wRcrkcO3fulDT3AsXNxe3atcPOnTuhUCjQvXt3cV1ISAjmzZuHWbNmISAgAJcvX8bcuXPRpEkTgylugOIm4W3btmHChAnw9PQ06Cc8efJkbN++HS+++CKmTJmCtm3bQqvVIiEhAQcPHsS0adNKnbf1SbYtzdy5cxEcHIy+ffti0qRJ0Gg0WLx4Mezs7MTuGzq6qXb27NmDBg0awN7eHj4+PuU6njFDhw5FfHw8FixYgHPnziE0NBQtWrSAVqvF7du3xR8C+tesPEaOHIlvv/0WI0eOxIIFC9CiRQvs378f4eHhT5z3GlEz41FYddCNsio5+ksnODjYYCRhWloavfPOO/TMM8+QjY0N9e7dWxxxtXz5ciIiGjRoEFlaWlJycnKpx37rrbfIwsKCkpKSxJGmixcvfmyez507RwDKHO156dIlAiCOohw1ahTZ2toaTZufn09ffPEFtWjRgiwtLcnFxYVeeuklydQtJRUWFtI333xD/fv3p0aNGpFSqSQrKyvy9fWlTz75hFJTU8W0unP7+uuvac6cOeTp6UmWlpbUvn17Cg8PN9j3jRs3aPTo0dSwYUNSKBRUr1496tatG82fP1+SLj09nSZNmkSNGjUihUJBbm5uFBwcTJcuXRLTHDp0iNq3b09KpZIAiCPtSpuGRWf9+vXk6+tLVlZW1KpVK9q2bVupo4NLXjPd6Ndff/1Vsvxx95pOWXm7f/8+2dnZSaaBiI6OpuDgYHJ2diaFQkENGzak4OBg8fgFBQU0btw4atu2LTk4OJC1tTX5+PjQrFmzKDc3V9zPP//8Q7179yZ7e3tycnKiN954Q5y+Q3+EdWn5K+0eKzkaVlc+GzdupIkTJ1K9evVIqVTSCy+8QH/99ZfRsihp165dFBQURA4ODqRUKqlx48b0+uuvS6bFISqekkl3X7ds2ZLWrFljcB1Lc/PmTerTpw/Z29sbzBJw69YtGjZsGLm4uJBCoSAfHx9avHixZJR4aXSjLI39042wLG10cHBwsNH9lRzFnJKSQhMnTqQmTZqQQqEgZ2dn6tixI82cOZNycnLEdGvWrCEfHx9SKpXUtGlTWrhwIa1evZoASKanKu3Yj9OlSxcCQNOnTzdYpxst3717d8nywsJCmj59OjVs2JCsrKyoQ4cOtGvXrlKvm0ajIS8vLwJAM2fONJqPnJwc+vzzz8nHx4csLS3FaaimTJlCSUlJkvMsORrX1G0B0IcffmhwbGP73LlzJ7Vp04YsLS2pUaNG9NVXX9HEiRPJyclJku7s2bPUvXt3srGxkdwfpX2W6N5b+lPplOXIkSP05ptvkqenJykUCrKxsaFWrVrR+PHjDd6LZY1qN3YP3rlzh1577TWys7Mje3t7eu211ygmJsYsRwcLRFVQX8qeKps3b8bbb7+NP//884lHHz5tbt68iSZNmmDx4sWYPn16TWeH1bCoqCgEBQXh119/FQdJMVaXqVQqPPfcc2jYsCEOHjxY09lhJXBzMJPYsmUL7t69izZt2kAmk+H48eNYvHgxXnzxRQ4AGWOMlWnMmDHo3bs3GjRogKSkJPzwww+4ePEili9fXtNZY0ZwEMgk7O3tsXXrVsyfPx+5ublo0KABQkNDMX/+/JrOGmOMsVouOzsb06dPR0pKChQKBTp06ID9+/c/8bynrGpwczBjjDHGWB3EU8QwxhhjjNVBHAQyxhhjjNVBHAQyxhhjjNVBPDCkCmi1Wty7dw/29vblfhwRY4wxxtiTICJkZ2fDw8OjzMemchBYBe7duyc+yJoxxhhjrCbcvn0bnp6epa7nILAK6B5Hc/v27Qo9jkuj0eDatWto1qyZ5NmXrOpwmVc/LvOaweVe/bjMq19dL/OsrCx4eXk99vF4HARWAV0TsIODQ4WDQDs7Ozg4ONTJm7cmcJlXPy7zmsHlXv24zKsfl3mxx3VJ44EhjDHGGGN1EAeBrMKICF+HXcLELWeg1fKc44wxxpg54SCQVVhCWh5WRl3D7nP3cDM1t6azwxhjjLFy4CCQVVhWvlr8W6XhmkDGGGPMnHAQyCpMrdUa/ZsxxhhjtR8HgazC1Hr9ANVcE8gYY4yZFQ4CWYXpB35qHhjCGGOMmRUOAlmFSZqDNdwczBhjjJkTDgJZhenX/mm4JpAxxhgzKxwEsgrj5mDGGGPMfHEQyCpMw6ODGWOMMbPFQSCrMB4dzBhjjJkvDgJZhXFzMGOMMWa+OAhkFSapCeQgkDHGGDMrHASyCtOfFoaniGGMMcbMCweBrMK4JpAxxhgzXxwEsgrT8DyBjDHGmNniIJBVmJYeBX7EMSBjjDFmVjgIZBWmH/gROApkjDHGzAkHgazC9MM+rglkjDHGzEutDAIXLlwIQRAwefJkcRkRYfbs2fDw8IC1tTUCAwMRHx8v2a6wsBAfffQRXF1dYWtri4EDB+LOnTuSNOnp6RgxYgQcHR3h6OiIESNGICMjQ5ImISEBAwYMgK2tLVxdXTFx4kQUFRVV1emaLZI0B3MUyBhjjJmTWhcExsXFYdWqVWjbtq1k+aJFi7B06VJ89913iIuLg7u7O3r37o3s7GwxzeTJk7Fz505s3boVx44dQ05ODkJCQqDRaMQ0w4YNw9mzZxEWFoawsDCcPXsWI0aMENdrNBoEBwcjNzcXx44dw9atW7F9+3ZMmzat6k/ezEibgxljjDFmTmpVEJiTk4O3334bP/30E5ycnMTlRIRly5Zh5syZePXVV+Hn54f169cjLy8PmzdvBgBkZmZi9erVWLJkCXr16oX27dtj06ZNuHDhAg4dOgQAuHjxIsLCwvDzzz/D398f/v7++Omnn7B3715cvnwZAHDw4EH8888/2LRpE9q3b49evXphyZIl+Omnn5CVlVX9hVKL6fcD5IpAxhhjzLxY1HQG9H344YcIDg5Gr169MH/+fHH5jRs3kJSUhD59+ojLlEolAgICEBMTg/fffx+nTp2CSqWSpPHw8ICfnx9iYmLQt29fxMbGwtHREV27dhXTPP/883B0dERMTAx8fHwQGxsLPz8/eHh4iGn69u2LwsJCnDp1CkFBQQb5LiwsRGFhofhaFyxqNBpJLaSpNBoNtFpthbatThrJs4Mrdq61hbmU+dOEy7xmcLlXPy7z6lfXy9zU8641QeDWrVtx+vRpxMXFGaxLSkoCANSvX1+yvH79+rh165aYxtLSUlKDqEuj2z4pKQlubm4G+3dzc5OkKXkcJycnWFpaimlKWrhwIebMmWOw/Nq1a7CzszO6TVm0Wi3S0tJw9epVyGS1qrJW4kFKuvh3SkoKrlxR1WBunoy5lPnThMu8ZnC5Vz8u8+pX18s8JyfHpHS1Igi8ffs2Jk2ahIMHD8LKyqrUdIIgSF4TkcGykkqmMZa+Imn0ffbZZ5g6dar4OisrC15eXmjWrBkcHBzKzJ8xGo0GV69eRfPmzSGXy8u9fXVxSbwOIA0A4OpaDy1aeNdofp6EuZT504TLvGZwuVc/LvPqV9fL3NTua7UiCDx16hSSk5PRsWNHcZlGo8GRI0fw3Xffif31kpKS0KBBAzFNcnKyWGvn7u6OoqIipKenS2oDk5OT0a1bNzHN/fv3DY6fkpIi2c+JEyck69PT06FSqQxqCHWUSiWUSqXBcrlcXuGbTyaTPdH21UI/KBaE2p1XE5hFmT9luMxrBpd79eMyr351ucxNPWeTg8Ddu3eXOxO9e/eGtbX1Y9P17NkTFy5ckCx755138Oyzz+LTTz9F06ZN4e7ujoiICLRv3x4AUFRUhOjoaHz99dcAgI4dO0KhUCAiIgJDhgwBACQmJuLvv//GokWLAAD+/v7IzMzEyZMn0aVLFwDAiRMnkJmZKQaK/v7+WLBgARITE8WA8+DBg1AqlZIglZUYHcwDQxhjjDGzYnIQOGjQoHLtWBAEXLlyBU2bNn1sWnt7e/j5+UmW2drawsXFRVw+efJkfPnll2jRogVatGiBL7/8EjY2Nhg2bBgAwNHREWPGjMG0adPg4uICZ2dnTJ8+HW3atEGvXr0AAL6+vujXrx/Gjh2LH3/8EQDw3nvvISQkBD4+PgCAPn36oFWrVhgxYgQWL16MtLQ0TJ8+HWPHjq1Q025dwU8MYYwxxsxLuZqDSxtYYYy9vX2FMlSaTz75BPn5+fjggw+Qnp6Orl274uDBg5LjfPvtt7CwsMCQIUOQn5+Pnj17Yt26dZJq0V9++QUTJ04URxEPHDgQ3333nbheLpdj3759+OCDD9C9e3dYW1tj2LBh+Oabbyr1fJ4G+hNEazkGZIwxxsyKyUHgqFGjTGra1Rk+fPgT1ZxFRUVJXguCgNmzZ2P27NmlbmNlZYUVK1ZgxYoVpaZxdnbGpk2byjx2o0aNsHfv3vJkt07ScnMwY4wxZrZMDgLXrl1brh2vXLmy3Jlh5kX6xBCOAhljjDFzUvcmz2GVhp8YwhhjjJmvcvUJlMlkj52XTxAEqNXqJ8oUMw/S0cEcBTLGGGPmpFxB4M6dO0tdFxMTgxUrVnAwUIfoX2m+7Iwxxph5KVcQ+Morrxgsu3TpEj777DPs2bMHb7/9NubNm1dpmWO1HI8OZowxxsxWhfsE3rt3D2PHjkXbtm2hVqtx9uxZrF+/Ho0aNarM/LFaTFITyANDGGOMMbNS7iAwMzMTn376KZo3b474+HgcPnwYe/bsMZjsmT39+IkhjDHGmPkqV3PwokWL8PXXX8Pd3R1btmwx2jzM6g4t6Y8O5iiQMcYYMyflCgJnzJgBa2trNG/eHOvXr8f69euNptuxY0elZI7VblTK34wxxhir/coVBI4cOfKxU8SwuoObgxljjDHzVa4gcN26dVWUDWaO9AeDaDkKZIwxxswKPzGEVRwZ/ZMxxhhjZqBcNYEAoNVqsW7dOuzYsQM3b96EIAho0qQJXn/9dYwYMYKbi+sQniyaMcYYM1/lqgkkIgwcOBDvvvsu7t69izZt2qB169a4desWQkNDMXjw4KrKJ6uFiEcHM8YYY2ar3H0Cjxw5gsOHDyMoKEiy7o8//sCgQYOwYcMGjBw5slIzyWon4uZgxhhjzGyVqyZwy5Yt+M9//mMQAALASy+9hBkzZuCXX36ptMyx2k0rGR3MYSBjjDFmTsoVBJ4/fx79+vUrdX3//v1x7ty5J84UMw/S0cE1mBHGGGOMlVu5gsC0tDTUr1+/1PX169dHenr6E2eKmQeeJ5AxxhgzX+UKAjUaDSwsSu9GKJfLoVarnzhTzPwQ9wpkjDHGzEq5BoYQEUJDQ6FUKo2uLywsrJRMMfMgHR1cgxlhjDHGWLlV+mPjeGRw3SGdJ5CjQMYYY8yc8GPjWIXxFDGMMcaY+TK5T+D58+eh1WpN3nF8fDz3D3zK8bODGWOMMfNlchDYvn17pKammrxjf39/JCQkVChTzDxoeXQwY4wxZrZMbg4mIvy///f/YGNjY1L6oqKiCmeKmQduDmaMMcbMl8lB4IsvvojLly+bvGN/f39YW1tXKFPMXPDoYMYYY8xcmRwERkVFVWE2mDkifmwcY4wxZrbKNVk0Y/r4iSGMMcaY+eIgkFWY/uhgfmIIY4wxZl44CGQVpl/7p+UYkDHGGDMrHASyCpM+MaTGssEYY4yxCuAgkFWY/gTR3BzMGGOMmZcKBYGHDh0qdd2PP/5Y4cwwM8MDQxhjjDGzVaEgMDg4GNOmTZNMCJ2SkoIBAwbgs88+q7TMsdpN2hzMUSBjjDFmTioUBB45cgR79uxB586dER8fj3379sHPzw85OTk4d+5cufe3cOFCdO7cGfb29nBzc8OgQYMMJqYmIsyePRseHh6wtrZGYGAg4uPjJWkKCwvx0UcfwdXVFba2thg4cCDu3LkjSZOeno4RI0bA0dERjo6OGDFiBDIyMiRpEhISMGDAANja2sLV1RUTJ07kJ6AYQZLmYMYYY4yZkwoFgV27dsWZM2fQtm1bdOzYEYMHD8a0adPwxx9/wMvLq9z7i46Oxocffojjx48jIiICarUaffr0QW5urphm0aJFWLp0Kb777jvExcXB3d0dvXv3RnZ2tphm8uTJ2LlzJ7Zu3Ypjx44hJycHISEh0Gg0Ypphw4bh7NmzCAsLQ1hYGM6ePYsRI0aI6zUaDYKDg5Gbm4tjx45h69at2L59O6ZNm1aRonqq6Qd+PDqYMcYYMy8mPzGkpMuXLyMuLg6enp64d+8eLl26hLy8PNja2pZ7X2FhYZLXa9euhZubG06dOoUXX3wRRIRly5Zh5syZePXVVwEA69evR/369bF582a8//77yMzMxOrVq7Fx40b06tULALBp0yZ4eXnh0KFD6Nu3Ly5evIiwsDAcP34cXbt2BQD89NNP8Pf3x+XLl+Hj44ODBw/in3/+we3bt+Hh4QEAWLJkCUJDQ7FgwQI4ODgY5L+wsBCFhYXi66ysLADFAaV+AGoqjUYDrVZboW2rk1Yv8jOH/JbFXMr8acJlXjO43Ksfl3n1q+tlbup5VygI/OqrrzBr1iy89957WLx4Ma5du4bhw4ejbdu22LRpE/z9/SuyW1FmZiYAwNnZGQBw48YNJCUloU+fPmIapVKJgIAAxMTE4P3338epU6egUqkkaTw8PODn54eYmBj07dsXsbGxcHR0FANAAHj++efh6OiImJgY+Pj4IDY2Fn5+fmIACAB9+/ZFYWEhTp06haCgIIP8Lly4EHPmzDFYfu3aNdjZ2ZX7/LVaLdLS0nD16lXIZLV3AHeWXi1sdnY2rly5UoO5eTLmUuZPEy7zmsHlXv24zKtfXS/znJwck9JVKAhcvnw5du3ahf79+wMAWrdujZMnT+I///kPAgMDJbVi5UVEmDp1Knr06AE/Pz8AQFJSEgCgfv36krT169fHrVu3xDSWlpZwcnIySKPbPikpCW5ubgbHdHNzk6QpeRwnJydYWlqKaUr67LPPMHXqVPF1VlYWvLy80KxZM6M1h4+j0Whw9epVNG/eHHK5vNzbVxe7U7kAim80Wzs7tGjRomYz9ATMpcyfJlzmNYPLvfpxmVe/ul7muhbJx6lQEHjhwgW4urpKlikUCixevBghISEV2aVowoQJOH/+PI4dO2awThAEyWsiMlhWUsk0xtJXJI0+pVIJpVJpsFwul1f45pPJZE+0fbWQlIdQu/NqArMo86cMl3nN4HKvflzm1a8ul7mp51yhOtKSAaC+gICAiuwSAPDRRx9h9+7diIyMhKenp7jc3d0dAAxq4pKTk8VaO3d3dxQVFSE9Pb3MNPfv3zc4bkpKiiRNyeOkp6dDpVIZ1BDWdZLRwTwwhLFSJWUWYOiq4wj7O7Gms8IYY6IK1QTOnTu3zPVffPFFufZHRPjoo4+wc+dOREVFoUmTJpL1TZo0gbu7OyIiItC+fXsAQFFREaKjo/H1118DADp27AiFQoGIiAgMGTIEAJCYmIi///4bixYtAgD4+/sjMzMTJ0+eRJcuXQAAJ06cQGZmJrp16yamWbBgARITE9GgQQMAwMGDB6FUKtGxY8dyndfTTvrsYI4CGSvNx7+dQ+z1VMReT8XNr4JrOjuMMQaggkHgzp07Ja9VKhVu3LgBCwsLNGvWrNxB4IcffojNmzfj999/h729vVgT5+joCGtrawiCgMmTJ+PLL79EixYt0KJFC3z55ZewsbHBsGHDxLRjxozBtGnT4OLiAmdnZ0yfPh1t2rQRRwv7+vqiX79+GDt2rPhkk/feew8hISHw8fEBAPTp0wetWrXCiBEjsHjxYqSlpWH69OkYO3Zshfr3Pc20PE8gYya5nZZX01lgjDEDFQoCz5w5Y7AsKysLoaGhGDx4cLn3t3LlSgBAYGCgZPnatWsRGhoKAPjkk0+Qn5+PDz74AOnp6ejatSsOHjwIe3t7Mf23334LCwsLDBkyBPn5+ejZsyfWrVsnaRv/5ZdfMHHiRHEU8cCBA/Hdd9+J6+VyOfbt24cPPvgA3bt3h7W1NYYNG4Zvvvmm3Of1tCN+bBxjJnGwVtR0FhhjzECF5wksycHBAXPnzkVISIhk8mVTmPLIMUEQMHv2bMyePbvUNFZWVlixYgVWrFhRahpnZ2ds2rSpzGM1atQIe/fufWye6jqtJAjkKJCx0vDbgzFWG1Xq5DkZGRniHH/s6cePjWPMNJKuExwRMsZqiQrVBP73v/+VvCYiJCYmYuPGjejXr1+lZIzVfvzFxphpNHrV5hotwUJe9tRWjDFWHSoUBH777beS1zKZDPXq1cOoUaPw2WefVUrGWO3Hzw5mzDT6P5hUGoJF3Zu2jDFWC1UoCLxx40Zl54OZIUmfwJrLBmO1nv57RaXVwhocBTLGal7de6AeqzTEzcGMmUSrFwWqNfxeYYzVDibXBOo/G/dxli5dWqHMMPPCU8QwZhqVVvvob422jJS1x55z9xBz7QFm9PeFI09xw9hTyeQg0NjcgMY87lm+7OkhnSyao0DGSqMXA5pNEDhnTzwe5BRBaSHH7IGtazo7jLEqYHIQGBkZievXr8Pb2xsyGbcis5Kjg2swI4zVcmq9KNAcmoNzCtV4kFMEAPj3fnYN54YxVlXKFc21aNECDx48EF+/+eabuH//fqVnipkH/c7u/OxgxkqnX/mnHxDWVtkFKvFvfmsz9vQqVxBYsvP//v37kZubW6kZYmaE+wQyZhKNXuBXpK79b5ZClV5+zaT5mjFWftyuyypMy08MYcwkJSeLru30A78iNQeB1SmvSI34e5k84wKrFuUKAgVBMBj4wQNB6i5+YghjppEEgWbwXpHUBHIQWK3GrPsLwf89hqjLKTWdFVYHlGuyaCJCaGgolEolAKCgoADjxo2Dra2tJN2OHTsqL4es1tJyczBjJtEP/MyhJrBQrRH/5ubg6qPSaBF7PRUAcOjifQQ961bDOWJPu3IFgaNGjZK8Hj58eKVmhpkXKuVvxpiU2TUH69X+Fao0ZaRklSklu1D8O7dQXYM5YXVFuYLAtWvXVlU+mBnSbwLm0cGMlc7cgsBCNQ8MqQlpuUXi3yozmEqImT8eGMIqjOcJZOzxiEjSdcLcgsBC7hNYbfQnEtdvkmesqnAQyCpMf7qz2v+1xljNKBn0mcXAEP0+gRwEVhv92j8Ovll14CCQVZikT2A1f7EVqDT45cQtPMgpfHxixmqQukQQqDVSE5hfpMGQH2MxZdvZWjHSXj8AMYeay6eFpCZQxUEgq3ocBLIKoxpsDv7t1B3M3Pk3ei6Jrt4DM1ZOJfvLlgwKASDqcjJO3kjDzjN3cTU5p7qyVir92j+1lsoVmO48cwcTt5xBDg9sKDduDmbVrVwDQxjTJ50sunqiwEK1BrN3x2PLydsAgMx8FTRaglzG81Wy2qlk0GesZi1Fr0b7QU4RWtSv8myVqWRTpJYAuQlvMSLClG3nAADerraY2rtlVWTvqcXNway6cU0gqzDJs4Or6fNqz7lEMQDUuZ9VUD0HZ6wCSjb/GhtJn1/0qNYnM7/IYH11K9kP0NTnHWcVPKr9u5ueX6l5qgv0awILeGoeVg04CGQVRjXw2LjMfJXBsrwi/rBktVfJmkBjzcEFev2/MvIM7/HqVrIp0tR+gfqBS1pu+fvrbj6RgAX7/qmzTaHS5mCuCWRVj5uDWYXpV2hUV2f2PCP9jOrqFwYzDwY1gcaCQL17uDb0pSsZgBgLXI3Rr9HMMPKDrSzZBSr8Z+cFAEATVzsM69qoXNs/Dbg5mFU3rglkFVYT8wTmGqn14w9LVpupTKgJ1A+eqvp+TskuxKazabjxILfUNCWbgzUmTlycr1cTWFDO0a33sx7VHNaGwTE1QTo6mH/csqrHQSCrMOlj46onCjT2KCXuO8NqM1XJQRZGgkD92uyqvp9X/3kTm86m481VJ0pNU7JJ2uSaQL28lzeI0X9kWkZezfeLrAncHMyqGweBrMJqpibQWHMwf1iaCyJCVkHN93mrTiUHVRibLFq/JrCygkCNlvC/v27janK2ZHl4/H0AQGpu6YHW7fQ8yWtTB4YUqCp+HsnZjwZ4lbcp+WlRcmoeNT+yj1UxDgJZhel/L1TXs4PzCo00B/OkqmaBiDB89Qk8/+VhnLqVXtPZeWLHr6fidlreY9MVqcs3MKS8zail2Xv+Hj757TyG/HhcEkw8LjgrUGlwusT1UZvYHCwJAsvx4+zAhURM2npWfG1sAFhdUPJ5wfzcZlbVOAhkFVYTo4ON1wRyc7A5SMkuxJ9XU5FXpEF4fFJNZ+eJHIxPwlurjmPcplOPTVuyFu1xA0MqqybwxI00AEBabhGupTzq/6dfc27sWOl5RVBrCQq5ADtl8dhBU0cH5xdVbIqTNX/ekLyuq108Stb88Q9cVtU4CGQVRqW+qDrG+gTyB6V50O/4/yDbvB/3tzj8MgAg/l6W0XtSX8naHZWR2h1Jc3AldW/I1puzTzeXJhFJAixjAzCy8ou3c7BSwOLhDNGm9gks2Rxs6qwB+SWCvroaBJa8N7irC6tqHASyCtPWRE2gseZgrgk0Cyk5BXp/m28QqNUSruuNrC3Zf66kkl/sxpr49AO//Eqa91K/SVXXDzMzXyUJLOLvZRpsp0vrYK2AxcMn8ZhcE6gXvGnJMAAujX7ACpTdJH43Ix8nb6QZrVE1d0UlyquuBsOs+nAQyCpM8sSQauoTyANDzJf+F31WLezz9X+RVzFu4ymklTFgAnj0qEKdW6llB4El+9MZC3AKJFPEVM4Xv34Z68o+qcTTda4bmSZGt52DlYX4OMaKDAwBpM3cZTEMAo1vl1uoRv9lRzDkx1j8dvqOSfs2J1wTyKobB4GswvSngKi20cE8RYzZ0v+iz64FEyLru59VgMXhlxEWn4RVR66XmbbkqNoDFxLLTG/4xW54v1ZFn0D9Udi6wC4xUxoElgy+9Lcrrgks/oowtUavZC2mKedCRMguMWK8tO0S0vLER9PFXks1KU/mxKBPYBlBdHpuUZ0dQMMqDweBrEKISDJxc/XVBPJk0eZK/0kYOUaCD32FatP7kz3O+TsZSMws+zm2+qN8z94ue+Ryaomm7L8eM9LZIAg0UhOo/+OmskYH6/r2AY+CvfslgkBj10G/T6CNpRwAkGekBt6YkjV/pvTXLVRrxSBz89iuD/djfLskvfzfeUwzvDkq2Rxc2mdb2N+JaD8vAr2WRtfKH8EFKg2m/u8sRq45KZn6h9U+HASW4vvvv0eTJk1gZWWFjh074ujRozWdpVqlUK2VNIkZq1GobCqN1uBJBrq8sNpPP+Ao69Fov5y4hWf/XxhGrjn5xIHgwfgkDPzuT/T99giSs0r/Mrqb8ShIfJBTdnOwrrnY08kaQHHtWlnzuZUcVGGsdierQD8IfPIvdSKSNAfravd0zcEPW3kNauAAveZgawvYWRWPDn5c0K5TsmbKlHPR5U0QgDYNHQEU90E0FnjqN2c/7jqZI1N+MADApuMJAIpH3Mffy6ryfJXXwX/uY8fpuzjybwrWHLtZ09lhZeAg0Iht27Zh8uTJmDlzJs6cOYMXXngB/fv3R0JCQk1nrdYo+SWema8yOuqxMhmbIxDg5mBz8UCvBi2vSFPqddt5+i6IgKNXHuCfRMMvOCIyeVDAvodNtVkFaoT/c7/UdPrNpCmPGbmsaw5+1t0BlhYyaLRk0Myqr2T/x5Jf7AUqjeTHjan96MpSqNZKBqCINYEPg6hGz1gCMD4pc9rDp3U4WCvEKWJMfZ5xyf6UptRq6vJmp7SAndIClnKZ0X0B0prA5KwCk38kqDVa/HbqDg5cSKy255xXhCldBwDgUtKjCcCvpdS+R+zpDzg6ft08mu2JCHvP38PPR69X2uAscyBQbX5H1JCuXbuiQ4cOWLlypbjM19cXgwYNwsKFCw3SFxYWorDw0RdHVlYWvLy8kJaWBgcHh3IfX6PR4OrVq2jevDnkcrnB+n/uZWHj8QRoiR7+K26OVWsJIEAm05vIWQAsZAKIih/tpqVHI/10V56IQA/3QcCjfWpJcowitRYFKg0K1FqxM7y1Qo4CtQZEQD07JZxtFbC0kEEhl0EhFyCXySCXCbCQCZALAvJVGnG9IDwK4ORCcdUEPcwXgR7+X7xAS8XTWdwv5Qv6hRau4t8KmQDZw/0BxTUMgiBAEIprQAQIgFB8MAJBEATIBSAnOxv29vYQBKHCo511R9UdT/ehrp+fkukFQSgue71rYCETIH9YZnkqDUCAXCYU57/Evkq+hUvmveQ73Ngj/gQI4v7Fa/Cw3NVaLawUcgiQDgYqi5YIl5KyYWspR2MXWxARfj8n7TvXpqEDnBQa2NnZQSYTIKC4zHbrpVNayBDkUw+yh2WhJcKpW+nIKlCjaxNnONkoIJcJ0D7Mry7vujyU3Fff1vUBFF8XlZpQpCkOlo5fT5PkLaClK+ytLMQ86d4zWiIc+Ls4mAzt1hjR/6bgxoM8eLvYwMfdHhoNQS4XxPtMrSUcNBJ8vtDCFUq5DGotIS23COfvSkfpBvnUg7VCLg7M0NFdO0u5TKwBlwnF106l0Yrnfi0lB5fvS4ODl/3csf/v4vkZg1vaY9+/xYGErkx01zziYjIAYO7AVoi9nooDf9+HXCbAv6nzo88MXX6I9P5+NDehjk99O/g2cAARQUP67+lH53IrNQ//JGbD4xkrHP04EN2/jkRSViGauNrA190BMlnxOcoEAQf+TpLU/Ae0dIWLraXkupd8P6i1hAt3s5DwsMm/s7cTvB7W4uqUfE8Bj97Lj9IYJCn+LNEj06tW0c8GESEzMxMOjo7Fx9JfB4JGW/z5ve9CkqSFxd1Bieebujz87Cq+/zPyVIi8nCKmcbNXolszF8iE4s8IMU8PP+OKNFrkFKqR9zCwcbJRwMFKITm+5LrSo+W68yjteqOU5XvOS9/rvX3dYGdlIX7eFf//8LNPW3xv3MsogKWFDA0crMSyVGuKy6X4vEpHeHTdZQ8/ewEgOysLzzg6QCaTST5fS37nERW3BsTdLO7a0cjZGp0aO0mOIQjF32MKefEPPy0ZfpIKeHSd9L+b5eKy4nRaItgrLTD3ldZlnNWTycrKgrOzc/F9V0YcwkFgCUVFRbCxscGvv/6KwYMHi8snTZqEs2fPIjo62mCb2bNnY86cOQbL4+LiYGdnV+48aLVapKWlwdnZGTKZYWXt8du5mH245ifbVcgEvNXuGaTkqBF2JfvxG1QSL0cFCtSEjh7WCL+SXW3T07DK4Wojx4M88/+l/XlgfZxNysfeS6Y1xw3ydcTuS5mlBtItXZVIzVMjtRLLxtVGjtQ8jeQ9IhOAeS/YYfHJPGQUlF5Tt/Tlhoi/n4/Vp9JKTWOMQgZ09bLFsVuGI4/L0qeFPaZ2d8MXhxJx8k7p/f0EAPZKGbIKn85uIDIBaOtujbOJZfdjre10v1+ewpl8KoWTtRxb3vSusv3n5OSgc+fOjw0CLaosB2bqwYMH0Gg0qF+/vmR5/fr1kZRkPPD67LPPMHXqVPG1riawWbNmVVITaOGUi+ly+4e/eISHvz4AC7kMAoqfTSp/+GtIV6On+xklf1hrIP6qeviH7pe2TChepl8zJH9Ys2Ypl0GpkMHKQg57awt4PmMN24fNRYmZBbiVmguNllD0sClKrX30C1ej94sOKG6e0RJgZVkc5OpqLh/VpD3KhyA8yrelhQz+TZ1h//CX7OX72biclP2odhOASq2V/CoVaxX1akN1x9KVkUqtwYMHD+Dq6mo08C7tV6j+55v+7yndL0BLC5lhwhLba4nEX6+6GkO1RgvNw2unVMggFwTx16eWDGsl9F/q12oY5Fsw+icIxb+6S+ZFQPH1183/VrJmqjREQD17JQpVGmQVqCETAJlMQNuGjvB2tcXB+PsoUKmRkpKCevXqFZ8QFdfa2FjK0fNZN8TdSkd2vqq49ppIvMYutpZwsbPEv/dzoNJoxTzrzl13XXX5797cBWm5RbiY+OgHg0JefD8rHtZKW8pl8HjGCi62lgiPv1+8z4fVoloqvm8JEN9XTevZoVtTZxSqteh1MRk5BWpoHt7f+rUAFjIBVgo53B2Ka2vG3s/Bv/ezkV2ghgDA0kIGmUyA0kKGl3zc8CCnEKcSMlD4sMa9tGbvIo0WlhbF73d6WG7FNe+CWENjIRfQp1V9XEvJxZX7OWI5Na9nAyd1KrZ3bIiY62nSe1MAFDIZvF1t0NnbGSFaQo82D5CWqxLvGYN7TxAk79vmbnZo6WaHqH8f4F5GPvKKNOL1L1kTBBTXsjZwtEJAy3qwUsixunETHLqYjKx8NbR6NYi62vLWHg5o6+mIAxeSkF2ohkqjldYu6d3TwKNa9XaezyAjvwiXkqQ1pKbUgxhLUrIeiMhwPkXxvUhapKamwcVF+sNet1ouk8HiYYtJO09HtPZwwNErD3A7PR+FDz/PiB61WlhbytHfzx3XUnJx4U6m+P7QlZGu9k4AoLCQwU4ph62lBQjFT4Qp2cez5DXU5Uv3+VtmWiPLIQBtGzrC4xlrRF1OQV6RGhp6VNbiZ7EWYk2vi60lijRaZOQV32saLcFCXlzzptaQ0ZrY0mip+DvnQeoDODu7FJ/Jw5pHmQDJZ60u3wq5gFYNHOBka4ljVx8Y9D/Xvc/UGm3xd6hgWDupu04yQRDPS1fbqX98mSDAWiFHixZepp9UOWVlmfbjlGsCS7h37x4aNmyImJgY+Pv7i8sXLFiAjRs34tKlS4/dR1ZWFhwdHR8bgZdGo9HgypUraNGihdEgkFU+LvPqx2VeM7jcqx+XefWr62VuahzCA0NKcHV1hVwuN6j1S05ONqgdZIwxxhgzVxwElmBpaYmOHTsiIiJCsjwiIgLdunWroVwxxhhjjFUu7hNoxNSpUzFixAh06tQJ/v7+WLVqFRISEjBu3DiTtte1sJvaJl+SRqNBTk4OsrKy6mQ1dk3gMq9+XOY1g8u9+nGZV7+6Xua6+ONxPf44CDTizTffRGpqKubOnYvExET4+flh//79aNy4sUnbZ2cXj5T18qq6Tp+MMcYYY2XJzs6Go6Njqet5YEgV0Gq1uHfvnjjnXHnpRhffvn27QgNLWPlxmVc/LvOaweVe/bjMq19dL3MiQnZ2Njw8PIzOeKHDNYFVQCaTwdPT84n34+DgUCdv3prEZV79uMxrBpd79eMyr351uczLqgHU4YEhjDHGGGN1EAeBjDHGGGN1EAeBtZBSqcSsWbOgVCprOit1Bpd59eMyrxlc7tWPy7z6cZmbhgeGMMYYY4zVQVwTyBhjjDFWB3EQyBhjjDFWB3EQyBhjjDFWB3EQyBhjjDFWB3EQWAVmz54NQRAk/9zd3cX1RITZs2fDw8MD1tbWCAwMRHx8vGQfhYWF+Oijj+Dq6gpbW1sMHDgQd+7ckaRJT0/HiBEj4OjoCEdHR4wYMQIZGRnVcYq10t27dzF8+HC4uLjAxsYGzz33HE6dOiWu53KvfN7e3gb3uiAI+PDDDwFwmVcFtVqNzz//HE2aNIG1tTWaNm2KuXPnQqvVimm43CtfdnY2Jk+ejMaNG8Pa2hrdunVDXFycuJ7L/MkcOXIEAwYMgIeHBwRBwK5duyTrq7N8ExISMGDAANja2sLV1RUTJ05EUVFRVZx2zSNW6WbNmkWtW7emxMRE8V9ycrK4/quvviJ7e3vavn07Xbhwgd58801q0KABZWVliWnGjRtHDRs2pIiICDp9+jQFBQVRu3btSK1Wi2n69etHfn5+FBMTQzExMeTn50chISHVeq61RVpaGjVu3JhCQ0PpxIkTdOPGDTp06BBdvXpVTMPlXvmSk5Ml93lERAQBoMjISCLiMq8K8+fPJxcXF9q7dy/duHGDfv31V7Kzs6Nly5aJabjcK9+QIUOoVatWFB0dTVeuXKFZs2aRg4MD3blzh4i4zJ/U/v37aebMmbR9+3YCQDt37pSsr67yVavV5OfnR0FBQXT69GmKiIggDw8PmjBhQpWXQU3gILAKzJo1i9q1a2d0nVarJXd3d/rqq6/EZQUFBeTo6Eg//PADERFlZGSQQqGgrVu3imnu3r1LMpmMwsLCiIjon3/+IQB0/PhxMU1sbCwBoEuXLlXBWdVun376KfXo0aPU9Vzu1WPSpEnUrFkz0mq1XOZVJDg4mEaPHi1Z9uqrr9Lw4cOJiO/1qpCXl0dyuZz27t0rWd6uXTuaOXMml3klKxkEVmf57t+/n2QyGd29e1dMs2XLFlIqlZSZmVkl51uTuDm4ily5cgUeHh5o0qQJ3nrrLVy/fh0AcOPGDSQlJaFPnz5iWqVSiYCAAMTExAAATp06BZVKJUnj4eEBPz8/MU1sbCwcHR3RtWtXMc3zzz8PR0dHMU1dsnv3bnTq1AlvvPEG3Nzc0L59e/z000/iei73qldUVIRNmzZh9OjREASBy7yK9OjRA4cPH8a///4LADh37hyOHTuGl19+GQDf61VBrVZDo9HAyspKstza2hrHjh3jMq9i1Vm+sbGx8PPzg4eHh5imb9++KCwslHQvelpwEFgFunbtig0bNiA8PBw//fQTkpKS0K1bN6SmpiIpKQkAUL9+fck29evXF9clJSXB0tISTk5OZaZxc3MzOLabm5uYpi65fv06Vq5ciRYtWiA8PBzjxo3DxIkTsWHDBgDgcq8Gu3btQkZGBkJDQwFwmVeVTz/9FEOHDsWzzz4LhUKB9u3bY/LkyRg6dCgALveqYG9vD39/f8ybNw/37t2DRqPBpk2bcOLECSQmJnKZV7HqLN+kpCSD4zg5OcHS0vKpvAYWNZ2Bp1H//v3Fv9u0aQN/f380a9YM69evx/PPPw8AEARBsg0RGSwrqWQaY+lN2c/TSKvVolOnTvjyyy8BAO3bt0d8fDxWrlyJkSNHium43KvO6tWr0b9/f8kvaIDLvLJt27YNmzZtwubNm9G6dWucPXsWkydPhoeHB0aNGiWm43KvXBs3bsTo0aPRsGFDyOVydOjQAcOGDcPp06fFNFzmVau6yrcuXQOuCawGtra2aNOmDa5cuSKOEi75iyI5OVn89eHu7o6ioiKkp6eXmeb+/fsGx0pJSTH4FVMXNGjQAK1atZIs8/X1RUJCAgBwuVexW7du4dChQ3j33XfFZVzmVePjjz/GjBkz8NZbb6FNmzYYMWIEpkyZgoULFwLgcq8qzZo1Q3R0NHJycnD79m2cPHkSKpUKTZo04TKvYtVZvu7u7gbHSU9Ph0qleiqvAQeB1aCwsBAXL15EgwYNxA+MiIgIcX1RURGio6PRrVs3AEDHjh2hUCgkaRITE/H333+Lafz9/ZGZmYmTJ0+KaU6cOIHMzEwxTV3SvXt3XL58WbLs33//RePGjQGAy72KrV27Fm5ubggODhaXcZlXjby8PMhk0o9uuVwuThHD5V61bG1t0aBBA6SnpyM8PByvvPIKl3kVq87y9ff3x99//43ExEQxzcGDB6FUKtGxY8cqPc8aUc0DUeqEadOmUVRUFF2/fp2OHz9OISEhZG9vTzdv3iSi4qHujo6OtGPHDrpw4QINHTrU6FB3T09POnToEJ0+fZpeeuklo0Pd27ZtS7GxsRQbG0tt2rSpE1MJGHPy5EmysLCgBQsW0JUrV+iXX34hGxsb2rRpk5iGy71qaDQaatSoEX366acG67jMK9+oUaOoYcOG4hQxO3bsIFdXV/rkk0/ENFzulS8sLIwOHDhA169fp4MHD1K7du2oS5cuVFRURERc5k8qOzubzpw5Q2fOnCEAtHTpUjpz5gzdunWLiKqvfHVTxPTs2ZNOnz5Nhw4dIk9PT54ihplON3+RQqEgDw8PevXVVyk+Pl5cr9VqadasWeTu7k5KpZJefPFFunDhgmQf+fn5NGHCBHJ2diZra2sKCQmhhIQESZrU1FR6++23yd7enuzt7entt9+m9PT06jjFWmnPnj3k5+dHSqWSnn32WVq1apVkPZd71QgPDycAdPnyZYN1XOaVLysriyZNmkSNGjUiKysratq0Kc2cOZMKCwvFNFzulW/btm3UtGlTsrS0JHd3d/rwww8pIyNDXM9l/mQiIyMJgMG/UaNGEVH1lu+tW7coODiYrK2tydnZmSZMmEAFBQVVefo1RiAiqsGKSMYYY4wxVgO4TyBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjDHGWB3EQSBjjLEnNmXKFLz11lvIysqq6awwxkzEQSBjjLEnptVqwY+iZ8y8cBDIGGNVKDAwEJMnT67pbACoWF4CAwMhCAIEQcDZs2dLTbd8+XJs27YNDg4O4rLQ0FBx2127dlUs04yxKsNBIGPMrP3www+wt7eHWq0Wl+Xk5EChUOCFF16QpD169CgEQcC///5b3dmsdpUZfI4dOxaJiYnw8/MzWBcTEwO5XI5+/foZrFu+fDkSExMrJQ+MscrHQSBjzKwFBQUhJycHf/31l7js6NGjcHd3R1xcHPLy8sTlUVFR8PDwQMuWLWsiq2bLxsYG7u7usLCwMFi3Zs0afPTRRzh27BgSEhIk6xwdHeHu7l5d2WSMlRMHgYwxs+bj4wMPDw9ERUWJy6KiovDKK6+gWbNmiImJkSwPCgoCAISFhaFHjx545pln4OLigpCQEFy7dk1M++OPP6Jhw4bQarWS4w0cOBCjRo0CABARFi1ahKZNm8La2hrt2rXDb7/9VmpeTUkfGBiIiRMn4pNPPoGzszPc3d0xe/ZsSZrs7Gy8/fbbsLW1RYMGDfDtt99Kav5CQ0MRHR2N5cuXi82xN2/eBFDcd6+sfZdHbm4u/ve//2H8+PEICQnBunXrKrwvxlj14yCQMWb2AgMDERkZKb6OjIxEYGAgAgICxOVFRUWIjY0Vg8Dc3FxMnToVcXFxOHz4MGQyGQYPHiwGfW+88QYePHgg2W96ejrCw8Px9ttvAwA+//xzrF27FitXrkR8fDymTJmC4cOHIzo62mg+TU2/fv162Nra4sSJE1i0aBHmzp2LiIgIcf3UqVPx559/Yvfu3YiIiMDRo0dx+vRpcf3y5cvh7+8vNuMmJibCy8vLpH2Xx7Zt2+Dj4wMfHx8MHz4ca9eu5cEhjJkTYowxM7dq1SqytbUllUpFWVlZZGFhQffv36etW7dSt27diIgoOjqaANC1a9eM7iM5OZkA0IULF8RlAwcOpNGjR4uvf/zxR3J3dye1Wk05OTlkZWVFMTExkv2MGTOGhg4dKr4OCAigSZMmlSt9jx49JGk6d+5Mn376KRERZWVlkUKhoF9//VVcn5GRQTY2NjRp0iSD4+p73L6NMbYfnW7dutGyZcuIiEilUpGrqytFREQYpANAO3fuLPUYjLGawTWBjDGzFxQUhNzcXMTFxeHo0aNo2bIl3NzcEBAQgLi4OOTm5iIqKgqNGjVC06ZNAQDXrl3DsGHD0LRpUzg4OKBJkyYAIOnX9vbbb2P79u0oLCwEAPzyyy946623IJfL8c8//6CgoAC9e/eGnZ2d+G/Dhg2SZmWd8qRv27at5HWDBg2QnJwMALh+/TpUKhW6dOkirnd0dISPj49JZVXWvsvj8uXLOHnyJN566y0AgIWFBd58802sWbOm3PtijNUMw16+jDFmZpo3bw5PT09ERkYiPT0dAQEBAAB3d3c0adIEf/75JyIjI/HSSy+J2wwYMABeXl746aef4OHhAa1WCz8/PxQVFUnSaLVa7Nu3D507d8bRo0exdOlSABCbjfft24eGDRtK8qNUKg3yWJ70CoVC8loQBHF7etjcKgiCJA2Z2Axb1r7LY/Xq1VCr1ZJzISIoFAqkp6fDycmp3PtkjFUvDgIZY0+FoKAgREVFIT09HR9//LG4PCAgAOHh4Th+/DjeeecdAEBqaiouXryIH3/8UZxG5tixYwb7tLa2xquvvopffvkFV69eRcuWLdGxY0cAQKtWraBUKpGQkCAGnWUpb/rSNGvWDAqFAidPnhT7+WVlZeHKlSuS/VpaWkKj0VT4OGVRq9XYsGEDlixZgj59+kjWvfbaa/jll18wYcKEKjk2Y6zycBDIGHsqBAUF4cMPP4RKpZIEQwEBARg/fjwKCgrEQSFOTk5wcXHBqlWr0KBBAyQkJGDGjBlG9/v2229jwIABiI+Px/Dhw8Xl9vb2mD59OqZMmQKtVosePXogKysLMTExsLOzE0cQVzR9aezt7TFq1Ch8/PHHcHZ2hpubG2bNmgWZTCapHfT29saJEydw8+ZN2NnZwdnZ2eSyfJy9e/ciPT0dY8aMgaOjo2Td66+/jtWrV3MQyJgZ4D6BjLGnQlBQEPLz89G8eXPUr19fXB4QEIDs7Gw0a9ZMrDmTyWTYunUrTp06BT8/P0yZMgWLFy82ut+XXnoJzs7OuHz5MoYNGyZZN2/ePHzxxRdYuHAhfH190bdvX+zZs0fsX1hSedOXZunSpfD390dISAh69eqF7t27w9fXF1ZWVmKa6dOnQy6Xo1WrVqhXr57BHH5PYvXq1ejVq5dBAAgU1wSePXtWMlqZMVY7CWRqRxLGGGO1Um5uLho2bIglS5ZgzJgxlbrvwMBAPPfcc1i2bFmF9yEIAnbu3IlBgwZVWr4YY0+OawIZY8zMnDlzBlu2bMG1a9dw+vRpcd7CV155pUqO9/3338POzg4XLlwo13bjxo2DnZ1dleSJMfbkuCaQMcbMzJkzZ/Duu+/i8uXLsLS0RMeOHbF06VK0adOm0o919+5d5OfnAwAaNWoES0tLk7dNTk5GVlYWgOKpaGxtbSs9f4yxiuMgkDHGGGOsDuLmYMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOoiDQMYYY4yxOuj/Ax3TjyJP+SzIAAAAAElFTkSuQmCC" }, - "execution_count": 8, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], - "source": [ - "arc_spectra[0].flux.sum()" - ] + "execution_count": 10 }, { - "cell_type": "markdown", - "id": "a64a7f45-9145-41ae-845c-389329762697", "metadata": {}, + "cell_type": "markdown", "source": [ - "## 3. Accessing the WCS Object\n", - "\n", - "Beyond resampling the flux array, the derived pixel-to-wavelength transformation is available as a standard [`gwcs`](https://gwcs.readthedocs.io/) (Generalized World Coordinate System) object via the `.wcs` property of the `WavelengthSolution1D` instance.\n", - "\n", - "This WCS object encapsulates the fitted model (`ws._p2w`) and defines the mapping between the input pixel coordinate frame and the output spectral coordinate frame (including units).\n", - "\n", - "**Uses:**\n", - "- **Applying the solution without rebinning:** You can use the WCS object directly with tools that understand GWCS (like `specutils`) to work with spectra while keeping the original pixel grid.\n", - "- **Interoperability:** Provides a standard way to represent the wavelength solution that can be understood by other Astropy-affiliated packages.\n", - "- **FITS Headers:** The GWCS object can be used to generate standard WCS keywords for inclusion in FITS file headers, making the wavelength calibration self-describing." - ] + "As a final check, let's make sure that the wavelength grid is indeed linear, and that the flux\n", + "was conserved as it should." + ], + "id": "21e11b25c0b14760" }, { "cell_type": "code", - "execution_count": 6, - "id": "04556935-222e-42f4-b352-cc19a87ec383", - "metadata": {}, + "id": "f15d4c4e-b721-4348-9f2b-078b3c348515", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.344842Z", + "start_time": "2025-04-24T10:19:56.342286Z" + } + }, + "source": [ + "np.diff(spectrum_wl.spectral_axis)" + ], "outputs": [ { "data": { - "text/latex": [ - "$[5095.0088,~5097.6018,~5100.1948,~\\dots,~10405.416,~10408.009,~10410.602] \\; \\mathrm{\\mathring{A}}$" - ], "text/plain": [ - "" - ] + "" + ], + "text/latex": "$[2.5969455,~2.5969455,~2.5969455,~\\dots,~2.5969455,~2.5969455,~2.5969455] \\; \\mathrm{\\mathring{A}}$" }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "spectrum_wl.spectral_axis" - ] + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 7, - "id": "5054d5e7-d9c4-4706-bc1e-fa3880e191c6", - "metadata": {}, + "id": "ae8c6cdb-2fa3-4916-9ba0-5dab3d19acdc", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.411956Z", + "start_time": "2025-04-24T10:19:56.409172Z" + } + }, + "source": [ + "spectrum_wl.flux.sum()" + ], "outputs": [ { "data": { "text/plain": [ - "\n", - "\n", - " [1]: \n", - "Parameters:\n", - " offset_0 c0_1 ... c3_1 c4_1 \n", - " -------- ----------------- ... ----------------------- ---------------------\n", - " -1000.0 7454.432054908556 ... -5.0351299187468156e-08 4.958031715494442e-12)>" - ] + "" + ], + "text/latex": "$839275 \\; \\mathrm{DN}$" }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ws.wcs" - ] + "execution_count": 8 }, { "cell_type": "code", - "execution_count": 8, - "id": "d0d1aed5-c16b-421e-9638-19b8ec38bc6d", - "metadata": {}, + "id": "960d0392-0a8c-4d37-ae94-940e84a1c600", + "metadata": { + "ExecuteTime": { + "end_time": "2025-04-24T10:19:56.497673Z", + "start_time": "2025-04-24T10:19:56.495039Z" + } + }, + "source": [ + "arc_spectra[0].flux.sum()" + ], "outputs": [ { "data": { - "text/latex": [ - "$[5300.1923,~5302.2961] \\; \\mathrm{\\mathring{A}}$" - ], "text/plain": [ - "" - ] + "" + ], + "text/latex": "$839275 \\; \\mathrm{DN}$" }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "ws.wcs.pixel_to_world([100, 101])" - ] + "execution_count": 9 }, { "cell_type": "markdown", From 11f8fcae53b558a1ff6c41128233a226bdcccb97 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:17:00 +0100 Subject: [PATCH 45/76] Cleaned up the wavelength calibration docs. --- .../wavelength_calibration.rst | 158 +++++++++--------- 1 file changed, 75 insertions(+), 83 deletions(-) diff --git a/docs/wavelength_calibration/wavelength_calibration.rst b/docs/wavelength_calibration/wavelength_calibration.rst index 44d51409..6513a7dc 100644 --- a/docs/wavelength_calibration/wavelength_calibration.rst +++ b/docs/wavelength_calibration/wavelength_calibration.rst @@ -37,17 +37,22 @@ The typical workflow involves these steps: arc spectra, and the initialization can also be done using a list of arc spectra (or a list of line arrays identified from multiple arc spectra) and a list of catalogs for each arc spectra. -2. **Line Identification (Optional)**: If an arc spectrum was provided, identify the pixel - locations of emission lines within it. +2. **Line Identification (Optional)**: If arc spectra were provided, identify the pixel + locations of emission lines within them. 3. **Matching and Fitting**: Determine the correspondence between observed line pixels and catalog wavelengths, and fit a model (a polynomial) to represent the pixel-to-wavelength transformation. This can be done manually by providing matched pairs or automatically using global optimization techniques. 4. **Inspection**: Evaluate the quality of the fit using residuals and diagnostic plots. -5. **Applying the Solution**: Use the fitted model (often accessed as a WCS object) to +5. **Applying the Solution**: Use the fitted model (often accessed as a `~gwcs.wcs.WCS` object) to calibrate science spectra or resample spectra onto a linear wavelength grid. -These steps are detailed in the following tutorials. +Tutorials +--------- + +The following tutorials provide hands-on examples demonstrating the usage of the +`~specreduce.wavecal1d.WavelengthCalibration1D` class. These step-by-step guides cover +both basic and advanced functionality to help you get started with wavelength calibration. .. toctree:: :maxdepth: 1 @@ -56,66 +61,74 @@ These steps are detailed in the following tutorials. wavecal1d_example_02.ipynb wavecal1d_example_03.ipynb -Detailed Steps --------------- +Quickstart +---------- -**1. Initialization** +1. Initialization +***************** You instantiate the :class:`~specreduce.wavecal1d.WavelengthCalibration1D` by providing basic information about your setup and data. A reference pixel (``ref_pixel``) is required, which serves -as the anchor point for the polynomial fit. +as the anchor point for the polynomial fit, and ``degree`` can be used to set the degree of the +polynomial for the fit. You must provide *either* a list of arc spectra (or a single arc spectrum) *or* a list of known observed line positions: * **Using an Arc Spectrum**: Provide the arc spectrum as a `specutils.Spectrum` object via the ``arc_spectra`` argument. You also need to provide a ``line_lists`` argument, - which can be a list of known catalog wavelengths (e.g., from an `astropy.table.QTable` or a - NumPy array with units) or the name(s) of standard line lists recognized by `specreduce` (e.g., - ``"ArI"``). + which can be a list of known catalog wavelengths or the name(s) of standard line lists + recognized by `specreduce` (e.g., ``"HeI"``). .. code-block:: python - import astropy.units as u - import numpy as np - from specreduce.compat import Spectrum - from specreduce.wavecal1d import WavelengthCalibration1D - - # Example arc spectrum (replace with your actual data) - arc_flux = np.random.rand(1024) * u.DN - arc_pixels = np.arange(1024) * u.pix # Dummy axis - arc_spectrum = Spectrum(flux=arc_flux, spectral_axis=arc_pixels) + wc = WavelengthCalibration1D(ref_pix=512, + arc_spectra=arc_hei, + line_lists=["HeI"]) - # Known ArI line wavelengths - known_ari_lines = [6965.43, 7067.22, 7383.98, 7503.87, 7635.11] * u.AA +* **Using multiple Arc Spectra**: - # Define reference pixel (e.g., center of the detector) - ref_pix = 512 + .. code-block:: python - ws = WavelengthCalibration1D(ref_pix, arc_spectra=arc_spectrum, line_lists=known_ari_lines) + wc = WavelengthCalibration1D(ref_pix=512, + arc_spectra=[arc_he, arc_hg_ar], + line_lists=[["HeI"], ["HgI", "ArI"]]) * **Using Observed Line Positions**: If you have already identified the pixel centroids of lines in your calibration spectrum, you can provide them directly via the ``obs_lines`` - argument (as a list or array). In this case, you *must* also provide the detector's pixel + argument (as a list of NumPy arrays). In this case, you *must* also provide the detector's pixel boundaries using ``pix_bounds`` (a tuple like ``(min_pixel, max_pixel)``). You still need to provide the ``line_lists`` containing the potential matching catalog wavelengths. .. code-block:: python # Assume observed line pixel centers were found previously - observed_pixels = np.array([105.3, 210.8, 455.1, 512.5, 680.2]) + obs_he = np.array([105.3, 210.8, 455.1, 512.5, 680.2]) # Pixel range of the detector pixel_bounds = (0, 1024) - ws = WavelengthCalibration1D(ref_pix, - obs_lines=observed_pixels, - line_lists=known_ari_lines, - pix_bounds=pixel_bounds) + wc = WavelengthCalibration1D(ref_pix=512, + obs_lines=obs_he, + line_lists=["HeI"], + pix_bounds=pixel_bounds) -You can also specify the ``degree`` of the polynomial to be used for the fit (defaults to 3). +* **Using Observed Line Positions From Multiple Arcs**: -**2. Finding Observed Lines** + .. code-block:: python + + obs_he = np.array([105.3, 210.8, 455.1, 512.5, 680.2]) + obs_hg_ar = np.array([234.2, 534.1, 768.2, 879.6]) + pixel_bounds = (0, 1024) + + wc = WavelengthCalibration1D(ref_pix=512, + obs_lines=[obs_he, obs_hg_ar], + line_lists=[["HeI"], ["HgI", "ArI"]], + pix_bounds=pixel_bounds) + + +2. Finding Observed Lines +************************* If you initialized the class with ``arc_spectra``, you need to detect the lines in it. Use the :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.find_lines` method: @@ -123,25 +136,25 @@ If you initialized the class with ``arc_spectra``, you need to detect the lines .. code-block:: python # Find lines with an estimated FWHM and noise factor - ws.find_lines(fwhm=3.5, noise_factor=5) + wc.find_lines(fwhm=3.5, noise_factor=5) # Access the found lines (pixel positions) - observed_lines = ws.observed_lines - print(observed_lines) + print(wc.observed_lines) This populates the `~specreduce.wavecal1d.WavelengthCalibration1D.observed_lines` attribute. -**3. Matching and Fitting the Solution** +3. Matching and Fitting the Solution +************************************ The core of the process is fitting the model that maps pixels to wavelengths. -* **Global Fitting (``fit_global``)**: If you have +* **Global Fitting for Automated Pipelines**: If you have `~specreduce.wavecal1d.WavelengthCalibration1D.observed_lines` (either found automatically or provided initially) and `~specreduce.wavecal1d.WavelengthCalibration1D.catalog_lines` (from ``line_lists``), but don't know the exact pixel-wavelength pairs, you can use :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_global`. This method uses a global - optimization algorithm (differential evolution) to find the best-fit polynomial parameters by + optimization algorithm to find the best-fit polynomial parameters by minimizing the distance between predicted line wavelengths and the nearest catalog lines. You need to provide estimated bounds for the wavelength and dispersion at the ``ref_pixel``. @@ -152,34 +165,35 @@ The core of the process is fitting the model that maps pixels to wavelengths. wavelength_bounds = (7450, 7550) dispersion_bounds = (1.8, 2.2) - ws.fit_global(wavelength_bounds, dispersion_bounds, popsize=30, refine_fit=True) + wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=30, refine_fit=True) Setting ``refine_fit=True`` automatically runs a least-squares refinement after the global fit finds an initial solution and matches lines. -* **Fitting Known Pairs (``fit_lines``)**: If you have already established explicit pairs of - observed pixel centers and their corresponding known wavelengths, you can use +* **Fitting Known Pairs for an Interactive Workflow**: If you have already established explicit + pairs of observed pixel centers and their corresponding known wavelengths, you can use :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_lines` to perform a direct least-squares fit. .. code-block:: python # Assume these are matched pairs - matched_pixels = np.array([105.3, 512.5, 780.1]) - matched_wavelengths = np.array([6965.43, 7503.87, 7723.76]) * u.AA + pixels = np.array([105.3, 512.5, 780.1]) + wavelengths = np.array([6965.43, 7503.87, 7723.76]) - # Create a temporary WS object or manually set attributes if needed - # (Ensure pix_bounds is set if ws was initialized without arc_spectra) - # ws.pix_bounds = (0, 1024) # If not set earlier - - ws.fit_lines(pixels=matched_pixels, wavelengths=matched_wavelengths) + wc.fit_lines(pixels=pixels, wavelengths=wavelengths, refine_fit=True) + When ``refine_fit=True`` is set, the method automatically identifies matching pairs between + observed and catalog lines, then performs a least-squares refinement using **all matching lines**. + This goes beyond the subset of lines provided to :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.fit_lines`, + resulting in a more complete wavelength calibration. After fitting (either way), the pixel-to-wavelength (`~specreduce.wavecal1d.WavelengthCalibration1D.pix_to_wav`) and wavelength-to-pixel (`~specreduce.wavecal1d.WavelengthCalibration1D.wav_to_pix`) model transforms are calculated. -**4. Inspecting the Fit** +4. Inspecting the Fit +********************* Several tools help assess the quality of the wavelength solution: @@ -188,8 +202,8 @@ Several tools help assess the quality of the wavelength solution: .. code-block:: python - rms_wave = ws.rms(space='wavelength') - rms_pix = ws.rms(space='pixel') + rms_wave = wc.rms(space='wavelength') + rms_pix = wc.rms(space='pixel') print(f"Fit RMS (wavelength): {rms_wave}") print(f"Fit RMS (pixel): {rms_pix}") @@ -206,16 +220,8 @@ Several tools help assess the quality of the wavelength solution: * :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.plot_catalog_lines`: Plots the catalog line positions (in wavelengths or mapped to pixels). - .. code-block:: python - - import matplotlib.pyplot as plt - - fig_fit = ws.plot_fit() - fig_resid = ws.plot_residuals(space='wavelength') - plt.show() - - -**5. Using the Solution** +5. Using the Solution +********************* Once satisfied with the fit, you can use the wavelength solution: @@ -226,36 +232,22 @@ Once satisfied with the fit, you can use the wavelength solution: .. code-block:: python pixels = np.array([100, 500, 900]) - wavelengths = ws.pix_to_wav(pixels) + wavelengths = wc.pix_to_wav(pixels) print(wavelengths) -* **Get WCS Object**: Access the `gwcs.WCS` object representing the solution via the +* **Get WCS Object**: Access the `~gwcs.wcs.WCS` object representing the solution via the :attr:`~specreduce.wavecal1d.WavelengthCalibration1D.wcs` attribute. This is particularly useful for attaching the calibration to a :class:`~specutils.Spectrum` object. - .. code-block:: python - - # Assuming 'science_spectrum' is a Spectrum1D object - # science_spectrum.wcs = ws.wcs - -* **Resample Spectrum**: Resample a spectrum (like your science target or the original arc lamp) - onto a new, potentially linearized, wavelength grid using - :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.resample`. +* **Rebin Spectrum**: Resample a spectrum onto a new wavelength grid using + :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.resample`. The rebinning is + flux-conserving, meaning the total flux in the output spectrum matches the total flux + in the input spectrum. .. code-block:: python - # Resample the original arc spectrum onto a grid of 1000 points + # Resample the original arc spectrum onto a linear grid of 1000 points resampled_arc = ws.resample(arc_spectrum, nbins=1000) # The resampled spectrum now has a linear wavelength axis print(resampled_arc.spectral_axis) - - -See Also -======== - -For hands-on examples, please refer to the wavelength calibration example notebooks provided with -``specreduce``. For detailed information on each method and its parameters, consult the API -documentation for :class:`~specreduce.wavecal1d.WavelengthCalibration1D`. - -.. _wavecal1d_doc: \ No newline at end of file From eb0ed36f93abd4c376565f8fbb59d1cc0f5cbd01 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:17:27 +0100 Subject: [PATCH 46/76] Codestyle fixes for the 1D wavelength calibration code. --- specreduce/tests/test_wavecal1d.py | 33 +++++++++---- specreduce/wavecal1d.py | 78 +++++++++++++++--------------- 2 files changed, 63 insertions(+), 48 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index c0d54d31..17d2cfa5 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -44,7 +44,9 @@ def mk_wc(mk_lines): @pytest.fixture def mk_good_wc_with_transform(mk_lines): obs_lines, cat_lines = mk_lines - wc = WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) + wc = WavelengthCalibration1D( + ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) + ) wc._p2w = p2w wc._calculate_p2w_inverse() wc._calculate_p2w_derivative() @@ -69,7 +71,9 @@ def test_init(mk_arc, mk_lines): arc = mk_arc obs_lines, cat_lines = mk_lines WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, arc_spectra=arc) - WavelengthCalibration1D(ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10)) + WavelengthCalibration1D( + ref_pixel, line_lists=cat_lines, obs_lines=obs_lines, pix_bounds=(0, 10) + ) with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): WavelengthCalibration1D(ref_pixel, arc_spectra=arc, obs_lines=obs_lines) @@ -83,7 +87,6 @@ def test_init(mk_arc, mk_lines): def test_init_line_list(mk_arc): - """Test the catalog line list initialization with various configurations of `arc_spectra` and `line_lists`.""" arc = mk_arc WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists=["ArI"]) WavelengthCalibration1D(ref_pixel, arc_spectra=arc, line_lists="ArI") @@ -92,8 +95,12 @@ def test_init_line_list(mk_arc): WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"], ["ArI"]]) WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", ["ArI"]]) WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=["ArI", "ArI"]) - WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])]) - WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ["ArI"]]) + WavelengthCalibration1D( + ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1]), array([0.1])] + ) + WavelengthCalibration1D( + ref_pixel, arc_spectra=[arc, arc], line_lists=[array([0.1, 0.3]), ["ArI"]] + ) with pytest.raises(ValueError, match="The number of line lists"): WavelengthCalibration1D(ref_pixel, arc_spectra=[arc, arc], line_lists=[["ArI"]]) @@ -156,7 +163,9 @@ def test_catalog_lines(mk_lines): wc = WavelengthCalibration1D(ref_pixel) assert wc.catalog_lines is None obs_lines, cat_lines = mk_lines - wc = WavelengthCalibration1D(ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=pix_bounds) + wc = WavelengthCalibration1D( + ref_pixel, obs_lines=obs_lines, line_lists=cat_lines, pix_bounds=pix_bounds + ) assert len(wc.catalog_lines) == 1 np.testing.assert_allclose(wc.catalog_lines[0].data, cat_lines) @@ -198,14 +207,18 @@ def test_fit_global(): lines_cat = array([500, 550, 600, 650, 700, 750, 800]) wavelength_bounds = (649, 651) dispersion_bounds = (49, 51) - wc = WavelengthCalibration1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) + wc = WavelengthCalibration1D( + 5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat + ) wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=10) np.testing.assert_allclose(wc._fit.x, [650.0, 50.0, 0.0, 0.0], atol=1e-4) assert wc._fit is not None assert wc._fit.success assert wc._p2w is not None - wc = WavelengthCalibration1D(5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat) + wc = WavelengthCalibration1D( + 5, pix_bounds=pix_bounds, obs_lines=lines_obs, line_lists=lines_cat + ) wc.fit_global(wavelength_bounds, dispersion_bounds, popsize=10, refine_fit=False) @@ -339,7 +352,9 @@ def test_plot_observed_lines(mk_good_wc_with_transform, mk_arc): wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] wc.arc_spectra = [mk_arc] for frames in [None, 0]: - fig = wc.plot_observed_lines(frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True) + fig = wc.plot_observed_lines( + frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True + ) assert isinstance(fig, Figure) assert fig.axes[0].has_data() assert len(fig.axes) == 1 diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 65230d57..61222981 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -20,6 +20,8 @@ from specreduce.calibration_data import load_pypeit_calibration_lines from specreduce.line_matching import find_arc_lines +__all__ = ["WavelengthCalibration1D"] + def _diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: """Compute the derivative of a Polynomial1D model. @@ -135,25 +137,25 @@ def _read_linelists( """ lines_wav = [] - for l in line_lists: - if isinstance(l, ndarray): - lines_wav.append(l) + for lst in line_lists: + if isinstance(lst, ndarray): + lines_wav.append(lst) else: lines = [] - if isinstance(l, str): - l = [l] - for ll in l: + if isinstance(lst, str): + lst = [lst] + for ll in lst: lines.append( load_pypeit_calibration_lines(ll, wave_air=wave_air)["wavelength"] .to(self.unit) .value ) lines_wav.append(np.ma.masked_array(np.sort(np.concatenate(lines)))) - for i, l in enumerate(lines_wav): - lines_wav[i] = l[(l >= line_list_bounds[0]) & (l <= line_list_bounds[1])] + for i, lst in enumerate(lines_wav): + lines_wav[i] = lst[(lst >= line_list_bounds[0]) & (lst <= line_list_bounds[1])] self.catalog_lines = lines_wav - self._trees = [KDTree(l.data[:, None]) for l in self._cat_lines] + self._trees = [KDTree(lst.data[:, None]) for lst in self._cat_lines] def find_lines(self, fwhm: float, noise_factor: float = 1.0) -> None: """Find lines in the provided arc spectra. @@ -205,15 +207,15 @@ def fit_lines( pixels A sequence of pixel positions corresponding to known spectral lines. wavelengths - A sequence of the same size as `pixels`, containing the known + A sequence of the same size as ``pixels``, containing the known wavelengths corresponding to the given pixel positions. match_obs - If True, snap the input `pixels` values to the nearest + If True, snap the input ``pixels`` values to the nearest pixel values found in `self._obs_lines` (if available). This helps ensure the fit uses the precise centroids detected by `find_lines` or provided initially. match_cat - If True, snap the input `wavelengths` values to the + If True, snap the input ``wavelengths`` values to the nearest wavelength values found in `self._cat_lines` (if available). This ensures the fit uses the precise catalog wavelengths. refine_fit @@ -410,21 +412,24 @@ def resample( ) -> Spectrum1D: """Bin the given pixel-space 1D spectrum to a wavelength space conserving the flux. - This method bins a pixel-space spectrum to a wavelength space using the computed pixel-to-wavelength and - wavelength-to-pixel transformations and their derivatives with respect to the spectral axis. The binning is - exact and conserves the total flux. + This method bins a pixel-space spectrum to a wavelength space using the computed + pixel-to-wavelength and wavelength-to-pixel transformations and their derivatives with + respect to the spectral axis. The binning is exact and conserves the total flux. Parameters ---------- spectrum A Spectrum1D instance containing the flux to be resampled over the wavelength space. nbins - The number of bins for resampling. If not provided, it defaults to the size of the input spectrum. + The number of bins for resampling. If not provided, it defaults to the size of the + input spectrum. wlbounds - A tuple specifying the starting and ending wavelengths for resampling. If not provided, the - wavelength bounds are inferred from the object's methods and the entire flux array is used. + A tuple specifying the starting and ending wavelengths for resampling. If not + provided, the wavelength bounds are inferred from the object's methods and the + entire flux array is used. bin_edges - Explicit bin edges in the wavelength space. If provided, `nbins` and `wlbounds` are ignored. + Explicit bin edges in the wavelength space. If provided, ``nbins`` and ``wlbounds`` + are ignored. Returns ------- @@ -520,11 +525,11 @@ def observed_lines(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | if not isinstance(lines_pix, Sequence): lines_pix = [lines_pix] self._obs_lines = [] - for l in lines_pix: - if isinstance(l, MaskedArray) and l.mask is not np.False_: - self._obs_lines.append(l) + for lst in lines_pix: + if isinstance(lst, MaskedArray) and lst.mask is not np.False_: + self._obs_lines.append(lst) else: - self._obs_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) + self._obs_lines.append(np.ma.masked_array(lst, mask=np.zeros(lst.size, bool))) @property def catalog_lines(self) -> list[MaskedArray]: @@ -536,11 +541,11 @@ def catalog_lines(self, lines_wav: MaskedArray | ndarray | list[MaskedArray] | l if not isinstance(lines_wav, Sequence): lines_wav = [lines_wav] self._cat_lines = [] - for l in lines_wav: - if isinstance(l, MaskedArray) and l.mask is not np.False_: - self._cat_lines.append(l) + for lst in lines_wav: + if isinstance(lst, MaskedArray) and lst.mask is not np.False_: + self._cat_lines.append(lst) else: - self._cat_lines.append(np.ma.masked_array(l, mask=np.zeros(l.size, bool))) + self._cat_lines.append(np.ma.masked_array(lst, mask=np.zeros(lst.size, bool))) @property def gwcs(self) -> gwcs.wcs.WCS: @@ -601,8 +606,8 @@ def match_lines(self, max_distance: float = 5) -> None: def remove_ummatched_lines(self): """Remove unmatched lines from observation and catalog line data.""" - self._obs_lines = [np.ma.masked_array(l.compressed()) for l in self._obs_lines] - self._cat_lines = [np.ma.masked_array(l.compressed()) for l in self._cat_lines] + self._obs_lines = [np.ma.masked_array(lst.compressed()) for lst in self._obs_lines] + self._cat_lines = [np.ma.masked_array(lst.compressed()) for lst in self._cat_lines] def rms(self, space: Literal["pixel", "wavelength"] = "wavelength") -> float: """Compute the RMS of the residuals between matched lines in the pixel or wavelength space. @@ -778,7 +783,7 @@ def plot_observed_lines( Axes object(s) to plot the spectral lines on. If None, new axes are created. figsize Dimensions of the figure to be created, specified as a tuple (width, height). Ignored - if `axes` is provided. + if ``axes`` is provided. plot_values If True, plots the numerical values of the observed lines at their respective locations on the graph. Default is True. @@ -845,7 +850,7 @@ def plot_fit( ---------- frames The indices of the frames to plot. If `None`, all frames from 0 to - `self.nframes - 1` are plotted. + ``self.nframes - 1`` are plotted. figsize Defines the width and height of the figure in inches. If `None`, the @@ -871,11 +876,6 @@ def plot_fit( else: frames = np.atleast_1d(frames) - if self._p2w is not None and obs_to_wav: - transform = self._p2w - else: - transform = lambda x: x - fig, axs = subplots(2 * frames.size, 1, constrained_layout=True, figsize=figsize) self.plot_catalog_lines( frames, @@ -918,10 +918,10 @@ def plot_residuals( Parameters ---------- ax - Matplotlib Axes object to plot on. If None, a new figure and axes are created. Default is None. + Matplotlib Axes object to plot on. If None, a new figure and axes are created. space - The reference space used for plotting residuals. Options are 'pixel' for residuals in pixel space or - 'wavelength' for residuals in wavelength space. + The reference space used for plotting residuals. Options are 'pixel' for residuals + in pixel space or 'wavelength' for residuals in wavelength space. figsize The size of the figure in inches, if a new figure is created. Default is None. From 25144b80227eb8c2d8ccf6005d1e6d4bd41c7949 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:17:45 +0100 Subject: [PATCH 47/76] Changed the documentation API. --- docs/api.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/api.rst b/docs/api.rst index 641f81b8..3f3937f9 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -23,7 +23,6 @@ API Index .. automodapi:: specreduce.wavecal1d :no-inheritance-diagram: - :include-all-objects: .. automodapi:: specreduce.calibration_data :no-inheritance-diagram: From 39a5d5cc60590fd150536bb73f5529c928077792 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:36:38 +0100 Subject: [PATCH 48/76] Brought back the original wavelength_calibration.py code so that we don't break backwards compatibility (quite yet). --- specreduce/__init__.py | 1 + specreduce/wavelength_calibration.py | 244 +++++++++++++++++++++++++++ 2 files changed, 245 insertions(+) create mode 100644 specreduce/wavelength_calibration.py diff --git a/specreduce/__init__.py b/specreduce/__init__.py index 8a9a0b47..d703b86b 100644 --- a/specreduce/__init__.py +++ b/specreduce/__init__.py @@ -1,6 +1,7 @@ # Licensed under a 3-clause BSD style license - see LICENSE.rst from specreduce.core import * # noqa +from specreduce.wavelength_calibration import * # noqa try: from .version import version as __version__ diff --git a/specreduce/wavelength_calibration.py b/specreduce/wavelength_calibration.py new file mode 100644 index 00000000..8d4ad620 --- /dev/null +++ b/specreduce/wavelength_calibration.py @@ -0,0 +1,244 @@ +from functools import cached_property + +import numpy as np +from astropy import units as u +from astropy.modeling.fitting import LMLSQFitter, LinearLSQFitter +from astropy.modeling.models import Linear1D +from astropy.table import QTable, hstack +from gwcs import coordinate_frames as cf +from gwcs import wcs + +from specreduce.compat import Spectrum + +__all__ = [ + 'WavelengthCalibration1D' +] + + +def _check_arr_monotonic(arr): + # returns True if ``arr`` is either strictly increasing or strictly + # decreasing, otherwise returns False. + + sorted_increasing = np.all(arr[1:] >= arr[:-1]) + sorted_decreasing = np.all(arr[1:] <= arr[:-1]) + return sorted_increasing or sorted_decreasing + + +class WavelengthCalibration1D(): + + def __init__(self, input_spectrum, matched_line_list=None, line_pixels=None, + line_wavelengths=None, catalog=None, input_model=Linear1D(), fitter=None): + """ + input_spectrum: `~specutils.Spectrum1D` + A one-dimensional Spectrum calibration spectrum from an arc lamp or similar. + matched_line_list: `~astropy.table.QTable`, optional + An `~astropy.table.QTable` table with (minimally) columns named + "pixel_center" and "wavelength" with known corresponding line pixel centers + and wavelengths populated. + line_pixels: list, array, `~astropy.table.QTable`, optional + List or array of line pixel locations to anchor the wavelength solution fit. + Can also be input as an `~astropy.table.QTable` table with (minimally) a column + named "pixel_center". + line_wavelengths: `~astropy.units.Quantity`, `~astropy.table.QTable`, optional + `astropy.units.Quantity` array of line wavelength values corresponding to the + line pixels defined in ``line_list``, assumed to be in the same + order. Can also be input as an `~astropy.table.QTable` with (minimally) + a "wavelength" column. + catalog: list, str, `~astropy.table.QTable`, optional + The name of a catalog of line wavelengths to load and use in automated and + template-matching line matching. NOTE: This option is currently not implemented. + input_model: `~astropy.modeling.Model` + The model to fit for the wavelength solution. Defaults to a linear model. + fitter: `~astropy.modeling.fitting.Fitter`, optional + The fitter to use in optimizing the model fit. Defaults to + `~astropy.modeling.fitting.LinearLSQFitter` if the model to fit is linear + or `~astropy.modeling.fitting.LMLSQFitter` if the model to fit is non-linear. + + Note that either ``matched_line_list`` or ``line_pixels`` must be specified, + and if ``matched_line_list`` is not input, at least one of ``line_wavelengths`` + or ``catalog`` must be specified. + """ + self._input_spectrum = input_spectrum + self._input_model = input_model + self._cached_properties = ['fitted_model', 'residuals', 'wcs'] + self.fitter = fitter + self._potential_wavelengths = None + self._catalog = catalog + + if not isinstance(input_spectrum, Spectrum): + raise ValueError('Input spectrum must be Spectrum.') + + # We use either line_pixels or matched_line_list to create self._matched_line_list, + # and check that various requirements are fulfilled by the input args. + if matched_line_list is not None: + pixel_arg = "matched_line_list" + if not isinstance(matched_line_list, QTable): + raise ValueError("matched_line_list must be an astropy.table.QTable.") + self._matched_line_list = matched_line_list + elif line_pixels is not None: + pixel_arg = "line_pixels" + if isinstance(line_pixels, (list, np.ndarray)): + self._matched_line_list = QTable([line_pixels], names=["pixel_center"]) + elif isinstance(line_pixels, QTable): + self._matched_line_list = line_pixels + else: + raise ValueError("Either matched_line_list or line_pixels must be specified.") + + if "pixel_center" not in self._matched_line_list.columns: + raise ValueError(f"{pixel_arg} must have a 'pixel_center' column.") + + if self._matched_line_list["pixel_center"].unit is None: + self._matched_line_list["pixel_center"].unit = u.pix + + # check that pixels are monotonic + if not _check_arr_monotonic(self._matched_line_list["pixel_center"]): + raise ValueError('Pixels must be strictly increasing or decreasing.') + + # now that pixels have been determined from input, figure out wavelengths. + if (line_wavelengths is None and catalog is None + and "wavelength" not in self._matched_line_list.columns): + raise ValueError("You must specify at least one of line_wavelengths, " + "catalog, or 'wavelength' column in matched_line_list.") + + # Sanity checks on line_wavelengths value + if line_wavelengths is not None: + if (isinstance(self._matched_line_list, QTable) and + "wavelength" in self._matched_line_list.columns): + raise ValueError("Cannot specify line_wavelengths separately if there is" + " a 'wavelength' column in matched_line_list.") + if len(line_wavelengths) != len(self._matched_line_list): + raise ValueError("If line_wavelengths is specified, it must have the same " + f"length as {pixel_arg}") + if not isinstance(line_wavelengths, (u.Quantity, QTable)): + raise ValueError("line_wavelengths must be specified as an astropy.units.Quantity" + " array or as an astropy.table.QTable") + + # make sure wavelengths (or freq) are monotonic and add wavelengths + # to _matched_line_list + if isinstance(line_wavelengths, u.Quantity): + if not _check_arr_monotonic(line_wavelengths): + if str(line_wavelengths.unit.physical_type) == "frequency": + raise ValueError('Frequencies must be strictly increasing or decreasing.') + raise ValueError('Wavelengths must be strictly increasing or decreasing.') + + self._matched_line_list["wavelength"] = line_wavelengths + + elif isinstance(line_wavelengths, QTable): + if not _check_arr_monotonic(line_wavelengths['wavelength']): + raise ValueError('Wavelengths must be strictly increasing or decreasing.') + self._matched_line_list = hstack([self._matched_line_list, line_wavelengths]) + + # Parse desired catalogs of lines for matching. + if catalog is not None: + # For now we avoid going into the later logic and just throw an error + raise NotImplementedError("No catalogs are available yet, please input " + "wavelengths with line_wavelengths or as a " + f"column in {pixel_arg}") + + if isinstance(catalog, QTable): + if "wavelength" not in catalog.columns: + raise ValueError("Catalog table must have a 'wavelength' column.") + self._catalog = catalog + else: + # This will need to be updated to match up with Tim's catalog code + if isinstance(catalog, list): + self._catalog = catalog + else: + self._catalog = [catalog] + for cat in self._catalog: + if isinstance(cat, str): + if cat not in self._available_catalogs: + raise ValueError(f"Line list '{cat}' is not an available catalog.") + + def identify_lines(self): + """ + ToDo: Code matching algorithm between line pixel locations and potential line + wavelengths from catalogs. + """ + pass + + def _clear_cache(self, *attrs): + """ + provide convenience function to clearing the cache for cached_properties + """ + if not len(attrs): + attrs = self._cached_properties + for attr in attrs: + if attr in self.__dict__: + del self.__dict__[attr] + + @property + def available_catalogs(self): + return self._available_catalogs + + @property + def input_spectrum(self): + return self._input_spectrum + + @input_spectrum.setter + def input_spectrum(self, new_spectrum): + # We want to clear the refined locations if a new calibration spectrum is provided + self._clear_cache() + self._input_spectrum = new_spectrum + + @property + def input_model(self): + return self._input_model + + @input_model.setter + def input_model(self, input_model): + self._clear_cache() + self._input_model = input_model + + @cached_property + def fitted_model(self): + # computes and returns WCS after fitting self.model to self.refined_pixels + x = self._matched_line_list["pixel_center"] + y = self._matched_line_list["wavelength"] + + if self.fitter is None: + # Flexible defaulting if self.fitter is None + if self.input_model.linear: + fitter = LinearLSQFitter(calc_uncertainties=True) + else: + fitter = LMLSQFitter(calc_uncertainties=True) + else: + fitter = self.fitter + + # Fit the model + return fitter(self.input_model, x, y) + + @cached_property + def residuals(self): + """ + calculate fit residuals between matched line list pixel centers and + wavelengths and the evaluated fit model. + """ + + x = self._matched_line_list["pixel_center"] + y = self._matched_line_list["wavelength"] + + # Get the fit residuals by evaulating model + return y - self.fitted_model(x) + + @cached_property + def wcs(self): + # Build a GWCS pipeline from the fitted model + pixel_frame = cf.CoordinateFrame(1, "SPECTRAL", [0,], axes_names=["x",], unit=[u.pix,]) + spectral_frame = cf.SpectralFrame(axes_names=["wavelength",], + unit=[self._matched_line_list["wavelength"].unit,]) + + pipeline = [(pixel_frame, self.fitted_model), (spectral_frame, None)] + + wcsobj = wcs.WCS(pipeline) + + return wcsobj + + def apply_to_spectrum(self, spectrum=None): + # returns Spectrum with wavelength calibration applied + # actual line refinement and WCS solution should already be done so that this can + # be called on multiple science sources + spectrum = self.input_spectrum if spectrum is None else spectrum + updated_spectrum = Spectrum(spectrum.flux, wcs=self.wcs, mask=spectrum.mask, + uncertainty=spectrum.uncertainty) + return updated_spectrum From 1d361f1c8fabe4ac9f0233dd61a20f954655f714 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:38:20 +0100 Subject: [PATCH 49/76] Fixed a broken WavelengthCalibration1D tests. --- specreduce/tests/test_wavecal1d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 17d2cfa5..105016b6 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -269,7 +269,7 @@ def test_wav_to_pix(mk_wc): def test_wcs_creates_valid_gwcs_object(mk_good_wc_with_transform): wc = mk_good_wc_with_transform - wcs_obj = wc.wcs + wcs_obj = wc.gwcs assert wcs_obj is not None assert isinstance(wcs_obj, wcs.WCS) assert wcs_obj.output_frame.unit[0] == u.angstrom From 1243b7fe77862c1d9b670d0e8882b98e65bd2e75 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 12:48:30 +0100 Subject: [PATCH 50/76] Added a docstring for the WavelengthCalibration1D initialiser. --- specreduce/wavecal1d.py | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 61222981..3190732f 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -57,7 +57,48 @@ def __init__( line_list_bounds: tuple[float, float] = (0, np.inf), wave_air: bool = False, ) -> None: + """A class for wavelength calibration of one-dimensional spectral data. + This class is designed to facilitate wavelength calibration of one-dimensional spectra, + with support for both direct input of line lists and observed spectra. It uses a polynomial + model for fitting the wavelength solution and offers features to incorporate catalog lines + and observed line positions. + + Parameters + ---------- + ref_pixel + The reference pixel in which the wavelength solution will be centered. + + unit + The unit of the wavelength calibration, by default ``astropy.units.Angstrom``. + + degree + The polynomial degree for the wavelength solution, by default 3. + + line_lists + Catalogs of spectral line wavelengths for wavelength calibration. Provide either an + array of line wavelengths or a list of catalog names. If `None`, no line lists are used. + + arc_spectra + Arc spectra provided as ``Spectrum`` objects for wavelength fitting, by default + None. This parameter and ``obs_lines`` cannot be provided simultaneously. + + obs_lines + Pixel positions of observed spectral lines for wavelength fitting, by default None. This + parameter and ``arc_spectra`` cannot be provided simultaneously. + + pix_bounds + Lower and upper pixel bounds for fitting, defined as a 2-tuple (min, max). If + ``obs_lines`` is provided, this parameter is mandatory. + + line_list_bounds + Wavelength bounds (inclusive) as a range (min, max) for filtering usable spectral + lines from the provided line lists, by default (0, np.inf). + + wave_air + Boolean indicating whether the input wavelengths correspond to air rather than vacuum; + by default `False`, meaning vacuum wavelengths. + """ self.unit = unit self._unit_str = unit.to_string("latex") self.degree = degree From f247cf756446459caacdd26cbc078dfac76ccf92 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 19:32:38 +0100 Subject: [PATCH 51/76] Small wavelength calibration documentation fixes. --- docs/wavelength_calibration/wavelength_calibration.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/wavelength_calibration/wavelength_calibration.rst b/docs/wavelength_calibration/wavelength_calibration.rst index 6513a7dc..c242cec8 100644 --- a/docs/wavelength_calibration/wavelength_calibration.rst +++ b/docs/wavelength_calibration/wavelength_calibration.rst @@ -75,7 +75,7 @@ polynomial for the fit. You must provide *either* a list of arc spectra (or a single arc spectrum) *or* a list of known observed line positions: -* **Using an Arc Spectrum**: Provide the arc spectrum as a `specutils.Spectrum` +* **Using an Arc Spectrum**: Provide the arc spectrum as a `specutils.Spectrum1D` object via the ``arc_spectra`` argument. You also need to provide a ``line_lists`` argument, which can be a list of known catalog wavelengths or the name(s) of standard line lists recognized by `specreduce` (e.g., ``"HeI"``). @@ -236,8 +236,8 @@ Once satisfied with the fit, you can use the wavelength solution: print(wavelengths) * **Get WCS Object**: Access the `~gwcs.wcs.WCS` object representing the solution via the - :attr:`~specreduce.wavecal1d.WavelengthCalibration1D.wcs` attribute. This is particularly useful - for attaching the calibration to a :class:`~specutils.Spectrum` object. + :attr:`~specreduce.wavecal1d.WavelengthCalibration1D.gwcs` attribute. This is particularly + useful for attaching the calibration to a :class:`~specutils.Spectrum1D` object. * **Rebin Spectrum**: Resample a spectrum onto a new wavelength grid using :meth:`~specreduce.wavecal1d.WavelengthCalibration1D.resample`. The rebinning is From fc8c0b9d8d38d013216dc3709847cd1e28071fff Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 19:42:08 +0100 Subject: [PATCH 52/76] Updated the changelog. --- CHANGES.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index 80fe9a14..847723a6 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -6,6 +6,9 @@ New Features - Added a ``disp_bounds`` argument to ``tracing.FitTrace``. The argument allows for adjusting the dispersion-axis window from which the trace peaks are estimated. +- Added a new ``specreduce.wavecal1d.WavelengthCalibration1D`` class for one-dimensional wavelength + calibration. The old ``specreduce.wavelength_calibration.WavelengthCalibration1D`` is + deprecated and will be removed in v. 2.0. 1.6.0 (2025-06-18) ------------------ From 6fa9b3cc2665237d10894d353d28a3dc76664ead Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 19:51:59 +0100 Subject: [PATCH 53/76] Made sure the basic WavelengthCalibration1D functionality can be used without matplotlib installed. --- specreduce/tests/test_wavecal1d.py | 156 ++++++++++++++++------------- specreduce/wavecal1d.py | 18 +++- 2 files changed, 99 insertions(+), 75 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 105016b6..e96ab5aa 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -6,10 +6,16 @@ from astropy.modeling.polynomial import Polynomial1D from astropy.nddata import StdDevUncertainty from gwcs import wcs -from matplotlib import pyplot as plt -from matplotlib.figure import Figure from numpy import array +try: + from matplotlib import pyplot as plt + from matplotlib.figure import Figure + with_matplotlib = True +except ImportError: + with_matplotlib = False + + from specreduce.wavecal1d import WavelengthCalibration1D, _diff_poly1d from specreduce.compat import Spectrum @@ -291,101 +297,107 @@ def test_remove_unmatched_lines(mk_good_wc_with_transform): def test_plot_lines_with_valid_input(): - wc = WavelengthCalibration1D(ref_pixel) - wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - wc._cat_lines = wc._obs_lines - fig = wc._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + if with_matplotlib: + wc = WavelengthCalibration1D(ref_pixel) + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc._cat_lines = wc._obs_lines + fig = wc._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig = wc._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, ax = plt.subplots(1, 1) - fig = wc._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, ax = plt.subplots(1, 1) + fig = wc._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, axs = plt.subplots(1, 2) - fig = wc._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, axs = plt.subplots(1, 2) + fig = wc._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig = wc._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() def test_plot_lines_raises_for_missing_transform(mk_wc): - wc = mk_wc - with pytest.raises(ValueError, match="Cannot map between pixels and"): - wc._plot_lines(kind="observed", map_x=True) + if with_matplotlib: + wc = mk_wc + with pytest.raises(ValueError, match="Cannot map between pixels and"): + wc._plot_lines(kind="observed", map_x=True) def test_plot_lines_calls_transform_correctly(mk_good_wc_with_transform): - wc = mk_good_wc_with_transform - wc._plot_lines(kind="observed", map_x=True) - wc._plot_lines(kind="catalog", map_x=True) + if with_matplotlib: + wc = mk_good_wc_with_transform + wc._plot_lines(kind="observed", map_x=True) + wc._plot_lines(kind="catalog", map_x=True) def test_plot_catalog_lines(mk_wc): - wc = mk_wc - wc._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] - fig = wc.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + if with_matplotlib: + wc = mk_wc + wc._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] + fig = wc.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, ax = plt.subplots(1, 1) - fig = wc.plot_catalog_lines(frames=0, axes=ax, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, ax = plt.subplots(1, 1) + fig = wc.plot_catalog_lines(frames=0, axes=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, axs = plt.subplots(1, 2) - fig = wc.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, axs = plt.subplots(1, 2) + fig = wc.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() def test_plot_observed_lines(mk_good_wc_with_transform, mk_arc): - wc = mk_good_wc_with_transform - wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - wc.arc_spectra = [mk_arc] - for frames in [None, 0]: - fig = wc.plot_observed_lines( - frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True - ) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() - assert len(fig.axes) == 1 + if with_matplotlib: + wc = mk_good_wc_with_transform + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = wc.plot_observed_lines( + frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True + ) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + assert len(fig.axes) == 1 def test_plot_fit(mk_arc, mk_good_wc_with_transform): - wc = mk_good_wc_with_transform - wc.arc_spectra = [mk_arc] - for frames in [None, 0]: - fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) - assert isinstance(fig, Figure) - assert len(fig.axes) == 2 - assert fig.axes[0].has_data() - assert fig.axes[1].has_data() - - fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) + if with_matplotlib: + wc = mk_good_wc_with_transform + wc.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) + assert isinstance(fig, Figure) + assert len(fig.axes) == 2 + assert fig.axes[0].has_data() + assert fig.axes[1].has_data() + wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) def test_plot_residuals(mk_good_wc_with_transform): - wc = mk_good_wc_with_transform + if with_matplotlib: + wc = mk_good_wc_with_transform - fig = wc.plot_residuals(space="pixel", figsize=(8, 4)) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc.plot_residuals(space="pixel", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig = wc.plot_residuals(space="wavelength", figsize=(8, 4)) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc.plot_residuals(space="wavelength", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, ax = plt.subplots(1, 1) - wc.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) + fig, ax = plt.subplots(1, 1) + wc.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) - with pytest.raises(ValueError, match="Invalid space specified"): - fig = wc.plot_residuals(space="wavelenght", figsize=(8, 4)) + with pytest.raises(ValueError, match="Invalid space specified"): + wc.plot_residuals(space="wavelenght", figsize=(8, 4)) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 3190732f..2f10ebf7 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -7,9 +7,6 @@ from astropy.modeling import models, Model, fitting from astropy.nddata import VarianceUncertainty from gwcs import coordinate_frames as cf -from matplotlib.axes import Axes -from matplotlib.figure import Figure -from matplotlib.pyplot import setp, subplots from numpy import ndarray from numpy.ma.core import MaskedArray from scipy import optimize @@ -17,6 +14,12 @@ from scipy.spatial import KDTree from specutils import Spectrum1D +try: + from matplotlib.pyplot import Axes, Figure, setp, subplots + with_matplotlib = True +except ImportError: + with_matplotlib = False + from specreduce.calibration_data import load_pypeit_calibration_lines from specreduce.line_matching import find_arc_lines @@ -685,6 +688,9 @@ def _plot_lines( value_fontsize: int | str | None = "small", ) -> Figure: + if not with_matplotlib: + raise ImportError("Matplotlib is required for plots.") + if frames is None: frames = np.arange(self.nframes) else: @@ -912,6 +918,9 @@ def plot_fit( matplotlib.figure.Figure The figure object containing the generated subplots. """ + if not with_matplotlib: + raise ImportError("Matplotlib is required for plots.") + if frames is None: frames = np.arange(self.nframes) else: @@ -970,6 +979,9 @@ def plot_residuals( ------- matplotlib.figure.Figure """ + if not with_matplotlib: + raise ImportError("Matplotlib is required for plots.") + if ax is None: fig, ax = subplots(figsize=figsize, constrained_layout=True) else: From f74c90462bea53f02be0e7196060f26349d7338b Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 21:08:05 +0100 Subject: [PATCH 54/76] Made matplotlib a basic dependecy. --- specreduce/tests/test_wavecal1d.py | 156 +++++++++++++---------------- specreduce/wavecal1d.py | 17 +--- 2 files changed, 72 insertions(+), 101 deletions(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index e96ab5aa..142f7d4a 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -7,15 +7,8 @@ from astropy.nddata import StdDevUncertainty from gwcs import wcs from numpy import array - -try: - from matplotlib import pyplot as plt - from matplotlib.figure import Figure - with_matplotlib = True -except ImportError: - with_matplotlib = False - - +from matplotlib import pyplot as plt +from matplotlib.figure import Figure from specreduce.wavecal1d import WavelengthCalibration1D, _diff_poly1d from specreduce.compat import Spectrum @@ -297,107 +290,100 @@ def test_remove_unmatched_lines(mk_good_wc_with_transform): def test_plot_lines_with_valid_input(): - if with_matplotlib: - wc = WavelengthCalibration1D(ref_pixel) - wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - wc._cat_lines = wc._obs_lines - fig = wc._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + wc = WavelengthCalibration1D(ref_pixel) + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc._cat_lines = wc._obs_lines + fig = wc._plot_lines(kind="observed", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig = wc._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc._plot_lines(kind="catalog", frames=0, figsize=(8, 4), plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, ax = plt.subplots(1, 1) - fig = wc._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, ax = plt.subplots(1, 1) + fig = wc._plot_lines(kind="catalog", frames=0, axs=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, axs = plt.subplots(1, 2) - fig = wc._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, axs = plt.subplots(1, 2) + fig = wc._plot_lines(kind="catalog", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig = wc._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc._plot_lines(kind="observed", frames=0, axs=axs, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() def test_plot_lines_raises_for_missing_transform(mk_wc): - if with_matplotlib: - wc = mk_wc - with pytest.raises(ValueError, match="Cannot map between pixels and"): - wc._plot_lines(kind="observed", map_x=True) + wc = mk_wc + with pytest.raises(ValueError, match="Cannot map between pixels and"): + wc._plot_lines(kind="observed", map_x=True) def test_plot_lines_calls_transform_correctly(mk_good_wc_with_transform): - if with_matplotlib: - wc = mk_good_wc_with_transform - wc._plot_lines(kind="observed", map_x=True) - wc._plot_lines(kind="catalog", map_x=True) + wc = mk_good_wc_with_transform + wc._plot_lines(kind="observed", map_x=True) + wc._plot_lines(kind="catalog", map_x=True) def test_plot_catalog_lines(mk_wc): - if with_matplotlib: - wc = mk_wc - wc._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] - fig = wc.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + wc = mk_wc + wc._cat_lines = [np.ma.masked_array([400, 500, 600], mask=[False, True, False])] + fig = wc.plot_catalog_lines(frames=0, figsize=(10, 6), plot_values=True, map_to_pix=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, ax = plt.subplots(1, 1) - fig = wc.plot_catalog_lines(frames=0, axes=ax, plot_values=True) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, ax = plt.subplots(1, 1) + fig = wc.plot_catalog_lines(frames=0, axes=ax, plot_values=True) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, axs = plt.subplots(1, 2) - fig = wc.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig, axs = plt.subplots(1, 2) + fig = wc.plot_catalog_lines(frames=[0], axes=axs, plot_values=False) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() def test_plot_observed_lines(mk_good_wc_with_transform, mk_arc): - if with_matplotlib: - wc = mk_good_wc_with_transform - wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] - wc.arc_spectra = [mk_arc] - for frames in [None, 0]: - fig = wc.plot_observed_lines( - frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True - ) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() - assert len(fig.axes) == 1 + wc = mk_good_wc_with_transform + wc._obs_lines = [np.ma.masked_array([100, 200, 300], mask=[False, True, False])] + wc.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = wc.plot_observed_lines( + frames=frames, figsize=(10, 5), plot_values=True, plot_spectra=True + ) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() + assert len(fig.axes) == 1 def test_plot_fit(mk_arc, mk_good_wc_with_transform): - if with_matplotlib: - wc = mk_good_wc_with_transform - wc.arc_spectra = [mk_arc] - for frames in [None, 0]: - fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) - assert isinstance(fig, Figure) - assert len(fig.axes) == 2 - assert fig.axes[0].has_data() - assert fig.axes[1].has_data() - wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) + wc = mk_good_wc_with_transform + wc.arc_spectra = [mk_arc] + for frames in [None, 0]: + fig = wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True) + assert isinstance(fig, Figure) + assert len(fig.axes) == 2 + assert fig.axes[0].has_data() + assert fig.axes[1].has_data() + wc.plot_fit(frames=frames, figsize=(12, 6), plot_values=True, obs_to_wav=True) def test_plot_residuals(mk_good_wc_with_transform): - if with_matplotlib: - wc = mk_good_wc_with_transform + wc = mk_good_wc_with_transform - fig = wc.plot_residuals(space="pixel", figsize=(8, 4)) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc.plot_residuals(space="pixel", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig = wc.plot_residuals(space="wavelength", figsize=(8, 4)) - assert isinstance(fig, Figure) - assert fig.axes[0].has_data() + fig = wc.plot_residuals(space="wavelength", figsize=(8, 4)) + assert isinstance(fig, Figure) + assert fig.axes[0].has_data() - fig, ax = plt.subplots(1, 1) - wc.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) + fig, ax = plt.subplots(1, 1) + wc.plot_residuals(ax=ax, space="wavelength", figsize=(8, 4)) - with pytest.raises(ValueError, match="Invalid space specified"): - wc.plot_residuals(space="wavelenght", figsize=(8, 4)) + with pytest.raises(ValueError, match="Invalid space specified"): + wc.plot_residuals(space="wavelenght", figsize=(8, 4)) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 2f10ebf7..1eb84887 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -13,12 +13,7 @@ from scipy.interpolate import interp1d from scipy.spatial import KDTree from specutils import Spectrum1D - -try: - from matplotlib.pyplot import Axes, Figure, setp, subplots - with_matplotlib = True -except ImportError: - with_matplotlib = False +from matplotlib.pyplot import Axes, Figure, setp, subplots from specreduce.calibration_data import load_pypeit_calibration_lines from specreduce.line_matching import find_arc_lines @@ -687,10 +682,6 @@ def _plot_lines( map_x: bool = False, value_fontsize: int | str | None = "small", ) -> Figure: - - if not with_matplotlib: - raise ImportError("Matplotlib is required for plots.") - if frames is None: frames = np.arange(self.nframes) else: @@ -918,9 +909,6 @@ def plot_fit( matplotlib.figure.Figure The figure object containing the generated subplots. """ - if not with_matplotlib: - raise ImportError("Matplotlib is required for plots.") - if frames is None: frames = np.arange(self.nframes) else: @@ -979,9 +967,6 @@ def plot_residuals( ------- matplotlib.figure.Figure """ - if not with_matplotlib: - raise ImportError("Matplotlib is required for plots.") - if ax is None: fig, ax = subplots(figsize=figsize, constrained_layout=True) else: From e4ed588e50ec03b2eca55da9254e749591a584ca Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 21:13:02 +0100 Subject: [PATCH 55/76] Revised wavelength calibration tests. --- specreduce/tests/test_wavecal1d.py | 1 - 1 file changed, 1 deletion(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 142f7d4a..3d4e651a 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -112,7 +112,6 @@ def test_find_lines(mocker, mk_arc): wc.find_lines(fwhm=2.0, noise_factor=1.5) assert wc._obs_lines is not None assert len(wc._obs_lines) == 1 - assert mock_find_arc_lines.called_once_with(arc, 2.0, noise_factor=1.5) wc = WavelengthCalibration1D(ref_pixel) with pytest.raises(ValueError, match="Must provide arc spectra to find lines."): From 1b2a9600fff037abb4b514453b285326ddb02832 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 24 Apr 2025 21:28:00 +0100 Subject: [PATCH 56/76] Codestyle fix. --- specreduce/tests/test_wavecal1d.py | 1 - 1 file changed, 1 deletion(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 3d4e651a..9290b01f 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -105,7 +105,6 @@ def test_init_line_list(mk_arc): def test_find_lines(mocker, mk_arc): - arc = mk_arc wc = WavelengthCalibration1D(ref_pixel, arc_spectra=mk_arc) mock_find_arc_lines = mocker.patch("specreduce.wavecal1d.find_arc_lines") mock_find_arc_lines.return_value = {"centroid": np.array([5.0]) * u.angstrom} From b349d036517ea2446fc4f8b859a1ac66b75daaf9 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Fri, 9 May 2025 17:44:21 +0100 Subject: [PATCH 57/76] Added a TiltCorrection class for 2D rectification. --- specreduce/tilt_correction.py | 306 ++++++++++++++++++++++++++++++++++ 1 file changed, 306 insertions(+) create mode 100644 specreduce/tilt_correction.py diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py new file mode 100644 index 00000000..4edb4652 --- /dev/null +++ b/specreduce/tilt_correction.py @@ -0,0 +1,306 @@ +import warnings +from typing import Iterable, Sequence + +import matplotlib.pyplot as plt +import astropy.units as u +import numpy as np +from astropy.modeling import models, Model, fitting +from astropy.nddata import StdDevUncertainty, NDData +from scipy.optimize import minimize +from scipy.spatial import KDTree + +from numpy import ndarray, concatenate, repeat, tile +from specutils import Spectrum1D + +from specreduce.line_matching import find_arc_lines +from specreduce.compat import Spectrum + + +def diff_poly2d_x(m): + coeffs = {} + for n in m.param_names: + ix, iy = int(n[1]), int(n[3]) + if ix > 0: + coeffs[f"c{ix-1}_{iy}"] = ix * getattr(m, n).value + return models.Polynomial2D(m.degree - 1, **coeffs) + + +class TiltCorrection: + def __init__( + self, + ref_pixel: tuple[float, float], + arc_frames: Sequence[NDData], + n_cd_samples: int = 10, + cd_sample_lims: tuple[float, float] | None = None, + cd_samples: Sequence[float] | None = None, + ): + self.ref_pixel = ref_pixel + self.arc_frames = arc_frames + self.nframes = len(arc_frames) + self.n_cd_pix = self.arc_frames[0].data.shape[0] + + self._lines_ref: Sequence[ndarray] | None = None + self._samples_rec_x: Sequence[ndarray] | None = None + self._samples_rec_y: Sequence[ndarray] | None = None + self._samples_det_x: Sequence[ndarray] | None = None + self._samples_det_y: Sequence[ndarray] | None = None + self._trees: Sequence[KDTree] | None = None + self._spectra: Sequence[Spectrum1D] | None = None + + # The rectified space -> detectoir space transform + self._r2d: Model | None = None + + if cd_samples is not None: + self.cd_samples = np.array(cd_samples) + else: + self.cd_samples = np.round( + np.arange(1, n_cd_samples + 1) * self.n_cd_pix / (n_cd_samples + 1) + ).astype(int) + self.ncd = self.cd_samples.size + self._shift = None + + def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0): + self._spectra = [] + self._samples_rec_x = [] + self._lines_ref = [] + samples_x = [] + samples_y = [] + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + + for i, d in enumerate(self.arc_frames): + self._spectra.append([]) + samples_x.append([]) + samples_y.append([]) + + # Find the line centroids for the reference row + spectrum = Spectrum( + d.data[self.ref_pixel[1]] * d.unit, + uncertainty=d[self.ref_pixel[1]].uncertainty.represent_as(StdDevUncertainty), + ) + lines = find_arc_lines(spectrum, fwhm, noise_factor=noise_factor) + self._lines_ref.append(lines["centroid"].value) + + # Find the line centroids for the sample rows + for s in self.cd_samples: + spectrum = Spectrum( + d.data[s] * d.unit, + uncertainty=d[s].uncertainty.represent_as(StdDevUncertainty), + ) + lines = find_arc_lines(spectrum, fwhm, noise_factor=noise_factor) + samples_x[i].append(lines["centroid"].value) + samples_y[i].append(np.full(len(lines), s)) + self._spectra[i].append(spectrum) + + self._samples_det_x = [np.concatenate(lpx) for lpx in samples_x] + self._samples_det_y = [np.concatenate(lpy) for lpy in samples_y] + self._samples_rec_y = [repeat(self.cd_samples, lref.size) for lref in self._lines_ref] + self._samples_rec_x = [tile(lref, self.cd_samples.size) for lref in self._lines_ref] + + self._trees = [ + KDTree(np.vstack([lx, ly]).T) for lx, ly in zip(self._samples_det_x, self._samples_det_y) + ] + + def fit(self): + self._shift = models.Shift(-self.ref_pixel[0]) & models.Shift(-self.ref_pixel[1]) + model = self._shift | models.Polynomial2D(3) + + coeffs = np.zeros(10) + coeffs[0] = self.ref_pixel[0] + coeffs[1] = 1 + transformed_points = [tile(a, (2, 1)).T.astype("d") for a in self._samples_rec_y] + + def minfun(x): + coeffs[4:] = x + distance_sum = 0.0 + for i, t in enumerate(self._trees): + transformed_points[i][:, 0] = model.evaluate( + self._samples_rec_x[i], self._samples_rec_y[i], -self.ref_pixel[0], -self.ref_pixel[1], *coeffs + ) + distance_sum += t.query(transformed_points[i])[0].sum() + return distance_sum + + res = minimize(minfun, np.zeros(6), method="powell") + coeffs[4:] = res.x + self._r2d = self._shift | models.Polynomial2D( + model[-1].degree, **{model[-1].param_names[i]: coeffs[i] for i in range(coeffs.size)} + ) + + self._calculate_derivative() + # self._refine_fit() + + def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + rx, ry, ox = self._match_lines(match_distance_bound) + model = self._shift | models.Polynomial2D( + degree, **{n: getattr(self._r2d[-1], n).value for n in self._r2d[-1].param_names} + ) + model.offset_0.fixed = True + model.offset_1.fixed = True + for i in range(degree + 1): + model.fixed[f"c{i}_0_2"] = True + + fitter = fitting.LMLSQFitter() + self._r2d = fitter(model, rx, ry, ox) + self._calculate_derivative() + + def _match_lines(self, upper_bound: float = 5): + matched_lines_obs = [] + matched_lines_ref_x = [] + matched_lines_ref_y = [] + for iframe, tree in enumerate(self._trees): + x_mapped = self._r2d(self._samples_rec_x[iframe], self._samples_rec_y[iframe]) + l, ix = tree.query( + np.array([x_mapped, self._samples_rec_y[iframe]]).T, distance_upper_bound=upper_bound + ) + m = np.isfinite(l) + matched_lines_obs.append(tree.data[ix[m], 0]) + matched_lines_ref_x.append(self._samples_rec_x[iframe][m]) + matched_lines_ref_y.append(self._samples_rec_y[iframe][m]) + return ( + np.concatenate(matched_lines_ref_x), + np.concatenate(matched_lines_ref_y), + np.concatenate(matched_lines_obs), + ) + + def _calculate_derivative(self): + """Calculate the derivative for the rectified space -> detector space transformation. + """ + if self._r2d is not None: + self._r2d_dxdx = self._shift | diff_poly2d_x(self._r2d[-1]) + + def rectify( + self, + flux: ndarray, + nbins: int | None = None, + bounds: tuple[float, float] | None = None, + bin_edges: Iterable[float] | None = None, + ): + """Resample a 2D spectrum from the detector space to a rectified space. + + Resample a 2D spectrum from the detector space to a rectified space where the wavelength + is constant along the rows. The grid edges are based on the specified number of bins, + bounds, or bin edges. The resampling is eaxct and conserves flux (as long as the + rectified space covers the whole detector space.) + + Parameters + ---------- + flux + 2D array representing the flux values of the distorted input image. The first + dimension corresponds to rows (typically the y-axis), and the second dimension + corresponds to columns (typically the x-axis). + nbins + Number of bins in the rectified space. If None, the number of bins will be set + to the number of columns in the `flux` input image. + bound + Tuple specifying the start and end coordinates for the rectified space along the + x-axis. If None, the bounds default to (0, number of columns in `flux`). + bin_edges + Explicitly provided edges of the bins in the rectified space. If None, bin + edges are automatically calculated as a uniform grid based on `nbins` and + `bounds`. + + Returns + ------- + rectified_flux : ndarray + 2D array containing the flux values rectified into the uniform grid defined + by `nbins`, `bounds`, or `bin_edges`. The output has the same number of rows + as the input `flux`, and its second dimension corresponds to the number of + rectified bins. + """ + ny, nx = flux.shape + ypix = np.arange(ny) + nbins = nx if nbins is None else nbins + l1, l2 = bounds if bounds is not None else (0, nx) + + bin_edges_rec = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) + bin_edges_det = np.clip(self._r2d(*np.meshgrid(bin_edges_rec, ypix)) , 0, nx - 1e-12) + bin_edge_ix = np.floor(bin_edges_det).astype(int) + bin_edge_w = bin_edges_det - bin_edge_ix + + rectified_flux = np.zeros((ny, nbins)) + weights = np.zeros((ny, nx)) + + # Calculate the derivative of the rectified space -> detector space transformation with + # respect to the detector coordinate (dx_rec / dx_det). This is needed for flux + # conservation, as it represents how the pixel width changes. + dtdx = self._r2d_dxdx(*np.meshgrid(np.arange(nx), np.arange(ny))) + + # Calculate a normalization factor 'n' for flux conservation. This factor accounts for the + # change in pixel size due to the distortion, and ensures that the total flux in each row + # is conserved after rectification + n = flux.sum(1) / (dtdx * flux).sum(1) + + ixs = np.tile(np.arange(flux.shape[1]), (flux.shape[0], 1)) + ys = np.arange(flux.shape[0]) + + for i in range(nbins): + # Get the detector pixel indices (left and right edges) for the current rectified bin. + i1, i2 = bin_edge_ix[:, i : i + 2].T + + # Create a mask 'm' where the left and right detector pixel edges are the same. + # This means the entire rectified bin falls within a single detector pixel. + m = i1 == i2 + + # For rows where the rectified bin falls within a single detector pixel, + # the rectified flux is the detector flux in that pixel, scaled by the width of the + # rectified bin in detector coordinates and the derivative dtdx. + if m.any(): + rectified_flux[:, i] = ( + (bin_edges_det[:, i + 1] - bin_edges_det[:, i]) * flux[ys, i1] * dtdx[ys, i1] + ) + + # For rows where the rectified bin spans multiple detector pixels, calculate the + # rectified flux as a weighted sum of the detector flux, multiplied by dtdx, + # within the span [imin, imax]. + if not m.all(): + imin, imax = i1.min(), i2.max() + 1 + ixc = ixs[:, imin:imax] + w = weights[:, imin:imax] + w[:] = 0.0 + w[(ixc > i1[:, None]) & (ixc < i2[:, None])] = 1 + w[ys, i1 - imin] = 1.0 - bin_edge_w[:, i] + w[ys, i2 - imin] = bin_edge_w[:, i + 1] + rectified_flux[~m, i] = (flux[~m, imin:imax] * dtdx[~m, imin:imax] * w[~m]).sum(1) + + # Apply the normalization factor to conserve flux + rectified_flux *= n[:, None] + return rectified_flux + + def plot_transform(self, frame: int = 0, nx: int = 50, ny: int = 100, ax=None, figsize=None, plot_lines: bool = False, cmap=None, vmax=None): + if ax is None: + fig, ax = plt.subplots(figsize=figsize) + else: + fig = ax.figure + + d = self.arc_frames[frame] + if plot_lines: + nx = self._lines_ref[frame].size + xs = tile(self._lines_ref[frame], (ny, 1)) + ys = tile(np.linspace(0, d.shape[0], ny)[:,None], (1, nx)) + else: + xs = tile(np.linspace(0, d.shape[1], nx), (ny, 1)) + ys = tile(np.linspace(0, d.shape[0], ny)[:,None], (1, nx)) + ax.imshow(d.data, aspect='auto', origin='lower', cmap=cmap, vmax=vmax) + ax.plot(self._r2d(xs, ys), ys, 'w--', lw=1, alpha=1) + plt.setp(ax, xlim=(0, d.shape[1]), ylim=(0, d.shape[0])) + return fig + + def plot_residuals(self, axes=None, model=None, figsize=None): + if axes is None: + fig, axes = plt.subplots( + self.nframes, + 1, + figsize=figsize, + constrained_layout=True, + sharex="all", + sharey="all", + ) + else: + fig = axes[0].figure + model = model if model is not None else self._p2w + trees = [KDTree(l[:, None]) for l in self.lines_wav] + for lamp, t in enumerate(trees): + l, ix = t.query( + model(self._samples_det_x[lamp], self._samples_det_y[lamp])[:, None], distance_upper_bound=5 + ) + axes[lamp].plot(self._samples_det_x[lamp], l, ".") \ No newline at end of file From b8f2fdc707a1a5b81a0dea84b6d8c8626804e529 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Fri, 9 May 2025 18:50:51 +0100 Subject: [PATCH 58/76] Refactored the tilt correction code to improve readability, structure, and functionality. Added detailed docstrings, refined methods for arc line identification, transformation fitting, and matching lines. Improved internal handling of parameters and optimized logic. --- specreduce/tilt_correction.py | 173 +++++++++++++++++++++++++--------- 1 file changed, 126 insertions(+), 47 deletions(-) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index 4edb4652..152b1ca8 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -34,6 +34,25 @@ def __init__( cd_sample_lims: tuple[float, float] | None = None, cd_samples: Sequence[float] | None = None, ): + """A class for 2D spectrum rectification. + + Parameters + ---------- + ref_pixel + A reference pixel position specified as a tuple of floating-point coordinates (x, y). + + arc_frames + A sequence of arc frames as `NDData` instances. + + n_cd_samples + Number of cross-dispersion (CD) samples to generate. + + cd_sample_lims + Tuple specifying the limits for calculating cross-dispersion sampling. + + cd_samples + A list of cross-dispersion locations to use. Overrides ``n_cd_samples`` if provided. + """ self.ref_pixel = ref_pixel self.arc_frames = arc_frames self.nframes = len(arc_frames) @@ -44,23 +63,40 @@ def __init__( self._samples_rec_y: Sequence[ndarray] | None = None self._samples_det_x: Sequence[ndarray] | None = None self._samples_det_y: Sequence[ndarray] | None = None + self._arc_spectra: Sequence[Spectrum1D] | None = None self._trees: Sequence[KDTree] | None = None - self._spectra: Sequence[Spectrum1D] | None = None + + self._shift = models.Shift(-self.ref_pixel[0]) & models.Shift(-self.ref_pixel[1]) # The rectified space -> detectoir space transform self._r2d: Model | None = None + # Calculate the cross-dispersion axis sample positions + slims = cd_sample_lims if cd_sample_lims is not None else (0, self.n_cd_pix) if cd_samples is not None: self.cd_samples = np.array(cd_samples) else: - self.cd_samples = np.round( - np.arange(1, n_cd_samples + 1) * self.n_cd_pix / (n_cd_samples + 1) + self.cd_samples = slims[0] + np.round( + np.arange(1, n_cd_samples + 1) * (slims[1] - slims[0]) / (n_cd_samples + 1) ).astype(int) self.ncd = self.cd_samples.size - self._shift = None - def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0): - self._spectra = [] + def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0) -> None: + """Find arc lines from the provided arc frames for all cross-dispersion samples. + + This method locates spectral arc lines from the provided arc frames, calculates + their centroids, and organizes them into reference lists and sample arrays + for further analysis. + + Parameters + ---------- + fwhm + Full width at half maximum of the spectral line to be detected, used + by the line-finding algorithm. + noise_factor + A multiplier for noise thresholding in the line-finding process. + """ + self._arc_spectra = [] self._samples_rec_x = [] self._lines_ref = [] samples_x = [] @@ -69,7 +105,7 @@ def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0): warnings.simplefilter("ignore") for i, d in enumerate(self.arc_frames): - self._spectra.append([]) + self._arc_spectra.append([]) samples_x.append([]) samples_y.append([]) @@ -90,7 +126,7 @@ def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0): lines = find_arc_lines(spectrum, fwhm, noise_factor=noise_factor) samples_x[i].append(lines["centroid"].value) samples_y[i].append(np.full(len(lines), s)) - self._spectra[i].append(spectrum) + self._arc_spectra[i].append(spectrum) self._samples_det_x = [np.concatenate(lpx) for lpx in samples_x] self._samples_det_y = [np.concatenate(lpy) for lpy in samples_y] @@ -98,11 +134,28 @@ def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0): self._samples_rec_x = [tile(lref, self.cd_samples.size) for lref in self._lines_ref] self._trees = [ - KDTree(np.vstack([lx, ly]).T) for lx, ly in zip(self._samples_det_x, self._samples_det_y) + KDTree(np.vstack([lx, ly]).T) + for lx, ly in zip(self._samples_det_x, self._samples_det_y) ] - def fit(self): - self._shift = models.Shift(-self.ref_pixel[0]) & models.Shift(-self.ref_pixel[1]) + def fit(self, degree: int = 3, method: str = "Powell", max_distance: float = 10) -> None: + """Fits a 2D polynomial transformation from rectified space to detector space. + + The transformation is calculated by minimizing the sum of distances between transformed + samples and their corresponding detector-space targets. The minimization is performed in + two stages: an initial minimization of a kd-tree based sum of line-line distances using + `scipy.optimize.minimize` and a refinement using least-squares optimization of matched + lines. + + Parameters + ---------- + degree + The degree of the final 2D polynomial model. + method + The optimization method used during the initial fitting stage. + max_distance + The maximum allowable distance to constrain the minimization.. + """ model = self._shift | models.Polynomial2D(3) coeffs = np.zeros(10) @@ -112,24 +165,43 @@ def fit(self): def minfun(x): coeffs[4:] = x - distance_sum = 0.0 + total_distance = 0.0 for i, t in enumerate(self._trees): transformed_points[i][:, 0] = model.evaluate( - self._samples_rec_x[i], self._samples_rec_y[i], -self.ref_pixel[0], -self.ref_pixel[1], *coeffs + self._samples_rec_x[i], + self._samples_rec_y[i], + -self.ref_pixel[0], + -self.ref_pixel[1], + *coeffs, ) - distance_sum += t.query(transformed_points[i])[0].sum() - return distance_sum + total_distance += np.clip(t.query(transformed_points[i])[0], 0, max_distance).sum() + return total_distance - res = minimize(minfun, np.zeros(6), method="powell") + self._initial_optimization_result = res = minimize(minfun, np.zeros(6), method=method) coeffs[4:] = res.x self._r2d = self._shift | models.Polynomial2D( model[-1].degree, **{model[-1].param_names[i]: coeffs[i] for i in range(coeffs.size)} ) + # Calculate the final fit using least-squares optimization between matched lines + self._refine_fit(degree) self._calculate_derivative() - # self._refine_fit() - def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): + def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0) -> None: + """Refine the rectified space -> detector space transformation model parameters. + + Refines the polynomial fit model parameters for matching features with a specified + degree and match distance bound. The refinement includes matching lines, + updating a polynomial model, and optimizing the parameters using a least squares + fitter. The derivative is recalculated after the optimization. + + Parameters + ---------- + degree + Degree of the polynomial used in the Polynomial2D model. + match_distance_bound + Maximum acceptable distance between features to be considered a match. + """ rx, ry, ox = self._match_lines(match_distance_bound) model = self._shift | models.Polynomial2D( degree, **{n: getattr(self._r2d[-1], n).value for n in self._r2d[-1].param_names} @@ -141,16 +213,34 @@ def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0): fitter = fitting.LMLSQFitter() self._r2d = fitter(model, rx, ry, ox) + self._refined_optimization_result = fitter.fit_info self._calculate_derivative() def _match_lines(self, upper_bound: float = 5): + """Match the reference arc line locations with the detector-space targets. + + Parameters + ---------- + upper_bound + Specifies the maximum allowed distance for matching lines. Matches beyond this distance will + be ignored. + + Returns + ------- + tuple of numpy.ndarray + A tuple containing three concatenated numpy arrays representing: + - Matched x-coordinates of reference lines. + - Matched y-coordinates of reference lines. + - Observational data points that matched. + """ matched_lines_obs = [] matched_lines_ref_x = [] matched_lines_ref_y = [] for iframe, tree in enumerate(self._trees): x_mapped = self._r2d(self._samples_rec_x[iframe], self._samples_rec_y[iframe]) l, ix = tree.query( - np.array([x_mapped, self._samples_rec_y[iframe]]).T, distance_upper_bound=upper_bound + np.array([x_mapped, self._samples_rec_y[iframe]]).T, + distance_upper_bound=upper_bound, ) m = np.isfinite(l) matched_lines_obs.append(tree.data[ix[m], 0]) @@ -163,8 +253,7 @@ def _match_lines(self, upper_bound: float = 5): ) def _calculate_derivative(self): - """Calculate the derivative for the rectified space -> detector space transformation. - """ + """Calculate the derivative for the rectified space -> detector space transformation.""" if self._r2d is not None: self._r2d_dxdx = self._shift | diff_poly2d_x(self._r2d[-1]) @@ -213,7 +302,7 @@ def rectify( l1, l2 = bounds if bounds is not None else (0, nx) bin_edges_rec = bin_edges if bin_edges is not None else np.linspace(l1, l2, num=nbins + 1) - bin_edges_det = np.clip(self._r2d(*np.meshgrid(bin_edges_rec, ypix)) , 0, nx - 1e-12) + bin_edges_det = np.clip(self._r2d(*np.meshgrid(bin_edges_rec, ypix)), 0, nx - 1e-12) bin_edge_ix = np.floor(bin_edges_det).astype(int) bin_edge_w = bin_edges_det - bin_edge_ix @@ -266,7 +355,17 @@ def rectify( rectified_flux *= n[:, None] return rectified_flux - def plot_transform(self, frame: int = 0, nx: int = 50, ny: int = 100, ax=None, figsize=None, plot_lines: bool = False, cmap=None, vmax=None): + def plot_transform( + self, + frame: int = 0, + nx: int = 50, + ny: int = 100, + ax=None, + figsize=None, + plot_lines: bool = False, + cmap=None, + vmax=None, + ): if ax is None: fig, ax = plt.subplots(figsize=figsize) else: @@ -276,31 +375,11 @@ def plot_transform(self, frame: int = 0, nx: int = 50, ny: int = 100, ax=None, f if plot_lines: nx = self._lines_ref[frame].size xs = tile(self._lines_ref[frame], (ny, 1)) - ys = tile(np.linspace(0, d.shape[0], ny)[:,None], (1, nx)) + ys = tile(np.linspace(0, d.shape[0], ny)[:, None], (1, nx)) else: xs = tile(np.linspace(0, d.shape[1], nx), (ny, 1)) - ys = tile(np.linspace(0, d.shape[0], ny)[:,None], (1, nx)) - ax.imshow(d.data, aspect='auto', origin='lower', cmap=cmap, vmax=vmax) - ax.plot(self._r2d(xs, ys), ys, 'w--', lw=1, alpha=1) + ys = tile(np.linspace(0, d.shape[0], ny)[:, None], (1, nx)) + ax.imshow(d.data, aspect="auto", origin="lower", cmap=cmap, vmax=vmax) + ax.plot(self._r2d(xs, ys), ys, "w--", lw=1, alpha=1) plt.setp(ax, xlim=(0, d.shape[1]), ylim=(0, d.shape[0])) return fig - - def plot_residuals(self, axes=None, model=None, figsize=None): - if axes is None: - fig, axes = plt.subplots( - self.nframes, - 1, - figsize=figsize, - constrained_layout=True, - sharex="all", - sharey="all", - ) - else: - fig = axes[0].figure - model = model if model is not None else self._p2w - trees = [KDTree(l[:, None]) for l in self.lines_wav] - for lamp, t in enumerate(trees): - l, ix = t.query( - model(self._samples_det_x[lamp], self._samples_det_y[lamp])[:, None], distance_upper_bound=5 - ) - axes[lamp].plot(self._samples_det_x[lamp], l, ".") \ No newline at end of file From 567ec43960fde9f881033419d5de6853b4bea45a Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Fri, 9 May 2025 18:52:29 +0100 Subject: [PATCH 59/76] Added type annotations and a docstring for the `diff_poly2d_x` function. --- specreduce/tilt_correction.py | 27 +++++++++++++++++++++++---- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index 152b1ca8..f17e1ae2 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -16,13 +16,32 @@ from specreduce.compat import Spectrum -def diff_poly2d_x(m): +def diff_poly2d_x(model: models.Polynomial2D) -> models.Polynomial2D: + """Compute the partial derivative of a 2D polynomial model with respect to x. + + Generates a new 2D polynomial model representing the derivative of the input + model in the x-direction. The coefficients of the resulting model are calculated + by multiplying the coefficients from the input model by their respective x + index and reducing the order in the x-dimension. + + Parameters + ---------- + model + An `astropy.modeling.models.Polynomial2D` model. + + Returns + ------- + models.Polynomial2D + A new 2D polynomial model representing the derivative of the input model + with respect to x. The degree of the resulting model will be decreased + by 1 in the x-dimension. + """ coeffs = {} - for n in m.param_names: + for n in model.param_names: ix, iy = int(n[1]), int(n[3]) if ix > 0: - coeffs[f"c{ix-1}_{iy}"] = ix * getattr(m, n).value - return models.Polynomial2D(m.degree - 1, **coeffs) + coeffs[f"c{ix-1}_{iy}"] = ix * getattr(model, n).value + return models.Polynomial2D(model.degree - 1, **coeffs) class TiltCorrection: From e21c95a0bf6a8c59269ca254a10a4e85729e088e Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 13 May 2025 16:27:05 +0100 Subject: [PATCH 60/76] Renamed private methods to public and adjusted their parameters for consistency and improved usability. Updated variable names and docstrings for better clarity and alignment with the code's functionality. Simplified return behavior in `match_lines` to support optional concatenation. --- specreduce/tilt_correction.py | 52 +++++++++++++++++++---------------- 1 file changed, 29 insertions(+), 23 deletions(-) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index f17e1ae2..393b675a 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -158,7 +158,7 @@ def find_arc_lines(self, fwhm: float, noise_factor: float = 5.0) -> None: ] def fit(self, degree: int = 3, method: str = "Powell", max_distance: float = 10) -> None: - """Fits a 2D polynomial transformation from rectified space to detector space. + """Fit a 2D polynomial transformation from rectified space to detector space. The transformation is calculated by minimizing the sum of distances between transformed samples and their corresponding detector-space targets. The minimization is performed in @@ -203,10 +203,10 @@ def minfun(x): ) # Calculate the final fit using least-squares optimization between matched lines - self._refine_fit(degree) + self.refine_fit(degree) self._calculate_derivative() - def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0) -> None: + def refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0) -> None: """Refine the rectified space -> detector space transformation model parameters. Refines the polynomial fit model parameters for matching features with a specified @@ -221,7 +221,7 @@ def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0) -> Non match_distance_bound Maximum acceptable distance between features to be considered a match. """ - rx, ry, ox = self._match_lines(match_distance_bound) + rx, ry, ox = self.match_lines(match_distance_bound) model = self._shift | models.Polynomial2D( degree, **{n: getattr(self._r2d[-1], n).value for n in self._r2d[-1].param_names} ) @@ -235,41 +235,47 @@ def _refine_fit(self, degree: int = 4, match_distance_bound: float = 5.0) -> Non self._refined_optimization_result = fitter.fit_info self._calculate_derivative() - def _match_lines(self, upper_bound: float = 5): + def match_lines( + self, max_distance: float = 5, concatenate: bool = True + ) -> tuple[ndarray, ndarray, ndarray] | tuple[list[ndarray], list[ndarray], list[ndarray]]: """Match the reference arc line locations with the detector-space targets. Parameters ---------- - upper_bound - Specifies the maximum allowed distance for matching lines. Matches beyond this distance will - be ignored. + max_distance + Specifies the maximum allowed distance for matching lines. Matches beyond this distance + will be ignored. Returns ------- tuple of numpy.ndarray A tuple containing three concatenated numpy arrays representing: - - Matched x-coordinates of reference lines. - - Matched y-coordinates of reference lines. - - Observational data points that matched. + - x-coordinates of matched rectified-space lines. + - y-coordinates of matched rectified-space lines. + - x-coordinates of matched detector-space lines. """ - matched_lines_obs = [] - matched_lines_ref_x = [] - matched_lines_ref_y = [] + matched_det_x = [] + matched_rec_x = [] + matched_rec_y = [] for iframe, tree in enumerate(self._trees): x_mapped = self._r2d(self._samples_rec_x[iframe], self._samples_rec_y[iframe]) l, ix = tree.query( np.array([x_mapped, self._samples_rec_y[iframe]]).T, - distance_upper_bound=upper_bound, + distance_upper_bound=max_distance, ) m = np.isfinite(l) - matched_lines_obs.append(tree.data[ix[m], 0]) - matched_lines_ref_x.append(self._samples_rec_x[iframe][m]) - matched_lines_ref_y.append(self._samples_rec_y[iframe][m]) - return ( - np.concatenate(matched_lines_ref_x), - np.concatenate(matched_lines_ref_y), - np.concatenate(matched_lines_obs), - ) + matched_det_x.append(tree.data[ix[m], 0]) + matched_rec_x.append(self._samples_rec_x[iframe][m]) + matched_rec_y.append(self._samples_rec_y[iframe][m]) + + if concatenate: + return ( + np.concatenate(matched_rec_x), + np.concatenate(matched_rec_y), + np.concatenate(matched_det_x), + ) + else: + return matched_rec_x, matched_rec_y, matched_det_x def _calculate_derivative(self): """Calculate the derivative for the rectified space -> detector space transformation.""" From ae49deb17e58caff46ea7e350b9c2d72679cb1a0 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 13 May 2025 16:27:38 +0100 Subject: [PATCH 61/76] Introduced a method to transform coordinates from rectified space to detector space. --- specreduce/tilt_correction.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index 393b675a..92477c62 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -282,6 +282,24 @@ def _calculate_derivative(self): if self._r2d is not None: self._r2d_dxdx = self._shift | diff_poly2d_x(self._r2d[-1]) + def rec_to_det(self, col: ndarray, row: ndarray) -> tuple[ndarray, ndarray]: + """Transform coordinates from the rectified space to detector space. + + Parameters + ---------- + col : ndarray + The dispersion-axis coordinates to be transformed. + row : ndarray + The cross-dispersion coordinates, returned as is. + + Returns + ------- + tuple of (ndarray, ndarray) + A tuple containing the transformed dispersion-axis coordinates as the first element + and the original cross-dispersion-axis coordinates as the second element.. + """ + return self._r2d(col, row), row + def rectify( self, flux: ndarray, From a65bcfa8be1b5521f0229f6c46e375b83cad9c86 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 13 May 2025 16:28:13 +0100 Subject: [PATCH 62/76] Refactored plot functionality and add fit quality visualization. --- specreduce/tilt_correction.py | 96 ++++++++++++++++++++++++++++------- 1 file changed, 77 insertions(+), 19 deletions(-) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index 92477c62..f109340a 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -398,31 +398,89 @@ def rectify( rectified_flux *= n[:, None] return rectified_flux - def plot_transform( + def plot_wavelength_contours( self, - frame: int = 0, - nx: int = 50, - ny: int = 100, - ax=None, - figsize=None, - plot_lines: bool = False, - cmap=None, - vmax=None, + ncol: int = 50, + nrow: int = 100, + cols: Sequence[float] | None = None, + ax: plt.Axes | None = None, + figsize: tuple[float, float] | None = None, + line_args: dict | None =None, ): + """Plot wavelength contour lines in detector space. + + Parameters + ---------- + ncol + The number of columns in the grid. + nrow + The number of rows in the grid. + cols + A sequence specifying the x-coordinates of the grid columns. If not + provided, it will be automatically calculated based on the arc frame + dimensions. + ax + The Matplotlib Axes on which to plot. If None, a new figure and Axes + are created. + figsize + Tuple specifying the size of the figure to create, applicable only if + `ax` is None.. + line_args + A dictionary of line properties (e.g., color, linewidth, linestyle). + These properties modify the default styling provided for grid lines. + If None, default styles are used. Default is None. + + Returns + ------- + figure : matplotlib.figure.Figure + The Matplotlib figure containing the plot. If an Axes instance is passed + to `ax`, the associated figure is returned. + """ + largs = {'c': 'k', 'lw': 0.5, 'alpha': 0.5, 'ls':'--'} + if line_args is not None: + largs.update(line_args) + if ax is None: fig, ax = plt.subplots(figsize=figsize) else: fig = ax.figure - d = self.arc_frames[frame] - if plot_lines: - nx = self._lines_ref[frame].size - xs = tile(self._lines_ref[frame], (ny, 1)) - ys = tile(np.linspace(0, d.shape[0], ny)[:, None], (1, nx)) + if cols is None: + cols = tile(np.linspace(0, self.arc_frames[0].data.shape[1], ncol), (nrow, 1)) else: - xs = tile(np.linspace(0, d.shape[1], nx), (ny, 1)) - ys = tile(np.linspace(0, d.shape[0], ny)[:, None], (1, nx)) - ax.imshow(d.data, aspect="auto", origin="lower", cmap=cmap, vmax=vmax) - ax.plot(self._r2d(xs, ys), ys, "w--", lw=1, alpha=1) - plt.setp(ax, xlim=(0, d.shape[1]), ylim=(0, d.shape[0])) + ncol = len(cols) + rows = tile(np.linspace(0, self.arc_frames[0].data.shape[0], nrow)[:, None], (1, ncol)) + + ax.plot(self._r2d(cols, rows), rows, **largs) + return fig + + def plot_fit_quality( + self, figsize=None, max_match_distance: float = 5, rlim: tuple[float, float] | None = None + ): + fig = plt.Figure(figsize=figsize, layout="constrained") + gs = plt.GridSpec(2, 2, width_ratios=(4, 1), height_ratios=(1, 3), figure=fig) + + ax1 = fig.add_subplot(gs[1, 0]) + ax2 = fig.add_subplot(gs[0, 0]) + ax3 = fig.add_subplot(gs[1, 1]) + + rxs, rys, dxs = self.match_lines(max_match_distance, concatenate=False) + for i, (rx, ry, dx) in enumerate(zip(rxs, rys, dxs)): + residuals = dx - self.rec_to_det(rx, ry)[0] + ax1.scatter(rx, ry, s=50 * abs(residuals), label=f"Arc {i+1}") + ax2.plot(rx, residuals, ".") + ax3.plot(residuals, ry, ".") + ax1.legend(loc="upper right") + ax2.set_xlim(ax1.get_xlim()) + ax3.set_ylim(ax1.get_ylim()) + plt.setp(ax2.get_xticklabels(), visible=False) + plt.setp(ax3.get_yticklabels(), visible=False) + plt.setp(ax1, xlabel="Dispersion axis [pix]", ylabel="Cross-dispersion axis [pix]") + ax2.set_ylabel("Residuals [pix]") + ax3.set_xlabel("Residuals [pix]") + + if rlim is not None: + ax2.set_ylim(rlim) + ax3.set_xlim(rlim) + return fig From 8cf8ffd05317aa3c6bdf86c970151cbc1db9a806 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 13 May 2025 16:28:41 +0100 Subject: [PATCH 63/76] Added tilt correction tutorial and sample data. --- docs/index.rst | 1 + docs/tilt_correction/common.py | 19 + .../osiris_arc_HgAr.fits.bz2 | Bin 0 -> 538668 bytes .../gtc_osiris_example/osiris_arc_Ne.fits.bz2 | Bin 0 -> 613358 bytes .../gtc_osiris_example/osiris_arc_Xe.fits.bz2 | Bin 0 -> 523691 bytes .../gtc_osiris_example/osiris_bias.fits.bz2 | Bin 0 -> 391504 bytes .../osiris_tres_3b.fits.bz2 | Bin 0 -> 528087 bytes docs/tilt_correction/tilt_correction.ipynb | 425 ++++++++++++++++++ 8 files changed, 445 insertions(+) create mode 100644 docs/tilt_correction/common.py create mode 100644 docs/tilt_correction/gtc_osiris_example/osiris_arc_HgAr.fits.bz2 create mode 100644 docs/tilt_correction/gtc_osiris_example/osiris_arc_Ne.fits.bz2 create mode 100644 docs/tilt_correction/gtc_osiris_example/osiris_arc_Xe.fits.bz2 create mode 100644 docs/tilt_correction/gtc_osiris_example/osiris_bias.fits.bz2 create mode 100644 docs/tilt_correction/gtc_osiris_example/osiris_tres_3b.fits.bz2 create mode 100644 docs/tilt_correction/tilt_correction.ipynb diff --git a/docs/index.rst b/docs/index.rst index 939e841c..4c479ffa 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,6 +50,7 @@ Calibration .. toctree:: :maxdepth: 1 + tilt_correction/tilt_correction.ipynb wavelength_calibration/wavelength_calibration.rst extinction.rst specphot_standards.rst diff --git a/docs/tilt_correction/common.py b/docs/tilt_correction/common.py new file mode 100644 index 00000000..edb606ac --- /dev/null +++ b/docs/tilt_correction/common.py @@ -0,0 +1,19 @@ +from pathlib import Path + +from astropy import units as u +from astropy.io import fits as pf +from astropy.nddata import CCDData, VarianceUncertainty + + +def read_file(fname, bias): + d = pf.getdata(fname).astype('d') + return CCDData(d - bias, unit=u.dn, uncertainty=VarianceUncertainty(d)) + + +def read_data(): + bias = pf.getdata('gtc_osiris_example/osiris_bias.fits.bz2').astype('d') + obj = read_file('gtc_osiris_example/osiris_tres_3b.fits.bz2', bias) + lamps = 'HgAr', 'Ne', 'Xe' + arc_files = sorted(Path('gtc_osiris_example').glob('*arc*')) + arcs = [read_file(f, bias) for f in arc_files] + return arcs, lamps, obj diff --git a/docs/tilt_correction/gtc_osiris_example/osiris_arc_HgAr.fits.bz2 b/docs/tilt_correction/gtc_osiris_example/osiris_arc_HgAr.fits.bz2 new file mode 100644 index 0000000000000000000000000000000000000000..6493fee96fc523332be7fcbc371282e4f2874c43 GIT binary patch literal 538668 zcmbq(t?ht7N1O=o- zN@6a*`w!d~cfC5Fv-UZAuk~4b?X}nQp!ID;`K9$(HT1qV{W!#>JPN@6zhvv<1`Z0q zk<124o(2GmLKM`5)X4Ea0CVS1ILg3W@?7#lJOF?kSoDDn00F4s2M6U&v!ejyY5;(( 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{}, + "cell_type": "markdown", + "source": [ + "# Tilt Correction\n", + "\n", + "In astronomical spectroscopy, tilt correction is a calibration step that addresses optical\n", + "distortions and misalignments in spectroscopic instruments. These distortions cause wavelength\n", + "not be constant along the cross-dispersion (spatial) axis: spectral features appear tilted or\n", + "curved across the detector instead of being perfectly aligned with detector columns.\n", + "\n", + "Tilt correction is carried out using a two-dimensional tilt function that describes how wavelength\n", + "positions shift across the spatial axis. The tilt function can be estimated from arc lamp\n", + "spectra, by measuring how the calibration line centroids vary along the cross-dispersion axis.\n", + "\n", + "This tilt function can then be used to transform the two-dimensional spectroscopy images so that\n", + "wavelengths are aligned along straight lines parallel to the detector axes (known as 2D\n", + "rectification). This is particularly important for accurate wavelength calibration and robust sky\n", + " subtraction.\n", + "\n", + "In `specreduce`, the tilt function is represented as a 2D polynomial (an `astropy.modeling.models\n", + ".Polynomial2d` instance) of a given degree. The `specreduce.tilt_correction.TiltCorrection` class\n", + " implements tilt correction by:\n", + "\n", + "1. Identifying emission lines in an arc lamp spectrum (or multiple arc lamp spectra)\n", + "2. Fitting a 2D polynomial model to characterize the geometric distortion\n", + "3. Computing a transformation that maps the tilted features to straight lines\n", + "4. Applying this transformation to rectify the science frames\n", + "\n", + "A single arc frame can often suffice, but the use of multiple arc lamps with different emission\n", + "line patterns helps constrain the transformation across the full wavelength range of the spectrograph." + ], + "id": "a86e5f303125a87" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from astropy.visualization import simple_norm\n", + "from specreduce.tilt_correction import TiltCorrection\n", + "from common import read_data\n", + "\n", + "plt.rc('figure', figsize=(6.3, 2))\n", + "plt.rc('font', size=8)" + ], + "id": "ba005ff705baee1e" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Read in the Arc Frames and Object Frame\n", + "\n", + "This tutorial demonstrates 2D tilt correction (rectification) using three arc lamp\n", + "spectra observed with the [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)\n", + "[Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/). The arcs (HgAr, Ne, and Xe)\n", + "were observed with the OSIRIS R1000R grism configuration, which covers approximately 5100-10000 Ã…\n", + "at moderate resolution.\n", + "\n", + "This example uses three arc lamp calibration frames and a single long-slit science frame, all observed with the OSIRIS instrument at the Gran Telescopio Canarias (GTC) in 2012. OSIRIS, which operated until 2023 before being upgraded to OSIRIS+, featured two 2048 x 4096 pixel Marconi CCD detectors. For simplicity and file size considerations, we use data from just one CCD that has been binned to 512 x 1024 pixels.\n", + "\n", + "OSIRIS spectra exhibit significant tilt distortion that must be corrected to achieve reliable background subtraction. The example science frame comes from a time-series of spectroscopic observations taken during an exoplanet transit (transmission spectroscopy). The scientific importance of proper tilt correction is highlighted by the original analysis of this dataset - insufficient correction of the tilt distortion led to residual time variations in telluric absorption lines that compromised the scientific results.\n" + ], + "id": "4bb3dc3c78942883" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "arcs, lamps, obj = read_data()\n", + "frames = arcs + [obj]\n", + "labels = lamps + ('Target',)" + ], + "id": "f218ac53060dd4a" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(frames):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=250_000))\n", + " axs[i].text(0.01, 0.9, labels[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + "plt.setp(axs, ylabel='CD axis [pix]')\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "id": "2f00cd7e37525ab1" + }, + { + "cell_type": "markdown", + "id": "ea64070e-7884-410e-9c43-01dcb6f101db", + "metadata": {}, + "source": [ + "## Initialize the Tilt Correction\n", + "\n", + "The `TiltCorrection` class is initialized with the following parameters:\n", + "\n", + "- Reference pixel: `(500, 300)` -\n", + "- Arc lamp data: List of `CCDData` objects containing arc lamp spectra \n", + "- Cross-dispersion sampling:\n", + " - Sample limits: `(50, 500)` - pixel range for sampling the cross-dispersion direction\n", + " - Number of samples: `8` - number of points to sample along cross-dispersion\n", + "\n", + "The initialization sets up the transformation model that will be used to map tilted spectral features to straight lines." + ] + }, + { + "cell_type": "code", + "id": "5e4b9637-a3b8-4805-8908-8ba6b50d713f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:46:29.633541Z", + "start_time": "2025-05-13T09:46:29.630908Z" + } + }, + "source": "s = TiltCorrection((500, 300), arcs, cd_sample_lims=(50, 500), n_cd_samples=7)", + "outputs": [], + "execution_count": 23 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Find Arc Lines\n", + "\n", + "After initializing the `TiltCorrection` object, the `find_arc_lines()` method detects emission peaks in the arc lamp spectra. This method takes a threshold parameter (in this case 5.0) to find significant emission lines above the background level.\n", + "\n", + "The detected lines are used as reference points for mapping the spectral tilt and curvature across the detector. For each input arc lamp (HgAr, Ne, Xe), the method:\n", + "\n", + "1. Analyzes spectra at multiple cross-dispersion positions\n", + "2. Identifies emission peaks above the threshold\n", + "3. Records the line positions to be used as fiducial points\n", + "4. Builds the dataset needed for fitting the geometric transformation\n", + "\n", + "This step is crucial as the identified lines form the basis for calculating the correction that will straighten tilted spectral features.\n" + ], + "id": "67439310-4340-4d7f-9bea-c41dafef371b" + }, + { + "cell_type": "code", + "id": "3b1815ec-bbc6-43fb-9130-ffd8108ce003", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:46:32.199926Z", + "start_time": "2025-05-13T09:46:30.196093Z" + } + }, + "source": "s.find_arc_lines(2.5, noise_factor=10)", + "outputs": [], + "execution_count": 24 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:46:45.372608Z", + "start_time": "2025-05-13T09:46:44.970984Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axs = plt.subplots(3, figsize=(6.3, 4), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(arcs):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=250_000))\n", + " axs[i].text(0.01, 0.9, lamps[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + " axs[i].plot(s._samples_det_x[i], s._samples_det_y[i], 'k.', ms=3)\n", + " axs[i].plot(s._lines_ref[i], np.full_like(s._lines_ref[i], s.ref_pixel[1]), 'r.', ms=3)\n", + "plt.setp(axs, ylabel='CD axis [pix]')\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "id": "f5084cc9fef3338c", + "outputs": [ + { + "data": { + "text/plain": [ + "

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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 26 + }, + { + "cell_type": "markdown", + "id": "fbf4d669-f594-4447-a7d3-ae7f153418bd", + "metadata": {}, + "source": [ + "## Fit the model\n", + "\n", + "The `fit()` method is used to calculate the geometric transformation that maps tilted spectral features to straight lines. This method:\n", + "\n", + "1. Takes a `degree` parameter (set to 4 here) that determines the polynomial order of the transformation\n", + "2. Uses the arc line positions detected earlier as reference points\n", + "3. Performs an initial optimization to find a rough transformation\n", + "4. Refines the solution through further optimization\n", + "5. Generates the final transformation model that will be used to rectify the spectra\n", + "\n", + "The fitted model captures both the tilt and any curvature present in the spectral features across the detector." + ] + }, + { + "cell_type": "code", + "id": "def37f07-78f1-43a2-b012-3a38b6ea7566", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:46:50.561746Z", + "start_time": "2025-05-13T09:46:49.793583Z" + } + }, + "source": [ + "s.fit(degree=4)" + ], + "outputs": [], + "execution_count": 27 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:46:50.817935Z", + "start_time": "2025-05-13T09:46:50.575640Z" + } + }, + "cell_type": "code", + "source": "s.plot_fit_quality(figsize=(6.3, 5), rlim=(-0.5, 0.5))", + "id": "8e7b1520eec71b8d", + "outputs": [ + { + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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" + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 28 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:47:15.893633Z", + "start_time": "2025-05-13T09:47:14.900989Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(frames):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=250_000))\n", + " axs[i].text(0.01, 0.9, labels[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + " s.plot_wavelength_contours(ax=axs[i])\n", + "plt.setp(axs, xlim=(0, arcs[0].shape[1]), ylabel='CD axis [pix]');\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "id": "9fd5b71f830d5fdf", + "outputs": [ + { + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 30 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "While there are some outliers present in the data, they don't appear to significantly impact the quality of the fit. If these outliers were causing issues with the fit accuracy, we could further refine the transformation model by calling `TiltCorrection.refine_fit()` with a restrictive `match_distance_bound` parameter (e.g., 0.5 pixels) to exclude these outlying points from the fit. This would ensure that only well-matched emission line positions are used to determine the transformation.\n", + "id": "d1345c25a71d68d8" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Rectify the frames\n", + "\n", + "After fitting the transformation model, we can apply it to rectify the spectral frames using the `TiltCorrection.rectify()` method. This method:\n", + "\n", + "1. Takes the input frame data as a CCData object.\n", + "2. Applies the fitted geometric transformation to rebin the pixels using an exact flux-conserving\n", + " approach.\n", + "3. Returns a new CCDData object where spectral features are aligned with detector rows/columns.\n", + "\n", + "In this example, we apply the rectification both to the arc lamp calibration frames and the science target frame. The resulting rectified frames show spectral features that are properly aligned horizontally, making subsequent analysis steps like wavelength calibration and spectral extraction more straightforward and accurate.\n" + ], + "id": "18b01edd419fa004" + }, + { + "cell_type": "code", + "id": "8ff2cb79-fe5c-4be1-8f0a-6c320ef7a646", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:47:19.243442Z", + "start_time": "2025-05-13T09:47:18.765257Z" + } + }, + "source": "rectified_data = [s.rectify(d.data) for d in frames]", + "outputs": [], + "execution_count": 31 + }, + { + "cell_type": "code", + "id": "576a031f-a4bc-4e41-b8de-c5d185bf9279", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:48:10.416622Z", + "start_time": "2025-05-13T09:48:09.784557Z" + } + }, + "source": [ + "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", + "for i,d in enumerate(rectified_data):\n", + " axs[i].imshow(d.data, origin='lower', aspect='auto', cmap=plt.cm.Blues,\n", + " norm=simple_norm(d.data, stretch='log', vmin=0, vmax=150_000))\n", + " axs[i].text(0.01, 0.9, labels[i], va='top', ha='left', c='k', transform=axs[i].transAxes)\n", + " axs[i].grid(which='both', axis='x')\n", + " axs[i].set_xticks(np.linspace(10, 1000, 19))\n", + "plt.setp(axs, ylabel='CD axis [pix]')\n", + "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 35 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Let's still compare spectral features before and after rectification by examining emission lines.\n", + "The plot shows the reference row spectrum from the original (non-rectified) arc frame overlaid with spectra from different rows of the rectified frame. The alignment of spectral features across multiple rows in the rectified data demonstrates successful correction of the tilt, with emission line positions matching to sub-pixel precision as expected from a proper rectification.\n" + ], + "id": "8c85cdf028427f3d" + }, + { + "cell_type": "code", + "id": "079c3cca-9e47-46f4-8557-233c50e6fa00", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-13T09:47:45.893443Z", + "start_time": "2025-05-13T09:47:45.824633Z" + } + }, + "source": [ + "fig, ax = plt.subplots(1, figsize=(6.3, 2), sharex='all', constrained_layout=True)\n", + "\n", + "ref_row = s.ref_pixel[1]\n", + "rec_rows = [s.ref_pixel[1], 150, 450]\n", + "\n", + "ax.plot(arcs[0].data[ref_row])\n", + "for i, r in enumerate(rec_rows):\n", + " ax.plot(50_000*(i+1) + rectified_data[0][r])\n", + "plt.setp(ax, xlabel='Dispersion axis [Pix]', yticks=[])\n", + "plt.setp(ax, xlim=(50, 200));" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 34 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From db5f96211a17d46d7e6554b20a5ed94bda2e39a2 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 15 May 2025 14:15:42 +0100 Subject: [PATCH 64/76] Introduced `disp_axis` and `mask_treatment` parameters to enhance flexibility in 2D spectrum rectification. Updated methods to use these parameters, ensuring proper alignment and control over mask handling. Also improved documentation and refactored variable naming. --- specreduce/tilt_correction.py | 147 +++++++++++++++++++++++----------- 1 file changed, 101 insertions(+), 46 deletions(-) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index f109340a..4a28361a 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -1,19 +1,18 @@ import warnings -from typing import Iterable, Sequence +from typing import Iterable, Sequence, Literal import matplotlib.pyplot as plt -import astropy.units as u import numpy as np from astropy.modeling import models, Model, fitting from astropy.nddata import StdDevUncertainty, NDData +from numpy import ndarray, repeat, tile from scipy.optimize import minimize from scipy.spatial import KDTree - -from numpy import ndarray, concatenate, repeat, tile from specutils import Spectrum1D -from specreduce.line_matching import find_arc_lines from specreduce.compat import Spectrum +from specreduce.core import _ImageParser +from specreduce.line_matching import find_arc_lines def diff_poly2d_x(model: models.Polynomial2D) -> models.Polynomial2D: @@ -52,16 +51,27 @@ def __init__( n_cd_samples: int = 10, cd_sample_lims: tuple[float, float] | None = None, cd_samples: Sequence[float] | None = None, + disp_axis: int = 1, + mask_treatment: Literal[ + "apply", + "ignore", + "propagate", + "zero_fill", + "nan_fill", + "apply_mask_only", + "apply_nan_only", + ] = "apply", ): """A class for 2D spectrum rectification. Parameters ---------- ref_pixel - A reference pixel position specified as a tuple of floating-point coordinates (x, y). + A reference pixel position specified as a tuple of floating-point coordinates + (dispersion axis, cross-dispersion axis). arc_frames - A sequence of arc frames as `NDData` instances. + A sequence of arc frames as `~astropy.nddata.NDData` instances. n_cd_samples Number of cross-dispersion (CD) samples to generate. @@ -71,11 +81,49 @@ def __init__( cd_samples A list of cross-dispersion locations to use. Overrides ``n_cd_samples`` if provided. + + disp_axis + The index of the image's dispersion axis. + + mask_treatment + Specifies how to handle masked or non-finite values in the input image. + The accepted values are: + + - ``apply``: The image remains unchanged, and any existing mask is combined with a mask + derived from non-finite values. + - ``ignore``: The image remains unchanged, and any existing mask is dropped. + - ``propagate``: The image remains unchanged, and any masked or non-finite pixel + causes the mask to extend across the entire cross-dispersion axis. + - ``zero_fill``: Pixels that are either masked or non-finite are replaced with 0.0, + and the mask is dropped. + - ``nan_fill``: Pixels that are either masked or non-finite are replaced with nan, + and the mask is dropped. + - ``apply_mask_only``: The image and mask are left unmodified. + - ``apply_nan_only``: The image is left unmodified, the old mask is dropped, + and a new mask is created based on non-finite values. """ self.ref_pixel = ref_pixel + self.disp_axis = disp_axis + self.mask_treatment = mask_treatment + + # IMPLEMENTATION NOTES: the code assumes that the image-parsing routines ensure that the + # cross-dispersion axis is aligned with the y-axis (1st array dimension) and the + # dispersion axis with the x-axis (2nd array dimension). However, this should not be + # visible to the end-user. The rectified spectra are returned with the original axis + # alignment given by the ``disp_axis`` argument. Also, I've decided to use `x` and `y` + # naming instead of `col` and `row` because this leads to (slightly) more readable code. + # The methods that are visible to the end-user use `disp` and `cdisp` naming. -HP + + # An ugly hack that should be changed after the refactoring of image parsing. + ip = _ImageParser() + self.arc_frames = [] + for f in arc_frames: + im = ip._parse_image(f, disp_axis=disp_axis, mask_treatment=mask_treatment) + self.arc_frames.append(NDData(im.flux, uncertainty=im.uncertainty, mask=im.mask)) + self.arc_frames = arc_frames self.nframes = len(arc_frames) - self.n_cd_pix = self.arc_frames[0].data.shape[0] + self._ny, self._nx = self.arc_frames[0].data.shape self._lines_ref: Sequence[ndarray] | None = None self._samples_rec_x: Sequence[ndarray] | None = None @@ -87,11 +135,11 @@ def __init__( self._shift = models.Shift(-self.ref_pixel[0]) & models.Shift(-self.ref_pixel[1]) - # The rectified space -> detectoir space transform + # The rectified space -> detector space transform self._r2d: Model | None = None # Calculate the cross-dispersion axis sample positions - slims = cd_sample_lims if cd_sample_lims is not None else (0, self.n_cd_pix) + slims = cd_sample_lims if cd_sample_lims is not None else (0, self._ny) if cd_samples is not None: self.cd_samples = np.array(cd_samples) else: @@ -282,14 +330,14 @@ def _calculate_derivative(self): if self._r2d is not None: self._r2d_dxdx = self._shift | diff_poly2d_x(self._r2d[-1]) - def rec_to_det(self, col: ndarray, row: ndarray) -> tuple[ndarray, ndarray]: + def rec_to_det(self, disp: ndarray, cdisp: ndarray) -> tuple[ndarray, ndarray]: """Transform coordinates from the rectified space to detector space. Parameters ---------- - col : ndarray + disp : ndarray The dispersion-axis coordinates to be transformed. - row : ndarray + cdisp : ndarray The cross-dispersion coordinates, returned as is. Returns @@ -298,11 +346,11 @@ def rec_to_det(self, col: ndarray, row: ndarray) -> tuple[ndarray, ndarray]: A tuple containing the transformed dispersion-axis coordinates as the first element and the original cross-dispersion-axis coordinates as the second element.. """ - return self._r2d(col, row), row + return self._r2d(disp, cdisp), cdisp def rectify( self, - flux: ndarray, + flux: NDData, nbins: int | None = None, bounds: tuple[float, float] | None = None, bin_edges: Iterable[float] | None = None, @@ -310,36 +358,40 @@ def rectify( """Resample a 2D spectrum from the detector space to a rectified space. Resample a 2D spectrum from the detector space to a rectified space where the wavelength - is constant along the rows. The grid edges are based on the specified number of bins, - bounds, or bin edges. The resampling is eaxct and conserves flux (as long as the - rectified space covers the whole detector space.) + is constant along the cross-dispersion axis. The grid edges are based on the specified + number of bins, bounds, or bin edges. The resampling is exact and conserves flux (as + long as the rectified space covers the whole detector space.) Parameters ---------- flux - 2D array representing the flux values of the distorted input image. The first - dimension corresponds to rows (typically the y-axis), and the second dimension - corresponds to columns (typically the x-axis). + 2D spectrum as an NDData instance. The dispersion and cross-dispersion axis alignment + should be the same as in the arc frames. nbins Number of bins in the rectified space. If None, the number of bins will be set to the number of columns in the `flux` input image. bound Tuple specifying the start and end coordinates for the rectified space along the - x-axis. If None, the bounds default to (0, number of columns in `flux`). + x-axis. If None, the bounds default to (0, number of columns in ``flux``). bin_edges Explicitly provided edges of the bins in the rectified space. If None, bin - edges are automatically calculated as a uniform grid based on `nbins` and - `bounds`. + edges are automatically calculated as a uniform grid based on ``nbins`` and + ``bounds``. Returns ------- rectified_flux : ndarray - 2D array containing the flux values rectified into the uniform grid defined - by `nbins`, `bounds`, or `bin_edges`. The output has the same number of rows - as the input `flux`, and its second dimension corresponds to the number of - rectified bins. + NDData instance containing the flux values rectified into the uniform grid + defined by ``nbins``, ``bounds``, or ``bin_edges``. """ - ny, nx = flux.shape + + # TODO: In the future, we want to make sure that we don't copy the data unless absolutely + # necessary. + ip = _ImageParser() + im = ip._parse_image(flux, disp_axis=self.disp_axis, mask_treatment=self.mask_treatment) + flux = im.flux + + ny, nx = flux.data.shape ypix = np.arange(ny) nbins = nx if nbins is None else nbins l1, l2 = bounds if bounds is not None else (0, nx) @@ -396,27 +448,30 @@ def rectify( # Apply the normalization factor to conserve flux rectified_flux *= n[:, None] - return rectified_flux + if self.disp_axis == 0: + rectified_flux = rectified_flux.T + + return NDData(rectified_flux*im.unit) def plot_wavelength_contours( self, - ncol: int = 50, - nrow: int = 100, - cols: Sequence[float] | None = None, + ndisp: int = 50, + ncdisp: int = 100, + disp_values: Sequence[float] | None = None, ax: plt.Axes | None = None, figsize: tuple[float, float] | None = None, - line_args: dict | None =None, + line_args: dict | None = None, ): """Plot wavelength contour lines in detector space. Parameters ---------- - ncol - The number of columns in the grid. - nrow - The number of rows in the grid. - cols - A sequence specifying the x-coordinates of the grid columns. If not + ndisp + The number of dispersion-axis lines. + ncdisp + The number of cross-dispersion axis points for each disp-axis line. + disp_values + A sequence specifying the dispersion-axis coordinates explicitly. If not provided, it will be automatically calculated based on the arc frame dimensions. ax @@ -436,7 +491,7 @@ def plot_wavelength_contours( The Matplotlib figure containing the plot. If an Axes instance is passed to `ax`, the associated figure is returned. """ - largs = {'c': 'k', 'lw': 0.5, 'alpha': 0.5, 'ls':'--'} + largs = {"c": "k", "lw": 0.5, "alpha": 0.5, "ls": "--"} if line_args is not None: largs.update(line_args) @@ -445,13 +500,13 @@ def plot_wavelength_contours( else: fig = ax.figure - if cols is None: - cols = tile(np.linspace(0, self.arc_frames[0].data.shape[1], ncol), (nrow, 1)) + if disp_values is None: + disp_values = tile(np.linspace(0, self.arc_frames[0].data.shape[1], ndisp), (ncdisp, 1)) else: - ncol = len(cols) - rows = tile(np.linspace(0, self.arc_frames[0].data.shape[0], nrow)[:, None], (1, ncol)) + ndisp = len(disp_values) + rows = tile(np.linspace(0, self.arc_frames[0].data.shape[0], ncdisp)[:, None], (1, ndisp)) - ax.plot(self._r2d(cols, rows), rows, **largs) + ax.plot(self._r2d(disp_values, rows), rows, **largs) return fig def plot_fit_quality( From bf503bf8dff43cb63e75db561fb619b820670b8c Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 15 May 2025 14:17:18 +0100 Subject: [PATCH 65/76] Added basic unit tests for TiltCorrection. Cover that the procedure runs, but must be improved to ensure that the results are correct. --- specreduce/tests/test_tilt_correction.py | 116 +++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 specreduce/tests/test_tilt_correction.py diff --git a/specreduce/tests/test_tilt_correction.py b/specreduce/tests/test_tilt_correction.py new file mode 100644 index 00000000..e88cad17 --- /dev/null +++ b/specreduce/tests/test_tilt_correction.py @@ -0,0 +1,116 @@ +import numpy as np +import pytest +from astropy.modeling import models +from astropy.nddata import NDData, StdDevUncertainty +from astropy.wcs import WCS + +from specreduce.tilt_correction import TiltCorrection, diff_poly2d_x +from specreduce.utils.synth_data import make_2d_arc_image + +# Arc frame creation code taken from Tim Pickering's example notebook +@pytest.fixture +def mk_arc_frames(): + + blue_channel_header = { + "CTYPE1": "AWAV-GRA", # Grating dispersion function with air wavelengths + "CUNIT1": "Angstrom", # Dispersion units + "CRPIX1": 1344 // 2, # Reference pixel [pix] + "CRVAL1": 5410, # Reference value [Angstrom] + "CDELT1": 1.19 * 2, # Linear dispersion [Angstrom/pix] + "PV1_0": 5.0e5, # Grating density [1/m] + "PV1_1": 1, # Diffraction order + "PV1_2": 8.05, # Incident angle [deg] + "CTYPE2": "PIXEL", # Spatial detector coordinates + "CUNIT2": "pix", # Spatial units + "CRPIX2": 1, # Reference pixel + "CRVAL2": 0, # Reference value + "CDELT2": 1, # Spatial units per pixel + } + blue_channel_wcs = WCS(header=blue_channel_header) + + tilt_mod = models.Legendre1D(degree=2, c0=25, c1=0, c2=50) + + arcs = [] + for ll in (["HeI", "NeI", "XeI"], ["ArI"]): + arc = make_2d_arc_image( + nx=512, + ny=128, + linelists=ll, + wcs=blue_channel_wcs, + line_fwhm=3, + tilt_func=tilt_mod, + amplitude_scale=1e-2, + ) + arc.wcs = None + arc.uncertainty = StdDevUncertainty(np.full_like(arc.data, 5)) + arcs.append(arc) + return arcs + + +def test_diff_poly2d_x_valid_derivative(): + model = models.Polynomial2D(degree=2, c0_0=1, c1_0=2, c2_0=3, c0_1=4, c1_1=5, c0_2=6) + derivative = diff_poly2d_x(model) + assert derivative.degree == 1 + assert derivative.c0_0 == 2 + assert derivative.c1_0 == 6 + assert derivative.c0_1 == 5 + + +def test_diff_poly2d_x_zero_x_coefficients(): + model = models.Polynomial2D(degree=2, c0_0=1, c0_1=2, c0_2=3) + derivative = diff_poly2d_x(model) + assert derivative.degree == 1 + assert derivative.c0_0 == 0 # All x coefficients are zero, so derivative is zero + assert derivative.c0_1 == 0 + + +def test_init_default_params(mk_arc_frames): + tilt_correction = TiltCorrection(ref_pixel=(128, 64), arc_frames=mk_arc_frames) + assert tilt_correction.ref_pixel == (128, 64) + assert tilt_correction.disp_axis == 1 + assert tilt_correction.mask_treatment == "apply" + assert len(tilt_correction.arc_frames) == 2 + + +def test_find_lines(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + np.testing.assert_array_equal(tc.cd_samples, np.array([14, 28, 43, 57, 71, 85, 100, 114])) + + +def test_fit(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + + +def test_plot_fit_quality(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + tc.plot_fit_quality() + + +def test_plot_wavelength_contours(mk_arc_frames): + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=mk_arc_frames, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + tc.plot_wavelength_contours() + + +def test_rectify(mk_arc_frames): + arcs = mk_arc_frames + tc = TiltCorrection( + ref_pixel=(256, 64), arc_frames=arcs, cd_sample_lims=(0, 128), n_cd_samples=8 + ) + tc.find_arc_lines(3.0, 5.0) + tc.fit(4) + tc.rectify(arcs[0]) From e7d399d5afaff3fda649ab5351461faebebb825b Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 15 May 2025 14:19:34 +0100 Subject: [PATCH 66/76] Updated docs related to tilt correction. --- docs/index.rst | 2 +- ..._correction.ipynb => osiris_example.ipynb} | 231 ++++++++++-------- docs/tilt_correction/tilt_correction.rst | 39 +++ 3 files changed, 170 insertions(+), 102 deletions(-) rename docs/tilt_correction/{tilt_correction.ipynb => osiris_example.ipynb} (79%) create mode 100644 docs/tilt_correction/tilt_correction.rst diff --git a/docs/index.rst b/docs/index.rst index 4c479ffa..e1422330 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -50,7 +50,7 @@ Calibration .. toctree:: :maxdepth: 1 - tilt_correction/tilt_correction.ipynb + tilt_correction/tilt_correction.rst wavelength_calibration/wavelength_calibration.rst extinction.rst specphot_standards.rst diff --git a/docs/tilt_correction/tilt_correction.ipynb b/docs/tilt_correction/osiris_example.ipynb similarity index 79% rename from docs/tilt_correction/tilt_correction.ipynb rename to docs/tilt_correction/osiris_example.ipynb index 6f9acf30..6f972d57 100644 --- a/docs/tilt_correction/tilt_correction.ipynb +++ b/docs/tilt_correction/osiris_example.ipynb @@ -4,41 +4,28 @@ "metadata": {}, "cell_type": "markdown", "source": [ - "# Tilt Correction\n", + "# Tilt Correction Tutorial 1\n", "\n", - "In astronomical spectroscopy, tilt correction is a calibration step that addresses optical\n", - "distortions and misalignments in spectroscopic instruments. These distortions cause wavelength\n", - "not be constant along the cross-dispersion (spatial) axis: spectral features appear tilted or\n", - "curved across the detector instead of being perfectly aligned with detector columns.\n", - "\n", - "Tilt correction is carried out using a two-dimensional tilt function that describes how wavelength\n", - "positions shift across the spatial axis. The tilt function can be estimated from arc lamp\n", - "spectra, by measuring how the calibration line centroids vary along the cross-dispersion axis.\n", - "\n", - "This tilt function can then be used to transform the two-dimensional spectroscopy images so that\n", - "wavelengths are aligned along straight lines parallel to the detector axes (known as 2D\n", - "rectification). This is particularly important for accurate wavelength calibration and robust sky\n", - " subtraction.\n", - "\n", - "In `specreduce`, the tilt function is represented as a 2D polynomial (an `astropy.modeling.models\n", - ".Polynomial2d` instance) of a given degree. The `specreduce.tilt_correction.TiltCorrection` class\n", - " implements tilt correction by:\n", - "\n", - "1. Identifying emission lines in an arc lamp spectrum (or multiple arc lamp spectra)\n", - "2. Fitting a 2D polynomial model to characterize the geometric distortion\n", - "3. Computing a transformation that maps the tilted features to straight lines\n", - "4. Applying this transformation to rectify the science frames\n", + "This tutorial demonstrates 2D tilt correction (rectification) using three arc lamp\n", + "spectra (HgAr, Ne, and Xe) and a single long-slit science frame, all observed with the\n", + "[Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/) at the\n", + "[Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/) using the R1000R grism\n", + "configuration in 2012.\n", "\n", - "A single arc frame can often suffice, but the use of multiple arc lamps with different emission\n", - "line patterns helps constrain the transformation across the full wavelength range of the spectrograph." + "OSIRIS, which operated until 2023 before being upgraded to OSIRIS+, featured two 2048 x 4096\n", + "pixel Marconi CCD detectors. For simplicity and file size considerations, we use data from just\n", + " one CCD that has been binned to 512 x 1024 pixels." ], - "id": "a86e5f303125a87" + "id": "35f5a105fadc80e7" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:38.741985Z", + "start_time": "2025-05-14T11:50:37.511090Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -50,43 +37,52 @@ "plt.rc('figure', figsize=(6.3, 2))\n", "plt.rc('font', size=8)" ], - "id": "ba005ff705baee1e" + "id": "744ee1d9a9ffd1ad", + "outputs": [], + "execution_count": 1 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "## Read in the Arc Frames and Object Frame\n", - "\n", - "This tutorial demonstrates 2D tilt correction (rectification) using three arc lamp\n", - "spectra observed with the [Gran Telescopio Canarias (GTC)](https://www.gtc.iac.es/)\n", - "[Osiris spectrograph](https://www.gtc.iac.es/instruments/osiris/). The arcs (HgAr, Ne, and Xe)\n", - "were observed with the OSIRIS R1000R grism configuration, which covers approximately 5100-10000 Ã…\n", - "at moderate resolution.\n", + "## 1. Read in the Arc Frames and Object Frame\n", "\n", - "This example uses three arc lamp calibration frames and a single long-slit science frame, all observed with the OSIRIS instrument at the Gran Telescopio Canarias (GTC) in 2012. OSIRIS, which operated until 2023 before being upgraded to OSIRIS+, featured two 2048 x 4096 pixel Marconi CCD detectors. For simplicity and file size considerations, we use data from just one CCD that has been binned to 512 x 1024 pixels.\n", + "We have hidden the data reading in `common.read_data` utility function as not to distract from\n", + "the main topic. The function returns a tuple of three arc frames as bias-subtracted `CCDData`\n", + "instances, a tuple containing the arc lamp names, and a single bias-subtracted `CCDData` instance\n", + "containing the science data. (We are cutting some corners what comes to basic data reduction and\n", + "skipping the steps not absolutely necessary for tilt correction, such as flat field correction,\n", + "but normally you would want to do these basic steps before.)\n", "\n", - "OSIRIS spectra exhibit significant tilt distortion that must be corrected to achieve reliable background subtraction. The example science frame comes from a time-series of spectroscopic observations taken during an exoplanet transit (transmission spectroscopy). The scientific importance of proper tilt correction is highlighted by the original analysis of this dataset - insufficient correction of the tilt distortion led to residual time variations in telluric absorption lines that compromised the scientific results.\n" + "Let's read in the data and take a look at it." ], - "id": "4bb3dc3c78942883" + "id": "7660d1df03a95655" }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:39.349943Z", + "start_time": "2025-05-14T11:50:38.887669Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ "arcs, lamps, obj = read_data()\n", "frames = arcs + [obj]\n", "labels = lamps + ('Target',)" ], - "id": "f218ac53060dd4a" + "id": "3e40fd913a645ec5", + "outputs": [], + "execution_count": 2 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-14T11:50:41.035690Z", + "start_time": "2025-05-14T11:50:40.431249Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ "fig, axs = plt.subplots(4, figsize=(6.3, 5), sharex='all', constrained_layout=True)\n", "for i,d in enumerate(frames):\n", @@ -96,22 +92,47 @@ "plt.setp(axs, ylabel='CD axis [pix]')\n", "plt.setp(axs[-1], xlabel='Dispersion axis [pix]');" ], - "id": "2f00cd7e37525ab1" + "id": "f7681e5a755a40d1", + "outputs": [ + { + "data": { + "text/plain": [ + "
    " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 }, { "cell_type": "markdown", "id": "ea64070e-7884-410e-9c43-01dcb6f101db", "metadata": {}, "source": [ - "## Initialize the Tilt Correction\n", + "We immediately see that OSIRIS spectra exhibit significant tilt distortion that must be corrected\n", + "to achieve reliable background subtraction. The example science frame comes from a time-series\n", + "of spectroscopic observations taken during an exoplanet transit (transmission spectroscopy). The\n", + "scientific importance of proper tilt correction is highlighted by the original analysis of this\n", + "dataset [(Parviainen et al. 2016)](https://ui.adsabs.harvard.edu/abs/2016A%26A...585A.114P/abstract):\n", + "insufficient correction of the tilt distortion led to residual time variations in\n", + "telluric absorption lines that compromised the scientific results.\n", + "\n", + "\n", + "## 2. Initialize the Tilt Correction Class\n", "\n", "The `TiltCorrection` class is initialized with the following parameters:\n", "\n", - "- Reference pixel: `(500, 300)` -\n", - "- Arc lamp data: List of `CCDData` objects containing arc lamp spectra \n", - "- Cross-dispersion sampling:\n", - " - Sample limits: `(50, 500)` - pixel range for sampling the cross-dispersion direction\n", - " - Number of samples: `8` - number of points to sample along cross-dispersion\n", + "- ``ref_pixel``: Reference pixel coordinate near detector center. The polynomial\n", + " fit centers around this pixel to improve numerical stability.\n", + "- ``arc_frames``: Either a single `CCDData` instance or a list of `CCDData` instances containing\n", + " the arc lamp spectra. These should be bias-subtracted and include uncertainties for the\n", + " internal line finding routine to work reliably.\n", + "- ``cd_sample_lims``: Pixel range for sampling the cross-dispersion direction.\n", + "- ``n_cd_samples``: Number of points to sample along cross-dispersion.\n", + "- ``cd_samples``: A list of cross-dispersion locations to use. Overrides ``n_cd_samples`` if provided.\n", "\n", "The initialization sets up the transformation model that will be used to map tilted spectral features to straight lines." ] @@ -121,30 +142,27 @@ "id": "5e4b9637-a3b8-4805-8908-8ba6b50d713f", "metadata": { "ExecuteTime": { - "end_time": "2025-05-13T09:46:29.633541Z", - "start_time": "2025-05-13T09:46:29.630908Z" + "end_time": "2025-05-14T11:50:44.337551Z", + "start_time": "2025-05-14T11:50:44.334742Z" } }, "source": "s = TiltCorrection((500, 300), arcs, cd_sample_lims=(50, 500), n_cd_samples=7)", "outputs": [], - "execution_count": 23 + "execution_count": 4 }, { "metadata": {}, "cell_type": "markdown", "source": [ - "## Find Arc Lines\n", + "## 3. Find Arc Lines\n", "\n", - "After initializing the `TiltCorrection` object, the `find_arc_lines()` method detects emission peaks in the arc lamp spectra. This method takes a threshold parameter (in this case 5.0) to find significant emission lines above the background level.\n", + "The `find_arc_lines()` method is used after initializing the `TiltCorrection` object to detect emission peaks in arc lamp spectra. The detection process employs the `specutils.fitting.find_lines_threshold` routine, which uses parameters for the expected line full-width half-maximum (FWHM) and noise threshold to identify significant emission lines above the background.\n", "\n", - "The detected lines are used as reference points for mapping the spectral tilt and curvature across the detector. For each input arc lamp (HgAr, Ne, Xe), the method:\n", + "The process begins by identifying lines at the reference row, which are stored separately. The\n", + "method then detects lines at specified cross-dispersion locations to generate a set of 2D points\n", + "in the *detector space*. These detector space points are essential for mapping the spectral tilt and curvature patterns across the detector. The points are stored in a kd-tree for each arc frame and will be used in the model fitting process.\n", "\n", - "1. Analyzes spectra at multiple cross-dispersion positions\n", - "2. Identifies emission peaks above the threshold\n", - "3. Records the line positions to be used as fiducial points\n", - "4. Builds the dataset needed for fitting the geometric transformation\n", - "\n", - "This step is crucial as the identified lines form the basis for calculating the correction that will straighten tilted spectral features.\n" + "The *rectified space* is defined by the points found in the reference row. The 2D rectification process maps the *detector space* columns to align with the wavelengths in the reference row across all elements (rows) of the cross-dispersion axis.\n" ], "id": "67439310-4340-4d7f-9bea-c41dafef371b" }, @@ -153,19 +171,30 @@ "id": "3b1815ec-bbc6-43fb-9130-ffd8108ce003", "metadata": { "ExecuteTime": { - "end_time": "2025-05-13T09:46:32.199926Z", - "start_time": "2025-05-13T09:46:30.196093Z" + "end_time": "2025-05-14T11:50:49.281261Z", + "start_time": "2025-05-14T11:50:47.282875Z" } }, - "source": "s.find_arc_lines(2.5, noise_factor=10)", + "source": "s.find_arc_lines(fwhm=2.5, noise_factor=10)", "outputs": [], - "execution_count": 24 + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Let's take a look at the data to see how we track the spectral lines using both reference-row\n", + "samples and detector-space samples across our arc frames. By using kd-trees, we can match up\n", + "spectral lines successfully even if they're not found at every cross-dispersion sample point.\n", + "This approach helps us get a solid final fit, handling any outliers that pop up in the data.\n" + ], + "id": "67bf1f15916b10c1" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-05-13T09:46:45.372608Z", - "start_time": "2025-05-13T09:46:44.970984Z" + "end_time": "2025-05-14T11:50:50.754949Z", + "start_time": "2025-05-14T11:50:50.361300Z" } }, "cell_type": "code", @@ -193,14 +222,14 @@ "output_type": "display_data" } ], - "execution_count": 26 + "execution_count": 6 }, { "cell_type": "markdown", "id": "fbf4d669-f594-4447-a7d3-ae7f153418bd", "metadata": {}, "source": [ - "## Fit the model\n", + "## 4. Fit the model\n", "\n", "The `fit()` method is used to calculate the geometric transformation that maps tilted spectral features to straight lines. This method:\n", "\n", @@ -218,21 +247,31 @@ "id": "def37f07-78f1-43a2-b012-3a38b6ea7566", "metadata": { "ExecuteTime": { - "end_time": "2025-05-13T09:46:50.561746Z", - "start_time": "2025-05-13T09:46:49.793583Z" + "end_time": "2025-05-14T12:03:26.500872Z", + "start_time": "2025-05-14T12:03:25.619511Z" } }, "source": [ "s.fit(degree=4)" ], "outputs": [], - "execution_count": 27 + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Let's examine how well our transformation fits the data using the `TiltCorrection.plot_fit_quality` method. This visualization displays both 2D and 1D residuals between the measured detector-space points and the transformed reference points.\n", + "\n", + "Our fit shows good accuracy across the detector. While the data contains some outlier points, they don't notably affect the overall fit quality in this example. If outliers do become problematic, we can enhance the transformation model by using `TiltCorrection.refine_fit()` with a tighter `match_distance_bound` parameter (such as 0.5 pixels). This refinement would focus the fit on only the most reliable emission line positions, ensuring better accuracy in the final transformation.\n" + ], + "id": "e4dd47b844051180" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-05-13T09:46:50.817935Z", - "start_time": "2025-05-13T09:46:50.575640Z" + "end_time": "2025-05-14T12:03:26.758088Z", + "start_time": "2025-05-14T12:03:26.523009Z" } }, "cell_type": "code", @@ -246,12 +285,18 @@ ], "image/png": 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" }, - "execution_count": 28, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 28 + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "You can see the transform directly in detector space with the `TiltCorrection.plot_wavelength_contours` method. This method creates an informative visualization by overlaying contour lines showing where wavelengths remain constant across the detector surface.\n", + "id": "a3e158126376faeb" }, { "metadata": { @@ -286,26 +331,13 @@ ], "execution_count": 30 }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "While there are some outliers present in the data, they don't appear to significantly impact the quality of the fit. If these outliers were causing issues with the fit accuracy, we could further refine the transformation model by calling `TiltCorrection.refine_fit()` with a restrictive `match_distance_bound` parameter (e.g., 0.5 pixels) to exclude these outlying points from the fit. This would ensure that only well-matched emission line positions are used to determine the transformation.\n", - "id": "d1345c25a71d68d8" - }, { "metadata": {}, "cell_type": "markdown", "source": [ - "### Rectify the frames\n", + "## 5. Rectify the frames\n", "\n", - "After fitting the transformation model, we can apply it to rectify the spectral frames using the `TiltCorrection.rectify()` method. This method:\n", - "\n", - "1. Takes the input frame data as a CCData object.\n", - "2. Applies the fitted geometric transformation to rebin the pixels using an exact flux-conserving\n", - " approach.\n", - "3. Returns a new CCDData object where spectral features are aligned with detector rows/columns.\n", - "\n", - "In this example, we apply the rectification both to the arc lamp calibration frames and the science target frame. The resulting rectified frames show spectral features that are properly aligned horizontally, making subsequent analysis steps like wavelength calibration and spectral extraction more straightforward and accurate.\n" + "Now that we have our fitted transformation model, we can rectify the spectral frames with the `TiltCorrection.rectify()` method. The process is straightforward - the method takes a CCData object containing the input frame data and applies the fitted geometric transformation to rebin the pixels. This rebinning uses an exact flux-conserving approach to ensure data quality. The output is a new CCDData object where all spectral features line up with the detector rows and columns." ], "id": "18b01edd419fa004" }, @@ -359,11 +391,8 @@ { "metadata": {}, "cell_type": "markdown", - "source": [ - "Let's still compare spectral features before and after rectification by examining emission lines.\n", - "The plot shows the reference row spectrum from the original (non-rectified) arc frame overlaid with spectra from different rows of the rectified frame. The alignment of spectral features across multiple rows in the rectified data demonstrates successful correction of the tilt, with emission line positions matching to sub-pixel precision as expected from a proper rectification.\n" - ], - "id": "8c85cdf028427f3d" + "source": "Let's check how well our rectification worked by comparing emission line positions. Here we overlay the reference row spectrum from the original arc frame with spectra sampled from different rows in the rectified data. You can see the spectral features line up beautifully across various rows after rectification - the emission lines match with sub-pixel precision, showing our tilt correction worked perfectly.\n", + "id": "7380d9d0da0475ca" }, { "cell_type": "code", diff --git a/docs/tilt_correction/tilt_correction.rst b/docs/tilt_correction/tilt_correction.rst new file mode 100644 index 00000000..82745609 --- /dev/null +++ b/docs/tilt_correction/tilt_correction.rst @@ -0,0 +1,39 @@ +Tilt Correction +=============== + +In astronomical spectroscopy, tilt correction is a calibration step that addresses optical +distortions and misalignments in spectroscopic instruments. These distortions cause wavelength +to vary along the cross-dispersion (spatial) axis, resulting in spectral features appearing +tilted or curved across the detector rather than being perfectly aligned with detector columns. + +Tilt correction is performed by modeling a two-dimensional tilt function that describes how +wavelength positions shift across the spatial axis. This function can be determined empirically +from arc lamp calibration spectra by measuring how the centroids of emission lines vary along +the cross-dispersion axis. + +Once characterized, the tilt function enables transformation of two-dimensional spectroscopic +images so that wavelengths become aligned along straight lines parallel to the detector axes (a +process known as 2D rectification). This alignment is essential for achieving accurate +wavelength calibration and performing robust sky subtraction. + +In the `specreduce` package, the tilt function is represented as a 2D polynomial using an +`~astropy.modeling.models.Polynomial2D` instance of a specified degree. The +`~specreduce.tilt_correction.TiltCorrection` class implements this correction through several steps: + +1. Identifying emission lines in one or more arc lamp calibration spectra for a given number of + cross-dispersion sample positions +2. Fitting a 2D polynomial model to characterize the geometric distortion +3. Computing a transformation that maps the tilted features to straight lines +4. Applying this transformation to rectify the observed frames + + +Tutorials +--------- + +The following tutorial provides hands-on examples demonstrating the usage of the +`~specreduce.tilt_correction.TiltCorrection` class. + +.. toctree:: + :maxdepth: 1 + + osiris_example.ipynb From f8e92b44096f90989cb752a0fea5c8451bd6bc26 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 15 May 2025 14:20:20 +0100 Subject: [PATCH 67/76] Codestyle fixes. --- specreduce/tests/test_tilt_correction.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/specreduce/tests/test_tilt_correction.py b/specreduce/tests/test_tilt_correction.py index e88cad17..66e20066 100644 --- a/specreduce/tests/test_tilt_correction.py +++ b/specreduce/tests/test_tilt_correction.py @@ -1,12 +1,13 @@ import numpy as np import pytest from astropy.modeling import models -from astropy.nddata import NDData, StdDevUncertainty +from astropy.nddata import StdDevUncertainty from astropy.wcs import WCS from specreduce.tilt_correction import TiltCorrection, diff_poly2d_x from specreduce.utils.synth_data import make_2d_arc_image + # Arc frame creation code taken from Tim Pickering's example notebook @pytest.fixture def mk_arc_frames(): From a4c98642ebf1a34d4ff4d416c6de2baba30d7ae2 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 15 May 2025 14:26:53 +0100 Subject: [PATCH 68/76] Documentation fixes. --- docs/tilt_correction/tilt_correction.rst | 2 +- specreduce/tilt_correction.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/tilt_correction/tilt_correction.rst b/docs/tilt_correction/tilt_correction.rst index 82745609..43f92e37 100644 --- a/docs/tilt_correction/tilt_correction.rst +++ b/docs/tilt_correction/tilt_correction.rst @@ -17,7 +17,7 @@ process known as 2D rectification). This alignment is essential for achieving ac wavelength calibration and performing robust sky subtraction. In the `specreduce` package, the tilt function is represented as a 2D polynomial using an -`~astropy.modeling.models.Polynomial2D` instance of a specified degree. The +``~astropy.modeling.models.Polynomial2D`` instance of a specified degree. The `~specreduce.tilt_correction.TiltCorrection` class implements this correction through several steps: 1. Identifying emission lines in one or more arc lamp calibration spectra for a given number of diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index 4a28361a..d188337f 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -14,6 +14,8 @@ from specreduce.core import _ImageParser from specreduce.line_matching import find_arc_lines +__all__ = ["TiltCorrection"] + def diff_poly2d_x(model: models.Polynomial2D) -> models.Polynomial2D: """Compute the partial derivative of a 2D polynomial model with respect to x. @@ -451,7 +453,7 @@ def rectify( if self.disp_axis == 0: rectified_flux = rectified_flux.T - return NDData(rectified_flux*im.unit) + return NDData(rectified_flux * im.unit) def plot_wavelength_contours( self, From bf4b47c38958773e951d61a117e36fc13b6034a3 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Thu, 15 May 2025 14:31:49 +0100 Subject: [PATCH 69/76] Documentation hack. Needs to be addressed correctly. --- docs/tilt_correction/tilt_correction.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/tilt_correction/tilt_correction.rst b/docs/tilt_correction/tilt_correction.rst index 43f92e37..9a63c12a 100644 --- a/docs/tilt_correction/tilt_correction.rst +++ b/docs/tilt_correction/tilt_correction.rst @@ -18,7 +18,8 @@ wavelength calibration and performing robust sky subtraction. In the `specreduce` package, the tilt function is represented as a 2D polynomial using an ``~astropy.modeling.models.Polynomial2D`` instance of a specified degree. The -`~specreduce.tilt_correction.TiltCorrection` class implements this correction through several steps: +``~specreduce.tilt_correction.TiltCorrection`` class implements this correction through several +steps: 1. Identifying emission lines in one or more arc lamp calibration spectra for a given number of cross-dispersion sample positions @@ -31,7 +32,7 @@ Tutorials --------- The following tutorial provides hands-on examples demonstrating the usage of the -`~specreduce.tilt_correction.TiltCorrection` class. +``~specreduce.tilt_correction.TiltCorrection`` class. .. toctree:: :maxdepth: 1 From f722a3ccb2fb4caba7fe40131165e7357d29fc86 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 17 Jun 2025 13:23:57 +0100 Subject: [PATCH 70/76] Removed an old Jupyter Notebook file. --- notebook_sandbox/wavecal_1d_shane.ipynb | 283 ------------------------ 1 file changed, 283 deletions(-) delete mode 100644 notebook_sandbox/wavecal_1d_shane.ipynb diff --git a/notebook_sandbox/wavecal_1d_shane.ipynb b/notebook_sandbox/wavecal_1d_shane.ipynb deleted file mode 100644 index c876312d..00000000 --- a/notebook_sandbox/wavecal_1d_shane.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "051529f9-7caf-428d-969c-51aa6c2f8f83", - "metadata": {}, - "source": [ - "# Specreduce 1D wavelength calibration example 1\n", - "\n", - "**Author:** Hannu Parviainen
    \n", - "**Modified:** 16 Jan. 2025" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "463038ad-ae02-4484-8226-36df99fbf810", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7853ed1c-5b05-42a2-9413-4054769c6032", - "metadata": {}, - "outputs": [], - "source": [ - "import astropy.units as u\n", - "import numpy as np\n", - "\n", - "from astropy.io.fits import getdata\n", - "from astropy.nddata import StdDevUncertainty\n", - "from matplotlib.pyplot import setp, subplots, close, rc\n", - "from specutils import Spectrum1D\n", - "\n", - "from specreduce.lswavecal1d import WavelengthSolution1D\n", - "\n", - "rc('figure', figsize=(11,3))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e3dd2a51-ef1a-4501-9bfb-62166676e88f", - "metadata": {}, - "outputs": [], - "source": [ - "flux = getdata('shane_kast_blue_600_4310_d55.fits', 1).astype('d')\n", - "arc_spectrum = Spectrum1D(flux*u.DN, uncertainty=StdDevUncertainty(2*np.sqrt(flux)))" - ] - }, - { - "cell_type": "markdown", - "id": "bf602a55-6b4b-4556-95f4-4f448cf39dd1", - "metadata": {}, - "source": [ - "### Initialize the wavelength solution and find the lines" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a130b965-4563-4959-8dc9-906445f5f07e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 81.1 ms, sys: 2.95 ms, total: 84 ms\n", - "Wall time: 84.9 ms\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%time\n", - "ws = WavelengthSolution1D(arc_spectra=arc_spectrum, line_lists=[['CdI', 'HgI', 'HeI']], wlbounds=(3200, 5700))\n", - "ws.find_lines(fwhm=4)\n", - "ws.plot_lines();" - ] - }, - { - "cell_type": "markdown", - "id": "e19324ce-7bd1-4d67-af52-f9278d949b26", - "metadata": {}, - "source": [ - "### Fit a pixel-wavelength transformation using the observed and theoretical line lists" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "84bae06f-fbec-4952-9a89-2d50d685deee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 493 ms, sys: 4.06 ms, total: 498 ms\n", - "Wall time: 498 ms\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%time\n", - "ws.fit(ref_pixel=1024, wavelength_bounds=(4400, 4500), dispersion_bounds=(1.0, 1.05), popsize=50)\n", - "ws.plot_solution();" - ] - }, - { - "cell_type": "markdown", - "id": "3daa7d49-c561-428e-a84a-dbb4d78fa405", - "metadata": {}, - "source": [ - "### Plot the residuals" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "12951d19-e8ae-4294-969f-06853ccd35b2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ws.plot_residuals(space='wavelength');" - ] - }, - { - "cell_type": "markdown", - "id": "5bcdadaa-baed-4676-94b5-ed04b0eca310", - "metadata": {}, - "source": [ - "### Plot the transformations" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2cc6f89f-39c6-49c3-8e23-0e26b7d2e951", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ws.plot_transforms(figsize=(11, 6));" - ] - }, - { - "cell_type": "markdown", - "id": "983fe041-41e9-47a7-a179-fead64e171ba", - "metadata": {}, - "source": [ - "### Rebin the spectrum to a linear spacing in wavelength space" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3640513e-b36f-40f6-b5f3-61b40e4b2766", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 25.7 ms, sys: 5.92 ms, total: 31.6 ms\n", - "Wall time: 28.2 ms\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%time\n", - "spectrum_wl = ws.resample(arc_spectrum)\n", - "fig, ax = subplots(constrained_layout=True)\n", - "ax.plot(spectrum_wl.spectral_axis, spectrum_wl.flux);" - ] - }, - { - "cell_type": "markdown", - "id": "4d21e847-568b-4c90-a945-205b16332f5e", - "metadata": {}, - "source": [ - "---" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 9fb67cef5aa12c422e6fa3816215071ddf83ef1b Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 17 Jun 2025 13:24:55 +0100 Subject: [PATCH 71/76] Added `matplotlib>=3.7` as a testing dependency in `pyproject.toml`. --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index f0943d82..315c06cc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ test = [ "pytest-astropy", "photutils>=1.0", + "matplotlib>=3.7", "tox", ] docs = [ From 77c63b87eddf2dd570cdf83113de20ceef6aa5fc Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 17 Jun 2025 13:35:29 +0100 Subject: [PATCH 72/76] Replaced `Spectrum1D` with `Spectrum` throughout `wavecal1d.py` for compatibility updates. --- specreduce/wavecal1d.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 1eb84887..fa02e65d 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -12,11 +12,11 @@ from scipy import optimize from scipy.interpolate import interp1d from scipy.spatial import KDTree -from specutils import Spectrum1D from matplotlib.pyplot import Axes, Figure, setp, subplots from specreduce.calibration_data import load_pypeit_calibration_lines from specreduce.line_matching import find_arc_lines +from specreduce.compat import Spectrum __all__ = ["WavelengthCalibration1D"] @@ -49,7 +49,7 @@ def __init__( unit: u.Unit = u.angstrom, degree: int = 3, line_lists: Sequence | None = None, - arc_spectra: Spectrum1D | Sequence[Spectrum1D] | None = None, + arc_spectra: Spectrum | Sequence[Spectrum] | None = None, obs_lines: ndarray | Sequence[ndarray] | None = None, pix_bounds: tuple[int, int] | None = None, line_list_bounds: tuple[float, float] = (0, np.inf), @@ -103,7 +103,7 @@ def __init__( self.ref_pixel = ref_pixel self.nframes = 0 - self.arc_spectra: list[Spectrum1D] | None = None + self.arc_spectra: list[Spectrum] | None = None self.bounds_pix: tuple[int, int] | None = pix_bounds self.bounds_wav: tuple[float, float] | None = None self._cat_lines: list[MaskedArray] | None = None @@ -124,7 +124,7 @@ def __init__( raise ValueError("Only one of arc_spectra or obs_lines can be provided.") if arc_spectra is not None: - self.arc_spectra = [arc_spectra] if isinstance(arc_spectra, Spectrum1D) else arc_spectra + self.arc_spectra = [arc_spectra] if isinstance(arc_spectra, Spectrum) else arc_spectra self.nframes = len(self.arc_spectra) for s in self.arc_spectra: if s.data.ndim > 1: @@ -444,11 +444,11 @@ def _calculate_p2w_inverse(self) -> None: def resample( self, - spectrum: Spectrum1D, + spectrum: Spectrum, nbins: int | None = None, wlbounds: tuple[float, float] | None = None, bin_edges: Sequence[float] | None = None, - ) -> Spectrum1D: + ) -> Spectrum: """Bin the given pixel-space 1D spectrum to a wavelength space conserving the flux. This method bins a pixel-space spectrum to a wavelength space using the computed @@ -516,7 +516,7 @@ def resample( flux_wl = (flux_wl * n) * spectrum.flux.unit ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(type(spectrum.uncertainty)) - return Spectrum1D(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) + return Spectrum(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) def pix_to_wav(self, pix: MaskedArray | ndarray | float) -> ndarray | float: """Map pixel values into wavelength values. From b46fdb5ded51da9a0ea41954e5e36bfc7d8784ef Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 17 Jun 2025 13:45:51 +0100 Subject: [PATCH 73/76] Revised wavecal1d tests. --- specreduce/tests/test_wavecal1d.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/specreduce/tests/test_wavecal1d.py b/specreduce/tests/test_wavecal1d.py index 9290b01f..5a5e9014 100644 --- a/specreduce/tests/test_wavecal1d.py +++ b/specreduce/tests/test_wavecal1d.py @@ -77,7 +77,8 @@ def test_init(mk_arc, mk_lines): with pytest.raises(ValueError, match="Only one of arc_spectra or obs_lines can be provided."): WavelengthCalibration1D(ref_pixel, arc_spectra=arc, obs_lines=obs_lines) - arc = Spectrum(flux=np.array([[1, 2]]) * u.DN, spectral_axis=np.array([1, 2]) * u.angstrom) + arc = Spectrum(flux=np.array([[1, 2, 3, 4, 5]]) * u.DN, + spectral_axis=np.array([1, 2, 3, 4, 5]) * u.angstrom) with pytest.raises(ValueError, match="The arc spectrum must be one dimensional."): WavelengthCalibration1D(ref_pixel, arc_spectra=arc) From 538d2b8d636c39ee9c4642d6a25b39f18d6387ff Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 17 Jun 2025 13:57:12 +0100 Subject: [PATCH 74/76] Fixed `flux` assignment in `tilt_correction.py` to ensure proper handling of unit-aware array values. --- specreduce/tilt_correction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/specreduce/tilt_correction.py b/specreduce/tilt_correction.py index d188337f..7f7d6320 100644 --- a/specreduce/tilt_correction.py +++ b/specreduce/tilt_correction.py @@ -391,7 +391,7 @@ def rectify( # necessary. ip = _ImageParser() im = ip._parse_image(flux, disp_axis=self.disp_axis, mask_treatment=self.mask_treatment) - flux = im.flux + flux = im.flux.value ny, nx = flux.data.shape ypix = np.arange(ny) From 786bded22c836d93a067c26288bfd04e558c455b Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 8 Jul 2025 20:46:26 +0100 Subject: [PATCH 75/76] Refactored observed line handling in `wavecal1d.py`, added amplitude support, and improved the plotting of observed lines. --- specreduce/wavecal1d.py | 186 +++++++++++++++++++++++++++------------- 1 file changed, 126 insertions(+), 60 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index fa02e65d..04e73305 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -21,6 +21,24 @@ __all__ = ["WavelengthCalibration1D"] +def unclutter_text_boxes(labels): + to_remove = set() + for i in range(len(labels)): + for j in range(i + 1, len(labels)): + l1 = labels[i] + l2 = labels[j] + bbox1 = l1.get_window_extent() + bbox2 = l2.get_window_extent() + if bbox1.overlaps(bbox2): + if l1.zorder < l2.zorder: + to_remove.add(l1) + else: + to_remove.add(l2) + + for label in to_remove: + label.remove() + + def _diff_poly1d(m: models.Polynomial1D) -> models.Polynomial1D: """Compute the derivative of a Polynomial1D model. @@ -216,10 +234,13 @@ def find_lines(self, fwhm: float, noise_factor: float = 1.0) -> None: with warnings.catch_warnings(): warnings.simplefilter("ignore") - lines_obs = [ + line_lists = [ find_arc_lines(sp, fwhm, noise_factor=noise_factor) for sp in self.arc_spectra ] - self._obs_lines = [np.ma.masked_array(lo["centroid"].value) for lo in lines_obs] + self.observed_lines = [ + np.ma.masked_array(np.transpose([ll["centroid"].value, ll["amplitude"].value])) + for ll in line_lists + ] def fit_lines( self, @@ -288,7 +309,7 @@ def fit_lines( if match_obs: if self._obs_lines is None: raise ValueError("Cannot fit without observed lines set.") - tree = KDTree(np.concatenate([c.data for c in self._obs_lines])[:, None]) + tree = KDTree(np.concatenate([c.data[:, 0] for c in self._obs_lines])[:, None]) ix = tree.query(pixels[:, None])[1] pixels = tree.data[ix][:, 0] @@ -417,7 +438,7 @@ def refine_fit(self, max_match_distance: float = 5.0, max_iter: int = 5) -> None rms = np.nan for i in range(max_iter): self.match_lines(max_match_distance) - matched_pix = np.ma.concatenate(self._obs_lines).compressed() + matched_pix = np.ma.concatenate(self.observed_lines).compressed() matched_wav = np.ma.concatenate(self._cat_lines).compressed() rms_new = np.sqrt(((matched_wav - self.pix_to_wav(matched_pix)) ** 2).mean()) if rms_new == rms: @@ -557,22 +578,35 @@ def wav_to_pix(self, wav: MaskedArray | ndarray | float) -> ndarray | float: @property def observed_lines(self) -> list[MaskedArray]: """Pixel positions of the observed lines as a list of masked arrays.""" - return self._obs_lines + return [lines[:, 0] for lines in self._obs_lines] @observed_lines.setter - def observed_lines(self, lines_pix: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): - if not isinstance(lines_pix, Sequence): - lines_pix = [lines_pix] + def observed_lines(self, line_lists: MaskedArray | ndarray | list[MaskedArray] | list[ndarray]): + if not isinstance(line_lists, Sequence): + line_lists = [line_lists] + self._obs_lines = [] - for lst in lines_pix: - if isinstance(lst, MaskedArray) and lst.mask is not np.False_: - self._obs_lines.append(lst) - else: - self._obs_lines.append(np.ma.masked_array(lst, mask=np.zeros(lst.size, bool))) + for lst in line_lists: + lst = MaskedArray(lst, copy=True) + + if (lst.ndim > 2) or (lst.ndim == 2 and lst.shape[1] > 2): + raise ValueError( + "Observed line lists must be 1D with a shape [n] (centroids) or " + "2D with a shape [n, 2] (centroids and amplitudes)." + ) + + if lst.mask is np.False_: + lst.mask = np.zeros(lst.shape[0], dtype=bool) + + if lst.ndim == 1: + lst = np.tile(lst[:, None], [1, 2]) + lst[:, 1] = np.arange(lst.shape[0]) + + self._obs_lines.append(lst) @property def catalog_lines(self) -> list[MaskedArray]: - """Catalogue line wavelengths as a list of masked arrays.""" + """Catalog line wavelengths as a list of masked arrays.""" return self._cat_lines @catalog_lines.setter @@ -615,11 +649,10 @@ def match_lines(self, max_distance: float = 5) -> None: The maximum allowed distance between the query points and the KD-tree data points for them to be considered a match. """ - matched_lines_wav = [] - matched_lines_pix = [] + for iframe, tree in enumerate(self._trees): l, ix = tree.query( - self._p2w(self._obs_lines[iframe].data)[:, None], + self._p2w(self._obs_lines[iframe].data[:, 0])[:, None], distance_upper_bound=max_distance, ) m = np.isfinite(l) @@ -636,12 +669,9 @@ def match_lines(self, max_distance: float = 5) -> None: r[np.argmin(l[s])] = True m[s] = r - matched_lines_wav.append(np.ma.masked_array(tree.data[:, 0], mask=True)) - matched_lines_wav[-1].mask[ix[m]] = False - matched_lines_pix.append(np.ma.masked_array(self._obs_lines[iframe].data, mask=~m)) - - self._obs_lines = matched_lines_pix - self._cat_lines = matched_lines_wav + self._cat_lines[iframe].mask[:] = True + self._cat_lines[iframe].mask[ix[m]] = False + self._obs_lines[iframe].mask[:, :] = ~m[:, None] def remove_ummatched_lines(self): """Remove unmatched lines from observation and catalog line data.""" @@ -804,10 +834,10 @@ def plot_observed_lines( frames: int | Sequence[int] | None = None, axes: Axes | Sequence[Axes] | None = None, figsize: tuple[float, float] | None = None, - plot_values: bool = True, + plot_labels: bool = True, plot_spectra: bool = True, map_to_wav: bool = False, - value_fontsize: int | str | None = "small", + label_kwargs: dict | None = None, ) -> Figure: """Plot observed spectral lines for the given arc spectra. @@ -822,55 +852,92 @@ def plot_observed_lines( figsize Dimensions of the figure to be created, specified as a tuple (width, height). Ignored if ``axes`` is provided. - plot_values + plot_labels If True, plots the numerical values of the observed lines at their respective locations on the graph. Default is True. plot_spectra If True, includes the arc spectra on the plot for comparison. Default is True. map_to_wav Determines whether to map the x-axis values to wavelengths. Default is False. + label_kwargs + Specifies the keyword arguments for the line label text objects. Returns ------- Figure The matplotlib figure containing the observed lines plot. """ - fig = self._plot_lines( - "observed", - frames=frames, - axs=axes, - figsize=figsize, - plot_values=plot_values, - map_x=map_to_wav, - value_fontsize=value_fontsize, - ) + + largs = dict(backgroundcolor="w", rotation=90, size="small") + if label_kwargs is not None: + largs.update(label_kwargs) + + if frames is None: + frames = np.arange(self.nframes) + else: + frames = np.atleast_1d(frames) if axes is None: - axes = np.atleast_1d(fig.axes) - - if self.arc_spectra is not None and plot_spectra: - if frames is None: - frames = np.arange(self.nframes) - elif np.isscalar(frames): - frames = [frames] - - transform = self._p2w if map_to_wav else lambda x: x - for i, frame in enumerate(frames): - axes[i].plot( - transform(self.arc_spectra[frame].spectral_axis.value), - self.arc_spectra[frame].data / (1.2 * self.arc_spectra[frame].data.max()), - c="k", - zorder=-10, - ) - setp( - axes, - xlim=transform( + fig, axes = subplots(frames.size, 1, figsize=figsize, constrained_layout=True) + elif isinstance(axes, Axes): + fig = axes.figure + axes = [axes] + else: + fig = axes[0].figure + axes = np.atleast_1d(axes) + + transform = self.pix_to_wav if map_to_wav else lambda x: x + xlabel = f"Wavelength [{self._unit_str}]" if map_to_wav else "Pixel" + + ypad = 1.3 + + for iax, iframe in enumerate(frames): + ax = axes.flat[iax] + if plot_spectra and self.arc_spectra is not None: + spc = self.arc_spectra[iframe] + vmax = spc.flux.value.max() + ax.plot(transform(spc.spectral_axis.value), spc.flux.value / vmax) + else: + vmax = 1.0 + + labels = [] + for i in range(self._obs_lines[iframe].shape[0]): + c, a = self._obs_lines[iframe].data[i] + if self._obs_lines[iframe].mask[i, 0] is True: + ls = ":" + else: + ls = "-" + + ax.plot(transform([c, c]), [a / vmax + 0.02, 1.27], "0.75", ls=ls) + if plot_labels: + labels.append( + ax.text( + transform(c), + ypad, + f"{transform(c):.0f}", + ha="center", + va="bottom", + **largs, + ) + ) + labels[-1].zorder = a / vmax + + if plot_labels: + fig.canvas.draw() + unclutter_text_boxes(labels) + tr = ax.transData.inverted() + ymax = max( [ - self.arc_spectra[0].spectral_axis.min().value, - self.arc_spectra[0].spectral_axis.max().value, + tr.transform_bbox(label.get_window_extent()).max[1] + for label in labels + if label.figure is not None ] - ), - ) + ) + else: + ymax = ypad + + setp(ax, xlabel=xlabel, yticks=[], ylim=(-0.02, ymax + 0.02)) + ax.autoscale(True, "x", tight=True) return fig def plot_fit( @@ -925,9 +992,8 @@ def plot_fit( self.plot_observed_lines( frames, axs[1::2], - plot_values=plot_values, + plot_labels=plot_values, map_to_wav=obs_to_wav, - value_fontsize=value_fontsize, ) xlims = np.array([ax.get_xlim() for ax in axs[::2]]) From 2785323dff56b9c672e1e85acd62345b11d471e9 Mon Sep 17 00:00:00 2001 From: Hannu Parviainen Date: Tue, 15 Jul 2025 12:55:33 +0100 Subject: [PATCH 76/76] Improved uncertainty type handling in WavelengthCalibration1D. --- specreduce/wavecal1d.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/specreduce/wavecal1d.py b/specreduce/wavecal1d.py index 04e73305..254b0a86 100644 --- a/specreduce/wavecal1d.py +++ b/specreduce/wavecal1d.py @@ -502,7 +502,12 @@ def resample( raise ValueError("Number of bins must be positive.") flux = spectrum.flux.value - ucty = spectrum.uncertainty.represent_as(VarianceUncertainty).array + if spectrum.uncertainty is not None: + ucty = spectrum.uncertainty.represent_as(VarianceUncertainty).array + ucty_type = type(spectrum.uncertainty) + else: + ucty = np.zeros_like(flux) + ucty_type = VarianceUncertainty npix = flux.size nbins = npix if nbins is None else nbins if wlbounds is None: @@ -535,8 +540,7 @@ def resample( flux_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * flux[i1] * dldx[i1] ucty_wl[i] = (bin_edges_pix[i + 1] - bin_edges_pix[i]) * ucty[i1] * dldx[i1] flux_wl = (flux_wl * n) * spectrum.flux.unit - ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(type(spectrum.uncertainty)) - + ucty_wl = VarianceUncertainty(ucty_wl * n).represent_as(ucty_type) return Spectrum(flux_wl, bin_centers_wav * u.angstrom, uncertainty=ucty_wl) def pix_to_wav(self, pix: MaskedArray | ndarray | float) -> ndarray | float:

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