diff --git a/src/toolbox_scs/detectors/__init__.py b/src/toolbox_scs/detectors/__init__.py
index 3f442878f734a9c4ad8568e15a6b187c31043a61..435b029f08e86a34b2e8df6cfe1d8c41cf07d788 100644
--- a/src/toolbox_scs/detectors/__init__.py
+++ b/src/toolbox_scs/detectors/__init__.py
@@ -7,6 +7,7 @@ from .dssc_misc import *
from .dssc_processing import *
from .gotthard2 import *
from .hrixs import *
+from .jf_hrixs import *
from .pes import *
from .viking import *
from .xgm import *
@@ -21,6 +22,7 @@ __all__ = (
+ dssc_processing.__all__
+ gotthard2.__all__
+ hrixs.__all__
+ + jf_hrixs.__all__
+ pes.__all__
+ viking.__all__
+ xgm.__all__
diff --git a/src/toolbox_scs/detectors/jf_hrixs.py b/src/toolbox_scs/detectors/jf_hrixs.py
new file mode 100644
index 0000000000000000000000000000000000000000..62d701d971858a2a77bc31fd650c9218f02cfadd
--- /dev/null
+++ b/src/toolbox_scs/detectors/jf_hrixs.py
@@ -0,0 +1,477 @@
+from functools import lru_cache
+import xarray as xr
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import leastsq
+from scipy.optimize import curve_fit
+from scipy.signal import fftconvolve
+
+import toolbox_scs as tb
+
+
+__all__ = [
+ 'JF_hRIXS',
+]
+
+
+# -----------------------------------------------------------------------------
+# Curvature
+
+def correct_curvature(image, factor=None, axis=1):
+ if factor is None:
+ return
+
+ if axis == 1:
+ image = image.T
+
+ ydim, xdim = image.shape
+ x = np.arange(xdim + 1)
+ y = np.arange(ydim + 1)
+ xx, yy = np.meshgrid(x[:-1] + 0.5, y[:-1] + 0.5)
+ xxn = xx - factor[0] * yy - factor[1] * yy ** 2
+ ret = np.histogramdd((xxn.flatten(), yy.flatten()),
+ bins=[x, y],
+ weights=image.flatten())[0]
+
+ return ret if axis == 1 else ret.T
+
+
+def get_spectrum(image, factor=None, axis=0,
+ pixel_range=None, energy_range=None, ):
+ start, stop = (0, image.shape[axis - 1])
+ if pixel_range is not None:
+ start = max(pixel_range[0] or start, start)
+ stop = min(pixel_range[1] or stop, stop)
+
+ edge = image.sum(axis=axis)[start:stop]
+ bins = np.arange(start, stop + 1)
+ centers = (bins[1:] + bins[:-1]) * 0.5
+ if factor is not None:
+ centers, edge = calibrate(centers, edge,
+ factor=factor,
+ range_=energy_range)
+
+ return centers, edge
+
+
+# -----------------------------------------------------------------------------
+# Energy calibration
+
+
+def energy_calibration(channels, energies):
+ return np.polyfit(channels, energies, deg=1)
+
+
+def calibrate(x, y=None, factor=None, range_=None):
+ if factor is not None:
+ x = np.polyval(factor, x)
+
+ if y is not None and range_ is not None:
+ start = np.argmin(np.abs((x - range_[0])))
+ stop = np.argmin(np.abs((x - range_[1])))
+ # Calibrated energies have a different direction
+ x, y = x[stop:start], y[stop:start]
+
+ return x, y
+
+
+# -----------------------------------------------------------------------------
+# Gaussian-related functions
+
+
+FWHM_COEFF = 2 * np.sqrt(2 * np.log(2))
+
+
+def gaussian_fit(x_data, y_data, offset=0):
+ """
+ Centre-of-mass and width. Lifted from image_processing.imageCentreofMass()
+ """
+
+ x0 = np.average(x_data, weights=y_data)
+ sx = np.sqrt(np.average((x_data - x0) ** 2, weights=y_data))
+
+ # Gaussian fit
+ baseline = y_data.min()
+ p_0 = (y_data.max(), x0 + offset, sx, baseline)
+ try:
+ p_f, _ = curve_fit(gauss1d, x_data, y_data, p_0, maxfev=10000)
+ return p_f
+ except (RuntimeError, TypeError) as e:
+ print(e)
+ return None
+
+
+def gauss1d(x, height, x0, sigma, offset):
+ return height * np.exp(-0.5 * ((x - x0) / sigma) ** 2) + offset
+
+
+def to_fwhm(sigma):
+ return abs(sigma * FWHM_COEFF)
+
+
+def decentroid(res):
+ res = np.array(res)
+ ret = np.zeros(shape=(res.max(axis=0) + 1).astype(int))
+ for cy, cx in res:
+ if cx > 0 and cy > 0:
+ ret[int(cy), int(cx)] += 1
+ return ret
+
+
+class JF_hRIXS:
+ """The JUNGFRAU hRIXS analysis, especially curvature correction
+
+ The objects of this class contain the meta-information about the settings
+ of the spectrometer, not the actual data, except possibly a dark image
+ for background subtraction.
+
+ The actual data is loaded into `xarray`s, and stays there.
+
+ Attributes
+ ----------
+
+ PROPOSAL: int
+ the number of the proposal
+ X_RANGE: slice
+ the slice to take in the dispersive direction, in pixels. Defaults
+ to the entire width.
+ Y_RANGE: slice
+ the slice to take in the energy direction
+ THRESHOLD: float
+ pixel counts above which a hit candidate is assumed, for centroiding.
+ use None if you want to give it in standard deviations instead.
+ STD_THRESHOLD:
+ same as THRESHOLD, in standard deviations.
+ DBL_THRESHOLD:
+ threshold controling whether a detected hit is considered to be a
+ double hit.
+ BINS: int
+ the number of bins used in centroiding
+ CURVE_A, CURVE_B: float
+ the coefficients of the parabola for the curvature correction
+ USE_DARK: bool
+ whether to do dark subtraction. Is initially `False`, magically
+ switches to `True` if a dark has been loaded, but may be reset.
+ ENERGY_INTERCEPT, ENERGY_SLOPE:
+ The calibration from pixel to energy
+ FIELDS:
+ the fields to be loaded from the data. Add additional fields if so
+ desired.
+
+ Example
+ -------
+
+ proposal = 3145
+ h = hRIXS(proposal)
+ h.Y_RANGE = slice(700, 900)
+ h.CURVE_B = -3.695346575286939e-07
+ h.CURVE_A = 0.024084479232443695
+ h.ENERGY_SLOPE = 0.018387
+ h.ENERGY_INTERCEPT = 498.27
+ """
+ def __init__(self, proposalNB):
+
+ self.PROPOSAL=proposalNB
+
+ # image range
+ self.X_RANGE = np.s_[:]
+ self.Y_RANGE = np.s_[:]
+
+ # curvature correction
+ self.CURVE_A = 0
+ self.CURVE_B = 0
+
+ # energy bins
+ self.BINS = 100
+ self.ENERGY_INTERCEPT = 0
+ self.ENERGY_SLOPE = 1
+
+ self.FIELDS = ['hRIXS_det', 'hRIXS_index', 'hRIXS_delay', 'hRIXS_norm', 'nrj']
+
+ def set_params(self, **params):
+ for key, value in params.items():
+ setattr(self, key.upper(), value)
+
+ def get_params(self, *params):
+ if not params:
+ params = ('proposal', 'x_range', 'y_range',
+ 'curve_a', 'curve_b',
+ 'bins', 'fields')
+ return {param: getattr(self, param.upper()) for param in params}
+
+ def from_run(self, runNB, proposal=None, extra_fields=(), drop_first=False):
+ """load a run
+
+ Load the run `runNB`. A thin wrapper around `toolbox.load`.
+ Parameters
+ ----------
+ drop_first: bool
+ if True, the first image in the run is removed from the dataset.
+
+ Example
+ -------
+
+ data = h.from_run(145) # load run 145
+
+ data1 = h.from_run(145) # load run 145
+ data2 = h.from_run(155) # load run 155
+ data = xarray.concat([data1, data2], 'trainId') # combine both
+ """
+ if proposal is None:
+ proposal = self.PROPOSAL
+ run, data = tb.load(proposal, runNB=runNB,
+ fields=self.FIELDS + list(extra_fields))
+ if drop_first is True:
+ data = data.isel(trainId=slice(1, None))
+ return data
+
+ def find_curvature(self, runNB, proposal=None, plot=True, args=None,
+ **kwargs):
+ """find the curvature correction coefficients
+
+ The hRIXS has some abberations which leads to the spectroscopic lines
+ being curved on the detector. We approximate these abberations with
+ a parabola for later correction.
+
+ Load a run and determine the curvature. The curvature is set in `self`,
+ and returned as a pair of floats.
+
+ Parameters
+ ----------
+
+ runNB: int
+ the run number to use
+ proposal: int
+ the proposal to use, default to the current proposal
+ plot: bool
+ whether to plot the found curvature onto the data
+ args: pair of float, optional
+ a starting value to prime the fitting routine
+
+ Example
+ -------
+
+ h.find_curvature(155) # use run 155 to fit the curvature
+ """
+ data = self.from_run(runNB, proposal)
+
+ image = data['hRIXS_det'].sum(dim='trainId') \
+ .values[self.X_RANGE, self.Y_RANGE].T
+ if args is None:
+ spec = (image - image[:10, :].mean()).mean(axis=1)
+ mean = np.average(np.arange(len(spec)), weights=spec)
+ args = (-2e-7, 0.02, mean - 0.02 * image.shape[1] / 2, 3,
+ spec.max(), image.mean())
+ args = _find_curvature(image, args, plot=plot, **kwargs)
+ self.CURVE_B, self.CURVE_A, *_ = args
+ return self.CURVE_A, self.CURVE_B
+
+ def find_curvature(img, args, plot=False, **kwargs):
+ """find the curvature correction coefficients
+
+ The hRIXS has some abberations which leads to the spectroscopic lines
+ being curved on the detector. We approximate these abberations with
+ a parabola for later correction.
+
+ Load a run and determine the curvature. The curvature is set in `self`,
+ and returned as a pair of floats.
+
+ Parameters
+ ----------
+
+ img: array
+ 2D average image
+ args: (a, b, c, s, h, o) initial coefficients
+ a,b,c are the parabola parameters, s is the width of the gaussian,
+ h the height and o an offset
+ plot: bool
+ whether to plot the found curvature onto the data
+ Example
+ -------
+
+ h.find_curvature(155) # use run 155 to fit the curvature
+ """
+
+ def parabola(x, a, b, c, s=0, h=0, o=0):
+ return (a*x + b)*x + c
+ def gauss(y, x, a, b, c, s, h, o=0):
+ return h * np.exp(-((y - parabola(x, a, b, c)) / (2 * s))**2) + o
+ x = np.arange(img.shape[1])[None, :]
+ y = np.arange(img.shape[0])[:, None]
+
+ if plot:
+ plt.figure(figsize=(10,10))
+ plt.imshow(img, cmap='gray', aspect='auto', interpolation='nearest', **kwargs)
+ plt.plot(x[0, :], parabola(x[0, :], *args))
+
+ args, _ = leastsq(lambda args: (gauss(y, x, *args) - img).ravel(), args)
+
+ if plot:
+ plt.plot(x[0, :], parabola(x[0, :], *args))
+ return args
+
+ def spectrum(self, fname):
+ """Bin photon hit data into spectrum.
+
+ Parameters
+ ----------
+
+ fname: string
+ file name of the data to load.
+ """
+
+ data_interp = xr.load_dataset(fname)
+ def hist_curv(x, y):
+ H, _ = np.histogram(
+ x - self.parabola(y), bins=self.BINS,
+ range=(0, self.Y_RANGE.stop - self.Y_RANGE.start))
+
+ return H
+
+ energy = (np.linspace(self.Y_RANGE.start,
+ self.Y_RANGE.stop,
+ self.BINS) * self.ENERGY_SLOPE + self.ENERGY_INTERCEPT)
+
+ spectrum = xr.apply_ufunc(hist_curv,
+ data_interp['y'],
+ data_interp['x'],
+ input_core_dims=[['hit'], ['hit']],
+ output_core_dims=[['energy']],
+ exclude_dims=set(('energy',)),
+ vectorize=True,
+ dask="parallelized",
+ output_dtypes=[np.float64],
+ output_sizes={'energy': len(energy)},
+ ).compute()
+ spectrum['energy'] = energy
+
+ return spectrum
+
+ def parabola(self, x):
+ return (self.CURVE_B * x + self.CURVE_A) * x
+
+ def integrate(self, data):
+ """calculate a spectrum by integration
+
+ This takes the `xarray` `data` and returns a copy of it, with a new
+ dataarray named `spectrum` added, which contains the energy spectrum
+ calculated for each hRIXS image.
+
+ First the energy that corresponds to each pixel is calculated.
+ Then all pixels within an energy range are summed, where the intensity
+ of one pixel is distributed among the two energy ranges the pixel
+ spans, proportionally to the overlap between the pixel and bin energy
+ ranges.
+
+ The resulting data is normalized to one pixel, so the average
+ intensity that arrived on one pixel.
+
+ Example
+ -------
+
+ h.integrate(data) # create spectrum by summing pixels
+ data.spectrum[0, :].plot() # plot the spectrum of the first image
+ """
+ bins = self.Y_RANGE.stop - self.Y_RANGE.start
+ margin = 10
+ ret = np.zeros((len(data["hRIXS_det"]), bins - 2 * margin))
+ if self.USE_DARK:
+ dark_image = self.dark_image.values[self.X_RANGE, self.Y_RANGE]
+ images = data["hRIXS_det"].values[:, self.X_RANGE, self.Y_RANGE]
+
+ x, y = np.ogrid[:images.shape[1], :images.shape[2]]
+ quo, rem = divmod(y - self.parabola(x), 1)
+ quo = np.array([quo, quo + 1])
+ rem = np.array([rem, 1 - rem])
+ wrong = (quo < margin) | (quo >= bins - margin)
+ quo[wrong] = margin
+ rem[wrong] = 0
+ quo = (quo - margin).astype(int).ravel()
+
+ for image, r in zip(images, ret):
+ if self.USE_DARK:
+ image = image - dark_image
+ r[:] = np.bincount(quo, weights=(rem * image).ravel())
+ ret /= np.bincount(quo, weights=rem.ravel())
+ data.coords["energy"] = (
+ np.arange(self.Y_RANGE.start + margin, self.Y_RANGE.stop - margin)
+ * self.ENERGY_SLOPE + self.ENERGY_INTERCEPT)
+ data['spectrum'] = (("trainId", "energy"), ret)
+ return data
+
+ aggregators = dict(
+ hRIXS_det=lambda x, dim: x.sum(dim=dim),
+ Delay=lambda x, dim: x.mean(dim=dim),
+ hRIXS_delay=lambda x, dim: x.mean(dim=dim),
+ hRIXS_norm=lambda x, dim: x.sum(dim=dim),
+ spectrum=lambda x, dim: x.sum(dim=dim),
+ dbl_spectrum=lambda x, dim: x.sum(dim=dim),
+ total_hits=lambda x, dim: x.sum(dim=dim),
+ dbl_hits=lambda x, dim: x.sum(dim=dim),
+ counts=lambda x, dim: x.sum(dim=dim)
+ )
+
+ def aggregator(self, da, dim):
+ agg = self.aggregators.get(da.name)
+ if agg is None:
+ return None
+ return agg(da, dim=dim)
+
+ def aggregate(self, ds, var=None, dim="trainId"):
+ """aggregate (i.e. mostly sum) all data within one dataset
+
+ take all images in a dataset and aggregate them and their metadata.
+ For images, spectra and normalizations that means adding them, for
+ others (e.g. delays) adding would not make sense, so we treat them
+ properly. The aggregation functions of each variable are defined
+ in the aggregators attribute of the class.
+ If var is specified, group the dataset by var prior to aggregation.
+ A new variable "counts" gives the number of frames aggregated in
+ each group.
+
+ Parameters
+ ----------
+ ds: xarray Dataset
+ the dataset containing RIXS data
+ var: string
+ One of the variables in the dataset. If var is specified, the
+ dataset is grouped by var prior to aggregation. This is useful
+ for sorting e.g. a dataset that contains multiple delays.
+ dim: string
+ the dimension over which to aggregate the data
+
+ Example
+ -------
+
+ h.centroid(data) # create spectra from finding photons
+ agg = h.aggregate(data) # sum all spectra
+ agg.spectrum.plot() # plot the resulting spectrum
+
+ agg2 = h.aggregate(data, 'hRIXS_delay') # group data by delay
+ agg2.spectrum[0, :].plot() # plot the spectrum for first value
+ """
+ ds["counts"] = xr.ones_like(ds[dim])
+ if var is not None:
+ groups = ds.groupby(var)
+ return groups.map(self.aggregate_ds, dim=dim)
+ return self.aggregate_ds(ds, dim)
+
+ def aggregate_ds(self, ds, dim='trainId'):
+ ret = ds.map(self.aggregator, dim=dim)
+ ret = ret.drop_vars([n for n in ret if n not in self.aggregators])
+ return ret
+
+ def normalize(self, data, which="hRIXS_norm"):
+ """ Adds a 'normalized' variable to the dataset defined as the
+ ration between 'spectrum' and 'which'
+
+ Parameters
+ ----------
+ data: xarray Dataset
+ the dataset containing hRIXS data
+ which: string, default="hRIXS_norm"
+ one of the variables of the dataset, usually "hRIXS_norm"
+ or "counts"
+ """
+ return data.assign(normalized=data["spectrum"] / data[which])