hRIXS functions

src/toolbox_scs/detectors/hrixs.py

+from functools import lru_cache
+
 import numpy as np
 from scipy.optimize import curve_fit
 from scipy.signal import fftconvolve
 
+import toolbox_scs as tb
+
 
 __all__ = [
     'find_curvature',
@@ -142,3 +146,197 @@ def gauss1d(x, height, x0, sigma, offset):
 
 def to_fwhm(sigma):
     return abs(sigma * FWHM_COEFF)
+
+
+# -----------------------------------------------------------------------------
+# Centroid
+
+THRESHOLD = 510  # pixel counts above which a hit candidate is assumed
+CURVE_A = 2.19042931e-02  # curvature parameters as determined elsewhere
+CURVE_B = -3.02191568e-07
+
+
+def centroid(image, threshold=THRESHOLD, curvature=(CURVE_A, CURVE_B)):
+    gs = 2
+    base = image.mean()
+    cp = np.argwhere(image[gs // 2: -gs // 2, gs // 2: -gs // 2] > threshold) + np.array([gs // 2, gs // 2])
+    if len(cp) > 100000:
+        raise RuntimeError('Threshold too low or acquisition time too long')
+
+    res = []
+    for cy, cx in cp:
+        spot = image[cy - gs // 2: cy + gs // 2 + 1, cx - gs // 2: cx + gs // 2 + 1] - base
+        spot[spot < 0] = 0
+        if (spot > image[cy, cx]).sum() == 0:
+            mx = np.average(np.arange(cx - gs // 2, cx + gs // 2 + 1), weights=spot.sum(axis=0))
+            my = np.average(np.arange(cy - gs // 2, cy + gs // 2 + 1), weights=spot.sum(axis=1))
+            my -= (curvature[0] + curvature[1] * mx) * mx
+            res.append((my, mx))
+    return res
+
+
+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
+
+
+# -----------------------------------------------------------------------------
+# Integral
+
+FACTOR = 3
+RANGE = [300, 400]
+BINS = abs(np.subtract(*RANGE)) * FACTOR
+
+
+def parabola(x, a, b, c=0):
+    return (a * x + b) * x + c
+
+
+def integrate(image, factor=FACTOR, range=RANGE, curvature=(CURVE_A, CURVE_B), ):
+    image = image - image.mean()
+    x = np.arange(image.shape[1])[None, :]
+    y = np.arange(image.shape[0])[:, None]
+    ys = factor * (y - parabola(x, curvature[1], curvature[0]))
+    ysf = np.floor(ys)
+    rang = (factor * range[0], factor * range[1])
+    bins = rang[1] - rang[0]
+    lhy, lhx = np.histogram(ysf.ravel(), bins=bins, weights=((ys - ysf) * image).ravel(), range=rang)
+    rhy, rhx = np.histogram((ysf + 1).ravel(), bins=bins, weights=(((ysf + 1) - ys) * image).ravel(), range=rang)
+    lvy, lvx = np.histogram(ysf.ravel(), bins=bins, weights=(ys - ysf).ravel(), range=rang)
+    rvy, rvx = np.histogram((ysf + 1).ravel(), bins=bins, weights=((ysf + 1) - ys).ravel(), range=rang)
+    return (lhy + rhy) / (lvy + rvy)
+
+
+# -----------------------------------------------------------------------------
+# hRIXS class
+
+
+class hRIXS:
+    # run
+    PROPOSAL = 2769
+
+    # image range
+    X_RANGE = np.s_[1300:-100]
+    Y_RANGE = np.s_[:]
+
+    # centroid
+    THRESHOLD = THRESHOLD  # pixel counts above which a hit candidate is assumed
+    CURVE_A = CURVE_A  # curvature parameters as determined elsewhere
+    CURVE_B = CURVE_B
+
+    # integral
+    FACTOR = FACTOR
+    RANGE = RANGE
+    BINS = abs(np.subtract(*RANGE)) * FACTOR
+
+    METHOD = 'centroid'  # ['centroid', 'integral']
+
+    @classmethod
+    def set_params(cls, **params):
+        for key, value in params.items():
+            setattr(cls, key.upper(), value)
+
+    def __set_params(self, **params):
+        self.__class__.set_params(**params)
+        self.refresh()
+
+    @classmethod
+    def get_params(cls, *params):
+        if not params:
+            params = ('proposal', 'x_range', 'y_range',
+                      'threshold', 'curve_a', 'curve_b',
+                      'factor', 'range', 'bins',
+                      'method')
+        return {param: getattr(cls, param.upper()) for param in params}
+
+    def refresh(self):
+        cls = self.__class__
+        for cached in ['_centroid', '_integral']:
+            getattr(cls, cached).fget.cache_clear()
+
+    def __init__(self, images, norm=None):
+        self.images = images
+        self.norm = norm
+
+        # class/instance method compatibility
+        self.set_params = self.__set_params
+
+    @classmethod
+    def from_run(cls, runNB, proposal=None, first_wrong=False):
+        if proposal is None:
+            proposal = cls.PROPOSAL
+
+        run, data = tb.load(proposal,
+                            runNB=runNB,
+                            fields=['hRIXS_det', 'SCS_slowTrain'])
+
+        # Get slow train data
+        mnemo = tb.mnemonics['SCS_slowTrain'][0]
+        slow_train = run[mnemo['source'], mnemo['key']].ndarray().sum()
+
+        return cls(images=data['hRIXS_det'][1 if first_wrong else 0:].data,
+                   norm=slow_train)
+
+    @property
+    @lru_cache()
+    def _centroid(self):
+        return sum((centroid(image[self.Y_RANGE, self.X_RANGE].T,
+                             threshold=self.THRESHOLD,
+                             curvature=(self.CURVE_A, self.CURVE_B), )
+                    for image in self.images), [])
+
+    def _centroid_spectrum(self, bins=None, range=None, normalize=True):
+        if bins is None:
+            bins = self.BINS
+        if range is None:
+            range = self.RANGE
+
+        r = np.array(self._centroid)
+        hy, hx = np.histogram(r[:, 0], bins=bins, range=range)
+        if normalize and self.norm is not None:
+            hy = hy / self.norm
+
+        return (hx[:-1] + hx[1:]) / 2, hy
+
+    @property
+    @lru_cache()
+    def _integral(self):
+        return sum((integrate(image[self.Y_RANGE, self.X_RANGE].T,
+                              factor=self.FACTOR,
+                              range=self.RANGE,
+                              curvature=(self.CURVE_A, self.CURVE_B))
+                    for image in self.images))
+
+    def _integral_spectrum(self, normalize=True):
+        values = self._integral
+        if normalize and self.norm is not None:
+            values = values / self.norm
+        return np.arange(values.size), values
+
+    @property
+    def corrected(self):
+        return decentroid(self._centroid)
+
+    def spectrum(self, normalize=True):
+        spec_func = (self._centroid_spectrum if self.METHOD.lower() == 'centroid'
+                     else self._integral_spectrum)
+        return spec_func(normalize=normalize)
+
+    def __sub__(self, other):
+        px, py = self.spectrum()
+        mx, my = other.spectrum()
+        return (px + mx) / 2, py - my
+
+    def __add__(self, other):
+        ix, iy = self.spectrum(normalize=False)
+        jx, jy = other.spectrum(normalize=False)
+
+        i_n = self.norm or 0
+        j_n = other.norm or 0
+        norm = ((i_n + j_n) or 1)
+
+        return ix, (iy + jy) / norm

[Martin Teichmann]

I don't like that the result of this is directly a spectrum, because this prevents us from adding more than two runs. run3 + run5 already returns a spectrum, doing run3 + run5 + run7 won't fly. So it would be better to require (run3 + run5 + run7).spectrum().

[Cammille Carinan]

Hmmm I have to think about doing it without copying the images.