more yolo

meteor/scale.py

@@ -1,100 +1,17 @@
-import numpy as np
-import scipy.optimize as opt
 
+import reciprocalspaceship as rs
 
-def scale_iso(data1, data2, ds):
-    """
-    Isotropic resolution-dependent scaling of data2 to data1.
-    (minimize [dataset1 - c*exp(-B*sintheta**2/lambda**2)*dataset2]
-
-    Input :
-
-    1. dataset1 in form of 1D numpy array
-    2. dataset2 in form of 1D numpy array
-    3. dHKLs for the datasets in form of 1D numpy array
-
-    Returns :
-
-    1. entire results from least squares fitting
-    2. c (as float)
-    3. B (as float)
-    2. scaled dataset2 in the form of a 1D numpy array
 
+def scale_structure_factors(reference: rs.DataSet, dataset_to_scale: rs.DataSet) -> rs.DataSet:
     """
+    Apply an anisotropic scaling so that `dataset_to_scale` is on the same scale as `reference`.
 
-    def scale_func(p, x1, x2, qs):
-        return x1 - (p[0] * np.exp(-p[1] * (qs**2))) * x2
+        C * exp{ -(h**2 B11 + k**2 B22 + l**2 B33 +
+                    2hk B12 + 2hl  B13 +  2kl B23) }
 
-    p0 = np.array([1.0, -20])
-    qs = 1 / (2 * ds)
-    matrix = opt.least_squares(scale_func, p0, args=(data1, data2, qs))
+    This is the same procedure implemented by CCP4's SCALEIT.
 
-    return (
-        matrix.x[0],
-        matrix.x[1],
-        (matrix.x[0] * np.exp(-matrix.x[1] * (qs**2))) * data2,
-    )
-
-
-def scale_aniso(x_dataset, y_dataset, Miller_indx):
-    """ "
-    Author: Virginia Apostolopoulou
-    Anisotropically scales y_dataset to x_dataset given an ndarray of Miller indices.
+    
+    .. https://www.ccp4.ac.uk/html/scaleit.html
     """
-
-    p0 = np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=np.float32)
-    matrix_ani = opt.least_squares(
-        aniso_scale_func, p0, args=(x_dataset, y_dataset, Miller_indx)
-    )
-
-    h = Miller_indx[:, 0]
-    k = Miller_indx[:, 1]
-    l = Miller_indx[:, 2]
-    h_sq = np.square(h)
-    k_sq = np.square(k)
-    l_sq = np.square(l)
-
-    hk_prod = h * k
-    hl_prod = h * l
-    kl_prod = k * l
-
-    t = -(
-        h_sq * matrix_ani.x[1]
-        + k_sq * matrix_ani.x[2]
-        + l_sq * matrix_ani.x[3]
-        + 2 * hk_prod * matrix_ani.x[4]
-        + 2 * hl_prod * matrix_ani.x[5]
-        + 2 * kl_prod * matrix_ani.x[6]
-    )
-
-    data_ani_scaled = (matrix_ani.x[0] * np.exp(t)) * y_dataset
-
-    return matrix_ani, t, data_ani_scaled
-
-
-def aniso_scale_func(p, x1, x2, H_arr):
-    "Author: Virginia Apostolopoulou"
-
-    h = H_arr[:, 0]
-    k = H_arr[:, 1]
-    l = H_arr[:, 2]
-
-    h_sq = np.square(h)
-    k_sq = np.square(k)
-    l_sq = np.square(l)
-
-    hk_prod = h * k
-    hl_prod = h * l
-    kl_prod = k * l
-
-    t = -(
-        h_sq * p[1]
-        + k_sq * p[2]
-        + l_sq * p[3]
-        + 2 * hk_prod * p[4]
-        + 2 * hl_prod * p[5]
-        + 2 * kl_prod * p[6]
-    )
-    expnt = np.exp(t)
-    r = x1 - p[0] * expnt * x2
-    return r
+    ...

pyproject.toml

@@ -9,7 +9,7 @@ authors = [
 dependencies = [
     "numpy",
     "scipy",
-    "skimage",
+    "scikit-image",
     "reciprocalspaceship",
 ]