script dump

compare_mtz.py

@@ -0,0 +1,207 @@
+import numpy as np
+import gemmi
+import matplotlib.pyplot as plt
+
+def riso_xtrapol8(F1, F2):
+    A1 = np.abs(F1)
+    A2 = np.abs(F2)
+
+    denom = 0.5 * (A1 + A2)
+    mask = denom > 0
+
+    if np.sum(mask) == 0:
+        return np.nan
+
+    num = np.sum(np.abs(A1[mask] - A2[mask]))
+    den = np.sum(denom[mask])
+
+    return num / den
+
+def cciso_xtrapol8(F1, F2):
+    A1 = np.abs(F1)
+    A2 = np.abs(F2)
+
+    mask = np.isfinite(A1) & np.isfinite(A2)
+
+    A1 = A1[mask]
+    A2 = A2[mask]
+
+    if len(A1) < 2:
+        return np.nan
+
+    A1m = A1 - np.mean(A1)
+    A2m = A2 - np.mean(A2)
+
+    num = np.sum(A1m * A2m)
+    den = np.sqrt(np.sum(A1m**2) * np.sum(A2m**2))
+
+    return num / den if den > 0 else np.nan
+
+def read_mtz_amplitudes(mtz_file, amp_label):
+    mtz = gemmi.read_mtz_file(mtz_file)
+
+    H = np.array(mtz.column_with_label('H'), dtype=int)
+    K = np.array(mtz.column_with_label('K'), dtype=int)
+    L = np.array(mtz.column_with_label('L'), dtype=int)
+
+    hkl = np.vstack([H, K, L]).T
+
+    F = np.array(mtz.column_with_label(amp_label), dtype=float)
+
+    cell = mtz.cell
+
+    return hkl, F, cell
+
+
+def match_reflections(hkl1, F1, hkl2, F2):
+    map1 = {tuple(h): f for h, f in zip(hkl1, F1)}
+    map2 = {tuple(h): f for h, f in zip(hkl2, F2)}
+
+    common = sorted(set(map1.keys()) & set(map2.keys()))
+
+    F1m = np.array([map1[h] for h in common])
+    F2m = np.array([map2[h] for h in common])
+
+    hklm = np.array(common, dtype=int)
+
+    return hklm, F1m, F2m
+
+def compute_resolution(cell, hkl):
+    return np.array([cell.calculate_d(list(h)) for h in hkl])
+
+def make_equal_count_bins(d, n_bins):
+    order = np.argsort(d)[::-1]  # low → high resolution
+    bins = np.array_split(order, n_bins)
+    return bins
+
+def make_equal_count_resolution_bins(d, n_bins):
+    """
+    Returns a list of index arrays, each containing
+    roughly the same number of reflections.
+    """
+    order = np.argsort(d)[::-1]  # low → high resolution
+    return np.array_split(order, n_bins)
+
+def riso_cciso_by_resolution(d, F1, F2, n_bins=20):
+    """
+    Compute Xtrapol8 Riso and CCiso in equal-count resolution bins.
+
+    Returns:
+        d_mid   : midpoint resolution per bin
+        Riso    : array
+        CCiso   : array
+        counts  : reflections per bin
+    """
+    bins = make_equal_count_resolution_bins(d, n_bins)
+
+    d_mid = []
+    Riso = []
+    CCiso = []
+    counts = []
+
+    for idx in bins:
+        if len(idx) < 10:
+            continue
+
+        d_bin = d[idx]
+        F1b = F1[idx]
+        F2b = F2[idx]
+
+        d_mid.append(0.5 * (d_bin.min() + d_bin.max()))
+        Riso.append(riso_xtrapol8(F1b, F2b))
+        CCiso.append(cciso_xtrapol8(F1b, F2b))
+        counts.append(len(idx))
+
+    return (
+        np.array(d_mid),
+        np.array(Riso),
+        np.array(CCiso),
+        np.array(counts),
+    )
+
+
+
+def plot_riso_cciso_vs_resolution(d_mid, Riso, CCiso):
+    fig, ax1 = plt.subplots(figsize=(6, 4))
+
+    # Riso
+    ax1.plot(1/d_mid, Riso, marker='o', label='Riso')
+    ax1.set_xlabel('Resolution (Å)')
+    ax1.set_ylabel('Riso')
+    ax1.grid(True, alpha=0.3)
+
+    # CCiso
+    ax2 = ax1.twinx()
+    ax2.plot(1/d_mid, CCiso, marker='s', linestyle='--', label='CCiso')
+    ax2.set_ylabel('CCiso')
+    ax2.set_ylim(0, 1)
+
+    # Combined legend
+    lines, labels = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines + lines2, labels + labels2, loc='best')
+
+    plt.tight_layout()
+    plt.show()
+
+
+def riso_cciso_from_mtz(
+    
+    mtz1, mtz2,
+    amp1_label, amp2_label,
+    n_bins=None
+):
+    hkl1, F1, cell1 = read_mtz_amplitudes(mtz1, amp1_label)
+    hkl2, F2, cell2 = read_mtz_amplitudes(mtz2, amp2_label)
+
+    hkl, F1m, F2m = match_reflections(hkl1, F1, hkl2, F2)
+
+    d = compute_resolution(cell1, hkl)
+
+    print(f"Matched reflections: {len(hkl)}")
+
+    # Overall statistics
+    Riso = riso_xtrapol8(F1m, F2m)
+    CCiso = cciso_xtrapol8(F1m, F2m)
+
+    print(f"\nOverall:")
+    print(f"  Riso  = {Riso:.4f}")
+    print(f"  CCiso = {CCiso:.4f}")
+
+    # Per-bin statistics
+    if n_bins is not None:
+        print(f"\nPer-resolution-bin statistics:")
+        bins = make_equal_count_bins(d, n_bins)
+
+        for i, idx in enumerate(bins):
+            if len(idx) < 10:
+                continue
+
+            Rb = riso_xtrapol8(F1m[idx], F2m[idx])
+            CCb = cciso_xtrapol8(F1m[idx], F2m[idx])
+
+            dmin = np.min(d[idx])
+            dmax = np.max(d[idx])
+
+            print(
+                f"Bin {i+1:02d}  "
+                f"{dmax:5.2f}–{dmin:5.2f} Å  "
+                f"Riso={Rb:6.3f}  CCiso={CCb:6.3f}  n={len(idx)}"
+            )
+
+    d_mid, Rb, CCb, nref = riso_cciso_by_resolution( d, F1m, F2m, n_bins=20 )
+
+    plot_riso_cciso_vs_resolution(d_mid, Rb, CCb)
+
+
+    return Riso, CCiso
+
+
+
+
+mtz1 = "../data/apo_100k_refine_5.mtz"
+mtz2 = "../data/on_100k_refine_141.mtz"
+amp1_label = "F-obs-filtered"
+amp2_label = "F-obs-filtered"
+
+riso_cciso_from_mtz( mtz1, mtz2, amp1_label, amp2_label, n_bins=None)