script dump

generate_argand_v5.py

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+import gemmi
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+from matplotlib.cm import ScalarMappable
+
+# -----------------------------
+# User input
+# -----------------------------
+#mtz_file = "../data/on_100k_refine_141.mtz"
+#F_label   = "F-model"
+#PHI_label = "PHIF-model"
+mtz_file = "../data/apo_100k_refine_5.mtz"
+F_label   = "F-obs-filtered"
+PHI_label = "PHIF-model"
+n_max = 10000
+n_bins = 10
+
+# -----------------------------
+# Load MTZ
+# -----------------------------
+mtz = gemmi.read_mtz_file(mtz_file)
+
+# Unit cell
+cell = mtz.cell
+
+# Structure factor data
+F   = np.array(mtz.column_with_label(F_label))
+PHI = np.array(mtz.column_with_label(PHI_label))
+
+# Gemmi-native HKL
+# HKL (must be integers!)
+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)
+
+# Remove invalid reflections
+mask = np.isfinite(F) & np.isfinite(PHI)
+
+F   = F[mask]
+PHI = PHI[mask]
+H   = H[mask]
+K   = K[mask]
+L   = L[mask]
+
+# Complex structure factors
+C = F * np.exp(1j * np.deg2rad(PHI))
+
+# -----------------------------
+# Compute resolution (d-spacing)
+# -----------------------------
+# d = 1 / |h*| where |h*| comes from reciprocal cell
+d = np.array([cell.calculate_d([h, k, l]) for h, k, l in zip(H, K, L)])
+
+# -----------------------------
+# Sort reflections: low → high resolution
+# (large d → small d)
+# -----------------------------
+idx = np.argsort(-d)
+
+C = C[idx][:n_max]
+d = d[idx][:n_max]
+
+# -----------------------------
+# Assign resolution bins
+# -----------------------------
+bins = np.linspace(d.min(), d.max(), n_bins + 1)
+colors_array = plt.cm.coolwarm(np.linspace(0, 1, n_bins))  # colormap
+
+bin_indices = np.digitize(d, bins) - 1
+bin_indices = np.clip(bin_indices, 0, n_bins - 1)   # <- fix
+# -----------------------------
+# Head-to-tail Argand walk
+# -----------------------------
+plt.figure(figsize=(7, 7))
+
+x, y = 0.0, 0.0   # first vector starts at origin
+
+for i, z in enumerate(C):
+    dx, dy = z.real, z.imag
+
+    # draw arrow FROM (x_start, y_start) TO (x_start+dx, y_start+dy)
+    plt.arrow(
+        x, y,
+        dx, dy,
+        color=colors_array[bin_indices[i]],
+        length_includes_head=True,
+        head_width=0.03 * abs(z),
+        alpha=0.6
+    )
+
+    # update start point for next vector
+    x += dx
+    y += dy
+
+# -----------------------------
+# Final tip on top
+# -----------------------------
+plt.scatter(x, y, c="red", s=80, label="Final tip")
+
+# -----------------------------
+# Resultant vector (sum of all SFs)
+# -----------------------------
+sum_C = np.sum(C)
+plt.arrow(
+    0, 0,
+    sum_C.real, sum_C.imag,
+    length_includes_head=True,
+    head_width=0.07 * abs(sum_C),
+    color="green",
+    alpha=0.9,
+    label="Resultant ΣF"
+)
+
+
+# Start / end markers
+plt.scatter(0, 0, c="black", s=40, label="Start")
+
+plt.xlabel("Re(F)")
+plt.ylabel("Im(F)")
+plt.title("Head-to-tail Argand walk (sorted by resolution)")
+plt.axis("equal")
+plt.grid(True)
+plt.legend()
+plt.show()
+
+
+# Assuming C is your filtered, complex structure factor array
+sum_C = np.sum(C)             # sum all complex vectors
+
+# Magnitude
+magnitude = np.abs(sum_C)
+
+# Phase in degrees (-180 to +180)
+phase_deg = np.angle(sum_C, deg=True)
+
+print(f"Resultant vector magnitude: {magnitude:.3f}")
+print(f"Resultant vector phase: {phase_deg:.2f}°")