Added Robins adjustments for FEL
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@ -4,7 +4,6 @@ from typing import Optional
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import numpy as np
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from numpy.typing import ArrayLike, NDArray
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from numpy.polynomial.polynomial import Polynomial as Poly
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import math
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from scipy.spatial import KDTree
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import pandas as pd
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import multiprocessing as mp
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@ -49,7 +48,7 @@ def _pbc_points_reduced(
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def _build_tree(points, box, r_max, pore_geometry):
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if np.all(np.diag(np.diag(box)) == box):
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tree = KDTree(points, boxsize=box)
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tree = KDTree(points % box, boxsize=box)
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points_pbc_index = None
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else:
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points_pbc, points_pbc_index = _pbc_points_reduced(
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@ -79,8 +78,7 @@ def occupation_matrix(
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z_bins = np.arange(0, box[2][2] + edge_length, edge_length)
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bins = [x_bins, y_bins, z_bins]
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# Trajectory is split for parallel computing
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size = math.ceil(len(frame_indices) / nodes)
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indices = [frame_indices[i : i + size] for i in range(0, len(frame_indices), size)]
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indices = np.array_split(frame_indices, nodes)
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pool = mp.Pool(nodes)
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results = pool.map(
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partial(_calc_histogram, trajectory=trajectory, bins=bins), indices
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@ -274,7 +272,11 @@ def distance_resolved_energies(
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def find_energy_maxima(
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energy_df: pd.DataFrame, r_min: float, r_max: float
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energy_df: pd.DataFrame,
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r_min: float,
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r_max: float,
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r_eval: float = None,
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degree: int = 2,
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) -> pd.DataFrame:
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distances = []
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energies = []
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@ -283,6 +285,9 @@ def find_energy_maxima(
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x = np.array(data_d["r"])
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y = np.array(data_d["energy"])
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mask = (x >= r_min) * (x <= r_max)
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p3 = Poly.fit(x[mask], y[mask], deg=2)
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energies.append(np.max(p3(np.linspace(r_min, r_max, 1000))))
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p3 = Poly.fit(x[mask], y[mask], deg=degree)
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if r_eval is None:
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energies.append(np.max(p3(np.linspace(r_min, r_max, 1000))))
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else:
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energies.append(p3(r_eval))
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return pd.DataFrame({"d": distances, "energy": energies})
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