佐久間 美波
301 lines
12 KiB
Python
Executable File
301 lines
12 KiB
Python
Executable File
#!/usr/bin/env python
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# ./dump2cube.py -i 050Li3PS4-050LiI.lammpstrj -m 160 160 160 -cut 8
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import numpy as np
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import argparse
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import re
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import os
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import time
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description = """This is a test program"""
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par = argparse.ArgumentParser(description=description)
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par.add_argument('-i', '--trjfile', default="", required=True,
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help='input trjfile')
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par.add_argument('-m', '--mesh', default=(30, 30, 30), required=False,
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nargs=3, type=int, help='mesh grid')
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par.add_argument('-cut', '--cutoff', default=7, required=False,
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type=float, help='mesh grid')
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args = par.parse_args()
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ang_borr = 0.5291772109217
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dirname = os.path.dirname(__file__)
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class LammpsTrj():
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def __init__(self):
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"""
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trjファイルを読み込んで、原子数、lattice、ステップ数を格納
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"""
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with open(args.trjfile) as o:
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data = o.read().split()
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self.atoms = int(data[7]) # 原子数
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data = np.array(data).reshape(-1, self.atoms*7+32)
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self.lattices = data[:, 17:23].astype(float) # (step, 6)
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self.data = data[:, 32:] # 座標データ
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self.steps = data.shape[0]
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self.mesh = args.mesh
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self.cutoff = args.cutoff
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def setLattice(self, lat):
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"""
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latticeの形によって操作を分岐
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self.M, self.M_を作成
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"""
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if lat.shape[0] == 6:
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M = np.array([[lat[1]-lat[0], 0, 0],
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[0, lat[3]-lat[2], 0],
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[0, 0, lat[5]-lat[4]]]).astype(float)
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if lat.shape[0] == 9:
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xlo_bound, xhi_bound, xy = lat[0, 0], lat[0, 1], lat[0, 2]
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ylo_bound, yhi_bound, xz = lat[1, 0], lat[1, 1], lat[1, 2]
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zlo_bound, zhi_bound, yz = lat[2, 0], lat[2, 1], lat[2, 2]
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xlo = xlo_bound - np.min([0.0, xy, xz, xy+xz])
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xhi = xhi_bound - np.max([0.0, xy, xz, xy+xz])
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ylo = ylo_bound - np.min([0.0, yz])
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yhi = yhi_bound - np.max([0.0, yz])
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zlo = zlo_bound
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zhi = zhi_bound
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lx = xhi - xlo
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ly = yhi - ylo
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lz = zhi - zlo
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a = lx
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b = np.sqrt(ly**2 + xy**2)
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c = np.sqrt(lz**2 + xz**2 + yz**2)
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alpha = np.arccos((xy*xz + ly*yz)/b/c)
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beta = np.arccos(xz/c)
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gamma = np.arccos(xy/b)
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v1 = [a, 0, 0]
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v2 = [b*np.cos(gamma), b*np.sin(gamma), 0]
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v3 = [c*np.cos(beta),
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c*(np.cos(alpha)-np.cos(beta)*np.cos(gamma))/np.sin(gamma),
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c*np.sqrt(1+2*np.cos(alpha)*np.cos(beta)*np.cos(gamma)
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- np.cos(alpha)**2-np.cos(beta)**2
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- np.cos(gamma)**2 / np.sin(gamma))]
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M = np.array([v1, v2, v3])
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return M
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def getOnestep(self, step):
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"""
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引数のstepにおける座標を、原子ごとにself.elems(辞書)に格納する
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self.elemsの座標は分率座標
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"""
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step_data = self.data[step, :].reshape(self.atoms, -1) # (atoms, 6)
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self.M = self.setLattice(self.lattices[step])
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self.M_ = np.linalg.inv(self.M)
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self.elems = {}
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for e in np.unique(step_data[:, 2]):
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d = step_data[step_data[:, 2] == e, 3:].astype(float)
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dxyz = d[:, 0:3]
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dxyz = dxyz @ self.M_
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dxyz = dxyz - np.floor(dxyz)
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d[:, 0:3] = dxyz
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self.elems[e] = d # 元素ごとに結晶座標とmol番号を格納
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return self.elems
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def makeCube(self, trjfile):
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"""
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すべてのP原子について処理を行う。
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P原子ごとに最も近い4つのI原子を選出し、回転・アライメントを行う。
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"""
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# Li用の座標リスト
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self.li_coords_list = []
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# I用のリスト (全てまとめて格納)
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self.i_coords_list = []
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# ヒストグラム結果格納用
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self.li_hist = None
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self.i_hist = None
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count_p = 0 # 処理したP原子の総数カウント用
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for step in range(self.steps):
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print(f"processing {step} step")
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self.elems = self.getOnestep(step)
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elems_ = {k: v.copy() for k, v in self.elems.items()} # copy
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# 全てのP原子に対してループ処理を行う
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for p_data in elems_["P"]:
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# 全てのI原子の中から、このP原子に最も近い4つのI原子を特定する
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i_diffs = elems_["I"][:, 0:3] - p_data[0:3]
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i_diffs = i_diffs - np.around(i_diffs) # 周期境界条件の考慮(分率)
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i_diffs_abs = i_diffs @ self.M # 絶対座標へ変換
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distances = np.linalg.norm(i_diffs_abs, axis=1)
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sorted_indices = np.argsort(distances)
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closest_Is = elems_["I"][sorted_indices[:4]] # 近い順に4つ抽出
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nearest_I_coords = closest_Is[0] # 回転定義に使用する「最も近いI」
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# S原子の処理 (同じmol番号のSを取得)
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s_data = elems_["S"][elems_["S"][:, 3] == p_data[3]]
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# 1つのPS4に注目して、Pを原点に配置するための距離計算
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si_diff = s_data[:, 0:3] - nearest_I_coords[0:3]
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si_diff = si_diff - np.around(si_diff)
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si_diff = np.linalg.norm(si_diff, axis=1)
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# 最も遠いSをz軸上に配置するため、そのインデックスを取得
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n_S_idx = np.argmax(si_diff)
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# その他のインデックスを取得
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s_indices = [0, 1, 2, 3]
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s_indices.remove(n_S_idx)
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n2_S_idx = s_indices[0]
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s_xyz = s_data[:, 0:3] - p_data[0:3]
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s_xyz = s_xyz - np.round(s_xyz)
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s_xyz = s_xyz @ self.M # 絶対座標へ
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# 1つのSをyz平面上に配置(z軸周り回転)
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theta = np.arctan2(s_xyz[n_S_idx][0], s_xyz[n_S_idx][1])
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self.Mat_z = np.array([[np.cos(-theta), np.sin(-theta), 0],
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[-np.sin(-theta), np.cos(-theta), 0],
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[0, 0, 1]])
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s_xyz = (self.Mat_z @ s_xyz.T).T
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# 1つのSをz軸上に配置(x軸周り回転)
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theta2 = np.arctan2(s_xyz[n_S_idx][1], s_xyz[n_S_idx][2])
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self.Mat_x = np.array([[1, 0, 0],
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[0, np.cos(-theta2), np.sin(-theta2)],
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[0, -np.sin(-theta2), np.cos(-theta2)]])
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s_xyz = (self.Mat_x @ s_xyz.T).T
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# もう1つのSをyz平面上に配置(z軸周り回転)
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theta3 = np.arctan2(s_xyz[n2_S_idx][0], s_xyz[n2_S_idx][1])
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self.Mat_z2 = np.array([[np.cos(-theta3), np.sin(-theta3), 0],
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[-np.sin(-theta3), np.cos(-theta3), 0],
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[0, 0, 1]])
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s_xyz = (self.Mat_z2 @ s_xyz.T).T
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self.s_xyz = s_xyz
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# PS4座標保存 (Output用)
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p_xyz = np.array([0, 0, 0])
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self.ps4_coord = np.vstack((p_xyz, s_xyz))
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self.ps4_coord = self.ps4_coord / ang_borr
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# 座標回転関数の定義
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def rotate_coords(coords_fractional):
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if len(coords_fractional) == 0:
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return np.array([])
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rot_xyz = coords_fractional[:, 0:3] - p_data[0:3]
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rot_xyz = rot_xyz - np.round(rot_xyz)
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rot_xyz = rot_xyz @ self.M
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rot_xyz = (self.Mat_z @ rot_xyz.T).T
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rot_xyz = (self.Mat_x @ rot_xyz.T).T
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rot_xyz = (self.Mat_z2 @ rot_xyz.T).T
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return rot_xyz
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# ヒストグラム範囲
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bounds = [[-(self.cutoff)/ang_borr,
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(self.cutoff)/ang_borr]] * 3
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# --- Liの処理: 座標をリストに追加 ---
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segment_xyz_Li = []
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for xyz in self.elems["Li"]:
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diff = xyz[0:3] - p_data[0:3]
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diff = diff - np.around(diff)
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diff = diff @ self.M
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diff = np.linalg.norm(diff)
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for xyzdata in xyz[diff < self.cutoff, :]:
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segment_xyz_Li.append(xyzdata)
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segment_xyz_Li = np.array(segment_xyz_Li, dtype=float)
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if len(segment_xyz_Li) > 0:
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rotationed_Li = rotate_coords(segment_xyz_Li)
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self.li_coords_list.extend(rotationed_Li.tolist())
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# --- Iの処理: 抽出した4つのIを回転させてまとめてリストに追加 ---
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rotationed_Is = rotate_coords(closest_Is)
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self.i_coords_list.extend(rotationed_Is.tolist())
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count_p += 1
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# ループ終了後にまとめてヒストグラム計算
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print("Calculating Histograms...")
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volume = (self.cutoff*2)**3
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bounds = [[-(self.cutoff)/ang_borr, (self.cutoff)/ang_borr]] * 3
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# Li
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if len(self.li_coords_list) > 0:
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print("num_Li: ", len(self.li_coords_list))
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li_arr = np.array(self.li_coords_list) / ang_borr
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li_hist, _ = np.histogramdd(li_arr, bins=self.mesh, range=bounds)
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self.li_hist = li_hist.ravel() / len(self.li_coords_list) / volume
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print("sum_Li: ", np.sum(self.li_hist) * volume)
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# I (All 4 atoms combined)
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if len(self.i_coords_list) > 0:
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print("num_I: ", len(self.i_coords_list))
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i_arr = np.array(self.i_coords_list) / ang_borr
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i_hist, _ = np.histogramdd(i_arr, bins=self.mesh, range=bounds)
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self.i_hist = i_hist.ravel() / len(self.i_coords_list) / volume
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print("sum_I: ", np.sum(self.i_hist) * volume)
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def outputCube(self):
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output_start_time = time.time()
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base = re.match(r"(\d{3}.*?LiI).*?",
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args.trjfile.split("/")[-1]).group(1)
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self.atomsDic = {'I': '53', 'Li': '3', 'P': '15', 'S': '16'}
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if self.li_hist is None and self.i_hist is None:
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print("No data found.")
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return
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def get_header():
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body = f"created from {__file__}, {args}\n"
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body += "Contains the selected quantity on a FFT grid\n"
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origin = [-(self.cutoff)/ang_borr] * 3
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body += "{:>5d}{:>12.7f}{:>12.7f}{:>12.7f}\n".format(
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5, *origin)
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body += "{:>5d}{:>12.7f}{:>12.7f}{:>12.7f}\n".format(
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self.mesh[0], (self.cutoff*2)/self.mesh[0]/ang_borr, 0, 0)
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body += "{:>5d}{:>12.7f}{:>12.7f}{:>12.7f}\n".format(
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self.mesh[1], 0, (self.cutoff*2)/self.mesh[1]/ang_borr, 0)
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body += "{:>5d}{:>12.7f}{:>12.7f}{:>12.7f}\n".format(
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self.mesh[2], 0, 0, (self.cutoff*2)/self.mesh[2]/ang_borr)
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# Pの座標
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body += "{:>5d}{:>12.7f}{:>12.7f}{:>12.7f}{:>12.7f}\n".format(
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int(self.atomsDic["P"]), float(self.atomsDic["P"]), *self.ps4_coord[0])
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# Sの座標
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for s_coord in self.ps4_coord[1:5]:
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body += "{:>5d}{:>12.7f}{:>12.7f}{:>12.7f}{:>12.7f}\n".format(
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int(self.atomsDic["S"]), float(self.atomsDic["S"]), *s_coord)
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return body
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# Liの出力
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if self.li_hist is not None:
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body = get_header()
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for idx, r in enumerate(self.li_hist):
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if idx % 6 == 5:
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body += "{:>13.5E}\n".format(r)
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else:
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body += "{:>13.5E}".format(r)
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outfile = f"{dirname}/{base}_PS4_Li.cube"
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with open(outfile, "w") as o:
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o.write(body)
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print(f"{outfile} was created.")
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# Iの出力 (まとめて1ファイルに出力)
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if self.i_hist is not None:
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body = get_header()
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for idx, r in enumerate(self.i_hist):
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if idx % 6 == 5:
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body += "{:>13.5E}\n".format(r)
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else:
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body += "{:>13.5E}".format(r)
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outfile = f"{dirname}/{base}_PS4_I.cube"
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with open(outfile, "w") as o:
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o.write(body)
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print(f"{outfile} was created.")
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output_end_time = time.time()
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print(f"output_time : {output_end_time - output_start_time} s")
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trj = LammpsTrj()
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trj.makeCube(args.trjfile)
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trj.outputCube()
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