XDSの回折点viewer

# -*- coding: utf-8 -*-
# ピラタスなどの2次元検出器で撮影しXDSで解析したときに反射のpredictionを連続的に見たい時があります
# ADXVを外部プログラムとしてcbfからjpegに変換し、XDS_ASCII.HKLにある反射predictionを重ね合わせるコードです、SPOT.XDSに変更してもよいです
# 0からframe_numまでをstep_numで飛ばしてframe_num/step_num枚のjpegを作成します
# 書き出し専用dirを作り、そこでImageJのstacking機能を利用してチェックします
# なのでPATHが設定済みのADXVが必要です、適宜dir、file名を書き換えてください
# コメントアウトしてますが、cv2を使ったrainbow疑似カラーのセットも作れるようになってます
# Linux terminal上で>>python3 xds-spotview.py 他お好みの方法で動かしてください
#共同利用者以外への利用責任はなく質問受付はしません。D2MatEのプロジェクト支援を受けて行われています
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import subprocess
#import cv2

# setting parameter
delta_omega = 0.1 # delta omega value
mosaicity = 0.5 # draw the frame in Evald_frame+-mosaicity/delta_omega
delta_frame = mosaicity/delta_omega
start_frame = 100 # start frame of cbf-to-jpeg
end_frame = 1800
step_num = 50 # prediction steps: frame_num/step_num frames will be converted
cbf_file = './image-tth30/220213_cytidine_m90-90_2_' # cbf file without numbering and extension
jpg_file = 'cytidine-tth30HS_' # jpg file without numbering and extension
jpgsp_file = './imagestack/cytidine-tth30HS_sp_' # jpg file with SPOT without numbering and extension
XDS_FullSPOT = 'XDS_ASCII.HKL' # XDS SPOT list
# jpg_col_file = 'test_col_' # jpg file without numbering and extension

# SPOT data reading to pandas DataFrame
df1 = pd.read_csv(XDS_FullSPOT, delim_whitespace=True, comment="!", header=None, usecols=[0,1,2], names=['h','k','l'], dtype='int')
df2 = pd.read_csv(XDS_FullSPOT, delim_whitespace=True, comment="!", header=None, usecols=[5,6,7], names=['dfx','dfy','dfz'], dtype='float')
df_s = pd.concat([df1, df2], axis=1).sort_values('dfz')  # conbine df1 (=dfx,dfy,dfz) and df2 (flag,h,k,l)

# main routine of SPOT selection, SPOT draw, convert cbf-to-jpg, jpg draw with SPOT
for Ewald_frame in range(start_frame, end_frame, step_num):

    # selection the SPOT data near the Bragg condiction
    bfEw_frame = int(Ewald_frame - delta_frame)
    afEw_frame = int(Ewald_frame + delta_frame)
    df_bfEw = df_s[(bfEw_frame < df_s['dfz']) & (df_s['dfz'] < Ewald_frame)]
    df_onEw = df_s[df_s['dfz'].astype(int) == Ewald_frame ]
    df_afEw = df_s[(Ewald_frame < df_s['dfz']) & (df_s['dfz'] < afEw_frame)]
    print(Ewald_frame)
    print(df_bfEw)
    print(df_onEw)
    print(df_afEw)

    # plot scatter figure of SPOT data
    fig, ax = plt.subplots(figsize=(10,10))
    plt.xlim(1, 981)
    plt.ylim(1034, 1)
    plt.rcParams["font.size"] = 18
    plt.xlabel('pixcel x')
    plt.ylabel('pixcel y')
    plt.scatter(df_bfEw['dfx'], df_bfEw['dfy'], marker="D", s=100, facecolor='None', edgecolors='blue')
    plt.scatter(df_onEw['dfx'], df_onEw['dfy'], marker="o", s=100, facecolor='None', edgecolors='blue')
    plt.scatter(df_afEw['dfx'], df_afEw['dfy'], marker="s", s=100, facecolor='None', edgecolors='blue')

    # convert cbf-to-jpg file on ADXV
    fnamecbf = cbf_file + f'{Ewald_frame:05d}' + '.cbf'
    fnamejpg = jpg_file + f'{Ewald_frame:05d}' + '.jpeg'
    #fnamejpg_col = jpg_col_file + f'{Ewald_frame:05d}' + '.jpeg'
    # change the {0:05d} part depending on the file number of digits
    cmd = 'adxv -sa -jpeg_scale 0.9 '+ fnamecbf + ' ' +fnamejpg
    print(cmd)
    subprocess.run(cmd, shell=True)

    # plot jpg data with the SPOT
    fnamejpgsp = jpgsp_file + f'{Ewald_frame:05d}' + '.jpeg'
    im = Image.open(fnamejpg)
    xlim_size = ax.get_xlim()
    ylim_size = ax.get_ylim()
    plt.imshow(im, extent=[*xlim_size, *ylim_size], aspect='auto', alpha=1)
    plt.savefig(fnamejpgsp)
    #pic=cv2.imread(fnamejpg)
    #rb_color = cv2.applyColorMap(pic, cv2.COLORMAP_RAINBOW)
    #cv2.imwrite(fnamejpg_col, np.array(rb_color))