#!/usr/bin/env python3 # author J.Beale, T.Mason """ # aim given a regular array of crystfel folders with different detector distances - naming covention = #.###/#.###.stream script will generate a graph analysing the detector distance as a function of the unit-cell constants # usage python update-geom-from-lab6.py -f either coarse or fine # output creates plots of the unit cell axis against clen """ # modules import pandas as pd import regex as re import os import numpy as np import matplotlib.pyplot as plt import argparse def scrub_clen( stream_pwd ): # get clen from stream name # example - /sf/cristallina/data/p20590/work/process/jhb/detector_refinement/coarse_scan/0.115/0.115.stream # scrub clen and return - else nan try: pattern = r"0\.\d+/(0\.\d+)\.stream" re_search = re.search( pattern, stream_pwd ) clen = re_search.group( 1 ) if AttributeError: return float( clen ) except AttributeError: return np.nan def find_streams( top_dir ): # create df for streams stream_df = pd.DataFrame() # search for all files that end with .stream for path, dirs, files in os.walk( top_dir ): for name in files: if name.endswith( ".stream" ): # get stream pwd stream_pwd = os.path.join( path, name ) # scrub clen from stream clen = scrub_clen( stream_pwd ) # put clen and stream pwd into df data = [ { "stream_pwd" : stream_pwd, "clen" : clen } ] stream_df_1 = pd.DataFrame( data ) stream_df = pd.concat( ( stream_df, stream_df_1 ) ) # sort df based on clen stream_df = stream_df.sort_values( by="clen" ) # reset df index stream_df = stream_df.reset_index( drop=True ) # return df of streams and clens return stream_df def scrub_us( stream ): # get uc values from stream file # example - Cell parameters 7.71784 7.78870 3.75250 nm, 90.19135 90.77553 90.19243 deg # scrub clen and return - else nan try: pattern = r"Cell\sparameters\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\snm,\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\sdeg" cells = re.findall( pattern, stream ) if AttributeError: return cells except AttributeError: return np.nan def find_clen_values(stats_df): def find_min_clen(col_name): min_val = stats_df[col_name].min() min_row = stats_df[stats_df[col_name] == min_val] min_clen = min_row['clen'].values[0] return min_val, min_clen min_alpha_val, min_alpha_clen = find_min_clen('std_alpha') min_beta_val, min_beta_clen = find_min_clen('std_beta') min_gamma_val, min_gamma_clen = find_min_clen('std_gamma') min_c_val, min_c_clen = find_min_clen('std_c') print("The value of clen for the minimum alpha value of {} is {}".format(min_alpha_val, min_alpha_clen)) print("The value of clen for the minimum beta value of {} is {}".format(min_beta_val, min_beta_clen)) print("The value of clen for the minimum gamma value of {} is {}".format(min_gamma_val, min_gamma_clen)) print("The value of clen for the minimum c value of {} is {}".format(min_c_val, min_c_clen)) # return min_alpha_clen, min_beta_clen, min_gamma_clen, min_c_clen, min_alpha_val, min_beta_val, min_gamma_val, min_c_val def plot_indexed_std( stats_df, ax1, ax2 ): # indexed images plot color = "tab:red" ax1.set_xlabel("clen") ax1.set_ylabel("indexed", color=color) ax1.plot(stats_df.clen, stats_df.indexed, color=color) ax1.tick_params(axis="y", labelcolor=color) # label color color = "tab:blue" ax2.set_ylabel("a,b,c st.deviation", color=color) ax2.tick_params(axis='y', labelcolor=color) # std_a plot color = "lightsteelblue" ax2.plot(stats_df.clen, stats_df.std_a, color=color) # std_b plot color = "cornflowerblue" ax2.plot(stats_df.clen, stats_df.std_b, color=color) # std_c plot color = "royalblue" ax2.plot(stats_df.clen, stats_df.std_c, color=color) def plot_indexed_std_alpha_beta_gamma( stats_df, ax1, ax2 ): # indexed images plot color = "tab:red" ax1.set_xlabel("clen") ax1.set_ylabel("indexed", color=color) ax1.plot(stats_df.clen, stats_df.indexed, color=color) ax1.tick_params(axis="y", labelcolor=color) # label color color = "tab:green" ax2.set_ylabel("alpha, beta, gamma st.deviation", color=color) ax2.tick_params(axis='y', labelcolor=color) # std_alpha plot color = "limegreen" ax2.plot(stats_df.clen, stats_df.std_alpha, color=color) # std_beta plot color = "darkgreen" ax2.plot(stats_df.clen, stats_df.std_beta, color=color) # std_gamma plot color = "green" ax2.plot(stats_df.clen, stats_df.std_gamma, color=color) def main( top_dir ): # find stream files from process directory print( "finding stream files" ) stream_df = find_streams( top_dir ) print( "done" ) # making results df for unit cell and index no. stats_df = pd.DataFrame() # loop through stream files and collect unit_cell information print( "looping through stream files to collect unit cell, indexed information" ) for index, row in stream_df.iterrows(): stream_pwd, clen = row[ "stream_pwd" ], row[ "clen" ] # open stream file print( "scrubbing stream for clen={0}".format( clen ) ) stream = open( stream_pwd, "r" ).read() # scrub unit cell information cells = scrub_us( stream ) # put cells in df cols = [ "a", "b", "c", "alpha", "beta", "gamma" ] cells_df = pd.DataFrame( cells, columns=cols ) cells_df = cells_df.astype( float ) # calc stats indexed = len( cells_df ) std_a = cells_df.a.std() std_b = cells_df.b.std() std_c = cells_df.c.std() std_alpha = cells_df.alpha.std() std_beta = cells_df.beta.std() std_gamma = cells_df.gamma.std() # put stats in results df stats = [ { "clen" : clen, "indexed" : indexed, "std_a" : std_a, "std_b" : std_b, "std_c" : std_c, "std_alpha" : std_alpha, "std_beta" : std_beta, "std_gamma" : std_gamma, } ] stats_df_1 = pd.DataFrame( stats ) stats_df = pd.concat( ( stats_df, stats_df_1 ) ) print( "done" ) # reset index stats_df = stats_df.reset_index( drop=True ) #print clen for minimum alpha, beta, and gamma values find_clen_values(stats_df) # plot results fig, (ax1, ax3) = plt.subplots(1, 2) ax2 = ax1.twinx() ax4 = ax3.twinx() plot_indexed_std(stats_df, ax1, ax2) plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4) fig.tight_layout() plt.show() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "-f", "--scan_folder", help="give the scan folder path used in the earlier part of the calculation", type=str, ) args = parser.parse_args() # run geom converter main( args.scan_folder )