From f4907ad02b556e4e5be9c575f4e2cfa76330570f Mon Sep 17 00:00:00 2001 From: Beale John Henry Date: Sun, 25 Jun 2023 23:06:02 +0200 Subject: [PATCH] updated to include argparse and figs in a fucntion --- clen_tools/distance-scan-analysis.py | 152 +++++++++++++++++++++------ 1 file changed, 120 insertions(+), 32 deletions(-) diff --git a/clen_tools/distance-scan-analysis.py b/clen_tools/distance-scan-analysis.py index 7bcbadc..ae5cfe8 100644 --- a/clen_tools/distance-scan-analysis.py +++ b/clen_tools/distance-scan-analysis.py @@ -1,4 +1,19 @@ +#!/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 @@ -6,6 +21,7 @@ import regex as re import os import numpy as np import matplotlib.pyplot as plt +import argparse def scrub_clen( stream_pwd ): @@ -67,6 +83,80 @@ def scrub_us( stream ): 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 ): @@ -76,7 +166,7 @@ def main( top_dir ): print( "done" ) # making results df for unit cell and index no. - results_df = pd.DataFrame() + stats_df = pd.DataFrame() # loop through stream files and collect unit_cell information print( "looping through stream files to collect unit cell, indexed information" ) @@ -101,55 +191,53 @@ def main( top_dir ): 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_c" : std_c, + "std_alpha" : std_alpha, + "std_beta" : std_beta, + "std_gamma" : std_gamma, } ] - results_df_1 = pd.DataFrame( stats ) - results_df = pd.concat( ( results_df, results_df_1 ) ) + stats_df_1 = pd.DataFrame( stats ) + stats_df = pd.concat( ( stats_df, stats_df_1 ) ) print( "done" ) # reset index - results_df = results_df.reset_index( drop=True ) + 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 = plt.subplots() - - # indexed images plot - color = "tab:red" - ax1.set_xlabel( "clen" ) - ax1.set_ylabel( "indexed", color=color ) - ax1.plot( results_df.clen, results_df.indexed, color=color) - ax1.tick_params( axis="y", labelcolor=color) - - # instantiate a second axes that shares the same x-axis + fig, (ax1, ax3) = plt.subplots(1, 2) ax2 = ax1.twinx() + ax4 = ax3.twinx() - # std_a plot - color = "tab:blue" - ax2.set_ylabel( "st.deviation", color=color ) - ax2.plot( results_df.clen, results_df.std_a, color=color ) - ax2.tick_params(axis='y', labelcolor=color) + plot_indexed_std(stats_df, ax1, ax2) + plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4) - # std_b plot - ax2.plot( results_df.clen, results_df.std_b, color=color ) - ax2.tick_params(axis='y', labelcolor=color) - - # std_b plot - ax2.plot( results_df.clen, results_df.std_c, color=color ) - ax2.tick_params(axis='y', labelcolor=color) - - fig.tight_layout() # otherwise the right y-label is slightly clipped + fig.tight_layout() plt.show() -# variables -top_dir = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/coarse_scan" +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 ) -main( top_dir )