updated to include argparse and figs in a fucntion

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 <path to top folder> 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 )
+        stats_df_1 = pd.DataFrame( stats )
-        results_df = pd.concat( ( results_df, results_df_1 ) )
+        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()
+    fig, (ax1, ax3) = plt.subplots(1, 2)
-
-    # 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
     ax2 = ax1.twinx()
+    ax4 = ax3.twinx()
 
-    # std_a plot
+    plot_indexed_std(stats_df, ax1, ax2)
-    color = "tab:blue"
+    plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4)
-    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)
 
-    # std_b plot
+    fig.tight_layout()
-    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
     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 )