labels to graph and removal of argparse

clen_tools/distance-scan-analysis.py

@@ -9,7 +9,7 @@ given a regular array of crystfel folders with different detector distances
 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
+python update-geom-from-lab6.py <path-to-scan-folder>
 
 # output
 creates plots of the unit cell axis against clen
@@ -21,7 +21,9 @@ import regex as re
 import os
 import numpy as np
 import matplotlib.pyplot as plt
-import argparse
+import sys
+from scipy.optimize import curve_fit
+from scipy.signal import peak_widths, find_peaks
 
 
 def scrub_clen( stream_pwd ):
@@ -84,7 +86,7 @@ def scrub_us( stream ):
     except AttributeError:
         return np.nan
 
-def find_clen_values(stats_df):
+def find_clen_values( stats_df ):
 
     def find_min_clen(col_name):
         min_val = stats_df[col_name].min()
@@ -92,17 +94,45 @@ def find_clen_values(stats_df):
         min_clen = min_row['clen'].values[0]
         return min_val, min_clen
 
+    def gauss(x, *p):
+        
+        A, mu, sigma = p
+        return A * np.exp(-(x-mu)**2/(2.*sigma**2))
+
+    p0 = [ 30, 0.111, 0.01 ]
+    parameters, covariance = curve_fit( gauss, stats_df.clen, stats_df.indexed, p0=p0 )
+
+    # Get the fitted curve
+    stats_df[ "gaus" ] = gauss( stats_df.clen, *parameters)
+    # find peak centre
+    peaks = find_peaks( stats_df.gaus.values )
+
+    # find full peak width
+    fwhm = peak_widths( stats_df.gaus.values, peaks[0], rel_height=0.5 )
+
+    fwhm_str = int( round( fwhm[2][0], 0 ) )
+    fwhm_end = int( round( fwhm[3][0], 0 ) )
+
+    # translate width into motor values
+    indexed_start = stats_df.iloc[ fwhm_str, 0 ]
+    indexed_end = stats_df.iloc[ fwhm_end, 0 ]
+    mid_gauss = stats_df.clen.iloc[ peaks[0] ].values[0]
+
+    # cut df to only include indexed patterns
+    stats_df = stats_df[ ( stats_df.clen < indexed_end ) & ( stats_df.clen > indexed_start ) ]
+    
+    # calculate minimum values
     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')
+
+    # find possible clens
+    suggested_clen = (min_alpha_clen + min_beta_clen + min_gamma_clen )/3
+    suggested_clen = round(suggested_clen, 4)
     
-    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
+    print( "middle of indexing gaussion fit of scan = {0}".format( mid_gauss ) )
+    print( "mean minimum of alpha, beta, gamma of scan = {0}".format( suggested_clen ) )
 
 
 def plot_indexed_std( stats_df, ax1, ax2 ):
@@ -120,17 +150,16 @@ def plot_indexed_std( stats_df, ax1, ax2 ):
     ax2.tick_params(axis='y', labelcolor=color)
 
     # std_a plot
-    color = "lightsteelblue"
-    ax2.plot(stats_df.clen, stats_df.std_a, color=color)
+    color = "turquoise"
+    ax2.plot(stats_df.clen, stats_df.std_a, color=color, label="a" )
 
     # std_b plot
-    color = "cornflowerblue"
-    ax2.plot(stats_df.clen, stats_df.std_b, color=color)
+    color = "deepskyblue"
+    ax2.plot(stats_df.clen, stats_df.std_b, color=color, label="b" )
 
     # std_c plot
     color = "royalblue"
-    ax2.plot(stats_df.clen, stats_df.std_c, color=color)
-
+    ax2.plot(stats_df.clen, stats_df.std_c, color=color, label="c" )
 
 def plot_indexed_std_alpha_beta_gamma( stats_df, ax1, ax2 ):
 
@@ -147,16 +176,16 @@ def plot_indexed_std_alpha_beta_gamma( stats_df, ax1, ax2 ):
     ax2.tick_params(axis='y', labelcolor=color)
 
     # std_alpha plot
-    color = "limegreen"
-    ax2.plot(stats_df.clen, stats_df.std_alpha, color=color)
+    color = "yellow"
+    ax2.plot(stats_df.clen, stats_df.std_alpha, color=color, label="alpha" )
 
     # std_beta plot
-    color = "darkgreen"
-    ax2.plot(stats_df.clen, stats_df.std_beta, color=color)
+    color = "green"
+    ax2.plot(stats_df.clen, stats_df.std_beta, color=color, label="beta" )
 
     # std_gamma plot
-    color = "green"
-    ax2.plot(stats_df.clen, stats_df.std_gamma, color=color)
+    color = "darkolivegreen"
+    ax2.plot(stats_df.clen, stats_df.std_gamma, color=color, label="gamma" )
     
 def main( top_dir ):
 
@@ -175,7 +204,6 @@ def main( top_dir ):
         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
@@ -208,7 +236,7 @@ def main( top_dir ):
         stats_df_1 = pd.DataFrame( stats )
         stats_df = pd.concat( ( stats_df, stats_df_1 ) )
 
-        print( "done" )
+    print( "done" )
 
     # reset index
     stats_df = stats_df.reset_index( drop=True )
@@ -224,20 +252,12 @@ def main( top_dir ):
     plot_indexed_std(stats_df, ax1, ax2)
     plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4)
 
+    fig.legend(loc="upper center")
     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 )
+    stream_pwd = sys.argv[1]
+    main( stream_pwd )