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updated to include argparse and figs in a fucntion

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This commit is contained in:
Beale John Henry
2023-06-25 23:06:02 +02:00
parent 882a4ebd29
commit f4907ad02b
1 changed files with 120 additions and 32 deletions
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152
clen_tools/distance-scan-analysis.py
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@@ -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 )
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 )