changed script name
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Beale John Henry
326
reduction_tools/stream_select_cell.py
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326
reduction_tools/stream_select_cell.py
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#!/usr/bin/env python3
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# author J.Beale
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"""
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# aim
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randomly select a series of crystals from a stream file and
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then compile them into the correctly formated .stream
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# usage
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python stream_random.py -s <path to stream>
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-o output file names
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-n sample size
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-r how many repeat random samples do you want?
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# output
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.stream file with random sample of xtals
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"""
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# modules
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import re
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import argparse
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import pandas as pd
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import numpy as np
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import os
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def extract_chunks( input_file ):
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# setup
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chunk_df = pd.DataFrame()
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image_no = []
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chunks = []
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hits = []
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collect_lines = False
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# Open the input file for reading
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with open(input_file, 'r') as f:
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for line in f:
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# Check for the start condition
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if line.startswith('----- Begin chunk -----'):
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hit = False
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collect_lines = True
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chunk_lines = []
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if collect_lines:
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chunk_lines.append(line)
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# find image_no
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if line.startswith( "Event:" ):
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image_search = re.findall( r"Event: //(\d+)", line )
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image = int(image_search[0])
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image_no.append( image )
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# is there a hit in chunk
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if line.startswith( "Cell parameters" ):
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hit = True
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if line.startswith('----- End chunk -----'):
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collect_lines = False # Stop collecting lines
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chunks.append( chunk_lines )
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hits.append( hit )
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chunk_df[ "chunks" ] = chunks
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chunk_df[ "image_no" ] = image_no
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chunk_df[ "hit" ] = hits
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return chunk_df
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def scrub_cells( line ):
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# get uc values from stream file
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# example - Cell parameters 7.71784 7.78870 3.75250 nm, 90.19135 90.77553 90.19243 deg
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pattern = r"Cell\sparameters\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\snm,\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\sdeg"
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a = re.search( pattern, line ).group(1)
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b = re.search( pattern, line ).group(2)
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c = re.search( pattern, line ).group(3)
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alpha = re.search( pattern, line ).group(4)
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beta = re.search( pattern, line ).group(5)
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gamma = re.search( pattern, line ).group(6)
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data = [ { "a" : float( a )*10,
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"b" : float( b )*10,
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"c" : float( c )*10,
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"alpha" : float( alpha ),
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"beta" : float( beta ),
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"gamma" : float( gamma )
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} ]
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cell_df = pd.DataFrame( data )
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return cell_df
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def extract_xtals( chunk ):
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# setup
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xtals = []
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cells_df = pd.DataFrame()
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collect_crystal_lines = False
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# Open the input file for reading
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for line in chunk:
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# Check for the xtals start condition
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if line.startswith('--- Begin crystal'):
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collect_crystal_lines = True
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xtal_lines = []
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if collect_crystal_lines:
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xtal_lines.append(line)
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if line.startswith('--- End crystal\n'):
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collect_crystal_lines = False # Stop collecting lines
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xtals.append( xtal_lines )
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if line.startswith( "Cell" ):
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cell_df = scrub_cells( line )
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cells_df = pd.concat( ( cells_df, cell_df ) )
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# reset index of cells_df
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cells_df = cells_df.reset_index( drop=True )
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return xtals, cells_df
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def extract_header( chunk ):
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# setup
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header = []
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collect_header_lines = False
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# Open the input file for reading
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for line in chunk:
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# Check for the xtals start condition
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if line.startswith('----- Begin chunk -----'):
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collect_header_lines = True
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header_lines = []
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if collect_header_lines:
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header_lines.append(line)
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if line.startswith('End of peak list'):
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collect_header_lines = False # Stop collecting lines
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header.append( header_lines )
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return header
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def get_header( header, input_file ):
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if header == "geom":
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start_keyword = "----- Begin geometry file -----"
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end_keyword = "----- End geometry file -----"
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if header == "cell":
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start_keyword = "----- Begin unit cell -----"
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end_keyword = "----- End unit cell -----"
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# setup
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collect_lines = False
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headers = []
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# Open the input file for reading
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with open(input_file, 'r') as f:
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for line in f:
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# Check for the start condition
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if line.strip() == start_keyword:
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collect_lines = True
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headers_lines = []
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# Collect lines between start and end conditions
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if collect_lines:
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headers_lines.append(line)
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# Check for the end condition
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if line.strip() == end_keyword:
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collect_lines = False # Stop collecting lines
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headers.append(headers_lines)
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return headers[0]
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def write_to_file( geom, cell, chunk_header, crystals, output_file ):
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# Write sections with matching cell parameters to the output file
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with open(output_file, 'w') as out_file:
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out_file.write('CrystFEL stream format 2.3\n')
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out_file.write('Generated by CrystFEL 0.10.2\n')
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out_file.writelines(geom)
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out_file.writelines(cell)
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for crystal, header in zip( crystals, chunk_header ):
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out_file.writelines( header )
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out_file.writelines( crystal )
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out_file.writelines( "----- End chunk -----\n" )
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def is_within_range( a, b, c, alpha, beta, gamma, cell_tolerance, angle_tolerance, ideal_centre ):
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cell = [ a, b, c, alpha, beta, gamma ]
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returner = False
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within_range = []
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# Function to check if cell parameters are within the specified range
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for i in range(3):
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if cell[i] < ideal_centre[i]+cell_tolerance and cell[i] > ideal_centre[i]-cell_tolerance:
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within_range.append(True)
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else:
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within_range.append(False)
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for i in range(3, 6):
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if cell[i] < ideal_centre[i]+angle_tolerance and cell[i] > ideal_centre[i]-angle_tolerance:
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within_range.append(True)
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else:
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within_range.append(False)
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if all(within_range):
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returner = True
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return returner
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def sort_xtals( chunk_df ):
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# extract xtals
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xtal_df = pd.DataFrame()
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counter = 0
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for index, row in chunk_df.iterrows():
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chunk, hit, image_no = row[ "chunks" ], row[ "hit" ], row[ "image_no" ]
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if hit:
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# find xtals and header
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header = extract_header( chunk )
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xtals, cells_df = extract_xtals( chunk )
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# make header same length as xtals
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header = header*len(xtals)
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# concat results
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xtal_df_1 = pd.DataFrame()
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xtal_df_1[ "header" ] = header
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xtal_df_1[ "xtals" ] = xtals
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xtal_df_1[ "image_no" ] = image_no
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xtal_df_1 = pd.concat( ( xtal_df_1, cells_df ), axis=1 )
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xtal_df = pd.concat( ( xtal_df, xtal_df_1 ) )
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# add count and print every 1000s
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counter = counter + len(xtals)
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if counter % 1000 == 0:
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print( counter, end='\r' )
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print( "done" )
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# sort by image no and reindex
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xtal_df = xtal_df.sort_values( by=[ "image_no" ] )
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xtal_df = xtal_df.reset_index( drop=True )
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return xtal_df
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def main( input_file, output, cell_tolerance, angle_tolerance, ideal_centre ):
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# get geom and cell file headers
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print( "getting header info from .stream file" )
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geom = get_header( "geom", input_file )
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cell = get_header( "cell", input_file )
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print( "done" )
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# extract chunks
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print( "finding chucks" )
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chunk_df = extract_chunks( input_file )
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# display no. of chunks
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print( "found {0} chunks".format( len(chunk_df) ) )
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# remove rows without xtals
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chunk_df = chunk_df.loc[chunk_df.hit, :]
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print( "found {0} hits (not including multiples)".format( len(chunk_df) ) )
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print( "done" )
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print( "sorting xtals from chunks" )
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xtal_df = sort_xtals( chunk_df )
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print( "done" )
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print( "finding cells that are within tolerance" )
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# select cells that are within tolerance
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xtal_df[ "select" ] = xtal_df.apply(lambda x: is_within_range( x[ "a" ], x[ "b" ], x[ "c" ],
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x[ "alpha" ], x[ "beta" ], x[ "gamma" ],
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cell_tolerance,
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angle_tolerance,
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ideal_centre ), axis=1
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)
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select_df = xtal_df[ xtal_df[ "select" ] == True ]
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print( "done" )
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print( "writing {0} to output file".format( len( select_df ) ) )
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crystals = select_df.xtals.to_list()
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chunk_header = select_df.header.to_list()
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output_file = "{0}.stream".format( output )
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write_to_file( geom, cell, chunk_header, crystals, output_file )
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print( "done" )
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def list_of_floats(arg):
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return list(map(float, arg.split(',')))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-s",
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"--stream",
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help="input stream file",
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required=True,
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type=os.path.abspath
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)
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parser.add_argument(
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"-o",
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"--output",
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help="output stream file name. '.stream will be added'",
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type=str,
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default="sample"
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)
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parser.add_argument(
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"-c",
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"--cell_tolerance",
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help="tolerance for cell lengths. 0.2 is default.",
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type=float,
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default=0.2
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)
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parser.add_argument(
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"-a",
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"--angle_tolerance",
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help="tolerance for cell angles. 0.25 is default.",
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type=float,
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default=0.25
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)
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parser.add_argument(
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"-i",
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"--ideal_centre",
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help="ideal lengths to be selected around. List of floats - e.g. 78.5, 78.5, 38.45, 90, 90, 90",
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type=list_of_floats,
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required=True
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)
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args = parser.parse_args()
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# run main
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main( args.stream, args.output, args.cell_tolerance, args.angle_tolerance, args.ideal_centre )
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