Usage of pands .append() method which is now depracated

[MatsveiTsishyn]

In analyze_flex_ddG.py in function calc_ddg, the line ddg_scores = ddg_scores.append( ... ) uses the append method on a 'DataFrame' object from the pandas package, which is now deprecated and thus causes the run crash on latest versions of pandas.

[Sinsilcobio]

@MatsveiTsishyn @kylebarlow any idea how to resolve this issue? Any update for the code? Thank you

[kylebarlow]

Have either of you had a chance to try changing it to pd.concat? Something like ddg_scores = pd.concat([ddg_scores, ...])

[Sinsilcobio]

@kylebarlow I just did and got this error: TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"

Please find the modified code below:

!/usr/bin/python3

import sys import os import sqlite3 import shutil import tempfile from pprint import pprint import pandas as pd import numpy as np import re import datetime import sys import collections import threading

rosetta_output_file_name = 'rosetta.out' output_database_name = 'ddG.db3' trajectory_stride = 5 script_output_folder = 'analysis_output'

zemu_gam_params = { 'fa_sol' : (6.940, -6.722), 'hbond_sc' : (1.902, -1.999), 'hbond_bb_sc' : (0.063, 0.452), 'fa_rep' : (1.659, -0.836), 'fa_elec' : (0.697, -0.122), 'hbond_lr_bb' : (2.738, -1.179), 'fa_atr' : (2.313, -1.649), }

def gam_function(x, score_term = None ): return -1.0 np.exp( zemu_gam_params[score_term][0] ) + 2.0 np.exp( zemu_gam_params[score_term][0] ) / ( 1.0 + np.exp( -1.0 x np.exp( zemu_gam_params[score_term][1] ) ) )

def apply_zemu_gam(scores): new_columns = list(scores.columns) new_columns.remove('total_score') scores = scores.copy()[ new_columns ] for score_term in zemu_gam_params: assert( score_term in scores.columns ) scores[score_term] = scores[score_term].apply( gam_function, score_term = score_term ) scores[ 'total_score' ] = scores[ list(zemu_gam_params.keys()) ].sum( axis = 1 ) scores[ 'score_function_name' ] = scores[ 'score_function_name' ] + '-gam' return scores

def rosetta_output_succeeded( potential_struct_dir ): path_to_rosetta_output = os.path.join( potential_struct_dir, rosetta_output_file_name ) if not os.path.isfile(path_to_rosetta_output): return False

db3_file = os.path.join( potential_struct_dir, output_database_name )
if not os.path.isfile( db3_file ):
    return False

success_line_found = False
no_more_batches_line_found = False
with open( path_to_rosetta_output, 'r' ) as f:
    for line in f:
        if line.startswith( 'protocols.jd2.JobDistributor' ) and 'reported success in' in line:
            success_line_found = True
        if line.startswith( 'protocols.jd2.JobDistributor' ) and 'no more batches to process' in line:
            no_more_batches_line_found = True

return no_more_batches_line_found and success_line_found

def find_finished_jobs( output_folder ): return_dict = {} job_dirs = [ os.path.abspath(os.path.join(output_folder, d)) for d in os.listdir(output_folder) if os.path.isdir( os.path.join(output_folder, d) )] for job_dir in job_dirs: completed_struct_dirs = [] for potential_struct_dir in sorted([ os.path.abspath(os.path.join(job_dir, d)) for d in os.listdir(job_dir) if os.path.isdir( os.path.join(job_dir, d) )]): if rosetta_output_succeeded( potential_struct_dir ): completed_struct_dirs.append( potential_struct_dir ) return_dict[job_dir] = completed_struct_dirs

return return_dict

def get_scores_from_db3_file(db3_file, struct_number, case_name): conn = sqlite3.connect(db3_file) conn.row_factory = sqlite3.Row c = conn.cursor()

num_batches = c.execute('SELECT max(batch_id) from batches').fetchone()[0]

scores = pd.read_sql_query('''
SELECT batches.name, structure_scores.struct_id, score_types.score_type_name, structure_scores.score_value, score_function_method_options.score_function_name from structure_scores
INNER JOIN batches ON batches.batch_id=structure_scores.batch_id
INNER JOIN score_function_method_options ON score_function_method_options.batch_id=batches.batch_id
INNER JOIN score_types ON score_types.batch_id=structure_scores.batch_id AND score_types.score_type_id=structure_scores.score_type_id
''', conn)

def renumber_struct_id( struct_id ):
    return trajectory_stride * ( 1 + (int(struct_id-1) // num_batches) )

scores['struct_id'] = scores['struct_id'].apply( renumber_struct_id )
scores['name'] = scores['name'].apply( lambda x: x[:-9] if x.endswith('_dbreport') else x )
scores = scores.pivot_table( index = ['name', 'struct_id', 'score_function_name'], columns = 'score_type_name', values = 'score_value' ).reset_index()
scores.rename( columns = {
    'name' : 'state',
    'struct_id' : 'backrub_steps',
}, inplace=True)
scores['struct_num'] = struct_number
scores['case_name'] = case_name

conn.close()

return scores

def process_finished_struct( output_path, case_name ): db3_file = os.path.join( output_path, output_database_name ) assert( os.path.isfile( db3_file ) ) struct_number = int( os.path.basename(output_path) ) scores_df = get_scores_from_db3_file( db3_file, struct_number, case_name )

return scores_df

def calc_ddg( scores ): total_structs = np.max( scores['struct_num'] )

nstructs_to_analyze = set([total_structs])
for x in range(10, total_structs):
    if x % 10 == 0:
        nstructs_to_analyze.add(x)
nstructs_to_analyze = sorted(nstructs_to_analyze)

all_ddg_scores = []
for nstructs in nstructs_to_analyze:
    ddg_scores = scores.loc[ ((scores['state'] == 'unbound_mut') | (scores['state'] == 'bound_wt')) & (scores['struct_num'] <= nstructs) ].copy()
    for column in ddg_scores.columns:
        if column not in ['state', 'case_name', 'backrub_steps', 'struct_num', 'score_function_name']:
            ddg_scores.loc[:,column] *= -1.0
    ddg_scores = pd.concat( scores.loc[ ((scores['state'] == 'unbound_wt') | (scores['state'] == 'bound_mut')) & (scores['struct_num'] <= nstructs) ].copy() )
    ddg_scores = ddg_scores.groupby( ['case_name', 'backrub_steps', 'struct_num', 'score_function_name'] ).sum().reset_index()

    if nstructs == total_structs:
        struct_scores = ddg_scores.copy()

    ddg_scores = ddg_scores.groupby( ['case_name', 'backrub_steps', 'score_function_name'] ).mean().round(decimals=5).reset_index()
    new_columns = list(ddg_scores.columns.values)
    new_columns.remove( 'struct_num' )
    ddg_scores = ddg_scores[new_columns]
    ddg_scores[ 'scored_state' ] = 'ddG'
    ddg_scores[ 'nstruct' ] = nstructs
    all_ddg_scores.append(ddg_scores)

return (pd.concat(all_ddg_scores), struct_scores)

def calc_dgs( scores ): l = []

total_structs = np.max( scores['struct_num'] )

nstructs_to_analyze = set([total_structs])
for x in range(10, total_structs):
    if x % 10 == 0:
        nstructs_to_analyze.add(x)
nstructs_to_analyze = sorted(nstructs_to_analyze)

for state in ['mut', 'wt']:
    for nstructs in nstructs_to_analyze:
        dg_scores = scores.loc[ (scores['state'].str.endswith(state)) & (scores['state'].str.startswith('unbound')) & (scores['struct_num'] <= nstructs) ].copy()
        for column in dg_scores.columns:
            if column not in ['state', 'case_name', 'backrub_steps', 'struct_num', 'score_function_name']:
                dg_scores.loc[:,column] *= -1.0
        dg_scores = dg_scores.append( scores.loc[ (scores['state'].str.endswith(state)) & (scores['state'].str.startswith('bound')) & (scores['struct_num'] <= nstructs) ].copy() )
        dg_scores = dg_scores.groupby( ['case_name', 'backrub_steps', 'struct_num', 'score_function_name'] ).sum().reset_index()
        dg_scores = dg_scores.groupby( ['case_name', 'backrub_steps', 'score_function_name'] ).mean().round(decimals=5).reset_index()
        new_columns = list(dg_scores.columns.values)
        new_columns.remove( 'struct_num' )
        dg_scores = dg_scores[new_columns]
        dg_scores[ 'scored_state' ] = state + '_dG'
        dg_scores[ 'nstruct' ] = nstructs
        l.append( dg_scores )
return l

def analyze_output_folder( output_folder ):

Pass in an outer output folder. Subdirectories are considered different mutation cases, with subdirectories of different structures.

finished_jobs = find_finished_jobs( output_folder )
if len(finished_jobs) == 0:
    print( 'No finished jobs found' )
    return

ddg_scores_dfs = []
struct_scores_dfs = []
for finished_job, finished_structs in finished_jobs.items():
    inner_scores_list = []
    for finished_struct in finished_structs:
        inner_scores = process_finished_struct( finished_struct, os.path.basename(finished_job) )
        inner_scores_list.append( inner_scores )
    scores = pd.concat( inner_scores_list )
    ddg_scores, struct_scores = calc_ddg( scores )
    struct_scores_dfs.append( struct_scores )
    ddg_scores_dfs.append( ddg_scores )
    ddg_scores_dfs.append( apply_zemu_gam(ddg_scores) )
    ddg_scores_dfs.extend( calc_dgs( scores ) )

if not os.path.isdir(script_output_folder):
    os.makedirs(script_output_folder)
basename = os.path.basename(output_folder)

pd.concat( struct_scores_dfs ).to_csv( os.path.join(script_output_folder, basename + '-struct_scores_results.csv' ) )

df = pd.concat( ddg_scores_dfs )
df.to_csv( os.path.join(script_output_folder, basename + '-results.csv') )

display_columns = ['backrub_steps', 'case_name', 'nstruct', 'score_function_name', 'scored_state', 'total_score']
for score_type in ['mut_dG', 'wt_dG', 'ddG']:
    print( score_type )
    print( df.loc[ df['scored_state'] == score_type ][display_columns].head( n = 20 ) )
    print( '' )

if name == 'main': for folder_to_analyze in sys.argv[1:]: if os.path.isdir( folder_to_analyze ): analyze_output_folder( folder_to_analyze )

[SHG369]

So does it solved now?