ERROR in run analyze_flex_ddG.py

[SHG369]

Because of the pands .append() method which is now depracated, so I modify the code as showed blow;
``#!/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 = ddg_scores._append( 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 )
``
erroe happend as

Unique values in 'state' column: ['bound_wt' 'unbound_mut']
Traceback (most recent call last):
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1874, in _agg_py_fallback
res_values = self.grouper.agg_series(ser, alt, preserve_dtype=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/ops.py", line 849, in agg_series
result = self._aggregate_series_pure_python(obj, func)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/ops.py", line 877, in _aggregate_series_pure_python
res = func(group)
^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 2380, in
alt=lambda x: Series(x).mean(numeric_only=numeric_only),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/series.py", line 6225, in mean
return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py", line 11992, in mean
return self._stat_function(
^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py", line 11949, in _stat_function
return self._reduce(
^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/series.py", line 6133, in _reduce
return op(delegate, skipna=skipna, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/nanops.py", line 147, in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/nanops.py", line 404, in new_func
result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/nanops.py", line 720, in nanmean
the_sum = _ensure_numeric(the_sum)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/nanops.py", line 1693, in _ensure_numeric
raise TypeError(f"Could not convert string '{x}' to numeric")
TypeError: Could not convert string 'bound_wtunbound_mutbound_mutunbound_wtbound_wtunbound_mutbound_mutunbound_wtbound_wtunbound_mutbound_mutunbound_wt' to numeric

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/ai/data/Software/flexddg/flex_ddG_tutorial/analyze_flex_ddG2.py", line 214, in
analyze_output_folder( folder_to_analyze )
File "/ai/data/Software/flexddg/flex_ddG_tutorial/analyze_flex_ddG2.py", line 190, in analyze_output_folder
ddg_scores, struct_scores = calc_ddg( scores )
^^^^^^^^^^^^^^^^^^
File "/ai/data/Software/flexddg/flex_ddG_tutorial/analyze_flex_ddG2.py", line 137, in calc_ddg
ddg_scores = ddg_scores.groupby( ['case_name', 'backrub_steps', 'score_function_name'] ).mean().round(decimals=5).reset_index()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 2378, in mean
result = self._cython_agg_general(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1929, in _cython_agg_general
new_mgr = data.grouped_reduce(array_func)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/internals/managers.py", line 1428, in grouped_reduce
applied = sb.apply(func)
^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/internals/blocks.py", line 366, in apply
result = func(self.values, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1926, in array_func
result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/root/anaconda3/lib/python3.11/site-packages/pandas/core/groupby/groupby.py", line 1878, in _agg_py_fallback
raise type(err)(msg) from err
TypeError: agg function failed [how->mean,dtype->object]

then the code blow were added in to populate the state column with the appropriate values
`state = os.path.basename(os.path.dirname(output_path)) if state.startswith("bound"): state = "bound_" + state.split("_")[1] elif state.startswith("unbound"): state = "unbound_" + `state.split("_")[1]
After that, I got a empty dataframe in the output file.

Unique values in 'state' column: []
mut_dG
Empty DataFrame
Columns: [backrub_steps, case_name, nstruct, score_function_name, scored_state, total_score]
Index: []

wt_dG
Empty DataFrame
Columns: [backrub_steps, case_name, nstruct, score_function_name, scored_state, total_score]
Index: []

ddG
Empty DataFrame
Columns: [backrub_steps, case_name, nstruct, score_function_name, scored_state, total_score]
Index: []