def main(argv):
parser = argparse.ArgumentParser(description='Extract frame-by-frame MMPSA/MMGBSA Energy Totals')
parser.add_argument('outfile', help='output file (CSV)')
parser.add_argument('calc', help='gb (for GBSA) or pb (for PBSA)')
parser.add_argument('-p', '--prefix', help='prefix for MMPBSA files (default _MMPBSA_)')
args = parser.parse_args()
if args.calc not in ['gb', 'pb']:
print('calc must be either "gb" or "pb"')
quit()
prefix = '_MMPBSA_' if args.prefix is None else args.prefix
data=MMPBSA_API.load_mmpbsa_info(prefix + 'info')
head = ['TOTAL']
with open(args.outfile, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=head)
writer.writeheader()
tot = 0
count = 0
for i in range(len(data[args.calc]['complex']['EEL'])):
row = {}
row['TOTAL'] = data[args.calc]['complex']['TOTAL'][i] - data[args.calc]['ligand']['TOTAL'][i] -data[args.calc]['receptor']['TOTAL'][i]
writer.writerow(row)
tot += row['TOTAL']
count += 1
print("%d frames in total. Mean %f.".format(count, tot/count))
def main():
from MMPBSA_mods import API as MMPBSA_API
data = MMPBSA_API.load_mmpbsa_info('_MMPBSA_info')
decomp_data = data['decomp']['gb']['complex']['TDC']
for residue in decomp_data:
energy_term = 'tot'
print('res = {}, energy={}'.format(residue, decomp_data[residue][energy_term]))
def getDataFrames(info_file, calculation_type='gb'):
'''
Reads an MMPBSA info file and returns a dictionary with four keys
'complex', 'receptor', 'ligand' and 'difference'
each with an associated pd.DataFrame with all the corresponding data
'''
data = MMPBSA_API.load_mmpbsa_info(info_file) # data is a dictionary
data_dfs = OrderedDict.fromkeys(['complex', 'receptor', 'ligand', 'difference'])
for key in data[calculation_type]:
df = pd.DataFrame.from_dict(data[calculation_type][key])
data_dfs[key] = df
# Calculate the difference between complex, receptor and ligand contributions
data_dfs['difference'] = data_dfs['complex'] - data_dfs['receptor'] - data_dfs['ligand']
return data_dfs
def getDataFrames(info_file, calculation_type='gb'):
'''
Reads an MMPBSA info file and returns a dictionary with four keys
'complex', 'receptor', 'ligand' and 'difference'
each with an associated pd.DataFrame with all the corresponding data
'''
data = MMPBSA_API.load_mmpbsa_info(info_file) # data is a dictionary
data_dfs = OrderedDict.fromkeys(
['complex', 'receptor', 'ligand', 'difference'])
for key in data[calculation_type]:
df = pd.DataFrame.from_dict(data[calculation_type][key])
data_dfs[key] = df
# Calculate the difference between complex, receptor and ligand contributions
data_dfs['difference'] = data_dfs['complex'] - data_dfs[
'receptor'] - data_dfs['ligand']
return data_dfs
def main():
from MMPBSA_mods import API as MMPBSA_API
data = MMPBSA_API.load_mmpbsa_info('_MMPBSA_info')
decomp_data = data['decomp']['gb']['complex']['TDC']
elist = ['tot', 'vdw', 'int', 'eel', 'pol', 'sas']
line0 = [','.join(['residue', ] + elist[:]),]
lines = []
for residue in sorted(decomp_data.keys()):
res_data = decomp_data[residue]
line = str(residue) + ',' + ','.join(str(np.mean(res_data[k])) for k in elist)
lines.append(line)
line0.extend(lines)
content = '\n'.join(line0)
with open('res.csv', 'w') as fh:
fh.write(content)
def main():
from MMPBSA_mods import API as MMPBSA_API
data = MMPBSA_API.load_mmpbsa_info('_MMPBSA_info')
decomp_data = data['decomp']['gb']['complex']['TDC']
pt.to_pickle(decomp_data, 'energy_per_residue_per_structure.pk')
#!/usr/bin/env python
from MMPBSA_mods import API as MMPBSA_API
# check http://ambermd.org/doc12/Amber15.pdf
# page 637 (Python API)
# data is nested python dict
data = MMPBSA_API.load_mmpbsa_info('_MMPBSA_info')
print(data.keys())
decomp_data = data['decomp']['gb']['complex']['TDC']
print(decomp_data.keys())
# res 1
print(decomp_data[1])
