def test_batch_fit_delta(self, tmp_path):
yaml_file = input_dir / 'data_states_deltas.yaml'
yaml_dict = yaml.safe_load(yaml_file.read_text())
hdxm_set = yaml_to_hdxmset(yaml_dict, data_dir=input_dir)
guess_output = csv_to_dataframe(output_dir / 'ecSecB_guess.csv')
gibbs_guess = hdxm_set[0].guess_deltaG(guess_output['rate'])
# broadcast single guess over samples
fr_global = fit_gibbs_global_batch(hdxm_set, gibbs_guess, epochs=200)
output = fr_global.output
check = csv_to_dataframe(output_dir / 'ecsecb_delta_batch' /
'fit_result.csv')
states = check.columns.unique(level=0)
for state in states:
from pandas.testing import assert_series_equal
result = output[state]['dG']
test = check[state]['dG']
assert_series_equal(result, test, rtol=0.1)
errors = fr_global.get_squared_errors()
assert errors.shape == (hdxm_set.Ns, hdxm_set.Np, hdxm_set.Nt)
assert not np.any(np.isnan(errors))
def test_load_from_yaml(self):
yaml_pth = Path(input_dir / 'data_states.yaml')
data_dict = yaml.safe_load(yaml_pth.read_text())
hdxm = yaml_to_hdxm(data_dict['SecB_tetramer'], data_dir=input_dir)
assert isinstance(hdxm, HDXMeasurement)
assert hdxm.metadata['temperature'] == data_dict['SecB_tetramer'][
'temperature']['value'] + 273.15
assert hdxm.name == 'SecB WT apo'
hdxm_set = yaml_to_hdxmset(data_dict, data_dir=input_dir)
assert isinstance(hdxm_set, HDXMeasurementSet)
assert hdxm_set.names == list(data_dict.keys())
from pyhdx.local_cluster import default_client
from pyhdx.fileIO import csv_to_protein
from pyhdx.models import HDXMeasurementSet
from pyhdx.support import pprint_df_to_file
from pyhdx import VERSION_STRING
current_dir = Path(__file__).parent
test_data_dir = current_dir.parent / 'tests' / 'test_data'
input_dir = test_data_dir / 'input'
yaml_stream = Path(current_dir / 'yaml_files' / 'SecB.yaml').read_text()
data_dict = yaml.safe_load(yaml_stream)
output_dir = current_dir / 'fit'
output_dir.mkdir(exist_ok=True)
hdx_set = yaml_to_hdxmset(data_dict, data_dir=input_dir)
rates_list = [
csv_to_protein(current_dir / 'guesses' / f'{name}_rates_guess.csv')['rate']
for name in data_dict.keys()
]
gibbs_guess = hdx_set.guess_deltaG(rates_list)
log_file = output_dir / f"fitting_log.txt"
now = datetime.now()
date = f'# {now.strftime("%Y/%m/%d %H:%M:%S")} ({int(now.timestamp())})'
lines = [VERSION_STRING, date]
r2 = 0.5
for r1 in [0, 0.01, 0.25, 0.5, 1]:
"""Obtain ΔG for ecSecB tetramer and dimer""" from pathlib import Path from pyhdx.batch_processing import yaml_to_hdxmset from pyhdx.fileIO import csv_to_dataframe, save_fitresult from pyhdx.fitting import fit_gibbs_global_batch import yaml cwd = Path(__file__).parent data_dir = cwd / 'test_data' / 'input' output_dir = cwd / 'test_data' / 'output' yaml_dict = yaml.safe_load(Path(data_dir / 'data_states.yaml').read_text()) hdx_set = yaml_to_hdxmset(yaml_dict, data_dir=data_dir) initial_guess_rates = csv_to_dataframe(output_dir / 'ecSecB_guess.csv') guesses = hdx_set[0].guess_deltaG(initial_guess_rates['rate']) fit_kwargs = yaml.safe_load(Path(data_dir / 'fit_settings.yaml').read_text()) fr = fit_gibbs_global_batch(hdx_set, guesses, **fit_kwargs) save_fitresult(output_dir / 'ecsecb_tetramer_dimer', fr)