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_batch_fit(self, tmp_path):
hdx_set = HDXMeasurementSet([self.hdxm_apo, self.hdxm_dimer])
guess = csv_to_dataframe(output_dir / 'ecSecB_guess.csv')
# Create rates dataframe
rates_df = pd.DataFrame(
{name: guess['rate']
for name in hdx_set.names})
gibbs_guess = hdx_set.guess_deltaG(rates_df)
fr_global = fit_gibbs_global_batch(hdx_set, gibbs_guess, epochs=1000)
fpath = Path(tmp_path) / 'fit_result_batch.csv'
fr_global.to_file(fpath)
df = csv_to_dataframe(fpath)
assert df.attrs['metadata'] == fr_global.metadata
output = fr_global.output
check_protein = csv_to_protein(output_dir / 'ecSecB_batch.csv')
states = ['SecB WT apo', 'SecB his dimer apo']
for state in states:
from pandas.testing import assert_series_equal
result = output[state]['dG']
test = check_protein[state]['dG']
assert_series_equal(result, test, rtol=0.1)
errors = fr_global.get_squared_errors()
assert errors.shape == (hdx_set.Ns, hdx_set.Np, hdx_set.Nt)
mock_alignment = {
'apo':
'MSEQNNTEMTFQIQRIYTKDI------------SFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLG-------------------EETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRG----TFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA',
'dimer':
'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVY--------------EVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGA----YCPNILFPAARECIASMVARGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA-----------------',
}
hdx_set.add_alignment(list(mock_alignment.values()))
gibbs_guess = hdx_set[0].guess_deltaG(
guess['rate']) # Guesses from first measurement
aligned_result = fit_gibbs_global_batch_aligned(hdx_set,
gibbs_guess,
r1=2,
r2=5,
epochs=1000)
output = aligned_result.output
check_protein = csv_to_protein(output_dir / 'ecSecB_batch_aligned.csv')
states = ['SecB WT apo', 'SecB his dimer apo']
for state in states:
from pandas.testing import assert_series_equal
result = output[state]['dG']
test = check_protein[state]['dG']
assert_series_equal(result, test, rtol=0.1)
def test_batch_fit(self):
hdx_set = HDXMeasurementSet([self.series_apo, self.series_dimer])
guess = csv_to_protein(
os.path.join(directory, 'test_data', 'ecSecB_guess.txt'))
gibbs_guess = hdx_set.guess_deltaG([guess['rate'], guess['rate']])
result = fit_gibbs_global_batch(hdx_set, gibbs_guess, epochs=1000)
output = result.output
check_protein = csv_to_protein(os.path.join(directory, 'test_data',
'ecSecB_batch.csv'),
column_depth=2)
states = ['SecB WT apo', 'SecB his dimer apo']
for state in states:
from pandas.testing import assert_series_equal
result = output[state]['deltaG']
test = check_protein[state]['deltaG']
assert_series_equal(result, test, rtol=0.1)
mock_alignment = {
'apo':
'MSEQNNTEMTFQIQRIYTKDI------------SFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLG-------------------EETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRG----TFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA',
'dimer':
'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVY--------------EVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGA----YCPNILFPAARECIASMVARGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA-----------------',
}
hdx_set.add_alignment(list(mock_alignment.values()))
gibbs_guess = hdx_set.guess_deltaG([guess['rate'], guess['rate']])
aligned_result = fit_gibbs_global_batch_aligned(hdx_set,
gibbs_guess,
r1=2,
r2=5,
epochs=1000)
output = aligned_result.output
check_protein = csv_to_protein(os.path.join(
directory, 'test_data', 'ecSecB_batch_aligned.csv'),
column_depth=2)
states = ['SecB WT apo', 'SecB his dimer apo']
for state in states:
from pandas.testing import assert_series_equal
result = output[state]['deltaG']
test = check_protein[state]['deltaG']
assert_series_equal(result, test, rtol=0.1)
pmt = PeptideMasterTable(data)
pmt.set_control(('Full deuteration control', 0.167*60))
st1 = HDXMeasurement(pmt.get_state('SecB his dimer apo'), pH=8, temperature=273.15 + 30)
st2 = HDXMeasurement(pmt.get_state('SecB WT apo'), pH=8, temperature=273.15 + 30)
hdx_set = HDXMeasurementSet([st1, st2])
guess = csv_to_protein(data_dir / 'output' / 'ecSecB_guess.csv')
gibbs_guess = hdx_set[0].guess_deltaG(guess['rate'])
# Example fit with only 5000 epochs and high learning rate
# Checkpoint stores model history every `epoch_step` epochs
checkpoint = CheckPoint(epoch_step=250)
result = fit_gibbs_global_batch(hdx_set, gibbs_guess, r1=0.5, r2=0.1, epochs=5000, lr=1e5, callbacks=[checkpoint])
print(f"MSE loss: {result.mse_loss:.2f}, "
f"Reg loss: {result.reg_loss:.2f}, "
f"Reg percent: {result.regularization_percentage:.0f}%")
df = checkpoint.to_dataframe(hdx_set.names)
dataframe_to_file(output_dir / 'model_history.csv', df)
dataframe_to_file(output_dir / 'model_history.txt', df, fmt='pprint')
# Checkpoint history scatter plot
# Note that these are raw dG values including interpolated values in regions of no coverage
history = checkpoint.model_history
num = len(history)
cmap = mpl.cm.get_cmap('winter')
hdx_set = HDXMeasurementSet(hdxm_list)
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]:
t0 = time.time()
result = fit_gibbs_global_batch(hdx_set,
gibbs_guess,
epochs=1000,
r1=r1,
r2=r2)
t1 = time.time()
block = '--------------------------'
regularizers = f'Regualizer 1: {r1} Regualizer 2: {r2}'
loss = f'Total_loss {result.total_loss:.2f}, mse_loss {result.mse_loss:.2f}, reg_loss {result.reg_loss:.2f}' \
f'({result.regularization_percentage:.2f}%)'
time_elapsed = f"Time elapsed: {(t1 - t0):.2f} s"
epochs = f"Number of epochs: {len(result.metadata['total_loss'])}"
result.output.to_csv(output_dir /
f"fit_output_r1_{r1}_r2_{r2}.csv") #, na_rep='NaN')
result.output.to_file(output_dir / f"fit_output_r1_{r1}_r2_{r2}.txt",
fmt='pprint',
output = wt_avg_result.output
output.to_file(directory / 'test_data' / 'ecSecB_guess.txt')
else:
output = csv_to_protein(directory / 'test_data' / 'ecSecB_guess.txt')
gibbs_guess = hdxm.guess_deltaG(output['rate'])
fr_torch = fit_gibbs_global(hdxm, gibbs_guess, epochs=epochs, r1=2)
fr_torch.output.to_file(directory / 'test_data' / 'ecSecB_torch_fit.txt')
hdxm_dimer = HDXMeasurement(pmt.get_state('SecB his dimer apo'), sequence=sequence_dimer,
temperature=temperature, pH=pH)
hdx_set = HDXMeasurementSet([hdxm_dimer, hdxm])
gibbs_guess = hdx_set.guess_deltaG([output['rate'], output['rate']])
batch_result = fit_gibbs_global_batch(hdx_set, gibbs_guess, epochs=epochs)
batch_result.output.to_file(directory / 'test_data' / 'ecSecB_batch.csv')
batch_result.output.to_file(directory / 'test_data' / 'ecSecB_batch.txt', fmt='pprint')
# Order is inverted compared to test!
mock_alignment = {
'dimer': 'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVY--------------EVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGA----YCPNILFPAARECIASMVARGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA-----------------',
'apo': 'MSEQNNTEMTFQIQRIYTKDI------------SFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLG-------------------EETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRG----TFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA',
}
hdx_set.add_alignment(list(mock_alignment.values()))
aligned_result = fit_gibbs_global_batch_aligned(hdx_set, gibbs_guess, r1=2, r2=5, epochs=1000)
from pyhdx.fileIO import csv_to_protein
current_dir = Path(__file__).parent
data_dir = current_dir.parent / 'tests' / 'test_data'
data = read_dynamx(data_dir / 'ecSecB_apo.csv', data_dir / 'ecSecB_dimer.csv')
pmt = PeptideMasterTable(data)
pmt.set_control(('Full deuteration control', 0.167))
st1 = HDXMeasurement(pmt.get_state('SecB his dimer apo'),
pH=8,
temperature=273.15 + 30)
st2 = HDXMeasurement(pmt.get_state('SecB WT apo'),
pH=8,
temperature=273.15 + 30)
hdx_set = HDXMeasurementSet([st1, st2])
guess = csv_to_protein(data_dir / 'ecSecB_guess.txt')
gibbs_guess = hdx_set.guess_deltaG([guess['rate'], guess['rate']])
# Example fit with only 1000 epochs and high regularizers
# For real data start with parameters r1=0.05, r2=0.5, epochs=100000
result = fit_gibbs_global_batch(hdx_set, gibbs_guess, r1=2, r2=5, epochs=1000)
#Human readable output
result.output.to_file('Batch_fit_result.txt', fmt='pprint')
#Machine readable output
result.output.to_file('Batch_fit_result.csv', fmt='csv')
fr_torch.output,
fmt='pprint')
# ----------
# Batch fits
# ----------
hdxm_dimer = HDXMeasurement(pmt.get_state('SecB his dimer apo'),
sequence=sequence_dimer,
temperature=temperature,
pH=pH)
hdx_set = HDXMeasurementSet([hdxm_dimer, hdxm])
gibbs_guess = hdx_set[0].guess_deltaG(guess_output['rate'])
batch_result = fit_gibbs_global_batch(hdx_set, gibbs_guess, epochs=epochs)
dataframe_to_file(output_dir / 'ecSecB_batch.csv', batch_result.output)
dataframe_to_file(output_dir / 'ecSecB_batch.txt',
batch_result.output,
fmt='pprint')
# Order is inverted compared to test!
mock_alignment = {
'dimer':
'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVY--------------EVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGA----YCPNILFPAARECIASMVARGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA-----------------',
'apo':
'MSEQNNTEMTFQIQRIYTKDI------------SFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLG-------------------EETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRG----TFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA',
}
hdx_set.add_alignment(list(mock_alignment.values()))
"""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)