def setup_class(cls):
fpath_apo = input_dir / 'ecSecB_apo.csv'
fpath_dimer = input_dir / 'ecSecB_dimer.csv'
data = read_dynamx(fpath_apo, fpath_dimer)
control = ('Full deuteration control', 0.167 * 60)
cls.temperature, cls.pH = 273.15 + 30, 8.
pf = PeptideMasterTable(data,
drop_first=1,
ignore_prolines=True,
remove_nan=False)
pf.set_control(control)
cls.hdxm_apo = HDXMeasurement(pf.get_state('SecB WT apo'),
temperature=cls.temperature,
pH=cls.pH)
cls.hdxm_dimer = HDXMeasurement(pf.get_state('SecB his dimer apo'),
temperature=cls.temperature,
pH=cls.pH)
data = pf.get_state('SecB WT apo')
reduced_data = data[data['end'] < 40]
cls.reduced_hdxm = HDXMeasurement(reduced_data)
cluster = LocalCluster()
cls.address = cluster.scheduler_address
def setup_class(cls):
fpath = directory / 'test_data' / 'ecSecB_apo.csv'
cls.pmt = PeptideMasterTable(read_dynamx(fpath))
cls.pmt.set_control(('Full deuteration control', 0.167))
d = cls.pmt.get_state('SecB WT apo')
cls.temperature, cls.pH = 273.15 + 30, 8.
cls.series = HDXMeasurement(d, temperature=cls.temperature, pH=cls.pH)
def setup_class(cls):
dtype = [('r_number', int), ('apple', float)]
array1 = np.empty(15, dtype=dtype)
array1['r_number'] = np.arange(15) + 3
array1['apple'] = np.ones(15) * 12
cls.array1 = array1
dtype = [('r_number', int), ('apple', float), ('grapes', float)]
array2 = np.empty(17, dtype=dtype)
array2['r_number'] = np.arange(17) + 6
array2['apple'] = np.ones(17) * 10
array2['grapes'] = np.ones(17) * 15 + np.random.rand(17)
cls.array2 = array2
dtype = [('r_number', int), ('pear', float), ('banana', float)]
array3 = np.empty(10, dtype=dtype)
array3['r_number'] = np.arange(10) + 1
array3['pear'] = np.random.rand(10) + 20
array3['banana'] = -(np.random.rand(10) + 20)
cls.array3 = array3
cls.protein = csv_to_protein(directory / 'test_data' / 'ecSecB_info.csv', column_depth=1)
fpath = directory / 'test_data' / 'ecSecB_apo.csv'
pf1 = PeptideMasterTable(read_dynamx(fpath))
#states = pf1.groupby_state(c_term=200)
cls.series = HDXMeasurement(pf1.get_state('SecB WT apo'), c_term=200)
def setup_class(cls):
fpath = input_dir / 'ecSecB_apo.csv'
cls.pmt = PeptideMasterTable(read_dynamx(fpath))
data = cls.pmt.get_state('SecB WT apo')
cls.hdxm = HDXMeasurement(data)
cls.sequence = 'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQM' \
'AHCLGAYCPNILFPYARECITSMVSRGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA'
def setup_class(cls):
fpath = input_dir / 'ecSecB_apo.csv'
cls.pmt = PeptideMasterTable(read_dynamx(fpath))
cls.pmt.set_control(('Full deuteration control', 0.167 * 60))
d = cls.pmt.get_state('SecB WT apo')
cls.temperature, cls.pH = 273.15 + 30, 8.
cls.hdxm = HDXMeasurement(d, temperature=cls.temperature, pH=cls.pH)
def setup_class(cls):
dtype = [('r_number', int), ('apple', float)]
array1 = np.empty(15, dtype=dtype)
array1['r_number'] = np.arange(15) + 3
array1['apple'] = np.ones(15) * 12
cls.array1 = array1
dtype = [('r_number', int), ('apple', float), ('grapes', float)]
array2 = np.empty(17, dtype=dtype)
array2['r_number'] = np.arange(17) + 6
array2['apple'] = np.ones(17) * 10
array2['grapes'] = np.ones(17) * 15 + np.random.rand(17)
cls.array2 = array2
dtype = [('r_number', int), ('pear', float), ('banana', float)]
array3 = np.empty(10, dtype=dtype)
array3['r_number'] = np.arange(10) + 1
array3['pear'] = np.random.rand(10) + 20
array3['banana'] = -(np.random.rand(10) + 20)
cls.array3 = array3
metadata = {
'temperature': 273.15,
'pH': 7.5,
'mutations': ['V123S', 'P234S']
}
cls.protein = csv_to_protein(output_dir / 'ecSecB_info.csv')
cls.protein.metadata = metadata
fpath = input_dir / 'ecSecB_apo.csv'
pf1 = PeptideMasterTable(read_dynamx(fpath))
cls.series = HDXMeasurement(pf1.get_state('SecB WT apo'), c_term=200)
def setup_class(cls):
cls.fpath = input_dir / 'ecSecB_apo.csv'
cls.pmt = PeptideMasterTable(read_dynamx(cls.fpath))
cls.state = 'SecB WT apo'
cls.control = ('Full deuteration control', 0.167*60)
cls.pmt.set_control(cls.control)
state_data = cls.pmt.get_state(cls.state)
cls.temperature, cls.pH = 273.15 + 30, 8.
cls.hdxm = HDXMeasurement(state_data, temperature=cls.temperature, pH=cls.pH)
cfg = ConfigurationSettings()
cfg.set('cluster', 'scheduler_address', f'127.0.0.1:{test_port}')
def setup_class(cls):
cls.fpath = directory / 'test_data' / 'ecSecB_apo.csv'
cls.pmt = PeptideMasterTable(read_dynamx(cls.fpath))
cls.state = 'SecB WT apo'
cls.control = ('Full deuteration control', 0.167)
cls.pmt.set_control(cls.control)
state_data = cls.pmt.get_state(cls.state)
cls.temperature, cls.pH = 273.15 + 30, 8.
cls.series = HDXMeasurement(state_data,
temperature=cls.temperature,
pH=cls.pH)
cls.prot_fit_result = csv_to_protein(directory / 'test_data' /
'ecSecB_torch_fit.txt')
cfg = ConfigurationSettings()
cfg.set('cluster', 'port', str(test_port))
from pathlib import Path
import numpy as np
from pyhdx import PeptideMasterTable, read_dynamx, HDXMeasurement
current_dir = Path(__file__).parent
np.random.seed(43)
fpath = current_dir.parent / 'tests' / 'test_data' / 'ecSecB_apo.csv'
data = read_dynamx(fpath)
pmt = PeptideMasterTable(data,
drop_first=1,
ignore_prolines=True,
remove_nan=False)
pmt.set_control(('Full deuteration control', 0.167))
sequence = 'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA'
hdxm = HDXMeasurement(pmt.get_state('SecB WT apo'), sequence=sequence)
print(hdxm.coverage.protein)
#hdxm.coverage.protein.to_file('test.txt', fmt='pprint')
sequence_dimer = 'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPAARECIASMVARGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA'
directory = Path(__file__).parent
test_data_dir = directory / 'test_data'
guess = False # guess true requires dask cluster at config defined ip/port
control = ('Full deuteration control', 0.167)
data = read_dynamx(test_data_dir / 'ecSecB_apo.csv', test_data_dir / 'ecSecB_dimer.csv')
pmt = PeptideMasterTable(data, drop_first=1, ignore_prolines=True, remove_nan=False)
pmt.set_control(control)
temperature, pH = 273.15 + 30, 8.
hdxm = HDXMeasurement(pmt.get_state('SecB WT apo'), sequence=sequence, temperature=temperature, pH=pH)
if guess:
client = default_client()
wt_avg_result = fit_rates_weighted_average(hdxm, bounds=(1e-2, 800))
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)
"""Load a HDX-MS dataset (peptide list with D-uptake per peptide, csv format)"""
from pathlib import Path
import numpy as np
from pyhdx import PeptideMasterTable, read_dynamx, HDXMeasurement
current_dir = Path(__file__).parent
output_dir = current_dir / 'output'
output_dir.mkdir(exist_ok=True)
np.random.seed(43)
fpath = current_dir.parent / 'tests' / 'test_data' / 'input' / 'ecSecB_apo.csv'
data = read_dynamx(fpath)
pmt = PeptideMasterTable(data,
drop_first=1,
ignore_prolines=True,
remove_nan=False)
pmt.set_control(('Full deuteration control', 0.167 * 60))
sequence = 'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA'
temperature, pH = 273.15 + 30, 8.
hdxm = HDXMeasurement(pmt.get_state('SecB WT apo'),
sequence=sequence,
pH=pH,
temperature=temperature)
hdxm.coverage.protein.to_file(output_dir / 'SecB_info.txt', fmt='pprint')
current_dir = Path(__file__).parent
#current_dir = Path().cwd() / 'templates' # pycharm scientific compat
output_dir = current_dir / 'output'
output_dir.mkdir(exist_ok=True)
test_data_dir = current_dir.parent / 'tests' / 'test_data'
input_dir = test_data_dir / 'input'
#%%
# Load the data of two Dynamx files, and combine the result to one table
data = read_dynamx(input_dir / 'ecSecB_apo.csv', input_dir / 'ecSecB_dimer.csv')
pmt = PeptideMasterTable(data, drop_first=1, ignore_prolines=True, remove_nan=False)
pmt.set_control(('Full deuteration control', 0.167*60))
temperature, pH = 273.15 + 30, 8.
hdxm = HDXMeasurement(pmt.get_state('SecB WT apo'), temperature=temperature, pH=pH)
#%%
if guess:
client = default_client()
wt_avg_result = fit_rates_weighted_average(hdxm, client=client)
init_guess = wt_avg_result.output
else:
init_guess = csv_to_dataframe(test_data_dir / 'output' / 'ecSecB_guess.csv')
gibbs_guess = hdxm.guess_deltaG(init_guess['rate'])
#%%
fr_torch = fit_gibbs_global(hdxm, gibbs_guess, **fit_kwargs)