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_apo = os.path.join(directory, 'test_data', 'ecSecB_apo.csv')
fpath_dimer = os.path.join(directory, 'test_data', 'ecSecB_dimer.csv')
data = read_dynamx(fpath_apo, fpath_dimer)
control = ('Full deuteration control', 0.167)
cls.temperature, cls.pH = 273.15 + 30, 8.
pf = PeptideMasterTable(data,
drop_first=1,
ignore_prolines=True,
remove_nan=False)
pf.set_control(control)
states = pf.groupby_state()
cls.series_apo = states['SecB WT apo']
cls.series_dimer = states['SecB his dimer apo']
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')
np.random.seed(43)
epochs = 1000
sequence = 'MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWQPEVKLDLDTASSQLADDVYEVVLRVTVTASLGEETAFLCEVQQGGIFSIAGIEGTQMAHCLGAYCPNILFPYARECITSMVSRGTFPQLNLAPVNFDALFMNYLQQQAGEGTEEHQDA'
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')
from pyhdx.support import get_reduced_blocks, get_original_blocks
from pyhdx.plot import plot_peptides
import matplotlib.pyplot as plt
import os
import numpy as np
data_dir = '../../tests/test_data'
filename = 'ecSecB_apo.csv'
refit = False
fpath = os.path.join(data_dir, filename)
data = read_dynamx(fpath)
master_table = PeptideMasterTable(data, drop_first=0, ignore_prolines=False)
master_table.set_control(('Full deuteration control', 0.167))
states = master_table.groupby_state()
print(states.keys())
series = states['SecB WT apo']
series.make_uniform()
split = series.split()
key = list(split)[1]
series = split[key]
kf = KineticsFitting(series, bounds=(0, 200))
print(kf.bounds)
if refit:
fr1 = kf.weighted_avg_fit()
arr = fr1.get_output(['tau1', 'tau2', 'r'])
epochs = 1000
directory = Path(__file__).parent
test_data_dir = directory / 'test_data'
guess = False
control = ('Full deuteration control', 0.167)
data = read_dynamx(test_data_dir / 'ecSecB_apo.csv',
test_data_dir / 'ecSecB_dimer.csv')
pf = PeptideMasterTable(data,
drop_first=1,
ignore_prolines=True,
remove_nan=False)
pf.set_control(control)
states = pf.groupby_state()
series = states['SecB WT apo']
temperature, pH = 273.15 + 30, 8.
kf = KineticsFitting(series,
bounds=(1e-2, 800),
temperature=temperature,
pH=pH)
if guess:
wt_avg_result = kf.weighted_avg_fit()
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')