def setup_class(cls):
cls.fpath = directory / 'test_data' / 'simulated_data_uptake.csv'
cls.pmt = PeptideMasterTable(read_dynamx(cls.fpath))
cls.state = 'state1'
cls.pmt.set_backexchange(0.)
states = cls.pmt.groupby_state()
cls.series = states[cls.state]
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))
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)
states = cls.pmt.groupby_state()
cls.series = states[cls.state]
cls.prot_fit_result = csv_to_protein(directory / 'test_data' /
'ecSecB_torch_fit.txt')
cls.ds_fit = DataSource(cls.prot_fit_result,
name='global_fit',
x='r_number',
tags=['mapping', 'pfact', 'deltaG'],
renderer='circle',
size=10)
from pyhdx import read_dynamx, PeptideMasterTable
from pyhdx.support import get_reduced_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'
fpath = os.path.join(data_dir, filename)
data = read_dynamx(fpath)
master_table = PeptideMasterTable(data, drop_first=0, ignore_prolines=False)
states = master_table.groupby_state()
print(states.keys())
series = states['SecB WT apo']
split = series.split()
key = list(split)[1]
cov = split[key].cov
def add_blocks(ax, positions, color):
for pos in positions:
ax.plot([pos, pos], [-40, 2], color=color,
linewidth=2) # linestyle=(0, (1, 1))
text_x = positions[:-1] + np.diff(positions) / 2
for i, x in enumerate(text_x[text_x < 58]):
ax.text(x,
from pathlib import Path
import numpy as np
from pyhdx import PeptideMasterTable, KineticsFitting, read_dynamx
from pyhdx.fileIO import txt_to_protein
guess = False
epochs = 100000
root_dir = Path().resolve().parent
test_data_dir = root_dir / 'tests' / 'test_data'
input_file_path = test_data_dir / 'ecSecB_apo.csv'
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(('Full deuteration control', 0.167))
states = pmt.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()
'stop_loss': 1e-6
}
#%%
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')
