def test_coverage(self):
tmpl, ctrl = _main_app()
ctrl.series = self.series
cov_figure = ctrl.figure_panels['CoverageFigure']
renderer = cov_figure.figure.renderers[0]
assert renderer.data_source.name == f'coverage_{self.series.state}'
def test_global_fit(self):
tmpl, ctrl = _main_app()
ctrl.cluster = None
ctrl.series = self.series
initial_guess = ctrl.control_panels['InitialGuessControl']
initial_guess._action_fit()
fit = ctrl.control_panels['FitControl']
fit.epochs = 20
fit._do_fitting()
assert 'global_fit' in ctrl.sources.keys()
def test_initial_guesses(self):
tmpl, ctrl = _main_app()
ctrl.cluster = None
ctrl.series = self.series
initial_guess = ctrl.control_panels['InitialGuessControl']
initial_guess._action_fit()
assert 'half-life' in ctrl.sources.keys()
initial_guess.fitting_model = 'Association'
initial_guess._action_fit()
assert 'fit1' in ctrl.sources.keys()
def test_classification(self):
tmpl, ctrl = _main_app()
ctrl.cluster = None
ctrl.series = self.series
initial_guess = ctrl.control_panels['InitialGuessControl']
initial_guess._action_fit()
assert 'half-life' in ctrl.sources.keys()
classification = ctrl.control_panels['ClassificationControl']
classification.target = 'half-life'
classification.quantity = 'rate'
classification._action_otsu()
def test_initial_guesses_and_fit(self):
tmpl, ctrl = _main_app()
ctrl.series = self.series
ctrl.control_panels['InitialGuessControl']._action_fit()
assert 'half-life' in ctrl.sources.keys()
fit_control = ctrl.control_panels['FitControl']
fit_control.epochs = 10
fit_control._do_fitting()
deltaG_figure = ctrl.figure_panels['DeltaGFigure']
renderer = deltaG_figure.figure.renderers[0]
assert renderer.data_source.name == 'global_fit'
def test_file_download_output(self):
tmpl, ctrl = _main_app()
ctrl.series = self.series
ctrl.publish_data('global_fit', self.ds_fit)
f_export = ctrl.control_panels['FileExportControl']
f_export.target = 'global_fit'
pml = f_export._make_pml('sadf')
assert pml == TEST_PML
io = f_export.linear_export_callback()
val = io.getvalue()
assert val.count('\n') == 148
def test_load_files(self):
with open(self.fpath, 'rb') as f:
binary = f.read()
tmpl, ctrl = _main_app()
file_input_control = ctrl.control_panels['PeptideFileInputControl']
file_input_control.file_selectors[0].value = binary
file_input_control._action_load()
file_input_control.norm_mode = 'Theory'
file_input_control.be_percent = 0.
file_input_control.exp_state = self.state
assert file_input_control.exp_exposures == [
0.167, 0.5, 1.0, 5.0, 10.0, 30., 100.
]
file_input_control._action_parse()
assert isinstance(ctrl.series, KineticsSeries)
assert isinstance(ctrl.peptides, PeptideMasterTable)
def test_load_files(self):
with open(self.fpath, 'rb') as f:
binary = f.read()
tmpl, ctrl = _main_app()
file_input_control = ctrl.control_panels['PeptideFileInputControl']
file_input_control.file_selectors[0].value = binary
file_input_control._action_load()
assert file_input_control.fd_state == 'Full deuteration control'
assert file_input_control.fd_exposure == 0.0
file_input_control.fd_state = self.control[0]
file_input_control.fdexposure = self.control[1]
file_input_control.exp_state = self.state
assert file_input_control.exp_exposures == [
0.0, 0.167, 0.5, 1.0, 5.0, 10.0, 100.000008
]
file_input_control._action_parse()
assert isinstance(ctrl.series, KineticsSeries)
assert isinstance(ctrl.peptides, PeptideMasterTable)
Reload SecB and fitted data and launch GUI
"""
from pyhdx.fileIO import read_dynamx, txt_to_np, csv_to_dataframe
from pyhdx import PeptideMasterTable
import pickle
from pyhdx.panel.apps import _main_app
from pyhdx.panel.utils import reload_previous
from pyhdx.panel.base import DEFAULT_COLORS, STATIC_DIR
from pyhdx.panel.data_sources import DataSource
import panel as pn
import numpy as np
from pathlib import Path
tmpl, ctrl = _main_app()
directory = Path(__file__).parent
fpath = directory / 'test_data' / 'ecSecB_apo.csv'
dic = {}
dic['file_path'] = directory / 'test_data' / 'ecSecB_apo.csv'
dic['norm_mode'] = 'Exp'
dic['fd_state'] = 'Full deuteration control'
dic['fd_exposure'] = 0.167
dic['exp_state'] = 'SecB WT apo'
src_file = directory / 'test_data' / 'ecSecB_torch_fit.txt'
df = csv_to_dataframe(src_file)
data_dict = df.to_dict(orient='series')
print(data_dict)
def test_coverage(self):
tmpl, ctrl = _main_app()
ctrl.series = self.series