def test_load_single_file(self):
with open(self.fpath, 'rb') as f:
binary = f.read()
ctrl = main_app()
file_input_control = ctrl.control_panels['PeptideFileInputControl']
file_input_control.input_files = [binary]
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.fd_exposure = 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_add_dataset()
assert self.state in ctrl.data_objects
series = ctrl.data_objects[self.state]
assert series.Nt == 7
assert series.Np == 63
assert series.Nr == 146
# Py39 result: 54.054873258574965
# assert np.nanmean(series.scores_stack) == 54.05487325857497
assert abs(np.nanmean(series.rfu_residues) - 0.540548732585749) < 1e-6
def test_load_files(self):
with open(self.fpath, 'rb') as f:
binary = f.read()
ctrl = main_app()
file_input_control = ctrl.control_panels['PeptideFileInputControl']
file_input_control.input_files = [binary]
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_add_dataset()
assert self.state in ctrl.fit_objects
fit_object = ctrl.fit_objects[self.state]
series = fit_object.series
assert series.Nt == 7
assert series.Np == 7
assert series.Nr == 38
def test_batch_mode(self):
fpath_1 = directory / 'test_data' / 'ecSecB_apo.csv'
fpath_2 = directory / 'test_data' / 'ecSecB_dimer.csv'
fpaths = [fpath_1, fpath_2]
files = [p.read_bytes() for p in fpaths]
ctrl = main_app(client=None)
file_input = ctrl.control_panels['PeptideFileInputControl']
file_input.input_files = files
file_input.fd_state = 'Full deuteration control'
file_input.fd_exposure = 0.167
file_input.exp_state = 'SecB WT apo'
file_input.dataset_name = 'testname_123'
file_input._action_add_dataset()
#assert ....
file_input.exp_state = 'SecB his dimer apo'
file_input.dataset_name = 'SecB his dimer apo' # todo catch error duplicate name
file_input._action_add_dataset()
initial_guess = ctrl.control_panels['InitialGuessControl']
initial_guess._action_fit()
# Wait until fitting futures are completed
while len(ctrl.future_queue) > 0:
ctrl.check_futures()
time.sleep(0.1)
#assert ....
fit_control = ctrl.control_panels['FitControl']
fit_control.epochs = 10
fit_control.fit_name = 'testfit_1'
fit_control._action_fit()
# Wait until fitting futures are completed
while len(ctrl.future_queue) > 0:
ctrl.check_futures()
time.sleep(0.1)
#assert ....
table = ctrl.sources['dataframe'].get('global_fit')
# Test classification
# todo test log space
# todo probably values should be fixed otherwise tests are co-dependent
values = table['testfit_1']['testname_123']['deltaG']
classification = ctrl.control_panels['ClassificationControl']
classification._action_otsu()
cmap, norm = classification.get_cmap_and_norm()
colors = cmap(norm(values), bytes=True)
assert colors.sum() == 68474
assert colors.std() == 109.39692637364178
classification.mode = 'Continuous'
classification._action_linear()
cmap, norm = classification.get_cmap_and_norm()
colors = cmap(norm(values), bytes=True)
assert colors.sum() == 73090
assert colors.std() == 89.41501408256475
value_widget = classification.widgets['value_2']
value_widget.value = 10e3
cmap, norm = classification.get_cmap_and_norm()
colors = cmap(norm(values), bytes=True)
assert colors.sum() == 73097
assert colors.std() == 91.9688274382922
classification.mode = 'Color map'
classification.library = 'colorcet'
classification.color_map = 'CET_C1'
cmap, norm = classification.get_cmap_and_norm()
colors = cmap(norm(values), bytes=True)
assert colors.sum() == 117289
assert colors.std() == 64.90120978241222
def test_global_fit(self):
ctrl = main_app()
def test_classification(self):
ctrl = main_app()
def test_initial_guesses(self):
ctrl = main_app()
def test_coverage(self):
ctrl = main_app()
def test_file_download_output(self):
ctrl = main_app()
def test_initial_guesses_and_fit(self):
ctrl = main_app(client=None)
ctrl.data_objects[self.series.name] = self.series
ctrl.control_panels['InitialGuessControl']._action_fit()
import panel as pn
from pyhdx.panel.apps import main_app #, diff_app, single_app, folding_app, full_deuteration_app
from pyhdx.panel.base import STATIC_DIR
import numpy as np
import torch
from pyhdx.panel.config import ConfigurationSettings
from pyhdx.support import verify_cluster
import logging
from functools import partial
from pathlib import Path
import datetime
APP_DICT = {
'main': lambda: main_app().template,
# 'single': lambda: single_app().template,
# 'diff': lambda: diff_app().template,
# 'folding': lambda: folding_app().template,
# 'full_deuteration': lambda: full_deuteration_app().template
}
def run_main():
np.random.seed(43)
torch.manual_seed(43)
cluster = ConfigurationSettings().cluster
if verify_cluster(cluster):
print("Welcome to the PyHDX server!")
"""
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.sources import DataSource
from pyhdx.batch_processing import load_from_yaml
from pyhdx.fileIO import csv_to_protein
import panel as pn
import numpy as np
from pathlib import Path
ctrl = main_app()
directory = Path(__file__).parent
root_dir = directory.parent.parent
data_dir = root_dir / 'tests' / 'test_data'
test_dir = directory / 'test_data'
fpath_1 = root_dir / 'tests' / 'test_data' / 'ecSecB_apo.csv'
fpath_2 = root_dir / 'tests' / 'test_data' / 'ecSecB_dimer.csv'
fpaths = [fpath_1, fpath_2]
files = [p.read_bytes() for p in fpaths]
d1 = {
'filenames': ['ecSecB_apo.csv', 'ecSecB_dimer.csv'],
'd_percentage': 95,