def load_from_yaml(yaml_dict, data_dir=None): #name: load what from yaml?
"""
Creates a :class:`~pyhdx.fitting.KineticsFitting` object from dictionary input.
Dictionary can be generated from .yaml format and should specifiy
Parameters
----------
yaml_dict : :obj:`dict`
Input dictionary specifying metadata and file location to load
data_dir : :obj:`str` or pathlib.Path object
Returns
-------
kf : :class:`~pyhdx.fititng.KineticsFitting`
:class:`~pyhdx.fititng.KineticsFitting` class as specified by input dict.
"""
if data_dir is not None:
input_files = [Path(data_dir) / fname for fname in yaml_dict['filenames']]
else:
input_files = yaml_dict['filenames']
data = read_dynamx(*input_files)
pmt = PeptideMasterTable(data, d_percentage=yaml_dict['d_percentage']) #todo add proline, n_term options
if 'control' in yaml_dict.keys(): # Use a FD control for back exchange correction
pmt.set_control(tuple(yaml_dict['control']))
elif 'be_percent' in yaml_dict.keys(): # Flat back exchange percentage for all peptides\
pmt.set_backexchange(yaml_dict['be_percent'])
else:
raise ValueError('No valid back exchange control method specified')
try:
c_term = yaml_dict.get('c_term', 0) or len(yaml_dict['sequence']) + 1
except KeyError:
raise ValueError("Must specify either 'c_term' or 'sequence'")
states = pmt.groupby_state(c_term=c_term)
series = states[yaml_dict['series_name']]
if yaml_dict['temperature_unit'].lower() == 'celsius':
temperature = yaml_dict['temperature'] + 273.15
elif yaml_dict['temperature_unit'].lower() == 'kelvin':
temperature = yaml_dict['temperature']
else:
raise ValueError("Invalid option for 'temperature_unit', must be 'Celsius' or 'Kelvin'")
kf = KineticsFitting(series, temperature=temperature, pH=yaml_dict['pH'])
return kf
def load_folding_from_yaml(yaml_dict, data_dir=None): #name: load what from yaml?
"""
Creates a :class:`~pyhdx.fitting.KineticsFitting` object from dictionary input.
Dictionary can be generated from .yaml format and should specifiy
Parameters
----------
yaml_dict : :obj:`dict`
Input dictionary specifying metadata and file location to load
data_dir : :obj:`str` or pathlib.Path object
Returns
-------
kf : :class:`~pyhdx.fititng.KineticsFitting`
:class:`~pyhdx.fititng.KineticsFitting` class as specified by input dict.
"""
if data_dir is not None:
input_files = [Path(data_dir) / fname for fname in yaml_dict['filenames']]
else:
input_files = yaml_dict['filenames']
data = read_dynamx(*input_files)
pmt = PeptideMasterTable(data, d_percentage=yaml_dict['d_percentage']) #todo add proline, n_term options
#todo merge this with the other func where it checks for control names to determine what to apply
pmt.set_control(control_100=tuple(yaml_dict['control_100']), control_0=tuple(yaml_dict['control_0']))
try:
c_term = yaml_dict.get('c_term', 0) or len(yaml_dict['sequence']) + 1
except KeyError:
raise ValueError("Must specify either 'c_term' or 'sequence'")
states = pmt.groupby_state(c_term=c_term)
series = states[yaml_dict['series_name']]
if yaml_dict['temperature_unit'].lower() == 'celsius':
temperature = yaml_dict['temperature'] + 273.15
elif yaml_dict['temperature_unit'].lower() == 'kelvin':
temperature = yaml_dict['temperature']
else:
raise ValueError("Invalid option for 'temperature_unit', must be 'Celsius' or 'Kelvin'")
kf = KineticsFitting(series, temperature=temperature, pH=yaml_dict['pH'])
return kf
def load_folding_from_yaml(yaml_dict,
data_dir=None): #name: load what from yaml?
"""
"""
raise NotImplementedError(
'Loading folding data from yaml currently not implemented')
if data_dir is not None:
input_files = [
Path(data_dir) / fname for fname in yaml_dict['filenames']
]
else:
input_files = yaml_dict['filenames']
data = read_dynamx(*input_files)
pmt = PeptideMasterTable(data, d_percentage=yaml_dict['d_percentage']
) #todo add proline, n_term options
#todo merge this with the other func where it checks for control names to determine what to apply
pmt.set_control(control_1=tuple(yaml_dict['control_1']),
control_0=tuple(yaml_dict['control_0']))
try:
c_term = yaml_dict.get('c_term', 0) or len(yaml_dict['sequence']) + 1
except KeyError:
raise ValueError("Must specify either 'c_term' or 'sequence'")
states = pmt.groupby_state(c_term=c_term)
series = states[yaml_dict['series_name']]
if yaml_dict['temperature_unit'].lower() == 'celsius':
temperature = yaml_dict['temperature'] + 273.15
elif yaml_dict['temperature_unit'].lower() == 'kelvin':
temperature = yaml_dict['temperature']
else:
raise ValueError(
"Invalid option for 'temperature_unit', must be 'Celsius' or 'Kelvin'"
)
kf = KineticsFitting(series, temperature=temperature, pH=yaml_dict['pH'])
return kf
from pyhdx.panel.sources import DataFrameSource
from pyhdx.panel.filters import MultiIndexSelectFilter
from lumen.sources import DerivedSource
directory = Path(__file__).parent
data_dir = directory / 'test_data'
data = read_dynamx(data_dir / 'ecSecB_apo.csv', data_dir / 'ecSecB_dimer.csv')
pmt = PeptideMasterTable(data)
pmt.set_control(('Full deuteration control', 0.167))
states = pmt.groupby_state()
st1 = states['SecB his dimer apo']
st2 = states['SecB WT apo']
df1 = pd.DataFrame(st1.full_data)
df2 = pd.DataFrame(st2.full_data)
rates_df = pd.read_csv(data_dir / 'ecSecB_rates.txt', index_col=0, header=[0, 1])
class TestLumenSources(object):
@classmethod
def setup_class(cls):
cls.df1 = df1
cls.df2 = df2