def import_HXcolumns(infile, sequence, name=None, percentD=False, conditions=None, error_estimate=5.0, n_fastamides=2, offset=0):
""" Function for converting a set of HX columns intoa Dataset object
"""
if type(conditions) is not Conditions:
print("Standard Conditions used. Please modify these in the script if you are not at 283K and pH=7")
conditions = Conditions()
f = open(infile,"r")
line = f.readline()
dataset = Dataset(name=name, sequence=sequence, conditions=conditions, error_estimate=error_estimate, offset=offset, input_file=infile)
column_headers = line.rstrip().split(",") # Pre-set to None. See #XXXX
for line in f.readlines():
fields = line.split(",")
sequence = fields[column_headers.index("peptide_seq")]
start_res = int(fields[column_headers.index("start_res")]) + offset
time = float(fields[column_headers.index("time")])
deut = float(fields[column_headers.index("D_inc")])
if not percentD:
deut = deut / tools.calculate_number_of_observable_amides(sequence, n_fastamides) * 100
score = float(fields[column_headers.index("score")])
new_peptide = dataset.create_peptide(sequence, start_res)
if new_peptide is not None:
# If the time is 0.0, that's weird. Ignore that.
if time==0.0:
continue
if deut < 105: # XXX Hard coded cap on %D value. Not ideal.
new_timepoint=True
# If timepoint is already in fragment, grab that timepoint
if time in [tp.time for tp in new_peptide.get_timepoints()]:
tp = new_peptide.get_timepoint_by_time(time)
#If not, add a new timepoint at this time. What to put for sigma??
else:
tp = new_peptide.add_timepoint(time)
# add the deuteration value as a replicate.
# Any other replicate information from the file should be added at this step.
tp.add_replicate(deut, score=score)
new_peptide.add_timepoint(time)
dataset.calculate_observable_rate_bounds()
dataset.calculate_observable_protection_factors()
return dataset
def import_json(infile, name=""):
# Imports a json written by Dataset.write_to_file()
import json
#print(infile)
with open(infile) as json_data:
d = json.load(json_data)
cond_dict = d["conditions"]
conditions = Conditions()
for a in cond_dict:
conditions.a = cond_dict[a]
dataset = Dataset(d["name"], conditions, d["sequence"],
input_file = d["raw_data_file"],
error_estimate = d["error_estimate"],
offset = d["offset"],
number_fast_exchanging_amides = d["num_fast_exchanging_amides"],
percent_deuterium = d["percent_deuterium"])
pep_dict = d['peptides']
for pep in pep_dict:
p = pep_dict[str(pep)]
peptide = dataset.create_peptide(p["sequence"], p["start_residue"],
peptide_id=pep,
charge_state=p["charge_state"],
retention_time=p["retention_time"],
sigma=p["sigma"])
tp_dict = p["timepoints"]
for tp in tp_dict:
timepoint = peptide.add_timepoint(tp_dict[str(tp)]["time"], tp_dict[str(tp)]["sigma"])
reps = tp_dict[str(tp)]["replicates"]
for rep in reps:
timepoint.add_replicate(reps[str(rep)]["deut"], experiment_id=reps[str(rep)]["experiment_id"],
score=reps[str(rep)]["reliability"], rt=reps[str(rep)]["rt"])
return dataset
