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from model import Model
import pandas as pd
from re import match
class Data:
"""Container for input and output data"""
# initialise empty model as static class member for efficiency
model = Model()
def parse_seq(self, src:str):
"parse input sequence"
self.seq = src.strip().upper()
if not all(x in self.model.alphabet for x in src):
raise RuntimeError("Unrecognised characters in sequence")
def parse_sub(self, trg:str):
"parse input substitutions"
self.mode = None
self.sub = list()
self.trg = trg.strip().upper()
# identify running mode
if len(self.trg.split()) == 1 and len(self.trg.split()[0]) == len(self.seq): # if single string of same length as sequence, seq vs seq mode
self.mode = 'SVS'
for resi,(src,trg) in enumerate(zip(self.seq, self.trg), 1):
if src != trg:
self.sub.append(f"{src}{resi}{trg}")
else:
self.trg = self.trg.split()
if all(match(r'\d+', x) for x in self.trg): # if all strings are numbers, deep mutational scanning mode
self.mode = 'DMS'
for resi in map(int, self.trg):
src = self.seq[resi-1]
for trg in "ACDEFGHIKLMNPQRSTVWY".replace(src,''):
self.sub.append(f"{src}{resi}{trg}")
elif all(match(r'[A-Z]\d+[A-Z]', x) for x in self.trg): # if all strings are of the form X#Y, single substitution mode
self.mode = 'MUT'
self.sub = self.trg
else:
raise RuntimeError("Unrecognised running mode; wrong inputs?")
self.sub = pd.DataFrame(self.sub, columns=['0'])
def __init__(self, src:str, trg:str, model_name:str, scoring_strategy:str, out_file):
"initialise data"
# if model has changed, load new model
if self.model.model_name != model_name:
self.model_name = model_name
self.model = Model(model_name)
self.parse_seq(src)
self.parse_sub(trg)
self.scoring_strategy = scoring_strategy
self.out = pd.DataFrame(self.sub, columns=['0', self.model_name])
self.out_buffer = out_file.name
def parse_output(self) -> str:
"format output data for visualisation"
if self.mode == 'MUT': # if single substitution mode, sort by score
self.out = self.out.sort_values(self.model_name, ascending=False)
elif self.mode == 'DMS': # if deep mutational scanning mode, sort by residue and score
self.out = pd.concat([(self.out.assign(resi=self.out['0'].str.extract(r'(\d+)', expand=False).astype(int)) # FIX: this doesn't work if there's jolly characters in the input sequence
.sort_values(['resi', self.model_name], ascending=[True,False])
.groupby(['resi'])
.head(19)
.drop(['resi'], axis=1)).iloc[19*x:19*(x+1)]
.reset_index(drop=True) for x in range(self.out.shape[0]//19)]
, axis=1).set_axis(range(self.out.shape[0]//19*2), axis='columns')
# save to temporary file to be downloaded
self.out.round(2).to_csv(self.out_buffer, index=False)
return (self.out.style
.format(lambda x: f'{x:.2f}' if isinstance(x, float) else x)
.hide(axis=0)
.hide(axis=1)
.background_gradient(cmap="RdYlGn", vmax=8, vmin=-8)
.to_html(justify='center'))
def calculate(self):
"run model and parse output"
self.model.run_model(self)
return self.parse_output()
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