Biology· code-generation
BioCoder
Yale University (Gerstein Lab) · 2023
Fuzz-tested benchmark evaluating LLMs on generating bioinformatics code with cross-file dependencies and domain knowledge.
Biology
Examples
Browse full dataset ↗promptSummaryOnly
This is in python
write a function that takes in a string as an argument and returns a sanitized version of the string suitable for LaTeX formatting. The function should follow these steps:
1. Replace any occurrences of four backslashes (\\\\) with the string \\textbackslash.
2. Escape certain characters, including $, %, _, }, {, &, and #, by adding a backslash before them.
3. Replace special characters such as tilde (~) with the corresponding LaTeX equivalent (e.g. $\\sim$).
The function should be named sanitize_tex and should have one argument named original_text. It should return the sanit …
test_case_id
61beb3529846e024cdff01d3e2ba1a1ec4212dd64426028deb1065f1975bd376
goldenCode
def sanitize_tex(original_text):
"""Sanitize TeX text.
Args:
original_text (str): the text to sanitize for LaTeX
Text is sanitized by following these steps:
1. Replaces ``\\\\`` by ``\\textbackslash``
2. Escapes certain characters (such as ``$``, ``%``, ``_``, ``}``, ``{``,
``&`` and ``#``) by adding a backslash (*e.g.* from ``&`` to ``\\&``).
3. Replaces special characters such as ``~`` by the LaTeX equivalent
(*e.g.* from ``~`` to ``$\\sim$``).
"""
sanitized_tex = original_text.replace('\\', '\\textbackslash ')
sanitized_tex = re. …
contextCode
import random
import hashlib
import numpy as np
import skimage
import skimage.measure
import scipy.ndimage
import os
import logging
from functools import wraps
from scipy import stats
import sys
import math
import subprocess
from pathlib import PurePath
from itertools import islice
import pysam
import pandas as pd
from scipy.signal import savgol_coeffs, savgol_filter
from scipy.stats import norm
import re
import fileinput
import warnings
from scipy.stats import scoreatpercentile, chisquare
from sklearn.preprocessing import scale
from sklearn.cluster import KMeans, AgglomerativeClustering
_char …
repository
pgxcentre/genipe
content
def sanitize_tex(original_text):
"""Sanitize TeX text.
Args:
original_text (str): the text to sanitize for LaTeX
Text is sanitized by following these steps:
1. Replaces ``\\\\`` by ``\\textbackslash``
2. Escapes certain characters (such as ``$``, ``%``, ``_``, ``}``, ``{``,
``&`` and ``#``) by adding a backslash (*e.g.* from ``&`` to ``\\&``).
3. Replaces special characters such as ``~`` by the LaTeX equivalent
(*e.g.* from ``~`` to ``$\\sim$``).
"""
sanitized_tex = original_text.replace('\\', '\\textbackslash ')
sanitized_tex = re. …
numLines
27
filePath
genipe/reporting/utils.py
lineEnd
84
comment
Sanitize TeX text.
Args:
original_text (str): the text to sanitize for LaTeX
Text is sanitized by following these steps:
1. Replaces ``\\`` by ``\textbackslash``
2. Escapes certain characters (such as ``$``, ``%``, ``_``, ``}``, ``{``,
``&`` and ``#``) by adding a backslash (*e.g.* from ``&`` to ``\&``).
3. Replaces special characters such as ``~`` by the LaTeX equivalent
(*e.g.* from ``~`` to ``$\sim$``).
signature
def sanitize_tex(original_text)
lineStart
58
language
Python
promptSummaryOnly
This is in python
Write a function called "filter_kmers" that takes in three parameters: "kmers", "kmer_len", and "rate". The function should return a clean set of k-mers in a tuple. The function should filter out low-complexity and low-frequency kmers. Inside the function, create a list called "low_comp" which uses a regular expression to match to each base in 'ACGTN' and divide "kmer_len" by 2. Initialize "i" and "x" both to -1. Use a while loop to iterate until "x" is equal to the length of "low_comp". Inside the loop, increment "i" by 1 and set "x" to the sum of the boolean output of each …
test_case_id
98b443b728881547cc9d48a5ccb5f26c65aa1cbafd482b962b85a4b2e4725d3f
goldenCode
def filter_kmers(kmers, kmer_len, rate):
"""Return a clean set of k-mers in tuple.
Filter low-complexity and low-frequency kmers.
"""
low_comp = [re.compile(base * (kmer_len // 2)) for base in 'ACGTN']
i, x = -1, -1
while x != len(low_comp):
i += 1
x = sum([(not p.findall(kmers[i][0])) for p in low_comp])
max_hits = kmers[i][1]
clean = []
total = 0
for s, n in kmers[i:]:
if sum([(not p.findall(s)) for p in low_comp]) != len(low_comp):
continue
if float(max_hits) / n > rate:
break
clean.a …
contextCode
import random
import hashlib
import numpy as np
import skimage
import skimage.measure
import scipy.ndimage
import os
import logging
from functools import wraps
from scipy import stats
import sys
import math
import subprocess
from pathlib import PurePath
from itertools import islice
import pysam
import pandas as pd
from scipy.signal import savgol_coeffs, savgol_filter
from scipy.stats import norm
import re
<<insert solution here>>
def main():
random.seed(<|int;range=0,100|>)
letters = ['A', 'C', 'G', 'T', 'N']
kmers = []
for _ in range(5):
kmers.append((''.join(random.ch …
repository
jnktsj/DNApi
content
def filter_kmers(kmers, kmer_len, rate):
"""Return a clean set of k-mers in tuple.
Filter low-complexity and low-frequency kmers.
"""
low_comp = [re.compile(base * (kmer_len // 2)) for base in 'ACGTN']
i, x = -1, -1
while x != len(low_comp):
i += 1
x = sum([(not p.findall(kmers[i][0])) for p in low_comp])
max_hits = kmers[i][1]
clean = []
total = 0
for s, n in kmers[i:]:
if sum([(not p.findall(s)) for p in low_comp]) != len(low_comp):
continue
if float(max_hits) / n > rate:
break
clean.a …
numLines
22
filePath
dnapilib/kmer.py
lineEnd
50
comment
Return a clean set of k-mers in tuple.
Filter low-complexity and low-frequency kmers.
signature
def filter_kmers(kmers, kmer_len, rate)
lineStart
29
language
Python
Real rows from the Hugging Face datasets server · long values truncated