add problem of dna search

Author: codejsha

README.md

@@ -943,6 +943,7 @@ Collections.binarySearch(arrayList, 3);   // for list
 
 - Find k-th smallest/largest element in an array (`FindKthSmallestElement`/`FindKthLargestElement`), EPI#11.8: [c++](cpp-algorithm/src/search) | Find the k-th smallest/largest element in an array using the quickselect algorithm (`QuickSelectAlgorithm`).
 - Find the minimum and maximum elements in an array (`FindMinMax`), EPI#11.7: [c++](cpp-algorithm/src/search)
+- Search a codon(combinations of three nucleotides) in a gene (`linear_contains`, `binary_contains`), CCSP#2.1: [python](python-algorithm/src/search) | Search a codon(combinations of three nucleotides) in a gene using linear search and binary search.
 - Search a sorted array for entry equal to its index (`SearchEntryEqualToItsIndex`), EPI#11.2: [c++](cpp-algorithm/src/search)
 - Search a sorted array for the first greater than a key (`SearchFirstGreaterThanKey`): [c++](cpp-algorithm/src/search)
 - Search a sorted array for the first occurrence of a key (`SearchFirstOfKey`), EPI#11.1: [c++](cpp-algorithm/src/search)
@@ -1153,6 +1154,7 @@ var str = collection.stream()
 - Discrete Mathematics and Its Applications, 8th Edition, by Kenneth H. Rosen
 - Cracking the Coding Interview, 6th Edition, by Gayle Laakmann McDowell
 - Elements of Programming Interviews, 2nd Edition, by Adnan Aziz, Tsung-Hsien Lee and Amit Prakash
+- Classic Computer Science Problems in Python, by David Kopec
 
 [:arrow_up_small: back to toc](#table-of-contents)
 

python-algorithm/algorithm/search/dna_search.py

@@ -0,0 +1,31 @@
+from enum import IntEnum
+from typing import Tuple, List
+
+Nucleotide = IntEnum('Nucleotide', ('A', 'C', 'G', 'T'))
+Codon = Tuple[Nucleotide, Nucleotide, Nucleotide]
+Gene = List[Codon]
+
+
+def string_to_gene(s: str) -> Gene:
+    gene = [(Nucleotide[s[i]], Nucleotide[s[i + 1]], Nucleotide[s[i + 2]])
+            for i in range(0, len(s), 3)
+            if i + 2 < len(s)]
+    return gene
+
+
+def linear_contains(gene: Gene, key_codon: Codon) -> bool:
+    return any(codon == key_codon for codon in gene)
+
+
+def binary_contains(gene: Gene, key_codon: Codon) -> bool:
+    low: int = 0
+    high: int = len(gene) - 1
+    while low <= high:
+        mid: int = (low + high) // 2
+        if gene[mid] < key_codon:
+            low = mid + 1
+        elif gene[mid] > key_codon:
+            high = mid - 1
+        else:
+            return True
+    return False

python-algorithm/algorithm/search/test_dna_search.py

@@ -0,0 +1,24 @@
+import pytest
+from algorithm.search.dna_search import Nucleotide, string_to_gene, linear_contains, binary_contains
+
+
+@pytest.mark.benchmark(group="linear_contains")
+@pytest.mark.parametrize("gene, key_codon, expected", [
+    ("ACGTGGCTCTCTAACGTACGTACGTACGGGGTTTATATATACCCTAGGACTCCCTTT", (Nucleotide.A, Nucleotide.C, Nucleotide.G), True),
+    ("ACGTGGCTCTCTAACGTACGTACGTACGGGGTTTATATATACCCTAGGACTCCCTTT", (Nucleotide.G, Nucleotide.A, Nucleotide.T), False),
+], ids=["successful", "failed"])
+def test_linear_contains(benchmark, gene_str, key_codon, expected):
+    gene = string_to_gene(gene_str)
+    result = benchmark(linear_contains, gene, key_codon)
+    assert expected == result
+
+
+@pytest.mark.benchmark(group="binary_contains")
+@pytest.mark.parametrize("gene, key_codon, expected", [
+    ("ACGTGGCTCTCTAACGTACGTACGTACGGGGTTTATATATACCCTAGGACTCCCTTT", (Nucleotide.A, Nucleotide.C, Nucleotide.G), True),
+    ("ACGTGGCTCTCTAACGTACGTACGTACGGGGTTTATATATACCCTAGGACTCCCTTT", (Nucleotide.G, Nucleotide.A, Nucleotide.T), False),
+], ids=["successful", "failed"])
+def test_binary_contains(benchmark, gene_str, key_codon, expected):
+    sorted_gene = sorted(string_to_gene(gene_str))
+    result = benchmark(binary_contains, sorted_gene, key_codon)
+    assert expected == result