使用Python 的str方法和循环 使用正则表达式 2. 性能示例代码 下面是性能测试的代码示例,通过时间统计来比较这几种方法的效率: importtimeimportredefstr_rfind(text,substring):returntext.rfind(substring)defregex_find(text,substring):matches=list(re.finditer(re.escape(substring),text))returnmatches[-1].start...
defstr2csv(filePath,s):''' 将字符串写入到本地csv文件中 :paramfilePath: csv文件路径 :params:待写入字符串(逗号分隔格式)'''withopen(filePath,'w',encoding='utf-8')asf:f.write(s)print('写入文件成功,请在'+filePath+'中查看') 结果如下: ...
Expand All@@ -273,6 +274,7 @@ def _occur_count( pw_dist:NDArrayA, labs_unique:NDArrayA, interval:NDArrayA, same_split:bool, )->NDArrayA: num=labs_unique.shape[0] out=np.zeros((num,num,interval.shape[0]-1),dtype=ft) Expand All@@ -281,22 +283,37 @@ def _occur_count( ...
python def divide(a, b): try: result = a / b print(f"The result of division is: {result}") except ZeroDivisionError as e: handle_division_error(e) # 调用错误处理函数 def handle_division_error(error): print(f"An error occurred: {error}") # 在这里可以添加更多的错误处理逻辑,如记录日...
Python Code:# Define a function that finds the first repeated character in a string with the smallest distance between the repetitions. def first_repeated_char_smallest_distance(str1): temp = {} # Create an empty dictionary to store characters and their indices. for ch in str1: # Iterate ...
Use the `str.replace()` method to remove the first occurrence of a character from a string, e.g. `result = my_str.replace('p', '', 1)`.
// Scala program to print the index of the// first occurrence of the given numberimportscala.collection.immutable._objectSample{// Main methoddefmain(args:Array[String]){varnums:Seq[Int]=Seq(10,20,30,20,56,67,78);varnum:Int=0;printf("Enter number: ");num=scala.io.StdIn.readInt();...
Python Code:from bisect import bisect_right def BinarySearch(a, x): i = bisect_right(a, x) if i != len(a)+1 and a[i-1] == x: return (i-1) else: return -1 nums = [1, 2, 3, 4, 8, 8, 10, 12] x = 8 num_position = BinarySearch(nums, x) if num_position == -...
De-replicated reads from all library subpools were then clustered (95% sequence similarity cut-off) using VSEARCH “--cluster_size” option with “--id 0.95 --iddef 1 --sizein --sizeout --centroids --uc” parameters. The output clustering table was filtered based on the following ...
''' parses the file input.txt in the same path as this python file and counts the occurrences of a keyword2 (kw2) in blocks around kw1 ''' import pathlib import re kw1 = "——" kw2 = "^[\d]+ nuit" def write_to_output(res_kw): fout = open("output.txt", 'a') for cnt...