grep uniq用法
时间: 2023-12-07 20:39:09 浏览: 40
以下是grep和uniq的用法示例:
1. grep用法示例:
假设我们有一个名为example.txt的文件,其中包含以下内容:
```
apple
banana
orange
apple
orange
```
我们可以使用grep命令来查找包含特定字符串的行,例如:
```shell
grep "apple" example.txt
```
输出:
```
apple
apple
```
我们也可以使用-v选项来查不包含特定字符串的行,例如:
```shell
grep -v "apple" example.txt
```
输出:
```
banana
orange
orange
```
2. uniq用法示例:
假设我们有一个名为example.txt的文件,其中包含以下内容:
```
apple
banana
orange
apple
orange
```
我们可以使用uniq命令来查找文件中的唯一行,例如:
```shell
uniq example.txt
```
输出:
```
apple
banana
orange
apple
orange
```
我们也可以使用-c选项来计算每个行的出现次数,例如:
```shell
uniq -c example.txt
```
输出:
```
2 apple
1 banana
2 orange
1 apple
1 orange
```
相关问题
请将下列shell语句转换为python语句:if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then # Make train dict echo "Make a dictionary" mkdir -p $(dirname $dict) echo "<blank> 0" > ${dict} # 0 will be used for "blank" in CTC echo "<unk> 1" >> ${dict} # <unk> must be 1 if [ "${token_type}" = bpe ]; then # we borrowed these code and scripts which are related bpe from ESPnet. cut -f 2- -d" " data/${train_set}/text | sort > data/lang_char/input.txt tools/spm_train --input=data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000 tools/spm_encode --model=${bpemodel}.model --output_format=piece < data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0 " " NR+1}' >> ${dict} elif [ "${token_type}" = char ]; then tools/text2token.py -s 1 -n 1 data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \ | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0 " " NR+1}' >> ${dict} fi num_token=$(cat $dict | wc -l) echo "<sos/eos> $num_token" >> $dict # <eos> fi
以下是将上述shell语句转换为Python语句的代码:
```python
if stage <= 2 and stop_stage >= 2:
# Make train dict
print("Make a dictionary")
os.makedirs(os.path.dirname(dict), exist_ok=True)
with open(dict, "w") as f:
f.write("<blank> 0\n") # 0 will be used for "blank" in CTC
f.write("<unk> 1\n") # <unk> must be 1
if token_type == "bpe":
# we borrowed these code and scripts which are related bpe from ESPnet.
with open(f"data/{train_set}/text") as text_file:
texts = [line.strip().split()[1:] for line in text_file]
with open("data/lang_char/input.txt", "w") as input_file:
for text in texts:
input_file.write(" ".join(text) + "\n")
subprocess.run(f"tools/spm_train --input=data/lang_char/input.txt --vocab_size={nbpe} --model_type={bpemode} --model_prefix={bpemodel} --input_sentence_size=100000000", shell=True)
with open(f"{bpemodel}.vocab") as vocab_file:
vocab = [line.strip().split()[0] for line in vocab_file]
with open(dict, "a") as dict_file:
for i, vocab_item in enumerate(vocab):
dict_file.write(f"{vocab_item} {i+2}\n")
elif token_type == "char":
with open(f"data/{train_set}/text") as text_file:
texts = [line.strip().split()[1] for line in text_file]
texts = " ".join(texts)
tokens = subprocess.check_output(f"echo '{texts}' | tools/spm_encode --model={bpemodel}.model --output_format=piece", shell=True)
tokens = tokens.decode().split("\n")
tokens = [token.strip() for token in tokens if token.strip() != ""]
tokens = sorted(set(tokens))
with open(dict, "a") as dict_file:
for i, token in enumerate(tokens):
dict_file.write(f"{token} {i+2}\n")
num_token = sum(1 for line in open(dict))
with open(dict, "a") as dict_file:
dict_file.write(f"<sos/eos> {num_token}\n") # <eos>
```
需要注意的是,在Python中也需要使用相应的模块和工具来实现shell语句中的一些操作,例如在此代码中使用了`os`、`subprocess`模块和`tools/spm_train`、`tools/spm_encode`脚本来进行文件操作和调用外部命令。另外,由于Python中没有直接对应的`$`符号,需要使用`f-string`或者`str.format()`方法来进行字符串格式化。
netstat -n
netstat命令是Linux系统中用于查询和统计网络连接状态和数据传输情况的常用工具。它可以帮助进行网络故障排除和性能调优。下面是netstat命令的一些常用参数及其用法:
- 显示每种类型TCP/UDP的连接数:`netstat -ano | awk '{print $1}' | sort | uniq -c`
- 显示每种网络状态的数量(TCP):`netstat -ano |grep TCP | awk '{print $4}' | sort | uniq -c`
- 显示指定进程ID网络状态的数量(TCP):`netstat -ano |grep 进程ID | awk '{print $4}' | sort | uniq -c`
- 显示端口占用数量:`netstat -ano | awk 'NR>2{print $1}' | sort | uniq -c | awk '{print $2 "占用了"$1"个端口"}'`
另外,如果你使用的是Windows系统,你可以使用win-netstat Golang中的Windows netstat实现来执行类似的操作。
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