用strftime的时候加个-符号




```python
print(datetime.datetime.now().strftime("%-Y  %-m  %-d   %-H:%-M:%-S"))
```

结果：

```python
2021  2  26   9:38:5
```

python datetime 前端不要带0 时间 小时不要0

Python





```python
select_this_hour_clickhouse_sql = "select * from default.zjk_forest_fire_risk_forecast_grid_data " \
                                          "where data_time='{}' and grid_id='{}'".format(now_datatime, name)
```

一个好看的sql写法 format写 字符串

python matplotlib





在绘图前 加一句

```python
plt.figure()
```

即可。

MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently

Python





```python
import numpy as np
import matplotlib.pyplot as plt
time_step = 0.02
period = 5.
time_vec = np.arange(0, 20, time_step)
sig = np.sin(2 * np.pi / period * time_vec) + \
       0.5 * np.random.randn(time_vec.size)
from scipy import fftpack
sample_freq = fftpack.fftfreq(sig.size, d=time_step)
sig_fft = fftpack.fft(sig)
pidxs = np.where(sample_freq > 0)
freqs = sample_freq[pidxs]
power = np.abs(sig_fft)[pidxs]
freq = freqs[power.argmax()]
print(np.allclose(freq, 1./period) ) # 检查是否找到了正确的频率
sig_fft[np.abs(sample_freq) > freq] = 0
main_sig = fftpack.ifft(sig_fft)
plt.figure()
plt.plot(time_vec, sig)
plt.plot(time_vec, main_sig, linewidth=3)
plt.title("signal")
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.show()
```

![在这里插入图片描述](/static/img/images/00752.png)

python scipy 信号去噪声

安装：




```python
pip install sklearn
```

一维数组聚类：
```python
from sklearn.cluster import KMeans
import numpy as np
x = np.random.random(1000).reshape(-1,1)
km = KMeans(n_clusters=5,max_iter=1000).fit(x)
print(km.cluster_centers_)
y = KMeans(n_clusters=5,max_iter=1000).fit_predict(x) #会得出每个sample属于哪一类
print(y)
```

二维数组聚类：

```python
from sklearn.cluster import KMeans
import numpy as np
x = np.random.random(1000).reshape(-1,2)
km = KMeans(n_clusters=5,max_iter=1000).fit(x)
print(km.cluster_centers_)
y = KMeans(n_clusters=5,max_iter=1000).fit_predict(x)#会得出每个sample属于哪一类
print(y)
```