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)
```

sklearn scikit-learn k-means 聚类算法 一维数组聚类 python 二维数组聚类

例子：





里面的坐标是WGC0084的。

```python
from geopandas import GeoDataFrame
from shapely.geometry import Polygon
import matplotlib.pyplot as plt
record_area = {'新地类': {0: '裸岩', 1: '乔木林'},
               '面积': {0: 2.46, 1: 0.12},
               'Shape_Length': {0: 0.010351519392398598, 1: 0.0023489068258685593},
               'Shape_Area': {0: 2.634822326382532e-06, 1: 1.246329599286182e-07},
               'geometry': {0: Polygon([(115.30992347400002, 41.03045785900008), (115.3098257480001, 41.03042899800005),
                                (115.30969286300001, 41.030391199000064), (115.30960737200007, 41.030363582000064),
                                (115.30965327800004, 41.03029873600008), (115.30983235100007, 41.03028598000003),
                                (115.31004008700006, 41.030290766000064), (115.31030859000009, 41.03026627100007),
                                (115.31056930500006, 41.03019715200003), (115.31077333600001, 41.03014305800008),
                                (115.31089930700011, 41.03007015400004), (115.31105807100005, 41.03001293300008),
                                (115.31135016400003, 41.029988169000035), (115.3116654150001, 41.029762223000034),
                                (115.3118061460001, 41.029661358000055), (115.31249887400008, 41.029374731000075),
                                (115.31273030900002, 41.029320978000044), (115.3130813470001, 41.029239447000066),
                                (115.3131580270001, 41.02922081600008), (115.313297379, 41.029186956000046),
                                (115.31355647200007, 41.02892670500006), (115.31357707600012, 41.02886651400007),
                                (115.31291644200007, 41.02877125500004), (115.31159314900003, 41.028738934000046),
                                (115.31138943700012, 41.02892181000004), (115.31061582600012, 41.02947425800005),
                                (115.30987346500001, 41.029959062000046), (115.30967965600007, 41.030048538000074),
                                (115.30934632200001, 41.03028793000004), (115.30921034800008, 41.03033235700008),
                                (115.3091987890001, 41.03033314600003), (115.30982119300006, 41.03049919700004),
                                (115.30992347400002, 41.03045785900008)]),
                            1: Polygon([(115.30751786600001, 41.03123731200003), (115.30736804700007, 41.03119306200006),
                                (115.30724995300011, 41.03118725400003), (115.30725101300004, 41.03124084600006),
                                (115.30736995600012, 41.03128952700007), (115.30771220700001, 41.03141429200008),
                                (115.30786805900004, 41.03147224600008), (115.30819713300002, 41.03159461300004),
                                (115.30831718700006, 41.03146102800008), (115.3081085990001, 41.03141336600004),
                                (115.30803719100004, 41.03138201500008), (115.3077433850001, 41.031301418000055),
                                (115.30762438200009, 41.03126877200003), (115.30751786600001, 41.03123731200003)])
                            }
               }
g = GeoDataFrame(record_area, crs="EPSG:4326")
print("地图")
print(g)
g.plot()
plt.show()
print("面积直接算 算的图形面积  不是地理面积：")
print(g.area)
g.to_crs(epsg=3310, inplace=True)
print("计算地图面积：")
print(g.area)
```

geopandas 计算面积 crs=“EPSG:4326“转成 3110后计算面积 还是比较准确的 不知道为什么

shapely介绍：





[https://blog.csdn.net/wiborgite/article/details/85167397](https://blog.csdn.net/wiborgite/article/details/85167397)

geopandas介绍：

[https://developer.ibm.com/zh/technologies/analytics/tutorials/working-with-geospatial-vector-data-in-python/](https://developer.ibm.com/zh/technologies/analytics/tutorials/working-with-geospatial-vector-data-in-python/)

官方资料：[https://geopandas.org/aggregation_with_dissolve.html](https://geopandas.org/aggregation_with_dissolve.html)

![在这里插入图片描述](/static/img/images/00753.png)

![在这里插入图片描述](/static/img/images/00754.png)

有一个geopandas的GeoDataFrame对象后：

```python
g['all']=1
qq=g.dissolve(by="all",aggfunc="sum")
qq.plot()
plt.show()
f=qq.loc[1,"geometry"] #拿出图形
fg={"bianjie":list(f.exterior.coords)}  #外环边界
print(list(f.interiors))  #内环
```

获取地图块边界 融合dissolve geopandas

**将python字典写入json文件：





这句就好用了。dumps可以把字典转成字符串，然后文件方法write写入这个字符串。**
```python
with open('jsonFile.json', 'w', encoding="utf-8") as f:
    f.write(json.dumps(zidian, ensure_ascii=False, indent=1))
```

**读取json文件 到python变量（可能是字典或者列表）：**

```python
with open('jsonFile.json', 'r', encoding="utf-8") as f:
    zidian=json.load(f)
```


 load、dump是针对文件的。

 

 loads、dumps是针对字符串的。

json python 读写json 读取和写入json文件 load dump

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