Python --我的常用函数

昆明地图

def kmdt(x):
    import matplotlib.pyplot as plt
    import json    

    with open('km.json','rb') as f:
        data=json.load(f)
        for fea in range(len(data['features'])):
            df=data['features'][fea]['geometry']['coordinates']
            for f1 in range(len(df)):
                df1=df[f1]
                for f2 in range(len(df1)):
                    d=df1[f2]
                    for i in range(len(d)):
                        km=plt.plot([d[i-1][0],d[i][0]],[d[i-1][1],d[i][1]],'k',linewidth=x
                            )
    return km

插值

def chazhi(x,xx,cz):
    import numpy as np
    from scipy import interpolate
    if cz=='':
        cz='nearest'
    lenx=len(x)
    weizhi=np.where(x==x)[0]
    lenw=len(weizhi)
    xh=np.array([0.0]*lenw)
    for i in range(0,lenw):
        xh[i]=x[weizhi[i]]
    f=interpolate.interp1d(weizhi,xh,kind=cz)
    ynew=f(xx)
    return ynew

一元回归

def huigui(x,y):
    from sklearn import linear_model
    import numpy as np
    model = linear_model.LinearRegression()
    xx=np.array(x).reshape(-1, 1)
    model.fit(xx,y)
    k=model.coef_
    b=model.intercept_
    ff='y = %.2f+%.2f'%(b,k)
    return(k,b,ff)

去空值，求平均

def offnan(x,y):
    import numpy as np
    a=np.array(x)
    b=np.array(y)
    aa=list(np.where(a==a)[0])
    bb=list(np.where(b==b)[0])
    c=list(set(aa)&set(bb))
    return (a[c],b[c])

def avern(x):
    import numpy as np
    a=np.array(x)
    le=len(a)
    su=0.0
    js=0.0
    for i in range(0,le):
        if a[i]==a[i]:
            su=su+a[i]
            js=js+1
    av=su/js
    return av

归一化

def guiyi(x,Type='oz'):
    import numpy as np
    a=np.array(x)
    ma=max(a)
    mi=min(a)
    me=np.mean(a)
    b=np.array([0.0]*len(a))
    if Type=='oz':
        for i in range(0,len(a)):
            b[i]=(a[i]-mi)/(ma-mi)
    if Type=='0':
        for i in range(0,len(a)):
            b[i]=(a[i]-me)/(ma-mi)

    return b

标准化

def biaozhun(x):
    from sklearn import preprocessing
    a=preprocessing.scale(x)
    return(a)


def zhongxin(x):
    import numpy as np
    a=np.array(x)
    b=np.mean(a)
    for i in range(0,len(a)):
        a[i]=a[i]-b
    return a

获取文件列表

def listfile(dirname,postfix = ''):
    import os
    filelist = []
    files = os.listdir(dirname)
    for item in files:
        #filelist.append([dirname,item])
        if os.path.isfile(dirname+item):
            if item.endswith(postfix):
                filelist.append([dirname,item])
        else:
            if os.path.isdir(dirname+item):
                filelist.extend(listfile(dirname+item+'/',postfix))
    return filelist

灰色关联

def huise(a,b,c,rho):
    import pandas as pd
    import numpy as np
    a=np.array(a)
    length=len(c)
    lent=len(a)
    a=a/a[0]
    aa=list(a)
    data=[]
    dataa=[]
    szi=[0.0]*lent
    zi=[]   
    for i in range(0,length):
        cc=c[i]
        bb=np.array(b[cc])
        bb=bb/bb[0]
        data.append(list(bb))
        dataa.append(aa)
    data=np.array(data)
    dataa=np.array(dataa)
    gmin=abs(data-dataa).min()
    gmax=abs(data-dataa).max()
    for i in range(0,length):
        cc=c[i]
        bb=np.array(b[cc])
        bb=bb/bb[0]
        for j in range(0,lent):
            szi[j]=(gmin+rho*gmax)/(abs(a[j]-bb[j])+rho*gmax)
        zi.append(list(szi))
    zi=np.array(zi)
    qz=np.mean(zi,1)
    return qz

多元回归

def dyhuigui(y,x):
    from sklearn import linear_model
    import numpy as np
    import pandas as pd

    model = linear_model.LinearRegression()
    # xx=np.array(x).reshape(-1, 1)
    model.fit(x,y)
    k=model.coef_
    b=model.intercept_
    r=model.score(x,y)
    return(k,b,r)

极射赤面投影地图

def jiquditu(axes):
	import matplotlib.pyplot as plt
	import numpy as np
	import matplotlib.path as mpath
	import matplotlib.ticker as mticker
	import cartopy.crs as ccrs
	ax=axes
	ax.set_extent([-180, 180, 0, 90], crs=ccrs.PlateCarree())
	theta = np.linspace(0, 2*np.pi, 1000)
	center, radius = [0.5, 0.5], 0.5
	verts = np.vstack([np.sin(theta), np.cos(theta)]).T
	circle = mpath.Path(verts * radius + center)
	ax.set_boundary(circle, transform=ax.transAxes)


	ax.coastlines()
	line=ax.gridlines(draw_labels=False)
	line.ylocator=mticker.FixedLocator([0,90])#手动设置x轴刻度
	line.xlocator=mticker.FixedLocator(np.arange(-180,181,30))#手动设置x轴刻度

	#创建要标注的labels字符串
	ticks=np.arange(0,181,30)
	etick=['0°']+['%d°E'%tick for tick in ticks if (tick !=0) & (tick!=180)]+['180°']
	ticks=np.arange(150,0,-30)
	wtick=['%d°W'%tick for tick in ticks if (tick !=0) & (tick!=180)]
	labels=etick+wtick

	#创建与labels对应的经纬度标注位置
	#xticks=[i for i in np.arange(0,210,30)]+[i for i in np.arange(-32,-180,-30)]
	for i in range(0,12):
	    p=-7
	    if i%6==0:
	        p=-3
	    if i%6==5 or i%6==1:
	        p=-5
	    if i%6==4 or i%6==2:
	        p=-6
	    ax.text(i*30,p,labels[i],transform=ccrs.PlateCarree(),
	            horizontalalignment='center',verticalalignment='center')

	ax.plot(np.linspace(-180,180,200),[30]*200,':',transform=ccrs.PlateCarree(),color='k',linewidth=0.4)
	ax.plot(np.linspace(-180,180,200),[60]*200,':',transform=ccrs.PlateCarree(),color='k',linewidth=0.4)
	return ax