def get_data_S():
'''
可以检测读取到的图片波段数与参数fac是否对应，不对应则try-except报错
'''
XY = read_xy(path_tiff + path1)
cdnX, cdnY = XY[0], XY[1]
CHLA = read_tiff(path_tiff + path1)
SD = read_tiff(path_tiff + path2)
TN = read_tiff(path_tiff + path3)
TP = read_tiff(path_tiff + path4)
NDVI = read_tiff(path_tiff + path5)
FAI = read_tiff(path_tiff + path6)
dict = {'X': cdnX, 'Y': cdnY, 'CHLA': CHLA,
'SD': SD, 'TP': TP, 'TN': TN,'NDVI':NDVI,'FAI':FAI}
df = pd.DataFrame(data=dict)
for col in df.columns: # df1.columns : 列名称的list
# print(col) # col为一个个列名
# df.drop(index=(df.loc[(df[col] == -999)].index), inplace=True)
df.drop(index=(df[df[col].isin([-999])].index), inplace=True) # 清洗4个指标
df.drop(index=(df[df[col].isin([-2])].index), inplace=True) # 清洗NDVI
# df.reset_index(inplace=True) # 重置索引，但会保留原索引
df.reset_index(drop=True, inplace=True) # 重置索引，不会保留原索引
Write(df,output_data_path)
return output_data_path
def read_single_band():
XY = read_xy(path_tiff + path1)
cdnX, cdnY = XY[0], XY[1]
CHLA = read_tiff(path_tiff + path1)
COD = read_tiff(path_tiff + path2)
NTU = read_tiff(path_tiff + path3)
TP = read_tiff(path_tiff + path4)
TSM = read_tiff(path_tiff + path5)
FAI01 = read_tiff(path_tiff + path6)
NDVI = read_tiff(path_tiff + path7, 1)
dict = {
'X': cdnX,
'Y': cdnY,
'CHLA': CHLA,
'COD': COD,
'NTU': NTU,
'TP': TP,
'TSM ': TSM,
'FAI01': FAI01,
'NDVI': NDVI
}
df = pd.DataFrame(data=dict)
'''
for col in df.columns: # df1.columns : 列名称的list
df.drop(index=(df[df[col].isin([-999])].index), inplace=True) # 清洗4个指标
df.drop(index=(df[df[col].isin([-2])].index), inplace=True) # 清洗NDVI
# df.reset_index(inplace=True) # 重置索引，但会保留原索引
df.reset_index(drop=True, inplace=True) # 重置索引，不会保留原索引
'''
Write(df, output_data_path)
"""写入部分"""
def Get_wenzi():
df = read_band(output_data_path)
surface = surface_rank(df=df, list_tar=["TP", "TN"])
TLI = TLI_rank(df=df, list_tar=["CHLA", "SD", "TP", "TN"])
TSI = TSI_rank(df=df, list_tar=["CHLA", "SD", "TP"])
df_tar = df[["X", "Y"]] # 多列时要写成列表形式
df_tar["surface"] = surface
df_tar["TLI"] = TLI
df_tar["TSI"] = TSI
Write(df, evaluate2_data_path)
def evaluation2():
'''读取部分'''
df = read_band(output_data_path, evaluate2_data_path)
'''计算部分'''
surface = surface_rank(df=df, list_tar=["TP"])
'''整合部分'''
df_tar = df[["X", "Y"]] # 多列时要写成列表形式
df_tar["surface"] = surface
'''写入部分'''
Write(df_tar, evaluate2_data_path)
def evaluation1():
'''读取部分'''
df = read_band(output_data_path, evaluate1_data_path)
'''计算部分'''
surface = surface_rank(df=df, list_tar=["TP"])
TLI = TLI_rank(df=df, list_tar=["CHLA", "COD", "TP"])
TSI = TSI_rank(df=df, list_tar=["CHLA", "TP"])
'''整合部分'''
df_tar = df[["X", "Y"]] # 多列时要写成列表形式
df_tar["surface"] = surface
df_tar["TLI"] = TLI
df_tar["TSI"] = TSI
df_tar["NDVI"] = df["NDVI"]
'''写入部分'''
Write(df_tar, evaluate1_data_path)
def Cal_NDVI():
#read_band(path6, target=output_multipband)
# 读全波段并生成excel
# exit()
XY = read_xy(path_tiff + path1)
cdnX, cdnY = XY[0], XY[1]
CHLA = read_tiff(path_tiff + path1)
COD = read_tiff(path_tiff + path2)
NTU = read_tiff(path_tiff + path3)
TP = read_tiff(path_tiff + path4)
TSM = read_tiff(path_tiff + path5)
NIR_8 = read_tiff(path6, num=8, key=True)
R = read_tiff(path6, num=4, key=True)
NDVI_8 = [0] * len(NIR_8)
for i in range(len(NIR_8)):
NDVI_8[i] = (NIR_8[i] - R[i]) / (NIR_8[i] + R[i])
# NDVI_9[i] = (NIR_9[i]-R[i]) / (NIR_9[i]+R[i])
dict = {
'X': cdnX,
'Y': cdnY,
'CHLA': CHLA,
'COD': COD,
'NTU': NTU,
'TP': TP,
'TSM': TSM,
'NDVI_8': NDVI_8
}
df = pd.DataFrame(data=dict)
"""清洗异常值"""
for col in df.columns: # df1.columns : 列名称的list
df.drop(index=(df[df[col].isin([-999])].index), inplace=True) # 清洗4个指标
#df.drop(index=(df[df[col].isin([-2])].index), inplace=True) # 清洗NDVI
df.drop(index=(df[df['NDVI_8'] <= 0].index), inplace=True) # 清洗NDVI
# df.reset_index(inplace=True) # 重置索引，但会保留原索引
df.reset_index(drop=True, inplace=True) # 重置索引，不会保留原索引
"""写入部分"""
Write(df, output_NDVI_path)
def Cal_FAI():
read_band(path6, target=output_multipband) # 读全波段并生成excel 与 SH4文件功能重复
XY = read_xy(path_tiff + path1)
cdnX, cdnY = XY[0], XY[1]
CHLA = read_tiff(path_tiff + path1)
COD = read_tiff(path_tiff + path2)
NTU = read_tiff(path_tiff + path3)
TP = read_tiff(path_tiff + path4)
TSM = read_tiff(path_tiff + path5)
'''FAI'''
# 红光波段 R665 是rand(4)
# 近红外波段有两个 R842 是rand(8); R865 是rand(9)
# 短波红外波段有两个 R1610 是rand(11); R2190 是rand(12)
R665 = read_tiff(path6, num=4, key=True)
R842 = read_tiff(path6, num=8, key=True)
R1610 = read_tiff(path6, num=11, key=True)
R842_ = [R665[i] + (R1610[i] - R665[i]) * (842 - 665) / (2190 - 665)
for i in range(len(R665))]
# FAI = R842 - R842_
FAI = [R842[i] - R842_[i] for i in range(len(R842))]
# FAI = [1 if i >= 0.02 else 0 for i in FAI]
dict = {'X': cdnX, 'Y': cdnY, 'CHLA': CHLA,
'COD': COD, 'NTU': NTU, 'TP': TP, 'TSM': TSM, 'FAI': FAI}
df = pd.DataFrame(data=dict)
"""清洗异常值"""
for col in df.columns: # df1.columns : 列名称的list
# print(col) # col为一个个列名
df.drop(index=(df.loc[(df[col] == -999)].index), inplace=True)
# df.reset_index(inplace=True) # 重置索引，但会保留原索引
df.reset_index(drop=True, inplace=True) # 重置索引，不会保留原索引
"""写入部分"""
Write(df,output_FAI_path)