profiler/mynumpy.py

import numpy as np
from pathlib import Path
TOKEN = ";"
SAVE_EXT_NAME = ".csv"

class MyNumpy(object):
    
    def __init__(self,):
        # self.first_ndarray = np.array([])
        # self.two_d_ndarray = np.ndarray([])
        pass
    
    def row_stack_nd_nd(self, nd1:np.ndarray, nd2:np.ndarray):
        if nd1.ndim == 1:
            np.stack(  (nd1, nd2), 0)  # 两个一位数组 ，0 上下叠加，1：转置水平叠加
            pass
        else:
            np.vstack(  (nd1, nd2))   # 垂直上下叠加
            pass
        pass
    
    def col_stack_nd_nd(self,nd1:np.ndarray, nd2:np.ndarray):
        if nd1.ndim == 1:
            np.stack(  (nd1, nd2), 1)  # 两个一位数组 ，0 上下叠加，1：转置水平叠加
            pass
        else:
            np.hstack( (nd1, nd2.reshape(-1,1)) )   # 垂直上下叠加
            pass
    
    def get_time_wl_data_from_csv(self, fpath:Path, sn,fname):
        data =[]
        
        file_name = fpath.joinpath( fname + SAVE_EXT_NAME)

        try:
            readout = np.loadtxt(file_name, dtype = float, delimiter = TOKEN)
        except IOError:
            # self.makeMsg( 2, file_name  + "  文件不存在 "  )
            readout = np.array([])
            raise Exception( "IOError : 文件不存在")
        except Exception as e :
            # self.makeMsg( 2,  "警告： %s"  %e   )
            readout = np.array([])  
            raise Exception( e)

        first_column    = readout[1:,0]
        header          = readout[0,1:]
        data            = readout[1:,1:]

        return first_column,header,data
        pass  

    def get_header_data_from_csv(self, fpath:Path, sn,fname):
        data =[]
        
        file_name = fpath.joinpath( fname + SAVE_EXT_NAME)

        try:
            readout = np.loadtxt(file_name, dtype = float, delimiter = TOKEN)
        except IOError:
            # self.makeMsg( 2, file_name  + "  文件不存在 "  )
            readout = np.array([])
            raise Exception( "IOError : 文件不存在")
        except Exception as e :
            # self.makeMsg( 2, "警告  %s"  % e   )
            readout = np.array([])  

        header  = readout[0,:]
        data    = readout[1:,:]
        
        return header,data
        pass  

    def combine_col_row_data (self,  col, row, data ):
        """ 
        @description  :  结合 时间  波长 数据，存到 csv   
                        考虑 data 是一维的情况，不考虑为空
                        col header data  皆为 ndarray 格式 
                        col data  二维 ,header 一维二维都可以  尽量用2维
        """ 

        print( "__combine_col_row_data  and  return" )

        # print( col.shape )        
        # print( row.shape )        
        # print( data.shape )    

        new_d = np.array( [ ] )   

        # 处理数据维度不符
        if col.shape[0] != data.shape[0]:
            raise Exception("ndarray shape 不对不能合并！")
            return new_d    

        if row.shape[1]  != col.shape[1] + data.shape[1]:
            raise Exception("ndarray shape 不对不能合并！")
            return new_d  

        # 给新数据    拼接上新的时间 column

        try:
            new_d = np.column_stack( ( col, data) )  
        except Exception as e :
            raise Exception("合并数据col  data 失败！")

        # 拼接上波长作为 头部           
        try:
            new_d = np.row_stack(  ( row ,new_d ) )  
        except Exception as e :
            raise Exception("合并数据row  data 失败！")
         
        # print( " new_d   %s " % new_d.shape)            
        return new_d

        pass

    def combine_col_row_data_to_csv(self,col,row,data, path_name:Path):
        """ 
        @description  :  结合 时间  波长 数据，存到 csv   
                        考虑 data 是一维的情况，不考虑为空
                        col header data  皆为 ndarray 格式 
                        col data  二维 ,header 一维二维都可以  尽量用2维
        """ 

        print( "__combine_col_row_data_to_csv" )

        # print( col.shape )        
        # print( row.shape )        
        # print( data.shape )    

        new_d = np.array( [ ] )   

        # 处理数据维度不符
        if col.shape[0] != data.shape[0]:
            return new_d    

        if row.shape[0]  != col.shape[1] + data.shape[1]:
            return new_d  

        # 给新数据    拼接上新的时间 column

        try:
            new_d = np.column_stack( ( col, data) )  
        except Exception as e :
            # self.makeMsg ( 2, "警告：拼接出错。 " +  e  )
            raise Exception("合并数据col  data 失败！")

        # 拼接上波长作为 头部           
        try:
            new_d = np.row_stack(  ( row ,new_d ) )  
        except Exception as e :
            # self.makeMsg ( 2, "警告：拼接出错。 " +  e  )
            raise Exception("合并数据row  data 失败！")
         
        # print( " new_d   %s " % new_d.shape)            
        try:
            np.savetxt( path_name, new_d, fmt='%8f', delimiter=TOKEN)
        except Exception as e:
            # self.makeMsg(2, "警告：%s    %s " % (e, path_name) )
            raise Exception("保存文件 失败！")

        pass

    pass

if __name__ == '__main__':
    mnp = MyNumpy()
    a = np.arange(0, 60, 5)
    a = a.reshape(3, 4)
    print(a)
    
    aa = np.array([1,2,3])
    bb = np.array([4,5,6])
    cc = np.array([7,8,9])
    s = np.stack( (aa,bb) ,0 )  # 行合并
 
    ss = np.vstack(  (s,cc) )
    sss = np.stack( (aa,bb) ,1 )
    ssss = np.hstack(  (sss,cc.reshape(-1,1)))  # 列合并,reshape(-1,1)
    aa = np.stack( (aa,bb) ,0 )  # 行合并
    
    print(s)
    print(ss)
    print(sss)
    print(cc)
    print(cc.reshape(-1,1))
    print(ssss)
    print(aa)
    pass

'''
np.stack( (aa,bb) ,0 )  增加了维度
np.stack( (aa,bb) ,1 )

np.vstack(  (s,cc) ) # 不增加维度

np.column_stack(  (sss,cc.T))
    
'''