【发布时间】：2017-03-24 21:30:28
【问题描述】：
注意：这是 Python 2.7，而不是 Py3
这是对较早问题的更新尝试。您要求我提供完整的代码、内容解释和示例输出文件。我会尽力把它格式化好。
此代码旨在从荧光“读板器”获取输入文件，并将读数转换为 DNA 浓度和质量。然后它会生成一个根据 8x12 板方案（DNA/分子工作的标准）组织的输出文件。行标记为“A、B、C、...、H”，列标记为 1 - 12。
根据用户输入，需要堆叠数组以格式化输出。但是，当数组在 UNIX 中堆叠（并打印或写入输出文件）时，它们仅限于第一个字符。
换句话说，在 Windows 中，如果数组中的数字是 247.5，它会打印完整的数字。但在 UNIX 环境（Linux/Ubuntu/MacOS）中，它会被截断为简单的“2”。 -2.7 的数字将在 Windows 中正常打印，但在 UNIX 中仅打印为“-”。
完整的代码可以在下面找到； 请注意，最后一块是代码中最相关的部分：
#!/usr/bin/env python
Usage = """
plate_calc.py - version 1.0
Convert a series of plate fluorescence readings
to total DNA mass per sample and print them to
a tab-delimited output file.
This program can take multiple files as inputs
(separated by a space) and generates a new
output file for each input file.
NOTE:
1) Input(s) must be an exported .txt file.
2) Standards must be in columns 1 and 2, or 11
and 12.
3) The program assumes equal volumes across wells.
Usage:
plate_calc.py input.txt input2.txt input3.txt
"""
import sys
import numpy as np
if len(sys.argv)<2:
print Usage
else:
#First, we want to extract the values of interest into a Numpy array
Filelist = sys.argv[1:]
input_DNA_vol = raw_input("Volume of sample used for AccuClear reading (uL): ")
remainder_vol = raw_input("Remaining volume per sample (uL): ")
orientation = raw_input("Are the standards on the LEFT (col. 1 & 2), or on the RIGHT (col. 11 and 12)? ")
orientation = orientation.lower()
for InfileName in Filelist:
with open(InfileName) as Infile:
fluor_list = []
Linenumber = 1
for line in Infile: #this will extract the relevant information and store as a list of lists
if Linenumber == 5:
line = line.strip('\n').strip('\r').strip('\t').split('\t')
fluor_list.append(line[1:])
elif Linenumber > 5 and Linenumber < 13:
line = line.strip('\n').strip('\r').strip('\t').split('\t')
fluor_list.append(line)
Linenumber += 1
fluor_list = [map(float, x) for x in fluor_list] #converts list items from strings to floats
fluor_array = np.asarray(fluor_list) #this takes our list of lists and converts it to a numpy array
这部分代码（上图）从输入文件（从读板器获得）中提取感兴趣的值，并将它们转换为数组。它还需要用户输入来获取计算和转换的信息，并确定放置标准的列。
最后一部分将在稍后发挥作用，当数组堆叠时 - 这就是问题行为发生的地方。
#Create conditional statement, depending on where the standards are, to split the array
if orientation == "right":
#Next, we want to average the 11th and 12th values of each of the 8 rows in our numpy array
stds = fluor_array[:,[10,11]] #Creates a sub-array with the standard values (last two columns, (8x2))
data = np.delete(fluor_array,(10,11),axis=1) #Creates a sub-array with the data (first 10 columns, (8x10))
elif orientation == "left":
#Next, we want to average the 1st and 2nd values of each of the 8 rows in our numpy array
stds = fluor_array[:,[0,1]] #Creates a sub-array with the standard values (first two columns, (8x2))
data = np.delete(fluor_array,(0,1),axis=1) #Creates a sub-array with the data (last 10 columns, (8x10))
else:
print "Error: answer must be 'LEFT' or 'RIGHT'"
std_av = np.mean(stds, axis=1) #creates an array of our averaged std values
#Then, we want to subtract the average value from row 1 (the BLANK) from each of the 8 averages (above)
std_av_st = std_av - std_av[0]
#Run a linear regression on the points in std_av_st against known concentration values (these data = y axis, need x axis)
x = np.array([0.00, 0.03, 0.10, 0.30, 1.00, 3.00, 10.00, 25.00])*10 #ng/uL*10 = ng/well
xi = np.vstack([x, np.zeros(len(x))]).T #creates new array of (x, 0) values (for the regression only); also ensures a zero-intercept (when we use (x, 1) values, the y-intercept is not forced to be zero, and the slope is slightly inflated)
m, c = np.linalg.lstsq(xi, std_av_st)[0] # m = slope for future calculations
#Now we want to subtract the average value from row 1 of std_av (the average BLANK value) from all data points in "data"
data_minus_blank = data - std_av[0]
#Now we want to divide each number in our "data" array by the value "m" derived above (to get total ng/well for each sample; y/m = x)
ng_per_well = data_minus_blank/m
#Now we need to account for the volume of sample put in to the AccuClear reading to calculate ng/uL
ng_per_microliter = ng_per_well/float(input_DNA_vol)
#Next, we multiply those values by the volume of DNA sample (variable "ng")
ng_total = ng_per_microliter*float(remainder_vol)
#Set number of decimal places to 1
ng_per_microliter = np.around(ng_per_microliter, decimals=1)
ng_total = np.around(ng_total, decimals=1)
上述代码执行必要的计算，以根据 DNA“标准”的线性回归计算给定样本中 DNA 的浓度 (ng/uL) 和总质量 (ng)，该标准可以在列中1 和 2（用户输入 =“左”）或第 11 和 12 列（用户输入 =“右”）。
#Create a row array (values A-H), and a filler array ('-') to add to existing arrays
col = [i for i in range(1,13)]
row = np.asarray(['A','B','C','D','E','F','G','H'])
filler = np.array(['-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-',]).reshape((8,2))
上面的代码创建了要与原始数组堆叠的数组。 “填充”数组是根据用户输入的“右”或“左”放置的（堆叠命令 np.c_[ ] 如下所示）。
#Create output
Outfile = open('Total_DNA_{0}'.format(InfileName),"w")
Outfile.write("DNA concentration (ng/uL):\n\n")
Outfile.write("\t"+"\t".join([str(n) for n in col])+"\n")
if orientation == "left": #Add filler to left, then add row to the left of filler
ng_per_microliter = np.c_[filler,ng_per_microliter]
ng_per_microliter = np.c_[row,ng_per_microliter]
Outfile.write("\n".join(["\t".join([n for n in item]) for item in ng_per_microliter.tolist()])+"\n\n")
elif orientation == "right": #Add rows to the left, and filler to the right
ng_per_microliter = np.c_[row,ng_per_microliter]
ng_per_microliter = np.c_[ng_per_microliter,filler]
Outfile.write("\n".join(["\t".join([n for n in item]) for item in ng_per_microliter.tolist()])+"\n\n")
Outfile.write("Total mass of DNA per sample (ng):\n\n")
Outfile.write("\t"+"\t".join([str(n) for n in col])+"\n")
if orientation == "left":
ng_total = np.c_[filler,ng_total]
ng_total = np.c_[row,ng_total]
Outfile.write("\n".join(["\t".join([n for n in item]) for item in ng_total.tolist()]))
elif orientation == "right":
ng_total = np.c_[row,ng_total]
ng_total = np.c_[ng_total,filler]
Outfile.write("\n".join(["\t".join([n for n in item]) for item in ng_total.tolist()]))
Outfile.close
最后，我们生成了输出文件。这就是问题行为发生的地方。
使用简单的打印命令，我发现堆叠命令 numpy.c_[ ] 是罪魁祸首（不是数组写入命令）。
看来 numpy.c_[ ] 在 Windows 中不会截断这些数字，但会在 UNIX 环境中将这些数字限制为第一个字符。 p>
有哪些替代方案可能适用于两个平台？如果不存在，我不介意制作一个特定于 UNIX 的脚本。
感谢大家的帮助和耐心。很抱歉没有提前提供所有必要的信息。
这些图片是屏幕截图，显示了 Windows 的正确输出以及我最终在 UNIX 中得到的结果（我试图为你格式化这些......但它们是一场噩梦）。当我简单地打印数组“ng_per_microliter”和“ng_total”时，我还包含了在终端中获得的输出截图。
【问题讨论】：
-
你走错了方向。使问题和示例更紧凑，专注于手头的问题，而不是更大更难理解。这看起来像一个格式问题。
ng_per_microliter和ng_total这样的值是否正确？我假设它们是具有形状和 dtype 的数组。 -
在我的辩护中，这就是我分解代码的原因，之前的评论者曾要求我这样做。
标签： python arrays windows unix numpy