假设我有一个包含两列数据file.dat
的文件。
我通常用
plot "file.dat" u 1:2
我希望平均超过10(例如)前一点和后面的10个点,并将其绘制在同一个图上。 我可以使用一些外部脚本轻松地做到这一点,我在其中创建另一列:
for(i=-10;i<=10;++i)
$3[j] += $2[j-i]
但是,我想知道在gnuplot中这样做的方法。 我的下一步是进行高斯平均。
答案 0 :(得分:8)
这也许是令人惊讶的,并没有内置于gnuplot。由于gnuplot如何将数据作为流处理,因此没有很好的方法来处理gnuplot中的单个数据点,也没有处理数据点的范围。
关于gnuplot的一个好处是它可以轻松地调用外部脚本和工具。如果你想使用外部脚本来处理gnuplot中的数据,你可以这样做:
plot "<script.py data.dat" u 1:2
例如,您可以使用下面的python脚本。它有点矫枉过正,但你可以在脚本或命令行中设置硬编码的参数值。
#!/usr/bin/python2.7
import sys
if (len(sys.argv) > 6):
print ""
print "This script takes one mandatory argument, the name of a file containing"
print "data to be plotted. It takes up to four optional arguments as follows:"
print " 1) the number of points before a data point to add into average."
print " 2) the number of points after a data point to add into average."
print " 3) the column number of y data (first column is column 1)"
print " 4) the column number of x data (first column is column 1)"
print ""
exit()
# set variable defaults
box_back = 10 # number of points before current point to add into average
box_front = 10 # number of points after current point to add into average
y_col = 2 # column number of y data (first column is column 1)
x_col = 1 # column number of x data (first column is column 1)
# assign variables from command line arguments
inputFileName = str(sys.argv[1])
if (len(sys.argv) > 2):
box_back = int(sys.argv[2])
if (len(sys.argv) > 3):
box_front = int(sys.argv[3])
if (len(sys.argv) > 4):
y_col = int(sys.argv[4])
if (len(sys.argv) > 5):
x_col = int(sys.argv[5])
# open input file
f = open(inputFileName)
# make list from lines in file
lines = f.readlines()
# make sure boxcar average will work
if ((box_back + box_front + 1) > len(lines)):
print ""
print "ERROR: too many points for boxcar averaging."
print ""
exit()
# this is the number of points encompassed in the boxcar average
num_points = box_back + box_front + 1
# this variable is the running sum.
sum_vals = 0
# add up values for first boxcar average
for i_ in range(0,num_points):
sum_vals += float(lines[i_].split()[y_col-1])
print float(lines[box_back].split()[x_col-1]),sum_vals/num_points
# each subsequent average differs only in the first and last points from the
# previous average.
for i_ in range(box_back+1,len(lines)-box_front):
sum_vals += float(lines[i_+box_front].split()[y_col-1])
sum_vals -= float(lines[i_-box_back-1].split()[y_col-1])
print float(lines[i_].split()[x_col-1]),sum_vals/num_points