Loading data files: Difference between revisions
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Please find all options here: | Please find all options here: | ||
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.loadtxt.html | https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.loadtxt.html | ||
Another opportunity is to use the python package [https://pandas.pydata.org/ pandas]: | |||
{{syntaxhighlight|lang=python|code= | |||
import pandas as pd | |||
file = "Name of my data file.txt" | |||
data = pd.read_csv('%s' % file, header=[0,1,2], delimiter='\t') | |||
# the first three lines of the header are read | |||
V = data[('Voltage', '[V]', 'SMU1')] | |||
I = data[('Current', '[A]', 'SMU2')] | |||
# Syntax: data[('<variable>', '<unit>', '<module label>')] | |||
}} | |||
== QtiPlot == | == QtiPlot == |
Revision as of 11:28, 19 September 2018
Origin
Just drag and drop the saved data files into Origin and select standard Ascii-import. Long name, unit and comment are automatically filled with the three header lines.
Python
The data files can easily be loaded using the numpy package:
import numpy as np # loading numpy package
data = np.genfromtxt("mydata.txt", usecols = (0,2,3), skip_header = 4, missing_values = "--", filling_values = float("nan"))
# this example loads data from the first, the third, and the fourth column, it skips the header and replaces missing values by nan (= "not a number")
# Load all columns be removing the usecols argument
Please find all options here: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.genfromtxt.html
A faster import can be realized by using the numpy function loadtxt. However, you have to make sure that all characters are numbers and can be converted to floats.
import numpy as np # loading numpy package
data = np.loadtxt("mydata.txt", usecols = (1,4,5), skiprows=4, delimiter="\t")
# this example loads data from the second, the fifth, and the sixth column and skips the header
# Load all columns be removing the usecols argument
Please find all options here: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.loadtxt.html
Another opportunity is to use the python package pandas:
import pandas as pd
file = "Name of my data file.txt"
data = pd.read_csv('%s' % file, header=[0,1,2], delimiter='\t')
# the first three lines of the header are read
V = data[('Voltage', '[V]', 'SMU1')]
I = data[('Current', '[A]', 'SMU2')]
# Syntax: data[('<variable>', '<unit>', '<module label>')]
QtiPlot
tbf
Gnuplot
tbf