Loading data files: Difference between revisions
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Last update with: Version 1.5.3.15 | |||
== Origin == | == Origin == | ||
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import numpy as np # loading numpy package | import numpy as np # loading numpy package | ||
data = np.genfromtxt("mydata.txt", usecols = (0,2,3), skip_header = | data = np.genfromtxt("mydata.txt", usecols = (0,2,3), skip_header = 3, 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") | # 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 | # Load all columns be removing the usecols argument | ||
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import numpy as np # loading numpy package | import numpy as np # loading numpy package | ||
data = np.loadtxt("mydata.txt", usecols = (1,4,5), skiprows= | data = np.loadtxt("mydata.txt", usecols = (1,4,5), skiprows=3, delimiter="\t") | ||
# this example loads data from the second, the fifth, and the sixth column and skips the header | # 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 | # Load all columns be removing the usecols argument | ||
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file = "Name of my data file.txt" | file = "Name of my data file.txt" | ||
data = pd.read_csv('%s' % file, header=[0,1 | data = pd.read_csv('%s' % file, header=[0,1], delimiter='\t') | ||
# the first | # the first two lines of the header are read | ||
V = data[('Voltage', '[V] | V = data[('SMU1 Voltage', '[V]')] | ||
I = data[('Current', '[A] | I = data[('SMU2 Current', '[A]')] | ||
# Syntax: data[('<variable>', '<unit | # Syntax: data[('<Module label + variable>', '<unit>')] | ||
}} | }} | ||
Revision as of 20:57, 24 March 2019
Last update with: Version 1.5.3.15
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. Make sure that the decimal separator is set to point in the options of Origin.
Python
The data files can easily be loaded using the package numpy:
import numpy as np # loading numpy package
data = np.genfromtxt("mydata.txt", usecols = (0,2,3), skip_header = 3, 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=3, 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 package pandas:
import pandas as pd
file = "Name of my data file.txt"
data = pd.read_csv('%s' % file, header=[0,1], delimiter='\t')
# the first two lines of the header are read
V = data[('SMU1 Voltage', '[V]')]
I = data[('SMU2 Current', '[A]')]
# Syntax: data[('<Module label + variable>', '<unit>')]
Please find all options here: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html
QtiPlot
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Gnuplot
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