I'd like to read a .mtx file using Python. The .mtx file is generated by Abaqus and looks like this:
1,1, 1,1, 1.939258533333333e-02 1,2, 1,2, 1.939258533333333e-02 2,1, 2,1, 1.889629366666666e-02
It seems that scipy.io.mmread would work, but when I ran the following code:
import scipy.io with open(abs_file_path) as mass_file: otpt = scipy.io.mmread(mass_file)
I got the following error:
Traceback (most recent call last): File "./test_read_mass_mtx.py", line 12, in <module> read_mass(file_path) File "/home/user/Desktop/Temp/python/data_functions/read_mass_mtx.py", line 6, in read_mass otpt = scipy.io.mmread(mass_file) File "/home/user/anaconda3/lib/python3.6/site-packages/scipy/io/mmio.py", line 76, in mmread return MMFile().read(source) File "/home/user/anaconda3/lib/python3.6/site-packages/scipy/io/mmio.py", line 414, in read self._parse_header(stream) File "/home/user/anaconda3/lib/python3.6/site-packages/scipy/io/mmio.py", line 478, in _parse_header self.__class__.info(stream) File "/home/user/anaconda3/lib/python3.6/site-packages/scipy/io/mmio.py", line 232, in info [asstr(part.strip()) for part in line.split()] ValueError: not enough values to unpack (expected 5, got 3)
Abaqus and SciPy don't agree on what a Matrix Market file should look like.
The first issue which causes your error is that SciPy expects whitespace between every column, hence your "expected 5, got 3" error. Changing your example input manually to contain whitespace:
1, 1, 1, 1, 1.939258533333333e-02 1, 2, 1, 2, 1.939258533333333e-02 2, 1, 2, 1, 1.889629366666666e-02
The error changes to
ValueError: source is not in Matrix Market format
This makes sense to me, because the docs of
mmread says the return value is
Dense or sparse matrix depending on the matrix format in the Matrix Market file.
Which might mean that there's at least some metadata (headers?) missing from the file, making it non-standard. So either
Either way you'll probably have to parse the file yourself. If you know what the first four columns mean you can probably easily parse it using
numpy.loadtxt or something similar.