I have been using textnets (python) to analyse a corpus. I need to export the resulting graph for further analysis / layout editing in Gephi. Having read the docs I am still confused on how to either save the resulting igraph Graph in the appropriate format or to access the pandas dataframe which could then be exported. For example using the tutorial from docs, if using:
from textnets import Corpus, Textnet
from textnets import examples
corpus = Corpus(examples.moon_landing)
tn = Textnet(corpus.tokenized(), min_docs=1)
print(tn)
I had thought I could either return a pandas data frame by calling 'tn' though this returns a 'Textnet' object.
I had also thought I could return an igraph.Graph object and then subsequently use Graph.write_gml() using something like tn.project(node_type='doc').write_gml('test.gml') to save the file in an appropriate format but this returns a ProjectedTextnet.
Any advise would be most welcome.
For the second part of your question, you can convert the textnet object to an igraph:
g = tn.graph
Then save as gml:
g.write_gml("test.gml")
Is it possible to read binary MATLAB .mat files in Python?
I've seen that SciPy has alleged support for reading .mat files, but I'm unsuccessful with it. I installed SciPy version 0.7.0, and I can't find the loadmat() method.
An import is required, import scipy.io...
import scipy.io
mat = scipy.io.loadmat('file.mat')
Neither scipy.io.savemat, nor scipy.io.loadmat work for MATLAB arrays version 7.3. But the good part is that MATLAB version 7.3 files are hdf5 datasets. So they can be read using a number of tools, including NumPy.
For Python, you will need the h5py extension, which requires HDF5 on your system.
import numpy as np
import h5py
f = h5py.File('somefile.mat','r')
data = f.get('data/variable1')
data = np.array(data) # For converting to a NumPy array
First save the .mat file as:
save('test.mat', '-v7')
After that, in Python, use the usual loadmat function:
import scipy.io as sio
test = sio.loadmat('test.mat')
There is a nice package called mat4py which can easily be installed using
pip install mat4py
It is straightforward to use (from the website):
Load data from a MAT-file
The function loadmat loads all variables stored in the MAT-file into a simple Python data structure, using only Python’s dict and list objects. Numeric and cell arrays are converted to row-ordered nested lists. Arrays are squeezed to eliminate arrays with only one element. The resulting data structure is composed of simple types that are compatible with the JSON format.
Example: Load a MAT-file into a Python data structure:
from mat4py import loadmat
data = loadmat('datafile.mat')
The variable data is a dict with the variables and values contained in the MAT-file.
Save a Python data structure to a MAT-file
Python data can be saved to a MAT-file, with the function savemat. Data has to be structured in the same way as for loadmat, i.e. it should be composed of simple data types, like dict, list, str, int, and float.
Example: Save a Python data structure to a MAT-file:
from mat4py import savemat
savemat('datafile.mat', data)
The parameter data shall be a dict with the variables.
Having MATLAB 2014b or newer installed, the MATLAB engine for Python could be used:
import matlab.engine
eng = matlab.engine.start_matlab()
content = eng.load("example.mat", nargout=1)
Reading the file
import scipy.io
mat = scipy.io.loadmat(file_name)
Inspecting the type of MAT variable
print(type(mat))
#OUTPUT - <class 'dict'>
The keys inside the dictionary are MATLAB variables, and the values are the objects assigned to those variables.
There is a great library for this task called: pymatreader.
Just do as follows:
Install the package: pip install pymatreader
Import the relevant function of this package: from pymatreader import read_mat
Use the function to read the matlab struct: data = read_mat('matlab_struct.mat')
use data.keys() to locate where the data is actually stored.
The keys will usually look like: dict_keys(['__header__', '__version__', '__globals__', 'data_opp']). Where data_opp will be the actual key which stores the data. The name of this key can ofcourse be changed between different files.
Last step - Create your dataframe: my_df = pd.DataFrame(data['data_opp'])
That's it :)
There is also the MATLAB Engine for Python by MathWorks itself. If you have MATLAB, this might be worth considering (I haven't tried it myself but it has a lot more functionality than just reading MATLAB files). However, I don't know if it is allowed to distribute it to other users (it is probably not a problem if those persons have MATLAB. Otherwise, maybe NumPy is the right way to go?).
Also, if you want to do all the basics yourself, MathWorks provides (if the link changes, try to google for matfile_format.pdf or its title MAT-FILE Format) a detailed documentation on the structure of the file format. It's not as complicated as I personally thought, but obviously, this is not the easiest way to go. It also depends on how many features of the .mat-files you want to support.
I've written a "small" (about 700 lines) Python script which can read some basic .mat-files. I'm neither a Python expert nor a beginner and it took me about two days to write it (using the MathWorks documentation linked above). I've learned a lot of new stuff and it was quite fun (most of the time). As I've written the Python script at work, I'm afraid I cannot publish it... But I can give some advice here:
First read the documentation.
Use a hex editor (such as HxD) and look into a reference .mat-file you want to parse.
Try to figure out the meaning of each byte by saving the bytes to a .txt file and annotate each line.
Use classes to save each data element (such as miCOMPRESSED, miMATRIX, mxDOUBLE, or miINT32)
The .mat-files' structure is optimal for saving the data elements in a tree data structure; each node has one class and subnodes
To read mat file to pandas dataFrame with mixed data types
import scipy.io as sio
mat=sio.loadmat('file.mat')# load mat-file
mdata = mat['myVar'] # variable in mat file
ndata = {n: mdata[n][0,0] for n in mdata.dtype.names}
Columns = [n for n, v in ndata.items() if v.size == 1]
d=dict((c, ndata[c][0]) for c in Columns)
df=pd.DataFrame.from_dict(d)
display(df)
Apart from scipy.io.loadmat for v4 (Level 1.0), v6, v7 to 7.2 matfiles and h5py.File for 7.3 format matfiles, there is anther type of matfiles in text data format instead of binary, usually created by Octave, which can't even be read in MATLAB.
Both of scipy.io.loadmat and h5py.File can't load them (tested on scipy 1.5.3 and h5py 3.1.0), and the only solution I found is numpy.loadtxt.
import numpy as np
mat = np.loadtxt('xxx.mat')
Can also use the hdf5storage library. official documentation here for details on matlab version support.
import hdf5storage
label_file = "./LabelTrain.mat"
out = hdf5storage.loadmat(label_file)
print(type(out)) # <class 'dict'>
from os.path import dirname, join as pjoin
import scipy.io as sio
data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data')
mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
mat_contents = sio.loadmat(mat_fname)
You can use above code to read the default saved .mat file in Python.
After struggling with this problem myself and trying other libraries (I have to say mat4py is a good one as well but with a few limitations) I have built this library ("matdata2py") that can handle most variable types and most importantly for me the "string" type. The .mat file needs to be saved in the -V7.3 version. I hope this can be useful for the community.
Installation:
pip install matdata2py
How to use this lib:
import matdata2py as mtp
To load the Matlab data file:
Variables_output = mtp.loadmatfile(file_Name, StructsExportLikeMatlab = True, ExportVar2PyEnv = False)
print(Variables_output.keys()) # with ExportVar2PyEnv = False the variables are as elements of the Variables_output dictionary.
with ExportVar2PyEnv = True you can see each variable separately as python variables with the same name as saved in the Mat file.
Flag descriptions
StructsExportLikeMatlab = True/False structures are exported in dictionary format (False) or dot-based format similar to Matlab (True)
ExportVar2PyEnv = True/False export all variables in a single dictionary (True) or as separate individual variables into the python environment (False)
scipy will work perfectly to load the .mat files.
And we can use the get() function to convert it to a numpy array.
mat = scipy.io.loadmat('point05m_matrix.mat')
x = mat.get("matrix")
print(type(x))
print(len(x))
plt.imshow(x, extent=[0,60,0,55], aspect='auto')
plt.show()
To Upload and Read mat files in python
Install mat4py in python.On successful installation we get:
Successfully installed mat4py-0.5.0.
Importing loadmat from mat4py.
Save file actual location inside a variable.
Load mat file format to a data value using python
pip install mat4py
from mat4py import loadmat
boston = r"E:\Downloads\boston.mat"
data = loadmat(boston, meta=False)
I am using the PlyFile library (https://pypi.org/project/plyfile) in Python.
I used vertex = plydata['vertex'] to generate a list of vertex co-ordinates. The datatype is as follows -
PlyElement('vertex', (PlyProperty('x', 'float'), PlyProperty('y', 'float'), PlyProperty('z', 'float')), count=2500086, comments=[])
After that I generated a list of all the x-axis values and put them into a numpy array and performed some operations on them. Then I replaced the original x-axis values in plydata['vertex'] with these new ones.
Now I want to write these values into a .ply file and create a new mesh. How would I go about it? I tried going through the docs but the code is quite messy.
Any insights would help, Thanks!
Saving a .ply file:
ply_data: PlyData
ply_data.text = True # for asci format
ply_data.write(path)
I have a recording of tracking data in .edf format (SR-RESEARCH eyelink). I want to convert it to ASC/CSV format in python. I have the GUI application but I want to do it programmatically (in Python).
I found the package pyEDFlib but couldn't find an example to how convert the eye-tracking .edf file to .asc or .csv.
What will the best best way to do it?
Thanks
If I trust the page here: http://pyedflib.readthedocs.io/en/latest, you can run through all the signals in the file this way:
import pyedflib
import numpy as np
f = pyedflib.EdfReader("data/test_generator.edf")
n = f.signals_in_file
signal_labels = f.getSignalLabels()
sigbufs = np.zeros((n, f.getNSamples()[0]))
for i in np.arange(n):
sigbufs[i, :] = f.readSignal(i)
The pyEDFlib library simply reads the file into an EdfReader object.
Then you just need to go through and make row for each.
I assume that signal_labels (in the code above) will be an array with all the labels so make a comma separated string out of them
signal_labels_row = ",".join(signal_labels)
Then do the same for each signal, 1 comma separated String for each
Then simply write them in a file.
I can see they provide an example of how to read a file and extract all the data you need here
https://github.com/holgern/pyedflib/blob/master/demo/readEDFFile.py
Based on your answers i have created this python3 script to export all singnals to multiple .csv files https://github.com/folkien/pyEdfToCsv
I have few lists which i want to save it to a *.mat file. But according to scipy.io.savemat command documentation i Need to create a dictionary with the lists and then use the command to save it to a *.mat file.
If i save it according to the way mentioned in the docs the mat file will have structure with variables as the Arrays which i used in the dictionary. Now i have a Problem here, I have another program (which is not editable) will use the mat files and load them to plot some Graphs from the data. The program cannot process the structure because it is written in a way where if it loads a mat files and then it will directly process the Arrays in it.
So is there a way to save the mat file without using dictionaries? Please see the Image for more understanding
Thanks
This is the sample algorithm i used to save my *.mat file
import os
os.getcwd()
os.chdir(os.getcwd())
import scipy.io as sio
x=[1,2,3,4,5]
y=[234,5445,778] #can be 1000 lists
data={}
data['x']=x
data['y']=y
sio.savemat('test.mat',{'interpolated_data':data})
How about
scipy.io.savemat('interpolated_data_max_compare.mat',
{'NA1_X_order10_ACCE_ms2': np.zeros((3000,1)),
'NA1_X_order10_DISP_mm': np.ones((3000,1))})
Should work fine...
According to the code you added in your question, instead of sio.savemat('...', {'interpolated_data':data}), just save
sio.savemat('...', data)
and you should be fine: data is already a dictionary you don't need to add an extra level with {'interpolated_data': data} when saving.
You could use the Writing primitives directly
import scipy.io.matlab as ml
f=open("something.mat","wb")
mw=ml.mio5.MatFile5Writer(f)
mw.put_variables({"testVar":22})