Develop Reference

Storing multiple GeoTiffs in HDF5 file in Python

I want to store multiple GeoTiff files in one HDF5 file to use it for further analysis since the function I am supposed to use can just deal with HDF5 (so basically like a raster stack in R but stored in a HDF5). I have to use Python. I am relatively new to HDF5 format (and geoanalysis in Python generally) and don't really know how to approach this issue. Especially keeping the geolocation/projection inforation seems tricky to me. So far I tried:
import h5py
import rasterio
r1 = rasterio.open("filename.tif")
r2 = rasterio.open("filename2.tif")
with h5py.File('path/test.h5', 'w') as hdf:
hdf.create_dataset('GeoTiff1', data=r1)
hdf.create_dataset('GeoTiff2', data=r2)
Yielding the following errror:
TypeError: Object dtype dtype('O') has no native HDF5 equivalent
I am pretty sure this not at all the correct approach and I'm happy about any suggestions.

What you can try is to do this:
import numpy as np
spec_dtype = h5py.special_dtype(vlen=np.dtype('float64'))
Just make a spec_dtype variable with float64 type then apply this to create_dataset:
with h5py.File('path/test.h5', 'w') as hdf:
hdf.create_dataset('GeoTiff1', data=r1,, dtype=spec_dtype)
hdf.create_dataset('GeoTiff2', data=r2,, dtype=spec_dtype)
Apply these and hopefully it will work.

Using HDFql in Python, your use-case could be solved as follows:
import HDFql
HDFql.execute("SHOW FILE SIZE filename.tif, filename2.tif")
HDFql.cursor_next()
HDFql.execute("CREATE DATASET path/test.h5 GeoTiff1 AS OPAQUE(%d) VALUES FROM BINARY FILE filename.tif" % HDFql.cursor_get_bigint())
HDFql.cursor_next()
HDFql.execute("CREATE DATASET path/test.h5 GeoTiff2 AS OPAQUE(%d) VALUES FROM BINARY FILE filename2.tif" % HDFql.cursor_get_bigint())

convert eye-tracking .edf file to ASC/CSV format

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

Creating *.mat file from Python without using dictionary

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})

Read *.au file in python

I am trying to read an audio file of type .au in python. I would like to have a vector with the amplitudes of the waveform contained in the audio file much in the same way as I can read *.wav files with scipy:
import scipy.io.wavfile as wav
rate,sig=wav.read('filename')
There is a built-in module for opening *.au files, i.e. sunau but I did not manage getting the amplitude vector using it. If I use the function 'sunau.Au_read' I can open a file but I am not able to extract the numbers I want but I can only do something like:
In [44]: f=sunau.Au_read('sound.au')
In [45]: f.readframes(10)
Out[45]: '\x0c\x02\x0c\x02,\x02L\x02\x14\x01,\x02,\x02\xec\x01L\x02\x04\x01'
I have no idea what to do with that string... In Matlab there is the function 'auread', is there an equivalent in Python?
Any help is greatly appreciated! :)

Read matlab file (*.mat) from zipped file without extracting to directory in Python

This specific questions stems from the attempt to handle large data sets produced by a MATLAB algorithm so that I can process them with python algorithms.
Background: I have large arrays in MATLAB (typically 20x20x40x15000 [i,j,k,frame]) and I want to use them in python. So I save the array to a *.mat file and use scipy.io.loadmat(fname) to read the *.mat file into a numpy array. However, a problem arises in that if I try to load the entire *.mat file in python, a memory error occurs. To get around this, I slice the *.mat file into pieces, so that I can load the pieces one at a time into a python array. If I divide up the *.mat by frame, I now have 15,000 *.mat files which quickly becomes a pain to work with (at least in windows). So my solution is to use zipped files.
Question: Can I use scipy to directly read a *.mat file from a zipped file without first unzipping the file to the current working directory?
Specs: Python 2.7, windows xp
Current code:
import scipy.io
import zipfile
import numpy as np
def readZip(zfilename,dim,frames):
data=np.zeros((dim[0],dim[1],dim[2],frames),dtype=np.float32)
zfile = zipfile.ZipFile( zfilename, "r" )
i=0
for info in zfile.infolist():
fname = info.filename
zfile.extract(fname)
mat=scipy.io.loadmat(fname)
data[:,:,:,i]=mat['export']
mat.clear()
i=i+1
return data
Tried code:
mat=scipy.io.loadmat(zfile.read(fname))
produces this error:
TypeError: file() argument 1 must be encoded string without NULL bytes, not str
mat=scipy.io.loadmat(zfile.open(fname))
produces this error:
fileobj.seek(0)
UnsupportedOperation: seek
Any other suggestions on handling the data are appreciated.
Thanks!

I am pretty sure that the answer to my question is NO and there are better ways to accomplish what I am trying to do.
Regardless, with the suggestion from J.F. Sebastian, I have devised a solution.
Solution: Save the data in MATLAB in the HDF5 format, namely hdf5write(fname, '/data', data_variable). This produces a *.h5 file which then can be read into python via h5py.
python code:
import h5py
r = h5py.File(fname, 'r+')
data = r['data']
I can now index directly into the data, however is stays on the hard drive.
print data[:,:,:,1]
Or I can load it into memory.
data_mem = data[:]
However, this once again gives memory errors. So, to get it into memory I can loop through each frame and add it to a numpy array.
h5py FTW!

In one of my frozen applications we bundle some files into the .bin file that py2exe creates, then pull them out like this:
z = zipfile.ZipFile(os.path.join(myDir, 'common.bin'))
data = z.read('schema-new.sql')
I am not certain if that would feed your .mat files into scipy, but I'd consider it worth a try.