I need to write a long list of ints and floats with Python the same way fwrite would do in C - in a binary form.
This is necessary to create input files for another piece of code I am working with.
What is the best way to do this?
You can do this quite simply with the struct module.
For example, to write a list of 32-bit integers in binary:
import struct
ints = [10,50,100,2500,256]
with open('output', 'w') as fh:
data = struct.pack('i' * len(ints), *ints)
fh.write(data)
Will write '\n\x00\x00\x002\x00\x00\x00d\x00\x00\x00\xc4\t\x00\x00\x00\x01\x00\x00'
Have a look at numpy: numpy tofile:
With the array-method 'tofile' you can write binary-data:
# define output-format
numdtype = num.dtype('2f')
# write data
myarray.tofile('filename', numdtype)
Another way is to use memmaps: numpy memmaps
# create memmap
data = num.memmap('filename', mode='w+', dtype=num.float, offset=myoffset, shape=(my_shape), order='C')
# put some data into in:
data[1:10] = num.random.rand(9)
# flush to disk:
data.flush()
del data
Related
I have a large numpy array and I'd like to dump it into a file using ASCII format. I would like to specify the format. This works:
import numpy
a = numpy.random.rand(5)
fmt = "{:.11e}\n"
with open("out.dat", "w") as f:
for item in a:
f.write(fmt.format(item))
but is slow because I manually loop over all entries of a. Is there a way to handle this in only one write operation?
Provided RAM is not an issue, you can try formatting the array to a string and then exporting it:
a_str = np.array2string(a, formatter={'float_kind':lambda x: "%.11f" % x}, separator='\n', threshold=np.inf)[1:-1]
with open("out.dat", "w") as f:
f.write(a_str)
I'm a python user for scientific computation. Now, I have some numpy arrays, and the size of each of them is huge. Thus, I can not short all of them in the memory at the same time. I want to save the arrays in the disk and read them one by one at each time to do some calculation. How to perform this process pythonicly?
I know if all the data are stored in the memory, I can create a list named array_list like this:
array_list = []
for i0 in range(n_array):
t_ayyay = do_some_calculate()
array_list.append(t_ayyay)
and when I want to use them:
for i0 in range(n_array):
t_ayyay = array_list[i0]
# do something.
How to save array_list in the disk, and I can read each object using the index without load all of them in the memory?
Thanks.
Pickle is your friend for serialization.
import pickle
some_list = [....]
pickle_out = open("some_list.pickle", "w")
pickle.dump(some_list, pickle_out)
pickle_out.close()
to open up your saved array
pickle_in = open("some_list.pickle", "r")
some_list = pickle.open(pickle_in)
How can I import an array to python (numpy.arry) from a file and that way the file must be written if it doesn't already exist.
For example, save out a matrix to a file then load it back.
Checkout the entry on the numpy example list. Here is the entry on .loadtxt()
>>> from numpy import *
>>>
>>> data = loadtxt("myfile.txt") # myfile.txt contains 4 columns of numbers
>>> t,z = data[:,0], data[:,3] # data is 2D numpy array
>>>
>>> t,x,y,z = loadtxt("myfile.txt", unpack=True) # to unpack all columns
>>> t,z = loadtxt("myfile.txt", usecols = (0,3), unpack=True) # to select just a few columns
>>> data = loadtxt("myfile.txt", skiprows = 7) # to skip 7 rows from top of file
>>> data = loadtxt("myfile.txt", comments = '!') # use '!' as comment char instead of '#'
>>> data = loadtxt("myfile.txt", delimiter=';') # use ';' as column separator instead of whitespace
>>> data = loadtxt("myfile.txt", dtype = int) # file contains integers instead of floats
Another option is numpy.genfromtxt, e.g:
import numpy as np
data = np.genfromtxt("myfile.dat",delimiter=",")
This will make data a numpy array with as many rows and columns as are in your file
(I know the question is old, but I think this might be good as a reference for people with similar questions)
If you want to load data from an ASCII/text file (which has the benefit or being more or less human-readable and easy to parse in other software), numpy.loadtxt is probably what you want:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html
If you just want to quickly save and load numpy arrays/matrices to and from a file, take a look at numpy.save and numpy.load:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.save.html
http://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html
In Python, Storing a bare python list as a numpy.array and then saving it out to file, then loading it back, and converting it back to a list takes some conversion tricks. The confusion is because python lists are not at all the same thing as numpy.arrays:
import numpy as np
foods = ['grape', 'cherry', 'mango']
filename = "./outfile.dat.npy"
np.save(filename, np.array(foods))
z = np.load(filename).tolist()
print("z is: " + str(z))
This prints:
z is: ['grape', 'cherry', 'mango']
Which is stored on disk as the filename: outfile.dat.npy
The important methods here are the tolist() and np.array(...) conversion functions.
Have a look at SciPy cookbook. It should give you an idea of some basic methods to import /export data.
If you save/load the files from your own Python programs, you may also want to consider the Pickle module, or cPickle.
I am trying to read a matlab file with the following code
import scipy.io
mat = scipy.io.loadmat('test.mat')
and it gives me the following error
raise NotImplementedError('Please use HDF reader for matlab v7.3 files')
NotImplementedError: Please use HDF reader for matlab v7.3 files
so could anyone please had the same problem and could please any sample code
thanks
I've created a small library to load MATLAB 7.3 files:
pip install mat73
To load a .mat 7.3 into Python as a dictionary:
import mat73
data_dict = mat73.loadmat('data.mat')
simple as that!
Try using h5py module
import h5py
with h5py.File('test.mat', 'r') as f:
f.keys()
import h5py
import numpy as np
filepath = '/path/to/data.mat'
arrays = {}
f = h5py.File(filepath)
for k, v in f.items():
arrays[k] = np.array(v)
you should end up with your data in the arrays dict, unless you have MATLAB structures, I suspect. Hope it helps!
Per Magu_'s answer on a related thread, check out the package hdf5storage which has convenience functions to read v7.3 matlab mat files; it is as simple as
import hdf5storage
mat = hdf5storage.loadmat('test.mat')
I had a look at this issue: https://github.com/h5py/h5py/issues/726. If you saved your mat file with -v7.3 option, you should generate the list of keys with (under Python 3.x):
import h5py
with h5py.File('test.mat', 'r') as file:
print(list(file.keys()))
In order to access the variable a for instance, you have to use the same trick:
with h5py.File('test.mat', 'r') as file:
a = list(file['a'])
According to the Scipy cookbook. http://wiki.scipy.org/Cookbook/Reading_mat_files,
Beginning at release 7.3 of Matlab, mat files are actually saved using the HDF5 format by default (except if you use the -vX flag at save time, see help save in Matlab). These files can be read in Python using, for instance, the PyTables or h5py package. Reading Matlab structures in mat files does not seem supported at this point.
Perhaps you could use Octave to re-save using the -vX flag.
Despite hours of searching I've not found how to access Matlab v7.3 structures either. Hopefully this partial answer will help someone, and I'd be very happy to see extra pointers.
So starting with (I think the [0][0] arises from Matlab giving everything to dimensions):
f = h5py.File('filename', 'r')
f['varname'][0][0]
gives: < HDF5 object reference >
Pass this reference to f again:
f[f['varname'][0][0]]
which gives an array:
convert this to a numpy array and extract the value (or, recursively, another < HDF5 object reference > :
np.array(f[f['varname'][0][0]])[0][0]
If accessing the disk is slow, maybe loading to memory would help.
Further edit: after much futile searching my final workaround (I really hope someone else has a better solution!) was calling Matlab from python which is pretty easy and fast:
eng = matlab.engine.start_matlab() # first fire up a Matlab instance
eng.quit()
eng = matlab.engine.connect_matlab() # or connect to an existing one
eng.sqrt(4.0)
x = 4.0
eng.workspace['y'] = x
a = eng.eval('sqrt(y)')
print(a)
x = eng.eval('parameterised_function_in_Matlab(1, 1)', nargout=1)
a = eng.eval('Structured_variable{1}{2}.object_name') # (nested cell, cell, object)
This function reads Matlab-produced HDF5 .mat files, and returns a structure of nested dicts of Numpy arrays. Matlab writes matrices in Fortran order, so this also transposes matrices and higher-dimensional arrays into conventional Numpy order arr[..., page, row, col].
import h5py
def read_matlab(filename):
def conv(path=''):
p = path or '/'
paths[p] = ret = {}
for k, v in f[p].items():
if type(v).__name__ == 'Group':
ret[k] = conv(f'{path}/{k}') # Nested struct
continue
v = v[()] # It's a Numpy array now
if v.dtype == 'object':
# HDF5ObjectReferences are converted into a list of actual pointers
ret[k] = [r and paths.get(f[r].name, f[r].name) for r in v.flat]
else:
# Matrices and other numeric arrays
ret[k] = v if v.ndim < 2 else v.swapaxes(-1, -2)
return ret
paths = {}
with h5py.File(filename, 'r') as f:
return conv()
If you are only reading in basic arrays and structs, see vikrantt's answer on a similar post. However, if you are working with a Matlab table, then IMHO the best solution is to avoid the save option altogether.
I've created a simple helper function to convert a Matlab table to a standard hdf5 file, and another helper function in Python to extract the data into a Pandas DataFrame.
Matlab Helper Function
function table_to_hdf5(T, path, group)
%TABLE_TO_HDF5 Save a Matlab table in an hdf5 file format
%
% TABLE_TO_HDF5(T) Saves the table T to the HDF5 file inputname.h5 at the root ('/')
% group, where inputname is the name of the input argument for T
%
% TABLE_TO_HDF5(T, path) Saves the table T to the HDF5 file specified by path at the
% root ('/') group.
%
% TABLE_TO_HDF5(T, path, group) Saves the table T to the HDF5 file specified by path
% at the group specified by group.
%
%%%
if nargin < 2
path = [inputname(1),'.h5']; % default file name to input argument
end
if nargin < 3
group = ''; % We will prepend '/' later, so this is effectively root
end
for field = T.Properties.VariableNames
% Prepare to write
field = field{:};
dataset_name = [group '/' field];
data = T.(field);
if ischar(data) || isstring(data)
warning('String columns not supported. Skipping...')
continue
end
% Write the data
h5create(path, dataset_name, size(data))
h5write(path, dataset_name, data)
end
end
Python Helper Function
import pandas as pd
import h5py
def h5_to_df(path, group = '/'):
"""
Load an hdf5 file into a pandas DataFrame
"""
df = pd.DataFrame()
with h5py.File(path, 'r') as f:
data = f[group]
for k,v in data.items():
if v.shape[0] > 1: # Multiple column field
for i in range(v.shape[0]):
k_new = f'{k}_{i}'
df[k_new] = v[i]
else:
df[k] = v[0]
return df
Important Notes
This will only work on numerical data. If you know how to add string data, please comment.
This will create the file if it does not already exist.
This will crash if the data already exists in the file. You'll want to include logic to handle those cases as you deem appropriate.
How can I import an array to python (numpy.arry) from a file and that way the file must be written if it doesn't already exist.
For example, save out a matrix to a file then load it back.
Checkout the entry on the numpy example list. Here is the entry on .loadtxt()
>>> from numpy import *
>>>
>>> data = loadtxt("myfile.txt") # myfile.txt contains 4 columns of numbers
>>> t,z = data[:,0], data[:,3] # data is 2D numpy array
>>>
>>> t,x,y,z = loadtxt("myfile.txt", unpack=True) # to unpack all columns
>>> t,z = loadtxt("myfile.txt", usecols = (0,3), unpack=True) # to select just a few columns
>>> data = loadtxt("myfile.txt", skiprows = 7) # to skip 7 rows from top of file
>>> data = loadtxt("myfile.txt", comments = '!') # use '!' as comment char instead of '#'
>>> data = loadtxt("myfile.txt", delimiter=';') # use ';' as column separator instead of whitespace
>>> data = loadtxt("myfile.txt", dtype = int) # file contains integers instead of floats
Another option is numpy.genfromtxt, e.g:
import numpy as np
data = np.genfromtxt("myfile.dat",delimiter=",")
This will make data a numpy array with as many rows and columns as are in your file
(I know the question is old, but I think this might be good as a reference for people with similar questions)
If you want to load data from an ASCII/text file (which has the benefit or being more or less human-readable and easy to parse in other software), numpy.loadtxt is probably what you want:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html
If you just want to quickly save and load numpy arrays/matrices to and from a file, take a look at numpy.save and numpy.load:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.save.html
http://docs.scipy.org/doc/numpy/reference/generated/numpy.load.html
In Python, Storing a bare python list as a numpy.array and then saving it out to file, then loading it back, and converting it back to a list takes some conversion tricks. The confusion is because python lists are not at all the same thing as numpy.arrays:
import numpy as np
foods = ['grape', 'cherry', 'mango']
filename = "./outfile.dat.npy"
np.save(filename, np.array(foods))
z = np.load(filename).tolist()
print("z is: " + str(z))
This prints:
z is: ['grape', 'cherry', 'mango']
Which is stored on disk as the filename: outfile.dat.npy
The important methods here are the tolist() and np.array(...) conversion functions.
Have a look at SciPy cookbook. It should give you an idea of some basic methods to import /export data.
If you save/load the files from your own Python programs, you may also want to consider the Pickle module, or cPickle.