Develop Reference

h5py file subset taking more space than parent file?

I have an existing h5py file that I downloaded which is ~18G in size. It has a number of nested datasets within it:
h5f = h5py.File('input.h5', 'r')
data = h5f['data']
latlong_data = data['lat_long'].value
I want to be able to some basic min/max scaling of the numerical data within latlong, so i want to put it in its own h5py file for easier use and lower memory usage.
However, when i try to write it out to its own file:
out = h5py.File('latlong_only.h5', 'w')
out.create_dataset('latlong', data=latlong)
out.close()
The output file is incredibly large. It's still not done writing to disk and is ~85GB in space. Why is the data being written to the new file not compressed?

Could be h5f['data/lat_long'] is using compression filters (and you aren't). To check the original dataset's compression settings, use this line:
print (h5f['data/latlong'].compression, h5f['data/latlong'].compression_opts)
After writing my answer, it occurred to me that you don't need to copy the data to another file to reduce the memory footprint. Your code reads the dataset into an array, which is not necessary in most use cases. A h5py dataset object behaves similar to a NumPy array. Instead, use this line: ds = h5f1['data/latlong'] to create a dataset object (instead of an array) and use it "like" it's a NumPy array. FYI, .value is a deprecated method to return the dataset as an array. Use this syntax instead arr = h5f1['data/latlong'][()]. Loading the dataset into an array also requires more memory than using an h5py object (which could be an issue with large datasets).
There are other ways to access the data. My suggestion to use dataset objects is 1 way. Your method (extracting data to a new file) is another way. I am not found of that approach because you now have 2 copies of the data; a bookkeeping nightmare. Another alternative is to create external links from the new file to the existing 18GB file. That way you have a small file that links to the big file (and no duplicate data). I describe that method in this post: [How can I combine multiple .h5 file?][1] Method 1: Create External Links.
If you still want to copy the data, here is what I would do. Your code reads the dataset into an array then writes the array to the new file (uncompressed). Instead, copy the dataset using h5py's group .copy() method, it will retain compression settings and attributes.
See below:
with h5py.File('input.h5', 'r') as h5f1, \
h5py.File('latlong_only.h5', 'w') as h5f2:
h5f1.copy(h5f1['data/latlong'], h5f2,'latlong')

writing files, reading them and call them with different moduls

I am trying to save variables after I run a program to a text file and read them in different module, to call them back in the original program. Point of that is to write plots with 4 different outcome of the main program.
attempt at coding
#main program
a = array([[0.05562032, 0.05386903, 0.05216994, 0.03045489, 0.03029977,
0.03014554],
[0. , 0.00175129, 0.00345037, 0.15353227, 0.1536874 ,
0.15384163]])
#save paramaters in external file
save_paramaters = open('save.txt','w')
save_paramaters.write(str(a))
save_paramaters.close()
I open the txt file in python module and save it as a variable, which I corrected manually(replacing spaces with commas)
#new program
dat = "save.txt"
b = open(dat, "r")
c = array(b.read())
In the main program I now call the variable with
a = array([[0.05562032, 0.05386903, 0.05216994, 0.03045489, 0.03029977,
0.03014554],
[0. , 0.00175129, 0.00345037, 0.15353227, 0.1536874 ,
0.15384163])
#save paramaters in external file
save_paramaters = open('save.txt','w')
save_paramaters.write(str(a))
save_paramaters.close()
#open the variable
from program import c
from matplotlib.pyplot import figure, plot
#and try to plot it
plot(c[1][:], label ='results2')
plot(c[0][:], label ='results1')
File "/Example.py", line 606, in example
plot(c[1][:], label ='results2') #model
IndexError: too many indices for array

If you want to save an array you can't just save it as text and expect python to figure it out. When you read it, you're reading it as text (as a string) and that's all your program can know.
If you want to save complex objects you have several other options:
You can save text (as you do) but parse it manually when reading it to turn it into an array. This is complex to write without bugs and will get even more complex if you have anything even more complex than an array.
You can save it using pickle - while this is a good solution for almost all objects, the file created wouldn't be readable to humans, and that's perhaps not what you want.
A good middle ground is to save objects as JSON - this is a standard for most datatypes and would work beautifully for dicts and lists and tuples (but will fail with more complex objects), and more importantly, it will be readable to humans such as yourself.
Let's say you go with JSON. You save a list like this:
import json
with open('save.txt','w') as f:
json.dump(your_object, f)
As simple as that. To read back the list:
import json
with open('save.txt','r') as f:
your_new_object = json.load(f)
This is fairly simple isn't it? Notice I used a with statement to open the files to make sure they close properly as well, but that's also more simple to write. Using pickles is fairly similar and even has the same syntax, but objects are saved as bytes and not text (so you have to use 'rb' and 'wb' modes on files to read and write, respectively).
To do the same thing with numpy array, we can also use numpy.save:
np.save('save', your_numpy_array)
And we read it back (with a npy extension) with numpy.load:
your_array = np.load('save.npy')
In readability terms, opening the file would be semi-readable (less than JSON, more than pickle)

Increase speed numpy.loadtxt?

I have hundred of thousands of data text files to read. As of now, I'm importing the data from text files every time I run the code. Perhaps the easy solution would be to simply reformat the data into a file faster to read.
Anyway, right now every text files I have look like:
User: unknown
Title : OE1_CHANNEL1_20181204_103805_01
Sample data
Wavelength OE1_CHANNEL1
185.000000 27.291955
186.000000 27.000877
187.000000 25.792290
188.000000 25.205620
189.000000 24.711882
.
.
.
The code where I read and import the txt files is:
# IMPORT DATA
path = 'T2'
if len(sys.argv) == 2:
path = sys.argv[1]
files = os.listdir(path)
trans_import = []
for index, item in enumerate(files):
trans_import.append(np.loadtxt(path+'/'+files[1], dtype=float, skiprows=4, usecols=(0,1)))
The resulting array looks in the variable explorer as:
{ndarray} = [[185. 27.291955]\n [186. 27.000877]\n ... ]
I'm wondering, how I could speed up this part? It takes a little too long as of now just to import ~4k text files. There are 841 lines inside every text files (spectrum). The output I get with this code is 841 * 2 = 1682. Obviously, it considers the \n as a line...

It would probably be much faster if you had one large file instead of many small ones. This is generally more efficient. Additionally, you might get a speedup from just saving the numpy array directly and loading that .npy file in instead of reading in a large text file. I'm not as sure about the last part though. As always when time is a concern, I would try both of these options and then measure the performance improvement.
If for some reason you really can't just have one large text file / .npy file, you could also probably get a speedup by using, e.g., multiprocessing to have multiple workers reading in the files at the same time. Then you can just concatenate the matrices together at the end.
Not your primary question but since it seems to be an issue - you can rewrite the text files to not have those extra newlines, but I don't think np.loadtxt can ignore them. If you're open to using pandas, though, pandas.read_csv with skip_blank_lines=True should handle that for you. To get a numpy.ndarray from a pandas.DataFrame, just do dataframe.values.

Let use pandas.read_csv (with C speed) instead of numpy.loadtxt. This is a very helpful post:
http://akuederle.com/stop-using-numpy-loadtxt

Saving python data for an application

I need to save multiple numpy arrays along with the user input that was used to compute the data these arrays contain in a single file. I'm having a hard time finding a good procedure to use to achieve this or even what file type to use. The only thing i can think of is too put the computed arrays along with the user input into one single array and then save it using numpy.save. Does anybody know any better alternatives or good file types for my use?

You could try using Pickle to serialize your arrays.

How about using pickle and then storing pickled array objects in a storage of your choice, like database or files?

I had this problem long ago so i dont have the code near to show you, but i used a binary write in a tmp file to get that done.
EDIT: Thats is, pickle is what i used. Thanks SpankMe and RoboInventor

Numpy provides functions to save arrays to files, e.g. savez():
outfile = '/tmp/data.dat'
x = np.arange(10)
y = np.sin(x)
np.savez(outfile, x=x, y=y)
npzfile = np.load(outfile)
print npzfile['x']
print npzfile['y']

get specific content from file python

I have a file test.txt which has an array:
array = [3,5,6,7,9,6,4,3,2,1,3,4,5,6,7,8,5,3,3,44,5,6,6,7]
Now what I want to do is get the content of array and perform some calculations with the array. But the problem is when I do open("test.txt") it outputs the content as the string. Actually the array is very big, and if I do a loop it might not be efficient. Is there any way to get the content without splitting , ? Any new ideas?

I recommend that you save the file as json instead, and read it in with the json module. Either that, or make it a .py file, and import it as python. A .txt file that looks like a python assignment is kind of odd.

Does your text file need to look like python syntax? A list of comma separated values would be the usual way to provide data:
1,2,3,4,5
Then you could read/write with the csv module or the numpy functions mentioned above. There's a lot of documentation about how to read csv data in efficiently. Once you had your csv reader data object set up, data could be stored with something like:
data = [ map( float, row) for row in csvreader]

If you want to store a python-like expression in a file, store only the expression (i.e. without array =) and parse it using ast.literal_eval().
However, consider using a different format such as JSON. Depending on the calculations you might also want to consider using a format where you do not need to load all data into memory at once.

Must the array be saved as a string? Could you use a pickle file and save it as a Python list?
If not, could you try lazy evaluation? Maybe only process sections of the array as needed.
Possibly, if there are calculations on the entire array that you must always do, it might be a good idea to pre-compute those results and store them in the txt file either in addition to the list or instead of the list.

You could also use numpy to load the data from the file using numpy.genfromtxt or numpy.loadtxt. Both are pretty fast and both have the ability to do the recasting on load. If the array is already loaded though, you can use numpy to convert it to an array of floats, and that is really fast.
import numpy as np
a = np.array(["1", "2", "3", "4"])
a = a.astype(np.float)

You could write a parser. They are very straightforward. And much much faster than regular expressions, please don't do that. Not that anyone suggested it.
# open up the file (r = read-only, b = binary)
stream = open("file_full_of_numbers.txt", "rb")
prefix = '' # end of the last chunk
full_number_list = []
# get a chunk of the file at a time
while True:
# just a small 1k chunk
buffer = stream.read(1024)
# no more data is left in the file
if '' == buffer:
break
# delemit this chunk of data by a comma
split_result = buffer.split(",")
# append the end of the last chunk to the first number
split_result[0] = prefix + split_result[0]
# save the end of the buffer (a partial number perhaps) for the next loop
prefix = split_result[-1]
# only work with full results, so skip the last one
numbers = split_result[0:-1]
# do something with the numbers we got (like save it into a full list)
full_number_list += numbers
# now full_number_list contains all the numbers in text format
You'll also have to add some logic to use the prefix when the buffer is blank. But I'll leave that code up to you.

OK, so the following methods ARE dangerous. Since they are used to attack systems by injecting code into them, used them at your own risk.
array = eval(open("test.txt", 'r').read().strip('array = '))
execfile('test.txt') # this is the fastest but most dangerous.
Safer methods.
import ast
array = ast.literal_eval(open("test.txt", 'r').read().strip('array = ')).
...
array = [float(value) for value in open('test.txt', 'r').read().strip('array = [').strip('\n]').split(',')]
The eassiest way to serialize python objects so you can load them later is to use pickle. Assuming you dont want a human readable format since this adds major head, either-wise, csv is fast and json is flexible.
import pickle
import random
array = random.sample(range(10**3), 20)
pickle.dump(array, open('test.obj', 'wb'))
loaded_array = pickle.load(open('test.obj', 'rb'))
assert array == loaded_array
pickle does have some overhead and if you need to serialize large objects you can specify the compression ratio, the default is 0 no compression, you can set it to pickle.HIGHEST_PROTOCOL pickle.dump(array, open('test.obj', 'wb'), pickle.HIGHEST_PROTOCOL)
If you are working with large numerical or scientific data sets then use numpy.tofile/numpy.fromfile or scipy.io.savemat/scipy.io.loadmat they have little overhead, but again only if you are already using numpy/scipy.
good luck.