According to RFC 1950 , the difference between the "OK" 0x789C and the "bad" 0x78DA is in the FLEVEL bit-field:

  FLEVEL (Compression level)
     These flags are available for use by specific compression
     methods.  The "deflate" method (CM = 8) sets these flags as
     follows:

        0 - compressor used fastest algorithm
        1 - compressor used fast algorithm
        2 - compressor used default algorithm
        3 - compressor used maximum compression, slowest algorithm

     The information in FLEVEL is not needed for decompression; it
     is there to indicate if recompression might be worthwhile.

"OK" uses 2, "bad" uses 3. So that difference in itself is not a problem.

To get any further, you might consider supplying the following information for each of compressing and (attempted) decompressing: what platform, what version of Python, what version of the zlib library, what was the actual code used to call the zlib module. Also supply the full traceback and error message from the failing decompression attempts. Have you tried to decompress the failing files with any other zlib-reading software? With what results? Please clarify what you have to work with: Does "Am I hosed?" mean that you don't have access to the original data? How did it get from a stream to a file? What guarantee do you have that the data was not mangled in transmission?

UPDATE Some observations based on partial clarifications published in your self-answer:

You are using Windows. Windows distinguishes between binary mode and text mode when reading and writing files. When reading in text mode, Python 2.x changes '\r\n' to '\n', and changes '\n' to '\r\n' when writing. This is not a good idea when dealing with non-text data. Worse, when reading in text mode, '\x1a' aka Ctrl-Z is treated as end-of-file.

To compress a file:

# imports and other superstructure left as a exercise
str_object1 = open('my_log_file', 'rb').read()
str_object2 = zlib.compress(str_object1, 9)
f = open('compressed_file', 'wb')
f.write(str_object2)
f.close()

To decompress a file:

str_object1 = open('compressed_file', 'rb').read()
str_object2 = zlib.decompress(str_object1)
f = open('my_recovered_log_file', 'wb')
f.write(str_object2)
f.close()

Aside: Better to use the gzip module which saves you having to think about nasssties like text mode, at the cost of a few bytes for the extra header info.

If you have been using 'rb' and 'wb' in your compression code but not in your decompression code [unlikely?], you are not hosed, you just need to flesh out the above decompression code and go for it.

Note carefully the use of "may", "should", etc in the following untested ideas.

If you have not been using 'rb' and 'wb' in your compression code, the probability that you have hosed yourself is rather high.

If there were any instances of '\x1a' in your original file, any data after the first such is lost -- but in that case it shouldn't fail on decompression (IOW this scenario doesn't match your symptoms).

If a Ctrl-Z was generated by zlib itself, this should cause an early EOF upon attempted decompression, which should of course cause an exception. In this case you may be able to gingerly reverse the process by reading the compressed file in binary mode and then substitute '\r\n' with '\n' [i.e. simulate text mode without the Ctrl-Z -> EOF gimmick]. Decompress the result. Edit Write the result out in TEXT mode. End edit

UPDATE 2 I can reproduce your symptoms -- with ANY level 1 to 9 -- with the following script:

import zlib, sys
fn = sys.argv[1]
level = int(sys.argv[2])
s1 = open(fn).read() # TEXT mode
s2 = zlib.compress(s1, level)
f = open(fn + '-ct', 'w') # TEXT mode
f.write(s2)
f.close()
# try to decompress in text mode
s1 = open(fn + '-ct').read() # TEXT mode
s2 = zlib.decompress(s1) # error -5
f = open(fn + '-dtt', 'w')
f.write(s2)
f.close()

Note: you will need a use a reasonably large text file (I used an 80kb source file) to ensure that the decompression result will contain a '\x1a'.

I can recover with this script:

import zlib, sys
fn = sys.argv[1]
# (1) reverse the text-mode write
# can't use text-mode read as it will stop at Ctrl-Z
s1 = open(fn, 'rb').read() # BINARY mode
s1 = s1.replace('\r\n', '\n')
# (2) reverse the compression
s2 = zlib.decompress(s1)
# (3) reverse the text mode read
f = open(fn + '-fixed', 'w') # TEXT mode
f.write(s2)
f.close()

NOTE: If there is a '\x1a' aka Ctrl-Z byte in the original file, and the file is read in text mode, that byte and all following bytes will NOT be included in the compressed file, and thus can NOT be recovered. For a text file (e.g. source code), this is no loss at all. For a binary file, you are most likely hosed.

Update 3 [following late revelation that there's an encryption/decryption layer involved in the problem]:

The "Error -5" message indicates that the data that you are trying to decompress has been mangled since it was compressed. If it's not caused by using text mode on the files, suspicion obviously(?) falls on your decryption and encryption wrappers. If you want help, you need to divulge the source of those wrappers. In fact what you should try to do is (like I did) put together a small script that reproduces the problem on more than one input file. Secondly (like I did) see whether you can reverse the process under what conditions. If you want help with the second stage, you need to divulge the problem-reproduction script.

As far as I know, there is no Python package that contains zlib because that's already included into the standard library.

Try the command below to see whether the zlib Python package is available and which version it has:

• for Python 2.x:

  python -c "import zlib; print(zlib.__version__)"
  

• for Python 3.x:

  python3 -c "import zlib; print(zlib.__version__)"
  

On my system, it outputs 1.0 for both Python versions.

One of the best options to compress numpy arrays is using np.savez_compressed. This will be nicer but will be slower. I don't think your compression code is correct

Copyimport numpy as np
import zlib
input_arr = np.arange(100)
dtype = input_arr.dtype
compressed_arr = zlib.compress(input_arr, 2)
decompressed_arr = np.fromstring(zlib.decompress(compressed_arr), dtype)

You can also use blosc which has even better performace