I'm having trouble reading an unformatted F77 binary file in Python.
I've tried the SciPy.io.FortraFile method and the NumPy.fromfile method, both to no avail. I have also read the file in IDL, which works, so I have a benchmark for what the data should look like. I'm hoping that someone can point out a silly mistake on my part -- there's nothing better than having an idiot moment and then washing your hands of it...
The data, bcube1, have dimensions 101x101x101x3, and is r*8 type. There are 3090903 entries in total. They are written using the following statement (not my code, copied from source).
open (unit=21, file=bendnm, status='new'
. ,form='unformatted')
write (21) bcube1
close (unit=21)
I can successfully read it in IDL using the following (also not my code, copied from colleague):
bcube=dblarr(101,101,101,3)
openr,lun,'bcube.0000000',/get_lun,/f77_unformatted,/swap_if_little_endian
readu,lun,bcube
free_lun,lun
The returned data (bcube) is double precision, with dimensions 101x101x101x3, so the header information for the file is aware of its dimensions (not flattend).
Now I try to get the same effect using Python, but no luck. I've tried the following methods.
In [30]: f = scipy.io.FortranFile('bcube.0000000', header_dtype='uint32')
In [31]: b = f.read_record(dtype='float64')
which returns the error Size obtained (3092150529) is not a multiple of the dtypes given (8). Changing the dtype changes the size obtained but it remains indivisible by 8.
Alternately, using fromfile results in no errors but returns one more value that is in the array (a footer perhaps?) and the individual array values are wildly wrong (should all be of order unity).
In [38]: f = np.fromfile('bcube.0000000')
In [39]: f.shape
Out[39]: (3090904,)
In [42]: f
Out[42]: array([ -3.09179121e-030, 4.97284231e-020, -1.06514594e+299, ...,
8.97359707e-029, 6.79921640e-316, -1.79102266e-037])
I've tried using byteswap to see if this makes the floating point values more reasonable but it does not.
It seems to me that the np.fromfile method is very close to working but there must be something wrong with the way it's reading the header information. Can anyone suggest how I can figure out what should be in the header file that allows IDL to know about the array dimensions and datatype? Is there a way to pass header information to fromfile so that it knows how to treat the leading entry?
I played a bit around with it, and I think I have an idea.
How Fortran stores unformatted data is not standardized, so you have to play a bit around with it, but you need three pieces of information:
The Format of the data. You suggest that is 64-bit reals, or 'f8' in python.
The type of the header. That is an unsigned integer, but you need the length in bytes. If unsure, try 4.
The header usually stores the length of the record in bytes, and is repeated at the end.
Then again, it is not standardized, so no guarantees.
The endianness, little or big.
Technically for both header and values, but I assume they're the same.
Python defaults to little endian, so if that were the the correct setting for your data, I think you would have already solved it.
When you open the file with scipy.io.FortranFile, you need to give the data type of the header. So if the data is stored big_endian, and you have a 4-byte unsigned integer header, you need this:
from scipy.io import FortranFile
ff = FortranFile('data.dat', 'r', '>u4')
When you read the data, you need the data type of the values. Again, assuming big_endian, you want type >f8:
vals = ff.read_reals('>f8')
Look here for a description of the syntax of the data type.
If you have control over the program that writes the data, I strongly suggest you write them into data streams, which can be more easily read by Python.
Fortran has record demarcations which are poorly documented, even in binary files.
So every write to an unformatted file:
integer*4 Test1
real*4 Matrix(3,3)
open(78,format='unformatted')
write(78) Test1
write(78) Matrix
close(78)
Should ultimately be padded by an np.int32 values. (I've seen references that this tells you the record length, but haven't verified persconally.)
The above could be read in Python via numpy as:
input_file = open(file_location,'rb')
datum = np.dtype([('P1',np.int32),('Test1',np.int32),('P2',np.int32),('P3',mp.int32),('MatrixT',(np.float32,(3,3))),('P4',np.int32)])
data = np.fromfile(input_file,datum)
Which should fully populate the data array with the individual data sets of the format above. Do note that numpy expects data to be packed in C format (row major) while Fortran format data is column major. For square matrix shapes like that above, this means getting the data out of the matrix requires a transpose as well, before using. For non square matrices, you will need to reshape and transpose:
Matrix = np.transpose(data[0]['MatrixT']
Transposing your 4-D data structure is going to need to be done carefully. You might look into SciPy for automated ways to do so; the SciPy package seems to have Fortran related utilities which I have not fully explored.
I find that the latest documentation about hstack/vstack note that "you should prefer np.concatenate or np.stack".
But I think their readability is better than concatenate(a, 0) or concatenate(a, 1)
All 3 'stack' functions use concatenate (as does np.append and column_stack). It's instructive to look at their code. np.source(np.hstack) for example.
What they all do is massage the dimensions of the input arrays, making sure they are are 1d or 2d etc, and then call concatenate with the appropriate axis. So in the long run it's a good idea to know how to use concatenate without the 'crutch' of the others.
But people will continue to use hstack and vstack where convenient. dstack and column_stack are less common. np.append is frequently misused and should be banished.
I think this 'preferred' note was added when np.stack was added. np.stack also uses concatenate, but in a somewhat more sophisticated way. It inserts a new axis (with expand_dims). I view it as a generalization of np.array. When given a list of matching arrays, np.array joins them on a new initial axis. np.stack does the same thing as a default, but lets us specify a different 'new' axis for concatenation.
I should qualify my answer. It is not official. Rather I'm making an educated guess based on knowledge of the code.
I am trying to extract information at a specific location (lat,lon) from different satellite images. These images are were given to me in the AREA format and I cooked up a simple jython script to extract temperature values like so.
While the script works, here is small snippet from it that prints out the data value at a point.
from edu.wisc.ssec.mcidas import AreaFile as af
url="adde://localhost/imagedata?&PORT=8113&COMPRESS=gzip&USER=idv&PROJ=0& VERSION=1&DEBUG=false&TRACE=0&GROUP=FL&DESCRIPTOR=8712C574&BAND=2&LATLON=29.7276 -85.0274 E&PLACE=ULEFT&SIZE=1 1&UNIT=TEMP&MAG=1 1&SPAC=4&NAV=X&AUX=YES&DOC=X&DAY=2012002 2012002&TIME=&POS=0&TRACK=0"
a=af(url);
value=a.getData();
print value
array([[I, [array([I, [array('i', [2826, 2833, 2841, 2853])])])
So what does this mean?
Please excuse me if the question seems trivial, while I am comfortable with python I am really new to dealing with scientific data.
Note
Here is a link to the entire script.
After asking around, I found out that the Area objects returns data in multiples of four. So the very first value is what I am looking for.
Grabbing the value is as simple as :
ar[0][0][0]
I have created a simple LabView program shown below that attempts to flatten an array [1,0,3] and then unflatten it and print out the contents.
However, I am unsuccessful in doing so. What am I doing wrong?
What am I doing wrong?
You're not going through tutorials or you're not reading the context help for the unflatten function (Ctrl+H) or you're not reading the full help for the function (right click>>Help) or you're not looking at the examples (from the help or Help>>Find Examples). Take your pick (preferably all four).
If you want an actual answer it is that LV is strictly typed, and therefore you need to tell the unflatten function which data type you want it to output (1D DBL array) and you're not doing that, but the real answer is what's in the previous paragraph - you should use those tools to learn how to find such an answer yourself.
The string returned by Flatten to String only contains the data, not the description of what data type was passed in, so in order to unflatten it again you need to tell Unflatten from String what type it was. You do this by wiring some data of the appropriate type (any data - if it's an array it can be an empty one) to the Type terminal.
I don't think this is immediately obvious from the LabVIEW 2012 help but I think it's fairly clear if you follow the link from the Unflatten from String help page to one of the examples. The Read Flattened Data.vi example has an array wired to the Type input.
Reverse words in a string (words are
separated by one or more spaces). Now
do it in-place.
What does in-place mean?
In-place means that you should update the original string rather than creating a new one.
Depending on the language/framework that you're using this could be impossible. (For example, strings are immutable in .NET and Java, so it would be impossible to perform an in-place update of a string without resorting to some evil hacks.)
In-place algorithms can only use O(1) extra space, essentially. Array reversal (essentially what the interview question boils down to) is a classic example. The following is taken from Wikipedia:
Suppose we want to reverse an array of n items. One simple way to do this is:
function reverse(a[0..n])
allocate b[0..n]
for i from 0 to n
b[n - i] = a[i]
return b
Unfortunately, this requires O(n) extra space to create the array b, and allocation is often a slow operation. If we no longer need a, we can instead overwrite it with its own reversal using this in-place algorithm:
function reverse-in-place(a[0..n])
for i from 0 to floor(n/2)
swap(a[i], a[n-i])
Sometimes doing something in-place is VERY HARD. A classic example is general non-square matrix transposition.
See also
In-place algorithm
In-place matrix transposition
You should change the content of the original string to the reverse without using a temporary storage variable to hold the string.