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Company Profile
Cantex Energy Corporation is an independent, managed risk, oil and gas exploration, development, and production company headquartered in San Antonio, Texas.
Recent News
Cantex Energy Corp. Announces Completion of the GPS Survey Today and the Mobilization of Seismic Crews for Big Canyon 2D Swath, Management would like to report The GPS surveying of our Big Canyon 2D Swath Geophysical program is being completed today. The crew that has been obtained to conduct the seismic survey (Quantum Geophysical) will be mobilizing May 30 (plus or minus 2 days) to the Big Canyon Prospect. It will take the crews about 3 to 4 days to get all the equipment (cable and geophones) laid out on the ground and then another day of testing so we should be in full production mode on or around the 4th or 5th of June. Once the first of three lines are shot we will then get data processed and report progress on a weekly basis.
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Cantex Energy Corp. (CTXE - News) is pleased to report the following on its Big Canyon Prospect in West Texas. Recent company announcements related to the acquisition of over 48,000 acres of a world-class prospect has captured the attention of many oil & gas industry experts and corporations, who have recently inquired into various participation opportunities ranging from sharing science technology to support findings or expertise to drill, operate and manage wells.
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Is there a reason why dtype's are unhashable ? (ouch)
On another point, is there a canonical list of dtype's ?
I'd like to test the dtype of an array, and I always
end up with something like this:
if array.dtype == numpy.dtype('l'): ...
When I would prefer to write something like:
if array.dtype == numpy.Int32: ...
(i can never remember these char codes !)
Alternatively, should dtype's __cmp__ promote the other arg
to a dtype before the compare ?
I guess not, since that would break a lot of code: eg. dtype(None)
is legal.
Simon.
--
Simon Burton, B.Sc.
Licensed PO Box 8066
ANU Canberra 2601
Australia
Ph. 61 02 6249 6940
http://arrowtheory.com
Hi everyone -
The "fromfile" method isn't working for Int8 in
ascii mode:
# cat test.dat
3
4
5
>>> import numpy as np
>>> np.__version__
'0.9.9.2547'
>>> np.fromfile('test.dat', sep='\n', dtype=np.Int16)
array([3, 4, 5], dtype=int16)
>>> np.fromfile('test.dat', sep='\n', dtype=np.Int8)
Traceback (most recent call last):
File "<stdin>", line 1, in ?
ValueError: don't know how to read character files with that array type
Was this intended?
Erin
Hi,
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V A L / u M
T r & m a d o I
S O m &
C i A L / S
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upper gates at once, he had precious small chance of ever finding the
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I am a user, not a numerics type,
so this is undoubtedly a naive question.
Might the sin function be written to give greater
accuracy for large real numbers? It seems that significant
digits are in some sense being discarded needlessly.
I.e., compare these:
>>> sin(linspace(0,10*pi,11))
array([ 0.00000000e+00, 1.22460635e-16, -2.44921271e-16,
3.67381906e-16, -4.89842542e-16, 6.12303177e-16,
-7.34763812e-16, 8.57224448e-16, -9.79685083e-16,
1.10214572e-15, -1.22460635e-15])
>>> sin(linspace(0,10*pi,11)%(2*pi))
array([ 0.00000000e+00, 1.22460635e-16, 0.00000000e+00,
1.22460635e-16, 0.00000000e+00, 1.22460635e-16,
0.00000000e+00, 1.22460635e-16, 0.00000000e+00,
1.22460635e-16, 0.00000000e+00])
Just wondering.
Cheers,
Alan Isaac
Hi list,
I searched the archives and found various threads regarding this issue
and I have not found a solution there.
software versions:
gfortran 4.0.1
atlas 3.6.0
lapack 3.0
Basically numpy spits out this message when I try to compile it:
gcc: numpy/linalg/lapack_litemodule.c
/usr/bin/gfortran -shared
build/temp.linux-x86_64-2.4/numpy/linalg/lapack_litemodule.o
-L/usr/local/lib/atlas -llapack -lptf77blas -lptcblas -latlas -lgfortran
-o build/lib.linux-x86_64-2.4/numpy/linalg/lapack_lite.so
/usr/bin/ld: /usr/local/lib/atlas/liblapack.a(dlamch.o): relocation
R_X86_64_32S against `a local symbol' can not be used when making a
shared object; recompile with -fPIC
/usr/local/lib/atlas/liblapack.a: could not read symbols: Bad value
collect2: ld returned 1 exit status
/usr/bin/ld: /usr/local/lib/atlas/liblapack.a(dlamch.o): relocation
R_X86_64_32S against `a local symbol' can not be used when making a
shared object; recompile with -fPIC
/usr/local/lib/atlas/liblapack.a: could not read symbols: Bad value
collect2: ld returned 1 exit status
error: Command "/usr/bin/gfortran -shared
build/temp.linux-x86_64-2.4/numpy/linalg/lapack_litemodule.o
-L/usr/local/lib/atlas -llapack -lptf77blas -lptcblas -latlas -lgfortran
-o build/lib.linux-x86_64-2.4/numpy/linalg/lapack_lite.so" failed with
exit status 1
However I have compiled all the software explicitly with the -fPIC flag
on. Attached is my make.inc for LAPACK and my Makefile for ATLAS. I
followed these instructions to create a hybrid LAPACK/ATLAS archive:
http://math-atlas.sourceforge.net/errata.html#completelp
Interestingly enough, if I just use the bare version of ATLAS, numpy
compiles fine. If I use the bare version of LAPACK, numpy compiles fine .
Any help would be greatly appreciated.
-Yi
I built and installed Numpy 0.9.8 successfully from the source using
python setup.py build
sudo python setup.py install
When I run the unit-test though, it fails with the following error:
[~] python
ActivePython 2.4.3 Build 11 (ActiveState Software Inc.) based on
Python 2.4.3 (#1, Apr 3 2006, 18:07:18)
[GCC 3.3 20030304 (Apple Computer, Inc. build 1666)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy
>>> numpy.__version__
'0.9.8'
>>> numpy.test(1,1)
Found 5 tests for numpy.distutils.misc_util
Found 3 tests for numpy.lib.getlimits
Found 30 tests for numpy.core.numerictypes
Found 13 tests for numpy.core.umath
Found 1 tests for numpy.core.scalarmath
Found 8 tests for numpy.lib.arraysetops
Found 42 tests for numpy.lib.type_check
Found 95 tests for numpy.core.multiarray
Found 3 tests for numpy.dft.helper
Found 36 tests for numpy.core.ma
Found 9 tests for numpy.lib.twodim_base
Found 2 tests for numpy.core.oldnumeric
Found 9 tests for numpy.core.defmatrix
Found 1 tests for numpy.lib.ufunclike
Found 35 tests for numpy.lib.function_base
Found 1 tests for numpy.lib.polynomial
Found 6 tests for numpy.core.records
Found 19 tests for numpy.core.numeric
Found 4 tests for numpy.lib.index_tricks
Found 46 tests for numpy.lib.shape_base
Found 0 tests for __main__
...................................................python(9463)
malloc: *** Deallocation of a pointer not malloced: 0xd668; This
could be a double free(), or free() called with the middle of an
allocated block; Try setting environment variable MallocHelp to see
tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd4e8; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd650; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd560; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd4a0; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd3f8; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd470; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd320; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd728; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd440; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd410; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd590; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
python(9463) malloc: *** Deallocation of a pointer not malloced:
0xd458; This could be a double free(), or free() called with the
middle of an allocated block; Try setting environment variable
MallocHelp to see tools to help debug
F............................................................................................................................................................................................................................................................................................................................
======================================================================
FAIL: check_types (numpy.core.tests.test_scalarmath.test_types)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/numpy/core/tests/test_scalarmath.py",
line 63, in check_types
assert val.dtype.num == typeconv[k,l] and \
AssertionError: error with (13,14)
----------------------------------------------------------------------
Ran 368 tests in 1.517s
FAILED (failures=1)
<unittest.TextTestRunner object at 0x1052910>
Any idea what is causing this?
Thanks
Rudolph
--
Rudolph van der Merwe
Hi,
I am new to working on the Windows XP OS and I am having a problem building numpy from source. I am attempting to build using the MinGW compilers on a Windows XP box which has Python 2.4.1 installed. I do not have nor do I intend to use any optimized libraries like ATLAS. I have checked out numpy revision 2542. This is as clean a build as you could probably get. I am attaching my build log to this message. If anyone has any helpful hints they would be most appreciated.
Thanks,
Chris
Is there a way to specify which dimensions I want dot to work over?
For example, if I have two arrays:
In [78]:ma = array([4,5,6]) # shape = (1,3)
In [79]:mb = ma.transpose() # shape = (3,1)
In [80]:dot(mb,ma)
Out[80]:
array([[16, 20, 24],
[20, 25, 30],
[24, 30, 36]])
No problems there.
Now I want to do that multiple times, threading over the first dimension
of larger arrays:
In [85]:mc = array([[[4,5,6]],[[7,8,9]]]) # shape = (2, 1, 3)
In [86]:md = array([[[4],[5],[6]],[[7],[8],[9]]]) #shape = (2, 3, 1)
and I want to calculate the two (3, 1) x (1, 3) dot products to get a
shape = (2, 3, 3) result, so that
res[i,...] == dot(md[i,...], mc[i,...])
>From my example above res[0,...] would be the same as dot(mb,ma) and
res[1,...] would be
In [108]:dot(md[1,...],mc[1,...])
Out[108]:
array([[49, 56, 63],
[56, 64, 72],
[63, 72, 81]])
I could do it by explicitly looping over the first dimension (as
suggested by my generic example), but it seems like there should be a
better way by specifying to the dot function the dimensions over which
it should be 'dotting'.
Cheers,
Angus.
--
Angus McMorland
email a.mcmorland(a)auckland.ac.nz
mobile +64-21-155-4906
PhD Student, Neurophysiology / Multiphoton & Confocal Imaging
Physiology, University of Auckland
phone +64-9-3737-599 x89707
Armourer, Auckland University Fencing
Secretary, Fencing North Inc.
