def get_infos():
import numpy
from numpy.distutils.system_info import get_info
# Print out full BLAS / LAPACK linkage info.
numpy.show_config()
if env_lib_dirs or env_libs:
print('using environment variables for blas/lapack libraries')
env_vars = {}
if env_lib_dirs:
env_vars['library_dirs'] = [env_lib_dirs]
if env_libs:
env_vars['libraries'] = env_libs.split(':')
return env_vars, {}
# environment variables not set, using defaults instead
blas_info = get_info('blas_opt')
if not blas_info:
blas_info = get_info('blas')
print(blas_info)
lapack_info = get_info('lapack_opt')
if not lapack_info:
lapack_info = get_info('lapack')
print(lapack_info)
return blas_info, lapack_info
def main():
subtitle(multiprocessing)
print "multiprocessing.cpu_count(): {}".format(multiprocessing.cpu_count())
subtitle(np)
np.show_config()
subtitle(os)
print_dictionary("os.environ", os.environ, [
'BOOST_NUMPY_LIB', 'HOME', 'LIBRARY_PATH', 'PI_HOME', 'PYENV_ROOT',
'PYTHONPATH', 'SHERLOCK'
])
print "os.getcwd(): {}".format(os.getcwd())
print "os.uname(): {}".format(os.uname())
subtitle(scipy)
scipy.__config__.show()
subtitle(sys)
print "sys.platform: {}".format(sys.platform)
print "sys.prefix: {}".format(sys.prefix)
print "sys.version: {}".format(sys.version)
print "sys.api_version: {}".format(sys.api_version)
print "sys.version_info: {}".format(sys.version_info)
print
def get_numpy_info():
"""
Retrieves information about numpy compilation.
"""
s = io.StringIO()
with contextlib.redirect_stdout(s):
numpy.show_config()
return s.getvalue()
def execute(execute=True, verbose=True, M=2000, N=2000, K=2000,
iters=10, order='C'):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
a = theano.shared(numpy.ones((M, N), dtype=theano.config.floatX,
order=order))
b = theano.shared(numpy.ones((N, K), dtype=theano.config.floatX,
order=order))
c = theano.shared(numpy.ones((M, K), dtype=theano.config.floatX,
order=order))
f = theano.function([], updates={c: 0.4 * c + .8 * T.dot(a, b)})
if verbose:
print 'Some Theano flags:'
print ' blas.ldflags=', theano.config.blas.ldflags
print ' compiledir=', theano.config.compiledir
print ' floatX=', theano.config.floatX
print 'Some environment variables:'
print ' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
print ' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')
print ' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS')
print
print ('Numpy config: (used when the Theano flag'
' "blas.ldflags" is empty)')
numpy.show_config()
print 'Numpy dot module:', numpy.dot.__module__
print 'Numpy location:', numpy.__file__
print 'Numpy version:', numpy.__version__
print
t0 = 0
t1 = -1
if any([x.op.__class__.__name__ == 'Gemm' for x in
f.maker.env.toposort()]):
impl = 'cpu'
elif any([x.op.__class__.__name__ == 'GpuGemm' for x in
f.maker.env.toposort()]):
impl = 'gpu'
else:
impl = 'ERROR, unable to tell if Theano used the cpu or the gpu:\n'
impl += str(f.maker.env.toposort())
if execute:
t0 = time.time()
for i in range(iters):
f()
t1 = time.time()
return t1 - t0, impl
def execute(execute=True, verbose=True):
a = theano.shared(numpy.ones(shapes, dtype=theano.config.floatX))
b = theano.shared(numpy.ones(shapes, dtype=theano.config.floatX))
c = theano.shared(numpy.ones(shapes, dtype=theano.config.floatX))
f = theano.function([], updates={c: 0.4 * c + .8 * T.dot(a, b)})
if verbose:
print 'Some theano flags:'
print ' blas.ldflags=', theano.config.blas.ldflags
print ' compiledir=', theano.config.compiledir
print ' floatX=', theano.config.floatX
print 'Some env flags:'
print ' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
print ' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')
print ' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS')
print
print(
'Numpy config: (used when the theano flags'
' "blas.ldflags" is empty)')
numpy.show_config()
print 'Numpy dot module:', numpy.dot.__module__
print 'Numpy file location that was loaded:', numpy.__file__
print 'Numpy version:', numpy.__version__
print
if any([
x.op.__class__.__name__ == 'Gemm'
for x in f.maker.env.toposort()
]):
print 'Used the cpu'
elif any([
x.op.__class__.__name__ == 'GpuGemm'
for x in f.maker.env.toposort()
]):
print 'Used the gpu'
else:
print 'ERROR, not able to tell if theano used the cpu or the gpu'
print f.maker.env.toposort()
t0 = 0
t1 = -1
if execute:
t0 = time.time()
for i in range(iters):
f()
t1 = time.time()
if verbose and execute:
print
print 'This execution time took %.2fs' % (t1 - t0)
print
print(
'Try to run this script a few times. Experience show that'
' the first time is not as fast as followings call. The'
' difference is not big, but consistent.')
return t1 - t0
def execute(execute=True, verbose=True, M=2000, N=2000, K=2000, iters=10, order="C"):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
if verbose:
print "Some Theano flags:"
print " blas.ldflags=", theano.config.blas.ldflags
print " compiledir=", theano.config.compiledir
print " floatX=", theano.config.floatX
print "Some environment variables:"
print " MKL_NUM_THREADS=", os.getenv("MKL_NUM_THREADS")
print " OMP_NUM_THREADS=", os.getenv("OMP_NUM_THREADS")
print " GOTO_NUM_THREADS=", os.getenv("GOTO_NUM_THREADS")
print
print ("Numpy config: (used when the Theano flag" ' "blas.ldflags" is empty)')
numpy.show_config()
print "Numpy dot module:", numpy.dot.__module__
print "Numpy location:", numpy.__file__
print "Numpy version:", numpy.__version__
print
a = theano.shared(numpy.ones((M, N), dtype=theano.config.floatX, order=order))
b = theano.shared(numpy.ones((N, K), dtype=theano.config.floatX, order=order))
c = theano.shared(numpy.ones((M, K), dtype=theano.config.floatX, order=order))
f = theano.function([], updates={c: 0.4 * c + 0.8 * T.dot(a, b)}, mode=theano.compile.ProfileMode())
if any([x.op.__class__.__name__ == "Gemm" for x in f.maker.env.toposort()]):
c_impl = f.profile.apply_cimpl.values()
assert len(c_impl) == 1
if c_impl[0]:
impl = "CPU (with direct Theano binding to blas)"
else:
impl = "CPU (without direct Theano binding to blas but with numpy/scipy binding to blas)"
elif any([x.op.__class__.__name__ == "GpuGemm" for x in f.maker.env.toposort()]):
impl = "GPU"
else:
impl = "ERROR, unable to tell if Theano used the cpu or the gpu:\n"
impl += str(f.maker.env.toposort())
t0 = 0
t1 = -1
if execute:
t0 = time.time()
for i in range(iters):
f()
t1 = time.time()
return t1 - t0, impl
def execute(execute=True, verbose=True):
a = theano.shared(numpy.ones(shapes, dtype=theano.config.floatX))
b = theano.shared(numpy.ones(shapes, dtype=theano.config.floatX))
c = theano.shared(numpy.ones(shapes, dtype=theano.config.floatX))
f = theano.function([], updates={c: 0.4 * c + .8 * T.dot(a, b)})
if verbose:
print 'Some theano flags:'
print ' blas.ldflags=', theano.config.blas.ldflags
print ' compiledir=', theano.config.compiledir
print ' floatX=', theano.config.floatX
print 'Some env flags:'
print ' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
print ' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')
print ' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS')
print
print ('Numpy config: (used when the theano flags'
' "blas.ldflags" is empty)')
numpy.show_config()
print 'Numpy dot module:', numpy.dot.__module__
print 'Numpy file location that was loaded:', numpy.__file__
print 'Numpy version:', numpy.__version__
print
if any([x.op.__class__.__name__ == 'Gemm' for x in
f.maker.env.toposort()]):
print 'Used the cpu'
elif any([x.op.__class__.__name__ == 'GpuGemm' for x in
f.maker.env.toposort()]):
print 'Used the gpu'
else:
print 'ERROR, not able to tell if theano used the cpu or the gpu'
print f.maker.env.toposort()
t0 = 0
t1 = -1
if execute:
t0 = time.time()
for i in range(iters):
f()
t1 = time.time()
if verbose and execute:
print
print 'This execution time took %.2fs' % (t1 - t0)
print
print ('Try to run this script a few times. Experience show that'
' the first time is not as fast as followings call. The'
' difference is not big, but consistent.')
return t1 - t0
def print_system_info():
log_msg('System information\n')
log_msg('\tpython: ' + sys.version)
log_msg('\tnumpy: ' + np.__version__)
log_msg('\tpandas: ' + pd.__version__)
# additional info about numpy libraries
if False:
old_stdout = sys.stdout
result = StringIO()
sys.stdout = result
np.show_config()
sys.stdout = old_stdout
log_msg(result.getvalue())
def getBlas():
file_ = open("npConfg_file.txt", "w")
with contextlib.redirect_stdout(file_):
numpy.show_config()
file_.close()
np_confg = open('npConfg_file.txt', 'r')
lib = ""
for line in np_confg:
if 'libraries' in line:
lib = line
break
np_confg.close()
os.remove("npConfg_file.txt")
if lib != "":
blas = lib.split('[')[1].split(',')[0]
return blas[1:len(blas) - 1]
return lib
def print_system_info():
log_msg('System information\n')
log_msg(f'\tlocal time zone: {datetime.utcnow().astimezone().tzinfo}')
log_msg(f'\tpython: {sys.version}')
log_msg(f'\tnumpy: {np.__version__}')
log_msg(f'\tpandas: {pd.__version__}')
# additional info about numpy libraries
if False:
from io import StringIO # only import this when actually needed
old_stdout = sys.stdout
result = StringIO()
sys.stdout = result
np.show_config()
sys.stdout = old_stdout
log_msg(result.getvalue())
def _get_numpy_libs():
from ._testing import SilenceStdout
with SilenceStdout() as capture:
np.show_config()
lines = capture.getvalue().split('\n')
libs = []
for li, line in enumerate(lines):
for key in ('lapack', 'blas'):
if line.startswith('%s_opt_info' % key):
lib = lines[li + 1]
if 'NOT AVAILABLE' in lib:
lib = 'unknown'
else:
try:
lib = lib.split('[')[1].split("'")[1]
except IndexError:
pass # keep whatever it was
libs += ['%s=%s' % (key, lib)]
libs = ', '.join(libs)
return libs
def main():
print('numpy config (python 3.7 image):')
print(numpy.__version__)
print(numpy.show_config())
print('')
print('scipy config:')
print(scipy.__version__)
print(scipy.show_config())
print('')
return 0
def main():
print('numpy config (miniconda:4.6.14 image):')
print(numpy.__version__)
print(numpy.show_config())
print('')
print('scipy config:')
print(scipy.__version__)
print(scipy.show_config())
print('')
return 0
def find_parameter(self):
for i in range(0, 10):
print(i)
temp = (self.Y == i).astype(np.float64)
temp_test = (self.Y_test == i).astype(np.float64)
self.regr.fit(self.X, temp)
self.theta1 = self.regr.coef_
intercept = self.regr.intercept_
self.theta[:,i] = np.column_stack((intercept, self.theta1))
self.score = self.regr.score(self.X_test, temp_test)
print(self.score)
print(np.show_config())
np.savetxt("parameter.txt",self.theta,delimiter=',',newline='\n')
def _get_numpy_libs():
"Return info on 'Blas/Lapack' lib linked to numpy."
# Import is nested to avoid long import time.
import numpy as np
with contextlib.redirect_stdout(io.StringIO()) as capture:
np.show_config()
lines = capture.getvalue().splitlines()
libs = []
for li, line in enumerate(lines):
for key in ("lapack", "blas"):
if line.startswith(f"{key}_opt_info"):
lib = lines[li + 1]
if "NOT AVAILABLE" in lib:
lib = "unknown"
else:
try:
lib = lib.split("[")[1].split("'")[1]
except IndexError:
pass # keep whatever it was
libs += [f"{key}={lib}"]
libs = ", ".join(libs)
return libs
def numpy_version_and_config():
# Task 2: Print numpy version and config
print(f"Task 2: {np.__version__}, type: {type(np.__version__)}")
np.show_config()
import numpy as np
print(np.__version__)
np.show_config() # include openblas atlas etc.
foo = np.zeros((10,10)) # float64, 8 bytes
print("%d bytes" % (foo.size * foo.itemsize)) # 800
np.info(np.add)
np.arange(10, 50)
nz = np.nonzero([1,2,0,0,4,0])
print nz
np.eye(3,4)
# array([[ 1., 0., 0., 0.],
# [ 0., 1., 0., 0.],
# [ 0., 0., 1., 0.]])
foo = np.random.random((3, 3))
foo.min()
foo.max()
foo = np.ones((5,5))
foo = np.pad(foo, pad_width=1, mode='constant', constant_values=0)
print(foo)
print(0.3 == 3 * 0.1) # False
# 3*0.1 = 0.30000000000000004
import numpy as np
import time
N = 6000
M = 10000
k_list = [64, 80, 96, 104, 112, 120, 128, 144, 160, 176, 192, 200, 208, 224, 240, 256, 384]
def get_gflops(M, N, K):
return M*N*(2.0*K-1.0) / 1000**3
np.show_config()
for K in k_list:
a = np.array(np.random.random((M, N)), dtype=np.double, order='C', copy=False)
b = np.array(np.random.random((N, K)), dtype=np.double, order='C', copy=False)
A = np.matrix(a, dtype=np.double, copy=False)
B = np.matrix(b, dtype=np.double, copy=False)
C = A*B
start = time.time()
C = A*B
C = A*B
C = A*B
C = A*B
C = A*B
end = time.time()
tm = (end-start) / 5.0
def execute(execute=True, verbose=True, M=2000, N=2000, K=2000,
iters=10, order='C'):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
if verbose:
print 'Some Theano flags:'
print ' blas.ldflags=', theano.config.blas.ldflags
print ' compiledir=', theano.config.compiledir
print ' floatX=', theano.config.floatX
print ' device=', theano.config.device
print 'Some OS information:'
print ' sys.platform=', sys.platform
print ' sys.version=', sys.version
print ' sys.prefix=', sys.prefix
print 'Some environment variables:'
print ' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
print ' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')
print ' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS')
print
print ('Numpy config: (used when the Theano flag'
' "blas.ldflags" is empty)')
numpy.show_config()
print 'Numpy dot module:', numpy.dot.__module__
print 'Numpy location:', numpy.__file__
print 'Numpy version:', numpy.__version__
if (theano.config.device.startswith("gpu") or
theano.config.init_gpu_device.startswith("gpu")):
print 'nvcc version:'
subprocess.call((theano.sandbox.cuda.nvcc_compiler.nvcc_path,
"--version"))
print
a = theano.shared(numpy.ones((M, N), dtype=theano.config.floatX,
order=order))
b = theano.shared(numpy.ones((N, K), dtype=theano.config.floatX,
order=order))
c = theano.shared(numpy.ones((M, K), dtype=theano.config.floatX,
order=order))
f = theano.function([], updates=[(c, 0.4 * c + .8 * T.dot(a, b))])
if any([x.op.__class__.__name__ == 'Gemm' for x in
f.maker.fgraph.toposort()]):
c_impl = [hasattr(thunk, 'cthunk')
for node, thunk in zip(f.fn.nodes, f.fn.thunks)
if node.op.__class__.__name__ == "Gemm"]
assert len(c_impl) == 1
if c_impl[0]:
impl = 'CPU (with direct Theano binding to blas)'
else:
impl = 'CPU (without direct Theano binding to blas but with numpy/scipy binding to blas)'
elif any([x.op.__class__.__name__ == 'GpuGemm' for x in
f.maker.fgraph.toposort()]):
impl = 'GPU'
else:
impl = 'ERROR, unable to tell if Theano used the cpu or the gpu:\n'
impl += str(f.maker.fgraph.toposort())
t0 = 0
t1 = -1
if execute:
sync = (hasattr(theano, "sandbox") and
hasattr(theano.sandbox, "cuda") and
theano.sandbox.cuda.cuda_available)
t0 = time.time()
for i in range(iters):
f()
if sync:
theano.sandbox.cuda.synchronize()
t1 = time.time()
return t1 - t0, impl
import os
import re
import subprocess
import numpy
numpy_root = os.path.dirname(numpy.__file__)
print "numpy root:", numpy_root
print
print "##### core files #####"
numpy_core = os.path.join(numpy_root, "core")
core_files = sorted(os.listdir(numpy_core))
for filename in core_files:
print filename
print
print "##### numpy packages #####"
process = subprocess.Popen("dpkg --get-selections".split(), stdout=subprocess.PIPE)
output = process.communicate()[0]
for line in output.split("\n"):
if line.find("numpy") >= 0:
print line
print
print "##### numpy.show_config() #####"
print numpy.show_config()
which += [('matplotlib')]
which += [('tables')]
which += [('contracts')]
which += [('geometry')]
which += [('compmake')]
which += [('reprep')]
which += [('procgraph')]
which += [('playerc')]
which += [('patience')]
which += [('cjson')]
which += [('PIL')]
which += [('Image')]
which += [('cv')]
which += [('pyparsing')]
which += [('decorator')]
which += [('nose')]
which += [('cairo')]
for x in which:
show_version(x)
#show_version('flydra.version', lambda m: m.__version__) #lambda m: m.version.version)
if False:
try:
import numpy
numpy.show_config()
except:
pass
def run_numpy_tests(*args, **kwargs):
import numpy
numpy.show_config()
numpy.test()
def sys_info(fid=None, show_paths=False):
"""Print the system information for debugging.
This function is useful for printing system information
to help triage bugs.
Parameters
----------
fid : file-like | None
The file to write to. Will be passed to :func:`print()`.
Can be None to use :data:`sys.stdout`.
show_paths : bool
If True, print paths for each module.
Examples
--------
Running this function with no arguments prints an output that is
useful when submitting bug reports::
>>> import mne
>>> mne.sys_info() # doctest: +SKIP
Platform: Linux-4.2.0-27-generic-x86_64-with-Ubuntu-15.10-wily
Python: 2.7.10 (default, Oct 14 2015, 16:09:02) [GCC 5.2.1 20151010]
Executable: /usr/bin/python
mne: 0.12.dev0
numpy: 1.12.0.dev0+ec5bd81 {lapack=mkl_rt, blas=mkl_rt}
scipy: 0.18.0.dev0+3deede3
matplotlib: 1.5.1+1107.g1fa2697
sklearn: 0.18.dev0
nibabel: 2.1.0dev
mayavi: 4.3.1
cupy: 4.1.0
pandas: 0.17.1+25.g547750a
dipy: 0.14.0
""" # noqa: E501
ljust = 15
out = 'Platform:'.ljust(ljust) + platform.platform() + '\n'
out += 'Python:'.ljust(ljust) + str(sys.version).replace('\n', ' ') + '\n'
out += 'Executable:'.ljust(ljust) + sys.executable + '\n'
out += 'CPU:'.ljust(ljust) + ('%s: ' % platform.processor())
try:
import multiprocessing
except ImportError:
out += ('number of processors unavailable ' +
'(requires "multiprocessing" package)\n')
else:
out += '%s cores\n' % multiprocessing.cpu_count()
out += 'Memory:'.ljust(ljust)
try:
import psutil
except ImportError:
out += 'Unavailable (requires "psutil" package)'
else:
out += '%0.1f GB\n' % (psutil.virtual_memory().total / float(2**30), )
out += '\n'
old_stdout = sys.stdout
capture = StringIO()
try:
sys.stdout = capture
np.show_config()
finally:
sys.stdout = old_stdout
lines = capture.getvalue().split('\n')
libs = []
for li, line in enumerate(lines):
for key in ('lapack', 'blas'):
if line.startswith('%s_opt_info' % key):
lib = lines[li + 1]
if 'NOT AVAILABLE' in lib:
lib = 'unknown'
else:
try:
lib = lib.split('[')[1].split("'")[1]
except IndexError:
pass # keep whatever it was
libs += ['%s=%s' % (key, lib)]
libs = ', '.join(libs)
for mod_name in ('mne', 'numpy', 'scipy', 'matplotlib', '', 'sklearn',
'nibabel', 'mayavi', 'cupy', 'pandas', 'dipy'):
if mod_name == '':
out += '\n'
continue
out += ('%s:' % mod_name).ljust(ljust)
try:
mod = __import__(mod_name)
if mod_name == 'mayavi':
# the real test
from mayavi import mlab # noqa, analysis:ignore
except Exception:
out += 'Not found\n'
else:
extra = (' (%s)' % op.dirname(mod.__file__)) if show_paths else ''
if mod_name == 'numpy':
extra = ' {%s}%s' % (libs, extra)
elif mod_name == 'matplotlib':
extra = ' {backend=%s}%s' % (mod.get_backend(), extra)
elif mod_name == 'mayavi':
try:
from pyface.qt import qt_api
except Exception:
qt_api = 'unknown'
if qt_api == 'pyqt5':
try:
from PyQt5.Qt import PYQT_VERSION_STR
qt_api += ', PyQt5=%s' % (PYQT_VERSION_STR, )
except Exception:
pass
extra = ' {qt_api=%s}%s' % (qt_api, extra)
out += '%s%s\n' % (mod.__version__, extra)
print(out, end='', file=fid)
#!/usr/bin/env python
import numpy as np
from mnist_numpy import read_mnist
from nnet_toolkit import nnet
import cluster_select_func as csf
from autoconvert import autoconvert
import sys
import time
#h5py used for saving results to a file
import h5py
print(str(np.show_config()))
#constants
MODE_P1 = 0
MODE_P2 = 1
#Get the parameters file from the command line
#use mnist_train__forget_params.py by default (no argument given)
if(len(sys.argv) > 1):
params_file = sys.argv[1]
else:
params_file = 'mnist_train_nonstationary_cluster_subset_params.py'
p = {}
execfile(params_file,p)
#grab extra parameters from command line
for i in range(2,len(sys.argv)):
(k,v) = sys.argv[i].split('=')
# numpy program to get the numpy version and show numpy build configuration
import numpy as np
print("Numpy version : ", np.__version__)
print("Numpy configuration : ", np.show_config())
'''
Numpy version : 1.18.4
blas_mkl_info:
NOT AVAILABLE
blis_info:
NOT AVAILABLE
openblas_info:
library_dirs = ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_info']
libraries = ['openblas_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
library_dirs = ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_info']
libraries = ['openblas_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
lapack_mkl_info:
NOT AVAILABLE
openblas_lapack_info:
library_dirs = ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_lapack_info']
libraries = ['openblas_lapack_info']
language = f77
define_macros = [('HAVE_CBLAS', None)]
lapack_opt_info:
library_dirs = ['C:\\projects\\numpy-wheels\\numpy\\build\\openblas_lapack_info']
libraries = ['openblas_lapack_info']
language = f77
#! usr/bin/env python3
# Filename: 100NumpyExercises.py
__author__ = 'Jerome Yao'
'''
'''
# 1. Import the numpy package under the name np (★☆☆)
import numpy as np
# 2. Print the numpy version and the configuration (★☆☆)
print('#2:\n', np.__version__)
np.show_config() # other way
# 3. Create a null vector of size 10 (★☆☆)
print('#3:\n', np.zeros(10))
# 4. How to find the memory size of any array (★☆☆)
array = np.zeros((10, 10))
print('#4:\n', '%d bytes' % (array.size * array.itemsize)) # 800bytes
# 待解决5. How to get the documentation of the numpy add function from the command line? (★☆☆) ##
# %run `python -c "import numpy; numpy.info(numpy.add)"`
# 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)
vector = np.zeros(10)
vector[4] = 1
print('#6:\n', vector)
# 7. Create a vector with values ranging from 10 to 49 (★☆☆)
def execute(execute=True,
verbose=True,
M=2000,
N=2000,
K=2000,
iters=10,
order='C'):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
a = theano.shared(
numpy.ones((M, N), dtype=theano.config.floatX, order=order))
b = theano.shared(
numpy.ones((N, K), dtype=theano.config.floatX, order=order))
c = theano.shared(
numpy.ones((M, K), dtype=theano.config.floatX, order=order))
f = theano.function([], updates={c: 0.4 * c + .8 * T.dot(a, b)})
if verbose:
print 'Some Theano flags:'
print ' blas.ldflags=', theano.config.blas.ldflags
print ' compiledir=', theano.config.compiledir
print ' floatX=', theano.config.floatX
print 'Some environment variables:'
print ' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
print ' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')
print ' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS')
print
print(
'Numpy config: (used when the Theano flag'
' "blas.ldflags" is empty)')
numpy.show_config()
print 'Numpy dot module:', numpy.dot.__module__
print 'Numpy location:', numpy.__file__
print 'Numpy version:', numpy.__version__
print
t0 = 0
t1 = -1
if any([x.op.__class__.__name__ == 'Gemm'
for x in f.maker.env.toposort()]):
impl = 'cpu'
elif any(
[x.op.__class__.__name__ == 'GpuGemm'
for x in f.maker.env.toposort()]):
impl = 'gpu'
else:
impl = 'ERROR, unable to tell if Theano used the cpu or the gpu:\n'
impl += str(f.maker.env.toposort())
if execute:
t0 = time.time()
for i in range(iters):
f()
t1 = time.time()
return t1 - t0, impl
import numpy as np
print(np.__version__)
np.show_config() # include openblas atlas etc.
foo = np.zeros((10, 10)) # float64, 8 bytes
print("%d bytes" % (foo.size * foo.itemsize)) # 800
np.info(np.add)
np.arange(10, 50)
nz = np.nonzero([1, 2, 0, 0, 4, 0])
print nz
np.eye(3, 4)
# array([[ 1., 0., 0., 0.],
# [ 0., 1., 0., 0.],
# [ 0., 0., 1., 0.]])
foo = np.random.random((3, 3))
foo.min()
foo.max()
foo = np.ones((5, 5))
foo = np.pad(foo, pad_width=1, mode='constant', constant_values=0)
print(foo)
print(0.3 == 3 * 0.1) # False
# 3*0.1 = 0.30000000000000004
if sys.argv[3] == "--preprocess-ref":
config.PREPROCESS_REFERENCE_VIDEO_ONLY = True;
elif sys.argv[3] == "--process-query-and-align-videos":
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False;
else:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = "do_all";
print("config.PREPROCESS_REFERENCE_VIDEO_ONLY = %s" % str(config.PREPROCESS_REFERENCE_VIDEO_ONLY));
AskFirst();
# Inspired from https://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using
print("numpy.version.version = %s" % str(np.version.version));
print("scipy.version.version = %s" % str(scipy.version.version));
#scipy.version.version
np.show_config();
scipy.show_config();
# See http://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
# We use 7 digits precision and suppress using scientific notation.
np.set_printoptions(precision=7, suppress=True, \
threshold=70000, linewidth=4000);
#threshold=7000000, linewidth=4000);
#threshold=7000, linewidth=300);
#threshold=1000000, linewidth=3000);
# Inspired from \OpenCV2-Python-Tutorials-master\source\py_tutorials\py_core\py_optimization
# normally returns True - relates to using the SIMD extensions of x86: SSX, AVX
which += [('tables')]
which += [('contracts')]
which += [('geometry')]
which += [('compmake')]
which += [('reprep')]
which += [('procgraph')]
which += [('playerc')]
which += [('patience')]
which += [('cjson')]
which += [('PIL')]
which += [('Image')]
which += [('cv')]
which += [('pyparsing')]
which += [('decorator')]
which += [('nose')]
which += [('cairo')]
for x in which:
show_version(x)
#show_version('flydra.version', lambda m: m.__version__) #lambda m: m.version.version)
if False:
try:
import numpy;
numpy.show_config()
except:
pass
#! usr/bin/env python3
# Filename: 100NumpyExercises.py
__author__ = 'Jerome Yao'
'''
'''
# 1. Import the numpy package under the name np (★☆☆)
import numpy as np
# 2. Print the numpy version and the configuration (★☆☆)
print('#2:\n', np.__version__)
np.show_config() # other way
# 3. Create a null vector of size 10 (★☆☆)
print('#3:\n', np.zeros(10))
# 4. How to find the memory size of any array (★☆☆)
array = np.zeros((10, 10))
print('#4:\n', '%d bytes' % (array.size * array.itemsize)) # 800bytes
# 待解决5. How to get the documentation of the numpy add function from the command line? (★☆☆) ##
# %run `python -c "import numpy; numpy.info(numpy.add)"`
# 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)
vector = np.zeros(10)
vector[4] = 1
print('#6:\n', vector)
# 7. Create a vector with values ranging from 10 to 49 (★☆☆)
if sys.argv[3] == "--preprocess-ref":
config.PREPROCESS_REFERENCE_VIDEO_ONLY = True;
elif sys.argv[3] == "--process-query-and-align-videos":
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False;
else:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = "do_all";
print("config.PREPROCESS_REFERENCE_VIDEO_ONLY = %s" % str(config.PREPROCESS_REFERENCE_VIDEO_ONLY));
AskFirst();
# Inspired from https://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using
print("numpy.version.version = %s" % str(np.version.version));
print("scipy.version.version = %s" % str(scipy.version.version));
#scipy.version.version
np.show_config();
scipy.show_config();
# See http://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
# We use 7 digits precision and suppress using scientific notation.
np.set_printoptions(precision=7, suppress=True, \
threshold=70000, linewidth=4000);
#threshold=7000000, linewidth=4000);
#threshold=7000, linewidth=300);
#threshold=1000000, linewidth=3000);
# Inspired from \OpenCV2-Python-Tutorials-master\source\py_tutorials\py_core\py_optimization
# normally returns True - relates to using the SIMD extensions of x86: SSX, AVX
def execute(execute=True,
verbose=True,
M=2000,
N=2000,
K=2000,
iters=10,
order='C'):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
if verbose:
print 'Some Theano flags:'
print ' blas.ldflags=', theano.config.blas.ldflags
print ' compiledir=', theano.config.compiledir
print ' floatX=', theano.config.floatX
print ' device=', theano.config.device
print 'Some OS information:'
print ' sys.platform=', sys.platform
print ' sys.version=', sys.version
print ' sys.prefix=', sys.prefix
print 'Some environment variables:'
print ' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS')
print ' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS')
print ' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS')
print
print(
'Numpy config: (used when the Theano flag'
' "blas.ldflags" is empty)')
numpy.show_config()
print 'Numpy dot module:', numpy.dot.__module__
print 'Numpy location:', numpy.__file__
print 'Numpy version:', numpy.__version__
if (theano.config.device.startswith("gpu")
or theano.config.init_gpu_device.startswith("gpu")):
print 'nvcc version:'
subprocess.call(
(theano.sandbox.cuda.nvcc_compiler.nvcc_path, "--version"))
print
a = theano.shared(
numpy.ones((M, N), dtype=theano.config.floatX, order=order))
b = theano.shared(
numpy.ones((N, K), dtype=theano.config.floatX, order=order))
c = theano.shared(
numpy.ones((M, K), dtype=theano.config.floatX, order=order))
f = theano.function([], updates=[(c, 0.4 * c + .8 * T.dot(a, b))])
if any(
[x.op.__class__.__name__ == 'Gemm'
for x in f.maker.fgraph.toposort()]):
c_impl = [
hasattr(thunk, 'cthunk')
for node, thunk in zip(f.fn.nodes, f.fn.thunks)
if node.op.__class__.__name__ == "Gemm"
]
assert len(c_impl) == 1
if c_impl[0]:
impl = 'CPU (with direct Theano binding to blas)'
else:
impl = 'CPU (without direct Theano binding to blas but with numpy/scipy binding to blas)'
elif any([
x.op.__class__.__name__ == 'GpuGemm'
for x in f.maker.fgraph.toposort()
]):
impl = 'GPU'
else:
impl = 'ERROR, unable to tell if Theano used the cpu or the gpu:\n'
impl += str(f.maker.fgraph.toposort())
t0 = 0
t1 = -1
if execute:
sync = (hasattr(theano, "sandbox") and hasattr(theano.sandbox, "cuda")
and theano.sandbox.cuda.cuda_available)
t0 = time.time()
for i in range(iters):
f()
if sync:
theano.sandbox.cuda.synchronize()
t1 = time.time()
return t1 - t0, impl
def execute(execute=True, verbose=True, M=2000, N=2000, K=2000,
iters=10, order='C'):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
if verbose:
print('Some Theano flags:')
print(' blas.ldflags=', theano.config.blas.ldflags)
print(' compiledir=', theano.config.compiledir)
print(' floatX=', theano.config.floatX)
print(' device=', theano.config.device)
print('Some OS information:')
print(' sys.platform=', sys.platform)
print(' sys.version=', sys.version)
print(' sys.prefix=', sys.prefix)
print('Some environment variables:')
print(' MKL_NUM_THREADS=', os.getenv('MKL_NUM_THREADS'))
print(' OMP_NUM_THREADS=', os.getenv('OMP_NUM_THREADS'))
print(' GOTO_NUM_THREADS=', os.getenv('GOTO_NUM_THREADS'))
print()
print('Numpy config: (used when the Theano flag'
' "blas.ldflags" is empty)')
np.show_config()
print('Numpy dot module:', np.dot.__module__)
print('Numpy location:', np.__file__)
print('Numpy version:', np.__version__)
a = theano.shared(np.ones((M, N), dtype=theano.config.floatX,
order=order))
b = theano.shared(np.ones((N, K), dtype=theano.config.floatX,
order=order))
c = theano.shared(np.ones((M, K), dtype=theano.config.floatX,
order=order))
f = theano.function([], updates=[(c, 0.4 * c + .8 * T.dot(a, b))])
if any([x.op.__class__.__name__ == 'Gemm' for x in
f.maker.fgraph.toposort()]):
c_impl = [hasattr(thunk, 'cthunk')
for node, thunk in zip(f.fn.nodes, f.fn.thunks)
if node.op.__class__.__name__ == "Gemm"]
assert len(c_impl) == 1
if c_impl[0]:
impl = 'CPU (with direct Theano binding to blas)'
else:
impl = 'CPU (without direct Theano binding to blas but with numpy/scipy binding to blas)'
elif any([x.op.__class__.__name__ == 'GpuGemm' for x in
f.maker.fgraph.toposort()]):
impl = 'GPU'
else:
impl = 'ERROR, unable to tell if Theano used the cpu or the gpu:\n'
impl += str(f.maker.fgraph.toposort())
t0 = 0
t1 = -1
f() # Ignore first function call to get representative time.
if execute:
sync = (hasattr(theano, "gpuarray") and
isinstance(c, theano.gpuarray.GpuArraySharedVariable))
if sync:
# Make sure we don't include the time from the first call
c.get_value(borrow=True, return_internal_type=True).sync()
t0 = time.time()
for i in range(iters):
f()
if sync:
c.get_value(borrow=True, return_internal_type=True).sync()
t1 = time.time()
return t1 - t0, impl
def task1(self):
print(np.__version__)
np.show_config()
def cache_info(cls):
f = io.StringIO()
with contextlib.redirect_stdout(f):
np.show_config()
cls._CACHE = f.getvalue()
def main(argv):
assert len(sys.argv) >= 3
if FLAGS.preprocess_ref:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = True
elif FLAGS.process_query_and_align_videos:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False
else:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False
print("config.PREPROCESS_REFERENCE_VIDEO_ONLY = %s" % str(
config.PREPROCESS_REFERENCE_VIDEO_ONLY))
ask_first()
# Inspired from https://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using
print("numpy.version.version = %s" % str(np.version.version))
print("scipy.version.version = %s" % str(scipy.version.version))
np.show_config()
scipy.show_config()
# See http://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
# We use 7 digits precision and suppress using scientific notation.
np.set_printoptions(precision=7, suppress=True,
threshold=70000, linewidth=4000)
# Inspired from \OpenCV2-Python-Tutorials-master\source\py_tutorials\py_core\py_optimization
# normally returns True - relates to using the SIMD extensions of x86:
# SSX, AVX
common.DebugPrint("cv2.useOptimized() is %s" % str(cv2.useOptimized()))
"""
From http://docs.opencv.org/modules/core/doc/utility_and_system_functions_and_macros.html#checkhardwaresupport
CV_CPU_MMX - MMX
CV_CPU_SSE - SSE
CV_CPU_SSE2 - SSE 2
CV_CPU_SSE3 - SSE 3
CV_CPU_SSSE3 - SSSE 3
CV_CPU_SSE4_1 - SSE 4.1
CV_CPU_SSE4_2 - SSE 4.2
CV_CPU_POPCNT - POPCOUNT
CV_CPU_AVX - AVX
"""
# TODO: Figure out the correct way to reference these in OpenCV 3.x
"""
# Need to setUseOptimized before calling checkHardwareSupport
cv2.setUseOptimized(True)
if config.OCV_OLD_PY_BINDINGS == False:
featDict = {cv2.CpuFeatures.CV_CPU_AVX: "AVX",
cv2.CPU_MMX: "MMX",
cv2.CPU_NEON: "NEON",
cv2.CPU_POPCNT: "POPCNT",
cv2.CPU_SSE: "SSE",
cv2.CPU_SSE2: "SSE2",
cv2.CPU_SSE3: "SSE3",
cv2.CPU_SSE4_1: "SSE4.1",
cv2.CPU_SSE4_2: "SSE4.2",
cv2.CPU_SSSE3: "SSSE3"}
for feat in featDict:
res = cv2.checkHardwareSupport(feat)
print("%s = %d" % (featDict[feat], res))
"""
# "Returns the number of logical CPUs available for the process."
common.DebugPrint("cv2.getNumberOfCPUs() (#logical CPUs) is %s" % str(
cv2.getNumberOfCPUs()))
common.DebugPrint(
"cv2.getTickFrequency() is %s" % str(cv2.getTickFrequency()))
video_file_q = sys.argv[1] # input/current video
video_file_r = sys.argv[2] # reference video
# TODO: use getopt() to run Evangelidis' or "Alex's" algorithm, etc
ReadVideo.main(video_file_q, video_file_r)
def benchmark_2d_ffts(mode='poppy', max_pow=13, verbose=False, savefig=False):
"""Benchmark FFT runtime vs array size, for multiple transform libraries
Parameters
----------
mode : string, 'poppy' or 'base'
What to test, either the full poppy usage of the 2D transform, including
surrounding setup and normalization code, or the basic transform on its own.
max_pow : int
Maximum power of 2 array size to test up to
verbose : bool
output numpy config info
savefig : bool
save plot result to a PDF?
"""
# modified based on https://github.com/numpy/numpy/issues/17839#issuecomment-733543850
try:
from simple_benchmark import benchmark
except ImportError:
raise RuntimeError("You must have simple_benchmark installed to run this function")
threads = multiprocessing.cpu_count()
if mode == 'base':
# Functions for benchmarking the low-level FFT functions in each library
def pocketfft(it):
np.fft.fft2(it)
def scipyfft(it):
scipy.fft.fft2(it)
def fftw_1thread(it):
# note, FFTW defaults to 1 thread, unless you override the config,
# but that's not a fair comparison
pyfftw.interfaces.numpy_fft.fft2(it, thread=1)
def fftw(it):
# explicitly try multithreaded here
pyfftw.interfaces.numpy_fft.fft2(it, threads=threads)
def mklfft(it):
mkl_fft.fft2(it)
funcs_to_test = [pocketfft, fftw, mklfft, scipyfft, fftw_1thread, ]
function_aliases = {pocketfft: 'numpy.fft', fftw: 'pyfftw.fft, multithreaded', scipyfft: 'scipy.fft',
mklfft: "MKL FFT", fftw_1thread: 'pyfftw.fft, single-thread'}
title = "Basic 2D FFT only"
elif mode == 'poppy':
# Functions for benchmarking poppy transforms using each library
def poppy_numpyfft(it):
global _USE_FFTW, _USE_MKL
_USE_FFTW = False
_USE_MKL = False
fft_2d(it, fftshift=False)
def poppy_fftw(it):
global _USE_FFTW, _USE_MKL
_USE_FFTW = True
_USE_MKL = False
fft_2d(it, fftshift=False)
def poppy_mklfft(it):
global _USE_FFTW, _USE_MKL
_USE_FFTW = False
_USE_MKL = True
fft_2d(it, fftshift=False)
funcs_to_test = [poppy_numpyfft, poppy_fftw, poppy_mklfft]
function_aliases = {poppy_numpyfft: 'poppy using numpy.fft', poppy_fftw: 'poppy using pyfftw.fft',
poppy_mklfft: "poppy using MKL FFT"}
title = 'full poppy.accel_math.fft_2d'
else:
raise ValueError(f"Unknown/invalid value for 'base' parameter: {base}")
def shp(len):
return (len, len)
if verbose:
print(np.version)
np.show_config()
# Turn on the cache for optimum performance
pyfftw.interfaces.cache.enable()
b_array = benchmark(
funcs_to_test,
arguments={2 ** i: np.random.uniform(size=shp(2 ** i)) + 1j * np.random.uniform(size=shp(2 ** i)) for i in
range(2, max_pow)},
argument_name='array size',
function_aliases=function_aliases
)
plt.figure(figsize=(12, 8))
b_array.plot()
plt.grid(which='both', alpha=0.2)
plt.grid(which='major', alpha=0.5)
plt.xlim(1, 2e4)
plt.ylim(1e-6, 1e1)
cpu_label = get_processor_name() + f", {get_physical_cpu_count()} cores"
plt.title(f"Time for 2D complex FFTs: {title}\n{cpu_label}", fontweight='bold')
if savefig:
plt.savefig(f"bench_ffts_{mode}.png")
def execute(execute=True, verbose=True, M=2000, N=2000, K=2000, iters=10, order="C"):
"""
:param execute: If True, execute a Theano function that should call gemm.
:param verbose: If True, will print some Theano flags and env variables.
:param M,N,K: The M,N,K size used by gemm.
:param iters: The number of calls to gemm to do.
:return: a tuple (execution time,
str that represents the implementation used)
"""
if verbose:
print("Some Theano flags:")
print(" blas.ldflags=", theano.config.blas.ldflags)
print(" compiledir=", theano.config.compiledir)
print(" floatX=", theano.config.floatX)
print(" device=", theano.config.device)
print("Some OS information:")
print(" sys.platform=", sys.platform)
print(" sys.version=", sys.version)
print(" sys.prefix=", sys.prefix)
print("Some environment variables:")
print(" MKL_NUM_THREADS=", os.getenv("MKL_NUM_THREADS"))
print(" OMP_NUM_THREADS=", os.getenv("OMP_NUM_THREADS"))
print(" GOTO_NUM_THREADS=", os.getenv("GOTO_NUM_THREADS"))
print()
print("Numpy config: (used when the Theano flag" ' "blas.ldflags" is empty)')
np.show_config()
print("Numpy dot module:", np.dot.__module__)
print("Numpy location:", np.__file__)
print("Numpy version:", np.__version__)
a = theano.shared(np.ones((M, N), dtype=theano.config.floatX, order=order))
b = theano.shared(np.ones((N, K), dtype=theano.config.floatX, order=order))
c = theano.shared(np.ones((M, K), dtype=theano.config.floatX, order=order))
f = theano.function([], updates=[(c, 0.4 * c + 0.8 * tt.dot(a, b))])
if any([x.op.__class__.__name__ == "Gemm" for x in f.maker.fgraph.toposort()]):
c_impl = [
hasattr(thunk, "cthunk")
for node, thunk in zip(f.fn.nodes, f.fn.thunks)
if node.op.__class__.__name__ == "Gemm"
]
assert len(c_impl) == 1
if c_impl[0]:
impl = "CPU (with direct Theano binding to blas)"
else:
impl = "CPU (without direct Theano binding to blas but with numpy/scipy binding to blas)"
elif any([x.op.__class__.__name__ == "GpuGemm" for x in f.maker.fgraph.toposort()]):
impl = "GPU"
else:
impl = "ERROR, unable to tell if Theano used the cpu or the gpu:\n"
impl += str(f.maker.fgraph.toposort())
t0 = 0
t1 = -1
f() # Ignore first function call to get representative time.
if execute:
sync = hasattr(theano, "gpuarray") and isinstance(
c, theano.gpuarray.GpuArraySharedVariable
)
if sync:
# Make sure we don't include the time from the first call
c.get_value(borrow=True, return_internal_type=True).sync()
t0 = time.time()
for i in range(iters):
f()
if sync:
c.get_value(borrow=True, return_internal_type=True).sync()
t1 = time.time()
return t1 - t0, impl
def sys_info(fid=None, show_paths=False):
"""Print the system information for debugging.
This function is useful for printing system information
to help triage bugs.
Parameters
----------
fid : file-like | None
The file to write to. Will be passed to :func:`print()`.
Can be None to use :data:`sys.stdout`.
show_paths : bool
If True, print paths for each module.
Examples
--------
Running this function with no arguments prints an output that is
useful when submitting bug reports::
>>> import mne
>>> mne.sys_info() # doctest: +SKIP
Platform: Linux-4.2.0-27-generic-x86_64-with-Ubuntu-15.10-wily
Python: 2.7.10 (default, Oct 14 2015, 16:09:02) [GCC 5.2.1 20151010]
Executable: /usr/bin/python
mne: 0.12.dev0
numpy: 1.12.0.dev0+ec5bd81 {lapack=mkl_rt, blas=mkl_rt}
scipy: 0.18.0.dev0+3deede3
matplotlib: 1.5.1+1107.g1fa2697
sklearn: 0.18.dev0
nibabel: 2.1.0dev
mayavi: 4.3.1
cupy: 4.1.0
pandas: 0.17.1+25.g547750a
dipy: 0.14.0
""" # noqa: E501
ljust = 15
out = 'Platform:'.ljust(ljust) + platform.platform() + '\n'
out += 'Python:'.ljust(ljust) + str(sys.version).replace('\n', ' ') + '\n'
out += 'Executable:'.ljust(ljust) + sys.executable + '\n'
out += 'CPU:'.ljust(ljust) + ('%s: %s cores\n' %
(platform.processor(),
multiprocessing.cpu_count()))
out += 'Memory:'.ljust(ljust)
try:
import psutil
except ImportError:
out += 'Unavailable (requires "psutil" package)'
else:
out += '%0.1f GB\n' % (psutil.virtual_memory().total / float(2 ** 30),)
out += '\n'
old_stdout = sys.stdout
capture = StringIO()
try:
sys.stdout = capture
np.show_config()
finally:
sys.stdout = old_stdout
lines = capture.getvalue().split('\n')
libs = []
for li, line in enumerate(lines):
for key in ('lapack', 'blas'):
if line.startswith('%s_opt_info' % key):
lib = lines[li + 1]
if 'NOT AVAILABLE' in lib:
lib = 'unknown'
else:
try:
lib = lib.split('[')[1].split("'")[1]
except IndexError:
pass # keep whatever it was
libs += ['%s=%s' % (key, lib)]
libs = ', '.join(libs)
for mod_name in ('mne', 'numpy', 'scipy', 'matplotlib', '', 'sklearn',
'nibabel', 'mayavi', 'cupy', 'pandas', 'dipy'):
if mod_name == '':
out += '\n'
continue
out += ('%s:' % mod_name).ljust(ljust)
try:
mod = __import__(mod_name)
if mod_name == 'mayavi':
# the real test
from mayavi import mlab # noqa, analysis:ignore
except Exception:
out += 'Not found\n'
else:
extra = (' (%s)' % op.dirname(mod.__file__)) if show_paths else ''
if mod_name == 'numpy':
extra = ' {%s}%s' % (libs, extra)
elif mod_name == 'matplotlib':
extra = ' {backend=%s}%s' % (mod.get_backend(), extra)
elif mod_name == 'mayavi':
try:
from pyface.qt import qt_api
except Exception:
qt_api = 'unknown'
if qt_api == 'pyqt5':
try:
from PyQt5.Qt import PYQT_VERSION_STR
qt_api += ', PyQt5=%s' % (PYQT_VERSION_STR,)
except Exception:
pass
extra = ' {qt_api=%s}%s' % (qt_api, extra)
out += '%s%s\n' % (mod.__version__, extra)
print(out, end='', file=fid)
# -*- coding: utf-8 -*- """ Created on Sun May 10 19:05:01 2020 @author: DELL.E5430.SSD """ import numpy as np print(np.__version__) print() print(np.show_config())
#1. Import the NUMPY package under the name np.
import numpy as np
#2. Print the NUMPY version and the configuration.
>>>np.__version__
'1.16.2'
>>> np.show_config()
mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/Users/julieG/anaconda3/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/Users/julieG/anaconda3/include']
blas_mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/Users/julieG/anaconda3/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/Users/julieG/anaconda3/include']
blas_opt_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/Users/julieG/anaconda3/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/Users/julieG/anaconda3/include']
lapack_mkl_info:
libraries = ['mkl_rt', 'pthread']
library_dirs = ['/Users/julieG/anaconda3/lib']
define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
include_dirs = ['/Users/julieG/anaconda3/include']
lapack_opt_info:
type(val) # # 6. Print the numpy version and the configuration # # In[22]: np.__version__ # In[28]: np.__config__ # In[30]: np.show_config() # # 7. Print the dimension of the array in question 3 # In[47]: #by there no. of ending prhantics we can easily see out this is 1-D ARRAY: val.ndim # # 8. Create a boolean array with all the True values # In[41]: array = np.array(range(10, 30), dtype=bool) array
def main(): print(np.__version__) np.show_config()
