def doCompute():
inlFactor = xa.SI['zstep'] / xa.SI['inldist'] * xa.SI['dipFactor']
crlFactor = xa.SI['zstep'] / xa.SI['crldist'] * xa.SI['dipFactor']
zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
filt = xa.params['Select']['Selection']
filtFunc = autojit(xl.vecmean) if filt == 0 else autojit(
xl.vmf_l1) if filt == 1 else autojit(xl.vmf_l2)
while True:
xa.doInput()
dx = -xa.Input['Inl_dip'] / inlFactor
dy = -xa.Input['Crl_dip'] / crlFactor
dz = np.ones(dx.shape)
s = np.sqrt(dx * dx + dy * dy + dz * dz)
#
# Apply the Vector Filter
out = np.empty((3, xa.TI['nrsamp']))
xl.vecFilter(np.array([dx / s, dy / s, dz / s]), zw, filtFunc, out)
#
# Get the output
xa.Output['Crl_dip'] = -out[1, :] / out[2, :] * crlFactor
xa.Output['Inl_dip'] = -out[0, :] / out[2, :] * inlFactor
xa.Output['True Dip'] = np.sqrt(
xa.Output['Crl_dip'] * xa.Output['Crl_dip'] +
xa.Output['Inl_dip'] * xa.Output['Inl_dip'])
xa.Output['Dip Azimuth'] = np.degrees(
np.arctan2(xa.Output['Inl_dip'], xa.Output['Crl_dip']))
xa.doOutput()
def compiled(self):
# Compile it if this is the first time.
if (not hasattr(self, '_compiled')) and NUMBA_AVAILABLE:
if self.autojit_kw is not None:
self._compiled = numba.autojit(**self.autojit_kw)(self.func)
else:
self._compiled = numba.autojit(self.func)
return self._compiled
def test_autojit(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
def dummy():
pass
autojit(dummy)
self.assertEqual(len(w), 1)
def compiled(self):
# Compile it if this is the first time.
if (not hasattr(self, '_compiled')) and NUMBA_AVAILABLE:
if self.autojit_kw is not None:
self._compiled = numba.autojit(**self.autojit_kw)(self.func)
else:
self._compiled = numba.autojit(self.func)
return self._compiled
def test_autojit(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
def dummy():
pass
autojit(dummy)
self.assertEqual(len(w), 1)
def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 filt = xa.params['Select']['Selection'] filtFunc = autojit(xl.vecmean) if filt==0 else autojit(xl.vmf_l1) if filt==1 else autojit(xl.vmf_l2) inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] band = xa.params['Par_1']['Value'] zw = min(2*int(xa.params['Par_0']['Value'])+1,3) N = xa.params['ZSampMargin']['Value'][1] kernel = xl.hilbert_kernel(N, band) while True: xa.doInput() indata = xa.Input['Input'] # # Analytic Signal # ansig = np.apply_along_axis(np.convolve,-1, indata, kernel, mode="same") sr = np.real(ansig) si = np.imag(ansig) # # Compute partial derivatives sx = xl.kroon3( sr, axis=0 ) sy = xl.kroon3( sr, axis=1 ) sz = xl.kroon3( sr, axis=2 ) shx = xl.kroon3( si, axis=0 ) shy = xl.kroon3( si, axis=1 ) shz = xl.kroon3( si, axis=2 ) px = sr[1:xs-1,1:ys-1,:] * shx[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sx[1:xs-1,1:ys-1,:] py = sr[1:xs-1,1:ys-1,:] * shy[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sy[1:xs-1,1:ys-1,:] pz = sr[1:xs-1,1:ys-1,:] * shz[1:xs-1,1:ys-1,:] - si[1:xs-1,1:ys-1,:] * sz[1:xs-1,1:ys-1,:] # # Normalise the gradients so Z component is positive p = np.sign(pz)/np.sqrt(px*px+py*py+pz*pz) px *= p py *= p pz *= p # # Filter out = np.empty((3,xa.TI['nrsamp'])) xl.vecFilter(np.array([px,py,pz]), zw, filtFunc, out) # # Get the output xa.Output['Crl_dip'] = -out[1,:]/out[2,:]*crlFactor xa.Output['Inl_dip'] = -out[0,:]/out[2,:]*inlFactor xa.Output['True Dip'] = np.sqrt(xa.Output['Crl_dip']*xa.Output['Crl_dip']+xa.Output['Inl_dip']*xa.Output['Inl_dip']) xa.Output['Dip Azimuth'] = np.degrees(np.arctan2(xa.Output['Inl_dip'],xa.Output['Crl_dip'])) xa.doOutput()
def compare_perf(fn, args, numba= True, cpython = True):
parakeet_fn = jit(fn)
name = fn.__name__
parakeet_result = None
numba_result = None
cpython_result = None
with timer('Parakeet #1 -- %s' % name):
parakeet_result = parakeet_fn(*args)
with timer('Parakeet #2 -- %s' % name):
parakeet_result = parakeet_fn(*args)
if numba:
numba_fn = autojit(fn)
with timer('Numba #1 -- %s' % name):
numba_result = numba_fn(*args)
with timer('Numba #2 -- %s' % name):
numba_result = numba_fn(*args)
if parakeet_result is not None and numba_result is not None:
assert np.allclose(parakeet_result, numba_result)
if cpython:
with timer('Python -- %s' % name):
python_result = fn(*args)
if cpython_result is not None and parakeet_result is not None:
assert np.allclose(parakeet_result, python_result)
def create_numba_funcs(scalar_type=SCALAR_TYPE):
this = sys.modules[__name__]
pixel_type = scalar_type[:]
image_type = scalar_type[:, :, :]
state_type = scalar_type[:, :, :]
this._numba_window_floor = jit(nopython=True,
argtypes=[size_t, size_t],
restype=size_t)(_py_window_floor)
this._numba_window_ceil = jit(nopython=True,
argtypes=[size_t, size_t, size_t],
restype=size_t)(_py_window_ceil)
this._numba_distance = jit(nopython=True,
argtypes=[image_type,
size_t, size_t, size_t, size_t],
restype=scalar_type)(_py_distance)
this._numba_np_distance = jit(nopython=False,
argtypes=[pixel_type, pixel_type],
restype=scalar_type)(_py_np_distance)
this._numba_g = jit(nopython=True,
argtypes=[scalar_type],
restype=scalar_type)(_py_g)
this._numba_np_g = jit(nopython=False,
argtypes=[pixel_type, pixel_type],
restype=scalar_type)(_py_np_g)
this._numba_kernel = autojit(nopython=True)(_py_kernel)
def sqr_dists_loops(X,Y):
xs = X.shape[0]
ys = Y.shape[0]
D = np.zeros((xs, ys), dtype=X.dtype)
for i in range(xs):
for j in range(ys):
D[i,j] = np.sqrt(np.sum((X[i,:] - Y[j,:])**2))
return D
ndims = 3000
nsamples = 1e5
nclusters = 1
X = np.random.randn(nsamples, ndims)
Y = np.random.randn(nclusters, ndims)
# scipy cdist - 20.83 secs
start = datetime.now()
dsci = cdist(X,Y, 'minkowski', p=2)
stop = datetime.now()
print("\n\n\t\tScript Running Time: %s"%str(stop - start))
## Numba - 0.54 secs
numba_dists = numba.autojit(sqr_dists_loops)
start = datetime.now()
dnumba = numba_dists(X,Y)
stop = datetime.now()
print("\n\n\t\tScript Running Time: %s"%str(stop - start))
def create_numba_funcs(scalar_type=SCALAR_TYPE):
this = sys.modules[__name__]
pixel_type = scalar_type[:]
image_type = scalar_type[:, :, :]
state_type = scalar_type[:, :, :]
this._numba_window_floor = jit(nopython=True,
argtypes=[size_t, size_t],
restype=size_t)(_py_window_floor)
this._numba_window_ceil = jit(nopython=True,
argtypes=[size_t, size_t, size_t],
restype=size_t)(_py_window_ceil)
this._numba_distance = jit(
nopython=True,
argtypes=[image_type, size_t, size_t, size_t, size_t],
restype=scalar_type)(_py_distance)
this._numba_np_distance = jit(nopython=False,
argtypes=[pixel_type, pixel_type],
restype=scalar_type)(_py_np_distance)
this._numba_g = jit(nopython=True,
argtypes=[scalar_type],
restype=scalar_type)(_py_g)
this._numba_np_g = jit(nopython=False,
argtypes=[pixel_type, pixel_type],
restype=scalar_type)(_py_np_g)
this._numba_kernel = autojit(nopython=True)(_py_kernel)
def compare_perf(fn, args, numba= True, cpython = True):
parakeet_fn = jit(fn)
with timer('Parakeet #1'):
parakeet_result = parakeet_fn(*args)
with timer('Parakeet #2'):
parakeet_result = parakeet_fn(*args)
if numba:
numba_fn = autojit(fn)
with timer('Numba #1'):
numba_result = numba_fn(*args)
with timer('Numba #2'):
numba_result = numba_fn(*args)
assert np.allclose(parakeet_result, numba_result)
if cpython:
with timer('Python'):
python_result = fn(*args)
assert np.allclose(parakeet_result, python_result)
def __init__(self, x, y, z, spatial_resolution, spectrum):
self.x = x
"""
Size of field in x-direction.
"""
self.y = y
"""
Size of field in y-direction.
"""
self.z = z
"""
Size of field in z-direction.
"""
self.spatial_resolution = spatial_resolution
"""
Spatial resolution.
"""
self.spectrum = spectrum
"""
Spectrum.
"""
try:
self._generate = numba.autojit(_generate)
except NameError:
self._generate = _generate
def doCompute(): xs = xa.SI['nrinl'] ys = xa.SI['nrcrl'] zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 zw = zs-2 filt = xa.params['Select']['Selection'] filtFunc = autojit(xl.vecmean) if filt==0 else autojit(xl.vmf_l1) if filt==1 else autojit(xl.vmf_l2) inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] while True: xa.doInput() s = xa.Input['Input'] sh = np.imag( hilbert(s) ) # # Compute partial derivatives sx = xl.kroon3( s, axis=0 ) sy = xl.kroon3( s, axis=1 ) sz = xl.kroon3( s, axis=2 ) shx = xl.kroon3( sh, axis=0 ) shy = xl.kroon3( sh, axis=1 ) shz = xl.kroon3( sh, axis=2 ) px = s[1:xs-1,1:ys-1,:] * shx[1:xs-1,1:ys-1,:] - sh[1:xs-1,1:ys-1,:] * sx[1:xs-1,1:ys-1,:] py = s[1:xs-1,1:ys-1,:] * shy[1:xs-1,1:ys-1,:] - sh[1:xs-1,1:ys-1,:] * sy[1:xs-1,1:ys-1,:] pz = s[1:xs-1,1:ys-1,:] * shz[1:xs-1,1:ys-1,:] - sh[1:xs-1,1:ys-1,:] * sz[1:xs-1,1:ys-1,:] # # Normalise the gradients so Z component is positive p = np.sign(pz)/np.sqrt(px*px+py*py+pz*pz) px *= p py *= p pz *= p # # Filter out = np.empty((3,xa.TI['nrsamp'])) xl.vecFilter(np.array([px,py,pz]), zw, filtFunc, out) # # Get the output xa.Output['Crl_dip'] = -out[1,:]/out[2,:]*crlFactor xa.Output['Inl_dip'] = -out[0,:]/out[2,:]*inlFactor xa.Output['True Dip'] = np.sqrt(xa.Output['Crl_dip']*xa.Output['Crl_dip']+xa.Output['Inl_dip']*xa.Output['Inl_dip']) xa.Output['Dip Azimuth'] = np.degrees(np.arctan2(xa.Output['Inl_dip'],xa.Output['Crl_dip'])) xa.doOutput()
def test(test_n=0):
entries=100
if entries == -1 or entries > fee.shape[0]:
entries = fee.shape[0]
res_tmp = {}
#res_tmp["entries"] = [entries]
if test_n==0:
start_t = time()
sase_map = dset_loop_chunk(fee, entries)
print "Numpy chunked loop: %s" % (time() - start_t)
res_tmp["np chunk"] = [time() - start_t,]
elif test_n==1:
start_t = time()
sase_map = dset_loop_chunk_loop(fee, entries)
print "Numpy chunked loop loop: %s" % (time() - start_t)
res_tmp["np chunk loop"] = [time() - start_t,]
elif test_n==2:
start_t = time()
sase_map = dset_loop_chunk_numba(fee, entries)
print "Numba chunked loop: %s" % (time() - start_t)
res_tmp["numba chunk"] = [time() - start_t,]
# very slow!
#if fee[0].shape[0] < 2000:
elif test_n==3:
dset_loop_numba = autojit(dset_loop)
start_t = time()
sase_map = dset_loop_numba(fee, entries)
print "Numba loop: %s" % (time() - start_t)
res_tmp["numba loop"] = [time() - start_t]
# very slow!
elif test_n==4:
start_t = time()
sase_map = dset_loop(fee, entries)
print "Python loop: %s" % (time() - start_t)
res_tmp["python loop"] = [time() - start_t]
elif test_n==5:
try:
start_t = time()
sase_map = fee[:entries].sum(axis=2)
print "Numpy: %s" % (time() - start_t)
res_tmp["np"] = [time() - start_t,]
except:
res_tmp["np"] = [np.nan]
#except:
# pass
return res_tmp
def doCompute():
xs = xa.SI['nrinl']
ys = xa.SI['nrcrl']
zs = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin'][
'Value'][0] + 1
zw = zs - 2
filt = xa.params['Select']['Selection']
filtFunc = autojit(xl.vecmean) if filt == 0 else autojit(
xl.vmf_l1) if filt == 1 else autojit(
xl.vmf_l2) if filt == 2 else autojit(xl.vmf_x3)
inlFactor = xa.SI['zstep'] / xa.SI['inldist'] * xa.SI['dipFactor']
crlFactor = xa.SI['zstep'] / xa.SI['crldist'] * xa.SI['dipFactor']
while True:
xa.doInput()
p = xa.Input['Input']
#
# Compute partial derivatives
px = xl.kroon3(p, axis=0)[1:xs - 1, 1:ys - 1, :]
py = xl.kroon3(p, axis=1)[1:xs - 1, 1:ys - 1, :]
pz = xl.kroon3(p, axis=2)[1:xs - 1, 1:ys - 1, :]
#
# Normalise the gradients so Z component is positive
p = np.sign(pz) / np.sqrt(px * px + py * py + pz * pz)
px *= p
py *= p
pz *= p
#
# Filter
out = np.empty((3, xa.TI['nrsamp']))
xl.vecFilter(np.array([px, py, pz]), zw, filtFunc, out)
#
# Get the output
xa.Output['Crl_dip'] = -out[1, :] / out[2, :] * crlFactor
xa.Output['Inl_dip'] = -out[0, :] / out[2, :] * inlFactor
xa.Output['True Dip'] = np.sqrt(
xa.Output['Crl_dip'] * xa.Output['Crl_dip'] +
xa.Output['Inl_dip'] * xa.Output['Inl_dip'])
xa.Output['Dip Azimuth'] = np.degrees(
np.arctan2(xa.Output['Inl_dip'], xa.Output['Crl_dip']))
xa.doOutput()
def doCompute(): inlFactor = xa.SI['zstep']/xa.SI['inldist'] * xa.SI['dipFactor'] crlFactor = xa.SI['zstep']/xa.SI['crldist'] * xa.SI['dipFactor'] zw = xa.params['ZSampMargin']['Value'][1] - xa.params['ZSampMargin']['Value'][0] + 1 filt = xa.params['Select']['Selection'] filtFunc = autojit(xl.vecmean) if filt==0 else autojit(xl.vmf_l1) if filt==1 else autojit(xl.vmf_l2) while True: xa.doInput() dx = -xa.Input['Inl_dip']/inlFactor dy = -xa.Input['Crl_dip']/crlFactor dz = np.ones(dx.shape) s = np.sqrt(dx*dx+dy*dy+dz*dz) # # Apply the Vector Filter out = np.empty((3,xa.TI['nrsamp'])) xl.vecFilter(np.array([dx/s,dy/s,dz/s]), zw, filtFunc, out) # # Get the output xa.Output['Crl_dip'] = -out[1,:]/out[2,:]*crlFactor xa.Output['Inl_dip'] = -out[0,:]/out[2,:]*inlFactor xa.Output['True Dip'] = np.sqrt(xa.Output['Crl_dip']*xa.Output['Crl_dip']+xa.Output['Inl_dip']*xa.Output['Inl_dip']) xa.Output['Dip Azimuth'] = np.degrees(np.arctan2(xa.Output['Inl_dip'],xa.Output['Crl_dip'])) xa.doOutput()
def main():
if len(sys.argv) > 1:
N = int(sys.argv[1])
else:
N = 10000
for name in [
'sqrt',
'log',
'exp',
]:
print '%s python: ' % name,
func = globals()[name + '_test']
t = time.time()
func(N)
print time.time() - t
print '%s numba: ' % name,
func = autojit(func)
t = time.time()
func(N)
print time.time() - t
def test_valid_compare():
array_nb = autojit(array)
a = np.random.rand(1e6)
assert np.allclose(array(a), array_nb(a))
# Authors: Olivier Grisel
# License: MIT
from pairwise import pairwise_python
from numba import autojit
benchmarks = (("pairwise_numba_nested_for_loops",
autojit(pairwise_python.pairwise_python_nested_for_loops)), )
####################
## TEST FUNCTIONS ##
####################
### TRIPLE FOR
def triplefor(taille):
count = 0
for i in xrange(taille):
for j in xrange(taille):
for k in xrange(taille):
count += i * j + k
return count / taille
tripleforN = autojit(triplefor)
#######################
## TIMINGS FUNCTIONS ##
#######################
def timings(nb_tries, start=1, stop=200, step=10):
values = range(start, stop, step)
setup = {
'triplefor': ('triplefor(%s)', 'from __main__ import triplefor'),
'tripleforN': ('tripleforN(%s)', 'from __main__ import tripleforN'),
'tripleforC': ('cython_test(%s)', 'from __main__ import cython_test'),
def arma(parameters,data,p=0,q=0):
T = data.size
errors = np.zeros_like(data)
for t in xrange(T):
errors[t] = data[t] - parameters[0]
for i in xrange(p):
if (t-i) >= 0:
errors[t] -= parameters[i+1] * data[t-i]
for i in xrange(q):
if (t-i) >= 0:
errors[t] -= parameters[i+p+1] * errors[t-i]
return errors
arma_autojit = autojit(arma)
arma_jit = jit(double[:](double[:],double[:],int32,int32))(arma)
parameters = np.zeros((3))
data = np.random.randn(10000)
p = 1
q = 1
if __name__=='__main__':
arma(parameters,data,p,q)
arma_autojit(parameters,data,p,q)
arma_jit(parameters,data,p,q)
arg.start + self.plus,
arg.stop + self.plus
)
def __rsub__(self, arg):
return type(arg)(
arg.start - self.mnus,
arg.stop - self.mnus
)
one = Shift(1,1)
sl = slice(2,5)
print sl+one
try:
from numba import autojit, jit, double
fast_f = autojit(f)
#fast_f = jit(restype=double[:],argtypes=[double[:], double[:], double[:]])(f)
t0 = time.time()
output = fast_f(Psi_l, Psi_r, CC)
print "time: numba", time.time() - t0
print "output numba", output
except ImportError:
print "Not using Numba nor Numbapro"
t1 = time.time()
output = f(Psi_l, Psi_r, CC)
print "time: numpy", time.time() - t1
print "output numpy", output
# ______________________________________________________________________
def _for_loop_fn_3(stop):
acc = 0
for i in range(stop):
for j in range(stop):
for k in range(stop):
for l in range(stop):
acc += 1
return acc
for_loop_fn_0 = autojit(backend='ast')(_for_loop_fn_0)
for_loop_fn_1 = autojit(backend='ast')(_for_loop_fn_1)
for_loop_fn_2 = autojit(backend='ast')(_for_loop_fn_2)
for_loop_fn_3 = autojit(backend='ast')(_for_loop_fn_3)
# ______________________________________________________________________
class TestForLoop(unittest.TestCase):
# @unittest.skipUnless(__debug__, "Requires implementation of iteration "
# "over arrays.")
def test_compiled_for_loop_fn_0(self):
for dtype in (np.float32, np.float64, np.int32, np.int64):
test_data = np.arange(10, dtype=dtype)
result = for_loop_fn_0(test_data)
self.assertEqual(result, 45)
def test_valid_compare():
array_nb = autojit(array)
a = np.random.rand(1e6)
assert np.allclose(array(a), array_nb(a))
def tdo_fft(inputfile, outputfile):
''' Perform Fourier transform and return frequency evolution '''
comp_expf = autojit(comp_exp)
# Input parameters
fourier_le = 1024 # Fourier length
time_le = 1024 # timewindow
dfmin = 100 # Frequency resolution
dt = 2e-8 # timestep of acquisition
# Lecture du fichier
fid = open(inputfile, 'rb')
fid.seek(512) # Skip useless header
V = fromfile(fid, int16, -1, '')
fid.close()
pstart = 1 # First timewindow
pend = int(floor((len(V) - fourier_le) / time_le)) # Last timewindow
V = V - mean(V) # Remove zero frequency contribution to FT
t = arange(0, fourier_le) * dt
# Approximation of main frequency
Vf = abs(real(fft(V[0:fourier_le])))
tf = fftfreq(fourier_le, dt)
fmax = zeros((pend + 1 - pstart, 3))
fmax[0, 1] = tf[argmax(Vf[0:int(fourier_le / 2)])]
fmax[0, 0] = time_le * dt / 2
# Calculation of constants
expon = -2j * pi * t
deltaf0 = tf[1] / 1000
# Windowing (uncomment desired window function)
# Rectangular
window = ones(fourier_le)
# Cosinus
#window = arange(0,fourier_le)
#window = 1-cos(window*2*pi/(fourier_le-1))
# Kaiser-Bessel
#window = arange(0,fourier_le)
#window = 0.402-0.498*cos(window*2*pi/(fourier_le-1))+0.098*cos(window*4*pi/(fourier_le-1))+0.001*cos(window*6*pi/(fourier_le-1))
# Precise determination of oscillating frequency via DFT
for i in xrange(pstart, pend):
# Utilisation de la dernière valeur comme point de depart
a = fmax[i - 1, 1]
V_temp = window * V[i * time_le:i * time_le + fourier_le]
# Previous frequency spectral weight
# Complex exponential time consuming ! Need a smarter way to perform this calculations
deltaf = deltaf0
essaimax = abs(local_trapz(V_temp * comp_expf(expon * a)))
# Calculation of local derivative of Fourier transform
# If derivative positive, then search for frequency in growing direction
if abs(local_trapz(V_temp * comp_expf(expon *
(a + deltaf)))) > essaimax:
while abs(deltaf) > dfmin:
F = abs(local_trapz(V_temp * comp_expf(expon * (a + deltaf))))
if F > essaimax:
essaimax = F
a += deltaf
else:
deltaf = -deltaf / 5
# Store frequency
fmax[i, 0:2] = [(i * time_le + fourier_le / 2) * dt,
a - 2.5 * deltaf]
# Lower frequency otherwise
else:
while abs(deltaf) > dfmin:
F = abs(local_trapz(V_temp * comp_expf(expon * (a - deltaf))))
if F > essaimax:
essaimax = F
a -= deltaf
else:
deltaf = -deltaf / 5
# Store frequency
fmax[i, 0:2] = [(i * time_le + fourier_le / 2) * dt,
a + 2.5 * deltaf]
# Save calculation in file
fmax[:, 2] = smooth(fmax[:, 1])[5:-5]
savetxt(outputfile, fmax)
from growcut import growcut_python
from numba import autojit
benchmarks = (
("growcut_numba",
autojit(growcut_python.growcut_python)),
)
def primes(limit):
# Keep only odd numbers in sieve, mapping from index to number is
# num = 2 * idx + 3
# The square of the number corresponding to idx then corresponds to:
# idx2 = 2*idx*idx + 6*idx + 3
sieve = [True] * (limit // 2)
prime_numbers = set([2])
for j in range(len(sieve)):
if sieve[j]:
new_prime = 2 * j + 3
prime_numbers.add(new_prime)
for k in range((2 * j + 6) * j + 3, len(sieve), new_prime):
sieve[k] = False
return list(prime_numbers)
numba_primes = autojit(primes)
start = time()
numba_primes(LIMIT)
end = time()
print("Numba: Time Taken : ", end - start)
start = time()
primes(LIMIT)
end = time()
print("Python: Time Taken : ", end - start)
import d2q9_nsnxny_loop as d2q9
from numba import double, jit, autojit
# m2f = jit('void(f8[:, :, :], f8[:, :, :])')(d2q9.m2f)
# f2m = jit('void(f8[:, :, :], f8[:, :, :])')(d2q9.f2m)
# transport = jit('void(f8[:, :, :])')(d2q9.transport)
# relaxation = jit('void(f8[:, :, :])')(d2q9.relaxation)
# periodic_bc = jit('void(f8[:, :, :])')(d2q9.periodic_bc)
m2f = autojit(d2q9.m2f)
f2m = autojit(d2q9.f2m)
transport = autojit(d2q9.transport)
relaxation = autojit(d2q9.relaxation)
periodic_bc = autojit(d2q9.periodic_bc)
@autojit
def one_time_step(f, m):
periodic_bc(f)
transport(f)
f2m(f, m)
relaxation(m)
m2f(m, f)
def compare_perf(fn,
args,
numba=True,
cpython=True,
extra={},
backends=('c', 'openmp', 'cuda'),
suppress_output=False,
propagate_exceptions=False):
parakeet_fn = jit(fn)
name = fn.__name__
parakeet_result = None
numba_result = None
cpython_result = None
kwargs = {
'suppress_stdout': suppress_output,
'suppress_stderr': suppress_output,
'propagate_exceptions': propagate_exceptions
}
backend = None
for backend in backends:
with timer('Parakeet (backend = %s) #1 -- %s' % (backend, name),
**kwargs):
parakeet_result = parakeet_fn(*args, _backend=backend)
with timer('Parakeet (backend = %s) #2 -- %s' % (backend, name),
**kwargs):
parakeet_result = parakeet_fn(*args, _backend=backend)
if numba:
from numba import autojit, config
numba_fn = autojit(fn)
with timer('Numba #1 -- %s' % name, **kwargs):
numba_result = numba_fn(*args)
with timer('Numba #2 -- %s' % name, **kwargs):
numba_result = numba_fn(*args)
if cpython:
with timer('Python -- %s' % name, **kwargs):
cpython_result = fn(*args)
for name, impl in extra.iteritems():
with timer("%s #1" % name, **kwargs):
impl(*args)
with timer("%s #2" % name, **kwargs):
extra_result = impl(*args)
if python_result is not None:
diffs = np.abs(parakeet_result - extra_result)
assert np.allclose(parakeet_result, extra_result, atol = atol, rtol = rtol), \
"Max elt difference between Parakeet and %s = %s (median = %s, min = %s)" % \
(name, np.max(diffs), np.median(diffs), np.min(diffs))
rtol = 0.0001
if backend in ('cuda', 'gpu'):
atol = 0.001
else:
atol = 0.00001
if parakeet_result is not None and cpython_result is not None:
diffs = np.abs(cpython_result - parakeet_result)
assert np.allclose(cpython_result, parakeet_result, atol = atol, rtol = rtol), \
"Max elt difference between Parakeet and CPython = %s (median = %s, min = %s)" % \
(np.max(diffs), np.median(diffs), np.min(diffs))
if numba_result is not None and cpython_result is not None:
diffs = np.abs(cpython_result - numba_result)
assert np.allclose(cpython_result, numba_result, atol = atol, rtol = rtol), \
"Max elt difference between Numba and CPython = %s (median = %s, min = %s)" % \
(np.max(diffs), np.median(diffs), np.min(diffs))
julia = np.empty((N, N), dtype=np.uint32)
grid_x = np.linspace(-bound, bound, N)
for i, x in enumerate(grid_x):
for j, y in enumerate(grid_x):
julia[i,j] = kernel(x, y, cr, ci, lim, cutoff=cutoff)
return julia
def julia(cr, ci, N, bound=1.5, lim=1000., cutoff=1e6):
grid_x = np.linspace(-bound, bound, N)
return np.array([[kernel(x,y,cr,ci,lim,cutoff=cutoff)
for x in grid_x]
for y in grid_x])
from compare_perf import compare_perf
cr=0.285
ci=0.01
N=1200
bound = 1.5
lim = 1000
cutoff = 1e6
extra = {}
try:
from numba import autojit
extra['numba'] = autojit(julia_loops)
except:
print "Failed to import Numba"
compare_perf(julia, [cr, ci, N, bound, lim, cutoff], numba = False, extra = extra)
# function to check if the point is on the polygon
EPSILON = 2
def pointOnPolygon(point, poly):
for i in range(len(poly)):
[a, b] = poly[i - 1], poly[i]
if abs(
vincenty(a, point, miles=False) +
vincenty(b, point, miles=False) -
vincenty(a, b, miles=False)) < EPSILON:
return True
return False
pointOnPolygon = autojit(pointOnPolygon)
# compute list of channels that are not available; chan_NA is the list of list of channels not available for the center node
df = pd.read_csv('Columbus_TVData_GradeB_SepDis.csv')
cols_to_use = df.columns
cols_to_use = []
for degree in range(1, 360, 2):
cols_to_use.append("Latitude." + str(degree))
cols_to_use.append("Longitude." + str(degree))
dfa = df[cols_to_use]
fr = dfa
fr.values.shape
poly_overall = fr.values.reshape((len(dfa), 180, 2))
path_overall = []
for row in range(0, len(dfa)):
# Authors: Yuancheng Peng
# License: MIT
from arc_distance import arc_distance_python as adp
from numba import autojit
benchmarks = (("arc_distance_numba_for_loops",
autojit(adp.arc_distance_python_nested_for_loops)), )
break
## Bin to the left
elif xr <= xnewl:
pass
## This condition should not happen
else:
pass
i += 1
## Add the sum to ynew
if weight != 0:
ynew[ii] = val/weight
ii += 1
return ynew
if 'numba' in sys.modules:
Interp_integrate = autojit(Interp_integrate)
def Resample_linlog(xold):
"""Resample_linlog(xold)
Resample a linear wavelength vector to a log space and
returns the new vector and the Doppler shift z.
The resampling is done such that the largest wavelength interval
is conserved in order to preserve the spectral resolution.
The Doppler shift is:
1+z = lambda_1 / lambda_0
In the non-relativistic limit:
z = v/c
import time
def f(n):
t0 = time.time()
result = 0.0
for i in range(n):
for j in range(n * i):
result += sin(pi / 2)
return int(result), time.time() - t0
n = 250
res_py = f(n)
print "Number of Loops %8d" % res_py[0]
print "Time in Sec for Python %8.3f" % res_py[1]
import numba as nb
f_nb = nb.autojit(f)
res_nb = f_nb(n)
print "Number of Loops %8d" % res_nb[0]
print "Time in Sec for Python %8.3f" % res_nb[1]
print "Number of Loops %8d" % res_py[0]
print "Speed-up of Numba %8d" % (res_py[1] / res_nb[1])
def tdo_fft(inputfile, outputfile):
''' Perform Fourier transform and return frequency evolution '''
comp_expf=autojit(comp_exp)
# Input parameters
fourier_le = 1024 # Fourier length
time_le = 1024 # timewindow
dfmin = 100 # Frequency resolution
dt = 2e-8 # timestep of acquisition
# Lecture du fichier
fid = open(inputfile, 'rb')
fid.seek(512) # Skip useless header
V = fromfile(fid, int16, -1, '')
fid.close()
pstart = 1 # First timewindow
pend = int(floor((len(V)-fourier_le)/time_le)) # Last timewindow
V = V-mean(V) # Remove zero frequency contribution to FT
t = arange(0, fourier_le)*dt
# Approximation of main frequency
Vf = abs(real(fft(V[0:fourier_le])))
tf = fftfreq(fourier_le, dt)
fmax = zeros((pend+1-pstart, 3))
fmax[0, 1] = tf[argmax(Vf[0:int(fourier_le/2)])]
fmax[0, 0] = time_le*dt/2
# Calculation of constants
expon = -2j*pi*t
deltaf0 = tf[1]/1000
# Windowing (uncomment desired window function)
# Rectangular
window = ones(fourier_le)
# Cosinus
#window = arange(0,fourier_le)
#window = 1-cos(window*2*pi/(fourier_le-1))
# Kaiser-Bessel
#window = arange(0,fourier_le)
#window = 0.402-0.498*cos(window*2*pi/(fourier_le-1))+0.098*cos(window*4*pi/(fourier_le-1))+0.001*cos(window*6*pi/(fourier_le-1))
# Precise determination of oscillating frequency via DFT
for i in xrange(pstart, pend):
# Utilisation de la dernière valeur comme point de depart
a = fmax[i-1, 1]
V_temp = window*V[i*time_le:i*time_le+fourier_le]
# Previous frequency spectral weight
# Complex exponential time consuming ! Need a smarter way to perform this calculations
deltaf = deltaf0
essaimax=abs(local_trapz(V_temp*comp_expf(expon*a)))
# Calculation of local derivative of Fourier transform
# If derivative positive, then search for frequency in growing direction
if abs(local_trapz(V_temp*comp_expf(expon*(a+deltaf)))) > essaimax:
while abs(deltaf)>dfmin:
F = abs(local_trapz(V_temp*comp_expf(expon*(a+deltaf))))
if F > essaimax:
essaimax = F
a += deltaf
else:
deltaf = -deltaf/5
# Store frequency
fmax[i, 0:2] = [(i*time_le+fourier_le/2)*dt, a-2.5*deltaf]
# Lower frequency otherwise
else:
while abs(deltaf)>dfmin:
F = abs(local_trapz(V_temp*comp_expf(expon*(a-deltaf))))
if F > essaimax:
essaimax = F
a -= deltaf
else:
deltaf = -deltaf/5
# Store frequency
fmax[i, 0:2] = [(i*time_le+fourier_le/2)*dt, a+2.5*deltaf]
# Save calculation in file
fmax[:,2] = smooth(fmax[:,1])[5:-5]
savetxt(outputfile, fmax)
def regridx(Variable):
'''Regrids Xp1 to X (Time averaged)
Future : check if 4 dimentions for
non timeaveraged?
'''
if len(np.shape(Variable)) == 4:
Vc = (Variable[:, :, :, 0:-1] + Variable[:, :, :, 1::]) / 2
else:
Vc = (Variable[:, :, 0:-1] + Variable[:, :, 1::]) / 2
return Vc
# Numba them
numba_regridy = autojit()(regridy)
numba_regridy.func_name = "numba_regridy"
numba_regridz = autojit()(regridz)
numba_regridz.func_name = "numba_regridz"
numba_regridx = autojit()(regridx)
numba_regridx.func_name = "numba_regridx"
def maxmag(var):
m1 = np.max(var, axis=0)
m2 = np.min(var, axis=0)
if m1 < abs(m2):
val = m2
else:
val = m1
with timer('Python (comprehensions)'):
sqr_dists(X, Y)
with timer('Python (loops)'):
sqr_dists_loops(X, Y)
#
# Numba
#
import numba
#
# Numba's @autojit just like Parakeet's @jit
#
numba_dists = numba.autojit(sqr_dists)
with timer('Numba (comprehensions) #1'):
numba_dists(X, Y)
with timer('Numba (comprehensions) #2'):
numba_dists(X, Y)
numba_dists_loops = numba.autojit(sqr_dists_loops)
with timer('Numba (loops) #1'):
numba_dists_loops(X, Y)
with timer('Numba (loops) #2'):
numba_dists_loops(X, Y)
with timer('Python (loops)'):
sqr_dists_loops(X,Y)
#
# Numba
#
import numba
#
# Numba's @autojit just like Parakeet's @jit
#
numba_dists = numba.autojit(sqr_dists)
with timer('Numba (comprehensions) #1'):
numba_dists(X,Y)
with timer('Numba (comprehensions) #2'):
numba_dists(X,Y)
numba_dists_loops = numba.autojit(sqr_dists_loops)
with timer('Numba (loops) #1'):
numba_dists_loops(X,Y)
with timer('Numba (loops) #2'):
numba_dists_loops(X,Y)
for t in range(1, 1501):
train_sum = np.zeros(x_train.shape[0])
test_sum = np.zeros(x_test.shape[0])
for i in range(t):
train_sum += alphas[i] * linearPred(x_train, ls_w_arr[i])
test_sum += alphas[i] * linearPred(x_test, ls_w_arr[i])
train_err_arr[t - 1] = np.sum(
y_train != np.sign(train_sum)) / y_train.shape[0]
test_err_arr[t -
1] = np.sum(y_test != np.sign(test_sum)) / y_test.shape[0]
return train_err_arr, test_err_arr
# In[105]:
autojit(linearPred)
autojit(train_test_errors)
# In[106]:
train_err_arr, test_err_arr = train_test_errors(bd.x_train, bd.x_test,
bd.y_train, bd.y_test, alphas,
ls_w_arr)
# In[107]:
plt.figure()
sb.axes_style("darkgrid")
plt.plot(range(1, 1501), train_err_arr, 'g', label="training error")
plt.plot(range(1, 1501), test_err_arr, 'r', label="testing error")
plt.xlabel("Iteratons t")
def compare_perf(fn, args, numba= True, cpython = True,
extra = {},
backends = ('c', 'openmp', 'cuda'),
suppress_output = False,
propagate_exceptions = False):
parakeet_fn = jit(fn)
name = fn.__name__
parakeet_result = None
numba_result = None
cpython_result = None
kwargs = {'suppress_stdout': suppress_output,
'suppress_stderr':suppress_output,
'propagate_exceptions' : propagate_exceptions
}
backend = None
for backend in backends:
with timer('Parakeet (backend = %s) #1 -- %s' % (backend, name), **kwargs):
parakeet_result = parakeet_fn(*args, _backend = backend)
with timer('Parakeet (backend = %s) #2 -- %s' % (backend, name), **kwargs):
parakeet_result = parakeet_fn(*args, _backend = backend)
if numba:
from numba import autojit, config
numba_fn = autojit(fn)
with timer('Numba #1 -- %s' % name, **kwargs):
numba_result = numba_fn(*args)
with timer('Numba #2 -- %s' % name, **kwargs):
numba_result = numba_fn(*args)
if cpython:
with timer('Python -- %s' % name, **kwargs):
cpython_result = fn(*args)
for name, impl in extra.iteritems():
with timer("%s #1" % name, **kwargs):
impl(*args)
with timer("%s #2" % name, **kwargs):
extra_result = impl(*args)
if python_result is not None:
diffs = np.abs(parakeet_result - extra_result)
assert np.allclose(parakeet_result, extra_result, atol = atol, rtol = rtol), \
"Max elt difference between Parakeet and %s = %s (median = %s, min = %s)" % \
(name, np.max(diffs), np.median(diffs), np.min(diffs))
rtol = 0.0001
if backend in ('cuda', 'gpu'):
atol = 0.001
else:
atol = 0.00001
if parakeet_result is not None and cpython_result is not None:
diffs = np.abs(cpython_result - parakeet_result)
assert np.allclose(cpython_result, parakeet_result, atol = atol, rtol = rtol), \
"Max elt difference between Parakeet and CPython = %s (median = %s, min = %s)" % \
(np.max(diffs), np.median(diffs), np.min(diffs))
if numba_result is not None and cpython_result is not None:
diffs = np.abs(cpython_result - numba_result)
assert np.allclose(cpython_result, numba_result, atol = atol, rtol = rtol), \
"Max elt difference between Numba and CPython = %s (median = %s, min = %s)" % \
(np.max(diffs), np.median(diffs), np.min(diffs))
# Authors: Olivier Grisel
# License: MIT
from pairwise import pairwise_python
from numba import autojit
benchmarks = (
("pairwise_numba_nested_for_loops",
autojit(pairwise_python.pairwise_python_nested_for_loops)),
)
ii] = np.apply_along_axis(interp, 0, ZFF, Z[::-1],
Vc[tt, ::-1, jj, ii])
for kk in range(len(ZFF)):
Tpff[tt, kk, jj,
ii] = find_nearest(Rho, Tpff[tt, kk, jj, ii])
Vprime = Vpff - VTavff
Tprime = Tpff[:] - TTavffbin[:]
VTprime = Vprime * Tprime
VTprimetav = np.mean(VTprime, axis=0)
VTprimetav20 = (VTprimetav20 + VTprimetav / (6 * total))
VTbar = np.mean(Vpff * Tpff, axis=0)
VTbar20 = VTbar20 + VTbar / (6 * total)
return VTprimetav20, VTbar20
numba_eddyfluxbin = autojit()(eddyfluxbin)
numba_eddyfluxbin.func_name = "eddyfluxbin"
# Here we go...
print 'Setup done... starting flux calc'
for file in lists:
print file # Where am i?
#VTprimetav20, VTbar20 = numba_eddyfluxbin(file, VTprimetav20,
# VTbar20, 'VVEL')
file2 = netCDF4.Dataset(file, 'r')
Temp = file2.variables['THETA'][:]
V = file2.variables['VVEL'][:]
Vc = numba_regridy(V)
for yr in range((6)):
yr1 = 100 * yr
yr2 = yr1 + 100
Vc = Vc[yr1:yr2]
def test():
f_ = autojit(f)
for v in range(-10,10):
assert f_(v)==f(v)
assert f_(float(v))==f(float(v))
from julia import julia_python
from numba import autojit
benchmarks = (("julia_numba_for_loops",
autojit(julia_python.julia_python_for_loops)), )
# Authors: Yuancheng Peng
# License: MIT
from arc_distance import arc_distance_python
from numba import autojit
benchmarks = (
("arc_distance_numba_for_loops",
autojit(arc_distance_python.arc_distance_python_nested_for_loops)),
)
def test_ifexp():
f_ = autojit(f)
for args in product(range(3), range(3)):
assert f_(*args) == f(*args)
def test_ifexp():
f_ = autojit(f)
for args in product(range(3), range(3)):
assert f_(*args)==f(*args)
num = 0.0
for ii in range(Mf):
for jj in range(Nf):
num += (filt[Mf - 1 - ii, Nf - 1 - jj] *
image[i - Mf2 + ii, j - Nf2 + jj])
result[i, j] = num
return result
# This kind of quadruply-nested for-loop is going to be quite slow.
# Using Numba we can compile this code to LLVM which then gets
# compiled to machine code:
# Now fastfilter_2d runs at speeds as if you had first translated
# it to C, compiled the code and wrapped it with Python
fastfilter_2d = jit(double[:, :](double[:, :], double[:, :]))(filter2d)
autofilter_2d = autojit(filter2d)
# Use utool to time this
imports = ut.codeblock(r'''
# STARTBLOCK
import numpy as np
from numba import double, jit, autojit
# ENDBLOCK
''')
datas = ut.codeblock(r'''
# STARTBLOCK
fastfilter_2d = jit(double[:, :](double[:, :], double[:, :]))(filter2d)
autofilter_2d = autojit(filter2d)
rng = np.random.RandomState(0)
image = rng.rand(100, 100)
from __future__ import print_function, division, absolute_import
import numpy as np
from numba import autojit
def test(a, b):
r = np.empty(3, dtype=bool)
for i in range(len(a)):
r[i] = a[i] != b[i]
return r
test_nb = autojit(test)
a = np.arange(3, dtype=complex)
b = np.arange(3, dtype=complex)
b[1] += 1j
assert np.array_equal(test(a, b), test_nb(a, b))
A_r = 255 - A_r + 0.001 #Prevent division by zero
out = np.empty_like(img, dtype=np.float32)
row, col = img.shape[:2]
for x in xrange(row):
for y in xrange(col):
x_init = max(x-1, 0)
x_final = min(x+2, row-1)
y_init = max(y-1,0)
y_final = min(y+2, col-1)
out[x,y,0] = np.min(img[x_init:x_final, y_init:y_final,0])/A_b
out[x,y,1] = np.min(img[x_init:x_final, y_init:y_final,1])/A_g
out[x,y,2] = np.min(255 - img[x_init:x_final, y_init:y_final,2])/A_r
out = np.max(1 - np.min(out, axis=2), 0.1) #0.1 is the minimum transmission map value
return out
tMap = autojit(calcTransmissionMap)
def waterlight_estimation(img):
"""Estimate brighest pixel in the image"""
img = np.float32(img)
b,g,r = cv2.split(np.float32(img))
minval,maxval,minloc,maxloc = cv2.minMaxLoc(r)
waterlight = img[maxloc[1], maxloc[0]]
return waterlight
def redchannelprior(img):
airlight = waterlight_estimation(img)
t = tMap(img, airlight)
return img
def minkowski_norm(grayimg, p):
def __init__(self, plus, mnus):
self.plus = plus
self.mnus = mnus
def __radd__(self, arg):
return type(arg)(arg.start + self.plus, arg.stop + self.plus)
def __rsub__(self, arg):
return type(arg)(arg.start - self.mnus, arg.stop - self.mnus)
one = Shift(1, 1)
sl = slice(2, 5)
print sl + one
try:
from numba import autojit, jit, double
fast_f = autojit(f)
#fast_f = jit(restype=double[:],argtypes=[double[:], double[:], double[:]])(f)
t0 = time.time()
output = fast_f(Psi_l, Psi_r, CC)
print "time: numba", time.time() - t0
print "output numba", output
except ImportError:
print "Not using Numba nor Numbapro"
t1 = time.time()
output = f(Psi_l, Psi_r, CC)
print "time: numpy", time.time() - t1
print "output numpy", output
@parakeet.jit
def parakeet_local_maxima(data, wsize, mode=wrap):
def is_max(pos):
def is_smaller_neighbor(offset):
neighbor_idx = tuple(mode(p, o-w/2, w) for (p, o, w) in zip(pos, offset, wsize))
return data[neighbor_idx] <= data[pos]
return np.all(parakeet.imap(is_smaller_neighbor, wsize))
return parakeet.imap(is_max, data.shape)
###
# Numba fails no matter what I do with the code, giving up for now
#
import numba
numba_local_maxima = numba.autojit(python_local_maxima)
if __name__ == '__main__':
from timer import timer
shape = (30,10,10,12)
x = np.random.randn(*shape)
wsize = (3,3,3,3)
with timer("Parakeet (first run)"):
parakeet_result = parakeet_local_maxima(x, wsize)
with timer("Parakeet (second run)"):
parakeet_result = parakeet_local_maxima(x, wsize)
with timer("Numba (first run)"):
numba_result = numba_local_maxima(x, wsize, wrap)
with timer("Numba (second run)"):
numba_result = numba_local_maxima(x, wsize, wrap)
with timer("Python"):
grid_x = np.linspace(-bound, bound, N)
for i, x in enumerate(grid_x):
for j, y in enumerate(grid_x):
julia[i, j] = kernel(x, y, cr, ci, lim, cutoff=cutoff)
return julia
def julia(cr, ci, N, bound=1.5, lim=1000., cutoff=1e6):
grid_x = np.linspace(-bound, bound, N)
return np.array(
[[kernel(x, y, cr, ci, lim, cutoff=cutoff) for x in grid_x]
for y in grid_x])
from compare_perf import compare_perf
cr = 0.285
ci = 0.01
N = 1200
bound = 1.5
lim = 1000
cutoff = 1e6
extra = {}
try:
from numba import autojit
extra['numba'] = autojit(julia_loops)
except:
print "Failed to import Numba"
compare_perf(julia, [cr, ci, N, bound, lim, cutoff], numba=False, extra=extra)
X, Y = np.meshgrid(x, y)
w = np.exp(-(X**2 + Y**2) / 2 / 17.5**2)
# Conversion to nanoseconds... 'D' is nanoseconds
Z = raster[:, :] - np.max(raster[:, :])
D = (abs(Z) / 0.15) + 10 # Start all recordings after 10 ns
for i in range(200):
for j in range(200):
ibin = np.int(D[i, j]) #finding ibin
#add power within pixel (i,j)-which is w(i,j) to the returned
#power in the bin number ibin
if ibin < 544:
cumpowr[ibin] = cumpowr[ibin] + w[i, j]
return cumpowr
wave_numba = autojit(waveformC)
folder = "/Users/sgrigsby/Desktop/April_21st_tile_output/"
idx = []
for i, filename in enumerate(os.listdir(folder)):
fn, ext = os.path.splitext(filename)
# source = gdal.Open(folder + filename)
# (source.RasterYSize & source.RasterXSize) == 200
idx.append(fn)
#April_20th_wf_no_conv = pd.DataFrame(index=idx,columns=np.r_[0:544:1])
space = np.zeros((len(idx), 544), dtype=float)
for i, filename in enumerate(tqdm(os.listdir(folder))):
#fn, ext = os.path.splitext(filename)
from growcut import growcut_python
from numba import autojit
benchmarks = (("growcut_numba", autojit(growcut_python.growcut_python)), )
## Bin to the left
elif xr <= xnewl:
pass
## This condition should not happen
else:
pass
i += 1
## Add the sum to ynew
if weight != 0:
ynew[ii] = val / weight
ii += 1
return ynew
if 'numba' in sys.modules:
Interp_integrate = autojit(Interp_integrate)
def Resample_linlog(xold):
"""Resample_linlog(xold)
Resample a linear wavelength vector to a log space and
returns the new vector and the Doppler shift z.
The resampling is done such that the largest wavelength interval
is conserved in order to preserve the spectral resolution.
The Doppler shift is:
1+z = lambda_1 / lambda_0
In the non-relativistic limit:
z = v/c
from julia import julia_python
from numba import autojit
benchmarks = (
("julia_numba_for_loops",
autojit(julia_python.julia_python_for_loops)),
)
break
## Bin to the left
elif xr <= xnewl:
pass
## This condition should not happen
else:
pass
i += 1
## Add the sum to ynew
if weight != 0:
ynew[ii] = val/weight
ii += 1
return ynew
if 'numba' in sys.modules:
Interp_linear_integrate = autojit(Interp_linear_integrate)
def Resample_linlog(xold):
"""
Resample a linear wavelength vector to a log space and
returns the new vector and the Doppler shift z.
The resampling is done such that the largest wavelength interval
is conserved in order to preserve the spectral resolution.
The Doppler shift is:
1+z = lambda_1 / lambda_0
In the non-relativistic limit:
z = v/c
