The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def render_lane(image, corners, ploty, fitx, ):
_, src, dst = perspective_transform(image, corners)
Minv = cv2.getPerspectiveTransform(dst, src)
# Create an image to draw the lines on
warp_zero = np.zeros_like(image[:,:,0]).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts = np.vstack((fitx,ploty)).astype(np.int32).T
# Draw the lane onto the warped blank image
#plt.plot(left_fitx, ploty, color='yellow')
cv2.polylines(color_warp, [pts], False, (0, 255, 0), 10)
#cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(image, 1, newwarp, 0.3, 0)
return result Example 2
def df_type_to_str(i):
'''
Convert into simple datatypes from pandas/numpy types
'''
if isinstance(i, np.bool_):
return bool(i)
if isinstance(i, np.int_):
return int(i)
if isinstance(i, np.float):
if np.isnan(i):
return 'NaN'
elif np.isinf(i):
return str(i)
return float(i)
if isinstance(i, np.uint):
return int(i)
if type(i) == bytes:
return i.decode('UTF-8')
if isinstance(i, (tuple, list)):
return str(i)
if i is pd.NaT: # not identified as a float null
return 'NaN'
return str(i) Example 3
def console_fill_foreground(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_foreground(con, cr, cg, cb) Example 4
def console_fill_background(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_background(con, cr, cg, cb) Example 5
def pickle_transitions_matrix_data():
transitions = pickle.load( open( "/ssd/ddimitrov/pickle/transitions", "rb" ) )
vocab = pickle.load( open( "/ssd/ddimitrov/pickle/vocab", "rb" ) )
i_indices = array.array(str("l"))
j_indices = array.array(str("l"))
values = array.array(str("d"))
for s, targets in transitions.iteritems():
for t, v in targets.iteritems():
i_indices.append(vocab[s])
j_indices.append(vocab[t])
values.append(v)
i_indices = np.frombuffer(i_indices, dtype=np.int_)
j_indices = np.frombuffer(j_indices, dtype=np.int_)
values = np.frombuffer(values, dtype=np.float64)
transition_matrix=[i_indices,j_indices,values]
pickle.dump(transition_matrix, open("/ssd/ddimitrov/pickle/transition_matrix", "wb"), protocol=pickle.HIGHEST_PROTOCOL)
print "transition_matrix" Example 6
def test_empty_tuple_index(self):
# Empty tuple index creates a view
a = np.array([1, 2, 3])
assert_equal(a[()], a)
assert_(a[()].base is a)
a = np.array(0)
assert_(isinstance(a[()], np.int_))
# Regression, it needs to fall through integer and fancy indexing
# cases, so need the with statement to ignore the non-integer error.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '', DeprecationWarning)
a = np.array([1.])
assert_(isinstance(a[0.], np.float_))
a = np.array([np.array(1)], dtype=object)
assert_(isinstance(a[0.], np.ndarray)) Example 7
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1)) Example 8
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1)) Example 9
def test_allclose(self):
# Tests allclose on arrays
a = np.random.rand(10)
b = a + np.random.rand(10) * 1e-8
self.assertTrue(allclose(a, b))
# Test allclose w/ infs
a[0] = np.inf
self.assertTrue(not allclose(a, b))
b[0] = np.inf
self.assertTrue(allclose(a, b))
# Test allclose w/ masked
a = masked_array(a)
a[-1] = masked
self.assertTrue(allclose(a, b, masked_equal=True))
self.assertTrue(not allclose(a, b, masked_equal=False))
# Test comparison w/ scalar
a *= 1e-8
a[0] = 0
self.assertTrue(allclose(a, 0, masked_equal=True))
# Test that the function works for MIN_INT integer typed arrays
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
self.assertTrue(allclose(a, a)) Example 10
def shrink_image(img, bins, return_integer=True, method='sum' ):
'''YG Dec 12, 2017 [email protected] shrink a two-d image by factor as bins, i.e., bins_x, bins_y, by summing up
input:
img: 2d array,
bins: integer list, eg. [2,2]
return_integer: if True, convert the output as integer
method: support sum/avg
output:
imgb: binned img
'''
m,n = img.shape
bx, by = bins
Nx, Ny = m//bx, n//by
#print(Nx*bx, Ny*by)
if method == 'sum':
d = img[:Nx*bx, :Ny*by].reshape( Nx,bx, Ny, by).sum(axis=(1,3) )
elif method == 'avg':
d = img[:Nx*bx, :Ny*by].reshape( Nx,bx, Ny, by).mean(axis=(1,3) )
if return_integer:
return np.int_( d )
else:
return d Example 11
def get_fra_num_by_dose( exp_dose, exp_time, att=1, dead_time =2 ):
'''
Calculate the frame number to be correlated by giving a X-ray exposure dose
Paramters:
exp_dose: a list, the exposed dose, e.g., in unit of exp_time(ms)*N(fram num)*att( attenuation)
exp_time: float, the exposure time for a xpcs time sereies
dead_time: dead time for the fast shutter reponse time, CHX = 2ms
Return:
noframes: the frame number to be correlated, exp_dose/( exp_time + dead_time )
e.g.,
no_dose_fra = get_fra_num_by_dose( exp_dose = [ 3.34* 20, 3.34*50, 3.34*100, 3.34*502, 3.34*505 ],
exp_time = 1.34, dead_time = 2)
--> no_dose_fra will be array([ 20, 50, 100, 502, 504])
'''
return np.int_( np.array( exp_dose )/( exp_time + dead_time)/ att ) Example 12
def create_time_slice( N, slice_num, slice_width, edges=None ):
'''create a ROI time regions '''
if edges is not None:
time_edge = edges
else:
if slice_num==1:
time_edge = [ [0,N] ]
else:
tstep = N // slice_num
te = np.arange( 0, slice_num +1 ) * tstep
tc = np.int_( (te[:-1] + te[1:])/2 )[1:-1]
if slice_width%2:
sw = slice_width//2 +1
time_edge = [ [0,slice_width], ] + [ [s-sw+1,s+sw] for s in tc ] + [ [N-slice_width,N]]
else:
sw= slice_width//2
time_edge = [ [0,slice_width], ] + [ [s-sw,s+sw] for s in tc ] + [ [N-slice_width,N]]
return np.array(time_edge) Example 13
def get_his_std_qi( data_pixel_qi, max_cts=None):
'''
YG. Dev 16, 2016
Calculate the photon histogram for one q by giving
Parameters:
data_pixel_qi: one-D array, for the photon counts
max_cts: for bin max, bin will be [0,1,2,..., max_cts]
Return:
bins
his
std
'''
if max_cts is None:
max_cts = np.max( data_pixel_qi ) +1
bins = np.arange(max_cts)
dqn, dqm = data_pixel_qi.shape
#get histogram here
H = np.apply_along_axis(np.bincount, 1, np.int_(data_pixel_qi), minlength= max_cts )/dqm
#do average for different frame
his = np.average( H, axis=0)
std = np.std( H, axis=0 )
#cal average photon counts
kmean= np.average(data_pixel_qi )
return bins, his, std, kmean Example 14
def default(self, obj):
# convert dates and numpy objects in a json serializable format
if isinstance(obj, datetime):
return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
elif isinstance(obj, date):
return obj.strftime('%Y-%m-%d')
elif type(obj) in (np.int_, np.intc, np.intp, np.int8, np.int16,
np.int32, np.int64, np.uint8, np.uint16,
np.uint32, np.uint64):
return int(obj)
elif type(obj) in (np.bool_,):
return bool(obj)
elif type(obj) in (np.float_, np.float16, np.float32, np.float64,
np.complex_, np.complex64, np.complex128):
return float(obj)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, obj) Example 15
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1)) Example 16
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1)) Example 17
def test_allclose(self):
# Tests allclose on arrays
a = np.random.rand(10)
b = a + np.random.rand(10) * 1e-8
self.assertTrue(allclose(a, b))
# Test allclose w/ infs
a[0] = np.inf
self.assertTrue(not allclose(a, b))
b[0] = np.inf
self.assertTrue(allclose(a, b))
# Test allclose w/ masked
a = masked_array(a)
a[-1] = masked
self.assertTrue(allclose(a, b, masked_equal=True))
self.assertTrue(not allclose(a, b, masked_equal=False))
# Test comparison w/ scalar
a *= 1e-8
a[0] = 0
self.assertTrue(allclose(a, 0, masked_equal=True))
# Test that the function works for MIN_INT integer typed arrays
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
self.assertTrue(allclose(a, a)) Example 18
def console_fill_foreground(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_foreground(con, cr, cg, cb) Example 19
def console_fill_background(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_background(con, cr, cg, cb) Example 20
def __tiledoutput__(self, net_op, batch_size, num_cols=8, net_recon_const=None):
num_rows = np.int_(np.ceil((batch_size*1.)/num_cols))
out_img = np.zeros((num_rows*self.outshape[0], num_cols*self.outshape[1], 3), dtype='uint8')
img_lab = np.zeros((self.outshape[0], self.outshape[1], 3), dtype='uint8')
c = 0
r = 0
for i in range(batch_size):
if(i % num_cols == 0 and i > 0):
r = r + 1
c = 0
img_lab[..., 0] = self.__decodeimg__(net_recon_const[i, 0, :, :].reshape(\
self.outshape[0], self.outshape[1]))
img_lab[..., 1] = self.__decodeimg__(net_op[i, 0, :, :].reshape(\
self.shape[0], self.shape[1]))
img_lab[..., 2] = self.__decodeimg__(net_op[i, 1, :, :].reshape(\
self.shape[0], self.shape[1]))
img_rgb = cv2.cvtColor(img_lab, cv2.COLOR_LAB2BGR)
out_img[r*self.outshape[0]:(r+1)*self.outshape[0], \
c*self.outshape[1]:(c+1)*self.outshape[1], ...] = img_rgb
c = c+1
return out_img Example 21
def test_constructors():
from pybind11_tests.array import default_constructors, converting_constructors
defaults = default_constructors()
for a in defaults.values():
assert a.size == 0
assert defaults["array"].dtype == np.array([]).dtype
assert defaults["array_t<int32>"].dtype == np.int32
assert defaults["array_t<double>"].dtype == np.float64
results = converting_constructors([1, 2, 3])
for a in results.values():
np.testing.assert_array_equal(a, [1, 2, 3])
assert results["array"].dtype == np.int_
assert results["array_t<int32>"].dtype == np.int32
assert results["array_t<double>"].dtype == np.float64 Example 22
def console_fill_foreground(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_foreground(con, cr, cg, cb) Example 23
def console_fill_background(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_background(con, cr, cg, cb) Example 24
def maybe_convert_indices(indices, n):
""" if we have negative indicies, translate to postive here
if have indicies that are out-of-bounds, raise an IndexError
"""
if isinstance(indices, list):
indices = np.array(indices)
if len(indices) == 0:
# If list is empty, np.array will return float and cause indexing
# errors.
return np.empty(0, dtype=np.int_)
mask = indices < 0
if mask.any():
indices[mask] += n
mask = (indices >= n) | (indices < 0)
if mask.any():
raise IndexError("indices are out-of-bounds")
return indices Example 25
def test_empty_fancy(self):
empty_farr = np.array([], dtype=np.float_)
empty_iarr = np.array([], dtype=np.int_)
empty_barr = np.array([], dtype=np.bool_)
# pd.DatetimeIndex is excluded, because it overrides getitem and should
# be tested separately.
for idx in [self.strIndex, self.intIndex, self.floatIndex]:
empty_idx = idx.__class__([])
self.assertTrue(idx[[]].identical(empty_idx))
self.assertTrue(idx[empty_iarr].identical(empty_idx))
self.assertTrue(idx[empty_barr].identical(empty_idx))
# np.ndarray only accepts ndarray of int & bool dtypes, so should
# Index.
self.assertRaises(IndexError, idx.__getitem__, empty_farr) Example 26
def test_empty_tuple_index(self):
# Empty tuple index creates a view
a = np.array([1, 2, 3])
assert_equal(a[()], a)
assert_(a[()].base is a)
a = np.array(0)
assert_(isinstance(a[()], np.int_))
# Regression, it needs to fall through integer and fancy indexing
# cases, so need the with statement to ignore the non-integer error.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '', DeprecationWarning)
a = np.array([1.])
assert_(isinstance(a[0.], np.float_))
a = np.array([np.array(1)], dtype=object)
assert_(isinstance(a[0.], np.ndarray)) Example 27
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, np.ones((1, 10)))
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1)) Example 28
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, np.ones((1, 10)))
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1)) Example 29
def test_allclose(self):
# Tests allclose on arrays
a = np.random.rand(10)
b = a + np.random.rand(10) * 1e-8
self.assertTrue(allclose(a, b))
# Test allclose w/ infs
a[0] = np.inf
self.assertTrue(not allclose(a, b))
b[0] = np.inf
self.assertTrue(allclose(a, b))
# Test all close w/ masked
a = masked_array(a)
a[-1] = masked
self.assertTrue(allclose(a, b, masked_equal=True))
self.assertTrue(not allclose(a, b, masked_equal=False))
# Test comparison w/ scalar
a *= 1e-8
a[0] = 0
self.assertTrue(allclose(a, 0, masked_equal=True))
# Test that the function works for MIN_INT integer typed arrays
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
self.assertTrue(allclose(a, a)) Example 30
def test_empty_tuple_index(self):
# Empty tuple index creates a view
a = np.array([1, 2, 3])
assert_equal(a[()], a)
assert_(a[()].base is a)
a = np.array(0)
assert_(isinstance(a[()], np.int_))
# Regression, it needs to fall through integer and fancy indexing
# cases, so need the with statement to ignore the non-integer error.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '', DeprecationWarning)
a = np.array([1.])
assert_(isinstance(a[0.], np.float_))
a = np.array([np.array(1)], dtype=object)
assert_(isinstance(a[0.], np.ndarray)) Example 31
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, np.ones((1, 10)))
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1)) Example 32
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, np.ones((1, 10)))
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1)) Example 33
def test_allclose(self):
# Tests allclose on arrays
a = np.random.rand(10)
b = a + np.random.rand(10) * 1e-8
self.assertTrue(allclose(a, b))
# Test allclose w/ infs
a[0] = np.inf
self.assertTrue(not allclose(a, b))
b[0] = np.inf
self.assertTrue(allclose(a, b))
# Test all close w/ masked
a = masked_array(a)
a[-1] = masked
self.assertTrue(allclose(a, b, masked_equal=True))
self.assertTrue(not allclose(a, b, masked_equal=False))
# Test comparison w/ scalar
a *= 1e-8
a[0] = 0
self.assertTrue(allclose(a, 0, masked_equal=True))
# Test that the function works for MIN_INT integer typed arrays
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
self.assertTrue(allclose(a, a)) Example 34
def console_fill_foreground(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_foreground(con, cr, cg, cb) Example 35
def console_fill_background(con,r,g,b) :
if len(r) != len(g) or len(r) != len(b):
raise TypeError('R, G and B must all have the same size.')
if (numpy_available and isinstance(r, numpy.ndarray) and
isinstance(g, numpy.ndarray) and isinstance(b, numpy.ndarray)):
#numpy arrays, use numpy's ctypes functions
r = numpy.ascontiguousarray(r, dtype=numpy.int_)
g = numpy.ascontiguousarray(g, dtype=numpy.int_)
b = numpy.ascontiguousarray(b, dtype=numpy.int_)
cr = r.ctypes.data_as(POINTER(c_int))
cg = g.ctypes.data_as(POINTER(c_int))
cb = b.ctypes.data_as(POINTER(c_int))
else:
# otherwise convert using ctypes arrays
cr = (c_int * len(r))(*r)
cg = (c_int * len(g))(*g)
cb = (c_int * len(b))(*b)
_lib.TCOD_console_fill_background(con, cr, cg, cb) Example 36
def test_get_all_route_shapes(self):
res = self.gtfs.get_all_route_shapes()
self.assertTrue(isinstance(res, list))
el = res[0]
keys = u"name type agency lats lons".split()
for key in keys:
self.assertTrue(key in el)
for el in res:
self.assertTrue(isinstance(el[u"name"], string_types), type(el[u"name"]))
self.assertTrue(isinstance(el[u"type"], (int, numpy.int_)), type(el[u'type']))
self.assertTrue(isinstance(el[u"agency"], string_types))
self.assertTrue(isinstance(el[u"lats"], list), type(el[u'lats']))
self.assertTrue(isinstance(el[u"lons"], list))
self.assertTrue(isinstance(el[u'lats'][0], float))
self.assertTrue(isinstance(el[u'lons'][0], float)) Example 37
def test_get_stop_count_data(self):
dt_start_query = datetime.datetime(2007, 1, 1, 7, 59, 59)
dt_end_query = datetime.datetime(2007, 1, 1, 10, 2, 1)
start_query = self.gtfs.unlocalized_datetime_to_ut_seconds(dt_start_query)
end_query = self.gtfs.unlocalized_datetime_to_ut_seconds(dt_end_query)
df = self.gtfs.get_stop_count_data(start_query, end_query)
self.assertTrue(isinstance(df, pandas.DataFrame))
columns = ["stop_I", "count", "lat", "lon", "name"]
for c in columns:
self.assertTrue(c in df.columns)
el = df[c].iloc[0]
if c in ["stop_I", "count"]:
self.assertTrue(isinstance(el, (int, numpy.int_)))
if c in ["lat", "lon"]:
self.assertTrue(isinstance(el, float))
if c in ["name"]:
self.assertTrue(isinstance(el, string_types), type(el))
self.assertTrue((df['count'].values > 0).any()) Example 38
def get_reads_base_sds(chrm_strand_reads, chrm_len, rev_strand):
base_sd_sums = np.zeros(chrm_len)
base_cov = np.zeros(chrm_len, dtype=np.int_)
for r_data in chrm_strand_reads:
# extract read means data so data across all chrms is not
# in RAM at one time
try:
read_data = h5py.File(r_data.fn, 'r')
except IOError:
# probably truncated file
continue
events_slot = '/'.join((
'/Analyses', r_data.corr_group, 'Events'))
if events_slot not in read_data:
continue
read_sds = read_data[events_slot]['norm_stdev']
if rev_strand:
read_sds = read_sds[::-1]
base_sd_sums[r_data.start:
r_data.start + len(read_sds)] += read_sds
base_cov[r_data.start:r_data.start + len(read_sds)] += 1
return base_sd_sums / base_cov Example 39
def get_reads_base_lengths(chrm_strand_reads, chrm_len, rev_strand):
base_length_sums = np.zeros(chrm_len)
base_cov = np.zeros(chrm_len, dtype=np.int_)
for r_data in chrm_strand_reads:
# extract read means data so data across all chrms is not
# in RAM at one time
try:
read_data = h5py.File(r_data.fn, 'r')
except IOError:
# probably truncated file
continue
events_slot = '/'.join((
'/Analyses', r_data.corr_group, 'Events'))
if events_slot not in read_data:
continue
read_lengths = read_data[events_slot]['length']
if rev_strand:
read_lengths = read_lengths[::-1]
base_length_sums[
r_data.start:
r_data.start + len(read_lengths)] += read_lengths
base_cov[r_data.start:r_data.start + len(read_lengths)] += 1
return base_length_sums / base_cov Example 40
def test_empty_tuple_index(self):
# Empty tuple index creates a view
a = np.array([1, 2, 3])
assert_equal(a[()], a)
assert_(a[()].base is a)
a = np.array(0)
assert_(isinstance(a[()], np.int_))
# Regression, it needs to fall through integer and fancy indexing
# cases, so need the with statement to ignore the non-integer error.
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '', DeprecationWarning)
a = np.array([1.])
assert_(isinstance(a[0.], np.float_))
a = np.array([np.array(1)], dtype=object)
assert_(isinstance(a[0.], np.ndarray)) Example 41
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1)) Example 42
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1)) Example 43
def test_allclose(self):
# Tests allclose on arrays
a = np.random.rand(10)
b = a + np.random.rand(10) * 1e-8
self.assertTrue(allclose(a, b))
# Test allclose w/ infs
a[0] = np.inf
self.assertTrue(not allclose(a, b))
b[0] = np.inf
self.assertTrue(allclose(a, b))
# Test allclose w/ masked
a = masked_array(a)
a[-1] = masked
self.assertTrue(allclose(a, b, masked_equal=True))
self.assertTrue(not allclose(a, b, masked_equal=False))
# Test comparison w/ scalar
a *= 1e-8
a[0] = 0
self.assertTrue(allclose(a, 0, masked_equal=True))
# Test that the function works for MIN_INT integer typed arrays
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
self.assertTrue(allclose(a, a)) Example 44
def default(self, obj):
# convert dates and numpy objects in a json serializable format
if isinstance(obj, datetime):
return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
elif isinstance(obj, date):
return obj.strftime('%Y-%m-%d')
elif type(obj) in [np.int_, np.intc, np.intp, np.int8, np.int16,
np.int32, np.int64, np.uint8, np.uint16,
np.uint32, np.uint64]:
return int(obj)
elif type(obj) in [np.bool_]:
return bool(obj)
elif type(obj) in [np.float_, np.float16, np.float32, np.float64,
np.complex_, np.complex64, np.complex128]:
return float(obj)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, obj) Example 45
def test_int_subclassing(self):
# Regression test for https://github.com/numpy/numpy/pull/3526
numpy_int = np.int_(0)
if sys.version_info[0] >= 3:
# On Py3k int_ should not inherit from int, because it's not
# fixed-width anymore
assert_equal(isinstance(numpy_int, int), False)
else:
# Otherwise, it should inherit from int...
assert_equal(isinstance(numpy_int, int), True)
# ... and fast-path checks on C-API level should also work
from numpy.core.multiarray_tests import test_int_subclass
assert_equal(test_int_subclass(numpy_int), True) Example 46
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1)) Example 47
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1)) Example 48
def test_allclose(self):
# Tests allclose on arrays
a = np.random.rand(10)
b = a + np.random.rand(10) * 1e-8
self.assertTrue(allclose(a, b))
# Test allclose w/ infs
a[0] = np.inf
self.assertTrue(not allclose(a, b))
b[0] = np.inf
self.assertTrue(allclose(a, b))
# Test allclose w/ masked
a = masked_array(a)
a[-1] = masked
self.assertTrue(allclose(a, b, masked_equal=True))
self.assertTrue(not allclose(a, b, masked_equal=False))
# Test comparison w/ scalar
a *= 1e-8
a[0] = 0
self.assertTrue(allclose(a, 0, masked_equal=True))
# Test that the function works for MIN_INT integer typed arrays
a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
self.assertTrue(allclose(a, a)) Example 49
def data_style_func(df):
'''
Default value that can be used as callback for data_style_func
Args:
df: the dataframe that will be used to build the presentation model
Returns:
a function table takes idx, col as arguments and returns a dictionary of html style attributes
'''
def _style_func(r, c):
if isinstance(df.at[r,c], (np.int_, np.float, np.uint)):
return td_style_to_str(default_numeric_td_style)
return td_style_to_str(default_td_style)
return _style_func Example 50
def __init__(self, max_value=10, **kwargs):
Metric.__init__(self, **kwargs)
self.counts = np.zeros(2+max_value, dtype=np.int_)
self.max_value = max_value 