Python numpy 模块，True_() 实例源码
我们从Python开源项目中，提取了以下33个代码示例，用于说明如何使用numpy.True_()。
def masked_matrix(matrix, all_zero=False):
"""
Returns masked version of HicMatrix. By default, all entries in zero-count
rows and columns are masked.
:param matrix: A numpy 2D matrix
:param all_zero: Mask ALL zero-count entries
:returns: MaskedArray with zero entries masked
"""
if all_zero:
return np.ma.MaskedArray(matrix, mask=np.isclose(matrix, 0.))
col_zero = np.isclose(np.sum(matrix, axis=0), 0.)
row_zero = np.isclose(np.sum(matrix, axis=1), 0.)
mask = np.zeros(matrix.shape, dtype=np.bool_)
mask[:, col_zero] = np.True_
mask[row_zero, :] = np.True_
return np.ma.MaskedArray(matrix, mask=mask)
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def _recalc_display_image_minmax(self):
finite_mask = np.isfinite(self.display_image)
if finite_mask.max() is np.True_:
self._display_image_min = self.display_image[finite_mask].min()
self._display_image_max = self.display_image[finite_mask].max()
else:
self._display_image_min = 0.
self._display_image_max = 0.
def _recalc_display_image_minmax(self):
finite_mask = np.isfinite(self.display_image)
if finite_mask.max() is np.True_:
self._display_image_min = self.display_image[finite_mask].min()
self._display_image_max = self.display_image[finite_mask].max()
else:
self._display_image_min = 0.
self._display_image_max = 0.
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
self.assertTrue((t and s) is s)
self.assertTrue((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
self.assertTrue((t | t) is t)
self.assertTrue((f | t) is t)
self.assertTrue((t | f) is t)
self.assertTrue((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
self.assertTrue((t & t) is t)
self.assertTrue((f & t) is f)
self.assertTrue((t & f) is f)
self.assertTrue((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
self.assertTrue((t ^ t) is f)
self.assertTrue((f ^ t) is t)
self.assertTrue((t ^ f) is t)
self.assertTrue((f ^ f) is f)
def aperture_phot(im, x, y, star_radius, sky_inner_radius, sky_outer_radius,
return_distances=False):
"""
im - 2-d numpy array
x,y - coordinates of center of star
star_radius - radius of photometry circle
sky_inner_radius, sky_outer_radius - defines annulus for determining sky
(if sky_inner_radius > sky_outer_radius, aperture_phot flips them)
----
Note that this is a very quick-and-dirty aperture photometry routine.
No error checking.
No partial pixels.
Many ways this could fail and/or give misleading results.
Not to be used within 12 hours of eating food.
Use only immediately after a large meal.
----
returns dictionary with:
flux - sky-subtracted flux inside star_radius
sky_per_pixel - sky counts per pixel determined from sky annulus
sky_per_pixel_err - estimated 1-sigma uncertainty in sky_per_pixel
sky_err - estimated 1-sigma uncertainty in sky subtraction from flux
n_star_pix - number of pixels in star_radius
n_sky_pix - number of pixels in sky annulus
x - input x
y - input y
star_radius - input star_radius
sky_inner_radius - input sky_inner_radius
sky_outer_radius - input sky_outer_radius
"""
if np.isnan(x) or np.isnan(y):
return {'error-msg':'One or both of x/y were NaN.', 'x':x, 'y':y, 'star_radius': star_radius,
'sky_inner_radius': sky_inner_radius, 'sky_outer_radius': sky_outer_radius,
'n_star_pix':0, 'n_sky_pix':0, 'sky_per_pixel':np.nan, 'sky_per_pixel_err':np.nan,
'flux':np.nan, 'sky_err':np.nan, 'distances':[]}
if sky_inner_radius > sky_outer_radius:
sky_inner_radius, sky_outer_radius = sky_outer_radius, sky_inner_radius
output = {'x': x, 'y': y, 'star_radius': star_radius,
'sky_inner_radius': sky_inner_radius, 'sky_outer_radius': sky_outer_radius}
xdist = np.outer(np.ones(im.shape[0]), np.arange(im.shape[1]) - x)
ydist = np.outer(np.arange(im.shape[0]) - y, np.ones(im.shape[1]))
dist = np.sqrt(xdist**2 + ydist**2)
star_mask = dist <= star_radius
star_pixels = im[star_mask]
sky_pixels = im[(dist >= sky_inner_radius) & (dist <= sky_outer_radius)]
output['n_star_pix'] = star_pixels.size
output['n_sky_pix'] = sky_pixels.size
finite_mask = np.isfinite(sky_pixels)
if finite_mask.max() is np.True_:
sky_per_pixel, median, stddev = sigma_clipped_stats(sky_pixels[finite_mask])
else:
sky_per_pixel, median, stddev = np.nan, np.nan, np.inf
sky_per_pixel_err = stddev/np.sqrt(finite_mask.sum())
output['sky_per_pixel'] = sky_per_pixel
# TODO: check that are doing sky_per_pixel_err right. In one quick test seemed high (but maybe wasn't a good test)
output['sky_per_pixel_err'] = sky_per_pixel_err
output['flux'] = star_pixels.sum() - sky_per_pixel*star_pixels.size
output['sky_err'] = sky_per_pixel_err*np.sqrt(star_pixels.size)
if return_distances:
output['distances'] = dist
return output
def aperture_phot(im, x, y, star_radius, sky_inner_radius, sky_outer_radius,
return_distances=False):
"""
im - 2-d numpy array
x,y - coordinates of center of star
star_radius - radius of photometry circle
sky_inner_radius, sky_outer_radius - defines annulus for determining sky
(if sky_inner_radius > sky_outer_radius, aperture_phot flips them)
----
Note that this is a very quick-and-dirty aperture photometry routine.
No error checking.
No partial pixels.
Many ways this could fail and/or give misleading results.
Not to be used within 12 hours of eating food.
Use only immediately after a large meal.
----
returns dictionary with:
flux - sky-subtracted flux inside star_radius
sky_per_pixel - sky counts per pixel determined from sky annulus
sky_per_pixel_err - estimated 1-sigma uncertainty in sky_per_pixel
sky_err - estimated 1-sigma uncertainty in sky subtraction from flux
n_star_pix - number of pixels in star_radius
n_sky_pix - number of pixels in sky annulus
x - input x
y - input y
star_radius - input star_radius
sky_inner_radius - input sky_inner_radius
sky_outer_radius - input sky_outer_radius
"""
if np.isnan(x) or np.isnan(y):
return {'error-msg':'One or both of x/y were NaN.', 'x':x, 'y':y, 'star_radius': star_radius,
'sky_inner_radius': sky_inner_radius, 'sky_outer_radius': sky_outer_radius,
'n_star_pix':0, 'n_sky_pix':0, 'sky_per_pixel':np.nan, 'sky_per_pixel_err':np.nan,
'flux':np.nan, 'sky_err':np.nan, 'distances':[]}
if sky_inner_radius > sky_outer_radius:
sky_inner_radius, sky_outer_radius = sky_outer_radius, sky_inner_radius
output = {'x': x, 'y': y, 'star_radius': star_radius,
'sky_inner_radius': sky_inner_radius, 'sky_outer_radius': sky_outer_radius}
xdist = np.outer(np.ones(im.shape[0]), np.arange(im.shape[1]) - x)
ydist = np.outer(np.arange(im.shape[0]) - y, np.ones(im.shape[1]))
dist = np.sqrt(xdist**2 + ydist**2)
star_mask = dist <= star_radius
star_pixels = im[star_mask]
sky_pixels = im[(dist >= sky_inner_radius) & (dist <= sky_outer_radius)]
output['n_star_pix'] = star_pixels.size
output['n_sky_pix'] = sky_pixels.size
finite_mask = np.isfinite(sky_pixels)
if finite_mask.max() is np.True_:
sky_per_pixel, median, stddev = sigma_clipped_stats(sky_pixels[finite_mask])
else:
sky_per_pixel, median, stddev = np.nan, np.nan, np.inf
sky_per_pixel_err = stddev/np.sqrt(finite_mask.sum())
output['sky_per_pixel'] = sky_per_pixel
# TODO: check that are doing sky_per_pixel_err right. In one quick test seemed high (but maybe wasn't a good test)
output['sky_per_pixel_err'] = sky_per_pixel_err
output['flux'] = star_pixels.sum() - sky_per_pixel*star_pixels.size
output['sky_err'] = sky_per_pixel_err*np.sqrt(star_pixels.size)
if return_distances:
output['distances'] = dist
return output