def get_distance(locA, locB):
# use haversine forumla
print "ayyo"
earth_rad = 6371.0
dlat = deg2rad(locB[0] - locA[0])
dlon = deg2rad(locB[1] - locA[1])
a = sin(dlat / 2) * sin(dlat / 2) + \
cos(deg2rad(locA[0])) * cos(deg2rad(locB[0])) * \
sin(dlon / 2) * sin(dlon / 2)
c = 2 * arctan2(sqrt(a), sqrt(1 - a))
return earth_rad * c
def angle(z, deg=0):
"""Return the angle of the complex argument z.
"""
if deg:
fact = 180/pi
else:
fact = 1.0
z = asarray(z)
if (issubclass(z.dtype.type, _nx.complexfloating)):
zimag = z.imag
zreal = z.real
else:
zimag = 0
zreal = z
return arctan2(zimag, zreal) * fact
def update_metric(self, pos_mat, ang_vec):
#This method allows us to update the flocking model with the Metric model
#Each new velocity is constructed by averaging over all of the velocities within
#the radius selected, self.r.
#Inputs -- x-coordinates, y-coordinates, trajectories for time = t
#Outputs -- x-coordinates, y-coordinates, trajectories for time = t + (delta t)
avg_sin = 0 * ang_vec
avg_cos = 0 * ang_vec
for j in range(0, self.N):
#find distances for all particles
dist_vec = self.calc_dist(pos_mat, j)
#find indicies that are within the radius
ngbs_idx_vec = np.where(dist_vec <= self.r)[0]
if len(ngbs_idx_vec) == 0:
avg_sin[j] = 0
avg_cos[j] = 0
else:
#find average velocity of those inside the radius
sint = np.average(sin(ang_vec[ngbs_idx_vec]))
cost = np.average(cos(ang_vec[ngbs_idx_vec]))
avg_sin[j] = sint
avg_cos[j] = cost
#construct the noise
noise = self.theta_noise()
#update velocities and positions
cosi = (self.ep) * avg_cos + (1 - self.ep) * np.cos(ang_vec)
sini = (self.ep) * avg_sin + (1 - self.ep) * np.sin(ang_vec)
new_ang_vec = arctan2(sini, cosi) + noise
mask = self.status_vec == 1
new_ang_vec[mask] += np.pi
new_mod_ang_vec = np.mod(new_ang_vec, 2 * np.pi)
pos_mat[:, 0] = pos_mat[:, 0] + self.dt * self.v * cos(new_mod_ang_vec)
pos_mat[:, 1] = pos_mat[:, 1] + self.dt * self.v * sin(new_mod_ang_vec)
#Make sure that the positions are not outside the boundary.
#If so, correct for periodicity
pos_mat = self.check_boundary(pos_mat)
#Outputs returned
return (pos_mat, new_ang_vec)
def sunposIntermediate(iYear, iMonth, iDay, dHours, dMinutes, dSeconds):
# Calculate difference in days between the current Julian Day
# and JD 2451545.0, which is noon 1 January 2000 Universal Time
# Calculate time of the day in UT decimal hours
dDecimalHours = dHours + (dMinutes + dSeconds / 60.0) / 60.0
# Calculate current Julian Day
liAux1 = (iMonth - 14) / 12
liAux2 = (1461 * (iYear + 4800 + liAux1)) / 4 + (
367 * (iMonth - 2 - 12 * liAux1)) / 12 - (3 * (
(iYear + 4900 + liAux1) / 100)) / 4 + iDay - 32075
dJulianDate = liAux2 - 0.5 + dDecimalHours / 24.0
# Calculate difference between current Julian Day and JD 2451545.0
dElapsedJulianDays = dJulianDate - 2451545.0
# Calculate ecliptic coordinates (ecliptic longitude and obliquity of the
# ecliptic in radians but without limiting the angle to be less than 2*Pi
# (i.e., the result may be greater than 2*Pi)
dOmega = 2.1429 - 0.0010394594 * dElapsedJulianDays
dMeanLongitude = 4.8950630 + 0.017202791698 * dElapsedJulianDays # Radians
dMeanAnomaly = 6.2400600 + 0.0172019699 * dElapsedJulianDays
dEclipticLongitude = dMeanLongitude + 0.03341607 * sin(
dMeanAnomaly) + 0.00034894 * sin(
2 * dMeanAnomaly) - 0.0001134 - 0.0000203 * sin(dOmega)
dEclipticObliquity = 0.4090928 - 6.2140e-9 * dElapsedJulianDays + 0.0000396 * cos(
dOmega)
# Calculate celestial coordinates ( right ascension and declination ) in radians
# but without limiting the angle to be less than 2*Pi (i.e., the result may be
# greater than 2*Pi)
dSin_EclipticLongitude = sin(dEclipticLongitude)
dY = cos(dEclipticObliquity) * dSin_EclipticLongitude
dX = cos(dEclipticLongitude)
dRightAscension = arctan2(dY, dX)
if dRightAscension < 0.0:
dRightAscension = dRightAscension + 2 * pi
dDeclination = arcsin(sin(dEclipticObliquity) * dSin_EclipticLongitude)
dGreenwichMeanSiderealTime = 6.6974243242 + 0.0657098283 * dElapsedJulianDays + dDecimalHours
return (dRightAscension, dDeclination, dGreenwichMeanSiderealTime)
def assert_arctan2_isnan(x, y):
assert_(np.isnan(ncu.arctan2(x, y)), "arctan(%s, %s) is %s, not nan" % (x, y, ncu.arctan2(x, y)))
def assert_arctan2_isnzero(x, y):
assert_(
(ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))),
"arctan(%s, %s) is %s, not -0" % (x, y, ncu.arctan2(x, y)),
)
def test_inf_any(self):
# atan2(+-infinity, x) returns +-pi/2 for finite x.
assert_almost_equal(ncu.arctan2(np.inf, 1), 0.5 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, 1), -0.5 * np.pi)
def test_inf_pinf(self):
# atan2(+-infinity, +infinity) returns +-pi/4.
assert_almost_equal(ncu.arctan2(np.inf, np.inf), 0.25 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, np.inf), -0.25 * np.pi)
def test_zero_negative(self):
# atan2(+-0, x) returns +-pi for x < 0.
assert_almost_equal(ncu.arctan2(np.PZERO, -1), np.pi)
assert_almost_equal(ncu.arctan2(np.NZERO, -1), -np.pi)
def test_negative_zero(self):
# atan2(y, +-0) returns -pi/2 for y < 0.
assert_almost_equal(ncu.arctan2(-1, np.PZERO), -0.5 * np.pi)
assert_almost_equal(ncu.arctan2(-1, np.NZERO), -0.5 * np.pi)
def test_any_ninf(self):
# atan2(+-y, -infinity) returns +-pi for finite y > 0.
assert_almost_equal(ncu.arctan2(1, np.NINF), np.pi)
assert_almost_equal(ncu.arctan2(-1, np.NINF), -np.pi)
def hog(image,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(3, 3),
visualise=False,
normalise=False):
"""Extract Histogram of Oriented Gradients (HOG) for a given image.
Compute a Histogram of Oriented Gradients (HOG) by
1. (optional) global image normalisation
2. computing the gradient image in x and y
3. computing gradient histograms
4. normalising across blocks
5. flattening into a feature vector
Parameters
----------
image : (M, N) ndarray
Input image (greyscale).
orientations : int
Number of orientation bins.
pixels_per_cell : 2 tuple (int, int)
Size (in pixels) of a cell.
cells_per_block : 2 tuple (int,int)
Number of cells in each block.
visualise : bool, optional
Also return an image of the HOG.
normalise : bool, optional
Apply power law compression to normalise the image before
processing.
Returns
-------
newarr : ndarray
HOG for the image as a 1D (flattened) array.
hog_image : ndarray (if visualise=True)
A visualisation of the HOG image.
References
----------
* http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients
* Dalal, N and Triggs, B, Histograms of Oriented Gradients for
Human Detection, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition 2005 San Diego, CA, USA
"""
image = np.atleast_2d(image)
"""
The first stage applies an optional global image normalisation
equalisation that is designed to reduce the influence of illumination
effects. In practice we use gamma (power law) compression, either
computing the square root or the log of each colour channel.
Image texture strength is typically proportional to the local surface
illumination so this compression helps to reduce the effects of local
shadowing and illumination variations.
"""
assert_nD(image, 2)
if normalise:
image = sqrt(image)
"""
The second stage computes first order image gradients. These capture
contour, silhouette and some texture information, while providing
further resistance to illumination variations. The locally dominant
colour channel is used, which provides colour invariance to a large
extent. Variant methods may also include second order image derivatives,
which act as primitive bar detectors - a useful feature for capturing,
e.g. bar like structures in bicycles and limbs in humans.
"""
if image.dtype.kind == 'u':
# convert uint image to float
# to avoid problems with subtracting unsigned numbers in np.diff()
image = image.astype('float')
gx = np.empty(image.shape, dtype=np.double)
gx[:, 0] = 0
gx[:, -1] = 0
gx[:, 1:-1] = image[:, 2:] - image[:, :-2]
gy = np.empty(image.shape, dtype=np.double)
gy[0, :] = 0
gy[-1, :] = 0
gy[1:-1, :] = image[2:, :] - image[:-2, :]
"""
The third stage aims to produce an encoding that is sensitive to
local image content while remaining resistant to small changes in
pose or appearance. The adopted method pools gradient orientation
information locally in the same way as the SIFT [Lowe 2004]
feature. The image window is divided into small spatial regions,
called "cells". For each cell we accumulate a local 1-D histogram
of gradient or edge orientations over all the pixels in the
cell. This combined cell-level 1-D histogram forms the basic
"orientation histogram" representation. Each orientation histogram
divides the gradient angle range into a fixed number of
predetermined bins. The gradient magnitudes of the pixels in the
cell are used to vote into the orientation histogram.
"""
magnitude = sqrt(gx**2 + gy**2)
orientation = arctan2(gy, gx) * (180 / pi) % 180
sy, sx = image.shape
cx, cy = pixels_per_cell
bx, by = cells_per_block
n_cellsx = int(np.floor(sx // cx)) # number of cells in x
n_cellsy = int(np.floor(sy // cy)) # number of cells in y
# compute orientations integral images
orientation_histogram = np.zeros((n_cellsy, n_cellsx, orientations))
subsample = np.index_exp[cy // 2:cy * n_cellsy:cy,
cx // 2:cx * n_cellsx:cx]
for i in range(orientations):
# create new integral image for this orientation
# isolate orientations in this range
temp_ori = np.where(orientation < 180.0 / orientations * (i + 1),
orientation, -1)
temp_ori = np.where(orientation >= 180.0 / orientations * i, temp_ori,
-1)
# select magnitudes for those orientations
cond2 = temp_ori > -1
temp_mag = np.where(cond2, magnitude, 0)
temp_filt = uniform_filter(temp_mag, size=(cy, cx))
orientation_histogram[:, :, i] = temp_filt[subsample]
# now for each cell, compute the histogram
hog_image = None
if visualise:
from .. import draw
radius = min(cx, cy) // 2 - 1
hog_image = np.zeros((sy, sx), dtype=float)
for x in range(n_cellsx):
for y in range(n_cellsy):
for o in range(orientations):
centre = tuple([y * cy + cy // 2, x * cx + cx // 2])
dx = radius * cos(float(o) / orientations * np.pi)
dy = radius * sin(float(o) / orientations * np.pi)
rr, cc = draw.line(int(centre[0] - dx),
int(centre[1] + dy),
int(centre[0] + dx),
int(centre[1] - dy))
hog_image[rr, cc] += orientation_histogram[y, x, o]
"""
The fourth stage computes normalisation, which takes local groups of
cells and contrast normalises their overall responses before passing
to next stage. Normalisation introduces better invariance to illumination,
shadowing, and edge contrast. It is performed by accumulating a measure
of local histogram "energy" over local groups of cells that we call
"blocks". The result is used to normalise each cell in the block.
Typically each individual cell is shared between several blocks, but
its normalisations are block dependent and thus different. The cell
thus appears several times in the final output vector with different
normalisations. This may seem redundant but it improves the performance.
We refer to the normalised block descriptors as Histogram of Oriented
Gradient (HOG) descriptors.
"""
n_blocksx = (n_cellsx - bx) + 1
n_blocksy = (n_cellsy - by) + 1
normalised_blocks = np.zeros((n_blocksy, n_blocksx, by, bx, orientations))
for x in range(n_blocksx):
for y in range(n_blocksy):
block = orientation_histogram[y:y + by, x:x + bx, :]
eps = 1e-5
normalised_blocks[y, x, :] = block / sqrt(block.sum()**2 + eps)
"""
The final step collects the HOG descriptors from all blocks of a dense
overlapping grid of blocks covering the detection window into a combined
feature vector for use in the window classifier.
"""
if visualise:
return normalised_blocks, hog_image
else:
return normalised_blocks.squeeze()
def test_zero_nzero(self):
# atan2(+-0, -0) returns +-pi.
assert_almost_equal(ncu.arctan2(np.PZERO, np.NZERO), np.pi)
assert_almost_equal(ncu.arctan2(np.NZERO, np.NZERO), -np.pi)
def assert_arctan2_isnzero(x, y):
assert ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))
def assert_arctan2_isninf(x, y):
assert np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0
def assert_arctan2_isnan(x, y):
assert np.isnan(ncu.arctan2(x, y))
def test_arctan2_special_values():
def assert_arctan2_isnan(x, y):
assert np.isnan(ncu.arctan2(x, y))
def assert_arctan2_ispinf(x, y):
assert np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) > 0
def assert_arctan2_isninf(x, y):
assert np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0
def assert_arctan2_ispzero(x, y):
assert ncu.arctan2(x, y) == 0 and not np.signbit(ncu.arctan2(x, y))
def assert_arctan2_isnzero(x, y):
assert ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))
# atan2(1, 1) returns pi/4.
yield assert_almost_equal, ncu.arctan2(1, 1), 0.25 * np.pi
yield assert_almost_equal, ncu.arctan2(-1, 1), -0.25 * np.pi
yield assert_almost_equal, ncu.arctan2(1, -1), 0.75 * np.pi
# atan2(+-0, -0) returns +-pi.
yield assert_almost_equal, ncu.arctan2(np.PZERO, np.NZERO), np.pi
yield assert_almost_equal, ncu.arctan2(np.NZERO, np.NZERO), -np.pi
# atan2(+-0, +0) returns +-0.
yield assert_arctan2_ispzero, np.PZERO, np.PZERO
yield assert_arctan2_isnzero, np.NZERO, np.PZERO
# atan2(+-0, x) returns +-pi for x < 0.
yield assert_almost_equal, ncu.arctan2(np.PZERO, -1), np.pi
yield assert_almost_equal, ncu.arctan2(np.NZERO, -1), -np.pi
# atan2(+-0, x) returns +-0 for x > 0.
yield assert_arctan2_ispzero, np.PZERO, 1
yield assert_arctan2_isnzero, np.NZERO, 1
# atan2(y, +-0) returns +pi/2 for y > 0.
yield assert_almost_equal, ncu.arctan2(1, np.PZERO), 0.5 * np.pi
yield assert_almost_equal, ncu.arctan2(1, np.NZERO), 0.5 * np.pi
# atan2(y, +-0) returns -pi/2 for y < 0.
yield assert_almost_equal, ncu.arctan2(-1, np.PZERO), -0.5 * np.pi
yield assert_almost_equal, ncu.arctan2(-1, np.NZERO), -0.5 * np.pi
# atan2(+-y, -infinity) returns +-pi for finite y > 0.
yield assert_almost_equal, ncu.arctan2(1, np.NINF), np.pi
yield assert_almost_equal, ncu.arctan2(-1, np.NINF), -np.pi
# atan2(+-y, +infinity) returns +-0 for finite y > 0.
yield assert_arctan2_ispzero, 1, np.inf
yield assert_arctan2_isnzero, -1, np.inf
# atan2(+-infinity, x) returns +-pi/2 for finite x.
yield assert_almost_equal, ncu.arctan2(np.inf, 1), 0.5 * np.pi
yield assert_almost_equal, ncu.arctan2(-np.inf, 1), -0.5 * np.pi
# atan2(+-infinity, -infinity) returns +-3*pi/4.
yield assert_almost_equal, ncu.arctan2(np.inf, -np.inf), 0.75 * np.pi
yield assert_almost_equal, ncu.arctan2(-np.inf, -np.inf), -0.75 * np.pi
# atan2(+-infinity, +infinity) returns +-pi/4.
yield assert_almost_equal, ncu.arctan2(np.inf, np.inf), 0.25 * np.pi
yield assert_almost_equal, ncu.arctan2(-np.inf, np.inf), -0.25 * np.pi
# atan2(nan, x) returns nan for any x, including inf
yield assert_arctan2_isnan, np.nan, np.inf
yield assert_arctan2_isnan, np.inf, np.nan
yield assert_arctan2_isnan, np.nan, np.nan
def test_positive_zero(self):
# atan2(y, +-0) returns +pi/2 for y > 0.
assert_almost_equal(ncu.arctan2(1, np.PZERO), 0.5 * np.pi)
assert_almost_equal(ncu.arctan2(1, np.NZERO), 0.5 * np.pi)
def getAngleToNegativeXAxis(self):
rad = arctan2( self[1], self[0]);
deg = (rad/pi)*180.0 + 180.0;
return deg
def test_inf_ninf(self):
# atan2(+-infinity, -infinity) returns +-3*pi/4.
assert_almost_equal(ncu.arctan2(np.inf, -np.inf), 0.75 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, -np.inf), -0.75 * np.pi)
def test_arctan2_special_values():
def assert_arctan2_isnan(x, y):
assert np.isnan(ncu.arctan2(x, y))
def assert_arctan2_ispinf(x, y):
assert np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) > 0
def assert_arctan2_isninf(x, y):
assert np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0
def assert_arctan2_ispzero(x, y):
assert ncu.arctan2(x, y) == 0 and not np.signbit(ncu.arctan2(x, y))
def assert_arctan2_isnzero(x, y):
assert ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))
# atan2(1, 1) returns pi/4.
yield assert_almost_equal, ncu.arctan2(1, 1), 0.25 * np.pi
yield assert_almost_equal, ncu.arctan2(-1, 1), -0.25 * np.pi
yield assert_almost_equal, ncu.arctan2(1, -1), 0.75 * np.pi
# atan2(+-0, -0) returns +-pi.
yield assert_almost_equal, ncu.arctan2(np.PZERO, np.NZERO), np.pi
yield assert_almost_equal, ncu.arctan2(np.NZERO, np.NZERO), -np.pi
# atan2(+-0, +0) returns +-0.
yield assert_arctan2_ispzero, np.PZERO, np.PZERO
yield assert_arctan2_isnzero, np.NZERO, np.PZERO
# atan2(+-0, x) returns +-pi for x < 0.
yield assert_almost_equal, ncu.arctan2(np.PZERO, -1), np.pi
yield assert_almost_equal, ncu.arctan2(np.NZERO, -1), -np.pi
# atan2(+-0, x) returns +-0 for x > 0.
yield assert_arctan2_ispzero, np.PZERO, 1
yield assert_arctan2_isnzero, np.NZERO, 1
# atan2(y, +-0) returns +pi/2 for y > 0.
yield assert_almost_equal, ncu.arctan2(1, np.PZERO), 0.5 * np.pi
yield assert_almost_equal, ncu.arctan2(1, np.NZERO), 0.5 * np.pi
# atan2(y, +-0) returns -pi/2 for y < 0.
yield assert_almost_equal, ncu.arctan2(-1, np.PZERO), -0.5 * np.pi
yield assert_almost_equal, ncu.arctan2(-1, np.NZERO), -0.5 * np.pi
# atan2(+-y, -infinity) returns +-pi for finite y > 0.
yield assert_almost_equal, ncu.arctan2(1, np.NINF), np.pi
yield assert_almost_equal, ncu.arctan2(-1, np.NINF), -np.pi
# atan2(+-y, +infinity) returns +-0 for finite y > 0.
yield assert_arctan2_ispzero, 1, np.inf
yield assert_arctan2_isnzero, -1, np.inf
# atan2(+-infinity, x) returns +-pi/2 for finite x.
yield assert_almost_equal, ncu.arctan2( np.inf, 1), 0.5 * np.pi
yield assert_almost_equal, ncu.arctan2(-np.inf, 1), -0.5 * np.pi
# atan2(+-infinity, -infinity) returns +-3*pi/4.
yield assert_almost_equal, ncu.arctan2( np.inf, -np.inf), 0.75 * np.pi
yield assert_almost_equal, ncu.arctan2(-np.inf, -np.inf), -0.75 * np.pi
# atan2(+-infinity, +infinity) returns +-pi/4.
yield assert_almost_equal, ncu.arctan2( np.inf, np.inf), 0.25 * np.pi
yield assert_almost_equal, ncu.arctan2(-np.inf, np.inf), -0.25 * np.pi
# atan2(nan, x) returns nan for any x, including inf
yield assert_arctan2_isnan, np.nan, np.inf
yield assert_arctan2_isnan, np.inf, np.nan
yield assert_arctan2_isnan, np.nan, np.nan
def test_zero_nzero(self):
# atan2(+-0, -0) returns +-pi.
assert_almost_equal(ncu.arctan2(np.PZERO, np.NZERO), np.pi)
assert_almost_equal(ncu.arctan2(np.NZERO, np.NZERO), -np.pi)
def assert_arctan2_isnan(x, y):
assert np.isnan(ncu.arctan2(x, y))
def test_positive_zero(self):
# atan2(y, +-0) returns +pi/2 for y > 0.
assert_almost_equal(ncu.arctan2(1, np.PZERO), 0.5 * np.pi)
assert_almost_equal(ncu.arctan2(1, np.NZERO), 0.5 * np.pi)
def assert_arctan2_isninf(x, y):
assert np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0
def test_any_ninf(self):
# atan2(+-y, -infinity) returns +-pi for finite y > 0.
assert_almost_equal(ncu.arctan2(1, np.NINF), np.pi)
assert_almost_equal(ncu.arctan2(-1, np.NINF), -np.pi)
def assert_arctan2_isnzero(x, y):
assert ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))
def test_inf_ninf(self):
# atan2(+-infinity, -infinity) returns +-3*pi/4.
assert_almost_equal(ncu.arctan2(np.inf, -np.inf), 0.75 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, -np.inf), -0.75 * np.pi)
def assert_arctan2_ispinf(x, y):
assert (np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) > 0
), "arctan(%s, %s) is %s, not +inf" % (x, y, ncu.arctan2(x, y))
def assert_arctan2_ispinf(x, y):
assert_((np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) > 0), "arctan(%s, %s) is %s, not +inf" % (x, y, ncu.arctan2(x, y)))
def assert_arctan2_ispzero(x, y):
assert (ncu.arctan2(x, y) == 0 and not np.signbit(ncu.arctan2(x, y))
), "arctan(%s, %s) is %s, not +0" % (x, y, ncu.arctan2(x, y))
def assert_arctan2_isninf(x, y):
assert_(
(np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0),
"arctan(%s, %s) is %s, not -inf" % (x, y, ncu.arctan2(x, y)),
)
def assert_arctan2_ispzero(x, y):
assert (ncu.arctan2(x, y) == 0 and not np.signbit(ncu.arctan2(x, y))), "arctan(%s, %s) is %s, not +0" % (x, y, ncu.arctan2(x, y))
def test_one_one(self):
# atan2(1, 1) returns pi/4.
assert_almost_equal(ncu.arctan2(1, 1), 0.25 * np.pi)
assert_almost_equal(ncu.arctan2(-1, 1), -0.25 * np.pi)
assert_almost_equal(ncu.arctan2(1, -1), 0.75 * np.pi)
def assert_arctan2_isnan(x, y):
assert_(np.isnan(ncu.arctan2(x, y)),
"arctan(%s, %s) is %s, not nan" % (x, y, ncu.arctan2(x, y)))
def test_zero_negative(self):
# atan2(+-0, x) returns +-pi for x < 0.
assert_almost_equal(ncu.arctan2(np.PZERO, -1), np.pi)
assert_almost_equal(ncu.arctan2(np.NZERO, -1), -np.pi)
def assert_arctan2_isninf(x, y):
assert_((np.isinf(ncu.arctan2(x, y)) and ncu.arctan2(x, y) < 0),
"arctan(%s, %s) is %s, not -inf" % (x, y, ncu.arctan2(x, y)))
def test_negative_zero(self):
# atan2(y, +-0) returns -pi/2 for y < 0.
assert_almost_equal(ncu.arctan2(-1, np.PZERO), -0.5 * np.pi)
assert_almost_equal(ncu.arctan2(-1, np.NZERO), -0.5 * np.pi)
def assert_arctan2_isnzero(x, y):
assert_((ncu.arctan2(x, y) == 0 and np.signbit(ncu.arctan2(x, y))),
"arctan(%s, %s) is %s, not -0" % (x, y, ncu.arctan2(x, y)))
def test_inf_any(self):
# atan2(+-infinity, x) returns +-pi/2 for finite x.
assert_almost_equal(ncu.arctan2(np.inf, 1), 0.5 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, 1), -0.5 * np.pi)
def test_one_one(self):
# atan2(1, 1) returns pi/4.
assert_almost_equal(ncu.arctan2(1, 1), 0.25 * np.pi)
assert_almost_equal(ncu.arctan2(-1, 1), -0.25 * np.pi)
assert_almost_equal(ncu.arctan2(1, -1), 0.75 * np.pi)
def test_inf_pinf(self):
# atan2(+-infinity, +infinity) returns +-pi/4.
assert_almost_equal(ncu.arctan2(np.inf, np.inf), 0.25 * np.pi)
assert_almost_equal(ncu.arctan2(-np.inf, np.inf), -0.25 * np.pi)
def anglePoints(x, y): # Angle between two points.
ang1 = arctan2(x[0], x[1])
ang2 = arctan2(y[0], y[1])
arctan2
return rad2deg((ang1 - ang2) % (2 * pi))
