def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
python类less()的实例源码
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2)
def ntron_pulse(amplitude=1.0, rise_time=80e-12, hold_time=170e-12, fall_time=1.0e-9, sample_rate=12e9):
delay = 2.0e-9 # Wait a few TCs for the rising edge
duration = delay + hold_time + 6.0*fall_time # Wait 6 TCs for the slow decay
pulse_points = int(duration*sample_rate)
if pulse_points < 320:
duration = 319/sample_rate
# times = np.arange(0, duration, 1/sample_rate)
times = np.linspace(0, duration, 320)
else:
pulse_points = 64*np.ceil(pulse_points/64.0)
duration = (pulse_points-1)/sample_rate
# times = np.arange(0, duration, 1/sample_rate)
times = np.linspace(0, duration, pulse_points)
rise_mask = np.less(times, delay)
hold_mask = np.less(times, delay + hold_time)*np.greater_equal(times, delay)
fall_mask = np.greater_equal(times, delay + hold_time)
wf = rise_mask*np.exp((times-delay)/rise_time)
wf += hold_mask
wf += fall_mask*np.exp(-(times-delay-hold_time)/fall_time)
return amplitude*wf
def ntron_pulse(amplitude=1.0, rise_time=80e-12, hold_time=170e-12, fall_time=1.0e-9, sample_rate=12e9):
delay = 2.0e-9 # Wait a few TCs for the rising edge
duration = delay + hold_time + 6.0*fall_time # Wait 6 TCs for the slow decay
pulse_points = int(duration*sample_rate)
if pulse_points < 320:
duration = 319/sample_rate
# times = np.arange(0, duration, 1/sample_rate)
times = np.linspace(0, duration, 320)
else:
pulse_points = 64*np.ceil(pulse_points/64.0)
duration = (pulse_points-1)/sample_rate
# times = np.arange(0, duration, 1/sample_rate)
times = np.linspace(0, duration, pulse_points)
rise_mask = np.less(times, delay)
hold_mask = np.less(times, delay + hold_time)*np.greater_equal(times, delay)
fall_mask = np.greater_equal(times, delay + hold_time)
wf = rise_mask*np.exp((times-delay)/rise_time)
wf += hold_mask
wf += fall_mask*np.exp(-(times-delay-hold_time)/fall_time)
return amplitude*wf
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode),
RuntimeWarning)
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2)
def get_local_minima(x, y):
"""
This function ...
:param x:
:param y:
:return:
"""
m = argrelextrema(y, np.less)[0].tolist()
# Find the indx of the absolute minimum (should also be included, is not for example when it is at the edge)
index = np.argmin(y)
if index not in m: m.append(index)
x_minima = [x[i] for i in m]
y_minima = [y[i] for i in m]
return x_minima, y_minima
# -----------------------------------------------------------------
def get_local_minima(x, y):
"""
This function ...
:param x:
:param y:
:return:
"""
m = argrelextrema(y, np.less)[0].tolist()
# Find the indx of the absolute minimum (should also be included, is not for example when it is at the edge)
index = np.argmin(y)
if index not in m: m.append(index)
x_minima = [x[i] for i in m]
y_minima = [y[i] for i in m]
return x_minima, y_minima
# -----------------------------------------------------------------
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
logging.warning('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode))
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4
def compute_by_noise_pow(self, signal, n_pow):
s_spec = np.fft.fftpack.fft(signal * self._window)
s_amp = np.absolute(s_spec)
s_phase = np.angle(s_spec)
gamma = self._calc_aposteriori_snr(s_amp, n_pow)
xi = self._calc_apriori_snr(gamma)
self._prevGamma = gamma
nu = gamma * xi / (1.0 + xi)
self._G = (self._gamma15 * np.sqrt(nu) / gamma) * np.exp(-nu / 2.0) *\
((1.0 + nu) * spc.i0(nu / 2.0) + nu * spc.i1(nu / 2.0))
idx = np.less(s_amp ** 2.0, n_pow)
self._G[idx] = self._constant
idx = np.isnan(self._G) + np.isinf(self._G)
self._G[idx] = xi[idx] / (xi[idx] + 1.0)
idx = np.isnan(self._G) + np.isinf(self._G)
self._G[idx] = self._constant
self._G = np.maximum(self._G, 0.0)
amp = self._G * s_amp
amp = np.maximum(amp, 0.0)
amp2 = self._ratio * amp + (1.0 - self._ratio) * s_amp
self._prevAmp = amp
spec = amp2 * np.exp(s_phase * 1j)
return np.real(np.fft.fftpack.ifft(spec))
def compute_by_noise_pow(self, signal, n_pow):
s_spec = np.fft.fftpack.fft(signal * self._window)
s_amp = np.absolute(s_spec)
s_phase = np.angle(s_spec)
gamma = self._calc_aposteriori_snr(s_amp, n_pow)
xi = self._calc_apriori_snr(gamma)
# xi = self._calc_apriori_snr2(gamma,n_pow)
self._prevGamma = gamma
nu = gamma * xi / (1.0 + xi)
self._G = xi / (1.0 + xi) * np.exp(0.5 * spc.exp1(nu))
idx = np.less(s_amp ** 2.0, n_pow)
self._G[idx] = self._constant
idx = np.isnan(self._G) + np.isinf(self._G)
self._G[idx] = xi[idx] / (xi[idx] + 1.0)
idx = np.isnan(self._G) + np.isinf(self._G)
self._G[idx] = self._constant
self._G = np.maximum(self._G, 0.0)
amp = self._G * s_amp
amp = np.maximum(amp, 0.0)
amp2 = self._ratio * amp + (1.0 - self._ratio) * s_amp
self._prevAmp = amp
spec = amp2 * np.exp(s_phase * 1j)
return np.real(np.fft.fftpack.ifft(spec))
def compute_by_noise_pow(self, signal, n_pow):
s_spec = np.fft.fftpack.fft(signal * self._window)
s_amp = np.absolute(s_spec)
s_phase = np.angle(s_spec)
gamma = self._calc_aposteriori_snr(s_amp, n_pow)
# xi = self._calc_apriori_snr2(gamma,n_pow)
xi = self._calc_apriori_snr(gamma)
self._prevGamma = gamma
u = 0.5 - self._mu / (4.0 * np.sqrt(gamma * xi))
self._G = u + np.sqrt(u ** 2.0 + self._tau / (gamma * 2.0))
idx = np.less(s_amp ** 2.0, n_pow)
self._G[idx] = self._constant
idx = np.isnan(self._G) + np.isinf(self._G)
self._G[idx] = xi[idx] / (xi[idx] + 1.0)
idx = np.isnan(self._G) + np.isinf(self._G)
self._G[idx] = self._constant
self._G = np.maximum(self._G, 0.0)
amp = self._G * s_amp
amp = np.maximum(amp, 0.0)
amp2 = self._ratio * amp + (1.0 - self._ratio) * s_amp
self._prevAmp = amp
spec = amp2 * np.exp(s_phase * 1j)
return np.real(np.fft.fftpack.ifft(spec))
def iterate_until_button_press(buttons, game_state, text_ending_place, text_starting_place):
# while a button was not clicked this method checks if mouse is in the button and if it is
# changes its colour
button_clicked = 0
while button_clicked == 0:
pygame.display.update()
user_events = event.events()
# the first button is the title which is unclickable, thus iterating from 1 to len(buttons)
for num in range(1, len(buttons)):
if np.all((np.less(text_starting_place[num] - config.menu_spacing, user_events["mouse_pos"]),
np.greater(text_ending_place[num] + config.menu_spacing, user_events["mouse_pos"]))):
if user_events["clicked"]:
button_clicked = num
else:
game_state.canvas.surface.blit(
buttons[num][1], text_starting_place[num])
else:
game_state.canvas.surface.blit(
buttons[num][0], text_starting_place[num])
if user_events["closed"] or user_events["quit_to_main_menu"]:
button_clicked = len(buttons)-1
return button_clicked
test_ufunc.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
阅读 33
收藏 0
点赞 0
评论 0
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
test_core.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
阅读 21
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def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2)
def __init__(self, monitor='val_loss', patience=0, verbose=0, mode='auto'):
super(Callback, self).__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.wait = 0
self.best_epoch = 0
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor:
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def __init__(self, monitor='val_loss', mode='auto', verbose=0):
super(BestWeight, self).__init__()
self.monitor = monitor
self.mode = mode
self.best_weights = None
self.verbose = verbose
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor:
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2)
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode),
RuntimeWarning)
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2)
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode),
RuntimeWarning)
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4
def plot_kde(data):
bw = 1.06 * st.stdev(data) / (len(data) ** .2)
kde = KernelDensity(kernel='gaussian', bandwidth=bw).fit(
np.array(data).reshape(-1, 1))
s = np.linspace(0, 1)
e = kde.score_samples(s.reshape(-1, 1))
plt.plot(s, e)
mi, ma = argrelextrema(e, np.less)[0], argrelextrema(e, np.greater)[0]
logger.info("Minima: %s" % s[mi])
logger.info("Maxima: %s" % s[ma])
plt.plot(s[:mi[0] + 1], e[:mi[0] + 1], 'r',
s[mi[0]:mi[1] + 1], e[mi[0]:mi[1] + 1], 'g',
s[mi[1]:], e[mi[1]:], 'b',
s[ma], e[ma], 'go',
s[mi], e[mi], 'ro')
plt.xlabel('Probability')
def on_epoch_end(self, epoch, logs={}):
current = self.monitor(self.previous_weights, self.model.get_weights())
self.previous_weights = self.model.get_weights()
if current is None:
warnings.warn('Early stopping requires %s available!' %
(self.monitor), RuntimeWarning)
if np.less(current, self.threshold_value):
if current == 0:
self.model.stop_training = True
if self.verbose > 0:
print('Epoch %05d: early stopping: ratio weights = 0' % (epoch))
elif self.wait >= self.patience:
if self.verbose > 0:
print('Epoch %05d: early stopping: ratio weights below %.4f' % (epoch, self.threshold_value))
self.model.stop_training = True
self.wait += 1
else:
self.wait = 0
def atmin(a,lowerlimit=None,dimension=None,inclusive=1):
"""
Returns the minimum value of a, along dimension, including only values less
than (or equal to, if inclusive=1) lowerlimit. If the limit is set to None,
all values in the array are used.
Usage: atmin(a,lowerlimit=None,dimension=None,inclusive=1)
"""
if inclusive: lowerfcn = N.greater
else: lowerfcn = N.greater_equal
if dimension == None:
a = N.ravel(a)
dimension = 0
if lowerlimit == None:
lowerlimit = N.minimum.reduce(N.ravel(a))-11
biggest = N.maximum.reduce(N.ravel(a))
ta = N.where(lowerfcn(a,lowerlimit),a,biggest)
return N.minimum.reduce(ta,dimension)
def atmax(a,upperlimit,dimension=None,inclusive=1):
"""
Returns the maximum value of a, along dimension, including only values greater
than (or equal to, if inclusive=1) upperlimit. If the limit is set to None,
a limit larger than the max value in the array is used.
Usage: atmax(a,upperlimit,dimension=None,inclusive=1)
"""
if inclusive: upperfcn = N.less
else: upperfcn = N.less_equal
if dimension == None:
a = N.ravel(a)
dimension = 0
if upperlimit == None:
upperlimit = N.maximum.reduce(N.ravel(a))+1
smallest = N.minimum.reduce(N.ravel(a))
ta = N.where(upperfcn(a,upperlimit),a,smallest)
return N.maximum.reduce(ta,dimension)
