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 mouse_drag(self, event):
'''
'''
if event.inaxes == self.ax and event.button == 1:
# Index of nearest point
i = np.nanargmin(((event.xdata - self.x) / self.nx) ** 2)
j = np.nanargmin(((event.ydata - self.y) / self.ny) ** 2)
if (i == self.last_i) and (j == self.last_j):
return
else:
self.last_i = i
self.last_j = j
# Toggle pixel
if self.aperture[j,i]:
self.aperture[j,i] = 0
else:
self.aperture[j,i] = 1
# Update the contour
self.update() Example 2
def mouse_click(self, event):
'''
'''
if event.mouseevent.inaxes == self.ax:
# Index of nearest point
i = np.nanargmin(((event.mouseevent.xdata - self.x) / self.nx) ** 2)
j = np.nanargmin(((event.mouseevent.ydata - self.y) / self.ny) ** 2)
self.last_i = i
self.last_j = j
# Toggle pixel
if self.aperture[j,i]:
self.aperture[j,i] = 0
else:
self.aperture[j,i] = 1
# Update the contour
self.update() Example 3
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list ``arx``.
Based on the smallest corresponding value in ``arf``,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
``xarchive`` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
try:
minidx = np.nanargmin(arf)
except ValueError:
return
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 4
def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt) Example 5
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
minidx = np.nanargmin(arf)
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 6
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
minidx = np.nanargmin(arf)
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 7
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
minidx = np.nanargmin(arf)
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 8
def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt) Example 9
def figure_mouse_pick(self, event):
"""
Trigger for when the mouse is used to select an item in the figure.
:param event:
The matplotlib event.
"""
ycol = "abundance"
xcol = {
self.ax_excitation_twin: "expot",
self.ax_line_strength_twin: "reduced_equivalent_width"
}[event.inaxes]
xscale = np.ptp(event.inaxes.get_xlim())
yscale = np.ptp(event.inaxes.get_ylim())
try:
distance = np.sqrt(
((self._state_transitions[ycol] - event.ydata)/yscale)**2 \
+ ((self._state_transitions[xcol] - event.xdata)/xscale)**2)
except AttributeError:
# Stellar parameters have not been measured yet
return None
index = np.nanargmin(distance)
# Because the state transitions are linked to the parent source model of
# the table view, we will have to get the proxy index.
proxy_index = self.table_view.model().mapFromSource(
self.proxy_spectral_models.sourceModel().createIndex(index, 0)).row()
self.table_view.selectRow(proxy_index)
return None Example 10
def find_min(x, bin_width = 10): xm = binned_average(x, bin_width=bin_width); imin = np.nanargmin(xm); return int((imin + 0.5) * bin_width);
Example 11
def find_min(x, bin_width = 10): xm = binned_average(x, bin_width=bin_width); imin = np.nanargmin(xm); return int((imin + 0.5) * bin_width);
Example 12
def find_min(x, bin_width = 10): xm = binned_average(x, bin_width=bin_width); imin = np.nanargmin(xm); return int((imin + 0.5) * bin_width);
Example 13
def find_min(x, bin_width = 10): xm = binned_average(x, bin_width=bin_width); imin = np.nanargmin(xm); return int((imin + 0.5) * bin_width);
Example 14
def find_min(x, bin_width = 10): xm = binned_average(x, bin_width=bin_width); imin = np.nanargmin(xm); return int((imin + 0.5) * bin_width);
Example 15
def Variogram(self):
"""
:return:
"""
self.run()
# find the best Variogram
idx = np.nanargmin(self.e)
return self.V[idx] Example 16
def _select_best_measure_index(curr_measures, args):
idx = None
try:
if args.measure == 'aicc':
# The best score for AICc is the minimum.
idx = np.nanargmin(curr_measures)
elif args.measure in ['hmm-distance', 'wasserstein', 'mahalanobis']:
# The best score for the l-d measure is the maximum.
idx = np.nanargmax(curr_measures)
except:
idx = random.choice(range(len(curr_measures)))
assert idx is not None
return idx Example 17
def initial_count(classify, test_set_x, data, valid):
(valid_st2,valid_st5,valid_st8) = valid
(ns_test_set_x_st2,ns_test_set_x_st5,ns_test_set_x_st8) = data
# classify st_2 it is always valid
(st2_count, st2_res, st2_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st2, 0, 0, 81) #100 - 19 etc.
# check if st5 is valid. if not return st2 count
if (valid_st5 == 1):
(st5_count, st5_res, st5_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st5, 0, 0, 21)
else:
st8_entropy = numpy.inf
if (valid_st8 == 1):
(st8_count, st8_res, st8_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st8, 0, 0, 6)
else:
st8_entropy = numpy.inf
winner = numpy.nanargmin(numpy.array([st2_entropy, st5_entropy, st8_entropy]))
if (winner == 0):
# winner is stride 2
return (st2_count, (st2_res*2/2,st2_res*2/5, st2_res*2/8))
if (winner == 1):
# winner is stride 5
return (st5_count, (st5_res*5/2,st5_res*5/5, st5_res*5/8))
if (winner == 2):
# winner is stride 8
return (st8_count, (st8_res*8/2,st8_res*8/5, st8_res*8/8)) Example 18
def get_next_count(classify, test_set_x, data, valid, global_count, curr_residue, start_frame):
(valid_st2,valid_st5,valid_st8) = valid
(ns_test_set_x_st2,ns_test_set_x_st5,ns_test_set_x_st8) = data
(curr_residue_st2, curr_residue_st5, curr_residue_st8) = curr_residue
# classify st_2 it is always valid
(st2_count, st2_res, st2_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st2, curr_residue_st2, (start_frame/2-19), (start_frame/2-19)+20)
# check if st5 is valid. if not return st2 count
if (valid_st5 == 1):
(st5_count, st5_res, st5_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st5, curr_residue_st5, (start_frame/5-19), (start_frame/5-19)+8)
else:
st5_entropy = numpy.inf
if (valid_st8 == 1):
(st8_count, st8_res, st8_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st8, curr_residue_st8, (start_frame/8-19), (start_frame/8-19)+5)
else:
st8_entropy = numpy.inf
winner = numpy.nanargmin(numpy.array([st2_entropy, st5_entropy, st8_entropy]))
if (winner == 0):
# winner is stride 2
return (global_count + st2_count, (st2_res*2/2,st2_res*2/5, st2_res*2/8))
if (winner == 1):
# winner is stride 5
return (global_count + st5_count, (st5_res*5/2,st5_res*5/5, st5_res*5/8))
if (winner == 2):
# winner is stride 8
return (global_count + st8_count, (st8_res*8/2,st8_res*8/5, st8_res*8/8)) Example 19
def get_remain_count(classify, test_set_x, data, valid, global_count, curr_residue, start_frame):
(valid_st2,valid_st5,valid_st8) = valid
(ns_test_set_x_st2,ns_test_set_x_st5,ns_test_set_x_st8) = data
(curr_residue_st2, curr_residue_st5, curr_residue_st8) = curr_residue
# classify st_2 it is always valid
(st2_count, st2_res, st2_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st2, curr_residue_st2, (start_frame/2-19), ns_test_set_x_st2.shape[0])
# check if st5 is valid. if not return st2 count
if (valid_st5 == 1):
(st5_count, st5_res, st5_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st5, curr_residue_st5, (start_frame/5-19), ns_test_set_x_st5.shape[0])
else:
st5_entropy = numpy.inf
if (valid_st8 == 1):
(st8_count, st8_res, st8_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st8, curr_residue_st8, (start_frame/8-19), ns_test_set_x_st8.shape[0])
else:
st8_entropy = numpy.inf
winner = numpy.nanargmin(numpy.array([st2_entropy, st5_entropy, st8_entropy]))
if (winner == 0):
# winner is stride 2
return (global_count + st2_count)
if (winner == 1):
# winner is stride 5
return (global_count + st5_count)
if (winner == 2):
# winner is stride 8
return (global_count + st8_count) Example 20
def count_entire_movie(classify, test_set_x, data, valid, global_count, curr_residue, start_frame):
(valid_st2,valid_st5,valid_st8) = valid
(ns_test_set_x_st2,ns_test_set_x_st5,ns_test_set_x_st8) = data
(curr_residue_st2, curr_residue_st5, curr_residue_st8) = curr_residue
# classify st_2 it is always valid
(st2_count, st2_res, st2_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st2, curr_residue_st2, 0, ns_test_set_x_st2.shape[0])
# check if st5 is valid. if not return st2 count
if (valid_st5 == 1):
(st5_count, st5_res, st5_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st5, curr_residue_st5, 0, ns_test_set_x_st5.shape[0])
else:
st5_entropy = numpy.inf
if (valid_st8 == 1):
(st8_count, st8_res, st8_entropy) = count_in_interval(classify, test_set_x, ns_test_set_x_st8, curr_residue_st8, 0, ns_test_set_x_st8.shape[0])
else:
st8_entropy = numpy.inf
winner = numpy.nanargmin(numpy.array([st2_entropy, st5_entropy, st8_entropy]))
if (winner == 0):
# winner is stride 2
return (global_count + st2_count)
if (winner == 1):
# winner is stride 5
return (global_count + st5_count)
if (winner == 2):
# winner is stride 8
return (global_count + st8_count) Example 21
def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt) Example 22
def __find_nearest_nodes(self, num, signal, mahar=True):
#if mahar: return self.__find_nearest_nodes_by_mahar(num, signal)
n = self.nodes.shape[0]
indexes = [0.0] * num
sq_dists = [0.0] * num
D = util.calc_distance(self.nodes, np.asarray([signal] * n))
for i in range(num):
indexes[i] = np.nanargmin(D)
sq_dists[i] = D[indexes[i]]
D[indexes[i]] = float('nan')
return indexes, sq_dists Example 23
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
minidx = np.nanargmin(arf)
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 24
def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt) Example 25
def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt) Example 26
def plot(self, ax=None, write_tau=True):
"""
Returns
-------
fig : matplotlib.figure.Figure
Figure instance containing the plot.
"""
check_is_fitted(self, 'neg_log_likelihood_')
if ax is None:
fig = plt.figure()
ax = fig.gca()
else:
fig = ax.figure
blue, green, red, purple, yellow, cyan = SEABORN_PALETTES['deep']
i_best = np.nanargmin(self.neg_log_likelihood_)
ax.plot(self.delays_ms_, self.neg_log_likelihood_, color=purple)
ax.plot(self.delays_ms_[i_best], self.neg_log_likelihood_[i_best], 'D',
color=red)
ax.set_xlabel('Delay (ms)')
ax.set_ylabel('Neg. log likelihood / T')
ax.grid('on')
if write_tau:
ax.text(0.5, 0.80, r'$\mathrm{Estimated}$',
horizontalalignment='center', transform=ax.transAxes)
ax.text(0.5, 0.66, r'$\tau_0 = %.0f \;\mathrm{ms}$' %
(self.delays_ms_[i_best], ), horizontalalignment='center',
transform=ax.transAxes)
return fig Example 27
def test_delay_shape():
est = fast_delay()
assert_equal(est.neg_log_likelihood_.shape, est.delays_ms_.shape)
assert_greater(est.neg_log_likelihood_.shape[0], 1)
i_best = np.nanargmin(est.neg_log_likelihood_)
assert_equal(est.best_delay_ms_, est.delays_ms_[i_best]) Example 28
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
minidx = np.nanargmin(arf)
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 29
def optimize_threshold_with_f1(f1c, thresholds, criterion='max'):
#f1c[np.isnan(f1c)] = 0
if criterion == 'max':
ti = np.nanargmax(f1c)
else:
ti = np.nanargmin(np.abs(thresholds-0.5*f1c))
#assert(np.all(thresholds>=0))
#idx = (thresholds>=f1c*0.5-mp) & (thresholds<=f1c*0.5+mp)
#assert(np.any(idx))
#ti = np.where(idx)[0][f1c[idx].argmax()]
return thresholds[ti], ti Example 30
def _(artist, event):
offsets = artist.get_offsets()
ds = np.hypot(
*(artist.axes.transData.transform(offsets) - [event.x, event.y]).T)
argmin = np.nanargmin(ds)
if ds[argmin] < artist.get_pickradius():
target = with_attrs(offsets[argmin], index=argmin)
return Selection(artist, target, ds[argmin], None, None)
else:
return None Example 31
def compute_integral(self, x_s, y_s):
if len(x_s) == 0:
return np.zeros((y_s.shape[0],)) * np.nan
closer = np.nanargmin(abs(x_s - self.limits[0]))
return y_s[:, closer] Example 32
def compute_draw_info(self, x, ys):
bs = self.compute_baseline(x, ys)
im = np.array([np.nanargmin(abs(x - self.limits[0]))])
dx = [self.limits[0], self.limits[0]]
dys = np.hstack((bs[:, im], ys[:, im]))
return [("curve", (dx, dys, INTEGRATE_DRAW_EDGE_PENARGS)), # line to value
("dot", (x[im], ys[:, im]))] Example 33
def update(self, arx, xarchive=None, arf=None, evals=None):
"""checks for better solutions in list `arx`.
Based on the smallest corresponding value in `arf`,
alternatively, `update` may be called with a `BestSolution`
instance like ``update(another_best_solution)`` in which case
the better solution becomes the current best.
`xarchive` is used to retrieve the genotype of a solution.
"""
if isinstance(arx, BestSolution):
if self.evalsall is None:
self.evalsall = arx.evalsall
elif arx.evalsall is not None:
self.evalsall = max((self.evalsall, arx.evalsall))
if arx.f is not None and arx.f < np.inf:
self.update([arx.x], xarchive, [arx.f], arx.evals)
return self
assert arf is not None
# find failsave minimum
minidx = np.nanargmin(arf)
if minidx is np.nan:
return
minarf = arf[minidx]
# minarf = reduce(lambda x, y: y if y and y is not np.nan
# and y < x else x, arf, np.inf)
if minarf < np.inf and (minarf < self.f or self.f is None):
self.x, self.f = arx[minidx], arf[minidx]
if xarchive is not None and xarchive.get(self.x) is not None:
self.x_geno = xarchive[self.x].get('geno')
else:
self.x_geno = None
self.evals = None if not evals else evals - len(arf) + minidx + 1
self.evalsall = evals
elif evals:
self.evalsall = evals
self.last.x = arx[minidx]
self.last.f = minarf Example 34
def test_nanargmin(self):
tgt = np.argmin(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmin(mat), tgt) Example 35
def nanmedoid(a, axis=1, indexonly=False):
"""
Compute the medoid along the specified axis, omitting
observations containing NaNs.
Returns the medoid of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : int
Axis along which the medoid is computed. The default
is to compute the median along the last axis of the array.
indexonly : bool, optional
If this is set to True, only the index of the medoid is returned.
Returns
-------
medoid : ndarray or int
"""
if axis == 1:
diff = a.T[:, None, :] - a.T
ssum = np.einsum('ijk,ijk->ij', diff, diff)
dist = np.nansum(np.sqrt(ssum), axis=1)
mask = np.isnan(a).any(axis=0)
dist[mask] = np.nan
idx = np.nanargmin(dist)
if indexonly:
return idx
else:
return a[:, idx]
if axis == 0:
diff = a[:, None, :] - a
ssum = np.einsum('ijk,ijk->ij', diff, diff)
dist = np.nansum(np.sqrt(ssum), axis=1)
mask = np.isnan(a).any(axis=1)
dist[mask] = np.nan
idx = np.nanargmin(dist)
if indexonly:
return idx
else:
return a[idx, :]
raise IndexError("axis {} out of bounds".format(axis)) Example 36
def nanargmin(a, axis=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])
"""
a, mask = _replace_nan(a, np.inf)
res = np.argmin(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 37
def nanargmax(a, axis=None):
"""
Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
results cannot be trusted if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax, nanargmin
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])
"""
a, mask = _replace_nan(a, -np.inf)
res = np.argmax(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 38
def nanargmin(a, axis=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])
"""
a, mask = _replace_nan(a, np.inf)
res = np.argmin(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 39
def nanargmax(a, axis=None):
"""
Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
results cannot be trusted if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax, nanargmin
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])
"""
a, mask = _replace_nan(a, -np.inf)
res = np.argmax(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 40
def nicecolorbar(self,
axcb=None,
reflevel=None,
label=None,
vmax=None,
vmin=None,
data=None,
loc='head right',
fontsize=8,
ticks = None):
if not axcb:
axcb = matplotlib.pyplot.gca()
divider = make_axes_locatable(axcb)
# this code is from
# http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html#axes-grid1
cax = divider.append_axes("right", size="2%", pad=0.15)
levels = numpy.asarray([0.001,0.0025,0.005,0.01,0.025,0.05,0.1,0.25,0.5,1,2.5,5,10,25,50,100,250,500,1000])
if vmax!= None and vmin != None:
level = levels[numpy.nanargmin(abs((vmax - vmin)/5 - levels))]
ticks = numpy.arange(vmin, vmax, level)
elif vmax :
level = levels[numpy.nanargmin(abs((vmax - numpy.nanmin(data))/5 - levels))]
ticks = numpy.arange(numpy.nanmin(data), vmax, level)
elif data is not None:
level = None #levels[numpy.nanargmin(abs((numpy.nanmax(data) - numpy.nanmin(data))/5 - levels))]
ticks = None #numpy.arange(numpy.nanmin(data), numpy.nanmax(data), level)
#ticks -= numpy.nanmin(abs(ticks))
cb = matplotlib.pyplot.colorbar(self,
cax=cax,
label=label,
orientation='vertical',
extend='both',
spacing='uniform',
ticks=ticks)
if vmax!= None and vmin != None:
#print(ticks,vmin,vmax)
cb.set_clim(vmin, vmax)
cb.ax.yaxis.set_ticks_position('right')
cb.ax.yaxis.set_label_position('right')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), rotation='vertical',fontsize=fontsize)
#if reflevel:
# cb.ax.axhline((reflevel-min(cb.get_clim()))/numpy.diff(cb.get_clim()),zorder=999,color='k',linewidth=2)
return cb Example 41
def nanargmin(a, axis=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])
"""
a, mask = _replace_nan(a, np.inf)
res = np.argmin(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 42
def nanargmax(a, axis=None):
"""
Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
results cannot be trusted if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax, nanargmin
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])
"""
a, mask = _replace_nan(a, -np.inf)
res = np.argmax(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 43
def nanargmin(a, axis=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])
"""
a, mask = _replace_nan(a, np.inf)
res = np.argmin(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 44
def nanargmax(a, axis=None):
"""
Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
results cannot be trusted if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax, nanargmin
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])
"""
a, mask = _replace_nan(a, -np.inf)
res = np.argmax(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 45
def nanargmin(a, axis=None):
"""
Return the indices of the minimum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
cannot be trusted if a slice contains only NaNs and Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmin, nanargmax
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmin(a)
0
>>> np.nanargmin(a)
2
>>> np.nanargmin(a, axis=0)
array([1, 1])
>>> np.nanargmin(a, axis=1)
array([1, 0])
"""
a, mask = _replace_nan(a, np.inf)
res = np.argmin(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 46
def nanargmax(a, axis=None):
"""
Return the indices of the maximum values in the specified axis ignoring
NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
results cannot be trusted if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax, nanargmin
Examples
--------
>>> a = np.array([[np.nan, 4], [2, 3]])
>>> np.argmax(a)
0
>>> np.nanargmax(a)
1
>>> np.nanargmax(a, axis=0)
array([1, 0])
>>> np.nanargmax(a, axis=1)
array([1, 1])
"""
a, mask = _replace_nan(a, -np.inf)
res = np.argmax(a, axis=axis)
if mask is not None:
mask = np.all(mask, axis=axis)
if np.any(mask):
raise ValueError("All-NaN slice encountered")
return res Example 47
def get_ray_lengths(self,x=None,y=None,PositionTol = 3,Coords='Display'):
# Work out ray lengths for all raytraced pixels
RayLength = np.sqrt(np.sum( (self.ray_end_coords - self.ray_start_coords) **2,axis=-1))
# If no x and y given, return them all
if x is None and y is None:
if self.fullchip:
if Coords.lower() == 'display':
return RayLength
else:
return self.transform.display_to_original_image(RayLength,binning=self.binning)
else:
return RayLength
else:
if self.x is None or self.y is None:
raise Exception('This ray data does not have x and y pixel indices!')
# Otherwise, return the ones at given x and y pixel coords.
if np.shape(x) != np.shape(y):
raise ValueError('x and y arrays must be the same shape!')
else:
if Coords.lower() == 'original':
x,y = self.transform.original_to_display_coords(x,y)
orig_shape = np.shape(x)
x = np.reshape(x,np.size(x),order='F')
y = np.reshape(y,np.size(y),order='F')
RL = np.zeros(np.shape(x))
RayLength = RayLength.flatten()
xflat = self.x.flatten()
yflat = self.y.flatten()
for pointno in range(x.size):
if np.isnan(x[pointno]) or np.isnan(y[pointno]):
RL[pointno] = np.nan
continue
deltaX = xflat - x[pointno]
deltaY = yflat - y[pointno]
deltaR = np.sqrt(deltaX**2 + deltaY**2)
if np.nanmin(deltaR) <= PositionTol:
RL[pointno] = RayLength[np.nanargmin(deltaR)]
else:
raise Exception('No ray-traced pixel within PositionTol of requested pixel!')
return np.reshape(RL,orig_shape,order='F')
# Return unit vectors of sight-line direction for each pixel. Example 48
def _compute_projection_pick(artist, path, xy):
"""Project *xy* on *path* to obtain a `Selection` for *artist*.
*path* is first transformed to screen coordinates using the artist
transform, and the target of the returned `Selection` is transformed
back to data coordinates using the artist *axes* inverse transform. The
`Selection` `index` is returned as a float. This function returns ``None``
for degenerate inputs.
The caller is responsible for converting the index to the proper class if
needed.
"""
transform = artist.get_transform().frozen()
tpath = (path.cleaned(transform) if transform.is_affine
# `cleaned` only handles affine transforms.
else transform.transform_path(path).cleaned())
# `cleaned` should return a path where the first element is `MOVETO`, the
# following are `LINETO` or `CLOSEPOLY`, and the last one is `STOP`, i.e.
# codes = path.codes
# assert (codes[0], codes[-1]) == (path.MOVETO, path.STOP)
# assert np.in1d(codes[1:-1], [path.LINETO, path.CLOSEPOLY]).all()
vertices = tpath.vertices[:-1]
codes = tpath.codes[:-1]
vertices[codes == tpath.CLOSEPOLY] = vertices[0]
# Unit vectors for each segment.
us = vertices[1:] - vertices[:-1]
ls = np.hypot(*us.T)
with np.errstate(invalid="ignore"):
# Results in 0/0 for repeated consecutive points.
us /= ls[:, None]
# Vectors from each vertex to the event (overwritten below).
vs = xy - vertices[:-1]
# Clipped dot products -- `einsum` cannot be done in place, `clip` can.
dot = np.clip(np.einsum("ij,ij->i", vs, us), 0, ls, out=vs[:, 0])
# Projections.
projs = vertices[:-1] + dot[:, None] * us
ds = np.hypot(*(xy - projs).T, out=vs[:, 1])
try:
argmin = np.nanargmin(ds)
dmin = ds[argmin]
except (ValueError, IndexError): # See above re: exceptions caught.
return
else:
target = AttrArray(
artist.axes.transData.inverted().transform_point(projs[argmin]))
target.index = (
(argmin + dot[argmin] / ls[argmin])
/ (path._interpolation_steps / tpath._interpolation_steps))
return Selection(artist, target, dmin, None, None) Example 49
def _(artist, event):
# No need to call `line.contains` because we're going to redo
# the work anyways (and it was broken for step plots up to
# matplotlib/matplotlib#6645).
# Always work in screen coordinates, as this is how we need to compute
# distances. Note that the artist transform may be different from the axes
# transform (e.g., for axvline).
xy = event.x, event.y
data_xy = artist.get_xydata()
sels = []
# If markers are visible, find the closest vertex.
if artist.get_marker() not in ["None", "none", " ", "", None]:
ds = np.hypot(*(xy - artist.get_transform().transform(data_xy)).T)
try:
argmin = np.nanargmin(ds)
dmin = ds[argmin]
except (ValueError, IndexError):
# numpy 1.7.0's `nanargmin([nan])` returns nan, so
# `ds[argmin]` raises IndexError. In later versions of numpy,
# `nanargmin([nan])` raises ValueError (the release notes for 1.8.0
# are incorrect on this topic).
pass
else:
# More precise than transforming back.
target = with_attrs(artist.get_xydata()[argmin], index=argmin)
sels.append(Selection(artist, target, dmin, None, None))
# If lines are visible, find the closest projection.
if (artist.get_linestyle() not in ["None", "none", " ", "", None]
and len(artist.get_xydata()) > 1):
sel = _compute_projection_pick(artist, artist.get_path(), xy)
if sel is not None:
sel.target.index = {
"_draw_lines": lambda _, index: index,
"_draw_steps_pre": Index.pre_index,
"_draw_steps_mid": Index.mid_index,
"_draw_steps_post": Index.post_index}[
Line2D.drawStyles[artist.get_drawstyle()]](
len(data_xy), sel.target.index)
sels.append(sel)
sel = min(sels, key=lambda sel: sel.dist, default=None)
return sel if sel and sel.dist < artist.get_pickradius() else None Example 50
def mouseMoved(self, evt):
pos = evt[0]
if self.plot.sceneBoundingRect().contains(pos):
mousePoint = self.plot.vb.mapSceneToView(pos)
posx, posy = mousePoint.x(), mousePoint.y()
labels = []
for a, vs in sorted(self.reports.items()):
for v in vs:
if isinstance(v, tuple) and len(v) == 2:
if v[0] == "x":
labels.append(("%0." + str(self.important_decimals[0]) + "f") % v[1])
continue
labels.append(str(v))
labels = " ".join(labels)
self.crosshair_hidden = bool(labels)
if self.location and not labels:
fs = "%0." + str(self.important_decimals[0]) + "f %0." + str(self.important_decimals[1]) + "f"
labels = fs % (posx, posy)
self.label.setText(labels, color=(0, 0, 0))
if self.curves and len(self.curves[0][0]): # need non-zero x axis!
cache = {}
bd = None
if self.markclosest and self.plot.vb.action != ZOOMING:
xpixel, ypixel = self.plot.vb.viewPixelSize()
distances = distancetocurves(self.curves[0], posx, posy, xpixel, ypixel, r=self.MOUSE_RADIUS,
cache=cache)
try:
mindi = np.nanargmin(distances)
if distances[mindi] < self.MOUSE_RADIUS:
bd = mindi
except ValueError: # if all distances are NaN
pass
if self.highlighted != bd:
QToolTip.hideText()
if self.highlighted is not None and bd is None:
self.highlighted = None
self.highlighted_curve.hide()
if bd is not None:
self.highlighted = bd
x = self.curves[0][0]
y = self.curves[0][1][self.highlighted]
self.highlighted_curve.setData(x=x, y=y)
self.highlighted_curve.show()
self.vLine.setPos(posx)
self.hLine.setPos(posy)
self.viewhelpers_show()
else:
self.viewhelpers_hide() 