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 guess(representation, sims, xi, a, a_, b):
sa = sims[xi[a]]
sa_ = sims[xi[a_]]
sb = sims[xi[b]]
add_sim = -sa+sa_+sb
if a in representation.wi:
add_sim[representation.wi[a]] = 0
if a_ in representation.wi:
add_sim[representation.wi[a_]] = 0
if b in representation.wi:
add_sim[representation.wi[b]] = 0
b_add = representation.iw[np.nanargmax(add_sim)]
mul_sim = sa_*sb*np.reciprocal(sa+0.01)
if a in representation.wi:
mul_sim[representation.wi[a]] = 0
if a_ in representation.wi:
mul_sim[representation.wi[a_]] = 0
if b in representation.wi:
mul_sim[representation.wi[b]] = 0
b_mul = representation.iw[np.nanargmax(mul_sim)]
return b_add, b_mul Example 2
def guess(representation, sims, xi, a, a_, b):
sa = sims[xi[a]]
sa_ = sims[xi[a_]]
sb = sims[xi[b]]
add_sim = -sa+sa_+sb
if a in representation.wi:
add_sim[representation.wi[a]] = 0
if a_ in representation.wi:
add_sim[representation.wi[a_]] = 0
if b in representation.wi:
add_sim[representation.wi[b]] = 0
b_add = representation.iw[np.nanargmax(add_sim)]
mul_sim = sa_*sb*np.reciprocal(sa+0.01)
if a in representation.wi:
mul_sim[representation.wi[a]] = 0
if a_ in representation.wi:
mul_sim[representation.wi[a_]] = 0
if b in representation.wi:
mul_sim[representation.wi[b]] = 0
b_mul = representation.iw[np.nanargmax(mul_sim)]
return b_add, b_mul Example 3
def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt) Example 4
def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt) Example 5
def generalized_esd(x, r, alpha=0.05, method='mean'):
"""Generalized ESD test for outliers
(http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h3.htm).
Args:
x (numpy.ndarray): the data
r (int): max number of outliers
alpha (float): the signifiance level
method (str): 'median' or 'mean'
Returns:
list[int]: list of the index of outliers
"""
x = np.asarray(x, dtype=np.float64)
fn = __get_pd_median if method == 'median' else __get_pd_mean
NaN = float('nan')
outliers = []
N = len(x)
for i in range(1, r + 1):
if np.any(~np.isnan(x)):
m, e = fn(x)
if e != 0.:
y = np.abs(x - m)
j = np.nanargmax(y)
R = y[j]
lam = __get_lambda_critical(N, i, alpha)
if R > lam * e:
outliers.append(j)
x[j] = NaN
else:
break
else:
break
else:
break
return outliers Example 6
def _select_best_score(scores, args):
return np.nanargmax(np.array(scores)) Example 7
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 8
def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt) Example 9
def choose_arm(x, experts, explore):
n_arms = len(experts)
# make predictions
preds = [expert.predict(x) for expert in experts]
# get best arm
arm_max = np.nanargmax(preds)
# create arm selection probabilities
P = [(1-explore)*(arm==arm_max) + explore/n_arms for arm in range(n_arms)]
# select an arm
chosen_arm = np.random.choice(np.arange(n_arms), p=P)
pred = preds[chosen_arm]
return chosen_arm, pred Example 10
def predict_ana( model, a, a2, b, realb2 ):
questWordIndices = [ model.word2id[x] for x in (a,a2,b) ]
# b2 is effectively iterating through the vocab. The row is all the cosine values
b2a2 = model.sim_row(a2)
b2a = model.sim_row(a)
b2b = model.sim_row(b)
addsims = b2a2 - b2a + b2b
addsims[questWordIndices] = -10000
iadd = np.nanargmax(addsims)
b2add = model.vocab[iadd]
# For debugging purposes
ia = model.word2id[a]
ia2 = model.word2id[a2]
ib = model.word2id[b]
ib2 = model.word2id[realb2]
realaddsim = addsims[ib2]
mulsims = ( b2a2 + 1 ) * ( b2b + 1 ) / ( b2a + 1.001 )
mulsims[questWordIndices] = -10000
imul = np.nanargmax(mulsims)
b2mul = model.vocab[imul]
return b2add, b2mul Example 11
def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt) Example 12
def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt) Example 13
def decode_location(likelihood, pos_centers, time_centers):
"""Finds the decoded location based on the centers of the position bins.
Parameters
----------
likelihood : np.array
With shape(n_timebins, n_positionbins)
pos_centers : np.array
time_centers : np.array
Returns
-------
decoded : nept.Position
Estimate of decoded position.
"""
prob_rows = np.sum(np.isnan(likelihood), axis=1) < likelihood.shape[1]
max_decoded_idx = np.nanargmax(likelihood[prob_rows], axis=1)
prob_decoded = pos_centers[max_decoded_idx]
decoded_pos = np.empty((likelihood.shape[0], pos_centers.shape[1])) * np.nan
decoded_pos[prob_rows] = prob_decoded
decoded_pos = np.squeeze(decoded_pos)
return nept.Position(decoded_pos, time_centers) Example 14
def maxabs(trace, starttime=None, endtime=None):
"""Returns the maximum of the absolute values of `trace`, and its occurrence time.
In other words, returns the point `(time, value)` where `value = max(abs(trace.data))`
and time (`UTCDateTime`) is the time occurrence of `value`
:param trace: the input obspy.core.Trace
:param starttime: (`obspy.UTCDateTime`) the start time (None or missing defaults to the trace
end): the maximum of the trace `abs` will be searched *from* this time. This argument,
if provided, does not affect the
returned `time` which will be always relative to the trace passed as argument
:param endtime: an obspy UTCDateTime object (or any value
`UTCDateTime` accepts, e.g. integer / `datetime` object) denoting
the end time (None or missing defaults to the trace end): the maximum of the trace `abs`
will be searched *until* this time
:return: the tuple (time, value) where `value = max(abs(trace.data))`, and time is
the value occurrence (`UTCDateTime`)
:return: the tuple `(time_of_max_abs, max_abs)`
"""
original_stime = None if starttime is None else trace.stats.starttime
if starttime is not None or endtime is not None:
# from the docs: "this returns a New Trace object
# Does not copy data but just passes a reference to it"
trace = trace.slice(starttime, endtime)
if trace.stats.npts < 1:
return np.nan
idx = np.nanargmax(np.abs(trace.data))
val = trace.data[idx]
tdelta = 0 if original_stime is None else trace.stats.starttime - original_stime
time = timeof(trace, idx) + tdelta
return (time, val) Example 15
def optimize_threshold_with_roc(roc, thresholds, criterion='dist'):
if roc.shape[1] > roc.shape[0]:
roc = roc.T
assert(roc.shape[0] == thresholds.shape[0])
if criterion == 'margin':
scores = roc[:,1]-roc[:,0]
else:
scores = -cdist(np.array([[0,1]]), roc)
ti = np.nanargmax(scores)
return thresholds[ti], ti Example 16
def optimize_threshold_with_prc(prc, thresholds, criterion='min'):
prc[np.isnan(prc)] = 0
if prc.shape[1] > prc.shape[0]:
prc = prc.T
assert(prc.shape[0] == thresholds.shape[0])
if criterion == 'sum':
scores = prc.sum(axis=1)
elif criterion.startswith('dist'):
scores = -cdist(np.array([[1,1]]), prc)
else:
scores = prc.min(axis=1)
ti = np.nanargmax(scores)
return thresholds[ti], ti Example 17
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 18
def compute_draw_info(self, x, ys):
bs = self.compute_baseline(x, ys)
im = np.nanargmax(ys-bs, axis=1)
lines = (x[im], bs[np.arange(bs.shape[0]), im]), (x[im], ys[np.arange(ys.shape[0]), im])
return [("curve", (x, self.compute_baseline(x, ys), INTEGRATE_DRAW_BASELINE_PENARGS)),
("curve", (x, ys, INTEGRATE_DRAW_BASELINE_PENARGS)),
("line", lines)] Example 19
def compute_integral(self, x_s, y_s):
y_s = y_s - self.compute_baseline(x_s, y_s)
if len(x_s) == 0:
return np.zeros((y_s.shape[0],)) * np.nan
# avoid whole nan rows
whole_nan_rows = np.isnan(y_s).all(axis=1)
y_s[whole_nan_rows] = 0
# select positions
pos = x_s[np.nanargmax(y_s, axis=1)]
# set unknown results
pos[whole_nan_rows] = np.nan
return pos Example 20
def compute_draw_info(self, x, ys):
bs = self.compute_baseline(x, ys)
im = np.nanargmax(ys-bs, axis=1)
lines = (x[im], bs[np.arange(bs.shape[0]), im]), (x[im], ys[np.arange(ys.shape[0]), im])
return [("curve", (x, self.compute_baseline(x, ys), INTEGRATE_DRAW_BASELINE_PENARGS)),
("curve", (x, ys, INTEGRATE_DRAW_BASELINE_PENARGS)),
("line", lines)] Example 21
def test_nanargmax(self):
tgt = np.argmax(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanargmax(mat), tgt) Example 22
def get_best_threshold(y_ref, y_pred_score, plot=False):
""" Get threshold on scores that maximizes f1 score.
Parameters
----------
y_ref : array
Reference labels (binary).
y_pred_score : array
Predicted scores.
plot : bool
If true, plot ROC curve
Returns
-------
best_threshold : float
threshold on score that maximized f1 score
max_fscore : float
f1 score achieved at best_threshold
"""
pos_weight = 1.0 - float(len(y_ref[y_ref == 1]))/float(len(y_ref))
neg_weight = 1.0 - float(len(y_ref[y_ref == 0]))/float(len(y_ref))
sample_weight = np.zeros(y_ref.shape)
sample_weight[y_ref == 1] = pos_weight
sample_weight[y_ref == 0] = neg_weight
print "max prediction value = %s" % np.max(y_pred_score)
print "min prediction value = %s" % np.min(y_pred_score)
precision, recall, thresholds = \
metrics.precision_recall_curve(y_ref, y_pred_score, pos_label=1,
sample_weight=sample_weight)
beta = 1.0
btasq = beta**2.0
fbeta_scores = (1.0 + btasq)*(precision*recall)/((btasq*precision)+recall)
max_fscore = fbeta_scores[np.nanargmax(fbeta_scores)]
best_threshold = thresholds[np.nanargmax(fbeta_scores)]
if plot:
plt.figure(1)
plt.subplot(1, 2, 1)
plt.plot(recall, precision, '.b', label='PR curve')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.legend(loc="lower right", frameon=True)
plt.subplot(1, 2, 2)
plt.plot(thresholds, fbeta_scores[:-1], '.r', label='f1-score')
plt.xlabel('Probability Threshold')
plt.ylabel('F1 score')
plt.show()
plot_data = (recall, precision, thresholds, fbeta_scores[:-1])
return best_threshold, max_fscore, plot_data Example 23
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 24
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 25
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 26
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 27
def evaluate_hyperparameters(dataset, iterator, args):
# Select features
if args.features is not None and args.features != dataset.feature_names:
print('selecting features ...')
features = _explode_features(args.features)
start = timeit.default_timer()
dataset = dataset.dataset_from_feature_names(features)
print('done, took %fs' % (timeit.default_timer() - start))
print('')
states = range(3, 22 + 1) # = [3,...,22]
topologies = ['full', 'left-to-right-full', 'left-to-right-1', 'left-to-right-2']
n_combinations = len(states) * len(topologies)
curr_step = 0
combinations = []
measures = []
for state in states:
for topology in topologies:
curr_step += 1
prefix = '%.3d_%d_%s' % (curr_step, state, topology)
print('(%.3d/%.3d) evaluating state=%d and topology=%s ...' % (curr_step, n_combinations, state, topology))
start = timeit.default_timer()
try:
# Configure args from which the HMMs are created
args.n_states = state
args.topology = topology
ll_stats = _compute_averaged_pos_and_neg_lls(dataset, iterator, prefix, args)
measure = _compute_measure(ll_stats, dataset, args)
except:
measure = np.nan
if measure is np.isnan(measure):
print('measure: not computable')
else:
print('measure: %f' % measure)
combinations.append((str(state), topology))
measures.append(measure)
print('done, took %fs' % (timeit.default_timer() - start))
print('')
best_idx = np.nanargmax(np.array(measures)) # get the argmax ignoring NaNs
print('best combination with score %f: %s' % (measures[best_idx], ', '.join(combinations[best_idx])))
print('detailed reports have been saved')
# Save results
assert len(combinations) == len(measures)
if args.output_dir is not None:
filename = '_results.csv'
with open(os.path.join(args.output_dir, filename), 'wb') as f:
writer = csv.writer(f, delimiter=';')
writer.writerow(['', 'idx', 'measure', 'combination'])
for idx, (measure, combination) in enumerate(zip(measures, combinations)):
selected = '*' if best_idx == idx else ''
writer.writerow([selected, '%d' % idx, '%f' % measure, ', '.join(combination)]) Example 28
def evaluate_pca(dataset, iterator, args):
# Select features
if args.features is not None and args.features != dataset.feature_names:
print('selecting features ...')
features = _explode_features(args.features)
start = timeit.default_timer()
dataset = dataset.dataset_from_feature_names(features)
print('done, took %fs' % (timeit.default_timer() - start))
print('')
pca_components = range(1, dataset.n_features)
total_steps = len(pca_components)
if 'pca' not in args.transformers:
args.transformers.append('pca')
curr_step = 0
measures = []
for n_components in pca_components:
curr_step += 1
prefix = '%.3d' % curr_step
print('(%.3d/%.3d) evaluating with %d pca components ...' % (curr_step, total_steps, n_components))
start = timeit.default_timer()
try:
args.pca_components = n_components
ll_stats = _compute_averaged_pos_and_neg_lls(dataset, iterator, prefix, args)
measure = _compute_measure(ll_stats, dataset, args)
except:
measure = np.nan
if measure is np.isnan(measure):
print('measure: not computable')
else:
print('measure: %f' % measure)
# Correct score. The problem is that it is computed given the dataset, which has too many features.
measure = (measure * float(dataset.n_features)) / float(n_components)
measures.append(measure)
print('done, took %fs' % (timeit.default_timer() - start))
print('')
assert len(pca_components) == len(measures)
best_idx = np.nanargmax(np.array(measures)) # get the argmax ignoring NaNs
print('best result with score %f: %d PCA components' % (measures[best_idx], pca_components[best_idx]))
print('detailed reports have been saved')
# Save results
if args.output_dir is not None:
filename = '_results.csv'
with open(os.path.join(args.output_dir, filename), 'wb') as f:
writer = csv.writer(f, delimiter=';')
writer.writerow(['', 'idx', 'measure', 'components'])
for idx, (measure, n_components) in enumerate(zip(measures, pca_components)):
selected = '*' if best_idx == idx else ''
writer.writerow([selected, '%d' % idx, '%f' % measure, '%d' % n_components]) Example 29
def evaluate_fhmms(dataset, iterator, args):
# Select features
if args.features is not None and args.features != dataset.feature_names:
print('selecting features ...')
features = _explode_features(args.features)
start = timeit.default_timer()
dataset = dataset.dataset_from_feature_names(features)
print('done, took %fs' % (timeit.default_timer() - start))
print('')
chains = [1, 2, 3, 4]
total_steps = len(chains)
curr_step = 0
measures = []
for chain in chains:
curr_step += 1
prefix = '%.3d_%d-chains' % (curr_step, chain)
print('(%.3d/%.3d) evaluating n_chains=%d ...' % (curr_step, total_steps, chain))
start = timeit.default_timer()
old_loglikelihood_method = args.loglikelihood_method
try:
# Configure args from which the HMMs are created
args.n_chains = chain
if chain == 1:
args.model = 'hmm'
args.loglikelihood_method = 'exact' # there's no approx loglikelihood method for HMMs
else:
args.model = 'fhmm-seq'
ll_stats = _compute_averaged_pos_and_neg_lls(dataset, iterator, prefix, args, save_model=True, compute_distances=False)
measure = _compute_measure(ll_stats, dataset, args)
except:
measure = np.nan
args.loglikelihood_method = old_loglikelihood_method
if measure is np.isnan(measure):
print('measure: not computable')
else:
print('measure: %f' % measure)
measures.append(measure)
print('done, took %fs' % (timeit.default_timer() - start))
print('')
best_idx = np.nanargmax(np.array(measures)) # get the argmax ignoring NaNs
print('best model with score %f: %d chains' % (measures[best_idx], chains[best_idx]))
print('detailed reports have been saved')
# Save results
assert len(chains) == len(measures)
if args.output_dir is not None:
filename = '_results.csv'
with open(os.path.join(args.output_dir, filename), 'wb') as f:
writer = csv.writer(f, delimiter=';')
writer.writerow(['', 'idx', 'measure', 'chains'])
for idx, (measure, chain) in enumerate(zip(measures, chains)):
selected = '*' if best_idx == idx else ''
writer.writerow([selected, '%d' % idx, '%f' % measure, '%d' % chain]) Example 30
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 31
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 32
def phase_shift( shotGather, num_vel=2048, min_frequency=5, max_frequency=100, min_velocity=1, max_velocity=1000 ):
# Ensure that min_velocity is greater than zero for numerical stability
if min_velocity < 1:
min_velocity = 1
# Frequency vector
freq = np.arange(0, shotGather.fnyq, shotGather.df)
# FFT of timeHistories (Equation 1 of Park et al. 1998).....................
U = np.fft.fft(shotGather.timeHistories, axis=0)
# Remove frequencies above/below specificied max/min frequencies and downsample (if required by zero padding)
fminID = np.argmin( np.absolute(freq-min_frequency) )
fmaxID = np.argmin( np.absolute(freq-max_frequency) )
freq_id = range(fminID,(fmaxID+1), shotGather.multiple)
freq = freq[freq_id]
U = U[freq_id, :]
# Trial velocities
v_vals = np.linspace( min_velocity, max_velocity, num_vel )
# Initialize variables
v_peak = np.zeros( np.shape(freq) )
V = np.zeros( (np.shape(v_vals)[0], len(freq)) )
pnorm = np.zeros( np.shape(V) )
# Transformation ...........................................................
# Loop through frequencies
for c in range( len(freq) ):
# Loop through trial velocities at each frequency
for r in range( np.shape(v_vals)[0] ):
# Set power equal to NaN at wavenumbers > kres
if v_vals[r] < (2*np.pi*freq[c]/shotGather.kres):
V[r,c] = float( 'nan' )
# (Equation 4 in Park et al. 1998)
else:
V[r,c] = np.abs( np.sum( U[c,:]/np.abs(U[c,:]) * np.exp( 1j*2*np.pi*freq[c]*shotGather.position / v_vals[r] ) ) )
# Identify index associated with peak power at current frequency
max_id = np.nanargmax( V[:,c] )
pnorm[:,c] = V[:,c] / V[max_id,c]
v_peak[c] = v_vals[max_id]
# Create instance of DispersionPower class..................................
dispersionPower = dctypes.DispersionPower( freq, v_peak, v_vals, 'velocity', shotGather.kres, pnorm )
return dispersionPower
#*******************************************************************************
# Slant-stack transform Example 33
def compose_ko(radargrids, qualitygrids):
"""Composes grids according to quality information using quality \
information as a knockout criterion.
The value of the composed pixel is taken from the radargrid whose
quality grid has the highest value.
Parameters
----------
radargrids : list of arrays
radar data to be composited. Each item in the list corresponds to the
data of one radar location. All items must have the same shape.
qualitygrids : list of arrays
quality data to decide upon which radar site will contribute its pixel
to the composite. Then length of this list must be the same as that
of `radargrids`. All items must have the same shape and be aligned with
the items in `radargrids`.
Returns
-------
composite : array
"""
# first add a fallback array for all pixels having missing values in all
# radargrids
radarfallback = (np.repeat(np.nan, np.prod(radargrids[0].shape))
.reshape(radargrids[0].shape))
radargrids.append(radarfallback)
radarinfo = np.array(radargrids)
# then do the same for the quality grids
qualityfallback = (np.repeat(-np.inf, np.prod(radargrids[0].shape))
.reshape(radargrids[0].shape))
qualitygrids.append(qualityfallback)
qualityinfo = np.array(qualitygrids)
select = np.nanargmax(qualityinfo, axis=0)
composite = (radarinfo.reshape((radarinfo.shape[0], -1))
[select.ravel(), np.arange(np.prod(radarinfo.shape[1:]))]
.reshape(radarinfo.shape[1:]))
radargrids.pop()
qualitygrids.pop()
return composite Example 34
def cum_beam_block_frac(pbb):
"""Cumulative beam blockage fraction along a beam.
Computes the cumulative beam blockage (cbb) along a beam from the partial
beam blockage (pbb) fraction of each bin along that beam. CBB in one bin
along a beam will always be at least as high as the maximum PBB of the
preceeding bins.
.. versionadded:: 0.10.0
Parameters
----------
pbb : :class:`numpy:numpy.ndarray`
2-D array of floats of shape (num beams, num range bins)
Partial beam blockage fraction of a bin along a beam [m]
Returns
-------
cbb : :class:`numpy:numpy.ndarray`
Array of floats of the same shape as pbb
Cumulative partial beam blockage fraction [unitless]
Examples
--------
>>> PBB = beam_block_frac(Th,Bh,a) #doctest: +SKIP
>>> CBB = cum_beam_block_frac(PBB) #doctest: +SKIP
See :ref:`notebooks/beamblockage/wradlib_beamblock.ipynb`.
"""
# This is the index of the maximum PBB along each beam
maxindex = np.nanargmax(pbb, axis=1)
cbb = np.copy(pbb)
# Iterate over all beams
for ii, index in enumerate(maxindex):
premax = 0.
for jj in range(index):
# Only iterate to max index to make this faster
if pbb[ii, jj] > premax:
cbb[ii, jj] = pbb[ii, jj]
premax = pbb[ii, jj]
else:
cbb[ii, jj] = premax
# beyond max index, everything is max anyway
cbb[ii, index:] = pbb[ii, index]
return cbb Example 35
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 36
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 37
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 38
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 39
def _auto_low_rank_model(data, mode, n_jobs, method_params, cv,
stop_early=True, verbose=None):
"""compute latent variable models."""
method_params = cp.deepcopy(method_params)
iter_n_components = method_params.pop('iter_n_components')
if iter_n_components is None:
iter_n_components = np.arange(5, data.shape[1], 5)
from sklearn.decomposition import PCA, FactorAnalysis
if mode == 'factor_analysis':
est = FactorAnalysis
elif mode == 'pca':
est = PCA
else:
raise ValueError('Come on, this is not a low rank estimator: %s' %
mode)
est = est(**method_params)
est.n_components = 1
scores = np.empty_like(iter_n_components, dtype=np.float64)
scores.fill(np.nan)
# make sure we don't empty the thing if it's a generator
max_n = max(list(cp.deepcopy(iter_n_components)))
if max_n > data.shape[1]:
warn('You are trying to estimate %i components on matrix '
'with %i features.' % (max_n, data.shape[1]))
for ii, n in enumerate(iter_n_components):
est.n_components = n
try: # this may fail depending on rank and split
score = _cross_val(data=data, est=est, cv=cv, n_jobs=n_jobs)
except ValueError:
score = np.inf
if np.isinf(score) or score > 0:
logger.info('... infinite values encountered. stopping estimation')
break
logger.info('... rank: %i - loglik: %0.3f' % (n, score))
if score != -np.inf:
scores[ii] = score
if (ii >= 3 and np.all(np.diff(scores[ii - 3:ii]) < 0.) and
stop_early is True):
# early stop search when loglik has been going down 3 times
logger.info('early stopping parameter search.')
break
# happens if rank is too low right form the beginning
if np.isnan(scores).all():
raise RuntimeError('Oh no! Could not estimate covariance because all '
'scores were NaN. Please contact the MNE-Python '
'developers.')
i_score = np.nanargmax(scores)
best = est.n_components = iter_n_components[i_score]
logger.info('... best model at rank = %i' % best)
runtime_info = {'ranks': np.array(iter_n_components),
'scores': scores,
'best': best,
'cv': cv}
return est, runtime_info Example 40
def get_multievent_sg(cum_trace, tmin, tmax, tstart,
threshold_inside_tmin_tmax_percent,
threshold_inside_tmin_tmax_sec, threshold_after_tmax_percent):
"""
Returns the tuple (or a list of tuples, if the first argument is a stream) of the
values (score, UTCDateTime of arrival)
where scores is: 0: no double event, 1: double event inside tmin_tmax,
2: double event after tmax, 3: both double event previously defined are detected
If score is 2 or 3, the second argument is the UTCDateTime denoting the occurrence of the
first sample triggering the double event after tmax
:param trace: the input obspy.core.Trace
"""
tmin = utcdatetime(tmin)
tmax = utcdatetime(tmax)
tstart = utcdatetime(tstart)
# split traces between tmin and tmax and after tmax
traces = [cum_trace.slice(tmin, tmax), cum_trace.slice(tmax, None)]
# calculate second derivative and normalize:
second_derivs = []
max_ = np.nan
for ttt in traces:
ttt.taper(type='cosine', max_percentage=0.05)
sec_der = savitzky_golay(ttt.data, 31, 2, deriv=2)
sec_der_abs = np.abs(sec_der)
idx = np.nanargmax(sec_der_abs)
# get max (global) for normalization:
max_ = np.nanmax([max_, sec_der_abs[idx]])
second_derivs.append(sec_der_abs)
# normalize second derivatives:
for der in second_derivs:
der /= max_
result = 0
# case A: see if after tmax we exceed a threshold
indices = np.where(second_derivs[1] >= threshold_after_tmax_percent)[0]
if len(indices):
result = 2
# case B: see if inside tmin tmax we exceed a threshold, and in case check the duration
deltatime = 0
indices = np.where(second_derivs[0] >= threshold_inside_tmin_tmax_percent)[0]
starttime = endtime = None
if len(indices) >= 2:
idx0 = indices[0]
idx1 = indices[-1]
starttime = timeof(traces[0], idx0)
endtime = timeof(traces[0], idx1)
deltatime = endtime - starttime
if deltatime >= threshold_inside_tmin_tmax_sec:
result += 1
return result, deltatime, starttime, endtime 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 