Here are the examples of the python api numpy.nonzero taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
162 Examples
5
Example 1
Project: tvb-library Source File: region_boundaries.py
def find_boundary_triangles(cortex):
"""
Identify triangles that cross a parcellation boundary
"""
tb01 = numpy.nonzero(cortex.region_mapping[cortex.triangles][:, 0] -
cortex.region_mapping[cortex.triangles][:, 1])
tb12 = numpy.nonzero(cortex.region_mapping[cortex.triangles][:, 1] -
cortex.region_mapping[cortex.triangles][:, 2])
tb20 = numpy.nonzero(cortex.region_mapping[cortex.triangles][:, 2] -
cortex.region_mapping[cortex.triangles][:, 0])
return numpy.unique(numpy.hstack((tb01, tb12, tb20)))
5
Example 2
Project: HPGL-GUI Source File: statistics_window.py
def makeCuts(self):
self.localMin = float(self.xMin.text())
self.localMax = float(self.xMax.text())
self.cutValues = self.clearValues[numpy.nonzero(self.clearValues >= self.localMin)]
self.cutValues = self.cutValues[numpy.nonzero(self.cutValues <= self.localMax)]
self.cutClearValues = self.cutValues[numpy.nonzero(self.cutValues != self.undefValue)]
self.max = '%.2f' % numpy.max(self.cutClearValues)
self.mean = '%.2f' % numpy.mean(self.cutClearValues)
self.min = '%.2f' % numpy.min(self.cutClearValues)
self.median = '%.2f' % numpy.median(self.cutClearValues)
self.variance = '%.2f' % numpy.var(self.cutClearValues)
self.defPoints = numpy.size(self.cutClearValues)
3
Example 3
def _compute_separator(self, K):
self._sv = np.nonzero(self._alphas.flat > self.sv_cutoff)
self._sv_alphas = self._alphas[self._sv]
self._sv_bags = [self._bags[i] for i in self._sv[0]]
self._sv_y = self._y[self._sv]
n = len(self._sv_bags)
if n == 0:
self._b = 0.0
self._bag_predictions = np.zeros(len(self._bags))
else:
_sv_all_K = K[self._sv]
_sv_K = _sv_all_K.T[self._sv].T
e = np.matrix(np.ones((n, 1)))
D = spdiag(self._sv_y)
self._b = float(e.T * D * e - self._sv_alphas.T * D * _sv_K * e) / n
self._bag_predictions = np.array(self._b
+ self._sv_alphas.T * D * _sv_all_K).reshape((-1,))
3
Example 4
def getAllDirEdges(self):
"""
Returns the set of directed edges of the current graph as a matrix in which each
row corresponds to an edge. For an undirected graph, there is an edge from
v1 to v2 and from v2 to v1 if v2!=v1.
:returns: A matrix with 2 columns, and each row corresponding to an edge.
"""
(rows, cols) = numpy.nonzero(self.W)
edges = numpy.c_[rows, cols]
return edges
3
Example 5
Project: ilastik-0.5 Source File: synapseDetectionFilter.py
def objectsSlow3d(self, cc):
#returns a dictionary, where the key is the point "intensity" (i.e. connected component number)
#and the value is a list of point coordinates [[x], [y], [z]]
objs = {}
nzindex = numpy.nonzero(cc)
for i in range(len(nzindex[0])):
value = cc[nzindex[0][i], nzindex[1][i], nzindex[2][i]]
if value > 0:
if value not in objs:
objs[value] = [[], [], []]
objs[value][0].append(nzindex[0][i])
objs[value][1].append(nzindex[1][i])
objs[value][2].append(nzindex[2][i])
return objs
3
Example 6
def set_context_undefined ( self, name, value ):
""" Sets the value of a currently undefined item.
"""
mask = self.context.context_selection
if mask is None:
super( SelectionReductionContext, self ).set_context_undefined(
name, value )
else:
data = self.context.get_context_undefined( name, value )
if data is not Undefined:
put( data, nonzero( mask ), value )
self.context.set_context_undefined( name, data )
3
Example 7
def select_rays(self, idxs):
"""
Inform the geometry manager that only the given rays are to be used,
so that internal data size is kept small.
Arguments:
idxs - an index array stating which rays of the working bundle
are active.
"""
self._idxs = idxs # For slicing ray bundles etc.
self._backside = N.nonzero(self._backside[idxs])[0]
v = self._working_bundle.get_vertices()[:,idxs]
d = self._working_bundle.get_directions()[:,idxs]
p = self._params[idxs]
del self._params
# Global coordinates on the surface:
self._global = v + p[None,:]*d
3
Example 8
Project: robothon Source File: arrayfns.py
def zmin_zmax(z, ireg):
z = asarray(z, dtype=float)
ireg = asarray(ireg, dtype=int)
if z.shape != ireg.shape or z.ndim != 2:
raise ValueError, "z and ireg must be the same shape and 2-d"
ix, iy = nx.nonzero(ireg)
# Now, add more indices
x1m = ix - 1
y1m = iy-1
i1 = x1m>=0
i2 = y1m>=0
i3 = i1 & i2
nix = nx.r_[ix, x1m[i1], x1m[i1], ix[i2] ]
niy = nx.r_[iy, iy[i1], y1m[i3], y1m[i2]]
# remove any negative indices
zres = z[nix,niy]
return zres.min().item(), zres.max().item()
3
Example 9
Project: ilastik-0.5 Source File: classificationMgr.py
def getTrainingMatrixRefForImage(self, dataItemImage):
prop = dataItemImage.module["Classification"]
if len(prop["trainingF"]) == 0 and prop["featureM"] is not None:
tempF = []
tempL = []
tempd = dataItemImage.overlayMgr["Classification/Labels"][:, :, :, :, 0].ravel()
indices = numpy.nonzero(tempd)[0]
tempL = dataItemImage.overlayMgr["Classification/Labels"][:,:,:,:,0].ravel()[indices]
tempL.shape += (1,)
prop["trainingIndices"] = indices
prop["trainingL"] = tempL
if len(indices) > 0:
prop["trainingF"] = self.getTrainingMforIndForImage(indices, dataItemImage)
else:
self.clearFeaturesAndTrainingForImage(dataItemImage)
return prop["trainingL"], prop["trainingF"], prop["trainingIndices"]
3
Example 10
def set_matrix_dimensions(self, bounds, xdensity, ydensity):
super(ModulatedLogSpectrogram, self).set_matrix_dimensions(bounds, xdensity, ydensity)
self._modulation_start_index = nonzero(self.frequency_spacing >= self.lower_freq_bound)[0][0]
self._modulation_end_index = nonzero(self.frequency_spacing >= self.upper_freq_bound)[0][0]
self._modulation = self.modulation_function(self._modulation_end_index-self._modulation_start_index)
self._modulation = reshape(self._modulation, [-1,1])
3
Example 11
def width(self,L):
m=0
for i,row in enumerate(L):
w=0
x,y = np.nonzero(row)
if len(y) > 0:
v = y-i
w=v.max()-v.min()+1
m = max(w,m)
return m
3
Example 12
def dense_to_sparse(voxel_data, dtype=np.int):
""" From dense representation to sparse (coordinate) representation.
No coordinate reordering.
"""
if voxel_data.ndim != 3:
raise ValueError('voxel_data is wrong shape; should be 3D array.')
return np.asarray(np.nonzero(voxel_data), dtype)
3
Example 13
def set_context_undefined ( self, name, value ):
""" Sets the value of a currently undefined item.
"""
mask = self._mask
if mask is None:
super( ReductionContext, self ).set_context_undefined( name, value )
else:
data = self.context.get_context_undefined( name, value )
if data is not Undefined:
put( data, nonzero( mask ), value )
self.context.set_context_undefined( name, data )
3
Example 14
def set_context_data ( self, name, value ):
""" Sets the value of a specified item.
"""
data = self.context.get_context_data( name )
mask = self.context.context_selection
if mask is not None:
put( data, nonzero( mask ), value )
self.context.set_context_data( name, data )
3
Example 15
Project: chaco Source File: subdivision_cells.py
def arg_find_runs(int_array, order='ascending'):
"""
This function is like find_runs(), but it returns a list of tuples
indicating the start and end indices of runs in the input *int_array*.
"""
if len(int_array) == 0:
return []
assert len(int_array.shape)==1, "find_runs() requires a 1D integer array."
if order == 'ascending':
increment = 1
elif order == 'descending':
increment = -1
else:
increment = 0
rshifted = right_shift(int_array, int_array[0]-increment)
start_indices = concatenate([[0], nonzero(int_array - (rshifted+increment))[0]])
end_indices = left_shift(start_indices, len(int_array))
return zip(start_indices, end_indices)
3
Example 16
def jaccard(v1, v2):
'''
Due to the idiosyncracies of my code the jaccard index is a bit
altered. The theory is the same but the implementation might be a bit
weird. I do not have two vectors containing the words of both docuements
but instead I have two equally sized vectors. The columns of the vectors
are the same and represent the words in the whole corpus. If an entry
is 1 then the word is present in the docuement. If it is 0 then it is not present.
SO first we find the indices of the words in each docuements and then jaccard is
calculated based on the indices.
'''
indices1 = numpy.nonzero(v1)[0].tolist()
indices2 = numpy.nonzero(v2)[0].tolist()
inter = len(set(indices1) & set(indices2))
un = len(set(indices1) | set(indices2))
dist = 1 - inter/float(un)
return dist
3
Example 17
Project: amen Source File: audio.py
def _create_zero_indexes(self):
"""
Create zero crossing indexes.
We use these in synthesis, and it is easier to make them here.
"""
zero_indexes = []
for channel_index in range(self.num_channels):
channel = self.raw_samples[channel_index]
zero_crossings = librosa.zero_crossings(channel)
zero_index = np.nonzero(zero_crossings)[0]
zero_indexes.append(zero_index)
return zero_indexes
3
Example 18
Project: chempy Source File: _equilibrium.py
def _solve_equilibrium_coord(c0, stoich, K, activity_product=None):
from scipy.optimize import brentq
mask, = np.nonzero(stoich)
stoich_m = stoich[mask]
c0_m = c0[mask]
lower, upper = _get_rc_interval(stoich_m, c0_m)
# span = upper - lower
return brentq(
equilibrium_residual,
lower, # + delta_frac*span,
upper, # - delta_frac*span,
(c0_m, stoich_m, K, activity_product)
)
3
Example 19
def trim_silence(audio, threshold):
'''Removes silence at the beginning and end of a sample.'''
energy = librosa.feature.rmse(audio)
frames = np.nonzero(energy > threshold)
indices = librosa.core.frames_to_samples(frames)[1]
# Note: indices can be an empty array, if the whole audio was silence.
return audio[indices[0]:indices[-1]] if indices.size else audio[0:0]
3
Example 20
def get_indices(self, i):
"""Row and column indices of the i'th bicluster.
Only works if ``rows_`` and ``columns_`` attributes exist.
Returns
-------
row_ind : np.array, dtype=np.intp
Indices of rows in the dataset that belong to the bicluster.
col_ind : np.array, dtype=np.intp
Indices of columns in the dataset that belong to the bicluster.
"""
rows = self.rows_[i]
columns = self.columns_[i]
return np.nonzero(rows)[0], np.nonzero(columns)[0]
3
Example 21
Project: hedge Source File: __init__.py
def map_if(self, expr):
bool_crit = self.rec(expr.condition)
then = self.rec(expr.then)
else_ = self.rec(expr.else_)
true_indices = np.nonzero(bool_crit)
false_indices = np.nonzero(~bool_crit)
result = self.discr.volume_empty(
kind=self.discr.compute_kind)
if isinstance(then, np.ndarray):
then = then[true_indices]
if isinstance(else_, np.ndarray):
else_ = else_[false_indices]
result[true_indices] = then
result[false_indices] = else_
return result
3
Example 22
Project: APGL Source File: DenseGraph.py
def neighbourOf(self, vertexIndex):
"""
Return an array of the indices of vertices than have an edge going to the input
vertex.
:param vertexIndex: the index of a vertex.
:type vertexIndex: :class:`int`
"""
Parameter.checkIndex(vertexIndex, 0, self.vList.getNumVertices())
nonZeroIndices = numpy.nonzero(self.W[:, vertexIndex])
neighbourIndices = nonZeroIndices[0]
return neighbourIndices
3
Example 23
def getAllDirEdges(self):
"""
Returns the set of directed edges of the current graph as a matrix in which each
row corresponds to an edge. For an undirected graph, there is an edge from
v1 to v2 and from v2 to v1 if v2!=v1.
:returns: A matrix with 2 columns, and each row corresponding to an edge.
"""
(rows, cols) = numpy.nonzero(self.W)
edges = numpy.c_[rows, cols]
return edges
3
Example 24
Project: GPy Source File: visualize.py
def modify_edges(self):
self.line_handle = []
if not self.connect==None:
self.I, self.J = np.nonzero(self.connect)
for rod, i, j in zip(self.rods, self.I, self.J):
rod.pos, rod.axis = self.pos_axis(i, j)
3
Example 25
Project: polar2grid Source File: mirs2swath.py
def filter_by_frequency(self, item, arr, freq):
freq_var = self[FREQ_VAR]
freq_idx = numpy.nonzero(freq_var[:] == freq)[0]
if freq_idx:
freq_idx = freq_idx[0]
else:
LOG.error("Frequency %f for variable %s does not exist" % (freq, item))
raise ValueError("Frequency %f for variable %s does not exist" % (freq, item))
freq_dim_idx = self[item].dimensions.index(freq_var.dimensions[0])
idx_obj = [slice(x) for x in arr.shape]
idx_obj[freq_dim_idx] = freq_idx
return arr[idx_obj]
3
Example 26
Project: qgisSpaceSyntaxToolkit Source File: test_branchings.py
def G2():
# Now we shift all the weights by -10.
# Should not affect optimal arborescence, but does affect optimal branching.
G = nx.DiGraph()
Garr = G_array.copy()
Garr[np.nonzero(Garr)] -= 10
G = nx.from_numpy_matrix(Garr, create_using=G)
G = nx.MultiDiGraph(G)
return G
3
Example 27
Project: deepchem Source File: datasets.py
def sparsify_features(X):
"""Extracts a sparse feature representation from dense feature array."""
n_samples = len(X)
X_sparse = []
for i in range(n_samples):
nonzero_inds = np.nonzero(X[i])[0]
nonzero_vals = X[i][nonzero_inds]
X_sparse.append((nonzero_inds, nonzero_vals))
X_sparse = np.array(X_sparse, dtype=object)
return X_sparse
3
Example 28
def set_context_data ( self, name, value ):
""" Sets the value of a specified item.
"""
mask = self._mask
if mask is not None:
data = self.context.get_context_data( name )
put( data, nonzero( mask ), value )
self.context.set_context_data( name, data )
else:
self.context.set_context_data( name, value )
3
Example 29
Project: pebl Source File: base.py
def _all_changes(self):
net = self.evaluator.network
changes = []
# edge removals
changes.extend((None, edge) for edge in net.edges)
# edge reversals
reverse = lambda edge: (edge[1], edge[0])
changes.extend((reverse(edge), edge) for edge in net.edges)
# edge additions
nz = N.nonzero(invert(net.edges.adjacency_matrix))
changes.extend( ((src,dest), None) for src,dest in zip(*nz) )
return changes
3
Example 30
def _set_limits(x, xmin, xmax):
below = numpy.nonzero(x < xmin)
if below.size > 0:
return 1
above = numpy.nonzero(x > xmax)
if above.size > 0:
return 1
return 0
3
Example 31
def arg_find_runs(int_array, order='ascending'):
"""
Like find_runs(), but returns a list of tuples indicating the start and
end indices of runs in the input *int_array*.
"""
n_points = len(int_array)
if n_points == 0:
return []
indices = nonzero(diff(int_array) - delta.get(order, 0))[0] + 1
result = empty(shape=(len(indices) + 1, 2), dtype=indices.dtype)
result[0, 0] = 0
result[-1, 1] = n_points
result[1:, 0] = indices
result[:-1, 1] = indices
return result
3
Example 32
Project: tvb-library Source File: region_boundaries.py
def find_region_neighbours(region_adjacency):
"""
"""
#NOTE: 'should only be doing 1 hemisphere here and then flipping, for symmetry.
number_of_regions = region_adjacency.shape[0]
xxx = numpy.nonzero(region_adjacency + region_adjacency.T)
neighbours = {}
for key in xrange(number_of_regions):
#Assign
neighbours[key] = list(xxx[1][xxx[0]==key])
# # Extend neighbours to include "colourbar neighbours"
# neighbours[key].append(numpy.mod(key+1, number_of_regions))
# neighbours[key].append(numpy.mod(key-1, number_of_regions))
return neighbours
3
Example 33
Project: CostSensitiveClassification Source File: bagging.py
def _create_stacking_set(estimators, estimators_features, estimators_weight, X, combination):
"""Private function used to create the stacking training set."""
n_samples = X.shape[0]
valid_estimators = np.nonzero(estimators_weight)[0]
n_valid_estimators = valid_estimators.shape[0]
X_stacking = np.zeros((n_samples, n_valid_estimators))
for e in range(n_valid_estimators):
if combination in ['stacking', 'stacking_bmr']:
X_stacking[:, e] = estimators[valid_estimators[e]].predict(X[:, estimators_features[valid_estimators[e]]])
elif combination in ['stacking_proba', 'stacking_proba_bmr']:
X_stacking[:, e] = estimators[valid_estimators[e]].predict_proba(X[:, estimators_features[valid_estimators[e]]])[:, 1]
return X_stacking
3
Example 34
Project: scikit-image Source File: watershed.py
def _compute_neighbors(image, structure, offset):
"""Compute neighborhood as an array of linear offsets into the image.
These are sorted according to Euclidean distance from the center (given
by `offset`), ensuring that immediate neighbors are visited first.
"""
structure[tuple(offset)] = 0 # ignore the center; it's not a neighbor
locations = np.transpose(np.nonzero(structure))
sqdistances = np.sum((locations - offset)**2, axis=1)
neighborhood = (np.ravel_multi_index(locations.T, image.shape) -
np.ravel_multi_index(offset, image.shape)).astype(np.int32)
sorted_neighborhood = neighborhood[np.argsort(sqdistances)]
return sorted_neighborhood
3
Example 35
def _compute_separator(self, K):
self._sv = np.nonzero(self._alphas.flat > self.sv_cutoff)
self._sv_alphas = self._alphas[self._sv]
self._sv_X = self._X[self._sv]
self._sv_y = self._y[self._sv]
n = len(self._sv_X)
if n == 0:
self._b = 0.0
self._predictions = np.zeros(len(self._X))
else:
_sv_all_K = K[self._sv]
_sv_K = _sv_all_K.T[self._sv].T
e = np.matrix(np.ones((n, 1)))
D = spdiag(self._sv_y)
self._b = float(e.T * D * e - self._sv_alphas.T * D * _sv_K * e) / n
self._predictions = np.array(self._b
+ self._sv_alphas.T * D * _sv_all_K).reshape((-1,))
3
Example 36
def select_rays(self, idxs):
"""
Inform the geometry manager that only the given rays are to be used,
so that internal data size is kept small.
Arguments:
idxs - an index array stating which rays of the working bundle
are active.
"""
self._idxs = idxs
self._backside = N.nonzero(self._backside[idxs])[0]
self._global = self._global[:,idxs].copy()
3
Example 37
def test_testMaPut(self):
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1]
i = numpy.nonzero(m)[0]
put(ym, i, zm)
assert all(take(ym, i, axis=0) == zm)
3
Example 38
Project: HPGL-GUI Source File: cube_list.py
def definedValues(self, index=0):
'''Returns values of cube without undefined'''
allValues = self.allValues(index)
undefined = self.undefValue(index)
# nonzero return indexes of defined values, but we need values
return allValues[numpy.nonzero(allValues != undefined)]
3
Example 39
def nonzero(a):
res = np.nonzero(a)
if len(res) == 1:
return res[0]
else:
raise ValueError, "Input argument must be 1d"
3
Example 40
def readline(self, size=None):
self._check_if_open()
remaining = len(self._ds) - self._position
if remaining == 0:
return self._convert('')
for l in range(min(100, remaining), remaining+100, 100):
data = self._ds[self._position:self._position+l]
eols = np.nonzero(data == 10)[0]
if len(eols) > 0:
n = eols[0]+1
self._position += n
return self._convert(data[:n].tostring())
self._position = len(self._ds)
return self._convert(data.tostring())
3
Example 41
Project: qutip Source File: test_sparse.py
def test_sp_bandwidth():
"Sparse: Bandwidth"
for kk in range(10):
A = sp.rand(100, 100, density=0.1, format='csr')
ans1 = sp_bandwidth(A)
A = A.toarray()
i, j = np.nonzero(A)
ans2 = ((j-i).max()+(i-j).max()+1, (i-j).max(), (j-i).max())
assert_equal(ans1, ans2)
for kk in range(10):
A = sp.rand(100, 100, density=0.1, format='csc')
ans1 = sp_bandwidth(A)
A = A.toarray()
i, j = np.nonzero(A)
ans2 = ((j-i).max()+(i-j).max()+1, (i-j).max(), (j-i).max())
assert_equal(ans1, ans2)
3
Example 42
def dense_to_sparse(voxel_data, dtype=np.int):
""" From dense representation to sparse (coordinate) representation.
No coordinate reordering.
"""
if voxel_data.ndim!=3:
raise ValueError('voxel_data is wrong shape; should be 3D array.')
return np.asarray(np.nonzero(voxel_data), dtype)
3
Example 43
def map_if_positive(self, expr):
crit = self.rec(expr.criterion)
bool_crit = crit > 0
then = self.rec(expr.then)
else_ = self.rec(expr.else_)
true_indices = np.nonzero(bool_crit)
false_indices = np.nonzero(~bool_crit)
result = np.empty_like(crit)
if isinstance(then, np.ndarray):
then = then[true_indices]
if isinstance(else_, np.ndarray):
else_ = else_[false_indices]
result[true_indices] = then
result[false_indices] = else_
return result
3
Example 44
Project: APGL Source File: DenseGraph.py
def neighbours(self, vertexIndex):
"""
Return an array of the indices of the neighbours of the given vertex.
:param vertexIndex: the index of a vertex.
:type vertexIndex: :class:`int`
"""
Parameter.checkIndex(vertexIndex, 0, self.vList.getNumVertices())
nonZeroIndices = numpy.nonzero(self.W[vertexIndex, :])
neighbourIndices = nonZeroIndices[0]
return neighbourIndices
3
Example 45
Project: trackpy Source File: find.py
def percentile_threshold(image, percentile):
"""Find grayscale threshold based on distribution in image."""
not_black = image[np.nonzero(image)]
if len(not_black) == 0:
return np.nan
return np.percentile(not_black, percentile)
3
Example 46
Project: ncpol2sdpa Source File: chordal_extension.py
def find_variable_cliques(variables, objective=0, inequalities=None,
equalities=None, momentinequalities=None,
momentequalities=None):
if objective == 0 and inequalities is None and equalities is None:
raise Exception("There is nothing to extract the chordal structure " +
"from!")
clique_set = generate_clique(variables, objective, inequalities,
equalities, momentinequalities,
momentequalities)
variable_sets = []
for clique in clique_set:
variable_sets.append([variables[i] for i in np.nonzero(clique)[0]])
return variable_sets
3
Example 47
Project: scikit-image Source File: peak.py
def _get_high_intensity_peaks(image, mask, num_peaks):
"""
Return the highest intensity peak coordinates.
"""
# get coordinates of peaks
coord = np.nonzero(mask)
# select num_peaks peaks
if len(coord[0]) > num_peaks:
intensities = image[coord]
idx_maxsort = np.argsort(intensities)
coord = np.transpose(coord)[idx_maxsort][-num_peaks:]
else:
coord = np.column_stack(coord)
return coord
3
Example 48
def draw_edges(self):
self.line_handle = []
if not self.connect==None:
x = []
y = []
z = []
self.I, self.J = np.nonzero(self.connect)
for i, j in zip(self.I, self.J):
x.append(self.vals[i, 0])
x.append(self.vals[j, 0])
x.append(np.NaN)
y.append(self.vals[i, 1])
y.append(self.vals[j, 1])
y.append(np.NaN)
z.append(self.vals[i, 2])
z.append(self.vals[j, 2])
z.append(np.NaN)
self.line_handle = self.axes.plot(np.array(x), np.array(y), np.array(z), '-', color=self.color)
3
Example 49
Project: deep_nets_iclr04 Source File: stitchparts.py
def plot_maximas_on_image(distribution):
# imgname = '/Users/ajain/Projects/MODEC/cropped-images/12-oclock-high-special-edition-00171221_00141.png'
# im = plt.imread(imgname)
plt.imshow(distribution)
rows, cols = numpy.nonzero(distribution)
xs = []
ys = []
ss = []
for idx in range(0, rows.shape[0]):
y = rows[idx]
x = cols[idx]
score = distribution[y, x]
xs.append(x)
ys.append(y)
ss.append(score)
# plt.scatter(xs, ys, c=ss, cmap=cm.coolwarm, s=5)
plt.show()
3
Example 50
Project: audfprint Source File: audfprint_match.py
def _calculate_time_ranges(self, hits, id, mode):
"""Given the id and mode, return the actual time support."""
match_times = sorted(hits[row, 3]
for row in np.nonzero(hits[:, 0]==id)[0]
if mode - self.window <= hits[row, 1]
and hits[row, 1] <= mode + self.window)
min_time = match_times[int(len(match_times)*self.time_quantile)]
max_time = match_times[int(len(match_times)*(1.0 - self.time_quantile)) - 1]
return min_time, max_time