Here are the examples of the python api numpy.sort taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
152 Examples
0
Example 151
Project: OSCAAR Source File: dataBank.py
def plotComparisonWeightings(self, apertureRadiusIndex=0):
"""
Plot histograms visualizing the relative weightings of the comparison
stars used to produce the "mean comparison star", from which the
light curve is calculated.
Parameters
----------
apertureRadiusIndex : int, optional (default=0)
Index of the aperture radius list corresponding to the aperture radius
from which to produce the plot.
"""
plt.ion()
weights = self.comparisonStarWeights[apertureRadiusIndex]
weights = np.sort(weights,axis=1)
width = 0.5
indices = weights[0,:]
coefficients = weights[1,:]
fig = plt.figure(num=None, figsize=(10, 8), facecolor='w',edgecolor='k')
fig.canvas.set_window_title('OSCAAR')
ax = fig.add_subplot(111)
ax.set_xlim([0,len(indices)+1])
ax.set_xticks(indices+width/2)
ax.set_xticklabels(["Star "+str(i) for i in range(len(indices))])
ax.set_xlabel('Comparison Star')
ax.set_ylabel('Normalized Weighting')
ax.set_title('Comparison Star Weights into the Composite Comparison Star for aperture radius %s' \
% self.apertureRadii[apertureRadiusIndex])
ax.axhline(xmin=0,xmax=1,y=1.0/len(indices),linestyle=':',color='k')
ax.bar(indices,coefficients,width,color='w')
plt.ioff()
plt.show()
0
Example 152
Project: astroML Source File: lumfunc.py
def binned_Cminus(x, y, xmax, ymax, xbins, ybins, normalize=False):
"""Compute the binned distributions using the Cminus method
Parameters
----------
x : array_like
array of x values
y : array_like
array of y values
xmax : array_like
array of maximum x values for each y value
ymax : array_like
array of maximum y values for each x value
xbins : array_like
array of bin edges for the x function: size=Nbins_x + 1
ybins : array_like
array of bin edges for the y function: size=Nbins_y + 1
normalize : boolean
if true, then returned distributions are normalized. Default
is False.
Returns
-------
dist_x, dist_y : ndarrays
distributions of size Nbins_x and Nbins_y
"""
Nx, Ny, cueml_x, cueml_y = Cminus(x, y, xmax, ymax)
# simple linear interpolation using a binary search
# interpolate the cuemulative distributions
x_sort = np.sort(x)
y_sort = np.sort(y)
Ix_edges = _sorted_interpolate(x_sort, cueml_x, xbins)
Iy_edges = _sorted_interpolate(y_sort, cueml_y, ybins)
if xbins[0] < x_sort[0]:
Ix_edges[0] = cueml_x[0]
if xbins[-1] > x_sort[-1]:
Ix_edges[-1] = cueml_x[-1]
if ybins[0] < y_sort[0]:
Iy_edges[0] = cueml_y[0]
if ybins[-1] > y_sort[-1]:
Iy_edges[-1] = cueml_y[-1]
x_dist = np.diff(Ix_edges) / np.diff(xbins)
y_dist = np.diff(Iy_edges) / np.diff(ybins)
if normalize:
x_dist /= len(x)
y_dist /= len(y)
return x_dist, y_dist