def densityPlot(x,
y,
bins2D=None,
bins=None,
xscale='log',
yscale='log',
nbinsX=1.5,
nbinsY=1.5,
weights=None):
"""
2-Plot static distance vs temporal distance
Plot fraction of Finite distances vs temporal distance
Plot the distance distribution
"""
x = np.array(x)
y = np.array(y)
if bins2D == None or bins == None:
xidx, yidx = getLimits2D(x, y, xscale, yscale)
minValueX, maxValueX = min(x[xidx]), max(x[xidx])
minValueY, maxValueY = min(y[yidx]), max(y[yidx])
print minValueX, maxValueX, minValueY, maxValueY
bins2D = binner.Bins2D(float, minValueX, maxValueX, xscale, nbinsX,
float, minValueY, maxValueY, yscale, nbinsY)
bins = binner.Bins(float, minValueX, maxValueX, xscale, nbinsX)
binned_data, b1, b2 = np.histogram2d(x,
y,
bins=bins2D.bin_limits,
weights=weights)
binned_data = binned_data.astype(np.float64)
X, Y = bins2D.edge_grids
z, b = np.histogram(x, bins=bins.bin_limits, weights=weights)
for i in np.nonzero(z)[0]:
binned_data[i, :] = binned_data[i, :] / z[i]
binned_data = binned_data.transpose()
binned_data = np.ma.masked_array(binned_data, binned_data == 0)
if weights == None:
z1, b = np.histogram(x, bins=bins.bin_limits, weights=y)
else:
z1, b = np.histogram(x, bins=bins.bin_limits, weights=y * weights)
z1 = z1.astype(np.float64)
for i in np.nonzero(z)[0]:
z1[i] = z1[i] / z[i]
return X, Y, binned_data, bins, z1
def setUp(self):
self.x_coords = [1, 1, 2, 4, 4, 3, 1, 2]
self.y_coords = [1, 4, 3, 5, 6, 1, 8, 6]
values = [1, 3, 7, 5, 6, 3, 1, 10]
weights = [1, 3, 1, 0, 100, 0, 1, 10]
self.coords = zip(self.x_coords, self.y_coords)
self.data = zip(self.x_coords, self.y_coords, values)
self.weighted_data = zip(self.x_coords, self.y_coords, values, weights)
bad_x_coords = [1, 1, 2, 4, 4, 5, 1, 2]
bad_y_coords = [1, 4, 0, 5, 6, 1, 8, 6]
self.bad_coords_A = zip(bad_x_coords, self.y_coords)
self.bad_coords_B = zip(self.x_coords, bad_y_coords)
self.bad_data_A = zip(bad_x_coords, self.y_coords, values)
self.bad_data_B = zip(self.x_coords, bad_y_coords, values)
self.bad_wdata_A = zip(bad_x_coords, self.y_coords, values, weights)
self.bad_wdata_B = zip(self.x_coords, bad_y_coords, values, weights)
self.bins = binner.Bins2D(int, 0, 0, 'custom', [1, 1.5, 4], int, 0, 0,
'custom', [1, 4.5, 5.5, 6.5, 10])