def plotCorrelation(tauArray,kappaMatrix,kappaLower=None,kappaUpper=None,CI=None,amplify=1):
"""Plots Pearson Correlation Coefficient K(t,tau) with rotated
axis to indicate absolute t, and relative time shift tau, between
two signals x(t),y(t).
Specified matrix has to be square with values -1 < p < +1
with corresponding time array giving the absolute time, t
of the centers of each correlated window."""
# defining tranformation for relative time shifts
def R(x, y):
x, y = asarray(x), asarray(y)
#return x,y
return (2*x - y)/2, (y + 2*x)/2
def Rt(x, y):
x, y = asarray(x), asarray(y)
#return x,y
return x + y, x - y
# create figure with rotated axes
fig = figure(figsize=(10, 10),frameon=False)
grid_locator = angle_helper.LocatorDMS(20)
grid_helper = GridHelperCurveLinear((R, Rt),
grid_locator1=grid_locator,
grid_locator2=grid_locator)
ax = Subplot(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax);ax.axis('off');
# copying over matrix
K = array(kappaMatrix)
# zero out correlations if confidence intervals overlap zero
if all(kappaLower != None) and all(kappaUpper != None) :
K[ (kappaLower<0) * (0<kappaUpper) ] = 0
# zero out statistically insignificant correlations
if all(CI != None) :
K[ abs(kappaMatrix) < CI ] = 0
# display pearson correlation matrix with +ive in red and -ive in blue
ax.imshow(K,cmap="RdBu_r",interpolation="none",origin="bottom",
extent = (tauArray[0],tauArray[-1],tauArray[0],tauArray[-1]),vmin=-1.0/amplify,vmax=1.0/amplify)
# display rotated axes time,t and time delay,tau
ax.axis["tau"] = tau = ax.new_floating_axis(0,0)
ax.axis["t"] = t = ax.new_floating_axis(1,0)
# setting axes options
ax.set_xlim(tauArray[0],tauArray[-1])
ax.set_ylim(tauArray[0],tauArray[-1])
ax.grid(which="both")
ax.set_aspect(1)
return fig
for x_T in x_T_levels:
ax.plot(x_T, y_all_p, color=(1.0, 0.5, 0.5))
for x_theta in x_thetas:
ax.plot(x_theta, y_all_p, color=(1.0, 0.7, 0.7))
for x_mixing_ratio in x_mixing_ratios:
good = p_all >= 600 # restrict mixing ratio lines to below 600 mb
ax.plot(x_mixing_ratio[good], y_all_p[good], color=(0.8, 0.8, 0.6))
n_moist = len(theta_ep_mesh)
moist_colors = ((0.6, 0.9, 0.7), ) * n_moist
ax.contour(x_from_Tp(mesh_T + C_to_K, mesh_p),
y_from_p(mesh_p),
theta_ep_mesh,
theta_ep_levels,
colors=moist_colors)
ax.axis((x_min, x_max, y_min, y_max))
def format_coord(x, y):
T, p = to_thermo(x, y)
return "{0:5.1f} C, {1:5.1f} mb".format(float(T), float(p))
ax.format_coord = format_coord
ax.plot(x_snd_Td, y_snd_p, linewidth=2, color='g')
ax.plot(x_snd_T, y_snd_p, linewidth=2, color='r')
plt.show()
for yi in y_p_levels:
ax.plot((x_min, x_max), (yi,yi), color = (1.0, 0.8, 0.8))
for x_T in x_T_levels:
ax.plot(x_T, y_all_p, color=(1.0, 0.5, 0.5))
for x_theta in x_thetas:
ax.plot(x_theta, y_all_p, color=(1.0, 0.7, 0.7))
for x_mixing_ratio in x_mixing_ratios:
good = p_all >= 600 # restrict mixing ratio lines to below 600 mb
ax.plot(x_mixing_ratio[good], y_all_p[good], color = (0.8, 0.8, 0.6))
n_moist = len(theta_ep_mesh)
moist_colors = ((0.6, 0.9, 0.7),)*n_moist
ax.contour(x_from_Tp(mesh_T+C_to_K, mesh_p), y_from_p(mesh_p),
theta_ep_mesh, theta_ep_levels, colors = moist_colors)
ax.axis((x_min, x_max, y_min, y_max))
def format_coord(x, y):
T, p = to_thermo(x, y)
return "{0:5.1f} C, {1:5.1f} mb".format(float(T), float(p))
ax.format_coord = format_coord
ax.plot(x_snd_Td, y_snd_p, linewidth = 2, color = 'g')
ax.plot(x_snd_T, y_snd_p, linewidth = 2, color = 'r')
plt.show()
