def _plot_trj_marker(wks, plot, x, y, angle, res):
''' Plots a single arrow head onto the a trajectory plot
pgon = _plot_trj_marker(wks, plot, x, y, angle, res)
wks : The identifier returned from calling Ngl.open_wks.
x,y : The X and Y coordinates of the arrow heads centre.
xMarker, yMarker : X and Y coordinates in data coordinates of the marker
polygon
angle: angle relative to the x axis of the marker
res: Resource list using following special resources:
Special resources:
trjArrowDirection : can be 'pos' or 'neg' to select direction of the arrows
trjArrowXShapeF : Array containing the x coordinates of the arrow head
relative to the heads centre.
trjArrowYShapeF : Array containing the y coordinates of the arrow head
relative to the heads centre.
'''
# get plot centre in data coordinates
xMid, yMid = [(ngl.get_float(plot, "tr" + ax + "MaxF")
+ ngl.get_float(plot, "tr" + ax + "MinF")) / 2.
for ax in "XY"]
# rotate arrow
if res.trjArrowDirection == "neg":
angle = angle - np.pi
xMarker = copy.deepcopy(res.trjArrowXShapeF)
yMarker = copy.deepcopy(res.trjArrowYShapeF)
# scale marker
xMarker = xMarker * res.trjArrowXScaleF * res.trjArrowSizeF + xMid
yMarker = yMarker * res.trjArrowYScaleF * res.trjArrowSizeF + yMid
xMarker_ndc, yMarker_ndc = ngl.datatondc(plot, xMarker, yMarker)
# move centre of mass to origin
xMarker_ndc, yMarker_ndc = [xMarker_ndc - np.mean(xMarker_ndc),
yMarker_ndc - np.mean(yMarker_ndc)]
# rotate marker
xMarker_ndc, yMarker_ndc = [
np.cos(angle) * xMarker_ndc - np.sin(angle) * yMarker_ndc,
np.sin(angle) * xMarker_ndc + np.cos(angle) * yMarker_ndc]
# shift to final position
xOffset_ndc, yOffset_ndc = ngl.datatondc(plot, x, y)
xMarker_ndc += xOffset_ndc
yMarker_ndc += yOffset_ndc
# convert back to coordinates
xMarker, yMarker = ngl.ndctodata(plot, xMarker_ndc, yMarker_ndc)
# filter attributes from res
for attr in dir(res):
if not attr[0:2] in ["gs", "__"] or attr[0:3] == "gsn":
delattr(res, attr)
return ngl.add_polygon(wks, plot, xMarker, yMarker, res)
npts = 400
x = Ngl.fspan(100., npts - 1, npts)
y = 500. + x * numpy.sin(0.031415926535898 * x)
wks = Ngl.open_wks("ps", "datandc")
xy = Ngl.xy(wks, x, y)
#
# Test with all in-range values and no missing values present.
#
x_dat = x
y_dat = numpy.absolute(y)
x_ndc, y_ndc = Ngl.datatondc(xy, x_dat, y_dat)
x_dat2, y_dat2 = Ngl.ndctodata(xy, x_ndc, y_ndc)
check_type(x_ndc)
check_type(y_ndc)
check_type(x_dat2)
check_type(y_dat2)
test_values("no oor or msg data: datatondc/ndctodata",
x_dat,
x_dat2,
delta=1e-4)
test_values("no oor or msg data: datatondc/ndctodata",
y_dat,
y_dat2,
delta=1e-4)
#
# Then draw markers.
#
gsres = Ngl.Resources()
gsres.gsMarkerColor = "Blue"
gsres.gsMarkerIndex = 16
gsres.gsMarkerSizeF = 17.0
Ngl.polymarker_ndc(wks, x_out[::8], y_out[::8], gsres)
gsres.gsMarkerColor = "Red"
gsres.gsMarkerIndex = 12
gsres.gsMarkerSizeF = 22.0
Ngl.polymarker_ndc(wks, x_out[4::8], y_out[4::8], gsres)
Ngl.frame(wks)
#
# Show that datatondc and ndctodata are inverse functions by
# converting a data point to NDC and then converting that point
# back to data space.
#
x_ndc, y_ndc = Ngl.datatondc(xy, 100., 600.)
x_dat, y_dat = Ngl.ndctodata(xy, x_ndc, y_ndc)
#
# Optional print.
#
# print("Original data: (100.0000, 600.0000) \nNDC: (%06.4f, %06.4f) \nBack to original: (%6.4f, %6.4f)" % (x_ndc,y_ndc,x_dat,y_dat))
Ngl.end()
