def cn_line_thickness(plot, val=0., factor=2.):
''' Increases the thickness of contour lines corresponding to a list of
values
cn_line_thickness(contour, val=0., factor=2.)
plot : plot Id of the plot to modify
val : scalar or list of values, which contour lines should be modified.
factor : scaling factor for line thickness
'''
cnLevels = ngl.get_float_array(plot, "cnLevels")
cnLineThicknesses = ngl.get_float_array(plot, "cnLineThicknesses")
try:
(v for v in val)
except TypeError:
val = [val]
for i, thickness in enumerate(cnLineThicknesses[:]):
if cnLevels[i] in val:
cnLineThicknesses[i] = thickness * factor
rlist = {"cnMonoLineThickness": False,
"cnLineThicknesses": cnLineThicknesses}
_set_values(plot, rlist)
def tm_lat_lon_labels(plot, ax="XB", direction=None, fmt="%d"):
'''Add degree symbols to the tickmark labels of one axis of a plot. This
function has to be called for each axis, that should get degree tickmark
labels
tm_lat_lon_labels(plot, ax="XB", direction="zonal")
plot : plot Id of the plot to modify
ax : one of "XB", "XT", "YL", "YR". Specifies the axis to work on.
direction : String that starts with either "z" or "m", to indicate the
direction of the axis (zonal or meridional). If omitted, a x-axis will be
assumed to be zonal. If the string starts with any other character, there
will be no hemisperic lables added, i.e N, S, W or E.
fmt : format string of the number
'''
def do_nothing(x):
return x
func_code = ngl.get_string(plot, "tm" + ax + "LabelFuncCode")
fmt_s = func_code.join([fmt, "S", "o", "N", ""])
if not direction:
if ax[0].lower() == "x":
direction = "zonal"
else:
direction = "meridional"
if direction[0].lower() == "z":
hem_label = ("W", "E")
func = abs
elif direction[0].lower() == "m":
hem_label = ("S", "N")
func = abs
else:
hem_label = ("", "")
func = do_nothing
tm_values = ngl.get_float_array(plot, "tm" + ax + "Values")
mtm_values = ngl.get_float_array(plot, "tm" + ax + "MinorValues")
labels = [fmt_s % func(val) + hem_label[0] if val < 0.
else fmt_s % func(val) + hem_label[1] for val in tm_values]
rlist = {"tm" + ax + "Mode": "Explicit",
"tm" + ax + "Values": tm_values,
"tm" + ax + "MinorValues": mtm_values,
"tm" + ax + "Labels": labels}
_set_values(plot, rlist)
def add_axis(wks, plot, ax="XB", offset=0., res=None):
val_ax = ("XT", "XB", "YL", "YR")
if not res:
res = {}
else:
res = _resource2dict(res)
resp = {}
keys = ["vpXF", "vpYF", "vpWidthF", "vpHeightF", "trXMinF", "trXMaxF",
"trYMinF", "trYMaxF"]
for a in val_ax:
for k in ("LabelFontHeight", "MajorOutwardLength",
"MinorOutwardLength"):
keys.append("tm{}{}F".format(a, k))
for k in keys:
resp[k] = ngl.get_float(plot, k)
for k in ("tm" + a + k for a in val_ax for k in ("Values", "MinorValues")):
resp[k] = ngl.get_float_array(plot, k)
for k in ("tm{}MinorPerMajor".format(a) for a in val_ax):
resp[k] = ngl.get_integer(plot, k)
for k in ("tm{}MinorOn".format(a) for a in val_ax):
resp[k] = (ngl.get_integer(plot, k) == 1)
for k in ("tm" + a + "Labels".format(a) for a in val_ax):
resp[k] = ngl.get_string_array(plot, k)
resp.update(res)
for a in ("XT", "XB", "YL", "YR"):
resp["tm{}Mode".format(a)] = "Explicit"
resp["tm{}On".format(a)] = (a == ax)
resp["tm{}BorderOn".format(a)] = (a == ax)
resp["nglDraw"] = False
resp["nglFrame"] = False
blank_plot = ngl.blank_plot(wks, _dict2Resource(resp))
amres = {"amJust": "CenterCenter"}
if ax[1].lower() in "lt":
ampos_sig = -1.
else:
ampos_sig = 1.
if ax[0].lower() == "x":
amres["amOrthogonalPosF"] = ampos_sig * offset
else:
amres["amParallelPosF"] = ampos_sig * offset
return ngl.add_annotation(plot, blank_plot, _dict2Resource(amres))
def cn_neg_dash_contours(plot, pattern=2):
'''Changes the line dash pattern of the negative contour lines.
cn_neg_dash_contours(plot, pattern=2)
plot : plot Id of the plot to modify
pattern : Pattern index. See http://www.ncl.ucar.edu/Document/Graphics/Images/dashpatterns.png
for a list of available patterns.
'''
levels = ngl.get_float_array(plot, "cnLevels")
patterns = np.zeros((len(levels)), 'i')
patterns[:] = 0 # solid line
for i in xrange(len(levels)):
if (levels[i] < 0.):
patterns[i] = pattern
rlist = {"cnLineDashPatterns": patterns,
"cnMonoLineDashPattern": False}
_set_values(plot, rlist)
def neg_dash_contours(contour):
#
# Retrieve the contour levels used.
#
levels = Ngl.get_float_array(contour,"cnLevels")
#
# Create an array to hold the line dash patterns.
#
patterns = numpy.zeros((len(levels)),'i')
patterns[:] = 0 # solid line
#
# Make contour lines above 0 dashed.
#
for i in xrange(len(levels)):
if (levels[i] > 0.):
patterns[i] = 2
rlist = Ngl.Resources()
rlist.cnLineDashPatterns = patterns
rlist.cnMonoLineDashPattern = False
Ngl.set_values(contour,rlist)
def neg_dash_contours(contour):
#
# Retrieve the contour levels used.
#
levels = Ngl.get_float_array(contour,"cnLevels")
#
# Create an array to hold the line dash patterns.
#
patterns = numpy.zeros((len(levels)),'i')
patterns[:] = 0 # solid line
#
# Make contour lines above 0 dashed.
#
for i in xrange(len(levels)):
if (levels[i] > 0.):
patterns[i] = 2
rlist = Ngl.Resources()
rlist.cnLineDashPatterns = patterns
rlist.cnMonoLineDashPattern = False
Ngl.set_values(contour,rlist)
res.sfYCellBounds = vlat #-- needed if set cnFillMode = "CellFill"
res.tiMainString = "ICON grid - raster fill"
res.tiMainOffsetYF = 0.03 #-- move main title towards plot
#-- create the contour plot to retrieve levels and colors
plot = Ngl.contour_map(wks,var,res)
Ngl.draw(plot)
Ngl.frame(wks)
del(levels)
#========== second plot ============
#-- retrieve the colors and levels from contour plot
levels = list(Ngl.get_float_array(plot.contour,"cnLevels"))
labels = list(Ngl.get_string_array(plot.contour,"cnLevels"))
nlevels = len(levels)
#-- define colormap (RGB value range 0-255)
colormap255 = np.array([[ 0, 0, 0], [255, 255, 255], [ 80, 255, 255], \
[ 27, 44, 98], [ 45, 102, 175], [ 66, 133, 196], \
[ 90, 166, 217], [119, 189, 230], [148, 211, 243], \
[215, 239, 249], [237, 248, 252], [255, 255, 255], \
[254, 248, 218], [253, 208, 107], [253, 167, 49], \
[247, 124, 43], [231, 75, 41], [203, 30, 38], \
[146, 21, 25]], int)
ncol = colormap255.shape[0] #-- number of colors
colormap = np.array(colormap255)/255. #-- convert to RGB value range 0.0-1.0
print("\n-----------printing some colormap values------------\n")
print("color index 5 (NavyBlue) = ", cmap[5, :])
print("color index 10 (Purple) = ", cmap[10, :])
print("color index 15 (Gold) = ", cmap[15, :])
#
# Retrieve some resource values of various types.
#
# Note that in order to retrieve some XY resources, like line colors,
# dash patterns, markers, etc, you need to use the "xydspec"
# attribute. This represents the DataSpec object which is created
# internally whenever an XY plot is created.
#
cmap_len = Ngl.get_integer(wks, "wkColorMapLen")
markers = Ngl.get_integer_array(xy.xydspec, "xyMarkers")
thicknesses = Ngl.get_float_array(xy.xydspec, "xyLineThicknesses")
line_mode = Ngl.get_string(xy.xydspec, "xyMarkLineMode")
style = Ngl.get_integer(xy, "xyYStyle")
print("\n-----------printing some resource values------------\n")
print("color map len = ", cmap_len, " (should be 23)")
print("markers = ", markers, " (should be [11 12 15 16])")
print("thicknesses = ", thicknesses, " should be ([3. 4. 5. 6.])")
print("mark/line mode = ", line_mode, " (should be 'MarkLines')")
print("style = ", style, " (should be 1)")
#
# Here's another way to retrieve the color map.
#
del cmap
cmap = Ngl.get_MDfloat_array(wks, "wkColorMap")
resources.cnFillOn = True # Turn on contour level fill.
resources.cnMonoFillColor = True # Use one fill color.
resources.cnMonoFillPattern = False # Use multiple fill patterns.
resources.cnLineLabelAngleF = 0. # Draw contour line labels right-side up.
resources.cnLevelSpacingF = 1.0
resources.nglDraw = False # Don't draw the plot or advance the
resources.nglFrame = False # frame in the call to Ngl.contour.
resources.nglMaximize = False
resources.pmLabelBarDisplayMode = "Never" # Turn off label bar.
contour = Ngl.contour(wks, unew, resources) # Create a contour plot.
levels = Ngl.get_float_array(contour,"cnLevels")
patterns = numpy.zeros((len(levels)+1),'i')
patterns[:] = -1
for i in xrange(len(levels)):
if (levels[i] <= -6.):
patterns[i] = 5
else:
if (levels[i] > 0.):
patterns[i] = 17.
patterns[-1] = 17 # last pattern
rlist = Ngl.Resources()
rlist.cnFillPatterns = patterns
rlist.cnFillScaleF = 0.8
resources.cnFillOn = True # Turn on contour level fill.
resources.cnMonoFillColor = True # Use one fill color.
resources.cnMonoFillPattern = False # Use multiple fill patterns.
resources.cnLineLabelAngleF = 0. # Draw contour line labels right-side up.
resources.cnLevelSpacingF = 1.0
resources.nglDraw = False # Don't draw the plot or advance the
resources.nglFrame = False # frame in the call to Ngl.contour.
resources.nglMaximize = False
resources.pmLabelBarDisplayMode = "Never" # Turn off label bar.
contour = Ngl.contour(wks, unew, resources) # Create a contour plot.
levels = Ngl.get_float_array(contour,"cnLevels")
patterns = numpy.zeros((len(levels)+1),'i')
patterns[:] = -1
for i in xrange(len(levels)):
if (levels[i] <= -6.):
patterns[i] = 5
else:
if (levels[i] > 0.):
patterns[i] = 17.
patterns[-1] = 17 # last pattern
rlist = Ngl.Resources()
rlist.cnFillPatterns = patterns
rlist.cnFillScaleF = 0.8
print "\n-----------printing some colormap values------------\n" print "color index 5 (NavyBlue) = ",cmap[5,:] print "color index 10 (Purple) = ",cmap[10,:] print "color index 15 (Gold) = ",cmap[15,:] # # Retrieve some resource values of various types. # # Note that in order to retrieve some XY resources, like line colors, # dash patterns, markers, etc, you need to use the "xydspec" # attribute. This represents the DataSpec object which is created # internally whenever an XY plot is created. # cmap_len = Ngl.get_integer(wks,"wkColorMapLen") markers = Ngl.get_integer_array(xy.xydspec,"xyMarkers") thicknesses = Ngl.get_float_array(xy.xydspec,"xyLineThicknesses") line_mode = Ngl.get_string(xy.xydspec,"xyMarkLineMode") style = Ngl.get_integer(xy,"xyYStyle") print "\n-----------printing some resource values------------\n" print "color map len = ",cmap_len," (should be 23)" print "markers = ",markers," (should be [11 12 15 16])" print "thicknesses = ",thicknesses," should be ([3. 4. 5. 6.])" print "mark/line mode = ",line_mode," (should be 'MarkLines')" print "style = ",style," (should be 1)" # # Here's another way to retrieve the color map. # del cmap cmap = Ngl.get_MDfloat_array(wks,"wkColorMap")
