def _plot_surface_data(lats,
lons,
data,
lat_min,
lat_max,
lon_min,
lon_max,
output_filename='noname.png',
title='',
units='',
cm_edge_values=None,
cb_tick_fmt="%.0f",
cb_labels=None,
cb_label_pos=None,
colormap_strategy='nonlinear',
cmp_name='jet',
colors=None,
extend='both',
fill_color='1.0',
plotStyle='contourf',
contourLines=True,
contourLabels=True,
smoothFactor=1,
proj=self._DEFAULT_PROJ,
product_label_str=None,
vlat=None,
vlon=None,
u=None,
v=None,
draw_every=1,
arrow_scale=10,
resolution=None,
area=None,
boundaryInUse='True',
shapefile=None):
'''
TODO
color map needs to be consilidated into one method. The existing discrete_colormap method is
less flexible. Actually, the overall plot method needs to be more flexible.
1. Introduce the colormap strategy
discrete: discrete_cmap
levels: from_levels_and_colors
2. Depending on the strategy, various combination of the arguments should be passed in.
discrete: cmp_name
extend
cm_edge_values
levels: color_array
extend
cm_edge_values
'''
if resolution is None and area is not None:
# try and get a resolution from the area default
resolution = regions[area][3].get('resolution', None)
if resolution is None:
# still no resolution? try and guess
resolution = guess_resolution(lat_min, lat_max, lon_min,
lon_max)
m = Basemap(projection=proj,
llcrnrlat=lat_min,
llcrnrlon=lon_min,
urcrnrlat=lat_max,
urcrnrlon=lon_max,
resolution=resolution)
#GAS this was removed because different colour ranges makes comparison difficult
#if cm_edge_values is None:
# cm_edge_values = get_tick_values(data.min(), data.max(), 10)[0]
n_colours = cm_edge_values.size - 1
if colormap_strategy == 'discrete':
d_cmap = discrete_cmap(cmp_name, n_colours, extend=extend)
norm = None
elif colormap_strategy == 'levels':
d_cmap, norm = from_levels_and_colors(cm_edge_values,
np.array(colors) / 255.0,
None,
extend=extend)
elif colormap_strategy == 'nonlinear':
d_cmap, norm = from_levels_and_colors(cm_edge_values,
None,
cmp_name,
extend=extend)
#GAS Smoothing section based on smoothFactor
if smoothFactor > 1:
#if smoothFactor > 1 and (lat_extent>20 or lon_extent>20):
size = int(smoothFactor)
x, y = np.mgrid[-size:size + 1, -size:size + 1]
g = np.exp(-(x**2 / float(size) + y**2 / float(size)))
g = g / g.sum()
#data=np.ma.masked_less(data,-998)a
data[data < -9.9] = 0
data[data > 1000] = 5
data = convolve2d(data, g, mode='same', boundary='symm')
#a=ma.masked_less(data,-998)
#np.savetxt('/data/comp/raster/filename.txt',data,delimiter=",")a
# Plot data
x, y = None, None
if plotStyle == 'contourf':
x, y = m(*np.meshgrid(lons, lats))
img = plt.contourf(x,
y,
data,
levels=cm_edge_values,
norm=norm,
shading='flat',
cmap=d_cmap,
extend=extend)
elif plotStyle == 'pcolormesh':
# Convert centre lat/lons to corner values required for
# pcolormesh
lons2 = get_grid_edges(lons)
lats2 = get_grid_edges(lats)
x2, y2 = m(*np.meshgrid(lons2, lats2))
img = m.pcolormesh(x2,
y2,
data,
shading='flat',
cmap=d_cmap,
norm=norm)
# Draw contours
if contourLines:
if x is None:
x, y = m(*np.meshgrid(lons, lats))
#GAS negative contour not to be dashed
plt.rcParams['contour.negative_linestyle'] = 'solid'
cnt = plt.contour(x,
y,
data,
levels=cm_edge_values,
norm=norm,
colors='k',
linewidths=0.4,
hold='on')
if contourLabels:
plt.clabel(cnt, inline=True, fmt=cb_tick_fmt, fontsize=8)
img.set_clim(cm_edge_values.min(), cm_edge_values.max())
# Plot vector data if provided
if (u is not None) and (v is not None) and \
(vlat is not None) and (vlon is not None):
# Draw vectors
if draw_every is not None:
draw_vector_plot(m,
vlon,
vlat,
u,
v,
draw_every=draw_every,
arrow_scale=arrow_scale)
if shapefile is not None:
if os.path.exists(shapefile + ".shp"):
front_info = m.readshapefile(shapefile, 'front')
marker = '_'
if hasattr(m, 'front'):
for front in m.front:
m.plot(front[0] if front[0] > 0 else front[0] + 360,
front[1],
color='k',
marker=marker,
markersize=1,
markeredgewidth=1)
# Draw land, coastlines, parallels, meridians and add title
m.drawmapboundary(linewidth=1.0, fill_color=fill_color)
m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8)
# m.fillcontinents(color='#F1EBB7', zorder=7)
m.fillcontinents(color='0.58', zorder=7)
parallels, p_dec_places = get_tick_values(lat_min, lat_max)
meridians, m_dec_places = get_tick_values(lon_min, lon_max)
m.drawparallels(parallels,
labels=[True, False, False, False],
fmt='%.' + str(p_dec_places) + 'f',
fontsize=6,
dashes=[3, 3],
color='gray')
m.drawmeridians(meridians,
labels=[False, False, False, True],
fmt='%.' + str(m_dec_places) + 'f',
fontsize=6,
dashes=[3, 3],
color='gray')
plt.title(title, fontsize=9)
# Draw colorbar
ax = plt.gca()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size=0.2, pad=0.3)
if cb_label_pos is None:
tick_pos = cm_edge_values
else:
tick_pos = cb_label_pos
if boundaryInUse == 'True':
cb = plt.colorbar(img,
cax=cax,
spacing='uniform',
drawedges='False',
norm=norm,
orientation='vertical',
extend=extend,
ticks=tick_pos)
#boundaries=cm_edge_values)
else:
cb = plt.colorbar(img,
cax=cax,
spacing='uniform',
drawedges='False',
orientation='vertical',
extend=extend,
ticks=tick_pos)
if cb_labels is None:
cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values])
else:
cb.set_ticklabels(cb_labels)
for tick in cb.ax.get_yticklabels():
tick.set_fontsize(7)
cb.set_label(units, fontsize=8)
# Patch for graphics bug that affects label positions for
# long/narrow plots
lat_extent = np.float(lat_max) - np.float(lat_min)
lon_extent = np.float(lon_max) - np.float(lon_min)
aspect_ratio = abs(lon_extent / lat_extent)
if aspect_ratio > 1.7:
copyright_label_yadj = -0.25
else:
copyright_label_yadj = -0.15
if aspect_ratio < 0.7:
copyright_label_xadj = -0.2
product_label_xadj = 1.4
else:
copyright_label_xadj = -0.1
product_label_xadj = 1.04
# Draw copyright and product labels
box = TextArea(getCopyright(),
textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(
loc=3,
child=box,
borderpad=0.1,
bbox_to_anchor=(copyright_label_xadj, copyright_label_yadj),
frameon=False,
bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
if product_label_str is not None:
box = TextArea(product_label_str,
textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(
loc=4,
child=box,
borderpad=0.1,
bbox_to_anchor=(product_label_xadj, copyright_label_yadj),
frameon=False,
bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
# Save figure
plt.savefig(output_filename,
dpi=150,
bbox_inches='tight',
pad_inches=0.6)
plt.close()
pngcrush(output_filename)
def _plot_surface_data(lats,
lons,
data,
lat_min,
lat_max,
lon_min,
lon_max,
output_filename='noname.png',
title='',
units='',
cm_edge_values=None,
cb_tick_fmt="%.0f",
cb_labels=None,
cb_label_pos=None,
colormap_strategy='nonlinear',
cmp_name='jet',
colors=None,
extend='both',
fill_color='1.0',
plotStyle='contourf',
contourLines=True,
contourLabels=True,
smoothFactor=1,
proj=self._DEFAULT_PROJ,
product_label_str=None,
vlat=None,
vlon=None,
u=None,
v=None,
draw_every=1,
arrow_scale=10,
vector=False,
overlay_grid=None,
resolution=None,
area=None,
boundaryInUse='True'):
cmArray = customColorMap[cmp_name]
if colormap_strategy == 'discrete':
n_colours = cm_edge_values.size - 1
d_cmap = discrete_cmap(cmp_name, n_colours, extend)
norm = None
elif colormap_strategy == 'levels':
d_cmap, norm = from_levels_and_colors(
customTicks[cm_edge_values],
np.array(cmArray) / 255.0)
elif colormap_strategy == 'nonlinear':
d_cmap, norm = from_levels_and_colors(
customTicks[cm_edge_values], None, cmp_name, extend)
cm_edge_values = np.array(customTicks[cm_edge_values])
m = Basemap(projection=proj,
llcrnrlat=lat_min,
llcrnrlon=lon_min,
urcrnrlat=lat_max,
urcrnrlon=lon_max,
resolution='i')
# Plot data
x2, y2 = m(*np.meshgrid(lons, lats))
img = m.contourf(x2,
y2,
data,
levels=cm_edge_values,
cmap=d_cmap,
norm=norm,
antialiased=True,
zorder=0)
img.set_clim(cm_edge_values.min(), cm_edge_values.max())
#plot contouring labels
if contourLabels:
labels = plt.clabel(img,
cm_edge_values[::4],
inline=True,
fmt='%.0f',
colors='k',
fontsize=5,
zorder=2)
bbox_props = dict(boxstyle="round", fc="w", ec="w", alpha=0.9)
for text in labels:
text.set_linespacing(1)
text.set_bbox(bbox_props)
#plot vectors
if vector:
every = 10
lons = lons[::every]
lats = lats[::every]
x2, y2 = m(*np.meshgrid(lons, lats))
if overlay_grid is not None:
radians_array = np.radians(overlay_grid)
radians_array = np.pi + radians_array
radians_array = radians_array[::every, ::every]
m.quiver(x2,
y2,
np.sin(radians_array),
np.cos(radians_array),
scale=60,
zorder=3)
# Draw land, coastlines, parallels, meridians and add title
m.drawmapboundary(linewidth=1.0, fill_color=fill_color)
m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8)
m.fillcontinents(color='0.58', zorder=7)
parallels, p_dec_places = get_tick_values(lat_min, lat_max)
meridians, m_dec_places = get_tick_values(lon_min, lon_max)
m.drawparallels(parallels,
labels=[True, False, False, False],
fmt='%.' + str(p_dec_places) + 'f',
fontsize=6,
dashes=[3, 3],
color='gray')
m.drawmeridians(meridians,
labels=[False, False, False, True],
fmt='%.' + str(m_dec_places) + 'f',
fontsize=6,
dashes=[3, 3],
color='gray')
plt.title(title, fontsize=9)
# Draw colorbar
ax = plt.gca()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size=0.2, pad=0.3)
if cb_label_pos is None:
tick_pos = cm_edge_values
else:
tick_pos = cb_label_pos
if boundaryInUse == 'True':
cb = plt.colorbar(
img,
cax=cax,
# spacing='proportional',
spacing='uniform',
drawedges='False',
orientation='vertical',
extend=extend,
ticks=tick_pos,
boundaries=cm_edge_values)
else:
cb = plt.colorbar(img,
cax=cax,
spacing='uniform',
drawedges='False',
orientation='vertical',
extend=extend,
ticks=tick_pos)
if cb_labels is None:
cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values])
else:
cb.set_ticklabels(cb_labels)
for tick in cb.ax.get_yticklabels():
tick.set_fontsize(7)
cb.set_label(units, fontsize=8)
# Patch for graphics bug that affects label positions for
# long/narrow plots
lat_extent = np.float(lat_max) - np.float(lat_min)
lon_extent = np.float(lon_max) - np.float(lon_min)
aspect_ratio = abs(lon_extent / lat_extent)
if aspect_ratio > 1.7:
copyright_label_yadj = -0.25
else:
copyright_label_yadj = -0.10
if aspect_ratio < 0.7:
copyright_label_xadj = -0.2
product_label_xadj = 1.4
else:
copyright_label_xadj = -0.1
product_label_xadj = 1.04
# Draw copyright and product labels
box = TextArea(getCopyright(),
textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(
loc=3,
child=box,
borderpad=0.1,
bbox_to_anchor=(copyright_label_xadj, copyright_label_yadj),
frameon=False,
bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
if product_label_str is not None:
box = TextArea(product_label_str,
textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(
loc=4,
child=box,
borderpad=0.1,
bbox_to_anchor=(product_label_xadj, copyright_label_yadj),
frameon=False,
bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
# Save figure
plt.savefig(output_filename,
dpi=150,
bbox_inches='tight',
pad_inches=0.6)
plt.close()
pngcrush(output_filename)
def plot_BRAN_depth_slice(depths, lats, lons, zonal_data, meridional_data, lats_all, lons_all, data_sf,
lat_cnt, lon_cnt, output_filename='noname.png', title='', units='m/s',
cb_ticks=None, cb_tick_fmt="%.0f", cmp_name='jet', proj='cyl',
product_label_str=None):
fg = plt.figure()
gs = mpl.gridspec.GridSpec(2,5)
ax1=plt.subplot(gs[1,0:2])
n_colours = cb_ticks.size - 1
d_cmap = discrete_cmap(cmp_name, n_colours)
# Draw contour
x, y = np.meshgrid(lons, depths)
ctr = plt.contour(x, y, zonal_data, levels=cb_ticks, colors='k', linewidths=0.4)
plt.clabel(ctr, inline=True, fmt=cb_tick_fmt, fontsize=8)
lons2 = get_grid_edges(lons)
depths2 = get_grid_edges(depths)
# Plot data
x2, y2 = np.meshgrid(lons2, depths2)
img = plt.pcolormesh(x2, y2, zonal_data, shading='flat', cmap=d_cmap, vmin=cb_ticks.min(), vmax=cb_ticks.max())
ax = plt.gca()
ax.set_ylim(0,300)
ax.set_ylim(ax.get_ylim()[::-1])
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(format_longitude))
ax.tick_params(axis='x', labelsize=8)
ax.tick_params(axis='y', labelsize=9)
plt.ylabel("Depth (m)", fontsize=10)
plt.xlabel("Longitude", fontsize=10)
plt.subplots_adjust(right=1.0)
ax2=plt.subplot(gs[1,2:4])
# Draw contour
x, y = np.meshgrid(lats, depths)
ctr = plt.contour(x, y, meridional_data, levels=cb_ticks, colors='k', linewidths=0.4)
plt.clabel(ctr, inline=True, fmt=cb_tick_fmt, fontsize=8)
lats2 = get_grid_edges(lats)
depths2 = get_grid_edges(depths)
# Plot data
x2, y2 = np.meshgrid(lats2, depths2)
img = plt.pcolormesh(x2, y2, meridional_data, shading='flat', cmap=d_cmap, vmin=cb_ticks.min(), vmax=cb_ticks.max())
ax = plt.gca()
ax.set_ylim(0,300)
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_yticklabels([''])
ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(format_latitude))
ax.tick_params(axis='x', labelsize=8)
plt.xlabel("Latitude", fontsize=10)
ax3=plt.subplot(gs[0,:-1])
lat_min = lats[0] - 5.0
lat_max = lats[-1] + 5.0
lon_min = lons[0] - 20.0
lon_max = lons[-1] + 20.0
m = Basemap(projection=proj, llcrnrlat=lat_min, llcrnrlon=lon_min, \
urcrnrlat=lat_max, urcrnrlon=lon_max, resolution='h')
m.drawcoastlines(linewidth=0.5, zorder=8, color='#505050')
m.fillcontinents(color='#F1EBB7', zorder=7)
plt.hold(True)
plt.plot([lon_cnt,lon_cnt],[lats[0],lats[-1]], color='k', linestyle='--', linewidth=2, zorder=8)
plt.plot([lons[0],lons[-1]],[lat_cnt,lat_cnt], color='k', linestyle='--', linewidth=2, zorder=8)
# Convert centre lat/lons to corner values required for pcolormesh
lons2 = get_grid_edges(lons_all)
lats2 = get_grid_edges(lats_all)
# Plot data
x2, y2 = m(*np.meshgrid(lons2, lats2))
img = m.pcolormesh(x2, y2, data_sf, shading='flat', cmap=d_cmap, vmin=cb_ticks.min(), vmax=cb_ticks.max())
plt.title(title, fontsize=12)
parallels, p_dec_places = get_tick_values(lat_min, lat_max, 3)
meridians, m_dec_places = get_tick_values(lon_min, lon_max)
m.drawparallels(parallels, labels=[True, False, False, False], fmt='%.' + str(p_dec_places) + 'f',
fontsize=8, dashes=[3, 3], color='gray')
m.drawmeridians(meridians, labels=[False, False, False, True], fmt='%.' + str(m_dec_places) + 'f',
fontsize=8, dashes=[3, 3], color='gray')
cbaxes = fg.add_axes([0.85, 0.15, 0.03, 0.7]) # setup colorbar axes.
cb = fg.colorbar(img, spacing='proportional', drawedges='True', cax=cbaxes,orientation='vertical',
extend='both', ticks=cb_ticks)
cb.set_ticklabels([cb_tick_fmt % k for k in cb_ticks])
cb.set_label(units)
box = TextArea(getCopyright(), textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(loc=3, child=box, bbox_to_anchor=(-1.3, -0.45), frameon=False, bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
box = TextArea(product_label_str, textprops=dict(color='k', fontsize=8))
copyrightBox = AnchoredOffsetbox(loc=4, child=box, bbox_to_anchor=(1.4, -0.45), frameon=False, bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
plt.savefig(output_filename, dpi=150, bbox_inches='tight', pad_inches=1.)
plt.close()
pngcrush(output_filename)
return
def plot_basemaps_and_colorbar(self, *args, **kwargs):
#Plots the image for the map overlay.
output_filename = kwargs.get('output_filename', 'noname.png')
overlay_grid = kwargs.get('overlay_grid', None)
monthly_clim_label_str = kwargs.get('monthly_clim_label_str', None)
annual_clim_label_str = kwargs.get('annual_clim_label_str', None)
fileName, fileExtension = os.path.splitext(output_filename)
colorbar_filename = fileName + COMMON_FILES['scale']
outputfile_map = fileName + COMMON_FILES['mapimg']
regions = [{
'lat_min': -90,
'lat_max': 90,
'lon_min': 110,
'lon_max': 290,
'output_filename': outputfile_map
}]
# Create colormap
cm_edge_values = kwargs.get('cm_edge_values', None)
cmp_name = kwargs.get('cmp_name', 'jet')
extend = kwargs.get('extend', 'both')
cb_label_pos = kwargs.get('cb_label_pos', None)
colormap_strategy = kwargs.get('colormap_strategy', 'nonlinear')
colors = kwargs.get('colors', None)
fill_color = kwargs.get('fill_color', '0.0')
n_colours = cm_edge_values.size - 1
if colormap_strategy == 'discrete':
d_cmap = discrete_cmap(cmp_name, n_colours, extend=extend)
norm = None
elif colormap_strategy == 'levels':
d_cmap, norm = from_levels_and_colors(cm_edge_values,
np.array(colors) / 255.0,
None,
extend=extend)
elif colormap_strategy == 'nonlinear':
d_cmap, norm = from_levels_and_colors(cm_edge_values,
None,
cmp_name,
extend=extend)
basemap_cmap, basemap_norm = from_levels_and_colors(cm_edge_values,
None,
'binary',
extend=extend)
if cm_edge_values is None:
cm_edge_values = get_tick_values(data.min(), data.max(), 10)[0]
if cb_label_pos is None:
tick_pos = cm_edge_values
else:
tick_pos = cb_label_pos
def _plot_basemap(region,
lats,
lons,
data,
units='',
cb_tick_fmt="%.0f",
cb_labels=None,
contourLines=True,
extend='both',
contourLabels=False,
proj=self._DEFAULT_PROJ,
**kwargs):
m = Basemap(projection=proj,
llcrnrlat=region['lat_min'],
llcrnrlon=region['lon_min'],
urcrnrlat=region['lat_max'],
urcrnrlon=region['lon_max'],
resolution='c')
# Convert centre lat/lons to corner values required for pcolormesh
lons2 = get_grid_edges(lons)
lats2 = get_grid_edges(lats)
# Plot data
m.drawmapboundary(linewidth=0.0, fill_color='0.02')
x2, y2 = m(*np.meshgrid(lons2, lats2))
img = m.pcolormesh(x2,
y2,
data,
shading='flat',
cmap=basemap_cmap,
norm=basemap_norm)
img.set_clim(cm_edge_values.min(), cm_edge_values.max())
plt.savefig(region['output_filename'],
dpi=120,
bbox_inches='tight',
pad_inches=0.0)
# generate shape file
gdal_process(region['output_filename'], region['lon_min'],
region['lat_max'], region['lon_max'],
region['lat_min'])
pngcrush(region['output_filename'])
baseName = os.path.splitext(region['output_filename'])[0]
# Draw contours
if contourLines:
plt.clf()
m.drawmapboundary(linewidth=0.0)
x, y = m(*np.meshgrid(lons, lats))
#GAS negative contour not to be dashed
plt.rcParams['contour.negative_linestyle'] = 'solid'
#cnt = plt.contour(x, y, data,levels = [29], colors=('purple',),linestyles=('-',),linewidths=(1,))
cnt = plt.contour(x,
y,
data,
levels=[29],
colors=('k', ),
linestyles=('-', ),
linewidths=(1, ),
antialiased=False)
if contourLabels:
plt.clabel(cnt,
inline=True,
fmt=cb_tick_fmt,
fontsize=8,
colors='purple')
contourFile = baseName + '_contour.png'
plt.savefig(contourFile,
dpi=120,
bbox_inches='tight',
pad_inches=0.0,
transparent=True)
# generate shape file
gdal_process(contourFile, region['lon_min'], region['lat_max'],
region['lon_max'], region['lat_min'])
pngcrush(contourFile)
#extract the overlay grid
if overlay_grid is not None:
plt.clf()
m.drawmapboundary(linewidth=0.0)
x, y = m(*np.meshgrid(overlay_grid.lons, overlay_grid.lats))
#cnt = plt.contour(x, y, overlay_grid.data,levels = [29], colors=('g',),linestyles=('-',),linewidths=(1,))
cnt = plt.contour(x,
y,
overlay_grid.data,
levels=[29],
colors=('k', ),
linestyles=('-', ),
linewidths=(1, ),
antialiased=False)
if contourLabels:
plt.clabel(cnt,
inline=True,
fmt=cb_tick_fmt,
fontsize=8,
colors='g',
zorder=3)
normalFile = baseName + '_normal.png'
plt.savefig(normalFile,
dpi=120,
bbox_inches='tight',
pad_inches=0.0,
transparent=True)
# generate shape file
gdal_process(normalFile, region['lon_min'], region['lat_max'],
region['lon_max'], region['lat_min'])
pngcrush(normalFile)
# m.drawmapboundary(linewidth=0.0, fill_color=fill_color)
# m.fillcontinents(color='0.58', zorder=7)
# Save figure
# plt.savefig(region['output_filename'], dpi=150,
# bbox_inches='tight', pad_inches=0.0)
plt.close()
def _plot_colorbar(lats,
lons,
data,
units='',
cb_tick_fmt="%.0f",
cb_labels=None,
extend='both',
proj=self._DEFAULT_PROJ,
**kwargs):
# Draw colorbar
fig = plt.figure(figsize=(1.5, 2))
ax1 = fig.add_axes([0.05, 0.05, 0.225, 1.5])
cb = mpl.colorbar.ColorbarBase(ax1,
cmap=d_cmap,
norm=norm,
orientation='vertical',
drawedges='True',
extend=extend,
ticks=tick_pos)
if cb_labels is None:
cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values])
else:
cb.set_ticklabels(cb_labels)
cb.set_label(units,
rotation='horizontal',
fontsize=10,
fontweight='bold')
cb.set_ticks(tick_pos)
for tick in cb.ax.get_yticklabels():
tick.set_fontsize(10)
tick.set_fontweight('bold')
self.plotConvergenceLegend(monthly_clim_label_str,
annual_clim_label_str, 12)
plt.savefig(colorbar_filename,
dpi=150,
transparent=True,
bbox_inches='tight',
pad_inches=0.9)
plt.close()
pngcrush(colorbar_filename)
for region in regions:
self.queue_plot(_plot_basemap, region, *args, **kwargs)
self.queue_plot(_plot_colorbar, *args, **kwargs)
def _plot_surface_data(lats,
lons,
data,
time,
lat_min,
lat_max,
lon_min,
lon_max,
output_filename='noname.png',
title='',
units='',
cm_edge_values=None,
cb_tick_fmt="%.0f",
cb_labels=None,
cb_label_pos=None,
colormap_strategy='nonlinear',
cmp_name='jet',
colors=None,
extend='both',
fill_color='1.0',
plotStyle='contourf',
contourLines=True,
contourLabels=True,
smoothFactor=1,
proj=self._DEFAULT_PROJ,
product_label_str=None,
vlat=None,
vlon=None,
u=None,
v=None,
draw_every=1,
arrow_scale=10,
resolution=None,
area=None,
boundaryInUse='True'):
d_cmap, norm = from_levels_and_colors(cm_edge_values,
None,
cmp_name,
extend=extend)
m = Basemap(projection=proj,
llcrnrlat=lat_min,
llcrnrlon=lon_min,
urcrnrlat=lat_max,
urcrnrlon=lon_max,
resolution='i')
# Plot data
x2, y2 = m(*np.meshgrid(lons, lats))
u = data[0][time]
v = data[1][time]
mag = np.sqrt(u**2 + v**2)
img = m.pcolormesh(x2,
y2,
mag,
shading='flat',
cmap=d_cmap,
norm=norm)
img.set_clim(cm_edge_values.min(), cm_edge_values.max())
#plot vectors
if area == 'pac':
every = 50
scale = 10
else:
every = 10
scale = 10
lons = lons[::every]
lats = lats[::every]
x2, y2 = m(*np.meshgrid(lons, lats))
u2 = u[::every, ::every]
v2 = v[::every, ::every]
rad = np.arctan2(v2, u2)
m.quiver(x2,
y2,
np.cos(rad),
np.sin(rad),
scale=scale,
zorder=3,
scale_units='inches',
pivot='middle')
# Draw land, coastlines, parallels, meridians and add title
m.drawmapboundary(linewidth=1.0, fill_color=fill_color)
m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8)
m.fillcontinents(color='0.58', zorder=7)
parallels, p_dec_places = get_tick_values(lat_min, lat_max)
meridians, m_dec_places = get_tick_values(lon_min, lon_max)
m.drawparallels(parallels,
labels=[True, False, False, False],
fmt='%.' + str(p_dec_places) + 'f',
fontsize=6,
dashes=[3, 3],
color='gray')
m.drawmeridians(meridians,
labels=[False, False, False, True],
fmt='%.' + str(m_dec_places) + 'f',
fontsize=6,
dashes=[3, 3],
color='gray')
plt.title(title, fontsize=9)
# Draw colorbar
ax = plt.gca()
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size=0.2, pad=0.3)
if cb_label_pos is None:
tick_pos = cm_edge_values
else:
tick_pos = cb_label_pos
if boundaryInUse == 'True':
cb = plt.colorbar(
img,
cax=cax,
# spacing='proportional',
spacing='uniform',
drawedges='False',
orientation='vertical',
extend=extend,
ticks=tick_pos,
boundaries=cm_edge_values)
else:
cb = plt.colorbar(img,
cax=cax,
spacing='uniform',
drawedges='False',
orientation='vertical',
extend=extend,
ticks=tick_pos)
if cb_labels is None:
cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values])
else:
cb.set_ticklabels(cb_labels)
for tick in cb.ax.get_yticklabels():
tick.set_fontsize(7)
cb.set_label(units, fontsize=8)
# Patch for graphics bug that affects label positions for
# long/narrow plots
lat_extent = np.float(lat_max) - np.float(lat_min)
lon_extent = np.float(lon_max) - np.float(lon_min)
aspect_ratio = abs(lon_extent / lat_extent)
if aspect_ratio > 1.7:
copyright_label_yadj = -0.25
else:
copyright_label_yadj = -0.10
if aspect_ratio < 0.7:
copyright_label_xadj = -0.2
product_label_xadj = 1.4
else:
copyright_label_xadj = -0.1
product_label_xadj = 1.04
# Draw copyright and product labels
box = TextArea(getCopyright(),
textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(
loc=3,
child=box,
borderpad=0.1,
bbox_to_anchor=(copyright_label_xadj, copyright_label_yadj),
frameon=False,
bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
if product_label_str is not None:
box = TextArea(product_label_str,
textprops=dict(color='k', fontsize=6))
copyrightBox = AnchoredOffsetbox(
loc=4,
child=box,
borderpad=0.1,
bbox_to_anchor=(product_label_xadj, copyright_label_yadj),
frameon=False,
bbox_transform=ax.transAxes)
ax.add_artist(copyrightBox)
# Save figure
plt.savefig(output_filename,
dpi=150,
bbox_inches='tight',
pad_inches=0.6)
plt.close()
pngcrush(output_filename)