def plotTidalGauge(outputFilename, saveData=True, **args):
"""
Plot tidal gauge data
"""
tidalGaugeId = args['tidalGaugeId']
tidalGaugeName = args['tidalGaugeName']
data = TideGauge(tidalGaugeId)
figure = plt.figure()
plt.rc('font', size=8)
plt.title("Monthly sea levels for " + tidalGaugeName)
plt.ylabel("Sea Level Height (metres)")
plt.xlabel("Year")
ax = figure.gca()
ax.grid(True)
maxPlot, = ax.plot(data.date, data.maximum, 'r-')
meanPlot, = ax.plot(data.date, data.mean_, 'k-')
minPlot, = ax.plot(data.date, data.minimum, 'b-')
# add legend
ax.legend([maxPlot, meanPlot, minPlot], ['Max', 'Mean', 'Min'],
ncol=3,
loc='lower right')
plt.axhline(y=0, color='k')
plt.figtext(0.02, 0.02, getCopyright(), fontsize=6)
plt.figtext(0.90,
0.05,
"0.0 = Tidal Gauge Zero",
fontsize=8,
horizontalalignment='right')
plt.savefig(outputFilename + '.png',
dpi=150,
bbox_inches='tight',
pad_inches=.1)
plt.close()
pngcrush(outputFilename + '.png')
with open(outputFilename + '.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(["# Monthly sea levels for " + tidalGaugeName])
writer.writerow(["# Data from tidalGaugeId"])
writer.writerow(
["# Sea Level Height (metres) relative to Tidal Gauge Zero"])
writer.writerow(data.headers[1:])
for row in data:
writer.writerow(list(row)[1:])
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 plotTimeseries(outputFilename, saveData=True, **args):
"""
Plot altimetry/reconstruction timeseries
"""
tidalGaugeName = args["tidalGaugeName"]
variable = args['variable']
lat = args['lat']
lon = args['lon']
titlePrefix = {
'alt': "Altimetry",
'rec': "Reconstruction",
}[variable]
grid = SeaLevelSeries(variable, latrange=(lat, lat), lonrange=(lon, lon))
if saveData:
with open(outputFilename + ".txt", 'w') as file:
writer = csv.writer(file, delimiter='\t')
writer.writerow(
('# Sea Level %s for %s' % (titlePrefix, tidalGaugeName), ))
if variable == 'alt':
writer.writerow([
'# Corrections: IB not removed; seasonal not removed; global trend not removed; GIA removed'
])
writer.writerow(['# Data from CSIRO CMAR'])
else:
writer.writerow([
'# Corrections: IB removed; seasonal cycle removed; global trend not removed; GIA removed'
])
writer.writerow(
['# Data from CSIRO CMAR, Church and White 2009'])
writer.writerow(['Date (YYYY-MM)', '%s (mm)' % titlePrefix])
for date, height in zip(grid.time, grid.data):
writer.writerow([date.strftime('%Y-%m'), height])
figure = plt.figure()
plt.rc('font', size=8)
plt.title("Sea Level %s for %s" % (titlePrefix, tidalGaugeName))
plt.ylabel('Sea-Surface Height (mm)')
plt.xlabel('Year')
ax = figure.gca()
ax.grid(True)
ax.set_ylim(-350, 350)
ax.plot(grid.time, grid.data, 'b-')
plt.axhline(y=0, color='k')
if variable == 'alt':
PRODUCT_NAME = """
Data from CSIRO CMAR
IB not removed; seasonal not removed; global trend not removed; GIA removed
"""
else:
PRODUCT_NAME = """
Data from CSIRO CMAR
IB removed; seasonal cycle removed; global trend not removed; GIA removed
"""
plt.figtext(0.02, 0.02, getCopyright(), fontsize=6)
plt.figtext(0.90,
0.02,
PRODUCT_NAME.strip(),
fontsize=6,
horizontalalignment='right')
plt.savefig(outputFilename + ".png",
dpi=150,
bbox_inches='tight',
pad_inches=.1)
plt.close()
pngcrush(outputFilename + ".png")
return 0
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_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)
def RosePlot(opath, wdir, units, lat, lon, ilat, ilon, xstr, title, var, binwd):
'''Plots a windrose of angular values in wdir, with annotations
Arguments:
wdir -- an array of angles in the range (0,360). (NumPy Array)
units -- measurements units for wdir variable. (float)
lat -- latitude of point. (float)
lon -- longitude of point. (float)
xstr -- x-axis label. (string)
title -- plot title. (string)
var -- the variable that is measured by wdir. (string)
binwd -- width of histogram bins. (float)
Returns:
rosefig -- An annotated windrose of wdir values.
'''
#set number of bins and max bin value.
N,wdnbin,wdmax = 8,8,2*np.pi
degree = ur'\u00b0'
if wdir[0] == -999.0:
raise LandError()
#flip directions as WWIII direction are given in meteorological convention.
if var == "Dm":
wdir = conv.dirflip(wdir)
wdir = conv.dirshift(wdir)
else:
wdir = conv.dirshift(wdir)
#make a basic histogram of wave directions over the range (-22.5,337.5).
try:
whist = np.histogram(wdir, wdnbin, (-22.5,337.5), density=False)
except TypeError:
# support older numpy
whist = np.histogram(wdir, wdnbin, (-22.5,337.5), normed=False)
#calculate mean bearing
meanb = meanbearing(wdir)
#force square figure and square axes looks better for polar
rosefig = figure(figsize=(10,7.5))
ax = rosefig.add_axes([0.075, 0.1, 0.6, 0.725], polar=True)
#plot radial gridlines at seperations of 45 degrees
plt.thetagrids((0,45,90,135,180,225,270,315),('N', '45' + degree, 'E', '135' + degree, 'S', '215' + degree, 'W', '315' + degree),frac = 1.1)
#rotate axis to zero at North and set direction of increasing angle clockwise.
plt.rgrids((0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9), angle=0)
plt.ylim(0.0,1.0)
try:
ax.set_theta_direction(-1)
ax.set_theta_zero_location('N')
except:
# hack around older matplotlib
pass
#lines, labels = plt.rgrids()
#set Nmax
Nmax = 1
#initialize prob and perc arrays
prob = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
perc = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
#variable to normalize bins so that they sum to 1 (probability)
normalizer = 1/float(len(wdir))
#normalize histogram to calculate probabilities and percentages
for i in range(0,N):
prob[i] = float(whist[0][i])*normalizer
perc[i] = round(100*prob[i],2)
units = '%'
theta = np.linspace(-2*np.pi/(2*N), 2*np.pi-2*np.pi/(2*N), N, endpoint=False)
#some basic settings for graphics
width = 2*np.pi/N
length = np.size(wdir)
#rounded mean bearing for labelling purposes
meanbr = round((meanb*180)/(np.pi),1)
#set up bar chart properties
bars = ax.bar(theta, prob, width, bottom=0.0)
my_cmap = hc.decile_rose()
#plot a line to indicate mean bearing
ax.arrow(0, 0, meanb, 1.0, edgecolor = 'r', facecolor = 'r', lw='3')
#plot bar chart of histogram in theta space
for r,bar in zip(prob, bars):
bar.set_facecolor(my_cmap(r))
bar.set_alpha(0.5)
lpack.rosepack(title)
formilat,formilon = NESWformat(ilat,ilon)
formlat,formlon = NESWformat(lat,lon)
#various annotations to plot
plt.figtext(0.76, 0.82,'Probabilities:', fontsize = 10, weight = 550)
plt.figtext(0.76, 0.575,'Location & Plot Data:', fontsize=10, weight = 550)
plt.figtext(0.76, 0.51, 'Input Lat/Lon:\n %s %s' % (formilat,formilon), fontsize=10)
plt.figtext(0.76, 0.46, 'Grid Lat/Lon:\n %s %s' % (formlat, formlon), fontsize=10)
plt.figtext(0.76, 0.425, 'Data points: %s' % length, fontsize=10)
plt.figtext(0.76, 0.4, 'Bins: %s' % int(N), fontsize=10)
plt.figtext(0.76, 0.375, 'Bin Width: %s %s' % (binwd, degree), fontsize=10)
plt.figtext(0.76, 0.275, 'Mean Bearing: %s%s %s' % (meanbr, degree,'T'), color='r', fontsize=10)
#display wave direction percentages for 8 primary compass points in the windrose
plt.figtext(0.76, 0.325, 'Directional Statistics:', fontsize=10, weight=550)
plt.figtext(0.76, 0.25, 'North: %s %s' % (perc[0], units), fontsize=10)
plt.figtext(0.76, 0.225, 'North East: %s %s' % (perc[1], units), fontsize=10)
plt.figtext(0.76, 0.2, 'East: %s %s' % (perc[2], units), fontsize=10)
plt.figtext(0.76, 0.175, 'South East: %s %s' % (perc[3], units), fontsize=10)
plt.figtext(0.76, 0.15, 'South: %s %s' % (perc[4], units), fontsize=10)
plt.figtext(0.76, 0.125, 'South West: %s %s' % (perc[5], units), fontsize=10)
plt.figtext(0.76, 0.1, 'West: %s %s' % (perc[6], units), fontsize=10)
plt.figtext(0.76, 0.075, 'North West: %s %s' % (perc[7], units), fontsize=10)
#Bureau of Meteorology Copyright
plt.figtext(0.58, 0.03, u'WAVEWATCH III$^{\u24C7}$', fontsize=10)
plt.figtext(0.02, 0.02, getCopyright(), fontsize=8)
#define image name
imgname = opath + '.png'
#write image file
plt.savefig(imgname)
plt.close()
pngcrush(imgname)
return
def HistPlot(opath, wheight, units, lat, lon, ilat, ilon, xstr, title, var, binwd):
'''Returns a normalized, annotated histogram for values of wheight
Arguments:
wheight -- an array of positive doubles. (NumPy Array)
units -- units associated with wheight. (string)
lat -- latitude of the point. (float)
lon -- longitude of the point. (float)
xstr -- x-axis label. (string)
title -- title of the plot. (string)
var -- variable associated with the values in wheight. (string)
binwd -- width of histogram bins. (float)
Returns:
histfig -- An annotated histogram of wheight values.
'''
#calculate the values of some pertinent statistical quantities
wavg = np.average(wheight)
wavgr = round(wavg,2)
maxwave = round(np.max(wheight),2)
minwave = round(np.min(wheight),2)
imaxwave = int(maxwave)+ 1
iminwave = int(minwave)- 1
Nmax = imaxwave
Nmin = iminwave
length = np.size(wheight)
debug = False
#calculate range of data
#binthresh = maxwave - minwave
#Error message if selected coordinates are out of range or on land
if maxwave == -999.0:
raise LandError()
#alter bin width depending on range of data
#if binthresh < 10:
# binwd = 0.1
#elif binthresh > 10:
# binwd = 0.2
#else:
# print "Error"
#bins per unit and total number of bins
binperunit = float(1/binwd)
binnum = (Nmax - Nmin)*binperunit
initial = round(0.001,3)
final = round(Nmax+0.001,3)
#histogram of selected variable
try:
whist,wbins = np.histogram(wheight, Nmax * binperunit,
(initial, final), density=True)
except TypeError:
# support older numpy
whist,wbins = np.histogram(wheight, Nmax * binperunit,
(initial, final), normed=True)
#print wbins
#calculate max and mins of histogram
maxy = np.max(whist*binwd)
maxx = np.max(wheight)
minx = np.min(wheight)
#do some basic statistics on histogram, std. deviation and quartiles.
q1 = round(sci.stats.scoreatpercentile(wheight,25),1)
q3 = round(sci.stats.scoreatpercentile(wheight,75),1)
x=linspace(0,maxx+0.5,Nmax*binperunit)
#set up figure
histfig=plt.figure(figsize = (10,7.5))
histax=histfig.add_axes([0.1,0.1,0.675,0.75])
plt.xlim(0,maxx+0.5)
plt.ylim(0,maxy+0.01)
whistnorm = whist*binwd
#np.hist(wheight,100,(0,Nmax),color='b',normed=True, histtype = 'stepfille)
#set up bar chart properties
bars=plt.bar(wbins[:-1],whistnorm,binwd,0)
if debug == True:
FILE = open('/home/jsmith/Test/verification/WW3' + '_' + str(lat) + '_' + str(lon) + '_' + var + '_array.txt', "w")
FILE.writelines(list( "%s \n" % i for i in whist))
FILE.close()
my_cmap = hc.quartile_colors(wheight,wavg,Nmax,binwd)
#bar chart of histogram
for r,bar in zip(wbins[:-1], bars):
bar.set_facecolor(my_cmap(r/Nmax))
bar.set_alpha(0.5)
#specify plot properties and plot guassian kde and average line
plt.xlim(minx-0.5,maxx+0.5)
plt.ylim(0,maxy+0.01)
#histax.plot(x, approximate_pdf(x), color = 'black', linewidth=3, alpha=1)
histax.axvline(wavg, color='r', lw='3')
#choose which legend to display based on variable
if var == 'Hs':
lpack.heightpack(title,wavg)
#plt.figtext(0.79,0.175, 'Rogue Wave Height: %s %s' % (2*wavgr,units), fontsize = 10, color = 'm')
elif var == 'Tm':
lpack.timepack(title)
formilat, formilon = NESWformat(ilat,ilon)
formlat, formlon = NESWformat(lat,lon)
#specify graph grid properties
xticks = [10,20,30,40,50,60,70,80,90,100]
plt.xticks = xticks, [('%s' % str(x/10))]
plt.grid(True)
#x,y axis labels
plt.xlabel('%s (%s)' % (xstr,units), fontsize=12)
plt.ylabel('Frequency', fontsize=12)
#various annotations for graphics
plt.figtext(0.79, 0.775, 'Distribution:', fontsize=10, weight=550)
plt.figtext(0.79, 0.615, 'Location & Plot Data:',fontsize = 10, weight = 550)
plt.figtext(0.79, 0.55, 'Input Lat/Lon:\n %s %s' % (formilat,formilon), fontsize=10)
plt.figtext(0.79, 0.50, 'Grid Lat/Lon:\n %s %s' % (formlat,formlon), fontsize=10)
plt.figtext(0.79, 0.465, 'Data points: %s' % length ,fontsize=10)
plt.figtext(0.79, 0.44, 'Bins: %s' % int(binnum), fontsize=10)
plt.figtext(0.79, 0.415, 'Bin Width: %s %s' % (binwd,units), fontsize=10)
plt.figtext(0.79, 0.365, 'Statistical Information:', fontsize=10, weight=550)
plt.figtext(0.79, 0.265, 'Mean: %s %s' % (round(wavgr,1), units), color='r', fontsize=10)
plt.figtext(0.79, 0.315, 'Max: %s %s' % (round(maxwave,1), units), fontsize=10)
plt.figtext(0.79, 0.215, 'Min: %s %s' % (round(minwave,1), units), fontsize=10)
plt.figtext(0.79, 0.24,'25th Percentile: %s %s' % (round(q1,1),units), fontsize=10)
plt.figtext(0.79, 0.29,'75th Percentile: %s %s' % (round(q3,1),units), fontsize=10)
#Bureau of Meteorology Copyright
plt.figtext(0.68, 0.03, u'WAVEWATCH III$^{\u24C7}$', fontsize=10)
plt.figtext(0.02, 0.02, getCopyright(), fontsize=8)
#define image name
imgname = opath + '.png'
#write image file
plt.savefig(imgname)
plt.close()
pngcrush(imgname)
return