def tricontouring_response(tri_subset, data, request, dpi=None):
"""
triang_subset is a matplotlib.Tri object in lat/lon units (will be converted to projected coordinates)
xmin, ymin, xmax, ymax is the bounding pox of the plot in PROJETED COORDINATES!!!
request is the original getMap request object
"""
dpi = dpi or 80.
bbox = request.GET['bbox']
width = request.GET['width']
height = request.GET['height']
colormap = request.GET['colormap']
colorscalerange = request.GET['colorscalerange']
cmin = colorscalerange.min
cmax = colorscalerange.max
crs = request.GET['crs']
nlvls = request.GET['numcontours']
EPSG4326 = pyproj.Proj(init='EPSG:4326')
tri_subset.x, tri_subset.y = pyproj.transform(EPSG4326, crs, tri_subset.x,
tri_subset.y)
fig = Figure(dpi=dpi, facecolor='none', edgecolor='none')
fig.set_alpha(0)
fig.set_figheight(height / dpi)
fig.set_figwidth(width / dpi)
ax = fig.add_axes([0., 0., 1., 1.], xticks=[], yticks=[])
ax.set_axis_off()
if request.GET['logscale'] is True:
norm_func = mpl.colors.LogNorm
else:
norm_func = mpl.colors.Normalize
# Set out of bound data to NaN so it shows transparent?
# Set to black like ncWMS?
# Configurable by user?
if cmin is not None and cmax is not None:
data[data > cmax] = cmax
data[data < cmin] = cmin
lvls = np.linspace(cmin, cmax, nlvls)
norm = norm_func(vmin=cmin, vmax=cmax)
else:
lvls = nlvls
norm = norm_func()
if request.GET['image_type'] == 'filledcontours':
ax.tricontourf(tri_subset, data, lvls, norm=norm, cmap=colormap)
elif request.GET['image_type'] == 'contours':
ax.tricontour(tri_subset, data, lvls, norm=norm, cmap=colormap)
ax.set_xlim(bbox.minx, bbox.maxx)
ax.set_ylim(bbox.miny, bbox.maxy)
ax.set_frame_on(False)
ax.set_clip_on(False)
ax.set_position([0., 0., 1., 1.])
return figure_response(fig, request)
def filledcontour(request):
# Create figure
fig, ax, norm = create_axis(request, get_position(request))
orientation = 'vertical'
if request.GET['horizontal'] is True:
orientation = 'horizontal'
csr = request.GET['colorscalerange']
if request.GET['logscale'] is True:
levs = np.hstack(
([csr.min - 3],
np.linspace(csr.min, csr.max,
request.GET['numcontours']), [csr.max + 40]))
x, y = np.meshgrid(np.ones(2), np.ones(2))
cs = ax.contourf(x,
y,
x,
levels=levs,
norm=norm,
cmap=get_cmap(request.GET['colormap']))
cb = colorbar(mappable=cs,
cax=ax,
orientation=orientation,
spacing='proportional',
extendrect=False,
use_gridspec=True)
if request.GET['showvalues'] is False:
cb.set_ticks([])
else:
cb.set_ticks(levs[1:-1])
cb.set_ticklabels(["%.1f" % x for x in levs[1:-1]])
else:
levs = np.linspace(csr.min, csr.max, request.GET['numcontours'])
x, y = np.meshgrid(np.ones(2), np.ones(2))
cs = ax.contourf(x,
y,
x,
levels=levs,
norm=norm,
cmap=get_cmap(request.GET['colormap']),
extend='both')
cb = colorbar(mappable=cs,
cax=ax,
orientation=orientation,
spacing='proportional',
extendrect=False,
use_gridspec=True)
if request.GET['showvalues'] is False:
cb.set_ticks([])
else:
cb.set_ticks(levs)
cb.set_ticklabels(["%.1f" % x for x in levs])
if request.GET['showlabel'] is True:
cb.set_label(request.GET['units'])
# Return HttpResponse
return figure_response(fig, request)
def gradiant(request):
# Create figure
fig, ax, norm = create_axis(request, get_position(request))
orientation = 'vertical'
if request.GET['horizontal'] is True:
orientation = 'horizontal'
cb = matplotlib.colorbar.ColorbarBase(ax,
cmap=get_cmap(
request.GET['colormap']),
norm=norm,
orientation=orientation)
if request.GET['showvalues'] is False:
cb.set_ticks([])
else:
csr = request.GET['colorscalerange']
ticks = np.linspace(csr.min, csr.max, 5)
cb.set_ticks(ticks)
cb.set_ticklabels(["%.1f" % x for x in ticks])
if request.GET['showlabel'] is True:
cb.set_label(request.GET['units'])
# Return HttpResponse
return figure_response(fig, request)
def pcolormesh_response(lon, lat, data, request, dpi=None):
dpi = dpi or 80.
bbox, width, height, colormap, cmin, cmax, crs = _get_common_params(
request)
EPSG4326 = pyproj.Proj(init='EPSG:4326')
x, y = pyproj.transform(EPSG4326, crs, lon, lat)
fig = Figure(dpi=dpi, facecolor='none', edgecolor='none')
fig.set_alpha(0)
fig.set_figheight(height / dpi)
fig.set_figwidth(width / dpi)
ax = fig.add_axes([0., 0., 1., 1.], xticks=[], yticks=[])
ax.set_axis_off()
if request.GET['logscale'] is True:
norm_func = mpl.colors.LogNorm
else:
norm_func = mpl.colors.Normalize
if cmin is not None and cmax is not None:
data[data > cmax] = cmax
data[data < cmin] = cmin
norm = norm = norm_func(vmin=cmin, vmax=cmax)
else:
norm = norm_func()
masked = np.ma.masked_invalid(data)
ax.pcolormesh(x, y, masked, norm=norm, cmap=colormap)
ax.set_xlim(bbox.minx, bbox.maxx)
ax.set_ylim(bbox.miny, bbox.maxy)
ax.set_frame_on(False)
ax.set_clip_on(False)
ax.set_position([0., 0., 1., 1.])
return figure_response(fig, request)
def contouring_response(lon, lat, data, request, dpi=None):
dpi = dpi or 80.
bbox, width, height, colormap, cmin, cmax, crs = _get_common_params(
request)
nlvls = request.GET['numcontours']
EPSG4326 = pyproj.Proj(init='EPSG:4326')
x, y = pyproj.transform(EPSG4326, crs, lon, lat)
fig = Figure(dpi=dpi, facecolor='none', edgecolor='none')
fig.set_alpha(0)
fig.set_figheight(height / dpi)
fig.set_figwidth(width / dpi)
ax = fig.add_axes([0., 0., 1., 1.], xticks=[], yticks=[])
ax.set_axis_off()
if request.GET['logscale'] is True:
norm_func = mpl.colors.LogNorm
else:
norm_func = mpl.colors.Normalize
if cmin is not None and cmax is not None:
data[data > cmax] = cmax
data[data < cmin] = cmin
lvls = np.linspace(cmin, cmax, nlvls)
norm = norm_func(vmin=cmin, vmax=cmax)
else:
lvls = nlvls
norm = norm_func()
if request.GET['image_type'] == 'filledcontours':
ax.contourf(x, y, data, lvls, norm=norm, cmap=colormap)
elif request.GET['image_type'] == 'contours':
ax.contour(x, y, data, lvls, norm=norm, cmap=colormap)
elif request.GET['image_type'] == 'filledhatches':
hatches = DEFAULT_HATCHES[:nlvls]
ax.contourf(x,
y,
data,
lvls,
norm=norm,
cmap=colormap,
hatches=hatches)
elif request.GET['image_type'] == 'hatches':
hatches = DEFAULT_HATCHES[:nlvls]
ax.contourf(x,
y,
data,
lvls,
norm=norm,
colors='none',
hatches=hatches)
ax.set_xlim(bbox.minx, bbox.maxx)
ax.set_ylim(bbox.miny, bbox.maxy)
ax.set_frame_on(False)
ax.set_clip_on(False)
ax.set_position([0., 0., 1., 1.])
return figure_response(fig, request)
def quiver_response(lon, lat, dx, dy, request, dpi=None):
dpi = dpi or 80.
bbox = request.GET['bbox']
width = request.GET['width']
height = request.GET['height']
colormap = request.GET['colormap']
colorscalerange = request.GET['colorscalerange']
vectorscale = request.GET['vectorscale']
cmin = colorscalerange.min
cmax = colorscalerange.max
crs = request.GET['crs']
unit_vectors = None # We don't support requesting these yet, but wouldn't be hard
EPSG4326 = pyproj.Proj(init='EPSG:4326')
x, y = pyproj.transform(EPSG4326, crs, lon,
lat) # TODO order for non-inverse?
fig = Figure(dpi=dpi, facecolor='none', edgecolor='none')
fig.set_alpha(0)
fig.set_figheight(height / dpi)
fig.set_figwidth(width / dpi)
ax = fig.add_axes([0., 0., 1., 1.], xticks=[], yticks=[])
ax.set_axis_off()
mags = np.sqrt(dx**2 + dy**2)
cmap = mpl.cm.get_cmap(colormap)
if request.GET['logscale'] is True:
norm_func = mpl.colors.LogNorm
else:
norm_func = mpl.colors.Normalize
# Set out of bound data to NaN so it shows transparent?
# Set to black like ncWMS?
# Configurable by user?
if cmin is not None and cmax is not None:
mags[mags > cmax] = cmax
mags[mags < cmin] = cmin
norm = norm_func(vmin=cmin, vmax=cmax)
else:
norm = norm_func()
# plot unit vectors
if unit_vectors:
ax.quiver(x,
y,
dx / mags,
dy / mags,
mags,
cmap=cmap,
norm=norm,
scale=vectorscale)
else:
ax.quiver(x, y, dx, dy, mags, cmap=cmap, norm=norm, scale=vectorscale)
ax.set_xlim(bbox.minx, bbox.maxx)
ax.set_ylim(bbox.miny, bbox.maxy)
ax.set_frame_on(False)
ax.set_clip_on(False)
ax.set_position([0., 0., 1., 1.])
return figure_response(fig, request)
def contour(request):
# Create figure
fig, ax, norm = create_axis(request)
ax.set_axis_off()
csr = request.GET['colorscalerange']
if request.GET['logscale'] is True:
levs = np.hstack(
([csr.min - 1],
np.linspace(csr.min, csr.max,
request.GET['numcontours']), [csr.max + 1]))
levs_labels = ["%.1f" % x for x in levs[1:-1]]
if request.GET['showvalues'] is False:
levs_labels = ['' for x in range(levs.size - 2)]
x, y = np.meshgrid(np.ones(2), np.ones(2))
cs = ax.contourf(x,
y,
x,
levels=levs,
norm=norm,
cmap=get_cmap(request.GET['colormap']))
proxy = [
plt.Rectangle((0, 0), 0, 0, fc=pc.get_facecolor()[0])
for pc in cs.collections
]
else:
levs = np.linspace(csr.min, csr.max, request.GET['numcontours'])
levs_labels = ["%.1f" % x for x in levs]
if request.GET['showvalues'] is False:
levs_labels = ['' for x in range(levs.size)]
x, y = np.meshgrid(np.ones(2), np.ones(2))
cs = ax.contourf(x,
y,
x,
levels=levs,
norm=norm,
cmap=get_cmap(request.GET['colormap']),
extend='max')
proxy = [
plt.Rectangle((0, 0), 0, 0, fc=pc.get_facecolor()[0])
for pc in cs.collections
]
params = dict()
if request.GET['horizontal'] is True:
columns = 5
if request.GET['numcontours'] > 20:
columns = request.GET['numcontours'] / 10
params = dict(labelspacing=0, mode="expand", ncol=columns)
cb = legend(proxy,
levs_labels,
loc=10,
borderaxespad=0.,
frameon=False,
**params)
if request.GET['showlabel'] is True:
cb.set_title(request.GET['units'])
# Return HttpResponse
return figure_response(fig, request, bbox_extra_artists=(cb, ))