def save_image_to_response(
self, response, framenr=0,
colormap=None, maxvalue=None):
if colormap is None:
colormap = 'PuBu'
if maxvalue is None:
maxvalue = self.maxvalue
dataset = gdal.Open(self.get_dataset_path(framenr))
colormap = get_mpl_cmap(colormap)
# Colormaps from CSVs have a fixed maxvalue, which is in the
# attribute 'csv_max_value'.
maxvalue = getattr(colormap, 'csv_max_value', maxvalue)
# Get data as masked array
data = np.ma.masked_less(
dataset.GetRasterBand(1).ReadAsArray(), LIMIT, copy=False)
# Normalize
normalize = colors.Normalize(vmin=0, vmax=maxvalue, clip=True)
# Apply colormap
rgba = colormap(normalize(data), bytes=True)
# Turn into PIL image
image = Image.fromarray(rgba)
# Save into response
response['Content-type'] = 'image/png'
image.save(response, 'png')
def get_response_for_getcounts(get_parameters):
""" Return json with curve coordinates. """
geometry = get_geometry(**get_parameters)
layers = get_parameters['layers'].split(',')
data = tuple(get_data(l, geometry) for l in layers)
bins = np.arange(0, 256)
histograms = [np.histogram(d.compressed(), bins)[0] for d in data]
nonzeros = [h.nonzero() for h in histograms]
nbins = [bins[:-1][n] for n in nonzeros]
nhistograms = [h[n] for n, h in zip(nonzeros, histograms)]
# Determine the ordering
argsorts = [h.argsort() for h in nhistograms]
arg10 = [a[:-10:-1] for a in argsorts]
argrest = [a[-10::-1] for a in argsorts]
# Use it to group
rests = [h[argrest].sum() for h, a in zip(nhistograms, argsorts)]
pairs = [np.array([b[arg10], h[arg10]]).transpose()
for b, h, a in zip(nbins, nhistograms, argsorts)]
# Prepare result data
styles = get_parameters['styles'].split(',')
colormaps = [
get_mpl_cmap(s.split(':')[0], settings_module=settings)
for s in styles]
result = [[dict(label=LANDUSE.get(b, b),
data=d,
color=colors.rgb2hex((c(b))))
for b, d in p.tolist()] for p, c in zip(pairs, colormaps)]
for r, s in zip(result, rests):
if s:
r.append(dict(label='Overig', data=float(s), color='#ffffff'))
return jsonify(result)
def get_response_for_getcounts(get_parameters):
""" Return json with curve coordinates. """
geometry = get_geometry(**get_parameters)
layers = get_parameters['layers'].split(',')
data = tuple(get_data(l, geometry) for l in layers)
bins = np.arange(0, 256)
histograms = [np.histogram(d.compressed(), bins)[0] for d in data]
nonzeros = [h.nonzero() for h in histograms]
nbins = [bins[:-1][n] for n in nonzeros]
nhistograms = [h[n] for n, h in zip(nonzeros, histograms)]
# Determine the ordering
argsorts = [h.argsort() for h in nhistograms]
arg10 = [a[:-10:-1] for a in argsorts]
argrest = [a[-10::-1] for a in argsorts]
# Use it to group
rests = [h[argrest].sum() for h, a in zip(nhistograms, argsorts)]
pairs = [
np.array([b[arg10], h[arg10]]).transpose()
for b, h, a in zip(nbins, nhistograms, argsorts)
]
# Prepare result data
styles = get_parameters['styles'].split(',')
colormaps = [
get_mpl_cmap(s.split(':')[0], settings_module=settings) for s in styles
]
result = [[
dict(label=LANDUSE.get(b, b), data=d, color=colors.rgb2hex((c(b))))
for b, d in p.tolist()
] for p, c in zip(pairs, colormaps)]
for r, s in zip(result, rests):
if s:
r.append(dict(label='Overig', data=float(s), color='#ffffff'))
return jsonify(result)
def get_image(data, style):
""" Return PIL image. """
parts = style.split(':')
colormap = get_mpl_cmap(parts[0] if parts[0] else 'jet',
settings_module=settings)
if isinstance(colormap, colors.ListedColormap):
normalize = lambda x: x
if (isinstance(colormap, colors.LinearSegmentedColormap)
and hasattr(colormap, 'csv_max_value')):
normalize = lambda x: x / colormap.csv_max_value
else:
normalize = colors.Normalize(*map(float_or_none, parts[1:]), clip=True)
rgba = colormap(normalize(data), bytes=True)
return Image.fromarray(rgba)
def get_image(data, style):
""" Return PIL image. """
parts = style.split(':')
colormap = get_mpl_cmap(
parts[0] if parts[0] else 'jet',
settings_module=settings)
if isinstance(colormap, colors.ListedColormap):
normalize = lambda x: x
if (isinstance(colormap, colors.LinearSegmentedColormap) and
hasattr(colormap, 'csv_max_value')):
normalize = lambda x: x / colormap.csv_max_value
else:
normalize = colors.Normalize(*map(float_or_none, parts[1:]), clip=True)
rgba = colormap(normalize(data), bytes=True)
return Image.fromarray(rgba)
def result_legend(result, presentationlayer, colormap=None, maxvalue=None):
presentationtype = presentationlayer.presentationtype
default_colormap, default_maxvalue = presentationtype.colormap_info(
project=result.scenario.main_project)
try:
maxvalue = float(maxvalue)
except (ValueError, TypeError):
maxvalue = default_maxvalue
if colormap is None:
colormap = default_colormap
if not colormap.endswith('.csv'):
show_maxvalue = True
# Matplotlib colormap -- use given maxvalue, and always do 10 steps
cmap = get_mpl_cmap(colormap)
arr = np.array(values_in_range(maxvalue, 10))
# Color the way that the grids are colored
normalize = colors.Normalize(vmin=0, vmax=maxvalue)
rgba = cmap(normalize(arr), bytes=True)
legend = [
(arr[i], rgba_to_html(tuple(rgba[i])))
for i in range(10)
]
else:
# Our own .csv colormaps.
show_maxvalue = False
cmap = ColorMap(os.path.join(
settings.FLOODING_LIB_COLORMAP_DIR, colormap))
legend = [
(value, rgba_to_html(cmap.value_to_color(value)))
for value in cmap.legend_values()
]
return {
'title': unicode(presentationtype),
'content': legend,
'colormaps': models.Colormap.colormaps(),
'active_colormap': colormap,
'current_maxvalue': maxvalue,
'show_maxvalue': show_maxvalue,
}
def result_legend(result, presentationlayer, colormap=None, maxvalue=None):
presentationtype = presentationlayer.presentationtype
default_colormap, default_maxvalue = presentationtype.colormap_info(
project=result.scenario.main_project)
try:
maxvalue = float(maxvalue)
except (ValueError, TypeError):
maxvalue = default_maxvalue
if colormap is None:
colormap = default_colormap
if not colormap.endswith('.csv'):
show_maxvalue = True
# Matplotlib colormap -- use given maxvalue, and always do 10 steps
cmap = get_mpl_cmap(colormap)
arr = np.array(values_in_range(maxvalue, 10))
# Color the way that the grids are colored
normalize = colors.Normalize(vmin=0, vmax=maxvalue)
rgba = cmap(normalize(arr), bytes=True)
legend = [
(arr[i], rgba_to_html(tuple(rgba[i])))
for i in range(10)
]
else:
# Our own .csv colormaps.
show_maxvalue = False
cmap = ColorMap(os.path.join(
settings.FLOODING_LIB_COLORMAP_DIR, colormap))
legend = [
(value, rgba_to_html(cmap.value_to_color(value)))
for value in cmap.legend_values()
]
return {
'title': unicode(presentationtype),
'content': legend,
'colormaps': models.Colormap.colormaps(),
'active_colormap': colormap,
'current_maxvalue': maxvalue,
'show_maxvalue': show_maxvalue,
}
