def plot_annloss_deagg_distmag(data, momag_labels, range_bins, pct_limits=None,
output_file=None, title=None,
show_graph=False, grid=False, colormap=None,
annotate=[]):
"""Plot annualised loss deaggregated distance/magnitude data.
Inputs:
data an MxN array containing % annualised loss values with axes
of moment/magnitude data (M) versus distance (N)
momag_labels an iterable containing the y-axis labels for each row of 'data'
range_bins an iterable of range values for each column of 'data'
pct_limits a tuple (min_pct, max_pct) for % range to plot
output_file path to required output plot file
title string used to title graph
show_graph if True shows graph in window on screen
grid if True puts a grid in the graph
colormap name of the colormap to use
annotate an iterable like [(x, y, str, dict), ...] where
x is X screen coordinate
y is Y screen coordinate
str is the annotation string to display at (x, y)
dict is a dictionary of key/value pairs as documented at
http://matplotlib.sourceforge.net/api/pyplot_api.html
(this is optional)
NOTE: if annotate=None is used, no automatic annotation occurs.
if annotate=[] is used, auto annotation is generated:
timestamp
clipping
max input data value
"""
# decide if we have a 'gui-less' plot or not.
import matplotlib
if output_file and not show_graph:
# no plot to be shown - no gui
matplotlib.use('Agg', warn=False)
elif show_graph:
# decide if we *can* show the plot - error if not
if sys.platform != 'win32':
try:
display = os.environ['DISPLAY']
except KeyError:
msg = ("No DISPLAY variable set. "
"Did you do 'ssh -X <machine>'?")
raise RuntimeError(msg)
# go ahead with the plot
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
# decide if we are going to plot
if output_file or show_graph:
# get colormap to use
(cmap_name, my_cmap) = uc.get_colormap(colormap)
# get maximum data value
#max_pct = scipy.max(data)
max_pct = num.max(data)
# limit values to within pct_limits here
if pct_limits:
(floor, ceil) = pct_limits
plot_data = scipy.clip(data, floor, ceil)
else:
plot_data = data
# drop last range bin row from data
plot_data = plot_data[:,:-1]
# Get data X and Y limits
max_x = range_bins[-1]
min_x = range_bins[0]
max_y = momag_labels[-1]
min_y = momag_labels[0]
max_z = num.max(plot_data)
min_z = num.min(plot_data)
# calculate required aspect ration (the *2 is a fudge)
(y_size, x_size) = scipy.shape(plot_data)
ratio = float(x_size/y_size) * 2
# start plot
fig = plt.figure()
ax = fig.add_subplot(111)
im = plt.imshow(plot_data, interpolation='nearest', cmap=my_cmap,
origin='lower', extent=[min_x,max_x,min_y,max_y])
# add color bar
for step in (1, 2, 5, 10, 20, 50, 100):
cb_ticks = [x for x in range(int(min_z), int(max_z+1), step)]
if len(cb_ticks) <= 12:
break
cb = plt.colorbar(orientation='horizontal', ticks=cb_ticks)
cb.set_label('% of annual loss')
# label axes
plt.xlabel('Distance (km)')
plt.ylabel('Moment Magnitude')
# set main plot aspect ratio
plt.axes().set_aspect(ratio)
# add graph 'furniture'
if title:
plt.title(title)
plt.grid(grid)
# add system annotations: the clipping in effect, time generated, etc
if isinstance(annotate, list):
pts.plot_timestamp(fig)
if pct_limits:
clip_str = 'Data values clipped to [%.1f,%.1f]' % (floor, ceil)
plt.text(0.98, 0.02, clip_str, fontsize=6,
transform=fig.transFigure, horizontalalignment='right')
pct_str = '%.2f%%' % max_pct
plt.text(0.91, 0.185, pct_str, horizontalalignment='left',
fontsize=8, transform=fig.transFigure, )
# add user annotations, if any
pua.plot_user_annotation(fig, annotate)
# show or save graph (or both?)
if output_file:
plt.savefig(output_file)
if show_graph:
plt.show()
plt.close()
def plot_hexbin_map(map_data, bins=100, title='', output_file=None, cblabel='',
cbformat='%.1f', show_graph=False, grid=True, colormap=None,
annotate=[], scale=None, np_posn=None,
hexbin_args=None, map_extent=None):
"""Plot data into a hexgrid with an underlay map.
Inputs:
map_data (nx3) array containing the data to plot. The first column
contains the latitude of a point, the second column contains
the longitude and the third column is the value to bin&plot.
bins Either a scalar (# bins in X and Y directions) or (nx, ny).
title the graph title string
output_file path of file to save plot picture in
cblabel label to display on the colorbar
cbformat format string used to show colormap values
show_graph show graph on screen if True
grid draw a grid on graph if True
colormap if supplied, the colormap name to use (string)
annotate an iterable like [(x, y, ann, dict), ...] where
x is X screen coordinate
y is Y screen coordinate
ann is the annotation string to display at (x, y)
dict is a dictionary of key/value pairs as documented at
http://matplotlib.sourceforge.net/api/pyplot_api.html
(this is optional)
NOTE: if annotate=None is used, no automatic annotation occurs.
if annotate=[] is used, auto annotation is generated:
timestamp
scale a tuple containing data used to draw a scale:
(posn, length, scale_args)
where 'posn' is where to place scale, one of:
'NW', 'NE', 'SW', 'SE'
(case-insensitive)
and 'scale_args' is an optional kwargs dictionary
see drawmapscale() function at
http://matplotlib.sourceforge.net/basemap/doc/html/api/basemap_api.html#mpl_toolkits.basemap.Basemap.drawmapscale
to see values in scale_args
np_posn position tuple or a string describing pointer position:
'NW', 'NE', 'SW' or 'SE'
(case-insensitive)
hexbin_args is a dictionary of hexbin() parameters
if not supplied, assume {'gridsize': 100,
'reduce_C_function': scipy.sum
}
Note: if hexbin_args is supplied but key 'reduce_C_function'
isn't supplied in dictionary, insert 'scipy.sum' function
Note: allowed values are documented at:
http://matplotlib.sourceforge.net/api/pyplot_api.html
map_extent sets the extent of the displayed map if supplied
(get extent from the data if not supplied)
"""
# decide if we have a 'gui-less' plot or not.
import matplotlib
if output_file and not show_graph:
# no plot to be shown - no gui
matplotlib.use('Agg', warn=False)
elif show_graph:
# decide if we *can* show the plot - error if not
if sys.platform != 'win32':
try:
display = os.environ['DISPLAY']
except KeyError:
msg = ("No DISPLAY variable set. "
"Did you do 'ssh -X <machine>'?")
raise RuntimeError(msg)
# go ahead with the plot
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from mpl_toolkits.basemap import Basemap
from matplotlib.mlab import griddata
# create a 'binned' plot on a map
if output_file or show_graph:
# decide on the colormap to use
(cmap_name, my_cmap) = uc.get_colormap(colormap)
# set the hexbin params
if hexbin_args is None:
hexbin_args = {'gridsize': 100,
'reduce_C_function': scipy.sum,
'linewidth': DEFAULT_LINEWIDTH}
try:
_ = hexbin_args['reduce_C_function']
except KeyError:
hexbin_args['reduce_C_function'] = scipy.sum
# here we fiddle with 'linewidth' to get around a bug in plotting
# if linewidth not set, hex bins overlap each other
# force linewidth to a small value if not specified
try:
_ = hexbin_args['linewidth']
except KeyError:
hexbin_args['linewidth'] = DEFAULT_LINEWIDTH
# if user doesn't define 'edgecolors' at all, use default
hexbin_args['edgecolors'] = hexbin_args.get('edgecolors', FILL_COLOUR)
# get extent of point data
if map_extent:
extent = map_extent
else:
extent = gxe.get_extent(map_data, margin=5)
(ll_lat, ll_lon, ur_lat, ur_lon) = extent
# start the plot
fig = plt.figure()
ax = fig.add_subplot(111)
# draw the map
m = Basemap(projection='cyl', llcrnrlat=ll_lat, urcrnrlat=ur_lat,
llcrnrlon=ll_lon, urcrnrlon=ur_lon, resolution='h',
suppress_ticks=False, area_thresh=0.5)
# m = Basemap(projection='cass', llcrnrlat=ll_lat, urcrnrlat=ur_lat,
# llcrnrlon=ll_lon, urcrnrlon=ur_lon, resolution='h',
# lat_0=(ll_lat+ur_lat)/2.0, lon_0=(ll_lon+ur_lon)/2.0,
# suppress_ticks=False) #, area_thresh=0.5)
# draw hexbin data on map
plt.hexbin(map_data[:,0], map_data[:,1], map_data[:,2],
cmap=my_cmap, zorder=4, **hexbin_args)
# draw the other minor stuff
m.fillcontinents(color=FILL_COLOUR, zorder=2)
m.drawcoastlines(linewidth=0.25, color='k', zorder=5)
m.drawmapboundary()
cb = plt.colorbar(cmap=my_cmap, format=cbformat)
cb.set_label(cblabel)
plt.xlabel('longitude', fontsize=8)
plt.ylabel('latitude', fontsize=8)
# if user asked for a NORTH pointer
if np_posn:
unp.plot_northpointer(m, (ll_lat, ll_lon, ur_lat, ur_lon),
np_posn)
# add graph 'furniture'
plt.title(title)
plt.grid(grid, color='#C0C0C0', linestyle='-', linewidth=0.25)
# add system annotations: time generated, etc
if isinstance(annotate, list):
pts.plot_timestamp(fig)
# add user annotations, if any
pua.plot_user_annotation(fig, annotate)
# show or save graph (or both?)
if output_file:
plt.savefig(output_file, dpi=300)
if show_graph:
plt.show()
plt.close()
def plot_hexbin_map(map_data,
bins=100,
title='',
output_file=None,
cblabel='',
cbformat='%.1f',
show_graph=False,
grid=True,
colormap=None,
annotate=[],
scale=None,
np_posn=None,
hexbin_args=None,
map_extent=None):
"""Plot data into a hexgrid with an underlay map.
Inputs:
map_data (nx3) array containing the data to plot. The first column
contains the latitude of a point, the second column contains
the longitude and the third column is the value to bin&plot.
bins Either a scalar (# bins in X and Y directions) or (nx, ny).
title the graph title string
output_file path of file to save plot picture in
cblabel label to display on the colorbar
cbformat format string used to show colormap values
show_graph show graph on screen if True
grid draw a grid on graph if True
colormap if supplied, the colormap name to use (string)
annotate an iterable like [(x, y, ann, dict), ...] where
x is X screen coordinate
y is Y screen coordinate
ann is the annotation string to display at (x, y)
dict is a dictionary of key/value pairs as documented at
http://matplotlib.sourceforge.net/api/pyplot_api.html
(this is optional)
NOTE: if annotate=None is used, no automatic annotation occurs.
if annotate=[] is used, auto annotation is generated:
timestamp
scale a tuple containing data used to draw a scale:
(posn, length, scale_args)
where 'posn' is where to place scale, one of:
'NW', 'NE', 'SW', 'SE'
(case-insensitive)
and 'scale_args' is an optional kwargs dictionary
see drawmapscale() function at
http://matplotlib.sourceforge.net/basemap/doc/html/api/basemap_api.html#mpl_toolkits.basemap.Basemap.drawmapscale
to see values in scale_args
np_posn position tuple or a string describing pointer position:
'NW', 'NE', 'SW' or 'SE'
(case-insensitive)
hexbin_args is a dictionary of hexbin() parameters
if not supplied, assume {'gridsize': 100,
'reduce_C_function': scipy.sum
}
Note: if hexbin_args is supplied but key 'reduce_C_function'
isn't supplied in dictionary, insert 'scipy.sum' function
Note: allowed values are documented at:
http://matplotlib.sourceforge.net/api/pyplot_api.html
map_extent sets the extent of the displayed map if supplied
(get extent from the data if not supplied)
"""
# decide if we have a 'gui-less' plot or not.
import matplotlib
if output_file and not show_graph:
# no plot to be shown - no gui
matplotlib.use('Agg', warn=False)
elif show_graph:
# decide if we *can* show the plot - error if not
if sys.platform != 'win32':
try:
display = os.environ['DISPLAY']
except KeyError:
msg = ("No DISPLAY variable set. "
"Did you do 'ssh -X <machine>'?")
raise RuntimeError(msg)
# go ahead with the plot
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from mpl_toolkits.basemap import Basemap
from matplotlib.mlab import griddata
# create a 'binned' plot on a map
if output_file or show_graph:
# decide on the colormap to use
(cmap_name, my_cmap) = uc.get_colormap(colormap)
# set the hexbin params
if hexbin_args is None:
hexbin_args = {
'gridsize': 100,
'reduce_C_function': scipy.sum,
'linewidth': DEFAULT_LINEWIDTH
}
try:
_ = hexbin_args['reduce_C_function']
except KeyError:
hexbin_args['reduce_C_function'] = scipy.sum
# here we fiddle with 'linewidth' to get around a bug in plotting
# if linewidth not set, hex bins overlap each other
# force linewidth to a small value if not specified
try:
_ = hexbin_args['linewidth']
except KeyError:
hexbin_args['linewidth'] = DEFAULT_LINEWIDTH
# if user doesn't define 'edgecolors' at all, use default
hexbin_args['edgecolors'] = hexbin_args.get('edgecolors', FILL_COLOUR)
# get extent of point data
if map_extent:
extent = map_extent
else:
extent = gxe.get_extent(map_data, margin=5)
(ll_lat, ll_lon, ur_lat, ur_lon) = extent
# start the plot
fig = plt.figure()
ax = fig.add_subplot(111)
# draw the map
m = Basemap(projection='cyl',
llcrnrlat=ll_lat,
urcrnrlat=ur_lat,
llcrnrlon=ll_lon,
urcrnrlon=ur_lon,
resolution='h',
suppress_ticks=False,
area_thresh=0.5)
# m = Basemap(projection='cass', llcrnrlat=ll_lat, urcrnrlat=ur_lat,
# llcrnrlon=ll_lon, urcrnrlon=ur_lon, resolution='h',
# lat_0=(ll_lat+ur_lat)/2.0, lon_0=(ll_lon+ur_lon)/2.0,
# suppress_ticks=False) #, area_thresh=0.5)
# draw hexbin data on map
plt.hexbin(map_data[:, 0],
map_data[:, 1],
map_data[:, 2],
cmap=my_cmap,
zorder=4,
**hexbin_args)
# draw the other minor stuff
m.fillcontinents(color=FILL_COLOUR, zorder=2)
m.drawcoastlines(linewidth=0.25, color='k', zorder=5)
m.drawmapboundary()
cb = plt.colorbar(cmap=my_cmap, format=cbformat)
cb.set_label(cblabel)
plt.xlabel('longitude', fontsize=8)
plt.ylabel('latitude', fontsize=8)
# if user asked for a NORTH pointer
if np_posn:
unp.plot_northpointer(m, (ll_lat, ll_lon, ur_lat, ur_lon), np_posn)
# add graph 'furniture'
plt.title(title)
plt.grid(grid, color='#C0C0C0', linestyle='-', linewidth=0.25)
# add system annotations: time generated, etc
if isinstance(annotate, list):
pts.plot_timestamp(fig)
# add user annotations, if any
pua.plot_user_annotation(fig, annotate)
# show or save graph (or both?)
if output_file:
plt.savefig(output_file, dpi=300)
if show_graph:
plt.show()
plt.close()
