def plot_pmlf(pml_data, title='', output_file=None, grid=True,
show_graph=False, annotate=[]):
"""Plot the PML fatality data. Optionally save a file or show the graph (or both).
Inputs:
pml_data (nx2) array containing the PMLf curve. The first column
contains the probability of exceedance (in one year) values,
the second column contains the direct fatality losses
for each of the probabilities of exceedance.
title if supplied, the graph title string
output_file path of file to save plot picture in
grid draw a grid on graph if True
show_graph show graph on screen if True
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
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
# create a plot of annualised loss versus return period if requested
if output_file or show_graph:
# unpack pml_data
[ProbExceedSmall, trghzd_agg] = pml_data
fig = plt.figure()
ax = fig.add_subplot(111)
plt.semilogy(trghzd_agg, ProbExceedSmall, color='b')
plt.xlabel('Direct fatality loss')
plt.ylabel('Probability of exceedance in one year')
# add graph 'furniture'
if title:
plt.title(title)
plt.grid(grid)
# 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)
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_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_pmlf(pml_data,
title='',
output_file=None,
grid=True,
show_graph=False,
annotate=[]):
"""Plot the PML fatality data. Optionally save a file or show the graph (or both).
Inputs:
pml_data (nx2) array containing the PMLf curve. The first column
contains the probability of exceedance (in one year) values,
the second column contains the direct fatality losses
for each of the probabilities of exceedance.
title if supplied, the graph title string
output_file path of file to save plot picture in
grid draw a grid on graph if True
show_graph show graph on screen if True
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
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
# create a plot of annualised loss versus return period if requested
if output_file or show_graph:
# unpack pml_data
[ProbExceedSmall, trghzd_agg] = pml_data
fig = plt.figure()
ax = fig.add_subplot(111)
plt.semilogy(trghzd_agg, ProbExceedSmall, color='b')
plt.xlabel('Direct fatality loss')
plt.ylabel('Probability of exceedance in one year')
# add graph 'furniture'
if title:
plt.title(title)
plt.grid(grid)
# 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)
if show_graph:
plt.show()
plt.close()
def plot_barchart(data, output_file=None, title='', xlabel='', ylabel='',
xrange=None, yrange=None, grid=True, show_graph=False,
bin_width=None, bardict=None, annotate=[]):
"""Plot a barchart. Optionally save a file or show the graph (or both).
Inputs:
data (nx2) array containing the barchart data. The first column
contains the X ordinal and the second column contains the
height of the bar
output_file path of file to save plot picture in
title the graph title string, if supplied
xlabel label to put on the X axis
ylabel label to put on the Y axis
xrange a scalar or iterable indicating min[,max] X value to plot
yrange a scalar or iterable indicating min[,max] Y value to plot
grid draw a grid on graph if True
show_graph show graph on screen if True
bin_width width of the bins
bardict a dictionary containing extra parameters to the
matplotlib.pyplot.bar() routine. Params are defined at
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.bar
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
matplotlib.sourceforge.net/api/pyplot_api.html
(this is optional)
NOTE: if annotate=None is used, no annotation occurs.
if annotate=[] is used, auto annotation is generated:
timestamp
X and Y range limits
"""
# 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
# create the plot if we want a file or must show it
if output_file or show_graph:
fig = plt.figure()
ax = fig.add_subplot(111)
# get bardict into correct form for bar() call below
bar_kwargs = {}
if bardict:
bar_kwargs = bardict
# plot the data
if bin_width:
plt.bar(data[:,0], data[:,1], width=bin_width, align='center',
**bar_kwargs)
else:
plt.bar(data[:,0], data[:,1], align='center', **bar_kwargs)
# set X and Y range limits
xrange = util.get_canonical_range(xrange)
yrange = util.get_canonical_range(yrange)
if xrange:
plt.xlim(xrange)
if yrange:
plt.ylim(yrange)
# do X and Y labels
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# add graph 'furniture'
if title:
plt.title(title)
plt.grid(grid)
# add system annotations: time generated, etc
if isinstance(annotate, list):
pts.plot_timestamp(fig)
# add range limits annotations
if annotate is None:
annotate = []
if xrange:
annotate.append((0.02, 0.05,
'X range forced to (%.2f,%.2f)' % xrange))
if yrange:
annotate.append((0.02, 0.03,
'Y range forced to (%.2f,%.2f)' % yrange))
# 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()
