def example_calc_histogram_plot_barchart(datafile, output_file=None):
"""An example of calulating a histogram and then plotting the result.
datafile the path to the file to plot
output_file path to the graph picture file to produce
"""
data = cxh.calc_xy_histogram(datafile, bins=50)
pb.plot_barchart(data, output_file=output_file,
title='example_calc_histogram_plot_barchart',
xlabel='IQ', ylabel='frequency',
xrange=None, yrange=None, grid=True,
show_graph=True, annotate=[])
def example_calc_histogram_plot_barchart(datafile, output_file=None):
"""An example of calulating a histogram and then plotting the result.
datafile the path to the file to plot
output_file path to the graph picture file to produce
"""
data = cxh.calc_xy_histogram(datafile, bins=50)
pb.plot_barchart(data,
output_file=output_file,
title='example_calc_histogram_plot_barchart',
xlabel='IQ',
ylabel='frequency',
xrange=None,
yrange=None,
grid=True,
show_graph=True,
annotate=[])
def plot_scen_loss_stats(input_dir, site_tag, output_dir,
# plot_file, save_file=None,
pre89=False, dollars89=False, resonly=False):
"""Plot a scenario loss statistics graph from scenario data.
input_dir input directory
site_tag event descriptor string
output_dir general output directory
## plot_file name of map output file to create in 'output_dir' directory
## save_file name of map data output file to create in 'output_dir' directory
pre89 if True consider buildings that existed before 1989 only
dollars89 if True express dollar values in pre-1989 dollars
resonly if True consider only residential buildings
"""
# read in raw data
data = cpr.obsolete_convert_Py2Mat_Risk(site_tag)
# filter data depending on various flags
temp_ecloss = data.ecloss
temp_bval2 = data.ecbval2
if pre89:
if resonly:
take_array = [x[2] and x[11] for x in data.structures]
temp_ecloss = scipy.take(data.ecloss, take_array)
temp_bval2 = scipy.take(data.ecbval2, take_array)
else:
take_array = [x[2] for x in data.structures]
temp_ecloss = scipy.take(data.ecloss, take_array)
temp_bval2 = scipy.take(data.ecbval2, take_array)
else:
if resonly:
take_array = [x[11] == 'RES1' for x in data.structures]
temp_ecloss = scipy.take(data.ecloss, resonly_array)
temp_bval2 = scipy.take(data.ecbval2, resonly_array)
else:
temp_ecloss = data.ecloss
temp_bval2 = data.ecbval2
# which dollars does user want
doll_str = 'in 2002 dollars'
cvt_doll = 1.0
if dollars89:
doll_str = 'in 1989 dollars'
cvt_doll = 1/1.37
f_ecloss = cvt_doll * temp_ecloss
f_bval2 = cvt_doll * temp_bval2
f_aggloss_bill = scipy.sum(f_bval2) / 1e9
print('f_ecloss=%s' % str(f_ecloss))
print('f_bval2=%s' % str(f_bval2))
print('f_aggbval2_bill=%s' % str(f_aggloss_bill))
h_data = cxh.calc_xy_histogram(f_aggloss_bill, bins=10)
plot_out = os.path.join(output_dir, 'test1.png')
title = 'Newc89 agg loss, pre89, 1989 dollars'
xlabel = '$ %s (x 1e+9)' % doll_str
pb.plot_barchart(h_data, output_file=plot_out, title=title,
xlabel=xlabel, ylabel='frequency',
xrange=None, yrange=None, grid=True,
show_graph=True, annotate=[])