from oasis_utils import read_csvtodf
# Parameters ------------------------------------------------------------------
vulnId = 1
summaryset_id = 1
# loss per location
ifile_vuln = "static/vulnerability.csv"
ifile_dmgmap = "static/damage_bin_dict.csv"
pd.options.display.max_rows = 10
# Script ----------------------------------------------------------------------
# Read the location losses from the ELT file
vuln0 = read_csvtodf(ifile_vuln)
# extract the vulnerability we're interested in
vuln0 = vuln0.loc[vuln0.vulnerability_id == vulnId]
vuln0.drop('vulnerability_id', 1, inplace=True)
# Set the index
vuln0 = vuln0.sort_values(['intensity_bin_id', 'damage_bin_id'],
axis=0,
ascending=[True, True])
vuln0 = vuln0.set_index(['intensity_bin_id', 'damage_bin_id'])
# Calculate cumulative and exceedance prob
vuln1 = vuln0.groupby('intensity_bin_id').cumsum().rename(
columns={'prob': 'cprob'})
# Show the plot
print('Pausing while plot is shown...')
pylab.show(block=True)
return 1
# Script ----------------------------------------------------------------------
# Read the footprint for the eventid we want
fp = read_footprint(ifile_fp, eventId)
# Read the intensity bins and join
fp = pd.merge(fp,
read_intensbins(ifile_intensbins),
left_index=True,
right_index=True)
# Get the exceedance probability for the intensity we want
fp = fp[fp.right >= intensval]['prob'].groupby(level=0).sum()
# Read location file
locs = read_csvtodf(ifile_loc)
locs.set_index('coverage_id', drop=True, inplace=True)
locs.index.names = ['locid']
# Join the footprint by event id
locs = pd.merge(locs, fp.to_frame(), left_on='areaperil_id', right_index=True)
# Plot
plot_footprintmap(locs)
# Formatting
ax.set_aspect('equal')
ax.autoscale(tight=True)
ax.grid()
# Add a color bar
plt.colorbar(cax, orientation='vertical', label='Loss')
# Add a title
plt.title("Expected GU loss by location, Type=%s" % losstype)
return 1
# Read location file
locs = read_csvtodf(ifile_loc)
# Read the location losses from the ELT file
loclosses0 = read_locationlosses(ifile_elt)
# Read the summary file to get the coverageid
summ = read_csvtodf(ifile_summ)
# Keep the location level losses
summ = summ.loc[summ.summaryset_id == summsetId]
# Get the coverage id per loss
loclosses = pd.merge(loclosses0, summ, left_index=True, right_on='summary_id')
# Get the location detail per loss
loclosses = pd.merge(loclosses,
""" Plot the output """
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from oasis_utils import read_csvtodf
# Parameters
ifile_getmodel = "./results/loccdf.csv"
pd.options.display.max_rows = 20
vulnId = 1
eventId = 1
# Read the output file
gm = read_csvtodf(ifile_getmodel)
gm = gm.loc[(gm.vulnerability_id == vulnId) & (gm.event_id == eventId)]
gm.drop(['vulnerability_id', 'event_id'], 1, inplace=True)
#gm3 = gm.groupby(["areaperil_id", "bin_mean"])['prob_to'].prod()
gm3 = gm.groupby(["areaperil_id", "bin_mean"])['prob_to'].max()
gm2 = gm3.to_frame().reset_index().pivot('areaperil_id', 'bin_mean', 'prob_to')
# Plot
f, ax2 = plt.subplots(figsize=(9, 6))
sns.heatmap(gm2, ax=ax2, cmap='Spectral')
plt.show()
ifile_item = "input/items.csv"
# Need the footprint to get the intensity per location
ifile_fp = "static/footprint.csv"
ifile_intensbins = "static/intensity_bin_dict.csv"
pd.options.display.max_rows = 10
# Script ----------------------------------------------------------------------
# Read the location losses from the ELT file
loclosses = read_locationlosses(ifile_elt_perloc)
# Read the summary file to get the coverageid
summ = read_csvtodf(ifile_summary)
summ = summ.loc[summ.summaryset_id == summaryset_id]
summ.drop('summaryset_id', 1, inplace=True)
# Get the coverage id per loss
loclosses = pd.merge(loclosses, summ, left_index=True, right_on='summary_id')
# Join with the items so we can get the areaperil
loclosses = pd.merge(loclosses, read_items(ifile_item), on="coverage_id")
# Join with the coverages so we can get the TIV
loclosses = pd.merge(loclosses,
read_coverages(ifile_cvg),
left_on="coverage_id",
right_index=True)