Fragility Curve Calculation with Detailed Damage Ratio Analysis

Setup and Environment Configuration¶

First, we'll set up the notebook environment with automatic reloading for development.

In [1]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

Let's check our current working directory and navigate to the package root if needed.

In [2]:

%pwd

%pwd

Out[2]:

'/home/mammad/Documents/EDU/cope/CHEERSafe/LossModel/examples'

In [3]:

%cd ..

%cd ..

/home/mammad/Documents/EDU/cope/CHEERSafe/LossModel

System Configuration¶

Let's check the available CPU cores for parallel processing configuration.

In [4]:

import os
cpu_count = os.cpu_count()
print(f"Available CPU cores: {cpu_count}")

import os cpu_count = os.cpu_count() print(f"Available CPU cores: {cpu_count}")

Available CPU cores: 24

Package Configuration and Imports¶

Configure the analysis parameters and import required modules.

Key Configuration for This Demo:

• EXPORT_DETAILED_DAMAGE_RATIOS = True: Enables export of detailed component-level damage ratios to CSV

When enabled, the following columns are exported per (building_type, cindex) combination:

• m: Building type identifier
• c: Component configuration index
• wind_orientation_index: Wind orientation index
• wind_speed_index: Wind speed index
• flood_depth_index: Flood depth index
• dr_total: Total building damage ratio
• dr_explicit: Explicit damage ratio (wind components)
• dr_implicit: Implicit damage ratio (interior + flood)
• dr_exp_wind_RC: Roof Coverings explicit damage ratio
• dr_exp_wind_RS: Roof Sheathings explicit damage ratio
• dr_exp_wind_RWC: Roof-Wall Connections explicit damage ratio
• dr_exp_wind_O: Openings explicit damage ratio
• dr_exp_wind_W: Walls explicit damage ratio
• dr_imp_wind_I: Interior implicit damage ratio
• dr_imp_wind_E: Electrical implicit damage ratio
• dr_imp_wind_M: Mechanical implicit damage ratio
• dr_imp_wind_P: Plumbing implicit damage ratio
• dr_imp_flood: Flood implicit damage ratio

In [5]:

from cheersafe.config import Config

# Configure analysis parameters
Config.NUM_OF_WORKERS = 20
Config.NUM_OF_ITERATIONS = 300
Config.INPUTS_PATH = "Inputs"
Config.OUTPUTS_PATH = "Outputs2"

# Enable detailed damage ratio export to CSV
Config.EXPORT_DETAILED_DAMAGE_RATIOS = True

print(f"EXPORT_DETAILED_DAMAGE_RATIOS: {Config.EXPORT_DETAILED_DAMAGE_RATIOS}")

# Import core analysis modules
from cheersafe.damage_probability_calculation import DamageProbabilityCalculation
from cheersafe.inventory import BuildingInventory
from cheersafe.tools.stats import Covariance, Sampling

from cheersafe.config import Config # Configure analysis parameters Config.NUM_OF_WORKERS = 20 Config.NUM_OF_ITERATIONS = 300 Config.INPUTS_PATH = "Inputs" Config.OUTPUTS_PATH = "Outputs2" # Enable detailed damage ratio export to CSV Config.EXPORT_DETAILED_DAMAGE_RATIOS = True print(f"EXPORT_DETAILED_DAMAGE_RATIOS: {Config.EXPORT_DETAILED_DAMAGE_RATIOS}") # Import core analysis modules from cheersafe.damage_probability_calculation import DamageProbabilityCalculation from cheersafe.inventory import BuildingInventory from cheersafe.tools.stats import Covariance, Sampling

EXPORT_DETAILED_DAMAGE_RATIOS: True

Initialize Core Components¶

Set up the building inventory and statistical analysis tools.

In [6]:

# Initialize building inventory with building geometry data
myBuildingInventory = BuildingInventory(buildings_file_name="BuildingGeometry_Old.json")

# Initialize statistical analysis components
myCovariance = Covariance()
mySampling = Sampling()

# Initialize building inventory with building geometry data myBuildingInventory = BuildingInventory(buildings_file_name="BuildingGeometry_Old.json") # Initialize statistical analysis components myCovariance = Covariance() mySampling = Sampling()

Create Main Analysis Engine¶

Initialize the damage probability calculation engine with our configured components.

In [7]:

# Initialize the main damage probability calculation engine
myDamageProbabilityCalculation = DamageProbabilityCalculation(
    BuildingInventory=myBuildingInventory,
    Sampling=mySampling,
    Covariance=myCovariance
)

# Initialize the main damage probability calculation engine myDamageProbabilityCalculation = DamageProbabilityCalculation( BuildingInventory=myBuildingInventory, Sampling=mySampling, Covariance=myCovariance )

Configure Analysis Parameters¶

Specify which building types and resistance indices to process in this analysis.

In [8]:

# Set building types to process
myDamageProbabilityCalculation.set_building_types_to_process([2])

# Set resistance indices to process
myDamageProbabilityCalculation.set_cindex_to_process([45])

# Set building types to process myDamageProbabilityCalculation.set_building_types_to_process([2]) # Set resistance indices to process myDamageProbabilityCalculation.set_cindex_to_process([45])

2026-04-08 12:00:18,018 - INFO - Set to process building types: [2]
2026-04-08 12:00:18,018 - INFO - Set to process component indices: [45]

Run Damage Probability Analysis¶

Execute the main damage probability calculation. With EXPORT_DETAILED_DAMAGE_RATIOS = True, this will also generate CSV files with detailed component-level damage ratios averaged over Monte Carlo iterations.

In [9]:

# Calculate average damage probability across all scenarios
# This will also export detailed damage ratios to CSV
myDamageProbabilityCalculation.get_average_damage_probability()

# Calculate average damage probability across all scenarios # This will also export detailed damage ratios to CSV myDamageProbabilityCalculation.get_average_damage_probability()

2026-04-08 12:00:18,028 - INFO - CIndex: 45
2026-04-08 12:00:18,029 - INFO - Building type: 2
2026-04-08 12:00:18,030 - INFO - Collecting detailed damage ratios for building 2, cIndex 45
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.8s
[Parallel(n_jobs=20)]: Done 160 tasks      | elapsed:    2.4s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.7s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.2s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 261 out of 300 | elapsed:    2.9s remaining:    0.4s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 261 out of 300 | elapsed:    2.9s remaining:    0.4s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.1s finished
[Parallel(n_jobs=20)]: Using backend LokyBackend with 20 concurrent workers.
[Parallel(n_jobs=20)]: Done  10 tasks      | elapsed:    0.2s
[Parallel(n_jobs=20)]: Done 261 out of 300 | elapsed:    2.8s remaining:    0.4s
[Parallel(n_jobs=20)]: Done 300 out of 300 | elapsed:    3.1s finished
2026-04-08 12:00:53,172 - INFO - Saved result for building type 2, cIndex 45, with 300 iterations to Outputs2/ProbDS_wfd_m2_c45_iter300.pkl
2026-04-08 12:01:12,902 - INFO - Saved detailed damage ratios for building type 2, cIndex 45 to Outputs2/DetailedDR_m2_c45_iter300.csv
2026-04-08 12:01:13,921 - INFO - All specified combinations processed and saved to individual files.

Out[9]:

True

Load and Explore the Detailed Damage Ratio CSV¶

The detailed damage ratios are saved to CSV files with the naming pattern: DetailedDR_m{building_type}_c{cindex}_iter{NUM_OF_ITERATIONS}.csv

Let's load and explore the generated CSV file.

In [10]:

import pandas as pd

# Define parameters matching our analysis
building_type = 2
c_index = 45
num_iterations = Config.NUM_OF_ITERATIONS

# Construct the CSV file path
csv_file_path = f"{Config.OUTPUTS_PATH}/DetailedDR_m{building_type}_c{c_index}_iter{num_iterations}.csv"
print(f"Loading: {csv_file_path}")

# Load the CSV file
df_detailed = pd.read_csv(csv_file_path)

print(f"\nDataFrame shape: {df_detailed.shape}")
print(f"Number of rows: {len(df_detailed)}")
print(f"Number of columns: {len(df_detailed.columns)}")

import pandas as pd # Define parameters matching our analysis building_type = 2 c_index = 45 num_iterations = Config.NUM_OF_ITERATIONS # Construct the CSV file path csv_file_path = f"{Config.OUTPUTS_PATH}/DetailedDR_m{building_type}_c{c_index}_iter{num_iterations}.csv" print(f"Loading: {csv_file_path}") # Load the CSV file df_detailed = pd.read_csv(csv_file_path) print(f"\nDataFrame shape: {df_detailed.shape}") print(f"Number of rows: {len(df_detailed)}") print(f"Number of columns: {len(df_detailed.columns)}")

Loading: Outputs2/DetailedDR_m2_c45_iter300.csv

DataFrame shape: (27200, 18)
Number of rows: 27200
Number of columns: 18

View Column Names¶

Let's see all the available columns in the detailed damage ratio output.

In [11]:

print("Available columns:")
for i, col in enumerate(df_detailed.columns, 1):
    print(f"  {i}. {col}")

print("Available columns:") for i, col in enumerate(df_detailed.columns, 1): print(f" {i}. {col}")

Available columns:
  1. m
  2. c
  3. wind_orientation_index
  4. wind_speed_index
  5. flood_depth_index
  6. dr_total
  7. dr_explicit
  8. dr_implicit
  9. dr_exp_wind_RC
  10. dr_exp_wind_RS
  11. dr_exp_wind_RWC
  12. dr_exp_wind_O
  13. dr_exp_wind_W
  14. dr_imp_wind_I
  15. dr_imp_wind_E
  16. dr_imp_wind_M
  17. dr_imp_wind_P
  18. dr_imp_flood

Preview the Data¶

Let's look at the first few rows of the detailed damage ratio data.

In [12]:

# Display first 10 rows
df_detailed.head(10)

# Display first 10 rows df_detailed.head(10)

Out[12]:

	m	c	wind_orientation_index	wind_speed_index	flood_depth_index	dr_total	dr_explicit	dr_implicit	dr_exp_wind_RC	dr_exp_wind_RS	dr_exp_wind_RWC	dr_exp_wind_O	dr_exp_wind_W	dr_imp_wind_I	dr_imp_wind_E	dr_imp_wind_M	dr_imp_wind_P	dr_imp_flood
0	2	45	0	0	0	0.000000	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000000
1	2	45	0	0	1	0.020333	0.0	0.020333	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.020333
2	2	45	0	0	2	0.031900	0.0	0.031900	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.031900
3	2	45	0	0	3	0.044267	0.0	0.044267	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.044267
4	2	45	0	0	4	0.101433	0.0	0.101433	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.101433
5	2	45	0	0	5	0.111900	0.0	0.111900	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.111900
6	2	45	0	0	6	0.103533	0.0	0.103533	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.103533
7	2	45	0	0	7	0.103267	0.0	0.103267	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.103267
8	2	45	0	0	8	0.103700	0.0	0.103700	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.103700
9	2	45	0	0	9	0.104667	0.0	0.104667	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.104667

Summary Statistics¶

Let's examine the statistical summary of the damage ratios.

In [13]:

# Get summary statistics for damage ratio columns
dr_columns = [col for col in df_detailed.columns if col.startswith('dr_')]
df_detailed[dr_columns].describe()

# Get summary statistics for damage ratio columns dr_columns = [col for col in df_detailed.columns if col.startswith('dr_')] df_detailed[dr_columns].describe()

Out[13]:

	dr_total	dr_explicit	dr_implicit	dr_exp_wind_RC	dr_exp_wind_RS	dr_exp_wind_RWC	dr_exp_wind_O	dr_exp_wind_W	dr_imp_wind_I	dr_imp_wind_E	dr_imp_wind_M	dr_imp_wind_P	dr_imp_flood
count	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000	27200.000000
mean	0.389094	0.041385	0.347710	0.141045	0.023409	0.131079	0.060146	0.099075	0.127035	0.091658	0.080754	0.074572	0.297269
std	0.238040	0.082886	0.181640	0.263299	0.065129	0.241197	0.137125	0.215998	0.237235	0.187577	0.168088	0.156553	0.156552
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.229824	0.000459	0.221171	0.002406	0.000656	0.000225	0.001296	0.000833	0.002548	0.001193	0.001105	0.000813	0.208600
50%	0.285464	0.003261	0.279783	0.015729	0.001750	0.004910	0.005926	0.008333	0.014932	0.009457	0.007542	0.006728	0.259200
75%	0.533996	0.021698	0.506483	0.097177	0.006385	0.094032	0.029861	0.035833	0.077234	0.043726	0.035814	0.032186	0.314167
max	1.309914	0.351574	0.964592	0.954000	0.481667	0.784279	0.789444	0.895000	0.898538	0.777176	0.720423	0.649599	0.700000

Analyze Damage Ratios by Wind Speed¶

Let's see how damage ratios vary with wind speed index.

In [14]:

# Group by wind speed index and calculate mean damage ratios
df_by_wind_speed = df_detailed.groupby('wind_speed_index')[dr_columns].mean()
df_by_wind_speed

# Group by wind speed index and calculate mean damage ratios df_by_wind_speed = df_detailed.groupby('wind_speed_index')[dr_columns].mean() df_by_wind_speed

Out[14]:

	dr_total	dr_explicit	dr_implicit	dr_exp_wind_RC	dr_exp_wind_RS	dr_exp_wind_RWC	dr_exp_wind_O	dr_exp_wind_W	dr_imp_wind_I	dr_imp_wind_E	dr_imp_wind_M	dr_imp_wind_P	dr_imp_flood
wind_speed_index
0	0.297433	0.000000	0.297433	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.297433
1	0.297453	0.000086	0.297367	0.000635	0.000281	0.000000	0.000370	0.000000	0.000331	0.000160	0.000135	0.000124	0.297281
2	0.297477	0.000112	0.297365	0.000797	0.000333	0.000051	0.000486	0.000000	0.000418	0.000224	0.000163	0.000151	0.297255
3	0.297825	0.000175	0.297649	0.001115	0.000365	0.000062	0.000579	0.000208	0.001036	0.000442	0.000363	0.000297	0.297356
4	0.297801	0.000254	0.297547	0.001609	0.000453	0.000101	0.000856	0.000312	0.001012	0.000486	0.000572	0.000434	0.297266
5	0.298245	0.000387	0.297858	0.002031	0.000578	0.000169	0.001181	0.000729	0.002201	0.000831	0.000810	0.000553	0.297261
6	0.298437	0.000507	0.297930	0.002672	0.000672	0.000298	0.001366	0.001042	0.002751	0.001425	0.001164	0.000977	0.297110
7	0.299056	0.000720	0.298336	0.003484	0.000740	0.000479	0.001690	0.001771	0.004021	0.002393	0.001882	0.001359	0.297148
8	0.299710	0.000979	0.298731	0.005005	0.000865	0.000760	0.002500	0.002187	0.004754	0.002528	0.002588	0.001924	0.297304
9	0.300959	0.001408	0.299550	0.006708	0.001078	0.001667	0.003356	0.003229	0.006921	0.003590	0.002814	0.003184	0.297409
10	0.302040	0.001876	0.300164	0.008937	0.001281	0.002432	0.004236	0.004375	0.009073	0.005125	0.004567	0.003611	0.297312
11	0.303861	0.002519	0.301342	0.011896	0.001469	0.003491	0.004838	0.006354	0.012571	0.007227	0.006348	0.005423	0.297252
12	0.305689	0.003307	0.302382	0.015615	0.001812	0.005788	0.006111	0.007917	0.014790	0.008613	0.006763	0.006875	0.297421
13	0.308394	0.004617	0.303777	0.020958	0.002198	0.010698	0.007894	0.010521	0.019925	0.011298	0.010181	0.009255	0.296995
14	0.311815	0.006302	0.305512	0.029625	0.002807	0.016318	0.010694	0.013229	0.024600	0.015405	0.011634	0.011737	0.297041
15	0.316396	0.008635	0.307761	0.039573	0.003609	0.025377	0.014259	0.017292	0.030323	0.018970	0.016010	0.014611	0.297518
16	0.322582	0.011553	0.311030	0.055328	0.004630	0.039809	0.017778	0.020104	0.042372	0.025248	0.021499	0.018402	0.297209
17	0.330461	0.015345	0.315116	0.072344	0.005510	0.060512	0.022824	0.023958	0.056231	0.031330	0.027421	0.022825	0.297235
18	0.341980	0.020631	0.321349	0.091755	0.006521	0.088204	0.028819	0.032187	0.075499	0.042312	0.034559	0.030671	0.297450
19	0.357897	0.027560	0.330336	0.115229	0.007708	0.119803	0.037338	0.045729	0.104589	0.056733	0.047052	0.043477	0.297106
20	0.432066	0.062350	0.369716	0.231755	0.018349	0.276976	0.084653	0.110937	0.223118	0.127202	0.104651	0.095318	0.297086
21	0.553979	0.118585	0.435394	0.409974	0.041214	0.481256	0.168056	0.239375	0.392066	0.241355	0.207342	0.187989	0.297213
22	0.703885	0.186866	0.517019	0.621974	0.085099	0.642016	0.270162	0.435104	0.564181	0.396285	0.350096	0.317450	0.297407
23	0.858093	0.252448	0.605645	0.832068	0.152943	0.729741	0.360903	0.650104	0.722590	0.563578	0.504040	0.477434	0.297475
24	0.993829	0.307392	0.686437	0.945026	0.244708	0.770968	0.452708	0.850208	0.860498	0.728690	0.656190	0.610219	0.297194

Visualize Component Damage Contributions¶

Let's visualize how different components contribute to the total damage ratio.

In [15]:

import matplotlib.pyplot as plt

# Explicit wind damage components
explicit_cols = ['dr_exp_wind_RC', 'dr_exp_wind_RS', 'dr_exp_wind_RWC', 'dr_exp_wind_O', 'dr_exp_wind_W']
explicit_labels = ['Roof Coverings', 'Roof Sheathings', 'Roof-Wall Conn.', 'Openings', 'Walls']

# Group by wind speed and get mean
df_wind_grouped = df_detailed.groupby('wind_speed_index')[explicit_cols].mean()

# Create the plot
fig, ax = plt.subplots(figsize=(12, 6))
df_wind_grouped.plot(kind='bar', ax=ax, width=0.8)
ax.set_xlabel('Wind Speed Index')
ax.set_ylabel('Mean Damage Ratio')
ax.set_title('Explicit Wind Damage Ratios by Wind Speed Index')
ax.legend(explicit_labels, loc='upper left', bbox_to_anchor=(1, 1))
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt # Explicit wind damage components explicit_cols = ['dr_exp_wind_RC', 'dr_exp_wind_RS', 'dr_exp_wind_RWC', 'dr_exp_wind_O', 'dr_exp_wind_W'] explicit_labels = ['Roof Coverings', 'Roof Sheathings', 'Roof-Wall Conn.', 'Openings', 'Walls'] # Group by wind speed and get mean df_wind_grouped = df_detailed.groupby('wind_speed_index')[explicit_cols].mean() # Create the plot fig, ax = plt.subplots(figsize=(12, 6)) df_wind_grouped.plot(kind='bar', ax=ax, width=0.8) ax.set_xlabel('Wind Speed Index') ax.set_ylabel('Mean Damage Ratio') ax.set_title('Explicit Wind Damage Ratios by Wind Speed Index') ax.legend(explicit_labels, loc='upper left', bbox_to_anchor=(1, 1)) plt.xticks(rotation=0) plt.tight_layout() plt.show()

Compare Explicit vs Implicit Damage¶

Let's compare the explicit and implicit damage contributions across wind speeds.

In [16]:

# Group by wind speed and get mean for total, explicit, and implicit
comparison_cols = ['dr_total', 'dr_explicit', 'dr_implicit']
df_comparison = df_detailed.groupby('wind_speed_index')[comparison_cols].mean()

# Create the plot
fig, ax = plt.subplots(figsize=(10, 6))
df_comparison.plot(kind='line', ax=ax, marker='o', linewidth=2, markersize=8)
ax.set_xlabel('Wind Speed Index')
ax.set_ylabel('Mean Damage Ratio')
ax.set_title('Total, Explicit, and Implicit Damage Ratios by Wind Speed Index')
ax.legend(['Total', 'Explicit (Wind)', 'Implicit (Interior + Flood)'])
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# Group by wind speed and get mean for total, explicit, and implicit comparison_cols = ['dr_total', 'dr_explicit', 'dr_implicit'] df_comparison = df_detailed.groupby('wind_speed_index')[comparison_cols].mean() # Create the plot fig, ax = plt.subplots(figsize=(10, 6)) df_comparison.plot(kind='line', ax=ax, marker='o', linewidth=2, markersize=8) ax.set_xlabel('Wind Speed Index') ax.set_ylabel('Mean Damage Ratio') ax.set_title('Total, Explicit, and Implicit Damage Ratios by Wind Speed Index') ax.legend(['Total', 'Explicit (Wind)', 'Implicit (Interior + Flood)']) ax.grid(True, alpha=0.3) plt.tight_layout() plt.show()

Reset Configuration¶

After the analysis, you may want to reset the configuration to default values.

In [17]:

# Reset all configuration to defaults
Config.reset_flags()
print(f"EXPORT_DETAILED_DAMAGE_RATIOS after reset: {Config.EXPORT_DETAILED_DAMAGE_RATIOS}")

# Reset all configuration to defaults Config.reset_flags() print(f"EXPORT_DETAILED_DAMAGE_RATIOS after reset: {Config.EXPORT_DETAILED_DAMAGE_RATIOS}")

EXPORT_DETAILED_DAMAGE_RATIOS after reset: False

Summary¶

This notebook demonstrated how to:

1. Enable detailed damage ratio export: Set Config.EXPORT_DETAILED_DAMAGE_RATIOS = True
2. Run the analysis: Execute Monte Carlo simulation which now also exports CSV files
3. Load and explore: Use pandas to load and analyze the detailed component-level damage ratios
4. Visualize: Create plots showing damage ratio contributions by component and wind speed

Output Files¶

The detailed damage ratio CSV files are saved to Config.OUTPUTS_PATH with the naming pattern:

• DetailedDR_m{building_type}_c{cindex}_iter{NUM_OF_ITERATIONS}.csv

Performance Note¶

Collecting detailed damage ratios is computationally more expensive than the standard analysis. Only enable EXPORT_DETAILED_DAMAGE_RATIOS when you need component-level breakdown for analysis or debugging.