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added risk data for june 2026 #29

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556 changes: 368 additions & 188 deletions RiskScoreModel/data/MASTER_VARIABLES.csv
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556 changes: 368 additions & 188 deletions RiskScoreModel/data/factor_scores_l1_exposure.csv
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17,670 changes: 8,925 additions & 8,745 deletions RiskScoreModel/data/factor_scores_l1_flood-hazard.csv
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6,214 changes: 3,197 additions & 3,017 deletions RiskScoreModel/data/factor_scores_l1_government-response.csv
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556 changes: 368 additions & 188 deletions RiskScoreModel/data/factor_scores_l1_vulnerability.csv
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22,502 changes: 11,341 additions & 11,161 deletions RiskScoreModel/data/risk_score.csv
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26,877 changes: 13,546 additions & 13,331 deletions RiskScoreModel/data/risk_score_final_district.csv
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16 changes: 8 additions & 8 deletions RiskScoreModel/scripts/govtresponse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,16 +22,16 @@ def get_financial_year(timeperiod):
#INPUT VARS
government_response_vars = ["total_tender_awarded_value",
#"total_expenditure_value",
#"SOPD_tenders_awarded_value",
"SOPD_tenders_awarded_value",
"SDRF_sanctions_awarded_value",
"SDRF_tenders_awarded_value",
#"RIDF_tenders_awarded_value",
#"LTIF_tenders_awarded_value",
# "CIDF_tenders_awarded_value",
# "Preparedness Measures_tenders_awarded_value",
# "Immediate Measures_tenders_awarded_value",
# "Others_tenders_awarded_value",
#'Repair and Restoration_tenders_awarded_value'
"RIDF_tenders_awarded_value",
"LTIF_tenders_awarded_value",
"CIDF_tenders_awarded_value",
"Preparedness Measures_tenders_awarded_value",
"Immediate Measures_tenders_awarded_value",
"Others_tenders_awarded_value",
'Repair and Restoration_tenders_awarded_value'
]

# Find cumsum in each FY of the government response vars
Expand Down
13 changes: 9 additions & 4 deletions RiskScoreModel/scripts/topis_riskscore_district.py
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Expand Up @@ -12,7 +12,7 @@
## MASTER DATA WITH FACTOR SCORES
print(os.getcwd())
## INPUT: FACTOR SCORES CSV
factor_scores_dfs = glob.glob(os.getcwd()+r'/RiskScoreModel/data/factor_scores_l1*.csv')
factor_scores_dfs = glob.glob(os.getcwd()+ '/RiskScoreModel/data/factor_scores_l1*.csv')

# Select only the columns that exist in both the DataFrame and the list
factors = ['exposure', 'flood-hazard', 'vulnerability', 'government-response']
Expand Down Expand Up @@ -46,9 +46,14 @@
cumulative_vars = [
"total_tender_awarded_value",
"SDRF_sanctions_awarded_value",
"SOPD_tenders_awarded_value",
"RIDF_tenders_awarded_value",
"LTIF_tenders_awarded_value",
"CIDF_tenders_awarded_value",
"SDRF_tenders_awarded_value",
"Preparedness Measures_tenders_awarded_value",
"Immediate Measures_tenders_awarded_value",
"Repair and Restoration_tenders_awarded_value",
"Others_tenders_awarded_value"
]

Expand Down Expand Up @@ -83,10 +88,10 @@
topsis.columns = [col.lower().replace('_', '-').replace(' ', '-') for col in topsis.columns]
print(topsis.columns)

topsis.to_csv(os.getcwd()+r'/RiskScoreModel/data/risk_score.csv', index=False)
topsis.to_csv(os.getcwd()+ '/RiskScoreModel/data/risk_score.csv', index=False)

## DISTRICT LEVEL SCORES
dist_ids = pd.read_csv(os.getcwd()+r'/RiskScoreModel/assets/district_objectid.csv')
dist_ids = pd.read_csv(os.getcwd()+ '/RiskScoreModel/assets/district_objectid.csv')

compositescorelabels = ['1','2','3','4','5']

Expand Down Expand Up @@ -401,7 +406,7 @@ def apply_rounding_rules(df, rounding_rules):
final["total-infrastructure-damage"] = final["total-house-fully-damaged"] + final["roads"] + final["bridge"]
final["total-female-population"] = final["sum-population"]* final["mean-sex-ratio"]/(1000 + final["mean-sex-ratio"])
final.rename(columns={'preparedness-measures-tenders-awarded-value': 'restoration-measures-tenders-awarded-value'}, inplace=True)
final.to_csv(os.getcwd()+r'/RiskScoreModel/data/risk_score_final_district.csv', index=False)
final.to_csv(os.getcwd()+ '/RiskScoreModel/data/risk_score_final_district.csv', index=False)

#dist.rename(columns={'preparedness-measures-tenders-awarded-value': 'restoration-measures-tenders-awarded-value'}, inplace=True)
#dist.to_csv(os.getcwd()+r'/IDS-DRR-Assam/RiskScoreModel/data/risk_score_final_dist.csv', index=False)
Expand Down