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Suggested fix for ImpfSetTropCyclone.get_countries_per_region() #1105 #1106

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Suggested fix for ImpfSetTropCyclone.get_countries_per_region() #1105 #1106

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aed57cf
Suggested fix
spjuhel Nov 4, 2025
4f77d6e
Lukas suggestions
spjuhel Dec 19, 2025
09ffb57
adds test for "all" and None
spjuhel Dec 19, 2025
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121 changes: 121 additions & 0 deletions climada/entity/impact_funcs/test/test_tc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -168,6 +168,127 @@ def test_get_countries_per_region(self):
self.assertListEqual(out[2], [124, 840])
self.assertListEqual(out[3], ["CAN", "USA"])

def test_get_countries_per_region_all_or_none(self):
ifs = ImpfSetTropCyclone()
out = ifs.get_countries_per_region()
out2 = ifs.get_countries_per_region("all")
self.assertEqual(out, out2)
self.assertDictEqual(
out[0],
{
"NA1": "Caribbean and Mexico",
"NA2": "USA and Canada",
"NI": "North Indian",
"OC": "Oceania",
"SI": "South Indian",
"WP1": "South East Asia",
"WP2": "Philippines",
"WP3": "China Mainland",
"WP4": "North West Pacific",
"ROW": "Rest of The World",
},
)
self.assertDictEqual(
out[1],
{
"NA1": 1,
"NA2": 2,
"NI": 3,
"OC": 4,
"SI": 5,
"WP1": 6,
"WP2": 7,
"WP3": 8,
"WP4": 9,
"ROW": 10,
},
)
# fmt: off
self.assertDictEqual(out[2],
{'NA1': [
533, 660, 32, 28, 44, 84, 60, 68, 52, 152, 170, 132, 188, 192,
136, 212, 214, 218, 238, 312, 308, 320, 254, 328, 340, 332,
388, 659, 662, 484, 500, 474, 558, 591, 604, 630, 600, 654,
222, 740, 534, 796, 780, 858, 670, 862, 92, 850
],
'NA2': [124, 840],
'NI': [
4, 784, 51, 31, 50, 48, 64, 262, 232, 231, 268, 356, 364,
368, 376, 400, 398, 417, 414, 422, 144, 462, 104, 496,
524, 512, 586, 634, 682, 706, 760, 762, 795, 800, 860, 887
],
'OC': [
16, 36, 184, 242, 583, 316, 296, 584, 580, 540, 574,
570, 520, 554, 612, 585, 598, 258, 90, 772, 626, 776,
798, 548, 876, 882
],
'SI': [
180, 174, 450, 466, 508, 480, 454, 748, 834, 710, 716
],
'WP1': [116, 360, 418, 458, 764, 704],
'WP2': [608],
'WP3': [156],
'WP4': [344, 392, 410, 446, 158],
'ROW': [
24, 248, 8, 20, 10, 260, 40, 108, 56, 204, 535, 854,
100, 70, 652, 112, 76, 96, 74, 72, 140, 166, 756, 384,
120, 178, 531, 162, 196, 203, 276, 208, 12, 818, 732,
724, 233, 246, 250, 234, 266, 826, 831, 288, 292, 324,
270, 624, 226, 300, 304, 334, 191, 348, 833, 86, 372,
352, 380, 832, 404, 430, 434, 438, 426, 440, 442, 428,
663, 504, 492, 498, 807, 470, 499, 478, 175, 516, 562,
566, 528, 578, 616, 408, 620, 275, 638, 642, 643, 646,
729, 686, 702, 239, 744, 694, 674, 666, 688, 728, 678,
703, 705, 752, 690, 148, 768, 788, 792, 804, 581, 336,
983, 894
]})

self.assertDictEqual(out[3],
{
"NA1": [
"ABW", "AIA", "ARG", "ATG", "BHS", "BLZ", "BMU", "BOL", "BRB", "CHL", "COL",
"CPV", "CRI", "CUB", "CYM", "DMA", "DOM", "ECU", "FLK", "GLP", "GRD", "GTM",
"GUF", "GUY", "HND", "HTI", "JAM", "KNA", "LCA", "MEX", "MSR", "MTQ", "NIC",
"PAN", "PER", "PRI", "PRY", "SHN", "SLV", "SUR", "SXM", "TCA", "TTO", "URY",
"VCT", "VEN", "VGB", "VIR",
],
"NA2": ["CAN", "USA"],
"NI": [
"AFG", "ARE", "ARM", "AZE", "BGD", "BHR", "BTN", "DJI", "ERI", "ETH", "GEO",
"IND", "IRN", "IRQ", "ISR", "JOR", "KAZ", "KGZ", "KWT", "LBN", "LKA", "MDV",
"MMR", "MNG", "NPL", "OMN", "PAK", "QAT", "SAU", "SOM", "SYR", "TJK", "TKM",
"UGA", "UZB", "YEM",
],
"OC": [
"ASM", "AUS", "COK", "FJI", "FSM", "GUM", "KIR", "MHL", "MNP", "NCL", "NFK",
"NIU", "NRU", "NZL", "PCN", "PLW", "PNG", "PYF", "SLB", "TKL", "TLS", "TON",
"TUV", "VUT", "WLF", "WSM",
],
"SI": [
"COD", "COM", "MDG", "MLI", "MOZ", "MUS", "MWI", "SWZ", "TZA", "ZAF", "ZWE",
],
"WP1": ["KHM", "IDN", "LAO", "MYS", "THA", "VNM"],
"WP2": ["PHL"],
"WP3": ["CHN"],
"WP4": ["HKG", "JPN", "KOR", "MAC", "TWN"],
"ROW": [
"AGO", "ALA", "ALB", "AND", "ATA", "ATF", "AUT", "BDI", "BEL", "BEN", "BES",
"BFA", "BGR", "BIH", "BLM", "BLR", "BRA", "BRN", "BVT", "BWA", "CAF", "CCK",
"CHE", "CIV", "CMR", "COG", "CUW", "CXR", "CYP", "CZE", "DEU", "DNK", "DZA",
"EGY", "ESH", "ESP", "EST", "FIN", "FRA", "FRO", "GAB", "GBR", "GGY", "GHA",
"GIB", "GIN", "GMB", "GNB", "GNQ", "GRC", "GRL", "HMD", "HRV", "HUN", "IMN",
"IOT", "IRL", "ISL", "ITA", "JEY", "KEN", "LBR", "LBY", "LIE", "LSO", "LTU",
"LUX", "LVA", "MAF", "MAR", "MCO", "MDA", "MKD", "MLT", "MNE", "MRT", "MYT",
"NAM", "NER", "NGA", "NLD", "NOR", "POL", "PRK", "PRT", "PSE", "REU", "ROU",
"RUS", "RWA", "SDN", "SEN", "SGP", "SGS", "SJM", "SLE", "SMR", "SPM", "SRB",
"SSD", "STP", "SVK", "SVN", "SWE", "SYC", "TCD", "TGO", "TUN", "TUR", "UKR",
"UMI", "VAT", "XKO", "ZMB",
],
}
)

# fmt: on

def test_get_imf_id_regions_per_countries(self):
"""Test get_impf_id_regions_per_countries()"""
ifs = ImpfSetTropCyclone()
Expand Down
25 changes: 19 additions & 6 deletions climada/entity/impact_funcs/trop_cyclone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,7 +92,7 @@ class CountryCode(Enum):
"NAM", "NER", "NGA", "NLD", "NOR", "POL", "PRK", "PRT", "PSE", "REU", "ROU",
"RUS", "RWA", "SDN", "SEN", "SGP", "SGS", "SJM", "SLE", "SMR", "SPM", "SRB",
"SSD", "STP", "SVK", "SVN", "SWE", "SYC", "TCD", "TGO", "TUN", "TUR", "UKR",
"UMI", "VAT", "XKX", "ZMB",
"UMI", "VAT", "XKO", "ZMB",
],
}

Expand Down Expand Up @@ -365,8 +365,18 @@ def calibrated_regional_vhalf(

@staticmethod
def get_countries_per_region(region=None):
"""Returns dictionaries with numerical (numeric) and alphabetical (alpha3) ISO3 codes
of all countries associated to a calibration region.
"""Returns countries within a TC calibration region and associated impact functions.

This method returns a tuple with numerical (numeric) and alphabetical (alpha3)
ISO3 codes of all countries associated to a calibration region.

If no region or "all" is provided as argument, the method return a tuple of
dictionaries with short name of the tropical cyclone calibration regions as
keys and the values for each of those.

Notes
-----

Only contains countries that were affected by tropical cyclones
between 1980 and 2017 according to EM-DAT.

Expand Down Expand Up @@ -395,9 +405,12 @@ def get_countries_per_region(region=None):
return (
CountryCode.REGION_NAME.value,
CountryCode.IMPF_ID.value,
coordinates.country_to_iso(
CountryCode.ALPHA3.value, representation="numeric"
),
{
reg: coordinates.country_to_iso(
CountryCode.ALPHA3.value[reg], representation="numeric"
)
for reg in CountryCode.REGION_NAME.value
},
CountryCode.ALPHA3.value,
)

Expand Down
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