|
| 1 | +""" |
| 2 | +This file is part of CLIMADA. |
| 3 | +
|
| 4 | +Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. |
| 5 | +
|
| 6 | +CLIMADA is free software: you can redistribute it and/or modify it under the |
| 7 | +terms of the GNU General Public License as published by the Free |
| 8 | +Software Foundation, version 3. |
| 9 | +
|
| 10 | +CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY |
| 11 | +WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A |
| 12 | +PARTICULAR PURPOSE. See the GNU General Public License for more details. |
| 13 | +
|
| 14 | +You should have received a copy of the GNU General Public License along |
| 15 | +with CLIMADA. If not, see <https://www.gnu.org/licenses/>. |
| 16 | +--- |
| 17 | +
|
| 18 | +A set of reusable fixtures for testing purpose. |
| 19 | +
|
| 20 | +The objective of this file is to provide minimalistic, understandable and consistent |
| 21 | +default objects for unit and integration testing. |
| 22 | +
|
| 23 | +Values are chosen such that: |
| 24 | + - Exposure value of the first points is 0. (First location should always have 0 impacts) |
| 25 | + - Category / Group id of all points is 1, except for third point, valued at 2000 (Impacts on that category are always a share of 2000) |
| 26 | + - Hazard centroids are the exposure centroids shifted by `HAZARD_JITTER` on both lon and lat. |
| 27 | + - There are 4 events, with frequencies == 0.03, 0.01, 0.006, 0.004, 0, |
| 28 | + such that impacts for RP250, 100 and 50 and 20 are at_event, |
| 29 | + (freq sorted cumulate to 1/250, 1/100, 1/50 and 1/20). |
| 30 | + - Hazard intensity is: |
| 31 | + * Event 1: zero everywhere (always no impact) |
| 32 | + * Event 2: max intensity at first centroid (also always no impact (first centroid is 0)) |
| 33 | + * Event 3: half max intensity at second centroid (impact == half second centroid) |
| 34 | + * Event 4: quarter max intensity everywhere (impact == 1/4 total value) |
| 35 | + * Event 5: max intensity everywhere (but zero frequency) |
| 36 | + With max intensity set at 100 |
| 37 | + - Impact function is the "identity function", x intensity is x% damages |
| 38 | + - Impact values should be: |
| 39 | + * AAI = 18 = 1000*1/2*0.006+(1000+2000+3000+4000+5000)*0.25*0.004 |
| 40 | + * RP20 = event1 = 0 |
| 41 | + * RP50 = event2 = 0 |
| 42 | + * RP100 = event3 = 500 = 1000*1/2 |
| 43 | + * RP250 = event4 = 3750 = (1000+2000+3000+4000+5000)*0.25 |
| 44 | +
|
| 45 | +""" |
| 46 | + |
| 47 | +import geopandas as gpd |
| 48 | +import numpy as np |
| 49 | +import pytest |
| 50 | +from scipy.sparse import csr_matrix |
| 51 | + |
| 52 | +from climada.entity import Exposures, ImpactFunc, ImpactFuncSet |
| 53 | +from climada.hazard import Centroids, Hazard |
| 54 | + |
| 55 | +# --------------------------------------------------------------------------- |
| 56 | +# Coordinate system and metadata |
| 57 | +# --------------------------------------------------------------------------- |
| 58 | +CRS_WGS84 = "EPSG:4326" |
| 59 | + |
| 60 | +# --------------------------------------------------------------------------- |
| 61 | +# Exposure attributes |
| 62 | +# --------------------------------------------------------------------------- |
| 63 | +EXP_DESC = "Test exposure dataset" |
| 64 | +EXPOSURE_REF_YEAR = 2020 |
| 65 | +EXPOSURE_VALUE_UNIT = "USD" |
| 66 | +VALUES = np.array([0, 1000, 2000, 3000, 4000, 5000]) |
| 67 | +CATEGORIES = np.array([1, 1, 2, 1, 1, 3]) |
| 68 | + |
| 69 | +# Exposure coordinates |
| 70 | +EXP_LONS = np.array([4, 4.25, 4.5, 4, 4.25, 4.5]) |
| 71 | +EXP_LATS = np.array([33, 33, 33, 33.25, 33.25, 33.25]) |
| 72 | + |
| 73 | +# --------------------------------------------------------------------------- |
| 74 | +# Hazard definition |
| 75 | +# --------------------------------------------------------------------------- |
| 76 | +HAZARD_TYPE = "TEST_HAZARD_TYPE" |
| 77 | +HAZARD_UNIT = "TEST_HAZARD_UNIT" |
| 78 | + |
| 79 | +# Hazard centroid positions |
| 80 | +HAZ_JITTER = 0.1 # To test centroid matching |
| 81 | +HAZ_LONS = EXP_LONS + HAZ_JITTER |
| 82 | +HAZ_LATS = EXP_LATS + HAZ_JITTER |
| 83 | + |
| 84 | +# Hazard events |
| 85 | +EVENT_IDS = np.array([1, 2, 3, 4, 5]) |
| 86 | +EVENT_NAMES = ["ev1", "ev2", "ev3", "ev4", "ev5"] |
| 87 | +DATES = np.array([1, 2, 3, 4, 5]) |
| 88 | + |
| 89 | +# Frequency are choosen so that they cumulate nicely |
| 90 | +# to correspond to 250, 100, 50, and 20y return periods (for impacts) |
| 91 | +FREQUENCY = np.array([0.03, 0.01, 0.006, 0.004, 0.0]) |
| 92 | +FREQUENCY_UNIT = "1/year" |
| 93 | + |
| 94 | +# Hazard maximum intensity |
| 95 | +# 100 to match 0 to 100% idea |
| 96 | +# also in line with linear 1:1 impact function |
| 97 | +# for easy mental calculus |
| 98 | +HAZARD_MAX_INTENSITY = 100 |
| 99 | + |
| 100 | +# --------------------------------------------------------------------------- |
| 101 | +# Impact function |
| 102 | +# --------------------------------------------------------------------------- |
| 103 | +IMPF_ID = 1 |
| 104 | +IMPF_NAME = "IMPF_1" |
| 105 | + |
| 106 | + |
| 107 | +@pytest.fixture |
| 108 | +def exposures_factory(): |
| 109 | + def _make_exposures( |
| 110 | + values=VALUES, |
| 111 | + exp_lons=EXP_LONS, |
| 112 | + exp_lats=EXP_LATS, |
| 113 | + value_factor=1.0, |
| 114 | + ref_year=EXPOSURE_REF_YEAR, |
| 115 | + hazard_type=HAZARD_TYPE, |
| 116 | + group_id=None, |
| 117 | + crs=CRS_WGS84, |
| 118 | + impf_id=IMPF_ID, |
| 119 | + description=EXP_DESC, |
| 120 | + value_unit=EXPOSURE_VALUE_UNIT, |
| 121 | + categories=None, |
| 122 | + ): |
| 123 | + gdf = gpd.GeoDataFrame( |
| 124 | + { |
| 125 | + "value": values * value_factor, |
| 126 | + f"impf_{hazard_type}": impf_id, |
| 127 | + "category": categories, |
| 128 | + "geometry": gpd.points_from_xy(exp_lons, exp_lats, crs=crs), |
| 129 | + }, |
| 130 | + crs=crs, |
| 131 | + ) |
| 132 | + if group_id is not None: |
| 133 | + gdf["group_id"] = group_id |
| 134 | + |
| 135 | + return Exposures( |
| 136 | + data=gdf, |
| 137 | + description=description, |
| 138 | + ref_year=ref_year, |
| 139 | + value_unit=value_unit, |
| 140 | + ) |
| 141 | + |
| 142 | + return _make_exposures |
| 143 | + |
| 144 | + |
| 145 | +@pytest.fixture |
| 146 | +def exposures(exposures_factory): |
| 147 | + return exposures_factory() |
| 148 | + |
| 149 | + |
| 150 | +def hazard_frequency_factory(base=FREQUENCY): |
| 151 | + def _make_frequency(scale=1.0): |
| 152 | + return base * scale |
| 153 | + |
| 154 | + return _make_frequency |
| 155 | + |
| 156 | + |
| 157 | +def hazard_frequency(): |
| 158 | + return hazard_frequency_factory() |
| 159 | + |
| 160 | + |
| 161 | +def hazard_intensity(max_intensity=HAZARD_MAX_INTENSITY, scale=1.0): |
| 162 | + """ |
| 163 | + Intensity matrix designed for analytical expectations: |
| 164 | + - Event 1: zero |
| 165 | + - Event 2: max intensity at first centroid |
| 166 | + - Event 3: half max intensity at second centroid |
| 167 | + - Event 4: quarter max intensity everywhere |
| 168 | + """ |
| 169 | + base = csr_matrix( |
| 170 | + [ |
| 171 | + [0, 0, 0, 0, 0, 0], |
| 172 | + [max_intensity, 0, 0, 0, 0, 0], |
| 173 | + [0, max_intensity / 2, 0, 0, 0, 0], |
| 174 | + [ |
| 175 | + max_intensity / 4, |
| 176 | + max_intensity / 4, |
| 177 | + max_intensity / 4, |
| 178 | + max_intensity / 4, |
| 179 | + max_intensity / 4, |
| 180 | + max_intensity / 4, |
| 181 | + ], |
| 182 | + [ |
| 183 | + max_intensity, |
| 184 | + max_intensity, |
| 185 | + max_intensity, |
| 186 | + max_intensity, |
| 187 | + max_intensity, |
| 188 | + max_intensity, |
| 189 | + ], |
| 190 | + ] |
| 191 | + ) |
| 192 | + |
| 193 | + return base * scale |
| 194 | + |
| 195 | + |
| 196 | +@pytest.fixture |
| 197 | +def centroids(): |
| 198 | + return Centroids(lat=HAZ_LATS, lon=HAZ_LONS, crs=CRS_WGS84) |
| 199 | + |
| 200 | + |
| 201 | +@pytest.fixture |
| 202 | +def hazard_factory(): |
| 203 | + def _make_hazard( |
| 204 | + intensity_matrix=None, |
| 205 | + frequency_array=FREQUENCY, |
| 206 | + max_intensity=HAZARD_MAX_INTENSITY, |
| 207 | + centroids=None, |
| 208 | + intensity_scale=1.0, |
| 209 | + frequency_scale=1.0, |
| 210 | + hazard_type=HAZARD_TYPE, |
| 211 | + hazard_unit=HAZARD_UNIT, |
| 212 | + lat=HAZ_LATS, |
| 213 | + lon=HAZ_LONS, |
| 214 | + crs=CRS_WGS84, |
| 215 | + event_id=EVENT_IDS, |
| 216 | + event_name=EVENT_NAMES, |
| 217 | + date=DATES, |
| 218 | + frequency_unit=FREQUENCY_UNIT, |
| 219 | + ): |
| 220 | + if intensity_matrix is None: |
| 221 | + intensity_matrix = hazard_intensity(max_intensity, intensity_scale) |
| 222 | + |
| 223 | + if centroids is None: |
| 224 | + centroids = Centroids(lat=lat, lon=lon, crs=crs) |
| 225 | + |
| 226 | + return Hazard( |
| 227 | + haz_type=hazard_type, |
| 228 | + units=hazard_unit, |
| 229 | + centroids=centroids, |
| 230 | + event_id=event_id, |
| 231 | + event_name=event_name, |
| 232 | + date=date, |
| 233 | + frequency=frequency_array * frequency_scale, |
| 234 | + frequency_unit=frequency_unit, |
| 235 | + intensity=intensity_matrix, |
| 236 | + ) |
| 237 | + |
| 238 | + return _make_hazard |
| 239 | + |
| 240 | + |
| 241 | +@pytest.fixture |
| 242 | +def hazard(hazard_factory): |
| 243 | + return hazard_factory() |
| 244 | + |
| 245 | + |
| 246 | +@pytest.fixture |
| 247 | +def impf_factory(): |
| 248 | + def _make_impf( |
| 249 | + paa_scale=1.0, |
| 250 | + max_intensity=HAZARD_MAX_INTENSITY, |
| 251 | + hazard_type=HAZARD_TYPE, |
| 252 | + hazard_unit=HAZARD_UNIT, |
| 253 | + impf_id=IMPF_ID, |
| 254 | + negative_intensities=False, |
| 255 | + ): |
| 256 | + intensity = np.array([0, max_intensity / 2, max_intensity]) |
| 257 | + mdd = np.array([0, 0.5, 1]) |
| 258 | + if negative_intensities: |
| 259 | + intensity = np.flip(intensity) * -1 |
| 260 | + mdd = np.flip(mdd) |
| 261 | + return ImpactFunc( |
| 262 | + haz_type=hazard_type, |
| 263 | + intensity_unit=hazard_unit, |
| 264 | + name=IMPF_NAME, |
| 265 | + intensity=intensity, |
| 266 | + mdd=mdd, |
| 267 | + paa=np.array([1, 1, 1]) * paa_scale, |
| 268 | + id=impf_id, |
| 269 | + ) |
| 270 | + |
| 271 | + return _make_impf |
| 272 | + |
| 273 | + |
| 274 | +@pytest.fixture |
| 275 | +def linear_impact_function(impf_factory): |
| 276 | + return impf_factory() |
| 277 | + |
| 278 | + |
| 279 | +@pytest.fixture |
| 280 | +def impfset_factory(impf_factory): |
| 281 | + def _make_impfset( |
| 282 | + paa_scale=1.0, |
| 283 | + max_intensity=HAZARD_MAX_INTENSITY, |
| 284 | + hazard_type=HAZARD_TYPE, |
| 285 | + hazard_unit=HAZARD_UNIT, |
| 286 | + impf_id=IMPF_ID, |
| 287 | + negative_intensities=False, |
| 288 | + ): |
| 289 | + return ImpactFuncSet( |
| 290 | + [ |
| 291 | + impf_factory( |
| 292 | + paa_scale, |
| 293 | + max_intensity, |
| 294 | + hazard_type, |
| 295 | + hazard_unit, |
| 296 | + impf_id, |
| 297 | + negative_intensities, |
| 298 | + ) |
| 299 | + ] |
| 300 | + ) |
| 301 | + |
| 302 | + return _make_impfset |
| 303 | + |
| 304 | + |
| 305 | +@pytest.fixture |
| 306 | +def impfset(impfset_factory): |
| 307 | + return impfset_factory() |
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