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Move data helper functions to new module; update/fix docstrings #1603

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Claire Simpson 2025-04-11 14:59:45 -06:00
parent 7385566b0a
commit 903c16e37f
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tests/crop_production/data_helpers.py Normal file
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"""Helper module for generating test data related to Crop Production tests."""
import pandas
def sample_nutrient_df():
"""Generate a sample nutrient DataFrame for crops.
This function creates a small DataFrame containing example nutrient
and production data for crops such as corn and soybean. It can be
used for testing nutrient calculations and validating model outputs.
Returns:
pandas.DataFrame: Nutrient and production data indexed by crop name.
"""
return pandas.DataFrame([
{'crop': 'corn', 'area (ha)': 21.0, 'production_observed': 0.2,
'percentrefuse': 7, 'protein': 42., 'lipid': 8, 'energy': 476.,
'ca': 27.0, 'fe': 15.7, 'mg': 280.0, 'ph': 704.0, 'k': 1727.0,
'na': 2.0, 'zn': 4.9, 'cu': 1.9, 'fl': 8, 'mn': 2.9, 'se': 0.1,
'vita': 3.0, 'betac': 16.0, 'alphac': 2.30, 'vite': 0.8,
'crypto': 1.6, 'lycopene': 0.36, 'lutein': 63.0, 'betat': 0.5,
'gammat': 2.1, 'deltat': 1.9, 'vitc': 6.8, 'thiamin': 0.4,
'riboflavin': 1.8, 'niacin': 8.2, 'pantothenic': 0.9,
'vitb6': 1.4, 'folate': 385.0, 'vitb12': 2.0, 'vitk': 41.0},
{'crop': 'soybean', 'area (ha)': 5., 'production_observed': 4.,
'percentrefuse': 9, 'protein': 33., 'lipid': 2., 'energy': 99.,
'ca': 257., 'fe': 15.7, 'mg': 280., 'ph': 704.0, 'k': 197.0,
'na': 2., 'zn': 4.9, 'cu': 1.6, 'fl': 3., 'mn': 5.2, 'se': 0.3,
'vita': 3.0, 'betac': 16.0, 'alphac': 1.0, 'vite': 0.8,
'crypto': 0.6, 'lycopene': 0.3, 'lutein': 61.0, 'betat': 0.5,
'gammat': 2.3, 'deltat': 1.2, 'vitc': 3.0, 'thiamin': 0.42,
'riboflavin': 0.82, 'niacin': 12.2, 'pantothenic': 0.92,
'vitb6': 5.4, 'folate': 305., 'vitb12': 3., 'vitk': 42.},
]).set_index('crop')
def tabulate_regr_results_table():
"""Generate expected output for unit tests of tabulated regression results.
This function returns a DataFrame that represents the expected output
of the `tabulate_regression_results` function, with the parameters
defined in the unit test `test_tabulate_regression_results`.
Returns:
pandas.DataFrame: Expected tabulated results for regression output.
"""
return pandas.DataFrame([
{'crop': 'corn', 'area (ha)': 20.0,
'production_observed': 80.0, 'production_modeled': 40.0,
'protein_modeled': 15624000.0, 'protein_observed': 31248000.0,
'lipid_modeled': 2976000.0, 'lipid_observed': 5952000.0,
'energy_modeled': 177072000.0, 'energy_observed': 354144000.0,
'ca_modeled': 10044000.0, 'ca_observed': 20088000.0,
'fe_modeled': 5840400.0, 'fe_observed': 11680800.0,
'mg_modeled': 104160000.0, 'mg_observed': 208320000.0,
'ph_modeled': 261888000.0, 'ph_observed': 523776000.0,
'k_modeled': 642444000.0, 'k_observed': 1284888000.0,
'na_modeled': 744000.0, 'na_observed': 1488000.0,
'zn_modeled': 1822800.0, 'zn_observed': 3645600.0,
'cu_modeled': 706800.0, 'cu_observed': 1413600.0,
'fl_modeled': 2976000.0, 'fl_observed': 5952000.0,
'mn_modeled': 1078800.0, 'mn_observed': 2157600.0,
'se_modeled': 37200.0, 'se_observed': 74400.0,
'vita_modeled': 1116000.0, 'vita_observed': 2232000.0,
'betac_modeled': 5952000.0, 'betac_observed': 11904000.0,
'alphac_modeled': 855600.0, 'alphac_observed': 1711200.0,
'vite_modeled': 297600.0, 'vite_observed': 595200.0,
'crypto_modeled': 595200.0, 'crypto_observed': 1190400.0,
'lycopene_modeled': 133920.0, 'lycopene_observed': 267840.0,
'lutein_modeled': 23436000.0, 'lutein_observed': 46872000.0,
'betat_modeled': 186000.0, 'betat_observed': 372000.0,
'gammat_modeled': 781200.0, 'gammat_observed': 1562400.0,
'deltat_modeled': 706800.0, 'deltat_observed': 1413600.0,
'vitc_modeled': 2529600.0, 'vitc_observed': 5059200.0,
'thiamin_modeled': 148800.0, 'thiamin_observed': 297600.0,
'riboflavin_modeled': 669600.0, 'riboflavin_observed': 1339200.0,
'niacin_modeled': 3050400.0, 'niacin_observed': 6100800.0,
'pantothenic_modeled': 334800.0, 'pantothenic_observed': 669600.0,
'vitb6_modeled': 520800.0, 'vitb6_observed': 1041600.0,
'folate_modeled': 143220000.0, 'folate_observed': 286440000.0,
'vitb12_modeled': 744000.0, 'vitb12_observed': 1488000.0,
'vitk_modeled': 15252000.0, 'vitk_observed': 30504000.0},
{'crop': 'soybean', 'area (ha)': 40.0,
'production_observed': 120.0, 'production_modeled': 70.0,
'protein_modeled': 21021000.0, 'protein_observed': 36036000.0,
'lipid_modeled': 1274000.0, 'lipid_observed': 2184000.0,
'energy_modeled': 63063000.0, 'energy_observed': 108108000.0,
'ca_modeled': 163709000.0, 'ca_observed': 280644000.0,
'fe_modeled': 10000900.0, 'fe_observed': 17144400.0,
'mg_modeled': 178360000.0, 'mg_observed': 305760000.0,
'ph_modeled': 448448000.0, 'ph_observed': 768768000.0,
'k_modeled': 125489000.0, 'k_observed': 215124000.0,
'na_modeled': 1274000.0, 'na_observed': 2184000.0,
'zn_modeled': 3121300.0, 'zn_observed': 5350800.0,
'cu_modeled': 1019200.0, 'cu_observed': 1747200.0,
'fl_modeled': 1911000.0, 'fl_observed': 3276000.0,
'mn_modeled': 3312400.0, 'mn_observed': 5678400.0,
'se_modeled': 191100.0, 'se_observed': 327600.0,
'vita_modeled': 1911000.0, 'vita_observed': 3276000.0,
'betac_modeled': 10192000.0, 'betac_observed': 17472000.0,
'alphac_modeled': 637000.0, 'alphac_observed': 1092000.0,
'vite_modeled': 509600.0, 'vite_observed': 873600.0,
'crypto_modeled': 382200.0, 'crypto_observed': 655200.0,
'lycopene_modeled': 191100.0, 'lycopene_observed': 327600.0,
'lutein_modeled': 38857000.0, 'lutein_observed': 66612000.0,
'betat_modeled': 318500.0, 'betat_observed': 546000.0,
'gammat_modeled': 1465100.0, 'gammat_observed': 2511600.0,
'deltat_modeled': 764400.0, 'deltat_observed': 1310400.0,
'vitc_modeled': 1911000.0, 'vitc_observed': 3276000.0,
'thiamin_modeled': 267540.0, 'thiamin_observed': 458640.0,
'riboflavin_modeled': 522340.0, 'riboflavin_observed': 895440.0,
'niacin_modeled': 7771400.0, 'niacin_observed': 13322400.0,
'pantothenic_modeled': 586040.0, 'pantothenic_observed': 1004640.0,
'vitb6_modeled': 3439800.0, 'vitb6_observed': 5896800.0,
'folate_modeled': 194285000.0, 'folate_observed': 333060000.0,
'vitb12_modeled': 1911000.0, 'vitb12_observed': 3276000.0,
'vitk_modeled': 26754000.0, 'vitk_observed': 45864000.0}])
def tabulate_pctl_results_table():
"""Generate expected output for unit tests of tabulated percentile results.
This function returns the expected DataFrame output of the
`tabulate_percentile_results` function, with the parameters
defined in the unit test `test_tabulate_percentile_results`.
Returns:
pandas.DataFrame: Expected tabulated results for percentile output.
"""
return pandas.DataFrame({
"crop": ["corn", "soybean"], "area (ha)": [2, 4],
"production_observed": [4, 7], "production_25": [1.25, 2.25],
"production_50": [2.5, 4.5], "production_75": [3.75, 6.75],
"protein_25": [488250, 675675], "protein_50": [976500, 1351350],
"protein_75": [1464750, 2027025],
"protein_observed": [1562400, 2102100],
"lipid_25": [93000, 40950], "lipid_50": [186000, 81900],
"lipid_75": [279000, 122850], "lipid_observed": [297600, 127400],
"energy_25": [5533500, 2027025], "energy_50": [11067000, 4054050],
"energy_75": [16600500, 6081075],
"energy_observed": [17707200, 6306300],
"ca_25": [313875, 5262075], "ca_50": [627750, 10524150],
"ca_75": [941625, 15786225], "ca_observed": [1004400, 16370900],
"fe_25": [182512.5, 321457.5], "fe_50": [365025, 642915],
"fe_75": [547537.5, 964372.5], "fe_observed": [584040, 1000090],
"mg_25": [3255000, 5733000], "mg_50": [6510000, 11466000],
"mg_75": [9765000, 17199000], "mg_observed": [10416000, 17836000],
"ph_25": [8184000, 14414400], "ph_50": [16368000, 28828800],
"ph_75": [24552000, 43243200], "ph_observed": [26188800, 44844800],
"k_25": [20076375, 4033575], "k_50": [40152750, 8067150],
"k_75": [60229125, 12100725], "k_observed": [64244400, 12548900],
"na_25": [23250, 40950], "na_50": [46500, 81900],
"na_75": [69750, 122850], "na_observed": [74400, 127400],
"zn_25": [56962.5, 100327.5], "zn_50": [113925, 200655],
"zn_75": [170887.5, 300982.5], "zn_observed": [182280, 312130],
"cu_25": [22087.5, 32760], "cu_50": [44175, 65520],
"cu_75": [66262.5, 98280], "cu_observed": [70680, 101920],
"fl_25": [93000, 61425], "fl_50": [186000, 122850],
"fl_75": [279000, 184275], "fl_observed": [297600, 191100],
"mn_25": [33712.5, 106470], "mn_50": [67425, 212940],
"mn_75": [101137.5, 319410], "mn_observed": [107880, 331240],
"se_25": [1162.5, 6142.5], "se_50": [2325, 12285],
"se_75": [3487.5, 18427.5], "se_observed": [3720, 19110],
"vita_25": [34875, 61425], "vita_50": [69750, 122850],
"vita_75": [104625, 184275], "vita_observed": [111600, 191100],
"betac_25": [186000, 327600], "betac_50": [372000, 655200],
"betac_75": [558000, 982800], "betac_observed": [595200, 1019200],
"alphac_25": [26737.5, 20475], "alphac_50": [53475, 40950],
"alphac_75": [80212.5, 61425], "alphac_observed": [85560, 63700],
"vite_25": [9300, 16380], "vite_50": [18600, 32760],
"vite_75": [27900, 49140], "vite_observed": [29760, 50960],
"crypto_25": [18600, 12285], "crypto_50": [37200, 24570],
"crypto_75": [55800, 36855], "crypto_observed": [59520, 38220],
"lycopene_25": [4185, 6142.5], "lycopene_50": [8370, 12285],
"lycopene_75": [12555, 18427.5], "lycopene_observed": [13392, 19110],
"lutein_25": [732375, 1248975], "lutein_50": [1464750, 2497950],
"lutein_75": [2197125, 3746925], "lutein_observed": [2343600, 3885700],
"betat_25": [5812.5, 10237.5], "betat_50": [11625, 20475],
"betat_75": [17437.5, 30712.5], "betat_observed": [18600, 31850],
"gammat_25": [24412.5, 47092.5], "gammat_50": [48825, 94185],
"gammat_75": [73237.5, 141277.5], "gammat_observed": [78120, 146510],
"deltat_25": [22087.5, 24570], "deltat_50": [44175, 49140],
"deltat_75": [66262.5, 73710], "deltat_observed": [70680, 76440],
"vitc_25": [79050, 61425], "vitc_50": [158100, 122850],
"vitc_75": [237150, 184275], "vitc_observed": [252960, 191100],
"thiamin_25": [4650, 8599.5], "thiamin_50": [9300, 17199],
"thiamin_75": [13950, 25798.5], "thiamin_observed": [14880, 26754],
"riboflavin_25": [20925, 16789.5], "riboflavin_50": [41850, 33579],
"riboflavin_75": [62775, 50368.5],
"riboflavin_observed": [66960, 52234], "niacin_25": [95325, 249795],
"niacin_50": [190650, 499590], "niacin_75": [285975, 749385],
"niacin_observed": [305040, 777140],
"pantothenic_25": [10462.5, 18837], "pantothenic_50": [20925, 37674],
"pantothenic_75": [31387.5, 56511],
"pantothenic_observed": [33480, 58604],
"vitb6_25": [16275, 110565], "vitb6_50": [32550, 221130],
"vitb6_75": [48825, 331695], "vitb6_observed": [52080, 343980],
"folate_25": [4475625, 6244875], "folate_50": [8951250, 12489750],
"folate_75": [13426875, 18734625],
"folate_observed": [14322000, 19428500],
"vitb12_25": [23250, 61425], "vitb12_50": [46500, 122850],
"vitb12_75": [69750, 184275], "vitb12_observed": [74400, 191100],
"vitk_25": [476625, 859950], "vitk_50": [953250, 1719900],
"vitk_75": [1429875, 2579850], "vitk_observed": [1525200, 2675400]
})
def aggregate_regr_polygons_table():
"""Generate expected aggregated nutrient results by polygon (e.g., FID).
This function returns the expected DataFrame output of the
`aggregate_regression_results_to_polygons` function for use in the
`test_aggregate_regression_results_to_polygons` unit test. It
summarizes nutrient production values for multiple crops grouped by
polygon identifiers (FIDs).
Returns:
pandas.DataFrame: Aggregated modeled and observed values by FID.
"""
return pandas.DataFrame([
{"FID": 0, "corn_modeled": 10, "corn_observed": 40,
"soybean_modeled": 20, "soybean_observed": 50,
"protein_modeled": 9912000, "protein_observed": 30639000,
"lipid_modeled": 1108000, "lipid_observed": 3886000,
"energy_modeled": 62286000, "energy_observed": 222117000,
"ca_modeled": 49285000, "ca_observed": 126979000,
"fe_modeled": 4317500, "fe_observed": 12983900,
"mg_modeled": 77000000, "mg_observed": 231560000,
"ph_modeled": 193600000, "ph_observed": 582208000,
"k_modeled": 196465000, "k_observed": 732079000,
"na_modeled": 550000, "na_observed": 1654000,
"zn_modeled": 1347500, "zn_observed": 4052300,
"cu_modeled": 467900, "cu_observed": 1434800,
"fl_modeled": 1290000, "fl_observed": 4341000,
"mn_modeled": 1216100, "mn_observed": 3444800,
"se_modeled": 63900, "se_observed": 173700,
"vita_modeled": 825000, "vita_observed": 2481000,
"betac_modeled": 4400000, "betac_observed": 13232000,
"alphac_modeled": 395900, "alphac_observed": 1310600,
"vite_modeled": 220000, "vite_observed": 661600,
"crypto_modeled": 258000, "crypto_observed": 868200,
"lycopene_modeled": 88080, "lycopene_observed": 270420,
"lutein_modeled": 16961000, "lutein_observed": 51191000,
"betat_modeled": 137500, "betat_observed": 413500,
"gammat_modeled": 613900, "gammat_observed": 1827700,
"deltat_modeled": 395100, "deltat_observed": 1252800,
"vitc_modeled": 1178400, "vitc_observed": 3894600,
"thiamin_modeled": 113640, "thiamin_observed": 339900,
"riboflavin_modeled": 316640, "riboflavin_observed": 1042700,
"niacin_modeled": 2983000, "niacin_observed": 8601400,
"pantothenic_modeled": 251140, "pantothenic_observed": 753400,
"vitb6_modeled": 1113000, "vitb6_observed": 2977800,
"folate_modeled": 91315000, "folate_observed": 281995000,
"vitb12_modeled": 732000, "vitb12_observed": 2109000,
"vitk_modeled": 11457000, "vitk_observed": 34362000},
{"FID": 1, "corn_modeled": 40, "corn_observed": 80,
"soybean_modeled": 70, "soybean_observed": 120,
"protein_modeled": 36645000, "protein_observed": 67284000,
"lipid_modeled": 4250000, "lipid_observed": 8136000,
"energy_modeled": 240135000, "energy_observed": 462252000,
"ca_modeled": 173753000, "ca_observed": 300732000,
"fe_modeled": 15841300, "fe_observed": 28825200,
"mg_modeled": 282520000, "mg_observed": 514080000,
"ph_modeled": 710336000, "ph_observed": 1292544000,
"k_modeled": 767933000, "k_observed": 1500012000,
"na_modeled": 2018000, "na_observed": 3672000,
"zn_modeled": 4944100, "zn_observed": 8996400,
"cu_modeled": 1726000, "cu_observed": 3160800,
"fl_modeled": 4887000, "fl_observed": 9228000,
"mn_modeled": 4391200, "mn_observed": 7836000,
"se_modeled": 228300, "se_observed": 402000,
"vita_modeled": 3027000, "vita_observed": 5508000,
"betac_modeled": 16144000, "betac_observed": 29376000,
"alphac_modeled": 1492600, "alphac_observed": 2803200,
"vite_modeled": 807200, "vite_observed": 1468800,
"crypto_modeled": 977400, "crypto_observed": 1845600,
"lycopene_modeled": 325020, "lycopene_observed": 595440,
"lutein_modeled": 62293000, "lutein_observed": 113484000,
"betat_modeled": 504500, "betat_observed": 918000,
"gammat_modeled": 2246300, "gammat_observed": 4074000,
"deltat_modeled": 1471200, "deltat_observed": 2724000,
"vitc_modeled": 4440600, "vitc_observed": 8335200,
"thiamin_modeled": 416340, "thiamin_observed": 756240,
"riboflavin_modeled": 1191940, "riboflavin_observed": 2234640,
"niacin_modeled": 10821800, "niacin_observed": 19423200,
"pantothenic_modeled": 920840, "pantothenic_observed": 1674240,
"vitb6_modeled": 3960600, "vitb6_observed": 6938400,
"folate_modeled": 337505000, "folate_observed": 619500000,
"vitb12_modeled": 2655000, "vitb12_observed": 4764000,
"vitk_modeled": 42006000, "vitk_observed": 76368000}
], dtype=float)
def aggregate_pctl_polygons_table():
"""Generate expected aggregated nutrient results by polygon (e.g., FID).
This function returns the expected DataFrame output of the
`aggregate_to_polygons` function for use in the
`test_aggregate_to_polygons` unit test. The DataFrame contains observed
and modeled (at different percentiles) crop yields and nutrient totals,
aggregated by polygon (FID).
Returns:
pandas.DataFrame: A table with observed and percentile-based modeled
values for crop yield and nutrient totals, grouped by FID.
"""
data = [
[0, 0.25, 0.5, 0.75, 1, 0.5, 1, 1.5, 2, 247800, 495600, 743400,
991200, 27700, 55400, 83100, 110800, 1557150, 3114300, 4671450,
6228600, 1232125, 2464250, 3696375, 4928500, 107937.5, 215875,
323812.5, 431750, 1925000, 3850000, 5775000, 7700000, 4840000,
9680000, 14520000, 19360000, 4911625, 9823250, 14734875,
19646500, 13750, 27500, 41250, 55000, 33687.5, 67375,
101062.5, 134750, 11697.5, 23395, 35092.5, 46790, 32250,
64500, 96750, 129000, 30402.5, 60805, 91207.5, 121610, 1597.5,
3195, 4792.5, 6390, 20625, 41250, 61875, 82500, 110000, 220000,
330000, 440000, 9897.5, 19795, 29692.5, 39590, 5500, 11000,
16500, 22000, 6450, 12900, 19350, 25800, 2202, 4404, 6606,
8808, 424025, 848050, 1272075, 1696100, 3437.5, 6875, 10312.5,
13750, 15347.5, 30695, 46042.5, 61390, 9877.5, 19755, 29632.5,
39510, 29460, 58920, 88380, 117840, 2841, 5682, 8523, 11364,
7916, 15832, 23748, 31664, 74575, 149150, 223725, 298300,
6278.5, 12557, 18835.5, 25114, 27825, 55650, 83475, 111300,
2282875, 4565750, 6848625, 9131500, 18300, 36600, 54900,
73200, 286425, 572850, 859275, 1145700],
[1, 1.25, 2.5, 3.75, 4, 2.25, 4.5, 6.75, 7, 1163925, 2327850,
3491775, 3664500, 133950, 267900, 401850, 425000, 7560525,
15121050, 22681575, 24013500, 5575950, 11151900, 16727850,
17375300, 503970, 1007940, 1511910, 1584130, 8988000,
17976000, 26964000, 28252000, 22598400, 45196800, 67795200,
71033600, 24109950, 48219900, 72329850, 76793300, 64200,
128400, 192600, 201800, 157290, 314580, 471870, 494410,
54847.5, 109695, 164542.5, 172600, 154425, 308850, 463275,
488700, 140182.5, 280365, 420547.5, 439120, 7305, 14610,
21915, 22830, 96300, 192600, 288900, 302700, 513600, 1027200,
1540800, 1614400, 47212.5, 94425, 141637.5, 149260, 25680,
51360, 77040, 80720, 30885, 61770, 92655, 97740, 10327.5,
20655, 30982.5, 32502, 1981350, 3962700, 5944050, 6229300,
16050, 32100, 48150, 50450, 71505, 143010, 214515, 224630,
46657.5, 93315, 139972.5, 147120, 140475, 280950, 421425,
444060, 13249.5, 26499, 39748.5, 41634, 37714.5, 75429,
113143.5, 119194, 345120, 690240, 1035360, 1082180, 29299.5,
58599, 87898.5, 92084, 126840, 253680, 380520, 396060,
10720500, 21441000, 32161500, 33750500, 84675, 169350, 254025,
265500, 1336575, 2673150, 4009725, 4200600]
]
columns = [
"FID", "corn_25", "corn_50", "corn_75", "corn_observed",
"soybean_25", "soybean_50", "soybean_75", "soybean_observed",
"protein_25", "protein_50", "protein_75", "protein_observed",
"lipid_25", "lipid_50", "lipid_75", "lipid_observed",
"energy_25", "energy_50", "energy_75", "energy_observed",
"ca_25", "ca_50", "ca_75", "ca_observed", "fe_25", "fe_50",
"fe_75", "fe_observed", "mg_25", "mg_50", "mg_75",
"mg_observed", "ph_25", "ph_50", "ph_75", "ph_observed",
"k_25", "k_50", "k_75", "k_observed", "na_25", "na_50",
"na_75", "na_observed", "zn_25", "zn_50", "zn_75",
"zn_observed", "cu_25", "cu_50", "cu_75", "cu_observed",
"fl_25", "fl_50", "fl_75", "fl_observed", "mn_25", "mn_50",
"mn_75", "mn_observed", "se_25", "se_50", "se_75",
"se_observed", "vita_25", "vita_50", "vita_75",
"vita_observed", "betac_25", "betac_50", "betac_75",
"betac_observed", "alphac_25", "alphac_50", "alphac_75",
"alphac_observed", "vite_25", "vite_50", "vite_75",
"vite_observed", "crypto_25", "crypto_50", "crypto_75",
"crypto_observed", "lycopene_25", "lycopene_50", "lycopene_75",
"lycopene_observed", "lutein_25", "lutein_50", "lutein_75",
"lutein_observed", "betat_25", "betat_50", "betat_75",
"betat_observed", "gammat_25", "gammat_50", "gammat_75",
"gammat_observed", "deltat_25", "deltat_50", "deltat_75",
"deltat_observed", "vitc_25", "vitc_50", "vitc_75",
"vitc_observed", "thiamin_25", "thiamin_50", "thiamin_75",
"thiamin_observed", "riboflavin_25", "riboflavin_50",
"riboflavin_75", "riboflavin_observed", "niacin_25",
"niacin_50", "niacin_75", "niacin_observed", "pantothenic_25",
"pantothenic_50", "pantothenic_75", "pantothenic_observed",
"vitb6_25", "vitb6_50", "vitb6_75", "vitb6_observed",
"folate_25", "folate_50", "folate_75", "folate_observed",
"vitb12_25", "vitb12_50", "vitb12_75", "vitb12_observed",
"vitk_25", "vitk_50", "vitk_75", "vitk_observed"
]
return pandas.DataFrame(data, columns=columns, dtype=float)

379
tests/test_crop_production.py
View File

@ -30,6 +30,7 @@ def _get_pixels_per_hectare(raster_path):
Returns:
A float representing the number of pixels per hectare.
"""
raster_info = pygeoprocessing.get_raster_info(raster_path)
pixel_area = abs(numpy.prod(raster_info['pixel_size']))
@ -37,12 +38,14 @@ def _get_pixels_per_hectare(raster_path):
def make_aggregate_vector(path_to_shp):
"""
Generate shapefile with two overlapping polygons
"""Generate shapefile with two overlapping polygons.
Args:
path_to_shp (str): path to store watershed results vector
Outputs:
path_to_shp (str): path to store results vector
Returns:
None
"""
# (xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)
shapely_geometry_list = [
@ -71,10 +74,13 @@ def make_aggregate_vector(path_to_shp):
def make_simple_raster(base_raster_path, array):
"""Create a raster on designated path with arbitrary values.
Args:
base_raster_path (str): the raster path for making the new raster.
Returns:
None.
None
"""
# UTM Zone 10N
srs = osr.SpatialReference()
@ -90,31 +96,6 @@ def make_simple_raster(base_raster_path, array):
base_raster_path)
def create_nutrient_df():
"""Creates a nutrient DataFrame for testing."""
return pandas.DataFrame([
{'crop': 'corn', 'area (ha)': 21.0, 'production_observed': 0.2,
'percentrefuse': 7, 'protein': 42., 'lipid': 8, 'energy': 476.,
'ca': 27.0, 'fe': 15.7, 'mg': 280.0, 'ph': 704.0, 'k': 1727.0,
'na': 2.0, 'zn': 4.9, 'cu': 1.9, 'fl': 8, 'mn': 2.9, 'se': 0.1,
'vita': 3.0, 'betac': 16.0, 'alphac': 2.30, 'vite': 0.8,
'crypto': 1.6, 'lycopene': 0.36, 'lutein': 63.0, 'betat': 0.5,
'gammat': 2.1, 'deltat': 1.9, 'vitc': 6.8, 'thiamin': 0.4,
'riboflavin': 1.8, 'niacin': 8.2, 'pantothenic': 0.9,
'vitb6': 1.4, 'folate': 385.0, 'vitb12': 2.0, 'vitk': 41.0},
{'crop': 'soybean', 'area (ha)': 5., 'production_observed': 4.,
'percentrefuse': 9, 'protein': 33., 'lipid': 2., 'energy': 99.,
'ca': 257., 'fe': 15.7, 'mg': 280., 'ph': 704.0, 'k': 197.0,
'na': 2., 'zn': 4.9, 'cu': 1.6, 'fl': 3., 'mn': 5.2, 'se': 0.3,
'vita': 3.0, 'betac': 16.0, 'alphac': 1.0, 'vite': 0.8,
'crypto': 0.6, 'lycopene': 0.3, 'lutein': 61.0, 'betat': 0.5,
'gammat': 2.3, 'deltat': 1.2, 'vitc': 3.0, 'thiamin': 0.42,
'riboflavin': 0.82, 'niacin': 12.2, 'pantothenic': 0.92,
'vitb6': 5.4, 'folate': 305., 'vitb12': 3., 'vitk': 42.},
]).set_index('crop')
def _create_crop_rasters(output_dir, crop_names, file_suffix):
"""Creates raster files for test setup."""
_OBSERVED_PRODUCTION_FILE_PATTERN = os.path.join(
@ -653,82 +634,10 @@ class CropProductionTests(unittest.TestCase):
"""Test `tabulate_regression_results`"""
from natcap.invest.crop_production_regression import \
tabulate_regression_results
from crop_production.data_helpers import sample_nutrient_df
from crop_production.data_helpers import tabulate_regr_results_table
def _create_expected_results():
"""Creates the expected results DataFrame."""
return pandas.DataFrame([
{'crop': 'corn', 'area (ha)': 20.0,
'production_observed': 80.0, 'production_modeled': 40.0,
'protein_modeled': 15624000.0, 'protein_observed': 31248000.0,
'lipid_modeled': 2976000.0, 'lipid_observed': 5952000.0,
'energy_modeled': 177072000.0, 'energy_observed': 354144000.0,
'ca_modeled': 10044000.0, 'ca_observed': 20088000.0,
'fe_modeled': 5840400.0, 'fe_observed': 11680800.0,
'mg_modeled': 104160000.0, 'mg_observed': 208320000.0,
'ph_modeled': 261888000.0, 'ph_observed': 523776000.0,
'k_modeled': 642444000.0, 'k_observed': 1284888000.0,
'na_modeled': 744000.0, 'na_observed': 1488000.0,
'zn_modeled': 1822800.0, 'zn_observed': 3645600.0,
'cu_modeled': 706800.0, 'cu_observed': 1413600.0,
'fl_modeled': 2976000.0, 'fl_observed': 5952000.0,
'mn_modeled': 1078800.0, 'mn_observed': 2157600.0,
'se_modeled': 37200.0, 'se_observed': 74400.0,
'vita_modeled': 1116000.0, 'vita_observed': 2232000.0,
'betac_modeled': 5952000.0, 'betac_observed': 11904000.0,
'alphac_modeled': 855600.0, 'alphac_observed': 1711200.0,
'vite_modeled': 297600.0, 'vite_observed': 595200.0,
'crypto_modeled': 595200.0, 'crypto_observed': 1190400.0,
'lycopene_modeled': 133920.0, 'lycopene_observed': 267840.0,
'lutein_modeled': 23436000.0, 'lutein_observed': 46872000.0,
'betat_modeled': 186000.0, 'betat_observed': 372000.0,
'gammat_modeled': 781200.0, 'gammat_observed': 1562400.0,
'deltat_modeled': 706800.0, 'deltat_observed': 1413600.0,
'vitc_modeled': 2529600.0, 'vitc_observed': 5059200.0,
'thiamin_modeled': 148800.0, 'thiamin_observed': 297600.0,
'riboflavin_modeled': 669600.0, 'riboflavin_observed': 1339200.0,
'niacin_modeled': 3050400.0, 'niacin_observed': 6100800.0,
'pantothenic_modeled': 334800.0, 'pantothenic_observed': 669600.0,
'vitb6_modeled': 520800.0, 'vitb6_observed': 1041600.0,
'folate_modeled': 143220000.0, 'folate_observed': 286440000.0,
'vitb12_modeled': 744000.0, 'vitb12_observed': 1488000.0,
'vitk_modeled': 15252000.0, 'vitk_observed': 30504000.0},
{'crop': 'soybean', 'area (ha)': 40.0,
'production_observed': 120.0, 'production_modeled': 70.0,
'protein_modeled': 21021000.0, 'protein_observed': 36036000.0,
'lipid_modeled': 1274000.0, 'lipid_observed': 2184000.0,
'energy_modeled': 63063000.0, 'energy_observed': 108108000.0,
'ca_modeled': 163709000.0, 'ca_observed': 280644000.0,
'fe_modeled': 10000900.0, 'fe_observed': 17144400.0,
'mg_modeled': 178360000.0, 'mg_observed': 305760000.0,
'ph_modeled': 448448000.0, 'ph_observed': 768768000.0,
'k_modeled': 125489000.0, 'k_observed': 215124000.0,
'na_modeled': 1274000.0, 'na_observed': 2184000.0,
'zn_modeled': 3121300.0, 'zn_observed': 5350800.0,
'cu_modeled': 1019200.0, 'cu_observed': 1747200.0,
'fl_modeled': 1911000.0, 'fl_observed': 3276000.0,
'mn_modeled': 3312400.0, 'mn_observed': 5678400.0,
'se_modeled': 191100.0, 'se_observed': 327600.0,
'vita_modeled': 1911000.0, 'vita_observed': 3276000.0,
'betac_modeled': 10192000.0, 'betac_observed': 17472000.0,
'alphac_modeled': 637000.0, 'alphac_observed': 1092000.0,
'vite_modeled': 509600.0, 'vite_observed': 873600.0,
'crypto_modeled': 382200.0, 'crypto_observed': 655200.0,
'lycopene_modeled': 191100.0, 'lycopene_observed': 327600.0,
'lutein_modeled': 38857000.0, 'lutein_observed': 66612000.0,
'betat_modeled': 318500.0, 'betat_observed': 546000.0,
'gammat_modeled': 1465100.0, 'gammat_observed': 2511600.0,
'deltat_modeled': 764400.0, 'deltat_observed': 1310400.0,
'vitc_modeled': 1911000.0, 'vitc_observed': 3276000.0,
'thiamin_modeled': 267540.0, 'thiamin_observed': 458640.0,
'riboflavin_modeled': 522340.0, 'riboflavin_observed': 895440.0,
'niacin_modeled': 7771400.0, 'niacin_observed': 13322400.0,
'pantothenic_modeled': 586040.0, 'pantothenic_observed': 1004640.0,
'vitb6_modeled': 3439800.0, 'vitb6_observed': 5896800.0,
'folate_modeled': 194285000.0, 'folate_observed': 333060000.0,
'vitb12_modeled': 1911000.0, 'vitb12_observed': 3276000.0,
'vitk_modeled': 26754000.0, 'vitk_observed': 45864000.0}])
nutrient_df = create_nutrient_df()
nutrient_df = sample_nutrient_df()
pixel_area_ha = 10
workspace_dir = self.workspace_dir
@ -755,7 +664,7 @@ class CropProductionTests(unittest.TestCase):
# Read only the first 2 crop's data (skipping total area)
actual_result_table = pandas.read_csv(target_table_path, nrows=2,
header=0)
expected_result_table = _create_expected_results()
expected_result_table = tabulate_regr_results_table()
# Compare expected vs actual
pandas.testing.assert_frame_equal(actual_result_table,
@ -765,82 +674,9 @@ class CropProductionTests(unittest.TestCase):
"""Test `aggregate_regression_results_to_polygons`"""
from natcap.invest.crop_production_regression import \
aggregate_regression_results_to_polygons
def _create_expected_agg_table():
"""Create expected output results"""
# Define the new values manually
return pandas.DataFrame([
{"FID": 0, "corn_modeled": 10, "corn_observed": 40,
"soybean_modeled": 20, "soybean_observed": 50,
"protein_modeled": 9912000, "protein_observed": 30639000,
"lipid_modeled": 1108000, "lipid_observed": 3886000,
"energy_modeled": 62286000, "energy_observed": 222117000,
"ca_modeled": 49285000, "ca_observed": 126979000,
"fe_modeled": 4317500, "fe_observed": 12983900,
"mg_modeled": 77000000, "mg_observed": 231560000,
"ph_modeled": 193600000, "ph_observed": 582208000,
"k_modeled": 196465000, "k_observed": 732079000,
"na_modeled": 550000, "na_observed": 1654000,
"zn_modeled": 1347500, "zn_observed": 4052300,
"cu_modeled": 467900, "cu_observed": 1434800,
"fl_modeled": 1290000, "fl_observed": 4341000,
"mn_modeled": 1216100, "mn_observed": 3444800,
"se_modeled": 63900, "se_observed": 173700,
"vita_modeled": 825000, "vita_observed": 2481000,
"betac_modeled": 4400000, "betac_observed": 13232000,
"alphac_modeled": 395900, "alphac_observed": 1310600,
"vite_modeled": 220000, "vite_observed": 661600,
"crypto_modeled": 258000, "crypto_observed": 868200,
"lycopene_modeled": 88080, "lycopene_observed": 270420,
"lutein_modeled": 16961000, "lutein_observed": 51191000,
"betat_modeled": 137500, "betat_observed": 413500,
"gammat_modeled": 613900, "gammat_observed": 1827700,
"deltat_modeled": 395100, "deltat_observed": 1252800,
"vitc_modeled": 1178400, "vitc_observed": 3894600,
"thiamin_modeled": 113640, "thiamin_observed": 339900,
"riboflavin_modeled": 316640, "riboflavin_observed": 1042700,
"niacin_modeled": 2983000, "niacin_observed": 8601400,
"pantothenic_modeled": 251140, "pantothenic_observed": 753400,
"vitb6_modeled": 1113000, "vitb6_observed": 2977800,
"folate_modeled": 91315000, "folate_observed": 281995000,
"vitb12_modeled": 732000, "vitb12_observed": 2109000,
"vitk_modeled": 11457000, "vitk_observed": 34362000},
{"FID": 1, "corn_modeled": 40, "corn_observed": 80,
"soybean_modeled": 70, "soybean_observed": 120,
"protein_modeled": 36645000, "protein_observed": 67284000,
"lipid_modeled": 4250000, "lipid_observed": 8136000,
"energy_modeled": 240135000, "energy_observed": 462252000,
"ca_modeled": 173753000, "ca_observed": 300732000,
"fe_modeled": 15841300, "fe_observed": 28825200,
"mg_modeled": 282520000, "mg_observed": 514080000,
"ph_modeled": 710336000, "ph_observed": 1292544000,
"k_modeled": 767933000, "k_observed": 1500012000,
"na_modeled": 2018000, "na_observed": 3672000,
"zn_modeled": 4944100, "zn_observed": 8996400,
"cu_modeled": 1726000, "cu_observed": 3160800,
"fl_modeled": 4887000, "fl_observed": 9228000,
"mn_modeled": 4391200, "mn_observed": 7836000,
"se_modeled": 228300, "se_observed": 402000,
"vita_modeled": 3027000, "vita_observed": 5508000,
"betac_modeled": 16144000, "betac_observed": 29376000,
"alphac_modeled": 1492600, "alphac_observed": 2803200,
"vite_modeled": 807200, "vite_observed": 1468800,
"crypto_modeled": 977400, "crypto_observed": 1845600,
"lycopene_modeled": 325020, "lycopene_observed": 595440,
"lutein_modeled": 62293000, "lutein_observed": 113484000,
"betat_modeled": 504500, "betat_observed": 918000,
"gammat_modeled": 2246300, "gammat_observed": 4074000,
"deltat_modeled": 1471200, "deltat_observed": 2724000,
"vitc_modeled": 4440600, "vitc_observed": 8335200,
"thiamin_modeled": 416340, "thiamin_observed": 756240,
"riboflavin_modeled": 1191940, "riboflavin_observed": 2234640,
"niacin_modeled": 10821800, "niacin_observed": 19423200,
"pantothenic_modeled": 920840, "pantothenic_observed": 1674240,
"vitb6_modeled": 3960600, "vitb6_observed": 6938400,
"folate_modeled": 337505000, "folate_observed": 619500000,
"vitb12_modeled": 2655000, "vitb12_observed": 4764000,
"vitk_modeled": 42006000, "vitk_observed": 76368000}
], dtype=float)
from crop_production.data_helpers import sample_nutrient_df
from crop_production.data_helpers import \
aggregate_regr_polygons_table
workspace = self.workspace_dir
@ -856,7 +692,7 @@ class CropProductionTests(unittest.TestCase):
landcover_raster_projection = spatial_ref.ExportToWkt()
crop_names = ['corn', 'soybean']
nutrient_df = create_nutrient_df()
nutrient_df = sample_nutrient_df()
output_dir = os.path.join(workspace, "OUTPUT")
os.makedirs(output_dir, exist_ok=True)
file_suffix = 'test'
@ -879,7 +715,7 @@ class CropProductionTests(unittest.TestCase):
actual_aggregate_table = pandas.read_csv(aggregate_table_path,
dtype=float)
expected_aggregate_table = _create_expected_agg_table()
expected_aggregate_table = aggregate_regr_polygons_table()
pandas.testing.assert_frame_equal(
actual_aggregate_table, expected_aggregate_table)
@ -888,90 +724,8 @@ class CropProductionTests(unittest.TestCase):
"""Test `aggregate_to_polygons`"""
from natcap.invest.crop_production_percentile import \
aggregate_to_polygons
def _create_expected_pctl_table():
"""Create expected output results"""
# Define the new values manually
data = [
[0, 0.25, 0.5, 0.75, 1, 0.5, 1, 1.5, 2, 247800, 495600, 743400,
991200, 27700, 55400, 83100, 110800, 1557150, 3114300, 4671450,
6228600, 1232125, 2464250, 3696375, 4928500, 107937.5, 215875,
323812.5, 431750, 1925000, 3850000, 5775000, 7700000, 4840000,
9680000, 14520000, 19360000, 4911625, 9823250, 14734875,
19646500, 13750, 27500, 41250, 55000, 33687.5, 67375,
101062.5, 134750, 11697.5, 23395, 35092.5, 46790, 32250,
64500, 96750, 129000, 30402.5, 60805, 91207.5, 121610, 1597.5,
3195, 4792.5, 6390, 20625, 41250, 61875, 82500, 110000, 220000,
330000, 440000, 9897.5, 19795, 29692.5, 39590, 5500, 11000,
16500, 22000, 6450, 12900, 19350, 25800, 2202, 4404, 6606,
8808, 424025, 848050, 1272075, 1696100, 3437.5, 6875, 10312.5,
13750, 15347.5, 30695, 46042.5, 61390, 9877.5, 19755, 29632.5,
39510, 29460, 58920, 88380, 117840, 2841, 5682, 8523, 11364,
7916, 15832, 23748, 31664, 74575, 149150, 223725, 298300,
6278.5, 12557, 18835.5, 25114, 27825, 55650, 83475, 111300,
2282875, 4565750, 6848625, 9131500, 18300, 36600, 54900,
73200, 286425, 572850, 859275, 1145700],
[1, 1.25, 2.5, 3.75, 4, 2.25, 4.5, 6.75, 7, 1163925, 2327850,
3491775, 3664500, 133950, 267900, 401850, 425000, 7560525,
15121050, 22681575, 24013500, 5575950, 11151900, 16727850,
17375300, 503970, 1007940, 1511910, 1584130, 8988000,
17976000, 26964000, 28252000, 22598400, 45196800, 67795200,
71033600, 24109950, 48219900, 72329850, 76793300, 64200,
128400, 192600, 201800, 157290, 314580, 471870, 494410,
54847.5, 109695, 164542.5, 172600, 154425, 308850, 463275,
488700, 140182.5, 280365, 420547.5, 439120, 7305, 14610,
21915, 22830, 96300, 192600, 288900, 302700, 513600, 1027200,
1540800, 1614400, 47212.5, 94425, 141637.5, 149260, 25680,
51360, 77040, 80720, 30885, 61770, 92655, 97740, 10327.5,
20655, 30982.5, 32502, 1981350, 3962700, 5944050, 6229300,
16050, 32100, 48150, 50450, 71505, 143010, 214515, 224630,
46657.5, 93315, 139972.5, 147120, 140475, 280950, 421425,
444060, 13249.5, 26499, 39748.5, 41634, 37714.5, 75429,
113143.5, 119194, 345120, 690240, 1035360, 1082180, 29299.5,
58599, 87898.5, 92084, 126840, 253680, 380520, 396060,
10720500, 21441000, 32161500, 33750500, 84675, 169350, 254025,
265500, 1336575, 2673150, 4009725, 4200600]
]
# Define column names
columns = [
"FID", "corn_25", "corn_50", "corn_75", "corn_observed",
"soybean_25", "soybean_50", "soybean_75", "soybean_observed",
"protein_25", "protein_50", "protein_75", "protein_observed",
"lipid_25", "lipid_50", "lipid_75", "lipid_observed",
"energy_25", "energy_50", "energy_75", "energy_observed",
"ca_25", "ca_50", "ca_75", "ca_observed", "fe_25", "fe_50",
"fe_75", "fe_observed", "mg_25", "mg_50", "mg_75",
"mg_observed", "ph_25", "ph_50", "ph_75", "ph_observed",
"k_25", "k_50", "k_75", "k_observed", "na_25", "na_50",
"na_75", "na_observed", "zn_25", "zn_50", "zn_75",
"zn_observed", "cu_25", "cu_50", "cu_75", "cu_observed",
"fl_25", "fl_50", "fl_75", "fl_observed", "mn_25", "mn_50",
"mn_75", "mn_observed", "se_25", "se_50", "se_75",
"se_observed", "vita_25", "vita_50", "vita_75",
"vita_observed", "betac_25", "betac_50", "betac_75",
"betac_observed", "alphac_25", "alphac_50", "alphac_75",
"alphac_observed", "vite_25", "vite_50", "vite_75",
"vite_observed", "crypto_25", "crypto_50", "crypto_75",
"crypto_observed", "lycopene_25", "lycopene_50", "lycopene_75",
"lycopene_observed", "lutein_25", "lutein_50", "lutein_75",
"lutein_observed", "betat_25", "betat_50", "betat_75",
"betat_observed", "gammat_25", "gammat_50", "gammat_75",
"gammat_observed", "deltat_25", "deltat_50", "deltat_75",
"deltat_observed", "vitc_25", "vitc_50", "vitc_75",
"vitc_observed", "thiamin_25", "thiamin_50", "thiamin_75",
"thiamin_observed", "riboflavin_25", "riboflavin_50",
"riboflavin_75", "riboflavin_observed", "niacin_25",
"niacin_50", "niacin_75", "niacin_observed", "pantothenic_25",
"pantothenic_50", "pantothenic_75", "pantothenic_observed",
"vitb6_25", "vitb6_50", "vitb6_75", "vitb6_observed",
"folate_25", "folate_50", "folate_75", "folate_observed",
"vitb12_25", "vitb12_50", "vitb12_75", "vitb12_observed",
"vitk_25", "vitk_50", "vitk_75", "vitk_observed"
]
# Create the DataFrame
return pandas.DataFrame(data, columns=columns, dtype=float)
from crop_production.data_helpers import sample_nutrient_df
from crop_production.data_helpers import aggregate_pctl_polygons_table
workspace = self.workspace_dir
@ -987,7 +741,7 @@ class CropProductionTests(unittest.TestCase):
landcover_raster_projection = spatial_ref.ExportToWkt()
crop_names = ['corn', 'soybean']
nutrient_df = create_nutrient_df()
nutrient_df = sample_nutrient_df()
yield_percentile_headers = ['25', '50', '75']
pixel_area_ha = 1
output_dir = os.path.join(workspace, "OUTPUT")
@ -1007,7 +761,7 @@ class CropProductionTests(unittest.TestCase):
actual_aggregate_pctl_table = pandas.read_csv(
target_aggregate_table_path, dtype=float)
expected_aggregate_pctl_table = _create_expected_pctl_table()
expected_aggregate_pctl_table = aggregate_pctl_polygons_table()
pandas.testing.assert_frame_equal(
actual_aggregate_pctl_table, expected_aggregate_pctl_table)
@ -1016,86 +770,10 @@ class CropProductionTests(unittest.TestCase):
"""Test `tabulate_results"""
from natcap.invest.crop_production_percentile import \
tabulate_results
from crop_production.data_helpers import sample_nutrient_df
from crop_production.data_helpers import tabulate_pctl_results_table
def _create_expected_result_table():
return pandas.DataFrame({
"crop": ["corn", "soybean"], "area (ha)": [2, 4],
"production_observed": [4, 7], "production_25": [1.25, 2.25],
"production_50": [2.5, 4.5], "production_75": [3.75, 6.75],
"protein_25": [488250, 675675], "protein_50": [976500, 1351350],
"protein_75": [1464750, 2027025],
"protein_observed": [1562400, 2102100],
"lipid_25": [93000, 40950], "lipid_50": [186000, 81900],
"lipid_75": [279000, 122850], "lipid_observed": [297600, 127400],
"energy_25": [5533500, 2027025], "energy_50": [11067000, 4054050],
"energy_75": [16600500, 6081075],
"energy_observed": [17707200, 6306300],
"ca_25": [313875, 5262075], "ca_50": [627750, 10524150],
"ca_75": [941625, 15786225], "ca_observed": [1004400, 16370900],
"fe_25": [182512.5, 321457.5], "fe_50": [365025, 642915],
"fe_75": [547537.5, 964372.5], "fe_observed": [584040, 1000090],
"mg_25": [3255000, 5733000], "mg_50": [6510000, 11466000],
"mg_75": [9765000, 17199000], "mg_observed": [10416000, 17836000],
"ph_25": [8184000, 14414400], "ph_50": [16368000, 28828800],
"ph_75": [24552000, 43243200], "ph_observed": [26188800, 44844800],
"k_25": [20076375, 4033575], "k_50": [40152750, 8067150],
"k_75": [60229125, 12100725], "k_observed": [64244400, 12548900],
"na_25": [23250, 40950], "na_50": [46500, 81900],
"na_75": [69750, 122850], "na_observed": [74400, 127400],
"zn_25": [56962.5, 100327.5], "zn_50": [113925, 200655],
"zn_75": [170887.5, 300982.5], "zn_observed": [182280, 312130],
"cu_25": [22087.5, 32760], "cu_50": [44175, 65520],
"cu_75": [66262.5, 98280], "cu_observed": [70680, 101920],
"fl_25": [93000, 61425], "fl_50": [186000, 122850],
"fl_75": [279000, 184275], "fl_observed": [297600, 191100],
"mn_25": [33712.5, 106470], "mn_50": [67425, 212940],
"mn_75": [101137.5, 319410], "mn_observed": [107880, 331240],
"se_25": [1162.5, 6142.5], "se_50": [2325, 12285],
"se_75": [3487.5, 18427.5], "se_observed": [3720, 19110],
"vita_25": [34875, 61425], "vita_50": [69750, 122850],
"vita_75": [104625, 184275], "vita_observed": [111600, 191100],
"betac_25": [186000, 327600], "betac_50": [372000, 655200],
"betac_75": [558000, 982800], "betac_observed": [595200, 1019200],
"alphac_25": [26737.5, 20475], "alphac_50": [53475, 40950],
"alphac_75": [80212.5, 61425], "alphac_observed": [85560, 63700],
"vite_25": [9300, 16380], "vite_50": [18600, 32760],
"vite_75": [27900, 49140], "vite_observed": [29760, 50960],
"crypto_25": [18600, 12285], "crypto_50": [37200, 24570],
"crypto_75": [55800, 36855], "crypto_observed": [59520, 38220],
"lycopene_25": [4185, 6142.5], "lycopene_50": [8370, 12285],
"lycopene_75": [12555, 18427.5], "lycopene_observed": [13392, 19110],
"lutein_25": [732375, 1248975], "lutein_50": [1464750, 2497950],
"lutein_75": [2197125, 3746925], "lutein_observed": [2343600, 3885700],
"betat_25": [5812.5, 10237.5], "betat_50": [11625, 20475],
"betat_75": [17437.5, 30712.5], "betat_observed": [18600, 31850],
"gammat_25": [24412.5, 47092.5], "gammat_50": [48825, 94185],
"gammat_75": [73237.5, 141277.5], "gammat_observed": [78120, 146510],
"deltat_25": [22087.5, 24570], "deltat_50": [44175, 49140],
"deltat_75": [66262.5, 73710], "deltat_observed": [70680, 76440],
"vitc_25": [79050, 61425], "vitc_50": [158100, 122850],
"vitc_75": [237150, 184275], "vitc_observed": [252960, 191100],
"thiamin_25": [4650, 8599.5], "thiamin_50": [9300, 17199],
"thiamin_75": [13950, 25798.5], "thiamin_observed": [14880, 26754],
"riboflavin_25": [20925, 16789.5], "riboflavin_50": [41850, 33579],
"riboflavin_75": [62775, 50368.5],
"riboflavin_observed": [66960, 52234], "niacin_25": [95325, 249795],
"niacin_50": [190650, 499590], "niacin_75": [285975, 749385],
"niacin_observed": [305040, 777140],
"pantothenic_25": [10462.5, 18837], "pantothenic_50": [20925, 37674],
"pantothenic_75": [31387.5, 56511],
"pantothenic_observed": [33480, 58604],
"vitb6_25": [16275, 110565], "vitb6_50": [32550, 221130],
"vitb6_75": [48825, 331695], "vitb6_observed": [52080, 343980],
"folate_25": [4475625, 6244875], "folate_50": [8951250, 12489750],
"folate_75": [13426875, 18734625],
"folate_observed": [14322000, 19428500],
"vitb12_25": [23250, 61425], "vitb12_50": [46500, 122850],
"vitb12_75": [69750, 184275], "vitb12_observed": [74400, 191100],
"vitk_25": [476625, 859950], "vitk_50": [953250, 1719900],
"vitk_75": [1429875, 2579850], "vitk_observed": [1525200, 2675400]
})
nutrient_df = create_nutrient_df()
nutrient_df = sample_nutrient_df()
output_dir = os.path.join(self.workspace_dir, "output")
os.makedirs(output_dir, exist_ok=True)
yield_percentile_headers = ['yield_25', 'yield_50', 'yield_75']
@ -1117,11 +795,12 @@ class CropProductionTests(unittest.TestCase):
output_dir, file_suffix, target_table_path)
actual_table = pandas.read_csv(target_table_path, nrows=2)
expected_table = _create_expected_result_table()
expected_table = tabulate_pctl_results_table()
pandas.testing.assert_frame_equal(actual_table, expected_table,
check_dtype=False)
class CropValidationTests(unittest.TestCase):
"""Tests for the Crop Productions' MODEL_SPEC and validation."""