655 lines
27 KiB
Python
655 lines
27 KiB
Python
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from statsmodels.compat.pandas import assert_series_equal, assert_frame_equal
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from io import StringIO
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from textwrap import dedent
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import numpy as np
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import numpy.testing as npt
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import numpy
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from numpy.testing import assert_equal
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import pandas
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import pytest
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from statsmodels.imputation import ros
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def load_basic_data():
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raw_csv = StringIO(
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"res,qual\n2.00,=\n4.20,=\n4.62,=\n5.00,ND\n5.00,ND\n5.50,ND\n"
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"5.57,=\n5.66,=\n5.75,ND\n5.86,=\n6.65,=\n6.78,=\n6.79,=\n7.50,=\n"
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"7.50,=\n7.50,=\n8.63,=\n8.71,=\n8.99,=\n9.50,ND\n9.50,ND\n9.85,=\n"
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"10.82,=\n11.00,ND\n11.25,=\n11.25,=\n12.20,=\n14.92,=\n16.77,=\n"
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"17.81,=\n19.16,=\n19.19,=\n19.64,=\n20.18,=\n22.97,=\n"
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)
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df = pandas.read_csv(raw_csv)
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df.loc[:, 'conc'] = df['res']
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df.loc[:, 'censored'] = df['qual'] == 'ND'
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return df
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def load_intermediate_data():
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df = pandas.DataFrame([
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{'censored': True, 'conc': 5.0, 'det_limit_index': 1, 'rank': 1},
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{'censored': True, 'conc': 5.0, 'det_limit_index': 1, 'rank': 2},
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{'censored': True, 'conc': 5.5, 'det_limit_index': 2, 'rank': 1},
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{'censored': True, 'conc': 5.75, 'det_limit_index': 3, 'rank': 1},
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{'censored': True, 'conc': 9.5, 'det_limit_index': 4, 'rank': 1},
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{'censored': True, 'conc': 9.5, 'det_limit_index': 4, 'rank': 2},
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{'censored': True, 'conc': 11.0, 'det_limit_index': 5, 'rank': 1},
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{'censored': False, 'conc': 2.0, 'det_limit_index': 0, 'rank': 1},
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{'censored': False, 'conc': 4.2, 'det_limit_index': 0, 'rank': 2},
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{'censored': False, 'conc': 4.62, 'det_limit_index': 0, 'rank': 3},
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{'censored': False, 'conc': 5.57, 'det_limit_index': 2, 'rank': 1},
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{'censored': False, 'conc': 5.66, 'det_limit_index': 2, 'rank': 2},
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{'censored': False, 'conc': 5.86, 'det_limit_index': 3, 'rank': 1},
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{'censored': False, 'conc': 6.65, 'det_limit_index': 3, 'rank': 2},
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{'censored': False, 'conc': 6.78, 'det_limit_index': 3, 'rank': 3},
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{'censored': False, 'conc': 6.79, 'det_limit_index': 3, 'rank': 4},
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{'censored': False, 'conc': 7.5, 'det_limit_index': 3, 'rank': 5},
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{'censored': False, 'conc': 7.5, 'det_limit_index': 3, 'rank': 6},
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{'censored': False, 'conc': 7.5, 'det_limit_index': 3, 'rank': 7},
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{'censored': False, 'conc': 8.63, 'det_limit_index': 3, 'rank': 8},
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{'censored': False, 'conc': 8.71, 'det_limit_index': 3, 'rank': 9},
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{'censored': False, 'conc': 8.99, 'det_limit_index': 3, 'rank': 10},
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{'censored': False, 'conc': 9.85, 'det_limit_index': 4, 'rank': 1},
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{'censored': False, 'conc': 10.82, 'det_limit_index': 4, 'rank': 2},
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{'censored': False, 'conc': 11.25, 'det_limit_index': 5, 'rank': 1},
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{'censored': False, 'conc': 11.25, 'det_limit_index': 5, 'rank': 2},
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{'censored': False, 'conc': 12.2, 'det_limit_index': 5, 'rank': 3},
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{'censored': False, 'conc': 14.92, 'det_limit_index': 5, 'rank': 4},
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{'censored': False, 'conc': 16.77, 'det_limit_index': 5, 'rank': 5},
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{'censored': False, 'conc': 17.81, 'det_limit_index': 5, 'rank': 6},
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{'censored': False, 'conc': 19.16, 'det_limit_index': 5, 'rank': 7},
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{'censored': False, 'conc': 19.19, 'det_limit_index': 5, 'rank': 8},
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{'censored': False, 'conc': 19.64, 'det_limit_index': 5, 'rank': 9},
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{'censored': False, 'conc': 20.18, 'det_limit_index': 5, 'rank': 10},
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{'censored': False, 'conc': 22.97, 'det_limit_index': 5, 'rank': 11}
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])
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return df
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def load_advanced_data():
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df = pandas.DataFrame([
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{'Zprelim': -1.4456202174142005, 'censored': True, 'conc': 5.0,
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'det_limit_index': 1, 'plot_pos': 0.07414187643020594, 'rank': 1},
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{'Zprelim': -1.2201035333697587, 'censored': True, 'conc': 5.0,
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'det_limit_index': 1, 'plot_pos': 0.11121281464530891, 'rank': 2},
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{'Zprelim': -1.043822530159519, 'censored': True, 'conc': 5.5,
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'det_limit_index': 2, 'plot_pos': 0.14828375286041187, 'rank': 1},
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{'Zprelim': -1.0438225301595188, 'censored': True, 'conc': 5.75,
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'det_limit_index': 3, 'plot_pos': 0.1482837528604119, 'rank': 1},
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{'Zprelim': -0.8109553641377003, 'censored': True, 'conc': 9.5,
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'det_limit_index': 4, 'plot_pos': 0.20869565217391303, 'rank': 1},
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{'Zprelim': -0.4046779045300476, 'censored': True, 'conc': 9.5,
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'det_limit_index': 4, 'plot_pos': 0.34285714285714286, 'rank': 2},
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{'Zprelim': -0.20857169501420522, 'censored': True, 'conc': 11.0,
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'det_limit_index': 5, 'plot_pos': 0.41739130434782606, 'rank': 1},
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{'Zprelim': -1.5927654676048002, 'censored': False, 'conc': 2.0,
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'det_limit_index': 0, 'plot_pos': 0.055606407322654455, 'rank': 1},
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{'Zprelim': -1.2201035333697587, 'censored': False, 'conc': 4.2,
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'det_limit_index': 0, 'plot_pos': 0.11121281464530891, 'rank': 2},
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{'Zprelim': -0.9668111610681008, 'censored': False, 'conc': 4.62,
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'det_limit_index': 0, 'plot_pos': 0.16681922196796337, 'rank': 3},
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{'Zprelim': -0.6835186393930371, 'censored': False, 'conc': 5.57,
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'det_limit_index': 2, 'plot_pos': 0.24713958810068648, 'rank': 1},
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{'Zprelim': -0.6072167256926887, 'censored': False, 'conc': 5.66,
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'det_limit_index': 2, 'plot_pos': 0.27185354691075514, 'rank': 2},
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{'Zprelim': -0.44953240276543616, 'censored': False, 'conc': 5.86,
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'det_limit_index': 3, 'plot_pos': 0.3265238194299979, 'rank': 1},
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{'Zprelim': -0.36788328223414807, 'censored': False, 'conc': 6.65,
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'det_limit_index': 3, 'plot_pos': 0.35648013313917204, 'rank': 2},
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{'Zprelim': -0.28861907892223937, 'censored': False, 'conc': 6.78,
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'det_limit_index': 3, 'plot_pos': 0.38643644684834616, 'rank': 3},
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{'Zprelim': -0.21113039741112186, 'censored': False, 'conc': 6.79,
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'det_limit_index': 3, 'plot_pos': 0.4163927605575203, 'rank': 4},
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{'Zprelim': -0.1348908823006299, 'censored': False, 'conc': 7.5,
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'det_limit_index': 3, 'plot_pos': 0.4463490742666944, 'rank': 5},
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{'Zprelim': -0.05942854708257491, 'censored': False, 'conc': 7.5,
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'det_limit_index': 3, 'plot_pos': 0.4763053879758685, 'rank': 6},
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{'Zprelim': 0.015696403006170083, 'censored': False, 'conc': 7.5,
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'det_limit_index': 3, 'plot_pos': 0.5062617016850427, 'rank': 7},
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{'Zprelim': 0.09091016994359362, 'censored': False, 'conc': 8.63,
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'det_limit_index': 3, 'plot_pos': 0.5362180153942168, 'rank': 8},
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{'Zprelim': 0.16664251178856201, 'censored': False, 'conc': 8.71,
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'det_limit_index': 3, 'plot_pos': 0.5661743291033909, 'rank': 9},
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{'Zprelim': 0.24334426739770573, 'censored': False, 'conc': 8.99,
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'det_limit_index': 3, 'plot_pos': 0.596130642812565, 'rank': 10},
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{'Zprelim': 0.3744432988606558, 'censored': False, 'conc': 9.85,
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'det_limit_index': 4, 'plot_pos': 0.6459627329192545, 'rank': 1},
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{'Zprelim': 0.4284507519609981, 'censored': False, 'conc': 10.82,
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'det_limit_index': 4, 'plot_pos': 0.6658385093167701, 'rank': 2},
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{'Zprelim': 0.5589578655042562, 'censored': False, 'conc': 11.25,
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'det_limit_index': 5, 'plot_pos': 0.7119047619047619, 'rank': 1},
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{'Zprelim': 0.6374841609623771, 'censored': False, 'conc': 11.25,
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'det_limit_index': 5, 'plot_pos': 0.7380952380952381, 'rank': 2},
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{'Zprelim': 0.7201566171385521, 'censored': False, 'conc': 12.2,
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'det_limit_index': 5, 'plot_pos': 0.7642857142857142, 'rank': 3},
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{'Zprelim': 0.8080746339118065, 'censored': False, 'conc': 14.92,
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'det_limit_index': 5, 'plot_pos': 0.7904761904761904, 'rank': 4},
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{'Zprelim': 0.9027347916438648, 'censored': False, 'conc': 16.77,
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'det_limit_index': 5, 'plot_pos': 0.8166666666666667, 'rank': 5},
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{'Zprelim': 1.0062699858608395, 'censored': False, 'conc': 17.81,
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'det_limit_index': 5, 'plot_pos': 0.8428571428571429, 'rank': 6},
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{'Zprelim': 1.1219004674623523, 'censored': False, 'conc': 19.16,
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'det_limit_index': 5, 'plot_pos': 0.8690476190476191, 'rank': 7},
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{'Zprelim': 1.2548759122271174, 'censored': False, 'conc': 19.19,
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'det_limit_index': 5, 'plot_pos': 0.8952380952380953, 'rank': 8},
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{'Zprelim': 1.414746425534976, 'censored': False, 'conc': 19.64,
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'det_limit_index': 5, 'plot_pos': 0.9214285714285714, 'rank': 9},
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{'Zprelim': 1.622193585315426, 'censored': False, 'conc': 20.18,
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'det_limit_index': 5, 'plot_pos': 0.9476190476190476, 'rank': 10},
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{'Zprelim': 1.9399896117517081, 'censored': False, 'conc': 22.97,
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'det_limit_index': 5, 'plot_pos': 0.9738095238095239, 'rank': 11}
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])
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return df
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def load_basic_cohn():
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cohn = pandas.DataFrame([
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{'lower_dl': 2.0, 'ncen_equal': 0.0, 'nobs_below': 0.0,
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'nuncen_above': 3.0, 'prob_exceedance': 1.0, 'upper_dl': 5.0},
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{'lower_dl': 5.0, 'ncen_equal': 2.0, 'nobs_below': 5.0,
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'nuncen_above': 0.0, 'prob_exceedance': 0.77757437070938218, 'upper_dl': 5.5},
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{'lower_dl': 5.5, 'ncen_equal': 1.0, 'nobs_below': 6.0,
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'nuncen_above': 2.0, 'prob_exceedance': 0.77757437070938218, 'upper_dl': 5.75},
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{'lower_dl': 5.75, 'ncen_equal': 1.0, 'nobs_below': 9.0,
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'nuncen_above': 10.0, 'prob_exceedance': 0.7034324942791762, 'upper_dl': 9.5},
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{'lower_dl': 9.5, 'ncen_equal': 2.0, 'nobs_below': 21.0,
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'nuncen_above': 2.0, 'prob_exceedance': 0.37391304347826088, 'upper_dl': 11.0},
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{'lower_dl': 11.0, 'ncen_equal': 1.0, 'nobs_below': 24.0,
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'nuncen_above': 11.0, 'prob_exceedance': 0.31428571428571428, 'upper_dl': numpy.inf},
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{'lower_dl': numpy.nan, 'ncen_equal': numpy.nan, 'nobs_below': numpy.nan,
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'nuncen_above': numpy.nan, 'prob_exceedance': 0.0, 'upper_dl': numpy.nan}
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])
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return cohn
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class Test__ros_sort:
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def setup_method(self):
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self.df = load_basic_data()
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self.expected_baseline = pandas.DataFrame([
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{'censored': True, 'conc': 5.0}, {'censored': True, 'conc': 5.0},
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{'censored': True, 'conc': 5.5}, {'censored': True, 'conc': 5.75},
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{'censored': True, 'conc': 9.5}, {'censored': True, 'conc': 9.5},
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{'censored': True, 'conc': 11.0}, {'censored': False, 'conc': 2.0},
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{'censored': False, 'conc': 4.2}, {'censored': False, 'conc': 4.62},
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{'censored': False, 'conc': 5.57}, {'censored': False, 'conc': 5.66},
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{'censored': False, 'conc': 5.86}, {'censored': False, 'conc': 6.65},
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{'censored': False, 'conc': 6.78}, {'censored': False, 'conc': 6.79},
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{'censored': False, 'conc': 7.5}, {'censored': False, 'conc': 7.5},
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{'censored': False, 'conc': 7.5}, {'censored': False, 'conc': 8.63},
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{'censored': False, 'conc': 8.71}, {'censored': False, 'conc': 8.99},
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{'censored': False, 'conc': 9.85}, {'censored': False, 'conc': 10.82},
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{'censored': False, 'conc': 11.25}, {'censored': False, 'conc': 11.25},
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{'censored': False, 'conc': 12.2}, {'censored': False, 'conc': 14.92},
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{'censored': False, 'conc': 16.77}, {'censored': False, 'conc': 17.81},
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{'censored': False, 'conc': 19.16}, {'censored': False, 'conc': 19.19},
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{'censored': False, 'conc': 19.64}, {'censored': False, 'conc': 20.18},
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{'censored': False, 'conc': 22.97},
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])[['conc', 'censored']]
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self.expected_with_warning = self.expected_baseline.iloc[:-1]
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def test_baseline(self):
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result = ros._ros_sort(self.df, 'conc', 'censored')
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assert_frame_equal(result, self.expected_baseline)
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def test_censored_greater_than_max(self):
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df = self.df.copy()
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max_row = df['conc'].idxmax()
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df.loc[max_row, 'censored'] = True
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result = ros._ros_sort(df, 'conc', 'censored')
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assert_frame_equal(result, self.expected_with_warning)
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class Test_cohn_numbers:
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def setup_method(self):
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self.df = load_basic_data()
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self.final_cols = ['lower_dl', 'upper_dl', 'nuncen_above', 'nobs_below',
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'ncen_equal', 'prob_exceedance']
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self.expected_baseline = pandas.DataFrame([
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{'lower_dl': 2.0, 'ncen_equal': 0.0, 'nobs_below': 0.0,
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'nuncen_above': 3.0, 'prob_exceedance': 1.0, 'upper_dl': 5.0},
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{'lower_dl': 5.0, 'ncen_equal': 2.0, 'nobs_below': 5.0,
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'nuncen_above': 0.0, 'prob_exceedance': 0.77757437070938218, 'upper_dl': 5.5},
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{'lower_dl': 5.5, 'ncen_equal': 1.0, 'nobs_below': 6.0,
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'nuncen_above': 2.0, 'prob_exceedance': 0.77757437070938218, 'upper_dl': 5.75},
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{'lower_dl': 5.75, 'ncen_equal': 1.0, 'nobs_below': 9.0,
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'nuncen_above': 10.0, 'prob_exceedance': 0.7034324942791762, 'upper_dl': 9.5},
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{'lower_dl': 9.5, 'ncen_equal': 2.0, 'nobs_below': 21.0,
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'nuncen_above': 2.0, 'prob_exceedance': 0.37391304347826088, 'upper_dl': 11.0},
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{'lower_dl': 11.0, 'ncen_equal': 1.0, 'nobs_below': 24.0,
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'nuncen_above': 11.0, 'prob_exceedance': 0.31428571428571428, 'upper_dl': numpy.inf},
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{'lower_dl': numpy.nan, 'ncen_equal': numpy.nan, 'nobs_below': numpy.nan,
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'nuncen_above': numpy.nan, 'prob_exceedance': 0.0, 'upper_dl': numpy.nan}
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||
|
|
])[self.final_cols]
|
||
|
|
|
||
|
|
|
||
|
|
def test_baseline(self):
|
||
|
|
result = ros.cohn_numbers(self.df, observations='conc', censorship='censored')
|
||
|
|
assert_frame_equal(result, self.expected_baseline)
|
||
|
|
|
||
|
|
def test_no_NDs(self):
|
||
|
|
_df = self.df.copy()
|
||
|
|
_df['qual'] = False
|
||
|
|
result = ros.cohn_numbers(_df, observations='conc', censorship='qual')
|
||
|
|
assert result.shape == (0, 6)
|
||
|
|
|
||
|
|
|
||
|
|
class Test__detection_limit_index:
|
||
|
|
def setup_method(self):
|
||
|
|
self.cohn = load_basic_cohn()
|
||
|
|
self.empty_cohn = pandas.DataFrame(numpy.empty((0, 7)))
|
||
|
|
|
||
|
|
def test_empty(self):
|
||
|
|
assert_equal(ros._detection_limit_index(None, self.empty_cohn), 0)
|
||
|
|
|
||
|
|
def test_populated(self):
|
||
|
|
assert_equal(ros._detection_limit_index(3.5, self.cohn), 0)
|
||
|
|
assert_equal(ros._detection_limit_index(6.0, self.cohn), 3)
|
||
|
|
assert_equal(ros._detection_limit_index(12.0, self.cohn), 5)
|
||
|
|
|
||
|
|
def test_out_of_bounds(self):
|
||
|
|
with pytest.raises(IndexError):
|
||
|
|
ros._detection_limit_index(0, self.cohn)
|
||
|
|
|
||
|
|
|
||
|
|
def test__ros_group_rank():
|
||
|
|
df = pandas.DataFrame({
|
||
|
|
'dl_idx': [1] * 12,
|
||
|
|
'params': list('AABCCCDE') + list('DCBA'),
|
||
|
|
'values': list(range(12))
|
||
|
|
})
|
||
|
|
|
||
|
|
result = ros._ros_group_rank(df, 'dl_idx', 'params')
|
||
|
|
expected = pandas.Series([1, 2, 1, 1, 2, 3, 1, 1, 2, 4, 2, 3], name='rank')
|
||
|
|
assert_series_equal(result.astype(int), expected.astype(int))
|
||
|
|
|
||
|
|
|
||
|
|
class Test__ros_plot_pos:
|
||
|
|
def setup_method(self):
|
||
|
|
self.cohn = load_basic_cohn()
|
||
|
|
|
||
|
|
def test_uncensored_1(self):
|
||
|
|
row = {'censored': False, 'det_limit_index': 2, 'rank': 1}
|
||
|
|
result = ros._ros_plot_pos(row, 'censored', self.cohn)
|
||
|
|
assert_equal(result, 0.24713958810068648)
|
||
|
|
|
||
|
|
def test_uncensored_2(self):
|
||
|
|
row = {'censored': False, 'det_limit_index': 2, 'rank': 12}
|
||
|
|
result = ros._ros_plot_pos(row, 'censored', self.cohn)
|
||
|
|
assert_equal(result, 0.51899313501144173)
|
||
|
|
|
||
|
|
def test_censored_1(self):
|
||
|
|
row = {'censored': True, 'det_limit_index': 5, 'rank': 4}
|
||
|
|
result = ros._ros_plot_pos(row, 'censored', self.cohn)
|
||
|
|
assert_equal(result, 1.3714285714285714)
|
||
|
|
|
||
|
|
def test_censored_2(self):
|
||
|
|
row = {'censored': True, 'det_limit_index': 4, 'rank': 2}
|
||
|
|
result = ros._ros_plot_pos(row, 'censored', self.cohn)
|
||
|
|
assert_equal(result, 0.41739130434782606)
|
||
|
|
|
||
|
|
|
||
|
|
def test__norm_plot_pos():
|
||
|
|
result = ros._norm_plot_pos([1, 2, 3, 4])
|
||
|
|
expected = numpy.array([ 0.159104, 0.385452, 0.614548, 0.840896])
|
||
|
|
npt.assert_array_almost_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_plotting_positions():
|
||
|
|
df = load_intermediate_data()
|
||
|
|
cohn = load_basic_cohn()
|
||
|
|
|
||
|
|
results = ros.plotting_positions(df, 'censored', cohn)
|
||
|
|
expected = numpy.array([
|
||
|
|
0.07414188, 0.11121281, 0.14828375, 0.14828375, 0.20869565,
|
||
|
|
0.34285714, 0.4173913 , 0.05560641, 0.11121281, 0.16681922,
|
||
|
|
0.24713959, 0.27185355, 0.32652382, 0.35648013, 0.38643645,
|
||
|
|
0.41639276, 0.44634907, 0.47630539, 0.5062617 , 0.53621802,
|
||
|
|
0.56617433, 0.59613064, 0.64596273, 0.66583851, 0.71190476,
|
||
|
|
0.73809524, 0.76428571, 0.79047619, 0.81666667, 0.84285714,
|
||
|
|
0.86904762, 0.8952381 , 0.92142857, 0.94761905, 0.97380952
|
||
|
|
])
|
||
|
|
npt.assert_array_almost_equal(results, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test__impute():
|
||
|
|
expected = numpy.array([
|
||
|
|
3.11279729, 3.60634338, 4.04602788, 4.04602788,
|
||
|
|
4.71008116, 6.14010906, 6.97841457, 2. ,
|
||
|
|
4.2 , 4.62 , 5.57 , 5.66 ,
|
||
|
|
5.86 , 6.65 , 6.78 , 6.79 ,
|
||
|
|
7.5 , 7.5 , 7.5 , 8.63 ,
|
||
|
|
8.71 , 8.99 , 9.85 , 10.82 ,
|
||
|
|
11.25 , 11.25 , 12.2 , 14.92 ,
|
||
|
|
16.77 , 17.81 , 19.16 , 19.19 ,
|
||
|
|
19.64 , 20.18 , 22.97
|
||
|
|
])
|
||
|
|
df = load_advanced_data()
|
||
|
|
df = ros._impute(df, 'conc', 'censored', numpy.log, numpy.exp)
|
||
|
|
result = df['final'].values
|
||
|
|
npt.assert_array_almost_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test__do_ros():
|
||
|
|
expected = numpy.array([
|
||
|
|
3.11279729, 3.60634338, 4.04602788, 4.04602788,
|
||
|
|
4.71008116, 6.14010906, 6.97841457, 2. ,
|
||
|
|
4.2 , 4.62 , 5.57 , 5.66 ,
|
||
|
|
5.86 , 6.65 , 6.78 , 6.79 ,
|
||
|
|
7.5 , 7.5 , 7.5 , 8.63 ,
|
||
|
|
8.71 , 8.99 , 9.85 , 10.82 ,
|
||
|
|
11.25 , 11.25 , 12.2 , 14.92 ,
|
||
|
|
16.77 , 17.81 , 19.16 , 19.19 ,
|
||
|
|
19.64 , 20.18 , 22.97
|
||
|
|
])
|
||
|
|
|
||
|
|
df = load_basic_data()
|
||
|
|
df = ros._do_ros(df, 'conc', 'censored', numpy.log, numpy.exp)
|
||
|
|
result = df['final'].values
|
||
|
|
npt.assert_array_almost_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
class CheckROSMixin:
|
||
|
|
def test_ros_df(self):
|
||
|
|
result = ros.impute_ros(self.rescol, self.cencol, df=self.df)
|
||
|
|
npt.assert_array_almost_equal(
|
||
|
|
sorted(result),
|
||
|
|
sorted(self.expected_final),
|
||
|
|
decimal=self.decimal
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_ros_arrays(self):
|
||
|
|
result = ros.impute_ros(self.df[self.rescol], self.df[self.cencol], df=None)
|
||
|
|
npt.assert_array_almost_equal(
|
||
|
|
sorted(result),
|
||
|
|
sorted(self.expected_final),
|
||
|
|
decimal=self.decimal
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_cohn(self):
|
||
|
|
cols = [
|
||
|
|
'nuncen_above', 'nobs_below',
|
||
|
|
'ncen_equal', 'prob_exceedance'
|
||
|
|
]
|
||
|
|
cohn = ros.cohn_numbers(self.df, self.rescol, self.cencol)
|
||
|
|
# Use round in place of the deprecated check_less_precise arg
|
||
|
|
assert_frame_equal(
|
||
|
|
np.round(cohn[cols], 3),
|
||
|
|
np.round(self.expected_cohn[cols], 3),
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class Test_ROS_HelselAppendixB(CheckROSMixin):
|
||
|
|
"""
|
||
|
|
Appendix B dataset from "Estimation of Descriptive Statists for
|
||
|
|
Multiply Censored Water Quality Data", Water Resources Research,
|
||
|
|
Vol 24, No 12, pp 1997 - 2004. December 1988.
|
||
|
|
"""
|
||
|
|
decimal = 2
|
||
|
|
res = numpy.array([
|
||
|
|
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10., 10., 10.,
|
||
|
|
3.0, 7.0, 9.0, 12., 15., 20., 27., 33., 50.
|
||
|
|
])
|
||
|
|
cen = numpy.array([
|
||
|
|
True, True, True, True, True, True, True, True, True,
|
||
|
|
False, False, False, False, False, False, False,
|
||
|
|
False, False
|
||
|
|
])
|
||
|
|
rescol = 'obs'
|
||
|
|
cencol = 'cen'
|
||
|
|
df = pandas.DataFrame({rescol: res, cencol: cen})
|
||
|
|
expected_final = numpy.array([
|
||
|
|
0.47, 0.85, 1.11, 1.27, 1.76, 2.34, 2.50, 3.00, 3.03,
|
||
|
|
4.80, 7.00, 9.00, 12.0, 15.0, 20.0, 27.0, 33.0, 50.0
|
||
|
|
])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([3.0, 6.0, numpy.nan]),
|
||
|
|
'nobs_below': numpy.array([6.0, 12.0, numpy.nan]),
|
||
|
|
'ncen_equal': numpy.array([6.0, 3.0, numpy.nan]),
|
||
|
|
'prob_exceedance': numpy.array([0.55556, 0.33333, 0.0]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_ROS_HelselArsenic(CheckROSMixin):
|
||
|
|
"""
|
||
|
|
Oahu arsenic data from Nondetects and Data Analysis by
|
||
|
|
Dennis R. Helsel (John Wiley, 2005)
|
||
|
|
|
||
|
|
Plotting positions are fudged since relative to source data since
|
||
|
|
modeled data is what matters and (source data plot positions are
|
||
|
|
not uniformly spaced, which seems weird)
|
||
|
|
"""
|
||
|
|
decimal = 2
|
||
|
|
res = numpy.array([
|
||
|
|
3.2, 2.8, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
|
||
|
|
2.0, 2.0, 1.7, 1.5, 1.0, 1.0, 1.0, 1.0,
|
||
|
|
0.9, 0.9, 0.7, 0.7, 0.6, 0.5, 0.5, 0.5
|
||
|
|
])
|
||
|
|
|
||
|
|
cen = numpy.array([
|
||
|
|
False, False, True, True, True, True, True,
|
||
|
|
True, True, True, False, False, True, True,
|
||
|
|
True, True, False, True, False, False, False,
|
||
|
|
False, False, False
|
||
|
|
])
|
||
|
|
rescol = 'obs'
|
||
|
|
cencol = 'cen'
|
||
|
|
df = pandas.DataFrame({rescol: res, cencol: cen})
|
||
|
|
expected_final = numpy.array([
|
||
|
|
3.20, 2.80, 1.42, 1.14, 0.95, 0.81, 0.68, 0.57,
|
||
|
|
0.46, 0.35, 1.70, 1.50, 0.98, 0.76, 0.58, 0.41,
|
||
|
|
0.90, 0.61, 0.70, 0.70, 0.60, 0.50, 0.50, 0.50
|
||
|
|
])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([6.0, 1.0, 2.0, 2.0, numpy.nan]),
|
||
|
|
'nobs_below': numpy.array([0.0, 7.0, 12.0, 22.0, numpy.nan]),
|
||
|
|
'ncen_equal': numpy.array([0.0, 1.0, 4.0, 8.0, numpy.nan]),
|
||
|
|
'prob_exceedance': numpy.array([1.0, 0.3125, 0.21429, 0.0833, 0.0]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_ROS_RNADAdata(CheckROSMixin):
|
||
|
|
decimal = 3
|
||
|
|
datastring = StringIO(dedent("""\
|
||
|
|
res cen
|
||
|
|
0.090 True
|
||
|
|
0.090 True
|
||
|
|
0.090 True
|
||
|
|
0.101 False
|
||
|
|
0.136 False
|
||
|
|
0.340 False
|
||
|
|
0.457 False
|
||
|
|
0.514 False
|
||
|
|
0.629 False
|
||
|
|
0.638 False
|
||
|
|
0.774 False
|
||
|
|
0.788 False
|
||
|
|
0.900 True
|
||
|
|
0.900 True
|
||
|
|
0.900 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 False
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.000 True
|
||
|
|
1.100 False
|
||
|
|
2.000 False
|
||
|
|
2.000 False
|
||
|
|
2.404 False
|
||
|
|
2.860 False
|
||
|
|
3.000 False
|
||
|
|
3.000 False
|
||
|
|
3.705 False
|
||
|
|
4.000 False
|
||
|
|
5.000 False
|
||
|
|
5.960 False
|
||
|
|
6.000 False
|
||
|
|
7.214 False
|
||
|
|
16.000 False
|
||
|
|
17.716 False
|
||
|
|
25.000 False
|
||
|
|
51.000 False"""))
|
||
|
|
rescol = 'res'
|
||
|
|
cencol = 'cen'
|
||
|
|
df = pandas.read_csv(datastring, sep=r'\s+')
|
||
|
|
expected_final = numpy.array([
|
||
|
|
0.01907990, 0.03826254, 0.06080717, 0.10100000, 0.13600000,
|
||
|
|
0.34000000, 0.45700000, 0.51400000, 0.62900000, 0.63800000,
|
||
|
|
0.77400000, 0.78800000, 0.08745914, 0.25257575, 0.58544205,
|
||
|
|
0.01711153, 0.03373885, 0.05287083, 0.07506079, 0.10081573,
|
||
|
|
1.00000000, 0.13070334, 0.16539309, 0.20569039, 0.25257575,
|
||
|
|
0.30725491, 0.37122555, 0.44636843, 0.53507405, 0.64042242,
|
||
|
|
0.76644378, 0.91850581, 1.10390531, 1.10000000, 2.00000000,
|
||
|
|
2.00000000, 2.40400000, 2.86000000, 3.00000000, 3.00000000,
|
||
|
|
3.70500000, 4.00000000, 5.00000000, 5.96000000, 6.00000000,
|
||
|
|
7.21400000, 16.00000000, 17.71600000, 25.00000000, 51.00000000
|
||
|
|
])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([9., 0.0, 18., numpy.nan]),
|
||
|
|
'nobs_below': numpy.array([3., 15., 32., numpy.nan]),
|
||
|
|
'ncen_equal': numpy.array([3., 3., 17., numpy.nan]),
|
||
|
|
'prob_exceedance': numpy.array([0.84, 0.36, 0.36, 0]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_NoOp_ZeroND(CheckROSMixin):
|
||
|
|
decimal = 2
|
||
|
|
numpy.random.seed(0)
|
||
|
|
N = 20
|
||
|
|
res = numpy.random.lognormal(size=N)
|
||
|
|
cen = [False] * N
|
||
|
|
rescol = 'obs'
|
||
|
|
cencol = 'cen'
|
||
|
|
df = pandas.DataFrame({rescol: res, cencol: cen})
|
||
|
|
expected_final = numpy.array([
|
||
|
|
0.38, 0.43, 0.81, 0.86, 0.90, 1.13, 1.15, 1.37, 1.40,
|
||
|
|
1.49, 1.51, 1.56, 2.14, 2.59, 2.66, 4.28, 4.46, 5.84,
|
||
|
|
6.47, 9.4
|
||
|
|
])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([]),
|
||
|
|
'nobs_below': numpy.array([]),
|
||
|
|
'ncen_equal': numpy.array([]),
|
||
|
|
'prob_exceedance': numpy.array([]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_ROS_OneND(CheckROSMixin):
|
||
|
|
decimal = 3
|
||
|
|
res = numpy.array([
|
||
|
|
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10., 10., 10.,
|
||
|
|
3.0, 7.0, 9.0, 12., 15., 20., 27., 33., 50.
|
||
|
|
])
|
||
|
|
cen = numpy.array([
|
||
|
|
True, False, False, False, False, False, False, False, False,
|
||
|
|
False, False, False, False, False, False, False,
|
||
|
|
False, False
|
||
|
|
])
|
||
|
|
rescol = 'conc'
|
||
|
|
cencol = 'cen'
|
||
|
|
df = pandas.DataFrame({rescol: res, cencol: cen})
|
||
|
|
expected_final = numpy.array([
|
||
|
|
0.24, 1.0, 1.0, 1.0, 1.0, 1.0, 10., 10., 10.,
|
||
|
|
3.0 , 7.0, 9.0, 12., 15., 20., 27., 33., 50.
|
||
|
|
])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([17.0, numpy.nan]),
|
||
|
|
'nobs_below': numpy.array([1.0, numpy.nan]),
|
||
|
|
'ncen_equal': numpy.array([1.0, numpy.nan]),
|
||
|
|
'prob_exceedance': numpy.array([0.94444, 0.0]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_HalfDLs_80pctNDs(CheckROSMixin):
|
||
|
|
decimal = 3
|
||
|
|
res = numpy.array([
|
||
|
|
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10., 10., 10.,
|
||
|
|
3.0, 7.0, 9.0, 12., 15., 20., 27., 33., 50.
|
||
|
|
])
|
||
|
|
cen = numpy.array([
|
||
|
|
True, True, True, True, True, True, True, True,
|
||
|
|
True, True, True, True, True, True, True, False,
|
||
|
|
False, False
|
||
|
|
])
|
||
|
|
rescol = 'value'
|
||
|
|
cencol = 'qual'
|
||
|
|
df = pandas.DataFrame({rescol: res, cencol: cen})
|
||
|
|
expected_final = numpy.array([
|
||
|
|
0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 5.0, 5.0, 5.0,
|
||
|
|
1.5, 3.5, 4.5, 6.0, 7.5, 10., 27., 33., 50.
|
||
|
|
])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([0., 0., 0., 0., 0., 0., 0., 3., numpy.nan]),
|
||
|
|
'nobs_below': numpy.array([6., 7., 8., 9., 12., 13., 14., 15., numpy.nan]),
|
||
|
|
'ncen_equal': numpy.array([6., 1., 1., 1., 3., 1., 1., 1., numpy.nan]),
|
||
|
|
'prob_exceedance': numpy.array([0.16667] * 8 + [0.]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_HaflDLs_OneUncensored(CheckROSMixin):
|
||
|
|
decimal = 3
|
||
|
|
res = numpy.array([1.0, 1.0, 12., 15., ])
|
||
|
|
cen = numpy.array([True, True, True, False ])
|
||
|
|
rescol = 'value'
|
||
|
|
cencol = 'qual'
|
||
|
|
df = pandas.DataFrame({rescol: res, cencol: cen})
|
||
|
|
expected_final = numpy.array([0.5, 0.5, 6. , 15.])
|
||
|
|
|
||
|
|
expected_cohn = pandas.DataFrame({
|
||
|
|
'nuncen_above': numpy.array([0., 1., numpy.nan]),
|
||
|
|
'nobs_below': numpy.array([2., 3., numpy.nan]),
|
||
|
|
'ncen_equal': numpy.array([2., 1., numpy.nan]),
|
||
|
|
'prob_exceedance': numpy.array([0.25, 0.25, 0.]),
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
class Test_ROS_MaxCen_GT_MaxUncen(Test_ROS_HelselAppendixB):
|
||
|
|
res = numpy.array([
|
||
|
|
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10., 10., 10.,
|
||
|
|
3.0, 7.0, 9.0, 12., 15., 20., 27., 33., 50.,
|
||
|
|
60, 70
|
||
|
|
])
|
||
|
|
cen = numpy.array([
|
||
|
|
True, True, True, True, True, True, True, True, True,
|
||
|
|
False, False, False, False, False, False, False,
|
||
|
|
False, False, True, True
|
||
|
|
])
|
||
|
|
|
||
|
|
|
||
|
|
class Test_ROS_OnlyDL_GT_MaxUncen(Test_NoOp_ZeroND):
|
||
|
|
numpy.random.seed(0)
|
||
|
|
N = 20
|
||
|
|
res = [
|
||
|
|
0.38, 0.43, 0.81, 0.86, 0.90, 1.13, 1.15, 1.37, 1.40,
|
||
|
|
1.49, 1.51, 1.56, 2.14, 2.59, 2.66, 4.28, 4.46, 5.84,
|
||
|
|
6.47, 9.40, 10.0, 10.0
|
||
|
|
]
|
||
|
|
cen = ([False] * N) + [True, True]
|