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import os, sys; sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) |
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import fnmatch |
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import numpy as np |
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import pytest |
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from lasio import read |
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test_dir = os.path.dirname(__file__) |
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egfn = lambda fn: os.path.join(os.path.dirname(__file__), "examples", fn) |
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stegfn = lambda vers, fn: os.path.join( |
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os.path.dirname(__file__), "examples", vers, fn) |
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NaN = np.nan |
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def test_read_v12_sample(): |
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l = read(stegfn("1.2", "sample.las")) |
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def test_read_v12_sample_big(): |
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l = read(stegfn("1.2", "sample_big.las")) |
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def test_read_v12_sample_curve_api(): |
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l = read(stegfn("1.2", "sample_curve_api.las")) |
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def test_read_v12_sample_minimal(): |
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l = read(stegfn("1.2", "sample_minimal.las")) |
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def test_read_v12_sample_wrapped(): |
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l = read(stegfn("1.2", "sample_wrapped.las")) |
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def test_read_v2_sample(): |
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l = read(stegfn("2.0", "sample_2.0.las")) |
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def test_read_v2_sample_based(): |
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l = read(stegfn("2.0", "sample_2.0_based.las")) |
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def test_read_v2_sample_minimal(): |
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l = read(stegfn("2.0", "sample_2.0_minimal.las")) |
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def test_read_v2_sample_wrapped(): |
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l = read(stegfn("2.0", "sample_2.0_wrapped.las")) |
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53
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def test_dodgy_param_sect(): |
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l = read(egfn("dodgy_param_sect.las")) |
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def test_mnemonic_good(): |
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l = read(egfn("mnemonic_good.las")) |
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assert [c.mnemonic for c in l.curves] == [ |
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"DEPT", "DT", "RHOB", "NPHI", "SFLU", "SFLA", "ILM", "ILD"] |
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def test_mnemonic_duplicate(): |
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l = read(egfn("mnemonic_duplicate.las")) |
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assert [c.mnemonic for c in l.curves] == [ |
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"DEPT", "DT", "RHOB", "NPHI", "SFLU:1", "SFLU:2", "ILM", "ILD"] |
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def test_mnemonic_leading_period(): |
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l = read(egfn("mnemonic_leading_period.las")) |
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assert [c.mnemonic for c in l.curves] == [ |
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"DEPT", "DT", "RHOB", "NPHI", "SFLU", "SFLA", "ILM", "ILD"] |
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def test_mnemonic_missing(): |
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l = read(egfn("mnemonic_missing.las")) |
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assert [c.mnemonic for c in l.curves] == [ |
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"DEPT", "DT", "RHOB", "NPHI", "UNKNOWN", "SFLA", "ILM", "ILD"] |
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def test_mnemonic_missing_multiple(): |
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l = read(egfn("mnemonic_missing_multiple.las")) |
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assert [c.mnemonic for c in l.curves] == [ |
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"DEPT", "DT", "RHOB", "NPHI", "UNKNOWN:1", "UNKNOWN:2", "ILM", "ILD"] |
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# ~A DEPTH DT RHOB NPHI SFLU SFLA |
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# 1.000 -999.25 -9999 0.450 123.450 1 |
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# 2.000 -999.25 -9999 0.460 123.460 2 |
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# 3.000 1 -9999 0.47 123.45 3 |
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# 4.000 2 3 0.48 123.46 4 |
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# 5.000 3 4 231.2 123.45 5 |
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# 6.000 4 5 231.2 1 6 |
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# 7.000 5 6 231.2 1 7 |
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# 8.000 6 7 231.2 1 8 |
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# 9.000 6 32767 231.2 -999.25 9 |
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96
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def test_null_policy_numeric_None(): |
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l = read(egfn("null_policy_numeric.las"), null_policy=None) |
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assert np.all(l['DT'] == [-999.25, -999.25, 1, 2, 3, 4, 5, 6, 6]) |
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assert np.all(l['RHOB'] == [-9999, -9999, -9999, 3, 4, 5, 6, 7, 32767]) |
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assert np.all(l['NPHI'] == [.45, .46, .47, .48, 231.2, 231.2, 231.2, 231.2, 231.2]) |
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assert np.all(l['SFLU'] == [123.45, 123.46, 123.45, 123.46, 123.45, 1, 1, 1, -999.25]) |
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assert np.all(l['SFLA'] == [1, 2, 3, 4, 5, 6, 7, 8, 9]) |
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def test_null_policy_numeric_NULL(): |
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l = read(egfn("null_policy_numeric.las"), null_policy='NULL') |
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assert np.all(np.isnan(l['DT']) == [True, True, False, False, False, False, False, False, False]) |
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assert np.isfinite(l['RHOB']).all() |
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assert np.isfinite(l['NPHI']).all() |
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assert np.all(np.isnan(l['SFLU']) == [False, False, False, False, False, False, False, False, True]) |
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assert np.isfinite(l['SFLA']).all() |
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def test_null_policy_numeric_common(): |
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l = read(egfn("null_policy_numeric.las"), null_policy='common') |
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assert np.all(np.isnan(l['DT']) == [True, True, False, False, False, False, False, False, False]) |
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assert np.all(np.isnan(l['RHOB']) == [True, True, True, False, False, False, False, False, True]) |
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assert np.isfinite(l['NPHI']).all() |
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assert np.all(np.isnan(l['SFLU']) == [False, False, False, False, False, False, False, False, True]) |
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assert np.isfinite(l['SFLA']).all() |
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def test_null_policy_numeric_aggressive(): |
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l = read(egfn("null_policy_numeric.las"), null_policy='aggressive') |
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assert np.all(np.isnan(l['DT']) == [True, True, False, False, False, False, False, True, True]) |
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assert np.all(np.isnan(l['RHOB']) == [True, True, True, False, False, False, False, False, True]) |
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assert np.all(np.isnan(l['NPHI']) == [False, False, False, False, True, True, True, True, True]) |
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assert np.all(np.isnan(l['SFLU']) == [False, False, False, False, False, True, True, True, True]) |
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assert np.isfinite(l['SFLA']).all() |
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def test_multi_curve_mnemonics(): |
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l = read(egfn('sample_issue105_a.las')) |
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assert l.keys() == [c.mnemonic for c in l.curves] == ['DEPT', 'RHO:1', 'RHO:2', 'RHO:3', 'PHI'] |
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def test_multi_missing_curve_mnemonics(): |
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l = read(egfn('sample_issue105_b.las')) |
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assert l.keys() == [c.mnemonic for c in l.curves] == ['DEPT', 'UNKNOWN:1', 'UNKNOWN:2', 'UNKNOWN:3', 'PHI'] |
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def test_multi_curve_mnemonics_gr(): |
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l = read(egfn('sample_issue105_c.las')) |
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assert l.keys() == [c.mnemonic for c in l.curves] == ['DEPT', 'GR:1', 'GR:2', 'GR[0]', 'GR[1]', 'GR[2]', 'GR[3]', 'GR[4]', 'GR[5]'] |
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# DEPT.M : 1 DEPTH |
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# GR.gAPI: mean gamma ray value |
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# GR.gAPI: corrected gamma ray value |
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# GR[0].gAPI: gamma ray image at angle 0 dega |
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# GR[1].gAPI: gamma ray image at angle 60 dega |
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# GR[2].gAPI: gamma ray image at angle 120 dega |
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# GR[3].gAPI: gamma ray image at angle 180 dega |
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# GR[4].gAPI: gamma ray image at angle 240 dega |
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# GR[5].gAPI: gamma ray image at angle 300 dega |
kinverarity1 /
lasio
