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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, las |
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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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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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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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# ~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 #N/A 4 231.2 123.45 5 |
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# 6.000 4 1.#INF 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 1.#IND |
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# 9.000 6 32767 -1.#IO -999.25 9 |
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def test_null_policy_alphanumeric_None(): |
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with pytest.raises(las.LASDataError): |
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l = read(egfn("null_policy_alphanumeric.las"), null_policy=None) |
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def test_null_policy_alphanumeric_NULL(): |
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with pytest.raises(las.LASDataError): |
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l = read(egfn("null_policy_alphanumeric.las"), null_policy='NULL') |
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def test_null_policy_alphanumeric_common(): |
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l = read(egfn("null_policy_alphanumeric.las"), null_policy='common') |
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assert np.all(np.isnan(l['DT']) == [True, True, False, False, True, False, False, False, False]) |
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assert np.all(np.isnan(l['RHOB']) == [True, True, True, False, False, True, False, False, True]) |
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assert np.all(np.isnan(l['NPHI']) == [False, False, False, False, False, False, False, False, True]) |
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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.all(np.isnan(l['SFLA']) == [False, False, False, False, False, False, False, True, False]) |
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def test_null_policy_alphanumeric_aggressive(): |
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l = read(egfn("null_policy_alphanumeric.las"), null_policy='aggressive') |
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assert np.all(np.isnan(l['DT']) == [True, True, False, False, True, False, False, True, True]) |
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assert np.all(np.isnan(l['RHOB']) == [True, True, True, False, False, True, 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.all(np.isnan(l['SFLA']) == [False, False, False, False, False, False, False, True, False]) |
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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
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Duplicated code is one of the most pungent code smells. If you need to duplicate the same code in three or more different places, we strongly encourage you to look into extracting the code into a single class or operation.
You can also find more detailed suggestions in the “Code” section of your repository.