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import pytest |
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@pytest.fixture |
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def test_datapoints_full_file_path(): |
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import os |
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return lambda file_name: os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'dts', file_name) |
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@pytest.fixture |
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def sample_json(test_datapoints_full_file_path): |
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return test_datapoints_full_file_path('sample-data.jsonlines') |
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@pytest.fixture |
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def sample_collaped_json(test_datapoints_full_file_path): |
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return test_datapoints_full_file_path('sample-data-collapsed.jsonlines') |
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@pytest.fixture |
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def datapoint_files_to_test(sample_collaped_json, sample_json): |
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import pandas as pd |
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import numpy as np |
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return { |
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'data_1': { |
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'data_path': sample_collaped_json, |
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'nb_rows': 100, |
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'nb_columns': 46, |
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'type_distros': { |
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'type': {str: 100}, |
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'flavors': {list: 98, type(None): 2}, |
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}, |
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'value_distros': { |
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'type': {'hybrid': 48, 'sativa': 19, 'indica': 33}, |
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}, |
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'row': { |
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0: { |
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'flavors': [lambda v: v == ["Chemical", "Pine", "Diesel"], |
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lambda v: type(v) == list, |
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], |
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'type': [lambda v: v == 'hybrid', |
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lambda v: type(v) == str, |
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], |
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}, |
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7: { |
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'flavors': [lambda v: v == ["Earthy", "Pungent", "Sweet"], |
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lambda v: type(v) == list, |
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], |
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'type': [lambda v: v == 'hybrid', |
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lambda v: type(v) == str, |
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], |
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}, |
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76: { |
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'flavors': [lambda v: v is None, |
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lambda v: pd.isnull(v), |
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lambda v: isinstance(v, type(None)), |
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], |
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}, |
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87: { |
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'flavors': [lambda v: v is None, |
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lambda v: type(v) == type(None), |
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], |
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}, |
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}, |
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'column_names': ( |
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'flavors', |
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'name', |
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'description', |
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'image_urls', |
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'parents', |
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'_id', |
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'type', |
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'image_paths', |
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'Aroused', |
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'Creative', |
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'Energetic', |
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'Euphoric', |
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'Focused', |
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'Giggly', |
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'Happy', |
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'Hungry', |
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'Relaxed', |
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'Sleepy', |
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'Talkative', |
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'Tingly', |
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'Uplifted', |
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'Cramps', |
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'Depression', |
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'Eye Pressure', |
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'Fatigue', |
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'Headaches', |
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'Inflammation', |
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'Insomnia', |
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'Lack of Appetite', |
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'Muscle Spasms', |
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'Nausea', |
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'Pain', |
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'Seizures', |
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'Spasticity', |
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'Stress', |
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'Anxious', |
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'Dizzy', |
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'Dry Eyes', |
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'Dry Mouth', |
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'Headache', |
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'Paranoid', |
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'difficulty', |
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'flowering', |
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'height', |
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'stretch', |
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'yield', |
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), |
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}, |
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'data_2': { |
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'data_path': sample_json, |
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'nb_rows': 100, |
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'nb_columns': 12, |
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'type_distros': { |
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'type': {str: 100}, |
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'flavors': {list: 98, float: 2}, |
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}, |
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'value_distros': { |
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'type': {'hybrid': 48, 'sativa': 19, 'indica': 33}, |
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}, |
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'row': { |
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0: { |
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'flavors': [lambda v: v == ["Chemical", "Pine", "Diesel"], |
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lambda v: type(v) == list, |
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], |
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'type': [lambda v: v == 'hybrid', |
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lambda v: type(v) == str |
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], |
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}, |
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7: { |
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'flavors': [lambda v: v == ["Earthy", "Pungent", "Sweet"], |
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lambda v: type(v) == list, |
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], |
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'type': [lambda v: v == 'hybrid', |
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lambda v: type(v) == str |
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], |
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}, |
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76: { |
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'flavors': [lambda v: np.isnan(v), |
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lambda v: pd.isnull(v), |
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lambda v: type(v) == float, |
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], |
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}, |
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87: { |
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'flavors': [lambda v: np.isnan(v), |
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lambda v: pd.isnull(v), |
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lambda v: type(v) == float, |
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], |
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}, |
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}, |
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'column_names': ( |
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'flavors', |
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'name', |
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'medical', |
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'description', |
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'image_urls', |
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'parents', |
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'negatives', |
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'grow_info', |
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'_id', |
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'type', |
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'image_paths', |
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'effects', |
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), |
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}, |
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} |
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boromir674 /
so-magic
