test_management.py 8.17 KB
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"""MIT License
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Copyright (c) 2019, Swiss Federal Institute of Technology (ETH Zurich), Matthias Meyer

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Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE."""
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import stuett
from tests.stuett.sample_data import *

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import datetime as dt
import pandas as pd
import base64
import os

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import pytest
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import zarr
from pathlib import Path

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test_data_dir = Path(__file__).absolute().parent.joinpath("..", "data")
stuett.global_config.set_setting("user_dir", test_data_dir.joinpath("user_dir/"))
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class TestSeismicSource(object):
    @pytest.mark.slow
    def test_seismic_source(self):
        # first test without config
        seismic_source = stuett.data.SeismicSource()

        # with config
        seismic_source = stuett.data.SeismicSource(config)

    @pytest.mark.slow
    def test_call(self):
        # first test without config
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        seismic_source = stuett.data.SeismicSource(use_arclink=True)
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        x = seismic_source(config)
        x = x.compute()
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        print(x)
        return

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        assert x.mean() == -55.73599312780376
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        # with config
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        seismic_source = stuett.data.SeismicSource(config, use_arclink=True)
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        request = {"start_time": start_time + offset, "end_time": end_time + offset}
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        x = seismic_source(request)
        x = x.compute()
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        assert x.mean() == -61.23491399790934
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        # TODO: make test to compare start_times
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@pytest.mark.slow
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def test_gsn():
    # test_data = pd.read_csv(test_data_dir + 'matterhorn_27_temperature_rock.csv',index_col='time')
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    gsn_node = stuett.data.GSNDataSource(
        deployment="matterhorn", position=30, vsensor="temperature_rock"
    )
    x = gsn_node({"start_time": "2017-07-01", "end_time": "2017-07-02"})

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    assert x.sum() == 1600959.34
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    # TODO: proper testing
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# test_gsn()
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def test_delayed():
    filename = Path(test_data_dir).joinpath(
        "timeseries", "MH30_temperature_rock_2017.csv"
    )
    node = stuett.data.CsvSource(filename)

    x = node(delayed=True)
    from dask.delayed import Delayed

    assert isinstance(x, Delayed)

    x = x.compute()

    non_delayed = node(delayed=False)

    assert x.mean() == 0.16201109532441677
    assert non_delayed.mean() == 0.16201109532441677


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def test_freeze():
    # with config
    # seismic_source = stuett.data.SeismicSource(config,use_arclink=True)
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    filename = Path(test_data_dir).joinpath(
        "timeseries", "MH30_temperature_rock_2017.csv"
    )
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    node = stuett.data.CsvSource(filename)

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    user_dir = stuett.global_config.get_setting("user_dir")
    store_name = user_dir.joinpath("frozen", "test.zarr")
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    import shutil
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    shutil.rmtree(store_name, ignore_errors=True)
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    store = zarr.DirectoryStore(store_name)
    # account_name = stuett.global_config.get_setting('azure')['account_name'] if stuett.global_config.setting_exists('azure') else "storageaccountperma8980"
    # account_key  = stuett.global_config.get_setting('azure')['account_key']  if stuett.global_config.setting_exists('azure')  else None
    # store = zarr.ABSStore(container='hackathon-on-permafrost', prefix='dataset/test.zarr', account_name=account_name, account_key=account_key, blob_service_kwargs={})
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    freezer = stuett.data.Freezer(store)
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    request = {"start_time": "2017-07-01", "end_time": "2017-08-01"}
    x = node(request=request)
    x = freezer(x)
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    request = {"start_time": "2017-09-01", "end_time": "2017-10-01"}
    x = freezer(node(request=request))
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    # x = freezer()
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    print("final", x)
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    shutil.rmtree(store_name, ignore_errors=True)


# test_freeze()
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def test_image_filenames():
    # first test without config
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    node = stuett.data.MHDSLRFilenames(base_directory=test_data_dir.joinpath("MHDSLR"))
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    start_time = dt.datetime(2017, 8, 6, 9, 56, 12, tzinfo=dt.timezone.utc)
    end_time = dt.datetime(2017, 8, 6, 10, 14, 10, tzinfo=dt.timezone.utc)

    offset = dt.timedelta(days=1)
    config_0 = {
        "channel": channels[0],
        "station": stations[0],
        "start_time": start_time,
        "end_time": end_time,
    }

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    data = node(request=config_0)
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    config_1 = config.copy()
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    # this should return and empty list
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    data = node(config_1)

    # Test if we do not provide a end_time
    del config_0["end_time"]
    data = node(config_0)

    del config_1["end_time"]
    data = node(config_1)

    config_1["start_time"] = dt.datetime(2018, 8, 6, 20, 0, 0, tzinfo=dt.timezone.utc)
    data = node(config_1)
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# test_image_filenames()


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def test_mhdslrimage():
    base_dir = Path(test_data_dir).joinpath("MHDSLR")
    node = stuett.data.MHDSLRImages(base_directory=base_dir)

    start_time = dt.datetime(2017, 8, 6, 9, 50, 12, tzinfo=dt.timezone.utc)
    end_time = dt.datetime(2017, 8, 6, 10, 12, 10, tzinfo=dt.timezone.utc)

    offset = dt.timedelta(days=1)
    config = {
        "start_time": start_time,
        "end_time": end_time,
    }

    data = node(config)

    config["output_format"] = "base64"
    data = node(config)

    from PIL import Image

    img = Image.open(base_dir.joinpath("2017-08-06", "20170806_095212.JPG"))
    img_base64 = base64.b64encode(img.tobytes())
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    assert data[0].values == img_base64
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    # Check a period where there is no image
    start_time = dt.datetime(2017, 8, 6, 9, 55, 12, tzinfo=dt.timezone.utc)
    end_time = dt.datetime(2017, 8, 6, 10, 10, 10, tzinfo=dt.timezone.utc)
    config = {
        "start_time": start_time,
        "end_time": end_time,
    }
    data = node(config)

    # print(data)
    assert data.shape == (0, 0, 0, 0)

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# test_mhdslrimage()


def test_csv():
    filename = Path(test_data_dir).joinpath(
        "timeseries", "MH30_temperature_rock_2017.csv"
    )
    node = stuett.data.CsvSource(filename)
    x = node()

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    assert len(x) == 8760
    assert x.mean() == 0.16201109532441677
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    # Test with store
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    directory = Path(test_data_dir).joinpath("timeseries")
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    store = stuett.DirectoryStore(directory)
    filename = "MH30_temperature_rock_2017.csv"
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    node = stuett.data.CsvSource(filename=filename, store=store)
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    x = node()
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    assert len(x) == 8760
    assert x.mean() == 0.16201109532441677

    # TODO: test with start and end time
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# test_csv()
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def test_annotations():
    filename = Path(test_data_dir).joinpath("annotations", "boundingbox_timeseries.csv")
    node = stuett.data.BoundingBoxAnnotation(filename)

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    targets = node()

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    assert targets[0] == "no_visibility"
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    filename = Path(test_data_dir).joinpath("annotations", "boundingbox_images.csv")
    node = stuett.data.BoundingBoxAnnotation(filename)

    targets = node()

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    targets = targets.swap_dims({"index": "start_time"})
    targets = targets.sortby("start_time")
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    assert targets.sel(start_time="2017-08-04T08:12:11") == "mountaineer"
    assert targets["start_y"][1] == 16

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# test_annotations()


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def test_datasets():
    filename = Path(test_data_dir).joinpath("annotations", "boundingbox_timeseries.csv")
    label = stuett.data.BoundingBoxAnnotation(filename)
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    filename = Path(test_data_dir).joinpath(
        "timeseries", "MH30_temperature_rock_2017.csv"
    )
    data = stuett.data.CsvSource(filename)
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    dataset = stuett.data.SegmentedDataset(
        data,
        label,
        dataset_slice={"time": slice("2017-08-01", "2017-08-03")},
        batch_dims={"time": pd.to_timedelta(24, "m")},
    )
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    # x = dataset[0]
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test_datasets()