def test_xyzalgorithm_uneccesary_channel_empty():
"""XYZAlgorithm_test.test_xyzalgorithm_uneccesary_channel_gaps()
confirms the process will run when an uneccesary channel is input
but contains gaps or is completely empty. ie. gaps in 'Z' channel
or and empty 'F' channel. This also makes sure the 'Z' and 'F' channels
are passed without any modification.
"""
algorithm = XYZAlgorithm('obs', 'mag')
timeseries = Stream()
timeseries += __create_trace('H', [1, 1])
timeseries += __create_trace('E', [1, 1])
timeseries += __create_trace('Z', [1, np.NaN])
timeseries += __create_trace('F', [np.NaN, np.NaN])
outstream = algorithm.process(timeseries)
assert_equals(outstream.select(channel='Z')[0].data.all(),
timeseries.select(channel='Z')[0].data.all())
assert_equals(outstream.select(channel='F')[0].data.all(),
timeseries.select(channel='F')[0].data.all())
ds = outstream.select(channel='D')
# there is 1 trace
assert_equals(len(ds), 1)
d = ds[0]
# d has 2 values (same as input)
assert_equals(len(d.data), 2)
# d has no NaN values
assert_equals(np.isnan(d).any(), False)
def test_xyzalgorithm_uneccesary_channel_empty():
"""XYZAlgorithm_test.test_xyzalgorithm_uneccesary_channel_gaps()
confirms the process will run when an uneccesary channel is input
but contains gaps or is completely empty. ie. gaps in 'Z' channel
or and empty 'F' channel. This also makes sure the 'Z' and 'F' channels
are passed without any modification.
"""
algorithm = XYZAlgorithm("obs", "mag")
timeseries = Stream()
timeseries += __create_trace("H", [1, 1])
timeseries += __create_trace("E", [1, 1])
timeseries += __create_trace("Z", [1, np.NaN])
timeseries += __create_trace("F", [np.NaN, np.NaN])
outstream = algorithm.process(timeseries)
assert_equal(
outstream.select(channel="Z")[0].data.all(),
timeseries.select(channel="Z")[0].data.all(),
)
assert_equal(
outstream.select(channel="F")[0].data.all(),
timeseries.select(channel="F")[0].data.all(),
)
ds = outstream.select(channel="D")
# there is 1 trace
assert_equal(len(ds), 1)
d = ds[0]
# d has 2 values (same as input)
assert_equal(len(d.data), 2)
# d has no NaN values
assert_equal(np.isnan(d).any(), False)
def test_xyzalgorithm_channels():
"""XYZAlgorithm_test.test_xyzalgorithm_channels()
confirms that the input/output channels are correct for the given
informat/outformat during instantiation.
"""
algorithm = XYZAlgorithm("obs", "geo")
inchannels = ["H", "E", "Z", "F"]
outchannels = ["X", "Y", "Z", "F"]
assert_equal(algorithm.get_input_channels(), inchannels)
assert_equal(algorithm.get_output_channels(), outchannels)
def test_xyzalgorithm_channels():
"""XYZAlgorithm_test.test_xyzalgorithm_channels()
confirms that the input/output channels are correct for the given
informat/outformat during instantiation.
"""
algorithm = XYZAlgorithm('obs', 'geo')
inchannels = ['H', 'E', 'Z', 'F']
outchannels = ['X', 'Y', 'Z', 'F']
assert_equals(algorithm.get_input_channels(), inchannels)
assert_equals(algorithm.get_output_channels(), outchannels)
def test_xyzalgorithm_channels():
"""XYZAlgorithm_test.test_xyzalgorithm_channels()
confirms that the input/output channels are correct for the given
informat/outformat during instantiation.
"""
algorithm = XYZAlgorithm('obs', 'geo')
inchannels = ['H', 'E', 'Z', 'F']
outchannels = ['X', 'Y', 'Z', 'F']
assert_equals(algorithm.get_input_channels(), inchannels)
assert_equals(algorithm.get_output_channels(), outchannels)
def test_xyzalgorithm_process():
"""XYZAlgorithm_test.test_xyzalgorithm_process()
confirms that a converted stream contains the correct outputchannels.
"""
algorithm = XYZAlgorithm("obs", "geo")
timeseries = Stream()
timeseries += __create_trace("H", [1, 1])
timeseries += __create_trace("E", [1, 1])
timeseries += __create_trace("Z", [1, 1])
timeseries += __create_trace("F", [1, 1])
outputstream = algorithm.process(timeseries)
assert_equal(outputstream[0].stats.channel, "X")
def test_xyzalgorithm_process():
"""XYZAlgorithm_test.test_xyzalgorithm_process()
confirms that a converted stream contains the correct outputchannels.
"""
algorithm = XYZAlgorithm('obs', 'geo')
timeseries = Stream()
timeseries += __create_trace('H', [1, 1])
timeseries += __create_trace('E', [1, 1])
timeseries += __create_trace('Z', [1, 1])
timeseries += __create_trace('F', [1, 1])
outputstream = algorithm.process(timeseries)
assert_is(outputstream[0].stats.channel, 'X')
def test_xyzalgorithm_process():
"""XYZAlgorithm_test.test_xyzalgorithm_process()
confirms that a converted stream contains the correct outputchannels.
"""
algorithm = XYZAlgorithm('obs', 'geo')
timeseries = Stream()
timeseries += __create_trace('H', [1, 1])
timeseries += __create_trace('E', [1, 1])
timeseries += __create_trace('Z', [1, 1])
timeseries += __create_trace('F', [1, 1])
outputstream = algorithm.process(timeseries)
assert_is(outputstream[0].stats.channel, 'X')
def fetch_usgs(out_path,
date,
stn,
type='variation',
interval='second',
he=False,
out_template='{stn}{date:%Y%m%d}v{suffix}.{suffix}'):
"""
Fetch USGS magnetometer data for *date* at *stn*. Store the data
in IAGA2002 format and return the file name (*out_template* serves
as a template). Limit to data *type* and *interval*. If *he*, then
include the H and E channels in the output (that is, local
magnetic north and east components).
"""
out_fname = os.path.join(
out_path,
out_template.format(stn=stn.lower(), date=date, suffix=interval[:3]))
input_factory = geomagio.edge.EdgeFactory()
timeseries = input_factory.get_timeseries(
observatory=stn,
channels=('H', 'E', 'Z', 'F'),
type=type,
interval=interval,
starttime=UTCDateTime('{date:%Y-%m-%d}T00:00:00Z'.format(date=date)),
endtime=UTCDateTime('{date:%Y-%m-%d}T23:59:59Z'.format(date=date)))
if all([NP.isnan(trace).all() for trace in timeseries.traces]):
raise ValueError('no data for {} on {:%Y-%m-%d} found'.format(
stn, date))
# convert from HEZF channels to XYZF channels
algorithm = XYZAlgorithm(informat='obs', outformat='geo')
xyzf = algorithm.process(timeseries)
with open(out_fname, 'w') as fid:
output_factory = geomagio.iaga2002.IAGA2002Factory()
output_factory.write_file(channels=('H', 'E', 'X', 'Y', 'Z',
'F') if he else
('X', 'Y', 'Z', 'F'),
fh=fid,
timeseries=xyzf)
return out_fname
def test_xyzalgorithm_limited_channels():
"""XYZAlgorithm_test.test_xyzalgorithm_limited_channels()
confirms that only the required channels are necessary for processing
ie. 'H' and 'E' are only needed to get 'X' and 'Y' without 'Z' or 'F'
"""
algorithm = XYZAlgorithm("obs", "mag")
count = 5
timeseries = Stream()
timeseries += __create_trace("H", [2] * count)
timeseries += __create_trace("E", [3] * count)
outstream = algorithm.process(timeseries)
ds = outstream.select(channel="D")
# there is 1 trace
assert_equal(len(ds), 1)
d = ds[0]
# d has `count` values (same as input)
assert_equal(len(d.data), count)
# d has no NaN values
assert_equal(np.isnan(d).any(), False)
def test_xyzalgorithm_limited_channels():
"""XYZAlgorithm_test.test_xyzalgorithm_limited_channels()
confirms that only the required channels are necessary for processing
ie. 'H' and 'E' are only needed to get 'X' and 'Y' without 'Z' or 'F'
"""
algorithm = XYZAlgorithm('obs', 'mag')
count = 5
timeseries = Stream()
timeseries += __create_trace('H', [2] * count)
timeseries += __create_trace('E', [3] * count)
outstream = algorithm.process(timeseries)
ds = outstream.select(channel='D')
# there is 1 trace
assert_equals(len(ds), 1)
d = ds[0]
# d has `count` values (same as input)
assert_equals(len(d.data), count)
# d has no NaN values
assert_equals(np.isnan(d).any(), False)