def test_date_time_range_mismatch(self):
cache_dir = os.path.join(self.temp_dir,
self.test_date_time_range_mismatch.__name__)
# missing first two entries
water_level_response = \
('Date Time, Water Level, Sigma, O or I (for verified), F, R, L, Quality\n'
'2000-10-30 12:12,1.003,0.003,0,0,0,0,v\n'
'2000-10-30 12:18,1.004,0.004,0,0,0,0,v\n'
'2000-10-30 12:24,1.005,0.005,0,0,0,0,v\n')
# missing last two entries
predictions_response = ('Date Time, Prediction\n'
'2000-10-30 12:00,1.101\n'
'2000-10-30 12:06,1.102\n'
'2000-10-30 12:12,1.103\n')
# monkey patch urllib to return mock data
def mock_read_response(url):
if 'product=water_level' in url:
return water_level_response
if 'product=predictions' in url:
return predictions_response
raise AssertionError
self._monkey_patch_urlopen(mock_read_response)
# should raise ValueError
fetch_noaa_tide_data(self.station,
self.begin_date,
self.end_date,
cache_dir=cache_dir)
def test_date_time_range_mismatch(self):
cache_dir = os.path.join(self.temp_dir,
self.test_date_time_range_mismatch.__name__)
# missing first two entries
water_level_response = \
('Date Time, Water Level, Sigma, O, F, R, L, Quality\n'
'2000-10-30 12:12,1.003,0.003,0,0,0,0,v\n'
'2000-10-30 12:18,1.004,0.004,0,0,0,0,v\n'
'2000-10-30 12:24,1.005,0.005,0,0,0,0,v\n')
# missing last two entries
predictions_response = ('Date Time, Prediction\n'
'2000-10-30 12:00,1.101\n'
'2000-10-30 12:06,1.102\n'
'2000-10-30 12:12,1.103\n')
# monkey patch urllib to return mock data
def mock_read_response(url):
if 'product=water_level' in url:
return water_level_response
if 'product=predictions' in url:
return predictions_response
raise AssertionError
self._monkey_patch_urlopen(mock_read_response)
# should raise ValueError
fetch_noaa_tide_data(self.station, self.begin_date, self.end_date,
cache_dir=cache_dir)
def test_api_error(self):
cache_dir = os.path.join(self.temp_dir, self.test_api_error.__name__)
# configure endpoint to return an error
self._monkey_patch_urlopen(lambda url: 'Something went wrong')
# should raise ValueError
fetch_noaa_tide_data(self.station, self.begin_date, self.end_date,
cache_dir=cache_dir)
def test_api_error(self):
cache_dir = os.path.join(self.temp_dir, self.test_api_error.__name__)
# configure endpoint to return an error
self._monkey_patch_urlopen(lambda url: 'Something went wrong')
# should raise ValueError
fetch_noaa_tide_data(self.station, self.begin_date, self.end_date,
cache_dir=cache_dir)
def gauge_afteraxes(cd):
axes = plt.gca()
surgeplot.plot_landfall_gauge(cd.gaugesoln, axes)
# fetch real data
noaaArr = [
"8557380", "8639348", "8662245", "2695540", "8531680", "8510560"
]
gaugeNumber = cd.gaugeno
if (gaugeNumber < 7):
# only looking at gauge 1-6 because rest of data not from NOAA Gauges
realData = geoutil.fetch_noaa_tide_data(
noaaArr[gaugeNumber - 1],
datetime.datetime(2015, 9, 30, hour=12),
datetime.datetime(2015, 10, 6, hour=6))
values = realData[1] - realData[2] # de-tide NOAA data
times = []
for time in realData[0]:
times.append(
(time - numpy.datetime64("2015-10-02T12:00")).astype(float)
/ 1440)
plt.plot(times, values, color="g", label="real")
# Fix up plot - in particular fix time labels
axes.set_title('Station %s' % cd.gaugeno)
axes.set_xlabel('Days relative to landfall')
axes.set_ylabel('Surface (m)')
axes.set_xlim([-2, 3.75])
axes.set_ylim([0, 4])
axes.set_xticks([-2, -1, 0, 1, 2, 3])
axes.set_xticklabels(
[r"$-2$", r"$-1$", r"$0$", r"$1$", r"$2$", r"$3$"])
axes.grid(True)
def _fetch_and_assert(self, date_time_expected, water_level_expected,
prediction_expected, cache_dir):
# fetch data
date_time, water_level, prediction = fetch_noaa_tide_data(
self.station, self.begin_date, self.end_date, cache_dir=cache_dir)
# make sure data was correctly retrieved
np.testing.assert_equal(date_time, date_time_expected)
np.testing.assert_equal(water_level, water_level_expected)
np.testing.assert_equal(prediction, prediction_expected)
def _fetch_and_assert(self, date_time_expected, water_level_expected,
prediction_expected, cache_dir):
# fetch data
date_time, water_level, prediction = fetch_noaa_tide_data(
self.station, self.begin_date, self.end_date, cache_dir=cache_dir)
# make sure data was correctly retrieved
np.testing.assert_equal(date_time, date_time_expected)
np.testing.assert_equal(water_level, water_level_expected)
np.testing.assert_equal(prediction, prediction_expected)
def get_actual_water_levels(station_id):
# Fetch water levels and tide predictions for given station
date_time, water_level, tide = fetch_noaa_tide_data(station_id,
begin_date, end_date)
# Calculate times relative to landfall
seconds_rel_landfall = (date_time - landfall_time) / np.timedelta64(1, 's')
# Subtract tide predictions from measured water levels
water_level -= tide
return seconds_rel_landfall, water_level
def get_actual_water_levels(station_id):
# Fetch water levels and tide predictions for given station
date_time, water_level, tide = fetch_noaa_tide_data(station_id,
begin_date, end_date)
# Calculate times relative to landfall
secs_rel_landfall = (date_time - landfall_time) / np.timedelta64(1, 's')
# Subtract tide predictions from measured water levels
water_level -= tide
return secs_rel_landfall, water_level
def test_api_error(self):
cache_dir = os.path.join(self.temp_dir, self.test_api_error.__name__)
# configure endpoint to return an error
self._monkey_patch_urlopen(lambda url: 'Something went wrong')
# should return None
d, w, p = fetch_noaa_tide_data(self.station,
self.begin_date,
self.end_date,
cache_dir=cache_dir)
assert d == None, '*** expected d == None'
def gauge_afteraxes(cd):
axes = plt.gca()
surgeplot.plot_landfall_gauge(cd.gaugesoln, axes)
gauge_id = ['8770822', '8768094', '8764227', '8761305', '8760922']
gauge_title = [
'Texas Point, Sabine Pass, TX', 'Calcasieu Pass, LA',
'LAWMA, Amerada Pass, LA', 'Shell Beach, LA',
'Pilots Station East, S.W. Pass, LA'
]
if (cd.gaugeno < 6):
realData = util.fetch_noaa_tide_data(gauge_id[cd.gaugeno - 1],
datetime.datetime(2019,
7,
10,
hour=12),
datetime.datetime(2019,
7,
16,
hour=12),
datum='MLLW')
values = realData[1] - realData[2]
times = []
for time in realData[0]:
times.append(
(time - numpy.datetime64("2019-07-13T15:00")).astype(float)
/ 1440)
plt.plot(times, values, color='orange', label='real')
axes.set_title('Gauge %s: %s' %
(cd.gaugeno,
gauge_title[cd.gaugeno - 1])) # i for i in gauge_title
axes.set_xlabel('Days relative to landfall')
axes.set_ylabel('Surface (m)')
axes.set_xlim([-2, 1])
axes.set_ylim([-1.0, 2.5])
axes.set_xticks([-2, -1, 0, 1])
axes.set_xticklabels([r"$-2$", r"$-1$", r"$0$", r"$1$"])
axes.grid(True)
