def test_getScores(self):
input = Input.Text("tests/example.txt")
obs = input.getScores("obs")
fcst = input.getScores("fcst")
self.assertEqual((1, 4, 4), obs.shape)
self.assertEqual((1, 4, 4), fcst.shape)
stations = input.getStations()
I0 = -1
for i in range(0, len(stations)):
if (stations[i] == Station.Station(0, 1, 1, 1)):
I0 = i
if (stations[i] == Station.Station(0, 0, 0, 2)):
I1 = i
self.assertEqual(12, obs[0, 0, I0])
self.assertTrue(np.isnan(obs[0, 1, I0]))
self.assertTrue(np.isnan(obs[0, 2, I0]))
self.assertEqual(5, obs[0, 3, I0])
self.assertEqual(13, fcst[0, 0, I0])
self.assertTrue(np.isnan(fcst[0, 1, I0]))
self.assertTrue(np.isnan(fcst[0, 2, I0]))
self.assertEqual(4, fcst[0, 3, I0])
for i in range(0, obs.shape[1]):
self.assertTrue(np.isnan(obs[0, i, I1]))
self.assertEqual(11, fcst[0, 0, I1])
for i in range(1, obs.shape[1]):
self.assertTrue(np.isnan(obs[0, i, I1]))
def test_getOffsets(self):
input = Input.Text("tests/example.txt")
offsets = input.getOffsets()
self.assertEqual(4, offsets.shape[0])
self.assertEqual(0, offsets[0])
self.assertEqual(1, offsets[1])
self.assertEqual(3, offsets[2])
self.assertEqual(22, offsets[3])
def test_getStations(self):
input = Input.Text("tests/example.txt")
stations = input.getStations()
stations = np.sort(stations)
self.assertEqual(4, len(stations))
self.assertTrue(Station.Station(0, 1, 1, 1) in stations)
self.assertTrue(Station.Station(0, 0, 0, 1) in stations)
self.assertTrue(Station.Station(0, 0, 0, 2) in stations)
self.assertTrue(Station.Station(0, 2, 2, 1) in stations)
def test_getDates(self):
input = Input.Text("tests/example.txt")
dates = input.getDates()
self.assertEqual(1, dates.shape[0])
self.assertEqual(20120101, dates[0])
def __init__(self,
filenames,
dates=None,
offsets=None,
locations=None,
latlonRange=None,
elevRange=None,
clim=None,
climType="subtract",
training=None,
legend=None,
removeMissingAcrossAll=True):
if (not isinstance(filenames, list)):
filenames = [filenames]
self._axis = "date"
self._index = 0
self._removeMissingAcrossAll = removeMissingAcrossAll
if (legend is not None and len(filenames) is not len(legend)):
Util.error("Need one legend entry for each filename")
self._legend = legend
# Organize files
self._files = list()
self._cache = list()
self._clim = None
for filename in filenames:
if (not os.path.exists(filename)):
Util.error("File '" + filename + "' does not exist")
if (Input.NetcdfCf.isValid(filename)):
file = Input.NetcdfCf(filename)
elif (Input.Comps.isValid(filename)):
file = Input.Comps(filename)
elif (Input.Text.isValid(filename)):
file = Input.Text(filename)
else:
Util.error("File '" + filename + "' is not a valid input file")
self._files.append(file)
self._cache.append(dict())
if (clim is not None):
if (not os.path.exists(clim)):
Util.error("File '" + clim + "' does not exist")
if (Input.NetcdfCf.isValid(clim)):
self._clim = Input.NetcdfCf(clim)
elif (Input.Comps.isValid(clim)):
self._clim = Input.Comps(clim)
elif (Input.Text.isValid(clim)):
self._clim = Input.Text(clim)
else:
Util.error("File '" + clim +
"' is not a valid climatology file")
self._cache.append(dict())
if (not (climType == "subtract" or climType == "divide")):
Util.error("Data: climType must be 'subtract' or 'divide")
self._climType = climType
# Climatology file
self._files = self._files + [self._clim]
# Latitude-Longitude range
if (latlonRange is not None):
lat = self._files[0].getLats()
lon = self._files[0].getLons()
locId = self._files[0].getStationIds()
latlonLocations = list()
minLon = latlonRange[0]
maxLon = latlonRange[1]
minLat = latlonRange[2]
maxLat = latlonRange[3]
for i in range(0, len(lat)):
currLat = float(lat[i])
currLon = float(lon[i])
if (currLat >= minLat and currLat <= maxLat
and currLon >= minLon and currLon <= maxLon):
latlonLocations.append(locId[i])
useLocations = list()
if (locations is not None):
for i in range(0, len(locations)):
currLocation = locations[i]
if (currLocation in latlonLocations):
useLocations.append(currLocation)
else:
useLocations = latlonLocations
if (len(useLocations) == 0):
Util.error("No available locations within lat/lon range")
elif locations is not None:
useLocations = locations
else:
useLocations = self._files[0].getStationIds()
# Elevation range
if (elevRange is not None):
stations = self._files[0].getStations()
minElev = elevRange[0]
maxElev = elevRange[1]
elevLocations = list()
for i in range(0, len(stations)):
currElev = float(stations[i].elev())
id = stations[i].id()
if (currElev >= minElev and currElev <= maxElev):
elevLocations.append(id)
useLocations = Util.intersect(useLocations, elevLocations)
if (len(useLocations) == 0):
Util.error("No available locations within elevation range")
# Find common indicies
self._datesI = Data._getUtilIndices(self._files, "Date", dates)
self._offsetsI = Data._getUtilIndices(self._files, "Offset", offsets)
self._locationsI = Data._getUtilIndices(self._files, "Location",
useLocations)
if (len(self._datesI[0]) == 0):
Util.error("No valid dates selected")
if (len(self._offsetsI[0]) == 0):
Util.error("No valid offsets selected")
if (len(self._locationsI[0]) == 0):
Util.error("No valid locations selected")
# Training
if (training is not None):
for f in range(0, len(self._datesI)):
if (len(self._datesI[f]) <= training):
Util.error("Training period too long for " +
self.getFilenames()[f] +
". Max training period is " +
str(len(self._datesI[f]) - 1) + ".")
self._datesI[f] = self._datesI[f][training:]
self._findex = 0