def test_get_clip_mask(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
coords = np.array([[2600020., 5630020.], [2600030., 5630030.],
[2600040., 5630040.], [2700100., 5630030.],
[2600040., 5640000.]])
mask = zonalstats.get_clip_mask(coords, self.npobj, proj_gk)
out = np.array([True, True, True, False, False])
np.testing.assert_array_equal(mask, out)
def test_get_clip_mask(self):
proj_gk = osr.SpatialReference()
proj_gk.ImportFromEPSG(31466)
coords = np.array([[2600020., 5630020.], [2600030., 5630030.],
[2600040., 5630040.], [2700100., 5630030.],
[2600040., 5640000.]])
mask = zonalstats.get_clip_mask(coords, self.npobj, proj_gk)
out = np.array([True, True, True, False, False])
np.testing.assert_array_equal(mask, out)
def read_trmm(filename1, filename2, bbox=None):
"""Reads TRMM files for matching with GR
Parameters
----------
filename1 : string
path of the TRMM 2A23 file
filename2 : string
path of the TRMM 2A25 file
bbox : dict
dictionary with bounding box coordinates (lon, lat),
defaults to None
Returns
-------
trmm_data : dict
dictionary of trmm data
Examples
--------
See :ref:`/notebooks/match3d/wradlib_match_workflow.ipynb`.
"""
# trmm 2A23 and 2A25 data is hdf4
pr_data1 = nc.Dataset(filename1, mode="r")
pr_data2 = nc.Dataset(filename2, mode="r")
lon = pr_data1.variables["Longitude"]
lat = pr_data1.variables["Latitude"]
if bbox is not None:
poly = [
[bbox["left"], bbox["bottom"]],
[bbox["left"], bbox["top"]],
[bbox["right"], bbox["top"]],
[bbox["right"], bbox["bottom"]],
[bbox["left"], bbox["bottom"]],
]
from wradlib.zonalstats import get_clip_mask
mask = get_clip_mask(np.dstack((lon[:], lat[:])), poly)
else:
mask = np.ones_like(lon, dtype=bool)
mask = np.nonzero(np.count_nonzero(mask, axis=1))
lon = pr_data1.variables["Longitude"][mask]
lat = pr_data1.variables["Latitude"][mask]
year = pr_data1.variables["Year"][mask]
month = pr_data1.variables["Month"][mask]
dayofmonth = pr_data1.variables["DayOfMonth"][mask]
# dayofyear = pr_data1.variables['DayOfYear'][mask]
hour = pr_data1.variables["Hour"][mask]
minute = pr_data1.variables["Minute"][mask]
second = pr_data1.variables["Second"][mask]
# secondofday = pr_data1.variables['scanTime_sec'][mask]
millisecond = pr_data1.variables["MilliSecond"][mask]
date_array = zip(
year,
month,
dayofmonth,
hour,
minute,
second,
millisecond.astype(np.int32) * 1000,
)
pr_time = np.array(
[dt.datetime(d[0], d[1], d[2], d[3], d[4], d[5], d[6]) for d in date_array]
)
pflag = pr_data1.variables["rainFlag"][mask]
ptype = pr_data1.variables["rainType"][mask]
status = pr_data1.variables["status"][mask]
zbb = pr_data1.variables["HBB"][mask].astype(np.float32)
bbwidth = pr_data1.variables["BBwidth"][mask].astype(np.float32)
quality = pr_data2.variables["dataQuality"][mask]
refl = pr_data2.variables["correctZFactor"][mask] / 100.0
zenith = pr_data2.variables["scLocalZenith"][mask]
pr_data1.close()
pr_data2.close()
# mask array
refl = np.ma.array(refl)
# Ground clutter
refl[refl == -8888.0] = np.ma.masked
# Misssing data
refl[refl == -9999.0] = np.ma.masked
# Scaling
refl /= 100.0
# Check for bad data
if max(quality) != 0:
raise ValueError("TRMM contains Bad Data")
# Determine the dimensions
ndim = refl.ndim
if ndim != 3:
raise ValueError(
"TRMM Dimensions do not match!" "Needed 3, given {0}".format(ndim)
)
tmp = refl.shape
nscan = tmp[0]
nray = tmp[1]
nbin = tmp[2]
# Reverse direction along the beam
refl = np.flip(refl, axis=-1)
# Simplify the precipitation flag
ipos = (pflag >= 10) & (pflag <= 20)
icer = pflag >= 20
pflag[ipos] = 1
pflag[icer] = 2
# Simplify the precipitation types
istr = (ptype >= 100) & (ptype <= 200)
icon = (ptype >= 200) & (ptype <= 300)
ioth = ptype >= 300
inone = ptype == -88
imiss = ptype == -99
ptype[istr] = 1
ptype[icon] = 2
ptype[ioth] = 3
ptype[inone] = 0
ptype[imiss] = -1
# Extract the surface type
sfc = np.zeros((nscan, nray), dtype=np.uint8)
i0 = status == 168
sfc[i0] = 0
i1 = status % 10 == 0
sfc[i1] = 1
i2 = (status - 1) % 10 == 0
sfc[i2] = 2
i3 = (status - 3) % 10 == 0
sfc[i3] = 3
i4 = (status - 4) % 10 == 0
sfc[i4] = 4
i5 = (status - 5) % 10 == 0
sfc[i5] = 5
i9 = (status - 9) % 10 == 0
sfc[i9] = 9
# Extract 2A23 quality
# TODO: Why is the `quality` variable overwritten?
quality = np.zeros((nscan, nray), dtype=np.uint8)
i0 = status == 168
quality[i0] = 0
i1 = status < 50
quality[i1] = 1
i2 = (status >= 50) & (status < 109)
quality[i2] = 2
trmm_data = {}
trmm_data.update(
{
"nscan": nscan,
"nray": nray,
"nbin": nbin,
"date": pr_time,
"lon": lon,
"lat": lat,
"pflag": pflag,
"ptype": ptype,
"zbb": zbb,
"bbwidth": bbwidth,
"sfc": sfc,
"quality": quality,
"refl": refl,
"zenith": zenith,
}
)
return trmm_data
def read_gpm(filename, bbox=None):
"""Reads GPM files for matching with GR
Parameters
----------
filename : string
path of the GPM file
bbox : dict
dictionary with bounding box coordinates (lon, lat),
defaults to None
Returns
-------
gpm_data : dict
dictionary of gpm data
Examples
--------
See :ref:`/notebooks/match3d/wradlib_match_workflow.ipynb`.
"""
pr_data = nc.Dataset(filename, mode="r")
lon = pr_data["NS"].variables["Longitude"]
lat = pr_data["NS"].variables["Latitude"]
if bbox is not None:
poly = [
[bbox["left"], bbox["bottom"]],
[bbox["left"], bbox["top"]],
[bbox["right"], bbox["top"]],
[bbox["right"], bbox["bottom"]],
[bbox["left"], bbox["bottom"]],
]
from wradlib.zonalstats import get_clip_mask
mask = get_clip_mask(np.dstack((lon[:], lat[:])), poly)
else:
mask = np.ones_like(lon, dtype=bool, subok=False)
mask = np.nonzero(np.count_nonzero(mask, axis=1))
lon = lon[mask]
lat = lat[mask]
year = pr_data["NS"]["ScanTime"].variables["Year"][mask]
month = pr_data["NS"]["ScanTime"].variables["Month"][mask]
dayofmonth = pr_data["NS"]["ScanTime"].variables["DayOfMonth"][mask]
# dayofyear = pr_data['NS']['ScanTime'].variables['DayOfYear'][mask]
hour = pr_data["NS"]["ScanTime"].variables["Hour"][mask]
minute = pr_data["NS"]["ScanTime"].variables["Minute"][mask]
second = pr_data["NS"]["ScanTime"].variables["Second"][mask]
# secondofday = pr_data['NS']['ScanTime'].variables['SecondOfDay'][mask]
millisecond = pr_data["NS"]["ScanTime"].variables["MilliSecond"][mask]
date_array = zip(
year,
month,
dayofmonth,
hour,
minute,
second,
millisecond.astype(np.int32) * 1000,
)
pr_time = np.array(
[dt.datetime(d[0], d[1], d[2], d[3], d[4], d[5], d[6]) for d in date_array]
)
sfc = pr_data["NS"]["PRE"].variables["landSurfaceType"][mask]
pflag = pr_data["NS"]["PRE"].variables["flagPrecip"][mask]
# bbflag = pr_data['NS']['CSF'].variables['flagBB'][mask]
zbb = pr_data["NS"]["CSF"].variables["heightBB"][mask]
# print(zbb.dtype)
bbwidth = pr_data["NS"]["CSF"].variables["widthBB"][mask]
qbb = pr_data["NS"]["CSF"].variables["qualityBB"][mask]
qtype = pr_data["NS"]["CSF"].variables["qualityTypePrecip"][mask]
ptype = pr_data["NS"]["CSF"].variables["typePrecip"][mask]
quality = pr_data["NS"]["scanStatus"].variables["dataQuality"][mask]
refl = pr_data["NS"]["SLV"].variables["zFactorCorrected"][mask]
# print(pr_data['NS']['SLV'].variables['zFactorCorrected'])
zenith = pr_data["NS"]["PRE"].variables["localZenithAngle"][mask]
pr_data.close()
# Check for bad data
if max(quality) != 0:
raise ValueError("GPM contains Bad Data")
pflag = pflag.astype(np.int8)
# Determine the dimensions
ndim = refl.ndim
if ndim != 3:
raise ValueError(
"GPM Dimensions do not match! " "Needed 3, given {0}".format(ndim)
)
tmp = refl.shape
nscan = tmp[0]
nray = tmp[1]
nbin = tmp[2]
# Reverse direction along the beam
refl = np.flip(refl, axis=-1)
# Change pflag=1 to pflag=2 to be consistent with 'Rain certain' in TRMM
pflag[pflag == 1] = 2
# Simplify the precipitation types
ptype = (ptype / 1e7).astype(np.int16)
# Simplify the surface types
imiss = sfc == -9999
sfc = (sfc / 1e2).astype(np.int16) + 1
sfc[imiss] = 0
# Set a quality indicator for the BB and precip type data
# TODO: Why is the `quality` variable overwritten?
quality = np.zeros((nscan, nray), dtype=np.uint8)
i1 = ((qbb == 0) | (qbb == 1)) & (qtype == 1)
quality[i1] = 1
i2 = (qbb > 1) | (qtype > 2)
quality[i2] = 2
gpm_data = {}
gpm_data.update(
{
"nscan": nscan,
"nray": nray,
"nbin": nbin,
"date": pr_time,
"lon": lon,
"lat": lat,
"pflag": pflag,
"ptype": ptype,
"zbb": zbb,
"bbwidth": bbwidth,
"sfc": sfc,
"quality": quality,
"refl": refl,
"zenith": zenith,
}
)
return gpm_data
def read_trmm(filename1, filename2, bbox=None):
"""Reads TRMM files for matching with GR
Parameters
----------
filename1 : string
path of the TRMM 2A23 file
filename2 : string
path of the TRMM 2A25 file
bbox : dict
dictionary with bounding box coordinates (lon, lat),
defaults to None
Returns
-------
trmm_data : dict
dictionary of trmm data
Examples
--------
See :ref:`/notebooks/match3d/wradlib_match_workflow.ipynb`.
"""
# trmm 2A23 and 2A25 data is hdf4
pr_data1 = nc.Dataset(filename1, mode="r")
pr_data2 = nc.Dataset(filename2, mode="r")
lon = pr_data1.variables['Longitude']
lat = pr_data1.variables['Latitude']
if bbox is not None:
poly = [[bbox['left'], bbox['bottom']], [bbox['left'], bbox['top']],
[bbox['right'], bbox['top']], [bbox['right'], bbox['bottom']],
[bbox['left'], bbox['bottom']]]
from wradlib.zonalstats import get_clip_mask
mask = get_clip_mask(np.dstack((lon[:], lat[:])), poly)
else:
mask = np.ones_like(lon, dtype=bool)
mask = np.nonzero(np.count_nonzero(mask, axis=1))
lon = pr_data1.variables['Longitude'][mask]
lat = pr_data1.variables['Latitude'][mask]
year = pr_data1.variables['Year'][mask]
month = pr_data1.variables['Month'][mask]
dayofmonth = pr_data1.variables['DayOfMonth'][mask]
# dayofyear = pr_data1.variables['DayOfYear'][mask]
hour = pr_data1.variables['Hour'][mask]
minute = pr_data1.variables['Minute'][mask]
second = pr_data1.variables['Second'][mask]
# secondofday = pr_data1.variables['scanTime_sec'][mask]
millisecond = pr_data1.variables['MilliSecond'][mask]
date_array = zip(year, month, dayofmonth, hour, minute, second,
millisecond.astype(np.int32) * 1000)
pr_time = np.array([
dt.datetime(d[0], d[1], d[2], d[3], d[4], d[5], d[6])
for d in date_array
])
pflag = pr_data1.variables['rainFlag'][mask]
ptype = pr_data1.variables['rainType'][mask]
status = pr_data1.variables['status'][mask]
zbb = pr_data1.variables['HBB'][mask].astype(np.float32)
bbwidth = pr_data1.variables['BBwidth'][mask].astype(np.float32)
quality = pr_data2.variables['dataQuality'][mask]
refl = pr_data2.variables['correctZFactor'][mask] / 100.
zenith = pr_data2.variables['scLocalZenith'][mask]
pr_data1.close()
pr_data2.close()
# mask array
refl = np.ma.array(refl)
# Ground clutter
refl[refl == -8888.] = np.ma.masked
# Misssing data
refl[refl == -9999.] = np.ma.masked
# Scaling
refl /= 100.
# Check for bad data
if max(quality) != 0:
raise ValueError('TRMM contains Bad Data')
# Determine the dimensions
ndim = refl.ndim
if ndim != 3:
raise ValueError('TRMM Dimensions do not match!'
'Needed 3, given {0}'.format(ndim))
tmp = refl.shape
nscan = tmp[0]
nray = tmp[1]
nbin = tmp[2]
# Reverse direction along the beam
refl = np.flip(refl, axis=-1)
# Simplify the precipitation flag
ipos = (pflag >= 10) & (pflag <= 20)
icer = (pflag >= 20)
pflag[ipos] = 1
pflag[icer] = 2
# Simplify the precipitation types
istr = (ptype >= 100) & (ptype <= 200)
icon = (ptype >= 200) & (ptype <= 300)
ioth = (ptype >= 300)
inone = (ptype == -88)
imiss = (ptype == -99)
ptype[istr] = 1
ptype[icon] = 2
ptype[ioth] = 3
ptype[inone] = 0
ptype[imiss] = -1
# Extract the surface type
sfc = np.zeros((nscan, nray), dtype=np.uint8)
i0 = (status == 168)
sfc[i0] = 0
i1 = (status % 10 == 0)
sfc[i1] = 1
i2 = ((status - 1) % 10 == 0)
sfc[i2] = 2
i3 = ((status - 3) % 10 == 0)
sfc[i3] = 3
i4 = ((status - 4) % 10 == 0)
sfc[i4] = 4
i5 = ((status - 5) % 10 == 0)
sfc[i5] = 5
i9 = ((status - 9) % 10 == 0)
sfc[i9] = 9
# Extract 2A23 quality
# TODO: Why is the `quality` variable overwritten?
quality = np.zeros((nscan, nray), dtype=np.uint8)
i0 = (status == 168)
quality[i0] = 0
i1 = (status < 50)
quality[i1] = 1
i2 = ((status >= 50) & (status < 109))
quality[i2] = 2
trmm_data = {}
trmm_data.update({
'nscan': nscan,
'nray': nray,
'nbin': nbin,
'date': pr_time,
'lon': lon,
'lat': lat,
'pflag': pflag,
'ptype': ptype,
'zbb': zbb,
'bbwidth': bbwidth,
'sfc': sfc,
'quality': quality,
'refl': refl,
'zenith': zenith
})
return trmm_data