def calculated_metadata(self, df=None, geometries=True, clean_cols=True, clean_rows=True):
if df is None:
df = self.to_dataframe(clean_cols=clean_cols, clean_rows=clean_rows)
trajectories = {}
for tid, tgroup in df.groupby('trajectory'):
tgroup = tgroup.sort_values('t')
first_row = tgroup.iloc[0]
first_loc = Point(first_row.x, first_row.y)
geometry = None
if geometries:
coords = list(unique_justseen(zip(tgroup.x, tgroup.y)))
if len(coords) > 1:
geometry = LineString(coords)
elif coords == 1:
geometry = first_loc
trajectory = namedtuple('Trajectory', ['min_z', 'max_z', 'min_t', 'max_t', 'first_loc', 'geometry'])
trajectories[tid] = trajectory(
min_z=tgroup.z.min(),
max_z=tgroup.z.max(),
min_t=tgroup.t.min(),
max_t=tgroup.t.max(),
first_loc=first_loc,
geometry=geometry
)
meta = namedtuple('Metadata', ['min_t', 'max_t', 'trajectories'])
return meta(
min_t=df.t.min(),
max_t=df.t.max(),
trajectories=trajectories
)
def calculated_metadata(self,
df=None,
geometries=True,
clean_cols=True,
clean_rows=True):
if df is None:
df = self.to_dataframe(clean_cols=clean_cols,
clean_rows=clean_rows)
trajectories = {}
for tid, tgroup in df.groupby('trajectory'):
tgroup = tgroup.sort_values('t')
profiles = {}
for pid, pgroup in tgroup.groupby('profile'):
pgroup = pgroup.sort_values('t')
first_row = pgroup.iloc[0]
profile = namedtuple('Profile',
['min_z', 'max_z', 't', 'x', 'y', 'loc'])
profiles[pid] = profile(min_z=pgroup.z.min(),
max_z=pgroup.z.max(),
t=first_row.t,
x=first_row.x,
y=first_row.y,
loc=Point(first_row.x, first_row.y))
geometry = None
first_row = tgroup.iloc[0]
first_loc = Point(first_row.x, first_row.y)
if geometries:
# only extract non-null pairs
null_coordinates = tgroup.x.isnull() | tgroup.y.isnull()
coords = list(
unique_justseen(
zip(tgroup.x[~null_coordinates].tolist(),
tgroup.y[~null_coordinates].tolist())))
if len(coords) > 1:
geometry = LineString(coords)
elif coords == 1:
geometry = first_loc
trajectory = namedtuple('Trajectory', [
'min_z', 'max_z', 'min_t', 'max_t', 'profiles', 'first_loc',
'geometry'
])
trajectories[tid] = trajectory(min_z=tgroup.z.min(),
max_z=tgroup.z.max(),
min_t=tgroup.t.min(),
max_t=tgroup.t.max(),
profiles=profiles,
first_loc=first_loc,
geometry=geometry)
meta = namedtuple('Metadata',
['min_z', 'max_z', 'min_t', 'max_t', 'trajectories'])
return meta(min_z=df.z.min(),
max_z=df.z.max(),
min_t=df.t.min(),
max_t=df.t.max(),
trajectories=trajectories)
def calculated_metadata(self, df=None, geometries=True, clean_cols=True, clean_rows=True):
if df is None:
df = self.to_dataframe(clean_cols=clean_cols, clean_rows=clean_rows)
profiles = {}
for pid, pgroup in df.groupby('profile'):
pgroup = pgroup.sort_values('t')
first_row = pgroup.iloc[0]
profile = namedtuple('Profile', ['min_z', 'max_z', 't', 'x', 'y', 'loc'])
profiles[pid] = profile(
min_z=pgroup.z.min(),
max_z=pgroup.z.max(),
t=first_row.t,
x=first_row.x,
y=first_row.y,
loc=Point(first_row.x, first_row.y)
)
geometry = None
first_row = df.iloc[0]
first_loc = Point(first_row.x, first_row.y)
if geometries:
coords = list(unique_justseen(zip(df.x, df.y)))
if len(coords) > 1:
geometry = LineString(coords) # noqa
elif len(coords) == 1:
geometry = first_loc # noqa
meta = namedtuple('Metadata', ['min_z', 'max_z', 'min_t', 'max_t', 'profiles', 'first_loc', 'geometry'])
return meta(
min_z=df.z.min(),
max_z=df.z.max(),
min_t=df.t.min(),
max_t=df.t.max(),
profiles=profiles,
first_loc=first_loc,
geometry=geometry
)
