def enforce_2D(self, ):
"""Make sure the grid is 2-D.
"""
msg = 'Pass a 2D array.'
for data in (self.raw_fcstdata, self.raw_verifdata):
if len(data.shape) == 1:
raise FormatError(msg)
elif len(data.shape) > 2:
for dim in N.arange(len(data.shape) - 2):
if data.shape[0] != 1:
raise FormatError(msg)
else:
print("Passed 2D grid check.")
return
def do_square_check(self):
"""Assert that the grid is a power of 2.
"""
for data in (self.raw_fcstdata, self.raw_verifdata):
for dim in range(len(data.shape)):
if not N.log2(data.shape[dim]).is_integer():
msg = "Data dimension not square/power of 2, but of shape {}.".format(
data.shape)
raise FormatError(msg)
elif data.shape[0] != data.shape[1]:
raise FormatError(msg)
else:
print("Passed square check.")
self.L = int(N.log2(data.shape[dim]))
return
def do_grid_check(self,):
""" Make sure grids are identical size.
"""
self.ydim, self.xdim = self.data_fcst.shape
if self.data_obs.shape != (self.ydim,self.xdim):
raise FormatError("Obs and forecast data not same size.")
return
def enforce_2D(self,):
"""Both data grids need to be 2D.
"""
for data in (self.data_obs,self.data_fcst):
shp = data.shape
if len(shp) == 2:
pass
elif len(shp) == 3:
if shp[0] == 0:
data = data[0,:,:]
elif len(shp) == 4:
if (shp[0] == 0) and (shp[1] == 0):
data = data[0,0,:,:]
else:
raise FormatError("Data needs to be 2D.")
return
def _enforce_same_dimensions(*args):
if not check_same_dimensions(*args):
raise FormatError("Input arrays not all same size.")
return
def __init__(self,
fcst_arr,
obs_arr,
thresh,
overunder='over',
CLs=None,
enforce_limits=True):
self.enforce_limits = enforce_limits
self.overunder = overunder
self.fcst_arr = fcst_arr
self.thresh = thresh
self.obs_arr = obs_arr
if fcst_arr.ndim == 3:
if fcst_arr.shape[0] == 1:
fcst_arr = fcst_arr[0, :, :]
self.FVens = False
else:
self.arr2x2s, self.J = self.compute_FV_prob()
self.FVens = True
else:
self.FVens = False
if self.FVens:
self.pod = dict()
self.pfd = dict()
self.pcli = dict()
self.kss = dict()
for j in self.J:
DS = DetScores(arr2x2=self.arr2x2s[j])
self.pod[j] = DS.compute_pod()
self.pfd[j] = DS.compute_pfd()
self.pcli[j] = DS.compute_pcli()
self.kss[j] = DS.compute_kss()
else:
super().__init__(fcst_arr=fcst_arr,
obs_arr=obs_arr,
thresh=thresh,
overunder=overunder)
self.pod = self.compute_pod()
self.pfd = self.compute_pfd()
self.pcli = self.compute_pcli()
self.kss = self.compute_kss()
if CLs is None:
CLs = N.arange(0.01, 1.01, 0.01)
elif isinstance(CLs, (int, float, N.ndarray, tuple, list)):
CLs = N.array(CLs)
else:
raise FormatError("CLs argument must be one of the following: \n"
"int, float, N.ndarray, tuple, list.")
self.CLs = CLs
self.FVs = []
for cl in self.CLs:
if self.FVens:
self.FVs.append(self.compute_FVens(cl))
else:
self.FVs.append(self.compute_FV(cl))
if self.enforce_limits:
clipped_FVs = []
overs = 0
unders = 0
for fv in self.FVs:
if fv > 1:
fv = 1
overs += 1
elif fv < 0:
fv = 0
unders += 1
clipped_FVs.append(fv)
print("{} FVs over 1 were set to 1.".format(overs))
print("{} FVs under 0 were set to 0.".format(unders))
self.raw_FVs = self.FVs
self.FVs = clipped_FVs