def cut_kernel(xpos, ypos, arr, date, lon, lat, t, parallax=False, rotate=False):
if parallax:
km, coords = u_gis.call_parallax_era(date.month, t, lon, lat, 0, 0)
lx, ly = km
lx = int(np.round(lx / 27.5))
ly = int(np.round(ly / 27.5)) # km into pixels
xpos = xpos - lx
ypos = ypos - ly
dist = 11
kernel = u_arrays.cut_kernel(arr,xpos, ypos,dist)
if rotate:
kernel = u_met.era_wind_rotate(kernel,date.month,lat,lon,level=700, ref_angle=90)
# if (np.sum(np.isfinite(kernel)) < 0.10 * kernel.size):
# return
kernel3 = kernel - np.nanmean(kernel)
cnt = np.zeros_like(kernel)
cnt[np.isfinite(kernel)] = 1
if kernel.shape != (dist*2+1, dist*2+1):
return None
return kernel, kernel3, cnt
def cut_kernel(xpos,
ypos,
arr,
date,
lon,
lat,
t,
parallax=False,
rotate=False,
probs=False):
if parallax:
km, coords = u_gis.call_parallax_era(date.month, t, lon, lat, 0, 0)
lx, ly = km
lx = int(np.round(lx / 3.))
ly = int(np.round(ly / 3.)) # km into pixels
xpos = xpos - lx
ypos = ypos - ly
dist = 200
kernel = u_arrays.cut_kernel(arr, xpos, ypos, dist)
if np.sum(probs) > 0:
prob = u_arrays.cut_kernel(probs, xpos, ypos, dist)
cnt2 = np.zeros_like(kernel)
cnt2[np.isfinite(prob)] = 1
else:
prob = np.zeros_like(kernel)
cnt2 = np.zeros_like(kernel)
if rotate:
kernel = u_met.era_wind_rotate(kernel,
date,
lat,
lon,
level=700,
ref_angle=90)
if (np.sum(np.isfinite(kernel)) < 2):
return
kernel3 = kernel - np.nanmean(kernel)
cnt = np.zeros_like(kernel)
cnt[np.isfinite(kernel)] = 1
if kernel.shape != (dist * 2 + 1, dist * 2 + 1):
pdb.set_trace()
return kernel, kernel3, cnt, prob, cnt2
def cut_kernel(xpos, ypos, arr, date, lon, lat, t, parallax=False, rotate=False, probs=False):
if parallax:
km, coords = u_gis.call_parallax_era(date.month, t, lon, lat, 0, 0)
lx, ly = km
lx = int(np.round(lx / 3.))
ly = int(np.round(ly / 3.)) # km into pixels
xpos = xpos - lx
ypos = ypos - ly
dist = 200
kernel = u_arrays.cut_kernel(arr,xpos, ypos,dist)
vdic = {}
for d in probs.data_vars:
var = u_arrays.cut_kernel(probs[d].values,xpos, ypos,dist)
vdic[d] = var
cnt2 = np.zeros_like(vdic[list(vdic.keys())[0]])
cnt2[np.isfinite(vdic[list(vdic.keys())[0]])] = 1
if rotate:
kernel = u_met.era_wind_rotate(kernel,date,lat,lon,level=700, ref_angle=90)
if (np.sum(np.isfinite(kernel)) < 2):
return
kernel3 = kernel - np.nanmean(kernel)
cnt = np.zeros_like(kernel)
cnt[np.isfinite(kernel)] = 1
if kernel.shape != (dist*2+1, dist*2+1):
pdb.set_trace()
return kernel, kernel3, cnt, cnt2, vdic
def cut_kernel(xpos,
ypos,
arr,
date,
lon,
lat,
t,
parallax=False,
rotate=False):
if parallax:
km, coords = u_gis.call_parallax_era(date.month, t, lon, lat, 0, 0)
lx, ly = km
lx = int(np.round(lx / 3.))
ly = int(np.round(ly / 3.)) # km into pixels
xpos = xpos - lx
ypos = ypos - ly
dist = 100
kernel = u_arrays.cut_kernel(arr, xpos, ypos, dist)
if rotate:
kernel = u_met.era_wind_rotate(kernel,
date,
lat,
lon,
level=700,
ref_angle=90)
if (np.sum(np.isfinite(kernel)) < 0.10 * kernel.size):
return
kernel3 = kernel - np.nanmean(kernel)
cnt = np.zeros_like(kernel)
cnt[np.isfinite(kernel)] = 1
if kernel.shape != (201, 201):
return
return kernel, kernel3, cnt
def cut_kernel(xpos, ypos, arr, date, lon, lat, t, parallax=False, probs=False):
if parallax:
km, coords = u_gis.call_parallax_era(date.month, t, lon, lat, 0, 0)
lx, ly = km
lx = int(np.round(lx / 3.))
ly = int(np.round(ly / 3.)) # km into pixels
xpos = xpos - lx
ypos = ypos - ly
dist = 200
kernel = u_arrays.cut_kernel(arr,xpos, ypos,dist)
if np.nansum(probs) > 0:
prob = u_arrays.cut_kernel(probs,xpos, ypos,dist)
cnt2 = np.zeros_like(kernel)
cnt2[np.isfinite(prob)] = 1
else:
prob = np.zeros_like(kernel)
cnt2 = np.zeros_like(kernel)
if (np.sum(np.isfinite(kernel)) < 2):
return
kernel3 = kernel - np.nanmean(kernel)
cnt = np.zeros_like(kernel)
cnt[np.isfinite(kernel)] = 1
if kernel.shape != (dist*2+1, dist*2+1):
pdb.set_trace()
return kernel, kernel3, cnt, prob, cnt2