def pointfind2(plat, plon, lat, lon, pdif=1):
""" return indeces and values of the grid point closest to lat/lon point
the same as pointfind but could be faster
Usage:
pointfind(plat, plon, lat, lon, pdif = 0.5)
Input:
plat - latitude of the point
plon - longitude of the point
lat - 2d array of latutudes
lon - 2d array of longitudes
pdif - we don't need it but leave it to have the same input as pointfind
Output:
indeces and values of the points that fulfil conditions
"""
dist_min = 1000000.
for i in range(lon.shape[0]):
for j in range(lon.shape[1]):
dist = Ngl.gc_dist(plat,plon,lat[i,j],lon[i,j])
if dist_min > dist:
dist_min = dist
i_min = i
j_min = j
lat_min = lat[i,j]
lon_min = lon[i,j]
print(i_min,j_min,lat_min,lon_min)
gg1 = i_min, j_min
return(gg1, lat_min, lon_min)
def pointfind2(plat, plon, lat, lon, pdif=1):
""" return indeces and values of the grid point closest to lat/lon point
the same as pointfind but could be faster
Usage:
pointfind(plat, plon, lat, lon, pdif = 0.5)
Input:
plat - latitude of the point
plon - longitude of the point
lat - 2d array of latutudes
lon - 2d array of longitudes
pdif - we don't need it but leave it to have the same input as pointfind
Output:
indeces and values of the points that fulfil conditions
"""
dist_min = 1000000.
for i in range(lon.shape[0]):
for j in range(lon.shape[1]):
dist = Ngl.gc_dist(plat, plon, lat[i, j], lon[i, j])
if dist_min > dist:
dist_min = dist
i_min = i
j_min = j
lat_min = lat[i, j]
lon_min = lon[i, j]
print(i_min, j_min, lat_min, lon_min)
gg1 = i_min, j_min
return (gg1, lat_min, lon_min)
def find_closest(lats, lons, lat, lon, mask=None):
mindist = 1e20
if mask == None:
mask = np.zeros([len(lats), len(lons)], dtype=np.bool)
for i in range(len(lons)):
for j in range(len(lats)):
if not mask[j, i]:
dist = Ngl.gc_dist(lons[i], lats[j], lon, lat)
if dist < mindist:
closest_i = i
closest_j = j
mindist = dist
return closest_i, closest_j
def find_closest(lats, lons, lat, lon, mask=None):
mindist = 1e20
if mask == None:
mask = np.zeros([len(lats), len(lons)], dtype=np.bool)
for i in range(len(lons)):
for j in range(len(lats)):
if not mask[j,i]:
dist = Ngl.gc_dist(lons[i], lats[j], lon, lat)
if dist < mindist:
closest_i = i
closest_j = j
mindist = dist
return closest_i, closest_j
def find_distances(x1, y1, x2, y2):
""" generalization of Ngl.gc_dist that can take arrays of lat and lon as input and return a ndarray of distances. """
narrays = 0
size_output = []
order_input = []
if isinstance(x1, np.ndarray):
IM1 = x1.size
narrays = narrays + 1
size_output.append(IM1)
order_input.append(0)
if isinstance(y1, np.ndarray):
JM1 = y1.size
narrays = narrays + 1
size_output.append(JM1)
order_input.append(1)
if isinstance(x2, np.ndarray):
IM2 = x2.size
narrays = narrays + 1
size_output.append(IM2)
order_input.append(2)
if isinstance(y2, np.ndarray):
JM2 = y2.size
narrays = narrays + 1
size_output.append(JM2)
order_input.append(3)
if narrays == 0:
output = Ngl.gc_dist(x1, y1, x2, y2)
else:
output = np.zeros(size_output)
if narrays == 1:
if order_input[0] == 0:
for i in range(0, IM1):
output[i] = Ngl.gc_dist(x1[i], y1, x2, y2)
if order_input[0] == 1:
for i in range(0, JM1):
output[i] = Ngl.gc_dist(x1, y1[i], x2, y2)
if order_input[0] == 2:
for i in range(0, IM2):
output[i] = Ngl.gc_dist(x1, y1, x2[i], y2)
if order_input[0] == 3:
for i in range(0, JM2):
output[i] = Ngl.gc_dist(x1, y1, x2, y2[i])
if narrays == 2:
if order_input[0] == 0:
if order_input[1] == 1:
for i in range(0, IM1):
for j in range(0, JM1):
output[i, j] = Ngl.gc_dist(x1[i], y1[j], x2, y2)
if order_input[1] == 2:
for i in range(0, IM1):
for j in range(0, IM2):
output[i, j] = Ngl.gc_dist(x1[i], y1, x2[j], y2)
if order_input[1] == 3:
for i in range(0, IM1):
for j in range(0, JM2):
output[i, j] = Ngl.gc_dist(x1[i], y1, x2, y2[j])
if order_input[0] == 1:
if order_input[1] == 2:
for i in range(0, JM1):
for j in range(0, IM2):
output[i, j] = Ngl.gc_dist(x1, y1[i], x2[j], y2)
if order_input[1] == 3:
for i in range(0, JM1):
for j in range(0, JM2):
output[i, j] = Ngl.gc_dist(x1, y1[i], x2, y2[j])
if order_input[0] == 2:
if order_input[1] == 3:
for i in range(0, IM2):
for j in range(0, JM2):
output[i, j] = Ngl.gc_dist(x1, y1, x2[i], y2[j])
if narrays == 3:
raise NotImplementedError
if narrays == 4:
for i in range(0, IM1):
for j in range(0, JM1):
for k in range(0, IM2):
for l in range(0, JM2):
output[i, j, k,
l] = Ngl.gc_dist(x1[i], y1[j], x2[k], y2[l])
return output
def get_transect(lat, lon, data, lat1, lon1, lat2, lon2, npoints = 10, pdif = 1, norep=False):
plat, plon = Ngl.gc_interp(lat1,lon1,lat2,lon2,npoints)
m_grid = numpy.zeros((plon.shape[0]))
n_grid = numpy.zeros((plat.shape[0]))
grid_lats = numpy.zeros((plat.shape[0]))
grid_lons = numpy.zeros((plat.shape[0]))
for k in range(plon.shape[0]):
coord, trash1, trash1 = pointfind2(plat[k], plon[k], lat, lon, pdif)
m_grid[k] = coord[0]
n_grid[k] = coord[1]
grid_lats[k] = lat[m_grid[k],n_grid[k]]
grid_lons[k] = lon[m_grid[k],n_grid[k]]
if len(data.shape) == 4:
data = data[0,:,:,:]
elif len(data.shape) == 5:
data = data[0,0,:,:,:]
data_prof = numpy.zeros(([data.shape[0],npoints]))
for j in range(data_prof.shape[1]):
data_prof[:,j] = data[:,m_grid[j],n_grid[j]]
x_kilometers = numpy.zeros((grid_lats.shape[0]))
for j in range(grid_lats.shape[0] - 1):
angle = Ngl.gc_dist(grid_lats[j], grid_lons[j], grid_lats[j+1], grid_lons[j+1] )
x_kilometers[j+1] = Ngl.gc_convert(angle, 2) + x_kilometers[j]
numpy.savez("outfile.npz", data_prof, x_kilometers)
if norep == True:
data_prof_norep = numpy.zeros(([data_prof.shape[0],1]))
x_kilometers_norep = numpy.array(([]))
m_grid_norep = numpy.array(([]))
n_grid_norep = numpy.array(([]))
present_location = -999999.999
for k in range(x_kilometers.shape[0]):
if int(x_kilometers[k]) != int(present_location):
present_location = x_kilometers[k]
data_prof_norep = numpy.hstack((data_prof_norep, data_prof[:,k:k+1]))
x_kilometers_norep = numpy.append(x_kilometers_norep, x_kilometers[k:k+1])
m_grid_norep = numpy.append(m_grid_norep, m_grid[k:k+1])
n_grid_norep = numpy.append(n_grid_norep, n_grid[k:k+1])
data_prof_norep = data_prof_norep[:,1:]
data_prof = data_prof_norep
x_kilometers = x_kilometers_norep
m_grid = m_grid_norep
n_grid = n_grid_norep
print(x_kilometers)
print(m_grid)
print(n_grid)
return(data_prof, x_kilometers, m_grid, n_grid )
def get_transect(lat,
lon,
data,
lat1,
lon1,
lat2,
lon2,
npoints=10,
pdif=1,
norep=False):
plat, plon = Ngl.gc_interp(lat1, lon1, lat2, lon2, npoints)
m_grid = numpy.zeros((plon.shape[0]))
n_grid = numpy.zeros((plat.shape[0]))
grid_lats = numpy.zeros((plat.shape[0]))
grid_lons = numpy.zeros((plat.shape[0]))
for k in range(plon.shape[0]):
coord, trash1, trash1 = pointfind2(plat[k], plon[k], lat, lon, pdif)
m_grid[k] = coord[0]
n_grid[k] = coord[1]
grid_lats[k] = lat[m_grid[k], n_grid[k]]
grid_lons[k] = lon[m_grid[k], n_grid[k]]
if len(data.shape) == 4:
data = data[0, :, :, :]
elif len(data.shape) == 5:
data = data[0, 0, :, :, :]
data_prof = numpy.zeros(([data.shape[0], npoints]))
for j in range(data_prof.shape[1]):
data_prof[:, j] = data[:, m_grid[j], n_grid[j]]
x_kilometers = numpy.zeros((grid_lats.shape[0]))
for j in range(grid_lats.shape[0] - 1):
angle = Ngl.gc_dist(grid_lats[j], grid_lons[j], grid_lats[j + 1],
grid_lons[j + 1])
x_kilometers[j + 1] = Ngl.gc_convert(angle, 2) + x_kilometers[j]
numpy.savez("outfile.npz", data_prof, x_kilometers)
if norep == True:
data_prof_norep = numpy.zeros(([data_prof.shape[0], 1]))
x_kilometers_norep = numpy.array(([]))
m_grid_norep = numpy.array(([]))
n_grid_norep = numpy.array(([]))
present_location = -999999.999
for k in range(x_kilometers.shape[0]):
if int(x_kilometers[k]) != int(present_location):
present_location = x_kilometers[k]
data_prof_norep = numpy.hstack(
(data_prof_norep, data_prof[:, k:k + 1]))
x_kilometers_norep = numpy.append(x_kilometers_norep,
x_kilometers[k:k + 1])
m_grid_norep = numpy.append(m_grid_norep, m_grid[k:k + 1])
n_grid_norep = numpy.append(n_grid_norep, n_grid[k:k + 1])
data_prof_norep = data_prof_norep[:, 1:]
data_prof = data_prof_norep
x_kilometers = x_kilometers_norep
m_grid = m_grid_norep
n_grid = n_grid_norep
print(x_kilometers)
print(m_grid)
print(n_grid)
return (data_prof, x_kilometers, m_grid, n_grid)
def find_distances(x1, y1, x2, y2):
""" generalization of Ngl.gc_dist that can take arrays of lat and lon as input and return a ndarray of distances. """
narrays = 0
size_output = []
order_input = []
if isinstance(x1, np.ndarray):
IM1 = x1.size
narrays = narrays+1
size_output.append(IM1)
order_input.append(0)
if isinstance(y1, np.ndarray):
JM1 = y1.size
narrays = narrays+1
size_output.append(JM1)
order_input.append(1)
if isinstance(x2, np.ndarray):
IM2 = x2.size
narrays = narrays+1
size_output.append(IM2)
order_input.append(2)
if isinstance(y2, np.ndarray):
JM2 = y2.size
narrays = narrays+1
size_output.append(JM2)
order_input.append(3)
if narrays == 0:
output = Ngl.gc_dist(x1, y1, x2, y2)
else:
output = np.zeros(size_output)
if narrays == 1:
if order_input[0] == 0:
for i in range(0,IM1):
output[i] = Ngl.gc_dist(x1[i], y1, x2, y2)
if order_input[0] == 1:
for i in range(0,JM1):
output[i] = Ngl.gc_dist(x1, y1[i], x2, y2)
if order_input[0] == 2:
for i in range(0,IM2):
output[i] = Ngl.gc_dist(x1, y1, x2[i], y2)
if order_input[0] == 3:
for i in range(0,JM2):
output[i] = Ngl.gc_dist(x1, y1, x2, y2[i])
if narrays == 2:
if order_input[0] == 0:
if order_input[1] == 1:
for i in range(0,IM1):
for j in range(0,JM1):
output[i,j] = Ngl.gc_dist(x1[i], y1[j], x2, y2)
if order_input[1] == 2:
for i in range(0,IM1):
for j in range(0,IM2):
output[i,j] = Ngl.gc_dist(x1[i], y1, x2[j], y2)
if order_input[1] == 3:
for i in range(0,IM1):
for j in range(0,JM2):
output[i,j] = Ngl.gc_dist(x1[i], y1, x2, y2[j])
if order_input[0] == 1:
if order_input[1] == 2:
for i in range(0,JM1):
for j in range(0,IM2):
output[i,j] = Ngl.gc_dist(x1, y1[i], x2[j], y2)
if order_input[1] == 3:
for i in range(0,JM1):
for j in range(0,JM2):
output[i,j] = Ngl.gc_dist(x1, y1[i], x2, y2[j])
if order_input[0] == 2:
if order_input[1] == 3:
for i in range(0,IM2):
for j in range(0,JM2):
output[i,j] = Ngl.gc_dist(x1, y1, x2[i], y2[j])
if narrays == 3:
raise NotImplementedError
if narrays == 4:
for i in range(0,IM1):
for j in range(0,JM1):
for k in range(0,IM2):
for l in range(0,JM2):
output[i,j,k,l] = Ngl.gc_dist(x1[i], y1[j], x2[k], y2[l])
return output
