import logging
import numpy as np
from scipy.ndimage import map_coordinates, grey_dilation
import logging; logging.captureWarnings(True); logger = logging.getLogger(__name__)
from scipy.interpolate import interp1d, LinearNDInterpolator
logger = logging.getLogger('opendrift') # using common logger
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def expand_numpy_array(data):
if isinstance(data, np.ma.MaskedArray):
logger.warning('Converting masked array to numpy array before interpolating')
data = np.ma.filled(data, fill_value=np.nan)
if not np.isfinite(data).any():
logger.warning('Only NaNs, returning')
return
mask = ~np.isfinite(data)
minval = np.finfo(data[mask].dtype).min
data[mask] = minval
data[mask] = grey_dilation(data, size=3)[mask]
data[data==minval] = np.nan
###########################
# 2D interpolator classes
###########################
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class Nearest2DInterpolator():
def __init__(self, xgrid, ygrid, x, y):
self.x = x
self.y = y
self.xi = (x - xgrid.min())/(xgrid.max()-xgrid.min())*len(xgrid)
self.yi = (y - ygrid.min())/(ygrid.max()-ygrid.min())*len(ygrid)
self.xi = np.round(self.xi).astype(np.uint32)
self.yi = np.round(self.yi).astype(np.uint32)
self.xi[self.xi >= len(xgrid)] = len(xgrid)-1
self.yi[self.yi >= len(ygrid)] = len(ygrid)-1
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def __call__(self, array2d):
return array2d[self.yi, self.xi]
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class NDImage2DInterpolator():
def __init__(self, xgrid, ygrid, x, y):
self.x = x
self.y = y
self.xi = (x - xgrid.min())/(xgrid.max()-xgrid.min())*len(xgrid)
self.yi = (y - ygrid.min())/(ygrid.max()-ygrid.min())*len(ygrid)
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def __call__(self, array2d):
try:
array2d = np.ma.array(array2d, mask=array2d.mask)
array2d[array2d.mask] = np.nan # Gives holes
except:
pass
return np.ma.masked_invalid(
map_coordinates(array2d, [self.yi, self.xi],
cval=np.nan, order=0))
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class LinearND2DInterpolator():
logger = logging.getLogger('opendrift')
def __init__(self, xgrid, ygrid, x, y):
self.block_x, self.block_y = np.meshgrid(xgrid, ygrid)
self.block_x = self.block_x.ravel()
self.block_y = self.block_y.ravel()
self.x = x
self.y = y
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def __call__(self, array2d):
array_ravel = array2d.ravel()
valid = np.isfinite(array_ravel)
#if isinstance(array2d.mask, np.ndarray):
# valid = ~array2d.ravel().mask
#elif array2d.mask == False:
# valid = np.ones(array_ravel.shape, dtype=bool)
#elif array2d.mask == True:
# valid = np.zeros(array_ravel.shape, dtype=bool)
if hasattr(self, 'interpolator'):
if not np.array_equal(valid, self.interpolator.valid):
logger.debug('Cannot reuse interpolator - validity of '
'array is different from original.')
if hasattr(self, 'interpolator') and (np.array_equal(
valid, self.interpolator.valid)):
# Reuse stored interpolator with new data
self.interpolator.values[:, 0] = \
(array_ravel[valid])
else:
# Make new interpolator for given x,y
self.interpolator = LinearNDInterpolator(
(self.block_y[valid],
self.block_x[valid]),
array_ravel[valid])
# Store valid array, to determine if can be used again
self.interpolator.valid = valid
# Call interpolator to avoid threading-problem:
# https://github.com/scipy/scipy/issues/8856
self.interpolator((0,0))
return self.interpolator(self.y, self.x)
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class Linear2DInterpolator():
logger = logging.getLogger('opendrift')
def __init__(self, xgrid, ygrid, x, y):
self.x = x
self.y = y
self.xi = (x - xgrid[0])/(xgrid[-1]-xgrid[0])*(len(xgrid)-1)
self.yi = (y - ygrid[0])/(ygrid[-1]-ygrid[0])*(len(ygrid)-1)
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def __call__(self, array2d):
if isinstance(array2d,np.ma.MaskedArray):
logger.debug('Converting masked array to numpy array for interpolation')
array2d = np.ma.filled(array2d, fill_value=np.nan)
if not np.isfinite(array2d).any():
logger.warning('Only NaNs input to linearNDFast - returning')
return np.nan*np.ones(len(self.xi))
# Fill NaN-values with nearby real values
interp = map_coordinates(array2d, [self.yi, self.xi],
cval=np.nan, order=1)
missing = np.where(~np.isfinite(interp))[0]
i=0
while len(missing) > 0:
i += 1
if i > 10:
logger.warning('Still NaN-values after 10 iterations, exiting!')
return interp
logger.debug('Linear2DInterpolator informational: NaN values for %i elements, expanding data %i' %
(len(missing), i))
expand_numpy_array(array2d)
interp[missing] = map_coordinates(
array2d, [self.yi[missing], self.xi[missing]],
cval=np.nan, order=1, mode='nearest')
missing = np.where(~np.isfinite(interp))[0]
return interp
horizontal_interpolation_methods = {
'nearest': Nearest2DInterpolator,
'ndimage': NDImage2DInterpolator,
'linearND': LinearND2DInterpolator,
'linearNDFast': Linear2DInterpolator}
###########################
# 1D interpolator classes
###########################
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class Nearest1DInterpolator():
def __init__(self, zgrid, z):
# Truncating above and below
z[z < zgrid.min()] = zgrid.min()
z[z > zgrid.max()] = zgrid.max()
# Interpolator zgrid -> index
if zgrid[1] > zgrid[0]: # increasing
z_interpolator = interp1d(zgrid, range(len(zgrid)))
else: # decreasing values, must flip for interpolator
z_interpolator = interp1d(zgrid[::-1], range(len(zgrid))[::-1])
# Indices corresponding to nearest value in zgrid
self.zi = np.round(z_interpolator(z)).astype(np.uint8)
self.zi[self.zi < 0] = 0
self.zi[self.zi >= len(zgrid)] = len(zgrid) - 1
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def __call__(self, array2d):
return array2d[self.zi, range(len(self.zi))]
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class Linear1DInterpolator():
def __init__(self, zgrid, z):
# Truncating above and below
z[z < zgrid.min()] = zgrid.min()
z[z > zgrid.max()] = zgrid.max()
# Interpolator zgrid -> index
if zgrid[1] > zgrid[0]: # increasing
z_interpolator = interp1d(zgrid, range(len(zgrid)))
else: # decreasing values, must flip for interpolator
z_interpolator = interp1d(zgrid[::-1], range(len(zgrid))[::-1])
z_interpolator(z[0]) # to prevent threading issues
# Indices corresponding to layers above and below
interp_zi = z_interpolator(z)
self.index_above = np.floor(interp_zi).astype(np.int8)
self.index_above[self.index_above < 0] = 0
self.index_below = np.minimum(self.index_above + 1, len(zgrid) - 1)
self.weight_above = 1 - (interp_zi - self.index_above)
self.xi = range(len(z))
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def __call__(self, array2d):
return array2d[self.index_above, self.xi]*self.weight_above + \
array2d[self.index_below, self.xi]*(1 - self.weight_above)
vertical_interpolation_methods = {
'nearest': Nearest1DInterpolator,
'linear': Linear1DInterpolator}
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def fill_NaN_towards_seafloor(array):
"""Extrapolate NaN-values (missing) towards seafloor"""
filled = False
for i in range(1, array.shape[0]):
mask = np.isnan(array[i,:,:])
if np.sum(mask) > 0:
array[i, mask] = array[i-1, mask]
filled = True
return filled