def getview(self, view, pbar):
import numpy as np
from pygeode.view import View, simplify
out = np.empty(view.shape, dtype=self.dtype)
out[()] = float('nan')
out_axes = view.clip().axes
# Loop over all available files.
N = 0 # Number of points covered so far
for filename, opener, axes in self._table:
subaxes = [
self._axis_manager._get_axis_intersection([a1, a2])
for a1, a2 in zip(out_axes, axes)
]
reorder = []
mask = []
if any(len(a) == 0 for a in subaxes): continue
for a1, a2 in zip(out_axes, subaxes):
# Figure out where the input chunk fits into the output
re = np.searchsorted(a2.values, a1.values)
# Mask out elements that we don't actually have in the chunk
m = [
r < len(a2.values) and a2.values[r] == v
for r, v in zip(re, a1.values)
]
m = np.array(m)
# Convert mask to integer indices
m = np.arange(len(m))[m]
# and then to a slice (where possible)
m = simplify(m)
re = re[m]
# Try to simplify the re-ordering array
if np.all(re == np.sort(re)):
re = simplify(re)
reorder.append(re)
mask.append(m)
var = [v for v in opener(filename) if v.name == self._varname][0]
v = View(subaxes)
chunk = v.get(var)
# Note: this may break if there is more than one axis with integer indices.
assert len([
r for r in reorder if isinstance(r, (tuple, np.ndarray))
]) <= 1, "Unhandled advanced indexing case."
assert len([m for m in mask if isinstance(m, (tuple, np.ndarray))
]) <= 1, "Unhandled advanced indexing case."
out[mask] = chunk[reorder]
N = N + chunk.size
pbar.update(100. * N / out.size)
return out
def getview (self, view, pbar):
import numpy as np
from pygeode.view import View, simplify
out = np.empty(view.shape, dtype=self.dtype)
out[()] = float('nan')
out_axes = view.clip().axes
# Loop over all available files.
N = 0 # Number of points covered so far
for filename, opener, axes in self._table:
subaxes = [self._axis_manager._get_axis_intersection([a1,a2]) for a1,a2 in zip(out_axes,axes)]
reorder = []
mask = []
if any(len(a)==0 for a in subaxes): continue
for a1,a2 in zip(out_axes,subaxes):
# Figure out where the input chunk fits into the output
re = np.searchsorted(a2.values, a1.values)
# Mask out elements that we don't actually have in the chunk
m = [r<len(a2.values) and a2.values[r]==v for r,v in zip(re,a1.values)]
m = np.array(m)
# Convert mask to integer indices
m = np.arange(len(m))[m]
# and then to a slice (where possible)
m = simplify(m)
re = re[m]
# Try to simplify the re-ordering array
if np.all(re == np.sort(re)):
re = simplify(re)
reorder.append(re)
mask.append(m)
var = [v for v in opener(filename) if v.name == self._varname][0]
v = View(subaxes)
chunk = v.get(var)
# Note: this may break if there is more than one axis with integer indices.
assert len([r for r in reorder if isinstance(r,(tuple,np.ndarray))]) <= 1, "Unhandled advanced indexing case."
assert len([m for m in mask if isinstance(m,(tuple,np.ndarray))]) <= 1, "Unhandled advanced indexing case."
out[mask] = chunk[reorder]
N = N + chunk.size
pbar.update(100.*N/out.size)
return out
