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poc.py
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poc.py
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import re
import numpy as np
import shapely
def nested_floats_to_shapely(nested):
raise NotImplementedError
def shapely_to_nested_floats(obj):
"""
Extract the underlying nested float64 data from a shapely object.
Notes
-----
This is hacked together. Someone who understands shapely can likely
put together something more robust.
"""
try:
obj.ctypes
except NotImplementedError:
# hasattr check returns True but access raises
out = np.asarray(obj)
for i, sub in enumerate(out):
out[i] = shapely_to_nested_floats(sub)
else:
out = np.asarray(obj.ctypes)
assert out.dtype == "f8", out.dtype
return out
def flatten_floats(nested):
"""
Convert a nested sequence of floats to a flat sequence.
nested is as produced by `shapely_to_nested_floats`. flat output
is in the format expected by `unpack_item`.
"""
# TODO: Could avoid lots of copies by pre-allocating.
if nested.dtype == "f8":
return nested
if not len(nested):
return np.array([], dtype=np.float64)
flats = [flatten_floats(x) for x in nested]
flats2 = [np.r_[np.inf, x, -np.inf] for x in flats]
out = np.r_[tuple(flats2)]
assert out.dtype == np.float64, out.dtype
return out
flat = nested[0]
for entry in nested:
flat = np.r_[flat, flatten_floats(entry)]
return flat
def unpack_item(item):
"""
Unpack a float64 array into a nested coordinate object similar to shapely.
Parameters
----------
item : ndarray[float64]
Notes
-----
*very* not-optimized
"""
assert isinstance(item, np.ndarray) and item.dtype == np.float64
# np.nan represents separation between items, so should not be present
# within an item.
assert not np.isnan(item).any()
# We should be working with coordinate pairs.
assert len(item) % 2 == 0
# For the proof of concept, we are going to let python do the parsing
# for us. This is very non-performant.
sitem = item.astype(str)
# +np.inf and -np.inf represent increment and decrement to the degree of
# nesting, respectively. In python we represent this as a nested tuple.
sitem[np.isposinf(item)] = "("
sitem[np.isneginf(item)] = ")"
raw = ",".join(sitem)
lrep = raw.replace("(,", "(").replace("(", ",(").lstrip(",")
rep = lrep.replace(")", "),").rstrip(",")
rep = re.sub(',+', ',', rep) # FIXME: kludge
nested = eval(rep)
return cast_nested(nested)
def cast_nested(nested):
"""
Cast nested tuples containing floats to nested ndarrays.
"""
try:
res = np.asarray(nested, dtype=np.float64)
except ValueError:
# nested, cant do it
pass
else:
if res.ndim == 1:
# Otherwise we might have a rectangular array by mistake,
# though in that case we could use res instead of tossing it here
return res
out = np.empty(len(nested), dtype=object)
for i, sub in enumerate(nested):
out[i] = cast_nested(sub)
# TODO: will this be right with exclusions?
return out
flat_to_nested = unpack_item # alias
def pack_item(item):
"""
Convert a shapely object into an ndarray[float64].
Parameters
----------
item: shapely.??
Returns
-------
ndarray[float64]
"""
raise NotImplementedError
class ShapelyArray:
def __init__(self, data):
assert isinstance(data, np.ndarray) and data.dtype == np.float64
self._data = data
# To support higher dimensions, we will need a more sophisticated
# implementation of _breaks
assert data.ndim == 1
breaks = np.isnan(data).nonzero()[0]
breaks = np.r_[0, breaks]
self._breaks = breaks
def __getitem__(self, key):
if not isinstance(key, int):
raise NotImplementedError(type(key))
if key < 0:
raise NotImplementedError(key)
start = self._breaks[key]
stop = self._breaks[key+1]
flat = self._data[start:stop]
nested = unpack_item(flat)
return nested_floats_to_shapely(item)
def roundtrip_check(obj):
# shapely obj
nested = shapely_to_nested_floats(obj)
flat = flatten_floats(nested)
unpacked = unpack_item(flat)
assert_nested_equal(nested, unpacked)
def assert_nested_equal(left, right):
assert left.shape == right.shape
assert left.dtype == right.dtype
if left.dtype == object:
for i in range(len(left)):
left2 = left[i]
right2 = right[i]
# in out case of nested floats, these should both be ndarrays
assert_nested_equal(left2, right2)
else:
np.testing.assert_equal(left, right)