from builtins import range, super
import logging
from ctypes import (addressof, byref, cast, c_double, c_float, c_int8, c_int16,
c_int32, c_int64, c_void_p, POINTER)
try:
import numpy as np
HAVE_NUMPY = True
except ImportError:
HAVE_NUMPY = False
from ..tecutil import _tecutil, _tecutil_connector
from ..constant import *
from ..exception import *
from .. import session
from ..tecutil import lock, lock_attributes
log = logging.getLogger(__name__)
[docs]class Array(c_void_p):
"""Low-level accessor for underlying data within a `Dataset`.
.. note::
The data manipulation context referred to below is currently being
developed and should show up in an up-coming revision.
This object exposes a list-like interface to the underlying data array.
Using it, values can be directly queried and modified. After any
modification to the data, the Tecplot Engine will have to be notified of
the change. This notification will happen automatically in most cases, but
can be turned off using the data manipulation context for a significant
performance increase on large datasets.
Accessing values within an `Array` is done through the standard ``[]``
syntax::
>>> print(array[3])
3.1415
The numbers passed are interpreted just like Python's built-in
:py:class:`slice` object::
>>> # print the values at indices: 5, 7, 9
>>> print(array[5:10:2])
[1.0, 1.0, 1.0]
Elements within an array can be manipulated in-place with the assignment
operator::
>>> array[3] = 5.0
>>> print(array[3])
5.0
Element-by-element access is *not* guaranteed to be performant and users
should avoid writing loops over indices in Python. Instead, whole arrays
should be used. This will effectively push the loop down to the underlying
native library and will be much faster in virtually all cases.
Consider this array of 10k elements::
>>> import tecplot as tp
>>> ds = tp.active_frame().create_dataset('Dataset', ['x'])
>>> zn = ds.add_ordered_zone('Zone', 10000)
>>> array = zn.values('x')
The following loop, which takes the sine of all values in the array will
require several Python function calls per element which is a tremendous
overhead::
>>> import math
>>> for i in range(len(ar)):
... ar[i] = math.sin(ar[i])
An immediate improvement on this can be made by looping over the elements
in Python only when reading the values, but assigning them using the
whole array. This will be several times faster for even modest arrays::
>>> ar[:] = [math.sin(x) for x in ar]
But there is still a large performance penalty for looping over elements
directly in Python and PyTecplot supports two solutions for large arrays:
`tecplot.data.operate.execute_equation` and `tecplot.extension.numpy`.
Please refer to these for details. Continuing with the example above, we
could accomplish the same thing with either of the following using
`execute_equation` (assuming the array is identified by the first zone,
first variable)::
>>> from tecplot.data.operate import execute_equation
>>> execute_equation('V1 = SIN(V1)', zones=[dataset.zone(0)])
or by using the `numpy` library::
>>> import numpy as np
>>> ar[:] = np.sin(ar[:])
In both of these cases, the calculation of the sine and loop over elements
is pushed to the low level library and is much faster.
"""
def __init__(self, zone, variable):
self.zone = zone
self.variable = variable
super().__init__(self._native_reference())
@property
def _cache(self):
if _tecutil_connector.suspended:
_tecutil_connector._delete_caches.append(self._delete_cache)
return True
else:
return False
def _delete_cache(self):
attrs = '''
_rnr _wnr _rrp _wrp
_location
_len
_data_type
'''.split()
for attr in attrs:
try:
delattr(self, attr)
except AttributeError:
pass
def _native_reference(self, writable=False):
if writable:
if self._cache:
if not hasattr(self, '_wnr'):
args = (self.zone.dataset.uid, self.zone.index + 1,
self.variable.index + 1)
with lock():
self._wnr = _tecutil.DataValueGetWritableNativeRefByUniqueID(*args)
return self._wnr
else:
args = (self.zone.dataset.uid, self.zone.index + 1,
self.variable.index + 1)
with lock():
return _tecutil.DataValueGetWritableNativeRefByUniqueID(*args)
else:
if self._cache:
if not hasattr(self, '_rnr'):
args = (self.zone.dataset.uid, self.zone.index + 1,
self.variable.index + 1)
with lock():
self._rnr = _tecutil.DataValueGetReadableNativeRefByUniqueID(*args)
return self._rnr
else:
args = (self.zone.dataset.uid, self.zone.index + 1,
self.variable.index + 1)
with lock():
return _tecutil.DataValueGetReadableNativeRefByUniqueID(*args)
@lock()
def _raw_pointer(self, writable=False):
if _tecutil_connector.connected:
msg = 'raw pointer access only available in batch-mode'
raise TecplotLogicError(msg)
elif writable:
if self._cache:
if not hasattr(self, '_wrp'):
ref = self._native_reference(writable=True)
_tecutil.handle.tecUtilDataValueGetWritableRawPtrByRef.restype = \
POINTER(self.c_type)
self._wrp = _tecutil.DataValueGetWritableRawPtrByRef(ref)
return self._wrp
else:
ref = self._native_reference(writable=True)
_tecutil.handle.tecUtilDataValueGetWritableRawPtrByRef.restype = \
POINTER(self.c_type)
return _tecutil.DataValueGetWritableRawPtrByRef(ref)
else:
if self._cache:
if not hasattr(self, '_readable_raw_pointer'):
_tecutil.handle.tecUtilDataValueGetReadableRawPtrByRef.restype = \
POINTER(self.c_type)
self._rrp = _tecutil.DataValueGetReadableRawPtrByRef(self)
return self._rrp
else:
_tecutil.handle.tecUtilDataValueGetReadableRawPtrByRef.restype = \
POINTER(self.c_type)
return _tecutil.DataValueGetReadableRawPtrByRef(self)
def __eq__(self, other):
self_addr = addressof(cast(self, POINTER(c_int64)).contents)
other_addr = addressof(cast(other, POINTER(c_int64)).contents)
return self_addr == other_addr
def __ne__(self, other):
return not (self == other)
def __len__(self):
"""The number of values in this array.
:type: `integer <int>`
Example showing size of ordered data::
>>> x = dataset.zone('Zone').values('X')
>>> print(x.shape)
(10, 10, 10)
>>> print(len(x))
1000
"""
if self._cache:
if not hasattr(self, '_len'):
self._len = _tecutil.DataValueGetCountByRef(self)
return self._len
else:
return _tecutil.DataValueGetCountByRef(self)
@property
def location(self):
"""The location of the data points with respect to the elements.
:type: `ValueLocation`
Possible values are `ValueLocation.CellCentered` and
`ValueLocation.Nodal`. Example usage::
>>> print(dataset.zone(0).values('X').location)
ValueLocation.Nodal
"""
if self._cache:
if not hasattr(self, '_location'):
self._location = _tecutil.DataValueGetLocation(
self.zone.index + 1, self.variable.index + 1)
return self._location
else:
return _tecutil.DataValueGetLocation(self.zone.index + 1,
self.variable.index + 1)
@property
def shape(self):
"""``(i, j, k)`` shape for this array.
:type: `tuple` of `floats <float>`
This is defined by the parent zone and can be used to reshape arrays.
The following example assumes 32-bit floating point array and copies
the Tecplot-owned ``data`` into the `numpy`-owned ``array``::
>>> import numpy as np
>>> data = dataset.zone('Zone').values('X')
>>> array = np.empty(data.shape, dtype=np.float32)
>>> arr_ptr = array.ctypes.data_as(POINTER(data.c_type))
>>> memmove(arr_ptr, data.copy(), sizeof(data.c_type) * len(data))
The data array presented is normally one-dimensional. For ordered data,
you may wish to reshape the array indexing according to the
dimensionality given by the ``shape`` attribute:
.. code-block:: python
:emphasize-lines: 10
>>> import numpy as np
>>> import tecplot as tp
>>> frame = tp.active_frame()
>>> dataset = frame.create_dataset('Dataset', ['x'])
>>> zone = dataset.add_ordered_zone('Zone', shape=(3,3,3))
>>> x = np.array(zone.values('X')[:])
>>> print(x)
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
>>> x.shape = zone.values('X').shape
>>> print(x)
[[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]]
"""
if self.zone.zone_type is ZoneType.Ordered:
array_shape = tuple(i for i in self.zone._shape if i > 1)
else:
array_shape = (self.zone.num_points,)
if self.location is ValueLocation.CellCentered:
array_shape = tuple(i - 1 for i in array_shape if i > 2)
if not array_shape:
array_shape = (1,)
return array_shape
@property
def c_type(self):
"""`ctypes` compatible data type of this array.
This is the `ctypes` equivalent of `Array.data_type` and will return
one of the following:
* `ctypes.c_float`
* `ctypes.c_double`
* `ctypes.c_int`
* `ctypes.c_int16`
* `ctypes.c_int8`
and can be used to create a `ctypes` array to store a copy of the
data::
import tecplot as tp
frame = tp.active_frame()
dataset = frame.create_dataset('Dataset', ['x'])
dataset.add_ordered_zone('Zone', (3,3,3))
x = dataset.zone('Zone').values('x')
# allocate array using Python's ctypes
x_array = (x.c_type * len(x))()
# copy values from Dataset into ctypes array
x_array[:] = x[:]
"""
_ctypes = {
FieldDataType.Float: c_float,
FieldDataType.Double: c_double,
FieldDataType.Int32: c_int32,
FieldDataType.Int16: c_int16,
FieldDataType.Byte: c_int8}
return _ctypes[self.data_type]
@property
def data_type(self):
"""`FieldDataType` indicating the underlying value type of this array.
:type: `FieldDataType`
Example usage::
>>> print(dataset.zone('Zone').values('X').data_type)
FieldDataType.Float
"""
return _tecutil.DataValueGetRefType(self)
[docs] @lock()
def copy(self, offset=0, size=None):
"""Copy the whole or part of the array into a ctypes array.
Parameters:
offset (`integer <int>`, optional): Zero-based offset for starting
index to copy. (default: 0)
size (`integer <int>`, optional): Number of values to copy into the
resulting array. A value of `None` will copy to the end of the
array. (default: `None`)
Here we will copy out chunks of the data, do some operation and set the
values back into the dataset::
>>> import tecplot as tp
>>> tp.new_layout()
>>> frame = tp.active_frame()
>>> dataset = frame.create_dataset('Dataset', ['x'])
>>> dataset.add_ordered_zone('Zone', (2, 2, 2))
>>> x = dataset.zone('Zone').values('x')
>>> # loop over array copying out 4 values at a time
>>> for i, offset in enumerate(range(0, len(x), 4)):
... x_array = x.copy(offset, 4)
... x_array[:] = [i] * 4
... x[offset:offset + 4] = x_array
>>> print(x[:])
[0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0]
"""
size = (len(self) - offset) if size is None else size
arr = (self.c_type * size)()
_tecutil.DataValueArrayGetByRef(self, offset + 1, size, arr)
return arr
def _slice_range(self, s):
start = s.start or 0
stop = s.stop or len(self)
step = s.step or 1
return range(start, stop, step)
def __getitem__(self, i):
if _tecutil_connector.connected:
if isinstance(i, slice):
s = self._slice_range(i)
if s.step == 1:
n = s.stop - s.start
return self.copy(s.start, n)[:]
else:
return [_tecutil.DataValueGetByRef(self, ii + 1)
for ii in s]
else:
return _tecutil.DataValueGetByRef(self, i + 1)
else:
if isinstance(i, slice):
data = self._raw_pointer()
return [data[ii] for ii in self._slice_range(i)]
else:
return self._raw_pointer()[i]
def __setitem__(self, i, val):
index = None
if _tecutil_connector.connected:
ref = self._native_reference(writable=True)
if isinstance(i, slice):
s = self._slice_range(i)
if s.step == 1:
n = s.stop - s.start
if HAVE_NUMPY:
data_ctype = self.c_type
nparr = np.asarray(val, dtype=data_ctype)
ptarr = nparr.ctypes.data_as(POINTER(data_ctype))
arr = (data_ctype * n).from_address(addressof(ptarr.contents))
else:
arr = (self.c_type * n)(*val)
_tecutil.DataValueArraySetByRef(ref, s.start + 1, n, arr)
else:
for ival, ii in enumerate(s):
_tecutil.DataValueSetByRef(ref, ii + 1, val[ival])
else:
_tecutil.DataValueSetByRef(ref, i + 1, val)
index = i + 1
else:
if isinstance(i, slice):
s = self._slice_range(i)
if len(s) != len(val):
raise TecplotIndexError('Array length mismatch')
data = self._raw_pointer(True)
for ival, ii in enumerate(self._slice_range(i)):
data[ii] = val[ival]
else:
self._raw_pointer(True)[i] = val
index = i + 1
session.data_altered(self.zone, self.variable, index)
def __iter__(self):
self.current_index = -1
self.current_length = len(self)
return self
def __next__(self):
self.current_index += 1
if self.current_index < self.current_length:
return self.__getitem__(self.current_index)
else:
del self.current_index
del self.current_length
raise StopIteration
def next(self): # if sys.version_info < (3,)
return self.__next__()
[docs] def minmax(self):
"""Limits of the values stored in this array.
:type: 2-tuple of `floats <float>`
This always returns `floats <float>` regardless of the underlying data
type::
>>> print(dataset.zone('Zone').values('x').minmax())
(0, 10)
"""
return _tecutil.DataValueGetMinMaxByRef(self)
[docs] def min(self):
"""Lower bound of the values stored in this array.
:type: `float`
This always returns a `float` regardless of the underlying data type::
>>> print(dataset.zone('Zone').values('x').min())
0
"""
return self.minmax()[0]
[docs] def max(self):
"""Upper bound of the values stored in this array.
:type: `float`
This always returns a `float` regardless of the underlying data type::
>>> print(dataset.zone('Zone').values('x').max())
10
"""
return self.minmax()[1]
@property
def shared_zones(self):
"""`list` of all `Zones <data_access>` sharing this array.
:type: `list` of `Zones <data_access>`
Example usage::
>>> dataset.zone('My Zone').copy(share_variables=True)
>>> for z in dataset.zone('My Zone').values(0).shared_zones:
... print(z.index)
0
1
"""
indices = _tecutil.DataValueGetShareZoneSet(self.zone.index + 1,
self.variable.index + 1)
ret = [self.zone.dataset.zone(i) for i in indices]
indices.dealloc()
return ret
@property
def passive(self):
"""`Boolean <bool>` indicating an unallocated zone-variable combination.
:type: `bool`
Passive variables are placeholders where no data is defined for a zone
variable combination. Passive variables will always return zero when
queried.
Example::
>>> import tecplot as tp
>>> ds = tp.active_page().add_frame().create_dataset('D', ['x','y'])
>>> z = ds.add_ordered_zone('Z1', (3,))
>>> z.values(0).passive
False
"""
return _tecutil.DataValueIsPassive(self.zone.index + 1,
self.variable.index + 1)