ImageArray Class
yt.data_objects.image_array.
ImageArray
[source]¶Bases: yt.units.yt_array.YTArray
A custom Numpy ndarray used for images.
This differs from ndarray in that you can optionally specify an info dictionary which is used later in saving, and can be accessed with ImageArray.info.
Parameters:  input_array (array_like) – A numpy ndarray, or list. 

Other Parameters:  
info (dictionary) – Contains information to be stored with image.  
Returns:  obj 
Return type:  ImageArray object 
Raises:  None 
See also
numpy.ndarray
Notes
References
Examples
These are written in doctest format, and should illustrate how to use the function. Use the variables ‘ds’ for the dataset, ‘pc’ for a plot collection, ‘c’ for a center, and ‘L’ for a vector.
>>> im = np.zeros([64,128,3])
>>> for i in range(im.shape[0]):
... for k in range(im.shape[2]):
... im[i,:,k] = np.linspace(0.,0.3*k, im.shape[1])
>>> myinfo = {'field':'dinosaurs', 'east_vector':np.array([1.,0.,0.]),
... 'north_vector':np.array([0.,0.,1.]), 'normal_vector':np.array([0.,1.,0.]),
... 'width':0.245, 'units':'cm', 'type':'rendering'}
>>> im_arr = ImageArray(im, info=myinfo)
>>> im_arr.save('test_ImageArray')
Numpy ndarray documentation appended:
T
¶Same as self.transpose(), except that self is returned if self.ndim < 2.
Examples
>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1., 2.],
[ 3., 4.]])
>>> x.T
array([[ 1., 3.],
[ 2., 4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1., 2., 3., 4.])
>>> x.T
array([ 1., 2., 3., 4.])
add_background_color
(background='black', inline=True)[source]¶Adds a background color to a 4channel ImageArray
This adds a background color to a 4channel ImageArray, by default doing so inline. The ImageArray must already be normalized to the [0,1] range.
Parameters: 


Returns:  out – The modified ImageArray with a background color added. 
Return type: 
Examples
>>> im = np.zeros([64,128,4])
>>> for i in range(im.shape[0]):
... for k in range(im.shape[2]):
... im[i,:,k] = np.linspace(0.,10.*k, im.shape[1])
>>> im_arr = ImageArray(im)
>>> im_arr.rescale()
>>> new_im = im_arr.add_background_color([1.,0.,0.,1.], inline=False)
>>> new_im.write_png('red_bg.png')
>>> im_arr.add_background_color('black')
>>> im_arr.write_png('black_bg.png')
all
(axis=None, out=None, keepdims=False)¶Returns True if all elements evaluate to True.
Refer to numpy.all for full documentation.
See also
numpy.all()
any
(axis=None, out=None, keepdims=False)¶Returns True if any of the elements of a evaluate to True.
Refer to numpy.any for full documentation.
See also
numpy.any()
argmax
(axis=None, out=None)¶Return indices of the maximum values along the given axis.
Refer to numpy.argmax for full documentation.
See also
numpy.argmax()
argmin
(axis=None, out=None)¶Return indices of the minimum values along the given axis of a.
Refer to numpy.argmin for detailed documentation.
See also
numpy.argmin()
argpartition
(kth, axis=1, kind='introselect', order=None)¶Returns the indices that would partition this array.
Refer to numpy.argpartition for full documentation.
New in version 1.8.0.
See also
numpy.argpartition()
argsort
(axis=1, kind='quicksort', order=None)¶Returns the indices that would sort this array.
Refer to numpy.argsort for full documentation.
See also
numpy.argsort()
astype
(dtype, order='K', casting='unsafe', subok=True, copy=True)¶Copy of the array, cast to a specified type.
Parameters: 


Returns:  arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order. 
Return type:  ndarray 
Notes
Starting in NumPy 1.9, astype method now returns an error if the string dtype to cast to is not long enough in ‘safe’ casting mode to hold the max value of integer/float array that is being casted. Previously the casting was allowed even if the result was truncated.
Raises:  ComplexWarning – When casting from complex to float or int. To avoid this,
one should use a.real.astype(t) . 

Examples
>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2])
base
¶Base object if memory is from some other object.
Examples
The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:]
>>> y.base is x
True
byteswap
(inplace)¶Swap the bytes of the array elements
Toggle between lowendian and bigendian data representation by returning a byteswapped array, optionally swapped inplace.
Parameters:  inplace (bool, optional) – If True , swap bytes inplace, default is False . 

Returns:  out – The byteswapped array. If inplace is True , this is
a view to self. 
Return type:  ndarray 
Examples
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(True)
array([ 256, 1, 13090], dtype=int16)
>>> map(hex, A)
['0x100', '0x1', '0x3322']
Arrays of strings are not swapped
>>> A = np.array(['ceg', 'fac'])
>>> A.byteswap()
array(['ceg', 'fac'],
dtype='S3')
choose
(choices, out=None, mode='raise')¶Use an index array to construct a new array from a set of choices.
Refer to numpy.choose for full documentation.
See also
numpy.choose()
clip
(min=None, max=None, out=None)¶Return an array whose values are limited to [min, max]
.
One of max or min must be given.
Refer to numpy.clip for full documentation.
See also
numpy.clip()
compress
(condition, axis=None, out=None)¶Return selected slices of this array along given axis.
Refer to numpy.compress for full documentation.
See also
numpy.compress()
conj
()¶Complexconjugate all elements.
Refer to numpy.conjugate for full documentation.
See also
numpy.conjugate()
conjugate
()¶Return the complex conjugate, elementwise.
Refer to numpy.conjugate for full documentation.
See also
numpy.conjugate()
convert_to_base
(unit_system='cgs')¶Convert the array and units to the equivalent base units in the specified unit system.
Parameters:  unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the default base units of cgs are used. 

Examples
>>> E = YTQuantity(2.5, "erg/s")
>>> E.convert_to_base(unit_system="galactic")
convert_to_cgs
()¶Convert the array and units to the equivalent cgs units.
convert_to_mks
()¶Convert the array and units to the equivalent mks units.
convert_to_units
(units)¶Convert the array and units to the given units.
Parameters:  units (Unit object or str) – The units you want to convert to. 

copy
(order='C')¶Return a copy of the array.
Parameters:  order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout of the copy. ‘C’ means Corder, ‘F’ means Forder, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and :func:numpy.copy are very similar, but have different default values for their order= arguments.) 

See also
numpy.copy()
, numpy.copyto()
Examples
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True
ctypes
¶An object to simplify the interaction of the array with the ctypes module.
This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.
Parameters:  None – 

Returns:  c – Possessing attributes data, shape, strides, etc. 
Return type:  Python object 
See also
numpy.ctypeslib
Notes
Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):
Be careful using the ctypes attribute  especially on temporary
arrays or arrays constructed on the fly. For example, calling
(a+b).ctypes.data_as(ctypes.c_void_p)
returns a pointer to memory
that is invalid because the array created as (a+b) is deallocated
before the next Python statement. You can avoid this problem using
either c=a+b
or ct=(a+b).ctypes
. In the latter case, ct will
hold a reference to the array until ct is deleted or reassigned.
If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as parameter attribute which will return an integer equal to the data attribute.
Examples
>>> import ctypes
>>> x
array([[0, 1],
[2, 3]])
>>> x.ctypes.data
30439712
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))
<ctypes.LP_c_long object at 0x01F01300>
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents
c_long(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents
c_longlong(4294967296L)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x01FFD580>
>>> x.ctypes.shape_as(ctypes.c_long)
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides_as(ctypes.c_longlong)
<numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>
cumprod
(axis=None, dtype=None, out=None)¶Return the cumulative product of the elements along the given axis.
Refer to numpy.cumprod for full documentation.
See also
numpy.cumprod()
cumsum
(axis=None, dtype=None, out=None)¶Return the cumulative sum of the elements along the given axis.
Refer to numpy.cumsum for full documentation.
See also
numpy.cumsum()
d
¶Get a view of the array data.
data
¶Python buffer object pointing to the start of the array’s data.
diagonal
(offset=0, axis1=0, axis2=1)¶Return specified diagonals. In NumPy 1.9 the returned array is a readonly view instead of a copy as in previous NumPy versions. In a future version the readonly restriction will be removed.
Refer to numpy.diagonal()
for full documentation.
See also
numpy.diagonal()
dot
(b, out=None)¶dtype
¶Datatype of the array’s elements.
Parameters:  None – 

Returns:  d 
Return type:  numpy dtype object 
See also
Examples
>>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
dump
(file)¶Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.
Parameters:  file (str) – A string naming the dump file. 

dumps
()¶Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.
Parameters:  None – 

fill
(value)¶Fill the array with a scalar value.
Parameters:  value (scalar) – All elements of a will be assigned this value. 

Examples
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([ 1., 1.])
flags
¶Information about the memory layout of the array.
C_CONTIGUOUS
(C)¶The data is in a single, Cstyle contiguous segment.
F_CONTIGUOUS
(F)¶The data is in a single, Fortranstyle contiguous segment.
OWNDATA
(O)¶The array owns the memory it uses or borrows it from another object.
WRITEABLE
(W)¶The data area can be written to. Setting this to False locks the data, making it readonly. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a nonwriteable array raises a RuntimeError exception.
ALIGNED
(A)¶The data and all elements are aligned appropriately for the hardware.
UPDATEIFCOPY
(U)¶This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array.
FNC
¶F_CONTIGUOUS and not C_CONTIGUOUS.
FORC
¶F_CONTIGUOUS or C_CONTIGUOUS (onesegment test).
BEHAVED
(B)¶ALIGNED and WRITEABLE.
CARRAY
(CA)¶BEHAVED and C_CONTIGUOUS.
FARRAY
(FA)¶BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
Notes
The flags object can be accessed dictionarylike (as in a.flags['WRITEABLE']
),
or by using lowercased attribute names (as in a.flags.writeable
). Short flag
names are only supported in dictionary access.
Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.
The array flags cannot be set arbitrarily:
False
.True
if the data is truly aligned.True
if the array owns its own memory
or the ultimate owner of the memory exposes a writeable buffer
interface or is a string.Arrays can be both Cstyle and Fortranstyle contiguous simultaneously. This is clear for 1dimensional arrays, but can also be true for higher dimensional arrays.
Even for contiguous arrays a stride for a given dimension
arr.strides[dim]
may be arbitrary if arr.shape[dim] == 1
or the array has no elements.
It does not generally hold that self.strides[1] == self.itemsize
for Cstyle contiguous arrays or self.strides[0] == self.itemsize
for
Fortranstyle contiguous arrays is true.
flat
¶A 1D iterator over the array.
This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s builtin iterator object.
Examples
>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
[2, 5],
[3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<type 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x
array([[3, 3, 3],
[3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
[3, 1, 3]])
flatten
(order='C')¶Return a copy of the array collapsed into one dimension.
Parameters:  order ({'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in rowmajor (Cstyle) order. ‘F’ means to flatten in columnmajor (Fortran style) order. ‘A’ means to flatten in columnmajor order if a is Fortran contiguous in memory, rowmajor order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’. 

Returns:  y – A copy of the input array, flattened to one dimension. 
Return type:  ndarray 
Examples
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
from_astropy
(arr, unit_registry=None)¶Convert an AstroPy “Quantity” to a YTArray or YTQuantity.
Parameters: 


from_hdf5
(filename, dataset_name=None, group_name=None)¶Attempts read in and convert a dataset in an hdf5 file into a YTArray.
Parameters: 


from_pint
(arr, unit_registry=None)¶Convert a Pint “Quantity” to a YTArray or YTQuantity.
Parameters: 


Examples
>>> from pint import UnitRegistry
>>> import numpy as np
>>> ureg = UnitRegistry()
>>> a = np.random.random(10)
>>> b = ureg.Quantity(a, "erg/cm**3")
>>> c = yt.YTArray.from_pint(b)
getfield
(dtype, offset=0)¶Returns a field of the given array as a certain type.
A field is a view of the array data with a given datatype. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16byte elements. If taking a view with a 32bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.
Parameters: 

Examples
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[ 1.+1.j, 0.+0.j],
[ 0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[ 1., 0.],
[ 0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the array for our view:
>>> x.getfield(np.float64, offset=8)
array([[ 1., 0.],
[ 0., 4.]])
has_equivalent
(equiv)¶Check to see if this YTArray or YTQuantity has an equivalent unit in equiv.
imag
¶The imaginary part of the array.
Examples
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0. , 0.70710678])
>>> x.imag.dtype
dtype('float64')
in_base
(unit_system='cgs')¶Creates a copy of this array with the data in the specified unit system, and returns it in that system’s base units.
Parameters:  unit_system (string, optional) – The unit system to be used in the conversion. If not specified, the default base units of cgs are used. 

Examples
>>> E = YTQuantity(2.5, "erg/s")
>>> E_new = E.in_base(unit_system="galactic")
in_cgs
()¶Creates a copy of this array with the data in the equivalent cgs units, and returns it.
Returns:  

Return type:  Quantity object with data converted to cgs units. 
in_mks
()¶Creates a copy of this array with the data in the equivalent mks units, and returns it.
Returns:  

Return type:  Quantity object with data converted to mks units. 
in_units
(units)¶Creates a copy of this array with the data in the supplied units, and returns it.
Parameters:  units (Unit object or string) – The units you want to get a new quantity in. 

Returns:  
Return type:  YTArray 
item
(*args)¶Copy an element of an array to a standard Python scalar and return it.
Parameters:  *args (Arguments (variable number and type)) –


Returns:  z – A copy of the specified element of the array as a suitable Python scalar 
Return type:  Standard Python scalar object 
Notes
When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.
item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.
Examples
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.item(3)
2
>>> x.item(7)
5
>>> x.item((0, 1))
1
>>> x.item((2, 2))
3
itemset
(*args)¶Insert scalar into an array (scalar is cast to array’s dtype, if possible)
There must be at least 1 argument, and define the last argument
as item. Then, a.itemset(*args)
is equivalent to but faster
than a[args] = item
. The item should be a scalar value and args
must select a single item in the array a.
Parameters:  *args (Arguments) – If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple. 

Notes
Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute lookup at each loop iteration.
Examples
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[3, 1, 7],
[2, 0, 3],
[8, 5, 9]])
itemsize
¶Length of one array element in bytes.
Examples
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
list_equivalencies
()¶Lists the possible equivalencies associated with this YTArray or YTQuantity.
max
(axis=None, out=None)¶Return the maximum along a given axis.
Refer to numpy.amax for full documentation.
See also
numpy.amax()
mean
(axis=None, dtype=None, out=None)¶min
(axis=None, out=None, keepdims=False)¶Return the minimum along a given axis.
Refer to numpy.amin for full documentation.
See also
numpy.amin()
nbytes
¶Total bytes consumed by the elements of the array.
Notes
Does not include memory consumed by nonelement attributes of the array object.
Examples
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
ndarray_view
()¶Returns a view into the array, but as an ndarray rather than ytarray.
Returns:  

Return type:  View of this array’s data. 
ndim
¶Number of array dimensions.
Examples
>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
ndview
¶Get a view of the array data.
newbyteorder
(new_order='S')¶Return the array with the same data viewed with a different byte order.
Equivalent to:
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and subarrays of the array data type.
Parameters:  new_order (string, optional) – Byte order to force; a value from the byte order specifications below. new_order codes can be any of:
The default value (‘S’) results in swapping the current byte order. The code does a caseinsensitive check on the first letter of new_order for the alternatives above. For example, any of ‘B’ or ‘b’ or ‘biggish’ are valid to specify bigendian. 

Returns:  new_arr – New array object with the dtype reflecting given change to the byte order. 
Return type:  array 
nonzero
()¶Return the indices of the elements that are nonzero.
Refer to numpy.nonzero for full documentation.
See also
numpy.nonzero()
partition
(kth, axis=1, kind='introselect', order=None)¶Rearranges the elements in the array in such a way that value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.
New in version 1.8.0.
Parameters: 


See also
numpy.partition()
argpartition()
sort()
Notes
See np.partition
for notes on the different algorithms.
Examples
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
array([1, 2, 3, 4])
prod
(axis=None, dtype=None, out=None)¶ptp
(axis=None, out=None)¶Peak to peak (maximum  minimum) value along a given axis.
Refer to numpy.ptp for full documentation.
See also
numpy.ptp()
put
(indices, values, mode='raise')¶Set a.flat[n] = values[n]
for all n in indices.
Refer to numpy.put for full documentation.
See also
numpy.put()
ravel
([order])¶Return a flattened array.
Refer to numpy.ravel for full documentation.
See also
numpy.ravel()
ndarray.flat()
real
¶The real part of the array.
Examples
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1. , 0.70710678])
>>> x.real.dtype
dtype('float64')
See also
numpy.real
repeat
(repeats, axis=None)¶Repeat elements of an array.
Refer to numpy.repeat for full documentation.
See also
numpy.repeat()
rescale
(cmax=None, amax=None, inline=True)[source]¶Rescales the image to be in [0,1] range.
Parameters: 


Returns:  out – The rescaled ImageArray, clipped to the [0,1] range. 
Return type: 
Notes
This requires that the shape of the ImageArray to have a length of 3, and for the third dimension to be >= 3. If the third dimension has a shape of 4, the alpha channel will also be rescaled.
Examples
>>> im = np.zeros([64,128,4])
>>> for i in range(im.shape[0]):
... for k in range(im.shape[2]):
... im[i,:,k] = np.linspace(0.,0.3*k, im.shape[1])
>>> im = ImageArray(im)
>>> im.write_png('original.png')
>>> im.rescale()
>>> im.write_png('normalized.png')
reshape
(shape, order='C')¶Returns an array containing the same data with a new shape.
Refer to numpy.reshape for full documentation.
See also
numpy.reshape()
resize
(new_shape, refcheck=True)¶Change shape and size of array inplace.
Parameters: 


Returns:  
Return type:  
Raises: 

See also
resize()
Notes
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be resized.
The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.
Examples
Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing...
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that has been referenced ...
Unless refcheck is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
round
(decimals=0, out=None)¶Return a with each element rounded to the given number of decimals.
Refer to numpy.around for full documentation.
See also
numpy.around()
save
(filename, png=True, hdf5=True)[source]¶Saves ImageArray.
Parameters:  filename – string This should not contain the extension type (.png, .h5, ...) 

searchsorted
(v, side='left', sorter=None)¶Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see numpy.searchsorted
See also
numpy.searchsorted()
setfield
(val, dtype, offset=0)¶Put a value into a specified place in a field defined by a datatype.
Place val into a‘s field defined by dtype and beginning offset bytes into the field.
Parameters:  

Returns:  
Return type: 
See also
Examples
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]])
>>> x
array([[ 1.00000000e+000, 1.48219694e323, 1.48219694e323],
[ 1.48219694e323, 1.00000000e+000, 1.48219694e323],
[ 1.48219694e323, 1.48219694e323, 1.00000000e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
setflags
(write=None, align=None, uic=None)¶Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
These Booleanvalued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)
Parameters: 

Notes
Array flags provide information about how the memory area used for the array is to be interpreted. There are 6 Boolean flags in use, only three of which can be changed by the user: UPDATEIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);
UPDATEIFCOPY (U) this array is a copy of some other array (referenced by .base). When this array is deallocated, the base array will be updated with the contents of this array.
All flags can be accessed using their first (upper case) letter as well as the full name.
Examples
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set UPDATEIFCOPY flag to True
shape
¶Tuple of array dimensions.
Notes
May be used to “reshape” the array, as long as this would not require a change in the total number of elements
Examples
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
size
¶Number of elements in the array.
Equivalent to np.prod(a.shape)
, i.e., the product of the array’s
dimensions.
Examples
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
sort
(axis=1, kind='quicksort', order=None)¶Sort an array, inplace.
Parameters: 


See also
numpy.sort()
argsort()
lexsort()
searchsorted()
partition()
Notes
See sort
for notes on the different sorting algorithms.
Examples
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the order keyword to specify a field to use when sorting a structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([('c', 1), ('a', 2)],
dtype=[('x', 'S1'), ('y', '<i4')])
squeeze
(axis=None)¶Remove singledimensional entries from the shape of a.
Refer to numpy.squeeze for full documentation.
See also
numpy.squeeze()
std
(axis=None, dtype=None, out=None, ddof=0)¶strides
¶Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element (i[0], i[1], ..., i[n])
in an array a
is:
offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.
Notes
Imagine an array of 32bit integers (each 4 bytes):
x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other
(known as a contiguous block of memory). The strides of an array tell
us how many bytes we have to skip in memory to move to the next position
along a certain axis. For example, we have to skip 4 bytes (1 value) to
move to the next column, but 20 bytes (5 values) to get to the same
position in the next row. As such, the strides for the array x will be
(20, 4)
.
See also
Examples
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813
sum
(axis=None, dtype=None, out=None)¶swapaxes
(axis1, axis2)¶Return a view of the array with axis1 and axis2 interchanged.
Refer to numpy.swapaxes for full documentation.
See also
numpy.swapaxes()
take
(indices, axis=None, out=None, mode='raise')¶Return an array formed from the elements of a at the given indices.
Refer to numpy.take for full documentation.
See also
numpy.take()
to
(units)¶An alias for YTArray.in_units().
See the docstrings of that function for details.
to_astropy
(**kwargs)¶Creates a new AstroPy quantity with the same unit information.
to_equivalent
(unit, equiv, **kwargs)¶Convert a YTArray or YTQuantity to an equivalent, e.g., something that is related by only a constant factor but not in the same units.
Parameters: 


Examples
>>> a = yt.YTArray(1.0e7,"K")
>>> a.to_equivalent("keV", "thermal")
to_ndarray
()¶Creates a copy of this array with the unit information stripped
to_pint
(unit_registry=None)¶Convert a YTArray or YTQuantity to a Pint Quantity.
Parameters: 


Examples
>>> a = YTQuantity(4.0, "cm**2/s")
>>> b = a.to_pint()
tobytes
(order='C')¶Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’, or ‘Any’ order (the default is ‘C’order). ‘Any’ order means Corder unless the F_CONTIGUOUS flag in the array is set, in which case it means ‘Fortran’ order.
New in version 1.9.0.
Parameters:  order ({'C', 'F', None}, optional) – Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array. 

Returns:  s – Python bytes exhibiting a copy of a‘s raw data. 
Return type:  bytes 
Examples
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'
tofile
(fid, sep="", format="%s")¶Write array to a file as text or binary (default).
Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().
Parameters: 


Notes
This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.
tolist
()¶Return the array as a (possibly nested) list.
Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible Python type.
Parameters:  none – 

Returns:  y – The possibly nested list of array elements. 
Return type:  list 
Notes
The array may be recreated, a = np.array(a.tolist())
.
Examples
>>> a = np.array([1, 2])
>>> a.tolist()
[1, 2]
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
tostring
(order='C')¶Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object can be produced in either ‘C’ or ‘Fortran’, or ‘Any’ order (the default is ‘C’order). ‘Any’ order means Corder unless the F_CONTIGUOUS flag in the array is set, in which case it means ‘Fortran’ order.
This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.
Parameters:  order ({'C', 'F', None}, optional) – Order of the data for multidimensional arrays: C, Fortran, or the same as for the original array. 

Returns:  s – Python bytes exhibiting a copy of a‘s raw data. 
Return type:  bytes 
Examples
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'
trace
(offset=0, axis1=0, axis2=1, dtype=None, out=None)¶Return the sum along diagonals of the array.
Refer to numpy.trace for full documentation.
See also
numpy.trace()
transpose
(*axes)¶Returns a view of the array with axes transposed.
For a 1D array, this has no effect. (To change between column and
row vectors, first cast the 1D array into a matrix object.)
For a 2D array, this is the usual matrix transpose.
For an nD array, if axes are given, their order indicates how the
axes are permuted (see Examples). If axes are not provided and
a.shape = (i[0], i[1], ... i[n2], i[n1])
, then
a.transpose().shape = (i[n1], i[n2], ... i[1], i[0])
.
Parameters:  axes (None, tuple of ints, or n ints) –


Returns:  out – View of a, with axes suitably permuted. 
Return type:  ndarray 
See also
ndarray.T()
Examples
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
ua
¶Get a YTArray filled with ones with the same unit and shape as this array
unit_array
¶Get a YTArray filled with ones with the same unit and shape as this array
unit_quantity
¶Get a YTQuantity with the same unit as this array and a value of 1.0
uq
¶Get a YTQuantity with the same unit as this array and a value of 1.0
v
¶Get a copy of the array data as a numpy ndarray
value
¶Get a copy of the array data as a numpy ndarray
var
(axis=None, dtype=None, out=None, ddof=0, keepdims=False)¶Returns the variance of the array elements, along given axis.
Refer to numpy.var for full documentation.
See also
numpy.var()
view
(dtype=None, type=None)¶New view of array with the same data.
Parameters: 


Notes
a.view()
is used two different ways:
a.view(some_dtype)
or a.view(dtype=some_dtype)
constructs a view
of the array’s memory with a different datatype. This can cause a
reinterpretation of the bytes of memory.
a.view(ndarray_subclass)
or a.view(type=ndarray_subclass)
just
returns an instance of ndarray_subclass that looks at the same array
(same shape, dtype, etc.) This does not cause a reinterpretation of the
memory.
For a.view(some_dtype)
, if some_dtype
has a different number of
bytes per entry than the previous dtype (for example, converting a
regular array to a structured array), then the behavior of the view
cannot be predicted just from the superficial appearance of a
(shown
by print(a)
). It also depends on exactly how a
is stored in
memory. Therefore if a
is Cordered versus fortranordered, versus
defined as a slice or transpose, etc., the view may give different
results.
Examples
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrixlib.defmatrix.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([ 2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> print(x)
[(1, 20) (3, 4)]
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortranordering, etc.:
>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
[4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array.
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
[(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
write_hdf5
(filename, dataset_name=None)[source]¶Writes ImageArray to hdf5 file.
Parameters: 

Examples
>>> im = np.zeros([64,128,3])
>>> for i in range(im.shape[0]):
... for k in range(im.shape[2]):
... im[i,:,k] = np.linspace(0.,0.3*k, im.shape[1])
>>> myinfo = {'field':'dinosaurs', 'east_vector':np.array([1.,0.,0.]),
... 'north_vector':np.array([0.,0.,1.]), 'normal_vector':np.array([0.,1.,0.]),
... 'width':0.245, 'units':'cm', 'type':'rendering'}
>>> im_arr = ImageArray(im, info=myinfo)
>>> im_arr.write_hdf5('test_ImageArray.h5')
write_image
(filename, color_bounds=None, channel=None, cmap_name=None, func=<function ImageArray.<lambda>>)[source]¶Writes a single channel of the ImageArray to a png file.
Parameters:  filename (string) – Note filename not be modified. 

Other Parameters:  


Returns:  scaled_image 
Return type:  uint8 image that has been saved 
Examples
>>> im = np.zeros([64,128])
>>> for i in range(im.shape[0]):
... im[i,:] = np.linspace(0.,0.3*i, im.shape[1])
>>> myinfo = {'field':'dinosaurs', 'east_vector':np.array([1.,0.,0.]),
... 'north_vector':np.array([0.,0.,1.]), 'normal_vector':np.array([0.,1.,0.]),
... 'width':0.245, 'units':'cm', 'type':'rendering'}
>>> im_arr = ImageArray(im, info=myinfo)
>>> im_arr.write_image('test_ImageArray.png')
write_png
(filename, sigma_clip=None, background='black', rescale=True, clip_ratio=None)[source]¶Writes ImageArray to png file.
Parameters: 


Examples
>>> im = np.zeros([64,128,4])
>>> for i in range(im.shape[0]):
... for k in range(im.shape[2]):
... im[i,:,k] = np.linspace(0.,10.*k, im.shape[1])
>>> im_arr = ImageArray(im)
>>> im_arr.write_png('standard.png')
>>> im_arr.write_png('nonscaled.png', rescale=False)
>>> im_arr.write_png('black_bg.png', background='black')
>>> im_arr.write_png('white_bg.png', background='white')
>>> im_arr.write_png('green_bg.png', background=[0,1,0,1])
>>> im_arr.write_png('transparent_bg.png', background=None)