# autolens.Grid1D¶

class autolens.Grid1D(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)
__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

 all([axis, out, keepdims]) Returns True if all elements evaluate to True. any([axis, out, keepdims]) Returns True if any of the elements of a evaluate to True. argmax([axis, out]) Return indices of the maximum values along the given axis. argmin([axis, out]) Return indices of the minimum values along the given axis of a. argpartition(kth[, axis, kind, order]) Returns the indices that would partition this array. argsort([axis, kind, order]) Returns the indices that would sort this array. astype(dtype[, order, casting, subok, copy]) Copy of the array, cast to a specified type. byteswap([inplace]) Swap the bytes of the array elements choose(choices[, out, mode]) Use an index array to construct a new array from a set of choices. clip([min, max, out]) Return an array whose values are limited to [min, max]. compress(condition[, axis, out]) Return selected slices of this array along given axis. conj() Complex-conjugate all elements. conjugate() Return the complex conjugate, element-wise. copy([order]) Return a copy of the array. cumprod([axis, dtype, out]) Return the cumulative product of the elements along the given axis. cumsum([axis, dtype, out]) Return the cumulative sum of the elements along the given axis. diagonal([offset, axis1, axis2]) Return specified diagonals. dot(b[, out]) Dot product of two arrays. dump(file) Dump a pickle of the array to the specified file. dumps() Returns the pickle of the array as a string. fill(value) Fill the array with a scalar value. flatten([order]) Return a copy of the array collapsed into one dimension. from_mask(mask) Create a Grid1D (see Grid1D.__new__) from a mask, where only unmasked pixels are included in the grid (if the grid is represented in its native 1D masked values are 0.0). getfield(dtype[, offset]) Returns a field of the given array as a certain type. item(*args) Copy an element of an array to a standard Python scalar and return it. itemset(*args) Insert scalar into an array (scalar is cast to array’s dtype, if possible) load(file_path, filename) manual_mask(grid, List[T]], mask) Create a Grid1D (see Grid1D.__new__) by inputting the grid coordinates in 1D with their corresponding mask. manual_native(grid, List[T]], pixel_scales, …) Create a Grid1D (see Grid1D.__new__) by inputting the grid coordinates in 1D, for example: manual_slim(grid, List[T]], pixel_scales, …) Create a Grid1D (see Grid1D.__new__) by inputting the grid coordinates in 1D, for example: max([axis, out, keepdims, initial, where]) Return the maximum along a given axis. mean([axis, dtype, out, keepdims]) Returns the average of the array elements along given axis. min([axis, out, keepdims, initial, where]) Return the minimum along a given axis. newbyteorder([new_order]) Return the array with the same data viewed with a different byte order. nonzero() Return the indices of the elements that are non-zero. output_to_fits(file_path, overwrite) padded_before_convolution_from(kernel_shape) partition(kth[, axis, kind, order]) Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. prod([axis, dtype, out, keepdims, initial, …]) Return the product of the array elements over the given axis project_to_radial_grid_2d(angle) Project the 1D grid of (y,x) coordinates to an irregular 2d grid of (y,x) coordinates. ptp([axis, out, keepdims]) Peak to peak (maximum - minimum) value along a given axis. put(indices, values[, mode]) Set a.flat[n] = values[n] for all n in indices. ravel([order]) Return a flattened array. repeat(repeats[, axis]) Repeat elements of an array. reshape(shape[, order]) Returns an array containing the same data with a new shape. resize(new_shape[, refcheck]) Change shape and size of array in-place. resized_from(new_shape) round([decimals, out]) Return a with each element rounded to the given number of decimals. save(file_path, filename) Save the tracer by serializing it with pickle. searchsorted(v[, side, sorter]) Find indices where elements of v should be inserted in a to maintain order. setfield(val, dtype[, offset]) Put a value into a specified place in a field defined by a data-type. setflags([write, align, uic]) Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. sort([axis, kind, order]) Sort an array in-place. squeeze([axis]) Remove single-dimensional entries from the shape of a. std([axis, dtype, out, ddof, keepdims]) Returns the standard deviation of the array elements along given axis. structure_2d_from(result) Convert a result from an ndarray to an aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows: structure_2d_list_from(result_list) Convert a result from a list of ndarrays to a list of aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows: sum([axis, dtype, out, keepdims, initial, where]) Return the sum of the array elements over the given axis. swapaxes(axis1, axis2) Return a view of the array with axis1 and axis2 interchanged. take(indices[, axis, out, mode]) Return an array formed from the elements of a at the given indices. tobytes([order]) Construct Python bytes containing the raw data bytes in the array. tofile(fid[, sep, format]) Write array to a file as text or binary (default). tolist() Return the array as an a.ndim-levels deep nested list of Python scalars. tostring([order]) A compatibility alias for tobytes, with exactly the same behavior. trace([offset, axis1, axis2, dtype, out]) Return the sum along diagonals of the array. transpose(*axes) Returns a view of the array with axes transposed. trimmed_after_convolution_from(kernel_shape) uniform(shape_native, pixel_scales, …) Create a Grid1D (see GridD.__new__) as a uniform grid of (x) values given an input shape_native and pixel_scales of the grid. uniform_from_zero(shape_native, …) Create a Grid1D (see GridD.__new__) as a uniform grid of (x) values given an input shape_native and pixel_scales of the grid, where the first (x) coordinate of the grid is 0.0 and all other values ascend positively. var([axis, dtype, out, ddof, keepdims]) Returns the variance of the array elements, along given axis. view([dtype][, type]) New view of array with the same data.

Attributes

 T The transposed array. base Base object if memory is from some other object. binned Convenience method to access the binned-up grid in its 1D representation, which is a Grid2D stored as an ndarray of shape [total_unmasked_pixels, 2]. ctypes An object to simplify the interaction of the array with the ctypes module. data Python buffer object pointing to the start of the array’s data. dtype Data-type of the array’s elements. flags Information about the memory layout of the array. flat A 1-D iterator over the array. imag The imaginary part of the array. itemsize Length of one array element in bytes. native Return a Grid1D where the data is stored in its native representation, which is an ndarray of shape [sub_size*total_x_pixels, 2]. nbytes Total bytes consumed by the elements of the array. ndim Number of array dimensions. origin pixel_scale pixel_scales real The real part of the array. shape Tuple of array dimensions. shape_native shape_slim size Number of elements in the array. slim Return a Grid1D where the data is stored its slim representation, which is an ndarray of shape [total_unmasked_pixels * sub_size, 2]. strides Tuple of bytes to step in each dimension when traversing an array. sub_shape_native sub_shape_slim sub_size total_pixels unmasked_grid
classmethod manual_slim(grid: Union[numpy.ndarray, List[T]], pixel_scales: Tuple[float], sub_size: int = 1, origin: Tuple[float] = (0.0,)) → autoarray.structures.grids.one_d.grid_1d.Grid1D

Create a Grid1D (see Grid1D.__new__) by inputting the grid coordinates in 1D, for example:

grid=np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])

grid=[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]]

Parameters: grid – The (y,x) coordinates of the grid input as an ndarray of shape [total_unmasked_pixells*(sub_size**2), 2] or a list of lists. pixel_scales – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure. sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-grid. origin – The origin of the grid’s mask.
classmethod manual_native(grid: Union[numpy.ndarray, List[T]], pixel_scales: Tuple[float], sub_size: int = 1, origin: Tuple[float] = (0.0,)) → autoarray.structures.grids.one_d.grid_1d.Grid1D

Create a Grid1D (see Grid1D.__new__) by inputting the grid coordinates in 1D, for example:

grid=np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]])

grid=[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0]]

Parameters: or list (grid) – The (y,x) coordinates of the grid input as an ndarray of shape [total_unmasked_pixells*(sub_size**2), 2] or a list of lists. pixel_scales – The (y,x) scaled units to pixel units conversion factors of every pixel. If this is input as a float, it is converted to a (float, float) structure. sub_size – The size (sub_size x sub_size) of each unmasked pixels sub-grid. origin – The origin of the grid’s mask.
classmethod manual_mask(grid: Union[numpy.ndarray, List[T]], mask: autoarray.mask.mask_1d.Mask1D) → autoarray.structures.grids.one_d.grid_1d.Grid1D

Create a Grid1D (see Grid1D.__new__) by inputting the grid coordinates in 1D with their corresponding mask.

See the manual_slim and manual_native methods for examples.

Parameters: or list (grid) – The (x) coordinates of the grid input as an ndarray of shape [total_coordinates*sub_size] or a list of lists. mask – The 1D mask associated with the grid, defining the pixels each grid coordinate is paired with and originates from.
classmethod from_mask(mask: autoarray.mask.mask_1d.Mask1D) → autoarray.structures.grids.one_d.grid_1d.Grid1D

Create a Grid1D (see Grid1D.__new__) from a mask, where only unmasked pixels are included in the grid (if the grid is represented in its native 1D masked values are 0.0).

The mask’s pixel_scales, sub_size and origin properties are used to compute the grid (x) coordinates.

classmethod uniform(shape_native: Tuple[float], pixel_scales: Union[float, Tuple[float]], sub_size: int = 1, origin: Tuple[float] = (0.0, 0.0)) → autoarray.structures.grids.one_d.grid_1d.Grid1D

Create a Grid1D (see GridD.__new__) as a uniform grid of (x) values given an input shape_native and pixel_scales of the grid.

Parameters: shape_native – The 1D shape of the uniform grid and the mask that it is paired with. pixel_scales – The (x) scaled units to pixel units conversion factor of every pixel. If this is input as a float, it is converted to a (float,) tuple. sub_size – The size (sub_size) of each unmasked pixels sub-grid. origin – The origin of the grid’s mask and coordinate system.
classmethod uniform_from_zero(shape_native: Tuple[float], pixel_scales: Union[float, Tuple[float, float]], sub_size: int = 1) → autoarray.structures.grids.one_d.grid_1d.Grid1D

Create a Grid1D (see GridD.__new__) as a uniform grid of (x) values given an input shape_native and pixel_scales of the grid, where the first (x) coordinate of the grid is 0.0 and all other values ascend positively.

Parameters: shape_native – The 1D shape of the uniform grid and the mask that it is paired with. pixel_scales – The (x) scaled units to pixel units conversion factor of every pixel. If this is input as a float, it is converted to a (float,) tuple. sub_size – The size (sub_size) of each unmasked pixels sub-grid.
structure_2d_from(result: numpy.ndarray) → Union[Grid2D, Grid2DTransformed, Grid2DTransformedNumpy]

Convert a result from an ndarray to an aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows:

• 1D np.ndarray -> aa.Array2D
• 2D np.ndarray -> aa.Grid2D

This function is used by the grid_2d_to_structure decorator to convert the output result of a function to an autoarray structure when a Grid2D instance is passed to the decorated function.

Parameters: or [np.ndarray] (result) – The input result (e.g. of a decorated function) that is converted to a PyAutoArray structure.
structure_2d_list_from(result_list: List[T]) → List[Union[Grid2D, Grid2DTransformed, Grid2DTransformedNumpy]]

Convert a result from a list of ndarrays to a list of aa.Array2D or aa.Grid2D structure, where the conversion depends on type(result) as follows:

• [1D np.ndarray] -> [aa.Array2D]
• [2D np.ndarray] -> [aa.Grid2D]

This function is used by the grid_like_list_to_structure-list decorator to convert the output result of a function to a list of autoarray structure when a Grid2D instance is passed to the decorated function.

Parameters: or [np.ndarray] (result_list) – The input result (e.g. of a decorated function) that is converted to a PyAutoArray structure.