Dimension Bot V1.3 Cracked
Violetta Wagganer
Here are my measurements for the new V1.3 Pi Zero power and USB test pads. I'd love it if anyone else could verify or correct. I also think I made a small error with the vertical dimension for PP22 and PP23 (although we never saw a problem with the original 5 prototype boards).
Hi thanks for making this, Been looking all over for something to cross reference. I have double checked your dimensions and you are right about the height of PP22 and PP23, I measure them a 1.9mm but don't think it would make all that much of a difference.
I think you may be correct. My method to measure these wasn't too precise. I have had a reliable connection using these dimensions for the Pi Platter. But when I examine the pads on a Pi that has been mounted on a Pi Platter it appears the dimple where the pogo pin contacted the pad is just off-center. Thanks.
An array is a collection of objects stored in a multi-dimensional grid. Zero-dimensional arrays are allowed, see this FAQ entry. In the most general case, an array may contain objects of type Any. For most computational purposes, arrays should contain objects of a more specific type, such as Float64 or Int32.
Many functions for constructing and initializing arrays are provided. In the following list of such functions, calls with a dims... argument can either take a single tuple of dimension sizes or a series of dimension sizes passed as a variable number of arguments. Most of these functions also accept a first input T, which is the element type of the array. If the type T is omitted it will default to Float64.
Just as ; and ;; concatenate in the first and second dimension, using more semicolons extends this same general scheme. The number of semicolons in the separator specifies the particular dimension, so ;;; concatenates in the third dimension, ;;;; in the 4th, and so on. Fewer semicolons take precedence, so the lower dimensions are generally concatenated first.
Like before, spaces (and tabs) for horizontal concatenation have a higher precedence than any number of semicolons. Thus, higher-dimensional arrays can also be written by specifying their rows first, with their elements textually arranged in a manner similar to their layout:
Although they both mean concatenation in the second dimension, spaces (or tabs) and ;; cannot appear in the same array expression unless the double semicolon is simply serving as a "line continuation" character. This allows a single horizontal concatenation to span multiple lines (without the line break being interpreted as a vertical concatenation).
The meaning of this form is that F(x,y,...) is evaluated with the variables x, y, etc. taking on each value in their given list of values. Values can be specified as any iterable object, but will commonly be ranges like 1:n or 2:(n-1), or explicit arrays of values like [1.2, 3.4, 5.7]. The result is an N-d dense array with dimensions that are the concatenation of the dimensions of the variable ranges rx, ry, etc. and each F(x,y,...) evaluation returns a scalar.
where each I_k may be a scalar integer, an array of integers, or any other supported index. This includes Colon (:) to select all indices within the entire dimension, ranges of the form a:c or a:b:c to select contiguous or strided subsections, and arrays of booleans to select elements at their true indices.
If all the indices are scalars, then the result X is a single element from the array A. Otherwise, X is an array with the same number of dimensions as the sum of the dimensionalities of all the indices.
The location i_1, i_2, i_3, ..., i_n+1 contains the value at A[I_1[i_1, i_2], I_2[i_3], ..., I_n[i_n+1]]. All dimensions indexed with scalars are dropped. For example, if J is an array of indices, then the result of A[2, J, 3] is an array with size size(J). Its jth element is populated by A[2, J[j], 3].
As a special part of this syntax, the end keyword may be used to represent the last index of each dimension within the indexing brackets, as determined by the size of the innermost array being indexed. Indexing syntax without the end keyword is equivalent to a call to getindex:
Just as in Indexing, the end keyword may be used to represent the last index of each dimension within the indexing brackets, as determined by the size of the array being assigned into. Indexed assignment syntax without the end keyword is equivalent to a call to setindex!:
Considered alone, this may seem relatively trivial; CartesianIndex simply gathers multiple integers together into one object that represents a single multidimensional index. When combined with other indexing forms and iterators that yield CartesianIndexes, however, this can produce very elegant and efficient code. See Iteration below, and for some more advanced examples, see this blog post on multidimensional algorithms and iteration.
Arrays of CartesianIndexN are also supported. They represent a collection of scalar indices that each span N dimensions, enabling a form of indexing that is sometimes referred to as pointwise indexing. For example, it enables accessing the diagonal elements from the first "page" of A from above:
CartesianIndex and arrays of CartesianIndex are not compatible with the end keyword to represent the last index of a dimension. Do not use end in indexing expressions that may contain either CartesianIndex or arrays thereof.
Often referred to as logical indexing or indexing with a logical mask, indexing by a boolean array selects elements at the indices where its values are true. Indexing by a boolean vector B is effectively the same as indexing by the vector of integers that is returned by findall(B). Similarly, indexing by a N-dimensional boolean array is effectively the same as indexing by the vector of CartesianIndexNs where its values are true. A logical index must be a vector of the same length as the dimension it indexes into, or it must be the only index provided and match the size and dimensionality of the array it indexes into. It is generally more efficient to use boolean arrays as indices directly instead of first calling findall.
The ordinary way to index into an N-dimensional array is to use exactly N indices; each index selects the position(s) in its particular dimension. For example, in the three-dimensional array A = rand(4, 3, 2), A[2, 3, 1] will select the number in the second row of the third column in the first "page" of the array. This is often referred to as cartesian indexing.
When exactly one index i is provided, that index no longer represents a location in a particular dimension of the array. Instead, it selects the ith element using the column-major iteration order that linearly spans the entire array. This is known as linear indexing. It essentially treats the array as though it had been reshaped into a one-dimensional vector with vec.
Indices may be omitted if the trailing dimensions that are not indexed into are all length one. In other words, trailing indices can be omitted only if there is only one possible value that those omitted indices could be for an in-bounds indexing expression. For example, a four-dimensional array with size (3, 4, 2, 1) may be indexed with only three indices as the dimension that gets skipped (the fourth dimension) has length one. Note that linear indexing takes precedence over this rule.
Similarly, more than N indices may be provided if all the indices beyond the dimensionality of the array are 1 (or more generally are the first and only element of axes(A, d) where d is that particular dimension number). This allows vectors to be indexed like one-column matrices, for example:
This is wasteful when dimensions get large, so Julia provides broadcast, which expands singleton dimensions in array arguments to match the corresponding dimension in the other array without using extra memory, and applies the given function elementwise:
The base array type in Julia is the abstract type AbstractArrayT,N. It is parameterized by the number of dimensions N and the element type T. AbstractVector and AbstractMatrix are aliases for the 1-d and 2-d cases. Operations on AbstractArray objects are defined using higher level operators and functions, in a way that is independent of the underlying storage. These operations generally work correctly as a fallback for any specific array implementation.
The stride of the second dimension is the spacing between elements in the same row, skipping as many elements as there are in a single column (5). Similarly, jumping between the two "pages" (in the third dimension) requires skipping 5*7 == 35 elements. The strides of this array is the tuple of these three numbers together:
In this particular case, the number of elements skipped in memory matches the number of linear indices skipped. This is only the case for contiguous arrays like Array (and other DenseArray subtypes) and is not true in general. Views with range indices are a good example of non-contiguous strided arrays; consider V = @view A[1:3:4, 2:2:6, 2:-1:1]. This view V refers to the same memory as A but is skipping and re-arranging some of its elements. The stride of the first dimension of V is 3 because we're only selecting every third row from our original array:
The Duet 3 Tool board is a CAN-FD connected expansion board for the Duet 3 Mainboard that is designed to provide complete control for a direct drive extruder, associated fans, filament sensor and probe. The mounting holes and connector locations match an e3d Hemera (v1.2) or Hemera XS(v1.3) , however it can be used with other direct drive extruders with suitable brackets. The stepper motor, heater, temperature sensor, fans and optionally Z probe and filament monitor all connect to the Duet 3 Tool board, leaving only CAN-FD and power wires to run back to the Duet 3 Mainboard or Tool Distribution Board.
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