“数组”的版本间差异
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无编辑摘要 |
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| （未显示2个用户的9个中间版本） | |||
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==数组== |
==数组== |
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* REFORM 数组变形 |
* REFORM 数组变形 |
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:将数组变为制定的维数 |
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A=indgen(20), B=reform(A,2,10) |
A=indgen(20), B=reform(A,2,10) |
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:reform在默认（无输入参数）的情况下将数组变为一维 |
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A＝reform(data[1,*]) |
A＝reform(data[1,*]) |
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* 中位值 |
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a=median(A,/even);偶数位的数据，取中间两个数的平均值 |
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* rotate |
* rotate |
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b=indgen(10) |
b=indgen(10) |
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c=[rotate(a,1),rotate(b,10] |
c=[rotate(a,1),rotate(b,10] |
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:转置 |
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IDL>print,transpose([a,b]) |
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* reverse |
* reverse |
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IDL>print,fan(indgen(3),2) |
IDL>print,fan(indgen(3),2) |
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IDL>print,fan(indgen(3),2,/trans) |
IDL>print,fan(indgen(3),2,/trans) |
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可以用rebin实现 |
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IDL>print,rebin(indgen(3),3,2) |
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* 多维数组的位置 |
* 多维数组的位置 |
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IDL prints: |
IDL prints: |
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Value at [3, 6] is 0.973381 |
Value at [3, 6] is 0.973381 |
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:数组的位数, |
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print,size(array) |
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:size 还可以用来确定变量的类型 Ctype=size(A,/type), |
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* 数组随机打乱（shuffle) |
* 数组随机打乱（shuffle) |
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Arr_S=arr[sort(randomu(seed,N_elements(arr))] |
Arr_S=arr[sort(randomu(seed,N_elements(arr))] |
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*数组去重 |
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Arr=Arr[uniq(Arr,sort(arr)) |
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*数组求和 |
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a=total(indgen(10),/cum)；输出为数组的累计分布 |
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==数组和矩阵运算== |
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*For A # B, where A and B are vectors, IDL performs A # TRANSPOSE(B). In this case, C = A # B is a matrix with Cij = Ai Bj. Mathematically, this is equivalent to the outer product, usually denoted by A Å B. |
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*For A ## B, where A and B are vectors, IDL performs TRANSPOSE(A) ## B. In this case, C = A ## B is a matrix with Cij = Bi Aj. |
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*To compute the dot product, usually denoted by A • B, use TRANSPOSE(A) # B. |
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*A # B = B ## A |
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*A # B = (BT # AT)T ;T表示转置运算 |
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2017年1月9日 (一) 04:37的最新版本
数组
- REFORM 数组变形
- 将数组变为制定的维数
A=indgen(20), B=reform(A,2,10)
- reform在默认（无输入参数）的情况下将数组变为一维
A＝reform(data[1,*])
- 中位值
a=median(A,/even);偶数位的数据，取中间两个数的平均值
- rotate
- 可以把数组旋转后组成矩阵，类似于列操作。
a=indgen(10) b=indgen(10) c=[rotate(a,1),rotate(b,10]
- 转置
IDL>print,transpose([a,b])
- reverse
- 数组反向
- 例：数组降序排列,结合sort（默认升序）
PRINT, 'Elements of A in descending order: ', A[REVERSE(SORT(A))]
- 扩维
IDL>print,fan(indgen(3),2) IDL>print,fan(indgen(3),2,/trans)
可以用rebin实现
IDL>print,rebin(indgen(3),3,2)
- 多维数组的位置
- 用array_indices
array = RANDOMU(seed, 10, 10) mx = MAX(array, location) ind=array_indices(array,location) print, ind, array[ind[0],ind[1]], format = '(%"Value at [%d, %d] is %f")'
IDL prints: Value at [3, 6] is 0.973381
- 数组的位数,
print,size(array)
- size 还可以用来确定变量的类型 Ctype=size(A,/type),
- 数组随机打乱（shuffle)
Arr_S=arr[sort(randomu(seed,N_elements(arr))]
- 数组去重
Arr=Arr[uniq(Arr,sort(arr))
- 数组求和
a=total(indgen(10),/cum)；输出为数组的累计分布
数组和矩阵运算
- For A # B, where A and B are vectors, IDL performs A # TRANSPOSE(B). In this case, C = A # B is a matrix with Cij = Ai Bj. Mathematically, this is equivalent to the outer product, usually denoted by A Å B.
- For A ## B, where A and B are vectors, IDL performs TRANSPOSE(A) ## B. In this case, C = A ## B is a matrix with Cij = Bi Aj.
- To compute the dot product, usually denoted by A • B, use TRANSPOSE(A) # B.
- A # B = B ## A
- A # B = (BT # AT)T ;T表示转置运算