I have these 2 arrays signal_arr and value_arr in Amibroker.
From these 2 arrays, I want to output an array output_arr such that when signal_arr is 1, it will follow the value of value_arr. When signal_arr is 0, output_arr will retain the value of value_arr when signal_arr was last 1.
This is best illustrated by an example.
signal_arr = [ 1 0 0 0 1 0 0 1 0 0 ]
value_arr = [0.5 0.6 0.4 0.2 0.8 0.7 0.6 0.2 0.3 0.4]
output_arr = [0.5 0.5 0.5 0.5 0.8 0.8 0.8 0.2 0.2 0.2]
Use ValueWhen.
output_arr = ValueWhen(signal_arr, value_arr);
Related
Say I have the following dataframe:
df = pd.DataFrame({'A' : [0, 0.3, 0.8, 1, 1.5, 2.3, 2.3, 2.9], 'B' : randn(8)})
df
Out[86]:
A B
0 0.0 0.130471
1 0.3 0.029251
2 0.8 0.790972
3 1.0 -0.870462
4 1.5 -0.700132
5 2.3 -0.361464
6 2.3 -1.100923
7 2.9 -1.003341
How could I split this dataframe based on the range of Col A values as in the following (0<=A<1, 1<=A<2, etc.)?:
A B
0 0.0 0.130471
1 0.3 0.029251
2 0.8 0.790972
A B
3 1.0 -0.870462
4 1.5 -0.700132
A B
5 2.3 -0.361464
6 2.3 -1.100923
7 2.9 -1.003341
I know that np.array could be used if this were to be split based on equal number of rows:
np.array_split(df, 3)
but does something similar exist that allows me to apply my condition here?
Try with groupby and floored division:
for k, d in df.groupby(df['A']//1):
print(d)
Output:
A B
0 0.0 0.130471
1 0.3 0.029251
2 0.8 0.790972
A B
3 1.0 -0.870462
4 1.5 -0.700132
A B
5 2.3 -0.361464
6 2.3 -1.100923
7 2.9 -1.003341
I have an array of doubles img which I use to multiple with a mask mask.*img where the mask will have values of 1 in the middle but go linearly to 0 at the borders e.g. for a 5x5 mask it would be something like
0.1 0.1 0.1 0.1 0.1
0 0.5 0.5 0.5 0.1
0.1 0.5 1 0.5 0.1
0.1 0.5 0.5 0.5 0.1
0.1 0.1 0.1 0.1 0.1
My idea for this currently is to create the center using x = ones(M)
and then create a sequence of decreasing values y = [0.9 0.5 0.3 0.1]
and then do
for k = 1: size(y)
x = padarray(x,[1 1], y(k))
which will add the values of y as a perimeter around x multiple times, one at a time. Is there a more clever way to create this kind of mask that tapers off at the perimeter?
An interesting way to do something similar might be. Where vector Taper is the same as the centre row of the 5 by 5 matrix. The rows are generated by comparing the corresponding element in the transpose with the vector Taper which is Taper.'.
Broken down into steps:
Row 1: min([0.1 0.5 1 0.5 0.1],[0.1]); → [0.1 0.1 0.1 0.1 0.1]
Row 2: min([0.1 0.5 1 0.5 0.1],[0.5]); → [0.1 0.5 0.5 0.5 0.1]
Row 3: min([0.1 0.5 1 0.5 0.1],[1]); → [0.1 0.5 1 0.5 0.1]
Row 4: min([0.1 0.5 1 0.5 0.1],[0.5]); → [0.1 0.5 0.5 0.5 0.1]
Row 5: min([0.1 0.5 1 0.5 0.1],[0.1]); → [0.1 0.1 0.1 0.1 0.1]
Taper = [0.1 0.5 1 0.5 0.1];
Result = min(Taper, Taper.');
Result
I need to merge this 2 files
File1
1
1
2
2
2
3
4
4
4
File2
1 A 0.2 0.8 0.3
2 B 0.4 0.3 0.2
3 C 0.8 0.9 0.5
4 D 0.6 0.7 0.8
Output should be
1 A 0.2 0.8 0.3
1 A 0.2 0.8 0.3
2 B 0.4 0.3 0.2
2 B 0.4 0.3 0.2
2 B 0.4 0.3 0.2
3 C 0.8 0.9 0.5
4 D 0.6 0.7 0.8
4 D 0.6 0.7 0.8
4 D 0.6 0.7 0.8
If you are using python and pandas then it's not too difficult I guess
d1 = pd.read_csv('doc1.txt',sep=" ",header=None)
d2 = pd.read_csv('doc2.txt',sep= " ",header=None)
data = d1.merge(d2,on=[0],how='left')
print(data)
There will be NAN values in data if second file does not have corresponding indices if you don't want that, you can change the type of join
I have a table in SQL Server like this:
Col1 Col2 Col3
----- ---- -----
1 1 1
0.5 0.5 2
0.3 0.1 3
What I would like to do is that for each value in Col 3, so 1,2,3, add a 4th column that contains the numbers 1-53 in sequence. So, something like:
Col1 Col2 Col3 Col 4
----- ---- ----- ------
1 1 1 1
1 1 1 2
1 1 1 3
And so forth.
How could I accomplish this in T-SQL / Microsoft SQL Server 2016?
Thanks!
Are these the results you're trying to get?
IF OBJECT_ID('tempdb..#TestData', 'U') IS NOT NULL
DROP TABLE #TestData;
CREATE TABLE #TestData (
Col1 DECIMAL(9,1) NOT NULL,
Col2 DECIMAL(9,1) NOT NULL,
Col3 INT NOT NULL
);
INSERT #TestData (Col1, Col2, Col3) VALUES
(1, 1 ,1), (0.5,0.5,2), (0.3,0.1,3);
SELECT
td.Col1, td.Col2, td.Col3, Col4 = t.n
FROM
#TestData td
CROSS APPLY dbo.tfn_Tally(53, 1) t;
Results...
Col1 Col2 Col3 Col4
----- ----- ---- -----
1.0 1.0 1 1
0.5 0.5 2 1
0.3 0.1 3 1
1.0 1.0 1 2
0.5 0.5 2 2
0.3 0.1 3 2
1.0 1.0 1 3
0.5 0.5 2 3
0.3 0.1 3 3
1.0 1.0 1 4
0.5 0.5 2 4
0.3 0.1 3 4
1.0 1.0 1 5
0.5 0.5 2 5
0.3 0.1 3 5
1.0 1.0 1 6
0.5 0.5 2 6
0.3 0.1 3 6
1.0 1.0 1 7
0.5 0.5 2 7
0.3 0.1 3 7
1.0 1.0 1 8
0.5 0.5 2 8
0.3 0.1 3 8
1.0 1.0 1 9
0.5 0.5 2 9
0.3 0.1 3 9
1.0 1.0 1 10
0.5 0.5 2 10
0.3 0.1 3 10
1.0 1.0 1 11
0.5 0.5 2 11
0.3 0.1 3 11
1.0 1.0 1 12
0.5 0.5 2 12
0.3 0.1 3 12
1.0 1.0 1 13
0.5 0.5 2 13
0.3 0.1 3 13
1.0 1.0 1 14
0.5 0.5 2 14
0.3 0.1 3 14
1.0 1.0 1 15
0.5 0.5 2 15
0.3 0.1 3 15
1.0 1.0 1 16
0.5 0.5 2 16
0.3 0.1 3 16
1.0 1.0 1 17
0.5 0.5 2 17
0.3 0.1 3 17
1.0 1.0 1 18
0.5 0.5 2 18
0.3 0.1 3 18
1.0 1.0 1 19
0.5 0.5 2 19
0.3 0.1 3 19
1.0 1.0 1 20
0.5 0.5 2 20
0.3 0.1 3 20
1.0 1.0 1 21
0.5 0.5 2 21
0.3 0.1 3 21
1.0 1.0 1 22
0.5 0.5 2 22
0.3 0.1 3 22
1.0 1.0 1 23
0.5 0.5 2 23
0.3 0.1 3 23
1.0 1.0 1 24
0.5 0.5 2 24
0.3 0.1 3 24
1.0 1.0 1 25
0.5 0.5 2 25
0.3 0.1 3 25
1.0 1.0 1 26
0.5 0.5 2 26
0.3 0.1 3 26
1.0 1.0 1 27
0.5 0.5 2 27
0.3 0.1 3 27
1.0 1.0 1 28
0.5 0.5 2 28
0.3 0.1 3 28
1.0 1.0 1 29
0.5 0.5 2 29
0.3 0.1 3 29
1.0 1.0 1 30
0.5 0.5 2 30
0.3 0.1 3 30
1.0 1.0 1 31
0.5 0.5 2 31
0.3 0.1 3 31
1.0 1.0 1 32
0.5 0.5 2 32
0.3 0.1 3 32
1.0 1.0 1 33
0.5 0.5 2 33
0.3 0.1 3 33
1.0 1.0 1 34
0.5 0.5 2 34
0.3 0.1 3 34
1.0 1.0 1 35
0.5 0.5 2 35
0.3 0.1 3 35
1.0 1.0 1 36
0.5 0.5 2 36
0.3 0.1 3 36
1.0 1.0 1 37
0.5 0.5 2 37
0.3 0.1 3 37
1.0 1.0 1 38
0.5 0.5 2 38
0.3 0.1 3 38
1.0 1.0 1 39
0.5 0.5 2 39
0.3 0.1 3 39
1.0 1.0 1 40
0.5 0.5 2 40
0.3 0.1 3 40
1.0 1.0 1 41
0.5 0.5 2 41
0.3 0.1 3 41
1.0 1.0 1 42
0.5 0.5 2 42
0.3 0.1 3 42
1.0 1.0 1 43
0.5 0.5 2 43
0.3 0.1 3 43
1.0 1.0 1 44
0.5 0.5 2 44
0.3 0.1 3 44
1.0 1.0 1 45
0.5 0.5 2 45
0.3 0.1 3 45
1.0 1.0 1 46
0.5 0.5 2 46
0.3 0.1 3 46
1.0 1.0 1 47
0.5 0.5 2 47
0.3 0.1 3 47
1.0 1.0 1 48
0.5 0.5 2 48
0.3 0.1 3 48
1.0 1.0 1 49
0.5 0.5 2 49
0.3 0.1 3 49
1.0 1.0 1 50
0.5 0.5 2 50
0.3 0.1 3 50
1.0 1.0 1 51
0.5 0.5 2 51
0.3 0.1 3 51
1.0 1.0 1 52
0.5 0.5 2 52
0.3 0.1 3 52
1.0 1.0 1 53
0.5 0.5 2 53
0.3 0.1 3 53
You'll have to invent a fake table with numbers in:
WITH nums as(
SELECT 1 as num
UNION ALL
SELECT num + 1 FROM nums
WHERE num <= 53
)
SELECT yourtable.*, num as col4 FROM
Yourtable
CROSS JOIN
nums
You can use below code. There are many ways to generate sequence (you can store it in temp table or use cte)
CREATE TABLE temp
(
Col1 DECIMAL(10,1),
Col2 DECIMAL(10,1),
Col3 INT
)
INSERT INTO temp
VALUES
(1,1,1)
,(0.5,0.5,2)
,(0.3,0.1,3)
DECLARE #Start INT =1
, #ENd INT = 53
SELECT
t.*
, seq.n AS Col4
FROM temp t
CROSS APPLY
(
SELECT DISTINCT n = number
FROM master..[spt_values]
WHERE number BETWEEN #start AND #end
) seq
RESULT:
Col1 Col2 Col3 Col4
--------------------------------------- --------------------------------------- ----------- -----------
1.0 1.0 1 1
1.0 1.0 1 2
1.0 1.0 1 3
1.0 1.0 1 4
1.0 1.0 1 5
1.0 1.0 1 6
1.0 1.0 1 7
1.0 1.0 1 8
1.0 1.0 1 9
1.0 1.0 1 10
1.0 1.0 1 11
1.0 1.0 1 12
1.0 1.0 1 13
1.0 1.0 1 14
1.0 1.0 1 15
1.0 1.0 1 16
1.0 1.0 1 17
1.0 1.0 1 18
1.0 1.0 1 19
1.0 1.0 1 20
1.0 1.0 1 21
1.0 1.0 1 22
1.0 1.0 1 23
1.0 1.0 1 24
1.0 1.0 1 25
1.0 1.0 1 26
1.0 1.0 1 27
1.0 1.0 1 28
1.0 1.0 1 29
1.0 1.0 1 30
1.0 1.0 1 31
1.0 1.0 1 32
1.0 1.0 1 33
1.0 1.0 1 34
1.0 1.0 1 35
1.0 1.0 1 36
1.0 1.0 1 37
1.0 1.0 1 38
1.0 1.0 1 39
1.0 1.0 1 40
1.0 1.0 1 41
1.0 1.0 1 42
1.0 1.0 1 43
1.0 1.0 1 44
1.0 1.0 1 45
1.0 1.0 1 46
1.0 1.0 1 47
1.0 1.0 1 48
1.0 1.0 1 49
1.0 1.0 1 50
1.0 1.0 1 51
1.0 1.0 1 52
1.0 1.0 1 53
0.5 0.5 2 1
0.5 0.5 2 2
0.5 0.5 2 3
0.5 0.5 2 4
0.5 0.5 2 5
0.5 0.5 2 6
and so on...
I am trying to make an array Z that have indexes of the most frequent occurring difference between two elements in the array X. So if the most frequent occurring difference between two elements in X is 3 then I would get all the indexes in X that have that difference into array Z.
x = [ 0.2 0.4 0.6 0.4 0.1 0.2 0.2 0.3 0.4 0.3 0.6];
ct = 0;
difference_x = diff(x);
unique_x = unique(difference_x);
for i = 1:length(unique_x)
for j = 1:length(x)
space_between_elements = abs(x(i)-x(i+1));
if space_between_elements == difference_x
ct = ct + 1;
space_set(i,ct) = j;
end
end
end
I Don´t get the indexes of X containing the most frequent difference from this code.
It appears you want to find how many unique differences there are, with "difference" interpreted in an absoulte-value sense; and also find how many times each difference occurs.
You can do that as follows:
x = [ 0.2 0.4 0.6 0.4 0.1 0.2 0.2 0.3 0.4 0.3 0.6]; %// data
difference_x = abs(diff(x));
unique_x = unique(difference_x); %// all differences
counts = histc(difference_x, unique_x); %// count for each difference
However, comparing reals for uniqueness (or equality) is problematic because of finite precision. You should rather apply a tolerance to declare two values as "equal":
x = [ 0.2 0.4 0.6 0.4 0.1 0.2 0.2 0.3 0.4 0.3 0.6]; %// data
tol = 1e-6; %// tolerance
difference_x = abs(diff(x));
difference_x = round(difference_x/tol)*tol; %// apply tolerance
unique_x = unique(difference_x); %// all differences
counts = histc(difference_x, unique_x); %// count for each difference
With your example x, the second approach gives
>> unique_x
unique_x =
0 0.1000 0.2000 0.3000
>> counts
counts =
1 4 3 2