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I have this simple code that doesn't compile.
const s = [_][_]int {
[_]int{08, 02, 22, 97, 38, 15, 00},
[_]int{49, 49, 99, 40, 17, 81, 18},
[_]int{81, 49, 31, 73, 55, 79, 14},
[_]int{52, 70, 95, 23, 04, 60, 11},
[_]int{22, 31, 16, 71, 51, 67, 63},
[_]int{24, 47, 32, 60, 99, 03, 45},
[_]int{32, 98, 81, 28, 64, 23, 67},
[_]int{67, 26, 20, 68, 02, 62, 12},
[_]int{24, 55, 58, 05, 66, 73, 99},
[_]int{21, 36, 23, 09, 75, 00, 76}
};
pub fn main() void
{
const w = s[0].len;
const h = s.len;
}
The compiler says:
./a.zig:1:14: error: inferred array size invalid here
const s = [_][_]int {
^
./a.zig:16:15: note: referenced here
const w = s[0].len;
What is the problem?
I'd be interested to know there's a deeper reason, but my simple understanding is that the current syntax [N]T allows for the array size to be elided using _, but not for more than one dimension.
So you can fix your problem using the following (N.B. I've used u8 because I'm unsure what your int is):
const s = [_][7]u8{
// Your items
}
I suspect this is because of the way the parsing rules are applied, so [7]u8 would be the type your nested array would hold, and will be used by the compiler to check contents are all of type [7]u8; you can confirm this by modifying one of your rows to have 6 elements and examining the resulting error.
If you want a variable number of items, you could start to look into an array of slices: [_][]u8, but I don't think that's what you're currently after.
I've just read
BBC: Samoa and Tokelau skip a day for dateline change, 30.12.2011
I wanted to see this with pytz, but everything I tried only showed an offset of -11, but not of +13 or +14:
>>> import pytz
>>> tz = pytz.timezone('Pacific/Samoa')
>>> tz_us = pytz.timezone('US/Samoa')
>>> import datetime
>>> datetime.datetime(2011, 12, 30, 9, 00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-29T22:00:00-11:00'
>>> datetime.datetime(2011, 12, 30, 10,00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-29T23:00:00-11:00'
>>> datetime.datetime(2011, 12, 30, 11, 00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-30T00:00:00-11:00'
>>> datetime.datetime(2011, 12, 31, 15, 00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-31T04:00:00-11:00'
>>> datetime.datetime(2015, 12, 31, 15, 00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2015-12-31T04:00:00-11:00'
>>> datetime.datetime(2011, 12, 31, 15, 00, tzinfo=datetime.timezone.utc).astimezone(tz_us).isoformat()
'2011-12-31T04:00:00-11:00'
>>> datetime.datetime(2015, 12, 31, 15, 00, tzinfo=datetime.timezone.utc).astimezone(tz_us).isoformat()
'2015-12-31T04:00:00-11:00'
Why can't I see the offset +13 / +14?
Both Pacific/Samoa and US/Samoa are aliases of Pacific/Pago_Pago, representing American Samoa, which is UTC-11 and did not skip that day.
For American Samoa, use Pacific/Pago_Pago
For the Independent State of Samoa, use Pacific/Apia
For Tokelau, use Pacific/Fakaofo
Personally, I prefer to only use canonical zone names. See the list on Wikipedia for reference.
See the timezone change with pytz
UTC time with offset:
>>> import pytz
>>> tz = pytz.timezone('Pacific/Apia')
>>> import datetime
>>> datetime.datetime(2011, 12, 30, 9, 59, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-29T23:59:00-10:00'
>>> datetime.datetime(2011, 12, 30, 10, 00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-31T00:00:00+14:00'
Local time:
>>> '{:%Y-%m-%d %H:%M}'.format(datetime.datetime(2011, 12, 30, 9, 59, tzinfo=datetime.timezone.utc).astimezone(tz))
'2011-12-29 23:59'
>>> '{:%Y-%m-%d %H:%M}'.format(datetime.datetime(2011, 12, 30, 10, 00, tzinfo=datetime.timezone.utc).astimezone(tz))
'2011-12-31 00:00'
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I have an array, whose elements are arrays of different sizes, say:
[[45, 96, 0.0, 96, 96, 96, 0.0], [04, 55, 06, 55, 04, 04, 02, 55]]
I want to find the sum of the two arrays, i.e.,
[49, 151, ...]
You can use something like this:
a.flat_map{|x| x.in_groups_of(a.max_by(&:size).size, 0)}.transpose.map(&:sum)
Or this:
a.max_by(&:size).map.with_index{|_, i| a.sum{|x| x[i]||0}}
Not very pretty, but works:
>> a = [[45, 96, 0.0, 96, 96, 96, 0.0], [04, 55, 06, 55, 04, 04, 02, 55]]
=> [[45, 96, 0.0, 96, 96, 96, 0.0], [4, 55, 6, 55, 4, 4, 2, 55]]
>> sorted_a = a.sort_by(&:size).reverse
=> [[4, 55, 6, 55, 4, 4, 2, 55], [45, 96, 0.0, 96, 96, 96, 0.0]]
>> zipped_a = sorted_a.first.zip(sorted_a.last)
=> [[4, 45], [55, 96], [6, 0.0], [55, 96], [4, 96], [4, 96], [2, 0.0], [55, nil]]
>> zipped_a.map{ |arr| arr.map{ |v| v || 0 } }.map(&:sum)
=> [49, 151, 6.0, 151, 100, 100, 2.0, 55]
First you have to sort the array starting the longest for zip to work properly. Zipping will then create nil values in the redundant values of the shorter arrays. So the next step is to replace these nils to zeroes (using the nested map) and finally you can sum the values.
You can try this way also
k =[]
for i in 0..ar.max_by(&:size).length-1 do
k << ar.map { |x| [x[i]] }
end
k.map(&:flatten).map{|a| a.compact.sum}
=> [49, 151, 6.0, 151, 100, 100, 2.0, 55]
a = [[45, 96, 0, 96, 96, 96, 0],
[ 4, 55, 6, 55, 4, 4, 2, 55]]
Array.new(a.max_by(&:size).size) { |i| a.reduce(0) { |t,e| t+e[i].to_i } }
#=>[49, 151, 6, 151, 100, 100, 2, 55]
Note that nil.to_i #=> 0 (ref).
Another example:
a = [[1], [2,3,4], [5,6]]
Array.new(a.max_by(&:size).size) { |i| a.reduce(0) { |t,e| t+e[i].to_i } }
#=> [8,9,4]
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I'm looking for a library/tool/image processing technique which can create vectors out of images (similar to text vectorization like TFIDF or so). Can anybody share some ideas how to proceed?
I am not sure what programming language you are using. Below is a sample i am using in R
This is how to use the Pixmap library to read in an image as a matrix.
library(pixmap)
the next command may only work on Linux
system("convert foo.tiff foo.ppm")
img <- read.pnm("foo.ppm")
To get info on your new object:
str(img)
Although included in the previous output, the size of the image can be extracted by:
img#size
Then to extract the red channel from the image for the first ten rows:
myextract <- img#red[1:10,]
Or to extract the entire red channel to an actual matrix:
red.mat<-matrix(NA,img#size[1],img#size[2])
red.mat<-img#red
Refer this : how to convert a JPEG to an image matrix in R
You can use Python- numpy also
>>> arr = np.array(im)
>>> arr = np.arange(150).reshape(5, 10, 3)
>>> x, y, z = arr.shape
>>> indices = np.vstack(np.unravel_index(np.arange(x*y), (y, x))).T
#or indices = np.hstack((np.repeat(np.arange(y), x)[:,np.newaxis], np.tile(np.arange(x), y)[:,np.newaxis]))
>>> np.hstack((arr.reshape(x*y, z), indices))
array([[ 0, 1, 2, 0, 0],
[ 3, 4, 5, 0, 1],
[ 6, 7, 8, 0, 2],
[ 9, 10, 11, 0, 3],
[ 12, 13, 14, 0, 4],
[ 15, 16, 17, 1, 0],
[ 18, 19, 20, 1, 1],
[ 21, 22, 23, 1, 2],
[ 24, 25, 26, 1, 3],
[ 27, 28, 29, 1, 4],
[ 30, 31, 32, 2, 0],
[ 33, 34, 35, 2, 1],
[ 36, 37, 38, 2, 2],
...
[129, 130, 131, 8, 3],
[132, 133, 134, 8, 4],
[135, 136, 137, 9, 0],
[138, 139, 140, 9, 1],
[141, 142, 143, 9, 2],
[144, 145, 146, 9, 3],
[147, 148, 149, 9, 4]])
Where arr = np.array(im) is my image
I want to list all weeknumbers together with year.
This is what I have:
start # 2012-05-10
ende # 2013-06-20
while start < ende
weeks << start.cweek
start += 1.week
end
List all weeknumbers:
#kws.each do |w|
w
end
I need some inspiration how to assign the corresponding year to each weeknumber..
So that I get 22 / 2012 23 / 2012 etc..
Thanks for help..
When in your while loop, you can also store the year, and one easy way is just as an array of arrays.
Then in your each loop later you can get access to both:
start = Date.new( 2012, 5, 10 )
ende = Date.new( 2013, 6, 20 )
weeks = []
while start < ende
weeks << [start.cweek, start.year] # <-- enhanced
start += 1.week
end
weeks.each do |w,y| # <-- take two arguments in the block
puts "#{w} / #{y}" # and print them both out
end
Results:
=>
19 / 2012
20 / 2012
21 / 2012
22 / 2012
23 / 2012
24 / 2012
25 / 2012
...
22 / 2013
23 / 2013
24 / 2013
Create a hash instead with key as a year and value as an array of week numbers
start # 2012-05-10
ende # 2013-06-20
weeks ={}
while start < ende
weeks[start.year] = [] unless weeks[start.year]
weeks[start.year] << start.cweek
start += 1.week
end
p weeks
and you get o/p
=> {2012=>[19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52],
2013=>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24]}
(10.weeks.ago.to_date..Date.today.to_date).map(&:beginning_of_week).uniq