How would I, for example, find out that 6pm is 50% between 4pm and 8pm?
Or that 12am Wednesday is 50% between 12pm Tuesday and 12pm Wednesday?
Convert the times to seconds, calculate the span in seconds, calculate the difference between your desired time and the first time in seconds, calculate the fraction of the whole span, and then multiply by 100%?
Example:
12 AM = 0 seconds (of day)
12 PM = 43200 seconds (of day)
Your desired time = 3 AM = 10800 seconds of day
Total time span = 43200 - 0 = 43200 seconds
Time difference of your desired time from first time = 10800 - 0 = 10800 seconds
Fraction = 10800 / 43200 = 0.25
Percentage = 0.25 * 100% = 25%
(Sorry don't know Ruby but there's the idea.)
require 'date'
start = Date.new(2008, 4, 10)
middle = Date.new(2009, 12, 12)
enddate = Date.new(2009, 4, 10)
duration = start - enddate #Duration of the whole time
desired = middle - start #Difference between desired + Start
fraction = desired / duration
percentage = fraction * 100
puts percentage.to_i
Thanks to 'John W' for the math.
Related
I am trying to write a simple script, where the input would be a start date, end date and a total amount of hours (150) and the script would generate a simple report containing random date-time intervals (with ideally weekdays) that would sum the entered amount of hours.
This is what I am trying to achieve:
Start: 2020-01-01
End: 2020-01-31
Total hours: 150
Report:
Jan 1, 2019, 08:02:20 – Jan 1, 2019, 08:55:00: sub time -> 52:40 (52 minutes 40 seconds)
Jan 1, 2019, 09:00:00 – Jan 1, 2019, 09:38:13: sub time -> 38:13 (38 minutes 13 seconds)
...
Jan 3, 2019, 13:15:00 – Jan 3, 2019, 14:45:13: sub time -> 01:30:13 (1 hour 30 minutes 13 seconds)
...
TOTAL TIME: 150 hours (or in minutes)
How do I generate time intervals where the total amount of minutes/hours would be equal to a given number of hours?
I assume the question is loosely-worded in the sense that "random" is not meant in a probability sense; that is, the intent is not to select a set of intervals (that total a given number of hours in length) with a mechanism that ensures all possible sets of such intervals have an equal likelihood of being selected. Rather, I understand that a set of intervals is to be chosen (e.g., for testing purposes) in a way that incorporates elements of randomness.
I have assumed the intervals are to be non-overlapping and the number of intervals is to be specified. I don't understand what "with ideally weekdays" means so I have disregarded that.
The heart of the approach I will propose is the following method.
def rnd_lengths(tot_secs, target_nbr)
max_secs = 2 * tot_secs/target_nbr - 1
arr = []
loop do
break(arr) if tot_secs.zero?
l = [(0.5 + max_secs * rand).round, tot_secs].min
arr << l
tot_secs -= l
end
end
The method generates an array of integers (lengths of intervals), measured in seconds, ideally having target_nbr elements. tot_secs is the required combined length of the "random" intervals (e.g., 150*3600).
Each element of the array is drawn randomly drawn from a uniform distribution that ranges from zero to max_secs (to be computed). This is done sequentially until tot_secs is reached. Should the last random value cause the total to exceed tot_secs it is reduced to make the total equal tot_secs.`
Suppose tot_secs equals 100 and we wish to generate 4 random intervals (target_nbr = 4). That means the average length of the intervals would be 25. As we are using a uniform distribution having an average of (1 + max_secs)/2, we may derive the value of max_secs from the expression
target_nbr * (1 + max_secs)/2 = tot_secs
which is
max_secs = 2 * tot_secs/target_nbr - 1
the first line of the method. For the example I mentioned, this would be
max_secs = 2 * 100/4 - 1
#=> 49
Let's try it.
rnd_lengths(100, 4)
#=> [49, 36, 15]
As you see the array that is returned sums to 100, as required, but it contains only 3 elements. That's why I named the argument target_nbr, as there is no assurance the array returned will have that number of elements. What to do? Try again!
rnd_lengths(100, 4)
#=> [14, 17, 26, 37, 6]
Still not 4 elements, so keep trying:
rnd_lengths(100, 4)
#=> [11, 37, 39, 13]
Success! It may take a few tries to get the correct number of elements, but for parameters likely to be used, and the nature of the probability distribution employed, I wouldn't expect that to be a problem.
Let's put this in a method.
def rdm_intervals(tot_secs, nbr_intervals)
loop do
arr = rnd_lengths(tot_secs, nbr_intervals)
break(arr) if arr.size == nbr_intervals
end
end
intervals = rdm_intervals(100, 4)
#=> [29, 26, 7, 38]
We can compute random gaps between intervals in the same way. Suppose the intervals fall within a range of 175 seconds (the number of seconds between the start time and end time). Then:
gaps = rdm_intervals(175-100, 5)
#=> [26, 5, 19, 4, 21]
As seen, the gaps sum to 75, as required. We can disregard the last element.
We can now form the intervals. The first interval begins at 26 seconds and ends at 26+29 #=> 55 seconds. The second interval begins at 55+5 #=> 60 seconds and ends at 60+26 #=> 86 seconds, and so on. We therefore find the intervals (each in ranges of seconds from zero) to be:
[26..55, 60..86, 105..112, 116..154]
Note that 175 - 154 = 21, the last element of gaps.
If one is uncomfortable with the fact that the last elements of intervals and gaps that are generally constrained in size one could of course randomly reposition those elements within their respective arrays.
One might not care if the number of intervals is exactly target_nbr. It would be simpler and faster to just use the first array of interval lengths produced. That's fine, but we still need the above methods to compute the random gaps, as their number must equal the number of intervals plus one:
gaps = rdm_intervals(175-100, intervals.size + 1)
We can now use these two methods to construct a method that will return the desired result. The argument tot_secs of this method equals total number of seconds spanned by the array intervals returned (e.g., 3600 * 150). The method returns an array containing nbr_intervals non-overlapping ranges of Time objects that fall between the given start and end dates.
require 'date'
def construct_intervals(start_date_str, end_date_str, tot_secs, nbr_intervals)
start_time = Date.strptime(start_date_str, '%Y-%m-%d').to_time
secs_in_period = Date.strptime(end_date_str, '%Y-%m-%d').to_time - start_time
intervals = rdm_intervals(tot_secs, nbr_intervals)
gaps = rdm_intervals(secs_in_period - tot_secs, nbr_intervals+1)
nbr_intervals.times.with_object([]) do |_,arr|
start_time += gaps.shift
end_time = start_time + intervals.shift
arr << (start_time..end_time)
start_time = end_time
end
end
See Date::strptime.
Let's try an example.
start_date_str = '2020-01-01'
end_date_str = '2020-01-31'
tot_secs = 3600*150
#=> 540000
construct_intervals(start_date_str, end_date_str, tot_secs, 4)
#=> [2020-01-06 18:05:04 -0800..2020-01-09 03:48:00 -0800,
# 2020-01-09 06:44:16 -0800..2020-01-11 23:33:44 -0800,
# 2020-01-20 20:30:21 -0800..2020-01-21 17:27:44 -0800,
# 2020-01-27 19:08:38 -0800..2020-01-28 01:38:51 -0800]
construct_intervals(start_date_str, end_date_str, tot_secs, 8)
#=> [2020-01-03 18:43:36 -0800..2020-01-04 10:49:14 -0800,
# 2020-01-08 07:55:44 -0800..2020-01-08 08:17:18 -0800,
# 2020-01-11 00:54:36 -0800..2020-01-11 23:00:53 -0800,
# 2020-01-14 05:20:14 -0800..2020-01-14 22:48:45 -0800,
# 2020-01-16 18:28:28 -0800..2020-01-17 22:50:24 -0800,
# 2020-01-22 02:59:31 -0800..2020-01-22 22:33:08 -0800,
# 2020-01-23 00:36:59 -0800..2020-01-24 12:15:37 -0800,
# 2020-01-29 11:22:21 -0800..2020-01-29 21:46:10 -0800]
See Date::strptime
START -xxx----xxx--x----xxxxx---xx--xx---xx-xx-x-xxx-- END
We need to fill a timespan with alternating periods of ON and OFF. This can be
denoted by a list of timestamps. Let's say that the period always starts with
an OFF period for simplicity's sake.
From the start/end of the timespan and the total seconds in ON state, we
gather useful facts:
the timespan's total size in seconds total_seconds
the second totals of both the ON (on_total_seconds) and the OFF (off_total_seconds) periods
Once we know these, a workable algorithm looks more or less like this - pardon
the functions without implementation:
# this can be a parameter as well
MIN_PERIODS = 10
MAX_PERIODS = 100
def fill_periods(start_date, end_date, on_total_seconds = 150*60*60)
total_seconds = get_total_seconds(start_date, end_date)
off_total_seconds = total_seconds - on_total_seconds
# establish two buckets to pull from alternately in populating our array of durations
on_bucket = on_total_seconds
off_bucket = off_total_seconds
result = []
# populate `result` with durations in seconds. `result` will sum to `total_seconds`
while on_bucket > 0 || off_bucket > 0 do
off_slice = rand(off_total_seconds / MAX_PERIODS / 2, off_total_seconds / MIN_PERIODS / 2).to_i
off_bucket -= [off_slice, off_bucket].min
on_slice = rand(on_total_seconds / MAX_PERIODS / 2, on_total_seconds / MIN_PERIODS / 2).to_i
on_bucket -= [on_slice, on_bucket].min
# randomness being random, we're going to hit 0 in one bucket before the
# other. when this happens, just add this (off, on) pair to the last one.
if off_slice == 0 || on_slice == 0
last_off, last_on = result.pop(2)
result << last_off + off_slice << last_on + on_slice
else
result << off_slice << on_slice
end
end
# build up an array of datetimes by progressively adding seconds to the last timestamp.
datetimes = result.each_with_object([start_date]) do |period, memo|
memo << add_seconds(memo.last, period)
end
# we want a list of datetime pairs denoting ON periods. since we know our
# timespan starts with OFF, we start our list of pairs with the second element.
datetimes.slice(1..-1).each_slice(2).to_a
end
I'm new to ruby and I'm having a hard time trying to figure out how to calculate a random travel time until it passes 1000 miles. So far, I can't figure out why it doesn't output the results, it just stays with the user input. Help please
def travel_time()
printf "Pick a vehicle: \n"
printf "1. Bicycle \n"
printf "2. Car \n"
printf "3. Jet Plane \n"
puts "Choose 1-3: \n"
vehicle = gets.chomp
case vehicle
when "1"
#Bicycle: 5-15 miles per hour
time = 0
distance = 0
until distance > 1000 do
speed = Random.rand(5...15)
distance = speed * 1
time = time + 1
end
puts "The number of hours it took to travel 1000 miles was #{time} hours"
when "2"
#Car: 20-70 miles per hour
time = 0
distance = 0
until distance > 1000 do
speed = Random.rand(20...70)
distance = speed * 1
time = time + 1
end
puts "The number of hours it took to travel 1000 miles was #{time} hours"
when "3"
#Jet Plane: 400-600 miles per hour
time = 0
distance = 0
until distance > 1000 do
speed = Random.rand(400...600)
distance = speed * 1
time = time + 1
end
puts "The number of hours it took to travel 1000 miles was #{time} hours"
end
end
travel_time
You have an infinite loop here:
until distance > 1000 do
speed = Random.rand(5...15)
distance = speed * 1
time = time + 1
end
This loop will never ende because the biggest value distance can get is 15, so i think you want to add to distance, not replace it; son try using += instead of =:
until distance > 1000 do
speed = Random.rand(5...15)
distance += speed * 1
time = time + 1
end
Same goes for all loops in each case.
How can I save the maximum speed and return it in a statement like I
have done?
One way to do it would be to add another variable (i.e. max_speed) and assign speed value to it whenever speed is greater than max_speed:
time = 0
distance = 0
max_speed = 0
until distance > 1000 do
speed = Random.rand(5...15)
max_speed = speed if speed > max_speed
distance += speed * 1
time = time + 1
end
puts "The maximum speed was #{max_speed} miles per hour"
Another way would be to use an array (although i prefer the first option):
speed = []
until distance > 1000 do
speed << Random.rand(5...15)
distance += speed.last * 1
time = time + 1
end
puts "The maximum speed was #{speed.max} miles per hour"
You're not actually summing up the distance - distance is never going to increase past 1000
until distance > 1000 do
speed = Random.rand(5...15)
distance = speed * 1 # will never equal more than 15
time = time + 1
end
You probably want
distance += speed * 1 # not sure why you're multiplying by 1 though
As a style comment: don't use case statements as control flow like if/then statements. Just use them to set values, and move everything else out. This can eliminate a lot of redundant code. Example:
time = 0
distance = 0
until distance > 1000 do
speed = case vehicle
when "1" then Random.rand(5...15) #Bicycle
when "2" then Random.rand(20...70) #Car
when "3" then Random.rand(400...600) #Plane
end
distance += speed * 1
time = time + 1
end
puts "The number of hours it took to travel 1000 miles was #{time} hours"
I need to subtract two DateTime objects in order to find out the difference in hours between them.
I try to do the following:
a = DateTime.new(2015, 6, 20, 16)
b = DateTime.new(2015, 6, 21, 16)
puts a - b
I get (-1/1), the object of class Rational.
So, the question is, how do I find out what the difference betweent the two dates is? In hours or days, or whatever.
And what does this Rational mean/represent when I subtract DateTimes just like that?
BTW:
When I try to subtract DateTime's with the difference of 1 year, I get (366/1), so when I do (366/1).to_i, I get the number of days. But when I tried subtracting two DateTime's with the difference of 1 hour, it gave me -1, the number of hours. So, how do I also find out the meaning of the returned value (hours, days, years, seconds)?
When you substract two datetimes, you'll get the difference in days, not hours.
You get a Rational type for the precision (some float numbers cannot be expressed exactly with computers)
To get a number of hours, multiply the result by 24, for minutes multiply by 24*60 etc...
a = DateTime.new(2015, 6, 20, 16)
b = DateTime.new(2015, 6, 21, 16)
(a - b).to_i
# days
# => -1
((a - b)* 24).to_i
# hours
# => -24
# ...
Here's a link to the official doc
If you do subtraction on them as a Time object it will return the result in seconds and then you can multiply accordingly to get minutes/hours/days/whatever.
a = DateTime.new(2015, 6, 20, 16)
b = DateTime.new(2015, 6, 21, 16)
diff = b.to_time - a.to_time # 86400
hours = diff / 60 / 60 # 24
Given a set of tasks:
T1(20,100) T2(30,250) T3(100,400) (execution time, deadline=peroid)
Now I want to constrict the deadlines as Di = f * Pi where Di is new deadline for ith task, Pi is the original period for ith task and f is the factor I want to figure out. What is the smallest value of f that the tasks will continue to meet their deadlines using rate monotonic scheduler?
This schema will repeat (synchronize) every 2000 time units. During this period
T1 must run 20 times, requiring 400 time units.
T2 must run 8 times, requiring 240 time units.
T3 must run 5 times, requiring 500 time units.
Total is 1140 time units per 2000 time unit interval.
f = 1140 / 2000 = 0.57
This assumes long-running tasks can be interrupted and resumed, to allow shorter-running tasks to run in between. Otherwise there will be no way for T1 to meet it's deadline once T3 has started.
The updated deadlines are:
T1(20,57)
T2(30,142.5)
T3(100,228)
These will repeat every 1851930 time units, and require the same time to complete.
A small simplification: When calculating factor, the period-time cancels out. This means you don't really need to calculate the period to get the factor:
Period = 2000
Required time = (Period / 100) * 20 + (Period / 250) * 30 + (Period / 400) * 100
f = Required time / Period = 20 / 100 + 30 / 250 + 100 / 400 = 0.57
f = Sum(Duration[i] / Period[i])
To calculate the period, you could do this:
Period(T1,T2) = lcm(100, 250) = 500
Period(T1,T2,T3) = lcm(500, 400) = 2000
where lcm(x,y) is the Least Common Multiple.
Given the following dataset for a single article on my site:
Article 1
2/1/2010 100
2/2/2010 80
2/3/2010 60
Article 2
2/1/2010 20000
2/2/2010 25000
2/3/2010 23000
where column 1 is the date and column 2 is the number of pageviews for an article. What is a basic velocity calculation that can be done to determine if this article is trending upwards or downwards for the most recent 3 days?
Caveats, the articles will not know the total number of pageviews only their own totals. Ideally with a number between 0 and 1. Any pointers to what this class of algorithms is called?
thanks!
update: Your data actually already is a list of velocities (pageviews/day). The following answer simply shows how to find the average velocity over the past three days. See my other answer for how to calculate pageview acceleration, which is the real statistic you are probably looking for.
Velocity is simply the change in a value (delta pageviews) over time:
For article 1 on 2/3/2010:
delta pageviews = 100 + 80 + 60
= 240 pageviews
delta time = 3 days
pageview velocity (over last three days) = [delta pageviews] / [delta time]
= 240 / 3
= 80 pageviews/day
For article 2 on 2/3/2010:
delta pageviews = 20000 + 25000 + 23000
= 68000 pageviews
delta time = 3 days
pageview velocity (over last three days) = [delta pageviews] / [delta time]
= 68,000 / 3
= 22,666 + 2/3 pageviews/day
Now that we know the maximum velocity, we can scale all the velocities to get relative velocities between 0 and 1 (or between 0% and 100%):
relative pageview velocity of article 1 = velocity / MAX_VELOCITY
= 240 / (22,666 + 2/3)
~ 0.0105882353
~ 1.05882353%
relative pageview velocity of article 2 = velocity / MAX_VELOCITY
= (22,666 + 2/3)/(22,666 + 2/3)
= 1
= 100%
"Pageview trend" likely refers to pageview acceleration, not velocity. Your dataset actually already is a list of velocities (pageviews/day). Pageviews are non-decreasing values, so pageview velocity can never be negative. The following describes how to calculate pageview acceleration, which may be negative.
PV_acceleration(t1,t2) = (PV_velocity{t2} - PV_velocity{t1}) / (t2 - t1)
("PV" == "Pageview")
Explanation:
Acceleration is simply change in velocity divided by change in time. Since your dataset is a list of page view velocities, you can plug them directly into the formula:
PV_acceleration("2/1/2010", "2/3/2010") = (60 - 100) / ("2/3/2010" - "2/1/2010")
= -40 / 2
= -20 pageviews per day per day
Note the data for "2/2/2010" was not used. An alternate method is to calculate three PV_accelerations (using a date range that goes back only a single day) and averaging them. There is not enough data in your example to do this for three days, but here is how to do it for the last two days:
PV_acceleration("2/3/2010", "2/2/2010") = (60 - 80) / ("2/3/2010" - "2/2/2010")
= -20 / 1
= -20 pageviews per day per day
PV_acceleration("2/2/2010", "2/1/2010") = (80 - 100) / ("2/2/2010" - "2/1/2010")
= -20 / 1
= -20 pageviews per day per day
PV_acceleration_average("2/3/2010", "2/2/2010") = -20 + -20 / 2
= -20 pageviews per day per day
This alternate method did not make a difference for the article 1 data because the page view acceleration did not change between the two days, but it will make a difference for article 2.
Just a link to an article about the 'trending' algorithm reddit, SUs and HN use among others.
http://www.seomoz.org/blog/reddit-stumbleupon-delicious-and-hacker-news-algorithms-exposed