I have a table containing as inputs the Date, Waketime, and Bedtime. Based on this data I want to calculate the sleep duration of the night before. Below you can see an example of the table I would like to obtain:
| Date | Waketime | Bedtime | Sleep |
| <2012-09-24 Mon> | 6:00 | 22:00 | |
| <2012-09-25 Tue> | 8:00 | 01:00 | 10:00 |
| <2012-09-26 Wed> | 7:00 | 23:00 | 6:00 |
When I apply the table formula: #+TBLFM: $4=if((24-$3)<12, (24-$3)+#-1$2, $3+#-1$2);t this doesn't give the wanted result, due to the integers (24, 12...) being interpreted as seconds.
Erroneous result:
| Date | Waketime | Bedtime | Sleep |
| <2012-09-24 Mon> | 6:00 | 22:00 | |
| <2012-09-25 Tue> | 8:00 | 01:00 | 5.01 |
| <2012-09-26 Wed> | 7:00 | 23:00 | -14.99 |
#+TBLFM: $4=if((24-$3)<12, (24-$3)+#-1$2, $3+#-1$2);t
How can I adjust this formula so it returns the correct sleep duration?
Notice that for computing sleep duration, I have an if-statement that computes the sleep duration differently, depending on the value of Bedtime. If bedtime is before 12h, Bedtime and Waketime are added. If bedtime is past 12h, 24 is first subtracted by Bedtime and subsequently added to Waketime
It's a hack, because org-mode does not seem to understand calc's HMS notation when creating formulas, but here you are:
#+TBLFM: $4=if($3 - (12 * 3600) < 0, (24 * 3600), 0) + $2 - $3;T
Related
I try to promote the usage of ISO8601. How can every month of any year and halves of theses months be representated in ISO 8601?
We use a perpetual calendar in Excel where there are 2 column headers : first row is January, February, etc. and below a column subdivided into 2, something like this following example:
| Tasks | January | February | March | ...
| | | | | | | | ...
| task1 | | X | | X | | | ...
| task2 | | | X | | | | ...
How to best merge these header rows into in a meaningful row, written Jan-<first half> | Jan-<second-half> | etc. in an easily readable form. I think January-01 | January-02 is obviously not the answer. If this is not the right way to do it, please, describe how to deal with this kind of repetition.
This question is different from the one about representing date ranges I've redirected to as in the later start and end years/dates are indicated. My question is about recurring approximate date spans.
Thanks
Given a Postgres table with columns highwater_datetime::timestamp and highwater::integer, I am trying to construct a select statement for a given highwater_datetime range, that generates rows with a column for the max highwater for each hour (first occurrence when dups) and another column showing the highwater_datetime when it occurred (truncated to the minute and order by highwater_datetime asc). e.g.
| highwater_datetime | max_highwater |
+--------------------+---------------+
| 2021-01-27 20:05 | 8 |
| 2021-01-27 21:00 | 7 |
| 2021-01-27 22:00 | 7 |
| 2021-01-27 23:00 | 7 |
| 2021-01-28 00:00 | 7 |
| 2021-01-28 01:32 | 7 |
| 2021-01-28 02:00 | 7 |
| 2021-01-28 03:00 | 7 |
| 2021-01-28 04:22 | 9 |
DISTINCT ON should do the trick:
SELECT DISTINCT ON (date_trunc('hour', highwater_datetime))
highwater_datetime,
highwater
FROM mytable
ORDER BY date_trunc('hour', highwater_datetime),
highwater DESC,
highwater_datetime;
DISTINCT ON will output the first row for each entry with the same hour according to the ORDER BY clause.
I have the following table with epoch timestamps in Postgres. I would like to select the timestamps where the time is from 20:00 to 21:00 in PST. I have tried the following partially but I can't seem to extract both hour and minutes.
SELECT timestamp from table where extract(‘hour’ from to_timestamp(created_at) at time zone ‘America/Los_angeles’) > 20
| created_at |
| 1526528788 |
| 1526442388 |
| 1526309188 |
| 1526359588 |
| 1526532388 |
| 1526489188 |
Expected result:
| created_at |
| 1526528788 |
| 1526442388 |
Any help would be greatly appreciated.
Why do you write America/Los Angeles when you mean PST? They are (sometimes) different.
Does that solve your problem:
... WHERE extract(hour FROM
to_timestamp(1526309188) AT TIME ZONE 'PST'
) BETWEEN 20 AND 21;
These are my two sample tables.
table "outage" (column formats are text, timestamp, timestamp)
+-------------------+----------------+----------------+
| outage_request_id | actual_start | actual_end |
+-------------------+----------------+----------------+
| 1-07697685 | 4/8/2015 4:48 | 4/8/2015 9:02 |
| 1-07223444 | 7/17/2015 4:24 | 8/01/2015 9:23 |
| 1-07223450 | 2/13/2015 4:24 | 4/29/2015 1:03 |
| 1-07223669 | 4/28/2017 9:20 | 4/30/2017 6:58 |
| 1-08985319 | 8/24/2015 3:18 | 8/24/2015 8:27 |
+-------------------+----------------+----------------+
and a second table "prices" (column format is numeric, timestamp)
+-------+---------------+
| price | stamp |
+-------+---------------+
| -2.31 | 2/1/2018 3:00 |
| -2.35 | 2/1/2018 4:00 |
| -1.77 | 2/1/2018 5:00 |
| -2.96 | 2/1/2018 6:00 |
| -5.14 | 2/1/2018 7:00 |
+-------+---------------+
My Goal: To sum the prices in between the start and stop times of each outage_request_id.
I have no idea how to go about properly joining the tables and getting a sum of prices in those outage timestamp ranges.
I can't promise this is efficient (in fact for very large tables I feel pretty confident it's not), but this should notionally get you what you want:
select
o.outage_request_id, o.actual_start, o.actual_end,
sum (p.price) as total_price
from
outage o
left join prices p on
p.stamp between o.actual_start and o.actual_end
group by
o.outage_request_id, o.actual_start, o.actual_end
I have a Spark DataFrame with the following entries:
| order id | time | amt |
| 1 | 2017-10-01 12:00 | 100 |
| 2 | 2017-10-01 15:00 | 100 |
| 3 | 2017-10-01 17:00 | 100 |
| 4 | 2017-10-02 16:00 | 100 |
| 5 | 2017-10-02 23:00 | 100 |
I want to add a column amount_prev_24h that has, for each order id, the sum of amt for all orders in the last 24 hours.
| order id | time | amt | amt_24h
| 1 | 2017-10-01 12:00 | 100 | 0
| 2 | 2017-10-01 15:00 | 100 | 100
| 3 | 2017-10-01 17:00 | 100 | 200
| 4 | 2017-10-02 16:00 | 100 | 100
| 5 | 2017-10-02 23:00 | 100 | 100
How would I go about doing it?
This is a pyspark code and similar to scala API.
df = df.withColumn('time_uts', unix_timestamp('time', format='yyyy-MM-dd HH:mm'))
df = df.withColumn('amt_24h', sum('amt').over(Window.orderBy('time_uts').rangeBetween(-24 * 3600, -1))).fillna(0, subset='amt_24h')
I hope this may help you.