I have a list of SECONDS.MICROSECONDS CLOCK_MONOTONIC timestamps like those below:
5795.944152
5795.952708
5795.952708
5795.960820
5795.960820
5795.969092
5795.969092
5795.977502
5795.977502
5795.986061
5795.986061
5795.994075
5795.994075
5796.002382
5796.002382
5796.010860
5796.010860
5796.019241
5796.019241
5796.027452
5796.027452
5796.035709
5796.035709
5796.044158
5796.044158
5796.052453
5796.052453
5796.060785
5796.060785
5796.069053
They each represent a particular action to be made.
What I need to do, in python preferably, but the programming language doesn't really matter, is to speed up the actions - something like being able to do a 2X, 3X, etc., speed increment on this list. So those values need to decrease in such a way to match the speed incrementation of ?X.
I thought of dividing each timestamp with the speed number I want, but it seems it doesn't work this way.
As described and suggested by #RobertDodier I have managed to find a quick and simple solution to my issue:
speed = 2
speedtimestamps = [(t0 + (t - t0)/speed for t in timestamps]
Just make sure to remove the first line containing the first t0 timestamp.
Related
I have been trying to find a nice way to format a timestamp of the current date & time with milliseconds using Python 3's native time library.
However there's no directive for milliseconds in the standard documentation https://docs.python.org/3/library/time.html#time.strftime.
There's undocumented directives though, like %s which will give the unix timestamp. Is there any other directives like this?
Code example:
>>> import time
>>> time.strftime('%Y-%m-%d %H:%M:%S %s')
'2017-08-28 09:27:04 1503912424'
Ideally I would just want to change the trailing %s to some directive for milliseconds.
I'm using Python 3.x.
I'm fully aware that it's quite simple to get milliseconds using the native datetime library, however I'm looking for a solution using the native time library solely.
If you insist on using time only:
miliSeconds = time.time()%1*1000
time() returns accurately the time since the epoch. Since you already have the the date up to a second, you don't really care this is a time delta, since the remaining fraction is what you need to add anyway to what you have already to get the accurate date. %1 retrieves the fraction and then I convert the numbers to millisecond by multiplying by 1000.
note
Note that even though the time is always returned as a floating point
number, not all systems provide time with a better precision than 1
second. While this function normally returns non-decreasing values, it
can return a lower value than a previous call if the system clock has
been set back between the two calls.
Taken from https://docs.python.org/3/library/time.html#time.time. But this means there is no way to do what you want. You may be able to do something more robust with process_time, but that would have to be elaborate.
Taking a very basic stock example such as the redify filter, with a large image (1200x1024) I was trying to determine why it takes (what I think) is too long. After some investigating, I find that the delay occurs in fabricjs::ApplyFilter, where replacement.src = canvasEl.toDataURL('image/png'); (line 17933 in 1.6.2). That take a long time, even compared to the complete pixel run through by the filter.
Is there some way around this? Can I do something differently to speed up the process? TIA
This is an interview question:
Say that you have an infinite amount of sorted data coming in, implement a way to find a specific time stamp.
What I can think of is to keep the data in a log file or something like that and use the sed command to find the log entry of that specific time stamp.
I do not know if what I think is correct or not.
Other solutions?
This seems like an open-ended question. Your solution was to keep the data in a log file, but with an infinite amount of data, you would then need an impractical amount of disk space as well. The problem is probably supposed to be analyzed in the following manner.
At first glance, with an infinite stream of data coming in, it is not stated what the rate of its coming is. Assuming the rate is r every second and you can check only n timestamps every second. It is not very interesting if r <= n.
If r > n you can only check one in every r/n integers. Then, that means you need to maintain a buffer of size r/n. The fact that the input is sorted implies that you can check the ends of the buffer and see if the desired timestamp is in range. If it is, you go through your buffer and identify the required timestamp.
Interview tip: It is often that these questions are open-ended in order to judge your thought processes. If it is underspecified (like it is here), you should ask for clarification.
I have the following problem:
I want to do the statistics of data that need to be constantly increasing. For example, the number of visits to the link. After some time be restarted these visit and start again from the beginning. To have a continuous increase, want to do the statistics somewhere. For this purpose, use a site that does this. In his condition can be used to COUNTER, GAUGE, AVERAGE, ... a.. I want to use the COUNTER. The system is built on Nagios.
My question is how to use this COUNTER. I guess it is the same as that of the RRD. But I met some strange things in the creation of such a COUNTER.
I submit the values ' 1 ' then ' 2 ' and the chart to come up 3. When I do it doesn't work. After the restart, for example, and submit again 1 to become 4
Anyone dealt with these things tell me briefly how it works with this COUNTER.
I saw that the COUNTER is used for traffic on routers, etc, but I want to apply for a regular graph, which just increases.
The RRD data type COUNTER will convert the input data into a rate, by taking the difference between this sample and the last sample, and dividing by the time interval (note that data normalisation also takes place and this is dependent on the Interval setting of the RRD)
Thus, updating with a constantly increasing count will result in a rate-of-change value to be graphed.
If you want to see your graph actually constantly increasing, IE showing the actual count of packets transferred (for example) rather than the rate of transfer, you would need to use type GAUGE which assumes any rate conversion has already been done.
If you want to submit the rate values (EG, 2 in the last minute), but display the overall constantly increasing total (in other words, the inverse of how the COUNTER data type works), then you would need to store the values as GAUGE, and use a CDEF in your RRDgraph command of the form CDEF:x=y,PREV,+ to obtain the ongoing total. Of course you would only have this relative to the start of the graph time window; maybe a separate call would let you determine what base value to use.
As you use Nagios, you may like to investigate Nagios add-ons such as pnp4nagios which will handle much of the graphing for you.
Everyone knows that MRTG needs at least one value to be passed on it's input.
In per-target options MRTG has 'gauge', 'absolute' and default (with no options) behavior of 'what to do with incoming data'. Or, how to count it.
Lets look at the elementary, yet popular example :
We pass cumulative data from network interface statistics of 'how much packets were recieved by the interface'.
We take it from '/proc/net/dev' or look at 'ifconfig' output for certain network interface. The number of recieved bytes is increasing every time. Its cumulative.
So as i can imagine there could be two types of possible statistics:
1. How fast this value changes upon the time interval. In oher words - activity.
2. Simple, as-is growing graphic that just draw every new value per every minute (or any other time interwal)
First graphic will be saltatory (activity). Second will just grow up every time.
I read twice rrdtool's and MRTG's docs and can't understand which option mentioned above counts what.
I suppose (i am not sure) that 'gauge' draw values as is, without any differentiation calculations (good for measuring how much memory or cpu is used every 5 minutes). And default or 'absolute' behavior tryes to calculate the speed between nearby measures, but what's the differencr between last two?
Can you, guys, explain in a simple manner which behavior stands after which option of three options possible?
Thanks in advance.
MRTG assumes that everything is being measured as a rate (even if it isnt a rate)
Type 'gauge' assumes that you have already calculated the rate; thus, the provided value is stored as-is (after Data Normalisation). This is appropriate for things like CPU usage.
Type 'absolute' assumes the value passed is the count since the last update. Thus, the value is divided by the number of seconds since the last update to get a rate in thingies per second. This is rarely used, and only for certain unusual data sources that reset their value on being read - eg, a script that counts the number of lines in a log file, then truncates the log file.
Type 'counter' (the default) assumes the value passed is a constantly growing count, possibly that wraps around at 16 or 64 bits. The difference between the value and its previous value is divided by the number of seconds since the last update to get a rate in thingies per second. If it sees the value decrease, it will assume a counter wraparound at 16 or 64 bit. This is appropriate for something like network traffic counters, which is why it is the default behaviour (MRTG was originally written for network traffic graphs)
Type 'derive' is like 'counter', but will allow the counter to decrease (resulting in a negative rate). This is not possible directly in MRTG but you can manually create the necessary RRD if you want.
All types subsequently perform Data Normalisation to adjust the timestamp to a multiple of the Interval. This will be more noticeable for Gauge types where the value is small than for counter types where the value is large.
For information on this, see Alex van der Bogaerdt's excellent tutorial