Graphite, averaging algorithm while displaying data - algorithm
I save data every second.
Here is definition of carbon's time series:
cat /etc/carbon/storage-schemas.conf
[requests]
pattern = ^requests\.
retentions = 1s:7d
For testing purposes I send to the graphite server values of step function:
setInterval(function() {
graphite.put('step', Math.round(Math.random()) );
}, 1000);
The function produces in a random way either 1 or 0 and puts it on requests.beryllium.step target.
Here is the graphite's graph shown for 1 minute:
/render?width=400&from=-2minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step
On the graph there are 60 data points as expected. I checked this by getting json data:
/render?width=400&from=-2minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=json
Result (60 points in the array, checked):
[{"target": "requests.beryllium.step", "datapoints": [[1.0, 1472502764], [0.0, 1472502765], [1.0, 1472502766], [0.0, 1472502767], [1.0, 1472502768], [0.0, 1472502769], [1.0, 1472502770], [0.0, 1472502771], [1.0, 1472502772], [1.0, 1472502773], [0.0, 1472502774], [1.0, 1472502775], [0.0, 1472502776], [1.0, 1472502777], [1.0, 1472502778], [0.0, 1472502779], [1.0, 1472502780], [0.0, 1472502781], [1.0, 1472502782], [1.0, 1472502783], [0.0, 1472502784], [1.0, 1472502785], [0.0, 1472502786], [1.0, 1472502787], [0.0, 1472502788], [0.0, 1472502789], [1.0, 1472502790], [1.0, 1472502791], [0.0, 1472502792], [1.0, 1472502793], [0.0, 1472502794], [1.0, 1472502795], [0.0, 1472502796], [1.0, 1472502797], [0.0, 1472502798], [1.0, 1472502799], [1.0, 1472502800], [0.0, 1472502801], [0.0, 1472502802], [0.0, 1472502803], [1.0, 1472502804], [1.0, 1472502805], [1.0, 1472502806], [1.0, 1472502807], [1.0, 1472502808], [0.0, 1472502809], [0.0, 1472502810], [1.0, 1472502811], [1.0, 1472502812], [1.0, 1472502813], [0.0, 1472502814], [1.0, 1472502815], [1.0, 1472502816], [0.0, 1472502817], [0.0, 1472502818], [0.0, 1472502819], [0.0, 1472502820], [0.0, 1472502821], [0.0, 1472502822], [1.0, 1472502823]]}]
All the data points are zeroes or ones and this is what we see on the graph, too. Till now, all is ok.
Now, I draw a graph for 15 mins:
/render?width=400&from=-16minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=png
On the graph we can see points that are not ones or zeroes. Let's check what are in the points array:
/render?width=400&from=-16minutes&until=-1minute&height=250&target=requests.beryllium.step&_uniq=0.06224050732088515&title=requests.beryllium.step&format=json
The result:
[{"target": "requests.beryllium.step", "datapoints": [[1.0, 1472502324], [0.0, 1472502325], [0.0, 1472502326], [0.0, 1472502327], [1.0, 1472502328], [1.0, 1472502329], [1.0, 1472502330], [0.0, 1472502331], [1.0, 1472502332], [1.0, 1472502333], [0.0, 1472502334], [1.0, 1472502335], [0.0, 1472502336], [0.0, 1472502337], [0.0, 1472502338], [0.0, 1472502339], [0.0, 1472502340], [1.0, 1472502341], [1.0, 1472502342], [1.0, 1472502343], [1.0, 1472502344], [0.0, 1472502345], [1.0, 1472502346], [1.0, 1472502347], [1.0, 1472502348], [1.0, 1472502349], [1.0, 1472502350], [0.0, 1472502351], [0.0, 1472502352], [1.0, 1472502353], [1.0, 1472502354], [0.0, 1472502355], [1.0, 1472502356], [1.0, 1472502357], [0.0, 1472502358], [0.0, 1472502359], [1.0, 1472502360], [1.0, 1472502361], [1.0, 1472502362], [1.0, 1472502363], [1.0, 1472502364], [0.0, 1472502365], [0.0, 1472502366], [1.0, 1472502367], [0.0, 1472502368], [0.0, 1472502369], [0.0, 1472502370], [0.0, 1472502371], [1.0, 1472502372], [1.0, 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[0.0, 1472503003], [1.0, 1472503004], [0.0, 1472503005], [0.0, 1472503006], [1.0, 1472503007], [1.0, 1472503008], [0.0, 1472503009], [0.0, 1472503010], [0.0, 1472503011], [0.0, 1472503012], [0.0, 1472503013], [1.0, 1472503014], [1.0, 1472503015], [1.0, 1472503016], [0.0, 1472503017], [0.0, 1472503018], [1.0, 1472503019], [0.0, 1472503020], [0.0, 1472503021], [0.0, 1472503022], [1.0, 1472503023], [1.0, 1472503024], [0.0, 1472503025], [0.0, 1472503026], [0.0, 1472503027], [0.0, 1472503028], [0.0, 1472503029], [1.0, 1472503030], [0.0, 1472503031], [0.0, 1472503032], [0.0, 1472503033], [0.0, 1472503034], [0.0, 1472503035], [1.0, 1472503036], [1.0, 1472503037], [1.0, 1472503038], [1.0, 1472503039], [0.0, 1472503040], [1.0, 1472503041], [0.0, 1472503042], [0.0, 1472503043], [1.0, 1472503044], [1.0, 1472503045], [1.0, 1472503046], [1.0, 1472503047], [0.0, 1472503048], [0.0, 1472503049], [1.0, 1472503050], [0.0, 1472503051], [1.0, 1472503052], [0.0, 1472503053], [1.0, 1472503054], [0.0, 1472503055], [1.0, 1472503056], [0.0, 1472503057], [1.0, 1472503058], [1.0, 1472503059], [0.0, 1472503060], [1.0, 1472503061], [1.0, 1472503062], [0.0, 1472503063], [0.0, 1472503064], [1.0, 1472503065], [0.0, 1472503066], [1.0, 1472503067], [1.0, 1472503068], [1.0, 1472503069], [0.0, 1472503070], [0.0, 1472503071], [0.0, 1472503072], [0.0, 1472503073], [1.0, 1472503074], [1.0, 1472503075], [1.0, 1472503076], [0.0, 1472503077], [1.0, 1472503078], [1.0, 1472503079], [1.0, 1472503080], [0.0, 1472503081], [0.0, 1472503082], [1.0, 1472503083], [1.0, 1472503084], [0.0, 1472503085], [1.0, 1472503086], [0.0, 1472503087], [0.0, 1472503088], [1.0, 1472503089], [1.0, 1472503090], [0.0, 1472503091], [1.0, 1472503092], [0.0, 1472503093], [1.0, 1472503094], [0.0, 1472503095], [1.0, 1472503096], [0.0, 1472503097], [1.0, 1472503098], [1.0, 1472503099], [0.0, 1472503100], [1.0, 1472503101], [0.0, 1472503102], [0.0, 1472503103], [1.0, 1472503104], [0.0, 1472503105], [1.0, 1472503106], [0.0, 1472503107], [1.0, 1472503108], [0.0, 1472503109], [1.0, 1472503110], [0.0, 1472503111], [0.0, 1472503112], [1.0, 1472503113], [0.0, 1472503114], [0.0, 1472503115], [1.0, 1472503116], [0.0, 1472503117], [0.0, 1472503118], [1.0, 1472503119], [1.0, 1472503120], [0.0, 1472503121], [1.0, 1472503122], [1.0, 1472503123], [0.0, 1472503124], [0.0, 1472503125], [1.0, 1472503126], [1.0, 1472503127], [0.0, 1472503128], [0.0, 1472503129], [1.0, 1472503130], [1.0, 1472503131], [0.0, 1472503132], [1.0, 1472503133], [0.0, 1472503134], [1.0, 1472503135], [1.0, 1472503136], [1.0, 1472503137], [1.0, 1472503138], [0.0, 1472503139], [1.0, 1472503140], [0.0, 1472503141], [0.0, 1472503142], [0.0, 1472503143], [0.0, 1472503144], [0.0, 1472503145], [1.0, 1472503146], [0.0, 1472503147], [1.0, 1472503148], [1.0, 1472503149], [0.0, 1472503150], [1.0, 1472503151], [0.0, 1472503152], [0.0, 1472503153], [0.0, 1472503154], [0.0, 1472503155], [1.0, 1472503156], [1.0, 1472503157], [1.0, 1472503158], [0.0, 1472503159], [1.0, 1472503160], [1.0, 1472503161], [1.0, 1472503162], [1.0, 1472503163], [1.0, 1472503164], [0.0, 1472503165], [1.0, 1472503166], [0.0, 1472503167], [0.0, 1472503168], [1.0, 1472503169], [0.0, 1472503170], [0.0, 1472503171], [0.0, 1472503172], [1.0, 1472503173], [0.0, 1472503174], [1.0, 1472503175], [1.0, 1472503176], [1.0, 1472503177], [1.0, 1472503178], [0.0, 1472503179], [1.0, 1472503180], [1.0, 1472503181], [0.0, 1472503182], [0.0, 1472503183], [1.0, 1472503184], [1.0, 1472503185], [0.0, 1472503186], [1.0, 1472503187], [0.0, 1472503188], [1.0, 1472503189], [1.0, 1472503190], [1.0, 1472503191], [0.0, 1472503192], [0.0, 1472503193], [0.0, 1472503194], [1.0, 1472503195], [0.0, 1472503196], [0.0, 1472503197], [0.0, 1472503198], [0.0, 1472503199], [0.0, 1472503200], [1.0, 1472503201], [0.0, 1472503202], [0.0, 1472503203], [1.0, 1472503204], [0.0, 1472503205], [1.0, 1472503206], [0.0, 1472503207], [1.0, 1472503208], [0.0, 1472503209], [0.0, 1472503210], [0.0, 1472503211], [0.0, 1472503212], [0.0, 1472503213], [0.0, 1472503214], [0.0, 1472503215], [1.0, 1472503216], [0.0, 1472503217], [1.0, 1472503218], [0.0, 1472503219], [0.0, 1472503220], [0.0, 1472503221], [1.0, 1472503222], [1.0, 1472503223]]}]
There are 900 points (15*60). Again, we can see that the data points are zeroes or ones only.
Therefore, there is some averaging algorithm that displays data on the graph which shows points that are not zeroes or ones.
I'm interested to know what calculation graphite does to draw graphs when I change time window from 1 min to some wider range.
Following the source code, here is the part responsible for calculation of how many points per pixel (best case 1 on 1):
def consolidateDataPoints(self):
numberOfPixels = self.graphWidth = self.area['xmax'] - self.area['xmin'] - (self.lineWidth + 1)
for series in self.data:
numberOfDataPoints = self.timeRange/series.step
minXStep = float( self.params.get('minXStep',1.0) )
divisor = self.timeRange / series.step
bestXStep = numberOfPixels / divisor
if bestXStep < minXStep:
drawableDataPoints = int( numberOfPixels / minXStep )
pointsPerPixel = math.ceil( float(numberOfDataPoints) / float(drawableDataPoints) )
series.consolidate(pointsPerPixel)
series.xStep = (numberOfPixels * pointsPerPixel) / numberOfDataPoints
else:
series.xStep = bestXStep
As you may note - adding minXStep=NUMBER to the query string can extend consolidation window. The default consolidation method is average.
Related
How to calculate line from a set of polygon points?
Let's say now I have an array that used to describe the shape of a polygon: [ [0.0, 1.0], [0.5, 1.0], [0.5, 0.3], [1.0, 0.3], [1.0, 0.0], [0.0, 0.0] ] As shown on diagram above, the blue line(s) or vector points list is the result I was looking for. Assuming the polygon only composed of one or more rectangular shapes, is there any way to extract/simplify the polygon to one or more lines?
Ruby stepping through decimals and casting to Array produces unexpected results [duplicate]
This question already has answers here: Is floating point math broken? (31 answers) Closed 5 years ago. So.. I'm trying to refactor a piece of code, namely: v = [0.0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.0] By using the .step method. Tried this but it's giving me some odd decimals for some of the numbers. Trying to figure out why this is? 0.0.step(by: 0.1, to: 1.0).to_a Gives me this result: => [0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9, 1.0] Ruby version: 2.3.0p0 How can I go about figuring out why this is happening? Each of those numbers returns a float.
Floats are inexact. Calculations (like adding) are more exact using Rationals. If you must use floats, convert to Floats after the calculations. p 0.step(1, 1/10r).map(&:to_f) 3 =>[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
Not able to remove NaN from ruby array
I have an array of values => [0.0, 4.76, 0.0, Infinity, NaN, 2.63, 0.74, 10.0, NaN, NaN, NaN, NaN, 0.0, NaN, NaN, NaN, NaN, NaN, Infinity, 5.26, NaN, 0.0, NaN, 3.45, 2.5, NaN, 10.0, 0.0, NaN, 2.94, NaN, NaN, 0.0, 2.04, 0.0, 11.11, NaN, NaN, 1.23, NaN, NaN, 11.11, NaN, NaN, NaN, 0.0, 9.68, NaN, NaN, 10.0, 5.0, 3.7, 10.0, Infinity, 0.0, 0.0, 1.41, NaN, 3.45, NaN] When I run this script to remove NaN's it removes some but not all NaN's. def remove_from_array(numArray) numArray.inject(0) do |i| if numArray[i].nan? numArray.delete_at(i) end i += 1 end numArray end What am I missing?
If you delete an item and move to the next index, you’re moving two items ahead, because the item at the current index no longer exists. Luckily, there’s a better way, using Array#reject!: numArray.reject! &:nan?
Rounding errors in Ruby matrix implementation
I'm doing a bit 'o matrix algebra in ruby. When testing the results, I'm seeing what I can only assume is a rounding error. All I'm doing is multiplying 3 matrices, but the values are fairly small: c_xy: [0.9702957262759965, 0.012661213742314235, -0.24159035004964077] [0, 0.9986295347545738, 0.05233595624294383] [0.24192189559966773, -0.050781354673095955, 0.9689659697053497] i2k = Matrix[[8.1144E-06, 0.0, 0.0], [0.0, 8.1144E-06, 0.0], [0.0, 0.0, 8.1144E-06]] c_yx: [0.9702957262759965, 0, 0.24192189559966773] [0.012661213742314235, 0.9986295347545738, -0.050781354673095955] [-0.24159035004964077, 0.05233595624294383, 0.9689659697053497] What I'm trying to do is c_xy * i2k * c_yx. Here's what I expect (this was done in Excel): 8.1144E-06 0 2.11758E-22 0 8.1144E-06 0 2.11758E-22 -5.29396E-23 8.1144E-06 And what I get: [8.1144e-06, 1.3234889800848443e-23, 6.352747104407253e-22] [0.0, 8.114399999999998e-06, -5.293955920339377e-23] [2.117582368135751e-22, 0.0, 8.1144e-06] As you can see, the first column matches, as does the diagonal. But then (in r,c indexing) (0,1) is wrong (though approaching 0), (0,2) is very wrong, and (1,2) and (2,1) seem to be transposed. I thought it had something to do with the8.1144e-6 value, and tried wrapping it in a BigDecimal to no avail. Any ideas on places I can look? I'm using the standard Ruby Matrix library edit here's the code. phi1 = 0.24434609527920614 phi2 = 0.05235987755982988 i2k = Matrix[[8.1144E-06, 0.0, 0.0], [0.0, 8.1144E-06, 0.0], [0.0, 0.0, 8.1144E-06]] c_x = Matrix[[1, 0, 0], [0, Math.cos(phi2), Math.sin(phi2)], [0, -Math.sin(phi2), Math.cos(phi2)]] c_y = Matrix[[Math.cos(phi1), 0, -Math.sin(phi1)], [0, 1, 0], [Math.sin(phi1), 0, Math.cos(phi1)]] c_xy = c_y * c_x c_yx = c_xy.transpose c_xy * i2k * c_yx
i2k is equal to the identity matrix times 8.1144E-06. This simplifies the answer to: c_xy * i2k * c_yx = 8.1144E-06 * c_xy * c_yx However since c_yx = c_xy.transpose and c_xy is a rotation matrix, the transpose of any rotation matrix is its inverse. So c_xy * c_yx is the identity matrix, and thus the exact answer is 8.1144E-06 times the identity matrix. Here is one way to calculate c_xy * c_yx without using the matrix algebra a priori: require 'matrix' require 'pp' phi1 = 14 * Math::PI/180 phi2 = 3 * Math::PI/180 c_x = Matrix[ [1,0,0], [0, Math.cos(phi2), Math.sin(phi2) ], [0, -Math.sin(phi2), Math.cos(phi2) ] ] c_y = Matrix[ [Math.cos(phi1), 0, -Math.sin(phi1) ], [0,1,0], [Math.sin(phi1), 0, Math.cos(phi1) ] ] c_xy = c_y * c_x c_yx = c_xy.transpose product = c_xy * c_yx pp *product clone = *product puts "\nApplying EPSILON:" product.each_with_index do |e,i,j| clone[i][j] = 0 if e.abs <= Float::EPSILON end pp clone Output: [1.0, 0.0, 2.7755575615628914e-17] [0.0, 0.9999999999999999, -6.938893903907228e-18] [2.7755575615628914e-17, -6.938893903907228e-18, 0.9999999999999999] Applying EPSILON: [1.0, 0, 0] [0, 0.9999999999999999, 0] [0, 0, 0.9999999999999999] which one can then surmise should be the identity matrix. This uses Float::EPSILON which is about 2.220446049250313e-16 in order to set values that have an absolute value no more than this equal to 0. These kinds of approximations are inevitable in floating point calculations; one must evaluate the appropriateness of these approximations on a case-by-case basis. An alternative is to do symbolic computation where possible rather than numeric.
Floating point numbers have a precision: puts Float::DIG # => 15 That's the number of decimal digits a Float can have on my, and probably your system. Numbers smaller than 1E-15 can not be represented with a float. You could try BigDecimal for arbitrary large precision.
How to sort an array of floats in Ruby?
Just wondering how to sort an array of floats in Ruby, since "sort" and "sort!" only work for integer arrays.
Arrays of floats can certainly be sorted: >> [6.2, 5.8, 1.1, 4.9, 13.4].sort => [1.1, 4.9, 5.8, 6.2, 13.4] Maybe you have a nil in your array, which can't be sorted with anything.
You can sort a float array without any problem like : irb(main):005:0> b = [2.0, 3.0, 1.0, 4.0] => [2.0, 3.0, 1.0, 4.0] irb(main):006:0> b.sort => [1.0, 2.0, 3.0, 4.0]
perhaps you have something like this in your array and haven't noticed: [1.0 , 3.0, 0/0, ...] the 0/0 will give you a NaN which is impossible to compare with a Float... in this case you should try to [2.3,nil,1].compact.sort # => [1,2.3] that or perhaps the same error with 1.0/0 wich yields infinity (but this error is detected by ruby)