{-# language ExtendedDefaultRules, ScopedTypeVariables, QuasiQuotes #-}
import Test.Tasty
import Test.Tasty.HUnit
import System.IO.Unsafe
import Graphics.Matplotlib
import System.IO.Temp
import System.Random
import Text.RawString.QQ
import Data.List
import Data.List.Split
-- * Random values for testing
uniforms :: (Random a, Num a) => [a]
uniforms = randoms (mkStdGen 42)
uniforms' lo hi = randomRs (lo,hi) (mkStdGen 42)
-- * Not so random values to enable some fully-reproducible tests
xs = [-0.54571992, 1.48409716, -0.57545561, 2.13058156, -0.75740497,
-1.27879086, -0.96008858, -1.65482373, -1.69086194, -1.41925464,
0.68144401, 1.44847131, 1.12124327, 1.32056244, -0.4555279 ,
1.96002923, -1.34810771, 0.01513306, 1.25298883, -1.07541677,
0.60920278, -0.13978163, 0.3975209 , -0.15211044, 0.0534633 ,
-0.39498474, -1.15320186, 0.6795936 , 0.50704333, 1.52951037,
0.90867393, -0.24833115, 1.39816295, -0.28642814, 0.96951915,
-1.20438266, -0.32642955, -0.62982941, 0.7245042 , -1.03169685,
-0.00542761, 0.54247125, -1.11559132, -2.6829048 , -0.13370841,
-0.74111166, 0.59198725, 2.73711931, 1.82122485, -0.73915212,
0.88290489, -1.17307876, 0.06753304, 0.40150672, 1.54455801,
-0.31133243, 1.66844302, -0.1290924 , 0.89657699, 0.41181393,
2.13382656, 1.58577659, -1.02449069, -1.10245954, -0.59691196,
-0.63040161, -0.51541836, 0.04139408, 0.54203055, -2.09082082,
-0.41295376, -0.77509336, 0.47612065, -1.69680096, 0.90195265,
0.23798858, -0.05112783, 1.00645056, -0.67012513, 0.52017487,
-0.42251472, 0.96513844, 1.00298933, 0.18257527, 0.54599979,
-1.50321042, 0.03949817, 0.35286613, 1.86994544, 1.16249707,
0.57421291, 1.21151469, 1.74863421, 0.42287859, -1.22785845,
-0.61650528, 1.76743253, -0.45818694, -1.16560907, 0.0677502 ]
ys = [ 1.28231455, 1.13480471, 0.57738712, 0.10268954, 1.00162163,
-0.85453571, -1.61049343, 1.33194242, 0.12054943, -0.56568776,
2.11550009, 0.03663454, 0.24889313, 0.85458325, 0.77326592,
0.58815223, -0.79997005, 0.54979418, 0.47711544, 0.73004143,
-0.65704545, 1.1946521 , -0.31119444, -0.0958055 , 0.37838453,
1.01281301, -0.53364162, 2.84609607, 0.09363483, -0.14821451,
-0.0481863 , -3.58277731, -1.7168244 , -0.87526525, -0.65430073,
1.0284506 , -0.81397895, 0.34868379, -0.51671293, 0.92879285,
0.04099886, 1.0828335 , 1.25991492, -1.48901447, 0.43657503,
0.78191509, 0.16633587, 1.99411663, -0.25542794, -0.43377353,
-0.82871869, -0.0402321 , -0.06278027, 0.28066445, 0.01185443,
1.42640101, -0.16627931, 0.82021257, -0.66684095, -0.21289723,
-1.25974667, -0.28681327, -2.11039334, -0.2722768 , -0.51622958,
0.01324637, -0.29277708, 1.35916036, -0.09089638, -1.00619256,
0.62707331, 1.17105748, -0.85636353, -0.6243519 , 0.1720141 ,
-0.15715394, 1.13488465, 2.43996937, 2.08224839, -0.23676918,
-0.24924999, 1.21629376, -0.12748227, 0.78319565, -0.10528614,
0.60177749, -1.03490762, -0.59163218, 0.16414076, 2.22783012,
-0.55178235, -0.69915414, 1.35454045, 0.42931902, -1.33656935,
-0.8023867 , -2.81354854, 0.39553427, -0.22235586, -1.34302011]
-- * Generate normally distributed random values; taken from normaldistribution==1.1.0.3
-- | Box-Muller method for generating two normally distributed
-- independent random values from two uniformly distributed
-- independent random values.
boxMuller :: Floating a => a -> a -> (a,a)
boxMuller u1 u2 = (r * cos t, r * sin t) where r = sqrt (-2 * log u1)
t = 2 * pi * u2
-- | Convert a list of uniformly distributed random values into a
-- list of normally distributed random values. The Box-Muller
-- algorithms converts values two at a time, so if the input list
-- has an uneven number of element the last one will be discarded.
boxMullers :: Floating a => [a] -> [a]
boxMullers (u1:u2:us) = n1:n2:boxMullers us where (n1,n2) = boxMuller u1 u2
boxMullers _ = []
-- | Plural variant of 'normal', producing an infinite list of
-- random values instead of returning a new generator. This function
-- is analogous to 'Random.randoms'.
normals = boxMullers $ randoms (mkStdGen 42)
-- | Analogous to 'normals' but uses the supplied (mean, standard
-- deviation).
normals' (mean, sigma) g = map (\x -> x * sigma + mean) $ normals
-- * Tests
main = defaultMain tests
tests :: TestTree
tests = testGroup "Tests" [unitTests]
-- | Test one plot; right now we just test that the command executed without
-- errors. We should visually compare plots somehow.
testPlot name fn = testCase name $ tryit fn @?= Right ""
where tryit fn = unsafePerformIO $ withSystemTempFile "a.png" (\file _ -> figure file fn)
-- | This generates examples from the test cases
testPlot' name fn = testCase name $ tryit fn name @?= Right ""
where tryit fn name = unsafePerformIO $ do
c <- code fn
print c
figure ("/tmp/imgs/" ++ name ++ ".png") fn
unitTests = testGroup "Unit tests"
[ testPlot "histogram" m1
, testPlot "cumulative" m2
, testPlot "scatter" m3
, testPlot "contour" m4
, testPlot "labelled-histogram" m5
-- TODO This test case is broken
-- , testPlot "sub-bars" m6
, testPlot "density-bandwidth" m7
, testPlot "density" m8
, testPlot "line-function" m9
, testPlot "quadratic" m10
, testPlot "projections" m11
, testPlot "line-options" m12
, testPlot "corr" mxcorr
, testPlot "tex" mtex
, testPlot "show-matrix" mmat
, testPlot "legend" mlegend
, testPlot "hist2DLog" mhist2DLog
, testPlot "eventplot" meventplot
, testPlot "errorbar" merrorbar
, testPlot "boxplot" mboxplot
]
-- * These tests are fully-reproducible, the output must be identical every time
m1 :: Matplotlib
m1 = histogram xs 8
m2 :: Matplotlib
m2 = histogram xs 8 @@ [o2 "cumulative" "True"]
m3 = scatter xs ys
degreesRadians a = a * pi / 180.0
radiansDegrees a = a * 180.0 / pi
m4 = contourF (\a b -> sin (degreesRadians a) + cos (degreesRadians b)) (-100) 100 (-200) 200 10
m5 = histogram xs 7
% yLabel "number of queries"
% xLabel "true positives"
m6 = subplotBarsLabelled
[[40, 50, 20, 50], [10, 20, 30, 40], [40, 50, 20, 50]]
["a", "b", "c", "d"] []
% title "Wee a title"
% xLabel "X"
% yLabel "Y"
m7 = densityBandwidth [2.1, 1.3, 0.4, 1.9, 5.1, 6.2] 1.5 (Just (-6, 11))
% ylim 0 0.2
m8 = density [2.1, 1.3, 0.4, 1.9, 5.1, 6.2] (Just (-6, 11))
m9 = lineF (\x -> x**2) [0,0.01..1]
m10 = plotMapLinear (\x -> x**2) (-2) 2 100 @@ [o1 "'.'"] % title "Quadratic function"
m11 = projectionsF (\a b -> cos (degreesRadians a) + sin (degreesRadians b)) (-100) 100 (-200) 200 10
m12 = plot [1,2,3,4,5,6] [1,3,2,5,2,4] @@ [o1 "'go-'", o2 "linewidth" "2"]
-- * These tests can be random and may not be exactly the same every time
-- | http://matplotlib.org/examples/pylab_examples/xcorr_demo.html
mxcorr = xacorr xs ys [o2 "usevlines" "True", o2 "maxlags" "50", o2 "normed" "True", o2 "lw" "2"]
where (xs :: [Double]) = take 100 normals
(ys :: [Double]) = take 100 normals
-- | http://matplotlib.org/examples/pylab_examples/tex_unicode_demo.html
mtex = plotMapLinear cos 0 1 100
% setTeX True
% setUnicode True
% xLabel [r|\textbf{time (s)}|]
% yLabel [r|\textit{Velocity (\u00B0/sec)}|] @@ [o2 "fontsize" "16"]
% title [r|\TeX\ is Number $\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!"|] @@ [o2 "fontsize" "16", o2 "color" "'r'"]
% grid True
mmat = pcolor (take 10 $ chunksOf 8 uniforms) @@ [o2 "edgecolors" "'k'", o2 "linewidth" "1"]
-- | http://matplotlib.org/examples/pylab_examples/legend_demo3.html
mlegend = plotMapLinear (\x -> x ** 2) 0 1 100 @@ [o2 "label" "'x^2'"]
% plotMapLinear (\x -> x ** 3) 0 1 100 @@ [o2 "label" "'x^3'"]
% legend @@ [o2 "fancybox" "True", o2 "shadow" "True", o2 "title" "'Legend'", o2 "loc" "'upper left'"]
-- | http://matplotlib.org/examples/pylab_examples/hist2d_log_demo.html
mhist2DLog = histogram2D x y @@ [o2 "bins" "40", o2 "norm" "mcolors.LogNorm()"]
% colorbar
where (x:y:_) = chunksOf 10000 normals
meventplot = plot xs ys
% mp # "ax.add_collection(mcollections.EventCollection(data[0], linelength=0.05))"
% mp # "ax.add_collection(mcollections.EventCollection(data[1], orientation='vertical', linelength=0.05))"
% text 0.1 0.6 "Ticks mark the actual data points"
where xs = sort $ take 10 uniforms
ys = map (\x -> x ** 2) xs
merrorbar = errorbar xs ys errs @@ [o2 "errorevery" "2"]
where xs = [0.1,0.2..4]
ys = map (\x -> exp $ -x) xs
errs = map (\x -> 0.1 + 0.1 * sqrt x) xs
mboxplot = subplots @@ [o2 "ncols" "2", o2 "sharey" "True"]
% setSubplot "0"
% boxplot (take 3 $ chunksOf 10 $ map (* 2) $ normals) @@ [o2 "labels" "['X', 'Y', 'Z']"]
% setSubplot "1"
% boxplot (take 3 $ chunksOf 10 $ map (* 2) $ normals) @@ [o2 "labels" "['A', 'B', 'C']", o2 "showbox" "False", o2 "showcaps" "False"]