Add rudimentary display to show axis information

Update README with new display

Author: mbauman

README.md

@@ -31,28 +31,23 @@ julia> fs = 40000 # Generate a 40kHz noisy signal, with spike-like stuff added f
        end
 
 julia> A = AxisArray([y 2y], Axis{:time}(0s:1s/fs:60s), Axis{:chan}([:c1, :c2]))
-2400001x2 AxisArrays.AxisArray{Float64,2,Array{Float64,2},Tuple{AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}},AxisArrays.Axis{:chan,Array{Symbol,1}}}}:
- -0.987931  -1.97586
- -0.719792  -1.43958
- -0.4038    -0.8076
- -1.12146   -2.24293
-  3.31236    6.62473
- -2.38934   -4.77868
- -3.65712   -7.31424
- -1.57186   -3.14373
- -3.89403   -7.78806
- -3.48901   -6.97803
+2-dimensional AxisArray{Float64,2,...} with axes:
+    :time, 0.0 s:2.5e-5 s:60.0 s
+    :chan, [:c1,:c2]
+And data, a 2400001x2 Array{Float64,2}:
+ -3.06091    -6.12181
+  0.152334    0.304668
+  7.86831    15.7366
+ -1.4144     -2.82879
+ -2.02881    -4.05763
+  9.87901    19.758
   ⋮
-  1.16204    2.32408
-  0.105888   0.211777
- -4.5175    -9.03501
- -0.792749  -1.5855
-  1.99229    3.98458
-  1.44092    2.88184
- -1.06677   -2.13353
-  3.03809    6.07619
- -2.90052   -5.80104
- -0.519704  -1.03941
+ -0.0254444  -0.0508888
+  0.204358    0.408717
+ -4.80093    -9.60186
+  5.39751    10.795
+  0.976276    1.95255
+  0.336558    0.673116
  ```
 
 AxisArrays behave like regular arrays, but they additionally use the axis
@@ -61,16 +56,21 @@ indices in *any* order, just so long as we annotate them with the axis name:
 
 ```jl
 julia> A[Axis{:time}(4)]
-1x2 AxisArrays.AxisArray{Float64,2,SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Colon},2},Tuple{AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}},AxisArrays.Axis{:chan,Array{Symbol,1}}}}:
- -1.12146  -2.24293
+2-dimensional AxisArray{Float64,2,...} with axes:
+    :time, 7.5e-5 s:2.5e-5 s:7.5e-5 s
+    :chan, [:c1,:c2]
+And data, a 1x2 SubArray{Float64,2,Array{Float64,2},Tuple{UnitRange{Int64},Colon},2}:
+ -1.4144  -2.82879
 
 julia> A[Axis{:chan}(:c2), Axis{:time}(1:5)]
-5-element AxisArrays.AxisArray{Float64,1,SubArray{Float64,1,Array{Float64,2},Tuple{UnitRange{Int64},Int64},2},Tuple{AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}}}}:
- -1.97586
- -1.43958
- -0.8076
- -2.24293
-  6.62473
+1-dimensional AxisArray{Float64,1,...} with axes:
+    :time, 0.0 s:2.5e-5 s:0.0001 s
+And data, a 5-element SubArray{Float64,1,Array{Float64,2},Tuple{UnitRange{Int64},Int64},2}:
+ -6.12181
+  0.304668
+ 15.7366
+ -2.82879
+ -4.05763
 ```
 
 We can also index by the *values* of each axis using an `Interval` type that
@@ -80,17 +80,19 @@ still has the correct time information for those datapoints!
 
 ```jl
 julia> A[40µs .. 220µs, :c1]
-7-element AxisArrays.AxisArray{Float64,1,SubArray{Float64,1,Array{Float64,2},Tuple{UnitRange{Int64},Int64},2},Tuple{AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}}}}:
- -0.4038
- -1.12146
-  3.31236
- -2.38934
- -3.65712
- -1.57186
- -3.89403
+1-dimensional AxisArray{Float64,1,...} with axes:
+    :time, 5.0e-5 s:2.5e-5 s:0.0002 s
+And data, a 7-element SubArray{Float64,1,Array{Float64,2},Tuple{UnitRange{Int64},Int64},2}:
+  7.86831
+ -1.4144
+ -2.02881
+  9.87901
+  0.463201
+  2.49211
+ -1.97716
 
 julia> axes(ans, 1)
-AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}}(5.0e-5 s:2.5e-5 s:0.00015 s)
+AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}}(5.0e-5 s:2.5e-5 s:0.0002 s)
 ```
 
 Sometimes, though, what we're really interested in is a window of time about a
@@ -101,14 +103,16 @@ we use the `atindex` function:
 
 ```jl
 julia> A[atindex(-90µs .. 90µs, 5), :c2]
-7-element AxisArrays.AxisArray{Float64,1,SubArray{Float64,1,Array{Float64,2},Tuple{UnitRange{Int64},Int64},2},Tuple{AxisArrays.Axis{:time,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}}}}:
- -1.43958
- -0.8076
- -2.24293
-  6.62473
- -4.77868
- -7.31424
- -3.14373
+1-dimensional AxisArray{Float64,1,...} with axes:
+    :time, SIUnits.SIQuantity{Float64,0,0,1,0,0,0,0,0,0}[5.0e-5 s,7.5e-5 s,0.0001 s,0.000125 s,0.00015 s,0.000175 s,0.0002 s]
+And data, a 7-element SubArray{Float64,1,Array{Float64,2},Tuple{AxisArrays.AxisArray{Int64,1,UnitRange{Int64},Tuple{AxisArrays.Axis{:sub,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}}}},Int64},0}:
+ 15.7366
+ -2.82879
+ -4.05763
+ 19.758
+  0.926402
+  4.98423
+ -3.95433
 ```
 
 This simple concept can be extended to some very powerful behaviors. For
@@ -117,31 +121,26 @@ crossings.
 
 ```jl
 julia> idxs = find(diff(A[:,:c1] .< -15) .> 0)
-248-element Array{Int64,1}: ...
+242-element Array{Int64,1}: ...
 
 julia> spks = A[atindex(-200µs .. 800µs, idxs), :c1]
-39x248 AxisArrays.AxisArray{Float64,2,Array{Float64,2},Tuple{AxisArrays.Axis{:time_sub,SIUnits.SIRange{FloatRange{Float64},Float64,0,0,1,0,0,0,0,0,0}},AxisArrays.Axis{:time_rep,Array{SIUnits.SIQuantity{Float64,0,0,1,0,0,0,0,0,0},1}}}}:
-   3.76269     3.20058      6.30581   …    9.6313      9.05193     0.214391
-   1.63657     3.26572      5.48104        1.4864      1.44608     6.1742
-   2.18868     5.87366      1.254          0.191431    1.69441     0.998004
-   3.8641      0.626106     0.147373      -1.66639    -2.91957     6.63631
-  -3.89523    -2.43706      2.54553        1.7135     -2.62467    -3.57186
-  -6.34036    -0.208273     2.06302   …   -5.43846    -5.53668    -6.3077
- -14.6912     -3.3506      -7.20661       -9.52052    -7.66351   -10.9802
- -26.3792    -16.0027     -20.6367       -16.4083    -17.2507    -23.289
- -31.6724    -25.7845     -19.683        -21.5722    -26.4421    -27.0657
- -40.0827    -29.7741     -29.1362       -31.2018    -33.5294    -28.8294
-   ⋮                                  ⋱    ⋮
-   2.65848     4.67792      2.62444        8.10507     0.972752    0.57176
-  -0.735043    7.30589      2.10037   …    4.99347     7.31926    -3.97361
-   1.91337    -4.53805     -3.3277         7.25753     1.24124     1.52025
-   4.52168    -1.21125      0.763654      -2.29234    -2.35595    -2.28334
-   1.48209    -0.79957     -6.21036        4.92486    -1.56463    -3.57588
-  -3.5987      1.98851      1.0221        -4.33494     3.96454     0.522113
-  -0.109871    3.17695      1.62774   …    0.998204    0.441668    6.64595
-   5.56824     0.0631867    2.73849        1.53731    -4.08166     4.67527
-  -1.43892    -5.00031      1.36733        3.70478    -0.25762    -1.40656
-   0.76075     3.90081     -4.59973       -2.91403     0.830114   -1.92139
+2-dimensional AxisArray{Float64,2,...} with axes:
+    :time_sub, -0.000175 s:2.5e-5 s:0.000775 s
+    :time_rep, SIUnits.SIQuantity{Float64,0,0,1,0,0,0,0,0,0}[0.178725 s,0.806825 s,0.88305 s,1.47485 s,1.50465 s,1.53805 s,1.541025 s,2.16365 s,2.368425 s,2.739 s  …  57.797925 s,57.924075 s,58.06075 s,58.215125 s,58.6403 s,58.96215 s,58.990225 s,59.001325 s,59.48395 s,59.611525 s]
+And data, a 39x242 Array{Float64,2}:
+ -1.53038     4.72882     5.8706    …  -0.231564      0.624714   3.44076
+ -2.24961     2.12414     5.69936       7.00179       2.30993    5.20432
+  5.96311     3.9713     -4.38335       1.32617      -0.686648   0.443454
+  3.86592     5.7466      2.32469       1.30803       3.44585    1.17781
+  3.56837    -3.32178     1.16106      -3.91796       2.41779   -6.17495
+ -9.52063    -2.07014    -1.18463   …  -3.55719       2.23117    1.76089
+  ⋮                                 ⋱                 ⋮
+  3.51708    -1.63627     0.281915     -2.41759       3.39403    0.101004
+  0.0421772  -2.13557    -4.71965       0.066912      3.25141   -0.445574
+  3.53238    -3.72221     1.68314   …  -4.15147      -5.25241   -1.77557
+ -4.38307     1.38275    -1.33641       3.40342       0.272826  -3.22013
+  2.54846    -0.0194032   2.58679      -0.000676503  -2.71147   -0.288483
+  0.260694   -4.1724     -0.111377      3.283         1.77147   -0.367888
 ```
 
 By indexing with a repeated interval, we have *added* a dimension to the
@@ -158,16 +157,17 @@ julia> using Sparklines
            print(t[i], ":\t")
            sparkln(spks[:, i])
        end
-0.37735 s:	▆▆▆▆▅▅▄▃▂▁▁▁▁▁▂▃▄▅▅▆▆▇▇█▇▇▇▇▆▆▅▆▆▆▅▅▆▅▆
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+d
+0.178725 s:	▄▄▅▄▄▃▃▂▁▁▂▃▅▆█▇▇▅▄▄▄▄▄▄▄▄▄▄▄▄▄▄▅▄▄▄▄▄▄
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+1.47485 s:	▅▆▆▆▅▅▄▃▂▁▁▁▁▁▂▂▄▅▅▇▇▇▇▇▇█▆▇▆▆▆▆▆▆▆▆▆▆▅
+1.50465 s:	▄▄▄▄▃▃▂▁▁▁▁▃▄▆█▇▆▅▃▃▃▃▃▄▄▄▄▄▄▄▄▄▃▃▄▄▄▄▄
+1.53805 s:	▄▃▄▄▄▃▃▂▁▁▁▂▅▆▇█▆▅▃▄▄▄▄▃▄▄▄▄▃▄▄▄▄▄▄▄▄▄▄
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 ```
 
 Fancier integration with plotting packages is a WIP.

src/core.jl

@@ -245,6 +245,19 @@ Base.similar{T}(A::AxisArray{T}, S, axs::Axis...) = similar(A, S, axs)
         AxisArray(d, $ax)
     end
 end
+# A simple display method to include axis information. It might be nice to
+# eventually display the axis labels alongside the data array, but that is
+# much more difficult.
+function Base.writemime{T,N}(io::IO, m::MIME"text/plain", A::AxisArray{T,N})
+    println(io, "$N-dimensional AxisArray{$T,$N,...} with axes:")
+    for (name, val) in zip(axisnames(A), axisvalues(A))
+        print(io, "    :$name, ")
+        Base.showlimited(io, val)
+        println(io)
+    end
+    print(io, "And data, a ")
+    writemime(io, m, A.data)
+end
 
 # Custom methods specific to AxisArrays
 @doc """

test/core.jl

@@ -109,3 +109,6 @@ A = AxisArray(reshape(1:24, 2,3,4),
 @test hash(Axis{:row}()) != hash(Axis{:col}())
 @test AxisArrays.axistype(Axis{1}(1:2)) == typeof(1:2)
 @test axisnames(Axis{1}, Axis{2}, Axis{3}) == (1,2,3)
+
+# Simply run the display method to ensure no stupid errors
+writemime(IOBuffer(),MIME("text/plain"),A)