diff --git a/README.md b/README.md
index 2722030..2e20b0c 100644
--- a/README.md
+++ b/README.md
@@ -37,10 +37,12 @@ Plot{Plots.PyPlotBackend() n=1}
 
 julia> c = wavelet(Morlet(π), β=2)
 
+CWT{Morlet mean 3.141592653589793, Father Wavelet, Q=8.0, β=2.0,aveLen=0.0, frame=1.0, norm=Inf, extraOctaves=0.0}
+
 julia> res = ContinuousWavelets.cwt(f, c)
 ┌ Warning: the lowest frequency wavelet has more than 1% its max at zero, so it may not be analytic. Think carefully
-│   lowAprxAnalyt = 0.06186323501016359
-└ @ ContinuousWavelets ~/work/ContinuousWavelets.jl/ContinuousWavelets.jl/src/sanityChecks.jl:6
+│   lowAprxAnalyt = 0.061863
+└ @ ContinuousWavelets ~/work/ContinuousWavelets.jl/ContinuousWavelets.jl/src/sanityChecks.jl:7
 2047×31 Matrix{ComplexF64}:
  -1.48637e-6+3.8241e-19im   …  0.000109978+9.67834e-5im
  -1.48602e-6+5.15534e-19im     -8.24922e-5+0.000130656im
@@ -90,6 +92,7 @@ julia> exs = cat(testfunction(n, "Doppler"), testfunction(n, "Blocks"), testfunc
 
 julia> c = wavelet(cDb2, β=2, extraOctaves=-0)
 
+CWT{Continuous db2, Father Wavelet, Q=8.0, β=2.0,aveLen=0.0, frame=1.0, norm=Inf, extraOctaves=0.0}
 
 julia> res = circshift(ContinuousWavelets.cwt(exs, c), (0, 1, 0))
 ┌ Warning: the highest frequency wavelet has more than 1% its max at the end, so it may not be analytic. Think carefully
diff --git a/docs/src/README.md b/docs/src/README.md
index 9b5e78d..54bb6e0 100644
--- a/docs/src/README.md
+++ b/docs/src/README.md
@@ -1,3 +1,12 @@
+```@meta ex
+DocTestFilters = [
+        r"\@ ContinuousWavelets .*",
+        r"[ +-][0-9]\.[0-9]{3,5}e-1[5-9]",
+        r"[ +-][0-9]\.[0-9]{3,5}e-[2-9][0-9]",
+        r"im {2,7}",
+    ]
+```
+
 # ContinuousWavelets
 
 [![Build Status](https://travis-ci.com/dsweber2/ContinuousWavelets.jl.svg?branch=master)](https://travis-ci.com/dsweber2/ContinuousWavelets.jl)
@@ -38,18 +47,18 @@ julia> f = testfunction(n, "Doppler");
 
 julia> c = wavelet(Morlet(π), β=2)
 
+CWT{Morlet mean 3.141592653589793, Father Wavelet, Q=8.0, β=2.0,aveLen=0.0, frame=1.0, norm=Inf, extraOctaves=0.0}
+
 julia> res = ContinuousWavelets.cwt(f, c)
 ┌ Warning: the lowest frequency wavelet has more than 1% its max at zero, so it may not be analytic. Think carefully
 │   lowAprxAnalyt = 0.061863
-└ @ ContinuousWavelets ~/work/ContinuousWavelets.jl/ContinuousWavelets.jl/src/sanityChecks.jl:6
+└ @ ContinuousWavelets ~/work/ContinuousWavelets.jl/ContinuousWavelets.jl/src/sanityChecks.jl:7
 2047×31 Matrix{ComplexF64}:
  -1.48637e-6+3.8241e-19im   …  0.000109978+9.67834e-5im
  -1.48602e-6+5.15534e-19im     -8.24922e-5+0.000130656im
             ⋮               ⋱             ⋮
  0.000435175+2.30636e-19im  …  -2.47195e-6-1.97048e-8im
  0.000435027-8.28725e-19im     -2.63499e-6+4.62331e-8im
-
-
 ```
 
 As the cwt frame is redundant, there are many choices of dual/inverse frames. There are three available in this package, `NaiveDelta()`, `PenroseDelta()`, and `DualFrames()`. As a toy example, lets knock out the middle time of the bumps function and apply a high pass filter:
@@ -104,6 +113,8 @@ julia> exs = cat(testfunction(n, "Doppler"), testfunction(n, "Blocks"), testfunc
 
 julia> c = wavelet(cDb2, β=2, extraOctaves=-0)
 
+CWT{Continuous db2, Father Wavelet, Q=8.0, β=2.0,aveLen=0.0, frame=1.0, norm=Inf, extraOctaves=0.0}
+
 julia> res = circshift(ContinuousWavelets.cwt(exs, c), (0, 1, 0))
 ┌ Warning: the highest frequency wavelet has more than 1% its max at the end, so it may not be analytic. Think carefully
 │   highAprxAnalyt = 0.26778