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Edit and run 





In [1]:



    


using TimeSeries
using Quandl

data = quandl("YAHOO/AAPL")["Adjusted Close"]



    


















    
Out[1]:








100x1 TimeSeries.TimeArray{Float64,1,Date,Array{Float64,1}} 2016-01-06 to 2016-05-27

             Adjusted Close  
2016-01-06 | 99.5504         
2016-01-07 | 95.3489         
2016-01-08 | 95.8531         
2016-01-11 | 97.4052         
⋮
2016-05-24 | 97.9            
2016-05-25 | 99.62           
2016-05-26 | 100.41          
2016-05-27 | 100.35          


















In [2]:



    


using Gadfly



    











In [3]:



    


p = plot(x = randn(3000), Geom.histogram(bincount = 100))



    


















    
Out[3]:









  
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In [4]:



    


data



    


















    
Out[4]:








100x1 TimeSeries.TimeArray{Float64,1,Date,Array{Float64,1}} 2016-01-06 to 2016-05-27

             Adjusted Close  
2016-01-06 | 99.5504         
2016-01-07 | 95.3489         
2016-01-08 | 95.8531         
2016-01-11 | 97.4052         
⋮
2016-05-24 | 97.9            
2016-05-25 | 99.62           
2016-05-26 | 100.41          
2016-05-27 | 100.35          


















In [5]:



    


using PyPlot



    


















    






INFO: Recompiling stale cache file /Users/davekensinger/.julia/lib/v0.4/PyPlot.ji for module PyPlot.
INFO: Recompiling stale cache file /Users/davekensinger/.julia/lib/v0.4/PyCall.ji for module PyCall.
INFO: Recompiling stale cache file /Users/davekensinger/.julia/lib/v0.4/LaTeXStrings.ji for module LaTeXStrings.
WARNING: using PyPlot.plot in module Main conflicts with an existing identifier.

















In [10]:



    


ts_length = 100
epsilon_values = randn(ts_length)
# print(epsilon_values)
# plot(epsilon_values, "b-")
plot(x=collect(1:100), y=epsilon_values, Geom.line)



    


















    
Out[10]:









  
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    y
  


  
  






















In [11]:



    


using Distributions



    











In [19]:



    


epsilon_values = rand(Laplace(), 500)
epsilon_values



    


















    
Out[19]:








500-element Array{Float64,1}:
  1.421     
 -0.181596  
 -3.23854   
  2.24198   
  1.20672   
 -0.863466  
 -1.31342   
  0.608764  
  0.791124  
 -2.51794   
  0.434766  
  1.5802    
  0.415043  
  ⋮         
  0.723574  
 -0.684548  
  0.285117  
  0.396163  
  0.793288  
 -1.74918   
  0.629827  
  0.0986058 
 -0.459962  
 -0.00368334
 -0.526847  
  0.016431  

















In [21]:



    


plot(x=epsilon_values, Geom.histogram)



    


















    
Out[21]:









  
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In [13]:



    


lp = Laplace()



    


















    
Out[13]:








Distributions.Laplace(μ=0.0, θ=1.0)

















In [16]:



    


plot_histogram(lp, 500)



    


















    






LoadError: MethodError: `plot` has no method matching plot(::Array{Float64,1}, ::Function)
Closest candidates are:
  plot(!Matched::Union{Array{Gadfly.Layer,1},Union{DataType,Function,Gadfly.Element,Gadfly.Theme}}...)
  plot(!Matched::Union{AbstractArray{T,2},DataFrames.AbstractDataFrame}, ::Union{Array{Gadfly.Layer,1},Union{DataType,Function,Gadfly.Element,Gadfly.Theme}}...)
  plot{T<:Union{DataType,Function}}(!Matched::Array{T<:Union{DataType,Function},1}, ::Any, !Matched::Any, !Matched::Union{DataType,Function,Gadfly.Element,Gadfly.Theme}...)
  ...
while loading In[16], in expression starting on line 1

 in plot_histogram at In[15]:3
















In [22]:



    


hist(lp)



    


















    






LoadError: MethodError: `hist` has no method matching hist(::Distributions.Laplace)
while loading In[22], in expression starting on line 1

















In [13]:



    


hist(epsilon_values)



    


















    
Out[13]:








(-3.0:1.0:3.0,[3,10,39,35,11,2])

















In [ ]:

Content source: dkensinger/julia

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