skedm example data



In [1]:

    
import sys
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('ticks')
sns.set_context(context='notebook',font_scale=1.3)
%matplotlib inline



In [2]:

    
%load_ext autoreload
%autoreload 2



In [3]:

    
# alter the line below to correspond to your file system
f = '../'
sys.path.append(f)



In [4]:

    
import skedm as edm
import skedm.data as data

1D Example Data

Logistic Map



In [5]:

    
x = data.logistic_map(sz=256, A=3.99, noise=.01, seed=36)
plt.plot(x);

Periodic



In [6]:

    
x = data.noisy_periodic(sz=256, freq=52, noise=0.5, seed=36)
plt.plot(x)









    Out[6]:





[<matplotlib.lines.Line2D at 0x116c7ce80>]

Noise



In [7]:

    
x = data.noise_1d(sz=256, seed=36)
plt.plot(x)









    Out[7]:





[<matplotlib.lines.Line2D at 0x116db90f0>]

Lorenz equations



In [8]:

    
X = data.lorenz(sz=10000, max_t=100.0, noise=0, parameters=(10.0, 2.6666666,28.0))

fig,ax = plt.subplots(nrows=3,sharex=True,figsize=(10,4))
ax[0].plot(X[:,0])
ax[1].plot(X[:,1])
ax[2].plot(X[:,2])









    Out[8]:





[<matplotlib.lines.Line2D at 0x116f52c88>]

2d Example Data

2d Logistic Map diffused in space



In [9]:

    
x = data.chaos_2d(sz=128, A=3.99, eps=1.0, noise=.1, seed=36)
plt.imshow(x, cmap='magma')
plt.colorbar()









    Out[9]:





<matplotlib.colorbar.Colorbar at 0x117218208>

2d horizontal sine wave added to a 2d vertial cosine wave



In [10]:

    
x = data.periodic_2d(sz=128, freq=36, noise=0.5, seed=36)
plt.imshow(x, cmap='magma')
plt.colorbar()









    Out[10]:





<matplotlib.colorbar.Colorbar at 0x1174d6978>

2d Brown noise

Takes roughly 1.5 minute to generate on a macbook air



In [11]:

    
x = data.brown_noise(sz=128, num_walks=500, walk_sz=100000, spread=1000, seed=3)
plt.imshow(x, cmap='magma')
plt.colorbar()









    Out[11]:





<matplotlib.colorbar.Colorbar at 0x116ba9f28>

Periodic Brown

Takes roughly 1.5 minutes to generate on a macbook air



In [12]:

    
x = data.periodic_brown(sz=128, freq=36, seed=15)
plt.imshow(x, cmap='magma')
plt.colorbar()









    Out[12]:





<matplotlib.colorbar.Colorbar at 0x11d747da0>

2d Noise



In [13]:

    
x = data.noise_2d(sz=128, seed=36)
plt.imshow(x, cmap='magma')
plt.colorbar()









    Out[13]:





<matplotlib.colorbar.Colorbar at 0x11db1e438>

Overlapping Circles



In [14]:

    
x = data.overlapping_circles(sz=256, rad=20.0, sigma=1, num_circles=1000)
plt.imshow(x, cmap='magma')
plt.colorbar()









    Out[14]:





<matplotlib.colorbar.Colorbar at 0x11dced470>

Concentric Circles



In [15]:

    
x = data.concentric_circles([2, 5, 10])
plt.imshow(x, cmap='jet')
plt.colorbar()









    



Circles Generated: 97






    Out[15]:





<matplotlib.colorbar.Colorbar at 0x11df78cf8>



In [16]:

    
np.unique(x)









    Out[16]:





array([ 0.,  1.,  2.,  3.])

Two different sized circles



In [17]:

    
x = data.small_and_large_circles()
plt.imshow(x, cmap='jet')
plt.colorbar()









    



Number of circles: 82






    Out[17]:





<matplotlib.colorbar.Colorbar at 0x10812c2b0>



In [18]:

    
np.unique(x)









    Out[18]:





array([ 0.,  1.,  2.])

Randomly sized circles



In [19]:

    
x = data.random_sized_circles([4,5,6], [5,7,8], sz=512, num_circs=3000)
plt.imshow(x, cmap='jet')
plt.colorbar()









    



1113






    Out[19]:





<matplotlib.colorbar.Colorbar at 0x11e81b668>



In [20]:

    
np.unique(x)









    Out[20]:





array([ 0.,  5.,  7.,  8.])

Voronoi Matrix



In [21]:

    
x = data.voronoi_matrix(sz=512, percent=0.01, num_classes=27)
plt.imshow(x, cmap='jet')
plt.colorbar()









    Out[21]:





<matplotlib.colorbar.Colorbar at 0x11f0e82b0>



In [22]:

    
np.unique(x)









    Out[22]:





array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,
        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,
        22.,  23.,  24.,  25.,  26.])

3d chaos



In [23]:

    
x = data.chaos_3d(sz=128, A=3.99, eps=1.0, steps=100, tstart=50)



In [24]:

    
x.shape









    Out[24]:





(128, 128, 100)



In [25]:

    
fig,axes = plt.subplots(3,3,figsize=(10,10))

for i, ax in enumerate(axes.ravel()):
    ax.imshow(x[:,:,i*10], cmap='magma')
    ax.set_title(i*10)



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