

In [1]:

    
%matplotlib notebook
import convis









    



/home/jacob_unencrypted/Environments/default/local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

The convis.samples module contains a few general purpose stimuli and convis.kernels contains convolution kernels.

Sample Stimuli



In [2]:

    
stim = convis.samples.pulse(t=2000,x=20,y=20,pulse_length=100)
convis.animate_to_video(stim,scrolling_plot=True,window_length=1000)









    Out[2]:



In [3]:

    
stim = convis.samples.chirp(t=2000, x=20, y=20,
                            pulse_on=500, pulse_off=1000,
                            amp_on=1500, amp_off=2500, amp_freq=5.0, amp_1=0.0, amp_2=1.0,
                            freq_on=3000, freq_off=4000, freq_1=0.1, freq_2=10.0,
                            stimulus_off=4500)
convis.animate_to_video(stim,scrolling_plot=True,window_length=1000)









    Out[3]:



In [4]:

    
stim = convis.samples.moving_grating(t=2000, x=20, y=20, vt=0.005, vx=3.0, vy=2.0, p=0.01)
convis.animate_to_video(stim,scrolling_plot=True,window_length=1000)









    Out[4]:



In [5]:

    
stim = convis.samples.moving_bar(t=2000, x=20, y=20,
                                 bar_pos=-1.0,
                                 bar_direction=0.0,
                                 bar_width=1.0,
                                 bar_v=0.01)
convis.animate_to_video(stim,scrolling_plot=True,window_length=1000)









    Out[5]:



In [6]:

    
stim = convis.samples.random_checker_stimulus(t=2000, x=20, y=20, checker_size=5, seed=123)
convis.animate_to_video(stim,scrolling_plot=True,window_length=1000)









    Out[6]:

Kernels



In [19]:

    
print '\n'.join(filter(lambda x: not x.startswith('_'), dir(convis.kernels)))









    



exponential_filter_1d
exponential_filter_5d
exponential_highpass_filter_1d
exponential_highpass_filter_5d
gabor_kernel
gauss_filter_2d
gauss_filter_3d
gauss_filter_5d
text_kernel



In [8]:

    
kernel = convis.kernels.gabor_kernel(phi=2.0, size=16, resolution=2.0, f=10.0, phase=0.0, sigma_x=1, sigma_y=1)
convis.describe(kernel)









    Out[8]:




Numpy array (16, 16)



In [9]:

    
kernel = convis.kernels.text_kernel()
convis.describe(kernel)









    Out[9]:




Numpy array (16, 16)



In [10]:

    
kernel = convis.kernels.exponential_filter_1d(tau=0.01,
                                                        n=0,
                                                        normalize=True)
convis.describe(kernel)









    Out[10]:




Numpy array (47,)



In [11]:

    
kernel = convis.kernels.exponential_highpass_filter_1d()
convis.describe(kernel)









    Out[11]:




1.0 (1,)



In [12]:

    
kernel = convis.kernels.gauss_filter_2d(0.1,0.1)
convis.describe(kernel)









    Out[12]:




Numpy array (7, 7)



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