Generating second-order figures from texture
The goal here is to use the MotionClouds library to generate figures with second-order contours, similar to those used in the P. Roelfsema's group.
Let's first initialize the notebook:
In [1]:
from __future__ import division, print_function
import numpy as np
np.set_printoptions(precision=6, suppress=True)
%matplotlib inline
import matplotlib.pyplot as plt
fig_width = 10
figsize = (fig_width, fig_width)
install and load the library¶
In [2]:
%pip install MotionClouds
In particular, we just generate one frame:
In [3]:
import MotionClouds as mc
mc.N_frame = 1
fx, fy, ft = mc.get_grids(mc.N_X, mc.N_Y, mc.N_frame)
name = 'texture'
env = mc.envelope_gabor(fx, fy, ft, theta=np.pi/4, V_X=0., V_Y=0., B_V=0)
z = mc.rectif(mc.random_cloud(env))
image = z.reshape((mc.N_X, mc.N_Y))
In [4]:
import matplotlib.pyplot as plt
%matplotlib inline
fig, ax = plt.subplots(figsize=(10,10))
_ = ax.imshow(image, cmap=plt.gray())
define a crop function¶
The library has a representation of space that we may take advantage of:
In [5]:
fig, ax = plt.subplots(figsize=(10,10))
_ = ax.imshow(fx, cmap=plt.gray())
print(fx.min(), fx.max())
We may easily define a central cropping mask:
In [6]:
rho = .2
mask = (np.abs(fx) < rho) * (np.abs(fy) < rho)
fig, ax = plt.subplots(figsize=(10,10))
_ = ax.imshow(mask, cmap=plt.gray())
print(mask.min(), mask.max())
From this we define a cropping function
In [7]:
def crop_and_merge(image, rho=.2, use_flip=True, use_fill=False, fill=.5):
N_X, N_Y = image.shape
fx, fy, ft = mc.get_grids(N_X, N_Y, 1)
image_fig = image.copy()
if use_flip: image_fig = np.fliplr(image_fig)
image_fig = np.roll(image_fig, N_X//4 + int(N_X//2*np.random.rand()), axis=0 ) # roll over one axis
image_fig = np.roll(image_fig, N_Y//4 + int(N_Y//2*np.random.rand()), axis=1 ) # roll over one axis
mask = (np.abs(fx[:, :, 0]) < rho) * (np.abs(fy[:, :, 0]) < rho)
if use_fill:
return image * (1-mask) + fill * mask
else:
return image * (1-mask) + image_fig * mask
usage¶
With a flip
In [8]:
fig, ax = plt.subplots(figsize=(10,10))
_ = ax.imshow(crop_and_merge(image, rho=.2, use_flip=True), cmap=plt.gray())
and without:
In [9]:
fig, ax = plt.subplots(figsize=(10,10))
_ = ax.imshow(crop_and_merge(image, rho=.2, use_flip=False), cmap=plt.gray())
We can also define a first-order figure:
In [10]:
fig, ax = plt.subplots(figsize=(10,10))
_ = ax.imshow(crop_and_merge(image, rho=.2, use_fill=True), cmap=plt.gray())
some book keeping for the notebook¶
In [12]:
%load_ext watermark
%watermark -i -h -m -v -p numpy,MotionClouds,matplotlib -r -g -b