@author: jakem
"""
from swellpy import Monodisperse
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# initialize the parameters in the class of methods (N,B,seed)
# The code in the monodisperse module lays out how each method works
N = 1000 #number of particles
B = 40 #box side length
seed = 125 #inital particle placement randomization
m = Monodisperse(N, B, seed)
#Define: important variables that you need. Natasha goes over these in her paper.
area_frac = 0.7 # area fraction
swell = m.equiv_swell(area_frac)
kick = .035
swell = m.equiv_swell(area_frac)
cycle_number = 5 #This is the number of shears that you do to your system.
pairs = m._tag(swell)
theta = m.find_angle2(pairs)
# rounded_theta = [ '%.1f' % elem for elem in theta ]
#print(rounded_theta)
x = pd.Series(theta, name="Theta")
sns.set_style('darkgrid')
# -*- coding: utf-8 -*- """ Created on Wed Jun 24 13:15:21 2020 @author: jakem """ from swellpy import Monodisperse import numpy as np # initialize the parameters in the class of methods (N,B,seed) # The code in the monodisperse module lays out how each method works N = 1000 #number of particles B = 40 #box side length seed = 125 #inital particle placement randomization m = Monodisperse(N, B, seed) #Define: important variables that you need. Natasha goes over these in her paper. area_frac = 0.7 # area fraction swell = m.equiv_swell(area_frac) kick = 1 swell = m.equiv_swell(area_frac) cycle_number = 50 #This is the number of shears that you do to your system. # Transform 'Box' in one direction # Tag particles that would interact # Transform 'Box' back to original magnitude(starting scale) # Apply kicks to particles that are tagged # Question: Is a particle tagged multiple times in varying directions if tagged # by multiple particles in varying directions? m.particle_plot(area_frac,
Created on Thu Jun 25 13:10:41 2020
@author: jakem
"""
from swellpy import Monodisperse
import numpy as np
'''
Normal Training Cycle: No transformations applied.
'''
# initialize the parameters in the class of methods (N,B,seed)
# The code in the monodisperse module lays out how each method works
N = 1000 #number of particles
B = 40 #box side length
seed = 125 #inital particle placement randomization
m = Monodisperse(N, B, seed)
#Define: important variables that you need. Natasha goes over these in her paper.
area_frac = 0.7 # area fraction
swell = m.equiv_swell(area_frac)
kick = .035
swell = m.equiv_swell(area_frac)
cycle_number = 500 #This is the number of shears that you do to your system.
m.particle_plot(area_frac,
show=True,
extend=True,
figsize=(7, 7),
filename=None)
m.train(area_frac, kick, cycle_number, noise=0)
m.particle_plot(area_frac,
"""
Created on Tue May 19 11:53:30 2020
@author: jkwak
"""
from swellpy import Monodisperse
import numpy as np
#import matplotlib.pyplot as plt
#from matplotlib.patches import Ellipse
#initialize the parameters in the class of methods (N,B,seed)
# The code in the monoddisperse module lays out how each method works
N = 1000 #number of particles
B = 40 #box side length
m = Monodisperse(N, B, 125)
#np.size ndim=1
#define important variables that you need. Natasha goes over these in her paper.
area_frac = 0.7 # area fraction
swell = m.equiv_swell(area_frac)
kick = 0.05
#gives the initial plot of a particle system
m.particle_plot(area_frac,
show=True,
extend=True,
figsize=(7, 7),
filename=None)
# -*- coding: utf-8 -*- """ Created on Tue Jun 30 11:14:14 2020 @author: jakem """ from swellpy import Monodisperse import numpy as np # initialize the parameters in the class of methods (N,B,seed) # The code in the monodisperse module lays out how each method works N = 1000 #number of particles B = 40 #box side length seed = 125 #inital particle placement randomization m = Monodisperse(N, B, seed) #Define: important variables that you need. Natasha goes over these in her paper. area_frac = 0.5 # area fraction swell = m.equiv_swell(area_frac) kick = .035 swell = m.equiv_swell(area_frac) cycle_number = 1 #This is the number of shears that you do to your system. # Actual Area Fraction: .78125 # Transform 'Box' in one direction # Tag particles that would interact # Transform 'Box' back to original magnitude(starting scale) # Apply kicks to particles that are tagged #m.particle_plot(area_frac, show=True, extend = True, figsize = (7,7), filename=None)
for i in self.centers:
[i[0], i[1]] = np.dot(r, [i[0], i[1]])
# def gen_scale(self, scale, theta):
# for i in self.centers: # Transform
# i[0] = i[0]*(1-(scale*np.cos(theta*np.pi/180)))
# for i in self.centers:
# i[1] = i[1]*(1-(scale*np.sin(theta*np.pi/180)))
# initialize the parameters in the class of methods (N,B,seed)
# The code in the monodisperse module lays out how each method works
N = 1000 #number of particles
B = 40 #box side length
seed = 125 #inital particle placement randomization
m = Monodisperse(N, B, seed)
#Define: important variables that you need. Natasha goes over these in her paper.
area_frac = 0.7 # area fraction
swell = m.equiv_swell(area_frac)
kick = .035
swell = m.equiv_swell(area_frac)
cycle_number = 1 #This is the number of shears that you do to your system.
m.particle_plot(area_frac,
show=True,
extend=True,
figsize=(7, 7),
filename=None)
rot_xform(m, 45, .8)