def make_illustration_snapshot():
fig = plt.figure()
ax = fig.gca()
model = vicsek_model_factory(model='angular',
n=200, L=100.0, eta=0.2, v_0=0.5)
for _ in range(100):
model.iterate()
ejm_rcparams.set_pretty_plots(use_latex=True, use_pgf=True)
ejm_rcparams.prettify_axes(ax)
ax.set_xlim(-model.L_half, model.L_half)
ax.set_ylim(-model.L_half, model.L_half)
ax.set_aspect('equal')
i_source = np.argmin(np.sum(np.square(model.r), axis=1))
r_i = model.r[i_source]
c = plt.Circle(r_i, radius=model.r_v, fill=False, edgecolor='red')
ax.add_patch(c)
colors = np.zeros([model.n])
colors[i_source] = 1.0
i_neighbs = model._neighbs(i_source)
for i_neighb in i_neighbs:
colors[i_neighb] = 0.5
s = 2.0
ax.quiver(model.r[:, 0], model.r[:, 1],
s * model.L * model.u[:, 0], s * model.L * model.u[:, 1],
colors, pivot='mid', edgecolor='none')
ax.axis('off')
plt.savefig('vicsek_snapshot_demo.pdf', bbox_inches='tight',
transparent=True)
def make_disordered_snapshot():
model = vicsek_model_factory(model='angular',
n=100, L=100.0, eta=0.8, v_0=0.5)
for _ in range(100):
model.iterate()
fig = plt.figure()
ax = fig.gca()
ejm_rcparams.set_pretty_plots(use_latex=True, use_pgf=True)
ejm_rcparams.prettify_axes(ax)
ax.set_xlim(-model.L_half, model.L_half)
ax.set_ylim(-model.L_half, model.L_half)
ax.set_aspect('equal')
s = 2.0
ax.quiver(model.r[:, 0], model.r[:, 1],
s * model.L * model.u[:, 0], s * model.L * model.u[:, 1],
pivot='mid', edgecolor='none')
ax.axis('off')
plt.savefig('vicsek_snapshot_disordered.pdf', bbox_inches='tight',
transparent=True)
import matplotlib.pyplot as plt
import dataset
import paths
from ciabatta import ejm_rcparams
save_flag = True
use_latex = save_flag
use_pgf = True
ejm_rcparams.set_pretty_plots(use_latex, use_pgf)
dr = 0.7
fig = plt.figure(figsize=(12, 12 * ejm_rcparams.golden_ratio))
ax = fig.add_subplot(111)
ejm_rcparams.prettify_axes(ax)
def plot_rdf(ax, dset_path, smooth):
dset = dataset.get_dset(dset_path)
rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
vp, vp_err = dset.get_vp()
R = dset.R
if use_latex:
label = r"$\SI{" + "{:.3g}".format(R) + r"}{\um}$, " + r"$\SI{" + "{:.2g}".format(vp) + r"}{\percent}$"
else:
label = r"$" + "{:.3g}".format(R) + r"\mu m$, $" + "{:.2g}".format(vp) + r"\%$"
if smooth:
label += r", Smooth swimming"
import matplotlib.pyplot as plt
from ciabatta import ejm_rcparams
from colors import *
from dataset import pickle_load
import numpy as np
import paths
save_flag = True
use_latex = save_flag
use_pgf = True
ejm_rcparams.set_pretty_plots(use_latex, use_pgf)
fig = plt.figure(figsize=(12, 12 * ejm_rcparams.golden_ratio))
ax = fig.add_subplot(111)
ejm_rcparams.prettify_axes(ax)
dr = 0.7
def get_stat(pickle_path, alg):
superset = pickle_load(pickle_path)
eta_0, eta_0_err = superset.get_eta_0()
f_peak, f_peak_err = superset.get_f_peak(alg, dr)
return eta_0, eta_0_err, f_peak, f_peak_err
eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_pickle_path, 'mean')
ax.errorbar(eta_0, f_peak, xerr=eta_0_err, yerr=f_peak_err, ls='none', label=r'Method A', c=color_exp)
eta_0, eta_0_err, f_peak, f_peak_err = get_stat(paths.exp_pickle_path, 'median')
from __future__ import print_function, division
import numpy as np
import scipy.stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import sklearn
from sklearn.cluster import KMeans
from ciabatta import ejm_rcparams
ejm_rcparams.set_pretty_plots(use_latex=False, use_pgf=True)
def get_data():
d = np.loadtxt('exercise.txt', delimiter=',').T
inds = np.arange(d.shape[1])
return inds, d
def remove_single_point_spikes(d):
d_filter = d.copy()
for i in range(1, d.shape[0] - 1):
if (d[i] != d[i - 1]) and (d[i - 1] == d[i + 1]):
d_filter[i] = d[i - 1]
return d_filter
def denoise_data(d):
d_denoise = d.copy()
for sample in d_denoise:
sample[:] = remove_single_point_spikes(sample)
return d_denoise
from __future__ import print_function
import matplotlib.pyplot as plt
from runner import get_filenames, filename_to_model
from ciabatta import pack, ejm_rcparams
from fipy import MatplotlibViewer, MatplotlibVectorViewer
import numpy as np
ejm_rcparams.set_pretty_plots(False, False)
def plot_coarse(dirname):
filenames = get_filenames(dirname)
model_0 = filename_to_model(filenames[0])
plt.show()
plt.ion()
fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
axs = axs.flatten()
ax_rho, ax_p, ax_food, ax_food_grad = axs
for ax in axs:
ax.set_xlim(-model_0.L[0] / 2.0, model_0.L[0] / 2.0)
ax.set_ylim(-model_0.L[1] / 2.0, model_0.L[1] / 2.0)
view_args = {'xmin': -model_0.L[0] / 2.0, 'xmax': model_0.L[0] / 2.0,
'ymin': -model_0.L[1] / 2.0, 'ymax': model_0.L[1] / 2.0,
'cmap': ejm_rcparams.reds}