def test_simulate():
pops = simulate(
model=logistic_map,
num_gens=200,
rate_min=3.7,
rate_max=3.9,
num_rates=100,
num_discard=100,
jit=True,
)
assert type(pops) == pd.DataFrame
assert pops.shape == (200, 100)
pops = simulate(
model=logistic_map,
num_gens=200,
rate_min=3.7,
rate_max=3.9,
num_rates=100,
num_discard=100,
jit=False,
)
assert type(pops) == pd.DataFrame
assert pops.shape == (200, 100)
def test_simulate():
pops = simulate(model=logistic_map, num_gens=200, rate_min=3.7, rate_max=3.9, num_rates=100, num_discard=100, jit=True)
assert type(pops)==pd.DataFrame
assert pops.shape==(200, 100)
pops = simulate(model=logistic_map, num_gens=200, rate_min=3.7, rate_max=3.9, num_rates=100, num_discard=100, jit=False)
assert type(pops)==pd.DataFrame
assert pops.shape==(200, 100)
def test_bifurcation_plot():
pops = simulate(model=logistic_map, num_gens=200, rate_min=0, rate_max=4, num_rates=100, num_discard=100)
assert type(pops)==pd.DataFrame
assert pops.shape==(200, 100)
# returns None
bifurcation_plot(pops, save=True, folder=_img_folder, filename='')
def test_bifurcation_plot():
pops = simulate(model=logistic_map,
num_gens=200,
rate_min=0,
rate_max=4,
num_rates=100,
num_discard=100)
assert type(pops) == pd.DataFrame
assert pops.shape == (200, 100)
# returns None
bifurcation_plot(pops, save=True, folder=_img_folder, filename='')
def test_phase_diagram_3d():
pops = simulate(model=cubic_map, num_gens=200, rate_min=3.5, num_rates=30, num_discard=100)
assert type(pops)==pd.DataFrame
assert pops.shape==(200, 30)
# returns None
phase_diagram_3d(pops, xmin=-1, xmax=1, ymin=-1, ymax=1, zmin=-1, zmax=1, alpha=0.2, color='viridis', azim=330,
legend=True, save=False, folder=_img_folder, filename='')
# returns None
phase_diagram_3d(pops, xmin=-1, xmax=1, ymin=-1, ymax=1, zmin=-1, zmax=1, alpha=0.2, color='inferno', azim=330,
legend=True, remove_ticks=False, save=False, folder=_img_folder, filename='')
def test_phase_diagram():
pops = simulate(model=singer_map, num_gens=200, rate_min=3.6, rate_max=4.0, num_rates=50, num_discard=100)
assert type(pops)==pd.DataFrame
assert pops.shape==(200, 50)
# returns None
phase_diagram(pops, xmin=0.25, xmax=0.75, ymin=0.8, ymax=1.01, size=7, discard_gens=10, color='b', color_reverse=True,
legend=True, save=False, folder=_img_folder, filename='')
fig_ax = phase_diagram(pops, xmin=0.25, xmax=0.75, ymin=0.8, ymax=1.01, size=7, discard_gens=10, color=['b', 'g'],
legend=True, save=False, show=False, folder=_img_folder, filename='')
assert type(fig_ax)==tuple
def test_phase_diagram_3d():
pops = simulate(model=cubic_map,
num_gens=200,
rate_min=3.5,
num_rates=30,
num_discard=100)
assert type(pops) == pd.DataFrame
assert pops.shape == (200, 30)
# returns None
phase_diagram_3d(
pops,
xmin=-1,
xmax=1,
ymin=-1,
ymax=1,
zmin=-1,
zmax=1,
alpha=0.2,
color="viridis",
azim=330,
legend=True,
save=False,
folder=_img_folder,
filename="",
)
# returns None
phase_diagram_3d(
pops,
xmin=-1,
xmax=1,
ymin=-1,
ymax=1,
zmin=-1,
zmax=1,
alpha=0.2,
color="inferno",
azim=330,
legend=True,
remove_ticks=False,
save=False,
folder=_img_folder,
filename="",
)
def test_phase_diagram():
pops = simulate(model=singer_map,
num_gens=200,
rate_min=3.6,
rate_max=4.0,
num_rates=50,
num_discard=100)
assert type(pops) == pd.DataFrame
assert pops.shape == (200, 50)
# returns None
phase_diagram(
pops,
xmin=0.25,
xmax=0.75,
ymin=0.8,
ymax=1.01,
size=7,
discard_gens=10,
color="b",
color_reverse=True,
legend=True,
save=False,
folder=_img_folder,
filename="",
)
fig_ax = phase_diagram(
pops,
xmin=0.25,
xmax=0.75,
ymin=0.8,
ymax=1.01,
size=7,
discard_gens=10,
color=["b", "g"],
legend=True,
save=False,
show=False,
folder=_img_folder,
filename="",
)
assert type(fig_ax) == tuple
def test_phase_diagram():
pops = simulate(model=singer_map, num_gens=200, rate_min=3.6, rate_max=4.0, num_rates=50, num_discard=100)
phase_diagram(pops, xmin=0.25, xmax=0.75, ymin=0.8, ymax=1.01, size=7, discard_gens=10, color='b', color_reverse=True,
legend=True, save=False, folder=_img_folder, filename='')
phase_diagram(pops, xmin=0.25, xmax=0.75, ymin=0.8, ymax=1.01, size=7, discard_gens=10, color=['b', 'g'],
legend=True, save=False, show=False, folder=_img_folder, filename='')
def test_bifurcation_plot():
pops = simulate(model=logistic_map, num_gens=200, rate_min=0, rate_max=4, num_rates=100, num_discard=100)
bifurcation_plot(pops, save=True, folder=_img_folder, filename='')
def test_simulate():
pops = simulate(model=logistic_map, num_gens=200, rate_min=3.7, rate_max=3.9, num_rates=100, num_discard=100, jit=True)
pops = simulate(model=logistic_map, num_gens=200, rate_min=3.7, rate_max=3.9, num_rates=100, num_discard=100, jit=False)
import pynamical from pynamical import simulate, bifurcation_plot, save_fig import pandas as pd, numpy as np, IPython.display as display, matplotlib.pyplot as plt, matplotlib.cm as cm from pynamical import logistic_map, simulate, bifurcation_plot pops = simulate(model=logistic_map, num_gens=100, rate_min=0, rate_max=4, num_rates=1000, num_discard=100) bifurcation_plot(pops)
import pynamical
from pynamical import simulate, bifurcation_plot, save_fig
import pandas as pd, numpy as np, matplotlib.pyplot as plt, matplotlib.cm as cm
pops = simulate(num_gens=100,
rate_min=0.,
rate_max=3.5,
num_rates=1000,
num_discard=100)
bifurcation_plot(pops, xmax=3.5, filename='logistic-map-bifurcation-1')
# ax.set_ylim(0, 1)
#
# ax.set_aspect('equal')
#
# ax.set_xlabel(r'$x_k$')
# ax.set_ylabel(r'$x_{k+1}$')
#
# plt.savefig('../imgs/chaos_vs_randomness_phase_plane.pdf', bbox_inches='tight', dpi=300)
pops = simulate(num_gens=100, rate_min=0, rate_max=4, num_rates=1000, num_discard=100, jit=True)
pops = pops.as_matrix()
mu = np.linspace(0, 4, 1000)
fig = plt.figure(figsize=(fig_width, fig_width/3))
ax = fig.gca()
for i in xrange(mu.size):
ax.plot(mu[i]*np.ones_like(pops[:, i]), pops[:, i], ',', color='royalblue')
# --> x-axis.
ax.set_xlim(0, 4)
ax.set_xlabel(r'$\mu$')
# --> y-axis.
import pynamical
from pynamical import simulate, bifurcation_plot, save_fig
import pandas as pd, numpy as np, matplotlib.pyplot as plt, matplotlib.cm as cm
# run the logistic model for 20 generations for 7 growth rates between 0.5 and 3.5 then view the output
pops = simulate(num_gens=20, rate_min=0.6, rate_max=3.6, num_rates=7)
pops.applymap(lambda x: '{:03.3f}'.format(x))
print(pops)
pops.to_csv("C:/Projects/GitHub/sjanzou/pynamical/Paper/out3.6.csv", sep=',')
def test_phase_diagram_3d():
pops = simulate(model=cubic_map, num_gens=200, rate_min=3.5, num_rates=30, num_discard=100)
phase_diagram_3d(pops, xmin=-1, xmax=1, ymin=-1, ymax=1, zmin=-1, zmax=1, alpha=0.2, color='viridis', azim=330,
legend=True, save=False, folder=_img_folder, filename='')
phase_diagram_3d(pops, xmin=-1, xmax=1, ymin=-1, ymax=1, zmin=-1, zmax=1, alpha=0.2, color='inferno', azim=330,
legend=True, remove_ticks=False, save=False, folder=_img_folder, filename='')
from pynamical import cobweb_plot, simulate, phase_diagram, bifurcation_plot
from numba import jit
@jit(nopython=True)
def sine_map(x, r):
return r * np.sin(np.pi * x)
if __name__ == '__main__':
pops = simulate(model=sine_map,
num_gens=100,
rate_min=0.6,
rate_max=1,
num_rates=1000,
num_discard=100,
jit=True)
pops = pops.as_matrix()
mu = np.linspace(0.6, 1, 1000)
fig = plt.figure(figsize=(w, w / 3))
ax = fig.gca()
for i in xrange(mu.size):
ax.plot(mu[i] * np.ones_like(pops[:, i]),
pops[:, i],
',',
color='royalblue')
'''
# Compute population size after breeding season based on y
x = y * np.exp(rb - alpha_b * y )
# Compute population after breeding seasone
ynew = x * np.exp(rnb - alpha_nb * x )
# Output new population size
return ynew
# Simulate to get bifurcation points
bif_data_x = simulate(model=ricker_b, num_gens=100, rate_min=-0.5, rate_max=rbEmp, num_rates=1000, num_discard=100)
bif_data_y = simulate(model=ricker_nb, num_gens=100, rate_min=-0.5, rate_max=rbEmp, num_rates=1000, num_discard=100)
# Bifurcation plot
bifurcation_plot(bif_data_x, title='Non-breeding population size',
xmin=-0.5, xmax=rbEmp, ymin=0, ymax=500, save=False,
xlabel='Breeding growth rate (rb)')
bifurcation_plot(bif_data_y, title='Breeding population size',
xmin=-0.5, xmax=rbEmp, ymin=0, ymax=500, save=False,
xlabel='Breeding growth rate (rb)')
# Model parameters (from Betini et al. (2013))
kb = 224 # Carrying capacity for breeding period
# Logistic model
@jit(nopython=True)
def logistic(x, rb):
# Output new population size
return x * (1 + rb * (1 - x / kb))
# Simulate to get bifurcation points
bif_data = simulate(model=logistic,
num_gens=100,
rate_min=0.5,
rate_max=5,
num_rates=1000,
num_discard=100)
# Make plot of bifurcation
bifurcation_plot(bif_data,
title='Ricker Bifurcation Diagram',
xmin=0,
xmax=5,
ymin=0,
ymax=500,
save=False)
# Export bifurcation points
bif_data.to_csv('../data_export/bif_data.csv')
from pynamical import logistic_map, simulate, bifurcation_plot
pops = simulate(model=logistic_map,
num_gens=100,
rate_min=0,
rate_max=4,
num_rates=1000,
num_discard=100)
bifurcation_plot(pops)
pops = simulate(model=logistic_map,
num_gens=100,
rate_min=3.7,
rate_max=3.9,
num_rates=1000,
num_discard=100)
bifurcation_plot(pops, xmin=3.7, xmax=3.9)
from pynamical import cubic_map, phase_diagram_3d
pops = simulate(model=cubic_map,
num_gens=3000,
rate_min=3.5,
num_rates=30,
num_discard=100)
phase_diagram_3d(pops,
xmin=-1,
xmax=1,
ymin=-1,
ymax=1,
zmin=-1,
zmax=1,
'''
# Compute population size after non-breeding season based on x
y = x * (1 + rnb * (1 - x / knb))
# Compute population after breeding season
xnew = y * (1 + (rb - a * x) * (1 - y / kb))
# Output new population size
return xnew
# Simulate to get bifurcation points
bif_data_x = simulate(model=ricker_b,
num_gens=100,
rate_min=0.5,
rate_max=5,
num_rates=1000,
num_discard=100)
# Obtain population size after non-breeding season by direct map from X_{t+1} to Y_{t+1}
def x_to_y(x):
return x * np.exp(rnb * (1 - x / knb))
# Compute bifurcation points for y by mapping from x
bif_data_y = x_to_y(bif_data_x)
# Make plot of bifurcation
bifurcation_plot(bif_data_x,
title='Ricker Bifurcation Diagram',
@jit(cache=True, nopython=True) # pragma: no cover
def map2(x, r):
"""
Define the equation for the cubic map.
Arguments
---------
x: float
current population value at time t
rate: float
growth rate parameter values
Returns
-------
float
result of map at time t+1
"""
return r - x - np.exp(x)
x = simulate(model=map2,
num_gens=100,
initial_pop=1,
rate_min=0,
rate_max=5,
num_rates=2000,
num_discard=100)
bifurcation_plot(x, xmin=-10, xmax=10, ymin=-10, ymax=10)
