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='')
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',
xmin=0,
xmax=5,
ymin=0,
ymax=500,
save=False)
bifurcation_plot(bif_data_y,
title='Ricker Bifurcation Diagram',
xmin=0,
xmax=5,
ymin=0,
ymax=500,
save=False)
# Export bifurcation points
bif_data_x.to_csv('../data_export/bif_data_x.csv')
bif_data_y.to_csv('../data_export/bif_data_y.csv')
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')
x - breeding population size
rb - growth rate during breeding season
Ouptut:
population size after the following breeding season.
'''
# Compute population size after non-breeding season based on x
xnew = x * np.exp(r - alpha * x)
# Output new population size
return xnew
# Simulate to get bifurcation points
bif_data_x = simulate(model=ricker,
num_gens=100,
rate_min=0,
rate_max=3.5,
num_rates=1000,
num_discard=100)
# Bifurcation plot
bifurcation_plot(bif_data_x,
title='Non-breeding population size',
xmin=0,
xmax=3.5,
ymin=0,
ymax=500,
save=False,
xlabel='Breeding growth rate (rb)')
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='')
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)
@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)
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,
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=4.0,
num_rates=200,
num_discard=100)
bifurcation_plot(pops, xmax=4.0, filename='logistic-map-bifurcation-6')
