In [1]:
from models import effort_model
In [2]:
results = effort_model.run_simulation(100, 1000)
In [3]:
results
Out[3]:
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In [4]:
import matplotlib.pyplot as plt
plt.figure()
plt.title("Locality and Effort")
plt.hold(True)
plt.plot(results['efforts'])
plt.plot(results['high_statuses'])
plt.legend(["Agents Making Effort", "Attaining High Status"], loc="best")
plt.xlabel("Period")
plt.ylabel("Count")
Out[4]:
<matplotlib.text.Text at 0x107344250>
In [30]:
figsize(6,4)
for i in xrange(10):
results = effort_model.run_simulation(100, 1000)
plt.figure()
plt.title("Effort and outcome")
plt.hold(True)
plt.plot(results['efforts'])
plt.plot(results['high_statuses'])
plt.legend(["Agents Making Effort", "Attaining High Status"], loc="best")
plt.xlabel("Period")
plt.ylabel("Count")
plt.ylim(0,100)
plt.savefig("effort-outcome-%d.pdf" % i)
In [ ]:
In [5]:
from models import local_dev_model
In [6]:
res = {}
for size in [10,20,30]:
res[size] = local_dev_model.run_simulation(size)
In [7]:
res
Out[7]:
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In [8]:
def plot_heatmap(res, n, title=""):
r = np.reshape(res, (n,n))
plt.figure()
plt.pcolor(r)
plt.colorbar()
#TODO make this plot square/save to files/maybe should combine two plus add title etc
In [31]:
figsize(11,4)
for size in [10,20,30]:
#plot_heatmap(res[size]['ave_effort'], size, "Average Effort")
plt.figure()
plt.subplot(1,2,1)
r = np.reshape(res[size]['ave_effort'], (size,size))
plt.title("Effort")
plt.pcolor(r)
plt.colorbar()
plt.subplot(1,2,2)
r = np.reshape(res[size]['ave_state'], (size,size))
plt.title("Status")
plt.pcolor(r)
plt.colorbar()
plt.savefig("local-%d.pdf" % size)
#plot_heatmap(res[size]['ave_state'], size, "Average Status")
In [33]:
figsize(6,4)
for size in [10,20,30]:
plt.figure()
#plt.title("Effort and outcome (with local learning)")
plt.hold(True)
plt.plot(res[size]['efforts'])
plt.plot(res[size]['high_statuses'])
plt.legend(["Agents Making Effort", "Attaining High Status"], loc="best")
plt.xlabel("Period")
plt.ylabel("Count")
ylim(0,size * size)
plt.savefig("aggregate-%d.pdf" % size)
In [11]:
from models import effort_model
import matplotlib.pyplot as plt
In [12]:
results = effort_model.run_simulation(n=100, periods=1000, beta=1.5, epsilon=0.1)
plt.figure()
plt.title("Locality and Effort")
plt.hold(True)
plt.plot(results['efforts'])
plt.plot(results['high_statuses'])
plt.legend(["Agents Making Effort", "Attaining High Status"], loc="best")
plt.xlabel("Period")
plt.ylabel("Count")
ylim(0,100)
Out[12]:
(0, 100)
In [12]:
In [12]:
Content source: jamesporter/endogenous-polarisation
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