In [1]:

    

import sklearn
import json
import operator
import matplotlib.pyplot as plt
import prettyplotlib as ppl
import seaborn as sns
sns.set(style='ticks', palette='Set1')
%matplotlib inline
from scipy.interpolate import interp1d
hist1 = sklearn.externals.joblib.load('agent1_indhistex')
hist2 = sklearn.externals.joblib.load('agent2_indhistex')


    

    




/usr/local/lib/python3.4/dist-packages/matplotlib/backends/backend_gtk3agg.py:18: UserWarning: The Gtk3Agg backend is known to not work on Python 3.x with pycairo. Try installing cairocffi.
  "The Gtk3Agg backend is known to not work on Python 3.x with pycairo. "

In [2]:

    

def loader(dictionary):
    def load_keys(k):
        unjsoned = json.loads(k)
        loaded = tuple( ((tuple(unjsoned[0])), unjsoned[1] ))
        return loaded
    return {load_keys(k):v for k,v in dictionary.items()}
def get_score(hist):
    loaded = {k:len(json.loads(v)) for k,v in loader(hist).items()}
    not_running = sum([v for k,v in loaded.items() if k[0][1] == 'sleeping' and k[0][3]=='sleeping'])
    zero = sum([v for k,v in loaded.items() if k[0][0] == 0])
    total = sum([v for k,v in loaded.items()])
    return not_running, zero, total, not_running/total, zero/total
print(get_score(hist1))
print(get_score(hist2))


    

    




(1532, 88, 6839, 0.22400935809328848, 0.012867378271677147)
(886, 90, 6839, 0.12955110396256764, 0.013159818686942536)

In [3]:

    

explore = sklearn.externals.joblib.load('ind_writer')
record = sklearn.externals.joblib.load('ind_exwriter1')

one_explore = [(i[0], i[1]['battery']) for i in explore if i[1]['_id']==0]
one_exploit = [(i[0], i[1]['battery']) for i in record if i[1]['_id']==0]
print(len(one_explore))


    

    




2689

In [23]:

    

fig,ax = plt.subplots(1)
#one_plot = one[1083:1120]
#two_plot = two[1083:1120]
one_plot = one_explore[0:600]
two_plot = one_exploit[0:200]
two_plot[75] = (two_plot[78][0], two_plot[76][1]+1)
#two_plot[75] = (two_plot[89][0], two_plot[89][1]-1)
x1 = [e[0]+0.3*i for i,e in enumerate(one_plot)]
x2 = [x1[-1]+i-x1[0] for i in x1[0:200]]
#y1 =  [e[0]+0.3*i for i,e in enumerate(two_plot)]
ppl.plot(ax, x1,[i[1] for i in one_plot], label = 'Sensor 1', linewidth = 1.25, color = '#F05F40'  )
ppl.plot(ax, x2,[i[1] for i in two_plot], label = 'Sensor 2', linewidth = 1.25, color = '#F05F40' )
ax.set_ylim([0,5])
ax.set_xlim([x1[0],x2[-1]])
ax.set_ylabel('Battery')
ax.set_xlabel('t')
ax.set_xticks([])
ax.set_yticks(range(6))
#legend = plt.legend()
ax.spines['bottom'].set_visible(False)
fig.savefig('exploring', format = 'svg')


    

    




/usr/local/lib/python3.4/dist-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))






    

In [5]:

    

agent1 = sklearn.externals.joblib.load('agent1_battex')
loader(agent1)


    

    
Out[5]:





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 ((4, 'sleeping', 5, 'running'), 'wakeup'): 90,
 ((5, 'running', 0, 'pending'), 'noop'): 100,
 ((5, 'running', 0, 'sleeping'), 'noop'): 122.2659316,
 ((5, 'running', 0, 'sleeping'), 'prep_sleep'): 95.72572887717426,
 ((5, 'running', 1, 'pending'), 'noop'): 429.4040332724562,
 ((5, 'running', 1, 'pending'), 'prep_sleep'): -10,
 ((5, 'running', 1, 'running'), 'noop'): 252.04662039365488,
 ((5, 'running', 1, 'sleeping'), 'noop'): 100,
 ((5, 'running', 2, 'running'), 'prep_sleep'): 90,
 ((5, 'running', 2, 'sleeping'), 'noop'): 316.37359064063105,
 ((5, 'running', 2, 'sleeping'), 'prep_sleep'): 95.003048,
 ((5, 'running', 3, 'running'), 'prep_sleep'): 90,
 ((5, 'running', 4, 'pending'), 'noop'): 100,
 ((5, 'running', 4, 'running'), 'prep_sleep'): 107.38836,
 ((5, 'running', 4, 'sleeping'), 'noop'): 100,
 ((5, 'running', 5, 'running'), 'wakeup'): 117.82,
 ((5, 'sleeping', 0, 'pending'), 'noop'): 0,
 ((5, 'sleeping', 0, 'pending'), 'wakeup'): 118.45285775656981,
 ((5, 'sleeping', 0, 'sleeping'), 'wakeup'): 90,
 ((5, 'sleeping', 1, 'pending'), 'noop'): 0,
 ((5, 'sleeping', 1, 'running'), 'noop'): 34.89073705244161,
 ((5, 'sleeping', 1, 'sleeping'), 'noop'): 100,
 ((5, 'sleeping', 2, 'pending'), 'wakeup'): 118.11744151793545,
 ((5, 'sleeping', 2, 'running'), 'wakeup'): 133.6952347640446,
 ((5, 'sleeping', 2, 'sleeping'), 'noop'): 100,
 ((5, 'sleeping', 2, 'sleeping'), 'wakeup'): 90,
 ((5, 'sleeping', 3, 'running'), 'noop'): 33.82,
 ((5, 'sleeping', 4, 'pending'), 'wakeup'): 90,
 ((5, 'sleeping', 5, 'running'), 'noop'): 0}

In [6]:

    

len(record)


    

    
Out[6]:





22902

In [7]:

    

len([i for i in record if i[1]['status']=='sleeping' and i[1]['nstat']=='sleeping'])


    

    
Out[7]:





2291

In [8]:

    

def time_sleeping(record):
    sleeper_indices = (ind for ind, el in enumerate(record) if
                       el[1]['status']=='sleeping' and el[1]['nstat']=='sleeping')
    times = (record[i+1][0]-record[i][0] for i in sleeper_indices)
    return sum(times)
def total_time(record):
    return record[-1][0]-record[0][0]
time_sleeping(record[0:480])/total_time(record[0:480])


    

    
Out[8]:





0.14198704536942627

In [9]:

    

two_plot


    

    
Out[9]:





[(0.7230737819991191, 4),
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 (0.7276136350046727, 2),
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 (2.559366414003307, 2),
 (2.57576364200213, 1),
 (2.580150854002568, 1)]

In [10]:

    

record


    

    
Out[10]:





[(0.7230737819991191,
  {'_id': 0,
   'battery': 4,
   'neighbour': 5,
   'nstat': 'running',
   'status': 'running'}),
 (0.7243523810029728,
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 (0.7262297440029215,
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 (0.7267493440012913,
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 (0.7276136350046727,
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 (0.7412905590026639,
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 (0.742179346998455,
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 (0.7585965960024623,
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 (0.7591534800012596,
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 (0.7599900860004709,
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 (0.7604812040008255,
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 (0.7612001790039358,
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 (0.7616623520007124,
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 (0.792406986001879,
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 (0.8644988830055809,
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 (0.8890420169991557,
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 (0.9122298950023833,
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 (0.9130367300022044,
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 (0.913501962000737,
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 (0.9209922560039558,
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 (0.9219368460035184,
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 (0.922849798000243,
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 (0.923298503003025,
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 (0.9413277960047708,
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 (0.9526247780013364,
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 (0.95332707700436,
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 (0.9537894189998042,
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 (0.9769054510034039,
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 (0.9780395109992241,
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 (0.9789126940013375,
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 (0.9793842370054335,
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 (0.9801892020041123,
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 (0.9806547260013758,
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 (1.005667411001923,
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 (1.008530505001545,
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 (1.009595141003956,
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In [11]:

    

1804/25256


    

    
Out[11]:





0.07142857142857142

In [12]:

    

x1


    

    
Out[12]:





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In [13]:

    

x2


    

    
Out[13]:





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In [14]:

    

x1[-1]+x1[0]


    

    
Out[14]:





121.62126903600002

In [ ]: