Are inhibitory connectivity motifs entirely random?

We will count the number of inhibitory connectivity motifs (electrical and chemical) and test whether this number is expected assuming an average connectivity found experimentally.



In [1]:

    
# loading python modules
import numpy as np
np.random.seed(0)

from matplotlib.pyplot import figure
from terminaltables import AsciiTable 
import matplotlib.pyplot as plt
%matplotlib inline  

from __future__ import division



In [2]:

    
# loading custom inet modules
from inet import DataLoader, __version__
from inet.motifs import iicounter, motifcounter
from inet.utils import chem_squarematrix, elec_squarematrix
print('Inet version {}'.format(__version__))









    



Inet version 0.0.12



In [3]:

    
# use the dataset to create the null hypothesis
mydataset = DataLoader('../data/PV')









    



 190 syn  files loaded

Collect number of experiments containing PV(+) cells



In [4]:

    
# e.g. mydataset.PV[2].values  will return the different configurations with 2 PV cells
nPV = range(9)
for i in nPV:
    nPV[i] = np.sum(mydataset.IN[i].values())



In [5]:

    
for i, experiment in enumerate(nPV):
    print('{:<3d} recordings with {:2d} PV-cells'.format(experiment, i))









    



0   recordings with  0 PV-cells
151 recordings with  1 PV-cells
31  recordings with  2 PV-cells
8   recordings with  3 PV-cells
0   recordings with  4 PV-cells
0   recordings with  5 PV-cells
0   recordings with  6 PV-cells
0   recordings with  7 PV-cells
0   recordings with  8 PV-cells



In [5]:

    
# for the moment, we only count experiments with 2 or 3 PVs
# later we use mydataset.PV[2:]
PV2 = sum(mydataset.IN[2].values())
PV3 = sum(mydataset.IN[3].values())
PV2, PV3









    Out[5]:





(31, 8)

Calculate empirical probabilities



In [10]:

    
PC = mydataset.motif.ii_chem_found/mydataset.motif.ii_chem_tested
PE = mydataset.motif.ii_elec_found/mydataset.motif.ii_elec_tested

PC2  = mydataset.motif.ii_c2_found/mydataset.motif.ii_c2_tested
Pdiv = mydataset.motif.ii_div_found/mydataset.motif.ii_div_tested
Pcon = mydataset.motif.ii_con_found/mydataset.motif.ii_con_tested
Plin = mydataset.motif.ii_lin_found/mydataset.motif.ii_lin_tested

PC1E = mydataset.motif.ii_c1e_found/mydataset.motif.ii_c1e_tested
PC2E = mydataset.motif.ii_c2e_found/mydataset.motif.ii_c2e_tested



info = [
    ['key', 'Probability', 'Motif', 'Value'],
    ['ii_chem', 'P(C)', 'chemical synapse',PC ],
    ['ii_elec', 'P(E)', 'electrical synapse',PE ],
    ['','',''],
    ['ii_c2', 'P(C U C)','bidirectional chemical synapse',PC2],
    ['ii_con', 'Pcon', 'convergent inhibitory motifs', Pcon],
    ['ii_div', 'Pdiv', 'divergent inhibitory motifs', Pdiv],
    ['ii_lin', 'Plin', 'linear inhibitory motifs', Plin],
    ['',''],
    ['ii_c1e', 'P(C U E)', 'electrical and unidirectional chemical', PC1E],
    ['ii_c2e', 'P(2C U E):','electrical and bidirectional chemical', PC2E],

]
print(AsciiTable(info).table)









    



+---------+-------------+----------------------------------------+-----------------+
| key     | Probability | Motif                                  | Value           |
+---------+-------------+----------------------------------------+-----------------+
| ii_chem | P(C)        | chemical synapse                       | 0.281818181818  |
| ii_elec | P(E)        | electrical synapse                     | 0.436363636364  |
|         |             |                                        |                 |
| ii_c2   | P(C U C)    | bidirectional chemical synapse         | 0.163636363636  |
| ii_con  | Pcon        | convergent inhibitory motifs           | 0.0416666666667 |
| ii_div  | Pdiv        | divergent inhibitory motifs            | 0.0833333333333 |
| ii_lin  | Plin        | linear inhibitory motifs               | 0.0833333333333 |
|         |             |                                        |                 |
| ii_c1e  | P(C U E)    | electrical and unidirectional chemical | 0.163636363636  |
| ii_c2e  | P(2C U E):  | electrical and bidirectional chemical  | 0.109090909091  |
+---------+-------------+----------------------------------------+-----------------+

Simulate random chemical synapses

from a random distribution whose probability is adjusted to the empirical probability found in the recordings.



In [11]:

    
def mychem_simulation():
    """
    simulate inhibitory chemical connections of the dataset
    
    Return
    ------
    A iicounter object 
    """
    mycount = iicounter()
    for _ in range(PV2):
        mycount += iicounter(chem_squarematrix(size=2,prob = PC))

    for _ in range(PV3):
        mycount += iicounter(chem_squarematrix(size=3, prob = PC))
        
    return(mycount)



In [12]:

    
print(mychem_simulation()) # one simulation, test the number of connection tested









    



+---------+-------+--------+
| Motif   | found | tested |
+---------+-------+--------+
| ii_c1e  | 0     | 110    |
| ii_c2   | 6     | 55     |
| ii_c2e  | 0     | 55     |
| ii_chem | 34    | 110    |
| ii_con  | 3     | 24     |
| ii_div  | 0     | 24     |
| ii_elec | 0     | 55     |
| ii_lin  | 7     | 24     |
+---------+-------+--------+



In [10]:

    
# must contain the same number of tested connections
for key in mychem_simulation().keys():
    print(key, mydataset.motif[key])









    



('ii_chem', {'tested': 110L, 'found': 31})
('ii_c1e', {'tested': 110L, 'found': 18})
('ii_div', {'tested': 24L, 'found': 2})
('ii_c2e', {'tested': 55L, 'found': 6})
('ii_elec', {'tested': 55L, 'found': 24})
('ii_chain', {'tested': 24L, 'found': 2})
('ii_con', {'tested': 24L, 'found': 1})
('ii_c2', {'tested': 55L, 'found': 9})



In [14]:

    
# simulate the whole data set 1,000 times
n_chem = list()

n_bichem = list()
n_div = list()
n_con = list()
n_chain = list()

for _ in range(1000):
    syn_counter = mychem_simulation()
    n_chem.append(syn_counter['ii_chem']['found']) # null hypothesis
    
    n_bichem.append(syn_counter['ii_c2']['found'])    
    n_con.append(syn_counter['ii_con']['found'])
    n_div.append(syn_counter['ii_div']['found'])
    n_chain.append(syn_counter['ii_lin']['found'])

If the null hypothesis is correctly implemented, we should see almost the same number of chemical synases as in the experiments.



In [15]:

    
np.mean(n_chem) # on average the same number of unidirectional connections









    Out[15]:





31.053000000000001



In [16]:

    
mydataset.motif['ii_chem']['found']









    Out[16]:





31

If we found a number which is different form the empirical, we must revise our null hypothese.



In [17]:

    
np.mean(n_bichem) # on average the same number of bidirectional connections????









    Out[17]:





4.3490000000000002

Define analiticaly the null hypothese:



In [18]:

    
PC*PC*mydataset.motif['ii_c2']['tested'] # null hypothesis









    Out[18]:





4.368181818181818



In [19]:

    
mydataset.motif['ii_c2']['found'] # however, we found more empirically









    Out[19]:





9



In [20]:

    
# Number of divergent connections found should be very similar to the ones calculates
np.mean(n_div)









    Out[20]:





1.9419999999999999



In [21]:

    
PC*PC*mydataset.motif['ii_div']['tested'] # null hypothesis









    Out[21]:





1.9061157024793387



In [22]:

    
np.mean(n_con)









    Out[22]:





1.893



In [23]:

    
PC*PC*mydataset.motif['ii_con']['tested'] # null hypothesis









    Out[23]:





1.9061157024793387

Simulate random electrical synapses

from a random distribution whose probability is adjusted to the empirical probability found in the recordings.



In [24]:

    
def myelec_simulation():
    """
    simulate inhibitory electrical connections of the dataset
    
    Return
    ------
    A iicounter object 
    """
    mycount = iicounter()
    for _ in range(PV2):
        mycount += iicounter(elec_squarematrix(size=2,prob = PE))

    for _ in range(PV3):
        mycount += iicounter(elec_squarematrix(size=3, prob = PE))
        
    return(mycount)



In [25]:

    
print(myelec_simulation()) # one simulation, test the number of connection tested









    



+---------+-------+--------+
| Motif   | found | tested |
+---------+-------+--------+
| ii_c1e  | 0     | 110    |
| ii_c2   | 0     | 55     |
| ii_c2e  | 0     | 55     |
| ii_chem | 0     | 110    |
| ii_con  | 0     | 24     |
| ii_div  | 0     | 24     |
| ii_elec | 26    | 55     |
| ii_lin  | 0     | 24     |
+---------+-------+--------+



In [26]:

    
# must contain the same number of tested connections
for key in myelec_simulation().keys():
    print(key, mydataset.motif[key])









    



('ii_chem', {'tested': 110, 'found': 31})
('ii_c1e', {'tested': 110, 'found': 18})
('ii_lin', {'tested': 24, 'found': 2})
('ii_div', {'tested': 24, 'found': 2})
('ii_c2e', {'tested': 55, 'found': 6})
('ii_elec', {'tested': 55, 'found': 24})
('ii_con', {'tested': 24, 'found': 1})
('ii_c2', {'tested': 55, 'found': 9})



In [27]:

    
n_elec = list()
for _ in range(1000):
    syn_elec = myelec_simulation()
    n_elec.append(syn_elec['ii_elec']['found'])

Similarly, we must see almost the same number of electrical connections as with the experiments



In [28]:

    
np.mean(n_elec)









    Out[28]:





23.975000000000001



In [29]:

    
mydataset.motif.ii_elec_found # voila!









    Out[29]:





24

Simulate electrical and chemical synapses independently



In [30]:

    
C = chem_squarematrix(size = 2, prob = PC)
E = elec_squarematrix(size = 2, prob = PE)
C + E # when a chemical (1) and electrical (2) synapse add , they have the motif 3









    Out[30]:





array([[0, 0],
       [0, 0]])



In [31]:

    
def myii_simulation():
    """
    simulate inhibitory electrical and chemical connections of the dataset
    
    Return
    ------
    A iicounter object 
    """
    mycount = iicounter()
    for _ in range(PV2):
        C = chem_squarematrix(size = 2, prob = PC)
        E = elec_squarematrix(size = 2, prob = PE)
        
        S = C + E
        x, y = np.where(S==2) # test to eliminate '1' from the oposite direction
        mycoor = zip(y,x)
        for i,j in mycoor:
            if S[i,j]==1:
                S[i,j]=3
                S[j,i]=0
                
        mycount += iicounter( S ) 

    for _ in range(PV3):
        C = chem_squarematrix(size = 3, prob = PC)
        E = elec_squarematrix(size = 3, prob = PE)

        S = C + E
        x, y = np.where(S==2) # test to eliminate '1' from the oposite direction
        mycoor = zip(y,x)
        for i,j in mycoor:
            if S[i,j]==1:
                S[i,j]=3
                S[j,i]=0
                
        mycount += iicounter( S ) 

    return(mycount)



In [32]:

    
myii_simulation()# one simulation, again, test the number of connections tested









    Out[32]:





{'ii_c1e': {'found': 11, 'tested': 110},
 'ii_c2': {'found': 4, 'tested': 55},
 'ii_c2e': {'found': 1, 'tested': 55},
 'ii_chem': {'found': 27, 'tested': 110},
 'ii_con': {'found': 2, 'tested': 24},
 'ii_div': {'found': 2, 'tested': 24},
 'ii_elec': {'found': 23, 'tested': 55},
 'ii_lin': {'found': 2, 'tested': 24}}



In [33]:

    
# must contain the same number of tested connections
for key in myii_simulation().keys():
    print(key, mydataset.motif[key])









    



('ii_chem', {'tested': 110, 'found': 31})
('ii_c1e', {'tested': 110, 'found': 18})
('ii_lin', {'tested': 24, 'found': 2})
('ii_div', {'tested': 24, 'found': 2})
('ii_c2e', {'tested': 55, 'found': 6})
('ii_elec', {'tested': 55, 'found': 24})
('ii_con', {'tested': 24, 'found': 1})
('ii_c2', {'tested': 55, 'found': 9})



In [35]:

    
# simulate the whole data set 1,000 times
n_chem = list()
n_elec = list()

n_c1e = list()
n_c2e = list()

n_c2 = list()
n_div = list()
n_con = list()
n_lin = list()

for _ in range(10000):
    syn_counter = myii_simulation()
    n_chem.append( syn_counter['ii_chem']['found'] ) # null hypothesis
    n_elec.append( syn_counter['ii_elec']['found'] ) # null hypothesis
    
    n_c1e.append( syn_counter['ii_c1e']['found'] )
    n_c2e.append( syn_counter['ii_c2e']['found'] )
    
    n_c2.append( syn_counter['ii_c2']['found'])
    n_con.append( syn_counter['ii_div']['found'])
    n_div.append( syn_counter['ii_div']['found'])
    n_lin.append( syn_counter['ii_lin']['found'])



In [37]:

    
info = [
    ['Syn Motif', 'Simulations', 'Empirical'], 
    ['chemical', np.mean(n_chem),  mydataset.motif['ii_chem']['found']],
    ['electrical', np.mean(n_elec),  mydataset.motif['ii_elec']['found']],
    [''],
    ['2 chem',np.mean(n_c2),mydataset.motif['ii_c2']['found']],
    ['convergent', np.mean(n_con), mydataset.motif['ii_con']['found']],
    ['divergent', np.mean(n_div), mydataset.motif['ii_div']['found']],
    ['chains', np.mean(n_chain), mydataset.motif['ii_lin']['found']],
    [''],
    ['1 chem + elec', np.mean(n_c1e),  mydataset.motif['ii_c1e']['found']],
    ['2 chem + elec', np.mean(n_c2e),  mydataset.motif['ii_c2e']['found']],
     ]
print(AsciiTable(info).table)









    



+---------------+-------------+-----------+
| Syn Motif     | Simulations | Empirical |
+---------------+-------------+-----------+
| chemical      | 30.9276     | 31        |
| electrical    | 23.9706     | 24        |
|               |             |           |
| 2 chem        | 4.3577      | 9         |
| convergent    | 1.8938      | 1         |
| divergent     | 1.8938      | 2         |
| chains        | 3.8087      | 2         |
|               |             |           |
| 1 chem + elec | 13.5081     | 18        |
| 2 chem + elec | 1.9215      | 6         |
+---------------+-------------+-----------+

Let's see if the connections found correspond to the theoretical values for the complex motifs.

A) Unidirectional chemical connections in the presence of an electrical synapse ii_c1e



In [38]:

    
mydataset.motif['ii_c1e']









    Out[38]:





{'found': 18, 'tested': 110}



In [39]:

    
PCE1 = mydataset.motif.ii_c1e_found /mydataset.motif.ii_c1e_tested
PCE1









    Out[39]:





0.16363636363636364



In [40]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PC*PE)*mydataset.motif.ii_c1e_tested









    Out[40]:





13.527272727272726

B) Bidirectional chemical connections in the presence of one electrical synapse ii_c2e



In [41]:

    
mydataset.motif['ii_c2e']









    Out[41]:





{'found': 6, 'tested': 55}



In [42]:

    
PCE2 = mydataset.motif.ii_c2e_found /mydataset.motif.ii_c2e_tested
PCE2









    Out[42]:





0.10909090909090909



In [43]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PE*PC*PC)*mydataset.motif.ii_c2e_tested #









    Out[43]:





1.9061157024793383

C) Only electrical synapses ii_elec



In [44]:

    
mydataset.motif['ii_elec']









    Out[44]:





{'found': 24, 'tested': 55}



In [45]:

    
PE = mydataset.motif.ii_elec_found /mydataset.motif.ii_elec_tested
PE









    Out[45]:





0.43636363636363634



In [46]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PE)*mydataset.motif.ii_elec_tested #









    Out[46]:





24.0

D) Unidirectionaly chemmical only ii_chem



In [47]:

    
mydataset.motif['ii_chem']









    Out[47]:





{'found': 31, 'tested': 110}



In [48]:

    
PC = mydataset.motif.ii_chem_found /mydataset.motif.ii_chem_tested
PC









    Out[48]:





0.2818181818181818



In [49]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PC)*mydataset.motif.ii_chem_tested #









    Out[49]:





30.999999999999996

E) Bidirectional chemical connections only ii_c2



In [50]:

    
mydataset.motif['ii_c2']









    Out[50]:





{'found': 9, 'tested': 55}



In [51]:

    
PC1 = mydataset.motif.ii_chem_found /mydataset.motif.ii_chem_tested
PC1









    Out[51]:





0.2818181818181818



In [52]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PC1*PC1)*mydataset.motif.ii_c2_tested









    Out[52]:





4.368181818181818



In [53]:

    
# calculate alfa_levels according to Zhao et al., 2001
alpha_rec = (PC1/(PC*PC))-1
print(alpha_rec)









    



2.54838709677

F) Divergent inhibitory connections ii_div



In [54]:

    
mydataset.motif['ii_div']









    Out[54]:





{'found': 2, 'tested': 24}



In [55]:

    
Pdiv = mydataset.motif.ii_div_found /mydataset.motif.ii_div_tested
Pdiv









    Out[55]:





0.08333333333333333



In [56]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PC*PC)*mydataset.motif.ii_div_tested









    Out[56]:





1.9061157024793387



In [57]:

    
# calculate alfa_levels according to Zhao et al., 2001
alpha_div = (Pdiv/(PC*PC))-1
print(alpha_div)









    



0.0492542490461

G) Convergent inhibitory connections ii_con



In [58]:

    
mydataset.motif['ii_con']









    Out[58]:





{'found': 1, 'tested': 24}



In [59]:

    
Pcon = mydataset.motif.ii_con_found / mydataset.motif.ii_con_tested
Pcon









    Out[59]:





0.041666666666666664



In [60]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PC*PC)*mydataset.motif.ii_con_tested









    Out[60]:





1.9061157024793387



In [61]:

    
# calculate alfa_levels according to Zhao et al., 2001
alpha_con = (Pcon/(PC*PC))-1
print(alpha_con)









    



-0.475372875477

H) Chain inhibitory connection ii_chain



In [63]:

    
mydataset.motif['ii_lin']









    Out[63]:





{'found': 2, 'tested': 24}



In [65]:

    
Pchain = mydataset.motif.ii_lin_found / mydataset.motif.ii_lin_tested
Pchain









    Out[65]:





0.08333333333333333



In [66]:

    
# definition of the null hypothese
# if this value is close to the simulation, we accept the null hypothese
(PC*PC)*mydataset.motif.ii_lin_tested









    Out[66]:





1.9061157024793387



In [67]:

    
# calculate alfa_levels according to Zhao et al., 2001
alpha_chain = (Pchain/(PC*PC))-1
print(alpha_chain)









    



0.0492542490461

Calculating P-Values



In [68]:

    
# operate with NumPY arrays rather than with lists
n_chem = np.array(n_chem)
n_elec = np.array(n_elec)


n_c2 = np.array(n_c2)
n_con = np.array(n_con)
n_div = np.array(n_div)
n_chain = np.array(n_chain)

n_c1e = np.array(n_c1e)
n_c2e = np.array(n_c2e)



In [71]:

    
pii_chem = len(n_chem[n_chem>mydataset.motif.ii_chem_found]) / n_chem.size
pii_elec = len(n_elec[n_elec>mydataset.motif.ii_elec_found])/ n_elec.size



pii_c2 =  len(n_c2[n_c2 > mydataset.motif.ii_c2_found])/ n_c2.size
pii_con = len(n_con[n_con < mydataset.motif.ii_con_found])/n_con.size # under-rep
pii_div = len(n_div[n_div > mydataset.motif.ii_div_found])/n_div.size
pii_chain = len(n_chain[n_chain < mydataset.motif.ii_lin_found])/n_chain.size # under-rep

pii_c1e = len(n_c1e[n_c1e > mydataset.motif.ii_c1e_found])/ n_c1e.size
pii_c2e = len(n_c2e[n_c2e > mydataset.motif.ii_c2e_found])/ n_c2e.size



In [73]:

    
info = [
    ['Syn Motif', 'Simulations', 'Empirical', 'P(Simulations)', 'alpha'], 
    ['chemical', np.mean(n_chem),  mydataset.motif.ii_chem_found, pii_chem],
    ['electrical', np.mean(n_elec),  mydataset.motif.ii_elec_found, pii_elec],
    [''],
    ['2 chem bidirect', np.mean(n_c2),  mydataset.motif.ii_c2_found, pii_c2,alpha_rec],
    ['convergent', np.mean(n_con),  mydataset.motif.ii_con_found, pii_con, alpha_con],    
    ['divergent', np.mean(n_div), mydataset.motif.ii_div_found, pii_div, alpha_div],
    ['chain', np.mean(n_chain), mydataset.motif.ii_lin_found, pii_chain, alpha_chain],    
    [''],
    ['1 chem + elec', np.mean(n_c1e),  mydataset.motif.ii_c1e_found, pii_c1e],
    ['2 chem + elec', np.mean(n_c2e),  mydataset.motif.ii_c2e_found, pii_c2e],
     ]
print(AsciiTable(info).table)









    



+-----------------+-------------+-----------+----------------+-----------------+
| Syn Motif       | Simulations | Empirical | P(Simulations) | alpha           |
+-----------------+-------------+-----------+----------------+-----------------+
| chemical        | 30.9276     | 31        | 0.4439         |                 |
| electrical      | 23.9706     | 24        | 0.4385         |                 |
|                 |             |           |                |                 |
| 2 chem bidirect | 4.3577      | 9         | 0.0098         | 2.54838709677   |
| convergent      | 1.8938      | 1         | 0.1405         | -0.475372875477 |
| divergent       | 1.8938      | 2         | 0.2962         | 0.0492542490461 |
| chain           | 3.8087      | 2         | 0.1398         | 0.0492542490461 |
|                 |             |           |                |                 |
| 1 chem + elec   | 13.5081     | 18        | 0.0924         |                 |
| 2 chem + elec   | 1.9215      | 6         | 0.0026         |                 |
+-----------------+-------------+-----------+----------------+-----------------+



In [74]:

    
from inet.plots import barplot

Plot chemical synapses alone



In [77]:

    
# This is our null hypothesis
fig = figure()
ax = fig.add_subplot(111)

ax = barplot(simulation = n_chem, n_found = mydataset.motif.ii_chem_found, larger=1);
ax.set_title('Chemical synapses', size=20);
ax.set_ylim(ymax=40);
ax.tick_params(labelsize=20)

fig.savefig('ii_chem.pdf')

Plot electrical synapses alone



In [78]:

    
fig = figure()
ax = fig.add_subplot(111)

ax = barplot(simulation = n_elec, n_found = mydataset.motif.ii_elec_found, larger=1);
ax.set_title('Electrical synapses',  size=20);
ax.set_ylim(ymax=40);
ax.tick_params(labelsize=20)

fig.savefig('ii_elec.pdf')

Plot bidirectional chemical synapses



In [79]:

    
fig = figure()
ax = fig.add_subplot(111)

ax = barplot(simulation = n_c2, n_found = mydataset.motif.ii_c2_found, larger=1);
ax.set_title('Bidirectional chemical',  size=20);
ax.set_ylim(ymax=10);
ax.tick_params(labelsize=20)

fig.savefig('ii_c2.pdf')

Plot convergent inhibitory



In [80]:

    
fig = figure()
ax = fig.add_subplot(111)

ax = barplot(simulation = n_con, n_found = mydataset.motif.ii_con_found, larger=False);
ax.set_title('Convergent inhibitory',  size=20);
ax.set_ylim(ymin=0, ymax=4);
ax.tick_params(labelsize=20)

fig.savefig('ii_con.pdf')

Plot divergent inhibitory



In [81]:

    
fig = figure()
ax = fig.add_subplot(111)

ax = barplot(simulation = n_div, n_found = mydataset.motif.ii_div_found, larger=1);
ax.set_title('Divergent inhibitory',  size=20);
ax.set_ylim(ymin=0, ymax=5);
ax.tick_params(labelsize=20)

fig.savefig('ii_div.pdf')

Plot linear chains



In [83]:

    
fig = figure()
ax = fig.add_subplot(111)

ax = barplot(simulation = n_chain, n_found = mydataset.motif.ii_lin_found, larger=False);
ax.set_title('Linear chains',  size=20);
ax.set_ylim(ymin=0, ymax=10);
ax.tick_params(labelsize=20)

fig.savefig('ii_chain.pdf')

#pii_chain # change this value in the plot!

Plot electrical and one chemical synapse alone



In [84]:

    
fig = figure() 
ax = fig.add_subplot(111)

ax = barplot(simulation = n_c1e, n_found = mydataset.motif.ii_c1e_found, larger=1);
ax.set_title('Electrical and one chemical',  size=20);
ax.set_ylim(ymax=25);
ax.tick_params(labelsize=20)

fig.savefig('ii_c1e.pdf')

Plot electrical and two chemical



In [85]:

    
fig = figure(5)
ax = fig.add_subplot(111)

ax = barplot(simulation = n_c2e, n_found = mydataset.motif.ii_c2e_found, larger=1);
ax.set_title('Electrical and two chemical',  size=20);
ax.set_ylim(ymin  = 0, ymax=10);
ax.tick_params(labelsize=20)

fig.savefig('ii_c2d.pdf')



In [ ]: