How to use the tellurium.plotArray function in tellurium

// Other declarations:
  const J0_VM1, J0_Keq1, J0_h, J4_V4, J4_KS4;
''')

# time vector
result = r.simulate (0, 20, 201, ['time'])

h_values = [r.J0_h + k for k in range(0,8)]
for h in h_values:
    r.reset()
    r.J0_h = h
    m = r.simulate(0, 20, 201, ['S1'])
    result = numpy.hstack([result, m])
    
te.plotArray(result, labels=['h={}'.format(int(h)) for h in h_values]);


# ### Compare simulations

# In[14]:

import tellurium as te

r = te.loada ('''
     v1: $Xo -> S1;  k1*Xo;
     v2: S1 -> $w;   k2*S1;

     //initialize.  Deterministic process.
     k1 = 1; k2 = 1; S1 = 20; Xo = 1;
''')

model = '''
  model pathway()
    S1 -> S2; k1*S1
    S2 -> S3; k2*S2
    S3 -> S4; k3*S3

    # Initialize values
    S1 = 5; S2 = 0; S3 = 0; S4 = 0;
    k1 = 0.1;  k2 = 0.55; k3 = 0.76
  end
'''

r = te.loada(model)
result = r.simulate(0, 20, 50)
te.plotArray(result)

import tellurium as te

r = te.loada('''
  model pathway()
    S1 -> S2; k1*S1
    S2 -> S3; k2*S2
    S3 -> S4; k3*S3

    # Initialize values
    S1 = 5; S2 = 0; S3 = 0; S4 = 0;
    k1 = 0.1;  k2 = 0.55; k3 = 0.76
  end
''')

result = r.simulate(0, 20, 51)
te.plotArray(result);

v1: $Xo -> S1;  k1*Xo;
     v2: S1 -> $w;   k2*S1;

     //initialize.  Deterministic process.
     k1 = 1; k2 = 1; S1 = 20; Xo = 1;
''')

m1 = r.simulate (0,20,100);

# Stochastic process
r.resetToOrigin()
r.setSeed(1234)
m2 = r.gillespie(0, 20, 100, ['time', 'S1'])

# plot all the results together
te.plotArray(m1, color="black", show=False)
te.plotArray(m2, color="blue");


# ### Sinus injection
# Example that show how to inject a sinusoidal into the model and use events to switch it off and on.

# In[15]:

import tellurium as te
import numpy

r = te.loada ('''
    # Inject sin wave into model    
    Xo := sin (time*0.5)*switch + 2;    
    
    # Model Definition

J1: $X0 -> S1; k1*X0;
    J2: S1 -> $X1; k2*S1;

    X0 = 1.0; S1 = 0.0; X1 = 0.0;
    k1 = 0.4; k2 = 2.3;
''')  
  
  
m = r.simulate (0, 4, 100, ["Time", "S1"])
for i in range (0,4):
    r.k1 = r.k1 + 0.1 
    r.reset()
    m = np.hstack([m, r.simulate(0, 4, 100, ['S1'])])

# use plotArray to plot merged data
te.plotArray(m);


# ### Merge multiple simulations
# Example of merging multiple simulations. In between simulations a parameter is changed.

# In[11]:

import tellurium as te
import numpy

r = te.loada ('''
    # Model Definition
    v1: $Xo -> S1;  k1*Xo;
    v2: S1 -> $w;   k2*S1;

    # Initialize constants

Xo = 0.09; X1 = 0.0;
S1 = 0.5; k1 = 3.2;
''')
print(r.selections)

initValue = 0.05
m = r.simulate (0, 4, 100, selections=["time", "S1"])

for i in range (0,12):
    r.reset()
    r['[S1]'] = initValue
    res = r.simulate (0, 4, 100, selections=["S1"])
    m = np.concatenate([m, res], axis=1)
    initValue += 1

te.plotArray(m, color="black", alpha=0.7, loc=None, 
             xlabel="time", ylabel="[S1]", title="Bistable system");


# ### Add plot elements

# In[5]:

import tellurium as te
import numpy
import matplotlib.pyplot as plt
import roadrunner

# Example showing how to embelise a graph, change title, axes labels.
# Example also uses an event to pulse S1

r = te.loada ('''

k1 = 0.1; k2 = 0.4; S1 = 10; S2 = 0;
   Km1 = 0.1; Km2 = 0.1;  
''')

r.conservedMoietyAnalysis = True

for i in range (1,8):
  numbers = np.linspace (0, 1.2, 200)
  result = np.empty ([0,2])
  for value in numbers:
      r.k1 = value
      r.steadyState()
      row = np.array ([value, r.S2])
      result = np.vstack ((result, row))
  te.plotArray(result, show=False, labels=['Km1={}'.format(r.Km1)],
               resetColorCycle=False,
               xlabel='k1', ylabel="S2", 
               title="Steady State S2 for different Km1 & Km2",
               ylim=[-0.1, 11], grid=True)
  r.k1 = 0.1
  r.Km1 = r.Km1 + 0.5;
  r.Km2 = r.Km2 + 0.5;