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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 It is assumed that this notebook is in the same folder as the python-files randomGraphsNumerical.py, transitionMatrix.py, transitionMatrixDirected.py, AmplifierQ.py and Plot.py. Also there has to be a subfolder called output. 
Generate random graphs and calculate fixation probability
Specify some parameters first.
Step1:  Now let's create the graphs, calculate the fixation probability and store it in one file per graph in the folder output.
Step2:  Classify graphs into amplifiers and suppressors
Step3:  Plotting
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Python Code:
popSize = 4 # This is the number of nodes in the network
update = 'BD' # Either 'BD' or 'DB' for Birth-death or death-Birth updating respectively
direction = 'undirected' # Either 'directed' or 'undirected' graphs are used
stepSize = 0.05 # Step size for the probability for each link in the network to be present independently. We used 0.05. 
numberOfGraphs = 500 # We used 500
Explanation: It is assumed that this notebook is in the same folder as the python-files randomGraphsNumerical.py, transitionMatrix.py, transitionMatrixDirected.py, AmplifierQ.py and Plot.py. Also there has to be a subfolder called output. 
Generate random graphs and calculate fixation probability
Specify some parameters first.
End of explanation
for probLinkConnect in np.arange(0.0,1.0+stepSize,stepSize):
    for graph in range(0, numberOfGraphs):
        !python randomGraphsNumerical.py $popSize $probLinkConnect $graph $update $direction
Explanation: Now let's create the graphs, calculate the fixation probability and store it in one file per graph in the folder output.
End of explanation
!python AmplifierQ.py $popSize $numberOfGraphs $stepSize $update $direction
Explanation: Classify graphs into amplifiers and suppressors
End of explanation
from Plot import *
Plot(popSize, numberOfGraphs, stepSize, update, direction)
Explanation: Plotting
End of explanation
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