In [1]:

    

import numpy as np
import pandas 
import matplotlib.pyplot as plt
%matplotlib inline
%cd data/
flow = pandas.read_table("flow-myosin-um-per-min.txt",header=None)


    

    




/home/guilhem/projects/polarity/polarikss/data

In [2]:

    

def interpolate(data,inserted_rows):
    """ Linearly Interpolate the matrix by adding rows
    
    Args:
        data (np.array): A timexSpace array
        new_number_row (int): Number of rows to insert between  
            each source row
        
    Returns:
        (np.array): with new_number_row lines
    
    """
    
    out = np.zeros((data.shape[0]*(inserted_rows),data.shape[1]))
    j = 0
    for i in range(out.shape[0]):
        k = i%inserted_rows
        if k==0:
            out[i,:] = data[j,:]
            if j+2 < data.shape[0]:
                j += 1
        else: 
            out[i,:] =  (data[j,:] 
                     +  float(k)/inserted_rows
                    * (data[j+1,:]- data[j,:]))
    return out


    

In [8]:

    

flow10 = interpolate(flow.values,40)

pandas.DataFrame(flow10).to_csv("flow-myosin-um-per-minb",index=None)
print "Wrote a {} matrix.".format(flow10.shape)
plt.subplot(1,2,1)
plt.imshow(flow.values,aspect="auto",interpolation="nearest")
plt.subplot(1,2,2)
plt.imshow(flow10,aspect="auto",interpolation="nearest")


    

    




Wrote a (3040, 65) matrix.






    
Out[8]:





<matplotlib.image.AxesImage at 0x6e33290>






    

In [6]:

    

flow10 = interpolate(flow.values,40)
flow10 = np.transpose(interpolate(np.transpose(flow10),40))

pandas.DataFrame(flow10).to_csv("flow-myosin-um-per-min_3040x2600linear-itp",index=None)
print "Wrote a {} matrix.".format(flow10.shape)
plt.subplot(1,2,1)
plt.imshow(flow.values,aspect="auto",interpolation="nearest")
plt.subplot(1,2,2)
plt.imshow(flow10,aspect="auto",interpolation="nearest")


    

    




Wrote a (3040, 2600) matrix.






    
Out[6]:





<matplotlib.image.AxesImage at 0x7f1e30251c90>






    

In [ ]: