How to make time from len of array…
Picture 1:
Picture 2:
How to make the picture 1 as picture 2
Picture 1 come from below:
`
x=[]
colors=['red','green','brown','teal','gray','black','maroon','orange','purple']
colors2=['green','red','orange','black','maroon','teal','blue','gray','brown']
for i in range(0,1950):
x.append(i)
for i in range(0,1):
plt.figure(figsize=(15,6))
# plt.figure()
plt.plot(x,out[0:1950,i],color=colors[i])
plt.plot(x,predictions[0:1950,i],markerfacecolor='none',color=colors2[i])
plt.title('LSTM Regression (Training Data)')
plt.ylabel('Force/Fz (N)')
plt.xlabel('Time/s')
plt.legend(['Real value', 'Predicted Value'], loc='upper right')
plt.savefig('Regression Result.png'[i])
plt.show()
`
>Solution :
@ahrensaj’s answer makes sense.
Here is your code modified to rescale the time scale.
x=[]
colors=['red','green','brown','teal','gray','black','maroon','orange','purple']
colors2=['green','red','orange','black','maroon','teal','blue','gray','brown']
# just update the x with correct scaling; use numpy array for faster computations
x = numpy.arange(0,1950)*40/1950
for i in range(0,1):
plt.figure(figsize=(15,6))
# plt.figure()
plt.plot(x,out[0:1950,i],color=colors[i])
plt.plot(x,predictions[0:1950,i],markerfacecolor='none',color=colors2[i])
plt.title('LSTM Regression (Training Data)')
plt.ylabel('Force/Fz (N)')
plt.xlabel('Time/s')
plt.legend(['Real value', 'Predicted Value'], loc='upper right')
plt.savefig('Regression Result.png'[i])
plt.show()
EDIT:
@minn’s comment made me curious about np.linspace vs np.arange. I often use np.arange and divide appropriately because it is one less command to remember. In any case, I benchmarked the two. np.arange seems to do better.
In [8]: %time [np.linspace(0,40,1950) for _ in range(10)]
CPU times: user 1.81 ms, sys: 146 µs, total: 1.96 ms
Wall time: 1.6 ms
In [9]: %time [np.arange(0,1950)/40 for _ in range(10)]
CPU times: user 794 µs, sys: 55 µs, total: 849 µs
Wall time: 1.09 ms