Comments (12)
Are you talking about the algorithms in the pyts.image module? For all of them it does not make much sense to have different width and height, since the idea overall is to get some information between time points: f(x_i, x_j)
.
from pyts.
from pyts.
If you give a float between 0 and 1, the size of the image will be proportional to the length of your time series.
So if you set image_size=p
, the size of the image will be (ceil(p * length), ceil(p * length))
.
With p=0.5
for instance, it would give:
ceil(0.5 * 1225) = 613
ceil(0.5 * 1248) = 624
from pyts.
from pyts.
I don't understand why you want different values as it does not make sense.
If you really want different width and height, just set image_size
to the larger value (39), you will get an image with shape (39, 39) and you can crop / downsample this image so that its new shape is (32, 39).
from pyts.
from pyts.
from pyts.
The thing is that there is no relationship of this kind. If you want to transform your 1D-time series into an 2D-array without performing in any modification of the values, you can use numpy.reshape
. But it is totally different from what the algorithms in the pyts.image
module do.
from pyts.
Is it not that, this entire module is to convert a time series to a matrix (image esentially)?
from pyts.
Yes, and the image will always be a square matrix.
from pyts.
from pyts.
No.
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