Hello!

I’m using NASA’s Ocean color data, chlorophyll to be precise, and a single day of data looks like this:

Basically, each pair of (lat,lon) has a single float value of chlor_a. There’s a whole bunch of daily data to be used.

How would I go about feeding this type of .nc data directly into a DCGAN, without reverting to using actual images (.png and .jpeg.)?

Thanks!

You could try to create an “image-like” input tensor in the shape `[batch_size, channels, height, width]`

, where the `channels`

could be set to 1, since you are dealing with a single float for each pixel location, and the `height`

and `width`

could be set to the `lat`

and `lon`

.

Thanks! Will give it a try in a few hours, and let you know how it works! Cheers

```
[batch_size, channels, height, width]
```

If I have 100 files of data, each representing a single day values, that would be

```
[100 (number of daily data/satellite "screenshots"), 1, 941 ( number of lat coordinates), 1200 (number of lon coordinates]
```

right?

Where do I then input the actual chlor_a values? Sorry if this is a dumb question, I’m very new to all of this and have trouble finding tutorials.

Does that mean that the `height, width`

part is just the` x = torch.from_numpy(x)`

where `x = np.array(nc.variables['chlor_a'])`

and where *nc* is the “xarray inputed” netCDF file? *x* already has `(lat, lon)`

dimensions.

In that case I think you can just `unsqueeze`

`dim0`

and `dim1`

as `x`

should already contain the values to create the batch and channel dimension.

This would create a tensor in the shape `[1, 1, lat, lon]`

which would be an “image-like” tensor.