Timedependens #11
Timedependens #11
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rettigl left a comment
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Generally, most changes make sense to me, but carefully check the order of your indices. I think the bug could be there.
| Delay: 1D array | None | ||
| Delay along time axis as numpy array. | ||
| I: 4D array | None | ||
| Measured intensity wrt momentum, time and energy, generated if None. |
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Do you consider the data as I(kx, ky, t, E) or I(kx, ky, E, t)?
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right now i only consider I(kx, ky, E, t), i will change the Description. The format I(kx, ky, E, t) comes from the nexus data set.
| ind1d = np.argmin(np.abs(EE - EE0), 1) | ||
| self.indEb = ind1d.reshape(self.E0.shape) | ||
| self.indEb = np.repeat(self.indEb[ :, :, np.newaxis], self.lengthdelay, axis=2) | ||
| # depends on the shape of E0, i assume E0 is the same for all times |
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Describe what you assume in the docstring, and possibly test for the shape
| | ||
| self.I = ndimage.gaussian_filter(self.I, sigma=sigma) | ||
| for t in range(len(self.delay)): | ||
| self.I[:,:,:,t] = ndimage.gaussian_filter(self.I[:,:,:,t], sigma=sigma) |
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So this also assumes I(kx, ky, E, t)
| for t in range(self.lengthdelay): | ||
| self.I[:,:,:,t] = mclahe( | ||
| self.I[:,:,:,t] , | ||
| kernel_size=kernel_size, | ||
| n_bins=n_bins, | ||
| clip_limit=clip_limit, | ||
| use_gpu=use_gpu, | ||
| ) |
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we should discuss if this makes sense. Maybe generate one set of parameters, and apply everywhere. Not sure if mclahe can do this rn.
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I think i already tried to do this for the 4d intensity. If i remenber correctly i got an error, but i can try again.
| logP -= (ECurv[self.indEb[indx, indy - 2]] - 2 * ECurv[self.indEb[indx, indy - 1]] + ECurv) ** 2 | ||
| if indy < (self.lengthKy - 1): | ||
| logP -= (ECurv[self.indEb[indx, indy - 1]] - 2 * ECurv + ECurv[self.indEb[indx, indy + 1]]) ** 2 | ||
| logP -= (ENN - ENN[self.indEb[indx, indy - 1, indt]]) ** 2 |
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The way this is defined here, it will take the same eta along all directions. This should be extended after testing to provide a len(3) eta array for the three dimensions.
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I did the extention and i am testing the results right now. If everything looks correct i will update this with the testing of len(3).
| logP -= np.sum( | ||
| (Eb[ 0 : (self.lengthKx - 1), :,:] - Eb[1 : self.lengthKx, :,:]) ** 2 | ||
| ) / (2 * self.eta**2) |
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I will update this when i find out were this bug with the "ripples" comes from.
| **Parameters**\n | ||
| kx, ky: 1D array, 1D array | None, None | ||
| kx, ky, time: 1D array, 1D array | None, None |
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They are indices and not 1d arrays, no?
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Yes, i will change this.
| Name of the colormap. | ||
| figsize: list/tuple | [9, 9] | ||
| Size of the figure produced. | ||
| equal_axes: bool | False |
| if time is None: | ||
| t = 0 | ||
| if time is not None: | ||
| t = time |
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I suppose this should be a time rather than an index like for the function above.
| ax.plot_surface(x, y, np.transpose(self.getEb())) | ||
| ax.plot_surface(x, y, np.transpose(Eb[:,:,t])) |
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does it matter if you use getEb or just Eb?
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I did matter in the beginning while i updatet the code for time dependens. I can check again if this is still needed.
2. added time constant
3. added data preprocessing steps
4. added time constant for plotting
5. changed the epochs from 150 to 100
For the mrfRec file: