Lateral drag / Shape factors #429

phigre · 2018-03-08T14:48:29Z

Closes Implement Cuffey and Patterson's shape factors to the flowline model #423
Passes Inversion Tests for runs without shape factors
Tests for inversion with shape factors added/passed
Tests for shape-factor interpolation from Adhikari /Nye added and passed
Fully documented, including whats-new.rst for all changes (remove if this change is small enough to be undocumented)
not yet to a sufficient amount

For first review (@fmaussion)

…nservation inversion. Shape factors are used as given in Adhikari 2012. A new cfg-param "use_shape_factor" is introduced. Needs further tests and eventually adaption in forward model.

Changed one comment/TODO in inversion routine.

fmaussion

Thanks so much! This already looks very good. I have a few minor comments only.

For the rest (and for future reference) it would be super nice to have some plots pasted in this PR:

curves for various zetas and for both methods
maybe some application test at HEF?

This we could talk bout it or I'll do it in a subsequent PR

fmaussion · 2018-03-08T15:06:19Z

oggm/cfg.py

@@ -274,6 +274,7 @@ def initialize(file=None):
    PARAMS['invert_with_sliding'] = cp.as_bool('invert_with_sliding')
    _k = 'optimize_inversion_params'
    PARAMS[_k] = cp.as_bool(_k)
+    PARAMS['use_shape_factor'] = cp.as_bool('use_shape_factor')


I think we should rename it to use_shape_factor_for_inversion. It is likely that we will want to separate between inversion and dynamical model's factors.

For testing between the factors it might be useful to have this option as a str: 'Huss' (or the original paper name), "Adhikari" or empty for no shape factor

Yes, I already also thought about an option like this, but just did not spend more time on it. Will do so.

fmaussion · 2018-03-08T15:07:13Z

oggm/core/inversion.py

+    according to Huss and Farinotti (2012)
+
+    Discouraged to use, as this does not make any difference
+    between parabolic and rectangular beds


I'm not sure to understand: wouldn't it be better to return a factor of 1 when the bed is rectangular in this case?

I could not really find any information on the assumed bed form in the paper. Therefore I did not go into too much detail yet. But you're right, I will adapt it this way.

fmaussion · 2018-03-08T15:07:40Z

oggm/core/inversion.py

+    Adhikari (2012)
+
+    TODO: should we expand this here to also include
+    the factors suggested for sliding?


Not yet I would say

fmaussion · 2018-03-08T15:08:32Z

oggm/core/inversion.py

+    factors_parabolic = np.array([0.251, 0.448, 0.653, 0.748,
+                                  0.803, 0.839, 0.917])
+    f_parab = interp1d(tabular_zetas, factors_parabolic,
+                       fill_value='extrapolate')


This entire block could live at the module scope instead

fmaussion · 2018-03-08T15:10:22Z

oggm/core/inversion.py

+    is_rectangular = is_rectangular.astype(bool)
+
+    # TODO: could check for division by 0, but at the moment
+    # this is covered by interpolation and clip, resulting in a factor of 1


It's just a problem if it throws numerical warnings. If you want you can put this statement in a catch as done here

In case is_rectangular is of type integer, these integers (0/1) are directly interpreted as indices, which is not what is intended here. In case of floats I have to admit that I did not check.

fmaussion · 2018-03-08T15:12:02Z

oggm/core/inversion.py

+    -------
+
+    """
+    a0s_shaped = a0s / (shape_factor ** 3)


you can safely overwrite a0s here

But then I cannot exploit the precomputed a0s in subsequent loops

Sorry, my mistake. Did not realize I was inside the function.

fmaussion · 2018-03-08T15:13:15Z

oggm/core/inversion.py

@@ -161,6 +273,11 @@ def mass_conservation_inversion(gdir, glen_a=cfg.A, fs=0., write=True,
    fd = 2. / (cfg.N+2) * glen_a
    a3 = fs / fd

+    # Shape factor params
+    sf_func = shape_factor_adhikari
+    sf_tol = 1e-2  # TODO: better as params in cfg?


This is too specific it's OK to leave it here

fmaussion · 2018-03-08T15:14:04Z

oggm/core/inversion.py

+        # Start iteration for shape factor with guess of 1
+        i = 0
+        sf = np.ones(slope.shape)
+        sf_diff = np.ones(slope.shape)


i = 0 and sf_diff could be defined in the if block below

Absolutely right

fmaussion · 2018-03-08T15:16:26Z

oggm/core/inversion.py

+
+    shape_factors = np.ones(widths.shape)
+
+    # TODO: higher order interpolation? (e.g. via InterpolatedUnivariateSpline)


Maybe? Depends on how the curve looks like

Already looks quite ok, i would say?

I can provide the notebook to this figure on demand

fmaussion · 2018-03-08T15:20:09Z

Ping @alexjarosch for info

phigre · 2018-03-08T16:45:54Z

oggm/core/inversion.py

+
+# TODO: how to handle zeta > 10? at the moment extrapolation
+# Table 1 from Adhikari (2012) and corresponding interpolation functions
+_ADHIKARI_TABLE_ZETAS = np.array([0.5, 1, 2, 3, 4, 5, 10])


Move this to top of file?

I'm not sure there is a pep rule for this, but yes I think it's slightly better

…ion of Nye-shape factor.

…ion of Nye-shape factor. Updated test to match change in shape factor calculation

fmaussion · 2018-03-20T13:28:31Z

oggm/params.cfg

@@ -189,6 +189,9 @@ optimize_inversion_params = False
 # If false, tell OGGM which should be used
 inversion_glen_a = 2.4e-24
 inversion_fs = 0.
+# Do you want to use shape factors to account for lateral drag?
+# Allowed is empty, "Adhikari", "Nye" equal to "Adhikari" or "Huss"


"Nye" (equivalent to "Adhikari")

fmaussion · 2018-03-20T13:30:14Z

oggm/core/inversion.py

@@ -161,6 +287,16 @@ def mass_conservation_inversion(gdir, glen_a=cfg.A, fs=0., write=True,
    fd = 2. / (cfg.N+2) * glen_a
    a3 = fs / fd

+    # Shape factor params
+    sf_func = None
+    if cfg.PARAMS['use_shape_factor_for_inversion'] == 'Adhikari' or \


create a new variable, like use_shape_factor = cfg.PARAMS['use_shape_factor_for_inversion'] and use it throughout the function to shorten the code a little bit

fmaussion · 2018-03-20T13:31:18Z

@phigre should we merge this as is and add the tests at a later stage or are you still working on this?

…shape factor in FluxbasedModel.step Currently, the shape factor methods of the inversion module are used for the calculation of shape factors.

…he shape factors for sections with height = 0 are no longer np.nan but are set to 1. (so shape factor is actually not influencing this section)

fmaussion · 2018-03-20T13:58:31Z

Ah, and don't forget to acknowledge your work in whats-new.rst !

…riables used for calculation of velocity

phigre · 2018-03-20T14:17:57Z

oggm/core/flowline.py

+            sf = sf_func(fl.widths_m, thick, fl.is_rectangular)
+            # TODO: avoid  unnecessary new array initialization by adding
+            # sf_stag to  self._stags
+            sf_stag = np.zeros(thick_stag.shape)


Staggered shape factors need to be computed to match with thick_stag and u_stag.
I decided to use shape factors from the unstaggered grid and combine upstream and downstream shape factors to obtain staggered values.
Directly computing shape factors on the staggered grid would make an additional width_stag necessary and the question arises what is_rectangular_stag would look like.

phigre · 2018-03-20T14:21:08Z

oggm/core/flowline.py

@@ -21,6 +21,7 @@
 from oggm import entity_task
 import oggm.core.massbalance as mbmods
 from oggm.core.centerlines import Centerline, line_order
+from oggm.core.inversion import shape_factor_adhikari, shape_factor_huss


I referenced the shape factor functions from the inversion module. IMHO, having them in a separate dedicated module and referencing this module from inversion and flowline would make more sense in regards of code structure.

Yes, probably. It could be added to utils for now (and the test moved to test_utils for now, even if the utils module is definitely overcrowded and could need a split-up one day. What do you think?

e.g. move to oggm.utils?

Introduced use_sf in flowline and inversion to shorten lines and avoid multiple calls to cfg.PARAMS[use_shape_...']

Using a proxy variable `use_sf` instead of `cfg.PARAMS['use_shape_...']`. This variable is set to None, if the acquired key is not found in PARAMS.

phigre · 2018-03-21T09:41:45Z

There are still some issues like the storage of sf_stag in self._stag and how to handle not set fl.is_rectangular (yes, that is possible), which I didn't want to decide on my own.

Otherwise ready to merge now?
A simple test for creating a synthetic glacier with shape factors in the FluxBasedModel and trying to recover the bed with mass-conservation inversion and the adequate shape factors for inversion worked out, so at least we ca assume that either both are right or broken ;)

…s. Moved shape factor function choice to init of class. Added tests with shape factors for inversion and FluxBasedModel doing a roundtrip.

phigre

Stiil a lot of testing necessary and some easy performance possible, but getting better

phigre · 2018-03-22T18:56:56Z

oggm/core/flowline.py

-            d = np.zeros(nx-1)
-            e = np.zeros(nx)
-            self._stags.append((a, b, c, d, e))
+            d = np.ones(nx+1)  # shape factor default is 1


could set to 1.0 in case shape factors are not wanted to make the array-array mutliplication an array-scalar mutliplication afterwards. May give some perfomance advantages (especially if numpy could identify multiplication by 1 as skippable)

phigre · 2018-03-22T18:58:04Z

oggm/core/flowline.py

-            rhogh = (RHO*G*slope_stag*sf_stag)**N
-            u_stag = (thick_stag**(N+1)) * self._fd * rhogh + \
+            rhogh = (RHO*G*slope_stag)**N
+            u_stag = (thick_stag**(N+1)) * self._fd * rhogh * sf_stag**N + \


shape factor as we use it only makes sense for the non-sliding part

phigre · 2018-03-22T19:01:06Z

oggm/tests/test_models.py

@@ -1377,6 +1377,96 @@ def test_inversion_parabolic(self):
            plt.plot(bed_shape_gl[:-3])
            plt.show()

+    def test_inversion_parabolic_sf_adhikari(self):


Tests for each case would probably be much better with one single callable function and write wrappers, which are actually called for testing and set the shape factor mode before calling the function.

phigre · 2018-03-22T19:03:09Z

oggm/tests/test_models.py

@@ -1377,6 +1377,96 @@ def test_inversion_parabolic(self):
            plt.plot(bed_shape_gl[:-3])
            plt.show()

+    def test_inversion_parabolic_sf_adhikari(self):


Much better to define one single function and then write wrappers for different shape factor modes, which are actually called for testing and do nothing else than setting shape factor cfg and call the function doing the work. Would avoid a lot of ugly copied code

phigre · 2018-03-22T19:04:07Z

oggm/core/flowline.py

-            d = np.zeros(nx-1)
-            e = np.zeros(nx)
-            self._stags.append((a, b, c, d, e))
+            d = np.ones(nx+1)  # shape factor default is 1


Set to 1.0 as a scalar if scalararray is faster than arrayarray in numpy. Would be cool, if numpy could skip multiplication by 1 automatically

phigre · 2018-03-22T19:05:13Z

oggm/utils.py

@@ -3161,10 +3161,10 @@ def shape_factor_huss(widths, heights, is_rectangular):

    # Ensure bool (for masking)
    is_rectangular = is_rectangular.astype(bool)


Avoid typecast necessary? Did not look into the implementation and cannot say anything about perfomance impacts

phigre · 2018-03-22T19:06:34Z

oggm/utils.py

@@ -3161,10 +3161,10 @@ def shape_factor_huss(widths, heights, is_rectangular):

    # Ensure bool (for masking)
    is_rectangular = is_rectangular.astype(bool)
-
    shape_factors = np.ones(widths.shape)


always repeating initialization could be substituted by storing shape factor array in the flowmodel and passing the reference to the shape factor functions

phigre · 2018-03-22T19:07:10Z

oggm/utils.py

    shape_factors = np.ones(widths.shape)
    shape_factors[~is_rectangular] = \
        (widths / (2 * heights + widths))[~is_rectangular]
+    shape_factors[heights <= 0.] = 1.


Need to avoid nans for areas without ice

fmaussion · 2018-03-27T11:01:16Z

This is a brilliant piece of work, thanks a lot! I will merge this now, we can always go back to some details later.

Experience shows that the global applications will require some tuning, but I'm eager to test this out. Note that this probably should become the default some day.

Philipp Gregor added 2 commits March 8, 2018 15:28

Added option for shape factors to account for lateral drag in mass-co…

50b0e5d

…nservation inversion. Shape factors are used as given in Adhikari 2012. A new cfg-param "use_shape_factor" is introduced. Needs further tests and eventually adaption in forward model.

Changed default value for param "use_shape_factor" from True to False.

5805051

Changed one comment/TODO in inversion routine.

fmaussion reviewed Mar 8, 2018

View reviewed changes

Implemented suggested changes from pull request.

70a50a8

phigre commented Mar 8, 2018

View reviewed changes

Philipp Gregor added 2 commits March 19, 2018 13:53

Fixed mistake where width instead of half-width was used for computat…

e2c4cdd

…ion of Nye-shape factor.

Fixed mistake where width instead of half-width was used for computat…

ddfbf55

…ion of Nye-shape factor. Updated test to match change in shape factor calculation

fmaussion reviewed Mar 20, 2018

View reviewed changes

Philipp Gregor added 2 commits March 20, 2018 14:42

Added cfg-Param 'use_shape_factor_for_fluxbasedmodel' and introduced …

c48320e

…shape factor in FluxbasedModel.step Currently, the shape factor methods of the inversion module are used for the calculation of shape factors.

Added correction of NaN-shape factors in shape_factor_adhikari. Now t…

af12226

…he shape factors for sections with height = 0 are no longer np.nan but are set to 1. (so shape factor is actually not influencing this section)

Changed shape factors from centered to staggered grid to match the va…

0ea774d

…riables used for calculation of velocity

phigre commented Mar 20, 2018

View reviewed changes

Philipp Gregor added 5 commits March 20, 2018 15:23

Changed formulation in comment of cfg

b1137c9

Fixed bug for sf_func call in flowline, when sf_func was not set.

ec04edd

Introduced use_sf in flowline and inversion to shorten lines and avoid multiple calls to cfg.PARAMS[use_shape_...']

Moved shape factor calculation to utils.

df78b06

Using a proxy variable `use_sf` instead of `cfg.PARAMS['use_shape_...']`. This variable is set to None, if the acquired key is not found in PARAMS.

Updated reference for shape_factor_adhikari in utils for testing

0b24173

Updated whats-new

96503d7

Fixed a bug with shape factors for tributaries. Added sf_stag to clas…

284783c

…s. Moved shape factor function choice to init of class. Added tests with shape factors for inversion and FluxBasedModel doing a roundtrip.

phigre commented Mar 22, 2018

View reviewed changes

fmaussion merged commit 5f98d9a into OGGM:master Mar 27, 2018


		shape_factors = np.ones(widths.shape)

		# TODO: higher order interpolation? (e.g. via InterpolatedUnivariateSpline)

		@@ -3161,10 +3161,10 @@ def shape_factor_huss(widths, heights, is_rectangular):

		# Ensure bool (for masking)
		is_rectangular = is_rectangular.astype(bool)

Lateral drag / Shape factors #429

Lateral drag / Shape factors #429

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phigre commented Mar 8, 2018 • edited

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fmaussion commented Mar 8, 2018

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fmaussion commented Mar 20, 2018

fmaussion commented Mar 20, 2018

phigre Mar 20, 2018 • edited

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phigre commented Mar 21, 2018

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fmaussion commented Mar 27, 2018

phigre commented Mar 8, 2018 •

edited

phigre Mar 20, 2018 •

edited