[MRG] Fix average_dipoles to allow for one trial #368

kenloi · 2021-06-18T03:43:56Z

Closes #306
Fixed bug in average_dipoles() function in dipole.py, now allowing for computation of an average even for n_of_1 simulation. I essentially had the function just return the first and only dipole instance in the input parameter dipoles list.

For n_of_1 trials, I decided to also include a message to let users know that when calling an average on a sample size of 1, they should consider simulating at least two trials to properly produce an average.

jasmainak · 2021-06-18T03:49:30Z

hnn_core/network.py

@@ -887,7 +887,7 @@ def _instantiate_drives(self, n_trials=1):
                    drive['name']]['events'].append(event_times)

    def add_tonic_bias(self, *, cell_type=None, amplitude=None,
-                       t0=None, T=None):
+                       t0=None, tstop=None):


@kenloi it looks like there are changes from your old pull request in this pull request. Every time you make a new PR, you should fetch a fresh copy of master and branch from there:

$ git fetch upstream master:master $ git checkout master $ git branch fix_average_dipoles $ git checkout fix_average_dipoles

now since you already started the work, I would recommend using git cherry-pick in the new branch. What you should do to fix the current situation is the following:

$ git branch -m fix_average_dipoles_old $ git fetch upstream master:master $ git checkout master $ git branch fix_average_dipoles $ git checkout fix_average_dipoles $ git cherry-pick <commit1-hash> $ git cherry-pick <commit2-hash> $ git push -f origin fix_average_dipoles

does that make sense?

Just read this^ @jasmainak. That makes sense. I'll try and fix my current branch. Thanks!

codecov-commenter · 2021-06-18T04:45:52Z

Codecov Report

Merging #368 (d20d01e) into master (50f3c7a) will not change coverage.
The diff coverage is 50.00%.

@@           Coverage Diff           @@
##           master     #368   +/-   ##
=======================================
  Coverage   89.94%   89.94%           
=======================================
  Files          16       16           
  Lines        2934     2934           
=======================================
  Hits         2639     2639           
  Misses        295      295

Impacted Files	Coverage Δ
hnn_core/dipole.py	`94.11% <50.00%> (ø)`

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 50f3c7a...d20d01e. Read the comment docs.

jasmainak · 2021-06-18T14:24:41Z

hnn_core/dipole.py

+        return dpls[0]
+    elif not dpls:
+        raise ValueError("Need at least one dipole object to return a"
+                         " dipole")


I would instead raise an error in the loop below if any of the objects in the list is not an instance of Dipole. Look around in the codebase to see how it's done

Could you also update a test after doing this? So you get familiar with the test suite and how it works?

Yea, that makes sense. I'll put the error detection into the loop and update a test.

Updated both the loop and the test. Haven't created test cases before so I hope that what I did makes sense. I used the # average two identical dipole objects as a guide for creating the single dipole object average test. Are there docs that explain the standards for developing test cases with examples?

jasmainak · 2021-06-18T14:27:07Z

hnn_core/dipole.py

-    if len(dpls) < 2:
-        raise ValueError("Need at least two dipole object to compute an"
-                         " average")
+    if len(dpls) == 1:


I think this function shouldn't throw any warnings if:

You get a list (with len > 0)

Each element of the list is a Dipole object

Also, if you really want to throw a warning, you should use the warnings module:

from warnings import warn warn('important message')

@jasmainak This is me just nitpicking but I was thinking that it might help to let users know that when calling the average_dipoles() function when only one trial was run, they are effectively not averaging anything. I'm cool with removing the message if you think it doesn't help much.

I also prefer to limit the number of warnings and only throw them when there's something to be careful about. I think average should work silently for a single instance, makes it more flexible.

Alrighty, I'll remove the message :)

ntolley · 2021-06-21T01:59:13Z

hnn_core/tests/test_dipole.py

@@ -48,6 +48,12 @@ def test_dipole(tmpdir, run_hnn_core_fixture):
                       "average of 2 trials"):
        dipole_avg = average_dipoles([dipole_avg, dipole_read])

+    # average single dipole object
+    dpl_avg1 = average_dipoles([dipole])


What about this for a test. You can create a Dipole object with two simulations, but second the data stored under the second trials to dpl[1].data = np.zeroes(len(dpl[0].times)).

Then your test can be

assert np.all(dpl_avg == dpl[0].data / 2)

I think the current test is good and should remain, but it'd be good to make sure the average function is actually doing some averaging too.

@ntolley Sounds good! I'll give it a try.

@ntolley I tried your implementation of setting the data for the second trial to zero. However the assertion

assert np.all(dpl_avg == dpl[0].data / 2)

causes the entire array to be set to 0. floats. This raises an IndexError later when trying to compute the average because numpy isn't able to compute a mean for:

[0. 0. 0. ... 0. 0. 0.]

I'll try and find a way around this in the meantime. I might have to rely on a different way to set the data to 0.

you should use np.allclose to compare arrays

jasmainak · 2021-06-21T02:34:59Z

hnn_core/dipole.py

    for dpl_idx, dpl in enumerate(dpls):
+        if not dpls:


this is not a type check. If I give dpls = True, then this check will not raise any error. You need to use isinstance ... and for dpl not dpls

Oops I'll take the heat for this, I think this was a suggestion I gave in a brief meeting with @kenloi. Misunderstood the purpose of the original check, isinstance() is definitely the way to go.

Alternatively, check out how _validate_type() is used in the code. It provides a boilerplate error message as these sorts of checks come up frequently.

@jasmainak That makes sense. I'll take care of it asap.
@ntolley Thanks for the suggestion, will check it out.

jasmainak · 2021-06-24T17:05:53Z

hnn_core/tests/test_dipole.py

+    single_dpl_avg = average_dipoles([dipole])
+    for dpl_key in single_dpl_avg.data.keys():
+        np.allclose(dipole_read.data[dpl_key],
+                    single_dpl_avg.data[dpl_key], rtol=0, atol=0.000051)


the test won't fail with this, sorry my bad. You have to use:

https://numpy.org/doc/stable/reference/generated/numpy.testing.assert_allclose.html

jasmainak · 2021-06-24T17:07:11Z

hnn_core/tests/test_dipole.py

+    dpl_avg = average_dipoles(dpl_1)
+    for dpl_key in dpl_avg.data.keys():
+        np.allclose(dpl_1[0].data[dpl_key],
+                    dpl_avg.data[dpl_key], rtol=0, atol=0.000051)


Suggested change

dpl_avg.data[dpl_key], rtol=0, atol=0.000051)

dpl_avg.data[dpl_key])

very strange these numbers are. I think we should just go with the defaults if it works

@jasmainak You're right, it works without the defaults. I set the numbers because I saw it used in the previous tests and figured that those were the accepted tolerances.

jasmainak · 2021-06-24T17:11:19Z

hnn_core/tests/test_dipole.py

+    dpl_null = dipole_read
+    dpl_null.data['agg'] = np.zeros(len(dpl_null.data['agg']))
+    dpl_null.data['L2'] = np.zeros(len(dpl_null.data['L2']))
+    dpl_null.data['L5'] = np.zeros(len(dpl_null.data['L5']))


why not just use the constructor: https://jonescompneurolab.github.io/hnn-core/dev/generated/hnn_core.dipole.Dipole.html#hnn_core.dipole.Dipole

n_times = len(dipole_read) dpl_zeros = Dipole(np.zeros(n_times, ), np.zeros(n_times, 3))

@jasmainak I like that. Much cleaner. Will go make the changes.

jasmainak · 2021-06-24T17:14:13Z

hnn_core/dipole.py

+            raise ValueError("Need at least one dipole object to return a"
+                             " dipole")


there needs to be a test to check that this works. Or is there already a test? Also, I think the text should be changed to reflect the fact that all objects in the list must instances of Dipole. Also, when you raise an error, you should tell the user what you got. Something like:

Suggested change

raise ValueError("Need at least one dipole object to return a"

" dipole")

raise ValueError(f"All elements in the list should be instances of Dipole. Got {type(dpl)}")

@jasmainak Yea, this test already exists. It's under line 51, the comment # average an n_of_1 dipole list which calls average_dipoles() on a list with only one dipole.

And thanks for the more verbose suggestion. I'll go make the change.

ntolley · 2021-06-25T01:42:43Z

hnn_core/tests/test_dipole.py

+    dpl_1 = [dipole, dpl_null]
+    dpl_avg = average_dipoles(dpl_1)
+    for dpl_key in dpl_avg.data.keys():
+        np.allclose(dpl_1[0].data[dpl_key],


Maybe I am misunderstanding the code I think this should fail. Since you're averaging a dipole of random numbers with zeros, shouldn't every value be cut in half?

And I see the tests passing so now I'm extra confused 😄

@ntolley Yea, I see what you're talking about. It shouldn't be working. I'll try debugging and see what's really happening.

it's because np.allclose returns a bool. You need an assert statement to make it fail

Thanks @jasmainak. I think I was able to fix it with your suggestion. Thanks @ntolley for bringing it to my attention.

kenloi · 2021-06-25T16:01:26Z

@ntolley @jasmainak @cjayb I think this issue is complete and ready for a final review. Took into account everyone's input. If anyone has the time, please take a look and let me know if there are any else to fix. I've also updated the whats_new.rst documentation.

ntolley

Looks great @kenloi, I think this is good to merge after a rebase (we'll make sure to merge this PR before any others since you've had to rebase a few times already...)

kenloi · 2021-06-25T17:55:04Z

Thanks @ntolley . I rebased again about an hour ago. Hopefully that resolves some of the conflicts?

ntolley · 2021-06-25T18:03:25Z

So it look like you have a merge conflict in test_dipole.py:

Esentially if there have been any pull requests merged since your last rebase, you need to rebase again. Your IDE should have an interface to help resolve conflicts similar to this: https://code.visualstudio.com/docs/editor/versioncontrol#_merge-conflicts.

The last PR merged added some code to test_dipole.py. Git is asking you to edit your commits on this PR as though you added your code to this new version of test_dipole.py.

Let me know if you want some help going through the steps for resolving a merge conflict

jasmainak · 2021-06-25T19:12:34Z

@kenloi you'll get the hang of github soon :) Remember for contributing to public repositories, only rebase is allowed. Don't merge the master in your branch as it will create a merge commit that is unrelated to your pull request. You should do:

$ git fetch upstream master:master
$ git rebase master

jasmainak · 2021-06-25T19:14:00Z

hnn_core/tests/test_dipole.py

+    # average an n_of_1 dipole list
+    single_dpl_avg = average_dipoles([dipole])
+    for dpl_key in single_dpl_avg.data.keys():
+        np.testing.assert_allclose(


Suggested change

np.testing.assert_allclose(

assert_allclose(

jasmainak · 2021-06-25T19:15:16Z

hnn_core/tests/test_dipole.py

+    dpl_1 = [dipole, dpl_null]
+    dpl_avg = average_dipoles(dpl_1)
+    for dpl_key in dpl_avg.data.keys():
+        assert np.allclose([data / 2 for data in dpl_1[0].data[dpl_key]],


Suggested change

assert np.allclose([data / 2 for data in dpl_1[0].data[dpl_key]],

assert_allclose(dpl_1[0].data[dpl_key] / 2.,

doc/whats_new.rst

X#

kenloi · 2021-06-25T22:26:53Z

I've rebased and implemented the changes suggested by @jasmainak. However, it looks like I've failed one of the tests which is reporting:
FAILED hnn_core/tests/test_parallel_backends.py::TestParallelBackends::test_run_mpibackend

I ran pytest on the test_parallel_backends.py and it passed all the test cases and returned a warning:
UserWarning: No currently running process to terminate

I'll try digging into the details of the report, but I'm not sure what this means.

ntolley · 2021-06-25T22:32:29Z

No need to worry about the tests, if only one of the builds is failing that usually means it's a problem on the server side. I just restarted the jobs and expect them to pass (for some reason our tests can be pretty finicky, I'm mildly suspicious it's a neuron problem...)

Also from the code review suggestions, if it looks reasonable you can actually commit them directly with the Commit suggestion button, and then git pull from your local computer to get the updated code.

kenloi · 2021-06-25T22:43:25Z

@ntolley Yea, I'm quickly realizing that my workflow is pretty inefficient. Thanks for the tip!

kenloi · 2021-06-26T01:31:15Z

@jasmainak @cjayb I think this PR is ready for another round of reviews. @ntolley has reviewed and approved. Just to double check, if I think the PR is ready to be merged, at what point do I switch the tag to [MRG]? Do I wait until I get at least two review approvals or before that? Appreciate all the feedback so far.

ntolley · 2021-06-26T03:22:52Z

You can go ahead and switch the tag! I'm not sure if we have an official system for [MRG], but in general we just use it to signal that we're ready for reviews (not that the PR is actually perfect). WIP is meant to indicate that we're not quite ready for input yet and are aware glaring errors.

jasmainak · 2021-06-26T04:38:35Z

yeah, [MRG} is to indicate you are yourself satisfied with what you have done and would be okay if the PR was merged as it stands. [WIP] means you are just beginning to work on it, early feedback is welcome but not nitpicks ...

hnn_core/tests/test_dipole.py

jasmainak · 2021-06-26T04:44:12Z

@kenloi two final nitpicks. Once you fix them, it's +1 for merge from my end.

Hope you're learning stuff and not losing motivation. Future PRs will definitely be easier and fun!

Co-authored-by: Mainak Jas <[email protected]>

jasmainak · 2021-06-27T03:38:56Z

Thanks @kenloi ! Looking forward to the next contribution :)

jasmainak reviewed Jun 18, 2021

View reviewed changes

kenloi force-pushed the fix_average_dipoles branch from 681f89a to 9310f28 Compare June 18, 2021 04:55

jasmainak reviewed Jun 18, 2021

View reviewed changes

ntolley reviewed Jun 21, 2021

View reviewed changes

jasmainak reviewed Jun 21, 2021

View reviewed changes

cjayb mentioned this pull request Jun 22, 2021

release 0.2 #365

Closed

15 tasks

jasmainak reviewed Jun 24, 2021

View reviewed changes

ntolley reviewed Jun 25, 2021

View reviewed changes

ntolley approved these changes Jun 25, 2021

View reviewed changes

jasmainak reviewed Jun 25, 2021

View reviewed changes

doc/whats_new.rst Outdated Show resolved Hide resolved

kenloi added 7 commits June 25, 2021 17:32

autopep8 to fix format

34c0621

X#

update loop and test

57badee

fixed type check and added tests

771d9dc

Added more verbose error message and simplified test

f9fe8da

Fix autopep8 format

faa70c6

Fixed test and updated docs

48a36ad

rebase and updated tests

46f52ea

kenloi force-pushed the fix_average_dipoles branch from 4556a75 to 46f52ea Compare June 25, 2021 22:00

jasmainak reviewed Jun 26, 2021

View reviewed changes

hnn_core/tests/test_dipole.py Outdated Show resolved Hide resolved

jasmainak reviewed Jun 26, 2021

View reviewed changes

hnn_core/tests/test_dipole.py Outdated Show resolved Hide resolved

Update hnn_core/tests/test_dipole.py

f330295

Co-authored-by: Mainak Jas <[email protected]>

kenloi changed the title ~~[MAINT] Fix average_dipoles to allow for one trial~~ [MRG] Fix average_dipoles to allow for one trial Jun 26, 2021

Update hnn_core/tests/test_dipole.py

d20d01e

Co-authored-by: Mainak Jas <[email protected]>

jasmainak merged commit abb1dca into jonescompneurolab:master Jun 27, 2021

	dpl_avg.data[dpl_key], rtol=0, atol=0.000051)
	dpl_avg.data[dpl_key])

		raise ValueError("Need at least one dipole object to return a"
		" dipole")

	raise ValueError("Need at least one dipole object to return a"
	" dipole")
	raise ValueError(f"All elements in the list should be instances of Dipole. Got {type(dpl)}")

	assert np.allclose([data / 2 for data in dpl_1[0].data[dpl_key]],
	assert_allclose(dpl_1[0].data[dpl_key] / 2.,

[MRG] Fix average_dipoles to allow for one trial #368

[MRG] Fix average_dipoles to allow for one trial #368

Conversation

kenloi commented Jun 18, 2021

Choose a reason for hiding this comment

Choose a reason for hiding this comment

codecov-commenter commented Jun 18, 2021 • edited Loading

Codecov Report

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

kenloi Jun 24, 2021 • edited Loading

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

kenloi Jun 25, 2021 • edited Loading

Choose a reason for hiding this comment

Choose a reason for hiding this comment

Choose a reason for hiding this comment

kenloi commented Jun 25, 2021

ntolley left a comment

Choose a reason for hiding this comment

kenloi commented Jun 25, 2021

ntolley commented Jun 25, 2021 • edited Loading

jasmainak commented Jun 25, 2021

Choose a reason for hiding this comment

Choose a reason for hiding this comment

kenloi commented Jun 25, 2021

ntolley commented Jun 25, 2021

kenloi commented Jun 25, 2021

kenloi commented Jun 26, 2021 • edited Loading

ntolley commented Jun 26, 2021

jasmainak commented Jun 26, 2021

jasmainak commented Jun 26, 2021

jasmainak commented Jun 27, 2021

codecov-commenter commented Jun 18, 2021 •

edited

Loading

kenloi Jun 24, 2021 •

edited

Loading

kenloi Jun 25, 2021 •

edited

Loading

ntolley commented Jun 25, 2021 •

edited

Loading

kenloi commented Jun 26, 2021 •

edited

Loading