autowisp.tests.test_expression_series module

Class Inheritance Diagram

Tests for reading one series’ values out of the project database.

These cover tier 2 of the expression layer: the part that knows the project database but not Django, and turns a SeriesKey into the {name: array} tier 1 evaluates against. What is asserted here is mostly alignment – that index i means the same image in every array – because every other property in the design rests on it and a violation of it produces a plot that looks entirely reasonable and is wrong.

The two-night fixture is the one test_diagnostics_views builds, imported rather than repeated: it is a project-database fixture rather than a view one, and the view tests and these want exactly the same rows.

class autowisp.tests.test_expression_series.SeriesValuesTestCase(methodName='runTest')

Bases: DiagnosticsViewTestCase

The two-night fixture, with the keys these tests ask about.

flats = (2, 'flat', ('R',), None)

two, recording bg_center and no quantile.

Type:

The same night’s flats

objects = (2, 'object', ('R',), None)

three, all recording everything.

Type:

The mixed night’s object frames

class autowisp.tests.test_expression_series.TestCanonicalImages(methodName='runTest')

Bases: SeriesValuesTestCase

The list every array is padded onto.

test_one_type_only()

The list is per type, which is what confines an aggregate.

test_ordered_by_time()

Ordered, because every array is aligned to this one by position.

test_the_channel_is_not_consulted()

Channels share an index space, so the list cannot depend on one.

class autowisp.tests.test_expression_series.TestCrossChannelCounts(methodName='runTest')

Bases: TestCrossChannelValues

Counting the images a binding draws, once the channels are chosen.

Shares the fixture above because the gap is what makes the question non-trivial: a session where every frame records both channels counts the same whichever way you ask.

counts_of(rows)

Return {session label: count} from what a counter returned.

test_a_binding_counts_the_images_having_all_of_it()

The intersection, not either channel’s own total.

Each session records four frames in R and three in B, so a quantity reading both draws three – and a count that quietly took one channel’s would claim four.

test_a_channel_never_recorded_counts_nothing()

And says so by absence rather than by a row reading zero.

test_nothing_required_counts_nothing()

A plot of the time against the time constrains no image.

test_one_channel_agrees_with_counting_within_a_channel()

The two questions only differ where more than one is involved.

Asked about a single channel, the cross-channel count must give what the per-channel one gives for that channel – otherwise one of them is wrong in a way no other test here would show.

class autowisp.tests.test_expression_series.TestCrossChannelValues(methodName='runTest')

Bases: TestCase

Reading one diagnostic in several channels, and counting by binding.

Its own fixture rather than the shared one, which records a single channel: giving that one a second would change what every series-table test sees.

classmethod _fill_database()

One session per gap position, four frames of two channels each.

drawn(quantity, channels, expressions, db_session)

Return one quantity’s values, bound to channels.

Sugar for the usual case here: a series binding exactly what the quantity is read in. Values come back keyed by quantity and binding, since two axes may be one quantity in two channels.

expected(channel, hole)

Return what channel reads for the session with that gap.

hole = 1

The one the tests about values use, the gap in the middle.

hole_positions = (0, 1, 3)

One observing session per position of the gap in channel B – first frame, middle, last – so the same assertions can be made with it moved. A gap only ever at the end would survive a column shifted by a frame; moving it is what pins the value to its own image, and a plot drawn from the wrong pairing looks entirely reasonable.

The gap is also what makes the two ways of counting differ: without it, counting across channels and counting within one give the same number.

key(channels, hole=None)

Return the series binding channels, of the session with hole.

missing_channel = 'G'

A channel this camera never recorded at all, for the case where a whole slot resolves to nothing.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

test_a_channel_never_recorded_is_undefined()

Not an error: the expression is simply NaN throughout.

One library is shared by every project, so an expression naming a channel this camera does not have is an ordinary thing to meet.

test_a_ratio_between_two_channels()

The quantity the whole of section 10 exists for.

Both columns are read against the same images, so the ratio is meaningful without anything being joined or matched up – and is undefined on the frame recording only one of them, rather than silently pairing that value with another frame’s.

test_a_value_stays_with_its_own_image()

The gap moved through the column, one session per position.

What is pinned is where the gap lands, not that dividing by NaN gives NaN. The values are read as a column and reshaped, so a column out by a frame would move every value onto its neighbour – and nothing downstream could tell, since the result is the right length and full of plausible numbers.

test_an_aggregate_spans_one_channel()

Each slot is read on its own, so a median is per channel.

R reads 8, 6, 4, 9 and B reads 2, 8, 5, so the medians are 7 and 5. Pool them and the median of all seven is 6, making this 1 rather than 2, and nothing would say so.

test_one_diagnostic_read_in_two_channels()

The plainest colour plot, with no expression in it at all.

Both axes are bg_center, told apart only by what the table bound each to. Asking per quantity rather than per binding would keep one of them and draw it on both axes – a diagonal line where a colour-colour plot belongs.

test_the_binding_decides_which_way_round()

The same expression, the channels swapped, is the reciprocal.

test_two_axes_of_different_kinds_resolve_in_one_query()

A plain diagnostic against a cross-channel expression.

Counting the statements as well as checking the numbers, because one read of the union is why both axes are asked for at once: two axes over the same diagnostic in overlapping channels must not become two reads of it, and a figure draws many series.

values_of = {'B': [2.0, 3.0, 8.0, 5.0], 'R': [8.0, 6.0, 4.0, 9.0]}

bg_center in each channel, per frame. Chosen so that every pairing of the two gives a distinct ratio: reading the wrong channel cannot land on the right answer by luck.

class autowisp.tests.test_expression_series.TestDiagnosticValues(methodName='runTest')

Bases: SeriesValuesTestCase

Padding several diagnostics onto that list in one query.

What is read is stated as {name: {channel tuples}} – the shape the walk that decides it produces – because a diagnostic may be wanted in more than one channel at once.

recorded = ('R',)

The fixture records everything in this one channel.

test_a_channel_nothing_was_recorded_in_is_all_nan()

Asking for a channel this camera never had is not an error.

It is the same padding one step further out: the join finds nothing, so the column is NaN throughout rather than absent, and an expression over it is undefined rather than broken.

test_a_diagnostic_the_type_lacks_is_all_nan()

Flats record no quantiles, and still owe a full-length column.

This is the case the padding exists for: an expression over a diagnostic some frames lack must produce a series of the right length with holes, not a shorter one that silently misaligns.

test_asking_for_nothing_still_gives_the_images()

The caller needs the image list even with no diagnostic wanted.

test_each_channel_is_asked_for_by_its_whole_key()

The shape that keeps this affordable however big the archive.

A session holds a manageable number of images; the image table will not, so the feature rests on anchoring to one observing session and reaching image_diagnostics by its unique index on (image_id, channel, diagnostic_id). Reading several channels adds a join each, and every one has to pin all three columns – drop the channel and the join matches every channel’s row, which is both wrong and a scan.

Asserted on the statement rather than on a query plan, because the predicates are what this module decides; which index to use is the database’s business, and SQLite’s answer would say nothing about the MariaDB servers that hold the large archives.

test_every_array_is_the_same_length()

Alignment is by position, so a short column would be a bug.

test_one_diagnostic_read_in_two_channels()

What an expression comparing channels needs, in one query.

Both columns come back against the same images, which is what makes a ratio between them meaningful without any joining.

test_time_comes_from_the_image_row()

jd is not in image_diagnostics and needs no row there.

test_values_land_against_their_own_images()

The fixture makes each value say which image it belongs to.

class autowisp.tests.test_expression_series.TestSeriesValues(methodName='runTest')

Bases: SeriesValuesTestCase

Resolving quantities, which is where expressions enter.

test_a_composed_expression_resolves_its_dependency()

Tier 1 orders them; this checks the values reach it to do so.

test_an_expression_is_evaluated_per_series()

The aggregate sees this series’ images and no others.

bg_center runs 100, 101, 102 for these frames and 500, 501 for the flats of the same night, so a median taken across the two would be nowhere near zero.

test_both_axes_resolve_together()

Two quantities, one call – the point of asking for both.

test_plain_diagnostics_need_no_library()

A diagnostic and the time, which is every plot’s usual x axis.

class autowisp.tests.test_expression_series.TestTiedJulianDates(methodName='runTest')

Bases: TestCase

Two images of one session sharing a jd.

Alignment is by position, so the order images come back in has to be total. Ordering by jd alone leaves a tie for the database to break however it likes, and two queries breaking one differently would pair a value with the wrong image – silently, since both plots look fine. The fixture here is the smallest thing that would expose it: two frames at the same instant with values that say which is which.

classmethod _fill_database()

Four frames, two of which share a Julian date.

classmethod setUpClass()

Hook method for setting up class fixture before running tests in the class.

classmethod tearDownClass()

Hook method for deconstructing the class fixture after running all tests in the class.

test_the_order_is_repeatable()

Two calls must not disagree about which image comes first.

test_values_stay_with_their_own_images()

The assertion the tiebreak exists for.

value_of = {}

bg_center of each image, by id, so a mispairing is visible.