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Cleaned tests and restricted numpy arrays in UnpackedArray to np.int3…
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…2 or np.uint32
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@evalott100
evalott100 committed Jul 18, 2023
1 parent 02265cd commit 6a06054
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Showing 2 changed files with 78 additions and 45 deletions.
5 changes: 1 addition & 4 deletions pandablocks/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,10 +7,7 @@

UnpackedArray = Union[
npt.NDArray[np.int32],
npt.NDArray[np.uint8],
npt.NDArray[np.uint16],
npt.NDArray[np.uint32],
npt.NDArray[np.bool_],
Sequence[str],
]

Expand Down Expand Up @@ -96,7 +93,7 @@ def table_to_words(
column = [field_details.labels.index(x) for x in column]

# PandA always handles tables in uint32 format
column_value = np.uint32(np.array(column))
column_value = np.array(column, dtype=np.uint32)

if packed is None:
# Create 1-D array sufficiently long to exactly hold the entire table, cast
Expand Down
118 changes: 77 additions & 41 deletions tests/test_utils.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
from itertools import chain
from typing import Dict, Iterable, List, OrderedDict

import numpy as np
Expand Down Expand Up @@ -158,50 +157,84 @@ def table_field_info(table_fields) -> TableFieldInfo:


@pytest.fixture
def table_1(table_fields: Dict[str, TableFieldDetails]) -> OrderedDict[str, np.ndarray]:
"""The unpacked equivalent of table_data"""
array_values: List[np.ndarray] = [
np.array([5, 0, 50000], dtype=np.uint16),
# Below labels correspond to numeric values [0, 6, 0]
np.array(["Immediate", "BITC=1", "Immediate"], dtype="<U9"),
np.array([-5, 678, 0], dtype=np.int32),
np.array([100, 0, 9], dtype=np.uint32),
np.array([0, 1, 1], dtype=np.uint8),
np.array([0, 0, 1], dtype=np.uint8),
np.array([0, 0, 1], dtype=np.uint8),
np.array([1, 0, 1], dtype=np.uint8),
np.array([0, 0, 1], dtype=np.uint8),
np.array([1, 0, 1], dtype=np.uint8),
np.array([0, 55, 9999], dtype=np.uint32),
np.array([0, 0, 1], dtype=np.uint8),
np.array([0, 0, 1], dtype=np.uint8),
np.array([1, 1, 1], dtype=np.uint8),
np.array([0, 0, 1], dtype=np.uint8),
np.array([0, 0, 1], dtype=np.uint8),
np.array([1, 0, 1], dtype=np.uint8),
]
return OrderedDict(zip(table_fields.keys(), array_values))
def table_1() -> OrderedDict[str, Iterable]:
return OrderedDict(
{
"REPEATS": [5, 0, 50000],
"TRIGGER": ["Immediate", "BITC=1", "Immediate"],
"POSITION": [-5, 678, 0],
"TIME1": [100, 0, 9],
"OUTA1": [0, 1, 1],
"OUTB1": [0, 0, 1],
"OUTC1": [0, 0, 1],
"OUTD1": [1, 0, 1],
"OUTE1": [0, 0, 1],
"OUTF1": [1, 0, 1],
"TIME2": [0, 55, 9999],
"OUTA2": [0, 0, 1],
"OUTB2": [0, 0, 1],
"OUTC2": [1, 1, 1],
"OUTD2": [0, 0, 1],
"OUTE2": [0, 0, 1],
"OUTF2": [1, 0, 1],
}
)


@pytest.fixture
def table_1_not_in_panda_order(
table_1: OrderedDict[str, np.ndarray]
) -> OrderedDict[str, np.ndarray]:
# To ensure the order is different: we want to check packed values
# come out in panda order
to_insert = list(table_1.items())
data: OrderedDict[str, np.ndarray] = OrderedDict()
for insert_iteration in chain(range(5), [7, 6, 5], range(8, len(to_insert))):
field_name, data_array = to_insert[insert_iteration]
data[str(field_name)] = data_array
def table_1_np_arrays() -> OrderedDict[str, Iterable]:
# Intentionally not in panda order. Whatever types the np arrays are,
# the outputs from words_to_table will be uint32 or int32.
return OrderedDict(
{
"POSITION": np.array([-5, 678, 0], dtype=np.int32),
"TIME1": np.array([100, 0, 9], dtype=np.uint32),
"OUTA1": np.array([0, 1, 1], dtype=np.uint8),
"OUTB1": np.array([0, 0, 1], dtype=np.uint8),
"OUTD1": np.array([1, 0, 1], dtype=np.uint8),
"OUTE1": np.array([0, 0, 1], dtype=np.uint8),
"OUTC1": np.array([0, 0, 1], dtype=np.uint8),
"OUTF1": np.array([1, 0, 1], dtype=np.uint8),
"TIME2": np.array([0, 55, 9999], dtype=np.uint32),
"OUTA2": np.array([0, 0, 1], dtype=np.uint8),
"OUTB2": np.array([0, 0, 1], dtype=np.uint8),
"REPEATS": np.array([5, 0, 50000], dtype=np.uint32),
"OUTC2": np.array([1, 1, 1], dtype=np.uint8),
"OUTD2": np.array([0, 0, 1], dtype=np.uint8),
"OUTE2": np.array([0, 0, 1], dtype=np.uint8),
"OUTF2": np.array([1, 0, 1], dtype=np.uint8),
"TRIGGER": np.array(["Immediate", "BITC=1", "Immediate"], dtype="<U9"),
}
)

return data

@pytest.fixture
def table_1_not_in_panda_order() -> OrderedDict[str, Iterable]:
return OrderedDict(
{
"REPEATS": [5, 0, 50000],
"TRIGGER": ["Immediate", "BITC=1", "Immediate"],
"POSITION": [-5, 678, 0],
"TIME1": [100, 0, 9],
"OUTA1": [0, 1, 1],
"OUTB1": [0, 0, 1],
"OUTC1": [0, 0, 1],
"OUTD1": [1, 0, 1],
"OUTF1": [1, 0, 1],
"OUTE1": [0, 0, 1],
"TIME2": [0, 55, 9999],
"OUTA2": [0, 0, 1],
"OUTC2": [1, 1, 1],
"OUTB2": [0, 0, 1],
"OUTD2": [0, 0, 1],
"OUTE2": [0, 0, 1],
"OUTF2": [1, 0, 1],
}
)


@pytest.fixture
def table_data_1() -> List[str]:
"""Table data associated with table_fields and table_field_info fixtures.
See table_unpacked_data for the unpacked equivalent"""
return [
"2457862149",
"4294967291",
Expand Down Expand Up @@ -267,7 +300,11 @@ def test_table_packing_pack_length_mismatched(

@pytest.mark.parametrize(
"table_fixture_name,table_data_fixture_name",
[("table_1_not_in_panda_order", "table_data_1"), ("table_2", "table_data_2")],
[
("table_1_not_in_panda_order", "table_data_1"),
("table_2", "table_data_2"),
("table_1_np_arrays", "table_data_1"),
],
)
def test_table_to_words_and_words_to_table(
table_fixture_name: str,
Expand Down Expand Up @@ -295,7 +332,7 @@ def test_table_to_words_and_words_to_table(


def test_table_packing_unpack(
table_1: OrderedDict[str, np.ndarray],
table_1_np_arrays: OrderedDict[str, np.ndarray],
table_field_info: TableFieldInfo,
table_data_1: List[str],
):
Expand All @@ -304,7 +341,7 @@ def test_table_packing_unpack(

actual: UnpackedArray
for field_name, actual in output_table.items():
expected = table_1[str(field_name)]
expected = table_1_np_arrays[str(field_name)]
np.testing.assert_array_equal(actual, expected)


Expand All @@ -313,7 +350,6 @@ def test_table_packing_pack(
table_field_info: TableFieldInfo,
table_data_1: List[str],
):
"""Test table unpacking works as expected"""
assert table_field_info.row_words
unpacked = table_to_words(table_1, table_field_info)

Expand Down

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