Update core and tango tests to match structure of epics tests #723
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jsouter
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| @@ -30,6 +31,7 @@ | |||
| | Array1D[np.float32] | |||
| | Array1D[np.float64] | |||
| | np.ndarray | |||
| | IntEnum | |||
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From my brief skim of the docs, PyTango enumerations look like:
class MyEnum(IntEnum):
ENUM_STRING1 = 0
ENUM_STRING2 = 1I guess this means that Tango enum string values can't contain spaces. The value is superfluous to us as we can set value by string like in EPICS.
I would prefer that we don't support this as a signal type, but instead may our own Enum to request, using the tango name as the value:
class MyEnum(StrictEnum): # or SubsetEnum
WHATEVER1 = "ENUM_STRING1"
WHATEVER2 = "ENUM_STRING2"We should then convert in the signal converter between the Tango IntEnum and the requested ophyd StrictEnum
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Okay, have figured out one way of doing converters for scalar/spectrum/image enum attributes. For the sake of working with assert_value etc it may be easiest if the converters return strs (or numpy arrays of strs), can create an Enum class in place in the converter but not sure if that provides much benefit.
The shift away from using IntEnums means we can currently use a StrictEnum with assert_value etc but we can't test those same values with the pytango provided assert_close.
will probably refactor soon, haven't tested puts via the converter yet.
| def get_every_signal_data(): | ||
| # list containing necessary info to construct a signal of each type for multiple | ||
| # transports e.g. soft/epics/tango | ||
| return [ | ||
| EverythingSignal("int", int, 1), | ||
| EverythingSignal("float", float, 1.234), | ||
| EverythingSignal("str", str, "test_string"), | ||
| EverythingSignal("bool", bool, True), | ||
| EverythingSignal("enum", ExampleEnum, ExampleEnum.B), | ||
| EverythingSignal("int8a", Array1D[np.int8], int_array_value(np.int8)), | ||
| EverythingSignal("uint8a", Array1D[np.uint8], int_array_value(np.uint8)), | ||
| EverythingSignal("int16a", Array1D[np.int16], int_array_value(np.int16)), | ||
| EverythingSignal("uint16a", Array1D[np.uint16], int_array_value(np.uint16)), | ||
| EverythingSignal("int32a", Array1D[np.int32], int_array_value(np.int32)), | ||
| EverythingSignal("uint32a", Array1D[np.uint32], int_array_value(np.uint32)), | ||
| EverythingSignal("int64a", Array1D[np.int64], int_array_value(np.int64)), | ||
| EverythingSignal("uint64a", Array1D[np.uint64], int_array_value(np.uint64)), | ||
| EverythingSignal( | ||
| "float32a", Array1D[np.float32], float_array_value(np.float32) | ||
| ), | ||
| EverythingSignal( | ||
| "float64a", Array1D[np.float64], float_array_value(np.float64) | ||
| ), | ||
| EverythingSignal("stra", Sequence[str], ["one", "two", "three"]), | ||
| EverythingSignal( | ||
| "enuma", Sequence[ExampleEnum], [ExampleEnum.A, ExampleEnum.C] | ||
| ), | ||
| EverythingSignal( | ||
| "table", | ||
| ExampleTable, | ||
| ExampleTable( | ||
| bool=np.array([False, False, True, True], np.bool_), | ||
| int=np.array([1, 8, -9, 32], np.int32), | ||
| float=np.array([1.8, 8.2, -6, 32.9887], np.float64), | ||
| str=["Hello", "World", "Foo", "Bar"], | ||
| enum=[ExampleEnum.A, ExampleEnum.B, ExampleEnum.A, ExampleEnum.C], | ||
| ), | ||
| ), | ||
| EverythingSignal("ndarray", np.ndarray, np.array(([1, 2, 3], [4, 5, 6]))), | ||
| ] |
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Although this saves typing, it's actually pretty useful to see how you would make the Tango Device and it's associated ophyd device (and the same for EPICS and sim). Rather than try to be clever here I think I'd prefer to just see the test data written out longhand, with this file not being imported from Tango...
| _array_vals = { | ||
| "int8a": np.array([-128, 127, 0, 1, 2, 3, 4], dtype=np.int8), | ||
| "uint8a": np.array([0, 255, 0, 1, 2, 3, 4], dtype=np.uint8), | ||
| "int16a": np.array([-32768, 32767, 0, 1, 2, 3, 4], dtype=np.int16), | ||
| "uint16a": np.array([0, 65535, 0, 1, 2, 3, 4], dtype=np.uint16), | ||
| "int32a": np.array([-2147483648, 2147483647, 0, 1, 2, 3, 4], dtype=np.int32), | ||
| "uint32a": np.array([0, 4294967295, 0, 1, 2, 3, 4], dtype=np.uint32), | ||
| "int64a": np.array( | ||
| [-9223372036854775808, 9223372036854775807, 0, 1, 2, 3, 4], | ||
| dtype=np.int64, | ||
| ), | ||
| "uint64a": np.array([0, 18446744073709551615, 0, 1, 2, 3, 4], dtype=np.uint64), | ||
| "float32a": np.array( | ||
| [ | ||
| -3.4028235e38, | ||
| 3.4028235e38, | ||
| 1.1754944e-38, | ||
| 1.4012985e-45, | ||
| 0.0000000e00, | ||
| 1.2340000e00, | ||
| 2.3400000e05, | ||
| 3.4499999e-06, | ||
| ], | ||
| dtype=np.float32, | ||
| ), | ||
| "float64a": np.array( | ||
| [ | ||
| -1.79769313e308, | ||
| 1.79769313e308, | ||
| 2.22507386e-308, | ||
| 4.94065646e-324, | ||
| 0.00000000e000, | ||
| 1.23400000e000, | ||
| 2.34000000e005, | ||
| 3.45000000e-006, | ||
| ], | ||
| dtype=np.float64, | ||
| ), |
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Although it would be fine to put these in a fixture and share these
…_reading Remove requirement for timestamp provider in MonitorQueue
…e and DevEnum tests
jsouter
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Still very WIP, sketching out how to handle tango enums, also probably some debug comments left in. |
test str image in tango add tests for comparing bool and enum arrays with assert_* methods
Closing #715
very WIP, I should rework the tango version of OneOfEverythingDevice to create signals for each type as scalar, spectrum and image, so I can combine test_tango_signals and test_tango_one_of_everything.
Also trying to rework MonitorQueue to use the existing assert_value/assert_reading methods