from asammdf import MDF, SUPPORTED_VERSIONS, Signal
import numpy as np
cycles = 100
sigs = []
mdf = MDF()
t = np.arange(cycles, dtype=np.float64)
# no conversion
sig = Signal(
np.ones(cycles, dtype=np.uint64),
t,
name='Channel_no_conversion',
unit='s',
conversion=None,
comment='Unsigned 64 bit channel {}',
)
sigs.append(sig)
# linear
conversion = {
'a': 2,
'b': -0.5,
}
sig = Signal(
np.ones(cycles, dtype=np.int64),
t,
name='Channel_linear_conversion',
unit='Nm',
conversion=conversion,
comment='Signed 64bit channel with linear conversion',
)
sigs.append(sig)
# algebraic
conversion = {
'formula': '2 * sin(X)',
}
sig = Signal(
np.arange(cycles, dtype=np.int32) / 100.0,
t,
name='Channel_algebraic',
unit='eV',
conversion=conversion,
comment='Sinus channel with algebraic conversion',
)
sigs.append(sig)
# rational
conversion = {
'P1': 0,
'P2': 4,
'P3': -0.5,
'P4': 0,
'P5': 0,
'P6': 1,
}
sig = Signal(
np.ones(cycles, dtype=np.int64),
t,
name='Channel_rational_conversion',
unit='Nm',
conversion=conversion,
comment='Channel with rational conversion',
)
sigs.append(sig)
# string channel
sig = [
'String channel sample {}'.format(j).encode('ascii')
for j in range(cycles)
]
sig = Signal(
np.array(sig),
t,
name='Channel_string',
comment='String channel',
encoding='latin-1',
)
sigs.append(sig)
# byte array
ones = np.ones(cycles, dtype=np.dtype('(8,)u1'))
sig = Signal(
ones*111,
t,
name='Channel_bytearay',
comment='Byte array channel',
)
sigs.append(sig)
# tabular
vals = 20
conversion = {
'raw_{}'.format(i): i
for i in range(vals)
}
conversion.update(
{
'phys_{}'.format(i): -i
for i in range(vals)
}
)
sig = Signal(
np.arange(cycles, dtype=np.uint32) % 20,
t,
name='Channel_tabular',
unit='-',
conversion=conversion,
comment='Tabular channel',
)
sigs.append(sig)
# value to text
vals = 20
conversion = {
'val_{}'.format(i): i
for i in range(vals)
}
conversion.update(
{
'text_{}'.format(i): 'key_{}'.format(i).encode('ascii')
for i in range(vals)
}
)
conversion['default'] = b'default key'
sig = Signal(
np.arange(cycles, dtype=np.uint32) % 30,
t,
name='Channel_value_to_text',
conversion=conversion,
comment='Value to text channel',
)
sigs.append(sig)
# tabular with range
vals = 20
conversion = {
'lower_{}'.format(i): i * 10
for i in range(vals)
}
conversion.update(
{
'upper_{}'.format(i): (i + 1) * 10
for i in range(vals)
}
)
conversion.update(
{
'phys_{}'.format(i): i
for i in range(vals)
}
)
conversion['default'] = -1
sig = Signal(
2 * np.arange(cycles, dtype=np.float64),
t,
name='Channel_value_range_to_value',
unit='order',
conversion=conversion,
comment='Value range to value channel',
)
sigs.append(sig)
# value range to text
vals = 20
conversion = {
'lower_{}'.format(i): i * 10
for i in range(vals)
}
conversion.update(
{
'upper_{}'.format(i): (i + 1) * 10
for i in range(vals)
}
)
conversion.update(
{
'text_{}'.format(i): 'Level {}'.format(i)
for i in range(vals)
}
)
conversion['default'] = b'Unknown level'
sig = Signal(
6 * np.arange(cycles, dtype=np.uint64) % 240,
t,
name='Channel_value_range_to_text',
conversion=conversion,
comment='Value range to text channel',
)
sigs.append(sig)
mdf.append(sigs, comment='single dimensional channels', common_timebase=True)
sigs = []
# lookup tabel with axis
samples = [
np.ones((cycles, 2, 3), dtype=np.uint64) * 1,
np.ones((cycles, 2), dtype=np.uint64) * 2,
np.ones((cycles, 3), dtype=np.uint64) * 3,
]
types = [
('Channel_lookup_with_axis', '(2, 3)<u8'),
('channel_axis_1', '(2, )<u8'),
('channel_axis_2', '(3, )<u8'),
]
sig = Signal(
np.core.records.fromarrays(samples, dtype=np.dtype(types)),
t,
name='Channel_lookup_with_axis',
unit='A',
comment='Array channel with axis',
)
sigs.append(sig)
# lookup tabel with default axis
samples = [
np.ones((cycles, 2, 3), dtype=np.uint64) * 4,
]
types = [
('Channel_lookup_with_default_axis', '(2, 3)<u8'),
]
sig = Signal(
np.core.records.fromarrays(samples, dtype=np.dtype(types)),
t,
name='Channel_lookup_with_default_axis',
unit='mA',
comment='Array channel with default axis',
)
sigs.append(sig)
# structure channel composition
samples = [
np.ones(cycles, dtype=np.uint8) * 10,
np.ones(cycles, dtype=np.uint16) * 20,
np.ones(cycles, dtype=np.uint32) * 30,
np.ones(cycles, dtype=np.uint64) * 40,
np.ones(cycles, dtype=np.int8) * -10,
np.ones(cycles, dtype=np.int16) * -20,
np.ones(cycles, dtype=np.int32) * -30,
np.ones(cycles, dtype=np.int64) * -40,
]
types = [
('struct_channel_0', np.uint8),
('struct_channel_1', np.uint16),
('struct_channel_2', np.uint32),
('struct_channel_3', np.uint64),
('struct_channel_4', np.int8),
('struct_channel_5', np.int16),
('struct_channel_6', np.int32),
('struct_channel_7', np.int64),
]
sig = Signal(
np.core.records.fromarrays(samples, dtype=np.dtype(types)),
t,
name='Channel_structure_composition',
comment='Structure channel composition',
)
sigs.append(sig)
# nested structures
l4_arr = [
np.ones(cycles, dtype=np.float64) * 41,
np.ones(cycles, dtype=np.float64) * 42,
np.ones(cycles, dtype=np.float64) * 43,
np.ones(cycles, dtype=np.float64) * 44,
]
types = [
('level41', np.float64),
('level42', np.float64),
('level43', np.float64),
('level44', np.float64),
]
l4_arr = np.core.records.fromarrays(l4_arr, dtype=types)
l3_arr = [
l4_arr,
l4_arr,
l4_arr,
]
types = [
('level31', l4_arr.dtype),
('level32', l4_arr.dtype),
('level33', l4_arr.dtype),
]
l3_arr = np.core.records.fromarrays(l3_arr, dtype=types)
l2_arr = [
l3_arr,
l3_arr,
]
types = [
('level21', l3_arr.dtype),
('level22', l3_arr.dtype),
]
l2_arr = np.core.records.fromarrays(l2_arr, dtype=types)
l1_arr = [
l2_arr,
]
types = [
('level11', l2_arr.dtype),
]
l1_arr = np.core.records.fromarrays(l1_arr, dtype=types)
sigs.append(
Signal(
l1_arr,
t,
name='Nested_structures',
)
)
mdf.append(sigs, comment='arrays', common_timebase=True)
mdf.save('demo.mf4', overwrite=True)