These notes are a discussion of the serialisation libraries available to
MicroPython plus a tutorial on the use of a library supporting Google Protocol
Buffers (here abbreviated to protobuf). The aim is not to replace official
documentation but to illustrate the relative merits and drawbacks of the
various approaches.
The need for serialisation arises whenever data must be stored on disk or communicated over an interface such as a socket, a UART or such interfaces as I2C or SPI. All these require the data to be presented as linear sequences of bytes. The problem is how to convert an arbitrary Python object to such a sequence, and how subsequently to restore the object.
I am aware of four ways of achieving this, each with their own advantages and drawbacks. In two cases the encoded strings comprise ASCII characters, in the other two they are binary (bytes can take all possible values).
- ujson (ASCII, official)
- pickle (ASCII, official)
- ustruct (binary, official)
- protobuf binary, unofficial
The first two are self-describing: the format includes a definition of its
structure. This means that the decoding process can re-create the object in the
absence of information on its structure, which may therefore change at runtime.
Further, ujson and pickle produce human-readable byte sequences which aid
debugging. The drawback is inefficiency: the byte sequences are relatively
long. They are variable length. This means that the receiving process must be
provided with a means to determine when a complete string has been received.
The ustruct and protobuf solutions are binary formats: the byte sequences
comprise binary data which is neither human readable nor self-describing.
Binary sequences require that the receiver has information on their structure
in order to decode them. In the case of ustruct sequences are of a fixed
length which can be determined from the structure. protobuf sequences are
variable length requiring handling discussed below.
The benefit of binary sequences is efficiency: sequence length is closer to the information-theoretic minimum, compared to the ASCII options.
These are very similar. ujson is documented
here. pickle has
identical methods so this doc may be used for both.
The advantage of ujson is that JSON strings can be accepted by CPython and by
other languages. The drawback is that only a subset of Python object types can
be converted to legal JSON strings; this is a limitation of the
JSON specification.
The advantage of pickle is that it will accept any Python object except for
instances of user defined classes. The extremely simple source may be found in
the official library.
The strings produced are incompatible with CPython's pickle, but can be
decoded in CPython by using the MicroPython decoder. There is a
bug in the
MicroPython implementation when running under MicroPython. A workround consists
of never encoding short strings which change repeatedly.
These may be copy-pasted to the MicroPython REPL.
Pickle:
import pickle
data = {1:'test', 2:1.414, 3: [11, 12, 13]}
s = pickle.dumps(data)
print('Human readable data:', s)
v = pickle.loads(s)
print('Decoded data (partial):', v[3])JSON. Note that dictionary keys must be strings:
import ujson
data = {'1':'test', '2':1.414, '3': [11, 12, 13]}
s = ujson.dumps(data)
print('Human readable data:', s)
v = ujson.loads(s)
print('Decoded data (partial):', v['3'])In real applications the data, and hence the string length, vary at runtime.
The receiving process needs to know when a complete string has been received or
read from a file. In practice ujson and pickle do not include newline
characters in encoded strings. If the data being encoded includes a newline, it
is escaped in the string:
import ujson
data = {'1':b'test\nmore', '2':1.414, '3': [11, 12, 13]}
s = ujson.dumps(data)
print('Human readable data:', s)
v = ujson.loads(s)
print('Decoded data (partial):', v['1'])If this is pasted at the REPL you will observe that the human readable data does not have a line break (while the decoded data does). Output:
Human readable data: {"2": 1.414, "3": [11, 12, 13], "1": "test\nmore"}
Decoded data (partial): test
more
Consequently encoded strings may be separated with '\n' before saving and
reading may be done using readline methods as in this code fragment where
u is a UART instance:
s = ujson.dumps(data)
# pickle produces a bytes object whereas ujson produces a string
# In the case of ujson it is probably wise to convert to bytes
u.write(s.encode())
# Pickle:
# u.write(s)
u.write(b'\n')
# receiver
s = u.readline()
v = ujson.loads(s) # ujson can cope with bytes object
# ujson and pickle can cope with trailing newline.This is documented here. The binary format is efficient, but the format of a sequence cannot change at runtime and must be "known" to the decoding process. Records are of fixed length. If data is to be stored in a binary random access file, the fixed record size means that the offset of a given record may readily be calculated.
Write a 100 record file. Each record comprises three 32-bit integers:
import ustruct
fmt = 'iii' # Record format: 3 signed ints
rlen = ustruct.calcsize(fmt) # Record length
buf = bytearray(rlen)
with open('myfile', 'wb') as f:
for x in range(100):
y = x * x
z = x * 10
ustruct.pack_into(fmt, buf, 0, x, y, z)
f.write(buf)Read record no. 10 from that file:
import ustruct
fmt = 'iii'
rlen = ustruct.calcsize(fmt) # Record length
buf = bytearray(rlen)
rnum = 10 # Record no.
with open('myfile', 'rb') as f:
f.seek(rnum * rlen)
f.readinto(buf)
result = ustruct.unpack_from(fmt, buf)
print(result)Owing to the fixed record length, integers must be constrained to fit the length declared in the format string.
Binary formats cannot use delimiters as any delimiter character may be present
in the data - however the fixed length of ustruct records means that this is
not a problem.
For performance oriented applications, ustruct is the only serialisation
approach which can be used in a non-allocating fashion, by using pre-allocated
buffers as in the above example.
In ustruct the s data type is normally prefixed by a length (defaulting to
1). This ensures that records are of fixed size, but is potentially inefficient
as shorter strings will still occupy the same amount of space. Longer strings
will silently be truncated. Short strings are packed with zeros.
import ustruct
fmt = 'ii30s'
rlen = ustruct.calcsize(fmt) # Record length
buf = bytearray(rlen)
ustruct.pack_into(fmt, buf, 0, 11, 22, 'the quick brown fox')
ustruct.unpack_from(fmt, buf)
ustruct.pack_into(fmt, buf, 0, 11, 22, 'rats')
ustruct.unpack_from(fmt, buf) # Packed with zeros
ustruct.pack_into(fmt, buf, 0, 11, 22, 'the quick brown fox jumps over the lazy dog')
ustruct.unpack_from(fmt, buf) # TruncationOutput:
(11, 22, b'the quick brown fox\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')
(11, 22, b'rats\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')
(11, 22, b'the quick brown fox jumps over')This is a Google standard
described in this Wikipedia article.
The aim is to provide a language independent, efficient, binary data interface.
Records are variable length, and strings and integers of arbitrary size may be
accommodated. The
implementation compatible with MicroPython
is a "micro" implementation: .proto files are not supported. However the data
format aims to be a subset of the Google standard and claims compatibility with
other platforms and languages.
The principal benefit to developers using only CPython/MicroPython is its efficient support for fields whose length varies at runtime. To my knowledge it is the sole solution for encoding such data in a compact binary format.
The following notes should be read in conjunction with the official docs. The notes aim to reduce the learning curve which I found a little challenging.
In normal use the object transmitted by minipb will be a dict with entries
having various predefined data types. Entries may be objects of variable length
including strings, lists and other dict instances. The structure of the
dict is defined using a schema. Sender and receiver share the schema with
each script using it to instantiate the Wire class. The Wire instance is
then repeatedly invoked to encode or decode the data.
The schema is a tuple defining the structure of the data dict. Each
element declares a key and its data type in an inner tuple. Elements of this
inner tuple are strings, with element 0 defining the field's key. Subsequent
elements define the field's data type; in most cases the data type is defined
by a single string.
The library comprises a single file minipb.py. It has a dependency, the
logging module logging.py which may be found in
micropython-lib.
On RAM constrained platforms minipb.py may be cross-compiled or frozen as
bytecode for even lower RAM consumption.
These are listed in the docs. Many of these are intended to maximise compatibility with the native data types of other languages. Where data will only be accessed by CPython or MicroPython, a subset may be used which maps onto Python data types:
- 'U' A UTF8 encoded string.
- 'a' A
bytesobject. - 'b' A
bool. - 'f' A
floatA 32-bit float: the usual MicroPython default. - 'z' An
int: a signed arbitrary length integer. Efficiently encoded with an ingenious algorithm. - 'd' A double precision 64-bit float. The default on Pyboard D SF6. Also on other platforms with special firmware builds.
- 'X' An empty field.
If a field is prefixed with * it is a required field, otherwise it is
optional. The field must still exist in the data: the only difference is that
a required field cannot be set to None. Optional fields can be useful,
notably for boolean types which can then represent three states.
The following is a minimal example which can be pasted at the REPL:
import minipb
schema = (('value', 'z'),) # Dict will hold a single integer
w = minipb.Wire(schema)
data = {'value': 0}
data['value'] = 150
tx = w.encode(data)
rx = w.decode(tx) # received data
print(rx)This example glosses over the fact that in a real application the data will
change and the length of the transmitted string tx will vary. The receiving
process needs to know the length of each string. Note that a consequence of the
binary format is that delimiters cannot be used. The length of each record must
be established and made available to the receiver. In the case where data is
being saved to a binary file, the file will need an index. Where data is to
be transmitted over and interface each string should be prepended with a fixed
length "size" field. The following example illustrates this.
These examples can't be cut and pasted at the REPL as they assume send(n) and
receive(n) functions which access the interface.
Sender example:
import minipb
schema = (('value', 'z'),
('float', 'f'),
('signed', 'z'),)
w = minipb.Wire(schema)
# Create a dict to hold the data
data = {'value': 0,
'float': 0.0,
'signed' : 0,}
while True:
# Update values then encode and transmit them, e.g.
# data['signed'] = get_signed_value()
tx = w.encode(data)
# Data lengths may change on each iteration
# here we encode the length in a single byte
dlen = len(tx).to_bytes(1, 'little')
send(dlen)
send(tx)Receiver example:
import minipb
# schema must match transmitter. Typically both would import this.
schema = (('value', 'z'),
('float', 'f'),
('signed', 'z'),)
w = minipb.Wire(schema)
while True:
dlen = receive(1) # Data length stored in 1 byte
data = receive(dlen) # Retrieve actual data
rx = w.decode(data)
# Do something with the received dictThis feature enables variable length lists to be encoded. List elements must
all be of the same (declared) data type. In this example the value and txt
fields are variable length lists denoted by the '+' prefix. The value field
holds a list of int values and txt holds strings:
import minipb
schema = (('value', '+z'),
('float', 'f'),
('txt', '+U'),
)
w = minipb.Wire(schema)
data = {'value': [150, 123, 456],
'float': 1.23,
'txt' : ['abc', 'def', 'ghi'],
}
tx = w.encode(data)
rx = w.decode(tx)
print(rx)
data['txt'][1] = 'the quick brown fox' # Strings have variable length
data['txt'].append('the end') # List has variable length
data['value'].append(999) # Variable length
tx = w.encode(data)
rx = w.decode(tx)
print(rx)The author of minipb does not recommend
their use. Their purpose appears to be in the context of fixed-length fields
which are outside the scope of pure Python programming.
The concept of message fields is a Protocol Buffer notion. In MicroPython
terminology a message field contains a dict whose contents are defined by
another schema. This enables nested dictionaries whose entries may be any valid
protobuf data type.
This is illustrated below. The example extends this by making the field a
variable length list of dict objects (with the '+[' specifier):
import minipb
# Schema for the nested dictionary instances
nested_schema = (('str2', 'U'),
('num2', 'z'),)
# Outer schema
schema = (('number', 'z'),
('string', 'U'),
('nested', '+[', nested_schema, ']'),
('num', 'z'),)
w = minipb.Wire(schema)
data = {
'number': 123,
'string': 'test',
'nested': [{'str2': 'string','num2': 888,},
{'str2': 'another_string', 'num2': 12345,}, ],
'num' : 42
}
tx = w.encode(data)
rx = w.decode(tx)
print(rx)
print(rx['nested'][0]['str2']) # Access inner dict instances
print(rx['nested'][1]['str2'])
# Appending to the nested list of dicts
data['nested'].append({'str2': 'rats', 'num2':999})
tx = w.encode(data)
rx = w.decode(tx)
print(rx)
print(rx['nested'][2]['str2']) # Access inner dict instancesThis is surely overkill in most MicroPython applications, but for the sake of completeness message fields can be recursive:
import minipb
inner_schema = (('str2', 'U'),
('num2', 'z'),)
nested_schema = (('inner', '+[', inner_schema, ']'),)
schema = (('number', 'z'),
('string', 'U'),
('nested', '[', nested_schema, ']'),
('num', 'z'),)
w = minipb.Wire(schema)
data = {
'number': 123,
'string': 'test',
'nested': {'inner':({'str2': 'string', 'num2': 888,},
{'str2': 'another_string','num2': 12345,}, ),},
'num' : 42
}
tx = w.encode(data)
rx = w.decode(tx)
print(rx)
print(rx['nested']['inner'][0]['str2'])