pycond 20230212

Description:

pycond 20230212

author: gk
version: 20230212

pycond: Lightweight Declarative Condition Expressions



Table Of Contents

What
Why

Alternatives


Mechanics

Parsing
Building
Structured Conditions
Evaluation
Default Lookup
Passing State
Deep Lookup / Nested State / Lists

Lookup Performance: Prebuilt Deep Getters
Best Practices


Prefixed Data
Attributes Access
Custom Lookup And Value Passing
Lazy Evaluation
Condition Operators (Comparators)

Using Symbolic Operators
Extending Condition Operators
Negation not
Reversal rev
Wrapping Condition Operators

Global Wrapping


Condition Local Wrapping
Combining Operations


Details

Debugging Lookups
Enabling/Disabling of Branches
Building Conditions From Text

Grammar
Atomic Conditions
Nesting


Tokenizing Details

Functioning
Separator sep
Apostrophes
Escaping


Building

Autoconv: Casting of values into python simple types




Context On Demand
Lookup Providers

Accepted Signatures
Parametrized Lookup Functions
Namespace


Caching
Extensions
Named Conditions: Qualification
Options
Partial Evaluation
Streaming Data

Filtering
Streaming Classification
Selective Classification


Treating of Booleans (Conditions, Not Names)
Asyncronous Operations

Asyncronous Filter






What
You have a bunch of data, possibly streaming...
id,first_name,last_name,email,gender,ip_address
1,Rufe,Morstatt,[email protected],Male,216.70.69.120
2,Kaela,Scott,[email protected],Female,73.248.145.44,2
(...)

... and you need to filter. For now lets say we have them already as list of dicts.
You can do it imperatively:
foo_users = [
u
for u in users
if (u['gender'] == 'Male' or u['last_name'] == 'Scott') and '@' in u['email']
]

or you have this module assemble a condition function from a declaration like:
from pycond import make_filter
cond = 'email contains .de and gender eq Male or last_name eq Scott'
is_foo = make_filter(cond) # the built filter function is first

and then apply as often as you need, against varying state / facts / models (...):
foo_users = filter(is_foo, users)

with roughly the same performance (factor 2-3) than the handcrafted python.

In real life performance is often better then using imperative code, due to
pycond's lazy evaluation feature.

Why
When the developer can decide upon the filters to apply on data he'll certainly
use Python's excellent expressive possibilities directly, e.g. as shown above
through list comprehensions.
But what if the filtering conditions are based on decisions outside of the program's
control? I.e. from an end user, hitting the program via the network, in a somehow serialized form, which is rarely directly evaluatable Python.
This is the main use case for this module.
Alternatives
But why yet another tool for such a standard job?
There is a list of great tools and frameworks where condition parsing is a (small) part of them, e.g. pyke or durable and many in the django world or from SQL statement parsers.
1. I just needed a very slim tool for only the parsing into functions - but this pretty transparent and customizable
pycond allows to customize

the list of condition operators
the list of combination operators
the general behavior of condition operators via global or condition local wrappers
their names
the tokenizer
the value lookup function

and ships as zero dependency single module.
All evaluation is done via partials and not lambdas, i.e. operations can be introspected and debugged very simply, through breakpoints or custom logging operator or lookup wrappers.
2. Simplicity of the grammar: Easy to type directly, readable by non
programmers but also synthesisable from structured data, e.g. from a web framework.
3. Performance: Good enough to have "pyconditions" used within stream filters.
With the current feature set we are sometimes a factor 2-3 worse but (due to lazy eval) often better,
compared with handcrafted list comprehensions.
Mechanics


Parsing
pycond parses the condition expressions according to a set of constraints given to the parser in the tokenizer function.
The result of the tokenizer is given to the builder.
import pycond as pc

expr = '[a eq b and [c lt 42 or foo eq bar]]'
cond = pc.to_struct(pc.tokenize(expr, sep=' ', brkts='[]'))
print('filter:', cond)
# test:
data = [
{'a': 'b', 'c': 1, 'foo': 42},
{'a': 'not b', 'c': 1},
]
filtered = list(filter(pc.make_filter(expr), data))
print('matching:', filtered)
return cond, len(filtered)

Output:
filter: [['a', 'eq', 'b', 'and', ['c', 'lt', '42', 'or', 'foo', 'eq', 'bar']]]
matching: [{'a': 'b', 'c': 1, 'foo': 42}]

Building
After parsing, the builder is assembling a nested set of operator functions,
combined via combining operators. The functions are partials, i.e. not yet
evaluated - but information about the necessary keys is already available:
f, meta = pc.parse_cond('foo eq bar')
assert meta['keys'] == ['foo']
assert f(state={'foo': 'bar'}) == True

Note: The make_filter function is actually a convencience function for
parse_cond, ignoring that meta information and calling with
state=<filter val>
Structured Conditions
Other processes may deliver condition structures via serializable formats (e.g.
json). If you pass such already tokenized constructs to the pycond function,
then the tokenizer is bypassed:
cond = [['a', 'eq', 'b'], 'or', ['c', 'in', ['foo', 'bar']]]
assert pc.pycond(cond)(state={'a': 'b'}) == True
# json support is built in:
cond_as_json = json.dumps(cond)
assert pc.pycond(cond_as_json)(state={'a': 'b'}) == True

Evaluation
The result of the builder is a 'pycondition', i.e. a function which can be run many times against varying state of the system.
How state is evaluated is customizable at build and run time.
Default Lookup
"Lookup" denotes the process of deriving the actual values to evaluate, from a given state. Can be simple gets, getattrs, walks into the structure - or arbitrary, via custom lookup functions.
The default is to get lookup keys within expressions from an initially empty State dict within the module. This is not thread safe, i.e. not to be used in async or non cooperative multitasking environments.
f = pc.pycond('foo eq bar')
assert f() == False
pc.State['foo'] = 'bar' # not thread safe!
assert f() == True

(pycond is a shortcut for parse_cond, when meta infos are not required).
Passing State
Using a state argument at evaluation is thread safe:
assert pc.pycond('a gt 2')(state={'a': 42}) == True
assert pc.pycond('a gt 2')(state={'a': -2}) == False

Deep Lookup / Nested State / Lists
You may supply a path seperator for diving into nested structures like so:
m = {'a': {'b': [{'c': 1}]}}
assert pc.pycond('a.b.0.c', deep='.')(state=m) == True
assert pc.pycond('a.b.1.c', deep='.')(state=m) == False
assert pc.pycond('a.b.0.c eq 1', deep='.')(state=m) == True
# convencience argument for string conditions:
assert pc.pycond('deep: a.b.0.c')(state=m) == True

# This is how you express deep access via structured conditions:
assert pc.pycond([('a', 'b', 0, 'c'), 'eq', 1])(state=m) == True

# Since tuples are not transferrable in json, we also allow deep paths as list:
# We apply heuristics to exclude expressions or conditions:
c = [[['a', 'b', 0, 'c'], 'eq', 1], 'and', 'a']
f, nfos = pc.parse_cond(c)
# sorting order for keys: tuples at end, sorted by len, rest default py sorted:
assert f(state=m) == True and nfos['keys'] == ['a', ('a', 'b', 0, 'c')]



The structure may also contain objects, then we use getattribute to get to the next value.


deep="." is actually just convience notation for supplying the following "lookup function" (see below):


m = {'a': {'b': [{'c': 1}]}}
assert pc.pycond('a.b.0.c', lookup=pc.state_get_deep)(state=m) == True

Lookup Performance: Prebuilt Deep Getters
The value lookup within nested structures can be stored into item and attribute getters (or , alternatively, an evaluated synthesized lookup function), built, when the first item has a matching structure.


Upside: Performance is a few times better compared to when the structure of items is explored each time, as with the 'deep' parameter.


Downside: The lookup remains as built for the first structurely matching item. Schematic changes like from a key within a dict to an attribute will not except but deliver always False for the
actual condition value matching.


pycond.Getters.state_get_deep2: A list of item and attribute getters is built at first successfull lookup evaluation.


pycond.Getters.state_get_evl: An expression like "lambda state=state['a'].b[0]['c']" is built and evaluated, then applied to the items.

Fastest way to get to the values at evaluation time.
Security: Round brackets within key names are forbidden and deliver always false - but an eval is an eval i.e. potentially evil.



These two additional "deep" lookup functions are conveniently made accessible by supplying a deep2 or deep3 argument:
m = {'a': {'b': [{'c': 1}]}}
# 3 times faster than deep. Safe.
assert pc.pycond('a.b.0.c', deep2='.')(state=m) == True
# 4 times faster than deep. Eval involved.
assert pc.pycond('a.b.0.c', deep3='.')(state=m) == True

The evaluation results for the keys are cached. The cache is cleared after 1Mio entries but can be cleared manually via pc.clear_caches() any time before that.
Best Practices

Lookup keys change all the time, not many items checked for specific key: Use deep
Many items to be checked with same keys, input from untrusted users: Use deep2
Many items to be checked with same keys, input from trusted users: Use deep3

Prefixed Data
When data is passed through processing pipelines, it often is passed with headers. So it may be useful to pass a global prefix to access the payload like so:
m = {'payload': {'b': [{'c': 1}], 'id': 123}}
assert pc.pycond('b.0.c', deep='.', prefix='payload')(state=m) == True

Attributes Access
Since version 20210221 we try attributes when objects are not dicts:
class MyObj:
val = {'a': 'b'}

m = {'payload': {'obj': MyObj()}}
cond = [['obj.val.a', 'eq', 'b']]
assert pc.pycond(cond, deep='.', prefix='payload')(state=m) == True

Custom Lookup And Value Passing
You can supply your own function for value acquisition.

Signature: See example.
Returns: The value for the key from the current state plus the
compare value for the operator function.

# must return a (key, value) tuple:
model = {'eve': {'last_host': 'somehost'}}

def my_lu(k, v, req, user, model=model):
print('user check. locals:', dict(locals()))
return (model.get(user) or {}).get(k), req[v]

f = pc.pycond('last_host eq host', lookup=my_lu)

req = {'host': 'somehost'}
assert f(req=req, user='joe') == False
assert f(req=req, user='eve') == True

Output:
user check. locals: {'k': 'last_host', 'v': 'host', 'req': {'host': 'somehost'}, 'user': 'joe', 'model': {'eve': {'last_host': 'somehost'}}}
user check. locals: {'k': 'last_host', 'v': 'host', 'req': {'host': 'somehost'}, 'user': 'eve', 'model': {'eve': {'last_host': 'somehost'}}}


as you can see in the example, the state parameter is just a convention
for pyconds' [title: default lookup function, fmatch:pycond.py, lmatch:def state_get] < SRC > .

Lazy Evaluation
This is avoiding unnecessary calculations in many cases:
When an evaluation branch contains an "and" or "and_not" combinator, then
at runtime we evaluate the first expression - and stop if it is already
False.
Same when first expression is True, followed by "or" or "or_not".
That way expensive deep branch evaluations are omitted or, when
the lookup is done lazy, the values won't be even fetched:
evaluated = []

def myget(key, val, cfg, state=None, **kw):
evaluated.append(key)
return pc.state_get(key, val, cfg, state, **kw)

f = pc.pycond('[a eq b] or foo eq bar and baz eq bar', lookup=myget)
assert f(state={'foo': 42}) == False
# the value for "baz" is not even fetched and the whole (possibly
# deep) branch after the last and is ignored:
assert evaluated == ['a', 'foo']
print(evaluated)
evaluated.clear()

f = pc.pycond('[[a eq b] or foo eq bar] and baz eq bar', lookup=myget)
assert f(state={'a': 'b', 'baz': 'bar'}) == True
# the value for "baz" is not even fetched and the whole (possibly
# deep) branch after the last and is ignored:
assert evaluated == ['a', 'baz']
print(evaluated)

Output:
['a', 'foo']
['a', 'baz']

Remember that all keys occurring in a condition(which may be provided by the user at runtime) are returned by the condition parser. Means that building of evaluation contexts[can be done]( # context-on-demand-and-lazy-evaluation), based on the data actually needed and not more.
Condition Operators (Comparators)
All booleanstandardlib operators
are available by default:
from pytest2md import html_table as tbl # just a table gen.
from pycond import get_ops

for k in 'nr', 'str':
s = 'Default supported ' + k + ' operators...(click to extend)'
print(tbl(get_ops()[k], [k + ' operator', 'alias'], summary=s))

Default supported nr operators...(click to extend)

nr operatoralias
add+
and_&
eq==
floordiv//
ge>=
gt>
iadd+=
iand&=
ifloordiv//=
ilshift<<=
imod%=
imul*=
ior|=
ipow**=
irshift>>=
is_is
is_notis
isub-=
itruediv/=
ixor^=
le<=
lshift<<
lt<
mod%
mul*
ne!=
or_|
pow**
rshift>>
sub-
truediv/
xor^
itemgetter
length_hint


Default supported str operators...(click to extend)

str operatoralias
attrgetter
concat+
contains
countOf
iconcat+=
indexOf
methodcaller


Using Symbolic Operators
By default pycond uses text style operators.

ops_use_symbolic switches processwide to symbolic style only.
ops_use_symbolic_and_txt switches processwide to both notations allowed.

pc.ops_use_symbolic()
pc.State['foo'] = 'bar'
assert pc.pycond('foo == bar')() == True
try:
# this raises now, text ops not known anymore:
pc.pycond('foo eq bar')
except:
pc.ops_use_symbolic_and_txt(allow_single_eq=True)
assert pc.pycond('foo = bar')() == True
assert pc.pycond('foo == bar')() == True
assert pc.pycond('foo eq bar')() == True
assert pc.pycond('foo != baz')() == True


Operator namespace(s) should be assigned at process start, they are global.

Extending Condition Operators
pc.OPS['maybe'] = lambda a, b: int(time.time()) % 2
# valid expression now:
assert pc.pycond('a maybe b')() in (True, False)

Negation not
Negates the result of the condition operator:
pc.State['foo'] = 'abc'
assert pc.pycond('foo eq abc')() == True
assert pc.pycond('foo not eq abc')() == False

Reversal rev
Reverses the arguments before calling the operator
pc.State['foo'] = 'abc'
assert pc.pycond('foo contains a')() == True
assert pc.pycond('foo rev contains abc')() == True


rev and not can be combined in any order.

Wrapping Condition Operators
Global Wrapping
You may globally wrap all evaluation time condition operations through a custom function:
l = []

def hk(f_op, a, b, l=l):
l.append((getattr(f_op, '__name__', ''), a, b))
return f_op(a, b)

pc.run_all_ops_thru(hk) # globally wrap the operators

pc.State.update({'a': 1, 'b': 2, 'c': 3})
f = pc.pycond('a gt 0 and b lt 3 and not c gt 4')
assert l == []
f()
expected_log = [('gt', 1, 0.0), ('lt', 2, 3.0), ('gt', 3, 4.0)]
assert l == expected_log
pc.ops_use_symbolic_and_txt()

You may compose such wrappers via repeated application of the run_all_ops_thru API function.
Condition Local Wrapping
This is done through the ops_thru parameter as shown:
def myhk(f_op, a, b):
return True

pc.State['a'] = 1
f = pc.pycond('a eq 2')
assert f() == False
f = pc.pycond('a eq 2', ops_thru=myhk)
assert f() == True


Using ops_thru is a good way to debug unexpected results, since you
can add breakpoints or loggers there.

Combining Operations
You can combine single conditions with

and
and not
or
or not
xor by default.

The combining functions are stored in pycond.COMB_OPS dict and may be extended.

Do not use spaces for the names of combining operators. The user may use them but they are replaced at before tokenizing time, like and not -> and_not.

Details
Debugging Lookups
pycond provides a key getter which prints out every lookup.
f = pc.pycond('[[a eq b] or foo eq bar] or [baz eq bar]', lookup=pc.dbg_get)
assert f(state={'foo': 'bar'}) == True

Output:
Lookup: a b -> None
Lookup: foo bar -> bar

Enabling/Disabling of Branches
Insert booleans like shown:
f = pc.pycond(['foo', 'and', ['bar', 'eq', 1]])
assert f(state={'foo': 1}) == False
f = pc.pycond(['foo', 'and', [True, 'or', ['bar', 'eq', 1]]])
assert f(state={'foo': 1}) == True

Building Conditions From Text
Condition functions are created internally from structured expressions -
but those are[hard to type]( # lazy-dynamic-context-assembly),
involving many apostropies.
The text based condition syntax is intended for situations when end users
type them into text boxes directly.
Grammar
Combine atomic conditions with boolean operators and nesting brackets like:
[< atom1 > < and | or | and not|... > <atom2 > ] < and|or... > [ [ < atom3 > ....

Atomic Conditions
[not] < lookup_key > [[rev] [not] < condition operator (co) > <value > ]


When just lookup_key is given, then co is set to the truthy function:

def truthy(key, val=None):
return operatur.truth(k)

so such an expression is valid and True:
pc.State.update({'foo': 1, 'bar': 'a', 'baz': []})
assert pc.pycond('[ foo and bar and not baz]')() == True


When not lookup_key is given, then co is set to the falsy
function:

m = {'x': 'y', 'falsy_val': {}}
# normal way
assert pc.pycond(['foo', 'eq', None])(state=m) == True
# using "not" as prefix:
assert pc.pycond('not foo')(state=m) == True
assert pc.pycond(['not', 'foo'])(state=m) == True
assert pc.pycond('not falsy_val')(state=m) == True
assert pc.pycond('x and not foo')(state=m) == True
assert pc.pycond('y and not falsy_val')(state=m) == False

Nesting
Combined conditions may be arbitrarily nested using brackets "[" and "]".

Via the brkts config parameter you may change those to other separators at build time.

Tokenizing Details

Brackets as strings in this flat list form, e.g. ['[', 'a', 'and' 'b', ']'...]

Functioning
The tokenizers job is to take apart expression strings for the builder.
Separator sep
Separates the different parts of an expression. Default is ' '.
pc.State['a'] = 42
assert pc.pycond('a.eq.42', sep='.')() == True


sep can be a any single character including binary.

Bracket characters do not need to be separated, the tokenizer will do:
# equal:
assert (
pc.pycond('[[a eq 42] and b]')() == pc.pycond('[ [ a eq 42 ] and b ]')()
)


The condition functions themselves do not evaluate equal - those
had been assembled two times.

Apostrophes
By putting strings into Apostrophes you can tell the tokenizer to not further inspect them, e.g. for the seperator:
pc.State['a'] = 'Hello World'
assert pc.pycond('a eq "Hello World"')() == True

Escaping
Tell the tokenizer to not interpret the next character:
pc.State['b'] = 'Hello World'
assert pc.pycond('b eq Hello\ World')() == True

Building
Autoconv: Casting of values into python simple types
Expression string values are automatically cast into bools and numbers via the public pycond.py_type function.
This can be prevented by setting the autoconv parameter to False or by using Apostrophes:
pc.State['a'] = '42'
assert pc.pycond('a eq 42')() == False
# compared as string now
assert pc.pycond('a eq "42"')() == True
# compared as string now
assert pc.pycond('a eq 42', autoconv=False)() == True

If you do not want to provide a custom lookup function(where you can do what you want)
but want to have looked up keys autoconverted then use:
for id in '1', 1:
pc.State['id'] = id
assert pc.pycond('id lt 42', autoconv_lookups=True)

Context On Demand
Often the conditions are in user space, applied on data streams under
the developer's control only at development time.
The end user might pick only a few keys from many offered within an API.
pycond's ctx_builder allows to only calculate those keys at runtime,
the user decided to base conditions upon:
At condition build time hand over a namespace for *all * functions which
are available to build the ctx.
pycon will return a context builder function for you, calling only those functions
which the condition actually requires.
pc.ops_use_symbolic_and_txt(allow_single_eq=True)

# Condition the end user configured, e.g. at program run time:
cond = [
['group_type', 'in', ['lab', 'first1k', 'friendly', 'auto']],
'and',
[
[
[
[
['cur_q', '<', 0.5],
'and',
['delta_q', '>=', 0.15],
],
'and',
['dt_last_enforce', '>', 28800],
],
'and',
['cur_hour', 'in', [3, 4, 5]],
],
'or',
[
[
[
['cur_q', '<', 0.5],
'and',
['delta_q', '>=', 0.15],
],
'and',
['dt_last_enforce', '>', 28800],
],
'and',
['clients', '=', 0],
],
],
]

# Getters for API keys offered to the user, involving potentially
# expensive to fetch context delivery functions:
# Signature must provide minimum a positional for the current
# state:
class ApiCtxFuncs:
def expensive_but_not_needed_here(ctx):
raise Exception("Won't run with cond. from above")

def cur_q(ctx):
print('Calculating cur_q')
return 0.1

def cur_hour(ctx):
print('Calculating cur_hour')
return 4

def dt_last_enforce(ctx):
print('Calculating dt_last_enforce')
return 10000000

def delta_q(ctx):
print('Calculating (expensive) delta_q')
time.sleep(0.1)
return 1

def clients(ctx):
print('Calculating clients')
return 0

if sys.version_info[0] < 3:
# we don't think it is a good idea to make the getter API stateful ;-)
p2m.convert_to_staticmethods(ApiCtxFuncs)

f, nfos = pc.parse_cond(cond, ctx_provider=ApiCtxFuncs)

# now we create (incomplete) data..
data1 = {'group_type': 'xxx'}, False
data2 = {'group_type': 'lab'}, True

# this key stores a context builder function, calculating the complete data:
make_ctx = nfos['complete_ctx']

t0 = time.time()
for event, expected in data1, data2:
assert f(state=make_ctx(event)) == expected

print('Calc.Time (delta_q was called twice):', round(time.time() - t0, 4)),
return cond, ApiCtxFuncs

Output:
Calculating clients
Calculating cur_hour
Calculating cur_q
Calculating (expensive) delta_q
Calculating dt_last_enforce
Calculating clients
Calculating cur_hour
Calculating cur_q
Calculating (expensive) delta_q
Calculating dt_last_enforce
Calc.Time (delta_q was called twice): 0.2006

Lookup Providers
ContextBuilders are interesting but we can do better.
We still calculated values for keys which might(dependent on the data) be not needed in dead ends of a lazily evaluated condition.
Lets avoid calculating these values, remembering the custom lookup function feature.
This is where lookup providers come in, providing namespaces for functions to be called conditionally.
Pycond treats the condition keys as function names within that namespace and calls them, when needed.
Accepted Signatures
Lookup provider functions may have the following signatures:
class F:
# simple data passing
def f1(data):
"""simple return a value being compared, getting passed the state/data"""
return data['a']

# simple, with ctx
def f2(data, **kw):
"""
simple return a value being compared, getting passed the state/data
All context information within kw, compare value not modifiable
"""
return data['b']

# full pycond compliant signature,
def f3(key, val, cfg, data, **kw):
"""
full pycond signature.
val is the value as defined by the condition, and which you could return modified
kw holds the cache, cfg holds the setup
v has to be returned:
"""
return data['c'], 100 # not 45!

# applied al
def f4(*a, **kw):
"""
Full variant(always when varargs are involved)
"""
return a[3]['d'], 'foo'

_ = 'and'
f = pc.pycond(
[
[':f1', 'eq', 42],
_,
[':f2', 'eq', 43, _, ':f3', 'eq', 45],
_,
[':f4', 'eq', 'foo'],
],
lookup_provider=F,
)
assert f(state={'a': 42, 'b': 43, 'c': 100, 'd': 'foo'}) == True

Parametrized Lookup Functions
Via the 'params' parameter you may supply keyword args to lookup functions:
class F:
def hello(k, v, cfg, data, count, **kw):
return data['foo'] == count, 0

m = pc.pycond([':hello'], lookup_provider=F, params={'hello': {'count': 2}})(
state={'foo': 2}
)
assert m == True

Namespace

Lookup functions can be found in nested class hirarchies or dicts. Separator is colon(':')
As shown above, if they are flat within a toplevel class or dict you should still prefix with ':', to get build time exception(MissingLookupFunction) when not present
You can switch that behaviour off per condition build as config arg, as shown below
You can switch that behaviour off globally via pc.prefixed_lookup_funcs=False

Warning: This is a breaking API change with pre-20200610 versions, where the prefix was not required to find functions in, back then, only flat namespaces. Use the global switch after import to get the old behaviour.
class F:
def a(data):
return data['foo']

class inner:
def b(data):
return data['bar']

m = {'c': {'d': {'func': lambda data: data['baz']}}}

# for the inner lookup the first prefix may be omitted:
_ = 'and'
cond = [
[':a', 'eq', 'foo1'],
_,
['inner:b', 'eq', 'bar1'],
_,
[
'c:d',
'eq',
'baz1',
],
]
c = pc.pycond(cond, lookup_provider=F, lookup_provider_dict=m)
assert c(state={'foo': 'foo1', 'bar': 'bar1', 'baz': 'baz1'}) == True

# Prefix checking on / off:
try:
pc.pycond([':xx', 'and', cond])
i = 9 / 0 # above will raise this:
except pc.MissingLookupFunction:
pass
try:
pc.pycond([':xx', 'and', cond], prefixed_lookup_funcs=False)
i = 9 / 0 # above will raise this:
except pc.MissingLookupFunction:
pass
cond[0] = 'a' # remove prefix, will still be found
c = pc.pycond(
['xx', 'or', cond],
lookup_provider=F,
lookup_provider_dict=m,
prefixed_lookup_funcs=False,
)
assert c(state={'foo': 'foo1', 'bar': 'bar1', 'baz': 'baz1'}) == True

You can switch that prefix needs off - and pycond will then check the state for key presence:
# we let pycond generate the lookup function (we use the simple signature type):
f = pc.pycond(cond, lookup_provider=ApiCtxFuncs, prefixed_lookup_funcs=False)
# Same events as above:
data1 = {'group_type': 'xxx'}, False
data2 = {'group_type': 'lab'}, True

t0 = time.time()
for event, expected in data1, data2:
# we will lookup only once:
assert f(state=event) == expected

print(
'Calc.Time (delta_q was called just once):',
round(time.time() - t0, 4),
)

# The deep switch keeps working:
cond2 = [cond, 'or', ['a-0-b', 'eq', 42]]
f = pc.pycond(
cond2,
lookup_provider=ApiCtxFuncs,
deep='-',
prefixed_lookup_funcs=False,
)
data2[0]['a'] = [{'b': 42}]
print('sample:', data2[0])
assert f(state=data2[0]) == True

Output:
Calculating cur_q
Calculating (expensive) delta_q
Calculating dt_last_enforce
Calculating cur_hour
Calc.Time (delta_q was called just once): 0.1004
sample: {'group_type': 'lab', 'a': [{'b': 42}]}
Calculating cur_q
Calculating (expensive) delta_q
Calculating dt_last_enforce
Calculating cur_hour

The output demonstrates that we did not even call the value provider functions for the dead branches of the condition.
NOTE: Instead of providing a class tree you may also provide a dict of functions as lookup_provider_dict argument, see qualify examples below.
Caching
Note: Currently you cannot override these defaults. Drop an issue if you need to.

Builtin state lookups: Not cached
Custom lookup functions: Not cached(you can implement caching within those functions)
Lookup provider return values: Cached, i.e. called only once, per data set
Named condition sets(see below): Cached

Extensions
We deliver a few lookup function extensions

for time checks
for os.environ checks(re-evaluated at runtime)

from datetime import datetime as dt
from os import environ as env

this_sec = dt.now().second
this_utc_hour = dt.utcnow().hour
f = pc.pycond(
[
['env:foo', 'eq', 'bar'],
'and',
# not breaking the build when the sec just jumps:
['dt:second', 'in', [this_sec, this_sec + 1, 0]],
'and',
['utc:hour', 'eq', this_utc_hour],
]
)
env['foo'] = 'bar'
assert f(state={'a': 1}) == True

Named Conditions: Qualification
Instead of just delivering booleans, pycond can be used to determine a whole set of
information about data declaratively, like so:
# We accept different forms of delivery.
# The first full text is restricted to simple flat dicts only:
for c in [
'one: a gt 10, two: a gt 10 or foo eq bar',
{'one': 'a gt 10', 'two': 'a gt 10 or foo eq bar'},
{
'one': ['a', 'gt', 10],
'two': ['a', 'gt', 10, 'or', 'foo', 'eq', 'bar'],
},
]:
f = pc.qualify(c)
r = f({'foo': 'bar', 'a': 0})
assert r == {'one': False, 'two': True}

We may refer to results of other named conditions and also can pass named condition sets as lists instead of dicts:
def run(q):
print('Running', q)

class F:
def custom(data):
return data.get('a')

f = pc.qualify(q, lookup_provider=F)

assert f({'a': 'b'}) == {
'first': True,
'listed': [False, False],
'thrd': True,
'zero': True,
'last': True,
}
res = f({'c': 'foo', 'x': 1})
assert res == {
'first': False,
'listed': [False, True],
'thrd': False,
'zero': True,
'last': True,
}

q = {
'thrd': ['k', 'or', ':first'],
'listed': [['foo'], ['c', 'eq', 'foo']],
'zero': [['x', 'eq', 1], 'or', ':thrd'],
'first': [':custom', 'eq', 'b'],
'last': True, # you might want to do this to always get at least one matcher, e.g. for data streaming
}
# as list of conditions:
run(q)

# as dict:
q = dict([[k, v] for k, v in q.items()])
run(q)

Output:
Running {'thrd': ['k', 'or', ':first'], 'listed': [['foo'], ['c', 'eq', 'foo']], 'zero': [['x', 'eq', 1], 'or', ':thrd'], 'first': [':custom', 'eq', 'b'], 'last': True}
Running {'thrd': ['k', 'or', ':first'], 'listed': [['foo'], ['c', 'eq', 'foo']], 'zero': [['x', 'eq', 1], 'or', ':thrd'], 'first': [':custom', 'eq', 'b'], 'last': True}

WARNING: For performance reasons there is no built in circular reference check. You'll run into python's built in recursion checker!
Options

into: Put the matched named conditions into the original data
prefix: Work from a prefix nested in the root
add_cached: Return also the data from function result cache

Here a few variants to parametrize behaviour, by example:
conds = {
0: ['foo'],
1: ['bar'],
2: ['func'],
3: ['n'],
'n': ['bar'],
}

class F:
def func(*a, **kw):
return True, 0

q = lambda d, **kw: pc.qualify(
conds, lookup_provider=F, prefixed_lookup_funcs=False, **kw
)(d)

m = q({'bar': 1})
assert m == {0: False, 1: True, 2: True, 3: True, 'n': True}

# return data, with matched conds in:
m = q({'bar': 1}, into='conds')
assert m == {
'bar': 1,
'conds': {0: False, 1: True, 2: True, 3: True, 'n': True},
}

def msg():
return {'bar': 1, 'pl': {'a': 1}}

# add_cached == True -> it's put into the cond results:
m = q(msg(), into='conds', add_cached=True)
assert m == {
'bar': 1,
'conds': {0: False, 1: True, 2: True, 3: True, 'n': True, 'func': True},
'pl': {'a': 1},
}

m = q(msg(), into='conds', add_cached='pl')
assert m == {
'bar': 1,
'conds': {0: False, 1: True, 2: True, 3: True, 'n': True},
# n had been put into the cache, was not evaled twice:
'pl': {'a': 1, 'func': True, 'n': True},
}

m = q({'bar': 1}, add_cached='pl')
assert m == {0: False, 1: True, 2: True, 3: True, 'n': True, 'func': True}

# prefix -> Nr 1, bar, should NOT be True, since not in pl now:
m = q(
msg(),
prefix='pl',
into='conds',
add_cached='pl',
)
assert m == {
'bar': 1,
'conds': {0: False, 1: False, 2: True, 3: False, 'n': False},
'pl': {'a': 1, 'func': True, 'n': False},
}

Partial Evaluation
If you either supply a key called 'root' OR supply it as argument to qualify, pycond will only evaluate named conditions required to calculate the root key:
called = []

def expensive_func(k, v, cfg, data, **kw):
called.append(data)
return 1, v

def xx(k, v, cfg, data, **kw):
called.append(data)
return data.get('a'), v

funcs = {'exp': {'func': expensive_func}, 'xx': {'func': xx}}
q = {
'root': ['foo', 'and', ':bar'],
'bar': [
['somecond'],
'or',
[[':exp', 'eq', 1], 'and', ':baz'],
],
'x': [':xx'],
'baz': [':exp', 'lt', 10],
}
qualifier = pc.qualify(q, lookup_provider_dict=funcs, add_cached=True)

d = {'foo': 1}
r = qualifier(d)

# root, bar, baz had been calculated, not x
assert r == {'root': True, 'bar': True, 'baz': True, 'exp': 1}
# expensive_func result, which was cached, is also returned.
# expensive_func only called once allthough result evaluated for bar and baz:
assert len(called) == 1

called.clear()
f = pc.qualify(q, lookup_provider_dict=funcs, root='x', add_cached=True)
assert f({'a': 1}) == {'x': True, 'xx': 1}
assert f({'b': 1}) == {'x': False, 'xx': None}
assert called == [{'a': 1}, {'b': 1}]

This means pycond can be used as a lightweight declarative function dispatching framework.
Streaming Data
Since version 20200601 and Python 3.x versions, pycond can deliverReactiveX compliant stream operators.
Lets first set up a test data stream, by defining a function rx_setup like so:
# simply `import rx as Rx and rx = rx.operators`:
# import pycond as pc, like always:
Rx, rx, GS = pc.import_rx('GS')

def push_through(*test_pipe, items=4):
"""
Function which takes a set of operators and runs an 'rx.interval' stream, until count items are through
"""

# stream sink result holder plus a stream completer:
l, compl = [], rx.take(items)
l.clear() # clear any previous results

def next_(x):
# simply remember what went through in a list:
l.append(x)

def err(*a):
# should never happen:
print('exception', a)

stream = Rx.interval(0.01) # numbers, each on its own thread

# turns the ints into dicts: {'i': 1}, then {'i': 2} and so on:
# (we start from 1, the first 0 we filter out)
stream = stream.pipe(
rx.filter(lambda i: i > 0), rx.map(lambda i: {'i': i})
)

# defines the stream through the tested operators:
test_pipe = test_pipe + (compl,)
s = stream.pipe(*test_pipe)

# runs the stream:
d = s.subscribe(
on_error=err,
on_next=next_,
on_completed=lambda: l.append('completed'),
)

# blocks until completed:
while not (l and l[-1] == 'completed'):
time.sleep(0.001)
l.pop() # removes completed indicator

return l # returns all processed messages

return Rx, rx, push_through

Lets test the setup by having some messages streamed through:
Rx, rx, push_through = rx_setup()
# test test setup:
r = push_through(items=3)
assert r == [{'i': 1}, {'i': 2}, {'i': 3}]

-> test setup works.
Filtering
This is the most simple operation: A simple stream filter.
Rx, rx, push_through = rx_setup()

# ask pycond for a stream filter based on a condition:
pcfilter = partial(pc.rxop, ['i', 'mod', 2])

r = push_through(pcfilter())
odds = [{'i': 1}, {'i': 3}, {'i': 5}, {'i': 7}]
assert r == odds

# try the stream filter with message headered data:
pl = 'payload'
r = push_through(rx.map(lambda i: {pl: i}), pcfilter(prefix=pl))
print('Full messages passed:', r)
r = [m[pl] for m in r]
assert len(r) == 4
assert r == odds

Output:
Full messages passed: [{'payload': {'i': 1}}, {'payload': {'i': 3}}, {'payload': {'i': 5}}, {'payload': {'i': 7}}]

Streaming Classification
Using named condition dicts we can classify data, i.e. tag it, in order to process subsequently:
Rx, rx, push_through = rx_setup()

# generate a set of classifiers:
conds = [['i', 'mod', i] for i in range(2, 4)]

def run(offs=0):

# and get a classifying operator from pycond, adding the results in place, at key 'mod':
r = push_through(pc.rxop(conds, into='mod'))
i, j = 0 + offs, 1 + offs
assert r == [
{'i': 1, 'mod': {i: 1, j: 1}},
{'i': 2, 'mod': {i: 0, j: 2}},
{'i': 3, 'mod': {i: 1, j: 0}},
{'i': 4, 'mod': {i: 0, j: 1}},
]

# this will automatically number the classifiers, from 0:
run()

# we can also provide the names of the classifiers by passing a dict:
# here we pass 2 and 3 as those names:
conds = dict([(i, ['i', 'mod', i]) for i in range(2, 4)])
run(2)

Normally the data has headers, so thats a good place to keep the classification tags.
Selective Classification
We fall back to an alternative condition evaluation(which could be a function call) * only * when a previous condition evaluation returns something falsy - by providing a * root condition*.
When it evaluated, possibly requiring evaluation of other conditions, we return:
Rx, rx, push_through = rx_setup()

# using the list style:
conds = [[i, [['i', 'mod', i], 'or', ':alt']] for i in range(2, 4)]
conds.append(['alt', ['i', 'gt', 1]])

# provide the root condition. Only when it evals falsy, the named "alt" condiction will be evaluated:
r = push_through(pc.rxop(conds, into='mod', root=2, add_cached=True))

assert r == [
# evaluation of alt was not required:
{'i': 1, 'mod': {2: True}},
# evaluation of alt was required:
{'i': 2, 'mod': {2: True, 'alt': True}},
{'i': 3, 'mod': {2: True}},
{'i': 4, 'mod': {2: True, 'alt': True}},
]

Treating of Booleans (Conditions, Not Names)
For the special case of booleans in a condition list we do not treat them as names.
# 2 unnamed conditions -> keys will be positional
qs = pc.qualify([True, False])
res = qs({'a': 1})
assert res == {0: True, 1: False} # and not {True: False}
# 2 named conds
qs = pc.qualify([[1, ['a', 'eq', 1]], [2, ['b', 'eq', 42]]])
res = qs({'a': 1})
assert res == {1: True, 2: False}

Asyncronous Operations
WARNING: Early Version. Only for the gevent platform.
Selective classification allows to call condition functions only when other criteria are met.
That makes it possible to read e.g. from a database only when data is really required - and not always, "just in case".
pycond allows to define, that blocking operations should be run * async* within the stream, possibly giving up order.
Asyncronous Filter
First a simple filter, which gives up order but does not block:
Rx, rx, push_through = rx_setup()

class F:
def check(k, v, cfg, data, t0=[], **kw):
# will be on different thread:
i, pointer = data['i'], ''
if not t0:
t0.append(now())
if i == 1:
# ints are fired at 0.01, i.e. the 1 will land 4 after 1:
time.sleep(0.048)
pointer = ' <----- not in order, blocked'
# demonstrate that item 1 is not blocking anything - just order is disturbed:
print('item %s: %.3fs %s' % (i, now() - t0[0], pointer))
return i % 2, v

# have the operator built for us - with a single condition filter:
rxop = pc.rxop(
[':check'],
into='mod',
lookup_provider=F,
asyn=['check'],
)
r = push_through(rxop, items=5)
assert [m['i'] for m in r] == [3, 5, 1, 7, 9]

Output:
item 2: 0.011s
item 3: 0.022s
item 4: 0.034s
item 5: 0.045s
item 1: 0.049s <----- not in order, blocked
item 6: 0.057s
item 7: 0.068s
item 8: 0.079s
item 9: 0.090s

Finally asyncronous classification, i.e. evaluation of multiple conditions:
def _thn(msg, data):
return print('thread:', cur_thread().name, msg, data)

# push_through just runs a stream of {'i': <nr>} through a given operator:
Rx, rx, push_through = rx_setup()

# Defining a simple 'set' of classifiers, here as list, with one single key: 42:
conds = [
[
42,
[
['i', 'lt', 100],
'and',
[[':odd', 'eq', 1], 'or', ['i', 'eq', 2]],
'and_not',
[':blocking', 'eq', 3],
],
]
]

class F:
"""
Namespace for condition lookup functions.
You may also pass a dict(lookup_provider_dict)

We provide the functions for 'odd' and 'blocking'.
"""

def odd(k, v, cfg, data, **kw):
# just print the threadname.
# will go up, interval stream has each nr on its own thread:
_thn('odd', data)
# fullfill condition only for odd numbers
# -> even nrs won't even run func 'blocking':
return data['i'] % 2, v

def blocking(k, v, cfg, data, **kw):
i = data['i']
# will be on different thread:
_thn('blocking', data)
if i == 1:
# two others will "overtake the i=1 item,
# since the interval stream is firing every 0.01 secs:
time.sleep(0.028)
elif i == 2:
# Exceptions, incl. timeouts, will simply be forwarded to cfg['err_handler']
# i.e. also timeout mgmt have to be done here, in the custom functions themselves.

# Rationale for not providing a timeout monitoring within pycond itself:
# Async ops are done with libs, which ship with their own timeout params.
# No need to re-invent / overlay with our own monitoring of that.

# In the err handler, then further arrangements can be done.
raise TimeoutError('ups')
elif i == 5:
1 / 0
return data['i'], v

errors = []

def handle_err(item, cfg, ctx, exc, t=errors, **kw):
# args are: [item, cfg]
if 'ups' in str(exc):
assert item['i'] == 2
assert exc.__class__ == TimeoutError
t.append(item)
else:
assert item['i'] == 5
assert exc.__class__ == ZeroDivisionError
t.append(item)

# have the operator built for us:
rxop = pc.rxop(
conds,
into='mod',
lookup_provider=F,
err_handler=handle_err,
asyn=['blocking'],
)
r = push_through(rxop, items=5)
assert [m['i'] for m in r] == [3, 1, 4, 6, 7]
assert [m['mod'][42] for m in r] == [False, True, False, False, True]
# item 2 caused a timeout:
assert [t['i'] for t in errors] == [2, 5]

Output:
thread: Thread-55 odd {'i': 1}
thread: Dummy-57 blocking {'i': 1}
thread: Thread-56 odd {'i': 2}
thread: Dummy-59 blocking {'i': 2}
thread: Thread-58 odd {'i': 3}
thread: Dummy-61 blocking {'i': 3}
thread: Thread-60 odd {'i': 4}
thread: Thread-62 odd {'i': 5}
thread: Dummy-64 blocking {'i': 5}
thread: Thread-63 odd {'i': 6}
thread: Thread-65 odd {'i': 7}
thread: Dummy-67 blocking {'i': 7}

*Auto generated by pytest2md, running ./tests/test_tutorial.py