def dict2pdfa(mapping, start: PA.State):
"""Convert nested dictionary into a PDFA.
- mapping is a nested dictionary of the form:
mapping = {
<State>: (<Label>, {
<Action>: {
<State>: <Probability>
}
}
}
"""
label_map = fn.walk_values(ig(0), mapping)
transition_map = fn.walk_values(ig(1), mapping)
outputs = set(bind(mapping).Values()[0].collect())
inputs = set(bind(mapping).Values()[1].Keys().collect())
return PA.pdfa(
start=start,
label=label_map.get,
inputs=inputs,
outputs=outputs,
**_encode_two_player_game(transition_map)
)
def visit_aag(self, _, children):
header, ios1, lgs1, ios2, lgs2, symbols, comments = children
ios, lgs = ios1 + ios2, lgs1 + lgs2
assert len(ios) == header.num_inputs + header.num_outputs
inputs, outputs = ios[:header.num_inputs], ios[header.num_inputs:]
assert len(lgs) == header.num_ands + header.num_latches
latches, gates = lgs[:header.num_latches], lgs[header.num_latches:]
# TODO: need to allow for inputs, outputs, latches not in
# symbol table.
inputs = {
symbols.inputs.inv.get(idx, f'i{idx}'): i
for idx, i in enumerate(inputs)
}
outputs = {
symbols.outputs.inv.get(idx, f'o{idx}'): i
for idx, i in enumerate(outputs)
}
latches = {
symbols.latches.inv.get(idx, f'l{idx}'): tuple(i)
for idx, i in enumerate(latches)
}
latches = fn.walk_values(lambda l: (l + (0, ))[:3], latches)
if len(comments) > 0:
assert comments[0].startswith('c\n')
comments[0] = comments[0][2:]
return AAG(inputs=inputs,
outputs=outputs,
latches=fn.walk_values(tuple, latches),
gates=fn.lmap(tuple, gates),
comments=tuple(comments))
def mutex_coins(name2prob, input_name=None, keep_seperate=False):
"""Mutually exclusive coins.
Encoded using the common denominator method.
"""
name2prob = fn.walk_values(utils.to_frac, name2prob)
assert sum(name2prob.values()) == 1
bots = [p.denominator for p in name2prob.values()]
lcm = reduce(utils.lcm, bots, 1)
word_len = max(math.ceil(math.log2(lcm)), 1)
max_val = 2**word_len
name2weight = fn.walk_values(
lambda p: p.numerator * (lcm // p.denominator), name2prob)
bits = atom(word_len, input_name, signed=False)
const_true = ~(bits @ 0)
total, coins = 0, []
for name, weight in name2weight.items():
lb = const_true if total == 0 else (bits >= total)
total += weight
ub = const_true if total == max_val else (bits < total)
expr = (lb & ub)
output = dict(expr.aigbv.output_map)[expr.output][0]
coins.append(expr.aig['o', {output: name}])
is_valid = const_true if lcm == max_val else bits < lcm
if keep_seperate:
return coins, is_valid
return reduce(lambda x, y: x | y, coins), is_valid
def get_balances(self):
available = {
'STEEM': Amount(self['balance']).amount,
'SBD': Amount(self['sbd_balance']).amount,
'VESTS': Amount(self['vesting_shares']).amount,
}
savings = {
'STEEM': Amount(self['savings_balance']).amount,
'SBD': Amount(self['savings_sbd_balance']).amount,
}
rewards = {
'STEEM': Amount(self['reward_steem_balance']).amount,
'SBD': Amount(self['reward_sbd_balance']).amount,
'VESTS': Amount(self['reward_vesting_balance']).amount,
}
totals = {
'STEEM':
sum([available['STEEM'], savings['STEEM'], rewards['STEEM']]),
'SBD': sum([available['SBD'], savings['SBD'], rewards['SBD']]),
'VESTS': sum([available['VESTS'], rewards['VESTS']]),
}
total = walk_values(rpartial(round, 3), totals)
return {
'available': available,
'savings': savings,
'rewards': rewards,
'total': total,
}
def get_balances(self):
available = {
'GOLOS': Amount(self['balance']).amount,
'GBG': Amount(self['sbd_balance']).amount,
'GESTS': Amount(self['vesting_shares']).amount,
}
savings = {
'GOLOS': Amount(self['savings_balance']).amount,
'GBG': Amount(self['savings_sbd_balance']).amount,
}
totals = {
'GOLOS': sum([available['GOLOS'], savings['GOLOS']]),
'GBG': sum([available['GBG'], savings['GBG']]),
'GESTS': sum([available['GESTS']]),
}
total = walk_values(rpartial(round, 3), totals)
return {
'available': available,
'savings': savings,
'total': total,
}
def simplify_fragment(obj):
""" Simplify and flatten individual fragment."""
# clean up the mess
def replace_values(val):
if type(val) == dict:
return walk_values(replace_values, val)
if val == "?" or val == 'None':
return 0
return val
obj = walk_values(replace_values, obj)
result = None
with suppress(Exception):
result = {
'symbol': obj['symbol'],
'category': obj['category'],
'supply': obj['availableSupply'],
'change_7d': round(float(obj['change7d']), 2),
'change_1d': round(float(obj['change24h']), 2),
'change_1h': round(float(obj['change1h']), 2),
'position': int(obj['position']),
'cap_usd': round(float(obj['marketCap']['usd'])),
'cap_btc': round(float(obj['marketCap']['btc'])),
'volume_btc': round(float(obj['volume24']['btc'])),
'price_usd': float(obj['price']['usd']),
'price_btc': float(obj['price']['btc']),
'timestamp': dt.datetime.fromtimestamp(obj['timestamp'])
}
return result
def evaluate(
self,
trace: Trace,
condition: Optional[Node] = None,
*,
dt=1.0,
time: Any = False,
quantitative=False,
logic: _ConnectivesDef = default
) -> Mapping[Node, Optional[bool]]:
"""Evaluate the truth values of the monitor conditions on the specified trace."""
evaluated_conditions: Iterable[Node] = (
self.conditions if condition is None else {condition}
)
results: MutableMapping[Node, Optional[bool]] = dict()
for phi in evaluated_conditions:
signals = {
k.id: v for k, v in trace.project(self.atoms(phi), logic).items()
}
# FIXME A default value is required by mtl even if no atoms required (TOP/BOT)
signals[None] = [(0, logic.const_false)]
if all(a.id in signals for a in self.atoms(phi)):
r = phi(signals, dt=dt, time=time, logic=logic)
if not quantitative:
if time is None:
r = funcy.walk_values(lambda v: v >= logic.const_true, r)
else:
r = r >= logic.const_true
results[phi] = r
else:
results[phi] = None
return results if condition is None else funcy.first(results.values())
def preprocess(tiles: Dict[int, np.ndarray]) -> Dict[int, HashTile]:
def _create_hash_tile(tile: np.ndarray) -> HashTile:
# just store the hashed borders and hash the plugs of each tile
edges_ = lmap(hashed, edges(tile))
plugs_ = hashed(plugs(tile))
return HashTile(tile=tile, edges=edges_, plugs=plugs_, neighbours=[])
def _compute_neighbours(no: int, hash_tiles: Dict[int, HashTile]) -> None:
# the hash tile numbers, except this one
other_nos = [i for i in hash_tiles.keys() if i != no]
# identify how many matching tiles each edge has in this tile
for e1, e2 in hash_tiles[no].edges:
neighbours = []
# check if this edge matches up with any of the other tile plugs
for other in other_nos:
other_plugs = hash_tiles[other].plugs
if e1 in other_plugs or e2 in other_plugs:
neighbours.append(other)
# fortunately, each edge has always only at most one matching tile pair
assert len(neighbours) <= 1
candidate = neighbours[0] if len(neighbours) > 0 else None
# the number of matching tiles for this border index
hash_tiles[no].neighbours.append(candidate)
# first create the hash tiles, then, compute the edge match list
result = walk_values(_create_hash_tile, tiles)
for key in result.keys():
_compute_neighbours(key, result)
return result
def extract_map(name_map, names):
lookup_root = fn.merge(*({v: k
for v in vals} for k, vals in name_map))
mapping = fn.group_by(lambda x: lookup_root[x.split('##time_')[0]],
names)
mapping = fn.walk_values(tuple, mapping) # Make hashable.
return frozenset(mapping.items())
def handle(self, *args, **options):
group = group_values(
[convert_date(attr),
attr.get('pubmed_id', '').split('|\n|')]
for attr in Series.objects.values_list('attrs', flat=True))
uniq_pmids = set([])
def count_uniq_pmids(pmids):
uniq_pmids.update(set(flatten(pmids)))
return len(uniq_pmids)
pmids = dict(walk_values(count_uniq_pmids, sorted(group.items())))
delta = CURRENT_DATE - START_DATE
keys = sorted(
set(
ceil_date(START_DATE + timedelta(days=index * 20))
for index in range(delta.days / 20 + 1)))
for index, date in enumerate(keys):
hc = HistoricalCounter.objects.filter(created_on=date).first()
if not hc:
continue
hc.counters['PMID'] = get_value(keys, index)(pmids)
hc.save()
def multi(coll):
def make_apply(el):
return lambda f: f(el) if callable(f) else f
if is_mapping(coll):
return lambda el: walk_values(make_apply(el), coll)
else:
return lambda el: lmap(make_apply(el), coll)
def r(stats):
"""Reduce collection of results"""
sorted_stats = reversed(sorted(walk_values(sum, stats), key=lambda s: s[1]))
return {
'words': len(stats),
'top': list(sorted_stats),
}
def get_vertical_cages(rows):
transpose_coordinates = lambda t: (t[1], t[0])
res = get_horizontal_cages(transpose(rows))
res = walk_keys(transpose_coordinates, res)
transpose_coordinates_lst = compose(list,
partial(map, transpose_coordinates))
res = walk_values(transpose_coordinates_lst, res)
return res
def cookiecutter(*args, **kwargs) -> str:
"""Call cookiecutter.main.cookiecutter after stringifying paths
Return:
project directory path
"""
args = fy.walk(_stringify_path, args)
kwargs = fy.walk_values(_stringify_path, kwargs)
return _cookiecutter(*args, **kwargs)
def _call_info(self, func, args, kwargs):
serialized_args = map(serialize, args)
serialized_kwargs = walk_values(serialize, kwargs)
parts = []
parts.extend(smart_str(a) for a in args)
parts.extend('%s=%s' % (k, smart_str(v)) for k, v in sorted(kwargs.items()))
parts.append(hash_args(serialized_args, serialized_kwargs))
dirname = '%s/%s' % (func.__name__, '.'.join(parts))
return dirname, serialized_args, serialized_kwargs
def adj_list(concept_class, parallel=True):
if parallel:
from pathos.multiprocessing import ProcessingPool
pool = ProcessingPool()
mapper = pool.map
else:
mapper = map
edge_generator = fn.cat(mapper(get_edges, possible_edges(concept_class)))
edge_lists = fn.walk_values(set, fn.group_values(edge_generator))
return defaultdict(set, edge_lists)
def _load_call_info(self, dirname):
path = os.path.join(self._path, dirname)
files = os.listdir(path)
arg_files = sorted(filter(r'^a', files))
args = tuple(map(self._read_data, (os.path.join(path, f) for f in arg_files)))
kwarg_files = filter(r'^k', files)
kwarg_files = {filename[1:]: os.path.join(path, filename) for filename in kwarg_files}
kwargs = walk_values(self._read_data, kwarg_files)
return args, kwargs
def export(self):
""" This method returns a dictionary that is type-safe to store as JSON or in a database.
"""
# Remove Steem instance object
safe_dict = remove_from_dict(self, ['steem'])
# Convert Amount class objects into pure dictionaries
def decompose_amounts(item):
if type(item) == Amount:
return item.__dict__
return item
return walk_values(decompose_amounts, safe_dict)
def setup_slug(sender, instance, **kwargs):
data = walk_values(compose(slugify, unicode),
instance.__dict__)
related = {item: data['_{0}_cache'.format(item)]
for item in map(lambda u: u[0:-3],
filter(lambda u: u.endswith('_id'),
data.keys()))}
data = merge(data, related)
slug = self.populate_from.format(
**data)[:self.max_length]
if slug != getattr(instance, name):
setattr(instance, name, slug)
instance.save()
def export(self):
""" This method returns a dictionary that is type-safe to store as JSON or in a database.
"""
self.refresh()
# Remove Steem instance object
safe_dict = remove_from_dict(self, ['steem'])
# Convert Amount class objects into pure dictionaries
def decompose_amounts(item):
if type(item) == Amount:
return item.__dict__
return item
return walk_values(decompose_amounts, safe_dict)
async def get_task(request):
app = request.app
raw_task_id = request.match_info['task_id']
if not _valid_task_id(raw_task_id):
return json_response({'errors': ['Parameter task_id is not valid']})
task_id = ObjectId(raw_task_id)
task = await store.get_task(app['db'], task_id)
if task is None:
return json_response(
{'errors': ['Task with provided id is not found']})
return json_response(walk_values(str, task))
def compute_greedy_policy(
values: ValueFunction,
policy: Policy,
rewards: Rewards,
transition_probabilities: TransitionProbabilities,
grid_size: int,
terminal_states: Set[GridState],
gamma: float,
) -> Policy:
"""Compute a greedy policy wrt a given value function & current policy."""
updated_policy: Policy = defaultdict(dict)
for state in get_states(grid_size):
# don't touch terminal states
if state in terminal_states:
updated_policy[state] = {
action: 0.0
for action in policy[state].keys()
}
continue
action_to_action_value = {
action: calculate_action_value(
state,
values,
action,
rewards,
transition_probabilities,
grid_size,
gamma,
)
for action in policy[state].keys()
}
max_action_value = max(action_to_action_value.values())
# if we have multiple actions that result in max value, set probability of 1 / n argmax actions
updated_policy_for_state = {
action: 1.0 if action_value == max_action_value else 0.0
for action, action_value in action_to_action_value.items()
}
n_argmax_actions = sum(updated_policy_for_state.values())
updated_policy_for_state = funcy.walk_values(
lambda is_action_argmax: is_action_argmax / n_argmax_actions,
updated_policy_for_state,
)
updated_policy[state] = updated_policy_for_state
return updated_policy
def values(self, *fields, **expressions):
"""
Extended version supporting renames:
.values('id', 'name', author__name='author')
"""
renames = select_values(isa(six.string_types), expressions)
if not renames:
return base.values(self, *fields, **expressions)
elif django.VERSION >= (1, 11):
rename_expressions = walk_values(F, renames)
expressions.update(rename_expressions)
return base.values(self, *fields, **expressions)
else:
f_to_name = flip(renames)
rename = lambda d: {f_to_name.get(k, k): v for k, v in d.items()}
return base.values(self, *chain(fields, f_to_name)).map(rename)
def mygene_fetch(platform, probes, scopes):
"""Queries mygene.info for current entrezid and sym, given an identifier."""
if scopes == "dna":
probes = get_dna_probes(platform, probes)
scopes = "accession"
def extract_queries(lines):
lines = remove(r'^(IMAGE:\d+|--[\w>-]+)$', lines)
queries = cat(re_iter(r'[\w+.-]+', l) for l in lines)
queries = remove(r'_at$|^\d+-\d+$', queries) # No such thing
return queries
# Clean unicode for mygene
# http://stackoverflow.com/questions/15321138/removing-unicode-u2026-like-characters
return [
q.decode('unicode_escape').encode('ascii', 'ignore')
for q in queries
]
_by_probe = group_values(probes.items())
queries_by_probe = walk_values(extract_queries, _by_probe)
# Collect all possible queries to make a single request to mygene
queries = set(cat(queries_by_probe.values()))
if not queries:
return []
mygenes = _mygene_fetch(queries, scopes, platform.specie)
# Form results into rows
results = []
dups = 0
for probe, queries in queries_by_probe.items():
matches = ldistinct(keep(mygenes.get, queries))
# Skip dups
if len(matches) > 1:
dups += 1
elif matches:
entrez, sym = matches[0]
results.append({
'probe': probe,
'mygene_sym': sym,
'mygene_entrez': entrez
})
if dups:
cprint('-> Produced %d dups' % dups, 'red')
return results
def ByUrl(mapping):
schemas = walk_values(Schema, mapping)
def validate(data):
if "url" not in data:
raise Invalid("expected 'url'")
parsed = urlparse(data["url"])
# Windows absolute paths should really have scheme == "" (local)
if os.name == "nt" and len(parsed.scheme) == 1 and parsed.netloc == "":
return schemas[""](data)
if parsed.scheme not in schemas:
raise Invalid(f"Unsupported URL type {parsed.scheme}://")
return schemas[parsed.scheme](data)
return validate
def copy_output_files(app):
"""
Creates a list of source file paths and destination directory names. Copies each
the source file to destination directory.
"""
path = lambda x: os.path.abspath(fs.get_task_file_path(app, x))
output_files = {'container_log': path('meta/log.txt')}
if fs.biobox_yaml_exists(app):
tmp_files = funcy.walk_values(lambda x: path("tmp/" + x),
image_type(app).output_file_paths(app))
output_files = funcy.merge(output_files, tmp_files)
else:
msg = "No biobox.yaml file created, cannot find paths of any container generated files"
app['logger'].warn(msg)
fs.copy_container_output_files(app, output_files)
def typify(value):
"""
typify takes a blockchain operation or dict/list/value,
and then it parses and converts string types into native data types where appropriate.
"""
if type(value) == dict:
return walk_values(typify, value)
if type(value) in [list, set]:
return list(map(typify, value))
if type(value) == str:
if re.match('^\d+\.\d+ (STEEM|SBD|VESTS)$', value):
return keep_in_dict(Amount(value).__dict__, ['amount', 'asset'])
if re.match('^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}$', value):
return parse_time(value)
return value
def processText(self, text):
(counters, llens, lines) = zip(*map(self._mapper, text.split("\n")))
# Reduce list of counters to one count
WC = self._reducer(counters)
# Total number of words and words per segment
num_words = sum(llens)
wps = num_words / 1.0 #TODO: consider segments
# Convert to percentages
WC = walk_values(lambda x: "{:.2f}".format(100 * x / float(num_words)),
dict(WC))
#
WC['num_words'] = num_words
WC['wps'] = wps
return WC
def get_mbta_station_info(cfg):
route_info = query_mbta_id("routes", cfg['route'])
stop_info = query_mbta_id("stops", cfg['stop'])
params = (('filter[stop]', cfg['stop']), ('filter[route]', cfg['route']),
('page[limit]', '10'))
arrivals = query_mbta('predictions', params)
by_direction = f.walk_values(
vectorize(f.compose(relative_ts, op.itemgetter('arrival_time'))),
f.group_by(op.itemgetter('direction_id'), arrivals))
return [
f.merge(
{
"station": stop_info['name'],
"route": cfg['route'],
"direction": route_info['direction_destinations'][k],
}, dict(zip(range(5), pad(v, 5))))
for k, v in by_direction.items()
]
def cutlatches(self, latches=None, renamer=None):
if renamer is None:
@fn.memoize
def renamer(_):
return common._fresh()
def renamer_bv(name):
root, idx = unpack_name(name)
return f"{renamer(root)}[{idx}]"
aig, lmap = self.aig.cutlatches(latches, renamer=renamer_bv)
circ = rebundle_aig(aig)
lmap = self.lmap.unblast(lmap)
def unblast_vals(vals):
name, _ = vals[0]
name = unpack_name(name)[0]
bdl = Bundle(size=len(vals), name=name)
return (name, bdl.unblast(dict(vals)))
lmap = fn.walk_values(unblast_vals, lmap)
return circ, lmap
def collection_health(mongo):
last_items = {
'Posts':
find_latest_item(mongo, 'Posts', 'created'),
'Comments':
find_latest_item(mongo, 'Comments', 'created'),
'Operations':
find_latest_item(mongo, 'Operations', 'timestamp'),
'AccountOperations':
find_latest_item(mongo, 'AccountOperations', 'timestamp'),
}
def time_delta(item_time):
delta = dt.datetime.utcnow().replace(tzinfo=None) - item_time.replace(
tzinfo=None)
return delta.seconds
timings = walk_values(time_delta, last_items)
return {
**timings, 'status':
'impaired' if any(lambda x: x > (60 * 10), timings.values()) else 'ok'
}
def get_series_tag_history():
series_tag_history = {
'created': defaultdict(int),
'validated': defaultdict(int),
'invalidated': defaultdict(int)
}
qs = SeriesTag.objects.filter(
is_active=True).prefetch_related('validations')
for tag in tqdm(qs, total=qs.count(), desc='series tag history'):
validations = list(tag.validations.all())
series_tag_history['created'][ceil_date(tag.created_on)] += 1
validated = silent(min)(v.created_on for v in validations
if v.annotation_kappa == 1)
if validated:
series_tag_history['validated'][ceil_date(validated)] += 1
invalidated = silent(min)(v.created_on for v in validations
if v.agrees_with is not None)
if invalidated:
series_tag_history['invalidated'][ceil_date(invalidated)] += 1
return walk_values(accumulate, series_tag_history)
def run(self: 'ReviewProject') -> None:
"Runs the setup task `'review'`."
reports = {
'pyflakes': ReviewProject.lint(),
'pycodestyle': ReviewProject.style(),
'mypy': ReviewProject.types(),
}
for tool in reports:
code, warn, err = reports[tool]
if code != 0:
ReviewProject.separator_line()
print(f'{tool}: code {code}')
print('WARN')
print(warn)
print('ERROR')
print(err)
issues = sum(funcy.walk_values(lambda x: x[0], reports).values())
if issues > 0:
ReviewProject.separator_line()
print()
print(f'{issues} issues found.')
def get_balances(self):
available = {
"GOLOS": Amount(self["balance"]).amount,
"GBG": Amount(self["sbd_balance"]).amount,
"GESTS": Amount(self["vesting_shares"]).amount,
}
savings = {
"GOLOS": Amount(self["savings_balance"]).amount,
"GBG": Amount(self["savings_sbd_balance"]).amount,
}
accumulative = {"GOLOS": Amount(self["accumulative_balance"]).amount}
tip = {"GOLOS": Amount(self["tip_balance"]).amount}
totals = {
"GOLOS":
sum([
available["GOLOS"], savings["GOLOS"], accumulative["GOLOS"],
tip["GOLOS"]
]),
"GBG":
sum([available["GBG"], savings["GBG"]]),
"GESTS":
sum([available["GESTS"]]),
}
total = walk_values(rpartial(round, 3), totals)
return {
"available": available,
"savings": savings,
"accumulative": accumulative,
"tip": tip,
"total": total,
}
def rebundle_names(names):
grouped_names = fn.group_values(map(unpack_name, names))
return BundleMap(pmap(fn.walk_values(to_size, grouped_names)))
def time_shift(t, xs):
return fn.walk_values(_time_shift(t), xs)
def get_state(t, xs):
return fn.walk_values(lens[0].get(), xs)
