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stringclasses 2
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stringclasses 2
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|---|---|---|
import time
import pytest
import examples
import progressbar
import original_examples
def test_examples(monkeypatch):
for example in examples.examples:
try:
example()
except ValueError:
pass
@pytest.mark.filterwarnings('ignore:.*maxval.*:DeprecationWarning')
@pytest.mark.parametrize('example', original_examples.examples)
def test_original_examples(example, monkeypatch):
monkeypatch.setattr(progressbar.ProgressBar,
'_MINIMUM_UPDATE_INTERVAL', 1)
monkeypatch.setattr(time, 'sleep', lambda t: None)
example()
@pytest.mark.parametrize('example', examples.examples)
def test_examples_nullbar(monkeypatch, example):
# Patch progressbar to use null bar instead of regular progress bar
monkeypatch.setattr(progressbar, 'ProgressBar', progressbar.NullBar)
assert progressbar.ProgressBar._MINIMUM_UPDATE_INTERVAL < 0.0001
example()
def test_reuse():
import progressbar
bar = progressbar.ProgressBar()
bar.start()
for i in range(10):
bar.update(i)
bar.finish()
bar.start(init=True)
for i in range(10):
bar.update(i)
bar.finish()
bar.start(init=False)
for i in range(10):
bar.update(i)
bar.finish()
def test_dirty():
import progressbar
bar = progressbar.ProgressBar()
bar.start()
for i in range(10):
bar.update(i)
bar.finish(dirty=True)
|
nilq/baby-python
|
python
|
from .default import Config
class DevelopmentConfig(Config):
"""
Configurations for Development.
"""
DEBUG = True
TESTING = True
SECRET = "DevelopSecret123!!" # pragma: allowlist secret
|
nilq/baby-python
|
python
|
import numpy as np
NUM_EXP = 1
def evaluate(job_id, params):
np.random.seed(NUM_EXP)
x = params['X']
y = params['Y']
z = params['Z']
a = params['A']
#print 'Evaluating at (%f, %f, %f, %f)' % (x, y, z, a)
obj1 = float(1.10471 * np.power(x,2.0) * z + 0.04811 * a * y * (14.0+z)) + np.random.normal(0,3.2)
obj2 = float(2.1952 / float((np.power(a,3.0)*y))) + np.random.normal(0,175)
c1 = (float(13600.0-np.power(np.power(6000.0/(np.power(2,0.5)*x*z),2.0)+ np.power( (6000.0*(14.0+0.5*z)*np.power(0.25*(np.power(z,2.0)+np.power(x+a,2.0)),0.5)/(2*np.power(2.0,0.5)*x*z*(np.power(z,2.0)/(12.0)+0.25*np.power(x+a,2.0)))) ,2.0) + z * 6000.0/(np.power(2,0.5)*x*z) * ((6000.0*(14.0+0.5*z)*np.power(0.25*(np.power(z,2.0)+np.power(x+a,2.0)),0.5)/(2*np.power(2.0,0.5)*x*z*(np.power(z,2.0)/(12.0)+0.25*np.power(x+a,2.0))))) / (np.power(0.25*(np.power(z,2.0)+np.power(x+a,2.0)),0.5)),0.5)) + np.random.normal(0,3)) / 75842.5359709
c2 = (30000.0-504000/(np.power(a,2.0)*y) + np.random.normal(0,0.5)) / 8526363.04783
c3 = (y - x + np.random.normal(0,0.05)) / 2.01692584516
c4 = (64746.022 * (1.0 - 0.0282346 * a) * a *np.power(y, 3.0) - 6000.0 + np.random.normal(0,0.05)) / 11617706.4105
return {
"o1" : obj1,
"o2" : obj2,
"c1" : c1,
"c2" : c2,
"c3" : c3,
"c4" : c4
}
def main(job_id, params):
try:
return evaluate(job_id, params)
except Exception as ex:
print ex
print 'An error occurred in mocotoy_con.py'
return np.nan
if __name__ == "__main__":
main(0, {u'X': np.array([ 5.0 ]), u'Y': np.array([ 2.8 ]), u'Z': np.array([ 5.0 ]), u'A': np.array([ 2.8 ])})
|
nilq/baby-python
|
python
|
class Config:
HOST_URL = "https://www.mirrativ.com"
USER_AGENT = "MR_APP/8.67.0/Android/GA00747-UK/5.1.1"
USER_ME = "/api/user/me"
PROFILE_EDIT = "/api/user/profile_edit"
FOLLOW = "/api/graph/follow"
COMMENT = "/api/live/live_comment"
LIVE = "/api/live/live"
EDIT_LIVE = "/api/live/live_edit"
CREATE_LIVE = "/api/live/live_create"
STREAM_URL = "/api/live/get_streaming_url"
GET_COMMENT = "/api/live/live_comments"
LIVE_POLLING = "/api/live/live_polling"
LIVE_REQUESTS = "/api/user/post_live_request"
EDIT_PROFILE = "/api/user/profile_edit"
BUY_AVATAR = "/api/avatar/purchase_avatars"
UPDATE_AVATAR = "/api/closet/update_closet_avatar"
|
nilq/baby-python
|
python
|
import os
import torch
import numpy as np
import pickle
from utils import *
def hook_fn(m, i, o):
try:
visualisation[m] = o.cpu().numpy()
except AttributeError:
visualisation[m] = o[0].cpu().numpy()
if __name__=='__main__':
with open('./results/cka/act_std.pkl', 'rb') as file:
act_std = pickle.load(file)
file.close()
file = open('./results/cka/act_adv.pkl', 'rb')
act_adv = pickle.load(file)
file.close()
file = open('./results/cka/act_bn0.pkl', 'rb')
act_bn0 = pickle.load(file)
file.close()
file = open('./results/cka/act_bn1.pkl', 'rb')
act_bn1 = pickle.load(file)
file.close()
'''
ckas_sa = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_self = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_aself = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_bns = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_bns_ = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_bna = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_bna_ = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_bnsa = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
'''
ckas_bn1 = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
ckas_bn0 = np.zeros((len(list(act_std.values())), len(list(act_std.values()))))
assert len(list(act_std.values())) == len(list(act_bn0.values()))
ckas = []
for i in range(len(list(act_std.values()))):
for j in range(len(list(act_std.values()))):
# X_s = list(act_std.values())[i].reshape(196, -1)
# X_a_ = list(act_adv.values())[i].reshape(196, -1)
# X_s_ = list(act_std.values())[j].reshape(196, -1)
# X_a = list(act_adv.values())[j].reshape(196, -1)
# try:
X_bn0_ = list(act_bn0.values())[i].reshape(196, -1)
X_bn0 = list(act_bn0.values())[j].reshape(196, -1)
X_bn1_ = list(act_bn1.values())[i].reshape(196, -1)
X_bn1 = list(act_bn1.values())[j].reshape(196, -1)
# except AttributeError:
# X_bn0_ = list(act_bn0.values())[i][0].reshape(196, -1)
# X_bn0 = list(act_bn0.values())[j][0].reshape(196, -1)
# X_bn1 = list(act_bn1.values())[j][0].reshape(196, -1)
'''
ckas_sa[i][j] = cka(gram_linear(X_s), gram_linear(X_a), debiased=True)
ckas_self[i][j] = cka(gram_linear(X_s), gram_linear(X_s_), debiased=True)
ckas_aself[i][j] = cka(gram_linear(X_a_), gram_linear(X_a), debiased=True)
ckas_bns[i][j] = cka(gram_linear(X_s), gram_linear(X_bn0), debiased=True)
ckas_bns_[i][j] = cka(gram_linear(X_s), gram_linear(X_bn1), debiased=True)
ckas_bnsa[i][j] = cka(gram_linear(X_bn0_), gram_linear(X_bn1), debiased=True)
ckas_bna[i][j] = cka(gram_linear(X_a_), gram_linear(X_bn0), debiased=True)
ckas_bna_[i][j] = cka(gram_linear(X_a_), gram_linear(X_bn1), debiased=True)
'''
ckas_bn0[i][j] = cka(gram_linear(X_bn0_), gram_linear(X_bn0), debiased=True)
ckas_bn1[i][j] = cka(gram_linear(X_bn1_), gram_linear(X_bn1), debiased=True)
# ckas.append(ckas_sa)
# ckas.append(ckas_self)
# ckas.append(ckas_aself)
# ckas.append(ckas_bns)
# ckas.append(ckas_bns_)
# ckas.append(ckas_bna)
# ckas.append(ckas_bna_)
# ckas.append(ckas_bnsa)
ckas.append(ckas_bn0)
ckas.append(ckas_bn1)
np.save('./results/ckas_.npy', np.array(ckas))
|
nilq/baby-python
|
python
|
# https://leetcode.com/problems/subsets-ii/description/
#
# algorithms
# Medium (40.24%)
# Total Accepted: 173.2K
# Total Submissions: 430.4K
# beats 100.0% of python submissions
class Solution(object):
def subsetsWithDup(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
length = len(nums)
res = set()
def resursive(idx, path):
res.add(tuple(sorted(path)))
if idx == length:
return
for i in xrange(idx, length):
resursive(i + 1, path + [nums[i]])
resursive(0, [])
return [list(path) for path in res]
|
nilq/baby-python
|
python
|
"""
retrieve environment variables and resolve references to AWS Parameter Store Parameters.
"""
from typing import Dict
import os
import boto3
def get(name: str, session: boto3.session.Session) -> str:
"""
gets the environment variable value specified by `name`. if the `value`
starts with ssm://, it will return the value of the SSM parameter with the specified name.
The resulting value is cached, so subsequent requests will return the same value.
"""
if name in _cache:
return _cache[name]
value = os.getenv(name)
if value and value.startswith("ssm://"):
response = session.client("ssm").get_parameter(
Name=value[6:], WithDecryption=True
)
value = response["Parameter"]["Value"]
_cache[name] = value
return value
# cache of retrieved environment variables
_cache: Dict[str, str] = {}
|
nilq/baby-python
|
python
|
"""
A repository of typed entities, retrievable by their external reference
Entity object API:
entity.entity_type --> string used for groupung
entity.external_ref --> lookup name
entity.origin --> one-time settable parameter, set by the entity store
entity.validate() --> must return True [for valid entities and False for invalid ones]
entity.name --> printable name
Optional:
entity.uuid --? used for entity retrieval
"""
from __future__ import print_function, unicode_literals
import uuid
import re
import os
from datetime import datetime
from collections import defaultdict
from antelope import local_ref
from ..from_json import to_json
# CatalogRef = namedtuple('CatalogRef', ['archive', 'id'])
ref_regex = re.compile('[a-z0-9_]+(\.[a-z0-9_]+)*', flags=re.IGNORECASE)
uuid_regex = re.compile('([0-9a-f]{8}-?([0-9a-f]{4}-?){3}[0-9a-f]{12})', flags=re.IGNORECASE)
def to_uuid(_in):
if _in is None:
return _in
if isinstance(_in, int):
return None
try:
g = uuid_regex.search(_in) # using the regexp test is 50% faster than asking the UUID library
except TypeError:
if isinstance(_in, uuid.UUID):
return str(_in)
g = None
if g is not None:
return g.groups()[0]
'''
# no regex match- let's see if uuid.UUID can handle the input
try:
_out = uuid.UUID(_in)
except ValueError:
return None
return str(_out)
''' ## NOTE: This is costly because it requires to instantiate a UUID for EVERY query, especially those that are
# already probably not valid UUIDs! There is every reason to expect the input is a string, and our regex already
# matches even non-RFC-compliant UUID strings. I'm going to leave it out for now
return None
class SourceAlreadyKnown(Exception):
pass
class EntityExists(Exception):
pass
class InvalidSemanticReference(Exception):
pass
class ReferenceCreationError(Exception):
pass
class EntityStore(object):
_entity_types = () # must be overridden
'''
_ns_uuid_required: specifies whether the archive must be supplied an ns_uuid (generally, archives that are
expected to generate persistent, deterministic IDs must have an externally specified ns_uuid)
If False: random ns_uuid generated if none is supplied
If True: ns_uuid must be supplied as an argument, will raise exception if missing
If None: ns_uuid forced to None - store does not have ns_uuid capabilities
'''
_ns_uuid_required = False
_origin = None # can be set when a catalog is assigning a ref
def _ref_to_uuid(self, key):
"""
This tries to find a UUID from a ref. Not sure what this is good for.
by default, to_uuid just returns a string matching the regex, or failing that, tries to generate a string
using uuid.UUID(key)
:param key:
:return:
"""
u = to_uuid(key) # check if key is already a uuid
if u is None:
return self._ref_to_nsuuid(key)
return u
def _ref_to_nsuuid(self, key):
if self._ns_uuid is None:
return None
if isinstance(key, int):
key = str(key)
return str(uuid.uuid3(self._ns_uuid, key))
def _ref_to_key(self, key):
"""
This method always returns a valid key into _entities, or None. May be overridden.
:param key:
:return:
"""
if key in self._entities:
return key
uu = self._ref_to_uuid(key)
if uu is not None:
if uu in self._entities:
return uu
def get_uuid(self, key):
"""
Deprecated.
:param key:
:return:
"""
return self._ref_to_uuid(key)
def _set_ns_uuid(self, ns_uuid):
print('%s: Setting NSUUID (%s) %s' % (self.ref, self._ns_uuid_required, ns_uuid))
if self._ns_uuid_required is None:
if ns_uuid is not None:
print('Ignoring ns_uuid specification')
return None
else:
if ns_uuid is None:
if self._ns_uuid_required is True:
raise AttributeError('ns_uuid specification required')
elif self._ns_uuid_required is False:
return uuid.uuid4()
else:
if isinstance(ns_uuid, uuid.UUID):
return ns_uuid
return uuid.UUID(ns_uuid)
def __init__(self, source, ref=None, quiet=True, upstream=None, static=False, dataReference=None, ns_uuid=None,
no_validate=None,
**kwargs):
"""
An EntityStore is a provenance structure for a collection of entities. Ostensibly, an EntityStore has a single
source from which entities are collected. The source is a resolvable URI that indicates a data resource from
which data describing the entities can be extracted. The exact manner of extracting data from resources is
subclass-dependent.
Internally, all entities are stored with UUID keys. If the external references do not contain UUIDs, it is
recommended to derive a UUID3 using an archive-specific, stable namespace ID. The class-level
_ns_uuid_required attribute governs this option:
- if True, an ns_uuid argument must be provided when the class is instantiated. This is consistent with a
use case in which it is desirable to have predictable, fixed UUIDs (i.e. to interface with a data system
that requires stable UUIDs)
- if False, a random ns_uuid is generated, and used to create a UUID anytime an entity is given a non-UUID
external_ref
- if None, UUID3 are not used and any supplied ns_uuid argument is ignored. external_refs must always be UUIDs.
There is still some refactoring to be done, to try to eliminate the need for externally visible UUIDs anywhere.
An archive has a single semantic reference that describes the data context from which its native entities
were gathered. The reference is given using dot-separated hierarchical terms in order of decreasing
semantic significance from left to right. The leftmost specifier should describe the maintainer of the
resource (which defaults to 'local' when a reference argument is not provided), followed by arbitrarily
more precise specifications. Some examples are:
local.lcia.traci.2.1.spreadsheet
ecoinvent.3.2.undefined
The purpose for the source / reference distinction is that in principle many different sources can all provide
the same semantic content: for instance, ecoinvent can be accessed from the website or from a file on the
user's computer. In principle, if the semantic reference for two archives is the same, the archives should
contain excerpts of the same data, even if drawn from different sources.
An entity is uniquely identified by its link property, which is made from concatenating the semantic origin and
a stable reference known as an 'external_ref', as 'origin/external_ref'. The first slash is the delimiter
between origin and reference. Examples:
elcd.3.2/processes/00043bd2-4563-4d73-8df8-b84b5d8902fc
uslci.ecospold/Acetic acid, at plant
Note that the inclusion of embedded whitespace, commas, and other characters indicate that these semantic
references are not proper URIs.
It is hoped that the user community will help develop and maintain a consistent and easily interpreted
namespace for semantic references. If this is done, it should be possible to identify any published entity
with a concise reference.
When an entity is first added to an archive, it is assigned that archive's *reference* as its origin, following
the expectation that data about the same reference from different sources is the same data.
When an entity with a different origin is added to an archive, it is good practice to add a mapping from that
origin to its source in the receiving archive's "catalog_names" dictionary. However, since the entity itself
does not know its archive's source, this cannot be done automatically.
:param source: physical data source-- where the information is being drawn from
:param ref: optional semantic reference for the data source. gets added to catalog_names.
:param quiet:
:param upstream:
:param static: [False] whether archive is expected to be unchanging.
:param dataReference: alternative to ref
:param ns_uuid: required to store entities by common name. Used to generate uuid3 from string inputs.
:param no_validate: if True, skip validation on entity add
:param kwargs: any other information that should be serialized with the archive
"""
self._source = source
if ref is None:
if dataReference is None:
ref = local_ref(source)
else:
ref = dataReference
self._entities = {} # uuid-indexed list of known entities
self._quiet = quiet # whether to print out a message every time a new entity is added / deleted / modified
self._serialize_dict = kwargs # this gets added to
self._counter = defaultdict(int)
self._ents_by_type = defaultdict(set)
self._upstream = None
self._no_validate = no_validate
self._loaded = False
self._static = static
self._descendant = False
if upstream is not None:
self.set_upstream(upstream)
self._catalog_names = defaultdict(set) # this is a place to map semantic references to data sources
self._add_name(ref, source)
self._serialize_dict['dataReference'] = ref
self._ns_uuid = self._set_ns_uuid(ns_uuid)
if self._ns_uuid is not None:
self._serialize_dict['ns_uuid'] = str(self._ns_uuid)
def _add_name(self, ref, source, rewrite=False):
"""
A source is not allowed to provide multiple semantic references
a ref must match the regexp ([A-Za-z0-9_]+(\.[A-Za-z0-9_])*)
:param ref:
:param source:
:param rewrite: [False] if True, if SourceAlreadyKnown, re-assign the source to the new ref. This may result
in the archive's ref changing, and should only be used when an authoritative source-ref pair is supplied
(e.g. a JSON file that was loaded into the archive)
:return:
"""
if not ref_regex.match(ref):
raise InvalidSemanticReference('%s' % ref)
for k, s in self._catalog_names.items():
if source in s and source is not None:
if source == self.source and k == local_ref(self.source):
'''if we're trying to add our own source and ref to the name index, and the source is currently
registered to the default local_ref, then we override it
'''
self._catalog_names[ref] = self._catalog_names.pop(k)
return
if k == ref or ref.startswith(k):
return
if rewrite:
self._catalog_names[k].remove(source)
print('%s: <source removed>' % k)
else:
raise SourceAlreadyKnown('Source %s already registered to name %s (vs: %s)' % (source, k, ref))
print('%s: %s' % (ref, source))
self._catalog_names[ref].add(source)
if ref == self.ref and self.source is None and rewrite:
self._source = source
def add_new_source(self, new_ref, new_source):
self._add_name(new_ref, new_source, rewrite=False)
@property
def source(self):
"""
The catalog's original source is the "master descriptor" of the catalog's content. This is required for
subclass methods to work properly, in the event that the original source is called upon.
:return:
"""
return self._source
def _set_source(self, new_ref, new_source):
self._source = new_source
self._add_name(new_ref, new_source)
self._descendant = True
def set_origin(self, origin):
self._serialize_dict['dataReference'] = origin
self._add_name(origin, self.source, rewrite=True)
self._origin = origin
@property
def ref(self):
if self._origin is not None:
return self._origin
try:
return next(k for k, s in self._catalog_names.items() if self.source in s)
except StopIteration:
return local_ref(self.source)
@property
def catalog_names(self):
for k in self._catalog_names.keys():
yield k
@property
def names(self):
"""
Return a mapping of data source to semantic reference, based on the catalog_names property. This is used by
a catalog interface to convert entity origins from physical to semantic.
If a single data source has multiple semantic references, only the most-downstream one will be kept. If there
are multiple semantic references for the same data source in the same archive, one will be kept at random.
This should be avoided and I should probably test for it when setting catalog_names.
:return:
"""
if self._upstream is None:
names = dict()
else:
names = self._upstream.names
for k, s in self._catalog_names.items():
for v in s:
names[v] = k
return names
def get_sources(self, name):
s = self._catalog_names[name]
if len(s) == 0:
for k, ss in self._catalog_names.items():
if k.startswith(name):
s = s.union(ss)
for d in s:
yield d
def construct_new_ref(self, signifier):
today = datetime.now().strftime('%Y%m%d')
if signifier is None:
new_tail = today
else:
if not bool(re.match('[A-Za-z0-9_-]+', signifier)):
raise ValueError('Invalid signifier %s' % signifier)
new_tail = '.'.join([signifier, datetime.now().strftime('%Y%m%d')])
if len(self.ref.split('.')) > 2: # must be true to be postfixing a postfix
old_tail = '.'.join(self.ref.split('.')[-2:])
if old_tail.startswith(new_tail):
hm = '.' + datetime.now().strftime('-%H%M')
if old_tail.startswith(new_tail + hm):
hm += datetime.now().strftime('%S')
if old_tail.startswith(new_tail + hm):
raise ReferenceCreationError('HMS? %s', (self.ref, hm))
new_tail += hm
elif old_tail.find('.' + today) >= 0 and signifier is not None:
# don't reprint the date if it already shows up
new_tail = signifier
new_ref = '.'.join([self.ref, new_tail])
return new_ref
def create_descendant(self, archive_path, signifier=None, force=False):
"""
Saves the archive to a new source with a new semantic reference. The new semantic ref is derived by
(a) first removing any trailing ref that matches [0-9]{8+}
(b) appending the descendant signifier
(c) appending the current date in YYYYMMDD format
After that:
1. The new semantic ref is added to catalog_names,
2. the source is set to archive_path/semantic.ref.json.gz,
3. load_all() is executed,
4. the archive is saved to the new source.
:param archive_path: where to store the archive
:param signifier: A nonzero-length string matching [A-Za-z0-9_-]+. If not supplied, then the semantic ref is
unchanged except for the date tag.
:param force: overwrite if file exists
:return: new semantic ref.
"""
if not os.path.exists(archive_path):
os.makedirs(archive_path)
new_ref = self.construct_new_ref(signifier)
if new_ref == self.ref:
raise KeyError('Refs are the same!') # KeyError bc it's a key in catalog_names
new_filename = new_ref + '.json.gz'
new_source = os.path.join(archive_path, new_filename)
if os.path.exists(new_source):
if force:
print('Overwriting existing archive')
else:
raise EnvironmentError('File %s exists: force=True to overwrite' % new_source)
try:
self.load_all()
except NotImplementedError:
pass
self._set_source(new_ref, new_source)
self.write_to_file(new_source, gzip=True, complete=True)
return new_ref
@property
def static(self):
return self._static or self._loaded
'''
@property
def ref(self):
"""
Deprecated. Archives have a source; catalogs have a ref.
:return:
"""
return self._source
'''
def entities(self):
for v in self._entities.values():
yield v
def set_upstream(self, upstream):
assert isinstance(upstream, EntityStore)
if upstream.source != self.source:
self._serialize_dict['upstreamReference'] = upstream.ref
self._upstream = upstream
'''
def truncate_upstream(self):
"""
BROKEN! / deprecated
removes upstream reference and rewrites entity uuids to match current index. note: deprecates the upstream
upstream_
:return:
"""
# TODO: this needs to be fixed: truncate needs localize all upstream entities (retaining their origins)
for k, e in self._entities.items():
e._uuid = k
self._upstream = None
if 'upstreamReference' in self._serialize_dict:
self._serialize_dict.pop('upstreamReference')
'''
def _print(self, *args):
if self._quiet is False:
print(*args)
def __str__(self):
count = sum(len(v) for v in self._ents_by_type.values())
s = '%s with %d entities at %s' % (self.__class__.__name__, count, self.source)
if self._upstream is not None:
s += ' [upstream %s]' % self._upstream.__class__.__name__
return s
def _get_entity(self, key):
"""
the fundamental method- retrieve an entity from LOCAL collection by key, nominally a UUID string.
If the string is not found, raises KeyError.
:param key: a uuid
:return: the LcEntity or None
"""
if key in self._entities:
return self._entities[key]
raise KeyError(key)
def __contains__(self, item):
return item in self._entities
def __getitem__(self, item):
"""
CLient-facing entity retrieval. item is a key that can be converted to a valid UUID from self._ref_to_key()--
either a literal UUID, or a string containing something matching a naive UUID regex.
First checks upstream, then local.
Returns None if nothing is found
:param item:
:return:
"""
if item is None:
return None
if self._upstream is not None:
e = self._upstream[item]
if e is not None:
return e
try:
if isinstance(item, int) and self._ns_uuid is not None:
return self._get_entity(self._ref_to_nsuuid(item))
return self._get_entity(self._ref_to_key(item))
except KeyError:
return None
def _ensure_valid_refs(self, entity):
"""
Hook to validate the incoming entity's references-- namely, to set its uuid
:param entity:
:return:
"""
if hasattr(entity, 'uuid') and entity.uuid is None:
uu = self._ref_to_uuid(entity.external_ref)
if uu is not None:
entity.uuid = uu
def _add(self, entity, key, quiet=False):
self._ensure_valid_refs(entity)
if key is None:
raise ValueError('Key not allowed to be None')
if key in self._entities:
raise EntityExists('Entity already exists: %s' % key)
if entity.entity_type not in self._entity_types:
raise TypeError('Entity type %s not valid!' % entity.entity_type)
if entity.is_entity and not self._no_validate:
if not entity.validate():
raise ValueError('Entity fails validation: %s' % repr(entity))
if not (self._quiet or quiet):
print('Adding %s entity with %s: %s' % (entity.entity_type, key, entity['Name']))
if entity.origin is None:
# TODO: uncomment / enforce this
# assert self._ref_to_key(entity.external_ref) == key, 'entity uuid must match origin repository key!'
entity.origin = self.ref
self._entities[key] = entity
if self._ns_uuid is not None: # ensure UUID3s work even if custom UUIDs are specified
nsuuid = self._ref_to_uuid(entity.external_ref)
if nsuuid is not None and nsuuid not in self._entities:
self._entities[nsuuid] = entity
self._counter[entity.entity_type] += 1
self._ents_by_type[entity.entity_type].add(key) # it's not ok to change an entity's type
def check_counter(self, entity_type=None):
if entity_type is None:
[self.check_counter(entity_type=k) for k in self._entity_types]
else:
print('%d new %s entities added (%d total)' % (self._counter[entity_type], entity_type,
self.count_by_type(entity_type)))
self._counter[entity_type] = 0
def find_partial_id(self, uid, upstream=False, startswith=True):
"""
:param uid: is a fragmentary (or complete) uuid string.
:param upstream: [False] whether to look upstream if it exists
:param startswith: [True] use .startswith instead of full regex
:return: result set
"""
if startswith:
def test(x, y):
return y.startswith(x)
else:
def test(x, y):
return bool(re.search(x, y))
result_set = [v for k, v in self._entities.items() if test(uid, k)]
if upstream and self._upstream is not None:
result_set += self._upstream.find_partial_id(uid, upstream=upstream, startswith=startswith)
return result_set
def _fetch(self, entity, **kwargs):
"""
Dummy function to fetch from archive. MUST be overridden.
Can't fetch from upstream.
:param entity:
:return:
"""
raise NotImplementedError
def retrieve_or_fetch_entity(self, key, **kwargs):
"""
Client-facing function to retrieve entity by ID, first checking in the archive, then from the source.
Input is flexible-- could be a UUID or key (partial uuid is just not useful)
:param key: the identifying string (uuid or external ref)
:param kwargs: used to pass provider-specific information
:return:
"""
if key is not None:
entity = self.__getitem__(key) # this checks upstream if it exists
if entity is not None:
# retrieve
return entity
# fetch
return self._fetch(key, **kwargs)
def get(self, key):
return self.retrieve_or_fetch_entity(key)
def validate_entity_list(self):
"""
This whole thing is crufty and untested and never used and should be abandoned
:return:
"""
count = 0
for k, v in self._entities.items():
valid = True
'''
# 1: confirm key is a UUID
if not isinstance(k, uuid.UUID):
print('Key %s is not a valid UUID.' % k)
valid = False
'''
if v.origin is None:
print("%s: No origin!" % k)
valid = False
if v.origin == self.source:
# 2: confirm entity's external key maps to its uuid
if self._ref_to_uuid(v.external_ref) != v.uuid:
print("%s: Key doesn't match UUID in origin!" % v.external_ref)
valid = False
# confirm entity is dict-like with keys() and with a set of common keys
try:
valid = valid & v.validate()
except AttributeError:
print('Key %s: not a valid LcEntity (no validate() method)' % k)
valid = False
if valid:
count += 1
print('%d entities validated out of %d' % (count, len(self._entities)))
return count
def _load_all(self, **kwargs):
"""
Must be overridden in subclass
:return:
"""
raise NotImplementedError
def load_all(self, **kwargs):
if self._loaded is False:
print('Loading %s' % self.source)
self._load_all(**kwargs)
self._loaded = True
def entities_by_type(self, entity_type):
for u in sorted(self._ents_by_type[entity_type]):
yield self._entities[u]
def count_by_type(self, entity_type):
return len(self._ents_by_type[entity_type])
@property
def init_args(self):
return self._serialize_dict
def serialize(self, **kwargs):
j = {
'dataSourceType': self.__class__.__name__,
'dataSource': self.source,
'catalogNames': {k: sorted(filter(None, s)) for k, s in self._catalog_names.items()},
'initArgs': self._serialize_dict
}
return j
def _serialize_all(self, **kwargs):
"""
To be overridden-- specify args necessary to make a complete copy
:param kwargs:
:return:
"""
return self.serialize(**kwargs)
def write_to_file(self, filename, gzip=False, complete=False, **kwargs):
"""
:param filename:
:param gzip:
:param complete:
:param kwargs: whatever is required by the subclass's serialize method
:return:
"""
if self._source is None:
self._set_source(self.ref, filename) # unless there was no source to begin with
elif filename not in self.names:
self._add_name(self.ref, filename)
if complete:
s = self._serialize_all(**kwargs)
if self._loaded:
s['loaded'] = True
else:
s = self.serialize(**kwargs)
to_json(s, filename, gzip=gzip)
|
nilq/baby-python
|
python
|
import bpy
from bpy.props import *
from ..node_socket import RenderNodeSocket, SocketBase, RenderNodeSocketmixin, RenderNodeSocketInterface
from ..node_socket import update_node
class RenderNodeSocketInterfaceRenderList(RenderNodeSocketmixin, RenderNodeSocketInterface,
bpy.types.NodeSocketInterface):
bl_idname = 'RSNodeSocketRenderList'
bl_socket_idname = 'RSNodeSocketRenderList'
bl_label = 'RenderList (RenderNode)'
shape = 'DIAMOND'
default_value = None
def init_from_socket(self, node, socket):
self.display_shape = self.shape
def draw(self, context, layout):
pass
def draw_color(self, context):
return 0.95, 0.95, 0.95, 1.0
class RSNodeSocketRenderList(bpy.types.NodeSocket, SocketBase):
bl_idname = 'RSNodeSocketRenderList'
bl_label = 'RSNodeSocketRenderList'
compatible_sockets = ['RenderNodeMerge','RSNodeSocketMergeSettings']
shape = 'DIAMOND'
default_value = None
def draw(self, context, layout, node, text):
layout.label(text=text)
def draw_color(self, context, node):
return 0.95, 0.95, 0.95, 1.0
def change_shape(self):
self.display_shape = self.shape
classes = (
RenderNodeSocketInterfaceRenderList,
RSNodeSocketRenderList,
)
def register():
for cls in classes:
bpy.utils.register_class(cls)
def unregister():
for cls in classes:
bpy.utils.unregister_class(cls)
|
nilq/baby-python
|
python
|
import ocaml
assert(ocaml.Result.get_ok(ocaml.Result.Ok(True)) == True)
|
nilq/baby-python
|
python
|
from opera.parser.yaml.node import Node
from ..entity import Entity
from ..path import Path
from ..string import String
class ImportDefinition(Entity):
ATTRS = dict(
file=Path,
repository=String,
namespace_prefix=String,
namespace_uri=String,
)
DEPRECATED = {
"namespace_uri",
}
@classmethod
def normalize(cls, yaml_node):
if not isinstance(yaml_node.value, (str, dict)):
cls.abort(
"Invalid import data. Expected string or dict.", yaml_node.loc,
)
if isinstance(yaml_node.value, str):
return Node({Node("file"): yaml_node})
return yaml_node
|
nilq/baby-python
|
python
|
#online = mongodb_online()
#print('mongodb-online: ', online)
#TODO: cron docker para mongo
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
import json
from django.db.models import Q
from rest_framework.exceptions import PermissionDenied
from rest_framework.generics import (
CreateAPIView,
ListCreateAPIView,
RetrieveAPIView,
RetrieveUpdateDestroyAPIView
)
from rest_framework.permissions import (
AllowAny,
IsAdminUser,
IsAuthenticated
)
from rest_framework.response import Response
from note.authentication import AuthorAndAllAdmins, IsAuthenticatedOrReadOnly
from note.controller import (
delete_user,
get_all_users,
get_user_name_by_id,
update_user
)
from note.models import User, Note
from note.serializers import UserSerializer, NoteSerializer
from note.utils import sanitize_json_input
class RegisterView(CreateAPIView):
queryset = User.objects.all()
permission_classes = (AllowAny,)
serializer_class = UserSerializer
class UsersAPIView(RetrieveAPIView):
permission_classes = (IsAdminUser, )
serializer_class = UserSerializer
def get(self, request):
users = get_all_users()
return Response(users)
class UserAPIView(RetrieveUpdateDestroyAPIView):
permission_classes = (IsAuthenticated, AuthorAndAllAdmins)
serializer_class = UserSerializer
def get_object(self):
return self.request.user
def get(self, request, user_id):
user_name = get_user_name_by_id(user_id)
content = {'user is': user_name}
return Response(content)
@sanitize_json_input
def put(self, request, *args, **kwargs):
data = json.loads(self.request.body)
uuid = kwargs.get('user_id')
user_name = update_user(request, data, uuid)
content = {'user {} has been updated'.format(self.request.user.name): user_name}
return Response(content)
def delete(self, request, *args, **kwargs):
user_name = get_user_name_by_id(kwargs.get('user_id'))
delete_user(kwargs.get('user_id'))
content = 'User {} has been deleted'.format(user_name)
return Response(content)
class NotesView(ListCreateAPIView):
permission_classes = (IsAuthenticatedOrReadOnly, )
serializer_class = NoteSerializer
def get_queryset(self):
visibility = self.request.user.is_authenticated
tags = dict(self.request.query_params).get('tag')
keyword = self.request.query_params.get('keyword')
filter = Q(user_id=self.request.user.id) if visibility else Q(is_private=visibility)
if tags:
filter &= Q(tags__title__in=tags)
if keyword:
filter &= Q(title__icontains=keyword) | Q(body__icontains=keyword) | Q(tags__title__icontains=keyword)
notes_obj = Note.objects.filter(filter).distinct()
return notes_obj
class NoteView(RetrieveUpdateDestroyAPIView):
permission_classes = (IsAuthenticatedOrReadOnly, )
serializer_class = NoteSerializer
def get_object(self):
notes_obj = Note.objects.get(id=self.kwargs.get('id'))
notes_obj = notes_obj if notes_obj.user_id == self.request.user or notes_obj.is_private == False else None
return notes_obj
@sanitize_json_input
def put(self, request, *args, **kwargs):
notes_obj = Note.objects.get(id=self.kwargs.get('id'))
if notes_obj.user_id == self.request.user:
return self.update(request, *args, **kwargs)
else:
raise PermissionDenied
def delete(self, request, *args, **kwargs):
notes_obj = Note.objects.get(id=self.kwargs.get('id'))
if notes_obj.user_id == self.request.user:
return self.destroy(request, *args, **kwargs)
else:
raise PermissionDenied
|
nilq/baby-python
|
python
|
from django.contrib import admin
# Register your models here.
from reg.models import UserProfile
from .models import *
class BookAuthorAdmin(admin.ModelAdmin):
list_display = ('author_last_name', 'author_first_name', 'author_middle_name')
search_fields = ('author_last_name', 'author_first_name', 'author_middle_name')
list_filter = ('author_last_name',)
ordering = ('-author_last_name',)
class LibraryBookAdmin(admin.ModelAdmin):
list_display = ('book_title', 'book_author_id', 'category','quantity', 'number_borrowed')
search_fields = ('book_title',)
fields = ('book_title', 'book_author_id', 'category')
class SingleBookAdmin(admin.ModelAdmin):
list_display = ('book_id', 'serial_number')
def save_model(self, request, obj, form, change):
admin.ModelAdmin.save_model(self, request, obj, form, change)
if not change:
obj.book_id.quantity += 1
if not obj.is_available_returned:
obj.book_id.number_borrowed += 1
if obj.is_available_returned and obj.book_id.number_borrowed > 0:
obj.book_id.number_borrowed -= 1
obj.book_id.save()
admin.site.register(UserProfile)
admin.site.register(LibraryBook, LibraryBookAdmin)
admin.site.register(SingleBook, SingleBookAdmin)
admin.site.register(BookAuthors, BookAuthorAdmin)
admin.site.register(BorrowingLog)
admin.site.register(BookCategory)
|
nilq/baby-python
|
python
|
import numpy as np
class ArgMaxPolicy(object):
def __init__(self, critic):
self.critic = critic
def get_action(self, obs):
if len(obs.shape) > 3:
observation = obs
else:
observation = obs[None]
## TODO return the action that maxinmizes the Q-value
# at the current observation as the output
# argmax(-1) returns the index of last dimension (action, in this case)
actions = self.critic.qa_values(observation).argmax(-1)
return actions.squeeze()
|
nilq/baby-python
|
python
|
# coding: utf-8
"""
DocuSign REST API
The DocuSign REST API provides you with a powerful, convenient, and simple Web services API for interacting with DocuSign.
OpenAPI spec version: v2
Contact: devcenter@docusign.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class MergeField(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, allow_sender_to_edit=None, configuration_type=None, path=None, row=None, write_back=None):
"""
MergeField - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'allow_sender_to_edit': 'str',
'configuration_type': 'str',
'path': 'str',
'row': 'str',
'write_back': 'str'
}
self.attribute_map = {
'allow_sender_to_edit': 'allowSenderToEdit',
'configuration_type': 'configurationType',
'path': 'path',
'row': 'row',
'write_back': 'writeBack'
}
self._allow_sender_to_edit = allow_sender_to_edit
self._configuration_type = configuration_type
self._path = path
self._row = row
self._write_back = write_back
@property
def allow_sender_to_edit(self):
"""
Gets the allow_sender_to_edit of this MergeField.
When set to **true**, the sender can modify the value of the custom tab during the sending process.
:return: The allow_sender_to_edit of this MergeField.
:rtype: str
"""
return self._allow_sender_to_edit
@allow_sender_to_edit.setter
def allow_sender_to_edit(self, allow_sender_to_edit):
"""
Sets the allow_sender_to_edit of this MergeField.
When set to **true**, the sender can modify the value of the custom tab during the sending process.
:param allow_sender_to_edit: The allow_sender_to_edit of this MergeField.
:type: str
"""
self._allow_sender_to_edit = allow_sender_to_edit
@property
def configuration_type(self):
"""
Gets the configuration_type of this MergeField.
If merge field's are being used, specifies the type of the merge field. The only supported value is **salesforce**.
:return: The configuration_type of this MergeField.
:rtype: str
"""
return self._configuration_type
@configuration_type.setter
def configuration_type(self, configuration_type):
"""
Sets the configuration_type of this MergeField.
If merge field's are being used, specifies the type of the merge field. The only supported value is **salesforce**.
:param configuration_type: The configuration_type of this MergeField.
:type: str
"""
self._configuration_type = configuration_type
@property
def path(self):
"""
Gets the path of this MergeField.
Sets the object associated with the custom tab. Currently this is the Salesforce Object.
:return: The path of this MergeField.
:rtype: str
"""
return self._path
@path.setter
def path(self, path):
"""
Sets the path of this MergeField.
Sets the object associated with the custom tab. Currently this is the Salesforce Object.
:param path: The path of this MergeField.
:type: str
"""
self._path = path
@property
def row(self):
"""
Gets the row of this MergeField.
Specifies the row number in a Salesforce table that the merge field value corresponds to.
:return: The row of this MergeField.
:rtype: str
"""
return self._row
@row.setter
def row(self, row):
"""
Sets the row of this MergeField.
Specifies the row number in a Salesforce table that the merge field value corresponds to.
:param row: The row of this MergeField.
:type: str
"""
self._row = row
@property
def write_back(self):
"""
Gets the write_back of this MergeField.
When wet to true, the information entered in the tab automatically updates the related Salesforce data when an envelope is completed.
:return: The write_back of this MergeField.
:rtype: str
"""
return self._write_back
@write_back.setter
def write_back(self, write_back):
"""
Sets the write_back of this MergeField.
When wet to true, the information entered in the tab automatically updates the related Salesforce data when an envelope is completed.
:param write_back: The write_back of this MergeField.
:type: str
"""
self._write_back = write_back
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
|
nilq/baby-python
|
python
|
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import sys
import json
import argparse
from modules.FIC_Core import FICCore
from modules.config import DEFAULT_DAYS, REPOSITORIES_FILE
class FICMainMenu(FICCore):
def __init__(self):
FICCore.__init__(self)
self.all = False
self.git_only = False
self.hg_only = False
self.repo_selection = False
self.logging = False
self.days = DEFAULT_DAYS
self.push = False
self.dev = False
self.skip_menu = False
self.parser = argparse.ArgumentParser()
self.arguments_set = False # Check to see if we set the Flag values or not. Helps to skip un-needed iterations.
def start(self):
"""
The entry point for script. Runs the entire logic depending of the arguments.
"""
# Set all argument flags, based on runtime arguments.
self._available_arguments()
# Check if we want to skip the menu or not.
if not self.skip_menu:
self._main_menu()
# Skip the menu.
else:
# TODO: Add ability to skip every menu. Not only ALL
# Check if we ONLY typed `python client.py -s/--skip-menu`
# If check is True, set self.all = True then run FIC main logic.
# We don't need to check if "-s/--skip-menu" is present, as this is the only way to
# enter this else statement.
self.all = True if len(sys.argv) <= 2 else self.all
self.run_fic(all=self.all,
git_only=self.git_only,
hg_only=self.hg_only,
days=self.days,
logging=self.logging)
def _available_arguments(self):
"""
This method reads and set all the arguments flags.
"""
self.parser.add_argument('-a', '--all', required=False, action='store_true', default=False,
help='Runs script for all available repositories')
self.parser.add_argument('-g', '--git', required=False, action='store_true', default=False,
help='Runs script only for repos that are on GitHub')
self.parser.add_argument('-hg', '--mercurial', required=False, action='store_true', default=False,
help='Runs script only for repos that are on Mercurial')
self.parser.add_argument("-r", "--repo", required=False, nargs="*",
help="Let the user choose for which repositories to run")
self.parser.add_argument("-l", "--logging", required=False, action='store_true', default=False,
help="Activate logger output in the console")
self.parser.add_argument("-days", "--days", required=False, action='store', default=DEFAULT_DAYS,
help="Generate the changelog.md for <int> amount of days.")
self.parser.add_argument("-p", "--push", required=False, action='store_true', default=False,
help="Runs for all available repositories and auto-push to github")
self.parser.add_argument("-dev", "--development", required=False, action='store_true', default=False,
help="Activate development mode")
self.parser.add_argument("-s", "--skip-menu", required=False, action="store_true", default=False,
help="Skip MainMenu. Used for automatization.")
self.args = self.parser.parse_args()
self._set_arguments_flags()
def _set_arguments_flags(self):
"""
This method changes the flags state depending of the arguments.
"""
# Check that we have parsed all arguments.
if not self.args:
self._available_arguments()
else:
pass
# Create and set flags.
if self.args.all:
self.all = True
if self.args.git:
self.git_only = True
if self.args.mercurial:
self.hg_only = True
# Check if Manual Repo Selection is present and in which mode:
# - If `-r` is missing. (Return: False)
# - If `-r` is present, but no list present. (Return: True)
# - If `-r` is present and a list of repos are present. (Return: List of repos)
repo_selection = False if isinstance(self.args.repo, type(None)) else self.args.repo
if repo_selection:
self.repo_selection = self.args.repo
if self.args.logging:
self.logging = True
if self.args.days:
if str(self.args.days).isdecimal():
self.days = int(self.args.days)
else:
print("When using -d/--days please insert a number of days.\n"
"Example: python3 client.py -d 30 or --days 10")
exit(4)
if self.args.push:
self.push = True
if self.args.development:
self.dev = True
if self.args.skip_menu:
self.skip_menu = True
self.arguments_set = True
def _construct_mainmenu_text(self):
"""
Creates the main-menu content and prepare it to be displayed.
:return: the main menu text
"""
if not self.arguments_set:
self._set_arguments_flags()
else:
pass
menu_header = "Welcome to Ciduty's Firefox Infra Changelog!\n" \
"You can use the options below to run the script according to your needs.\n"
menu_logging = "==== Logging is active ====\n"
menu_dev = "==== Dev Mode is active ====\n"
menu_days = f"==== Generating Changelog for {self.days} days ====\n"
menu_notifications = (menu_logging if self.logging else "") + \
(menu_dev if self.dev else "") + \
(menu_days if self.days is not DEFAULT_DAYS else "")
menu_options = "1. Run script for all available repositories \n" \
"2. Run script only for repositories that are on GitHub\n" \
"3. Run script only for repositories that are on Mercurial\n" \
"4. Run script for repositories that you choose\n" \
"5. Activates logger output in console\n" \
"6. Generates changelog.md for the amount of days set by user\n" \
"7. Run the script for all repositories and push changes to Github\n" \
"0. Exit application."
return menu_header + menu_notifications + menu_options
def _main_menu(self):
"""
This method prints the main menu and reads the chosen options.
"""
print(self._construct_mainmenu_text())
self.choice = int(input())
self._run_selected_menu(choice=self.choice)
def _run_selected_menu(self, choice):
"""
This method calls the run_fic method depending of the chosen option.
:param choice: the chosen option by user
"""
if choice == 1:
self.LOGGER.info(f"Script running for choice {choice}: ALL Repositories.")
self.run_fic(all=True,
logging=self.logging,
days=self.days)
if choice == 2:
self.LOGGER.info(f"Script running for choice {choice}: Git Repositories Only.")
self.run_fic(git_only=True,
logging=self.logging,
days=self.days)
if choice == 3:
self.LOGGER.info(f"Script running for choice {choice}: HG Repositories Only.")
self.run_fic(hg_only=True,
logging=self.logging,
days=self.days)
if choice == 4:
self.LOGGER.info(f"Script running for choice {choice}: Custom Repositories.")
self._repo_selection_menu()
self.run_fic(repo_list=self.repo_selection)
if choice == 5:
self.logging = not self.logging
if self.logging:
self.LOGGER.info("Console Logging has been activated.")
else:
self.LOGGER.info("Console Logging has been deactivated.")
self._main_menu()
if choice == 6:
print("Please input the amount of days `changelog.md` will be generated for:")
days = input()
if str(days).isdecimal():
self.days = int(days)
self.LOGGER.info(f"DEFAULT_DAYS parameter has been changed to: {self.days} day(s)")
self._main_menu()
else:
print("Amount of days need to be an integer!\n"
"Moving back to Main Menu.")
self._main_menu()
if choice == 7:
self.LOGGER.info(f"Script running for choice {choice}: ALL Repositories and PUSH changes to GitHub")
self.run_fic(all=True,
push=True,
logging=self.logging,
days=self.days)
if choice == 0:
exit()
def _repo_selection_menu(self):
"""
Load available repositories and prepares them for user selection.
"""
repo_list = json.load(self.load(None, REPOSITORIES_FILE))
temp_list = []
# Argument "-r" provided, but no list of repositories is included.
# Enter Selection Menu.
if not self.repo_selection or (len(self.repo_selection) == 0):
self._construct_repo_selection(repo_list)
# Argument "-r" provided and list of repositories is included.
# Skip Selection Menu
else:
for key in repo_list:
for repo in repo_list.get(key):
for selection in self.repo_selection:
if int(selection) == repo_list.get(key).get(repo).get("order"):
temp_list.append((int(selection), repo, key))
self.repo_selection = []
for _, repo, key in temp_list:
self.repo_selection.append((repo, key))
def _construct_repo_selection(self, repo_list):
"""
The method that creates the list of the repositories chosen by user.
"""
temp_list = []
self.repo_selection = []
for key in repo_list:
for repo in repo_list.get(key):
temp_list.append((repo_list.get(key).get(repo).get("order"), repo, key))
print("Available Repositories:")
for entry in sorted(temp_list):
print(entry[0], entry[1])
print("Enter the number of the repositorie(s) you want to run, separated by comma.\n"
"Example: 1, 5, 20, 3, 2")
choices = input()
choices = choices.split(",")
self.repo_selection = []
for key in repo_list:
for repo in repo_list.get(key):
for choice in choices:
if int(choice) == repo_list.get(key).get(repo).get("order"):
self.repo_selection.append((repo, key))
|
nilq/baby-python
|
python
|
from django import forms
from django.contrib.admin import widgets
import os
CHOICE = {
('0','キュート'),
('1','クール'),
('2','パッション'),
}
form SampleForm(forms.Form):
select = forms.ChoiceField(label='属性', widget=forms.RadioSelect, choices= CHOICE, initial=0)
|
nilq/baby-python
|
python
|
L=[[[*map(int,v.split(','))]for v in l.split('->')]for l in open("inputday5")]
r,m=lambda n,x,y:x<=n<=y or y<=n<=x,max(max(max(p)for p in l)for l in L)+1
c=lambda a,b,f,s,w:(r(a,f[0],s[0])and r(b,f[1],s[1])and(f[0]==s[0]or f[1]==s[1]or(w and abs(f[0]-a)==abs(f[1]-b))))
print(*(sum(a)for a in((sum(c(i,j,f,s,b)for f,s in L)>1 for j in range(m)for i in range(m))for b in(0,1))))
|
nilq/baby-python
|
python
|
# Filename: HCm_UV_v5.0.py
#####################
###### IMPORTS ######
#####################
import string
import numpy as np
import sys
#sys.stderr = open('errorlog.txt', 'w')
import warnings
warnings.filterwarnings("ignore")
#######################
###### FUNCTIONS ######
#######################
#Function for interpolation of grids
def interpolate(grid,z,zmin,zmax,n):
#Columns of the library
n_comments = 0
with open('Libraries_uv/C17_POPSTAR_1myr_uv.dat', 'r') as file1:
for line in file1:
if line[0] == '#':
n_comments += 1
auxiliar_labels = np.genfromtxt('Libraries_uv/C17_POPSTAR_1myr_uv.dat', dtype=None, names=True, encoding = 'ascii', skip_header=n_comments).dtype.names
ncol = len(auxiliar_labels)
vec = []
if z == 2:
label_z = 'logU'
if z == 1:
label_z = 'logCO'
if z == 0:
label_z = '12logOH'
type_list_names = []
for col in auxiliar_labels:
inter = 0
no_inter = 0
type_list_names.append((col, float))
for row in range(0,len(grid)):
if grid[label_z][row] < zmin or grid[label_z][row] > zmax: continue
if z == 2: x = '12logOH'; y = 'logCO'
if z == 1: x = '12logOH'; y = 'logU'
if z == 0: x = 'logCO'; y = 'logU'
if row == (len(grid)-1):
vec.append(grid[col][row])
no_inter = no_inter + 1
elif grid[x][row] < grid[x][row+1] or grid[y][row] < grid[y][row+1] :
vec.append(grid[col][row])
no_inter = no_inter + 1
else:
inter = inter + 1
for index in range(0,n):
i = grid[col][row]+(index)*(grid[col][row+1]-grid[col][row])/n
vec.append(i)
out_aux = np.transpose(np.reshape(vec,(-1,n*inter+no_inter)))
out = np.zeros(out_aux.shape[0], dtype=type_list_names)
for col_n in range(0, len(auxiliar_labels)):
out[auxiliar_labels[col_n]] = out_aux[:, col_n]
return out
################################
###### INITIAL ITERATIONS ######
################################
#Description of the code
print ('-------------------------------------------------')
print ('This is HII-CHI-mistry for UV version 5.0')
print ('See Perez-Montero, & Amorin (2017) for details')
print ('Insert the name of your input text file with some or all of the following columns:')
print (' Lya 1216')
print (' NV] 1239')
print (' CIV 1549')
print (' HeII 1640')
print (' OIII 1665')
print (' CIII 1909')
print (' Hb 4861')
print (' OIII 5007')
print ('in arbitrary units and reddening corrected. Each column must be given with labels for the lines and their corresponding flux errors.')
print ('-------------------------------------------------')
# Input file reading
if len(sys.argv) == 1:
if int(sys.version[0]) < 3:
input00 = raw_input('Insert input file name:')
else:
input00 = input('Insert input file name:')
else:
input00 = str(sys.argv[1])
try:
#Counting comments:
n_comments = 0
with open(input00, 'r') as file2:
for line in file2:
if line[0] == '#':
n_comments += 1
input0 = np.genfromtxt(input00,dtype=None,names=True, encoding = 'ascii', skip_header = n_comments)
print ('The input file is:'+input00)
except:
print ('Input file error: It does not exist or has wrong format')
sys.exit
print ('')
if input0.size == 1:
input1 = np.stack((input0,input0))
else:
input1 = input0
# Iterations for Montecarlo error derivation
if len(sys.argv) < 3:
n = 25
else:
n = int(sys.argv[2])
print ('The number of iterations for MonteCarlo simulation is: ',n)
print ('')
#############################################
###### SELECTION OF THE GRID OF MODELS ######
#############################################
#Interface with the user
print ('')
question = True
while question:
print ('-------------------------------------------------')
print ('Default SEDs')
print ('------------')
print ('(1) POPSTAR with Chabrier IMF, age = 1 Myr')
print ('(2) BPASS v.2.1 a_IMF = 1.35, Mup = 300, age = 1Myr with binaries')
print ('(3) AGN, double component, a(UV) = -1.0')
print ('')
print ('Other SED')
print ('---------')
print ('(4) Different library')
print ('-------------------------------------------------')
if int(sys.version[0]) < 3:
sed = raw_input('Choose SED of the models: ')
else:
sed = input('Choose SED of the models: ')
if sed == '1' or sed == '2' or sed == '3' or sed == '4': question = False
print ('')
#Further questions on the AGN models
if sed == '3':
#SLOPE ALPHA
question = True
while question:
if int(sys.version[0]) < 3:
alpha = raw_input('Choose value for alpha(OX) in the AGN models: [1] -0.8 [2] -1.2: ')
else:
alpha = input('Choose value for alpha(OX) in the AGN models: [1] -0.8 [2] -1.2: ')
if alpha == '1' or alpha == '2': question = False
print ('')
#FRACTION OF FREE ELECTRONS
question = True
while question:
if int(sys.version[0]) < 3:
efrac = raw_input('Choose stop criterion in the AGN models: [1] 2% free electrons [2] 98% free electrons: ')
else:
efrac = input('Choose stop criterion in the AGN models: [1] 2% free electrons [2] 98% free electrons: ')
if efrac == '1' or efrac == '2': question = False
#Presence or absence of dust in the models
question = True
while question:
if int(sys.version[0]) < 3:
grains = raw_input('Choose AGN models with [1] or without [2] dust grains: ')
else:
grains = input('Choose AGN models with [1] or without [2] dust grains: ')
if grains == '1' or grains == '2': question = False
print ('')
#Particular file introduced by the user
if sed == '4':
question = True
while question:
print ('Introduce name of the file containing the models. It must be located in the folder "Libraries_uv".')
print (' ')
if int(sys.version[0]) < 3:
new_library = raw_input('Name of file: ')
else:
new_library = input('Name of file: ')
#Searching for the file
try:
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+new_library, 'r') as file3:
for line in file3:
if line[0] == '#':
n_comments += 1
library_user = np.genfromtxt('Libraries_uv/'+new_library, dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
print (' ')
print ('Loading library '+new_library+'. Checking correct format of the file.')
question = False
except:
print (' ')
print ('Library was not found in folder "Libraries_uv" or file does not exist.')
question = True
while question:
try:
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+new_library, 'r') as file4:
for line in file4:
if line[0] == '#':
n_comments += 1
library_user = np.genfromtxt('Libraries_uv/'+new_library, dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
#Checking correct format:
#Counting comments:
n_comments = 0
with open('Libraries_uv/C17_POPSTAR_1myr_uv.dat', 'r') as file5:
for line in file5:
if line[0] == '#':
n_comments += 1
auxiliar_labels = np.genfromtxt('Libraries_uv/C17_POPSTAR_1myr_uv.dat', dtype=None, names=True, encoding = 'ascii', skip_header=n_comments).dtype.names
missing_labels = []
for label in auxiliar_labels:
if label in library_user.dtype.names:
continue
else:
missing_labels.append(label)
#Displaying message for the user:
print('Succesfully reading of the file')
if len(missing_labels) == 0:
print ('File presents the correct format')
question = False
else:
print ('File does not present the correct format. The following columns are missing:')
for need_label in missing_labels:
print('- '+need_label)
print ('More details on the correct format for the library are found in readme file.')
print (' ')
print ('Reintroduce name of the file with fixed format:')
print (' ')
if int(sys.version[0]) < 3:
new_library = raw_input('Name of file: ')
else:
new_library = input('Name of file: ')
except:
print ('Something went wrong while reading file. Please, reintroduce name of the file:')
print ('')
if int(sys.version[0]) < 3:
new_library = raw_input('Name of file: ')
else:
new_library = input('Name of file: ')
#Interpolation in the grid of models
question = True
print ('')
while question:
if int(sys.version[0]) < 3:
inter = raw_input('Choose models [0] No interpolated [1] Interpolated: ')
else:
inter = input('Choose models [0] No interpolated [1] Interpolated: ')
if inter == '0' or inter == '1': question = False
print ('')
sed = int(sed)
inter = int(inter)
alpha = int(alpha)
efrac = int(efrac)
grains = int(grains)
#POPSTAR MODEL
if sed==1:
file_lib = 'C17_POPSTAR_1myr_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file6:
for line in file6:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'POPSTAR, age = 1 Myr, Chabrier IMF. No interpolation.'
print ('No interpolation for the POPSTAR models is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
print ('')
res_CO = 0.125
elif inter == 1:
sed_type = 'POPSTAR, age = 1 Myr, Chabrier IMF. Interpolation.'
print ('Interpolation for the POPSTAR models is going to be used.')
print ('The grid has a resolution of 0.01dex for O/H and 0.0125dex for C/O.')
print ('')
res_CO = 0.125
#BPASS MODEL
elif sed==2:
file_lib = 'C17_BPASS_IMF135_mup300_1myr_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file7:
for line in file7:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'BPASS a_IMF = 1.35, M_up = 300, age = 1Myr, with binaries. No interpolation.'
print ('No interpolation for the BPASS models is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
print ('')
res_CO = 0.125
elif inter == 1:
sed_type = 'BPASS v.2.1, a_IMF = 1.35, M_up = 300, age = 1Myr. Interpolation.'
print ('Interpolation for the BPASS models is going to be used.')
print ('The grid has a resolution of 0.01dex for O/H and 0.0125dex for C/O.')
print ('')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -0.8, efrac = 2%, with dust grains
elif sed==3 and alpha ==1 and efrac == 1 and grains == 1:
file_lib = 'C17_AGN_alpha08_efrac02_CNfix_grains_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -0.8 and free electron fraction = 2% with dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -0.8 with 2% free electrons and dust grains models is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -0.8, free electron fraction = 2% and with dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -0.8, 2% free electrons and with dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -0.8, efrac = 2%, without dust grains
elif sed==3 and alpha ==1 and efrac == 1 and grains == 2:
file_lib = 'C17_AGN_alpha08_efrac02_CNfix_nograins_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -0.8 and free electron fraction = 2% without dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -0.8 with 2% free electrons models without grains is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -0.8, free electron fraction = 2% and without dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -0.8, 2% free electrons and without dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -0.8, efrac = 98%, with dust grains
elif sed==3 and alpha ==1 and efrac == 2 and grains == 1:
file_lib = 'C17_AGN_alpha08_efrac98_CNfix_grains_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -0.8 and free electron fraction = 98% with dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -0.8 with 98% free electrons and dust grains models is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -0.8, free electron fraction = 98% and with dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -0.8, 98% free electrons and with dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -0.8, efrac = 98%, without dust grains
elif sed==3 and alpha ==1 and efrac == 2 and grains == 2:
file_lib = 'C17_AGN_alpha08_efrac98_CNfix_nograins_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -0.8 and free electron fraction = 98% without dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -0.8 with 98% free electrons models without grains is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -0.8, free electron fraction = 98% and without dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -0.8, 98% free electrons and without dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -1.2, efrac = 2%, with dust grains
elif sed==3 and alpha ==2 and efrac == 1 and grains == 1:
file_lib = 'C17_AGN_alpha12_efrac02_CNfix_grains_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -1.2 and free electron fraction = 2% with dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -1.2 with 2% free electrons and dust grains models is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -1.2, free electron fraction = 2% and with dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -1.2, 2% free electrons and with dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -1.2, efrac = 2%, without dust grains
elif sed==3 and alpha ==2 and efrac == 1 and grains == 2:
file_lib = 'C17_AGN_alpha12_efrac02_CNfix_nograins_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -1.2 and free electron fraction = 2% without dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -1.2 with 2% free electrons models without grains is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -1.2, free electron fraction = 2% and without dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -1.2, 2% free electrons and without dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -1.2, efrac = 98%, with dust grains
elif sed==3 and alpha ==2 and efrac == 2 and grains == 1:
file_lib = 'C17_AGN_alpha12_efrac98_CNfix_grains_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -1.2 and free electron fraction = 98% with dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -1.2 with 98% free electrons and dust grains models is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_CO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -1.2, free electron fraction = 98% and with dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -1.2, 98% free electrons and with dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_CO = 0.125
#AGN MODEL FOR alpha_OX = -1.2, efrac = 98%, without dust grains
elif sed==3 and alpha ==2 and efrac == 2 and grains == 2:
file_lib = 'C17_AGN_alpha12_efrac98_CNfix_nograins_uv.dat'
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'Double composite AGN, a(OX) = -1.2 and free electron fraction = 98% without dust grains. No interpolation.'
print ('No interpolation for the AGN a(ox) = -1.2 with 98% free electrons models without grains is going to be used.')
print ('The grid has a resolution of 0.1dex for O/H and 0.125dex for C/O.')
res_NO = 0.125
elif inter == 1:
sed_type = 'Double composite AGN, a(OX) = -1.2, free electron fraction = 98% and without dust grains. Interpolation.'
print ('Interpolation for the AGN a(ox) = -1.2, 98% free electrons and without dust models is going to be used.')
print ('The grid has a resolution of 0.01 dex for O/H and 0.0125 dex for C/O.')
res_NO = 0.125
#Different library
elif sed==4:
file_lib = new_library
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+new_library, 'r') as file8:
for line in file8:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+new_library,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if inter == 0:
sed_type = 'User file ' + new_library + ' used as library for the models no interpolated'
print ('No interpolation for the library '+new_library)
res_CO = 0.125
elif inter == 1:
sed_type = 'User file ' + new_library + ' used as library for the models interpolated'
print ('Interpolation for the library '+new_library)
res_CO = 0.125
#Valuable columns of the files
uv_lin = ['12logOH', 'logCO', 'logU', 'Lya_1216', 'CIV_1549', 'HeII_1640', 'OIII_1665', 'CIII_1909', 'OIII_5007']
lin_uv_label = ['12+log(O/H)', 'log(C/O)', 'log(U)', 'Lya_1216', 'CIV_1549', 'HeII_1640', 'OIII_1665', 'CIII_1909', 'OIII_5007']
########################################
###### SORTING THE GRID OF MODELS ######
########################################
print (' ')
print ('Sorting the grid of models')
print (' ')
index_OH_CO_U_sorted = [] #storing the correct order of the indexes
#Sorting abundances 12+log(O/H)
OH_values = grid_aux['12logOH'] #Oxygen abundances
if len(OH_values) != 1:
sorted_list_OH = sorted(range(len(OH_values)),key=OH_values.__getitem__)
if len(OH_values) == 1:
sorted_list_OH = [0]
#Sorting abundance ratios log(C/O)
OH_values_diff = list(set(OH_values[sorted_list_OH]))
OH_values_diff.sort() #It is necessary to sort again the list of different elements
for OH_num in OH_values_diff:
index_OH_fix = np.where(OH_values == OH_num)[0] #Index(es) for a particular abundance 12+log(O/H)
CO_values = grid_aux['logCO'][index_OH_fix]
if len(CO_values) != 1:
sorted_list_CO = sorted(range(len(CO_values)), key=CO_values.__getitem__)
if len(CO_values) == 1:
sorted_list_CO = [0]
CO_values_diff = list(set(CO_values[sorted_list_CO]))
CO_values_diff.sort() #It s necessary to sort again the list of different elements
for CO_num in CO_values_diff:
index_OH_CO_fix = np.where(CO_values == CO_num)[0] #Index(es) for particular abundances 12+log(O/H) and log(C/O)
#Sorting ionization parameters
U_values = grid_aux['logU'][index_OH_fix[index_OH_CO_fix]]
if len(U_values) != 1:
sorted_list_U = sorted(range(len(U_values)), key=U_values.__getitem__)
if len(U_values) == 1:
sorted_list_U = [0]
index_OH_CO_U = index_OH_fix[index_OH_CO_fix[sorted_list_U]] #Sorted index(es) for U at fixed O/H and C/O
for index_sort in index_OH_CO_U:
index_OH_CO_U_sorted.append(index_sort) #Adding index in the correct order
#Generating new library file
list_comments = [] #Storing comments in the file:
with open('Libraries_uv/'+file_lib, 'r') as file_aux:
for line in file_aux:
if line[0] == '#':
list_comments.append(line)
#Storing columns:
lin_uv_col = []
#Retrieving each column of the grid
for label in uv_lin:
aux_col = grid_aux[label].tolist()
lin_uv_col.append(aux_col)
#Comments
grid_to_write = open('Libraries_uv/'+file_lib, 'w')
for line_com in list_comments:
grid_to_write.write(line_com)
#Header line
label_line = '{:15} '.format(lin_uv_label[0].replace(' ',''))
for ind in range(1, len(lin_uv_label)-1):
label_line += '\t {:15} '.format(lin_uv_label[ind].replace(' ',''))
label_line += '\t {:15}\n'.format(lin_uv_label[-1].replace(' ',''))
grid_to_write.write(label_line)
#Values:
for ind_val in index_OH_CO_U_sorted:
val_line = '{:7.7f} '.format(lin_uv_col[0][ind_val])
for ind2 in range(1, len(lin_uv_label)-1):
val_line += '\t {:7.7f} '.format(lin_uv_col[ind2][ind_val])
val_line += '\t {:7.7f}\n'.format(lin_uv_col[-1][ind_val])
grid_to_write.write(val_line)
grid_to_write.close()
#Opening sorted grid of models
n_comments = 0
with open('Libraries_uv/'+file_lib, 'r') as file12:
for line in file12:
if line[0] == '#':
n_comments += 1
grid_aux = np.genfromtxt('Libraries_uv/'+file_lib, dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
################################################
###### CONSTRAINTS FOR THE GRID OF MODELS ######
################################################
#Reading constraints and creating library with constraints
print (' ')
print ('Select a file with the constraint laws to be used to limit the grid of models when the measurement of a quantity is impossible without any relation.')
print (' ')
print ('')
question = True
while question:
print ('-------------------------------------------------')
print ('Default constraints')
print ('-------------------')
print ('(1) Constraints for Star-Forming Galaxies')
print ('(2) Constraints for Extreme Emission Line Galaxies')
print ('(3) Constraints for AGNs (no restriction in the ionization parameter)')
print ('')
print ('Other constraints')
print ('-----------------')
print ('(4) Different constraint file')
print ('-------------------------------------------------')
if int(sys.version[0]) < 3:
const = raw_input('Choose constraint for the grids: ')
else:
const = input('Choose constraint for the grids: ')
if const == '1' or const == '2' or const == '3' or const == '4': question = False
print ('')
#Particular file introduced by the user
if const == '4':
question = True
while question:
print ('Introduce name of the file containing the constraints for the grids. It must be located in the folder "Constraints".')
print (' ')
if int(sys.version[0]) < 3:
new_const = raw_input('Name of file: ')
else:
new_const = input('Name of file: ')
#Searching for the file
try:
#Counting comments:
n_comments = 0
with open('Constraints/'+new_const, 'r') as file9:
for line in file9:
if line[0] == '#':
n_comments += 1
const_user = np.genfromtxt('Constraints/'+new_const, dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
print (' ')
print ('Loading constraint file '+new_const+'. Checking correct format of the file.')
question = False
except:
print (' ')
print ('File was not found in folder "Constraints" or file does not exist.')
question = True
while question:
try:
#Counting comments:
n_comments = 0
with open('Constraints/'+new_const, 'r') as file10:
for line in file10:
if line[0] == '#':
n_comments += 1
const_user = np.genfromtxt('Constraints/'+new_const, dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
#Checking correct format:
#Counting comments:
n_comments = 0
with open('Constraints/template_OH.dat', 'r') as file11:
for line in file11:
if line[0] == '#':
n_comments += 1
auxiliar_labels = np.genfromtxt('Constraints/template_OH.dat', dtype=None, names=True, encoding = 'ascii', skip_header=n_comments).dtype.names
missing_labels = []
for label in auxiliar_labels:
if label in const_user.dtype.names:
continue
else:
missing_labels.append(label)
#Displaying message for the user:
print ('Succesfully reading of the file')
if len(missing_labels) == 0:
print ('File presents the correct format')
question = False
else:
print ('File does not present the correct format. The following columns are missing:')
for need_label in missing_labels:
print('- '+need_label)
print ('More details on the correct format for the library are found in readme file.')
print (' ')
print ('Reintroduce name of the file with fixed format:')
print (' ')
if int(sys.version[0]) < 3:
new_const = raw_input('Name of file: ')
else:
new_const = input('Name of file: ')
except:
print ('Something went wrong while reading file. Please, reintroduce name of the file:')
print (' ')
if int(sys.version[0]) < 3:
new_const = raw_input('Name of file: ')
else:
new_const = input('Name of file: ')
#Generation of grids with constraints laws:
if const == '1' or const == '2' or const == '3' or const == '4':
#First grid does not change
grid1 = grid_aux
file_lib_2 = file_lib
#Generating libraries for the constraints in the files
if const == '1': #Star-Forming Galaxies
const_file = 'template_OH.dat'
name_const = 'Constraints/template_OH.dat'
n_comments = 0
with open(name_const, 'r') as file12:
for line in file12:
if line[0] == '#':
n_comments += 1
const_data = np.genfromtxt(name_const,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if const == '2':
const_file = 'template_OH_eelg.dat'
name_const = 'Constraints/template_OH_eelg.dat'
n_comments = 0
with open(name_const, 'r') as file13:
for line in file13:
if line[0] == '#':
n_comments += 1
const_data = np.genfromtxt(name_const,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if const == '3':
name_const = 'Constraints/template_OH_agn.dat'
const_file = 'template_OH_agn.dat'
n_comments = 0
with open(name_const, 'r') as file18:
for line in file18:
if line[0] == '#':
n_comments += 1
const_data = np.genfromtxt(name_const,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
if const == '4':
const_file = new_const
name_const = 'Constraints/'+new_const
n_comments = 0
with open(name_const, 'r') as file14:
for line in file14:
if line[0] == '#':
n_comments += 1
const_data = np.genfromtxt(name_const,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
#Limiting the grids:
lin_uv_val = []
#The initial grid need to be constrained in the ionization parameter
#Retrieving each column of the grid
for label in uv_lin:
aux_col = grid1[label].tolist()
lin_uv_val.append(aux_col)
#Creation of the grids
name_OH_U = '.'.join(file_lib_2.split('.')[0:-1])+'_OH_U_constrained.'+file_lib.split('.')[-1]
name_OH_U_CO = '.'.join(file_lib_2.split('.')[0:-1])+'_OH_U_CO_constrained.'+file_lib.split('.')[-1]
file_open = open('Libraries_uv/'+ name_OH_U, 'w') #OH and U relation
file_open_2 = open('Libraries_uv/'+name_OH_U_CO, 'w') #OH, CO and U relation
file_open.write('#Constrained by relation between 12+log(O/H) and log(U)\n')
file_open_2.write('#Constrained by relation between 12+log(O/H), log(U) and log(C/O)\n')
#Header line
label_line = '{:15} '.format(lin_uv_label[0].replace(' ',''))
for ind in range(1, len(lin_uv_label)-1):
label_line += '\t {:15} '.format(lin_uv_label[ind].replace(' ',''))
label_line += '\t {:15}\n'.format(lin_uv_label[-1].replace(' ',''))
file_open.write(label_line)
file_open_2.write(label_line)
#Values:
for ind_val in range(0, len(lin_uv_val[0])):
index_desired = np.where(const_data['12logOH'] == lin_uv_val[0][ind_val])[0][0] #Searching for constrain in given value of O/H
if lin_uv_val[2][ind_val] <= const_data['logU_max'][index_desired] and lin_uv_val[2][ind_val] >= const_data['logU_min'][index_desired]:
val_line = '{:7.7f} '.format(lin_uv_val[0][ind_val])
for ind2 in range(1, len(lin_uv_label)-1):
val_line += '\t {:7.7f} '.format(lin_uv_val[ind2][ind_val])
val_line += '\t {:7.7f}\n'.format(lin_uv_val[-1][ind_val])
file_open.write(val_line)
if lin_uv_val[2][ind_val] <= const_data['logU_max'][index_desired] and lin_uv_val[2][ind_val] >= const_data['logU_min'][index_desired] and lin_uv_val[1][ind_val] <= const_data['logCO_max'][index_desired] and lin_uv_val[1][ind_val] >= const_data['logCO_min'][index_desired]:
val_line = '{:7.7f} '.format(lin_uv_val[0][ind_val])
for ind2 in range(1, len(lin_uv_label)-1):
val_line += '\t {:7.7f} '.format(lin_uv_val[ind2][ind_val])
val_line += '\t {:7.7f}\n'.format(lin_uv_val[-1][ind_val])
file_open_2.write(val_line)
file_open.close()
file_open_2.close()
#Counting comments:
n_comments = 0
with open('Libraries_uv/'+name_OH_U, 'r') as file15:
for line in file15:
if line[0] == '#':
n_comments += 1
grid2 = np.genfromtxt('Libraries_uv/'+name_OH_U,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
n_comments = 0
with open('Libraries_uv/'+name_OH_U_CO, 'r') as file:
for line in file:
if line[0] == '#':
n_comments += 1
grid3 = np.genfromtxt('Libraries_uv/'+name_OH_U_CO,dtype=None,names=True, encoding = 'ascii', skip_header=n_comments)
#Residual in CO
if inter==0:
res_CO = np.max([sorted(set(grid1['logCO']))[ind+1]-sorted(set(grid1['logCO']))[ind] for ind in range(0, len(set(grid1['logCO']))-1)])
if inter==1:
res_CO = np.max([sorted(set(grid1['logCO']))[ind+1]-sorted(set(grid1['logCO']))[ind] for ind in range(0, len(set(grid1['logCO']))-1)])/10
###########################################
###### SUMMARY OF THE GRID OF MODELS ######
###########################################
print ('-------------------------------------------------')
print ('Summary of the models')
print ('---------------------')
print ('Libraries generated with the constraints. The following grids are going to be used:')
print ('- Full library (Grid#1): '+file_lib_2)
print (' Total number of models: ' + str(len(grid1)))
print ('- Library constrained by 12+log(O/H) - log(U) relation (Grid#2): '+name_OH_U)
print (' Total number of models: ' + str(len(grid2)))
print ('- Library constrained by 12+log(O/H) - log(U) - log(C/O) relation (Grid#3): '+name_OH_U_CO)
print (' Total number of models: ' + str(len(grid3)))
print ('-------------------------------------------------')
print (' ')
#################################################
###### CREATING ARRAY TO STORE ESTIMATIONS ######
#################################################
grids = []
OHffs = []
eOHffs = []
COffs = []
eCOffs = []
logUffs = []
elogUffs = []
Label_ID = False
Label_Lya = False
Label_eLya = False
Label_NV = False
Label_eNV = False
Label_CIV = False
Label_eCIV = False
Label_HeII = False
Label_eHeII = False
Label_OIII_1665 = False
Label_eOIII_1665 = False
Label_CIII = False
Label_eCIII = False
Label_OIII_5007 = False
Label_eOIII_5007 = False
Label_Hbeta = False
Label_eHbeta = False
#Checking input information
for col in range(0,len(input1.dtype.names),1):
if input1.dtype.names[col] == 'ID':
Label_ID = True
if input1.dtype.names[col] == 'Lya_1216':
Label_Lya = True
if input1.dtype.names[col] == 'eLya_1216':
Label_eLya = True
if input1.dtype.names[col] == 'NV_1239':
Label_NV = True
if input1.dtype.names[col] == 'eNV_1239':
Label_eNV = True
if input1.dtype.names[col] == 'CIV_1549':
Label_CIV = True
if input1.dtype.names[col] == 'eCIV_1549':
Label_eCIV = True
if input1.dtype.names[col] == 'HeII_1640':
Label_HeII = True
if input1.dtype.names[col] == 'eHeII_1640':
Label_eHeII = True
if input1.dtype.names[col] == 'OIII_1665':
Label_OIII_1665 = True
if input1.dtype.names[col] == 'eOIII_1665':
Label_eOIII_1665 = True
if input1.dtype.names[col] == 'CIII_1909':
Label_CIII = True
if input1.dtype.names[col] == 'eCIII_1909':
Label_eCIII = True
if input1.dtype.names[col] == 'Hb_4861':
Label_Hbeta = True
if input1.dtype.names[col] == 'eHb_4861':
Label_eHbeta = True
if input1.dtype.names[col] == 'OIII_5007':
Label_OIII_5007 = True
if input1.dtype.names[col] == 'eOIII_5007':
Label_eOIII_5007 = True
#Adapting final output with information from given input
if Label_ID == False:
Names = np.arange(1,input1.size+1,1)
else:
Names = input1['ID']
if Label_Lya == False:
Lya_1216 = np.zeros(input1.size)
else:
Lya_1216 = input1['Lya_1216']
if Label_eLya == False:
eLya_1216 = np.zeros(input1.size)
else:
eLya_1216 = input1['eLya_1216']
if Label_NV == False:
NV_1239 = np.zeros(input1.size)
else:
NV_1239 = input1['NV_1239']
if Label_eNV == False:
eNV_1239 = np.zeros(input1.size)
else:
eNV_1239 = input1['eNV_1239']
if Label_CIV == False:
CIV_1549 = np.zeros(input1.size)
else:
CIV_1549 = input1['CIV_1549']
if Label_eCIV == False:
eCIV_1549 = np.zeros(input1.size)
else:
eCIV_1549 = input1['eCIV_1549']
if Label_HeII == False:
HeII_1640 = np.zeros(input1.size)
else:
HeII_1640 = input1['HeII_1640']
if Label_eHeII == False:
eHeII_1640 = np.zeros(input1.size)
else:
eHeII_1640 = input1['eHeII_1640']
if Label_OIII_1665 == False:
OIII_1665 = np.zeros(input1.size)
else:
OIII_1665 = input1['OIII_1665']
if Label_eOIII_1665 == False:
eOIII_1665 = np.zeros(input1.size)
else:
eOIII_1665 = input1['eOIII_1665']
if Label_CIII == False:
CIII_1909 = np.zeros(input1.size)
else:
CIII_1909 = input1['CIII_1909']
if Label_eCIII == False:
eCIII_1909 = np.zeros(input1.size)
else:
eCIII_1909 = input1['eCIII_1909']
if Label_Hbeta == False:
Hb_4861 = np.zeros(len(input1))
else:
Hb_4861 = input1['Hb_4861']
if Label_eHbeta == False:
eHb_4861 = np.zeros(input1.size)
else:
eHb_4861 = input1['eHb_4861']
if Label_OIII_5007 == False:
OIII_5007 = np.zeros(input1.size)
else:
OIII_5007 = input1['OIII_5007']
if Label_eOIII_5007 == False:
eOIII_5007 = np.zeros(input1.size)
else:
eOIII_5007 = input1['eOIII_5007']
################################################################
###### OUTPUT FORMAT AND INFORMATION: ONLY EMISSION LINES ######
################################################################
#Creation of output only with information from inputs
aux_list = []
aux_list.append(('ID','U12'))
if Label_Lya == True:
aux_list.append(('Lya_1216', float))
if Label_eLya == True:
aux_list.append(('eLya_1216', float))
if Label_NV == True:
aux_list.append(('NV_1239', float))
if Label_eNV == True:
aux_list.append(('eNV_1239', float))
if Label_CIV == True:
aux_list.append(('CIV_1549', float))
if Label_eCIV == True:
aux_list.append(('eCIV_1549', float))
if Label_HeII == True:
aux_list.append(('HeII_1640', float))
if Label_eHeII == True:
aux_list.append(('eHeII_1640', float))
if Label_OIII_1665 == True:
aux_list.append(('OIII_1665', float))
if Label_eOIII_1665 == True:
aux_list.append(('eOIII_1665', float))
if Label_CIII == True:
aux_list.append(('CIII_1909', float))
if Label_eCIII == True:
aux_list.append(('eCIII_1909', float))
if Label_Hbeta == True:
aux_list.append(('Hb_4861', float))
if Label_eHbeta == True:
aux_list.append(('eHb_4861', float))
if Label_OIII_5007 == True:
aux_list.append(('OIII_5007', float))
if Label_eOIII_5007 == True:
aux_list.append(('eOIII_5007', float))
aux_list.append(('grid', int))
aux_list.append(('OH', float))
aux_list.append(('eOH', float))
aux_list.append(('CO', float))
aux_list.append(('eCO', float))
aux_list.append(('logU', float))
aux_list.append(('elogU', float))
output = np.zeros(input1.size, dtype=aux_list)
output['ID'] = Names
if Label_Lya == True:
output['Lya_1216'] = Lya_1216
if Label_eLya == True:
output['eLya_1216'] = eLya_1216
if Label_NV == True:
output['NV_1239'] = NV_1239
if Label_eNV == True:
output['eNV_1239'] = eNV_1239
if Label_CIV == True:
output['CIV_1549'] = CIV_1549
if Label_eCIV == True:
output['eCIV_1549'] = eCIV_1549
if Label_HeII == True:
output['HeII_1640'] = HeII_1640
if Label_eHeII == True:
output['eHeII_1640'] = eHeII_1640
if Label_OIII_1665 == True:
output['OIII_1665'] = OIII_1665
if Label_eOIII_1665 == True:
output['eOIII_1665'] = eOIII_1665
if Label_CIII == True:
output['CIII_1909'] = CIII_1909
if Label_eCIII == True:
output['eCIII_1909'] = eCIII_1909
if Label_Hbeta == True:
output['Hb_4861'] = Hb_4861
if Label_eHbeta == True:
output['eHb_4861'] = eHb_4861
if Label_OIII_5007 == True:
output['OIII_5007'] = OIII_5007
if Label_eOIII_5007 == True:
output['eOIII_5007'] = eOIII_5007
################################################
###### ESTIMATIONS OF CHEMICAL ABUNDANCES ######
################################################
#Display for the user
print ('Calculating....')
print ('')
print ('')
print ('----------------------------------------------------------------')
print ('(%) ID Grid 12+log(O/H) log(C/O) log(U)')
print ('----------------------------------------------------------------')
# Beginning of loop of calculation
count = 0
for tab in range(0,len(input1),1):
count = count + 1
OH_mc = []
CO_mc = []
logU_mc = []
OHe_mc = []
COe_mc = []
logUe_mc = []
#Starting Montecarlo
for monte in range(0,n,1):
OH_p = 0
logU_p = 0
CO_p = 0
den_OH = 0
den_CO = 0
OH_e = 0
CO_e = 0
logU_e = 0
den_OH_e = 0
den_CO_e = 0
tol_max = 1e3
#Generating observable values for emission lines
Lya_1216_obs = 0
if Lya_1216[tab] <= 0:
Lya_1216_obs = 0
else:
while Lya_1216_obs <= 0:
Lya_1216_obs = np.random.normal(Lya_1216[tab],eLya_1216[tab]+1e-5)
NV_1239_obs = 0
if NV_1239[tab]<= 0:
NV_1239_obs = 0
else:
while NV_1239_obs <= 0:
NV_1239_obs = np.random.normal(NV_1239[tab],eNV_1239[tab]+1e-5)
CIV_1549_obs = 0
if CIV_1549[tab] <= 0:
CIV_1549_obs = 0
else:
while CIV_1549_obs <= 0:
CIV_1549_obs = np.random.normal(CIV_1549[tab],eCIV_1549[tab]+1e-5)
HeII_1640_obs = 0
if HeII_1640[tab] <= 0:
HeII_1640_obs = 0
else:
if HeII_1640_obs <= 0:
HeII_1640_obs = np.random.normal(HeII_1640[tab],eHeII_1640[tab]+1e-5)
OIII_1665_obs = 0
if OIII_1665[tab] == 0:
OIII_1665_obs = 0
else:
while OIII_1665_obs <= 0:
OIII_1665_obs = np.random.normal(OIII_1665[tab],eOIII_1665[tab]+1e-5)
CIII_1909_obs = 0
if CIII_1909[tab] <= 0:
CIII_1909_obs = 0
else:
while CIII_1909_obs <= 0:
CIII_1909_obs = np.random.normal(CIII_1909[tab],eCIII_1909[tab]+1e-5)
Hb_4861_obs = 0
if Hb_4861[tab] <= 0:
Hb_4861_obs = 0
else:
while Hb_4861_obs <= 0:
Hb_4861_obs = np.random.normal(Hb_4861[tab],eHb_4861[tab]+1e-5)
OIII_5007_obs = 0
if OIII_5007[tab] <= 0:
OIII_5007_obs = 0
else:
while OIII_5007_obs <= 0:
OIII_5007_obs = np.random.normal(OIII_5007[tab],eOIII_5007[tab]+1e-5)
#Observables
if OIII_1665_obs <= 0 or OIII_5007_obs <= 0:
ROIII_obs = 0
else:
ROIII_obs = OIII_5007_obs/OIII_1665_obs
if Lya_1216_obs == 0 or NV_1239_obs == 0:
N5_obs = 0
else:
N5_obs = (NV_1239_obs ) / (Lya_1216_obs)
if HeII_1640_obs == 0 or NV_1239_obs == 0:
N5He2_obs = 0
else:
N5He2_obs = (NV_1239_obs) / (HeII_1640_obs)
if Lya_1216_obs <= 0 or CIII_1909_obs <= 0 or CIV_1549_obs <= 0:
C34_obs = 0
else:
C34_obs = (CIII_1909_obs + CIV_1549_obs) / (Lya_1216_obs)
if HeII_1640_obs <= 0 or CIII_1909_obs <= 0 or CIV_1549_obs <= 0:
C34He2_obs = 0
else:
C34He2_obs = (CIII_1909_obs + CIV_1549_obs) / (HeII_1640_obs)
if CIII_1909_obs <= 0 or OIII_1665_obs <= 0 or CIV_1549_obs <= 0:
C3O3_obs = -10
else:
C3O3_obs = np.log10((CIII_1909_obs) / (OIII_1665_obs))
if CIII_1909_obs <= 0 or CIV_1549_obs <= 0:
C3C4_obs = 0
else:
C3C4_obs = (CIII_1909_obs/CIV_1549_obs)
if CIII_1909_obs <= 0 or Hb_4861_obs <= 0:
C34Hb_obs = 0
else:
C34Hb_obs = (CIII_1909_obs + CIV_1549_obs) / Hb_4861_obs
# Selection of grid
if OIII_1665[tab] > 0 and OIII_5007[tab] > 0:
grid = grid1
if monte == n-1: grids.append(1)
grid_type = 1
elif OIII_1665[tab] > 0 and CIII_1909[tab] > 0:
grid = grid2
if monte == n-1: grids.append(2)
grid_type = 2
else:
grid = grid3
if monte == n-1: grids.append(3)
grid_type = 3
######################
# Calculation of C/O #
######################
if C3O3_obs == -10:
CO = -10
else:
CHI_ROIII = 0
CHI_C3O3 = 0
CHI_CO = 0
for index in grid:
if ROIII_obs == 0:
CHI_ROIII = 0
elif index['OIII_1665'] == 0 or index['OIII_5007'] == 0:
CHI_ROIII = tol_max
else:
CHI_ROIII = (index['OIII_5007']/index['OIII_1665'] - ROIII_obs)**2/(index['OIII_5007']/index['OIII_1665'])
if C3O3_obs == -10:
CHI_C3O3 = 0
elif index['CIII_1909'] == 0 or index['OIII_1665'] == 0:
CHI_C3O3 = tol_max
else:
CHI_C3O3 =(np.log10((index['CIII_1909'])/index['OIII_1665']) - C3O3_obs)**2/np.log10((index['CIII_1909'])/(index['OIII_1665']+1e-5))
CHI_CO = (CHI_ROIII**2 + CHI_C3O3**2 )**0.5
if CHI_CO == 0:
CO_p = CO_p
den_CO = den_CO
else:
CO_p = index['logCO'] /(CHI_CO)**2 + CO_p
den_CO = 1 / (CHI_CO)**2 + den_CO
CO = CO_p / den_CO
# Calculation of C/O error
if C3O3_obs == -10:
eCO = 0
else:
CHI_ROIII = 0
CHI_C3O3 = 0
CHI_CO = 0
for index in grid:
if ROIII_obs == 0:
CHI_ROIII = 0
elif index['OIII_1665'] == 0 or index['OIII_5007'] == 0:
CHI_ROIII = tol_max
else:
CHI_ROIII = (index['OIII_5007']/index['OIII_1665'] - ROIII_obs)**2/(index['OIII_5007']/index['OIII_1665'])
if C3O3_obs == -10:
CHI_C3O3 = 0
elif index['CIII_1909'] == 0 or index['OIII_1665'] == 0:
CHI_C3O3 = tol_max
else:
CHI_C3O3 =(np.log10((index['CIII_1909'])/index['OIII_1665']) - C3O3_obs)**2/np.log10((index['CIII_1909'])/(index['OIII_1665']+1e-5))
CHI_CO = (CHI_ROIII**2 + CHI_C3O3**2 )**0.5
if CHI_CO == 0:
CO_e = CO_e
den_CO_e = den_CO_e
else:
CO_e = (index['logCO'] - CO)**2 / (CHI_CO)**2 + CO_e
den_CO_e = 1 /(CHI_CO)**2 + den_CO_e
eCO = CO_e / den_CO_e
###############################
# Calculation of O/H and logU #
###############################
if C34_obs == 0 and ROIII_obs == 0 and C34Hb_obs == 0 and C34He2_obs == 0 and N5_obs == 0 and N5He2_obs == 0:
OH = 0
logU = 0
else:
CHI_ROIII = 0
CHI_C3C4 = 0
CHI_C34He2 = 0
CHI_C34 = 0
CHI_C34Hb = 0
CHI_N5 = 0
CHI_N5He2 = 0
CHI_OH = 0
for index in grid:
if CO > -10 and np.abs(index['logCO'] - CO) > np.abs(eCO+0.125):
continue
if NV_1239_obs > 0 and index['NV_1239'] == 0:
continue
if CIV_1549_obs > 0 and index['CIV_1549'] == 0:
continue
if HeII_1640_obs > 0 and index['HeII_1640'] == 0:
continue
else:
if ROIII_obs == 0:
CHI_ROIII = 0
elif index['OIII_1665'] == 0 or index['OIII_5007'] == 0:
CHI_ROIII = tol_max
else:
CHI_ROIII = (index['OIII_5007']/index['OIII_1665'] - ROIII_obs)**2/(index['OIII_5007']/index['OIII_1665'])
if N5_obs == 0:
CHI_N5 = 0
elif index['Lya_1216'] == 0 or index['NV_1239'] == 0:
CHI_N5 = tol_max
else:
CHI_N5 = ((index['NV_1239'])/index['Lya_1216'] - N5_obs)**2/((index['NV_1239'])/index['Lya_1216'])
if N5He2_obs == 0:
CHI_N5He2 = 0
elif index['HeII_1640'] == 0 or index['NV_1239'] == 0:
CHI_N5He2 = tol_max
else:
CHI_N5He2 = ((index['NV_1239'])/index['HeII_1640'] - N5He2_obs)**2/((index['NV_1239'])/index['HeII_1640'])
if C34_obs == 0:
CHI_C34 = 0
elif index['Lya_1216'] == 0 or index['CIII_1909'] == 0:
CHI_C34 = tol_max
else:
CHI_C34 = ((index['CIII_1909']+index['CIV_1549'])/index['Lya_1216'] - C34_obs)**2/((index['CIII_1909']+index['CIV_1549'])/index['Lya_1216'])
if C34He2_obs == 0:
CHI_C34He2 = 0
elif index['HeII_1640'] == 0 or index['CIII_1909'] == 0:
CHI_C34He2 = tol_max
else:
CHI_C34He2 = ((index['CIII_1909']+index['CIV_1549'])/index['HeII_1640'] - C34He2_obs)**2/((index['CIII_1909']+index['CIV_1549'])/index['HeII_1640'])
if C34Hb_obs == 0:
CHI_C34Hb = 0
elif index['CIII_1909'] == 0:
CHI_C34Hb = tol_max
else:
CHI_C34Hb = (index['CIII_1909']+index['CIV_1549'] - C34Hb_obs)**2/(index['CIII_1909']+index['CIV_1549'])
if C3C4_obs == 0:
CHI_C3C4 = 0
elif index['CIV_1549'] == 0 or index['CIII_1909'] == 0:
CHI_C3C4 = tol_max
else:
CHI_C3C4 = (index['CIII_1909']/index['CIV_1549'] - C3C4_obs)**2/(index['CIII_1909']/index['CIV_1549'])
if C34Hb_obs > 0:
CHI_OH = (CHI_ROIII**2 + CHI_C34Hb**2 + CHI_C3C4**2)**0.5
else:
CHI_OH = (CHI_ROIII**2 + CHI_C34**2 + CHI_C34He2**2 + CHI_N5**2 + CHI_N5He2**2 + CHI_C3C4**2 )**0.5
if CHI_OH == 0:
OH_p = OH_p
logU_p = logU_p
den_OH = den_OH
else:
OH_p = index['12logOH'] / (CHI_OH)**2 + OH_p
logU_p = index['logU'] / (CHI_OH)**2 + logU_p
den_OH = 1 /(CHI_OH)**2 + den_OH
if OH_p == 0:
OH = 0
else:
OH = OH_p / den_OH
if logU_p == 0:
logU = 0
else:
logU = logU_p / den_OH
#Impossibility for AGN in the estimation
if sed == 3 and Lya_1216[tab] == 0 and HeII_1640[tab] == 0 and Hb_4861[tab] == 0:
OH = 0
# Calculation of error of O/H and logU
if C34_obs == 0 and ROIII_obs == 0 and C34Hb_obs == 0 and C34He2_obs == 0 and N5_obs == 0 and N5He2_obs == 0:
eOH = 0
elogU = 0
else:
CHI_ROIII = 0
CHI_N5 = 0
CHI_N5He2 = 0
CHI_C3C4 = 0
CHI_C34 = 0
CHI_C34He2 = 0
CHI_C34Hb = 0
CHI_OH = 0
for index in grid:
if CO > -10 and np.abs(index['logCO'] - CO) > np.abs(eCO+res_CO):
continue
if NV_1239_obs > 0 and index['NV_1239'] == 0:
continue
if CIV_1549_obs > 0 and index['CIV_1549'] == 0:
continue
if HeII_1640_obs > 0 and index['HeII_1640'] == 0:
continue
else:
if ROIII_obs == 0:
CHI_ROIII = 0
elif index['OIII_1665'] == 0 or index['OIII_5007'] == 0:
CHI_ROIII = tol_max
else:
CHI_ROIII = (index['OIII_5007']/index['OIII_1665'] - ROIII_obs)**2/(index['OIII_5007']/index['OIII_1665'])
if N5_obs == 0:
CHI_N5 = 0
elif index['Lya_1216'] == 0 or index['NV_1239'] == 0:
CHI_N5 = tol_max
else:
CHI_N5 = ((index['NV_1239'])/index['Lya_1216'] - N5_obs)**2/((index['NV_1239'])/index['Lya_1216'])
if N5He2_obs == 0:
CHI_N5He2 = 0
elif index['HeII_1640'] == 0 or index['NV_1239'] == 0:
CHI_N5He2 = tol_max
else:
CHI_N5He2 = ((index['NV_1239'])/index['HeII_1640'] - N5He2_obs)**2/((index['NV_1239'])/index['HeII_1640'])
if C34_obs == 0:
CHI_C34 = 0
elif index['Lya_1216'] == 0 or index['CIII_1909'] == 0:
CHI_C34 = tol_max
else:
CHI_C34 = ((index['CIII_1909']+index['CIV_1549'])/index['Lya_1216'] - C34_obs)**2/((index['CIII_1909']+index['CIV_1549'])/index['Lya_1216'])
if C34He2_obs == 0:
CHI_C34He2 = 0
elif index['HeII_1640'] == 0 or index['CIII_1909'] == 0:
CHI_C34He2 = tol_max
else:
CHI_C34He2 = ((index['CIII_1909']+index['CIV_1549'])/index['HeII_1640'] - C34He2_obs)**2/((index['CIII_1909']+index['CIV_1549'])/index['HeII_1640'])
if C34Hb_obs == 0:
CHI_C34Hb = 0
elif index['CIII_1909'] == 0:
CHI_C34Hb = tol_max
else:
CHI_C34Hb = (index['CIII_1909']+index['CIV_1549'] - C34Hb_obs)**2/(index['CIII_1909']+index['CIV_1549'])
if C3C4_obs == 0:
CHI_C3C4 = 0
elif index['CIV_1549'] == 0 or index['CIII_1909'] == 0:
CHI_C3C4 = tol_max
else:
CHI_C3C4 = (index['CIII_1909']/index['CIV_1549'] - C3C4_obs)**2/(index['CIII_1909']/index['CIV_1549'])
if C34Hb_obs > 0:
CHI_OH = (CHI_ROIII**2 + CHI_C34Hb**2 + CHI_C3C4**2)**0.5
else:
CHI_OH = (CHI_ROIII**2 + CHI_C34**2 + CHI_C34He2**2 + CHI_N5**2 + CHI_N5He2**2 + CHI_C3C4**2 )**0.5
if CHI_OH == 0:
OH_e = OH_e
logU_e = logU_e
den_OH_e = den_OH_e
else:
OH_e = (index['12logOH'] - OH)**2 /(CHI_OH)**2 + OH_e
logU_e = (index['logU'] - logU)**2 /(CHI_OH)**2 + logU_e
den_OH_e = 1 /(CHI_OH)**2 + den_OH_e
if OH_e == 0:
eOH = 0
else:
eOH = OH_e / den_OH_e
if logU_e == 0:
elogU = 0
else:
elogU = logU_e / den_OH_e
#Impossiiblity in AGNs to determine O/H without recombination lines
if sed == 3 and Lya_1216[tab] == 0 and HeII_1640[tab] == 0 and Hb_4861[tab] == 0:
eOH = 0
# Iterations for interpolated models
if inter == 0 or (OH == 0 and CO == -10):
COf = CO
OHf = OH
logUf = logU
elif inter == 1:
if OH == 0:
igrid = grid
else:
igrid = interpolate(grid,2,logU-elogU-0.25,logU+elogU+0.25,10)
igrid = igrid[np.lexsort((igrid['logCO'],igrid['logU']))]
igrid = interpolate(igrid,0,OH-eOH-0.1,OH+eOH+0.1,10)
if CO == -10:
igrid = igrid
else:
igrid = igrid[np.lexsort((igrid['12logOH'],igrid['logU']))]
igrid = interpolate(igrid,1,CO-eCO-0.125,CO+eCO+0.125,10)
CHI_ROIII = 0
CHI_C3O3 = 0
CHI_C3C4 = 0
CHI_N5 = 0
CHI_N5He2 = 0
CHI_C34He2 = 0
CHI_C34 = 0
CHI_C34Hb = 0
CHI_OH = 0
CHI_CO = 0
for index in igrid:
if ROIII_obs == 0:
CHI_ROIII = 0
elif index['OIII_1665'] == 0 or index['OIII_5007'] == 0:
CHI_ROIII = tol_max
else:
CHI_ROIII = (index['OIII_5007']/index['OIII_1665'] - ROIII_obs)**2/(index['OIII_5007']/index['OIII_1665'])
if N5_obs == 0:
CHI_N5 = 0
elif index['Lya_1216'] == 0 or index['NV_1239'] == 0:
CHI_N5 = tol_max
else:
CHI_N5 = ((index['NV_1239'])/index['Lya_1216'] - N5_obs)**2/((index['NV_1239'])/index['Lya_1216'])
if N5He2_obs == 0:
CHI_N5He2 = 0
elif index['HeII_1640'] == 0 or index['NV_1239'] == 0:
CHI_N5He2 = tol_max
else:
CHI_N5He2 = ((index['NV_1239'])/index['HeII_1640'] - N5He2_obs)**2/((index['NV_1239'])/index['HeII_1640'])
if C3O3_obs == -10:
CHI_C3O3 = 0
elif index['CIII_1909'] == 0 or index['OIII_1665'] == 0:
CHI_C3O3 = tol_max
else:
CHI_C3O3 =(np.log10((index['CIII_1909'])/index['OIII_1665']) - C3O3_obs)**2/np.log10((index['CIII_1909'])/(index['OIII_1665']+1e-5))
if C34_obs == 0:
CHI_C34 = 0
elif index['Lya_1216'] == 0:
CHI_C34 = tol_max
else:
CHI_C34 = ((index['CIV_1549']+index['CIII_1909'])/index['Lya_1216'] - C34_obs)**2/((index['CIV_1549']+index['CIII_1909'])/index['Lya_1216'])
if C34Hb_obs == 0:
CHI_C34Hb = 0
elif index['CIV_1549'] == 0:
CHI_C34Hb = tol_max
else:
CHI_C34Hb = (index['CIV_1549']+index['CIII_1909'] - C34_obs)**2/(index['CIV_1549']+index['CIII_1909'])
if C3C4_obs == 0:
CHI_C3C4 = 0
elif index['CIII_1909'] == 0 or index['CIV_1549'] == 0:
CHI_C3C4 = tol_max
else:
CHI_C3C4 = (index['CIV_1549']/index['CIII_1909'] - C3C4_obs)**2/(index['CIV_1549']/index['CIII_1909'])
if C34Hb_obs > 0:
CHI_OH = (CHI_ROIII**2 + CHI_C34Hb**2 + CHI_C3C4**2)**0.5
else:
CHI_OH = (CHI_ROIII**2 + CHI_N5**2 + CHI_N5He2**2 + CHI_C34**2 + CHI_C34He2**2 + CHI_C3C4**2 )**0.5
if CHI_OH == 0:
OH_p = OH_p
logU_p = logU_p
den_OH = den_OH
else:
OH_p = index['12logOH'] /(CHI_OH)**2 + OH_p
logU_p = index['logU'] /(CHI_OH)**2 + logU_p
den_OH = 1 /(CHI_OH)**2 + den_OH
CHI_CO = (CHI_ROIII**2 + CHI_C3O3**2 )**0.5
if CHI_CO == 0:
CO_p = CO_p
den_CO = den_CO
else:
CO_p = index['logCO'] /(CHI_CO)**2**2 + CO_p
den_CO = 1 /(CHI_CO)**2**2 + den_CO
if CO == -10:
COf = -10
else:
COf = CO_p / den_CO
if OH == 0:
OHf = 0
logUf = 0
else:
OHf = OH_p / den_OH
logUf = logU_p / den_OH
if OHf > 0: OH_mc.append(OHf)
if COf > -10: CO_mc.append(COf)
if logUf < 0: logU_mc.append(logUf)
if OHf > 0: OHe_mc.append(eOH)
if COf > -10: COe_mc.append(eCO)
if logUf < 0: logUe_mc.append(elogU)
if len(OH_mc) > 0:
OHff = np.mean(OH_mc)
eOHff = (np.std(OH_mc)**2+np.mean(OHe_mc)**2)**0.5
else:
OHff = 0
eOHff = 0
if len(logU_mc) > 0:
logUff = np.mean(logU_mc)
elogUff = (np.std(logU_mc)**2+np.mean(logUe_mc)**2)**0.5
else:
elogUff = 0
logUff = 0
if len(CO_mc) > 0:
COff = np.mean(CO_mc)
eCOff = (np.std(CO_mc)**2+np.mean(COe_mc)**2)**0.5
else:
COff = -10
eCOff = 0
OHffs.append(OHff)
eOHffs.append(eOHff)
COffs.append(COff)
eCOffs.append(eCOff)
logUffs.append(logUff)
elogUffs.append(elogUff)
##################################
# Displaying results in terminal #
##################################
if input0.size == 1 and tab==0: continue
print (round(100*(count)/float(input1.size),1),'%',Names[tab],grid_type,'', round(OHff,2), round(eOHff,2),'',round(COff,2), round(eCOff,2), '',round(logUff,2), round(elogUff,2))
####################################################
###### OUTPUT FORMAT AND INFORMATION: RESULTS ######
####################################################
#Grid used and results from the free parameters
output['grid'] = grids
output['OH'] = OHffs
output['eOH'] = eOHffs
output['CO'] = COffs
output['eCO'] = eCOffs
output['logU'] = logUffs
output['elogU'] = elogUffs
if input0.size == 1: output = np.delete(output,obj=1,axis=0)
#Header comments for the file
lineas_header = [' HII-CHI-mistry_UV v.5.0 output file', 'Input file:'+input00,'Iterations for MonteCarlo: '+str(n),'Used models: '+sed_type,'Library file used : '+file_lib_2, 'Template used to constraint grid of models: '+const_file,'']
#Labels for columns (emission lines)
line_label = '{:30} '.format(output.dtype.names[0])
for ind2 in range(1, len(output.dtype.names)):
line_label += '{:30} '.format(output.dtype.names[ind2])
#Labels for columns
lineas_header.append(line_label)
header = '\n'.join(lineas_header)
#Results
np.savetxt('.'.join(input00.split('.')[:-1])+'_hcm-uv-output.dat',output,fmt=' '.join(['%s']*1+['%.3f']*(len(output.dtype.names)-8)+['%i']+['%.2f']*6), header=header)
lines_stor = []
with open('.'.join(input00.split('.')[:-1])+'_hcm-uv-output.dat', 'r+') as output_file:
for line in output_file:
lines_stor.append(line)
#Reformating output for better reading of the table
file_overwrite = open('.'.join(input00.split('.')[:-1])+'_hcm-uv-output.dat', 'r+')
file_overwrite.seek(0)
for line_n in lines_stor:
if line_n[0] == '#' and line_n[2:4] == 'ID':
file_overwrite.write(line_n[2:])
else:
file_overwrite.write(line_n)
file_overwrite.truncate()
file_overwrite.close()
print ('-------------------------------------------------')
print ('Results are stored in ' + '.'.join(input00.split('.')[:-1]) + '_hcm-uv-output.dat')
print ('-------------------------------------------------')
#############################################
###### INFORMATION AND CONTACT DETAILS ######
#############################################
# Enrique Perez-Montero, epm@iaa.es
# Borja Perez-Diaz, bperez@iaa.es
#################
###### END ######
#################
|
nilq/baby-python
|
python
|
__all__ = [
'apply',
'applyCSS',
'change',
'changeCSS',
'delete',
'forwarddelete',
'insert',
'queryEnabled',
'queryIndeterm',
'queryState',
'querySupported',
'queryValue',
'selection',
'unapply',
'unapplyCSS'
]
|
nilq/baby-python
|
python
|
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from urllib.parse import urlparse
from typing import Dict, Type
from .base import Client, Server
_scheme_to_client_types: Dict[str, Type[Client]] = dict()
_scheme_to_server_types: Dict[str, Type[Server]] = dict()
def register_client(client_type: Type[Client]):
_scheme_to_client_types[client_type.scheme] = client_type
return client_type
def register_server(server_type: Type[Server]):
_scheme_to_server_types[server_type.scheme] = server_type
return server_type
def _check_scheme(scheme: str, types: Dict):
if scheme == '':
scheme = None
if scheme not in types: # pragma: no cover
raise ValueError(f'address illegal, address scheme '
f'should be one of '
f'{", ".join(types)}, '
f'got {scheme}')
return scheme
def get_client_type(address: str) -> Type[Client]:
if '://' not in address:
scheme = None
else:
scheme = urlparse(address).scheme
scheme = _check_scheme(scheme, _scheme_to_client_types)
return _scheme_to_client_types[scheme]
def get_server_type(address: str) -> Type[Server]:
if '://' not in address:
scheme = None
else:
scheme = urlparse(address).scheme
scheme = _check_scheme(scheme, _scheme_to_server_types)
return _scheme_to_server_types[scheme]
def gen_internal_address(process_index: int) -> str:
return f'unixsocket:///{process_index}'
|
nilq/baby-python
|
python
|
from Application import Application
if __name__ == '__main__':
app = Application()
|
nilq/baby-python
|
python
|
# determines whether a matrix is orthogonal. A square matrix is orthogonal,
# if its columns and rows are orthogonal unit vectors,
# which is equivalent to: MT M = I
import numpy as np
def check_orthogonal(M):
# make sure the input is a matrix
if len(np.shape(M)) !=2:
print("error: input is not a matrix")
return
# make sure the input is not a square matrix
dim = np.shape(M)[0]
if dim != np.shape(M)[1]:
print("error: input is not a square matrix")
return
A = np.dot(M, M.T)
# if np.array_equal(A, np.identity(dim)):
[rows, cols] = A.shape
I = np.identity(dim)
for i in range(rows):
for j in range(cols):
if not (A[i, j] - I[i, j] <= 10e-3):
print("matrix is not orthogonal")
return
print("matrix is orthogonal")
if __name__ == '__main__':
# Verify check_orthogonal function
D = 1. / 3. * np.array(
[[2, 2, -1],
[2, -1, 2],
[-1, 2, 2]])
check_orthogonal(D)
# Test 2
R = np.array([[np.cos(np.pi / 4), -np.sin(np.pi / 4)],
[np.sin(np.pi / 4), np.cos(np.pi / 4)]])
check_orthogonal(R)
|
nilq/baby-python
|
python
|
"""
抽象工厂 代码实例
Abstract Factory Code Demo
家具工厂
"""
from __future__ import annotations
from abc import ABC, abstractmethod
class Chair(ABC):
"""
product interface 1: Chair
"""
@abstractmethod
def sit_on(self) -> str:
pass
class Sofa(ABC):
"""
product interface 2: Sofa
"""
@abstractmethod
def lie_on(self) -> str:
pass
class ModernChair(Chair):
"""
product implement Chair: ModernChair
"""
def sit_on(self) -> str:
return 'I sit on a Modern Chair'
class ClassicChair(Chair):
"""
product implement Chair: ClassicChair
"""
def sit_on(self) -> str:
return 'I sit on a Classic Chair'
class ModernSofa(Sofa):
"""
product implement Sofa: ModernSofa
"""
def lie_on(self) -> str:
return 'I sit on a Modern Sofa'
class ClassicSofa(Sofa):
"""
product implement Sofa: ClassicSofa
"""
def lie_on(self) -> str:
return 'I sit on a Classic Sofa'
class FurnitureFactory(ABC):
"""
一个抽象工厂接口 定义了一系列方法，用来返回不同的抽象产品
The Abstract Factory interface declares a set of methods that return different abstract products.
家具工厂生成沙发和椅子 Furniture Factory produce Chair and SOfa
"""
@abstractmethod
def produce_chair(self) -> Chair:
pass
@abstractmethod
def produce_sofa(self) -> Sofa:
pass
class ModernFurnitureFactory(FurnitureFactory):
"""
一个抽象工厂的实现类 implement FurnitureFactory to produce true product
"""
def produce_chair(self) -> Chair:
print('ModernFurnitureFactory produce chair ...')
return ModernChair()
def produce_sofa(self) -> Sofa:
print('ModernFurnitureFactory produce sofa ...')
return ModernSofa()
class ClassicFurnitureFactory(FurnitureFactory):
"""
一个抽象工厂的实现类 implement FurnitureFactory to produce true product
"""
def produce_chair(self) -> Chair:
print('ClassicFurnitureFactory produce chair ...')
return ClassicChair()
def produce_sofa(self) -> Sofa:
print('ClassicFurnitureFactory produce sofa ...')
return ClassicSofa()
def client_code(factory: FurnitureFactory):
chair = factory.produce_chair()
print(chair.sit_on())
sofa = factory.produce_sofa()
print(sofa.lie_on())
if __name__ == '__main__':
print('\r\n--- I want some Modern Furniture ---\r\n')
client_code(ModernFurnitureFactory())
print('\r\n--- I want some Classic Furniture ---\r\n')
client_code(ClassicFurnitureFactory())
|
nilq/baby-python
|
python
|
import requests
import time
import datetime
import json
import csv
# def get(t):
# res_text=requests.get('http://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=0000011,3990012&sty=CTBFTA&st=z&sr=&p=&ps=&cb=&token=70f12f2f4f091e459a279469fe49eca5').text
# data=eval(res_text)
# dh=data[0].split(',')
# ds=data[1].split(',')
# # 超大单流入
# data_1='%.4f'%((float(dh[7]) + float(ds[7])) / 100000000)
# data_2='%.4f'%((float(dh[8]) + float(ds[8])) / 100000000)
# data_3='%.4f'%((float(dh[11]) + float(ds[11])) / 100000000)
# data_4='%.4f'%((float(dh[12]) + float(ds[12])) / 100000000)
# data_5='%.4f'%((float(dh[15]) + float(ds[15])) / 100000000)
# data_6='%.4f'%((float(dh[16]) + float(ds[16])) / 100000000)
# data_7='%.4f'%((float(dh[19]) + float(ds[19])) / 100000000)
# data_8='%.4f'%((float(dh[20]) + float(ds[20])) / 100000000)
# datalist=[str(t)[11:16],data_1,data_2,data_3,data_4,data_5,data_6,data_7,data_8]
# print(datalist)
# targetData.append(datalist)
# targetData=[]
# while True:
# nowTime=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
# if int(str(nowTime)[11:13])>=15 and int(str(nowTime)[14:16])>0:
# writetoCsv('实时成交'+nowTime[:10]+'.csv',targetData)
# break
# elif int(str(nowTime)[11:13])==11 and int(str(nowTime)[14:16])==30:
# writetoCsv('实时成交'+nowTime[:10]+'.csv',targetData)
# targetData=[]
# time.sleep(5340.125)
# else:
# try:
# get(nowTime)
# except e:
# writetoCsv('实时成交'+nowTime[:10]+'.csv',targetData)
# targetData=[]
# print('error,attempingting,please wait')
# get(nowTime)
# time.sleep(59.875)
import time
from threading import Timer
#需要补齐包
def getdata():
nowTime=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
w.start()
data0= #需要补齐的内容
target=[]
target.append(str(nowTime)[11:19])
for d in data0.Data :
target.append(d[0])
print(target)
writetoCsv('实时成交'+str(nowTime)[:10]+'.csv',target)
t=Timer(60,getdata()).start()
def writetoCsv(filename,writelist,header=None):
out=open(filename, 'a+',encoding='gb18030',newline = '')
csv_write=csv.writer(out)
csv_write.writerow(wlist)
t=Timer(60,getdata()).start()
while True:
if int(str(nowTime)[11:13])>=15 and int(str(nowTime)[14:16])>2:
t.cancel()
break
time.sleep(120)
|
nilq/baby-python
|
python
|
import socket
from socket import *
from win32clipboard import *
from win32con import *
print "ClipCross Alpha"
host = "" #Accept connection from any machine.
port = 6000 #We will communicate over port 6000 of this machine. Ports 0-1024 are restricted, ports 1025-65535 are not.
s=""
try:
sock = socket() #Create a network socket. By default, it is a TCP socket
print "Socket successfully created"
sock.bind((host,port)) #Binds to the port
print "Socket successfully bound to port %d" %(port)
sock.listen(1) #We want to listen only to one connection at a time
print "Socket listening for connections..."
con, address = sock.accept()
print "Recieved connection from %s" %(str(address))
from Tkinter import *
import tkMessageBox
root = Tk()
root.withdraw()
query = tkMessageBox.askquestion('Incoming Clipboard Data', 'Do you wish to recieve clipboard data from %s?' %(str(address[0])), icon = 'warning')
if query == 'yes':
s= str(con.recv(65536))
try:
OpenClipboard()
EmptyClipboard()
SetClipboardData(CF_TEXT, s)
CloseClipboard()
except:
print "Error in accessing clipboard data!!!"
sys.exit()
print "Recieved clipboard data from client"
con.send("Thank you for connecting. Your data was successfully recieved.")
else:
con.send("The user you were trying to send data to declined your clipboard data.")
except:
print "Error in networking!"
sys.exit()
finally:
con.close()
|
nilq/baby-python
|
python
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
from .. import _utilities, _tables
__all__ = ['Instance']
class Instance(pulumi.CustomResource):
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
alternative_location_id: Optional[pulumi.Input[str]] = None,
authorized_network: Optional[pulumi.Input[str]] = None,
connect_mode: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
location_id: Optional[pulumi.Input[str]] = None,
memory_size_gb: Optional[pulumi.Input[float]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
redis_configs: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
redis_version: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
reserved_ip_range: Optional[pulumi.Input[str]] = None,
tier: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
A Google Cloud Redis instance.
To get more information about Instance, see:
* [API documentation](https://cloud.google.com/memorystore/docs/redis/reference/rest/)
* How-to Guides
* [Official Documentation](https://cloud.google.com/memorystore/docs/redis/)
## Example Usage
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] alternative_location_id: Only applicable to STANDARD_HA tier which protects the instance
against zonal failures by provisioning it across two zones.
If provided, it must be a different zone from the one provided in
[locationId].
:param pulumi.Input[str] authorized_network: The full name of the Google Compute Engine network to which the
instance is connected. If left unspecified, the default network
will be used.
:param pulumi.Input[str] connect_mode: The connection mode of the Redis instance.
Default value is `DIRECT_PEERING`.
Possible values are `DIRECT_PEERING` and `PRIVATE_SERVICE_ACCESS`.
:param pulumi.Input[str] display_name: An arbitrary and optional user-provided name for the instance.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] labels: Resource labels to represent user provided metadata.
:param pulumi.Input[str] location_id: The zone where the instance will be provisioned. If not provided,
the service will choose a zone for the instance. For STANDARD_HA tier,
instances will be created across two zones for protection against
zonal failures. If [alternativeLocationId] is also provided, it must
be different from [locationId].
:param pulumi.Input[float] memory_size_gb: Redis memory size in GiB.
:param pulumi.Input[str] name: The ID of the instance or a fully qualified identifier for the instance.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] redis_configs: Redis configuration parameters, according to http://redis.io/topics/config.
Please check Memorystore documentation for the list of supported parameters:
https://cloud.google.com/memorystore/docs/redis/reference/rest/v1/projects.locations.instances#Instance.FIELDS.redis_configs
:param pulumi.Input[str] redis_version: The version of Redis software. If not provided, latest supported
version will be used. Currently, the supported values are:
- REDIS_5_0 for Redis 5.0 compatibility
- REDIS_4_0 for Redis 4.0 compatibility
- REDIS_3_2 for Redis 3.2 compatibility
:param pulumi.Input[str] region: The name of the Redis region of the instance.
:param pulumi.Input[str] reserved_ip_range: The CIDR range of internal addresses that are reserved for this
instance. If not provided, the service will choose an unused /29
block, for example, 10.0.0.0/29 or 192.168.0.0/29. Ranges must be
unique and non-overlapping with existing subnets in an authorized
network.
:param pulumi.Input[str] tier: The service tier of the instance. Must be one of these values:
- BASIC: standalone instance
- STANDARD_HA: highly available primary/replica instances
Default value is `BASIC`.
Possible values are `BASIC` and `STANDARD_HA`.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['alternative_location_id'] = alternative_location_id
__props__['authorized_network'] = authorized_network
__props__['connect_mode'] = connect_mode
__props__['display_name'] = display_name
__props__['labels'] = labels
__props__['location_id'] = location_id
if memory_size_gb is None:
raise TypeError("Missing required property 'memory_size_gb'")
__props__['memory_size_gb'] = memory_size_gb
__props__['name'] = name
__props__['project'] = project
__props__['redis_configs'] = redis_configs
__props__['redis_version'] = redis_version
__props__['region'] = region
__props__['reserved_ip_range'] = reserved_ip_range
__props__['tier'] = tier
__props__['create_time'] = None
__props__['current_location_id'] = None
__props__['host'] = None
__props__['persistence_iam_identity'] = None
__props__['port'] = None
super(Instance, __self__).__init__(
'gcp:redis/instance:Instance',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
alternative_location_id: Optional[pulumi.Input[str]] = None,
authorized_network: Optional[pulumi.Input[str]] = None,
connect_mode: Optional[pulumi.Input[str]] = None,
create_time: Optional[pulumi.Input[str]] = None,
current_location_id: Optional[pulumi.Input[str]] = None,
display_name: Optional[pulumi.Input[str]] = None,
host: Optional[pulumi.Input[str]] = None,
labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
location_id: Optional[pulumi.Input[str]] = None,
memory_size_gb: Optional[pulumi.Input[float]] = None,
name: Optional[pulumi.Input[str]] = None,
persistence_iam_identity: Optional[pulumi.Input[str]] = None,
port: Optional[pulumi.Input[float]] = None,
project: Optional[pulumi.Input[str]] = None,
redis_configs: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
redis_version: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
reserved_ip_range: Optional[pulumi.Input[str]] = None,
tier: Optional[pulumi.Input[str]] = None) -> 'Instance':
"""
Get an existing Instance resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] alternative_location_id: Only applicable to STANDARD_HA tier which protects the instance
against zonal failures by provisioning it across two zones.
If provided, it must be a different zone from the one provided in
[locationId].
:param pulumi.Input[str] authorized_network: The full name of the Google Compute Engine network to which the
instance is connected. If left unspecified, the default network
will be used.
:param pulumi.Input[str] connect_mode: The connection mode of the Redis instance.
Default value is `DIRECT_PEERING`.
Possible values are `DIRECT_PEERING` and `PRIVATE_SERVICE_ACCESS`.
:param pulumi.Input[str] create_time: The time the instance was created in RFC3339 UTC "Zulu" format, accurate to nanoseconds.
:param pulumi.Input[str] current_location_id: The current zone where the Redis endpoint is placed. For Basic Tier instances, this will always be the same as the
[locationId] provided by the user at creation time. For Standard Tier instances, this can be either [locationId] or
[alternativeLocationId] and can change after a failover event.
:param pulumi.Input[str] display_name: An arbitrary and optional user-provided name for the instance.
:param pulumi.Input[str] host: Hostname or IP address of the exposed Redis endpoint used by clients to connect to the service.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] labels: Resource labels to represent user provided metadata.
:param pulumi.Input[str] location_id: The zone where the instance will be provisioned. If not provided,
the service will choose a zone for the instance. For STANDARD_HA tier,
instances will be created across two zones for protection against
zonal failures. If [alternativeLocationId] is also provided, it must
be different from [locationId].
:param pulumi.Input[float] memory_size_gb: Redis memory size in GiB.
:param pulumi.Input[str] name: The ID of the instance or a fully qualified identifier for the instance.
:param pulumi.Input[str] persistence_iam_identity: Output only. Cloud IAM identity used by import / export operations to transfer data to/from Cloud Storage. Format is
"serviceAccount:". The value may change over time for a given instance so should be checked before each import/export
operation.
:param pulumi.Input[float] port: The port number of the exposed Redis endpoint.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] redis_configs: Redis configuration parameters, according to http://redis.io/topics/config.
Please check Memorystore documentation for the list of supported parameters:
https://cloud.google.com/memorystore/docs/redis/reference/rest/v1/projects.locations.instances#Instance.FIELDS.redis_configs
:param pulumi.Input[str] redis_version: The version of Redis software. If not provided, latest supported
version will be used. Currently, the supported values are:
- REDIS_5_0 for Redis 5.0 compatibility
- REDIS_4_0 for Redis 4.0 compatibility
- REDIS_3_2 for Redis 3.2 compatibility
:param pulumi.Input[str] region: The name of the Redis region of the instance.
:param pulumi.Input[str] reserved_ip_range: The CIDR range of internal addresses that are reserved for this
instance. If not provided, the service will choose an unused /29
block, for example, 10.0.0.0/29 or 192.168.0.0/29. Ranges must be
unique and non-overlapping with existing subnets in an authorized
network.
:param pulumi.Input[str] tier: The service tier of the instance. Must be one of these values:
- BASIC: standalone instance
- STANDARD_HA: highly available primary/replica instances
Default value is `BASIC`.
Possible values are `BASIC` and `STANDARD_HA`.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["alternative_location_id"] = alternative_location_id
__props__["authorized_network"] = authorized_network
__props__["connect_mode"] = connect_mode
__props__["create_time"] = create_time
__props__["current_location_id"] = current_location_id
__props__["display_name"] = display_name
__props__["host"] = host
__props__["labels"] = labels
__props__["location_id"] = location_id
__props__["memory_size_gb"] = memory_size_gb
__props__["name"] = name
__props__["persistence_iam_identity"] = persistence_iam_identity
__props__["port"] = port
__props__["project"] = project
__props__["redis_configs"] = redis_configs
__props__["redis_version"] = redis_version
__props__["region"] = region
__props__["reserved_ip_range"] = reserved_ip_range
__props__["tier"] = tier
return Instance(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="alternativeLocationId")
def alternative_location_id(self) -> pulumi.Output[str]:
"""
Only applicable to STANDARD_HA tier which protects the instance
against zonal failures by provisioning it across two zones.
If provided, it must be a different zone from the one provided in
[locationId].
"""
return pulumi.get(self, "alternative_location_id")
@property
@pulumi.getter(name="authorizedNetwork")
def authorized_network(self) -> pulumi.Output[str]:
"""
The full name of the Google Compute Engine network to which the
instance is connected. If left unspecified, the default network
will be used.
"""
return pulumi.get(self, "authorized_network")
@property
@pulumi.getter(name="connectMode")
def connect_mode(self) -> pulumi.Output[Optional[str]]:
"""
The connection mode of the Redis instance.
Default value is `DIRECT_PEERING`.
Possible values are `DIRECT_PEERING` and `PRIVATE_SERVICE_ACCESS`.
"""
return pulumi.get(self, "connect_mode")
@property
@pulumi.getter(name="createTime")
def create_time(self) -> pulumi.Output[str]:
"""
The time the instance was created in RFC3339 UTC "Zulu" format, accurate to nanoseconds.
"""
return pulumi.get(self, "create_time")
@property
@pulumi.getter(name="currentLocationId")
def current_location_id(self) -> pulumi.Output[str]:
"""
The current zone where the Redis endpoint is placed. For Basic Tier instances, this will always be the same as the
[locationId] provided by the user at creation time. For Standard Tier instances, this can be either [locationId] or
[alternativeLocationId] and can change after a failover event.
"""
return pulumi.get(self, "current_location_id")
@property
@pulumi.getter(name="displayName")
def display_name(self) -> pulumi.Output[Optional[str]]:
"""
An arbitrary and optional user-provided name for the instance.
"""
return pulumi.get(self, "display_name")
@property
@pulumi.getter
def host(self) -> pulumi.Output[str]:
"""
Hostname or IP address of the exposed Redis endpoint used by clients to connect to the service.
"""
return pulumi.get(self, "host")
@property
@pulumi.getter
def labels(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource labels to represent user provided metadata.
"""
return pulumi.get(self, "labels")
@property
@pulumi.getter(name="locationId")
def location_id(self) -> pulumi.Output[str]:
"""
The zone where the instance will be provisioned. If not provided,
the service will choose a zone for the instance. For STANDARD_HA tier,
instances will be created across two zones for protection against
zonal failures. If [alternativeLocationId] is also provided, it must
be different from [locationId].
"""
return pulumi.get(self, "location_id")
@property
@pulumi.getter(name="memorySizeGb")
def memory_size_gb(self) -> pulumi.Output[float]:
"""
Redis memory size in GiB.
"""
return pulumi.get(self, "memory_size_gb")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
The ID of the instance or a fully qualified identifier for the instance.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="persistenceIamIdentity")
def persistence_iam_identity(self) -> pulumi.Output[str]:
"""
Output only. Cloud IAM identity used by import / export operations to transfer data to/from Cloud Storage. Format is
"serviceAccount:". The value may change over time for a given instance so should be checked before each import/export
operation.
"""
return pulumi.get(self, "persistence_iam_identity")
@property
@pulumi.getter
def port(self) -> pulumi.Output[float]:
"""
The port number of the exposed Redis endpoint.
"""
return pulumi.get(self, "port")
@property
@pulumi.getter
def project(self) -> pulumi.Output[str]:
"""
The ID of the project in which the resource belongs.
If it is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@property
@pulumi.getter(name="redisConfigs")
def redis_configs(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Redis configuration parameters, according to http://redis.io/topics/config.
Please check Memorystore documentation for the list of supported parameters:
https://cloud.google.com/memorystore/docs/redis/reference/rest/v1/projects.locations.instances#Instance.FIELDS.redis_configs
"""
return pulumi.get(self, "redis_configs")
@property
@pulumi.getter(name="redisVersion")
def redis_version(self) -> pulumi.Output[str]:
"""
The version of Redis software. If not provided, latest supported
version will be used. Currently, the supported values are:
- REDIS_5_0 for Redis 5.0 compatibility
- REDIS_4_0 for Redis 4.0 compatibility
- REDIS_3_2 for Redis 3.2 compatibility
"""
return pulumi.get(self, "redis_version")
@property
@pulumi.getter
def region(self) -> pulumi.Output[str]:
"""
The name of the Redis region of the instance.
"""
return pulumi.get(self, "region")
@property
@pulumi.getter(name="reservedIpRange")
def reserved_ip_range(self) -> pulumi.Output[str]:
"""
The CIDR range of internal addresses that are reserved for this
instance. If not provided, the service will choose an unused /29
block, for example, 10.0.0.0/29 or 192.168.0.0/29. Ranges must be
unique and non-overlapping with existing subnets in an authorized
network.
"""
return pulumi.get(self, "reserved_ip_range")
@property
@pulumi.getter
def tier(self) -> pulumi.Output[Optional[str]]:
"""
The service tier of the instance. Must be one of these values:
- BASIC: standalone instance
- STANDARD_HA: highly available primary/replica instances
Default value is `BASIC`.
Possible values are `BASIC` and `STANDARD_HA`.
"""
return pulumi.get(self, "tier")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
|
nilq/baby-python
|
python
|
import logging
from pkg_resources import DistributionNotFound, get_distribution
from feast.infra.offline_stores.bigquery_source import BigQuerySource
from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
SparkSource,
)
from feast.infra.offline_stores.file_source import FileSource
from feast.infra.offline_stores.redshift_source import RedshiftSource
from feast.infra.offline_stores.snowflake_source import SnowflakeSource
from .data_source import KafkaSource, KinesisSource, SourceType
from .entity import Entity
from .feature import Feature
from .feature_service import FeatureService
from .feature_store import FeatureStore
from .feature_view import FeatureView
from .on_demand_feature_view import OnDemandFeatureView
from .repo_config import RepoConfig
from .request_feature_view import RequestFeatureView
from .value_type import ValueType
logging.basicConfig(
format="%(asctime)s %(levelname)s:%(message)s",
datefmt="%m/%d/%Y %I:%M:%S %p",
level=logging.INFO,
)
try:
__version__ = get_distribution(__name__).version
except DistributionNotFound:
# package is not installed
pass
__all__ = [
"Entity",
"KafkaSource",
"KinesisSource",
"Feature",
"FeatureService",
"FeatureStore",
"FeatureView",
"OnDemandFeatureView",
"RepoConfig",
"SourceType",
"ValueType",
"BigQuerySource",
"FileSource",
"RedshiftSource",
"RequestFeatureView",
"SnowflakeSource",
"SparkSource",
]
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 8 10:41:43 2018
@author: Haosong
"""
import sys
sys.path.append("C:\\CSCI1001_Project\\PrepareData.py")
# Replace the thing in the braces above with the current path PrepareData.py is in
import PrepareData
PrepareData.prepareData()
|
nilq/baby-python
|
python
|
from django.shortcuts import render, get_object_or_404, redirect
from rest_framework.views import APIView
from rest_framework.response import Response
import json
from .functions import classify_passenger, load_model
class get_classification(APIView):
def post(self, request):
model = load_model('./api/titanic_model.pk')
data = request.data
prediction = classify_passenger(model = model, data = data)
return(Response(prediction))
|
nilq/baby-python
|
python
|
# -*-coding:utf-8-*-
from mobpush.model.BasePush import BasePush
class IosNotify(BasePush):
serialVersionUID = 6316980682876425791
BADGE_TYPE_SET = 1
BADGE_TYPE_ADD = 2
SLIENT = 1
def __init__(self, title=None, subtitle=None, attachment=None,
attachmentType=None, mutableContent=None, contentAvailable=None,
slientPush=None, category=None, badgeType=None, badge=None, sound='default'):
self.data = {
'title': title,
# 标题- 不填写则为应用名称
'subtitle': subtitle,
# 副标题
'sound': sound,
# APNs通知，通过这个字段指定声音。默认为default，即系统默认声音。 如果设置为空值，则为静音。
# 如果设置为特殊的名称，则需要你的App里配置了该声音才可以正常。
'badge': badge,
# 可直接指定 APNs 推送通知的 badge，未设置这个值角标则不带角标推送
'badgeType': badgeType,
# badgeAdd=true 时，增加badge对应的角标数，负数时，算减法
# 当这个数值设置了值时，会更新数据库数据
# 未设置这个值角标则不带角标推送
# 1: 绝对值，2: 修改值
'category': category,
# 只有IOS8及以上系统才支持此参数推送
'slientPush': slientPush,
# 如果只携带content-available: 1,不携带任何badge，sound 和消息内容等参数，
# 则可以不打扰用户的情况下进行内容更新等操作即为“Silent Remote Notifications”。
'contentAvailable': contentAvailable,
# 将该键设为 1 则表示有新的可用内容。带上这个键值，意味着你的 App 在后台启动了或恢复运行了，application:didReceiveRemoteNotification:fetchCompletionHandler:被调用了。
'mutableContent': mutableContent,
# 需要在附加字段中配置相应参数
'attachmentType': attachmentType,
# ios富文本0无 ；1 图片 ；2 视频 ；3 音频
'attachment': attachment,
}
class AndroidNotify(BasePush):
def __init__(self, appName=None, title=None, sound=None, warn='12', style=0, content=None):
self.data = {
'appName': appName,
# 通知标题
'title': title,
# 如果不设置，则默认的通知标题为应用的名称。
# max = 20, message = "推送标题最大长度20"
'warn': warn,
# warn: 提醒类型： 1提示音；2震动；3指示灯
# 如果多个组合则对应编号组合如：12 标识提示音+震动
'style': style,
# 显示样式标识 0、默认通知无； 1、长内容则为内容数据； 2、大图则为图片地址； 3、横幅则为多行内容
# values = {0, 1, 2, 3}, message = "安卓消息格式参数错误"
'content': content,
# content: style样式具体内容
'sound': sound,
# 自定义声音
}
class CustomNotify(BasePush):
def __init__(self, customType=None, customTitle=None):
self.data = {
'customType': customType,
# 自定义消息类型：text 文本消息
'customTitle': customTitle
# 自定义类型标题
}
class PushNotify(BasePush):
def __init__(self, taskCron=0, taskTime=None, plats=[1, 2], iosProduction=1, offlineSeconds=3600,
content=None, title=None, type=1, customNotify=None, androidNotify=None, iosNotify=None,
url=None, extrasMapList=[]):
self.data = {
'taskCron': taskCron,
# 是否是定时任务：0否，1是，默认0
'taskTime': taskTime,
# 定时消息 发送时间
'speed': 0,
# 定速推送, 设置平均每秒推送速度
# 0: 不限制
# 其他限制速度
# 例如: 每秒1条,每秒100条, 建议最小设置为100条
# 这个只是模糊的控制, 只保证推送整体上的平均数值, 比如设置为1, 每5秒推送一条
'plats': plats,
# 可使用平台，1 android;2 ios ;3 winphone(暂不使用) ;
'iosProduction': iosProduction,
# plat = 2下，0测试环境，1生产环境，默认1
'offlineSeconds': offlineSeconds,
# 离线时间，秒
'content': content,
# 推送内容
'title': title,
# 推送标题
'type': type,
# 推送类型：1通知；2自定义
# values = {1, 2}, message = "消息类型1：通知，2：自定义"
'customNotify': customNotify,
# 自定义内容, type=2
'androidNotify': androidNotify,
# android通知消息, type=1, android
'iosNotify': iosNotify,
# ios通知消息, type=1, ios
'url': url,
# 打开链接
'extrasMapList': extrasMapList,
# 附加字段键值对的方式
}
|
nilq/baby-python
|
python
|
from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
from itsdangerous import URLSafeSerializer as URLSafeSerializer
def generate_auth_token(secret_key, username, password):
serializer = Serializer(secret_key, expires_in=15000)
token = serializer.dumps({'username': username, "password": password})
return token.decode()
def deserialize_auth_token(secret_key, token):
serializer = Serializer(secret_key)
return serializer.loads(token)
def generate_res_token(secret_key, body):
serializer = URLSafeSerializer(secret_key)
token = serializer.dumps(body)
return token
def deserialize_res_token(secret_key, token):
serializer = URLSafeSerializer(secret_key)
return serializer.loads(token)
|
nilq/baby-python
|
python
|
import mlflow
from threading import Thread
import os
import time
from sapsan.core.models import ExperimentBackend
class MLflowBackend(ExperimentBackend):
def __init__(self, name: str = 'experiment',
host: str = 'localhost',
port: int = 9000):
super().__init__(name)
self.host = host
self.port = port
self.mlflow_url = "http://{host}:{port}".format(host=host,
port=port)
mlflow.set_tracking_uri(self.mlflow_url)
try:
self.experiment_id = mlflow.set_experiment(name)
print("mlflow ui is already running at %s:%s"%(self.host, self.port))
except:
print("starting mlflow ui, please wait ...")
self.start_ui()
self.experiment_id = mlflow.set_experiment(name)
print("mlflow ui is running at %s:%s"%(self.host, self.port))
def start_ui(self):
mlflow_thread = Thread(target=
os.system("mlflow ui --host %s --port %s &"%(self.host, self.port)))
mlflow_thread.start()
time.sleep(5)
def start(self, run_name: str, nested = False):
mlflow.start_run(run_name = run_name, nested = nested)
def log_metric(self, name: str, value: float):
mlflow.log_metric(name, value)
def log_parameter(self, name: str, value: str):
mlflow.log_param(name, value)
def log_artifact(self, path: str):
mlflow.log_artifact(path)
def close_active_run(self):
if mlflow.active_run()!=None: mlflow.end_run()
def end(self):
mlflow.end_run()
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
# Copyright 2019 HTCondor Team, Computer Sciences Department,
# University of Wisconsin-Madison, WI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from pathlib import Path
import cloudpickle
def main(uid, input_file):
func_path = Path.cwd() / f'{uid}.func'
with func_path.open(mode = 'rb') as f:
func = cloudpickle.load(f)
input_file_path = Path.cwd() / Path(input_file).name
output_file_path = Path.cwd() / f'{uid}.output'
func(input_file_path, output_file_path)
if __name__ == '__main__':
main(uid = sys.argv[1], input_file = sys.argv[2])
|
nilq/baby-python
|
python
|
from core.buckets import BucketExtend
from core.sampler import Sampler
class LowDiscrepancySampler(Sampler):
def __init__(self, bucket_extend: BucketExtend, samples_count: int, shutterOpen: float, shutterClose: float):
super().__init__(bucket_extend, samples_count, shutterOpen, shutterClose)
self.samples_count = samples_count
self.pos_x = self.bucket_extend.start_x
self.pos_y = self.bucket_extend.start_y
self.image_samples = [float, float] * samples_count
self.lens_samples = [float, float] * samples_count
self.time_samples = [float] * samples_count
self.sample_pos = 0
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# coding: utf-8
# In[2]:
from pathlib import Path
import numpy as np
import pandas as pd
train = pd.read_csv("corpus/imdb/labeledTrainData.tsv", header=0,
delimiter="\t", quoting=3)
test = pd.read_csv("corpus/imdb/testData.tsv", header=0,
delimiter="\t", quoting=3)
train_texts = train["review"].tolist()
train_labels = train["sentiment"].tolist()
test_texts = test["review"].tolist()
from sklearn.model_selection import train_test_split
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)
import tensorflow as tf
train_dataset = tf.data.Dataset.from_tensor_slices((
dict(train_encodings),
train_labels
))
val_dataset = tf.data.Dataset.from_tensor_slices((
dict(val_encodings),
val_labels
))
# test_labels = [1]*len(test1)
test_dataset = tf.data.Dataset.from_tensor_slices((
dict(test_encodings)
))
from transformers import TFDistilBertForSequenceClassification
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
# In[3]:
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
# In[4]:
history = model.fit(train_dataset.batch(5), epochs=5)
# In[5]:
model.evaluate(val_dataset.batch(5))
# In[6]:
labels_pred = model.predict(test_dataset.batch(5))
# In[9]:
from matplotlib import pyplot as plt
plt.plot(history.history['acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# 绘制训练 & 验证的损失值
plt.plot(history.history['loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# In[10]:
y = labels_pred.logits
y_pred = np.argmax(y,axis = 1)
# In[15]:
y
# In[12]:
y_pred
# In[13]:
result_output = pd.DataFrame(data={"id": test["id"], "sentiment": y_pred})
result_output.to_csv("bert.csv", index=False, quoting=3)
# In[14]:
model.save("TFDistilBertForSequenceClassification")
|
nilq/baby-python
|
python
|
# Available debug categories.
DEBUG_CATEGORIES = {
'architects': False,
'callbacks': False,
'controllers': False,
'drivers': False,
'emitters': False,
'imap': False,
'managers': False,
'workers': False,
'all': False,
}
# Default categories for the 'all' keyword.
DEBUG_ALL_CATEGORIES = [
'callbacks',
'controllers',
'drivers',
'emitters',
'imap',
'managers',
'workers',
]
ARC = 'architects'
CLB = 'callbacks'
CTL = 'controllers'
DRV = 'drivers'
EMT = 'emitters'
MGR = 'managers'
WRK = 'workers'
IMAP = 'imap'
# Time to sleep for a response of another worker. This value is used by the edmp
# module where appropriate. This allows not eating too much CPU.
#TODO: expose to the rascal.
SLEEP = 0.02
|
nilq/baby-python
|
python
|
class Solution:
def sortColors(self, nums):
return nums.sort()
if __name__ == '__main__':
nums = [0, 1, 2, 2, 1, 1, 2, 2, 0, 0, 0, 0, 2, 1]
print("Before Sort: ")
print(nums)
# [0, 1, 2, 2, 1, 1, 2, 2, 0, 0, 0, 0, 2, 1]
Solution().sortColors(nums)
print("After Sort: ")
print(nums)
# [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2]
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
"""
Check Lisp examples in a Markdown file.
To run (assuming this repo is a submodule in a dir called .cl-make):
$ pip3 install -r .cl-make/requirements.txt
$ .cl-make/readme.py README.md
The return code is zero iff all Lisp examples in the file run without
errors in an SBCL REPL and their outputs match the given outputs. Such
output can be specified in a language-less code block immediately
following the Lisp code block.
The whole REPL session is printed to stdout. If the REPL session exits
unexpectedly, or any evaluation takes longer than 30 seconds, or an
error occurs, or the output doesn't match, then a descriptive error
message is printed to stderr and an exit code of 1 is returned. A
standalone Lisp file is created to reproduce the environment for the
failing Lisp form, and all this reproduction information is included in
the error message.
This script uses pytest internally, and thus can also return other exit
codes: https://docs.pytest.org/en/6.0.1/usage.html#possible-exit-codes
"""
import argparse
import difflib
import logging
import os
import pathlib
import sys
import tempfile
import marko.block as block
from marko.ext.gfm import gfm
import pexpect
import pytest
def pairwise(things):
"""
Return a list of pairs of adjacent elements from things.
The last element of this list is the pair (things[-1], None).
>>> list(pairwise(['a', 'b', 'c']))
[('a', 'b'), ('b', 'c'), ('c', None)]
>>> list(pairwise([]))
[]
"""
return zip(things, things[1:] + [None])
def is_code_block(element):
"""
Return truthy iff the Marko element is a code block.
>>> is_code_block(gfm.parse(''' foo''').children[0])
True
>>> is_code_block(gfm.parse('''```
... bar
... ```''').children[0])
True
>>> is_code_block(gfm.parse('''> baz''').children[0])
False
"""
types = [block.CodeBlock, block.FencedCode]
return any(isinstance(element, t) for t in types)
def code_block_to_dict(code_block):
r"""
Return a dict of the lang and text of the Marko code block.
>>> code_block_to_dict(gfm.parse('''```lisp
... (+ 2
... 2)
... ```''').children[0])
{'lang': 'lisp', 'text': '(+ 2\n 2)\n'}
>>> code_block_to_dict(gfm.parse(''' foo''').children[0])
{'lang': '', 'text': 'foo\n'}
"""
return {
'lang': code_block.lang,
# should only have one child but just in case; also, children of
# the child is just a string holding the text
'text': ''.join(child.children for child in code_block.children),
}
def slurp(filename):
"""
Return the contents of filename as a string.
>>> 'public domain' in slurp('LICENSE.txt')
True
"""
with open(filename) as file:
return file.read()
def lisp_examples(element):
r"""
Return a list of all Lisp examples in the Marko element.
A Lisp example is a code block whose language is 'lisp', and is
returned as a dictionary whose key 'code' holds the text of that
code block. If the Lisp code block is immediately followed by
another code block whose language is the empty string, then the text
of that second block is also included in the dictionary, under the
key 'output'.
>>> from pprint import pprint
>>> examples = lisp_examples(gfm.parse(slurp('test/example.md')))
>>> pprint(examples, width=68)
[{'code': '(format t "Hello, world 1!")\n',
'output': 'Hello, world 1!\nNIL\n'},
{'code': '(format t "Hello, world 4!")\n',
'output': 'Hello, world 4!\nNIL\n'},
{'code': '(format nil "Hello, world 5!")\n'}]
"""
examples = []
if hasattr(element, 'children'):
children = element.children
# sometimes the children are just a string holding the text
if isinstance(children, list):
# don't let blank lines get in the middle of an example
pared = [x for x in children if not isinstance(x, block.BlankLine)]
for a, b in pairwise(pared):
if is_code_block(a):
code = code_block_to_dict(a)
if code['lang'] == 'lisp':
example = {'code': code['text']}
if is_code_block(b):
output = code_block_to_dict(b)
if not output['lang']:
example['output'] = output['text']
examples.append(example)
else:
# will safely skip when a has no grandchildren
examples.extend(lisp_examples(a))
return examples
def quicklisp():
"""
Return the path to the Quicklisp directory.
"""
# Quicklisp sets this variable on installation
if 'QUICK_LISP' in os.environ:
return os.environ['QUICK_LISP']
else:
# but it doesn't show up in a Docker image without using ENV, so
# in particular SEL doesn't have $QUICK_LISP at time of writing
return f'{os.environ["HOME"]}/quicklisp'
# regex matching the default SBCL prompt, only at the start of a line
prompt = r'(?<![^\n])\* '
# possibilities when we eval
patterns = [prompt, pexpect.EOF, pexpect.TIMEOUT]
class ExitException(Exception):
pass
class TimeoutException(Exception):
pass
class MismatchException(Exception):
def __init__(self, actual):
self.actual = actual
class ReadmeItem(pytest.Item):
def __init__(self, name, parent, code, output):
super().__init__(name, parent)
self.code = code
self.output = output
def runtest(self):
code = self.code
repl.send(code)
index = repl.expect(patterns)
# Pexpect returns CR/LF
actual = repl.before.replace('\r\n', '\n')
# print nicely as if input/output were in actual REPL session
logging.info('* ' + '\n '.join(code.splitlines()) + f'\n{actual}')
if index == patterns.index(pexpect.EOF):
raise ExitException()
elif index == patterns.index(pexpect.TIMEOUT):
# the error is (?) shown in the log to stdout
raise TimeoutException()
else:
expected = self.output
if expected and expected != actual:
# the actual output is (?) shown in the log to stdout
raise MismatchException(actual)
else:
# track all the forms we successfully evaluate up until
# the first error (if any)
forms.append(code)
def reportinfo(self):
return self.fspath, 0, f'[readme] Lisp example #{self.name}'
def repr_failure(self, excinfo):
tmp = tempfile.NamedTemporaryFile(
mode='w',
suffix='.lisp',
prefix=f'{pathlib.Path(self.parent.fspath).stem}_',
delete=False,
)
repro = tmp.name
tmp.write('\n'.join(forms))
tmp.close()
if isinstance(excinfo.value, ExitException):
reason = 'Exited REPL unexpectedly.\n'
if isinstance(excinfo.value, TimeoutException):
# the error is shown in the log to stdout
reason = 'Timeout: either took too long or an error occurred.\n'
if isinstance(excinfo.value, MismatchException):
diff = list(difflib.ndiff(
self.output.splitlines(keepends=True),
excinfo.value.actual.splitlines(keepends=True),
))
# the full actual output is shown in the log to stdout
reason = ' '.join(
['Differences (ndiff with -expected +actual):\n\n'] + diff
)
return '\n'.join([
reason,
'To reproduce this in a REPL, first evaluate all the forms up to',
'but not including this one by running the following command:',
'',
f' sbcl --load {repro}',
'',
'Then evaluate the erroneous form:',
'',
] + [f' {line}' for line in self.code.splitlines()])
class ReadmeFile(pytest.File):
def collect(self):
examples = lisp_examples(gfm.parse(slurp(self.fspath)))
for index, example in enumerate(examples):
yield ReadmeItem.from_parent(
self,
name=str(index+1),
code=example['code'], # mandatory
output=example.get('output'), # might not be present
)
class ReadmePlugin:
def pytest_collect_file(self, parent, path):
# we don't check the path because our pytest invocation
# specifies only one file, and we assume user gave us Markdown
return ReadmeFile.from_parent(parent, fspath=path)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--timeout',
type=float,
help='seconds allowed for each REPL form',
)
parser.add_argument('file', help='a Markdown file name')
cli_args = parser.parse_args()
# aggregate all the forms that we evaluate successfully, so that if
# an error occurs, the user can easily reproduce it
forms = []
# Quicklisp isn't present by default in a raw SBCL in the Docker
# image, but it is installed already so we just need to load it
args = ['--load', f'{quicklisp()}/setup.lisp']
repl = pexpect.spawn(
'sbcl',
args,
echo=False, # otherwise we have to strip input from repl.before
encoding='utf-8', # otherwise repl.before gives binary strings
timeout=cli_args.timeout,
)
# nothing should go wrong before we eval anything
repl.expect(prompt)
exit_code = pytest.main(
['--exitfirst', # the REPL can get messed up if error or exit
'--log-cli-level=INFO', # print every input and output
'--log-format=%(message)s',
'--show-capture=no', # don't reprint input/output on failure
'--', # don't choke on filenames starting with dashes
cli_args.file],
plugins=[ReadmePlugin()]
)
sys.exit(exit_code)
|
nilq/baby-python
|
python
|
#!/usr/bin/python
import serial
import time
import sys
if len(sys.argv) != 2:
print "Usage: %s <serial port>" % sys.argv[0]
sys.exit()
def getResponse():
time.sleep(0.25)
s = ser.readline()
print "RECV: "
print s
if "NMI:" in s:
print "NMI signal received"
#sys.exit()
s = ser.readline()
print "RECV: "
print s
if "IRQ:" in s:
print "IRQ signal received"
s = ser.readline()
print "RECV: "
print s
ser = serial.Serial(sys.argv[1], 115200, timeout=5)
getResponse() # initial ready message
for i in range(99):
ser.write(b"WD000%02X\n" % i)
getResponse()
ser.write(b"WD00100\n")
getResponse()
ser.write(b"WD001FF\n")
getResponse()
ser.write(b"WD0003F\n")
getResponse()
ser.write(b"WD00100\n")
getResponse()
ser.write(b"WD001FF\n")
getResponse()
ser.close()
|
nilq/baby-python
|
python
|
import cfscrape
from flask import request
from flask_restplus import Resource, Namespace, fields, abort
from Servers.AnimeFLV.scraper import getList, scrapeEpisodeList, scrapeEpisode, scrapeGenre, scrapeGenreList, scrapeFeed, scrapeLastAnimeAdded
cfscraper = cfscrape.create_scraper(delay=10)
animeflv_api = Namespace('AnimeFLV', description='AnimeFLV API')
search_model = animeflv_api.model('Search AnimeFLV', {
'value': fields.String,
'page': fields.Integer
})
episodes_list_model = animeflv_api.model('Episodes List AnimeFLV', {
'last_id': fields.Integer,
'slug': fields.String,
'page': fields.Integer
})
watch_episode_model = animeflv_api.model('Watch Episode AnimeFLV', {
'id_episode': fields.Integer,
'slug': fields.String,
'no_episode': fields.Integer
})
genre_model = animeflv_api.model('Genre search AnimeFLV', {
'type': fields.String,
'page': fields.Integer
})
@animeflv_api.route('/')
class Home(Resource):
@animeflv_api.doc(description='Index endpoint',
responses={200: 'Server is OK'})
def get(self):
return {'server': 'AnimeFLV'}
@animeflv_api.route('/search')
class Search(Resource):
@animeflv_api.expect(search_model)
@animeflv_api.doc(description='Search for an anime in AnimeFLV',
responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
},
params={
'value': 'String to search in AnimeFLV',
'page': 'Current page'
})
def post(self):
params = request.get_json()
anime_name = params['value'].lower()
page = params['page']
if not anime_name or not page:
abort(400, 'Bad request')
try:
anime_list = getList()
directory = [anime for anime in anime_list if anime_name in anime['title'].lower()]
page-=1
length = len(directory)
start_range = page * 24
end_range = start_range + 24 if start_range + 24 < length else length
filtered_anime = [directory[i] for i in range(start_range, end_range)]
return filtered_anime
except:
abort(500, 'Something ocurred while searching the anime')
@animeflv_api.route('/episodes')
class Episodes(Resource):
@animeflv_api.expect(episodes_list_model)
@animeflv_api.doc(description='Search an anime episodes list',
responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
},
params={
'last_id': 'Anime last Id',
'slug': 'Anime name used in AnimeFLV endpoint',
'page': 'Current page'
})
def post(self):
params = request.get_json()
last_id = params['last_id']
slug = params['slug']
page = params['page']
if not slug or not last_id or not page:
abort(400, 'Bad request')
try:
episodes = scrapeEpisodeList(last_id, slug)
page-=1
length = len(episodes)
start_range = page * 24
end_range = start_range + 24 if start_range + 24 < length else length
results = [episodes[i] for i in range(start_range, end_range)]
return results
except:
abort(500, 'Something ocurred while retrieving the episodes list')
@animeflv_api.route('/watch')
class Watch(Resource):
@animeflv_api.expect(watch_episode_model)
@animeflv_api.doc(description='Get episode streaming options',
responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
}, params={
'id_episode': 'Episode id',
'slug': 'Anime name used in AnimeFLV endpoint',
'no_episode': 'Eposide number'
})
def post(self):
params = request.get_json()
id_episode = params['id_episode']
slug = params['slug']
no_episode = params['no_episode']
if not id_episode or not slug or not no_episode:
abort(400, 'Bad request')
try:
return scrapeEpisode(id_episode, slug, no_episode)
except:
abort(500, 'Something ocurred while retrieving streaming options')
@animeflv_api.route('/genre')
class Genre(Resource):
@animeflv_api.expect(genre_model)
@animeflv_api.doc(description='Get animes related with specific genre',
responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
}, params={
'type': 'Genre type',
'page': 'Current page'
})
def post(self):
params = request.get_json()
genre_type = params['type']
page = params['page']
if not genre_type or not page:
abort(400, 'Bad request')
try:
return scrapeGenre(genre_type, page)
except:
abort(500, 'Something ocurred while retrieving animes')
@animeflv_api.route('/genre/list')
class GenreList(Resource):
@animeflv_api.doc(description='Get genre list',
responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
})
def get(self):
try:
return scrapeGenreList()
except:
abort(500, 'Something ocurred while retrieving genre list')
@animeflv_api.route('/feed')
class Feed(Resource):
@animeflv_api.doc(description='Get today feed', responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
})
def get(self):
try:
return scrapeFeed()
except:
abort(500, 'Something ocurred while retrieving today feed')
@animeflv_api.route('/last')
class LastAnimeAdded(Resource):
@animeflv_api.doc(description='Get last anime added', responses={
200: 'Request was successful',
400: 'Bad request',
500: 'Internal server error'
})
def get(self):
try:
return scrapeLastAnimeAdded()
except:
abort(500, 'Something ocurred while retrieving last anime added')
|
nilq/baby-python
|
python
|
from threading import Lock
from twisted.internet import protocol, reactor
class ClientProtocol(protocol.Protocol):
def dataReceived(self, data):
self.server_protocol.transport.write(data)
def connectionLost(self, reason):
self.server_protocol.transport.loseConnection()
class ClientFactory(protocol.ClientFactory):
def __init__(self, server_protocol):
self.server_protocol = server_protocol
def buildProtocol(self, addr):
client_protocol = ClientProtocol()
client_protocol.server_protocol = self.server_protocol
self.server_protocol.client_protocol = client_protocol
return client_protocol
class ServerProtocol(protocol.Protocol):
def __init__(self, dst_ip, dst_port):
self.dst_ip = dst_ip
self.dst_port = dst_port
self.client_protocol = None
self.buffer = []
def connectionMade(self):
reactor.connectTCP(self.dst_ip, self.dst_port, ClientFactory(self))
def dataReceived(self, data):
self.buffer.append(data)
self.sendData()
def sendData(self):
if not self.client_protocol:
reactor.callLater(0.5, self.sendData)
return
for packet in self.buffer:
self.client_protocol.transport.write(packet)
self.buffer = []
def connectionLost(self, reason):
if self.client_protocol:
self.client_protocol.transport.loseConnection()
class ServerFactory(protocol.Factory):
def __init__(self, dst_ip, dst_port):
self.dst_ip = dst_ip
self.dst_port = dst_port
def buildProtocol(self, addr):
return ServerProtocol(self.dst_ip, self.dst_port)
class NATService:
"""
This service provides a NAT-like service when the backend pool is located in a remote machine.
Guests are bound to a local IP (e.g., 192.168.150.0/24), and so not accessible from a remote Cowrie.
This class provides TCP proxies that associate accessible IPs in the backend pool's machine to the internal
IPs used by guests, like a NAT.
"""
def __init__(self):
self.bindings = {}
self.lock = Lock() # we need to be thread-safe just in case, this is accessed from multiple clients
def request_binding(self, guest_id, dst_ip, ssh_port, telnet_port):
self.lock.acquire()
try:
# see if binding is already created
if dst_ip in self.bindings:
# increase connected
self.bindings[guest_id][0] += 1
return self.bindings[guest_id][1]._realPortNumber, self.bindings[guest_id][2]._realPortNumber
else:
nat_ssh = reactor.listenTCP(0, ServerFactory(dst_ip, ssh_port), interface='0.0.0.0')
nat_telnet = reactor.listenTCP(0, ServerFactory(dst_ip, telnet_port), interface='0.0.0.0')
self.bindings[guest_id] = [0, nat_ssh, nat_telnet]
return nat_ssh._realPortNumber, nat_telnet._realPortNumber
finally:
self.lock.release()
def free_binding(self, guest_id):
self.lock.acquire()
try:
self.bindings[guest_id][0] -= 1
# stop listening if no-one connected
if self.bindings[guest_id][0] == 0:
self.bindings[guest_id][1].stopListening()
self.bindings[guest_id][2].stopListening()
finally:
self.lock.release()
|
nilq/baby-python
|
python
|
from pathlib import Path
from collections import defaultdict
import sys
TEST_MODE = bool(len(sys.argv) > 1 and sys.argv[1] == "test")
CARD = ['E', 'S', 'W', 'N']
DIRECTIONS = [(1,0),(0,1),(-1,0),(0,-1)]
ROTATIONS = [(1,0,0,1),(0,-1,1,0),(-1,0,0,-1),(0,1,-1,0)]
def phase1(data):
pos = [0,0]
facing = 0
for l, val in data:
if l in CARD:
pos[0] += DIRECTIONS[CARD.index(l)][0] * val
pos[1] += DIRECTIONS[CARD.index(l)][1] * val
elif l == 'F':
pos[0] += DIRECTIONS[facing][0] * val
pos[1] += DIRECTIONS[facing][1] * val
elif l == 'L':
facing = (facing - val//90) % 4
elif l == 'R':
facing = (facing + val//90) % 4
return abs(pos[0])+abs(pos[1])
def phase2(data):
pos = [0,0]
wp = [10,-1]
for l, val in data:
if l in CARD:
wp[0] += DIRECTIONS[CARD.index(l)][0] * val
wp[1] += DIRECTIONS[CARD.index(l)][1] * val
elif l == 'F':
pos[0] += wp[0] * val
pos[1] += wp[1] * val
else:
direction = 1 if l == 'R' else -1
matrix = ROTATIONS[direction*val//90]
wp = [wp[0]*matrix[0]+wp[1]*matrix[1],wp[0]*matrix[2]+wp[1]*matrix[3]]
return abs(pos[0])+abs(pos[1])
if __name__ == "__main__":
with Path(__file__).parent.joinpath("input/day12_sample" if TEST_MODE else "input/day12").open() as f:
INSTRUCTIONS = [(line[0], int(line[1:].strip())) for line in f]
print(f'Phase 1: {phase1(INSTRUCTIONS)}')
print(f'Phase 2: {phase2(INSTRUCTIONS)}')
|
nilq/baby-python
|
python
|
import pytest
from rotkehlchen.tests.utils.ethereum import ETHEREUM_TEST_PARAMETERS
@pytest.mark.parametrize(*ETHEREUM_TEST_PARAMETERS)
def test_get_block_by_number(ethereum_manager):
block = ethereum_manager.get_block_by_number(10304885)
assert block['timestamp'] == 1592686213
assert block['number'] == 10304885
assert block['hash'] == '0xe2217ba1639c6ca2183f40b0f800185b3901faece2462854b3162d4c5077752c'
@pytest.mark.parametrize(*ETHEREUM_TEST_PARAMETERS)
def test_get_transaction_receipt(ethereum_manager):
result = ethereum_manager.get_transaction_receipt(
'0x12d474b6cbba04fd1a14e55ef45b1eb175985612244631b4b70450c888962a89',
)
block_hash = '0x6f3a7838a8788c3371b88df170c3643d19bad896c915a7368681292882b6ad61'
assert result['blockHash'] == block_hash
assert len(result['logs']) == 2
assert result['gasUsed'] == '0x232ae'
|
nilq/baby-python
|
python
|
# Copyright 2021 Alibaba Group Holding Limited. All Rights Reserved.
import os
import random
import numpy as np
import torch
def set_random_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
|
nilq/baby-python
|
python
|
# Copyright 2006-2012 Mark Diekhans
import re
from pycbio.hgdata.autoSql import intArraySplit, intArrayJoin, strArraySplit, strArrayJoin
from pycbio.tsv.tabFile import TabFileReader
from pycbio.hgdata import dnaOps
from pycbio.hgdata.cigar import ExonerateCigar
from collections import defaultdict
from deprecation import deprecated
# FIXME: drop sequence support, it is almost never used
# Notes:
# - terms plus and minus are used because `positive' is long and `pos' abbreviation is
# often used for position.
def reverseCoords(start, end, size):
return (size - end, size - start)
def reverseStrand(s):
"return reverse of a strand character"
return "+" if (s == "-") else "-"
def dropQueryUniq(qName):
"""if a suffix in the form -[0-9]+(.[0-9]+)? is append to make the name unique,
drop it"""
return re.match('^(.+?)(-[0-9]+(.[0-9]+)*)?$', qName).group(1)
class PslBlock(object):
"""Block of a PSL"""
__slots__ = ("psl", "iBlk", "qStart", "tStart", "size", "qSeq", "tSeq")
def __init__(self, qStart, tStart, size, qSeq=None, tSeq=None):
"sets iBlk base on being added in ascending order"
self.psl = None
self.iBlk = None
self.qStart = qStart
self.tStart = tStart
self.size = size
self.qSeq = qSeq
self.tSeq = tSeq
def __len__(self):
return self.size
def __str__(self):
return "{}..{} <=> {}..{}".format(self.qStart, self.qEnd, self.tStart, self.tEnd)
@property
def qEnd(self):
return self.qStart + self.size
@property
def tEnd(self):
return self.tStart + self.size
@property
def qStartPlus(self):
"get qStart for the block on positive strand"
if self.psl.qStrand == '+':
return self.qStart
else:
return self.psl.qSize - self.qEnd
@property
def qEndPlus(self):
"get qEnd for the block on positive strand"
if self.psl.qStrand == '+':
return self.qEnd
else:
return self.psl.qSize - self.qStart
@property
def tStartPlus(self):
"get tStart for the block on positive strand"
if self.psl.tStrand == '+':
return self.tStart
else:
return self.psl.tSize - self.tEnd
@property
def tEndPlus(self):
"get tEnd for the block on positive strand"
if self.psl.tStrand == '+':
return self.tEnd
else:
return self.psl.tSize - self.tStart
@deprecated()
def getQStartPos(self):
return self.qStartPlus
@deprecated()
def getQEndPos(self):
return self.qEndPlus
@deprecated()
def getTStartPos(self):
return self.tStartPlus
@deprecated()
def getTEndPos(self):
return self.tEndPlus
def sameAlign(self, other):
"compare for equality of alignment."
return (other is not None) and (self.qStart == other.qStart) and (self.tStart == other.tStart) and (self.size == other.size) and (self.qSeq == other.qSeq) and (self.tSeq == other.tSeq)
def reverseComplement(self, newPsl):
"construct a block that is the reverse complement of this block"
return PslBlock(self.psl.qSize - self.qEnd,
self.psl.tSize - self.tEnd, self.size,
(dnaOps.reverseComplement(self.qSeq) if (self.qSeq is not None) else None),
(dnaOps.reverseComplement(self.tSeq) if (self.tSeq is not None) else None))
def swapSides(self, newPsl):
"construct a block with query and target swapped "
return PslBlock(self.tStart, self.qStart, self.size, self.tSeq, self.qSeq)
def swapSidesReverseComplement(self, newPsl):
"construct a block with query and target swapped and reverse complemented "
return PslBlock(self.psl.tSize - self.tEnd,
self.psl.qSize - self.qEnd, self.size,
(dnaOps.reverseComplement(self.tSeq) if (self.tSeq is not None) else None),
(dnaOps.reverseComplement(self.qSeq) if (self.qSeq is not None) else None))
class Psl(object):
"""Object containing data from a PSL record."""
__slots__ = ("match", "misMatch", "repMatch", "nCount", "qNumInsert", "qBaseInsert", "tNumInsert", "tBaseInsert", "strand", "qName", "qSize", "qStart", "qEnd", "tName", "tSize", "tStart", "tEnd", "blocks")
@classmethod
def _parseBlocks(cls, psl, blockCount, blockSizesStr, qStartsStr, tStartsStr, qSeqsStr, tSeqsStr):
"convert parallel arrays to PslBlock objects"
blockSizes = intArraySplit(blockSizesStr)
qStarts = intArraySplit(qStartsStr)
tStarts = intArraySplit(tStartsStr)
haveSeqs = (qSeqsStr is not None)
if haveSeqs:
qSeqs = strArraySplit(qSeqsStr)
tSeqs = strArraySplit(tSeqsStr)
for i in range(blockCount):
psl.addBlock(PslBlock(qStarts[i], tStarts[i], blockSizes[i],
(qSeqs[i] if haveSeqs else None),
(tSeqs[i] if haveSeqs else None)))
def __init__(self, qName=None, qSize=0, qStart=0, qEnd=0,
tName=None, tSize=0, tStart=0, tEnd=0,
strand=None):
"create a new PSL with no blocks"
self.match = 0
self.misMatch = 0
self.repMatch = 0
self.nCount = 0
self.qNumInsert = 0
self.qBaseInsert = 0
self.tNumInsert = 0
self.tBaseInsert = 0
self.strand = strand
self.qName = qName
self.qSize = qSize
self.qStart = qStart
self.qEnd = qEnd
self.tName = tName
self.tSize = tSize
self.tStart = tStart
self.tEnd = tEnd
self.blocks = []
@classmethod
def fromRow(cls, row):
""""Create PSL from a text row of columns, usually split from a tab
file line"""
psl = Psl(qName=row[9], qSize=int(row[10]), qStart=int(row[11]), qEnd=int(row[12]),
tName=row[13], tSize=int(row[14]), tStart=int(row[15]), tEnd=int(row[16]),
strand=row[8])
psl.match = int(row[0])
psl.misMatch = int(row[1])
psl.repMatch = int(row[2])
psl.nCount = int(row[3])
psl.qNumInsert = int(row[4])
psl.qBaseInsert = int(row[5])
psl.tNumInsert = int(row[6])
psl.tBaseInsert = int(row[7])
blockCount = int(row[17])
haveSeqs = len(row) > 21
cls._parseBlocks(psl, blockCount, row[18], row[19], row[20],
(row[21] if haveSeqs else None),
(row[22] if haveSeqs else None))
return psl
@classmethod
def fromDbRow(cls, row, dbColIdxMap):
""""Create PSL from a database row"""
# FIXME: change to use DictCursor
psl = Psl(qName=row[dbColIdxMap["qName"]],
qSize=row[dbColIdxMap["qSize"]],
qStart=row[dbColIdxMap["qStart"]],
qEnd=row[dbColIdxMap["qEnd"]],
tName=row[dbColIdxMap["tName"]],
tSize=row[dbColIdxMap["tSize"]],
tStart=row[dbColIdxMap["tStart"]],
tEnd=row[dbColIdxMap["tEnd"]],
strand=row[dbColIdxMap["strand"]],)
psl.match = row[dbColIdxMap["matches"]]
psl.misMatch = row[dbColIdxMap["misMatches"]]
psl.repMatch = row[dbColIdxMap["repMatches"]]
psl.nCount = row[dbColIdxMap["nCount"]]
psl.qNumInsert = row[dbColIdxMap["qNumInsert"]]
psl.qBaseInsert = row[dbColIdxMap["qBaseInsert"]]
psl.tNumInsert = row[dbColIdxMap["tNumInsert"]]
psl.tBaseInsert = row[dbColIdxMap["tBaseInsert"]]
blockCount = row[dbColIdxMap["blockCount"]]
haveSeqs = "qSeqs" in dbColIdxMap
cls._parseBlocks(psl, blockCount, row[dbColIdxMap["blockSizes"]],
row[dbColIdxMap["qStarts"]], row[dbColIdxMap["tStarts"]],
(row[dbColIdxMap["qSeqs"]] if haveSeqs else None),
(row[dbColIdxMap["tSeqs"]] if haveSeqs else None))
return psl
@classmethod
def create(cls,
qName=None, qSize=0, qStart=0, qEnd=0,
tName=None, tSize=0, tStart=0, tEnd=0,
strand=None):
"create a new PSL"
psl = Psl(qName=qName, qSize=qSize, qStart=qStart, qEnd=qEnd,
tName=tName, tSize=tSize, tStart=tStart, tEnd=tEnd,
strand=strand)
return psl
def addBlock(self, blk):
blk.psl = self
blk.iBlk = len(self.blocks)
self.blocks.append(blk)
@property
def blockCount(self):
return len(self.blocks)
@property
def qStrand(self):
return self.strand[0]
@property
def tStrand(self):
return (self.strand[1] if len(self.strand) > 1 else "+")
@deprecated()
def getQStrand(self):
return self.qStrand
@deprecated()
def getTStrand(self):
return self.tStrand
@deprecated()
def qRevRange(self, start, end):
"reverse a query range to the other strand (dropping, this is dumb)"
return (self.qSize - end, self.qSize - start)
@deprecated()
def tRevRange(self, start, end):
"reverse a query range to the other strand (dropping, this is dumb)"
return (self.tSize - end, self.tSize - start)
@deprecated()
def qRangeToPos(self, start, end):
"convert a query range in alignment coordinates to positive strand coordinates"
if self.qStrand == "+":
return (start, end)
else:
return (self.qSize - end, self.qSize - start)
@deprecated()
def tRangeToPos(self, start, end):
"convert a target range in alignment coordinates to positive strand coordinates"
if self.tStrand == "+":
return (start, end)
else:
return (self.tSize - end, self.tSize - start)
def isProtein(self):
lastBlock = self.blockCount - 1
if len(self.strand) < 2:
return False
return (((self.strand[1] == '+') and
(self.tEnd == self.tStarts[lastBlock] + 3 * self.blockSizes[lastBlock]))
or
((self.strand[1] == '-') and
(self.tStart == (self.tSize - (self.tStarts[lastBlock] + 3 * self.blockSizes[lastBlock])))))
@property
def tLength(self):
return self.tEnd - self.tStart
@property
def qLength(self):
return self.qEnd - self.qStart
def tOverlap(self, tName, tStart, tEnd):
"test for overlap of target range"
return (tName == self.tName) and (tStart < self.tEnd) and (tEnd > self.tStart)
def tBlkOverlap(self, tStart, tEnd, iBlk):
"does the specified block overlap the target range"
return (tStart < self.getTEndPos(iBlk)) and (tEnd > self.getTStartPos(iBlk))
def toRow(self):
"convert PSL to array of strings"
row = [str(self.match),
str(self.misMatch),
str(self.repMatch),
str(self.nCount),
str(self.qNumInsert),
str(self.qBaseInsert),
str(self.tNumInsert),
str(self.tBaseInsert),
self.strand,
self.qName,
str(self.qSize),
str(self.qStart),
str(self.qEnd),
self.tName,
str(self.tSize),
str(self.tStart),
str(self.tEnd),
str(self.blockCount),
intArrayJoin([b.size for b in self.blocks]),
intArrayJoin([b.qStart for b in self.blocks]),
intArrayJoin([b.tStart for b in self.blocks])]
if self.blocks[0].qSeq is not None:
row.append(strArrayJoin([b.qSeq for b in self.blocks]))
row.append(strArrayJoin([b.tSeq for b in self.blocks]))
return row
def __str__(self):
"return psl as a tab-separated string"
return "\t".join(self.toRow())
def write(self, fh):
"""write psl to a tab-seperated file"""
fh.write(str(self))
fh.write('\n')
@staticmethod
def queryKey(psl):
"sort key using query address"
return (psl.qName, psl.qStart, psl.qEnd)
@staticmethod
def targetKey(psl):
"sort key using target address"
return (psl.tName, psl.tStart, psl.tEnd)
def __eq__(self, other):
"compare for equality of alignment"
if ((not isinstance(other, self.__class__))
or (self.match != other.match)
or (self.misMatch != other.misMatch)
or (self.repMatch != other.repMatch)
or (self.nCount != other.nCount)
or (self.qNumInsert != other.qNumInsert)
or (self.qBaseInsert != other.qBaseInsert)
or (self.tNumInsert != other.tNumInsert)
or (self.tBaseInsert != other.tBaseInsert)
or (self.strand != other.strand)
or (self.qName != other.qName)
or (self.qSize != other.qSize)
or (self.qStart != other.qStart)
or (self.qEnd != other.qEnd)
or (self.tName != other.tName)
or (self.tSize != other.tSize)
or (self.tStart != other.tStart)
or (self.tEnd != other.tEnd)
or (self.blockCount != other.blockCount)):
return False
for i in range(self.blockCount):
if not self.blocks[i].sameAlign(other.blocks[i]):
return False
return True
def __ne__(self, other):
return not self.__eq__(other)
def sameAlign(self, other):
"compare for equality of alignment. The stats fields are not compared."
if ((other is None)
or (self.strand != other.strand)
or (self.qName != other.qName)
or (self.qSize != other.qSize)
or (self.qStart != other.qStart)
or (self.qEnd != other.qEnd)
or (self.tName != other.tName)
or (self.tSize != other.tSize)
or (self.tStart != other.tStart)
or (self.tEnd != other.tEnd)
or (self.blockCount != other.blockCount)):
return False
for i in range(self.blockCount):
if not self.blocks[i].sameAlign(other.blocks[i]):
return False
return True
def __hash__(self):
return hash(self.tName) + hash(self.tStart)
def identity(self):
# FIXME: make property
aligned = float(self.match + self.misMatch + self.repMatch)
if aligned == 0.0:
return 0.0 # just matches Ns
else:
return (self.match + self.repMatch) / aligned
def basesAligned(self):
# FIXME: make property
return self.match + self.misMatch + self.repMatch
def queryAligned(self):
# FIXME: make property
return (self.match + self.misMatch + self.repMatch) / self.qSize
def reverseComplement(self):
"create a new PSL that is reverse complemented"
rc = Psl(qName=self.qName, qSize=self.qSize, qStart=self.qStart, qEnd=self.qEnd,
tName=self.tName, tSize=self.tSize, tStart=self.tStart, tEnd=self.tEnd,
strand=reverseStrand(self.qStrand) + reverseStrand(self.tStrand))
rc.match = self.match
rc.misMatch = self.misMatch
rc.repMatch = self.repMatch
rc.nCount = self.nCount
rc.qNumInsert = self.qNumInsert
rc.qBaseInsert = self.qBaseInsert
rc.tNumInsert = self.tNumInsert
rc.tBaseInsert = self.tBaseInsert
for i in range(self.blockCount - 1, -1, -1):
rc.addBlock(self.blocks[i].reverseComplement(rc))
return rc
def _swapStrand(self, keepTStrandImplicit, doRc):
# don't make implicit if already explicit
if keepTStrandImplicit and (len(self.strand) == 1):
qs = reverseStrand(self.tStrand) if doRc else self.tStrand
ts = ""
else:
# swap and make|keep explicit
qs = self.tStrand
ts = self.qStrand
return qs + ts
def swapSides(self, keepTStrandImplicit=False):
"""Create a new PSL with target and query swapped,
If keepTStrandImplicit is True the psl has an implicit positive target strand, reverse
complement to keep the target strand positive and implicit.
If keepTStrandImplicit is False, don't reverse complement untranslated
alignments to keep target positive strand. This will make the target
strand explicit."""
doRc = (keepTStrandImplicit and (len(self.strand) == 1) and (self.qStrand == "-"))
swap = Psl(qName=self.tName, qSize=self.tSize,
qStart=self.tStart, qEnd=self.tEnd,
tName=self.qName, tSize=self.qSize,
tStart=self.qStart, tEnd=self.qEnd,
strand=self._swapStrand(keepTStrandImplicit, doRc))
swap.match = self.match
swap.misMatch = self.misMatch
swap.repMatch = self.repMatch
swap.nCount = self.nCount
swap.qNumInsert = self.tNumInsert
swap.qBaseInsert = self.tBaseInsert
swap.tNumInsert = self.qNumInsert
swap.tBaseInsert = self.qBaseInsert
if doRc:
for i in range(self.blockCount - 1, -1, -1):
swap.addBlock(self.blocks[i].swapSidesReverseComplement(swap))
else:
for i in range(self.blockCount):
swap.addBlock(self.blocks[i].swapSides(swap))
return swap
class PslReader(object):
"""Generator to read PSLs from a tab file or file-like object"""
def __init__(self, fspec):
self.fspec = fspec
def __iter__(self):
for psl in TabFileReader(self.fspec, rowClass=Psl.fromRow, hashAreComments=True, skipBlankLines=True):
yield psl
class PslTbl(list):
"""Table of PSL objects loaded from a tab-file
"""
def __init__(self, fileName, qNameIdx=False, tNameIdx=False, qUniqDrop=False):
for psl in PslReader(fileName):
self.append(psl)
self.qNameMap = self.tNameMap = None
if qNameIdx:
self._mkQNameIdx(qUniqDrop)
if tNameIdx:
self._mkTNameIdx()
def _mkQNameIdx(self, qUniqDrop):
self.qNameMap = defaultdict(list)
for psl in self:
n = dropQueryUniq(psl.qName) if qUniqDrop else psl.qName
self.qNameMap[n].append(psl)
def _mkTNameIdx(self):
self.tNameMap = defaultdict(list)
for psl in self:
self.tNameMap[psl.tName](psl)
self.tNameMap.default_factory = None
def getQNames(self):
return list(self.qNameMap.keys())
def haveQName(self, qName):
return (self.qNameMap.get(qName) is not None)
def genByQName(self, qName):
"""generator to get PSL for a give qName"""
ent = self.qNameMap.get(qName)
if ent is not None:
for psl in ent:
yield psl
def getByQName(self, qName):
"""get list of PSLs for a give qName"""
return list(self.genByQName(qName))
def getTNames(self):
return list(self.tNameMap.keys())
def haveTName(self, tName):
return (self.tNameMap.get(tName) is not None)
def genByTName(self, tName):
"""generator to get PSL for a give tName"""
ent = self.tNameMap.get(tName)
if ent is not None:
for psl in ent:
yield psl
def getByTName(self, tName):
"""get a list PSL for a give tName"""
return list(self.genByTName(tName))
def pslFromExonerateCigar(qName, qSize, qStart, qEnd, qStrand, tName, tSize, tStart, tEnd, tStrand, cigarStr):
"create a PSL from an Ensembl-style cigar formatted alignment"
def processMatch(psl, size, qNext, tNext):
psl.addBlock(PslBlock(qNext, tNext, size))
psl.match += size
return (qNext + size, tNext + size)
def processInsert(psl, size, tNext):
psl.tNumInsert += 1
psl.tBaseInsert += size
return tNext + size
def processDelete(psl, size, qNext):
psl.qNumInsert += 1
psl.qBaseInsert += size
return qNext + size
cigar = ExonerateCigar(cigarStr)
psl = Psl.create(qName=qName, qSize=qSize, qStart=qStart, qEnd=qEnd,
tName=tName, tSize=tSize, tStart=tStart, tEnd=tEnd,
strand=qStrand + tStrand)
qNext = qStart
qBlkEnd = qEnd
if qStrand == '-':
qNext, qBlkEnd = reverseCoords(qNext, qBlkEnd, qSize)
tNext = tStart
tBlkEnd = tEnd
if tStrand == '-':
tNext, tBlkEnd = reverseCoords(tNext, tBlkEnd, tSize)
for op in cigar:
if op.aligned:
qNext, tNext = processMatch(psl, op.count, qNext, tNext)
elif op.tinsert:
tNext = processInsert(psl, op.count, tNext)
elif op.tdelete:
qNext = processDelete(psl, op.count, qNext)
else:
raise Exception("invalid CIGAR op {} in {}".format(op, cigar))
if qNext != qBlkEnd:
raise Exception("CIGAR length does not match aligned query range: {} {}".format(qName, cigar))
if tNext != tBlkEnd:
raise Exception("CIGAR length does not match aligned target range: {} {}".format(qName, cigar))
if psl.tStrand == '-':
psl = psl.reverseComplement()
psl.strand = psl.strand[0] # BLAT convention
return psl
|
nilq/baby-python
|
python
|
from pydantic import BaseSettings
class Settings(BaseSettings):
MONGO_URI: str = "mongodb://localhost:27017/"
APP_DB: str = "ultraapp"
JWT_SECRET: str = "S3CR3T" # jwt secret
JWT_LIFETIME: int = 3600 * 24
settings = Settings()
|
nilq/baby-python
|
python
|
import time
from typing import List
class Solution:
def evalRPN(self, tokens: List[str]) -> int:
stack = []
for token in tokens:
if not stack:
stack.append(token)
if token in {'+', '-', '*', '/'}:
y = stack.pop()
x = stack.pop()
if token == '+':
stack.append(x+y)
elif token == '-':
stack.append(x-y)
elif token == '*':
stack.append(x*y)
elif token == '/':
stack.append(int(x/y))
else:
stack.append(int(token))
return stack.pop()
if __name__ == "__main__":
testCases = [
(["2", "1", "+", "3", "*"], 9),
(["4", "13", "5", "/", "+"], 6),
(["10", "6", "9", "3", "+", "-11", "*", "/", "*", "17", "+", "5", "+"], 22)
]
for i, testCase in enumerate(testCases):
tokens, ans = testCase
tic = time.time()
ret = Solution().evalRPN(tokens)
toc = time.time()
print(f"{i}: {ret == ans}, return {ret} in {toc-tic:.3f}s.")
|
nilq/baby-python
|
python
|
"""
Create an OpenVINO model package to upload to Azure Blob Storage and use IoT Hub module update twin to update the Azure Percept AzureEyeModule.
"""
import argparse
import os
import json
import zipfile
import datetime
from azure.storage.blob import (
BlockBlobService,
BlobPermissions,
)
from azure.iot.hub import IoTHubRegistryManager
from azure.iot.hub.models import Twin, TwinProperties
def create_openvino_image_classification_model_config(model_filepath, label_filename='labels.txt'):
"""
Create the AzureEyeModule config.json file for an image classification model. Returns the config filepath.
"""
# Create the config.json file
config = {
"DomainType": "classification",
"LabelFileName": label_filename,
"ModelFileName": os.path.basename(model_filepath) # model filepath is the .xml openvino model file
}
# write the config.json file in the model directory
config_filepath = os.path.join(os.path.dirname(model_filepath), "config.json")
with open(config_filepath, "w") as f:
json.dump(config, f)
return config_filepath
def zip_openvino_image_classification_model_package(config_filepath):
"""
Zip the model directory for uploading to IoT Hub. Return the zip filepath.
"""
# read the config json
with open(config_filepath, "r") as f:
config = json.load(f)
# create the zip file from config.json, the label file, and the model xml and bin files
config_dirname = os.path.dirname(os.path.abspath(config_filepath))
model_no_ext = os.path.splitext(config["ModelFileName"])[0]
model_bin_filename = f"{model_no_ext}.bin" # get the model .bin filename from the .xml file name
# create the zip filepath from the model name
zip_filepath = os.path.join(os.path.dirname(config_filepath), f"{model_no_ext}.zip")
with zipfile.ZipFile(zip_filepath, "w") as zf:
zf.write(config_filepath, arcname="config.json")
zf.write(os.path.join(config_dirname, config["LabelFileName"]), arcname=config["LabelFileName"])
zf.write(os.path.join(config_dirname, config["ModelFileName"]), arcname=config["ModelFileName"])
zf.write(os.path.join(config_dirname, model_bin_filename), arcname=os.path.basename(model_bin_filename))
return zip_filepath
def upload_model_zip(model_zip_filepath, model_container_name, storage_account_name, storage_account_key):
"""
Upload the OpenVINO model package to Azure Blob Storage and return the download URL.
"""
# create a BlockBlobService object with Azure storage account name and key
block_blob_service = BlockBlobService(account_name=storage_account_name, account_key=storage_account_key)
# create a container for the model
block_blob_service.create_container(model_container_name, fail_on_exist=False)
# upload the model package to the container
model_blob_name = os.path.basename(model_zip_filepath)
block_blob_service.create_blob_from_path(
container_name=model_container_name,
blob_name=model_blob_name,
file_path=model_zip_filepath,
)
# get the model download URL
model_download_url = block_blob_service.make_blob_url(
model_container_name,
model_blob_name,
protocol='https',
sas_token=block_blob_service.generate_blob_shared_access_signature(
container_name=model_container_name,
blob_name=model_blob_name,
permission=BlobPermissions.READ,
expiry=datetime.datetime.utcnow() + datetime.timedelta(hours=1)
)
)
return model_download_url
def update_percept_module_twin(model_download_url, connection_string, device_id, module_id='azureeyemodule'):
"""
Update the Azure IoT Hub module twin to use the new model download URL, which will cause the Percept kit to
download and run the new model.
connection_string, device_id come from IoT Hub:
# Go to https://portal.azure.com
# Select your IoT Hub
# Click on Shared access policies
# Click 'service' policy on the right (or another policy having 'service connect' permission)
# Copy Connection string--primary key
"""
iothub_registry_manager = IoTHubRegistryManager(connection_string)
module_twin = iothub_registry_manager.get_module_twin(device_id, module_id)
print (f"Module twin properties before update:\n{module_twin.properties}")
# Update twin
twin_patch = Twin()
twin_patch.properties = TwinProperties(desired={"ModelZipUrl": model_download_url})
updated_module_twin = iothub_registry_manager.update_module_twin(device_id, module_id, twin_patch, module_twin.etag)
print (f"Module twin properties after update:\n{updated_module_twin.properties}")
if __name__ == '__main__':
# Create a command line parser with the model filepath, Azure Storage account name, key, and model container name options
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", required=True, help="Path to the OpenVINO model .xml file")
parser.add_argument('--storage-account-name', type=str, required=True, help='Azure Storage account name')
parser.add_argument('--storage-account-key', type=str, required=True, help='Azure Storage account key')
parser.add_argument('--storage-container-name', type=str, required=True, help='Azure Storage model container name')
parser.add_argument('--iothub-connection-string', type=str, required=True, help='IoT Hub connection string')
parser.add_argument('--device-id', type=str, required=True, help='IoT Hub Percept device id')
# Parse the command line arguments
args = parser.parse_args()
# Create the OpenVINO model package
config_filepath = create_openvino_image_classification_model_config(args.model)
# Zip the model package
zip_filepath = zip_openvino_image_classification_model_package(config_filepath)
# Upload the model package to Azure Storage
model_download_url = upload_model_zip(zip_filepath, args.storage_container_name, args.storage_account_name, args.storage_account_key)
# Update the Azure IoT Hub module twin to use the new model package version
update_percept_module_twin(model_download_url, args.iothub_connection_string, args.device_id)
|
nilq/baby-python
|
python
|
EPSILON = 0
UNICODE_LATIN_START = 32
UNICODE_LATIN_END = 127
SEEK_RULE = 1
SEEK_RULE_NAME = 2
SEEK_ST_COLON = 3
SEEK_ND_COLON = 4
SEEK_EQUALS = 5
SEEK_ST_PROD = 6
SEEK_ST_TERM = 7
SEEK_ST_NTERM = 8
SEEK_ST_ESC = 9
SEEK_PROD = 10
SEEK_TERM = 11
SEEK_NTERM = 12
SEEK_ESC = 13
SEEK_SPECIAL_TERM = 14
SEEK_SPECIAL_NTERM = 15
SEEK_SPECIAL_DONE = 16
EXPECTED_LT = -1
EMPTY_RULENAME = -2
LT_FOBIDDEN = -3
EXPECTED_COLON = -4
EXPECTED_EQUALS = -5
EMPY_PRODUCTION = -6
INVALID_TOKEN = -7
INVALID_ESCAPE = -8
DUPLICATED_RULE = -9
INVALID_REGULAR = -10
PLUS_BEFORE = -11
|
nilq/baby-python
|
python
|
'''
Script to do analysis
'''
import argparse
import logging
import time
import torch
import transformers
import itertools
from collections import defaultdict
from models import MTModel
# Use with care: logging error only while printing analysis for reading sanity
transformers.utils.logging.set_verbosity_error()
def output_diff(alignment, translation):
pass
def get_out_token(src_idx, s2t, output):
#get 1-best ali
out_idx = list(s2t[src_idx])[0]
#get token from idx
tmp = output.split()
out_token = tmp[out_idx]
return out_token
# Align source and target word sequences with the awesome aligner (expects non-tokenized input)
def align(src, tgt):
model = transformers.BertModel.from_pretrained('bert-base-multilingual-cased')
tokenizer = transformers.BertTokenizer.from_pretrained('bert-base-multilingual-cased')
# pre-processing
sent_src, sent_tgt = src.strip().split(), tgt.strip().split()
token_src, token_tgt = [tokenizer.tokenize(word) for word in sent_src], [tokenizer.tokenize(word) for word in sent_tgt]
wid_src, wid_tgt = [tokenizer.convert_tokens_to_ids(x) for x in token_src], [tokenizer.convert_tokens_to_ids(x) for x in token_tgt]
ids_src, ids_tgt = tokenizer.prepare_for_model(list(itertools.chain(*wid_src)), return_tensors='pt', model_max_length=tokenizer.model_max_length, truncation=True)['input_ids'], tokenizer.prepare_for_model(list(itertools.chain(*wid_tgt)), return_tensors='pt', truncation=True, model_max_length=tokenizer.model_max_length)['input_ids']
sub2word_map_src = []
for i, word_list in enumerate(token_src):
sub2word_map_src += [i for x in word_list]
sub2word_map_tgt = []
for i, word_list in enumerate(token_tgt):
sub2word_map_tgt += [i for x in word_list]
# alignment
align_layer = 8
threshold = 1e-3
model.eval()
with torch.no_grad():
out_src = model(ids_src.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]
out_tgt = model(ids_tgt.unsqueeze(0), output_hidden_states=True)[2][align_layer][0, 1:-1]
dot_prod = torch.matmul(out_src, out_tgt.transpose(-1, -2))
softmax_srctgt = torch.nn.Softmax(dim=-1)(dot_prod)
softmax_tgtsrc = torch.nn.Softmax(dim=-2)(dot_prod)
softmax_inter = (softmax_srctgt > threshold)*(softmax_tgtsrc > threshold)
# src2tgt is a dict mapping src words to their set of aligned tgt words; align_words is the set of alignments for printing alis etc
align_subwords = torch.nonzero(softmax_inter, as_tuple=False)
align_words = set()
src2tgt = defaultdict(set)
for i, j in align_subwords:
align_words.add( (sub2word_map_src[i], sub2word_map_tgt[j]) )
src2tgt[sub2word_map_src[i]].add(sub2word_map_tgt[j])
return src2tgt, align_words
def print_alignments(align_words):
for i, j in sorted(align_words):
print(f'{color.BOLD}{color.BLUE}{sent_src[i]}{color.END}==={color.BOLD}{color.RED}{sent_tgt[j]}{color.END}')
return
# printing
class color:
PURPLE = '\033[95m'
CYAN = '\033[96m'
DARKCYAN = '\033[36m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
END = '\033[0m'
#example: python analysis.py --lang_pair en-es --src "this is a test" --swap_idx 3 --swap_val sentence
if __name__=="__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--lang_pair')
parser.add_argument('--src')
parser.add_argument('--swap_idx', action='store', type=int)
parser.add_argument('--swap_val')
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')
# -- swap analysis --
start = time.time()
#instantiate model
model = MTModel(args.lang_pair)
src_idx = args.swap_idx
#standard setting
src = args.src
out = model.translation_from_string(src)
s2t, _ = align(src,out)
#noised source
src_word = src.split()[ src_idx ]
src_swap = args.swap_val
src_cos = model.compute_cos(model.get_embed_from_text(src_word), model.get_embed_from_text(src_swap))
print("cossim between src (%s) and sub (%s) is: %f." % (src_word, src_swap, src_cos))
#do swap
tmp = src.split()
tmp[src_idx] = src_swap
swap_src = ' '.join(tmp)
swap_out = model.translation_from_string(swap_src)
swap_s2t, _ = align(swap_src,swap_out)
#noised output
out_word = get_out_token(src_idx, s2t, out)
out_swap = get_out_token(src_idx, swap_s2t, swap_out)
out_cos = model.compute_cos(model.get_embed_from_text(out_word), model.get_embed_from_text(out_swap))
print("cossim between output (%s) and sub (%s) is: %f." % (out_word, out_swap, out_cos))
print(out)
print(swap_out)
end = time.time()
logging.info(f'Time to run script: {end-start} secs')
|
nilq/baby-python
|
python
|
from __future__ import annotations
import numpy as np
from PySide2.QtCore import QPoint, QRect
from PySide2.QtWidgets import QMdiSubWindow
class DataViewerSubWindow(QMdiSubWindow):
def __init__(self, viewer: DataViewer):
super().__init__()
self.viewer = viewer
self.layout_anchors = None
self._laying_out = False
self.update_window_title()
@property
def viewer(self):
return self.widget()
@viewer.setter
def viewer(self, value):
self.setWidget(value)
def update_window_title(self):
self.setWindowTitle(self.viewer.data_path_name)
def lay_out_to_anchors(self):
if self.layout_anchors is None:
return
mdi = self.mdiArea()
mdi_size = np.array([mdi.width(), mdi.height()])
layout_rect_angle_point_coords = self.layout_anchors * mdi_size
layout_rect = QRect(QPoint(*layout_rect_angle_point_coords[0]), QPoint(*layout_rect_angle_point_coords[1]))
self._laying_out = True
self.setGeometry(layout_rect)
self._laying_out = False
def show_normal(self):
if self.isHidden():
self.show()
self.viewer.show()
if self.isMinimized():
self.showNormal()
def resizeEvent(self, resize_event: QResizeEvent):
super().resizeEvent(resize_event)
if not self._laying_out and self.layout_anchors is not None:
mdi = self.mdiArea()
top_left_point = self.mapTo(mdi, self.rect().topLeft())
bottom_right_point = self.mapTo(mdi, self.rect().bottomRight())
mdi_size = np.array([mdi.width(), mdi.height()])
self.layout_anchors[0] = np.array([top_left_point.x(), top_left_point.y()]) / mdi_size
self.layout_anchors[1] = np.array([bottom_right_point.x(), bottom_right_point.y()]) / mdi_size
|
nilq/baby-python
|
python
|
class AMQPError(Exception):
message = 'An unspecified AMQP error has occurred: %s'
def __repr__(self):
return "<%s: %s>" % (self.__class__.__name__, self.message % self.args)
# Backward compatibility
AMQPException = AMQPError
class AMQPConnectionError(AMQPError):
message = 'Connection can not be opened'
class IncompatibleProtocolError(AMQPConnectionError):
message = 'The protocol returned by the server is not supported'
class AuthenticationError(AMQPConnectionError):
message = (
'Server and client could not negotiate use of the '
'authentication mechanisms. Server supports only %r, '
'but client supports only %r.'
)
class ProbableAuthenticationError(AMQPConnectionError):
message = (
'Client was disconnected at a connection stage indicating a '
'probable authentication error: %s'
)
class ConnectionClosed(AMQPConnectionError):
message = 'The AMQP connection was closed (%s) %s'
class ConnectionSyntaxError(ConnectionClosed):
message = ('The sender sent a frame that contained illegal values for '
'one or more fields. This strongly implies a programming error '
'in the sending peer: %r')
class ConnectionFrameError(ConnectionClosed):
message = ('The sender sent a malformed frame that the recipient could '
'not decode. This strongly implies a programming error '
'in the sending peer: %r')
class ConnectionCommandInvalid(ConnectionClosed):
message = ('The client sent an invalid sequence of frames, attempting to '
'perform an operation that was considered invalid by the server.'
' This usually implies a programming error in the client: %r')
class ConnectionChannelError(ConnectionClosed):
message = ('The client attempted to work with a channel that had not been '
'correctly opened. This most likely indicates a fault in the '
'client layer: %r')
class ConnectionUnexpectedFrame(ConnectionClosed):
message = ("The peer sent a frame that was not expected, usually in the "
"context of a content header and body. This strongly indicates "
"a fault in the peer's content processing: %r")
class ConnectionResourceError(ConnectionClosed):
message = ("The server could not complete the method because it lacked "
"sufficient resources. This may be due to the client creating "
"too many of some type of entity: %r")
class ConnectionNotAllowed(ConnectionClosed):
message = ("The client tried to work with some entity in a manner that is "
"prohibited by the server, due to security settings or by "
"some other criteria: %r")
class ConnectionNotImplemented(ConnectionClosed):
message = ("The client tried to use functionality that is "
"not implemented in the server: %r")
class ConnectionInternalError(ConnectionClosed):
message = (" The server could not complete the method because of an "
"internal error. The server may require intervention by an "
"operator in order to resume normal operations: %r")
class AMQPChannelError(AMQPError):
message = 'An unspecified AMQP channel error has occurred'
class ChannelClosed(AMQPChannelError):
message = 'The channel was closed (%s) %s'
class ChannelAccessRefused(ChannelClosed):
message = ('The client attempted to work with a server entity to '
'which it has no access due to security settings: %r')
class ChannelNotFoundEntity(ChannelClosed):
message = ('The client attempted to work with a server '
'entity that does not exist: %r')
class ChannelLockedResource(ChannelClosed):
message = ('The client attempted to work with a server entity to '
'which it has no access because another client is working '
'with it: %r')
class ChannelPreconditionFailed(ChannelClosed):
message = ('The client requested a method that was not allowed because '
'some precondition failed: %r')
class DuplicateConsumerTag(ChannelClosed):
message = 'The consumer tag specified already exists for this channel: %s'
class ProtocolSyntaxError(AMQPError):
message = 'An unspecified protocol syntax error occurred'
class InvalidFrameError(ProtocolSyntaxError):
message = 'Invalid frame received: %r'
class MethodNotImplemented(AMQPError):
pass
class DeliveryError(AMQPError):
__slots__ = 'message', 'frame'
def __init__(self, message, frame):
self.message = message
self.frame = frame
super().__init__()
|
nilq/baby-python
|
python
|
import sys
import time
from collections import deque
from datetime import timedelta
from rich import get_console
from rich.progress import BarColumn, Progress, ProgressColumn, SpinnerColumn, TextColumn
class TimeRemainingColumn(ProgressColumn):
"""Renders estimated time remaining."""
# Only refresh twice a second to prevent jitter
max_refresh = 0.5
def __init__(self, *args, **kwargs):
self.start_time = time.time()
super().__init__(*args, **kwargs)
def render(self, *args, **kwargs):
delta = timedelta(seconds=int(time.time() - self.start_time))
return str(delta)
class IterationsPerSecond:
def format(self, task):
if "times" in dir(task) and len(task.times):
speed = len(task.times) / task.times[-1]
return f"{speed:.2f}it/s"
return "0.00it/s"
class IndefeniteProgressBar:
def __init__(self):
with get_console() as console:
self.pbar = Progress(
SpinnerColumn(style=""),
TextColumn("{task.completed}it"),
BarColumn(console.width),
TextColumn(IterationsPerSecond()),
TimeRemainingColumn(),
console=console,
expand=True,
)
self.pbar.start()
self.pbar.add_task(None, start=False)
self.pbar.tasks[0].times = deque(maxlen=100)
self.start_time = time.time()
def print(self, *args, sep=" ", end="\n"):
msg = sep.join(map(str, args))
sys.stdout.writelines(msg + end)
def update(self):
task = self.pbar.tasks[0]
task.completed += 1
task.times.append(time.time() - self.start_time)
def close(self):
self.pbar.stop()
|
nilq/baby-python
|
python
|
import arcpy
arcpy.env.overwriteOutput = True
# Note: Script assumes data from Pro SDK community samples are installed under C:\Data, as follows:
inFC = r"E:\GISTech\2021\ProProjects\PythonUsage\PythonUsage.gdb\FCL_Lijn"
outFC = r"E:\GISTech\2021\ProProjects\PythonUsage\PythonUsage.gdb\ViaScript"
# Buffer the input features creating three buffer distance feature classes
arcpy.Buffer_analysis(inFC, outFC, "500 meter")
# The following message will be included in the message box from the calling button's OnClick routine
print("Python script uitgevoerd.")
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
from bitshares import BitShares
from bitshares.instance import set_shared_bitshares_instance
from bitshares.amount import Amount
from bitshares.price import Price
from bitshares.asset import Asset
import unittest
class Testcases(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(Testcases, self).__init__(*args, **kwargs)
bitshares = BitShares("wss://node.bitshares.eu", nobroadcast=True,)
set_shared_bitshares_instance(bitshares)
def test_init(self):
# self.assertEqual(1, 1)
Price("0.315 USD/BTS")
Price(1.0, "USD/GOLD")
Price(0.315, base="USD", quote="BTS")
Price(0.315, base=Asset("USD"), quote=Asset("BTS"))
Price(
{
"base": {"amount": 1, "asset_id": "1.3.0"},
"quote": {"amount": 10, "asset_id": "1.3.106"},
}
)
Price(
{
"receives": {"amount": 1, "asset_id": "1.3.0"},
"pays": {"amount": 10, "asset_id": "1.3.106"},
},
base_asset=Asset("1.3.0"),
)
Price(quote="10 GOLD", base="1 USD")
Price("10 GOLD", "1 USD")
Price(Amount("10 GOLD"), Amount("1 USD"))
def test_multiplication(self):
p1 = Price(10.0, "USD/GOLD")
p2 = Price(5.0, "EUR/USD")
p3 = p1 * p2
p4 = p3.as_base("GOLD")
self.assertEqual(p4["quote"]["symbol"], "EUR")
self.assertEqual(p4["base"]["symbol"], "GOLD")
# 10 USD/GOLD * 0.2 EUR/USD = 50 EUR/GOLD = 0.02 GOLD/EUR
self.assertEqual(float(p4), 0.02)
# Inline multiplication
p5 = p1
p5 *= p2
p4 = p5.as_base("GOLD")
self.assertEqual(p4["quote"]["symbol"], "EUR")
self.assertEqual(p4["base"]["symbol"], "GOLD")
# 10 USD/GOLD * 0.2 EUR/USD = 2 EUR/GOLD = 0.02 GOLD/EUR
self.assertEqual(float(p4), 0.02)
def test_div(self):
p1 = Price(10.0, "USD/GOLD")
p2 = Price(5.0, "USD/EUR")
# 10 USD/GOLD / 5 USD/EUR = 2 EUR/GOLD
p3 = p1 / p2
p4 = p3.as_base("EUR")
self.assertEqual(p4["base"]["symbol"], "EUR")
self.assertEqual(p4["quote"]["symbol"], "GOLD")
# 10 USD/GOLD * 0.2 EUR/USD = 2 EUR/GOLD = 0.5 GOLD/EUR
self.assertEqual(float(p4), 2)
def test_div2(self):
p1 = Price(10.0, "USD/GOLD")
p2 = Price(5.0, "USD/GOLD")
# 10 USD/GOLD / 5 USD/EUR = 2 EUR/GOLD
p3 = p1 / p2
self.assertTrue(isinstance(p3, (float, int)))
self.assertEqual(float(p3), 2.0)
|
nilq/baby-python
|
python
|
from ._version import VERSION
from ._chat_client import ChatClient
from ._chat_thread_client import ChatThreadClient
from ._generated.models import (
SendChatMessageResult,
ChatThreadInfo,
ChatMessageType
)
from ._shared.user_credential import CommunicationTokenCredential
from ._shared.user_token_refresh_options import CommunicationTokenRefreshOptions
from ._models import (
ChatThreadParticipant,
ChatMessage,
ChatThread,
ChatMessageReadReceipt,
ChatMessageContent
)
from ._shared.models import CommunicationUserIdentifier
__all__ = [
'ChatClient',
'ChatThreadClient',
'ChatMessage',
'ChatMessageContent',
'ChatMessageReadReceipt',
'SendChatMessageResult',
'ChatThread',
'ChatThreadInfo',
'CommunicationTokenCredential',
'CommunicationTokenRefreshOptions',
'CommunicationUserIdentifier',
'ChatThreadParticipant',
'ChatMessageType'
]
__version__ = VERSION
|
nilq/baby-python
|
python
|
from django.test import TestCase
from wagtailmenus.conf import constants
from wagtailmenus.models import MainMenu
from wagtailmenus.tests import base, utils
Page = utils.get_page_model()
class MainMenuTestCase(TestCase):
"""A base TestCase class for testing MainMenu model class methods"""
fixtures = ['test.json']
def get_random_menu_instance_with_opt_vals_set(self):
obj = MainMenu.objects.order_by('?').first()
obj._option_vals = utils.make_optionvals_instance()
return obj
def get_test_menu_instance(self):
return MainMenu.objects.first()
class TestMainMenuGeneralMethods(MainMenuTestCase):
def test_create_from_collected_values_is_not_implemented(self):
# Model-based menus use get_from_collected_values() instead of
# create_from_collected_values(), because existing objects are reused,
# rather than recreated each time
menu = self.get_test_menu_instance()
with self.assertRaises(NotImplementedError):
menu.create_from_collected_values(None, None)
class TestTopLevelItems(MainMenuTestCase):
# ------------------------------------------------------------------------
# MainMenu.top_level_items
# ------------------------------------------------------------------------
def test_uses_many_queries_when_menu_items_link_to_pages(self):
# 6 queries in total:
# 1. Fetch menu items
# 2. Fetch vanilla pages
# 3-7: Fetch specific pages (HomePage, TopLevelPage, LowLevelPage, ArticleListPage, ContactPage)
menu = self.get_test_menu_instance()
with self.assertNumQueries(7):
menu.top_level_items
def test_uses_a_single_query_when_no_menu_items_link_to_pages(self):
# Replace any menu items that link to pages with links
# to custom urls
menu = self.get_test_menu_instance()
for i, item in enumerate(
menu.get_menu_items_manager().all()
):
if item.link_page_id:
item.link_page = None
item.link_url = '/test/{}/'.format(i)
item.save()
# If no menu items link to pages, no further queries are needed
with self.assertNumQueries(1):
menu.top_level_items
class TestGetPagesForDisplay(MainMenuTestCase):
# ------------------------------------------------------------------------
# MainMenu.pages_for_display
# ------------------------------------------------------------------------
def test_result(self):
menu = MainMenu.objects.get(pk=1)
# And a `max_levels` value of 2
self.assertEqual(menu.max_levels, 2)
# Every page returned by `pages_for_display` should be a
# live, not expired and meant to appear in menus
for p in menu.pages_for_display.values():
self.assertTrue(p.live)
self.assertFalse(p.expired)
self.assertTrue(p.show_in_menus)
# Their should be 12 pages total, 1 for each item, plus children:
# 1. <HomePage: Home>,
# 2. <TopLevelPage: About us>
# 3. <LowLevelPage: Meet the team>
# 4. <LowLevelPage: Our heritage>
# 5. <LowLevelPage: Our mission and values>
# X. <TopLevelPage: Superheroes> - not included (show_in_menus=False)
# 6. <LowLevelPage: Marvel Comics>
# 7. <LowLevelPage: D.C. Comics>
# 8. <TopLevelPage: News & events>
# 9. <LowLevelPage: Latest news>
# 10. <LowLevelPage: Upcoming events>
# 11. <LowLevelPage: In the press>
# 12. <ContactPage: Contact us>
self.assertEqual(len(menu.pages_for_display), 12)
# After being called once, pages_for_display should be cached, so
# accessing it again shouldn't trigger any database queries
with self.assertNumQueries(0):
list(menu.pages_for_display.values())
class TestAddMenuItemsForPages(MainMenuTestCase):
# ------------------------------------------------------------------------
# MainMenu.add_menu_items_for_pages()
# ------------------------------------------------------------------------
def test_add_menu_items_for_pages(self):
menu = MainMenu.objects.get(pk=1)
# The current number of menu items is 6
self.assertEqual(menu.get_menu_items_manager().count(), 6)
# 'Superheroes' has 2 children: 'D.C. Comics' & 'Marvel Comics'
superheroes_page = Page.objects.get(title="Superheroes")
children_of_superheroes = superheroes_page.get_children()
self.assertEqual(children_of_superheroes.count(), 2)
# Use 'add_menu_items_for_pages' to add pages for the above pages
menu.add_menu_items_for_pages(children_of_superheroes)
# The number of menu items should now be 8
self.assertEqual(menu.get_menu_items_manager().count(), 8)
# Evaluate menu items to a list
menu_items = list(menu.get_menu_items_manager().all())
# The last item should be a link to the 'D.C. Comics' page, and the
# sort_order on the item should be 7
dc_item = menu_items.pop()
self.assertEqual(dc_item.link_page.title, 'D.C. Comics')
self.assertEqual(dc_item.sort_order, 7)
# The '2nd to last' item should be a link to the 'Marvel Comics' page,
# and the sort_order on the item should be 6
marvel_item = menu_items.pop()
self.assertEqual(marvel_item.link_page.title, 'Marvel Comics')
self.assertEqual(marvel_item.sort_order, 6)
class TestGetSpecifiedSubMenuTemplateName(MainMenuTestCase):
# ------------------------------------------------------------------------
# MainMenu._get_specified_sub_menu_template_name()
# (inherited from mixins.DefinesSubMenuTemplatesMixin)
# ------------------------------------------------------------------------
def test_returns_none_if_no_templates_specified(self):
menu = self.get_random_menu_instance_with_opt_vals_set()
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=2), None
)
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=3), None
)
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=4), None
)
def test_returns_last_template_when_no_template_specified_for_level(self):
menu = MainMenu.objects.all().first()
menu._option_vals = utils.make_optionvals_instance(
sub_menu_template_names=('single_template.html',)
)
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=2),
'single_template.html'
)
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=3),
'single_template.html'
)
def test_preference_order_of_specified_values(self):
menu = MainMenu.objects.all().first()
menu._option_vals = utils.make_optionvals_instance(
sub_menu_template_name='single_template_as_option.html',
sub_menu_template_names=('option_one.html', 'option_two.html')
)
menu.sub_menu_template_name = 'single_template_as_attr.html'
menu.sub_menu_template_names = utils.SUB_MENU_TEMPLATE_LIST
# While both 'sub_menu_template_name' and 'sub_menu_template_names' are
# specified as option values, the 'sub_menu_template_name' value will
# be preferred
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=4),
'single_template_as_option.html'
)
# If only 'sub_menu_template_names' is specified as an option value,
# that will be preferred
menu._option_vals = utils.make_optionvals_instance(
sub_menu_template_name=None,
sub_menu_template_names=('option_one.html', 'option_two.html')
)
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=4),
'option_two.html',
)
# If no templates have been specified via options, the
# 'sub_menu_template_name' attribute is preferred
menu._option_vals = utils.make_optionvals_instance(
sub_menu_template_name=None,
sub_menu_template_names=None
)
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=4),
'single_template_as_attr.html'
)
# If the 'sub_menu_template_name' attribute is None, the method
# should prefer the 'sub_menu_template_names' attribute
menu.sub_menu_template_name = None
self.assertEqual(
menu._get_specified_sub_menu_template_name(level=4),
menu.sub_menu_template_names[1]
)
class TestGetSubMenuTemplateNames(
MainMenuTestCase, base.GetSubMenuTemplateNamesMethodTestCase
):
"""
Tests MainMenu.get_sub_menu_template_names() using common test cases
from base.GetTemplateNamesMethodTestCase
"""
expected_default_result_length = 4
class TestGetTemplateNames(
MainMenuTestCase, base.GetTemplateNamesMethodTestCase
):
"""
Tests MainMenu.get_template_names() using common test cases from
base.GetTemplateNamesMethodTestCase
"""
expected_default_result_length = 3
def mock_relative_url_method(self, site=None):
return ''
|
nilq/baby-python
|
python
|
"""
Receba a altura do degrau de uma escada e a altura que o usuário deseja alcançar subindo a escada.
Calcule e mostre quantos degraus o usuário deverá subir para atingir o seu objetivo.
"""
a = float(input('Qual é a altura do degrau da escada (cm)? '))
ab = float(input('Qual é a altura que você deseja alcançar subindo a escada (metros)? '))
x = (ab * 100) / a
print(f'O usuário deverá subir {x:.0f} degraus para alcançar o objetivo.')
|
nilq/baby-python
|
python
|
import numpy as np
from netCDF4 import Dataset
from .utils import popEntries,setDimensions
from .OBSstruct import OBSstruct
import pandas as pd
def remove_duplicates(S, coordinate = 'fractional'):
'''
This function identifies duplicated observations
and makes sure all observation on output are unique.
Input:
OBS - OBSstruct object or observation netcdf file
coordinate - Whether to base method on fractional grid coordinates (default)
or use lon/lat/depth 'geographical'
'''
if not isinstance(S,OBSstruct):
fid = Dataset(S)
OBS = OBSstruct(fid)
else:
OBS=OBSstruct(S)
# New method
OBSout = OBSstruct()
OBSout.variance = OBS.variance
OBSout.Nstate = OBS.Nstate
OBSout.spherical = OBS.spherical
OBSout.globalatts = OBS.globalatts
# Create a pandas dataframe from the observation object:
data = {}
for name in OBS.getfieldlist():
data[name] = getattr(OBS, name)
if coordinate == 'fractional':
identifyers = {'X' : 'Xgrid', 'Y':'Ygrid', 'Z':'Zgrid'}
elif coordinate == 'geographical':
identifyers = {'X' : 'lon', 'Y':'lat', 'Z':'depth'}
identifyers['T'] = 'time'
identifyers['V'] = 'value'
# expand data with rounded values that will be used to test uniqueness
for name in identifyers.keys():
data[name] = np.round(getattr(OBS, identifyers[name]), 3)
# Finally, the dataframe:
df = pd.DataFrame(data)
df=df.drop_duplicates(subset = ["T","X","Y","Z","V","type"])
# Convert the reduced data set back to observation object
for name in OBS.getfieldlist():
setattr(OBSout, name, df[name].values)
OBSout = setDimensions(OBSout)
return OBSout
|
nilq/baby-python
|
python
|
import matplotlib.pyplot as plt
import csv
import random
import numpy as np
import math
import matplotlib.patches as patches
data = {}
with open('datasets/data_boston.csv', 'r') as csvfile:
csvfile.readline()
file = csv.reader(csvfile, delimiter=',')
for row in file:
if data.has_key(row[5]):
data[row[5]].append([float(row[14]), float(row[15]), row[5]])
else:
data[row[5]] = [[float(row[14]), float(row[15]), row[5]]]
data_list = []
lat_min = 99
lat_max = -99
long_min = 99
long_max = -99
print "data done"
violation_map = {}
i=0
for key,value in data.iteritems():
random.shuffle(value)
if len(value) > 20000:
violation_map[key] = i
i = i+1
for val in value[:20000]:
if val[0] > lat_max:
lat_max = val[0]
if val[0] < lat_min:
lat_min = val[0]
if val[1] > long_max:
long_max = val[1]
if val[1] < long_min:
long_min = val[1]
data_list.append(val)
print "data list done"
del data
count = {}
print lat_max, lat_min, long_max, long_min
lat_range_min = 999
lat_range_max = -999
long_range_min = 999
long_range_max = -999
for l in data_list:
lat_key = int(math.floor((l[0]-lat_min)*1000))
long_key = int(math.floor(math.fabs(l[1]-long_min)*1000))
if lat_key > lat_range_max:
lat_range_max = lat_key
if lat_key < lat_range_min:
lat_range_min = lat_key
if long_key > long_range_max:
long_range_max = long_key
if long_key < long_range_min:
long_range_min = long_key
if not count.has_key((lat_key, long_key)):
count[(lat_key, long_key)] = [0 for j in range(len(violation_map))]
count[(lat_key, long_key)][violation_map[l[2]]] = count[(lat_key, long_key)][violation_map[l[2]]] + 1
print lat_range_min, lat_range_max, long_range_min, long_range_max
"""
for key,value in count.iteritems():
print key, value
"""
"""
lat_range_min = int(math.floor((lat_min-math.floor(lat_min))*1000))
lat_range_max = int(math.floor((lat_max-math.floor(lat_max))*1000))
long_range_min = int(math.floor((long_min-math.floor(long_min))*1000))
long_range_max = int(math.floor((long_max-math.floor(long_max))*1000))
"""
fig = plt.figure()
ax = fig.add_subplot(111, aspect='equal')
ax.set_xlim([lat_range_min, lat_range_max])
ax.set_ylim([long_range_min, long_range_max])
print lat_range_min, lat_range_max, long_range_min, long_range_max
for i in range(lat_range_min, lat_range_max):
for j in range(long_range_min, long_range_max):
#print i,j
if count.has_key((i,j)):
tot = count[(i,j)][0]+count[(i,j)][1]+count[(i,j)][2]
red = int(count[(i,j)][0]*255/tot)
blue = int(count[(i,j)][1]*255/tot)
green = int(count[(i,j)][2]*255/tot)
color = '#'+('0'+str(hex(red).split('x')[1]))[-2:] + ('0'+str(hex(blue).split('x')[1]))[-2:] +('0'+str(hex(green).split('x')[1]))[-2:]
ax.add_patch(
patches.Rectangle(
(i, j),
1,
1,
facecolor=color,
linewidth=0,
)
)
fig.savefig('rect.png', dpi=1000, bbox_inches='tight')
plt.show()
"""
fig1 = plt.figure()
ax1 = fig1.add_subplot(111, aspect='equal')
ax1.add_patch(
patches.Rectangle(
(0.1, 0.1), # (x,y)
0.5, # width
0.5, # height
facecolor = color,
)
)
fig1.savefig('rect1.png', dpi=90, bbox_inches='tight')
plt.show()
"""
"""
division = 1000
lat_interval = (lat_max-lat_min)/division
long_interval = (long_max-long_min)/division
count_in_grid = [[[0,0,0,0] for i in range(division)] for j in range(division)]
print "array init done"
for i in range(division):
print i, " of ", division
for j in range(division):
for l in data_list:
if l[0] < lat_min + (i+1)*lat_interval and l[0] > lat_min + i*lat_interval and l[1] < long_min + (i+1)*long_interval and l[1] > long_min + i*long_interval:
count_in_grid[i][j][violation_map[l[3]]] = count_in_grid[i][j][violation_map[l[3]]] + 1
print count_in_grid
"""
|
nilq/baby-python
|
python
|
#!/usr/bin/python
#coding:utf-8
import os
import re
import string
import linecache
import shutil
#Get file name from given directory
directoryPath = os.getcwd()
#directoryPath2 = os.getcwd() + '\\New'
file_extension = ".md"
if __name__ == '__main__':
for fileName in os.listdir(directoryPath):
if(fileName.endswith(file_extension)):
file1 = directoryPath + '\\' + fileName
file2 = directoryPath + '\\New\\' + fileName
with open(file1, "r") as f1, open(file2, "w") as f2:
for line in f1:
if '<br /><p style="text-align:center"><a href="https://www.seeedstudio.com/act-4.html" target="_blank"><img src="https://github.com/SeeedDocument/Wiki_Banner/raw/master/new_product.jpg" /></a></p>' in line:
line = line.replace('<br /><p style="text-align:center"><a href="https://www.seeedstudio.com/act-4.html" target="_blank"><img src="https://github.com/SeeedDocument/Wiki_Banner/raw/master/new_product.jpg" /></a></p>', '<br /><p style="text-align:center"><a href="https://www.seeedstudio.com/act-4.html?utm_source=wiki&utm_medium=wikibanner&utm_campaign=newproducts" target="_blank"><img src="https://github.com/SeeedDocument/Wiki_Banner/raw/master/new_product.jpg" /></a></p>')
f2.write(line)
os.remove(file1)
os.rename(file2, file1)
|
nilq/baby-python
|
python
|
from .point_cloud import PointCloud, PointCloudMeta, PointCloudSpatial # noqa
|
nilq/baby-python
|
python
|
def empty_graph(n):
res = []
for i in range(n):
res.append([0]*n)
return res
def convert(graph):
matrix = []
for i in range(len(graph)):
matrix.append([0]*len(graph))
for j in graph[i]:
matrix[i][j] = 1
return matrix
def prims_algo(graph):
graph1 = convert(graph)
n = len(graph1)
tree = empty_graph(n)
con =[0]
while len(con) < n :
found = False
for i in con:
for j in range(n):
if j not in con and graph1[i][j] == 1:
tree[i][j] =1
tree[j][i] =1
con += [j]
found = True
break
if found :
break
return tree
matrix = [[0, 1, 1, 1, 0, 1, 1, 0, 0],
[1, 0, 0, 1, 0, 0, 1, 1, 0],
[1, 0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 1, 0, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0, 0, 0]]
lst = [[1,2,3,5,6],[0,3,6,7],[0,3],[0,1,2,4],[3,5,8],[0,4,8],[0,1],[1],[4,5]]
print("From graph to spanning tree:\n")
print(prims_algo(lst))
|
nilq/baby-python
|
python
|
#
# customization fragment to run L1 GT emulator starting from a RAW file
#
# V.M. Ghete 2010-06-09
import FWCore.ParameterSet.Config as cms
def customise(process):
#
# (re-)run the L1 GT emulator starting from a RAW file
#
from L1Trigger.Configuration.L1Trigger_custom import customiseL1GtEmulatorFromRaw
process=customiseL1GtEmulatorFromRaw(process)
#
# special configuration cases (change to desired configuration in customize_l1TriggerConfiguration)
#
from L1Trigger.Configuration.customise_l1TriggerConfiguration import customiseL1TriggerConfiguration
process=customiseL1TriggerConfiguration(process)
#
# customization of output commands
#
from L1Trigger.Configuration.L1Trigger_custom import customiseOutputCommands
process=customiseOutputCommands(process)
#
# print the L1 trigger report
# comment/un-comment the corresponding flag
#
#printL1TriggerReport = False
printL1TriggerReport = True
if printL1TriggerReport == True :
from L1Trigger.Configuration.L1Trigger_custom import customiseL1TriggerReport
process=customiseL1TriggerReport(process)
process.SimL1Emulator_L1TriggerReport = cms.Sequence(process.SimL1Emulator*process.l1GtTrigReport)
process.L1simulation_step.replace(process.SimL1Emulator,process.SimL1Emulator_L1TriggerReport)
process.l1GtTrigReport.L1GtRecordInputTag = "simGtDigis"
#
return (process)
|
nilq/baby-python
|
python
|
"""
Overview:
Useful functions for build representation format of object.
"""
from typing import List, Tuple
__all__ = [
'get_repr_info',
]
def get_repr_info(cls: type, args: List[Tuple]) -> str:
"""
Overview:
Get representation information for object.
Can be used in ``__repr__`` method for class.
Arguments:
- cls (:obj:`type`): Object's type.
- args (:obj:`List[Tuple]`): Argument display information.
Returns:
- repr (:obj:`str`): Representation string.
Examples::
>>> from hbutils.model import get_repr_info
>>> class Sum:
... def __init__(self, a, b):
... self.__a = a
... self.__b = b
... def __repr__(self):
... return get_repr_info(
... cls=self.__class__,
... args=[
... ('b', lambda: self.__b, lambda: self.__b is not None),
... ('a', lambda: self.__a),
... ]
... )
...
>>> Sum(1, 2)
<Sum b: 2, a: 1>
>>> Sum(1, None)
<Sum a: 1>
>>> Sum(None, None)
<Sum a: None>
"""
_data_items = []
for item in args:
if isinstance(item, tuple):
if len(item) == 2:
name, fd = item
if isinstance(fd, tuple):
_data_func, _present_func = fd
else:
_data_func, _present_func = fd, lambda: True
elif len(item) == 3:
name, _data_func, _present_func = item
else:
raise ValueError('Tuple\'s length should be 2 or 3 but {actual} found.'.format(actual=repr(len(item))))
if _present_func():
_data_items.append('{name}: {data}'.format(name=name, data=_data_func()))
else:
raise TypeError(
'Argument item should be tuple but {actual} found.'.format(actual=repr(type(item).__name__)))
if _data_items:
return '<{cls} {data}>'.format(cls=cls.__name__, data=', '.join(_data_items))
else:
return '<{cls}>'.format(cls=cls.__name__)
|
nilq/baby-python
|
python
|
# -*- Mode: Python; tab-width: 4 -*-
# Copyright (c) 2005-2010 Slide, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of the author nor the names of other
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
'''error
Definitions for access/service return code errors/exceptions.
'''
import exceptions
SUCCESS = 0
#
# old style, pass through rc values
#
UNKNOWN = 1
DUPLICATE_KEY = 2
EXEC_TRACEBACK = 5
AFFINITY_ERROR = 6
#
# new style exceptions.
#
table = {}
lookup = lambda i, *a: table.get(i, AccessError)(*a)
ACCESS_ERROR_MASK = 0x400 #starting at 1K to avoid collision.
class AccessError(exceptions.Exception):
id = 0x400 + 0
class DatabaseUnavailable(AccessError):
'''DatabaseUnavailable
Database was unavailable to service the request
'''
id = 0x400 + 1
class NoServiceHandler(AccessError):
'''NoServiceHandler
The requested service handler does not exist.
'''
id = 0x400 + 2
class ServiceTraceback(AccessError):
'''ServiceTraceback
Unknown/Unhandled exception occured while executing the request.
'''
id = 0x400 + 3
class LockTimeout(AccessError):
'''LockTimeout
resource lock timed out/heavy lock contention
'''
id = 0x400 + 4
class ParameterError(AccessError):
'''ParameterError
The request had incorrect/inconsistent parameters.
'''
id = 0x400 + 5
class NoServiceDefined(AccessError):
'''NoServiceDefined
The request was made with no service defined.
'''
id = 0x400 + 6
#
# Build ID/exception table
#
for v in locals().values():
try:
if issubclass(v, AccessError):
table[v.id] = v
except TypeError:
pass
table[None] = AccessError
#
# end..
|
nilq/baby-python
|
python
|
from bnop_source.b_code.bnop_facades import BnopFacades
from bnop_source.b_code.core.object_model.bnop_repositories import BnopRepositories
from bnop_source.b_code.core.object_model.objects.bnop_objects import BnopObjects
from boro_common_source.ckids.boro_object_ckids import BoroObjectCkIds
from nf_common_source.code.constants.standard_constants import DEFAULT_NULL_VALUE
from nf_common_source.code.nf.types.nf_column_types import NfColumnTypes
from nf_common_source.code.services.dataframe_service.dataframe_mergers import inner_merge_dataframes
from nf_ea_common_tools_source.b_code.nf_ea_common.common_knowledge.ea_connector_types import EaConnectorTypes
from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.common_knowledge.collection_types.nf_ea_com_collection_types import NfEaComCollectionTypes
from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.common_knowledge.column_types.nf_ea_com_column_types import NfEaComColumnTypes
from nf_ea_common_tools_source.b_code.services.general.nf_ea.com.nf_ea_com_universes import NfEaComUniverses
INSTANCE_UML_NAMES_COLUMN = \
'instance_uml_names'
TYPE_UML_NAMES_COLUMN = \
'type_uml_names'
def migrate_ea_connectors_in_scope_of_typing_pattern(
nf_ea_com_universe: NfEaComUniverses,
bnop_repository: BnopRepositories):
typing_ea_connectors = \
__get_typing_connectors(
nf_ea_com_universe=nf_ea_com_universe)
__migrate_typing_connectors(
ea_connectors=typing_ea_connectors,
bnop_repository=bnop_repository)
def __get_typing_connectors(
nf_ea_com_universe: NfEaComUniverses) \
-> list:
ea_connectors = \
nf_ea_com_universe.nf_ea_com_registry.dictionary_of_collections[NfEaComCollectionTypes.EA_CONNECTORS]
ea_classifiers = \
nf_ea_com_universe.nf_ea_com_registry.dictionary_of_collections[NfEaComCollectionTypes.EA_CLASSIFIERS]
typing_ea_connectors = \
ea_connectors[ea_connectors[
NfEaComColumnTypes.CONNECTORS_ELEMENT_TYPE_NAME.column_name] == EaConnectorTypes.DEPENDENCY.type_name]
typing_ea_connectors_with_uml_names_dataframe = \
inner_merge_dataframes(
master_dataframe=typing_ea_connectors,
master_dataframe_key_columns=[
NfEaComColumnTypes.ELEMENTS_CLIENT_PLACE2_END_CONNECTORS.column_name],
merge_suffixes=['', '_type_uml_names'],
foreign_key_dataframe=ea_classifiers,
foreign_key_dataframe_fk_columns=[NfColumnTypes.NF_UUIDS.column_name],
foreign_key_dataframe_other_column_rename_dictionary=
{
NfEaComColumnTypes.EXPLICIT_OBJECTS_EA_OBJECT_NAME.column_name: TYPE_UML_NAMES_COLUMN
})
typing_ea_connectors_with_uml_names_dataframe = \
inner_merge_dataframes(
master_dataframe=typing_ea_connectors_with_uml_names_dataframe,
master_dataframe_key_columns=[
NfEaComColumnTypes.ELEMENTS_SUPPLIER_PLACE1_END_CONNECTORS.column_name],
merge_suffixes=['', '_instance_uml_names'],
foreign_key_dataframe=ea_classifiers,
foreign_key_dataframe_fk_columns=[NfColumnTypes.NF_UUIDS.column_name],
foreign_key_dataframe_other_column_rename_dictionary=
{
NfEaComColumnTypes.EXPLICIT_OBJECTS_EA_OBJECT_NAME.column_name: INSTANCE_UML_NAMES_COLUMN
})
typing_ea_connectors_with_uml_names_dataframe.fillna(
value=DEFAULT_NULL_VALUE,
inplace=True)
typing_ea_connectors_with_uml_names = \
typing_ea_connectors_with_uml_names_dataframe.to_dict(
orient='records')
return \
typing_ea_connectors_with_uml_names
def __migrate_typing_connectors(
ea_connectors: list,
bnop_repository: BnopRepositories):
for ea_connector in ea_connectors:
__migrate_typing_connector(
ea_connector=ea_connector,
bnop_repository=bnop_repository)
def __migrate_typing_connector(
bnop_repository: BnopRepositories,
ea_connector: dict):
typing_tuple_nf_uuid = \
ea_connector[NfColumnTypes.NF_UUIDS.column_name]
instance_nf_uuid = \
ea_connector[NfEaComColumnTypes.ELEMENTS_SUPPLIER_PLACE1_END_CONNECTORS.column_name]
instance_uml_name = \
ea_connector[INSTANCE_UML_NAMES_COLUMN]
type_nf_uuid = \
ea_connector[NfEaComColumnTypes.ELEMENTS_CLIENT_PLACE2_END_CONNECTORS.column_name]
type_uml_name = \
ea_connector[TYPE_UML_NAMES_COLUMN]
if instance_nf_uuid in BnopObjects.registry_keyed_on_uuid:
bnop_instance = \
BnopObjects.registry_keyed_on_uuid[instance_nf_uuid]
else:
bnop_instance = \
BnopFacades.create_bnop_object(
object_uuid=instance_nf_uuid,
owning_repository_uuid=bnop_repository.uuid,
presentation_name=instance_uml_name)
if type_nf_uuid in BnopObjects.registry_keyed_on_uuid:
bnop_type = \
BnopObjects.registry_keyed_on_uuid[type_nf_uuid]
else:
bnop_type = \
BnopFacades.create_bnop_type(
type_uuid=type_nf_uuid,
owning_repository_uuid=bnop_repository.uuid,
presentation_name=type_uml_name)
BnopFacades.create_bnop_tuple_from_two_placed_objects(
tuple_uuid=typing_tuple_nf_uuid,
placed1_object=bnop_type,
placed2_object=bnop_instance,
immutable_minor_composition_couple_type_boro_object_ckid=BoroObjectCkIds.TypesInstances,
owning_repository_uuid=bnop_repository.uuid)
|
nilq/baby-python
|
python
|
"""
Unit tests for flat_file.py
See: https://code.visualstudio.com/docs/python/testing
"""
import unittest
from cred_manage.flat_file import FlatFileCredContainer
import os
FLAT_FILE_THAT_EXISTS='/tmp/file_that_exist.txt'
FLAT_FILE_THAT_DOES_NOT_EXIST='/tmp/file_that_not_exists.txt'
def setUpModule():
"""
Boilerplate to ensure the conditions are right for these tests
"""
# See that there is a flat file that actually exists
with open(FLAT_FILE_THAT_EXISTS, 'w') as f:
f.write("There is content in this file.\n")
# Ensure that there is no such file on disk with the name in FLAT_FILE_THAT_DOES_NOT_EXIST
if os.path.exists(FLAT_FILE_THAT_DOES_NOT_EXIST):
os.remove(FLAT_FILE_THAT_DOES_NOT_EXIST)
def tearDownModule():
"""
Post-testing cleanup
"""
# Clean up the flat file we generated as part of setUpModule
if os.path.exists(FLAT_FILE_THAT_EXISTS):
os.remove(FLAT_FILE_THAT_EXISTS) # It exists no longer
class Test_FlatFileCredContainer(unittest.TestCase):
def test_init_with_bad_file_name(self):
"""
Assert that a FileNotFoundError is raised when we try to init FlatFileCredContainer with a bad file name
"""
self.assertRaises(FileNotFoundError, FlatFileCredContainer, file_path=FLAT_FILE_THAT_DOES_NOT_EXIST)
def test_init_with_valid_file_name(self):
"""
Assert that no Exceptions are raised by __ini__ for FlatFileCredContainer when instantiating with a valid file name
"""
# Armed with a file that exists, init the object. We expect no exceptions to be raised
try:
o = FlatFileCredContainer(file_path=FLAT_FILE_THAT_EXISTS, allow_broad_permissions=True)
except Exception as ex:
self.fail(f"An unexpected exception occurred when instantiating the FlatFileCredContainer during the test: {str(type(ex))}")
def test_get_cred_method_implemented(self):
"""
Asserts that the get_cred method has been implemented.
The superclass will raise a NotImplementedError otherwise
"""
o = FlatFileCredContainer(file_path=FLAT_FILE_THAT_EXISTS, allow_broad_permissions=True)
try:
c = o.get_cred(self)
except NotImplementedError as ex:
self.fail(f"The get_cred() method has not been implemented in the subclass: {type(o)}")
#TODO: Add a test to see that set cred is implemented
#TODO: Add a test to see that delete cred is implemented
|
nilq/baby-python
|
python
|
from marshmallow import fields, validate
from app import ma
from nfmanagementapi.models import FilterRule
class FilterRuleSchema(ma.SQLAlchemyAutoSchema):
class Meta:
model = FilterRule
ordered = True
uuid = fields.UUID(required=True, description="Unique Identifier", dump_only=True)
name = fields.String(required=True, description="Rule name")
description = fields.String(required=False, description="Description")
source = fields.List(fields.UUID(), required=False, description="list of Source object UUIDs")
destination = fields.List(fields.UUID(), required=False, description="list of Destination object UUIDs")
service = fields.List(fields.UUID(), required=False, description="list of Service UUIDs")
action = fields.String(required=True, description="Action to apply", validate=validate.OneOf(["accept", "drop"]))
ctime = fields.DateTime(required=True, description="Creation time", dump_only=True)
mtime = fields.DateTime(required=True, description="Modification time", dump_only=True)
|
nilq/baby-python
|
python
|
import sys
import Adafruit_DHT
import Adafruit_BMP.BMP085 as BMP085
import requests
def getReadings():
humidity, dht_temp = Adafruit_DHT.read_retry(22, 4)
if humidity is not None and dht_temp is not None:
bmp_sensor = BMP085.BMP085()
pressure = bmp_sensor.read_pressure()
bmp_temp = bmp_sensor.read_temperature()
if pressure is not None and bmp_temp is not None:
data = {}
data['temperatureBmp'] = bmp_temp
data['temperatureDht'] = dht_temp
data['humidity'] = humidity
data['pressure'] = pressure
return data
return None
data = getReadings()
print(data)
requests.post('http://pharylonapi.azurewebsites.net/api/weather/reading', data)
|
nilq/baby-python
|
python
|
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, val=0, next=None):
# self.val = val
# self.next = next
class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
if not l1:
return l2
if not l2:
return l1
if l1.val < l2.val:
return ListNode(l1.val, self.mergeTwoLists(l1.next, l2))
else:
return ListNode(l2.val, self.mergeTwoLists(l1, l2.next))
|
nilq/baby-python
|
python
|
import bug_killer_client.network.project as project_client
from bug_killer_api_interface.schemas.request.project import CreateProjectPayload, UpdateProjectPayload
from bug_killer_api_interface.schemas.response import UserProjectsResponse, ProjectResponse
async def get_user_projects(auth: str) -> UserProjectsResponse:
"""
Get the projects that the user is a manager or member of
auth: The cognito user's id token
"""
raw_rsp = await project_client.get_user_projects(auth)
return UserProjectsResponse.parse_obj(raw_rsp)
async def get_project(auth: str, project_id: str) -> ProjectResponse:
"""
Get project by its id
auth: The cognito user's id token
project_id: The id of the project to get
"""
raw_rsp = await project_client.get_project(auth, project_id)
return ProjectResponse.parse_obj(raw_rsp)
async def create_project(auth: str, payload: CreateProjectPayload) -> ProjectResponse:
"""
Creates a project
auth: The cognito user's id token
payload: The details of the project to create
"""
raw_rsp = await project_client.create_project(auth, payload.api_dict())
return ProjectResponse.parse_obj(raw_rsp)
async def update_project(auth: str, project_id: str, payload: UpdateProjectPayload) -> ProjectResponse:
"""
Updates a project by its id
auth: The cognito user's id token
project_id: The id of the project to update
payload: The details of the project to update
"""
raw_rsp = await project_client.update_project(auth, project_id, payload.api_dict())
return ProjectResponse.parse_obj(raw_rsp)
async def delete_project(auth: str, project_id: str) -> ProjectResponse:
"""
Deletes a project by its id
auth: The cognito user's id token
project_id: The id of the project to delete
"""
raw_rsp = await project_client.delete_project(auth, project_id)
return ProjectResponse.parse_obj(raw_rsp)
|
nilq/baby-python
|
python
|
import imageio
import pandas as pd
import matplotlib.pyplot as plt
import trackviz.static
tracks = pd.read_csv('sample_data/ant_tracking_res.csv').rename(columns={'frame': 't'})
fig, ax = trackviz.static.trajectory_3d(tracks, color='t', line_kws=dict(linewidths=0.5))
fig.savefig('output/static_3d_color_frame.png')
# plt.show()
|
nilq/baby-python
|
python
|
from typing import Any
from typing import Optional
import aiohttp
from ...box import box
from ...command import argument
from ...command import option
from ...event import Message
from ...utils import json
box.assert_config_required('NAVER_CLIENT_ID', str)
box.assert_config_required('NAVER_CLIENT_SECRET', str)
LANGUAGE_MAP: dict[Optional[str], Optional[str]] = {
None: None,
'auto': None,
'자동': None,
'자동감지': None,
'ko': 'ko',
'korea': 'ko',
'korean': 'ko',
'한': 'ko',
'한글': 'ko',
'한국': 'ko',
'한국어': 'ko',
'en': 'en',
'eng': 'en',
'english': 'en',
'영': 'en',
'영어': 'en',
'ja': 'ja',
'japan': 'ja',
'japanese': 'ja',
'일': 'ja',
'일어': 'ja',
'일본': 'ja',
'일본어': 'ja',
'zh': 'zh-CN',
'zh-cn': 'zh-CN',
'중': 'zh-CN',
'중국': 'zh-CN',
'중국어': 'zh-CN',
'중국어간체': 'zh-CN',
'중국어 간체': 'zh-CN',
'간체': 'zh-CN',
'중국어번체': 'zh-TW',
'중국어 번체': 'zh-TW',
'번체': 'zh-TW',
'es': 'es',
'스페인': 'es',
'스페인어': 'es',
'fr': 'fr',
'프랑스': 'fr',
'프랑스어': 'fr',
'러시아': 'ru',
'러시아어': 'ru',
'vi': 'vi',
'베트남': 'vi',
'베트남어': 'vi',
'th': 'th',
'태국': 'th',
'태국어': 'th',
'이탈리아': 'it',
'이탈리아어': 'it',
'id': 'id',
'인도네시아': 'id',
'인도네시아어': 'id',
'de': 'de',
'독일': 'de',
'독일어': 'de',
}
LANGUAGE_NAME: dict[str, str] = {
'ko': '한국어',
'ja': '일본어',
'zh-CN': '중국어 간체',
'zh-TW': '중국어 번체',
'hi': '힌디어',
'en': '영어',
'es': '스페인어',
'fr': '프랑스어',
'de': '독일어',
'pt': '포르투갈어',
'vi': '베트남어',
'id': '인도네시아어',
'fa': '페르시아어',
'ar': '아랍어',
'mm': '미얀마어',
'th': '태국어',
'ru': '러시아어',
'it': '이탈리아어',
}
AVAILABLE_COMBINATIONS: set[tuple[str, str]] = {
('ko', 'en'),
('ko', 'ja'),
('ko', 'zh-CN'),
('ko', 'zh-TW'),
('ko', 'vi'),
('ko', 'id'),
('ko', 'th'),
('ko', 'de'),
('ko', 'ru'),
('ko', 'es'),
('ko', 'it'),
('ko', 'fr'),
('en', 'ja'),
('en', 'fr'),
('en', 'zh-CN'),
('en', 'zh-TW'),
('ja', 'zh-CN'),
('ja', 'zh-TW'),
('zh-CN', 'zh-TW'),
}
AVAILABLE_COMBINATIONS |= {(t, s) for s, t in AVAILABLE_COMBINATIONS}
async def detect_language(headers: dict[str, str], text: str) -> str:
url = 'https://openapi.naver.com/v1/papago/detectLangs'
async with aiohttp.ClientSession(headers=headers) as session:
async with session.post(url, data={'query': text}) as resp:
result: dict[str, Any] = await resp.json(loads=json.loads)
return result['langCode']
async def _translate(
headers: dict[str, str],
source: str,
target: str,
text: str,
) -> str:
url = 'https://openapi.naver.com/v1/papago/n2mt'
data = {
'source': source,
'target': target,
'text': text,
}
async with aiohttp.ClientSession(headers=headers) as session:
async with session.post(url, data=data) as resp:
result: dict[str, Any] = await resp.json(loads=json.loads)
return result['message']['result']['translatedText']
@box.command('번역', aliases=['번역기', 'translate', 'tr', 't'], use_shlex=False)
@option('--source', '-s')
@option('--target', '-t', default='ko')
@argument('text', nargs=-1, concat=True)
async def translate(bot, event: Message, source, target, text: str):
"""
번역
파파고 NMT 번역을 활용하여 주어진 문장을 다른 언어로 번역합니다.
`{PREFIX}번역 ソードアート・オンライン` (주어진 문장의 언어를 자동으로 추론해서 한국어로 번역)
`{PREFIX}번역 --source=ja ソードアート・オンライン` (`--source` 옵션으로 원문 언어 지정)
`{PREFIX}번역 --target=en ソードアート・オンライン` (`--target` 옵션으로 결과 언어 지정)
쾌적한 Slack 환경 유지를 위해 번역할 원문 문장은 최대 500자까지만 지원합니다.
`--source`/`-s`와 `--target`/`-t` 옵션은 한국어도 인식합니다. (`--target=일본어`)
"""
if len(text) > 500:
await bot.say(event.channel, '500자 이상의 긴 문장의 번역은 다른 번역기를 사용해주세요!')
return
headers = {
'X-Naver-Client-Id': bot.config.NAVER_CLIENT_ID,
'X-Naver-Client-Secret': bot.config.NAVER_CLIENT_SECRET,
}
source_code = LANGUAGE_MAP.get(source, 'error')
target_code = LANGUAGE_MAP.get(target, 'error')
if source_code is None:
source_code = await detect_language(headers, text)
if source_code == target_code == 'ko':
target_code = 'en'
if source_code == 'error':
await bot.say(event.channel, '원문 언어가 올바르지 않아요!')
elif source_code == 'unk':
await bot.say(event.channel, '원문 언어를 추론하는데에 실패했어요!')
elif target_code is None or target_code == 'error':
await bot.say(event.channel, '결과값 언어가 올바르지 않아요!')
elif source_code == target_code:
await bot.say(event.channel, '원문 언어와 결과값 언어가 같아요!')
elif (source_code, target_code) not in AVAILABLE_COMBINATIONS:
await bot.say(
event.channel,
f'{LANGUAGE_NAME[source_code]}에서 {LANGUAGE_NAME[target_code]}로의'
f' 번역은 현재 지원되지 않아요!',
)
else:
result = await _translate(headers, source_code, target_code, text)
await bot.say(
event.channel,
f'{LANGUAGE_NAME[source_code]} 원문: {text}\n'
f'{LANGUAGE_NAME[target_code]} 번역: {result}',
)
|
nilq/baby-python
|
python
|
"""Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
Original C++ source file: image_ops.cc
"""
import collections as _collections
from tensorflow.python.eager import execute as _execute
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import tensor_shape as _tensor_shape
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
def adjust_contrast(images, contrast_factor, min_value, max_value, name=None):
r"""Deprecated. Disallowed in GraphDef version >= 2.
Args:
images: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `float32`, `float64`.
contrast_factor: A `Tensor` of type `float32`.
min_value: A `Tensor` of type `float32`.
max_value: A `Tensor` of type `float32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"AdjustContrast", images=images, contrast_factor=contrast_factor,
min_value=min_value, max_value=max_value, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_attr_T = _attr_T.as_datatype_enum
contrast_factor = _ops.convert_to_tensor(contrast_factor, _dtypes.float32)
min_value = _ops.convert_to_tensor(min_value, _dtypes.float32)
max_value = _ops.convert_to_tensor(max_value, _dtypes.float32)
_inputs_flat = [images, contrast_factor, min_value, max_value]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"AdjustContrast", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"AdjustContrast", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _adjust_contrastv2(images, contrast_factor, name=None):
r"""Adjust the contrast of one or more images.
`images` is a tensor of at least 3 dimensions. The last 3 dimensions are
interpreted as `[height, width, channels]`. The other dimensions only
represent a collection of images, such as `[batch, height, width, channels].`
Contrast is adjusted independently for each channel of each image.
For each channel, the Op first computes the mean of the image pixels in the
channel and then adjusts each component of each pixel to
`(x - mean) * contrast_factor + mean`.
Args:
images: A `Tensor` of type `float32`. Images to adjust. At least 3-D.
contrast_factor: A `Tensor` of type `float32`.
A float multiplier for adjusting contrast.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. The contrast-adjusted image or images.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"AdjustContrastv2", images=images, contrast_factor=contrast_factor,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
else:
images = _ops.convert_to_tensor(images, _dtypes.float32)
contrast_factor = _ops.convert_to_tensor(contrast_factor, _dtypes.float32)
_inputs_flat = [images, contrast_factor]
_attrs = None
_result = _execute.execute(b"AdjustContrastv2", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"AdjustContrastv2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def adjust_hue(images, delta, name=None):
r"""Adjust the hue of one or more images.
`images` is a tensor of at least 3 dimensions. The last dimension is
interpretted as channels, and must be three.
The input image is considered in the RGB colorspace. Conceptually, the RGB
colors are first mapped into HSV. A delta is then applied all the hue values,
and then remapped back to RGB colorspace.
Args:
images: A `Tensor` of type `float32`. Images to adjust. At least 3-D.
delta: A `Tensor` of type `float32`. A float delta to add to the hue.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. The hue-adjusted image or images.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"AdjustHue", images=images, delta=delta, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
else:
images = _ops.convert_to_tensor(images, _dtypes.float32)
delta = _ops.convert_to_tensor(delta, _dtypes.float32)
_inputs_flat = [images, delta]
_attrs = None
_result = _execute.execute(b"AdjustHue", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"AdjustHue", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def adjust_saturation(images, scale, name=None):
r"""Adjust the saturation of one or more images.
`images` is a tensor of at least 3 dimensions. The last dimension is
interpretted as channels, and must be three.
The input image is considered in the RGB colorspace. Conceptually, the RGB
colors are first mapped into HSV. A scale is then applied all the saturation
values, and then remapped back to RGB colorspace.
Args:
images: A `Tensor` of type `float32`. Images to adjust. At least 3-D.
scale: A `Tensor` of type `float32`.
A float scale to add to the saturation.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. The hue-adjusted image or images.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"AdjustSaturation", images=images, scale=scale, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
else:
images = _ops.convert_to_tensor(images, _dtypes.float32)
scale = _ops.convert_to_tensor(scale, _dtypes.float32)
_inputs_flat = [images, scale]
_attrs = None
_result = _execute.execute(b"AdjustSaturation", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"AdjustSaturation", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def crop_and_resize(image, boxes, box_ind, crop_size, method="bilinear", extrapolation_value=0, name=None):
r"""Extracts crops from the input image tensor and bilinearly resizes them (possibly
with aspect ratio change) to a common output size specified by `crop_size`. This
is more general than the `crop_to_bounding_box` op which extracts a fixed size
slice from the input image and does not allow resizing or aspect ratio change.
Returns a tensor with `crops` from the input `image` at positions defined at the
bounding box locations in `boxes`. The cropped boxes are all resized (with
bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The
result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`.
Args:
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
Both `image_height` and `image_width` need to be positive.
boxes: A `Tensor` of type `float32`.
A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
specifies the coordinates of a box in the `box_ind[i]` image and is specified
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
`[0, 1]` interval of normalized image height is mapped to
`[0, image_height - 1]` in image height coordinates. We do allow `y1` > `y2`, in
which case the sampled crop is an up-down flipped version of the original
image. The width dimension is treated similarly. Normalized coordinates
outside the `[0, 1]` range are allowed, in which case we use
`extrapolation_value` to extrapolate the input image values.
box_ind: A `Tensor` of type `int32`.
A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
crop_size: A `Tensor` of type `int32`.
A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All
cropped image patches are resized to this size. The aspect ratio of the image
content is not preserved. Both `crop_height` and `crop_width` need to be
positive.
method: An optional `string` from: `"bilinear"`. Defaults to `"bilinear"`.
A string specifying the interpolation method. Only 'bilinear' is
supported for now.
extrapolation_value: An optional `float`. Defaults to `0`.
Value used for extrapolation, when applicable.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
"""
if method is None:
method = "bilinear"
method = _execute.make_str(method, "method")
if extrapolation_value is None:
extrapolation_value = 0
extrapolation_value = _execute.make_float(extrapolation_value, "extrapolation_value")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"CropAndResize", image=image, boxes=boxes, box_ind=box_ind,
crop_size=crop_size, method=method,
extrapolation_value=extrapolation_value, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "method", _op.get_attr("method"),
"extrapolation_value", _op.get_attr("extrapolation_value"))
else:
_attr_T, (image,) = _execute.args_to_matching_eager([image], _ctx)
_attr_T = _attr_T.as_datatype_enum
boxes = _ops.convert_to_tensor(boxes, _dtypes.float32)
box_ind = _ops.convert_to_tensor(box_ind, _dtypes.int32)
crop_size = _ops.convert_to_tensor(crop_size, _dtypes.int32)
_inputs_flat = [image, boxes, box_ind, crop_size]
_attrs = ("T", _attr_T, "method", method, "extrapolation_value",
extrapolation_value)
_result = _execute.execute(b"CropAndResize", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"CropAndResize", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def crop_and_resize_grad_boxes(grads, image, boxes, box_ind, method="bilinear", name=None):
r"""Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
Args:
grads: A `Tensor` of type `float32`.
A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
Both `image_height` and `image_width` need to be positive.
boxes: A `Tensor` of type `float32`.
A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
specifies the coordinates of a box in the `box_ind[i]` image and is specified
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
`[0, 1]` interval of normalized image height is mapped to
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
which case the sampled crop is an up-down flipped version of the original
image. The width dimension is treated similarly. Normalized coordinates
outside the `[0, 1]` range are allowed, in which case we use
`extrapolation_value` to extrapolate the input image values.
box_ind: A `Tensor` of type `int32`.
A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
method: An optional `string` from: `"bilinear"`. Defaults to `"bilinear"`.
A string specifying the interpolation method. Only 'bilinear' is
supported for now.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. A 2-D tensor of shape `[num_boxes, 4]`.
"""
if method is None:
method = "bilinear"
method = _execute.make_str(method, "method")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"CropAndResizeGradBoxes", grads=grads, image=image, boxes=boxes,
box_ind=box_ind, method=method, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "method", _op.get_attr("method"))
else:
_attr_T, (image,) = _execute.args_to_matching_eager([image], _ctx)
_attr_T = _attr_T.as_datatype_enum
grads = _ops.convert_to_tensor(grads, _dtypes.float32)
boxes = _ops.convert_to_tensor(boxes, _dtypes.float32)
box_ind = _ops.convert_to_tensor(box_ind, _dtypes.int32)
_inputs_flat = [grads, image, boxes, box_ind]
_attrs = ("T", _attr_T, "method", method)
_result = _execute.execute(b"CropAndResizeGradBoxes", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"CropAndResizeGradBoxes", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def crop_and_resize_grad_image(grads, boxes, box_ind, image_size, T, method="bilinear", name=None):
r"""Computes the gradient of the crop_and_resize op wrt the input image tensor.
Args:
grads: A `Tensor` of type `float32`.
A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
boxes: A `Tensor` of type `float32`.
A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor
specifies the coordinates of a box in the `box_ind[i]` image and is specified
in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of
`y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the
`[0, 1]` interval of normalized image height is mapped to
`[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in
which case the sampled crop is an up-down flipped version of the original
image. The width dimension is treated similarly. Normalized coordinates
outside the `[0, 1]` range are allowed, in which case we use
`extrapolation_value` to extrapolate the input image values.
box_ind: A `Tensor` of type `int32`.
A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`.
The value of `box_ind[i]` specifies the image that the `i`-th box refers to.
image_size: A `Tensor` of type `int32`.
A 1-D tensor with value `[batch, image_height, image_width, depth]`
containing the original image size. Both `image_height` and `image_width` need
to be positive.
T: A `tf.DType` from: `tf.float32, tf.half, tf.float64`.
method: An optional `string` from: `"bilinear"`. Defaults to `"bilinear"`.
A string specifying the interpolation method. Only 'bilinear' is
supported for now.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `T`.
A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
"""
T = _execute.make_type(T, "T")
if method is None:
method = "bilinear"
method = _execute.make_str(method, "method")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"CropAndResizeGradImage", grads=grads, boxes=boxes, box_ind=box_ind,
image_size=image_size, T=T, method=method, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "method", _op.get_attr("method"))
else:
grads = _ops.convert_to_tensor(grads, _dtypes.float32)
boxes = _ops.convert_to_tensor(boxes, _dtypes.float32)
box_ind = _ops.convert_to_tensor(box_ind, _dtypes.int32)
image_size = _ops.convert_to_tensor(image_size, _dtypes.int32)
_inputs_flat = [grads, boxes, box_ind, image_size]
_attrs = ("T", T, "method", method)
_result = _execute.execute(b"CropAndResizeGradImage", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"CropAndResizeGradImage", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def decode_and_crop_jpeg(contents, crop_window, channels=0, ratio=1, fancy_upscaling=True, try_recover_truncated=False, acceptable_fraction=1, dct_method="", name=None):
r"""Decode and Crop a JPEG-encoded image to a uint8 tensor.
The attr `channels` indicates the desired number of color channels for the
decoded image.
Accepted values are:
* 0: Use the number of channels in the JPEG-encoded image.
* 1: output a grayscale image.
* 3: output an RGB image.
If needed, the JPEG-encoded image is transformed to match the requested number
of color channels.
The attr `ratio` allows downscaling the image by an integer factor during
decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
downscaling the image later.
It is equivalent to a combination of decode and crop, but much faster by only
decoding partial jpeg image.
Args:
contents: A `Tensor` of type `string`. 0-D. The JPEG-encoded image.
crop_window: A `Tensor` of type `int32`.
1-D. The crop window: [crop_y, crop_x, crop_height, crop_width].
channels: An optional `int`. Defaults to `0`.
Number of color channels for the decoded image.
ratio: An optional `int`. Defaults to `1`. Downscaling ratio.
fancy_upscaling: An optional `bool`. Defaults to `True`.
If true use a slower but nicer upscaling of the
chroma planes (yuv420/422 only).
try_recover_truncated: An optional `bool`. Defaults to `False`.
If true try to recover an image from truncated input.
acceptable_fraction: An optional `float`. Defaults to `1`.
The minimum required fraction of lines before a truncated
input is accepted.
dct_method: An optional `string`. Defaults to `""`.
string specifying a hint about the algorithm used for
decompression. Defaults to "" which maps to a system-specific
default. Currently valid values are ["INTEGER_FAST",
"INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
jpeg library changes to a version that does not have that specific
option.)
name: A name for the operation (optional).
Returns:
A `Tensor` of type `uint8`. 3-D with shape `[height, width, channels]`..
"""
if channels is None:
channels = 0
channels = _execute.make_int(channels, "channels")
if ratio is None:
ratio = 1
ratio = _execute.make_int(ratio, "ratio")
if fancy_upscaling is None:
fancy_upscaling = True
fancy_upscaling = _execute.make_bool(fancy_upscaling, "fancy_upscaling")
if try_recover_truncated is None:
try_recover_truncated = False
try_recover_truncated = _execute.make_bool(try_recover_truncated, "try_recover_truncated")
if acceptable_fraction is None:
acceptable_fraction = 1
acceptable_fraction = _execute.make_float(acceptable_fraction, "acceptable_fraction")
if dct_method is None:
dct_method = ""
dct_method = _execute.make_str(dct_method, "dct_method")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"DecodeAndCropJpeg", contents=contents, crop_window=crop_window,
channels=channels, ratio=ratio, fancy_upscaling=fancy_upscaling,
try_recover_truncated=try_recover_truncated,
acceptable_fraction=acceptable_fraction, dct_method=dct_method,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("channels", _op.get_attr("channels"), "ratio",
_op.get_attr("ratio"), "fancy_upscaling",
_op.get_attr("fancy_upscaling"), "try_recover_truncated",
_op.get_attr("try_recover_truncated"), "acceptable_fraction",
_op.get_attr("acceptable_fraction"), "dct_method",
_op.get_attr("dct_method"))
else:
contents = _ops.convert_to_tensor(contents, _dtypes.string)
crop_window = _ops.convert_to_tensor(crop_window, _dtypes.int32)
_inputs_flat = [contents, crop_window]
_attrs = ("channels", channels, "ratio", ratio, "fancy_upscaling",
fancy_upscaling, "try_recover_truncated", try_recover_truncated,
"acceptable_fraction", acceptable_fraction, "dct_method",
dct_method)
_result = _execute.execute(b"DecodeAndCropJpeg", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DecodeAndCropJpeg", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def decode_bmp(contents, channels=0, name=None):
r"""Decode the first frame of a BMP-encoded image to a uint8 tensor.
The attr `channels` indicates the desired number of color channels for the
decoded image.
Accepted values are:
* 0: Use the number of channels in the BMP-encoded image.
* 3: output an RGB image.
* 4: output an RGBA image.
Args:
contents: A `Tensor` of type `string`. 0-D. The BMP-encoded image.
channels: An optional `int`. Defaults to `0`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `uint8`.
3-D with shape `[height, width, channels]`. RGB order
"""
if channels is None:
channels = 0
channels = _execute.make_int(channels, "channels")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"DecodeBmp", contents=contents, channels=channels, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("channels", _op.get_attr("channels"))
else:
contents = _ops.convert_to_tensor(contents, _dtypes.string)
_inputs_flat = [contents]
_attrs = ("channels", channels)
_result = _execute.execute(b"DecodeBmp", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DecodeBmp", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def decode_gif(contents, name=None):
r"""Decode the first frame of a GIF-encoded image to a uint8 tensor.
GIF with frame or transparency compression are not supported
convert animated GIF from compressed to uncompressed by:
convert $src.gif -coalesce $dst.gif
This op also supports decoding JPEGs and PNGs, though it is cleaner to use
`tf.image.decode_image`.
Args:
contents: A `Tensor` of type `string`. 0-D. The GIF-encoded image.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `uint8`.
4-D with shape `[num_frames, height, width, 3]`. RGB order
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"DecodeGif", contents=contents, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
else:
contents = _ops.convert_to_tensor(contents, _dtypes.string)
_inputs_flat = [contents]
_attrs = None
_result = _execute.execute(b"DecodeGif", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DecodeGif", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def decode_jpeg(contents, channels=0, ratio=1, fancy_upscaling=True, try_recover_truncated=False, acceptable_fraction=1, dct_method="", name=None):
r"""Decode a JPEG-encoded image to a uint8 tensor.
The attr `channels` indicates the desired number of color channels for the
decoded image.
Accepted values are:
* 0: Use the number of channels in the JPEG-encoded image.
* 1: output a grayscale image.
* 3: output an RGB image.
If needed, the JPEG-encoded image is transformed to match the requested number
of color channels.
The attr `ratio` allows downscaling the image by an integer factor during
decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
downscaling the image later.
This op also supports decoding PNGs and non-animated GIFs since the interface is
the same, though it is cleaner to use `tf.image.decode_image`.
Args:
contents: A `Tensor` of type `string`. 0-D. The JPEG-encoded image.
channels: An optional `int`. Defaults to `0`.
Number of color channels for the decoded image.
ratio: An optional `int`. Defaults to `1`. Downscaling ratio.
fancy_upscaling: An optional `bool`. Defaults to `True`.
If true use a slower but nicer upscaling of the
chroma planes (yuv420/422 only).
try_recover_truncated: An optional `bool`. Defaults to `False`.
If true try to recover an image from truncated input.
acceptable_fraction: An optional `float`. Defaults to `1`.
The minimum required fraction of lines before a truncated
input is accepted.
dct_method: An optional `string`. Defaults to `""`.
string specifying a hint about the algorithm used for
decompression. Defaults to "" which maps to a system-specific
default. Currently valid values are ["INTEGER_FAST",
"INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
jpeg library changes to a version that does not have that specific
option.)
name: A name for the operation (optional).
Returns:
A `Tensor` of type `uint8`. 3-D with shape `[height, width, channels]`..
"""
if channels is None:
channels = 0
channels = _execute.make_int(channels, "channels")
if ratio is None:
ratio = 1
ratio = _execute.make_int(ratio, "ratio")
if fancy_upscaling is None:
fancy_upscaling = True
fancy_upscaling = _execute.make_bool(fancy_upscaling, "fancy_upscaling")
if try_recover_truncated is None:
try_recover_truncated = False
try_recover_truncated = _execute.make_bool(try_recover_truncated, "try_recover_truncated")
if acceptable_fraction is None:
acceptable_fraction = 1
acceptable_fraction = _execute.make_float(acceptable_fraction, "acceptable_fraction")
if dct_method is None:
dct_method = ""
dct_method = _execute.make_str(dct_method, "dct_method")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"DecodeJpeg", contents=contents, channels=channels, ratio=ratio,
fancy_upscaling=fancy_upscaling,
try_recover_truncated=try_recover_truncated,
acceptable_fraction=acceptable_fraction, dct_method=dct_method,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("channels", _op.get_attr("channels"), "ratio",
_op.get_attr("ratio"), "fancy_upscaling",
_op.get_attr("fancy_upscaling"), "try_recover_truncated",
_op.get_attr("try_recover_truncated"), "acceptable_fraction",
_op.get_attr("acceptable_fraction"), "dct_method",
_op.get_attr("dct_method"))
else:
contents = _ops.convert_to_tensor(contents, _dtypes.string)
_inputs_flat = [contents]
_attrs = ("channels", channels, "ratio", ratio, "fancy_upscaling",
fancy_upscaling, "try_recover_truncated", try_recover_truncated,
"acceptable_fraction", acceptable_fraction, "dct_method",
dct_method)
_result = _execute.execute(b"DecodeJpeg", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DecodeJpeg", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def decode_png(contents, channels=0, dtype=_dtypes.uint8, name=None):
r"""Decode a PNG-encoded image to a uint8 or uint16 tensor.
The attr `channels` indicates the desired number of color channels for the
decoded image.
Accepted values are:
* 0: Use the number of channels in the PNG-encoded image.
* 1: output a grayscale image.
* 3: output an RGB image.
* 4: output an RGBA image.
If needed, the PNG-encoded image is transformed to match the requested number
of color channels.
This op also supports decoding JPEGs and non-animated GIFs since the interface
is the same, though it is cleaner to use `tf.image.decode_image`.
Args:
contents: A `Tensor` of type `string`. 0-D. The PNG-encoded image.
channels: An optional `int`. Defaults to `0`.
Number of color channels for the decoded image.
dtype: An optional `tf.DType` from: `tf.uint8, tf.uint16`. Defaults to `tf.uint8`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`. 3-D with shape `[height, width, channels]`.
"""
if channels is None:
channels = 0
channels = _execute.make_int(channels, "channels")
if dtype is None:
dtype = _dtypes.uint8
dtype = _execute.make_type(dtype, "dtype")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"DecodePng", contents=contents, channels=channels, dtype=dtype,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("channels", _op.get_attr("channels"), "dtype",
_op.get_attr("dtype"))
else:
contents = _ops.convert_to_tensor(contents, _dtypes.string)
_inputs_flat = [contents]
_attrs = ("channels", channels, "dtype", dtype)
_result = _execute.execute(b"DecodePng", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DecodePng", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def draw_bounding_boxes(images, boxes, name=None):
r"""Draw bounding boxes on a batch of images.
Outputs a copy of `images` but draws on top of the pixels zero or more bounding
boxes specified by the locations in `boxes`. The coordinates of the each
bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
height of the underlying image.
For example, if an image is 100 x 200 pixels (height x width) and the bounding
box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of
the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates).
Parts of the bounding box may fall outside the image.
Args:
images: A `Tensor`. Must be one of the following types: `float32`, `half`.
4-D with shape `[batch, height, width, depth]`. A batch of images.
boxes: A `Tensor` of type `float32`.
3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding
boxes.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `images`.
4-D with the same shape as `images`. The batch of input images with
bounding boxes drawn on the images.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"DrawBoundingBoxes", images=images, boxes=boxes, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx, _dtypes.float32)
_attr_T = _attr_T.as_datatype_enum
boxes = _ops.convert_to_tensor(boxes, _dtypes.float32)
_inputs_flat = [images, boxes]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"DrawBoundingBoxes", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DrawBoundingBoxes", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def encode_jpeg(image, format="", quality=95, progressive=False, optimize_size=False, chroma_downsampling=True, density_unit="in", x_density=300, y_density=300, xmp_metadata="", name=None):
r"""JPEG-encode an image.
`image` is a 3-D uint8 Tensor of shape `[height, width, channels]`.
The attr `format` can be used to override the color format of the encoded
output. Values can be:
* `''`: Use a default format based on the number of channels in the image.
* `grayscale`: Output a grayscale JPEG image. The `channels` dimension
of `image` must be 1.
* `rgb`: Output an RGB JPEG image. The `channels` dimension
of `image` must be 3.
If `format` is not specified or is the empty string, a default format is picked
in function of the number of channels in `image`:
* 1: Output a grayscale image.
* 3: Output an RGB image.
Args:
image: A `Tensor` of type `uint8`.
3-D with shape `[height, width, channels]`.
format: An optional `string` from: `"", "grayscale", "rgb"`. Defaults to `""`.
Per pixel image format.
quality: An optional `int`. Defaults to `95`.
Quality of the compression from 0 to 100 (higher is better and slower).
progressive: An optional `bool`. Defaults to `False`.
If True, create a JPEG that loads progressively (coarse to fine).
optimize_size: An optional `bool`. Defaults to `False`.
If True, spend CPU/RAM to reduce size with no quality change.
chroma_downsampling: An optional `bool`. Defaults to `True`.
See http://en.wikipedia.org/wiki/Chroma_subsampling.
density_unit: An optional `string` from: `"in", "cm"`. Defaults to `"in"`.
Unit used to specify `x_density` and `y_density`:
pixels per inch (`'in'`) or centimeter (`'cm'`).
x_density: An optional `int`. Defaults to `300`.
Horizontal pixels per density unit.
y_density: An optional `int`. Defaults to `300`.
Vertical pixels per density unit.
xmp_metadata: An optional `string`. Defaults to `""`.
If not empty, embed this XMP metadata in the image header.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `string`. 0-D. JPEG-encoded image.
"""
if format is None:
format = ""
format = _execute.make_str(format, "format")
if quality is None:
quality = 95
quality = _execute.make_int(quality, "quality")
if progressive is None:
progressive = False
progressive = _execute.make_bool(progressive, "progressive")
if optimize_size is None:
optimize_size = False
optimize_size = _execute.make_bool(optimize_size, "optimize_size")
if chroma_downsampling is None:
chroma_downsampling = True
chroma_downsampling = _execute.make_bool(chroma_downsampling, "chroma_downsampling")
if density_unit is None:
density_unit = "in"
density_unit = _execute.make_str(density_unit, "density_unit")
if x_density is None:
x_density = 300
x_density = _execute.make_int(x_density, "x_density")
if y_density is None:
y_density = 300
y_density = _execute.make_int(y_density, "y_density")
if xmp_metadata is None:
xmp_metadata = ""
xmp_metadata = _execute.make_str(xmp_metadata, "xmp_metadata")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"EncodeJpeg", image=image, format=format, quality=quality,
progressive=progressive, optimize_size=optimize_size,
chroma_downsampling=chroma_downsampling, density_unit=density_unit,
x_density=x_density, y_density=y_density, xmp_metadata=xmp_metadata,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("format", _op.get_attr("format"), "quality",
_op.get_attr("quality"), "progressive",
_op.get_attr("progressive"), "optimize_size",
_op.get_attr("optimize_size"), "chroma_downsampling",
_op.get_attr("chroma_downsampling"), "density_unit",
_op.get_attr("density_unit"), "x_density",
_op.get_attr("x_density"), "y_density",
_op.get_attr("y_density"), "xmp_metadata",
_op.get_attr("xmp_metadata"))
else:
image = _ops.convert_to_tensor(image, _dtypes.uint8)
_inputs_flat = [image]
_attrs = ("format", format, "quality", quality, "progressive",
progressive, "optimize_size", optimize_size,
"chroma_downsampling", chroma_downsampling, "density_unit",
density_unit, "x_density", x_density, "y_density", y_density,
"xmp_metadata", xmp_metadata)
_result = _execute.execute(b"EncodeJpeg", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"EncodeJpeg", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def encode_png(image, compression=-1, name=None):
r"""PNG-encode an image.
`image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]`
where `channels` is:
* 1: for grayscale.
* 2: for grayscale + alpha.
* 3: for RGB.
* 4: for RGBA.
The ZLIB compression level, `compression`, can be -1 for the PNG-encoder
default or a value from 0 to 9. 9 is the highest compression level, generating
the smallest output, but is slower.
Args:
image: A `Tensor`. Must be one of the following types: `uint8`, `uint16`.
3-D with shape `[height, width, channels]`.
compression: An optional `int`. Defaults to `-1`. Compression level.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `string`. 0-D. PNG-encoded image.
"""
if compression is None:
compression = -1
compression = _execute.make_int(compression, "compression")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"EncodePng", image=image, compression=compression, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("compression", _op.get_attr("compression"), "T",
_op.get_attr("T"))
else:
_attr_T, (image,) = _execute.args_to_matching_eager([image], _ctx, _dtypes.uint8)
_attr_T = _attr_T.as_datatype_enum
_inputs_flat = [image]
_attrs = ("compression", compression, "T", _attr_T)
_result = _execute.execute(b"EncodePng", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"EncodePng", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def extract_glimpse(input, size, offsets, centered=True, normalized=True, uniform_noise=True, name=None):
r"""Extracts a glimpse from the input tensor.
Returns a set of windows called glimpses extracted at location
`offsets` from the input tensor. If the windows only partially
overlaps the inputs, the non overlapping areas will be filled with
random noise.
The result is a 4-D tensor of shape `[batch_size, glimpse_height,
glimpse_width, channels]`. The channels and batch dimensions are the
same as that of the input tensor. The height and width of the output
windows are specified in the `size` parameter.
The argument `normalized` and `centered` controls how the windows are built:
* If the coordinates are normalized but not centered, 0.0 and 1.0
correspond to the minimum and maximum of each height and width
dimension.
* If the coordinates are both normalized and centered, they range from
-1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper
left corner, the lower right corner is located at (1.0, 1.0) and the
center is at (0, 0).
* If the coordinates are not normalized they are interpreted as
numbers of pixels.
Args:
input: A `Tensor` of type `float32`.
A 4-D float tensor of shape `[batch_size, height, width, channels]`.
size: A `Tensor` of type `int32`.
A 1-D tensor of 2 elements containing the size of the glimpses
to extract. The glimpse height must be specified first, following
by the glimpse width.
offsets: A `Tensor` of type `float32`.
A 2-D integer tensor of shape `[batch_size, 2]` containing
the y, x locations of the center of each window.
centered: An optional `bool`. Defaults to `True`.
indicates if the offset coordinates are centered relative to
the image, in which case the (0, 0) offset is relative to the center
of the input images. If false, the (0,0) offset corresponds to the
upper left corner of the input images.
normalized: An optional `bool`. Defaults to `True`.
indicates if the offset coordinates are normalized.
uniform_noise: An optional `bool`. Defaults to `True`.
indicates if the noise should be generated using a
uniform distribution or a Gaussian distribution.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
A tensor representing the glimpses `[batch_size,
glimpse_height, glimpse_width, channels]`.
"""
if centered is None:
centered = True
centered = _execute.make_bool(centered, "centered")
if normalized is None:
normalized = True
normalized = _execute.make_bool(normalized, "normalized")
if uniform_noise is None:
uniform_noise = True
uniform_noise = _execute.make_bool(uniform_noise, "uniform_noise")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ExtractGlimpse", input=input, size=size, offsets=offsets,
centered=centered, normalized=normalized, uniform_noise=uniform_noise,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("centered", _op.get_attr("centered"), "normalized",
_op.get_attr("normalized"), "uniform_noise",
_op.get_attr("uniform_noise"))
else:
input = _ops.convert_to_tensor(input, _dtypes.float32)
size = _ops.convert_to_tensor(size, _dtypes.int32)
offsets = _ops.convert_to_tensor(offsets, _dtypes.float32)
_inputs_flat = [input, size, offsets]
_attrs = ("centered", centered, "normalized", normalized, "uniform_noise",
uniform_noise)
_result = _execute.execute(b"ExtractGlimpse", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ExtractGlimpse", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def extract_jpeg_shape(contents, output_type=_dtypes.int32, name=None):
r"""Extract the shape information of a JPEG-encoded image.
This op only parses the image header, so it is much faster than DecodeJpeg.
Args:
contents: A `Tensor` of type `string`. 0-D. The JPEG-encoded image.
output_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
(Optional) The output type of the operation (int32 or int64).
Defaults to int32.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `output_type`.
1-D. The image shape with format [height, width, channels].
"""
if output_type is None:
output_type = _dtypes.int32
output_type = _execute.make_type(output_type, "output_type")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ExtractJpegShape", contents=contents, output_type=output_type,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("output_type", _op.get_attr("output_type"))
else:
contents = _ops.convert_to_tensor(contents, _dtypes.string)
_inputs_flat = [contents]
_attrs = ("output_type", output_type)
_result = _execute.execute(b"ExtractJpegShape", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ExtractJpegShape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def hsv_to_rgb(images, name=None):
r"""Convert one or more images from HSV to RGB.
Outputs a tensor of the same shape as the `images` tensor, containing the RGB
value of the pixels. The output is only well defined if the value in `images`
are in `[0,1]`.
See `rgb_to_hsv` for a description of the HSV encoding.
Args:
images: A `Tensor`. Must be one of the following types: `float32`, `float64`.
1-D or higher rank. HSV data to convert. Last dimension must be size 3.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `images`. `images` converted to RGB.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"HSVToRGB", images=images, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx, _dtypes.float32)
_attr_T = _attr_T.as_datatype_enum
_inputs_flat = [images]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"HSVToRGB", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"HSVToRGB", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _non_max_suppression(boxes, scores, max_output_size, iou_threshold=0.5, name=None):
r"""Greedily selects a subset of bounding boxes in descending order of score,
pruning away boxes that have high intersection-over-union (IOU) overlap
with previously selected boxes. Bounding boxes are supplied as
[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
diagonal pair of box corners and the coordinates can be provided as normalized
(i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
is agnostic to where the origin is in the coordinate system. Note that this
algorithm is invariant to orthogonal transformations and translations
of the coordinate system; thus translating or reflections of the coordinate
system result in the same boxes being selected by the algorithm.
The output of this operation is a set of integers indexing into the input
collection of bounding boxes representing the selected boxes. The bounding
box coordinates corresponding to the selected indices can then be obtained
using the `tf.gather operation`. For example:
selected_indices = tf.image.non_max_suppression(
boxes, scores, max_output_size, iou_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
Args:
boxes: A `Tensor` of type `float32`.
A 2-D float tensor of shape `[num_boxes, 4]`.
scores: A `Tensor` of type `float32`.
A 1-D float tensor of shape `[num_boxes]` representing a single
score corresponding to each box (each row of boxes).
max_output_size: A `Tensor` of type `int32`.
A scalar integer tensor representing the maximum number of
boxes to be selected by non max suppression.
iou_threshold: An optional `float`. Defaults to `0.5`.
A float representing the threshold for deciding whether boxes
overlap too much with respect to IOU.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int32`.
A 1-D integer tensor of shape `[M]` representing the selected
indices from the boxes tensor, where `M <= max_output_size`.
"""
if iou_threshold is None:
iou_threshold = 0.5
iou_threshold = _execute.make_float(iou_threshold, "iou_threshold")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"NonMaxSuppression", boxes=boxes, scores=scores,
max_output_size=max_output_size, iou_threshold=iou_threshold,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("iou_threshold", _op.get_attr("iou_threshold"))
else:
boxes = _ops.convert_to_tensor(boxes, _dtypes.float32)
scores = _ops.convert_to_tensor(scores, _dtypes.float32)
max_output_size = _ops.convert_to_tensor(max_output_size, _dtypes.int32)
_inputs_flat = [boxes, scores, max_output_size]
_attrs = ("iou_threshold", iou_threshold)
_result = _execute.execute(b"NonMaxSuppression", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"NonMaxSuppression", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _non_max_suppression_v2(boxes, scores, max_output_size, iou_threshold, name=None):
r"""Greedily selects a subset of bounding boxes in descending order of score,
pruning away boxes that have high intersection-over-union (IOU) overlap
with previously selected boxes. Bounding boxes are supplied as
[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
diagonal pair of box corners and the coordinates can be provided as normalized
(i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm
is agnostic to where the origin is in the coordinate system. Note that this
algorithm is invariant to orthogonal transformations and translations
of the coordinate system; thus translating or reflections of the coordinate
system result in the same boxes being selected by the algorithm.
The output of this operation is a set of integers indexing into the input
collection of bounding boxes representing the selected boxes. The bounding
box coordinates corresponding to the selected indices can then be obtained
using the `tf.gather operation`. For example:
selected_indices = tf.image.non_max_suppression_v2(
boxes, scores, max_output_size, iou_threshold)
selected_boxes = tf.gather(boxes, selected_indices)
Args:
boxes: A `Tensor` of type `float32`.
A 2-D float tensor of shape `[num_boxes, 4]`.
scores: A `Tensor` of type `float32`.
A 1-D float tensor of shape `[num_boxes]` representing a single
score corresponding to each box (each row of boxes).
max_output_size: A `Tensor` of type `int32`.
A scalar integer tensor representing the maximum number of
boxes to be selected by non max suppression.
iou_threshold: A `Tensor` of type `float32`.
A 0-D float tensor representing the threshold for deciding whether
boxes overlap too much with respect to IOU.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int32`.
A 1-D integer tensor of shape `[M]` representing the selected
indices from the boxes tensor, where `M <= max_output_size`.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"NonMaxSuppressionV2", boxes=boxes, scores=scores,
max_output_size=max_output_size, iou_threshold=iou_threshold,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = None
else:
boxes = _ops.convert_to_tensor(boxes, _dtypes.float32)
scores = _ops.convert_to_tensor(scores, _dtypes.float32)
max_output_size = _ops.convert_to_tensor(max_output_size, _dtypes.int32)
iou_threshold = _ops.convert_to_tensor(iou_threshold, _dtypes.float32)
_inputs_flat = [boxes, scores, max_output_size, iou_threshold]
_attrs = None
_result = _execute.execute(b"NonMaxSuppressionV2", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"NonMaxSuppressionV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_quantized_resize_bilinear_outputs = ["resized_images", "out_min", "out_max"]
_QuantizedResizeBilinearOutput = _collections.namedtuple(
"QuantizedResizeBilinear", _quantized_resize_bilinear_outputs)
def quantized_resize_bilinear(images, size, min, max, align_corners=False, name=None):
r"""Resize quantized `images` to `size` using quantized bilinear interpolation.
Input images and output images must be quantized types.
Args:
images: A `Tensor`. Must be one of the following types: `quint8`, `qint32`, `float32`.
4-D with shape `[batch, height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
min: A `Tensor` of type `float32`.
max: A `Tensor` of type `float32`.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale input by (new_height - 1) / (height - 1), which
exactly aligns the 4 corners of images and resized images. If false, rescale
by new_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (resized_images, out_min, out_max).
resized_images: A `Tensor`. Has the same type as `images`. 4-D with shape
`[batch, new_height, new_width, channels]`.
out_min: A `Tensor` of type `float32`.
out_max: A `Tensor` of type `float32`.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizedResizeBilinear", images=images, size=size, min=min, max=max,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int32)
min = _ops.convert_to_tensor(min, _dtypes.float32)
max = _ops.convert_to_tensor(max, _dtypes.float32)
_inputs_flat = [images, size, min, max]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"QuantizedResizeBilinear", 3,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"QuantizedResizeBilinear", _inputs_flat, _attrs, _result, name)
_result = _QuantizedResizeBilinearOutput._make(_result)
return _result
def rgb_to_hsv(images, name=None):
r"""Converts one or more images from RGB to HSV.
Outputs a tensor of the same shape as the `images` tensor, containing the HSV
value of the pixels. The output is only well defined if the value in `images`
are in `[0,1]`.
`output[..., 0]` contains hue, `output[..., 1]` contains saturation, and
`output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0
corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue.
Args:
images: A `Tensor`. Must be one of the following types: `float32`, `float64`.
1-D or higher rank. RGB data to convert. Last dimension must be size 3.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `images`. `images` converted to HSV.
"""
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"RGBToHSV", images=images, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx, _dtypes.float32)
_attr_T = _attr_T.as_datatype_enum
_inputs_flat = [images]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"RGBToHSV", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"RGBToHSV", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _random_crop(image, size, seed=0, seed2=0, name=None):
r"""Randomly crop `image`.
`size` is a 1-D int64 tensor with 2 elements representing the crop height and
width. The values must be non negative.
This Op picks a random location in `image` and crops a `height` by `width`
rectangle from that location. The random location is picked so the cropped
area will fit inside the original image.
Args:
image: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `float32`, `float64`.
3-D of shape `[height, width, channels]`.
size: A `Tensor` of type `int64`.
1-D of length 2 containing: `crop_height`, `crop_width`..
seed: An optional `int`. Defaults to `0`.
If either seed or seed2 are set to be non-zero, the random number
generator is seeded by the given seed. Otherwise, it is seeded by a
random seed.
seed2: An optional `int`. Defaults to `0`.
An second seed to avoid seed collision.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `image`.
3-D of shape `[crop_height, crop_width, channels].`
"""
if seed is None:
seed = 0
seed = _execute.make_int(seed, "seed")
if seed2 is None:
seed2 = 0
seed2 = _execute.make_int(seed2, "seed2")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"RandomCrop", image=image, size=size, seed=seed, seed2=seed2,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "seed", _op.get_attr("seed"), "seed2",
_op.get_attr("seed2"))
else:
_attr_T, (image,) = _execute.args_to_matching_eager([image], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int64)
_inputs_flat = [image, size]
_attrs = ("T", _attr_T, "seed", seed, "seed2", seed2)
_result = _execute.execute(b"RandomCrop", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"RandomCrop", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def resize_area(images, size, align_corners=False, name=None):
r"""Resize `images` to `size` using area interpolation.
Input images can be of different types but output images are always float.
Each output pixel is computed by first transforming the pixel's footprint into
the input tensor and then averaging the pixels that intersect the footprint. An
input pixel's contribution to the average is weighted by the fraction of its
area that intersects the footprint. This is the same as OpenCV's INTER_AREA.
Args:
images: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
4-D with shape `[batch, height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale input by (new_height - 1) / (height - 1), which
exactly aligns the 4 corners of images and resized images. If false, rescale
by new_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. 4-D with shape
`[batch, new_height, new_width, channels]`.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeArea", images=images, size=size, align_corners=align_corners,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int32)
_inputs_flat = [images, size]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeArea", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ResizeArea", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def resize_bicubic(images, size, align_corners=False, name=None):
r"""Resize `images` to `size` using bicubic interpolation.
Input images can be of different types but output images are always float.
Args:
images: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
4-D with shape `[batch, height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale input by (new_height - 1) / (height - 1), which
exactly aligns the 4 corners of images and resized images. If false, rescale
by new_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. 4-D with shape
`[batch, new_height, new_width, channels]`.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeBicubic", images=images, size=size,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int32)
_inputs_flat = [images, size]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeBicubic", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ResizeBicubic", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _resize_bicubic_grad(grads, original_image, align_corners=False, name=None):
r"""Computes the gradient of bicubic interpolation.
Args:
grads: A `Tensor` of type `float32`.
4-D with shape `[batch, height, width, channels]`.
original_image: A `Tensor`. Must be one of the following types: `float32`, `float64`.
4-D with shape `[batch, orig_height, orig_width, channels]`,
The image tensor that was resized.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale grads by (orig_height - 1) / (height - 1), which
exactly aligns the 4 corners of grads and original_image. If false, rescale by
orig_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `original_image`.
4-D with shape `[batch, orig_height, orig_width, channels]`.
Gradients with respect to the input image. Input image must have been
float or double.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeBicubicGrad", grads=grads, original_image=original_image,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (original_image,) = _execute.args_to_matching_eager([original_image], _ctx)
_attr_T = _attr_T.as_datatype_enum
grads = _ops.convert_to_tensor(grads, _dtypes.float32)
_inputs_flat = [grads, original_image]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeBicubicGrad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ResizeBicubicGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def resize_bilinear(images, size, align_corners=False, name=None):
r"""Resize `images` to `size` using bilinear interpolation.
Input images can be of different types but output images are always float.
Args:
images: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
4-D with shape `[batch, height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale input by (new_height - 1) / (height - 1), which
exactly aligns the 4 corners of images and resized images. If false, rescale
by new_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`. 4-D with shape
`[batch, new_height, new_width, channels]`.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeBilinear", images=images, size=size,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int32)
_inputs_flat = [images, size]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeBilinear", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ResizeBilinear", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _resize_bilinear_grad(grads, original_image, align_corners=False, name=None):
r"""Computes the gradient of bilinear interpolation.
Args:
grads: A `Tensor` of type `float32`.
4-D with shape `[batch, height, width, channels]`.
original_image: A `Tensor`. Must be one of the following types: `float32`, `half`, `float64`.
4-D with shape `[batch, orig_height, orig_width, channels]`,
The image tensor that was resized.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale grads by (orig_height - 1) / (height - 1), which
exactly aligns the 4 corners of grads and original_image. If false, rescale by
orig_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `original_image`.
4-D with shape `[batch, orig_height, orig_width, channels]`.
Gradients with respect to the input image. Input image must have been
float or double.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeBilinearGrad", grads=grads, original_image=original_image,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (original_image,) = _execute.args_to_matching_eager([original_image], _ctx)
_attr_T = _attr_T.as_datatype_enum
grads = _ops.convert_to_tensor(grads, _dtypes.float32)
_inputs_flat = [grads, original_image]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeBilinearGrad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ResizeBilinearGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def resize_nearest_neighbor(images, size, align_corners=False, name=None):
r"""Resize `images` to `size` using nearest neighbor interpolation.
Args:
images: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`, `half`, `float32`, `float64`.
4-D with shape `[batch, height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
new size for the images.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale input by (new_height - 1) / (height - 1), which
exactly aligns the 4 corners of images and resized images. If false, rescale
by new_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `images`. 4-D with shape
`[batch, new_height, new_width, channels]`.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeNearestNeighbor", images=images, size=size,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int32)
_inputs_flat = [images, size]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeNearestNeighbor", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ResizeNearestNeighbor", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _resize_nearest_neighbor_grad(grads, size, align_corners=False, name=None):
r"""Computes the gradient of nearest neighbor interpolation.
Args:
grads: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `half`, `float32`, `float64`.
4-D with shape `[batch, height, width, channels]`.
size: A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The
original input size.
align_corners: An optional `bool`. Defaults to `False`.
If true, rescale grads by (orig_height - 1) / (height - 1), which
exactly aligns the 4 corners of grads and original_image. If false, rescale by
orig_height / height. Treat similarly the width dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `grads`.
4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients
with respect to the input image.
"""
if align_corners is None:
align_corners = False
align_corners = _execute.make_bool(align_corners, "align_corners")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"ResizeNearestNeighborGrad", grads=grads, size=size,
align_corners=align_corners, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "align_corners",
_op.get_attr("align_corners"))
else:
_attr_T, (grads,) = _execute.args_to_matching_eager([grads], _ctx)
_attr_T = _attr_T.as_datatype_enum
size = _ops.convert_to_tensor(size, _dtypes.int32)
_inputs_flat = [grads, size]
_attrs = ("T", _attr_T, "align_corners", align_corners)
_result = _execute.execute(b"ResizeNearestNeighborGrad", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ResizeNearestNeighborGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
__sample_distorted_bounding_box_outputs = ["begin", "size", "bboxes"]
_SampleDistortedBoundingBoxOutput = _collections.namedtuple(
"SampleDistortedBoundingBox", __sample_distorted_bounding_box_outputs)
def _sample_distorted_bounding_box(image_size, bounding_boxes, seed=0, seed2=0, min_object_covered=0.1, aspect_ratio_range=[0.75, 1.33], area_range=[0.05, 1], max_attempts=100, use_image_if_no_bounding_boxes=False, name=None):
r"""Generate a single randomly distorted bounding box for an image.
Bounding box annotations are often supplied in addition to ground-truth labels
in image recognition or object localization tasks. A common technique for
training such a system is to randomly distort an image while preserving
its content, i.e. *data augmentation*. This Op outputs a randomly distorted
localization of an object, i.e. bounding box, given an `image_size`,
`bounding_boxes` and a series of constraints.
The output of this Op is a single bounding box that may be used to crop the
original image. The output is returned as 3 tensors: `begin`, `size` and
`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
what the bounding box looks like.
Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
height of the underlying image.
For example,
```python
# Generate a single distorted bounding box.
begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
tf.shape(image),
bounding_boxes=bounding_boxes)
# Draw the bounding box in an image summary.
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
bbox_for_draw)
tf.image_summary('images_with_box', image_with_box)
# Employ the bounding box to distort the image.
distorted_image = tf.slice(image, begin, size)
```
Note that if no bounding box information is available, setting
`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
false and no bounding boxes are supplied, an error is raised.
Args:
image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
1-D, containing `[height, width, channels]`.
bounding_boxes: A `Tensor` of type `float32`.
3-D with shape `[batch, N, 4]` describing the N bounding boxes
associated with the image.
seed: An optional `int`. Defaults to `0`.
If either `seed` or `seed2` are set to non-zero, the random number
generator is seeded by the given `seed`. Otherwise, it is seeded by a random
seed.
seed2: An optional `int`. Defaults to `0`.
A second seed to avoid seed collision.
min_object_covered: An optional `float`. Defaults to `0.1`.
The cropped area of the image must contain at least this
fraction of any bounding box supplied. The value of this parameter should be
non-negative. In the case of 0, the cropped area does not need to overlap
any of the bounding boxes supplied.
aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
The cropped area of the image must have an aspect ratio =
width / height within this range.
area_range: An optional list of `floats`. Defaults to `[0.05, 1]`.
The cropped area of the image must contain a fraction of the
supplied image within in this range.
max_attempts: An optional `int`. Defaults to `100`.
Number of attempts at generating a cropped region of the image
of the specified constraints. After `max_attempts` failures, return the entire
image.
use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
Controls behavior if no bounding boxes supplied.
If true, assume an implicit bounding box covering the whole input. If false,
raise an error.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (begin, size, bboxes).
begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
`tf.slice`.
size: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
`tf.slice`.
bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Provide as input to `tf.image.draw_bounding_boxes`.
"""
if seed is None:
seed = 0
seed = _execute.make_int(seed, "seed")
if seed2 is None:
seed2 = 0
seed2 = _execute.make_int(seed2, "seed2")
if min_object_covered is None:
min_object_covered = 0.1
min_object_covered = _execute.make_float(min_object_covered, "min_object_covered")
if aspect_ratio_range is None:
aspect_ratio_range = [0.75, 1.33]
if not isinstance(aspect_ratio_range, (list, tuple)):
raise TypeError(
"Expected list for 'aspect_ratio_range' argument to "
"'sample_distorted_bounding_box' Op, not %r." % aspect_ratio_range)
aspect_ratio_range = [_execute.make_float(_f, "aspect_ratio_range") for _f in aspect_ratio_range]
if area_range is None:
area_range = [0.05, 1]
if not isinstance(area_range, (list, tuple)):
raise TypeError(
"Expected list for 'area_range' argument to "
"'sample_distorted_bounding_box' Op, not %r." % area_range)
area_range = [_execute.make_float(_f, "area_range") for _f in area_range]
if max_attempts is None:
max_attempts = 100
max_attempts = _execute.make_int(max_attempts, "max_attempts")
if use_image_if_no_bounding_boxes is None:
use_image_if_no_bounding_boxes = False
use_image_if_no_bounding_boxes = _execute.make_bool(use_image_if_no_bounding_boxes, "use_image_if_no_bounding_boxes")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"SampleDistortedBoundingBox", image_size=image_size,
bounding_boxes=bounding_boxes, seed=seed, seed2=seed2,
min_object_covered=min_object_covered,
aspect_ratio_range=aspect_ratio_range, area_range=area_range,
max_attempts=max_attempts,
use_image_if_no_bounding_boxes=use_image_if_no_bounding_boxes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "seed", _op.get_attr("seed"), "seed2",
_op.get_attr("seed2"), "min_object_covered",
_op.get_attr("min_object_covered"), "aspect_ratio_range",
_op.get_attr("aspect_ratio_range"), "area_range",
_op.get_attr("area_range"), "max_attempts",
_op.get_attr("max_attempts"), "use_image_if_no_bounding_boxes",
_op.get_attr("use_image_if_no_bounding_boxes"))
else:
_attr_T, (image_size,) = _execute.args_to_matching_eager([image_size], _ctx)
_attr_T = _attr_T.as_datatype_enum
bounding_boxes = _ops.convert_to_tensor(bounding_boxes, _dtypes.float32)
_inputs_flat = [image_size, bounding_boxes]
_attrs = ("T", _attr_T, "seed", seed, "seed2", seed2,
"min_object_covered", min_object_covered, "aspect_ratio_range",
aspect_ratio_range, "area_range", area_range, "max_attempts",
max_attempts, "use_image_if_no_bounding_boxes",
use_image_if_no_bounding_boxes)
_result = _execute.execute(b"SampleDistortedBoundingBox", 3,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"SampleDistortedBoundingBox", _inputs_flat, _attrs, _result, name)
_result = _SampleDistortedBoundingBoxOutput._make(_result)
return _result
__sample_distorted_bounding_box_v2_outputs = ["begin", "size", "bboxes"]
_SampleDistortedBoundingBoxV2Output = _collections.namedtuple(
"SampleDistortedBoundingBoxV2",
__sample_distorted_bounding_box_v2_outputs)
def _sample_distorted_bounding_box_v2(image_size, bounding_boxes, min_object_covered, seed=0, seed2=0, aspect_ratio_range=[0.75, 1.33], area_range=[0.05, 1], max_attempts=100, use_image_if_no_bounding_boxes=False, name=None):
r"""Generate a single randomly distorted bounding box for an image.
Bounding box annotations are often supplied in addition to ground-truth labels
in image recognition or object localization tasks. A common technique for
training such a system is to randomly distort an image while preserving
its content, i.e. *data augmentation*. This Op outputs a randomly distorted
localization of an object, i.e. bounding box, given an `image_size`,
`bounding_boxes` and a series of constraints.
The output of this Op is a single bounding box that may be used to crop the
original image. The output is returned as 3 tensors: `begin`, `size` and
`bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the
image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize
what the bounding box looks like.
Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The
bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and
height of the underlying image.
For example,
```python
# Generate a single distorted bounding box.
begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box(
tf.shape(image),
bounding_boxes=bounding_boxes)
# Draw the bounding box in an image summary.
image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0),
bbox_for_draw)
tf.image_summary('images_with_box', image_with_box)
# Employ the bounding box to distort the image.
distorted_image = tf.slice(image, begin, size)
```
Note that if no bounding box information is available, setting
`use_image_if_no_bounding_boxes = true` will assume there is a single implicit
bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is
false and no bounding boxes are supplied, an error is raised.
Args:
image_size: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int16`, `int32`, `int64`.
1-D, containing `[height, width, channels]`.
bounding_boxes: A `Tensor` of type `float32`.
3-D with shape `[batch, N, 4]` describing the N bounding boxes
associated with the image.
min_object_covered: A `Tensor` of type `float32`.
The cropped area of the image must contain at least this
fraction of any bounding box supplied. The value of this parameter should be
non-negative. In the case of 0, the cropped area does not need to overlap
any of the bounding boxes supplied.
seed: An optional `int`. Defaults to `0`.
If either `seed` or `seed2` are set to non-zero, the random number
generator is seeded by the given `seed`. Otherwise, it is seeded by a random
seed.
seed2: An optional `int`. Defaults to `0`.
A second seed to avoid seed collision.
aspect_ratio_range: An optional list of `floats`. Defaults to `[0.75, 1.33]`.
The cropped area of the image must have an aspect ratio =
width / height within this range.
area_range: An optional list of `floats`. Defaults to `[0.05, 1]`.
The cropped area of the image must contain a fraction of the
supplied image within in this range.
max_attempts: An optional `int`. Defaults to `100`.
Number of attempts at generating a cropped region of the image
of the specified constraints. After `max_attempts` failures, return the entire
image.
use_image_if_no_bounding_boxes: An optional `bool`. Defaults to `False`.
Controls behavior if no bounding boxes supplied.
If true, assume an implicit bounding box covering the whole input. If false,
raise an error.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (begin, size, bboxes).
begin: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to
`tf.slice`.
size: A `Tensor`. Has the same type as `image_size`. 1-D, containing `[target_height, target_width, -1]`. Provide as input to
`tf.slice`.
bboxes: A `Tensor` of type `float32`. 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Provide as input to `tf.image.draw_bounding_boxes`.
"""
if seed is None:
seed = 0
seed = _execute.make_int(seed, "seed")
if seed2 is None:
seed2 = 0
seed2 = _execute.make_int(seed2, "seed2")
if aspect_ratio_range is None:
aspect_ratio_range = [0.75, 1.33]
if not isinstance(aspect_ratio_range, (list, tuple)):
raise TypeError(
"Expected list for 'aspect_ratio_range' argument to "
"'sample_distorted_bounding_box_v2' Op, not %r." % aspect_ratio_range)
aspect_ratio_range = [_execute.make_float(_f, "aspect_ratio_range") for _f in aspect_ratio_range]
if area_range is None:
area_range = [0.05, 1]
if not isinstance(area_range, (list, tuple)):
raise TypeError(
"Expected list for 'area_range' argument to "
"'sample_distorted_bounding_box_v2' Op, not %r." % area_range)
area_range = [_execute.make_float(_f, "area_range") for _f in area_range]
if max_attempts is None:
max_attempts = 100
max_attempts = _execute.make_int(max_attempts, "max_attempts")
if use_image_if_no_bounding_boxes is None:
use_image_if_no_bounding_boxes = False
use_image_if_no_bounding_boxes = _execute.make_bool(use_image_if_no_bounding_boxes, "use_image_if_no_bounding_boxes")
_ctx = _context.context()
if _ctx.in_graph_mode():
_, _, _op = _op_def_lib._apply_op_helper(
"SampleDistortedBoundingBoxV2", image_size=image_size,
bounding_boxes=bounding_boxes, min_object_covered=min_object_covered,
seed=seed, seed2=seed2, aspect_ratio_range=aspect_ratio_range,
area_range=area_range, max_attempts=max_attempts,
use_image_if_no_bounding_boxes=use_image_if_no_bounding_boxes,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "seed", _op.get_attr("seed"), "seed2",
_op.get_attr("seed2"), "aspect_ratio_range",
_op.get_attr("aspect_ratio_range"), "area_range",
_op.get_attr("area_range"), "max_attempts",
_op.get_attr("max_attempts"), "use_image_if_no_bounding_boxes",
_op.get_attr("use_image_if_no_bounding_boxes"))
else:
_attr_T, (image_size,) = _execute.args_to_matching_eager([image_size], _ctx)
_attr_T = _attr_T.as_datatype_enum
bounding_boxes = _ops.convert_to_tensor(bounding_boxes, _dtypes.float32)
min_object_covered = _ops.convert_to_tensor(min_object_covered, _dtypes.float32)
_inputs_flat = [image_size, bounding_boxes, min_object_covered]
_attrs = ("T", _attr_T, "seed", seed, "seed2", seed2,
"aspect_ratio_range", aspect_ratio_range, "area_range",
area_range, "max_attempts", max_attempts,
"use_image_if_no_bounding_boxes",
use_image_if_no_bounding_boxes)
_result = _execute.execute(b"SampleDistortedBoundingBoxV2", 3,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"SampleDistortedBoundingBoxV2", _inputs_flat, _attrs, _result, name)
_result = _SampleDistortedBoundingBoxV2Output._make(_result)
return _result
def _InitOpDefLibrary(op_list_proto_bytes):
op_list = _op_def_pb2.OpList()
op_list.ParseFromString(op_list_proto_bytes)
_op_def_registry.register_op_list(op_list)
op_def_lib = _op_def_library.OpDefLibrary()
op_def_lib.add_op_list(op_list)
return op_def_lib
# op {
# name: "AdjustContrast"
# input_arg {
# name: "images"
# type_attr: "T"
# }
# input_arg {
# name: "contrast_factor"
# type: DT_FLOAT
# }
# input_arg {
# name: "min_value"
# type: DT_FLOAT
# }
# input_arg {
# name: "max_value"
# type: DT_FLOAT
# }
# output_arg {
# name: "output"
# type: DT_FLOAT
# }
# attr {
# name: "T"
# type: "type"
# allowed_values {
# list {
# type: DT_UINT8
# type: DT_INT8
# type: DT_INT16
# type: DT_INT32
# type: DT_INT64
# type: DT_FLOAT
# type: DT_DOUBLE
# }
# }
# }
# deprecation {
# version: 2
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# name: "use_image_if_no_bounding_boxes"
# type: "bool"
# default_value {
# b: false
# }
# }
# is_stateful: true
# }
_op_def_lib = _InitOpDefLibrary(b"\n\226\001\n\016AdjustContrast\022\013\n\006images\"\001T\022\023\n\017contrast_factor\030\001\022\r\n\tmin_value\030\001\022\r\n\tmax_value\030\001\032\n\n\006output\030\001\"\026\n\001T\022\004type:\013\n\t2\007\004\006\005\003\t\001\002B \010\002\022\034Use AdjustContrastv2 instead\n?\n\020AdjustContrastv2\022\n\n\006images\030\001\022\023\n\017contrast_factor\030\001\032\n\n\006output\030\001\n.\n\tAdjustHue\022\n\n\006images\030\001\022\t\n\005delta\030\001\032\n\n\006output\030\001\n5\n\020AdjustSaturation\022\n\n\006images\030\001\022\t\n\005scale\030\001\032\n\n\006output\030\001\n\267\001\n\rCropAndResize\022\n\n\005image\"\001T\022\t\n\005boxes\030\001\022\013\n\007box_ind\030\003\022\r\n\tcrop_size\030\003\032\t\n\005crops\030\001\"\027\n\001T\022\004type:\014\n\n2\010\004\006\005\003\t\023\001\002\"*\n\006method\022\006string\032\n\022\010bilinear:\014\n\n\022\010bilinear\"#\n\023extrapolation_value\022\005float\032\005%\000\000\000\000\n\230\001\n\026CropAndResizeGradBoxes\022\t\n\005grads\030\001\022\n\n\005image\"\001T\022\t\n\005boxes\030\001\022\013\n\007box_ind\030\003\032\n\n\006output\030\001\"\027\n\001T\022\004type:\014\n\n2\010\004\006\005\003\t\023\001\002\"*\n\006method\022\006string\032\n\022\010bilinear:\014\n\n\022\010bilinear\n\230\001\n\026CropAndResizeGradImage\022\t\n\005grads\030\001\022\t\n\005boxes\030\001\022\013\n\007box_ind\030\003\022\016\n\nimage_size\030\003\032\013\n\006output\"\001T\"\022\n\001T\022\004type:\007\n\0052\003\001\023\002\"*\n\006method\022\006string\032\n\022\010bilinear:\014\n\n\022\010bilinear\n\343\001\n\021DecodeAndCropJpeg\022\014\n\010contents\030\007\022\017\n\013crop_window\030\003\032\t\n\005image\030\004\"\023\n\010channels\022\003int\032\002\030\000\"\020\n\005ratio\022\003int\032\002\030\001\"\033\n\017fancy_upscaling\022\004bool\032\002(\001\"!\n\025try_recover_truncated\022\004bool\032\002(\000\"#\n\023acceptable_fraction\022\005float\032\005%\000\000\200?\"\030\n\ndct_method\022\006string\032\002\022\000\n9\n\tDecodeBmp\022\014\n\010contents\030\007\032\t\n\005image\030\004\"\023\n\010channels\022\003int\032\002\030\000\n$\n\tDecodeGif\022\014\n\010contents\030\007\032\t\n\005image\030\004\n\313\001\n\nDecodeJpeg\022\014\n\010contents\030\007\032\t\n\005image\030\004\"\023\n\010channels\022\003int\032\002\030\000\"\020\n\005ratio\022\003int\032\002\030\001\"\033\n\017fancy_upscaling\022\004bool\032\002(\001\"!\n\025try_recover_truncated\022\004bool\032\002(\000\"#\n\023acceptable_fraction\022\005float\032\005%\000\000\200?\"\030\n\ndct_method\022\006string\032\002\022\000\nY\n\tDecodePng\022\014\n\010contents\030\007\032\016\n\005image\"\005dtype\"\023\n\010channels\022\003int\032\002\030\000\"\031\n\005dtype\022\004type\032\0020\004:\006\n\0042\002\004\021\nO\n\021DrawBoundingBoxes\022\013\n\006images\"\001T\022\t\n\005boxes\030\001\032\013\n\006output\"\001T\"\025\n\001T\022\004type\032\0020\001:\006\n\0042\002\001\023\n\256\002\n\nEncodeJpeg\022\t\n\005image\030\004\032\014\n\010contents\030\007\"*\n\006format\022\006string\032\002\022\000:\024\n\022\022\000\022\tgrayscale\022\003rgb\"\022\n\007quality\022\003int\032\002\030_\"\027\n\013progressive\022\004bool\032\002(\000\"\031\n\roptimize_size\022\004bool\032\002(\000\"\037\n\023chroma_downsampling\022\004bool\032\002(\001\"(\n\014density_unit\022\006string\032\004\022\002in:\n\n\010\022\002in\022\002cm\"\025\n\tx_density\022\003int\032\003\030\254\002\"\025\n\ty_density\022\003int\032\003\030\254\002\"\032\n\014xmp_metadata\022\006string\032\002\022\000\n]\n\tEncodePng\022\n\n\005image\"\001T\032\014\n\010contents\030\007\"\037\n\013compression\022\003int\032\013\030\377\377\377\377\377\377\377\377\377\001\"\025\n\001T\022\004type\032\0020\004:\006\n\0042\002\004\021\n\210\001\n\016ExtractGlimpse\022\t\n\005input\030\001\022\010\n\004size\030\003\022\013\n\007offsets\030\001\032\013\n\007glimpse\030\001\"\024\n\010centered\022\004bool\032\002(\001\"\026\n\nnormalized\022\004bool\032\002(\001\"\031\n\runiform_noise\022\004bool\032\002(\001\n]\n\020ExtractJpegShape\022\014\n\010contents\030\007\032\032\n\013image_shape\"\013output_type\"\037\n\013output_type\022\004type\032\0020\003:\006\n\0042\002\003\t\n;\n\010HSVToRGB\022\013\n\006images\"\001T\032\013\n\006output\"\001T\"\025\n\001T\022\004type\032\0020\001:\006\n\0042\002\001\002\nt\n\021NonMaxSuppression\022\t\n\005boxes\030\001\022\n\n\006scores\030\001\022\023\n\017max_output_size\030\003\032\024\n\020selected_indices\030\003\"\035\n\riou_threshold\022\005float\032\005%\000\000\000?\nj\n\023NonMaxSuppressionV2\022\t\n\005boxes\030\001\022\n\n\006scores\030\001\022\023\n\017max_output_size\030\003\022\021\n\riou_threshold\030\001\032\024\n\020selected_indices\030\003\n\240\001\n\027QuantizedResizeBilinear\022\013\n\006images\"\001T\022\010\n\004size\030\003\022\007\n\003min\030\001\022\007\n\003max\030\001\032\023\n\016resized_images\"\001T\032\013\n\007out_min\030\001\032\013\n\007out_max\030\001\"\022\n\001T\022\004type:\007\n\0052\003\014\r\001\"\031\n\ralign_corners\022\004bool\032\002(\000\n;\n\010RGBToHSV\022\013\n\006images\"\001T\032\013\n\006output\"\001T\"\025\n\001T\022\004type\032\0020\001:\006\n\0042\002\001\002\n\221\001\n\nRandomCrop\022\n\n\005image\"\001T\022\010\n\004size\030\t\032\013\n\006output\"\001T\"\026\n\001T\022\004type:\013\n\t2\007\004\006\005\003\t\001\002\"\017\n\004seed\022\003int\032\002\030\000\"\020\n\005seed2\022\003int\032\002\030\000B\"\010\010\022\036Random crop is now pure Python\210\001\001\nk\n\nResizeArea\022\013\n\006images\"\001T\022\010\n\004size\030\003\032\022\n\016resized_images\030\001\"\027\n\001T\022\004type:\014\n\n2\010\004\006\005\003\t\023\001\002\"\031\n\ralign_corners\022\004bool\032\002(\000\nn\n\rResizeBicubic\022\013\n\006images\"\001T\022\010\n\004size\030\003\032\022\n\016resized_images\030\001\"\027\n\001T\022\004type:\014\n\n2\010\004\006\005\003\t\023\001\002\"\031\n\ralign_corners\022\004bool\032\002(\000\nn\n\021ResizeBicubicGrad\022\t\n\005grads\030\001\022\023\n\016original_image\"\001T\032\013\n\006output\"\001T\"\021\n\001T\022\004type:\006\n\0042\002\001\002\"\031\n\ralign_corners\022\004bool\032\002(\000\no\n\016ResizeBilinear\022\013\n\006images\"\001T\022\010\n\004size\030\003\032\022\n\016resized_images\030\001\"\027\n\001T\022\004type:\014\n\n2\010\004\006\005\003\t\023\001\002\"\031\n\ralign_corners\022\004bool\032\002(\000\np\n\022ResizeBilinearGrad\022\t\n\005grads\030\001\022\023\n\016original_image\"\001T\032\013\n\006output\"\001T\"\022\n\001T\022\004type:\007\n\0052\003\001\023\002\"\031\n\ralign_corners\022\004bool\032\002(\000\nw\n\025ResizeNearestNeighbor\022\013\n\006images\"\001T\022\010\n\004size\030\003\032\023\n\016resized_images\"\001T\"\027\n\001T\022\004type:\014\n\n2\010\004\006\005\003\t\023\001\002\"\031\n\ralign_corners\022\004bool\032\002(\000\np\n\031ResizeNearestNeighborGrad\022\n\n\005grads\"\001T\022\010\n\004size\030\003\032\013\n\006output\"\001T\"\025\n\001T\022\004type:\n\n\0102\006\004\006\003\023\001\002\"\031\n\ralign_corners\022\004bool\032\002(\000\n\343\002\n\032SampleDistortedBoundingBox\022\017\n\nimage_size\"\001T\022\022\n\016bounding_boxes\030\001\032\n\n\005begin\"\001T\032\t\n\004size\"\001T\032\n\n\006bboxes\030\001\"\024\n\001T\022\004type:\t\n\0072\005\004\006\005\003\t\"\017\n\004seed\022\003int\032\002\030\000\"\020\n\005seed2\022\003int\032\002\030\000\"\"\n\022min_object_covered\022\005float\032\005%\315\314\314=\"/\n\022aspect_ratio_range\022\013list(float)\032\014\n\n\"\010\000\000@?q=\252?\"\'\n\narea_range\022\013list(float)\032\014\n\n\"\010\315\314L=\000\000\200?\"\027\n\014max_attempts\022\003int\032\002\030d\"*\n\036use_image_if_no_bounding_boxes\022\004bool\032\002(\000\210\001\001\n\331\002\n\034SampleDistortedBoundingBoxV2\022\017\n\nimage_size\"\001T\022\022\n\016bounding_boxes\030\001\022\026\n\022min_object_covered\030\001\032\n\n\005begin\"\001T\032\t\n\004size\"\001T\032\n\n\006bboxes\030\001\"\024\n\001T\022\004type:\t\n\0072\005\004\006\005\003\t\"\017\n\004seed\022\003int\032\002\030\000\"\020\n\005seed2\022\003int\032\002\030\000\"/\n\022aspect_ratio_range\022\013list(float)\032\014\n\n\"\010\000\000@?q=\252?\"\'\n\narea_range\022\013list(float)\032\014\n\n\"\010\315\314L=\000\000\200?\"\027\n\014max_attempts\022\003int\032\002\030d\"*\n\036use_image_if_no_bounding_boxes\022\004bool\032\002(\000\210\001\001")
|
nilq/baby-python
|
python
|
"""
Copyright 2018 Ederson Bilhante
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import logging
import time
from random import choice
from splinter import Browser
from .email import tempmailaddress
LOGGER = logging.getLogger(__file__)
class HappnCredits(object):
def __init__(self, name, lastname, password, sponsorship):
self.name = name
self.lastname = lastname
self.password = password
self.sponsorship = sponsorship
self.email = None
with Browser('chrome') as fb:
self.fb_browser = fb
self.get_new_email()
self.fb()
self.confirm_email_fb()
self.happn()
self.fb_browser.quit()
def fb(self):
LOGGER.info('Starting Facebook')
url = 'https://www.facebook.com'
LOGGER.info('Visiting https://www.facebook.com')
self.fb_browser.visit(url)
time.sleep(5)
field = self.fb_browser.find_by_name('firstname')
if field:
LOGGER.info('Filling Name')
field.fill(self.name)
time.sleep(1)
else:
raise Exception('Error')
field = self.fb_browser.find_by_name('lastname')
if field:
LOGGER.info('Filling lastname')
field.fill(self.lastname)
time.sleep(1)
else:
raise Exception('Error')
field = self.fb_browser.find_by_name('reg_email__')
if field:
LOGGER.info('Filling email')
field.type(self.email)
time.sleep(1)
else:
raise Exception('Error')
field = self.fb_browser.find_by_name('reg_email_confirmation__')
if field:
LOGGER.info('Filling confirm email')
field.type(self.email)
time.sleep(1)
else:
raise Exception('Error')
field = self.fb_browser.find_by_name('reg_passwd__')
if field:
LOGGER.info('Filling password')
field.fill(self.password)
time.sleep(1)
else:
raise Exception('Error')
field = self.fb_browser.find_by_name('birthday_day')
if field:
LOGGER.info('Set Bday')
day = choice(range(1, 20))
self.fb_browser.select('birthday_day', day)
time.sleep(1)
else:
raise Exception('Error')
field = self.fb_browser.find_by_name('sex')
if field:
LOGGER.info('Set Sex')
self.fb_browser.choose('sex', '1')
time.sleep(1)
else:
raise Exception('Error')
button = self.fb_browser.find_by_name('websubmit')
if button:
LOGGER.info('Send command')
button.first.click()
time.sleep(10)
else:
raise Exception('Error')
def happn(self):
LOGGER.info('Starting happn')
url = 'https://www.happn.com/invite/{}'.format(self.sponsorship)
LOGGER.info('Visiting www.happn.com/invite')
self.fb_browser.visit(url)
time.sleep(1)
LOGGER.info('Accepting cookie')
self.fb_browser.find_by_css('#cookie-button')[0].click()
LOGGER.info('Registering')
self.fb_browser.find_by_css('.button-register')[0].click()
time.sleep(5)
happn_window = self.fb_browser.windows.current
self.fb_browser.windows.current = self.fb_browser.windows.current.next
time.sleep(2)
LOGGER.info('Confirming')
self.fb_browser.find_by_name('__CONFIRM__')[0].click()
self.fb_browser.windows.current = happn_window
time.sleep(2)
self.fb_browser.find_by_css('.submit')[0].click()
LOGGER.info('Downloading happn')
LOGGER.info('Finishing happn')
time.sleep(10)
def get_new_email(self):
tempmailaddress.get_new_email(self)
def confirm_email_fb(self):
tempmailaddress.confirm_email_fb(self)
|
nilq/baby-python
|
python
|
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from einops import rearrange
from torch import Tensor
device = "cuda" if torch.cuda.is_available() else "cpu"
DEVICE = device
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
self.max_len = max_len
self.d_model = d_model
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self) -> Tensor:
x = self.pe[0, : self.max_len]
return self.dropout(x).unsqueeze(0)
class ResNetFeatureExtractor(nn.Module):
def __init__(self):
super().__init__()
# Making the resnet 50 model, which was used in the docformer for the purpose of visual feature extraction
resnet50 = models.resnet50(pretrained=False)
modules = list(resnet50.children())[:-2]
self.resnet50 = nn.Sequential(*modules)
# Applying convolution and linear layer
self.conv1 = nn.Conv2d(2048, 768, 1)
self.relu1 = F.relu
self.linear1 = nn.Linear(192, 512)
def forward(self, x):
x = self.resnet50(x)
x = self.conv1(x)
x = self.relu1(x)
x = rearrange(x, "b e w h -> b e (w h)") # b -> batch, e -> embedding dim, w -> width, h -> height
x = self.linear1(x)
x = rearrange(x, "b e s -> b s e") # b -> batch, e -> embedding dim, s -> sequence length
return x
class DocFormerEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super(DocFormerEmbeddings, self).__init__()
self.config = config
self.position_embeddings_v = PositionalEncoding(
d_model=config["hidden_size"],
dropout=0.1,
max_len=config["max_position_embeddings"],
)
self.x_topleft_position_embeddings_v = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.x_bottomright_position_embeddings_v = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.w_position_embeddings_v = nn.Embedding(config["max_2d_position_embeddings"], config["shape_size"])
self.x_topleft_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.x_bottomleft_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.x_topright_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.x_bottomright_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.x_centroid_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_topleft_position_embeddings_v = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.y_bottomright_position_embeddings_v = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.h_position_embeddings_v = nn.Embedding(config["max_2d_position_embeddings"], config["shape_size"])
self.y_topleft_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_bottomleft_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_topright_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_bottomright_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_centroid_distance_to_prev_embeddings_v = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.position_embeddings_t = PositionalEncoding(
d_model=config["hidden_size"],
dropout=0.1,
max_len=config["max_position_embeddings"],
)
self.x_topleft_position_embeddings_t = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.x_bottomright_position_embeddings_t = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.w_position_embeddings_t = nn.Embedding(config["max_2d_position_embeddings"], config["shape_size"])
self.x_topleft_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"]+1, config["shape_size"])
self.x_bottomleft_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"]+1, config["shape_size"])
self.x_topright_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.x_bottomright_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.x_centroid_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_topleft_position_embeddings_t = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.y_bottomright_position_embeddings_t = nn.Embedding(config["max_2d_position_embeddings"], config["coordinate_size"])
self.h_position_embeddings_t = nn.Embedding(config["max_2d_position_embeddings"], config["shape_size"])
self.y_topleft_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_bottomleft_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_topright_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_bottomright_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.y_centroid_distance_to_prev_embeddings_t = nn.Embedding(2*config["max_2d_position_embeddings"] + 1, config["shape_size"])
self.LayerNorm = nn.LayerNorm(config["hidden_size"], eps=config["layer_norm_eps"])
self.dropout = nn.Dropout(config["hidden_dropout_prob"])
def forward(self, x_feature, y_feature):
"""
Arguments:
x_features of shape, (batch size, seq_len, 8)
y_features of shape, (batch size, seq_len, 8)
Outputs:
(V-bar-s, T-bar-s) of shape (batch size, 512,768),(batch size, 512,768)
What are the features:
0 -> top left x/y
1 -> bottom right x/y
2 -> width/height
3 -> diff top left x/y
4 -> diff bottom left x/y
5 -> diff top right x/y
6 -> diff bottom right x/y
7 -> centroids diff x/y
"""
batch, seq_len = x_feature.shape[:-1]
hidden_size = self.config["hidden_size"]
num_feat = x_feature.shape[-1]
sub_dim = hidden_size // num_feat
# Clamping and adding a bias for handling negative values
x_feature[:,:,3:] = torch.clamp(x_feature[:,:,3:],-self.config["max_2d_position_embeddings"],self.config["max_2d_position_embeddings"])
x_feature[:,:,3:]+= self.config["max_2d_position_embeddings"]
y_feature[:,:,3:] = torch.clamp(y_feature[:,:,3:],-self.config["max_2d_position_embeddings"],self.config["max_2d_position_embeddings"])
y_feature[:,:,3:]+= self.config["max_2d_position_embeddings"]
x_topleft_position_embeddings_v = self.x_topleft_position_embeddings_v(x_feature[:,:,0])
x_bottomright_position_embeddings_v = self.x_bottomright_position_embeddings_v(x_feature[:,:,1])
w_position_embeddings_v = self.w_position_embeddings_v(x_feature[:,:,2])
x_topleft_distance_to_prev_embeddings_v = self.x_topleft_distance_to_prev_embeddings_v(x_feature[:,:,3])
x_bottomleft_distance_to_prev_embeddings_v = self.x_bottomleft_distance_to_prev_embeddings_v(x_feature[:,:,4])
x_topright_distance_to_prev_embeddings_v = self.x_topright_distance_to_prev_embeddings_v(x_feature[:,:,5])
x_bottomright_distance_to_prev_embeddings_v = self.x_bottomright_distance_to_prev_embeddings_v(x_feature[:,:,6])
x_centroid_distance_to_prev_embeddings_v = self.x_centroid_distance_to_prev_embeddings_v(x_feature[:,:,7])
x_calculated_embedding_v = torch.cat(
[
x_topleft_position_embeddings_v,
x_bottomright_position_embeddings_v,
w_position_embeddings_v,
x_topleft_distance_to_prev_embeddings_v,
x_bottomleft_distance_to_prev_embeddings_v,
x_topright_distance_to_prev_embeddings_v,
x_bottomright_distance_to_prev_embeddings_v ,
x_centroid_distance_to_prev_embeddings_v
],
dim = -1
)
y_topleft_position_embeddings_v = self.y_topleft_position_embeddings_v(y_feature[:,:,0])
y_bottomright_position_embeddings_v = self.y_bottomright_position_embeddings_v(y_feature[:,:,1])
h_position_embeddings_v = self.h_position_embeddings_v(y_feature[:,:,2])
y_topleft_distance_to_prev_embeddings_v = self.y_topleft_distance_to_prev_embeddings_v(y_feature[:,:,3])
y_bottomleft_distance_to_prev_embeddings_v = self.y_bottomleft_distance_to_prev_embeddings_v(y_feature[:,:,4])
y_topright_distance_to_prev_embeddings_v = self.y_topright_distance_to_prev_embeddings_v(y_feature[:,:,5])
y_bottomright_distance_to_prev_embeddings_v = self.y_bottomright_distance_to_prev_embeddings_v(y_feature[:,:,6])
y_centroid_distance_to_prev_embeddings_v = self.y_centroid_distance_to_prev_embeddings_v(y_feature[:,:,7])
x_calculated_embedding_v = torch.cat(
[
x_topleft_position_embeddings_v,
x_bottomright_position_embeddings_v,
w_position_embeddings_v,
x_topleft_distance_to_prev_embeddings_v,
x_bottomleft_distance_to_prev_embeddings_v,
x_topright_distance_to_prev_embeddings_v,
x_bottomright_distance_to_prev_embeddings_v ,
x_centroid_distance_to_prev_embeddings_v
],
dim = -1
)
y_calculated_embedding_v = torch.cat(
[
y_topleft_position_embeddings_v,
y_bottomright_position_embeddings_v,
h_position_embeddings_v,
y_topleft_distance_to_prev_embeddings_v,
y_bottomleft_distance_to_prev_embeddings_v,
y_topright_distance_to_prev_embeddings_v,
y_bottomright_distance_to_prev_embeddings_v ,
y_centroid_distance_to_prev_embeddings_v
],
dim = -1
)
v_bar_s = x_calculated_embedding_v + y_calculated_embedding_v + self.position_embeddings_v()
x_topleft_position_embeddings_t = self.x_topleft_position_embeddings_t(x_feature[:,:,0])
x_bottomright_position_embeddings_t = self.x_bottomright_position_embeddings_t(x_feature[:,:,1])
w_position_embeddings_t = self.w_position_embeddings_t(x_feature[:,:,2])
x_topleft_distance_to_prev_embeddings_t = self.x_topleft_distance_to_prev_embeddings_t(x_feature[:,:,3])
x_bottomleft_distance_to_prev_embeddings_t = self.x_bottomleft_distance_to_prev_embeddings_t(x_feature[:,:,4])
x_topright_distance_to_prev_embeddings_t = self.x_topright_distance_to_prev_embeddings_t(x_feature[:,:,5])
x_bottomright_distance_to_prev_embeddings_t = self.x_bottomright_distance_to_prev_embeddings_t(x_feature[:,:,6])
x_centroid_distance_to_prev_embeddings_t = self.x_centroid_distance_to_prev_embeddings_t(x_feature[:,:,7])
x_calculated_embedding_t = torch.cat(
[
x_topleft_position_embeddings_t,
x_bottomright_position_embeddings_t,
w_position_embeddings_t,
x_topleft_distance_to_prev_embeddings_t,
x_bottomleft_distance_to_prev_embeddings_t,
x_topright_distance_to_prev_embeddings_t,
x_bottomright_distance_to_prev_embeddings_t ,
x_centroid_distance_to_prev_embeddings_t
],
dim = -1
)
y_topleft_position_embeddings_t = self.y_topleft_position_embeddings_t(y_feature[:,:,0])
y_bottomright_position_embeddings_t = self.y_bottomright_position_embeddings_t(y_feature[:,:,1])
h_position_embeddings_t = self.h_position_embeddings_t(y_feature[:,:,2])
y_topleft_distance_to_prev_embeddings_t = self.y_topleft_distance_to_prev_embeddings_t(y_feature[:,:,3])
y_bottomleft_distance_to_prev_embeddings_t = self.y_bottomleft_distance_to_prev_embeddings_t(y_feature[:,:,4])
y_topright_distance_to_prev_embeddings_t = self.y_topright_distance_to_prev_embeddings_t(y_feature[:,:,5])
y_bottomright_distance_to_prev_embeddings_t = self.y_bottomright_distance_to_prev_embeddings_t(y_feature[:,:,6])
y_centroid_distance_to_prev_embeddings_t = self.y_centroid_distance_to_prev_embeddings_t(y_feature[:,:,7])
x_calculated_embedding_t = torch.cat(
[
x_topleft_position_embeddings_t,
x_bottomright_position_embeddings_t,
w_position_embeddings_t,
x_topleft_distance_to_prev_embeddings_t,
x_bottomleft_distance_to_prev_embeddings_t,
x_topright_distance_to_prev_embeddings_t,
x_bottomright_distance_to_prev_embeddings_t ,
x_centroid_distance_to_prev_embeddings_t
],
dim = -1
)
y_calculated_embedding_t = torch.cat(
[
y_topleft_position_embeddings_t,
y_bottomright_position_embeddings_t,
h_position_embeddings_t,
y_topleft_distance_to_prev_embeddings_t,
y_bottomleft_distance_to_prev_embeddings_t,
y_topright_distance_to_prev_embeddings_t,
y_bottomright_distance_to_prev_embeddings_t ,
y_centroid_distance_to_prev_embeddings_t
],
dim = -1
)
t_bar_s = x_calculated_embedding_t + y_calculated_embedding_t + self.position_embeddings_t()
return v_bar_s, t_bar_s
# fmt: off
class PreNorm(nn.Module):
def __init__(self, dim, fn):
# Fig 1: http://proceedings.mlr.press/v119/xiong20b/xiong20b.pdf
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class PreNormAttn(nn.Module):
def __init__(self, dim, fn):
# Fig 1: http://proceedings.mlr.press/v119/xiong20b/xiong20b.pdf
super().__init__()
self.norm_t_bar = nn.LayerNorm(dim)
self.norm_v_bar = nn.LayerNorm(dim)
self.norm_t_bar_s = nn.LayerNorm(dim)
self.norm_v_bar_s = nn.LayerNorm(dim)
self.fn = fn
def forward(self, t_bar, v_bar, t_bar_s, v_bar_s, **kwargs):
return self.fn(self.norm_t_bar(t_bar),
self.norm_v_bar(v_bar),
self.norm_t_bar_s(t_bar_s),
self.norm_v_bar_s(v_bar_s), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout=0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class RelativePosition(nn.Module):
def __init__(self, num_units, max_relative_position, max_seq_length):
super().__init__()
self.num_units = num_units
self.max_relative_position = max_relative_position
self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
self.max_length = max_seq_length
range_vec_q = torch.arange(max_seq_length)
range_vec_k = torch.arange(max_seq_length)
distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
final_mat = distance_mat_clipped + self.max_relative_position
self.final_mat = torch.LongTensor(final_mat)
nn.init.xavier_uniform_(self.embeddings_table)
def forward(self, length_q, length_k):
embeddings = self.embeddings_table[self.final_mat[:length_q, :length_k]]
return embeddings
class MultiModalAttentionLayer(nn.Module):
def __init__(self, embed_dim, n_heads, max_relative_position, max_seq_length, dropout):
super().__init__()
assert embed_dim % n_heads == 0
self.embed_dim = embed_dim
self.n_heads = n_heads
self.head_dim = embed_dim // n_heads
self.relative_positions_text = RelativePosition(self.head_dim, max_relative_position, max_seq_length)
self.relative_positions_img = RelativePosition(self.head_dim, max_relative_position, max_seq_length)
# text qkv embeddings
self.fc_k_text = nn.Linear(embed_dim, embed_dim)
self.fc_q_text = nn.Linear(embed_dim, embed_dim)
self.fc_v_text = nn.Linear(embed_dim, embed_dim)
# image qkv embeddings
self.fc_k_img = nn.Linear(embed_dim, embed_dim)
self.fc_q_img = nn.Linear(embed_dim, embed_dim)
self.fc_v_img = nn.Linear(embed_dim, embed_dim)
# spatial qk embeddings (shared for visual and text)
self.fc_k_spatial = nn.Linear(embed_dim, embed_dim)
self.fc_q_spatial = nn.Linear(embed_dim, embed_dim)
self.dropout = nn.Dropout(dropout)
self.to_out = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.Dropout(dropout)
)
self.scale = torch.sqrt(torch.FloatTensor([embed_dim]))
def forward(self, text_feat, img_feat, text_spatial_feat, img_spatial_feat):
text_feat = text_feat
img_feat = img_feat
text_spatial_feat = text_spatial_feat
img_spatial_feat = img_spatial_feat
seq_length = text_feat.shape[1]
# self attention of text
# b -> batch, t -> time steps (l -> length has same meaning), head -> # of heads, k -> head dim.
key_text_nh = rearrange(self.fc_k_text(text_feat), 'b t (head k) -> head b t k', head=self.n_heads).to(DEVICE)
query_text_nh = rearrange(self.fc_q_text(text_feat), 'b l (head k) -> head b l k', head=self.n_heads).to(DEVICE)
value_text_nh = rearrange(self.fc_v_text(text_feat), 'b t (head k) -> head b t k', head=self.n_heads).to(DEVICE)
dots_text = torch.einsum('hblk,hbtk->hblt', query_text_nh, key_text_nh) / self.scale.to(DEVICE)
# 1D relative positions (query, key)
rel_pos_embed_text = self.relative_positions_text(seq_length, seq_length)
rel_pos_key_text = torch.einsum('bhrd,lrd->bhlr', key_text_nh, rel_pos_embed_text)
rel_pos_query_text = torch.einsum('bhld,lrd->bhlr', query_text_nh, rel_pos_embed_text)
# shared spatial <-> text hidden features
key_spatial_text = self.fc_k_spatial(text_spatial_feat)
query_spatial_text = self.fc_q_spatial(text_spatial_feat)
key_spatial_text_nh = rearrange(key_spatial_text, 'b t (head k) -> head b t k', head=self.n_heads)
query_spatial_text_nh = rearrange(query_spatial_text, 'b l (head k) -> head b l k', head=self.n_heads)
dots_text_spatial = torch.einsum('hblk,hbtk->hblt', query_spatial_text_nh, key_spatial_text_nh) / self.scale.to(DEVICE)
# Line 38 of pseudo-code
text_attn_scores = dots_text + rel_pos_key_text + rel_pos_query_text + dots_text_spatial
# self-attention of image
key_img_nh = rearrange(self.fc_k_img(img_feat), 'b t (head k) -> head b t k', head=self.n_heads).to(DEVICE)
query_img_nh = rearrange(self.fc_q_img(img_feat), 'b l (head k) -> head b l k', head=self.n_heads).to(DEVICE)
value_img_nh = rearrange(self.fc_v_img(img_feat), 'b t (head k) -> head b t k', head=self.n_heads).to(DEVICE)
dots_img = torch.einsum('hblk,hbtk->hblt', query_img_nh, key_img_nh) / self.scale.to(DEVICE)
# 1D relative positions (query, key)
rel_pos_embed_img = self.relative_positions_img(seq_length, seq_length)
rel_pos_key_img = torch.einsum('bhrd,lrd->bhlr', key_img_nh, rel_pos_embed_text)
rel_pos_query_img = torch.einsum('bhld,lrd->bhlr', query_img_nh, rel_pos_embed_text)
# shared spatial <-> image features
key_spatial_img = self.fc_k_spatial(img_spatial_feat)
query_spatial_img = self.fc_q_spatial(img_spatial_feat)
key_spatial_img_nh = rearrange(key_spatial_img, 'b t (head k) -> head b t k', head=self.n_heads)
query_spatial_img_nh = rearrange(query_spatial_img, 'b l (head k) -> head b l k', head=self.n_heads)
dots_img_spatial = torch.einsum('hblk,hbtk->hblt', query_spatial_img_nh, key_spatial_img_nh) / self.scale.to(DEVICE)
# Line 59 of pseudo-code
img_attn_scores = dots_img + rel_pos_key_img + rel_pos_query_img + dots_img_spatial
text_attn_probs = self.dropout(torch.softmax(text_attn_scores, dim=-1))
img_attn_probs = self.dropout(torch.softmax(img_attn_scores, dim=-1))
text_context = torch.einsum('hblt,hbtv->hblv', text_attn_probs, value_text_nh)
img_context = torch.einsum('hblt,hbtv->hblv', img_attn_probs, value_img_nh)
context = text_context + img_context
embeddings = rearrange(context, 'head b t d -> b t (head d)')
return self.to_out(embeddings)
class DocFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([])
for _ in range(config['num_hidden_layers']):
encoder_block = nn.ModuleList([
PreNormAttn(config['hidden_size'],
MultiModalAttentionLayer(config['hidden_size'],
config['num_attention_heads'],
config['max_relative_positions'],
config['max_position_embeddings'],
config['hidden_dropout_prob'],
)
),
PreNorm(config['hidden_size'],
FeedForward(config['hidden_size'],
config['hidden_size'] * config['intermediate_ff_size_factor'],
dropout=config['hidden_dropout_prob']))
])
self.layers.append(encoder_block)
def forward(
self,
text_feat, # text feat or output from last encoder block
img_feat,
text_spatial_feat,
img_spatial_feat,
):
# Fig 1 encoder part (skip conn for both attn & FF): https://arxiv.org/abs/1706.03762
# TODO: ensure 1st skip conn (var "skip") in such a multimodal setting makes sense (most likely does)
for attn, ff in self.layers:
skip = text_feat + img_feat + text_spatial_feat + img_spatial_feat
x = attn(text_feat, img_feat, text_spatial_feat, img_spatial_feat) + skip
x = ff(x) + x
text_feat = x
return x
class LanguageFeatureExtractor(nn.Module):
def __init__(self):
super().__init__()
from transformers import LayoutLMForTokenClassification
layoutlm_dummy = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=1)
self.embedding_vector = nn.Embedding.from_pretrained(layoutlm_dummy.layoutlm.embeddings.word_embeddings.weight)
def forward(self, x):
return self.embedding_vector(x)
class ExtractFeatures(nn.Module):
'''
Inputs: dictionary
Output: v_bar, t_bar, v_bar_s, t_bar_s
'''
def __init__(self, config):
super().__init__()
self.visual_feature = ResNetFeatureExtractor()
self.language_feature = LanguageFeatureExtractor()
self.spatial_feature = DocFormerEmbeddings(config)
def forward(self, encoding):
image = encoding['resized_scaled_img']
language = encoding['input_ids']
x_feature = encoding['x_features']
y_feature = encoding['y_features']
v_bar = self.visual_feature(image)
t_bar = self.language_feature(language)
v_bar_s, t_bar_s = self.spatial_feature(x_feature, y_feature)
return v_bar, t_bar, v_bar_s, t_bar_s
class DocFormer(nn.Module):
'''
Easy boiler plate, because this model will just take as an input, the dictionary which is obtained from create_features function
'''
def __init__(self, config):
super().__init__()
self.config = config
self.extract_feature = ExtractFeatures(config)
self.encoder = DocFormerEncoder(config)
self.dropout = nn.Dropout(config['hidden_dropout_prob'])
def forward(self, x ,use_tdi=False):
v_bar, t_bar, v_bar_s, t_bar_s = self.extract_feature(x,use_tdi)
features = {'v_bar': v_bar, 't_bar': t_bar, 'v_bar_s': v_bar_s, 't_bar_s': t_bar_s}
output = self.encoder(features['t_bar'], features['v_bar'], features['t_bar_s'], features['v_bar_s'])
output = self.dropout(output)
return output
|
nilq/baby-python
|
python
|
# teste = list()
# teste.append('Henrique')
# teste.append(15)
#
# galera = list()
# galera.append(teste[:])
#
# teste[0] = 'Maria'
# teste[1] = 22
#
# galera.append(teste[:])
# print(galera)
galera = [['João', 19], ['Alana', 16], ['Maria', 33], ['Pedro', 25]]
for p in galera:
print(f'Nome: {p[0]} \nIdade: {p[1]}')
print('')
|
nilq/baby-python
|
python
|
from django.db import IntegrityError
from django.db.models import Q
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from cajas.boxes.services.box_daily_square_manager import BoxDailySquareManager
from cajas.users.models.employee import Employee
from cajas.users.models.partner import Partner
from cajas.users.services.user_service import UserManager
from cajas.users.services.partner_service import PartnerManager
from cajas.movement.services.partner_service import MovementPartnerManager
from cajas.movement.services.daily_square_service import MovementDailySquareManager
from cajas.office.models.officeCountry import OfficeCountry
user_manager = UserManager()
class UserCreate(APIView):
"""
"""
def post(self, request, format=None):
user_manager.create_user(request.data)
return Response(
'Se ha creado el usuario exitosamente.',
status=status.HTTP_201_CREATED
)
|
nilq/baby-python
|
python
|
#!/usr/bin/env python3
# Copyright (C) 2018 Adrian Herrera
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
Inspects the current operating system and determines which Clang binary to
download. If a valid version is found, the suffix of the file to download is
printed to stdout. Otherwise an error message is printed to stderr.
Note: This script is only really meant to be used by the S2E Makefile. It has
no real use outside of this.
"""
import distro
import sys
# Supported operating systems for Clang binary downloads
SUPPORTED_OS = ('ubuntu', 'debian')
def eprint(*args, **kwargs):
"""Print to stderr and exit."""
print(*args, file=sys.stderr, **kwargs)
sys.exit(1)
def _get_debian_version(version_string):
"""
Determine the Clang binary to download from the version string returned by
``distro.linux_distribution``.
"""
version = int(version_string)
if version >= 8:
return 'x86_64-linux-gnu-debian8'
else:
return None
def _get_ubuntu_version(version_string):
"""
Determine the Clang binary to downoad from the version string returned by
``distro.linux_distribution``.
"""
major_version, minor_version = list(map(int, version_string.split('.')))
if major_version == 14 and minor_version >= 4:
return 'x86_64-linux-gnu-ubuntu-14.04',
elif major_version == 15:
return 'x86_64-linux-gnu-ubuntu-14.04',
elif major_version == 16 and minor_version >= 4:
return 'x86_64-linux-gnu-ubuntu-16.04',
elif major_version == 18:
return 'x86_64-linux-gnu-ubuntu-18.04',
elif major_version == 20:
return 'x86_64-linux-gnu-ubuntu-18.04',
else:
return None
def main():
"""The main function."""
name = distro.name()
version = distro.version()
clang_ver_to_download = None
if name.lower() == 'darwin':
clang_ver_to_download = 'x86_64-darwin-apple'
elif name.lower() == 'debian':
clang_ver_to_download = _get_debian_version(version)
elif name.lower() == 'ubuntu':
clang_ver_to_download = _get_ubuntu_version(version)
else:
eprint('Linux distro %s is not supported' % name)
if clang_ver_to_download:
print('%s' % clang_ver_to_download)
else:
eprint('%s %s is not supported' % (distro, version))
if __name__ == '__main__':
main()
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.12.8)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
\x00\x00\xb4\xdf\
\xff\
\xd8\xff\xe0\x00\x10\x4a\x46\x49\x46\x00\x01\x01\x00\x00\x01\x00\
\x01\x00\x00\xff\xdb\x00\x43\x00\x05\x03\x04\x04\x04\x03\x05\x04\
\x04\x04\x05\x05\x05\x06\x07\x0c\x08\x07\x07\x07\x07\x0f\x0b\x0b\
\x09\x0c\x11\x0f\x12\x12\x11\x0f\x11\x11\x13\x16\x1c\x17\x13\x14\
\x1a\x15\x11\x11\x18\x21\x18\x1a\x1d\x1d\x1f\x1f\x1f\x13\x17\x22\
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\x02\x4b\x48\x48\x48\x02\x4b\x48\x48\x48\x02\x4b\x48\x48\x02\x4b\
\x48\x48\x48\x02\x4b\x48\x48\x48\x02\x4b\x48\x48\x48\x02\x4b\x48\
\x48\x02\x4b\x48\x48\x48\x02\x4b\x48\x48\x84\x04\xff\x0f\x45\x7a\
\xf5\x15\x6a\xfa\x91\x24\x00\x00\x00\x00\x49\x45\x4e\x44\xae\x42\
\x60\x82\
"
qt_resource_name = b"\
\x00\x05\
\x00\x7a\xec\x35\
\x00\x74\
\x00\x68\x00\x65\x00\x6d\x00\x65\
\x00\x0b\
\x0c\xd3\x40\x27\
\x00\x63\
\x00\x69\x00\x72\x00\x63\x00\x75\x00\x69\x00\x74\x00\x2e\x00\x6a\x00\x70\x00\x67\
\x00\x08\
\x03\x6a\x59\xa7\
\x00\x70\
\x00\x6c\x00\x6f\x00\x77\x00\x2e\x00\x70\x00\x6e\x00\x67\
"
qt_resource_struct_v1 = b"\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x02\
\x00\x00\x00\x2c\x00\x00\x00\x00\x00\x01\x00\x00\xb4\xe3\
\x00\x00\x00\x10\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\
"
qt_resource_struct_v2 = b"\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x02\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\x2c\x00\x00\x00\x00\x00\x01\x00\x00\xb4\xe3\
\x00\x00\x01\x7b\x42\x8c\xa1\x00\
\x00\x00\x00\x10\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\
\x00\x00\x01\x7b\x0d\x8c\x68\x4c\
"
qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
nilq/baby-python
|
python
|
class RunnerTemplate:
def exec(self, data):
pass
|
nilq/baby-python
|
python
|
import asyncio
import logging
import re
from io import BytesIO
import discord
from redbot.core import checks, commands
from redbot.core.bot import Red
from redbot.core.utils.chat_formatting import box, inline
from tsutils.emoji import char_to_emoji, fix_emojis_for_server, replace_emoji_names_with_code
logger = logging.getLogger('red.misc-cogs.fancysay')
class FancySay(commands.Cog):
"""Allows the user to make the bot say things in a variety of ways."""
def __init__(self, bot: Red, *args, **kwargs):
super().__init__(*args, **kwargs)
self.bot = bot
async def red_get_data_for_user(self, *, user_id):
"""Get a user's personal data."""
data = "No data is stored for user with ID {}.\n".format(user_id)
return {"user_data.txt": BytesIO(data.encode())}
async def red_delete_data_for_user(self, *, requester, user_id):
"""Delete a user's personal data.
No personal data is stored in this cog.
"""
return
@commands.group()
@commands.guild_only()
@checks.mod_or_permissions(manage_messages=True)
async def fancysay(self, ctx):
"""Make the bot say fancy things (via embeds)."""
@fancysay.command()
async def pingrole(self, ctx, role: discord.Role, *, text):
"""[p]fancysay pingrole rolename this is the text to ping
1) Converts a role to mentionable
2) Posts the message + ping in the current channel
3) Sets the role to unmentionable
4) Deletes the input message
The role must be unmentionable before this command for safety.
"""
if role.mentionable:
await ctx.send(inline('Error: role is already mentionable'))
return
try:
await role.edit(mentionable=True)
except Exception as ex:
await ctx.send(inline('Error: failed to set role mentionable'))
if ex.text == "Missing Permissions":
message = await ctx.send(inline('Make sure this bot\'s role is higher than the one you\'re mentioning'))
await asyncio.sleep(3)
await message.delete()
return
await ctx.message.delete()
await asyncio.sleep(1)
await ctx.send('From {}:\n{}\n{}'.format(ctx.author.mention, role.mention, text))
try:
await role.edit(mentionable=False)
except Exception as ex:
await ctx.send(inline('Error: failed to set role unmentionable'))
return
@fancysay.command()
async def emoji(self, ctx, *, text):
"""Speak the provided text as emojis, deleting the original request"""
await ctx.message.delete()
new_msg = ""
for char in text:
if char.isalpha():
new_msg += char_to_emoji(char) + ' '
elif char == ' ':
new_msg += ' '
elif char.isspace():
new_msg += char
if len(new_msg):
await ctx.send(new_msg)
@commands.command()
@checks.mod_or_permissions(add_reactions=True)
async def emojireact(self, ctx, *, text):
"""React to a message with emoji"""
EXTRA = {
"a": ["\N{NEGATIVE SQUARED LATIN CAPITAL LETTER A}"],
"b": ["\N{NEGATIVE SQUARED LATIN CAPITAL LETTER B}"],
"o": ["\N{NEGATIVE SQUARED LATIN CAPITAL LETTER O}"],
}
*text, message_t = text.split()
try:
message = await commands.MessageConverter().convert(ctx, message_t)
text = "".join(text)
except:
message = None
text = "".join(text + [message_t])
if message is None:
message = (await ctx.channel.history(limit=2).flatten())[1]
text = re.sub(r'[^a-z0-9]', '', text.lower())
if len(message.reactions) + len(text) > 20:
await ctx.send("I don't have enough room to spell this.")
return
for char in text:
if text.count(char) > len(EXTRA.get(char, [])) + 1:
await ctx.send("It is not possible to make this using emoji.")
return
await ctx.message.delete()
used = ""
for char in text:
emote = ([char_to_emoji(char)] + EXTRA.get(char, []))[used.count(char)]
await message.add_reaction(emote)
used += char
@fancysay.command(aliases=['tdif'])
@checks.bot_has_permissions(embed_links=True)
async def title_description_image_footer(self, ctx, title, description, image, footer):
"""[title] [description] [image_url] [footer_text]
You must specify a title. You can omit any of description, image, or footer.
To omit an item use empty quotes. For the text fields, wrap your text in quotes.
The bot will automatically delete your 'say' command if it can
e.g. say with all fields:
fancysay title_description_image_footer "My title text" "Description text" "xyz.com/image.png" "source: xyz.com"
e.g. say with only title and image:
fancysay title_descirption_image_footer "My title" "" "xyz.com/image.png" ""
"""
embed = discord.Embed()
if len(title):
embed.title = title
if len(description):
embed.description = description
if len(image):
embed.set_image(url=image)
if len(footer):
embed.set_footer(text=footer)
try:
await ctx.send(embed=embed)
await ctx.message.delete()
except Exception as error:
await ctx.send(box(error.text))
@commands.command(aliases=["parrot", "repeat"])
@checks.mod_or_permissions(manage_messages=True)
async def say(self, ctx, *, message):
"""Make the bot parrot a phrase."""
message = self.emojify(message)
await ctx.send(message)
@commands.command(aliases=["testparrot", "testrepeat"])
@checks.mod_or_permissions(manage_messages=True)
async def testsay(self, ctx, *, message):
"""Make the bot parrot a phrase without smart emoji replacements."""
await ctx.send(message)
@commands.command()
@checks.mod_or_permissions(manage_messages=True)
async def mask(self, ctx, *, message):
"""Sends a message as the bot."""
message = self.emojify(message)
await ctx.message.delete()
await ctx.send(message)
@commands.command()
@checks.mod_or_permissions(manage_messages=True)
async def yell(self, ctx, *, message):
"""Yells some text."""
message = self.emojify(message)
await ctx.send(message.upper().rstrip(",.!?") + "!!!!!!")
def emojify(self, message):
emojis = list()
for guild in self.bot.guilds:
emojis.extend(guild.emojis)
message = replace_emoji_names_with_code(emojis, message)
return fix_emojis_for_server(emojis, message)
|
nilq/baby-python
|
python
|
# Lint as: python3
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for fenced_doctest."""
from typing import List, Optional, Tuple
from absl.testing import absltest
from absl.testing import parameterized
from tensorflow.tools.docs import fenced_doctest_lib
EXAMPLES = [
# pyformat: disable
('simple', [('code', None)], """
Hello
``` python
code
```
Goodbye
"""),
('output', [('code', 'result')], """
Hello
``` python
code
```
```
result
```
Goodbye
"""),
('not-output', [('code', None)], """
Hello
``` python
code
```
``` bash
result
```
Goodbye
"""),
('first', [('code', None)], """
``` python
code
```
Goodbye
"""[1:]),
('last', [('code', None)], """
Hello
``` python
code
```"""),
('last_output', [('code', 'result')], """
Hello
``` python
code
```
```
result
```"""),
('skip-unlabeled', [], """
Hello
```
skip
```
Goodbye
"""),
('skip-wrong-label', [], """
Hello
``` sdkfjgsd
skip
```
Goodbye
"""),
('doctest_skip', [], """
Hello
``` python
doctest: +SKIP
```
Goodbye
"""),
('skip_all', [], """
<!-- doctest: skip-all -->
Hello
``` python
a
```
``` python
b
```
Goodbye
"""),
('two', [('a', None), ('b', None)], """
Hello
``` python
a
```
``` python
b
```
Goodbye
"""),
('two-outputs', [('a', 'A'), ('b', 'B')], """
Hello
``` python
a
```
```
A
```
``` python
b
```
```
B
```
Goodbye
"""),
('list', [('a', None), ('b', 'B'), ('c', 'C'), ('d', None)], """
Hello
``` python
a
```
``` python
b
```
```
B
```
List:
* first
``` python
c
```
```
C
```
``` python
d
```
* second
Goodbye
"""),
('multiline', [('a\nb', 'A\nB')], """
Hello
``` python
a
b
```
```
A
B
```
Goodbye
""")
]
ExampleTuples = List[Tuple[str, Optional[str]]]
class G3DoctestTest(parameterized.TestCase):
def _do_test(self, expected_example_tuples, string):
parser = fenced_doctest_lib.FencedCellParser(fence_label='python')
example_tuples = []
for example in parser.get_examples(string, name=self._testMethodName):
source = example.source.rstrip('\n')
want = example.want
if want is not None:
want = want.rstrip('\n')
example_tuples.append((source, want))
self.assertEqual(expected_example_tuples, example_tuples)
@parameterized.named_parameters(*EXAMPLES)
def test_parser(self, expected_example_tuples: ExampleTuples, string: str):
self._do_test(expected_example_tuples, string)
@parameterized.named_parameters(*EXAMPLES)
def test_parser_no_blanks(self, expected_example_tuples: ExampleTuples,
string: str):
string = string.replace('\n\n', '\n')
self._do_test(expected_example_tuples, string)
if __name__ == '__main__':
absltest.main()
|
nilq/baby-python
|
python
|
# LIBRARIES
from django.db import models
from django.db.utils import IntegrityError
from django.contrib.contenttypes.models import ContentType
# DJANGAE
from djangae.db import transaction
from djangae.fields import (
ComputedCharField,
GenericRelationField,
ListField,
RelatedSetField,
RelatedListField,
ShardedCounterField,
SetField,
)
from djangae.fields.counting import DEFAULT_SHARD_COUNT
from djangae.models import CounterShard
from djangae.test import TestCase
class ComputedFieldModel(models.Model):
def computer(self):
return "%s_%s" % (self.int_field, self.char_field)
int_field = models.IntegerField()
char_field = models.CharField(max_length=50)
test_field = ComputedCharField(computer, max_length=50)
class Meta:
app_label = "djangae"
class ComputedFieldTests(TestCase):
def test_computed_field(self):
instance = ComputedFieldModel(int_field=1, char_field="test")
instance.save()
self.assertEqual(instance.test_field, "1_test")
# Try getting and saving the instance again
instance = ComputedFieldModel.objects.get(test_field="1_test")
instance.save()
class ModelWithCounter(models.Model):
counter = ShardedCounterField()
class Meta:
app_label = "djangae"
class ModelWithManyCounters(models.Model):
counter1 = ShardedCounterField()
counter2 = ShardedCounterField()
class Meta:
app_label = "djangae"
class ISOther(models.Model):
name = models.CharField(max_length=500)
def __unicode__(self):
return "%s:%s" % (self.pk, self.name)
class Meta:
app_label = "djangae"
class RelationWithoutReverse(models.Model):
name = models.CharField(max_length=500)
class Meta:
app_label = "djangae"
class RelationWithOverriddenDbTable(models.Model):
class Meta:
db_table = "bananarama"
app_label = "djangae"
class GenericRelationModel(models.Model):
relation_to_anything = GenericRelationField(null=True)
unique_relation_to_anything = GenericRelationField(null=True, unique=True)
class Meta:
app_label = "djangae"
class ISModel(models.Model):
related_things = RelatedSetField(ISOther)
related_list = RelatedListField(ISOther, related_name="ismodel_list")
limted_related = RelatedSetField(RelationWithoutReverse, limit_choices_to={'name': 'banana'}, related_name="+")
children = RelatedSetField("self", related_name="+")
class Meta:
app_label = "djangae"
class IterableFieldModel(models.Model):
set_field = SetField(models.CharField(max_length=1))
list_field = ListField(models.CharField(max_length=1))
class Meta:
app_label = "djangae"
class ShardedCounterTest(TestCase):
def test_basic_usage(self):
instance = ModelWithCounter.objects.create()
self.assertEqual(0, instance.counter.value())
instance.counter.increment()
self.assertEqual(1, instance.counter.value())
instance.counter.increment()
self.assertEqual(2, instance.counter.value())
instance.counter.decrement()
self.assertEqual(1, instance.counter.value())
instance.counter.decrement()
self.assertEqual(0, instance.counter.value())
def test_negative_counts(self):
instance = ModelWithCounter.objects.create()
self.assertEqual(instance.counter.value(), 0)
instance.counter.decrement(5)
instance.counter.increment()
self.assertEqual(instance.counter.value(), -4)
def test_create_in_transaction(self):
""" ShardedCounterField shouldn't prevent us from saving the model object inside a transaction.
"""
with transaction.atomic():
ModelWithCounter.objects.create()
def test_increment_step(self):
""" Test the behvaviour of incrementing in steps of more than 1. """
instance = ModelWithCounter.objects.create()
self.assertEqual(instance.counter.value(), 0)
instance.counter.increment(3)
instance.counter.increment(2)
self.assertEqual(instance.counter.value(), 5)
def test_decrement_step(self):
""" Test the behvaviour of decrementing in steps of more than 1. """
instance = ModelWithCounter.objects.create()
self.assertEqual(instance.counter.value(), 0)
instance.counter.increment(2)
instance.counter.increment(7)
instance.counter.increment(3)
instance.counter.decrement(7)
self.assertEqual(instance.counter.value(), 5)
def test_reset(self):
""" Test the behaviour of calling reset() on the field. """
instance = ModelWithCounter.objects.create()
self.assertEqual(instance.counter.value(), 0)
instance.counter.increment(7)
self.assertEqual(instance.counter.value(), 7)
instance.counter.reset()
self.assertEqual(instance.counter.value(), 0)
def test_populate(self):
""" Test that the populate() method correctly generates all of the CounterShard objects. """
instance = ModelWithCounter.objects.create()
# Initially, none of the CounterShard objects should have been created
self.assertEqual(len(instance.counter), 0)
self.assertEqual(CounterShard.objects.count(), 0)
instance.counter.populate()
expected_num_shards = instance._meta.get_field('counter').shard_count
self.assertEqual(len(instance.counter), expected_num_shards)
def test_populate_is_idempotent_across_threads(self):
""" Edge case test to make sure that 2 different threads calling .populate() on a field
don't cause it to exceed the corrent number of shards.
"""
instance = ModelWithCounter.objects.create()
same_instance = ModelWithCounter.objects.get()
instance.counter.populate()
same_instance.counter.populate()
# Now reload it from the DB and check that it has the correct number of shards
instance = ModelWithCounter.objects.get()
self.assertEqual(instance.counter.all().count(), DEFAULT_SHARD_COUNT)
def test_label_reference_is_saved(self):
""" Test that each CounterShard which the field creates is saved with the label of the
model and field to which it belongs.
"""
instance = ModelWithCounter.objects.create()
instance.counter.populate()
expected_shard_label = '%s.%s' % (ModelWithCounter._meta.db_table, 'counter')
self.assertEqual(
CounterShard.objects.filter(label=expected_shard_label).count(),
len(instance.counter)
)
def test_many_counters_on_one_model(self):
""" Test that have multiple counters on the same model doesn't cause any issues.
This is mostly to test that the multiple reverse relations to the CounterShard model
don't clash.
"""
instance = ModelWithManyCounters.objects.create()
instance.counter1.increment(5)
instance.counter1.increment(5)
instance.counter2.increment(1)
self.assertEqual(instance.counter1.value(), 10)
self.assertEqual(instance.counter2.value(), 1)
instance.counter1.reset()
self.assertEqual(instance.counter1.value(), 0)
self.assertEqual(instance.counter2.value(), 1)
class IterableFieldTests(TestCase):
def test_filtering_on_iterable_fields(self):
list1 = IterableFieldModel.objects.create(
list_field=['A', 'B', 'C', 'D', 'E', 'F', 'G'],
set_field=set(['A', 'B', 'C', 'D', 'E', 'F', 'G']))
list2 = IterableFieldModel.objects.create(
list_field=['A', 'B', 'C', 'H', 'I', 'J'],
set_field=set(['A', 'B', 'C', 'H', 'I', 'J']))
# filtering using exact lookup with ListField:
qry = IterableFieldModel.objects.filter(list_field='A')
self.assertEqual(sorted(x.pk for x in qry), sorted([list1.pk, list2.pk]))
qry = IterableFieldModel.objects.filter(list_field='H')
self.assertEqual(sorted(x.pk for x in qry), [list2.pk,])
# filtering using exact lookup with SetField:
qry = IterableFieldModel.objects.filter(set_field='A')
self.assertEqual(sorted(x.pk for x in qry), sorted([list1.pk, list2.pk]))
qry = IterableFieldModel.objects.filter(set_field='H')
self.assertEqual(sorted(x.pk for x in qry), [list2.pk,])
# filtering using in lookup with ListField:
qry = IterableFieldModel.objects.filter(list_field__in=['A', 'B', 'C'])
self.assertEqual(sorted(x.pk for x in qry), sorted([list1.pk, list2.pk,]))
qry = IterableFieldModel.objects.filter(list_field__in=['H', 'I', 'J'])
self.assertEqual(sorted(x.pk for x in qry), sorted([list2.pk,]))
# filtering using in lookup with SetField:
qry = IterableFieldModel.objects.filter(set_field__in=set(['A', 'B']))
self.assertEqual(sorted(x.pk for x in qry), sorted([list1.pk, list2.pk]))
qry = IterableFieldModel.objects.filter(set_field__in=set(['H']))
self.assertEqual(sorted(x.pk for x in qry), [list2.pk,])
def test_empty_iterable_fields(self):
""" Test that an empty set field always returns set(), not None """
instance = IterableFieldModel()
# When assigning
self.assertEqual(instance.set_field, set())
self.assertEqual(instance.list_field, [])
instance.save()
instance = IterableFieldModel.objects.get()
# When getting it from the db
self.assertEqual(instance.set_field, set())
self.assertEqual(instance.list_field, [])
def test_list_field(self):
instance = IterableFieldModel.objects.create()
self.assertEqual([], instance.list_field)
instance.list_field.append("One")
self.assertEqual(["One"], instance.list_field)
instance.save()
self.assertEqual(["One"], instance.list_field)
instance = IterableFieldModel.objects.get(pk=instance.pk)
self.assertEqual(["One"], instance.list_field)
instance.list_field = None
# Or anything else for that matter!
with self.assertRaises(ValueError):
instance.list_field = "Bananas"
instance.save()
results = IterableFieldModel.objects.filter(list_field="One")
self.assertEqual([instance], list(results))
self.assertEqual([1, 2], ListField(models.IntegerField).to_python("[1, 2]"))
def test_set_field(self):
instance = IterableFieldModel.objects.create()
self.assertEqual(set(), instance.set_field)
instance.set_field.add("One")
self.assertEqual(set(["One"]), instance.set_field)
instance.save()
self.assertEqual(set(["One"]), instance.set_field)
instance = IterableFieldModel.objects.get(pk=instance.pk)
self.assertEqual(set(["One"]), instance.set_field)
instance.set_field = None
# Or anything else for that matter!
with self.assertRaises(ValueError):
instance.set_field = "Bananas"
instance.save()
self.assertEqual({1, 2}, SetField(models.IntegerField).to_python("{1, 2}"))
def test_empty_list_queryable_with_is_null(self):
instance = IterableFieldModel.objects.create()
self.assertTrue(IterableFieldModel.objects.filter(set_field__isnull=True).exists())
instance.set_field.add(1)
instance.save()
self.assertFalse(IterableFieldModel.objects.filter(set_field__isnull=True).exists())
self.assertTrue(IterableFieldModel.objects.filter(set_field__isnull=False).exists())
self.assertFalse(IterableFieldModel.objects.exclude(set_field__isnull=False).exists())
self.assertTrue(IterableFieldModel.objects.exclude(set_field__isnull=True).exists())
class InstanceListFieldTests(TestCase):
def test_deserialization(self):
i1 = ISOther.objects.create(pk=1)
i2 = ISOther.objects.create(pk=2)
# Does the to_python need to return ordered list? SetField test only passes because the set
# happens to order it correctly
self.assertItemsEqual([i1, i2], ISModel._meta.get_field("related_list").to_python("[1, 2]"))
def test_save_and_load_empty(self):
"""
Create a main object with no related items,
get a copy of it back from the db and try to read items.
"""
main = ISModel.objects.create()
main_from_db = ISModel.objects.get(pk=main.pk)
# Fetch the container from the database and read its items
self.assertItemsEqual(main_from_db.related_list.all(), [])
def test_basic_usage(self):
main = ISModel.objects.create()
other = ISOther.objects.create(name="test")
other2 = ISOther.objects.create(name="test2")
main.related_list.add(other)
main.save()
self.assertEqual([other.pk,], main.related_list_ids)
self.assertEqual(list(ISOther.objects.filter(pk__in=main.related_list_ids)), list(main.related_list.all()))
self.assertEqual([main], list(other.ismodel_list.all()))
main.related_list.remove(other)
self.assertFalse(main.related_list)
main.related_list = [other2, ]
self.assertEqual([other2.pk, ], main.related_list_ids)
with self.assertRaises(AttributeError):
other.ismodel_list = [main, ]
without_reverse = RelationWithoutReverse.objects.create(name="test3")
self.assertFalse(hasattr(without_reverse, "ismodel_list"))
def test_add_to_empty(self):
"""
Create a main object with no related items,
get a copy of it back from the db and try to add items.
"""
main = ISModel.objects.create()
main_from_db = ISModel.objects.get(pk=main.pk)
other = ISOther.objects.create()
main_from_db.related_list.add(other)
main_from_db.save()
def test_add_another(self):
"""
Create a main object with related items,
get a copy of it back from the db and try to add more.
"""
main = ISModel.objects.create()
other1 = ISOther.objects.create()
main.related_things.add(other1)
main.save()
main_from_db = ISModel.objects.get(pk=main.pk)
other2 = ISOther.objects.create()
main_from_db.related_list.add(other2)
main_from_db.save()
def test_multiple_objects(self):
main = ISModel.objects.create()
other1 = ISOther.objects.create()
other2 = ISOther.objects.create()
main.related_list.add(other1, other2)
main.save()
main_from_db = ISModel.objects.get(pk=main.pk)
self.assertEqual(main_from_db.related_list.count(), 2)
def test_deletion(self):
"""
Delete one of the objects referred to by the related field
"""
main = ISModel.objects.create()
other = ISOther.objects.create()
main.related_list.add(other)
main.save()
other.delete()
self.assertEqual(main.related_list.count(), 0)
def test_ordering_is_maintained(self):
main = ISModel.objects.create()
other = ISOther.objects.create()
other1 = ISOther.objects.create()
other2 = ISOther.objects.create()
other3 = ISOther.objects.create()
main.related_list.add(other, other1, other2, other3)
main.save()
self.assertEqual(main.related_list.count(), 4)
self.assertEqual([other.pk, other1.pk, other2.pk, other3.pk, ], main.related_list_ids)
self.assertItemsEqual([other, other1, other2, other3, ], main.related_list.all())
main.related_list.clear()
main.save()
self.assertEqual([], main.related_list_ids)
def test_duplicates_maintained(self):
"""
For whatever reason you might want many of the same relation in the
list
"""
main = ISModel.objects.create()
other = ISOther.objects.create()
other1 = ISOther.objects.create()
other2 = ISOther.objects.create()
other3 = ISOther.objects.create()
main.related_list.add(other, other1, other2, other1, other3,)
main.save()
self.assertEqual([other.pk, other1.pk, other2.pk, other1.pk, other3.pk, ], main.related_list_ids)
self.assertItemsEqual([other, other1, other2, other1, other3, ], main.related_list.all())
def test_slicing(self):
main = ISModel.objects.create()
other = ISOther.objects.create()
other1 = ISOther.objects.create()
other2 = ISOther.objects.create()
other3 = ISOther.objects.create()
main.related_list.add(other, other1, other2, other1, other3,)
main.save()
self.assertItemsEqual([other, other1, ], main.related_list.all()[:2])
self.assertItemsEqual([other1, ], main.related_list.all()[1:2])
self.assertEqual(other1, main.related_list.all()[1:2][0])
def test_filtering(self):
main = ISModel.objects.create()
other = ISOther.objects.create(name="one")
other1 = ISOther.objects.create(name="two")
other2 = ISOther.objects.create(name="one")
other3 = ISOther.objects.create(name="three")
main.related_list.add(other, other1, other2, other1, other2,)
main.save()
self.assertItemsEqual([other, other2, other2], main.related_list.filter(name="one"))
class InstanceSetFieldTests(TestCase):
def test_deserialization(self):
i1 = ISOther.objects.create(pk=1)
i2 = ISOther.objects.create(pk=2)
self.assertEqual(set([i1, i2]), ISModel._meta.get_field("related_things").to_python("[1, 2]"))
def test_basic_usage(self):
main = ISModel.objects.create()
other = ISOther.objects.create(name="test")
other2 = ISOther.objects.create(name="test2")
main.related_things.add(other)
main.save()
self.assertEqual({other.pk}, main.related_things_ids)
self.assertEqual(list(ISOther.objects.filter(pk__in=main.related_things_ids)), list(main.related_things.all()))
self.assertEqual([main], list(other.ismodel_set.all()))
main.related_things.remove(other)
self.assertFalse(main.related_things_ids)
main.related_things = {other2}
self.assertEqual({other2.pk}, main.related_things_ids)
with self.assertRaises(AttributeError):
other.ismodel_set = {main}
without_reverse = RelationWithoutReverse.objects.create(name="test3")
self.assertFalse(hasattr(without_reverse, "ismodel_set"))
def test_save_and_load_empty(self):
"""
Create a main object with no related items,
get a copy of it back from the db and try to read items.
"""
main = ISModel.objects.create()
main_from_db = ISModel.objects.get(pk=main.pk)
# Fetch the container from the database and read its items
self.assertItemsEqual(main_from_db.related_things.all(), [])
def test_add_to_empty(self):
"""
Create a main object with no related items,
get a copy of it back from the db and try to add items.
"""
main = ISModel.objects.create()
main_from_db = ISModel.objects.get(pk=main.pk)
other = ISOther.objects.create()
main_from_db.related_things.add(other)
main_from_db.save()
def test_add_another(self):
"""
Create a main object with related items,
get a copy of it back from the db and try to add more.
"""
main = ISModel.objects.create()
other1 = ISOther.objects.create()
main.related_things.add(other1)
main.save()
main_from_db = ISModel.objects.get(pk=main.pk)
other2 = ISOther.objects.create()
main_from_db.related_things.add(other2)
main_from_db.save()
def test_multiple_objects(self):
main = ISModel.objects.create()
other1 = ISOther.objects.create()
other2 = ISOther.objects.create()
main.related_things.add(other1, other2)
main.save()
main_from_db = ISModel.objects.get(pk=main.pk)
self.assertEqual(main_from_db.related_things.count(), 2)
def test_deletion(self):
"""
Delete one of the objects referred to by the related field
"""
main = ISModel.objects.create()
other = ISOther.objects.create()
main.related_things.add(other)
main.save()
other.delete()
self.assertEqual(main.related_things.count(), 0)
def test_querying_with_isnull(self):
obj = ISModel.objects.create()
self.assertItemsEqual([obj], ISModel.objects.filter(related_things__isnull=True))
self.assertItemsEqual([obj], ISModel.objects.filter(related_things_ids__isnull=True))
class TestGenericRelationField(TestCase):
def test_basic_usage(self):
instance = GenericRelationModel.objects.create()
self.assertIsNone(instance.relation_to_anything)
thing = ISOther.objects.create()
instance.relation_to_anything = thing
instance.save()
self.assertTrue(instance.relation_to_anything_id)
instance = GenericRelationModel.objects.get()
self.assertEqual(thing, instance.relation_to_anything)
def test_overridden_dbtable(self):
""" Check that the related object having a custom `db_table` doesn't affect the functionality. """
instance = GenericRelationModel.objects.create()
self.assertIsNone(instance.relation_to_anything)
weird = RelationWithOverriddenDbTable.objects.create()
instance.relation_to_anything = weird
instance.save()
self.assertTrue(instance.relation_to_anything)
instance = GenericRelationModel.objects.get()
self.assertEqual(weird, instance.relation_to_anything)
def test_querying(self):
thing = ISOther.objects.create()
instance = GenericRelationModel.objects.create(relation_to_anything=thing)
self.assertEqual(GenericRelationModel.objects.filter(relation_to_anything=thing)[0], instance)
def test_unique(self):
thing = ISOther.objects.create()
instance = GenericRelationModel.objects.create(unique_relation_to_anything=thing)
# Trying to create another instance which relates to the same 'thing' should fail
self.assertRaises(IntegrityError, GenericRelationModel.objects.create, unique_relation_to_anything=thing)
# But creating 2 objects which both have `unique_relation_to_anything` set to None should be fine
instance.unique_relation_to_anything = None
instance.save()
GenericRelationModel.objects.create(unique_relation_to_anything=None)
GenericRelationModel.objects.create() # It should work even if we don't explicitly set it to None
|
nilq/baby-python
|
python
|
class DocumentOCRViewTestMixin(object):
def _request_document_content_view(self):
return self.get(
viewname='ocr:document_ocr_content', kwargs={
'document_id': self.test_document.pk
}
)
def _request_document_content_delete_view(self):
return self.post(
viewname='ocr:document_ocr_content_delete', kwargs={
'document_id': self.test_document.pk
}
)
def _request_document_page_content_view(self):
return self.get(
viewname='ocr:document_page_ocr_content', kwargs={
'document_page_id': self.test_document.pages.first().pk
}
)
def _request_document_submit_view(self):
return self.post(
viewname='ocr:document_submit', kwargs={
'document_id': self.test_document.pk
}
)
def _request_multiple_document_submit_view(self):
return self.post(
viewname='ocr:document_submit_multiple', data={
'id_list': self.test_document.pk,
}
)
def _request_document_ocr_download_view(self):
return self.get(
viewname='ocr:document_ocr_download', kwargs={
'document_id': self.test_document.pk
}
)
class DocumentTypeOCRViewTestMixin(object):
def _request_document_type_ocr_settings_view(self):
return self.get(
viewname='ocr:document_type_ocr_settings', kwargs={
'document_type_id': self.test_document_type.pk
}
)
|
nilq/baby-python
|
python
|
import attr
import aiohttp
import asyncio
from typing import Any, Optional
# Not frozen, since that doesn't work in PyPy
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class FacebookError(Exception):
"""Base class for all custom exceptions raised by ``fbchat``.
All exceptions in the module inherit this.
"""
#: A message describing the error
message: str
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class HTTPError(FacebookError):
"""Base class for errors with the HTTP(s) connection to Facebook."""
#: The returned HTTP status code, if relevant
status_code: Optional[int] = None
def __str__(self):
if not self.status_code:
return self.message
return "Got {} response: {}".format(self.status_code, self.message)
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class ParseError(FacebookError):
"""Raised when we fail parsing a response from Facebook.
This may contain sensitive data, so should not be logged to file.
"""
data_file: str = ""
data: Any = None
def __str__(self):
if self.data:
return f"{self.message}. Please report this, along with the data below:\n{self.data}"
elif self.data_file:
return f"{self.message}. Please report this, along with the data in {self.data_file}"
else:
return self.message
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class NotLoggedIn(FacebookError):
"""Raised by Facebook if the client has been logged out."""
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class NotConnected(FacebookError):
"""Raised by Facebook if the client has been logged out."""
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class ExternalError(FacebookError):
"""Base class for errors that Facebook return."""
#: The error message that Facebook returned (Possibly in the user's own language)
description: str
#: The error code that Facebook returned
code: Optional[int] = None
def __str__(self):
if self.code:
return "#{} {}: {}".format(self.code, self.message, self.description)
return "{}: {}".format(self.message, self.description)
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class GraphQLError(ExternalError):
"""Raised by Facebook if there was an error in the GraphQL query."""
# TODO: Handle multiple errors
#: Query debug information
debug_info: Optional[str] = None
def __str__(self):
if self.debug_info:
return "{}, {}".format(super().__str__(), self.debug_info)
return super().__str__()
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class InvalidParameters(ExternalError):
"""Raised by Facebook if:
- Some function supplied invalid parameters.
- Some content is not found.
- Some content is no longer available.
"""
@attr.s(slots=True, auto_exc=True, auto_attribs=True)
class PleaseRefresh(ExternalError):
"""Raised by Facebook if the client has been inactive for too long.
This error usually happens after 1-2 days of inactivity.
"""
code: int = 1357004
def handle_payload_error(j):
if "error" not in j:
return
code = j["error"]
if code == 1357001:
raise NotLoggedIn(j["errorSummary"])
elif code == 1357004:
error_cls = PleaseRefresh
elif code in (1357031, 1545010, 1545003):
error_cls = InvalidParameters
else:
error_cls = ExternalError
raise error_cls(j["errorSummary"], description=j["errorDescription"], code=code)
def handle_graphql_errors(j):
errors = []
if j.get("error"):
errors = [j["error"]]
if "errors" in j:
errors = j["errors"]
if errors:
error = errors[0] # TODO: Handle multiple errors
# TODO: Use `severity`
raise GraphQLError(
# TODO: What data is always available?
message=error.get("summary", "Unknown error"),
description=error.get("message") or error.get("description") or "",
code=error.get("code"),
debug_info=error.get("debug_info"),
)
def handle_http_error(code):
if code == 404:
raise HTTPError(
"This might be because you provided an invalid id"
+ " (Facebook usually require integer ids)",
status_code=code,
)
if code == 500:
raise HTTPError(
"There is probably an error on the endpoint, or it might be rate limited",
status_code=code,
)
if 400 <= code < 600:
raise HTTPError("Failed sending request", status_code=code)
def handle_requests_error(e):
if isinstance(e, (aiohttp.ClientConnectionError, aiohttp.ServerConnectionError)):
raise HTTPError("Connection error") from e
if isinstance(e, aiohttp.ClientResponseError):
pass # Raised when using .raise_for_status, so should never happen
if isinstance(e, aiohttp.InvalidURL):
pass # Should never happen, we always prove valid URLs
if isinstance(e, aiohttp.TooManyRedirects):
pass # TODO: Consider using allow_redirects=False to prevent this
if isinstance(e, (aiohttp.ServerTimeoutError, asyncio.TimeoutError)):
pass # Should never happen, we don't set timeouts
raise HTTPError("Requests error") from e
|
nilq/baby-python
|
python
|
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from azure.cli.core.help_files import helps # pylint: disable=unused-import
# pylint: disable=line-too-long, too-many-lines
helps['storage entity insert'] = """
type: command
short-summary: Insert a new entity into the table.
long-summary: Inserts a new entity into the table. When inserting an entity into a table, you must specify values for the PartitionKey and RowKey system properties. Together, these properties form the primary key and must be unique within the table. Both the PartitionKey and RowKey values may be up to 64 KB in size. If you are using an integer value as a key, you should convert the integer to a fixed-width string, because they are canonically sorted. For example, you should convert the value 1 to 0000001 to ensure proper sorting.
parameters:
- name: --table-name -t
type: string
short-summary: 'The name of the table to insert the entity into.'
- name: --entity -e
type: list
short-summary: 'A space-separated list of key=value pairs. Must contain a PartitionKey and a RowKey.'
- name: --if-exists
type: string
short-summary: 'Specify what should happen if an entity already exists for the specified PartitionKey and RowKey.'
- name: --timeout
short-summary: The server timeout, expressed in seconds.
"""
helps['storage'] = """
type: group
short-summary: Durable, highly available, and massively scalable cloud storage
"""
helps['storage account'] = """
type: group
short-summary: Manage storage accounts.
"""
helps['storage account keys'] = """
type: group
short-summary: Manage storage account keys.
"""
helps['storage blob'] = """
type: group
short-summary: Object storage for unstructured data
"""
helps['storage blob exists'] = """
type: command
short-summary: Returns a boolean indicating whether the blob exists.
"""
helps['storage blob list'] = """
type: command
short-summary: List blobs in a given container.
"""
helps['storage blob copy'] = """
type: group
short-summary: Manage blob copy operations.
"""
helps['storage blob lease'] = """
type: group
short-summary: Manage storage blob leases.
"""
helps['storage blob metadata'] = """
type: group
short-summary: Manage blob metadata.
"""
helps['storage blob service-properties'] = """
type: group
short-summary: Manage storage blob service properties.
"""
helps['storage container'] = """
type: group
short-summary: Manage blob storage containers.
"""
helps['storage container exists'] = """
type: command
short-summary: Returns a boolean indicating whether the container exists.
"""
helps['storage container list'] = """
type: command
short-summary: List containers in a storage account.
"""
helps['storage container lease'] = """
type: group
short-summary: Manage blob storage container leases.
"""
helps['storage container metadata'] = """
type: group
short-summary: Manage container metadata.
"""
helps['storage container policy'] = """
type: group
short-summary: Manage container stored access policies.
"""
helps['storage cors'] = """
type: group
short-summary: Manage Storage service Cross-Orgin Resource Sharing (CORS)
"""
helps['storage cors add'] = """
type: command
short-summary: Add a CORS rule to a storage account.
"""
helps['storage cors clear'] = """
type: command
short-summary: Remove all CORS rules from a storage account.
"""
helps['storage cors list'] = """
type: command
short-summary: List all CORS rules for a storage account.
"""
helps['storage directory'] = """
type: group
short-summary: Manage file storage directories.
"""
helps['storage directory exists'] = """
type: command
short-summary: Returns a boolean indicating whether the directory exists.
"""
helps['storage directory metadata'] = """
type: group
short-summary: Manage file storage directory metadata.
"""
helps['storage directory list'] = """
type: command
short-summary: List directories in the specified share.
"""
helps['storage entity'] = """
type: group
short-summary: Manage table storage entities.
"""
helps['storage entity query'] = """
type: command
short-summary: List entities which satisfy a given query.
"""
helps['storage file'] = """
type: group
short-summary: File shares that use the standard SMB 3.0 protocol
"""
helps['storage file exists'] = """
type: command
short-summary: Returns a boolean indicating whether the file exists.
"""
helps['storage file list'] = """
type: command
short-summary: List files and directories in the specified share.
parameters:
- name: --exclude-dir
type: bool
short-summary: List only files in the specified share.
"""
helps['storage file copy'] = """
type: group
short-summary: Manage file copy operations.
"""
helps['storage file metadata'] = """
type: group
short-summary: Manage file metadata.
"""
helps['storage file upload-batch'] = """
type: command
short-summary: Upload files from local directory to Azure Storage File Share in batch
parameters:
- name: --source -s
type: string
short-summary: The directory from which the files should to be uploaded.
- name: --destination -d
type: string
short-summary: The string represents the destination of this upload operation. The
destination can be the file share URL or the share name. When the
destination is the share URL, the storage account name will parsed
from the URL.
- name: --pattern
type: string
short-summary: The pattern is used for files globbing. The supported patterns are '*',
'?', '[seq', and '[!seq]'.
- name: --dryrun
type: bool
short-summary: Output the list of files which would be uploaded. No actual data transfer
will occur.
- name: --max-connections
type: integer
short-summary: Maximum number of parallel connections to use. Default value is 1.
- name: --validate-content
type: bool
short-summary: If set, calculates an MD5 hash for each range of the file. The storage
service checks the hash of the content that has arrived with the hash that
was sent. This is primarily valuable for detecting bitflips on the wire if
using http instead of https as https (the default) will already validate.
Note that this MD5 hash is not stored with the file.
"""
helps['storage file download-batch'] = """
type: command
short-summary: Download files from Azure Storage File Share to a local directory in batch
parameters:
- name: --source -s
type: string
short-summary: The string represents the source of this file download operation. The
source can be the file share URL or the share name. When the source is
the share URL, the storage account name will parsed from the URL.
- name: --destination -d
type: string
short-summary: The directory where the files to be downloaded. The directory must exist.
- name: --pattern
type: string
short-summary: The pattern is used for files globbing. The supported patterns are '*',
'?', '[seq', and '[!seq]'.
- name: --dryrun
type: bool
short-summary: Output the list of files which would be downloaded. No actual data transfer
will occur.
- name: --max-connections
type: integer
short-summary: Maximum number of parallel connections to use. Default value is 1.
- name: --validate-content
type: bool
short-summary: If set, calculates an MD5 hash for each range of the file. The storage
service checks the hash of the content that has arrived with the hash that
was sent. This is primarily valuable for detecting bitflips on the wire if
using http instead of https as https (the default) will already validate.
Note that this MD5 hash is not stored with the file.
"""
helps['storage file copy start-batch'] = """
type: command
short-summary: Copy multiple files to file share asynchronously.
parameters:
- name: --destination-share
type: string
short-summary: The file share where the specified source files or blobs to be copied to.
- name: --destination-path
type: string
short-summary: The directory where the specified source files or blobs to be copied to. If
omitted, the files or blobs will be copied to the root directory.
- name: --pattern
type: string
short-summary: The pattern is used for globbing files or blobs in the source. The
supported patterns are '*', '?', '[seq', and '[!seq]'.
- name: --dryrun
type: bool
short-summary: Output the list of files or blobs which would be uploaded. No actual data
transfer will occur.
- name: --source-account
type: string
short-summary: The source storage account from which the files or blobs will be copied to
the destination. If omitted, it is assumed that source is in the same
storage account as destination
- name: --source-key
type: string
short-summary: The account key for the source storage account.
- name: --source-container
type: string
short-summary: The source container from which the blobs will be copied to the destination
- name: --source-share
type: string
short-summary: The source share from which the files will be copied to the destination
- name: --source-uri
type: string
short-summary: A URI specifies an file share or blob container from which the files or
blobs will be copied to the destination. If the source is in another
account, the source must either be public or must be authenticated via a
shared access signature. If the source is public, no authentication is
required.
- name: --source-sas
type: string
short-summary: The shared access signature for the source storage account.
"""
helps['storage logging'] = """
type: group
short-summary: Manage Storage service logging information.
"""
helps['storage logging show'] = """
type: command
short-summary: Show logging settings for a storage account.
"""
helps['storage logging update'] = """
type: command
short-summary: Update logging settings for a storage account.
"""
helps['storage message'] = """
type: group
short-summary: Manage queue storage messages.
"""
helps['storage metrics'] = """
type: group
short-summary: Manage Storage service metrics.
"""
helps['storage metrics show'] = """
type: command
short-summary: Show metrics settings for a storage account.
"""
helps['storage metrics update'] = """
type: command
short-summary: Update metrics settings for a storage account.
"""
helps['storage queue'] = """
type: group
short-summary: Effectively scale apps according to traffic using queues.
"""
helps['storage queue list'] = """
type: command
short-summary: List queues in a storage account.
"""
helps['storage queue metadata'] = """
type: group
short-summary: Manage storage queue metadata.
"""
helps['storage queue policy'] = """
type: group
short-summary: Manage storage queue shared access policies.
"""
helps['storage share'] = """
type: group
short-summary: Manage file shares.
"""
helps['storage share exists'] = """
type: command
short-summary: Returns a boolean indicating whether the share exists.
"""
helps['storage share list'] = """
type: command
short-summary: List file shares in a storage account.
"""
helps['storage share metadata'] = """
type: group
short-summary: Manage file share metadata.
"""
helps['storage share policy'] = """
type: group
short-summary: Manage storage file share shared access policies.
"""
helps['storage table'] = """
type: group
short-summary: NoSQL key-value storage using semi-structured datasets.
"""
helps['storage table list'] = """
type: command
short-summary: List tables in a storage account.
"""
helps['storage table policy'] = """
type: group
short-summary: Manage storage table shared access policies.
"""
|
nilq/baby-python
|
python
|
"""Lambda pocket-to-kindle create_epub."""
from datetime import datetime
from os import environ as env
from os import stat
from shlex import join
from subprocess import (run, CalledProcessError, TimeoutExpired)
from tempfile import NamedTemporaryFile
from uuid import uuid4
import utils
import utils.aws as aws
import utils.handlers as handlers
import utils.helpers as helpers
def create_epub(event: utils.LambdaEvent) -> str:
"""Build EPUB file from URL source and store it to S3."""
utils.Log.info("Fetch content from %s", event["url"])
requests = helpers.import_non_stdlib_module("requests")
response = requests.get(url=event["url"])
if not response.status_code == 200:
raise utils.HandledError("Error downloading %s: "
"HTTP status code %d" % (event["ur"], response.status_code),
status_code=response.status_code)
utils.Log.info("Create Markdown text from %s source", event["url"])
html2text = helpers.import_non_stdlib_module("html2text")
markdown_maker = html2text.HTML2Text()
markdown_maker.ignore_links = True
markdown = markdown_maker.handle(response.text)
utils.Log.debug("Markdown content:\n%s", markdown)
utils.Log.info("Create temporary file to store epub content")
epub = NamedTemporaryFile(suffix=".epub")
utils.Log.debug("tempfile created: %s", epub.name)
try:
completed = run(["pandoc", "--version"], check=True, capture_output=True, text=True)
utils.Log.debug(completed.stdout)
pandoc_cmd = [
"pandoc",
"--quiet",
"--from=markdown",
"--to=epub",
f"--metadata=title:'{event['title']}'",
f"--output={epub.name}",
]
timeout = 200
utils.Log.info("Executing %s", join(pandoc_cmd))
run(pandoc_cmd, input=bytes(markdown, encoding="utf-8"), check=True, timeout=timeout)
utils.Log.info("EPUB creation completed (%d bytes)", stat(epub.name).st_size)
except TimeoutExpired:
raise utils.HandledError("Error: pandoc execution exceeded timeout of %d seconds" % timeout,
status_code=500)
except CalledProcessError as error:
raise utils.HandledError("Error: %s" % error, status_code=500) from error
now = datetime.utcnow()
file_name = f"pocket-{event['item_id']}" if "item_id" in event else uuid4()
key_name = now.strftime(f"%Y/%m/%d/{file_name}.epub")
aws.put_object_to_s3_bucket(key=key_name, bucket=env["EPUB_BUCKET"], body=epub)
file_url = f"s3://{env['EPUB_BUCKET']}/{key_name}"
utils.Log.info("File %s created successfully", file_url)
return f"success: {file_url}"
def handler(event, context) -> utils.Response:
"""Lambda entry point."""
return handlers.EventHandler(
name="pocket_create_epub",
event=utils.LambdaEvent(event),
context=utils.LambdaContext(context),
action=create_epub,
).response
|
nilq/baby-python
|
python
|
# default_settings.py
#
# Author(s):
# Exequiel Ceasar Navarrete <esnavarrete1@up.edu.ph>
#
# Licensed under MIT
# Version 1.0.0
import os
#========================================================
# [Flask-specific configuration] ::start
#========================================================
DEBUG = False
CACHE_TYPE = "filesystem"
CACHE_DIRECTORY = os.path.join(os.getcwd(), "data/cache")
#========================================================
# [Flask-specific configuration] ::end
#========================================================
#========================================================
# [Application-specific configuration] ::start
#========================================================
APP_TITLE = "CMSC 265 Image Search Engine for Skin"
SKIN_DETECT_CACHE_KEY = "detected_skins"
SKIN_DETECT_CACHE_TTL = 900
SKIN_DETECT_INPUT_DIR = os.path.join(os.getcwd(), "assets/img/input-for-skin-detection")
SKIN_DETECT_OUTPUT_DIR = os.path.join(os.getcwd(), "app/static/img/detected-skins")
SKIN_DETECT_IM_WIDTH = 800
SKIN_DETECT_IM_HEIGHT = 450
SKIN_DETECT_RESULTS_PER_PAGE = 50
#========================================================
# [Application-specific configuration] ::end
#========================================================
|
nilq/baby-python
|
python
|
from __future__ import annotations
import typing
if typing.TYPE_CHECKING:
from src.typehints import AnyCallableT
__all__: tuple[str, ...] = ("is_classvar",)
def is_classvar(fn: AnyCallableT) -> bool:
return hasattr(fn, "__classvar__")
|
nilq/baby-python
|
python
|
#!/usr/bin/python
from urllib2 import urlopen
import json, re
from json import loads
def print_json(j):
j = json.dumps(j, sort_keys=True, indent=2)
# j = re.sub(j, r'\n', '\\n')
print j
def my_urlopen(url):
print "\nurlopen:", url
return urlopen(url)
print "\nget all projects"
req = urlopen('http://172.16.2.164:8080/api/json')
res = req.read()
data = loads(res)
print_json(data.keys())
print_json(data['jobs'][0].keys())
for i,job in enumerate(data['jobs']):
print "\njob", i
print_json(job['name'])
if job['name']=='h2o_release_tests':
jobIndex = i
print "\nfull url to job", jobIndex
req = my_urlopen('%s%s' % (data['jobs'][jobIndex]['url'], 'api/json'))
res = req.read()
job = loads(res)
print_json(job.keys())
# [ "url", "number" ]
print "I think this job is h2o_release_tests: ", job['name']
print "\nwhen did", job['name'], "last run to success?"
print "job['lastCompletedBuild']:"
print_json(job['lastCompletedBuild'])
print_json(job['lastCompletedBuild'].keys())
# first part has the trailing / already
req = my_urlopen('%s%s' % (job['lastCompletedBuild']['url'], 'testReport/api/json'))
res = req.read()
testReport = loads(res)
# [ "suites", "failCount", "skipCount", "duration", "passCount", "empty" ]
# printed = 0
print testReport.keys()
printed = 0
for i in testReport['suites']:
print "#######################################################"
noKeysList = []
if isinstance(i, dict) and printed<8:
print "i.keys", i.keys()
# i.keys [u'name', u'stdout', u'timestamp', u'stderr', u'duration', u'cases', u'id']
# print_json(i)
printed += 1
# {
# "type":"object", "properties":{
# "age": { "type":"number", },
# "className": { "type":"string", },
# "duration": { "type":"number", },
# "errorDetails": { "type":"string", },
# "errorStackTrace": { "type":"string", },
# "failedSince": { "type":"number", },
# "name": { "type":"string", },
# "skippedMessage": { "type":"string", },
# "skipped": { "type":"boolean", },
# "status": { "type":"string", },
# "stderr": { "type":"string", },
# "stdout": { "type":"string", }
# }
# }
|
nilq/baby-python
|
python
|
"""Tests for the Update integration init."""
from __future__ import annotations
import asyncio
from collections.abc import Awaitable, Callable
from typing import Any
from unittest.mock import Mock, patch
from aiohttp import ClientWebSocketResponse
import pytest
from homeassistant.components.update import (
DOMAIN,
IntegrationUpdateFailed,
UpdateDescription,
)
from homeassistant.core import HomeAssistant
from homeassistant.setup import async_setup_component
from tests.common import mock_platform
async def setup_mock_domain(
hass: HomeAssistant,
async_list_updates: Callable[[HomeAssistant], Awaitable[list[UpdateDescription]]]
| None = None,
async_perform_update: Callable[[HomeAssistant, str, str], Awaitable[bool]]
| None = None,
) -> None:
"""Set up a mock domain."""
async def _mock_async_list_updates(hass: HomeAssistant) -> list[UpdateDescription]:
return [
UpdateDescription(
identifier="lorem_ipsum",
name="Lorem Ipsum",
current_version="1.0.0",
available_version="1.0.1",
)
]
async def _mock_async_perform_update(
hass: HomeAssistant,
identifier: str,
version: str,
**kwargs: Any,
) -> bool:
return True
mock_platform(
hass,
"some_domain.update",
Mock(
async_list_updates=async_list_updates or _mock_async_list_updates,
async_perform_update=async_perform_update or _mock_async_perform_update,
),
)
assert await async_setup_component(hass, "some_domain", {})
async def gather_update_info(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> list[dict]:
"""Gather all info."""
client = await hass_ws_client(hass)
await client.send_json({"id": 1, "type": "update/info"})
resp = await client.receive_json()
return resp["result"]
async def test_update_updates(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test getting updates."""
await setup_mock_domain(hass)
assert await async_setup_component(hass, DOMAIN, {})
with patch(
"homeassistant.components.update.storage.Store.async_load",
return_value={"skipped": []},
):
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
data = data[0] == {
"domain": "some_domain",
"identifier": "lorem_ipsum",
"name": "Lorem Ipsum",
"current_version": "1.0.0",
"available_version": "1.0.1",
"changelog_url": None,
"icon_url": None,
}
async def test_update_updates_with_timeout_error(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test timeout while getting updates."""
async def mock_async_list_updates(hass: HomeAssistant) -> list[UpdateDescription]:
raise asyncio.TimeoutError()
await setup_mock_domain(hass, async_list_updates=mock_async_list_updates)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 0
async def test_update_updates_with_exception(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test exception while getting updates."""
async def mock_async_list_updates(hass: HomeAssistant) -> list[UpdateDescription]:
raise Exception()
await setup_mock_domain(hass, async_list_updates=mock_async_list_updates)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 0
async def test_update_update(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test performing an update."""
await setup_mock_domain(hass)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
update = data[0]
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/update",
"domain": update["domain"],
"identifier": update["identifier"],
"version": update["available_version"],
}
)
resp = await client.receive_json()
assert resp["success"]
async def test_skip_update(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test skipping updates."""
await setup_mock_domain(hass)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
update = data[0]
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/skip",
"domain": update["domain"],
"identifier": update["identifier"],
"version": update["available_version"],
}
)
resp = await client.receive_json()
assert resp["success"]
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 0
async def test_skip_non_existing_update(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test skipping non-existing updates."""
await setup_mock_domain(hass)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/skip",
"domain": "non_existing",
"identifier": "non_existing",
"version": "non_existing",
}
)
resp = await client.receive_json()
assert not resp["success"]
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
async def test_update_update_non_existing(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test that we fail when trying to update something that does not exist."""
await setup_mock_domain(hass)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/update",
"domain": "does_not_exist",
"identifier": "does_not_exist",
"version": "non_existing",
}
)
resp = await client.receive_json()
assert not resp["success"]
assert resp["error"]["code"] == "not_found"
async def test_update_update_failed(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test that we correctly handle failed updates."""
async def mock_async_perform_update(
hass: HomeAssistant,
identifier: str,
version: str,
**kwargs,
) -> bool:
raise IntegrationUpdateFailed("Test update failed")
await setup_mock_domain(hass, async_perform_update=mock_async_perform_update)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
update = data[0]
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/update",
"domain": update["domain"],
"identifier": update["identifier"],
"version": update["available_version"],
}
)
resp = await client.receive_json()
assert not resp["success"]
assert resp["error"]["code"] == "update_failed"
assert resp["error"]["message"] == "Test update failed"
async def test_update_update_failed_generic(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
caplog: pytest.LogCaptureFixture,
) -> None:
"""Test that we correctly handle failed updates."""
async def mock_async_perform_update(
hass: HomeAssistant,
identifier: str,
version: str,
**kwargs,
) -> bool:
raise TypeError("Test update failed")
await setup_mock_domain(hass, async_perform_update=mock_async_perform_update)
assert await async_setup_component(hass, DOMAIN, {})
data = await gather_update_info(hass, hass_ws_client)
assert len(data) == 1
update = data[0]
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/update",
"domain": update["domain"],
"identifier": update["identifier"],
"version": update["available_version"],
}
)
resp = await client.receive_json()
assert not resp["success"]
assert resp["error"]["code"] == "update_failed"
assert resp["error"]["message"] == "Unknown Error"
assert "Test update failed" in caplog.text
async def test_update_before_info(
hass: HomeAssistant,
hass_ws_client: Callable[[HomeAssistant], Awaitable[ClientWebSocketResponse]],
) -> None:
"""Test that we fail when trying to update something that does not exist."""
await setup_mock_domain(hass)
assert await async_setup_component(hass, DOMAIN, {})
client = await hass_ws_client(hass)
await client.send_json(
{
"id": 1,
"type": "update/update",
"domain": "does_not_exist",
"identifier": "does_not_exist",
"version": "non_existing",
}
)
resp = await client.receive_json()
assert not resp["success"]
assert resp["error"]["code"] == "not_found"
|
nilq/baby-python
|
python
|
class Events:
def __getattr__(self, name):
if hasattr(self.__class__, '__events__'):
assert name in self.__class__.__events__, \
"Event '%s' is not declared" % name
self.__dict__[name] = ev = _EventSlot(name)
return ev
def __repr__(self):
return 'Events' + str(list(self))
__str__ = __repr__
def __len__(self):
return NotImplemented
def __iter__(self):
def gen(dictitems=self.__dict__.items()):
for val in dictitems.itervalues():
if isinstance(val, _EventSlot):
yield val
return gen()
class _EventSlot:
def __init__(self, name):
self.targets = []
self.__name__ = name
def __repr__(self):
return 'event ' + self.__name__
def __call__(self, *a, **kw):
for f in self.targets: f(*a, **kw)
def __iadd__(self, f):
self.targets.append(f)
return self
def __isub__(self, f):
while f in self.targets:
self.targets.remove(f)
return self
|
nilq/baby-python
|
python
|
"""
Code modified from PyTorch DCGAN examples: https://github.com/pytorch/examples/tree/master/dcgan
"""
import argparse
import os
import random
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.utils.data
def get_parsers():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', required=True, choices=['celebA'], help='celebA')
parser.add_argument('--dataroot', required=True, help='path to dataset')
parser.add_argument('--batch_size', type=int, default=100, help='input batch size')
parser.add_argument('--image_size', type=int, default=128, help='the height / width of the input image to network')
parser.add_argument('--nz', type=int, default=64, help='size of the latent z vector, noise')
parser.add_argument('--niter', type=int, default=25, help='number of epochs to train for')
parser.add_argument('--print_every', type=int, default=10, help='number iterations to print out statements')
parser.add_argument('--lr', type=float, default=0.0005, help='learning rate, default=0.0002')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--e_pretrain', action='store_true', help='if pretrain encoder')
parser.add_argument('--e_pretrain_sample_size', type=int, default=256, help='sample size for encoder pretrain')
parser.add_argument('--e_pretrain_iters', type=int, default=1, help='max epochs to pretrain the encoder')
parser.add_argument('--input_normalize_sym', action='store_true', help='for tanh of GAN outputs')
parser.add_argument('--ngpu', type=int, default=1, help='number of GPUs to use')
parser.add_argument('--checkpoint', default='', help="path to checkpoint (to continue training)")
parser.add_argument('--outf', default='.', help='folder to output images and model checkpoints')
parser.add_argument('--noise', default='gaussian', choices=['gaussian', 'add_noise'], help='noise type for WAE, | gaussian | add_noise |')
parser.add_argument('--seed', type=int, default=None, help='manual seed')
parser.add_argument('--gpu_id', type=int, default=0, help='The ID of the specified GPU')
parser.add_argument('--LAMBDA', type=float, default=100, help='LAMBDA for WAE')
parser.add_argument('--img_norm', type=float, default=None, help='normalization of images')
parser.add_argument('--mode', type=str, default='gan', choices=['gan', 'mmd'], help='| gan | mmd |')
parser.add_argument('--kernel', type=str, default='IMQ', choices=['RBF', 'IMQ'], help='| RBF | IMQ |')
parser.add_argument('--pz_scale', type=float, default=1., help='sacling of sample noise')
opt = parser.parse_args()
print(opt)
return opt
def main():
opt = get_parsers()
# specify the gpu id if using only 1 gpu
if opt.ngpu == 1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu_id)
# output directory
os.makedirs(opt.outf, exist_ok=True)
# random seeds
if opt.seed is None:
opt.seed = random.randint(1, 10000)
print("Random Seed: ", opt.seed)
random.seed(opt.seed)
torch.manual_seed(opt.seed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.seed)
# use cuda
cudnn.benchmark = True
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
# main training
if opt.mode == 'gan':
from train_wae_gan import train
elif opt.mode == 'mmd':
from train_wae_mmd import train
else:
raise NotImplementedError
train(opt)
if __name__ == "__main__":
main()
|
nilq/baby-python
|
python
|
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class FunctionRepresentation(nn.Module):
"""Function to represent a single datapoint. For example this could be a
function that takes pixel coordinates as input and returns RGB values, i.e.
f(x, y) = (r, g, b).
Args:
coordinate_dim (int): Dimension of input (coordinates).
feature_dim (int): Dimension of output (features).
layer_sizes (tuple of ints): Specifies size of each hidden layer.
encoding (torch.nn.Module): Encoding layer, usually one of
Identity or FourierFeatures.
final_non_linearity (torch.nn.Module): Final non linearity to use.
Usually nn.Sigmoid() or nn.Tanh().
"""
def __init__(self, coordinate_dim, feature_dim, layer_sizes, encoding,
non_linearity=nn.ReLU(), final_non_linearity=nn.Sigmoid()):
super(FunctionRepresentation, self).__init__()
self.coordinate_dim = coordinate_dim
self.feature_dim = feature_dim
self.layer_sizes = layer_sizes
self.encoding = encoding
self.non_linearity = non_linearity
self.final_non_linearity = final_non_linearity
self._init_neural_net()
def _init_neural_net(self):
"""
"""
# First layer transforms coordinates into a positional encoding
# Check output dimension of positional encoding
if isinstance(self.encoding, nn.Identity):
prev_num_units = self.coordinate_dim # No encoding, so same output dimension
else:
prev_num_units = self.encoding.feature_dim
# Build MLP layers
forward_layers = []
for num_units in self.layer_sizes:
forward_layers.append(nn.Linear(prev_num_units, num_units))
forward_layers.append(self.non_linearity)
prev_num_units = num_units
forward_layers.append(nn.Linear(prev_num_units, self.feature_dim))
forward_layers.append(self.final_non_linearity)
self.forward_layers = nn.Sequential(*forward_layers)
def forward(self, coordinates):
"""Forward pass. Given a set of coordinates, returns feature at every
coordinate.
Args:
coordinates (torch.Tensor): Shape (batch_size, coordinate_dim)
"""
encoded = self.encoding(coordinates)
return self.forward_layers(encoded)
def get_weight_shapes(self):
"""Returns lists of shapes of weights and biases in the network."""
weight_shapes = []
bias_shapes = []
for param in self.forward_layers.parameters():
if len(param.shape) == 1:
bias_shapes.append(param.shape)
if len(param.shape) == 2:
weight_shapes.append(param.shape)
return weight_shapes, bias_shapes
def get_weights_and_biases(self):
"""Returns list of weights and biases in the network."""
weights = []
biases = []
for param in self.forward_layers.parameters():
if len(param.shape) == 1:
biases.append(param)
if len(param.shape) == 2:
weights.append(param)
return weights, biases
def set_weights_and_biases(self, weights, biases):
"""Sets weights and biases in the network.
Args:
weights (list of torch.Tensor):
biases (list of torch.Tensor):
Notes:
The inputs to this function should have the same form as the outputs
of self.get_weights_and_biases.
"""
weight_idx = 0
bias_idx = 0
with torch.no_grad():
for param in self.forward_layers.parameters():
if len(param.shape) == 1:
param.copy_(biases[bias_idx])
bias_idx += 1
if len(param.shape) == 2:
param.copy_(weights[weight_idx])
weight_idx += 1
def duplicate(self):
"""Returns a FunctionRepresentation instance with random weights."""
# Extract device
device = next(self.parameters()).device
# Create new function representation and put it on same device
return FunctionRepresentation(self.coordinate_dim, self.feature_dim,
self.layer_sizes, self.encoding,
self.non_linearity,
self.final_non_linearity).to(device)
def sample_grid(self, data_converter, resolution=None):
"""Returns function values evaluated on grid.
Args:
data_converter (data.conversion.DataConverter):
resolution (tuple of ints): Resolution of grid on which to evaluate
features. If None uses default resolution.
"""
# Predict features at every coordinate in a grid
if resolution is None:
coordinates = data_converter.coordinates
else:
coordinates = data_converter.superresolve_coordinates(resolution)
features = self(coordinates)
# Convert features into appropriate data format (e.g. images)
return data_converter.to_data(coordinates, features, resolution)
def stateless_forward(self, coordinates, weights, biases):
"""Computes forward pass of function representation given a set of
weights and biases without using the state of the PyTorch module.
Args:
coordinates (torch.Tensor): Tensor of shape (num_points, coordinate_dim).
weights (list of torch.Tensor): List of tensors containing weights
of linear layers of neural network.
biases (list of torch.Tensor): List of tensors containing biases of
linear layers of neural network.
Notes:
This is useful for computing forward pass for a specific function
representation (i.e. for a given set of weights and biases). However,
it might be easiest to just change the weights of the network directly
and then perform forward pass.
Doing the current way is definitely more error prone because we have
to mimic the forward pass, instead of just directly using it.
Return:
Returns a tensor of shape (num_points, feature_dim)
"""
# Positional encoding is first layer of function representation
# model, so apply this transformation to coordinates
hidden = self.encoding(coordinates)
# Apply linear layers and non linearities
for i in range(len(weights)):
hidden = F.linear(hidden, weights[i], biases[i])
if i == len(weights) - 1:
hidden = self.final_non_linearity(hidden)
else:
hidden = self.non_linearity(hidden)
return hidden
def batch_stateless_forward(self, coordinates, weights, biases):
"""Stateless forward pass for multiple function representations.
Args:
coordinates (torch.Tensor): Batch of coordinates of shape
(batch_size, num_points, coordinate_dim).
weights (dict of list of torch.Tensor): Batch of list of tensors
containing weights of linear layers for each neural network.
biases (dict of list of torch.Tensor): Batch of list of tensors
containing biases of linear layers for each neural network.
Return:
Returns a tensor of shape (batch_size, num_points, feature_dim).
"""
features = []
for i in range(coordinates.shape[0]):
features.append(
self.stateless_forward(coordinates[i], weights[i], biases[i]).unsqueeze(0)
)
return torch.cat(features, dim=0)
def _get_config(self):
return {"coordinate_dim": self.coordinate_dim,
"feature_dim": self.feature_dim,
"layer_sizes": self.layer_sizes,
"encoding": self.encoding,
"non_linearity": self.non_linearity,
"final_non_linearity": self.final_non_linearity}
class FourierFeatures(nn.Module):
"""Random Fourier features.
Args:
frequency_matrix (torch.Tensor): Matrix of frequencies to use
for Fourier features. Shape (num_frequencies, num_coordinates).
This is referred to as B in the paper.
learnable_features (bool): If True, fourier features are learnable,
otherwise they are fixed.
"""
def __init__(self, frequency_matrix, learnable_features=False):
super(FourierFeatures, self).__init__()
if learnable_features:
self.frequency_matrix = nn.Parameter(frequency_matrix)
else:
# Register buffer adds a key to the state dict of the model. This will
# track the attribute without registering it as a learnable parameter.
# We require this so frequency matrix will also be moved to GPU when
# we call .to(device) on the model
self.register_buffer('frequency_matrix', frequency_matrix)
self.learnable_features = learnable_features
self.num_frequencies = frequency_matrix.shape[0]
self.coordinate_dim = frequency_matrix.shape[1]
# Factor of 2 since we consider both a sine and cosine encoding
self.feature_dim = 2 * self.num_frequencies
def forward(self, coordinates):
"""Creates Fourier features from coordinates.
Args:
coordinates (torch.Tensor): Shape (num_points, coordinate_dim)
"""
# The coordinates variable contains a batch of vectors of dimension
# coordinate_dim. We want to perform a matrix multiply of each of these
# vectors with the frequency matrix. I.e. given coordinates of
# shape (num_points, coordinate_dim) we perform a matrix multiply by
# the transposed frequency matrix of shape (coordinate_dim, num_frequencies)
# to obtain an output of shape (num_points, num_frequencies).
prefeatures = torch.matmul(coordinates, self.frequency_matrix.T)
# Calculate cosine and sine features
cos_features = torch.cos(2 * math.pi * prefeatures)
sin_features = torch.sin(2 * math.pi * prefeatures)
# Concatenate sine and cosine features
return torch.cat((cos_features, sin_features), dim=1)
|
nilq/baby-python
|
python
|
import random
import os
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
plt.ion()
PROJECT_FOLDER = os.path.dirname(__file__)
DATASET_FOLDER = PROJECT_FOLDER + '/data/columbia-prcg-datasets'
PHOTO_FOLDER = DATASET_FOLDER + '/google_images/'
CG_FOLDER = DATASET_FOLDER + '/prcg_images/'
NUM_IMAGES_PER_CLASS = 10
class classification:
cg = 1
photo = 0
def pick_random_images(folder, num_images):
files = random.sample(os.listdir(folder), num_images)
return [folder + file for file in files]
def get_user_class_from_image(image_path):
img = mpimg.imread(image_path)
# show image
imgplot = plt.imshow(img)
plt.show(block=False)
# get user classification
user_input = None
while user_input != 'p' and user_input != 'c':
user_input = input(
'Enter a classification: (p)hoto or (c)omputer-generated')
plt.close('all')
return classification.photo if user_input == 'p' else classification.cg
def main():
# load in images
image_paths_with_labels = []
for img in pick_random_images(PHOTO_FOLDER, NUM_IMAGES_PER_CLASS):
image_paths_with_labels.append((img, classification.photo))
for img in pick_random_images(CG_FOLDER, NUM_IMAGES_PER_CLASS):
image_paths_with_labels.append((img, classification.cg))
print("Loaded {} images.".format(len(image_paths_with_labels)))
# randomize images
random.shuffle(image_paths_with_labels)
# print(image_paths_with_labels)
user_classifications = []
for (image_path, label) in image_paths_with_labels:
user_classifications.append(get_user_class_from_image(image_path))
# process results
total_correct = 0
for i in range(len(user_classifications)):
total_correct += int(
user_classifications[i] == image_paths_with_labels[i][1])
accuracy = total_correct / len(user_classifications)
print("Accuracy: {}".format(accuracy))
if __name__ == "__main__":
main()
|
nilq/baby-python
|
python
|
import os
import tempfile
from ..spectrify import load_data, spectrify_audios
import numpy as np
def test_spectrify():
out_bins = 80
samplerate = 44100
frame_len = 0.1
input_fnames = [os.path.dirname(__file__)+"/test.mp3"]
spec_datas = [load_data(filename,samplerate) for filename in input_fnames]
spec_outs = spectrify_audios(
spec_datas,
out_bins,
samplerate,
frame_len,
)
assert len(spec_outs) == 1
assert spec_outs[0].shape == (int(30/frame_len), out_bins)
# np.save("test.npy",spec_outs[0])
|
nilq/baby-python
|
python
|
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